diff --git a/.azure-pipelines/azure-pipelines-base.yml b/.azure-pipelines/azure-pipelines-base.yml deleted file mode 100644 index 0b479ea505..0000000000 --- a/.azure-pipelines/azure-pipelines-base.yml +++ /dev/null @@ -1,115 +0,0 @@ -jobs: -- job: BaseTests - pool: - vmImage: 'ubuntu-latest' - variables: - - name: NUMBA_DISABLE_JIT - value: 1 - timeoutInMinutes: 360 - strategy: - matrix: - Python_312_optionals: - python.version: "3.12" - Nightlies: false - OptionalRequirements: true - name: "Python 3.12" - Python_312: - python.version: "3.12" - Nightlies: false - OptionalRequirements: false - name: "Python 3.12" - Python_311_optionals: - python.version: "3.11" - Nightlies: false - OptionalRequirements: true - name: "Python 3.11" - Python_311_preview: - python.version: "3.11" - Nightlies: true - OptionalRequirements: false - name: "Python 3.11" - Python_310_optionals: - python.version: "3.10" - Nightlies: false - OptionalRequirements: true - name: "Python 3.10" - - - steps: - - task: UsePythonVersion@0 - inputs: - versionSpec: '$(python.version)' - displayName: 'Use Python $(python.version)' - - - script: | - sudo apt-get update - sudo apt-get install -y ffmpeg - displayName: 'Install external libraries' - - - script: | - ls -ahl - env - pwd - gcc --version - python --version - displayName: 'Debug information' - - - script: | - python -m pip install --upgrade pip - python -m pip install wheel - python -m pip install --no-cache-dir -r requirements.txt - python -m pip install --no-cache-dir -r requirements-dev.txt - python -m pip install pytest-azurepipelines - displayName: 'Install base requirements' - - - script: | - python -m pip install --no-cache-dir -r requirements-optional.txt - condition: and(succeeded(), eq(variables.OptionalRequirements, true)) - displayName: 'Install optional requirements' - - - script: | - python -m pip install --no-cache-dir --upgrade --pre -r requirements.txt - python -m pip install --no-cache-dir --force-reinstall --no-deps --pre -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple contourpy matplotlib numpy pandas scipy xarray - condition: and(succeeded(), eq(variables.Nightlies, true)) - displayName: 'Update with nightlies' - - - script: | - python -m pip install . - displayName: 'Install ArviZ package' - - - script: | - python -m pip freeze - displayName: 'Print packages' - - - script: | - ARVIZ_REQUIRE_ALL_DEPS=TRUE python -m pytest arviz/tests/base_tests --cov arviz --cov-report=xml - condition: and(succeeded(), eq(variables.OptionalRequirements, true)) - displayName: 'pytest (require all dependencies)' - - - script: | - python -m pytest arviz/tests/base_tests --cov arviz --cov-report=xml - condition: and(succeeded(), eq(variables.OptionalRequirements, false)) - displayName: 'pytest (skip if dependency missing)' - - - script: | - ls -ahl - env - displayName: 'Debug information 2' - - - task: PublishTestResults@2 - condition: succeededOrFailed() - inputs: - testResultsFiles: '$(System.DefaultWorkingDirectory)/test-*.xml' - testRunTitle: 'Publish test results for Python $(python.version)' - displayName: 'Publish Test Results' - - - script: | - curl https://keybase.io/codecovsecurity/pgp_keys.asc | gpg --no-default-keyring --keyring trustedkeys.gpg --import # One-time step - curl -Os https://uploader.codecov.io/latest/linux/codecov - curl -Os https://uploader.codecov.io/latest/linux/codecov.SHA256SUM - curl -Os https://uploader.codecov.io/latest/linux/codecov.SHA256SUM.sig - gpgv codecov.SHA256SUM.sig codecov.SHA256SUM - shasum -a 256 -c codecov.SHA256SUM - chmod +x codecov - ./codecov -n "$(NAME)" - displayName: 'upload coverage' diff --git a/.azure-pipelines/azure-pipelines-benchmarks.yml b/.azure-pipelines/azure-pipelines-benchmarks.yml deleted file mode 100644 index b2d8206333..0000000000 --- a/.azure-pipelines/azure-pipelines-benchmarks.yml +++ /dev/null @@ -1,57 +0,0 @@ -jobs: -- job: Benchmarks - pool: - vmImage: 'ubuntu-latest' - timeoutInMinutes: 360 - strategy: - matrix: - Python_311: - python.version: "3.11" - name: "Python 3.11 - benchmarks" - - steps: - - - task: UsePythonVersion@0 - inputs: - versionSpec: '$(python.version)' - displayName: 'Use Python $(python.version)' - - - script: | - ls -ahl - env - pwd - gcc --version - python --version - displayName: 'Debug information' - - - script: | - python -m pip install --upgrade pip - python -m pip install wheel build - python -m pip install --no-cache-dir -r requirements.txt - python -m pip install --no-cache-dir -r requirements-optional.txt - python -m pip install asv - displayName: 'Install requirements' - - - script: | - python -m pip freeze - displayName: 'Debug information 2' - - - script: | - python -m asv machine --yes - git log --pretty=format:'%h' -n 1 > hashestobenchmark.txt - cat hashestobenchmark.txt - python -m asv run HASHFILE:hashestobenchmark.txt - displayName: 'Run the benchmarks' - workingDirectory: asv_benchmarks/ - - - script: | - git log --pretty=format:'%h' -n 1 | xargs python -m asv show - git log --pretty=format:'%h' -n 1 | xargs python -m asv show > ../benchmark_results.txt - displayName: 'Print and save results' - workingDirectory: asv_benchmarks/ - - - task: PublishBuildArtifacts@1 - inputs: - pathtoPublish: 'benchmark_results.txt' - artifactName: 'Benchmarks' - displayName: 'Publish the benchmarks' diff --git a/.azure-pipelines/azure-pipelines-external.yml b/.azure-pipelines/azure-pipelines-external.yml deleted file mode 100644 index b414a2a323..0000000000 --- a/.azure-pipelines/azure-pipelines-external.yml +++ /dev/null @@ -1,124 +0,0 @@ -jobs: -- job: ExternalTests - pool: - vmImage: 'ubuntu-22.04' - variables: - - name: NUMBA_DISABLE_JIT - value: 1 - - name: ARVIZ_REQUIRE_ALL_DEPS - value: 1 - timeoutInMinutes: 360 - strategy: - matrix: - Python_311_Latest: - python.version: "3.11" - cmdstanpy.version: "latest" - emcee.version: "latest" - name: "External latest" - - Python_311_Special_versions: - python.version: "3.11" - cmdstanpy.version: "github" - emcee.version: 2 - name: "External special" - - steps: - - task: UsePythonVersion@0 - inputs: - versionSpec: '$(python.version)' - displayName: 'Use Python $(python.version): $(name)' - - - script: | - ls - env - pwd - gcc --version - python --version - displayName: 'Debug information' - - - script: | - sudo apt-get update - python -m pip install --upgrade pip - python -m pip install wheel - - python -m pip --no-cache-dir install torch --index-url https://download.pytorch.org/whl/cpu - - if [ "$(cmdstanpy.version)" = "latest" ]; then - python -m pip --no-cache-dir install cmdstanpy - else - if [ "$(cmdstanpy.version)" = "github" ]; then - python -m pip --no-cache-dir install git+https://github.com/stan-dev/cmdstanpy@develop#egg=cmdstanpy - else - python -m pip --no-cache-dir install cmdstanpy=="$(cmdstanpy.version)" - fi - fi - - if [ "$(emcee.version)" = "latest" ]; then - python -m pip --no-cache-dir install emcee h5py - else - python -m pip --no-cache-dir install "emcee<3" - fi - - grep -Ev '^cmdstanpy|^emcee' requirements-external.txt | xargs python -m pip install "jax<0.7" - - displayName: 'Install packages' - - - script: | - python -m pip install --no-cache-dir -r requirements.txt - python -m pip install --no-cache-dir -r requirements-dev.txt - python -m pip install --no-cache-dir -r requirements-optional.txt - python -m pip install pytest-azurepipelines - displayName: 'Install requirements' - - - script: | - python -m pip install . - displayName: 'Install ArviZ package' - - - script: | - python -m pip freeze - displayName: 'Print packages' - - - script: | - python -m pylint arviz - displayName: 'pylint' - - - script: | - python -m pydocstyle arviz - displayName: 'pydocstyle' - - - script: | - python -m black --check --diff arviz examples asv_benchmarks - displayName: 'black' - - - script: | - absolufy-imports $(find arviz -name '*.py') --never - displayName: 'Use relative imports' - - - script: | - python -m madforhooks.no_print_statements $(find arviz -name '*.py' -not -path 'arviz/tests/*') - displayName: 'Disallow debugging print statements (use `file=sys.stdout` if not debugging)' - - - script: | - pytest arviz/tests/helpers.py - displayName: 'precompile models' - - - script: | - python -m pytest arviz/tests/external_tests --cov arviz --cov-report=xml - displayName: 'pytest' - - - task: PublishTestResults@2 - condition: succeededOrFailed() - inputs: - testResultsFiles: '$(System.DefaultWorkingDirectory)/test-*.xml' - testRunTitle: 'Publish test results for Python $(python.version)' - - - script: | - curl https://keybase.io/codecovsecurity/pgp_keys.asc | gpg --no-default-keyring --keyring trustedkeys.gpg --import # One-time step - curl -Os https://uploader.codecov.io/latest/linux/codecov - curl -Os https://uploader.codecov.io/latest/linux/codecov.SHA256SUM - curl -Os https://uploader.codecov.io/latest/linux/codecov.SHA256SUM.sig - gpgv codecov.SHA256SUM.sig codecov.SHA256SUM - shasum -a 256 -c codecov.SHA256SUM - chmod +x codecov - ./codecov -n "$(NAME)" - displayName: 'upload coverage' diff --git a/.azure-pipelines/azure-pipelines-wheel.yml b/.azure-pipelines/azure-pipelines-wheel.yml deleted file mode 100644 index 7031e8a8d1..0000000000 --- a/.azure-pipelines/azure-pipelines-wheel.yml +++ /dev/null @@ -1,68 +0,0 @@ -jobs: -- job: BuildWheel - dependsOn: - - BaseTests - - ExternalTests - condition: and(succeeded(), startsWith(variables['Build.SourceBranch'], 'refs/tags/')) - pool: - vmImage: 'ubuntu-latest' - variables: - - name: NUMBA_DISABLE_JIT - value: 1 - timeoutInMinutes: 360 - strategy: - matrix: - Python_311: - python.version: "3.11" - name: "Python 3.11 - wheel" - - steps: - - - task: UsePythonVersion@0 - inputs: - versionSpec: '$(python.version)' - displayName: 'Use Python $(python.version)' - - - script: | - ls -ahl - env - pwd - gcc --version - python --version - displayName: 'Debug information' - - - script: | - python -m pip install --upgrade pip - python -m pip install wheel - python -m pip install --no-cache-dir -r requirements.txt - python -m pip install twine - displayName: 'Install requirements' - - - script: | - python setup.py sdist bdist_wheel - ls -lh dist - displayName: 'Build a wheel' - - - script: | - mkdir install_test - cd install_test - ls -lh ../dist - python -m pip install ../dist/*.whl - python -c "import arviz as az; print(az);print(az.summary(az.load_arviz_data('non_centered_eight')))" - cd .. - displayName: 'Install and test the wheel' - - - script: | - ls -ahl - env - displayName: 'Debug information 2' - - - task: PublishBuildArtifacts@1 - inputs: - pathtoPublish: 'dist' - artifactName: 'arviz_wheel_dist' - displayName: 'Publish the wheel' - - - script: | - python -m twine upload -u __token__ -p $(PYPI_PASSWORD) --skip-existing dist/* - displayName: 'Upload wheel to PyPI' diff --git a/.dockerignore b/.dockerignore deleted file mode 100644 index 3b2fb9519e..0000000000 --- a/.dockerignore +++ /dev/null @@ -1,7 +0,0 @@ -**/Dockerfile -**/container.ps1 -**/container.sh -**/*.egg-info -**/.pytest_cache -**/__pycache__ -**/.git diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000000..d94dd2bda8 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,12 @@ +version: 2 +updates: +- package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "monthly" + cooldown: + default-days: 14 + groups: + actions: + patterns: + - "*" diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 0000000000..bdfb23abb4 --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,50 @@ +name: Publish library + +on: + push: + branches: + - main + tags: + # Don't try to be smart about PEP 440 compliance, + # see https://www.python.org/dev/peps/pep-0440/#appendix-b-parsing-version-strings-with-regular-expressions + - v* + +jobs: + build-package: + runs-on: ubuntu-latest + permissions: + # write attestations and id-token are necessary for attest-build-provenance-github + attestations: write + id-token: write + steps: + - uses: actions/checkout@v5 + with: + fetch-depth: 0 + persist-credentials: false + - uses: hynek/build-and-inspect-python-package@v2 + with: + # Prove that the packages were built in the context of this workflow. + attest-build-provenance-github: true + publish: + runs-on: ubuntu-latest + if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags') + # Use the `release` GitHub environment to protect the Trusted Publishing (OIDC) + # workflow by requiring signoff from a maintainer. + environment: + name: publish + url: https://pypi.org/p/arviz + needs: build-package + permissions: + # write id-token is necessary for trusted publishing (OIDC) + id-token: write + steps: + - name: Download Distribution Artifacts + uses: actions/download-artifact@v6 + with: + # The build-and-inspect-python-package action invokes upload-artifact. + # These are the correct arguments from that action. + name: Packages + path: dist + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + # Implicitly attests that the packages were uploaded in the context of this workflow. diff --git a/.github/workflows/rtd_link_description.yaml b/.github/workflows/rtd_link_description.yaml deleted file mode 100644 index b68d2fcf87..0000000000 --- a/.github/workflows/rtd_link_description.yaml +++ /dev/null @@ -1,16 +0,0 @@ -name: Read the Docs Pull Request Preview -on: - pull_request_target: - types: - - opened - -permissions: - pull-requests: write - -jobs: - documentation-links: - runs-on: ubuntu-latest - steps: - - uses: readthedocs/actions/preview@v1 - with: - project-slug: "arviz" diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 0000000000..0f9adda3ef --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,27 @@ +name: Run tests +on: + pull_request: + push: + branches: [main] + paths-ignore: + - "docs/" + +jobs: + test: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.11", "3.12", "3.13"] + fail-fast: false + steps: + - uses: actions/checkout@v5 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install tox tox-gh-actions + - name: Test with tox + run: SKIP=no-commit-to-branch tox diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000000..5a2ee951b7 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,25 @@ +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: check-added-large-files + - id: check-toml + - id: check-merge-conflict + - id: end-of-file-fixer + - id: no-commit-to-branch + args: [--branch, main] + - id: trailing-whitespace + +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.11.13 + hooks: + - id: ruff + args: [--fix, --exit-non-zero-on-fix] + types: [ python ] + - id: ruff-format + types: [ python ] + +- repo: https://github.com/MarcoGorelli/madforhooks + rev: 0.4.1 + hooks: + - id: no-print-statements diff --git a/.projections.json b/.projections.json deleted file mode 100644 index ab4abaaec7..0000000000 --- a/.projections.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "arviz/plots/backends/matplotlib/*.py": { - "alternate": "arviz/plots/backends/bokeh/{}.py", - "related": "arviz/plots/{}.py", - "type": "mpl" - }, - "arviz/plots/backends/bokeh/*.py": { - "alternate": "arviz/plots/backends/matplotlib/{}.py", - "related": "arviz/plots/{}.py", - "type": "bokeh" - }, - "arviz/plots/*.py": { - "alternate": "arviz/plots/backends/matplotlib/{}.py", - "related": "arviz/plots/backends/bokeh/{}.py", - "type": "base" - }, - "arviz/data/io_*.py": { - "alternate": "arviz/tests/external_tests/test_data_{}.py", - "type": "converter" - }, - "arviz/tests/external_tests/test_data_*.py": { - "alternate": "arviz/data/io_{}.py", - "type": "test" - } -} diff --git a/.pydocstyle.ini b/.pydocstyle.ini deleted file mode 100644 index 38cc2164bf..0000000000 --- a/.pydocstyle.ini +++ /dev/null @@ -1,3 +0,0 @@ -[pydocstyle] -convention = numpy -match-dir = '^(?!example_data).*' diff --git a/.pylintrc b/.pylintrc deleted file mode 100644 index a72dd1788c..0000000000 --- a/.pylintrc +++ /dev/null @@ -1,506 +0,0 @@ -[MASTER] - -# A comma-separated list of package or module names from where C extensions may -# be loaded. Extensions are loading into the active Python interpreter and may -# run arbitrary code -extension-pkg-whitelist= - -# Add files or directories to the blacklist. They should be base names, not -# paths. -ignore=CVS - -# Add files or directories matching the regex patterns to the blacklist. The -# regex matches against base names, not paths. -ignore-patterns= - -# Python code to execute, usually for sys.path manipulation such as -# pygtk.require(). -#init-hook= - -# Use multiple processes to speed up Pylint. -jobs=1 - -# List of plugins (as comma separated values of python modules names) to load, -# usually to register additional checkers. -load-plugins= - -# Pickle collected data for later comparisons. -persistent=yes - -# Specify a configuration file. -#rcfile= - -# Allow loading of arbitrary C extensions. Extensions are imported into the -# active Python interpreter and may run arbitrary code. -unsafe-load-any-extension=no - - -[MESSAGES CONTROL] - -# Only show warnings with the listed confidence levels. Leave empty to show -# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED -confidence= - -# Disable the message, report, category or checker with the given id(s). You -# can either give multiple identifiers separated by comma (,) or put this -# option multiple times (only on the command line, not in the configuration -# file where it should appear only once).You can also use "--disable=all" to -# disable everything first and then reenable specific checks. For example, if -# you want to run only the similarities checker, you can use "--disable=all -# --enable=similarities". If you want to run only the classes checker, but have -# no Warning level messages displayed, use"--disable=all --enable=classes -# --disable=W" -disable=missing-docstring, - no-else-return, - len-as-condition, - too-many-arguments, - too-many-locals, - too-many-branches, - too-many-statements, - too-many-positional-arguments, - too-few-public-methods, - import-outside-toplevel, - no-else-continue, - unnecessary-comprehension, - unsubscriptable-object, - cyclic-import, - ungrouped-imports, - not-an-iterable, - no-member, - #TODO: Remove this once todos are done - fixme, - consider-using-with, - consider-using-f-string, - # TODO: Remove this once update the code base. - unnecessary-lambda-assignment, - use-dict-literal, - possibly-used-before-assignment, - # TODO (maybe): Remove this if the exess of issues related to that are fixed - # https://github.com/pylint-dev/pylint/issues?q=is%3Aissue+is%3Aopen+used-before-assignment+label%3A%22C%3A+used-before-assignment%22 - used-before-assignment - -# Enable the message, report, category or checker with the given id(s). You can -# either give multiple identifier separated by comma (,) or put this option -# multiple time (only on the command line, not in the configuration file where -# it should appear only once). See also the "--disable" option for examples. -enable=c-extension-no-member - - -[REPORTS] - -# Python expression which should return a note less than 10 (10 is the highest -# note). You have access to the variables errors warning, statement which -# respectively contain the number of errors / warnings messages and the total -# number of statements analyzed. This is used by the global evaluation report -# (RP0004). -evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) - -# Template used to display messages. This is a python new-style format string -# used to format the message information. See doc for all details -#msg-template= - -# Set the output format. Available formats are text, parseable, colorized, json -# and msvs (visual studio).You can also give a reporter class, eg -# mypackage.mymodule.MyReporterClass. -output-format=text - -# Tells whether to display a full report or only the messages -reports=no - -# Activate the evaluation score. -score=yes - - -[REFACTORING] - -# Maximum number of nested blocks for function / method body -max-nested-blocks=5 - -# Complete name of functions that never returns. When checking for -# inconsistent-return-statements if a never returning function is called then -# it will be considered as an explicit return statement and no message will be -# printed. -never-returning-functions=optparse.Values,sys.exit - - -[FORMAT] - -# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. -expected-line-ending-format= - -# Regexp for a line that is allowed to be longer than the limit. -ignore-long-lines=^\s*(# )??$ - -# Number of spaces of indent required inside a hanging or continued line. -indent-after-paren=4 - -# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 -# tab). -indent-string=' ' - -# Maximum number of characters on a single line. -max-line-length=100 - -# Maximum number of lines in a module -max-module-lines=1000 - -# Allow the body of a class to be on the same line as the declaration if body -# contains single statement. -single-line-class-stmt=no - -# Allow the body of an if to be on the same line as the test if there is no -# else. -single-line-if-stmt=no - - -[VARIABLES] - -# List of additional names supposed to be defined in builtins. Remember that -# you should avoid to define new builtins when possible. -additional-builtins= - -# Tells whether unused global variables should be treated as a violation. -allow-global-unused-variables=yes - -# List of strings which can identify a callback function by name. A callback -# name must start or end with one of those strings. -callbacks=cb_, - _cb - -# A regular expression matching the name of dummy variables (i.e. expectedly -# not used). -dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_ - -# Argument names that match this expression will be ignored. Default to name -# with leading underscore -ignored-argument-names=_.*|^ignored_|^unused_ - -# Tells whether we should check for unused import in __init__ files. -init-import=no - -# List of qualified module names which can have objects that can redefine -# builtins. -redefining-builtins-modules=six.moves,past.builtins,future.builtins - - -[BASIC] - -# Naming style matching correct argument names -argument-naming-style=snake_case - -# Regular expression matching correct argument names. Overrides argument- -# naming-style -#argument-rgx= - -# Naming style matching correct attribute names -attr-naming-style=snake_case - -# Regular expression matching correct attribute names. Overrides attr-naming- -# style -#attr-rgx= - -# Bad variable names which should always be refused, separated by a comma -bad-names=foo, - bar, - baz, - toto, - tutu, - tata - -# Naming style matching correct class attribute names -class-attribute-naming-style=any - -# Regular expression matching correct class attribute names. Overrides class- -# attribute-naming-style -#class-attribute-rgx= - -# Naming style matching correct class names -class-naming-style=PascalCase - -# Regular expression matching correct class names. Overrides class-naming-style -#class-rgx= - -# Naming style matching correct constant names -const-naming-style=UPPER_CASE - -# Regular expression matching correct constant names. Overrides const-naming- -# style -#const-rgx= - -# Minimum line length for functions/classes that require docstrings, shorter -# ones are exempt. -docstring-min-length=-1 - -# Naming style matching correct function names -function-naming-style=snake_case - -# Regular expression matching correct function names. Overrides function- -# naming-style -#function-rgx= - -# Good variable names which should always be accepted, separated by a comma -good-names=b, - e, - i, - j, - k, - n, - m, - t, - q, - x, - y, - z, - ax, - bw, - df, - dx, - ex, - gs, - ic, - mu, - ok, - sd, - se, - tr, - eta, - Run, - _log, - _ - - -# Include a hint for the correct naming format with invalid-name -include-naming-hint=no - -# Naming style matching correct inline iteration names -inlinevar-naming-style=any - -# Regular expression matching correct inline iteration names. Overrides -# inlinevar-naming-style -#inlinevar-rgx= - -# Naming style matching correct method names -method-naming-style=snake_case - -# Regular expression matching correct method names. Overrides method-naming- -# style -#method-rgx= - -# Naming style matching correct module names -module-naming-style=snake_case - -# Regular expression matching correct module names. Overrides module-naming- -# style -#module-rgx= - -# Colon-delimited sets of names that determine each other's naming style when -# the name regexes allow several styles. -name-group= - -# Regular expression which should only match function or class names that do -# not require a docstring. -no-docstring-rgx=^_ - -# List of decorators that produce properties, such as abc.abstractproperty. Add -# to this list to register other decorators that produce valid properties. -property-classes=abc.abstractproperty - -# Naming style matching correct variable names -variable-naming-style=snake_case - -# Regular expression matching correct variable names. Overrides variable- -# naming-style -#variable-rgx= - - -[LOGGING] - -# Logging modules to check that the string format arguments are in logging -# function parameter format -logging-modules=logging - - -[SIMILARITIES] - -# Ignore comments when computing similarities. -ignore-comments=yes - -# Ignore docstrings when computing similarities. -ignore-docstrings=yes - -# Ignore imports when computing similarities. -ignore-imports=no - -# Minimum lines number of a similarity. -min-similarity-lines=50 - - -[TYPECHECK] - -# List of decorators that produce context managers, such as -# contextlib.contextmanager. Add to this list to register other decorators that -# produce valid context managers. -contextmanager-decorators=contextlib.contextmanager - -# List of members which are set dynamically and missed by pylint inference -# system, and so shouldn't trigger E1101 when accessed. Python regular -# expressions are accepted. -generated-members= - -# Tells whether missing members accessed in mixin class should be ignored. A -# mixin class is detected if its name ends with "mixin" (case insensitive). -ignore-mixin-members=yes - -# This flag controls whether pylint should warn about no-member and similar -# checks whenever an opaque object is returned when inferring. The inference -# can return multiple potential results while evaluating a Python object, but -# some branches might not be evaluated, which results in partial inference. In -# that case, it might be useful to still emit no-member and other checks for -# the rest of the inferred objects. -ignore-on-opaque-inference=yes - -# List of class names for which member attributes should not be checked (useful -# for classes with dynamically set attributes). This supports the use of -# qualified names. -ignored-classes=optparse.Values,thread._local,_thread._local,netCDF4 - -# List of module names for which member attributes should not be checked -# (useful for modules/projects where namespaces are manipulated during runtime -# and thus existing member attributes cannot be deduced by static analysis. It -# supports qualified module names, as well as Unix pattern matching. -ignored-modules= - -# Show a hint with possible names when a member name was not found. The aspect -# of finding the hint is based on edit distance. -missing-member-hint=yes - -# The minimum edit distance a name should have in order to be considered a -# similar match for a missing member name. -missing-member-hint-distance=1 - -# The total number of similar names that should be taken in consideration when -# showing a hint for a missing member. -missing-member-max-choices=1 - - -[MISCELLANEOUS] - -# List of note tags to take in consideration, separated by a comma. -notes=FIXME, - XXX, - TODO - - -[SPELLING] - -# Limits count of emitted suggestions for spelling mistakes -max-spelling-suggestions=4 - -# Spelling dictionary name. Available dictionaries: none. To make it working -# install python-enchant package. -spelling-dict= - -# List of comma separated words that should not be checked. -spelling-ignore-words= - -# A path to a file that contains private dictionary; one word per line. -spelling-private-dict-file= - -# Tells whether to store unknown words to indicated private dictionary in -# --spelling-private-dict-file option instead of raising a message. -spelling-store-unknown-words=no - - -[CLASSES] - -# List of method names used to declare (i.e. assign) instance attributes. -defining-attr-methods=__init__, - __new__, - setUp - -# List of member names, which should be excluded from the protected access -# warning. -exclude-protected=_asdict, - _fields, - _replace, - _source, - _make - -# List of valid names for the first argument in a class method. -valid-classmethod-first-arg=cls - -# List of valid names for the first argument in a metaclass class method. -valid-metaclass-classmethod-first-arg=mcs - - -[IMPORTS] - -# Allow wildcard imports from modules that define __all__. -allow-wildcard-with-all=no - -# Analyse import fallback blocks. This can be used to support both Python 2 and -# 3 compatible code, which means that the block might have code that exists -# only in one or another interpreter, leading to false positives when analysed. -analyse-fallback-blocks=no - -# Deprecated modules which should not be used, separated by a comma -deprecated-modules=optparse,tkinter.tix - -# Create a graph of external dependencies in the given file (report RP0402 must -# not be disabled) -ext-import-graph= - -# Create a graph of every (i.e. internal and external) dependencies in the -# given file (report RP0402 must not be disabled) -import-graph= - -# Create a graph of internal dependencies in the given file (report RP0402 must -# not be disabled) -int-import-graph= - -# Force import order to recognize a module as part of the standard -# compatibility libraries. -known-standard-library= - -# Force import order to recognize a module as part of a third party library. -known-third-party=enchant - - -[DESIGN] - -# Maximum number of arguments for function / method -max-args=10 - -# Maximum number of attributes for a class (see R0902). -max-attributes=10 - -# Maximum number of boolean expressions in a if statement -max-bool-expr=5 - -# Maximum number of branch for function / method body -max-branches=12 - -# Maximum number of locals for function / method body -max-locals=15 - -# Maximum number of parents for a class (see R0901). -max-parents=7 - -# Maximum number of public methods for a class (see R0904). -max-public-methods=20 - -# Maximum number of return / yield for function / method body -max-returns=6 - -# Maximum number of statements in function / method body -max-statements=50 - -# Minimum number of public methods for a class (see R0903). -min-public-methods=2 - - -[EXCEPTIONS] - -# Exceptions that will emit a warning when being caught. Defaults to -# "builtins.Exception" -overgeneral-exceptions=builtins.Exception diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 1a1c98a8d9..901d67bad5 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -3,16 +3,19 @@ version: 2 build: - os: ubuntu-22.04 + os: ubuntu-lts-latest tools: - python: "3.11" + python: "3.12" + jobs: + pre_build: + - python docs/scripts/homepage_miniatures.py sphinx: - configuration: doc/source/conf.py + configuration: docs/source/conf.py python: install: - - requirements: requirements.txt - - requirements: requirements-docs.txt - method: pip path: . + extra_requirements: + - doc diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 59b9fcac58..e9d0b72e42 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -4,7 +4,7 @@ ArviZ adopts the NumFOCUS Code of Conduct directly. In other words, we expect our community to treat others with kindness and understanding. -# THE SHORT VERSION +# THE SHORT VERSION Be kind to others. Do not insult or put down others. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate. @@ -30,7 +30,7 @@ emergency, we urge you to contact local law enforcement before making a report. (In the U.S., dial 911.)** We are committed to promptly addressing any reported issues. -If you have experienced or witnessed behavior that violates this +If you have experienced or witnessed behavior that violates this Code of Conduct, please complete the form below to make a report. @@ -46,5 +46,5 @@ https://www.numfocus.org/privacy-policy # Full Code of Conduct The full text of the NumFOCUS/ArviZ Code of Conduct can be found on -NumFOCUS's website +NumFOCUS's website https://numfocus.org/code-of-conduct diff --git a/LICENSE b/LICENSE index 8f71f43fee..8dada3edaf 100644 --- a/LICENSE +++ b/LICENSE @@ -199,4 +199,3 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. - diff --git a/MANIFEST.in b/MANIFEST.in index 7d83fc33a8..22e11150db 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -2,8 +2,3 @@ include *.md include *.txt include *.toml include LICENSE -include arviz/py.typed - -recursive-include arviz *.mplstyle -recursive-include arviz/static * -recursive-include arviz/data/example_data *.nc *.json diff --git a/Makefile b/Makefile deleted file mode 100644 index fc13da8064..0000000000 --- a/Makefile +++ /dev/null @@ -1,27 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -b html -SPHINXBUILD = sphinx-build -SPHINXPROJ = ArviZ -SOURCEDIR = doc/source -BUILDDIR = doc/build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -html: - sphinx-build "$(SOURCEDIR)" "$(BUILDDIR)" -b html - -livehtml: - sphinx-autobuild "$(SOURCEDIR)" "$(BUILDDIR)" -b html - -cleandocs: - rm -r "$(BUILDDIR)" "doc/jupyter_execute" "$(SOURCEDIR)/api/generated" "$(SOURCEDIR)/examples" - -preview: - python -m webbrowser "$(BUILDDIR)/index.html" diff --git a/arviz/__init__.py b/arviz/__init__.py deleted file mode 100644 index 569eeff00f..0000000000 --- a/arviz/__init__.py +++ /dev/null @@ -1,367 +0,0 @@ -# pylint: disable=wildcard-import,invalid-name,wrong-import-position -"""ArviZ is a library for exploratory analysis of Bayesian models.""" -__version__ = "0.23.0.dev0" - -import logging -import os -import warnings -import datetime -from pathlib import Path - -from matplotlib.colors import LinearSegmentedColormap -from matplotlib.pyplot import style -import matplotlib as mpl -from packaging import version - - -def _warn_once_per_day(): - warning_dir = Path.home() / "arviz_data" - warning_dir.mkdir(exist_ok=True) - - stamp_file = warning_dir / "daily_warning" - today = datetime.date.today() - - if stamp_file.exists(): - last_date = datetime.date.fromisoformat(stamp_file.read_text().strip()) - else: - last_date = None - - if last_date != today: - warnings.warn( - "\nArviZ is undergoing a major refactor to improve flexibility and extensibility " - "while maintaining a user-friendly interface." - "\nSome upcoming changes may be backward incompatible." - "\nFor details and migration guidance, visit: " - "https://python.arviz.org/en/latest/user_guide/migration_guide.html", - FutureWarning, - ) - - stamp_file.write_text(today.isoformat()) - - -_warn_once_per_day() - - -class Logger(logging.Logger): - """Override Logger to avoid repeated messages.""" - - def __init__(self, name, level=logging.NOTSET): - super().__init__(name=name, level=level) - self.cache = [] - - def _log( - self, level, msg, *args, **kwargs - ): # pylint: disable=signature-differs, arguments-differ - msg_hash = hash(msg) - if msg_hash in self.cache: - return - self.cache.append(msg_hash) - super()._log(level, msg, *args, **kwargs) - - -_log = Logger("arviz") - - -from .data import * -from .plots import * -from .plots.backends import * -from .stats import * -from .rcparams import rc_context, rcParams -from .utils import Numba, Dask, interactive_backend -from .wrappers import * -from . import preview - -# add ArviZ's styles to matplotlib's styles -_arviz_style_path = os.path.join(os.path.dirname(__file__), "plots", "styles") -if version.parse(mpl.__version__) >= version.parse("3.11.0.dev0"): - style.USER_LIBRARY_PATHS.append(_arviz_style_path) - style.reload_library() -else: - style.core.USER_LIBRARY_PATHS.append(_arviz_style_path) - style.core.reload_library() - - -if not logging.root.handlers: - _handler = logging.StreamHandler() - _formatter = logging.Formatter("%(name)s - %(levelname)s - %(message)s") - _handler.setFormatter(_formatter) - _log.setLevel(logging.INFO) - _log.addHandler(_handler) - - -# adds perceptually uniform grey scale from colorcet -_linear_grey_10_95_c0 = [ - [0.10767, 0.1077, 0.1077], - [0.11032, 0.11035, 0.11035], - [0.11295, 0.11298, 0.11297], - [0.11554, 0.11558, 0.11557], - [0.1182, 0.11824, 0.11823], - [0.12079, 0.12083, 0.12082], - [0.12344, 0.12348, 0.12347], - [0.12615, 0.12618, 0.12618], - [0.12879, 0.12882, 0.12881], - [0.13149, 0.13152, 0.13151], - [0.13418, 0.13421, 0.1342], - [0.13684, 0.13688, 0.13687], - [0.13951, 0.13955, 0.13954], - [0.14226, 0.1423, 0.14229], - [0.14499, 0.14503, 0.14502], - [0.1477, 0.14774, 0.14773], - [0.15042, 0.15046, 0.15045], - [0.15313, 0.15317, 0.15316], - [0.15591, 0.15595, 0.15594], - [0.15866, 0.1587, 0.15869], - [0.16142, 0.16147, 0.16145], - [0.16418, 0.16423, 0.16422], - [0.16695, 0.16699, 0.16698], - [0.16973, 0.16977, 0.16976], - [0.17248, 0.17253, 0.17252], - [0.17529, 0.17533, 0.17532], - [0.17811, 0.17815, 0.17814], - [0.18087, 0.18092, 0.1809], - [0.18369, 0.18374, 0.18372], - [0.18652, 0.18656, 0.18655], - [0.18934, 0.18939, 0.18938], - [0.19217, 0.19221, 0.1922], - [0.19502, 0.19506, 0.19505], - [0.19785, 0.1979, 0.19788], - [0.20068, 0.20073, 0.20072], - [0.20357, 0.20362, 0.20361], - [0.20645, 0.2065, 0.20649], - [0.20929, 0.20934, 0.20933], - [0.21219, 0.21224, 0.21222], - [0.21504, 0.21509, 0.21508], - [0.21795, 0.218, 0.21799], - [0.22086, 0.22091, 0.2209], - [0.22374, 0.22379, 0.22377], - [0.22666, 0.22671, 0.22669], - [0.22954, 0.2296, 0.22958], - [0.23248, 0.23253, 0.23252], - [0.23542, 0.23547, 0.23546], - [0.23832, 0.23838, 0.23836], - [0.24127, 0.24133, 0.24131], - [0.24419, 0.24425, 0.24424], - [0.24716, 0.24722, 0.2472], - [0.25009, 0.25015, 0.25014], - [0.25308, 0.25313, 0.25312], - [0.25603, 0.25608, 0.25607], - [0.25902, 0.25908, 0.25906], - [0.26198, 0.26204, 0.26203], - [0.26496, 0.26502, 0.265], - [0.26794, 0.268, 0.26798], - [0.27095, 0.27101, 0.271], - [0.27395, 0.27401, 0.274], - [0.27695, 0.27702, 0.277], - [0.27996, 0.28002, 0.28], - [0.28298, 0.28304, 0.28303], - [0.28598, 0.28604, 0.28603], - [0.28902, 0.28909, 0.28907], - [0.29205, 0.29212, 0.2921], - [0.29508, 0.29514, 0.29513], - [0.29812, 0.29818, 0.29817], - [0.30116, 0.30123, 0.30121], - [0.30422, 0.30429, 0.30427], - [0.30728, 0.30734, 0.30733], - [0.31036, 0.31043, 0.31041], - [0.31342, 0.31349, 0.31347], - [0.31649, 0.31656, 0.31654], - [0.31957, 0.31964, 0.31962], - [0.32266, 0.32273, 0.32271], - [0.32572, 0.3258, 0.32578], - [0.32883, 0.32891, 0.32889], - [0.33193, 0.332, 0.33198], - [0.33504, 0.33512, 0.3351], - [0.33813, 0.3382, 0.33818], - [0.34125, 0.34133, 0.34131], - [0.34436, 0.34444, 0.34442], - [0.3475, 0.34757, 0.34755], - [0.35063, 0.3507, 0.35068], - [0.35374, 0.35382, 0.3538], - [0.35689, 0.35697, 0.35695], - [0.36002, 0.3601, 0.36008], - [0.36317, 0.36325, 0.36323], - [0.36633, 0.36641, 0.36639], - [0.36948, 0.36956, 0.36954], - [0.37263, 0.37272, 0.3727], - [0.3758, 0.37589, 0.37587], - [0.37897, 0.37906, 0.37904], - [0.38214, 0.38223, 0.38221], - [0.38532, 0.3854, 0.38538], - [0.38852, 0.3886, 0.38858], - [0.3917, 0.39179, 0.39177], - [0.39489, 0.39498, 0.39496], - [0.3981, 0.39818, 0.39816], - [0.4013, 0.40138, 0.40136], - [0.40449, 0.40458, 0.40456], - [0.40771, 0.4078, 0.40778], - [0.41093, 0.41102, 0.411], - [0.41415, 0.41423, 0.41421], - [0.41738, 0.41747, 0.41744], - [0.4206, 0.42068, 0.42066], - [0.42383, 0.42392, 0.4239], - [0.42708, 0.42717, 0.42715], - [0.43031, 0.43041, 0.43038], - [0.43355, 0.43364, 0.43362], - [0.43681, 0.43691, 0.43688], - [0.44007, 0.44016, 0.44014], - [0.44333, 0.44342, 0.4434], - [0.44659, 0.44668, 0.44666], - [0.44986, 0.44995, 0.44993], - [0.45313, 0.45322, 0.4532], - [0.4564, 0.4565, 0.45647], - [0.45968, 0.45978, 0.45976], - [0.46296, 0.46306, 0.46303], - [0.46625, 0.46635, 0.46633], - [0.46956, 0.46966, 0.46963], - [0.47284, 0.47294, 0.47292], - [0.47615, 0.47625, 0.47623], - [0.47946, 0.47956, 0.47953], - [0.48276, 0.48286, 0.48284], - [0.48607, 0.48618, 0.48615], - [0.48939, 0.48949, 0.48947], - [0.49271, 0.49281, 0.49279], - [0.49603, 0.49614, 0.49611], - [0.49936, 0.49947, 0.49944], - [0.5027, 0.5028, 0.50278], - [0.50603, 0.50614, 0.50612], - [0.50938, 0.50949, 0.50946], - [0.51273, 0.51284, 0.51281], - [0.51607, 0.51618, 0.51615], - [0.51943, 0.51954, 0.51951], - [0.52279, 0.5229, 0.52287], - [0.52615, 0.52626, 0.52624], - [0.52952, 0.52963, 0.5296], - [0.53289, 0.533, 0.53297], - [0.53626, 0.53637, 0.53634], - [0.53963, 0.53974, 0.53971], - [0.54302, 0.54313, 0.5431], - [0.54641, 0.54652, 0.54649], - [0.54979, 0.5499, 0.54987], - [0.55317, 0.55329, 0.55326], - [0.55657, 0.55669, 0.55666], - [0.55998, 0.56009, 0.56007], - [0.56338, 0.5635, 0.56347], - [0.56679, 0.56691, 0.56688], - [0.57019, 0.57031, 0.57028], - [0.57361, 0.57373, 0.5737], - [0.57703, 0.57715, 0.57712], - [0.58045, 0.58057, 0.58054], - [0.58387, 0.58399, 0.58396], - [0.5873, 0.58742, 0.58739], - [0.59073, 0.59086, 0.59083], - [0.59417, 0.5943, 0.59427], - [0.59761, 0.59774, 0.59771], - [0.60106, 0.60118, 0.60115], - [0.60451, 0.60463, 0.6046], - [0.60796, 0.60809, 0.60806], - [0.61141, 0.61154, 0.61151], - [0.61486, 0.61499, 0.61496], - [0.61833, 0.61846, 0.61843], - [0.62179, 0.62192, 0.62189], - [0.62527, 0.62539, 0.62536], - [0.62874, 0.62887, 0.62884], - [0.63222, 0.63235, 0.63231], - [0.63569, 0.63583, 0.63579], - [0.63918, 0.63931, 0.63928], - [0.64267, 0.6428, 0.64276], - [0.64615, 0.64629, 0.64625], - [0.64965, 0.64978, 0.64975], - [0.65314, 0.65327, 0.65324], - [0.65665, 0.65678, 0.65675], - [0.66015, 0.66028, 0.66025], - [0.66366, 0.6638, 0.66376], - [0.66717, 0.6673, 0.66727], - [0.67069, 0.67082, 0.67079], - [0.6742, 0.67434, 0.6743], - [0.67772, 0.67786, 0.67783], - [0.68124, 0.68138, 0.68134], - [0.68477, 0.68491, 0.68488], - [0.68831, 0.68845, 0.68841], - [0.69184, 0.69198, 0.69195], - [0.69538, 0.69552, 0.69548], - [0.69891, 0.69906, 0.69902], - [0.70246, 0.7026, 0.70257], - [0.70601, 0.70616, 0.70612], - [0.70956, 0.70971, 0.70967], - [0.71311, 0.71326, 0.71322], - [0.71668, 0.71682, 0.71679], - [0.72023, 0.72038, 0.72034], - [0.7238, 0.72394, 0.72391], - [0.72736, 0.72751, 0.72748], - [0.73093, 0.73108, 0.73105], - [0.73451, 0.73466, 0.73463], - [0.73808, 0.73823, 0.7382], - [0.74167, 0.74182, 0.74178], - [0.74525, 0.7454, 0.74536], - [0.74884, 0.74899, 0.74895], - [0.75243, 0.75258, 0.75254], - [0.75602, 0.75618, 0.75614], - [0.75961, 0.75977, 0.75973], - [0.76322, 0.76337, 0.76333], - [0.76682, 0.76697, 0.76694], - [0.77043, 0.77058, 0.77055], - [0.77403, 0.77419, 0.77415], - [0.77765, 0.77781, 0.77777], - [0.78126, 0.78142, 0.78138], - [0.78489, 0.78504, 0.785], - [0.78851, 0.78867, 0.78863], - [0.79213, 0.79229, 0.79225], - [0.79576, 0.79592, 0.79588], - [0.79939, 0.79955, 0.79951], - [0.80302, 0.80318, 0.80314], - [0.80666, 0.80683, 0.80679], - [0.8103, 0.81046, 0.81042], - [0.81394, 0.81411, 0.81406], - [0.81759, 0.81775, 0.81771], - [0.82124, 0.82141, 0.82136], - [0.82489, 0.82506, 0.82501], - [0.82855, 0.82872, 0.82867], - [0.83221, 0.83237, 0.83233], - [0.83587, 0.83604, 0.83599], - [0.83953, 0.8397, 0.83966], - [0.8432, 0.84337, 0.84333], - [0.84687, 0.84704, 0.847], - [0.85054, 0.85071, 0.85067], - [0.85422, 0.85439, 0.85435], - [0.8579, 0.85807, 0.85803], - [0.86158, 0.86176, 0.86171], - [0.86527, 0.86544, 0.8654], - [0.86896, 0.86913, 0.86909], - [0.87265, 0.87282, 0.87278], - [0.87634, 0.87652, 0.87647], - [0.88004, 0.88022, 0.88017], - [0.88374, 0.88392, 0.88388], - [0.88744, 0.88762, 0.88758], - [0.89115, 0.89133, 0.89128], - [0.89486, 0.89504, 0.895], - [0.89857, 0.89875, 0.89871], - [0.90229, 0.90247, 0.90242], - [0.90601, 0.90619, 0.90614], - [0.90972, 0.90991, 0.90986], - [0.91345, 0.91363, 0.91359], - [0.91718, 0.91736, 0.91732], - [0.9209, 0.92108, 0.92104], - [0.92463, 0.92482, 0.92477], - [0.92837, 0.92855, 0.92851], - [0.9321, 0.93229, 0.93224], - [0.93585, 0.93604, 0.93599], - [0.93959, 0.93978, 0.93973], - [0.94334, 0.94353, 0.94348], -] - - -def _mpl_cm(name, colorlist): - cmap = LinearSegmentedColormap.from_list(name, colorlist, N=256) - if "cet_" + name not in mpl.colormaps(): - mpl.colormaps.register(cmap, name="cet_" + name) - - -try: - import colorcet -except ModuleNotFoundError: - _mpl_cm("gray", _linear_grey_10_95_c0) - _mpl_cm("gray_r", list(reversed(_linear_grey_10_95_c0))) - - -# clean namespace -del os, logging, LinearSegmentedColormap, Logger, mpl diff --git a/arviz/data/__init__.py b/arviz/data/__init__.py deleted file mode 100644 index aa5866dd76..0000000000 --- a/arviz/data/__init__.py +++ /dev/null @@ -1,55 +0,0 @@ -"""Code for loading and manipulating data structures.""" - -from .base import CoordSpec, DimSpec, dict_to_dataset, numpy_to_data_array, pytree_to_dataset -from .converters import convert_to_dataset, convert_to_inference_data -from .datasets import clear_data_home, list_datasets, load_arviz_data -from .inference_data import InferenceData, concat -from .io_beanmachine import from_beanmachine -from .io_cmdstan import from_cmdstan -from .io_cmdstanpy import from_cmdstanpy -from .io_datatree import from_datatree, to_datatree -from .io_dict import from_dict, from_pytree -from .io_emcee import from_emcee -from .io_json import from_json, to_json -from .io_netcdf import from_netcdf, to_netcdf -from .io_numpyro import from_numpyro -from .io_pyjags import from_pyjags -from .io_pyro import from_pyro -from .io_pystan import from_pystan -from .io_zarr import from_zarr, to_zarr -from .utils import extract, extract_dataset - -__all__ = [ - "InferenceData", - "concat", - "load_arviz_data", - "list_datasets", - "clear_data_home", - "numpy_to_data_array", - "extract", - "extract_dataset", - "dict_to_dataset", - "convert_to_dataset", - "convert_to_inference_data", - "from_beanmachine", - "from_pyjags", - "from_pystan", - "from_emcee", - "from_cmdstan", - "from_cmdstanpy", - "from_datatree", - "from_dict", - "from_pytree", - "from_json", - "from_pyro", - "from_numpyro", - "from_netcdf", - "pytree_to_dataset", - "to_datatree", - "to_json", - "to_netcdf", - "from_zarr", - "to_zarr", - "CoordSpec", - "DimSpec", -] diff --git a/arviz/data/base.py b/arviz/data/base.py deleted file mode 100644 index ef5850c062..0000000000 --- a/arviz/data/base.py +++ /dev/null @@ -1,596 +0,0 @@ -"""Low level converters usually used by other functions.""" - -import datetime -import functools -import importlib -import re -import warnings -from copy import deepcopy -from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar, Union - -import numpy as np -import xarray as xr - -try: - import tree -except ImportError: - tree = None - -try: - import ujson as json -except ImportError: - # mypy struggles with conditional imports expressed as catching ImportError: - # https://github.com/python/mypy/issues/1153 - import json # type: ignore - -from .. import __version__, utils -from ..rcparams import rcParams - -CoordSpec = Dict[str, List[Any]] -DimSpec = Dict[str, List[str]] -RequiresArgTypeT = TypeVar("RequiresArgTypeT") -RequiresReturnTypeT = TypeVar("RequiresReturnTypeT") - - -class requires: # pylint: disable=invalid-name - """Decorator to return None if an object does not have the required attribute. - - If the decorator is called various times on the same function with different - attributes, it will return None if one of them is missing. If instead a list - of attributes is passed, it will return None if all attributes in the list are - missing. Both functionalities can be combined as desired. - """ - - def __init__(self, *props: Union[str, List[str]]) -> None: - self.props: Tuple[Union[str, List[str]], ...] = props - - # Until typing.ParamSpec (https://www.python.org/dev/peps/pep-0612/) is available - # in all our supported Python versions, there is no way to simultaneously express - # the following two properties: - # - the input function may take arbitrary args/kwargs, and - # - the output function takes those same arbitrary args/kwargs, but has a different return type. - # We either have to limit the input function to e.g. only allowing a "self" argument, - # or we have to adopt the current approach of annotating the returned function as if - # it was defined as "def f(*args: Any, **kwargs: Any) -> Optional[RequiresReturnTypeT]". - # - # Since all functions decorated with @requires currently only accept a single argument, - # we choose to limit application of @requires to only functions of one argument. - # When typing.ParamSpec is available, this definition can be updated to use it. - # See https://github.com/arviz-devs/arviz/pull/1504 for more discussion. - def __call__( - self, func: Callable[[RequiresArgTypeT], RequiresReturnTypeT] - ) -> Callable[[RequiresArgTypeT], Optional[RequiresReturnTypeT]]: # noqa: D202 - """Wrap the decorated function.""" - - def wrapped(cls: RequiresArgTypeT) -> Optional[RequiresReturnTypeT]: - """Return None if not all props are available.""" - for prop in self.props: - prop = [prop] if isinstance(prop, str) else prop - if all((getattr(cls, prop_i) is None for prop_i in prop)): - return None - return func(cls) - - return wrapped - - -def _yield_flat_up_to(shallow_tree, input_tree, path=()): - """Yields (path, value) pairs of input_tree flattened up to shallow_tree. - - Adapted from dm-tree (https://github.com/google-deepmind/tree) to allow - lists as leaves. - - Args: - shallow_tree: Nested structure. Traverse no further than its leaf nodes. - input_tree: Nested structure. Return the paths and values from this tree. - Must have the same upper structure as shallow_tree. - path: Tuple. Optional argument, only used when recursing. The path from the - root of the original shallow_tree, down to the root of the shallow_tree - arg of this recursive call. - - Yields: - Pairs of (path, value), where path the tuple path of a leaf node in - shallow_tree, and value is the value of the corresponding node in - input_tree. - """ - # pylint: disable=protected-access - if tree is None: - raise ImportError("Missing optional dependency 'dm-tree'. Use pip or conda to install it") - - if isinstance(shallow_tree, tree._TEXT_OR_BYTES) or not ( - isinstance(shallow_tree, tree.collections_abc.Mapping) - or tree._is_namedtuple(shallow_tree) - or tree._is_attrs(shallow_tree) - ): - yield (path, input_tree) - else: - input_tree = dict(tree._yield_sorted_items(input_tree)) - for shallow_key, shallow_subtree in tree._yield_sorted_items(shallow_tree): - subpath = path + (shallow_key,) - input_subtree = input_tree[shallow_key] - for leaf_path, leaf_value in _yield_flat_up_to( - shallow_subtree, input_subtree, path=subpath - ): - yield (leaf_path, leaf_value) - # pylint: enable=protected-access - - -def _flatten_with_path(structure): - return list(_yield_flat_up_to(structure, structure)) - - -def generate_dims_coords( - shape, - var_name, - dims=None, - coords=None, - default_dims=None, - index_origin=None, - skip_event_dims=None, -): - """Generate default dimensions and coordinates for a variable. - - Parameters - ---------- - shape : tuple[int] - Shape of the variable - var_name : str - Name of the variable. If no dimension name(s) is provided, ArviZ - will generate a default dimension name using ``var_name``, e.g., - ``"foo_dim_0"`` for the first dimension if ``var_name`` is ``"foo"``. - dims : list - List of dimensions for the variable - coords : dict[str] -> list[str] - Map of dimensions to coordinates - default_dims : list[str] - Dimension names that are not part of the variable's shape. For example, - when manipulating Monte Carlo traces, the ``default_dims`` would be - ``["chain" , "draw"]`` which ArviZ uses as its own names for dimensions - of MCMC traces. - index_origin : int, optional - Starting value of integer coordinate values. Defaults to the value in rcParam - ``data.index_origin``. - skip_event_dims : bool, default False - - Returns - ------- - list[str] - Default dims - dict[str] -> list[str] - Default coords - """ - if index_origin is None: - index_origin = rcParams["data.index_origin"] - if default_dims is None: - default_dims = [] - if dims is None: - dims = [] - if skip_event_dims is None: - skip_event_dims = False - - if coords is None: - coords = {} - - coords = deepcopy(coords) - dims = deepcopy(dims) - - ndims = len([dim for dim in dims if dim not in default_dims]) - if ndims > len(shape): - if skip_event_dims: - dims = dims[: len(shape)] - else: - warnings.warn( - ( - "In variable {var_name}, there are " - + "more dims ({dims_len}) given than exist ({shape_len}). " - + "Passed array should have shape ({defaults}*shape)" - ).format( - var_name=var_name, - dims_len=len(dims), - shape_len=len(shape), - defaults=",".join(default_dims) + ", " if default_dims is not None else "", - ), - UserWarning, - ) - if skip_event_dims: - # this is needed in case the reduction keeps the dimension with size 1 - for i, (dim, dim_size) in enumerate(zip(dims, shape)): - if (dim in coords) and (dim_size != len(coords[dim])): - dims = dims[:i] - break - - for i, dim_len in enumerate(shape): - idx = i + len([dim for dim in default_dims if dim in dims]) - if len(dims) < idx + 1: - dim_name = f"{var_name}_dim_{i}" - dims.append(dim_name) - elif dims[idx] is None: - dim_name = f"{var_name}_dim_{i}" - dims[idx] = dim_name - dim_name = dims[idx] - if dim_name not in coords: - coords[dim_name] = np.arange(index_origin, dim_len + index_origin) - coords = { - key: coord - for key, coord in coords.items() - if any(key == dim for dim in dims + default_dims) - } - return dims, coords - - -def numpy_to_data_array( - ary, - *, - var_name="data", - coords=None, - dims=None, - default_dims=None, - index_origin=None, - skip_event_dims=None, -): - """Convert a numpy array to an xarray.DataArray. - - By default, the first two dimensions will be (chain, draw), and any remaining - dimensions will be "shape". - * If the numpy array is 1d, this dimension is interpreted as draw - * If the numpy array is 2d, it is interpreted as (chain, draw) - * If the numpy array is 3 or more dimensions, the last dimensions are kept as shapes. - - To modify this behaviour, use ``default_dims``. - - Parameters - ---------- - ary : np.ndarray - A numpy array. If it has 2 or more dimensions, the first dimension should be - independent chains from a simulation. Use `np.expand_dims(ary, 0)` to add a - single dimension to the front if there is only 1 chain. - var_name : str - If there are no dims passed, this string is used to name dimensions - coords : dict[str, iterable] - A dictionary containing the values that are used as index. The key - is the name of the dimension, the values are the index values. - dims : List(str) - A list of coordinate names for the variable - default_dims : list of str, optional - Passed to :py:func:`generate_dims_coords`. Defaults to ``["chain", "draw"]``, and - an empty list is accepted - index_origin : int, optional - Passed to :py:func:`generate_dims_coords` - skip_event_dims : bool - - Returns - ------- - xr.DataArray - Will have the same data as passed, but with coordinates and dimensions - """ - # manage and transform copies - if default_dims is None: - default_dims = ["chain", "draw"] - if "chain" in default_dims and "draw" in default_dims: - ary = utils.two_de(ary) - n_chains, n_samples, *_ = ary.shape - if n_chains > n_samples: - warnings.warn( - "More chains ({n_chains}) than draws ({n_samples}). " - "Passed array should have shape (chains, draws, *shape)".format( - n_chains=n_chains, n_samples=n_samples - ), - UserWarning, - ) - else: - ary = utils.one_de(ary) - - dims, coords = generate_dims_coords( - ary.shape[len(default_dims) :], - var_name, - dims=dims, - coords=coords, - default_dims=default_dims, - index_origin=index_origin, - skip_event_dims=skip_event_dims, - ) - - # reversed order for default dims: 'chain', 'draw' - if "draw" not in dims and "draw" in default_dims: - dims = ["draw"] + dims - if "chain" not in dims and "chain" in default_dims: - dims = ["chain"] + dims - - index_origin = rcParams["data.index_origin"] - if "chain" not in coords and "chain" in default_dims: - coords["chain"] = np.arange(index_origin, n_chains + index_origin) - if "draw" not in coords and "draw" in default_dims: - coords["draw"] = np.arange(index_origin, n_samples + index_origin) - - # filter coords based on the dims - coords = {key: xr.IndexVariable((key,), data=np.asarray(coords[key])) for key in dims} - return xr.DataArray(ary, coords=coords, dims=dims) - - -def dict_to_dataset( - data, - *, - attrs=None, - library=None, - coords=None, - dims=None, - default_dims=None, - index_origin=None, - skip_event_dims=None, -): - """Convert a dictionary or pytree of numpy arrays to an xarray.Dataset. - - ArviZ itself supports conversion of flat dictionaries. - Suport for pytrees requires ``dm-tree`` which is an optional dependency. - See https://jax.readthedocs.io/en/latest/pytrees.html for what a pytree is, but - this inclues at least dictionaries and tuple types. - - Parameters - ---------- - data : dict of {str : array_like or dict} or pytree - Data to convert. Keys are variable names. - attrs : dict, optional - Json serializable metadata to attach to the dataset, in addition to defaults. - library : module, optional - Library used for performing inference. Will be attached to the attrs metadata. - coords : dict of {str : ndarray}, optional - Coordinates for the dataset - dims : dict of {str : list of str}, optional - Dimensions of each variable. The keys are variable names, values are lists of - coordinates. - default_dims : list of str, optional - Passed to :py:func:`numpy_to_data_array` - index_origin : int, optional - Passed to :py:func:`numpy_to_data_array` - skip_event_dims : bool, optional - If True, cut extra dims whenever present to match the shape of the data. - Necessary for PPLs which have the same name in both observed data and log - likelihood groups, to account for their different shapes when observations are - multivariate. - - Returns - ------- - xarray.Dataset - In case of nested pytrees, the variable name will be a tuple of individual names. - - Notes - ----- - This function is available through two aliases: ``dict_to_dataset`` or ``pytree_to_dataset``. - - Examples - -------- - Convert a dictionary with two 2D variables to a Dataset. - - .. ipython:: - - In [1]: import arviz as az - ...: import numpy as np - ...: az.dict_to_dataset({'x': np.random.randn(4, 100), 'y': np.random.rand(4, 100)}) - - Note that unlike the :class:`xarray.Dataset` constructor, ArviZ has added extra - information to the generated Dataset such as default dimension names for sampled - dimensions and some attributes. - - The function is also general enough to work on pytrees such as nested dictionaries: - - .. ipython:: - - In [1]: az.pytree_to_dataset({'top': {'second': 1.}, 'top2': 1.}) - - which has two variables (as many as leafs) named ``('top', 'second')`` and ``top2``. - - Dimensions and co-ordinates can be defined as usual: - - .. ipython:: - - In [1]: datadict = { - ...: "top": {"a": np.random.randn(100), "b": np.random.randn(1, 100, 10)}, - ...: "d": np.random.randn(100), - ...: } - ...: az.dict_to_dataset( - ...: datadict, - ...: coords={"c": np.arange(10)}, - ...: dims={("top", "b"): ["c"]} - ...: ) - - """ - if dims is None: - dims = {} - - if tree is not None: - try: - data = {k[0] if len(k) == 1 else k: v for k, v in _flatten_with_path(data)} - except TypeError: # probably unsortable keys -- the function will still work if - pass # it is an honest dictionary. - - data_vars = { - key: numpy_to_data_array( - values, - var_name=key, - coords=coords, - dims=dims.get(key), - default_dims=default_dims, - index_origin=index_origin, - skip_event_dims=skip_event_dims, - ) - for key, values in data.items() - } - return xr.Dataset(data_vars=data_vars, attrs=make_attrs(attrs=attrs, library=library)) - - -pytree_to_dataset = dict_to_dataset - - -def make_attrs(attrs=None, library=None): - """Make standard attributes to attach to xarray datasets. - - Parameters - ---------- - attrs : dict (optional) - Additional attributes to add or overwrite - - Returns - ------- - dict - attrs - """ - default_attrs = { - "created_at": datetime.datetime.now(datetime.timezone.utc).isoformat(), - "arviz_version": __version__, - } - if library is not None: - library_name = library.__name__ - default_attrs["inference_library"] = library_name - try: - version = importlib.metadata.version(library_name) - default_attrs["inference_library_version"] = version - except importlib.metadata.PackageNotFoundError: - if hasattr(library, "__version__"): - version = library.__version__ - default_attrs["inference_library_version"] = version - if attrs is not None: - default_attrs.update(attrs) - return default_attrs - - -def _extend_xr_method(func, doc=None, description="", examples="", see_also=""): - """Make wrapper to extend methods from xr.Dataset to InferenceData Class. - - Parameters - ---------- - func : callable - An xr.Dataset function - doc : str - docstring for the func - description : str - the description of the func to be added in docstring - examples : str - the examples of the func to be added in docstring - see_also : str, list - the similar methods of func to be included in See Also section of docstring - - """ - # pydocstyle requires a non empty line - - @functools.wraps(func) - def wrapped(self, *args, **kwargs): - _filter = kwargs.pop("filter_groups", None) - _groups = kwargs.pop("groups", None) - _inplace = kwargs.pop("inplace", False) - - out = self if _inplace else deepcopy(self) - - groups = self._group_names(_groups, _filter) # pylint: disable=protected-access - for group in groups: - xr_data = getattr(out, group) - xr_data = func(xr_data, *args, **kwargs) # pylint: disable=not-callable - setattr(out, group, xr_data) - - return None if _inplace else out - - description_default = """{method_name} method is extended from xarray.Dataset methods. - - {description} - - For more info see :meth:`xarray:xarray.Dataset.{method_name}`. - In addition to the arguments available in the original method, the following - ones are added by ArviZ to adapt the method to being called on an ``InferenceData`` object. - """.format( - description=description, method_name=func.__name__ # pylint: disable=no-member - ) - params = """ - Other Parameters - ---------------- - groups: str or list of str, optional - Groups where the selection is to be applied. Can either be group names - or metagroup names. - filter_groups: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret groups as the real group or metagroup names. - If "like", interpret groups as substrings of the real group or metagroup names. - If "regex", interpret groups as regular expressions on the real group or - metagroup names. A la `pandas.filter`. - inplace: bool, optional - If ``True``, modify the InferenceData object inplace, - otherwise, return the modified copy. - """ - - if not isinstance(see_also, str): - see_also = "\n".join(see_also) - see_also_basic = """ - See Also - -------- - xarray.Dataset.{method_name} - {custom_see_also} - """.format( - method_name=func.__name__, custom_see_also=see_also # pylint: disable=no-member - ) - wrapped.__doc__ = ( - description_default + params + examples + see_also_basic if doc is None else doc - ) - - return wrapped - - -def _make_json_serializable(data: dict) -> dict: - """Convert `data` with numpy.ndarray-like values to JSON-serializable form.""" - ret = {} - for key, value in data.items(): - try: - json.dumps(value) - except (TypeError, OverflowError): - pass - else: - ret[key] = value - continue - if isinstance(value, dict): - ret[key] = _make_json_serializable(value) - elif isinstance(value, np.ndarray): - ret[key] = np.asarray(value).tolist() - else: - raise TypeError( - f"Value associated with variable `{type(value)}` is not JSON serializable." - ) - return ret - - -def infer_stan_dtypes(stan_code): - """Infer Stan integer variables from generated quantities block.""" - # Remove old deprecated comments - stan_code = "\n".join( - line if "#" not in line else line[: line.find("#")] for line in stan_code.splitlines() - ) - pattern_remove_comments = re.compile( - r'//.*?$|/\*.*?\*/|\'(?:\\.|[^\\\'])*\'|"(?:\\.|[^\\"])*"', re.DOTALL | re.MULTILINE - ) - stan_code = re.sub(pattern_remove_comments, "", stan_code) - - # Check generated quantities - if "generated quantities" not in stan_code: - return {} - - # Extract generated quantities block - gen_quantities_location = stan_code.index("generated quantities") - block_start = gen_quantities_location + stan_code[gen_quantities_location:].index("{") - - curly_bracket_count = 0 - block_end = None - for block_end, char in enumerate(stan_code[block_start:], block_start + 1): - if char == "{": - curly_bracket_count += 1 - elif char == "}": - curly_bracket_count -= 1 - - if curly_bracket_count == 0: - break - - stan_code = stan_code[block_start:block_end] - - stan_integer = r"int" - stan_limits = r"(?:\<[^\>]+\>)*" # ignore group: 0 or more <....> - stan_param = r"([^;=\s\[]+)" # capture group: ends= ";", "=", "[" or whitespace - stan_ws = r"\s*" # 0 or more whitespace - stan_ws_one = r"\s+" # 1 or more whitespace - pattern_int = re.compile( - "".join((stan_integer, stan_ws_one, stan_limits, stan_ws, stan_param)), re.IGNORECASE - ) - dtypes = {key.strip(): "int" for key in re.findall(pattern_int, stan_code)} - return dtypes diff --git a/arviz/data/converters.py b/arviz/data/converters.py deleted file mode 100644 index cb6e61128b..0000000000 --- a/arviz/data/converters.py +++ /dev/null @@ -1,203 +0,0 @@ -"""High level conversion functions.""" - -import numpy as np -import xarray as xr -import pandas as pd - -try: - from tree import is_nested -except ImportError: - is_nested = lambda obj: False - -from .base import dict_to_dataset -from .inference_data import InferenceData -from .io_beanmachine import from_beanmachine -from .io_cmdstan import from_cmdstan -from .io_cmdstanpy import from_cmdstanpy -from .io_emcee import from_emcee -from .io_numpyro import from_numpyro -from .io_pyro import from_pyro -from .io_pystan import from_pystan - - -# pylint: disable=too-many-return-statements -def convert_to_inference_data(obj, *, group="posterior", coords=None, dims=None, **kwargs): - r"""Convert a supported object to an InferenceData object. - - This function sends `obj` to the right conversion function. It is idempotent, - in that it will return arviz.InferenceData objects unchanged. - - Parameters - ---------- - obj : dict, str, np.ndarray, xr.Dataset, pystan fit - A supported object to convert to InferenceData: - | InferenceData: returns unchanged - | str: Attempts to load the cmdstan csv or netcdf dataset from disk - | pystan fit: Automatically extracts data - | cmdstanpy fit: Automatically extracts data - | cmdstan csv-list: Automatically extracts data - | emcee sampler: Automatically extracts data - | pyro MCMC: Automatically extracts data - | beanmachine MonteCarloSamples: Automatically extracts data - | xarray.Dataset: adds to InferenceData as only group - | xarray.DataArray: creates an xarray dataset as the only group, gives the - array an arbitrary name, if name not set - | dict: creates an xarray dataset as the only group - | numpy array: creates an xarray dataset as the only group, gives the - array an arbitrary name - | object with __array__: converts to numpy array, then creates an xarray dataset as - the only group, gives the array an arbitrary name - group : str - If `obj` is a dict or numpy array, assigns the resulting xarray - dataset to this group. Default: "posterior". - coords : dict[str, iterable] - A dictionary containing the values that are used as index. The key - is the name of the dimension, the values are the index values. - dims : dict[str, List(str)] - A mapping from variables to a list of coordinate names for the variable - kwargs - Rest of the supported keyword arguments transferred to conversion function. - - Returns - ------- - InferenceData - """ - kwargs[group] = obj - kwargs["coords"] = coords - kwargs["dims"] = dims - - # Cases that convert to InferenceData - if isinstance(obj, InferenceData): - if coords is not None or dims is not None: - raise TypeError("Cannot use coords or dims arguments with InferenceData value.") - return obj - elif isinstance(obj, str): - if obj.endswith(".csv"): - if group == "sample_stats": - kwargs["posterior"] = kwargs.pop(group) - elif group == "sample_stats_prior": - kwargs["prior"] = kwargs.pop(group) - return from_cmdstan(**kwargs) - else: - if coords is not None or dims is not None: - raise TypeError( - "Cannot use coords or dims arguments reading InferenceData from netcdf." - ) - return InferenceData.from_netcdf(obj) - elif ( - obj.__class__.__name__ in {"StanFit4Model", "CmdStanMCMC"} - or obj.__class__.__module__ == "stan.fit" - ): - if group == "sample_stats": - kwargs["posterior"] = kwargs.pop(group) - elif group == "sample_stats_prior": - kwargs["prior"] = kwargs.pop(group) - if obj.__class__.__name__ == "CmdStanMCMC": - return from_cmdstanpy(**kwargs) - else: # pystan or pystan3 - return from_pystan(**kwargs) - elif obj.__class__.__name__ == "EnsembleSampler": # ugly, but doesn't make emcee a requirement - return from_emcee(sampler=kwargs.pop(group), **kwargs) - elif obj.__class__.__name__ == "MonteCarloSamples": - return from_beanmachine(sampler=kwargs.pop(group), **kwargs) - elif obj.__class__.__name__ == "MCMC" and obj.__class__.__module__.startswith("pyro"): - return from_pyro(posterior=kwargs.pop(group), **kwargs) - elif obj.__class__.__name__ == "MCMC" and obj.__class__.__module__.startswith("numpyro"): - return from_numpyro(posterior=kwargs.pop(group), **kwargs) - - # Cases that convert to xarray - if isinstance(obj, xr.Dataset): - dataset = obj - elif isinstance(obj, xr.DataArray): - if obj.name is None: - obj.name = "x" - dataset = obj.to_dataset() - elif isinstance(obj, dict): - dataset = dict_to_dataset(obj, coords=coords, dims=dims) - elif is_nested(obj) and not isinstance(obj, (list, tuple)): - dataset = dict_to_dataset(obj, coords=coords, dims=dims) - elif isinstance(obj, np.ndarray): - dataset = dict_to_dataset({"x": obj}, coords=coords, dims=dims) - elif ( - hasattr(obj, "__array__") - and callable(getattr(obj, "__array__")) - and (not isinstance(obj, pd.DataFrame)) - ): - obj = obj.__array__() - dataset = dict_to_dataset({"x": obj}, coords=coords, dims=dims) - elif isinstance(obj, (list, tuple)) and isinstance(obj[0], str) and obj[0].endswith(".csv"): - if group == "sample_stats": - kwargs["posterior"] = kwargs.pop(group) - elif group == "sample_stats_prior": - kwargs["prior"] = kwargs.pop(group) - return from_cmdstan(**kwargs) - else: - allowable_types = ( - "xarray dataarray", - "xarray dataset", - "dict", - "pytree (if 'dm-tree' is installed)", - "netcdf filename", - "numpy array", - "object with __array__", - "pystan fit", - "emcee fit", - "pyro mcmc fit", - "numpyro mcmc fit", - "cmdstan fit csv filename", - "cmdstanpy fit", - ) - raise ValueError( - f'Can only convert {", ".join(allowable_types)} to InferenceData, ' - f"not {obj.__class__.__name__}" - ) - - return InferenceData(**{group: dataset}) - - -def convert_to_dataset(obj, *, group="posterior", coords=None, dims=None): - """Convert a supported object to an xarray dataset. - - This function is idempotent, in that it will return xarray.Dataset functions - unchanged. Raises `ValueError` if the desired group can not be extracted. - - Note this goes through a DataInference object. See `convert_to_inference_data` - for more details. Raises ValueError if it can not work out the desired - conversion. - - Parameters - ---------- - obj : dict, str, np.ndarray, xr.Dataset, pystan fit - A supported object to convert to InferenceData: - - - InferenceData: returns unchanged - - str: Attempts to load the netcdf dataset from disk - - pystan fit: Automatically extracts data - - xarray.Dataset: adds to InferenceData as only group - - xarray.DataArray: creates an xarray dataset as the only group, gives the - array an arbitrary name, if name not set - - dict: creates an xarray dataset as the only group - - numpy array: creates an xarray dataset as the only group, gives the - array an arbitrary name - - group : str - If `obj` is a dict or numpy array, assigns the resulting xarray - dataset to this group. - coords : dict[str, iterable] - A dictionary containing the values that are used as index. The key - is the name of the dimension, the values are the index values. - dims : dict[str, List(str)] - A mapping from variables to a list of coordinate names for the variable - - Returns - ------- - xarray.Dataset - """ - inference_data = convert_to_inference_data(obj, group=group, coords=coords, dims=dims) - dataset = getattr(inference_data, group, None) - if dataset is None: - raise ValueError( - "Can not extract {group} from {obj}! See {filename} for other " - "conversion utilities.".format(group=group, obj=obj, filename=__file__) - ) - return dataset diff --git a/arviz/data/datasets.py b/arviz/data/datasets.py deleted file mode 100644 index 4e0272503f..0000000000 --- a/arviz/data/datasets.py +++ /dev/null @@ -1,161 +0,0 @@ -"""Base IO code for all datasets. Heavily influenced by scikit-learn's implementation.""" - -import hashlib -import itertools -import json -import os -import shutil -from collections import namedtuple -from urllib.request import urlretrieve - -from ..rcparams import rcParams -from .io_netcdf import from_netcdf - -LocalFileMetadata = namedtuple("LocalFileMetadata", ["name", "filename", "description"]) - -RemoteFileMetadata = namedtuple( - "RemoteFileMetadata", ["name", "filename", "url", "checksum", "description"] -) -_EXAMPLE_DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "example_data") -_LOCAL_DATA_DIR = os.path.join(_EXAMPLE_DATA_DIR, "data") - -with open(os.path.join(_EXAMPLE_DATA_DIR, "data_local.json"), "r", encoding="utf-8") as f: - LOCAL_DATASETS = { - entry["name"]: LocalFileMetadata( - name=entry["name"], - filename=os.path.join(_LOCAL_DATA_DIR, entry["filename"]), - description=entry["description"], - ) - for entry in json.load(f) - } - -with open(os.path.join(_EXAMPLE_DATA_DIR, "data_remote.json"), "r", encoding="utf-8") as f: - REMOTE_DATASETS = {entry["name"]: RemoteFileMetadata(**entry) for entry in json.load(f)} - - -def get_data_home(data_home=None): - """Return the path of the arviz data dir. - - This folder is used by some dataset loaders to avoid downloading the - data several times. - - By default the data dir is set to a folder named 'arviz_data' in the - user home folder. - - Alternatively, it can be set by the 'ARVIZ_DATA' environment - variable or programmatically by giving an explicit folder path. The '~' - symbol is expanded to the user home folder. - - If the folder does not already exist, it is automatically created. - - Parameters - ---------- - data_home : str | None - The path to arviz data dir. - """ - if data_home is None: - data_home = os.environ.get("ARVIZ_DATA", os.path.join("~", "arviz_data")) - data_home = os.path.expanduser(data_home) - if not os.path.exists(data_home): - os.makedirs(data_home) - return data_home - - -def clear_data_home(data_home=None): - """Delete all the content of the data home cache. - - Parameters - ---------- - data_home : str | None - The path to arviz data dir. - """ - data_home = get_data_home(data_home) - shutil.rmtree(data_home) - - -def _sha256(path): - """Calculate the sha256 hash of the file at path.""" - sha256hash = hashlib.sha256() - chunk_size = 8192 - with open(path, "rb") as buff: - while True: - buffer = buff.read(chunk_size) - if not buffer: - break - sha256hash.update(buffer) - return sha256hash.hexdigest() - - -def load_arviz_data(dataset=None, data_home=None, **kwargs): - """Load a local or remote pre-made dataset. - - Run with no parameters to get a list of all available models. - - The directory to save to can also be set with the environment - variable `ARVIZ_HOME`. The checksum of the dataset is checked against a - hardcoded value to watch for data corruption. - - Run `az.clear_data_home` to clear the data directory. - - Parameters - ---------- - dataset : str - Name of dataset to load. - data_home : str, optional - Where to save remote datasets - **kwargs : dict, optional - Keyword arguments passed to :func:`arviz.from_netcdf`. - - Returns - ------- - xarray.Dataset - - """ - if dataset in LOCAL_DATASETS: - resource = LOCAL_DATASETS[dataset] - return from_netcdf(resource.filename, **kwargs) - - elif dataset in REMOTE_DATASETS: - remote = REMOTE_DATASETS[dataset] - home_dir = get_data_home(data_home=data_home) - file_path = os.path.join(home_dir, remote.filename) - - if not os.path.exists(file_path): - http_type = rcParams["data.http_protocol"] - - # Replaces http type. Redundant if http_type is http, useful if http_type is https - url = remote.url.replace("http", http_type) - urlretrieve(url, file_path) - - checksum = _sha256(file_path) - if remote.checksum != checksum: - raise IOError( - f"{file_path} has an SHA256 checksum ({checksum}) differing from expected " - "({remote.checksum}), file may be corrupted. " - "Run `arviz.clear_data_home()` and try again, or please open an issue." - ) - return from_netcdf(file_path, **kwargs) - else: - if dataset is None: - return dict(itertools.chain(LOCAL_DATASETS.items(), REMOTE_DATASETS.items())) - else: - raise ValueError( - "Dataset {} not found! The following are available:\n{}".format( - dataset, list_datasets() - ) - ) - - -def list_datasets(): - """Get a string representation of all available datasets with descriptions.""" - lines = [] - for name, resource in itertools.chain(LOCAL_DATASETS.items(), REMOTE_DATASETS.items()): - if isinstance(resource, LocalFileMetadata): - location = f"local: {resource.filename}" - elif isinstance(resource, RemoteFileMetadata): - location = f"remote: {resource.url}" - else: - location = "unknown" - lines.append(f"{name}\n{'=' * len(name)}\n{resource.description}\n\n{location}") - - return f"\n\n{10 * '-'}\n\n".join(lines) diff --git a/arviz/data/example_data/.gitignore b/arviz/data/example_data/.gitignore deleted file mode 100644 index aae4008f7f..0000000000 --- a/arviz/data/example_data/.gitignore +++ /dev/null @@ -1,130 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints -.virtual_documents - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ diff --git a/arviz/data/example_data/LICENSE b/arviz/data/example_data/LICENSE deleted file mode 100644 index 261eeb9e9f..0000000000 --- a/arviz/data/example_data/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/arviz/data/example_data/README.md b/arviz/data/example_data/README.md deleted file mode 100644 index f2962333d1..0000000000 --- a/arviz/data/example_data/README.md +++ /dev/null @@ -1,8 +0,0 @@ -# ArviZ `InferenceData` examples - -This repository contains metadata of ArviZ InferenceData examples and the code used to generate some of the examples. -Example models stored remotely are listed in `data_remote.json`. -Example models stored locally are listed in `data_local.json`, and the data themselves are stored in `data/`. -Inclusion of the code serves both as extra information on the models in the JSON files and to ease updating these examples whenever necessary. - -When applicable, code for a model should be added as an executable file (e.g. `.py` or `.ipynb`) to a directory in `code/` of the same name as the model. diff --git a/arviz/data/example_data/code/centered_eight/centered_eight.ipynb b/arviz/data/example_data/code/centered_eight/centered_eight.ipynb deleted file mode 100644 index a6308fd10d..0000000000 --- a/arviz/data/example_data/code/centered_eight/centered_eight.ipynb +++ /dev/null @@ -1,3948 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# centered_eight example" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pathlib\n", - "import arviz as az\n", - "import pymc as pm\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "draws = 500\n", - "chains = 4\n", - "\n", - "J = 8\n", - "scores = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])\n", - "sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])\n", - "schools = np.array(\n", - " [\n", - " \"Choate\",\n", - " \"Deerfield\",\n", - " \"Phillips Andover\",\n", - " \"Phillips Exeter\",\n", - " \"Hotchkiss\",\n", - " \"Lawrenceville\",\n", - " \"St. Paul's\",\n", - " \"Mt. Hermon\",\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [mu, obs, tau, theta]\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [mu, tau, theta]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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arviz.InferenceData
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      <xarray.Dataset>\n",
-       "Dimensions:  (chain: 4, draw: 500, school: 8)\n",
-       "Coordinates:\n",
-       "  * chain    (chain) int64 0 1 2 3\n",
-       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
-       "  * school   (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
-       "Data variables:\n",
-       "    mu       (chain, draw) float64 7.872 3.385 9.1 7.304 ... 1.767 3.486 3.404\n",
-       "    theta    (chain, draw, school) float64 12.32 9.905 14.95 ... 6.762 1.295\n",
-       "    tau      (chain, draw) float64 4.726 3.909 4.844 1.857 ... 2.741 2.932 4.461\n",
-       "Attributes:\n",
-       "    created_at:                 2022-10-13T14:37:37.315398\n",
-       "    arviz_version:              0.13.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  4.2.2\n",
-       "    sampling_time:              7.480114936828613\n",
-       "    tuning_steps:               1000

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-       "Dimensions:  (chain: 4, draw: 500, school: 8)\n",
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-       "  * chain    (chain) int64 0 1 2 3\n",
-       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
-       "  * school   (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
-       "Data variables:\n",
-       "    obs      (chain, draw, school) float64 15.9 -1.73 27.99 ... 7.052 6.946\n",
-       "Attributes:\n",
-       "    created_at:                 2022-10-13T14:37:41.460544\n",
-       "    arviz_version:              0.13.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  4.2.2

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-       "Dimensions:  (chain: 4, draw: 500, school: 8)\n",
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-       "  * chain    (chain) int64 0 1 2 3\n",
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-       "  * school   (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
-       "Data variables:\n",
-       "    obs      (chain, draw, school) float64 -4.173 -3.24 -4.321 ... -3.853 -3.986\n",
-       "Attributes:\n",
-       "    created_at:                 2022-10-13T14:37:37.487399\n",
-       "    arviz_version:              0.13.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  4.2.2

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      <xarray.Dataset>\n",
-       "Dimensions:              (chain: 4, draw: 500)\n",
-       "Coordinates:\n",
-       "  * chain                (chain) int64 0 1 2 3\n",
-       "  * draw                 (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499\n",
-       "Data variables: (12/16)\n",
-       "    max_energy_error     (chain, draw) float64 -0.645 0.1782 ... 0.7405 -0.5795\n",
-       "    energy_error         (chain, draw) float64 0.001383 0.1622 ... 0.07346\n",
-       "    lp                   (chain, draw) float64 -60.33 -57.8 ... -53.5 -55.58\n",
-       "    index_in_trajectory  (chain, draw) int64 -17 -12 -6 4 6 3 ... -6 5 -5 4 3 6\n",
-       "    acceptance_rate      (chain, draw) float64 0.9942 0.9635 ... 0.7359 0.8777\n",
-       "    diverging            (chain, draw) bool False False False ... False False\n",
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-       "    step_size_bar        (chain, draw) float64 0.2573 0.2573 ... 0.2685 0.2685\n",
-       "    step_size            (chain, draw) float64 0.4435 0.4435 ... 0.2938 0.2938\n",
-       "    energy               (chain, draw) float64 69.12 63.2 63.15 ... 61.2 60.12\n",
-       "    tree_depth           (chain, draw) int64 5 4 5 3 4 4 4 4 ... 4 4 4 4 3 3 3 3\n",
-       "    perf_counter_diff    (chain, draw) float64 0.005272 0.002766 ... 0.001373\n",
-       "Attributes:\n",
-       "    created_at:                 2022-10-13T14:37:37.324929\n",
-       "    arviz_version:              0.13.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  4.2.2\n",
-       "    sampling_time:              7.480114936828613\n",
-       "    tuning_steps:               1000

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-       "Dimensions:  (chain: 1, draw: 500, school: 8)\n",
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-       "  * chain    (chain) int64 0\n",
-       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
-       "  * school   (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
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-       "    tau      (chain, draw) float64 1.941 3.388 4.208 ... 0.8353 0.06893 2.145\n",
-       "    theta    (chain, draw, school) float64 4.866 4.59 -0.7404 ... -2.031 6.045\n",
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-       "Attributes:\n",
-       "    created_at:                 2022-10-13T14:37:26.602116\n",
-       "    arviz_version:              0.13.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  4.2.2

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-       "  * school   (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
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-       "    obs      (chain, draw, school) float64 12.39 -10.1 -12.09 ... 3.084 6.079\n",
-       "Attributes:\n",
-       "    created_at:                 2022-10-13T14:37:26.604969\n",
-       "    arviz_version:              0.13.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  4.2.2

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-       "  * school   (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
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-       "    obs      (school) float64 28.0 8.0 -3.0 7.0 -1.0 1.0 18.0 12.0\n",
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-       "    created_at:                 2022-10-13T14:37:26.606375\n",
-       "    arviz_version:              0.13.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  4.2.2

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-       "    arviz_version:              0.13.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  4.2.2

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"idata" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PosixPath('../../data/non_centered_eight.nc')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Storing the model to .nc format\n", - "idata.to_netcdf(pathlib.Path(\"..\", \"..\", \"data\", \"non_centered_eight.nc\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.10" - }, - "vscode": { - "interpreter": { - "hash": "a3357ae232635c60236cc6fd817e8b6b9d1594a7c105bd2f3bd11f1b30a66c24" - 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60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,61,61,61,61,61,62,62,62,63, - 63,63,63,63,63,63,63,63,63,63,64,64,65,65,65,65,65,65,65,65,65,65,65,65,65, - 65,66,66,66,66,66,66,66,66,66,66,66,66,66,67,67,67,67,67,67,67,67,68,68,68, - 68,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69, - 69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69, - 69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69, - 69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69, - 69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,70,70,70,70,70,70,70,70, - 70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,71,71,71,71,71,71,71,71, - 71,71,72,72,73,73,73,73,74,74,74,75,75,75,75,76,76,76,76,76,76,76,77,77,77, - 77,77,78,78,78,78,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79, - 79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79, - 79,79,80,80,80,81,82,82,82,82,82,82,82,82,82,82,82,82,82,83,83,83,83,83,83, - 83,83,83,83,83,83,83,84,84], - "county_name" : ["AITKIN","ANOKA","BECKER","BELTRAMI","BENTON","BIG STONE", - "BLUE EARTH","BROWN","CARLTON","CARVER","CASS","CHIPPEWA","CHISAGO","CLAY", - "CLEARWATER","COOK","COTTONWOOD","CROW WING","DAKOTA","DODGE","DOUGLAS", - "FARIBAULT","FILLMORE","FREEBORN","GOODHUE","HENNEPIN","HOUSTON","HUBBARD", - "ISANTI","ITASCA","JACKSON","KANABEC","KANDIYOHI","KITTSON","KOOCHICHING", - "LAC QUI PARLE","LAKE","LAKE OF THE WOODS","LE SUEUR","LINCOLN","LYON", - "MAHNOMEN","MARSHALL","MARTIN","MCLEOD","MEEKER","MILLE LACS","MORRISON", - "MOWER","MURRAY","NICOLLET","NOBLES","NORMAN","OLMSTED","OTTER TAIL", - "PENNINGTON","PINE","PIPESTONE","POLK","POPE","RAMSEY","REDWOOD","RENVILLE", - "RICE","ROCK","ROSEAU","SCOTT","SHERBURNE","SIBLEY","ST LOUIS","STEARNS", - "STEELE","STEVENS","SWIFT","TODD","TRAVERSE","WABASHA","WADENA","WASECA", - "WASHINGTON","WATONWAN","WILKIN","WINONA","WRIGHT","YELLOW MEDICINE"], - "u" : [-0.6890475953545,-0.847312860499705,-0.11345877376814,-0.593352525607471, - -0.142890481153028,0.387056708424809,0.271613664272716,0.277578704889093, - -0.332315487884133,0.0958645715553609,-0.608219806930943,0.273684562807927, - -0.735320087151157,0.343781175420031,-0.0598604138977053,-0.504995982525783, - 0.339560320720446,-0.633390700175141,-0.0241451624685385,0.263855459775217, - 0.155712316979839,0.295025046707039,0.414913662927776,0.224206985667406, - 0.196610646424232,-0.09652081232424,0.503529068533353,-0.400596976711304, - -0.751872232701787,-0.663347630601429,0.309020284839334,-0.0533860089036706, - 0.10973294265158,-0.00780336722984165,-0.881828920506582,0.311029878756367, - -0.691596383667291,-0.681708848419116,0.194447736591795,0.444903746232023, - 0.39473440617801,0.14960034272844,0.0137648285757133,0.16586183575527, - 0.140422593662249,0.0239508741557865,-0.210059521766286,-0.0932266518569275, - 0.260932470709068,0.398849942735755,0.248046873411091,0.405451774685941, - 0.265221716521598,0.24315007938984,-0.20473036924229,-0.0740276676859043, - -0.163292169547294,0.478604039464906,0.266111082523638,0.281148273600784, - -0.418053510614464,0.366322258590098,0.380577976600215,0.193146092877103, - 0.528024865192056,-0.212045364648176,0.0631156343140753,-0.683436482376788, - 0.23721212323308,-0.474673717150503,0.116395406677788,0.269805738660408, - 0.470778329088307,0.316028975591628,-0.0468400668458182,0.497594476916848, - 0.150082415651304,-0.672029731746026,0.212414197101124,-0.147484283042756, - 0.183237803578308,0.236036084296147,0.463211867357523,-0.0900242748478867, - 0.355286981163884] -} diff --git a/arviz/data/example_data/code/radon/radon.py b/arviz/data/example_data/code/radon/radon.py deleted file mode 100644 index 29704519a2..0000000000 --- a/arviz/data/example_data/code/radon/radon.py +++ /dev/null @@ -1,49 +0,0 @@ -import arviz as az -import pymc3 as pm -import numpy as np -import ujson as json - -with open("radon.json", "rb") as f: - radon_data = json.load(f) - -radon_data = { - key: np.array(value) if isinstance(value, list) else value - for key, value in radon_data.items() -} - -coords = { - "Level": ["Basement", "Floor"], - "obs_id": np.arange(radon_data["y"].size), - "County": radon_data["county_name"], - "g_coef": ["intercept", "slope"], -} - -with pm.Model(coords=coords): - floor_idx = pm.Data("floor_idx", radon_data["x"], dims="obs_id") - county_idx = pm.Data("county_idx", radon_data["county"], dims="obs_id") - uranium = pm.Data("uranium", radon_data["u"], dims="County") - - # Hyperpriors: - g = pm.Normal("g", mu=0.0, sigma=10.0, dims="g_coef") - sigma_a = pm.Exponential("sigma_a", 1.0) - - # Varying intercepts uranium model: - a = pm.Deterministic("a", g[0] + g[1] * uranium, dims="County") - za_county = pm.Normal("za_county", mu=0.0, sigma=1.0, dims="County") - a_county = pm.Deterministic("a_county", a + za_county * sigma_a, dims="County") - # Common slope: - b = pm.Normal("b", mu=0.0, sigma=1.0) - - # Expected value per county: - theta = a_county[county_idx] + b * floor_idx - # Model error: - sigma = pm.Exponential("sigma", 1.0) - - y = pm.Normal("y", theta, sigma=sigma, observed=radon_data["y"], dims="obs_id") - - prior = pm.sample_prior_predictive(500) - trace = pm.sample( - 1000, tune=500, target_accept=0.99, random_seed=117 - ) - post_pred = pm.sample_posterior_predictive(trace) - idata = az.from_pymc3(trace, prior=prior, posterior_predictive=post_pred) diff --git a/arviz/data/example_data/code/rugby/rugby.csv b/arviz/data/example_data/code/rugby/rugby.csv deleted file mode 100644 index 0d2470de48..0000000000 --- a/arviz/data/example_data/code/rugby/rugby.csv +++ /dev/null @@ -1,61 +0,0 @@ -home_team,away_team,home_score,away_score,i_home,i_away -Wales,Italy,23,15,0,4 -France,England,26,24,1,5 -Ireland,Scotland,28,6,2,3 -Ireland,Wales,26,3,2,0 -Scotland,England,0,20,3,5 -France,Italy,30,10,1,4 -Wales,France,27,6,0,1 -Italy,Scotland,20,21,4,3 -England,Ireland,13,10,5,2 -Ireland,Italy,46,7,2,4 -Scotland,France,17,19,3,1 -England,Wales,29,18,5,0 -Italy,England,11,52,4,5 -Wales,Scotland,51,3,0,3 -France,Ireland,20,22,1,2 -Wales,England,16,21,0,5 -Italy,Ireland,3,26,4,2 -France,Scotland,15,8,1,3 -England,Italy,47,17,5,4 -Ireland,France,18,11,2,1 -Scotland,Wales,23,26,3,0 -Scotland,Italy,19,22,3,4 -France,Wales,13,20,1,0 -Ireland,England,19,9,2,5 -Wales,Ireland,23,16,0,2 -England,Scotland,25,13,5,3 -Italy,France,0,29,4,1 -Italy,Wales,20,61,4,0 -Scotland,Ireland,10,40,3,2 -England,France,55,35,5,1 -France,Italy,23,21,1,4 -Scotland,England,9,15,3,5 -Ireland,Wales,16,16,2,0 -France,Ireland,10,9,1,2 -Wales,Scotland,27,23,0,3 -Italy,England,9,40,4,5 -Wales,France,19,10,0,1 -Italy,Scotland,20,36,4,3 -England,Ireland,21,10,5,2 -Ireland,Italy,58,15,2,4 -England,Wales,25,21,5,0 -Scotland,France,29,18,3,1 -Wales,Italy,67,14,0,4 -Ireland,Scotland,35,25,2,3 -France,England,21,31,1,5 -Scotland,Ireland,27,22,3,2 -England,France,19,16,5,1 -Italy,Wales,7,33,4,0 -Italy,Ireland,10,63,4,2 -Wales,England,16,21,0,5 -France,Scotland,22,16,1,3 -Scotland,Wales,29,13,3,0 -Ireland,France,19,9,2,1 -England,Italy,36,15,5,4 -Wales,Ireland,22,9,0,2 -Italy,France,18,40,4,1 -England,Scotland,61,21,5,3 -Scotland,Italy,29,0,3,4 -France,Wales,20,18,1,0 -Ireland,England,13,9,2,5 diff --git a/arviz/data/example_data/code/rugby/rugby.ipynb b/arviz/data/example_data/code/rugby/rugby.ipynb deleted file mode 100644 index 0a20449706..0000000000 --- a/arviz/data/example_data/code/rugby/rugby.ipynb +++ /dev/null @@ -1,5262 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Loading libraries\n", - "import arviz as az\n", - "import pymc as pm\n", - "import pytensor.tensor as pt\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Reading the data\n", - "df = pd.read_csv('rugby.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "observed_home_goals = df.home_score.values\n", - "observed_away_goals = df.away_score.values\n", - "\n", - "home_team = df.i_home.values\n", - "away_team = df.i_away.values\n", - "\n", - "teams = np.array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'])\n", - "matches = [f\"{home} {away}\" for home, away in zip(df.home_team, df.away_team)]\n", - "coords = {\"team\": teams, \"match\": matches}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# building the model\n", - "with pm.Model(coords=coords) as model:\n", - " # global model parameters\n", - " home = pm.Normal('home', mu=0, sigma=1)\n", - " sd_att = pm.HalfNormal('sd_att', sigma=2)\n", - " sd_def = pm.HalfNormal('sd_def', sigma=2)\n", - " intercept = pm.Normal('intercept', mu=3, sigma=1)\n", - " \n", - " # team-specific model parameters\n", - " atts_star = pm.Normal(\"atts_star\", mu=0, sigma=sd_att, dims=\"team\")\n", - " defs_star = pm.Normal(\"defs_star\", mu=0, sigma=sd_def, dims=\"team\")\n", - " \n", - " atts = pm.Deterministic('atts', atts_star - pt.mean(atts_star), dims=\"team\")\n", - " defs = pm.Deterministic('defs', defs_star - pt.mean(defs_star), dims=\"team\")\n", - " home_theta = pt.exp(intercept + home + atts[home_team] + defs[away_team])\n", - " away_theta = pt.exp(intercept + atts[away_team] + defs[home_team])\n", - " \n", - " # likelihood of observed data\n", - " pm.Poisson('home_points', mu=home_theta, observed=observed_home_goals, dims=\"match\")\n", - " pm.Poisson('away_points', mu=away_theta, observed=observed_away_goals, dims=\"match\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [atts_star, away_points, defs_star, home, home_points, intercept, sd_att, sd_def]\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [home, sd_att, sd_def, intercept, atts_star, defs_star]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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arviz.InferenceData
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      <xarray.Dataset>\n",
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-       "  * team       (team) <U8 'Wales' 'France' 'Ireland' ... 'Italy' 'England'\n",
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-       "    home       (chain, draw) float64 0.1341 0.2025 0.2146 ... 0.1988 0.1177\n",
-       "    intercept  (chain, draw) float64 2.949 2.907 2.888 ... 2.908 2.876 2.954\n",
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-       "    created_at:                 2024-03-06T20:46:23.841916\n",
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-       "Attributes:\n",
-       "    created_at:                 2024-03-06T20:46:09.480812\n",
-       "    arviz_version:              0.17.0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.10.4+7.g34d2a5d9

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      <xarray.Dataset>\n",
-       "Dimensions:    (chain: 4, draw: 500, team: 6)\n",
-       "Coordinates:\n",
-       "  * chain      (chain) int64 0 1 2 3\n",
-       "  * draw       (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499\n",
-       "  * team       (team) <U8 'Wales' 'France' 'Ireland' ... 'Italy' 'England'\n",
-       "Data variables:\n",
-       "    home       (chain, draw) float64 -0.9279 -0.9394 -0.942 ... -0.9387 -0.9259\n",
-       "    sd_att     (chain, draw) float64 -0.9305 -0.9221 -0.9238 ... -0.9391 -0.9299\n",
-       "    sd_def     (chain, draw) float64 -0.9601 -0.9487 -0.9321 ... -0.9333 -0.9349\n",
-       "    intercept  (chain, draw) float64 -0.9202 -0.9233 -0.9252 ... -0.9267 -0.92\n",
-       "    atts_star  (chain, draw, team) float64 -0.3334 0.2247 ... -0.2554 -0.5093\n",
-       "    defs_star  (chain, draw, team) float64 -0.6467 -0.6082 ... -0.5865 -0.8016\n",
-       "Attributes:\n",
-       "    created_at:                 2024-03-06T20:46:24.377610\n",
-       "    arviz_version:              0.17.0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.10.4+7.g34d2a5d9

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-       "Dimensions:      (chain: 4, draw: 500, match: 60)\n",
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-       "  * chain        (chain) int64 0 1 2 3\n",
-       "  * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
-       "  * match        (match) <U16 'Wales Italy' ... 'Ireland England'\n",
-       "  * home_team    (match) <U8 'Wales' 'France' 'Ireland' ... 'France' 'Ireland'\n",
-       "  * away_team    (match) <U8 'Italy' 'England' 'Scotland' ... 'Wales' 'England'\n",
-       "Data variables:\n",
-       "    home_points  (chain, draw, match) int64 48 22 23 22 19 38 ... 16 48 33 18 12\n",
-       "    away_points  (chain, draw, match) int64 12 24 10 19 24 12 ... 38 12 15 28 15\n",
-       "Attributes:\n",
-       "    created_at:                 2024-03-06T20:46:25.689246\n",
-       "    arviz_version:              0.17.0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.10.4+7.g34d2a5d9

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-       "Dimensions:  (chain: 4, draw: 500)\n",
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-       "  * chain    (chain) int64 0 1 2 3\n",
-       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
-       "Data variables:\n",
-       "    sd_att   (chain, draw) float64 -1.189 -1.834 -1.627 ... -0.9111 -1.217\n",
-       "    sd_def   (chain, draw) float64 -0.5553 -0.7182 -1.126 ... -1.083 -1.028\n",
-       "Attributes:\n",
-       "    sd_att:   pymc.logprob.transforms.LogTransform\n",
-       "    sd_def:   pymc.logprob.transforms.LogTransform

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\n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> prior\n", - "\t> prior_predictive\n", - "\t> observed_data\n", - "\t> log_prior\n", - "\t> posterior_predictive\n", - "\t> unconstrained_posterior" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idata" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'rugby.nc'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Storing the model to .nc format\n", - "idata.to_netcdf('rugby.nc')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/arviz/data/example_data/code/rugby_field/rugby_field.ipynb b/arviz/data/example_data/code/rugby_field/rugby_field.ipynb deleted file mode 100644 index b311a14625..0000000000 --- a/arviz/data/example_data/code/rugby_field/rugby_field.ipynb +++ /dev/null @@ -1,270 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Loading libraries\n", - "import arviz as az\n", - "import pymc as pm\n", - "import pytensor.tensor as pt\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Reading the data\n", - "df = pd.read_csv('../rugby/rugby.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "observed_home_goals = df.home_score.values\n", - "observed_away_goals = df.away_score.values\n", - "\n", - "home_team = df.i_home.values\n", - "away_team = df.i_away.values\n", - "\n", - "num_teams = len(df.i_home.drop_duplicates())\n", - "num_games = len(home_team)\n", - "\n", - "teams = np.array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'])\n", - "matches = [f\"{home} {away}\" for home, away in zip(df.home_team, df.away_team)]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# building the model\n", - "coords = {\n", - " \"team\": teams,\n", - " \"match\": matches,\n", - " \"field\": [\"home\", \"away\"],\n", - "}\n", - "with pm.Model(coords=coords) as model:\n", - " # global model parameters\n", - " sd_att = pm.HalfNormal('sd_att', sigma=2)\n", - " sd_def = pm.HalfNormal('sd_def', sigma=2)\n", - " sd_att_field = pm.HalfNormal('sd_att_field', sigma=2)\n", - " sd_def_field = pm.HalfNormal('sd_def_field', sigma=2)\n", - " intercept = pm.Normal('intercept', mu=3, sigma=1, dims=\"field\")\n", - " \n", - " # team-specific model parameters\n", - " atts_team = pm.Normal(\"atts_team\", mu=0, sigma=1, dims=\"team\") * sd_att\n", - " defs_team = pm.Normal(\"defs_team\", mu=0, sigma=1, dims=\"team\") * sd_def\n", - "\n", - " # team-field specific parameters\n", - " atts = pm.Normal(\"atts\", mu=0, sigma=1, dims=(\"field\", \"team\")) * sd_att_field + atts_team\n", - " defs = pm.Normal(\"defs\", mu=0, sigma=1, dims=(\"field\", \"team\")) * sd_def_field + defs_team\n", - " \n", - " #atts_star = pm.Deterministic('atts_star', atts - pt.mean(atts), dims=(\"field\", \"team\"))\n", - " #defs_star = pm.Deterministic('defs_star', defs - pt.mean(defs), dims=(\"field\", \"team\"))\n", - " atts_star = atts - pt.mean(atts, axis=0)\n", - " defs_star = defs - pt.mean(defs, axis=0)\n", - " home_theta = pt.exp(intercept[0] + atts_star[0, home_team] + defs_star[1, away_team])\n", - " away_theta = pt.exp(intercept[1] + atts_star[1, away_team] + defs_star[0, home_team])\n", - " \n", - " # likelihood of observed data\n", - " pm.Poisson('home_points', mu=home_theta, observed=observed_home_goals, dims=\"match\")\n", - " pm.Poisson('away_points', mu=away_theta, observed=observed_away_goals, dims=\"match\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [atts, atts_team, away_points, defs, defs_team, home_points, intercept, sd_att, sd_att_field, sd_def, sd_def_field]\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [sd_att, sd_def, sd_att_field, sd_def_field, intercept, atts_team, defs_team, atts, defs]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "with model:\n", - " idata = pm.sample_prior_predictive()\n", - " idata.extend(\n", - " pm.sample(\n", - " 500,\n", - " tune=1000,\n", - " target_accept=0.9,\n", - " random_seed=11,\n", - " idata_kwargs={\"log_likelihood\": True, \"log_prior\": True}\n", - " )\n", - " )\n", - " pm.sample_posterior_predictive(idata, extend_inferencedata=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'rugby_field.nc'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Storing the model to .nc format\n", - "idata.to_netcdf('rugby_field.nc')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/arviz/data/example_data/data/centered_eight.nc b/arviz/data/example_data/data/centered_eight.nc deleted file mode 100644 index ac839c5f2d..0000000000 Binary files a/arviz/data/example_data/data/centered_eight.nc and /dev/null differ diff --git a/arviz/data/example_data/data/non_centered_eight.nc b/arviz/data/example_data/data/non_centered_eight.nc deleted file mode 100644 index e7e475322c..0000000000 Binary files a/arviz/data/example_data/data/non_centered_eight.nc and /dev/null differ diff --git a/arviz/data/example_data/data_local.json b/arviz/data/example_data/data_local.json deleted file mode 100644 index 23e8b36094..0000000000 --- a/arviz/data/example_data/data_local.json +++ /dev/null @@ -1,12 +0,0 @@ -[ - { - "name": "centered_eight", - "filename": "centered_eight.nc", - "description": "A centered parameterization of the eight schools model. Provided as an example of a model that NUTS has trouble fitting. Compare to `non_centered_eight`.\n\nThe eight schools model is a hierarchical model used for an analysis of the effectiveness of classes that were designed to improve students' performance on the Scholastic Aptitude Test.\n\nSee Bayesian Data Analysis (Gelman et. al.) for more details." - }, - { - "name": "non_centered_eight", - "filename": "non_centered_eight.nc", - "description": "A non-centered parameterization of the eight schools model. This is a hierarchical model where sampling problems may be fixed by a non-centered parametrization. Compare to `centered_eight`.\n\nThe eight schools model is a hierarchical model used for an analysis of the effectiveness of classes that were designed to improve students' performance on the Scholastic Aptitude Test.\n\nSee Bayesian Data Analysis (Gelman et. al.) for more details." - } -] \ No newline at end of file diff --git a/arviz/data/example_data/data_remote.json b/arviz/data/example_data/data_remote.json deleted file mode 100644 index 4247b87565..0000000000 --- a/arviz/data/example_data/data_remote.json +++ /dev/null @@ -1,58 +0,0 @@ -[ - { - "name": "radon", - "filename": "radon_hierarchical.nc", - "url": "http://ndownloader.figshare.com/files/24067472", - "checksum": "a9b2b4adf1bf9c5728e5bdc97107e69c4fc8d8b7d213e9147233b57be8b4587b", - "description": "Radon is a radioactive gas that enters homes through contact points with the ground. It is a carcinogen that is the primary cause of lung cancer in non-smokers. Radon levels vary greatly from household to household.\n\nThis example uses an EPA study of radon levels in houses in Minnesota to construct a model with a hierarchy over households within a county. The model includes estimates (gamma) for contextual effects of the uranium per household.\n\nSee Gelman and Hill (2006) for details on the example, or https://docs.pymc.io/notebooks/multilevel_modeling.html by Chris Fonnesbeck for details on this implementation." - }, - { - "name": "rugby", - "filename": "rugby.nc", - "url": "http://figshare.com/ndownloader/files/44916469", - "checksum": "f4a5e699a8a4cc93f722eb97929dd7c4895c59a2183f05309f5082f3f81eb228", - "description": "The Six Nations Championship is a yearly rugby competition between Italy, Ireland, Scotland, England, France and Wales. Fifteen games are played each year, representing all combinations of the six teams.\n\nThis example uses and includes results from 2014 - 2017, comprising 60 total games. It models latent parameters for each team's attack and defense, as well as a global parameter for home team advantage.\n\nSee https://github.com/arviz-devs/arviz_example_data/blob/main/code/rugby/rugby.ipynb for the whole model specification." - }, - { - "name": "rugby_field", - "filename": "rugby_field.nc", - "url": "http://figshare.com/ndownloader/files/44667112", - "checksum": "53a99da7ac40d82cd01bb0b089263b9633ee016f975700e941b4c6ea289a1fb0", - "description": "A variant of the 'rugby' example dataset. The Six Nations Championship is a yearly rugby competition between Italy, Ireland, Scotland, England, France and Wales. Fifteen games are played each year, representing all combinations of the six teams.\n\nThis example uses and includes results from 2014 - 2017, comprising 60 total games. It models latent parameters for each team's attack and defense, with each team having different values depending on them being home or away team.\n\nSee https://github.com/arviz-devs/arviz_example_data/blob/main/code/rugby_field/rugby_field.ipynb for the whole model specification." - }, - { - "name": "regression1d", - "filename": "regression1d.nc", - "url": "http://ndownloader.figshare.com/files/16254899", - "checksum": "909e8ffe344e196dad2730b1542881ab5729cb0977dd20ba645a532ffa427278", - "description": "A synthetic one dimensional linear regression dataset with latent slope, intercept, and noise (\"eps\"). One hundred data points, fit with PyMC3.\n\nTrue slope and intercept are included as deterministic variables." - }, - { - "name": "regression10d", - "filename": "regression10d.nc", - "url": "http://ndownloader.figshare.com/files/16255736", - "checksum": "c6716ec7e19926ad2a52d6ae4c1d1dd5ddb747e204c0d811757c8e93fcf9f970", - "description": "A synthetic multi-dimensional (10 dimensions) linear regression dataset with latent weights (\"w\"), intercept, and noise (\"eps\"). Five hundred data points, fit with PyMC3.\n\nTrue weights and intercept are included as deterministic variables." - }, - { - "name": "classification1d", - "filename": "classification1d.nc", - "url": "http://ndownloader.figshare.com/files/16256678", - "checksum": "1cf3806e72c14001f6864bb69d89747dcc09dd55bcbca50aba04e9939daee5a0", - "description": "A synthetic one dimensional logistic regression dataset with latent slope and intercept, passed into a Bernoulli random variable. One hundred data points, fit with PyMC3.\n\nTrue slope and intercept are included as deterministic variables." - }, - { - "name": "classification10d", - "filename": "classification10d.nc", - "url": "http://ndownloader.figshare.com/files/16256681", - "checksum": "16c9a45e1e6e0519d573cafc4d266d761ba347e62b6f6a79030aaa8e2fde1367", - "description": "A synthetic multi dimensional (10 dimensions) logistic regression dataset with latent weights (\"w\") and intercept, passed into a Bernoulli random variable. Five hundred data points, fit with PyMC3.\n\nTrue weights and intercept are included as deterministic variables." - }, - { - "name": "glycan_torsion_angles", - "filename": "glycan_torsion_angles.nc", - "url": "http://ndownloader.figshare.com/files/22882652", - "checksum": "4622621fe7a1d3075c18c4c34af8cc57c59eabbb3501b20c6e2d9c6c4737034c", - "description": "Torsion angles phi and psi are critical for determining the three dimensional structure of bio-molecules. Combinations of phi and psi torsion angles that produce clashes between atoms in the bio-molecule result in high energy, unlikely structures.\n\nThis model uses a Von Mises distribution to propose torsion angles for the structure of a glycan molecule (pdb id: 2LIQ), and a Potential to estimate the proposed structure's energy. Said Potential is bound by Boltzman's law." - } -] \ No newline at end of file diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py deleted file mode 100644 index 7749a6a08c..0000000000 --- a/arviz/data/inference_data.py +++ /dev/null @@ -1,2386 +0,0 @@ -# pylint: disable=too-many-lines,too-many-public-methods -"""Data structure for using netcdf groups with xarray.""" -import os -import re -import sys -import uuid -import warnings -from collections import OrderedDict, defaultdict -from collections.abc import MutableMapping, Sequence -from copy import copy as ccopy -from copy import deepcopy -import datetime -from html import escape -from typing import ( - TYPE_CHECKING, - Any, - Iterable, - Iterator, - List, - Mapping, - Optional, - Tuple, - TypeVar, - Union, - overload, -) - -import numpy as np -import xarray as xr -from packaging import version - -from ..rcparams import rcParams -from ..utils import HtmlTemplate, _subset_list, _var_names, either_dict_or_kwargs -from .base import _extend_xr_method, _make_json_serializable, dict_to_dataset - -if sys.version_info[:2] >= (3, 9): - # As of 3.9, collections.abc types support generic parameters themselves. - from collections.abc import ItemsView, ValuesView -else: - # These typing imports are deprecated in 3.9, and moved to collections.abc instead. - from typing import ItemsView, ValuesView - -if TYPE_CHECKING: - from typing_extensions import Literal - -try: - import ujson as json -except ImportError: - # mypy struggles with conditional imports expressed as catching ImportError: - # https://github.com/python/mypy/issues/1153 - import json # type: ignore - - -SUPPORTED_GROUPS = [ - "posterior", - "posterior_predictive", - "predictions", - "log_likelihood", - "log_prior", - "sample_stats", - "prior", - "prior_predictive", - "sample_stats_prior", - "observed_data", - "constant_data", - "predictions_constant_data", - "unconstrained_posterior", - "unconstrained_prior", -] - -WARMUP_TAG = "warmup_" - -SUPPORTED_GROUPS_WARMUP = [ - f"{WARMUP_TAG}posterior", - f"{WARMUP_TAG}posterior_predictive", - f"{WARMUP_TAG}predictions", - f"{WARMUP_TAG}sample_stats", - f"{WARMUP_TAG}log_likelihood", - f"{WARMUP_TAG}log_prior", -] - -SUPPORTED_GROUPS_ALL = SUPPORTED_GROUPS + SUPPORTED_GROUPS_WARMUP - -InferenceDataT = TypeVar("InferenceDataT", bound="InferenceData") - - -def _compressible_dtype(dtype): - """Check basic dtypes for automatic compression.""" - if dtype.kind == "V": - return all(_compressible_dtype(item) for item, _ in dtype.fields.values()) - return dtype.kind in {"b", "i", "u", "f", "c", "S"} - - -class InferenceData(Mapping[str, xr.Dataset]): - """Container for inference data storage using xarray. - - For a detailed introduction to ``InferenceData`` objects and their usage, see - :ref:`xarray_for_arviz`. This page provides help and documentation - on ``InferenceData`` methods and their low level implementation. - """ - - def __init__( - self, - attrs: Union[None, Mapping[Any, Any]] = None, - warn_on_custom_groups: bool = False, - **kwargs: Union[xr.Dataset, List[xr.Dataset], Tuple[xr.Dataset, xr.Dataset]], - ) -> None: - """Initialize InferenceData object from keyword xarray datasets. - - Parameters - ---------- - attrs : dict - sets global attribute for InferenceData object. - warn_on_custom_groups : bool, default False - Emit a warning when custom groups are present in the InferenceData. - "custom group" means any group whose name isn't defined in :ref:`schema` - kwargs : - Keyword arguments of xarray datasets - - Examples - -------- - Initiate an InferenceData object from scratch, not recommended. InferenceData - objects should be initialized using ``from_xyz`` methods, see :ref:`data_api` for more - details. - - .. ipython:: - - In [1]: import arviz as az - ...: import numpy as np - ...: import xarray as xr - ...: dataset = xr.Dataset( - ...: { - ...: "a": (["chain", "draw", "a_dim"], np.random.normal(size=(4, 100, 3))), - ...: "b": (["chain", "draw"], np.random.normal(size=(4, 100))), - ...: }, - ...: coords={ - ...: "chain": (["chain"], np.arange(4)), - ...: "draw": (["draw"], np.arange(100)), - ...: "a_dim": (["a_dim"], ["x", "y", "z"]), - ...: } - ...: ) - ...: idata = az.InferenceData(posterior=dataset, prior=dataset) - ...: idata - - We have created an ``InferenceData`` object with two groups. Now we can check its - contents: - - .. ipython:: - - In [1]: idata.posterior - - """ - self._groups: List[str] = [] - self._groups_warmup: List[str] = [] - self._attrs: Union[None, dict] = dict(attrs) if attrs is not None else None - save_warmup = kwargs.pop("save_warmup", False) - key_list = [key for key in SUPPORTED_GROUPS_ALL if key in kwargs] - for key in kwargs: - if key not in SUPPORTED_GROUPS_ALL: - key_list.append(key) - if warn_on_custom_groups: - warnings.warn( - f"{key} group is not defined in the InferenceData scheme", UserWarning - ) - for key in key_list: - dataset = kwargs[key] - dataset_warmup = None - if dataset is None: - continue - elif isinstance(dataset, (list, tuple)): - dataset, dataset_warmup = dataset - elif not isinstance(dataset, xr.Dataset): - raise ValueError( - "Arguments to InferenceData must be xarray Datasets " - f"(argument '{key}' was type '{type(dataset)}')" - ) - if not key.startswith(WARMUP_TAG): - if dataset: - setattr(self, key, dataset) - self._groups.append(key) - elif key.startswith(WARMUP_TAG): - if dataset: - setattr(self, key, dataset) - self._groups_warmup.append(key) - if save_warmup and dataset_warmup is not None and dataset_warmup: - key = f"{WARMUP_TAG}{key}" - setattr(self, key, dataset_warmup) - self._groups_warmup.append(key) - - @property - def attrs(self) -> dict: - """Attributes of InferenceData object.""" - if self._attrs is None: - self._attrs = {} - return self._attrs - - @attrs.setter - def attrs(self, value) -> None: - self._attrs = dict(value) - - def __repr__(self) -> str: - """Make string representation of InferenceData object.""" - msg = "Inference data with groups:\n\t> {options}".format( - options="\n\t> ".join(self._groups) - ) - if self._groups_warmup: - msg += f"\n\nWarmup iterations saved ({WARMUP_TAG}*)." - return msg - - def _repr_html_(self) -> str: - """Make html representation of InferenceData object.""" - try: - from xarray.core.options import OPTIONS - - display_style = OPTIONS["display_style"] - if display_style == "text": - html_repr = f"
{escape(repr(self))}
" - else: - elements = "".join( - [ - HtmlTemplate.element_template.format( - group_id=group + str(uuid.uuid4()), - group=group, - xr_data=getattr( # pylint: disable=protected-access - self, group - )._repr_html_(), - ) - for group in self._groups_all - ] - ) - formatted_html_template = ( # pylint: disable=possibly-unused-variable - HtmlTemplate.html_template.format(elements) - ) - css_template = HtmlTemplate.css_template # pylint: disable=possibly-unused-variable - html_repr = f"{locals()['formatted_html_template']}{locals()['css_template']}" - except: # pylint: disable=bare-except - html_repr = f"
{escape(repr(self))}
" - return html_repr - - def __delattr__(self, group: str) -> None: - """Delete a group from the InferenceData object.""" - if group in self._groups: - self._groups.remove(group) - elif group in self._groups_warmup: - self._groups_warmup.remove(group) - object.__delattr__(self, group) - - def __delitem__(self, key: str) -> None: - """Delete an item from the InferenceData object using del idata[key].""" - self.__delattr__(key) - - @property - def _groups_all(self) -> List[str]: - return self._groups + self._groups_warmup - - def __len__(self) -> int: - """Return the number of groups in this InferenceData object.""" - return len(self._groups_all) - - def __iter__(self) -> Iterator[str]: - """Iterate over groups in InferenceData object.""" - yield from self._groups_all - - def __contains__(self, key: object) -> bool: - """Return True if the named item is present, and False otherwise.""" - return key in self._groups_all - - def __getitem__(self, key: str) -> xr.Dataset: - """Get item by key.""" - if key not in self._groups_all: - raise KeyError(key) - return getattr(self, key) - - def __setitem__(self, key: str, value: xr.Dataset): - """Set item by key and update group list accordingly.""" - if key.startswith(WARMUP_TAG): - self._groups_warmup.append(key) - else: - self._groups.append(key) - setattr(self, key, value) - - def groups(self) -> List[str]: - """Return all groups present in InferenceData object.""" - return self._groups_all - - class InferenceDataValuesView(ValuesView[xr.Dataset]): - """ValuesView implementation for InferenceData, to allow it to implement Mapping.""" - - def __init__( # pylint: disable=super-init-not-called - self, parent: "InferenceData" - ) -> None: - """Create a new InferenceDataValuesView from an InferenceData object.""" - self.parent = parent - - def __len__(self) -> int: - """Return the number of groups in the parent InferenceData.""" - return len(self.parent._groups_all) - - def __iter__(self) -> Iterator[xr.Dataset]: - """Iterate through the Xarray datasets present in the InferenceData object.""" - parent = self.parent - for group in parent._groups_all: - yield getattr(parent, group) - - def __contains__(self, key: object) -> bool: - """Return True if the given Xarray dataset is one of the values, and False otherwise.""" - if not isinstance(key, xr.Dataset): - return False - - for dataset in self: - if dataset.equals(key): - return True - - return False - - def values(self) -> "InferenceData.InferenceDataValuesView": - """Return a view over the Xarray Datasets present in the InferenceData object.""" - return InferenceData.InferenceDataValuesView(self) - - class InferenceDataItemsView(ItemsView[str, xr.Dataset]): - """ItemsView implementation for InferenceData, to allow it to implement Mapping.""" - - def __init__( # pylint: disable=super-init-not-called - self, parent: "InferenceData" - ) -> None: - """Create a new InferenceDataItemsView from an InferenceData object.""" - self.parent = parent - - def __len__(self) -> int: - """Return the number of groups in the parent InferenceData.""" - return len(self.parent._groups_all) - - def __iter__(self) -> Iterator[Tuple[str, xr.Dataset]]: - """Iterate through the groups and corresponding Xarray datasets in the InferenceData.""" - parent = self.parent - for group in parent._groups_all: - yield group, getattr(parent, group) - - def __contains__(self, key: object) -> bool: - """Return True if the (group, dataset) tuple is present, and False otherwise.""" - parent = self.parent - if not isinstance(key, tuple) or len(key) != 2: - return False - - group, dataset = key - if group not in parent._groups_all: - return False - - if not isinstance(dataset, xr.Dataset): - return False - - existing_dataset = getattr(parent, group) - return existing_dataset.equals(dataset) - - def items(self) -> "InferenceData.InferenceDataItemsView": - """Return a view over the groups and datasets present in the InferenceData object.""" - return InferenceData.InferenceDataItemsView(self) - - @staticmethod - def from_netcdf( - filename, - *, - engine="h5netcdf", - group_kwargs=None, - regex=False, - base_group: str = "/", - ) -> "InferenceData": - """Initialize object from a netcdf file. - - Expects that the file will have groups, each of which can be loaded by xarray. - By default, the datasets of the InferenceData object will be lazily loaded instead - of being loaded into memory. This - behaviour is regulated by the value of ``az.rcParams["data.load"]``. - - Parameters - ---------- - filename : str - location of netcdf file - engine : {"h5netcdf", "netcdf4"}, default "h5netcdf" - Library used to read the netcdf file. - group_kwargs : dict of {str: dict}, optional - Keyword arguments to be passed into each call of :func:`xarray.open_dataset`. - The keys of the higher level should be group names or regex matching group - names, the inner dicts re passed to ``open_dataset`` - This feature is currently experimental. - regex : bool, default False - Specifies where regex search should be used to extend the keyword arguments. - This feature is currently experimental. - base_group : str, default "/" - The group in the netCDF file where the InferenceData is stored. By default, - assumes that the file only contains an InferenceData object. - - Returns - ------- - InferenceData - """ - groups = {} - attrs = {} - - if engine == "h5netcdf": - import h5netcdf - elif engine == "netcdf4": - import netCDF4 as nc - else: - raise ValueError( - f"Invalid value for engine: {engine}. Valid options are: h5netcdf or netcdf4" - ) - - try: - with ( - h5netcdf.File(filename, mode="r") - if engine == "h5netcdf" - else nc.Dataset(filename, mode="r") - ) as file_handle: - if base_group == "/": - data = file_handle - else: - data = file_handle[base_group] - - data_groups = list(data.groups) - - for group in data_groups: - group_kws = {} - - group_kws = {} - if group_kwargs is not None and regex is False: - group_kws = group_kwargs.get(group, {}) - if group_kwargs is not None and regex is True: - for key, kws in group_kwargs.items(): - if re.search(key, group): - group_kws = kws - group_kws.setdefault("engine", engine) - data = xr.open_dataset(filename, group=f"{base_group}/{group}", **group_kws) - if rcParams["data.load"] == "eager": - with data: - groups[group] = data.load() - else: - groups[group] = data - - with xr.open_dataset(filename, engine=engine, group=base_group) as data: - attrs.update(data.load().attrs) - - return InferenceData(attrs=attrs, **groups) - except OSError as err: - if err.errno == -101: - raise type(err)( - str(err) - + ( - " while reading a NetCDF file. This is probably an error in HDF5, " - "which happens because your OS does not support HDF5 file locking. See " - "https://stackoverflow.com/questions/49317927/" - "errno-101-netcdf-hdf-error-when-opening-netcdf-file#49317928" - " for a possible solution." - ) - ) from err - raise err - - def to_netcdf( - self, - filename: str, - compress: bool = True, - groups: Optional[List[str]] = None, - engine: str = "h5netcdf", - base_group: str = "/", - overwrite_existing: bool = True, - ) -> str: - """Write InferenceData to netcdf4 file. - - Parameters - ---------- - filename : str - Location to write to - compress : bool, optional - Whether to compress result. Note this saves disk space, but may make - saving and loading somewhat slower (default: True). - groups : list, optional - Write only these groups to netcdf file. - engine : {"h5netcdf", "netcdf4"}, default "h5netcdf" - Library used to read the netcdf file. - base_group : str, default "/" - The group in the netCDF file where the InferenceData is will be stored. - By default, will write to the root of the netCDF file - overwrite_existing : bool, default True - Whether to overwrite the existing file or append to it. - - Returns - ------- - str - Location of netcdf file - """ - if base_group is None: - base_group = "/" - - if os.path.exists(filename) and not overwrite_existing: - mode = "a" - else: - mode = "w" # overwrite first, then append - - if self._attrs: - xr.Dataset(attrs=self._attrs).to_netcdf( - filename, mode=mode, engine=engine, group=base_group - ) - mode = "a" - - if self._groups_all: # check's whether a group is present or not. - if groups is None: - groups = self._groups_all - else: - groups = [group for group in self._groups_all if group in groups] - - for group in groups: - data = getattr(self, group) - kwargs = {"engine": engine} - if compress: - kwargs["encoding"] = { - var_name: {"zlib": True} - for var_name, values in data.variables.items() - if _compressible_dtype(values.dtype) - } - data.to_netcdf(filename, mode=mode, group=f"{base_group}/{group}", **kwargs) - data.close() - mode = "a" - elif not self._attrs: # creates a netcdf file for an empty InferenceData object. - if engine == "h5netcdf": - import h5netcdf - - empty_netcdf_file = h5netcdf.File(filename, mode="w") - elif engine == "netcdf4": - import netCDF4 as nc - - empty_netcdf_file = nc.Dataset(filename, mode="w", format="NETCDF4") - empty_netcdf_file.close() - return filename - - def to_datatree(self): - """Convert InferenceData object to a :class:`~xarray.DataTree`.""" - try: - from xarray import DataTree - except ImportError as err: - raise ImportError( - "xarray must be have DataTree in order to use InferenceData.to_datatree. " - "Update to xarray>=2024.11.0" - ) from err - dt = DataTree.from_dict({group: ds for group, ds in self.items()}) - dt.attrs = self.attrs - return dt - - @staticmethod - def from_datatree(datatree): - """Create an InferenceData object from a :class:`~xarray.DataTree`. - - Parameters - ---------- - datatree : DataTree - """ - return InferenceData( - attrs=datatree.attrs, - **{group: child.to_dataset() for group, child in datatree.children.items()}, - ) - - def to_dict(self, groups=None, filter_groups=None): - """Convert InferenceData to a dictionary following xarray naming conventions. - - Parameters - ---------- - groups : list, optional - Groups where the transformation is to be applied. Can either be group names - or metagroup names. - filter_groups: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret groups as the real group or metagroup names. - If "like", interpret groups as substrings of the real group or metagroup names. - If "regex", interpret groups as regular expressions on the real group or - metagroup names. A la `pandas.filter`. - - Returns - ------- - dict - A dictionary containing all groups of InferenceData object. - When `data=False` return just the schema. - """ - ret = defaultdict(dict) - if self._groups_all: # check's whether a group is present or not. - if groups is None: - groups = self._group_names(groups, filter_groups) - else: - groups = [group for group in self._groups_all if group in groups] - - for group in groups: - dataset = getattr(self, group) - data = {} - for var_name, dataarray in dataset.items(): - data[var_name] = dataarray.values - dims = [] - for coord_name, coord_values in dataarray.coords.items(): - if coord_name not in ("chain", "draw") and not coord_name.startswith( - f"{var_name}_dim_" - ): - dims.append(coord_name) - ret["coords"][coord_name] = coord_values.values - - if group in ( - "predictions", - "predictions_constant_data", - ): - dims_key = "pred_dims" - else: - dims_key = "dims" - if len(dims) > 0: - ret[dims_key][var_name] = dims - ret[group] = data - ret[f"{group}_attrs"] = dataset.attrs - - ret["attrs"] = self.attrs - return ret - - def to_json(self, filename, groups=None, filter_groups=None, **kwargs): - """Write InferenceData to a json file. - - Parameters - ---------- - filename : str - Location to write to - groups : list, optional - Groups where the transformation is to be applied. Can either be group names - or metagroup names. - filter_groups: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret groups as the real group or metagroup names. - If "like", interpret groups as substrings of the real group or metagroup names. - If "regex", interpret groups as regular expressions on the real group or - metagroup names. A la `pandas.filter`. - kwargs : dict - kwargs passed to json.dump() - - Returns - ------- - str - Location of json file - """ - idata_dict = _make_json_serializable( - self.to_dict(groups=groups, filter_groups=filter_groups) - ) - - with open(filename, "w", encoding="utf8") as file: - json.dump(idata_dict, file, **kwargs) - - return filename - - def to_dataframe( - self, - groups=None, - filter_groups=None, - var_names=None, - filter_vars=None, - include_coords=True, - include_index=True, - index_origin=None, - ): - """Convert InferenceData to a :class:`pandas.DataFrame` following xarray naming conventions. - - This returns dataframe in a "wide" -format, where each item in ndimensional array is - unpacked. To access "tidy" -format, use xarray functionality found for each dataset. - - In case of a multiple groups, function adds a group identification to the var name. - - Data groups ("observed_data", "constant_data", "predictions_constant_data") are - skipped implicitly. - - Raises TypeError if no valid groups are found. - Raises ValueError if no data are selected. - - Parameters - ---------- - groups: str or list of str, optional - Groups where the transformation is to be applied. Can either be group names - or metagroup names. - filter_groups: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret groups as the real group or metagroup names. - If "like", interpret groups as substrings of the real group or metagroup names. - If "regex", interpret groups as regular expressions on the real group or - metagroup names. A la `pandas.filter`. - var_names : str or list of str, optional - Variables to be extracted. Prefix the variables by `~` when you want to exclude them. - filter_vars: {None, "like", "regex"}, optional - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - `pandas.filter`. - Like with plotting, sometimes it's easier to subset saying what to exclude - instead of what to include - include_coords: bool - Add coordinate values to column name (tuple). - include_index: bool - Add index information for multidimensional arrays. - index_origin: {0, 1}, optional - Starting index for multidimensional objects. 0- or 1-based. - Defaults to rcParams["data.index_origin"]. - - Returns - ------- - pandas.DataFrame - A pandas DataFrame containing all selected groups of InferenceData object. - """ - # pylint: disable=too-many-nested-blocks - if not include_coords and not include_index: - raise TypeError("Both include_coords and include_index can not be False.") - if index_origin is None: - index_origin = rcParams["data.index_origin"] - if index_origin not in [0, 1]: - raise TypeError(f"index_origin must be 0 or 1, saw {index_origin}") - - group_names = list( - filter(lambda x: "data" not in x, self._group_names(groups, filter_groups)) - ) - - if not group_names: - raise TypeError(f"No valid groups found: {groups}") - - dfs = {} - for group in group_names: - dataset = self[group] - group_var_names = _var_names(var_names, dataset, filter_vars, "ignore") - if (group_var_names is not None) and not group_var_names: - continue - if group_var_names is not None: - dataset = dataset[[var_name for var_name in group_var_names if var_name in dataset]] - df = None - coords_to_idx = { - name: dict(map(reversed, enumerate(dataset.coords[name].values, index_origin))) - for name in list(filter(lambda x: x not in ("chain", "draw"), dataset.coords)) - } - for data_array in dataset.values(): - dataframe = data_array.to_dataframe() - if list(filter(lambda x: x not in ("chain", "draw"), data_array.dims)): - levels = [ - idx - for idx, dim in enumerate(data_array.dims) - if dim not in ("chain", "draw") - ] - dataframe = dataframe.unstack(level=levels) - tuple_columns = [] - for name, *coords in dataframe.columns: - if include_index: - idxs = [] - for coordname, coorditem in zip(dataframe.columns.names[1:], coords): - idxs.append(coords_to_idx[coordname][coorditem]) - if include_coords: - tuple_columns.append( - (f"{name}[{','.join(map(str, idxs))}]", *coords) - ) - else: - tuple_columns.append(f"{name}[{','.join(map(str, idxs))}]") - else: - tuple_columns.append((name, *coords)) - - dataframe.columns = tuple_columns - dataframe.sort_index(axis=1, inplace=True) - if df is None: - df = dataframe - continue - df = df.join(dataframe, how="outer") - if df is not None: - df = df.reset_index() - dfs[group] = df - if not dfs: - raise ValueError("No data selected for the dataframe.") - if len(dfs) > 1: - for group, df in dfs.items(): - df.columns = [ - ( - col - if col in ("draw", "chain") - else (group, *col) if isinstance(col, tuple) else (group, col) - ) - for col in df.columns - ] - dfs, *dfs_tail = list(dfs.values()) - for df in dfs_tail: - dfs = dfs.merge(df, how="outer", copy=False) - else: - (dfs,) = dfs.values() # pylint: disable=unbalanced-dict-unpacking - return dfs - - def to_zarr(self, store=None): - """Convert InferenceData to a :class:`zarr.hierarchy.Group`. - - The zarr storage is using the same group names as the InferenceData. - - Raises - ------ - TypeError - If no valid store is found. - - Parameters - ---------- - store: zarr.storage i.e MutableMapping or str, optional - Zarr storage class or path to desired DirectoryStore. - - Returns - ------- - zarr.hierarchy.group - A zarr hierarchy group containing the InferenceData. - - References - ---------- - https://zarr.readthedocs.io/ - """ - try: - import zarr - except ImportError as err: - raise ImportError("'to_zarr' method needs Zarr (>=2.5.0,<3) installed.") from err - if version.parse(zarr.__version__) < version.parse("2.5.0"): - raise ImportError( - "Found zarr<2.5.0, please upgrade to a zarr (>=2.5.0,<3) to use 'to_zarr'" - ) - if version.parse(zarr.__version__) >= version.parse("3.0.0.dev0"): - raise ImportError( - "Found zarr>=3, which is not supported by ArviZ. Instead, you can use " - "'dt = InferenceData.to_datatree' followed by 'dt.to_zarr()' " - "(needs xarray>=2024.11.0)" - ) - - # Check store type and create store if necessary - if store is None: - store = zarr.storage.TempStore(suffix="arviz") - elif isinstance(store, str): - store = zarr.storage.DirectoryStore(path=store) - elif not isinstance(store, MutableMapping): - raise TypeError(f"No valid store found: {store}") - - groups = self.groups() - - if not groups: - raise TypeError("No valid groups found!") - - # order matters here, saving attrs after the groups will erase the groups. - if self.attrs: - xr.Dataset(attrs=self.attrs).to_zarr(store=store, mode="w") - - for group in groups: - # Create zarr group in store with same group name - getattr(self, group).to_zarr(store=store, group=group, mode="w") - - return zarr.open(store) # Open store to get overarching group - - @staticmethod - def from_zarr(store) -> "InferenceData": - """Initialize object from a zarr store or path. - - Expects that the zarr store will have groups, each of which can be loaded by xarray. - By default, the datasets of the InferenceData object will be lazily loaded instead - of being loaded into memory. This - behaviour is regulated by the value of ``az.rcParams["data.load"]``. - - Parameters - ---------- - store: MutableMapping or zarr.hierarchy.Group or str. - Zarr storage class or path to desired Store. - - Returns - ------- - InferenceData object - - References - ---------- - https://zarr.readthedocs.io/ - """ - try: - import zarr - except ImportError as err: - raise ImportError("'from_zarr' method needs Zarr (>=2.5.0,<3) installed.") from err - if version.parse(zarr.__version__) < version.parse("2.5.0"): - raise ImportError( - "Found zarr<2.5.0, please upgrade to a zarr (>=2.5.0,<3) to use 'from_zarr'" - ) - if version.parse(zarr.__version__) >= version.parse("3.0.0.dev0"): - raise ImportError( - "Found zarr>=3, which is not supported by ArviZ. Instead, you can use " - "'xarray.open_datatree' followed by 'arviz.InferenceData.from_datatree' " - "(needs xarray>=2024.11.0)" - ) - - # Check store type and create store if necessary - if isinstance(store, str): - store = zarr.storage.DirectoryStore(path=store) - elif isinstance(store, zarr.hierarchy.Group): - store = store.store - elif not isinstance(store, MutableMapping): - raise TypeError(f"No valid store found: {store}") - - groups = {} - zarr_handle = zarr.open(store, mode="r") - - # Open each group via xarray method - for key_group, _ in zarr_handle.groups(): - with xr.open_zarr(store=store, group=key_group) as data: - groups[key_group] = data.load() if rcParams["data.load"] == "eager" else data - - with xr.open_zarr(store=store) as root: - attrs = root.attrs - - return InferenceData(attrs=attrs, **groups) - - def __add__(self, other: "InferenceData") -> "InferenceData": - """Concatenate two InferenceData objects.""" - return concat(self, other, copy=True, inplace=False) - - def sel( - self: InferenceDataT, - groups: Optional[Union[str, List[str]]] = None, - filter_groups: Optional["Literal['like', 'regex']"] = None, - inplace: bool = False, - chain_prior: Optional[bool] = None, - **kwargs: Any, - ) -> Optional[InferenceDataT]: - """Perform an xarray selection on all groups. - - Loops groups to perform Dataset.sel(key=item) - for every kwarg if key is a dimension of the dataset. - One example could be performing a burn in cut on the InferenceData object - or discarding a chain. The selection is performed on all relevant groups (like - posterior, prior, sample stats) while non relevant groups like observed data are - omitted. See :meth:`xarray.Dataset.sel ` - - Parameters - ---------- - groups : str or list of str, optional - Groups where the selection is to be applied. Can either be group names - or metagroup names. - filter_groups : {None, "like", "regex"}, optional, default=None - If `None` (default), interpret groups as the real group or metagroup names. - If "like", interpret groups as substrings of the real group or metagroup names. - If "regex", interpret groups as regular expressions on the real group or - metagroup names. A la `pandas.filter`. - inplace : bool, optional - If ``True``, modify the InferenceData object inplace, - otherwise, return the modified copy. - chain_prior : bool, optional, deprecated - If ``False``, do not select prior related groups using ``chain`` dim. - Otherwise, use selection on ``chain`` if present. Default=False - kwargs : dict, optional - It must be accepted by Dataset.sel(). - - Returns - ------- - InferenceData - A new InferenceData object by default. - When `inplace==True` perform selection in-place and return `None` - - Examples - -------- - Use ``sel`` to discard one chain of the InferenceData object. We first check the - dimensions of the original object: - - .. jupyter-execute:: - - import arviz as az - idata = az.load_arviz_data("centered_eight") - idata - - In order to remove the third chain: - - .. jupyter-execute:: - - idata_subset = idata.sel(chain=[0, 1, 3], groups="posterior_groups") - idata_subset - - See Also - -------- - xarray.Dataset.sel : - Returns a new dataset with each array indexed by tick labels along the specified - dimension(s). - isel : Returns a new dataset with each array indexed along the specified dimension(s). - """ - if chain_prior is not None: - warnings.warn( - "chain_prior has been deprecated. Use groups argument and " - "rcParams['data.metagroups'] instead.", - DeprecationWarning, - ) - else: - chain_prior = False - group_names = self._group_names(groups, filter_groups) - - out = self if inplace else deepcopy(self) - for group in group_names: - dataset = getattr(self, group) - valid_keys = set(kwargs.keys()).intersection(dataset.dims) - if not chain_prior and "prior" in group: - valid_keys -= {"chain"} - dataset = dataset.sel(**{key: kwargs[key] for key in valid_keys}) - setattr(out, group, dataset) - if inplace: - return None - else: - return out - - def isel( - self: InferenceDataT, - groups: Optional[Union[str, List[str]]] = None, - filter_groups: Optional["Literal['like', 'regex']"] = None, - inplace: bool = False, - **kwargs: Any, - ) -> Optional[InferenceDataT]: - """Perform an xarray selection on all groups. - - Loops groups to perform Dataset.isel(key=item) - for every kwarg if key is a dimension of the dataset. - One example could be performing a burn in cut on the InferenceData object - or discarding a chain. The selection is performed on all relevant groups (like - posterior, prior, sample stats) while non relevant groups like observed data are - omitted. See :meth:`xarray:xarray.Dataset.isel` - - Parameters - ---------- - groups : str or list of str, optional - Groups where the selection is to be applied. Can either be group names - or metagroup names. - filter_groups : {None, "like", "regex"}, optional - If `None` (default), interpret groups as the real group or metagroup names. - If "like", interpret groups as substrings of the real group or metagroup names. - If "regex", interpret groups as regular expressions on the real group or - metagroup names. A la `pandas.filter`. - inplace : bool, optional - If ``True``, modify the InferenceData object inplace, - otherwise, return the modified copy. - kwargs : dict, optional - It must be accepted by :meth:`xarray:xarray.Dataset.isel`. - - Returns - ------- - InferenceData - A new InferenceData object by default. - When `inplace==True` perform selection in-place and return `None` - - Examples - -------- - Use ``isel`` to discard one chain of the InferenceData object. We first check the - dimensions of the original object: - - .. jupyter-execute:: - - import arviz as az - idata = az.load_arviz_data("centered_eight") - idata - - In order to remove the third chain: - - .. jupyter-execute:: - - idata_subset = idata.isel(chain=[0, 1, 3], groups="posterior_groups") - idata_subset - - You can expand the groups and coords in each group to see how now only the chains 0, 1 and - 3 are present. - - See Also - -------- - xarray.Dataset.isel : - Returns a new dataset with each array indexed along the specified dimension(s). - sel : - Returns a new dataset with each array indexed by tick labels along the specified - dimension(s). - """ - group_names = self._group_names(groups, filter_groups) - - out = self if inplace else deepcopy(self) - for group in group_names: - dataset = getattr(self, group) - valid_keys = set(kwargs.keys()).intersection(dataset.dims) - dataset = dataset.isel(**{key: kwargs[key] for key in valid_keys}) - setattr(out, group, dataset) - if inplace: - return None - else: - return out - - def stack( - self, - dimensions=None, - groups=None, - filter_groups=None, - inplace=False, - **kwargs, - ): - """Perform an xarray stacking on all groups. - - Stack any number of existing dimensions into a single new dimension. - Loops groups to perform Dataset.stack(key=value) - for every kwarg if value is a dimension of the dataset. - The selection is performed on all relevant groups (like - posterior, prior, sample stats) while non relevant groups like observed data are - omitted. See :meth:`xarray:xarray.Dataset.stack` - - Parameters - ---------- - dimensions : dict, optional - Names of new dimensions, and the existing dimensions that they replace. - groups: str or list of str, optional - Groups where the selection is to be applied. Can either be group names - or metagroup names. - filter_groups : {None, "like", "regex"}, optional - If `None` (default), interpret groups as the real group or metagroup names. - If "like", interpret groups as substrings of the real group or metagroup names. - If "regex", interpret groups as regular expressions on the real group or - metagroup names. A la `pandas.filter`. - inplace : bool, optional - If ``True``, modify the InferenceData object inplace, - otherwise, return the modified copy. - kwargs : dict, optional - It must be accepted by :meth:`xarray:xarray.Dataset.stack`. - - Returns - ------- - InferenceData - A new InferenceData object by default. - When `inplace==True` perform selection in-place and return `None` - - Examples - -------- - Use ``stack`` to stack any number of existing dimensions into a single new dimension. - We first check the original object: - - .. jupyter-execute:: - - import arviz as az - idata = az.load_arviz_data("rugby") - idata - - In order to stack two dimensions ``chain`` and ``draw`` to ``sample``, we can use: - - .. jupyter-execute:: - - idata.stack(sample=["chain", "draw"], inplace=True) - idata - - We can also take the example of custom InferenceData object and perform stacking. We first - check the original object: - - .. jupyter-execute:: - - import numpy as np - datadict = { - "a": np.random.randn(100), - "b": np.random.randn(1, 100, 10), - "c": np.random.randn(1, 100, 3, 4), - } - coords = { - "c1": np.arange(3), - "c99": np.arange(4), - "b1": np.arange(10), - } - dims = {"c": ["c1", "c99"], "b": ["b1"]} - idata = az.from_dict( - posterior=datadict, posterior_predictive=datadict, coords=coords, dims=dims - ) - idata - - In order to stack two dimensions ``c1`` and ``c99`` to ``z``, we can use: - - .. jupyter-execute:: - - idata.stack(z=["c1", "c99"], inplace=True) - idata - - See Also - -------- - xarray.Dataset.stack : Stack any number of existing dimensions into a single new dimension. - unstack : Perform an xarray unstacking on all groups of InferenceData object. - """ - groups = self._group_names(groups, filter_groups) - - dimensions = {} if dimensions is None else dimensions - dimensions.update(kwargs) - out = self if inplace else deepcopy(self) - for group in groups: - dataset = getattr(self, group) - kwarg_dict = {} - for key, value in dimensions.items(): - try: - if not set(value).difference(dataset.dims): - kwarg_dict[key] = value - except TypeError: - kwarg_dict[key] = value - dataset = dataset.stack(**kwarg_dict) - setattr(out, group, dataset) - if inplace: - return None - else: - return out - - def unstack(self, dim=None, groups=None, filter_groups=None, inplace=False): - """Perform an xarray unstacking on all groups. - - Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions. - Loops groups to perform Dataset.unstack(key=value). - The selection is performed on all relevant groups (like posterior, prior, - sample stats) while non relevant groups like observed data are omitted. - See :meth:`xarray:xarray.Dataset.unstack` - - Parameters - ---------- - dim : Hashable or iterable of Hashable, optional - Dimension(s) over which to unstack. By default unstacks all MultiIndexes. - groups : str or list of str, optional - Groups where the selection is to be applied. Can either be group names - or metagroup names. - filter_groups : {None, "like", "regex"}, optional - If `None` (default), interpret groups as the real group or metagroup names. - If "like", interpret groups as substrings of the real group or metagroup names. - If "regex", interpret groups as regular expressions on the real group or - metagroup names. A la `pandas.filter`. - inplace : bool, optional - If ``True``, modify the InferenceData object inplace, - otherwise, return the modified copy. - - Returns - ------- - InferenceData - A new InferenceData object by default. - When `inplace==True` perform selection in place and return `None` - - Examples - -------- - Use ``unstack`` to unstack existing dimensions corresponding to MultiIndexes into - multiple new dimensions. We first stack two dimensions ``c1`` and ``c99`` to ``z``: - - .. jupyter-execute:: - - import arviz as az - import numpy as np - datadict = { - "a": np.random.randn(100), - "b": np.random.randn(1, 100, 10), - "c": np.random.randn(1, 100, 3, 4), - } - coords = { - "c1": np.arange(3), - "c99": np.arange(4), - "b1": np.arange(10), - } - dims = {"c": ["c1", "c99"], "b": ["b1"]} - idata = az.from_dict( - posterior=datadict, posterior_predictive=datadict, coords=coords, dims=dims - ) - idata.stack(z=["c1", "c99"], inplace=True) - idata - - In order to unstack the dimension ``z``, we use: - - .. jupyter-execute:: - - idata.unstack(inplace=True) - idata - - See Also - -------- - xarray.Dataset.unstack : - Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions. - stack : Perform an xarray stacking on all groups of InferenceData object. - """ - groups = self._group_names(groups, filter_groups) - if isinstance(dim, str): - dim = [dim] - - out = self if inplace else deepcopy(self) - for group in groups: - dataset = getattr(self, group) - valid_dims = set(dim).intersection(dataset.dims) if dim is not None else dim - dataset = dataset.unstack(dim=valid_dims) - setattr(out, group, dataset) - if inplace: - return None - else: - return out - - def rename(self, name_dict=None, groups=None, filter_groups=None, inplace=False): - """Perform xarray renaming of variable and dimensions on all groups. - - Loops groups to perform Dataset.rename(name_dict) - for every key in name_dict if key is a dimension/data_vars of the dataset. - The renaming is performed on all relevant groups (like - posterior, prior, sample stats) while non relevant groups like observed data are - omitted. See :meth:`xarray:xarray.Dataset.rename` - - Parameters - ---------- - name_dict : dict - Dictionary whose keys are current variable or dimension names - and whose values are the desired names. - groups : str or list of str, optional - Groups where the selection is to be applied. Can either be group names - or metagroup names. - filter_groups : {None, "like", "regex"}, optional - If `None` (default), interpret groups as the real group or metagroup names. - If "like", interpret groups as substrings of the real group or metagroup names. - If "regex", interpret groups as regular expressions on the real group or - metagroup names. A la `pandas.filter`. - inplace : bool, optional - If ``True``, modify the InferenceData object inplace, - otherwise, return the modified copy. - - Returns - ------- - InferenceData - A new InferenceData object by default. - When `inplace==True` perform renaming in-place and return `None` - - Examples - -------- - Use ``rename`` to renaming of variable and dimensions on all groups of the InferenceData - object. We first check the original object: - - .. jupyter-execute:: - - import arviz as az - idata = az.load_arviz_data("rugby") - idata - - In order to rename the dimensions and variable, we use: - - .. jupyter-execute:: - - idata.rename({"team": "team_new", "match":"match_new"}, inplace=True) - idata - - See Also - -------- - xarray.Dataset.rename : Returns a new object with renamed variables and dimensions. - rename_vars : - Perform xarray renaming of variable or coordinate names on all groups of an - InferenceData object. - rename_dims : Perform xarray renaming of dimensions on all groups of InferenceData object. - """ - groups = self._group_names(groups, filter_groups) - if "chain" in name_dict.keys() or "draw" in name_dict.keys(): - raise KeyError("'chain' or 'draw' dimensions can't be renamed") - out = self if inplace else deepcopy(self) - - for group in groups: - dataset = getattr(self, group) - expected_keys = list(dataset.data_vars) + list(dataset.dims) - valid_keys = set(name_dict.keys()).intersection(expected_keys) - dataset = dataset.rename({key: name_dict[key] for key in valid_keys}) - setattr(out, group, dataset) - if inplace: - return None - else: - return out - - def rename_vars(self, name_dict=None, groups=None, filter_groups=None, inplace=False): - """Perform xarray renaming of variable or coordinate names on all groups. - - Loops groups to perform Dataset.rename_vars(name_dict) - for every key in name_dict if key is a variable or coordinate names of the dataset. - The renaming is performed on all relevant groups (like - posterior, prior, sample stats) while non relevant groups like observed data are - omitted. See :meth:`xarray:xarray.Dataset.rename_vars` - - Parameters - ---------- - name_dict : dict - Dictionary whose keys are current variable or coordinate names - and whose values are the desired names. - groups : str or list of str, optional - Groups where the selection is to be applied. Can either be group names - or metagroup names. - filter_groups : {None, "like", "regex"}, optional - If `None` (default), interpret groups as the real group or metagroup names. - If "like", interpret groups as substrings of the real group or metagroup names. - If "regex", interpret groups as regular expressions on the real group or - metagroup names. A la `pandas.filter`. - inplace : bool, optional - If ``True``, modify the InferenceData object inplace, - otherwise, return the modified copy. - - - Returns - ------- - InferenceData - A new InferenceData object with renamed variables including coordinates by default. - When `inplace==True` perform renaming in-place and return `None` - - Examples - -------- - Use ``rename_vars`` to renaming of variable and coordinates on all groups of the - InferenceData object. We first check the data variables of original object: - - .. jupyter-execute:: - - import arviz as az - idata = az.load_arviz_data("rugby") - idata - - In order to rename the data variables, we use: - - .. jupyter-execute:: - - idata.rename_vars({"home": "home_new"}, inplace=True) - idata - - See Also - -------- - xarray.Dataset.rename_vars : - Returns a new object with renamed variables including coordinates. - rename : - Perform xarray renaming of variable and dimensions on all groups of an InferenceData - object. - rename_dims : Perform xarray renaming of dimensions on all groups of InferenceData object. - """ - groups = self._group_names(groups, filter_groups) - - out = self if inplace else deepcopy(self) - for group in groups: - dataset = getattr(self, group) - valid_keys = set(name_dict.keys()).intersection(dataset.data_vars) - dataset = dataset.rename_vars({key: name_dict[key] for key in valid_keys}) - setattr(out, group, dataset) - if inplace: - return None - else: - return out - - def rename_dims(self, name_dict=None, groups=None, filter_groups=None, inplace=False): - """Perform xarray renaming of dimensions on all groups. - - Loops groups to perform Dataset.rename_dims(name_dict) - for every key in name_dict if key is a dimension of the dataset. - The renaming is performed on all relevant groups (like - posterior, prior, sample stats) while non relevant groups like observed data are - omitted. See :meth:`xarray:xarray.Dataset.rename_dims` - - Parameters - ---------- - name_dict : dict - Dictionary whose keys are current dimension names and whose values are the desired - names. - groups : str or list of str, optional - Groups where the selection is to be applied. Can either be group names - or metagroup names. - filter_groups : {None, "like", "regex"}, optional - If `None` (default), interpret groups as the real group or metagroup names. - If "like", interpret groups as substrings of the real group or metagroup names. - If "regex", interpret groups as regular expressions on the real group or - metagroup names. A la `pandas.filter`. - inplace : bool, optional - If ``True``, modify the InferenceData object inplace, - otherwise, return the modified copy. - - Returns - ------- - InferenceData - A new InferenceData object with renamed dimension by default. - When `inplace==True` perform renaming in-place and return `None` - - Examples - -------- - Use ``rename_dims`` to renaming of dimensions on all groups of the InferenceData - object. We first check the dimensions of original object: - - .. jupyter-execute:: - - import arviz as az - idata = az.load_arviz_data("rugby") - idata - - In order to rename the dimensions, we use: - - .. jupyter-execute:: - - idata.rename_dims({"team": "team_new"}, inplace=True) - idata - - See Also - -------- - xarray.Dataset.rename_dims : Returns a new object with renamed dimensions only. - rename : - Perform xarray renaming of variable and dimensions on all groups of an InferenceData - object. - rename_vars : - Perform xarray renaming of variable or coordinate names on all groups of an - InferenceData object. - """ - groups = self._group_names(groups, filter_groups) - if "chain" in name_dict.keys() or "draw" in name_dict.keys(): - raise KeyError("'chain' or 'draw' dimensions can't be renamed") - - out = self if inplace else deepcopy(self) - for group in groups: - dataset = getattr(self, group) - valid_keys = set(name_dict.keys()).intersection(dataset.dims) - dataset = dataset.rename_dims({key: name_dict[key] for key in valid_keys}) - setattr(out, group, dataset) - if inplace: - return None - else: - return out - - def add_groups( - self, group_dict=None, coords=None, dims=None, warn_on_custom_groups=False, **kwargs - ): - """Add new groups to InferenceData object. - - Parameters - ---------- - group_dict : dict of {str : dict or xarray.Dataset}, optional - Groups to be added - coords : dict of {str : array_like}, optional - Coordinates for the dataset - dims : dict of {str : list of str}, optional - Dimensions of each variable. The keys are variable names, values are lists of - coordinates. - warn_on_custom_groups : bool, default False - Emit a warning when custom groups are present in the InferenceData. - "custom group" means any group whose name isn't defined in :ref:`schema` - kwargs : dict, optional - The keyword arguments form of group_dict. One of group_dict or kwargs must be provided. - - Examples - -------- - Add a ``log_likelihood`` group to the "rugby" example InferenceData after loading. - - .. jupyter-execute:: - - import arviz as az - idata = az.load_arviz_data("rugby") - del idata.log_likelihood - idata2 = idata.copy() - post = idata.posterior - obs = idata.observed_data - idata - - Knowing the model, we can compute it manually. In this case however, - we will generate random samples with the right shape. - - .. jupyter-execute:: - - import numpy as np - rng = np.random.default_rng(73) - ary = rng.normal(size=(post.sizes["chain"], post.sizes["draw"], obs.sizes["match"])) - idata.add_groups( - log_likelihood={"home_points": ary}, - dims={"home_points": ["match"]}, - ) - idata - - This is fine if we have raw data, but a bit inconvenient if we start with labeled - data already. Why provide dims and coords manually again? - Let's generate a fake log likelihood (doesn't match the model but it serves just - the same for illustration purposes here) working from the posterior and - observed_data groups manually: - - .. jupyter-execute:: - - import xarray as xr - from xarray_einstats.stats import XrDiscreteRV - from scipy.stats import poisson - dist = XrDiscreteRV(poisson, np.exp(post["atts"])) - log_lik = dist.logpmf(obs["home_points"]).to_dataset(name="home_points") - idata2.add_groups({"log_likelihood": log_lik}) - idata2 - - Note that in the first example we have used the ``kwargs`` argument - and in the second we have used the ``group_dict`` one. - - See Also - -------- - extend : Extend InferenceData with groups from another InferenceData. - concat : Concatenate InferenceData objects. - """ - group_dict = either_dict_or_kwargs(group_dict, kwargs, "add_groups") - if not group_dict: - raise ValueError("One of group_dict or kwargs must be provided.") - repeated_groups = [group for group in group_dict.keys() if group in self._groups] - if repeated_groups: - raise ValueError(f"{repeated_groups} group(s) already exists.") - for group, dataset in group_dict.items(): - if warn_on_custom_groups and group not in SUPPORTED_GROUPS_ALL: - warnings.warn( - f"The group {group} is not defined in the InferenceData scheme", - UserWarning, - ) - if dataset is None: - continue - elif isinstance(dataset, dict): - if ( - group in ("observed_data", "constant_data", "predictions_constant_data") - or group not in SUPPORTED_GROUPS_ALL - ): - warnings.warn( - "the default dims 'chain' and 'draw' will be added automatically", - UserWarning, - ) - dataset = dict_to_dataset(dataset, coords=coords, dims=dims) - elif isinstance(dataset, xr.DataArray): - if dataset.name is None: - dataset.name = "x" - dataset = dataset.to_dataset() - elif not isinstance(dataset, xr.Dataset): - raise ValueError( - "Arguments to add_groups() must be xr.Dataset, xr.Dataarray or dicts\ - (argument '{}' was type '{}')".format( - group, type(dataset) - ) - ) - if dataset: - setattr(self, group, dataset) - if group.startswith(WARMUP_TAG): - supported_order = [ - key for key in SUPPORTED_GROUPS_ALL if key in self._groups_warmup - ] - if (supported_order == self._groups_warmup) and (group in SUPPORTED_GROUPS_ALL): - group_order = [ - key - for key in SUPPORTED_GROUPS_ALL - if key in self._groups_warmup + [group] - ] - group_idx = group_order.index(group) - self._groups_warmup.insert(group_idx, group) - else: - self._groups_warmup.append(group) - else: - supported_order = [key for key in SUPPORTED_GROUPS_ALL if key in self._groups] - if (supported_order == self._groups) and (group in SUPPORTED_GROUPS_ALL): - group_order = [ - key for key in SUPPORTED_GROUPS_ALL if key in self._groups + [group] - ] - group_idx = group_order.index(group) - self._groups.insert(group_idx, group) - else: - self._groups.append(group) - - def extend(self, other, join="left", warn_on_custom_groups=False): - """Extend InferenceData with groups from another InferenceData. - - Parameters - ---------- - other : InferenceData - InferenceData to be added - join : {'left', 'right'}, default 'left' - Defines how the two decide which group to keep when the same group is - present in both objects. 'left' will discard the group in ``other`` whereas 'right' - will keep the group in ``other`` and discard the one in ``self``. - warn_on_custom_groups : bool, default False - Emit a warning when custom groups are present in the InferenceData. - "custom group" means any group whose name isn't defined in :ref:`schema` - - Examples - -------- - Take two InferenceData objects, and extend the first with the groups it doesn't have - but are present in the 2nd InferenceData object. - - First InferenceData: - - .. jupyter-execute:: - - import arviz as az - idata = az.load_arviz_data("radon") - - Second InferenceData: - - .. jupyter-execute:: - - other_idata = az.load_arviz_data("rugby") - - Call the ``extend`` method: - - .. jupyter-execute:: - - idata.extend(other_idata) - idata - - See how now the first InferenceData has more groups, with the data from the - second one, but the groups it originally had have not been modified, - even if also present in the second InferenceData. - - See Also - -------- - add_groups : Add new groups to InferenceData object. - concat : Concatenate InferenceData objects. - - """ - if not isinstance(other, InferenceData): - raise ValueError("Extending is possible between two InferenceData objects only.") - if join not in ("left", "right"): - raise ValueError(f"join must be either 'left' or 'right', found {join}") - for group in other._groups_all: # pylint: disable=protected-access - if hasattr(self, group) and join == "left": - continue - if warn_on_custom_groups and group not in SUPPORTED_GROUPS_ALL: - warnings.warn( - f"{group} group is not defined in the InferenceData scheme", UserWarning - ) - dataset = getattr(other, group) - setattr(self, group, dataset) - if group.startswith(WARMUP_TAG): - if group not in self._groups_warmup: - supported_order = [ - key for key in SUPPORTED_GROUPS_ALL if key in self._groups_warmup - ] - if (supported_order == self._groups_warmup) and (group in SUPPORTED_GROUPS_ALL): - group_order = [ - key - for key in SUPPORTED_GROUPS_ALL - if key in self._groups_warmup + [group] - ] - group_idx = group_order.index(group) - self._groups_warmup.insert(group_idx, group) - else: - self._groups_warmup.append(group) - elif group not in self._groups: - supported_order = [key for key in SUPPORTED_GROUPS_ALL if key in self._groups] - if (supported_order == self._groups) and (group in SUPPORTED_GROUPS_ALL): - group_order = [ - key for key in SUPPORTED_GROUPS_ALL if key in self._groups + [group] - ] - group_idx = group_order.index(group) - self._groups.insert(group_idx, group) - else: - self._groups.append(group) - - set_index = _extend_xr_method(xr.Dataset.set_index, see_also="reset_index") - get_index = _extend_xr_method(xr.Dataset.get_index) - reset_index = _extend_xr_method(xr.Dataset.reset_index, see_also="set_index") - set_coords = _extend_xr_method(xr.Dataset.set_coords, see_also="reset_coords") - reset_coords = _extend_xr_method(xr.Dataset.reset_coords, see_also="set_coords") - assign = _extend_xr_method(xr.Dataset.assign) - assign_coords = _extend_xr_method(xr.Dataset.assign_coords) - sortby = _extend_xr_method(xr.Dataset.sortby) - chunk = _extend_xr_method(xr.Dataset.chunk) - unify_chunks = _extend_xr_method(xr.Dataset.unify_chunks) - load = _extend_xr_method(xr.Dataset.load) - compute = _extend_xr_method(xr.Dataset.compute) - persist = _extend_xr_method(xr.Dataset.persist) - quantile = _extend_xr_method(xr.Dataset.quantile) - close = _extend_xr_method(xr.Dataset.close) - - # The following lines use methods on xr.Dataset that are dynamically defined and attached. - # As a result mypy cannot see them, so we have to suppress the resulting mypy errors. - mean = _extend_xr_method(xr.Dataset.mean, see_also="median") # type: ignore[attr-defined] - median = _extend_xr_method(xr.Dataset.median, see_also="mean") # type: ignore[attr-defined] - min = _extend_xr_method(xr.Dataset.min, see_also=["max", "sum"]) # type: ignore[attr-defined] - max = _extend_xr_method(xr.Dataset.max, see_also=["min", "sum"]) # type: ignore[attr-defined] - cumsum = _extend_xr_method(xr.Dataset.cumsum, see_also="sum") # type: ignore[attr-defined] - sum = _extend_xr_method(xr.Dataset.sum, see_also="cumsum") # type: ignore[attr-defined] - - def _group_names( - self, - groups: Optional[Union[str, List[str]]], - filter_groups: Optional["Literal['like', 'regex']"] = None, - ) -> List[str]: - """Handle expansion of group names input across arviz. - - Parameters - ---------- - groups: str, list of str or None - group or metagroup names. - idata: xarray.Dataset - Posterior data in an xarray - filter_groups: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret groups as the real group or metagroup names. - If "like", interpret groups as substrings of the real group or metagroup names. - If "regex", interpret groups as regular expressions on the real group or - metagroup names. A la `pandas.filter`. - - Returns - ------- - groups: list - """ - if filter_groups not in {None, "like", "regex"}: - raise ValueError( - f"'filter_groups' can only be None, 'like', or 'regex', got: '{filter_groups}'" - ) - - all_groups = self._groups_all - if groups is None: - return all_groups - if isinstance(groups, str): - groups = [groups] - sel_groups = [] - metagroups = rcParams["data.metagroups"] - for group in groups: - if group[0] == "~": - sel_groups.extend( - [f"~{item}" for item in metagroups[group[1:]] if item in all_groups] - if group[1:] in metagroups - else [group] - ) - else: - sel_groups.extend( - [item for item in metagroups[group] if item in all_groups] - if group in metagroups - else [group] - ) - - try: - group_names = _subset_list(sel_groups, all_groups, filter_items=filter_groups) - except KeyError as err: - msg = " ".join(("groups:", f"{err}", "in InferenceData")) - raise KeyError(msg) from err - return group_names - - def map(self, fun, groups=None, filter_groups=None, inplace=False, args=None, **kwargs): - """Apply a function to multiple groups. - - Applies ``fun`` groupwise to the selected ``InferenceData`` groups and overwrites the - group with the result of the function. - - Parameters - ---------- - fun : callable - Function to be applied to each group. Assumes the function is called as - ``fun(dataset, *args, **kwargs)``. - groups : str or list of str, optional - Groups where the selection is to be applied. Can either be group names - or metagroup names. - filter_groups : {None, "like", "regex"}, optional - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - `pandas.filter`. - inplace : bool, optional - If ``True``, modify the InferenceData object inplace, - otherwise, return the modified copy. - args : array_like, optional - Positional arguments passed to ``fun``. - **kwargs : mapping, optional - Keyword arguments passed to ``fun``. - - Returns - ------- - InferenceData - A new InferenceData object by default. - When `inplace==True` perform selection in place and return `None` - - Examples - -------- - Shift observed_data, prior_predictive and posterior_predictive. - - .. jupyter-execute:: - - import arviz as az - import numpy as np - idata = az.load_arviz_data("non_centered_eight") - idata_shifted_obs = idata.map(lambda x: x + 3, groups="observed_vars") - idata_shifted_obs - - Rename and update the coordinate values in both posterior and prior groups. - - .. jupyter-execute:: - - idata = az.load_arviz_data("radon") - idata = idata.map( - lambda ds: ds.rename({"g_coef": "uranium_coefs"}).assign( - uranium_coefs=["intercept", "u_slope"] - ), - groups=["posterior", "prior"] - ) - idata - - Add extra coordinates to all groups containing observed variables - - .. jupyter-execute:: - - idata = az.load_arviz_data("rugby") - home_team, away_team = np.array([ - m.split() for m in idata.observed_data.match.values - ]).T - idata = idata.map( - lambda ds, **kwargs: ds.assign_coords(**kwargs), - groups="observed_vars", - home_team=("match", home_team), - away_team=("match", away_team), - ) - idata - - """ - if args is None: - args = [] - groups = self._group_names(groups, filter_groups) - - out = self if inplace else deepcopy(self) - for group in groups: - dataset = getattr(self, group) - dataset = fun(dataset, *args, **kwargs) - setattr(out, group, dataset) - if inplace: - return None - else: - return out - - def _wrap_xarray_method( - self, method, groups=None, filter_groups=None, inplace=False, args=None, **kwargs - ): - """Extend and xarray.Dataset method to InferenceData object. - - Parameters - ---------- - method: str - Method to be extended. Must be a ``xarray.Dataset`` method. - groups: str or list of str, optional - Groups where the selection is to be applied. Can either be group names - or metagroup names. - inplace: bool, optional - If ``True``, modify the InferenceData object inplace, - otherwise, return the modified copy. - **kwargs: mapping, optional - Keyword arguments passed to the xarray Dataset method. - - Returns - ------- - InferenceData - A new InferenceData object by default. - When `inplace==True` perform selection in place and return `None` - - Examples - -------- - Compute the mean of `posterior_groups`: - - .. ipython:: - - In [1]: import arviz as az - ...: idata = az.load_arviz_data("non_centered_eight") - ...: idata_means = idata._wrap_xarray_method("mean", groups="latent_vars") - ...: print(idata_means.posterior) - ...: print(idata_means.observed_data) - - .. ipython:: - - In [1]: idata_stack = idata._wrap_xarray_method( - ...: "stack", - ...: groups=["posterior_groups", "prior_groups"], - ...: sample=["chain", "draw"] - ...: ) - ...: print(idata_stack.posterior) - ...: print(idata_stack.prior) - ...: print(idata_stack.observed_data) - - """ - if args is None: - args = [] - groups = self._group_names(groups, filter_groups) - - method = getattr(xr.Dataset, method) - - out = self if inplace else deepcopy(self) - for group in groups: - dataset = getattr(self, group) - dataset = method(dataset, *args, **kwargs) - setattr(out, group, dataset) - if inplace: - return None - else: - return out - - def copy(self) -> "InferenceData": - """Return a fresh copy of the ``InferenceData`` object.""" - return deepcopy(self) - - -@overload -def concat( - *args, - dim: Optional[str] = None, - copy: bool = True, - inplace: "Literal[True]", - reset_dim: bool = True, -) -> None: ... - - -@overload -def concat( - *args, - dim: Optional[str] = None, - copy: bool = True, - inplace: "Literal[False]", - reset_dim: bool = True, -) -> InferenceData: ... - - -@overload -def concat( - ids: Iterable[InferenceData], - dim: Optional[str] = None, - *, - copy: bool = True, - inplace: "Literal[False]", - reset_dim: bool = True, -) -> InferenceData: ... - - -@overload -def concat( - ids: Iterable[InferenceData], - dim: Optional[str] = None, - *, - copy: bool = True, - inplace: "Literal[True]", - reset_dim: bool = True, -) -> None: ... - - -@overload -def concat( - ids: Iterable[InferenceData], - dim: Optional[str] = None, - *, - copy: bool = True, - inplace: bool = False, - reset_dim: bool = True, -) -> Optional[InferenceData]: ... - - -# pylint: disable=protected-access, inconsistent-return-statements -def concat(*args, dim=None, copy=True, inplace=False, reset_dim=True): - """Concatenate InferenceData objects. - - Concatenates over `group`, `chain` or `draw`. - By default concatenates over unique groups. - To concatenate over `chain` or `draw` function - needs identical groups and variables. - - The `variables` in the `data` -group are merged if `dim` are not found. - - - Parameters - ---------- - *args : InferenceData - Variable length InferenceData list or - Sequence of InferenceData. - dim : str, optional - If defined, concatenated over the defined dimension. - Dimension which is concatenated. If None, concatenates over - unique groups. - copy : bool - If True, groups are copied to the new InferenceData object. - Used only if `dim` is None. - inplace : bool - If True, merge args to first object. - reset_dim : bool - Valid only if dim is not None. - - Returns - ------- - InferenceData - A new InferenceData object by default. - When `inplace==True` merge args to first arg and return `None` - - See Also - -------- - add_groups : Add new groups to InferenceData object. - extend : Extend InferenceData with groups from another InferenceData. - - Examples - -------- - Use ``concat`` method to concatenate InferenceData objects. This will concatenates over - unique groups by default. We first create an ``InferenceData`` object: - - .. ipython:: - - In [1]: import arviz as az - ...: import numpy as np - ...: data = { - ...: "a": np.random.normal(size=(4, 100, 3)), - ...: "b": np.random.normal(size=(4, 100)), - ...: } - ...: coords = {"a_dim": ["x", "y", "z"]} - ...: dataA = az.from_dict(data, coords=coords, dims={"a": ["a_dim"]}) - ...: dataA - - We have created an ``InferenceData`` object with default group 'posterior'. Now, we will - create another ``InferenceData`` object: - - .. ipython:: - - In [1]: dataB = az.from_dict(prior=data, coords=coords, dims={"a": ["a_dim"]}) - ...: dataB - - We have created another ``InferenceData`` object with group 'prior'. Now, we will concatenate - these two ``InferenceData`` objects: - - .. ipython:: - - In [1]: az.concat(dataA, dataB) - - Now, we will concatenate over chain (or draw). It requires identical groups and variables. - Here we are concatenating two identical ``InferenceData`` objects over dimension chain: - - .. ipython:: - - In [1]: az.concat(dataA, dataA, dim="chain") - - It will create an ``InferenceData`` with the original group 'posterior'. In similar way, - we can also concatenate over draws. - - """ - # pylint: disable=undefined-loop-variable, too-many-nested-blocks - if len(args) == 0: - if inplace: - return - return InferenceData() - - if len(args) == 1 and isinstance(args[0], Sequence): - args = args[0] - - # assert that all args are InferenceData - for i, arg in enumerate(args): - if not isinstance(arg, InferenceData): - raise TypeError( - "Concatenating is supported only" - "between InferenceData objects. Input arg {} is {}".format(i, type(arg)) - ) - - if dim is not None and dim.lower() not in {"group", "chain", "draw"}: - msg = f'Invalid `dim`: {dim}. Valid `dim` are {{"group", "chain", "draw"}}' - raise TypeError(msg) - dim = dim.lower() if dim is not None else dim - - if len(args) == 1 and isinstance(args[0], InferenceData): - if inplace: - return None - else: - if copy: - return deepcopy(args[0]) - else: - return args[0] - - current_time = datetime.datetime.now(datetime.timezone.utc).isoformat() - combined_attr = defaultdict(list) - for idata in args: - for key, val in idata.attrs.items(): - combined_attr[key].append(val) - - for key, val in combined_attr.items(): - all_same = True - for indx in range(len(val) - 1): - if val[indx] != val[indx + 1]: - all_same = False - break - if all_same: - combined_attr[key] = val[0] - if inplace: - setattr(args[0], "_attrs", dict(combined_attr)) - - if not inplace: - # Keep order for python 3.5 - inference_data_dict = OrderedDict() - - if dim is None: - arg0 = args[0] - arg0_groups = ccopy(arg0._groups_all) - args_groups = {} - # check if groups are independent - # Concat over unique groups - for arg in args[1:]: - for group in arg._groups_all: - if group in args_groups or group in arg0_groups: - msg = ( - "Concatenating overlapping groups is not supported unless `dim` is defined." - " Valid dimensions are `chain` and `draw`. Alternatively, use extend to" - " combine InferenceData with overlapping groups" - ) - raise TypeError(msg) - group_data = getattr(arg, group) - args_groups[group] = deepcopy(group_data) if copy else group_data - # add arg0 to args_groups if inplace is False - # otherwise it will merge args_groups to arg0 - # inference data object - if not inplace: - for group in arg0_groups: - group_data = getattr(arg0, group) - args_groups[group] = deepcopy(group_data) if copy else group_data - - other_groups = [group for group in args_groups if group not in SUPPORTED_GROUPS_ALL] - - for group in SUPPORTED_GROUPS_ALL + other_groups: - if group not in args_groups: - continue - if inplace: - if group.startswith(WARMUP_TAG): - arg0._groups_warmup.append(group) - else: - arg0._groups.append(group) - setattr(arg0, group, args_groups[group]) - else: - inference_data_dict[group] = args_groups[group] - if inplace: - other_groups = [ - group for group in arg0_groups if group not in SUPPORTED_GROUPS_ALL - ] + other_groups - sorted_groups = [ - group for group in SUPPORTED_GROUPS + other_groups if group in arg0._groups - ] - setattr(arg0, "_groups", sorted_groups) - sorted_groups_warmup = [ - group - for group in SUPPORTED_GROUPS_WARMUP + other_groups - if group in arg0._groups_warmup - ] - setattr(arg0, "_groups_warmup", sorted_groups_warmup) - else: - arg0 = args[0] - arg0_groups = arg0._groups_all - for arg in args[1:]: - for group0 in arg0_groups: - if group0 not in arg._groups_all: - if group0 == "observed_data": - continue - msg = "Mismatch between the groups." - raise TypeError(msg) - for group in arg._groups_all: - # handle data groups separately - if group not in ["observed_data", "constant_data", "predictions_constant_data"]: - # assert that groups are equal - if group not in arg0_groups: - msg = "Mismatch between the groups." - raise TypeError(msg) - - # assert that variables are equal - group_data = getattr(arg, group) - group_vars = group_data.data_vars - - if not inplace and group in inference_data_dict: - group0_data = inference_data_dict[group] - else: - group0_data = getattr(arg0, group) - group0_vars = group0_data.data_vars - - for var in group0_vars: - if var not in group_vars: - msg = "Mismatch between the variables." - raise TypeError(msg) - - for var in group_vars: - if var not in group0_vars: - msg = "Mismatch between the variables." - raise TypeError(msg) - var_dims = group_data[var].dims - var0_dims = group0_data[var].dims - if var_dims != var0_dims: - msg = "Mismatch between the dimensions." - raise TypeError(msg) - - if dim not in var_dims or dim not in var0_dims: - msg = f"Dimension {dim} missing." - raise TypeError(msg) - - # xr.concat - concatenated_group = xr.concat((group0_data, group_data), dim=dim) - if reset_dim: - concatenated_group[dim] = range(concatenated_group[dim].size) - - # handle attrs - if hasattr(group0_data, "attrs"): - group0_attrs = deepcopy(getattr(group0_data, "attrs")) - else: - group0_attrs = OrderedDict() - - if hasattr(group_data, "attrs"): - group_attrs = getattr(group_data, "attrs") - else: - group_attrs = {} - - # gather attrs results to group0_attrs - for attr_key, attr_values in group_attrs.items(): - group0_attr_values = group0_attrs.get(attr_key, None) - equality = attr_values == group0_attr_values - if hasattr(equality, "__iter__"): - equality = np.all(equality) - if equality: - continue - # handle special cases: - if attr_key in ("created_at", "previous_created_at"): - # check the defaults - if not hasattr(group0_attrs, "previous_created_at"): - group0_attrs["previous_created_at"] = [] - if group0_attr_values is not None: - group0_attrs["previous_created_at"].append(group0_attr_values) - # check previous values - if attr_key == "previous_created_at": - if not isinstance(attr_values, list): - attr_values = [attr_values] - group0_attrs["previous_created_at"].extend(attr_values) - continue - # update "created_at" - if group0_attr_values != current_time: - group0_attrs[attr_key] = current_time - group0_attrs["previous_created_at"].append(attr_values) - - elif attr_key in group0_attrs: - combined_key = f"combined_{attr_key}" - if combined_key not in group0_attrs: - group0_attrs[combined_key] = [group0_attr_values] - group0_attrs[combined_key].append(attr_values) - else: - group0_attrs[attr_key] = attr_values - # update attrs - setattr(concatenated_group, "attrs", group0_attrs) - - if inplace: - setattr(arg0, group, concatenated_group) - else: - inference_data_dict[group] = concatenated_group - else: - # observed_data, "constant_data", "predictions_constant_data", - if group not in arg0_groups: - setattr(arg0, group, deepcopy(group_data) if copy else group_data) - arg0._groups.append(group) - continue - - # assert that variables are equal - group_data = getattr(arg, group) - group_vars = group_data.data_vars - - group0_data = getattr(arg0, group) - if not inplace: - group0_data = deepcopy(group0_data) - group0_vars = group0_data.data_vars - - for var in group_vars: - if var not in group0_vars: - var_data = group_data[var] - getattr(arg0, group)[var] = var_data - else: - var_data = group_data[var] - var0_data = group0_data[var] - if dim in var_data.dims and dim in var0_data.dims: - concatenated_var = xr.concat((group_data, group0_data), dim=dim) - group0_data[var] = concatenated_var - - # handle attrs - if hasattr(group0_data, "attrs"): - group0_attrs = getattr(group0_data, "attrs") - else: - group0_attrs = OrderedDict() - - if hasattr(group_data, "attrs"): - group_attrs = getattr(group_data, "attrs") - else: - group_attrs = {} - - # gather attrs results to group0_attrs - for attr_key, attr_values in group_attrs.items(): - group0_attr_values = group0_attrs.get(attr_key, None) - equality = attr_values == group0_attr_values - if hasattr(equality, "__iter__"): - equality = np.all(equality) - if equality: - continue - # handle special cases: - if attr_key in ("created_at", "previous_created_at"): - # check the defaults - if not hasattr(group0_attrs, "previous_created_at"): - group0_attrs["previous_created_at"] = [] - if group0_attr_values is not None: - group0_attrs["previous_created_at"].append(group0_attr_values) - # check previous values - if attr_key == "previous_created_at": - if not isinstance(attr_values, list): - attr_values = [attr_values] - group0_attrs["previous_created_at"].extend(attr_values) - continue - # update "created_at" - if group0_attr_values != current_time: - group0_attrs[attr_key] = current_time - group0_attrs["previous_created_at"].append(attr_values) - - elif attr_key in group0_attrs: - combined_key = f"combined_{attr_key}" - if combined_key not in group0_attrs: - group0_attrs[combined_key] = [group0_attr_values] - group0_attrs[combined_key].append(attr_values) - - else: - group0_attrs[attr_key] = attr_values - # update attrs - setattr(group0_data, "attrs", group0_attrs) - - if inplace: - setattr(arg0, group, group0_data) - else: - inference_data_dict[group] = group0_data - - if not inplace: - inference_data_dict["attrs"] = combined_attr - - return None if inplace else InferenceData(**inference_data_dict) diff --git a/arviz/data/io_beanmachine.py b/arviz/data/io_beanmachine.py deleted file mode 100644 index 3b2fa543ee..0000000000 --- a/arviz/data/io_beanmachine.py +++ /dev/null @@ -1,112 +0,0 @@ -"""beanmachine-specific conversion code.""" - -from .inference_data import InferenceData -from .base import dict_to_dataset, requires - - -class BMConverter: - """Encapsulate Bean Machine specific logic.""" - - def __init__( - self, - *, - sampler=None, - coords=None, - dims=None, - ) -> None: - self.sampler = sampler - self.coords = coords - self.dims = dims - - import beanmachine.ppl as bm # pylint: disable=import-error - - self.beanm = bm - - if "posterior" in self.sampler.namespaces: - self.posterior = self.sampler.namespaces["posterior"].samples - else: - self.posterior = None - - if "posterior_predictive" in self.sampler.namespaces: - self.posterior_predictive = self.sampler.namespaces["posterior_predictive"].samples - else: - self.posterior_predictive = None - - if self.sampler.log_likelihoods is not None: - self.log_likelihoods = self.sampler.log_likelihoods - else: - self.log_likelihoods = None - - if self.sampler.observations is not None: - self.observations = self.sampler.observations - else: - self.observations = None - - @requires("posterior") - def posterior_to_xarray(self): - """Convert the posterior to an xarray dataset.""" - data = {k: v.detach().cpu().numpy() for k, v in self.posterior.items()} - return dict_to_dataset(data, library=self.beanm, coords=self.coords, dims=self.dims) - - @requires("posterior_predictive") - def posterior_predictive_to_xarray(self): - """Convert posterior_predictive samples to xarray.""" - data = {k: v.detach().cpu().numpy() for k, v in self.posterior_predictive.items()} - return dict_to_dataset(data, library=self.beanm, coords=self.coords, dims=self.dims) - - @requires("log_likelihoods") - def log_likelihood_to_xarray(self): - data = {k: v.detach().cpu().numpy() for k, v in self.log_likelihoods.items()} - return dict_to_dataset(data, library=self.beanm, coords=self.coords, dims=self.dims) - - @requires("observations") - def observed_data_to_xarray(self): - """Convert observed data to xarray.""" - data = {k: v.detach().cpu().numpy() for k, v in self.observations.items()} - return dict_to_dataset( - data, library=self.beanm, coords=self.coords, dims=self.dims, default_dims=[] - ) - - def to_inference_data(self): - """Convert all available data to an InferenceData object.""" - return InferenceData( - **{ - "posterior": self.posterior_to_xarray(), - "posterior_predictive": self.posterior_predictive_to_xarray(), - "log_likelihood": self.log_likelihood_to_xarray(), - "observed_data": self.observed_data_to_xarray(), - } - ) - - -def from_beanmachine( - sampler=None, - *, - coords=None, - dims=None, -): - """Convert Bean Machine MonteCarloSamples object into an InferenceData object. - - For a usage example read the - :ref:`Creating InferenceData section on from_beanmachine ` - - - Parameters - ---------- - sampler : bm.MonteCarloSamples - Fitted MonteCarloSamples object from Bean Machine - coords : dict of {str : array-like} - Map of dimensions to coordinates - dims : dict of {str : list of str} - Map variable names to their coordinates - - Warnings - -------- - `beanmachine` is no longer under active development, and therefore, it - is not possible to test this converter in ArviZ's CI. - """ - return BMConverter( - sampler=sampler, - coords=coords, - dims=dims, - ).to_inference_data() diff --git a/arviz/data/io_cmdstan.py b/arviz/data/io_cmdstan.py deleted file mode 100644 index b8e256632f..0000000000 --- a/arviz/data/io_cmdstan.py +++ /dev/null @@ -1,1036 +0,0 @@ -# pylint: disable=too-many-lines -"""CmdStan-specific conversion code.""" -try: - import ujson as json -except ImportError: - # Can't find ujson using json - # mypy struggles with conditional imports expressed as catching ImportError: - # https://github.com/python/mypy/issues/1153 - import json # type: ignore -import logging -import os -import re -from collections import defaultdict -from glob import glob -from pathlib import Path -from typing import Dict, List, Optional, Union - -import numpy as np - -from .. import utils -from ..rcparams import rcParams -from .base import CoordSpec, DimSpec, dict_to_dataset, infer_stan_dtypes, requires -from .inference_data import InferenceData - -_log = logging.getLogger(__name__) - - -def check_glob(path, group, disable_glob): - """Find files with glob.""" - if isinstance(path, str) and (not disable_glob): - path_glob = glob(path) - if path_glob: - path = sorted(path_glob) - msg = "\n".join(f"{i}: {os.path.normpath(fpath)}" for i, fpath in enumerate(path, 1)) - len_p = len(path) - _log.info("glob found %d files for '%s':\n%s", len_p, group, msg) - return path - - -class CmdStanConverter: - """Encapsulate CmdStan specific logic.""" - - # pylint: disable=too-many-instance-attributes - - def __init__( - self, - *, - posterior=None, - posterior_predictive=None, - predictions=None, - prior=None, - prior_predictive=None, - observed_data=None, - observed_data_var=None, - constant_data=None, - constant_data_var=None, - predictions_constant_data=None, - predictions_constant_data_var=None, - log_likelihood=None, - index_origin=None, - coords=None, - dims=None, - disable_glob=False, - save_warmup=None, - dtypes=None, - ): - self.posterior_ = check_glob(posterior, "posterior", disable_glob) - self.posterior_predictive = check_glob( - posterior_predictive, "posterior_predictive", disable_glob - ) - self.predictions = check_glob(predictions, "predictions", disable_glob) - self.prior_ = check_glob(prior, "prior", disable_glob) - self.prior_predictive = check_glob(prior_predictive, "prior_predictive", disable_glob) - self.log_likelihood = check_glob(log_likelihood, "log_likelihood", disable_glob) - self.observed_data = observed_data - self.observed_data_var = observed_data_var - self.constant_data = constant_data - self.constant_data_var = constant_data_var - self.predictions_constant_data = predictions_constant_data - self.predictions_constant_data_var = predictions_constant_data_var - self.coords = coords if coords is not None else {} - self.dims = dims if dims is not None else {} - - self.posterior = None - self.prior = None - self.attrs = None - self.attrs_prior = None - - self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup - self.index_origin = index_origin - - if dtypes is None: - dtypes = {} - elif isinstance(dtypes, str): - dtypes_path = Path(dtypes) - if dtypes_path.exists(): - with dtypes_path.open("r", encoding="UTF-8") as f_obj: - model_code = f_obj.read() - else: - model_code = dtypes - - dtypes = infer_stan_dtypes(model_code) - - self.dtypes = dtypes - - # populate posterior and sample_stats - self._parse_posterior() - self._parse_prior() - - if ( - self.log_likelihood is None - and self.posterior_ is not None - and any(name.split(".")[0] == "log_lik" for name in self.posterior_columns) - ): - self.log_likelihood = ["log_lik"] - elif isinstance(self.log_likelihood, bool): - self.log_likelihood = None - - @requires("posterior_") - def _parse_posterior(self): - """Read csv paths to list of ndarrays.""" - paths = self.posterior_ - if isinstance(paths, str): - paths = [paths] - - chain_data = [] - columns = None - for path in paths: - output_data = _read_output(path) - chain_data.append(output_data) - if columns is None: - columns = output_data - - self.posterior = ( - [item["sample"] for item in chain_data], - [item["sample_warmup"] for item in chain_data], - ) - self.posterior_columns = columns["sample_columns"] - self.sample_stats_columns = columns["sample_stats_columns"] - - attrs = {} - for item in chain_data: - for key, value in item["configuration_info"].items(): - if key not in attrs: - attrs[key] = [] - attrs[key].append(value) - self.attrs = attrs - - @requires("prior_") - def _parse_prior(self): - """Read csv paths to list of ndarrays.""" - paths = self.prior_ - if isinstance(paths, str): - paths = [paths] - - chain_data = [] - columns = None - for path in paths: - output_data = _read_output(path) - chain_data.append(output_data) - if columns is None: - columns = output_data - - self.prior = ( - [item["sample"] for item in chain_data], - [item["sample_warmup"] for item in chain_data], - ) - self.prior_columns = columns["sample_columns"] - self.sample_stats_prior_columns = columns["sample_stats_columns"] - - attrs = {} - for item in chain_data: - for key, value in item["configuration_info"].items(): - if key not in attrs: - attrs[key] = [] - attrs[key].append(value) - self.attrs_prior = attrs - - @requires("posterior") - def posterior_to_xarray(self): - """Extract posterior samples from output csv.""" - columns = self.posterior_columns - - # filter posterior_predictive, predictions and log_likelihood - posterior_predictive = self.posterior_predictive - if posterior_predictive is None or ( - isinstance(posterior_predictive, str) and posterior_predictive.lower().endswith(".csv") - ): - posterior_predictive = [] - elif isinstance(posterior_predictive, str): - posterior_predictive = [ - col for col in columns if posterior_predictive == col.split(".")[0] - ] - else: - posterior_predictive = [ - col - for col in columns - if any(item == col.split(".")[0] for item in posterior_predictive) - ] - - predictions = self.predictions - if predictions is None or ( - isinstance(predictions, str) and predictions.lower().endswith(".csv") - ): - predictions = [] - elif isinstance(predictions, str): - predictions = [col for col in columns if predictions == col.split(".")[0]] - else: - predictions = [ - col for col in columns if any(item == col.split(".")[0] for item in predictions) - ] - - log_likelihood = self.log_likelihood - if log_likelihood is None or ( - isinstance(log_likelihood, str) and log_likelihood.lower().endswith(".csv") - ): - log_likelihood = [] - elif isinstance(log_likelihood, str): - log_likelihood = [col for col in columns if log_likelihood == col.split(".")[0]] - elif isinstance(log_likelihood, dict): - log_likelihood = [ - col - for col in columns - if any(item == col.split(".")[0] for item in log_likelihood.values()) - ] - else: - log_likelihood = [ - col for col in columns if any(item == col.split(".")[0] for item in log_likelihood) - ] - - invalid_cols = posterior_predictive + predictions + log_likelihood - valid_cols = {col: idx for col, idx in columns.items() if col not in invalid_cols} - data = _unpack_ndarrays(self.posterior[0], valid_cols, self.dtypes) - data_warmup = _unpack_ndarrays(self.posterior[1], valid_cols, self.dtypes) - return ( - dict_to_dataset( - data, - coords=self.coords, - dims=self.dims, - attrs=self.attrs, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - coords=self.coords, - dims=self.dims, - attrs=self.attrs, - index_origin=self.index_origin, - ), - ) - - @requires("posterior") - @requires("sample_stats_columns") - def sample_stats_to_xarray(self): - """Extract sample_stats from fit.""" - dtypes = {"diverging": bool, "n_steps": np.int64, "tree_depth": np.int64, **self.dtypes} - rename_dict = { - "divergent": "diverging", - "n_leapfrog": "n_steps", - "treedepth": "tree_depth", - "stepsize": "step_size", - "accept_stat": "acceptance_rate", - } - - columns_new = {} - for key, idx in self.sample_stats_columns.items(): - name = re.sub("__$", "", key) - name = rename_dict.get(name, name) - columns_new[name] = idx - - data = _unpack_ndarrays(self.posterior[0], columns_new, dtypes) - data_warmup = _unpack_ndarrays(self.posterior[1], columns_new, dtypes) - return ( - dict_to_dataset( - data, - coords=self.coords, - dims=self.dims, - attrs={item: key for key, item in rename_dict.items()}, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - coords=self.coords, - dims=self.dims, - attrs={item: key for key, item in rename_dict.items()}, - index_origin=self.index_origin, - ), - ) - - @requires("posterior") - @requires("posterior_predictive") - def posterior_predictive_to_xarray(self): - """Convert posterior_predictive samples to xarray.""" - posterior_predictive = self.posterior_predictive - - if ( - isinstance(posterior_predictive, (tuple, list)) - and posterior_predictive[0].endswith(".csv") - ) or (isinstance(posterior_predictive, str) and posterior_predictive.endswith(".csv")): - if isinstance(posterior_predictive, str): - posterior_predictive = [posterior_predictive] - chain_data = [] - chain_data_warmup = [] - columns = None - attrs = {} - for path in posterior_predictive: - parsed_output = _read_output(path) - chain_data.append(parsed_output["sample"]) - chain_data_warmup.append(parsed_output["sample_warmup"]) - if columns is None: - columns = parsed_output["sample_columns"] - - for key, value in parsed_output["configuration_info"].items(): - if key not in attrs: - attrs[key] = [] - attrs[key].append(value) - - data = _unpack_ndarrays(chain_data, columns, self.dtypes) - data_warmup = _unpack_ndarrays(chain_data_warmup, columns, self.dtypes) - - else: - if isinstance(posterior_predictive, str): - posterior_predictive = [posterior_predictive] - columns = { - col: idx - for col, idx in self.posterior_columns.items() - if any(item == col.split(".")[0] for item in posterior_predictive) - } - data = _unpack_ndarrays(self.posterior[0], columns, self.dtypes) - data_warmup = _unpack_ndarrays(self.posterior[1], columns, self.dtypes) - - attrs = None - return ( - dict_to_dataset( - data, - coords=self.coords, - dims=self.dims, - attrs=attrs, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - coords=self.coords, - dims=self.dims, - attrs=attrs, - index_origin=self.index_origin, - ), - ) - - @requires("posterior") - @requires("predictions") - def predictions_to_xarray(self): - """Convert out of sample predictions samples to xarray.""" - predictions = self.predictions - - if (isinstance(predictions, (tuple, list)) and predictions[0].endswith(".csv")) or ( - isinstance(predictions, str) and predictions.endswith(".csv") - ): - if isinstance(predictions, str): - predictions = [predictions] - chain_data = [] - chain_data_warmup = [] - columns = None - attrs = {} - for path in predictions: - parsed_output = _read_output(path) - chain_data.append(parsed_output["sample"]) - chain_data_warmup.append(parsed_output["sample_warmup"]) - if columns is None: - columns = parsed_output["sample_columns"] - - for key, value in parsed_output["configuration_info"].items(): - if key not in attrs: - attrs[key] = [] - attrs[key].append(value) - - data = _unpack_ndarrays(chain_data, columns, self.dtypes) - data_warmup = _unpack_ndarrays(chain_data_warmup, columns, self.dtypes) - else: - if isinstance(predictions, str): - predictions = [predictions] - columns = { - col: idx - for col, idx in self.posterior_columns.items() - if any(item == col.split(".")[0] for item in predictions) - } - data = _unpack_ndarrays(self.posterior[0], columns, self.dtypes) - data_warmup = _unpack_ndarrays(self.posterior[1], columns, self.dtypes) - - attrs = None - return ( - dict_to_dataset( - data, - coords=self.coords, - dims=self.dims, - attrs=attrs, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - coords=self.coords, - dims=self.dims, - attrs=attrs, - index_origin=self.index_origin, - ), - ) - - @requires("posterior") - @requires("log_likelihood") - def log_likelihood_to_xarray(self): - """Convert elementwise log_likelihood samples to xarray.""" - log_likelihood = self.log_likelihood - - if (isinstance(log_likelihood, (tuple, list)) and log_likelihood[0].endswith(".csv")) or ( - isinstance(log_likelihood, str) and log_likelihood.endswith(".csv") - ): - if isinstance(log_likelihood, str): - log_likelihood = [log_likelihood] - - chain_data = [] - chain_data_warmup = [] - columns = None - attrs = {} - for path in log_likelihood: - parsed_output = _read_output(path) - chain_data.append(parsed_output["sample"]) - chain_data_warmup.append(parsed_output["sample_warmup"]) - - if columns is None: - columns = parsed_output["sample_columns"] - - for key, value in parsed_output["configuration_info"].items(): - if key not in attrs: - attrs[key] = [] - attrs[key].append(value) - data = _unpack_ndarrays(chain_data, columns, self.dtypes) - data_warmup = _unpack_ndarrays(chain_data_warmup, columns, self.dtypes) - else: - if isinstance(log_likelihood, dict): - log_lik_to_obs_name = {v: k for k, v in log_likelihood.items()} - columns = { - col.replace(col_name, log_lik_to_obs_name[col_name]): idx - for col, col_name, idx in ( - (col, col.split(".")[0], idx) for col, idx in self.posterior_columns.items() - ) - if any(item == col_name for item in log_likelihood.values()) - } - else: - if isinstance(log_likelihood, str): - log_likelihood = [log_likelihood] - columns = { - col: idx - for col, idx in self.posterior_columns.items() - if any(item == col.split(".")[0] for item in log_likelihood) - } - data = _unpack_ndarrays(self.posterior[0], columns, self.dtypes) - data_warmup = _unpack_ndarrays(self.posterior[1], columns, self.dtypes) - attrs = None - return ( - dict_to_dataset( - data, - coords=self.coords, - dims=self.dims, - attrs=attrs, - index_origin=self.index_origin, - skip_event_dims=True, - ), - dict_to_dataset( - data_warmup, - coords=self.coords, - dims=self.dims, - attrs=attrs, - index_origin=self.index_origin, - skip_event_dims=True, - ), - ) - - @requires("prior") - def prior_to_xarray(self): - """Convert prior samples to xarray.""" - # filter prior_predictive - prior_predictive = self.prior_predictive - - columns = self.prior_columns - - if prior_predictive is None or ( - isinstance(prior_predictive, str) and prior_predictive.lower().endswith(".csv") - ): - prior_predictive = [] - elif isinstance(prior_predictive, str): - prior_predictive = [col for col in columns if prior_predictive == col.split(".")[0]] - else: - prior_predictive = [ - col - for col in columns - if any(item == col.split(".")[0] for item in prior_predictive) - ] - - invalid_cols = prior_predictive - valid_cols = {col: idx for col, idx in columns.items() if col not in invalid_cols} - data = _unpack_ndarrays(self.prior[0], valid_cols, self.dtypes) - data_warmup = _unpack_ndarrays(self.prior[1], valid_cols, self.dtypes) - return ( - dict_to_dataset( - data, - coords=self.coords, - dims=self.dims, - attrs=self.attrs_prior, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - coords=self.coords, - dims=self.dims, - attrs=self.attrs_prior, - index_origin=self.index_origin, - ), - ) - - @requires("prior") - @requires("sample_stats_prior_columns") - def sample_stats_prior_to_xarray(self): - """Extract sample_stats from fit.""" - dtypes = {"diverging": bool, "n_steps": np.int64, "tree_depth": np.int64, **self.dtypes} - rename_dict = { - "divergent": "diverging", - "n_leapfrog": "n_steps", - "treedepth": "tree_depth", - "stepsize": "step_size", - "accept_stat": "acceptance_rate", - } - - columns_new = {} - for key, idx in self.sample_stats_prior_columns.items(): - name = re.sub("__$", "", key) - name = rename_dict.get(name, name) - columns_new[name] = idx - - data = _unpack_ndarrays(self.posterior[0], columns_new, dtypes) - data_warmup = _unpack_ndarrays(self.posterior[1], columns_new, dtypes) - return ( - dict_to_dataset( - data, - coords=self.coords, - dims=self.dims, - attrs={item: key for key, item in rename_dict.items()}, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - coords=self.coords, - dims=self.dims, - attrs={item: key for key, item in rename_dict.items()}, - index_origin=self.index_origin, - ), - ) - - @requires("prior") - @requires("prior_predictive") - def prior_predictive_to_xarray(self): - """Convert prior_predictive samples to xarray.""" - prior_predictive = self.prior_predictive - - if ( - isinstance(prior_predictive, (tuple, list)) and prior_predictive[0].endswith(".csv") - ) or (isinstance(prior_predictive, str) and prior_predictive.endswith(".csv")): - if isinstance(prior_predictive, str): - prior_predictive = [prior_predictive] - chain_data = [] - chain_data_warmup = [] - columns = None - attrs = {} - for path in prior_predictive: - parsed_output = _read_output(path) - chain_data.append(parsed_output["sample"]) - chain_data_warmup.append(parsed_output["sample_warmup"]) - if columns is None: - columns = parsed_output["sample_columns"] - for key, value in parsed_output["configuration_info"].items(): - if key not in attrs: - attrs[key] = [] - attrs[key].append(value) - data = _unpack_ndarrays(chain_data, columns, self.dtypes) - data_warmup = _unpack_ndarrays(chain_data_warmup, columns, self.dtypes) - else: - if isinstance(prior_predictive, str): - prior_predictive = [prior_predictive] - columns = { - col: idx - for col, idx in self.prior_columns.items() - if any(item == col.split(".")[0] for item in prior_predictive) - } - data = _unpack_ndarrays(self.prior[0], columns, self.dtypes) - data_warmup = _unpack_ndarrays(self.prior[1], columns, self.dtypes) - attrs = None - return ( - dict_to_dataset( - data, - coords=self.coords, - dims=self.dims, - attrs=attrs, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - coords=self.coords, - dims=self.dims, - attrs=attrs, - index_origin=self.index_origin, - ), - ) - - @requires("observed_data") - def observed_data_to_xarray(self): - """Convert observed data to xarray.""" - observed_data_raw = _read_data(self.observed_data) - variables = self.observed_data_var - if isinstance(variables, str): - variables = [variables] - observed_data = { - key: utils.one_de(vals) - for key, vals in observed_data_raw.items() - if variables is None or key in variables - } - return dict_to_dataset( - observed_data, - coords=self.coords, - dims=self.dims, - default_dims=[], - index_origin=self.index_origin, - ) - - @requires("constant_data") - def constant_data_to_xarray(self): - """Convert constant data to xarray.""" - constant_data_raw = _read_data(self.constant_data) - variables = self.constant_data_var - if isinstance(variables, str): - variables = [variables] - constant_data = { - key: utils.one_de(vals) - for key, vals in constant_data_raw.items() - if variables is None or key in variables - } - return dict_to_dataset( - constant_data, - coords=self.coords, - dims=self.dims, - default_dims=[], - index_origin=self.index_origin, - ) - - @requires("predictions_constant_data") - def predictions_constant_data_to_xarray(self): - """Convert predictions constant data to xarray.""" - predictions_constant_data_raw = _read_data(self.predictions_constant_data) - variables = self.predictions_constant_data_var - if isinstance(variables, str): - variables = [variables] - predictions_constant_data = {} - for key, vals in predictions_constant_data_raw.items(): - if variables is not None and key not in variables: - continue - vals = utils.one_de(vals) - predictions_constant_data[key] = utils.one_de(vals) - return dict_to_dataset( - predictions_constant_data, - coords=self.coords, - dims=self.dims, - default_dims=[], - index_origin=self.index_origin, - ) - - def to_inference_data(self): - """Convert all available data to an InferenceData object. - - Note that if groups can not be created (i.e., there is no `output`, so - the `posterior` and `sample_stats` can not be extracted), then the InferenceData - will not have those groups. - """ - return InferenceData( - save_warmup=self.save_warmup, - **{ - "posterior": self.posterior_to_xarray(), - "sample_stats": self.sample_stats_to_xarray(), - "log_likelihood": self.log_likelihood_to_xarray(), - "posterior_predictive": self.posterior_predictive_to_xarray(), - "prior": self.prior_to_xarray(), - "sample_stats_prior": self.sample_stats_prior_to_xarray(), - "prior_predictive": self.prior_predictive_to_xarray(), - "observed_data": self.observed_data_to_xarray(), - "constant_data": self.constant_data_to_xarray(), - "predictions": self.predictions_to_xarray(), - "predictions_constant_data": self.predictions_constant_data_to_xarray(), - }, - ) - - -def _process_configuration(comments): - """Extract sampling information.""" - results = { - "comments": "\n".join(comments), - "stan_version": {}, - } - - comments_gen = iter(comments) - - for comment in comments_gen: - comment = re.sub(r"^\s*#\s*|\s*\(Default\)\s*$", "", comment).strip() - if comment.startswith("stan_version_"): - key, val = re.sub(r"^\s*stan_version_", "", comment).split("=") - results["stan_version"][key.strip()] = val.strip() - elif comment.startswith("Step size"): - _, val = comment.split("=") - results["step_size"] = float(val.strip()) - elif "inverse mass matrix" in comment: - comment = re.sub(r"^\s*#\s*", "", next(comments_gen)).strip() - results["inverse_mass_matrix"] = [float(item) for item in comment.split(",")] - elif ("seconds" in comment) and any( - item in comment for item in ("(Warm-up)", "(Sampling)", "(Total)") - ): - value = re.sub( - ( - r"^Elapsed\s*Time:\s*|" - r"\s*seconds\s*\(Warm-up\)\s*|" - r"\s*seconds\s*\(Sampling\)\s*|" - r"\s*seconds\s*\(Total\)\s*" - ), - "", - comment, - ) - key = ( - "warmup_time_seconds" - if "(Warm-up)" in comment - else "sampling_time_seconds" if "(Sampling)" in comment else "total_time_seconds" - ) - results[key] = float(value) - elif "=" in comment: - match_int = re.search(r"^(\S+)\s*=\s*([-+]?[0-9]+)$", comment) - match_float = re.search(r"^(\S+)\s*=\s*([-+]?[0-9]+\.[0-9]+)$", comment) - match_str_bool = re.search(r"^(\S+)\s*=\s*(true|false)$", comment) - match_str = re.search(r"^(\S+)\s*=\s*(\S+)$", comment) - match_empty = re.search(r"^(\S+)\s*=\s*$", comment) - if match_int: - key, value = match_int.group(1), match_int.group(2) - results[key] = int(value) - elif match_float: - key, value = match_float.group(1), match_float.group(2) - results[key] = float(value) - elif match_str_bool: - key, value = match_str_bool.group(1), match_str_bool.group(2) - results[key] = int(value == "true") - elif match_str: - key, value = match_str.group(1), match_str.group(2) - results[key] = value - elif match_empty: - key = match_empty.group(1) - results[key] = None - - results = {key: str(results[key]) for key in sorted(results)} - return results - - -def _read_output_file(path): - """Read Stan csv file to ndarray.""" - comments = [] - data = [] - columns = None - with open(path, "rb") as f_obj: - # read header - for line in f_obj: - if line.startswith(b"#"): - comments.append(line.strip().decode("utf-8")) - continue - columns = {key: idx for idx, key in enumerate(line.strip().decode("utf-8").split(","))} - break - # read data - for line in f_obj: - line = line.strip() - if line.startswith(b"#"): - comments.append(line.decode("utf-8")) - continue - if line: - data.append(np.array(line.split(b","), dtype=np.float64)) - - return columns, np.array(data, dtype=np.float64), comments - - -def _read_output(path): - """Read CmdStan output csv file. - - Parameters - ---------- - path : str - - Returns - ------- - Dict[str, Any] - """ - # Read data - columns, data, comments = _read_output_file(path) - - pconf = _process_configuration(comments) - - # split dataframe to warmup and draws - saved_warmup = ( - int(pconf.get("save_warmup", 0)) - * int(pconf.get("num_warmup", 0)) - // int(pconf.get("thin", 1)) - ) - - data_warmup = data[:saved_warmup] - data = data[saved_warmup:] - - # Split data to sample_stats and sample - sample_stats_columns = {col: idx for col, idx in columns.items() if col.endswith("__")} - sample_columns = {col: idx for col, idx in columns.items() if col not in sample_stats_columns} - - return { - "sample": data, - "sample_warmup": data_warmup, - "sample_columns": sample_columns, - "sample_stats_columns": sample_stats_columns, - "configuration_info": pconf, - } - - -def _process_data_var(string): - """Transform datastring to key, values pair. - - All values are transformed to floating point values. - - Parameters - ---------- - string : str - - Returns - ------- - Tuple[Str, Str] - key, values pair - """ - key, var = string.split("<-") - if "structure" in var: - var, dim = var.replace("structure(", "").replace(",", "").split(".Dim") - # dtype = int if '.' not in var and 'e' not in var.lower() else float - dtype = float - var = var.replace("c(", "").replace(")", "").strip().split() - dim = dim.replace("=", "").replace("c(", "").replace(")", "").strip().split() - dim = tuple(map(int, dim)) - var = np.fromiter(map(dtype, var), dtype).reshape(dim, order="F") - elif "c(" in var: - # dtype = int if '.' not in var and 'e' not in var.lower() else float - dtype = float - var = var.replace("c(", "").replace(")", "").split(",") - var = np.fromiter(map(dtype, var), dtype) - else: - # dtype = int if '.' not in var and 'e' not in var.lower() else float - dtype = float - var = dtype(var) - return key.strip(), var - - -def _read_data(path): - """Read Rdump or JSON output to dictionary. - - Parameters - ---------- - path : str - - Returns - ------- - Dict - key, values pairs from Rdump/JSON formatted data. - """ - data = {} - with open(path, "r", encoding="utf8") as f_obj: - if path.lower().endswith(".json"): - return json.load(f_obj) - var = "" - for line in f_obj: - if "<-" in line: - if len(var): - key, var = _process_data_var(var) - data[key] = var - var = "" - var += f" {line.strip()}" - if len(var): - key, var = _process_data_var(var) - data[key] = var - return data - - -def _unpack_ndarrays(arrays, columns, dtypes=None): - """Transform a list of ndarrays to dictionary containing ndarrays. - - Parameters - ---------- - arrays : List[np.ndarray] - columns: Dict[str, int] - dtypes: Dict[str, Any] - - Returns - ------- - Dict - key, values pairs. Values are formatted to shape = (nchain, ndraws, *shape) - """ - col_groups = defaultdict(list) - for col, col_idx in columns.items(): - key, *loc = col.split(".") - loc = tuple(int(i) - 1 for i in loc) - col_groups[key].append((col_idx, loc)) - - chains = len(arrays) - draws = len(arrays[0]) - sample = {} - if draws: - for key, cols_locs in col_groups.items(): - ndim = np.array([loc for _, loc in cols_locs]).max(0) + 1 - dtype = dtypes.get(key, np.float64) - sample[key] = np.zeros((chains, draws, *ndim), dtype=dtype) - for col, loc in cols_locs: - for chain_id, arr in enumerate(arrays): - draw = arr[:, col] - if loc == (): - sample[key][chain_id, :] = draw - else: - axis1_all = range(sample[key].shape[1]) - slicer = (chain_id, axis1_all, *loc) - sample[key][slicer] = draw - return sample - - -def from_cmdstan( - posterior: Optional[Union[str, List[str]]] = None, - *, - posterior_predictive: Optional[Union[str, List[str]]] = None, - predictions: Optional[Union[str, List[str]]] = None, - prior: Optional[Union[str, List[str]]] = None, - prior_predictive: Optional[Union[str, List[str]]] = None, - observed_data: Optional[str] = None, - observed_data_var: Optional[Union[str, List[str]]] = None, - constant_data: Optional[str] = None, - constant_data_var: Optional[Union[str, List[str]]] = None, - predictions_constant_data: Optional[str] = None, - predictions_constant_data_var: Optional[Union[str, List[str]]] = None, - log_likelihood: Optional[Union[str, List[str]]] = None, - index_origin: Optional[int] = None, - coords: Optional[CoordSpec] = None, - dims: Optional[DimSpec] = None, - disable_glob: Optional[bool] = False, - save_warmup: Optional[bool] = None, - dtypes: Optional[Dict] = None, -) -> InferenceData: - """Convert CmdStan data into an InferenceData object. - - For a usage example read the - :ref:`Creating InferenceData section on from_cmdstan ` - - Parameters - ---------- - posterior : str or list of str, optional - List of paths to output.csv files. - posterior_predictive : str or list of str, optional - Posterior predictive samples for the fit. If endswith ".csv" assumes file. - predictions : str or list of str, optional - Out of sample predictions samples for the fit. If endswith ".csv" assumes file. - prior : str or list of str, optional - List of paths to output.csv files - prior_predictive : str or list of str, optional - Prior predictive samples for the fit. If endswith ".csv" assumes file. - observed_data : str, optional - Observed data used in the sampling. Path to data file in Rdump or JSON format. - observed_data_var : str or list of str, optional - Variable(s) used for slicing observed_data. If not defined, all - data variables are imported. - constant_data : str, optional - Constant data used in the sampling. Path to data file in Rdump or JSON format. - constant_data_var : str or list of str, optional - Variable(s) used for slicing constant_data. If not defined, all - data variables are imported. - predictions_constant_data : str, optional - Constant data for predictions used in the sampling. - Path to data file in Rdump or JSON format. - predictions_constant_data_var : str or list of str, optional - Variable(s) used for slicing predictions_constant_data. - If not defined, all data variables are imported. - log_likelihood : dict of {str: str}, list of str or str, optional - Pointwise log_likelihood for the data. log_likelihood is extracted from the - posterior. It is recommended to use this argument as a dictionary whose keys - are observed variable names and its values are the variables storing log - likelihood arrays in the Stan code. In other cases, a dictionary with keys - equal to its values is used. By default, if a variable ``log_lik`` is - present in the Stan model, it will be retrieved as pointwise log - likelihood values. Use ``False`` to avoid this behaviour. - index_origin : int, optional - Starting value of integer coordinate values. Defaults to the value in rcParam - ``data.index_origin``. - coords : dict of {str: array_like}, optional - A dictionary containing the values that are used as index. The key - is the name of the dimension, the values are the index values. - dims : dict of {str: list of str}, optional - A mapping from variables to a list of coordinate names for the variable. - disable_glob : bool - Don't use glob for string input. This means that all string input is - assumed to be variable names (samples) or a path (data). - save_warmup : bool - Save warmup iterations into InferenceData object, if found in the input files. - If not defined, use default defined by the rcParams. - dtypes : dict or str - A dictionary containing dtype information (int, float) for parameters. - If input is a string, it is assumed to be a model code or path to model code file. - - Returns - ------- - InferenceData object - """ - return CmdStanConverter( - posterior=posterior, - posterior_predictive=posterior_predictive, - predictions=predictions, - prior=prior, - prior_predictive=prior_predictive, - observed_data=observed_data, - observed_data_var=observed_data_var, - constant_data=constant_data, - constant_data_var=constant_data_var, - predictions_constant_data=predictions_constant_data, - predictions_constant_data_var=predictions_constant_data_var, - log_likelihood=log_likelihood, - index_origin=index_origin, - coords=coords, - dims=dims, - disable_glob=disable_glob, - save_warmup=save_warmup, - dtypes=dtypes, - ).to_inference_data() diff --git a/arviz/data/io_cmdstanpy.py b/arviz/data/io_cmdstanpy.py deleted file mode 100644 index 9acc56d888..0000000000 --- a/arviz/data/io_cmdstanpy.py +++ /dev/null @@ -1,1233 +0,0 @@ -# pylint: disable=too-many-lines -"""CmdStanPy-specific conversion code.""" -import logging -import re -from collections import defaultdict -from copy import deepcopy -from pathlib import Path - -import numpy as np - -from ..rcparams import rcParams -from .base import dict_to_dataset, infer_stan_dtypes, make_attrs, requires -from .inference_data import InferenceData - -_log = logging.getLogger(__name__) - - -class CmdStanPyConverter: - """Encapsulate CmdStanPy specific logic.""" - - # pylint: disable=too-many-instance-attributes - - def __init__( - self, - *, - posterior=None, - posterior_predictive=None, - predictions=None, - prior=None, - prior_predictive=None, - observed_data=None, - constant_data=None, - predictions_constant_data=None, - log_likelihood=None, - index_origin=None, - coords=None, - dims=None, - save_warmup=None, - dtypes=None, - ): - self.posterior = posterior # CmdStanPy CmdStanMCMC object - self.posterior_predictive = posterior_predictive - self.predictions = predictions - self.prior = prior - self.prior_predictive = prior_predictive - self.observed_data = observed_data - self.constant_data = constant_data - self.predictions_constant_data = predictions_constant_data - self.log_likelihood = ( - rcParams["data.log_likelihood"] if log_likelihood is None else log_likelihood - ) - self.index_origin = index_origin - self.coords = coords - self.dims = dims - - self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup - - import cmdstanpy # pylint: disable=import-error - - if dtypes is None: - dtypes = {} - elif isinstance(dtypes, cmdstanpy.model.CmdStanModel): - model_code = dtypes.code() - dtypes = infer_stan_dtypes(model_code) - elif isinstance(dtypes, str): - dtypes_path = Path(dtypes) - if dtypes_path.exists(): - with dtypes_path.open("r", encoding="UTF-8") as f_obj: - model_code = f_obj.read() - else: - model_code = dtypes - dtypes = infer_stan_dtypes(model_code) - - self.dtypes = dtypes - - if hasattr(self.posterior, "metadata") and hasattr( - self.posterior.metadata, "stan_vars_cols" - ): - if self.log_likelihood is True and "log_lik" in self.posterior.metadata.stan_vars_cols: - self.log_likelihood = ["log_lik"] - elif hasattr(self.posterior, "metadata") and hasattr( - self.posterior.metadata, "stan_vars_cols" - ): - if self.log_likelihood is True and "log_lik" in self.posterior.metadata.stan_vars_cols: - self.log_likelihood = ["log_lik"] - elif hasattr(self.posterior, "stan_vars_cols"): - if self.log_likelihood is True and "log_lik" in self.posterior.stan_vars_cols: - self.log_likelihood = ["log_lik"] - elif hasattr(self.posterior, "metadata") and hasattr(self.posterior.metadata, "stan_vars"): - if self.log_likelihood is True and "log_lik" in self.posterior.metadata.stan_vars: - self.log_likelihood = ["log_lik"] - elif ( - self.log_likelihood is True - and self.posterior is not None - and hasattr(self.posterior, "column_names") - and any(name.split("[")[0] == "log_lik" for name in self.posterior.column_names) - ): - self.log_likelihood = ["log_lik"] - - if isinstance(self.log_likelihood, bool): - self.log_likelihood = None - - self.cmdstanpy = cmdstanpy - - @requires("posterior") - def posterior_to_xarray(self): - """Extract posterior samples from output csv.""" - if not (hasattr(self.posterior, "metadata") or hasattr(self.posterior, "stan_vars_cols")): - return self.posterior_to_xarray_pre_v_0_9_68() - if ( - hasattr(self.posterior, "metadata") - and hasattr(self.posterior.metadata, "stan_vars_cols") - ) or hasattr(self.posterior, "stan_vars_cols"): - return self.posterior_to_xarray_pre_v_1_0_0() - if hasattr(self.posterior, "metadata") and hasattr( - self.posterior.metadata, "stan_vars_cols" - ): - return self.posterior_to_xarray_pre_v_1_2_0() - - items = list(self.posterior.metadata.stan_vars) - if self.posterior_predictive is not None: - try: - items = _filter(items, self.posterior_predictive) - except ValueError: - pass - if self.predictions is not None: - try: - items = _filter(items, self.predictions) - except ValueError: - pass - if self.log_likelihood is not None: - try: - items = _filter(items, self.log_likelihood) - except ValueError: - pass - - valid_cols = [] - for item in items: - if hasattr(self.posterior, "metadata"): - if item in self.posterior.metadata.stan_vars: - valid_cols.append(item) - - data, data_warmup = _unpack_fit( - self.posterior, - items, - self.save_warmup, - self.dtypes, - ) - - dims = deepcopy(self.dims) if self.dims is not None else {} - coords = deepcopy(self.coords) if self.coords is not None else {} - - return ( - dict_to_dataset( - data, - library=self.cmdstanpy, - coords=coords, - dims=dims, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=self.cmdstanpy, - coords=coords, - dims=dims, - index_origin=self.index_origin, - ), - ) - - @requires("posterior") - def sample_stats_to_xarray(self): - """Extract sample_stats from prosterior fit.""" - return self.stats_to_xarray(self.posterior) - - @requires("prior") - def sample_stats_prior_to_xarray(self): - """Extract sample_stats from prior fit.""" - return self.stats_to_xarray(self.prior) - - def stats_to_xarray(self, fit): - """Extract sample_stats from fit.""" - if not (hasattr(fit, "metadata") or hasattr(fit, "sampler_vars_cols")): - return self.sample_stats_to_xarray_pre_v_0_9_68(fit) - if (hasattr(fit, "metadata") and hasattr(fit.metadata, "stan_vars_cols")) or hasattr( - fit, "stan_vars_cols" - ): - return self.sample_stats_to_xarray_pre_v_1_0_0(fit) - if hasattr(fit, "metadata") and hasattr(fit.metadata, "stan_vars_cols"): - return self.sample_stats_to_xarray_pre_v_1_2_0(fit) - - dtypes = { - "divergent__": bool, - "n_leapfrog__": np.int64, - "treedepth__": np.int64, - **self.dtypes, - } - - items = list(fit.method_variables()) # pylint: disable=protected-access - - rename_dict = { - "divergent": "diverging", - "n_leapfrog": "n_steps", - "treedepth": "tree_depth", - "stepsize": "step_size", - "accept_stat": "acceptance_rate", - } - - data, data_warmup = _unpack_fit( - fit, - items, - self.save_warmup, - self.dtypes, - ) - for item in items: - name = re.sub("__$", "", item) - name = rename_dict.get(name, name) - data[name] = data.pop(item).astype(dtypes.get(item, float)) - if data_warmup: - data_warmup[name] = data_warmup.pop(item).astype(dtypes.get(item, float)) - - return ( - dict_to_dataset( - data, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - ) - - @requires("posterior") - @requires("posterior_predictive") - def posterior_predictive_to_xarray(self): - """Convert posterior_predictive samples to xarray.""" - return self.predictive_to_xarray(self.posterior_predictive, self.posterior) - - @requires("prior") - @requires("prior_predictive") - def prior_predictive_to_xarray(self): - """Convert prior_predictive samples to xarray.""" - return self.predictive_to_xarray(self.prior_predictive, self.prior) - - def predictive_to_xarray(self, names, fit): - """Convert predictive samples to xarray.""" - predictive = _as_set(names) - - if not (hasattr(fit, "metadata") or hasattr(fit, "stan_vars_cols")): # pre_v_0_9_68 - valid_cols = _filter_columns(fit.column_names, predictive) - data, data_warmup = _unpack_frame( - fit, - fit.column_names, - valid_cols, - self.save_warmup, - self.dtypes, - ) - elif (hasattr(fit, "metadata") and hasattr(fit.metadata, "sample_vars_cols")) or hasattr( - fit, "stan_vars_cols" - ): # pre_v_1_0_0 - data, data_warmup = _unpack_fit_pre_v_1_0_0( - fit, - predictive, - self.save_warmup, - self.dtypes, - ) - elif hasattr(fit, "metadata") and hasattr(fit.metadata, "stan_vars_cols"): # pre_v_1_2_0 - data, data_warmup = _unpack_fit_pre_v_1_2_0( - fit, - predictive, - self.save_warmup, - self.dtypes, - ) - else: - data, data_warmup = _unpack_fit( - fit, - predictive, - self.save_warmup, - self.dtypes, - ) - - return ( - dict_to_dataset( - data, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - ) - - @requires("posterior") - @requires("predictions") - def predictions_to_xarray(self): - """Convert out of sample predictions samples to xarray.""" - predictions = _as_set(self.predictions) - - if not ( - hasattr(self.posterior, "metadata") or hasattr(self.posterior, "stan_vars_cols") - ): # pre_v_0_9_68 - columns = self.posterior.column_names - valid_cols = _filter_columns(columns, predictions) - data, data_warmup = _unpack_frame( - self.posterior, - columns, - valid_cols, - self.save_warmup, - self.dtypes, - ) - elif ( - hasattr(self.posterior, "metadata") - and hasattr(self.posterior.metadata, "sample_vars_cols") - ) or hasattr( - self.posterior, "stan_vars_cols" - ): # pre_v_1_0_0 - data, data_warmup = _unpack_fit_pre_v_1_0_0( - self.posterior, - predictions, - self.save_warmup, - self.dtypes, - ) - elif hasattr(self.posterior, "metadata") and hasattr( - self.posterior.metadata, "stan_vars_cols" - ): # pre_v_1_2_0 - data, data_warmup = _unpack_fit_pre_v_1_2_0( - self.posterior, - predictions, - self.save_warmup, - self.dtypes, - ) - else: - data, data_warmup = _unpack_fit( - self.posterior, - predictions, - self.save_warmup, - self.dtypes, - ) - - return ( - dict_to_dataset( - data, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - ) - - @requires("posterior") - @requires("log_likelihood") - def log_likelihood_to_xarray(self): - """Convert elementwise log likelihood samples to xarray.""" - log_likelihood = _as_set(self.log_likelihood) - - if not ( - hasattr(self.posterior, "metadata") or hasattr(self.posterior, "stan_vars_cols") - ): # pre_v_0_9_68 - columns = self.posterior.column_names - valid_cols = _filter_columns(columns, log_likelihood) - data, data_warmup = _unpack_frame( - self.posterior, - columns, - valid_cols, - self.save_warmup, - self.dtypes, - ) - elif ( - hasattr(self.posterior, "metadata") - and hasattr(self.posterior.metadata, "sample_vars_cols") - ) or hasattr( - self.posterior, "stan_vars_cols" - ): # pre_v_1_0_0 - data, data_warmup = _unpack_fit_pre_v_1_0_0( - self.posterior, - log_likelihood, - self.save_warmup, - self.dtypes, - ) - elif hasattr(self.posterior, "metadata") and hasattr( - self.posterior.metadata, "stan_vars_cols" - ): # pre_v_1_2_0 - data, data_warmup = _unpack_fit_pre_v_1_2_0( - self.posterior, - log_likelihood, - self.save_warmup, - self.dtypes, - ) - else: - data, data_warmup = _unpack_fit( - self.posterior, - log_likelihood, - self.save_warmup, - self.dtypes, - ) - - if isinstance(self.log_likelihood, dict): - data = {obs_name: data[lik_name] for obs_name, lik_name in self.log_likelihood.items()} - if data_warmup: - data_warmup = { - obs_name: data_warmup[lik_name] - for obs_name, lik_name in self.log_likelihood.items() - } - return ( - dict_to_dataset( - data, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - skip_event_dims=True, - ), - dict_to_dataset( - data_warmup, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - skip_event_dims=True, - ), - ) - - @requires("prior") - def prior_to_xarray(self): - """Convert prior samples to xarray.""" - if not ( - hasattr(self.prior, "metadata") or hasattr(self.prior, "stan_vars_cols") - ): # pre_v_0_9_68 - columns = self.prior.column_names - prior_predictive = _as_set(self.prior_predictive) - prior_predictive = _filter_columns(columns, prior_predictive) - - invalid_cols = set(prior_predictive + [col for col in columns if col.endswith("__")]) - valid_cols = [col for col in columns if col not in invalid_cols] - - data, data_warmup = _unpack_frame( - self.prior, - columns, - valid_cols, - self.save_warmup, - self.dtypes, - ) - elif ( - hasattr(self.prior, "metadata") and hasattr(self.prior.metadata, "sample_vars_cols") - ) or hasattr( - self.prior, "stan_vars_cols" - ): # pre_v_1_0_0 - if hasattr(self.prior, "metadata"): - items = list(self.prior.metadata.stan_vars_cols.keys()) - else: - items = list(self.prior.stan_vars_cols.keys()) - if self.prior_predictive is not None: - try: - items = _filter(items, self.prior_predictive) - except ValueError: - pass - data, data_warmup = _unpack_fit_pre_v_1_0_0( - self.prior, - items, - self.save_warmup, - self.dtypes, - ) - elif hasattr(self.prior, "metadata") and hasattr( - self.prior.metadata, "stan_vars_cols" - ): # pre_v_1_2_0 - items = list(self.prior.metadata.stan_vars_cols.keys()) - if self.prior_predictive is not None: - try: - items = _filter(items, self.prior_predictive) - except ValueError: - pass - data, data_warmup = _unpack_fit_pre_v_1_2_0( - self.prior, - items, - self.save_warmup, - self.dtypes, - ) - else: - items = list(self.prior.metadata.stan_vars.keys()) - if self.prior_predictive is not None: - try: - items = _filter(items, self.prior_predictive) - except ValueError: - pass - data, data_warmup = _unpack_fit( - self.prior, - items, - self.save_warmup, - self.dtypes, - ) - - return ( - dict_to_dataset( - data, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - ) - - @requires("observed_data") - def observed_data_to_xarray(self): - """Convert observed data to xarray.""" - return dict_to_dataset( - self.observed_data, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - default_dims=[], - index_origin=self.index_origin, - ) - - @requires("constant_data") - def constant_data_to_xarray(self): - """Convert constant data to xarray.""" - return dict_to_dataset( - self.constant_data, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - default_dims=[], - index_origin=self.index_origin, - ) - - @requires("predictions_constant_data") - def predictions_constant_data_to_xarray(self): - """Convert constant data to xarray.""" - return dict_to_dataset( - self.predictions_constant_data, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - attrs=make_attrs(library=self.cmdstanpy), - default_dims=[], - index_origin=self.index_origin, - ) - - def to_inference_data(self): - """Convert all available data to an InferenceData object. - - Note that if groups can not be created (i.e., there is no `output`, so - the `posterior` and `sample_stats` can not be extracted), then the InferenceData - will not have those groups. - """ - return InferenceData( - save_warmup=self.save_warmup, - **{ - "posterior": self.posterior_to_xarray(), - "sample_stats": self.sample_stats_to_xarray(), - "posterior_predictive": self.posterior_predictive_to_xarray(), - "predictions": self.predictions_to_xarray(), - "prior": self.prior_to_xarray(), - "sample_stats_prior": self.sample_stats_prior_to_xarray(), - "prior_predictive": self.prior_predictive_to_xarray(), - "observed_data": self.observed_data_to_xarray(), - "constant_data": self.constant_data_to_xarray(), - "predictions_constant_data": self.predictions_constant_data_to_xarray(), - "log_likelihood": self.log_likelihood_to_xarray(), - }, - ) - - def posterior_to_xarray_pre_v_1_2_0(self): - items = list(self.posterior.metadata.stan_vars_cols) - if self.posterior_predictive is not None: - try: - items = _filter(items, self.posterior_predictive) - except ValueError: - pass - if self.predictions is not None: - try: - items = _filter(items, self.predictions) - except ValueError: - pass - if self.log_likelihood is not None: - try: - items = _filter(items, self.log_likelihood) - except ValueError: - pass - - valid_cols = [] - for item in items: - if hasattr(self.posterior, "metadata"): - if item in self.posterior.metadata.stan_vars_cols: - valid_cols.append(item) - - data, data_warmup = _unpack_fit_pre_v_1_2_0( - self.posterior, - items, - self.save_warmup, - self.dtypes, - ) - - dims = deepcopy(self.dims) if self.dims is not None else {} - coords = deepcopy(self.coords) if self.coords is not None else {} - - return ( - dict_to_dataset( - data, - library=self.cmdstanpy, - coords=coords, - dims=dims, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=self.cmdstanpy, - coords=coords, - dims=dims, - index_origin=self.index_origin, - ), - ) - - @requires("posterior") - def posterior_to_xarray_pre_v_1_0_0(self): - if hasattr(self.posterior, "metadata"): - items = list(self.posterior.metadata.stan_vars_cols.keys()) - else: - items = list(self.posterior.stan_vars_cols.keys()) - if self.posterior_predictive is not None: - try: - items = _filter(items, self.posterior_predictive) - except ValueError: - pass - if self.predictions is not None: - try: - items = _filter(items, self.predictions) - except ValueError: - pass - if self.log_likelihood is not None: - try: - items = _filter(items, self.log_likelihood) - except ValueError: - pass - - valid_cols = [] - for item in items: - if hasattr(self.posterior, "metadata"): - valid_cols.extend(self.posterior.metadata.stan_vars_cols[item]) - else: - valid_cols.extend(self.posterior.stan_vars_cols[item]) - - data, data_warmup = _unpack_fit_pre_v_1_0_0( - self.posterior, - items, - self.save_warmup, - self.dtypes, - ) - - dims = deepcopy(self.dims) if self.dims is not None else {} - coords = deepcopy(self.coords) if self.coords is not None else {} - - return ( - dict_to_dataset( - data, - library=self.cmdstanpy, - coords=coords, - dims=dims, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=self.cmdstanpy, - coords=coords, - dims=dims, - index_origin=self.index_origin, - ), - ) - - @requires("posterior") - def posterior_to_xarray_pre_v_0_9_68(self): - """Extract posterior samples from output csv.""" - columns = self.posterior.column_names - - # filter posterior_predictive, predictions and log_likelihood - posterior_predictive = self.posterior_predictive - if posterior_predictive is None: - posterior_predictive = [] - elif isinstance(posterior_predictive, str): - posterior_predictive = [ - col for col in columns if posterior_predictive == col.split("[")[0].split(".")[0] - ] - else: - posterior_predictive = [ - col - for col in columns - if any(item == col.split("[")[0].split(".")[0] for item in posterior_predictive) - ] - - predictions = self.predictions - if predictions is None: - predictions = [] - elif isinstance(predictions, str): - predictions = [col for col in columns if predictions == col.split("[")[0].split(".")[0]] - else: - predictions = [ - col - for col in columns - if any(item == col.split("[")[0].split(".")[0] for item in predictions) - ] - - log_likelihood = self.log_likelihood - if log_likelihood is None: - log_likelihood = [] - elif isinstance(log_likelihood, str): - log_likelihood = [ - col for col in columns if log_likelihood == col.split("[")[0].split(".")[0] - ] - else: - log_likelihood = [ - col - for col in columns - if any(item == col.split("[")[0].split(".")[0] for item in log_likelihood) - ] - - invalid_cols = set( - posterior_predictive - + predictions - + log_likelihood - + [col for col in columns if col.endswith("__")] - ) - valid_cols = [col for col in columns if col not in invalid_cols] - data, data_warmup = _unpack_frame( - self.posterior, - columns, - valid_cols, - self.save_warmup, - self.dtypes, - ) - - return ( - dict_to_dataset( - data, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - ) - - def sample_stats_to_xarray_pre_v_1_2_0(self, fit): - dtypes = { - "divergent__": bool, - "n_leapfrog__": np.int64, - "treedepth__": np.int64, - **self.dtypes, - } - - items = list(fit.metadata.method_vars_cols.keys()) # pylint: disable=protected-access - - rename_dict = { - "divergent": "diverging", - "n_leapfrog": "n_steps", - "treedepth": "tree_depth", - "stepsize": "step_size", - "accept_stat": "acceptance_rate", - } - - data, data_warmup = _unpack_fit_pre_v_1_2_0( - fit, - items, - self.save_warmup, - self.dtypes, - ) - for item in items: - name = re.sub("__$", "", item) - name = rename_dict.get(name, name) - data[name] = data.pop(item).astype(dtypes.get(item, float)) - if data_warmup: - data_warmup[name] = data_warmup.pop(item).astype(dtypes.get(item, float)) - - return ( - dict_to_dataset( - data, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - ) - - def sample_stats_to_xarray_pre_v_1_0_0(self, fit): - """Extract sample_stats from fit.""" - dtypes = { - "divergent__": bool, - "n_leapfrog__": np.int64, - "treedepth__": np.int64, - **self.dtypes, - } - if hasattr(fit, "metadata"): - items = list(fit.metadata._method_vars_cols.keys()) # pylint: disable=protected-access - else: - items = list(fit.sampler_vars_cols.keys()) - rename_dict = { - "divergent": "diverging", - "n_leapfrog": "n_steps", - "treedepth": "tree_depth", - "stepsize": "step_size", - "accept_stat": "acceptance_rate", - } - - data, data_warmup = _unpack_fit_pre_v_1_0_0( - fit, - items, - self.save_warmup, - self.dtypes, - ) - for item in items: - name = re.sub("__$", "", item) - name = rename_dict.get(name, name) - data[name] = data.pop(item).astype(dtypes.get(item, float)) - if data_warmup: - data_warmup[name] = data_warmup.pop(item).astype(dtypes.get(item, float)) - return ( - dict_to_dataset( - data, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - ) - - def sample_stats_to_xarray_pre_v_0_9_68(self, fit): - """Extract sample_stats from fit.""" - dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64} - columns = fit.column_names - valid_cols = [col for col in columns if col.endswith("__")] - data, data_warmup = _unpack_frame( - fit, - columns, - valid_cols, - self.save_warmup, - self.dtypes, - ) - for s_param in list(data.keys()): - s_param_, *_ = s_param.split(".") - name = re.sub("__$", "", s_param_) - name = "diverging" if name == "divergent" else name - data[name] = data.pop(s_param).astype(dtypes.get(s_param, float)) - if data_warmup: - data_warmup[name] = data_warmup.pop(s_param).astype(dtypes.get(s_param, float)) - return ( - dict_to_dataset( - data, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=self.cmdstanpy, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ), - ) - - -def _as_set(spec): - """Uniform representation for args which be name or list of names.""" - if spec is None: - return [] - if isinstance(spec, str): - return [spec] - try: - return set(spec.values()) - except AttributeError: - return set(spec) - - -def _filter(names, spec): - """Remove names from list of names.""" - if isinstance(spec, str): - names.remove(spec) - elif isinstance(spec, list): - for item in spec: - names.remove(item) - elif isinstance(spec, dict): - for item in spec.values(): - names.remove(item) - return names - - -def _filter_columns(columns, spec): - """Parse variable name from column label, removing element index, if any.""" - return [col for col in columns if col.split("[")[0].split(".")[0] in spec] - - -def _unpack_fit(fit, items, save_warmup, dtypes): - num_warmup = 0 - if save_warmup: - if not fit._save_warmup: # pylint: disable=protected-access - save_warmup = False - else: - num_warmup = fit.num_draws_warmup - - nchains = fit.chains - sample = {} - sample_warmup = {} - stan_vars_cols = list(fit.metadata.stan_vars) - sampler_vars = fit.method_variables() - for item in items: - if item in stan_vars_cols: - raw_draws = fit.stan_variable(item, inc_warmup=save_warmup) - raw_draws = np.swapaxes( - raw_draws.reshape((-1, nchains, *raw_draws.shape[1:]), order="F"), 0, 1 - ) - elif item in sampler_vars: - raw_draws = np.swapaxes(sampler_vars[item], 0, 1) - else: - raise ValueError(f"fit data, unknown variable: {item}") - raw_draws = raw_draws.astype(dtypes.get(item)) - if save_warmup: - sample_warmup[item] = raw_draws[:, :num_warmup, ...] - sample[item] = raw_draws[:, num_warmup:, ...] - else: - sample[item] = raw_draws - - return sample, sample_warmup - - -def _unpack_fit_pre_v_1_2_0(fit, items, save_warmup, dtypes): - num_warmup = 0 - if save_warmup: - if not fit._save_warmup: # pylint: disable=protected-access - save_warmup = False - else: - num_warmup = fit.num_draws_warmup - - nchains = fit.chains - sample = {} - sample_warmup = {} - stan_vars_cols = list(fit.metadata.stan_vars_cols) - sampler_vars = fit.method_variables() - for item in items: - if item in stan_vars_cols: - raw_draws = fit.stan_variable(item, inc_warmup=save_warmup) - raw_draws = np.swapaxes( - raw_draws.reshape((-1, nchains, *raw_draws.shape[1:]), order="F"), 0, 1 - ) - elif item in sampler_vars: - raw_draws = np.swapaxes(sampler_vars[item], 0, 1) - else: - raise ValueError(f"fit data, unknown variable: {item}") - raw_draws = raw_draws.astype(dtypes.get(item)) - if save_warmup: - sample_warmup[item] = raw_draws[:, :num_warmup, ...] - sample[item] = raw_draws[:, num_warmup:, ...] - else: - sample[item] = raw_draws - - return sample, sample_warmup - - -def _unpack_fit_pre_v_1_0_0(fit, items, save_warmup, dtypes): - """Transform fit to dictionary containing ndarrays. - - Parameters - ---------- - data: cmdstanpy.CmdStanMCMC - items: list - save_warmup: bool - dtypes: dict - - Returns - ------- - dict - key, values pairs. Values are formatted to shape = (chains, draws, *shape) - """ - num_warmup = 0 - if save_warmup: - if not fit._save_warmup: # pylint: disable=protected-access - save_warmup = False - else: - num_warmup = fit.num_draws_warmup - - nchains = fit.chains - draws = np.swapaxes(fit.draws(inc_warmup=save_warmup), 0, 1) - sample = {} - sample_warmup = {} - - stan_vars_cols = fit.metadata.stan_vars_cols if hasattr(fit, "metadata") else fit.stan_vars_cols - sampler_vars_cols = ( - fit.metadata._method_vars_cols # pylint: disable=protected-access - if hasattr(fit, "metadata") - else fit.sampler_vars_cols - ) - for item in items: - if item in stan_vars_cols: - col_idxs = stan_vars_cols[item] - raw_draws = fit.stan_variable(item, inc_warmup=save_warmup) - raw_draws = np.swapaxes( - raw_draws.reshape((-1, nchains, *raw_draws.shape[1:]), order="F"), 0, 1 - ) - elif item in sampler_vars_cols: - col_idxs = sampler_vars_cols[item] - raw_draws = draws[..., col_idxs[0]] - else: - raise ValueError(f"fit data, unknown variable: {item}") - raw_draws = raw_draws.astype(dtypes.get(item)) - if save_warmup: - sample_warmup[item] = raw_draws[:, :num_warmup, ...] - sample[item] = raw_draws[:, num_warmup:, ...] - else: - sample[item] = raw_draws - - return sample, sample_warmup - - -def _unpack_frame(fit, columns, valid_cols, save_warmup, dtypes): - """Transform fit to dictionary containing ndarrays. - - Called when fit object created by cmdstanpy version < 0.9.68 - - Parameters - ---------- - data: cmdstanpy.CmdStanMCMC - columns: list - valid_cols: list - save_warmup: bool - dtypes: dict - - Returns - ------- - dict - key, values pairs. Values are formatted to shape = (chains, draws, *shape) - """ - if save_warmup and not fit._save_warmup: # pylint: disable=protected-access - save_warmup = False - if hasattr(fit, "draws"): - data = fit.draws(inc_warmup=save_warmup) - if save_warmup: - num_warmup = fit._draws_warmup # pylint: disable=protected-access - data_warmup = data[:num_warmup] - data = data[num_warmup:] - else: - data = fit.sample - if save_warmup: - data_warmup = fit.warmup[: data.shape[0]] - - draws, chains, *_ = data.shape - if save_warmup: - draws_warmup, *_ = data_warmup.shape - - column_groups = defaultdict(list) - column_locs = defaultdict(list) - # iterate flat column names - for i, col in enumerate(columns): - if "." in col: - # parse parameter names e.g. X.1.2 --> X, (1,2) - col_base, *col_tail = col.split(".") - else: - # parse parameter names e.g. X[1,2] --> X, (1,2) - col_base, *col_tail = col.replace("]", "").replace("[", ",").split(",") - if len(col_tail): - # gather nD array locations - column_groups[col_base].append(tuple(map(int, col_tail))) - # gather raw data locations for each parameter - column_locs[col_base].append(i) - # gather parameter dimensions (assumes dense arrays) - dims = { - colname: tuple(np.array(col_dims).max(0)) for colname, col_dims in column_groups.items() - } - sample = {} - sample_warmup = {} - valid_base_cols = [] - # get list of parameters for extraction (basename) X.1.2 --> X - for col in valid_cols: - base_col = col.split("[")[0].split(".")[0] - if base_col not in valid_base_cols: - valid_base_cols.append(base_col) - - # extract each wanted parameter to ndarray with correct shape - for key in valid_base_cols: - ndim = dims.get(key, None) - shape_location = column_groups.get(key, None) - if ndim is not None: - sample[key] = np.full((chains, draws, *ndim), np.nan) - if save_warmup: - sample_warmup[key] = np.full((chains, draws_warmup, *ndim), np.nan) - if shape_location is None: - # reorder draw, chain -> chain, draw - (i,) = column_locs[key] - sample[key] = np.swapaxes(data[..., i], 0, 1) - if save_warmup: - sample_warmup[key] = np.swapaxes(data_warmup[..., i], 0, 1) - else: - for i, shape_loc in zip(column_locs[key], shape_location): - # location to insert extracted array - shape_loc = tuple([Ellipsis] + [j - 1 for j in shape_loc]) - # reorder draw, chain -> chain, draw and insert to ndarray - sample[key][shape_loc] = np.swapaxes(data[..., i], 0, 1) - if save_warmup: - sample_warmup[key][shape_loc] = np.swapaxes(data_warmup[..., i], 0, 1) - - for key, dtype in dtypes.items(): - if key in sample: - sample[key] = sample[key].astype(dtype) - if save_warmup and key in sample_warmup: - sample_warmup[key] = sample_warmup[key].astype(dtype) - return sample, sample_warmup - - -def from_cmdstanpy( - posterior=None, - *, - posterior_predictive=None, - predictions=None, - prior=None, - prior_predictive=None, - observed_data=None, - constant_data=None, - predictions_constant_data=None, - log_likelihood=None, - index_origin=None, - coords=None, - dims=None, - save_warmup=None, - dtypes=None, -): - """Convert CmdStanPy data into an InferenceData object. - - For a usage example read the - :ref:`Creating InferenceData section on from_cmdstanpy ` - - Parameters - ---------- - posterior : CmdStanMCMC object - CmdStanPy CmdStanMCMC - posterior_predictive : str, list of str - Posterior predictive samples for the fit. - predictions : str, list of str - Out of sample prediction samples for the fit. - prior : CmdStanMCMC - CmdStanPy CmdStanMCMC - prior_predictive : str, list of str - Prior predictive samples for the fit. - observed_data : dict - Observed data used in the sampling. - constant_data : dict - Constant data used in the sampling. - predictions_constant_data : dict - Constant data for predictions used in the sampling. - log_likelihood : str, list of str, dict of {str: str}, optional - Pointwise log_likelihood for the data. If a dict, its keys should represent var_names - from the corresponding observed data and its values the stan variable where the - data is stored. By default, if a variable ``log_lik`` is present in the Stan model, - it will be retrieved as pointwise log likelihood values. Use ``False`` - or set ``data.log_likelihood`` to false to avoid this behaviour. - index_origin : int, optional - Starting value of integer coordinate values. Defaults to the value in rcParam - ``data.index_origin``. - coords : dict of str or dict of iterable - A dictionary containing the values that are used as index. The key - is the name of the dimension, the values are the index values. - dims : dict of str or list of str - A mapping from variables to a list of coordinate names for the variable. - save_warmup : bool - Save warmup iterations into InferenceData object, if found in the input files. - If not defined, use default defined by the rcParams. - dtypes: dict or str or cmdstanpy.CmdStanModel - A dictionary containing dtype information (int, float) for parameters. - If input is a string, it is assumed to be a model code or path to model code file. - Model code can extracted from cmdstanpy.CmdStanModel object. - - Returns - ------- - InferenceData object - """ - return CmdStanPyConverter( - posterior=posterior, - posterior_predictive=posterior_predictive, - predictions=predictions, - prior=prior, - prior_predictive=prior_predictive, - observed_data=observed_data, - constant_data=constant_data, - predictions_constant_data=predictions_constant_data, - log_likelihood=log_likelihood, - index_origin=index_origin, - coords=coords, - dims=dims, - save_warmup=save_warmup, - dtypes=dtypes, - ).to_inference_data() diff --git a/arviz/data/io_datatree.py b/arviz/data/io_datatree.py deleted file mode 100644 index d28f00a1d3..0000000000 --- a/arviz/data/io_datatree.py +++ /dev/null @@ -1,23 +0,0 @@ -"""Conversion between InferenceData and DataTree.""" - -from .inference_data import InferenceData - - -def to_datatree(data): - """Convert InferenceData object to a :class:`~xarray.DataTree`. - - Parameters - ---------- - data : InferenceData - """ - return data.to_datatree() - - -def from_datatree(datatree): - """Create an InferenceData object from a :class:`~xarray.DataTree`. - - Parameters - ---------- - datatree : DataTree - """ - return InferenceData.from_datatree(datatree) diff --git a/arviz/data/io_dict.py b/arviz/data/io_dict.py deleted file mode 100644 index ebff5276e9..0000000000 --- a/arviz/data/io_dict.py +++ /dev/null @@ -1,462 +0,0 @@ -"""Dictionary specific conversion code.""" - -import warnings -from typing import Optional - -from ..rcparams import rcParams -from .base import dict_to_dataset, requires -from .inference_data import WARMUP_TAG, InferenceData - - -# pylint: disable=too-many-instance-attributes -class DictConverter: - """Encapsulate Dictionary specific logic.""" - - def __init__( - self, - *, - posterior=None, - posterior_predictive=None, - predictions=None, - sample_stats=None, - log_likelihood=None, - prior=None, - prior_predictive=None, - sample_stats_prior=None, - observed_data=None, - constant_data=None, - predictions_constant_data=None, - warmup_posterior=None, - warmup_posterior_predictive=None, - warmup_predictions=None, - warmup_log_likelihood=None, - warmup_sample_stats=None, - save_warmup=None, - index_origin=None, - coords=None, - dims=None, - pred_dims=None, - pred_coords=None, - attrs=None, - **kwargs, - ): - self.posterior = posterior - self.posterior_predictive = posterior_predictive - self.predictions = predictions - self.sample_stats = sample_stats - self.log_likelihood = log_likelihood - self.prior = prior - self.prior_predictive = prior_predictive - self.sample_stats_prior = sample_stats_prior - self.observed_data = observed_data - self.constant_data = constant_data - self.predictions_constant_data = predictions_constant_data - self.warmup_posterior = warmup_posterior - self.warmup_posterior_predictive = warmup_posterior_predictive - self.warmup_predictions = warmup_predictions - self.warmup_log_likelihood = warmup_log_likelihood - self.warmup_sample_stats = warmup_sample_stats - self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup - self.coords = ( - coords - if pred_coords is None - else pred_coords if coords is None else {**coords, **pred_coords} - ) - self.index_origin = index_origin - self.coords = coords - self.dims = dims - self.pred_dims = dims if pred_dims is None else pred_dims - self.attrs = {} if attrs is None else attrs - self.attrs.pop("created_at", None) - self.attrs.pop("arviz_version", None) - self._kwargs = kwargs - - def _init_dict(self, attr_name): - dict_or_none = getattr(self, attr_name, {}) - return {} if dict_or_none is None else dict_or_none - - @requires(["posterior", f"{WARMUP_TAG}posterior"]) - def posterior_to_xarray(self): - """Convert posterior samples to xarray.""" - data = self._init_dict("posterior") - data_warmup = self._init_dict(f"{WARMUP_TAG}posterior") - if not isinstance(data, dict): - raise TypeError("DictConverter.posterior is not a dictionary") - if not isinstance(data_warmup, dict): - raise TypeError("DictConverter.warmup_posterior is not a dictionary") - - if "log_likelihood" in data: - warnings.warn( - "log_likelihood variable found in posterior group." - " For stats functions log likelihood data needs to be in log_likelihood group.", - UserWarning, - ) - posterior_attrs = self._kwargs.get("posterior_attrs") - posterior_warmup_attrs = self._kwargs.get("posterior_warmup_attrs") - return ( - dict_to_dataset( - data, - library=None, - coords=self.coords, - dims=self.dims, - attrs=posterior_attrs, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=None, - coords=self.coords, - dims=self.dims, - attrs=posterior_warmup_attrs, - index_origin=self.index_origin, - ), - ) - - @requires(["sample_stats", f"{WARMUP_TAG}sample_stats"]) - def sample_stats_to_xarray(self): - """Convert sample_stats samples to xarray.""" - data = self._init_dict("sample_stats") - data_warmup = self._init_dict(f"{WARMUP_TAG}sample_stats") - if not isinstance(data, dict): - raise TypeError("DictConverter.sample_stats is not a dictionary") - if not isinstance(data_warmup, dict): - raise TypeError("DictConverter.warmup_sample_stats is not a dictionary") - - if "log_likelihood" in data: - warnings.warn( - "log_likelihood variable found in sample_stats." - " Storing log_likelihood data in sample_stats group will be deprecated in " - "favour of storing them in the log_likelihood group.", - PendingDeprecationWarning, - ) - sample_stats_attrs = self._kwargs.get("sample_stats_attrs") - sample_stats_warmup_attrs = self._kwargs.get("sample_stats_warmup_attrs") - return ( - dict_to_dataset( - data, - library=None, - coords=self.coords, - dims=self.dims, - attrs=sample_stats_attrs, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=None, - coords=self.coords, - dims=self.dims, - attrs=sample_stats_warmup_attrs, - index_origin=self.index_origin, - ), - ) - - @requires(["log_likelihood", f"{WARMUP_TAG}log_likelihood"]) - def log_likelihood_to_xarray(self): - """Convert log_likelihood samples to xarray.""" - data = self._init_dict("log_likelihood") - data_warmup = self._init_dict(f"{WARMUP_TAG}log_likelihood") - if not isinstance(data, dict): - raise TypeError("DictConverter.log_likelihood is not a dictionary") - if not isinstance(data_warmup, dict): - raise TypeError("DictConverter.warmup_log_likelihood is not a dictionary") - log_likelihood_attrs = self._kwargs.get("log_likelihood_attrs") - log_likelihood_warmup_attrs = self._kwargs.get("log_likelihood_warmup_attrs") - return ( - dict_to_dataset( - data, - library=None, - coords=self.coords, - dims=self.dims, - attrs=log_likelihood_attrs, - index_origin=self.index_origin, - skip_event_dims=True, - ), - dict_to_dataset( - data_warmup, - library=None, - coords=self.coords, - dims=self.dims, - attrs=log_likelihood_warmup_attrs, - index_origin=self.index_origin, - skip_event_dims=True, - ), - ) - - @requires(["posterior_predictive", f"{WARMUP_TAG}posterior_predictive"]) - def posterior_predictive_to_xarray(self): - """Convert posterior_predictive samples to xarray.""" - data = self._init_dict("posterior_predictive") - data_warmup = self._init_dict(f"{WARMUP_TAG}posterior_predictive") - if not isinstance(data, dict): - raise TypeError("DictConverter.posterior_predictive is not a dictionary") - if not isinstance(data_warmup, dict): - raise TypeError("DictConverter.warmup_posterior_predictive is not a dictionary") - posterior_predictive_attrs = self._kwargs.get("posterior_predictive_attrs") - posterior_predictive_warmup_attrs = self._kwargs.get("posterior_predictive_warmup_attrs") - return ( - dict_to_dataset( - data, - library=None, - coords=self.coords, - dims=self.dims, - attrs=posterior_predictive_attrs, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=None, - coords=self.coords, - dims=self.dims, - attrs=posterior_predictive_warmup_attrs, - index_origin=self.index_origin, - ), - ) - - @requires(["predictions", f"{WARMUP_TAG}predictions"]) - def predictions_to_xarray(self): - """Convert predictions to xarray.""" - data = self._init_dict("predictions") - data_warmup = self._init_dict(f"{WARMUP_TAG}predictions") - if not isinstance(data, dict): - raise TypeError("DictConverter.predictions is not a dictionary") - if not isinstance(data_warmup, dict): - raise TypeError("DictConverter.warmup_predictions is not a dictionary") - predictions_attrs = self._kwargs.get("predictions_attrs") - predictions_warmup_attrs = self._kwargs.get("predictions_warmup_attrs") - return ( - dict_to_dataset( - data, - library=None, - coords=self.coords, - dims=self.pred_dims, - attrs=predictions_attrs, - index_origin=self.index_origin, - ), - dict_to_dataset( - data_warmup, - library=None, - coords=self.coords, - dims=self.pred_dims, - attrs=predictions_warmup_attrs, - index_origin=self.index_origin, - ), - ) - - @requires("prior") - def prior_to_xarray(self): - """Convert prior samples to xarray.""" - data = self.prior - if not isinstance(data, dict): - raise TypeError("DictConverter.prior is not a dictionary") - prior_attrs = self._kwargs.get("prior_attrs") - return dict_to_dataset( - data, - library=None, - coords=self.coords, - dims=self.dims, - attrs=prior_attrs, - index_origin=self.index_origin, - ) - - @requires("sample_stats_prior") - def sample_stats_prior_to_xarray(self): - """Convert sample_stats_prior samples to xarray.""" - data = self.sample_stats_prior - if not isinstance(data, dict): - raise TypeError("DictConverter.sample_stats_prior is not a dictionary") - sample_stats_prior_attrs = self._kwargs.get("sample_stats_prior_attrs") - return dict_to_dataset( - data, - library=None, - coords=self.coords, - dims=self.dims, - attrs=sample_stats_prior_attrs, - index_origin=self.index_origin, - ) - - @requires("prior_predictive") - def prior_predictive_to_xarray(self): - """Convert prior_predictive samples to xarray.""" - data = self.prior_predictive - if not isinstance(data, dict): - raise TypeError("DictConverter.prior_predictive is not a dictionary") - prior_predictive_attrs = self._kwargs.get("prior_predictive_attrs") - return dict_to_dataset( - data, - library=None, - coords=self.coords, - dims=self.dims, - attrs=prior_predictive_attrs, - index_origin=self.index_origin, - ) - - def data_to_xarray(self, data, group, dims=None): - """Convert data to xarray.""" - if not isinstance(data, dict): - raise TypeError(f"DictConverter.{group} is not a dictionary") - if dims is None: - dims = {} if self.dims is None else self.dims - return dict_to_dataset( - data, - library=None, - coords=self.coords, - dims=self.dims, - default_dims=[], - attrs=self.attrs, - index_origin=self.index_origin, - ) - - @requires("observed_data") - def observed_data_to_xarray(self): - """Convert observed_data to xarray.""" - return self.data_to_xarray(self.observed_data, group="observed_data", dims=self.dims) - - @requires("constant_data") - def constant_data_to_xarray(self): - """Convert constant_data to xarray.""" - return self.data_to_xarray(self.constant_data, group="constant_data") - - @requires("predictions_constant_data") - def predictions_constant_data_to_xarray(self): - """Convert predictions_constant_data to xarray.""" - return self.data_to_xarray( - self.predictions_constant_data, group="predictions_constant_data", dims=self.pred_dims - ) - - def to_inference_data(self): - """Convert all available data to an InferenceData object. - - Note that if groups can not be created, then the InferenceData - will not have those groups. - """ - return InferenceData( - **{ - "posterior": self.posterior_to_xarray(), - "sample_stats": self.sample_stats_to_xarray(), - "log_likelihood": self.log_likelihood_to_xarray(), - "posterior_predictive": self.posterior_predictive_to_xarray(), - "predictions": self.predictions_to_xarray(), - "prior": self.prior_to_xarray(), - "sample_stats_prior": self.sample_stats_prior_to_xarray(), - "prior_predictive": self.prior_predictive_to_xarray(), - "observed_data": self.observed_data_to_xarray(), - "constant_data": self.constant_data_to_xarray(), - "predictions_constant_data": self.predictions_constant_data_to_xarray(), - "save_warmup": self.save_warmup, - "attrs": self.attrs, - } - ) - - -# pylint: disable=too-many-instance-attributes -def from_dict( - posterior=None, - *, - posterior_predictive=None, - predictions=None, - sample_stats=None, - log_likelihood=None, - prior=None, - prior_predictive=None, - sample_stats_prior=None, - observed_data=None, - constant_data=None, - predictions_constant_data=None, - warmup_posterior=None, - warmup_posterior_predictive=None, - warmup_predictions=None, - warmup_log_likelihood=None, - warmup_sample_stats=None, - save_warmup=None, - index_origin: Optional[int] = None, - coords=None, - dims=None, - pred_dims=None, - pred_coords=None, - attrs=None, - **kwargs, -): - """Convert Dictionary data into an InferenceData object. - - For a usage example read the - :ref:`Creating InferenceData section on from_dict ` - - Parameters - ---------- - posterior : dict, optional - posterior_predictive : dict, optional - predictions: dict, optional - sample_stats : dict, optional - log_likelihood : dict, optional - For stats functions, log likelihood data should be stored here. - prior : dict, optional - prior_predictive : dict, optional - observed_data : dict, optional - constant_data : dict, optional - predictions_constant_data: dict, optional - warmup_posterior : dict, optional - warmup_posterior_predictive : dict, optional - warmup_predictions : dict, optional - warmup_log_likelihood : dict, optional - warmup_sample_stats : dict, optional - save_warmup : bool, optional - Save warmup iterations InferenceData object. If not defined, use default - defined by the rcParams. - index_origin : int, optional - coords : dict of {str : list}, optional - A dictionary containing the values that are used as index. The key - is the name of the dimension, the values are the index values. - dims : dict of {str : list of str}, optional - A mapping from variables to a list of coordinate names for the variable. - pred_dims : dict of {str : list of str}, optional - A mapping from variables to a list of coordinate names for predictions. - pred_coords : dict of {str : list}, optional - A mapping from variables to a list of coordinate values for predictions. - attrs : dict, optional - A dictionary containing attributes for different groups. - kwargs : dict, optional - A dictionary containing group attrs. Accepted kwargs are: - - - posterior_attrs, posterior_warmup_attrs : attrs for posterior group - - sample_stats_attrs, sample_stats_warmup_attrs : attrs for sample_stats group - - log_likelihood_attrs, log_likelihood_warmup_attrs : attrs for log_likelihood group - - posterior_predictive_attrs, posterior_predictive_warmup_attrs : attrs for - posterior_predictive group - - predictions_attrs, predictions_warmup_attrs : attrs for predictions group - - prior_attrs : attrs for prior group - - sample_stats_prior_attrs : attrs for sample_stats_prior group - - prior_predictive_attrs : attrs for prior_predictive group - - Returns - ------- - InferenceData - """ - return DictConverter( - posterior=posterior, - posterior_predictive=posterior_predictive, - predictions=predictions, - sample_stats=sample_stats, - log_likelihood=log_likelihood, - prior=prior, - prior_predictive=prior_predictive, - sample_stats_prior=sample_stats_prior, - observed_data=observed_data, - constant_data=constant_data, - predictions_constant_data=predictions_constant_data, - warmup_posterior=warmup_posterior, - warmup_posterior_predictive=warmup_posterior_predictive, - warmup_predictions=warmup_predictions, - warmup_log_likelihood=warmup_log_likelihood, - warmup_sample_stats=warmup_sample_stats, - save_warmup=save_warmup, - index_origin=index_origin, - coords=coords, - dims=dims, - pred_dims=pred_dims, - pred_coords=pred_coords, - attrs=attrs, - **kwargs, - ).to_inference_data() - - -from_pytree = from_dict diff --git a/arviz/data/io_emcee.py b/arviz/data/io_emcee.py deleted file mode 100644 index d08c5f8d89..0000000000 --- a/arviz/data/io_emcee.py +++ /dev/null @@ -1,317 +0,0 @@ -"""emcee-specific conversion code.""" - -import warnings -from collections import OrderedDict - -import numpy as np -import xarray as xr - -from .. import utils -from .base import dict_to_dataset, generate_dims_coords, make_attrs -from .inference_data import InferenceData - - -def _verify_names(sampler, var_names, arg_names, slices): - """Make sure var_names and arg_names are assigned reasonably. - - This is meant to run before loading emcee objects into InferenceData. - In case var_names or arg_names is None, will provide defaults. If they are - not None, it verifies there are the right number of them. - - Throws a ValueError in case validation fails. - - Parameters - ---------- - sampler : emcee.EnsembleSampler - Fitted emcee sampler - var_names : list[str] or None - Names for the emcee parameters - arg_names : list[str] or None - Names for the args/observations provided to emcee - slices : list[seq] or None - slices to select the variables (used for multidimensional variables) - - Returns - ------- - list[str], list[str], list[seq] - Defaults for var_names, arg_names and slices - """ - # There are 3 possible cases: emcee2, emcee3 and sampler read from h5 file (emcee3 only) - if hasattr(sampler, "args"): - ndim = sampler.chain.shape[-1] - num_args = len(sampler.args) - elif hasattr(sampler, "log_prob_fn"): - ndim = sampler.get_chain().shape[-1] - num_args = len(sampler.log_prob_fn.args) - else: - ndim = sampler.get_chain().shape[-1] - num_args = 0 # emcee only stores the posterior samples - - if slices is None: - slices = utils.arange(ndim) - num_vars = ndim - else: - num_vars = len(slices) - indices = utils.arange(ndim) - slicing_try = np.concatenate([utils.one_de(indices[idx]) for idx in slices]) - if len(set(slicing_try)) != ndim: - warnings.warn( - "Check slices: Not all parameters in chain captured. " - f"{ndim} are present, and {len(slicing_try)} have been captured.", - UserWarning, - ) - if len(slicing_try) != len(set(slicing_try)): - warnings.warn(f"Overlapping slices. Check the index present: {slicing_try}", UserWarning) - - if var_names is None: - var_names = [f"var_{idx}" for idx in range(num_vars)] - if arg_names is None: - arg_names = [f"arg_{idx}" for idx in range(num_args)] - - if len(var_names) != num_vars: - raise ValueError( - f"The sampler has {num_vars} variables, " - f"but only {len(var_names)} var_names were provided!" - ) - - if len(arg_names) != num_args: - raise ValueError( - f"The sampler has {num_args} args, " - f"but only {len(arg_names)} arg_names were provided!" - ) - return var_names, arg_names, slices - - -# pylint: disable=too-many-instance-attributes -class EmceeConverter: - """Encapsulate emcee specific logic.""" - - def __init__( - self, - sampler, - var_names=None, - slices=None, - arg_names=None, - arg_groups=None, - blob_names=None, - blob_groups=None, - index_origin=None, - coords=None, - dims=None, - ): - var_names, arg_names, slices = _verify_names(sampler, var_names, arg_names, slices) - self.sampler = sampler - self.var_names = var_names - self.slices = slices - self.arg_names = arg_names - self.arg_groups = arg_groups - self.blob_names = blob_names - self.blob_groups = blob_groups - self.index_origin = index_origin - self.coords = coords - self.dims = dims - import emcee - - self.emcee = emcee - - def posterior_to_xarray(self): - """Convert the posterior to an xarray dataset.""" - # Use emcee3 syntax, else use emcee2 - if hasattr(self.sampler, "get_chain"): - samples_ary = self.sampler.get_chain().swapaxes(0, 1) - else: - samples_ary = self.sampler.chain - - data = { - var_name: (samples_ary[(..., idx)]) - for idx, var_name in zip(self.slices, self.var_names) - } - return dict_to_dataset( - data, - library=self.emcee, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ) - - def args_to_xarray(self): - """Convert emcee args to observed and constant_data xarray Datasets.""" - dims = {} if self.dims is None else self.dims - if self.arg_groups is None: - self.arg_groups = ["observed_data" for _ in self.arg_names] - if len(self.arg_names) != len(self.arg_groups): - raise ValueError( - "arg_names and arg_groups must have the same length, or arg_groups be None" - ) - arg_groups_set = set(self.arg_groups) - bad_groups = [ - group for group in arg_groups_set if group not in ("observed_data", "constant_data") - ] - if bad_groups: - raise SyntaxError( - "all arg_groups values should be either 'observed_data' or 'constant_data' , " - f"not {bad_groups}" - ) - obs_const_dict = {group: OrderedDict() for group in arg_groups_set} - for idx, (arg_name, group) in enumerate(zip(self.arg_names, self.arg_groups)): - # Use emcee3 syntax, else use emcee2 - arg_array = np.atleast_1d( - self.sampler.log_prob_fn.args[idx] - if hasattr(self.sampler, "log_prob_fn") - else self.sampler.args[idx] - ) - arg_dims = dims.get(arg_name) - arg_dims, coords = generate_dims_coords( - arg_array.shape, - arg_name, - dims=arg_dims, - coords=self.coords, - index_origin=self.index_origin, - ) - # filter coords based on the dims - coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in arg_dims} - obs_const_dict[group][arg_name] = xr.DataArray(arg_array, dims=arg_dims, coords=coords) - for key, values in obs_const_dict.items(): - obs_const_dict[key] = xr.Dataset(data_vars=values, attrs=make_attrs(library=self.emcee)) - return obs_const_dict - - def blobs_to_dict(self): - """Convert blobs to dictionary {groupname: xr.Dataset}. - - It also stores lp values in sample_stats group. - """ - store_blobs = self.blob_names is not None - self.blob_names = [] if self.blob_names is None else self.blob_names - if self.blob_groups is None: - self.blob_groups = ["log_likelihood" for _ in self.blob_names] - if len(self.blob_names) != len(self.blob_groups): - raise ValueError( - "blob_names and blob_groups must have the same length, or blob_groups be None" - ) - if store_blobs: - if int(self.emcee.__version__[0]) >= 3: - blobs = self.sampler.get_blobs() - else: - blobs = np.array(self.sampler.blobs, dtype=object) - if (blobs is None or blobs.size == 0) and self.blob_names: - raise ValueError("No blobs in sampler, blob_names must be None") - if len(blobs.shape) == 2: - blobs = np.expand_dims(blobs, axis=-1) - blobs = blobs.swapaxes(0, 2) - nblobs, nwalkers, ndraws, *_ = blobs.shape - if len(self.blob_names) != nblobs and len(self.blob_names) > 1: - raise ValueError( - "Incorrect number of blob names. " - f"Expected {nblobs}, found {len(self.blob_names)}" - ) - blob_groups_set = set(self.blob_groups) - blob_groups_set.add("sample_stats") - idata_groups = ("posterior", "observed_data", "constant_data") - if np.any(np.isin(list(blob_groups_set), idata_groups)): - raise SyntaxError( - f"{idata_groups} groups should not come from blobs. " - "Using them here would overwrite their actual values" - ) - blob_dict = {group: OrderedDict() for group in blob_groups_set} - if len(self.blob_names) == 1: - blob_dict[self.blob_groups[0]][self.blob_names[0]] = blobs.swapaxes(0, 2).swapaxes(0, 1) - else: - for i_blob, (name, group) in enumerate(zip(self.blob_names, self.blob_groups)): - # for coherent blobs (all having the same dimensions) one line is enough - blob = blobs[i_blob] - # for blobs of different size, we get an array of arrays, which we convert - # to an ndarray per blob_name - if blob.dtype == object: - blob = blob.reshape(-1) - blob = np.stack(blob) - blob = blob.reshape((nwalkers, ndraws, -1)) - blob_dict[group][name] = np.squeeze(blob) - - # store lp in sample_stats group - blob_dict["sample_stats"]["lp"] = ( - self.sampler.get_log_prob().swapaxes(0, 1) - if hasattr(self.sampler, "get_log_prob") - else self.sampler.lnprobability - ) - for key, values in blob_dict.items(): - blob_dict[key] = dict_to_dataset( - values, - library=self.emcee, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ) - return blob_dict - - def to_inference_data(self): - """Convert all available data to an InferenceData object.""" - blobs_dict = self.blobs_to_dict() - obs_const_dict = self.args_to_xarray() - return InferenceData( - **{"posterior": self.posterior_to_xarray(), **obs_const_dict, **blobs_dict} - ) - - -def from_emcee( - sampler=None, - var_names=None, - slices=None, - arg_names=None, - arg_groups=None, - blob_names=None, - blob_groups=None, - index_origin=None, - coords=None, - dims=None, -): - """Convert emcee data into an InferenceData object. - - For a usage example read :ref:`emcee_conversion` - - - Parameters - ---------- - sampler : emcee.EnsembleSampler - Fitted sampler from emcee. - var_names : list of str, optional - A list of names for variables in the sampler - slices : list of array-like or slice, optional - A list containing the indexes of each variable. Should only be used - for multidimensional variables. - arg_names : list of str, optional - A list of names for args in the sampler - arg_groups : list of str, optional - A list of the group names (either ``observed_data`` or ``constant_data``) where - args in the sampler are stored. If None, all args will be stored in observed - data group. - blob_names : list of str, optional - A list of names for blobs in the sampler. When None, - blobs are omitted, independently of them being present - in the sampler or not. - blob_groups : list of str, optional - A list of the groups where blob_names variables - should be assigned respectively. If blob_names!=None - and blob_groups is None, all variables are assigned - to log_likelihood group - coords : dict of {str : array_like}, optional - Map of dimensions to coordinates - dims : dict of {str : list of str}, optional - Map variable names to their coordinates - - Returns - ------- - arviz.InferenceData - - """ - return EmceeConverter( - sampler=sampler, - var_names=var_names, - slices=slices, - arg_names=arg_names, - arg_groups=arg_groups, - blob_names=blob_names, - blob_groups=blob_groups, - index_origin=index_origin, - coords=coords, - dims=dims, - ).to_inference_data() diff --git a/arviz/data/io_json.py b/arviz/data/io_json.py deleted file mode 100644 index 82ada77009..0000000000 --- a/arviz/data/io_json.py +++ /dev/null @@ -1,54 +0,0 @@ -"""Input and output support for data.""" - -from .io_dict import from_dict - -try: - import ujson as json -except ImportError: - # Can't find ujson using json - # mypy struggles with conditional imports expressed as catching ImportError: - # https://github.com/python/mypy/issues/1153 - import json # type: ignore - - -def from_json(filename): - """Initialize object from a json file. - - Will use the faster `ujson` (https://github.com/ultrajson/ultrajson) if it is available. - - Parameters - ---------- - filename : str - location of json file - - Returns - ------- - InferenceData object - """ - with open(filename, "rb") as file: - idata_dict = json.load(file) - - return from_dict(**idata_dict, save_warmup=True) - - -def to_json(idata, filename): - """Save dataset as a json file. - - Will use the faster `ujson` (https://github.com/ultrajson/ultrajson) if it is available. - - WARNING: Only idempotent in case `idata` is InferenceData. - - Parameters - ---------- - idata : InferenceData - Object to be saved - filename : str - name or path of the file to load trace - - Returns - ------- - str - filename saved to - """ - file_name = idata.to_json(filename) - return file_name diff --git a/arviz/data/io_netcdf.py b/arviz/data/io_netcdf.py deleted file mode 100644 index bd20e20921..0000000000 --- a/arviz/data/io_netcdf.py +++ /dev/null @@ -1,68 +0,0 @@ -"""Input and output support for data.""" - -from .converters import convert_to_inference_data -from .inference_data import InferenceData - - -def from_netcdf(filename, *, engine="h5netcdf", group_kwargs=None, regex=False): - """Load netcdf file back into an arviz.InferenceData. - - Parameters - ---------- - filename : str - name or path of the file to load trace - engine : {"h5netcdf", "netcdf4"}, default "h5netcdf" - Library used to read the netcdf file. - group_kwargs : dict of {str: dict} - Keyword arguments to be passed into each call of :func:`xarray.open_dataset`. - The keys of the higher level should be group names or regex matching group - names, the inner dicts re passed to ``open_dataset``. - This feature is currently experimental - regex : str - Specifies where regex search should be used to extend the keyword arguments. - - Returns - ------- - InferenceData object - - Notes - ----- - By default, the datasets of the InferenceData object will be lazily loaded instead - of loaded into memory. This behaviour is regulated by the value of - ``az.rcParams["data.load"]``. - """ - if group_kwargs is None: - group_kwargs = {} - return InferenceData.from_netcdf( - filename, engine=engine, group_kwargs=group_kwargs, regex=regex - ) - - -def to_netcdf(data, filename, *, group="posterior", engine="h5netcdf", coords=None, dims=None): - """Save dataset as a netcdf file. - - WARNING: Only idempotent in case `data` is InferenceData - - Parameters - ---------- - data : InferenceData, or any object accepted by `convert_to_inference_data` - Object to be saved - filename : str - name or path of the file to load trace - group : str (optional) - In case `data` is not InferenceData, this is the group it will be saved to - engine : {"h5netcdf", "netcdf4"}, default "h5netcdf" - Library used to read the netcdf file. - coords : dict (optional) - See `convert_to_inference_data` - dims : dict (optional) - See `convert_to_inference_data` - - Returns - ------- - str - filename saved to - """ - inference_data = convert_to_inference_data(data, group=group, coords=coords, dims=dims) - file_name = inference_data.to_netcdf(filename, engine=engine) - return file_name diff --git a/arviz/data/io_numpyro.py b/arviz/data/io_numpyro.py deleted file mode 100644 index d49d6db619..0000000000 --- a/arviz/data/io_numpyro.py +++ /dev/null @@ -1,497 +0,0 @@ -"""NumPyro-specific conversion code.""" - -from collections import defaultdict -import logging -from typing import Any, Callable, Optional, Dict, List, Tuple - -import numpy as np - -from .. import utils -from ..rcparams import rcParams -from .base import dict_to_dataset, requires -from .inference_data import InferenceData - -_log = logging.getLogger(__name__) - - -def _add_dims(dims_a: Dict[str, List[str]], dims_b: Dict[str, List[str]]) -> Dict[str, List[str]]: - merged = defaultdict(list) - - for k, v in dims_a.items(): - merged[k].extend(v) - - for k, v in dims_b.items(): - merged[k].extend(v) - - # Convert back to a regular dict - return dict(merged) - - -def infer_dims( - model: Callable, - model_args: Optional[Tuple[Any, ...]] = None, - model_kwargs: Optional[Dict[str, Any]] = None, -) -> Dict[str, List[str]]: - - from numpyro import handlers, distributions as dist - from numpyro.ops.pytree import PytreeTrace - from numpyro.infer.initialization import init_to_sample - import jax - - model_args = tuple() if model_args is None else model_args - model_kwargs = dict() if model_args is None else model_kwargs - - def _get_dist_name(fn): - if isinstance(fn, (dist.Independent, dist.ExpandedDistribution, dist.MaskedDistribution)): - return _get_dist_name(fn.base_dist) - return type(fn).__name__ - - def get_trace(): - # We use `init_to_sample` to get around ImproperUniform distribution, - # which does not have `sample` method. - subs_model = handlers.substitute( - handlers.seed(model, 0), - substitute_fn=init_to_sample, - ) - trace = handlers.trace(subs_model).get_trace(*model_args, **model_kwargs) - # Work around an issue where jax.eval_shape does not work - # for distribution output (e.g. the function `lambda: dist.Normal(0, 1)`) - # Here we will remove `fn` and store its name in the trace. - for _, site in trace.items(): - if site["type"] == "sample": - site["fn_name"] = _get_dist_name(site.pop("fn")) - elif site["type"] == "deterministic": - site["fn_name"] = "Deterministic" - return PytreeTrace(trace) - - # We use eval_shape to avoid any array computation. - trace = jax.eval_shape(get_trace).trace - - named_dims = {} - - for name, site in trace.items(): - batch_dims = [frame.name for frame in sorted(site["cond_indep_stack"], key=lambda x: x.dim)] - event_dims = list(site.get("infer", {}).get("event_dims", [])) - if site["type"] in ["sample", "deterministic"] and (batch_dims or event_dims): - named_dims[name] = batch_dims + event_dims - - return named_dims - - -class NumPyroConverter: - """Encapsulate NumPyro specific logic.""" - - # pylint: disable=too-many-instance-attributes - - model = None # type: Optional[Callable] - nchains = None # type: int - ndraws = None # type: int - - def __init__( - self, - *, - posterior=None, - prior=None, - posterior_predictive=None, - predictions=None, - constant_data=None, - predictions_constant_data=None, - log_likelihood=None, - index_origin=None, - coords=None, - dims=None, - pred_dims=None, - extra_event_dims=None, - num_chains=1, - ): - """Convert NumPyro data into an InferenceData object. - - Parameters - ---------- - posterior : numpyro.mcmc.MCMC - Fitted MCMC object from NumPyro - prior: dict - Prior samples from a NumPyro model - posterior_predictive : dict - Posterior predictive samples for the posterior - predictions: dict - Out of sample predictions - constant_data: dict - Dictionary containing constant data variables mapped to their values. - predictions_constant_data: dict - Constant data used for out-of-sample predictions. - index_origin : int, optional - coords : dict[str] -> list[str] - Map of dimensions to coordinates - dims : dict[str] -> list[str] - Map variable names to their coordinates. Will be inferred if they are not provided. - pred_dims: dict - Dims for predictions data. Map variable names to their coordinates. - extra_event_dims: dict - Extra event dims for deterministic sites. Maps event dims that couldnt be inferred to - their coordinates. - num_chains: int - Number of chains used for sampling. Ignored if posterior is present. - """ - import jax - import numpyro - - self.posterior = posterior - self.prior = jax.device_get(prior) - self.posterior_predictive = jax.device_get(posterior_predictive) - self.predictions = predictions - self.constant_data = constant_data - self.predictions_constant_data = predictions_constant_data - self.log_likelihood = ( - rcParams["data.log_likelihood"] if log_likelihood is None else log_likelihood - ) - self.index_origin = rcParams["data.index_origin"] if index_origin is None else index_origin - self.coords = coords - self.dims = dims - self.pred_dims = pred_dims - self.extra_event_dims = extra_event_dims - self.numpyro = numpyro - - def arbitrary_element(dct): - return next(iter(dct.values())) - - if posterior is not None: - samples = jax.device_get(self.posterior.get_samples(group_by_chain=True)) - if hasattr(samples, "_asdict"): - # In case it is easy to convert to a dictionary, as in the case of namedtuples - samples = samples._asdict() - if not isinstance(samples, dict): - # handle the case we run MCMC with a general potential_fn - # (instead of a NumPyro model) whose args is not a dictionary - # (e.g. f(x) = x ** 2) - tree_flatten_samples = jax.tree_util.tree_flatten(samples)[0] - samples = { - f"Param:{i}": jax.device_get(v) for i, v in enumerate(tree_flatten_samples) - } - self._samples = samples - self.nchains, self.ndraws = ( - posterior.num_chains, - posterior.num_samples // posterior.thinning, - ) - self.model = self.posterior.sampler.model - # model arguments and keyword arguments - self._args = self.posterior._args # pylint: disable=protected-access - self._kwargs = self.posterior._kwargs # pylint: disable=protected-access - self.dims = self.dims if self.dims is not None else self.infer_dims() - self.pred_dims = ( - self.pred_dims if self.pred_dims is not None else self.infer_pred_dims() - ) - else: - self.nchains = num_chains - get_from = None - if predictions is not None: - get_from = predictions - elif posterior_predictive is not None: - get_from = posterior_predictive - elif prior is not None: - get_from = prior - if get_from is None and constant_data is None and predictions_constant_data is None: - raise ValueError( - "When constructing InferenceData must have at least" - " one of posterior, prior, posterior_predictive or predictions." - ) - if get_from is not None: - aelem = arbitrary_element(get_from) - self.ndraws = aelem.shape[0] // self.nchains - - observations = {} - if self.model is not None: - # we need to use an init strategy to generate random samples for ImproperUniform sites - seeded_model = numpyro.handlers.substitute( - numpyro.handlers.seed(self.model, jax.random.PRNGKey(0)), - substitute_fn=numpyro.infer.init_to_sample, - ) - trace = numpyro.handlers.trace(seeded_model).get_trace(*self._args, **self._kwargs) - observations = { - name: site["value"] - for name, site in trace.items() - if site["type"] == "sample" and site["is_observed"] - } - self.observations = observations if observations else None - - @requires("posterior") - def posterior_to_xarray(self): - """Convert the posterior to an xarray dataset.""" - data = self._samples - return dict_to_dataset( - data, - library=self.numpyro, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ) - - @requires("posterior") - def sample_stats_to_xarray(self): - """Extract sample_stats from NumPyro posterior.""" - rename_key = { - "potential_energy": "lp", - "adapt_state.step_size": "step_size", - "num_steps": "n_steps", - "accept_prob": "acceptance_rate", - } - data = {} - for stat, value in self.posterior.get_extra_fields(group_by_chain=True).items(): - if isinstance(value, (dict, tuple)): - continue - name = rename_key.get(stat, stat) - value = value.copy() - if stat == "potential_energy": - data[name] = -value - else: - data[name] = value - if stat == "num_steps": - data["tree_depth"] = np.log2(value).astype(int) + 1 - return dict_to_dataset( - data, - library=self.numpyro, - dims=None, - coords=self.coords, - index_origin=self.index_origin, - ) - - @requires("posterior") - @requires("model") - def log_likelihood_to_xarray(self): - """Extract log likelihood from NumPyro posterior.""" - if not self.log_likelihood: - return None - data = {} - if self.observations is not None: - samples = self.posterior.get_samples(group_by_chain=False) - if hasattr(samples, "_asdict"): - samples = samples._asdict() - log_likelihood_dict = self.numpyro.infer.log_likelihood( - self.model, samples, *self._args, **self._kwargs - ) - for obs_name, log_like in log_likelihood_dict.items(): - shape = (self.nchains, self.ndraws) + log_like.shape[1:] - data[obs_name] = np.reshape(np.asarray(log_like), shape) - return dict_to_dataset( - data, - library=self.numpyro, - dims=self.dims, - coords=self.coords, - index_origin=self.index_origin, - skip_event_dims=True, - ) - - def translate_posterior_predictive_dict_to_xarray(self, dct, dims): - """Convert posterior_predictive or prediction samples to xarray.""" - data = {} - for k, ary in dct.items(): - shape = ary.shape - if shape[0] == self.nchains and shape[1] == self.ndraws: - data[k] = ary - elif shape[0] == self.nchains * self.ndraws: - data[k] = ary.reshape((self.nchains, self.ndraws, *shape[1:])) - else: - data[k] = utils.expand_dims(ary) - _log.warning( - "posterior predictive shape not compatible with number of chains and draws. " - "This can mean that some draws or even whole chains are not represented." - ) - return dict_to_dataset( - data, - library=self.numpyro, - coords=self.coords, - dims=dims, - index_origin=self.index_origin, - ) - - @requires("posterior_predictive") - def posterior_predictive_to_xarray(self): - """Convert posterior_predictive samples to xarray.""" - return self.translate_posterior_predictive_dict_to_xarray( - self.posterior_predictive, self.dims - ) - - @requires("predictions") - def predictions_to_xarray(self): - """Convert predictions to xarray.""" - return self.translate_posterior_predictive_dict_to_xarray(self.predictions, self.pred_dims) - - def priors_to_xarray(self): - """Convert prior samples (and if possible prior predictive too) to xarray.""" - if self.prior is None: - return {"prior": None, "prior_predictive": None} - if self.posterior is not None: - prior_vars = list(self._samples.keys()) - prior_predictive_vars = [key for key in self.prior.keys() if key not in prior_vars] - else: - prior_vars = self.prior.keys() - prior_predictive_vars = None - priors_dict = { - group: ( - None - if var_names is None - else dict_to_dataset( - {k: utils.expand_dims(self.prior[k]) for k in var_names}, - library=self.numpyro, - coords=self.coords, - dims=self.dims, - index_origin=self.index_origin, - ) - ) - for group, var_names in zip( - ("prior", "prior_predictive"), (prior_vars, prior_predictive_vars) - ) - } - return priors_dict - - @requires("observations") - @requires("model") - def observed_data_to_xarray(self): - """Convert observed data to xarray.""" - return dict_to_dataset( - self.observations, - library=self.numpyro, - dims=self.dims, - coords=self.coords, - default_dims=[], - index_origin=self.index_origin, - ) - - @requires("constant_data") - def constant_data_to_xarray(self): - """Convert constant_data to xarray.""" - return dict_to_dataset( - self.constant_data, - library=self.numpyro, - dims=self.dims, - coords=self.coords, - default_dims=[], - index_origin=self.index_origin, - ) - - @requires("predictions_constant_data") - def predictions_constant_data_to_xarray(self): - """Convert predictions_constant_data to xarray.""" - return dict_to_dataset( - self.predictions_constant_data, - library=self.numpyro, - dims=self.pred_dims, - coords=self.coords, - default_dims=[], - index_origin=self.index_origin, - ) - - def to_inference_data(self): - """Convert all available data to an InferenceData object. - - Note that if groups can not be created (i.e., there is no `trace`, so - the `posterior` and `sample_stats` can not be extracted), then the InferenceData - will not have those groups. - """ - return InferenceData( - **{ - "posterior": self.posterior_to_xarray(), - "sample_stats": self.sample_stats_to_xarray(), - "log_likelihood": self.log_likelihood_to_xarray(), - "posterior_predictive": self.posterior_predictive_to_xarray(), - "predictions": self.predictions_to_xarray(), - **self.priors_to_xarray(), - "observed_data": self.observed_data_to_xarray(), - "constant_data": self.constant_data_to_xarray(), - "predictions_constant_data": self.predictions_constant_data_to_xarray(), - } - ) - - @requires("posterior") - @requires("model") - def infer_dims(self) -> Dict[str, List[str]]: - dims = infer_dims(self.model, self._args, self._kwargs) - if self.extra_event_dims: - dims = _add_dims(dims, self.extra_event_dims) - return dims - - @requires("posterior") - @requires("model") - @requires("predictions") - def infer_pred_dims(self) -> Dict[str, List[str]]: - dims = infer_dims(self.model, self._args, self._kwargs) - if self.extra_event_dims: - dims = _add_dims(dims, self.extra_event_dims) - return dims - - -def from_numpyro( - posterior=None, - *, - prior=None, - posterior_predictive=None, - predictions=None, - constant_data=None, - predictions_constant_data=None, - log_likelihood=None, - index_origin=None, - coords=None, - dims=None, - pred_dims=None, - extra_event_dims=None, - num_chains=1, -): - """Convert NumPyro data into an InferenceData object. - - If no dims are provided, this will infer batch dim names from NumPyro model plates. - For event dim names, such as with the ZeroSumNormal, `infer={"event_dims":dim_names}` - can be provided in numpyro.sample, i.e.:: - - # equivalent to dims entry, {"gamma": ["groups"]} - gamma = numpyro.sample( - "gamma", - dist.ZeroSumNormal(1, event_shape=(n_groups,)), - infer={"event_dims":["groups"]} - ) - - There is also an additional `extra_event_dims` input to cover any edge cases, for instance - deterministic sites with event dims (which dont have an `infer` argument to provide metadata). - - For a usage example read the - :ref:`Creating InferenceData section on from_numpyro ` - - Parameters - ---------- - posterior : numpyro.mcmc.MCMC - Fitted MCMC object from NumPyro - prior: dict - Prior samples from a NumPyro model - posterior_predictive : dict - Posterior predictive samples for the posterior - predictions: dict - Out of sample predictions - constant_data: dict - Dictionary containing constant data variables mapped to their values. - predictions_constant_data: dict - Constant data used for out-of-sample predictions. - index_origin : int, optional - coords : dict[str] -> list[str] - Map of dimensions to coordinates - dims : dict[str] -> list[str] - Map variable names to their coordinates. Will be inferred if they are not provided. - pred_dims: dict - Dims for predictions data. Map variable names to their coordinates. Default behavior is to - infer dims if this is not provided - num_chains: int - Number of chains used for sampling. Ignored if posterior is present. - """ - return NumPyroConverter( - posterior=posterior, - prior=prior, - posterior_predictive=posterior_predictive, - predictions=predictions, - constant_data=constant_data, - predictions_constant_data=predictions_constant_data, - log_likelihood=log_likelihood, - index_origin=index_origin, - coords=coords, - dims=dims, - pred_dims=pred_dims, - extra_event_dims=extra_event_dims, - num_chains=num_chains, - ).to_inference_data() diff --git a/arviz/data/io_pyjags.py b/arviz/data/io_pyjags.py deleted file mode 100644 index bc323d893f..0000000000 --- a/arviz/data/io_pyjags.py +++ /dev/null @@ -1,378 +0,0 @@ -"""Convert PyJAGS sample dictionaries to ArviZ inference data objects.""" - -import typing as tp -from collections import OrderedDict -from collections.abc import Iterable - -import numpy as np -import xarray - -from .inference_data import InferenceData - -from ..rcparams import rcParams -from .base import dict_to_dataset - - -class PyJAGSConverter: - """Encapsulate PyJAGS specific logic.""" - - def __init__( - self, - *, - posterior: tp.Optional[tp.Mapping[str, np.ndarray]] = None, - prior: tp.Optional[tp.Mapping[str, np.ndarray]] = None, - log_likelihood: tp.Optional[ - tp.Union[str, tp.List[str], tp.Tuple[str, ...], tp.Mapping[str, str]] - ] = None, - coords=None, - dims=None, - save_warmup: tp.Optional[bool] = None, - warmup_iterations: int = 0, - ) -> None: - self.posterior: tp.Optional[tp.Mapping[str, np.ndarray]] - self.log_likelihood: tp.Optional[tp.Dict[str, np.ndarray]] - if log_likelihood is not None and posterior is not None: - posterior_copy = dict(posterior) # create a shallow copy of the dictionary - - if isinstance(log_likelihood, str): - log_likelihood = [log_likelihood] - if isinstance(log_likelihood, (list, tuple)): - log_likelihood = {name: name for name in log_likelihood} - - self.log_likelihood = { - obs_var_name: posterior_copy.pop(log_like_name) - for obs_var_name, log_like_name in log_likelihood.items() - } - self.posterior = posterior_copy - else: - self.posterior = posterior - self.log_likelihood = None - self.prior = prior - self.coords = coords - self.dims = dims - self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup - self.warmup_iterations = warmup_iterations - - import pyjags # pylint: disable=import-error - - self.pyjags = pyjags - - def _pyjags_samples_to_xarray( - self, pyjags_samples: tp.Mapping[str, np.ndarray] - ) -> tp.Tuple[xarray.Dataset, xarray.Dataset]: - data, data_warmup = get_draws( - pyjags_samples=pyjags_samples, - warmup_iterations=self.warmup_iterations, - warmup=self.save_warmup, - ) - - return ( - dict_to_dataset(data, library=self.pyjags, coords=self.coords, dims=self.dims), - dict_to_dataset( - data_warmup, - library=self.pyjags, - coords=self.coords, - dims=self.dims, - ), - ) - - def posterior_to_xarray(self) -> tp.Optional[tp.Tuple[xarray.Dataset, xarray.Dataset]]: - """Extract posterior samples from fit.""" - if self.posterior is None: - return None - - return self._pyjags_samples_to_xarray(self.posterior) - - def prior_to_xarray(self) -> tp.Optional[tp.Tuple[xarray.Dataset, xarray.Dataset]]: - """Extract posterior samples from fit.""" - if self.prior is None: - return None - - return self._pyjags_samples_to_xarray(self.prior) - - def log_likelihood_to_xarray(self) -> tp.Optional[tp.Tuple[xarray.Dataset, xarray.Dataset]]: - """Extract log likelihood samples from fit.""" - if self.log_likelihood is None: - return None - - return self._pyjags_samples_to_xarray(self.log_likelihood) - - def to_inference_data(self): - """Convert all available data to an InferenceData object.""" - # obs_const_dict = self.observed_and_constant_data_to_xarray() - # predictions_const_data = self.predictions_constant_data_to_xarray() - save_warmup = self.save_warmup and self.warmup_iterations > 0 - # self.posterior is not None - - idata_dict = { - "posterior": self.posterior_to_xarray(), - "prior": self.prior_to_xarray(), - "log_likelihood": self.log_likelihood_to_xarray(), - "save_warmup": save_warmup, - } - - return InferenceData(**idata_dict) - - -def get_draws( - pyjags_samples: tp.Mapping[str, np.ndarray], - variables: tp.Optional[tp.Union[str, tp.Iterable[str]]] = None, - warmup: bool = False, - warmup_iterations: int = 0, -) -> tp.Tuple[tp.Mapping[str, np.ndarray], tp.Mapping[str, np.ndarray]]: - """ - Convert PyJAGS samples dictionary to ArviZ format and split warmup samples. - - Parameters - ---------- - pyjags_samples: a dictionary mapping variable names to NumPy arrays of MCMC - chains of samples with shape - (parameter_dimension, chain_length, number_of_chains) - - variables: the variables to extract from the samples dictionary - warmup: whether or not to return warmup draws in data_warmup - warmup_iterations: the number of warmup iterations if any - - Returns - ------- - A tuple of two samples dictionaries in ArviZ format - """ - data_warmup: tp.Mapping[str, np.ndarray] = OrderedDict() - - if variables is None: - variables = list(pyjags_samples.keys()) - elif isinstance(variables, str): - variables = [variables] - - if not isinstance(variables, Iterable): - raise TypeError("variables must be of type Sequence or str") - - variables = tuple(variables) - - if warmup_iterations > 0: - ( - warmup_samples, - actual_samples, - ) = _split_pyjags_dict_in_warmup_and_actual_samples( - pyjags_samples=pyjags_samples, - warmup_iterations=warmup_iterations, - variable_names=variables, - ) - - data = _convert_pyjags_dict_to_arviz_dict(samples=actual_samples, variable_names=variables) - - if warmup: - data_warmup = _convert_pyjags_dict_to_arviz_dict( - samples=warmup_samples, variable_names=variables - ) - else: - data = _convert_pyjags_dict_to_arviz_dict(samples=pyjags_samples, variable_names=variables) - - return data, data_warmup - - -def _split_pyjags_dict_in_warmup_and_actual_samples( - pyjags_samples: tp.Mapping[str, np.ndarray], - warmup_iterations: int, - variable_names: tp.Optional[tp.Tuple[str, ...]] = None, -) -> tp.Tuple[tp.Mapping[str, np.ndarray], tp.Mapping[str, np.ndarray]]: - """ - Split a PyJAGS samples dictionary into actual samples and warmup samples. - - Parameters - ---------- - pyjags_samples: a dictionary mapping variable names to NumPy arrays of MCMC - chains of samples with shape - (parameter_dimension, chain_length, number_of_chains) - - warmup_iterations: the number of draws to be split off for warmum - variable_names: the variables in the dictionary to use; if None use all - - Returns - ------- - A tuple of two pyjags samples dictionaries in PyJAGS format - """ - if variable_names is None: - variable_names = tuple(pyjags_samples.keys()) - - warmup_samples: tp.Dict[str, np.ndarray] = {} - actual_samples: tp.Dict[str, np.ndarray] = {} - - for variable_name, chains in pyjags_samples.items(): - if variable_name in variable_names: - warmup_samples[variable_name] = chains[:, :warmup_iterations, :] - actual_samples[variable_name] = chains[:, warmup_iterations:, :] - - return warmup_samples, actual_samples - - -def _convert_pyjags_dict_to_arviz_dict( - samples: tp.Mapping[str, np.ndarray], - variable_names: tp.Optional[tp.Tuple[str, ...]] = None, -) -> tp.Mapping[str, np.ndarray]: - """ - Convert a PyJAGS dictionary to an ArviZ dictionary. - - Takes a python dictionary of samples that has been generated by the sample - method of a model instance and returns a dictionary of samples in ArviZ - format. - - Parameters - ---------- - samples: a dictionary mapping variable names to P arrays with shape - (parameter_dimension, chain_length, number_of_chains) - - Returns - ------- - a dictionary mapping variable names to NumPy arrays with shape - (number_of_chains, chain_length, parameter_dimension) - """ - # pyjags returns a dictionary of NumPy arrays with shape - # (parameter_dimension, chain_length, number_of_chains) - # but arviz expects samples with shape - # (number_of_chains, chain_length, parameter_dimension) - - variable_name_to_samples_map = {} - - if variable_names is None: - variable_names = tuple(samples.keys()) - - for variable_name, chains in samples.items(): - if variable_name in variable_names: - parameter_dimension, _, _ = chains.shape - if parameter_dimension == 1: - variable_name_to_samples_map[variable_name] = chains[0, :, :].transpose() - else: - variable_name_to_samples_map[variable_name] = np.swapaxes(chains, 0, 2) - - return variable_name_to_samples_map - - -def _extract_arviz_dict_from_inference_data( - idata, -) -> tp.Mapping[str, np.ndarray]: - """ - Extract the samples dictionary from an ArviZ inference data object. - - Extracts a dictionary mapping parameter names to NumPy arrays of samples - with shape (number_of_chains, chain_length, parameter_dimension) from an - ArviZ inference data object. - - Parameters - ---------- - idata: InferenceData - - Returns - ------- - a dictionary mapping variable names to NumPy arrays with shape - (number_of_chains, chain_length, parameter_dimension) - - """ - variable_name_to_samples_map = { - key: np.array(value["data"]) - for key, value in idata.posterior.to_dict()["data_vars"].items() - } - - return variable_name_to_samples_map - - -def _convert_arviz_dict_to_pyjags_dict( - samples: tp.Mapping[str, np.ndarray], -) -> tp.Mapping[str, np.ndarray]: - """ - Convert and ArviZ dictionary to a PyJAGS dictionary. - - Takes a python dictionary of samples in ArviZ format and returns the samples - as a dictionary in PyJAGS format. - - Parameters - ---------- - samples: dict of {str : array_like} - a dictionary mapping variable names to NumPy arrays with shape - (number_of_chains, chain_length, parameter_dimension) - - Returns - ------- - a dictionary mapping variable names to NumPy arrays with shape - (parameter_dimension, chain_length, number_of_chains) - - """ - # pyjags returns a dictionary of NumPy arrays with shape - # (parameter_dimension, chain_length, number_of_chains) - # but arviz expects samples with shape - # (number_of_chains, chain_length, parameter_dimension) - - variable_name_to_samples_map = {} - - for variable_name, chains in samples.items(): - if chains.ndim == 2: - number_of_chains, chain_length = chains.shape - chains = chains.reshape((number_of_chains, chain_length, 1)) - - variable_name_to_samples_map[variable_name] = np.swapaxes(chains, 0, 2) - - return variable_name_to_samples_map - - -def from_pyjags( - posterior: tp.Optional[tp.Mapping[str, np.ndarray]] = None, - prior: tp.Optional[tp.Mapping[str, np.ndarray]] = None, - log_likelihood: tp.Optional[tp.Mapping[str, str]] = None, - coords=None, - dims=None, - save_warmup=None, - warmup_iterations: int = 0, -) -> InferenceData: - """ - Convert PyJAGS posterior samples to an ArviZ inference data object. - - Takes a python dictionary of samples that has been generated by the sample - method of a model instance and returns an Arviz inference data object. - For a usage example read the - :ref:`Creating InferenceData section on from_pyjags ` - - Parameters - ---------- - posterior: dict of {str : array_like}, optional - a dictionary mapping variable names to NumPy arrays containing - posterior samples with shape - (parameter_dimension, chain_length, number_of_chains) - - prior: dict of {str : array_like}, optional - a dictionary mapping variable names to NumPy arrays containing - prior samples with shape - (parameter_dimension, chain_length, number_of_chains) - - log_likelihood: dict of {str: str}, list of str or str, optional - Pointwise log_likelihood for the data. log_likelihood is extracted from the - posterior. It is recommended to use this argument as a dictionary whose keys - are observed variable names and its values are the variables storing log - likelihood arrays in the JAGS code. In other cases, a dictionary with keys - equal to its values is used. - - coords: dict[str, iterable] - A dictionary containing the values that are used as index. The key - is the name of the dimension, the values are the index values. - - dims: dict[str, List(str)] - A mapping from variables to a list of coordinate names for the variable. - - save_warmup : bool, optional - Save warmup iterations in InferenceData. If not defined, use default defined by the rcParams. - - warmup_iterations: int, optional - Number of warmup iterations - - Returns - ------- - InferenceData - """ - return PyJAGSConverter( - posterior=posterior, - prior=prior, - log_likelihood=log_likelihood, - dims=dims, - coords=coords, - save_warmup=save_warmup, - warmup_iterations=warmup_iterations, - ).to_inference_data() diff --git a/arviz/data/io_pyro.py b/arviz/data/io_pyro.py deleted file mode 100644 index 501be5ea38..0000000000 --- a/arviz/data/io_pyro.py +++ /dev/null @@ -1,333 +0,0 @@ -"""Pyro-specific conversion code.""" - -import logging -from typing import Callable, Optional -import warnings - -import numpy as np -from packaging import version - -from .. import utils -from ..rcparams import rcParams -from .base import dict_to_dataset, requires -from .inference_data import InferenceData - -_log = logging.getLogger(__name__) - - -class PyroConverter: - """Encapsulate Pyro specific logic.""" - - # pylint: disable=too-many-instance-attributes - - model = None # type: Optional[Callable] - nchains = None # type: int - ndraws = None # type: int - - def __init__( - self, - *, - posterior=None, - prior=None, - posterior_predictive=None, - log_likelihood=None, - predictions=None, - constant_data=None, - predictions_constant_data=None, - coords=None, - dims=None, - pred_dims=None, - num_chains=1, - ): - """Convert Pyro data into an InferenceData object. - - Parameters - ---------- - posterior : pyro.infer.MCMC - Fitted MCMC object from Pyro - prior: dict - Prior samples from a Pyro model - posterior_predictive : dict - Posterior predictive samples for the posterior - predictions: dict - Out of sample predictions - constant_data: dict - Dictionary containing constant data variables mapped to their values. - predictions_constant_data: dict - Constant data used for out-of-sample predictions. - coords : dict[str] -> list[str] - Map of dimensions to coordinates - dims : dict[str] -> list[str] - Map variable names to their coordinates - pred_dims: dict - Dims for predictions data. Map variable names to their coordinates. - num_chains: int - Number of chains used for sampling. Ignored if posterior is present. - """ - self.posterior = posterior - self.prior = prior - self.posterior_predictive = posterior_predictive - self.log_likelihood = ( - rcParams["data.log_likelihood"] if log_likelihood is None else log_likelihood - ) - self.predictions = predictions - self.constant_data = constant_data - self.predictions_constant_data = predictions_constant_data - self.coords = coords - self.dims = {} if dims is None else dims - self.pred_dims = {} if pred_dims is None else pred_dims - import pyro - - def arbitrary_element(dct): - return next(iter(dct.values())) - - self.pyro = pyro - if posterior is not None: - self.nchains, self.ndraws = posterior.num_chains, posterior.num_samples - if version.parse(pyro.__version__) >= version.parse("1.0.0"): - self.model = self.posterior.kernel.model - # model arguments and keyword arguments - self._args = self.posterior._args # pylint: disable=protected-access - self._kwargs = self.posterior._kwargs # pylint: disable=protected-access - else: - self.nchains = num_chains - get_from = None - if predictions is not None: - get_from = predictions - elif posterior_predictive is not None: - get_from = posterior_predictive - elif prior is not None: - get_from = prior - if get_from is None and constant_data is None and predictions_constant_data is None: - raise ValueError( - "When constructing InferenceData must have at least" - " one of posterior, prior, posterior_predictive or predictions." - ) - if get_from is not None: - aelem = arbitrary_element(get_from) - self.ndraws = aelem.shape[0] // self.nchains - - observations = {} - if self.model is not None: - trace = pyro.poutine.trace(self.model).get_trace( # pylint: disable=not-callable - *self._args, **self._kwargs - ) - observations = { - name: site["value"].cpu() - for name, site in trace.nodes.items() - if site["type"] == "sample" and site["is_observed"] - } - self.observations = observations if observations else None - - @requires("posterior") - def posterior_to_xarray(self): - """Convert the posterior to an xarray dataset.""" - data = self.posterior.get_samples(group_by_chain=True) - data = {k: v.detach().cpu().numpy() for k, v in data.items()} - return dict_to_dataset(data, library=self.pyro, coords=self.coords, dims=self.dims) - - @requires("posterior") - def sample_stats_to_xarray(self): - """Extract sample_stats from Pyro posterior.""" - divergences = self.posterior.diagnostics()["divergences"] - diverging = np.zeros((self.nchains, self.ndraws), dtype=bool) - for i, k in enumerate(sorted(divergences)): - diverging[i, divergences[k]] = True - data = {"diverging": diverging} - return dict_to_dataset(data, library=self.pyro, coords=self.coords, dims=None) - - @requires("posterior") - @requires("model") - def log_likelihood_to_xarray(self): - """Extract log likelihood from Pyro posterior.""" - if not self.log_likelihood: - return None - data = {} - if self.observations is not None: - try: - samples = self.posterior.get_samples(group_by_chain=False) - predictive = self.pyro.infer.Predictive(self.model, samples) - vectorized_trace = predictive.get_vectorized_trace(*self._args, **self._kwargs) - for obs_name in self.observations.keys(): - obs_site = vectorized_trace.nodes[obs_name] - log_like = obs_site["fn"].log_prob(obs_site["value"]).detach().cpu().numpy() - shape = (self.nchains, self.ndraws) + log_like.shape[1:] - data[obs_name] = np.reshape(log_like, shape) - except: # pylint: disable=bare-except - # cannot get vectorized trace - warnings.warn( - "Could not get vectorized trace, log_likelihood group will be omitted. " - "Check your model vectorization or set log_likelihood=False" - ) - return None - return dict_to_dataset( - data, library=self.pyro, coords=self.coords, dims=self.dims, skip_event_dims=True - ) - - def translate_posterior_predictive_dict_to_xarray(self, dct, dims): - """Convert posterior_predictive or prediction samples to xarray.""" - data = {} - for k, ary in dct.items(): - ary = ary.detach().cpu().numpy() - shape = ary.shape - if shape[0] == self.nchains and shape[1] == self.ndraws: - data[k] = ary - elif shape[0] == self.nchains * self.ndraws: - data[k] = ary.reshape((self.nchains, self.ndraws, *shape[1:])) - else: - data[k] = utils.expand_dims(ary) - _log.warning( - "posterior predictive shape not compatible with number of chains and draws." - "This can mean that some draws or even whole chains are not represented." - ) - return dict_to_dataset(data, library=self.pyro, coords=self.coords, dims=dims) - - @requires("posterior_predictive") - def posterior_predictive_to_xarray(self): - """Convert posterior_predictive samples to xarray.""" - return self.translate_posterior_predictive_dict_to_xarray( - self.posterior_predictive, self.dims - ) - - @requires("predictions") - def predictions_to_xarray(self): - """Convert predictions to xarray.""" - return self.translate_posterior_predictive_dict_to_xarray(self.predictions, self.pred_dims) - - def priors_to_xarray(self): - """Convert prior samples (and if possible prior predictive too) to xarray.""" - if self.prior is None: - return {"prior": None, "prior_predictive": None} - if self.posterior is not None: - prior_vars = list(self.posterior.get_samples().keys()) - prior_predictive_vars = [key for key in self.prior.keys() if key not in prior_vars] - else: - prior_vars = self.prior.keys() - prior_predictive_vars = None - priors_dict = { - group: ( - None - if var_names is None - else dict_to_dataset( - { - k: utils.expand_dims(np.squeeze(self.prior[k].detach().cpu().numpy())) - for k in var_names - }, - library=self.pyro, - coords=self.coords, - dims=self.dims, - ) - ) - for group, var_names in zip( - ("prior", "prior_predictive"), (prior_vars, prior_predictive_vars) - ) - } - return priors_dict - - @requires("observations") - @requires("model") - def observed_data_to_xarray(self): - """Convert observed data to xarray.""" - dims = {} if self.dims is None else self.dims - return dict_to_dataset( - self.observations, library=self.pyro, coords=self.coords, dims=dims, default_dims=[] - ) - - @requires("constant_data") - def constant_data_to_xarray(self): - """Convert constant_data to xarray.""" - return dict_to_dataset( - self.constant_data, - library=self.pyro, - coords=self.coords, - dims=self.dims, - default_dims=[], - ) - - @requires("predictions_constant_data") - def predictions_constant_data_to_xarray(self): - """Convert predictions_constant_data to xarray.""" - return dict_to_dataset( - self.predictions_constant_data, - library=self.pyro, - coords=self.coords, - dims=self.pred_dims, - default_dims=[], - ) - - def to_inference_data(self): - """Convert all available data to an InferenceData object.""" - return InferenceData( - **{ - "posterior": self.posterior_to_xarray(), - "sample_stats": self.sample_stats_to_xarray(), - "log_likelihood": self.log_likelihood_to_xarray(), - "posterior_predictive": self.posterior_predictive_to_xarray(), - "predictions": self.predictions_to_xarray(), - "constant_data": self.constant_data_to_xarray(), - "predictions_constant_data": self.predictions_constant_data_to_xarray(), - **self.priors_to_xarray(), - "observed_data": self.observed_data_to_xarray(), - } - ) - - -def from_pyro( - posterior=None, - *, - prior=None, - posterior_predictive=None, - log_likelihood=None, - predictions=None, - constant_data=None, - predictions_constant_data=None, - coords=None, - dims=None, - pred_dims=None, - num_chains=1, -): - """Convert Pyro data into an InferenceData object. - - For a usage example read the - :ref:`Creating InferenceData section on from_pyro ` - - - Parameters - ---------- - posterior : pyro.infer.MCMC - Fitted MCMC object from Pyro - prior: dict - Prior samples from a Pyro model - posterior_predictive : dict - Posterior predictive samples for the posterior - log_likelihood : bool, optional - Calculate and store pointwise log likelihood values. Defaults to the value - of rcParam ``data.log_likelihood``. - predictions: dict - Out of sample predictions - constant_data: dict - Dictionary containing constant data variables mapped to their values. - predictions_constant_data: dict - Constant data used for out-of-sample predictions. - coords : dict[str] -> list[str] - Map of dimensions to coordinates - dims : dict[str] -> list[str] - Map variable names to their coordinates - pred_dims: dict - Dims for predictions data. Map variable names to their coordinates. - num_chains: int - Number of chains used for sampling. Ignored if posterior is present. - """ - return PyroConverter( - posterior=posterior, - prior=prior, - posterior_predictive=posterior_predictive, - log_likelihood=log_likelihood, - predictions=predictions, - constant_data=constant_data, - predictions_constant_data=predictions_constant_data, - coords=coords, - dims=dims, - pred_dims=pred_dims, - num_chains=num_chains, - ).to_inference_data() diff --git a/arviz/data/io_pystan.py b/arviz/data/io_pystan.py deleted file mode 100644 index 480a1be638..0000000000 --- a/arviz/data/io_pystan.py +++ /dev/null @@ -1,1095 +0,0 @@ -# pylint: disable=too-many-instance-attributes,too-many-lines -"""PyStan-specific conversion code.""" -import re -from collections import OrderedDict -from copy import deepcopy -from math import ceil - -import numpy as np -import xarray as xr - -from .. import _log -from ..rcparams import rcParams -from .base import dict_to_dataset, generate_dims_coords, infer_stan_dtypes, make_attrs, requires -from .inference_data import InferenceData - -try: - import ujson as json -except ImportError: - # Can't find ujson using json - # mypy struggles with conditional imports expressed as catching ImportError: - # https://github.com/python/mypy/issues/1153 - import json # type: ignore - - -class PyStanConverter: - """Encapsulate PyStan specific logic.""" - - def __init__( - self, - *, - posterior=None, - posterior_predictive=None, - predictions=None, - prior=None, - prior_predictive=None, - observed_data=None, - constant_data=None, - predictions_constant_data=None, - log_likelihood=None, - coords=None, - dims=None, - save_warmup=None, - dtypes=None, - ): - self.posterior = posterior - self.posterior_predictive = posterior_predictive - self.predictions = predictions - self.prior = prior - self.prior_predictive = prior_predictive - self.observed_data = observed_data - self.constant_data = constant_data - self.predictions_constant_data = predictions_constant_data - self.log_likelihood = ( - rcParams["data.log_likelihood"] if log_likelihood is None else log_likelihood - ) - self.coords = coords - self.dims = dims - self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup - self.dtypes = dtypes - - if ( - self.log_likelihood is True - and self.posterior is not None - and "log_lik" in self.posterior.sim["pars_oi"] - ): - self.log_likelihood = ["log_lik"] - elif isinstance(self.log_likelihood, bool): - self.log_likelihood = None - - import pystan # pylint: disable=import-error - - self.pystan = pystan - - @requires("posterior") - def posterior_to_xarray(self): - """Extract posterior samples from fit.""" - posterior = self.posterior - # filter posterior_predictive and log_likelihood - posterior_predictive = self.posterior_predictive - if posterior_predictive is None: - posterior_predictive = [] - elif isinstance(posterior_predictive, str): - posterior_predictive = [posterior_predictive] - predictions = self.predictions - if predictions is None: - predictions = [] - elif isinstance(predictions, str): - predictions = [predictions] - log_likelihood = self.log_likelihood - if log_likelihood is None: - log_likelihood = [] - elif isinstance(log_likelihood, str): - log_likelihood = [log_likelihood] - elif isinstance(log_likelihood, dict): - log_likelihood = list(log_likelihood.values()) - - ignore = posterior_predictive + predictions + log_likelihood + ["lp__"] - - data, data_warmup = get_draws( - posterior, ignore=ignore, warmup=self.save_warmup, dtypes=self.dtypes - ) - attrs = get_attrs(posterior) - return ( - dict_to_dataset( - data, library=self.pystan, attrs=attrs, coords=self.coords, dims=self.dims - ), - dict_to_dataset( - data_warmup, library=self.pystan, attrs=attrs, coords=self.coords, dims=self.dims - ), - ) - - @requires("posterior") - def sample_stats_to_xarray(self): - """Extract sample_stats from posterior.""" - posterior = self.posterior - - data, data_warmup = get_sample_stats(posterior, warmup=self.save_warmup) - - # lp__ - stat_lp, stat_lp_warmup = get_draws( - posterior, variables="lp__", warmup=self.save_warmup, dtypes=self.dtypes - ) - data["lp"] = stat_lp["lp__"] - if stat_lp_warmup: - data_warmup["lp"] = stat_lp_warmup["lp__"] - - attrs = get_attrs(posterior) - return ( - dict_to_dataset( - data, library=self.pystan, attrs=attrs, coords=self.coords, dims=self.dims - ), - dict_to_dataset( - data_warmup, library=self.pystan, attrs=attrs, coords=self.coords, dims=self.dims - ), - ) - - @requires("posterior") - @requires("log_likelihood") - def log_likelihood_to_xarray(self): - """Store log_likelihood data in log_likelihood group.""" - fit = self.posterior - - # log_likelihood values - log_likelihood = self.log_likelihood - if isinstance(log_likelihood, str): - log_likelihood = [log_likelihood] - if isinstance(log_likelihood, (list, tuple)): - log_likelihood = {name: name for name in log_likelihood} - log_likelihood_draws, log_likelihood_draws_warmup = get_draws( - fit, - variables=list(log_likelihood.values()), - warmup=self.save_warmup, - dtypes=self.dtypes, - ) - data = { - obs_var_name: log_likelihood_draws[log_like_name] - for obs_var_name, log_like_name in log_likelihood.items() - if log_like_name in log_likelihood_draws - } - - data_warmup = { - obs_var_name: log_likelihood_draws_warmup[log_like_name] - for obs_var_name, log_like_name in log_likelihood.items() - if log_like_name in log_likelihood_draws_warmup - } - - return ( - dict_to_dataset( - data, library=self.pystan, coords=self.coords, dims=self.dims, skip_event_dims=True - ), - dict_to_dataset( - data_warmup, - library=self.pystan, - coords=self.coords, - dims=self.dims, - skip_event_dims=True, - ), - ) - - @requires("posterior") - @requires("posterior_predictive") - def posterior_predictive_to_xarray(self): - """Convert posterior_predictive samples to xarray.""" - posterior = self.posterior - posterior_predictive = self.posterior_predictive - data, data_warmup = get_draws( - posterior, variables=posterior_predictive, warmup=self.save_warmup, dtypes=self.dtypes - ) - return ( - dict_to_dataset(data, library=self.pystan, coords=self.coords, dims=self.dims), - dict_to_dataset(data_warmup, library=self.pystan, coords=self.coords, dims=self.dims), - ) - - @requires("posterior") - @requires("predictions") - def predictions_to_xarray(self): - """Convert predictions samples to xarray.""" - posterior = self.posterior - predictions = self.predictions - data, data_warmup = get_draws( - posterior, variables=predictions, warmup=self.save_warmup, dtypes=self.dtypes - ) - return ( - dict_to_dataset(data, library=self.pystan, coords=self.coords, dims=self.dims), - dict_to_dataset(data_warmup, library=self.pystan, coords=self.coords, dims=self.dims), - ) - - @requires("prior") - def prior_to_xarray(self): - """Convert prior samples to xarray.""" - prior = self.prior - # filter posterior_predictive and log_likelihood - prior_predictive = self.prior_predictive - if prior_predictive is None: - prior_predictive = [] - elif isinstance(prior_predictive, str): - prior_predictive = [prior_predictive] - - ignore = prior_predictive + ["lp__"] - - data, _ = get_draws(prior, ignore=ignore, warmup=False, dtypes=self.dtypes) - attrs = get_attrs(prior) - return dict_to_dataset( - data, library=self.pystan, attrs=attrs, coords=self.coords, dims=self.dims - ) - - @requires("prior") - def sample_stats_prior_to_xarray(self): - """Extract sample_stats_prior from prior.""" - prior = self.prior - data, _ = get_sample_stats(prior, warmup=False) - - # lp__ - stat_lp, _ = get_draws(prior, variables="lp__", warmup=False, dtypes=self.dtypes) - data["lp"] = stat_lp["lp__"] - - attrs = get_attrs(prior) - return dict_to_dataset( - data, library=self.pystan, attrs=attrs, coords=self.coords, dims=self.dims - ) - - @requires("prior") - @requires("prior_predictive") - def prior_predictive_to_xarray(self): - """Convert prior_predictive samples to xarray.""" - prior = self.prior - prior_predictive = self.prior_predictive - data, _ = get_draws(prior, variables=prior_predictive, warmup=False, dtypes=self.dtypes) - return dict_to_dataset(data, library=self.pystan, coords=self.coords, dims=self.dims) - - @requires("posterior") - @requires(["observed_data", "constant_data", "predictions_constant_data"]) - def data_to_xarray(self): - """Convert observed, constant data and predictions constant data to xarray.""" - posterior = self.posterior - dims = {} if self.dims is None else self.dims - obs_const_dict = {} - for group_name in ("observed_data", "constant_data", "predictions_constant_data"): - names = getattr(self, group_name) - if names is None: - continue - names = [names] if isinstance(names, str) else names - data = OrderedDict() - for key in names: - vals = np.atleast_1d(posterior.data[key]) - val_dims = dims.get(key) - val_dims, coords = generate_dims_coords( - vals.shape, key, dims=val_dims, coords=self.coords - ) - data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) - obs_const_dict[group_name] = xr.Dataset( - data_vars=data, attrs=make_attrs(library=self.pystan) - ) - return obs_const_dict - - def to_inference_data(self): - """Convert all available data to an InferenceData object. - - Note that if groups can not be created (i.e., there is no `fit`, so - the `posterior` and `sample_stats` can not be extracted), then the InferenceData - will not have those groups. - """ - data_dict = self.data_to_xarray() - return InferenceData( - save_warmup=self.save_warmup, - **{ - "posterior": self.posterior_to_xarray(), - "sample_stats": self.sample_stats_to_xarray(), - "log_likelihood": self.log_likelihood_to_xarray(), - "posterior_predictive": self.posterior_predictive_to_xarray(), - "predictions": self.predictions_to_xarray(), - "prior": self.prior_to_xarray(), - "sample_stats_prior": self.sample_stats_prior_to_xarray(), - "prior_predictive": self.prior_predictive_to_xarray(), - **({} if data_dict is None else data_dict), - }, - ) - - -class PyStan3Converter: - """Encapsulate PyStan3 specific logic.""" - - # pylint: disable=too-many-instance-attributes - def __init__( - self, - *, - posterior=None, - posterior_model=None, - posterior_predictive=None, - predictions=None, - prior=None, - prior_model=None, - prior_predictive=None, - observed_data=None, - constant_data=None, - predictions_constant_data=None, - log_likelihood=None, - coords=None, - dims=None, - save_warmup=None, - dtypes=None, - ): - self.posterior = posterior - self.posterior_model = posterior_model - self.posterior_predictive = posterior_predictive - self.predictions = predictions - self.prior = prior - self.prior_model = prior_model - self.prior_predictive = prior_predictive - self.observed_data = observed_data - self.constant_data = constant_data - self.predictions_constant_data = predictions_constant_data - self.log_likelihood = ( - rcParams["data.log_likelihood"] if log_likelihood is None else log_likelihood - ) - self.coords = coords - self.dims = dims - self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup - self.dtypes = dtypes - - if ( - self.log_likelihood is True - and self.posterior is not None - and "log_lik" in self.posterior.param_names - ): - self.log_likelihood = ["log_lik"] - elif isinstance(self.log_likelihood, bool): - self.log_likelihood = None - - import stan # pylint: disable=import-error - - self.stan = stan - - @requires("posterior") - def posterior_to_xarray(self): - """Extract posterior samples from fit.""" - posterior = self.posterior - posterior_model = self.posterior_model - # filter posterior_predictive and log_likelihood - posterior_predictive = self.posterior_predictive - if posterior_predictive is None: - posterior_predictive = [] - elif isinstance(posterior_predictive, str): - posterior_predictive = [posterior_predictive] - predictions = self.predictions - if predictions is None: - predictions = [] - elif isinstance(predictions, str): - predictions = [predictions] - log_likelihood = self.log_likelihood - if log_likelihood is None: - log_likelihood = [] - elif isinstance(log_likelihood, str): - log_likelihood = [log_likelihood] - elif isinstance(log_likelihood, dict): - log_likelihood = list(log_likelihood.values()) - - ignore = posterior_predictive + predictions + log_likelihood - - data, data_warmup = get_draws_stan3( - posterior, - model=posterior_model, - ignore=ignore, - warmup=self.save_warmup, - dtypes=self.dtypes, - ) - attrs = get_attrs_stan3(posterior, model=posterior_model) - return ( - dict_to_dataset( - data, library=self.stan, attrs=attrs, coords=self.coords, dims=self.dims - ), - dict_to_dataset( - data_warmup, library=self.stan, attrs=attrs, coords=self.coords, dims=self.dims - ), - ) - - @requires("posterior") - def sample_stats_to_xarray(self): - """Extract sample_stats from posterior.""" - posterior = self.posterior - posterior_model = self.posterior_model - data, data_warmup = get_sample_stats_stan3( - posterior, ignore="lp__", warmup=self.save_warmup, dtypes=self.dtypes - ) - data_lp, data_warmup_lp = get_sample_stats_stan3( - posterior, variables="lp__", warmup=self.save_warmup - ) - data["lp"] = data_lp["lp"] - if data_warmup_lp: - data_warmup["lp"] = data_warmup_lp["lp"] - - attrs = get_attrs_stan3(posterior, model=posterior_model) - return ( - dict_to_dataset( - data, library=self.stan, attrs=attrs, coords=self.coords, dims=self.dims - ), - dict_to_dataset( - data_warmup, library=self.stan, attrs=attrs, coords=self.coords, dims=self.dims - ), - ) - - @requires("posterior") - @requires("log_likelihood") - def log_likelihood_to_xarray(self): - """Store log_likelihood data in log_likelihood group.""" - fit = self.posterior - - log_likelihood = self.log_likelihood - model = self.posterior_model - if isinstance(log_likelihood, str): - log_likelihood = [log_likelihood] - if isinstance(log_likelihood, (list, tuple)): - log_likelihood = {name: name for name in log_likelihood} - log_likelihood_draws, log_likelihood_draws_warmup = get_draws_stan3( - fit, - model=model, - variables=list(log_likelihood.values()), - warmup=self.save_warmup, - dtypes=self.dtypes, - ) - data = { - obs_var_name: log_likelihood_draws[log_like_name] - for obs_var_name, log_like_name in log_likelihood.items() - if log_like_name in log_likelihood_draws - } - data_warmup = { - obs_var_name: log_likelihood_draws_warmup[log_like_name] - for obs_var_name, log_like_name in log_likelihood.items() - if log_like_name in log_likelihood_draws_warmup - } - - return ( - dict_to_dataset(data, library=self.stan, coords=self.coords, dims=self.dims), - dict_to_dataset(data_warmup, library=self.stan, coords=self.coords, dims=self.dims), - ) - - @requires("posterior") - @requires("posterior_predictive") - def posterior_predictive_to_xarray(self): - """Convert posterior_predictive samples to xarray.""" - posterior = self.posterior - posterior_model = self.posterior_model - posterior_predictive = self.posterior_predictive - data, data_warmup = get_draws_stan3( - posterior, - model=posterior_model, - variables=posterior_predictive, - warmup=self.save_warmup, - dtypes=self.dtypes, - ) - return ( - dict_to_dataset(data, library=self.stan, coords=self.coords, dims=self.dims), - dict_to_dataset(data_warmup, library=self.stan, coords=self.coords, dims=self.dims), - ) - - @requires("posterior") - @requires("predictions") - def predictions_to_xarray(self): - """Convert predictions samples to xarray.""" - posterior = self.posterior - posterior_model = self.posterior_model - predictions = self.predictions - data, data_warmup = get_draws_stan3( - posterior, - model=posterior_model, - variables=predictions, - warmup=self.save_warmup, - dtypes=self.dtypes, - ) - return ( - dict_to_dataset(data, library=self.stan, coords=self.coords, dims=self.dims), - dict_to_dataset(data_warmup, library=self.stan, coords=self.coords, dims=self.dims), - ) - - @requires("prior") - def prior_to_xarray(self): - """Convert prior samples to xarray.""" - prior = self.prior - prior_model = self.prior_model - # filter posterior_predictive and log_likelihood - prior_predictive = self.prior_predictive - if prior_predictive is None: - prior_predictive = [] - elif isinstance(prior_predictive, str): - prior_predictive = [prior_predictive] - - ignore = prior_predictive - - data, data_warmup = get_draws_stan3( - prior, model=prior_model, ignore=ignore, warmup=self.save_warmup, dtypes=self.dtypes - ) - attrs = get_attrs_stan3(prior, model=prior_model) - return ( - dict_to_dataset( - data, library=self.stan, attrs=attrs, coords=self.coords, dims=self.dims - ), - dict_to_dataset( - data_warmup, library=self.stan, attrs=attrs, coords=self.coords, dims=self.dims - ), - ) - - @requires("prior") - def sample_stats_prior_to_xarray(self): - """Extract sample_stats_prior from prior.""" - prior = self.prior - prior_model = self.prior_model - data, data_warmup = get_sample_stats_stan3( - prior, warmup=self.save_warmup, dtypes=self.dtypes - ) - attrs = get_attrs_stan3(prior, model=prior_model) - return ( - dict_to_dataset( - data, library=self.stan, attrs=attrs, coords=self.coords, dims=self.dims - ), - dict_to_dataset( - data_warmup, library=self.stan, attrs=attrs, coords=self.coords, dims=self.dims - ), - ) - - @requires("prior") - @requires("prior_predictive") - def prior_predictive_to_xarray(self): - """Convert prior_predictive samples to xarray.""" - prior = self.prior - prior_model = self.prior_model - prior_predictive = self.prior_predictive - data, data_warmup = get_draws_stan3( - prior, - model=prior_model, - variables=prior_predictive, - warmup=self.save_warmup, - dtypes=self.dtypes, - ) - return ( - dict_to_dataset(data, library=self.stan, coords=self.coords, dims=self.dims), - dict_to_dataset(data_warmup, library=self.stan, coords=self.coords, dims=self.dims), - ) - - @requires("posterior_model") - @requires(["observed_data", "constant_data"]) - def observed_and_constant_data_to_xarray(self): - """Convert observed data to xarray.""" - posterior_model = self.posterior_model - dims = {} if self.dims is None else self.dims - obs_const_dict = {} - for group_name in ("observed_data", "constant_data"): - names = getattr(self, group_name) - if names is None: - continue - names = [names] if isinstance(names, str) else names - data = OrderedDict() - for key in names: - vals = np.atleast_1d(posterior_model.data[key]) - val_dims = dims.get(key) - val_dims, coords = generate_dims_coords( - vals.shape, key, dims=val_dims, coords=self.coords - ) - data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) - obs_const_dict[group_name] = xr.Dataset( - data_vars=data, attrs=make_attrs(library=self.stan) - ) - return obs_const_dict - - @requires("posterior_model") - @requires("predictions_constant_data") - def predictions_constant_data_to_xarray(self): - """Convert observed data to xarray.""" - posterior_model = self.posterior_model - dims = {} if self.dims is None else self.dims - names = self.predictions_constant_data - names = [names] if isinstance(names, str) else names - data = OrderedDict() - for key in names: - vals = np.atleast_1d(posterior_model.data[key]) - val_dims = dims.get(key) - val_dims, coords = generate_dims_coords( - vals.shape, key, dims=val_dims, coords=self.coords - ) - data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset(data_vars=data, attrs=make_attrs(library=self.stan)) - - def to_inference_data(self): - """Convert all available data to an InferenceData object. - - Note that if groups can not be created (i.e., there is no `fit`, so - the `posterior` and `sample_stats` can not be extracted), then the InferenceData - will not have those groups. - """ - obs_const_dict = self.observed_and_constant_data_to_xarray() - predictions_const_data = self.predictions_constant_data_to_xarray() - return InferenceData( - save_warmup=self.save_warmup, - **{ - "posterior": self.posterior_to_xarray(), - "sample_stats": self.sample_stats_to_xarray(), - "log_likelihood": self.log_likelihood_to_xarray(), - "posterior_predictive": self.posterior_predictive_to_xarray(), - "predictions": self.predictions_to_xarray(), - "prior": self.prior_to_xarray(), - "sample_stats_prior": self.sample_stats_prior_to_xarray(), - "prior_predictive": self.prior_predictive_to_xarray(), - **({} if obs_const_dict is None else obs_const_dict), - **( - {} - if predictions_const_data is None - else {"predictions_constant_data": predictions_const_data} - ), - }, - ) - - -def get_draws(fit, variables=None, ignore=None, warmup=False, dtypes=None): - """Extract draws from PyStan fit.""" - if ignore is None: - ignore = [] - if fit.mode == 1: - msg = "Model in mode 'test_grad'. Sampling is not conducted." - raise AttributeError(msg) - - if fit.mode == 2 or fit.sim.get("samples") is None: - msg = "Fit doesn't contain samples." - raise AttributeError(msg) - - if dtypes is None: - dtypes = {} - - dtypes = {**infer_dtypes(fit), **dtypes} - - if variables is None: - variables = fit.sim["pars_oi"] - elif isinstance(variables, str): - variables = [variables] - variables = list(variables) - - for var, dim in zip(fit.sim["pars_oi"], fit.sim["dims_oi"]): - if var in variables and np.prod(dim) == 0: - del variables[variables.index(var)] - - ndraws_warmup = fit.sim["warmup2"] - if max(ndraws_warmup) == 0: - warmup = False - ndraws = [s - w for s, w in zip(fit.sim["n_save"], ndraws_warmup)] - nchain = len(fit.sim["samples"]) - - # check if the values are in 0-based (<=2.17) or 1-based indexing (>=2.18) - shift = 1 - if any(dim and np.prod(dim) != 0 for dim in fit.sim["dims_oi"]): - # choose variable with lowest number of dims > 1 - par_idx = min( - (dim, i) for i, dim in enumerate(fit.sim["dims_oi"]) if (dim and np.prod(dim) != 0) - )[1] - offset = int(sum(map(np.prod, fit.sim["dims_oi"][:par_idx]))) - par_offset = int(np.prod(fit.sim["dims_oi"][par_idx])) - par_keys = fit.sim["fnames_oi"][offset : offset + par_offset] - shift = len(par_keys) - for item in par_keys: - _, shape = item.replace("]", "").split("[") - shape_idx_min = min(int(shape_value) for shape_value in shape.split(",")) - shift = min(shift, shape_idx_min) - # If shift is higher than 1, this will probably mean that Stan - # has implemented sparse structure (saves only non-zero parts), - # but let's hope that dims are still corresponding to the full shape - shift = int(min(shift, 1)) - - var_keys = OrderedDict((var, []) for var in fit.sim["pars_oi"]) - for key in fit.sim["fnames_oi"]: - var, *tails = key.split("[") - loc = [Ellipsis] - for tail in tails: - loc = [] - for i in tail[:-1].split(","): - loc.append(int(i) - shift) - var_keys[var].append((key, loc)) - - shapes = dict(zip(fit.sim["pars_oi"], fit.sim["dims_oi"])) - - variables = [var for var in variables if var not in ignore] - - data = OrderedDict() - data_warmup = OrderedDict() - - for var in variables: - if var in data: - continue - keys_locs = var_keys.get(var, [(var, [Ellipsis])]) - shape = shapes.get(var, []) - dtype = dtypes.get(var) - - ndraw = max(ndraws) - ary_shape = [nchain, ndraw] + shape - ary = np.empty(ary_shape, dtype=dtype, order="F") - - if warmup: - nwarmup = max(ndraws_warmup) - ary_warmup_shape = [nchain, nwarmup] + shape - ary_warmup = np.empty(ary_warmup_shape, dtype=dtype, order="F") - - for chain, (pyholder, ndraw, ndraw_warmup) in enumerate( - zip(fit.sim["samples"], ndraws, ndraws_warmup) - ): - axes = [chain, slice(None)] - for key, loc in keys_locs: - ary_slice = tuple(axes + loc) - ary[ary_slice] = pyholder.chains[key][-ndraw:] - if warmup: - ary_warmup[ary_slice] = pyholder.chains[key][:ndraw_warmup] - data[var] = ary - if warmup: - data_warmup[var] = ary_warmup - return data, data_warmup - - -def get_sample_stats(fit, warmup=False, dtypes=None): - """Extract sample stats from PyStan fit.""" - if dtypes is None: - dtypes = {} - dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64, **dtypes} - - rename_dict = { - "divergent": "diverging", - "n_leapfrog": "n_steps", - "treedepth": "tree_depth", - "stepsize": "step_size", - "accept_stat": "acceptance_rate", - } - - ndraws_warmup = fit.sim["warmup2"] - if max(ndraws_warmup) == 0: - warmup = False - ndraws = [s - w for s, w in zip(fit.sim["n_save"], ndraws_warmup)] - - extraction = OrderedDict() - extraction_warmup = OrderedDict() - for chain, (pyholder, ndraw, ndraw_warmup) in enumerate( - zip(fit.sim["samples"], ndraws, ndraws_warmup) - ): - if chain == 0: - for key in pyholder["sampler_param_names"]: - extraction[key] = [] - if warmup: - extraction_warmup[key] = [] - for key, values in zip(pyholder["sampler_param_names"], pyholder["sampler_params"]): - extraction[key].append(values[-ndraw:]) - if warmup: - extraction_warmup[key].append(values[:ndraw_warmup]) - - data = OrderedDict() - for key, values in extraction.items(): - values = np.stack(values, axis=0) - dtype = dtypes.get(key) - values = values.astype(dtype) - name = re.sub("__$", "", key) - name = rename_dict.get(name, name) - data[name] = values - - data_warmup = OrderedDict() - if warmup: - for key, values in extraction_warmup.items(): - values = np.stack(values, axis=0) - values = values.astype(dtypes.get(key)) - name = re.sub("__$", "", key) - name = rename_dict.get(name, name) - data_warmup[name] = values - - return data, data_warmup - - -def get_attrs(fit): - """Get attributes from PyStan fit object.""" - attrs = {} - - try: - attrs["args"] = [deepcopy(holder.args) for holder in fit.sim["samples"]] - except Exception as exp: # pylint: disable=broad-except - _log.warning("Failed to fetch args from fit: %s", exp) - if "args" in attrs: - for arg in attrs["args"]: - if isinstance(arg["init"], bytes): - arg["init"] = arg["init"].decode("utf-8") - attrs["args"] = json.dumps(attrs["args"]) - try: - attrs["inits"] = [holder.inits for holder in fit.sim["samples"]] - except Exception as exp: # pylint: disable=broad-except - _log.warning("Failed to fetch `args` from fit: %s", exp) - else: - attrs["inits"] = json.dumps(attrs["inits"]) - - attrs["step_size"] = [] - attrs["metric"] = [] - attrs["inv_metric"] = [] - for holder in fit.sim["samples"]: - try: - step_size = float( - re.search( - r"step\s*size\s*=\s*([0-9]+.?[0-9]+)\s*", - holder.adaptation_info, - flags=re.IGNORECASE, - ).group(1) - ) - except AttributeError: - step_size = np.nan - attrs["step_size"].append(step_size) - - inv_metric_match = re.search( - r"mass matrix:\s*(.*)\s*$", holder.adaptation_info, flags=re.DOTALL - ) - if inv_metric_match: - inv_metric_str = inv_metric_match.group(1) - if "Diagonal elements of inverse mass matrix" in holder.adaptation_info: - metric = "diag_e" - inv_metric = [float(item) for item in inv_metric_str.strip(" #\n").split(",")] - else: - metric = "dense_e" - inv_metric = [ - list(map(float, item.split(","))) - for item in re.sub(r"#\s", "", inv_metric_str).splitlines() - ] - else: - metric = "unit_e" - inv_metric = None - - attrs["metric"].append(metric) - attrs["inv_metric"].append(inv_metric) - attrs["inv_metric"] = json.dumps(attrs["inv_metric"]) - - if not attrs["step_size"]: - del attrs["step_size"] - - attrs["adaptation_info"] = fit.get_adaptation_info() - attrs["stan_code"] = fit.get_stancode() - - return attrs - - -def get_draws_stan3(fit, model=None, variables=None, ignore=None, warmup=False, dtypes=None): - """Extract draws from PyStan3 fit.""" - if ignore is None: - ignore = [] - - if dtypes is None: - dtypes = {} - - if model is not None: - dtypes = {**infer_dtypes(fit, model), **dtypes} - - if not fit.save_warmup: - warmup = False - - num_warmup = ceil((fit.num_warmup * fit.save_warmup) / fit.num_thin) - - if variables is None: - variables = fit.param_names - elif isinstance(variables, str): - variables = [variables] - variables = list(variables) - - data = OrderedDict() - data_warmup = OrderedDict() - - for var in variables: - if var in ignore: - continue - if var in data: - continue - dtype = dtypes.get(var) - - new_shape = (*fit.dims[fit.param_names.index(var)], -1, fit.num_chains) - if 0 in new_shape: - continue - values = fit._draws[fit._parameter_indexes(var), :] # pylint: disable=protected-access - values = values.reshape(new_shape, order="F") - values = np.moveaxis(values, [-2, -1], [1, 0]) - values = values.astype(dtype) - if warmup: - data_warmup[var] = values[:, num_warmup:] - data[var] = values[:, num_warmup:] - - return data, data_warmup - - -def get_sample_stats_stan3(fit, variables=None, ignore=None, warmup=False, dtypes=None): - """Extract sample stats from PyStan3 fit.""" - if dtypes is None: - dtypes = {} - dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64, **dtypes} - - rename_dict = { - "divergent": "diverging", - "n_leapfrog": "n_steps", - "treedepth": "tree_depth", - "stepsize": "step_size", - "accept_stat": "acceptance_rate", - } - - if isinstance(variables, str): - variables = [variables] - if isinstance(ignore, str): - ignore = [ignore] - - if not fit.save_warmup: - warmup = False - - num_warmup = ceil((fit.num_warmup * fit.save_warmup) / fit.num_thin) - - data = OrderedDict() - data_warmup = OrderedDict() - for key in fit.sample_and_sampler_param_names: - if (variables and key not in variables) or (ignore and key in ignore): - continue - new_shape = -1, fit.num_chains - values = fit._draws[fit._parameter_indexes(key)] # pylint: disable=protected-access - values = values.reshape(new_shape, order="F") - values = np.moveaxis(values, [-2, -1], [1, 0]) - dtype = dtypes.get(key) - values = values.astype(dtype) - name = re.sub("__$", "", key) - name = rename_dict.get(name, name) - if warmup: - data_warmup[name] = values[:, :num_warmup] - data[name] = values[:, num_warmup:] - - return data, data_warmup - - -def get_attrs_stan3(fit, model=None): - """Get attributes from PyStan3 fit and model object.""" - attrs = {} - for key in ["num_chains", "num_samples", "num_thin", "num_warmup", "save_warmup"]: - try: - attrs[key] = getattr(fit, key) - except AttributeError as exp: - _log.warning("Failed to access attribute %s in fit object %s", key, exp) - - if model is not None: - for key in ["model_name", "program_code", "random_seed"]: - try: - attrs[key] = getattr(model, key) - except AttributeError as exp: - _log.warning("Failed to access attribute %s in model object %s", key, exp) - - return attrs - - -def infer_dtypes(fit, model=None): - """Infer dtypes from Stan model code. - - Function strips out generated quantities block and searches for `int` - dtypes after stripping out comments inside the block. - """ - if model is None: - stan_code = fit.get_stancode() - model_pars = fit.model_pars - else: - stan_code = model.program_code - model_pars = fit.param_names - - dtypes = {key: item for key, item in infer_stan_dtypes(stan_code).items() if key in model_pars} - return dtypes - - -# pylint disable=too-many-instance-attributes -def from_pystan( - posterior=None, - *, - posterior_predictive=None, - predictions=None, - prior=None, - prior_predictive=None, - observed_data=None, - constant_data=None, - predictions_constant_data=None, - log_likelihood=None, - coords=None, - dims=None, - posterior_model=None, - prior_model=None, - save_warmup=None, - dtypes=None, -): - """Convert PyStan data into an InferenceData object. - - For a usage example read the - :ref:`Creating InferenceData section on from_pystan ` - - Parameters - ---------- - posterior : StanFit4Model or stan.fit.Fit - PyStan fit object for posterior. - posterior_predictive : str, a list of str - Posterior predictive samples for the posterior. - predictions : str, a list of str - Out-of-sample predictions for the posterior. - prior : StanFit4Model or stan.fit.Fit - PyStan fit object for prior. - prior_predictive : str, a list of str - Posterior predictive samples for the prior. - observed_data : str or a list of str - observed data used in the sampling. - Observed data is extracted from the `posterior.data`. - PyStan3 needs model object for the extraction. - See `posterior_model`. - constant_data : str or list of str - Constants relevant to the model (i.e. x values in a linear - regression). - predictions_constant_data : str or list of str - Constants relevant to the model predictions (i.e. new x values in a linear - regression). - log_likelihood : dict of {str: str}, list of str or str, optional - Pointwise log_likelihood for the data. log_likelihood is extracted from the - posterior. It is recommended to use this argument as a dictionary whose keys - are observed variable names and its values are the variables storing log - likelihood arrays in the Stan code. In other cases, a dictionary with keys - equal to its values is used. By default, if a variable ``log_lik`` is - present in the Stan model, it will be retrieved as pointwise log - likelihood values. Use ``False`` or set ``data.log_likelihood`` to - false to avoid this behaviour. - coords : dict[str, iterable] - A dictionary containing the values that are used as index. The key - is the name of the dimension, the values are the index values. - dims : dict[str, List(str)] - A mapping from variables to a list of coordinate names for the variable. - posterior_model : stan.model.Model - PyStan3 specific model object. Needed for automatic dtype parsing - and for the extraction of observed data. - prior_model : stan.model.Model - PyStan3 specific model object. Needed for automatic dtype parsing. - save_warmup : bool - Save warmup iterations into InferenceData object. If not defined, use default - defined by the rcParams. - dtypes: dict - A dictionary containing dtype information (int, float) for parameters. - By default dtype information is extracted from the model code. - Model code is extracted from fit object in PyStan 2 and from model object - in PyStan 3. - - Returns - ------- - InferenceData object - """ - check_posterior = (posterior is not None) and (type(posterior).__module__ == "stan.fit") - check_prior = (prior is not None) and (type(prior).__module__ == "stan.fit") - if check_posterior or check_prior: - return PyStan3Converter( - posterior=posterior, - posterior_model=posterior_model, - posterior_predictive=posterior_predictive, - predictions=predictions, - prior=prior, - prior_model=prior_model, - prior_predictive=prior_predictive, - observed_data=observed_data, - constant_data=constant_data, - predictions_constant_data=predictions_constant_data, - log_likelihood=log_likelihood, - coords=coords, - dims=dims, - save_warmup=save_warmup, - dtypes=dtypes, - ).to_inference_data() - else: - return PyStanConverter( - posterior=posterior, - posterior_predictive=posterior_predictive, - predictions=predictions, - prior=prior, - prior_predictive=prior_predictive, - observed_data=observed_data, - constant_data=constant_data, - predictions_constant_data=predictions_constant_data, - log_likelihood=log_likelihood, - coords=coords, - dims=dims, - save_warmup=save_warmup, - dtypes=dtypes, - ).to_inference_data() diff --git a/arviz/data/io_zarr.py b/arviz/data/io_zarr.py deleted file mode 100644 index 069d149f56..0000000000 --- a/arviz/data/io_zarr.py +++ /dev/null @@ -1,46 +0,0 @@ -"""Input and output support for zarr data.""" - -from .converters import convert_to_inference_data -from .inference_data import InferenceData - - -def from_zarr(store): - return InferenceData.from_zarr(store) - - -from_zarr.__doc__ = InferenceData.from_zarr.__doc__ - - -def to_zarr(data, store=None, **kwargs): - """ - Convert data to zarr, optionally saving to disk if ``store`` is provided. - - The zarr storage is using the same group names as the InferenceData. - - Parameters - ---------- - store : zarr.storage, MutableMapping or str, optional - Zarr storage class or path to desired DirectoryStore. - Default (None) a store is created in a temporary directory. - **kwargs : dict, optional - Passed to :py:func:`convert_to_inference_data`. - - Returns - ------- - zarr.hierarchy.group - A zarr hierarchy group containing the InferenceData. - - Raises - ------ - TypeError - If no valid store is found. - - - References - ---------- - https://zarr.readthedocs.io/ - - """ - inference_data = convert_to_inference_data(data, **kwargs) - zarr_group = inference_data.to_zarr(store=store) - return zarr_group diff --git a/arviz/data/utils.py b/arviz/data/utils.py deleted file mode 100644 index 67d56b396c..0000000000 --- a/arviz/data/utils.py +++ /dev/null @@ -1,139 +0,0 @@ -"""Data specific utilities.""" - -import warnings -import numpy as np - -from ..utils import _var_names -from .converters import convert_to_dataset - - -def extract_dataset( - data, - group="posterior", - combined=True, - var_names=None, - filter_vars=None, - num_samples=None, - rng=None, -): - """Extract an InferenceData group or subset of it. - - .. deprecated:: 0.13 - `extract_dataset` will be removed in ArviZ 0.14, it is replaced by - `extract` because the latter allows to obtain both DataSets and DataArrays. - """ - warnings.warn( - "extract_dataset has been deprecated, please use extract", FutureWarning, stacklevel=2 - ) - - data = extract( - data=data, - group=group, - combined=combined, - var_names=var_names, - filter_vars=filter_vars, - num_samples=num_samples, - rng=rng, - ) - return data - - -def extract( - data, - group="posterior", - combined=True, - var_names=None, - filter_vars=None, - num_samples=None, - keep_dataset=False, - rng=None, -): - """Extract an InferenceData group or subset of it. - - Parameters - ---------- - idata : InferenceData or InferenceData_like - InferenceData from which to extract the data. - group : str, optional - Which InferenceData data group to extract data from. - combined : bool, optional - Combine ``chain`` and ``draw`` dimensions into ``sample``. Won't work if - a dimension named ``sample`` already exists. - var_names : str or list of str, optional - Variables to be extracted. Prefix the variables by `~` when you want to exclude them. - filter_vars: {None, "like", "regex"}, optional - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - `pandas.filter`. - Like with plotting, sometimes it's easier to subset saying what to exclude - instead of what to include - num_samples : int, optional - Extract only a subset of the samples. Only valid if ``combined=True`` - keep_dataset : bool, optional - If true, always return a DataSet. If false (default) return a DataArray - when there is a single variable. - rng : bool, int, numpy.Generator, optional - Shuffle the samples, only valid if ``combined=True``. By default, - samples are shuffled if ``num_samples`` is not ``None``, and are left - in the same order otherwise. This ensures that subsetting the samples doesn't return - only samples from a single chain and consecutive draws. - - Returns - ------- - xarray.DataArray or xarray.Dataset - - Examples - -------- - The default behaviour is to return the posterior group after stacking the chain and - draw dimensions. - - .. jupyter-execute:: - - import arviz as az - idata = az.load_arviz_data("centered_eight") - az.extract(idata) - - You can also indicate a subset to be returned, but in variables and in samples: - - .. jupyter-execute:: - - az.extract(idata, var_names="theta", num_samples=100) - - To keep the chain and draw dimensions, use ``combined=False``. - - .. jupyter-execute:: - - az.extract(idata, group="prior", combined=False) - - """ - if num_samples is not None and not combined: - raise ValueError("num_samples is only compatible with combined=True") - if rng is None: - rng = num_samples is not None - if rng is not False and not combined: - raise ValueError("rng is only compatible with combined=True") - data = convert_to_dataset(data, group=group) - var_names = _var_names(var_names, data, filter_vars) - if var_names is not None: - if len(var_names) == 1 and not keep_dataset: - var_names = var_names[0] - data = data[var_names] - if combined: - data = data.stack(sample=("chain", "draw")) - # 0 is a valid seed se we need to check for rng being exactly boolean - if rng is not False: - if rng is True: - rng = np.random.default_rng() - # default_rng takes ints or sequences of ints - try: - rng = np.random.default_rng(rng) - random_subset = rng.permutation(np.arange(len(data["sample"]))) - except TypeError as err: - raise TypeError("Unable to initializate numpy random Generator from rng") from err - except AttributeError as err: - raise AttributeError("Unable to use rng to generate a permutation") from err - data = data.isel(sample=random_subset) - if num_samples is not None: - data = data.isel(sample=slice(None, num_samples)) - return data diff --git a/arviz/labels.py b/arviz/labels.py deleted file mode 100644 index 6bb81240cb..0000000000 --- a/arviz/labels.py +++ /dev/null @@ -1,210 +0,0 @@ -# pylint: disable=unused-argument -"""Utilities to generate labels from xarray objects.""" -from typing import Union - -__all__ = [ - "mix_labellers", - "BaseLabeller", - "DimCoordLabeller", - "DimIdxLabeller", - "MapLabeller", - "NoVarLabeller", - "NoModelLabeller", -] - - -def mix_labellers(labellers, class_name="MixtureLabeller"): - """Combine Labeller classes dynamically. - - The Labeller class aims to split plot labeling in ArviZ into atomic tasks to maximize - extensibility, and the few classes provided are designed with small deviations - from the base class, in many cases only one method is modified by the child class. - It is to be expected then to want to use multiple classes "at once". - - This functions helps combine classes dynamically. - - For a general overview of ArviZ label customization, including - ``mix_labellers``, see the :ref:`label_guide` page. - - Parameters - ---------- - labellers : iterable of types - Iterable of Labeller types to combine - class_name : str, optional - The name of the generated class - - Returns - ------- - type - Mixture class object. **It is not initialized**, and it should be - initialized before passing it to ArviZ functions. - - Examples - -------- - Combine the :class:`~arviz.labels.DimCoordLabeller` with the - :class:`~arviz.labels.MapLabeller` to generate labels in the style of the - ``DimCoordLabeller`` but using the mappings defined by ``MapLabeller``. - Note that this works even though both modify the same methods because - ``MapLabeller`` implements the mapping and then calls `super().method`. - - .. jupyter-execute:: - - from arviz.labels import mix_labellers, DimCoordLabeller, MapLabeller - l1 = DimCoordLabeller() - sel = {"dim1": "a", "dim2": "top"} - print(f"Output of DimCoordLabeller alone > {l1.sel_to_str(sel, sel)}") - l2 = MapLabeller(dim_map={"dim1": "$d_1$", "dim2": r"$d_2$"}) - print(f"Output of MapLabeller alone > {l2.sel_to_str(sel, sel)}") - l3 = mix_labellers( - (MapLabeller, DimCoordLabeller) - )(dim_map={"dim1": "$d_1$", "dim2": r"$d_2$"}) - print(f"Output of mixture labeller > {l3.sel_to_str(sel, sel)}") - - We can see how the mappings are taken into account as well as the dim+coord style. However, - he order in the ``labellers`` arg iterator is important! See for yourself: - - .. jupyter-execute:: - - l4 = mix_labellers( - (DimCoordLabeller, MapLabeller) - )(dim_map={"dim1": "$d_1$", "dim2": r"$d_2$"}) - print(f"Output of inverted mixture labeller > {l4.sel_to_str(sel, sel)}") - - """ - return type(class_name, labellers, {}) - - -class BaseLabeller: - """WIP.""" - - def dim_coord_to_str(self, dim, coord_val, coord_idx): - """WIP.""" - return f"{coord_val}" - - def sel_to_str(self, sel: dict, isel: dict): - """WIP.""" - if sel: - return ", ".join( - [ - self.dim_coord_to_str(dim, v, i) - for (dim, v), (_, i) in zip(sel.items(), isel.items()) - ] - ) - return "" - - def var_name_to_str(self, var_name: Union[str, None]): - """WIP.""" - return var_name - - def var_pp_to_str(self, var_name, pp_var_name): - """WIP.""" - var_name_str = self.var_name_to_str(var_name) - pp_var_name_str = self.var_name_to_str(pp_var_name) - if var_name_str == pp_var_name_str: - return f"{var_name_str}" - return f"{var_name_str} / {pp_var_name_str}" - - def model_name_to_str(self, model_name): - """WIP.""" - return model_name - - def make_label_vert(self, var_name: Union[str, None], sel: dict, isel: dict): - """WIP.""" - var_name_str = self.var_name_to_str(var_name) - sel_str = self.sel_to_str(sel, isel) - if not sel_str: - return "" if var_name_str is None else var_name_str - if var_name_str is None: - return sel_str - return f"{var_name_str}\n{sel_str}" - - def make_label_flat(self, var_name: str, sel: dict, isel: dict): - """WIP.""" - var_name_str = self.var_name_to_str(var_name) - sel_str = self.sel_to_str(sel, isel) - if not sel_str: - return "" if var_name_str is None else var_name_str - if var_name_str is None: - return sel_str - return f"{var_name_str}[{sel_str}]" - - def make_pp_label(self, var_name, pp_var_name, sel, isel): - """WIP.""" - names = self.var_pp_to_str(var_name, pp_var_name) - return self.make_label_vert(names, sel, isel) - - def make_model_label(self, model_name, label): - """WIP.""" - model_name_str = self.model_name_to_str(model_name) - if model_name_str is None: - return label - if label is None or label == "": - return model_name_str - return f"{model_name_str}: {label}" - - -class DimCoordLabeller(BaseLabeller): - """WIP.""" - - def dim_coord_to_str(self, dim, coord_val, coord_idx): - """WIP.""" - return f"{dim}: {coord_val}" - - -class IdxLabeller(BaseLabeller): - """WIP.""" - - def dim_coord_to_str(self, dim, coord_val, coord_idx): - """WIP.""" - return f"{coord_idx}" - - -class DimIdxLabeller(BaseLabeller): - """WIP.""" - - def dim_coord_to_str(self, dim, coord_val, coord_idx): - """WIP.""" - return f"{dim}#{coord_idx}" - - -class MapLabeller(BaseLabeller): - """WIP.""" - - def __init__(self, var_name_map=None, dim_map=None, coord_map=None, model_name_map=None): - """WIP.""" - self.var_name_map = {} if var_name_map is None else var_name_map - self.dim_map = {} if dim_map is None else dim_map - self.coord_map = {} if coord_map is None else coord_map - self.model_name_map = {} if model_name_map is None else model_name_map - - def dim_coord_to_str(self, dim, coord_val, coord_idx): - """WIP.""" - dim_str = self.dim_map.get(dim, dim) - coord_str = self.coord_map.get(dim, {}).get(coord_val, coord_val) - return super().dim_coord_to_str(dim_str, coord_str, coord_idx) - - def var_name_to_str(self, var_name): - """WIP.""" - var_name_str = self.var_name_map.get(var_name, var_name) - return super().var_name_to_str(var_name_str) - - def model_name_to_str(self, model_name): - """WIP.""" - model_name_str = self.var_name_map.get(model_name, model_name) - return super().model_name_to_str(model_name_str) - - -class NoVarLabeller(BaseLabeller): - """WIP.""" - - def var_name_to_str(self, var_name): - """WIP.""" - return None - - -class NoModelLabeller(BaseLabeller): - """WIP.""" - - def make_model_label(self, model_name, label): - """WIP.""" - return label diff --git a/arviz/plots/__init__.py b/arviz/plots/__init__.py deleted file mode 100644 index ddc8c6fd3b..0000000000 --- a/arviz/plots/__init__.py +++ /dev/null @@ -1,61 +0,0 @@ -"""Plotting functions.""" - -from .autocorrplot import plot_autocorr -from .bpvplot import plot_bpv -from .bfplot import plot_bf -from .compareplot import plot_compare -from .densityplot import plot_density -from .distcomparisonplot import plot_dist_comparison -from .distplot import plot_dist -from .dotplot import plot_dot -from .ecdfplot import plot_ecdf -from .elpdplot import plot_elpd -from .energyplot import plot_energy -from .essplot import plot_ess -from .forestplot import plot_forest -from .hdiplot import plot_hdi -from .kdeplot import plot_kde -from .khatplot import plot_khat -from .lmplot import plot_lm -from .loopitplot import plot_loo_pit -from .mcseplot import plot_mcse -from .pairplot import plot_pair -from .parallelplot import plot_parallel -from .posteriorplot import plot_posterior -from .ppcplot import plot_ppc -from .rankplot import plot_rank -from .separationplot import plot_separation -from .traceplot import plot_trace -from .tsplot import plot_ts -from .violinplot import plot_violin - -__all__ = [ - "plot_autocorr", - "plot_bpv", - "plot_bf", - "plot_compare", - "plot_density", - "plot_dist", - "plot_dot", - "plot_ecdf", - "plot_elpd", - "plot_energy", - "plot_ess", - "plot_forest", - "plot_hdi", - "plot_kde", - "plot_khat", - "plot_lm", - "plot_loo_pit", - "plot_mcse", - "plot_pair", - "plot_parallel", - "plot_posterior", - "plot_ppc", - "plot_dist_comparison", - "plot_rank", - "plot_trace", - "plot_ts", - "plot_violin", - "plot_separation", -] diff --git a/arviz/plots/autocorrplot.py b/arviz/plots/autocorrplot.py deleted file mode 100644 index 2aeddaafb6..0000000000 --- a/arviz/plots/autocorrplot.py +++ /dev/null @@ -1,171 +0,0 @@ -"""Autocorrelation plot of data.""" - -from ..data import convert_to_dataset -from ..labels import BaseLabeller -from ..sel_utils import xarray_var_iter -from ..rcparams import rcParams -from ..utils import _var_names, get_coords -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function - - -def plot_autocorr( - data, - var_names=None, - filter_vars=None, - max_lag=None, - combined=False, - coords=None, - grid=None, - figsize=None, - textsize=None, - labeller=None, - ax=None, - backend=None, - backend_config=None, - backend_kwargs=None, - show=None, -): - r"""Bar plot of the autocorrelation function (ACF) for a sequence of data. - - The ACF plots are helpful as a convergence diagnostic for posteriors from MCMC - samples which display autocorrelation. - - Parameters - ---------- - data : InferenceData - Any object that can be converted to an :class:`arviz.InferenceData` object - refer to documentation of :func:`arviz.convert_to_dataset` for details - var_names : list of str, optional - Variables to be plotted. Prefix the variables by ``~`` when you want to exclude - them from the plot. See :ref:`this section ` for usage examples. - filter_vars : {None, "like", "regex"}, default None - If `None` (default), interpret `var_names` as the real variables names. If "like", - interpret `var_names` as substrings of the real variables names. If "regex", - interpret `var_names` as regular expressions on the real variables names. See - :ref:`this section ` for usage examples. - coords: mapping, optional - Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel` - max_lag : int, optional - Maximum lag to calculate autocorrelation. By Default, the plot displays the - first 100 lag or the total number of draws, whichever is smaller. - combined : bool, default False - Flag for combining multiple chains into a single chain. If False, chains will be - plotted separately. - grid : tuple, optional - Number of rows and columns. Defaults to None, the rows and columns are - automatically inferred. See :ref:`this section ` for usage examples. - figsize : (float, float), optional - Figure size. If None it will be defined automatically. - Note this is not used if `ax` is supplied. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If None it will be autoscaled based - on `figsize`. - labeller : Labeller, optional - Class providing the method ``make_label_vert`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - ax : 2D array-like of matplotlib_axes or bokeh_figure, optional - A 2D array of locations into which to plot the densities. If not supplied, ArviZ will create - its own array of plot areas (and return it). - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_config : dict, optional - Currently specifies the bounds to use for bokeh axes. Defaults to value set in ``rcParams``. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - - Returns - ------- - axes : matplotlib_axes or bokeh_figures - - See Also - -------- - autocov : Compute autocovariance estimates for every lag for the input array. - autocorr : Compute autocorrelation using FFT for every lag for the input array. - - Examples - -------- - Plot default autocorrelation - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> data = az.load_arviz_data('centered_eight') - >>> az.plot_autocorr(data) - - Plot subset variables by specifying variable name exactly - - .. plot:: - :context: close-figs - - >>> az.plot_autocorr(data, var_names=['mu', 'tau'] ) - - - Combine chains by variable and select variables by excluding some with partial naming - - .. plot:: - :context: close-figs - - >>> az.plot_autocorr(data, var_names=['~thet'], filter_vars="like", combined=True) - - - Specify maximum lag (x axis bound) - - .. plot:: - :context: close-figs - - >>> az.plot_autocorr(data, var_names=['mu', 'tau'], max_lag=200, combined=True) - """ - data = convert_to_dataset(data, group="posterior") - var_names = _var_names(var_names, data, filter_vars) - - # Default max lag to 100 or max length of chain - if max_lag is None: - max_lag = min(100, data["draw"].shape[0]) - - if coords is None: - coords = {} - - if labeller is None: - labeller = BaseLabeller() - - plotters = filter_plotters_list( - list( - xarray_var_iter( - get_coords(data, coords), var_names, combined, dim_order=["chain", "draw"] - ) - ), - "plot_autocorr", - ) - rows, cols = default_grid(len(plotters), grid=grid) - - autocorr_plot_args = dict( - axes=ax, - plotters=plotters, - max_lag=max_lag, - figsize=figsize, - rows=rows, - cols=cols, - combined=combined, - textsize=textsize, - labeller=labeller, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - if backend == "bokeh": - autocorr_plot_args.update(backend_config=backend_config) - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_autocorr", "autocorrplot", backend) - axes = plot(**autocorr_plot_args) - - return axes diff --git a/arviz/plots/backends/__init__.py b/arviz/plots/backends/__init__.py deleted file mode 100644 index bd08073377..0000000000 --- a/arviz/plots/backends/__init__.py +++ /dev/null @@ -1,223 +0,0 @@ -# pylint: disable=no-member,invalid-name,redefined-outer-name -"""ArviZ plotting backends.""" -import re - -import numpy as np -from pandas import DataFrame - -from ...rcparams import rcParams - -__all__ = [ - "to_cds", - "output_notebook", - "output_file", - "ColumnDataSource", - "create_layout", - "show_layout", -] - - -def to_cds( - data, - var_names=None, - groups=None, - dimensions=None, - group_info=True, - var_name_format=None, - index_origin=None, -): - """Transform data to ColumnDataSource (CDS) compatible with Bokeh. - - Uses `_ARVIZ_GROUP_` and `_ARVIZ_CDS_SELECTION_` to separate var_name - from group and dimensions in CDS columns. - - Parameters - ---------- - data : obj - Any object that can be converted to an az.InferenceData object - Refer to documentation of az.convert_to_inference_data for details - var_names : str or list of str, optional - Variables to be processed, if None all variables are processed. - groups : str or list of str, optional - Select groups for CDS. Default groups are {"posterior_groups", "prior_groups", - "posterior_groups_warmup"} - - - posterior_groups: posterior, posterior_predictive, sample_stats - - prior_groups: prior, prior_predictive, sample_stats_prior - - posterior_groups_warmup: warmup_posterior, warmup_posterior_predictive, - warmup_sample_stats - - ignore_groups : str or list of str, optional - Ignore specific groups from CDS. - dimension : str, or list of str, optional - Select dimensions along to slice the data. By default uses ("chain", "draw"). - group_info : bool - Add group info for `var_name_format` - var_name_format : str or tuple of tuple of string, optional - Select column name format for non-scalar input. - Predefined options are {"brackets", "underscore", "cds"} - - "brackets": - - add_group_info == False: ``theta[0,0]`` - - add_group_info == True: ``theta_posterior[0,0]`` - "underscore": - - add_group_info == False: ``theta_0_0`` - - add_group_info == True: ``theta_posterior_0_0_`` - "cds": - - add_group_info == False: ``theta_ARVIZ_CDS_SELECTION_0_0`` - - add_group_info == True: ``theta_ARVIZ_GROUP_posterior__ARVIZ_CDS_SELECTION_0_0`` - tuple: - Structure: - - - tuple: (dim_info, group_info) - - - dim_info: (str: `.join` separator, - str: dim_separator_start, - str: dim_separator_end) - - group_info: (str: group separator start, str: group separator end) - - Example: ((",", "[", "]"), ("_", "")) - - - add_group_info == False: ``theta[0,0]`` - - add_group_info == True: ``theta_posterior[0,0]`` - - index_origin : int, optional - Start parameter indices from `index_origin`. Either 0 or 1. - - Returns - ------- - bokeh.models.ColumnDataSource object - """ - from ...utils import flatten_inference_data_to_dict - - if var_name_format is None: - var_name_format = "cds" - - cds_dict = flatten_inference_data_to_dict( - data=data, - var_names=var_names, - groups=groups, - dimensions=dimensions, - group_info=group_info, - index_origin=index_origin, - var_name_format=var_name_format, - ) - cds_data = ColumnDataSource(DataFrame.from_dict(cds_dict, orient="columns")) - return cds_data - - -def output_notebook(*args, **kwargs): - """Wrap func:`bokeh.plotting.output_notebook`.""" - import bokeh.plotting as bkp - - return bkp.output_notebook(*args, **kwargs) - - -def output_file(*args, **kwargs): - """Wrap :func:`bokeh.plotting.output_file`.""" - import bokeh.plotting as bkp - - return bkp.output_file(*args, **kwargs) - - -def ColumnDataSource(*args, **kwargs): - """Wrap bokeh.models.ColumnDataSource.""" - from bokeh.models import ColumnDataSource - - return ColumnDataSource(*args, **kwargs) - - -def create_layout(ax, force_layout=False): - """Transform bokeh array of figures to layout.""" - ax = np.atleast_2d(ax) - subplot_order = rcParams["plot.bokeh.layout.order"] - if force_layout: - from bokeh.layouts import gridplot as layout - - ax = ax.tolist() - layout_args = { - "sizing_mode": rcParams["plot.bokeh.layout.sizing_mode"], - "toolbar_location": rcParams["plot.bokeh.layout.toolbar_location"], - } - elif any(item in subplot_order for item in ("row", "column")): - # check number of rows - match = re.match(r"(\d*)(row|column)", subplot_order) - n = int(match.group(1)) if match.group(1) is not None else 1 - subplot_order = match.group(2) - # set up 1D list of axes - ax = [item for item in ax.ravel().tolist() if item is not None] - layout_args = {"sizing_mode": rcParams["plot.bokeh.layout.sizing_mode"]} - if subplot_order == "row" and n == 1: - from bokeh.layouts import row as layout - elif subplot_order == "column" and n == 1: - from bokeh.layouts import column as layout - else: - from bokeh.layouts import layout - - if n != 1: - ax = np.array(ax + [None for _ in range(int(np.ceil(len(ax) / n)) - len(ax))]) - ax = ax.reshape(n, -1) if subplot_order == "row" else ax.reshape(-1, n) - ax = ax.tolist() - else: - if subplot_order in ("square", "square_trimmed"): - ax = [item for item in ax.ravel().tolist() if item is not None] - n = int(np.ceil(len(ax) ** 0.5)) - ax = ax + [None for _ in range(n**2 - len(ax))] - ax = np.array(ax).reshape(n, n) - ax = ax.tolist() - if (subplot_order == "square_trimmed") and any( - all(item is None for item in row) for row in ax - ): - from bokeh.layouts import layout - - ax = [row for row in ax if any(item is not None for item in row)] - layout_args = {"sizing_mode": rcParams["plot.bokeh.layout.sizing_mode"]} - else: - from bokeh.layouts import gridplot as layout - - layout_args = { - "sizing_mode": rcParams["plot.bokeh.layout.sizing_mode"], - "toolbar_location": rcParams["plot.bokeh.layout.toolbar_location"], - } - # ignore "fixed" sizing_mode without explicit width and height - if layout_args.get("sizing_mode", "") == "fixed": - layout_args.pop("sizing_mode") - return layout(ax, **layout_args) - - -def show_layout(ax, show=True, force_layout=False): - """Create a layout and call bokeh show.""" - if show is None: - show = rcParams["plot.bokeh.show"] - if show: - import bokeh.plotting as bkp - - layout = create_layout(ax, force_layout=force_layout) - bkp.show(layout) - - -def _copy_docstring(lib, function): - """Extract docstring from function.""" - import importlib - - try: - module = importlib.import_module(lib) - func = getattr(module, function) - doc = func.__doc__ - except ImportError: - doc = f"Failed to import function {function} from {lib}" - - if not isinstance(doc, str): - doc = "" - return doc - - -# TODO: try copying substitutions too, or autoreplace them ourselves -if output_notebook.__doc__ is not None: # if run with python -OO, __doc__ is stripped - output_notebook.__doc__ += "\n\n" + _copy_docstring( - "bokeh.plotting", "output_notebook" - ).replace("|save|", "save").replace("|show|", "show") - output_file.__doc__ += "\n\n" + _copy_docstring("bokeh.plotting", "output_file").replace( - "|save|", "save" - ).replace("|show|", "show") - ColumnDataSource.__doc__ += "\n\n" + _copy_docstring("bokeh.models", "ColumnDataSource") diff --git a/arviz/plots/backends/bokeh/__init__.py b/arviz/plots/backends/bokeh/__init__.py deleted file mode 100644 index dbc327cbf4..0000000000 --- a/arviz/plots/backends/bokeh/__init__.py +++ /dev/null @@ -1,166 +0,0 @@ -# pylint: disable=wrong-import-position -"""Bokeh Plotting Backend.""" -from bokeh.plotting import figure -from numpy import array -from packaging import version - -from ....rcparams import rcParams - - -def backend_kwarg_defaults(*args, **kwargs): - """Get default kwargs for backend. - - For args add a tuple with key and rcParam key pair. - """ - defaults = {**kwargs} - # add needed default args from arviz.rcParams - for key, arg in args: - defaults.setdefault(key, rcParams[arg]) - - for key, arg in { - "toolbar_location": "plot.bokeh.layout.toolbar_location", - "tools": "plot.bokeh.tools", - "output_backend": "plot.bokeh.output_backend", - "height": "plot.bokeh.figure.height", - "width": "plot.bokeh.figure.width", - }.items(): - # by default, ignore height and width if dpi is used - if key in ("height", "width") and "dpi" in defaults: - continue - defaults.setdefault(key, rcParams[arg]) - return defaults - - -def create_axes_grid( - length_plotters, - rows=1, - cols=1, - figsize=None, - squeeze=False, - sharex=False, - sharey=False, - polar=False, - backend_kwargs=None, -): - """Create figure and axes for grids with multiple plots. - - Parameters - ---------- - length_plotters : int - Number of figures required - rows : int - Number of rows - cols : int - Number of columns - figsize : tuple - Figure size in inches - squeeze : bool - Return bokeh.figure object if True else ndarray of bokeh.figure objects - sharex : bool - Share x axis between the figures - sharey : bool - Share y axis between the figures - polar : bool - Set up polar coordinates plot - backend_kwargs: dict, optional - kwargs for backend figure. - - Returns - ------- - figures : bokeh figure or np.array of bokeh.figure - """ - if backend_kwargs is None: - backend_kwargs = {} - - if figsize is not None: - backend_kwargs = { - **backend_kwarg_defaults( - ("dpi", "plot.bokeh.figure.dpi"), - ), - **backend_kwargs, - } - dpi = backend_kwargs.pop("dpi") - backend_kwargs.setdefault("width", int(figsize[0] * dpi / cols)) - backend_kwargs.setdefault("height", int(figsize[1] * dpi / rows)) - else: - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figures = [] - - if polar: - backend_kwargs.setdefault("x_axis_type", None) - backend_kwargs.setdefault("y_axis_type", None) - - for row in range(rows): - row_figures = [] - for col in range(cols): - if (row == 0) and (col == 0) and (sharex or sharey): - p = figure(**backend_kwargs) # pylint: disable=invalid-name - row_figures.append(p) - if sharex: - backend_kwargs["x_range"] = p.x_range - if sharey: - backend_kwargs["y_range"] = p.y_range - elif row * cols + (col + 1) > length_plotters: - row_figures.append(None) - else: - row_figures.append(figure(**backend_kwargs)) - figures.append(row_figures) - figures = array(figures) - if figures.size == 1 and squeeze: - figures = figures[0, 0] - return figures - - -def dealiase_sel_kwargs(kwargs, prop_dict, idx): - """Generate kwargs dict from kwargs and prop_dict. - - Gets property at position ``idx`` for each property in prop_dict and adds it to - ``kwargs``. Values in prop_dict are dealiased and overwrite values in - kwargs with the same key . - - Parameters - ---------- - kwargs : dict - prop_dict : dict of {str : array_like} - idx : int - """ - return { - **kwargs, - **{prop: props[idx] for prop, props in prop_dict.items()}, - } - - -from .autocorrplot import plot_autocorr -from .compareplot import plot_compare -from .densityplot import plot_density -from .distplot import plot_dist -from .elpdplot import plot_elpd -from .energyplot import plot_energy -from .essplot import plot_ess -from .forestplot import plot_forest -from .hdiplot import plot_hdi -from .kdeplot import plot_kde -from .khatplot import plot_khat -from .loopitplot import plot_loo_pit -from .mcseplot import plot_mcse -from .pairplot import plot_pair -from .parallelplot import plot_parallel -from .ppcplot import plot_ppc -from .posteriorplot import plot_posterior -from .rankplot import plot_rank -from .traceplot import plot_trace -from .violinplot import plot_violin - - -def check_bokeh_version(): - """Check minimum bokeh version.""" - try: - import bokeh - - assert version.parse(bokeh.__version__) >= version.parse("1.4.0") - except (ImportError, AssertionError) as err: - raise ImportError("'bokeh' backend needs Bokeh (1.4.0+) installed.") from err diff --git a/arviz/plots/backends/bokeh/autocorrplot.py b/arviz/plots/backends/bokeh/autocorrplot.py deleted file mode 100644 index dced230308..0000000000 --- a/arviz/plots/backends/bokeh/autocorrplot.py +++ /dev/null @@ -1,101 +0,0 @@ -"""Bokeh Autocorrplot.""" - -import numpy as np -from bokeh.models import DataRange1d, BoxAnnotation -from bokeh.models.annotations import Title - - -from ....stats import autocorr -from ...plot_utils import _scale_fig_size -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_autocorr( - axes, - plotters, - max_lag, - figsize, - rows, - cols, - combined, - textsize, - labeller, - backend_config, - backend_kwargs, - show, -): - """Bokeh autocorrelation plot.""" - if backend_config is None: - backend_config = {} - - len_y = plotters[0][-1].size - backend_config.setdefault("bounds_x_range", (0, len_y)) - - backend_config = { - **backend_kwarg_defaults( - ("bounds_y_range", "plot.bokeh.bounds_y_range"), - ), - **backend_config, - } - - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults( - ("dpi", "plot.bokeh.figure.dpi"), - ), - **backend_kwargs, - } - - figsize, _, _, _, line_width, _ = _scale_fig_size(figsize, textsize, rows, cols) - - if axes is None: - axes = create_axes_grid( - len(plotters), - rows, - cols, - figsize=figsize, - sharex=True, - sharey=True, - backend_kwargs=backend_kwargs, - ) - else: - axes = np.atleast_2d(axes) - - data_range_x = DataRange1d( - start=0, end=max_lag, bounds=backend_config["bounds_x_range"], min_interval=5 - ) - data_range_y = DataRange1d( - start=-1, end=1, bounds=backend_config["bounds_y_range"], min_interval=0.1 - ) - - for (var_name, selection, isel, x), ax in zip( - plotters, (item for item in axes.flatten() if item is not None) - ): - x_prime = x - if combined: - x_prime = x.flatten() - c_i = 1.96 / x_prime.size**0.5 - y = autocorr(x_prime) - - ax.add_layout(BoxAnnotation(bottom=-c_i, top=c_i, fill_color="gray")) - ax.segment( - x0=np.arange(len(y)), - y0=0, - x1=np.arange(len(y)), - y1=y, - line_width=line_width, - line_color="black", - ) - - title = Title() - title.text = labeller.make_label_vert(var_name, selection, isel) - ax.title = title - ax.x_range = data_range_x - ax.y_range = data_range_y - - show_layout(axes, show) - - return axes diff --git a/arviz/plots/backends/bokeh/bfplot.py b/arviz/plots/backends/bokeh/bfplot.py deleted file mode 100644 index fd3c97cac1..0000000000 --- a/arviz/plots/backends/bokeh/bfplot.py +++ /dev/null @@ -1,23 +0,0 @@ -"""Bokeh Bayes Factor plot.""" - - -def plot_bf( - ax, - nvars, - ngroups, - figsize, - dc_plotters, - legend, - groups, - textsize, - labeller, - prior_kwargs, - posterior_kwargs, - observed_kwargs, - backend_kwargs, - show, -): - """Bokeh Bayes Factor plot.""" - raise NotImplementedError( - "The bokeh backend is still under development. Use matplotlib backend." - ) diff --git a/arviz/plots/backends/bokeh/bpvplot.py b/arviz/plots/backends/bokeh/bpvplot.py deleted file mode 100644 index ab48624b24..0000000000 --- a/arviz/plots/backends/bokeh/bpvplot.py +++ /dev/null @@ -1,193 +0,0 @@ -"""Bokeh Bayesian p-value Posterior predictive plot.""" - -import numpy as np -from bokeh.models import BoxAnnotation -from bokeh.models.annotations import Title -from scipy import stats - -from ....stats.density_utils import kde -from ....stats.stats_utils import smooth_data -from ...kdeplot import plot_kde -from ...plot_utils import ( - _scale_fig_size, - is_valid_quantile, - sample_reference_distribution, - vectorized_to_hex, -) -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_bpv( - ax, - length_plotters, - rows, - cols, - obs_plotters, - pp_plotters, - total_pp_samples, - kind, - t_stat, - bpv, - plot_mean, - reference, - mse, - n_ref, - hdi_prob, - color, - figsize, - textsize, - labeller, - plot_ref_kwargs, - backend_kwargs, - show, - smoothing, -): - """Bokeh bpv plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - color = vectorized_to_hex(color) - - if plot_ref_kwargs is None: - plot_ref_kwargs = {} - if kind == "p_value" and reference == "analytical": - plot_ref_kwargs.setdefault("line_color", "black") - plot_ref_kwargs.setdefault("line_dash", "dashed") - else: - plot_ref_kwargs.setdefault("alpha", 0.1) - plot_ref_kwargs.setdefault("line_color", color) - - (figsize, ax_labelsize, _, _, linewidth, markersize) = _scale_fig_size( - figsize, textsize, rows, cols - ) - - if ax is None: - axes = create_axes_grid( - length_plotters, - rows, - cols, - figsize=figsize, - backend_kwargs=backend_kwargs, - ) - else: - axes = np.atleast_2d(ax) - - if len([item for item in axes.ravel() if not None]) != length_plotters: - raise ValueError( - f"Found {length_plotters} variables to plot but {len(axes)} axes instances. " - "They must be equal." - ) - - for i, ax_i in enumerate((item for item in axes.flatten() if item is not None)): - var_name, sel, isel, obs_vals = obs_plotters[i] - pp_var_name, _, _, pp_vals = pp_plotters[i] - - obs_vals = obs_vals.flatten() - pp_vals = pp_vals.reshape(total_pp_samples, -1) - - if (obs_vals.dtype.kind == "i" or pp_vals.dtype.kind == "i") and smoothing is True: - obs_vals, pp_vals = smooth_data(obs_vals, pp_vals) - - if kind == "p_value": - tstat_pit = np.mean(pp_vals <= obs_vals, axis=-1) - x_s, tstat_pit_dens = kde(tstat_pit) - ax_i.line(x_s, tstat_pit_dens, line_width=linewidth, line_color=color) - if reference is not None: - dist = stats.beta(obs_vals.size / 2, obs_vals.size / 2) - if reference == "analytical": - lwb = dist.ppf((1 - 0.9999) / 2) - upb = 1 - lwb - x = np.linspace(lwb, upb, 500) - dens_ref = dist.pdf(x) - ax_i.line(x, dens_ref, **plot_ref_kwargs) - elif reference == "samples": - x_ss, u_dens = sample_reference_distribution( - dist, - ( - tstat_pit_dens.size, - n_ref, - ), - ) - ax_i.multi_line( - list(x_ss.T), list(u_dens.T), line_width=linewidth, **plot_ref_kwargs - ) - - elif kind == "u_value": - tstat_pit = np.mean(pp_vals <= obs_vals, axis=0) - x_s, tstat_pit_dens = kde(tstat_pit) - ax_i.line(x_s, tstat_pit_dens, color=color) - if reference is not None: - if reference == "analytical": - n_obs = obs_vals.size - hdi_ = stats.beta(n_obs / 2, n_obs / 2).ppf((1 - hdi_prob) / 2) - hdi_odds = (hdi_ / (1 - hdi_), (1 - hdi_) / hdi_) - ax_i.add_layout( - BoxAnnotation( - bottom=hdi_odds[1], - top=hdi_odds[0], - fill_alpha=plot_ref_kwargs.pop("alpha"), - fill_color=plot_ref_kwargs.pop("line_color"), - **plot_ref_kwargs, - ) - ) - ax_i.line([0, 1], [1, 1], line_color="white") - elif reference == "samples": - dist = stats.uniform(0, 1) - x_ss, u_dens = sample_reference_distribution(dist, (tstat_pit_dens.size, n_ref)) - for x_ss_i, u_dens_i in zip(x_ss.T, u_dens.T): - ax_i.line(x_ss_i, u_dens_i, line_width=linewidth, **plot_ref_kwargs) - if mse: - ax_i.line(0, 0, legend_label=f"mse={np.mean((1 - tstat_pit_dens)**2) * 100:.2f}") - - ax_i.line(0, 0) - else: - if t_stat in ["mean", "median", "std"]: - if t_stat == "mean": - tfunc = np.mean - elif t_stat == "median": - tfunc = np.median - elif t_stat == "std": - tfunc = np.std - obs_vals = tfunc(obs_vals) - pp_vals = tfunc(pp_vals, axis=1) - elif hasattr(t_stat, "__call__"): - obs_vals = t_stat(obs_vals.flatten()) - pp_vals = t_stat(pp_vals) - elif is_valid_quantile(t_stat): - t_stat = float(t_stat) - obs_vals = np.quantile(obs_vals, q=t_stat) - pp_vals = np.quantile(pp_vals, q=t_stat, axis=1) - else: - raise ValueError(f"T statistics {t_stat} not implemented") - - plot_kde(pp_vals, ax=ax_i, plot_kwargs={"color": color}, backend="bokeh", show=False) - # ax_i.set_yticks([]) - if bpv: - p_value = np.mean(pp_vals <= obs_vals) - ax_i.line(0, 0, legend_label=f"bpv={p_value:.2f}", alpha=0) - - if plot_mean: - ax_i.scatter( - obs_vals.mean(), - 0, - fill_color=color, - line_color="black", - size=markersize, - marker="circle", - ) - - _title = Title() - _title.text = labeller.make_pp_label(var_name, pp_var_name, sel, isel) - ax_i.title = _title - size = str(int(ax_labelsize)) - ax_i.title.text_font_size = f"{size}pt" - - show_layout(axes, show) - - return axes diff --git a/arviz/plots/backends/bokeh/compareplot.py b/arviz/plots/backends/bokeh/compareplot.py deleted file mode 100644 index a3a87c5e17..0000000000 --- a/arviz/plots/backends/bokeh/compareplot.py +++ /dev/null @@ -1,167 +0,0 @@ -"""Bokeh Compareplot.""" - -from bokeh.models import Span -from bokeh.models.annotations import Title, Legend - - -from ...plot_utils import _scale_fig_size -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_compare( - ax, - comp_df, - legend, - title, - figsize, - plot_ic_diff, - plot_standard_error, - insample_dev, - yticks_pos, - yticks_labels, - plot_kwargs, - textsize, - information_criterion, - step, - backend_kwargs, - show, -): - """Bokeh compareplot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, _, _, _, line_width, _ = _scale_fig_size(figsize, textsize, 1, 1) - - if ax is None: - ax = create_axes_grid( - 1, - figsize=figsize, - squeeze=True, - backend_kwargs=backend_kwargs, - ) - - yticks_pos = list(yticks_pos) - - labels = [] - - if plot_ic_diff: - ax.yaxis.ticker = yticks_pos[::2] - ax.yaxis.major_label_overrides = { - dtype(key): value - for key, value in zip(yticks_pos, yticks_labels) - for dtype in (int, float) - if (dtype(key) - key == 0) - } - - # create the coordinates for the errorbars - err_xs = [] - err_ys = [] - - for x, y, xerr in zip( - comp_df[information_criterion].iloc[1:], yticks_pos[1::2], comp_df.dse[1:] - ): - err_xs.append((x - xerr, x + xerr)) - err_ys.append((y, y)) - - # plot them - dif_tri = ax.scatter( - comp_df[information_criterion].iloc[1:], - yticks_pos[1::2], - line_color=plot_kwargs.get("color_dse", "grey"), - fill_color=plot_kwargs.get("color_dse", "grey"), - line_width=2, - size=6, - marker="triangle", - ) - dif_line = ax.multi_line(err_xs, err_ys, line_color=plot_kwargs.get("color_dse", "grey")) - - labels.append(("ELPD difference", [dif_tri, dif_line])) - - else: - ax.yaxis.ticker = yticks_pos[::2] - ax.yaxis.major_label_overrides = dict(zip(yticks_pos[::2], yticks_labels)) - - elpd_circ = ax.scatter( - comp_df[information_criterion], - yticks_pos[::2], - line_color=plot_kwargs.get("color_ic", "black"), - fill_color=None, - line_width=2, - size=6, - marker="circle", - ) - elpd_label = [elpd_circ] - - if plot_standard_error: - # create the coordinates for the errorbars - err_xs = [] - err_ys = [] - - for x, y, xerr in zip(comp_df[information_criterion], yticks_pos[::2], comp_df.se): - err_xs.append((x - xerr, x + xerr)) - err_ys.append((y, y)) - - # plot them - elpd_line = ax.multi_line(err_xs, err_ys, line_color=plot_kwargs.get("color_ic", "black")) - elpd_label.append(elpd_line) - - labels.append(("ELPD", elpd_label)) - - scale = comp_df["scale"].iloc[0] - - if insample_dev: - p_ic = comp_df[f"p_{information_criterion.split('_')[1]}"] - if scale == "log": - correction = p_ic - elif scale == "negative_log": - correction = -p_ic - elif scale == "deviance": - correction = -(2 * p_ic) - insample_circ = ax.scatter( - comp_df[information_criterion] + correction, - yticks_pos[::2], - line_color=plot_kwargs.get("color_insample_dev", "black"), - fill_color=plot_kwargs.get("color_insample_dev", "black"), - line_width=2, - size=6, - marker="circle", - ) - labels.append(("In-sample ELPD", [insample_circ])) - - vline = Span( - location=comp_df[information_criterion].iloc[0], - dimension="height", - line_color=plot_kwargs.get("color_ls_min_ic", "grey"), - line_width=line_width, - line_dash=plot_kwargs.get("ls_min_ic", "dashed"), - ) - - ax.renderers.append(vline) - - if legend: - legend = Legend(items=labels, orientation="vertical", location="top_right") - ax.add_layout(legend, "above") - ax.legend.click_policy = "hide" - - if title: - _title = Title() - _title.text = f"Model comparison\n{'higher' if scale == 'log' else 'lower'} is better" - ax.title = _title - - if scale == "negative_log": - scale = "-log" - - ax.xaxis.axis_label = f"{information_criterion} ({scale})" - ax.yaxis.axis_label = "ranked models" - ax.y_range._property_values["start"] = -1 + step # pylint: disable=protected-access - ax.y_range._property_values["end"] = 0 - step # pylint: disable=protected-access - - show_layout(ax, show) - - return ax diff --git a/arviz/plots/backends/bokeh/densityplot.py b/arviz/plots/backends/bokeh/densityplot.py deleted file mode 100644 index 4f8e78b0e5..0000000000 --- a/arviz/plots/backends/bokeh/densityplot.py +++ /dev/null @@ -1,239 +0,0 @@ -"""Bokeh Densityplot.""" - -from collections import defaultdict -from itertools import cycle - -import matplotlib.pyplot as plt -import numpy as np -from bokeh.models.annotations import Legend, Title - -from ....stats import hdi -from ....stats.density_utils import get_bins, histogram, kde -from ...plot_utils import _scale_fig_size, calculate_point_estimate, vectorized_to_hex -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_density( - ax, - all_labels, - to_plot, - colors, - bw, - circular, - figsize, - length_plotters, - rows, - cols, - textsize, - labeller, - hdi_prob, - point_estimate, - hdi_markers, - outline, - shade, - n_data, - data_labels, - backend_kwargs, - show, -): - """Bokeh density plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - if colors == "cycle": - colors = [ - prop - for _, prop in zip( - range(n_data), cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"]) - ) - ] - elif isinstance(colors, str): - colors = [colors for _ in range(n_data)] - colors = vectorized_to_hex(colors) - - (figsize, _, _, _, line_width, markersize) = _scale_fig_size(figsize, textsize, rows, cols) - - if ax is None: - ax = create_axes_grid( - length_plotters, - rows, - cols, - figsize=figsize, - squeeze=False, - backend_kwargs=backend_kwargs, - ) - else: - ax = np.atleast_2d(ax) - - axis_map = dict(zip(all_labels, (item for item in ax.flatten() if item is not None))) - - if data_labels is None: - data_labels = {} - - legend_items = defaultdict(list) - for m_idx, plotters in enumerate(to_plot): - for var_name, selection, isel, values in plotters: - label = labeller.make_label_vert(var_name, selection, isel) - - data_label = data_labels[m_idx] if data_labels else None - - plotted = _d_helper( - values.flatten(), - label, - colors[m_idx], - bw, - circular, - line_width, - markersize, - hdi_prob, - point_estimate, - hdi_markers, - outline, - shade, - axis_map[label], - ) - if data_label is not None: - legend_items[axis_map[label]].append((data_label, plotted)) - - for ax1, legend in legend_items.items(): - legend = Legend( - items=legend, - location="center_right", - orientation="horizontal", - ) - ax1.add_layout(legend, "above") - ax1.legend.click_policy = "hide" - - show_layout(ax, show) - - return ax - - -def _d_helper( - vec, - vname, - color, - bw, - circular, - line_width, - markersize, - hdi_prob, - point_estimate, - hdi_markers, - outline, - shade, - ax, -): - extra = {} - plotted = [] - - if vec.dtype.kind == "f": - if hdi_prob != 1: - hdi_ = hdi(vec, hdi_prob, multimodal=False) - new_vec = vec[(vec >= hdi_[0]) & (vec <= hdi_[1])] - else: - new_vec = vec - - x, density = kde(new_vec, circular=circular, bw=bw) - density *= hdi_prob - xmin, xmax = x[0], x[-1] - ymin, ymax = density[0], density[-1] - - if outline: - plotted.append(ax.line(x, density, line_color=color, line_width=line_width, **extra)) - plotted.append( - ax.line( - [xmin, xmin], - [-ymin / 100, ymin], - line_color=color, - line_dash="solid", - line_width=line_width, - muted_color=color, - muted_alpha=0.2, - ) - ) - plotted.append( - ax.line( - [xmax, xmax], - [-ymax / 100, ymax], - line_color=color, - line_dash="solid", - line_width=line_width, - muted_color=color, - muted_alpha=0.2, - ) - ) - - if shade: - plotted.append( - ax.patch( - np.r_[x[::-1], x, x[-1:]], - np.r_[np.zeros_like(x), density, [0]], - fill_color=color, - fill_alpha=shade, - muted_color=color, - muted_alpha=0.2, - **extra - ) - ) - - else: - xmin, xmax = hdi(vec, hdi_prob, multimodal=False) - bins = get_bins(vec) - - _, hist, edges = histogram(vec, bins=bins) - - if outline: - plotted.append( - ax.quad( - top=hist, - bottom=0, - left=edges[:-1], - right=edges[1:], - line_color=color, - fill_color=None, - muted_color=color, - muted_alpha=0.2, - **extra - ) - ) - else: - plotted.append( - ax.quad( - top=hist, - bottom=0, - left=edges[:-1], - right=edges[1:], - line_color=color, - fill_color=color, - fill_alpha=shade, - muted_color=color, - muted_alpha=0.2, - **extra - ) - ) - - if hdi_markers: - plotted.append(ax.diamond(xmin, 0, line_color="black", fill_color=color, size=markersize)) - plotted.append(ax.diamond(xmax, 0, line_color="black", fill_color=color, size=markersize)) - - if point_estimate is not None: - est = calculate_point_estimate(point_estimate, vec, bw, circular) - plotted.append( - ax.scatter( - est, 0, marker="circle", fill_color=color, line_color="black", size=markersize - ) - ) - - _title = Title() - _title.text = vname - ax.title = _title - ax.title.text_font_size = "13pt" - - return plotted diff --git a/arviz/plots/backends/bokeh/distcomparisonplot.py b/arviz/plots/backends/bokeh/distcomparisonplot.py deleted file mode 100644 index 38d5bcdc58..0000000000 --- a/arviz/plots/backends/bokeh/distcomparisonplot.py +++ /dev/null @@ -1,23 +0,0 @@ -"""Bokeh Density Comparison plot.""" - - -def plot_dist_comparison( - ax, - nvars, - ngroups, - figsize, - dc_plotters, - legend, - groups, - textsize, - labeller, - prior_kwargs, - posterior_kwargs, - observed_kwargs, - backend_kwargs, - show, -): - """Bokeh Density Comparison plot.""" - raise NotImplementedError( - "The bokeh backend is still under development. Use matplotlib backend." - ) diff --git a/arviz/plots/backends/bokeh/distplot.py b/arviz/plots/backends/bokeh/distplot.py deleted file mode 100644 index 00ec9d9e1e..0000000000 --- a/arviz/plots/backends/bokeh/distplot.py +++ /dev/null @@ -1,183 +0,0 @@ -"""Bokeh Distplot.""" - -import matplotlib.pyplot as plt -import numpy as np - -from ....stats.density_utils import get_bins, histogram -from ...kdeplot import plot_kde -from ...plot_utils import ( - _scale_fig_size, - set_bokeh_circular_ticks_labels, - vectorized_to_hex, - _init_kwargs_dict, -) -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_dist( - values, - values2, - color, - kind, - cumulative, - label, - rotated, - rug, - bw, - quantiles, - contour, - fill_last, - figsize, - textsize, - plot_kwargs, - fill_kwargs, - rug_kwargs, - contour_kwargs, - contourf_kwargs, - pcolormesh_kwargs, - hist_kwargs, - is_circular, - ax, - backend_kwargs, - show, -): - """Bokeh distplot.""" - backend_kwargs = _init_kwargs_dict(backend_kwargs) - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, *_ = _scale_fig_size(figsize, textsize) - - color = vectorized_to_hex(color) - - hist_kwargs = _init_kwargs_dict(hist_kwargs) - if kind == "hist": - hist_kwargs.setdefault("cumulative", cumulative) - hist_kwargs.setdefault("fill_color", color) - hist_kwargs.setdefault("line_color", color) - hist_kwargs.setdefault("line_alpha", 0) - if label is not None: - hist_kwargs.setdefault("legend_label", str(label)) - - if ax is None: - ax = create_axes_grid( - 1, - figsize=figsize, - squeeze=True, - polar=is_circular, - backend_kwargs=backend_kwargs, - ) - - if kind == "auto": - kind = "hist" if values.dtype.kind == "i" else "kde" - - if kind == "hist": - _histplot_bokeh_op( - values=values, - values2=values2, - rotated=rotated, - ax=ax, - hist_kwargs=hist_kwargs, - is_circular=is_circular, - ) - elif kind == "kde": - plot_kwargs = _init_kwargs_dict(plot_kwargs) - if color is None: - color = plt.rcParams["axes.prop_cycle"].by_key()["color"][0] - plot_kwargs.setdefault("line_color", color) - legend = label is not None - - plot_kde( - values, - values2, - cumulative=cumulative, - rug=rug, - label=label, - bw=bw, - is_circular=is_circular, - quantiles=quantiles, - rotated=rotated, - contour=contour, - legend=legend, - fill_last=fill_last, - plot_kwargs=plot_kwargs, - fill_kwargs=fill_kwargs, - rug_kwargs=rug_kwargs, - contour_kwargs=contour_kwargs, - contourf_kwargs=contourf_kwargs, - pcolormesh_kwargs=pcolormesh_kwargs, - ax=ax, - backend="bokeh", - backend_kwargs={}, - show=False, - ) - else: - raise TypeError(f'Invalid "kind":{kind}. Select from {{"auto","kde","hist"}}') - - show_layout(ax, show) - - return ax - - -def _histplot_bokeh_op(values, values2, rotated, ax, hist_kwargs, is_circular): - """Add a histogram for the data to the axes.""" - if values2 is not None: - raise NotImplementedError("Insert hexbin plot here") - - color = hist_kwargs.pop("color", False) - if color: - hist_kwargs["fill_color"] = color - hist_kwargs["line_color"] = color - - bins = hist_kwargs.pop("bins", None) - if bins is None: - bins = get_bins(values) - hist, hist_dens, edges = histogram(np.asarray(values).flatten(), bins=bins) - if hist_kwargs.pop("density", True): - hist = hist_dens - if hist_kwargs.pop("cumulative", False): - hist = np.cumsum(hist) - hist /= hist[-1] - if values.dtype.kind == "i": - edges = edges.astype(float) - 0.5 - - if is_circular: - if is_circular == "degrees": - edges = np.deg2rad(edges) - labels = ["0°", "45°", "90°", "135°", "180°", "225°", "270°", "315°"] - else: - labels = [ - r"0", - r"π/4", - r"π/2", - r"3π/4", - r"π", - r"5π/4", - r"3π/2", - r"7π/4", - ] - - delta = np.mean(np.diff(edges) / 2) - - ax.annular_wedge( - x=0, - y=0, - inner_radius=0, - outer_radius=hist, - start_angle=edges[1:] - delta, - end_angle=edges[:-1] - delta, - direction="clock", - **hist_kwargs, - ) - - ax = set_bokeh_circular_ticks_labels(ax, hist, labels) - elif rotated: - ax.quad(top=edges[:-1], bottom=edges[1:], left=0, right=hist, **hist_kwargs) - else: - ax.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], **hist_kwargs) - - return ax diff --git a/arviz/plots/backends/bokeh/dotplot.py b/arviz/plots/backends/bokeh/dotplot.py deleted file mode 100644 index 327f17776c..0000000000 --- a/arviz/plots/backends/bokeh/dotplot.py +++ /dev/null @@ -1,113 +0,0 @@ -"""Bokeh dotplot.""" - -import math -import warnings -import numpy as np - -from ...plot_utils import _scale_fig_size, vectorized_to_hex -from .. import show_layout -from . import create_axes_grid -from ...plot_utils import plot_point_interval -from ...dotplot import wilkinson_algorithm, layout_stacks - - -def plot_dot( - values, - binwidth, - dotsize, - stackratio, - hdi_prob, - quartiles, - rotated, - dotcolor, - intervalcolor, - markersize, - markercolor, - marker, - figsize, - linewidth, - point_estimate, - nquantiles, - point_interval, - ax, - show, - backend_kwargs, - plot_kwargs, -): - """Bokeh dotplot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs.setdefault("match_aspect", True) - - (figsize, _, _, _, auto_linewidth, auto_markersize) = _scale_fig_size(figsize, None) - dotcolor = vectorized_to_hex(dotcolor) - intervalcolor = vectorized_to_hex(intervalcolor) - markercolor = vectorized_to_hex(markercolor) - - if plot_kwargs is None: - plot_kwargs = {} - else: - plot_kwargs = plot_kwargs.copy() - plot_kwargs.setdefault("color", dotcolor) - plot_kwargs.setdefault("marker", "circle") - - if linewidth is None: - linewidth = auto_linewidth - - if markersize is None: - markersize = auto_markersize - - if ax is None: - ax = create_axes_grid( - 1, - figsize=figsize, - squeeze=True, - backend_kwargs=backend_kwargs, - ) - - if point_interval: - ax = plot_point_interval( - ax, - values, - point_estimate, - hdi_prob, - quartiles, - linewidth, - markersize, - markercolor, - marker, - rotated, - intervalcolor, - "bokeh", - ) - - if nquantiles > values.shape[0]: - warnings.warn( - "nquantiles must be less than or equal to the number of data points", UserWarning - ) - nquantiles = values.shape[0] - else: - qlist = np.linspace(1 / (2 * nquantiles), 1 - 1 / (2 * nquantiles), nquantiles) - values = np.quantile(values, qlist) - - if binwidth is None: - binwidth = math.sqrt((values[-1] - values[0] + 1) ** 2 / (2 * nquantiles * np.pi)) - - ## Wilkinson's Algorithm - stack_locs, stack_count = wilkinson_algorithm(values, binwidth) - x, y = layout_stacks(stack_locs, stack_count, binwidth, stackratio, rotated) - - ax.scatter(x, y, radius=dotsize * (binwidth / 2), **plot_kwargs, radius_dimension="y") - if rotated: - ax.xaxis.major_tick_line_color = None - ax.xaxis.minor_tick_line_color = None - ax.xaxis.major_label_text_font_size = "0pt" - else: - ax.yaxis.major_tick_line_color = None - ax.yaxis.minor_tick_line_color = None - ax.yaxis.major_label_text_font_size = "0pt" - - show_layout(ax, show) - - return ax diff --git a/arviz/plots/backends/bokeh/ecdfplot.py b/arviz/plots/backends/bokeh/ecdfplot.py deleted file mode 100644 index 6df3413efa..0000000000 --- a/arviz/plots/backends/bokeh/ecdfplot.py +++ /dev/null @@ -1,73 +0,0 @@ -"""Bokeh ecdfplot.""" - -from matplotlib.colors import to_hex - -from ...plot_utils import _scale_fig_size -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_ecdf( - x_coord, - y_coord, - x_bands, - lower, - higher, - plot_kwargs, - fill_kwargs, - plot_outline_kwargs, - figsize, - fill_band, - ax, - show, - backend_kwargs, -): - """Bokeh ecdfplot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - (figsize, *_) = _scale_fig_size(figsize, None) - - if ax is None: - ax = create_axes_grid( - 1, - figsize=figsize, - squeeze=True, - backend_kwargs=backend_kwargs, - ) - - if plot_kwargs is None: - plot_kwargs = {} - - plot_kwargs.setdefault("mode", "after") - - if fill_band: - if fill_kwargs is None: - fill_kwargs = {} - fill_kwargs.setdefault("fill_color", to_hex("C0")) - fill_kwargs.setdefault("fill_alpha", 0.2) - else: - if plot_outline_kwargs is None: - plot_outline_kwargs = {} - plot_outline_kwargs.setdefault("color", to_hex("C0")) - plot_outline_kwargs.setdefault("alpha", 0.2) - - if x_bands is not None: - ax.step(x_coord, y_coord, **plot_kwargs) - - if fill_band: - ax.varea(x_bands, lower, higher, **fill_kwargs) - else: - ax.line(x_bands, lower, **plot_outline_kwargs) - ax.line(x_bands, higher, **plot_outline_kwargs) - else: - ax.step(x_coord, y_coord, **plot_kwargs) - - show_layout(ax, show) - - return ax diff --git a/arviz/plots/backends/bokeh/elpdplot.py b/arviz/plots/backends/bokeh/elpdplot.py deleted file mode 100644 index fd6484be13..0000000000 --- a/arviz/plots/backends/bokeh/elpdplot.py +++ /dev/null @@ -1,203 +0,0 @@ -"""Bokeh ELPDPlot.""" - -import warnings - -import bokeh.plotting as bkp -import numpy as np -from bokeh.models import ColumnDataSource -from bokeh.models.annotations import Title -from bokeh.models.glyphs import Scatter -from ....rcparams import _validate_bokeh_marker, rcParams -from ...plot_utils import _scale_fig_size, color_from_dim, vectorized_to_hex -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_elpd( - ax, - models, - pointwise_data, - numvars, - figsize, - textsize, - plot_kwargs, - xlabels, - coord_labels, - xdata, - threshold, - legend, # pylint: disable=unused-argument - color, - backend_kwargs, - show, -): - """Bokeh elpd plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults( - ("dpi", "plot.bokeh.figure.dpi"), - ), - **backend_kwargs, - } - - plot_kwargs = {} if plot_kwargs is None else plot_kwargs - plot_kwargs.setdefault("marker", rcParams["plot.bokeh.marker"]) - if isinstance(color, str) and color in pointwise_data[0].dims: - colors, _ = color_from_dim(pointwise_data[0], color) - plot_kwargs.setdefault("color", vectorized_to_hex(colors)) - plot_kwargs.setdefault("color", vectorized_to_hex(color)) - - # flatten data (data must be flattened after selecting, labeling and coloring) - pointwise_data = [pointwise.values.flatten() for pointwise in pointwise_data] - - if numvars == 2: - (figsize, _, _, _, _, markersize) = _scale_fig_size( - figsize, textsize, numvars - 1, numvars - 1 - ) - plot_kwargs.setdefault("s", markersize) - - if ax is None: - ax = create_axes_grid( - 1, - figsize=figsize, - squeeze=True, - backend_kwargs=backend_kwargs, - ) - ydata = pointwise_data[0] - pointwise_data[1] - _plot_atomic_elpd( - ax, xdata, ydata, *models, threshold, coord_labels, xlabels, True, True, plot_kwargs - ) - - else: - max_plots = ( - numvars**2 if rcParams["plot.max_subplots"] is None else rcParams["plot.max_subplots"] - ) - vars_to_plot = np.sum(np.arange(numvars).cumsum() < max_plots) - if vars_to_plot < numvars: - warnings.warn( - "rcParams['plot.max_subplots'] ({max_plots}) is smaller than the number " - "of resulting ELPD pairwise plots with these variables, generating only a " - "{side}x{side} grid".format(max_plots=max_plots, side=vars_to_plot), - UserWarning, - ) - numvars = vars_to_plot - - (figsize, _, _, _, _, markersize) = _scale_fig_size( - figsize, textsize, numvars - 2, numvars - 2 - ) - plot_kwargs.setdefault("s", markersize) - - if ax is None: - dpi = backend_kwargs.pop("dpi") - ax = [] - for row in range(numvars - 1): - ax_row = [] - for col in range(numvars - 1): - if row == 0 and col == 0: - ax_first = bkp.figure( - width=int(figsize[0] / (numvars - 1) * dpi), - height=int(figsize[1] / (numvars - 1) * dpi), - **backend_kwargs, - ) - ax_row.append(ax_first) - elif row < col: - ax_row.append(None) - else: - ax_row.append( - bkp.figure( - width=int(figsize[0] / (numvars - 1) * dpi), - height=int(figsize[1] / (numvars - 1) * dpi), - x_range=ax_first.x_range, - y_range=ax_first.y_range, - **backend_kwargs, - ) - ) - ax.append(ax_row) - ax = np.array(ax) - - for i in range(0, numvars - 1): - var1 = pointwise_data[i] - - for j in range(0, numvars - 1): - if j < i: - continue - - var2 = pointwise_data[j + 1] - ydata = var1 - var2 - _plot_atomic_elpd( - ax[j, i], - xdata, - ydata, - models[i], - models[j + 1], - threshold, - coord_labels, - xlabels, - j == numvars - 2, - i == 0, - plot_kwargs, - ) - - show_layout(ax, show) - - return ax - - -def _plot_atomic_elpd( - ax_, - xdata, - ydata, - model1, - model2, - threshold, - coord_labels, - xlabels, - xlabels_shown, - ylabels_shown, - plot_kwargs, -): - marker = _validate_bokeh_marker(plot_kwargs.get("marker")) - sizes = np.ones(len(xdata)) * plot_kwargs.get("s") - glyph = Scatter( - x="xdata", - y="ydata", - size="sizes", - line_color=plot_kwargs.get("color", "black"), - marker=marker, - ) - source = ColumnDataSource(dict(xdata=xdata, ydata=ydata, sizes=sizes)) - ax_.add_glyph(source, glyph) - if threshold is not None: - diff_abs = np.abs(ydata - ydata.mean()) - bool_ary = diff_abs > threshold * ydata.std() - if coord_labels is None: - coord_labels = xdata.astype(str) - outliers = np.nonzero(bool_ary)[0] - for outlier in outliers: - label = coord_labels[outlier] - ax_.text( - x=[outlier], - y=[ydata[outlier]], - text=label, - text_color="black", - ) - if ylabels_shown: - ax_.yaxis.axis_label = "ELPD difference" - else: - ax_.yaxis.minor_tick_line_color = None - ax_.yaxis.major_label_text_font_size = "0pt" - - if xlabels_shown: - if xlabels: - ax_.xaxis.ticker = np.arange(0, len(coord_labels)) - ax_.xaxis.major_label_overrides = { - str(key): str(value) - for key, value in zip(np.arange(0, len(coord_labels)), list(coord_labels)) - } - else: - ax_.xaxis.minor_tick_line_color = None - ax_.xaxis.major_label_text_font_size = "0pt" - title = Title() - title.text = f"{model1} - {model2}" - ax_.title = title diff --git a/arviz/plots/backends/bokeh/energyplot.py b/arviz/plots/backends/bokeh/energyplot.py deleted file mode 100644 index 6e24b2f493..0000000000 --- a/arviz/plots/backends/bokeh/energyplot.py +++ /dev/null @@ -1,155 +0,0 @@ -"""Bokeh energyplot.""" - -from itertools import cycle - -import numpy as np -from bokeh.models import Label -from bokeh.models.annotations import Legend -from matplotlib.pyplot import rcParams as mpl_rcParams - -from ....stats import bfmi as e_bfmi -from ...kdeplot import plot_kde -from ...plot_utils import _scale_fig_size, vectorized_to_hex -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid -from .distplot import _histplot_bokeh_op - - -def plot_energy( - ax, - energy, - kind, - bfmi, - figsize, - textsize, - fill_alpha, - fill_color, - fill_kwargs, - plot_kwargs, - bw, - legend, - backend_kwargs, - show, -): - """Bokeh energy plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(("dpi", "plot.bokeh.figure.dpi")), - **backend_kwargs, - } - dpi = backend_kwargs.pop("dpi") - - figsize, _, _, _, line_width, _ = _scale_fig_size(figsize, textsize, 1, 1) - - fill_kwargs = {} if fill_kwargs is None else fill_kwargs - plot_kwargs = {} if plot_kwargs is None else plot_kwargs - plot_kwargs.setdefault("line_width", line_width) - if kind == "hist": - legend = False - - if ax is None: - ax = create_axes_grid( - 1, - figsize=figsize, - squeeze=True, - backend_kwargs=backend_kwargs, - ) - - _colors = [ - prop for _, prop in zip(range(10), cycle(mpl_rcParams["axes.prop_cycle"].by_key()["color"])) - ] - if (fill_color[0].startswith("C") and len(fill_color[0]) == 2) and ( - fill_color[1].startswith("C") and len(fill_color[1]) == 2 - ): - fill_color = tuple((_colors[int(color[1:]) % 10] for color in fill_color)) - elif fill_color[0].startswith("C") and len(fill_color[0]) == 2: - fill_color = tuple([_colors[int(fill_color[0][1:]) % 10]] + list(fill_color[1:])) - elif fill_color[1].startswith("C") and len(fill_color[1]) == 2: - fill_color = tuple(list(fill_color[1:]) + [_colors[int(fill_color[0][1:]) % 10]]) - - series = zip( - fill_alpha, - fill_color, - ("Marginal Energy", "Energy transition"), - (energy - energy.mean(), np.diff(energy)), - ) - - labels = [] - - if kind == "kde": - for alpha, color, label, value in series: - fill_kwargs["fill_alpha"] = alpha - fill_kwargs["fill_color"] = vectorized_to_hex(color) - plot_kwargs["line_alpha"] = alpha - plot_kwargs["line_color"] = vectorized_to_hex(color) - _, glyph = plot_kde( - value, - bw=bw, - label=label, - fill_kwargs=fill_kwargs, - plot_kwargs=plot_kwargs, - ax=ax, - legend=legend, - backend="bokeh", - backend_kwargs={}, - show=False, - return_glyph=True, - ) - labels.append( - ( - label, - glyph, - ) - ) - - elif kind == "hist": - hist_kwargs = plot_kwargs.copy() - hist_kwargs.update(**fill_kwargs) - - for alpha, color, label, value in series: - hist_kwargs["fill_alpha"] = alpha - hist_kwargs["fill_color"] = vectorized_to_hex(color) - hist_kwargs["line_color"] = None - hist_kwargs["line_alpha"] = alpha - _histplot_bokeh_op( - value.flatten(), - values2=None, - rotated=False, - ax=ax, - hist_kwargs=hist_kwargs, - is_circular=False, - ) - - else: - raise ValueError(f"Plot type {kind} not recognized.") - - if bfmi: - for idx, val in enumerate(e_bfmi(energy)): - bfmi_info = Label( - x=int(figsize[0] * dpi * 0.58), - y=int(figsize[1] * dpi * 0.73) - 20 * idx, - x_units="screen", - y_units="screen", - text=f"chain {idx:>2} BFMI = {val:.2f}", - border_line_color=None, - border_line_alpha=0.0, - background_fill_color="white", - background_fill_alpha=1.0, - ) - - ax.add_layout(bfmi_info) - - if legend and label is not None: - legend = Legend( - items=labels, - location="center_right", - orientation="horizontal", - ) - ax.add_layout(legend, "above") - ax.legend.click_policy = "hide" - - show_layout(ax, show) - - return ax diff --git a/arviz/plots/backends/bokeh/essplot.py b/arviz/plots/backends/bokeh/essplot.py deleted file mode 100644 index 617e309f47..0000000000 --- a/arviz/plots/backends/bokeh/essplot.py +++ /dev/null @@ -1,176 +0,0 @@ -# pylint: disable=all -"""Bokeh ESS plots.""" -import numpy as np -from bokeh.models import ColumnDataSource, Span -from bokeh.models.annotations import Legend, Title -from scipy.stats import rankdata - -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid -from ...plot_utils import _scale_fig_size -from bokeh.models.glyphs import Scatter - - -def plot_ess( - ax, - plotters, - xdata, - ess_tail_dataset, - mean_ess, - sd_ess, - idata, - data, - kind, - extra_methods, - textsize, - rows, - cols, - figsize, - kwargs, - extra_kwargs, - text_kwargs, - n_samples, - relative, - min_ess, - labeller, - ylabel, - rug, - rug_kind, - rug_kwargs, - hline_kwargs, - backend_kwargs, - show, -): - """Bokeh essplot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - (figsize, *_, _linewidth, _markersize) = _scale_fig_size(figsize, textsize, rows, cols) - - if hline_kwargs is None: - hline_kwargs = {} - hline_kwargs.setdefault("line_color", "red") - hline_kwargs.setdefault("line_width", 3) - hline_kwargs.setdefault("line_dash", "dashed") - hline_kwargs.setdefault("line_alpha", 1) - - if ax is None: - ax = create_axes_grid( - len(plotters), - rows, - cols, - figsize=figsize, - backend_kwargs=backend_kwargs, - ) - else: - ax = np.atleast_2d(ax) - - for (var_name, selection, isel, x), ax_ in zip( - plotters, (item for item in ax.flatten() if item is not None) - ): - bulk_points = ax_.scatter(np.asarray(xdata), np.asarray(x), marker="circle", size=6) - if kind == "evolution": - bulk_line = ax_.line(np.asarray(xdata), np.asarray(x)) - ess_tail = ess_tail_dataset[var_name].sel(**selection) - tail_points = ax_.line(np.asarray(xdata), np.asarray(ess_tail), color="orange") - tail_line = ax_.scatter( - np.asarray(xdata), np.asarray(ess_tail), marker="circle", size=6, color="orange" - ) - elif rug: - if rug_kwargs is None: - rug_kwargs = {} - if not hasattr(idata, "sample_stats"): - raise ValueError("InferenceData object must contain sample_stats for rug plot") - if not hasattr(idata.sample_stats, rug_kind): - raise ValueError(f"InferenceData does not contain {rug_kind} data") - - rug_kwargs.setdefault("space", 0.1) - _rug_kwargs = {} - _rug_kwargs.setdefault("size", 8) - _rug_kwargs.setdefault("line_color", rug_kwargs.get("line_color", "black")) - _rug_kwargs.setdefault("line_width", 1) - _rug_kwargs.setdefault("line_alpha", 0.35) - _rug_kwargs.setdefault("angle", np.pi / 2) - - values = data[var_name].sel(**selection).values.flatten() - mask = idata.sample_stats[rug_kind].values.flatten() - values = rankdata(values, method="average")[mask] - rug_space = np.max(x) * rug_kwargs.pop("space") - rug_x, rug_y = values / (len(mask) - 1), np.zeros_like(values) - rug_space - - glyph = Scatter(x="rug_x", y="rug_y", marker="dash", **_rug_kwargs) - cds_rug = ColumnDataSource({"rug_x": np.asarray(rug_x), "rug_y": np.asarray(rug_y)}) - ax_.add_glyph(cds_rug, glyph) - - hline = Span( - location=0, - dimension="width", - line_color="black", - line_width=_linewidth, - line_alpha=0.7, - ) - - ax_.renderers.append(hline) - - if extra_methods: - mean_ess_i = mean_ess[var_name].sel(**selection).values.item() - sd_ess_i = sd_ess[var_name].sel(**selection).values.item() - - hline = Span( - location=mean_ess_i, - dimension="width", - line_color="black", - line_width=2, - line_dash="dashed", - line_alpha=1.0, - ) - - ax_.renderers.append(hline) - - hline = Span( - location=sd_ess_i, - dimension="width", - line_color="black", - line_width=1, - line_dash="dashed", - line_alpha=1.0, - ) - - ax_.renderers.append(hline) - - if relative and kind == "evolution": - thin_xdata = np.linspace(xdata.min(), xdata.max(), 100) - ax_.line(thin_xdata, min_ess / thin_xdata, **hline_kwargs) - else: - hline = Span( - location=min_ess / n_samples if relative else min_ess, - dimension="width", - **hline_kwargs, - ) - - ax_.renderers.append(hline) - - if kind == "evolution": - legend = Legend( - items=[("bulk", [bulk_points, bulk_line]), ("tail", [tail_line, tail_points])], - location="center_right", - orientation="horizontal", - ) - ax_.add_layout(legend, "above") - ax_.legend.click_policy = "hide" - - title = Title() - title.text = labeller.make_label_vert(var_name, selection, isel) - ax_.title = title - - ax_.xaxis.axis_label = "Total number of draws" if kind == "evolution" else "Quantile" - ax_.yaxis.axis_label = ylabel.format("Relative ESS" if relative else "ESS") - - show_layout(ax, show) - - return ax diff --git a/arviz/plots/backends/bokeh/forestplot.py b/arviz/plots/backends/bokeh/forestplot.py deleted file mode 100644 index c6a52fb55c..0000000000 --- a/arviz/plots/backends/bokeh/forestplot.py +++ /dev/null @@ -1,772 +0,0 @@ -# pylint: disable=all -"""Bokeh forestplot.""" -from collections import OrderedDict, defaultdict -from itertools import cycle, tee - -import bokeh.plotting as bkp -import matplotlib.pyplot as plt -import numpy as np -from bokeh.models import Band, ColumnDataSource, DataRange1d -from bokeh.models.annotations import Title, Legend -from bokeh.models.tickers import FixedTicker - -from ....sel_utils import xarray_var_iter -from ....rcparams import rcParams -from ....stats import hdi -from ....stats.density_utils import get_bins, histogram, kde -from ....stats.diagnostics import _ess, _rhat -from ...plot_utils import _scale_fig_size -from .. import show_layout -from . import backend_kwarg_defaults - - -def pairwise(iterable): - """From itertools cookbook. [a, b, c, ...] -> (a, b), (b, c), ...""" - first, second = tee(iterable) - next(second, None) - return zip(first, second) - - -def plot_forest( - ax, - datasets, - var_names, - model_names, - combined, - combine_dims, - colors, - figsize, - width_ratios, - linewidth, - markersize, - kind, - ncols, - hdi_prob, - quartiles, - rope, - ridgeplot_overlap, - ridgeplot_alpha, - ridgeplot_kind, - ridgeplot_truncate, - ridgeplot_quantiles, - textsize, - legend, - labeller, - ess, - r_hat, - backend_config, - backend_kwargs, - show, -): - """Bokeh forest plot.""" - plot_handler = PlotHandler( - datasets, - var_names=var_names, - model_names=model_names, - combined=combined, - combine_dims=combine_dims, - colors=colors, - labeller=labeller, - ) - - if figsize is None: - if kind == "ridgeplot": - figsize = (min(14, sum(width_ratios) * 3), plot_handler.fig_height() * 3) - else: - figsize = (min(12, sum(width_ratios) * 2), plot_handler.fig_height()) - - (figsize, _, _, _, auto_linewidth, auto_markersize) = _scale_fig_size(figsize, textsize, 1.1, 1) - - if linewidth is None: - linewidth = auto_linewidth - - if markersize is None: - markersize = auto_markersize - - if backend_config is None: - backend_config = {} - - backend_config = { - **backend_kwarg_defaults( - ("bounds_x_range", "plot.bokeh.bounds_x_range"), - ("bounds_y_range", "plot.bokeh.bounds_y_range"), - ), - **backend_config, - } - - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults( - ("dpi", "plot.bokeh.figure.dpi"), - ), - **backend_kwargs, - } - dpi = backend_kwargs.pop("dpi") - - if ax is None: - axes = [] - - for i, width_r in zip(range(ncols), width_ratios): - backend_kwargs_i = backend_kwargs.copy() - backend_kwargs_i.setdefault("height", int(figsize[1] * dpi)) - backend_kwargs_i.setdefault( - "width", int(figsize[0] * (width_r / sum(width_ratios)) * dpi * 1.25) - ) - ax = bkp.figure( - **backend_kwargs_i, - ) - if i == 0: - backend_kwargs.setdefault("y_range", ax.y_range) - axes.append(ax) - else: - axes = ax - - axes = np.atleast_2d(axes) - - plotted = defaultdict(list) - - if kind == "forestplot": - plot_handler.forestplot( - hdi_prob, - quartiles, - linewidth, - markersize, - axes[0, 0], - rope, - plotted, - ) - elif kind == "ridgeplot": - plot_handler.ridgeplot( - hdi_prob, - ridgeplot_overlap, - linewidth, - markersize, - ridgeplot_alpha, - ridgeplot_kind, - ridgeplot_truncate, - ridgeplot_quantiles, - axes[0, 0], - plotted, - ) - else: - raise TypeError( - f"Argument 'kind' must be one of 'forestplot' or 'ridgeplot' (you provided {kind})" - ) - - idx = 1 - if ess: - plotted_ess = defaultdict(list) - plot_handler.plot_neff(axes[0, idx], markersize, plotted_ess) - if legend: - plot_handler.legend(axes[0, idx], plotted_ess) - idx += 1 - - if r_hat: - plotted_r_hat = defaultdict(list) - plot_handler.plot_rhat(axes[0, idx], markersize, plotted_r_hat) - if legend: - plot_handler.legend(axes[0, idx], plotted_r_hat) - idx += 1 - - all_plotters = list(plot_handler.plotters.values()) - y_max = plot_handler.y_max() - all_plotters[-1].group_offset - if kind == "ridgeplot": # space at the top - y_max += ridgeplot_overlap - - for i, ax_ in enumerate(axes.ravel()): - if kind == "ridgeplot": - ax_.xgrid.grid_line_color = None - ax_.ygrid.grid_line_color = None - else: - ax_.ygrid.grid_line_color = None - - if i != 0: - ax_.yaxis.visible = False - - ax_.outline_line_color = None - ax_.x_range = DataRange1d(bounds=backend_config["bounds_x_range"], min_interval=1) - ax_.y_range = DataRange1d(bounds=backend_config["bounds_y_range"], min_interval=2) - - ax_.y_range._property_values["start"] = -all_plotters[ # pylint: disable=protected-access - 0 - ].group_offset - ax_.y_range._property_values["end"] = y_max # pylint: disable=protected-access - - labels, ticks = plot_handler.labels_and_ticks() - ticks = [int(tick) if (tick).is_integer() else tick for tick in ticks] - - axes[0, 0].yaxis.ticker = FixedTicker(ticks=ticks) - axes[0, 0].yaxis.major_label_overrides = dict(zip(map(str, ticks), map(str, labels))) - - if legend: - plot_handler.legend(axes[0, 0], plotted) - show_layout(axes, show) - - return axes - - -class PlotHandler: - """Class to handle logic from ForestPlot.""" - - # pylint: disable=inconsistent-return-statements - - def __init__(self, datasets, var_names, model_names, combined, combine_dims, colors, labeller): - self.data = datasets - - if model_names is None: - if len(self.data) > 1: - model_names = [f"Model {idx}" for idx, _ in enumerate(self.data)] - else: - model_names = [""] - elif len(model_names) != len(self.data): - raise ValueError("The number of model names does not match the number of models") - - self.model_names = list(reversed(model_names)) # y-values are upside down - - if var_names is None: - if len(self.data) > 1: - self.var_names = list( - set().union(*[OrderedDict(datum.data_vars) for datum in self.data]) - ) - else: - self.var_names = list( - reversed(*[OrderedDict(datum.data_vars) for datum in self.data]) - ) - else: - self.var_names = list(reversed(var_names)) # y-values are upside down - - self.combined = combined - self.combine_dims = combine_dims - - if colors == "cycle": - colors = [ - prop - for _, prop in zip( - range(len(self.data)), cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"]) - ) - ] - elif isinstance(colors, str): - colors = [colors for _ in self.data] - - self.colors = list(reversed(colors)) # y-values are upside down - self.labeller = labeller - - self.plotters = self.make_plotters() - - def make_plotters(self): - """Initialize an object for each variable to be plotted.""" - plotters, y = {}, 0 - for var_name in self.var_names: - plotters[var_name] = VarHandler( - var_name, - self.data, - y, - model_names=self.model_names, - combined=self.combined, - combine_dims=self.combine_dims, - colors=self.colors, - labeller=self.labeller, - ) - y = plotters[var_name].y_max() - return plotters - - def labels_and_ticks(self): - """Collect labels and ticks from plotters.""" - val = self.plotters.values() - - def label_idxs(): - labels, idxs = [], [] - for plotter in val: - sub_labels, sub_idxs, _, _, _ = plotter.labels_ticks_and_vals() - labels_to_idxs = defaultdict(list) - for label, idx in zip(sub_labels, sub_idxs): - labels_to_idxs[label].append(idx) - sub_idxs = [] - sub_labels = [] - for label, all_idx in labels_to_idxs.items(): - sub_labels.append(label) - sub_idxs.append(np.mean([j for j in all_idx])) - labels.append(sub_labels) - idxs.append(sub_idxs) - return np.concatenate(labels), np.concatenate(idxs) - - return label_idxs() - - def legend(self, ax, plotted): - """Add interactive legend with colorcoded model info.""" - legend_it = [] - for model_name, glyphs in plotted.items(): - legend_it.append((model_name, glyphs)) - - legend = Legend(items=legend_it, orientation="vertical", location="top_left") - ax.add_layout(legend, "above") - ax.legend.click_policy = "hide" - - def display_multiple_ropes( - self, rope, ax, y, linewidth, var_name, selection, plotted, model_name - ): - """Display ROPE when more than one interval is provided.""" - for sel in rope.get(var_name, []): - # pylint: disable=line-too-long - if all(k in selection and selection[k] == v for k, v in sel.items() if k != "rope"): - vals = sel["rope"] - plotted[model_name].append( - ax.line( - vals, - (y + 0.05, y + 0.05), - line_width=linewidth * 2, - color=[ - color - for _, color in zip( - range(3), cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"]) - ) - ][2], - line_alpha=0.7, - ) - ) - return ax - - def ridgeplot( - self, - hdi_prob, - mult, - linewidth, - markersize, - alpha, - ridgeplot_kind, - ridgeplot_truncate, - ridgeplot_quantiles, - ax, - plotted, - ): - """Draw ridgeplot for each plotter. - - Parameters - ---------- - hdi_prob : float - Probability for the highest density interval. - mult : float - How much to multiply height by. Set this to greater than 1 to have some overlap. - linewidth : float - Width of line on border of ridges - markersize : float - Size of marker in center of forestplot line - alpha : float - Transparency of ridges - ridgeplot_kind : string - By default ("auto") continuous variables are plotted using KDEs and discrete ones using - histograms. To override this use "hist" to plot histograms and "density" for KDEs - ridgeplot_truncate: bool - Whether to truncate densities according to the value of hdi_prop. Defaults to True - ridgeplot_quantiles: list - Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles. - Defaults to None. - ax : Axes - Axes to draw on - plotted : dict - Contains glyphs for each model - """ - if alpha is None: - alpha = 1.0 - for plotter in list(self.plotters.values())[::-1]: - for x, y_min, y_max, hdi_, y_q, color, model_name in plotter.ridgeplot( - hdi_prob, mult, ridgeplot_kind - ): - if alpha == 0: - border = color - facecolor = None - else: - border = "black" - facecolor = color - if x.dtype.kind == "i": - if ridgeplot_truncate: - y_max = y_max[(x >= hdi_[0]) & (x <= hdi_[1])] - x = x[(x >= hdi_[0]) & (x <= hdi_[1])] - else: - facecolor = color - alpha = [alpha if ci else 0 for ci in ((x >= hdi_[0]) & (x <= hdi_[1]))] - y_min = np.ones_like(x) * y_min - plotted[model_name].append( - ax.vbar( - x=x, - top=y_max - y_min, - bottom=y_min, - width=0.9, - line_color=border, - color=facecolor, - fill_alpha=alpha, - ) - ) - else: - tr_x = x[(x >= hdi_[0]) & (x <= hdi_[1])] - tr_y_min = np.ones_like(tr_x) * y_min - tr_y_max = y_max[(x >= hdi_[0]) & (x <= hdi_[1])] - y_min = np.ones_like(x) * y_min - patch = ax.patch( - np.concatenate([tr_x, tr_x[::-1]]), - np.concatenate([tr_y_min, tr_y_max[::-1]]), - fill_color=color, - fill_alpha=alpha, - line_width=0, - ) - patch.level = "overlay" - plotted[model_name].append(patch) - if ridgeplot_truncate: - plotted[model_name].append( - ax.line( - x, y_max, line_dash="solid", line_width=linewidth, line_color=border - ) - ) - plotted[model_name].append( - ax.line( - x, y_min, line_dash="solid", line_width=linewidth, line_color=border - ) - ) - else: - plotted[model_name].append( - ax.line( - tr_x, - tr_y_max, - line_dash="solid", - line_width=linewidth, - line_color=border, - ) - ) - plotted[model_name].append( - ax.line( - tr_x, - tr_y_min, - line_dash="solid", - line_width=linewidth, - line_color=border, - ) - ) - if ridgeplot_quantiles is not None: - quantiles = [x[np.sum(y_q < quant)] for quant in ridgeplot_quantiles] - plotted[model_name].append( - ax.diamond( - quantiles, - np.ones_like(quantiles) * y_min[0], - line_color="black", - fill_color="black", - size=markersize, - ) - ) - - return ax - - def forestplot(self, hdi_prob, quartiles, linewidth, markersize, ax, rope, plotted): - """Draw forestplot for each plotter. - - Parameters - ---------- - hdi_prob : float - Probability for the highest density interval. Width of each line. - quartiles : bool - Whether to mark quartiles - linewidth : float - Width of forestplot line - markersize : float - Size of marker in center of forestplot line - ax : Axes - Axes to draw on - plotted : dict - Contains glyphs for each model - """ - if rope is None or isinstance(rope, dict): - pass - elif len(rope) == 2: - cds = ColumnDataSource( - { - "x": rope, - "lower": [-2 * self.y_max(), -2 * self.y_max()], - "upper": [self.y_max() * 2, self.y_max() * 2], - } - ) - - band = Band( - base="x", - lower="lower", - upper="upper", - fill_color=[ - color - for _, color in zip( - range(4), cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"]) - ) - ][2], - line_alpha=0.5, - source=cds, - ) - - ax.renderers.append(band) - else: - raise ValueError( - "Argument `rope` must be None, a dictionary like" - '{"var_name": {"rope": (lo, hi)}}, or an ' - "iterable of length 2" - ) - # Quantiles to be calculated - endpoint = 100 * (1 - hdi_prob) / 2 - if quartiles: - qlist = [endpoint, 25, 50, 75, 100 - endpoint] - else: - qlist = [endpoint, 50, 100 - endpoint] - - for plotter in self.plotters.values(): - for y, model_name, selection, values, color in plotter.treeplot(qlist, hdi_prob): - if isinstance(rope, dict): - self.display_multiple_ropes( - rope, ax, y, linewidth, plotter.var_name, selection, plotted, model_name - ) - - mid = len(values) // 2 - param_iter = zip( - np.linspace(2 * linewidth, linewidth, mid, endpoint=True)[-1::-1], range(mid) - ) - for width, j in param_iter: - plotted[model_name].append( - ax.line( - [values[j], values[-(j + 1)]], - [y, y], - line_width=width, - line_color=color, - ) - ) - plotted[model_name].append( - ax.scatter( - x=values[mid], - y=y, - marker="circle", - size=markersize * 0.75, - fill_color=color, - ) - ) - _title = Title() - _title.text = f"{hdi_prob:.1%} HDI" - ax.title = _title - - return ax - - def plot_neff(self, ax, markersize, plotted): - """Draw effective n for each plotter.""" - max_ess = 0 - for plotter in self.plotters.values(): - for y, ess, color, model_name in plotter.ess(): - if ess is not None: - plotted[model_name].append( - ax.scatter( - x=ess, - y=y, - marker="circle", - fill_color=color, - size=markersize, - line_color="black", - ) - ) - if ess > max_ess: - max_ess = ess - ax.x_range._property_values["start"] = 0 # pylint: disable=protected-access - ax.x_range._property_values["end"] = 1.07 * max_ess # pylint: disable=protected-access - - _title = Title() - _title.text = "ess" - ax.title = _title - - ax.xaxis[0].ticker.desired_num_ticks = 3 - - return ax - - def plot_rhat(self, ax, markersize, plotted): - """Draw r-hat for each plotter.""" - for plotter in self.plotters.values(): - for y, r_hat, color, model_name in plotter.r_hat(): - if r_hat is not None: - plotted[model_name].append( - ax.scatter( - x=r_hat, - y=y, - marker="circle", - fill_color=color, - size=markersize, - line_color="black", - ) - ) - ax.x_range._property_values["start"] = 0.9 # pylint: disable=protected-access - ax.x_range._property_values["end"] = 2.1 # pylint: disable=protected-access - - _title = Title() - _title.text = "r_hat" - ax.title = _title - - ax.xaxis[0].ticker.desired_num_ticks = 3 - - return ax - - def fig_height(self): - """Figure out the height of this plot.""" - # hand-tuned - return ( - 4 - + len(self.data) * len(self.var_names) - - 1 - + 0.1 * sum(1 for j in self.plotters.values() for _ in j.iterator()) - ) - - def y_max(self): - """Get maximum y value for the plot.""" - return max(p.y_max() for p in self.plotters.values()) - - -class VarHandler: - """Handle individual variable logic.""" - - def __init__( - self, var_name, data, y_start, model_names, combined, combine_dims, colors, labeller - ): - self.var_name = var_name - self.data = data - self.y_start = y_start - self.model_names = model_names - self.combined = combined - self.combine_dims = combine_dims - self.colors = colors - self.labeller = labeller - self.model_color = dict(zip(self.model_names, self.colors)) - max_chains = max(datum.chain.max().values for datum in data) - self.chain_offset = len(data) * 0.45 / max(1, max_chains) - self.var_offset = 1.5 * self.chain_offset - self.group_offset = 2 * self.var_offset - - def iterator(self): - """Iterate over models and chains for each variable.""" - if self.combined: - grouped_data = [[(0, datum)] for datum in self.data] - skip_dims = self.combine_dims.union({"chain"}) - else: - grouped_data = [datum.groupby("chain", squeeze=False) for datum in self.data] - skip_dims = self.combine_dims - - label_dict = OrderedDict() - selection_list = [] - for name, grouped_datum in zip(self.model_names, grouped_data): - for _, sub_data in grouped_datum: - datum_iter = xarray_var_iter( - sub_data.squeeze(), - var_names=[self.var_name], - skip_dims=skip_dims, - reverse_selections=True, - ) - datum_list = list(datum_iter) - for _, selection, isel, values in datum_list: - selection_list.append(selection) - if not selection or not len(selection_list) % len(datum_list): - var_name = self.var_name - else: - var_name = "" - label = self.labeller.make_label_flat(var_name, selection, isel) - if label not in label_dict: - label_dict[label] = OrderedDict() - if name not in label_dict[label]: - label_dict[label][name] = [] - label_dict[label][name].append(values) - - y = self.y_start - for idx, (label, model_data) in enumerate(label_dict.items()): - for model_name, value_list in model_data.items(): - row_label = self.labeller.make_model_label(model_name, label) - for values in value_list: - yield y, row_label, model_name, label, selection_list[ - idx - ], values, self.model_color[model_name] - y += self.chain_offset - y += self.var_offset - y += self.group_offset - - def labels_ticks_and_vals(self): - """Get labels, ticks, values, and colors for the variable.""" - y_ticks = defaultdict(list) - for y, label, model_name, _, _, vals, color in self.iterator(): - y_ticks[label].append((y, vals, color, model_name)) - labels, ticks, vals, colors, model_names = [], [], [], [], [] - for label, all_data in y_ticks.items(): - for data in all_data: - labels.append(label) - ticks.append(data[0]) - vals.append(np.array(data[1])) - model_names.append(data[3]) - colors.append(data[2]) # the colors are all the same - return labels, ticks, vals, colors, model_names - - def treeplot(self, qlist, hdi_prob): - """Get data for each treeplot for the variable.""" - for y, _, model_name, _, selection, values, color in self.iterator(): - ntiles = np.percentile(values.flatten(), qlist) - ntiles[0], ntiles[-1] = hdi(values.flatten(), hdi_prob, multimodal=False) - yield y, model_name, selection, ntiles, color - - def ridgeplot(self, hdi_prob, mult, ridgeplot_kind): - """Get data for each ridgeplot for the variable.""" - xvals, hdi_vals, yvals, pdfs, pdfs_q, colors, model_names = [], [], [], [], [], [], [] - - for y, _, model_name, *_, values, color in self.iterator(): - yvals.append(y) - colors.append(color) - model_names.append(model_name) - values = values.flatten() - values = values[np.isfinite(values)] - - if hdi_prob != 1: - hdi_ = hdi(values, hdi_prob, multimodal=False) - else: - hdi_ = min(values), max(values) - - if ridgeplot_kind == "auto": - kind = "hist" if np.all(np.mod(values, 1) == 0) else "density" - else: - kind = ridgeplot_kind - - if kind == "hist": - bins = get_bins(values) - _, density, x = histogram(values, bins=bins) - x = x[:-1] - elif kind == "density": - x, density = kde(values) - - density_q = density.cumsum() / density.sum() - - xvals.append(x) - pdfs.append(density) - pdfs_q.append(density_q) - hdi_vals.append(hdi_) - - scaling = max(np.max(j) for j in pdfs) - for y, x, hdi_val, pdf, pdf_q, color, model_name in zip( - yvals, xvals, hdi_vals, pdfs, pdfs_q, colors, model_names - ): - yield x, y, mult * pdf / scaling + y, hdi_val, pdf_q, color, model_name - - def ess(self): - """Get effective n data for the variable.""" - _, y_vals, values, colors, model_names = self.labels_ticks_and_vals() - for y, value, color, model_name in zip(y_vals, values, colors, model_names): - yield y, _ess(value), color, model_name - - def r_hat(self): - """Get rhat data for the variable.""" - _, y_vals, values, colors, model_names = self.labels_ticks_and_vals() - for y, value, color, model_name in zip(y_vals, values, colors, model_names): - if value.ndim != 2 or value.shape[0] < 2: - yield y, None, color, model_name - else: - yield y, _rhat(value), color, model_name - - def y_max(self): - """Get max y value for the variable.""" - end_y = max(y for y, *_ in self.iterator()) - - if self.combined: - end_y += self.group_offset - - return end_y + 2 * self.group_offset diff --git a/arviz/plots/backends/bokeh/hdiplot.py b/arviz/plots/backends/bokeh/hdiplot.py deleted file mode 100644 index b3711040f0..0000000000 --- a/arviz/plots/backends/bokeh/hdiplot.py +++ /dev/null @@ -1,54 +0,0 @@ -"""Bokeh hdiplot.""" - -import numpy as np - -from ...plot_utils import _scale_fig_size, vectorized_to_hex -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_hdi(ax, x_data, y_data, color, figsize, plot_kwargs, fill_kwargs, backend_kwargs, show): - """Bokeh HDI plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - plot_kwargs = {} if plot_kwargs is None else plot_kwargs - plot_kwargs["color"] = vectorized_to_hex(plot_kwargs.get("color", color)) - plot_kwargs.setdefault("alpha", 0) - - fill_kwargs = {} if fill_kwargs is None else fill_kwargs.copy() - # Convert matplotlib color to bokeh fill_color if needed - if "color" in fill_kwargs and "fill_color" not in fill_kwargs: - fill_kwargs["fill_color"] = vectorized_to_hex(fill_kwargs.pop("color")) - else: - fill_kwargs["fill_color"] = vectorized_to_hex(fill_kwargs.get("fill_color", color)) - fill_kwargs.setdefault("fill_alpha", fill_kwargs.pop("alpha", 0.5)) - - figsize, *_ = _scale_fig_size(figsize, None) - - if ax is None: - ax = create_axes_grid( - 1, - figsize=figsize, - squeeze=True, - backend_kwargs=backend_kwargs, - ) - - plot_kwargs.setdefault("line_color", plot_kwargs.pop("color")) - plot_kwargs.setdefault("line_alpha", plot_kwargs.pop("alpha", 0)) - - ax.patch( - np.concatenate((x_data, x_data[::-1])), - np.concatenate((y_data[:, 0], y_data[:, 1][::-1])), - **fill_kwargs, - **plot_kwargs - ) - - show_layout(ax, show) - - return ax diff --git a/arviz/plots/backends/bokeh/kdeplot.py b/arviz/plots/backends/bokeh/kdeplot.py deleted file mode 100644 index 3b89075908..0000000000 --- a/arviz/plots/backends/bokeh/kdeplot.py +++ /dev/null @@ -1,268 +0,0 @@ -# pylint: disable=c-extension-no-member -"""Bokeh KDE Plot.""" -from collections.abc import Callable -from numbers import Integral - -import numpy as np -from bokeh.models import ColumnDataSource -from bokeh.models.glyphs import Scatter -from matplotlib import colormaps -from matplotlib.colors import rgb2hex -from matplotlib.pyplot import rcParams as mpl_rcParams - -from ...plot_utils import _scale_fig_size, _init_kwargs_dict -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_kde( - density, - lower, - upper, - density_q, - xmin, - xmax, - ymin, - ymax, - gridsize, - values, - values2, - rug, - label, # pylint: disable=unused-argument - quantiles, - rotated, - contour, - fill_last, - figsize, - textsize, # pylint: disable=unused-argument - plot_kwargs, - fill_kwargs, - rug_kwargs, - contour_kwargs, - contourf_kwargs, - pcolormesh_kwargs, - is_circular, # pylint: disable=unused-argument - ax, - legend, # pylint: disable=unused-argument - backend_kwargs, - show, - return_glyph, -): - """Bokeh kde plot.""" - backend_kwargs = _init_kwargs_dict(backend_kwargs) - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, *_ = _scale_fig_size(figsize, textsize) - - if ax is None: - ax = create_axes_grid( - 1, - figsize=figsize, - squeeze=True, - backend_kwargs=backend_kwargs, - ) - - glyphs = [] - if values2 is None: - plot_kwargs = _init_kwargs_dict(plot_kwargs) - plot_kwargs.setdefault("line_color", mpl_rcParams["axes.prop_cycle"].by_key()["color"][0]) - - fill_kwargs = _init_kwargs_dict(fill_kwargs) - fill_kwargs.setdefault("fill_color", mpl_rcParams["axes.prop_cycle"].by_key()["color"][0]) - - if rug: - rug_kwargs = _init_kwargs_dict(rug_kwargs) - - if "cds" in rug_kwargs: - cds_rug = rug_kwargs.pop("cds") - rug_varname = rug_kwargs.pop("y", "y") - else: - rug_varname = "y" - cds_rug = ColumnDataSource({rug_varname: np.asarray(values)}) - - rug_kwargs.setdefault("size", 8) - rug_kwargs.setdefault("line_color", plot_kwargs["line_color"]) - rug_kwargs.setdefault("line_width", 1) - rug_kwargs.setdefault("line_alpha", 0.35) - if not rotated: - rug_kwargs.setdefault("angle", np.pi / 2) - if isinstance(cds_rug, dict): - for _cds_rug in cds_rug.values(): - if not rotated: - glyph = Scatter(x=rug_varname, y=0.0, marker="dash", **rug_kwargs) - else: - glyph = Scatter(x=0.0, y=rug_varname, marker="dash", **rug_kwargs) - ax.add_glyph(_cds_rug, glyph) - else: - if not rotated: - glyph = Scatter(x=rug_varname, y=0.0, marker="dash", **rug_kwargs) - else: - glyph = Scatter(x=0.0, y=rug_varname, marker="dash", **rug_kwargs) - ax.add_glyph(cds_rug, glyph) - glyphs.append(glyph) - - x = np.linspace(lower, upper, len(density)) - - if quantiles is not None: - fill_kwargs.setdefault("fill_alpha", 0.75) - fill_kwargs.setdefault("line_color", None) - - quantiles = sorted(np.clip(quantiles, 0, 1)) - if quantiles[0] != 0: - quantiles = [0] + quantiles - if quantiles[-1] != 1: - quantiles = quantiles + [1] - - for quant_0, quant_1 in zip(quantiles[:-1], quantiles[1:]): - idx = (density_q > quant_0) & (density_q < quant_1) - if idx.sum(): - patch_x = np.concatenate((x[idx], [x[idx][-1]], x[idx][::-1], [x[idx][0]])) - patch_y = np.concatenate( - (np.zeros_like(density[idx]), [density[idx][-1]], density[idx][::-1], [0]) - ) - if not rotated: - patch = ax.patch(patch_x, patch_y, **fill_kwargs) - else: - patch = ax.patch(patch_y, patch_x, **fill_kwargs) - glyphs.append(patch) - else: - if fill_kwargs.get("fill_alpha", False): - patch_x = np.concatenate((x, [x[-1]], x[::-1], [x[0]])) - patch_y = np.concatenate( - (np.zeros_like(density), [density[-1]], density[::-1], [0]) - ) - if not rotated: - patch = ax.patch(patch_x, patch_y, **fill_kwargs) - else: - patch = ax.patch(patch_y, patch_x, **fill_kwargs) - glyphs.append(patch) - - if label is not None: - plot_kwargs.setdefault("legend_label", label) - if not rotated: - line = ax.line(x, density, **plot_kwargs) - else: - line = ax.line(density, x, **plot_kwargs) - glyphs.append(line) - - else: - try: - import contourpy - except ImportError as err: - raise ImportError( - "'bokeh' kde contour plots needs ContourPy installed (pip install countourpy)." - ) from err - - contour_kwargs = _init_kwargs_dict(contour_kwargs) - contourf_kwargs = _init_kwargs_dict(contourf_kwargs) - pcolormesh_kwargs = _init_kwargs_dict(pcolormesh_kwargs) - - g_s = complex(gridsize[0]) - x_x, y_y = np.mgrid[xmin:xmax:g_s, ymin:ymax:g_s] - - if contour: - scaled_density, *scaled_density_args = _scale_axis(density) - - contourpy_kwargs = _init_kwargs_dict(contour_kwargs.pop("contourpy_kwargs", {})) - contourpy_kwargs.setdefault("name", "serial") - contour_generator = contourpy.contour_generator( - x=x_x, y=y_y, z=scaled_density, **contourpy_kwargs - ) - - levels = 9 - if "levels" in contourf_kwargs: - levels = contourf_kwargs.pop("levels") - if "levels" in contour_kwargs: - levels = contour_kwargs.pop("levels") - - if isinstance(levels, Integral): - levels_scaled = np.linspace(0, 1, levels + 2) - levels = _rescale_axis(levels_scaled, scaled_density_args) - else: - levels_scaled_nonclip, *_ = _scale_axis(np.asarray(levels), scaled_density_args) - levels_scaled = np.clip(levels_scaled_nonclip, 0, 1) - - cmap = contourf_kwargs.pop("cmap", "viridis") - if isinstance(cmap, str): - cmap = colormaps[cmap] - if isinstance(cmap, Callable): - colors = [rgb2hex(item) for item in cmap(np.linspace(0, 1, len(levels_scaled) + 1))] - else: - colors = cmap - - contour_kwargs.update(contourf_kwargs) - contour_kwargs.setdefault("line_alpha", 0.25) - contour_kwargs.setdefault("fill_alpha", 1) - - for i, (level, level_upper, color) in enumerate( - zip(levels_scaled[:-1], levels_scaled[1:], colors[1:]) - ): - if not fill_last and (i == 0): - continue - contour_kwargs_ = contour_kwargs.copy() - contour_kwargs_.setdefault("line_color", color) - contour_kwargs_.setdefault("fill_color", color) - vertices, _ = contour_generator.filled(level, level_upper) - for seg in vertices: - # ax.multi_polygon would be better, but input is - # currently not suitable - # seg is 1 line that defines an area - # multi_polygon would need inner and outer edges - # as a line - patch = ax.patch(*seg.T, **contour_kwargs_) - glyphs.append(patch) - - if fill_last: - ax.background_fill_color = colors[0] - - ax.xgrid.grid_line_color = None - ax.ygrid.grid_line_color = None - - else: - cmap = pcolormesh_kwargs.pop("cmap", "viridis") - if isinstance(cmap, str): - cmap = colormaps[cmap] - if isinstance(cmap, Callable): - colors = [rgb2hex(item) for item in cmap(np.linspace(0, 1, 256))] - else: - colors = cmap - - image = ax.image( - image=[density.T], - x=xmin, - y=ymin, - dw=(xmax - xmin) / density.shape[0], - dh=(ymax - ymin) / density.shape[1], - palette=colors, - **pcolormesh_kwargs - ) - glyphs.append(image) - ax.x_range.range_padding = ax.y_range.range_padding = 0 - - show_layout(ax, show) - - if return_glyph: - return ax, glyphs - - return ax - - -def _scale_axis(arr, args=None): - if args: - amin, amax = args - else: - amin, amax = arr.min(), arr.max() - scaled_arr = arr - amin - scaled_arr /= amax - amin - return scaled_arr, amin, amax - - -def _rescale_axis(arr, args): - amin, amax = args - rescaled_arr = arr * (amax - amin) - rescaled_arr += amin - return rescaled_arr diff --git a/arviz/plots/backends/bokeh/khatplot.py b/arviz/plots/backends/bokeh/khatplot.py deleted file mode 100644 index 0635da5aec..0000000000 --- a/arviz/plots/backends/bokeh/khatplot.py +++ /dev/null @@ -1,163 +0,0 @@ -"""Bokeh pareto shape plot.""" - -from collections.abc import Iterable - -from matplotlib import cm -import matplotlib.pyplot as plt -import numpy as np -from bokeh.models import Span -from matplotlib.colors import to_rgba_array - -from ....stats.density_utils import histogram -from ...plot_utils import _scale_fig_size, color_from_dim, vectorized_to_hex -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_khat( - hover_label, # pylint: disable=unused-argument - hover_format, # pylint: disable=unused-argument - ax, - figsize, - xdata, - khats, - good_k, - kwargs, - threshold, - coord_labels, - show_hlines, - show_bins, - hlines_kwargs, - xlabels, # pylint: disable=unused-argument - legend, # pylint: disable=unused-argument - color, - dims, - textsize, - markersize, # pylint: disable=unused-argument - n_data_points, - bin_format, - backend_kwargs, - show, -): - """Bokeh khat plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults( - ("dpi", "plot.bokeh.figure.dpi"), - ), - **backend_kwargs, - } - - (figsize, *_, line_width, _) = _scale_fig_size(figsize, textsize) - - if hlines_kwargs is None: - hlines_kwargs = {} - - if good_k is None: - good_k = 0.7 - - hlines_kwargs.setdefault("hlines", [0, good_k, 1]) - - cmap = None - if isinstance(color, str): - if color in dims: - colors, _ = color_from_dim(khats, color) - cmap_name = kwargs.get("cmap", plt.rcParams["image.cmap"]) - cmap = getattr(cm, cmap_name) - rgba_c = cmap(colors) - else: - legend = False - rgba_c = to_rgba_array(np.full(n_data_points, color)) - else: - legend = False - try: - rgba_c = to_rgba_array(color) - except ValueError: - cmap_name = kwargs.get("cmap", plt.rcParams["image.cmap"]) - cmap = getattr(cm, cmap_name) - rgba_c = cmap(color) - - khats = khats if isinstance(khats, np.ndarray) else khats.values.flatten() - alphas = 0.5 + 0.2 * (khats > good_k) + 0.3 * (khats > 1) - - rgba_c = vectorized_to_hex(rgba_c) - - if ax is None: - ax = create_axes_grid( - 1, - figsize=figsize, - squeeze=True, - backend_kwargs=backend_kwargs, - ) - - if not isinstance(rgba_c, str) and isinstance(rgba_c, Iterable): - for idx, (alpha, rgba_c_) in enumerate(zip(alphas, rgba_c)): - ax.scatter( - xdata[idx], - khats[idx], - marker="cross", - line_color=rgba_c_, - fill_color=rgba_c_, - line_alpha=alpha, - fill_alpha=alpha, - size=10, - ) - else: - ax.scatter( - xdata, - khats, - marker="cross", - line_color=rgba_c, - fill_color=rgba_c, - size=10, - line_alpha=alphas, - fill_alpha=alphas, - ) - - if threshold is not None: - idxs = xdata[khats > threshold] - for idx in idxs: - ax.text(x=[idx], y=[khats[idx]], text=[coord_labels[idx]]) - - if show_hlines: - for hline in hlines_kwargs.pop("hlines"): - _hline = Span( - location=hline, - dimension="width", - line_color="grey", - line_width=line_width, - line_dash="dashed", - ) - ax.renderers.append(_hline) - - ymin = min(khats) - ymax = max(khats) - xmax = len(khats) - - if show_bins: - bin_edges = np.array([ymin, good_k, 1, ymax]) - bin_edges = bin_edges[(bin_edges >= ymin) & (bin_edges <= ymax)] - hist, _, _ = histogram(khats, bin_edges) - for idx, count in enumerate(hist): - ax.text( - x=[(n_data_points - 1 + xmax) / 2], - y=[np.mean(bin_edges[idx : idx + 2])], - text=[bin_format.format(count, count / n_data_points * 100)], - ) - ax.x_range._property_values["end"] = xmax + 1 # pylint: disable=protected-access - - ax.xaxis.axis_label = "Data Point" - ax.yaxis.axis_label = "Shape parameter k" - - if ymin > 0: - ax.y_range._property_values["start"] = -0.02 # pylint: disable=protected-access - if ymax < 1: - ax.y_range._property_values["end"] = 1.02 # pylint: disable=protected-access - elif ymax > 1 & threshold: - ax.y_range._property_values["end"] = 1.1 * ymax # pylint: disable=protected-access - - show_layout(ax, show) - - return ax diff --git a/arviz/plots/backends/bokeh/lmplot.py b/arviz/plots/backends/bokeh/lmplot.py deleted file mode 100644 index 8c9d2ee653..0000000000 --- a/arviz/plots/backends/bokeh/lmplot.py +++ /dev/null @@ -1,185 +0,0 @@ -"""Bokeh linear regression plot.""" - -import numpy as np -from bokeh.models.annotations import Legend - -from ...hdiplot import plot_hdi - -from ...plot_utils import _scale_fig_size -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_lm( - x, - y, - y_model, - y_hat, - num_samples, - kind_pp, - kind_model, - length_plotters, - xjitter, - rows, - cols, - y_kwargs, - y_hat_plot_kwargs, - y_hat_fill_kwargs, - y_model_plot_kwargs, - y_model_fill_kwargs, - y_model_mean_kwargs, - backend_kwargs, - show, - figsize, - textsize, - axes, - legend, - grid, # pylint: disable=unused-argument -): - """Bokeh linreg plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, *_ = _scale_fig_size(figsize, textsize, rows, cols) - if axes is None: - axes = create_axes_grid(length_plotters, rows, cols, backend_kwargs=backend_kwargs) - - if y_kwargs is None: - y_kwargs = {} - else: - y_kwargs = y_kwargs.copy() - y_kwargs.setdefault("marker", "circle") - y_kwargs.setdefault("fill_color", "red") - y_kwargs.setdefault("line_width", 0) - y_kwargs.setdefault("size", 3) - - if y_hat_plot_kwargs is None: - y_hat_plot_kwargs = {} - else: - y_hat_plot_kwargs = y_hat_plot_kwargs.copy() - y_hat_plot_kwargs.setdefault("marker", "circle") - y_hat_plot_kwargs.setdefault("fill_color", "orange") - y_hat_plot_kwargs.setdefault("line_width", 0) - - if y_hat_fill_kwargs is None: - y_hat_fill_kwargs = {} - else: - y_hat_fill_kwargs = y_hat_fill_kwargs.copy() - # Convert matplotlib color to bokeh fill_color if needed - if "color" in y_hat_fill_kwargs and "fill_color" not in y_hat_fill_kwargs: - y_hat_fill_kwargs["fill_color"] = y_hat_fill_kwargs.pop("color") - y_hat_fill_kwargs.setdefault("fill_color", "orange") - y_hat_fill_kwargs.setdefault("fill_alpha", 0.5) - - if y_model_plot_kwargs is None: - y_model_plot_kwargs = {} - y_model_plot_kwargs.setdefault("line_color", "black") - y_model_plot_kwargs.setdefault("line_alpha", 0.5) - y_model_plot_kwargs.setdefault("line_width", 0.5) - - if y_model_fill_kwargs is None: - y_model_fill_kwargs = {} - else: - y_model_fill_kwargs = y_model_fill_kwargs.copy() - # Convert matplotlib color to bokeh fill_color if needed - if "color" in y_model_fill_kwargs and "fill_color" not in y_model_fill_kwargs: - y_model_fill_kwargs["fill_color"] = y_model_fill_kwargs.pop("color") - y_model_fill_kwargs.setdefault("fill_color", "black") - y_model_fill_kwargs.setdefault("fill_alpha", 0.5) - - if y_model_mean_kwargs is None: - y_model_mean_kwargs = {} - y_model_mean_kwargs.setdefault("line_color", "yellow") - y_model_mean_kwargs.setdefault("line_width", 2) - - for i, ax_i in enumerate((item for item in axes.flatten() if item is not None)): - _, _, _, y_plotters = y[i] - _, _, _, x_plotters = x[i] - legend_it = [] - observed_legend = ax_i.scatter(x_plotters, y_plotters, **y_kwargs) - legend_it.append(("Observed", [observed_legend])) - - if y_hat is not None: - _, _, _, y_hat_plotters = y_hat[i] - if kind_pp == "samples": - posterior_legend = [] - for j in range(num_samples): - if xjitter is True: - jitter_scale = x_plotters[1] - x_plotters[0] - scale_high = jitter_scale * 0.2 - x_plotters_jitter = x_plotters + np.random.uniform( - low=-scale_high, high=scale_high, size=len(x_plotters) - ) - posterior_circle = ax_i.scatter( - x_plotters_jitter, - y_hat_plotters[..., j], - alpha=0.2, - **y_hat_plot_kwargs, - ) - else: - posterior_circle = ax_i.scatter( - x_plotters, y_hat_plotters[..., j], alpha=0.2, **y_hat_plot_kwargs - ) - posterior_legend.append(posterior_circle) - legend_it.append(("Posterior predictive samples", posterior_legend)) - - else: - plot_hdi( - x_plotters, - y_hat_plotters, - ax=ax_i, - backend="bokeh", - fill_kwargs=y_hat_fill_kwargs, - show=False, - ) - - if y_model is not None: - _, _, _, y_model_plotters = y_model[i] - if kind_model == "lines": - model_legend = ax_i.multi_line( - [np.tile(np.array(x_plotters, dtype=object), (num_samples, 1))], - [np.transpose(y_model_plotters)], - **y_model_plot_kwargs, - ) - legend_it.append(("Uncertainty in mean", [model_legend])) - - y_model_mean = np.mean(y_model_plotters, axis=1) - else: - plot_hdi( - x_plotters, - y_model_plotters, - fill_kwargs=y_model_fill_kwargs, - ax=ax_i, - backend="bokeh", - show=False, - ) - - y_model_mean = np.mean(y_model_plotters, axis=(0, 1)) - # Plot mean line across all x values instead of just edges - mean_legend = ax_i.line(x_plotters, y_model_mean, **y_model_mean_kwargs) - legend_it.append(("Mean", [mean_legend])) - continue # Skip the edge plotting since we plotted full line - - x_plotters_edge = [min(x_plotters), max(x_plotters)] - y_model_mean_edge = [min(y_model_mean), max(y_model_mean)] - mean_legend = ax_i.line(x_plotters_edge, y_model_mean_edge, **y_model_mean_kwargs) - legend_it.append(("Mean", [mean_legend])) - - if legend: - legend = Legend( - items=legend_it, - location="top_left", - orientation="vertical", - ) - ax_i.add_layout(legend) - if textsize is not None: - ax_i.legend.label_text_font_size = f"{textsize}pt" - ax_i.legend.click_policy = "hide" - - show_layout(axes, show) - return axes diff --git a/arviz/plots/backends/bokeh/loopitplot.py b/arviz/plots/backends/bokeh/loopitplot.py deleted file mode 100644 index 0884e04d48..0000000000 --- a/arviz/plots/backends/bokeh/loopitplot.py +++ /dev/null @@ -1,211 +0,0 @@ -"""Bokeh loopitplot.""" - -import numpy as np -from bokeh.models import BoxAnnotation -from matplotlib.colors import hsv_to_rgb, rgb_to_hsv, to_hex, to_rgb -from xarray import DataArray - -from ....stats.density_utils import kde -from ...plot_utils import _scale_fig_size -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_loo_pit( - ax, - figsize, - ecdf, - loo_pit, - loo_pit_ecdf, - unif_ecdf, - p975, - p025, - fill_kwargs, - ecdf_fill, - use_hdi, - x_vals, - hdi_kwargs, - hdi_odds, - n_unif, - unif, - plot_unif_kwargs, - loo_pit_kde, - legend, # pylint: disable=unused-argument - y_hat, - y, - color, - textsize, - labeller, - hdi_prob, - plot_kwargs, - backend_kwargs, - show, -): - """Bokeh loo pit plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - (figsize, *_, linewidth, _) = _scale_fig_size(figsize, textsize, 1, 1) - - if ax is None: - backend_kwargs.setdefault("x_range", (0, 1)) - ax = create_axes_grid( - 1, - figsize=figsize, - squeeze=True, - backend_kwargs=backend_kwargs, - ) - - plot_kwargs = {} if plot_kwargs is None else plot_kwargs - plot_kwargs.setdefault("color", to_hex(color)) - plot_kwargs.setdefault("linewidth", linewidth * 1.4) - if isinstance(y, str): - xlabel = y - elif isinstance(y, DataArray) and y.name is not None: - xlabel = y.name - elif isinstance(y_hat, str): - xlabel = y_hat - elif isinstance(y_hat, DataArray) and y_hat.name is not None: - xlabel = y_hat.name - else: - xlabel = "" - label = "LOO-PIT ECDF" if ecdf else "LOO-PIT" - xlabel = labeller.var_name_to_str(xlabel) - - plot_kwargs.setdefault("legend_label", label) - - plot_unif_kwargs = {} if plot_unif_kwargs is None else plot_unif_kwargs - light_color = rgb_to_hsv(to_rgb(plot_kwargs.get("color"))) - light_color[1] /= 2 # pylint: disable=unsupported-assignment-operation - light_color[2] += (1 - light_color[2]) / 2 # pylint: disable=unsupported-assignment-operation - plot_unif_kwargs.setdefault("color", to_hex(hsv_to_rgb(light_color))) - plot_unif_kwargs.setdefault("alpha", 0.5) - plot_unif_kwargs.setdefault("linewidth", 0.6 * linewidth) - - if ecdf: - n_data_points = loo_pit.size - plot_kwargs.setdefault("drawstyle", "steps-mid" if n_data_points < 100 else "default") - plot_unif_kwargs.setdefault("drawstyle", "steps-mid" if n_data_points < 100 else "default") - - if ecdf_fill: - if fill_kwargs is None: - fill_kwargs = {} - fill_kwargs.setdefault("color", to_hex(hsv_to_rgb(light_color))) - fill_kwargs.setdefault("alpha", 0.5) - fill_kwargs.setdefault( - "step", "mid" if plot_kwargs["drawstyle"] == "steps-mid" else None - ) - fill_kwargs.setdefault("legend_label", f"{hdi_prob * 100:.3g}% credible interval") - elif use_hdi: - if hdi_kwargs is None: - hdi_kwargs = {} - hdi_kwargs.setdefault("color", to_hex(hsv_to_rgb(light_color))) - hdi_kwargs.setdefault("alpha", 0.35) - - if ecdf: - if plot_kwargs.get("drawstyle") == "steps-mid": - ax.step( - np.hstack((0, loo_pit, 1)), - np.hstack((0, loo_pit - loo_pit_ecdf, 0)), - line_color=plot_kwargs.get("color", "black"), - line_alpha=plot_kwargs.get("alpha", 1.0), - line_width=plot_kwargs.get("linewidth", 3.0), - mode="center", - ) - else: - ax.line( - np.hstack((0, loo_pit, 1)), - np.hstack((0, loo_pit - loo_pit_ecdf, 0)), - line_color=plot_kwargs.get("color", "black"), - line_alpha=plot_kwargs.get("alpha", 1.0), - line_width=plot_kwargs.get("linewidth", 3.0), - ) - - if ecdf_fill: - if ( - fill_kwargs.get("drawstyle") == "steps-mid" - or fill_kwargs.get("drawstyle") != "steps-mid" - ): - # use step patch when you find out how to do that - ax.patch( - np.concatenate((unif_ecdf, unif_ecdf[::-1])), - np.concatenate((p975 - unif_ecdf, (p025 - unif_ecdf)[::-1])), - fill_color=fill_kwargs.get("color"), - fill_alpha=fill_kwargs.get("alpha", 1.0), - ) - elif fill_kwargs is not None and fill_kwargs.get("drawstyle") == "steps-mid": - ax.step( - unif_ecdf, - p975 - unif_ecdf, - line_color=plot_unif_kwargs.get("color", "black"), - line_alpha=plot_unif_kwargs.get("alpha", 1.0), - line_width=plot_kwargs.get("linewidth", 1.0), - mode="center", - ) - ax.step( - unif_ecdf, - p025 - unif_ecdf, - line_color=plot_unif_kwargs.get("color", "black"), - line_alpha=plot_unif_kwargs.get("alpha", 1.0), - line_width=plot_unif_kwargs.get("linewidth", 1.0), - mode="center", - ) - else: - ax.line( - unif_ecdf, - p975 - unif_ecdf, - line_color=plot_unif_kwargs.get("color", "black"), - line_alpha=plot_unif_kwargs.get("alpha", 1.0), - line_width=plot_unif_kwargs.get("linewidth", 1.0), - ) - ax.line( - unif_ecdf, - p025 - unif_ecdf, - line_color=plot_unif_kwargs.get("color", "black"), - line_alpha=plot_unif_kwargs.get("alpha", 1.0), - line_width=plot_unif_kwargs.get("linewidth", 1.0), - ) - else: - if use_hdi: - patch = BoxAnnotation( - bottom=hdi_odds[1], - top=hdi_odds[0], - fill_alpha=hdi_kwargs.pop("alpha"), - fill_color=hdi_kwargs.pop("color"), - **hdi_kwargs, - ) - patch.level = "underlay" - ax.add_layout(patch) - - # Adds horizontal reference line - ax.line([0, 1], [1, 1], line_color="white", line_width=1.5) - else: - for idx in range(n_unif): - x_s, unif_density = kde(unif[idx, :]) - ax.line( - x_s, - unif_density, - line_color=plot_unif_kwargs.get("color", "black"), - line_alpha=plot_unif_kwargs.get("alpha", 0.1), - line_width=plot_unif_kwargs.get("linewidth", 1.0), - ) - ax.line( - x_vals, - loo_pit_kde, - line_color=plot_kwargs.get("color", "black"), - line_alpha=plot_kwargs.get("alpha", 1.0), - line_width=plot_kwargs.get("linewidth", 3.0), - ) - - # Sets xlim(0, 1) - ax.xaxis.axis_label = xlabel - ax.line(0, 0) - ax.line(1, 0) - show_layout(ax, show) - - return ax diff --git a/arviz/plots/backends/bokeh/mcseplot.py b/arviz/plots/backends/bokeh/mcseplot.py deleted file mode 100644 index 549bab4769..0000000000 --- a/arviz/plots/backends/bokeh/mcseplot.py +++ /dev/null @@ -1,184 +0,0 @@ -"""Bokeh mcseplot.""" - -import numpy as np -from bokeh.models import ColumnDataSource, Span -from bokeh.models.glyphs import Scatter -from bokeh.models.annotations import Title -from scipy.stats import rankdata - -from ....stats.stats_utils import quantile as _quantile -from ...plot_utils import _scale_fig_size -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_mcse( - ax, - plotters, - length_plotters, - rows, - cols, - figsize, - errorbar, - rug, - data, - probs, - kwargs, # pylint: disable=unused-argument - extra_methods, - mean_mcse, - sd_mcse, - textsize, - labeller, - text_kwargs, # pylint: disable=unused-argument - rug_kwargs, - extra_kwargs, - idata, - rug_kind, - backend_kwargs, - show, -): - """Bokeh mcse plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - (figsize, *_, _linewidth, _markersize) = _scale_fig_size(figsize, textsize, rows, cols) - - extra_kwargs = {} if extra_kwargs is None else extra_kwargs - extra_kwargs.setdefault("linewidth", _linewidth / 2) - extra_kwargs.setdefault("color", "black") - extra_kwargs.setdefault("alpha", 0.5) - - if ax is None: - ax = create_axes_grid( - length_plotters, - rows, - cols, - figsize=figsize, - backend_kwargs=backend_kwargs, - ) - else: - ax = np.atleast_2d(ax) - - for (var_name, selection, isel, x), ax_ in zip( - plotters, (item for item in ax.flatten() if item is not None) - ): - if errorbar or rug: - values = data[var_name].sel(**selection).values.flatten() - if errorbar: - quantile_values = _quantile(values, probs) - ax_.scatter(probs, quantile_values, marker="dash") - ax_.multi_line( - list(zip(probs, probs)), - [(quant - err, quant + err) for quant, err in zip(quantile_values, x)], - ) - else: - ax_.scatter(probs, x, marker="circle") - if extra_methods: - mean_mcse_i = mean_mcse[var_name].sel(**selection).values.item() - sd_mcse_i = sd_mcse[var_name].sel(**selection).values.item() - hline_mean = Span( - location=mean_mcse_i, - dimension="width", - line_color=extra_kwargs["color"], - line_width=extra_kwargs["linewidth"] * 2, - line_alpha=extra_kwargs["alpha"], - ) - - ax_.renderers.append(hline_mean) - - hline_sd = Span( - location=sd_mcse_i, - dimension="width", - line_color="black", - line_width=extra_kwargs["linewidth"], - line_alpha=extra_kwargs["alpha"], - ) - - ax_.renderers.append(hline_sd) - - if rug: - if rug_kwargs is None: - rug_kwargs = {} - if not hasattr(idata, "sample_stats"): - raise ValueError("InferenceData object must contain sample_stats for rug plot") - if not hasattr(idata.sample_stats, rug_kind): - raise ValueError(f"InferenceData does not contain {rug_kind} data") - rug_kwargs.setdefault("space", 0.1) - - _rug_kwargs = {} - _rug_kwargs.setdefault("size", 8) - _rug_kwargs.setdefault("line_color", rug_kwargs.get("line_color", "black")) - _rug_kwargs.setdefault("line_width", 1) - _rug_kwargs.setdefault("line_alpha", 0.35) - _rug_kwargs.setdefault("angle", np.pi / 2) - - mask = idata.sample_stats[rug_kind].values.flatten() - values = rankdata(values, method="average")[mask] - if errorbar: - rug_x, rug_y = ( - values / (len(mask) - 1), - np.full_like( - values, - min( - 0, - min(quantile_values) - - (max(quantile_values) - min(quantile_values)) * 0.05, - ), - ), - ) - - hline = Span( - location=min( - 0, - min(quantile_values) - (max(quantile_values) - min(quantile_values)) * 0.05, - ), - dimension="width", - line_color="black", - line_width=_linewidth, - line_alpha=0.7, - ) - - else: - rug_x, rug_y = ( - values / (len(mask) - 1), - np.full_like( - values, - 0, - ), - ) - - hline = Span( - location=0, - dimension="width", - line_color="black", - line_width=_linewidth, - line_alpha=0.7, - ) - - ax_.renderers.append(hline) - - glyph = Scatter(x="rug_x", y="rug_y", marker="dash", **_rug_kwargs) - cds_rug = ColumnDataSource({"rug_x": np.asarray(rug_x), "rug_y": np.asarray(rug_y)}) - ax_.add_glyph(cds_rug, glyph) - - title = Title() - title.text = labeller.make_label_vert(var_name, selection, isel) - ax_.title = title - - ax_.xaxis.axis_label = "Quantile" - ax_.yaxis.axis_label = ( - r"Value $\pm$ MCSE for quantiles" if errorbar else "MCSE for quantiles" - ) - - if not errorbar: - ax_.y_range._property_values["start"] = -0.05 # pylint: disable=protected-access - ax_.y_range._property_values["end"] = 1 # pylint: disable=protected-access - - show_layout(ax, show) - - return ax diff --git a/arviz/plots/backends/bokeh/pairplot.py b/arviz/plots/backends/bokeh/pairplot.py deleted file mode 100644 index 7359e9340a..0000000000 --- a/arviz/plots/backends/bokeh/pairplot.py +++ /dev/null @@ -1,328 +0,0 @@ -"""Bokeh pairplot.""" - -import warnings -from copy import deepcopy -from uuid import uuid4 - -import bokeh.plotting as bkp -import numpy as np -from bokeh.models import CDSView, ColumnDataSource, GroupFilter, Span - -from ....rcparams import rcParams -from ...distplot import plot_dist -from ...kdeplot import plot_kde -from ...plot_utils import ( - _scale_fig_size, - calculate_point_estimate, - vectorized_to_hex, - _init_kwargs_dict, -) -from .. import show_layout -from . import backend_kwarg_defaults - - -def plot_pair( - ax, - plotters, - numvars, - figsize, - textsize, - kind, - scatter_kwargs, # pylint: disable=unused-argument - kde_kwargs, - hexbin_kwargs, - gridsize, # pylint: disable=unused-argument - colorbar, # pylint: disable=unused-argument - divergences, - diverging_mask, - divergences_kwargs, - flat_var_names, - flat_ref_slices, - flat_var_labels, - backend_kwargs, - marginal_kwargs, - show, - marginals, - point_estimate, - point_estimate_kwargs, - point_estimate_marker_kwargs, - reference_values, - reference_values_kwargs, -): - """Bokeh pair plot.""" - backend_kwargs = _init_kwargs_dict(backend_kwargs) - - backend_kwargs = { - **backend_kwarg_defaults( - ("dpi", "plot.bokeh.figure.dpi"), - ), - **backend_kwargs, - } - - hexbin_kwargs = _init_kwargs_dict(hexbin_kwargs) - hexbin_kwargs.setdefault("size", 0.5) - - marginal_kwargs = _init_kwargs_dict(marginal_kwargs) - point_estimate_kwargs = _init_kwargs_dict(point_estimate_kwargs) - kde_kwargs = _init_kwargs_dict(kde_kwargs) - - if kind != "kde": - kde_kwargs.setdefault("contourf_kwargs", {}) - kde_kwargs["contourf_kwargs"].setdefault("fill_alpha", 0) - kde_kwargs.setdefault("contour_kwargs", {}) - kde_kwargs["contour_kwargs"].setdefault("line_color", "black") - kde_kwargs["contour_kwargs"].setdefault("line_alpha", 1) - - if reference_values: - difference = set(flat_var_names).difference(set(reference_values.keys())) - - if difference: - warnings.warn( - "Argument reference_values does not include reference value for: {}".format( - ", ".join(difference) - ), - UserWarning, - ) - - reference_values_kwargs = _init_kwargs_dict(reference_values_kwargs) - reference_values_kwargs.setdefault("marker", "circle") - reference_values_kwargs.setdefault("line_color", "black") - reference_values_kwargs.setdefault("fill_color", vectorized_to_hex("C2")) - reference_values_kwargs.setdefault("line_width", 1) - reference_values_kwargs.setdefault("size", 10) - - divergences_kwargs = _init_kwargs_dict(divergences_kwargs) - divergences_kwargs.setdefault("marker", "circle") - divergences_kwargs.setdefault("line_color", "black") - divergences_kwargs.setdefault("fill_color", vectorized_to_hex("C1")) - divergences_kwargs.setdefault("line_width", 1) - divergences_kwargs.setdefault("size", 10) - - dpi = backend_kwargs.pop("dpi") - max_plots = ( - numvars**2 if rcParams["plot.max_subplots"] is None else rcParams["plot.max_subplots"] - ) - vars_to_plot = np.sum(np.arange(numvars).cumsum() < max_plots) - if vars_to_plot < numvars: - warnings.warn( - "rcParams['plot.max_subplots'] ({max_plots}) is smaller than the number " - "of resulting pair plots with these variables, generating only a " - "{side}x{side} grid".format(max_plots=max_plots, side=vars_to_plot), - UserWarning, - ) - numvars = vars_to_plot - - if numvars == 2: - offset = 1 - else: - offset = 2 - (figsize, _, _, _, _, markersize) = _scale_fig_size( - figsize, textsize, numvars - offset, numvars - offset - ) - - point_estimate_marker_kwargs = _init_kwargs_dict(point_estimate_marker_kwargs) - point_estimate_marker_kwargs.setdefault("marker", "square") - point_estimate_marker_kwargs.setdefault("size", markersize) - point_estimate_marker_kwargs.setdefault("color", "black") - point_estimate_kwargs.setdefault("line_color", "black") - point_estimate_kwargs.setdefault("line_width", 2) - point_estimate_kwargs.setdefault("line_dash", "solid") - - tmp_flat_var_names = None - if len(flat_var_names) == len(list(set(flat_var_names))): - source_dict = dict(zip(flat_var_names, [list(post[-1].flatten()) for post in plotters])) - else: - tmp_flat_var_names = [f"{name}__{str(uuid4())}" for name in flat_var_names] - source_dict = dict(zip(tmp_flat_var_names, [list(post[-1].flatten()) for post in plotters])) - if divergences: - divergenve_name = f"divergences_{str(uuid4())}" - source_dict[divergenve_name] = np.array(diverging_mask).astype(bool).astype(int).astype(str) - - source = ColumnDataSource(data=source_dict) - - if divergences: - source_nondiv = CDSView(filter=GroupFilter(column_name=divergenve_name, group="0")) - source_div = CDSView(filter=GroupFilter(column_name=divergenve_name, group="1")) - - def get_width_and_height(jointplot, rotate): - """Compute subplots dimensions for two or more variables.""" - if jointplot: - if rotate: - width = int(figsize[0] / (numvars - 1) + 2 * dpi) - height = int(figsize[1] / (numvars - 1) * dpi) - else: - width = int(figsize[0] / (numvars - 1) * dpi) - height = int(figsize[1] / (numvars - 1) + 2 * dpi) - else: - width = int(figsize[0] / (numvars - 1) * dpi) - height = int(figsize[1] / (numvars - 1) * dpi) - return width, height - - if marginals: - marginals_offset = 0 - else: - marginals_offset = 1 - - if ax is None: - ax = [] - backend_kwargs.setdefault("width", int(figsize[0] / (numvars - 1) * dpi)) - backend_kwargs.setdefault("height", int(figsize[1] / (numvars - 1) * dpi)) - for row in range(numvars - marginals_offset): - row_ax = [] - var1 = ( - flat_var_names[row + marginals_offset] - if tmp_flat_var_names is None - else tmp_flat_var_names[row + marginals_offset] - ) - for col in range(numvars - marginals_offset): - var2 = ( - flat_var_names[col] if tmp_flat_var_names is None else tmp_flat_var_names[col] - ) - backend_kwargs_copy = backend_kwargs.copy() - if "scatter" in kind: - tooltips = [ - (var2, f"@{{{var2}}}"), - (var1, f"@{{{var1}}}"), - ] - backend_kwargs_copy.setdefault("tooltips", tooltips) - else: - tooltips = None - if row < col: - row_ax.append(None) - else: - jointplot = row == col and numvars == 2 and marginals - rotate = col == 1 - width, height = get_width_and_height(jointplot, rotate) - if jointplot: - ax_ = bkp.figure(width=width, height=height, tooltips=tooltips) - else: - ax_ = bkp.figure(**backend_kwargs_copy) - row_ax.append(ax_) - ax.append(row_ax) - ax = np.array(ax) - else: - assert ax.shape == (numvars - marginals_offset, numvars - marginals_offset) - - # pylint: disable=too-many-nested-blocks - for i in range(0, numvars - marginals_offset): - var1 = flat_var_names[i] if tmp_flat_var_names is None else tmp_flat_var_names[i] - - for j in range(0, numvars - marginals_offset): - var2 = ( - flat_var_names[j + marginals_offset] - if tmp_flat_var_names is None - else tmp_flat_var_names[j + marginals_offset] - ) - - if j == i and marginals: - rotate = numvars == 2 and j == 1 - var1_dist = plotters[i][-1].flatten() - plot_dist( - var1_dist, - ax=ax[j, i], - show=False, - backend="bokeh", - rotated=rotate, - **marginal_kwargs, - ) - - ax[j, i].xaxis.axis_label = flat_var_labels[i] - ax[j, i].yaxis.axis_label = flat_var_labels[j + marginals_offset] - - elif j + marginals_offset > i: - if "scatter" in kind: - if divergences: - ax[j, i].scatter( - var1, var2, marker="circle", source=source, view=source_nondiv - ) - else: - ax[j, i].scatter(var1, var2, marker="circle", source=source) - - if "kde" in kind: - var1_kde = plotters[i][-1].flatten() - var2_kde = plotters[j + marginals_offset][-1].flatten() - plot_kde( - var1_kde, - var2_kde, - ax=ax[j, i], - backend="bokeh", - backend_kwargs={}, - show=False, - **deepcopy(kde_kwargs), - ) - - if "hexbin" in kind: - var1_hexbin = plotters[i][-1].flatten() - var2_hexbin = plotters[j + marginals_offset][-1].flatten() - ax[j, i].grid.visible = False - ax[j, i].hexbin( - var1_hexbin, - var2_hexbin, - **hexbin_kwargs, - ) - - if divergences: - ax[j, i].scatter( - var1, - var2, - source=source, - view=source_div, - **divergences_kwargs, - ) - - if point_estimate: - var1_pe = plotters[i][-1].flatten() - var2_pe = plotters[j][-1].flatten() - pe_x = calculate_point_estimate(point_estimate, var1_pe) - pe_y = calculate_point_estimate(point_estimate, var2_pe) - ax[j, i].scatter(pe_x, pe_y, **point_estimate_marker_kwargs) - - ax_hline = Span( - location=pe_y, - dimension="width", - **point_estimate_kwargs, - ) - ax_vline = Span( - location=pe_x, - dimension="height", - **point_estimate_kwargs, - ) - ax[j, i].add_layout(ax_hline) - ax[j, i].add_layout(ax_vline) - - if marginals: - ax[j - 1, i].add_layout(ax_vline) - - pe_last = calculate_point_estimate(point_estimate, plotters[-1][-1]) - ax_pe_vline = Span( - location=pe_last, - dimension="height", - **point_estimate_kwargs, - ) - ax[-1, -1].add_layout(ax_pe_vline) - - if numvars == 2: - ax_pe_hline = Span( - location=pe_last, - dimension="width", - **point_estimate_kwargs, - ) - ax[-1, -1].add_layout(ax_pe_hline) - - if reference_values: - x_name = flat_var_names[j + marginals_offset] - y_name = flat_var_names[i] - if (x_name not in difference) and (y_name not in difference): - ax[j, i].scatter( - np.array(reference_values[y_name])[flat_ref_slices[i]], - np.array(reference_values[x_name])[ - flat_ref_slices[j + marginals_offset] - ], - **reference_values_kwargs, - ) - ax[j, i].xaxis.axis_label = flat_var_labels[i] - ax[j, i].yaxis.axis_label = flat_var_labels[j + marginals_offset] - - show_layout(ax, show) - - return ax diff --git a/arviz/plots/backends/bokeh/parallelplot.py b/arviz/plots/backends/bokeh/parallelplot.py deleted file mode 100644 index a6d042b71c..0000000000 --- a/arviz/plots/backends/bokeh/parallelplot.py +++ /dev/null @@ -1,81 +0,0 @@ -"""Bokeh Parallel coordinates plot.""" - -import numpy as np -from bokeh.models import DataRange1d -from bokeh.models.tickers import FixedTicker - -from ...plot_utils import _scale_fig_size -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_parallel( - ax, - colornd, # pylint: disable=unused-argument - colord, # pylint: disable=unused-argument - shadend, # pylint: disable=unused-argument - diverging_mask, - posterior, - textsize, - var_names, - legend, # pylint: disable=unused-argument - figsize, - backend_kwargs, - backend_config, - show, -): - """Bokeh parallel plot.""" - if backend_config is None: - backend_config = {} - - backend_config = { - **backend_kwarg_defaults( - ("bounds_x_range", "plot.bokeh.bounds_x_range"), - ("bounds_y_range", "plot.bokeh.bounds_y_range"), - ), - **backend_config, - } - - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, *_ = _scale_fig_size(figsize, textsize, 1, 1) - - if ax is None: - ax = create_axes_grid( - 1, - figsize=figsize, - squeeze=True, - backend_kwargs=backend_kwargs, - ) - - non_div = list(posterior[:, ~diverging_mask].T) - x_non_div = [list(range(len(non_div[0]))) for _ in range(len(non_div))] - - ax.multi_line( - x_non_div, - non_div, - line_color="black", - line_alpha=0.05, - ) - - if np.any(diverging_mask): - div = list(posterior[:, diverging_mask].T) - x_non_div = [list(range(len(div[0]))) for _ in range(len(div))] - ax.multi_line(x_non_div, div, color="lime", line_width=1, line_alpha=0.5) - - ax.xaxis.ticker = FixedTicker(ticks=list(range(len(var_names)))) - ax.xaxis.major_label_overrides = dict(zip(map(str, range(len(var_names))), map(str, var_names))) - ax.xaxis.major_label_orientation = np.pi / 2 - - ax.x_range = DataRange1d(bounds=backend_config["bounds_x_range"], min_interval=2) - ax.y_range = DataRange1d(bounds=backend_config["bounds_y_range"], min_interval=5) - - show_layout(ax, show) - - return ax diff --git a/arviz/plots/backends/bokeh/posteriorplot.py b/arviz/plots/backends/bokeh/posteriorplot.py deleted file mode 100644 index 2cee3c749e..0000000000 --- a/arviz/plots/backends/bokeh/posteriorplot.py +++ /dev/null @@ -1,324 +0,0 @@ -"""Bokeh Plot posterior densities.""" - -from numbers import Number -from typing import Optional - -import numpy as np -from bokeh.models.annotations import Title - -from ....stats import hdi -from ....stats.density_utils import get_bins, histogram -from ...kdeplot import plot_kde -from ...plot_utils import ( - _scale_fig_size, - calculate_point_estimate, - format_sig_figs, - round_num, - vectorized_to_hex, -) -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_posterior( - ax, - length_plotters, - rows, - cols, - figsize, - plotters, - bw, - circular, - bins, - kind, - point_estimate, - round_to, - hdi_prob, - multimodal, - skipna, - textsize, - ref_val, - rope, - ref_val_color, - rope_color, - labeller, - kwargs, - backend_kwargs, - show, -): - """Bokeh posterior plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults( - ("dpi", "plot.bokeh.figure.dpi"), - ), - **backend_kwargs, - } - - (figsize, ax_labelsize, *_, linewidth, _) = _scale_fig_size(figsize, textsize, rows, cols) - - if ax is None: - ax = create_axes_grid( - length_plotters, - rows, - cols, - figsize=figsize, - backend_kwargs=backend_kwargs, - ) - else: - ax = np.atleast_2d(ax) - idx = 0 - for (var_name, selection, isel, x), ax_ in zip( - plotters, (item for item in ax.flatten() if item is not None) - ): - _plot_posterior_op( - idx, - x.flatten(), - var_name, - selection, - ax=ax_, - bw=bw, - circular=circular, - bins=bins, - kind=kind, - point_estimate=point_estimate, - round_to=round_to, - hdi_prob=hdi_prob, - multimodal=multimodal, - skipna=skipna, - linewidth=linewidth, - ref_val=ref_val, - rope=rope, - ref_val_color=ref_val_color, - rope_color=rope_color, - ax_labelsize=ax_labelsize, - **kwargs, - ) - idx += 1 - _title = Title() - _title.text = labeller.make_label_vert(var_name, selection, isel) - ax_.title = _title - - show_layout(ax, show) - - return ax - - -def _plot_posterior_op( - idx, - values, - var_name, - selection, - ax, - bw, - circular, - linewidth, - bins, - kind, - point_estimate, - hdi_prob, - multimodal, - skipna, - ref_val, - rope, - ref_val_color, - rope_color, - ax_labelsize, - round_to: Optional[int] = None, - **kwargs, -): # noqa: D202 - """Artist to draw posterior.""" - - def format_as_percent(x, round_to=0): - return "{0:.{1:d}f}%".format(100 * x, round_to) - - def display_ref_val(max_data): - if ref_val is None: - return - elif isinstance(ref_val, dict): - val = None - for sel in ref_val.get(var_name, []): - if all( - k in selection and selection[k] == v for k, v in sel.items() if k != "ref_val" - ): - val = sel["ref_val"] - break - if val is None: - return - elif isinstance(ref_val, list): - val = ref_val[idx] - elif isinstance(ref_val, Number): - val = ref_val - else: - raise ValueError( - "Argument `ref_val` must be None, a constant, a list or a " - 'dictionary like {"var_name": [{"ref_val": ref_val}]}' - ) - less_than_ref_probability = (values < val).mean() - greater_than_ref_probability = (values >= val).mean() - ref_in_posterior = "{} <{:g}< {}".format( - format_as_percent(less_than_ref_probability, 1), - val, - format_as_percent(greater_than_ref_probability, 1), - ) - ax.line( - [val, val], - [0, 0.8 * max_data], - line_color=vectorized_to_hex(ref_val_color), - line_alpha=0.65, - ) - - ax.text( - x=[values.mean()], - y=[max_data * 0.6], - text=[ref_in_posterior], - text_color=vectorized_to_hex(ref_val_color), - text_align="center", - ) - - def display_rope(max_data): - if rope is None: - return - elif isinstance(rope, dict): - vals = None - for sel in rope.get(var_name, []): - # pylint: disable=line-too-long - if all(k in selection and selection[k] == v for k, v in sel.items() if k != "rope"): - vals = sel["rope"] - break - if vals is None: - return - elif len(rope) == 2: - vals = rope - else: - raise ValueError( - "Argument `rope` must be None, a dictionary like" - '{"var_name": {"rope": (lo, hi)}}, or an' - "iterable of length 2" - ) - rope_text = [f"{val:.{format_sig_figs(val, round_to)}g}" for val in vals] - - ax.line( - vals, - (max_data * 0.02, max_data * 0.02), - line_width=linewidth * 5, - line_color=vectorized_to_hex(rope_color), - line_alpha=0.7, - ) - probability_within_rope = ((values > vals[0]) & (values <= vals[1])).mean() - text_props = dict( - text_color=vectorized_to_hex(rope_color), - text_align="center", - ) - ax.text( - x=values.mean(), - y=[max_data * 0.45], - text=[f"{format_as_percent(probability_within_rope, 1)} in ROPE"], - **text_props, - ) - - ax.text( - x=vals, - y=[max_data * 0.2, max_data * 0.2], - text_font_size=f"{ax_labelsize}pt", - text=rope_text, - **text_props, - ) - - def display_point_estimate(max_data): - if not point_estimate: - return - point_value = calculate_point_estimate(point_estimate, values, bw, circular) - sig_figs = format_sig_figs(point_value, round_to) - point_text = "{point_estimate}={point_value:.{sig_figs}g}".format( - point_estimate=point_estimate, point_value=point_value, sig_figs=sig_figs - ) - - ax.text(x=[point_value], y=[max_data * 0.8], text=[point_text], text_align="center") - - def display_hdi(max_data): - # np.ndarray with 2 entries, min and max - # pylint: disable=line-too-long - hdi_probs = hdi( - values, hdi_prob=hdi_prob, circular=circular, multimodal=multimodal, skipna=skipna - ) # type: np.ndarray - - for hdi_i in np.atleast_2d(hdi_probs): - ax.line( - hdi_i, - (max_data * 0.02, max_data * 0.02), - line_width=linewidth * 2, - line_color="black", - ) - - ax.text( - x=list(hdi_i) + [(hdi_i[0] + hdi_i[1]) / 2], - y=[max_data * 0.07, max_data * 0.07, max_data * 0.3], - text=( - list(map(str, map(lambda x: round_num(x, round_to), hdi_i))) - + [f"{format_as_percent(hdi_prob)} HDI"] - ), - text_align="center", - ) - - def format_axes(): - ax.yaxis.visible = False - ax.yaxis.major_tick_line_color = None - ax.yaxis.minor_tick_line_color = None - ax.yaxis.major_label_text_font_size = "0pt" - ax.xgrid.grid_line_color = None - ax.ygrid.grid_line_color = None - - if skipna: - values = values[~np.isnan(values)] - - if kind == "kde" and values.dtype.kind == "f": - kwargs.setdefault("line_width", linewidth) - plot_kde( - values, - bw=bw, - is_circular=circular, - fill_kwargs={"fill_alpha": kwargs.pop("fill_alpha", 0)}, - plot_kwargs=kwargs, - ax=ax, - rug=False, - backend="bokeh", - backend_kwargs={}, - show=False, - ) - max_data = values.max() - elif values.dtype.kind == "i" or (values.dtype.kind == "f" and kind == "hist"): - if bins is None: - bins = get_bins(values) - kwargs.setdefault("align", "left") - kwargs.setdefault("color", "blue") - _, hist, edges = histogram(values, bins=bins) - max_data = hist.max() - ax.quad( - top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_alpha=0.35, line_alpha=0.35 - ) - elif values.dtype.kind == "b": - if bins is None: - bins = "auto" - kwargs.setdefault("color", "blue") - - hist = np.array([(~values).sum(), values.sum()]) - max_data = hist.max() - edges = np.array([-0.5, 0.5, 1.5]) - ax.quad( - top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_alpha=0.35, line_alpha=0.35 - ) - hdi_prob = "hide" - ax.xaxis.ticker = [0, 1] - ax.xaxis.major_label_overrides = {0: "False", 1: "True"} - else: - raise TypeError("Values must be float, integer or boolean") - - format_axes() - if hdi_prob != "hide": - display_hdi(max_data) - display_point_estimate(max_data) - display_ref_val(max_data) - display_rope(max_data) diff --git a/arviz/plots/backends/bokeh/ppcplot.py b/arviz/plots/backends/bokeh/ppcplot.py deleted file mode 100644 index a27a3ea447..0000000000 --- a/arviz/plots/backends/bokeh/ppcplot.py +++ /dev/null @@ -1,379 +0,0 @@ -"""Bokeh Posterior predictive plot.""" - -import numpy as np -from bokeh.models.annotations import Legend -from bokeh.models.glyphs import Scatter -from bokeh.models import ColumnDataSource - - -from ....stats.density_utils import get_bins, histogram, kde -from ...kdeplot import plot_kde -from ...plot_utils import _scale_fig_size, vectorized_to_hex - - -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_ppc( - ax, - length_plotters, - rows, - cols, - figsize, - animated, - obs_plotters, - pp_plotters, - predictive_dataset, - pp_sample_ix, - kind, - alpha, - colors, - textsize, - mean, - observed, - observed_rug, - jitter, - total_pp_samples, - legend, # pylint: disable=unused-argument - labeller, - group, # pylint: disable=unused-argument - animation_kwargs, # pylint: disable=unused-argument - num_pp_samples, - backend_kwargs, - show, -): - """Bokeh ppc plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults( - ("dpi", "plot.bokeh.figure.dpi"), - ), - **backend_kwargs, - } - - colors = vectorized_to_hex(colors) - - (figsize, *_, linewidth, markersize) = _scale_fig_size(figsize, textsize, rows, cols) - if ax is None: - axes = create_axes_grid( - length_plotters, - rows, - cols, - figsize=figsize, - backend_kwargs=backend_kwargs, - ) - else: - axes = np.atleast_2d(ax) - - if len([item for item in axes.ravel() if not None]) != length_plotters: - raise ValueError( - f"Found {length_plotters} variables to plot but {len(axes)} axes instances. " - "They must be equal." - ) - - if alpha is None: - if animated: - alpha = 1 - else: - if kind.lower() == "scatter": - alpha = 0.7 - else: - alpha = 0.2 - - if jitter is None: - jitter = 0.0 - if jitter < 0.0: - raise ValueError("jitter must be >=0.") - - for i, ax_i in enumerate((item for item in axes.flatten() if item is not None)): - var_name, sel, isel, obs_vals = obs_plotters[i] - pp_var_name, _, _, pp_vals = pp_plotters[i] - dtype = predictive_dataset[pp_var_name].dtype.kind - legend_it = [] - - if dtype not in ["i", "f"]: - raise ValueError( - f"The data type of the predictive data must be one of 'i' or 'f', but is '{dtype}'" - ) - - # flatten non-specified dimensions - obs_vals = obs_vals.flatten() - pp_vals = pp_vals.reshape(total_pp_samples, -1) - pp_sampled_vals = pp_vals[pp_sample_ix] - cds_rug = ColumnDataSource({"_": np.array(obs_vals)}) - - if kind == "kde": - plot_kwargs = { - "line_color": colors[0], - "line_alpha": alpha, - "line_width": 0.5 * linewidth, - } - - pp_densities = [] - pp_xs = [] - for vals in pp_sampled_vals: - vals = np.array([vals]).flatten() - if dtype == "f": - pp_x, pp_density = kde(vals) - pp_densities.append(pp_density) - pp_xs.append(pp_x) - else: - bins = get_bins(vals) - _, hist, bin_edges = histogram(vals, bins=bins) - hist = np.concatenate((hist[:1], hist)) - pp_densities.append(hist) - pp_xs.append(bin_edges) - - if dtype == "f": - multi_line = ax_i.multi_line(pp_xs, pp_densities, **plot_kwargs) - legend_it.append((f"{group.capitalize()} predictive", [multi_line])) - else: - all_steps = [] - for x_s, y_s in zip(pp_xs, pp_densities): - step = ax_i.step(x_s, y_s, **plot_kwargs) - all_steps.append(step) - legend_it.append((f"{group.capitalize()} predictive", all_steps)) - - if observed: - label = "Observed" - if dtype == "f": - _, glyph = plot_kde( - obs_vals, - plot_kwargs={"line_color": colors[1], "line_width": linewidth}, - fill_kwargs={"alpha": 0}, - ax=ax_i, - backend="bokeh", - backend_kwargs={}, - show=False, - return_glyph=True, - ) - legend_it.append((label, glyph)) - if observed_rug: - glyph = Scatter( - x="_", - y=0.0, - marker="dash", - angle=np.pi / 2, - line_color=colors[1], - line_width=linewidth, - ) - ax_i.add_glyph(cds_rug, glyph) - else: - bins = get_bins(obs_vals) - _, hist, bin_edges = histogram(obs_vals, bins=bins) - hist = np.concatenate((hist[:1], hist)) - step = ax_i.step( - bin_edges, - hist, - line_color=colors[1], - line_width=linewidth, - mode="center", - ) - legend_it.append((label, [step])) - - if mean: - label = f"{group.capitalize()} predictive mean" - if dtype == "f": - rep = len(pp_densities) - len_density = len(pp_densities[0]) - - new_x = np.linspace(np.min(pp_xs), np.max(pp_xs), len_density) - new_d = np.zeros((rep, len_density)) - bins = np.digitize(pp_xs, new_x, right=True) - new_x -= (new_x[1] - new_x[0]) / 2 - for irep in range(rep): - new_d[irep][bins[irep]] = pp_densities[irep] - line = ax_i.line( - new_x, - new_d.mean(0), - color=colors[2], - line_dash="dashed", - line_width=linewidth, - ) - legend_it.append((label, [line])) - else: - vals = pp_vals.flatten() - bins = get_bins(vals) - _, hist, bin_edges = histogram(vals, bins=bins) - hist = np.concatenate((hist[:1], hist)) - step = ax_i.step( - bin_edges, - hist, - line_color=colors[2], - line_width=linewidth, - line_dash="dashed", - mode="center", - ) - legend_it.append((label, [step])) - ax_i.yaxis.major_tick_line_color = None - ax_i.yaxis.minor_tick_line_color = None - ax_i.yaxis.major_label_text_font_size = "0pt" - - elif kind == "cumulative": - if observed: - label = "Observed" - if dtype == "f": - glyph = ax_i.line( - *_empirical_cdf(obs_vals), - line_color=colors[1], - line_width=linewidth, - ) - glyph.level = "overlay" - legend_it.append((label, [glyph])) - - else: - step = ax_i.step( - *_empirical_cdf(obs_vals), - line_color=colors[1], - line_width=linewidth, - mode="center", - ) - legend_it.append((label, [step])) - - if observed_rug: - glyph = Scatter( - x="_", - y=0.0, - marker="dash", - angle=np.pi / 2, - line_color=colors[1], - line_width=linewidth, - ) - ax_i.add_glyph(cds_rug, glyph) - - pp_densities = np.empty((2 * len(pp_sampled_vals), pp_sampled_vals[0].size)) - for idx, vals in enumerate(pp_sampled_vals): - vals = np.array([vals]).flatten() - pp_x, pp_density = _empirical_cdf(vals) - pp_densities[2 * idx] = pp_x - pp_densities[2 * idx + 1] = pp_density - multi_line = ax_i.multi_line( - list(pp_densities[::2]), - list(pp_densities[1::2]), - line_alpha=alpha, - line_color=colors[0], - line_width=linewidth, - ) - legend_it.append((f"{group.capitalize()} predictive", [multi_line])) - if mean: - label = f"{group.capitalize()} predictive mean" - line = ax_i.line( - *_empirical_cdf(pp_vals.flatten()), - color=colors[2], - line_dash="dashed", - line_width=linewidth, - ) - legend_it.append((label, [line])) - - elif kind == "scatter": - if mean: - label = f"{group.capitalize()} predictive mean" - if dtype == "f": - _, glyph = plot_kde( - pp_vals.flatten(), - plot_kwargs={ - "line_color": colors[2], - "line_dash": "dashed", - "line_width": linewidth, - }, - ax=ax_i, - backend="bokeh", - backend_kwargs={}, - show=False, - return_glyph=True, - ) - legend_it.append((label, glyph)) - else: - vals = pp_vals.flatten() - bins = get_bins(vals) - _, hist, bin_edges = histogram(vals, bins=bins) - hist = np.concatenate((hist[:1], hist)) - step = ax_i.step( - bin_edges, - hist, - color=colors[2], - line_width=linewidth, - line_dash="dashed", - mode="center", - ) - legend_it.append((label, [step])) - - jitter_scale = 0.1 - y_rows = np.linspace(0, 0.1, num_pp_samples + 1) - scale_low = 0 - scale_high = jitter_scale * jitter - - if observed: - label = "Observed" - obs_yvals = np.zeros_like(obs_vals, dtype=np.float64) - if jitter: - obs_yvals += np.random.uniform( - low=scale_low, high=scale_high, size=len(obs_vals) - ) - glyph = ax_i.scatter( - obs_vals, - obs_yvals, - marker="circle", - line_color=colors[1], - fill_color=colors[1], - size=markersize, - line_alpha=alpha, - ) - glyph.level = "overlay" - legend_it.append((label, [glyph])) - - all_scatter = [] - for vals, y in zip(pp_sampled_vals, y_rows[1:]): - vals = np.ravel(vals) - yvals = np.full_like(vals, y, dtype=np.float64) - if jitter: - yvals += np.random.uniform(low=scale_low, high=scale_high, size=len(vals)) - scatter = ax_i.scatter( - vals, - yvals, - line_color=colors[0], - fill_color=colors[0], - size=markersize, - fill_alpha=alpha, - ) - all_scatter.append(scatter) - - legend_it.append((f"{group.capitalize()} predictive", all_scatter)) - ax_i.yaxis.major_tick_line_color = None - ax_i.yaxis.minor_tick_line_color = None - ax_i.yaxis.major_label_text_font_size = "0pt" - - if legend: - legend = Legend( - items=legend_it, - location="top_left", - orientation="vertical", - ) - ax_i.add_layout(legend) - if textsize is not None: - ax_i.legend.label_text_font_size = f"{textsize}pt" - ax_i.legend.click_policy = "hide" - ax_i.xaxis.axis_label = labeller.make_pp_label(var_name, pp_var_name, sel, isel) - - show_layout(axes, show) - - return axes - - -def _empirical_cdf(data): - """Compute empirical cdf of a numpy array. - - Parameters - ---------- - data : np.array - 1d array - - Returns - ------- - np.array, np.array - x and y coordinates for the empirical cdf of the data - """ - return np.sort(data), np.linspace(0, 1, len(data)) diff --git a/arviz/plots/backends/bokeh/rankplot.py b/arviz/plots/backends/bokeh/rankplot.py deleted file mode 100644 index b75a367e0c..0000000000 --- a/arviz/plots/backends/bokeh/rankplot.py +++ /dev/null @@ -1,149 +0,0 @@ -"""Bokeh rankplot.""" - -import numpy as np - -from bokeh.models import Span -from bokeh.models.annotations import Title -from bokeh.models.tickers import FixedTicker - -from ....stats.density_utils import histogram -from ...plot_utils import _scale_fig_size, compute_ranks -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_rank( - axes, - length_plotters, - rows, - cols, - figsize, - plotters, - bins, - kind, - colors, - ref_line, - labels, - labeller, - ref_line_kwargs, - bar_kwargs, - vlines_kwargs, - marker_vlines_kwargs, - backend_kwargs, - show, -): - """Bokeh rank plot.""" - if ref_line_kwargs is None: - ref_line_kwargs = {} - ref_line_kwargs.setdefault("line_dash", "dashed") - ref_line_kwargs.setdefault("line_color", "black") - - if bar_kwargs is None: - bar_kwargs = {} - bar_kwargs.setdefault("line_color", "white") - - if vlines_kwargs is None: - vlines_kwargs = {} - vlines_kwargs.setdefault("line_width", 2) - vlines_kwargs.setdefault("line_dash", "solid") - - if marker_vlines_kwargs is None: - marker_vlines_kwargs = {} - marker_vlines_kwargs.setdefault("marker", "circle") - - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults( - ("dpi", "plot.bokeh.figure.dpi"), - ), - **backend_kwargs, - } - figsize, *_ = _scale_fig_size(figsize, None, rows=rows, cols=cols) - if axes is None: - axes = create_axes_grid( - length_plotters, - rows, - cols, - figsize=figsize, - sharex=True, - sharey=True, - backend_kwargs=backend_kwargs, - ) - else: - axes = np.atleast_2d(axes) - - for ax, (var_name, selection, isel, var_data) in zip( - (item for item in axes.flatten() if item is not None), plotters - ): - ranks = compute_ranks(var_data) - bin_ary = np.histogram_bin_edges(ranks, bins=bins, range=(0, ranks.size)) - all_counts = np.empty((len(ranks), len(bin_ary) - 1)) - for idx, row in enumerate(ranks): - _, all_counts[idx], _ = histogram(row, bins=bin_ary) - counts_normalizer = all_counts.max() / 0.95 - gap = 1 - width = bin_ary[1] - bin_ary[0] - - bar_kwargs.setdefault("width", width) - # Center the bins - bin_ary = (bin_ary[1:] + bin_ary[:-1]) / 2 - - y_ticks = [] - if kind == "bars": - for idx, counts in enumerate(all_counts): - counts = counts / counts_normalizer - y_ticks.append(idx * gap) - ax.vbar( - x=bin_ary, - top=y_ticks[-1] + counts, - bottom=y_ticks[-1], - fill_color=colors[idx], - **bar_kwargs, - ) - if ref_line: - hline = Span(location=y_ticks[-1] + counts.mean(), **ref_line_kwargs) - ax.add_layout(hline) - if labels: - ax.yaxis.axis_label = "Chain" - elif kind == "vlines": - ymin = np.full(len(all_counts), all_counts.mean()) - for idx, counts in enumerate(all_counts): - ax.scatter( - bin_ary, - counts, - fill_color=colors[idx], - line_color=colors[idx], - **marker_vlines_kwargs, - ) - x_locations = [(bin, bin) for bin in bin_ary] - y_locations = [(ymin[idx], counts_) for counts_ in counts] - ax.multi_line(x_locations, y_locations, line_color=colors[idx], **vlines_kwargs) - - if ref_line: - hline = Span(location=all_counts.mean(), **ref_line_kwargs) - ax.add_layout(hline) - - if labels: - ax.xaxis.axis_label = "Rank (all chains)" - - ax.yaxis.ticker = FixedTicker(ticks=y_ticks) - ax.xaxis.major_label_overrides = dict( - zip(map(str, y_ticks), map(str, range(len(y_ticks)))) - ) - - else: - ax.yaxis.major_tick_line_color = None - ax.yaxis.minor_tick_line_color = None - - ax.xaxis.major_label_text_font_size = "0pt" - ax.yaxis.major_label_text_font_size = "0pt" - - _title = Title() - _title.text = labeller.make_label_vert(var_name, selection, isel) - ax.title = _title - - show_layout(axes, show) - - return axes diff --git a/arviz/plots/backends/bokeh/separationplot.py b/arviz/plots/backends/bokeh/separationplot.py deleted file mode 100644 index b0aacb0dda..0000000000 --- a/arviz/plots/backends/bokeh/separationplot.py +++ /dev/null @@ -1,107 +0,0 @@ -"""Bokeh separation plot.""" - -import numpy as np - -from ...plot_utils import _scale_fig_size, vectorized_to_hex -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_separation( - y, - y_hat, - y_hat_line, - label_y_hat, - expected_events, - figsize, - textsize, - color, - legend, - locs, - width, - ax, - plot_kwargs, - y_hat_line_kwargs, - exp_events_kwargs, - backend_kwargs, - show, -): - """Matplotlib separation plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - if plot_kwargs is None: - plot_kwargs = {} - - # plot_kwargs.setdefault("color", "#2a2eec") - # if color: - plot_kwargs["color"] = vectorized_to_hex(color) - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - if y_hat_line_kwargs is None: - y_hat_line_kwargs = {} - - y_hat_line_kwargs.setdefault("color", "black") - y_hat_line_kwargs.setdefault("line_width", 2) - - if exp_events_kwargs is None: - exp_events_kwargs = {} - - exp_events_kwargs.setdefault("color", "black") - exp_events_kwargs.setdefault("size", 15) - - if legend: - y_hat_line_kwargs.setdefault("legend_label", label_y_hat) - exp_events_kwargs.setdefault( - "legend_label", - "Expected events", - ) - - figsize, *_ = _scale_fig_size(figsize, textsize) - - idx = np.argsort(y_hat) - - backend_kwargs["x_range"] = (0, 1) - backend_kwargs["y_range"] = (0, 1) - - if ax is None: - ax = create_axes_grid(1, figsize=figsize, squeeze=True, backend_kwargs=backend_kwargs) - - for i, loc in enumerate(locs): - positive = not y[idx][i] == 0 - alpha = 1 if positive else 0.3 - ax.vbar( - loc, - top=1, - width=width, - fill_alpha=alpha, - line_alpha=alpha, - **plot_kwargs, - ) - - if y_hat_line: - ax.line( - np.linspace(0, 1, len(y_hat)), - y_hat[idx], - **y_hat_line_kwargs, - ) - - if expected_events: - expected_events = int(np.round(np.sum(y_hat))) - ax.triangle( - y_hat[idx][len(y_hat) - expected_events - 1], - 0, - **exp_events_kwargs, - ) - - ax.axis.visible = False - ax.xgrid.grid_line_color = None - ax.ygrid.grid_line_color = None - - show_layout(ax, show) - - return ax diff --git a/arviz/plots/backends/bokeh/traceplot.py b/arviz/plots/backends/bokeh/traceplot.py deleted file mode 100644 index 9a55deb2bc..0000000000 --- a/arviz/plots/backends/bokeh/traceplot.py +++ /dev/null @@ -1,436 +0,0 @@ -"""Bokeh Traceplot.""" - -import warnings -from collections.abc import Iterable -from itertools import cycle - -import bokeh.plotting as bkp -import matplotlib.pyplot as plt -import numpy as np -from bokeh.models import ColumnDataSource, DataRange1d, Span -from bokeh.models.glyphs import Scatter -from bokeh.models.annotations import Title - -from ...distplot import plot_dist -from ...plot_utils import _scale_fig_size -from ...rankplot import plot_rank -from .. import show_layout -from . import backend_kwarg_defaults, dealiase_sel_kwargs -from ....sel_utils import xarray_var_iter - - -def plot_trace( - data, - var_names, - divergences, - kind, - figsize, - rug, - lines, - circ_var_names, # pylint: disable=unused-argument - circ_var_units, # pylint: disable=unused-argument - compact, - compact_prop, - combined, - chain_prop, - legend, - labeller, - plot_kwargs, - fill_kwargs, - rug_kwargs, - hist_kwargs, - trace_kwargs, - rank_kwargs, - plotters, - divergence_data, - axes, - backend_kwargs, - backend_config, - show, -): - """Bokeh traceplot.""" - # If divergences are plotted they must be provided - if divergences is not False: - assert divergence_data is not None - - if backend_config is None: - backend_config = {} - - backend_config = { - **backend_kwarg_defaults( - ("bounds_y_range", "plot.bokeh.bounds_y_range"), - ), - **backend_config, - } - - # Set plot default backend kwargs - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults( - ("dpi", "plot.bokeh.figure.dpi"), - ), - **backend_kwargs, - } - dpi = backend_kwargs.pop("dpi") - - if figsize is None: - figsize = (12, len(plotters) * 2) - - figsize, _, _, _, linewidth, _ = _scale_fig_size(figsize, 10, rows=len(plotters), cols=2) - - backend_kwargs.setdefault("height", int(figsize[1] * dpi // len(plotters))) - backend_kwargs.setdefault("width", int(figsize[0] * dpi // 2)) - - if lines is None: - lines = () - - num_chain_props = len(data.chain) + 1 if combined else len(data.chain) - if not compact: - chain_prop = ( - {"line_color": plt.rcParams["axes.prop_cycle"].by_key()["color"]} - if chain_prop is None - else chain_prop - ) - else: - chain_prop = ( - { - "line_dash": ("solid", "dotted", "dashed", "dashdot"), - } - if chain_prop is None - else chain_prop - ) - compact_prop = ( - {"line_color": plt.rcParams["axes.prop_cycle"].by_key()["color"]} - if compact_prop is None - else compact_prop - ) - - if isinstance(chain_prop, str): - chain_prop = {chain_prop: plt.rcParams["axes.prop_cycle"].by_key()[chain_prop]} - if isinstance(chain_prop, tuple): - warnings.warn( - "chain_prop as a tuple will be deprecated in a future warning, use a dict instead", - FutureWarning, - ) - chain_prop = {chain_prop[0]: chain_prop[1]} - chain_prop = { - prop_name: [prop for _, prop in zip(range(num_chain_props), cycle(props))] - for prop_name, props in chain_prop.items() - } - - if isinstance(compact_prop, str): - compact_prop = {compact_prop: plt.rcParams["axes.prop_cycle"].by_key()[compact_prop]} - if isinstance(compact_prop, tuple): - warnings.warn( - "compact_prop as a tuple will be deprecated in a future warning, use a dict instead", - FutureWarning, - ) - compact_prop = {compact_prop[0]: compact_prop[1]} - - trace_kwargs = {} if trace_kwargs is None else trace_kwargs - trace_kwargs.setdefault("alpha", 0.35) - - if hist_kwargs is None: - hist_kwargs = {} - hist_kwargs.setdefault("alpha", 0.35) - - if plot_kwargs is None: - plot_kwargs = {} - if fill_kwargs is None: - fill_kwargs = {} - if rug_kwargs is None: - rug_kwargs = {} - if rank_kwargs is None: - rank_kwargs = {} - - trace_kwargs.setdefault("line_width", linewidth) - plot_kwargs.setdefault("line_width", linewidth) - - if rank_kwargs is None: - rank_kwargs = {} - - if axes is None: - axes = [] - backend_kwargs_copy = backend_kwargs.copy() - for i in range(len(plotters)): - if not i: - _axes = [bkp.figure(**backend_kwargs), bkp.figure(**backend_kwargs_copy)] - backend_kwargs_copy.setdefault("x_range", _axes[1].x_range) - else: - _axes = [ - bkp.figure(**backend_kwargs), - bkp.figure(**backend_kwargs_copy), - ] - axes.append(_axes) - - axes = np.atleast_2d(axes) - - cds_data = {} - cds_var_groups = {} - draw_name = "draw" - - for var_name, selection, isel, value in list( - xarray_var_iter(data, var_names=var_names, combined=True) - ): - if selection: - cds_name = "{}_ARVIZ_CDS_SELECTION_{}".format( - var_name, - "_".join( - str(item) - for key, value in selection.items() - for item in ( - [key, value] - if (isinstance(value, str) or not isinstance(value, Iterable)) - else [key, *value] - ) - ), - ) - else: - cds_name = var_name - - if var_name not in cds_var_groups: - cds_var_groups[var_name] = [] - cds_var_groups[var_name].append(cds_name) - - for chain_idx, _ in enumerate(data.chain.values): - if chain_idx not in cds_data: - cds_data[chain_idx] = {} - _data = value[chain_idx] - cds_data[chain_idx][cds_name] = _data - - while any(key == draw_name for key in cds_data[0]): - draw_name += "w" - - for chain in cds_data.values(): - chain[draw_name] = data.draw.values - - cds_data = {chain_idx: ColumnDataSource(cds) for chain_idx, cds in cds_data.items()} - - for idx, (var_name, selection, isel, value) in enumerate(plotters): - value = np.atleast_2d(value) - - if len(value.shape) == 2: - y_name = ( - var_name - if not selection - else "{}_ARVIZ_CDS_SELECTION_{}".format( - var_name, - "_".join( - str(item) - for key, value in selection.items() - for item in ( - (key, value) - if (isinstance(value, str) or not isinstance(value, Iterable)) - else (key, *value) - ) - ), - ) - ) - if rug: - rug_kwargs["y"] = y_name - _plot_chains_bokeh( - ax_density=axes[idx, 0], - ax_trace=axes[idx, 1], - data=cds_data, - x_name=draw_name, - y_name=y_name, - chain_prop=chain_prop, - combined=combined, - rug=rug, - kind=kind, - legend=legend, - trace_kwargs=trace_kwargs, - hist_kwargs=hist_kwargs, - plot_kwargs=plot_kwargs, - fill_kwargs=fill_kwargs, - rug_kwargs=rug_kwargs, - rank_kwargs=rank_kwargs, - ) - else: - for y_name in cds_var_groups[var_name]: - if rug: - rug_kwargs["y"] = y_name - _plot_chains_bokeh( - ax_density=axes[idx, 0], - ax_trace=axes[idx, 1], - data=cds_data, - x_name=draw_name, - y_name=y_name, - chain_prop=chain_prop, - combined=combined, - rug=rug, - kind=kind, - legend=legend, - trace_kwargs=trace_kwargs, - hist_kwargs=hist_kwargs, - plot_kwargs=plot_kwargs, - fill_kwargs=fill_kwargs, - rug_kwargs=rug_kwargs, - rank_kwargs=rank_kwargs, - ) - - for col in (0, 1): - _title = Title() - _title.text = labeller.make_label_vert(var_name, selection, isel) - axes[idx, col].title = _title - axes[idx, col].y_range = DataRange1d( - bounds=backend_config["bounds_y_range"], min_interval=0.1 - ) - - for _, _, vlines in (j for j in lines if j[0] == var_name and j[1] == selection): - if isinstance(vlines, (float, int)): - line_values = [vlines] - else: - line_values = np.atleast_1d(vlines).ravel() - - for line_value in line_values: - vline = Span( - location=line_value, - dimension="height", - line_color="black", - line_width=1.5, - line_alpha=0.75, - ) - hline = Span( - location=line_value, - dimension="width", - line_color="black", - line_width=1.5, - line_alpha=trace_kwargs["alpha"], - ) - - axes[idx, 0].renderers.append(vline) - axes[idx, 1].renderers.append(hline) - - if legend: - for col in (0, 1): - axes[idx, col].legend.location = "top_left" - axes[idx, col].legend.click_policy = "hide" - else: - for col in (0, 1): - if axes[idx, col].legend: - axes[idx, col].legend.visible = False - - if divergences: - div_density_kwargs = {} - div_density_kwargs.setdefault("size", 14) - div_density_kwargs.setdefault("line_color", "red") - div_density_kwargs.setdefault("line_width", 2) - div_density_kwargs.setdefault("line_alpha", 0.50) - div_density_kwargs.setdefault("angle", np.pi / 2) - - div_trace_kwargs = {} - div_trace_kwargs.setdefault("size", 14) - div_trace_kwargs.setdefault("line_color", "red") - div_trace_kwargs.setdefault("line_width", 2) - div_trace_kwargs.setdefault("line_alpha", 0.50) - div_trace_kwargs.setdefault("angle", np.pi / 2) - - div_selection = {k: v for k, v in selection.items() if k in divergence_data.dims} - divs = divergence_data.sel(**div_selection).values - divs = np.atleast_2d(divs) - - for chain, chain_divs in enumerate(divs): - div_idxs = np.arange(len(chain_divs))[chain_divs] - if div_idxs.size > 0: - values = value[chain, div_idxs] - tmp_cds = ColumnDataSource({"y": values, "x": div_idxs}) - if divergences == "top": - y_div_trace = value.max() - else: - y_div_trace = value.min() - glyph_density = Scatter(x="y", y=0.0, marker="dash", **div_density_kwargs) - if kind == "trace": - glyph_trace = Scatter( - x="x", y=y_div_trace, marker="dash", **div_trace_kwargs - ) - axes[idx, 1].add_glyph(tmp_cds, glyph_trace) - - axes[idx, 0].add_glyph(tmp_cds, glyph_density) - - show_layout(axes, show) - - return axes - - -def _plot_chains_bokeh( - ax_density, - ax_trace, - data, - x_name, - y_name, - chain_prop, - combined, - rug, - kind, - legend, - trace_kwargs, - hist_kwargs, - plot_kwargs, - fill_kwargs, - rug_kwargs, - rank_kwargs, -): - marker = trace_kwargs.pop("marker", True) - for chain_idx, cds in data.items(): - if kind == "trace": - if legend: - trace_kwargs["legend_label"] = f"chain {chain_idx}" - ax_trace.line( - x=x_name, - y=y_name, - source=cds, - **dealiase_sel_kwargs(trace_kwargs, chain_prop, chain_idx), - ) - if marker: - ax_trace.scatter( - x=x_name, - y=y_name, - marker="circle", - source=cds, - radius=0.30, - alpha=0.5, - **dealiase_sel_kwargs({}, chain_prop, chain_idx), - ) - if not combined: - rug_kwargs["cds"] = cds - if legend: - plot_kwargs["legend_label"] = f"chain {chain_idx}" - plot_dist( - cds.data[y_name], - ax=ax_density, - rug=rug, - hist_kwargs=hist_kwargs, - plot_kwargs=dealiase_sel_kwargs(plot_kwargs, chain_prop, chain_idx), - fill_kwargs=fill_kwargs, - rug_kwargs=rug_kwargs, - backend="bokeh", - backend_kwargs={}, - show=False, - ) - - if kind == "rank_bars": - value = np.array([item.data[y_name] for item in data.values()]) - plot_rank(value, kind="bars", ax=ax_trace, backend="bokeh", show=False, **rank_kwargs) - elif kind == "rank_vlines": - value = np.array([item.data[y_name] for item in data.values()]) - plot_rank(value, kind="vlines", ax=ax_trace, backend="bokeh", show=False, **rank_kwargs) - - if combined: - rug_kwargs["cds"] = data - if legend: - plot_kwargs["legend_label"] = "combined chains" - plot_dist( - np.concatenate([item.data[y_name] for item in data.values()]).flatten(), - ax=ax_density, - rug=rug, - hist_kwargs=hist_kwargs, - plot_kwargs=dealiase_sel_kwargs(plot_kwargs, chain_prop, -1), - fill_kwargs=fill_kwargs, - rug_kwargs=rug_kwargs, - backend="bokeh", - backend_kwargs={}, - show=False, - ) diff --git a/arviz/plots/backends/bokeh/violinplot.py b/arviz/plots/backends/bokeh/violinplot.py deleted file mode 100644 index e3564a2be1..0000000000 --- a/arviz/plots/backends/bokeh/violinplot.py +++ /dev/null @@ -1,164 +0,0 @@ -"""Bokeh Violinplot.""" - -import numpy as np -from bokeh.models.annotations import Title - -from ....stats import hdi -from ....stats.density_utils import get_bins, histogram, kde -from ...plot_utils import _scale_fig_size -from .. import show_layout -from . import backend_kwarg_defaults, create_axes_grid - - -def plot_violin( - ax, - plotters, - figsize, - rows, - cols, - sharex, - sharey, - shade_kwargs, - shade, - rug, - side, - rug_kwargs, - bw, - textsize, - labeller, - circular, - hdi_prob, - quartiles, - backend_kwargs, - show, -): - """Bokeh violin plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults( - ("dpi", "plot.bokeh.figure.dpi"), - ), - **backend_kwargs, - } - (figsize, *_, linewidth, _) = _scale_fig_size(figsize, textsize, rows, cols) - - shade_kwargs = {} if shade_kwargs is None else shade_kwargs - rug_kwargs = {} if rug_kwargs is None else rug_kwargs - rug_kwargs.setdefault("fill_alpha", 0.1) - rug_kwargs.setdefault("line_alpha", 0.1) - if ax is None: - ax = create_axes_grid( - len(plotters), - rows, - cols, - sharex=sharex, - sharey=sharey, - figsize=figsize, - backend_kwargs=backend_kwargs, - ) - else: - ax = np.atleast_2d(ax) - - for (var_name, selection, isel, x), ax_ in zip( - plotters, (item for item in ax.flatten() if item is not None) - ): - val = x.flatten() - if val[0].dtype.kind == "i": - dens = cat_hist(val, rug, side, shade, ax_, **shade_kwargs) - else: - dens = _violinplot(val, rug, side, shade, bw, circular, ax_, **shade_kwargs) - if rug: - rug_x = -np.abs(np.random.normal(scale=max(dens) / 3.5, size=len(val))) - ax_.scatter(rug_x, val, **rug_kwargs) - - per = np.nanpercentile(val, [25, 75, 50]) - hdi_probs = hdi(val, hdi_prob, multimodal=False, skipna=True) - - if quartiles: - ax_.line( - [0, 0], per[:2], line_width=linewidth * 3, line_color="black", line_cap="round" - ) - ax_.line([0, 0], hdi_probs, line_width=linewidth, line_color="black", line_cap="round") - ax_.scatter( - 0, - per[-1], - marker="circle", - line_color="white", - fill_color="white", - size=linewidth * 1.5, - line_width=linewidth, - ) - - _title = Title() - _title.align = "center" - _title.text = labeller.make_label_vert(var_name, selection, isel) - ax_.title = _title - ax_.xaxis.major_tick_line_color = None - ax_.xaxis.minor_tick_line_color = None - ax_.xaxis.major_label_text_font_size = "0pt" - - show_layout(ax, show) - - return ax - - -def _violinplot(val, rug, side, shade, bw, circular, ax, **shade_kwargs): - """Auxiliary function to plot violinplots.""" - if bw == "default": - bw = "taylor" if circular else "experimental" - x, density = kde(val, circular=circular, bw=bw) - - if rug and side == "both": - side = "right" - - if side == "left": - dens = -density - elif side == "right": - x = x[::-1] - dens = density[::-1] - elif side == "both": - x = np.concatenate([x, x[::-1]]) - dens = np.concatenate([-density, density[::-1]]) - - ax.harea(y=x, x1=dens, x2=np.zeros_like(dens), fill_alpha=shade, **shade_kwargs) - - return density - - -def cat_hist(val, rug, side, shade, ax, **shade_kwargs): - """Auxiliary function to plot discrete-violinplots.""" - bins = get_bins(val) - _, binned_d, _ = histogram(val, bins=bins) - - bin_edges = np.linspace(np.min(val), np.max(val), len(bins)) - heights = np.diff(bin_edges) - centers = bin_edges[:-1] + heights.mean() / 2 - bar_length = 0.5 * binned_d - - if rug and side == "both": - side = "right" - - if side == "right": - left = 0 - right = bar_length - elif side == "left": - left = -bar_length - right = 0 - elif side == "both": - left = -bar_length - right = bar_length - - ax.hbar( - y=centers, - left=left, - right=right, - height=heights, - fill_alpha=shade, - line_alpha=shade, - line_color=None, - **shade_kwargs - ) - - return binned_d diff --git a/arviz/plots/backends/matplotlib/__init__.py b/arviz/plots/backends/matplotlib/__init__.py deleted file mode 100644 index 5962339045..0000000000 --- a/arviz/plots/backends/matplotlib/__init__.py +++ /dev/null @@ -1,124 +0,0 @@ -# pylint: disable=wrong-import-position -"""Matplotlib Plotting Backend.""" -import matplotlib as mpl - -from matplotlib.cbook import normalize_kwargs -from matplotlib.pyplot import subplots -from numpy import ndenumerate - -from ....rcparams import rcParams - - -def backend_kwarg_defaults(*args, **kwargs): - """Get default kwargs for backend. - - For args add a tuple with key and rcParam key pair. - """ - defaults = {**kwargs} - # add needed default args from arviz.rcParams - for key, arg in args: - defaults.setdefault(key, rcParams[arg]) - return defaults - - -def backend_show(show): - """Set default behaviour for show if not explicitly defined.""" - if show is None: - show = rcParams["plot.matplotlib.show"] - return show - - -def create_axes_grid(length_plotters, rows=1, cols=1, backend_kwargs=None): - """Create figure and axes for grids with multiple plots. - - Parameters - ---------- - length_plotters : int - Number of axes required - rows : int - Number of rows - cols : int - Number of columns - backend_kwargs: dict, optional - kwargs for backend figure. - - Returns - ------- - fig : matplotlib figure - ax : matplotlib axes - """ - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = {**backend_kwarg_defaults(), **backend_kwargs} - - fig, axes = subplots(rows, cols, **backend_kwargs) - extra = (rows * cols) - length_plotters - if extra > 0: - for (row, col), ax in ndenumerate(axes): - if (row * cols + col + 1) > length_plotters: - ax.set_axis_off() - return fig, axes - - -def matplotlib_kwarg_dealiaser(args, kind): - """De-aliase the kwargs passed to plots.""" - if args is None: - return {} - matplotlib_kwarg_dealiaser_dict = { - "scatter": mpl.collections.PathCollection, - "plot": mpl.lines.Line2D, - "hist": mpl.patches.Patch, - "bar": mpl.patches.Rectangle, - "hexbin": mpl.collections.PolyCollection, - "fill_between": mpl.collections.PolyCollection, - "hlines": mpl.collections.LineCollection, - "text": mpl.text.Text, - "contour": mpl.contour.ContourSet, - "pcolormesh": mpl.collections.QuadMesh, - } - return normalize_kwargs(args, getattr(matplotlib_kwarg_dealiaser_dict[kind], "_alias_map", {})) - - -def dealiase_sel_kwargs(kwargs, prop_dict, idx): - """Generate kwargs dict from kwargs and prop_dict. - - Gets property at position ``idx`` for each property in prop_dict and adds it to - ``kwargs``. Values in prop_dict are dealiased and overwrite values in - kwargs with the same key . - - Parameters - ---------- - kwargs : dict - prop_dict : dict of {str : array_like} - idx : int - """ - return { - **kwargs, - **matplotlib_kwarg_dealiaser( - {prop: props[idx] for prop, props in prop_dict.items()}, "plot" - ), - } - - -from .autocorrplot import plot_autocorr -from .bpvplot import plot_bpv -from .compareplot import plot_compare -from .densityplot import plot_density -from .distplot import plot_dist -from .elpdplot import plot_elpd -from .energyplot import plot_energy -from .essplot import plot_ess -from .forestplot import plot_forest -from .hdiplot import plot_hdi -from .kdeplot import plot_kde -from .khatplot import plot_khat -from .loopitplot import plot_loo_pit -from .mcseplot import plot_mcse -from .pairplot import plot_pair -from .parallelplot import plot_parallel -from .posteriorplot import plot_posterior -from .ppcplot import plot_ppc -from .rankplot import plot_rank -from .traceplot import plot_trace -from .violinplot import plot_violin diff --git a/arviz/plots/backends/matplotlib/autocorrplot.py b/arviz/plots/backends/matplotlib/autocorrplot.py deleted file mode 100644 index e1e871140f..0000000000 --- a/arviz/plots/backends/matplotlib/autocorrplot.py +++ /dev/null @@ -1,72 +0,0 @@ -"""Matplotlib Autocorrplot.""" - -import matplotlib.pyplot as plt -import numpy as np - -from ....stats import autocorr -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, backend_show, create_axes_grid - - -def plot_autocorr( - axes, - plotters, - max_lag, - figsize, - rows, - cols, - combined, - textsize, - labeller, - backend_kwargs, - show, -): - """Matplotlib autocorrplot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, _, titlesize, xt_labelsize, linewidth, _ = _scale_fig_size( - figsize, textsize, rows, cols - ) - - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs.setdefault("sharex", True) - backend_kwargs.setdefault("sharey", True) - backend_kwargs.setdefault("squeeze", True) - - if axes is None: - _, axes = create_axes_grid( - len(plotters), - rows, - cols, - backend_kwargs=backend_kwargs, - ) - - for (var_name, selection, isel, x), ax in zip(plotters, np.ravel(axes)): - x_prime = x - if combined: - x_prime = x.flatten() - c_i = 1.96 / x_prime.size**0.5 - y = autocorr(x_prime) - - ax.fill_between([0, max_lag], -c_i, c_i, color="0.75") - ax.vlines(x=np.arange(0, max_lag), ymin=0, ymax=y[0:max_lag], lw=linewidth) - - ax.set_title( - labeller.make_label_vert(var_name, selection, isel), fontsize=titlesize, wrap=True - ) - ax.tick_params(labelsize=xt_labelsize) - - if np.asarray(axes).size > 0: - np.asarray(axes).item(0).set_xlim(0, max_lag) - np.asarray(axes).item(0).set_ylim(-1, 1) - - if backend_show(show): - plt.show() - - return axes diff --git a/arviz/plots/backends/matplotlib/bfplot.py b/arviz/plots/backends/matplotlib/bfplot.py deleted file mode 100644 index d975c34d70..0000000000 --- a/arviz/plots/backends/matplotlib/bfplot.py +++ /dev/null @@ -1,78 +0,0 @@ -import matplotlib.pyplot as plt - -from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser -from ...distplot import plot_dist -from ...plot_utils import _scale_fig_size - - -def plot_bf( - ax, - bf_10, - bf_01, - prior, - posterior, - ref_val, - prior_at_ref_val, - posterior_at_ref_val, - var_name, - colors, - figsize, - textsize, - plot_kwargs, - hist_kwargs, - backend_kwargs, - show, -): - """Matplotlib Bayes Factor plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - if hist_kwargs is None: - hist_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, _, _, _, linewidth, _ = _scale_fig_size(figsize, textsize, 1, 1) - - plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") - plot_kwargs.setdefault("linewidth", linewidth) - hist_kwargs.setdefault("alpha", 0.5) - - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs.setdefault("squeeze", True) - - if ax is None: - _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs) - - plot_dist( - prior, - color=colors[0], - label="Prior", - ax=ax, - plot_kwargs=plot_kwargs, - hist_kwargs=hist_kwargs, - ) - plot_dist( - posterior, - color=colors[1], - label="Posterior", - ax=ax, - plot_kwargs=plot_kwargs, - hist_kwargs=hist_kwargs, - ) - - ax.plot(ref_val, posterior_at_ref_val, "ko", lw=1.5) - ax.plot(ref_val, prior_at_ref_val, "ko", lw=1.5) - ax.axvline(ref_val, color="k", ls="--") - ax.set_xlabel(var_name) - ax.set_ylabel("Density") - ax.set_title(f"The BF_10 is {bf_10:.2f}\nThe BF_01 is {bf_01:.2f}") - plt.legend() - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/bpvplot.py b/arviz/plots/backends/matplotlib/bpvplot.py deleted file mode 100644 index a87592758d..0000000000 --- a/arviz/plots/backends/matplotlib/bpvplot.py +++ /dev/null @@ -1,177 +0,0 @@ -"""Matplotib Bayesian p-value Posterior predictive plot.""" - -import matplotlib.pyplot as plt -import numpy as np -from scipy import stats - -from ....stats.density_utils import kde -from ....stats.stats_utils import smooth_data -from ...kdeplot import plot_kde -from ...plot_utils import ( - _scale_fig_size, - is_valid_quantile, - sample_reference_distribution, -) -from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser - - -def plot_bpv( - ax, - length_plotters, - rows, - cols, - obs_plotters, - pp_plotters, - total_pp_samples, - kind, - t_stat, - bpv, - plot_mean, - reference, - mse, - n_ref, - hdi_prob, - color, - figsize, - textsize, - labeller, - plot_ref_kwargs, - backend_kwargs, - show, - smoothing, -): - """Matplotlib bpv plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, ax_labelsize, _, _, linewidth, markersize = _scale_fig_size( - figsize, textsize, rows, cols - ) - - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs.setdefault("squeeze", True) - - if (kind == "u_value") and (reference == "analytical"): - plot_ref_kwargs = matplotlib_kwarg_dealiaser(plot_ref_kwargs, "fill_between") - else: - plot_ref_kwargs = matplotlib_kwarg_dealiaser(plot_ref_kwargs, "plot") - - if kind == "p_value" and reference == "analytical": - plot_ref_kwargs.setdefault("color", "k") - plot_ref_kwargs.setdefault("linestyle", "--") - elif kind == "u_value" and reference == "analytical": - plot_ref_kwargs.setdefault("color", "k") - plot_ref_kwargs.setdefault("alpha", 0.2) - else: - plot_ref_kwargs.setdefault("alpha", 0.1) - plot_ref_kwargs.setdefault("color", color) - - if ax is None: - _, axes = create_axes_grid(length_plotters, rows, cols, backend_kwargs=backend_kwargs) - else: - axes = np.asarray(ax) - if axes.size < length_plotters: - raise ValueError( - f"Found {length_plotters} variables to plot but {axes.size} axes instances. " - "Axes instances must at minimum be equal to variables." - ) - - for i, ax_i in enumerate(np.ravel(axes)[:length_plotters]): - var_name, selection, isel, obs_vals = obs_plotters[i] - pp_var_name, _, _, pp_vals = pp_plotters[i] - - obs_vals = obs_vals.flatten() - pp_vals = pp_vals.reshape(total_pp_samples, -1) - - if (obs_vals.dtype.kind == "i" or pp_vals.dtype.kind == "i") and smoothing is True: - obs_vals, pp_vals = smooth_data(obs_vals, pp_vals) - - if kind == "p_value": - tstat_pit = np.mean(pp_vals <= obs_vals, axis=-1) - x_s, tstat_pit_dens = kde(tstat_pit) - ax_i.plot(x_s, tstat_pit_dens, linewidth=linewidth, color=color) - ax_i.set_yticks([]) - if reference is not None: - dist = stats.beta(obs_vals.size / 2, obs_vals.size / 2) - if reference == "analytical": - lwb = dist.ppf((1 - 0.9999) / 2) - upb = 1 - lwb - x = np.linspace(lwb, upb, 500) - dens_ref = dist.pdf(x) - ax_i.plot(x, dens_ref, zorder=1, **plot_ref_kwargs) - elif reference == "samples": - x_ss, u_dens = sample_reference_distribution( - dist, - ( - tstat_pit_dens.size, - n_ref, - ), - ) - ax_i.plot(x_ss, u_dens, linewidth=linewidth, **plot_ref_kwargs) - - elif kind == "u_value": - tstat_pit = np.mean(pp_vals <= obs_vals, axis=0) - x_s, tstat_pit_dens = kde(tstat_pit) - ax_i.plot(x_s, tstat_pit_dens, color=color) - if reference is not None: - if reference == "analytical": - n_obs = obs_vals.size - hdi_ = stats.beta(n_obs / 2, n_obs / 2).ppf((1 - hdi_prob) / 2) - hdi_odds = (hdi_ / (1 - hdi_), (1 - hdi_) / hdi_) - ax_i.axhspan(*hdi_odds, **plot_ref_kwargs) - ax_i.axhline(1, color="w", zorder=1) - elif reference == "samples": - dist = stats.uniform(0, 1) - x_ss, u_dens = sample_reference_distribution(dist, (tstat_pit_dens.size, n_ref)) - ax_i.plot(x_ss, u_dens, linewidth=linewidth, **plot_ref_kwargs) - if mse: - ax_i.plot(0, 0, label=f"mse={np.mean((1 - tstat_pit_dens)**2) * 100:.2f}") - ax_i.legend() - - ax_i.set_ylim(0, None) - ax_i.set_xlim(0, 1) - else: - if t_stat in ["mean", "median", "std"]: - if t_stat == "mean": - tfunc = np.mean - elif t_stat == "median": - tfunc = np.median - elif t_stat == "std": - tfunc = np.std - obs_vals = tfunc(obs_vals) - pp_vals = tfunc(pp_vals, axis=1) - elif hasattr(t_stat, "__call__"): - obs_vals = t_stat(obs_vals.flatten()) - pp_vals = t_stat(pp_vals) - elif is_valid_quantile(t_stat): - t_stat = float(t_stat) - obs_vals = np.quantile(obs_vals, q=t_stat) - pp_vals = np.quantile(pp_vals, q=t_stat, axis=1) - else: - raise ValueError(f"T statistics {t_stat} not implemented") - - plot_kde(pp_vals, ax=ax_i, plot_kwargs={"color": color}) - ax_i.set_yticks([]) - if bpv: - p_value = np.mean(pp_vals <= obs_vals) - ax_i.plot(obs_vals, 0, label=f"bpv={p_value:.2f}", alpha=0) - ax_i.legend() - - if plot_mean: - ax_i.plot( - obs_vals.mean(), 0, "o", color=color, markeredgecolor="k", markersize=markersize - ) - - ax_i.set_title( - labeller.make_pp_label(var_name, pp_var_name, selection, isel), fontsize=ax_labelsize - ) - - if backend_show(show): - plt.show() - - return axes diff --git a/arviz/plots/backends/matplotlib/compareplot.py b/arviz/plots/backends/matplotlib/compareplot.py deleted file mode 100644 index 948f5ee08e..0000000000 --- a/arviz/plots/backends/matplotlib/compareplot.py +++ /dev/null @@ -1,135 +0,0 @@ -"""Matplotlib Compareplot.""" - -import matplotlib.pyplot as plt - -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, backend_show, create_axes_grid - - -def plot_compare( - ax, - comp_df, - legend, - title, - figsize, - plot_ic_diff, - plot_standard_error, - insample_dev, - yticks_pos, - yticks_labels, - plot_kwargs, - information_criterion, - textsize, - step, - backend_kwargs, - show, -): - """Matplotlib compare plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - if figsize is None: - figsize = (6, len(comp_df)) - - figsize, ax_labelsize, _, xt_labelsize, linewidth, _ = _scale_fig_size(figsize, textsize, 1, 1) - - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - - if ax is None: - _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs) - - if plot_standard_error: - ax.errorbar( - x=comp_df[information_criterion], - y=yticks_pos[::2], - xerr=comp_df.se, - label="ELPD", - color=plot_kwargs.get("color_ic", "k"), - fmt=plot_kwargs.get("marker_ic", "o"), - mfc=plot_kwargs.get("marker_fc", "white"), - mew=linewidth, - lw=linewidth, - ) - else: - ax.plot( - comp_df[information_criterion], - yticks_pos[::2], - label="ELPD", - color=plot_kwargs.get("color_ic", "k"), - marker=plot_kwargs.get("marker_ic", "o"), - mfc=plot_kwargs.get("marker_fc", "white"), - mew=linewidth, - lw=0, - zorder=3, - ) - - if plot_ic_diff: - ax.set_yticks(yticks_pos) - ax.errorbar( - x=comp_df[information_criterion].iloc[1:], - y=yticks_pos[1::2], - xerr=comp_df.dse[1:], - label="ELPD difference", - color=plot_kwargs.get("color_dse", "grey"), - fmt=plot_kwargs.get("marker_dse", "^"), - mew=linewidth, - elinewidth=linewidth, - ) - - else: - ax.set_yticks(yticks_pos[::2]) - - scale = comp_df["scale"].iloc[0] - - if insample_dev: - p_ic = comp_df[f"p_{information_criterion.split('_')[1]}"] - if scale == "log": - correction = p_ic - elif scale == "negative_log": - correction = -p_ic - elif scale == "deviance": - correction = -(2 * p_ic) - ax.plot( - comp_df[information_criterion] + correction, - yticks_pos[::2], - label="In-sample ELPD", - color=plot_kwargs.get("color_insample_dev", "k"), - marker=plot_kwargs.get("marker_insample_dev", "o"), - mew=linewidth, - lw=0, - ) - - ax.axvline( - comp_df[information_criterion].iloc[0], - ls=plot_kwargs.get("ls_min_ic", "--"), - color=plot_kwargs.get("color_ls_min_ic", "grey"), - lw=linewidth, - ) - if legend: - ax.legend(bbox_to_anchor=(1.01, 1), loc="upper left", ncol=1, fontsize=ax_labelsize) - - if title: - ax.set_title( - f"Model comparison\n{'higher' if scale == 'log' else 'lower'} is better", - fontsize=ax_labelsize, - ) - - if scale == "negative_log": - scale = "-log" - - ax.set_xlabel(f"{information_criterion} ({scale})", fontsize=ax_labelsize) - ax.set_ylabel("ranked models", fontsize=ax_labelsize) - ax.set_yticklabels(yticks_labels) - ax.set_ylim(-1 + step, 0 - step) - ax.tick_params(labelsize=xt_labelsize) - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/densityplot.py b/arviz/plots/backends/matplotlib/densityplot.py deleted file mode 100644 index b72641985c..0000000000 --- a/arviz/plots/backends/matplotlib/densityplot.py +++ /dev/null @@ -1,194 +0,0 @@ -"""Matplotlib Densityplot.""" - -from itertools import cycle - -import matplotlib.pyplot as plt -import numpy as np - -from ....stats import hdi -from ....stats.density_utils import get_bins, kde -from ...plot_utils import _scale_fig_size, calculate_point_estimate -from . import backend_kwarg_defaults, backend_show, create_axes_grid - - -def plot_density( - ax, - all_labels, - to_plot, - colors, - bw, - circular, - figsize, - length_plotters, - rows, - cols, - textsize, - labeller, - hdi_prob, - point_estimate, - hdi_markers, - outline, - shade, - n_data, - data_labels, - backend_kwargs, - show, -): - """Matplotlib densityplot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - if colors == "cycle": - colors = [ - prop - for _, prop in zip( - range(n_data), cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"]) - ) - ] - elif isinstance(colors, str): - colors = [colors for _ in range(n_data)] - - (figsize, _, titlesize, xt_labelsize, linewidth, markersize) = _scale_fig_size( - figsize, textsize, rows, cols - ) - - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs.setdefault("squeeze", False) - if ax is None: - _, ax = create_axes_grid( - length_plotters, - rows, - cols, - backend_kwargs=backend_kwargs, - ) - - axis_map = dict(zip(all_labels, np.ravel(ax))) - - for m_idx, plotters in enumerate(to_plot): - for var_name, selection, isel, values in plotters: - label = labeller.make_label_vert(var_name, selection, isel) - _d_helper( - values.flatten(), - label, - colors[m_idx], - bw, - circular, - titlesize, - xt_labelsize, - linewidth, - markersize, - hdi_prob, - point_estimate, - hdi_markers, - outline, - shade, - axis_map[label], - ) - - if n_data > 1: - for m_idx, label in enumerate(data_labels): - np.ravel(ax).item(0).plot([], label=label, c=colors[m_idx], markersize=markersize) - np.ravel(ax).item(0).legend(fontsize=xt_labelsize) - - if backend_show(show): - plt.show() - - return ax - - -def _d_helper( - vec, - vname, - color, - bw, - circular, - titlesize, - xt_labelsize, - linewidth, - markersize, - hdi_prob, - point_estimate, - hdi_markers, - outline, - shade, - ax, -): - """Plot an individual dimension. - - Parameters - ---------- - vec : array - 1D array from trace - vname : str - variable name - color : str - matplotlib color - bw: float or str, optional - If numeric, indicates the bandwidth and must be positive. - If str, indicates the method to estimate the bandwidth and must be - one of "scott", "silverman", "isj" or "experimental" when `circular` is False - and "taylor" (for now) when `circular` is True. - titlesize : float - font size for title - xt_labelsize : float - fontsize for xticks - linewidth : float - Thickness of lines - markersize : float - Size of markers - hdi_prob : float - Probability for the highest density interval. Defaults to 0.94 - point_estimate : Optional[str] - Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None. - Defaults to 'auto' i.e. it falls back to default set in rcParams. - shade : float - Alpha blending value for the shaded area under the curve, between 0 (no shade) and 1 - (opaque). Defaults to 0. - ax : matplotlib axes - """ - if vec.dtype.kind == "f": - if hdi_prob != 1: - hdi_ = hdi(vec, hdi_prob, multimodal=False) - new_vec = vec[(vec >= hdi_[0]) & (vec <= hdi_[1])] - else: - new_vec = vec - - x, density = kde(new_vec, circular=circular, bw=bw) - density *= hdi_prob - xmin, xmax = x[0], x[-1] - ymin, ymax = density[0], density[-1] - - if outline: - ax.plot(x, density, color=color, lw=linewidth) - ax.plot([xmin, xmin], [-ymin / 100, ymin], color=color, ls="-", lw=linewidth) - ax.plot([xmax, xmax], [-ymax / 100, ymax], color=color, ls="-", lw=linewidth) - - if shade: - ax.fill_between(x, density, color=color, alpha=shade) - - else: - xmin, xmax = hdi(vec, hdi_prob, multimodal=False) - bins = get_bins(vec) - if outline: - ax.hist(vec, bins=bins, color=color, histtype="step", align="left") - if shade: - ax.hist(vec, bins=bins, color=color, alpha=shade) - - if hdi_markers: - ax.plot(xmin, 0, hdi_markers, color=color, markeredgecolor="k", markersize=markersize) - ax.plot(xmax, 0, hdi_markers, color=color, markeredgecolor="k", markersize=markersize) - - if point_estimate is not None: - est = calculate_point_estimate(point_estimate, vec, bw) - ax.plot(est, 0, "o", color=color, markeredgecolor="k", markersize=markersize) - - ax.set_yticks([]) - ax.set_title(vname, fontsize=titlesize, wrap=True) - for pos in ["left", "right", "top"]: - ax.spines[pos].set_visible(False) - ax.tick_params(labelsize=xt_labelsize) diff --git a/arviz/plots/backends/matplotlib/distcomparisonplot.py b/arviz/plots/backends/matplotlib/distcomparisonplot.py deleted file mode 100644 index 66305f097b..0000000000 --- a/arviz/plots/backends/matplotlib/distcomparisonplot.py +++ /dev/null @@ -1,119 +0,0 @@ -"""Matplotlib Density Comparison plot.""" - -import matplotlib.pyplot as plt -import numpy as np - -from ...distplot import plot_dist -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, backend_show - - -def plot_dist_comparison( - ax, - nvars, - ngroups, - figsize, - dc_plotters, - legend, - groups, - textsize, - labeller, - prior_kwargs, - posterior_kwargs, - observed_kwargs, - backend_kwargs, - show, -): - """Matplotlib Density Comparison plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - if prior_kwargs is None: - prior_kwargs = {} - - if posterior_kwargs is None: - posterior_kwargs = {} - - if observed_kwargs is None: - observed_kwargs = {} - - if backend_kwargs is None: - backend_kwargs = {} - - (figsize, _, _, _, linewidth, _) = _scale_fig_size(figsize, textsize, 2 * nvars, ngroups) - - backend_kwargs.setdefault("figsize", figsize) - - posterior_kwargs.setdefault("plot_kwargs", {}) - posterior_kwargs["plot_kwargs"]["color"] = posterior_kwargs["plot_kwargs"].get("color", "C0") - posterior_kwargs["plot_kwargs"].setdefault("linewidth", linewidth) - posterior_kwargs.setdefault("hist_kwargs", {}) - posterior_kwargs["hist_kwargs"].setdefault("alpha", 0.5) - - prior_kwargs.setdefault("plot_kwargs", {}) - prior_kwargs["plot_kwargs"]["color"] = prior_kwargs["plot_kwargs"].get("color", "C1") - prior_kwargs["plot_kwargs"].setdefault("linewidth", linewidth) - prior_kwargs.setdefault("hist_kwargs", {}) - prior_kwargs["hist_kwargs"].setdefault("alpha", 0.5) - - observed_kwargs.setdefault("plot_kwargs", {}) - observed_kwargs["plot_kwargs"]["color"] = observed_kwargs["plot_kwargs"].get("color", "C2") - observed_kwargs["plot_kwargs"].setdefault("linewidth", linewidth) - observed_kwargs.setdefault("hist_kwargs", {}) - observed_kwargs["hist_kwargs"].setdefault("alpha", 0.5) - - if ax is None: - axes = np.empty((nvars, ngroups + 1), dtype=object) - fig = plt.figure(**backend_kwargs) - gs = fig.add_gridspec(ncols=ngroups, nrows=nvars * 2) - for i in range(nvars): - for j in range(ngroups): - axes[i, j] = fig.add_subplot(gs[2 * i, j]) - axes[i, -1] = fig.add_subplot(gs[2 * i + 1, :]) - - else: - axes = ax - if ax.shape != (nvars, ngroups + 1): - raise ValueError( - f"Found {axes.shape} shape of axes, " - f"which is not equal to data shape {(nvars, ngroups + 1)}." - ) - - for idx, plotter in enumerate(dc_plotters): - group = groups[idx] - kwargs = ( - prior_kwargs - if group.startswith("prior") - else posterior_kwargs if group.startswith("posterior") else observed_kwargs - ) - for idx2, ( - var_name, - sel, - isel, - data, - ) in enumerate(plotter): - label = f"{group}" - plot_dist( - data, - label=label if legend else None, - ax=axes[idx2, idx], - **kwargs, - ) - plot_dist( - data, - label=label if legend else None, - ax=axes[idx2, -1], - **kwargs, - ) - if idx == 0: - axes[idx2, -1].set_xlabel(labeller.make_label_vert(var_name, sel, isel)) - - if backend_show(show): - plt.show() - - return axes diff --git a/arviz/plots/backends/matplotlib/distplot.py b/arviz/plots/backends/matplotlib/distplot.py deleted file mode 100644 index afd539de5b..0000000000 --- a/arviz/plots/backends/matplotlib/distplot.py +++ /dev/null @@ -1,178 +0,0 @@ -"""Matplotlib distplot.""" - -import matplotlib.pyplot as plt -from matplotlib import _pylab_helpers -import numpy as np - -from ....stats.density_utils import get_bins -from ...kdeplot import plot_kde -from ...plot_utils import _scale_fig_size, _init_kwargs_dict -from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser - - -def plot_dist( - values, - values2, - color, - kind, - cumulative, - label, - rotated, - rug, - bw, - quantiles, - contour, - fill_last, - figsize, - textsize, - plot_kwargs, - fill_kwargs, - rug_kwargs, - contour_kwargs, - contourf_kwargs, - pcolormesh_kwargs, - hist_kwargs, - is_circular, - ax, - backend_kwargs, - show, -): - """Matplotlib distplot.""" - backend_kwargs = _init_kwargs_dict(backend_kwargs) - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, *_ = _scale_fig_size(figsize, textsize) - - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - backend_kwargs.setdefault("subplot_kw", {}) - backend_kwargs["subplot_kw"].setdefault("polar", is_circular) - - if ax is None: - fig_manager = _pylab_helpers.Gcf.get_active() - if fig_manager is not None: - ax = fig_manager.canvas.figure.gca() - else: - _, ax = create_axes_grid( - 1, - backend_kwargs=backend_kwargs, - ) - - if kind == "hist": - hist_kwargs = matplotlib_kwarg_dealiaser(hist_kwargs, "hist") - hist_kwargs.setdefault("cumulative", cumulative) - hist_kwargs.setdefault("color", color) - hist_kwargs.setdefault("label", label) - hist_kwargs.setdefault("rwidth", 0.9) - hist_kwargs.setdefault("density", True) - - if rotated: - hist_kwargs.setdefault("orientation", "horizontal") - else: - hist_kwargs.setdefault("orientation", "vertical") - - ax = _histplot_mpl_op( - values=values, - values2=values2, - rotated=rotated, - ax=ax, - hist_kwargs=hist_kwargs, - is_circular=is_circular, - ) - - elif kind == "kde": - plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") - plot_kwargs.setdefault("color", color) - legend = label is not None - - ax = plot_kde( - values, - values2, - cumulative=cumulative, - rug=rug, - label=label, - bw=bw, - quantiles=quantiles, - rotated=rotated, - contour=contour, - legend=legend, - fill_last=fill_last, - textsize=textsize, - plot_kwargs=plot_kwargs, - fill_kwargs=fill_kwargs, - rug_kwargs=rug_kwargs, - contour_kwargs=contour_kwargs, - contourf_kwargs=contourf_kwargs, - pcolormesh_kwargs=pcolormesh_kwargs, - ax=ax, - backend="matplotlib", - backend_kwargs=backend_kwargs, - is_circular=is_circular, - show=show, - ) - - if backend_show(show): - plt.show() - - return ax - - -def _histplot_mpl_op(values, values2, rotated, ax, hist_kwargs, is_circular): - """Add a histogram for the data to the axes.""" - bins = hist_kwargs.pop("bins", None) - - if is_circular == "degrees": - if bins is None: - bins = get_bins(values) - values = np.deg2rad(values) - bins = np.deg2rad(bins) - - elif is_circular: - labels = [ - "0", - f"{np.pi/4:.2f}", - f"{np.pi/2:.2f}", - f"{3*np.pi/4:.2f}", - f"{np.pi:.2f}", - f"{-3*np.pi/4:.2f}", - f"{-np.pi/2:.2f}", - f"{-np.pi/4:.2f}", - ] - - ax.set_xticklabels(labels) - - if values2 is not None: - raise NotImplementedError("Insert hexbin plot here") - - if bins is None: - bins = get_bins(values) - - if values.dtype.kind == "i": - hist_kwargs.setdefault("align", "left") - else: - hist_kwargs.setdefault("align", "mid") - - n, bins, _ = ax.hist(np.asarray(values).flatten(), bins=bins, **hist_kwargs) - - if values.dtype.kind == "i": - ticks = bins[:-1] - else: - ticks = (bins[1:] + bins[:-1]) / 2 - - if rotated: - ax.set_yticks(ticks) - elif not is_circular: - ax.set_xticks(ticks) - - if is_circular: - ax.set_ylim(0, 1.5 * n.max()) - ax.set_yticklabels([]) - - if hist_kwargs.get("label") is not None: - ax.legend() - - return ax diff --git a/arviz/plots/backends/matplotlib/dotplot.py b/arviz/plots/backends/matplotlib/dotplot.py deleted file mode 100644 index 73250bed28..0000000000 --- a/arviz/plots/backends/matplotlib/dotplot.py +++ /dev/null @@ -1,116 +0,0 @@ -"""Matplotlib dotplot.""" - -import math -import warnings -import numpy as np -import matplotlib.pyplot as plt -from matplotlib import _pylab_helpers - -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, create_axes_grid, backend_show -from ...plot_utils import plot_point_interval -from ...dotplot import wilkinson_algorithm, layout_stacks - - -def plot_dot( - values, - binwidth, - dotsize, - stackratio, - hdi_prob, - quartiles, - rotated, - dotcolor, - intervalcolor, - markersize, - markercolor, - marker, - figsize, - linewidth, - point_estimate, - nquantiles, - point_interval, - ax, - show, - backend_kwargs, - plot_kwargs, -): - """Matplotlib dotplot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = {**backend_kwarg_defaults(), **backend_kwargs} - - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - - (figsize, _, _, _, auto_linewidth, auto_markersize) = _scale_fig_size(figsize, None) - - if plot_kwargs is None: - plot_kwargs = {} - plot_kwargs.setdefault("color", dotcolor) - - if linewidth is None: - linewidth = auto_linewidth - - if markersize is None: - markersize = auto_markersize - - if ax is None: - fig_manager = _pylab_helpers.Gcf.get_active() - if fig_manager is not None: - ax = fig_manager.canvas.figure.gca() - else: - _, ax = create_axes_grid( - 1, - backend_kwargs=backend_kwargs, - ) - - if point_interval: - ax = plot_point_interval( - ax, - values, - point_estimate, - hdi_prob, - quartiles, - linewidth, - markersize, - markercolor, - marker, - rotated, - intervalcolor, - "matplotlib", - ) - - if nquantiles > values.shape[0]: - warnings.warn( - "nquantiles must be less than or equal to the number of data points", UserWarning - ) - nquantiles = values.shape[0] - else: - qlist = np.linspace(1 / (2 * nquantiles), 1 - 1 / (2 * nquantiles), nquantiles) - values = np.quantile(values, qlist) - - if binwidth is None: - binwidth = math.sqrt((values[-1] - values[0] + 1) ** 2 / (2 * nquantiles * np.pi)) - - ## Wilkinson's Algorithm - stack_locs, stack_count = wilkinson_algorithm(values, binwidth) - x, y = layout_stacks(stack_locs, stack_count, binwidth, stackratio, rotated) - - for x_i, y_i in zip(x, y): - dot = plt.Circle((x_i, y_i), dotsize * binwidth / 2, **plot_kwargs) - ax.add_patch(dot) - - if rotated: - ax.tick_params(bottom=False, labelbottom=False) - else: - ax.tick_params(left=False, labelleft=False) - - ax.set_aspect("equal", adjustable="datalim") - ax.autoscale() - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/ecdfplot.py b/arviz/plots/backends/matplotlib/ecdfplot.py deleted file mode 100644 index bb9764cdb2..0000000000 --- a/arviz/plots/backends/matplotlib/ecdfplot.py +++ /dev/null @@ -1,70 +0,0 @@ -"""Matplotlib ecdfplot.""" - -import matplotlib.pyplot as plt -from matplotlib.colors import to_hex - -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, create_axes_grid, backend_show - - -def plot_ecdf( - x_coord, - y_coord, - x_bands, - lower, - higher, - plot_kwargs, - fill_kwargs, - plot_outline_kwargs, - figsize, - fill_band, - ax, - show, - backend_kwargs, -): - """Matplotlib ecdfplot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - (figsize, _, _, _, _, _) = _scale_fig_size(figsize, None) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - - if ax is None: - _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs) - - if plot_kwargs is None: - plot_kwargs = {} - - plot_kwargs.setdefault("where", "post") - - if fill_band: - if fill_kwargs is None: - fill_kwargs = {} - fill_kwargs.setdefault("step", "post") - fill_kwargs.setdefault("color", to_hex("C0")) - fill_kwargs.setdefault("alpha", 0.2) - else: - if plot_outline_kwargs is None: - plot_outline_kwargs = {} - plot_outline_kwargs.setdefault("where", "post") - plot_outline_kwargs.setdefault("color", to_hex("C0")) - plot_outline_kwargs.setdefault("alpha", 0.2) - - ax.step(x_coord, y_coord, **plot_kwargs) - - if x_bands is not None: - if fill_band: - ax.fill_between(x_bands, lower, higher, **fill_kwargs) - else: - ax.plot(x_bands, lower, x_bands, higher, **plot_outline_kwargs) - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/elpdplot.py b/arviz/plots/backends/matplotlib/elpdplot.py deleted file mode 100644 index 456beb6557..0000000000 --- a/arviz/plots/backends/matplotlib/elpdplot.py +++ /dev/null @@ -1,189 +0,0 @@ -"""Matplotlib ELPDPlot.""" - -import warnings - -from matplotlib import cm -import matplotlib.pyplot as plt -import numpy as np -from matplotlib.lines import Line2D - -from ....rcparams import rcParams -from ...plot_utils import _scale_fig_size, color_from_dim, set_xticklabels -from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser - - -def plot_elpd( - ax, - models, - pointwise_data, - numvars, - figsize, - textsize, - plot_kwargs, - xlabels, - coord_labels, - xdata, - threshold, - legend, - color, - backend_kwargs, - show, -): - """Matplotlib elpd plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - backend_kwargs.setdefault("constrained_layout", not xlabels) - - plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "scatter") - - markersize = None - - if isinstance(color, str) and color in pointwise_data[0].dims: - colors, color_mapping = color_from_dim(pointwise_data[0], color) - cmap_name = plot_kwargs.pop("cmap", plt.rcParams["image.cmap"]) - markersize = plot_kwargs.pop("s", plt.rcParams["lines.markersize"]) - cmap = getattr(cm, cmap_name) - handles = [ - Line2D([], [], color=cmap(float_color), label=coord, ms=markersize, lw=0, **plot_kwargs) - for coord, float_color in color_mapping.items() - ] - plot_kwargs.setdefault("cmap", cmap_name) - plot_kwargs.setdefault("s", markersize**2) - plot_kwargs.setdefault("c", colors) - else: - legend = False - plot_kwargs.setdefault("c", color) - - # flatten data (data must be flattened after selecting, labeling and coloring) - pointwise_data = [pointwise.values.flatten() for pointwise in pointwise_data] - - if numvars == 2: - (figsize, ax_labelsize, titlesize, xt_labelsize, _, markersize) = _scale_fig_size( - figsize, textsize, numvars - 1, numvars - 1 - ) - plot_kwargs.setdefault("s", markersize**2) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - if ax is None: - fig, ax = create_axes_grid( - 1, - backend_kwargs=backend_kwargs, - ) - else: - fig = ax.get_figure() - - ydata = pointwise_data[0] - pointwise_data[1] - ax.scatter(xdata, ydata, **plot_kwargs) - if threshold is not None: - diff_abs = np.abs(ydata - ydata.mean()) - bool_ary = diff_abs > threshold * ydata.std() - if coord_labels is None: - coord_labels = xdata.astype(str) - outliers = np.nonzero(bool_ary)[0] - for outlier in outliers: - label = coord_labels[outlier] - ax.text( - outlier, - ydata[outlier], - label, - horizontalalignment="center", - verticalalignment="bottom" if ydata[outlier] > 0 else "top", - fontsize=0.8 * xt_labelsize, - ) - - ax.set_title("{} - {}".format(*models), fontsize=titlesize, wrap=True) - ax.set_ylabel("ELPD difference", fontsize=ax_labelsize, wrap=True) - ax.tick_params(labelsize=xt_labelsize) - if xlabels: - set_xticklabels(ax, coord_labels) - fig.autofmt_xdate() - fig.tight_layout() - if legend: - ncols = len(handles) // 6 + 1 - ax.legend(handles=handles, ncol=ncols, title=color) - - else: - max_plots = ( - numvars**2 if rcParams["plot.max_subplots"] is None else rcParams["plot.max_subplots"] - ) - vars_to_plot = np.sum(np.arange(numvars).cumsum() < max_plots) - if vars_to_plot < numvars: - warnings.warn( - "rcParams['plot.max_subplots'] ({max_plots}) is smaller than the number " - "of resulting ELPD pairwise plots with these variables, generating only a " - "{side}x{side} grid".format(max_plots=max_plots, side=vars_to_plot), - UserWarning, - ) - numvars = vars_to_plot - - (figsize, ax_labelsize, titlesize, xt_labelsize, _, markersize) = _scale_fig_size( - figsize, textsize, numvars - 2, numvars - 2 - ) - plot_kwargs.setdefault("s", markersize**2) - - if ax is None: - fig, ax = plt.subplots( - numvars - 1, - numvars - 1, - figsize=figsize, - squeeze=False, - constrained_layout=not xlabels, - sharey="row", - sharex="col", - ) - else: - fig = ax.ravel()[0].get_figure() - - for i in range(0, numvars - 1): - var1 = pointwise_data[i] - - for j in range(0, numvars - 1): - if j < i: - ax[j, i].axis("off") - continue - - var2 = pointwise_data[j + 1] - ax[j, i].scatter(xdata, var1 - var2, **plot_kwargs) - if threshold is not None: - ydata = var1 - var2 - diff_abs = np.abs(ydata - ydata.mean()) - bool_ary = diff_abs > threshold * ydata.std() - if coord_labels is None: - coord_labels = xdata.astype(str) - outliers = np.nonzero(bool_ary)[0] - for outlier in outliers: - label = coord_labels[outlier] - ax[j, i].text( - outlier, - ydata[outlier], - label, - horizontalalignment="center", - verticalalignment="bottom" if ydata[outlier] > 0 else "top", - fontsize=0.8 * xt_labelsize, - ) - - if i == 0: - ax[j, i].set_ylabel("ELPD difference", fontsize=ax_labelsize, wrap=True) - - ax[j, i].tick_params(labelsize=xt_labelsize) - ax[j, i].set_title(f"{models[i]} - {models[j + 1]}", fontsize=titlesize, wrap=True) - if xlabels: - for i in range(len(ax)): - set_xticklabels(ax[-1, i], coord_labels) - fig.autofmt_xdate() - fig.tight_layout() - if legend: - ncols = len(handles) // 6 + 1 - ax[0, 1].legend( - handles=handles, ncol=ncols, title=color, bbox_to_anchor=(0, 1), loc="upper left" - ) - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/energyplot.py b/arviz/plots/backends/matplotlib/energyplot.py deleted file mode 100644 index 3c263248c0..0000000000 --- a/arviz/plots/backends/matplotlib/energyplot.py +++ /dev/null @@ -1,113 +0,0 @@ -"""Matplotlib energyplot.""" - -from itertools import cycle - -import matplotlib.pyplot as plt -import numpy as np -from matplotlib.pyplot import rcParams as mpl_rcParams - -from ....stats import bfmi as e_bfmi -from ...kdeplot import plot_kde -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser - - -def plot_energy( - ax, - energy, - kind, - bfmi, - figsize, - textsize, - fill_alpha, - fill_color, - fill_kwargs, - plot_kwargs, - bw, - legend, - backend_kwargs, - show, -): - """Matplotlib energy plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, _, _, xt_labelsize, linewidth, _ = _scale_fig_size(figsize, textsize, 1, 1) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - if ax is None: - _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs) - - fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, "hexbin") - types = "hist" if kind == "hist" else "plot" - plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, types) - - _colors = [ - prop for _, prop in zip(range(10), cycle(mpl_rcParams["axes.prop_cycle"].by_key()["color"])) - ] - if (fill_color[0].startswith("C") and len(fill_color[0]) == 2) and ( - fill_color[1].startswith("C") and len(fill_color[1]) == 2 - ): - fill_color = tuple((_colors[int(color[1:]) % 10] for color in fill_color)) - elif fill_color[0].startswith("C") and len(fill_color[0]) == 2: - fill_color = tuple([_colors[int(fill_color[0][1:]) % 10]] + list(fill_color[1:])) - elif fill_color[1].startswith("C") and len(fill_color[1]) == 2: - fill_color = tuple(list(fill_color[1:]) + [_colors[int(fill_color[0][1:]) % 10]]) - - series = zip( - fill_alpha, - fill_color, - ("Marginal Energy", "Energy transition"), - (energy - energy.mean(), np.diff(energy)), - ) - - if kind == "kde": - for alpha, color, label, value in series: - fill_kwargs["alpha"] = alpha - fill_kwargs["color"] = color - plot_kwargs.setdefault("color", color) - plot_kwargs.setdefault("alpha", 0) - plot_kwargs.setdefault("linewidth", linewidth) - plot_kde( - value, - bw=bw, - label=label, - textsize=xt_labelsize, - fill_kwargs=fill_kwargs, - plot_kwargs=plot_kwargs, - ax=ax, - legend=False, - ) - elif kind == "hist": - for alpha, color, label, value in series: - ax.hist( - value.flatten(), - bins="auto", - density=True, - alpha=alpha, - label=label, - color=color, - **plot_kwargs, - ) - - else: - raise ValueError(f"Plot type {kind} not recognized.") - - if bfmi: - for idx, val in enumerate(e_bfmi(energy)): - ax.plot([], label=f"chain {idx:>2} BFMI = {val:.2f}", alpha=0) - if legend: - ax.legend() - - ax.set_xticks([]) - ax.set_yticks([]) - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/essplot.py b/arviz/plots/backends/matplotlib/essplot.py deleted file mode 100644 index 8071b88405..0000000000 --- a/arviz/plots/backends/matplotlib/essplot.py +++ /dev/null @@ -1,180 +0,0 @@ -"""Matplotlib energyplot.""" - -import matplotlib.pyplot as plt -import numpy as np -from scipy.stats import rankdata - -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser - - -def plot_ess( - ax, - plotters, - xdata, - ess_tail_dataset, - mean_ess, - sd_ess, - idata, - data, - kind, - extra_methods, - textsize, - rows, - cols, - figsize, - kwargs, - extra_kwargs, - text_kwargs, - n_samples, - relative, - min_ess, - labeller, - ylabel, - rug, - rug_kind, - rug_kwargs, - hline_kwargs, - backend_kwargs, - show, -): - """Matplotlib ess plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - (figsize, ax_labelsize, titlesize, xt_labelsize, _linewidth, _markersize) = _scale_fig_size( - figsize, textsize, rows, cols - ) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - - kwargs = matplotlib_kwarg_dealiaser(kwargs, "plot") - _linestyle = "-" if kind == "evolution" else "none" - kwargs.setdefault("linestyle", _linestyle) - kwargs.setdefault("linewidth", _linewidth) - kwargs.setdefault("markersize", _markersize) - kwargs.setdefault("marker", "o") - kwargs.setdefault("zorder", 3) - - extra_kwargs = matplotlib_kwarg_dealiaser(extra_kwargs, "plot") - if kind == "evolution": - extra_kwargs = { - **extra_kwargs, - **{key: item for key, item in kwargs.items() if key not in extra_kwargs}, - } - kwargs.setdefault("label", "bulk") - extra_kwargs.setdefault("label", "tail") - else: - extra_kwargs.setdefault("linewidth", _linewidth / 2) - extra_kwargs.setdefault("color", "k") - extra_kwargs.setdefault("alpha", 0.5) - kwargs.setdefault("label", kind) - - hline_kwargs = matplotlib_kwarg_dealiaser(hline_kwargs, "plot") - hline_kwargs.setdefault("linewidth", _linewidth) - hline_kwargs.setdefault("linestyle", "--") - hline_kwargs.setdefault("color", "gray") - hline_kwargs.setdefault("alpha", 0.7) - if extra_methods: - text_kwargs = matplotlib_kwarg_dealiaser(text_kwargs, "text") - text_x = text_kwargs.pop("x", 1) - text_kwargs.setdefault("fontsize", xt_labelsize * 0.7) - text_kwargs.setdefault("alpha", extra_kwargs["alpha"]) - text_kwargs.setdefault("color", extra_kwargs["color"]) - text_kwargs.setdefault("horizontalalignment", "right") - text_va = text_kwargs.pop("verticalalignment", None) - - if ax is None: - _, ax = create_axes_grid( - len(plotters), - rows, - cols, - backend_kwargs=backend_kwargs, - ) - - for (var_name, selection, isel, x), ax_ in zip(plotters, np.ravel(ax)): - ax_.plot(xdata, x, **kwargs) - if kind == "evolution": - ess_tail = ess_tail_dataset[var_name].sel(**selection) - ax_.plot(xdata, ess_tail, **extra_kwargs) - elif rug: - rug_kwargs = matplotlib_kwarg_dealiaser(rug_kwargs, "plot") - if not hasattr(idata, "sample_stats"): - raise ValueError("InferenceData object must contain sample_stats for rug plot") - if not hasattr(idata.sample_stats, rug_kind): - raise ValueError(f"InferenceData does not contain {rug_kind} data") - rug_kwargs.setdefault("marker", "|") - rug_kwargs.setdefault("linestyle", rug_kwargs.pop("ls", "None")) - rug_kwargs.setdefault("color", rug_kwargs.pop("c", kwargs.get("color", "C0"))) - rug_kwargs.setdefault("space", 0.1) - rug_kwargs.setdefault("markersize", rug_kwargs.pop("ms", 2 * _markersize)) - - values = data[var_name].sel(**selection).values.flatten() - mask = idata.sample_stats[rug_kind].values.flatten() - values = rankdata(values, method="average")[mask] - rug_space = np.max(x) * rug_kwargs.pop("space") - rug_x, rug_y = values / (len(mask) - 1), np.zeros_like(values) - rug_space - ax_.plot(rug_x, rug_y, **rug_kwargs) - ax_.axhline(0, color="k", linewidth=_linewidth, alpha=0.7) - if extra_methods: - mean_ess_i = mean_ess[var_name].sel(**selection).values.item() - sd_ess_i = sd_ess[var_name].sel(**selection).values.item() - ax_.axhline(mean_ess_i, **extra_kwargs) - ax_.annotate( - "mean", - (text_x, mean_ess_i), - va=( - text_va - if text_va is not None - else "bottom" if mean_ess_i >= sd_ess_i else "top" - ), - **text_kwargs, - ) - ax_.axhline(sd_ess_i, **extra_kwargs) - ax_.annotate( - "sd", - (text_x, sd_ess_i), - va=text_va if text_va is not None else "bottom" if sd_ess_i > mean_ess_i else "top", - **text_kwargs, - ) - - if relative and kind == "evolution": - thin_xdata = np.linspace(xdata.min(), xdata.max(), 100) - ax_.plot(thin_xdata, min_ess / thin_xdata, **hline_kwargs) - else: - hline = min_ess / n_samples if relative else min_ess - ax_.axhline(hline, **hline_kwargs) - - ax_.set_title( - labeller.make_label_vert(var_name, selection, isel), fontsize=titlesize, wrap=True - ) - ax_.tick_params(labelsize=xt_labelsize) - ax_.set_xlabel( - "Total number of draws" if kind == "evolution" else "Quantile", fontsize=ax_labelsize - ) - ax_.set_ylabel( - ylabel.format("Relative ESS" if relative else "ESS"), fontsize=ax_labelsize, wrap=True - ) - if kind == "evolution": - ax_.legend(title="Method", fontsize=xt_labelsize, title_fontsize=xt_labelsize) - else: - ax_.set_xlim(0, 1) - if rug: - ax_.yaxis.get_major_locator().set_params(nbins="auto", steps=[1, 2, 5, 10]) - _, ymax = ax_.get_ylim() - yticks = ax_.get_yticks().astype(np.int64) - yticks = yticks[(yticks >= 0) & (yticks < ymax)] - ax_.set_yticks(yticks) - ax_.set_yticklabels(yticks) - else: - ax_.set_ylim(bottom=0) - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/forestplot.py b/arviz/plots/backends/matplotlib/forestplot.py deleted file mode 100644 index 25a085b60c..0000000000 --- a/arviz/plots/backends/matplotlib/forestplot.py +++ /dev/null @@ -1,656 +0,0 @@ -"""Matplotlib forestplot.""" - -from collections import OrderedDict, defaultdict -from itertools import tee - -import matplotlib.pyplot as plt -import numpy as np -from matplotlib.colors import to_rgba -from matplotlib.lines import Line2D - -from ....stats import hdi -from ....stats.density_utils import get_bins, histogram, kde -from ....stats.diagnostics import _ess, _rhat -from ....sel_utils import xarray_var_iter -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, backend_show - - -def pairwise(iterable): - """From itertools cookbook. [a, b, c, ...] -> (a, b), (b, c), ...""" - first, second = tee(iterable) - next(second, None) - return zip(first, second) - - -def plot_forest( - ax, - datasets, - var_names, - model_names, - combined, - combine_dims, - colors, - figsize, - width_ratios, - linewidth, - markersize, - kind, - ncols, - hdi_prob, - quartiles, - rope, - ridgeplot_overlap, - ridgeplot_alpha, - ridgeplot_kind, - ridgeplot_truncate, - ridgeplot_quantiles, - textsize, - legend, - labeller, - ess, - r_hat, - backend_kwargs, - backend_config, # pylint: disable=unused-argument - show, -): - """Matplotlib forest plot.""" - plot_handler = PlotHandler( - datasets, - var_names=var_names, - model_names=model_names, - combined=combined, - combine_dims=combine_dims, - colors=colors, - labeller=labeller, - ) - - if figsize is None: - if kind == "ridgeplot": - figsize = (min(14, sum(width_ratios) * 4), plot_handler.fig_height() * 1.2) - else: - figsize = (min(12, sum(width_ratios) * 2), plot_handler.fig_height()) - - (figsize, _, titlesize, xt_labelsize, auto_linewidth, auto_markersize) = _scale_fig_size( - figsize, textsize, 1.1, 1 - ) - - if linewidth is None: - linewidth = auto_linewidth - - if markersize is None: - markersize = auto_markersize - - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - if ax is None: - _, axes = plt.subplots( - nrows=1, - ncols=ncols, - figsize=figsize, - gridspec_kw={"width_ratios": width_ratios}, - sharey=True, - **backend_kwargs, - ) - else: - axes = ax - - axes = np.atleast_1d(axes) - if kind == "forestplot": - plot_handler.forestplot( - hdi_prob, - quartiles, - xt_labelsize, - titlesize, - linewidth, - markersize, - axes[0], - rope, - ) - elif kind == "ridgeplot": - plot_handler.ridgeplot( - hdi_prob, - ridgeplot_overlap, - linewidth, - markersize, - ridgeplot_alpha, - ridgeplot_kind, - ridgeplot_truncate, - ridgeplot_quantiles, - axes[0], - ) - else: - raise TypeError( - f"Argument 'kind' must be one of 'forestplot' " f"or 'ridgeplot' (you provided {kind})" - ) - - idx = 1 - if ess: - plot_handler.plot_neff(axes[idx], xt_labelsize, titlesize, markersize) - idx += 1 - - if r_hat: - plot_handler.plot_rhat(axes[idx], xt_labelsize, titlesize, markersize) - idx += 1 - - for ax_ in axes: - if kind == "ridgeplot": - ax_.grid(False) - else: - ax_.grid(False, axis="y") - # Remove ticklines on y-axes - ax_.tick_params(axis="y", left=False, right=False) - - for loc, spine in ax_.spines.items(): - if loc in ["left", "right"]: - spine.set_visible(False) - - if len(plot_handler.data) > 1: - plot_handler.make_bands(ax_) - - labels, ticks = plot_handler.labels_and_ticks() - axes[0].set_yticks(ticks) - axes[0].set_yticklabels(labels) - all_plotters = list(plot_handler.plotters.values()) - y_max = plot_handler.y_max() - all_plotters[-1].group_offset - if kind == "ridgeplot": # space at the top - y_max += ridgeplot_overlap - axes[0].set_ylim(-all_plotters[0].group_offset, y_max) - if legend: - plot_handler.legend(ax=axes[0]) - - if backend_show(show): - plt.show() - - return axes - - -class PlotHandler: - """Class to handle logic from ForestPlot.""" - - # pylint: disable=inconsistent-return-statements - - def __init__(self, datasets, var_names, model_names, combined, combine_dims, colors, labeller): - self.data = datasets - - if model_names is None: - if len(self.data) > 1: - model_names = [f"Model {idx}" for idx, _ in enumerate(self.data)] - else: - model_names = [None] - elif len(model_names) != len(self.data): - raise ValueError("The number of model names does not match the number of models") - - self.model_names = list(reversed(model_names)) # y-values are upside down - - if var_names is None: - if len(self.data) > 1: - self.var_names = list( - set().union(*[OrderedDict(datum.data_vars) for datum in self.data]) - ) - else: - self.var_names = list( - reversed(*[OrderedDict(datum.data_vars) for datum in self.data]) - ) - else: - self.var_names = list(reversed(var_names)) # y-values are upside down - - self.combined = combined - self.combine_dims = combine_dims - - if colors == "cycle": - # TODO: Use matplotlib prop cycle instead - colors = [f"C{idx}" for idx, _ in enumerate(self.data)] - elif isinstance(colors, str): - colors = [colors for _ in self.data] - - self.colors = list(reversed(colors)) # y-values are upside down - self.labeller = labeller - - self.plotters = self.make_plotters() - - def make_plotters(self): - """Initialize an object for each variable to be plotted.""" - plotters, y = {}, 0 - for var_name in self.var_names: - plotters[var_name] = VarHandler( - var_name, - self.data, - y, - model_names=self.model_names, - combined=self.combined, - combine_dims=self.combine_dims, - colors=self.colors, - labeller=self.labeller, - ) - y = plotters[var_name].y_max() - return plotters - - def labels_and_ticks(self): - """Collect labels and ticks from plotters.""" - val = self.plotters.values() - - def label_idxs(): - labels, idxs = [], [] - for plotter in val: - sub_labels, sub_idxs, _, _ = plotter.labels_ticks_and_vals() - labels_to_idxs = defaultdict(list) - for label, idx in zip(sub_labels, sub_idxs): - labels_to_idxs[label].append(idx) - sub_idxs = [] - sub_labels = [] - for label, all_idx in labels_to_idxs.items(): - sub_labels.append(label) - sub_idxs.append(np.mean([j for j in all_idx])) - labels.append(sub_labels) - idxs.append(sub_idxs) - return np.concatenate(labels), np.concatenate(idxs) - - return label_idxs() - - def legend(self, ax): - """Add legend with colorcoded model info.""" - handles = [Line2D([], [], color=c) for c in self.colors] - ax.legend(handles=handles, labels=self.model_names) - - def display_multiple_ropes(self, rope, ax, y, linewidth, var_name, selection): - """Display ROPE when more than one interval is provided.""" - for sel in rope.get(var_name, []): - # pylint: disable=line-too-long - if all(k in selection and selection[k] == v for k, v in sel.items() if k != "rope"): - vals = sel["rope"] - ax.plot( - vals, - (y + 0.05, y + 0.05), - lw=linewidth * 2, - color="C2", - solid_capstyle="round", - zorder=0, - alpha=0.7, - ) - return ax - - def ridgeplot( - self, - hdi_prob, - mult, - linewidth, - markersize, - alpha, - ridgeplot_kind, - ridgeplot_truncate, - ridgeplot_quantiles, - ax, - ): - """Draw ridgeplot for each plotter. - - Parameters - ---------- - hdi_prob : float - Probability for the highest density interval. - mult : float - How much to multiply height by. Set this to greater than 1 to have some overlap. - linewidth : float - Width of line on border of ridges - markersize : float - Size of marker in center of forestplot line - alpha : float - Transparency of ridges - ridgeplot_kind : string - By default ("auto") continuous variables are plotted using KDEs and discrete ones using - histograms. To override this use "hist" to plot histograms and "density" for KDEs - ridgeplot_truncate: bool - Whether to truncate densities according to the value of hdi_prop. Defaults to True - ridgeplot_quantiles: list - Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles. - Defaults to None. - ax : Axes - Axes to draw on - """ - if alpha is None: - alpha = 1.0 - zorder = 0 - for plotter in self.plotters.values(): - for x, y_min, y_max, hdi_, y_q, color in plotter.ridgeplot( - hdi_prob, mult, ridgeplot_kind - ): - if alpha == 0: - border = color - facecolor = "None" - else: - border = "k" - if x.dtype.kind == "i": - if ridgeplot_truncate: - facecolor = to_rgba(color, alpha) - y_max = y_max[(x >= hdi_[0]) & (x <= hdi_[1])] - x = x[(x >= hdi_[0]) & (x <= hdi_[1])] - else: - facecolor = [ - to_rgba(color, alpha) if ci else "None" - for ci in ((x >= hdi_[0]) & (x <= hdi_[1])) - ] - y_min = np.ones_like(x) * y_min - ax.bar( - x, - y_max - y_min, - bottom=y_min, - linewidth=linewidth, - ec=border, - color=facecolor, - alpha=None, - zorder=zorder, - ) - else: - tr_x = x[(x >= hdi_[0]) & (x <= hdi_[1])] - tr_y_min = np.ones_like(tr_x) * y_min - tr_y_max = y_max[(x >= hdi_[0]) & (x <= hdi_[1])] - y_min = np.ones_like(x) * y_min - if ridgeplot_truncate: - ax.plot( - tr_x, tr_y_max, "-", linewidth=linewidth, color=border, zorder=zorder - ) - ax.plot( - tr_x, tr_y_min, "-", linewidth=linewidth, color=border, zorder=zorder - ) - else: - ax.plot(x, y_max, "-", linewidth=linewidth, color=border, zorder=zorder) - ax.plot(x, y_min, "-", linewidth=linewidth, color=border, zorder=zorder) - ax.fill_between( - tr_x, tr_y_max, tr_y_min, alpha=alpha, color=color, zorder=zorder - ) - - if ridgeplot_quantiles is not None: - quantiles = [x[np.sum(y_q < quant)] for quant in ridgeplot_quantiles] - ax.plot( - quantiles, - np.ones_like(quantiles) * y_min[0], - "d", - mfc=border, - mec=border, - ms=markersize, - ) - zorder -= 1 - return ax - - def forestplot( - self, hdi_prob, quartiles, xt_labelsize, titlesize, linewidth, markersize, ax, rope - ): - """Draw forestplot for each plotter. - - Parameters - ---------- - hdi_prob : float - Probability for the highest density interval. Width of each line. - quartiles : bool - Whether to mark quartiles - xt_textsize : float - Size of tick text - titlesize : float - Size of title text - linewidth : float - Width of forestplot line - markersize : float - Size of marker in center of forestplot line - ax : Axes - Axes to draw on - """ - # Quantiles to be calculated - endpoint = 100 * (1 - hdi_prob) / 2 - if quartiles: - qlist = [endpoint, 25, 50, 75, 100 - endpoint] - else: - qlist = [endpoint, 50, 100 - endpoint] - - for plotter in self.plotters.values(): - for y, selection, values, color in plotter.treeplot(qlist, hdi_prob): - if isinstance(rope, dict): - self.display_multiple_ropes(rope, ax, y, linewidth, plotter.var_name, selection) - - mid = len(values) // 2 - param_iter = zip( - np.linspace(2 * linewidth, linewidth, mid, endpoint=True)[-1::-1], range(mid) - ) - for width, j in param_iter: - ax.hlines(y, values[j], values[-(j + 1)], linewidth=width, color=color) - ax.plot( - values[mid], - y, - "o", - mfc=ax.get_facecolor(), - markersize=markersize * 0.75, - color=color, - ) - ax.tick_params(labelsize=xt_labelsize) - ax.set_title(f"{hdi_prob:.1%} HDI", fontsize=titlesize, wrap=True) - if rope is None or isinstance(rope, dict): - return - elif len(rope) == 2: - ax.axvspan(rope[0], rope[1], 0, self.y_max(), color="C2", alpha=0.5) - else: - raise ValueError( - "Argument `rope` must be None, a dictionary like" - '{"var_name": {"rope": (lo, hi)}}, or an ' - "iterable of length 2" - ) - return ax - - def plot_neff(self, ax, xt_labelsize, titlesize, markersize): - """Draw effective n for each plotter.""" - for plotter in self.plotters.values(): - for y, ess, color in plotter.ess(): - if ess is not None: - ax.plot( - ess, - y, - "o", - color=color, - clip_on=False, - markersize=markersize, - markeredgecolor="k", - ) - ax.set_xlim(left=0) - ax.set_title("ess", fontsize=titlesize, wrap=True) - ax.tick_params(labelsize=xt_labelsize) - return ax - - def plot_rhat(self, ax, xt_labelsize, titlesize, markersize): - """Draw r-hat for each plotter.""" - for plotter in self.plotters.values(): - for y, r_hat, color in plotter.r_hat(): - if r_hat is not None: - ax.plot(r_hat, y, "o", color=color, markersize=markersize, markeredgecolor="k") - ax.set_xlim(left=0.9, right=2.1) - ax.set_xticks([1, 2]) - ax.tick_params(labelsize=xt_labelsize) - ax.set_title("r_hat", fontsize=titlesize, wrap=True) - return ax - - def make_bands(self, ax): - """Draw shaded horizontal bands for each plotter.""" - y_vals, y_prev, is_zero = [0], None, False - prev_color_index = 0 - for plotter in self.plotters.values(): - for y, *_, color in plotter.iterator(): - if self.colors.index(color) < prev_color_index: - if not is_zero and y_prev is not None: - y_vals.append((y + y_prev) * 0.5) - is_zero = True - else: - is_zero = False - prev_color_index = self.colors.index(color) - y_prev = y - - offset = plotter.group_offset # pylint: disable=undefined-loop-variable - - y_vals.append(y_prev + offset) - for idx, (y_start, y_stop) in enumerate(pairwise(y_vals)): - ax.axhspan(y_start, y_stop, color="k", alpha=0.1 * (idx % 2)) - return ax - - def fig_height(self): - """Figure out the height of this plot.""" - # hand-tuned - return ( - 4 - + len(self.data) * len(self.var_names) - - 1 - + 0.1 * sum(1 for j in self.plotters.values() for _ in j.iterator()) - ) - - def y_max(self): - """Get maximum y value for the plot.""" - return max(p.y_max() for p in self.plotters.values()) - - -# pylint: disable=too-many-instance-attributes -class VarHandler: - """Handle individual variable logic.""" - - def __init__( - self, var_name, data, y_start, model_names, combined, combine_dims, colors, labeller - ): - self.var_name = var_name - self.data = data - self.y_start = y_start - self.model_names = model_names - self.combined = combined - self.combine_dims = combine_dims - self.colors = colors - self.labeller = labeller - self.model_color = dict(zip(self.model_names, self.colors)) - max_chains = max(datum.chain.max().values for datum in data) - self.chain_offset = len(data) * 0.45 / max(1, max_chains) - self.var_offset = 1.5 * self.chain_offset - self.group_offset = 2 * self.var_offset - - def iterator(self): - """Iterate over models and chains for each variable.""" - if self.combined: - grouped_data = [[(0, datum)] for datum in self.data] - skip_dims = self.combine_dims.union({"chain"}) - else: - grouped_data = [datum.groupby("chain", squeeze=False) for datum in self.data] - skip_dims = self.combine_dims - - label_dict = OrderedDict() - selection_list = [] - for name, grouped_datum in zip(self.model_names, grouped_data): - for _, sub_data in grouped_datum: - datum_iter = xarray_var_iter( - sub_data.squeeze(), - var_names=[self.var_name], - skip_dims=skip_dims, - reverse_selections=True, - ) - datum_list = list(datum_iter) - for _, selection, isel, values in datum_list: - selection_list.append(selection) - if not selection or not len(selection_list) % len(datum_list): - var_name = self.var_name - else: - var_name = "" - label = self.labeller.make_label_flat(var_name, selection, isel) - if label not in label_dict: - label_dict[label] = OrderedDict() - if name not in label_dict[label]: - label_dict[label][name] = [] - label_dict[label][name].append(values) - - y = self.y_start - for idx, (label, model_data) in enumerate(label_dict.items()): - for model_name, value_list in model_data.items(): - row_label = self.labeller.make_model_label(model_name, label) - for values in value_list: - yield y, row_label, label, selection_list[idx], values, self.model_color[ - model_name - ] - y += self.chain_offset - y += self.var_offset - y += self.group_offset - - def labels_ticks_and_vals(self): - """Get labels, ticks, values, and colors for the variable.""" - y_ticks = defaultdict(list) - for y, label, _, _, vals, color in self.iterator(): - y_ticks[label].append((y, vals, color)) - labels, ticks, vals, colors = [], [], [], [] - for label, all_data in y_ticks.items(): - for data in all_data: - labels.append(label) - ticks.append(data[0]) - vals.append(np.array(data[1])) - colors.append(data[2]) # the colors are all the same - return labels, ticks, vals, colors - - def treeplot(self, qlist, hdi_prob): - """Get data for each treeplot for the variable.""" - for y, _, _, selection, values, color in self.iterator(): - ntiles = np.percentile(values.flatten(), qlist) - ntiles[0], ntiles[-1] = hdi(values.flatten(), hdi_prob, multimodal=False) - yield y, selection, ntiles, color - - def ridgeplot(self, hdi_prob, mult, ridgeplot_kind): - """Get data for each ridgeplot for the variable.""" - xvals, hdi_vals, yvals, pdfs, pdfs_q, colors = [], [], [], [], [], [] - for y, *_, values, color in self.iterator(): - yvals.append(y) - colors.append(color) - values = values.flatten() - values = values[np.isfinite(values)] - - if hdi_prob != 1: - hdi_ = hdi(values, hdi_prob, multimodal=False) - else: - hdi_ = min(values), max(values) - - if ridgeplot_kind == "auto": - kind = "hist" if np.all(np.mod(values, 1) == 0) else "density" - else: - kind = ridgeplot_kind - - if kind == "hist": - _, density, x = histogram(values, bins=get_bins(values)) - x = x[:-1] - elif kind == "density": - x, density = kde(values) - - density_q = density.cumsum() / density.sum() - - xvals.append(x) - pdfs.append(density) - pdfs_q.append(density_q) - hdi_vals.append(hdi_) - - scaling = max(np.max(j) for j in pdfs) - for y, x, hdi_val, pdf, pdf_q, color in zip(yvals, xvals, hdi_vals, pdfs, pdfs_q, colors): - yield x, y, mult * pdf / scaling + y, hdi_val, pdf_q, color - - def ess(self): - """Get effective n data for the variable.""" - _, y_vals, values, colors = self.labels_ticks_and_vals() - for y, value, color in zip(y_vals, values, colors): - yield y, _ess(value), color - - def r_hat(self): - """Get rhat data for the variable.""" - _, y_vals, values, colors = self.labels_ticks_and_vals() - for y, value, color in zip(y_vals, values, colors): - if value.ndim != 2 or value.shape[0] < 2: - yield y, None, color - else: - yield y, _rhat(value), color - - def y_max(self): - """Get max y value for the variable.""" - end_y = max(y for y, *_ in self.iterator()) - - if self.combined: - end_y += self.group_offset - - return end_y + 2 * self.group_offset diff --git a/arviz/plots/backends/matplotlib/hdiplot.py b/arviz/plots/backends/matplotlib/hdiplot.py deleted file mode 100644 index a8600d5a0f..0000000000 --- a/arviz/plots/backends/matplotlib/hdiplot.py +++ /dev/null @@ -1,48 +0,0 @@ -"""Matplotlib hdiplot.""" - -import matplotlib.pyplot as plt -from matplotlib import _pylab_helpers - -from ...plot_utils import _scale_fig_size, vectorized_to_hex -from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser - - -def plot_hdi(ax, x_data, y_data, color, figsize, plot_kwargs, fill_kwargs, backend_kwargs, show): - """Matplotlib HDI plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") - plot_kwargs["color"] = vectorized_to_hex(plot_kwargs.get("color", color)) - plot_kwargs.setdefault("alpha", 0) - - fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, "fill_between") - fill_kwargs["color"] = vectorized_to_hex(fill_kwargs.get("color", color)) - fill_kwargs.setdefault("alpha", 0.5) - - figsize, *_ = _scale_fig_size(figsize, None) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - - if ax is None: - fig_manager = _pylab_helpers.Gcf.get_active() - if fig_manager is not None: - ax = fig_manager.canvas.figure.gca() - else: - _, ax = create_axes_grid( - 1, - backend_kwargs=backend_kwargs, - ) - - ax.plot(x_data, y_data, **plot_kwargs) - ax.fill_between(x_data, y_data[:, 0], y_data[:, 1], **fill_kwargs) - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/kdeplot.py b/arviz/plots/backends/matplotlib/kdeplot.py deleted file mode 100644 index 5fff7ef9b2..0000000000 --- a/arviz/plots/backends/matplotlib/kdeplot.py +++ /dev/null @@ -1,177 +0,0 @@ -"""Matplotlib kdeplot.""" - -import numpy as np -from matplotlib import pyplot as plt -from matplotlib import _pylab_helpers -import matplotlib.ticker as mticker - - -from ...plot_utils import _scale_fig_size, _init_kwargs_dict -from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser - - -def plot_kde( - density, - lower, - upper, - density_q, - xmin, - xmax, - ymin, - ymax, - gridsize, - values, - values2, - rug, - label, - quantiles, - rotated, - contour, - fill_last, - figsize, - textsize, - plot_kwargs, - fill_kwargs, - rug_kwargs, - contour_kwargs, - contourf_kwargs, - pcolormesh_kwargs, - is_circular, - ax, - legend, - backend_kwargs, - show, - return_glyph, # pylint: disable=unused-argument -): - """Matplotlib kde plot.""" - backend_kwargs = _init_kwargs_dict(backend_kwargs) - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, *_, xt_labelsize, linewidth, markersize = _scale_fig_size(figsize, textsize) - - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - backend_kwargs.setdefault("subplot_kw", {}) - backend_kwargs["subplot_kw"].setdefault("polar", is_circular) - - if ax is None: - fig_manager = _pylab_helpers.Gcf.get_active() - if fig_manager is not None: - ax = fig_manager.canvas.figure.gca() - else: - _, ax = create_axes_grid( - 1, - backend_kwargs=backend_kwargs, - ) - - if values2 is None: - plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") - plot_kwargs.setdefault("color", "C0") - - default_color = plot_kwargs.get("color") - - fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, "hexbin") - fill_kwargs.setdefault("color", default_color) - - rug_kwargs = matplotlib_kwarg_dealiaser(rug_kwargs, "plot") - rug_kwargs.setdefault("marker", "_" if rotated else "|") - rug_kwargs.setdefault("linestyle", "None") - rug_kwargs.setdefault("color", default_color) - rug_kwargs.setdefault("space", 0.2) - - plot_kwargs.setdefault("linewidth", linewidth) - rug_kwargs.setdefault("markersize", 2 * markersize) - - rug_space = max(density) * rug_kwargs.pop("space") - - if is_circular: - if is_circular == "radians": - labels = [ - "0", - f"{np.pi/4:.2f}", - f"{np.pi/2:.2f}", - f"{3*np.pi/4:.2f}", - f"{np.pi:.2f}", - f"{-3*np.pi/4:.2f}", - f"{-np.pi/2:.2f}", - f"{-np.pi/4:.2f}", - ] - - ticks_loc = ax.get_xticks() - ax.xaxis.set_major_locator(mticker.FixedLocator(ticks_loc)) - ax.set_xticklabels(labels) - - x = np.linspace(-np.pi, np.pi, len(density)) - ax.set_yticklabels([]) - - else: - x = np.linspace(lower, upper, len(density)) - - fill_func = ax.fill_between - fill_x, fill_y = x, density - if rotated: - x, density = density, x - fill_func = ax.fill_betweenx - - ax.tick_params(labelsize=xt_labelsize) - - if rotated: - ax.set_xlim(0, auto=True) - rug_x, rug_y = np.zeros_like(values) - rug_space, values - else: - ax.set_ylim(0, auto=True) - rug_x, rug_y = values, np.zeros_like(values) - rug_space - - if rug: - ax.plot(rug_x, rug_y, **rug_kwargs) - - if quantiles is not None: - fill_kwargs.setdefault("alpha", 0.75) - - idx = [np.sum(density_q < quant) for quant in quantiles] - - fill_func( - fill_x, - fill_y, - where=np.isin(fill_x, fill_x[idx], invert=True, assume_unique=True), - **fill_kwargs, - ) - else: - fill_kwargs.setdefault("alpha", 0) - if fill_kwargs.get("alpha") == 0: - label = plot_kwargs.setdefault("label", label) - else: - label = fill_kwargs.setdefault("label", label) - ax.plot(x, density, **plot_kwargs) - fill_func(fill_x, fill_y, **fill_kwargs) - if legend and label: - ax.legend() - else: - contour_kwargs = matplotlib_kwarg_dealiaser(contour_kwargs, "contour") - contour_kwargs.setdefault("colors", "0.5") - contourf_kwargs = matplotlib_kwarg_dealiaser(contourf_kwargs, "contour") - pcolormesh_kwargs = matplotlib_kwarg_dealiaser(pcolormesh_kwargs, "pcolormesh") - pcolormesh_kwargs.setdefault("shading", "auto") - - g_s = complex(gridsize[0]) - x_x, y_y = np.mgrid[xmin:xmax:g_s, ymin:ymax:g_s] - - ax.grid(False) - if contour: - qcfs = ax.contourf(x_x, y_y, density, antialiased=True, **contourf_kwargs) - ax.contour(x_x, y_y, density, **contour_kwargs) - if not fill_last: - alpha = np.ones(len(qcfs.allsegs), dtype=float) - alpha[0] = 0 - qcfs.set_alpha(alpha) - else: - ax.pcolormesh(x_x, y_y, density, **pcolormesh_kwargs) - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/khatplot.py b/arviz/plots/backends/matplotlib/khatplot.py deleted file mode 100644 index bb76c7a563..0000000000 --- a/arviz/plots/backends/matplotlib/khatplot.py +++ /dev/null @@ -1,241 +0,0 @@ -"""Matplotlib khatplot.""" - -import warnings - -import matplotlib as mpl -from matplotlib import cm -import matplotlib.pyplot as plt -import numpy as np -from matplotlib.colors import to_rgba_array -from packaging import version - -from ....stats.density_utils import histogram -from ...plot_utils import _scale_fig_size, color_from_dim, set_xticklabels, vectorized_to_hex -from . import backend_show, create_axes_grid, matplotlib_kwarg_dealiaser - - -def plot_khat( - hover_label, - hover_format, - ax, - figsize, - xdata, - khats, - good_k, - kwargs, - threshold, - coord_labels, - show_hlines, - show_bins, - hlines_kwargs, - xlabels, - legend, - color, - dims, - textsize, - markersize, - n_data_points, - bin_format, - backend_kwargs, - show, -): - """Matplotlib khat plot.""" - if version.parse(mpl.__version__) >= version.parse("3.9.0.dev0"): - interactive_backends = mpl.backends.backend_registry.list_builtin( - mpl.backends.BackendFilter.INTERACTIVE - ) - else: - interactive_backends = mpl.rcsetup.interactive_bk - if hover_label and mpl.get_backend() not in interactive_backends: - hover_label = False - warnings.warn( - "hover labels are only available with interactive backends. To switch to an " - "interactive backend from ipython or jupyter, use `%matplotlib` there should be " - "no need to restart the kernel. For other cases, see " - "https://matplotlib.org/3.1.0/tutorials/introductory/usage.html#backends", - UserWarning, - ) - - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwargs, - } - - (figsize, ax_labelsize, _, xt_labelsize, linewidth, scaled_markersize) = _scale_fig_size( - figsize, textsize - ) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - - if good_k is None: - good_k = 0.7 - - hlines_kwargs = matplotlib_kwarg_dealiaser(hlines_kwargs, "hlines") - hlines_kwargs.setdefault("hlines", [0, good_k, 1]) - hlines_kwargs.setdefault("linestyle", [":", "-.", "--", "-"]) - hlines_kwargs.setdefault("alpha", 0.7) - hlines_kwargs.setdefault("zorder", -1) - hlines_kwargs.setdefault("color", "C1") - hlines_kwargs["color"] = vectorized_to_hex(hlines_kwargs["color"]) - - if markersize is None: - markersize = scaled_markersize**2 # s in scatter plot mus be markersize square - # for dots to have the same size - - kwargs = matplotlib_kwarg_dealiaser(kwargs, "scatter") - kwargs.setdefault("s", markersize) - kwargs.setdefault("marker", "+") - - c_kwarg = kwargs.get("c", None) - - if c_kwarg is None: - color_mapping = None - cmap = None - if isinstance(color, str): - if color in dims: - colors, color_mapping = color_from_dim(khats, color) - cmap_name = kwargs.get("cmap", plt.rcParams["image.cmap"]) - cmap = getattr(cm, cmap_name) - rgba_c = cmap(colors) - else: - legend = False - rgba_c = to_rgba_array(np.full(n_data_points, color)) - else: - legend = False - try: - rgba_c = to_rgba_array(color) - except ValueError: - cmap_name = kwargs.get("cmap", plt.rcParams["image.cmap"]) - cmap = getattr(cm, cmap_name) - norm_fun = kwargs.get("norm", mpl.colors.Normalize(color.min(), color.max())) - rgba_c = cmap(norm_fun(color)) - - khats = khats if isinstance(khats, np.ndarray) else khats.values.flatten() - alphas = 0.5 + 0.2 * (khats > good_k) + 0.3 * (khats > 1) - rgba_c[:, 3] = alphas - rgba_c = vectorized_to_hex(rgba_c) - kwargs["c"] = rgba_c - else: - if isinstance(c_kwarg, str): - if c_kwarg in dims: - colors, color_mapping = color_from_dim(khats, c_kwarg) - else: - legend = False - else: - legend = False - - if ax is None: - fig, ax = create_axes_grid( - 1, - backend_kwargs=backend_kwargs, - ) - else: - fig = ax.get_figure() - - sc_plot = ax.scatter(xdata, khats, **kwargs) - - if threshold is not None: - idxs = xdata[khats > threshold] - for idx in idxs: - ax.text( - idx, - khats[idx], - coord_labels[idx], - horizontalalignment="center", - verticalalignment="bottom", - fontsize=0.8 * xt_labelsize, - ) - - xmin, xmax = ax.get_xlim() - if show_bins: - xmax += n_data_points / 12 - ylims1 = ax.get_ylim() - ylims2 = ax.get_ylim() - ymin = min(ylims1[0], ylims2[0]) - ymax = min(ylims1[1], ylims2[1]) - - if show_hlines: - ax.hlines( - hlines_kwargs.pop("hlines"), xmin=xmin, xmax=xmax, linewidth=linewidth, **hlines_kwargs - ) - - if show_bins: - bin_edges = np.array([ymin, good_k, 1, ymax]) - bin_edges = bin_edges[(bin_edges >= ymin) & (bin_edges <= ymax)] - hist, _, _ = histogram(khats, bin_edges) - for idx, count in enumerate(hist): - ax.text( - (n_data_points - 1 + xmax) / 2, - np.mean(bin_edges[idx : idx + 2]), - bin_format.format(count, count / n_data_points * 100), - horizontalalignment="center", - verticalalignment="center", - ) - - ax.set_xlabel("Data Point", fontsize=ax_labelsize) - ax.set_ylabel(r"Shape parameter k", fontsize=ax_labelsize) - ax.tick_params(labelsize=xt_labelsize) - if xlabels: - set_xticklabels(ax, coord_labels) - fig.autofmt_xdate() - fig.tight_layout() - if legend: - kwargs.pop("c") - ncols = len(color_mapping) // 6 + 1 - for label, float_color in color_mapping.items(): - ax.scatter([], [], c=[cmap(float_color)], label=label, **kwargs) - ax.legend(ncol=ncols, title=color) - - if hover_label and mpl.get_backend() in mpl.rcsetup.interactive_bk: - _make_hover_annotation(fig, ax, sc_plot, coord_labels, rgba_c, hover_format) - - if backend_show(show): - plt.show() - - return ax - - -def _make_hover_annotation(fig, ax, sc_plot, coord_labels, rgba_c, hover_format): - """Show data point label when hovering over it with mouse.""" - annot = ax.annotate( - "", - xy=(0, 0), - xytext=(0, 0), - textcoords="offset points", - bbox=dict(boxstyle="round", fc="w", alpha=0.4), - arrowprops=dict(arrowstyle="->"), - ) - annot.set_visible(False) - xmid = np.mean(ax.get_xlim()) - ymid = np.mean(ax.get_ylim()) - offset = 10 - - def update_annot(ind): - idx = ind["ind"][0] - pos = sc_plot.get_offsets()[idx] - annot_text = hover_format.format(idx, coord_labels[idx]) - annot.xy = pos - annot.set_position( - (-offset if pos[0] > xmid else offset, -offset if pos[1] > ymid else offset) - ) - annot.set_text(annot_text) - annot.get_bbox_patch().set_facecolor(rgba_c[idx]) - annot.set_ha("right" if pos[0] > xmid else "left") - annot.set_va("top" if pos[1] > ymid else "bottom") - - def hover(event): - vis = annot.get_visible() - if event.inaxes == ax: - cont, ind = sc_plot.contains(event) - if cont: - update_annot(ind) - annot.set_visible(True) - fig.canvas.draw_idle() - else: - if vis: - annot.set_visible(False) - fig.canvas.draw_idle() - - fig.canvas.mpl_connect("motion_notify_event", hover) diff --git a/arviz/plots/backends/matplotlib/lmplot.py b/arviz/plots/backends/matplotlib/lmplot.py deleted file mode 100644 index 112a97ea16..0000000000 --- a/arviz/plots/backends/matplotlib/lmplot.py +++ /dev/null @@ -1,149 +0,0 @@ -"""Matplotlib plot linear regression figure.""" - -import matplotlib.pyplot as plt -import numpy as np - -from ...plot_utils import _scale_fig_size -from ...hdiplot import plot_hdi -from . import create_axes_grid, matplotlib_kwarg_dealiaser, backend_show, backend_kwarg_defaults - - -def plot_lm( - x, - y, - y_model, - y_hat, - num_samples, - kind_pp, - kind_model, - xjitter, - length_plotters, - rows, - cols, - y_kwargs, - y_hat_plot_kwargs, - y_hat_fill_kwargs, - y_model_plot_kwargs, - y_model_fill_kwargs, - y_model_mean_kwargs, - backend_kwargs, - show, - figsize, - textsize, - axes, - legend, - grid, -): - """Matplotlib Linear Regression.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, _, _, xt_labelsize, _, _ = _scale_fig_size(figsize, textsize, rows, cols) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs.setdefault("squeeze", False) - - if axes is None: - _, axes = create_axes_grid(length_plotters, rows, cols, backend_kwargs=backend_kwargs) - - for i, ax_i in enumerate(np.ravel(axes)[:length_plotters]): - # All the kwargs are defined here beforehand - y_kwargs = matplotlib_kwarg_dealiaser(y_kwargs, "plot") - y_kwargs.setdefault("color", "C3") - y_kwargs.setdefault("marker", ".") - y_kwargs.setdefault("markersize", 15) - y_kwargs.setdefault("linewidth", 0) - y_kwargs.setdefault("zorder", 10) - y_kwargs.setdefault("label", "observed_data") - - y_hat_plot_kwargs = matplotlib_kwarg_dealiaser(y_hat_plot_kwargs, "plot") - y_hat_plot_kwargs.setdefault("color", "C1") - y_hat_plot_kwargs.setdefault("alpha", 0.3) - y_hat_plot_kwargs.setdefault("markersize", 10) - y_hat_plot_kwargs.setdefault("marker", ".") - y_hat_plot_kwargs.setdefault("linewidth", 0) - - y_hat_fill_kwargs = matplotlib_kwarg_dealiaser(y_hat_fill_kwargs, "fill_between") - y_hat_fill_kwargs.setdefault("color", "C3") - - y_model_plot_kwargs = matplotlib_kwarg_dealiaser(y_model_plot_kwargs, "plot") - y_model_plot_kwargs.setdefault("color", "C6") - y_model_plot_kwargs.setdefault("alpha", 0.5) - y_model_plot_kwargs.setdefault("linewidth", 0.5) - y_model_plot_kwargs.setdefault("zorder", 9) - - y_model_fill_kwargs = matplotlib_kwarg_dealiaser(y_model_fill_kwargs, "fill_between") - y_model_fill_kwargs.setdefault("color", "C0") - y_model_fill_kwargs.setdefault("linewidth", 0.5) - y_model_fill_kwargs.setdefault("zorder", 9) - y_model_fill_kwargs.setdefault("alpha", 0.5) - - y_model_mean_kwargs = matplotlib_kwarg_dealiaser(y_model_mean_kwargs, "plot") - y_model_mean_kwargs.setdefault("color", "C6") - y_model_mean_kwargs.setdefault("linewidth", 0.8) - y_model_mean_kwargs.setdefault("zorder", 11) - - y_var_name, _, _, y_plotters = y[i] - x_var_name, _, _, x_plotters = x[i] - ax_i.plot(x_plotters, y_plotters, **y_kwargs) - ax_i.set_xlabel(x_var_name) - ax_i.set_ylabel(y_var_name) - - if y_hat is not None: - _, _, _, y_hat_plotters = y_hat[i] - if kind_pp == "samples": - for j in range(num_samples): - if xjitter is True: - jitter_scale = x_plotters[1] - x_plotters[0] - scale_high = jitter_scale * 0.2 - x_plotters_jitter = x_plotters + np.random.uniform( - low=-scale_high, high=scale_high, size=len(x_plotters) - ) - ax_i.plot(x_plotters_jitter, y_hat_plotters[..., j], **y_hat_plot_kwargs) - else: - ax_i.plot(x_plotters, y_hat_plotters[..., j], **y_hat_plot_kwargs) - ax_i.plot([], **y_hat_plot_kwargs, label="Posterior predictive samples") - else: - plot_hdi(x_plotters, y_hat_plotters, ax=ax_i, **y_hat_fill_kwargs) - ax_i.plot( - [], color=y_hat_fill_kwargs["color"], label="Posterior predictive samples" - ) - - if y_model is not None: - _, _, _, y_model_plotters = y_model[i] - - if kind_model == "lines": - # y_model_plotters should be (points, samples) - y_points = y_model_plotters.shape[0] - if x_plotters.shape[0] == y_points: - for j in range(num_samples): - ax_i.plot(x_plotters, y_model_plotters[:, j], **y_model_plot_kwargs) - - ax_i.plot([], **y_model_plot_kwargs, label="Uncertainty in mean") - y_model_mean = np.mean(y_model_plotters, axis=1) - ax_i.plot(x_plotters, y_model_mean, **y_model_mean_kwargs, label="Mean") - - else: - plot_hdi( - x_plotters, - y_model_plotters, - fill_kwargs=y_model_fill_kwargs, - ax=ax_i, - ) - - ax_i.plot([], color=y_model_fill_kwargs["color"], label="Uncertainty in mean") - y_model_mean = np.mean(y_model_plotters, axis=0) - ax_i.plot(x_plotters, y_model_mean, **y_model_mean_kwargs, label="Mean") - - if legend: - ax_i.legend(fontsize=xt_labelsize, loc="upper left") - if grid: - ax_i.grid(True) - - if backend_show(show): - plt.show() - return axes diff --git a/arviz/plots/backends/matplotlib/loopitplot.py b/arviz/plots/backends/matplotlib/loopitplot.py deleted file mode 100644 index 4f3f618df2..0000000000 --- a/arviz/plots/backends/matplotlib/loopitplot.py +++ /dev/null @@ -1,144 +0,0 @@ -"""Matplotlib loopitplot.""" - -import matplotlib.pyplot as plt -import numpy as np -from matplotlib.colors import hsv_to_rgb, rgb_to_hsv, to_hex, to_rgb -from xarray import DataArray - -from ....stats.density_utils import kde -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser - - -def plot_loo_pit( - ax, - figsize, - ecdf, - loo_pit, - loo_pit_ecdf, - unif_ecdf, - p975, - p025, - fill_kwargs, - ecdf_fill, - use_hdi, - x_vals, - hdi_kwargs, - hdi_odds, - n_unif, - unif, - plot_unif_kwargs, - loo_pit_kde, - legend, - labeller, - y_hat, - y, - color, - textsize, - hdi_prob, - plot_kwargs, - backend_kwargs, - show, -): - """Matplotlib loo pit plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - (figsize, _, _, xt_labelsize, linewidth, _) = _scale_fig_size(figsize, textsize, 1, 1) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - - if ax is None: - _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs) - - plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") - plot_kwargs["color"] = to_hex(color) - plot_kwargs.setdefault("linewidth", linewidth * 1.4) - if isinstance(y, str): - xlabel = y - elif isinstance(y, DataArray) and y.name is not None: - xlabel = y.name - elif isinstance(y_hat, str): - xlabel = y_hat - elif isinstance(y_hat, DataArray) and y_hat.name is not None: - xlabel = y_hat.name - else: - xlabel = "" - label = "LOO-PIT ECDF" if ecdf else "LOO-PIT" - xlabel = labeller.var_name_to_str(y) - - plot_kwargs.setdefault("label", label) - plot_kwargs.setdefault("zorder", 5) - - plot_unif_kwargs = matplotlib_kwarg_dealiaser(plot_unif_kwargs, "plot") - light_color = rgb_to_hsv(to_rgb(plot_kwargs.get("color"))) - light_color[1] /= 2 # pylint: disable=unsupported-assignment-operation - light_color[2] += (1 - light_color[2]) / 2 # pylint: disable=unsupported-assignment-operation - plot_unif_kwargs.setdefault("color", to_hex(hsv_to_rgb(light_color))) - plot_unif_kwargs.setdefault("alpha", 0.5) - plot_unif_kwargs.setdefault("linewidth", 0.6 * linewidth) - - if ecdf: - n_data_points = loo_pit.size - plot_kwargs.setdefault("drawstyle", "steps-mid" if n_data_points < 100 else "default") - plot_unif_kwargs.setdefault("drawstyle", "steps-mid" if n_data_points < 100 else "default") - - if ecdf_fill: - if fill_kwargs is None: - fill_kwargs = {} - fill_kwargs.setdefault("color", to_hex(hsv_to_rgb(light_color))) - fill_kwargs.setdefault("alpha", 0.5) - fill_kwargs.setdefault( - "step", "mid" if plot_kwargs["drawstyle"] == "steps-mid" else None - ) - fill_kwargs.setdefault("label", f"{hdi_prob * 100:.3g}% credible interval") - elif use_hdi: - if hdi_kwargs is None: - hdi_kwargs = {} - hdi_kwargs.setdefault("color", to_hex(hsv_to_rgb(light_color))) - hdi_kwargs.setdefault("alpha", 0.35) - hdi_kwargs.setdefault("label", "Uniform HDI") - - if ecdf: - ax.plot( - np.hstack((0, loo_pit, 1)), np.hstack((0, loo_pit - loo_pit_ecdf, 0)), **plot_kwargs - ) - - if ecdf_fill: - ax.fill_between(unif_ecdf, p975 - unif_ecdf, p025 - unif_ecdf, **fill_kwargs) - else: - ax.plot(unif_ecdf, p975 - unif_ecdf, unif_ecdf, p025 - unif_ecdf, **plot_unif_kwargs) - else: - x_ss = np.empty((n_unif, len(loo_pit_kde))) - u_dens = np.empty((n_unif, len(loo_pit_kde))) - if use_hdi: - ax.axhspan(*hdi_odds, **hdi_kwargs) - - # Adds horizontal reference line - ax.axhline(1, color="w", zorder=1) - else: - for idx in range(n_unif): - x_s, unif_density = kde(unif[idx, :]) - x_ss[idx] = x_s - u_dens[idx] = unif_density - ax.plot(x_ss.T, u_dens.T, **plot_unif_kwargs) - ax.plot(x_vals, loo_pit_kde, **plot_kwargs) - ax.set_xlim(0, 1) - ax.set_ylim(0, None) - ax.set_xlabel(xlabel) - ax.tick_params(labelsize=xt_labelsize) - if legend: - if not (use_hdi or (ecdf and ecdf_fill)): - label = f"{hdi_prob * 100:.3g}% credible interval" if ecdf else "Uniform" - ax.plot([], label=label, **plot_unif_kwargs) - ax.legend() - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/mcseplot.py b/arviz/plots/backends/matplotlib/mcseplot.py deleted file mode 100644 index 7f1084470c..0000000000 --- a/arviz/plots/backends/matplotlib/mcseplot.py +++ /dev/null @@ -1,161 +0,0 @@ -"""Matplotlib mcseplot.""" - -import matplotlib.pyplot as plt -import numpy as np -from scipy.stats import rankdata - -from ....stats.stats_utils import quantile as _quantile -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser - - -def plot_mcse( - ax, - plotters, - length_plotters, - rows, - cols, - figsize, - errorbar, - rug, - data, - probs, - kwargs, - extra_methods, - mean_mcse, - sd_mcse, - textsize, - labeller, - text_kwargs, - rug_kwargs, - extra_kwargs, - idata, - rug_kind, - backend_kwargs, - show, -): - """Matplotlib mcseplot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - (figsize, ax_labelsize, titlesize, xt_labelsize, _linewidth, _markersize) = _scale_fig_size( - figsize, textsize, rows, cols - ) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - - kwargs = matplotlib_kwarg_dealiaser(kwargs, "plot") - kwargs.setdefault("linestyle", "none") - kwargs.setdefault("linewidth", _linewidth) - kwargs.setdefault("markersize", _markersize) - kwargs.setdefault("marker", "_" if errorbar else "o") - kwargs.setdefault("zorder", 3) - - extra_kwargs = matplotlib_kwarg_dealiaser(extra_kwargs, "plot") - extra_kwargs.setdefault("linestyle", "-") - extra_kwargs.setdefault("linewidth", _linewidth / 2) - extra_kwargs.setdefault("color", "k") - extra_kwargs.setdefault("alpha", 0.5) - text_x = None - text_va = None - if extra_methods: - text_kwargs = matplotlib_kwarg_dealiaser(text_kwargs, "text") - text_x = text_kwargs.pop("x", 1) - text_kwargs.setdefault("fontsize", xt_labelsize * 0.7) - text_kwargs.setdefault("alpha", extra_kwargs["alpha"]) - text_kwargs.setdefault("color", extra_kwargs["color"]) - text_kwargs.setdefault("horizontalalignment", "right") - text_va = text_kwargs.pop("verticalalignment", None) - - if ax is None: - _, ax = create_axes_grid( - length_plotters, - rows, - cols, - backend_kwargs=backend_kwargs, - ) - - for (var_name, selection, isel, x), ax_ in zip(plotters, np.ravel(ax)): - if errorbar or rug: - values = data[var_name].sel(**selection).values.flatten() - if errorbar: - quantile_values = _quantile(values, probs) - ax_.errorbar(probs, quantile_values, yerr=x, **kwargs) - else: - ax_.plot(probs, x, label="quantile", **kwargs) - if extra_methods: - mean_mcse_i = mean_mcse[var_name].sel(**selection).values.item() - sd_mcse_i = sd_mcse[var_name].sel(**selection).values.item() - ax_.axhline(mean_mcse_i, **extra_kwargs) - ax_.annotate( - "mean", - (text_x, mean_mcse_i), - va=( - text_va - if text_va is not None - else "bottom" if mean_mcse_i > sd_mcse_i else "top" - ), - **text_kwargs, - ) - ax_.axhline(sd_mcse_i, **extra_kwargs) - ax_.annotate( - "sd", - (text_x, sd_mcse_i), - va=( - text_va - if text_va is not None - else "bottom" if sd_mcse_i >= mean_mcse_i else "top" - ), - **text_kwargs, - ) - if rug: - rug_kwargs = matplotlib_kwarg_dealiaser(rug_kwargs, "plot") - if not hasattr(idata, "sample_stats"): - raise ValueError("InferenceData object must contain sample_stats for rug plot") - if not hasattr(idata.sample_stats, rug_kind): - raise ValueError(f"InferenceData does not contain {rug_kind} data") - rug_kwargs.setdefault("marker", "|") - rug_kwargs.setdefault("linestyle", rug_kwargs.pop("ls", "None")) - rug_kwargs.setdefault("color", rug_kwargs.pop("c", kwargs.get("color", "C0"))) - rug_kwargs.setdefault("space", 0.1) - rug_kwargs.setdefault("markersize", rug_kwargs.pop("ms", 2 * _markersize)) - - mask = idata.sample_stats[rug_kind].values.flatten() - values = rankdata(values, method="average")[mask] - y_min, y_max = ax_.get_ylim() - y_min = y_min if errorbar else 0 - rug_space = (y_max - y_min) * rug_kwargs.pop("space") - rug_x, rug_y = values / (len(mask) - 1), np.full_like(values, y_min) - rug_space - ax_.plot(rug_x, rug_y, **rug_kwargs) - ax_.axhline(y_min, color="k", linewidth=_linewidth, alpha=0.7) - - ax_.set_title( - labeller.make_label_vert(var_name, selection, isel), fontsize=titlesize, wrap=True - ) - ax_.tick_params(labelsize=xt_labelsize) - ax_.set_xlabel("Quantile", fontsize=ax_labelsize, wrap=True) - ax_.set_ylabel( - r"Value $\pm$ MCSE for quantiles" if errorbar else "MCSE for quantiles", - fontsize=ax_labelsize, - wrap=True, - ) - ax_.set_xlim(0, 1) - if rug: - ax_.yaxis.get_major_locator().set_params(nbins="auto", steps=[1, 2, 5, 10]) - y_min, y_max = ax_.get_ylim() - yticks = ax_.get_yticks() - yticks = yticks[(yticks >= y_min) & (yticks < y_max)] - ax_.set_yticks(yticks) - ax_.set_yticklabels([f"{ytick:.3g}" for ytick in yticks]) - elif not errorbar: - ax_.set_ylim(bottom=0) - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/pairplot.py b/arviz/plots/backends/matplotlib/pairplot.py deleted file mode 100644 index ccb42582b4..0000000000 --- a/arviz/plots/backends/matplotlib/pairplot.py +++ /dev/null @@ -1,355 +0,0 @@ -"""Matplotlib pairplot.""" - -import warnings -from copy import deepcopy - -import matplotlib.pyplot as plt -import numpy as np -from mpl_toolkits.axes_grid1 import make_axes_locatable - -from ....rcparams import rcParams -from ...distplot import plot_dist -from ...kdeplot import plot_kde -from ...plot_utils import _scale_fig_size, calculate_point_estimate, _init_kwargs_dict -from . import backend_kwarg_defaults, backend_show, matplotlib_kwarg_dealiaser - - -def plot_pair( - ax, - plotters, - numvars, - figsize, - textsize, - kind, - scatter_kwargs, - kde_kwargs, - hexbin_kwargs, - gridsize, - colorbar, - divergences, - diverging_mask, - divergences_kwargs, - flat_var_names, - flat_ref_slices, - flat_var_labels, - backend_kwargs, - marginal_kwargs, - show, - marginals, - point_estimate, - point_estimate_kwargs, - point_estimate_marker_kwargs, - reference_values, - reference_values_kwargs, -): - """Matplotlib pairplot.""" - backend_kwargs = _init_kwargs_dict(backend_kwargs) - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - scatter_kwargs = matplotlib_kwarg_dealiaser(scatter_kwargs, "scatter") - - scatter_kwargs.setdefault("marker", ".") - scatter_kwargs.setdefault("lw", 0) - # Sets the default zorder higher than zorder of grid, which is 0.5 - scatter_kwargs.setdefault("zorder", 0.6) - - kde_kwargs = _init_kwargs_dict(kde_kwargs) - - hexbin_kwargs = matplotlib_kwarg_dealiaser(hexbin_kwargs, "hexbin") - hexbin_kwargs.setdefault("mincnt", 1) - - divergences_kwargs = matplotlib_kwarg_dealiaser(divergences_kwargs, "plot") - divergences_kwargs.setdefault("marker", "o") - divergences_kwargs.setdefault("markeredgecolor", "k") - divergences_kwargs.setdefault("color", "C1") - divergences_kwargs.setdefault("lw", 0) - - marginal_kwargs = _init_kwargs_dict(marginal_kwargs) - - point_estimate_kwargs = matplotlib_kwarg_dealiaser(point_estimate_kwargs, "fill_between") - point_estimate_kwargs.setdefault("color", "k") - - if kind != "kde": - kde_kwargs.setdefault("contourf_kwargs", {}) - kde_kwargs["contourf_kwargs"].setdefault("alpha", 0) - kde_kwargs.setdefault("contour_kwargs", {}) - kde_kwargs["contour_kwargs"].setdefault("colors", "k") - - if reference_values: - difference = set(flat_var_names).difference(set(reference_values.keys())) - - if difference: - warnings.warn( - "Argument reference_values does not include reference value for: {}".format( - ", ".join(difference) - ), - UserWarning, - ) - - reference_values_kwargs = matplotlib_kwarg_dealiaser(reference_values_kwargs, "plot") - - reference_values_kwargs.setdefault("color", "C2") - reference_values_kwargs.setdefault("markeredgecolor", "k") - reference_values_kwargs.setdefault("marker", "o") - - point_estimate_marker_kwargs = matplotlib_kwarg_dealiaser( - point_estimate_marker_kwargs, "scatter" - ) - point_estimate_marker_kwargs.setdefault("marker", "s") - point_estimate_marker_kwargs.setdefault("color", "k") - - # pylint: disable=too-many-nested-blocks - if numvars == 2: - (figsize, ax_labelsize, _, xt_labelsize, linewidth, markersize) = _scale_fig_size( - figsize, textsize, numvars - 1, numvars - 1 - ) - backend_kwargs.setdefault("figsize", figsize) - - marginal_kwargs.setdefault("plot_kwargs", {}) - marginal_kwargs["plot_kwargs"].setdefault("linewidth", linewidth) - - point_estimate_marker_kwargs.setdefault("s", markersize + 50) - - # Flatten data - x = plotters[0][-1].flatten() - y = plotters[1][-1].flatten() - if ax is None: - if marginals: - # Instantiate figure and grid - widths = [2, 2, 2, 1] - heights = [1.4, 2, 2, 2] - fig = plt.figure(**backend_kwargs) - grid = plt.GridSpec( - 4, - 4, - hspace=0.1, - wspace=0.1, - figure=fig, - width_ratios=widths, - height_ratios=heights, - ) - # Set up main plot - ax = fig.add_subplot(grid[1:, :-1]) - # Set up top KDE - ax_hist_x = fig.add_subplot(grid[0, :-1], sharex=ax) - ax_hist_x.set_yticks([]) - # Set up right KDE - ax_hist_y = fig.add_subplot(grid[1:, -1], sharey=ax) - ax_hist_y.set_xticks([]) - ax_return = np.array([[ax_hist_x, None], [ax, ax_hist_y]]) - - for val, ax_, rotate in ((x, ax_hist_x, False), (y, ax_hist_y, True)): - plot_dist(val, textsize=xt_labelsize, rotated=rotate, ax=ax_, **marginal_kwargs) - - # Personalize axes - ax_hist_x.tick_params(labelleft=False, labelbottom=False) - ax_hist_y.tick_params(labelleft=False, labelbottom=False) - else: - fig, ax = plt.subplots(numvars - 1, numvars - 1, **backend_kwargs) - else: - if marginals: - assert ax.shape == (numvars, numvars) - if ax[0, 1] is not None and ax[0, 1].get_figure() is not None: - ax[0, 1].remove() - ax_return = ax - ax_hist_x = ax[0, 0] - ax_hist_y = ax[1, 1] - ax = ax[1, 0] - for val, ax_, rotate in ((x, ax_hist_x, False), (y, ax_hist_y, True)): - plot_dist(val, textsize=xt_labelsize, rotated=rotate, ax=ax_, **marginal_kwargs) - else: - ax = np.atleast_2d(ax)[0, 0] - - if "scatter" in kind: - ax.scatter(x, y, **scatter_kwargs) - if "kde" in kind: - plot_kde(x, y, ax=ax, **kde_kwargs) - if "hexbin" in kind: - hexbin = ax.hexbin( - x, - y, - gridsize=gridsize, - **hexbin_kwargs, - ) - ax.grid(False) - - if kind == "hexbin" and colorbar: - cbar = ax.figure.colorbar(hexbin, ticks=[hexbin.norm.vmin, hexbin.norm.vmax], ax=ax) - cbar.ax.set_yticklabels(["low", "high"], fontsize=ax_labelsize) - - if divergences: - ax.plot( - x[diverging_mask], - y[diverging_mask], - **divergences_kwargs, - ) - - if point_estimate: - pe_x = calculate_point_estimate(point_estimate, x) - pe_y = calculate_point_estimate(point_estimate, y) - if marginals: - ax_hist_x.axvline(pe_x, **point_estimate_kwargs) - ax_hist_y.axhline(pe_y, **point_estimate_kwargs) - - ax.axvline(pe_x, **point_estimate_kwargs) - ax.axhline(pe_y, **point_estimate_kwargs) - - ax.scatter(pe_x, pe_y, **point_estimate_marker_kwargs) - - if reference_values: - ax.plot( - np.array(reference_values[flat_var_names[0]])[flat_ref_slices[0]], - np.array(reference_values[flat_var_names[1]])[flat_ref_slices[1]], - **reference_values_kwargs, - ) - ax.set_xlabel(f"{flat_var_labels[0]}", fontsize=ax_labelsize, wrap=True) - ax.set_ylabel(f"{flat_var_labels[1]}", fontsize=ax_labelsize, wrap=True) - ax.tick_params(labelsize=xt_labelsize) - - else: - not_marginals = int(not marginals) - num_subplot_cols = numvars - not_marginals - max_plots = ( - num_subplot_cols**2 - if rcParams["plot.max_subplots"] is None - else rcParams["plot.max_subplots"] - ) - cols_to_plot = np.sum(np.arange(1, num_subplot_cols + 1).cumsum() <= max_plots) - if cols_to_plot < num_subplot_cols: - vars_to_plot = cols_to_plot - warnings.warn( - "rcParams['plot.max_subplots'] ({max_plots}) is smaller than the number " - "of resulting pair plots with these variables, generating only a " - "{side}x{side} grid".format(max_plots=max_plots, side=vars_to_plot), - UserWarning, - ) - else: - vars_to_plot = numvars - not_marginals - - (figsize, ax_labelsize, _, xt_labelsize, _, markersize) = _scale_fig_size( - figsize, textsize, vars_to_plot, vars_to_plot - ) - backend_kwargs.setdefault("figsize", figsize) - point_estimate_marker_kwargs.setdefault("s", markersize + 50) - - if ax is None: - if backend_kwargs.pop("sharex", None) is not None: - warnings.warn( - "'sharex' keyword is ignored. For non-standard sharing, provide 'ax'.", - UserWarning, - ) - if backend_kwargs.pop("sharey", None) is not None: - warnings.warn( - "'sharey' keyword is ignored. For non-standard sharing, provide 'ax'.", - UserWarning, - ) - backend_kwargs["sharex"] = "col" - if not_marginals: - backend_kwargs["sharey"] = "row" - fig, ax = plt.subplots( - vars_to_plot, - vars_to_plot, - **backend_kwargs, - ) - if backend_kwargs.get("sharey") is None: - for j in range(0, vars_to_plot): - for i in range(0, j): - ax[j, i].axes.sharey(ax[j, 0]) - - hexbin_values = [] - for i in range(0, vars_to_plot): - var1 = plotters[i][-1].flatten() - - for j in range(0, vars_to_plot): - var2 = plotters[j + not_marginals][-1].flatten() - if i > j: - if ax[j, i].get_figure() is not None: - ax[j, i].remove() - continue - - elif i == j and marginals: - loc = "right" - plot_dist(var1, ax=ax[i, j], **marginal_kwargs) - - else: - if i == j: - loc = "left" - - if "scatter" in kind: - ax[j, i].scatter(var1, var2, **scatter_kwargs) - - if "kde" in kind: - plot_kde( - var1, - var2, - ax=ax[j, i], - **deepcopy(kde_kwargs), - ) - - if "hexbin" in kind: - ax[j, i].grid(False) - hexbin = ax[j, i].hexbin(var1, var2, gridsize=gridsize, **hexbin_kwargs) - - if divergences: - ax[j, i].plot( - var1[diverging_mask], var2[diverging_mask], **divergences_kwargs - ) - - if kind == "hexbin" and colorbar: - hexbin_values.append(hexbin.norm.vmin) - hexbin_values.append(hexbin.norm.vmax) - divider = make_axes_locatable(ax[-1, -1]) - cax = divider.append_axes(loc, size="7%", pad="5%") - cbar = fig.colorbar( - hexbin, ticks=[hexbin.norm.vmin, hexbin.norm.vmax], cax=cax - ) - cbar.ax.set_yticklabels(["low", "high"], fontsize=ax_labelsize) - - if point_estimate: - pe_x = calculate_point_estimate(point_estimate, var1) - pe_y = calculate_point_estimate(point_estimate, var2) - ax[j, i].axvline(pe_x, **point_estimate_kwargs) - ax[j, i].axhline(pe_y, **point_estimate_kwargs) - - if marginals: - ax[j - 1, i].axvline(pe_x, **point_estimate_kwargs) - pe_last = calculate_point_estimate(point_estimate, plotters[-1][-1]) - ax[-1, -1].axvline(pe_last, **point_estimate_kwargs) - - ax[j, i].scatter(pe_x, pe_y, **point_estimate_marker_kwargs) - - if reference_values: - x_name = flat_var_names[i] - y_name = flat_var_names[j + not_marginals] - if (x_name not in difference) and (y_name not in difference): - ax[j, i].plot( - np.array(reference_values[x_name])[flat_ref_slices[i]], - np.array(reference_values[y_name])[ - flat_ref_slices[j + not_marginals] - ], - **reference_values_kwargs, - ) - - if j != vars_to_plot - 1: - plt.setp(ax[j, i].get_xticklabels(), visible=False) - else: - ax[j, i].set_xlabel(f"{flat_var_labels[i]}", fontsize=ax_labelsize, wrap=True) - if i != 0: - plt.setp(ax[j, i].get_yticklabels(), visible=False) - else: - ax[j, i].set_ylabel( - f"{flat_var_labels[j + not_marginals]}", - fontsize=ax_labelsize, - wrap=True, - ) - ax[j, i].tick_params(labelsize=xt_labelsize) - - if backend_show(show): - plt.show() - - if marginals and numvars == 2: - return ax_return - return ax diff --git a/arviz/plots/backends/matplotlib/parallelplot.py b/arviz/plots/backends/matplotlib/parallelplot.py deleted file mode 100644 index dee07fb18b..0000000000 --- a/arviz/plots/backends/matplotlib/parallelplot.py +++ /dev/null @@ -1,58 +0,0 @@ -"""Matplotlib Parallel coordinates plot.""" - -import matplotlib.pyplot as plt -import numpy as np - -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, backend_show, create_axes_grid - - -def plot_parallel( - ax, - colornd, - colord, - shadend, - diverging_mask, - posterior, - textsize, - var_names, - legend, - figsize, - backend_kwargs, - backend_config, # pylint: disable=unused-argument - show, -): - """Matplotlib parallel plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, _, _, xt_labelsize, _, _ = _scale_fig_size(figsize, textsize, 1, 1) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - if ax is None: - _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs) - - ax.plot(posterior[:, ~diverging_mask], color=colornd, alpha=shadend) - - if np.any(diverging_mask): - ax.plot(posterior[:, diverging_mask], color=colord, lw=1) - - ax.tick_params(labelsize=textsize) - ax.set_xticks(range(len(var_names))) - ax.set_xticklabels(var_names) - - if legend: - ax.plot([], color=colornd, label="non-divergent") - if np.any(diverging_mask): - ax.plot([], color=colord, label="divergent") - ax.legend(fontsize=xt_labelsize) - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/posteriorplot.py b/arviz/plots/backends/matplotlib/posteriorplot.py deleted file mode 100644 index f67b4ad60d..0000000000 --- a/arviz/plots/backends/matplotlib/posteriorplot.py +++ /dev/null @@ -1,348 +0,0 @@ -"""Matplotlib Plot posterior densities.""" - -from numbers import Number - -import matplotlib.pyplot as plt -import numpy as np - -from ....stats import hdi -from ....stats.density_utils import get_bins -from ...kdeplot import plot_kde -from ...plot_utils import ( - _scale_fig_size, - calculate_point_estimate, - format_sig_figs, - round_num, - vectorized_to_hex, -) -from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser - - -def plot_posterior( - ax, - length_plotters, - rows, - cols, - figsize, - plotters, - bw, - circular, - bins, - kind, - point_estimate, - round_to, - hdi_prob, - multimodal, - skipna, - textsize, - ref_val, - rope, - ref_val_color, - rope_color, - labeller, - kwargs, - backend_kwargs, - show, -): - """Matplotlib posterior plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - (figsize, ax_labelsize, titlesize, xt_labelsize, _linewidth, _) = _scale_fig_size( - figsize, textsize, rows, cols - ) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs.setdefault("squeeze", True) - - if kind == "hist": - kwargs = matplotlib_kwarg_dealiaser(kwargs, "hist") - else: - kwargs = matplotlib_kwarg_dealiaser(kwargs, "plot") - kwargs.setdefault("linewidth", _linewidth) - - if ax is None: - _, ax = create_axes_grid( - length_plotters, - rows, - cols, - backend_kwargs=backend_kwargs, - ) - idx = 0 - for (var_name, selection, isel, x), ax_ in zip(plotters, np.ravel(ax)): - _plot_posterior_op( - idx, - x.flatten(), - var_name, - selection, - ax=ax_, - bw=bw, - circular=circular, - bins=bins, - kind=kind, - point_estimate=point_estimate, - round_to=round_to, - hdi_prob=hdi_prob, - multimodal=multimodal, - skipna=skipna, - ref_val=ref_val, - rope=rope, - ref_val_color=ref_val_color, - rope_color=rope_color, - ax_labelsize=ax_labelsize, - xt_labelsize=xt_labelsize, - **kwargs, - ) - idx += 1 - ax_.set_title( - labeller.make_label_vert(var_name, selection, isel), fontsize=titlesize, wrap=True - ) - - if backend_show(show): - plt.show() - - return ax - - -def _plot_posterior_op( - idx, - values, - var_name, - selection, - ax, - bw, - circular, - linewidth, - bins, - kind, - point_estimate, - hdi_prob, - multimodal, - skipna, - ref_val, - rope, - ref_val_color, - rope_color, - ax_labelsize, - xt_labelsize, - round_to=None, - **kwargs, -): # noqa: D202 - """Artist to draw posterior.""" - - def format_as_percent(x, round_to=0): - return "{0:.{1:d}f}%".format(100 * x, round_to) - - def display_ref_val(): - if ref_val is None: - return - elif isinstance(ref_val, dict): - val = None - for sel in ref_val.get(var_name, []): - if all( - k in selection and selection[k] == v for k, v in sel.items() if k != "ref_val" - ): - val = sel["ref_val"] - break - if val is None: - return - elif isinstance(ref_val, list): - val = ref_val[idx] - elif isinstance(ref_val, Number): - val = ref_val - else: - raise ValueError( - "Argument `ref_val` must be None, a constant, a list or a " - 'dictionary like {"var_name": [{"ref_val": ref_val}]}' - ) - less_than_ref_probability = (values < val).mean() - greater_than_ref_probability = (values >= val).mean() - ref_in_posterior = "{} <{:g}< {}".format( - format_as_percent(less_than_ref_probability, 1), - val, - format_as_percent(greater_than_ref_probability, 1), - ) - ax.axvline( - val, - ymin=0.05, - ymax=0.75, - lw=linewidth, - alpha=0.65, - color=vectorized_to_hex(ref_val_color), - ) - ax.text( - values.mean(), - plot_height * 0.6, - ref_in_posterior, - size=ax_labelsize, - weight="semibold", - horizontalalignment="center", - color=vectorized_to_hex(ref_val_color), - ) - - def display_rope(): - if rope is None: - return - elif isinstance(rope, dict): - vals = None - for sel in rope.get(var_name, []): - # pylint: disable=line-too-long - if all(k in selection and selection[k] == v for k, v in sel.items() if k != "rope"): - vals = sel["rope"] - break - if vals is None: - return - elif len(rope) == 2: - vals = rope - else: - raise ValueError( - "Argument `rope` must be None, a dictionary like" - '{"var_name": {"rope": (lo, hi)}}, or an' - "iterable of length 2" - ) - rope_text = [f"{val:.{format_sig_figs(val, round_to)}g}" for val in vals] - ax.plot( - vals, - (plot_height * 0.02, plot_height * 0.02), - lw=linewidth * 5, - solid_capstyle="butt", - zorder=0, - alpha=0.7, - color=vectorized_to_hex(rope_color), - ) - probability_within_rope = ((values > vals[0]) & (values <= vals[1])).mean() - ax.text( - values.mean(), - plot_height * 0.45, - f"{format_as_percent(probability_within_rope, 1)} in ROPE", - weight="semibold", - horizontalalignment="center", - size=ax_labelsize, - color=vectorized_to_hex(rope_color), - ) - ax.text( - vals[0], - plot_height * 0.2, - rope_text[0], - weight="semibold", - horizontalalignment="right", - size=ax_labelsize, - color=vectorized_to_hex(rope_color), - ) - ax.text( - vals[1], - plot_height * 0.2, - rope_text[1], - weight="semibold", - horizontalalignment="left", - size=ax_labelsize, - color=vectorized_to_hex(rope_color), - ) - - def display_point_estimate(): - if not point_estimate: - return - point_value = calculate_point_estimate(point_estimate, values, bw, circular, skipna) - sig_figs = format_sig_figs(point_value, round_to) - point_text = "{point_estimate}={point_value:.{sig_figs}g}".format( - point_estimate=point_estimate, point_value=point_value, sig_figs=sig_figs - ) - ax.text( - point_value, - plot_height * 0.8, - point_text, - size=ax_labelsize, - horizontalalignment="center", - ) - - def display_hdi(): - # np.ndarray with 2 entries, min and max - # pylint: disable=line-too-long - hdi_probs = hdi( - values, hdi_prob=hdi_prob, circular=circular, multimodal=multimodal, skipna=skipna - ) # type: np.ndarray - - for hdi_i in np.atleast_2d(hdi_probs): - ax.plot( - hdi_i, - (plot_height * 0.02, plot_height * 0.02), - lw=linewidth * 2, - color="k", - solid_capstyle="butt", - ) - ax.text( - hdi_i[0], - plot_height * 0.07, - f"{round_num(hdi_i[0], round_to)} ", - size=ax_labelsize, - horizontalalignment="right", - ) - ax.text( - hdi_i[1], - plot_height * 0.07, - f" {round_num(hdi_i[1], round_to)}", - size=ax_labelsize, - horizontalalignment="left", - ) - ax.text( - (hdi_i[0] + hdi_i[1]) / 2, - plot_height * 0.3, - f"{format_as_percent(hdi_prob)} HDI", - size=ax_labelsize, - horizontalalignment="center", - ) - - def format_axes(): - ax.yaxis.set_ticks([]) - ax.spines["top"].set_visible(False) - ax.spines["right"].set_visible(False) - ax.spines["left"].set_visible(False) - ax.spines["bottom"].set_visible(True) - ax.xaxis.set_ticks_position("bottom") - ax.tick_params( - axis="x", direction="out", width=1, length=3, color="0.5", labelsize=xt_labelsize - ) - ax.spines["bottom"].set_color("0.5") - - if kind == "kde" and values.dtype.kind == "f": - kwargs.setdefault("linewidth", linewidth) - plot_kde( - values, - bw=bw, - is_circular=circular, - fill_kwargs={"alpha": kwargs.pop("fill_alpha", 0)}, - plot_kwargs=kwargs, - ax=ax, - rug=False, - show=False, - ) - elif values.dtype.kind == "i" or (values.dtype.kind == "f" and kind == "hist"): - if bins is None: - xmin = values.min() - xmax = values.max() - bins = get_bins(values) - ax.set_xlim(xmin - 0.5, xmax + 0.5) - kwargs.setdefault("align", "left") - kwargs.setdefault("color", "C0") - ax.hist(values, bins=bins, alpha=0.35, **kwargs) - elif values.dtype.kind == "b": - if bins is None: - bins = "auto" - kwargs.setdefault("color", "C0") - ax.bar(["False", "True"], [(~values).sum(), values.sum()], alpha=0.35, **kwargs) - hdi_prob = "hide" - else: - raise TypeError("Values must be float, integer or boolean") - - plot_height = ax.get_ylim()[1] - - format_axes() - if hdi_prob != "hide": - display_hdi() - display_point_estimate() - display_ref_val() - display_rope() diff --git a/arviz/plots/backends/matplotlib/ppcplot.py b/arviz/plots/backends/matplotlib/ppcplot.py deleted file mode 100644 index 2b4118760b..0000000000 --- a/arviz/plots/backends/matplotlib/ppcplot.py +++ /dev/null @@ -1,478 +0,0 @@ -"""Matplotlib Posterior predictive plot.""" - -import logging -import platform - -import matplotlib.pyplot as plt -import numpy as np -from matplotlib import animation, get_backend - -from ....stats.density_utils import get_bins, histogram, kde -from ...kdeplot import plot_kde -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, backend_show, create_axes_grid - -_log = logging.getLogger(__name__) - - -def plot_ppc( - ax, - length_plotters, - rows, - cols, - figsize, - animated, - obs_plotters, - pp_plotters, - predictive_dataset, - pp_sample_ix, - kind, - alpha, - colors, - textsize, - mean, - observed, - observed_rug, - jitter, - total_pp_samples, - legend, - labeller, - group, - animation_kwargs, - num_pp_samples, - backend_kwargs, - show, -): - """Matplotlib ppc plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - if animation_kwargs is None: - animation_kwargs = {} - if platform.system() == "Linux": - animation_kwargs.setdefault("blit", True) - else: - animation_kwargs.setdefault("blit", False) - - if alpha is None: - if animated: - alpha = 1 - else: - if kind.lower() == "scatter": - alpha = 0.7 - else: - alpha = 0.2 - - if jitter is None: - jitter = 0.0 - if jitter < 0.0: - raise ValueError("jitter must be >=0") - - if animated: - try: - shell = get_ipython().__class__.__name__ - if shell == "ZMQInteractiveShell" and get_backend() != "nbAgg": - raise Warning( - "To run animations inside a notebook you have to use the nbAgg backend. " - "Try with `%matplotlib notebook` or `%matplotlib nbAgg`. You can switch " - "back to the default backend with `%matplotlib inline` or " - "`%matplotlib auto`." - ) - except NameError: - pass - - if animation_kwargs["blit"] and platform.system() != "Linux": - _log.warning( - "If you experience problems rendering the animation try setting " - "`animation_kwargs({'blit':False}) or changing the plotting backend " - "(e.g. to TkAgg)" - ) - - (figsize, ax_labelsize, _, xt_labelsize, linewidth, markersize) = _scale_fig_size( - figsize, textsize, rows, cols - ) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs.setdefault("squeeze", True) - if ax is None: - fig, axes = create_axes_grid(length_plotters, rows, cols, backend_kwargs=backend_kwargs) - else: - axes = np.ravel(ax) - if len(axes) != length_plotters: - raise ValueError( - "Found {} variables to plot but {} axes instances. They must be equal.".format( - length_plotters, len(axes) - ) - ) - if animated: - fig = axes[0].get_figure() - if not all((ax.get_figure() is fig for ax in axes)): - raise ValueError("All axes must be on the same figure for animation to work") - - for i, ax_i in enumerate(np.ravel(axes)[:length_plotters]): - var_name, selection, isel, obs_vals = obs_plotters[i] - pp_var_name, _, _, pp_vals = pp_plotters[i] - dtype = predictive_dataset[pp_var_name].dtype.kind - - if dtype not in ["i", "f"]: - raise ValueError( - f"The data type of the predictive data must be one of 'i' or 'f', but is '{dtype}'" - ) - - # flatten non-specified dimensions - obs_vals = obs_vals.flatten() - pp_vals = pp_vals.reshape(total_pp_samples, -1) - pp_sampled_vals = pp_vals[pp_sample_ix] - - if kind == "kde": - plot_kwargs = {"color": colors[0], "alpha": alpha, "linewidth": 0.5 * linewidth} - if dtype == "i": - plot_kwargs["drawstyle"] = "steps-pre" - ax_i.plot([], color=colors[0], label=f"{group.capitalize()} predictive") - if observed: - if dtype == "f": - plot_kde( - obs_vals, - rug=observed_rug, - label="Observed", - plot_kwargs={"color": colors[1], "linewidth": linewidth, "zorder": 3}, - fill_kwargs={"alpha": 0}, - ax=ax_i, - legend=legend, - ) - else: - bins = get_bins(obs_vals) - _, hist, bin_edges = histogram(obs_vals, bins=bins) - hist = np.concatenate((hist[:1], hist)) - ax_i.plot( - bin_edges, - hist, - label="Observed", - color=colors[1], - linewidth=linewidth, - zorder=3, - drawstyle=plot_kwargs["drawstyle"], - ) - - pp_densities = [] - pp_xs = [] - for vals in pp_sampled_vals: - vals = np.array([vals]).flatten() - if dtype == "f": - pp_x, pp_density = kde(vals) - pp_densities.append(pp_density) - pp_xs.append(pp_x) - else: - bins = get_bins(vals) - _, hist, bin_edges = histogram(vals, bins=bins) - hist = np.concatenate((hist[:1], hist)) - pp_densities.append(hist) - pp_xs.append(bin_edges) - - if animated: - animate, init = _set_animation( - pp_sampled_vals, ax_i, dtype=dtype, kind=kind, plot_kwargs=plot_kwargs - ) - - else: - if dtype == "f": - ax_i.plot(np.transpose(pp_xs), np.transpose(pp_densities), **plot_kwargs) - else: - for x_s, y_s in zip(pp_xs, pp_densities): - ax_i.plot(x_s, y_s, **plot_kwargs) - - if mean: - label = f"{group.capitalize()} predictive mean" - if dtype == "f": - rep = len(pp_densities) - len_density = len(pp_densities[0]) - - new_x = np.linspace(np.min(pp_xs), np.max(pp_xs), len_density) - new_d = np.zeros((rep, len_density)) - bins = np.digitize(pp_xs, new_x, right=True) - new_x -= (new_x[1] - new_x[0]) / 2 - for irep in range(rep): - new_d[irep][bins[irep]] = pp_densities[irep] - ax_i.plot( - new_x, - new_d.mean(0), - color=colors[2], - linestyle="--", - linewidth=linewidth * 1.5, - zorder=2, - label=label, - ) - else: - vals = pp_vals.flatten() - bins = get_bins(vals) - _, hist, bin_edges = histogram(vals, bins=bins) - hist = np.concatenate((hist[:1], hist)) - ax_i.plot( - bin_edges, - hist, - color=colors[2], - linewidth=linewidth * 1.5, - label=label, - zorder=2, - linestyle="--", - drawstyle=plot_kwargs["drawstyle"], - ) - ax_i.tick_params(labelsize=xt_labelsize) - ax_i.set_yticks([]) - - elif kind == "cumulative": - drawstyle = "default" if dtype == "f" else "steps-pre" - if observed: - ax_i.plot( - *_empirical_cdf(obs_vals), - color=colors[1], - linewidth=linewidth, - label="Observed", - drawstyle=drawstyle, - zorder=3, - ) - if observed_rug: - ax_i.plot( - obs_vals, - np.zeros_like(obs_vals) - 0.1, - ls="", - marker="|", - color=colors[1], - ) - if animated: - animate, init = _set_animation( - pp_sampled_vals, - ax_i, - kind=kind, - alpha=alpha, - drawstyle=drawstyle, - linewidth=linewidth, - ) - - else: - pp_densities = np.empty((2 * len(pp_sampled_vals), pp_sampled_vals[0].size)) - for idx, vals in enumerate(pp_sampled_vals): - vals = np.array([vals]).flatten() - pp_x, pp_density = _empirical_cdf(vals) - pp_densities[2 * idx] = pp_x - pp_densities[2 * idx + 1] = pp_density - - ax_i.plot( - *pp_densities, - alpha=alpha, - color=colors[0], - drawstyle=drawstyle, - linewidth=linewidth, - ) - ax_i.plot([], color=colors[0], label=f"{group.capitalize()} predictive") - if mean: - ax_i.plot( - *_empirical_cdf(pp_vals.flatten()), - color=colors[2], - linestyle="--", - linewidth=linewidth * 1.5, - drawstyle=drawstyle, - label=f"{group.capitalize()} predictive mean", - ) - ax_i.set_yticks([0, 0.5, 1]) - - elif kind == "scatter": - if mean: - if dtype == "f": - plot_kde( - pp_vals.flatten(), - plot_kwargs={ - "color": colors[2], - "linestyle": "--", - "linewidth": linewidth * 1.5, - "zorder": 3, - }, - label=f"{group.capitalize()} predictive mean", - ax=ax_i, - legend=legend, - ) - else: - vals = pp_vals.flatten() - bins = get_bins(vals) - _, hist, bin_edges = histogram(vals, bins=bins) - hist = np.concatenate((hist[:1], hist)) - ax_i.plot( - bin_edges, - hist, - color=colors[2], - linewidth=linewidth * 1.5, - label=f"{group.capitalize()} predictive mean", - zorder=3, - linestyle="--", - drawstyle="steps-pre", - ) - - _, limit = ax_i.get_ylim() - limit *= 1.05 - y_rows = np.linspace(0, limit, num_pp_samples + 1) - jitter_scale = y_rows[1] - y_rows[0] - scale_low = 0 - scale_high = jitter_scale * jitter - - if observed: - obs_yvals = np.zeros_like(obs_vals, dtype=np.float64) - if jitter: - obs_yvals += np.random.uniform( - low=scale_low, high=scale_high, size=len(obs_vals) - ) - ax_i.plot( - obs_vals, - obs_yvals, - "o", - color=colors[1], - markersize=markersize, - alpha=alpha, - label="Observed", - zorder=4, - ) - - if animated: - animate, init = _set_animation( - pp_sampled_vals, - ax_i, - kind=kind, - color=colors[0], - height=y_rows.mean() * 0.5, - markersize=markersize, - ) - - else: - for vals, y in zip(pp_sampled_vals, y_rows[1:]): - vals = np.ravel(vals) - yvals = np.full_like(vals, y, dtype=np.float64) - if jitter: - yvals += np.random.uniform(low=scale_low, high=scale_high, size=len(vals)) - ax_i.plot( - vals, - yvals, - "o", - zorder=2, - color=colors[0], - markersize=markersize, - alpha=alpha, - ) - - ax_i.plot([], color=colors[0], marker="o", label=f"{group.capitalize()} predictive") - - ax_i.set_yticks([]) - - ax_i.set_xlabel( - labeller.make_pp_label(var_name, pp_var_name, selection, isel), fontsize=ax_labelsize - ) - - if legend: - if i == 0: - ax_i.legend(fontsize=xt_labelsize * 0.75) - - if backend_show(show): - plt.show() - - if animated: - ani = animation.FuncAnimation( - fig, animate, np.arange(0, num_pp_samples), init_func=init, **animation_kwargs - ) - return axes, ani - else: - return axes - - -def _set_animation( - pp_sampled_vals, - ax, - dtype=None, - kind="density", - alpha=None, - color=None, - drawstyle=None, - linewidth=None, - height=None, - markersize=None, - plot_kwargs=None, -): - if kind == "kde": - length = len(pp_sampled_vals) - if dtype == "f": - x_vals, y_vals = kde(pp_sampled_vals[0]) - max_max = max(max(kde(pp_sampled_vals[i])[1]) for i in range(length)) - ax.set_ylim(0, max_max) - (line,) = ax.plot(x_vals, y_vals, **plot_kwargs) - - def animate(i): - x_vals, y_vals = kde(pp_sampled_vals[i]) - line.set_data(x_vals, y_vals) - return (line,) - - else: - vals = pp_sampled_vals[0] - bins = get_bins(vals) - _, y_vals, x_vals = histogram(vals, bins=bins) - (line,) = ax.plot(x_vals[:-1], y_vals, **plot_kwargs) - - max_max = max( - max(histogram(pp_sampled_vals[i], bins=get_bins(pp_sampled_vals[i]))[1]) - for i in range(length) - ) - - ax.set_ylim(0, max_max) - - def animate(i): - pp_vals = pp_sampled_vals[i] - _, y_vals, x_vals = histogram(pp_vals, bins=get_bins(pp_vals)) - line.set_data(x_vals[:-1], y_vals) - return (line,) - - elif kind == "cumulative": - x_vals, y_vals = _empirical_cdf(pp_sampled_vals[0]) - (line,) = ax.plot( - x_vals, y_vals, alpha=alpha, color=color, drawstyle=drawstyle, linewidth=linewidth - ) - - def animate(i): - x_vals, y_vals = _empirical_cdf(pp_sampled_vals[i]) - line.set_data(x_vals, y_vals) - return (line,) - - elif kind == "scatter": - x_vals = pp_sampled_vals[0] - y_vals = np.full_like(x_vals, height, dtype=np.float64) - (line,) = ax.plot( - x_vals, y_vals, "o", zorder=2, color=color, markersize=markersize, alpha=alpha - ) - - def animate(i): - line.set_xdata(np.ravel(pp_sampled_vals[i])) - return (line,) - - def init(): - if kind != "scatter": - line.set_data([], []) - else: - line.set_xdata([]) - return (line,) - - return animate, init - - -def _empirical_cdf(data): - """Compute empirical cdf of a numpy array. - - Parameters - ---------- - data : np.array - 1d array - - Returns - ------- - np.array, np.array - x and y coordinates for the empirical cdf of the data - """ - return np.sort(data), np.linspace(0, 1, len(data)) diff --git a/arviz/plots/backends/matplotlib/rankplot.py b/arviz/plots/backends/matplotlib/rankplot.py deleted file mode 100644 index 5e64e2e82d..0000000000 --- a/arviz/plots/backends/matplotlib/rankplot.py +++ /dev/null @@ -1,119 +0,0 @@ -"""Matplotlib rankplot.""" - -import matplotlib.pyplot as plt -import numpy as np - -from ....stats.density_utils import histogram -from ...plot_utils import _scale_fig_size, compute_ranks -from . import backend_kwarg_defaults, backend_show, create_axes_grid - - -def plot_rank( - axes, - length_plotters, - rows, - cols, - figsize, - plotters, - bins, - kind, - colors, - ref_line, - labels, - labeller, - ref_line_kwargs, - bar_kwargs, - vlines_kwargs, - marker_vlines_kwargs, - backend_kwargs, - show, -): - """Matplotlib rankplot..""" - if ref_line_kwargs is None: - ref_line_kwargs = {} - ref_line_kwargs.setdefault("linestyle", "--") - ref_line_kwargs.setdefault("color", "k") - - if bar_kwargs is None: - bar_kwargs = {} - bar_kwargs.setdefault("align", "center") - - if vlines_kwargs is None: - vlines_kwargs = {} - vlines_kwargs.setdefault("lw", 2) - - if marker_vlines_kwargs is None: - marker_vlines_kwargs = {} - marker_vlines_kwargs.setdefault("marker", "o") - marker_vlines_kwargs.setdefault("lw", 0) - - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - figsize, ax_labelsize, titlesize, _, _, _ = _scale_fig_size(figsize, None, rows=rows, cols=cols) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs.setdefault("squeeze", True) - if axes is None: - _, axes = create_axes_grid( - length_plotters, - rows, - cols, - backend_kwargs=backend_kwargs, - ) - - for ax, (var_name, selection, isel, var_data) in zip(np.ravel(axes), plotters): - ranks = compute_ranks(var_data) - bin_ary = np.histogram_bin_edges(ranks, bins=bins, range=(0, ranks.size)) - all_counts = np.empty((len(ranks), len(bin_ary) - 1)) - for idx, row in enumerate(ranks): - _, all_counts[idx], _ = histogram(row, bins=bin_ary) - gap = 2 / ranks.size - width = bin_ary[1] - bin_ary[0] - - bar_kwargs.setdefault("width", width) - bar_kwargs.setdefault("edgecolor", ax.get_facecolor()) - # Center the bins - bin_ary = (bin_ary[1:] + bin_ary[:-1]) / 2 - - y_ticks = [] - if kind == "bars": - for idx, counts in enumerate(all_counts): - y_ticks.append(idx * gap) - ax.bar( - bin_ary, - counts, - bottom=y_ticks[-1], - color=colors[idx], - **bar_kwargs, - ) - if ref_line: - ax.axhline(y=y_ticks[-1] + counts.mean(), **ref_line_kwargs) - if labels: - ax.set_ylabel("Chain", fontsize=ax_labelsize) - elif kind == "vlines": - ymin = all_counts.mean() - - for idx, counts in enumerate(all_counts): - ax.plot(bin_ary, counts, color=colors[idx], **marker_vlines_kwargs) - ax.vlines(bin_ary, ymin, counts, colors=colors[idx], **vlines_kwargs) - ax.set_ylim(0, all_counts.mean() * 2) - if ref_line: - ax.axhline(y=ymin, **ref_line_kwargs) - - if labels: - ax.set_xlabel("Rank (all chains)", fontsize=ax_labelsize) - ax.set_yticks(y_ticks) - ax.set_yticklabels(np.arange(len(y_ticks))) - ax.set_title(labeller.make_label_vert(var_name, selection, isel), fontsize=titlesize) - else: - ax.set_yticks([]) - - if backend_show(show): - plt.show() - - return axes diff --git a/arviz/plots/backends/matplotlib/separationplot.py b/arviz/plots/backends/matplotlib/separationplot.py deleted file mode 100644 index f709c8613a..0000000000 --- a/arviz/plots/backends/matplotlib/separationplot.py +++ /dev/null @@ -1,97 +0,0 @@ -"""Matplotlib separation plot.""" - -import matplotlib.pyplot as plt -import numpy as np - -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, backend_show, create_axes_grid - - -def plot_separation( - y, - y_hat, - y_hat_line, - label_y_hat, - expected_events, - figsize, - textsize, - color, - legend, - locs, - width, - ax, - plot_kwargs, - y_hat_line_kwargs, - exp_events_kwargs, - backend_kwargs, - show, -): - """Matplotlib separation plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - if plot_kwargs is None: - plot_kwargs = {} - - # plot_kwargs.setdefault("color", "C0") - # if color: - plot_kwargs["color"] = color - - if y_hat_line_kwargs is None: - y_hat_line_kwargs = {} - - y_hat_line_kwargs.setdefault("color", "k") - - if exp_events_kwargs is None: - exp_events_kwargs = {} - - exp_events_kwargs.setdefault("color", "k") - exp_events_kwargs.setdefault("marker", "^") - exp_events_kwargs.setdefault("s", 100) - exp_events_kwargs.setdefault("zorder", 2) - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - (figsize, *_) = _scale_fig_size(figsize, textsize, 1, 1) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs["squeeze"] = True - - if ax is None: - _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs) - - idx = np.argsort(y_hat) - - for i, loc in enumerate(locs): - positive = not y[idx][i] == 0 - alpha = 1 if positive else 0.3 - ax.bar(loc, 1, width=width, alpha=alpha, **plot_kwargs) - - if y_hat_line: - ax.plot(np.linspace(0, 1, len(y_hat)), y_hat[idx], label=label_y_hat, **y_hat_line_kwargs) - - if expected_events: - expected_events = int(np.round(np.sum(y_hat))) - ax.scatter( - y_hat[idx][len(y_hat) - expected_events - 1], - 0, - label="Expected events", - **exp_events_kwargs - ) - - if legend and (expected_events or y_hat_line): - handles, labels = ax.get_legend_handles_labels() - labels_dict = dict(zip(labels, handles)) - ax.legend(labels_dict.values(), labels_dict.keys()) - - ax.set_xticks([]) - ax.set_yticks([]) - ax.set_xlim(0, 1) - ax.set_ylim(0, 1) - - if backend_show(show): - plt.show() - - return ax diff --git a/arviz/plots/backends/matplotlib/traceplot.py b/arviz/plots/backends/matplotlib/traceplot.py deleted file mode 100644 index b00baa5daa..0000000000 --- a/arviz/plots/backends/matplotlib/traceplot.py +++ /dev/null @@ -1,526 +0,0 @@ -"""Matplotlib traceplot.""" - -import warnings -from itertools import cycle - -from matplotlib import gridspec -import matplotlib.pyplot as plt -import numpy as np -from matplotlib.lines import Line2D -import matplotlib.ticker as mticker - -from ....stats.density_utils import get_bins -from ...distplot import plot_dist -from ...plot_utils import _scale_fig_size, format_coords_as_labels -from ...rankplot import plot_rank -from . import backend_kwarg_defaults, backend_show, dealiase_sel_kwargs, matplotlib_kwarg_dealiaser - - -def plot_trace( - data, - var_names, # pylint: disable=unused-argument - divergences, - kind, - figsize, - rug, - lines, - circ_var_names, - circ_var_units, - compact, - compact_prop, - combined, - chain_prop, - legend, - labeller, - plot_kwargs, - fill_kwargs, - rug_kwargs, - hist_kwargs, - trace_kwargs, - rank_kwargs, - plotters, - divergence_data, - axes, - backend_kwargs, - backend_config, # pylint: disable=unused-argument - show, -): - """Plot distribution (histogram or kernel density estimates) and sampled values. - - If `divergences` data is available in `sample_stats`, will plot the location of divergences as - dashed vertical lines. - - Parameters - ---------- - data : obj - Any object that can be converted to an az.InferenceData object - Refer to documentation of az.convert_to_dataset for details - var_names : string, or list of strings - One or more variables to be plotted. - divergences : {"bottom", "top", None, False} - Plot location of divergences on the traceplots. Options are "bottom", "top", or False-y. - kind : {"trace", "rank_bar", "rank_vlines"}, optional - Choose between plotting sampled values per iteration and rank plots. - figsize : figure size tuple - If None, size is (12, variables * 2) - rug : bool - If True adds a rugplot. Defaults to False. Ignored for 2D KDE. Only affects continuous - variables. - lines : tuple or list - List of tuple of (var_name, {'coord': selection}, [line_positions]) to be overplotted as - vertical lines on the density and horizontal lines on the trace. - circ_var_names : string, or list of strings - List of circular variables to account for when plotting KDE. - circ_var_units : str - Whether the variables in `circ_var_names` are in "degrees" or "radians". - combined : bool - Flag for combining multiple chains into a single line. If False (default), chains will be - plotted separately. - legend : bool - Add a legend to the figure with the chain color code. - plot_kwargs : dict - Extra keyword arguments passed to `arviz.plot_dist`. Only affects continuous variables. - fill_kwargs : dict - Extra keyword arguments passed to `arviz.plot_dist`. Only affects continuous variables. - rug_kwargs : dict - Extra keyword arguments passed to `arviz.plot_dist`. Only affects continuous variables. - hist_kwargs : dict - Extra keyword arguments passed to `arviz.plot_dist`. Only affects discrete variables. - trace_kwargs : dict - Extra keyword arguments passed to `plt.plot` - rank_kwargs : dict - Extra keyword arguments passed to `arviz.plot_rank` - Returns - ------- - axes : matplotlib axes - - - Examples - -------- - Plot a subset variables - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> data = az.load_arviz_data('non_centered_eight') - >>> coords = {'school': ['Choate', 'Lawrenceville']} - >>> az.plot_trace(data, var_names=('theta_t', 'theta'), coords=coords) - - Show all dimensions of multidimensional variables in the same plot - - .. plot:: - :context: close-figs - - >>> az.plot_trace(data, compact=True) - - Combine all chains into one distribution - - .. plot:: - :context: close-figs - - >>> az.plot_trace(data, var_names=('theta_t', 'theta'), coords=coords, combined=True) - - - Plot reference lines against distribution and trace - - .. plot:: - :context: close-figs - - >>> lines = (('theta_t',{'school': "Choate"}, [-1]),) - >>> az.plot_trace(data, var_names=('theta_t', 'theta'), coords=coords, lines=lines) - - """ - # Set plot default backend kwargs - if backend_kwargs is None: - backend_kwargs = {} - - if circ_var_names is None: - circ_var_names = [] - - backend_kwargs = {**backend_kwarg_defaults(), **backend_kwargs} - - if lines is None: - lines = () - - num_chain_props = len(data.chain) + 1 if combined else len(data.chain) - if not compact: - chain_prop = "color" if chain_prop is None else chain_prop - else: - chain_prop = ( - { - "linestyle": ("solid", "dotted", "dashed", "dashdot"), - } - if chain_prop is None - else chain_prop - ) - compact_prop = "color" if compact_prop is None else compact_prop - - if isinstance(chain_prop, str): - chain_prop = {chain_prop: plt.rcParams["axes.prop_cycle"].by_key()[chain_prop]} - if isinstance(chain_prop, tuple): - warnings.warn( - "chain_prop as a tuple will be deprecated in a future warning, use a dict instead", - FutureWarning, - ) - chain_prop = {chain_prop[0]: chain_prop[1]} - chain_prop = { - prop_name: [prop for _, prop in zip(range(num_chain_props), cycle(props))] - for prop_name, props in chain_prop.items() - } - - if isinstance(compact_prop, str): - compact_prop = {compact_prop: plt.rcParams["axes.prop_cycle"].by_key()[compact_prop]} - if isinstance(compact_prop, tuple): - warnings.warn( - "compact_prop as a tuple will be deprecated in a future warning, use a dict instead", - FutureWarning, - ) - compact_prop = {compact_prop[0]: compact_prop[1]} - - if figsize is None: - figsize = (12, len(plotters) * 2) - - backend_kwargs.setdefault("figsize", figsize) - - trace_kwargs = matplotlib_kwarg_dealiaser(trace_kwargs, "plot") - trace_kwargs.setdefault("alpha", 0.35) - - hist_kwargs = matplotlib_kwarg_dealiaser(hist_kwargs, "hist") - hist_kwargs.setdefault("alpha", 0.35) - - plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") - fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, "fill_between") - rug_kwargs = matplotlib_kwarg_dealiaser(rug_kwargs, "scatter") - rank_kwargs = matplotlib_kwarg_dealiaser(rank_kwargs, "bar") - if compact: - rank_kwargs.setdefault("bar_kwargs", {}) - rank_kwargs["bar_kwargs"].setdefault("alpha", 0.2) - - textsize = plot_kwargs.pop("textsize", 10) - - figsize, _, titlesize, xt_labelsize, linewidth, _ = _scale_fig_size( - figsize, textsize, rows=len(plotters), cols=2 - ) - - trace_kwargs.setdefault("linewidth", linewidth) - plot_kwargs.setdefault("linewidth", linewidth) - - # Check the input for lines - if lines is not None: - all_var_names = set(plotter[0] for plotter in plotters) - - invalid_var_names = set() - for line in lines: - if line[0] not in all_var_names: - invalid_var_names.add(line[0]) - if invalid_var_names: - warnings.warn( - "A valid var_name should be provided, found {} expected from {}".format( - invalid_var_names, all_var_names - ) - ) - - if axes is None: - fig = plt.figure(**backend_kwargs) - spec = gridspec.GridSpec(ncols=2, nrows=len(plotters), figure=fig) - - # pylint: disable=too-many-nested-blocks - for idx, (var_name, selection, isel, value) in enumerate(plotters): - for idy in range(2): - value = np.atleast_2d(value) - - circular = var_name in circ_var_names and not idy - if var_name in circ_var_names and idy: - circ_units_trace = circ_var_units - else: - circ_units_trace = False - - if axes is None: - ax = fig.add_subplot(spec[idx, idy], polar=circular) - else: - ax = axes[idx, idy] - - if len(value.shape) == 2: - if compact_prop: - aux_plot_kwargs = dealiase_sel_kwargs(plot_kwargs, compact_prop, 0) - aux_trace_kwargs = dealiase_sel_kwargs(trace_kwargs, compact_prop, 0) - else: - aux_plot_kwargs = plot_kwargs - aux_trace_kwargs = trace_kwargs - - ax = _plot_chains_mpl( - ax, - idy, - value, - data, - chain_prop, - combined, - xt_labelsize, - rug, - kind, - aux_trace_kwargs, - hist_kwargs, - aux_plot_kwargs, - fill_kwargs, - rug_kwargs, - rank_kwargs, - circular, - circ_var_units, - circ_units_trace, - ) - - else: - sub_data = data[var_name].sel(**selection) - legend_labels = format_coords_as_labels(sub_data, skip_dims=("chain", "draw")) - legend_title = ", ".join( - [ - f"{coord_name}" - for coord_name in sub_data.coords - if coord_name not in {"chain", "draw"} - ] - ) - value = value.reshape((value.shape[0], value.shape[1], -1)) - compact_prop_iter = { - prop_name: [prop for _, prop in zip(range(value.shape[2]), cycle(props))] - for prop_name, props in compact_prop.items() - } - handles = [] - for sub_idx, label in zip(range(value.shape[2]), legend_labels): - aux_plot_kwargs = dealiase_sel_kwargs(plot_kwargs, compact_prop_iter, sub_idx) - aux_trace_kwargs = dealiase_sel_kwargs(trace_kwargs, compact_prop_iter, sub_idx) - ax = _plot_chains_mpl( - ax, - idy, - value[..., sub_idx], - data, - chain_prop, - combined, - xt_labelsize, - rug, - kind, - aux_trace_kwargs, - hist_kwargs, - aux_plot_kwargs, - fill_kwargs, - rug_kwargs, - rank_kwargs, - circular, - circ_var_units, - circ_units_trace, - ) - if legend: - handles.append( - Line2D( - [], - [], - label=label, - **dealiase_sel_kwargs(aux_plot_kwargs, chain_prop, 0), - ) - ) - if legend and idy == 0: - ax.legend(handles=handles, title=legend_title) - - if value[0].dtype.kind == "i" and idy == 0: - xticks = get_bins(value) - ax.set_xticks(xticks[:-1]) - y = 1 / textsize - if not idy: - ax.set_yticks([]) - if circular: - y = 0.13 if selection else 0.12 - ax.set_title( - labeller.make_label_vert(var_name, selection, isel), - fontsize=titlesize, - wrap=True, - y=textsize * y, - ) - ax.tick_params(labelsize=xt_labelsize) - - xlims = ax.get_xlim() - ylims = ax.get_ylim() - - if divergences: - div_selection = {k: v for k, v in selection.items() if k in divergence_data.dims} - divs = divergence_data.sel(**div_selection).values - # if combined: - # divs = divs.flatten() - divs = np.atleast_2d(divs) - - for chain, chain_divs in enumerate(divs): - div_draws = data.draw.values[chain_divs] - div_idxs = np.arange(len(chain_divs))[chain_divs] - if div_idxs.size > 0: - if divergences == "top": - ylocs = ylims[1] - else: - ylocs = ylims[0] - values = value[chain, div_idxs] - - if circular: - tick = [ax.get_rmin() + ax.get_rmax() * 0.60, ax.get_rmax()] - for val in values: - ax.plot( - [val, val], - tick, - color="black", - markeredgewidth=1.5, - markersize=30, - alpha=trace_kwargs["alpha"], - zorder=0.6, - ) - else: - if kind == "trace" and idy: - ax.plot( - div_draws, - np.zeros_like(div_idxs) + ylocs, - marker="|", - color="black", - markeredgewidth=1.5, - markersize=30, - linestyle="None", - alpha=hist_kwargs["alpha"], - zorder=0.6, - ) - elif not idy: - ax.plot( - values, - np.zeros_like(values) + ylocs, - marker="|", - color="black", - markeredgewidth=1.5, - markersize=30, - linestyle="None", - alpha=trace_kwargs["alpha"], - zorder=0.6, - ) - - for _, _, vlines in (j for j in lines if j[0] == var_name and j[1] == selection): - if isinstance(vlines, (float, int)): - line_values = [vlines] - else: - line_values = np.atleast_1d(vlines).ravel() - if not np.issubdtype(line_values.dtype, np.number): - raise ValueError(f"line-positions should be numeric, found {line_values}") - if idy: - ax.hlines( - line_values, - xlims[0], - xlims[1], - colors="black", - linewidth=1.5, - alpha=trace_kwargs["alpha"], - ) - - else: - ax.vlines( - line_values, - ylims[0], - ylims[1], - colors="black", - linewidth=1.5, - alpha=trace_kwargs["alpha"], - ) - - if kind == "trace" and idy: - ax.set_xlim(left=data.draw.min(), right=data.draw.max()) - - if legend: - legend_kwargs = trace_kwargs if combined else plot_kwargs - handles = [ - Line2D( - [], [], label=chain_id, **dealiase_sel_kwargs(legend_kwargs, chain_prop, chain_id) - ) - for chain_id in range(data.sizes["chain"]) - ] - if combined: - handles.insert( - 0, - Line2D( - [], [], label="combined", **dealiase_sel_kwargs(plot_kwargs, chain_prop, -1) - ), - ) - ax.figure.axes[1].legend(handles=handles, title="chain", loc="upper right") - - if axes is None: - axes = np.array(ax.figure.axes).reshape(-1, 2) - - if backend_show(show): - plt.show() - - return axes - - -def _plot_chains_mpl( - axes, - idy, - value, - data, - chain_prop, - combined, - xt_labelsize, - rug, - kind, - trace_kwargs, - hist_kwargs, - plot_kwargs, - fill_kwargs, - rug_kwargs, - rank_kwargs, - circular, - circ_var_units, - circ_units_trace, -): - if not circular: - circ_var_units = False - - for chain_idx, row in enumerate(value): - if kind == "trace": - aux_kwargs = dealiase_sel_kwargs(trace_kwargs, chain_prop, chain_idx) - if idy: - axes.plot(data.draw.values, row, **aux_kwargs) - if circ_units_trace == "degrees": - y_tick_locs = axes.get_yticks() - y_tick_labels = [i + 2 * 180 if i < 0 else i for i in np.rad2deg(y_tick_locs)] - axes.yaxis.set_major_locator(mticker.FixedLocator(y_tick_locs)) - axes.set_yticklabels([f"{i:.0f}°" for i in y_tick_labels]) - - if not combined: - aux_kwargs = dealiase_sel_kwargs(plot_kwargs, chain_prop, chain_idx) - if not idy: - axes = plot_dist( - values=row, - textsize=xt_labelsize, - rug=rug, - ax=axes, - hist_kwargs=hist_kwargs, - plot_kwargs=aux_kwargs, - fill_kwargs=fill_kwargs, - rug_kwargs=rug_kwargs, - backend="matplotlib", - show=False, - is_circular=circ_var_units, - ) - - if kind == "rank_bars" and idy: - axes = plot_rank(data=value, kind="bars", ax=axes, **rank_kwargs) - elif kind == "rank_vlines" and idy: - axes = plot_rank(data=value, kind="vlines", ax=axes, **rank_kwargs) - - if combined: - aux_kwargs = dealiase_sel_kwargs(plot_kwargs, chain_prop, -1) - if not idy: - axes = plot_dist( - values=value.flatten(), - textsize=xt_labelsize, - rug=rug, - ax=axes, - hist_kwargs=hist_kwargs, - plot_kwargs=aux_kwargs, - fill_kwargs=fill_kwargs, - rug_kwargs=rug_kwargs, - backend="matplotlib", - show=False, - is_circular=circ_var_units, - ) - return axes diff --git a/arviz/plots/backends/matplotlib/tsplot.py b/arviz/plots/backends/matplotlib/tsplot.py deleted file mode 100644 index f4471cdeef..0000000000 --- a/arviz/plots/backends/matplotlib/tsplot.py +++ /dev/null @@ -1,121 +0,0 @@ -"""Matplotlib plot time series figure.""" - -import matplotlib.pyplot as plt -import numpy as np - -from ...plot_utils import _scale_fig_size -from . import create_axes_grid, backend_show, matplotlib_kwarg_dealiaser, backend_kwarg_defaults - - -def plot_ts( - x_plotters, - y_plotters, - y_mean_plotters, - y_hat_plotters, - y_holdout_plotters, - x_holdout_plotters, - y_forecasts_plotters, - y_forecasts_mean_plotters, - num_samples, - backend_kwargs, - y_kwargs, - y_hat_plot_kwargs, - y_mean_plot_kwargs, - vline_kwargs, - length_plotters, - rows, - cols, - textsize, - figsize, - legend, - axes, - show, -): - """Matplotlib time series.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - if figsize is None: - figsize = (12, rows * 5) - - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs.setdefault("squeeze", False) - - figsize, _, _, xt_labelsize, _, _ = _scale_fig_size(figsize, textsize, rows, cols) - - if axes is None: - _, axes = create_axes_grid(length_plotters, rows, cols, backend_kwargs=backend_kwargs) - - y_kwargs = matplotlib_kwarg_dealiaser(y_kwargs, "plot") - y_kwargs.setdefault("color", "blue") - y_kwargs.setdefault("zorder", 10) - - y_hat_plot_kwargs = matplotlib_kwarg_dealiaser(y_hat_plot_kwargs, "plot") - y_hat_plot_kwargs.setdefault("color", "grey") - y_hat_plot_kwargs.setdefault("alpha", 0.1) - - y_mean_plot_kwargs = matplotlib_kwarg_dealiaser(y_mean_plot_kwargs, "plot") - y_mean_plot_kwargs.setdefault("color", "red") - y_mean_plot_kwargs.setdefault("linestyle", "dashed") - - vline_kwargs = matplotlib_kwarg_dealiaser(vline_kwargs, "plot") - vline_kwargs.setdefault("color", "black") - vline_kwargs.setdefault("linestyle", "dashed") - - for i, ax_i in enumerate(np.ravel(axes)[:length_plotters]): - y_var_name, _, _, y_plotters_i = y_plotters[i] - x_var_name, _, _, x_plotters_i = x_plotters[i] - - ax_i.plot(x_plotters_i, y_plotters_i, **y_kwargs) - ax_i.plot([], label="Actual", **y_kwargs) - if y_hat_plotters is not None or y_forecasts_plotters is not None: - ax_i.plot([], label="Fitted", **y_mean_plot_kwargs) - ax_i.plot([], label="Uncertainty", **y_hat_plot_kwargs) - - ax_i.set_xlabel(x_var_name) - ax_i.set_ylabel(y_var_name) - - if y_hat_plotters is not None: - *_, y_hat_plotters_i = y_hat_plotters[i] - *_, x_hat_plotters_i = x_plotters[i] - for j in range(num_samples): - ax_i.plot(x_hat_plotters_i, y_hat_plotters_i[..., j], **y_hat_plot_kwargs) - - *_, x_mean_plotters_i = x_plotters[i] - *_, y_mean_plotters_i = y_mean_plotters[i] - ax_i.plot(x_mean_plotters_i, y_mean_plotters_i, **y_mean_plot_kwargs) - - if y_holdout_plotters is not None: - *_, y_holdout_plotters_i = y_holdout_plotters[i] - *_, x_holdout_plotters_i = x_holdout_plotters[i] - - ax_i.plot(x_holdout_plotters_i, y_holdout_plotters_i, **y_kwargs) - ax_i.axvline(x_plotters_i[-1], **vline_kwargs) - - if y_forecasts_plotters is not None: - *_, y_forecasts_plotters_i = y_forecasts_plotters[i] - *_, x_forecasts_plotters_i = x_holdout_plotters[i] - for j in range(num_samples): - ax_i.plot( - x_forecasts_plotters_i, y_forecasts_plotters_i[..., j], **y_hat_plot_kwargs - ) - - *_, x_forecasts_mean_plotters_i = x_holdout_plotters[i] - *_, y_forecasts_mean_plotters_i = y_forecasts_mean_plotters[i] - ax_i.plot( - x_forecasts_mean_plotters_i, y_forecasts_mean_plotters_i, **y_mean_plot_kwargs - ) - ax_i.axvline(x_plotters_i[-1], **vline_kwargs) - - if legend: - ax_i.legend(fontsize=xt_labelsize, loc="upper left") - - if backend_show(show): - plt.show() - - return axes diff --git a/arviz/plots/backends/matplotlib/violinplot.py b/arviz/plots/backends/matplotlib/violinplot.py deleted file mode 100644 index b7ae8be7ca..0000000000 --- a/arviz/plots/backends/matplotlib/violinplot.py +++ /dev/null @@ -1,148 +0,0 @@ -"""Matplotlib Violinplot.""" - -import matplotlib.pyplot as plt -import numpy as np - -from ....stats import hdi -from ....stats.density_utils import get_bins, histogram, kde -from ...plot_utils import _scale_fig_size -from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser - - -def plot_violin( - ax, - plotters, - figsize, - rows, - cols, - sharex, - sharey, - shade_kwargs, - shade, - rug, - rug_kwargs, - side, - bw, - textsize, - labeller, - circular, - hdi_prob, - quartiles, - backend_kwargs, - show, -): - """Matplotlib violin plot.""" - if backend_kwargs is None: - backend_kwargs = {} - - backend_kwargs = { - **backend_kwarg_defaults(), - **backend_kwargs, - } - - (figsize, ax_labelsize, _, xt_labelsize, linewidth, _) = _scale_fig_size( - figsize, textsize, rows, cols - ) - backend_kwargs.setdefault("figsize", figsize) - backend_kwargs.setdefault("sharex", sharex) - backend_kwargs.setdefault("sharey", sharey) - backend_kwargs.setdefault("squeeze", True) - - shade_kwargs = matplotlib_kwarg_dealiaser(shade_kwargs, "hexbin") - rug_kwargs = matplotlib_kwarg_dealiaser(rug_kwargs, "plot") - rug_kwargs.setdefault("alpha", 0.1) - rug_kwargs.setdefault("marker", ".") - rug_kwargs.setdefault("linestyle", "") - - if ax is None: - fig, ax = create_axes_grid( - len(plotters), - rows, - cols, - backend_kwargs=backend_kwargs, - ) - fig.set_layout_engine("none") - fig.subplots_adjust(wspace=0) - - ax = np.atleast_1d(ax) - - current_col = 0 - for (var_name, selection, isel, x), ax_ in zip(plotters, ax.flatten()): - val = x.flatten() - if val[0].dtype.kind == "i": - dens = cat_hist(val, rug, side, shade, ax_, **shade_kwargs) - else: - dens = _violinplot(val, rug, side, shade, bw, circular, ax_, **shade_kwargs) - - if rug: - rug_x = -np.abs(np.random.normal(scale=max(dens) / 3.5, size=len(val))) - ax_.plot(rug_x, val, **rug_kwargs) - - per = np.nanpercentile(val, [25, 75, 50]) - hdi_probs = hdi(val, hdi_prob, multimodal=False, skipna=True) - - if quartiles: - ax_.plot([0, 0], per[:2], lw=linewidth * 3, color="k", solid_capstyle="round") - ax_.plot([0, 0], hdi_probs, lw=linewidth, color="k", solid_capstyle="round") - ax_.plot(0, per[-1], "wo", ms=linewidth * 1.5) - - ax_.set_title(labeller.make_label_vert(var_name, selection, isel), fontsize=ax_labelsize) - ax_.set_xticks([]) - ax_.tick_params(labelsize=xt_labelsize) - ax_.grid(None, axis="x") - if current_col != 0: - ax_.spines["left"].set_visible(False) - ax_.yaxis.set_ticks_position("none") - current_col += 1 - if current_col == cols: - current_col = 0 - - if backend_show(show): - plt.show() - - return ax - - -def _violinplot(val, rug, side, shade, bw, circular, ax, **shade_kwargs): - """Auxiliary function to plot violinplots.""" - if bw == "default": - bw = "taylor" if circular else "experimental" - x, density = kde(val, circular=circular, bw=bw) - - if rug and side == "both": - side = "right" - - if side == "left": - dens = -density - elif side == "right": - x = x[::-1] - dens = density[::-1] - elif side == "both": - x = np.concatenate([x, x[::-1]]) - dens = np.concatenate([-density, density[::-1]]) - - ax.fill_betweenx(x, dens, alpha=shade, lw=0, **shade_kwargs) - return density - - -def cat_hist(val, rug, side, shade, ax, **shade_kwargs): - """Auxiliary function to plot discrete-violinplots.""" - bins = get_bins(val) - _, binned_d, _ = histogram(val, bins=bins) - - bin_edges = np.linspace(np.min(val), np.max(val), len(bins)) - heights = np.diff(bin_edges) - centers = bin_edges[:-1] + heights.mean() / 2 - - if rug and side == "both": - side = "right" - - if side == "right": - left = None - elif side == "left": - left = -binned_d - elif side == "both": - left = -0.5 * binned_d - - ax.barh(centers, binned_d, height=heights, left=left, alpha=shade, **shade_kwargs) - return binned_d diff --git a/arviz/plots/bfplot.py b/arviz/plots/bfplot.py deleted file mode 100644 index b95fa3f9c7..0000000000 --- a/arviz/plots/bfplot.py +++ /dev/null @@ -1,128 +0,0 @@ -# Plotting and reporting Bayes Factor given idata, var name, prior distribution and reference value -# pylint: disable=unbalanced-tuple-unpacking -import logging - -from ..data.utils import extract -from .plot_utils import get_plotting_function -from ..stats import bayes_factor - -_log = logging.getLogger(__name__) - - -def plot_bf( - idata, - var_name, - prior=None, - ref_val=0, - colors=("C0", "C1"), - figsize=None, - textsize=None, - hist_kwargs=None, - plot_kwargs=None, - ax=None, - backend=None, - backend_kwargs=None, - show=None, -): - r"""Approximated Bayes Factor for comparing hypothesis of two nested models. - - The Bayes factor is estimated by comparing a model (H1) against a model in which the - parameter of interest has been restricted to be a point-null (H0). This computation - assumes the models are nested and thus H0 is a special case of H1. - - Notes - ----- - The bayes Factor is approximated as the Savage-Dickey density ratio - algorithm presented in [1]_. - - Parameters - ---------- - idata : InferenceData - Any object that can be converted to an :class:`arviz.InferenceData` object - Refer to documentation of :func:`arviz.convert_to_dataset` for details. - var_name : str, optional - Name of variable we want to test. - prior : numpy.array, optional - In case we want to use different prior, for example for sensitivity analysis. - ref_val : int, default 0 - Point-null for Bayes factor estimation. - colors : tuple, default ('C0', 'C1') - Tuple of valid Matplotlib colors. First element for the prior, second for the posterior. - figsize : (float, float), optional - Figure size. If `None` it will be defined automatically. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If `None` it will be auto - scaled based on `figsize`. - plot_kwargs : dict, optional - Additional keywords passed to :func:`matplotlib.pyplot.plot`. - hist_kwargs : dict, optional - Additional keywords passed to :func:`arviz.plot_dist`. Only works for discrete variables. - ax : axes, optional - :class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`. - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - - Returns - ------- - dict : A dictionary with BF10 (Bayes Factor 10 (H1/H0 ratio), and BF01 (H0/H1 ratio). - axes : matplotlib_axes or bokeh_figure - - References - ---------- - .. [1] Heck, D., 2019. A caveat on the Savage-Dickey density ratio: - The case of computing Bayes factors for regression parameters. - - Examples - -------- - Moderate evidence indicating that the parameter "a" is different from zero. - - .. plot:: - :context: close-figs - - >>> import numpy as np - >>> import arviz as az - >>> idata = az.from_dict(posterior={"a":np.random.normal(1, 0.5, 5000)}, - ... prior={"a":np.random.normal(0, 1, 5000)}) - >>> az.plot_bf(idata, var_name="a", ref_val=0) - - """ - - if prior is None: - prior = extract(idata, var_names=var_name, group="prior").values - - bf, p_at_ref_val = bayes_factor( - idata, var_name, prior=prior, ref_val=ref_val, return_ref_vals=True - ) - bf_10 = bf["BF10"] - bf_01 = bf["BF01"] - - posterior = extract(idata, var_names=var_name) - - bfplot_kwargs = dict( - ax=ax, - bf_10=bf_10.item(), - bf_01=bf_01.item(), - prior=prior, - posterior=posterior, - ref_val=ref_val, - prior_at_ref_val=p_at_ref_val["prior"], - posterior_at_ref_val=p_at_ref_val["posterior"], - var_name=var_name, - colors=colors, - figsize=figsize, - textsize=textsize, - hist_kwargs=hist_kwargs, - plot_kwargs=plot_kwargs, - backend_kwargs=backend_kwargs, - show=show, - ) - - plot = get_plotting_function("plot_bf", "bfplot", backend) - axes = plot(**bfplot_kwargs) - return {"BF10": bf_10, "BF01": bf_01}, axes diff --git a/arviz/plots/bpvplot.py b/arviz/plots/bpvplot.py deleted file mode 100644 index 63eaeedfa6..0000000000 --- a/arviz/plots/bpvplot.py +++ /dev/null @@ -1,308 +0,0 @@ -"""Bayesian p-value Posterior/Prior predictive plot.""" - -import numpy as np - -from ..labels import BaseLabeller -from ..rcparams import rcParams -from ..utils import _var_names -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function -from ..sel_utils import xarray_var_iter - - -def plot_bpv( - data, - kind="u_value", - t_stat="median", - bpv=True, - plot_mean=True, - reference="analytical", - smoothing=None, - mse=False, - n_ref=100, - hdi_prob=0.94, - color="C0", - grid=None, - figsize=None, - textsize=None, - labeller=None, - data_pairs=None, - var_names=None, - filter_vars=None, - coords=None, - flatten=None, - flatten_pp=None, - ax=None, - backend=None, - plot_ref_kwargs=None, - backend_kwargs=None, - group="posterior", - show=None, -): - r"""Plot Bayesian p-value for observed data and Posterior/Prior predictive. - - Parameters - ---------- - data : InferenceData - :class:`arviz.InferenceData` object containing the observed and - posterior/prior predictive data. - kind : {"u_value", "p_value", "t_stat"}, default "u_value" - Specify the kind of plot: - - * The ``kind="p_value"`` computes :math:`p := p(y* \leq y | y)`. - This is the probability of the data y being larger or equal than the predicted data y*. - The ideal value is 0.5 (half the predictions below and half above the data). - * The ``kind="u_value"`` argument computes :math:`p_i := p(y_i* \leq y_i | y)`. - i.e. like a p_value but per observation :math:`y_i`. This is also known as marginal - p_value. The ideal distribution is uniform. This is similar to the LOO-PIT - calculation/plot, the difference is than in LOO-pit plot we compute - :math:`pi = p(y_i* r \leq y_i | y_{-i} )`, where :math:`y_{-i}`, - is all other data except :math:`y_i`. - * The ``kind="t_stat"`` argument computes :math:`:= p(T(y)* \leq T(y) | y)` - where T is any test statistic. See ``t_stat`` argument below for details - of available options. - - t_stat : str, float, or callable, default "median" - Test statistics to compute from the observations and predictive distributions. - Allowed strings are “mean”, “median” or “std”. Alternative a quantile can be passed - as a float (or str) in the interval (0, 1). Finally a user defined function is also - acepted, see examples section for details. - bpv : bool, default True - If True add the Bayesian p_value to the legend when ``kind = t_stat``. - plot_mean : bool, default True - Whether or not to plot the mean test statistic. - reference : {"analytical", "samples", None}, default "analytical" - How to compute the distributions used as reference for ``kind=u_values`` - or ``kind=p_values``. Use `None` to not plot any reference. - smoothing : bool, optional - If True and the data has integer dtype, smooth the data before computing the p-values, - u-values or tstat. By default, True when `kind` is "u_value" and False otherwise. - mse : bool, default False - Show scaled mean square error between uniform distribution and marginal p_value - distribution. - n_ref : int, default 100 - Number of reference distributions to sample when ``reference=samples``. - hdi_prob : float, optional - Probability for the highest density interval for the analytical reference distribution when - ``kind=u_values``. Should be in the interval (0, 1]. Defaults to the - rcParam ``stats.ci_prob``. See :ref:`this section ` for usage examples. - color : str, optional - Matplotlib color - grid : tuple, optional - Number of rows and columns. By default, the rows and columns are - automatically inferred. See :ref:`this section ` for usage examples. - figsize : (float, float), optional - Figure size. If None it will be defined automatically. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If None it will be autoscaled based - on `figsize`. - data_pairs : dict, optional - Dictionary containing relations between observed data and posterior/prior predictive data. - Dictionary structure: - - - key = data var_name - - value = posterior/prior predictive var_name - - For example, ``data_pairs = {'y' : 'y_hat'}`` - If None, it will assume that the observed data and the posterior/prior - predictive data have the same variable name. - Labeller : Labeller, optional - Class providing the method ``make_pp_label`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - var_names : list of str, optional - Variables to be plotted. If `None` all variable are plotted. Prefix the variables by ``~`` - when you want to exclude them from the plot. See the :ref:`this section ` - for usage examples. See :ref:`this section ` for usage examples. - filter_vars : {None, "like", "regex"}, default None - If `None` (default), interpret `var_names` as the real variables names. If "like", - interpret `var_names` as substrings of the real variables names. If "regex", - interpret `var_names` as regular expressions on the real variables names. See - :ref:`this section ` for usage examples. - coords : dict, optional - Dictionary mapping dimensions to selected coordinates to be plotted. - Dimensions without a mapping specified will include all coordinates for - that dimension. Defaults to including all coordinates for all - dimensions if None. See :ref:`this section ` for usage examples. - flatten : list, optional - List of dimensions to flatten in observed_data. Only flattens across the coordinates - specified in the coords argument. Defaults to flattening all of the dimensions. - flatten_pp : list, optional - List of dimensions to flatten in posterior_predictive/prior_predictive. Only flattens - across the coordinates specified in the coords argument. Defaults to flattening all - of the dimensions. Dimensions should match flatten excluding dimensions for data_pairs - parameters. If `flatten` is defined and `flatten_pp` is None, then ``flatten_pp=flatten``. - legend : bool, default True - Add legend to figure. - ax : 2D array-like of matplotlib_axes or bokeh_figure, optional - A 2D array of locations into which to plot the densities. If not supplied, ArviZ will create - its own array of plot areas (and return it). - backend : str, optional - Select plotting backend {"matplotlib", "bokeh"}. Default "matplotlib". - plot_ref_kwargs : dict, optional - Extra keyword arguments to control how reference is represented. - Passed to :meth:`matplotlib.axes.Axes.plot` or - :meth:`matplotlib.axes.Axes.axhspan` (when ``kind=u_value`` - and ``reference=analytical``). - backend_kwargs : bool, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - group : {"posterior", "prior"}, default "posterior" - Specifies which InferenceData group should be plotted. If "posterior", then the values - in `posterior_predictive` group are compared to the ones in `observed_data`, if "prior" then - the same comparison happens, but with the values in `prior_predictive` group. - show : bool, optional - Call backend show function. - - Returns - ------- - axes : 2D ndarray of matplotlib_axes or bokeh_figure - - See Also - -------- - plot_ppc : Plot for posterior/prior predictive checks. - plot_loo_pit : Plot Leave-One-Out probability integral transformation (PIT) predictive checks. - plot_dist_comparison : Plot to compare fitted and unfitted distributions. - - References - ---------- - * Gelman et al. (2013) see http://www.stat.columbia.edu/~gelman/book/ pages 151-153 for details - - Notes - ----- - Discrete data is smoothed before computing either p-values or u-values using the - function :func:`~arviz.smooth_data` if the data is integer type - and the smoothing parameter is True. - - Examples - -------- - Plot Bayesian p_values. - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> data = az.load_arviz_data("regression1d") - >>> az.plot_bpv(data, kind="p_value") - - Plot custom test statistic comparison. - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> data = az.load_arviz_data("regression1d") - >>> az.plot_bpv(data, kind="t_stat", t_stat=lambda x:np.percentile(x, q=50, axis=-1)) - """ - if group not in ("posterior", "prior"): - raise TypeError("`group` argument must be either `posterior` or `prior`") - - for groups in (f"{group}_predictive", "observed_data"): - if not hasattr(data, groups): - raise TypeError(f'`data` argument must have the group "{groups}"') - - if kind.lower() not in ("t_stat", "u_value", "p_value"): - raise TypeError("`kind` argument must be either `t_stat`, `u_value`, or `p_value`") - - if reference is not None and reference.lower() not in ("analytical", "samples"): - raise TypeError("`reference` argument must be either `analytical`, `samples`, or `None`") - - if hdi_prob is None: - hdi_prob = rcParams["stats.ci_prob"] - elif not 1 >= hdi_prob > 0: - raise ValueError("The value of hdi_prob should be in the interval (0, 1]") - - if smoothing is None: - smoothing = kind.lower() == "u_value" - - if data_pairs is None: - data_pairs = {} - - if labeller is None: - labeller = BaseLabeller() - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - observed = data.observed_data - - if group == "posterior": - predictive_dataset = data.posterior_predictive - elif group == "prior": - predictive_dataset = data.prior_predictive - - if var_names is None: - var_names = list(observed.data_vars) - var_names = _var_names(var_names, observed, filter_vars) - pp_var_names = [data_pairs.get(var, var) for var in var_names] - pp_var_names = _var_names(pp_var_names, predictive_dataset, filter_vars) - - if flatten_pp is None: - if flatten is None: - flatten_pp = list(predictive_dataset.dims) - else: - flatten_pp = flatten - if flatten is None: - flatten = list(observed.dims) - - if coords is None: - coords = {} - - total_pp_samples = predictive_dataset.sizes["chain"] * predictive_dataset.sizes["draw"] - - for key in coords.keys(): - coords[key] = np.where(np.isin(observed[key], coords[key]))[0] - - obs_plotters = filter_plotters_list( - list( - xarray_var_iter( - observed.isel(coords), skip_dims=set(flatten), var_names=var_names, combined=True - ) - ), - "plot_t_stats", - ) - length_plotters = len(obs_plotters) - pp_plotters = [ - tup - for _, tup in zip( - range(length_plotters), - xarray_var_iter( - predictive_dataset.isel(coords), - var_names=pp_var_names, - skip_dims=set(flatten_pp), - combined=True, - ), - ) - ] - rows, cols = default_grid(length_plotters, grid=grid) - - bpvplot_kwargs = dict( - ax=ax, - length_plotters=length_plotters, - rows=rows, - cols=cols, - obs_plotters=obs_plotters, - pp_plotters=pp_plotters, - total_pp_samples=total_pp_samples, - kind=kind, - bpv=bpv, - t_stat=t_stat, - reference=reference, - mse=mse, - n_ref=n_ref, - hdi_prob=hdi_prob, - plot_mean=plot_mean, - color=color, - figsize=figsize, - textsize=textsize, - labeller=labeller, - plot_ref_kwargs=plot_ref_kwargs, - backend_kwargs=backend_kwargs, - show=show, - smoothing=smoothing, - ) - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_bpv", "bpvplot", backend) - axes = plot(**bpvplot_kwargs) - return axes diff --git a/arviz/plots/compareplot.py b/arviz/plots/compareplot.py deleted file mode 100644 index a17ca0176c..0000000000 --- a/arviz/plots/compareplot.py +++ /dev/null @@ -1,177 +0,0 @@ -"""Summary plot for model comparison.""" - -import numpy as np - -from ..labels import BaseLabeller -from ..rcparams import rcParams -from .plot_utils import get_plotting_function - - -def plot_compare( - comp_df, - insample_dev=False, - plot_standard_error=True, - plot_ic_diff=False, - order_by_rank=True, - legend=False, - title=True, - figsize=None, - textsize=None, - labeller=None, - plot_kwargs=None, - ax=None, - backend=None, - backend_kwargs=None, - show=None, -): - r"""Summary plot for model comparison. - - Models are compared based on their expected log pointwise predictive density (ELPD). - This plot is in the style of the one used in [2]_. Chapter 6 in the first edition - or 7 in the second. - - Notes - ----- - The ELPD is estimated either by Pareto smoothed importance sampling leave-one-out - cross-validation (LOO) or using the widely applicable information criterion (WAIC). - We recommend LOO in line with the work presented by [1]_. - - Parameters - ---------- - comp_df : pandas.DataFrame - Result of the :func:`arviz.compare` method. - insample_dev : bool, default False - Plot in-sample ELPD, that is the value of the information criteria without the - penalization given by the effective number of parameters (p_loo or p_waic). - plot_standard_error : bool, default True - Plot the standard error of the ELPD. - plot_ic_diff : bool, default False - Plot standard error of the difference in ELPD between each model - and the top-ranked model. - order_by_rank : bool, default True - If True ensure the best model is used as reference. - legend : bool, default False - Add legend to figure. - figsize : (float, float), optional - If `None`, size is (6, num of models) inches. - title : bool, default True - Show a tittle with a description of how to interpret the plot. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If `None` it will be autoscaled based - on `figsize`. - labeller : Labeller, optional - Class providing the method ``make_label_vert`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - plot_kwargs : dict, optional - Optional arguments for plot elements. Currently accepts 'color_ic', - 'marker_ic', 'color_insample_dev', 'marker_insample_dev', 'color_dse', - 'marker_dse', 'ls_min_ic' 'color_ls_min_ic', 'fontsize' - ax : matplotlib_axes or bokeh_figure, optional - Matplotlib axes or bokeh figure. - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_kwargs : bool, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - - Returns - ------- - axes : matplotlib_axes or bokeh_figure - - See Also - -------- - plot_elpd : Plot pointwise elpd differences between two or more models. - compare : Compare models based on PSIS-LOO loo or WAIC waic cross-validation. - loo : Compute Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO-CV). - waic : Compute the widely applicable information criterion. - - References - ---------- - .. [1] Vehtari et al. (2016). Practical Bayesian model evaluation using leave-one-out - cross-validation and WAIC https://arxiv.org/abs/1507.04544 - - .. [2] McElreath R. (2022). Statistical Rethinking A Bayesian Course with Examples in - R and Stan, Second edition, CRC Press. - - Examples - -------- - Show default compare plot - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> model_compare = az.compare({'Centered 8 schools': az.load_arviz_data('centered_eight'), - >>> 'Non-centered 8 schools': az.load_arviz_data('non_centered_eight')}) - >>> az.plot_compare(model_compare) - - Include the in-sample ELDP - - .. plot:: - :context: close-figs - - >>> az.plot_compare(model_compare, insample_dev=True) - - """ - if plot_kwargs is None: - plot_kwargs = {} - - if labeller is None: - labeller = BaseLabeller() - - yticks_pos, step = np.linspace(0, -1, (comp_df.shape[0] * 2) - 1, retstep=True) - yticks_pos[1::2] = yticks_pos[1::2] + step / 2 - labels = [labeller.model_name_to_str(model_name) for model_name in comp_df.index] - - if plot_ic_diff: - yticks_labels = [""] * len(yticks_pos) - yticks_labels[0] = labels[0] - yticks_labels[2::2] = labels[1:] - else: - yticks_labels = labels - - _information_criterion = ["elpd_loo", "elpd_waic"] - column_index = [c.lower() for c in comp_df.columns] - for information_criterion in _information_criterion: - if information_criterion in column_index: - break - else: - raise ValueError( - "comp_df must contain one of the following " - f"information criterion: {_information_criterion}" - ) - - if order_by_rank: - comp_df.sort_values(by="rank", inplace=True) - - compareplot_kwargs = dict( - ax=ax, - comp_df=comp_df, - legend=legend, - title=title, - figsize=figsize, - plot_ic_diff=plot_ic_diff, - plot_standard_error=plot_standard_error, - insample_dev=insample_dev, - yticks_pos=yticks_pos, - yticks_labels=yticks_labels, - plot_kwargs=plot_kwargs, - information_criterion=information_criterion, - textsize=textsize, - step=step, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_compare", "compareplot", backend) - ax = plot(**compareplot_kwargs) - - return ax diff --git a/arviz/plots/densityplot.py b/arviz/plots/densityplot.py deleted file mode 100644 index 1dc28a0368..0000000000 --- a/arviz/plots/densityplot.py +++ /dev/null @@ -1,284 +0,0 @@ -"""KDE and histogram plots for multiple variables.""" - -import warnings - -from ..data import convert_to_dataset -from ..labels import BaseLabeller -from ..sel_utils import ( - xarray_var_iter, -) -from ..rcparams import rcParams -from ..utils import _var_names -from .plot_utils import default_grid, get_plotting_function - - -# pylint:disable-msg=too-many-function-args -def plot_density( - data, - group="posterior", - data_labels=None, - var_names=None, - filter_vars=None, - combine_dims=None, - transform=None, - hdi_prob=None, - point_estimate="auto", - colors="cycle", - outline=True, - hdi_markers="", - shade=0.0, - bw="default", - circular=False, - grid=None, - figsize=None, - textsize=None, - labeller=None, - ax=None, - backend=None, - backend_kwargs=None, - show=None, -): - r"""Generate KDE plots for continuous variables and histograms for discrete ones. - - Plots are truncated at their 100*(1-alpha)% highest density intervals. Plots are grouped per - variable and colors assigned to models. - - Parameters - ---------- - data : InferenceData or iterable of InferenceData - Any object that can be converted to an :class:`arviz.InferenceData` object, or an Iterator - returning a sequence of such objects. - Refer to documentation of :func:`arviz.convert_to_dataset` for details. - group : {"posterior", "prior"}, default "posterior" - Specifies which InferenceData group should be plotted. If "posterior", then the values - in `posterior_predictive` group are compared to the ones in `observed_data`, if "prior" then - the same comparison happens, but with the values in `prior_predictive` group. - data_labels : list of str, default None - List with names for the datasets passed as "data." Useful when plotting more than one - dataset. Must be the same shape as the data parameter. - var_names : list of str, optional - List of variables to plot. If multiple datasets are supplied and `var_names` is not None, - will print the same set of variables for each dataset. Defaults to None, which results in - all the variables being plotted. - filter_vars : {None, "like", "regex"}, default None - If `None` (default), interpret `var_names` as the real variables names. If "like", - interpret `var_names` as substrings of the real variables names. If "regex", - interpret `var_names` as regular expressions on the real variables names. See - :ref:`this section ` for usage examples. - combine_dims : set_like of str, optional - List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. - See :ref:`this section ` for usage examples. - transform : callable - Function to transform data (defaults to `None` i.e. the identity function). - hdi_prob : float, default 0.94 - Probability for the highest density interval. Should be in the interval (0, 1]. - See :ref:`this section ` for usage examples. - point_estimate : str, optional - Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None. - Defaults to 'auto' i.e. it falls back to default set in ``rcParams``. - colors : str or list of str, optional - List with valid matplotlib colors, one color per model. Alternative a string can be passed. - If the string is `cycle`, it will automatically choose a color per model from matplotlib's - cycle. If a single color is passed, e.g. 'k', 'C2' or 'red' this color will be used for all - models. Defaults to `cycle`. - outline : bool, default True - Use a line to draw KDEs and histograms. - hdi_markers : str - A valid `matplotlib.markers` like 'v', used to indicate the limits of the highest density - interval. Defaults to empty string (no marker). - shade : float, default 0 - Alpha blending value for the shaded area under the curve, between 0 (no shade) and 1 - (opaque). - bw : float or str, optional - If numeric, indicates the bandwidth and must be positive. - If str, indicates the method to estimate the bandwidth and must be - one of "scott", "silverman", "isj" or "experimental" when `circular` is False - and "taylor" (for now) when `circular` is True. - Defaults to "default" which means "experimental" when variable is not circular - and "taylor" when it is. - circular : bool, default False - If True, it interprets the values passed are from a circular variable measured in radians - and a circular KDE is used. Only valid for 1D KDE. - grid : tuple, optional - Number of rows and columns. Defaults to ``None``, the rows and columns are - automatically inferred. See :ref:`this section ` for usage examples. - figsize : (float, float), optional - Figure size. If `None` it will be defined automatically. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If `None` it will be autoscaled based - on `figsize`. - labeller : Labeller, optional - Class providing the method ``make_label_vert`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - ax : 2D array-like of matplotlib_axes or bokeh_figure, optional - A 2D array of locations into which to plot the densities. If not supplied, ArviZ will create - its own array of plot areas (and return it). - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - - Returns - ------- - axes : 2D ndarray of matplotlib_axes or bokeh_figure - - See Also - -------- - plot_dist : Plot distribution as histogram or kernel density estimates. - plot_posterior : Plot Posterior densities in the style of John K. Kruschke's book. - - Examples - -------- - Plot default density plot - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> centered = az.load_arviz_data('centered_eight') - >>> non_centered = az.load_arviz_data('non_centered_eight') - >>> az.plot_density([centered, non_centered]) - - Plot variables in a 4x5 grid - - .. plot:: - :context: close-figs - - >>> az.plot_density([centered, non_centered], grid=(4, 5)) - - Plot subset variables by specifying variable name exactly - - .. plot:: - :context: close-figs - - >>> az.plot_density([centered, non_centered], var_names=["mu"]) - - Plot a specific `az.InferenceData` group - - .. plot:: - :context: close-figs - - >>> az.plot_density([centered, non_centered], var_names=["mu"], group="prior") - - Specify highest density interval - - .. plot:: - :context: close-figs - - >>> az.plot_density([centered, non_centered], var_names=["mu"], hdi_prob=.5) - - Shade plots and/or remove outlines - - .. plot:: - :context: close-figs - - >>> az.plot_density([centered, non_centered], var_names=["mu"], outline=False, shade=.8) - - Specify binwidth for kernel density estimation - - .. plot:: - :context: close-figs - - >>> az.plot_density([centered, non_centered], var_names=["mu"], bw=.9) - """ - if isinstance(data, (list, tuple)): - datasets = [convert_to_dataset(datum, group=group) for datum in data] - else: - datasets = [convert_to_dataset(data, group=group)] - - if transform is not None: - datasets = [transform(dataset) for dataset in datasets] - - if labeller is None: - labeller = BaseLabeller() - - var_names = _var_names(var_names, datasets, filter_vars) - - n_data = len(datasets) - - if data_labels is None: - data_labels = [f"{idx}" for idx in range(n_data)] if n_data > 1 else [""] - elif len(data_labels) != n_data: - raise ValueError( - f"The number of names for the models ({len(data_labels)}) " - f"does not match the number of models ({n_data})" - ) - - if hdi_prob is None: - hdi_prob = rcParams["stats.ci_prob"] - elif not 1 >= hdi_prob > 0: - raise ValueError("The value of hdi_prob should be in the interval (0, 1]") - - to_plot = [ - list(xarray_var_iter(data, var_names, combined=True, skip_dims=combine_dims)) - for data in datasets - ] - all_labels = [] - length_plotters = [] - for plotters in to_plot: - length_plotters.append(len(plotters)) - for var_name, selection, isel, _ in plotters: - label = labeller.make_label_vert(var_name, selection, isel) - if label not in all_labels: - all_labels.append(label) - length_plotters = len(all_labels) - max_plots = rcParams["plot.max_subplots"] - max_plots = length_plotters if max_plots is None else max_plots - if length_plotters > max_plots: - warnings.warn( - "rcParams['plot.max_subplots'] ({max_plots}) is smaller than the number " - "of variables to plot ({len_plotters}) in plot_density, generating only " - "{max_plots} plots".format(max_plots=max_plots, len_plotters=length_plotters), - UserWarning, - ) - all_labels = all_labels[:max_plots] - to_plot = [ - [ - (var_name, selection, values) - for var_name, selection, isel, values in plotters - if labeller.make_label_vert(var_name, selection, isel) in all_labels - ] - for plotters in to_plot - ] - length_plotters = max_plots - rows, cols = default_grid(length_plotters, grid=grid, max_cols=3) - - if bw == "default": - bw = "taylor" if circular else "experimental" - - plot_density_kwargs = dict( - ax=ax, - all_labels=all_labels, - to_plot=to_plot, - colors=colors, - bw=bw, - circular=circular, - figsize=figsize, - length_plotters=length_plotters, - rows=rows, - cols=cols, - textsize=textsize, - labeller=labeller, - hdi_prob=hdi_prob, - point_estimate=point_estimate, - hdi_markers=hdi_markers, - outline=outline, - shade=shade, - n_data=n_data, - data_labels=data_labels, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_density", "densityplot", backend) - ax = plot(**plot_density_kwargs) - return ax diff --git a/arviz/plots/distcomparisonplot.py b/arviz/plots/distcomparisonplot.py deleted file mode 100644 index 92475eea6e..0000000000 --- a/arviz/plots/distcomparisonplot.py +++ /dev/null @@ -1,197 +0,0 @@ -"""Density Comparison plot.""" - -import warnings -from ..labels import BaseLabeller -from ..rcparams import rcParams -from ..utils import _var_names, get_coords -from .plot_utils import get_plotting_function -from ..sel_utils import xarray_var_iter, xarray_sel_iter - - -def plot_dist_comparison( - data, - kind="latent", - figsize=None, - textsize=None, - var_names=None, - coords=None, - combine_dims=None, - transform=None, - legend=True, - labeller=None, - ax=None, - prior_kwargs=None, - posterior_kwargs=None, - observed_kwargs=None, - backend=None, - backend_kwargs=None, - show=None, -): - r"""Plot to compare fitted and unfitted distributions. - - The resulting plots will show the compared distributions both on - separate axes (particularly useful when one of them is substantially tighter - than another), and plotted together, displaying a grid of three plots per - distribution. - - Parameters - ---------- - data : InferenceData - Any object that can be converted to an :class:`arviz.InferenceData` object - containing the posterior/prior data. Refer to documentation of - :func:`arviz.convert_to_dataset` for details. - kind : {"latent", "observed"}, default "latent" - kind of plot to display The "latent" option includes {"prior", "posterior"}, - and the "observed" option includes - {"observed_data", "prior_predictive", "posterior_predictive"}. - figsize : (float, float), optional - Figure size. If ``None`` it will be defined automatically. - textsize : float - Text size scaling factor for labels, titles and lines. If ``None`` it will be - autoscaled based on `figsize`. - var_names : str, list, list of lists, optional - if str, plot the variable. if list, plot all the variables in list - of all groups. if list of lists, plot the vars of groups in respective lists. - See :ref:`this section ` for usage examples. - coords : dict - Dictionary mapping dimensions to selected coordinates to be plotted. - Dimensions without a mapping specified will include all coordinates for - that dimension. See :ref:`this section ` for usage examples. - combine_dims : set_like of str, optional - List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. - See :ref:`this section ` for usage examples. - transform : callable - Function to transform data (defaults to `None` i.e. the identity function). - legend : bool - Add legend to figure. By default True. - labeller : Labeller, optional - Class providing the method ``make_pp_label`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - ax : (nvars, 3) array-like of matplotlib_axes, optional - Matplotlib axes: The ax argument should have shape (nvars, 3), where the - last column is for the combined before/after plots and columns 0 and 1 are - for the before and after plots, respectively. - prior_kwargs : dicts, optional - Additional keywords passed to :func:`arviz.plot_dist` for prior/predictive groups. - posterior_kwargs : dicts, optional - Additional keywords passed to :func:`arviz.plot_dist` for posterior/predictive groups. - observed_kwargs : dicts, optional - Additional keywords passed to :func:`arviz.plot_dist` for observed_data group. - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - - Returns - ------- - axes : 2D ndarray of matplotlib_axes - Returned object will have shape (nvars, 3), - where the last column is the combined plot and the first columns are the single plots. - - See Also - -------- - plot_bpv : Plot Bayesian p-value for observed data and Posterior/Prior predictive. - - Examples - -------- - Plot the prior/posterior plot for specified vars and coords. - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> data = az.load_arviz_data('rugby') - >>> az.plot_dist_comparison(data, var_names=["defs"], coords={"team" : ["Italy"]}) - - """ - all_groups = ["prior", "posterior"] - - if kind == "observed": - all_groups = ["observed_data", "prior_predictive", "posterior_predictive"] - - if coords is None: - coords = {} - - if labeller is None: - labeller = BaseLabeller() - - datasets = [] - groups = [] - for group in all_groups: - try: - datasets.append(getattr(data, group)) - groups.append(group) - except: # pylint: disable=bare-except - pass - - if var_names is None: - var_names = list(datasets[0].data_vars) - - if isinstance(var_names, str): - var_names = [var_names] - - if isinstance(var_names[0], str): - var_names = [var_names for _ in datasets] - - var_names = [_var_names(vars, dataset) for vars, dataset in zip(var_names, datasets)] - - if transform is not None: - datasets = [transform(dataset) for dataset in datasets] - - datasets = get_coords(datasets, coords) - len_plots = rcParams["plot.max_subplots"] // (len(groups) + 1) - len_plots = len_plots or 1 - dc_plotters = [ - list(xarray_var_iter(data, var_names=var, combined=True, skip_dims=combine_dims))[ - :len_plots - ] - for data, var in zip(datasets, var_names) - ] - - total_plots = sum( - 1 for _ in xarray_sel_iter(datasets[0], var_names=var_names[0], combined=True) - ) * (len(groups) + 1) - maxplots = len(dc_plotters[0]) * (len(groups) + 1) - - if total_plots > rcParams["plot.max_subplots"]: - warnings.warn( - "rcParams['plot.max_subplots'] ({rcParam}) is smaller than the number " - "of subplots to plot ({len_plotters}), generating only {max_plots} " - "plots".format( - rcParam=rcParams["plot.max_subplots"], len_plotters=total_plots, max_plots=maxplots - ), - UserWarning, - ) - - nvars = len(dc_plotters[0]) - ngroups = len(groups) - - distcomparisonplot_kwargs = dict( - ax=ax, - nvars=nvars, - ngroups=ngroups, - figsize=figsize, - dc_plotters=dc_plotters, - legend=legend, - groups=groups, - textsize=textsize, - labeller=labeller, - prior_kwargs=prior_kwargs, - posterior_kwargs=posterior_kwargs, - observed_kwargs=observed_kwargs, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_dist_comparison", "distcomparisonplot", backend) - axes = plot(**distcomparisonplot_kwargs) - return axes diff --git a/arviz/plots/distplot.py b/arviz/plots/distplot.py deleted file mode 100644 index 67233b1d37..0000000000 --- a/arviz/plots/distplot.py +++ /dev/null @@ -1,233 +0,0 @@ -# pylint: disable=unexpected-keyword-arg -"""Plot distribution as histogram or kernel density estimates.""" -import numpy as np -import xarray as xr - -from ..data import InferenceData -from ..rcparams import rcParams -from .plot_utils import get_plotting_function - - -def plot_dist( - values, - values2=None, - color="C0", - kind="auto", - cumulative=False, - label=None, - rotated=False, - rug=False, - bw="default", - quantiles=None, - contour=True, - fill_last=True, - figsize=None, - textsize=None, - plot_kwargs=None, - fill_kwargs=None, - rug_kwargs=None, - contour_kwargs=None, - contourf_kwargs=None, - pcolormesh_kwargs=None, - hist_kwargs=None, - is_circular=False, - ax=None, - backend=None, - backend_kwargs=None, - show=None, - **kwargs, -): - r"""Plot distribution as histogram or kernel density estimates. - - By default continuous variables are plotted using KDEs and discrete ones using histograms - - Parameters - ---------- - values : array-like - Values to plot from an unknown continuous or discrete distribution. - values2 : array-like, optional - Values to plot. If present, a 2D KDE or a hexbin will be estimated. - color : string - valid matplotlib color. - kind : string, default "auto" - By default ("auto") continuous variables will use the kind defined by rcParam - ``plot.density_kind`` and discrete ones will use histograms. - To override this use "hist" to plot histograms and "kde" for KDEs. - cumulative : bool, default False - If true plot the estimated cumulative distribution function. Defaults to False. - Ignored for 2D KDE. - label : string - Text to include as part of the legend. - rotated : bool, default False - Whether to rotate the 1D KDE plot 90 degrees. - rug : bool, default False - Add a `rug plot `_ for a specific subset - of values. Ignored for 2D KDE. - bw : float or str, optional - If numeric, indicates the bandwidth and must be positive. - If str, indicates the method to estimate the bandwidth and must be - one of "scott", "silverman", "isj" or "experimental" when ``is_circular`` is False - and "taylor" (for now) when ``is_circular`` is True. - Defaults to "experimental" when variable is not circular and "taylor" when it is. - quantiles : list, optional - Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles. - contour : bool, default True - If True plot the 2D KDE using contours, otherwise plot a smooth 2D KDE. - fill_last : bool, default True - If True fill the last contour of the 2D KDE plot. - figsize : (float, float), optional - Figure size. If `None` it will be defined automatically. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If `None` it will be autoscaled based - on `figsize`. Not implemented for bokeh backend. - plot_kwargs : dict - Keywords passed to the pdf line of a 1D KDE. Passed to :func:`arviz.plot_kde` as - ``plot_kwargs``. - fill_kwargs : dict - Keywords passed to the fill under the line (use fill_kwargs={'alpha': 0} to disable fill). - Ignored for 2D KDE. Passed to :func:`arviz.plot_kde` as ``fill_kwargs``. - rug_kwargs : dict - Keywords passed to the rug plot. Ignored if ``rug=False`` or for 2D KDE - Use ``space`` keyword (float) to control the position of the rugplot. - The larger this number the lower the rugplot. Passed to - :func:`arviz.plot_kde` as ``rug_kwargs``. - contour_kwargs : dict - Keywords passed to the contourplot. Ignored for 1D KDE. - contourf_kwargs : dict - Keywords passed to :meth:`matplotlib.axes.Axes.contourf`. Ignored for 1D KDE. - pcolormesh_kwargs : dict - Keywords passed to :meth:`matplotlib.axes.Axes.pcolormesh`. Ignored for 1D KDE. - hist_kwargs : dict - Keyword arguments used to customize the histogram. Ignored when plotting a KDE. - They are passed to :meth:`matplotlib.axes.Axes.hist` if using matplotlib, - or to :meth:`bokeh.plotting.figure.quad` if using bokeh. In bokeh case, - the following extra keywords are also supported: - - * ``color``: replaces the ``fill_color`` and ``line_color`` of the ``quad`` method - * ``bins``: taken from ``hist_kwargs`` and passed to :func:`numpy.histogram` instead - * ``density``: normalize histogram to represent a probability density function, - Defaults to ``True`` - - * ``cumulative``: plot the cumulative counts. Defaults to ``False``. - - is_circular : {False, True, "radians", "degrees"}, default False - Select input type {"radians", "degrees"} for circular histogram or KDE plot. If True, - default input type is "radians". When this argument is present, it interprets the - values passed are from a circular variable measured in radians and a circular KDE is - used. Inputs in "degrees" will undergo an internal conversion to radians. Only valid - for 1D KDE. - ax : matplotlib_axes or bokeh_figure, optional - Matplotlib or bokeh targets on which to plot. If not supplied, Arviz will create - its own plot area (and return it). - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_kwargs :dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - - Returns - ------- - axes : matplotlib axes or bokeh figure - - See Also - -------- - plot_posterior : Plot Posterior densities in the style of John K. Kruschke's book. - plot_density : Generate KDE plots for continuous variables and histograms for discrete ones. - plot_kde : 1D or 2D KDE plot taking into account boundary conditions. - - Examples - -------- - Plot an integer distribution - - .. plot:: - :context: close-figs - - >>> import numpy as np - >>> import arviz as az - >>> a = np.random.poisson(4, 1000) - >>> az.plot_dist(a) - - Plot a continuous distribution - - .. plot:: - :context: close-figs - - >>> b = np.random.normal(0, 1, 1000) - >>> az.plot_dist(b) - - Add a rug under the Gaussian distribution - - .. plot:: - :context: close-figs - - >>> az.plot_dist(b, rug=True) - - Segment into quantiles - - .. plot:: - :context: close-figs - - >>> az.plot_dist(b, rug=True, quantiles=[.25, .5, .75]) - - Plot as the cumulative distribution - - .. plot:: - :context: close-figs - - >>> az.plot_dist(b, rug=True, quantiles=[.25, .5, .75], cumulative=True) - """ - values = np.asarray(values) - - if isinstance(values, (InferenceData, xr.Dataset)): - raise ValueError( - "InferenceData or xarray.Dataset object detected," - " use plot_posterior, plot_density or plot_pair" - " instead of plot_dist" - ) - - if kind not in ["auto", "kde", "hist"]: - raise TypeError(f'Invalid "kind":{kind}. Select from {{"auto","kde","hist"}}') - - if kind == "auto": - kind = "hist" if values.dtype.kind == "i" else rcParams["plot.density_kind"] - - dist_plot_args = dict( - # User Facing API that can be simplified - values=values, - values2=values2, - color=color, - kind=kind, - cumulative=cumulative, - label=label, - rotated=rotated, - rug=rug, - bw=bw, - quantiles=quantiles, - contour=contour, - fill_last=fill_last, - figsize=figsize, - textsize=textsize, - plot_kwargs=plot_kwargs, - fill_kwargs=fill_kwargs, - rug_kwargs=rug_kwargs, - contour_kwargs=contour_kwargs, - contourf_kwargs=contourf_kwargs, - pcolormesh_kwargs=pcolormesh_kwargs, - hist_kwargs=hist_kwargs, - ax=ax, - backend_kwargs=backend_kwargs, - is_circular=is_circular, - show=show, - **kwargs, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - plot = get_plotting_function("plot_dist", "distplot", backend) - ax = plot(**dist_plot_args) - return ax diff --git a/arviz/plots/dotplot.py b/arviz/plots/dotplot.py deleted file mode 100644 index 90ecbf7e74..0000000000 --- a/arviz/plots/dotplot.py +++ /dev/null @@ -1,233 +0,0 @@ -"""Plot distribution as dot plot or quantile dot plot.""" - -import numpy as np - - -from ..rcparams import rcParams -from .plot_utils import get_plotting_function - - -def plot_dot( - values, - binwidth=None, - dotsize=1, - stackratio=1, - hdi_prob=None, - rotated=False, - dotcolor="C0", - intervalcolor="C3", - markersize=None, - markercolor="C0", - marker="o", - figsize=None, - linewidth=None, - point_estimate="auto", - nquantiles=50, - quartiles=True, - point_interval=False, - ax=None, - show=None, - plot_kwargs=None, - backend=None, - backend_kwargs=None, - **kwargs -): - r"""Plot distribution as dot plot or quantile dot plot. - - This function uses the Wilkinson's Algorithm [1]_ to allot dots to bins. - The quantile dot plots was inspired from [2]_. - - Parameters - ---------- - values : array-like - Values to plot from an unknown continuous or discrete distribution. - binwidth : float, optional - Width of the bin for drawing the dot plot. - dotsize : float, default 1 - The size of the dots relative to the bin width. The default makes dots be - just about as wide as the bin width. - stackratio : float, default 1 - The distance between the center of the dots in the same stack relative to the bin height. - The default makes dots in the same stack just touch each other. - point_interval : bool, default False - Plots the point interval. Uses ``hdi_prob`` to plot the HDI interval - point_estimate : str, optional - Plot point estimate per variable. Values should be ``mean``, ``median``, ``mode`` or None. - Defaults to ``auto`` i.e. it falls back to default set in rcParams. - dotcolor : string, optional - The color of the dots. Should be a valid matplotlib color. - intervalcolor : string, optional - The color of the interval. Should be a valid matplotlib color. - linewidth : int, default None - Line width throughout. If None it will be autoscaled based on `figsize`. - markersize : int, default None - Markersize throughout. If None it will be autoscaled based on `figsize`. - markercolor : string, optional - The color of the marker when plot_interval is True. Should be a valid matplotlib color. - marker : string, default "o" - The shape of the marker. Valid for matplotlib backend. - hdi_prob : float, optional - Valid only when point_interval is True. Plots HDI for chosen percentage of density. - Defaults to ``stats.ci_prob`` rcParam. See :ref:`this section ` - for usage examples. - rotated : bool, default False - Whether to rotate the dot plot by 90 degrees. - nquantiles : int, default 50 - Number of quantiles to plot, used for quantile dot plots. - quartiles : bool, default True - If True then the quartile interval will be plotted with the HDI. - figsize : (float,float), optional - Figure size. If ``None`` it will be defined automatically. - plot_kwargs : dict, optional - Keywords passed for customizing the dots. Passed to :class:`mpl:matplotlib.patches.Circle` - in matplotlib and :meth:`bokeh.plotting.figure.circle` in bokeh. - backend :{"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - ax : axes, optional - Matplotlib_axes or bokeh_figure. - show : bool, optional - Call backend show function. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - - Returns - ------- - axes : matplotlib_axes or bokeh_figure - - See Also - -------- - plot_dist : Plot distribution as histogram or kernel density estimates. - - References - ---------- - .. [1] Leland Wilkinson (1999) Dot Plots, The American Statistician, 53:3, 276-281, - DOI: 10.1080/00031305.1999.10474474 - .. [2] Matthew Kay, Tara Kola, Jessica R. Hullman, - and Sean A. Munson. 2016. When (ish) is My Bus? User-centered Visualizations of Uncertainty - in Everyday, Mobile Predictive Systems. DOI:https://doi.org/10.1145/2858036.2858558 - - Examples - -------- - Plot dot plot for a set of data points - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> import numpy as np - >>> values = np.random.normal(0, 1, 500) - >>> az.plot_dot(values) - - Manually adjust number of quantiles to plot - - .. plot:: - :context: close-figs - - >>> az.plot_dot(values, nquantiles=100) - - Add a point interval under the dot plot - - .. plot:: - :context: close-figs - - >>> az.plot_dot(values, point_interval=True) - - Rotate the dot plots by 90 degrees i.e swap x and y axis - - .. plot:: - :context: close-figs - - >>> az.plot_dot(values, point_interval=True, rotated=True) - - """ - if nquantiles == 0: - raise ValueError("Number of quantiles should be greater than 0") - - if marker != "o" and backend == "bokeh": - raise ValueError("marker argument is valid only for matplotlib backend") - - values = np.ravel(values) - values = values[np.isfinite(values)] - values.sort() - - if hdi_prob is None: - hdi_prob = rcParams["stats.ci_prob"] - elif not 1 >= hdi_prob > 0: - raise ValueError("The value of hdi_prob should be in the interval (0, 1]") - - if point_estimate == "auto": - point_estimate = rcParams["plot.point_estimate"] - elif point_estimate not in {"mean", "median", "mode", None}: - raise ValueError("The value of point_estimate must be either mean, median, mode or None.") - - if not isinstance(nquantiles, int): - raise TypeError("nquantiles must be of integer type, refer to docs for further details") - - dot_plot_args = dict( - values=values, - binwidth=binwidth, - dotsize=dotsize, - stackratio=stackratio, - hdi_prob=hdi_prob, - quartiles=quartiles, - rotated=rotated, - dotcolor=dotcolor, - intervalcolor=intervalcolor, - markersize=markersize, - markercolor=markercolor, - marker=marker, - figsize=figsize, - linewidth=linewidth, - point_estimate=point_estimate, - nquantiles=nquantiles, - point_interval=point_interval, - ax=ax, - show=show, - backend_kwargs=backend_kwargs, - plot_kwargs=plot_kwargs, - **kwargs - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - plot = get_plotting_function("plot_dot", "dotplot", backend) - ax = plot(**dot_plot_args) - - return ax - - -def wilkinson_algorithm(values, binwidth): - """Wilkinson's algorithm to distribute dots into horizontal stacks.""" - ndots = len(values) - count = 0 - stack_locs, stack_counts = [], [] - - while count < ndots: - stack_first_dot = values[count] - num_dots_stack = 0 - while values[count] < (binwidth + stack_first_dot): - num_dots_stack += 1 - count += 1 - if count == ndots: - break - stack_locs.append((stack_first_dot + values[count - 1]) / 2) - stack_counts.append(num_dots_stack) - - return stack_locs, stack_counts - - -def layout_stacks(stack_locs, stack_counts, binwidth, stackratio, rotated): - """Use count and location of stacks to get coordinates of dots.""" - dotheight = stackratio * binwidth - binradius = binwidth / 2 - - x = np.repeat(stack_locs, stack_counts) - y = np.hstack([dotheight * np.arange(count) + binradius for count in stack_counts]) - if rotated: - x, y = y, x - - return x, y diff --git a/arviz/plots/ecdfplot.py b/arviz/plots/ecdfplot.py deleted file mode 100644 index bc92f48e5f..0000000000 --- a/arviz/plots/ecdfplot.py +++ /dev/null @@ -1,372 +0,0 @@ -"""Plot ecdf or ecdf-difference plot with confidence bands.""" - -import warnings - -import numpy as np -from scipy.stats import uniform - -try: - from scipy.stats import ecdf as scipy_ecdf -except ImportError: - scipy_ecdf = None - -from ..rcparams import rcParams -from ..stats.ecdf_utils import ecdf_confidence_band, _get_ecdf_points -from ..utils import BehaviourChangeWarning -from .plot_utils import get_plotting_function - - -def plot_ecdf( - values, - values2=None, - eval_points=None, - cdf=None, - difference=False, - confidence_bands=False, - ci_prob=None, - num_trials=500, - rvs=None, - random_state=None, - figsize=None, - fill_band=True, - plot_kwargs=None, - fill_kwargs=None, - plot_outline_kwargs=None, - ax=None, - show=None, - backend=None, - backend_kwargs=None, - npoints=100, - pointwise=False, - fpr=None, - pit=False, - **kwargs, -): - r"""Plot ECDF or ECDF-Difference Plot with Confidence bands. - - Plots of the empirical cumulative distribution function (ECDF) of an array. Optionally, A `cdf` - argument representing a reference CDF may be provided for comparison using a difference ECDF - plot and/or confidence bands. - - Alternatively, the PIT for a single dataset may be visualized. - - Notes - ----- - This plot computes the confidence bands with the simulated based algorithm presented in [1]_. - - Parameters - ---------- - values : array-like - Values to plot from an unknown continuous or discrete distribution. - values2 : array-like, optional - values to compare to the original sample. - - .. deprecated:: 0.18.0 - Instead use ``cdf=scipy.stats.ecdf(values2).cdf.evaluate``. - cdf : callable, optional - Cumulative distribution function of the distribution to compare the original sample. - The function must take as input a numpy array of draws from the distribution. - difference : bool, default False - If True then plot ECDF-difference plot otherwise ECDF plot. - confidence_bands : str or bool - - - False: No confidence bands are plotted (default). - - True: Plot bands computed with the default algorithm (subject to change) - - "pointwise": Compute the pointwise (i.e. marginal) confidence band. - - "optimized": Use optimization to estimate a simultaneous confidence band. - - "simulated": Use Monte Carlo simulation to estimate a simultaneous confidence - band. - - For simultaneous confidence bands to be correctly calibrated, provide `eval_points` that - are not dependent on the `values`. - ci_prob : float, default 0.94 - The probability that the true ECDF lies within the confidence band. If `confidence_bands` - is "pointwise", this is the marginal probability instead of the joint probability. - eval_points : array-like, optional - The points at which to evaluate the ECDF. If None, `npoints` uniformly spaced points - between the data bounds will be used. - rvs: callable, optional - A function that takes an integer `ndraws` and optionally the object passed to - `random_state` and returns an array of `ndraws` samples from the same distribution - as the original dataset. Required if `method` is "simulated" and variable is discrete. - random_state : int, numpy.random.Generator or numpy.random.RandomState, optional - num_trials : int, default 500 - The number of random ECDFs to generate for constructing simultaneous confidence bands - (if `confidence_bands` is "simulated"). - figsize : (float,float), optional - Figure size. If `None` it will be defined automatically. - fill_band : bool, default True - If True it fills in between to mark the area inside the confidence interval. Otherwise, - plot the border lines. - plot_kwargs : dict, optional - Additional kwargs passed to :func:`mpl:matplotlib.pyplot.step` or - :meth:`bokeh.plotting.figure.step` - fill_kwargs : dict, optional - Additional kwargs passed to :func:`mpl:matplotlib.pyplot.fill_between` or - :meth:`bokeh:bokeh.plotting.Figure.varea` - plot_outline_kwargs : dict, optional - Additional kwargs passed to :meth:`mpl:matplotlib.axes.Axes.plot` or - :meth:`bokeh:bokeh.plotting.Figure.line` - ax :axes, optional - Matplotlib axes or bokeh figures. - show : bool, optional - Call backend show function. - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - npoints : int, default 100 - The number of evaluation points for the ecdf or ecdf-difference plots, if `eval_points` is - not provided or `pit` is `True`. - - .. deprecated:: 0.18.0 - Instead specify ``eval_points=np.linspace(np.min(values), np.max(values), npoints)`` - unless `pit` is `True`. - pointwise : bool, default False - - .. deprecated:: 0.18.0 - Instead use `confidence_bands="pointwise"`. - fpr : float, optional - - .. deprecated:: 0.18.0 - Instead use `ci_prob=1-fpr`. - pit : bool, default False - If True plots the ECDF or ECDF-diff of PIT of sample. - - .. deprecated:: 0.18.0 - See below example instead. - - Returns - ------- - axes : matplotlib_axes or bokeh_figure - - References - ---------- - .. [1] Säilynoja, T., Bürkner, P.C. and Vehtari, A. (2022). Graphical Test for - Discrete Uniformity and its Applications in Goodness of Fit Evaluation and - Multiple Sample Comparison. Statistics and Computing, 32(32). - - Examples - -------- - In a future release, the default behaviour of ``plot_ecdf`` will change. - To maintain the original behaviour you should do: - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> import numpy as np - >>> from scipy.stats import uniform, norm - >>> - >>> sample = norm(0,1).rvs(1000) - >>> npoints = 100 - >>> az.plot_ecdf(sample, eval_points=np.linspace(sample.min(), sample.max(), npoints)) - - However, seeing this warning isn't an indicator of anything being wrong, - if you are happy to get different behaviour as ArviZ improves and adds - new algorithms you can ignore it like so: - - .. plot:: - :context: close-figs - - >>> import warnings - >>> warnings.filterwarnings("ignore", category=az.utils.BehaviourChangeWarning) - - Plot an ECDF plot for a given sample evaluated at the sample points. This will become - the new behaviour when `eval_points` is not provided: - - .. plot:: - :context: close-figs - - >>> az.plot_ecdf(sample, eval_points=np.unique(sample)) - - Plot an ECDF plot with confidence bands for comparing a given sample to a given distribution. - We manually specify evaluation points independent of the values so that the confidence bands - are correctly calibrated. - - .. plot:: - :context: close-figs - - >>> distribution = norm(0,1) - >>> eval_points = np.linspace(*distribution.ppf([0.001, 0.999]), 100) - >>> az.plot_ecdf( - >>> sample, eval_points=eval_points, - >>> cdf=distribution.cdf, confidence_bands=True - >>> ) - - Plot an ECDF-difference plot with confidence bands for comparing a given sample - to a given distribution. - - .. plot:: - :context: close-figs - - >>> az.plot_ecdf( - >>> sample, cdf=distribution.cdf, - >>> confidence_bands=True, difference=True - >>> ) - - Plot an ECDF plot with confidence bands for the probability integral transform (PIT) of a - continuous sample. If drawn from the reference distribution, the PIT values should be uniformly - distributed. - - .. plot:: - :context: close-figs - - >>> pit_vals = distribution.cdf(sample) - >>> uniform_dist = uniform(0, 1) - >>> az.plot_ecdf( - >>> pit_vals, cdf=uniform_dist.cdf, confidence_bands=True, - >>> ) - - Plot an ECDF-difference plot of PIT values. - - .. plot:: - :context: close-figs - - >>> az.plot_ecdf( - >>> pit_vals, cdf = uniform_dist.cdf, confidence_bands = True, - >>> difference = True - >>> ) - """ - if confidence_bands is True: - if pointwise: - warnings.warn( - "`pointwise` has been deprecated. Use `confidence_bands='pointwise'` instead.", - FutureWarning, - ) - confidence_bands = "pointwise" - else: - confidence_bands = "auto" - # if pointwise specified, confidence_bands must be a bool or 'pointwise' - elif confidence_bands not in [False, "pointwise"] and pointwise: - raise ValueError( - f"Cannot specify both `confidence_bands='{confidence_bands}'` and `pointwise=True`" - ) - - if fpr is not None: - warnings.warn( - "`fpr` has been deprecated. Use `ci_prob=1-fpr` or set `rcParam['stats.ci_prob']` to" - "`1-fpr`.", - FutureWarning, - ) - if ci_prob is not None: - raise ValueError("Cannot specify both `fpr` and `ci_prob`") - ci_prob = 1 - fpr - - if ci_prob is None: - ci_prob = rcParams["stats.ci_prob"] - - if values2 is not None: - if cdf is not None: - raise ValueError("You cannot specify both `values2` and `cdf`") - if scipy_ecdf is None: - raise ValueError( - "The `values2` argument is deprecated and `scipy.stats.ecdf` is not available. " - "Please use `cdf` instead." - ) - warnings.warn( - "`values2` has been deprecated. Use `cdf=scipy.stats.ecdf(values2).cdf.evaluate` " - "instead.", - FutureWarning, - ) - cdf = scipy_ecdf(np.ravel(values2)).cdf.evaluate - - if cdf is None: - if confidence_bands: - raise ValueError("For confidence bands you must specify cdf") - if difference is True: - raise ValueError("For ECDF difference plot you must specify cdf") - if pit: - raise ValueError("For PIT plot you must specify cdf") - - values = np.ravel(values) - values.sort() - - if pit: - warnings.warn( - "`pit` has been deprecated. Specify `values=cdf(values)` instead.", - FutureWarning, - ) - values = cdf(values) - cdf = uniform(0, 1).cdf - rvs = uniform(0, 1).rvs - eval_points = np.linspace(1 / npoints, 1, npoints) - - if eval_points is None: - warnings.warn( - "In future versions, if `eval_points` is not provided, then the ECDF will be evaluated" - " at the unique values of the sample. To keep the current behavior, provide " - "`eval_points` explicitly.", - BehaviourChangeWarning, - ) - if confidence_bands in ["optimized", "simulated"]: - warnings.warn( - "For simultaneous bands to be correctly calibrated, specify `eval_points` " - "independent of the `values`" - ) - eval_points = np.linspace(values[0], values[-1], npoints) - else: - eval_points = np.asarray(eval_points) - - if difference or confidence_bands: - cdf_at_eval_points = cdf(eval_points) - else: - cdf_at_eval_points = np.zeros_like(eval_points) - - x_coord, y_coord = _get_ecdf_points(values, eval_points, difference) - - if difference: - y_coord -= cdf_at_eval_points - - if confidence_bands: - ndraws = len(values) - if confidence_bands == "auto": - if ndraws < 200 or num_trials >= 250 * np.sqrt(ndraws): - confidence_bands = "optimized" - else: - confidence_bands = "simulated" - x_bands = eval_points - lower, higher = ecdf_confidence_band( - ndraws, - eval_points, - cdf_at_eval_points, - method=confidence_bands, - prob=ci_prob, - num_trials=num_trials, - rvs=rvs, - random_state=random_state, - ) - - if difference: - lower -= cdf_at_eval_points - higher -= cdf_at_eval_points - else: - x_bands, lower, higher = None, None, None - - ecdf_plot_args = dict( - x_coord=x_coord, - y_coord=y_coord, - x_bands=x_bands, - lower=lower, - higher=higher, - figsize=figsize, - fill_band=fill_band, - plot_kwargs=plot_kwargs, - fill_kwargs=fill_kwargs, - plot_outline_kwargs=plot_outline_kwargs, - ax=ax, - show=show, - backend_kwargs=backend_kwargs, - **kwargs, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - plot = get_plotting_function("plot_ecdf", "ecdfplot", backend) - ax = plot(**ecdf_plot_args) - - return ax diff --git a/arviz/plots/elpdplot.py b/arviz/plots/elpdplot.py deleted file mode 100644 index eef0486de1..0000000000 --- a/arviz/plots/elpdplot.py +++ /dev/null @@ -1,174 +0,0 @@ -"""Plot pointwise elpd estimations of inference data.""" - -import numpy as np - -from ..rcparams import rcParams -from ..stats import _calculate_ics -from ..utils import get_coords -from .plot_utils import format_coords_as_labels, get_plotting_function - - -def plot_elpd( - compare_dict, - color="C0", - xlabels=False, - figsize=None, - textsize=None, - coords=None, - legend=False, - threshold=None, - ax=None, - ic=None, - scale=None, - var_name=None, - plot_kwargs=None, - backend=None, - backend_kwargs=None, - show=None, -): - r"""Plot pointwise elpd differences between two or more models. - - Pointwise model comparison based on their expected log pointwise predictive density (ELPD). - - Notes - ----- - The ELPD is estimated either by Pareto smoothed importance sampling leave-one-out - cross-validation (LOO) or using the widely applicable information criterion (WAIC). - We recommend LOO in line with the work presented by [1]_. - - Parameters - ---------- - compare_dict : mapping of {str : ELPDData or InferenceData} - A dictionary mapping the model name to the object containing inference data or the result - of :func:`arviz.loo` or :func:`arviz.waic` functions. - Refer to :func:`arviz.convert_to_inference_data` for details on possible dict items. - color : str or array_like, default "C0" - Colors of the scatter plot. If color is a str all dots will have the same color. - If it is the size of the observations, each dot will have the specified color. - Otherwise, it will be interpreted as a list of the dims to be used for the color code. - xlabels : bool, default False - Use coords as xticklabels. - figsize : (float, float), optional - If `None`, size is (8 + numvars, 8 + numvars). - textsize : float, optional - Text size for labels. If `None` it will be autoscaled based on `figsize`. - coords : mapping, optional - Coordinates of points to plot. **All** values are used for computation, but only a - subset can be plotted for convenience. See :ref:`this section ` - for usage examples. - legend : bool, default False - Include a legend to the plot. Only taken into account when color argument is a dim name. - threshold : float, optional - If some elpd difference is larger than ``threshold * elpd.std()``, show its label. If - `None`, no observations will be highlighted. - ic : str, optional - Information Criterion ("loo" for PSIS-LOO, "waic" for WAIC) used to compare models. - Defaults to ``rcParams["stats.information_criterion"]``. - Only taken into account when input is :class:`arviz.InferenceData`. - scale : str, optional - Scale argument passed to :func:`arviz.loo` or :func:`arviz.waic`, see their docs for - details. Only taken into account when values in ``compare_dict`` are - :class:`arviz.InferenceData`. - var_name : str, optional - Argument passed to to :func:`arviz.loo` or :func:`arviz.waic`, see their docs for - details. Only taken into account when values in ``compare_dict`` are - :class:`arviz.InferenceData`. - plot_kwargs : dicts, optional - Additional keywords passed to :meth:`matplotlib.axes.Axes.scatter`. - ax : axes, optional - :class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`. - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - - Returns - ------- - axes : matplotlib_axes or bokeh_figure - - See Also - -------- - plot_compare : Summary plot for model comparison. - loo : Compute Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO-CV). - waic : Compute the widely applicable information criterion. - - References - ---------- - .. [1] Vehtari et al. (2016). Practical Bayesian model evaluation using leave-one-out - cross-validation and WAIC https://arxiv.org/abs/1507.04544 - - Examples - -------- - Compare pointwise PSIS-LOO for centered and non centered models of the 8-schools problem - using matplotlib. - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> idata1 = az.load_arviz_data("centered_eight") - >>> idata2 = az.load_arviz_data("non_centered_eight") - >>> az.plot_elpd( - >>> {"centered model": idata1, "non centered model": idata2}, - >>> xlabels=True - >>> ) - - .. bokeh-plot:: - :source-position: above - - import arviz as az - idata1 = az.load_arviz_data("centered_eight") - idata2 = az.load_arviz_data("non_centered_eight") - az.plot_elpd( - {"centered model": idata1, "non centered model": idata2}, - backend="bokeh" - ) - - """ - try: - (compare_dict, _, ic) = _calculate_ics(compare_dict, scale=scale, ic=ic, var_name=var_name) - except Exception as e: - raise e.__class__("Encountered error in ic computation of plot_elpd.") from e - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - numvars = len(compare_dict) - models = list(compare_dict.keys()) - - if coords is None: - coords = {} - - pointwise_data = [get_coords(compare_dict[model][f"{ic}_i"], coords) for model in models] - xdata = np.arange(pointwise_data[0].size) - coord_labels = format_coords_as_labels(pointwise_data[0]) if xlabels else None - - if numvars < 2: - raise ValueError("Number of models to compare must be 2 or greater.") - - elpd_plot_kwargs = dict( - ax=ax, - models=models, - pointwise_data=pointwise_data, - numvars=numvars, - figsize=figsize, - textsize=textsize, - plot_kwargs=plot_kwargs, - xlabels=xlabels, - coord_labels=coord_labels, - xdata=xdata, - threshold=threshold, - legend=legend, - color=color, - backend_kwargs=backend_kwargs, - show=show, - ) - - plot = get_plotting_function("plot_elpd", "elpdplot", backend) - ax = plot(**elpd_plot_kwargs) - return ax diff --git a/arviz/plots/energyplot.py b/arviz/plots/energyplot.py deleted file mode 100644 index 34cabf2f00..0000000000 --- a/arviz/plots/energyplot.py +++ /dev/null @@ -1,147 +0,0 @@ -"""Plot energy transition distribution in HMC inference.""" - -import warnings - -from ..data import convert_to_dataset -from ..rcparams import rcParams -from .plot_utils import get_plotting_function - - -def plot_energy( - data, - kind=None, - bfmi=True, - figsize=None, - legend=True, - fill_alpha=(1, 0.75), - fill_color=("C0", "C5"), - bw="experimental", - textsize=None, - fill_kwargs=None, - plot_kwargs=None, - ax=None, - backend=None, - backend_kwargs=None, - show=None, -): - r"""Plot energy transition distribution and marginal energy distribution in HMC algorithms. - - This may help to diagnose poor exploration by gradient-based algorithms like HMC or NUTS. - The energy function in HMC can identify posteriors with heavy tailed distributions, that - in practice are challenging for sampling. - - This plot is in the style of the one used in [1]_. - - Parameters - ---------- - data : obj - :class:`xarray.Dataset`, or any object that can be converted (must represent - ``sample_stats`` and have an ``energy`` variable). - kind : str, optional - Type of plot to display ("kde", "hist"). - bfmi : bool, default True - If True add to the plot the value of the estimated Bayesian fraction of missing - information. - figsize : (float, float), optional - Figure size. If `None` it will be defined automatically. - legend : bool, default True - Flag for plotting legend. - fill_alpha : tuple, default (1, 0.75) - Alpha blending value for the shaded area under the curve, between 0 - (no shade) and 1 (opaque). - fill_color : tuple of valid matplotlib color, default ('C0', 'C5') - Color for Marginal energy distribution and Energy transition distribution. - bw : float or str, optional - If numeric, indicates the bandwidth and must be positive. - If str, indicates the method to estimate the bandwidth and must be - one of "scott", "silverman", "isj" or "experimental". Defaults to "experimental". - Only works if ``kind='kde'``. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If `None` it will be autoscaled - based on `figsize`. - fill_kwargs : dicts, optional - Additional keywords passed to :func:`arviz.plot_kde` (to control the shade). - plot_kwargs : dicts, optional - Additional keywords passed to :func:`arviz.plot_kde` or :func:`matplotlib.pyplot.hist` - (if ``type='hist'``). - ax : axes, optional - :class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`. - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - - Returns - ------- - axes : matplotlib axes or bokeh figures - - See Also - -------- - bfmi : Calculate the estimated Bayesian fraction of missing information (BFMI). - - References - ---------- - .. [1] Betancourt (2016). Diagnosing Suboptimal Cotangent Disintegrations in - Hamiltonian Monte Carlo https://arxiv.org/abs/1604.00695 - - Examples - -------- - Plot a default energy plot - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> data = az.load_arviz_data('centered_eight') - >>> az.plot_energy(data) - - Represent energy plot via histograms - - .. plot:: - :context: close-figs - - >>> az.plot_energy(data, kind='hist') - - """ - energy = convert_to_dataset(data, group="sample_stats").energy.transpose("chain", "draw").values - - if kind == "histogram": - warnings.warn( - "kind histogram will be deprecated in a future release. Use `hist` " - "or set rcParam `plot.density_kind` to `hist`", - FutureWarning, - ) - kind = "hist" - - if kind is None: - kind = rcParams["plot.density_kind"] - - plot_energy_kwargs = dict( - ax=ax, - energy=energy, - kind=kind, - bfmi=bfmi, - figsize=figsize, - textsize=textsize, - fill_alpha=fill_alpha, - fill_color=fill_color, - fill_kwargs=fill_kwargs, - plot_kwargs=plot_kwargs, - bw=bw, - legend=legend, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_energy", "energyplot", backend) - ax = plot(**plot_energy_kwargs) - return ax diff --git a/arviz/plots/essplot.py b/arviz/plots/essplot.py deleted file mode 100644 index a7c7bc1a98..0000000000 --- a/arviz/plots/essplot.py +++ /dev/null @@ -1,319 +0,0 @@ -"""Plot quantile or local effective sample sizes.""" - -import numpy as np -import xarray as xr - -from ..data import convert_to_dataset -from ..labels import BaseLabeller -from ..rcparams import rcParams -from ..sel_utils import xarray_var_iter -from ..stats import ess -from ..utils import _var_names, get_coords -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function - - -def plot_ess( - idata, - var_names=None, - filter_vars=None, - kind="local", - relative=False, - coords=None, - figsize=None, - grid=None, - textsize=None, - rug=False, - rug_kind="diverging", - n_points=20, - extra_methods=False, - min_ess=400, - labeller=None, - ax=None, - extra_kwargs=None, - text_kwargs=None, - hline_kwargs=None, - rug_kwargs=None, - backend=None, - backend_kwargs=None, - show=None, - **kwargs, -): - r"""Generate quantile, local, or evolution ESS plots. - - The local and the quantile ESS plots are recommended for checking - that there are enough samples for all the explored regions of the - parameter space. Checking local and quantile ESS is particularly - relevant when working with HDI intervals as opposed to ESS bulk, - which is suitable for point estimates. - - Parameters - ---------- - idata : InferenceData - Any object that can be converted to an :class:`arviz.InferenceData` object - Refer to documentation of :func:`arviz.convert_to_dataset` for details. - var_names : list of str, optional - Variables to be plotted. Prefix the variables by ``~`` when you want to exclude - them from the plot. See :ref:`this section ` for usage examples. - filter_vars : {None, "like", "regex"}, default None - If `None` (default), interpret `var_names` as the real variables names. If "like", - interpret `var_names` as substrings of the real variables names. If "regex", - interpret `var_names` as regular expressions on the real variables names. See - :ref:`this section ` for usage examples. - kind : {"local", "quantile", "evolution"}, default "local" - Specify the kind of plot: - - * The ``kind="local"`` argument generates the ESS' local efficiency for - estimating quantiles of a desired posterior. - * The ``kind="quantile"`` argument generates the ESS' local efficiency - for estimating small-interval probability of a desired posterior. - * The ``kind="evolution"`` argument generates the estimated ESS' - with incrised number of iterations of a desired posterior. - - relative : bool, default False - Show relative ess in plot ``ress = ess / N``. - coords : dict, optional - Coordinates of `var_names` to be plotted. Passed to :meth:`xarray.Dataset.sel`. - See :ref:`this section ` for usage examples. - grid : tuple, optional - Number of rows and columns. By default, the rows and columns are - automatically inferred. See :ref:`this section ` for usage examples. - figsize : (float, float), optional - Figure size. If ``None`` it will be defined automatically. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If ``None`` it will be autoscaled - based on `figsize`. - rug : bool, default False - Add a `rug plot `_ for a specific subset of values. - rug_kind : str, default "diverging" - Variable in sample stats to use as rug mask. Must be a boolean variable. - n_points : int, default 20 - Number of points for which to plot their quantile/local ess or number of subsets - in the evolution plot. - extra_methods : bool, default False - Plot mean and sd ESS as horizontal lines. Not taken into account if ``kind = 'evolution'``. - min_ess : int, default 400 - Minimum number of ESS desired. If ``relative=True`` the line is plotted at - ``min_ess / n_samples`` for local and quantile kinds and as a curve following - the ``min_ess / n`` dependency in evolution kind. - labeller : Labeller, optional - Class providing the method ``make_label_vert`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - ax : 2D array-like of matplotlib_axes or bokeh_figure, optional - A 2D array of locations into which to plot the densities. If not supplied, ArviZ will create - its own array of plot areas (and return it). - extra_kwargs : dict, optional - If evolution plot, `extra_kwargs` is used to plot ess tail and differentiate it - from ess bulk. Otherwise, passed to extra methods lines. - text_kwargs : dict, optional - Only taken into account when ``extra_methods=True``. kwargs passed to ax.annotate - for extra methods lines labels. It accepts the additional - key ``x`` to set ``xy=(text_kwargs["x"], mcse)`` - hline_kwargs : dict, optional - kwargs passed to :func:`~matplotlib.axes.Axes.axhline` or to :class:`~bokeh.models.Span` - depending on the backend for the horizontal minimum ESS line. - For relative ess evolution plots the kwargs are passed to - :func:`~matplotlib.axes.Axes.plot` or to :class:`~bokeh.plotting.figure.line` - rug_kwargs : dict - kwargs passed to rug plot. - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - **kwargs - Passed as-is to :meth:`mpl:matplotlib.axes.Axes.hist` or - :meth:`mpl:matplotlib.axes.Axes.plot` function depending on the - value of `kind`. - - Returns - ------- - axes : matplotlib_axes or bokeh_figure - - See Also - -------- - ess : Calculate estimate of the effective sample size. - - References - ---------- - .. [1] Vehtari et al. (2021). Rank-normalization, folding, and - localization: An improved Rhat for assessing convergence of - MCMC. Bayesian analysis, 16(2):667-718. - - Examples - -------- - Plot local ESS. - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> idata = az.load_arviz_data("centered_eight") - >>> coords = {"school": ["Choate", "Lawrenceville"]} - >>> az.plot_ess( - ... idata, kind="local", var_names=["mu", "theta"], coords=coords - ... ) - - Plot ESS evolution as the number of samples increase. When the model is converging properly, - both lines in this plot should be roughly linear. - - .. plot:: - :context: close-figs - - >>> az.plot_ess( - ... idata, kind="evolution", var_names=["mu", "theta"], coords=coords - ... ) - - Customize local ESS plot to look like reference paper. - - .. plot:: - :context: close-figs - - >>> az.plot_ess( - ... idata, kind="local", var_names=["mu"], drawstyle="steps-mid", color="k", - ... linestyle="-", marker=None, rug=True, rug_kwargs={"color": "r"} - ... ) - - Customize ESS evolution plot to look like reference paper. - - .. plot:: - :context: close-figs - - >>> extra_kwargs = {"color": "lightsteelblue"} - >>> az.plot_ess( - ... idata, kind="evolution", var_names=["mu"], - ... color="royalblue", extra_kwargs=extra_kwargs - ... ) - - """ - valid_kinds = ("local", "quantile", "evolution") - kind = kind.lower() - if kind not in valid_kinds: - raise ValueError(f"Invalid kind, kind must be one of {valid_kinds} not {kind}") - - if coords is None: - coords = {} - if "chain" in coords or "draw" in coords: - raise ValueError("chain and draw are invalid coordinates for this kind of plot") - if labeller is None: - labeller = BaseLabeller() - extra_methods = False if kind == "evolution" else extra_methods - - data = get_coords(convert_to_dataset(idata, group="posterior"), coords) - var_names = _var_names(var_names, data, filter_vars) - n_draws = data.sizes["draw"] - n_samples = n_draws * data.sizes["chain"] - - ess_tail_dataset = None - mean_ess = None - sd_ess = None - - if kind == "quantile": - probs = np.linspace(1 / n_points, 1 - 1 / n_points, n_points) - xdata = probs - ylabel = "{} for quantiles" - ess_dataset = xr.concat( - [ - ess(data, var_names=var_names, relative=relative, method="quantile", prob=p) - for p in probs - ], - dim="ess_dim", - ) - elif kind == "local": - probs = np.linspace(0, 1, n_points, endpoint=False) - xdata = probs - ylabel = "{} for small intervals" - ess_dataset = xr.concat( - [ - ess( - data, - var_names=var_names, - relative=relative, - method="local", - prob=[p, p + 1 / n_points], - ) - for p in probs - ], - dim="ess_dim", - ) - else: - first_draw = data.draw.values[0] - ylabel = "{}" - xdata = np.linspace(n_samples / n_points, n_samples, n_points) - draw_divisions = np.linspace(n_draws // n_points, n_draws, n_points, dtype=int) - ess_dataset = xr.concat( - [ - ess( - data.sel(draw=slice(first_draw + draw_div)), - var_names=var_names, - relative=relative, - method="bulk", - ) - for draw_div in draw_divisions - ], - dim="ess_dim", - ) - ess_tail_dataset = xr.concat( - [ - ess( - data.sel(draw=slice(first_draw + draw_div)), - var_names=var_names, - relative=relative, - method="tail", - ) - for draw_div in draw_divisions - ], - dim="ess_dim", - ) - - plotters = filter_plotters_list( - list(xarray_var_iter(ess_dataset, var_names=var_names, skip_dims={"ess_dim"})), "plot_ess" - ) - length_plotters = len(plotters) - rows, cols = default_grid(length_plotters, grid=grid) - - if extra_methods: - mean_ess = ess(data, var_names=var_names, method="mean", relative=relative) - sd_ess = ess(data, var_names=var_names, method="sd", relative=relative) - - essplot_kwargs = dict( - ax=ax, - plotters=plotters, - xdata=xdata, - ess_tail_dataset=ess_tail_dataset, - mean_ess=mean_ess, - sd_ess=sd_ess, - idata=idata, - data=data, - kind=kind, - extra_methods=extra_methods, - textsize=textsize, - rows=rows, - cols=cols, - figsize=figsize, - kwargs=kwargs, - extra_kwargs=extra_kwargs, - text_kwargs=text_kwargs, - n_samples=n_samples, - relative=relative, - min_ess=min_ess, - labeller=labeller, - ylabel=ylabel, - rug=rug, - rug_kind=rug_kind, - rug_kwargs=rug_kwargs, - hline_kwargs=hline_kwargs, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_ess", "essplot", backend) - ax = plot(**essplot_kwargs) - return ax diff --git a/arviz/plots/forestplot.py b/arviz/plots/forestplot.py deleted file mode 100644 index a3da7cf027..0000000000 --- a/arviz/plots/forestplot.py +++ /dev/null @@ -1,304 +0,0 @@ -"""Forest plot.""" - -from ..data import convert_to_dataset -from ..labels import BaseLabeller, NoModelLabeller -from ..rcparams import rcParams -from ..utils import _var_names, get_coords -from .plot_utils import get_plotting_function - - -def plot_forest( - data, - kind="forestplot", - model_names=None, - var_names=None, - filter_vars=None, - transform=None, - coords=None, - combined=False, - combine_dims=None, - hdi_prob=None, - rope=None, - quartiles=True, - ess=False, - r_hat=False, - colors="cycle", - textsize=None, - linewidth=None, - markersize=None, - legend=True, - labeller=None, - ridgeplot_alpha=None, - ridgeplot_overlap=2, - ridgeplot_kind="auto", - ridgeplot_truncate=True, - ridgeplot_quantiles=None, - figsize=None, - ax=None, - backend=None, - backend_config=None, - backend_kwargs=None, - show=None, -): - r"""Forest plot to compare HDI intervals from a number of distributions. - - Generate forest or ridge plots to compare distributions from a model or list of models. - Additionally, the function can display effective sample sizes (ess) and Rhats to visualize - convergence diagnostics alongside the distributions. - - Parameters - ---------- - data : InferenceData - Any object that can be converted to an :class:`arviz.InferenceData` object - Refer to documentation of :func:`arviz.convert_to_dataset` for details. - kind : {"forestplot", "ridgeplot"}, default "forestplot" - Specify the kind of plot: - - * The ``kind="forestplot"`` generates credible intervals, where the central points are the - estimated posterior median, the thick lines are the central quartiles, and the thin lines - represent the :math:`100\times(hdi\_prob)\%` highest density intervals. - * The ``kind="ridgeplot"`` option generates density plots (kernel density estimate or - histograms) in the same graph. Ridge plots can be configured to have different overlap, - truncation bounds and quantile markers. - - model_names : list of str, optional - List with names for the models in the list of data. Useful when plotting more that one - dataset. - var_names : list of str, optional - Variables to be plotted. Prefix the variables by ``~`` when you want to exclude - them from the plot. See :ref:`this section ` for usage examples. - combine_dims : set_like of str, optional - List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. - See :ref:`this section ` for usage examples. - filter_vars : {None, "like", "regex"}, default None - If `None` (default), interpret `var_names` as the real variables names. If "like", - interpret `var_names` as substrings of the real variables names. If "regex", - interpret `var_names` as regular expressions on the real variables names. See - :ref:`this section ` for usage examples. - transform : callable or dict, optional - Function to transform the data. Defaults to None, i.e., the identity function. - coords : dict, optional - Coordinates of ``var_names`` to be plotted. Passed to :meth:`xarray.Dataset.sel`. - See :ref:`this section ` for usage examples. - combined : bool, default False - Flag for combining multiple chains into a single chain. If False, chains will - be plotted separately. See :ref:`this section ` for usage examples. - hdi_prob : float, default 0.94 - Plots highest posterior density interval for chosen percentage of density. - See :ref:`this section ` for usage examples. - rope : list, tuple or dictionary of {str : tuples or lists}, optional - A dictionary of tuples with the lower and upper values of the Region Of Practical - Equivalence. See :ref:`this section ` for usage examples. - quartiles : bool, default True - Flag for plotting the interquartile range, in addition to the ``hdi_prob`` intervals. - r_hat : bool, default False - Flag for plotting Split R-hat statistics. Requires 2 or more chains. - ess : bool, default False - Flag for plotting the effective sample size. - colors : list or string, optional - list with valid matplotlib colors, one color per model. Alternative a string can be passed. - If the string is `cycle`, it will automatically chose a color per model from the matplotlibs - cycle. If a single color is passed, eg 'k', 'C2', 'red' this color will be used for all - models. Defaults to 'cycle'. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If `None` it will be autoscaled based - on ``figsize``. - linewidth : int, optional - Line width throughout. If `None` it will be autoscaled based on ``figsize``. - markersize : int, optional - Markersize throughout. If `None` it will be autoscaled based on ``figsize``. - legend : bool, optional - Show a legend with the color encoded model information. - Defaults to True, if there are multiple models. - labeller : Labeller, optional - Class providing the method ``make_label_vert`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - ridgeplot_alpha: float, optional - Transparency for ridgeplot fill. If ``ridgeplot_alpha=0``, border is colored by model, - otherwise a `black` outline is used. - ridgeplot_overlap : float, default 2 - Overlap height for ridgeplots. - ridgeplot_kind : string, optional - By default ("auto") continuous variables are plotted using KDEs and discrete ones using - histograms. To override this use "hist" to plot histograms and "density" for KDEs. - ridgeplot_truncate : bool, default True - Whether to truncate densities according to the value of ``hdi_prob``. - ridgeplot_quantiles : list, optional - Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles. - figsize : (float, float), optional - Figure size. If `None`, it will be defined automatically. - ax : axes, optional - :class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`. - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_config : dict, optional - Currently specifies the bounds to use for bokeh axes. Defaults to value set in ``rcParams``. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - - Returns - ------- - 1D ndarray of matplotlib_axes or bokeh_figures - - See Also - -------- - plot_posterior : Plot Posterior densities in the style of John K. Kruschke's book. - plot_density : Generate KDE plots for continuous variables and histograms for discrete ones. - summary : Create a data frame with summary statistics. - - Examples - -------- - Forestplot - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> non_centered_data = az.load_arviz_data('non_centered_eight') - >>> axes = az.plot_forest(non_centered_data, - >>> kind='forestplot', - >>> var_names=["^the"], - >>> filter_vars="regex", - >>> combined=True, - >>> figsize=(9, 7)) - >>> axes[0].set_title('Estimated theta for 8 schools model') - - Forestplot with multiple datasets - - .. plot:: - :context: close-figs - - >>> centered_data = az.load_arviz_data('centered_eight') - >>> axes = az.plot_forest([non_centered_data, centered_data], - >>> model_names = ["non centered eight", "centered eight"], - >>> kind='forestplot', - >>> var_names=["^the"], - >>> filter_vars="regex", - >>> combined=True, - >>> figsize=(9, 7)) - >>> axes[0].set_title('Estimated theta for 8 schools models') - - Ridgeplot - - .. plot:: - :context: close-figs - - >>> axes = az.plot_forest(non_centered_data, - >>> kind='ridgeplot', - >>> var_names=['theta'], - >>> combined=True, - >>> ridgeplot_overlap=3, - >>> colors='white', - >>> figsize=(9, 7)) - >>> axes[0].set_title('Estimated theta for 8 schools model') - - Ridgeplot non-truncated and with quantiles - - .. plot:: - :context: close-figs - - >>> axes = az.plot_forest(non_centered_data, - >>> kind='ridgeplot', - >>> var_names=['theta'], - >>> combined=True, - >>> ridgeplot_truncate=False, - >>> ridgeplot_quantiles=[.25, .5, .75], - >>> ridgeplot_overlap=0.7, - >>> colors='white', - >>> figsize=(9, 7)) - >>> axes[0].set_title('Estimated theta for 8 schools model') - """ - if not isinstance(data, (list, tuple)): - data = [data] - if len(data) == 1: - legend = False - - if coords is None: - coords = {} - - if combine_dims is None: - combine_dims = set() - - if labeller is None: - labeller = NoModelLabeller() if legend else BaseLabeller() - - datasets = [convert_to_dataset(datum) for datum in reversed(data)] - if transform is not None: - if callable(transform): - datasets = [transform(dataset) for dataset in datasets] - elif isinstance(transform, dict): - transformed_datasets = [] - for dataset in datasets: - new_dataset = dataset.copy() - for var_name, func in transform.items(): - if var_name in new_dataset: - new_dataset[var_name] = func(new_dataset[var_name]) - transformed_datasets.append(new_dataset) - datasets = transformed_datasets - else: - raise ValueError("transform must be either a callable or a dict {var_name: callable}") - datasets = get_coords( - datasets, list(reversed(coords)) if isinstance(coords, (list, tuple)) else coords - ) - - var_names = _var_names(var_names, datasets, filter_vars) - - ncols, width_ratios = 1, [3] - - if ess: - ncols += 1 - width_ratios.append(1) - - if r_hat: - ncols += 1 - width_ratios.append(1) - - if hdi_prob is None: - hdi_prob = rcParams["stats.ci_prob"] - elif not 1 >= hdi_prob > 0: - raise ValueError("The value of hdi_prob should be in the interval (0, 1]") - - plot_forest_kwargs = dict( - ax=ax, - datasets=datasets, - var_names=var_names, - model_names=model_names, - combined=combined, - combine_dims=combine_dims, - colors=colors, - figsize=figsize, - width_ratios=width_ratios, - linewidth=linewidth, - markersize=markersize, - kind=kind, - ncols=ncols, - hdi_prob=hdi_prob, - quartiles=quartiles, - rope=rope, - ridgeplot_overlap=ridgeplot_overlap, - ridgeplot_alpha=ridgeplot_alpha, - ridgeplot_kind=ridgeplot_kind, - ridgeplot_truncate=ridgeplot_truncate, - ridgeplot_quantiles=ridgeplot_quantiles, - textsize=textsize, - legend=legend, - labeller=labeller, - ess=ess, - r_hat=r_hat, - backend_kwargs=backend_kwargs, - backend_config=backend_config, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_forest", "forestplot", backend) - axes = plot(**plot_forest_kwargs) - return axes diff --git a/arviz/plots/hdiplot.py b/arviz/plots/hdiplot.py deleted file mode 100644 index 546281634f..0000000000 --- a/arviz/plots/hdiplot.py +++ /dev/null @@ -1,211 +0,0 @@ -"""Plot highest density intervals for regression data.""" - -import warnings - -import numpy as np -from scipy.interpolate import griddata -from scipy.signal import savgol_filter -from xarray import Dataset - -from ..rcparams import rcParams -from ..stats import hdi -from .plot_utils import get_plotting_function - - -def plot_hdi( - x, - y=None, - hdi_prob=None, - hdi_data=None, - color="C1", - circular=False, - smooth=True, - smooth_kwargs=None, - figsize=None, - fill_kwargs=None, - plot_kwargs=None, - hdi_kwargs=None, - ax=None, - backend=None, - backend_kwargs=None, - show=None, -): - r"""Plot HDI intervals for regression data. - - Parameters - ---------- - x : array-like - Values to plot. - y : array-like, optional - Values from which to compute the HDI. Assumed shape ``(chain, draw, \*shape)``. - Only optional if ``hdi_data`` is present. - hdi_data : array_like, optional - Precomputed HDI values to use. Assumed shape is ``(*x.shape, 2)``. - hdi_prob : float, optional - Probability for the highest density interval. Defaults to ``stats.ci_prob`` rcParam. - See :ref:`this section ` for usage examples. - color : str, default "C1" - Color used for the limits of the HDI and fill. Should be a valid matplotlib color. - circular : bool, default False - Whether to compute the HDI taking into account ``x`` is a circular variable - (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables). - smooth : boolean, default True - If True the result will be smoothed by first computing a linear interpolation of the data - over a regular grid and then applying the Savitzky-Golay filter to the interpolated data. - smooth_kwargs : dict, optional - Additional keywords modifying the Savitzky-Golay filter. See - :func:`scipy:scipy.signal.savgol_filter` for details. - figsize : (float, float), optional - Figure size. If ``None``, it will be defined automatically. - fill_kwargs : dict, optional - Keywords passed to :meth:`mpl:matplotlib.axes.Axes.fill_between` - (use ``fill_kwargs={'alpha': 0}`` to disable fill) or to - :meth:`bokeh.plotting.Figure.patch`. - plot_kwargs : dict, optional - HDI limits keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.plot` or - :meth:`bokeh.plotting.Figure.patch`. - hdi_kwargs : dict, optional - Keyword arguments passed to :func:`~arviz.hdi`. Ignored if ``hdi_data`` is present. - ax : axes, optional - Matplotlib axes or bokeh figures. - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - - Returns - ------- - axes : matplotlib axes or bokeh figures - - See Also - -------- - hdi : Calculate highest density interval (HDI) of array for given probability. - - Examples - -------- - Plot HDI interval of simulated random-walk data using `y` argument: - - .. plot:: - :context: close-figs - - >>> import numpy as np - >>> import arviz as az - >>> # time-steps random walk - >>> x_data =np.arange(0,100) - >>> # Mean random walk - >>> mu = np.zeros(100) - >>> for i in x_data: mu[i] = mu[i-1] + np.random.normal(0, 1, 1) - >>> # Simulated pp samples form the random walk time series - >>> y_data = np.random.normal(2 + mu * 0.5, 0.5, size = (2, 50, 100)) - >>> az.plot_hdi(x_data, y_data) - - ``plot_hdi`` can also be given precalculated values with the argument ``hdi_data``. This example - shows how to use :func:`~arviz.hdi` to precalculate the values and pass these values to - ``plot_hdi``. Similarly to an example in ``hdi`` we are using the ``input_core_dims`` - argument of :func:`~arviz.wrap_xarray_ufunc` to manually define the dimensions over which - to calculate the HDI. - - .. plot:: - :context: close-figs - - >>> hdi_data = az.hdi(y_data, input_core_dims=[["draw"]]) - >>> ax = az.plot_hdi(x_data, hdi_data=hdi_data[0], color="r", fill_kwargs={"alpha": .2}) - >>> az.plot_hdi(x_data, hdi_data=hdi_data[1], color="k", ax=ax, fill_kwargs={"alpha": .2}) - - ``plot_hdi`` can also be used with Inference Data objects. Here we use the posterior predictive - to plot the HDI interval. - - .. plot:: - :context: close-figs - - >>> X = np.random.normal(0,1,100) - >>> Y = np.random.normal(2 + X * 0.5, 0.5, size=(2,10,100)) - >>> idata = az.from_dict(posterior={"y": Y}, constant_data={"x":X}) - >>> x_data = idata.constant_data.x - >>> y_data = idata.posterior.y - >>> az.plot_hdi(x_data, y_data) - - """ - if hdi_kwargs is None: - hdi_kwargs = {} - - x = np.asarray(x) - x_shape = x.shape - - if isinstance(x[0], str): - raise NotImplementedError( - "The `arviz.plot_hdi()` function does not support categorical data. " - "Consider using `arviz.plot_forest()`." - ) - if y is None and hdi_data is None: - raise ValueError("One of {y, hdi_data} is required") - if hdi_data is not None and y is not None: - warnings.warn("Both y and hdi_data arguments present, ignoring y") - elif hdi_data is not None: - hdi_prob = ( - hdi_data.hdi.attrs.get("hdi_prob", np.nan) if hasattr(hdi_data, "hdi") else np.nan - ) - if isinstance(hdi_data, Dataset): - data_vars = list(hdi_data.data_vars) - if len(data_vars) != 1: - raise ValueError( - "Found several variables in hdi_data. Only single variable Datasets are " - "supported." - ) - hdi_data = hdi_data[data_vars[0]] - else: - y = np.asarray(y) - if hdi_prob is None: - hdi_prob = rcParams["stats.ci_prob"] - elif not 1 >= hdi_prob > 0: - raise ValueError("The value of hdi_prob should be in the interval (0, 1]") - hdi_data = hdi(y, hdi_prob=hdi_prob, circular=circular, multimodal=False, **hdi_kwargs) - - hdi_shape = hdi_data.shape - if hdi_shape[:-1] != x_shape: - msg = ( - "Dimension mismatch for x: {} and hdi: {}. Check the dimensions of y and" - "hdi_kwargs to make sure they are compatible" - ) - raise TypeError(msg.format(x_shape, hdi_shape)) - - if smooth: - if isinstance(x[0], np.datetime64): - raise TypeError("Cannot deal with x as type datetime. Recommend setting smooth=False.") - - if smooth_kwargs is None: - smooth_kwargs = {} - smooth_kwargs.setdefault("window_length", 55) - smooth_kwargs.setdefault("polyorder", 2) - x_data = np.linspace(x.min(), x.max(), 200) - x_data[0] = (x_data[0] + x_data[1]) / 2 - hdi_interp = griddata(x, hdi_data, x_data) - y_data = savgol_filter(hdi_interp, axis=0, **smooth_kwargs) - else: - idx = np.argsort(x) - x_data = x[idx] - y_data = hdi_data[idx] - - hdiplot_kwargs = dict( - ax=ax, - x_data=x_data, - y_data=y_data, - color=color, - figsize=figsize, - plot_kwargs=plot_kwargs, - fill_kwargs=fill_kwargs, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - plot = get_plotting_function("plot_hdi", "hdiplot", backend) - ax = plot(**hdiplot_kwargs) - return ax diff --git a/arviz/plots/kdeplot.py b/arviz/plots/kdeplot.py deleted file mode 100644 index 7e2dc2b2bf..0000000000 --- a/arviz/plots/kdeplot.py +++ /dev/null @@ -1,357 +0,0 @@ -# pylint: disable=unexpected-keyword-arg -"""One-dimensional kernel density estimate plots.""" -import warnings - -import xarray as xr - -from ..data import InferenceData -from ..rcparams import rcParams -from ..stats.density_utils import _fast_kde_2d, kde, _find_hdi_contours -from .plot_utils import get_plotting_function, _init_kwargs_dict - - -def plot_kde( - values, - values2=None, - cumulative=False, - rug=False, - label=None, - bw="default", - adaptive=False, - quantiles=None, - rotated=False, - contour=True, - hdi_probs=None, - fill_last=False, - figsize=None, - textsize=None, - plot_kwargs=None, - fill_kwargs=None, - rug_kwargs=None, - contour_kwargs=None, - contourf_kwargs=None, - pcolormesh_kwargs=None, - is_circular=False, - ax=None, - legend=True, - backend=None, - backend_kwargs=None, - show=None, - return_glyph=False, - **kwargs -): - """1D or 2D KDE plot taking into account boundary conditions. - - Parameters - ---------- - values : array-like - Values to plot - values2 : array-like, optional - Values to plot. If present, a 2D KDE will be estimated - cumulative : bool, dafault False - If True plot the estimated cumulative distribution function. Ignored for 2D KDE. - rug : bool, default False - Add a `rug plot `_ for a specific subset of - values. Ignored for 2D KDE. - label : string, optional - Text to include as part of the legend. - bw : float or str, optional - If numeric, indicates the bandwidth and must be positive. - If str, indicates the method to estimate the bandwidth and must be - one of "scott", "silverman", "isj" or "experimental" when ``is_circular`` is False - and "taylor" (for now) when ``is_circular`` is True. - Defaults to "default" which means "experimental" when variable is not circular - and "taylor" when it is. - adaptive : bool, default False - If True, an adaptative bandwidth is used. Only valid for 1D KDE. - quantiles : list, optional - Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles. - rotated : bool, default False - Whether to rotate the 1D KDE plot 90 degrees. - contour : bool, default True - If True plot the 2D KDE using contours, otherwise plot a smooth 2D KDE. - hdi_probs : list, optional - Plots highest density credibility regions for the provided probabilities for a 2D KDE. - Defaults to [0.5, 0.8, 0.94]. - fill_last : bool, default False - If True fill the last contour of the 2D KDE plot. - figsize : (float, float), optional - Figure size. If ``None`` it will be defined automatically. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If ``None`` it will be autoscaled - based on ``figsize``. Not implemented for bokeh backend. - plot_kwargs : dict, optional - Keywords passed to the pdf line of a 1D KDE. See :meth:`mpl:matplotlib.axes.Axes.plot` - or :meth:`bokeh:bokeh.plotting.Figure.line` for a description of accepted values. - fill_kwargs : dict, optional - Keywords passed to the fill under the line (use ``fill_kwargs={'alpha': 0}`` - to disable fill). Ignored for 2D KDE. Passed to - :meth:`bokeh.plotting.Figure.patch`. - rug_kwargs : dict, optional - Keywords passed to the rug plot. Ignored if ``rug=False`` or for 2D KDE - Use ``space`` keyword (float) to control the position of the rugplot. The larger this number - the lower the rugplot. Passed to :class:`bokeh:bokeh.models.glyphs.Scatter`. - contour_kwargs : dict, optional - Keywords passed to :meth:`mpl:matplotlib.axes.Axes.contour` - to draw contour lines or :meth:`bokeh.plotting.Figure.patch`. - Ignored for 1D KDE. - contourf_kwargs : dict, optional - Keywords passed to :meth:`mpl:matplotlib.axes.Axes.contourf` - to draw filled contours. Ignored for 1D KDE. - pcolormesh_kwargs : dict, optional - Keywords passed to :meth:`mpl:matplotlib.axes.Axes.pcolormesh` or - :meth:`bokeh.plotting.Figure.image`. - Ignored for 1D KDE. - is_circular : {False, True, "radians", "degrees"}. Default False - Select input type {"radians", "degrees"} for circular histogram or KDE plot. If True, - default input type is "radians". When this argument is present, it interprets ``values`` - as a circular variable measured in radians and a circular KDE is used. Inputs in - "degrees" will undergo an internal conversion to radians. - ax : axes, optional - Matplotlib axes or bokeh figures. - legend : bool, default True - Add legend to the figure. - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - return_glyph : bool, optional - Internal argument to return glyphs for bokeh. - - Returns - ------- - axes : matplotlib.Axes or bokeh.plotting.Figure - Object containing the kde plot - glyphs : list, optional - Bokeh glyphs present in plot. Only provided if ``return_glyph`` is True. - - See Also - -------- - kde : One dimensional density estimation. - plot_dist : Plot distribution as histogram or kernel density estimates. - - Examples - -------- - Plot default KDE - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> non_centered = az.load_arviz_data('non_centered_eight') - >>> mu_posterior = np.concatenate(non_centered.posterior["mu"].values) - >>> tau_posterior = np.concatenate(non_centered.posterior["tau"].values) - >>> az.plot_kde(mu_posterior) - - - Plot KDE with rugplot - - .. plot:: - :context: close-figs - - >>> az.plot_kde(mu_posterior, rug=True) - - Plot KDE with adaptive bandwidth - - .. plot:: - :context: close-figs - - >>> az.plot_kde(mu_posterior, adaptive=True) - - Plot KDE with a different bandwidth estimator - - .. plot:: - :context: close-figs - - >>> az.plot_kde(mu_posterior, bw="scott") - - Plot KDE with a bandwidth specified manually - - .. plot:: - :context: close-figs - - >>> az.plot_kde(mu_posterior, bw=0.4) - - Plot KDE for a circular variable - - .. plot:: - :context: close-figs - - >>> rvs = np.random.vonmises(mu=np.pi, kappa=2, size=500) - >>> az.plot_kde(rvs, is_circular=True) - - - Plot a cumulative distribution - - .. plot:: - :context: close-figs - - >>> az.plot_kde(mu_posterior, cumulative=True) - - - - Rotate plot 90 degrees - - .. plot:: - :context: close-figs - - >>> az.plot_kde(mu_posterior, rotated=True) - - - Plot 2d contour KDE - - .. plot:: - :context: close-figs - - >>> az.plot_kde(mu_posterior, values2=tau_posterior) - - - Plot 2d contour KDE, without filling and contour lines using viridis cmap - - .. plot:: - :context: close-figs - - >>> az.plot_kde(mu_posterior, values2=tau_posterior, - ... contour_kwargs={"colors":None, "cmap":plt.cm.viridis}, - ... contourf_kwargs={"alpha":0}); - - Plot 2d contour KDE, set the number of levels to 3. - - .. plot:: - :context: close-figs - - >>> az.plot_kde( - ... mu_posterior, values2=tau_posterior, - ... contour_kwargs={"levels":3}, contourf_kwargs={"levels":3} - ... ); - - Plot 2d contour KDE with 30%, 60% and 90% HDI contours. - - .. plot:: - :context: close-figs - - >>> az.plot_kde(mu_posterior, values2=tau_posterior, hdi_probs=[0.3, 0.6, 0.9]) - - Plot 2d smooth KDE - - .. plot:: - :context: close-figs - - >>> az.plot_kde(mu_posterior, values2=tau_posterior, contour=False) - - """ - if isinstance(values, xr.Dataset): - raise ValueError( - "Xarray dataset object detected. Use plot_posterior, plot_density " - "or plot_pair instead of plot_kde" - ) - if isinstance(values, InferenceData): - raise ValueError( - " Inference Data object detected. Use plot_posterior " - "or plot_pair instead of plot_kde" - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - if values2 is None: - if bw == "default": - bw = "taylor" if is_circular else "experimental" - - grid, density = kde(values, is_circular, bw=bw, adaptive=adaptive, cumulative=cumulative) - lower, upper = grid[0], grid[-1] - - density_q = density if cumulative else density.cumsum() / density.sum() - - # This is just a hack placeholder for now - xmin, xmax, ymin, ymax, gridsize = [None] * 5 - else: - gridsize = (128, 128) if contour else (256, 256) - density, xmin, xmax, ymin, ymax = _fast_kde_2d(values, values2, gridsize=gridsize) - - if hdi_probs is None: - hdi_probs = [0.5, 0.8, 0.94] - - if hdi_probs is not None: - # Check hdi probs are within bounds (0, 1) - if min(hdi_probs) <= 0 or max(hdi_probs) >= 1: - raise ValueError("Highest density interval probabilities must be between 0 and 1") - - # Calculate contour levels and sort for matplotlib - contour_levels = _find_hdi_contours(density, hdi_probs) - contour_levels.sort() - - contour_level_list = [0] + list(contour_levels) + [density.max()] - - # Add keyword arguments to contour, contourf - contour_kwargs = _init_kwargs_dict(contour_kwargs) - if "levels" in contour_kwargs: - warnings.warn( - "Both 'levels' in contour_kwargs and 'hdi_probs' have been specified." - "Using 'hdi_probs' in favor of 'levels'.", - UserWarning, - ) - - if backend == "bokeh": - contour_kwargs["levels"] = contour_level_list - elif backend == "matplotlib": - contour_kwargs["levels"] = contour_level_list[1:] - - contourf_kwargs = _init_kwargs_dict(contourf_kwargs) - if "levels" in contourf_kwargs: - warnings.warn( - "Both 'levels' in contourf_kwargs and 'hdi_probs' have been specified." - "Using 'hdi_probs' in favor of 'levels'.", - UserWarning, - ) - contourf_kwargs["levels"] = contour_level_list - - lower, upper, density_q = [None] * 3 - - kde_plot_args = dict( - # Internal API - density=density, - lower=lower, - upper=upper, - density_q=density_q, - xmin=xmin, - xmax=xmax, - ymin=ymin, - ymax=ymax, - gridsize=gridsize, - # User Facing API that can be simplified - values=values, - values2=values2, - rug=rug, - label=label, - quantiles=quantiles, - rotated=rotated, - contour=contour, - fill_last=fill_last, - figsize=figsize, - textsize=textsize, - plot_kwargs=plot_kwargs, - fill_kwargs=fill_kwargs, - rug_kwargs=rug_kwargs, - contour_kwargs=contour_kwargs, - contourf_kwargs=contourf_kwargs, - pcolormesh_kwargs=pcolormesh_kwargs, - is_circular=is_circular, - ax=ax, - legend=legend, - backend_kwargs=backend_kwargs, - show=show, - return_glyph=return_glyph, - **kwargs, - ) - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_kde", "kdeplot", backend) - ax = plot(**kde_plot_args) - - return ax diff --git a/arviz/plots/khatplot.py b/arviz/plots/khatplot.py deleted file mode 100644 index 3493f69eb8..0000000000 --- a/arviz/plots/khatplot.py +++ /dev/null @@ -1,236 +0,0 @@ -"""Pareto tail indices plot.""" - -import logging -import warnings - -import numpy as np -from xarray import DataArray - -from ..rcparams import rcParams -from ..stats import ELPDData -from ..utils import get_coords -from .plot_utils import format_coords_as_labels, get_plotting_function - -_log = logging.getLogger(__name__) - - -def plot_khat( - khats, - color="C0", - xlabels=False, - show_hlines=False, - show_bins=False, - bin_format="{1:.1f}%", - annotate=False, - threshold=None, - hover_label=False, - hover_format="{1}", - figsize=None, - textsize=None, - coords=None, - legend=False, - markersize=None, - ax=None, - hlines_kwargs=None, - backend=None, - backend_kwargs=None, - show=None, - **kwargs -): - r"""Plot Pareto tail indices :math:`\hat{k}` for diagnosing convergence in PSIS-LOO. - - Parameters - ---------- - khats : ELPDData - The input Pareto tail indices to be plotted. - color : str or array_like, default "C0" - Colors of the scatter plot, if color is a str all dots will have the same color, - if it is the size of the observations, each dot will have the specified color, - otherwise, it will be interpreted as a list of the dims to be used for the color - code. If Matplotlib c argument is passed, it will override the color argument. - xlabels : bool, default False - Use coords as xticklabels. - show_hlines : bool, default False - Show the horizontal lines, by default at the values [0, 0.5, 0.7, 1]. - show_bins : bool, default False - Show the percentage of khats falling in each bin, as delimited by hlines. - bin_format : str, optional - The string is used as formatting guide calling ``bin_format.format(count, pct)``. - threshold : float, optional - Show the labels of k values larger than `threshold`. If ``None`` (default), no - observations will be highlighted. - hover_label : bool, default False - Show the datapoint label when hovering over it with the mouse. Requires an interactive - backend. - hover_format : str, default "{1}" - String used to format the hover label via ``hover_format.format(idx, coord_label)`` - figsize : (float, float), optional - Figure size. If ``None`` it will be defined automatically. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If ``None`` it will be autoscaled - based on `figsize`. - coords : mapping, optional - Coordinates of points to plot. **All** values are used for computation, but only a - a subset can be plotted for convenience. See :ref:`this section ` for - usage examples. - legend : bool, default False - Include a legend to the plot. Only taken into account when color argument is a dim name. - markersize : int, optional - markersize for scatter plot. Defaults to ``None`` in which case it will - be chosen based on autoscaling for figsize. - ax : axes, optional - Matplotlib axes or bokeh figures. - hlines_kwargs : dict, optional - Additional keywords passed to - :meth:`matplotlib.axes.Axes.hlines`. - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show : bool, optional - Call backend show function. - kwargs : - Additional keywords passed to - :meth:`matplotlib.axes.Axes.scatter`. - - Returns - ------- - axes : matplotlib_axes or bokeh_figures - - See Also - -------- - psislw : Pareto smoothed importance sampling (PSIS). - - Examples - -------- - Plot estimated pareto shape parameters showing how many fall in each category. - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> radon = az.load_arviz_data("radon") - >>> loo_radon = az.loo(radon, pointwise=True) - >>> az.plot_khat(loo_radon, show_bins=True) - - Show xlabels - - .. plot:: - :context: close-figs - - >>> centered_eight = az.load_arviz_data("centered_eight") - >>> khats = az.loo(centered_eight, pointwise=True).pareto_k - >>> az.plot_khat(khats, xlabels=True, threshold=1) - - Use custom color scheme - - .. plot:: - :context: close-figs - - >>> counties = radon.posterior.County[radon.constant_data.county_idx].values - >>> colors = [ - ... "blue" if county[-1] in ("A", "N") else "green" for county in counties - ... ] - >>> az.plot_khat(loo_radon, color=colors) - - Notes - ----- - The Generalized Pareto distribution (GPD) diagnoses convergence rates for importance - sampling. GPD has parameters offset, scale, and shape. The shape parameter (:math:`k`) - tells the distribution's number of finite moments. The pre-asymptotic convergence rate - of importance sampling can be estimated based on the fractional number of finite moments - of the importance ratio distribution. GPD is fitted to the largest importance ratios and - interprets the estimated shape parameter :math:`k`, i.e., :math:`\hat{k}` can then be - used as a diagnostic (most importantly if :math:`\hat{k} > 0.7`, then the convergence - rate is impractically low). See [1]_. - - References - ---------- - .. [1] Vehtari, A., Simpson, D., Gelman, A., Yao, Y., Gabry, J. (2024). - Pareto Smoothed Importance Sampling. Journal of Machine Learning - Research, 25(72):1-58. - - """ - if annotate: - _log.warning("annotate will be deprecated, please use threshold instead") - threshold = annotate - - if coords is None: - coords = {} - - if color is None: - color = "C0" - - if isinstance(khats, np.ndarray): - warnings.warn( - "support for arrays will be deprecated, please use ELPDData." - "The reason for this, is that we need to know the numbers of draws" - "sampled from the posterior", - FutureWarning, - ) - khats = khats.flatten() - xlabels = False - legend = False - dims = [] - good_k = None - else: - if isinstance(khats, ELPDData): - good_k = khats.good_k - khats = khats.pareto_k - else: - good_k = None - warnings.warn( - "support for DataArrays will be deprecated, please use ELPDData." - "The reason for this, is that we need to know the numbers of draws" - "sampled from the posterior", - FutureWarning, - ) - if not isinstance(khats, DataArray): - raise ValueError("Incorrect khat data input. Check the documentation") - - khats = get_coords(khats, coords) - dims = khats.dims - - n_data_points = khats.size - xdata = np.arange(n_data_points) - if isinstance(khats, DataArray): - coord_labels = format_coords_as_labels(khats) - else: - coord_labels = xdata.astype(str) - - plot_khat_kwargs = dict( - hover_label=hover_label, - hover_format=hover_format, - ax=ax, - figsize=figsize, - xdata=xdata, - khats=khats, - good_k=good_k, - kwargs=kwargs, - threshold=threshold, - coord_labels=coord_labels, - show_hlines=show_hlines, - show_bins=show_bins, - hlines_kwargs=hlines_kwargs, - xlabels=xlabels, - legend=legend, - color=color, - dims=dims, - textsize=textsize, - markersize=markersize, - n_data_points=n_data_points, - bin_format=bin_format, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_khat", "khatplot", backend) - axes = plot(**plot_khat_kwargs) - return axes diff --git a/arviz/plots/lmplot.py b/arviz/plots/lmplot.py deleted file mode 100644 index 3c55524187..0000000000 --- a/arviz/plots/lmplot.py +++ /dev/null @@ -1,380 +0,0 @@ -"""Plot regression figure.""" - -import warnings -from numbers import Integral -from itertools import repeat - -import xarray as xr -import numpy as np -from xarray.core.dataarray import DataArray - -from ..sel_utils import xarray_var_iter -from ..rcparams import rcParams -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function - - -def _repeat_flatten_list(lst, n): - return [item for sublist in repeat(lst, n) for item in sublist] - - -def plot_lm( - y, - idata=None, - x=None, - y_model=None, - y_hat=None, - num_samples=50, - kind_pp="samples", - kind_model="lines", - xjitter=False, - plot_dim=None, - backend=None, - y_kwargs=None, - y_hat_plot_kwargs=None, - y_hat_fill_kwargs=None, - y_model_plot_kwargs=None, - y_model_fill_kwargs=None, - y_model_mean_kwargs=None, - backend_kwargs=None, - show=None, - figsize=None, - textsize=None, - axes=None, - legend=True, - grid=True, -): - """Posterior predictive and mean plots for regression-like data. - - Parameters - ---------- - y : str or DataArray or ndarray - If str, variable name from ``observed_data``. - idata : InferenceData, Optional - Optional only if ``y`` is not str. - x : str, tuple of strings, DataArray or array-like, optional - If str or tuple, variable name from ``constant_data``. - If ndarray, could be 1D, or 2D for multiple plots. - If None, coords name of ``y`` (``y`` should be DataArray). - y_model : str or Sequence, Optional - If str, variable name from ``posterior``. - Its dimensions should be same as ``y`` plus added chains and draws. - y_hat : str, Optional - If str, variable name from ``posterior_predictive``. - Its dimensions should be same as ``y`` plus added chains and draws. - num_samples : int, Optional, Default 50 - Significant if ``kind_pp`` is "samples" or ``kind_model`` is "lines". - Number of samples to be drawn from posterior predictive or - kind_pp : {"samples", "hdi"}, Default "samples" - Options to visualize uncertainty in data. - kind_model : {"lines", "hdi"}, Default "lines" - Options to visualize uncertainty in mean of the data. - plot_dim : str, Optional - Necessary if ``y`` is multidimensional. - backend : str, Optional - Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". - y_kwargs : dict, optional - Passed to :meth:`mpl:matplotlib.axes.Axes.plot` in matplotlib - and :meth:`bokeh:bokeh.plotting.Figure.circle` in bokeh - y_hat_plot_kwargs : dict, optional - Passed to :meth:`mpl:matplotlib.axes.Axes.plot` in matplotlib - and :meth:`bokeh:bokeh.plotting.Figure.circle` in bokeh - y_hat_fill_kwargs : dict, optional - Passed to :func:`arviz.plot_hdi` - y_model_plot_kwargs : dict, optional - Passed to :meth:`mpl:matplotlib.axes.Axes.plot` in matplotlib - and :meth:`bokeh:bokeh.plotting.Figure.line` in bokeh - y_model_fill_kwargs : dict, optional - Significant if ``kind_model`` is "hdi". Passed to :func:`arviz.plot_hdi` - y_model_mean_kwargs : dict, optional - Passed to :meth:`mpl:matplotlib.axes.Axes.plot` in matplotlib - and :meth:`bokeh:bokeh.plotting.Figure.line` in bokeh - backend_kwargs : dict, optional - These are kwargs specific to the backend being used. Passed to - :func:`matplotlib.pyplot.subplots` or - :func:`bokeh.plotting.figure`. - figsize : (float, float), optional - Figure size. If None it will be defined automatically. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If None it will be - autoscaled based on ``figsize``. - axes : 2D numpy array-like of matplotlib_axes or bokeh_figures, optional - A 2D array of locations into which to plot the densities. If not supplied, Arviz will create - its own array of plot areas (and return it). - show : bool, optional - Call backend show function. - legend : bool, optional - Add legend to figure. By default True. - grid : bool, optional - Add grid to figure. By default True. - - - Returns - ------- - axes: matplotlib axes or bokeh figures - - See Also - -------- - plot_ts : Plot timeseries data - plot_ppc : Plot for posterior/prior predictive checks - - Examples - -------- - Plot regression default plot - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> import numpy as np - >>> import xarray as xr - >>> idata = az.load_arviz_data('regression1d') - >>> x = xr.DataArray(np.linspace(0, 1, 100)) - >>> idata.posterior["y_model"] = idata.posterior["intercept"] + idata.posterior["slope"]*x - >>> az.plot_lm(idata=idata, y="y", x=x) - - Plot regression data and mean uncertainty - - .. plot:: - :context: close-figs - - >>> az.plot_lm(idata=idata, y="y", x=x, y_model="y_model") - - Plot regression data and mean uncertainty in hdi form - - .. plot:: - :context: close-figs - - >>> az.plot_lm( - ... idata=idata, y="y", x=x, y_model="y_model", kind_pp="hdi", kind_model="hdi" - ... ) - - Plot regression data for multi-dimensional y using plot_dim - - .. plot:: - :context: close-figs - - >>> data = az.from_dict( - ... observed_data = { "y": np.random.normal(size=(5, 7)) }, - ... posterior_predictive = {"y": np.random.randn(4, 1000, 5, 7) / 2}, - ... dims={"y": ["dim1", "dim2"]}, - ... coords={"dim1": range(5), "dim2": range(7)} - ... ) - >>> az.plot_lm(idata=data, y="y", plot_dim="dim1") - """ - if kind_pp not in ("samples", "hdi"): - raise ValueError("kind_ppc should be either samples or hdi") - - if kind_model not in ("lines", "hdi"): - raise ValueError("kind_model should be either lines or hdi") - - if y_hat is None and isinstance(y, str): - y_hat = y - - if isinstance(y, str): - y = idata.observed_data[y] - elif not isinstance(y, DataArray): - y = xr.DataArray(y) - - if len(y.dims) > 1 and plot_dim is None: - raise ValueError("Argument plot_dim is needed in case of multidimensional data") - - x_var_names = None - if isinstance(x, str): - x = idata.constant_data[x] - x_skip_dims = x.dims - elif isinstance(x, tuple): - x_var_names = x - x = idata.constant_data - x_skip_dims = x.dims - elif isinstance(x, DataArray): - x_skip_dims = x.dims - elif x is None: - x = y.coords[y.dims[0]] if plot_dim is None else y.coords[plot_dim] - x_skip_dims = x.dims - else: - x = xr.DataArray(x) - x_skip_dims = [x.dims[-1]] - - # If posterior is present in idata and y_hat is there, get its values - if isinstance(y_model, str): - if "posterior" not in idata.groups(): - warnings.warn("Posterior not found in idata", UserWarning) - y_model = None - elif hasattr(idata.posterior, y_model): - y_model = idata.posterior[y_model] - else: - warnings.warn("y_model not found in posterior", UserWarning) - y_model = None - - # If posterior_predictive is present in idata and y_hat is there, get its values - if isinstance(y_hat, str): - if "posterior_predictive" not in idata.groups(): - warnings.warn("posterior_predictive not found in idata", UserWarning) - y_hat = None - elif hasattr(idata.posterior_predictive, y_hat): - y_hat = idata.posterior_predictive[y_hat] - else: - warnings.warn("y_hat not found in posterior_predictive", UserWarning) - y_hat = None - - # Check if num_pp_smaples is valid and generate num_pp_smaples number of random indexes. - # Only needed if kind_pp="samples" or kind_model="lines". Not req for plotting hdi - pp_sample_ix = None - if (y_hat is not None and kind_pp == "samples") or ( - y_model is not None and kind_model == "lines" - ): - if y_hat is not None: - total_pp_samples = y_hat.sizes["chain"] * y_hat.sizes["draw"] - else: - total_pp_samples = y_model.sizes["chain"] * y_model.sizes["draw"] - - if ( - not isinstance(num_samples, Integral) - or num_samples < 1 - or num_samples > total_pp_samples - ): - raise TypeError(f"`num_samples` must be an integer between 1 and {total_pp_samples}.") - - pp_sample_ix = np.random.choice(total_pp_samples, size=num_samples, replace=False) - - # crucial step in case of multidim y - if plot_dim is None: - skip_dims = list(y.dims) - elif isinstance(plot_dim, str): - skip_dims = [plot_dim] - elif isinstance(plot_dim, tuple): - skip_dims = list(plot_dim) - - # Generate x axis plotters. - x = filter_plotters_list( - plotters=list( - xarray_var_iter( - x, - var_names=x_var_names, - skip_dims=set(x_skip_dims), - combined=True, - ) - ), - plot_kind="plot_lm", - ) - - # Generate y axis plotters - y = filter_plotters_list( - plotters=list( - xarray_var_iter( - y, - skip_dims=set(skip_dims), - combined=True, - ) - ), - plot_kind="plot_lm", - ) - - # If there are multiple x and multidimensional y, we need total of len(x)*len(y) graphs - len_y = len(y) - len_x = len(x) - length_plotters = len_x * len_y - y = _repeat_flatten_list(y, len_x) - x = _repeat_flatten_list(x, len_y) - - # Filter out the required values to generate plotters - if y_hat is not None: - if kind_pp == "samples": - y_hat = y_hat.stack(__sample__=("chain", "draw"))[..., pp_sample_ix] - skip_dims += ["__sample__"] - - y_hat = [ - tup - for _, tup in zip( - range(len_y), - xarray_var_iter( - y_hat, - skip_dims=set(skip_dims), - combined=True, - ), - ) - ] - - y_hat = _repeat_flatten_list(y_hat, len_x) - - # Filter out the required values to generate plotters - if y_model is not None: - if kind_model == "lines": - var_name = y_model.name if y_model.name else "y_model" - data = y_model.values - - total_samples = data.shape[0] * data.shape[1] - data = data.reshape(total_samples, *data.shape[2:]) - - if pp_sample_ix is not None: - data = data[pp_sample_ix] - - if plot_dim is not None: - # For plot_dim case, transpose to get dimension first - data = data.transpose(1, 0, 2)[..., 0] - - # Create plotter tuple(s) - if plot_dim is not None: - y_model = [(var_name, {}, {}, data) for _ in range(length_plotters)] - else: - y_model = [(var_name, {}, {}, data)] - y_model = _repeat_flatten_list(y_model, len_x) - - elif kind_model == "hdi": - var_name = y_model.name if y_model.name else "y_model" - data = y_model.values - - if plot_dim is not None: - # First transpose to get plot_dim first - data = data.transpose(2, 0, 1, 3) - # For plot_dim case, we just want HDI for first dimension - data = data[..., 0] - - # Reshape to (samples, points) - data = data.transpose(1, 2, 0).reshape(-1, data.shape[0]) - y_model = [(var_name, {}, {}, data) for _ in range(length_plotters)] - - else: - data = data.reshape(-1, data.shape[-1]) - y_model = [(var_name, {}, {}, data)] - y_model = _repeat_flatten_list(y_model, len_x) - - if len(y_model) == 1: - y_model = _repeat_flatten_list(y_model, len_x) - - rows, cols = default_grid(length_plotters) - - lmplot_kwargs = dict( - x=x, - y=y, - y_model=y_model, - y_hat=y_hat, - num_samples=num_samples, - kind_pp=kind_pp, - kind_model=kind_model, - length_plotters=length_plotters, - xjitter=xjitter, - rows=rows, - cols=cols, - y_kwargs=y_kwargs, - y_hat_plot_kwargs=y_hat_plot_kwargs, - y_hat_fill_kwargs=y_hat_fill_kwargs, - y_model_plot_kwargs=y_model_plot_kwargs, - y_model_fill_kwargs=y_model_fill_kwargs, - y_model_mean_kwargs=y_model_mean_kwargs, - backend_kwargs=backend_kwargs, - show=show, - figsize=figsize, - textsize=textsize, - axes=axes, - legend=legend, - grid=grid, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - plot = get_plotting_function("plot_lm", "lmplot", backend) - ax = plot(**lmplot_kwargs) - return ax diff --git a/arviz/plots/loopitplot.py b/arviz/plots/loopitplot.py deleted file mode 100644 index ae832c76ac..0000000000 --- a/arviz/plots/loopitplot.py +++ /dev/null @@ -1,224 +0,0 @@ -"""Plot LOO-PIT predictive checks of inference data.""" - -import numpy as np -from scipy import stats - -from ..labels import BaseLabeller -from ..rcparams import rcParams -from ..stats import loo_pit as _loo_pit -from ..stats.density_utils import kde -from .plot_utils import get_plotting_function - - -def plot_loo_pit( - idata=None, - y=None, - y_hat=None, - log_weights=None, - ecdf=False, - ecdf_fill=True, - n_unif=100, - use_hdi=False, - hdi_prob=None, - figsize=None, - textsize=None, - labeller=None, - color="C0", - legend=True, - ax=None, - plot_kwargs=None, - plot_unif_kwargs=None, - hdi_kwargs=None, - fill_kwargs=None, - backend=None, - backend_kwargs=None, - show=None, -): - """Plot Leave-One-Out (LOO) probability integral transformation (PIT) predictive checks. - - Parameters - ---------- - idata : InferenceData - :class:`arviz.InferenceData` object. - y : array, DataArray or str - Observed data. If str, ``idata`` must be present and contain the observed data group - y_hat : array, DataArray or str - Posterior predictive samples for ``y``. It must have the same shape as y plus an - extra dimension at the end of size n_samples (chains and draws stacked). If str or - None, ``idata`` must contain the posterior predictive group. If None, ``y_hat`` is taken - equal to y, thus, y must be str too. - log_weights : array or DataArray - Smoothed log_weights. It must have the same shape as ``y_hat`` - ecdf : bool, optional - Plot the difference between the LOO-PIT Empirical Cumulative Distribution Function - (ECDF) and the uniform CDF instead of LOO-PIT kde. - In this case, instead of overlaying uniform distributions, the beta ``hdi_prob`` - around the theoretical uniform CDF is shown. This approximation only holds - for large S and ECDF values not very close to 0 nor 1. For more information, see - `Vehtari et al. (2021)`, `Appendix G `_. - ecdf_fill : bool, optional - Use :meth:`matplotlib.axes.Axes.fill_between` to mark the area - inside the credible interval. Otherwise, plot the - border lines. - n_unif : int, optional - Number of datasets to simulate and overlay from the uniform distribution. - use_hdi : bool, optional - Compute expected hdi values instead of overlaying the sampled uniform distributions. - hdi_prob : float, optional - Probability for the highest density interval. Works with ``use_hdi=True`` or ``ecdf=True``. - figsize : (float, float), optional - If None, size is (8 + numvars, 8 + numvars) - textsize : int, optional - Text size for labels. If None it will be autoscaled based on ``figsize``. - labeller : Labeller, optional - Class providing the method ``make_pp_label`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - color : str or array_like, optional - Color of the LOO-PIT estimated pdf plot. If ``plot_unif_kwargs`` has no "color" key, - a slightly lighter color than this argument will be used for the uniform kde lines. - This will ensure that LOO-PIT kde and uniform kde have different default colors. - legend : bool, optional - Show the legend of the figure. - ax : axes, optional - Matplotlib axes or bokeh figures. - plot_kwargs : dict, optional - Additional keywords passed to :meth:`matplotlib.axes.Axes.plot` - for LOO-PIT line (kde or ECDF) - plot_unif_kwargs : dict, optional - Additional keywords passed to :meth:`matplotlib.axes.Axes.plot` for - overlaid uniform distributions or for beta credible interval - lines if ``ecdf=True`` - hdi_kwargs : dict, optional - Additional keywords passed to :meth:`matplotlib.axes.Axes.axhspan` - fill_kwargs : dict, optional - Additional kwargs passed to :meth:`matplotlib.axes.Axes.fill_between` - backend : str, optional - Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". - backend_kwargs : bool, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or - :func:`bokeh.plotting.figure`. For additional documentation - check the plotting method of the backend. - show : bool, optional - Call backend show function. - - Returns - ------- - axes : matplotlib_axes or bokeh_figures - - See Also - -------- - plot_bpv : Plot Bayesian p-value for observed data and Posterior/Prior predictive. - loo_pit : Compute leave one out (PSIS-LOO) probability integral transform (PIT) values. - - References - ---------- - * Gabry et al. (2017) see https://arxiv.org/abs/1709.01449 - * https://mc-stan.org/bayesplot/reference/PPC-loo.html - * Gelman et al. BDA (2014) Section 6.3 - - Examples - -------- - Plot LOO-PIT predictive checks overlaying the KDE of the LOO-PIT values to several - realizations of uniform variable sampling with the same number of observations. - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> idata = az.load_arviz_data("radon") - >>> az.plot_loo_pit(idata=idata, y="y") - - Fill the area containing the 94% highest density interval of the difference between uniform - variables empirical CDF and the real uniform CDF. A LOO-PIT ECDF clearly outside of these - theoretical boundaries indicates that the observations and the posterior predictive - samples do not follow the same distribution. - - .. plot:: - :context: close-figs - - >>> az.plot_loo_pit(idata=idata, y="y", ecdf=True) - - """ - if ecdf and use_hdi: - raise ValueError("use_hdi is incompatible with ecdf plot") - - if labeller is None: - labeller = BaseLabeller() - - loo_pit = _loo_pit(idata=idata, y=y, y_hat=y_hat, log_weights=log_weights) - loo_pit = loo_pit.flatten() if isinstance(loo_pit, np.ndarray) else loo_pit.values.flatten() - - loo_pit_ecdf = None - unif_ecdf = None - p975 = None - p025 = None - loo_pit_kde = None - hdi_odds = None - unif = None - x_vals = None - - if hdi_prob is None: - hdi_prob = rcParams["stats.ci_prob"] - elif not 1 >= hdi_prob > 0: - raise ValueError("The value of hdi_prob should be in the interval (0, 1]") - - if ecdf: - loo_pit.sort() - n_data_points = loo_pit.size - loo_pit_ecdf = np.arange(n_data_points) / n_data_points - # ideal unnormalized ECDF of uniform distribution with n_data_points points - # it is used indistinctively as x or p(u>> import arviz as az - >>> idata = az.load_arviz_data("centered_eight") - >>> coords = {"school": ["Deerfield", "Lawrenceville"]} - >>> az.plot_mcse( - ... idata, var_names=["mu", "theta"], coords=coords - ... ) - - """ - mean_mcse = None - sd_mcse = None - - if coords is None: - coords = {} - if "chain" in coords or "draw" in coords: - raise ValueError("chain and draw are invalid coordinates for this kind of plot") - - if labeller is None: - labeller = BaseLabeller() - - data = get_coords(convert_to_dataset(idata, group="posterior"), coords) - var_names = _var_names(var_names, data, filter_vars) - - probs = np.linspace(1 / n_points, 1 - 1 / n_points, n_points) - mcse_dataset = xr.concat( - [mcse(data, var_names=var_names, method="quantile", prob=p) for p in probs], dim="mcse_dim" - ) - - plotters = filter_plotters_list( - list(xarray_var_iter(mcse_dataset, var_names=var_names, skip_dims={"mcse_dim"})), - "plot_mcse", - ) - length_plotters = len(plotters) - rows, cols = default_grid(length_plotters, grid=grid) - - if extra_methods: - mean_mcse = mcse(data, var_names=var_names, method="mean") - sd_mcse = mcse(data, var_names=var_names, method="sd") - - mcse_kwargs = dict( - ax=ax, - plotters=plotters, - length_plotters=length_plotters, - rows=rows, - cols=cols, - figsize=figsize, - errorbar=errorbar, - rug=rug, - data=data, - probs=probs, - kwargs=kwargs, - extra_methods=extra_methods, - mean_mcse=mean_mcse, - sd_mcse=sd_mcse, - textsize=textsize, - labeller=labeller, - text_kwargs=text_kwargs, - rug_kwargs=rug_kwargs, - extra_kwargs=extra_kwargs, - idata=idata, - rug_kind=rug_kind, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_mcse", "mcseplot", backend) - ax = plot(**mcse_kwargs) - return ax diff --git a/arviz/plots/pairplot.py b/arviz/plots/pairplot.py deleted file mode 100644 index 9eb6a9ffd4..0000000000 --- a/arviz/plots/pairplot.py +++ /dev/null @@ -1,281 +0,0 @@ -"""Plot a scatter, kde and/or hexbin of sampled parameters.""" - -import warnings -from typing import List, Optional, Union - -import numpy as np - -from ..data import convert_to_dataset -from ..labels import BaseLabeller -from ..sel_utils import xarray_to_ndarray, xarray_var_iter -from .plot_utils import get_plotting_function -from ..rcparams import rcParams -from ..utils import _var_names, get_coords - - -def plot_pair( - data, - group="posterior", - var_names: Optional[List[str]] = None, - filter_vars: Optional[str] = None, - combine_dims=None, - coords=None, - marginals=False, - figsize=None, - textsize=None, - kind: Union[str, List[str]] = "scatter", - gridsize="auto", - divergences=False, - colorbar=False, - labeller=None, - ax=None, - divergences_kwargs=None, - scatter_kwargs=None, - kde_kwargs=None, - hexbin_kwargs=None, - backend=None, - backend_kwargs=None, - marginal_kwargs=None, - point_estimate=None, - point_estimate_kwargs=None, - point_estimate_marker_kwargs=None, - reference_values=None, - reference_values_kwargs=None, - show=None, -): - """ - Plot a scatter, kde and/or hexbin matrix with (optional) marginals on the diagonal. - - Parameters - ---------- - data: obj - Any object that can be converted to an :class:`arviz.InferenceData` object. - Refer to documentation of :func:`arviz.convert_to_dataset` for details - group: str, optional - Specifies which InferenceData group should be plotted. Defaults to 'posterior'. - var_names: list of variable names, optional - Variables to be plotted, if None all variable are plotted. Prefix the - variables by ``~`` when you want to exclude them from the plot. - filter_vars: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - ``pandas.filter``. - combine_dims : set_like of str, optional - List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. - See the :ref:`this section ` for usage examples. - coords: mapping, optional - Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel`. - marginals: bool, optional - If True pairplot will include marginal distributions for every variable - figsize: figure size tuple - If None, size is (8 + numvars, 8 + numvars) - textsize: int - Text size for labels. If None it will be autoscaled based on ``figsize``. - kind : str or List[str] - Type of plot to display (scatter, kde and/or hexbin) - gridsize: int or (int, int), optional - Only works for ``kind=hexbin``. The number of hexagons in the x-direction. - The corresponding number of hexagons in the y-direction is chosen - such that the hexagons are approximately regular. Alternatively, gridsize - can be a tuple with two elements specifying the number of hexagons - in the x-direction and the y-direction. - divergences: Boolean - If True divergences will be plotted in a different color, only if group is either 'prior' - or 'posterior'. - colorbar: bool - If True a colorbar will be included as part of the plot (Defaults to False). - Only works when ``kind=hexbin`` - labeller : labeller instance, optional - Class providing the method ``make_label_vert`` to generate the labels in the plot. - Read the :ref:`label_guide` for more details and usage examples. - ax: axes, optional - Matplotlib axes or bokeh figures. - divergences_kwargs: dicts, optional - Additional keywords passed to :meth:`matplotlib.axes.Axes.scatter` for divergences - scatter_kwargs: - Additional keywords passed to :meth:`matplotlib.axes.Axes.scatter` when using scatter kind - kde_kwargs: dict, optional - Additional keywords passed to :func:`arviz.plot_kde` when using kde kind - hexbin_kwargs: dict, optional - Additional keywords passed to :meth:`matplotlib.axes.Axes.hexbin` when - using hexbin kind - backend: str, optional - Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". - backend_kwargs: bool, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or - :func:`bokeh.plotting.figure`. - marginal_kwargs: dict, optional - Additional keywords passed to :func:`arviz.plot_dist`, modifying the - marginal distributions plotted in the diagonal. - point_estimate: str, optional - Select point estimate from 'mean', 'mode' or 'median'. The point estimate will be - plotted using a scatter marker and vertical/horizontal lines. - point_estimate_kwargs: dict, optional - Additional keywords passed to :meth:`matplotlib.axes.Axes.axvline`, - :meth:`matplotlib.axes.Axes.axhline` (matplotlib) or - :class:`bokeh:bokeh.models.Span` (bokeh) - point_estimate_marker_kwargs: dict, optional - Additional keywords passed to :meth:`matplotlib.axes.Axes.scatter` - or :meth:`bokeh:bokeh.plotting.Figure.square` in point - estimate plot. Not available in bokeh - reference_values: dict, optional - Reference values for the plotted variables. The Reference values will be plotted - using a scatter marker - reference_values_kwargs: dict, optional - Additional keywords passed to :meth:`matplotlib.axes.Axes.plot` or - :meth:`bokeh:bokeh.plotting.Figure.circle` in reference values plot - show: bool, optional - Call backend show function. - - Returns - ------- - axes: matplotlib axes or bokeh figures - - Examples - -------- - KDE Pair Plot - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> centered = az.load_arviz_data('centered_eight') - >>> coords = {'school': ['Choate', 'Deerfield']} - >>> az.plot_pair(centered, - >>> var_names=['theta', 'mu', 'tau'], - >>> kind='kde', - >>> coords=coords, - >>> divergences=True, - >>> textsize=18) - - Hexbin pair plot - - .. plot:: - :context: close-figs - - >>> az.plot_pair(centered, - >>> var_names=['theta', 'mu'], - >>> coords=coords, - >>> textsize=18, - >>> kind='hexbin') - - Pair plot showing divergences and select variables with regular expressions - - .. plot:: - :context: close-figs - - >>> az.plot_pair(centered, - ... var_names=['^t', 'mu'], - ... filter_vars="regex", - ... coords=coords, - ... divergences=True, - ... textsize=18) - """ - valid_kinds = ["scatter", "kde", "hexbin"] - kind_boolean: Union[bool, List[bool]] - if isinstance(kind, str): - kind_boolean = kind in valid_kinds - else: - kind_boolean = [kind[i] in valid_kinds for i in range(len(kind))] - if not np.all(kind_boolean): - raise ValueError(f"Plot type {kind} not recognized. Plot type must be in {valid_kinds}") - - if coords is None: - coords = {} - - if labeller is None: - labeller = BaseLabeller() - - # Get posterior draws and combine chains - dataset = convert_to_dataset(data, group=group) - var_names = _var_names(var_names, dataset, filter_vars) - plotters = list( - xarray_var_iter( - get_coords(dataset, coords), var_names=var_names, skip_dims=combine_dims, combined=True - ) - ) - flat_var_names = [] - flat_ref_slices = [] - flat_var_labels = [] - for var_name, sel, isel, _ in plotters: - dims = [dim for dim in dataset[var_name].dims if dim not in ["chain", "draw"]] - flat_var_names.append(var_name) - flat_ref_slices.append(tuple(isel[dim] if dim in isel else slice(None) for dim in dims)) - flat_var_labels.append(labeller.make_label_vert(var_name, sel, isel)) - - divergent_data = None - diverging_mask = None - - # Assigning divergence group based on group param - if group == "posterior": - divergent_group = "sample_stats" - elif group == "prior": - divergent_group = "sample_stats_prior" - else: - divergences = False - - # Get diverging draws and combine chains - if divergences: - if hasattr(data, divergent_group) and hasattr(getattr(data, divergent_group), "diverging"): - divergent_data = convert_to_dataset(data, group=divergent_group) - _, diverging_mask = xarray_to_ndarray( - divergent_data, var_names=("diverging",), combined=True - ) - diverging_mask = np.squeeze(diverging_mask) - else: - divergences = False - warnings.warn( - "Divergences data not found, plotting without divergences. " - "Make sure the sample method provides divergences data and " - "that it is present in the `diverging` field of `sample_stats` " - "or `sample_stats_prior` or set divergences=False", - UserWarning, - ) - - if gridsize == "auto": - gridsize = int(dataset.sizes["draw"] ** 0.35) - - numvars = len(flat_var_names) - - if numvars < 2: - raise ValueError("Number of variables to be plotted must be 2 or greater.") - - pairplot_kwargs = dict( - ax=ax, - plotters=plotters, - numvars=numvars, - figsize=figsize, - textsize=textsize, - kind=kind, - scatter_kwargs=scatter_kwargs, - kde_kwargs=kde_kwargs, - hexbin_kwargs=hexbin_kwargs, - gridsize=gridsize, - colorbar=colorbar, - divergences=divergences, - diverging_mask=diverging_mask, - divergences_kwargs=divergences_kwargs, - flat_var_names=flat_var_names, - flat_ref_slices=flat_ref_slices, - flat_var_labels=flat_var_labels, - backend_kwargs=backend_kwargs, - marginal_kwargs=marginal_kwargs, - show=show, - marginals=marginals, - point_estimate=point_estimate, - point_estimate_kwargs=point_estimate_kwargs, - point_estimate_marker_kwargs=point_estimate_marker_kwargs, - reference_values=reference_values, - reference_values_kwargs=reference_values_kwargs, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_pair", "pairplot", backend) - ax = plot(**pairplot_kwargs) - return ax diff --git a/arviz/plots/parallelplot.py b/arviz/plots/parallelplot.py deleted file mode 100644 index ec026facda..0000000000 --- a/arviz/plots/parallelplot.py +++ /dev/null @@ -1,204 +0,0 @@ -"""Parallel coordinates plot showing posterior points with and without divergences marked.""" - -import numpy as np -from scipy.stats import rankdata - -from ..data import convert_to_dataset -from ..labels import BaseLabeller -from ..sel_utils import xarray_to_ndarray -from ..rcparams import rcParams -from ..stats.stats_utils import stats_variance_2d as svar -from ..utils import _numba_var, _var_names, get_coords -from .plot_utils import get_plotting_function - - -def plot_parallel( - data, - var_names=None, - filter_vars=None, - coords=None, - figsize=None, - textsize=None, - legend=True, - colornd="k", - colord="C1", - shadend=0.025, - labeller=None, - ax=None, - norm_method=None, - backend=None, - backend_config=None, - backend_kwargs=None, - show=None, -): - """ - Plot parallel coordinates plot showing posterior points with and without divergences. - - Described by https://arxiv.org/abs/1709.01449 - - Parameters - ---------- - data: obj - Any object that can be converted to an :class:`arviz.InferenceData` object - refer to documentation of :func:`arviz.convert_to_dataset` for details - var_names: list of variable names - Variables to be plotted, if `None` all variables are plotted. Can be used to change the - order of the plotted variables. Prefix the variables by ``~`` when you want to exclude - them from the plot. - filter_vars: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - ``pandas.filter``. - coords: mapping, optional - Coordinates of ``var_names`` to be plotted. - Passed to :meth:`xarray.Dataset.sel`. - figsize: tuple - Figure size. If None it will be defined automatically. - textsize: float - Text size scaling factor for labels, titles and lines. If None it will be autoscaled based - on ``figsize``. - legend: bool - Flag for plotting legend (defaults to True) - colornd: valid matplotlib color - color for non-divergent points. Defaults to 'k' - colord: valid matplotlib color - color for divergent points. Defaults to 'C1' - shadend: float - Alpha blending value for non-divergent points, between 0 (invisible) and 1 (opaque). - Defaults to .025 - labeller : labeller instance, optional - Class providing the method ``make_label_vert`` to generate the labels in the plot. - Read the :ref:`label_guide` for more details and usage examples. - ax: axes, optional - Matplotlib axes or bokeh figures. - norm_method: str - Method for normalizing the data. Methods include normal, minmax and rank. - Defaults to none. - backend: str, optional - Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". - backend_config: dict, optional - Currently specifies the bounds to use for bokeh axes. - Defaults to value set in ``rcParams``. - backend_kwargs: bool, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or - :func:`bokeh.plotting.figure`. - show: bool, optional - Call backend show function. - - Returns - ------- - axes: matplotlib axes or bokeh figures - - See Also - -------- - plot_pair : Plot a scatter, kde and/or hexbin matrix with (optional) marginals on the diagonal. - plot_trace : Plot distribution (histogram or kernel density estimates) and sampled values - or rank plot - - Examples - -------- - Plot default parallel plot - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> data = az.load_arviz_data('centered_eight') - >>> az.plot_parallel(data, var_names=["mu", "tau"]) - - - Plot parallel plot with normalization - - .. plot:: - :context: close-figs - - >>> az.plot_parallel(data, var_names=["theta", "tau", "mu"], norm_method="normal") - - Plot parallel plot with minmax - - .. plot:: - :context: close-figs - - >>> ax = az.plot_parallel(data, var_names=["theta", "tau", "mu"], norm_method="minmax") - >>> ax.set_xticklabels(ax.get_xticklabels(), rotation=45) - - Plot parallel plot with rank - - .. plot:: - :context: close-figs - - >>> ax = az.plot_parallel(data, var_names=["theta", "tau", "mu"], norm_method="rank") - >>> ax.set_xticklabels(ax.get_xticklabels(), rotation=45) - """ - if coords is None: - coords = {} - - if labeller is None: - labeller = BaseLabeller() - - # Get diverging draws and combine chains - divergent_data = convert_to_dataset(data, group="sample_stats") - _, diverging_mask = xarray_to_ndarray( - divergent_data, - var_names=("diverging",), - combined=True, - ) - diverging_mask = np.squeeze(diverging_mask) - - # Get posterior draws and combine chains - posterior_data = convert_to_dataset(data, group="posterior") - var_names = _var_names(var_names, posterior_data, filter_vars) - var_names, _posterior = xarray_to_ndarray( - get_coords(posterior_data, coords), - var_names=var_names, - combined=True, - label_fun=labeller.make_label_vert, - ) - if len(var_names) < 2: - raise ValueError("Number of variables to be plotted must be 2 or greater.") - if norm_method is not None: - if norm_method == "normal": - mean = np.mean(_posterior, axis=1) - if _posterior.ndim <= 2: - standard_deviation = np.sqrt(_numba_var(svar, np.var, _posterior, axis=1)) - else: - standard_deviation = np.std(_posterior, axis=1) - for i in range(0, np.shape(mean)[0]): - _posterior[i, :] = (_posterior[i, :] - mean[i]) / standard_deviation[i] - elif norm_method == "minmax": - min_elem = np.min(_posterior, axis=1) - max_elem = np.max(_posterior, axis=1) - for i in range(0, np.shape(min_elem)[0]): - _posterior[i, :] = ((_posterior[i, :]) - min_elem[i]) / (max_elem[i] - min_elem[i]) - elif norm_method == "rank": - _posterior = rankdata(_posterior, axis=1, method="average") - else: - raise ValueError(f"{norm_method} is not supported. Use normal, minmax or rank.") - - parallel_kwargs = dict( - ax=ax, - colornd=colornd, - colord=colord, - shadend=shadend, - diverging_mask=diverging_mask, - posterior=_posterior, - textsize=textsize, - var_names=var_names, - legend=legend, - figsize=figsize, - backend_kwargs=backend_kwargs, - backend_config=backend_config, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_parallel", "parallelplot", backend) - ax = plot(**parallel_kwargs) - - return ax diff --git a/arviz/plots/plot_utils.py b/arviz/plots/plot_utils.py deleted file mode 100644 index af5ec992de..0000000000 --- a/arviz/plots/plot_utils.py +++ /dev/null @@ -1,599 +0,0 @@ -"""Utilities for plotting.""" - -import importlib -import warnings -from typing import Any, Dict - -import matplotlib as mpl -import numpy as np -import packaging -from matplotlib.colors import to_hex -from scipy.stats import mode, rankdata -from scipy.interpolate import CubicSpline - - -from ..rcparams import rcParams -from ..stats.density_utils import kde -from ..stats import hdi - -KwargSpec = Dict[str, Any] - - -def make_2d(ary): - """Convert any array into a 2d numpy array. - - In case the array is already more than 2 dimensional, will ravel the - dimensions after the first. - """ - dim_0, *_ = np.atleast_1d(ary).shape - return ary.reshape(dim_0, -1, order="F") - - -def _scale_fig_size(figsize, textsize, rows=1, cols=1): - """Scale figure properties according to rows and cols. - - Parameters - ---------- - figsize : float or None - Size of figure in inches - textsize : float or None - fontsize - rows : int - Number of rows - cols : int - Number of columns - - Returns - ------- - figsize : float or None - Size of figure in inches - ax_labelsize : int - fontsize for axes label - titlesize : int - fontsize for title - xt_labelsize : int - fontsize for axes ticks - linewidth : int - linewidth - markersize : int - markersize - """ - params = mpl.rcParams - rc_width, rc_height = tuple(params["figure.figsize"]) - rc_ax_labelsize = params["axes.labelsize"] - rc_titlesize = params["axes.titlesize"] - rc_xt_labelsize = params["xtick.labelsize"] - rc_linewidth = params["lines.linewidth"] - rc_markersize = params["lines.markersize"] - if isinstance(rc_ax_labelsize, str): - rc_ax_labelsize = 15 - if isinstance(rc_titlesize, str): - rc_titlesize = 16 - if isinstance(rc_xt_labelsize, str): - rc_xt_labelsize = 14 - - if figsize is None: - width, height = rc_width, rc_height - sff = 1 if (rows == cols == 1) else 1.15 - width = width * cols * sff - height = height * rows * sff - else: - width, height = figsize - - if textsize is not None: - scale_factor = textsize / rc_xt_labelsize - elif rows == cols == 1: - scale_factor = ((width * height) / (rc_width * rc_height)) ** 0.5 - else: - scale_factor = 1 - - ax_labelsize = rc_ax_labelsize * scale_factor - titlesize = rc_titlesize * scale_factor - xt_labelsize = rc_xt_labelsize * scale_factor - linewidth = rc_linewidth * scale_factor - markersize = rc_markersize * scale_factor - - return (width, height), ax_labelsize, titlesize, xt_labelsize, linewidth, markersize - - -def default_grid(n_items, grid=None, max_cols=4, min_cols=3): # noqa: D202 - """Make a grid for subplots. - - Tries to get as close to sqrt(n_items) x sqrt(n_items) as it can, - but allows for custom logic - - Parameters - ---------- - n_items : int - Number of panels required - grid : tuple - Number of rows and columns - max_cols : int - Maximum number of columns, inclusive - min_cols : int - Minimum number of columns, inclusive - - Returns - ------- - (int, int) - Rows and columns, so that rows * columns >= n_items - """ - - if grid is None: - - def in_bounds(val): - return np.clip(val, min_cols, max_cols) - - if n_items <= max_cols: - return 1, n_items - ideal = in_bounds(round(n_items**0.5)) - - for offset in (0, 1, -1, 2, -2): - cols = in_bounds(ideal + offset) - rows, extra = divmod(n_items, cols) - if extra == 0: - return rows, cols - return n_items // ideal + 1, ideal - else: - rows, cols = grid - if rows * cols < n_items: - raise ValueError("The number of rows times columns is less than the number of subplots") - if (rows * cols) - n_items >= cols: - warnings.warn("The number of rows times columns is larger than necessary") - return rows, cols - - -def format_sig_figs(value, default=None): - """Get a default number of significant figures. - - Gives the integer part or `default`, whichever is bigger. - - Examples - -------- - 0.1234 --> 0.12 - 1.234 --> 1.2 - 12.34 --> 12 - 123.4 --> 123 - """ - if default is None: - default = 2 - if value == 0: - return 1 - return max(int(np.log10(np.abs(value))) + 1, default) - - -def round_num(n, round_to): - """ - Return a string representing a number with `round_to` significant figures. - - Parameters - ---------- - n : float - number to round - round_to : int - number of significant figures - """ - sig_figs = format_sig_figs(n, round_to) - return "{n:.{sig_figs}g}".format(n=n, sig_figs=sig_figs) - - -def color_from_dim(dataarray, dim_name): - """Return colors and color mapping of a DataArray using coord values as color code. - - Parameters - ---------- - dataarray : xarray.DataArray - dim_name : str - dimension whose coordinates will be used as color code. - - Returns - ------- - colors : array of floats - Array of colors (as floats for use with a cmap) for each element in the dataarray. - color_mapping : mapping coord_value -> float - Mapping from coord values to corresponding color - """ - present_dims = dataarray.dims - coord_values = dataarray[dim_name].values - unique_coords = set(coord_values) - color_mapping = {coord: num / len(unique_coords) for num, coord in enumerate(unique_coords)} - if len(present_dims) > 1: - multi_coords = dataarray.coords.to_index() - coord_idx = present_dims.index(dim_name) - colors = [color_mapping[coord[coord_idx]] for coord in multi_coords] - else: - colors = [color_mapping[coord] for coord in coord_values] - return colors, color_mapping - - -def vectorized_to_hex(c_values, keep_alpha=False): - """Convert a color (including vector of colors) to hex. - - Parameters - ---------- - c: Matplotlib color - - keep_alpha: boolean - to select if alpha values should be kept in the final hex values. - - Returns - ------- - rgba_hex : vector of hex values - """ - try: - hex_color = to_hex(c_values, keep_alpha) - - except ValueError: - hex_color = [to_hex(color, keep_alpha) for color in c_values] - return hex_color - - -def format_coords_as_labels(dataarray, skip_dims=None): - """Format 1d or multi-d dataarray coords as strings. - - Parameters - ---------- - dataarray : xarray.DataArray - DataArray whose coordinates will be converted to labels. - skip_dims : str of list_like, optional - Dimensions whose values should not be included in the labels - """ - if skip_dims is None: - coord_labels = dataarray.coords.to_index() - else: - coord_labels = dataarray.coords.to_index().droplevel(skip_dims).drop_duplicates() - coord_labels = coord_labels.values - if isinstance(coord_labels[0], tuple): - fmt = ", ".join(["{}" for _ in coord_labels[0]]) - return np.array([fmt.format(*x) for x in coord_labels]) - return np.array([f"{s}" for s in coord_labels]) - - -def set_xticklabels(ax, coord_labels): - """Set xticklabels to label list using Matplotlib default formatter.""" - ax.xaxis.get_major_locator().set_params(nbins=9, steps=[1, 2, 5, 10]) - xticks = ax.get_xticks().astype(np.int64) - xticks = xticks[(xticks >= 0) & (xticks < len(coord_labels))] - if len(xticks) > len(coord_labels): - ax.set_xticks(np.arange(len(coord_labels))) - ax.set_xticklabels(coord_labels) - else: - ax.set_xticks(xticks) - ax.set_xticklabels(coord_labels[xticks]) - - -def filter_plotters_list(plotters, plot_kind): - """Cut list of plotters so that it is at most of length "plot.max_subplots".""" - max_plots = rcParams["plot.max_subplots"] - max_plots = len(plotters) if max_plots is None else max_plots - if len(plotters) > max_plots: - warnings.warn( - "rcParams['plot.max_subplots'] ({max_plots}) is smaller than the number " - "of variables to plot ({len_plotters}) in {plot_kind}, generating only " - "{max_plots} plots".format( - max_plots=max_plots, len_plotters=len(plotters), plot_kind=plot_kind - ), - UserWarning, - ) - return plotters[:max_plots] - return plotters - - -def get_plotting_function(plot_name, plot_module, backend): - """Return plotting function for correct backend.""" - _backend = { - "mpl": "matplotlib", - "bokeh": "bokeh", - "matplotlib": "matplotlib", - } - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - try: - backend = _backend[backend] - except KeyError as err: - raise KeyError( - f"Backend {backend} is not implemented. Try backend in {set(_backend.values())}" - ) from err - - if backend == "bokeh": - try: - import bokeh - - assert packaging.version.parse(bokeh.__version__) >= packaging.version.parse("1.4.0") - - except (ImportError, AssertionError) as err: - raise ImportError( - "'bokeh' backend needs Bokeh (1.4.0+) installed. Please upgrade or install" - ) from err - - # Perform import of plotting method - # TODO: Convert module import to top level for all plots - module = importlib.import_module(f"arviz.plots.backends.{backend}.{plot_module}") - - plotting_method = getattr(module, plot_name) - - return plotting_method - - -def calculate_point_estimate(point_estimate, values, bw="default", circular=False, skipna=False): - """Validate and calculate the point estimate. - - Parameters - ---------- - point_estimate : Optional[str] - Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None. - Defaults to 'auto' i.e. it falls back to default set in rcParams. - values : 1-d array - bw: Optional[float or str] - If numeric, indicates the bandwidth and must be positive. - If str, indicates the method to estimate the bandwidth and must be - one of "scott", "silverman", "isj" or "experimental" when `circular` is False - and "taylor" (for now) when `circular` is True. - Defaults to "default" which means "experimental" when variable is not circular - and "taylor" when it is. - circular: Optional[bool] - If True, it interprets the values passed are from a circular variable measured in radians - and a circular KDE is used. Only valid for 1D KDE. Defaults to False. - skipna=True, - If true ignores nan values when computing the hdi. Defaults to false. - - Returns - ------- - point_value : float - best estimate of data distribution - """ - point_value = None - if point_estimate == "auto": - point_estimate = rcParams["plot.point_estimate"] - elif point_estimate not in ("mean", "median", "mode", None): - raise ValueError( - f"Point estimate should be 'mean', 'median', 'mode' or None, not {point_estimate}" - ) - if point_estimate == "mean": - point_value = np.nanmean(values) if skipna else np.mean(values) - elif point_estimate == "mode": - if values.dtype.kind == "f": - if bw == "default": - bw = "taylor" if circular else "experimental" - x, density = kde(values, circular=circular, bw=bw) - point_value = x[np.argmax(density)] - else: - point_value = int(mode(values).mode) - elif point_estimate == "median": - point_value = np.nanmedian(values) if skipna else np.median(values) - return point_value - - -def plot_point_interval( - ax, - values, - point_estimate, - hdi_prob, - quartiles, - linewidth, - markersize, - markercolor, - marker, - rotated, - intervalcolor, - backend="matplotlib", -): - """Plot point intervals. - - Translates the data and represents them as point and interval summaries. - - Parameters - ---------- - ax : axes - Matplotlib axes - values : array-like - Values to plot - point_estimate : str - Plot point estimate per variable. - linewidth : int - Line width throughout. - quartiles : bool - If True then the quartile interval will be plotted with the HDI. - markersize : int - Markersize throughout. - markercolor: string - Color of the marker. - marker: string - Shape of the marker. - hdi_prob : float - Valid only when point_interval is True. Plots HDI for chosen percentage of density. - rotated : bool - Whether to rotate the dot plot by 90 degrees. - intervalcolor : string - Color of the interval. - backend : string, optional - Matplotlib or Bokeh. - """ - endpoint = (1 - hdi_prob) / 2 - if quartiles: - qlist_interval = [endpoint, 0.25, 0.75, 1 - endpoint] - else: - qlist_interval = [endpoint, 1 - endpoint] - quantiles_interval = np.quantile(values, qlist_interval) - - quantiles_interval[0], quantiles_interval[-1] = hdi( - values.flatten(), hdi_prob, multimodal=False - ) - mid = len(quantiles_interval) // 2 - param_iter = zip(np.linspace(2 * linewidth, linewidth, mid, endpoint=True)[-1::-1], range(mid)) - - if backend == "matplotlib": - for width, j in param_iter: - if rotated: - ax.vlines( - 0, - quantiles_interval[j], - quantiles_interval[-(j + 1)], - linewidth=width, - color=intervalcolor, - ) - else: - ax.hlines( - 0, - quantiles_interval[j], - quantiles_interval[-(j + 1)], - linewidth=width, - color=intervalcolor, - ) - - if point_estimate: - point_value = calculate_point_estimate(point_estimate, values) - if rotated: - ax.plot( - 0, - point_value, - marker, - markersize=markersize, - color=markercolor, - ) - else: - ax.plot( - point_value, - 0, - marker, - markersize=markersize, - color=markercolor, - ) - else: - for width, j in param_iter: - if rotated: - ax.line( - [0, 0], - [quantiles_interval[j], quantiles_interval[-(j + 1)]], - line_width=width, - color=intervalcolor, - ) - else: - ax.line( - [quantiles_interval[j], quantiles_interval[-(j + 1)]], - [0, 0], - line_width=width, - color=intervalcolor, - ) - - if point_estimate: - point_value = calculate_point_estimate(point_estimate, values) - if rotated: - ax.scatter( - x=0, - y=point_value, - marker="circle", - size=markersize, - fill_color=markercolor, - ) - else: - ax.scatter( - x=point_value, - y=0, - marker="circle", - size=markersize, - fill_color=markercolor, - ) - - return ax - - -def is_valid_quantile(value): - """Check if value is a number between 0 and 1.""" - try: - value = float(value) - return 0 < value < 1 - except ValueError: - return False - - -def sample_reference_distribution(dist, shape): - """Generate samples from a scipy distribution with a given shape.""" - x_ss = [] - densities = [] - dist_rvs = dist.rvs(size=shape) - for idx in range(shape[1]): - x_s, density = kde(dist_rvs[:, idx]) - x_ss.append(x_s) - densities.append(density) - return np.array(x_ss).T, np.array(densities).T - - -def set_bokeh_circular_ticks_labels(ax, hist, labels): - """Place ticks and ticklabels on Bokeh's circular histogram.""" - ticks = np.linspace(-np.pi, np.pi, len(labels), endpoint=False) - ax.annular_wedge( - x=0, - y=0, - inner_radius=0, - outer_radius=np.max(hist) * 1.1, - start_angle=ticks, - end_angle=ticks, - line_color="grey", - ) - - radii_circles = np.linspace(0, np.max(hist) * 1.1, 4) - ax.scatter(0, 0, marker="circle", radius=radii_circles, fill_color=None, line_color="grey") - - offset = np.max(hist * 1.05) * 0.15 - ticks_labels_pos_1 = np.max(hist * 1.05) - ticks_labels_pos_2 = ticks_labels_pos_1 * np.sqrt(2) / 2 - - ax.text( - [ - ticks_labels_pos_1 + offset, - ticks_labels_pos_2 + offset, - 0, - -ticks_labels_pos_2 - offset, - -ticks_labels_pos_1 - offset, - -ticks_labels_pos_2 - offset, - 0, - ticks_labels_pos_2 + offset, - ], - [ - 0, - ticks_labels_pos_2 + offset / 2, - ticks_labels_pos_1 + offset, - ticks_labels_pos_2 + offset / 2, - 0, - -ticks_labels_pos_2 - offset, - -ticks_labels_pos_1 - offset, - -ticks_labels_pos_2 - offset, - ], - text=labels, - text_align="center", - ) - - return ax - - -def compute_ranks(ary): - """Compute ranks for continuous and discrete variables.""" - if ary.dtype.kind == "i": - ary_shape = ary.shape - ary = ary.flatten() - min_ary, max_ary = min(ary), max(ary) - x = np.linspace(min_ary, max_ary, len(ary)) - csi = CubicSpline(x, ary) - ary = csi(np.linspace(min_ary + 0.001, max_ary - 0.001, len(ary))).reshape(ary_shape) - ranks = rankdata(ary, method="average").reshape(ary.shape) - - return ranks - - -def _init_kwargs_dict(kwargs): - """Initialize kwargs dict. - - If the input is a dictionary, it returns - a copy of the dictionary, otherwise it - returns an empty dictionary. - - Parameters - ---------- - kwargs : dict or None - kwargs dict to initialize - """ - return {} if kwargs is None else kwargs.copy() diff --git a/arviz/plots/posteriorplot.py b/arviz/plots/posteriorplot.py deleted file mode 100644 index 6b3e7b5045..0000000000 --- a/arviz/plots/posteriorplot.py +++ /dev/null @@ -1,298 +0,0 @@ -"""Plot posterior densities.""" - -from ..data import convert_to_dataset -from ..labels import BaseLabeller -from ..sel_utils import xarray_var_iter -from ..utils import _var_names, get_coords -from ..rcparams import rcParams -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function - - -def plot_posterior( - data, - var_names=None, - filter_vars=None, - combine_dims=None, - transform=None, - coords=None, - grid=None, - figsize=None, - textsize=None, - hdi_prob=None, - multimodal=False, - skipna=False, - round_to=None, - point_estimate="auto", - group="posterior", - rope=None, - ref_val=None, - rope_color="C2", - ref_val_color="C1", - kind=None, - bw="default", - circular=False, - bins=None, - labeller=None, - ax=None, - backend=None, - backend_kwargs=None, - show=None, - **kwargs -): - r"""Plot Posterior densities in the style of John K. Kruschke's book. - - Parameters - ---------- - data: obj - Any object that can be converted to an :class:`arviz.InferenceData` object. - Refer to the documentation of :func:`arviz.convert_to_dataset` for details - var_names: list of variable names - Variables to be plotted, two variables are required. Prefix the variables with ``~`` - when you want to exclude them from the plot. - filter_vars: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - ``pandas.filter``. - combine_dims : set_like of str, optional - List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. - See the :ref:`this section ` for usage examples. - transform: callable - Function to transform data (defaults to None i.e.the identity function) - coords: mapping, optional - Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel` - grid : tuple - Number of rows and columns. Defaults to None, the rows and columns are - automatically inferred. - figsize: tuple - Figure size. If None it will be defined automatically. - textsize: float - Text size scaling factor for labels, titles and lines. If None it will be autoscaled based - on ``figsize``. - hdi_prob: float, optional - Plots highest density interval for chosen percentage of density. - Use 'hide' to hide the highest density interval. Defaults to 0.94. - multimodal: bool - If true (default) it may compute more than one credible interval if the distribution is - multimodal and the modes are well separated. - skipna : bool - If true ignores nan values when computing the hdi and point estimates. Defaults to false. - round_to: int, optional - Controls formatting of floats. Defaults to 2 or the integer part, whichever is bigger. - point_estimate: Optional[str] - Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None. - Defaults to 'auto' i.e. it falls back to default set in rcParams. - group: str, optional - Specifies which InferenceData group should be plotted. Defaults to 'posterior'. - rope : list, tuple or dictionary of {str: tuples or lists}, optional - A dictionary of tuples with the lower and upper values of the Region Of Practical - Equivalence. See :ref:`this section ` for usage examples. - ref_val: float or dictionary of floats - display the percentage below and above the values in ref_val. Must be None (default), - a constant, a list or a dictionary like see an example below. If a list is provided, its - length should match the number of variables. - rope_color: str, optional - Specifies the color of ROPE and displayed percentage within ROPE - ref_val_color: str, optional - Specifies the color of the displayed percentage - kind: str - Type of plot to display (kde or hist) For discrete variables this argument is ignored and - a histogram is always used. Defaults to rcParam ``plot.density_kind`` - bw: float or str, optional - If numeric, indicates the bandwidth and must be positive. - If str, indicates the method to estimate the bandwidth and must be - one of "scott", "silverman", "isj" or "experimental" when `circular` is False - and "taylor" (for now) when `circular` is True. - Defaults to "default" which means "experimental" when variable is not circular - and "taylor" when it is. Only works if `kind == kde`. - circular: bool, optional - If True, it interprets the values passed are from a circular variable measured in radians - and a circular KDE is used. Only valid for 1D KDE. Defaults to False. - Only works if `kind == kde`. - bins: integer or sequence or 'auto', optional - Controls the number of bins,accepts the same keywords :func:`matplotlib.pyplot.hist` does. - Only works if `kind == hist`. If None (default) it will use `auto` for continuous variables - and `range(xmin, xmax + 1)` for discrete variables. - labeller : labeller instance, optional - Class providing the method ``make_label_vert`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - ax: numpy array-like of matplotlib axes or bokeh figures, optional - A 2D array of locations into which to plot the densities. If not supplied, Arviz will create - its own array of plot areas (and return it). - backend: str, optional - Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". - backend_kwargs: bool, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure` - show: bool, optional - Call backend show function. - **kwargs - Passed as-is to :func:`matplotlib.pyplot.hist` or :func:`matplotlib.pyplot.plot` function - depending on the value of `kind`. - - Returns - ------- - axes: matplotlib axes or bokeh figures - - See Also - -------- - plot_dist : Plot distribution as histogram or kernel density estimates. - plot_density : Generate KDE plots for continuous variables and histograms for discrete ones. - plot_forest : Forest plot to compare HDI intervals from a number of distributions. - - Examples - -------- - Show a default kernel density plot following style of John Kruschke - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> data = az.load_arviz_data('centered_eight') - >>> az.plot_posterior(data) - - Plot subset variables by specifying variable name exactly - - .. plot:: - :context: close-figs - - >>> az.plot_posterior(data, var_names=['mu']) - - Plot Region of Practical Equivalence (rope) and select variables with regular expressions - - .. plot:: - :context: close-figs - - >>> az.plot_posterior(data, var_names=['mu', '^the'], filter_vars="regex", rope=(-1, 1)) - - Plot Region of Practical Equivalence for selected distributions - - .. plot:: - :context: close-figs - - >>> rope = {'mu': [{'rope': (-2, 2)}], 'theta': [{'school': 'Choate', 'rope': (2, 4)}]} - >>> az.plot_posterior(data, var_names=['mu', 'theta'], rope=rope) - - Using `coords` argument to plot only a subset of data - - .. plot:: - :context: close-figs - - >>> coords = {"school": ["Choate","Phillips Exeter"]} - >>> az.plot_posterior(data, var_names=["mu", "theta"], coords=coords) - - Add reference lines - - .. plot:: - :context: close-figs - - >>> az.plot_posterior(data, var_names=['mu', 'theta'], ref_val=0) - - Show point estimate of distribution - - .. plot:: - :context: close-figs - - >>> az.plot_posterior(data, var_names=['mu', 'theta'], point_estimate='mode') - - Show reference values using variable names and coordinates - - .. plot:: - :context: close-figs - - >>> az.plot_posterior(data, ref_val= {"theta": [{"school": "Deerfield", "ref_val": 4}, - ... {"school": "Choate", "ref_val": 3}]}) - - Show reference values using a list - - .. plot:: - :context: close-figs - - >>> az.plot_posterior(data, ref_val=[1] + [5] * 8 + [1]) - - - Plot posterior as a histogram - - .. plot:: - :context: close-figs - - >>> az.plot_posterior(data, var_names=['mu'], kind='hist') - - Change size of highest density interval - - .. plot:: - :context: close-figs - - >>> az.plot_posterior(data, var_names=['mu'], hdi_prob=.75) - """ - data = convert_to_dataset(data, group=group) - if transform is not None: - data = transform(data) - var_names = _var_names(var_names, data, filter_vars) - - if coords is None: - coords = {} - - if labeller is None: - labeller = BaseLabeller() - - if hdi_prob is None: - hdi_prob = rcParams["stats.ci_prob"] - elif hdi_prob not in (None, "hide"): - if not 1 >= hdi_prob > 0: - raise ValueError("The value of hdi_prob should be in the interval (0, 1]") - - if point_estimate == "auto": - point_estimate = rcParams["plot.point_estimate"] - elif point_estimate not in {"mean", "median", "mode", None}: - raise ValueError("The value of point_estimate must be either mean, median, mode or None.") - - if kind is None: - kind = rcParams["plot.density_kind"] - - plotters = filter_plotters_list( - list( - xarray_var_iter( - get_coords(data, coords), var_names=var_names, combined=True, skip_dims=combine_dims - ) - ), - "plot_posterior", - ) - length_plotters = len(plotters) - rows, cols = default_grid(length_plotters, grid=grid) - - posteriorplot_kwargs = dict( - ax=ax, - length_plotters=length_plotters, - rows=rows, - cols=cols, - figsize=figsize, - plotters=plotters, - bw=bw, - circular=circular, - bins=bins, - kind=kind, - point_estimate=point_estimate, - round_to=round_to, - hdi_prob=hdi_prob, - multimodal=multimodal, - skipna=skipna, - textsize=textsize, - ref_val=ref_val, - rope=rope, - ref_val_color=ref_val_color, - rope_color=rope_color, - labeller=labeller, - kwargs=kwargs, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_posterior", "posteriorplot", backend) - ax = plot(**posteriorplot_kwargs) - return ax diff --git a/arviz/plots/ppcplot.py b/arviz/plots/ppcplot.py deleted file mode 100644 index 618c5e34fc..0000000000 --- a/arviz/plots/ppcplot.py +++ /dev/null @@ -1,369 +0,0 @@ -"""Posterior/Prior predictive plot.""" - -import logging -import warnings -from numbers import Integral - -import numpy as np - -from ..labels import BaseLabeller -from ..sel_utils import xarray_var_iter -from ..rcparams import rcParams -from ..utils import _var_names -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function - -_log = logging.getLogger(__name__) - - -def plot_ppc( - data, - kind="kde", - alpha=None, - mean=True, - observed=None, - observed_rug=False, - color=None, - colors=None, - grid=None, - figsize=None, - textsize=None, - data_pairs=None, - var_names=None, - filter_vars=None, - coords=None, - flatten=None, - flatten_pp=None, - num_pp_samples=None, - random_seed=None, - jitter=None, - animated=False, - animation_kwargs=None, - legend=True, - labeller=None, - ax=None, - backend=None, - backend_kwargs=None, - group="posterior", - show=None, -): - """ - Plot for posterior/prior predictive checks. - - Parameters - ---------- - data : InferenceData - :class:`arviz.InferenceData` object containing the observed and posterior/prior - predictive data. - kind : str, default "kde" - Type of plot to display ("kde", "cumulative", or "scatter"). - alpha : float, optional - Opacity of posterior/prior predictive density curves. - Defaults to 0.2 for ``kind = kde`` and cumulative, for scatter defaults to 0.7. - mean : bool, default True - Whether or not to plot the mean posterior/prior predictive distribution. - observed : bool, optional - Whether or not to plot the observed data. Defaults to True for ``group = posterior`` - and False for ``group = prior``. - observed_rug : bool, default False - Whether or not to plot a rug plot for the observed data. Only valid if `observed` is - `True` and for kind `kde` or `cumulative`. - color : list, optional - List with valid matplotlib colors corresponding to the posterior/prior predictive - distribution, observed data and mean of the posterior/prior predictive distribution. - Defaults to ["C0", "k", "C1"]. - grid : tuple, optional - Number of rows and columns. Defaults to None, the rows and columns are - automatically inferred. - figsize : tuple, optional - Figure size. If None, it will be defined automatically. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If None, it will be - autoscaled based on ``figsize``. - data_pairs : dict, optional - Dictionary containing relations between observed data and posterior/prior predictive data. - Dictionary structure: - - - key = data var_name - - value = posterior/prior predictive var_name - - For example, ``data_pairs = {'y' : 'y_hat'}`` - If None, it will assume that the observed data and the posterior/prior - predictive data have the same variable name. - var_names : list of str, optional - Variables to be plotted, if `None` all variable are plotted. Prefix the - variables by ``~`` when you want to exclude them from the plot. - filter_vars : {None, "like", "regex"}, default None - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - ``pandas.filter``. - coords : dict, optional - Dictionary mapping dimensions to selected coordinates to be plotted. - Dimensions without a mapping specified will include all coordinates for - that dimension. Defaults to including all coordinates for all - dimensions if None. - flatten : list - List of dimensions to flatten in ``observed_data``. Only flattens across the coordinates - specified in the ``coords`` argument. Defaults to flattening all of the dimensions. - flatten_pp : list - List of dimensions to flatten in posterior_predictive/prior_predictive. Only flattens - across the coordinates specified in the ``coords`` argument. Defaults to flattening all - of the dimensions. Dimensions should match flatten excluding dimensions for ``data_pairs`` - parameters. If ``flatten`` is defined and ``flatten_pp`` is None, then - ``flatten_pp = flatten``. - num_pp_samples : int - The number of posterior/prior predictive samples to plot. For ``kind`` = 'scatter' and - ``animation = False`` if defaults to a maximum of 5 samples and will set jitter to 0.7. - unless defined. Otherwise it defaults to all provided samples. - random_seed : int - Random number generator seed passed to ``numpy.random.seed`` to allow - reproducibility of the plot. By default, no seed will be provided - and the plot will change each call if a random sample is specified - by ``num_pp_samples``. - jitter : float, default 0 - If ``kind`` is "scatter", jitter will add random uniform noise to the height - of the ppc samples and observed data. - animated : bool, default False - Create an animation of one posterior/prior predictive sample per frame. - Only works with matploblib backend. - To run animations inside a notebook you have to use the `nbAgg` matplotlib's backend. - Try with `%matplotlib notebook` or `%matplotlib nbAgg`. You can switch back to the - default matplotlib's backend with `%matplotlib inline` or `%matplotlib auto`. - If switching back and forth between matplotlib's backend, you may need to run twice the cell - with the animation. - If you experience problems rendering the animation try setting - ``animation_kwargs({'blit':False})`` or changing the matplotlib's backend (e.g. to TkAgg) - If you run the animation from a script write ``ax, ani = az.plot_ppc(.)`` - animation_kwargs : dict - Keywords passed to :class:`matplotlib.animation.FuncAnimation`. Ignored with - matplotlib backend. - legend : bool, default True - Add legend to figure. - labeller : labeller, optional - Class providing the method ``make_pp_label`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - ax : numpy array-like of matplotlib_axes or bokeh figures, optional - A 2D array of locations into which to plot the densities. If not supplied, Arviz will create - its own array of plot areas (and return it). - backend : str, optional - Select plotting backend {"matplotlib","bokeh"}. Default to "matplotlib". - backend_kwargs : dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - group : {"prior", "posterior"}, optional - Specifies which InferenceData group should be plotted. Defaults to 'posterior'. - Other value can be 'prior'. - show : bool, optional - Call backend show function. - - Returns - ------- - axes : matplotlib_axes or bokeh_figures - ani : matplotlib.animation.FuncAnimation, optional - Only provided if `animated` is ``True``. - - See Also - -------- - plot_bpv : Plot Bayesian p-value for observed data and Posterior/Prior predictive. - plot_loo_pit : Plot for posterior predictive checks using cross validation. - plot_lm : Posterior predictive and mean plots for regression-like data. - plot_ts : Plot timeseries data. - - Examples - -------- - Plot the observed data KDE overlaid on posterior predictive KDEs. - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> data = az.load_arviz_data('radon') - >>> az.plot_ppc(data, data_pairs={"y":"y"}) - - Plot the overlay with empirical CDFs. - - .. plot:: - :context: close-figs - - >>> az.plot_ppc(data, kind='cumulative') - - Use the ``coords`` and ``flatten`` parameters to plot selected variable dimensions - across multiple plots. We will now modify the dimension ``obs_id`` to contain - indicate the name of the county where the measure was taken. The change has to - be done on both ``posterior_predictive`` and ``observed_data`` groups, which is - why we will use :meth:`~arviz.InferenceData.map` to apply the same function to - both groups. Afterwards, we will select the counties to be plotted with the - ``coords`` arg. - - .. plot:: - :context: close-figs - - >>> obs_county = data.posterior["County"][data.constant_data["county_idx"]] - >>> data = data.assign_coords(obs_id=obs_county, groups="observed_vars") - >>> az.plot_ppc(data, coords={'obs_id': ['ANOKA', 'BELTRAMI']}, flatten=[]) - - Plot the overlay using a stacked scatter plot that is particularly useful - when the sample sizes are small. - - .. plot:: - :context: close-figs - - >>> az.plot_ppc(data, kind='scatter', flatten=[], - >>> coords={'obs_id': ['AITKIN', 'BELTRAMI']}) - - Plot random posterior predictive sub-samples. - - .. plot:: - :context: close-figs - - >>> az.plot_ppc(data, num_pp_samples=30, random_seed=7) - """ - if group not in ("posterior", "prior"): - raise TypeError("`group` argument must be either `posterior` or `prior`") - - for groups in (f"{group}_predictive", "observed_data"): - if not hasattr(data, groups): - raise TypeError(f'`data` argument must have the group "{groups}" for ppcplot') - - if kind.lower() not in ("kde", "cumulative", "scatter"): - raise TypeError("`kind` argument must be either `kde`, `cumulative`, or `scatter`") - - if colors is None: - colors = ["C0", "k", "C1"] - - if isinstance(colors, str): - raise TypeError("colors should be a list with 3 items.") - - if len(colors) != 3: - raise ValueError("colors should be a list with 3 items.") - - if color is not None: - warnings.warn("color has been deprecated in favor of colors", FutureWarning) - colors[0] = color - - if data_pairs is None: - data_pairs = {} - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - if backend == "bokeh" and animated: - raise TypeError("Animation option is only supported with matplotlib backend.") - - observed_data = data.observed_data - - if group == "posterior": - predictive_dataset = data.posterior_predictive - if observed is None: - observed = True - elif group == "prior": - predictive_dataset = data.prior_predictive - if observed is None: - observed = False - - if var_names is None: - var_names = list(observed_data.data_vars) - var_names = _var_names(var_names, observed_data, filter_vars) - pp_var_names = [data_pairs.get(var, var) for var in var_names] - pp_var_names = _var_names(pp_var_names, predictive_dataset, filter_vars) - - if flatten_pp is None: - if flatten is None: - flatten_pp = list(predictive_dataset.dims) - else: - flatten_pp = flatten - if flatten is None: - flatten = list(observed_data.dims) - - if coords is None: - coords = {} - else: - coords = coords.copy() - - if labeller is None: - labeller = BaseLabeller() - - if random_seed is not None: - np.random.seed(random_seed) - - total_pp_samples = predictive_dataset.sizes["chain"] * predictive_dataset.sizes["draw"] - if num_pp_samples is None: - if kind == "scatter" and not animated: - num_pp_samples = min(5, total_pp_samples) - else: - num_pp_samples = total_pp_samples - - if ( - not isinstance(num_pp_samples, Integral) - or num_pp_samples < 1 - or num_pp_samples > total_pp_samples - ): - raise TypeError(f"`num_pp_samples` must be an integer between 1 and {total_pp_samples}.") - - pp_sample_ix = np.random.choice(total_pp_samples, size=num_pp_samples, replace=False) - - for key in coords.keys(): - coords[key] = np.where(np.isin(observed_data[key], coords[key]))[0] - - obs_plotters = filter_plotters_list( - list( - xarray_var_iter( - observed_data.isel(coords), - skip_dims=set(flatten), - var_names=var_names, - combined=True, - dim_order=["chain", "draw"], - ) - ), - "plot_ppc", - ) - length_plotters = len(obs_plotters) - pp_plotters = [ - tup - for _, tup in zip( - range(length_plotters), - xarray_var_iter( - predictive_dataset.isel(coords), - var_names=pp_var_names, - skip_dims=set(flatten_pp), - combined=True, - dim_order=["chain", "draw"], - ), - ) - ] - rows, cols = default_grid(length_plotters, grid=grid) - - ppcplot_kwargs = dict( - ax=ax, - length_plotters=length_plotters, - rows=rows, - cols=cols, - figsize=figsize, - animated=animated, - obs_plotters=obs_plotters, - pp_plotters=pp_plotters, - predictive_dataset=predictive_dataset, - pp_sample_ix=pp_sample_ix, - kind=kind, - alpha=alpha, - colors=colors, - jitter=jitter, - textsize=textsize, - mean=mean, - observed=observed, - observed_rug=observed_rug, - total_pp_samples=total_pp_samples, - legend=legend, - labeller=labeller, - group=group, - animation_kwargs=animation_kwargs, - num_pp_samples=num_pp_samples, - backend_kwargs=backend_kwargs, - show=show, - ) - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_ppc", "ppcplot", backend) - axes = plot(**ppcplot_kwargs) - return axes diff --git a/arviz/plots/rankplot.py b/arviz/plots/rankplot.py deleted file mode 100644 index 04ffad19e5..0000000000 --- a/arviz/plots/rankplot.py +++ /dev/null @@ -1,232 +0,0 @@ -"""Histograms of ranked posterior draws, plotted for each chain.""" - -from itertools import cycle - -import matplotlib.pyplot as plt - -from ..data import convert_to_dataset -from ..labels import BaseLabeller -from ..sel_utils import xarray_var_iter -from ..rcparams import rcParams -from ..stats.density_utils import _sturges_formula -from ..utils import _var_names -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function - - -def plot_rank( - data, - var_names=None, - filter_vars=None, - transform=None, - coords=None, - bins=None, - kind="bars", - colors="cycle", - ref_line=True, - labels=True, - labeller=None, - grid=None, - figsize=None, - ax=None, - backend=None, - ref_line_kwargs=None, - bar_kwargs=None, - vlines_kwargs=None, - marker_vlines_kwargs=None, - backend_kwargs=None, - show=None, -): - """Plot rank order statistics of chains. - - From the paper: Rank plots are histograms of the ranked posterior draws (ranked over all - chains) plotted separately for each chain. - If all of the chains are targeting the same posterior, we expect the ranks in each chain to be - uniform, whereas if one chain has a different location or scale parameter, this will be - reflected in the deviation from uniformity. If rank plots of all chains look similar, this - indicates good mixing of the chains. - - This plot was introduced by Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, - Paul-Christian Burkner (2021): Rank-normalization, folding, and localization: - An improved R-hat for assessing convergence of MCMC. Bayesian analysis, 16(2):667-718. - - - Parameters - ---------- - data: obj - Any object that can be converted to an :class:`arviz.InferenceData` object. - Refer to documentation of :func:`arviz.convert_to_dataset` for details - var_names: string or list of variable names - Variables to be plotted. Prefix the variables by ``~`` when you want to exclude - them from the plot. - filter_vars: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - ``pandas.filter``. - transform: callable - Function to transform data (defaults to None i.e.the identity function) - coords: mapping, optional - Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel` - bins: None or passed to np.histogram - Binning strategy used for histogram. By default uses twice the result of Sturges' formula. - See :func:`numpy.histogram` documentation for, other available arguments. - kind: string - If bars (defaults), ranks are represented as stacked histograms (one per chain). If vlines - ranks are represented as vertical lines above or below ``ref_line``. - colors: string or list of strings - List with valid matplotlib colors, one color per model. Alternative a string can be passed. - If the string is `cycle`, it will automatically choose a color per model from matplotlib's - cycle. If a single color is passed, e.g. 'k', 'C2' or 'red' this color will be used for all - models. Defaults to `cycle`. - ref_line: boolean - Whether to include a dashed line showing where a uniform distribution would lie - labels: bool - whether to plot or not the x and y labels, defaults to True - labeller : labeller instance, optional - Class providing the method ``make_label_vert`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - grid : tuple - Number of rows and columns. Defaults to None, the rows and columns are - automatically inferred. - figsize: tuple - Figure size. If None it will be defined automatically. - ax: numpy array-like of matplotlib axes or bokeh figures, optional - A 2D array of locations into which to plot the densities. If not supplied, ArviZ will create - its own array of plot areas (and return it). - backend: str, optional - Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". - ref_line_kwargs : dict, optional - Reference line keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.axhline` or - :class:`bokeh:bokeh.models.Span`. - bar_kwargs : dict, optional - Bars keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.bar` or - :meth:`bokeh:bokeh.plotting.Figure.vbar`. - vlines_kwargs : dict, optional - Vlines keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.vlines` or - :meth:`bokeh:bokeh.plotting.Figure.multi_line`. - marker_vlines_kwargs : dict, optional - Marker for the vlines keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.plot` or - :meth:`bokeh:bokeh.plotting.Figure.circle`. - backend_kwargs: bool, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or - :func:`bokeh.plotting.figure`. For additional documentation - check the plotting method of the backend. - show: bool, optional - Call backend show function. - - Returns - ------- - axes: matplotlib axes or bokeh figures - - See Also - -------- - plot_trace : Plot distribution (histogram or kernel density estimates) and - sampled values or rank plot. - - Examples - -------- - Show a default rank plot - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> data = az.load_arviz_data('centered_eight') - >>> az.plot_rank(data) - - Recreate Figure 13 from the arxiv preprint - - .. plot:: - :context: close-figs - - >>> data = az.load_arviz_data('centered_eight') - >>> az.plot_rank(data, var_names='tau') - - Use vlines to compare results for centered vs noncentered models - - .. plot:: - :context: close-figs - - >>> import matplotlib.pyplot as plt - >>> centered_data = az.load_arviz_data('centered_eight') - >>> noncentered_data = az.load_arviz_data('non_centered_eight') - >>> _, ax = plt.subplots(1, 2, figsize=(12, 3)) - >>> az.plot_rank(centered_data, var_names="mu", kind='vlines', ax=ax[0]) - >>> az.plot_rank(noncentered_data, var_names="mu", kind='vlines', ax=ax[1]) - - Change the aesthetics using kwargs - - .. plot:: - :context: close-figs - - >>> az.plot_rank(noncentered_data, var_names="mu", kind="vlines", - >>> vlines_kwargs={'lw':0}, marker_vlines_kwargs={'lw':3}); - """ - if transform is not None: - data = transform(data) - posterior_data = convert_to_dataset(data, group="posterior") - if coords is not None: - posterior_data = posterior_data.sel(**coords) - var_names = _var_names(var_names, posterior_data, filter_vars) - plotters = filter_plotters_list( - list( - xarray_var_iter( - posterior_data, - var_names=var_names, - combined=True, - dim_order=["chain", "draw"], - ) - ), - "plot_rank", - ) - length_plotters = len(plotters) - - if bins is None: - bins = _sturges_formula(posterior_data, mult=2) - - if labeller is None: - labeller = BaseLabeller() - - rows, cols = default_grid(length_plotters, grid=grid) - - chains = len(posterior_data.chain) - if colors == "cycle": - colors = [ - prop - for _, prop in zip( - range(chains), cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"]) - ) - ] - elif isinstance(colors, str): - colors = [colors] * chains - - rankplot_kwargs = dict( - axes=ax, - length_plotters=length_plotters, - rows=rows, - cols=cols, - figsize=figsize, - plotters=plotters, - bins=bins, - kind=kind, - colors=colors, - ref_line=ref_line, - labels=labels, - labeller=labeller, - ref_line_kwargs=ref_line_kwargs, - bar_kwargs=bar_kwargs, - vlines_kwargs=vlines_kwargs, - marker_vlines_kwargs=marker_vlines_kwargs, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_rank", "rankplot", backend) - axes = plot(**rankplot_kwargs) - return axes diff --git a/arviz/plots/separationplot.py b/arviz/plots/separationplot.py deleted file mode 100644 index 3476dfea7d..0000000000 --- a/arviz/plots/separationplot.py +++ /dev/null @@ -1,167 +0,0 @@ -"""Separation plot for discrete outcome models.""" - -import warnings - -import numpy as np -import xarray as xr - -from ..data import InferenceData -from ..rcparams import rcParams -from .plot_utils import get_plotting_function - - -def plot_separation( - idata=None, - y=None, - y_hat=None, - y_hat_line=False, - expected_events=False, - figsize=None, - textsize=None, - color="C0", - legend=True, - ax=None, - plot_kwargs=None, - y_hat_line_kwargs=None, - exp_events_kwargs=None, - backend=None, - backend_kwargs=None, - show=None, -): - """Separation plot for binary outcome models. - - Model predictions are sorted and plotted using a color code according to - the observed data. - - Parameters - ---------- - idata : InferenceData - :class:`arviz.InferenceData` object. - y : array, DataArray or str - Observed data. If str, ``idata`` must be present and contain the observed data group - y_hat : array, DataArray or str - Posterior predictive samples for ``y``. It must have the same shape as ``y``. If str or - None, ``idata`` must contain the posterior predictive group. - y_hat_line : bool, optional - Plot the sorted ``y_hat`` predictions. - expected_events : bool, optional - Plot the total number of expected events. - figsize : figure size tuple, optional - If None, size is (8 + numvars, 8 + numvars) - textsize: int, optional - Text size for labels. If None it will be autoscaled based on ``figsize``. - color : str, optional - Color to assign to the positive class. The negative class will be plotted using the - same color and an `alpha=0.3` transparency. - legend : bool, optional - Show the legend of the figure. - ax: axes, optional - Matplotlib axes or bokeh figures. - plot_kwargs : dict, optional - Additional keywords passed to :meth:`mpl:matplotlib.axes.Axes.bar` or - :meth:`bokeh:bokeh.plotting.Figure.vbar` for separation plot. - y_hat_line_kwargs : dict, optional - Additional keywords passed to ax.plot for ``y_hat`` line. - exp_events_kwargs : dict, optional - Additional keywords passed to ax.scatter for ``expected_events`` marker. - backend: str, optional - Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". - backend_kwargs: bool, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or - :func:`bokeh.plotting.figure`. - show : bool, optional - Call backend show function. - - Returns - ------- - axes : matplotlib axes or bokeh figures - - See Also - -------- - plot_ppc : Plot for posterior/prior predictive checks. - - References - ---------- - .. [1] Greenhill, B. *et al.*, The Separation Plot: A New Visual Method - for Evaluating the Fit of Binary Models, *American Journal of - Political Science*, (2011) see https://doi.org/10.1111/j.1540-5907.2011.00525.x - - Examples - -------- - Separation plot for a logistic regression model. - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> idata = az.load_arviz_data('classification10d') - >>> az.plot_separation(idata=idata, y='outcome', y_hat='outcome', figsize=(8, 1)) - - """ - label_y_hat = "y_hat" - if idata is not None and not isinstance(idata, InferenceData): - raise ValueError("idata must be of type InferenceData or None") - - if idata is None: - if not all(isinstance(arg, (np.ndarray, xr.DataArray)) for arg in (y, y_hat)): - raise ValueError( - "y and y_hat must be array or DataArray when idata is None " - f"but they are of types {[type(arg) for arg in (y, y_hat)]}" - ) - - else: - if y_hat is None and isinstance(y, str): - label_y_hat = y - y_hat = y - elif y_hat is None: - raise ValueError("y_hat cannot be None if y is not a str") - - if isinstance(y, str): - y = idata.observed_data[y].values - elif not isinstance(y, (np.ndarray, xr.DataArray)): - raise ValueError(f"y must be of types array, DataArray or str, not {type(y)}") - - if isinstance(y_hat, str): - label_y_hat = y_hat - y_hat = idata.posterior_predictive[y_hat].mean(dim=("chain", "draw")).values - elif not isinstance(y_hat, (np.ndarray, xr.DataArray)): - raise ValueError(f"y_hat must be of types array, DataArray or str, not {type(y_hat)}") - - if len(y) != len(y_hat): - warnings.warn( - "y and y_hat must be the same length", - UserWarning, - ) - - locs = np.linspace(0, 1, len(y_hat)) - width = np.diff(locs).mean() - - separation_kwargs = dict( - y=y, - y_hat=y_hat, - y_hat_line=y_hat_line, - label_y_hat=label_y_hat, - expected_events=expected_events, - figsize=figsize, - textsize=textsize, - color=color, - legend=legend, - locs=locs, - width=width, - ax=ax, - plot_kwargs=plot_kwargs, - y_hat_line_kwargs=y_hat_line_kwargs, - exp_events_kwargs=exp_events_kwargs, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - plot = get_plotting_function("plot_separation", "separationplot", backend) - axes = plot(**separation_kwargs) - - return axes diff --git a/arviz/plots/styles/arviz-bluish.mplstyle b/arviz/plots/styles/arviz-bluish.mplstyle deleted file mode 100644 index 0670a6594a..0000000000 --- a/arviz/plots/styles/arviz-bluish.mplstyle +++ /dev/null @@ -1 +0,0 @@ -axes.prop_cycle: cycler('color', ['0045b9ff', '0045b999', '0045b966', '0045b933', '000000']) diff --git a/arviz/plots/styles/arviz-brownish.mplstyle b/arviz/plots/styles/arviz-brownish.mplstyle deleted file mode 100644 index a2eef125cc..0000000000 --- a/arviz/plots/styles/arviz-brownish.mplstyle +++ /dev/null @@ -1 +0,0 @@ -axes.prop_cycle: cycler('color', ['bb5100ff', 'ec7a00ff', 'ec7a00aa', 'ec7a0066', '000000']) diff --git a/arviz/plots/styles/arviz-colors.mplstyle b/arviz/plots/styles/arviz-colors.mplstyle deleted file mode 100644 index a909d51a5c..0000000000 --- a/arviz/plots/styles/arviz-colors.mplstyle +++ /dev/null @@ -1,2 +0,0 @@ -# color-blind friendly cycle designed using https://colorcyclepicker.mpetroff.net/ -axes.prop_cycle: cycler('color', ['2a2eec', 'fa7c17', '328c06', 'c10c90', '933708', '65e5f3', 'e6e135', '1ccd6a', 'bd8ad5', 'b16b57']) diff --git a/arviz/plots/styles/arviz-cyanish.mplstyle b/arviz/plots/styles/arviz-cyanish.mplstyle deleted file mode 100644 index 987587684a..0000000000 --- a/arviz/plots/styles/arviz-cyanish.mplstyle +++ /dev/null @@ -1 +0,0 @@ -axes.prop_cycle: cycler('color', ['008182ff', '008182aa', '00818266', '00818233', '000000']) diff --git a/arviz/plots/styles/arviz-darkgrid.mplstyle b/arviz/plots/styles/arviz-darkgrid.mplstyle deleted file mode 100644 index 300c2ae9cc..0000000000 --- a/arviz/plots/styles/arviz-darkgrid.mplstyle +++ /dev/null @@ -1,40 +0,0 @@ -# This style is based on Seaborn-darkgrid parameters, the main differences are -# Default matplotlib font (no problem using Greek letters!) -# Larger font size for several elements -# Colorblind friendly color cycle -figure.figsize: 7.2, 4.8 -figure.dpi: 100.0 -figure.facecolor: white -figure.constrained_layout.use: True -text.color: .15 -axes.labelcolor: .15 -legend.frameon: False -legend.numpoints: 1 -legend.scatterpoints: 1 -xtick.direction: out -ytick.direction: out -xtick.color: .15 -ytick.color: .15 -axes.axisbelow: True -grid.linestyle: - -lines.solid_capstyle: round - -axes.labelsize: 15 -axes.titlesize: 16 -xtick.labelsize: 14 -ytick.labelsize: 14 -legend.fontsize: 14 - -axes.grid: True -axes.facecolor: eeeeee -axes.edgecolor: white -axes.linewidth: 0 -grid.color: white -image.cmap: viridis -xtick.major.size: 0 -ytick.major.size: 0 -xtick.minor.size: 0 -ytick.minor.size: 0 - -# color-blind friendly cycle designed using https://colorcyclepicker.mpetroff.net/ -axes.prop_cycle: cycler('color', ['2a2eec', 'fa7c17', '328c06', 'c10c90', '933708', '65e5f3', 'e6e135', '1ccd6a', 'bd8ad5', 'b16b57']) diff --git a/arviz/plots/styles/arviz-doc.mplstyle b/arviz/plots/styles/arviz-doc.mplstyle deleted file mode 100644 index 41c64e2241..0000000000 --- a/arviz/plots/styles/arviz-doc.mplstyle +++ /dev/null @@ -1,88 +0,0 @@ -## *************************************************************************** -## * FIGURE * -## *************************************************************************** - -figure.facecolor: white # broken white outside box -figure.edgecolor: None # broken white outside box -figure.titleweight: bold # weight of the figure title -figure.titlesize: 18 - -figure.figsize: 11.5, 5 -figure.dpi: 300.0 -figure.constrained_layout.use: True - -## *************************************************************************** -## * FONT * -## *************************************************************************** - -font.style: normal -font.variant: normal -font.weight: normal -font.stretch: normal - -text.color: .15 - -## *************************************************************************** -## * AXES * -## *************************************************************************** - -axes.facecolor: white -axes.edgecolor: .33 # axes edge color -axes.linewidth: 0.8 # edge line width - -axes.grid: False # do not show grid -# axes.grid.axis: y # which axis the grid should apply to -axes.grid.which: major # grid lines at {major, minor, both} ticks -axes.axisbelow: True # keep grid layer in the back - -grid.color: .8 # grid color -grid.linestyle: - # solid -grid.linewidth: 0.8 # in points -grid.alpha: 1.0 # transparency, between 0.0 and 1.0 - -lines.solid_capstyle: round - -axes.spines.right: False # do not show right spine -axes.spines.top: False # do not show top spine - -axes.titlesize: 16 -axes.titleweight: bold # font weight of title - -axes.labelsize: 14 -axes.labelcolor: .15 -axes.labelweight: normal # weight of the x and y labels - -# color-blind friendly cycle designed using https://colorcyclepicker.mpetroff.net/ -# see preview and check for colorblindness here https://coolors.co/107591-00c0bf-f69a48-fdcd49-8da798-a19368-525252-a6761d-7035b7-cf166e -axes.prop_cycle: cycler(color=['107591','00c0bf','f69a48','fdcd49','8da798','a19368','525252','a6761d','7035b7','cf166e']) - -image.cmap: viridis - -## *************************************************************************** -## * TICKS * -## *************************************************************************** - -xtick.labelsize: 14 -xtick.color: .15 -xtick.top: False -xtick.bottom: True -xtick.direction: out - -ytick.labelsize: 14 -ytick.color: .15 -ytick.left: True -ytick.right: False -ytick.direction: out - -## *************************************************************************** -## * LEGEND * -## *************************************************************************** - -legend.framealpha: 0.5 -legend.frameon: False # do not draw on background patch -legend.fancybox: False # do not round corners - -legend.numpoints: 1 -legend.scatterpoints: 1 - -legend.fontsize: 14 diff --git a/arviz/plots/styles/arviz-docgrid.mplstyle b/arviz/plots/styles/arviz-docgrid.mplstyle deleted file mode 100644 index 08c70af662..0000000000 --- a/arviz/plots/styles/arviz-docgrid.mplstyle +++ /dev/null @@ -1,88 +0,0 @@ -## *************************************************************************** -## * FIGURE * -## *************************************************************************** - -figure.facecolor: white # broken white outside box -figure.edgecolor: None # broken white outside box -figure.titleweight: bold # weight of the figure title -figure.titlesize: 18 - -figure.figsize: 11.5, 5 -figure.dpi: 300.0 -figure.constrained_layout.use: True - -## *************************************************************************** -## * FONT * -## *************************************************************************** - -font.style: normal -font.variant: normal -font.weight: normal -font.stretch: normal - -text.color: .15 - -## *************************************************************************** -## * AXES * -## *************************************************************************** - -axes.facecolor: white -axes.edgecolor: .33 # axes edge color -axes.linewidth: 0.8 # edge line width - -axes.grid: True # show grid -# axes.grid.axis: y # which axis the grid should apply to -axes.grid.which: major # grid lines at {major, minor, both} ticks -axes.axisbelow: True # keep grid layer in the back - -grid.color: .8 # grid color -grid.linestyle: - # solid -grid.linewidth: 0.8 # in points -grid.alpha: 1.0 # transparency, between 0.0 and 1.0 - -lines.solid_capstyle: round - -axes.spines.right: False # do not show right spine -axes.spines.top: False # do not show top spine - -axes.titlesize: 16 -axes.titleweight: bold # font weight of title - -axes.labelsize: 14 -axes.labelcolor: .15 -axes.labelweight: normal # weight of the x and y labels - -# color-blind friendly cycle designed using https://colorcyclepicker.mpetroff.net/ -# see preview and check for colorblindness here https://coolors.co/107591-00c0bf-f69a48-fdcd49-8da798-a19368-525252-a6761d-7035b7-cf166e -axes.prop_cycle: cycler(color=['107591','00c0bf','f69a48','fdcd49','8da798','a19368','525252','a6761d','7035b7','cf166e']) - -image.cmap: viridis - -## *************************************************************************** -## * TICKS * -## *************************************************************************** - -xtick.labelsize: 14 -xtick.color: .15 -xtick.top: False -xtick.bottom: True -xtick.direction: out - -ytick.labelsize: 14 -ytick.color: .15 -ytick.left: True -ytick.right: False -ytick.direction: out - -## *************************************************************************** -## * LEGEND * -## *************************************************************************** - -legend.framealpha: 0.5 -legend.frameon: False # do not draw on background patch -legend.fancybox: False # do not round corners - -legend.numpoints: 1 -legend.scatterpoints: 1 - -legend.fontsize: 14 diff --git a/arviz/plots/styles/arviz-grayscale.mplstyle b/arviz/plots/styles/arviz-grayscale.mplstyle deleted file mode 100644 index 15a0eb7a2c..0000000000 --- a/arviz/plots/styles/arviz-grayscale.mplstyle +++ /dev/null @@ -1,41 +0,0 @@ -# This style is based on Seaborn-white parameters, the main differences are -# Default matplotlib font (no problem using Greek letters!) -# Larger font size for several elements -# Colorblind friendly color cycle -figure.figsize: 7.2, 4.8 -figure.dpi: 100.0 -figure.facecolor: white -figure.constrained_layout.use: True -text.color: .15 -axes.labelcolor: .15 -legend.frameon: False -legend.numpoints: 1 -legend.scatterpoints: 1 -xtick.direction: out -ytick.direction: out -xtick.color: .15 -ytick.color: .15 -axes.axisbelow: True -lines.solid_capstyle: round - -axes.labelsize: 15 -axes.titlesize: 16 -xtick.labelsize: 14 -ytick.labelsize: 14 -legend.fontsize: 14 - -axes.grid: False -axes.facecolor: white -axes.edgecolor: 0 -axes.linewidth: 1 -axes.spines.top: False -axes.spines.right: False -image.cmap: cet_gray # perceptually uniform gray scale from colorcet (linear_grey_10_95_c0) -xtick.major.size: 0 -ytick.major.size: 0 -xtick.minor.size: 0 -ytick.minor.size: 0 - -# First 4 colors are from colorcet and the last one is the "ArviZ-blue" -# [to_hex(_linear_grey_0_100_c0[i]) for i in np.linspace(0, 195, 4).astype(int)] -axes.prop_cycle: cycler('color', ["000000", "4a4a4a", "7e7f7f", "b8b8b8", "2a2eec"]) diff --git a/arviz/plots/styles/arviz-greenish.mplstyle b/arviz/plots/styles/arviz-greenish.mplstyle deleted file mode 100644 index 8384619fa4..0000000000 --- a/arviz/plots/styles/arviz-greenish.mplstyle +++ /dev/null @@ -1 +0,0 @@ -axes.prop_cycle: cycler('color', ['259516ff', '259516aa', '25951666', '25951633', '000000']) diff --git a/arviz/plots/styles/arviz-orangish.mplstyle b/arviz/plots/styles/arviz-orangish.mplstyle deleted file mode 100644 index a69ae5277e..0000000000 --- a/arviz/plots/styles/arviz-orangish.mplstyle +++ /dev/null @@ -1 +0,0 @@ -axes.prop_cycle: cycler('color', ['fd5800ff', 'fd5800aa', 'fd580066', 'fd580033', '000000']) diff --git a/arviz/plots/styles/arviz-plasmish.mplstyle b/arviz/plots/styles/arviz-plasmish.mplstyle deleted file mode 100644 index 38285670f6..0000000000 --- a/arviz/plots/styles/arviz-plasmish.mplstyle +++ /dev/null @@ -1 +0,0 @@ -axes.prop_cycle: cycler('color', ['0d0887', '8e0ca4', 'de6164', 'fdc627', '000000']) diff --git a/arviz/plots/styles/arviz-purplish.mplstyle b/arviz/plots/styles/arviz-purplish.mplstyle deleted file mode 100644 index bbf0c895a7..0000000000 --- a/arviz/plots/styles/arviz-purplish.mplstyle +++ /dev/null @@ -1 +0,0 @@ -axes.prop_cycle: cycler('color', ['820076ff', '820076aa', '82007666', '82007633', '000000']) diff --git a/arviz/plots/styles/arviz-redish.mplstyle b/arviz/plots/styles/arviz-redish.mplstyle deleted file mode 100644 index 91890cd160..0000000000 --- a/arviz/plots/styles/arviz-redish.mplstyle +++ /dev/null @@ -1 +0,0 @@ -axes.prop_cycle: cycler('color', ['bf0700ff', 'bf0700aa', 'bf070066', 'bf070033', '000000']) diff --git a/arviz/plots/styles/arviz-royish.mplstyle b/arviz/plots/styles/arviz-royish.mplstyle deleted file mode 100644 index 4e2b329b4f..0000000000 --- a/arviz/plots/styles/arviz-royish.mplstyle +++ /dev/null @@ -1 +0,0 @@ -axes.prop_cycle: cycler('color', ['bf0700', 'fd5800', 'ffba02', 'ffba0277', '000000']) diff --git a/arviz/plots/styles/arviz-viridish.mplstyle b/arviz/plots/styles/arviz-viridish.mplstyle deleted file mode 100644 index 50aaae5b15..0000000000 --- a/arviz/plots/styles/arviz-viridish.mplstyle +++ /dev/null @@ -1 +0,0 @@ -axes.prop_cycle: cycler('color', ['481d6f', '2f6b8e', '25ab82', 'b0dd2f', '000000']) diff --git a/arviz/plots/styles/arviz-white.mplstyle b/arviz/plots/styles/arviz-white.mplstyle deleted file mode 100644 index 6b7eeeddf5..0000000000 --- a/arviz/plots/styles/arviz-white.mplstyle +++ /dev/null @@ -1,40 +0,0 @@ -# This style is based on Seaborn-white parameters, the main differences are -# Default matplotlib font (no problem using Greek letters!) -# Larger font size for several elements -# Colorblind friendly color cycle -figure.figsize: 7.2, 4.8 -figure.dpi: 100.0 -figure.facecolor: white -figure.constrained_layout.use: True -text.color: .15 -axes.labelcolor: .15 -legend.frameon: False -legend.numpoints: 1 -legend.scatterpoints: 1 -xtick.direction: out -ytick.direction: out -xtick.color: .15 -ytick.color: .15 -axes.axisbelow: True -lines.solid_capstyle: round - -axes.labelsize: 15 -axes.titlesize: 16 -xtick.labelsize: 14 -ytick.labelsize: 14 -legend.fontsize: 14 - -axes.grid: False -axes.facecolor: white -axes.edgecolor: 0 -axes.linewidth: 1 -axes.spines.top: False -axes.spines.right: False -image.cmap: viridis -xtick.major.size: 0 -ytick.major.size: 0 -xtick.minor.size: 0 -ytick.minor.size: 0 - -# color-blind friendly cycle designed using https://colorcyclepicker.mpetroff.net/ -axes.prop_cycle: cycler('color', ['2a2eec', 'fa7c17', '328c06', 'c10c90', '933708', '65e5f3', 'e6e135', '1ccd6a', 'bd8ad5', 'b16b57']) diff --git a/arviz/plots/styles/arviz-whitegrid.mplstyle b/arviz/plots/styles/arviz-whitegrid.mplstyle deleted file mode 100644 index f6887ea52d..0000000000 --- a/arviz/plots/styles/arviz-whitegrid.mplstyle +++ /dev/null @@ -1,40 +0,0 @@ -# This style is based on Seaborn-whitegrid parameters, the main differences are -# Default matplotlib font (no problem using Greek letters!) -# Larger font size for several elements -# Colorblind friendly color cycle -figure.figsize: 7.2, 4.8 -figure.dpi: 100.0 -figure.facecolor: white -figure.constrained_layout.use: True -text.color: .15 -axes.labelcolor: .15 -legend.frameon: False -legend.numpoints: 1 -legend.scatterpoints: 1 -xtick.direction: out -ytick.direction: out -xtick.color: .15 -ytick.color: .15 -axes.axisbelow: True -grid.linestyle: - -lines.solid_capstyle: round - -axes.labelsize: 15 -axes.titlesize: 16 -xtick.labelsize: 14 -ytick.labelsize: 14 -legend.fontsize: 14 - -axes.grid: True -axes.facecolor: white -axes.edgecolor: .8 -axes.linewidth: 1 -grid.color: .8 -image.cmap: viridis -xtick.major.size: 0 -ytick.major.size: 0 -xtick.minor.size: 0 -ytick.minor.size: 0 - -# color-blind friendly cycle designed using https://colorcyclepicker.mpetroff.net/ -axes.prop_cycle: cycler('color', ['2a2eec', 'fa7c17', '328c06', 'c10c90', '933708', '65e5f3', 'e6e135', '1ccd6a', 'bd8ad5', 'b16b57']) diff --git a/arviz/plots/traceplot.py b/arviz/plots/traceplot.py deleted file mode 100644 index ab8734ec18..0000000000 --- a/arviz/plots/traceplot.py +++ /dev/null @@ -1,273 +0,0 @@ -"""Plot kde or histograms and values from MCMC samples.""" - -import warnings -from typing import Any, Callable, List, Mapping, Optional, Tuple, Union, Sequence - -from ..data import CoordSpec, InferenceData, convert_to_dataset -from ..labels import BaseLabeller -from ..rcparams import rcParams -from ..sel_utils import xarray_var_iter -from ..utils import _var_names, get_coords -from .plot_utils import KwargSpec, get_plotting_function - - -def plot_trace( - data: InferenceData, - var_names: Optional[Sequence[str]] = None, - filter_vars: Optional[str] = None, - transform: Optional[Callable] = None, - coords: Optional[CoordSpec] = None, - divergences: Optional[str] = "auto", - kind: Optional[str] = "trace", - figsize: Optional[Tuple[float, float]] = None, - rug: bool = False, - lines: Optional[List[Tuple[str, CoordSpec, Any]]] = None, - circ_var_names: Optional[List[str]] = None, - circ_var_units: str = "radians", - compact: bool = True, - compact_prop: Optional[Union[str, Mapping[str, Any]]] = None, - combined: bool = False, - chain_prop: Optional[Union[str, Mapping[str, Any]]] = None, - legend: bool = False, - plot_kwargs: Optional[KwargSpec] = None, - fill_kwargs: Optional[KwargSpec] = None, - rug_kwargs: Optional[KwargSpec] = None, - hist_kwargs: Optional[KwargSpec] = None, - trace_kwargs: Optional[KwargSpec] = None, - rank_kwargs: Optional[KwargSpec] = None, - labeller=None, - axes=None, - backend: Optional[str] = None, - backend_config: Optional[KwargSpec] = None, - backend_kwargs: Optional[KwargSpec] = None, - show: Optional[bool] = None, -): - """Plot distribution (histogram or kernel density estimates) and sampled values or rank plot. - - If `divergences` data is available in `sample_stats`, will plot the location of divergences as - dashed vertical lines. - - Parameters - ---------- - data: obj - Any object that can be converted to an :class:`arviz.InferenceData` object - Refer to documentation of :func:`arviz.convert_to_dataset` for details - var_names: str or list of str, optional - One or more variables to be plotted. Prefix the variables by ``~`` when you want - to exclude them from the plot. - filter_vars: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - ``pandas.filter``. - coords: dict of {str: slice or array_like}, optional - Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel` - divergences: {"bottom", "top", None}, optional - Plot location of divergences on the traceplots. - kind: {"trace", "rank_bars", "rank_vlines"}, optional - Choose between plotting sampled values per iteration and rank plots. - transform: callable, optional - Function to transform data (defaults to None i.e.the identity function) - figsize: tuple of (float, float), optional - If None, size is (12, variables * 2) - rug: bool, optional - If True adds a rugplot of samples. Defaults to False. Ignored for 2D KDE. - Only affects continuous variables. - lines: list of tuple of (str, dict, array_like), optional - List of (var_name, {'coord': selection}, [line, positions]) to be overplotted as - vertical lines on the density and horizontal lines on the trace. - circ_var_names : str or list of str, optional - List of circular variables to account for when plotting KDE. - circ_var_units : str - Whether the variables in ``circ_var_names`` are in "degrees" or "radians". - compact: bool, optional - Plot multidimensional variables in a single plot. - compact_prop: str or dict {str: array_like}, optional - Defines the property name and the property values to distinguish different - dimensions with compact=True. - When compact=True it defaults to color, it is - ignored otherwise. - combined: bool, optional - Flag for combining multiple chains into a single line. If False (default), chains will be - plotted separately. - chain_prop: str or dict {str: array_like}, optional - Defines the property name and the property values to distinguish different chains. - If compact=True it defaults to linestyle, - otherwise it uses the color to distinguish - different chains. - legend: bool, optional - Add a legend to the figure with the chain color code. - plot_kwargs, fill_kwargs, rug_kwargs, hist_kwargs: dict, optional - Extra keyword arguments passed to :func:`arviz.plot_dist`. Only affects continuous - variables. - trace_kwargs: dict, optional - Extra keyword arguments passed to :meth:`matplotlib.axes.Axes.plot` - labeller : labeller instance, optional - Class providing the method ``make_label_vert`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - rank_kwargs : dict, optional - Extra keyword arguments passed to :func:`arviz.plot_rank` - axes: axes, optional - Matplotlib axes or bokeh figures. - backend: {"matplotlib", "bokeh"}, optional - Select plotting backend. - backend_config: dict, optional - Currently specifies the bounds to use for bokeh axes. Defaults to value set in rcParams. - backend_kwargs: dict, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or - :func:`bokeh.plotting.figure`. - show: bool, optional - Call backend show function. - - Returns - ------- - axes: matplotlib axes or bokeh figures - - See Also - -------- - plot_rank : Plot rank order statistics of chains. - - Examples - -------- - Plot a subset variables and select them with partial naming - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> data = az.load_arviz_data('non_centered_eight') - >>> coords = {'school': ['Choate', 'Lawrenceville']} - >>> az.plot_trace(data, var_names=('theta'), filter_vars="like", coords=coords) - - Show all dimensions of multidimensional variables in the same plot - - .. plot:: - :context: close-figs - - >>> az.plot_trace(data, compact=True) - - Display a rank plot instead of trace - - .. plot:: - :context: close-figs - - >>> az.plot_trace(data, var_names=["mu", "tau"], kind="rank_bars") - - Combine all chains into one distribution and select variables with regular expressions - - .. plot:: - :context: close-figs - - >>> az.plot_trace( - >>> data, var_names=('^theta'), filter_vars="regex", coords=coords, combined=True - >>> ) - - - Plot reference lines against distribution and trace - - .. plot:: - :context: close-figs - - >>> lines = (('theta_t',{'school': "Choate"}, [-1]),) - >>> az.plot_trace(data, var_names=('theta_t', 'theta'), coords=coords, lines=lines) - - """ - if kind not in {"trace", "rank_vlines", "rank_bars"}: - raise ValueError("The value of kind must be either trace, rank_vlines or rank_bars.") - - if divergences == "auto": - divergences = "top" if rug else "bottom" - if divergences: - try: - divergence_data = convert_to_dataset(data, group="sample_stats").diverging.transpose( - "chain", "draw" - ) - except (ValueError, AttributeError): # No sample_stats, or no `.diverging` - divergences = None - - if coords is None: - coords = {} - - if labeller is None: - labeller = BaseLabeller() - - if divergences: - divergence_data = get_coords( - divergence_data, {k: v for k, v in coords.items() if k in ("chain", "draw")} - ) - else: - divergence_data = False - - coords_data = get_coords(convert_to_dataset(data, group="posterior"), coords) - - if transform is not None: - coords_data = transform(coords_data) - - var_names = _var_names(var_names, coords_data, filter_vars) - - skip_dims = set(coords_data.dims) - {"chain", "draw"} if compact else set() - - plotters = list( - xarray_var_iter( - coords_data, - var_names=var_names, - combined=True, - skip_dims=skip_dims, - dim_order=["chain", "draw"], - ) - ) - max_plots = rcParams["plot.max_subplots"] - max_plots = len(plotters) if max_plots is None else max(max_plots // 2, 1) - if len(plotters) > max_plots: - warnings.warn( - "rcParams['plot.max_subplots'] ({max_plots}) is smaller than the number " - "of variables to plot ({len_plotters}), generating only {max_plots} " - "plots".format(max_plots=max_plots, len_plotters=len(plotters)), - UserWarning, - ) - plotters = plotters[:max_plots] - - # TODO: Check if this can be further simplified - trace_plot_args = dict( - # User Kwargs - data=coords_data, - var_names=var_names, - # coords = coords, - divergences=divergences, - kind=kind, - figsize=figsize, - rug=rug, - lines=lines, - circ_var_names=circ_var_names, - circ_var_units=circ_var_units, - plot_kwargs=plot_kwargs, - fill_kwargs=fill_kwargs, - rug_kwargs=rug_kwargs, - hist_kwargs=hist_kwargs, - trace_kwargs=trace_kwargs, - rank_kwargs=rank_kwargs, - compact=compact, - compact_prop=compact_prop, - combined=combined, - chain_prop=chain_prop, - legend=legend, - labeller=labeller, - # Generated kwargs - divergence_data=divergence_data, - # skip_dims=skip_dims, - plotters=plotters, - axes=axes, - backend_config=backend_config, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - plot = get_plotting_function("plot_trace", "traceplot", backend) - axes = plot(**trace_plot_args) - - return axes diff --git a/arviz/plots/tsplot.py b/arviz/plots/tsplot.py deleted file mode 100644 index 8085f22ba8..0000000000 --- a/arviz/plots/tsplot.py +++ /dev/null @@ -1,440 +0,0 @@ -"""Plot timeseries data.""" - -import warnings -import numpy as np - -from ..sel_utils import xarray_var_iter -from ..rcparams import rcParams -from .plot_utils import default_grid, get_plotting_function - - -def plot_ts( - idata, - y, - x=None, - y_hat=None, - y_holdout=None, - y_forecasts=None, - x_holdout=None, - plot_dim=None, - holdout_dim=None, - num_samples=100, - backend=None, - backend_kwargs=None, - y_kwargs=None, - y_hat_plot_kwargs=None, - y_mean_plot_kwargs=None, - vline_kwargs=None, - textsize=None, - figsize=None, - legend=True, - axes=None, - show=None, -): - """Plot timeseries data. - - Parameters - ---------- - idata : InferenceData - :class:`arviz.InferenceData` object. - y : str - Variable name from ``observed_data``. - Values to be plotted on y-axis before holdout. - x : str, Optional - Values to be plotted on x-axis before holdout. - If None, coords of ``y`` dims is chosen. - y_hat : str, optional - Variable name from ``posterior_predictive``. - Assumed to be of shape ``(chain, draw, *y_dims)``. - y_holdout : str, optional - Variable name from ``observed_data``. - It represents the observed data after the holdout period. - Useful while testing the model, when you want to compare - observed test data with predictions/forecasts. - y_forecasts : str, optional - Variable name from ``posterior_predictive``. - It represents forecasts (posterior predictive) values after holdout period. - Useful to compare observed vs predictions/forecasts. - Assumed shape ``(chain, draw, *shape)``. - x_holdout : str, Defaults to coords of y. - Variable name from ``constant_data``. - If None, coords of ``y_holdout`` or - coords of ``y_forecast`` (either of the two available) is chosen. - plot_dim: str, Optional - Should be present in ``y.dims``. - Necessary for selection of ``x`` if ``x`` is None and ``y`` is multidimensional. - holdout_dim: str, Optional - Should be present in ``y_holdout.dims`` or ``y_forecats.dims``. - Necessary to choose ``x_holdout`` if ``x`` is None and - if ``y_holdout`` or ``y_forecasts`` is multidimensional. - num_samples : int, default 100 - Number of posterior predictive samples drawn from ``y_hat`` and ``y_forecasts``. - backend : {"matplotlib", "bokeh"}, default "matplotlib" - Select plotting backend. - y_kwargs : dict, optional - Passed to :meth:`matplotlib.axes.Axes.plot` in matplotlib. - y_hat_plot_kwargs : dict, optional - Passed to :meth:`matplotlib.axes.Axes.plot` in matplotlib. - y_mean_plot_kwargs : dict, optional - Passed to :meth:`matplotlib.axes.Axes.plot` in matplotlib. - vline_kwargs : dict, optional - Passed to :meth:`matplotlib.axes.Axes.axvline` in matplotlib. - backend_kwargs : dict, optional - These are kwargs specific to the backend being used. Passed to - :func:`matplotlib.pyplot.subplots`. - figsize : tuple, optional - Figure size. If None, it will be defined automatically. - textsize : float, optional - Text size scaling factor for labels, titles and lines. If None, it will be - autoscaled based on ``figsize``. - - - Returns - ------- - axes: matplotlib axes or bokeh figures. - - See Also - -------- - plot_lm : Posterior predictive and mean plots for regression-like data. - plot_ppc : Plot for posterior/prior predictive checks. - - Examples - -------- - Plot timeseries default plot - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> nchains, ndraws = (4, 500) - >>> obs_data = { - ... "y": 2 * np.arange(1, 9) + 3, - ... "z": 2 * np.arange(8, 12) + 3, - ... } - >>> posterior_predictive = { - ... "y": np.random.normal( - ... (obs_data["y"] * 1.2) - 3, size=(nchains, ndraws, len(obs_data["y"])) - ... ), - ... "z": np.random.normal( - ... (obs_data["z"] * 1.2) - 3, size=(nchains, ndraws, len(obs_data["z"])) - ... ), - ... } - >>> idata = az.from_dict( - ... observed_data=obs_data, - ... posterior_predictive=posterior_predictive, - ... coords={"obs_dim": np.arange(1, 9), "pred_dim": np.arange(8, 12)}, - ... dims={"y": ["obs_dim"], "z": ["pred_dim"]}, - ... ) - >>> ax = az.plot_ts(idata=idata, y="y", y_holdout="z") - - Plot timeseries multidim plot - - .. plot:: - :context: close-figs - - >>> ndim1, ndim2 = (5, 7) - >>> data = { - ... "y": np.random.normal(size=(ndim1, ndim2)), - ... "z": np.random.normal(size=(ndim1, ndim2)), - ... } - >>> posterior_predictive = { - ... "y": np.random.randn(nchains, ndraws, ndim1, ndim2), - ... "z": np.random.randn(nchains, ndraws, ndim1, ndim2), - ... } - >>> const_data = {"x": np.arange(1, 6), "x_pred": np.arange(5, 10)} - >>> idata = az.from_dict( - ... observed_data=data, - ... posterior_predictive=posterior_predictive, - ... constant_data=const_data, - ... dims={ - ... "y": ["dim1", "dim2"], - ... "z": ["holdout_dim1", "holdout_dim2"], - ... }, - ... coords={ - ... "dim1": range(ndim1), - ... "dim2": range(ndim2), - ... "holdout_dim1": range(ndim1 - 1, ndim1 + 4), - ... "holdout_dim2": range(ndim2 - 1, ndim2 + 6), - ... }, - ... ) - >>> az.plot_ts( - ... idata=idata, - ... y="y", - ... plot_dim="dim1", - ... y_holdout="z", - ... holdout_dim="holdout_dim1", - ... ) - - """ - # Assign default values if none is provided - y_hat = y if y_hat is None and isinstance(y, str) else y_hat - y_forecasts = y_holdout if y_forecasts is None and isinstance(y_holdout, str) else y_forecasts - # holdout_dim = plot_dim if holdout_dim is None and plot_dim is not None else holdout_dim - - if isinstance(y, str): - y = idata.observed_data[y] - - if isinstance(y_holdout, str): - y_holdout = idata.observed_data[y_holdout] - - if len(y.dims) > 1 and plot_dim is None: - raise ValueError("Argument plot_dim is needed in case of multidimensional data") - - if y_holdout is not None and len(y_holdout.dims) > 1 and holdout_dim is None: - raise ValueError("Argument holdout_dim is needed in case of multidimensional data") - - # Assigning values to x - x_var_names = None - if isinstance(x, str): - x = idata.constant_data[x] - elif isinstance(x, tuple): - x_var_names = x - x = idata.constant_data - elif x is None: - if plot_dim is None: - x = y.coords[y.dims[0]] - else: - x = y.coords[plot_dim] - - # If posterior_predictive is present in idata and y_hat is there, get its values - if isinstance(y_hat, str): - if "posterior_predictive" not in idata.groups(): - warnings.warn("posterior_predictive not found in idata", UserWarning) - y_hat = None - elif hasattr(idata.posterior_predictive, y_hat): - y_hat = idata.posterior_predictive[y_hat] - else: - warnings.warn("y_hat not found in posterior_predictive", UserWarning) - y_hat = None - - # If posterior_predictive is present in idata and y_forecasts is there, get its values - x_holdout_var_names = None - if isinstance(y_forecasts, str): - if "posterior_predictive" not in idata.groups(): - warnings.warn("posterior_predictive not found in idata", UserWarning) - y_forecasts = None - elif hasattr(idata.posterior_predictive, y_forecasts): - y_forecasts = idata.posterior_predictive[y_forecasts] - else: - warnings.warn("y_hat not found in posterior_predictive", UserWarning) - y_forecasts = None - - # Assign values to y_holdout - if isinstance(y_holdout, str): - y_holdout = idata.observed_data[y_holdout] - - # Assign values to x_holdout. - if y_holdout is not None or y_forecasts is not None: - if x_holdout is None: - if holdout_dim is None: - if y_holdout is None: - x_holdout = y_forecasts.coords[y_forecasts.dims[-1]] - else: - x_holdout = y_holdout.coords[y_holdout.dims[-1]] - elif y_holdout is None: - x_holdout = y_forecasts.coords[holdout_dim] - else: - x_holdout = y_holdout.coords[holdout_dim] - elif isinstance(x_holdout, str): - x_holdout = idata.constant_data[x_holdout] - elif isinstance(x_holdout, tuple): - x_holdout_var_names = x_holdout - x_holdout = idata.constant_data - - # Choose dims to generate y plotters - if plot_dim is None: - skip_dims = list(y.dims) - elif isinstance(plot_dim, str): - skip_dims = [plot_dim] - elif isinstance(plot_dim, tuple): - skip_dims = list(plot_dim) - - # Choose dims to generate y_holdout plotters - if holdout_dim is None: - if y_holdout is not None: - skip_holdout_dims = list(y_holdout.dims) - elif y_forecasts is not None: - skip_holdout_dims = list(y_forecasts.dims) - elif isinstance(holdout_dim, str): - skip_holdout_dims = [holdout_dim] - elif isinstance(holdout_dim, tuple): - skip_holdout_dims = list(holdout_dim) - - # Compulsory plotters - y_plotters = list( - xarray_var_iter( - y, - skip_dims=set(skip_dims), - combined=True, - ) - ) - - # Compulsory plotters - x_plotters = list( - xarray_var_iter( - x, - var_names=x_var_names, - skip_dims=set(x.dims), - combined=True, - ) - ) - # Necessary when multidim y - # If there are multiple x and multidimensional y, we need total of len(x)*len(y) graphs - len_y = len(y_plotters) - len_x = len(x_plotters) - length_plotters = len_x * len_y - # TODO: Incompatible types in assignment (expression has type "ndarray[Any, dtype[Any]]", - # TODO: variable has type "List[Any]") [assignment] - y_plotters = np.tile(np.array(y_plotters, dtype=object), (len_x, 1)) # type: ignore[assignment] - x_plotters = np.tile(np.array(x_plotters, dtype=object), (len_y, 1)) # type: ignore[assignment] - - # Generate plotters for all the available data - y_mean_plotters = None - y_hat_plotters = None - if y_hat is not None: - total_samples = y_hat.sizes["chain"] * y_hat.sizes["draw"] - pp_sample_ix = np.random.choice(total_samples, size=num_samples, replace=False) - - y_hat_satcked = y_hat.stack(__sample__=("chain", "draw"))[..., pp_sample_ix] - - y_hat_plotters = list( - xarray_var_iter( - y_hat_satcked, - skip_dims=set(skip_dims + ["__sample__"]), - combined=True, - ) - ) - - y_mean = y_hat.mean(("chain", "draw")) - y_mean_plotters = list( - xarray_var_iter( - y_mean, - skip_dims=set(skip_dims), - combined=True, - ) - ) - - # Necessary when multidim y - # If there are multiple x and multidimensional y, we need total of len(x)*len(y) graphs - # TODO: Incompatible types in assignment (expression has type "ndarray[Any, dtype[Any]]", - # TODO: variable has type "List[Any]") [assignment] - y_hat_plotters = np.tile( - np.array(y_hat_plotters, dtype=object), (len_x, 1) - ) # type: ignore[assignment] - y_mean_plotters = np.tile( - np.array(y_mean_plotters, dtype=object), (len_x, 1) - ) # type: ignore[assignment] - - y_holdout_plotters = None - x_holdout_plotters = None - if y_holdout is not None: - y_holdout_plotters = list( - xarray_var_iter( - y_holdout, - skip_dims=set(skip_holdout_dims), - combined=True, - ) - ) - - x_holdout_plotters = list( - xarray_var_iter( - x_holdout, - var_names=x_holdout_var_names, - skip_dims=set(x_holdout.dims), - combined=True, - ) - ) - - # Necessary when multidim y - # If there are multiple x and multidimensional y, we need total of len(x)*len(y) graphs - # TODO: Incompatible types in assignment (expression has type "ndarray[Any, dtype[Any]]", - # TODO: variable has type "List[Any]") [assignment] - y_holdout_plotters = np.tile( - np.array(y_holdout_plotters, dtype=object), (len_x, 1) - ) # type: ignore[assignment] - x_holdout_plotters = np.tile( - np.array(x_holdout_plotters, dtype=object), (len_y, 1) - ) # type: ignore[assignment] - - y_forecasts_plotters = None - y_forecasts_mean_plotters = None - if y_forecasts is not None: - total_samples = y_forecasts.sizes["chain"] * y_forecasts.sizes["draw"] - pp_sample_ix = np.random.choice(total_samples, size=num_samples, replace=False) - - y_forecasts_satcked = y_forecasts.stack(__sample__=("chain", "draw"))[..., pp_sample_ix] - - y_forecasts_plotters = list( - xarray_var_iter( - y_forecasts_satcked, - skip_dims=set(skip_holdout_dims + ["__sample__"]), - combined=True, - ) - ) - - y_forecasts_mean = y_forecasts.mean(("chain", "draw")) - y_forecasts_mean_plotters = list( - xarray_var_iter( - y_forecasts_mean, - skip_dims=set(skip_holdout_dims), - combined=True, - ) - ) - - x_holdout_plotters = list( - xarray_var_iter( - x_holdout, - var_names=x_holdout_var_names, - skip_dims=set(x_holdout.dims), - combined=True, - ) - ) - - # Necessary when multidim y - # If there are multiple x and multidimensional y, we need total of len(x)*len(y) graphs - # TODO: Incompatible types in assignment (expression has type "ndarray[Any, dtype[Any]]", - # TODO: variable has type "List[Any]") [assignment] - y_forecasts_mean_plotters = np.tile( - np.array(y_forecasts_mean_plotters, dtype=object), (len_x, 1) - ) # type: ignore[assignment] - y_forecasts_plotters = np.tile( - np.array(y_forecasts_plotters, dtype=object), (len_x, 1) - ) # type: ignore[assignment] - x_holdout_plotters = np.tile( - np.array(x_holdout_plotters, dtype=object), (len_y, 1) - ) # type: ignore[assignment] - - rows, cols = default_grid(length_plotters) - - tsplot_kwargs = dict( - x_plotters=x_plotters, - y_plotters=y_plotters, - y_mean_plotters=y_mean_plotters, - y_hat_plotters=y_hat_plotters, - y_holdout_plotters=y_holdout_plotters, - x_holdout_plotters=x_holdout_plotters, - y_forecasts_plotters=y_forecasts_plotters, - y_forecasts_mean_plotters=y_forecasts_mean_plotters, - num_samples=num_samples, - length_plotters=length_plotters, - rows=rows, - cols=cols, - backend_kwargs=backend_kwargs, - y_kwargs=y_kwargs, - y_hat_plot_kwargs=y_hat_plot_kwargs, - y_mean_plot_kwargs=y_mean_plot_kwargs, - vline_kwargs=vline_kwargs, - textsize=textsize, - figsize=figsize, - legend=legend, - axes=axes, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - plot = get_plotting_function("plot_ts", "tsplot", backend) - ax = plot(**tsplot_kwargs) - return ax diff --git a/arviz/plots/violinplot.py b/arviz/plots/violinplot.py deleted file mode 100644 index c3af7707aa..0000000000 --- a/arviz/plots/violinplot.py +++ /dev/null @@ -1,192 +0,0 @@ -"""Plot posterior traces as violin plot.""" - -from ..data import convert_to_dataset -from ..labels import BaseLabeller -from ..sel_utils import xarray_var_iter -from ..utils import _var_names -from ..rcparams import rcParams -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function - - -def plot_violin( - data, - var_names=None, - combine_dims=None, - filter_vars=None, - transform=None, - quartiles=True, - rug=False, - side="both", - hdi_prob=None, - shade=0.35, - bw="default", - circular=False, - sharex=True, - sharey=True, - grid=None, - figsize=None, - textsize=None, - labeller=None, - ax=None, - shade_kwargs=None, - rug_kwargs=None, - backend=None, - backend_kwargs=None, - show=None, -): - """Plot posterior of traces as violin plot. - - Notes - ----- - If multiple chains are provided for a variable they will be combined - - Parameters - ---------- - data: obj - Any object that can be converted to an :class:`arviz.InferenceData` object - Refer to documentation of :func:`arviz.convert_to_dataset` for details - var_names: list of variable names, optional - Variables to be plotted, if None all variable are plotted. Prefix the - variables by ``~`` when you want to exclude them from the plot. - combine_dims : set_like of str, optional - List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. - See the :ref:`this section ` for usage examples. - filter_vars: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - ``pandas.filter``. - transform: callable - Function to transform data (defaults to None i.e. the identity function). - quartiles: bool, optional - Flag for plotting the interquartile range, in addition to the ``hdi_prob`` * 100% - intervals. Defaults to ``True``. - rug: bool - If ``True`` adds a jittered rugplot. Defaults to ``False``. - side : {"both", "left", "right"}, default "both" - If ``both``, both sides of the violin plot are rendered. If ``left`` or ``right``, only - the respective side is rendered. By separately plotting left and right halfs with - different data, split violin plots can be achieved. - hdi_prob: float, optional - Plots highest posterior density interval for chosen percentage of density. - Defaults to 0.94. - shade: float - Alpha blending value for the shaded area under the curve, between 0 - (no shade) and 1 (opaque). Defaults to 0. - bw: float or str, optional - If numeric, indicates the bandwidth and must be positive. - If str, indicates the method to estimate the bandwidth and must be - one of "scott", "silverman", "isj" or "experimental" when ``circular`` is ``False`` - and "taylor" (for now) when ``circular`` is ``True``. - Defaults to "default" which means "experimental" when variable is not circular - and "taylor" when it is. - circular: bool, optional. - If ``True``, it interprets `values` is a circular variable measured in radians - and a circular KDE is used. Defaults to ``False``. - grid : tuple - Number of rows and columns. Defaults to None, the rows and columns are - automatically inferred. - figsize: tuple - Figure size. If None it will be defined automatically. - textsize: int - Text size of the point_estimates, axis ticks, and highest density interval. If None it will - be autoscaled based on ``figsize``. - labeller : labeller instance, optional - Class providing the method ``make_label_vert`` to generate the labels in the plot titles. - Read the :ref:`label_guide` for more details and usage examples. - sharex: bool - Defaults to ``True``, violinplots share a common x-axis scale. - sharey: bool - Defaults to ``True``, violinplots share a common y-axis scale. - ax: numpy array-like of matplotlib axes or bokeh figures, optional - A 2D array of locations into which to plot the densities. If not supplied, Arviz will create - its own array of plot areas (and return it). - shade_kwargs: dicts, optional - Additional keywords passed to :meth:`matplotlib.axes.Axes.fill_between`, or - :meth:`matplotlib.axes.Axes.barh` to control the shade. - rug_kwargs: dict - Keywords passed to the rug plot. If true only the right half side of the violin will be - plotted. - backend: str, optional - Select plotting backend {"matplotlib","bokeh"}. Default to "matplotlib". - backend_kwargs: bool, optional - These are kwargs specific to the backend being used, passed to - :func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`. - For additional documentation check the plotting method of the backend. - show: bool, optional - Call backend show function. - - Returns - ------- - axes: matplotlib axes or bokeh figures - - See Also - -------- - plot_forest: Forest plot to compare HDI intervals from a number of distributions. - - Examples - -------- - Show a default violin plot - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> data = az.load_arviz_data('centered_eight') - >>> az.plot_violin(data) - - """ - if labeller is None: - labeller = BaseLabeller() - - data = convert_to_dataset(data, group="posterior") - if transform is not None: - data = transform(data) - var_names = _var_names(var_names, data, filter_vars) - - plotters = filter_plotters_list( - list(xarray_var_iter(data, var_names=var_names, combined=True, skip_dims=combine_dims)), - "plot_violin", - ) - - rows, cols = default_grid(len(plotters), grid=grid) - - if hdi_prob is None: - hdi_prob = rcParams["stats.ci_prob"] - elif not 1 >= hdi_prob > 0: - raise ValueError("The value of hdi_prob should be in the interval (0, 1]") - - violinplot_kwargs = dict( - ax=ax, - plotters=plotters, - figsize=figsize, - rows=rows, - cols=cols, - sharex=sharex, - sharey=sharey, - shade_kwargs=shade_kwargs, - shade=shade, - rug=rug, - rug_kwargs=rug_kwargs, - side=side, - bw=bw, - textsize=textsize, - labeller=labeller, - circular=circular, - hdi_prob=hdi_prob, - quartiles=quartiles, - backend_kwargs=backend_kwargs, - show=show, - ) - - if backend is None: - backend = rcParams["plot.backend"] - backend = backend.lower() - - if side not in ("both", "left", "right"): - raise ValueError(f"'side' can only be 'both', 'left', or 'right', got: '{side}'") - - # TODO: Add backend kwargs - plot = get_plotting_function("plot_violin", "violinplot", backend) - ax = plot(**violinplot_kwargs) - return ax diff --git a/arviz/preview.py b/arviz/preview.py deleted file mode 100644 index 8fc1dced18..0000000000 --- a/arviz/preview.py +++ /dev/null @@ -1,48 +0,0 @@ -# pylint: disable=unused-import,unused-wildcard-import,wildcard-import,invalid-name -"""Expose features from arviz-xyz refactored packages inside ``arviz.preview`` namespace.""" -import logging - -_log = logging.getLogger(__name__) - -info = "" - -try: - from arviz_base import * - - status = "arviz_base available, exposing its functions as part of arviz.preview" - _log.info(status) -except ModuleNotFoundError: - status = "arviz_base not installed" - _log.info(status) -except ImportError: - status = "Unable to import arviz_base" - _log.info(status, exc_info=True) - -info += status + "\n" - -try: - from arviz_stats import * - - status = "arviz_stats available, exposing its functions as part of arviz.preview" - _log.info(status) -except ModuleNotFoundError: - status = "arviz_stats not installed" - _log.info(status) -except ImportError: - status = "Unable to import arviz_stats" - _log.info(status, exc_info=True) -info += status + "\n" - -try: - from arviz_plots import * - - status = "arviz_plots available, exposing its functions as part of arviz.preview" - _log.info(status) -except ModuleNotFoundError: - status = "arviz_plots not installed" - _log.info(status) -except ImportError: - status = "Unable to import arviz_plots" - _log.info(status, exc_info=True) - -info += status + "\n" diff --git a/arviz/py.typed b/arviz/py.typed deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/arviz/rcparams.py b/arviz/rcparams.py deleted file mode 100644 index 668c867373..0000000000 --- a/arviz/rcparams.py +++ /dev/null @@ -1,606 +0,0 @@ -"""ArviZ rcparams. Based on matplotlib's implementation.""" - -import locale -import logging -import os -import pprint -import re -import sys -import warnings -from collections.abc import MutableMapping -from pathlib import Path -from typing import Any, Dict -from typing_extensions import Literal - -NO_GET_ARGS: bool = False # pylint: disable=invalid-name -try: - from typing_extensions import get_args -except ImportError: - NO_GET_ARGS = True # pylint: disable=invalid-name - - -import numpy as np - -_log = logging.getLogger(__name__) - -ScaleKeyword = Literal["log", "negative_log", "deviance"] -ICKeyword = Literal["loo", "waic"] - -_identity = lambda x: x - - -def _make_validate_choice(accepted_values, allow_none=False, typeof=str): - """Validate value is in accepted_values. - - Parameters - ---------- - accepted_values : iterable - Iterable containing all accepted_values. - allow_none: boolean, optional - Whether to accept ``None`` in addition to the values in ``accepted_values``. - typeof: type, optional - Type the values should be converted to. - """ - # no blank lines allowed after function docstring by pydocstyle, - # but black requires white line before function - - def validate_choice(value): - if allow_none and (value is None or isinstance(value, str) and value.lower() == "none"): - return None - try: - value = typeof(value) - except (ValueError, TypeError) as err: - raise ValueError(f"Could not convert to {typeof.__name__}") from err - if isinstance(value, str): - value = value.lower() - - if value in accepted_values: - # Convert value to python boolean if string matches - value = {"true": True, "false": False}.get(value, value) - return value - raise ValueError( - f'{value} is not one of {accepted_values}{" nor None" if allow_none else ""}' - ) - - return validate_choice - - -def _make_validate_choice_regex(accepted_values, accepted_values_regex, allow_none=False): - """Validate value is in accepted_values with regex. - - Parameters - ---------- - accepted_values : iterable - Iterable containing all accepted_values. - accepted_values_regex : iterable - Iterable containing all accepted_values with regex string. - allow_none: boolean, optional - Whether to accept ``None`` in addition to the values in ``accepted_values``. - typeof: type, optional - Type the values should be converted to. - """ - # no blank lines allowed after function docstring by pydocstyle, - # but black requires white line before function - - def validate_choice_regex(value): - if allow_none and (value is None or isinstance(value, str) and value.lower() == "none"): - return None - value = str(value) - if isinstance(value, str): - value = value.lower() - - if value in accepted_values: - # Convert value to python boolean if string matches - value = {"true": True, "false": False}.get(value, value) - return value - elif any(re.match(pattern, value) for pattern in accepted_values_regex): - return value - raise ValueError( - f"{value} is not one of {accepted_values} " - f'or in regex {accepted_values_regex}{" nor None" if allow_none else ""}' - ) - - return validate_choice_regex - - -def _validate_positive_int(value): - """Validate value is a natural number.""" - try: - value = int(value) - except ValueError as err: - raise ValueError("Could not convert to int") from err - if value > 0: - return value - else: - raise ValueError("Only positive values are valid") - - -def _validate_float(value): - """Validate value is a float.""" - try: - value = float(value) - except ValueError as err: - raise ValueError("Could not convert to float") from err - return value - - -def _validate_probability(value): - """Validate a probability: a float between 0 and 1.""" - value = _validate_float(value) - if (value < 0) or (value > 1): - raise ValueError("Only values between 0 and 1 are valid.") - return value - - -def _validate_boolean(value): - """Validate value is a float.""" - if isinstance(value, str): - value = value.lower() - if value not in {True, False, "true", "false"}: - raise ValueError("Only boolean values are valid.") - return value is True or value == "true" - - -def _add_none_to_validator(base_validator): - """Create a validator function that catches none and then calls base_fun.""" - # no blank lines allowed after function docstring by pydocstyle, - # but black requires white line before function - - def validate_with_none(value): - if value is None or isinstance(value, str) and value.lower() == "none": - return None - return base_validator(value) - - return validate_with_none - - -def _validate_bokeh_marker(value): - """Validate the markers.""" - try: - from bokeh.core.enums import MarkerType - except ImportError: - MarkerType = [ - "asterisk", - "circle", - "circle_cross", - "circle_dot", - "circle_x", - "circle_y", - "cross", - "dash", - "diamond", - "diamond_cross", - "diamond_dot", - "dot", - "hex", - "hex_dot", - "inverted_triangle", - "plus", - "square", - "square_cross", - "square_dot", - "square_pin", - "square_x", - "star", - "star_dot", - "triangle", - "triangle_dot", - "triangle_pin", - "x", - "y", - ] - all_markers = list(MarkerType) - if value not in all_markers: - raise ValueError(f"{value} is not one of {all_markers}") - return value - - -def _validate_dict_of_lists(values): - if isinstance(values, dict): - return {key: tuple(item) for key, item in values.items()} - validated_dict = {} - for value in values: - tup = value.split(":", 1) - if len(tup) != 2: - raise ValueError(f"Could not interpret '{value}' as key: list or str") - key, vals = tup - key = key.strip(' "') - vals = [val.strip(' "') for val in vals.strip(" [],").split(",")] - if key in validated_dict: - warnings.warn(f"Repeated key {key} when validating dict of lists") - validated_dict[key] = tuple(vals) - return validated_dict - - -def make_iterable_validator(scalar_validator, length=None, allow_none=False, allow_auto=False): - """Validate value is an iterable datatype.""" - # based on matplotlib's _listify_validator function - - def validate_iterable(value): - if allow_none and (value is None or isinstance(value, str) and value.lower() == "none"): - return None - if isinstance(value, str): - if allow_auto and value.lower() == "auto": - return "auto" - value = tuple(v.strip("([ ])") for v in value.split(",") if v.strip()) - if np.iterable(value) and not isinstance(value, (set, frozenset)): - val = tuple(scalar_validator(v) for v in value) - if length is not None and len(val) != length: - raise ValueError(f"Iterable must be of length: {length}") - return val - raise ValueError("Only ordered iterable values are valid") - - return validate_iterable - - -_validate_float_or_none = _add_none_to_validator(_validate_float) -_validate_positive_int_or_none = _add_none_to_validator(_validate_positive_int) -_validate_bokeh_bounds = make_iterable_validator( # pylint: disable=invalid-name - _validate_float_or_none, length=2, allow_none=True, allow_auto=True -) - -METAGROUPS = { - "posterior_groups": ["posterior", "posterior_predictive", "sample_stats", "log_likelihood"], - "prior_groups": ["prior", "prior_predictive", "sample_stats_prior"], - "posterior_groups_warmup": [ - "_warmup_posterior", - "_warmup_posterior_predictive", - "_warmup_sample_stats", - ], - "latent_vars": ["posterior", "prior"], - "observed_vars": ["posterior_predictive", "observed_data", "prior_predictive"], -} - -defaultParams = { # pylint: disable=invalid-name - "data.http_protocol": ("https", _make_validate_choice({"https", "http"})), - "data.load": ("lazy", _make_validate_choice({"lazy", "eager"})), - "data.metagroups": (METAGROUPS, _validate_dict_of_lists), - "data.index_origin": (0, _make_validate_choice({0, 1}, typeof=int)), - "data.log_likelihood": (True, _validate_boolean), - "data.save_warmup": (False, _validate_boolean), - "plot.backend": ("matplotlib", _make_validate_choice({"matplotlib", "bokeh"})), - "plot.density_kind": ("kde", _make_validate_choice({"kde", "hist"})), - "plot.max_subplots": (40, _validate_positive_int_or_none), - "plot.point_estimate": ( - "mean", - _make_validate_choice({"mean", "median", "mode"}, allow_none=True), - ), - "plot.bokeh.bounds_x_range": ("auto", _validate_bokeh_bounds), - "plot.bokeh.bounds_y_range": ("auto", _validate_bokeh_bounds), - "plot.bokeh.figure.dpi": (60, _validate_positive_int), - "plot.bokeh.figure.height": (500, _validate_positive_int), - "plot.bokeh.figure.width": (500, _validate_positive_int), - "plot.bokeh.layout.order": ( - "default", - _make_validate_choice_regex( - {"default", r"column", r"row", "square", "square_trimmed"}, {r"\d*column", r"\d*row"} - ), - ), - "plot.bokeh.layout.sizing_mode": ( - "fixed", - _make_validate_choice( - { - "fixed", - "stretch_width", - "stretch_height", - "stretch_both", - "scale_width", - "scale_height", - "scale_both", - } - ), - ), - "plot.bokeh.layout.toolbar_location": ( - "above", - _make_validate_choice({"above", "below", "left", "right"}, allow_none=True), - ), - "plot.bokeh.marker": ("cross", _validate_bokeh_marker), - "plot.bokeh.output_backend": ("webgl", _make_validate_choice({"canvas", "svg", "webgl"})), - "plot.bokeh.show": (True, _validate_boolean), - "plot.bokeh.tools": ( - "reset,pan,box_zoom,wheel_zoom,lasso_select,undo,save,hover", - lambda x: x, - ), - "plot.matplotlib.show": (False, _validate_boolean), - "stats.ci_prob": (0.94, _validate_probability), - "stats.information_criterion": ( - "loo", - _make_validate_choice({"loo", "waic"} if NO_GET_ARGS else set(get_args(ICKeyword))), - ), - "stats.ic_pointwise": (True, _validate_boolean), - "stats.ic_scale": ( - "log", - _make_validate_choice( - {"log", "negative_log", "deviance"} if NO_GET_ARGS else set(get_args(ScaleKeyword)) - ), - ), - "stats.ic_compare_method": ( - "stacking", - _make_validate_choice({"stacking", "bb-pseudo-bma", "pseudo-bma"}), - ), -} - -# map from deprecated params to (version, new_param, fold2new, fnew2old) -deprecated_map = {"stats.hdi_prob": ("0.18.0", "stats.ci_prob", _identity, _identity)} - - -class RcParams(MutableMapping): - """Class to contain ArviZ default parameters. - - It is implemented as a dict with validation when setting items. - """ - - validate = {key: validate_fun for key, (_, validate_fun) in defaultParams.items()} - - # validate values on the way in - def __init__(self, *args, **kwargs): - self._underlying_storage: Dict[str, Any] = {} - super().__init__() - self.update(*args, **kwargs) - - def __setitem__(self, key, val): - """Add validation to __setitem__ function.""" - if key in deprecated_map: - version, key_new, fold2new, _ = deprecated_map[key] - warnings.warn( - f"{key} is deprecated since {version}, use {key_new} instead", - FutureWarning, - ) - key = key_new - val = fold2new(val) - - try: - try: - cval = self.validate[key](val) - except ValueError as verr: - raise ValueError(f"Key {key}: {str(verr)}") from verr - self._underlying_storage[key] = cval - except KeyError as err: - raise KeyError( - f"{key} is not a valid rc parameter " - f"(see rcParams.keys() for a list of valid parameters)" - ) from err - - def __getitem__(self, key): - """Use underlying dict's getitem method.""" - if key in deprecated_map: - version, key_new, _, fnew2old = deprecated_map[key] - warnings.warn( - f"{key} is deprecated since {version}, use {key_new} instead", - FutureWarning, - ) - if key not in self._underlying_storage: - key = key_new - else: - fnew2old = _identity - - return fnew2old(self._underlying_storage[key]) - - def __delitem__(self, key): - """Raise TypeError if someone ever tries to delete a key from RcParams.""" - raise TypeError("RcParams keys cannot be deleted") - - def clear(self): - """Raise TypeError if someone ever tries to delete all keys from RcParams.""" - raise TypeError("RcParams keys cannot be deleted") - - def pop(self, key, default=None): - """Raise TypeError if someone ever tries to delete a key from RcParams.""" - raise TypeError( - "RcParams keys cannot be deleted. Use .get(key) of RcParams[key] to check values" - ) - - def popitem(self): - """Raise TypeError if someone ever tries to delete a key from RcParams.""" - raise TypeError( - "RcParams keys cannot be deleted. Use .get(key) of RcParams[key] to check values" - ) - - def setdefault(self, key, default=None): - """Raise error when using setdefault, defaults are handled on initialization.""" - raise TypeError( - "Defaults in RcParams are handled on object initialization during library" - "import. Use arvizrc file instead." - "" - ) - - def __repr__(self): - """Customize repr of RcParams objects.""" - class_name = self.__class__.__name__ - indent = len(class_name) + 1 - repr_split = pprint.pformat( - self._underlying_storage, - indent=1, - width=80 - indent, - ).split("\n") - repr_indented = ("\n" + " " * indent).join(repr_split) - return f"{class_name}({repr_indented})" - - def __str__(self): - """Customize str/print of RcParams objects.""" - return "\n".join(map("{0[0]:<22}: {0[1]}".format, sorted(self._underlying_storage.items()))) - - def __iter__(self): - """Yield sorted list of keys.""" - yield from sorted(self._underlying_storage.keys()) - - def __len__(self): - """Use underlying dict's len method.""" - return len(self._underlying_storage) - - def find_all(self, pattern): - """ - Find keys that match a regex pattern. - - Return the subset of this RcParams dictionary whose keys match, - using :func:`re.search`, the given ``pattern``. - - Notes - ----- - Changes to the returned dictionary are *not* propagated to - the parent RcParams dictionary. - """ - pattern_re = re.compile(pattern) - return RcParams((key, value) for key, value in self.items() if pattern_re.search(key)) - - def copy(self): - """Get a copy of the RcParams object.""" - return dict(self._underlying_storage) - - -def get_arviz_rcfile(): - """Get arvizrc file. - - The file location is determined in the following order: - - - ``$PWD/arvizrc`` - - ``$ARVIZ_DATA/arvizrc`` - - On Linux, - - ``$XDG_CONFIG_HOME/arviz/arvizrc`` (if ``$XDG_CONFIG_HOME`` - is defined) - - or ``$HOME/.config/arviz/arvizrc`` (if ``$XDG_CONFIG_HOME`` - is not defined) - - On other platforms, - - ``$HOME/.arviz/arvizrc`` if ``$HOME`` is defined - - Otherwise, the default defined in ``rcparams.py`` file will be used. - """ - # no blank lines allowed after function docstring by pydocstyle, - # but black requires white line before function - - def gen_candidates(): - yield os.path.join(os.getcwd(), "arvizrc") - arviz_data_dir = os.environ.get("ARVIZ_DATA") - if arviz_data_dir: - yield os.path.join(arviz_data_dir, "arvizrc") - xdg_base = os.environ.get("XDG_CONFIG_HOME", str(Path.home() / ".config")) - if sys.platform.startswith(("linux", "freebsd")): - configdir = str(Path(xdg_base, "arviz")) - else: - configdir = str(Path.home() / ".arviz") - yield os.path.join(configdir, "arvizrc") - - for fname in gen_candidates(): - if os.path.exists(fname) and not os.path.isdir(fname): - return fname - - return None - - -def read_rcfile(fname): - """Return :class:`arviz.RcParams` from the contents of the given file. - - Unlike `rc_params_from_file`, the configuration class only contains the - parameters specified in the file (i.e. default values are not filled in). - """ - _error_details_fmt = 'line #%d\n\t"%s"\n\tin file "%s"' - - config = RcParams() - with open(fname, "r", encoding="utf8") as rcfile: - try: - multiline = False - for line_no, line in enumerate(rcfile, 1): - strippedline = line.split("#", 1)[0].strip() - if not strippedline: - continue - if multiline: - if strippedline == "}": - multiline = False - val = aux_val - else: - aux_val.append(strippedline) - continue - else: - tup = strippedline.split(":", 1) - if len(tup) != 2: - error_details = _error_details_fmt % (line_no, line, fname) - _log.warning("Illegal %s", error_details) - continue - key, val = tup - key = key.strip() - val = val.strip() - if key in config: - _log.warning("Duplicate key in file %r line #%d.", fname, line_no) - if key in {"data.metagroups"}: - aux_val = [] - multiline = True - continue - try: - config[key] = val - except ValueError as verr: - error_details = _error_details_fmt % (line_no, line, fname) - raise ValueError(f"Bad val {val} on {error_details}\n\t{str(verr)}") from verr - - except UnicodeDecodeError: - _log.warning( - "Cannot decode configuration file %s with encoding " - "%s, check LANG and LC_* variables.", - fname, - locale.getpreferredencoding(do_setlocale=False) or "utf-8 (default)", - ) - raise - - return config - - -def rc_params(ignore_files=False): - """Read and validate arvizrc file.""" - fname = None if ignore_files else get_arviz_rcfile() - defaults = RcParams([(key, default) for key, (default, _) in defaultParams.items()]) - if fname is not None: - file_defaults = read_rcfile(fname) - defaults.update(file_defaults) - return defaults - - -rcParams = rc_params() # pylint: disable=invalid-name - - -class rc_context: # pylint: disable=invalid-name - """ - Return a context manager for managing rc settings. - - Parameters - ---------- - rc : dict, optional - Mapping containing the rcParams to modify temporally. - fname : str, optional - Filename of the file containing the rcParams to use inside the rc_context. - - Examples - -------- - This allows one to do:: - - with az.rc_context(fname='pystan.rc'): - idata = az.load_arviz_data("radon") - az.plot_posterior(idata, var_names=["gamma"]) - - The plot would have settings from 'screen.rc' - - A dictionary can also be passed to the context manager:: - - with az.rc_context(rc={'plot.max_subplots': None}, fname='pystan.rc'): - idata = az.load_arviz_data("radon") - az.plot_posterior(idata, var_names=["gamma"]) - - The 'rc' dictionary takes precedence over the settings loaded from - 'fname'. Passing a dictionary only is also valid. - """ - - # Based on mpl.rc_context - - def __init__(self, rc=None, fname=None): - self._orig = rcParams.copy() - if fname: - file_rcparams = read_rcfile(fname) - rcParams.update(file_rcparams) - if rc: - rcParams.update(rc) - - def __enter__(self): - """Define enter method of context manager.""" - return self - - def __exit__(self, exc_type, exc_value, exc_tb): - """Define exit method of context manager.""" - rcParams.update(self._orig) diff --git a/arviz/sel_utils.py b/arviz/sel_utils.py deleted file mode 100644 index c538b5e1f7..0000000000 --- a/arviz/sel_utils.py +++ /dev/null @@ -1,223 +0,0 @@ -"""Utilities for selecting and iterating on xarray objects.""" - -from itertools import product, tee - -import numpy as np -import xarray as xr - -from .labels import BaseLabeller - -__all__ = ["xarray_sel_iter", "xarray_var_iter", "xarray_to_ndarray"] - - -def selection_to_string(selection): - """Convert dictionary of coordinates to a string for labels. - - Parameters - ---------- - selection : dict[Any] -> Any - - Returns - ------- - str - key1: value1, key2: value2, ... - """ - return ", ".join([f"{v}" for _, v in selection.items()]) - - -def make_label(var_name, selection, position="below"): - """Consistent labelling for plots. - - Parameters - ---------- - var_name : str - Name of the variable - - selection : dict[Any] -> Any - Coordinates of the variable - position : str - Whether to position the coordinates' label "below" (default) or "beside" - the name of the variable - - Returns - ------- - label - A text representation of the label - """ - if selection: - sel = selection_to_string(selection) - if position == "below": - base = "{}\n{}" - elif position == "beside": - base = "{}[{}]" - else: - sel = "" - base = "{}{}" - return base.format(var_name, sel) - - -def _dims(data, var_name, skip_dims): - return [dim for dim in data[var_name].dims if dim not in skip_dims] - - -def _zip_dims(new_dims, vals): - return [dict(zip(new_dims, prod)) for prod in product(*vals)] - - -def xarray_sel_iter(data, var_names=None, combined=False, skip_dims=None, reverse_selections=False): - """Convert xarray data to an iterator over variable names and selections. - - Iterates over each var_name and all of its coordinates, returning the variable - names and selections that allow properly obtain the data from ``data`` as desired. - - Parameters - ---------- - data : xarray.Dataset - Posterior data in an xarray - - var_names : iterator of strings (optional) - Should be a subset of data.data_vars. Defaults to all of them. - - combined : bool - Whether to combine chains or leave them separate - - skip_dims : set - dimensions to not iterate over - - reverse_selections : bool - Whether to reverse selections before iterating. - - Returns - ------- - Iterator of (var_name: str, selection: dict(str, any)) - The string is the variable name, the dictionary are coordinate names to values,. - To get the values of the variable at these coordinates, do - ``data[var_name].sel(**selection)``. - """ - if skip_dims is None: - skip_dims = set() - - if combined: - skip_dims = skip_dims.union({"chain", "draw"}) - else: - skip_dims.add("draw") - - if var_names is None: - if isinstance(data, xr.Dataset): - var_names = list(data.data_vars) - elif isinstance(data, xr.DataArray): - var_names = [data.name] - data = {data.name: data} - - for var_name in var_names: - if var_name in data: - new_dims = _dims(data, var_name, skip_dims) - vals = [list(dict.fromkeys(data[var_name][dim].values)) for dim in new_dims] - dims = _zip_dims(new_dims, vals) - idims = _zip_dims(new_dims, [range(len(v)) for v in vals]) - if reverse_selections: - dims = reversed(dims) - idims = reversed(idims) - - for selection, iselection in zip(dims, idims): - yield var_name, selection, iselection - - -def xarray_var_iter( - data, var_names=None, combined=False, skip_dims=None, reverse_selections=False, dim_order=None -): - """Convert xarray data to an iterator over vectors. - - Iterates over each var_name and all of its coordinates, returning the 1d - data. - - Parameters - ---------- - data : xarray.Dataset - Posterior data in an xarray - - var_names : iterator of strings (optional) - Should be a subset of data.data_vars. Defaults to all of them. - - combined : bool - Whether to combine chains or leave them separate - - skip_dims : set - dimensions to not iterate over - - reverse_selections : bool - Whether to reverse selections before iterating. - - dim_order: list - Order for the first dimensions. Skips dimensions not found in the variable. - - Returns - ------- - Iterator of (str, dict(str, any), np.array) - The string is the variable name, the dictionary are coordinate names to values, - and the array are the values of the variable at those coordinates. - """ - data_to_sel = data - if var_names is None and isinstance(data, xr.DataArray): - data_to_sel = {data.name: data} - - if isinstance(dim_order, str): - dim_order = [dim_order] - - for var_name, selection, iselection in xarray_sel_iter( - data, - var_names=var_names, - combined=combined, - skip_dims=skip_dims, - reverse_selections=reverse_selections, - ): - selected_data = data_to_sel[var_name].sel(**selection) - if dim_order is not None: - dim_order_selected = [dim for dim in dim_order if dim in selected_data.dims] - if dim_order_selected: - selected_data = selected_data.transpose(*dim_order_selected, ...) - yield var_name, selection, iselection, selected_data.values - - -def xarray_to_ndarray(data, *, var_names=None, combined=True, label_fun=None): - """Take xarray data and unpacks into variables and data into list and numpy array respectively. - - Assumes that chain and draw are in coordinates - - Parameters - ---------- - data: xarray.DataSet - Data in an xarray from an InferenceData object. Examples include posterior or sample_stats - - var_names: iter - Should be a subset of data.data_vars not including chain and draws. Defaults to all of them - - combined: bool - Whether to combine chain into one array - - Returns - ------- - var_names: list - List of variable names - data: np.array - Data values - """ - if label_fun is None: - label_fun = BaseLabeller().make_label_vert - data_to_sel = data - if var_names is None and isinstance(data, xr.DataArray): - data_to_sel = {data.name: data} - - iterator1, iterator2 = tee(xarray_sel_iter(data, var_names=var_names, combined=combined)) - vars_and_sel = list(iterator1) - unpacked_var_names = [ - label_fun(var_name, selection, isel) for var_name, selection, isel in vars_and_sel - ] - - # Merge chains and variables, check dtype to be compatible with divergences data - data0 = data_to_sel[vars_and_sel[0][0]].sel(**vars_and_sel[0][1]) - unpacked_data = np.empty((len(unpacked_var_names), data0.size), dtype=data0.dtype) - for idx, (var_name, selection, _) in enumerate(iterator2): - unpacked_data[idx] = data_to_sel[var_name].sel(**selection).values.flatten() - - return unpacked_var_names, unpacked_data diff --git a/arviz/static/css/style.css b/arviz/static/css/style.css deleted file mode 100644 index f2d5dc60f1..0000000000 --- a/arviz/static/css/style.css +++ /dev/null @@ -1,340 +0,0 @@ -/* CSS stylesheet for displaying InferenceData objects in jupyterlab. - * - */ - -:root { - --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1)); - --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54)); - --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38)); - --xr-border-color: var(--jp-border-color2, #e0e0e0); - --xr-disabled-color: var(--jp-layout-color3, #bdbdbd); - --xr-background-color: var(--jp-layout-color0, white); - --xr-background-color-row-even: var(--jp-layout-color1, white); - --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee); -} - -html[theme=dark], -body.vscode-dark { - --xr-font-color0: rgba(255, 255, 255, 1); - --xr-font-color2: rgba(255, 255, 255, 0.54); - --xr-font-color3: rgba(255, 255, 255, 0.38); - --xr-border-color: #1F1F1F; - --xr-disabled-color: #515151; - --xr-background-color: #111111; - --xr-background-color-row-even: #111111; - --xr-background-color-row-odd: #313131; -} - -.xr-wrap { - display: block; - min-width: 300px; - max-width: 700px; -} - -.xr-text-repr-fallback { - /* fallback to plain text repr when CSS is not injected (untrusted notebook) */ - display: none; -} - -.xr-header { - padding-top: 6px; - padding-bottom: 6px; - margin-bottom: 4px; - border-bottom: solid 1px var(--xr-border-color); -} - -.xr-header > div, -.xr-header > ul { - display: inline; - margin-top: 0; - margin-bottom: 0; -} - -.xr-obj-type, -.xr-array-name { - margin-left: 2px; - margin-right: 10px; -} - -.xr-obj-type { - color: var(--xr-font-color2); -} - -.xr-sections { - padding-left: 0 !important; - display: grid; - grid-template-columns: 150px auto auto 1fr 20px 20px; -} - -.xr-sections.group-sections { - grid-template-columns: auto; -} - -.xr-section-item { - display: contents; -} - -.xr-section-item input { - display: none; -} - -.xr-section-item input + label { - color: var(--xr-disabled-color); -} - -.xr-section-item input:enabled + label { - cursor: pointer; - color: var(--xr-font-color2); -} - -.xr-section-item input:enabled + label:hover { - color: var(--xr-font-color0); -} - -.xr-section-summary { - grid-column: 1; - color: var(--xr-font-color2); - font-weight: 500; -} - -.xr-section-summary > span { - display: inline-block; - padding-left: 0.5em; -} - -.xr-section-summary-in:disabled + label { - color: var(--xr-font-color2); -} - -.xr-section-summary-in + label:before { - display: inline-block; - content: '►'; - font-size: 11px; - width: 15px; - text-align: center; -} - -.xr-section-summary-in:disabled + label:before { - color: var(--xr-disabled-color); -} - -.xr-section-summary-in:checked + label:before { - content: '▼'; -} - -.xr-section-summary-in:checked + label > span { - display: none; -} - -.xr-section-summary, -.xr-section-inline-details { - padding-top: 4px; - padding-bottom: 4px; -} - -.xr-section-inline-details { - grid-column: 2 / -1; -} - -.xr-section-details { - display: none; - grid-column: 1 / -1; - margin-bottom: 5px; -} - -.xr-section-summary-in:checked ~ .xr-section-details { - display: contents; -} - -.xr-array-wrap { - grid-column: 1 / -1; - display: grid; - grid-template-columns: 20px auto; -} - -.xr-array-wrap > label { - grid-column: 1; - vertical-align: top; -} - -.xr-preview { - color: var(--xr-font-color3); -} - -.xr-array-preview, -.xr-array-data { - padding: 0 5px !important; - grid-column: 2; -} - -.xr-array-data, -.xr-array-in:checked ~ .xr-array-preview { - display: none; -} - -.xr-array-in:checked ~ .xr-array-data, -.xr-array-preview { - display: inline-block; -} - -.xr-dim-list { - display: inline-block !important; - list-style: none; - padding: 0 !important; - margin: 0; -} - -.xr-dim-list li { - display: inline-block; - padding: 0; - margin: 0; -} - -.xr-dim-list:before { - content: '('; -} - -.xr-dim-list:after { - content: ')'; -} - -.xr-dim-list li:not(:last-child):after { - content: ','; - padding-right: 5px; -} - -.xr-has-index { - font-weight: bold; -} - -.xr-var-list, -.xr-var-item { - display: contents; -} - -.xr-var-item > div, -.xr-var-item label, -.xr-var-item > .xr-var-name span { - background-color: var(--xr-background-color-row-even); - margin-bottom: 0; -} - -.xr-var-item > .xr-var-name:hover span { - padding-right: 5px; -} - -.xr-var-list > li:nth-child(odd) > div, -.xr-var-list > li:nth-child(odd) > label, -.xr-var-list > li:nth-child(odd) > .xr-var-name span { - background-color: var(--xr-background-color-row-odd); -} - -.xr-var-name { - grid-column: 1; -} - -.xr-var-dims { - grid-column: 2; -} - -.xr-var-dtype { - grid-column: 3; - text-align: right; - color: var(--xr-font-color2); -} - -.xr-var-preview { - grid-column: 4; -} - -.xr-var-name, -.xr-var-dims, -.xr-var-dtype, -.xr-preview, -.xr-attrs dt { - white-space: nowrap; - overflow: hidden; - text-overflow: ellipsis; - padding-right: 10px; -} - -.xr-var-name:hover, -.xr-var-dims:hover, -.xr-var-dtype:hover, -.xr-attrs dt:hover { - overflow: visible; - width: auto; - z-index: 1; -} - -.xr-var-attrs, -.xr-var-data { - display: none; - background-color: var(--xr-background-color) !important; - padding-bottom: 5px !important; -} - -.xr-var-attrs-in:checked ~ .xr-var-attrs, -.xr-var-data-in:checked ~ .xr-var-data { - display: block; -} - -.xr-var-data > table { - float: right; -} - -.xr-var-name span, -.xr-var-data, -.xr-attrs { - padding-left: 25px !important; -} - -.xr-attrs, -.xr-var-attrs, -.xr-var-data { - grid-column: 1 / -1; -} - -dl.xr-attrs { - padding: 0; - margin: 0; - display: grid; - grid-template-columns: 125px auto; -} - -.xr-attrs dt, -.xr-attrs dd { - padding: 0; - margin: 0; - float: left; - padding-right: 10px; - width: auto; -} - -.xr-attrs dt { - font-weight: normal; - grid-column: 1; -} - -.xr-attrs dt:hover span { - display: inline-block; - background: var(--xr-background-color); - padding-right: 10px; -} - -.xr-attrs dd { - grid-column: 2; - white-space: pre-wrap; - word-break: break-all; -} - -.xr-icon-database, -.xr-icon-file-text2 { - display: inline-block; - vertical-align: middle; - width: 1em; - height: 1.5em !important; - stroke-width: 0; - stroke: currentColor; - fill: currentColor; -} diff --git a/arviz/static/html/icons-svg-inline.html b/arviz/static/html/icons-svg-inline.html deleted file mode 100644 index b0e837a26c..0000000000 --- a/arviz/static/html/icons-svg-inline.html +++ /dev/null @@ -1,15 +0,0 @@ - - - - - - - - - - - - - - - diff --git a/arviz/stats/__init__.py b/arviz/stats/__init__.py deleted file mode 100644 index e3fb0c14c8..0000000000 --- a/arviz/stats/__init__.py +++ /dev/null @@ -1,37 +0,0 @@ -# pylint: disable=wildcard-import -"""Statistical tests and diagnostics for ArviZ.""" -from .density_utils import * -from .diagnostics import * -from .stats import * -from .stats import _calculate_ics -from .stats_refitting import * -from .stats_utils import * - -__all__ = [ - "apply_test_function", - "bayes_factor", - "bfmi", - "compare", - "hdi", - "kde", - "loo", - "loo_pit", - "psislw", - "r2_samples", - "r2_score", - "summary", - "waic", - "weight_predictions", - "ELPDData", - "ess", - "rhat", - "mcse", - "autocorr", - "autocov", - "make_ufunc", - "smooth_data", - "wrap_xarray_ufunc", - "reloo", - "_calculate_ics", - "psens", -] diff --git a/arviz/stats/density_utils.py b/arviz/stats/density_utils.py deleted file mode 100644 index 91c18cd559..0000000000 --- a/arviz/stats/density_utils.py +++ /dev/null @@ -1,1013 +0,0 @@ -# pylint: disable=invalid-name,too-many-lines -"""Density estimation functions for ArviZ.""" -import warnings - -import numpy as np -from scipy.fftpack import fft -from scipy.optimize import brentq -from scipy.signal import convolve, convolve2d -from scipy.signal.windows import gaussian -from scipy.sparse import coo_matrix -from scipy.special import ive # pylint: disable=no-name-in-module - -from ..utils import _cov, _dot, _stack, conditional_jit - -__all__ = ["kde"] - - -def _bw_scott(x, x_std=None, **kwargs): # pylint: disable=unused-argument - """Scott's Rule.""" - if x_std is None: - x_std = np.std(x) - bw = 1.06 * x_std * len(x) ** (-0.2) - return bw - - -def _bw_silverman(x, x_std=None, **kwargs): # pylint: disable=unused-argument - """Silverman's Rule.""" - if x_std is None: - x_std = np.std(x) - q75, q25 = np.percentile(x, [75, 25]) - x_iqr = q75 - q25 - a = min(x_std, x_iqr / 1.34) - bw = 0.9 * a * len(x) ** (-0.2) - return bw - - -def _bw_isj(x, grid_counts=None, x_std=None, x_range=None): - """Improved Sheather-Jones bandwidth estimation. - - Improved Sheather and Jones method as explained in [1]_. This method is used internally by the - KDE estimator, resulting in saved computation time as minimums, maximums and the grid are - pre-computed. - - References - ---------- - .. [1] Kernel density estimation via diffusion. - Z. I. Botev, J. F. Grotowski, and D. P. Kroese. - Ann. Statist. 38 (2010), no. 5, 2916--2957. - """ - x_len = len(x) - if x_range is None: - x_min = np.min(x) - x_max = np.max(x) - x_range = x_max - x_min - - # Relative frequency per bin - if grid_counts is None: - x_std = np.std(x) - grid_len = 256 - grid_min = x_min - 0.5 * x_std - grid_max = x_max + 0.5 * x_std - grid_counts, _, _ = histogram(x, grid_len, (grid_min, grid_max)) - else: - grid_len = len(grid_counts) - 1 - - grid_relfreq = grid_counts / x_len - - # Discrete cosine transform of the data - a_k = _dct1d(grid_relfreq) - - k_sq = np.arange(1, grid_len) ** 2 - a_sq = a_k[range(1, grid_len)] ** 2 - - t = _root(_fixed_point, x_len, args=(x_len, k_sq, a_sq), x=x) - h = t**0.5 * x_range - return h - - -def _bw_experimental(x, grid_counts=None, x_std=None, x_range=None): - """Experimental bandwidth estimator.""" - bw_silverman = _bw_silverman(x, x_std=x_std) - bw_isj = _bw_isj(x, grid_counts=grid_counts, x_range=x_range) - return 0.5 * (bw_silverman + bw_isj) - - -def _bw_taylor(x): - """Taylor's rule for circular bandwidth estimation. - - This function implements a rule-of-thumb for choosing the bandwidth of a von Mises kernel - density estimator that assumes the underlying distribution is von Mises as introduced in [1]_. - It is analogous to Scott's rule for the Gaussian KDE. - - Circular bandwidth has a different scale from linear bandwidth. Unlike linear scale, low - bandwidths are associated with oversmoothing and high values with undersmoothing. - - References - ---------- - .. [1] C.C Taylor (2008). Automatic bandwidth selection for circular - density estimation. - Computational Statistics and Data Analysis, 52, 7, 3493–3500. - """ - x_len = len(x) - kappa = _kappa_mle(x) - num = 3 * x_len * kappa**2 * ive(2, 2 * kappa) - den = 4 * np.pi**0.5 * ive(0, kappa) ** 2 - return (num / den) ** 0.4 - - -_BW_METHODS_LINEAR = { - "scott": _bw_scott, - "silverman": _bw_silverman, - "isj": _bw_isj, - "experimental": _bw_experimental, -} - - -def _get_bw(x, bw, grid_counts=None, x_std=None, x_range=None): - """Compute bandwidth for a given data `x` and `bw`. - - Also checks `bw` is correctly specified. - - Parameters - ---------- - x : 1-D numpy array - 1 dimensional array of sample data from the - variable for which a density estimate is desired. - bw: int, float or str - If numeric, indicates the bandwidth and must be positive. - If str, indicates the method to estimate the bandwidth. - - Returns - ------- - bw: float - Bandwidth - """ - if isinstance(bw, bool): - raise ValueError( - ( - "`bw` must not be of type `bool`.\n" - "Expected a positive numeric or one of the following strings:\n" - f"{list(_BW_METHODS_LINEAR)}." - ) - ) - if isinstance(bw, (int, float)): - if bw < 0: - raise ValueError(f"Numeric `bw` must be positive.\nInput: {bw:.4f}.") - elif isinstance(bw, str): - bw_lower = bw.lower() - - if bw_lower not in _BW_METHODS_LINEAR: - raise ValueError( - "Unrecognized bandwidth method.\n" - f"Input is: {bw_lower}.\n" - f"Expected one of: {list(_BW_METHODS_LINEAR)}." - ) - - bw_fun = _BW_METHODS_LINEAR[bw_lower] - bw = bw_fun(x, grid_counts=grid_counts, x_std=x_std, x_range=x_range) - else: - raise ValueError( - "Unrecognized `bw` argument.\n" - "Expected a positive numeric or one of the following strings:\n" - f"{list(_BW_METHODS_LINEAR)}." - ) - return bw - - -def _vonmises_pdf(x, mu, kappa): - """Calculate vonmises_pdf.""" - if kappa <= 0: - raise ValueError("Argument 'kappa' must be positive.") - pdf = 1 / (2 * np.pi * ive(0, kappa)) * np.exp(np.cos(x - mu) - 1) ** kappa - return pdf - - -def _a1inv(x): - """Compute inverse function. - - Inverse function of the ratio of the first and - zeroth order Bessel functions of the first kind. - - Returns the value k, such that a1inv(x) = k, i.e. a1(k) = x. - """ - if 0 <= x < 0.53: - return 2 * x + x**3 + (5 * x**5) / 6 - elif x < 0.85: - return -0.4 + 1.39 * x + 0.43 / (1 - x) - else: - return 1 / (x**3 - 4 * x**2 + 3 * x) - - -def _kappa_mle(x): - mean = _circular_mean(x) - kappa = _a1inv(np.mean(np.cos(x - mean))) - return kappa - - -def _dct1d(x): - """Discrete Cosine Transform in 1 Dimension. - - Parameters - ---------- - x : numpy array - 1 dimensional array of values for which the - DCT is desired - - Returns - ------- - output : DTC transformed values - """ - x_len = len(x) - - even_increasing = np.arange(0, x_len, 2) - odd_decreasing = np.arange(x_len - 1, 0, -2) - - x = np.concatenate((x[even_increasing], x[odd_decreasing])) - - w_1k = np.r_[1, (2 * np.exp(-(0 + 1j) * (np.arange(1, x_len)) * np.pi / (2 * x_len)))] - output = np.real(w_1k * fft(x)) - - return output - - -def _fixed_point(t, N, k_sq, a_sq): - """Calculate t-zeta*gamma^[l](t). - - Implementation of the function t-zeta*gamma^[l](t) derived from equation (30) in [1]. - - References - ---------- - .. [1] Kernel density estimation via diffusion. - Z. I. Botev, J. F. Grotowski, and D. P. Kroese. - Ann. Statist. 38 (2010), no. 5, 2916--2957. - """ - k_sq = np.asarray(k_sq, dtype=np.float64) - a_sq = np.asarray(a_sq, dtype=np.float64) - - l = 7 - f = np.sum(np.power(k_sq, l) * a_sq * np.exp(-k_sq * np.pi**2 * t)) - f *= 0.5 * np.pi ** (2.0 * l) - - for j in np.arange(l - 1, 2 - 1, -1): - c1 = (1 + 0.5 ** (j + 0.5)) / 3 - c2 = np.prod(np.arange(1.0, 2 * j + 1, 2, dtype=np.float64)) - c2 /= (np.pi / 2) ** 0.5 - t_j = np.power((c1 * (c2 / (N * f))), (2.0 / (3.0 + 2.0 * j))) - f = np.sum(k_sq**j * a_sq * np.exp(-k_sq * np.pi**2.0 * t_j)) - f *= 0.5 * np.pi ** (2 * j) - - out = t - (2 * N * np.pi**0.5 * f) ** (-0.4) - return out - - -def _root(function, N, args, x): - # The right bound is at most 0.01 - found = False - N = max(min(1050, N), 50) - tol = 10e-12 + 0.01 * (N - 50) / 1000 - - while not found: - try: - bw, res = brentq(function, 0, 0.01, args=args, full_output=True, disp=False) - found = res.converged - except ValueError: - bw = 0 - tol *= 2.0 - found = False - if bw <= 0 or tol >= 1: - bw = (_bw_silverman(x) / np.ptp(x)) ** 2 - return bw - return bw - - -def _check_custom_lims(custom_lims, x_min, x_max): - """Check if `custom_lims` are of the correct type. - - It accepts numeric lists/tuples of length 2. - - Parameters - ---------- - custom_lims : Object whose type is checked. - - Returns - ------- - None: Object of type None - """ - if not isinstance(custom_lims, (list, tuple)): - raise TypeError( - "`custom_lims` must be a numeric list or tuple of length 2.\n" - f"Not an object of {type(custom_lims)}." - ) - - if len(custom_lims) != 2: - raise AttributeError(f"`len(custom_lims)` must be 2, not {len(custom_lims)}.") - - any_bool = any(isinstance(i, bool) for i in custom_lims) - if any_bool: - raise TypeError("Elements of `custom_lims` must be numeric or None, not bool.") - - custom_lims = list(custom_lims) # convert to a mutable object - if custom_lims[0] is None: - custom_lims[0] = x_min - - if custom_lims[1] is None: - custom_lims[1] = x_max - - all_numeric = all(isinstance(i, (int, float, np.integer, np.number)) for i in custom_lims) - if not all_numeric: - raise TypeError( - "Elements of `custom_lims` must be numeric or None.\nAt least one of them is not." - ) - - if not custom_lims[0] < custom_lims[1]: - raise ValueError("`custom_lims[0]` must be smaller than `custom_lims[1]`.") - - if custom_lims[0] > x_min or custom_lims[1] < x_max: - raise ValueError("Some observations are outside `custom_lims` boundaries.") - - return custom_lims - - -def _get_grid( - x_min, x_max, x_std, extend_fct, grid_len, custom_lims, extend=True, bound_correction=False -): - """Compute the grid that bins the data used to estimate the density function. - - Parameters - ---------- - x_min : float - Minimum value of the data - x_max: float - Maximum value of the data. - x_std: float - Standard deviation of the data. - extend_fct: bool - Indicates the factor by which `x_std` is multiplied - to extend the range of the data. - grid_len: int - Number of bins - custom_lims: tuple or list - Custom limits for the domain of the density estimation. - Must be numeric of length 2. Overrides `extend`. - extend: bool, optional - Whether to extend the range of the data or not. - Default is True. - bound_correction: bool, optional - Whether the density estimations performs boundary correction or not. - This does not impacts directly in the output, but is used - to override `extend`. Overrides `extend`. - Default is False. - - Returns - ------- - grid_len: int - Number of bins - grid_min: float - Minimum value of the grid - grid_max: float - Maximum value of the grid - """ - # Set up number of bins. - grid_len = max(int(grid_len), 100) - - # Set up domain - if custom_lims is not None: - custom_lims = _check_custom_lims(custom_lims, x_min, x_max) - grid_min = custom_lims[0] - grid_max = custom_lims[1] - elif extend and not bound_correction: - grid_extend = extend_fct * x_std - grid_min = x_min - grid_extend - grid_max = x_max + grid_extend - else: - grid_min = x_min - grid_max = x_max - return grid_min, grid_max, grid_len - - -def kde(x, circular=False, **kwargs): - """One dimensional density estimation. - - It is a wrapper around ``kde_linear()`` and ``kde_circular()``. - - Parameters - ---------- - x : 1D numpy array - Data used to calculate the density estimation. - circular : bool, optional - Whether ``x`` is a circular variable or not. Defaults to False. - kwargs : dict, optional - Arguments passed to ``kde_linear()`` and ``kde_circular()``. - See their documentation for more info. - - Returns - ------- - grid : numpy.ndarray - Gridded numpy array for the x values. - pdf : numpy.ndarray - Numpy array for the density estimates. - bw : float - The estimated bandwidth. Only returned if requested. - - Examples - -------- - Default density estimation for linear data - - .. plot:: - :context: close-figs - - >>> import numpy as np - >>> import matplotlib.pyplot as plt - >>> from arviz import kde - >>> - >>> rng = np.random.default_rng(49) - >>> rvs = rng.gamma(shape=1.8, size=1000) - >>> grid, pdf = kde(rvs) - >>> plt.plot(grid, pdf) - - Density estimation for linear data with Silverman's rule bandwidth - - .. plot:: - :context: close-figs - - >>> grid, pdf = kde(rvs, bw="silverman") - >>> plt.plot(grid, pdf) - - Density estimation for linear data with scaled bandwidth - - .. plot:: - :context: close-figs - - >>> # bw_fct > 1 means more smoothness. - >>> grid, pdf = kde(rvs, bw_fct=2.5) - >>> plt.plot(grid, pdf) - - Default density estimation for linear data with extended limits - - .. plot:: - :context: close-figs - - >>> grid, pdf = kde(rvs, bound_correction=False, extend=True, extend_fct=0.5) - >>> plt.plot(grid, pdf) - - Default density estimation for linear data with custom limits - - .. plot:: - :context: close-figs - - >>> # It accepts tuples and lists of length 2. - >>> grid, pdf = kde(rvs, bound_correction=False, custom_lims=(0, 11)) - >>> plt.plot(grid, pdf) - - Default density estimation for circular data - - .. plot:: - :context: close-figs - - >>> rvs = np.random.vonmises(mu=np.pi, kappa=1, size=500) - >>> grid, pdf = kde(rvs, circular=True) - >>> plt.plot(grid, pdf) - - Density estimation for circular data with scaled bandwidth - - .. plot:: - :context: close-figs - - >>> rvs = np.random.vonmises(mu=np.pi, kappa=1, size=500) - >>> # bw_fct > 1 means less smoothness. - >>> grid, pdf = kde(rvs, circular=True, bw_fct=3) - >>> plt.plot(grid, pdf) - - Density estimation for circular data with custom limits - - .. plot:: - :context: close-figs - - >>> # This is still experimental, does not always work. - >>> rvs = np.random.vonmises(mu=0, kappa=30, size=500) - >>> grid, pdf = kde(rvs, circular=True, custom_lims=(-1, 1)) - >>> plt.plot(grid, pdf) - - See Also - -------- - plot_kde : Compute and plot a kernel density estimate. - """ - x = x[np.isfinite(x)] - if x.size == 0 or np.all(x == x[0]): - warnings.warn("Your data appears to have a single value or no finite values") - - return np.zeros(2), np.array([np.nan] * 2) - - if circular: - if circular == "degrees": - x = np.radians(x) - kde_fun = _kde_circular - else: - kde_fun = _kde_linear - - return kde_fun(x, **kwargs) - - -def _kde_linear( - x, - bw="experimental", - adaptive=False, - extend=False, - bound_correction=True, - extend_fct=0, - bw_fct=1, - bw_return=False, - custom_lims=None, - cumulative=False, - grid_len=512, - **kwargs, # pylint: disable=unused-argument -): - """One dimensional density estimation for linear data. - - Given an array of data points `x` it returns an estimate of - the probability density function that generated the samples in `x`. - - Parameters - ---------- - x : 1D numpy array - Data used to calculate the density estimation. - bw: int, float or str, optional - If numeric, indicates the bandwidth and must be positive. - If str, indicates the method to estimate the bandwidth and must be one of "scott", - "silverman", "isj" or "experimental". Defaults to "experimental". - adaptive: boolean, optional - Indicates if the bandwidth is adaptive or not. - It is the recommended approach when there are multiple modes with different spread. - It is not compatible with convolution. Defaults to False. - extend: boolean, optional - Whether to extend the observed range for `x` in the estimation. - It extends each bound by a multiple of the standard deviation of `x` given by `extend_fct`. - Defaults to False. - bound_correction: boolean, optional - Whether to perform boundary correction on the bounds of `x` or not. - Defaults to True. - extend_fct: float, optional - Number of standard deviations used to widen the lower and upper bounds of `x`. - Defaults to 0.5. - bw_fct: float, optional - A value that multiplies `bw` which enables tuning smoothness by hand. - Must be positive. Values below 1 decrease smoothness while values above 1 decrease it. - Defaults to 1 (no modification). - bw_return: bool, optional - Whether to return the estimated bandwidth in addition to the other objects. - Defaults to False. - custom_lims: list or tuple, optional - A list or tuple of length 2 indicating custom bounds for the range of `x`. - Defaults to None which disables custom bounds. - cumulative: bool, optional - Whether return the PDF or the cumulative PDF. Defaults to False. - grid_len: int, optional - The number of intervals used to bin the data points i.e. the length of the grid used in - the estimation. Defaults to 512. - - Returns - ------- - grid : Gridded numpy array for the x values. - pdf : Numpy array for the density estimates. - bw: optional, the estimated bandwidth. - """ - # Check `bw_fct` is numeric and positive - if not isinstance(bw_fct, (int, float, np.integer, np.floating)): - raise TypeError(f"`bw_fct` must be a positive number, not an object of {type(bw_fct)}.") - - if bw_fct <= 0: - raise ValueError(f"`bw_fct` must be a positive number, not {bw_fct}.") - - # Preliminary calculations - x_min = x.min() - x_max = x.max() - x_std = np.std(x) - x_range = x_max - x_min - - # Determine grid - grid_min, grid_max, grid_len = _get_grid( - x_min, x_max, x_std, extend_fct, grid_len, custom_lims, extend, bound_correction - ) - grid_counts, _, grid_edges = histogram(x, grid_len, (grid_min, grid_max)) - - # Bandwidth estimation - bw = bw_fct * _get_bw(x, bw, grid_counts, x_std, x_range) - - # Density estimation - if adaptive: - grid, pdf = _kde_adaptive(x, bw, grid_edges, grid_counts, grid_len, bound_correction) - else: - grid, pdf = _kde_convolution(x, bw, grid_edges, grid_counts, grid_len, bound_correction) - - if cumulative: - pdf = pdf.cumsum() / pdf.sum() - - if bw_return: - return grid, pdf, bw - else: - return grid, pdf - - -def _kde_circular( - x, - bw="taylor", - bw_fct=1, - bw_return=False, - custom_lims=None, - cumulative=False, - grid_len=512, - **kwargs, # pylint: disable=unused-argument -): - """One dimensional density estimation for circular data. - - Given an array of data points `x` measured in radians, it returns an estimate of the - probability density function that generated the samples in `x`. - - Parameters - ---------- - x : 1D numpy array - Data used to calculate the density estimation. - bw: int, float or str, optional - If numeric, indicates the bandwidth and must be positive. - If str, indicates the method to estimate the bandwidth and must be "taylor" since it is the - only option supported so far. Defaults to "taylor". - bw_fct: float, optional - A value that multiplies `bw` which enables tuning smoothness by hand. Must be positive. - Values above 1 decrease smoothness while values below 1 decrease it. - Defaults to 1 (no modification). - bw_return: bool, optional - Whether to return the estimated bandwidth in addition to the other objects. - Defaults to False. - custom_lims: list or tuple, optional - A list or tuple of length 2 indicating custom bounds for the range of `x`. - Defaults to None which means the estimation limits are [-pi, pi]. - cumulative: bool, optional - Whether return the PDF or the cumulative PDF. Defaults to False. - grid_len: int, optional - The number of intervals used to bin the data point i.e. the length of the grid used in the - estimation. Defaults to 512. - """ - # All values between -pi and pi - x = _normalize_angle(x) - - # Check `bw_fct` is numeric and positive - if not isinstance(bw_fct, (int, float, np.integer, np.floating)): - raise TypeError(f"`bw_fct` must be a positive number, not an object of {type(bw_fct)}.") - - if bw_fct <= 0: - raise ValueError(f"`bw_fct` must be a positive number, not {bw_fct}.") - - # Determine bandwidth - if isinstance(bw, bool): - raise ValueError("`bw` can't be of type `bool`.\nExpected a positive numeric or 'taylor'") - if isinstance(bw, (int, float)) and bw < 0: - raise ValueError(f"Numeric `bw` must be positive.\nInput: {bw:.4f}.") - if isinstance(bw, str): - if bw == "taylor": - bw = _bw_taylor(x) - else: - raise ValueError(f"`bw` must be a positive numeric or `taylor`, not {bw}") - bw *= bw_fct - - # Determine grid - if custom_lims is not None: - custom_lims = _check_custom_lims(custom_lims, x.min(), x.max()) - grid_min = custom_lims[0] - grid_max = custom_lims[1] - assert grid_min >= -np.pi, "Lower limit can't be smaller than -pi" - assert grid_max <= np.pi, "Upper limit can't be larger than pi" - else: - grid_min = -np.pi - grid_max = np.pi - - bins = np.linspace(grid_min, grid_max, grid_len + 1) - bin_counts, _, bin_edges = histogram(x, bins=bins) - grid = 0.5 * (bin_edges[1:] + bin_edges[:-1]) - - kern = _vonmises_pdf(x=grid, mu=0, kappa=bw) - pdf = np.fft.fftshift(np.fft.irfft(np.fft.rfft(kern) * np.fft.rfft(bin_counts))) - pdf /= len(x) - - if cumulative: - pdf = pdf.cumsum() / pdf.sum() - - if bw_return: - return grid, pdf, bw - else: - return grid, pdf - - -# pylint: disable=unused-argument -def _kde_convolution(x, bw, grid_edges, grid_counts, grid_len, bound_correction, **kwargs): - """Kernel density with convolution. - - One dimensional Gaussian kernel density estimation via convolution of the binned relative - frequencies and a Gaussian filter. This is an internal function used by `kde()`. - """ - # Calculate relative frequencies per bin - bin_width = grid_edges[1] - grid_edges[0] - f = grid_counts / bin_width / len(x) - - # Bandwidth must consider the bin width - bw /= bin_width - - # See: https://stackoverflow.com/questions/2773606/gaussian-filter-in-matlab - - grid = (grid_edges[1:] + grid_edges[:-1]) / 2 - - kernel_n = int(bw * 2 * np.pi) - if kernel_n == 0: - kernel_n = 1 - - kernel = gaussian(kernel_n, bw) - - if bound_correction: - npad = int(grid_len / 5) - f = np.concatenate([f[npad - 1 :: -1], f, f[grid_len : grid_len - npad - 1 : -1]]) - pdf = convolve(f, kernel, mode="same", method="direct")[npad : npad + grid_len] - else: - pdf = convolve(f, kernel, mode="same", method="direct") - pdf /= bw * (2 * np.pi) ** 0.5 - - return grid, pdf - - -def _kde_adaptive(x, bw, grid_edges, grid_counts, grid_len, bound_correction, **kwargs): - """Compute Adaptive Kernel Density Estimation. - - One dimensional adaptive Gaussian kernel density estimation. The implementation uses the binning - technique. Since there is not an unique `bw`, the convolution is not possible. The alternative - implemented in this function is known as Abramson's method. - This is an internal function used by `kde()`. - """ - # Pilot computations used for bandwidth adjustment - pilot_grid, pilot_pdf = _kde_convolution( - x, bw, grid_edges, grid_counts, grid_len, bound_correction - ) - - # Adds to avoid np.log(0) and zero division - pilot_pdf += 1e-9 - - # Determine the modification factors - pdf_interp = np.interp(x, pilot_grid, pilot_pdf) - geom_mean = np.exp(np.mean(np.log(pdf_interp))) - - # Power of c = 0.5 -> Abramson's method - adj_factor = (geom_mean / pilot_pdf) ** 0.5 - bw_adj = bw * adj_factor - - # Estimation of Gaussian KDE via binned method (convolution not possible) - grid = pilot_grid - - if bound_correction: - grid_npad = int(grid_len / 5) - grid_width = grid_edges[1] - grid_edges[0] - grid_pad = grid_npad * grid_width - grid_padded = np.linspace( - grid_edges[0] - grid_pad, - grid_edges[grid_len - 1] + grid_pad, - num=grid_len + 2 * grid_npad, - ) - grid_counts = np.concatenate( - [ - grid_counts[grid_npad - 1 :: -1], - grid_counts, - grid_counts[grid_len : grid_len - grid_npad - 1 : -1], - ] - ) - bw_adj = np.concatenate( - [bw_adj[grid_npad - 1 :: -1], bw_adj, bw_adj[grid_len : grid_len - grid_npad - 1 : -1]] - ) - pdf_mat = (grid_padded - grid_padded[:, None]) / bw_adj[:, None] - pdf_mat = np.exp(-0.5 * pdf_mat**2) * grid_counts[:, None] - pdf_mat /= (2 * np.pi) ** 0.5 * bw_adj[:, None] - pdf = np.sum(pdf_mat[:, grid_npad : grid_npad + grid_len], axis=0) / len(x) - - else: - pdf_mat = (grid - grid[:, None]) / bw_adj[:, None] - pdf_mat = np.exp(-0.5 * pdf_mat**2) * grid_counts[:, None] - pdf_mat /= (2 * np.pi) ** 0.5 * bw_adj[:, None] - pdf = np.sum(pdf_mat, axis=0) / len(x) - - return grid, pdf - - -def _fast_kde_2d(x, y, gridsize=(128, 128), circular=False): - """ - 2D fft-based Gaussian kernel density estimate (KDE). - - The code was adapted from https://github.com/mfouesneau/faststats - - Parameters - ---------- - x : Numpy array or list - y : Numpy array or list - gridsize : tuple - Number of points used to discretize data. Use powers of 2 for fft optimization - circular: bool - If True use circular boundaries. Defaults to False - - Returns - ------- - grid: A gridded 2D KDE of the input points (x, y) - xmin: minimum value of x - xmax: maximum value of x - ymin: minimum value of y - ymax: maximum value of y - """ - x = np.asarray(x, dtype=float) - x = x[np.isfinite(x)] - y = np.asarray(y, dtype=float) - y = y[np.isfinite(y)] - - xmin, xmax = x.min(), x.max() - ymin, ymax = y.min(), y.max() - - len_x = len(x) - weights = np.ones(len_x) - n_x, n_y = gridsize - - d_x = (xmax - xmin) / (n_x - 1) - d_y = (ymax - ymin) / (n_y - 1) - - xyi = _stack(x, y).T - xyi -= [xmin, ymin] - xyi /= [d_x, d_y] - xyi = np.floor(xyi, xyi).T - - scotts_factor = len_x ** (-1 / 6) - cov = _cov(xyi) - std_devs = np.diag(cov) ** 0.5 - kern_nx, kern_ny = np.round(scotts_factor * 2 * np.pi * std_devs) - - inv_cov = np.linalg.inv(cov * scotts_factor**2) - - x_x = np.arange(kern_nx) - kern_nx / 2 - y_y = np.arange(kern_ny) - kern_ny / 2 - x_x, y_y = np.meshgrid(x_x, y_y) - - kernel = _stack(x_x.flatten(), y_y.flatten()) - kernel = _dot(inv_cov, kernel) * kernel - kernel = np.exp(-kernel.sum(axis=0) / 2) - kernel = kernel.reshape((int(kern_ny), int(kern_nx))) - - boundary = "wrap" if circular else "symm" - - grid = coo_matrix((weights, xyi), shape=(n_x, n_y)).toarray() - grid = convolve2d(grid, kernel, mode="same", boundary=boundary) - - norm_factor = np.linalg.det(2 * np.pi * cov * scotts_factor**2) - norm_factor = len_x * d_x * d_y * norm_factor**0.5 - - grid /= norm_factor - - return grid, xmin, xmax, ymin, ymax - - -def get_bins(values): - """ - Automatically compute the number of bins for discrete variables. - - Parameters - ---------- - values = numpy array - values - - Returns - ------- - array with the bins - - Notes - ----- - Computes the width of the bins by taking the maximum of the Sturges and the Freedman-Diaconis - estimators. According to numpy `np.histogram` this provides good all around performance. - - The Sturges is a very simplistic estimator based on the assumption of normality of the data. - This estimator has poor performance for non-normal data, which becomes especially obvious for - large data sets. The estimate depends only on size of the data. - - The Freedman-Diaconis rule uses interquartile range (IQR) to estimate the binwidth. - It is considered a robust version of the Scott rule as the IQR is less affected by outliers - than the standard deviation. However, the IQR depends on fewer points than the standard - deviation, so it is less accurate, especially for long tailed distributions. - """ - dtype = values.dtype.kind - - if dtype == "i": - x_min = values.min().astype(int) - x_max = values.max().astype(int) - else: - x_min = values.min().astype(float) - x_max = values.max().astype(float) - - # Sturges histogram bin estimator - bins_sturges = (x_max - x_min) / (np.log2(values.size) + 1) - - # The Freedman-Diaconis histogram bin estimator. - iqr = np.subtract(*np.percentile(values, [75, 25])) # pylint: disable=assignment-from-no-return - bins_fd = 2 * iqr * values.size ** (-1 / 3) - - if dtype == "i": - width = np.round(np.max([1, bins_sturges, bins_fd])).astype(int) - bins = np.arange(x_min, x_max + width + 1, width) - else: - width = np.max([bins_sturges, bins_fd]) - if np.isclose(x_min, x_max): - width = 1e-3 - bins = np.arange(x_min, x_max + width, width) - - return bins - - -def _sturges_formula(dataset, mult=1): - """Use Sturges' formula to determine number of bins. - - See https://en.wikipedia.org/wiki/Histogram#Sturges'_formula - or https://doi.org/10.1080%2F01621459.1926.10502161 - - Parameters - ---------- - dataset: xarray.DataSet - Must have the `draw` dimension - - mult: float - Used to scale the number of bins up or down. Default is 1 for Sturges' formula. - - Returns - ------- - int - Number of bins to use - """ - return int(np.ceil(mult * np.log2(dataset.draw.size)) + 1) - - -def _circular_mean(x): - """Compute mean of circular variable measured in radians. - - The result is between -pi and pi. - """ - sinr = np.sum(np.sin(x)) - cosr = np.sum(np.cos(x)) - mean = np.arctan2(sinr, cosr) - - return mean - - -def _normalize_angle(x, zero_centered=True): - """Normalize angles. - - Normalize angles in radians to [-pi, pi) or [0, 2 * pi) according to `zero_centered`. - """ - if zero_centered: - return (x + np.pi) % (2 * np.pi) - np.pi - else: - return x % (2 * np.pi) - - -@conditional_jit(cache=True, nopython=True) -def histogram(data, bins, range_hist=None): - """Conditionally jitted histogram. - - Parameters - ---------- - data : array-like - Input data. Passed as first positional argument to ``np.histogram``. - bins : int or array-like - Passed as keyword argument ``bins`` to ``np.histogram``. - range_hist : (float, float), optional - Passed as keyword argument ``range`` to ``np.histogram``. - - Returns - ------- - hist : array - The number of counts per bin. - density : array - The density corresponding to each bin. - bin_edges : array - The edges of the bins used. - """ - hist, bin_edges = np.histogram(data, bins=bins, range=range_hist) - hist_dens = hist / (hist.sum() * np.diff(bin_edges)) - return hist, hist_dens, bin_edges - - -def _find_hdi_contours(density, hdi_probs): - """ - Find contours enclosing regions of highest posterior density. - - Parameters - ---------- - density : array-like - A 2D KDE on a grid with cells of equal area. - hdi_probs : array-like - An array of highest density interval confidence probabilities. - - Returns - ------- - contour_levels : array - The contour levels corresponding to the given HDI probabilities. - """ - # Using the algorithm from corner.py - sorted_density = np.sort(density, axis=None)[::-1] - sm = sorted_density.cumsum() - sm /= sm[-1] - - contours = np.empty_like(hdi_probs) - for idx, hdi_prob in enumerate(hdi_probs): - try: - contours[idx] = sorted_density[sm <= hdi_prob][-1] - except IndexError: - contours[idx] = sorted_density[0] - - return contours diff --git a/arviz/stats/diagnostics.py b/arviz/stats/diagnostics.py deleted file mode 100644 index e7dba32bab..0000000000 --- a/arviz/stats/diagnostics.py +++ /dev/null @@ -1,1013 +0,0 @@ -# pylint: disable=too-many-lines, too-many-function-args, redefined-outer-name -"""Diagnostic functions for ArviZ.""" -import warnings -from collections.abc import Sequence - -import numpy as np -import packaging -import pandas as pd -import scipy -from scipy import stats - -from ..data import convert_to_dataset -from ..utils import Numba, _numba_var, _stack, _var_names -from .density_utils import histogram as _histogram -from .stats_utils import _circular_standard_deviation, _sqrt -from .stats_utils import autocov as _autocov -from .stats_utils import not_valid as _not_valid -from .stats_utils import quantile as _quantile -from .stats_utils import stats_variance_2d as svar -from .stats_utils import wrap_xarray_ufunc as _wrap_xarray_ufunc - -__all__ = ["bfmi", "ess", "rhat", "mcse"] - - -def bfmi(data): - r"""Calculate the estimated Bayesian fraction of missing information (BFMI). - - BFMI quantifies how well momentum resampling matches the marginal energy distribution. For more - information on BFMI, see https://arxiv.org/pdf/1604.00695v1.pdf. The current advice is that - values smaller than 0.3 indicate poor sampling. However, this threshold is - provisional and may change. See - `pystan_workflow `_ - for more information. - - Parameters - ---------- - data : obj - Any object that can be converted to an :class:`arviz.InferenceData` object. - Refer to documentation of :func:`arviz.convert_to_dataset` for details. - If InferenceData, energy variable needs to be found. - - Returns - ------- - z : array - The Bayesian fraction of missing information of the model and trace. One element per - chain in the trace. - - See Also - -------- - plot_energy : Plot energy transition distribution and marginal energy - distribution in HMC algorithms. - - Examples - -------- - Compute the BFMI of an InferenceData object - - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data('radon') - ...: az.bfmi(data) - - """ - if isinstance(data, np.ndarray): - return _bfmi(data) - - dataset = convert_to_dataset(data, group="sample_stats") - if not hasattr(dataset, "energy"): - raise TypeError("Energy variable was not found.") - return _bfmi(dataset.energy.transpose("chain", "draw")) - - -def ess( - data, - *, - var_names=None, - method="bulk", - relative=False, - prob=None, - dask_kwargs=None, -): - r"""Calculate estimate of the effective sample size (ess). - - Parameters - ---------- - data : obj - Any object that can be converted to an :class:`arviz.InferenceData` object. - Refer to documentation of :func:`arviz.convert_to_dataset` for details. - For ndarray: shape = (chain, draw). - For n-dimensional ndarray transform first to dataset with :func:`arviz.convert_to_dataset`. - var_names : str or list of str - Names of variables to include in the return value Dataset. - method : str, optional, default "bulk" - Select ess method. Valid methods are: - - - "bulk" - - "tail" # prob, optional - - "quantile" # prob - - "mean" (old ess) - - "sd" - - "median" - - "mad" (mean absolute deviance) - - "z_scale" - - "folded" - - "identity" - - "local" - relative : bool - Return relative ess - ``ress = ess / n`` - prob : float, or tuple of two floats, optional - probability value for "tail", "quantile" or "local" ess functions. - dask_kwargs : dict, optional - Dask related kwargs passed to :func:`~arviz.wrap_xarray_ufunc`. - - Returns - ------- - xarray.Dataset - Return the effective sample size, :math:`\hat{N}_{eff}` - - Notes - ----- - The basic ess (:math:`N_{\mathit{eff}}`) diagnostic is computed by: - - .. math:: \hat{N}_{\mathit{eff}} = \frac{MN}{\hat{\tau}} - - .. math:: \hat{\tau} = -1 + 2 \sum_{t'=0}^K \hat{P}_{t'} - - where :math:`M` is the number of chains, :math:`N` the number of draws, - :math:`\hat{\rho}_t` is the estimated _autocorrelation at lag :math:`t`, and - :math:`K` is the last integer for which :math:`\hat{P}_{K} = \hat{\rho}_{2K} + - \hat{\rho}_{2K+1}` is still positive. - - The current implementation is similar to Stan, which uses Geyer's initial monotone sequence - criterion (Geyer, 1992; Geyer, 2011). - - References - ---------- - * Vehtari et al. (2021). Rank-normalization, folding, and - localization: An improved Rhat for assessing convergence of - MCMC. Bayesian analysis, 16(2):667-718. - * https://mc-stan.org/docs/reference-manual/analysis.html#effective-sample-size.section - * Gelman et al. BDA3 (2013) Formula 11.8 - - See Also - -------- - arviz.rhat : Compute estimate of rank normalized splitR-hat for a set of traces. - arviz.mcse : Calculate Markov Chain Standard Error statistic. - plot_ess : Plot quantile, local or evolution of effective sample sizes (ESS). - arviz.summary : Create a data frame with summary statistics. - - Examples - -------- - Calculate the effective_sample_size using the default arguments: - - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data('non_centered_eight') - ...: az.ess(data) - - Calculate the ress of some of the variables - - .. ipython:: - - In [1]: az.ess(data, relative=True, var_names=["mu", "theta_t"]) - - Calculate the ess using the "tail" method, leaving the `prob` argument at its default - value. - - .. ipython:: - - In [1]: az.ess(data, method="tail") - - """ - methods = { - "bulk": _ess_bulk, - "tail": _ess_tail, - "quantile": _ess_quantile, - "mean": _ess_mean, - "sd": _ess_sd, - "median": _ess_median, - "mad": _ess_mad, - "z_scale": _ess_z_scale, - "folded": _ess_folded, - "identity": _ess_identity, - "local": _ess_local, - } - - if method not in methods: - raise TypeError(f"ess method {method} not found. Valid methods are:\n{', '.join(methods)}") - ess_func = methods[method] - - if (method == "quantile") and prob is None: - raise TypeError("Quantile (prob) information needs to be defined.") - - if isinstance(data, np.ndarray): - data = np.atleast_2d(data) - if len(data.shape) < 3: - if prob is not None: - return ess_func( # pylint: disable=unexpected-keyword-arg - data, prob=prob, relative=relative - ) - - return ess_func(data, relative=relative) - - msg = ( - "Only uni-dimensional ndarray variables are supported." - " Please transform first to dataset with `az.convert_to_dataset`." - ) - raise TypeError(msg) - - dataset = convert_to_dataset(data, group="posterior") - var_names = _var_names(var_names, dataset) - - dataset = dataset if var_names is None else dataset[var_names] - - ufunc_kwargs = {"ravel": False} - func_kwargs = {"relative": relative} if prob is None else {"prob": prob, "relative": relative} - return _wrap_xarray_ufunc( - ess_func, - dataset, - ufunc_kwargs=ufunc_kwargs, - func_kwargs=func_kwargs, - dask_kwargs=dask_kwargs, - ) - - -def rhat(data, *, var_names=None, method="rank", dask_kwargs=None): - r"""Compute estimate of rank normalized splitR-hat for a set of traces. - - The rank normalized R-hat diagnostic tests for lack of convergence by comparing the variance - between multiple chains to the variance within each chain. If convergence has been achieved, - the between-chain and within-chain variances should be identical. To be most effective in - detecting evidence for nonconvergence, each chain should have been initialized to starting - values that are dispersed relative to the target distribution. - - Parameters - ---------- - data : obj - Any object that can be converted to an :class:`arviz.InferenceData` object. - Refer to documentation of :func:`arviz.convert_to_dataset` for details. - At least 2 posterior chains are needed to compute this diagnostic of one or more - stochastic parameters. - For ndarray: shape = (chain, draw). - For n-dimensional ndarray transform first to dataset with ``az.convert_to_dataset``. - var_names : list - Names of variables to include in the rhat report - method : str - Select R-hat method. Valid methods are: - - "rank" # recommended by Vehtari et al. (2021) - - "split" - - "folded" - - "z_scale" - - "identity" - dask_kwargs : dict, optional - Dask related kwargs passed to :func:`~arviz.wrap_xarray_ufunc`. - - Returns - ------- - xarray.Dataset - Returns dataset of the potential scale reduction factors, :math:`\hat{R}` - - See Also - -------- - ess : Calculate estimate of the effective sample size (ess). - mcse : Calculate Markov Chain Standard Error statistic. - plot_forest : Forest plot to compare HDI intervals from a number of distributions. - - Notes - ----- - The diagnostic is computed by: - - .. math:: \hat{R} = \sqrt{\frac{\hat{V}}{W}} - - where :math:`W` is the within-chain variance and :math:`\hat{V}` is the posterior variance - estimate for the pooled rank-traces. This is the potential scale reduction factor, which - converges to unity when each of the traces is a sample from the target posterior. Values - greater than one indicate that one or more chains have not yet converged. - - Rank values are calculated over all the chains with ``scipy.stats.rankdata``. - Each chain is split in two and normalized with the z-transform following - Vehtari et al. (2021). - - References - ---------- - * Vehtari et al. (2021). Rank-normalization, folding, and - localization: An improved Rhat for assessing convergence of - MCMC. Bayesian analysis, 16(2):667-718. - * Gelman et al. BDA3 (2013) - * Brooks and Gelman (1998) - * Gelman and Rubin (1992) - - Examples - -------- - Calculate the R-hat using the default arguments: - - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data("non_centered_eight") - ...: az.rhat(data) - - Calculate the R-hat of some variables using the folded method: - - .. ipython:: - - In [1]: az.rhat(data, var_names=["mu", "theta_t"], method="folded") - - """ - methods = { - "rank": _rhat_rank, - "split": _rhat_split, - "folded": _rhat_folded, - "z_scale": _rhat_z_scale, - "identity": _rhat_identity, - } - if method not in methods: - raise TypeError( - f"R-hat method {method} not found. Valid methods are:\n{', '.join(methods)}" - ) - rhat_func = methods[method] - - if isinstance(data, np.ndarray): - data = np.atleast_2d(data) - if len(data.shape) < 3: - return rhat_func(data) - - msg = ( - "Only uni-dimensional ndarray variables are supported." - " Please transform first to dataset with `az.convert_to_dataset`." - ) - raise TypeError(msg) - - dataset = convert_to_dataset(data, group="posterior") - var_names = _var_names(var_names, dataset) - - dataset = dataset if var_names is None else dataset[var_names] - - ufunc_kwargs = {"ravel": False} - func_kwargs = {} - return _wrap_xarray_ufunc( - rhat_func, - dataset, - ufunc_kwargs=ufunc_kwargs, - func_kwargs=func_kwargs, - dask_kwargs=dask_kwargs, - ) - - -def mcse(data, *, var_names=None, method="mean", prob=None, dask_kwargs=None): - """Calculate Markov Chain Standard Error statistic. - - Parameters - ---------- - data : obj - Any object that can be converted to an :class:`arviz.InferenceData` object - Refer to documentation of :func:`arviz.convert_to_dataset` for details - For ndarray: shape = (chain, draw). - For n-dimensional ndarray transform first to dataset with ``az.convert_to_dataset``. - var_names : list - Names of variables to include in the rhat report - method : str - Select mcse method. Valid methods are: - - "mean" - - "sd" - - "median" - - "quantile" - - prob : float - Quantile information. - dask_kwargs : dict, optional - Dask related kwargs passed to :func:`~arviz.wrap_xarray_ufunc`. - - Returns - ------- - xarray.Dataset - Return the msce dataset - - See Also - -------- - ess : Compute autocovariance estimates for every lag for the input array. - summary : Create a data frame with summary statistics. - plot_mcse : Plot quantile or local Monte Carlo Standard Error. - - Examples - -------- - Calculate the Markov Chain Standard Error using the default arguments: - - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data("non_centered_eight") - ...: az.mcse(data) - - Calculate the Markov Chain Standard Error using the quantile method: - - .. ipython:: - - In [1]: az.mcse(data, method="quantile", prob=0.7) - - """ - methods = { - "mean": _mcse_mean, - "sd": _mcse_sd, - "median": _mcse_median, - "quantile": _mcse_quantile, - } - if method not in methods: - raise TypeError( - "mcse method {} not found. Valid methods are:\n{}".format( - method, "\n ".join(methods) - ) - ) - mcse_func = methods[method] - - if method == "quantile" and prob is None: - raise TypeError("Quantile (prob) information needs to be defined.") - - if isinstance(data, np.ndarray): - data = np.atleast_2d(data) - if len(data.shape) < 3: - if prob is not None: - return mcse_func(data, prob=prob) # pylint: disable=unexpected-keyword-arg - - return mcse_func(data) - - msg = ( - "Only uni-dimensional ndarray variables are supported." - " Please transform first to dataset with `az.convert_to_dataset`." - ) - raise TypeError(msg) - - dataset = convert_to_dataset(data, group="posterior") - var_names = _var_names(var_names, dataset) - - dataset = dataset if var_names is None else dataset[var_names] - - ufunc_kwargs = {"ravel": False} - func_kwargs = {} if prob is None else {"prob": prob} - return _wrap_xarray_ufunc( - mcse_func, - dataset, - ufunc_kwargs=ufunc_kwargs, - func_kwargs=func_kwargs, - dask_kwargs=dask_kwargs, - ) - - -def ks_summary(pareto_tail_indices): - """Display a summary of Pareto tail indices. - - Parameters - ---------- - pareto_tail_indices : array - Pareto tail indices. - - Returns - ------- - df_k : dataframe - Dataframe containing k diagnostic values. - """ - _numba_flag = Numba.numba_flag - if _numba_flag: - bins = np.asarray([-np.inf, 0.5, 0.7, 1, np.inf]) - kcounts, *_ = _histogram(pareto_tail_indices, bins) - else: - kcounts, *_ = _histogram(pareto_tail_indices, bins=[-np.inf, 0.5, 0.7, 1, np.inf]) - kprop = kcounts / len(pareto_tail_indices) * 100 - df_k = pd.DataFrame( - dict(_=["(good)", "(ok)", "(bad)", "(very bad)"], Count=kcounts, Pct=kprop) - ).rename(index={0: "(-Inf, 0.5]", 1: " (0.5, 0.7]", 2: " (0.7, 1]", 3: " (1, Inf)"}) - - if np.sum(kcounts[1:]) == 0: - warnings.warn("All Pareto k estimates are good (k < 0.5)") - elif np.sum(kcounts[2:]) == 0: - warnings.warn("All Pareto k estimates are ok (k < 0.7)") - - return df_k - - -def _bfmi(energy): - r"""Calculate the estimated Bayesian fraction of missing information (BFMI). - - BFMI quantifies how well momentum resampling matches the marginal energy distribution. For more - information on BFMI, see https://arxiv.org/pdf/1604.00695v1.pdf. The current advice is that - values smaller than 0.3 indicate poor sampling. However, this threshold is provisional and may - change. See http://mc-stan.org/users/documentation/case-studies/pystan_workflow.html for more - information. - - Parameters - ---------- - energy : NumPy array - Should be extracted from a gradient based sampler, such as in Stan or PyMC3. Typically, - after converting a trace or fit to InferenceData, the energy will be in - `data.sample_stats.energy`. - - Returns - ------- - z : array - The Bayesian fraction of missing information of the model and trace. One element per - chain in the trace. - """ - energy_mat = np.atleast_2d(energy) - num = np.square(np.diff(energy_mat, axis=1)).mean(axis=1) # pylint: disable=no-member - if energy_mat.ndim == 2: - den = _numba_var(svar, np.var, energy_mat, axis=1, ddof=1) - else: - den = np.var(energy, axis=1, ddof=1) - return num / den - - -def _backtransform_ranks(arr, c=3 / 8): # pylint: disable=invalid-name - """Backtransformation of ranks. - - Parameters - ---------- - arr : np.ndarray - Ranks array - c : float - Fractional offset. Defaults to c = 3/8 as recommended by Blom (1958). - - Returns - ------- - np.ndarray - - References - ---------- - Blom, G. (1958). Statistical Estimates and Transformed Beta-Variables. Wiley; New York. - """ - arr = np.asarray(arr) - size = arr.size - return (arr - c) / (size - 2 * c + 1) - - -def _z_scale(ary): - """Calculate z_scale. - - Parameters - ---------- - ary : np.ndarray - - Returns - ------- - np.ndarray - """ - ary = np.asarray(ary) - if packaging.version.parse(scipy.__version__) < packaging.version.parse("1.10.0.dev0"): - rank = stats.rankdata(ary, method="average") - else: - # the .ravel part is only needed to overcom a bug in scipy 1.10.0.rc1 - rank = stats.rankdata( # pylint: disable=unexpected-keyword-arg - ary, method="average", nan_policy="omit" - ) - rank = _backtransform_ranks(rank) - z = stats.norm.ppf(rank) - z = z.reshape(ary.shape) - return z - - -def _split_chains(ary): - """Split and stack chains.""" - ary = np.asarray(ary) - if len(ary.shape) <= 1: - ary = np.atleast_2d(ary) - _, n_draw = ary.shape - half = n_draw // 2 - return _stack(ary[:, :half], ary[:, -half:]) - - -def _z_fold(ary): - """Fold and z-scale values.""" - ary = np.asarray(ary) - ary = abs(ary - np.median(ary)) - ary = _z_scale(ary) - return ary - - -def _rhat(ary): - """Compute the rhat for a 2d array.""" - _numba_flag = Numba.numba_flag - ary = np.asarray(ary, dtype=float) - if _not_valid(ary, check_shape=False): - return np.nan - _, num_samples = ary.shape - - # Calculate chain mean - chain_mean = np.mean(ary, axis=1) - # Calculate chain variance - chain_var = _numba_var(svar, np.var, ary, axis=1, ddof=1) - # Calculate between-chain variance - between_chain_variance = num_samples * _numba_var(svar, np.var, chain_mean, axis=None, ddof=1) - # Calculate within-chain variance - within_chain_variance = np.mean(chain_var) - # Estimate of marginal posterior variance - rhat_value = np.sqrt( - (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples) - ) - return rhat_value - - -def _rhat_rank(ary): - """Compute the rank normalized rhat for 2d array. - - Computation follows https://arxiv.org/abs/1903.08008 - """ - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=2)): - return np.nan - split_ary = _split_chains(ary) - rhat_bulk = _rhat(_z_scale(split_ary)) - - split_ary_folded = abs(split_ary - np.median(split_ary)) - rhat_tail = _rhat(_z_scale(split_ary_folded)) - - rhat_rank = max(rhat_bulk, rhat_tail) - return rhat_rank - - -def _rhat_folded(ary): - """Calculate split-Rhat for folded z-values.""" - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=2)): - return np.nan - ary = _z_fold(_split_chains(ary)) - return _rhat(ary) - - -def _rhat_z_scale(ary): - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=2)): - return np.nan - return _rhat(_z_scale(_split_chains(ary))) - - -def _rhat_split(ary): - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=2)): - return np.nan - return _rhat(_split_chains(ary)) - - -def _rhat_identity(ary): - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=2)): - return np.nan - return _rhat(ary) - - -def _ess(ary, relative=False): - """Compute the effective sample size for a 2D array.""" - _numba_flag = Numba.numba_flag - ary = np.asarray(ary, dtype=float) - if _not_valid(ary, check_shape=False): - return np.nan - if (np.max(ary) - np.min(ary)) < np.finfo(float).resolution: # pylint: disable=no-member - return ary.size - if len(ary.shape) < 2: - ary = np.atleast_2d(ary) - n_chain, n_draw = ary.shape - acov = _autocov(ary, axis=1) - chain_mean = ary.mean(axis=1) - mean_var = np.mean(acov[:, 0]) * n_draw / (n_draw - 1.0) - var_plus = mean_var * (n_draw - 1.0) / n_draw - if n_chain > 1: - var_plus += _numba_var(svar, np.var, chain_mean, axis=None, ddof=1) - - rho_hat_t = np.zeros(n_draw) - rho_hat_even = 1.0 - rho_hat_t[0] = rho_hat_even - rho_hat_odd = 1.0 - (mean_var - np.mean(acov[:, 1])) / var_plus - rho_hat_t[1] = rho_hat_odd - - # Geyer's initial positive sequence - t = 1 - while t < (n_draw - 3) and (rho_hat_even + rho_hat_odd) > 0.0: - rho_hat_even = 1.0 - (mean_var - np.mean(acov[:, t + 1])) / var_plus - rho_hat_odd = 1.0 - (mean_var - np.mean(acov[:, t + 2])) / var_plus - if (rho_hat_even + rho_hat_odd) >= 0: - rho_hat_t[t + 1] = rho_hat_even - rho_hat_t[t + 2] = rho_hat_odd - t += 2 - - max_t = t - 2 - # improve estimation - if rho_hat_even > 0: - rho_hat_t[max_t + 1] = rho_hat_even - # Geyer's initial monotone sequence - t = 1 - while t <= max_t - 2: - if (rho_hat_t[t + 1] + rho_hat_t[t + 2]) > (rho_hat_t[t - 1] + rho_hat_t[t]): - rho_hat_t[t + 1] = (rho_hat_t[t - 1] + rho_hat_t[t]) / 2.0 - rho_hat_t[t + 2] = rho_hat_t[t + 1] - t += 2 - - ess = n_chain * n_draw - tau_hat = -1.0 + 2.0 * np.sum(rho_hat_t[: max_t + 1]) + np.sum(rho_hat_t[max_t + 1 : max_t + 2]) - tau_hat = max(tau_hat, 1 / np.log10(ess)) - ess = (1 if relative else ess) / tau_hat - if np.isnan(rho_hat_t).any(): - ess = np.nan - return ess - - -def _ess_bulk(ary, relative=False): - """Compute the effective sample size for the bulk.""" - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - z_scaled = _z_scale(_split_chains(ary)) - ess_bulk = _ess(z_scaled, relative=relative) - return ess_bulk - - -def _ess_tail(ary, prob=None, relative=False): - """Compute the effective sample size for the tail. - - If `prob` defined, ess = min(qess(prob), qess(1-prob)) - """ - if prob is None: - prob = (0.05, 0.95) - elif not isinstance(prob, Sequence): - prob = (prob, 1 - prob) - - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - - prob_low, prob_high = prob - quantile_low_ess = _ess_quantile(ary, prob_low, relative=relative) - quantile_high_ess = _ess_quantile(ary, prob_high, relative=relative) - return min(quantile_low_ess, quantile_high_ess) - - -def _ess_mean(ary, relative=False): - """Compute the effective sample size for the mean.""" - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - return _ess(_split_chains(ary), relative=relative) - - -def _ess_sd(ary, relative=False): - """Compute the effective sample size for the sd.""" - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - ary = (ary - ary.mean()) ** 2 - return _ess(_split_chains(ary), relative=relative) - - -def _ess_quantile(ary, prob, relative=False): - """Compute the effective sample size for the specific residual.""" - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - if prob is None: - raise TypeError("Prob not defined.") - (quantile,) = _quantile(ary, prob) - iquantile = ary <= quantile - return _ess(_split_chains(iquantile), relative=relative) - - -def _ess_local(ary, prob, relative=False): - """Compute the effective sample size for the specific residual.""" - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - if prob is None: - raise TypeError("Prob not defined.") - if len(prob) != 2: - raise ValueError("Prob argument in ess local must be upper and lower bound") - quantile = _quantile(ary, prob) - iquantile = (quantile[0] <= ary) & (ary <= quantile[1]) - return _ess(_split_chains(iquantile), relative=relative) - - -def _ess_z_scale(ary, relative=False): - """Calculate ess for z-scaLe.""" - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - return _ess(_z_scale(_split_chains(ary)), relative=relative) - - -def _ess_folded(ary, relative=False): - """Calculate split-ess for folded data.""" - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - return _ess(_z_fold(_split_chains(ary)), relative=relative) - - -def _ess_median(ary, relative=False): - """Calculate split-ess for median.""" - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - return _ess_quantile(ary, 0.5, relative=relative) - - -def _ess_mad(ary, relative=False): - """Calculate split-ess for mean absolute deviance.""" - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - ary = abs(ary - np.median(ary)) - ary = ary <= np.median(ary) - ary = _z_scale(_split_chains(ary)) - return _ess(ary, relative=relative) - - -def _ess_identity(ary, relative=False): - """Calculate ess.""" - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - return _ess(ary, relative=relative) - - -def _mcse_mean(ary): - """Compute the Markov Chain mean error.""" - _numba_flag = Numba.numba_flag - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - ess = _ess_mean(ary) - if _numba_flag: - sd = _sqrt(svar(np.ravel(ary), ddof=1), np.zeros(1)) - else: - sd = np.std(ary, ddof=1) - mcse_mean_value = sd / np.sqrt(ess) - return mcse_mean_value - - -def _mcse_sd(ary): - """Compute the Markov Chain sd error.""" - _numba_flag = Numba.numba_flag - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - sims_c2 = (ary - ary.mean()) ** 2 - ess = _ess_mean(sims_c2) - evar = (sims_c2).mean() - varvar = ((sims_c2**2).mean() - evar**2) / ess - varsd = varvar / evar / 4 - if _numba_flag: - mcse_sd_value = float(_sqrt(np.ravel(varsd), np.zeros(1))) - else: - mcse_sd_value = np.sqrt(varsd) - return mcse_sd_value - - -def _mcse_median(ary): - """Compute the Markov Chain median error.""" - return _mcse_quantile(ary, 0.5) - - -def _mcse_quantile(ary, prob): - """Compute the Markov Chain quantile error at quantile=prob.""" - ary = np.asarray(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - return np.nan - ess = _ess_quantile(ary, prob) - probability = [0.1586553, 0.8413447] - with np.errstate(invalid="ignore"): - ppf = stats.beta.ppf(probability, ess * prob + 1, ess * (1 - prob) + 1) - sorted_ary = np.sort(ary.ravel()) - size = sorted_ary.size - ppf_size = ppf * size - 1 - th1 = sorted_ary[int(np.floor(np.nanmax((ppf_size[0], 0))))] - th2 = sorted_ary[int(np.ceil(np.nanmin((ppf_size[1], size - 1))))] - return (th2 - th1) / 2 - - -def _mc_error(ary, batches=5, circular=False): - """Calculate the simulation standard error, accounting for non-independent samples. - - The trace is divided into batches, and the standard deviation of the batch - means is calculated. - - Parameters - ---------- - ary : Numpy array - An array containing MCMC samples - batches : integer - Number of batches - circular : bool - Whether to compute the error taking into account `ary` is a circular variable - (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables). - - Returns - ------- - mc_error : float - Simulation standard error - """ - _numba_flag = Numba.numba_flag - if ary.ndim > 1: - dims = np.shape(ary) - trace = np.transpose([t.ravel() for t in ary]) - - return np.reshape([_mc_error(t, batches) for t in trace], dims[1:]) - - else: - if _not_valid(ary, check_shape=False): - return np.nan - if batches == 1: - if circular: - if _numba_flag: - std = _circular_standard_deviation(ary, high=np.pi, low=-np.pi) - else: - std = stats.circstd(ary, high=np.pi, low=-np.pi) - elif _numba_flag: - std = float(_sqrt(svar(ary), np.zeros(1)).item()) - else: - std = np.std(ary) - return std / np.sqrt(len(ary)) - - batched_traces = np.resize(ary, (batches, int(len(ary) / batches))) - - if circular: - means = stats.circmean(batched_traces, high=np.pi, low=-np.pi, axis=1) - if _numba_flag: - std = _circular_standard_deviation(means, high=np.pi, low=-np.pi) - else: - std = stats.circstd(means, high=np.pi, low=-np.pi) - else: - means = np.mean(batched_traces, 1) - std = _sqrt(svar(means), np.zeros(1)) if _numba_flag else np.std(means) - return std / np.sqrt(batches) - - -def _multichain_statistics(ary, focus="mean"): - """Calculate efficiently multichain statistics for summary. - - Parameters - ---------- - ary : numpy.ndarray - focus : select focus for the statistics. Deafault is mean. - - Returns - ------- - tuple - Order of return parameters is - If focus equals "mean" - - mcse_mean, mcse_sd, ess_bulk, ess_tail, r_hat - Else if focus equals "median" - - mcse_median, ess_median, ess_tail, r_hat - """ - ary = np.atleast_2d(ary) - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): - if focus == "mean": - return np.nan, np.nan, np.nan, np.nan, np.nan - return np.nan, np.nan, np.nan, np.nan - - z_split = _z_scale(_split_chains(ary)) - - # ess tail - quantile05, quantile95 = _quantile(ary, [0.05, 0.95]) - iquantile05 = ary <= quantile05 - quantile05_ess = _ess(_split_chains(iquantile05)) - iquantile95 = ary <= quantile95 - quantile95_ess = _ess(_split_chains(iquantile95)) - ess_tail_value = min(quantile05_ess, quantile95_ess) - - if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=2)): - rhat_value = np.nan - else: - # r_hat - rhat_bulk = _rhat(z_split) - ary_folded = np.abs(ary - np.median(ary)) - rhat_tail = _rhat(_z_scale(_split_chains(ary_folded))) - rhat_value = max(rhat_bulk, rhat_tail) - - if focus == "mean": - # ess mean - ess_mean_value = _ess_mean(ary) - - # mcse_mean - sims_c2 = (ary - ary.mean()) ** 2 - sims_c2_sum = sims_c2.sum() - var = sims_c2_sum / (sims_c2.size - 1) - mcse_mean_value = np.sqrt(var / ess_mean_value) - - # ess bulk - ess_bulk_value = _ess(z_split) - - # mcse_sd - evar = sims_c2_sum / sims_c2.size - ess_mean_sims = _ess_mean(sims_c2) - varvar = ((sims_c2**2).mean() - evar**2) / ess_mean_sims - varsd = varvar / evar / 4 - mcse_sd_value = np.sqrt(varsd) - - return ( - mcse_mean_value, - mcse_sd_value, - ess_bulk_value, - ess_tail_value, - rhat_value, - ) - - # ess median - ess_median_value = _ess_median(ary) - - # mcse_median - mcse_median_value = _mcse_median(ary) - - return ( - mcse_median_value, - ess_median_value, - ess_tail_value, - rhat_value, - ) diff --git a/arviz/stats/ecdf_utils.py b/arviz/stats/ecdf_utils.py deleted file mode 100644 index 1985446c68..0000000000 --- a/arviz/stats/ecdf_utils.py +++ /dev/null @@ -1,324 +0,0 @@ -"""Functions for evaluating ECDFs and their confidence bands.""" - -import math -from typing import Any, Callable, Optional, Tuple -import warnings - -import numpy as np -from scipy.stats import uniform, binom -from scipy.optimize import minimize_scalar - -try: - from numba import jit, vectorize -except ImportError: - - def jit(*args, **kwargs): # pylint: disable=unused-argument - return lambda f: f - - def vectorize(*args, **kwargs): # pylint: disable=unused-argument - return lambda f: f - - -from ..utils import Numba - - -def compute_ecdf(sample: np.ndarray, eval_points: np.ndarray) -> np.ndarray: - """Compute ECDF of the sorted `sample` at the evaluation points.""" - return np.searchsorted(sample, eval_points, side="right") / len(sample) - - -def _get_ecdf_points( - sample: np.ndarray, eval_points: np.ndarray, difference: bool -) -> Tuple[np.ndarray, np.ndarray]: - """Compute the coordinates for the ecdf points using compute_ecdf.""" - x = eval_points - y = compute_ecdf(sample, eval_points) - - if not difference and y[0] > 0: - x = np.insert(x, 0, x[0]) - y = np.insert(y, 0, 0) - return x, y - - -def _call_rvs(rvs, ndraws, random_state): - if random_state is None: - return rvs(ndraws) - else: - return rvs(ndraws, random_state=random_state) - - -def _simulate_ecdf( - ndraws: int, - eval_points: np.ndarray, - rvs: Callable[[int, Optional[Any]], np.ndarray], - random_state: Optional[Any] = None, -) -> np.ndarray: - """Simulate ECDF at the `eval_points` using the given random variable sampler""" - sample = _call_rvs(rvs, ndraws, random_state) - sample.sort() - return compute_ecdf(sample, eval_points) - - -def _fit_pointwise_band_probability( - ndraws: int, - ecdf_at_eval_points: np.ndarray, - cdf_at_eval_points: np.ndarray, -) -> float: - """Compute the smallest marginal probability of a pointwise confidence band that - contains the ECDF.""" - ecdf_scaled = (ndraws * ecdf_at_eval_points).astype(int) - prob_lower_tail = np.amin(binom.cdf(ecdf_scaled, ndraws, cdf_at_eval_points)) - prob_upper_tail = np.amin(binom.sf(ecdf_scaled - 1, ndraws, cdf_at_eval_points)) - prob_pointwise = 1 - 2 * min(prob_lower_tail, prob_upper_tail) - return prob_pointwise - - -def _get_pointwise_confidence_band( - prob: float, ndraws: int, cdf_at_eval_points: np.ndarray -) -> Tuple[np.ndarray, np.ndarray]: - """Compute the `prob`-level pointwise confidence band.""" - count_lower, count_upper = binom.interval(prob, ndraws, cdf_at_eval_points) - prob_lower = count_lower / ndraws - prob_upper = count_upper / ndraws - return prob_lower, prob_upper - - -def ecdf_confidence_band( - ndraws: int, - eval_points: np.ndarray, - cdf_at_eval_points: np.ndarray, - prob: float = 0.95, - method="optimized", - **kwargs, -) -> Tuple[np.ndarray, np.ndarray]: - """Compute the `prob`-level confidence band for the ECDF. - - Arguments - --------- - ndraws : int - Number of samples in the original dataset. - eval_points : np.ndarray - Points at which the ECDF is evaluated. If these are dependent on the sample - values, simultaneous confidence bands may not be correctly calibrated. - cdf_at_eval_points : np.ndarray - CDF values at the evaluation points. - prob : float, default 0.95 - The target probability that a true ECDF lies within the confidence band. - method : string, default "simulated" - The method used to compute the confidence band. Valid options are: - - "pointwise": Compute the pointwise (i.e. marginal) confidence band. - - "optimized": Use optimization to estimate a simultaneous confidence band. - - "simulated": Use Monte Carlo simulation to estimate a simultaneous confidence band. - `rvs` must be provided. - rvs: callable, optional - A function that takes an integer `ndraws` and optionally the object passed to - `random_state` and returns an array of `ndraws` samples from the same distribution - as the original dataset. Required if `method` is "simulated" and variable is discrete. - num_trials : int, default 500 - The number of random ECDFs to generate for constructing simultaneous confidence bands - (if `method` is "simulated"). - random_state : int, numpy.random.Generator or numpy.random.RandomState, optional - - Returns - ------- - prob_lower : np.ndarray - Lower confidence band for the ECDF at the evaluation points. - prob_upper : np.ndarray - Upper confidence band for the ECDF at the evaluation points. - """ - if not 0 < prob < 1: - raise ValueError(f"Invalid value for `prob`. Expected 0 < prob < 1, but got {prob}.") - - if method == "pointwise": - prob_pointwise = prob - elif method == "optimized": - prob_pointwise = _optimize_simultaneous_ecdf_band_probability( - ndraws, eval_points, cdf_at_eval_points, prob=prob, **kwargs - ) - elif method == "simulated": - prob_pointwise = _simulate_simultaneous_ecdf_band_probability( - ndraws, eval_points, cdf_at_eval_points, prob=prob, **kwargs - ) - else: - raise ValueError( - f"Unknown method {method}. Valid options are 'pointwise', 'optimized', or 'simulated'." - ) - - prob_lower, prob_upper = _get_pointwise_confidence_band( - prob_pointwise, ndraws, cdf_at_eval_points - ) - - return prob_lower, prob_upper - - -def _update_ecdf_band_interior_probabilities( - prob_left: np.ndarray, - interval_left: np.ndarray, - interval_right: np.ndarray, - p: float, - ndraws: int, -) -> np.ndarray: - """Update the probability that an ECDF has been within the envelope including at the current - point. - - Arguments - --------- - prob_left : np.ndarray - For each point in the interior at the previous point, the joint probability that it and all - points before are in the interior. - interval_left : np.ndarray - The set of points in the interior at the previous point. - interval_right : np.ndarray - The set of points in the interior at the current point. - p : float - The probability of any given point found between the previous point and the current one. - ndraws : int - Number of draws in the original dataset. - - Returns - ------- - prob_right : np.ndarray - For each point in the interior at the current point, the joint probability that it and all - previous points are in the interior. - """ - interval_left = interval_left[:, np.newaxis] - prob_conditional = binom.pmf(interval_right, ndraws - interval_left, p, loc=interval_left) - prob_right = prob_left.dot(prob_conditional) - return prob_right - - -@vectorize(["float64(int64, int64, float64, int64)"]) -def _binom_pmf(k, n, p, loc): - k -= loc - if k < 0 or k > n: - return 0.0 - if p == 0: - return 1.0 if k == 0 else 0.0 - if p == 1: - return 1.0 if k == n else 0.0 - if k == 0: - return (1 - p) ** n - if k == n: - return p**n - lbinom = math.lgamma(n + 1) - math.lgamma(k + 1) - math.lgamma(n - k + 1) - return np.exp(lbinom + k * np.log(p) + (n - k) * np.log1p(-p)) - - -@jit(nopython=True) -def _update_ecdf_band_interior_probabilities_numba( - prob_left: np.ndarray, - interval_left: np.ndarray, - interval_right: np.ndarray, - p: float, - ndraws: int, -) -> np.ndarray: - interval_left = interval_left[:, np.newaxis] - prob_conditional = _binom_pmf(interval_right, ndraws - interval_left, p, interval_left) - prob_right = prob_left.dot(prob_conditional) - return prob_right - - -def _ecdf_band_interior_probability(prob_between_points, ndraws, lower_count, upper_count): - interval_left = np.arange(1) - prob_interior = np.ones(1) - for i in range(prob_between_points.shape[0]): - interval_right = np.arange(lower_count[i], upper_count[i]) - prob_interior = _update_ecdf_band_interior_probabilities( - prob_interior, interval_left, interval_right, prob_between_points[i], ndraws - ) - interval_left = interval_right - return prob_interior.sum() - - -@jit(nopython=True) -def _ecdf_band_interior_probability_numba(prob_between_points, ndraws, lower_count, upper_count): - interval_left = np.arange(1) - prob_interior = np.ones(1) - for i in range(prob_between_points.shape[0]): - interval_right = np.arange(lower_count[i], upper_count[i]) - prob_interior = _update_ecdf_band_interior_probabilities_numba( - prob_interior, interval_left, interval_right, prob_between_points[i], ndraws - ) - interval_left = interval_right - return prob_interior.sum() - - -def _ecdf_band_optimization_objective( - prob_pointwise: float, - cdf_at_eval_points: np.ndarray, - ndraws: int, - prob_target: float, -) -> float: - """Objective function for optimizing the simultaneous confidence band probability.""" - lower, upper = _get_pointwise_confidence_band(prob_pointwise, ndraws, cdf_at_eval_points) - lower_count = (lower * ndraws).astype(int) - upper_count = (upper * ndraws).astype(int) + 1 - cdf_with_zero = np.insert(cdf_at_eval_points[:-1], 0, 0) - prob_between_points = (cdf_at_eval_points - cdf_with_zero) / (1 - cdf_with_zero) - if Numba.numba_flag: - prob_interior = _ecdf_band_interior_probability_numba( - prob_between_points, ndraws, lower_count, upper_count - ) - else: - prob_interior = _ecdf_band_interior_probability( - prob_between_points, ndraws, lower_count, upper_count - ) - return abs(prob_interior - prob_target) - - -def _optimize_simultaneous_ecdf_band_probability( - ndraws: int, - eval_points: np.ndarray, # pylint: disable=unused-argument - cdf_at_eval_points: np.ndarray, - prob: float = 0.95, - **kwargs, # pylint: disable=unused-argument -): - """Estimate probability for simultaneous confidence band using optimization. - - This function simulates the pointwise probability needed to construct pointwise confidence bands - that form a `prob`-level confidence envelope for the ECDF of a sample. - """ - cdf_at_eval_points = np.unique(cdf_at_eval_points) - objective = lambda p: _ecdf_band_optimization_objective(p, cdf_at_eval_points, ndraws, prob) - prob_pointwise = minimize_scalar(objective, bounds=(prob, 1), method="bounded").x - return prob_pointwise - - -def _simulate_simultaneous_ecdf_band_probability( - ndraws: int, - eval_points: np.ndarray, - cdf_at_eval_points: np.ndarray, - prob: float = 0.95, - rvs: Optional[Callable[[int, Optional[Any]], np.ndarray]] = None, - num_trials: int = 500, - random_state: Optional[Any] = None, -) -> float: - """Estimate probability for simultaneous confidence band using simulation. - - This function simulates the pointwise probability needed to construct pointwise - confidence bands that form a `prob`-level confidence envelope for the ECDF - of a sample. - """ - if rvs is None: - warnings.warn( - "Assuming variable is continuous for calibration of pointwise bands. " - "If the variable is discrete, specify random variable sampler `rvs`.", - UserWarning, - ) - # if variable continuous, we can calibrate the confidence band using a uniform - # distribution - rvs = uniform(0, 1).rvs - eval_points_sim = cdf_at_eval_points - else: - eval_points_sim = eval_points - - probs_pointwise = np.empty(num_trials) - for i in range(num_trials): - ecdf_at_eval_points = _simulate_ecdf( - ndraws, eval_points_sim, rvs, random_state=random_state - ) - prob_pointwise = _fit_pointwise_band_probability( - ndraws, ecdf_at_eval_points, cdf_at_eval_points - ) - probs_pointwise[i] = prob_pointwise - return np.quantile(probs_pointwise, prob) diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py deleted file mode 100644 index 7d1d893841..0000000000 --- a/arviz/stats/stats.py +++ /dev/null @@ -1,2422 +0,0 @@ -# pylint: disable=too-many-lines -"""Statistical functions in ArviZ.""" - -import warnings -from copy import deepcopy -from typing import List, Optional, Tuple, Union, Mapping, cast, Callable - -import numpy as np -import pandas as pd -import scipy.stats as st -from xarray_einstats import stats -import xarray as xr -from scipy.optimize import minimize, LinearConstraint, Bounds -from typing_extensions import Literal - -NO_GET_ARGS: bool = False # pylint: disable=invalid-name -try: - from typing_extensions import get_args -except ImportError: - NO_GET_ARGS = True # pylint: disable=invalid-name - -from .. import _log -from ..data import InferenceData, convert_to_dataset, convert_to_inference_data, extract -from ..rcparams import rcParams, ScaleKeyword, ICKeyword -from ..utils import Numba, _numba_var, _var_names, get_coords -from .density_utils import get_bins as _get_bins -from .density_utils import histogram as _histogram -from .density_utils import kde as _kde -from .density_utils import _kde_linear -from .diagnostics import _mc_error, _multichain_statistics, ess -from .stats_utils import ELPDData, _circular_standard_deviation, smooth_data -from .stats_utils import get_log_likelihood as _get_log_likelihood -from .stats_utils import get_log_prior as _get_log_prior -from .stats_utils import logsumexp as _logsumexp -from .stats_utils import make_ufunc as _make_ufunc -from .stats_utils import stats_variance_2d as svar -from .stats_utils import wrap_xarray_ufunc as _wrap_xarray_ufunc -from ..sel_utils import xarray_var_iter -from ..labels import BaseLabeller - - -__all__ = [ - "apply_test_function", - "bayes_factor", - "compare", - "hdi", - "loo", - "loo_pit", - "psislw", - "r2_samples", - "r2_score", - "summary", - "waic", - "weight_predictions", - "_calculate_ics", - "psens", -] - - -def compare( - compare_dict: Mapping[str, InferenceData], - ic: Optional[ICKeyword] = None, - method: Literal["stacking", "BB-pseudo-BMA", "pseudo-BMA"] = "stacking", - b_samples: int = 1000, - alpha: float = 1, - seed=None, - scale: Optional[ScaleKeyword] = None, - var_name: Optional[str] = None, -): - r"""Compare models based on their expected log pointwise predictive density (ELPD). - - The ELPD is estimated either by Pareto smoothed importance sampling leave-one-out - cross-validation (LOO) or using the widely applicable information criterion (WAIC). - We recommend loo. Read more theory here - in a paper by some of the - leading authorities on model comparison dx.doi.org/10.1111/1467-9868.00353 - - Parameters - ---------- - compare_dict: dict of {str: InferenceData or ELPDData} - A dictionary of model names and :class:`arviz.InferenceData` or ``ELPDData``. - ic: str, optional - Method to estimate the ELPD, available options are "loo" or "waic". Defaults to - ``rcParams["stats.information_criterion"]``. - method: str, optional - Method used to estimate the weights for each model. Available options are: - - - 'stacking' : stacking of predictive distributions. - - 'BB-pseudo-BMA' : pseudo-Bayesian Model averaging using Akaike-type - weighting. The weights are stabilized using the Bayesian bootstrap. - - 'pseudo-BMA': pseudo-Bayesian Model averaging using Akaike-type - weighting, without Bootstrap stabilization (not recommended). - - For more information read https://arxiv.org/abs/1704.02030 - b_samples: int, optional default = 1000 - Number of samples taken by the Bayesian bootstrap estimation. - Only useful when method = 'BB-pseudo-BMA'. - Defaults to ``rcParams["stats.ic_compare_method"]``. - alpha: float, optional - The shape parameter in the Dirichlet distribution used for the Bayesian bootstrap. Only - useful when method = 'BB-pseudo-BMA'. When alpha=1 (default), the distribution is uniform - on the simplex. A smaller alpha will keeps the final weights more away from 0 and 1. - seed: int or np.random.RandomState instance, optional - If int or RandomState, use it for seeding Bayesian bootstrap. Only - useful when method = 'BB-pseudo-BMA'. Default None the global - :mod:`numpy.random` state is used. - scale: str, optional - Output scale for IC. Available options are: - - - `log` : (default) log-score (after Vehtari et al. (2017)) - - `negative_log` : -1 * (log-score) - - `deviance` : -2 * (log-score) - - A higher log-score (or a lower deviance) indicates a model with better predictive - accuracy. - var_name: str, optional - If there is more than a single observed variable in the ``InferenceData``, which - should be used as the basis for comparison. - - Returns - ------- - A DataFrame, ordered from best to worst model (measured by the ELPD). - The index reflects the key with which the models are passed to this function. The columns are: - rank: The rank-order of the models. 0 is the best. - elpd: ELPD estimated either using (PSIS-LOO-CV `elpd_loo` or WAIC `elpd_waic`). - Higher ELPD indicates higher out-of-sample predictive fit ("better" model). - If `scale` is `deviance` or `negative_log` smaller values indicates - higher out-of-sample predictive fit ("better" model). - pIC: Estimated effective number of parameters. - elpd_diff: The difference in ELPD between two models. - If more than two models are compared, the difference is computed relative to the - top-ranked model, that always has a elpd_diff of 0. - weight: Relative weight for each model. - This can be loosely interpreted as the probability of each model (among the compared model) - given the data. By default the uncertainty in the weights estimation is considered using - Bayesian bootstrap. - SE: Standard error of the ELPD estimate. - If method = BB-pseudo-BMA these values are estimated using Bayesian bootstrap. - dSE: Standard error of the difference in ELPD between each model and the top-ranked model. - It's always 0 for the top-ranked model. - warning: A value of 1 indicates that the computation of the ELPD may not be reliable. - This could be indication of WAIC/LOO starting to fail see - http://arxiv.org/abs/1507.04544 for details. - scale: Scale used for the ELPD. - - Examples - -------- - Compare the centered and non centered models of the eight school problem: - - .. ipython:: - :okwarning: - - In [1]: import arviz as az - ...: data1 = az.load_arviz_data("non_centered_eight") - ...: data2 = az.load_arviz_data("centered_eight") - ...: compare_dict = {"non centered": data1, "centered": data2} - ...: az.compare(compare_dict) - - Compare the models using PSIS-LOO-CV, returning the ELPD in log scale and calculating the - weights using the stacking method. - - .. ipython:: - :okwarning: - - In [1]: az.compare(compare_dict, ic="loo", method="stacking", scale="log") - - See Also - -------- - loo : - Compute the ELPD using the Pareto smoothed importance sampling Leave-one-out - cross-validation method. - waic : Compute the ELPD using the widely applicable information criterion. - plot_compare : Summary plot for model comparison. - - References - ---------- - .. [1] Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using - leave-one-out cross-validation and WAIC. Stat Comput 27, 1413–1432 (2017) - see https://doi.org/10.1007/s11222-016-9696-4 - - """ - try: - (ics_dict, scale, ic) = _calculate_ics(compare_dict, scale=scale, ic=ic, var_name=var_name) - except Exception as e: - raise e.__class__("Encountered error in ELPD computation of compare.") from e - names = list(ics_dict.keys()) - if ic in {"loo", "waic"}: - df_comp = pd.DataFrame( - { - "rank": pd.Series(index=names, dtype="int"), - f"elpd_{ic}": pd.Series(index=names, dtype="float"), - f"p_{ic}": pd.Series(index=names, dtype="float"), - "elpd_diff": pd.Series(index=names, dtype="float"), - "weight": pd.Series(index=names, dtype="float"), - "se": pd.Series(index=names, dtype="float"), - "dse": pd.Series(index=names, dtype="float"), - "warning": pd.Series(index=names, dtype="boolean"), - "scale": pd.Series(index=names, dtype="str"), - } - ) - else: - raise NotImplementedError(f"The information criterion {ic} is not supported.") - - if scale == "log": - scale_value = 1 - ascending = False - else: - if scale == "negative_log": - scale_value = -1 - else: - scale_value = -2 - ascending = True - - method = rcParams["stats.ic_compare_method"] if method is None else method - if method.lower() not in ["stacking", "bb-pseudo-bma", "pseudo-bma"]: - raise ValueError(f"The method {method}, to compute weights, is not supported.") - - p_ic = f"p_{ic}" - ic_i = f"{ic}_i" - - ics = pd.DataFrame.from_dict(ics_dict, orient="index") - ics.sort_values(by=f"elpd_{ic}", inplace=True, ascending=ascending) - ics[ic_i] = ics[ic_i].apply(lambda x: x.values.flatten()) - - if method.lower() == "stacking": - rows, cols, ic_i_val = _ic_matrix(ics, ic_i) - exp_ic_i = np.exp(ic_i_val / scale_value) - - def log_score(weights): - return -np.sum(np.log(exp_ic_i @ weights)) - - def gradient(weights): - denominator = exp_ic_i @ weights - return -np.sum(exp_ic_i / denominator[:, np.newaxis], axis=0) - - theta = np.full(cols, 1.0 / cols) - bounds = Bounds(lb=np.zeros(cols), ub=np.ones(cols)) - constraints = LinearConstraint(np.ones(cols), lb=1.0, ub=1.0) - - minimize_result = minimize( - fun=log_score, x0=theta, jac=gradient, bounds=bounds, constraints=constraints - ) - - weights = minimize_result["x"] - ses = ics["se"] - - elif method.lower() == "bb-pseudo-bma": - rows, cols, ic_i_val = _ic_matrix(ics, ic_i) - ic_i_val = ic_i_val * rows - - b_weighting = st.dirichlet.rvs(alpha=[alpha] * rows, size=b_samples, random_state=seed) - weights = np.zeros((b_samples, cols)) - z_bs = np.zeros_like(weights) - for i in range(b_samples): - z_b = np.dot(b_weighting[i], ic_i_val) - u_weights = np.exp((z_b - np.max(z_b)) / scale_value) - z_bs[i] = z_b # pylint: disable=unsupported-assignment-operation - weights[i] = u_weights / np.sum(u_weights) - - weights = weights.mean(axis=0) - ses = pd.Series(z_bs.std(axis=0), index=ics.index) # pylint: disable=no-member - - elif method.lower() == "pseudo-bma": - min_ic = ics.iloc[0][f"elpd_{ic}"] - z_rv = np.exp((ics[f"elpd_{ic}"] - min_ic) / scale_value) - weights = (z_rv / np.sum(z_rv)).to_numpy() - ses = ics["se"] - - if np.any(weights): - min_ic_i_val = ics[ic_i].iloc[0] - for idx, val in enumerate(ics.index): - res = ics.loc[val] - if scale_value < 0: - diff = res[ic_i] - min_ic_i_val - else: - diff = min_ic_i_val - res[ic_i] - d_ic = np.sum(diff) - d_std_err = np.sqrt(len(diff) * np.var(diff)) - std_err = ses.loc[val] - weight = weights[idx] - df_comp.loc[val] = ( - idx, - res[f"elpd_{ic}"], - res[p_ic], - d_ic, - weight, - std_err, - d_std_err, - res["warning"], - res["scale"], - ) - - df_comp["rank"] = df_comp["rank"].astype(int) - df_comp["warning"] = df_comp["warning"].astype(bool) - return df_comp.sort_values(by=f"elpd_{ic}", ascending=ascending) - - -def _ic_matrix(ics, ic_i): - """Store the previously computed pointwise predictive accuracy values (ics) in a 2D matrix.""" - cols, _ = ics.shape - rows = len(ics[ic_i].iloc[0]) - ic_i_val = np.zeros((rows, cols)) - - for idx, val in enumerate(ics.index): - ic = ics.loc[val][ic_i] - - if len(ic) != rows: - raise ValueError("The number of observations should be the same across all models") - - ic_i_val[:, idx] = ic - - return rows, cols, ic_i_val - - -def _calculate_ics( - compare_dict, - scale: Optional[ScaleKeyword] = None, - ic: Optional[ICKeyword] = None, - var_name: Optional[str] = None, -): - """Calculate LOO or WAIC only if necessary. - - It always calls the ic function with ``pointwise=True``. - - Parameters - ---------- - compare_dict : dict of {str : InferenceData or ELPDData} - A dictionary of model names and InferenceData or ELPDData objects - scale : str, optional - Output scale for IC. Available options are: - - - `log` : (default) log-score (after Vehtari et al. (2017)) - - `negative_log` : -1 * (log-score) - - `deviance` : -2 * (log-score) - - A higher log-score (or a lower deviance) indicates a model with better predictive accuracy. - ic : str, optional - Information Criterion (PSIS-LOO `loo` or WAIC `waic`) used to compare models. - Defaults to ``rcParams["stats.information_criterion"]``. - var_name : str, optional - Name of the variable storing pointwise log likelihood values in ``log_likelihood`` group. - - - Returns - ------- - compare_dict : dict of ELPDData - scale : str - ic : str - - """ - precomputed_elpds = { - name: elpd_data - for name, elpd_data in compare_dict.items() - if isinstance(elpd_data, ELPDData) - } - precomputed_ic = None - precomputed_scale = None - if precomputed_elpds: - _, arbitrary_elpd = precomputed_elpds.popitem() - precomputed_ic = arbitrary_elpd.index[0].split("_")[1] - precomputed_scale = arbitrary_elpd["scale"] - raise_non_pointwise = f"{precomputed_ic}_i" not in arbitrary_elpd - if any( - elpd_data.index[0].split("_")[1] != precomputed_ic - for elpd_data in precomputed_elpds.values() - ): - raise ValueError( - "All information criteria to be compared must be the same " - "but found both loo and waic." - ) - if any(elpd_data["scale"] != precomputed_scale for elpd_data in precomputed_elpds.values()): - raise ValueError("All information criteria to be compared must use the same scale") - if ( - any(f"{precomputed_ic}_i" not in elpd_data for elpd_data in precomputed_elpds.values()) - or raise_non_pointwise - ): - raise ValueError("Not all provided ELPDData have been calculated with pointwise=True") - if ic is not None and ic.lower() != precomputed_ic: - warnings.warn( - "Provided ic argument is incompatible with precomputed elpd data. " - f"Using ic from precomputed elpddata: {precomputed_ic}" - ) - ic = precomputed_ic - if scale is not None and scale.lower() != precomputed_scale: - warnings.warn( - "Provided scale argument is incompatible with precomputed elpd data. " - f"Using scale from precomputed elpddata: {precomputed_scale}" - ) - scale = precomputed_scale - - if ic is None and precomputed_ic is None: - ic = cast(ICKeyword, rcParams["stats.information_criterion"]) - elif ic is None: - ic = precomputed_ic - else: - ic = cast(ICKeyword, ic.lower()) - allowable = ["loo", "waic"] if NO_GET_ARGS else get_args(ICKeyword) - if ic not in allowable: - raise ValueError(f"{ic} is not a valid value for ic: must be in {allowable}") - - if scale is None and precomputed_scale is None: - scale = cast(ScaleKeyword, rcParams["stats.ic_scale"]) - elif scale is None: - scale = precomputed_scale - else: - scale = cast(ScaleKeyword, scale.lower()) - allowable = ["log", "negative_log", "deviance"] if NO_GET_ARGS else get_args(ScaleKeyword) - if scale not in allowable: - raise ValueError(f"{scale} is not a valid value for scale: must be in {allowable}") - - if ic == "loo": - ic_func: Callable = loo - elif ic == "waic": - ic_func = waic - else: - raise NotImplementedError(f"The information criterion {ic} is not supported.") - - compare_dict = deepcopy(compare_dict) - for name, dataset in compare_dict.items(): - if not isinstance(dataset, ELPDData): - try: - compare_dict[name] = ic_func( - convert_to_inference_data(dataset), - pointwise=True, - scale=scale, - var_name=var_name, - ) - except Exception as e: - raise e.__class__( - f"Encountered error trying to compute {ic} from model {name}." - ) from e - return (compare_dict, scale, ic) - - -def hdi( - ary, - hdi_prob=None, - circular=False, - multimodal=False, - skipna=False, - group="posterior", - var_names=None, - filter_vars=None, - coords=None, - max_modes=10, - dask_kwargs=None, - **kwargs, -): - """ - Calculate highest density interval (HDI) of array for given probability. - - The HDI is the minimum width Bayesian credible interval (BCI). - - Parameters - ---------- - ary: obj - object containing posterior samples. - Any object that can be converted to an :class:`arviz.InferenceData` object. - Refer to documentation of :func:`arviz.convert_to_dataset` for details. - hdi_prob: float, optional - Prob for which the highest density interval will be computed. Defaults to - ``stats.ci_prob`` rcParam. - circular: bool, optional - Whether to compute the hdi taking into account `x` is a circular variable - (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables). - Only works if multimodal is False. - multimodal: bool, optional - If true it may compute more than one hdi if the distribution is multimodal and the - modes are well separated. - skipna: bool, optional - If true ignores nan values when computing the hdi. Defaults to false. - group: str, optional - Specifies which InferenceData group should be used to calculate hdi. - Defaults to 'posterior' - var_names: list, optional - Names of variables to include in the hdi report. Prefix the variables by ``~`` - when you want to exclude them from the report: `["~beta"]` instead of `["beta"]` - (see :func:`arviz.summary` for more details). - filter_vars: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - ``pandas.filter``. - coords: mapping, optional - Specifies the subset over to calculate hdi. - max_modes: int, optional - Specifies the maximum number of modes for multimodal case. - dask_kwargs : dict, optional - Dask related kwargs passed to :func:`~arviz.wrap_xarray_ufunc`. - kwargs: dict, optional - Additional keywords passed to :func:`~arviz.wrap_xarray_ufunc`. - - Returns - ------- - np.ndarray or xarray.Dataset, depending upon input - lower(s) and upper(s) values of the interval(s). - - See Also - -------- - plot_hdi : Plot highest density intervals for regression data. - xarray.Dataset.quantile : Calculate quantiles of array for given probabilities. - - Examples - -------- - Calculate the HDI of a Normal random variable: - - .. ipython:: - - In [1]: import arviz as az - ...: import numpy as np - ...: data = np.random.normal(size=2000) - ...: az.hdi(data, hdi_prob=.68) - - Calculate the HDI of a dataset: - - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data('centered_eight') - ...: az.hdi(data) - - We can also calculate the HDI of some of the variables of dataset: - - .. ipython:: - - In [1]: az.hdi(data, var_names=["mu", "theta"]) - - By default, ``hdi`` is calculated over the ``chain`` and ``draw`` dimensions. We can use the - ``input_core_dims`` argument of :func:`~arviz.wrap_xarray_ufunc` to change this. In this example - we calculate the HDI also over the ``school`` dimension: - - .. ipython:: - - In [1]: az.hdi(data, var_names="theta", input_core_dims = [["chain","draw", "school"]]) - - We can also calculate the hdi over a particular selection: - - .. ipython:: - - In [1]: az.hdi(data, coords={"chain":[0, 1, 3]}, input_core_dims = [["draw"]]) - - """ - if hdi_prob is None: - hdi_prob = rcParams["stats.ci_prob"] - elif not 1 >= hdi_prob > 0: - raise ValueError("The value of hdi_prob should be in the interval (0, 1]") - - func_kwargs = { - "hdi_prob": hdi_prob, - "skipna": skipna, - "out_shape": (max_modes, 2) if multimodal else (2,), - } - kwargs.setdefault("output_core_dims", [["mode", "hdi"] if multimodal else ["hdi"]]) - if not multimodal: - func_kwargs["circular"] = circular - else: - func_kwargs["max_modes"] = max_modes - - func = _hdi_multimodal if multimodal else _hdi - - isarray = isinstance(ary, np.ndarray) - if isarray and ary.ndim <= 1: - func_kwargs.pop("out_shape") - hdi_data = func(ary, **func_kwargs) # pylint: disable=unexpected-keyword-arg - return hdi_data[~np.isnan(hdi_data).all(axis=1), :] if multimodal else hdi_data - - if isarray and ary.ndim == 2: - warnings.warn( - "hdi currently interprets 2d data as (draw, shape) but this will change in " - "a future release to (chain, draw) for coherence with other functions", - FutureWarning, - stacklevel=2, - ) - ary = np.expand_dims(ary, 0) - - ary = convert_to_dataset(ary, group=group) - if coords is not None: - ary = get_coords(ary, coords) - var_names = _var_names(var_names, ary, filter_vars) - ary = ary[var_names] if var_names else ary - - hdi_coord = xr.DataArray(["lower", "higher"], dims=["hdi"], attrs=dict(hdi_prob=hdi_prob)) - hdi_data = _wrap_xarray_ufunc( - func, ary, func_kwargs=func_kwargs, dask_kwargs=dask_kwargs, **kwargs - ).assign_coords({"hdi": hdi_coord}) - hdi_data = hdi_data.dropna("mode", how="all") if multimodal else hdi_data - return hdi_data.x.values if isarray else hdi_data - - -def _hdi(ary, hdi_prob, circular, skipna): - """Compute hpi over the flattened array.""" - ary = ary.flatten() - if skipna: - nans = np.isnan(ary) - if not nans.all(): - ary = ary[~nans] - n = len(ary) - - if circular: - mean = st.circmean(ary, high=np.pi, low=-np.pi) - ary = ary - mean - ary = np.arctan2(np.sin(ary), np.cos(ary)) - - ary = np.sort(ary) - interval_idx_inc = int(np.floor(hdi_prob * n)) - n_intervals = n - interval_idx_inc - interval_width = np.subtract(ary[interval_idx_inc:], ary[:n_intervals], dtype=np.float64) - - if len(interval_width) == 0: - raise ValueError("Too few elements for interval calculation. ") - - min_idx = np.argmin(interval_width) - hdi_min = ary[min_idx] - hdi_max = ary[min_idx + interval_idx_inc] - - if circular: - hdi_min = hdi_min + mean - hdi_max = hdi_max + mean - hdi_min = np.arctan2(np.sin(hdi_min), np.cos(hdi_min)) - hdi_max = np.arctan2(np.sin(hdi_max), np.cos(hdi_max)) - - hdi_interval = np.array([hdi_min, hdi_max]) - - return hdi_interval - - -def _hdi_multimodal(ary, hdi_prob, skipna, max_modes): - """Compute HDI if the distribution is multimodal.""" - ary = ary.flatten() - if skipna: - ary = ary[~np.isnan(ary)] - - if ary.dtype.kind == "f": - bins, density = _kde(ary) - lower, upper = bins[0], bins[-1] - range_x = upper - lower - dx = range_x / len(density) - else: - bins = _get_bins(ary) - _, density, _ = _histogram(ary, bins=bins) - dx = np.diff(bins)[0] - - density *= dx - - idx = np.argsort(-density) - intervals = bins[idx][density[idx].cumsum() <= hdi_prob] - intervals.sort() - - intervals_splitted = np.split(intervals, np.where(np.diff(intervals) >= dx * 1.1)[0] + 1) - - hdi_intervals = np.full((max_modes, 2), np.nan) - for i, interval in enumerate(intervals_splitted): - if i == max_modes: - warnings.warn( - f"found more modes than {max_modes}, returning only the first {max_modes} modes" - ) - break - if interval.size == 0: - hdi_intervals[i] = np.asarray([bins[0], bins[0]]) - else: - hdi_intervals[i] = np.asarray([interval[0], interval[-1]]) - - return np.array(hdi_intervals) - - -def loo(data, pointwise=None, var_name=None, reff=None, scale=None): - """Compute Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO-CV). - - Estimates the expected log pointwise predictive density (elpd) using Pareto-smoothed - importance sampling leave-one-out cross-validation (PSIS-LOO-CV). Also calculates LOO's - standard error and the effective number of parameters. Read more theory here - https://arxiv.org/abs/1507.04544 and here https://arxiv.org/abs/1507.02646 - - Parameters - ---------- - data: obj - Any object that can be converted to an :class:`arviz.InferenceData` object. - Refer to documentation of - :func:`arviz.convert_to_dataset` for details. - pointwise: bool, optional - If True the pointwise predictive accuracy will be returned. Defaults to - ``stats.ic_pointwise`` rcParam. - var_name : str, optional - The name of the variable in log_likelihood groups storing the pointwise log - likelihood data to use for loo computation. - reff: float, optional - Relative MCMC efficiency, ``ess / n`` i.e. number of effective samples divided by the number - of actual samples. Computed from trace by default. - scale: str - Output scale for loo. Available options are: - - - ``log`` : (default) log-score - - ``negative_log`` : -1 * log-score - - ``deviance`` : -2 * log-score - - A higher log-score (or a lower deviance or negative log_score) indicates a model with - better predictive accuracy. - - Returns - ------- - ELPDData object (inherits from :class:`pandas.Series`) with the following row/attributes: - elpd_loo: approximated expected log pointwise predictive density (elpd) - se: standard error of the elpd - p_loo: effective number of parameters - n_samples: number of samples - n_data_points: number of data points - warning: bool - True if the estimated shape parameter of Pareto distribution is greater than - ``good_k``. - loo_i: :class:`~xarray.DataArray` with the pointwise predictive accuracy, - only if pointwise=True - pareto_k: array of Pareto shape values, only if pointwise True - scale: scale of the elpd - good_k: For a sample size S, the thresold is compute as min(1 - 1/log10(S), 0.7) - - The returned object has a custom print method that overrides pd.Series method. - - See Also - -------- - compare : Compare models based on PSIS-LOO loo or WAIC waic cross-validation. - waic : Compute the widely applicable information criterion. - plot_compare : Summary plot for model comparison. - plot_elpd : Plot pointwise elpd differences between two or more models. - plot_khat : Plot Pareto tail indices for diagnosing convergence. - - Examples - -------- - Calculate LOO of a model: - - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data("centered_eight") - ...: az.loo(data) - - Calculate LOO of a model and return the pointwise values: - - .. ipython:: - - In [2]: data_loo = az.loo(data, pointwise=True) - ...: data_loo.loo_i - """ - inference_data = convert_to_inference_data(data) - log_likelihood = _get_log_likelihood(inference_data, var_name=var_name) - pointwise = rcParams["stats.ic_pointwise"] if pointwise is None else pointwise - - log_likelihood = log_likelihood.stack(__sample__=("chain", "draw")) - shape = log_likelihood.shape - n_samples = shape[-1] - n_data_points = np.prod(shape[:-1]) - scale = rcParams["stats.ic_scale"] if scale is None else scale.lower() - - if scale == "deviance": - scale_value = -2 - elif scale == "log": - scale_value = 1 - elif scale == "negative_log": - scale_value = -1 - else: - raise TypeError('Valid scale values are "deviance", "log", "negative_log"') - - if reff is None: - if not hasattr(inference_data, "posterior"): - raise TypeError("Must be able to extract a posterior group from data.") - posterior = inference_data.posterior - n_chains = len(posterior.chain) - if n_chains == 1: - reff = 1.0 - else: - ess_p = ess(posterior, method="mean") - # this mean is over all data variables - reff = ( - np.hstack([ess_p[v].values.flatten() for v in ess_p.data_vars]).mean() / n_samples - ) - - log_weights, pareto_shape = psislw(-log_likelihood, reff) - log_weights += log_likelihood - - warn_mg = False - good_k = min(1 - 1 / np.log10(n_samples), 0.7) - - if np.any(pareto_shape > good_k): - warnings.warn( - f"Estimated shape parameter of Pareto distribution is greater than {good_k:.2f} " - "for one or more samples. You should consider using a more robust model, this is " - "because importance sampling is less likely to work well if the marginal posterior " - "and LOO posterior are very different. This is more likely to happen with a " - "non-robust model and highly influential observations." - ) - warn_mg = True - - ufunc_kwargs = {"n_dims": 1, "ravel": False} - kwargs = {"input_core_dims": [["__sample__"]]} - loo_lppd_i = scale_value * _wrap_xarray_ufunc( - _logsumexp, log_weights, ufunc_kwargs=ufunc_kwargs, **kwargs - ) - loo_lppd = loo_lppd_i.values.sum() - loo_lppd_se = (n_data_points * np.var(loo_lppd_i.values)) ** 0.5 - - lppd = np.sum( - _wrap_xarray_ufunc( - _logsumexp, - log_likelihood, - func_kwargs={"b_inv": n_samples}, - ufunc_kwargs=ufunc_kwargs, - **kwargs, - ).values - ) - p_loo = lppd - loo_lppd / scale_value - - if not pointwise: - return ELPDData( - data=[loo_lppd, loo_lppd_se, p_loo, n_samples, n_data_points, warn_mg, scale, good_k], - index=[ - "elpd_loo", - "se", - "p_loo", - "n_samples", - "n_data_points", - "warning", - "scale", - "good_k", - ], - ) - if np.equal(loo_lppd, loo_lppd_i).all(): # pylint: disable=no-member - warnings.warn( - "The point-wise LOO is the same with the sum LOO, please double check " - "the Observed RV in your model to make sure it returns element-wise logp." - ) - return ELPDData( - data=[ - loo_lppd, - loo_lppd_se, - p_loo, - n_samples, - n_data_points, - warn_mg, - loo_lppd_i.rename("loo_i"), - pareto_shape, - scale, - good_k, - ], - index=[ - "elpd_loo", - "se", - "p_loo", - "n_samples", - "n_data_points", - "warning", - "loo_i", - "pareto_k", - "scale", - "good_k", - ], - ) - - -def psislw(log_weights, reff=1.0, normalize=True): - """ - Pareto smoothed importance sampling (PSIS). - - Notes - ----- - If the ``log_weights`` input is an :class:`~xarray.DataArray` with a dimension - named ``__sample__`` (recommended) ``psislw`` will interpret this dimension as samples, - and all other dimensions as dimensions of the observed data, looping over them to - calculate the psislw of each observation. If no ``__sample__`` dimension is present or - the input is a numpy array, the last dimension will be interpreted as ``__sample__``. - - Parameters - ---------- - log_weights : DataArray or (..., N) array-like - Array of size (n_observations, n_samples) - reff : float, default 1 - relative MCMC efficiency, ``ess / n`` - normalize : bool, default True - return normalized log weights - - Returns - ------- - lw_out : DataArray or (..., N) ndarray - Smoothed, truncated and possibly normalized log weights. - kss : DataArray or (...) ndarray - Estimates of the shape parameter *k* of the generalized Pareto - distribution. - - References - ---------- - * Vehtari et al. (2024). Pareto smoothed importance sampling. Journal of Machine - Learning Research, 25(72):1-58. - - See Also - -------- - loo : Compute Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO-CV). - - Examples - -------- - Get Pareto smoothed importance sampling (PSIS) log weights: - - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data("non_centered_eight") - ...: log_likelihood = data.log_likelihood["obs"].stack( - ...: __sample__=["chain", "draw"] - ...: ) - ...: az.psislw(-log_likelihood, reff=0.8) - - """ - log_weights = deepcopy(log_weights) - if hasattr(log_weights, "__sample__"): - n_samples = len(log_weights.__sample__) - shape = [ - size for size, dim in zip(log_weights.shape, log_weights.dims) if dim != "__sample__" - ] - else: - n_samples = log_weights.shape[-1] - shape = log_weights.shape[:-1] - # precalculate constants - cutoff_ind = -int(np.ceil(min(n_samples / 5.0, 3 * (n_samples / reff) ** 0.5))) - 1 - cutoffmin = np.log(np.finfo(float).tiny) # pylint: disable=no-member, assignment-from-no-return - - # create output array with proper dimensions - out = np.empty_like(log_weights), np.empty(shape) - - # define kwargs - func_kwargs = { - "cutoff_ind": cutoff_ind, - "cutoffmin": cutoffmin, - "out": out, - "normalize": normalize, - } - ufunc_kwargs = {"n_dims": 1, "n_output": 2, "ravel": False, "check_shape": False} - kwargs = {"input_core_dims": [["__sample__"]], "output_core_dims": [["__sample__"], []]} - log_weights, pareto_shape = _wrap_xarray_ufunc( - _psislw, - log_weights, - ufunc_kwargs=ufunc_kwargs, - func_kwargs=func_kwargs, - **kwargs, - ) - if isinstance(log_weights, xr.DataArray): - log_weights = log_weights.rename("log_weights") - if isinstance(pareto_shape, xr.DataArray): - pareto_shape = pareto_shape.rename("pareto_shape") - return log_weights, pareto_shape - - -def _psislw(log_weights, cutoff_ind, cutoffmin, normalize): - """ - Pareto smoothed importance sampling (PSIS) for a 1D vector. - - Parameters - ---------- - log_weights: array - Array of length n_observations - cutoff_ind: int - cutoffmin: float - normalize: bool - - Returns - ------- - lw_out: array - Smoothed log weights - kss: float - Pareto tail index - """ - x = np.asarray(log_weights) - - # improve numerical accuracy - max_x = np.max(x) - x -= max_x - # sort the array - x_sort_ind = np.argsort(x) - # divide log weights into body and right tail - xcutoff = max(x[x_sort_ind[cutoff_ind]], cutoffmin) - - expxcutoff = np.exp(xcutoff) - (tailinds,) = np.where(x > xcutoff) # pylint: disable=unbalanced-tuple-unpacking - x_tail = x[tailinds] - tail_len = len(x_tail) - if tail_len <= 4: - # not enough tail samples for gpdfit - k = np.inf - else: - # order of tail samples - x_tail_si = np.argsort(x_tail) - # fit generalized Pareto distribution to the right tail samples - x_tail = np.exp(x_tail) - expxcutoff - k, sigma = _gpdfit(x_tail[x_tail_si]) - - if np.isfinite(k): - # no smoothing if GPD fit failed - # compute ordered statistic for the fit - sti = np.arange(0.5, tail_len) / tail_len - smoothed_tail = _gpinv(sti, k, sigma) - smoothed_tail = np.log( # pylint: disable=assignment-from-no-return - smoothed_tail + expxcutoff - ) - # place the smoothed tail into the output array - x[tailinds[x_tail_si]] = smoothed_tail - # truncate smoothed values to the largest raw weight 0 - x[x > 0] = 0 - - # renormalize weights - if normalize: - x -= _logsumexp(x) - else: - x += max_x - - return x, k - - -def _gpdfit(ary): - """Estimate the parameters for the Generalized Pareto Distribution (GPD). - - Empirical Bayes estimate for the parameters of the generalized Pareto - distribution given the data. - - Parameters - ---------- - ary: array - sorted 1D data array - - Returns - ------- - k: float - estimated shape parameter - sigma: float - estimated scale parameter - """ - prior_bs = 3 - prior_k = 10 - n = len(ary) - m_est = 30 + int(n**0.5) - - b_ary = 1 - np.sqrt(m_est / (np.arange(1, m_est + 1, dtype=float) - 0.5)) - b_ary /= prior_bs * ary[int(n / 4 + 0.5) - 1] - b_ary += 1 / ary[-1] - - k_ary = np.log1p(-b_ary[:, None] * ary).mean(axis=1) # pylint: disable=no-member - len_scale = n * (np.log(-(b_ary / k_ary)) - k_ary - 1) - weights = 1 / np.exp(len_scale - len_scale[:, None]).sum(axis=1) - - # remove negligible weights - real_idxs = weights >= 10 * np.finfo(float).eps - if not np.all(real_idxs): - weights = weights[real_idxs] - b_ary = b_ary[real_idxs] - # normalise weights - weights /= weights.sum() - - # posterior mean for b - b_post = np.sum(b_ary * weights) - # estimate for k - k_post = np.log1p(-b_post * ary).mean() # pylint: disable=invalid-unary-operand-type,no-member - # add prior for k_post - sigma = -k_post / b_post - k_post = (n * k_post + prior_k * 0.5) / (n + prior_k) - - return k_post, sigma - - -def _gpinv(probs, kappa, sigma): - """Inverse Generalized Pareto distribution function.""" - # pylint: disable=unsupported-assignment-operation, invalid-unary-operand-type - x = np.full_like(probs, np.nan) - if sigma <= 0: - return x - ok = (probs > 0) & (probs < 1) - if np.all(ok): - if np.abs(kappa) < np.finfo(float).eps: - x = -np.log1p(-probs) - else: - x = np.expm1(-kappa * np.log1p(-probs)) / kappa - x *= sigma - else: - if np.abs(kappa) < np.finfo(float).eps: - x[ok] = -np.log1p(-probs[ok]) - else: - x[ok] = np.expm1(-kappa * np.log1p(-probs[ok])) / kappa - x *= sigma - x[probs == 0] = 0 - x[probs == 1] = np.inf if kappa >= 0 else -sigma / kappa - return x - - -def r2_samples(y_true, y_pred): - """R² samples for Bayesian regression models. Only valid for linear models. - - Parameters - ---------- - y_true: array-like of shape = (n_outputs,) - Ground truth (correct) target values. - y_pred: array-like of shape = (n_posterior_samples, n_outputs) - Estimated target values. - - Returns - ------- - Pandas Series with the following indices: - Bayesian R² samples. - - See Also - -------- - plot_lm : Posterior predictive and mean plots for regression-like data. - - Examples - -------- - Calculate R² samples for Bayesian regression models : - - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data('regression1d') - ...: y_true = data.observed_data["y"].values - ...: y_pred = data.posterior_predictive.stack(sample=("chain", "draw"))["y"].values.T - ...: az.r2_samples(y_true, y_pred) - - """ - _numba_flag = Numba.numba_flag - if y_pred.ndim == 1: - var_y_est = _numba_var(svar, np.var, y_pred) - var_e = _numba_var(svar, np.var, (y_true - y_pred)) - else: - var_y_est = _numba_var(svar, np.var, y_pred, axis=1) - var_e = _numba_var(svar, np.var, (y_true - y_pred), axis=1) - r_squared = var_y_est / (var_y_est + var_e) - - return r_squared - - -def r2_score(y_true, y_pred): - """R² for Bayesian regression models. Only valid for linear models. - - Parameters - ---------- - y_true: array-like of shape = (n_outputs,) - Ground truth (correct) target values. - y_pred: array-like of shape = (n_posterior_samples, n_outputs) - Estimated target values. - - Returns - ------- - Pandas Series with the following indices: - r2: Bayesian R² - r2_std: standard deviation of the Bayesian R². - - See Also - -------- - plot_lm : Posterior predictive and mean plots for regression-like data. - - Examples - -------- - Calculate R² for Bayesian regression models : - - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data('regression1d') - ...: y_true = data.observed_data["y"].values - ...: y_pred = data.posterior_predictive.stack(sample=("chain", "draw"))["y"].values.T - ...: az.r2_score(y_true, y_pred) - - """ - r_squared = r2_samples(y_true=y_true, y_pred=y_pred) - return pd.Series([np.mean(r_squared), np.std(r_squared)], index=["r2", "r2_std"]) - - -def summary( - data, - var_names: Optional[List[str]] = None, - filter_vars=None, - group=None, - fmt: "Literal['wide', 'long', 'xarray']" = "wide", - kind: "Literal['all', 'stats', 'diagnostics']" = "all", - round_to=None, - circ_var_names=None, - stat_focus="mean", - stat_funcs=None, - extend=True, - hdi_prob=None, - skipna=False, - labeller=None, - coords=None, - index_origin=None, - order=None, -) -> Union[pd.DataFrame, xr.Dataset]: - """Create a data frame with summary statistics. - - Parameters - ---------- - data: obj - Any object that can be converted to an :class:`arviz.InferenceData` object - Refer to documentation of :func:`arviz.convert_to_dataset` for details - var_names: list - Names of variables to include in summary. Prefix the variables by ``~`` when you - want to exclude them from the summary: `["~beta"]` instead of `["beta"]` (see - examples below). - filter_vars: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - ``pandas.filter``. - coords: Dict[str, List[Any]], optional - Coordinate subset for which to calculate the summary. - group: str - Select a group for summary. Defaults to "posterior", "prior" or first group - in that order, depending what groups exists. - fmt: {'wide', 'long', 'xarray'} - Return format is either pandas.DataFrame {'wide', 'long'} or xarray.Dataset {'xarray'}. - kind: {'all', 'stats', 'diagnostics'} - Whether to include the `stats`: `mean`, `sd`, `hdi_3%`, `hdi_97%`, or the `diagnostics`: - `mcse_mean`, `mcse_sd`, `ess_bulk`, `ess_tail`, and `r_hat`. Default to include `all` of - them. - round_to: int - Number of decimals used to round results. Defaults to 2. Use "none" to return raw numbers. - circ_var_names: list - A list of circular variables to compute circular stats for - stat_focus : str, default "mean" - Select the focus for summary. - stat_funcs: dict - A list of functions or a dict of functions with function names as keys used to calculate - statistics. By default, the mean, standard deviation, simulation standard error, and - highest posterior density intervals are included. - - The functions will be given one argument, the samples for a variable as an nD array, - The functions should be in the style of a ufunc and return a single number. For example, - :func:`numpy.mean`, or ``scipy.stats.var`` would both work. - extend: boolean - If True, use the statistics returned by ``stat_funcs`` in addition to, rather than in place - of, the default statistics. This is only meaningful when ``stat_funcs`` is not None. - hdi_prob: float, optional - Highest density interval to compute. Defaults to 0.94. This is only meaningful when - ``stat_funcs`` is None. - skipna: bool - If true ignores nan values when computing the summary statistics, it does not affect the - behaviour of the functions passed to ``stat_funcs``. Defaults to false. - labeller : labeller instance, optional - Class providing the method `make_label_flat` to generate the labels in the plot titles. - For more details on ``labeller`` usage see :ref:`label_guide` - credible_interval: float, optional - deprecated: Please see hdi_prob - order - deprecated: order is now ignored. - index_origin - deprecated: index_origin is now ignored, modify the coordinate values to change the - value used in summary. - - Returns - ------- - pandas.DataFrame or xarray.Dataset - Return type dicated by `fmt` argument. - - Return value will contain summary statistics for each variable. Default statistics depend on - the value of ``stat_focus``: - - ``stat_focus="mean"``: `mean`, `sd`, `hdi_3%`, `hdi_97%`, `mcse_mean`, `mcse_sd`, - `ess_bulk`, `ess_tail`, and `r_hat` - - ``stat_focus="median"``: `median`, `mad`, `eti_3%`, `eti_97%`, `mcse_median`, `ess_median`, - `ess_tail`, and `r_hat` - - `r_hat` is only computed for traces with 2 or more chains. - - See Also - -------- - waic : Compute the widely applicable information criterion. - loo : Compute Pareto-smoothed importance sampling leave-one-out - cross-validation (PSIS-LOO-CV). - ess : Calculate estimate of the effective sample size (ess). - rhat : Compute estimate of rank normalized splitR-hat for a set of traces. - mcse : Calculate Markov Chain Standard Error statistic. - - Examples - -------- - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data("centered_eight") - ...: az.summary(data, var_names=["mu", "tau"]) - - You can use ``filter_vars`` to select variables without having to specify all the exact - names. Use ``filter_vars="like"`` to select based on partial naming: - - .. ipython:: - - In [1]: az.summary(data, var_names=["the"], filter_vars="like") - - Use ``filter_vars="regex"`` to select based on regular expressions, and prefix the variables - you want to exclude by ``~``. Here, we exclude from the summary all the variables - starting with the letter t: - - .. ipython:: - - In [1]: az.summary(data, var_names=["~^t"], filter_vars="regex") - - Other statistics can be calculated by passing a list of functions - or a dictionary with key, function pairs. - - .. ipython:: - - In [1]: import numpy as np - ...: def median_sd(x): - ...: median = np.percentile(x, 50) - ...: sd = np.sqrt(np.mean((x-median)**2)) - ...: return sd - ...: - ...: func_dict = { - ...: "std": np.std, - ...: "median_std": median_sd, - ...: "5%": lambda x: np.percentile(x, 5), - ...: "median": lambda x: np.percentile(x, 50), - ...: "95%": lambda x: np.percentile(x, 95), - ...: } - ...: az.summary( - ...: data, - ...: var_names=["mu", "tau"], - ...: stat_funcs=func_dict, - ...: extend=False - ...: ) - - Use ``stat_focus`` to change the focus of summary statistics obatined to median: - - .. ipython:: - - In [1]: az.summary(data, stat_focus="median") - - """ - _log.cache = [] - - if coords is None: - coords = {} - - if index_origin is not None: - warnings.warn( - "index_origin has been deprecated. summary now shows coordinate values, " - "to change the label shown, modify the coordinate values before calling summary", - DeprecationWarning, - ) - index_origin = rcParams["data.index_origin"] - if labeller is None: - labeller = BaseLabeller() - if hdi_prob is None: - hdi_prob = rcParams["stats.ci_prob"] - elif not 1 >= hdi_prob > 0: - raise ValueError("The value of hdi_prob should be in the interval (0, 1]") - - if isinstance(data, InferenceData): - if group is None: - if not data.groups(): - raise TypeError("InferenceData does not contain any groups") - if "posterior" in data: - dataset = data["posterior"] - elif "prior" in data: - dataset = data["prior"] - else: - warnings.warn(f"Selecting first found group: {data.groups()[0]}") - dataset = data[data.groups()[0]] - elif group in data.groups(): - dataset = data[group] - else: - raise TypeError(f"InferenceData does not contain group: {group}") - else: - dataset = convert_to_dataset(data, group="posterior") - var_names = _var_names(var_names, dataset, filter_vars) - dataset = dataset if var_names is None else dataset[var_names] - dataset = get_coords(dataset, coords) - - fmt_group = ("wide", "long", "xarray") - if not isinstance(fmt, str) or (fmt.lower() not in fmt_group): - raise TypeError(f"Invalid format: '{fmt}'. Formatting options are: {fmt_group}") - - kind_group = ("all", "stats", "diagnostics") - if not isinstance(kind, str) or kind not in kind_group: - raise TypeError(f"Invalid kind: '{kind}'. Kind options are: {kind_group}") - - focus_group = ("mean", "median") - if not isinstance(stat_focus, str) or (stat_focus not in focus_group): - raise TypeError(f"Invalid format: '{stat_focus}'. Focus options are: {focus_group}") - - if stat_focus != "mean" and circ_var_names is not None: - raise TypeError(f"Invalid format: Circular stats not supported for '{stat_focus}'") - - if order is not None: - warnings.warn( - "order has been deprecated. summary now shows coordinate values.", DeprecationWarning - ) - - alpha = 1 - hdi_prob - - extra_metrics = [] - extra_metric_names = [] - - if stat_funcs is not None: - if isinstance(stat_funcs, dict): - for stat_func_name, stat_func in stat_funcs.items(): - extra_metrics.append( - xr.apply_ufunc( - _make_ufunc(stat_func), dataset, input_core_dims=(("chain", "draw"),) - ) - ) - extra_metric_names.append(stat_func_name) - else: - for stat_func in stat_funcs: - extra_metrics.append( - xr.apply_ufunc( - _make_ufunc(stat_func), dataset, input_core_dims=(("chain", "draw"),) - ) - ) - extra_metric_names.append(stat_func.__name__) - - metrics: List[xr.Dataset] = [] - metric_names: List[str] = [] - if extend and kind in ["all", "stats"]: - if stat_focus == "mean": - mean = dataset.mean(dim=("chain", "draw"), skipna=skipna) - - sd = dataset.std(dim=("chain", "draw"), ddof=1, skipna=skipna) - - hdi_post = hdi(dataset, hdi_prob=hdi_prob, multimodal=False, skipna=skipna) - hdi_lower = hdi_post.sel(hdi="lower", drop=True) - hdi_higher = hdi_post.sel(hdi="higher", drop=True) - metrics.extend((mean, sd, hdi_lower, hdi_higher)) - metric_names.extend( - ("mean", "sd", f"hdi_{100 * alpha / 2:g}%", f"hdi_{100 * (1 - alpha / 2):g}%") - ) - elif stat_focus == "median": - median = dataset.median(dim=("chain", "draw"), skipna=skipna) - - mad = stats.median_abs_deviation(dataset, dims=("chain", "draw")) - eti_post = dataset.quantile( - (alpha / 2, 1 - alpha / 2), dim=("chain", "draw"), skipna=skipna - ) - eti_lower = eti_post.isel(quantile=0, drop=True) - eti_higher = eti_post.isel(quantile=1, drop=True) - metrics.extend((median, mad, eti_lower, eti_higher)) - metric_names.extend( - ("median", "mad", f"eti_{100 * alpha / 2:g}%", f"eti_{100 * (1 - alpha / 2):g}%") - ) - - if circ_var_names: - nan_policy = "omit" if skipna else "propagate" - circ_mean = stats.circmean( - dataset, dims=["chain", "draw"], high=np.pi, low=-np.pi, nan_policy=nan_policy - ) - _numba_flag = Numba.numba_flag - if _numba_flag: - circ_sd = xr.apply_ufunc( - _make_ufunc(_circular_standard_deviation), - dataset, - kwargs=dict(high=np.pi, low=-np.pi, skipna=skipna), - input_core_dims=(("chain", "draw"),), - ) - else: - circ_sd = stats.circstd( - dataset, dims=["chain", "draw"], high=np.pi, low=-np.pi, nan_policy=nan_policy - ) - circ_mcse = xr.apply_ufunc( - _make_ufunc(_mc_error), - dataset, - kwargs=dict(circular=True), - input_core_dims=(("chain", "draw"),), - ) - - circ_hdi = hdi(dataset, hdi_prob=hdi_prob, circular=True, skipna=skipna) - circ_hdi_lower = circ_hdi.sel(hdi="lower", drop=True) - circ_hdi_higher = circ_hdi.sel(hdi="higher", drop=True) - - if kind in ["all", "diagnostics"] and extend: - diagnostics_names: Tuple[str, ...] - if stat_focus == "mean": - diagnostics = xr.apply_ufunc( - _make_ufunc(_multichain_statistics, n_output=5, ravel=False), - dataset, - input_core_dims=(("chain", "draw"),), - output_core_dims=tuple([] for _ in range(5)), - ) - diagnostics_names = ( - "mcse_mean", - "mcse_sd", - "ess_bulk", - "ess_tail", - "r_hat", - ) - - elif stat_focus == "median": - diagnostics = xr.apply_ufunc( - _make_ufunc(_multichain_statistics, n_output=4, ravel=False), - dataset, - kwargs=dict(focus="median"), - input_core_dims=(("chain", "draw"),), - output_core_dims=tuple([] for _ in range(4)), - ) - diagnostics_names = ( - "mcse_median", - "ess_median", - "ess_tail", - "r_hat", - ) - metrics.extend(diagnostics) - metric_names.extend(diagnostics_names) - - if circ_var_names and kind != "diagnostics" and stat_focus == "mean": - for metric, circ_stat in zip( - # Replace only the first 5 statistics for their circular equivalent - metrics[:5], - (circ_mean, circ_sd, circ_hdi_lower, circ_hdi_higher, circ_mcse), - ): - for circ_var in circ_var_names: - metric[circ_var] = circ_stat[circ_var] - - metrics.extend(extra_metrics) - metric_names.extend(extra_metric_names) - joined = ( - xr.concat(metrics, dim="metric").assign_coords(metric=metric_names).reset_coords(drop=True) - ) - n_metrics = len(metric_names) - n_vars = np.sum([joined[var].size // n_metrics for var in joined.data_vars]) - - if fmt.lower() == "wide": - summary_df = pd.DataFrame( - (np.full((cast(int, n_vars), n_metrics), np.nan)), columns=metric_names - ) - indices = [] - for i, (var_name, sel, isel, values) in enumerate( - xarray_var_iter(joined, skip_dims={"metric"}) - ): - summary_df.iloc[i] = values - indices.append(labeller.make_label_flat(var_name, sel, isel)) - summary_df.index = indices - elif fmt.lower() == "long": - df = joined.to_dataframe().reset_index().set_index("metric") - df.index = list(df.index) - summary_df = df - else: - # format is 'xarray' - summary_df = joined - if (round_to is not None) and (round_to not in ("None", "none")): - summary_df = summary_df.round(round_to) - elif round_to not in ("None", "none") and (fmt.lower() in ("long", "wide")): - # Don't round xarray object by default (even with "none") - decimals = { - col: 3 if col not in {"ess_bulk", "ess_tail", "r_hat"} else 2 if col == "r_hat" else 0 - for col in summary_df.columns - } - summary_df = summary_df.round(decimals) - - return summary_df - - -def waic(data, pointwise=None, var_name=None, scale=None, dask_kwargs=None): - """Compute the widely applicable information criterion. - - Estimates the expected log pointwise predictive density (elpd) using WAIC. Also calculates the - WAIC's standard error and the effective number of parameters. - Read more theory here https://arxiv.org/abs/1507.04544 and here https://arxiv.org/abs/1004.2316 - - Parameters - ---------- - data: obj - Any object that can be converted to an :class:`arviz.InferenceData` object. - Refer to documentation of :func:`arviz.convert_to_inference_data` for details. - pointwise: bool - If True the pointwise predictive accuracy will be returned. Defaults to - ``stats.ic_pointwise`` rcParam. - var_name : str, optional - The name of the variable in log_likelihood groups storing the pointwise log - likelihood data to use for waic computation. - scale: str - Output scale for WAIC. Available options are: - - - `log` : (default) log-score - - `negative_log` : -1 * log-score - - `deviance` : -2 * log-score - - A higher log-score (or a lower deviance or negative log_score) indicates a model with - better predictive accuracy. - dask_kwargs : dict, optional - Dask related kwargs passed to :func:`~arviz.wrap_xarray_ufunc`. - - Returns - ------- - ELPDData object (inherits from :class:`pandas.Series`) with the following row/attributes: - elpd_waic: approximated expected log pointwise predictive density (elpd) - se: standard error of the elpd - p_waic: effective number parameters - n_samples: number of samples - n_data_points: number of data points - warning: bool - True if posterior variance of the log predictive densities exceeds 0.4 - waic_i: :class:`~xarray.DataArray` with the pointwise predictive accuracy, - only if pointwise=True - scale: scale of the elpd - - The returned object has a custom print method that overrides pd.Series method. - - See Also - -------- - loo : Compute Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO-CV). - compare : Compare models based on PSIS-LOO-CV or WAIC. - plot_compare : Summary plot for model comparison. - - Examples - -------- - Calculate WAIC of a model: - - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data("centered_eight") - ...: az.waic(data) - - Calculate WAIC of a model and return the pointwise values: - - .. ipython:: - - In [2]: data_waic = az.waic(data, pointwise=True) - ...: data_waic.waic_i - """ - inference_data = convert_to_inference_data(data) - log_likelihood = _get_log_likelihood(inference_data, var_name=var_name) - scale = rcParams["stats.ic_scale"] if scale is None else scale.lower() - pointwise = rcParams["stats.ic_pointwise"] if pointwise is None else pointwise - - if scale == "deviance": - scale_value = -2 - elif scale == "log": - scale_value = 1 - elif scale == "negative_log": - scale_value = -1 - else: - raise TypeError('Valid scale values are "deviance", "log", "negative_log"') - - log_likelihood = log_likelihood.stack(__sample__=("chain", "draw")) - shape = log_likelihood.shape - n_samples = shape[-1] - n_data_points = np.prod(shape[:-1]) - - ufunc_kwargs = {"n_dims": 1, "ravel": False} - kwargs = {"input_core_dims": [["__sample__"]]} - lppd_i = _wrap_xarray_ufunc( - _logsumexp, - log_likelihood, - func_kwargs={"b_inv": n_samples}, - ufunc_kwargs=ufunc_kwargs, - dask_kwargs=dask_kwargs, - **kwargs, - ) - - vars_lpd = log_likelihood.var(dim="__sample__") - warn_mg = False - if np.any(vars_lpd > 0.4): - warnings.warn( - ( - "For one or more samples the posterior variance of the log predictive " - "densities exceeds 0.4. This could be indication of WAIC starting to fail. \n" - "See http://arxiv.org/abs/1507.04544 for details" - ) - ) - warn_mg = True - - waic_i = scale_value * (lppd_i - vars_lpd) - waic_se = (n_data_points * np.var(waic_i.values)) ** 0.5 - waic_sum = np.sum(waic_i.values) - p_waic = np.sum(vars_lpd.values) - - if not pointwise: - return ELPDData( - data=[waic_sum, waic_se, p_waic, n_samples, n_data_points, warn_mg, scale], - index=[ - "waic", - "se", - "p_waic", - "n_samples", - "n_data_points", - "warning", - "scale", - ], - ) - if np.equal(waic_sum, waic_i).all(): # pylint: disable=no-member - warnings.warn( - """The point-wise WAIC is the same with the sum WAIC, please double check - the Observed RV in your model to make sure it returns element-wise logp. - """ - ) - return ELPDData( - data=[ - waic_sum, - waic_se, - p_waic, - n_samples, - n_data_points, - warn_mg, - waic_i.rename("waic_i"), - scale, - ], - index=[ - "elpd_waic", - "se", - "p_waic", - "n_samples", - "n_data_points", - "warning", - "waic_i", - "scale", - ], - ) - - -def loo_pit(idata=None, *, y=None, y_hat=None, log_weights=None): - """Compute leave one out (PSIS-LOO) probability integral transform (PIT) values. - - Parameters - ---------- - idata: InferenceData - :class:`arviz.InferenceData` object. - y: array, DataArray or str - Observed data. If str, ``idata`` must be present and contain the observed data group - y_hat: array, DataArray or str - Posterior predictive samples for ``y``. It must have the same shape as y plus an - extra dimension at the end of size n_samples (chains and draws stacked). If str or - None, ``idata`` must contain the posterior predictive group. If None, y_hat is taken - equal to y, thus, y must be str too. - log_weights: array or DataArray - Smoothed log_weights. It must have the same shape as ``y_hat`` - dask_kwargs : dict, optional - Dask related kwargs passed to :func:`~arviz.wrap_xarray_ufunc`. - - Returns - ------- - loo_pit: array or DataArray - Value of the LOO-PIT at each observed data point. - - See Also - -------- - plot_loo_pit : Plot Leave-One-Out probability integral transformation (PIT) predictive checks. - loo : Compute Pareto-smoothed importance sampling leave-one-out - cross-validation (PSIS-LOO-CV). - plot_elpd : Plot pointwise elpd differences between two or more models. - plot_khat : Plot Pareto tail indices for diagnosing convergence. - - Examples - -------- - Calculate LOO-PIT values using as test quantity the observed values themselves. - - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data("centered_eight") - ...: az.loo_pit(idata=data, y="obs") - - Calculate LOO-PIT values using as test quantity the square of the difference between - each observation and `mu`. Both ``y`` and ``y_hat`` inputs will be array-like, - but ``idata`` will still be passed in order to calculate the ``log_weights`` from - there. - - .. ipython:: - - In [1]: T = data.observed_data.obs - data.posterior.mu.median(dim=("chain", "draw")) - ...: T_hat = data.posterior_predictive.obs - data.posterior.mu - ...: T_hat = T_hat.stack(__sample__=("chain", "draw")) - ...: az.loo_pit(idata=data, y=T**2, y_hat=T_hat**2) - - """ - y_str = "" - if idata is not None and not isinstance(idata, InferenceData): - raise ValueError("idata must be of type InferenceData or None") - - if idata is None: - if not all(isinstance(arg, (np.ndarray, xr.DataArray)) for arg in (y, y_hat, log_weights)): - raise ValueError( - "all 3 y, y_hat and log_weights must be array or DataArray when idata is None " - f"but they are of types {[type(arg) for arg in (y, y_hat, log_weights)]}" - ) - - else: - if y_hat is None and isinstance(y, str): - y_hat = y - elif y_hat is None: - raise ValueError("y_hat cannot be None if y is not a str") - if isinstance(y, str): - y_str = y - y = idata.observed_data[y].values - elif not isinstance(y, (np.ndarray, xr.DataArray)): - raise ValueError(f"y must be of types array, DataArray or str, not {type(y)}") - if isinstance(y_hat, str): - y_hat = idata.posterior_predictive[y_hat].stack(__sample__=("chain", "draw")).values - elif not isinstance(y_hat, (np.ndarray, xr.DataArray)): - raise ValueError(f"y_hat must be of types array, DataArray or str, not {type(y_hat)}") - if log_weights is None: - if y_str: - try: - log_likelihood = _get_log_likelihood(idata, var_name=y_str) - except TypeError: - log_likelihood = _get_log_likelihood(idata) - else: - log_likelihood = _get_log_likelihood(idata) - log_likelihood = log_likelihood.stack(__sample__=("chain", "draw")) - posterior = convert_to_dataset(idata, group="posterior") - n_chains = len(posterior.chain) - n_samples = len(log_likelihood.__sample__) - ess_p = ess(posterior, method="mean") - # this mean is over all data variables - reff = ( - (np.hstack([ess_p[v].values.flatten() for v in ess_p.data_vars]).mean() / n_samples) - if n_chains > 1 - else 1 - ) - log_weights = psislw(-log_likelihood, reff=reff)[0].values - elif not isinstance(log_weights, (np.ndarray, xr.DataArray)): - raise ValueError( - f"log_weights must be None or of types array or DataArray, not {type(log_weights)}" - ) - - if len(y.shape) + 1 != len(y_hat.shape): - raise ValueError( - f"y_hat must have 1 more dimension than y, but y_hat has {len(y_hat.shape)} dims and " - f"y has {len(y.shape)} dims" - ) - - if y.shape != y_hat.shape[:-1]: - raise ValueError( - f"y has shape: {y.shape} which should be equal to y_hat shape (omitting the last " - f"dimension): {y_hat.shape}" - ) - - if y_hat.shape != log_weights.shape: - raise ValueError( - "y_hat and log_weights must have the same shape but have shapes " - f"{y_hat.shape,} and {log_weights.shape}" - ) - - kwargs = { - "input_core_dims": [[], ["__sample__"], ["__sample__"]], - "output_core_dims": [[]], - "join": "left", - } - ufunc_kwargs = {"n_dims": 1} - - if y.dtype.kind == "i" or y_hat.dtype.kind == "i": - y, y_hat = smooth_data(y, y_hat) - - return _wrap_xarray_ufunc( - _loo_pit, - y, - y_hat, - log_weights, - ufunc_kwargs=ufunc_kwargs, - **kwargs, - ) - - -def _loo_pit(y, y_hat, log_weights): - """Compute LOO-PIT values.""" - sel = y_hat <= y - if np.sum(sel) > 0: - value = np.exp(_logsumexp(log_weights[sel])) - return min(1, value) - else: - return 0 - - -def apply_test_function( - idata, - func, - group="both", - var_names=None, - pointwise=False, - out_data_shape=None, - out_pp_shape=None, - out_name_data="T", - out_name_pp=None, - func_args=None, - func_kwargs=None, - ufunc_kwargs=None, - wrap_data_kwargs=None, - wrap_pp_kwargs=None, - inplace=True, - overwrite=None, -): - """Apply a Bayesian test function to an InferenceData object. - - Parameters - ---------- - idata: InferenceData - :class:`arviz.InferenceData` object on which to apply the test function. - This function will add new variables to the InferenceData object - to store the result without modifying the existing ones. - func: callable - Callable that calculates the test function. It must have the following call signature - ``func(y, theta, *args, **kwargs)`` (where ``y`` is the observed data or posterior - predictive and ``theta`` the model parameters) even if not all the arguments are - used. - group: str, optional - Group on which to apply the test function. Can be observed_data, posterior_predictive - or both. - var_names: dict group -> var_names, optional - Mapping from group name to the variables to be passed to func. It can be a dict of - strings or lists of strings. There is also the option of using ``both`` as key, - in which case, the same variables are used in observed data and posterior predictive - groups - pointwise: bool, optional - If True, apply the test function to each observation and sample, otherwise, apply - test function to each sample. - out_data_shape, out_pp_shape: tuple, optional - Output shape of the test function applied to the observed/posterior predictive data. - If None, the default depends on the value of pointwise. - out_name_data, out_name_pp: str, optional - Name of the variables to add to the observed_data and posterior_predictive datasets - respectively. ``out_name_pp`` can be ``None``, in which case will be taken equal to - ``out_name_data``. - func_args: sequence, optional - Passed as is to ``func`` - func_kwargs: mapping, optional - Passed as is to ``func`` - wrap_data_kwargs, wrap_pp_kwargs: mapping, optional - kwargs passed to :func:`~arviz.wrap_xarray_ufunc`. By default, some suitable input_core_dims - are used. - inplace: bool, optional - If True, add the variables inplace, otherwise, return a copy of idata with the variables - added. - overwrite: bool, optional - Overwrite data in case ``out_name_data`` or ``out_name_pp`` are already variables in - dataset. If ``None`` it will be the opposite of inplace. - - Returns - ------- - idata: InferenceData - Output InferenceData object. If ``inplace=True``, it is the same input object modified - inplace. - - See Also - -------- - plot_bpv : Plot Bayesian p-value for observed data and Posterior/Prior predictive. - - Notes - ----- - This function is provided for convenience to wrap scalar or functions working on low - dims to inference data object. It is not optimized to be faster nor as fast as vectorized - computations. - - Examples - -------- - Use ``apply_test_function`` to wrap ``numpy.min`` for illustration purposes. And plot the - results. - - .. plot:: - :context: close-figs - - >>> import arviz as az - >>> idata = az.load_arviz_data("centered_eight") - >>> az.apply_test_function(idata, lambda y, theta: np.min(y)) - >>> T = idata.observed_data.T.item() - >>> az.plot_posterior(idata, var_names=["T"], group="posterior_predictive", ref_val=T) - - """ - out = idata if inplace else deepcopy(idata) - - valid_groups = ("observed_data", "posterior_predictive", "both") - if group not in valid_groups: - raise ValueError(f"Invalid group argument. Must be one of {valid_groups} not {group}.") - if overwrite is None: - overwrite = not inplace - - if out_name_pp is None: - out_name_pp = out_name_data - - if func_args is None: - func_args = tuple() - - if func_kwargs is None: - func_kwargs = {} - - if ufunc_kwargs is None: - ufunc_kwargs = {} - ufunc_kwargs.setdefault("check_shape", False) - ufunc_kwargs.setdefault("ravel", False) - - if wrap_data_kwargs is None: - wrap_data_kwargs = {} - if wrap_pp_kwargs is None: - wrap_pp_kwargs = {} - if var_names is None: - var_names = {} - - both_var_names = var_names.pop("both", None) - var_names.setdefault("posterior", list(out.posterior.data_vars)) - - in_posterior = out.posterior[var_names["posterior"]] - if isinstance(in_posterior, xr.Dataset): - in_posterior = in_posterior.to_array().squeeze() - - groups = ("posterior_predictive", "observed_data") if group == "both" else [group] - for grp in groups: - out_group_shape = out_data_shape if grp == "observed_data" else out_pp_shape - out_name_group = out_name_data if grp == "observed_data" else out_name_pp - wrap_group_kwargs = wrap_data_kwargs if grp == "observed_data" else wrap_pp_kwargs - if not hasattr(out, grp): - raise ValueError(f"InferenceData object must have {grp} group") - if not overwrite and out_name_group in getattr(out, grp).data_vars: - raise ValueError( - f"Should overwrite: {out_name_group} variable present in group {grp}," - " but overwrite is False" - ) - var_names.setdefault( - grp, list(getattr(out, grp).data_vars) if both_var_names is None else both_var_names - ) - in_group = getattr(out, grp)[var_names[grp]] - if isinstance(in_group, xr.Dataset): - in_group = in_group.to_array(dim=f"{grp}_var").squeeze() - - if pointwise: - out_group_shape = in_group.shape if out_group_shape is None else out_group_shape - elif grp == "observed_data": - out_group_shape = () if out_group_shape is None else out_group_shape - elif grp == "posterior_predictive": - out_group_shape = in_group.shape[:2] if out_group_shape is None else out_group_shape - loop_dims = in_group.dims[: len(out_group_shape)] - - wrap_group_kwargs.setdefault( - "input_core_dims", - [ - [dim for dim in dataset.dims if dim not in loop_dims] - for dataset in [in_group, in_posterior] - ], - ) - func_kwargs["out"] = np.empty(out_group_shape) - - out_group = getattr(out, grp) - try: - out_group[out_name_group] = _wrap_xarray_ufunc( - func, - in_group.values, - in_posterior.values, - func_args=func_args, - func_kwargs=func_kwargs, - ufunc_kwargs=ufunc_kwargs, - **wrap_group_kwargs, - ) - except IndexError: - excluded_dims = set( - wrap_group_kwargs["input_core_dims"][0] + wrap_group_kwargs["input_core_dims"][1] - ) - out_group[out_name_group] = _wrap_xarray_ufunc( - func, - *xr.broadcast(in_group, in_posterior, exclude=excluded_dims), - func_args=func_args, - func_kwargs=func_kwargs, - ufunc_kwargs=ufunc_kwargs, - **wrap_group_kwargs, - ) - setattr(out, grp, out_group) - - return out - - -def weight_predictions(idatas, weights=None): - """ - Generate weighted posterior predictive samples from a list of InferenceData - and a set of weights. - - Parameters - --------- - idatas : list[InferenceData] - List of :class:`arviz.InferenceData` objects containing the groups `posterior_predictive` - and `observed_data`. Observations should be the same for all InferenceData objects. - weights : array-like, optional - Individual weights for each model. Weights should be positive. If they do not sum up to 1, - they will be normalized. Default, same weight for each model. - Weights can be computed using many different methods including those in - :func:`arviz.compare`. - - Returns - ------- - idata: InferenceData - Output InferenceData object with the groups `posterior_predictive` and `observed_data`. - - See Also - -------- - compare : Compare models based on PSIS-LOO `loo` or WAIC `waic` cross-validation - """ - if len(idatas) < 2: - raise ValueError("You should provide a list with at least two InferenceData objects") - - if not all("posterior_predictive" in idata.groups() for idata in idatas): - raise ValueError( - "All the InferenceData objects must contain the `posterior_predictive` group" - ) - - if not all(idatas[0].observed_data.equals(idata.observed_data) for idata in idatas[1:]): - raise ValueError("The observed data should be the same for all InferenceData objects") - - if weights is None: - weights = np.ones(len(idatas)) / len(idatas) - elif len(idatas) != len(weights): - raise ValueError( - "The number of weights should be the same as the number of InferenceData objects" - ) - - weights = np.array(weights, dtype=float) - weights /= weights.sum() - - len_idatas = [ - idata.posterior_predictive.sizes["chain"] * idata.posterior_predictive.sizes["draw"] - for idata in idatas - ] - - if not all(len_idatas): - raise ValueError("At least one of your idatas has 0 samples") - - new_samples = (np.min(len_idatas) * weights).astype(int) - - new_idatas = [ - extract(idata, group="posterior_predictive", num_samples=samples).reset_coords() - for samples, idata in zip(new_samples, idatas) - ] - - weighted_samples = InferenceData( - posterior_predictive=xr.concat(new_idatas, dim="sample"), - observed_data=idatas[0].observed_data, - ) - - return weighted_samples - - -def psens( - data, - *, - component="prior", - component_var_names=None, - component_coords=None, - var_names=None, - coords=None, - filter_vars=None, - delta=0.01, - dask_kwargs=None, -): - """Compute power-scaling sensitivity diagnostic. - - Power-scales the prior or likelihood and calculates how much the posterior is affected. - - Parameters - ---------- - data : obj - Any object that can be converted to an :class:`arviz.InferenceData` object. - Refer to documentation of :func:`arviz.convert_to_dataset` for details. - For ndarray: shape = (chain, draw). - For n-dimensional ndarray transform first to dataset with ``az.convert_to_dataset``. - component : {"prior", "likelihood"}, default "prior" - When `component` is "likelihood", the log likelihood values are retrieved - from the ``log_likelihood`` group as pointwise log likelihood and added - together. With "prior", the log prior values are retrieved from the - ``log_prior`` group. - component_var_names : str, optional - Name of the prior or log likelihood variables to use - component_coords : dict, optional - Coordinates defining a subset over the component element for which to - compute the prior sensitivity diagnostic. - var_names : list of str, optional - Names of posterior variables to include in the power scaling sensitivity diagnostic - coords : dict, optional - Coordinates defining a subset over the posterior. Only these variables will - be used when computing the prior sensitivity. - filter_vars: {None, "like", "regex"}, default None - If ``None`` (default), interpret var_names as the real variables names. - If "like", interpret var_names as substrings of the real variables names. - If "regex", interpret var_names as regular expressions on the real variables names. - delta : float - Value for finite difference derivative calculation. - dask_kwargs : dict, optional - Dask related kwargs passed to :func:`~arviz.wrap_xarray_ufunc`. - - Returns - ------- - xarray.Dataset - Returns dataset of power-scaling sensitivity diagnostic values. - Higher sensitivity values indicate greater sensitivity. - Prior sensitivity above 0.05 indicates informative prior. - Likelihood sensitivity below 0.05 indicates weak or nonin-formative likelihood. - - Examples - -------- - Compute the likelihood sensitivity for the non centered eight model: - - .. ipython:: - - In [1]: import arviz as az - ...: data = az.load_arviz_data("non_centered_eight") - ...: az.psens(data, component="likelihood") - - To compute the prior sensitivity, we need to first compute the log prior - at each posterior sample. In our case, we know mu has a normal prior :math:`N(0, 5)`, - tau is a half cauchy prior with scale/beta parameter 5, - and theta has a standard normal as prior. - We add this information to the ``log_prior`` group before computing powerscaling - check with ``psens`` - - .. ipython:: - - In [1]: from xarray_einstats.stats import XrContinuousRV - ...: from scipy.stats import norm, halfcauchy - ...: post = data.posterior - ...: log_prior = { - ...: "mu": XrContinuousRV(norm, 0, 5).logpdf(post["mu"]), - ...: "tau": XrContinuousRV(halfcauchy, scale=5).logpdf(post["tau"]), - ...: "theta_t": XrContinuousRV(norm, 0, 1).logpdf(post["theta_t"]), - ...: } - ...: data.add_groups({"log_prior": log_prior}) - ...: az.psens(data, component="prior") - - Notes - ----- - The diagnostic is computed by power-scaling the specified component (prior or likelihood) - and determining the degree to which the posterior changes as described in [1]_. - It uses Pareto-smoothed importance sampling to avoid refitting the model. - - References - ---------- - .. [1] Kallioinen et al, *Detecting and diagnosing prior and likelihood sensitivity with - power-scaling*, 2022, https://arxiv.org/abs/2107.14054 - - """ - dataset = extract(data, var_names=var_names, filter_vars=filter_vars, group="posterior") - if coords is None: - dataset = dataset.sel(coords) - - if component == "likelihood": - component_draws = _get_log_likelihood(data, var_name=component_var_names, single_var=False) - elif component == "prior": - component_draws = _get_log_prior(data, var_names=component_var_names) - else: - raise ValueError("Value for `component` argument not recognized") - - component_draws = component_draws.stack(__sample__=("chain", "draw")) - if component_coords is None: - component_draws = component_draws.sel(component_coords) - if isinstance(component_draws, xr.DataArray): - component_draws = component_draws.to_dataset() - if len(component_draws.dims): - component_draws = component_draws.to_stacked_array( - "latent-obs_var", sample_dims=("__sample__",) - ).sum("latent-obs_var") - # from here component_draws is a 1d object with dimensions (sample,) - - # calculate lower and upper alpha values - lower_alpha = 1 / (1 + delta) - upper_alpha = 1 + delta - - # calculate importance sampling weights for lower and upper alpha power-scaling - lower_w = np.exp(_powerscale_lw(component_draws=component_draws, alpha=lower_alpha)) - lower_w = lower_w / np.sum(lower_w) - - upper_w = np.exp(_powerscale_lw(component_draws=component_draws, alpha=upper_alpha)) - upper_w = upper_w / np.sum(upper_w) - - ufunc_kwargs = {"n_dims": 1, "ravel": False} - func_kwargs = {"lower_weights": lower_w.values, "upper_weights": upper_w.values, "delta": delta} - - # calculate the sensitivity diagnostic based on the importance weights and draws - return _wrap_xarray_ufunc( - _powerscale_sens, - dataset, - ufunc_kwargs=ufunc_kwargs, - func_kwargs=func_kwargs, - dask_kwargs=dask_kwargs, - input_core_dims=[["sample"]], - ) - - -def _powerscale_sens(draws, *, lower_weights=None, upper_weights=None, delta=0.01): - """ - Calculate power-scaling sensitivity by finite difference - second derivative of CJS - """ - lower_cjs = max( - _cjs_dist(draws=draws, weights=lower_weights), - _cjs_dist(draws=-1 * draws, weights=lower_weights), - ) - upper_cjs = max( - _cjs_dist(draws=draws, weights=upper_weights), - _cjs_dist(draws=-1 * draws, weights=upper_weights), - ) - logdiffsquare = 2 * np.log2(1 + delta) - grad = (lower_cjs + upper_cjs) / logdiffsquare - - return grad - - -def _powerscale_lw(alpha, component_draws): - """ - Calculate log weights for power-scaling component by alpha. - """ - log_weights = (alpha - 1) * component_draws - log_weights = psislw(log_weights)[0] - - return log_weights - - -def _cjs_dist(draws, weights): - """ - Calculate the cumulative Jensen-Shannon distance between original draws and weighted draws. - """ - - # sort draws and weights - order = np.argsort(draws) - draws = draws[order] - weights = weights[order] - - binwidth = np.diff(draws) - - # ecdfs - cdf_p = np.linspace(1 / len(draws), 1 - 1 / len(draws), len(draws) - 1) - cdf_q = np.cumsum(weights / np.sum(weights))[:-1] - - # integrals of ecdfs - cdf_p_int = np.dot(cdf_p, binwidth) - cdf_q_int = np.dot(cdf_q, binwidth) - - # cjs calculation - pq_numer = np.log2(cdf_p, out=np.zeros_like(cdf_p), where=cdf_p != 0) - qp_numer = np.log2(cdf_q, out=np.zeros_like(cdf_q), where=cdf_q != 0) - - denom = 0.5 * (cdf_p + cdf_q) - denom = np.log2(denom, out=np.zeros_like(denom), where=denom != 0) - - cjs_pq = np.sum(binwidth * (cdf_p * (pq_numer - denom))) + 0.5 / np.log(2) * ( - cdf_q_int - cdf_p_int - ) - - cjs_qp = np.sum(binwidth * (cdf_q * (qp_numer - denom))) + 0.5 / np.log(2) * ( - cdf_p_int - cdf_q_int - ) - - cjs_pq = max(0, cjs_pq) - cjs_qp = max(0, cjs_qp) - - bound = cdf_p_int + cdf_q_int - - return np.sqrt((cjs_pq + cjs_qp) / bound) - - -def bayes_factor(idata, var_name, ref_val=0, prior=None, return_ref_vals=False): - r"""Approximated Bayes Factor for comparing hypothesis of two nested models. - - The Bayes factor is estimated by comparing a model (H1) against a model in which the - parameter of interest has been restricted to be a point-null (H0). This computation - assumes the models are nested and thus H0 is a special case of H1. - - Notes - ----- - The bayes Factor is approximated as the Savage-Dickey density ratio - algorithm presented in [1]_. - - Parameters - ---------- - idata : InferenceData - Any object that can be converted to an :class:`arviz.InferenceData` object - Refer to documentation of :func:`arviz.convert_to_dataset` for details. - var_name : str, optional - Name of variable we want to test. - ref_val : int, default 0 - Point-null for Bayes factor estimation. - prior : numpy.array, optional - In case we want to use different prior, for example for sensitivity analysis. - return_ref_vals : bool, optional - Whether to return the values of the prior and posterior at the reference value. - Used by :func:`arviz.plot_bf` to display the distribution comparison. - - - Returns - ------- - dict : A dictionary with BF10 (Bayes Factor 10 (H1/H0 ratio), and BF01 (H0/H1 ratio). - - References - ---------- - .. [1] Heck, D., 2019. A caveat on the Savage-Dickey density ratio: - The case of computing Bayes factors for regression parameters. - - Examples - -------- - Moderate evidence indicating that the parameter "a" is different from zero. - - .. ipython:: - - In [1]: import numpy as np - ...: import arviz as az - ...: idata = az.from_dict(posterior={"a":np.random.normal(1, 0.5, 5000)}, - ...: prior={"a":np.random.normal(0, 1, 5000)}) - ...: az.bayes_factor(idata, var_name="a", ref_val=0) - - """ - - posterior = extract(idata, var_names=var_name).values - - if ref_val > posterior.max() or ref_val < posterior.min(): - _log.warning( - "The reference value is outside of the posterior. " - "This translate into infinite support for H1, which is most likely an overstatement." - ) - - if posterior.ndim > 1: - _log.warning("Posterior distribution has {posterior.ndim} dimensions") - - if prior is None: - prior = extract(idata, var_names=var_name, group="prior").values - - if posterior.dtype.kind == "f": - posterior_grid, posterior_pdf, *_ = _kde_linear(posterior) - prior_grid, prior_pdf, *_ = _kde_linear(prior) - posterior_at_ref_val = np.interp(ref_val, posterior_grid, posterior_pdf) - prior_at_ref_val = np.interp(ref_val, prior_grid, prior_pdf) - - elif posterior.dtype.kind == "i": - posterior_at_ref_val = (posterior == ref_val).mean() - prior_at_ref_val = (prior == ref_val).mean() - - bf_10 = prior_at_ref_val / posterior_at_ref_val - bf = {"BF10": bf_10, "BF01": 1 / bf_10} - - if return_ref_vals: - return (bf, {"prior": prior_at_ref_val, "posterior": posterior_at_ref_val}) - else: - return bf diff --git a/arviz/stats/stats_refitting.py b/arviz/stats/stats_refitting.py deleted file mode 100644 index dbe3a5d49f..0000000000 --- a/arviz/stats/stats_refitting.py +++ /dev/null @@ -1,119 +0,0 @@ -"""Stats functions that require refitting the model.""" - -import logging -import warnings - -import numpy as np - -from .stats import loo -from .stats_utils import logsumexp as _logsumexp - -__all__ = ["reloo"] - -_log = logging.getLogger(__name__) - - -def reloo(wrapper, loo_orig=None, k_thresh=0.7, scale=None, verbose=True): - """Recalculate exact Leave-One-Out cross validation refitting where the approximation fails. - - ``az.loo`` estimates the values of Leave-One-Out (LOO) cross validation using Pareto - Smoothed Importance Sampling (PSIS) to approximate its value. PSIS works well when - the posterior and the posterior_i (excluding observation i from the data used to fit) - are similar. In some cases, there are highly influential observations for which PSIS - cannot approximate the LOO-CV, and a warning of a large Pareto shape is sent by ArviZ. - This cases typically have a handful of bad or very bad Pareto shapes and a majority of - good or ok shapes. - - Therefore, this may not indicate that the model is not robust enough - nor that these observations are inherently bad, only that PSIS cannot approximate LOO-CV - correctly. Thus, we can use PSIS for all observations where the Pareto shape is below a - threshold and refit the model to perform exact cross validation for the handful of - observations where PSIS cannot be used. This approach allows to properly approximate - LOO-CV with only a handful of refits, which in most cases is still much less computationally - expensive than exact LOO-CV, which needs one refit per observation. - - Parameters - ---------- - wrapper: SamplingWrapper-like - Class (preferably a subclass of ``az.SamplingWrapper``, see :ref:`wrappers_api` - for details) implementing the methods described - in the SamplingWrapper docs. This allows ArviZ to call **any** sampling backend - (like PyStan or emcee) using always the same syntax. - loo_orig : ELPDData, optional - ELPDData instance with pointwise loo results. The pareto_k attribute will be checked - for values above the threshold. - k_thresh : float, optional - Pareto shape threshold. Each pareto shape value above ``k_thresh`` will trigger - a refit excluding that observation. - scale : str, optional - Only taken into account when loo_orig is None. See ``az.loo`` for valid options. - - Returns - ------- - ELPDData - ELPDData instance containing the PSIS approximation where possible and the exact - LOO-CV result where PSIS failed. The Pareto shape of the observations where exact - LOO-CV was performed is artificially set to 0, but as PSIS is not performed, it - should be ignored. - - Notes - ----- - It is strongly recommended to first compute ``az.loo`` on the inference results to - confirm that the number of values above the threshold is small enough. Otherwise, - prohibitive computation time may be needed to perform all required refits. - - As an extreme case, artificially assigning all ``pareto_k`` values to something - larger than the threshold would make ``reloo`` perform the whole exact LOO-CV. - This is not generally recommended - nor intended, however, if needed, this function can be used to achieve the result. - - Warnings - -------- - Sampling wrappers are an experimental feature in a very early stage. Please use them - with caution. - """ - required_methods = ("sel_observations", "sample", "get_inference_data", "log_likelihood__i") - not_implemented = wrapper.check_implemented_methods(required_methods) - if not_implemented: - raise TypeError( - "Passed wrapper instance does not implement all methods required for reloo " - f"to work. Check the documentation of SamplingWrapper. {not_implemented} must be " - "implemented and were not found." - ) - if loo_orig is None: - loo_orig = loo(wrapper.idata_orig, pointwise=True, scale=scale) - loo_refitted = loo_orig.copy() - khats = loo_refitted.pareto_k - loo_i = loo_refitted.loo_i - scale = loo_orig.scale - - if scale.lower() == "deviance": - scale_value = -2 - elif scale.lower() == "log": - scale_value = 1 - elif scale.lower() == "negative_log": - scale_value = -1 - lppd_orig = loo_orig.p_loo + loo_orig.elpd_loo / scale_value - n_data_points = loo_orig.n_data_points - - if verbose: - warnings.warn("reloo is an experimental and untested feature", UserWarning) - - if np.any(khats > k_thresh): - for idx in np.argwhere(khats.values > k_thresh): - if verbose: - _log.info("Refitting model excluding observation %d", idx) - new_obs, excluded_obs = wrapper.sel_observations(idx) - fit = wrapper.sample(new_obs) - idata_idx = wrapper.get_inference_data(fit) - log_like_idx = wrapper.log_likelihood__i(excluded_obs, idata_idx).values.flatten() - loo_lppd_idx = scale_value * _logsumexp(log_like_idx, b_inv=len(log_like_idx)) - khats[idx] = 0 - loo_i[idx] = loo_lppd_idx - loo_refitted.elpd_loo = loo_i.values.sum() - loo_refitted.se = (n_data_points * np.var(loo_i.values)) ** 0.5 - loo_refitted.p_loo = lppd_orig - loo_refitted.elpd_loo / scale_value - return loo_refitted - else: - _log.info("No problematic observations") - return loo_orig diff --git a/arviz/stats/stats_utils.py b/arviz/stats/stats_utils.py deleted file mode 100644 index c3e636207d..0000000000 --- a/arviz/stats/stats_utils.py +++ /dev/null @@ -1,609 +0,0 @@ -"""Stats-utility functions for ArviZ.""" - -import warnings -from collections.abc import Sequence -from copy import copy as _copy -from copy import deepcopy as _deepcopy - -import numpy as np -import pandas as pd -from scipy.fftpack import next_fast_len -from scipy.interpolate import CubicSpline -from scipy.stats.mstats import mquantiles -from xarray import apply_ufunc - -from .. import _log -from ..utils import conditional_jit, conditional_vect, conditional_dask -from .density_utils import histogram as _histogram - - -__all__ = ["autocorr", "autocov", "ELPDData", "make_ufunc", "smooth_data", "wrap_xarray_ufunc"] - - -def autocov(ary, axis=-1): - """Compute autocovariance estimates for every lag for the input array. - - Parameters - ---------- - ary : Numpy array - An array containing MCMC samples - - Returns - ------- - acov: Numpy array same size as the input array - """ - axis = axis if axis > 0 else len(ary.shape) + axis - n = ary.shape[axis] - m = next_fast_len(2 * n) - - ary = ary - ary.mean(axis, keepdims=True) - - # added to silence tuple warning for a submodule - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - - ifft_ary = np.fft.rfft(ary, n=m, axis=axis) - ifft_ary *= np.conjugate(ifft_ary) - - shape = tuple( - slice(None) if dim_len != axis else slice(0, n) for dim_len, _ in enumerate(ary.shape) - ) - cov = np.fft.irfft(ifft_ary, n=m, axis=axis)[shape] - cov /= n - - return cov - - -def autocorr(ary, axis=-1): - """Compute autocorrelation using FFT for every lag for the input array. - - See https://en.wikipedia.org/wiki/autocorrelation#Efficient_computation - - Parameters - ---------- - ary : Numpy array - An array containing MCMC samples - - Returns - ------- - acorr: Numpy array same size as the input array - """ - corr = autocov(ary, axis=axis) - axis = axis = axis if axis > 0 else len(corr.shape) + axis - norm = tuple( - slice(None, None) if dim != axis else slice(None, 1) for dim, _ in enumerate(corr.shape) - ) - with np.errstate(invalid="ignore"): - corr /= corr[norm] - return corr - - -def make_ufunc( - func, n_dims=2, n_output=1, n_input=1, index=Ellipsis, ravel=True, check_shape=None -): # noqa: D202 - """Make ufunc from a function taking 1D array input. - - Parameters - ---------- - func : callable - n_dims : int, optional - Number of core dimensions not broadcasted. Dimensions are skipped from the end. - At minimum n_dims > 0. - n_output : int, optional - Select number of results returned by `func`. - If n_output > 1, ufunc returns a tuple of objects else returns an object. - n_input : int, optional - Number of **array** inputs to func, i.e. ``n_input=2`` means that func is called - with ``func(ary1, ary2, *args, **kwargs)`` - index : int, optional - Slice ndarray with `index`. Defaults to `Ellipsis`. - ravel : bool, optional - If true, ravel the ndarray before calling `func`. - check_shape: bool, optional - If false, do not check if the shape of the output is compatible with n_dims and - n_output. By default, True only for n_input=1. If n_input is larger than 1, the last - input array is used to check the shape, however, shape checking with multiple inputs - may not be correct. - - Returns - ------- - callable - ufunc wrapper for `func`. - """ - if n_dims < 1: - raise TypeError("n_dims must be one or higher.") - - if n_input == 1 and check_shape is None: - check_shape = True - elif check_shape is None: - check_shape = False - - def _ufunc(*args, out=None, out_shape=None, **kwargs): - """General ufunc for single-output function.""" - arys = args[:n_input] - n_dims_out = None - if out is None: - if out_shape is None: - out = np.empty(arys[-1].shape[:-n_dims]) - else: - out = np.empty((*arys[-1].shape[:-n_dims], *out_shape)) - n_dims_out = -len(out_shape) - elif check_shape: - if out.shape != arys[-1].shape[:-n_dims]: - msg = f"Shape incorrect for `out`: {out.shape}." - msg += f" Correct shape is {arys[-1].shape[:-n_dims]}" - raise TypeError(msg) - for idx in np.ndindex(out.shape[:n_dims_out]): - arys_idx = [ary[idx].ravel() if ravel else ary[idx] for ary in arys] - out_idx = np.asarray(func(*arys_idx, *args[n_input:], **kwargs))[index] - if n_dims_out is None: - out_idx = out_idx.item() - out[idx] = out_idx - return out - - def _multi_ufunc(*args, out=None, out_shape=None, **kwargs): - """General ufunc for multi-output function.""" - arys = args[:n_input] - element_shape = arys[-1].shape[:-n_dims] - if out is None: - if out_shape is None: - out = tuple(np.empty(element_shape) for _ in range(n_output)) - else: - out = tuple(np.empty((*element_shape, *out_shape[i])) for i in range(n_output)) - - elif check_shape: - raise_error = False - correct_shape = tuple(element_shape for _ in range(n_output)) - if isinstance(out, tuple): - out_shape = tuple(item.shape for item in out) - if out_shape != correct_shape: - raise_error = True - else: - raise_error = True - out_shape = "not tuple, type={type(out)}" - if raise_error: - msg = f"Shapes incorrect for `out`: {out_shape}." - msg += f" Correct shapes are {correct_shape}" - raise TypeError(msg) - for idx in np.ndindex(element_shape): - arys_idx = [ary[idx].ravel() if ravel else ary[idx] for ary in arys] - results = func(*arys_idx, *args[n_input:], **kwargs) - for i, res in enumerate(results): - out[i][idx] = np.asarray(res)[index] - return out - - if n_output > 1: - ufunc = _multi_ufunc - else: - ufunc = _ufunc - - update_docstring(ufunc, func, n_output) - return ufunc - - -@conditional_dask -def wrap_xarray_ufunc( - ufunc, - *datasets, - ufunc_kwargs=None, - func_args=None, - func_kwargs=None, - dask_kwargs=None, - **kwargs, -): - """Wrap make_ufunc with xarray.apply_ufunc. - - Parameters - ---------- - ufunc : callable - *datasets : xarray.Dataset - ufunc_kwargs : dict - Keyword arguments passed to `make_ufunc`. - - 'n_dims', int, by default 2 - - 'n_output', int, by default 1 - - 'n_input', int, by default len(datasets) - - 'index', slice, by default Ellipsis - - 'ravel', bool, by default True - func_args : tuple - Arguments passed to 'ufunc'. - func_kwargs : dict - Keyword arguments passed to 'ufunc'. - - 'out_shape', int, by default None - dask_kwargs : dict - Dask related kwargs passed to :func:`xarray:xarray.apply_ufunc`. - Use ``enable_dask`` method of :class:`arviz.Dask` to set default kwargs. - **kwargs - Passed to :func:`xarray.apply_ufunc`. - - Returns - ------- - xarray.Dataset - """ - if ufunc_kwargs is None: - ufunc_kwargs = {} - ufunc_kwargs.setdefault("n_input", len(datasets)) - if func_args is None: - func_args = tuple() - if func_kwargs is None: - func_kwargs = {} - if dask_kwargs is None: - dask_kwargs = {} - - kwargs.setdefault( - "input_core_dims", tuple(("chain", "draw") for _ in range(len(func_args) + len(datasets))) - ) - ufunc_kwargs.setdefault("n_dims", len(kwargs["input_core_dims"][-1])) - kwargs.setdefault("output_core_dims", tuple([] for _ in range(ufunc_kwargs.get("n_output", 1)))) - - callable_ufunc = make_ufunc(ufunc, **ufunc_kwargs) - - return apply_ufunc( - callable_ufunc, *datasets, *func_args, kwargs=func_kwargs, **dask_kwargs, **kwargs - ) - - -def update_docstring(ufunc, func, n_output=1): - """Update ArviZ generated ufunc docstring.""" - module = "" - name = "" - docstring = "" - if hasattr(func, "__module__") and isinstance(func.__module__, str): - module += func.__module__ - if hasattr(func, "__name__"): - name += func.__name__ - if hasattr(func, "__doc__") and isinstance(func.__doc__, str): - docstring += func.__doc__ - ufunc.__doc__ += "\n\n" - if module or name: - ufunc.__doc__ += "This function is a ufunc wrapper for " - ufunc.__doc__ += module + "." + name - ufunc.__doc__ += "\n" - ufunc.__doc__ += 'Call ufunc with n_args from xarray against "chain" and "draw" dimensions:' - ufunc.__doc__ += "\n\n" - input_core_dims = 'tuple(("chain", "draw") for _ in range(n_args))' - if n_output > 1: - output_core_dims = f" tuple([] for _ in range({n_output}))" - msg = f"xr.apply_ufunc(ufunc, dataset, input_core_dims={input_core_dims}, " - msg += f"output_core_dims={ output_core_dims})" - else: - output_core_dims = "" - msg = f"xr.apply_ufunc(ufunc, dataset, input_core_dims={input_core_dims})" - ufunc.__doc__ += msg - ufunc.__doc__ += "\n\n" - ufunc.__doc__ += "For example: np.std(data, ddof=1) --> n_args=2" - if docstring: - ufunc.__doc__ += "\n\n" - ufunc.__doc__ += module - ufunc.__doc__ += name - ufunc.__doc__ += " docstring:" - ufunc.__doc__ += "\n\n" - ufunc.__doc__ += docstring - - -def logsumexp(ary, *, b=None, b_inv=None, axis=None, keepdims=False, out=None, copy=True): - """Stable logsumexp when b >= 0 and b is scalar. - - b_inv overwrites b unless b_inv is None. - """ - # check dimensions for result arrays - ary = np.asarray(ary) - if ary.dtype.kind == "i": - ary = ary.astype(np.float64) - dtype = ary.dtype.type - shape = ary.shape - shape_len = len(shape) - if isinstance(axis, Sequence): - axis = tuple(axis_i if axis_i >= 0 else shape_len + axis_i for axis_i in axis) - agroup = axis - else: - axis = axis if (axis is None) or (axis >= 0) else shape_len + axis - agroup = (axis,) - shape_max = ( - tuple(1 for _ in shape) - if axis is None - else tuple(1 if i in agroup else d for i, d in enumerate(shape)) - ) - # create result arrays - if out is None: - if not keepdims: - out_shape = ( - tuple() - if axis is None - else tuple(d for i, d in enumerate(shape) if i not in agroup) - ) - else: - out_shape = shape_max - out = np.empty(out_shape, dtype=dtype) - if b_inv == 0: - return np.full_like(out, np.inf, dtype=dtype) if out.shape else np.inf - if b_inv is None and b == 0: - return np.full_like(out, -np.inf) if out.shape else -np.inf - ary_max = np.empty(shape_max, dtype=dtype) - # calculations - ary.max(axis=axis, keepdims=True, out=ary_max) - if copy: - ary = ary.copy() - ary -= ary_max - np.exp(ary, out=ary) - ary.sum(axis=axis, keepdims=keepdims, out=out) - np.log(out, out=out) - if b_inv is not None: - ary_max -= np.log(b_inv) - elif b: - ary_max += np.log(b) - out += ary_max if keepdims else ary_max.squeeze() - # transform to scalar if possible - return out if out.shape else dtype(out) - - -def quantile(ary, q, axis=None, limit=None): - """Use same quantile function as R (Type 7).""" - if limit is None: - limit = tuple() - return mquantiles(ary, q, alphap=1, betap=1, axis=axis, limit=limit) - - -def not_valid(ary, check_nan=True, check_shape=True, nan_kwargs=None, shape_kwargs=None): - """Validate ndarray. - - Parameters - ---------- - ary : numpy.ndarray - check_nan : bool - Check if any value contains NaN. - check_shape : bool - Check if array has correct shape. Assumes dimensions in order (chain, draw, *shape). - For 1D arrays (shape = (n,)) assumes chain equals 1. - nan_kwargs : dict - Valid kwargs are: - axis : int, - Defaults to None. - how : str, {"all", "any"} - Default to "any". - shape_kwargs : dict - Valid kwargs are: - min_chains : int - Defaults to 1. - min_draws : int - Defaults to 4. - - Returns - ------- - bool - """ - ary = np.asarray(ary) - - nan_error = False - draw_error = False - chain_error = False - - if check_nan: - if nan_kwargs is None: - nan_kwargs = {} - - isnan = np.isnan(ary) - axis = nan_kwargs.get("axis", None) - if nan_kwargs.get("how", "any").lower() == "all": - nan_error = isnan.all(axis) - else: - nan_error = isnan.any(axis) - - if (isinstance(nan_error, bool) and nan_error) or nan_error.any(): - _log.warning("Array contains NaN-value.") - - if check_shape: - shape = ary.shape - - if shape_kwargs is None: - shape_kwargs = {} - - min_chains = shape_kwargs.get("min_chains", 2) - min_draws = shape_kwargs.get("min_draws", 4) - error_msg = f"Shape validation failed: input_shape: {shape}, " - error_msg += f"minimum_shape: (chains={min_chains}, draws={min_draws})" - - chain_error = ((min_chains > 1) and (len(shape) < 2)) or (shape[0] < min_chains) - draw_error = ((len(shape) < 2) and (shape[0] < min_draws)) or ( - (len(shape) > 1) and (shape[1] < min_draws) - ) - - if chain_error or draw_error: - _log.warning(error_msg) - - return nan_error | chain_error | draw_error - - -def get_log_likelihood(idata, var_name=None, single_var=True): - """Retrieve the log likelihood dataarray of a given variable.""" - if ( - not hasattr(idata, "log_likelihood") - and hasattr(idata, "sample_stats") - and hasattr(idata.sample_stats, "log_likelihood") - ): - warnings.warn( - "Storing the log_likelihood in sample_stats groups has been deprecated", - DeprecationWarning, - ) - return idata.sample_stats.log_likelihood - if not hasattr(idata, "log_likelihood"): - raise TypeError("log likelihood not found in inference data object") - if var_name is None: - var_names = list(idata.log_likelihood.data_vars) - if len(var_names) > 1: - if single_var: - raise TypeError( - f"Found several log likelihood arrays {var_names}, var_name cannot be None" - ) - return idata.log_likelihood[var_names] - return idata.log_likelihood[var_names[0]] - else: - try: - log_likelihood = idata.log_likelihood[var_name] - except KeyError as err: - raise TypeError(f"No log likelihood data named {var_name} found") from err - return log_likelihood - - -BASE_FMT = """Computed from {{n_samples}} posterior samples and \ -{{n_points}} observations log-likelihood matrix. - -{{0:{0}}} Estimate SE -{{scale}}_{{kind}} {{1:8.2f}} {{2:7.2f}} -p_{{kind:{1}}} {{3:8.2f}} -""" -POINTWISE_LOO_FMT = """------ - -Pareto k diagnostic values: - {{0:>{0}}} {{1:>6}} -(-Inf, {{8:.2f}}] (good) {{2:{0}d}} {{5:6.1f}}% - ({{8:.2f}}, 1] (bad) {{3:{0}d}} {{6:6.1f}}% - (1, Inf) (very bad) {{4:{0}d}} {{7:6.1f}}% -""" -SCALE_DICT = {"deviance": "deviance", "log": "elpd", "negative_log": "-elpd"} - - -class ELPDData(pd.Series): # pylint: disable=too-many-ancestors - """Class to contain the data from elpd information criterion like waic or loo.""" - - def __str__(self): - """Print elpd data in a user friendly way.""" - kind = self.index[0].split("_")[1] - - if kind not in ("loo", "waic"): - raise ValueError("Invalid ELPDData object") - - scale_str = SCALE_DICT[self["scale"]] - padding = len(scale_str) + len(kind) + 1 - base = BASE_FMT.format(padding, padding - 2) - base = base.format( - "", - kind=kind, - scale=scale_str, - n_samples=self.n_samples, - n_points=self.n_data_points, - *self.values, - ) - - if self.warning: - base += "\n\nThere has been a warning during the calculation. Please check the results." - - if kind == "loo" and "pareto_k" in self: - bins = np.asarray([-np.inf, self.good_k, 1, np.inf]) - counts, *_ = _histogram(self.pareto_k.values, bins) - extended = POINTWISE_LOO_FMT.format(max(4, len(str(np.max(counts))))) - extended = extended.format( - "Count", - "Pct.", - *[*counts, *(counts / np.sum(counts) * 100)], - self.good_k, - ) - base = "\n".join([base, extended]) - return base - - def __repr__(self): - """Alias to ``__str__``.""" - return self.__str__() - - def copy(self, deep=True): # pylint:disable=overridden-final-method - """Perform a pandas deep copy of the ELPDData plus a copy of the stored data.""" - copied_obj = pd.Series.copy(self) - for key in copied_obj.keys(): - if deep: - copied_obj[key] = _deepcopy(copied_obj[key]) - else: - copied_obj[key] = _copy(copied_obj[key]) - return ELPDData(copied_obj) - - -@conditional_jit(nopython=True) -def stats_variance_1d(data, ddof=0): - a_a, b_b = 0, 0 - for i in data: - a_a = a_a + i - b_b = b_b + i * i - var = b_b / (len(data)) - ((a_a / (len(data))) ** 2) - var = var * (len(data) / (len(data) - ddof)) - return var - - -def stats_variance_2d(data, ddof=0, axis=1): - if data.ndim == 1: - return stats_variance_1d(data, ddof=ddof) - a_a, b_b = data.shape - if axis == 1: - var = np.zeros(a_a) - for i in range(a_a): - var[i] = stats_variance_1d(data[i], ddof=ddof) - else: - var = np.zeros(b_b) - for i in range(b_b): - var[i] = stats_variance_1d(data[:, i], ddof=ddof) - - return var - - -@conditional_vect -def _sqrt(a_a, b_b): - return (a_a + b_b) ** 0.5 - - -def _circfunc(samples, high, low, skipna): - samples = np.asarray(samples) - if skipna: - samples = samples[~np.isnan(samples)] - if samples.size == 0: - return np.nan - return _angle(samples, low, high, np.pi) - - -@conditional_vect -def _angle(samples, low, high, p_i=np.pi): - ang = (samples - low) * 2.0 * p_i / (high - low) - return ang - - -def _circular_standard_deviation(samples, high=2 * np.pi, low=0, skipna=False, axis=None): - ang = _circfunc(samples, high, low, skipna) - s_s = np.sin(ang).mean(axis=axis) - c_c = np.cos(ang).mean(axis=axis) - r_r = np.hypot(s_s, c_c) - return ((high - low) / 2.0 / np.pi) * np.sqrt(-2 * np.log(r_r)) - - -def smooth_data(obs_vals, pp_vals): - """Smooth data using a cubic spline. - - Helper function for discrete data in plot_pbv, loo_pit and plot_loo_pit. - - Parameters - ---------- - obs_vals : (N) array-like - Observed data - pp_vals : (S, N) array-like - Posterior predictive samples. ``N`` is the number of observations, - and ``S`` is the number of samples (generally n_chains*n_draws). - - Returns - ------- - obs_vals : (N) ndarray - Smoothed observed data - pp_vals : (S, N) ndarray - Smoothed posterior predictive samples - """ - x = np.linspace(0, 1, len(obs_vals)) - csi = CubicSpline(x, obs_vals) - obs_vals = csi(np.linspace(0.01, 0.99, len(obs_vals))) - - x = np.linspace(0, 1, pp_vals.shape[1]) - csi = CubicSpline(x, pp_vals, axis=1) - pp_vals = csi(np.linspace(0.01, 0.99, pp_vals.shape[1])) - - return obs_vals, pp_vals - - -def get_log_prior(idata, var_names=None): - """Retrieve the log prior dataarray of a given variable.""" - if not hasattr(idata, "log_prior"): - raise TypeError("log prior not found in inference data object") - if var_names is None: - var_names = list(idata.log_prior.data_vars) - return idata.log_prior[var_names] diff --git a/arviz/tests/__init__.py b/arviz/tests/__init__.py deleted file mode 100644 index db49e82c9f..0000000000 --- a/arviz/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Test suite.""" diff --git a/arviz/tests/bad.rcparams b/arviz/tests/bad.rcparams deleted file mode 100644 index 54d50fe4cb..0000000000 --- a/arviz/tests/bad.rcparams +++ /dev/null @@ -1 +0,0 @@ -stats.ic_scale : positive_log diff --git a/arviz/tests/base_tests/__init__.py b/arviz/tests/base_tests/__init__.py deleted file mode 100644 index a247e2137b..0000000000 --- a/arviz/tests/base_tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Base test suite.""" diff --git a/arviz/tests/base_tests/test_data.py b/arviz/tests/base_tests/test_data.py deleted file mode 100644 index 40c026cbe1..0000000000 --- a/arviz/tests/base_tests/test_data.py +++ /dev/null @@ -1,1679 +0,0 @@ -# pylint: disable=no-member, invalid-name, redefined-outer-name -# pylint: disable=too-many-lines - -import importlib -import os -import warnings -from collections import namedtuple -from copy import deepcopy -from html import escape -from typing import Dict -from tempfile import TemporaryDirectory -from urllib.parse import urlunsplit - -import numpy as np -import pytest -import xarray as xr -from xarray.core.options import OPTIONS -from xarray.testing import assert_identical - -from ... import ( - InferenceData, - clear_data_home, - concat, - convert_to_dataset, - convert_to_inference_data, - from_datatree, - from_dict, - from_json, - from_netcdf, - list_datasets, - load_arviz_data, - to_netcdf, - extract, -) - -from ...data.base import ( - dict_to_dataset, - generate_dims_coords, - infer_stan_dtypes, - make_attrs, - numpy_to_data_array, -) -from ...data.datasets import LOCAL_DATASETS, REMOTE_DATASETS, RemoteFileMetadata -from ..helpers import ( # pylint: disable=unused-import - chains, - check_multiple_attrs, - create_data_random, - data_random, - draws, - eight_schools_params, - models, -) - -# Check if dm-tree is installed -dm_tree_installed = importlib.util.find_spec("tree") is not None # pylint: disable=invalid-name -skip_tests = (not dm_tree_installed) and ("ARVIZ_REQUIRE_ALL_DEPS" not in os.environ) - - -@pytest.fixture(autouse=True) -def no_remote_data(monkeypatch, tmpdir): - """Delete all remote data and replace it with a local dataset.""" - keys = list(REMOTE_DATASETS) - for key in keys: - monkeypatch.delitem(REMOTE_DATASETS, key) - - centered = LOCAL_DATASETS["centered_eight"] - filename = os.path.join(str(tmpdir), os.path.basename(centered.filename)) - - url = urlunsplit(("file", "", centered.filename, "", "")) - - monkeypatch.setitem( - REMOTE_DATASETS, - "test_remote", - RemoteFileMetadata( - name="test_remote", - filename=filename, - url=url, - checksum="8efc3abafe0c796eb9aea7b69490d4e2400a33c57504ef4932e1c7105849176f", - description=centered.description, - ), - ) - monkeypatch.setitem( - REMOTE_DATASETS, - "bad_checksum", - RemoteFileMetadata( - name="bad_checksum", - filename=filename, - url=url, - checksum="bad!", - description=centered.description, - ), - ) - UnknownFileMetaData = namedtuple( - "UnknownFileMetaData", ["filename", "url", "checksum", "description"] - ) - monkeypatch.setitem( - REMOTE_DATASETS, - "test_unknown", - UnknownFileMetaData( - filename=filename, - url=url, - checksum="9ae00c83654b3f061d32c882ec0a270d10838fa36515ecb162b89a290e014849", - description="Test bad REMOTE_DATASET", - ), - ) - - -def test_load_local_arviz_data(): - inference_data = load_arviz_data("centered_eight") - assert isinstance(inference_data, InferenceData) - assert set(inference_data.observed_data.obs.coords["school"].values) == { - "Hotchkiss", - "Mt. Hermon", - "Choate", - "Deerfield", - "Phillips Andover", - "St. Paul's", - "Lawrenceville", - "Phillips Exeter", - } - assert inference_data.posterior["theta"].dims == ("chain", "draw", "school") - - -@pytest.mark.parametrize("fill_attrs", [True, False]) -def test_local_save(fill_attrs): - inference_data = load_arviz_data("centered_eight") - assert isinstance(inference_data, InferenceData) - - if fill_attrs: - inference_data.attrs["test"] = 1 - with TemporaryDirectory(prefix="arviz_tests_") as tmp_dir: - path = os.path.join(tmp_dir, "test_file.nc") - inference_data.to_netcdf(path) - - inference_data2 = from_netcdf(path) - if fill_attrs: - assert "test" in inference_data2.attrs - assert inference_data2.attrs["test"] == 1 - # pylint: disable=protected-access - assert all(group in inference_data2 for group in inference_data._groups_all) - # pylint: enable=protected-access - - -def test_clear_data_home(): - resource = REMOTE_DATASETS["test_remote"] - assert not os.path.exists(resource.filename) - load_arviz_data("test_remote") - assert os.path.exists(resource.filename) - clear_data_home(data_home=os.path.dirname(resource.filename)) - assert not os.path.exists(resource.filename) - - -def test_load_remote_arviz_data(): - assert load_arviz_data("test_remote") - - -def test_bad_checksum(): - with pytest.raises(IOError): - load_arviz_data("bad_checksum") - - -def test_missing_dataset(): - with pytest.raises(ValueError): - load_arviz_data("does not exist") - - -def test_list_datasets(): - dataset_string = list_datasets() - # make sure all the names of the data sets are in the dataset description - for key in ( - "centered_eight", - "non_centered_eight", - "test_remote", - "bad_checksum", - "test_unknown", - ): - assert key in dataset_string - - -def test_dims_coords(): - shape = 4, 20, 5 - var_name = "x" - dims, coords = generate_dims_coords(shape, var_name) - assert "x_dim_0" in dims - assert "x_dim_1" in dims - assert "x_dim_2" in dims - assert len(coords["x_dim_0"]) == 4 - assert len(coords["x_dim_1"]) == 20 - assert len(coords["x_dim_2"]) == 5 - - -@pytest.mark.parametrize( - "in_dims", (["dim1", "dim2"], ["draw", "dim1", "dim2"], ["chain", "draw", "dim1", "dim2"]) -) -def test_dims_coords_default_dims(in_dims): - shape = 4, 7 - var_name = "x" - dims, coords = generate_dims_coords( - shape, - var_name, - dims=in_dims, - coords={"chain": ["a", "b", "c"]}, - default_dims=["chain", "draw"], - ) - assert "dim1" in dims - assert "dim2" in dims - assert ("chain" in dims) == ("chain" in in_dims) - assert ("draw" in dims) == ("draw" in in_dims) - assert len(coords["dim1"]) == 4 - assert len(coords["dim2"]) == 7 - assert len(coords["chain"]) == 3 - assert "draw" not in coords - - -def test_dims_coords_extra_dims(): - shape = 4, 20 - var_name = "x" - with pytest.warns(UserWarning): - dims, coords = generate_dims_coords(shape, var_name, dims=["xx", "xy", "xz"]) - assert "xx" in dims - assert "xy" in dims - assert "xz" in dims - assert len(coords["xx"]) == 4 - assert len(coords["xy"]) == 20 - - -@pytest.mark.parametrize("shape", [(4, 20), (4, 20, 1)]) -def test_dims_coords_skip_event_dims(shape): - coords = {"x": np.arange(4), "y": np.arange(20), "z": np.arange(5)} - dims, coords = generate_dims_coords( - shape, "name", dims=["x", "y", "z"], coords=coords, skip_event_dims=True - ) - assert "x" in dims - assert "y" in dims - assert "z" not in dims - assert len(coords["x"]) == 4 - assert len(coords["y"]) == 20 - assert "z" not in coords - - -@pytest.mark.parametrize("dims", [None, ["chain", "draw"], ["chain", "draw", None]]) -def test_numpy_to_data_array_with_dims(dims): - da = numpy_to_data_array( - np.empty((4, 500, 7)), - var_name="a", - dims=dims, - default_dims=["chain", "draw"], - ) - assert list(da.dims) == ["chain", "draw", "a_dim_0"] - - -def test_make_attrs(): - extra_attrs = {"key": "Value"} - attrs = make_attrs(attrs=extra_attrs) - assert "key" in attrs - assert attrs["key"] == "Value" - - -@pytest.mark.parametrize("copy", [True, False]) -@pytest.mark.parametrize("inplace", [True, False]) -@pytest.mark.parametrize("sequence", [True, False]) -def test_concat_group(copy, inplace, sequence): - idata1 = from_dict( - posterior={"A": np.random.randn(2, 10, 2), "B": np.random.randn(2, 10, 5, 2)} - ) - if copy and inplace: - original_idata1_posterior_id = id(idata1.posterior) - idata2 = from_dict(prior={"C": np.random.randn(2, 10, 2), "D": np.random.randn(2, 10, 5, 2)}) - idata3 = from_dict(observed_data={"E": np.random.randn(100), "F": np.random.randn(2, 100)}) - # basic case - assert concat(idata1, idata2, copy=True, inplace=False) is not None - if sequence: - new_idata = concat((idata1, idata2, idata3), copy=copy, inplace=inplace) - else: - new_idata = concat(idata1, idata2, idata3, copy=copy, inplace=inplace) - if inplace: - assert new_idata is None - new_idata = idata1 - assert new_idata is not None - test_dict = {"posterior": ["A", "B"], "prior": ["C", "D"], "observed_data": ["E", "F"]} - fails = check_multiple_attrs(test_dict, new_idata) - assert not fails - if copy: - if inplace: - assert id(new_idata.posterior) == original_idata1_posterior_id - else: - assert id(new_idata.posterior) != id(idata1.posterior) - assert id(new_idata.prior) != id(idata2.prior) - assert id(new_idata.observed_data) != id(idata3.observed_data) - else: - assert id(new_idata.posterior) == id(idata1.posterior) - assert id(new_idata.prior) == id(idata2.prior) - assert id(new_idata.observed_data) == id(idata3.observed_data) - - -@pytest.mark.parametrize("dim", ["chain", "draw"]) -@pytest.mark.parametrize("copy", [True, False]) -@pytest.mark.parametrize("inplace", [True, False]) -@pytest.mark.parametrize("sequence", [True, False]) -@pytest.mark.parametrize("reset_dim", [True, False]) -def test_concat_dim(dim, copy, inplace, sequence, reset_dim): - idata1 = from_dict( - posterior={"A": np.random.randn(2, 10, 2), "B": np.random.randn(2, 10, 5, 2)}, - observed_data={"C": np.random.randn(100), "D": np.random.randn(2, 100)}, - ) - if inplace: - original_idata1_id = id(idata1) - idata2 = from_dict( - posterior={"A": np.random.randn(2, 10, 2), "B": np.random.randn(2, 10, 5, 2)}, - observed_data={"C": np.random.randn(100), "D": np.random.randn(2, 100)}, - ) - idata3 = from_dict( - posterior={"A": np.random.randn(2, 10, 2), "B": np.random.randn(2, 10, 5, 2)}, - observed_data={"C": np.random.randn(100), "D": np.random.randn(2, 100)}, - ) - # basic case - assert ( - concat(idata1, idata2, dim=dim, copy=copy, inplace=False, reset_dim=reset_dim) is not None - ) - if sequence: - new_idata = concat( - (idata1, idata2, idata3), copy=copy, dim=dim, inplace=inplace, reset_dim=reset_dim - ) - else: - new_idata = concat( - idata1, idata2, idata3, dim=dim, copy=copy, inplace=inplace, reset_dim=reset_dim - ) - if inplace: - assert new_idata is None - new_idata = idata1 - assert new_idata is not None - test_dict = {"posterior": ["A", "B"], "observed_data": ["C", "D"]} - fails = check_multiple_attrs(test_dict, new_idata) - assert not fails - if inplace: - assert id(new_idata) == original_idata1_id - else: - assert id(new_idata) != id(idata1) - assert getattr(new_idata.posterior, dim).size == 6 if dim == "chain" else 30 - if reset_dim: - assert np.all( - getattr(new_idata.posterior, dim).values - == (np.arange(6) if dim == "chain" else np.arange(30)) - ) - - -@pytest.mark.parametrize("copy", [True, False]) -@pytest.mark.parametrize("inplace", [True, False]) -@pytest.mark.parametrize("sequence", [True, False]) -def test_concat_edgecases(copy, inplace, sequence): - idata = from_dict(posterior={"A": np.random.randn(2, 10, 2), "B": np.random.randn(2, 10, 5, 2)}) - empty = concat() - assert empty is not None - if sequence: - new_idata = concat([idata], copy=copy, inplace=inplace) - else: - new_idata = concat(idata, copy=copy, inplace=inplace) - if inplace: - assert new_idata is None - new_idata = idata - else: - assert new_idata is not None - test_dict = {"posterior": ["A", "B"]} - fails = check_multiple_attrs(test_dict, new_idata) - assert not fails - if copy and not inplace: - assert id(new_idata.posterior) != id(idata.posterior) - else: - assert id(new_idata.posterior) == id(idata.posterior) - - -def test_concat_bad(): - with pytest.raises(TypeError): - concat("hello", "hello") - idata = from_dict(posterior={"A": np.random.randn(2, 10, 2), "B": np.random.randn(2, 10, 5, 2)}) - idata2 = from_dict(posterior={"A": np.random.randn(2, 10, 2)}) - idata3 = from_dict(prior={"A": np.random.randn(2, 10, 2)}) - with pytest.raises(TypeError): - concat(idata, np.array([1, 2, 3, 4, 5])) - with pytest.raises(TypeError): - concat(idata, idata, dim=None) - with pytest.raises(TypeError): - concat(idata, idata2, dim="chain") - with pytest.raises(TypeError): - concat(idata2, idata, dim="chain") - with pytest.raises(TypeError): - concat(idata, idata3, dim="chain") - with pytest.raises(TypeError): - concat(idata3, idata, dim="chain") - - -def test_inference_concat_keeps_all_fields(): - """From failures observed in issue #907""" - idata1 = from_dict(posterior={"A": [1, 2, 3, 4]}, sample_stats={"B": [2, 3, 4, 5]}) - idata2 = from_dict(prior={"C": [1, 2, 3, 4]}, observed_data={"D": [2, 3, 4, 5]}) - - idata_c1 = concat(idata1, idata2) - idata_c2 = concat(idata2, idata1) - - test_dict = {"posterior": ["A"], "sample_stats": ["B"], "prior": ["C"], "observed_data": ["D"]} - - fails_c1 = check_multiple_attrs(test_dict, idata_c1) - assert not fails_c1 - fails_c2 = check_multiple_attrs(test_dict, idata_c2) - assert not fails_c2 - - -@pytest.mark.parametrize( - "model_code,expected", - [ - ("data {int y;} models {y ~ poisson(3);} generated quantities {int X;}", {"X": "int"}), - ( - "data {real y;} models {y ~ normal(0,1);} generated quantities {int Y; real G;}", - {"Y": "int"}, - ), - ], -) -def test_infer_stan_dtypes(model_code, expected): - """Test different examples for dtypes in Stan models.""" - res = infer_stan_dtypes(model_code) - assert res == expected - - -class TestInferenceData: # pylint: disable=too-many-public-methods - def test_addition(self): - idata1 = from_dict( - posterior={"A": np.random.randn(2, 10, 2), "B": np.random.randn(2, 10, 5, 2)} - ) - idata2 = from_dict( - prior={"C": np.random.randn(2, 10, 2), "D": np.random.randn(2, 10, 5, 2)} - ) - new_idata = idata1 + idata2 - assert new_idata is not None - test_dict = {"posterior": ["A", "B"], "prior": ["C", "D"]} - fails = check_multiple_attrs(test_dict, new_idata) - assert not fails - - def test_iter(self, models): - idata = models.model_1 - for group in idata: - assert group in idata._groups_all # pylint: disable=protected-access - - def test_groups(self, models): - idata = models.model_1 - for group in idata.groups(): - assert group in idata._groups_all # pylint: disable=protected-access - - def test_values(self, models): - idata = models.model_1 - datasets = idata.values() - for group in idata.groups(): - assert group in idata._groups_all # pylint: disable=protected-access - dataset = getattr(idata, group) - assert dataset in datasets - - def test_items(self, models): - idata = models.model_1 - for group, dataset in idata.items(): - assert group in idata._groups_all # pylint: disable=protected-access - assert dataset.equals(getattr(idata, group)) - - @pytest.mark.parametrize("inplace", [True, False]) - def test_extend_xr_method(self, data_random, inplace): - idata = data_random - idata_copy = deepcopy(idata) - kwargs = {"groups": "posterior_groups"} - if inplace: - idata_copy.sum(dim="draw", inplace=inplace, **kwargs) - else: - idata2 = idata_copy.sum(dim="draw", inplace=inplace, **kwargs) - assert idata2 is not idata_copy - idata_copy = idata2 - assert_identical(idata_copy.posterior, idata.posterior.sum(dim="draw")) - assert_identical( - idata_copy.posterior_predictive, idata.posterior_predictive.sum(dim="draw") - ) - assert_identical(idata_copy.observed_data, idata.observed_data) - - @pytest.mark.parametrize("inplace", [False, True]) - def test_sel(self, data_random, inplace): - idata = data_random - original_groups = getattr(idata, "_groups") - ndraws = idata.posterior.draw.values.size - kwargs = {"draw": slice(200, None), "chain": slice(None, None, 2), "b_dim_0": [1, 2, 7]} - if inplace: - idata.sel(inplace=inplace, **kwargs) - else: - idata2 = idata.sel(inplace=inplace, **kwargs) - assert idata2 is not idata - idata = idata2 - groups = getattr(idata, "_groups") - assert np.all(np.isin(groups, original_groups)) - for group in groups: - dataset = getattr(idata, group) - assert "b_dim_0" in dataset.dims - assert np.all(dataset.b_dim_0.values == np.array(kwargs["b_dim_0"])) - if group != "observed_data": - assert np.all(np.isin(["chain", "draw"], dataset.dims)) - assert np.all(dataset.chain.values == np.arange(0, 4, 2)) - assert np.all(dataset.draw.values == np.arange(200, ndraws)) - - def test_sel_chain_prior(self): - idata = load_arviz_data("centered_eight") - original_groups = getattr(idata, "_groups") - idata_subset = idata.sel(inplace=False, chain_prior=False, chain=[0, 1, 3]) - groups = getattr(idata_subset, "_groups") - assert np.all(np.isin(groups, original_groups)) - for group in groups: - dataset_subset = getattr(idata_subset, group) - dataset = getattr(idata, group) - if "chain" in dataset.dims: - assert "chain" in dataset_subset.dims - if "prior" not in group: - assert np.all(dataset_subset.chain.values == np.array([0, 1, 3])) - else: - assert "chain" not in dataset_subset.dims - with pytest.raises(KeyError): - idata.sel(inplace=False, chain_prior=True, chain=[0, 1, 3]) - - @pytest.mark.parametrize("use", ("del", "delattr", "delitem")) - def test_del(self, use): - # create inference data object - data = np.random.normal(size=(4, 500, 8)) - idata = from_dict( - posterior={"a": data[..., 0], "b": data}, - sample_stats={"a": data[..., 0], "b": data}, - observed_data={"b": data[0, 0, :]}, - posterior_predictive={"a": data[..., 0], "b": data}, - ) - - # assert inference data object has all attributes - test_dict = { - "posterior": ("a", "b"), - "sample_stats": ("a", "b"), - "observed_data": ["b"], - "posterior_predictive": ("a", "b"), - } - fails = check_multiple_attrs(test_dict, idata) - assert not fails - # assert _groups attribute contains all groups - groups = getattr(idata, "_groups") - assert all((group in groups for group in test_dict)) - - # Use del method - if use == "del": - del idata.sample_stats - elif use == "delitem": - del idata["sample_stats"] - else: - delattr(idata, "sample_stats") - - # assert attribute has been removed - test_dict.pop("sample_stats") - fails = check_multiple_attrs(test_dict, idata) - assert not fails - assert not hasattr(idata, "sample_stats") - # assert _groups attribute has been updated - assert "sample_stats" not in getattr(idata, "_groups") - - @pytest.mark.parametrize( - "args_res", - ( - ([("posterior", "sample_stats")], ("posterior", "sample_stats")), - (["posterior", "like"], ("posterior", "warmup_posterior", "posterior_predictive")), - (["^posterior", "regex"], ("posterior", "posterior_predictive")), - ( - [("~^warmup", "~^obs"), "regex"], - ("posterior", "sample_stats", "posterior_predictive"), - ), - ( - ["~observed_vars"], - ("posterior", "sample_stats", "warmup_posterior", "warmup_sample_stats"), - ), - ), - ) - def test_group_names(self, args_res): - args, result = args_res - ds = dict_to_dataset({"a": np.random.normal(size=(3, 10))}) - idata = InferenceData( - posterior=(ds, ds), - sample_stats=(ds, ds), - observed_data=ds, - posterior_predictive=ds, - ) - group_names = idata._group_names(*args) # pylint: disable=protected-access - assert np.all([name in result for name in group_names]) - - def test_group_names_invalid_args(self): - ds = dict_to_dataset({"a": np.random.normal(size=(3, 10))}) - idata = InferenceData(posterior=(ds, ds)) - msg = r"^\'filter_groups\' can only be None, \'like\', or \'regex\', got: 'foo'$" - with pytest.raises(ValueError, match=msg): - idata._group_names( # pylint: disable=protected-access - ("posterior",), filter_groups="foo" - ) - - @pytest.mark.parametrize("inplace", [False, True]) - def test_isel(self, data_random, inplace): - idata = data_random - original_groups = getattr(idata, "_groups") - ndraws = idata.posterior.draw.values.size - kwargs = {"draw": slice(200, None), "chain": slice(None, None, 2), "b_dim_0": [1, 2, 7]} - if inplace: - idata.isel(inplace=inplace, **kwargs) - else: - idata2 = idata.isel(inplace=inplace, **kwargs) - assert idata2 is not idata - idata = idata2 - groups = getattr(idata, "_groups") - assert np.all(np.isin(groups, original_groups)) - for group in groups: - dataset = getattr(idata, group) - assert "b_dim_0" in dataset.dims - assert np.all(dataset.b_dim_0.values == np.array(kwargs["b_dim_0"])) - if group != "observed_data": - assert np.all(np.isin(["chain", "draw"], dataset.dims)) - assert np.all(dataset.chain.values == np.arange(0, 4, 2)) - assert np.all(dataset.draw.values == np.arange(200, ndraws)) - - def test_rename(self, data_random): - idata = data_random - original_groups = getattr(idata, "_groups") - renamed_idata = idata.rename({"b": "b_new"}) - for group in original_groups: - xr_data = getattr(renamed_idata, group) - assert "b_new" in list(xr_data.data_vars) - assert "b" not in list(xr_data.data_vars) - - renamed_idata = idata.rename({"b_dim_0": "b_new"}) - for group in original_groups: - xr_data = getattr(renamed_idata, group) - assert "b_new" in list(xr_data.dims) - assert "b_dim_0" not in list(xr_data.dims) - - def test_rename_vars(self, data_random): - idata = data_random - original_groups = getattr(idata, "_groups") - renamed_idata = idata.rename_vars({"b": "b_new"}) - for group in original_groups: - xr_data = getattr(renamed_idata, group) - assert "b_new" in list(xr_data.data_vars) - assert "b" not in list(xr_data.data_vars) - - renamed_idata = idata.rename_vars({"b_dim_0": "b_new"}) - for group in original_groups: - xr_data = getattr(renamed_idata, group) - assert "b_new" not in list(xr_data.dims) - assert "b_dim_0" in list(xr_data.dims) - - def test_rename_dims(self, data_random): - idata = data_random - original_groups = getattr(idata, "_groups") - renamed_idata = idata.rename_dims({"b_dim_0": "b_new"}) - for group in original_groups: - xr_data = getattr(renamed_idata, group) - assert "b_new" in list(xr_data.dims) - assert "b_dim_0" not in list(xr_data.dims) - - renamed_idata = idata.rename_dims({"b": "b_new"}) - for group in original_groups: - xr_data = getattr(renamed_idata, group) - assert "b_new" not in list(xr_data.data_vars) - assert "b" in list(xr_data.data_vars) - - def test_stack_unstack(self): - datadict = { - "a": np.random.randn(100), - "b": np.random.randn(1, 100, 10), - "c": np.random.randn(1, 100, 3, 4), - } - coords = { - "c1": np.arange(3), - "c99": np.arange(4), - "b1": np.arange(10), - } - dims = {"c": ["c1", "c99"], "b": ["b1"]} - dataset = from_dict(posterior=datadict, coords=coords, dims=dims) - assert_identical( - dataset.stack(z=["c1", "c99"]).posterior, dataset.posterior.stack(z=["c1", "c99"]) - ) - assert_identical(dataset.stack(z=["c1", "c99"]).unstack().posterior, dataset.posterior) - assert_identical( - dataset.stack(z=["c1", "c99"]).unstack(dim="z").posterior, dataset.posterior - ) - - def test_stack_bool(self): - datadict = { - "a": np.random.randn(100), - "b": np.random.randn(1, 100, 10), - "c": np.random.randn(1, 100, 3, 4), - } - coords = { - "c1": np.arange(3), - "c99": np.arange(4), - "b1": np.arange(10), - } - dims = {"c": ["c1", "c99"], "b": ["b1"]} - dataset = from_dict(posterior=datadict, coords=coords, dims=dims) - assert_identical( - dataset.stack(z=["c1", "c99"], create_index=False).posterior, - dataset.posterior.stack(z=["c1", "c99"], create_index=False), - ) - - def test_to_dict(self, models): - idata = models.model_1 - test_data = from_dict(**idata.to_dict()) - assert test_data - for group in idata._groups_all: # pylint: disable=protected-access - xr_data = getattr(idata, group) - test_xr_data = getattr(test_data, group) - assert xr_data.equals(test_xr_data) - - def test_to_dict_warmup(self): - idata = create_data_random( - groups=[ - "posterior", - "sample_stats", - "observed_data", - "warmup_posterior", - "warmup_posterior_predictive", - ] - ) - test_data = from_dict(**idata.to_dict(), save_warmup=True) - assert test_data - for group in idata._groups_all: # pylint: disable=protected-access - xr_data = getattr(idata, group) - test_xr_data = getattr(test_data, group) - assert xr_data.equals(test_xr_data) - - @pytest.mark.parametrize( - "kwargs", - ( - { - "groups": "posterior", - "include_coords": True, - "include_index": True, - "index_origin": 0, - }, - { - "groups": ["posterior", "sample_stats"], - "include_coords": False, - "include_index": True, - "index_origin": 0, - }, - { - "groups": "posterior_groups", - "include_coords": True, - "include_index": False, - "index_origin": 1, - }, - ), - ) - def test_to_dataframe(self, kwargs): - idata = from_dict( - posterior={"a": np.random.randn(4, 100, 3, 4, 5), "b": np.random.randn(4, 100)}, - sample_stats={"a": np.random.randn(4, 100, 3, 4, 5), "b": np.random.randn(4, 100)}, - observed_data={"a": np.random.randn(3, 4, 5), "b": np.random.randn(4)}, - ) - test_data = idata.to_dataframe(**kwargs) - assert not test_data.empty - groups = kwargs.get("groups", idata._groups_all) # pylint: disable=protected-access - for group in idata._groups_all: # pylint: disable=protected-access - if "data" in group: - continue - assert test_data.shape == ( - (4 * 100, 3 * 4 * 5 + 1 + 2) - if groups == "posterior" - else (4 * 100, (3 * 4 * 5 + 1) * 2 + 2) - ) - if groups == "posterior": - if kwargs.get("include_coords", True) and kwargs.get("include_index", True): - assert any( - f"[{kwargs.get('index_origin', 0)}," in item[0] - for item in test_data.columns - if isinstance(item, tuple) - ) - if kwargs.get("include_coords", True): - assert any(isinstance(item, tuple) for item in test_data.columns) - else: - assert not any(isinstance(item, tuple) for item in test_data.columns) - else: - if not kwargs.get("include_index", True): - assert all( - item in test_data.columns - for item in (("posterior", "a", 1, 1, 1), ("posterior", "b")) - ) - assert all(item in test_data.columns for item in ("chain", "draw")) - - @pytest.mark.parametrize( - "kwargs", - ( - { - "var_names": ["parameter_1", "parameter_2", "variable_1", "variable_2"], - "filter_vars": None, - "var_results": [ - ("posterior", "parameter_1"), - ("posterior", "parameter_2"), - ("prior", "parameter_1"), - ("prior", "parameter_2"), - ("posterior", "variable_1"), - ("posterior", "variable_2"), - ], - }, - { - "var_names": "parameter", - "filter_vars": "like", - "groups": "posterior", - "var_results": ["parameter_1", "parameter_2"], - }, - { - "var_names": "~parameter", - "filter_vars": "like", - "groups": "posterior", - "var_results": ["variable_1", "variable_2", "custom_name"], - }, - { - "var_names": [".+_2$", "custom_name"], - "filter_vars": "regex", - "groups": "posterior", - "var_results": ["parameter_2", "variable_2", "custom_name"], - }, - { - "var_names": ["lp"], - "filter_vars": "regex", - "groups": "sample_stats", - "var_results": ["lp"], - }, - ), - ) - def test_to_dataframe_selection(self, kwargs): - results = kwargs.pop("var_results") - idata = from_dict( - posterior={ - "parameter_1": np.random.randn(4, 100), - "parameter_2": np.random.randn(4, 100), - "variable_1": np.random.randn(4, 100), - "variable_2": np.random.randn(4, 100), - "custom_name": np.random.randn(4, 100), - }, - prior={ - "parameter_1": np.random.randn(4, 100), - "parameter_2": np.random.randn(4, 100), - }, - sample_stats={ - "lp": np.random.randn(4, 100), - }, - ) - test_data = idata.to_dataframe(**kwargs) - assert not test_data.empty - assert set(test_data.columns).symmetric_difference(results) == set(["chain", "draw"]) - - def test_to_dataframe_bad(self): - idata = from_dict( - posterior={"a": np.random.randn(4, 100, 3, 4, 5), "b": np.random.randn(4, 100)}, - sample_stats={"a": np.random.randn(4, 100, 3, 4, 5), "b": np.random.randn(4, 100)}, - observed_data={"a": np.random.randn(3, 4, 5), "b": np.random.randn(4)}, - ) - with pytest.raises(TypeError): - idata.to_dataframe(index_origin=2) - - with pytest.raises(TypeError): - idata.to_dataframe(include_coords=False, include_index=False) - - with pytest.raises(TypeError): - idata.to_dataframe(groups=["observed_data"]) - - with pytest.raises(KeyError): - idata.to_dataframe(groups=["invalid_group"]) - - with pytest.raises(ValueError): - idata.to_dataframe(var_names=["c"]) - - @pytest.mark.parametrize("use", (None, "args", "kwargs")) - def test_map(self, use): - idata = load_arviz_data("centered_eight") - args = [] - kwargs = {} - if use is None: - fun = lambda x: x + 3 - elif use == "args": - fun = lambda x, a: x + a - args = [3] - else: - fun = lambda x, a: x + a - kwargs = {"a": 3} - groups = ("observed_data", "posterior_predictive") - idata_map = idata.map(fun, groups, args=args, **kwargs) - groups_map = idata_map._groups # pylint: disable=protected-access - assert groups_map == idata._groups # pylint: disable=protected-access - assert np.allclose( - idata_map.observed_data.obs, fun(idata.observed_data.obs, *args, **kwargs) - ) - assert np.allclose( - idata_map.posterior_predictive.obs, fun(idata.posterior_predictive.obs, *args, **kwargs) - ) - assert np.allclose(idata_map.posterior.mu, idata.posterior.mu) - - def test_repr_html(self): - """Test if the function _repr_html is generating html.""" - idata = load_arviz_data("centered_eight") - display_style = OPTIONS["display_style"] - xr.set_options(display_style="html") - html = idata._repr_html_() # pylint: disable=protected-access - - assert html is not None - assert " 0 - inference_data2 = from_netcdf(filepath) - fails = check_multiple_attrs(test_dict, inference_data2) - assert not fails - os.remove(filepath) - assert not os.path.exists(filepath) - - @pytest.mark.parametrize("base_group", ["/", "test_group", "group/subgroup"]) - @pytest.mark.parametrize("groups_arg", [False, True]) - @pytest.mark.parametrize("compress", [True, False]) - @pytest.mark.parametrize("engine", ["h5netcdf", "netcdf4"]) - def test_io_method(self, data, eight_schools_params, groups_arg, base_group, compress, engine): - # create InferenceData and check it has been properly created - inference_data = self.get_inference_data( # pylint: disable=W0612 - data, eight_schools_params - ) - if engine == "h5netcdf": - try: - import h5netcdf # pylint: disable=unused-import - except ImportError: - pytest.skip("h5netcdf not installed") - elif engine == "netcdf4": - try: - import netCDF4 # pylint: disable=unused-import - except ImportError: - pytest.skip("netcdf4 not installed") - test_dict = { - "posterior": ["eta", "theta", "mu", "tau"], - "posterior_predictive": ["eta", "theta", "mu", "tau"], - "sample_stats": ["eta", "theta", "mu", "tau"], - "prior": ["eta", "theta", "mu", "tau"], - "prior_predictive": ["eta", "theta", "mu", "tau"], - "sample_stats_prior": ["eta", "theta", "mu", "tau"], - "observed_data": ["J", "y", "sigma"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - # check filename does not exist and use to_netcdf method - here = os.path.dirname(os.path.abspath(__file__)) - data_directory = os.path.join(here, "..", "saved_models") - filepath = os.path.join(data_directory, "io_method_testfile.nc") - assert not os.path.exists(filepath) - # InferenceData method - inference_data.to_netcdf( - filepath, - groups=("posterior", "observed_data") if groups_arg else None, - compress=compress, - base_group=base_group, - ) - - # assert file has been saved correctly - assert os.path.exists(filepath) - assert os.path.getsize(filepath) > 0 - inference_data2 = InferenceData.from_netcdf(filepath, base_group=base_group) - if groups_arg: # if groups arg, update test dict to contain only saved groups - test_dict = { - "posterior": ["eta", "theta", "mu", "tau"], - "observed_data": ["J", "y", "sigma"], - } - assert not hasattr(inference_data2, "sample_stats") - fails = check_multiple_attrs(test_dict, inference_data2) - assert not fails - - os.remove(filepath) - assert not os.path.exists(filepath) - - def test_empty_inference_data_object(self): - inference_data = InferenceData() - here = os.path.dirname(os.path.abspath(__file__)) - data_directory = os.path.join(here, "..", "saved_models") - filepath = os.path.join(data_directory, "empty_test_file.nc") - assert not os.path.exists(filepath) - inference_data.to_netcdf(filepath) - assert os.path.exists(filepath) - os.remove(filepath) - assert not os.path.exists(filepath) - - -class TestJSON: - def test_json_converters(self, models): - idata = models.model_1 - - filepath = os.path.realpath("test.json") - idata.to_json(filepath) - - idata_copy = from_json(filepath) - for group in idata._groups_all: # pylint: disable=protected-access - xr_data = getattr(idata, group) - test_xr_data = getattr(idata_copy, group) - assert xr_data.equals(test_xr_data) - - os.remove(filepath) - assert not os.path.exists(filepath) - - -class TestDataTree: - def test_datatree(self): - idata = load_arviz_data("centered_eight") - dt = idata.to_datatree() - idata_back = from_datatree(dt) - for group, ds in idata.items(): - assert_identical(ds, idata_back[group]) - assert all(group in dt.children for group in idata.groups()) - - def test_datatree_attrs(self): - idata = load_arviz_data("centered_eight") - idata.attrs = {"not": "empty"} - assert idata.attrs - dt = idata.to_datatree() - idata_back = from_datatree(dt) - assert dt.attrs == idata.attrs - assert idata_back.attrs == idata.attrs - - -class TestConversions: - def test_id_conversion_idempotent(self): - stored = load_arviz_data("centered_eight") - inference_data = convert_to_inference_data(stored) - assert isinstance(inference_data, InferenceData) - assert set(inference_data.observed_data.obs.coords["school"].values) == { - "Hotchkiss", - "Mt. Hermon", - "Choate", - "Deerfield", - "Phillips Andover", - "St. Paul's", - "Lawrenceville", - "Phillips Exeter", - } - assert inference_data.posterior["theta"].dims == ("chain", "draw", "school") - - def test_dataset_conversion_idempotent(self): - inference_data = load_arviz_data("centered_eight") - data_set = convert_to_dataset(inference_data.posterior) - assert isinstance(data_set, xr.Dataset) - assert set(data_set.coords["school"].values) == { - "Hotchkiss", - "Mt. Hermon", - "Choate", - "Deerfield", - "Phillips Andover", - "St. Paul's", - "Lawrenceville", - "Phillips Exeter", - } - assert data_set["theta"].dims == ("chain", "draw", "school") - - def test_id_conversion_args(self): - stored = load_arviz_data("centered_eight") - IVIES = ["Yale", "Harvard", "MIT", "Princeton", "Cornell", "Dartmouth", "Columbia", "Brown"] - # test dictionary argument... - # I reverse engineered a dictionary out of the centered_eight - # data. That's what this block of code does. - d = stored.posterior.to_dict() - d = d["data_vars"] - test_dict = {} # type: Dict[str, np.ndarray] - for var_name in d: - data = d[var_name]["data"] - # this is a list of chains that is a list of samples... - chain_arrs = [] - for chain in data: # list of samples - chain_arrs.append(np.array(chain)) - data_arr = np.stack(chain_arrs) - test_dict[var_name] = data_arr - - inference_data = convert_to_inference_data( - test_dict, dims={"theta": ["Ivies"]}, coords={"Ivies": IVIES} - ) - - assert isinstance(inference_data, InferenceData) - assert set(inference_data.posterior.coords["Ivies"].values) == set(IVIES) - assert inference_data.posterior["theta"].dims == ("chain", "draw", "Ivies") - - -class TestDataArrayToDataset: - def test_1d_dataset(self): - size = 100 - dataset = convert_to_dataset( - xr.DataArray(np.random.randn(1, size), name="plot", dims=("chain", "draw")) - ) - assert len(dataset.data_vars) == 1 - assert "plot" in dataset.data_vars - assert dataset.chain.shape == (1,) - assert dataset.draw.shape == (size,) - - def test_nd_to_dataset(self): - shape = (1, 2, 3, 4, 5) - dataset = convert_to_dataset( - xr.DataArray(np.random.randn(*shape), dims=("chain", "draw", "dim_0", "dim_1", "dim_2")) - ) - var_name = list(dataset.data_vars)[0] - - assert len(dataset.data_vars) == 1 - assert dataset.chain.shape == shape[:1] - assert dataset.draw.shape == shape[1:2] - assert dataset[var_name].shape == shape - - def test_nd_to_inference_data(self): - shape = (1, 2, 3, 4, 5) - inference_data = convert_to_inference_data( - xr.DataArray( - np.random.randn(*shape), dims=("chain", "draw", "dim_0", "dim_1", "dim_2") - ), - group="prior", - ) - var_name = list(inference_data.prior.data_vars)[0] - - assert hasattr(inference_data, "prior") - assert len(inference_data.prior.data_vars) == 1 - assert inference_data.prior.chain.shape == shape[:1] - assert inference_data.prior.draw.shape == shape[1:2] - assert inference_data.prior[var_name].shape == shape - - -class TestExtractDataset: - def test_default(self): - idata = load_arviz_data("centered_eight") - post = extract(idata) - assert isinstance(post, xr.Dataset) - assert "sample" in post.dims - assert post.theta.size == (4 * 500 * 8) - - def test_seed(self): - idata = load_arviz_data("centered_eight") - post = extract(idata, rng=7) - post_pred = extract(idata, group="posterior_predictive", rng=7) - assert all(post.sample == post_pred.sample) - - def test_no_combine(self): - idata = load_arviz_data("centered_eight") - post = extract(idata, combined=False) - assert "sample" not in post.dims - assert post.sizes["chain"] == 4 - assert post.sizes["draw"] == 500 - - def test_var_name_group(self): - idata = load_arviz_data("centered_eight") - prior = extract(idata, group="prior", var_names="the", filter_vars="like") - assert {} == prior.attrs - assert "theta" in prior.name - - def test_keep_dataset(self): - idata = load_arviz_data("centered_eight") - prior = extract( - idata, group="prior", var_names="the", filter_vars="like", keep_dataset=True - ) - assert prior.attrs == idata.prior.attrs - assert "theta" in prior.data_vars - assert "mu" not in prior.data_vars - - def test_subset_samples(self): - idata = load_arviz_data("centered_eight") - post = extract(idata, num_samples=10) - assert post.sizes["sample"] == 10 - assert post.attrs == idata.posterior.attrs - - -def test_convert_to_inference_data_with_array_like(): - class ArrayLike: - def __init__(self, data): - self._data = np.asarray(data) - - def __array__(self): - return self._data - - array_like = ArrayLike(np.random.randn(4, 100)) - idata = convert_to_inference_data(array_like, group="posterior") - - assert hasattr(idata, "posterior") - assert "x" in idata.posterior.data_vars - assert idata.posterior["x"].shape == (4, 100) diff --git a/arviz/tests/base_tests/test_data_zarr.py b/arviz/tests/base_tests/test_data_zarr.py deleted file mode 100644 index c93b0253dd..0000000000 --- a/arviz/tests/base_tests/test_data_zarr.py +++ /dev/null @@ -1,143 +0,0 @@ -# pylint: disable=redefined-outer-name -import os -from collections.abc import MutableMapping -from tempfile import TemporaryDirectory -from typing import Mapping - -import numpy as np -import pytest - -from ... import InferenceData, from_dict -from ... import to_zarr, from_zarr - -from ..helpers import ( # pylint: disable=unused-import - chains, - check_multiple_attrs, - draws, - eight_schools_params, - importorskip, -) - -zarr = importorskip("zarr") # pylint: disable=invalid-name - - -class TestDataZarr: - @pytest.fixture(scope="class") - def data(self, draws, chains): - class Data: - # fake 8-school output - shapes: Mapping[str, list] = {"mu": [], "tau": [], "eta": [8], "theta": [8]} - obj = {key: np.random.randn(chains, draws, *shape) for key, shape in shapes.items()} - - return Data - - def get_inference_data(self, data, eight_schools_params, fill_attrs): - return from_dict( - posterior=data.obj, - posterior_predictive=data.obj, - sample_stats=data.obj, - prior=data.obj, - prior_predictive=data.obj, - sample_stats_prior=data.obj, - observed_data=eight_schools_params, - coords={"school": np.arange(8)}, - dims={"theta": ["school"], "eta": ["school"]}, - attrs={"test": 1} if fill_attrs else None, - ) - - @pytest.mark.parametrize("store", [0, 1, 2]) - @pytest.mark.parametrize("fill_attrs", [True, False]) - def test_io_method(self, data, eight_schools_params, store, fill_attrs): - # create InferenceData and check it has been properly created - inference_data = self.get_inference_data( # pylint: disable=W0612 - data, eight_schools_params, fill_attrs - ) - test_dict = { - "posterior": ["eta", "theta", "mu", "tau"], - "posterior_predictive": ["eta", "theta", "mu", "tau"], - "sample_stats": ["eta", "theta", "mu", "tau"], - "prior": ["eta", "theta", "mu", "tau"], - "prior_predictive": ["eta", "theta", "mu", "tau"], - "sample_stats_prior": ["eta", "theta", "mu", "tau"], - "observed_data": ["J", "y", "sigma"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - if fill_attrs: - assert inference_data.attrs["test"] == 1 - else: - assert "test" not in inference_data.attrs - - # check filename does not exist and use to_zarr method - with TemporaryDirectory(prefix="arviz_tests_") as tmp_dir: - filepath = os.path.join(tmp_dir, "zarr") - - # InferenceData method - if store == 0: - # Tempdir - store = inference_data.to_zarr(store=None) - assert isinstance(store, MutableMapping) - elif store == 1: - inference_data.to_zarr(store=filepath) - # assert file has been saved correctly - assert os.path.exists(filepath) - assert os.path.getsize(filepath) > 0 - elif store == 2: - store = zarr.storage.DirectoryStore(filepath) - inference_data.to_zarr(store=store) - # assert file has been saved correctly - assert os.path.exists(filepath) - assert os.path.getsize(filepath) > 0 - - if isinstance(store, MutableMapping): - inference_data2 = InferenceData.from_zarr(store) - else: - inference_data2 = InferenceData.from_zarr(filepath) - - # Everything in dict still available in inference_data2 ? - fails = check_multiple_attrs(test_dict, inference_data2) - assert not fails - - if fill_attrs: - assert inference_data2.attrs["test"] == 1 - else: - assert "test" not in inference_data2.attrs - - def test_io_function(self, data, eight_schools_params): - # create InferenceData and check it has been properly created - inference_data = self.get_inference_data( # pylint: disable=W0612 - data, - eight_schools_params, - fill_attrs=True, - ) - test_dict = { - "posterior": ["eta", "theta", "mu", "tau"], - "posterior_predictive": ["eta", "theta", "mu", "tau"], - "sample_stats": ["eta", "theta", "mu", "tau"], - "prior": ["eta", "theta", "mu", "tau"], - "prior_predictive": ["eta", "theta", "mu", "tau"], - "sample_stats_prior": ["eta", "theta", "mu", "tau"], - "observed_data": ["J", "y", "sigma"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - assert inference_data.attrs["test"] == 1 - - # check filename does not exist and use to_zarr method - with TemporaryDirectory(prefix="arviz_tests_") as tmp_dir: - filepath = os.path.join(tmp_dir, "zarr") - - to_zarr(inference_data, store=filepath) - # assert file has been saved correctly - assert os.path.exists(filepath) - assert os.path.getsize(filepath) > 0 - - inference_data2 = from_zarr(filepath) - - # Everything in dict still available in inference_data2 ? - fails = check_multiple_attrs(test_dict, inference_data2) - assert not fails - - assert inference_data2.attrs["test"] == 1 diff --git a/arviz/tests/base_tests/test_diagnostics.py b/arviz/tests/base_tests/test_diagnostics.py deleted file mode 100644 index 69e2946a86..0000000000 --- a/arviz/tests/base_tests/test_diagnostics.py +++ /dev/null @@ -1,511 +0,0 @@ -"""Test Diagnostic methods""" - -# pylint: disable=redefined-outer-name, no-member, too-many-public-methods -import os - -import numpy as np -import packaging -import pandas as pd -import pytest -import scipy -from numpy.testing import assert_almost_equal - -from ...data import from_cmdstan, load_arviz_data -from ...rcparams import rcParams -from ...sel_utils import xarray_var_iter -from ...stats import bfmi, ess, mcse, rhat -from ...stats.diagnostics import ( - _ess, - _ess_quantile, - _mc_error, - _mcse_quantile, - _multichain_statistics, - _rhat, - _rhat_rank, - _split_chains, - _z_scale, - ks_summary, -) - -# For tests only, recommended value should be closer to 1.01-1.05 -# See discussion in https://github.com/stan-dev/rstan/pull/618 -GOOD_RHAT = 1.1 - -rcParams["data.load"] = "eager" - - -@pytest.fixture(scope="session") -def data(): - centered_eight = load_arviz_data("centered_eight") - return centered_eight.posterior - - -class TestDiagnostics: - def test_bfmi(self): - energy = np.array([1, 2, 3, 4]) - assert_almost_equal(bfmi(energy), 0.6) - - def test_bfmi_dataset(self): - data = load_arviz_data("centered_eight") - assert bfmi(data).all() - - def test_bfmi_dataset_bad(self): - data = load_arviz_data("centered_eight") - del data.sample_stats["energy"] - with pytest.raises(TypeError): - bfmi(data) - - def test_bfmi_correctly_transposed(self): - data = load_arviz_data("centered_eight") - vals1 = bfmi(data) - data.sample_stats["energy"] = data.sample_stats["energy"].T - vals2 = bfmi(data) - assert_almost_equal(vals1, vals2) - - def test_deterministic(self): - """ - Test algorithm against posterior (R) convergence functions. - - posterior: https://github.com/stan-dev/posterior - R code: - ``` - library("posterior") - data2 <- read.csv("blocker.2.csv", comment.char = "#") - data1 <- read.csv("blocker.1.csv", comment.char = "#") - output <- matrix(ncol=17, nrow=length(names(data1))-4) - j = 0 - for (i in 1:length(names(data1))) { - name = names(data1)[i] - ary = matrix(c(data1[,name], data2[,name]), 1000, 2) - if (!endsWith(name, "__")) - j <- j + 1 - output[j,] <- c( - posterior::rhat(ary), - posterior::rhat_basic(ary, FALSE), - posterior::ess_bulk(ary), - posterior::ess_tail(ary), - posterior::ess_mean(ary), - posterior::ess_sd(ary), - posterior::ess_median(ary), - posterior::ess_basic(ary, FALSE), - posterior::ess_quantile(ary, 0.01), - posterior::ess_quantile(ary, 0.1), - posterior::ess_quantile(ary, 0.3), - posterior::mcse_mean(ary), - posterior::mcse_sd(ary), - posterior::mcse_median(ary), - posterior::mcse_quantile(ary, prob=0.01), - posterior::mcse_quantile(ary, prob=0.1), - posterior::mcse_quantile(ary, prob=0.3)) - } - df = data.frame(output, row.names = names(data1)[5:ncol(data1)]) - colnames(df) <- c("rhat_rank", - "rhat_raw", - "ess_bulk", - "ess_tail", - "ess_mean", - "ess_sd", - "ess_median", - "ess_raw", - "ess_quantile01", - "ess_quantile10", - "ess_quantile30", - "mcse_mean", - "mcse_sd", - "mcse_median", - "mcse_quantile01", - "mcse_quantile10", - "mcse_quantile30") - write.csv(df, "reference_posterior.csv") - ``` - Reference file: - - Created: 2024-12-20 - System: Ubuntu 24.04.1 LTS - R version 4.4.2 (2024-10-31) - posterior version from https://github.com/stan-dev/posterior/pull/388 - (after release 1.6.0 but before the fixes in the PR were released). - """ - # download input files - here = os.path.dirname(os.path.abspath(__file__)) - data_directory = os.path.join(here, "..", "saved_models") - path = os.path.join(data_directory, "stan_diagnostics", "blocker.[0-9].csv") - posterior = from_cmdstan(path) - reference_path = os.path.join(data_directory, "stan_diagnostics", "reference_posterior.csv") - reference = ( - pd.read_csv(reference_path, index_col=0, float_precision="high") - .sort_index(axis=1) - .sort_index(axis=0) - ) - # test arviz functions - funcs = { - "rhat_rank": lambda x: rhat(x, method="rank"), - "rhat_raw": lambda x: rhat(x, method="identity"), - "ess_bulk": lambda x: ess(x, method="bulk"), - "ess_tail": lambda x: ess(x, method="tail"), - "ess_mean": lambda x: ess(x, method="mean"), - "ess_sd": lambda x: ess(x, method="sd"), - "ess_median": lambda x: ess(x, method="median"), - "ess_raw": lambda x: ess(x, method="identity"), - "ess_quantile01": lambda x: ess(x, method="quantile", prob=0.01), - "ess_quantile10": lambda x: ess(x, method="quantile", prob=0.1), - "ess_quantile30": lambda x: ess(x, method="quantile", prob=0.3), - "mcse_mean": lambda x: mcse(x, method="mean"), - "mcse_sd": lambda x: mcse(x, method="sd"), - "mcse_median": lambda x: mcse(x, method="median"), - "mcse_quantile01": lambda x: mcse(x, method="quantile", prob=0.01), - "mcse_quantile10": lambda x: mcse(x, method="quantile", prob=0.1), - "mcse_quantile30": lambda x: mcse(x, method="quantile", prob=0.3), - } - results = {} - for key, coord_dict, _, vals in xarray_var_iter(posterior.posterior, combined=True): - if coord_dict: - key = f"{key}.{list(coord_dict.values())[0] + 1}" - results[key] = {func_name: func(vals) for func_name, func in funcs.items()} - arviz_data = pd.DataFrame.from_dict(results).T.sort_index(axis=1).sort_index(axis=0) - - # check column names - assert set(arviz_data.columns) == set(reference.columns) - - # check parameter names - assert set(arviz_data.index) == set(reference.index) - - # show print with pytests '-s' tag - np.set_printoptions(16) - print(abs(reference - arviz_data).max()) - - # test absolute accuracy - assert (abs(reference - arviz_data).values < 1e-8).all(None) - - @pytest.mark.parametrize("method", ("rank", "split", "folded", "z_scale", "identity")) - @pytest.mark.parametrize("var_names", (None, "mu", ["mu", "tau"])) - def test_rhat(self, data, var_names, method): - """Confirm R-hat statistic is close to 1 for a large - number of samples. Also checks the correct shape""" - rhat_data = rhat(data, var_names=var_names, method=method) - for r_hat in rhat_data.data_vars.values(): - assert ((1 / GOOD_RHAT < r_hat.values) | (r_hat.values < GOOD_RHAT)).all() - - # In None case check that all varnames from rhat_data match input data - if var_names is None: - assert list(rhat_data.data_vars) == list(data.data_vars) - - @pytest.mark.parametrize("method", ("rank", "split", "folded", "z_scale", "identity")) - def test_rhat_nan(self, method): - """Confirm R-hat statistic returns nan.""" - data = np.random.randn(4, 100) - data[0, 0] = np.nan # pylint: disable=unsupported-assignment-operation - rhat_data = rhat(data, method=method) - assert np.isnan(rhat_data) - if method == "rank": - assert np.isnan(_rhat(rhat_data)) - - @pytest.mark.parametrize("method", ("rank", "split", "folded", "z_scale", "identity")) - @pytest.mark.parametrize("chain", (None, 1, 2)) - @pytest.mark.parametrize("draw", (1, 2, 3, 4)) - def test_rhat_shape(self, method, chain, draw): - """Confirm R-hat statistic returns nan.""" - data = np.random.randn(draw) if chain is None else np.random.randn(chain, draw) - if (chain in (None, 1)) or (draw < 4): - rhat_data = rhat(data, method=method) - assert np.isnan(rhat_data) - else: - rhat_data = rhat(data, method=method) - assert not np.isnan(rhat_data) - - def test_rhat_bad(self): - """Confirm rank normalized Split R-hat statistic is - far from 1 for a small number of samples.""" - r_hat = rhat(np.vstack([20 + np.random.randn(1, 100), np.random.randn(1, 100)])) - assert 1 / GOOD_RHAT > r_hat or GOOD_RHAT < r_hat - - def test_rhat_bad_method(self): - with pytest.raises(TypeError): - rhat(np.random.randn(2, 300), method="wrong_method") - - def test_rhat_ndarray(self): - with pytest.raises(TypeError): - rhat(np.random.randn(2, 300, 10)) - - @pytest.mark.parametrize( - "method", - ( - "bulk", - "tail", - "quantile", - "local", - "mean", - "sd", - "median", - "mad", - "z_scale", - "folded", - "identity", - ), - ) - @pytest.mark.parametrize("relative", (True, False)) - def test_effective_sample_size_array(self, data, method, relative): - n_low = 100 / 400 if relative else 100 - n_high = 800 / 400 if relative else 800 - if method in ("quantile", "tail"): - ess_hat = ess(data, method=method, prob=0.34, relative=relative) - if method == "tail": - assert ess_hat > n_low - assert ess_hat < n_high - ess_hat = ess(np.random.randn(4, 100), method=method, relative=relative) - assert ess_hat > n_low - assert ess_hat < n_high - ess_hat = ess( - np.random.randn(4, 100), method=method, prob=(0.2, 0.8), relative=relative - ) - elif method == "local": - ess_hat = ess( - np.random.randn(4, 100), method=method, prob=(0.2, 0.3), relative=relative - ) - else: - ess_hat = ess(np.random.randn(4, 100), method=method, relative=relative) - assert ess_hat > n_low - assert ess_hat < n_high - - @pytest.mark.parametrize( - "method", - ( - "bulk", - "tail", - "quantile", - "local", - "mean", - "sd", - "median", - "mad", - "z_scale", - "folded", - "identity", - ), - ) - @pytest.mark.parametrize("relative", (True, False)) - @pytest.mark.parametrize("chain", (None, 1, 2)) - @pytest.mark.parametrize("draw", (1, 2, 3, 4)) - @pytest.mark.parametrize("use_nan", (True, False)) - def test_effective_sample_size_nan(self, method, relative, chain, draw, use_nan): - data = np.random.randn(draw) if chain is None else np.random.randn(chain, draw) - if use_nan: - data[0] = np.nan - if method in ("quantile", "tail"): - ess_value = ess(data, method=method, prob=0.34, relative=relative) - elif method == "local": - ess_value = ess(data, method=method, prob=(0.2, 0.3), relative=relative) - else: - ess_value = ess(data, method=method, relative=relative) - if (draw < 4) or use_nan: - assert np.isnan(ess_value) - else: - assert not np.isnan(ess_value) - # test following only once tests are run - if (method == "bulk") and (not relative) and (chain is None) and (draw == 4): - if use_nan: - assert np.isnan(_ess(data)) - else: - assert not np.isnan(_ess(data)) - - @pytest.mark.parametrize("relative", (True, False)) - def test_effective_sample_size_missing_prob(self, relative): - with pytest.raises(TypeError): - ess(np.random.randn(4, 100), method="quantile", relative=relative) - with pytest.raises(TypeError): - _ess_quantile(np.random.randn(4, 100), prob=None, relative=relative) - with pytest.raises(TypeError): - ess(np.random.randn(4, 100), method="local", relative=relative) - - @pytest.mark.parametrize("relative", (True, False)) - def test_effective_sample_size_too_many_probs(self, relative): - with pytest.raises(ValueError): - ess(np.random.randn(4, 100), method="local", prob=[0.1, 0.2, 0.9], relative=relative) - - def test_effective_sample_size_constant(self): - assert ess(np.ones((4, 100))) == 400 - - def test_effective_sample_size_bad_method(self): - with pytest.raises(TypeError): - ess(np.random.randn(4, 100), method="wrong_method") - - def test_effective_sample_size_ndarray(self): - with pytest.raises(TypeError): - ess(np.random.randn(2, 300, 10)) - - @pytest.mark.parametrize( - "method", - ( - "bulk", - "tail", - "quantile", - "local", - "mean", - "sd", - "median", - "mad", - "z_scale", - "folded", - "identity", - ), - ) - @pytest.mark.parametrize("relative", (True, False)) - @pytest.mark.parametrize("var_names", (None, "mu", ["mu", "tau"])) - def test_effective_sample_size_dataset(self, data, method, var_names, relative): - n_low = 100 / (data.chain.size * data.draw.size) if relative else 100 - if method in ("quantile", "tail"): - ess_hat = ess(data, var_names=var_names, method=method, prob=0.34, relative=relative) - elif method == "local": - ess_hat = ess( - data, var_names=var_names, method=method, prob=(0.2, 0.3), relative=relative - ) - else: - ess_hat = ess(data, var_names=var_names, method=method, relative=relative) - assert np.all(ess_hat.mu.values > n_low) # This might break if the data is regenerated - - @pytest.mark.parametrize("mcse_method", ("mean", "sd", "median", "quantile")) - def test_mcse_array(self, mcse_method): - if mcse_method == "quantile": - mcse_hat = mcse(np.random.randn(4, 100), method=mcse_method, prob=0.34) - else: - mcse_hat = mcse(np.random.randn(4, 100), method=mcse_method) - assert mcse_hat - - def test_mcse_ndarray(self): - with pytest.raises(TypeError): - mcse(np.random.randn(2, 300, 10)) - - @pytest.mark.parametrize("mcse_method", ("mean", "sd", "median", "quantile")) - @pytest.mark.parametrize("var_names", (None, "mu", ["mu", "tau"])) - def test_mcse_dataset(self, data, mcse_method, var_names): - if mcse_method == "quantile": - mcse_hat = mcse(data, var_names=var_names, method=mcse_method, prob=0.34) - else: - mcse_hat = mcse(data, var_names=var_names, method=mcse_method) - assert mcse_hat # This might break if the data is regenerated - - @pytest.mark.parametrize("mcse_method", ("mean", "sd", "median", "quantile")) - @pytest.mark.parametrize("chain", (None, 1, 2)) - @pytest.mark.parametrize("draw", (1, 2, 3, 4)) - @pytest.mark.parametrize("use_nan", (True, False)) - def test_mcse_nan(self, mcse_method, chain, draw, use_nan): - data = np.random.randn(draw) if chain is None else np.random.randn(chain, draw) - if use_nan: - data[0] = np.nan - if mcse_method == "quantile": - mcse_hat = mcse(data, method=mcse_method, prob=0.34) - else: - mcse_hat = mcse(data, method=mcse_method) - if draw < 4 or use_nan: - assert np.isnan(mcse_hat) - else: - assert not np.isnan(mcse_hat) - - @pytest.mark.parametrize("method", ("wrong_method", "quantile")) - def test_mcse_bad_method(self, data, method): - with pytest.raises(TypeError): - mcse(data, method=method, prob=None) - - @pytest.mark.parametrize("draws", (3, 4, 100)) - @pytest.mark.parametrize("chains", (None, 1, 2)) - def test_multichain_summary_array(self, draws, chains): - """Test multichain statistics against individual functions.""" - if chains is None: - ary = np.random.randn(draws) - else: - ary = np.random.randn(chains, draws) - - mcse_mean_hat = mcse(ary, method="mean") - mcse_sd_hat = mcse(ary, method="sd") - ess_bulk_hat = ess(ary, method="bulk") - ess_tail_hat = ess(ary, method="tail") - rhat_hat = _rhat_rank(ary) - ( - mcse_mean_hat_, - mcse_sd_hat_, - ess_bulk_hat_, - ess_tail_hat_, - rhat_hat_, - ) = _multichain_statistics(ary) - if draws == 3: - assert np.isnan( - ( - mcse_mean_hat, - mcse_sd_hat, - ess_bulk_hat, - ess_tail_hat, - rhat_hat, - ) - ).all() - assert np.isnan( - ( - mcse_mean_hat_, - mcse_sd_hat_, - ess_bulk_hat_, - ess_tail_hat_, - rhat_hat_, - ) - ).all() - else: - assert_almost_equal(mcse_mean_hat, mcse_mean_hat_) - assert_almost_equal(mcse_sd_hat, mcse_sd_hat_) - assert_almost_equal(ess_bulk_hat, ess_bulk_hat_) - assert_almost_equal(ess_tail_hat, ess_tail_hat_) - if chains in (None, 1): - assert np.isnan(rhat_hat) - assert np.isnan(rhat_hat_) - else: - assert round(rhat_hat, 3) == round(rhat_hat_, 3) - - def test_ks_summary(self): - """Instead of psislw data, this test uses fake data.""" - pareto_tail_indices = np.array([0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2]) - with pytest.warns(UserWarning): - summary = ks_summary(pareto_tail_indices) - assert summary is not None - pareto_tail_indices2 = np.array([0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.6]) - with pytest.warns(UserWarning): - summary2 = ks_summary(pareto_tail_indices2) - assert summary2 is not None - - @pytest.mark.parametrize("size", [100, 101]) - @pytest.mark.parametrize("batches", [1, 2, 3, 5, 7]) - @pytest.mark.parametrize("ndim", [1, 2, 3]) - @pytest.mark.parametrize("circular", [False, True]) - def test_mc_error(self, size, batches, ndim, circular): - x = np.random.randn(size, ndim).squeeze() # pylint: disable=no-member - assert _mc_error(x, batches=batches, circular=circular) is not None - - @pytest.mark.parametrize("size", [100, 101]) - @pytest.mark.parametrize("ndim", [1, 2, 3]) - def test_mc_error_nan(self, size, ndim): - x = np.random.randn(size, ndim).squeeze() # pylint: disable=no-member - x[0] = np.nan - if ndim != 1: - assert np.isnan(_mc_error(x)).all() - else: - assert np.isnan(_mc_error(x)) - - @pytest.mark.parametrize("func", ("_mcse_quantile", "_z_scale")) - def test_nan_behaviour(self, func): - data = np.random.randn(100, 4) - data[0, 0] = np.nan # pylint: disable=unsupported-assignment-operation - if func == "_mcse_quantile": - assert np.isnan(_mcse_quantile(data, 0.5)).all(None) - elif packaging.version.parse(scipy.__version__) < packaging.version.parse("1.10.0.dev0"): - assert not np.isnan(_z_scale(data)).all(None) - assert not np.isnan(_z_scale(data)).any(None) - else: - assert np.isnan(_z_scale(data)).sum() == 1 - - @pytest.mark.parametrize("chains", (None, 1, 2, 3)) - @pytest.mark.parametrize("draws", (2, 3, 100, 101)) - def test_split_chain_dims(self, chains, draws): - if chains is None: - data = np.random.randn(draws) - else: - data = np.random.randn(chains, draws) - split_data = _split_chains(data) - if chains is None: - chains = 1 - assert split_data.shape == (chains * 2, draws // 2) diff --git a/arviz/tests/base_tests/test_diagnostics_numba.py b/arviz/tests/base_tests/test_diagnostics_numba.py deleted file mode 100644 index d775eac345..0000000000 --- a/arviz/tests/base_tests/test_diagnostics_numba.py +++ /dev/null @@ -1,87 +0,0 @@ -"""Test Diagnostic methods""" - -# pylint: disable=redefined-outer-name, no-member, too-many-public-methods -import numpy as np -import pytest - -from ...data import load_arviz_data -from ...rcparams import rcParams -from ...stats import bfmi, mcse, rhat -from ...stats.diagnostics import _mc_error, ks_summary -from ...utils import Numba -from ..helpers import importorskip -from .test_diagnostics import data # pylint: disable=unused-import - -importorskip("numba") - -rcParams["data.load"] = "eager" - - -def test_numba_bfmi(): - """Numba test for bfmi.""" - state = Numba.numba_flag - school = load_arviz_data("centered_eight") - data_md = np.random.rand(100, 100, 10) - Numba.disable_numba() - non_numba = bfmi(school.posterior["mu"].values) - non_numba_md = bfmi(data_md) - Numba.enable_numba() - with_numba = bfmi(school.posterior["mu"].values) - with_numba_md = bfmi(data_md) - assert np.allclose(non_numba_md, with_numba_md) - assert np.allclose(with_numba, non_numba) - assert state == Numba.numba_flag - - -@pytest.mark.parametrize("method", ("rank", "split", "folded", "z_scale", "identity")) -def test_numba_rhat(method): - """Numba test for mcse.""" - state = Numba.numba_flag - school = np.random.rand(100, 100) - Numba.disable_numba() - non_numba = rhat(school, method=method) - Numba.enable_numba() - with_numba = rhat(school, method=method) - assert np.allclose(with_numba, non_numba) - assert Numba.numba_flag == state - - -@pytest.mark.parametrize("method", ("mean", "sd", "quantile")) -def test_numba_mcse(method, prob=None): - """Numba test for mcse.""" - state = Numba.numba_flag - school = np.random.rand(100, 100) - if method == "quantile": - prob = 0.80 - Numba.disable_numba() - non_numba = mcse(school, method=method, prob=prob) - Numba.enable_numba() - with_numba = mcse(school, method=method, prob=prob) - assert np.allclose(with_numba, non_numba) - assert Numba.numba_flag == state - - -def test_ks_summary_numba(): - """Numba test for ks_summary.""" - state = Numba.numba_flag - data = np.random.randn(100, 100) - Numba.disable_numba() - non_numba = (ks_summary(data)["Count"]).values - Numba.enable_numba() - with_numba = (ks_summary(data)["Count"]).values - assert np.allclose(non_numba, with_numba) - assert Numba.numba_flag == state - - -@pytest.mark.parametrize("batches", (1, 20)) -@pytest.mark.parametrize("circular", (True, False)) -def test_mcse_error_numba(batches, circular): - """Numba test for mcse_error.""" - data = np.random.randn(100, 100) - state = Numba.numba_flag - Numba.disable_numba() - non_numba = _mc_error(data, batches=batches, circular=circular) - Numba.enable_numba() - with_numba = _mc_error(data, batches=batches, circular=circular) - assert np.allclose(non_numba, with_numba) - assert state == Numba.numba_flag diff --git a/arviz/tests/base_tests/test_helpers.py b/arviz/tests/base_tests/test_helpers.py deleted file mode 100644 index c0bbf43379..0000000000 --- a/arviz/tests/base_tests/test_helpers.py +++ /dev/null @@ -1,18 +0,0 @@ -import pytest -from _pytest.outcomes import Skipped - -from ..helpers import importorskip - - -def test_importorskip_local(monkeypatch): - """Test ``importorskip`` run on local machine with non-existent module, which should skip.""" - monkeypatch.delenv("ARVIZ_REQUIRE_ALL_DEPS", raising=False) - with pytest.raises(Skipped): - importorskip("non-existent-function") - - -def test_importorskip_ci(monkeypatch): - """Test ``importorskip`` run on CI machine with non-existent module, which should fail.""" - monkeypatch.setenv("ARVIZ_REQUIRE_ALL_DEPS", 1) - with pytest.raises(ModuleNotFoundError): - importorskip("non-existent-function") diff --git a/arviz/tests/base_tests/test_labels.py b/arviz/tests/base_tests/test_labels.py deleted file mode 100644 index 40d2986ff0..0000000000 --- a/arviz/tests/base_tests/test_labels.py +++ /dev/null @@ -1,69 +0,0 @@ -"""Tests for labeller classes.""" - -import pytest - -from ...labels import ( - BaseLabeller, - DimCoordLabeller, - DimIdxLabeller, - IdxLabeller, - MapLabeller, - NoModelLabeller, - NoVarLabeller, -) - - -class Data: - def __init__(self): - self.sel = { - "instrument": "a", - "experiment": 3, - } - self.isel = { - "instrument": 0, - "experiment": 4, - } - - -@pytest.fixture -def multidim_sels(): - return Data() - - -class Labellers: - def __init__(self): - self.labellers = { - "BaseLabeller": BaseLabeller(), - "DimCoordLabeller": DimCoordLabeller(), - "IdxLabeller": IdxLabeller(), - "DimIdxLabeller": DimIdxLabeller(), - "MapLabeller": MapLabeller(), - "NoVarLabeller": NoVarLabeller(), - "NoModelLabeller": NoModelLabeller(), - } - - -@pytest.fixture -def labellers(): - return Labellers() - - -@pytest.mark.parametrize( - "args", - [ - ("BaseLabeller", "theta\na, 3"), - ("DimCoordLabeller", "theta\ninstrument: a, experiment: 3"), - ("IdxLabeller", "theta\n0, 4"), - ("DimIdxLabeller", "theta\ninstrument#0, experiment#4"), - ("MapLabeller", "theta\na, 3"), - ("NoVarLabeller", "a, 3"), - ("NoModelLabeller", "theta\na, 3"), - ], -) -class TestLabellers: - # pylint: disable=redefined-outer-name - def test_make_label_vert(self, args, multidim_sels, labellers): - name, expected_label = args - labeller_arg = labellers.labellers[name] - label = labeller_arg.make_label_vert("theta", multidim_sels.sel, multidim_sels.isel) - assert label == expected_label diff --git a/arviz/tests/base_tests/test_plot_utils.py b/arviz/tests/base_tests/test_plot_utils.py deleted file mode 100644 index 471bd91f27..0000000000 --- a/arviz/tests/base_tests/test_plot_utils.py +++ /dev/null @@ -1,342 +0,0 @@ -# pylint: disable=redefined-outer-name -import importlib -import os - -import numpy as np -import pytest -import xarray as xr - -from ...data import from_dict -from ...plots.backends.matplotlib import dealiase_sel_kwargs, matplotlib_kwarg_dealiaser -from ...plots.plot_utils import ( - compute_ranks, - filter_plotters_list, - format_sig_figs, - get_plotting_function, - make_2d, - set_bokeh_circular_ticks_labels, - vectorized_to_hex, -) -from ...rcparams import rc_context -from ...sel_utils import xarray_sel_iter, xarray_to_ndarray -from ...stats.density_utils import get_bins -from ...utils import get_coords - -# Check if Bokeh is installed -bokeh_installed = importlib.util.find_spec("bokeh") is not None # pylint: disable=invalid-name -skip_tests = (not bokeh_installed) and ("ARVIZ_REQUIRE_ALL_DEPS" not in os.environ) - - -@pytest.mark.parametrize( - "value, default, expected", - [ - (123.456, 2, 3), - (-123.456, 3, 3), - (-123.456, 4, 4), - (12.3456, 2, 2), - (1.23456, 2, 2), - (0.123456, 2, 2), - ], -) -def test_format_sig_figs(value, default, expected): - assert format_sig_figs(value, default=default) == expected - - -@pytest.fixture(scope="function") -def sample_dataset(): - mu = np.arange(1, 7).reshape(2, 3) - tau = np.arange(7, 13).reshape(2, 3) - - chain = [0, 1] - draws = [0, 1, 2] - - data = xr.Dataset( - {"mu": (["chain", "draw"], mu), "tau": (["chain", "draw"], tau)}, - coords={"draw": draws, "chain": chain}, - ) - - return mu, tau, data - - -def test_make_2d(): - """Touches code that is hard to reach.""" - assert len(make_2d(np.array([2, 3, 4])).shape) == 2 - - -def test_get_bins(): - """Touches code that is hard to reach.""" - assert get_bins(np.array([1, 2, 3, 100])) is not None - - -def test_dataset_to_numpy_not_combined(sample_dataset): # pylint: disable=invalid-name - mu, tau, data = sample_dataset - var_names, data = xarray_to_ndarray(data, combined=False) - - # 2 vars x 2 chains - assert len(var_names) == 4 - mu_tau = np.concatenate((mu, tau), axis=0) - tau_mu = np.concatenate((tau, mu), axis=0) - deqmt = data == mu_tau - deqtm = data == tau_mu - assert deqmt.all() or deqtm.all() - - -def test_dataset_to_numpy_combined(sample_dataset): - mu, tau, data = sample_dataset - var_names, data = xarray_to_ndarray(data, combined=True) - - assert len(var_names) == 2 - assert (data[var_names.index("mu")] == mu.reshape(1, 6)).all() - assert (data[var_names.index("tau")] == tau.reshape(1, 6)).all() - - -def test_xarray_sel_iter_ordering(): - """Assert that coordinate names stay the provided order""" - coords = list("dcba") - data = from_dict( # pylint: disable=no-member - {"x": np.random.randn(1, 100, len(coords))}, - coords={"in_order": coords}, - dims={"x": ["in_order"]}, - ).posterior - - coord_names = [sel["in_order"] for _, sel, _ in xarray_sel_iter(data)] - assert coord_names == coords - - -def test_xarray_sel_iter_ordering_combined(sample_dataset): # pylint: disable=invalid-name - """Assert that varname order stays consistent when chains are combined""" - _, _, data = sample_dataset - var_names = [var for (var, _, _) in xarray_sel_iter(data, var_names=None, combined=True)] - assert set(var_names) == {"mu", "tau"} - - -def test_xarray_sel_iter_ordering_uncombined(sample_dataset): # pylint: disable=invalid-name - """Assert that varname order stays consistent when chains are not combined""" - _, _, data = sample_dataset - var_names = [(var, selection) for (var, selection, _) in xarray_sel_iter(data, var_names=None)] - - assert len(var_names) == 4 - for var_name in var_names: - assert var_name in [ - ("mu", {"chain": 0}), - ("mu", {"chain": 1}), - ("tau", {"chain": 0}), - ("tau", {"chain": 1}), - ] - - -def test_xarray_sel_data_array(sample_dataset): # pylint: disable=invalid-name - """Assert that varname order stays consistent when chains are combined - - Touches code that is hard to reach. - """ - _, _, data = sample_dataset - var_names = [var for (var, _, _) in xarray_sel_iter(data.mu, var_names=None, combined=True)] - assert set(var_names) == {"mu"} - - -class TestCoordsExceptions: - # test coord exceptions on datasets - def test_invalid_coord_name(self, sample_dataset): # pylint: disable=invalid-name - """Assert that nicer exception appears when user enters wrong coords name""" - _, _, data = sample_dataset - coords = {"NOT_A_COORD_NAME": [1]} - - with pytest.raises( - (KeyError, ValueError), - match=( - r"Coords " - r"({'NOT_A_COORD_NAME'} are invalid coordinate keys" - r"|should follow mapping format {coord_name:\[dim1, dim2\]})" - ), - ): - get_coords(data, coords) - - def test_invalid_coord_value(self, sample_dataset): # pylint: disable=invalid-name - """Assert that nicer exception appears when user enters wrong coords value""" - _, _, data = sample_dataset - coords = {"draw": [1234567]} - - with pytest.raises( - KeyError, match=r"Coords should follow mapping format {coord_name:\[dim1, dim2\]}" - ): - get_coords(data, coords) - - def test_invalid_coord_structure(self, sample_dataset): # pylint: disable=invalid-name - """Assert that nicer exception appears when user enters wrong coords datatype""" - _, _, data = sample_dataset - coords = {"draw"} - - with pytest.raises(TypeError): - get_coords(data, coords) - - # test coord exceptions on dataset list - def test_invalid_coord_name_list(self, sample_dataset): # pylint: disable=invalid-name - """Assert that nicer exception appears when user enters wrong coords name""" - _, _, data = sample_dataset - coords = {"NOT_A_COORD_NAME": [1]} - - with pytest.raises( - (KeyError, ValueError), - match=( - r"data\[1\]:.+Coords " - r"({'NOT_A_COORD_NAME'} are invalid coordinate keys" - r"|should follow mapping format {coord_name:\[dim1, dim2\]})" - ), - ): - get_coords((data, data), ({"draw": [0, 1]}, coords)) - - def test_invalid_coord_value_list(self, sample_dataset): # pylint: disable=invalid-name - """Assert that nicer exception appears when user enters wrong coords value""" - _, _, data = sample_dataset - coords = {"draw": [1234567]} - - with pytest.raises( - KeyError, - match=r"data\[0\]:.+Coords should follow mapping format {coord_name:\[dim1, dim2\]}", - ): - get_coords((data, data), (coords, {"draw": [0, 1]})) - - -def test_filter_plotter_list(): - plotters = list(range(7)) - with rc_context({"plot.max_subplots": 10}): - plotters_filtered = filter_plotters_list(plotters, "") - assert plotters == plotters_filtered - - -def test_filter_plotter_list_warning(): - plotters = list(range(7)) - with rc_context({"plot.max_subplots": 5}): - with pytest.warns(UserWarning, match="test warning"): - plotters_filtered = filter_plotters_list(plotters, "test warning") - assert len(plotters_filtered) == 5 - - -@pytest.mark.skipif(skip_tests, reason="test requires bokeh which is not installed") -def test_bokeh_import(): - """Tests that correct method is returned on bokeh import""" - plot = get_plotting_function("plot_dist", "distplot", "bokeh") - - from ...plots.backends.bokeh.distplot import plot_dist - - assert plot is plot_dist - - -@pytest.mark.parametrize( - "params", - [ - { - "input": ( - { - "dashes": "-", - }, - "scatter", - ), - "output": "linestyle", - }, - { - "input": ( - {"mfc": "blue", "c": "blue", "line_width": 2}, - "plot", - ), - "output": ("markerfacecolor", "color", "line_width"), - }, - {"input": ({"ec": "blue", "fc": "black"}, "hist"), "output": ("edgecolor", "facecolor")}, - { - "input": ({"edgecolors": "blue", "lw": 3}, "hlines"), - "output": ("edgecolor", "linewidth"), - }, - ], -) -def test_matplotlib_kwarg_dealiaser(params): - dealiased = matplotlib_kwarg_dealiaser(params["input"][0], kind=params["input"][1]) - for returned in dealiased: - assert returned in params["output"] - - -@pytest.mark.parametrize("c_values", ["#0000ff", "blue", [0, 0, 1]]) -def test_vectorized_to_hex_scalar(c_values): - output = vectorized_to_hex(c_values) - assert output == "#0000ff" - - -@pytest.mark.parametrize( - "c_values", [["blue", "blue"], ["blue", "#0000ff"], np.array([[0, 0, 1], [0, 0, 1]])] -) -def test_vectorized_to_hex_array(c_values): - output = vectorized_to_hex(c_values) - assert np.all([item == "#0000ff" for item in output]) - - -def test_mpl_dealiase_sel_kwargs(): - """Check mpl dealiase_sel_kwargs behaviour. - - Makes sure kwargs are overwritten when necessary even with alias involved and that - they are not modified when not included in props. - """ - kwargs = {"linewidth": 3, "alpha": 0.4, "line_color": "red"} - props = {"lw": [1, 2, 4, 5], "linestyle": ["-", "--", ":"]} - res = dealiase_sel_kwargs(kwargs, props, 2) - assert "linewidth" in res - assert res["linewidth"] == 4 - assert "linestyle" in res - assert res["linestyle"] == ":" - assert "alpha" in res - assert res["alpha"] == 0.4 - assert "line_color" in res - assert res["line_color"] == "red" - - -@pytest.mark.skipif(skip_tests, reason="test requires bokeh which is not installed") -def test_bokeh_dealiase_sel_kwargs(): - """Check bokeh dealiase_sel_kwargs behaviour. - - Makes sure kwargs are overwritten when necessary even with alias involved and that - they are not modified when not included in props. - """ - from ...plots.backends.bokeh import dealiase_sel_kwargs - - kwargs = {"line_width": 3, "line_alpha": 0.4, "line_color": "red"} - props = {"line_width": [1, 2, 4, 5], "line_dash": ["dashed", "dashed", "dashed"]} - res = dealiase_sel_kwargs(kwargs, props, 2) - assert "line_width" in res - assert res["line_width"] == 4 - assert "line_dash" in res - assert res["line_dash"] == "dashed" - assert "line_alpha" in res - assert res["line_alpha"] == 0.4 - assert "line_color" in res - assert res["line_color"] == "red" - - -@pytest.mark.skipif(skip_tests, reason="test requires bokeh which is not installed") -def test_set_bokeh_circular_ticks_labels(): - """Assert the axes returned after placing ticks and tick labels for circular plots.""" - import bokeh.plotting as bkp - - ax = bkp.figure(x_axis_type=None, y_axis_type=None) - hist = np.linspace(0, 1, 10) - labels = ["0°", "45°", "90°", "135°", "180°", "225°", "270°", "315°"] - ax = set_bokeh_circular_ticks_labels(ax, hist, labels) - renderers = ax.renderers - assert len(renderers) == 3 - assert renderers[2].data_source.data["text"] == labels - assert len(renderers[0].data_source.data["start_angle"]) == len(labels) - - -def test_compute_ranks(): - pois_data = np.array([[5, 4, 1, 4, 0], [2, 8, 2, 1, 1]]) - expected = np.array([[9.0, 7.0, 3.0, 8.0, 1.0], [5.0, 10.0, 6.0, 2.0, 4.0]]) - ranks = compute_ranks(pois_data) - np.testing.assert_equal(ranks, expected) - - norm_data = np.array( - [ - [0.2644187, -1.3004813, -0.80428456, 1.01319068, 0.62631143], - [1.34498018, -0.13428933, -0.69855487, -0.9498981, -0.34074092], - ] - ) - expected = np.array([[7.0, 1.0, 3.0, 9.0, 8.0], [10.0, 6.0, 4.0, 2.0, 5.0]]) - ranks = compute_ranks(norm_data) - np.testing.assert_equal(ranks, expected) diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py deleted file mode 100644 index c9bbf46a7f..0000000000 --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ /dev/null @@ -1,1288 +0,0 @@ -# pylint: disable=redefined-outer-name,too-many-lines -"""Tests use the 'bokeh' backend.""" -from copy import deepcopy - -import numpy as np -import pytest -from pandas import DataFrame # pylint: disable=wrong-import-position -from scipy.stats import norm # pylint: disable=wrong-import-position - -from ...data import from_dict, load_arviz_data # pylint: disable=wrong-import-position -from ...labels import MapLabeller # pylint: disable=wrong-import-position -from ...plots import ( # pylint: disable=wrong-import-position - plot_autocorr, - plot_bpv, - plot_compare, - plot_density, - plot_dist, - plot_dist_comparison, - plot_dot, - plot_ecdf, - plot_elpd, - plot_energy, - plot_ess, - plot_forest, - plot_hdi, - plot_kde, - plot_khat, - plot_lm, - plot_loo_pit, - plot_mcse, - plot_pair, - plot_parallel, - plot_posterior, - plot_ppc, - plot_rank, - plot_separation, - plot_trace, - plot_violin, -) -from ...rcparams import rc_context, rcParams # pylint: disable=wrong-import-position -from ...stats import compare, hdi, loo, waic # pylint: disable=wrong-import-position -from ..helpers import ( # pylint: disable=unused-import, wrong-import-position - create_model, - create_multidimensional_model, - eight_schools_params, - importorskip, - models, - multidim_models, -) - -# Skip tests if bokeh not installed -bkp = importorskip("bokeh.plotting") # pylint: disable=invalid-name - - -rcParams["data.load"] = "eager" - - -@pytest.fixture(scope="module") -def data(eight_schools_params): - data = eight_schools_params - return data - - -@pytest.fixture(scope="module") -def df_trace(): - return DataFrame({"a": np.random.poisson(2.3, 100)}) - - -@pytest.fixture(scope="module") -def discrete_model(): - """Simple fixture for random discrete model""" - return {"x": np.random.randint(10, size=100), "y": np.random.randint(10, size=100)} - - -@pytest.fixture(scope="module") -def continuous_model(): - """Simple fixture for random continuous model""" - return {"x": np.random.beta(2, 5, size=100), "y": np.random.beta(2, 5, size=100)} - - -@pytest.mark.parametrize( - "kwargs", - [ - {"point_estimate": "mean"}, - {"point_estimate": "median"}, - {"hdi_prob": 0.94}, - {"hdi_prob": 1}, - {"outline": True}, - {"hdi_markers": ["v"]}, - {"shade": 1}, - ], -) -def test_plot_density_float(models, kwargs): - obj = [getattr(models, model_fit) for model_fit in ["model_1", "model_2"]] - axes = plot_density(obj, backend="bokeh", show=False, **kwargs) - assert axes.shape[0] >= 6 - assert axes.shape[0] >= 3 - - -def test_plot_density_discrete(discrete_model): - axes = plot_density(discrete_model, shade=0.9, backend="bokeh", show=False) - assert axes.shape[0] == 1 - - -def test_plot_density_no_subset(): - """Test plot_density works when variables are not subset of one another (#1093).""" - model_ab = from_dict( - { - "a": np.random.normal(size=200), - "b": np.random.normal(size=200), - } - ) - model_bc = from_dict( - { - "b": np.random.normal(size=200), - "c": np.random.normal(size=200), - } - ) - axes = plot_density([model_ab, model_bc], backend="bokeh", show=False) - assert axes.size == 3 - - -def test_plot_density_one_var(): - """Test plot_density works when there is only one variable (#1401).""" - model_ab = from_dict( - { - "a": np.random.normal(size=200), - } - ) - model_bc = from_dict( - { - "a": np.random.normal(size=200), - } - ) - axes = plot_density([model_ab, model_bc], backend="bokeh", show=False) - assert axes.size == 1 - - -def test_plot_density_bad_kwargs(models): - obj = [getattr(models, model_fit) for model_fit in ["model_1", "model_2"]] - with pytest.raises(ValueError): - plot_density(obj, point_estimate="bad_value", backend="bokeh", show=False) - - with pytest.raises(ValueError): - plot_density( - obj, - data_labels=[f"bad_value_{i}" for i in range(len(obj) + 10)], - backend="bokeh", - show=False, - ) - - with pytest.raises(ValueError): - plot_density(obj, hdi_prob=2, backend="bokeh", show=False) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"y_hat_line": True}, - {"expected_events": True}, - {"y_hat_line_kwargs": {"linestyle": "dotted"}}, - {"exp_events_kwargs": {"marker": "o"}}, - ], -) -def test_plot_separation(kwargs): - idata = load_arviz_data("classification10d") - ax = plot_separation(idata=idata, y="outcome", backend="bokeh", show=False, **kwargs) - assert ax - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"var_names": "mu"}, - {"var_names": ["mu", "tau"]}, - {"combined": True, "rug": True}, - {"compact": True, "legend": True}, - {"combined": True, "compact": True, "legend": True}, - {"divergences": "top"}, - {"divergences": False}, - {"kind": "rank_vlines"}, - {"kind": "rank_bars"}, - {"lines": [("mu", {}, [1, 2])]}, - {"lines": [("mu", {}, 8)]}, - ], -) -def test_plot_trace(models, kwargs): - axes = plot_trace(models.model_1, backend="bokeh", show=False, **kwargs) - assert axes.shape - - -def test_plot_trace_discrete(discrete_model): - axes = plot_trace(discrete_model, backend="bokeh", show=False) - assert axes.shape - - -def test_plot_trace_max_subplots_warning(models): - with pytest.warns(UserWarning): - with rc_context(rc={"plot.max_subplots": 2}): - axes = plot_trace(models.model_1, backend="bokeh", show=False) - assert axes.shape - - -@pytest.mark.parametrize( - "kwargs", - [ - {"plot_kwargs": {"line_dash": "solid"}}, - {"contour": True, "fill_last": False}, - { - "contour": True, - "contourf_kwargs": {"cmap": "plasma"}, - "contour_kwargs": {"line_width": 1}, - }, - {"contour": False}, - {"contour": False, "pcolormesh_kwargs": {"cmap": "plasma"}}, - ], -) -def test_plot_kde(continuous_model, kwargs): - axes = plot_kde( - continuous_model["x"], continuous_model["y"], backend="bokeh", show=False, **kwargs - ) - assert axes - - -@pytest.mark.parametrize( - "kwargs", - [ - {"cumulative": True}, - {"cumulative": True, "plot_kwargs": {"line_dash": "dashed"}}, - {"rug": True}, - {"rug": True, "rug_kwargs": {"line_alpha": 0.2}, "rotated": True}, - ], -) -def test_plot_kde_cumulative(continuous_model, kwargs): - axes = plot_kde(continuous_model["x"], backend="bokeh", show=False, **kwargs) - assert axes - - -@pytest.mark.parametrize( - "kwargs", - [ - {"kind": "hist"}, - {"kind": "kde"}, - {"is_circular": False}, - {"is_circular": False, "kind": "hist"}, - {"is_circular": True}, - {"is_circular": True, "kind": "hist"}, - {"is_circular": "radians"}, - {"is_circular": "radians", "kind": "hist"}, - {"is_circular": "degrees"}, - {"is_circular": "degrees", "kind": "hist"}, - ], -) -def test_plot_dist(continuous_model, kwargs): - axes = plot_dist(continuous_model["x"], backend="bokeh", show=False, **kwargs) - assert axes - - -def test_plot_kde_1d(continuous_model): - axes = plot_kde(continuous_model["y"], backend="bokeh", show=False) - assert axes - - -@pytest.mark.parametrize( - "kwargs", - [ - {"contour": True, "fill_last": False}, - {"contour": True, "contourf_kwargs": {"cmap": "plasma"}}, - {"contour": False}, - {"contour": False, "pcolormesh_kwargs": {"cmap": "plasma"}}, - {"contour": True, "contourf_kwargs": {"levels": 3}}, - {"contour": True, "contourf_kwargs": {"levels": [0.1, 0.2, 0.3]}}, - {"hdi_probs": [0.3, 0.9, 0.6]}, - {"hdi_probs": [0.3, 0.6, 0.9], "contourf_kwargs": {"cmap": "Blues"}}, - {"hdi_probs": [0.9, 0.6, 0.3], "contour_kwargs": {"alpha": 0}}, - ], -) -def test_plot_kde_2d(continuous_model, kwargs): - axes = plot_kde( - continuous_model["x"], continuous_model["y"], backend="bokeh", show=False, **kwargs - ) - assert axes - - -@pytest.mark.parametrize( - "kwargs", [{"plot_kwargs": {"line_dash": "solid"}}, {"cumulative": True}, {"rug": True}] -) -def test_plot_kde_quantiles(continuous_model, kwargs): - axes = plot_kde( - continuous_model["x"], quantiles=[0.05, 0.5, 0.95], backend="bokeh", show=False, **kwargs - ) - assert axes - - -def test_plot_autocorr_short_chain(): - """Check that logic for small chain defaulting doesn't cause exception""" - chain = np.arange(10) - axes = plot_autocorr(chain, backend="bokeh", show=False) - assert axes - - -def test_plot_autocorr_uncombined(models): - axes = plot_autocorr(models.model_1, combined=False, backend="bokeh", show=False) - assert axes.shape[0] == 10 - max_subplots = ( - np.inf if rcParams["plot.max_subplots"] is None else rcParams["plot.max_subplots"] - ) - assert len([ax for ax in axes.ravel() if ax is not None]) == min(72, max_subplots) - - -def test_plot_autocorr_combined(models): - axes = plot_autocorr(models.model_1, combined=True, backend="bokeh", show=False) - assert axes.shape[0] == 6 - assert axes.shape[1] == 3 - - -@pytest.mark.parametrize("var_names", (None, "mu", ["mu", "tau"])) -def test_plot_autocorr_var_names(models, var_names): - axes = plot_autocorr( - models.model_1, var_names=var_names, combined=True, backend="bokeh", show=False - ) - assert axes.shape - - -@pytest.mark.parametrize( - "kwargs", [{"insample_dev": False}, {"plot_standard_error": False}, {"plot_ic_diff": False}] -) -def test_plot_compare(models, kwargs): - model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2}) - - axes = plot_compare(model_compare, backend="bokeh", show=False, **kwargs) - assert axes - - -def test_plot_compare_no_ic(models): - """Check exception is raised if model_compare doesn't contain a valid information criterion""" - model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2}) - - # Drop column needed for plotting - model_compare = model_compare.drop("elpd_loo", axis=1) - with pytest.raises(ValueError) as err: - plot_compare(model_compare, backend="bokeh", show=False) - - assert "comp_df must contain one of the following" in str(err.value) - assert "['elpd_loo', 'elpd_waic']" in str(err.value) - - -def test_plot_ecdf_basic(): - data = np.random.randn(4, 1000) - axes = plot_ecdf(data, backend="bokeh", show=False) - assert axes is not None - - -def test_plot_ecdf_values2(): - data = np.random.randn(4, 1000) - data2 = np.random.randn(4, 500) - axes = plot_ecdf(data, data2, backend="bokeh", show=False) - assert axes is not None - - -def test_plot_ecdf_cdf(): - data = np.random.randn(4, 1000) - cdf = norm(0, 1).cdf - axes = plot_ecdf(data, cdf=cdf, backend="bokeh", show=False) - assert axes is not None - - -@pytest.mark.parametrize( - "kwargs", [{}, {"ic": "loo"}, {"xlabels": True, "scale": "log"}, {"threshold": 2}] -) -@pytest.mark.parametrize("add_model", [False, True]) -@pytest.mark.parametrize("use_elpddata", [False, True]) -def test_plot_elpd(models, add_model, use_elpddata, kwargs): - model_dict = {"Model 1": models.model_1, "Model 2": models.model_2} - if add_model: - model_dict["Model 3"] = create_model(seed=12) - - if use_elpddata: - ic = kwargs.get("ic", "waic") - scale = kwargs.get("scale", "deviance") - if ic == "waic": - model_dict = {k: waic(v, scale=scale, pointwise=True) for k, v in model_dict.items()} - else: - model_dict = {k: loo(v, scale=scale, pointwise=True) for k, v in model_dict.items()} - - axes = plot_elpd(model_dict, backend="bokeh", show=False, **kwargs) - assert np.any(axes) - if add_model: - assert axes.shape[0] == axes.shape[1] - assert axes.shape[0] == len(model_dict) - 1 - - -@pytest.mark.parametrize("kwargs", [{}, {"ic": "loo"}, {"xlabels": True, "scale": "log"}]) -@pytest.mark.parametrize("add_model", [False, True]) -@pytest.mark.parametrize("use_elpddata", [False, True]) -def test_plot_elpd_multidim(multidim_models, add_model, use_elpddata, kwargs): - model_dict = {"Model 1": multidim_models.model_1, "Model 2": multidim_models.model_2} - if add_model: - model_dict["Model 3"] = create_multidimensional_model(seed=12) - - if use_elpddata: - ic = kwargs.get("ic", "waic") - scale = kwargs.get("scale", "deviance") - if ic == "waic": - model_dict = {k: waic(v, scale=scale, pointwise=True) for k, v in model_dict.items()} - else: - model_dict = {k: loo(v, scale=scale, pointwise=True) for k, v in model_dict.items()} - - axes = plot_elpd(model_dict, backend="bokeh", show=False, **kwargs) - assert np.any(axes) - if add_model: - assert axes.shape[0] == axes.shape[1] - assert axes.shape[0] == len(model_dict) - 1 - - -@pytest.mark.parametrize("kind", ["kde", "hist"]) -def test_plot_energy(models, kind): - assert plot_energy(models.model_1, kind=kind, backend="bokeh", show=False) - - -def test_plot_energy_bad(models): - with pytest.raises(ValueError): - plot_energy(models.model_1, kind="bad_kind", backend="bokeh", show=False) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"var_names": ["theta"], "relative": True, "color": "r"}, - {"coords": {"school": slice(4)}, "n_points": 10}, - {"min_ess": 600, "hline_kwargs": {"line_color": "red"}}, - ], -) -@pytest.mark.parametrize("kind", ["local", "quantile", "evolution"]) -def test_plot_ess(models, kind, kwargs): - """Test plot_ess arguments common to all kind of plots.""" - idata = models.model_1 - ax = plot_ess(idata, kind=kind, backend="bokeh", show=False, **kwargs) - assert np.all(ax) - - -@pytest.mark.parametrize( - "kwargs", - [ - {"rug": True}, - {"rug": True, "rug_kind": "max_depth", "rug_kwargs": {"color": "c"}}, - {"extra_methods": True}, - {"extra_methods": True, "extra_kwargs": {"ls": ":"}, "text_kwargs": {"x": 0, "ha": "left"}}, - {"extra_methods": True, "rug": True}, - ], -) -@pytest.mark.parametrize("kind", ["local", "quantile"]) -def test_plot_ess_local_quantile(models, kind, kwargs): - """Test specific arguments in kinds local and quantile of plot_ess.""" - idata = models.model_1 - ax = plot_ess(idata, kind=kind, backend="bokeh", show=False, **kwargs) - assert np.all(ax) - - -def test_plot_ess_evolution(models): - """Test specific arguments in evolution kind of plot_ess.""" - idata = models.model_1 - ax = plot_ess( - idata, - kind="evolution", - extra_kwargs={"linestyle": "--"}, - color="b", - backend="bokeh", - show=False, - ) - assert np.all(ax) - - -def test_plot_ess_bad_kind(models): - """Test error when plot_ess receives an invalid kind.""" - idata = models.model_1 - with pytest.raises(ValueError, match="Invalid kind"): - plot_ess(idata, kind="bad kind", backend="bokeh", show=False) - - -@pytest.mark.parametrize("dim", ["chain", "draw"]) -def test_plot_ess_bad_coords(models, dim): - """Test error when chain or dim are used as coords to select a data subset.""" - idata = models.model_1 - with pytest.raises(ValueError, match="invalid coordinates"): - plot_ess(idata, coords={dim: slice(3)}, backend="bokeh", show=False) - - -def test_plot_ess_no_sample_stats(models): - """Test error when rug=True but sample_stats group is not present.""" - idata = models.model_1 - with pytest.raises(ValueError, match="must contain sample_stats"): - plot_ess(idata.posterior, rug=True, backend="bokeh", show=False) - - -def test_plot_ess_no_divergences(models): - """Test error when rug=True, but the variable defined by rug_kind is missing.""" - idata = deepcopy(models.model_1) - idata.sample_stats = idata.sample_stats.rename({"diverging": "diverging_missing"}) - with pytest.raises(ValueError, match="not contain diverging"): - plot_ess(idata, rug=True, backend="bokeh", show=False) - - -@pytest.mark.parametrize("model_fits", [["model_1"], ["model_1", "model_2"]]) -@pytest.mark.parametrize( - "args_expected", - [ - ({}, 1), - ({"var_names": "mu"}, 1), - ({"var_names": "mu", "rope": (-1, 1)}, 1), - ({"r_hat": True, "quartiles": False}, 2), - ({"var_names": ["mu"], "colors": "black", "ess": True, "combined": True}, 2), - ( - { - "kind": "ridgeplot", - "ridgeplot_truncate": False, - "ridgeplot_quantiles": [0.25, 0.5, 0.75], - }, - 1, - ), - ({"kind": "ridgeplot", "r_hat": True, "ess": True}, 3), - ({"kind": "ridgeplot", "r_hat": True, "ess": True, "ridgeplot_alpha": 0}, 3), - ( - { - "var_names": ["mu", "tau"], - "rope": { - "mu": [{"rope": (-0.1, 0.1)}], - "theta": [{"school": "Choate", "rope": (0.2, 0.5)}], - }, - }, - 1, - ), - ], -) -def test_plot_forest(models, model_fits, args_expected): - obj = [getattr(models, model_fit) for model_fit in model_fits] - args, expected = args_expected - axes = plot_forest(obj, backend="bokeh", show=False, **args) - assert axes.shape == (1, expected) - - -def test_plot_forest_rope_exception(): - with pytest.raises(ValueError) as err: - plot_forest({"x": [1]}, rope="not_correct_format", backend="bokeh", show=False) - assert "Argument `rope` must be None, a dictionary like" in str(err.value) - - -def test_plot_forest_single_value(): - axes = plot_forest({"x": [1]}, backend="bokeh", show=False) - assert axes.shape - - -@pytest.mark.parametrize("model_fits", [["model_1"], ["model_1", "model_2"]]) -def test_plot_forest_bad(models, model_fits): - obj = [getattr(models, model_fit) for model_fit in model_fits] - with pytest.raises(TypeError): - plot_forest(obj, kind="bad_kind", backend="bokeh", show=False) - - with pytest.raises(ValueError): - plot_forest( - obj, - model_names=[f"model_name_{i}" for i in range(len(obj) + 10)], - backend="bokeh", - show=False, - ) - - -@pytest.mark.parametrize( - "kwargs", - [ - {"color": "C5", "circular": True}, - {"hdi_data": True, "fill_kwargs": {"alpha": 0}}, - {"plot_kwargs": {"alpha": 0}}, - {"smooth_kwargs": {"window_length": 33, "polyorder": 5, "mode": "mirror"}}, - {"hdi_data": True, "smooth": False, "color": "xkcd:jade"}, - ], -) -def test_plot_hdi(models, data, kwargs): - hdi_data = kwargs.pop("hdi_data", None) - y_data = models.model_1.posterior["theta"] - if hdi_data: - hdi_data = hdi(y_data) - axis = plot_hdi(data["y"], hdi_data=hdi_data, backend="bokeh", show=False, **kwargs) - else: - axis = plot_hdi(data["y"], y_data, backend="bokeh", show=False, **kwargs) - assert axis - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"xlabels": True}, - {"color": "obs_dim", "xlabels": True, "show_bins": True, "bin_format": "{0}"}, - {"color": "obs_dim", "legend": True, "hover_label": True}, - {"color": "blue", "coords": {"obs_dim": slice(2, 4)}}, - {"color": np.random.uniform(size=8), "show_bins": True}, - { - "color": np.random.uniform(size=(8, 3)), - "show_bins": True, - "show_hlines": True, - "threshold": 1, - }, - ], -) -@pytest.mark.parametrize("input_type", ["elpd_data", "data_array", "array"]) -def test_plot_khat(models, input_type, kwargs): - khats_data = loo(models.model_1, pointwise=True) - - if input_type == "data_array": - khats_data = khats_data.pareto_k - elif input_type == "array": - khats_data = khats_data.pareto_k.values - if "color" in kwargs and isinstance(kwargs["color"], str) and kwargs["color"] == "obs_dim": - kwargs["color"] = None - - axes = plot_khat(khats_data, backend="bokeh", show=False, **kwargs) - assert axes - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"xlabels": True}, - {"color": "dim1", "xlabels": True, "show_bins": True, "bin_format": "{0}"}, - {"color": "dim2", "legend": True, "hover_label": True}, - {"color": "blue", "coords": {"dim2": slice(2, 4)}}, - {"color": np.random.uniform(size=35), "show_bins": True}, - { - "color": np.random.uniform(size=(35, 3)), - "show_bins": True, - "show_hlines": True, - "threshold": 1, - }, - ], -) -@pytest.mark.parametrize("input_type", ["elpd_data", "data_array", "array"]) -def test_plot_khat_multidim(multidim_models, input_type, kwargs): - khats_data = loo(multidim_models.model_1, pointwise=True) - - if input_type == "data_array": - khats_data = khats_data.pareto_k - elif input_type == "array": - khats_data = khats_data.pareto_k.values - if ( - "color" in kwargs - and isinstance(kwargs["color"], str) - and kwargs["color"] in ("dim1", "dim2") - ): - kwargs["color"] = None - - axes = plot_khat(khats_data, backend="bokeh", show=False, **kwargs) - assert axes - - -def test_plot_khat_threshold(): - khats = np.array([0, 0, 0.6, 0.6, 0.8, 0.9, 0.9, 2, 3, 4, 1.5]) - axes = plot_khat(khats, threshold=1, backend="bokeh", show=False) - assert axes - - -def test_plot_khat_bad_input(models): - with pytest.raises(ValueError): - plot_khat(models.model_1.sample_stats, backend="bokeh", show=False) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"n_unif": 50}, - {"use_hdi": True, "color": "gray"}, - {"use_hdi": True, "hdi_prob": 0.68}, - {"use_hdi": True, "hdi_kwargs": {"line_dash": "dashed", "alpha": 0}}, - {"ecdf": True}, - {"ecdf": True, "ecdf_fill": False, "plot_unif_kwargs": {"line_dash": "--"}}, - {"ecdf": True, "hdi_prob": 0.97, "fill_kwargs": {"color": "red"}}, - ], -) -def test_plot_loo_pit(models, kwargs): - axes = plot_loo_pit(idata=models.model_1, y="y", backend="bokeh", show=False, **kwargs) - assert axes - - -def test_plot_loo_pit_incompatible_args(models): - """Test error when both ecdf and use_hdi are True.""" - with pytest.raises(ValueError, match="incompatible"): - plot_loo_pit( - idata=models.model_1, y="y", ecdf=True, use_hdi=True, backend="bokeh", show=False - ) - - -@pytest.mark.parametrize( - "args", - [ - {"y": "str"}, - {"y": "DataArray", "y_hat": "str"}, - {"y": "ndarray", "y_hat": "str"}, - {"y": "ndarray", "y_hat": "DataArray"}, - {"y": "ndarray", "y_hat": "ndarray"}, - ], -) -def test_plot_loo_pit_label(models, args): - if args["y"] == "str": - y = "y" - elif args["y"] == "DataArray": - y = models.model_1.observed_data.y - elif args["y"] == "ndarray": - y = models.model_1.observed_data.y.values - - if args.get("y_hat") == "str": - y_hat = "y" - elif args.get("y_hat") == "DataArray": - y_hat = models.model_1.posterior_predictive.y.stack(__sample__=("chain", "draw")) - elif args.get("y_hat") == "ndarray": - y_hat = models.model_1.posterior_predictive.y.stack(__sample__=("chain", "draw")).values - else: - y_hat = None - - ax = plot_loo_pit(idata=models.model_1, y=y, y_hat=y_hat, backend="bokeh", show=False) - assert ax - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"var_names": ["theta"], "color": "r"}, - {"rug": True, "rug_kwargs": {"color": "r"}}, - {"errorbar": True, "rug": True, "rug_kind": "max_depth"}, - {"errorbar": True, "coords": {"school": slice(4)}, "n_points": 10}, - {"extra_methods": True, "rug": True}, - {"extra_methods": True, "extra_kwargs": {"ls": ":"}, "text_kwargs": {"x": 0, "ha": "left"}}, - ], -) -def test_plot_mcse(models, kwargs): - idata = models.model_1 - ax = plot_mcse(idata, backend="bokeh", show=False, **kwargs) - assert np.all(ax) - - -@pytest.mark.parametrize("dim", ["chain", "draw"]) -def test_plot_mcse_bad_coords(models, dim): - """Test error when chain or dim are used as coords to select a data subset.""" - idata = models.model_1 - with pytest.raises(ValueError, match="invalid coordinates"): - plot_mcse(idata, coords={dim: slice(3)}, backend="bokeh", show=False) - - -def test_plot_mcse_no_sample_stats(models): - """Test error when rug=True but sample_stats group is not present.""" - idata = models.model_1 - with pytest.raises(ValueError, match="must contain sample_stats"): - plot_mcse(idata.posterior, rug=True, backend="bokeh", show=False) - - -def test_plot_mcse_no_divergences(models): - """Test error when rug=True, but the variable defined by rug_kind is missing.""" - idata = deepcopy(models.model_1) - idata.sample_stats = idata.sample_stats.rename({"diverging": "diverging_missing"}) - with pytest.raises(ValueError, match="not contain diverging"): - plot_mcse(idata, rug=True, backend="bokeh", show=False) - - -@pytest.mark.slow -@pytest.mark.parametrize( - "kwargs", - [ - {"var_names": "theta", "divergences": True, "coords": {"school": [0, 1]}}, - {"divergences": True, "var_names": ["theta", "mu"]}, - {"kind": "kde", "var_names": ["theta"]}, - {"kind": "hexbin", "var_names": ["theta"]}, - { - "kind": "hexbin", - "var_names": ["theta"], - "coords": {"school": [0, 1]}, - "textsize": 20, - }, - { - "point_estimate": "mean", - "reference_values": {"mu": 0, "tau": 0}, - "reference_values_kwargs": {"line_color": "blue"}, - }, - { - "var_names": ["mu", "tau"], - "reference_values": {"mu": 0, "tau": 0}, - "labeller": MapLabeller({"mu": r"$\mu$", "theta": r"$\theta"}), - }, - { - "var_names": ["theta"], - "reference_values": {"theta": [0.0] * 8}, - "labeller": MapLabeller({"theta": r"$\theta$"}), - }, - { - "var_names": ["theta"], - "reference_values": {"theta": np.zeros(8)}, - "labeller": MapLabeller({"theta": r"$\theta$"}), - }, - ], -) -def test_plot_pair(models, kwargs): - ax = plot_pair(models.model_1, backend="bokeh", show=False, **kwargs) - assert np.any(ax) - - -@pytest.mark.parametrize("kwargs", [{"kind": "scatter"}, {"kind": "kde"}, {"kind": "hexbin"}]) -def test_plot_pair_2var(discrete_model, kwargs): - ax = plot_pair( - discrete_model, ax=np.atleast_2d(bkp.figure()), backend="bokeh", show=False, **kwargs - ) - assert ax - - -def test_plot_pair_bad(models): - with pytest.raises(ValueError): - plot_pair(models.model_1, kind="bad_kind", backend="bokeh", show=False) - with pytest.raises(Exception): - plot_pair(models.model_1, var_names=["mu"], backend="bokeh", show=False) - - -@pytest.mark.parametrize("has_sample_stats", [True, False]) -def test_plot_pair_divergences_warning(has_sample_stats): - data = load_arviz_data("centered_eight") - if has_sample_stats: - # sample_stats present, diverging field missing - data.sample_stats = data.sample_stats.rename({"diverging": "diverging_missing"}) - else: - # sample_stats missing - data = data.posterior # pylint: disable=no-member - with pytest.warns(UserWarning): - ax = plot_pair(data, divergences=True, backend="bokeh", show=False) - assert np.any(ax) - - -def test_plot_parallel_raises_valueerror(df_trace): # pylint: disable=invalid-name - with pytest.raises(ValueError): - plot_parallel(df_trace, backend="bokeh", show=False) - - -@pytest.mark.parametrize("norm_method", [None, "normal", "minmax", "rank"]) -def test_plot_parallel(models, norm_method): - assert plot_parallel( - models.model_1, - var_names=["mu", "tau"], - norm_method=norm_method, - backend="bokeh", - show=False, - ) - - -@pytest.mark.parametrize("var_names", [None, "mu", ["mu", "tau"]]) -def test_plot_parallel_exception(models, var_names): - """Ensure that correct exception is raised when one variable is passed.""" - with pytest.raises(ValueError): - assert plot_parallel( - models.model_1, var_names=var_names, norm_method="foo", backend="bokeh", show=False - ) - - -@pytest.mark.parametrize("var_names", (None, "mu", ["mu", "tau"])) -@pytest.mark.parametrize("side", ["both", "left", "right"]) -@pytest.mark.parametrize("rug", [True]) -def test_plot_violin(models, var_names, side, rug): - axes = plot_violin( - models.model_1, var_names=var_names, side=side, rug=rug, backend="bokeh", show=False - ) - assert axes.shape - - -def test_plot_violin_ax(models): - ax = bkp.figure() - axes = plot_violin(models.model_1, var_names="mu", ax=ax, backend="bokeh", show=False) - assert axes.shape - - -def test_plot_violin_layout(models): - axes = plot_violin( - models.model_1, var_names=["mu", "tau"], sharey=False, backend="bokeh", show=False - ) - assert axes.shape - - -def test_plot_violin_discrete(discrete_model): - axes = plot_violin(discrete_model, backend="bokeh", show=False) - assert axes.shape - - -@pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) -@pytest.mark.parametrize("alpha", [None, 0.2, 1]) -@pytest.mark.parametrize("observed", [True, False]) -@pytest.mark.parametrize("observed_rug", [False, True]) -def test_plot_ppc(models, kind, alpha, observed, observed_rug): - axes = plot_ppc( - models.model_1, - kind=kind, - alpha=alpha, - observed=observed, - observed_rug=observed_rug, - random_seed=3, - backend="bokeh", - show=False, - ) - assert axes - - -def test_plot_ppc_textsize(models): - axes = plot_ppc( - models.model_1, - textsize=10, - random_seed=3, - backend="bokeh", - show=False, - ) - assert axes - - -@pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) -@pytest.mark.parametrize("jitter", [None, 0, 0.1, 1, 3]) -def test_plot_ppc_multichain(kind, jitter): - data = from_dict( - posterior_predictive={ - "x": np.random.randn(4, 100, 30), - "y_hat": np.random.randn(4, 100, 3, 10), - }, - observed_data={"x": np.random.randn(30), "y": np.random.randn(3, 10)}, - ) - axes = plot_ppc( - data, - kind=kind, - data_pairs={"y": "y_hat"}, - jitter=jitter, - random_seed=3, - backend="bokeh", - show=False, - ) - assert np.all(axes) - - -@pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) -def test_plot_ppc_discrete(kind): - data = from_dict( - observed_data={"obs": np.random.randint(1, 100, 15)}, - posterior_predictive={"obs": np.random.randint(1, 300, (1, 20, 15))}, - ) - - axes = plot_ppc(data, kind=kind, backend="bokeh", show=False) - assert axes - - -def test_plot_ppc_grid(models): - axes = plot_ppc(models.model_1, kind="scatter", flatten=[], backend="bokeh", show=False) - assert len(axes.ravel()) == 8 - axes = plot_ppc( - models.model_1, - kind="scatter", - flatten=[], - coords={"obs_dim": [1, 2, 3]}, - backend="bokeh", - show=False, - ) - assert len(axes.ravel()) == 3 - axes = plot_ppc( - models.model_1, - kind="scatter", - flatten=["obs_dim"], - coords={"obs_dim": [1, 2, 3]}, - backend="bokeh", - show=False, - ) - assert len(axes.ravel()) == 1 - - -@pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) -def test_plot_ppc_bad(models, kind): - data = from_dict(posterior={"mu": np.random.randn()}) - with pytest.raises(TypeError): - plot_ppc(data, kind=kind, backend="bokeh", show=False) - with pytest.raises(TypeError): - plot_ppc(models.model_1, kind="bad_val", backend="bokeh", show=False) - with pytest.raises(TypeError): - plot_ppc(models.model_1, num_pp_samples="bad_val", backend="bokeh", show=False) - - -@pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) -def test_plot_ppc_ax(models, kind): - """Test ax argument of plot_ppc.""" - ax = bkp.figure() - axes = plot_ppc(models.model_1, kind=kind, ax=ax, backend="bokeh", show=False) - assert axes[0, 0] is ax - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"var_names": "mu"}, - {"var_names": ("mu", "tau")}, - {"rope": (-2, 2)}, - {"rope": {"mu": [{"rope": (-2, 2)}], "theta": [{"school": "Choate", "rope": (2, 4)}]}}, - {"point_estimate": "mode"}, - {"point_estimate": "median"}, - {"point_estimate": None}, - {"hdi_prob": "hide", "legend_label": ""}, - {"ref_val": 0}, - {"ref_val": None}, - {"ref_val": {"mu": [{"ref_val": 1}]}}, - {"bins": None, "kind": "hist"}, - { - "ref_val": { - "theta": [ - # {"school": ["Choate", "Deerfield"], "ref_val": -1}, this is not working - {"school": "Lawrenceville", "ref_val": 3} - ] - } - }, - ], -) -def test_plot_posterior(models, kwargs): - axes = plot_posterior(models.model_1, backend="bokeh", show=False, **kwargs) - assert axes.shape - - -@pytest.mark.parametrize("kwargs", [{}, {"point_estimate": "mode"}, {"bins": None, "kind": "hist"}]) -def test_plot_posterior_discrete(discrete_model, kwargs): - axes = plot_posterior(discrete_model, backend="bokeh", show=False, **kwargs) - assert axes.shape - - -def test_plot_posterior_boolean(): - data = np.random.choice(a=[False, True], size=(4, 100)) - axes = plot_posterior(data, backend="bokeh", show=False) - assert axes.shape - - -def test_plot_posterior_bad_type(): - with pytest.raises(TypeError): - plot_posterior(np.array(["a", "b", "c"]), backend="bokeh", show=False) - - -def test_plot_posterior_bad(models): - with pytest.raises(ValueError): - plot_posterior(models.model_1, backend="bokeh", show=False, rope="bad_value") - with pytest.raises(ValueError): - plot_posterior(models.model_1, ref_val="bad_value", backend="bokeh", show=False) - with pytest.raises(ValueError): - plot_posterior(models.model_1, point_estimate="bad_value", backend="bokeh", show=False) - - -@pytest.mark.parametrize("point_estimate", ("mode", "mean", "median")) -def test_plot_posterior_point_estimates(models, point_estimate): - axes = plot_posterior( - models.model_1, - var_names=("mu", "tau"), - point_estimate=point_estimate, - backend="bokeh", - show=False, - ) - assert axes.shape == (1, 2) - - -def test_plot_posterior_skipna(): - sample = np.linspace(0, 1) - sample[:10] = np.nan - plot_posterior({"a": sample}, backend="bokeh", show=False, skipna=True) - with pytest.raises(ValueError): - plot_posterior({"a": sample}, backend="bokeh", show=False, skipna=False) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"var_names": "mu"}, - {"var_names": ("mu", "tau"), "coords": {"school": [0, 1]}}, - {"var_names": "mu", "ref_line": True}, - { - "var_names": "mu", - "ref_line_kwargs": {"line_width": 2, "line_color": "red"}, - "bar_kwargs": {"width": 50}, - }, - {"var_names": "mu", "ref_line": False}, - {"var_names": "mu", "kind": "vlines"}, - { - "var_names": "mu", - "kind": "vlines", - "vlines_kwargs": {"line_width": 0}, - "marker_vlines_kwargs": {"radius": 20}, - }, - ], -) -def test_plot_rank(models, kwargs): - axes = plot_rank(models.model_1, backend="bokeh", show=False, **kwargs) - assert axes.shape - - -def test_plot_dist_comparison_warn(models): - with pytest.raises(NotImplementedError, match="The bokeh backend.+Use matplotlib backend."): - plot_dist_comparison(models.model_1, backend="bokeh") - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"reference": "analytical"}, - {"kind": "p_value"}, - {"kind": "t_stat", "t_stat": "std"}, - {"kind": "t_stat", "t_stat": 0.5, "bpv": True}, - ], -) -def test_plot_bpv(models, kwargs): - axes = plot_bpv(models.model_1, backend="bokeh", show=False, **kwargs) - assert axes.shape - - -def test_plot_bpv_discrete(): - fake_obs = {"a": np.random.poisson(2.5, 100)} - fake_pp = {"a": np.random.poisson(2.5, (1, 10, 100))} - fake_model = from_dict(posterior_predictive=fake_pp, observed_data=fake_obs) - axes = plot_bpv( - fake_model, - backend="bokeh", - show=False, - ) - assert axes.shape - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - { - "binwidth": 0.5, - "stackratio": 2, - "nquantiles": 20, - }, - {"point_interval": True}, - { - "point_interval": True, - "dotsize": 1.2, - "point_estimate": "median", - "plot_kwargs": {"color": "grey"}, - }, - { - "point_interval": True, - "plot_kwargs": {"color": "grey"}, - "nquantiles": 100, - "hdi_prob": 0.95, - "intervalcolor": "green", - }, - { - "point_interval": True, - "plot_kwargs": {"color": "grey"}, - "quartiles": False, - "linewidth": 2, - }, - ], -) -def test_plot_dot(continuous_model, kwargs): - data = continuous_model["x"] - ax = plot_dot(data, **kwargs, backend="bokeh", show=False) - assert ax - - -@pytest.mark.parametrize( - "kwargs", - [ - {"rotated": True}, - { - "point_interval": True, - "rotated": True, - "dotcolor": "grey", - "binwidth": 0.5, - }, - { - "rotated": True, - "point_interval": True, - "plot_kwargs": {"color": "grey"}, - "nquantiles": 100, - "dotsize": 0.8, - "hdi_prob": 0.95, - "intervalcolor": "green", - }, - ], -) -def test_plot_dot_rotated(continuous_model, kwargs): - data = continuous_model["x"] - ax = plot_dot(data, **kwargs, backend="bokeh", show=False) - assert ax - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"y_hat": "bad_name"}, - {"x": "x1"}, - {"x": ("x1", "x2")}, - { - "x": ("x1", "x2"), - "y_kwargs": {"fill_color": "blue"}, - "y_hat_plot_kwargs": {"fill_color": "orange"}, - "legend": True, - }, - {"x": ("x1", "x2"), "y_model_plot_kwargs": {"line_color": "red"}}, - { - "x": ("x1", "x2"), - "kind_pp": "hdi", - "kind_model": "hdi", - "y_model_fill_kwargs": {"color": "red"}, - "y_hat_fill_kwargs": {"color": "cyan"}, - }, - ], -) -def test_plot_lm_1d(models, kwargs): - """Test functionality for 1D data.""" - idata = models.model_1 - if "constant_data" not in idata.groups(): - y = idata.observed_data["y"] - x1data = y.coords[y.dims[0]] - idata.add_groups({"constant_data": {"_": x1data}}) - idata.constant_data["x1"] = x1data - idata.constant_data["x2"] = x1data - - axes = plot_lm( - idata=idata, y="y", y_model="eta", backend="bokeh", xjitter=True, show=False, **kwargs - ) - assert np.all(axes) - - -def test_plot_lm_multidim(multidim_models): - """Test functionality for multidimentional data.""" - idata = multidim_models.model_1 - axes = plot_lm(idata=idata, y="y", plot_dim="dim1", show=False, backend="bokeh") - assert np.any(axes) - - -def test_plot_lm_list(): - """Test the plots when input data is list or ndarray.""" - y = [1, 2, 3, 4, 5] - assert plot_lm(y=y, x=np.arange(len(y)), show=False, backend="bokeh") - - -def generate_lm_1d_data(): - rng = np.random.default_rng() - return from_dict( - observed_data={"y": rng.normal(size=7)}, - posterior_predictive={"y": rng.normal(size=(4, 1000, 7)) / 2}, - posterior={"y_model": rng.normal(size=(4, 1000, 7))}, - dims={"y": ["dim1"]}, - coords={"dim1": range(7)}, - ) - - -def generate_lm_2d_data(): - rng = np.random.default_rng() - return from_dict( - observed_data={"y": rng.normal(size=(5, 7))}, - posterior_predictive={"y": rng.normal(size=(4, 1000, 5, 7)) / 2}, - posterior={"y_model": rng.normal(size=(4, 1000, 5, 7))}, - dims={"y": ["dim1", "dim2"]}, - coords={"dim1": range(5), "dim2": range(7)}, - ) - - -@pytest.mark.parametrize("data", ("1d", "2d")) -@pytest.mark.parametrize("kind", ("lines", "hdi")) -@pytest.mark.parametrize("use_y_model", (True, False)) -def test_plot_lm(data, kind, use_y_model): - if data == "1d": - idata = generate_lm_1d_data() - else: - idata = generate_lm_2d_data() - - kwargs = {"idata": idata, "y": "y", "kind_model": kind, "backend": "bokeh", "show": False} - if data == "2d": - kwargs["plot_dim"] = "dim1" - if use_y_model: - kwargs["y_model"] = "y_model" - if kind == "lines": - kwargs["num_samples"] = 50 - - ax = plot_lm(**kwargs) - assert ax is not None diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py deleted file mode 100644 index a0a2ae5caa..0000000000 --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ /dev/null @@ -1,2197 +0,0 @@ -"""Tests use the default backend.""" - -# pylint: disable=redefined-outer-name,too-many-lines -import os -import re -from copy import deepcopy - -import matplotlib.pyplot as plt -import numpy as np -import pytest -import xarray as xr -from matplotlib import animation -from pandas import DataFrame -from scipy.stats import gaussian_kde, norm - -from ...data import from_dict, load_arviz_data -from ...labels import MapLabeller -from ...plots import ( - plot_autocorr, - plot_bf, - plot_bpv, - plot_compare, - plot_density, - plot_dist, - plot_dist_comparison, - plot_dot, - plot_ecdf, - plot_elpd, - plot_energy, - plot_ess, - plot_forest, - plot_hdi, - plot_kde, - plot_khat, - plot_lm, - plot_loo_pit, - plot_mcse, - plot_pair, - plot_parallel, - plot_posterior, - plot_ppc, - plot_rank, - plot_separation, - plot_trace, - plot_ts, - plot_violin, -) -from ...plots.dotplot import wilkinson_algorithm -from ...plots.plot_utils import plot_point_interval -from ...rcparams import rc_context, rcParams -from ...stats import compare, hdi, loo, waic -from ...stats.density_utils import kde as _kde -from ...utils import BehaviourChangeWarning, _cov -from ..helpers import ( # pylint: disable=unused-import - RandomVariableTestClass, - create_model, - create_multidimensional_model, - does_not_warn, - eight_schools_params, - models, - multidim_models, -) - -rcParams["data.load"] = "eager" - - -@pytest.fixture(scope="function", autouse=True) -def clean_plots(request, save_figs): - """Close plots after each test, optionally save if --save is specified during test invocation""" - - def fin(): - if save_figs is not None: - plt.savefig(f"{os.path.join(save_figs, request.node.name)}.png") - plt.close("all") - - request.addfinalizer(fin) - - -@pytest.fixture(scope="module") -def data(eight_schools_params): - data = eight_schools_params - return data - - -@pytest.fixture(scope="module") -def df_trace(): - return DataFrame({"a": np.random.poisson(2.3, 100)}) - - -@pytest.fixture(scope="module") -def discrete_model(): - """Simple fixture for random discrete model""" - return {"x": np.random.randint(10, size=100), "y": np.random.randint(10, size=100)} - - -@pytest.fixture(scope="module") -def discrete_multidim_model(): - """Simple fixture for random discrete model""" - idata = from_dict( - {"x": np.random.randint(10, size=(2, 50, 3)), "y": np.random.randint(10, size=(2, 50))}, - dims={"x": ["school"]}, - ) - return idata - - -@pytest.fixture(scope="module") -def continuous_model(): - """Simple fixture for random continuous model""" - return {"x": np.random.beta(2, 5, size=100), "y": np.random.beta(2, 5, size=100)} - - -@pytest.fixture(scope="function") -def fig_ax(): - fig, ax = plt.subplots(1, 1) - return fig, ax - - -@pytest.fixture(scope="module") -def data_random(): - return np.random.randint(1, 100, size=20) - - -@pytest.fixture(scope="module") -def data_list(): - return list(range(11, 31)) - - -@pytest.mark.parametrize( - "kwargs", - [ - {"point_estimate": "mean"}, - {"point_estimate": "median"}, - {"hdi_prob": 0.94}, - {"hdi_prob": 1}, - {"outline": True}, - {"colors": ["g", "b", "r", "y"]}, - {"colors": "k"}, - {"hdi_markers": ["v"]}, - {"shade": 1}, - {"transform": lambda x: x + 1}, - {"ax": plt.subplots(6, 3)[1]}, - ], -) -def test_plot_density_float(models, kwargs): - obj = [getattr(models, model_fit) for model_fit in ["model_1", "model_2"]] - axes = plot_density(obj, **kwargs) - assert axes.shape == (6, 3) - - -def test_plot_density_discrete(discrete_model): - axes = plot_density(discrete_model, shade=0.9) - assert axes.size == 2 - - -def test_plot_density_no_subset(): - """Test plot_density works when variables are not subset of one another (#1093).""" - model_ab = from_dict( - { - "a": np.random.normal(size=200), - "b": np.random.normal(size=200), - } - ) - model_bc = from_dict( - { - "b": np.random.normal(size=200), - "c": np.random.normal(size=200), - } - ) - axes = plot_density([model_ab, model_bc]) - assert axes.size == 3 - - -def test_plot_density_nonstring_varnames(): - """Test plot_density works when variables are not strings.""" - rv1 = RandomVariableTestClass("a") - rv2 = RandomVariableTestClass("b") - rv3 = RandomVariableTestClass("c") - model_ab = from_dict( - { - rv1: np.random.normal(size=200), - rv2: np.random.normal(size=200), - } - ) - model_bc = from_dict( - { - rv2: np.random.normal(size=200), - rv3: np.random.normal(size=200), - } - ) - axes = plot_density([model_ab, model_bc]) - assert axes.size == 3 - - -def test_plot_density_bad_kwargs(models): - obj = [getattr(models, model_fit) for model_fit in ["model_1", "model_2"]] - with pytest.raises(ValueError): - plot_density(obj, point_estimate="bad_value") - - with pytest.raises(ValueError): - plot_density(obj, data_labels=[f"bad_value_{i}" for i in range(len(obj) + 10)]) - - with pytest.raises(ValueError): - plot_density(obj, hdi_prob=2) - - with pytest.raises(ValueError): - plot_density(obj, filter_vars="bad_value") - - -def test_plot_density_discrete_combinedims(discrete_model): - axes = plot_density(discrete_model, combine_dims={"school"}, shade=0.9) - assert axes.size == 2 - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"y_hat_line": True}, - {"expected_events": True}, - {"y_hat_line_kwargs": {"linestyle": "dotted"}}, - {"exp_events_kwargs": {"marker": "o"}}, - ], -) -def test_plot_separation(kwargs): - idata = load_arviz_data("classification10d") - ax = plot_separation(idata=idata, y="outcome", **kwargs) - assert ax - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"var_names": "mu"}, - {"var_names": ["mu", "tau"]}, - {"combined": True}, - {"compact": True}, - {"combined": True, "compact": True, "legend": True}, - {"divergences": "top", "legend": True}, - {"divergences": False}, - {"kind": "rank_vlines"}, - {"kind": "rank_bars"}, - {"lines": [("mu", {}, [1, 2])]}, - {"lines": [("mu", {}, 8)]}, - {"circ_var_names": ["mu"]}, - {"circ_var_names": ["mu"], "circ_var_units": "degrees"}, - ], -) -def test_plot_trace(models, kwargs): - axes = plot_trace(models.model_1, **kwargs) - assert axes.shape - - -@pytest.mark.parametrize( - "compact", - [True, False], -) -@pytest.mark.parametrize( - "combined", - [True, False], -) -def test_plot_trace_legend(compact, combined): - idata = load_arviz_data("rugby") - axes = plot_trace( - idata, var_names=["home", "atts_star"], compact=compact, combined=combined, legend=True - ) - assert axes[0, 1].get_legend() - compact_legend = axes[1, 0].get_legend() - if compact: - assert axes.shape == (2, 2) - assert compact_legend - else: - assert axes.shape == (7, 2) - assert not compact_legend - - -def test_plot_trace_discrete(discrete_model): - axes = plot_trace(discrete_model) - assert axes.shape - - -def test_plot_trace_max_subplots_warning(models): - with pytest.warns(UserWarning): - with rc_context(rc={"plot.max_subplots": 6}): - axes = plot_trace(models.model_1) - assert axes.shape == (3, 2) - - -def test_plot_dist_comparison_warning(models): - with pytest.warns(UserWarning): - with rc_context(rc={"plot.max_subplots": 6}): - axes = plot_dist_comparison(models.model_1) - assert axes.shape == (2, 3) - - -@pytest.mark.parametrize("kwargs", [{"var_names": ["mu", "tau"], "lines": [("hey", {}, [1])]}]) -def test_plot_trace_invalid_varname_warning(models, kwargs): - with pytest.warns(UserWarning, match="valid var.+should be provided"): - axes = plot_trace(models.model_1, **kwargs) - assert axes.shape - - -def test_plot_trace_diverging_correctly_transposed(): - idata = load_arviz_data("centered_eight") - idata.sample_stats["diverging"] = idata.sample_stats.diverging.T - plot_trace(idata, divergences="bottom") - - -@pytest.mark.parametrize( - "bad_kwargs", [{"var_names": ["mu", "tau"], "lines": [("mu", {}, ["hey"])]}] -) -def test_plot_trace_bad_lines_value(models, bad_kwargs): - with pytest.raises(ValueError, match="line-positions should be numeric"): - plot_trace(models.model_1, **bad_kwargs) - - -@pytest.mark.parametrize("prop", ["chain_prop", "compact_prop"]) -def test_plot_trace_futurewarning(models, prop): - with pytest.warns(FutureWarning, match=f"{prop} as a tuple.+deprecated"): - ax = plot_trace(models.model_1, **{prop: ("ls", ("-", "--"))}) - assert ax.shape - - -@pytest.mark.parametrize("model_fits", [["model_1"], ["model_1", "model_2"]]) -@pytest.mark.parametrize( - "args_expected", - [ - ({}, 1), - ({"var_names": "mu", "transform": lambda x: x + 1}, 1), - ({"var_names": "mu", "rope": (-1, 1), "combine_dims": {"school"}}, 1), - ({"r_hat": True, "quartiles": False}, 2), - ({"var_names": ["mu"], "colors": "C0", "ess": True, "combined": True}, 2), - ( - { - "kind": "ridgeplot", - "ridgeplot_truncate": False, - "ridgeplot_quantiles": [0.25, 0.5, 0.75], - }, - 1, - ), - ({"kind": "ridgeplot", "r_hat": True, "ess": True}, 3), - ({"kind": "ridgeplot", "r_hat": True, "ess": True}, 3), - ({"kind": "ridgeplot", "r_hat": True, "ess": True, "ridgeplot_alpha": 0}, 3), - ( - { - "var_names": ["mu", "theta"], - "rope": { - "mu": [{"rope": (-0.1, 0.1)}], - "theta": [{"school": "Choate", "rope": (0.2, 0.5)}], - }, - }, - 1, - ), - ], -) -def test_plot_forest(models, model_fits, args_expected): - obj = [getattr(models, model_fit) for model_fit in model_fits] - args, expected = args_expected - axes = plot_forest(obj, **args) - assert axes.size == expected - - -def test_plot_forest_rope_exception(): - with pytest.raises(ValueError) as err: - plot_forest({"x": [1]}, rope="not_correct_format") - assert "Argument `rope` must be None, a dictionary like" in str(err.value) - - -def test_plot_forest_single_value(): - axes = plot_forest({"x": [1]}) - assert axes.shape - - -def test_plot_forest_ridge_discrete(discrete_model): - axes = plot_forest(discrete_model, kind="ridgeplot") - assert axes.shape - - -@pytest.mark.parametrize("model_fits", [["model_1"], ["model_1", "model_2"]]) -def test_plot_forest_bad(models, model_fits): - obj = [getattr(models, model_fit) for model_fit in model_fits] - with pytest.raises(TypeError): - plot_forest(obj, kind="bad_kind") - - with pytest.raises(ValueError): - plot_forest(obj, model_names=[f"model_name_{i}" for i in range(len(obj) + 10)]) - - -@pytest.mark.parametrize("kind", ["kde", "hist"]) -def test_plot_energy(models, kind): - assert plot_energy(models.model_1, kind=kind) - - -def test_plot_energy_bad(models): - with pytest.raises(ValueError): - plot_energy(models.model_1, kind="bad_kind") - - -def test_plot_energy_correctly_transposed(): - idata = load_arviz_data("centered_eight") - idata.sample_stats["energy"] = idata.sample_stats.energy.T - ax = plot_energy(idata) - # legend has one entry for each KDE and 1 BFMI for each chain - assert len(ax.legend_.texts) == 2 + len(idata.sample_stats.chain) - - -def test_plot_parallel_raises_valueerror(df_trace): # pylint: disable=invalid-name - with pytest.raises(ValueError): - plot_parallel(df_trace) - - -@pytest.mark.parametrize("norm_method", [None, "normal", "minmax", "rank"]) -def test_plot_parallel(models, norm_method): - assert plot_parallel(models.model_1, var_names=["mu", "tau"], norm_method=norm_method) - - -@pytest.mark.parametrize("var_names", [None, "mu", ["mu", "tau"]]) -def test_plot_parallel_exception(models, var_names): - """Ensure that correct exception is raised when one variable is passed.""" - with pytest.raises(ValueError): - assert plot_parallel(models.model_1, var_names=var_names, norm_method="foo") - - -@pytest.mark.parametrize( - "kwargs", - [ - {"plot_kwargs": {"linestyle": "-"}}, - {"contour": True, "fill_last": False}, - { - "contour": True, - "contourf_kwargs": {"cmap": "plasma"}, - "contour_kwargs": {"linewidths": 1}, - }, - {"contour": False}, - {"contour": False, "pcolormesh_kwargs": {"cmap": "plasma"}}, - {"is_circular": False}, - {"is_circular": True}, - {"is_circular": "radians"}, - {"is_circular": "degrees"}, - {"adaptive": True}, - {"hdi_probs": [0.3, 0.9, 0.6]}, - {"hdi_probs": [0.3, 0.6, 0.9], "contourf_kwargs": {"cmap": "Blues"}}, - {"hdi_probs": [0.9, 0.6, 0.3], "contour_kwargs": {"alpha": 0}}, - ], -) -def test_plot_kde(continuous_model, kwargs): - axes = plot_kde(continuous_model["x"], continuous_model["y"], **kwargs) - axes1 = plot_kde(continuous_model["x"], continuous_model["y"], **kwargs) - assert axes - assert axes is axes1 - - -@pytest.mark.parametrize( - "kwargs", - [ - {"hdi_probs": [1, 2, 3]}, - {"hdi_probs": [-0.3, 0.6, 0.9]}, - {"hdi_probs": [0, 0.3, 0.6]}, - {"hdi_probs": [0.3, 0.6, 1]}, - ], -) -def test_plot_kde_hdi_probs_bad(continuous_model, kwargs): - """Ensure invalid hdi probabilities are rejected.""" - with pytest.raises(ValueError): - plot_kde(continuous_model["x"], continuous_model["y"], **kwargs) - - -@pytest.mark.parametrize( - "kwargs", - [ - {"hdi_probs": [0.3, 0.6, 0.9], "contourf_kwargs": {"levels": [0, 0.5, 1]}}, - {"hdi_probs": [0.3, 0.6, 0.9], "contour_kwargs": {"levels": [0, 0.5, 1]}}, - ], -) -def test_plot_kde_hdi_probs_warning(continuous_model, kwargs): - """Ensure warning is raised when too many keywords are specified.""" - with pytest.warns(UserWarning): - axes = plot_kde(continuous_model["x"], continuous_model["y"], **kwargs) - assert axes - - -@pytest.mark.parametrize("shape", [(8,), (8, 8), (8, 8, 8)]) -def test_cov(shape): - x = np.random.randn(*shape) - if x.ndim <= 2: - assert np.allclose(_cov(x), np.cov(x)) - else: - with pytest.raises(ValueError): - _cov(x) - - -@pytest.mark.parametrize( - "kwargs", - [ - {"cumulative": True}, - {"cumulative": True, "plot_kwargs": {"linestyle": "--"}}, - {"rug": True}, - {"rug": True, "rug_kwargs": {"alpha": 0.2}, "rotated": True}, - ], -) -def test_plot_kde_cumulative(continuous_model, kwargs): - axes = plot_kde(continuous_model["x"], quantiles=[0.25, 0.5, 0.75], **kwargs) - assert axes - - -@pytest.mark.parametrize( - "kwargs", - [ - {"kind": "hist"}, - {"kind": "kde"}, - {"is_circular": False}, - {"is_circular": False, "kind": "hist"}, - {"is_circular": True}, - {"is_circular": True, "kind": "hist"}, - {"is_circular": "radians"}, - {"is_circular": "radians", "kind": "hist"}, - {"is_circular": "degrees"}, - {"is_circular": "degrees", "kind": "hist"}, - ], -) -def test_plot_dist(continuous_model, kwargs): - axes = plot_dist(continuous_model["x"], **kwargs) - axes1 = plot_dist(continuous_model["x"], **kwargs) - assert axes - assert axes is axes1 - - -def test_plot_dist_hist(data_random): - axes = plot_dist(data_random, hist_kwargs=dict(bins=30)) - assert axes - - -def test_list_conversion(data_list): - axes = plot_dist(data_list, hist_kwargs=dict(bins=30)) - assert axes - - -@pytest.mark.parametrize( - "kwargs", - [ - {"plot_kwargs": {"linestyle": "-"}}, - {"contour": True, "fill_last": False}, - {"contour": False}, - ], -) -def test_plot_dist_2d_kde(continuous_model, kwargs): - axes = plot_dist(continuous_model["x"], continuous_model["y"], **kwargs) - assert axes - - -@pytest.mark.parametrize( - "kwargs", [{"plot_kwargs": {"linestyle": "-"}}, {"cumulative": True}, {"rug": True}] -) -def test_plot_kde_quantiles(continuous_model, kwargs): - axes = plot_kde(continuous_model["x"], quantiles=[0.05, 0.5, 0.95], **kwargs) - assert axes - - -def test_plot_kde_inference_data(models): - """ - Ensure that an exception is raised when plot_kde - is used with an inference data or Xarray dataset object. - """ - with pytest.raises(ValueError, match="Inference Data"): - plot_kde(models.model_1) - with pytest.raises(ValueError, match="Xarray"): - plot_kde(models.model_1.posterior) - - -@pytest.mark.slow -@pytest.mark.parametrize( - "kwargs", - [ - { - "var_names": "theta", - "divergences": True, - "coords": {"school": [0, 1]}, - "scatter_kwargs": {"marker": "x", "c": "C0"}, - "divergences_kwargs": {"marker": "*", "c": "C0"}, - }, - { - "divergences": True, - "scatter_kwargs": {"marker": "x", "c": "C0"}, - "divergences_kwargs": {"marker": "*", "c": "C0"}, - "var_names": ["theta", "mu"], - }, - {"kind": "kde", "var_names": ["theta"]}, - {"kind": "hexbin", "colorbar": False, "var_names": ["theta"]}, - {"kind": "hexbin", "colorbar": True, "var_names": ["theta"]}, - { - "kind": "hexbin", - "var_names": ["theta"], - "coords": {"school": [0, 1]}, - "colorbar": True, - "hexbin_kwargs": {"cmap": "viridis"}, - "textsize": 20, - }, - { - "point_estimate": "mean", - "reference_values": {"mu": 0, "tau": 0}, - "reference_values_kwargs": {"c": "C0", "marker": "*"}, - }, - { - "var_names": ["mu", "tau"], - "reference_values": {"mu": 0, "tau": 0}, - "labeller": MapLabeller({"mu": r"$\mu$", "theta": r"$\theta"}), - }, - { - "var_names": ["theta"], - "reference_values": {"theta": [0.0] * 8}, - "labeller": MapLabeller({"theta": r"$\theta$"}), - }, - { - "var_names": ["theta"], - "reference_values": {"theta": np.zeros(8)}, - "labeller": MapLabeller({"theta": r"$\theta$"}), - }, - ], -) -def test_plot_pair(models, kwargs): - ax = plot_pair(models.model_1, **kwargs) - assert np.all(ax) - - -@pytest.mark.parametrize( - "kwargs", [{"kind": "scatter"}, {"kind": "kde"}, {"kind": "hexbin", "colorbar": True}] -) -def test_plot_pair_2var(discrete_model, fig_ax, kwargs): - _, ax = fig_ax - ax = plot_pair(discrete_model, ax=ax, **kwargs) - assert ax - - -def test_plot_pair_bad(models): - with pytest.raises(ValueError): - plot_pair(models.model_1, kind="bad_kind") - with pytest.raises(Exception): - plot_pair(models.model_1, var_names=["mu"]) - - -@pytest.mark.parametrize("has_sample_stats", [True, False]) -def test_plot_pair_divergences_warning(has_sample_stats): - data = load_arviz_data("centered_eight") - if has_sample_stats: - # sample_stats present, diverging field missing - data.sample_stats = data.sample_stats.rename({"diverging": "diverging_missing"}) - else: - # sample_stats missing - data = data.posterior # pylint: disable=no-member - with pytest.warns(UserWarning): - ax = plot_pair(data, divergences=True) - assert np.all(ax) - - -@pytest.mark.parametrize( - "kwargs", [{}, {"marginals": True}, {"marginals": True, "var_names": ["mu", "tau"]}] -) -def test_plot_pair_overlaid(models, kwargs): - ax = plot_pair(models.model_1, **kwargs) - ax2 = plot_pair(models.model_2, ax=ax, **kwargs) - assert ax is ax2 - assert ax.shape - - -@pytest.mark.parametrize("marginals", [True, False]) -def test_plot_pair_combinedims(models, marginals): - ax = plot_pair( - models.model_1, var_names=["eta", "theta"], combine_dims={"school"}, marginals=marginals - ) - if marginals: - assert ax.shape == (2, 2) - else: - assert not isinstance(ax, np.ndarray) - - -@pytest.mark.parametrize("marginals", [True, False]) -@pytest.mark.parametrize("max_subplots", [True, False]) -def test_plot_pair_shapes(marginals, max_subplots): - rng = np.random.default_rng() - idata = from_dict({"a": rng.standard_normal((4, 500, 5))}) - if max_subplots: - with rc_context({"plot.max_subplots": 6}): - with pytest.warns(UserWarning, match="3x3 grid"): - ax = plot_pair(idata, marginals=marginals) - else: - ax = plot_pair(idata, marginals=marginals) - side = 3 if max_subplots else (4 + marginals) - assert ax.shape == (side, side) - - -@pytest.mark.parametrize("sharex", ["col", None]) -@pytest.mark.parametrize("sharey", ["row", None]) -@pytest.mark.parametrize("marginals", [True, False]) -def test_plot_pair_shared(sharex, sharey, marginals): - # Generate fake data and plot - rng = np.random.default_rng() - idata = from_dict({"a": rng.standard_normal((4, 500, 5))}) - numvars = 5 - (not marginals) - if sharex is None and sharey is None: - ax = plot_pair(idata, marginals=marginals) - else: - backend_kwargs = {} - if sharex is not None: - backend_kwargs["sharex"] = sharex - if sharey is not None: - backend_kwargs["sharey"] = sharey - with pytest.warns(UserWarning): - ax = plot_pair(idata, marginals=marginals, backend_kwargs=backend_kwargs) - - # Check x axes shared correctly - for i in range(numvars): - num_shared_x = numvars - i - assert len(ax[-1, i].get_shared_x_axes().get_siblings(ax[-1, i])) == num_shared_x - - # Check y axes shared correctly - for j in range(numvars): - if marginals: - num_shared_y = j - - # Check diagonal has unshared axis - assert len(ax[j, j].get_shared_y_axes().get_siblings(ax[j, j])) == 1 - - if j == 0: - continue - else: - num_shared_y = j + 1 - assert len(ax[j, 0].get_shared_y_axes().get_siblings(ax[j, 0])) == num_shared_y - - -@pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) -@pytest.mark.parametrize("alpha", [None, 0.2, 1]) -@pytest.mark.parametrize("animated", [False, True]) -@pytest.mark.parametrize("observed", [True, False]) -@pytest.mark.parametrize("observed_rug", [False, True]) -def test_plot_ppc(models, kind, alpha, animated, observed, observed_rug): - if animation and not animation.writers.is_available("ffmpeg"): - pytest.skip("matplotlib animations within ArviZ require ffmpeg") - animation_kwargs = {"blit": False} - axes = plot_ppc( - models.model_1, - kind=kind, - alpha=alpha, - observed=observed, - observed_rug=observed_rug, - animated=animated, - animation_kwargs=animation_kwargs, - random_seed=3, - ) - if animated: - assert axes[0] - assert axes[1] - assert axes - - -def test_plot_ppc_transposed(): - idata = load_arviz_data("rugby") - idata.map( - lambda ds: ds.assign(points=xr.concat((ds.home_points, ds.away_points), "field")), - groups="observed_vars", - inplace=True, - ) - assert idata.posterior_predictive.points.dims == ("field", "chain", "draw", "match") - ax = plot_ppc( - idata, - kind="scatter", - var_names="points", - flatten=["field"], - coords={"match": ["Wales Italy"]}, - random_seed=3, - num_pp_samples=8, - ) - x, y = ax.get_lines()[2].get_data() - assert not np.isclose(y[0], 0) - assert np.all(np.array([47, 44, 15, 11]) == x) - - -@pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) -@pytest.mark.parametrize("jitter", [None, 0, 0.1, 1, 3]) -@pytest.mark.parametrize("animated", [False, True]) -def test_plot_ppc_multichain(kind, jitter, animated): - if animation and not animation.writers.is_available("ffmpeg"): - pytest.skip("matplotlib animations within ArviZ require ffmpeg") - data = from_dict( - posterior_predictive={ - "x": np.random.randn(4, 100, 30), - "y_hat": np.random.randn(4, 100, 3, 10), - }, - observed_data={"x": np.random.randn(30), "y": np.random.randn(3, 10)}, - ) - animation_kwargs = {"blit": False} - axes = plot_ppc( - data, - kind=kind, - data_pairs={"y": "y_hat"}, - jitter=jitter, - animated=animated, - animation_kwargs=animation_kwargs, - random_seed=3, - ) - if animated: - assert np.all(axes[0]) - assert np.all(axes[1]) - else: - assert np.all(axes) - - -@pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) -@pytest.mark.parametrize("animated", [False, True]) -def test_plot_ppc_discrete(kind, animated): - if animation and not animation.writers.is_available("ffmpeg"): - pytest.skip("matplotlib animations within ArviZ require ffmpeg") - data = from_dict( - observed_data={"obs": np.random.randint(1, 100, 15)}, - posterior_predictive={"obs": np.random.randint(1, 300, (1, 20, 15))}, - ) - - animation_kwargs = {"blit": False} - axes = plot_ppc(data, kind=kind, animated=animated, animation_kwargs=animation_kwargs) - if animated: - assert np.all(axes[0]) - assert np.all(axes[1]) - assert axes - - -@pytest.mark.skipif( - not animation.writers.is_available("ffmpeg"), - reason="matplotlib animations within ArviZ require ffmpeg", -) -@pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) -def test_plot_ppc_save_animation(models, kind): - animation_kwargs = {"blit": False} - axes, anim = plot_ppc( - models.model_1, - kind=kind, - animated=True, - animation_kwargs=animation_kwargs, - num_pp_samples=5, - random_seed=3, - ) - assert axes - assert anim - animations_folder = "../saved_animations" - os.makedirs(animations_folder, exist_ok=True) - path = os.path.join(animations_folder, f"ppc_{kind}_animation.mp4") - anim.save(path) - assert os.path.exists(path) - assert os.path.getsize(path) - - -@pytest.mark.skipif( - not animation.writers.is_available("ffmpeg"), - reason="matplotlib animations within ArviZ require ffmpeg", -) -@pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) -def test_plot_ppc_discrete_save_animation(kind): - data = from_dict( - observed_data={"obs": np.random.randint(1, 100, 15)}, - posterior_predictive={"obs": np.random.randint(1, 300, (1, 20, 15))}, - ) - animation_kwargs = {"blit": False} - axes, anim = plot_ppc( - data, - kind=kind, - animated=True, - animation_kwargs=animation_kwargs, - num_pp_samples=5, - random_seed=3, - ) - assert axes - assert anim - animations_folder = "../saved_animations" - os.makedirs(animations_folder, exist_ok=True) - path = os.path.join(animations_folder, f"ppc_discrete_{kind}_animation.mp4") - anim.save(path) - assert os.path.exists(path) - assert os.path.getsize(path) - - -@pytest.mark.skipif( - not animation.writers.is_available("ffmpeg"), - reason="matplotlib animations within ArviZ require ffmpeg", -) -@pytest.mark.parametrize("system", ["Windows", "Darwin"]) -def test_non_linux_blit(models, monkeypatch, system, caplog): - import platform - - def mock_system(): - return system - - monkeypatch.setattr(platform, "system", mock_system) - - animation_kwargs = {"blit": True} - axes, anim = plot_ppc( - models.model_1, - kind="kde", - animated=True, - animation_kwargs=animation_kwargs, - num_pp_samples=5, - random_seed=3, - ) - records = caplog.records - assert len(records) == 1 - assert records[0].levelname == "WARNING" - assert axes - assert anim - - -@pytest.mark.parametrize( - "kwargs", - [ - {"flatten": []}, - {"flatten": [], "coords": {"obs_dim": [1, 2, 3]}}, - {"flatten": ["obs_dim"], "coords": {"obs_dim": [1, 2, 3]}}, - ], -) -def test_plot_ppc_grid(models, kwargs): - axes = plot_ppc(models.model_1, kind="scatter", **kwargs) - if not kwargs.get("flatten") and not kwargs.get("coords"): - assert axes.size == 8 - elif not kwargs.get("flatten"): - assert axes.size == 3 - else: - assert not isinstance(axes, np.ndarray) - assert np.ravel(axes).size == 1 - - -@pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) -def test_plot_ppc_bad(models, kind): - data = from_dict(posterior={"mu": np.random.randn()}) - with pytest.raises(TypeError): - plot_ppc(data, kind=kind) - with pytest.raises(TypeError): - plot_ppc(models.model_1, kind="bad_val") - with pytest.raises(TypeError): - plot_ppc(models.model_1, num_pp_samples="bad_val") - - -@pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) -def test_plot_ppc_ax(models, kind, fig_ax): - """Test ax argument of plot_ppc.""" - _, ax = fig_ax - axes = plot_ppc(models.model_1, kind=kind, ax=ax) - assert np.asarray(axes).item(0) is ax - - -@pytest.mark.skipif( - not animation.writers.is_available("ffmpeg"), - reason="matplotlib animations within ArviZ require ffmpeg", -) -def test_plot_ppc_bad_ax(models, fig_ax): - _, ax = fig_ax - _, ax2 = plt.subplots(1, 2) - with pytest.raises(ValueError, match="same figure"): - plot_ppc( - models.model_1, ax=[ax, *ax2], flatten=[], coords={"obs_dim": [1, 2, 3]}, animated=True - ) - with pytest.raises(ValueError, match="2 axes"): - plot_ppc(models.model_1, ax=ax2) - - -def test_plot_legend(models): - axes = plot_ppc(models.model_1) - legend_texts = axes.get_legend().get_texts() - result = [i.get_text() for i in legend_texts] - expected = ["Posterior predictive", "Observed", "Posterior predictive mean"] - assert result == expected - - -@pytest.mark.parametrize("var_names", (None, "mu", ["mu", "tau"])) -@pytest.mark.parametrize("side", ["both", "left", "right"]) -@pytest.mark.parametrize("rug", [True]) -def test_plot_violin(models, var_names, side, rug): - axes = plot_violin(models.model_1, var_names=var_names, side=side, rug=rug) - assert axes.shape - - -def test_plot_violin_ax(models): - _, ax = plt.subplots(1) - axes = plot_violin(models.model_1, var_names="mu", ax=ax) - assert axes.shape - - -def test_plot_violin_layout(models): - axes = plot_violin(models.model_1, var_names=["mu", "tau"], sharey=False) - assert axes.shape - - -def test_plot_violin_discrete(discrete_model): - axes = plot_violin(discrete_model) - assert axes.shape - - -@pytest.mark.parametrize("var_names", (None, "mu", ["mu", "tau"])) -def test_plot_violin_combinedims(models, var_names): - axes = plot_violin(models.model_1, var_names=var_names, combine_dims={"school"}) - assert axes.shape - - -def test_plot_violin_ax_combinedims(models): - _, ax = plt.subplots(1) - axes = plot_violin(models.model_1, var_names="mu", combine_dims={"school"}, ax=ax) - assert axes.shape - - -def test_plot_violin_layout_combinedims(models): - axes = plot_violin( - models.model_1, var_names=["mu", "tau"], combine_dims={"school"}, sharey=False - ) - assert axes.shape - - -def test_plot_violin_discrete_combinedims(discrete_model): - axes = plot_violin(discrete_model, combine_dims={"school"}) - assert axes.shape - - -def test_plot_autocorr_short_chain(): - """Check that logic for small chain defaulting doesn't cause exception""" - chain = np.arange(10) - axes = plot_autocorr(chain) - assert axes - - -def test_plot_autocorr_uncombined(models): - axes = plot_autocorr(models.model_1, combined=False) - assert axes.size - max_subplots = ( - np.inf if rcParams["plot.max_subplots"] is None else rcParams["plot.max_subplots"] - ) - assert axes.size == min(72, max_subplots) - - -def test_plot_autocorr_combined(models): - axes = plot_autocorr(models.model_1, combined=True) - assert axes.size == 18 - - -@pytest.mark.parametrize("var_names", (None, "mu", ["mu"], ["mu", "tau"])) -def test_plot_autocorr_var_names(models, var_names): - axes = plot_autocorr(models.model_1, var_names=var_names, combined=True) - if (isinstance(var_names, list) and len(var_names) == 1) or isinstance(var_names, str): - assert not isinstance(axes, np.ndarray) - else: - assert axes.shape - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"var_names": "mu"}, - {"var_names": ("mu", "tau"), "coords": {"school": [0, 1]}}, - {"var_names": "mu", "ref_line": True}, - { - "var_names": "mu", - "ref_line_kwargs": {"lw": 2, "color": "C2"}, - "bar_kwargs": {"width": 0.7}, - }, - {"var_names": "mu", "ref_line": False}, - {"var_names": "mu", "kind": "vlines"}, - { - "var_names": "mu", - "kind": "vlines", - "vlines_kwargs": {"lw": 0}, - "marker_vlines_kwargs": {"lw": 3}, - }, - ], -) -def test_plot_rank(models, kwargs): - axes = plot_rank(models.model_1, **kwargs) - var_names = kwargs.get("var_names", []) - if isinstance(var_names, str): - assert not isinstance(axes, np.ndarray) - else: - assert axes.shape - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"var_names": "mu"}, - {"var_names": ("mu", "tau")}, - {"rope": (-2, 2)}, - {"rope": {"mu": [{"rope": (-2, 2)}], "theta": [{"school": "Choate", "rope": (2, 4)}]}}, - {"point_estimate": "mode"}, - {"point_estimate": "median"}, - {"hdi_prob": "hide", "label": ""}, - {"point_estimate": None}, - {"ref_val": 0}, - {"ref_val": None}, - {"ref_val": {"mu": [{"ref_val": 1}]}}, - {"bins": None, "kind": "hist"}, - { - "ref_val": { - "theta": [ - # {"school": ["Choate", "Deerfield"], "ref_val": -1}, this is not working - {"school": "Lawrenceville", "ref_val": 3} - ] - } - }, - ], -) -def test_plot_posterior(models, kwargs): - axes = plot_posterior(models.model_1, **kwargs) - if isinstance(kwargs.get("var_names"), str): - assert not isinstance(axes, np.ndarray) - else: - assert axes.shape - - -def test_plot_posterior_boolean(): - data = np.random.choice(a=[False, True], size=(4, 100)) - axes = plot_posterior(data) - assert axes - plt.draw() - labels = [label.get_text() for label in axes.get_xticklabels()] - assert all(item in labels for item in ("True", "False")) - - -@pytest.mark.parametrize("kwargs", [{}, {"point_estimate": "mode"}, {"bins": None, "kind": "hist"}]) -def test_plot_posterior_discrete(discrete_model, kwargs): - axes = plot_posterior(discrete_model, **kwargs) - assert axes.shape - - -def test_plot_posterior_bad_type(): - with pytest.raises(TypeError): - plot_posterior(np.array(["a", "b", "c"])) - - -def test_plot_posterior_bad(models): - with pytest.raises(ValueError): - plot_posterior(models.model_1, rope="bad_value") - with pytest.raises(ValueError): - plot_posterior(models.model_1, ref_val="bad_value") - with pytest.raises(ValueError): - plot_posterior(models.model_1, point_estimate="bad_value") - - -@pytest.mark.parametrize("point_estimate", ("mode", "mean", "median")) -def test_plot_posterior_point_estimates(models, point_estimate): - axes = plot_posterior(models.model_1, var_names=("mu", "tau"), point_estimate=point_estimate) - assert axes.size == 2 - - -def test_plot_posterior_skipna(): - sample = np.linspace(0, 1) - sample[:10] = np.nan - plot_posterior({"a": sample}, skipna=True) - with pytest.raises(ValueError): - plot_posterior({"a": sample}, skipna=False) - - -@pytest.mark.parametrize("kwargs", [{"var_names": ["mu", "theta"]}]) -def test_plot_posterior_combinedims(models, kwargs): - axes = plot_posterior(models.model_1, combine_dims={"school"}, **kwargs) - if isinstance(kwargs.get("var_names"), str): - assert not isinstance(axes, np.ndarray) - else: - assert axes.shape - - -@pytest.mark.parametrize("kwargs", [{}, {"point_estimate": "mode"}, {"bins": None, "kind": "hist"}]) -def test_plot_posterior_discrete_combinedims(discrete_multidim_model, kwargs): - axes = plot_posterior(discrete_multidim_model, combine_dims={"school"}, **kwargs) - assert axes.size == 2 - - -@pytest.mark.parametrize("point_estimate", ("mode", "mean", "median")) -def test_plot_posterior_point_estimates_combinedims(models, point_estimate): - axes = plot_posterior( - models.model_1, - var_names=("mu", "tau"), - combine_dims={"school"}, - point_estimate=point_estimate, - ) - assert axes.size == 2 - - -def test_plot_posterior_skipna_combinedims(): - idata = load_arviz_data("centered_eight") - idata.posterior["theta"].loc[dict(school="Deerfield")] = np.nan - with pytest.raises(ValueError): - plot_posterior(idata, var_names="theta", combine_dims={"school"}, skipna=False) - ax = plot_posterior(idata, var_names="theta", combine_dims={"school"}, skipna=True) - assert not isinstance(ax, np.ndarray) - - -@pytest.mark.parametrize( - "kwargs", [{"insample_dev": True}, {"plot_standard_error": False}, {"plot_ic_diff": False}] -) -def test_plot_compare(models, kwargs): - model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2}) - - axes = plot_compare(model_compare, **kwargs) - assert axes - - -def test_plot_compare_no_ic(models): - """Check exception is raised if model_compare doesn't contain a valid information criterion""" - model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2}) - - # Drop column needed for plotting - model_compare = model_compare.drop("elpd_loo", axis=1) - with pytest.raises(ValueError) as err: - plot_compare(model_compare) - - assert "comp_df must contain one of the following" in str(err.value) - assert "['elpd_loo', 'elpd_waic']" in str(err.value) - - -@pytest.mark.parametrize( - "kwargs", - [ - {"color": "0.5", "circular": True}, - {"hdi_data": True, "fill_kwargs": {"alpha": 0}}, - {"plot_kwargs": {"alpha": 0}}, - {"smooth_kwargs": {"window_length": 33, "polyorder": 5, "mode": "mirror"}}, - {"hdi_data": True, "smooth": False}, - ], -) -def test_plot_hdi(models, data, kwargs): - hdi_data = kwargs.pop("hdi_data", None) - if hdi_data: - hdi_data = hdi(models.model_1.posterior["theta"]) - ax = plot_hdi(data["y"], hdi_data=hdi_data, **kwargs) - else: - ax = plot_hdi(data["y"], models.model_1.posterior["theta"], **kwargs) - assert ax - - -def test_plot_hdi_warning(): - """Check using both y and hdi_data sends a warning.""" - x_data = np.random.normal(0, 1, 100) - y_data = np.random.normal(2 + x_data * 0.5, 0.5, (1, 200, 100)) - hdi_data = hdi(y_data) - with pytest.warns(UserWarning, match="Both y and hdi_data"): - ax = plot_hdi(x_data, y=y_data, hdi_data=hdi_data) - assert ax - - -def test_plot_hdi_missing_arg_error(): - """Check that both y and hdi_data missing raises an error.""" - with pytest.raises(ValueError, match="One of {y, hdi_data"): - plot_hdi(np.arange(20)) - - -def test_plot_hdi_dataset_error(models): - """Check hdi_data as multiple variable Dataset raises an error.""" - hdi_data = hdi(models.model_1) - with pytest.raises(ValueError, match="Only single variable Dataset"): - plot_hdi(np.arange(8), hdi_data=hdi_data) - - -def test_plot_hdi_string_error(): - """Check x as type string raises an error.""" - x_data = ["a", "b", "c", "d"] - y_data = np.random.normal(0, 5, (1, 200, len(x_data))) - hdi_data = hdi(y_data) - with pytest.raises( - NotImplementedError, - match=re.escape( - ( - "The `arviz.plot_hdi()` function does not support categorical data. " - "Consider using `arviz.plot_forest()`." - ) - ), - ): - plot_hdi(x=x_data, y=y_data, hdi_data=hdi_data) - - -def test_plot_hdi_datetime_error(): - """Check x as datetime raises an error.""" - x_data = np.arange(start="2022-01-01", stop="2022-03-01", dtype=np.datetime64) - y_data = np.random.normal(0, 5, (1, 200, x_data.shape[0])) - hdi_data = hdi(y_data) - with pytest.raises(TypeError, match="Cannot deal with x as type datetime."): - plot_hdi(x=x_data, y=y_data, hdi_data=hdi_data) - - -@pytest.mark.parametrize("limits", [(-10.0, 10.0), (-5, 5), (None, None)]) -def test_kde_scipy(limits): - """ - Evaluates if sum of density is the same for our implementation - and the implementation in scipy - """ - data = np.random.normal(0, 1, 10000) - grid, density_own = _kde(data, custom_lims=limits) - density_sp = gaussian_kde(data).evaluate(grid) - np.testing.assert_almost_equal(density_own.sum(), density_sp.sum(), 1) - - -@pytest.mark.parametrize("limits", [(-10.0, 10.0), (-5, 5), (None, None)]) -def test_kde_cumulative(limits): - """ - Evaluates if last value of cumulative density is 1 - """ - data = np.random.normal(0, 1, 1000) - density = _kde(data, custom_lims=limits, cumulative=True)[1] - np.testing.assert_almost_equal(round(density[-1], 3), 1) - - -def test_plot_ecdf_basic(): - data = np.random.randn(4, 1000) - axes = plot_ecdf(data) - assert axes is not None - - -def test_plot_ecdf_eval_points(): - """Check that BehaviourChangeWarning is raised if eval_points is not specified.""" - data = np.random.randn(4, 1000) - eval_points = np.linspace(-3, 3, 100) - with pytest.warns(BehaviourChangeWarning): - axes = plot_ecdf(data) - assert axes is not None - with does_not_warn(BehaviourChangeWarning): - axes = plot_ecdf(data, eval_points=eval_points) - assert axes is not None - - -@pytest.mark.parametrize("confidence_bands", [True, "pointwise", "optimized", "simulated"]) -@pytest.mark.parametrize("ndraws", [100, 10_000]) -def test_plot_ecdf_confidence_bands(confidence_bands, ndraws): - """Check that all confidence_bands values correctly accepted""" - data = np.random.randn(4, ndraws // 4) - axes = plot_ecdf(data, confidence_bands=confidence_bands, cdf=norm(0, 1).cdf) - assert axes is not None - - -def test_plot_ecdf_values2(): - data = np.random.randn(4, 1000) - data2 = np.random.randn(4, 1000) - axes = plot_ecdf(data, data2) - assert axes is not None - - -def test_plot_ecdf_cdf(): - data = np.random.randn(4, 1000) - cdf = norm(0, 1).cdf - axes = plot_ecdf(data, cdf=cdf) - assert axes is not None - - -def test_plot_ecdf_error(): - """Check that all error conditions are correctly raised.""" - dist = norm(0, 1) - data = dist.rvs(1000) - - # cdf not specified - with pytest.raises(ValueError): - plot_ecdf(data, confidence_bands=True) - plot_ecdf(data, confidence_bands=True, cdf=dist.cdf) - with pytest.raises(ValueError): - plot_ecdf(data, difference=True) - plot_ecdf(data, difference=True, cdf=dist.cdf) - with pytest.raises(ValueError): - plot_ecdf(data, pit=True) - plot_ecdf(data, pit=True, cdf=dist.cdf) - - # contradictory confidence band types - with pytest.raises(ValueError): - plot_ecdf(data, cdf=dist.cdf, confidence_bands="simulated", pointwise=True) - with pytest.raises(ValueError): - plot_ecdf(data, cdf=dist.cdf, confidence_bands="optimized", pointwise=True) - plot_ecdf(data, cdf=dist.cdf, confidence_bands=True, pointwise=True) - plot_ecdf(data, cdf=dist.cdf, confidence_bands="pointwise") - - # contradictory band probabilities - with pytest.raises(ValueError): - plot_ecdf(data, cdf=dist.cdf, confidence_bands=True, ci_prob=0.9, fpr=0.1) - plot_ecdf(data, cdf=dist.cdf, confidence_bands=True, ci_prob=0.9) - plot_ecdf(data, cdf=dist.cdf, confidence_bands=True, fpr=0.1) - - # contradictory reference - data2 = dist.rvs(200) - with pytest.raises(ValueError): - plot_ecdf(data, data2, cdf=dist.cdf, difference=True) - plot_ecdf(data, data2, difference=True) - plot_ecdf(data, cdf=dist.cdf, difference=True) - - -def test_plot_ecdf_deprecations(): - """Check that deprecations are raised correctly.""" - dist = norm(0, 1) - data = dist.rvs(1000) - # base case, no deprecations - with does_not_warn(FutureWarning): - axes = plot_ecdf(data, cdf=dist.cdf, confidence_bands=True) - assert axes is not None - - # values2 is deprecated - data2 = dist.rvs(200) - with pytest.warns(FutureWarning): - axes = plot_ecdf(data, values2=data2, difference=True) - - # pit is deprecated - with pytest.warns(FutureWarning): - axes = plot_ecdf(data, cdf=dist.cdf, pit=True) - assert axes is not None - - # fpr is deprecated - with does_not_warn(FutureWarning): - axes = plot_ecdf(data, cdf=dist.cdf, ci_prob=0.9) - with pytest.warns(FutureWarning): - axes = plot_ecdf(data, cdf=dist.cdf, confidence_bands=True, fpr=0.1) - assert axes is not None - - # pointwise is deprecated - with does_not_warn(FutureWarning): - axes = plot_ecdf(data, cdf=dist.cdf, confidence_bands="pointwise") - with pytest.warns(FutureWarning): - axes = plot_ecdf(data, cdf=dist.cdf, confidence_bands=True, pointwise=True) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"ic": "loo"}, - {"xlabels": True, "scale": "log"}, - {"color": "obs_dim", "xlabels": True}, - {"color": "obs_dim", "legend": True}, - {"ic": "loo", "color": "blue", "coords": {"obs_dim": slice(2, 5)}}, - {"color": np.random.uniform(size=8), "threshold": 0.1}, - {"threshold": 2}, - ], -) -@pytest.mark.parametrize("add_model", [False, True]) -@pytest.mark.parametrize("use_elpddata", [False, True]) -def test_plot_elpd(models, add_model, use_elpddata, kwargs): - model_dict = {"Model 1": models.model_1, "Model 2": models.model_2} - if add_model: - model_dict["Model 3"] = create_model(seed=12) - - if use_elpddata: - ic = kwargs.get("ic", "waic") - scale = kwargs.get("scale", "deviance") - if ic == "waic": - model_dict = {k: waic(v, scale=scale, pointwise=True) for k, v in model_dict.items()} - else: - model_dict = {k: loo(v, scale=scale, pointwise=True) for k, v in model_dict.items()} - - axes = plot_elpd(model_dict, **kwargs) - assert np.all(axes) - if add_model: - assert axes.shape[0] == axes.shape[1] - assert axes.shape[0] == len(model_dict) - 1 - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"ic": "loo"}, - {"xlabels": True, "scale": "log"}, - {"color": "dim1", "xlabels": True}, - {"color": "dim2", "legend": True}, - {"ic": "loo", "color": "blue", "coords": {"dim2": slice(2, 4)}}, - {"color": np.random.uniform(size=35), "threshold": 0.1}, - ], -) -@pytest.mark.parametrize("add_model", [False, True]) -@pytest.mark.parametrize("use_elpddata", [False, True]) -def test_plot_elpd_multidim(multidim_models, add_model, use_elpddata, kwargs): - model_dict = {"Model 1": multidim_models.model_1, "Model 2": multidim_models.model_2} - if add_model: - model_dict["Model 3"] = create_multidimensional_model(seed=12) - - if use_elpddata: - ic = kwargs.get("ic", "waic") - scale = kwargs.get("scale", "deviance") - if ic == "waic": - model_dict = {k: waic(v, scale=scale, pointwise=True) for k, v in model_dict.items()} - else: - model_dict = {k: loo(v, scale=scale, pointwise=True) for k, v in model_dict.items()} - - axes = plot_elpd(model_dict, **kwargs) - assert np.all(axes) - if add_model: - assert axes.shape[0] == axes.shape[1] - assert axes.shape[0] == len(model_dict) - 1 - - -def test_plot_elpd_bad_ic(models): - model_dict = { - "Model 1": waic(models.model_1, pointwise=True), - "Model 2": loo(models.model_2, pointwise=True), - } - with pytest.raises(ValueError): - plot_elpd(model_dict, ic="bad_ic") - - -def test_plot_elpd_ic_error(models): - model_dict = { - "Model 1": waic(models.model_1, pointwise=True), - "Model 2": loo(models.model_2, pointwise=True), - } - with pytest.raises(ValueError): - plot_elpd(model_dict) - - -def test_plot_elpd_scale_error(models): - model_dict = { - "Model 1": waic(models.model_1, pointwise=True, scale="log"), - "Model 2": waic(models.model_2, pointwise=True, scale="deviance"), - } - with pytest.raises(ValueError): - plot_elpd(model_dict) - - -def test_plot_elpd_one_model(models): - model_dict = {"Model 1": models.model_1} - with pytest.raises(Exception): - plot_elpd(model_dict) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"xlabels": True}, - {"color": "obs_dim", "xlabels": True, "show_bins": True, "bin_format": "{0}"}, - {"color": "obs_dim", "legend": True, "hover_label": True}, - {"color": "blue", "coords": {"obs_dim": slice(2, 4)}}, - {"color": np.random.uniform(size=8), "show_bins": True}, - { - "color": np.random.uniform(size=(8, 3)), - "show_bins": True, - "show_hlines": True, - "threshold": 1, - }, - ], -) -@pytest.mark.parametrize("input_type", ["elpd_data", "data_array", "array"]) -def test_plot_khat(models, input_type, kwargs): - khats_data = loo(models.model_1, pointwise=True) - - if input_type == "data_array": - khats_data = khats_data.pareto_k - elif input_type == "array": - khats_data = khats_data.pareto_k.values - if "color" in kwargs and isinstance(kwargs["color"], str) and kwargs["color"] == "obs_dim": - kwargs["color"] = None - - axes = plot_khat(khats_data, **kwargs) - assert axes - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"xlabels": True}, - {"color": "dim1", "xlabels": True, "show_bins": True, "bin_format": "{0}"}, - {"color": "dim2", "legend": True, "hover_label": True}, - {"color": "blue", "coords": {"dim2": slice(2, 4)}}, - {"color": np.random.uniform(size=35), "show_bins": True}, - { - "color": np.random.uniform(size=(35, 3)), - "show_bins": True, - "show_hlines": True, - "threshold": 1, - }, - ], -) -@pytest.mark.parametrize("input_type", ["elpd_data", "data_array", "array"]) -def test_plot_khat_multidim(multidim_models, input_type, kwargs): - khats_data = loo(multidim_models.model_1, pointwise=True) - - if input_type == "data_array": - khats_data = khats_data.pareto_k - elif input_type == "array": - khats_data = khats_data.pareto_k.values - if ( - "color" in kwargs - and isinstance(kwargs["color"], str) - and kwargs["color"] in ("dim1", "dim2") - ): - kwargs["color"] = None - - axes = plot_khat(khats_data, **kwargs) - assert axes - - -def test_plot_khat_threshold(): - khats = np.array([0, 0, 0.6, 0.6, 0.8, 0.9, 0.9, 2, 3, 4, 1.5]) - axes = plot_khat(khats, threshold=1) - assert axes - - -def test_plot_khat_bad_input(models): - with pytest.raises(ValueError): - plot_khat(models.model_1.sample_stats) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"var_names": ["theta"], "relative": True, "color": "r"}, - {"coords": {"school": slice(4)}, "n_points": 10}, - {"min_ess": 600, "hline_kwargs": {"color": "r"}}, - ], -) -@pytest.mark.parametrize("kind", ["local", "quantile", "evolution"]) -def test_plot_ess(models, kind, kwargs): - """Test plot_ess arguments common to all kind of plots.""" - idata = models.model_1 - ax = plot_ess(idata, kind=kind, **kwargs) - assert np.all(ax) - - -@pytest.mark.parametrize( - "kwargs", - [ - {"rug": True}, - {"rug": True, "rug_kind": "max_depth", "rug_kwargs": {"color": "c"}}, - {"extra_methods": True}, - {"extra_methods": True, "extra_kwargs": {"ls": ":"}, "text_kwargs": {"x": 0, "ha": "left"}}, - {"extra_methods": True, "rug": True}, - ], -) -@pytest.mark.parametrize("kind", ["local", "quantile"]) -def test_plot_ess_local_quantile(models, kind, kwargs): - """Test specific arguments in kinds local and quantile of plot_ess.""" - idata = models.model_1 - ax = plot_ess(idata, kind=kind, **kwargs) - assert np.all(ax) - - -def test_plot_ess_evolution(models): - """Test specific arguments in evolution kind of plot_ess.""" - idata = models.model_1 - ax = plot_ess(idata, kind="evolution", extra_kwargs={"linestyle": "--"}, color="b") - assert np.all(ax) - - -def test_plot_ess_bad_kind(models): - """Test error when plot_ess receives an invalid kind.""" - idata = models.model_1 - with pytest.raises(ValueError, match="Invalid kind"): - plot_ess(idata, kind="bad kind") - - -@pytest.mark.parametrize("dim", ["chain", "draw"]) -def test_plot_ess_bad_coords(models, dim): - """Test error when chain or dim are used as coords to select a data subset.""" - idata = models.model_1 - with pytest.raises(ValueError, match="invalid coordinates"): - plot_ess(idata, coords={dim: slice(3)}) - - -def test_plot_ess_no_sample_stats(models): - """Test error when rug=True but sample_stats group is not present.""" - idata = models.model_1 - with pytest.raises(ValueError, match="must contain sample_stats"): - plot_ess(idata.posterior, rug=True) - - -def test_plot_ess_no_divergences(models): - """Test error when rug=True, but the variable defined by rug_kind is missing.""" - idata = deepcopy(models.model_1) - idata.sample_stats = idata.sample_stats.rename({"diverging": "diverging_missing"}) - with pytest.raises(ValueError, match="not contain diverging"): - plot_ess(idata, rug=True) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"n_unif": 50, "legend": False}, - {"use_hdi": True, "color": "gray"}, - {"use_hdi": True, "hdi_prob": 0.68}, - {"use_hdi": True, "hdi_kwargs": {"fill": 0.1}}, - {"ecdf": True}, - {"ecdf": True, "ecdf_fill": False, "plot_unif_kwargs": {"ls": "--"}}, - {"ecdf": True, "hdi_prob": 0.97, "fill_kwargs": {"hatch": "/"}}, - ], -) -def test_plot_loo_pit(models, kwargs): - axes = plot_loo_pit(idata=models.model_1, y="y", **kwargs) - assert axes - - -def test_plot_loo_pit_incompatible_args(models): - """Test error when both ecdf and use_hdi are True.""" - with pytest.raises(ValueError, match="incompatible"): - plot_loo_pit(idata=models.model_1, y="y", ecdf=True, use_hdi=True) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"var_names": ["theta"], "color": "r"}, - {"rug": True, "rug_kwargs": {"color": "r"}}, - {"errorbar": True, "rug": True, "rug_kind": "max_depth"}, - {"errorbar": True, "coords": {"school": slice(4)}, "n_points": 10}, - {"extra_methods": True, "rug": True}, - {"extra_methods": True, "extra_kwargs": {"ls": ":"}, "text_kwargs": {"x": 0, "ha": "left"}}, - ], -) -def test_plot_mcse(models, kwargs): - idata = models.model_1 - ax = plot_mcse(idata, **kwargs) - assert np.all(ax) - - -@pytest.mark.parametrize("dim", ["chain", "draw"]) -def test_plot_mcse_bad_coords(models, dim): - """Test error when chain or dim are used as coords to select a data subset.""" - idata = models.model_1 - with pytest.raises(ValueError, match="invalid coordinates"): - plot_mcse(idata, coords={dim: slice(3)}) - - -def test_plot_mcse_no_sample_stats(models): - """Test error when rug=True but sample_stats group is not present.""" - idata = models.model_1 - with pytest.raises(ValueError, match="must contain sample_stats"): - plot_mcse(idata.posterior, rug=True) - - -def test_plot_mcse_no_divergences(models): - """Test error when rug=True, but the variable defined by rug_kind is missing.""" - idata = deepcopy(models.model_1) - idata.sample_stats = idata.sample_stats.rename({"diverging": "diverging_missing"}) - with pytest.raises(ValueError, match="not contain diverging"): - plot_mcse(idata, rug=True) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"var_names": ["theta"]}, - {"var_names": ["theta"], "coords": {"school": [0, 1]}}, - {"var_names": ["eta"], "posterior_kwargs": {"rug": True, "rug_kwargs": {"color": "r"}}}, - {"var_names": ["mu"], "prior_kwargs": {"fill_kwargs": {"alpha": 0.5}}}, - { - "var_names": ["tau"], - "prior_kwargs": {"plot_kwargs": {"color": "r"}}, - "posterior_kwargs": {"plot_kwargs": {"color": "b"}}, - }, - {"var_names": ["y"], "kind": "observed"}, - ], -) -def test_plot_dist_comparison(models, kwargs): - idata = models.model_1 - ax = plot_dist_comparison(idata, **kwargs) - assert np.all(ax) - - -def test_plot_dist_comparison_different_vars(): - data = from_dict( - posterior={ - "x": np.random.randn(4, 100, 30), - }, - prior={"x_hat": np.random.randn(4, 100, 30)}, - ) - with pytest.raises(KeyError): - plot_dist_comparison(data, var_names="x") - ax = plot_dist_comparison(data, var_names=[["x_hat"], ["x"]]) - assert np.all(ax) - - -def test_plot_dist_comparison_combinedims(models): - idata = models.model_1 - ax = plot_dist_comparison(idata, combine_dims={"school"}) - assert np.all(ax) - - -def test_plot_dist_comparison_different_vars_combinedims(): - data = from_dict( - posterior={ - "x": np.random.randn(4, 100, 30), - }, - prior={"x_hat": np.random.randn(4, 100, 30)}, - dims={"x": ["3rd_dim"], "x_hat": ["3rd_dim"]}, - ) - with pytest.raises(KeyError): - plot_dist_comparison(data, var_names="x", combine_dims={"3rd_dim"}) - ax = plot_dist_comparison(data, var_names=[["x_hat"], ["x"]], combine_dims={"3rd_dim"}) - assert np.all(ax) - assert ax.size == 3 - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"reference": "analytical"}, - {"kind": "p_value"}, - {"kind": "t_stat", "t_stat": "std"}, - {"kind": "t_stat", "t_stat": 0.5, "bpv": True}, - ], -) -def test_plot_bpv(models, kwargs): - axes = plot_bpv(models.model_1, **kwargs) - assert not isinstance(axes, np.ndarray) - - -def test_plot_bpv_discrete(): - fake_obs = {"a": np.random.poisson(2.5, 100)} - fake_pp = {"a": np.random.poisson(2.5, (1, 10, 100))} - fake_model = from_dict(posterior_predictive=fake_pp, observed_data=fake_obs) - axes = plot_bpv(fake_model) - assert not isinstance(axes, np.ndarray) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - { - "binwidth": 0.5, - "stackratio": 2, - "nquantiles": 20, - }, - {"point_interval": True}, - { - "point_interval": True, - "dotsize": 1.2, - "point_estimate": "median", - "plot_kwargs": {"color": "grey"}, - }, - { - "point_interval": True, - "plot_kwargs": {"color": "grey"}, - "nquantiles": 100, - "hdi_prob": 0.95, - "intervalcolor": "green", - }, - { - "point_interval": True, - "plot_kwargs": {"color": "grey"}, - "quartiles": False, - "linewidth": 2, - }, - ], -) -def test_plot_dot(continuous_model, kwargs): - data = continuous_model["x"] - ax = plot_dot(data, **kwargs) - assert ax - - -@pytest.mark.parametrize( - "kwargs", - [ - {"rotated": True}, - { - "point_interval": True, - "rotated": True, - "dotcolor": "grey", - "binwidth": 0.5, - }, - { - "rotated": True, - "point_interval": True, - "plot_kwargs": {"color": "grey"}, - "nquantiles": 100, - "dotsize": 0.8, - "hdi_prob": 0.95, - "intervalcolor": "green", - }, - ], -) -def test_plot_dot_rotated(continuous_model, kwargs): - data = continuous_model["x"] - ax = plot_dot(data, **kwargs) - assert ax - - -@pytest.mark.parametrize( - "kwargs", - [ - { - "point_estimate": "mean", - "hdi_prob": 0.95, - "quartiles": False, - "linewidth": 2, - "markersize": 2, - "markercolor": "red", - "marker": "o", - "rotated": False, - "intervalcolor": "green", - }, - ], -) -def test_plot_point_interval(continuous_model, kwargs): - _, ax = plt.subplots() - data = continuous_model["x"] - values = np.sort(data) - ax = plot_point_interval(ax, values, **kwargs) - assert ax - - -def test_wilkinson_algorithm(continuous_model): - data = continuous_model["x"] - values = np.sort(data) - _, stack_counts = wilkinson_algorithm(values, 0.5) - assert np.sum(stack_counts) == len(values) - stack_locs, stack_counts = wilkinson_algorithm([0.0, 1.0, 1.8, 3.0, 5.0], 1.0) - assert stack_locs == [0.0, 1.4, 3.0, 5.0] - assert stack_counts == [1, 2, 1, 1] - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"y_hat": "bad_name"}, - {"x": "x1"}, - {"x": ("x1", "x2")}, - { - "x": ("x1", "x2"), - "y_kwargs": {"color": "blue", "marker": "^"}, - "y_hat_plot_kwargs": {"color": "cyan"}, - }, - {"x": ("x1", "x2"), "y_model_plot_kwargs": {"color": "red"}}, - { - "x": ("x1", "x2"), - "kind_pp": "hdi", - "kind_model": "hdi", - "y_model_fill_kwargs": {"color": "red"}, - "y_hat_fill_kwargs": {"color": "cyan"}, - }, - ], -) -def test_plot_lm_1d(models, kwargs): - """Test functionality for 1D data.""" - idata = models.model_1 - if "constant_data" not in idata.groups(): - y = idata.observed_data["y"] - x1data = y.coords[y.dims[0]] - idata.add_groups({"constant_data": {"_": x1data}}) - idata.constant_data["x1"] = x1data - idata.constant_data["x2"] = x1data - - axes = plot_lm(idata=idata, y="y", y_model="eta", xjitter=True, **kwargs) - assert np.all(axes) - - -def test_plot_lm_multidim(multidim_models): - """Test functionality for multidimentional data.""" - idata = multidim_models.model_1 - axes = plot_lm( - idata=idata, - x=idata.observed_data["y"].coords["dim1"].values, - y="y", - xjitter=True, - plot_dim="dim1", - show=False, - figsize=(4, 16), - ) - assert np.all(axes) - - -@pytest.mark.parametrize( - "val_err_kwargs", - [ - {}, - {"kind_pp": "bad_kind"}, - {"kind_model": "bad_kind"}, - ], -) -def test_plot_lm_valueerror(multidim_models, val_err_kwargs): - """Test error plot_dim gets no value for multidim data and wrong value in kind_... args.""" - idata2 = multidim_models.model_1 - with pytest.raises(ValueError): - plot_lm(idata=idata2, y="y", **val_err_kwargs) - - -@pytest.mark.parametrize( - "warn_kwargs", - [ - {"y_hat": "bad_name"}, - {"y_model": "bad_name"}, - ], -) -def test_plot_lm_warning(models, warn_kwargs): - """Test Warning when needed groups or variables are not there in idata.""" - idata1 = models.model_1 - with pytest.warns(UserWarning): - plot_lm( - idata=from_dict(observed_data={"y": idata1.observed_data["y"].values}), - y="y", - **warn_kwargs, - ) - with pytest.warns(UserWarning): - plot_lm(idata=idata1, y="y", **warn_kwargs) - - -def test_plot_lm_typeerror(models): - """Test error when invalid value passed to num_samples.""" - idata1 = models.model_1 - with pytest.raises(TypeError): - plot_lm(idata=idata1, y="y", num_samples=-1) - - -def test_plot_lm_list(): - """Test the plots when input data is list or ndarray.""" - y = [1, 2, 3, 4, 5] - assert plot_lm(y=y, x=np.arange(len(y)), show=False) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"y_hat": "bad_name"}, - {"x": "x"}, - {"x": ("x", "x")}, - {"y_holdout": "z"}, - {"y_holdout": "z", "x_holdout": "x_pred"}, - {"x": ("x", "x"), "y_holdout": "z", "x_holdout": ("x_pred", "x_pred")}, - {"y_forecasts": "z"}, - {"y_holdout": "z", "y_forecasts": "bad_name"}, - ], -) -def test_plot_ts(kwargs): - """Test timeseries plots basic functionality.""" - nchains = 4 - ndraws = 500 - obs_data = { - "y": 2 * np.arange(1, 9) + 3, - "z": 2 * np.arange(8, 12) + 3, - } - - posterior_predictive = { - "y": np.random.normal( - (obs_data["y"] * 1.2) - 3, size=(nchains, ndraws, len(obs_data["y"])) - ), - "z": np.random.normal( - (obs_data["z"] * 1.2) - 3, size=(nchains, ndraws, len(obs_data["z"])) - ), - } - - const_data = {"x": np.arange(1, 9), "x_pred": np.arange(8, 12)} - - idata = from_dict( - observed_data=obs_data, - posterior_predictive=posterior_predictive, - constant_data=const_data, - coords={"obs_dim": np.arange(1, 9), "pred_dim": np.arange(8, 12)}, - dims={"y": ["obs_dim"], "z": ["pred_dim"]}, - ) - - ax = plot_ts(idata=idata, y="y", **kwargs) - assert np.all(ax) - - -@pytest.mark.parametrize( - "kwargs", - [ - {}, - { - "y_holdout": "z", - "holdout_dim": "holdout_dim1", - "x": ("x", "x"), - "x_holdout": ("x_pred", "x_pred"), - }, - {"y_forecasts": "z", "holdout_dim": "holdout_dim1"}, - ], -) -def test_plot_ts_multidim(kwargs): - """Test timeseries plots multidim functionality.""" - nchains = 4 - ndraws = 500 - ndim1 = 5 - ndim2 = 7 - data = { - "y": np.random.normal(size=(ndim1, ndim2)), - "z": np.random.normal(size=(ndim1, ndim2)), - } - - posterior_predictive = { - "y": np.random.randn(nchains, ndraws, ndim1, ndim2), - "z": np.random.randn(nchains, ndraws, ndim1, ndim2), - } - - const_data = {"x": np.arange(1, 6), "x_pred": np.arange(5, 10)} - - idata = from_dict( - observed_data=data, - posterior_predictive=posterior_predictive, - constant_data=const_data, - dims={ - "y": ["dim1", "dim2"], - "z": ["holdout_dim1", "holdout_dim2"], - }, - coords={ - "dim1": range(ndim1), - "dim2": range(ndim2), - "holdout_dim1": range(ndim1 - 1, ndim1 + 4), - "holdout_dim2": range(ndim2 - 1, ndim2 + 6), - }, - ) - - ax = plot_ts(idata=idata, y="y", plot_dim="dim1", **kwargs) - assert np.all(ax) - - -@pytest.mark.parametrize("val_err_kwargs", [{}, {"plot_dim": "dim1", "y_holdout": "y"}]) -def test_plot_ts_valueerror(multidim_models, val_err_kwargs): - """Test error plot_dim gets no value for multidim data and wrong value in kind_... args.""" - idata2 = multidim_models.model_1 - with pytest.raises(ValueError): - plot_ts(idata=idata2, y="y", **val_err_kwargs) - - -def test_plot_bf(): - idata = from_dict( - posterior={"a": np.random.normal(1, 0.5, 5000)}, prior={"a": np.random.normal(0, 1, 5000)} - ) - _, bf_plot = plot_bf(idata, var_name="a", ref_val=0) - assert bf_plot is not None - - -def generate_lm_1d_data(): - rng = np.random.default_rng() - return from_dict( - observed_data={"y": rng.normal(size=7)}, - posterior_predictive={"y": rng.normal(size=(4, 1000, 7)) / 2}, - posterior={"y_model": rng.normal(size=(4, 1000, 7))}, - dims={"y": ["dim1"]}, - coords={"dim1": range(7)}, - ) - - -def generate_lm_2d_data(): - rng = np.random.default_rng() - return from_dict( - observed_data={"y": rng.normal(size=(5, 7))}, - posterior_predictive={"y": rng.normal(size=(4, 1000, 5, 7)) / 2}, - posterior={"y_model": rng.normal(size=(4, 1000, 5, 7))}, - dims={"y": ["dim1", "dim2"]}, - coords={"dim1": range(5), "dim2": range(7)}, - ) - - -@pytest.mark.parametrize("data", ("1d", "2d")) -@pytest.mark.parametrize("kind", ("lines", "hdi")) -@pytest.mark.parametrize("use_y_model", (True, False)) -def test_plot_lm(data, kind, use_y_model): - if data == "1d": - idata = generate_lm_1d_data() - else: - idata = generate_lm_2d_data() - - kwargs = {"idata": idata, "y": "y", "kind_model": kind} - if data == "2d": - kwargs["plot_dim"] = "dim1" - if use_y_model: - kwargs["y_model"] = "y_model" - if kind == "lines": - kwargs["num_samples"] = 50 - - ax = plot_lm(**kwargs) - assert ax is not None - - -@pytest.mark.parametrize( - "coords, expected_vars", - [ - ({"school": ["Choate"]}, ["theta"]), - ({"school": ["Lawrenceville"]}, ["theta"]), - ({}, ["theta"]), - ], -) -def test_plot_autocorr_coords(coords, expected_vars): - """Test plot_autocorr with coords kwarg.""" - idata = load_arviz_data("centered_eight") - - axes = plot_autocorr(idata, var_names=expected_vars, coords=coords, show=False) - assert axes is not None - - -def test_plot_forest_with_transform(): - """Test if plot_forest runs successfully with a transform dictionary.""" - data = xr.Dataset( - { - "var1": (["chain", "draw"], np.array([[1, 2, 3], [4, 5, 6]])), - "var2": (["chain", "draw"], np.array([[7, 8, 9], [10, 11, 12]])), - }, - coords={"chain": [0, 1], "draw": [0, 1, 2]}, - ) - transform_dict = { - "var1": lambda x: x + 1, - "var2": lambda x: x * 2, - } - - axes = plot_forest(data, transform=transform_dict, show=False) - assert axes is not None diff --git a/arviz/tests/base_tests/test_rcparams.py b/arviz/tests/base_tests/test_rcparams.py deleted file mode 100644 index ab1af1f624..0000000000 --- a/arviz/tests/base_tests/test_rcparams.py +++ /dev/null @@ -1,317 +0,0 @@ -# pylint: disable=redefined-outer-name -import os - -import numpy as np -import pytest -from xarray.core.indexing import MemoryCachedArray - -from ...data import datasets, load_arviz_data -from ...rcparams import ( - _make_validate_choice, - _make_validate_choice_regex, - _validate_float_or_none, - _validate_positive_int_or_none, - _validate_probability, - make_iterable_validator, - rc_context, - rc_params, - rcParams, - read_rcfile, -) -from ...stats import compare -from ..helpers import models # pylint: disable=unused-import - - -### Test rcparams classes ### -def test_rc_context_dict(): - rcParams["data.load"] = "lazy" - with rc_context(rc={"data.load": "eager"}): - assert rcParams["data.load"] == "eager" - assert rcParams["data.load"] == "lazy" - - -def test_rc_context_file(): - path = os.path.dirname(os.path.abspath(__file__)) - rcParams["data.load"] = "lazy" - with rc_context(fname=os.path.join(path, "../test.rcparams")): - assert rcParams["data.load"] == "eager" - assert rcParams["data.load"] == "lazy" - - -def test_bad_rc_file(): - """Test bad value raises error.""" - path = os.path.dirname(os.path.abspath(__file__)) - with pytest.raises(ValueError, match="Bad val "): - read_rcfile(os.path.join(path, "../bad.rcparams")) - - -def test_warning_rc_file(caplog): - """Test invalid lines and duplicated keys log warnings and bad value raises error.""" - path = os.path.dirname(os.path.abspath(__file__)) - read_rcfile(os.path.join(path, "../test.rcparams")) - records = caplog.records - assert len(records) == 1 - assert records[0].levelname == "WARNING" - assert "Duplicate key" in caplog.text - - -def test_bad_key(): - """Test the error when using unexistent keys in rcParams is correct.""" - with pytest.raises(KeyError, match="bad_key is not a valid rc"): - rcParams["bad_key"] = "nothing" - - -def test_del_key_error(): - """Check that rcParams keys cannot be deleted.""" - with pytest.raises(TypeError, match="keys cannot be deleted"): - del rcParams["data.load"] - - -def test_clear_error(): - """Check that rcParams cannot be cleared.""" - with pytest.raises(TypeError, match="keys cannot be deleted"): - rcParams.clear() - - -def test_pop_error(): - """Check rcParams pop error.""" - with pytest.raises(TypeError, match=r"keys cannot be deleted.*get\(key\)"): - rcParams.pop("data.load") - - -def test_popitem_error(): - """Check rcParams popitem error.""" - with pytest.raises(TypeError, match=r"keys cannot be deleted.*get\(key\)"): - rcParams.popitem() - - -def test_setdefaults_error(): - """Check rcParams popitem error.""" - with pytest.raises(TypeError, match="Use arvizrc"): - rcParams.setdefault("data.load", "eager") - - -def test_rcparams_find_all(): - data_rcparams = rcParams.find_all("data") - assert len(data_rcparams) - - -def test_rcparams_repr_str(): - """Check both repr and str print all keys.""" - repr_str = repr(rcParams) - str_str = str(rcParams) - assert repr_str.startswith("RcParams") - for string in (repr_str, str_str): - assert all((key in string for key in rcParams.keys())) - - -### Test arvizrc.template file is up to date ### -def test_rctemplate_updated(): - fname = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../../arvizrc.template") - rc_pars_template = read_rcfile(fname) - rc_defaults = rc_params(ignore_files=True) - assert all((key in rc_pars_template.keys() for key in rc_defaults.keys())), [ - key for key in rc_defaults.keys() if key not in rc_pars_template - ] - assert all((value == rc_pars_template[key] for key, value in rc_defaults.items())), [ - key for key, value in rc_defaults.items() if value != rc_pars_template[key] - ] - - -### Test validation functions ### -@pytest.mark.parametrize("param", ["data.load", "stats.information_criterion"]) -def test_choice_bad_values(param): - """Test error messages are correct for rcParams validated with _make_validate_choice.""" - msg = "{}: bad_value is not one of".format(param.replace(".", r"\.")) - with pytest.raises(ValueError, match=msg): - rcParams[param] = "bad_value" - - -@pytest.mark.parametrize("args", [("stats.hdi_prob", "stats.ci_prob", 0.7, 0.7)]) -def test_deprecated_param(args): - """Test value and warning message correctly set for deprecated rcparams.""" - param_old, param_new, val_old, val_new = args - assert param_new in rcParams - assert not np.isclose(rcParams[param_new], val_new) - msg = f"{param_old} is deprecated since .*, use {param_new} instead" - with pytest.warns(FutureWarning, match=msg): - with rc_context(rc={param_old: val_old}): - assert np.isclose(rcParams[param_new], val_new) - - -@pytest.mark.parametrize("allow_none", (True, False)) -@pytest.mark.parametrize("typeof", (str, int)) -@pytest.mark.parametrize("args", [("not one", 10), (False, None), (False, 4)]) -def test_make_validate_choice(args, allow_none, typeof): - accepted_values = set(typeof(value) for value in (0, 1, 4, 6)) - validate_choice = _make_validate_choice(accepted_values, allow_none=allow_none, typeof=typeof) - raise_error, value = args - if value is None and not allow_none: - raise_error = "not one of" if typeof == str else "Could not convert" - if raise_error: - with pytest.raises(ValueError, match=raise_error): - validate_choice(value) - else: - value = validate_choice(value) - assert value in accepted_values or value is None - - -@pytest.mark.parametrize("allow_none", (True, False)) -@pytest.mark.parametrize( - "args", - [ - (False, None), - (False, "row"), - (False, "54row"), - (False, "4column"), - ("or in regex", "square"), - ], -) -def test_make_validate_choice_regex(args, allow_none): - accepted_values = {"row", "column"} - accepted_values_regex = {r"\d*row", r"\d*column"} - validate_choice = _make_validate_choice_regex( - accepted_values, accepted_values_regex, allow_none=allow_none - ) - raise_error, value = args - if value is None and not allow_none: - raise_error = "or in regex" - if raise_error: - with pytest.raises(ValueError, match=raise_error): - validate_choice(value) - else: - value_result = validate_choice(value) - assert value == value_result - - -@pytest.mark.parametrize("allow_none", (True, False)) -@pytest.mark.parametrize("allow_auto", (True, False)) -@pytest.mark.parametrize("value", [(1, 2), "auto", None, "(1, 4)"]) -def test_make_iterable_validator_none_auto(value, allow_auto, allow_none): - scalar_validator = _validate_float_or_none - validate_iterable = make_iterable_validator( - scalar_validator, allow_auto=allow_auto, allow_none=allow_none - ) - raise_error = False - if value is None and not allow_none: - raise_error = "Only ordered iterable" - if value == "auto" and not allow_auto: - raise_error = "Could not convert" - if raise_error: - with pytest.raises(ValueError, match=raise_error): - validate_iterable(value) - else: - value = validate_iterable(value) - assert np.iterable(value) or value is None or value == "auto" - - -@pytest.mark.parametrize("length", (2, None)) -@pytest.mark.parametrize("value", [(1, 5), (1, 3, 5), "(3, 4, 5)"]) -def test_make_iterable_validator_length(value, length): - scalar_validator = _validate_float_or_none - validate_iterable = make_iterable_validator(scalar_validator, length=length) - raise_error = False - if length is not None and len(value) != length: - raise_error = "Iterable must be of length" - if raise_error: - with pytest.raises(ValueError, match=raise_error): - validate_iterable(value) - else: - value = validate_iterable(value) - assert np.iterable(value) - - -@pytest.mark.parametrize( - "args", - [ - ("Only ordered iterable", set(["a", "b", "c"])), - ("Could not convert", "johndoe"), - ("Only ordered iterable", 15), - ], -) -def test_make_iterable_validator_illegal(args): - scalar_validator = _validate_float_or_none - validate_iterable = make_iterable_validator(scalar_validator) - raise_error, value = args - with pytest.raises(ValueError, match=raise_error): - validate_iterable(value) - - -@pytest.mark.parametrize( - "args", - [("Only positive", -1), ("Could not convert", "1.3"), (False, "2"), (False, None), (False, 1)], -) -def test_validate_positive_int_or_none(args): - raise_error, value = args - if raise_error: - with pytest.raises(ValueError, match=raise_error): - _validate_positive_int_or_none(value) - else: - value = _validate_positive_int_or_none(value) - assert isinstance(value, int) or value is None - - -@pytest.mark.parametrize( - "args", - [ - ("Only.+between 0 and 1", -1), - ("Only.+between 0 and 1", "1.3"), - ("not convert to float", "word"), - (False, "0.6"), - (False, 0), - (False, 1), - ], -) -def test_validate_probability(args): - raise_error, value = args - if raise_error: - with pytest.raises(ValueError, match=raise_error): - _validate_probability(value) - else: - value = _validate_probability(value) - assert isinstance(value, float) - - -### Test integration of rcParams in ArviZ ### -def test_data_load(): - rcParams["data.load"] = "lazy" - idata_lazy = load_arviz_data("centered_eight") - assert isinstance( - idata_lazy.posterior.mu.variable._data, # pylint: disable=protected-access - MemoryCachedArray, - ) - assert rcParams["data.load"] == "lazy" - rcParams["data.load"] = "eager" - idata_eager = load_arviz_data("centered_eight") - assert isinstance( - idata_eager.posterior.mu.variable._data, np.ndarray # pylint: disable=protected-access - ) - assert rcParams["data.load"] == "eager" - - -def test_stats_information_criterion(models): - rcParams["stats.information_criterion"] = "waic" - df_comp = compare({"model1": models.model_1, "model2": models.model_2}) - assert "elpd_waic" in df_comp.columns - rcParams["stats.information_criterion"] = "loo" - df_comp = compare({"model1": models.model_1, "model2": models.model_2}) - assert "elpd_loo" in df_comp.columns - - -def test_http_type_request(monkeypatch): - def _urlretrive(url, _): - raise ValueError(f"URL Retrieved: {url}") - - # Hijack url retrieve to inspect url passed - monkeypatch.setattr(datasets, "urlretrieve", _urlretrive) - - # Test HTTPS default - with pytest.raises(ValueError) as error: - datasets.load_arviz_data("radon") - assert "https://" in str(error) - - # Test HTTP setting - with pytest.raises(ValueError) as error: - rcParams["data.http_protocol"] = "http" - datasets.load_arviz_data("radon") - assert "http://" in str(error) diff --git a/arviz/tests/base_tests/test_stats.py b/arviz/tests/base_tests/test_stats.py deleted file mode 100644 index bc6f4d0739..0000000000 --- a/arviz/tests/base_tests/test_stats.py +++ /dev/null @@ -1,925 +0,0 @@ -# pylint: disable=redefined-outer-name, no-member -from copy import deepcopy - -import numpy as np -import pytest -from numpy.testing import ( - assert_allclose, - assert_array_almost_equal, - assert_almost_equal, - assert_array_equal, -) -from scipy.special import logsumexp -from scipy.stats import linregress, norm, halfcauchy -from xarray import DataArray, Dataset -from xarray_einstats.stats import XrContinuousRV - -from ...data import concat, convert_to_inference_data, from_dict, load_arviz_data, InferenceData -from ...rcparams import rcParams -from ...stats import ( - apply_test_function, - bayes_factor, - compare, - ess, - hdi, - loo, - loo_pit, - psens, - psislw, - r2_score, - summary, - waic, - weight_predictions, - _calculate_ics, -) -from ...stats.stats import _gpinv -from ...stats.stats_utils import get_log_likelihood -from ..helpers import check_multiple_attrs, multidim_models # pylint: disable=unused-import - -rcParams["data.load"] = "eager" - - -@pytest.fixture(scope="session") -def centered_eight(): - centered_eight = load_arviz_data("centered_eight") - return centered_eight - - -@pytest.fixture(scope="session") -def non_centered_eight(): - non_centered_eight = load_arviz_data("non_centered_eight") - return non_centered_eight - - -@pytest.fixture(scope="module") -def multivariable_log_likelihood(centered_eight): - centered_eight = centered_eight.copy() - new_arr = DataArray( - np.zeros(centered_eight.log_likelihood["obs"].values.shape), - dims=["chain", "draw", "school"], - coords=centered_eight.log_likelihood.coords, - ) - centered_eight.log_likelihood["decoy"] = new_arr - return centered_eight - - -def test_hdp(): - normal_sample = np.random.randn(5000000) - interval = hdi(normal_sample) - assert_array_almost_equal(interval, [-1.88, 1.88], 2) - - -def test_hdp_2darray(): - normal_sample = np.random.randn(12000, 5) - msg = ( - r"hdi currently interprets 2d data as \(draw, shape\) but this will " - r"change in a future release to \(chain, draw\) for coherence with other functions" - ) - with pytest.warns(FutureWarning, match=msg): - result = hdi(normal_sample) - assert result.shape == (5, 2) - - -def test_hdi_multidimension(): - normal_sample = np.random.randn(12000, 10, 3) - result = hdi(normal_sample) - assert result.shape == (3, 2) - - -def test_hdi_idata(centered_eight): - data = centered_eight.posterior - result = hdi(data) - assert isinstance(result, Dataset) - assert dict(result.sizes) == {"school": 8, "hdi": 2} - - result = hdi(data, input_core_dims=[["chain"]]) - assert isinstance(result, Dataset) - assert result.sizes == {"draw": 500, "hdi": 2, "school": 8} - - -def test_hdi_idata_varnames(centered_eight): - data = centered_eight.posterior - result = hdi(data, var_names=["mu", "theta"]) - assert isinstance(result, Dataset) - assert result.sizes == {"hdi": 2, "school": 8} - assert list(result.data_vars.keys()) == ["mu", "theta"] - - -def test_hdi_idata_group(centered_eight): - result_posterior = hdi(centered_eight, group="posterior", var_names="mu") - result_prior = hdi(centered_eight, group="prior", var_names="mu") - assert result_prior.sizes == {"hdi": 2} - range_posterior = result_posterior.mu.values[1] - result_posterior.mu.values[0] - range_prior = result_prior.mu.values[1] - result_prior.mu.values[0] - assert range_posterior < range_prior - - -def test_hdi_coords(centered_eight): - data = centered_eight.posterior - result = hdi(data, coords={"chain": [0, 1, 3]}, input_core_dims=[["draw"]]) - assert_array_equal(result.coords["chain"], [0, 1, 3]) - - -def test_hdi_multimodal(): - normal_sample = np.concatenate( - (np.random.normal(-4, 1, 2500000), np.random.normal(2, 0.5, 2500000)) - ) - intervals = hdi(normal_sample, multimodal=True) - assert_array_almost_equal(intervals, [[-5.8, -2.2], [0.9, 3.1]], 1) - - -def test_hdi_multimodal_multivars(): - size = 2500000 - var1 = np.concatenate((np.random.normal(-4, 1, size), np.random.normal(2, 0.5, size))) - var2 = np.random.normal(8, 1, size * 2) - sample = Dataset( - { - "var1": (("chain", "draw"), var1[np.newaxis, :]), - "var2": (("chain", "draw"), var2[np.newaxis, :]), - }, - coords={"chain": [0], "draw": np.arange(size * 2)}, - ) - intervals = hdi(sample, multimodal=True) - assert_array_almost_equal(intervals.var1, [[-5.8, -2.2], [0.9, 3.1]], 1) - assert_array_almost_equal(intervals.var2, [[6.1, 9.9], [np.nan, np.nan]], 1) - - -def test_hdi_circular(): - normal_sample = np.random.vonmises(np.pi, 1, 5000000) - interval = hdi(normal_sample, circular=True) - assert_array_almost_equal(interval, [0.6, -0.6], 1) - - -def test_hdi_bad_ci(): - normal_sample = np.random.randn(10) - with pytest.raises(ValueError): - hdi(normal_sample, hdi_prob=2) - - -def test_hdi_skipna(): - normal_sample = np.random.randn(500) - interval = hdi(normal_sample[10:]) - normal_sample[:10] = np.nan - interval_ = hdi(normal_sample, skipna=True) - assert_array_almost_equal(interval, interval_) - - -def test_r2_score(): - x = np.linspace(0, 1, 100) - y = np.random.normal(x, 1) - y_pred = x + np.random.randn(300, 100) - res = linregress(x, y) - assert_allclose(res.rvalue**2, r2_score(y, y_pred).r2, 2) - - -@pytest.mark.parametrize("method", ["stacking", "BB-pseudo-BMA", "pseudo-BMA"]) -@pytest.mark.parametrize("multidim", [True, False]) -def test_compare_same(centered_eight, multidim_models, method, multidim): - if multidim: - data_dict = {"first": multidim_models.model_1, "second": multidim_models.model_1} - else: - data_dict = {"first": centered_eight, "second": centered_eight} - - weight = compare(data_dict, method=method)["weight"].to_numpy() - assert_allclose(weight[0], weight[1]) - assert_allclose(np.sum(weight), 1.0) - - -def test_compare_unknown_ic_and_method(centered_eight, non_centered_eight): - model_dict = {"centered": centered_eight, "non_centered": non_centered_eight} - with pytest.raises(ValueError): - compare(model_dict, ic="Unknown", method="stacking") - with pytest.raises(ValueError): - compare(model_dict, ic="loo", method="Unknown") - - -@pytest.mark.parametrize("ic", ["loo", "waic"]) -@pytest.mark.parametrize("method", ["stacking", "BB-pseudo-BMA", "pseudo-BMA"]) -@pytest.mark.parametrize("scale", ["log", "negative_log", "deviance"]) -def test_compare_different(centered_eight, non_centered_eight, ic, method, scale): - model_dict = {"centered": centered_eight, "non_centered": non_centered_eight} - weight = compare(model_dict, ic=ic, method=method, scale=scale)["weight"] - assert weight["non_centered"] > weight["centered"] - assert_allclose(np.sum(weight), 1.0) - - -@pytest.mark.parametrize("ic", ["loo", "waic"]) -@pytest.mark.parametrize("method", ["stacking", "BB-pseudo-BMA", "pseudo-BMA"]) -def test_compare_different_multidim(multidim_models, ic, method): - model_dict = {"model_1": multidim_models.model_1, "model_2": multidim_models.model_2} - weight = compare(model_dict, ic=ic, method=method)["weight"] - - # this should hold because the same seed is always used - assert weight["model_1"] > weight["model_2"] - assert_allclose(np.sum(weight), 1.0) - - -def test_compare_different_size(centered_eight, non_centered_eight): - centered_eight = deepcopy(centered_eight) - centered_eight.posterior = centered_eight.posterior.drop("Choate", "school") - centered_eight.log_likelihood = centered_eight.log_likelihood.drop("Choate", "school") - centered_eight.posterior_predictive = centered_eight.posterior_predictive.drop( - "Choate", "school" - ) - centered_eight.prior = centered_eight.prior.drop("Choate", "school") - centered_eight.observed_data = centered_eight.observed_data.drop("Choate", "school") - model_dict = {"centered": centered_eight, "non_centered": non_centered_eight} - with pytest.raises(ValueError): - compare(model_dict, ic="waic", method="stacking") - - -@pytest.mark.parametrize("ic", ["loo", "waic"]) -def test_compare_multiple_obs(multivariable_log_likelihood, centered_eight, non_centered_eight, ic): - compare_dict = { - "centered_eight": centered_eight, - "non_centered_eight": non_centered_eight, - "problematic": multivariable_log_likelihood, - } - with pytest.raises(TypeError, match="several log likelihood arrays"): - get_log_likelihood(compare_dict["problematic"]) - with pytest.raises(TypeError, match="error in ELPD computation"): - compare(compare_dict, ic=ic) - assert compare(compare_dict, ic=ic, var_name="obs") is not None - - -@pytest.mark.parametrize("ic", ["loo", "waic"]) -def test_calculate_ics(centered_eight, non_centered_eight, ic): - ic_func = loo if ic == "loo" else waic - idata_dict = {"centered": centered_eight, "non_centered": non_centered_eight} - elpddata_dict = {key: ic_func(value) for key, value in idata_dict.items()} - mixed_dict = {"centered": idata_dict["centered"], "non_centered": elpddata_dict["non_centered"]} - idata_out, _, _ = _calculate_ics(idata_dict, ic=ic) - elpddata_out, _, _ = _calculate_ics(elpddata_dict, ic=ic) - mixed_out, _, _ = _calculate_ics(mixed_dict, ic=ic) - for model in idata_dict: - ic_ = f"elpd_{ic}" - assert idata_out[model][ic_] == elpddata_out[model][ic_] - assert idata_out[model][ic_] == mixed_out[model][ic_] - assert idata_out[model][f"p_{ic}"] == elpddata_out[model][f"p_{ic}"] - assert idata_out[model][f"p_{ic}"] == mixed_out[model][f"p_{ic}"] - - -def test_calculate_ics_ic_error(centered_eight, non_centered_eight): - in_dict = {"centered": loo(centered_eight), "non_centered": waic(non_centered_eight)} - with pytest.raises(ValueError, match="found both loo and waic"): - _calculate_ics(in_dict) - - -def test_calculate_ics_ic_override(centered_eight, non_centered_eight): - in_dict = {"centered": centered_eight, "non_centered": waic(non_centered_eight)} - with pytest.warns(UserWarning, match="precomputed elpddata: waic"): - out_dict, _, ic = _calculate_ics(in_dict, ic="loo") - assert ic == "waic" - assert out_dict["centered"]["elpd_waic"] == waic(centered_eight)["elpd_waic"] - - -def test_summary_ndarray(): - array = np.random.randn(4, 100, 2) - summary_df = summary(array) - assert summary_df.shape - - -@pytest.mark.parametrize("var_names_expected", ((None, 10), ("mu", 1), (["mu", "tau"], 2))) -def test_summary_var_names(centered_eight, var_names_expected): - var_names, expected = var_names_expected - summary_df = summary(centered_eight, var_names=var_names) - assert len(summary_df.index) == expected - - -@pytest.mark.parametrize("missing_groups", (None, "posterior", "prior")) -def test_summary_groups(centered_eight, missing_groups): - if missing_groups == "posterior": - centered_eight = deepcopy(centered_eight) - del centered_eight.posterior - elif missing_groups == "prior": - centered_eight = deepcopy(centered_eight) - del centered_eight.posterior - del centered_eight.prior - if missing_groups == "prior": - with pytest.warns(UserWarning): - summary_df = summary(centered_eight) - else: - summary_df = summary(centered_eight) - assert summary_df.shape - - -def test_summary_group_argument(centered_eight): - summary_df_posterior = summary(centered_eight, group="posterior") - summary_df_prior = summary(centered_eight, group="prior") - assert list(summary_df_posterior.index) != list(summary_df_prior.index) - - -def test_summary_wrong_group(centered_eight): - with pytest.raises(TypeError, match=r"InferenceData does not contain group: InvalidGroup"): - summary(centered_eight, group="InvalidGroup") - - -METRICS_NAMES = [ - "mean", - "sd", - "hdi_3%", - "hdi_97%", - "mcse_mean", - "mcse_sd", - "ess_bulk", - "ess_tail", - "r_hat", - "median", - "mad", - "eti_3%", - "eti_97%", - "mcse_median", - "ess_median", - "ess_tail", - "r_hat", -] - - -@pytest.mark.parametrize( - "params", - ( - ("mean", "all", METRICS_NAMES[:9]), - ("mean", "stats", METRICS_NAMES[:4]), - ("mean", "diagnostics", METRICS_NAMES[4:9]), - ("median", "all", METRICS_NAMES[9:17]), - ("median", "stats", METRICS_NAMES[9:13]), - ("median", "diagnostics", METRICS_NAMES[13:17]), - ), -) -def test_summary_focus_kind(centered_eight, params): - stat_focus, kind, metrics_names_ = params - summary_df = summary(centered_eight, stat_focus=stat_focus, kind=kind) - assert_array_equal(summary_df.columns, metrics_names_) - - -def test_summary_wrong_focus(centered_eight): - with pytest.raises(TypeError, match=r"Invalid format: 'WrongFocus'.*"): - summary(centered_eight, stat_focus="WrongFocus") - - -@pytest.mark.parametrize("fmt", ["wide", "long", "xarray"]) -def test_summary_fmt(centered_eight, fmt): - assert summary(centered_eight, fmt=fmt) is not None - - -def test_summary_labels(): - coords1 = list("abcd") - coords2 = np.arange(1, 6) - data = from_dict( - {"a": np.random.randn(4, 100, 4, 5)}, - coords={"dim1": coords1, "dim2": coords2}, - dims={"a": ["dim1", "dim2"]}, - ) - az_summary = summary(data, fmt="wide") - assert az_summary is not None - column_order = [] - for coord1 in coords1: - for coord2 in coords2: - column_order.append(f"a[{coord1}, {coord2}]") - for col1, col2 in zip(list(az_summary.index), column_order): - assert col1 == col2 - - -@pytest.mark.parametrize( - "stat_funcs", [[np.var], {"var": np.var, "var2": lambda x: np.var(x) ** 2}] -) -def test_summary_stat_func(centered_eight, stat_funcs): - arviz_summary = summary(centered_eight, stat_funcs=stat_funcs) - assert arviz_summary is not None - assert hasattr(arviz_summary, "var") - - -def test_summary_nan(centered_eight): - centered_eight = deepcopy(centered_eight) - centered_eight.posterior["theta"].loc[{"school": "Deerfield"}] = np.nan - summary_xarray = summary(centered_eight) - assert summary_xarray is not None - assert summary_xarray.loc["theta[Deerfield]"].isnull().all() - assert ( - summary_xarray.loc[[ix for ix in summary_xarray.index if ix != "theta[Deerfield]"]] - .notnull() - .all() - .all() - ) - - -def test_summary_skip_nan(centered_eight): - centered_eight = deepcopy(centered_eight) - centered_eight.posterior["theta"].loc[{"draw": slice(10), "school": "Deerfield"}] = np.nan - summary_xarray = summary(centered_eight) - theta_1 = summary_xarray.loc["theta[Deerfield]"].isnull() - assert summary_xarray is not None - assert ~theta_1[:4].all() - assert theta_1[4:].all() - - -@pytest.mark.parametrize("fmt", [1, "bad_fmt"]) -def test_summary_bad_fmt(centered_eight, fmt): - with pytest.raises(TypeError, match="Invalid format"): - summary(centered_eight, fmt=fmt) - - -def test_summary_order_deprecation(centered_eight): - with pytest.warns(DeprecationWarning, match="order"): - summary(centered_eight, order="C") - - -def test_summary_index_origin_deprecation(centered_eight): - with pytest.warns(DeprecationWarning, match="index_origin"): - summary(centered_eight, index_origin=1) - - -@pytest.mark.parametrize("scale", ["log", "negative_log", "deviance"]) -@pytest.mark.parametrize("multidim", (True, False)) -def test_waic(centered_eight, multidim_models, scale, multidim): - """Test widely available information criterion calculation""" - if multidim: - assert waic(multidim_models.model_1, scale=scale) is not None - waic_pointwise = waic(multidim_models.model_1, pointwise=True, scale=scale) - else: - assert waic(centered_eight, scale=scale) is not None - waic_pointwise = waic(centered_eight, pointwise=True, scale=scale) - assert waic_pointwise is not None - assert "waic_i" in waic_pointwise - - -def test_waic_bad(centered_eight): - """Test widely available information criterion calculation""" - centered_eight = deepcopy(centered_eight) - delattr(centered_eight, "log_likelihood") - with pytest.raises(TypeError): - waic(centered_eight) - - -def test_waic_bad_scale(centered_eight): - """Test widely available information criterion calculation with bad scale.""" - with pytest.raises(TypeError): - waic(centered_eight, scale="bad_value") - - -def test_waic_warning(centered_eight): - centered_eight = deepcopy(centered_eight) - centered_eight.log_likelihood["obs"][:, :250, 1] = 10 - with pytest.warns(UserWarning): - assert waic(centered_eight, pointwise=True) is not None - # this should throw a warning, but due to numerical issues it fails - centered_eight.log_likelihood["obs"][:, :, :] = 0 - with pytest.warns(UserWarning): - assert waic(centered_eight, pointwise=True) is not None - - -@pytest.mark.parametrize("scale", ["log", "negative_log", "deviance"]) -def test_waic_print(centered_eight, scale): - waic_data = repr(waic(centered_eight, scale=scale)) - waic_pointwise = repr(waic(centered_eight, scale=scale, pointwise=True)) - assert waic_data is not None - assert waic_pointwise is not None - assert waic_data == waic_pointwise - - -@pytest.mark.parametrize("scale", ["log", "negative_log", "deviance"]) -@pytest.mark.parametrize("multidim", (True, False)) -def test_loo(centered_eight, multidim_models, scale, multidim): - """Test approximate leave one out criterion calculation""" - if multidim: - assert loo(multidim_models.model_1, scale=scale) is not None - loo_pointwise = loo(multidim_models.model_1, pointwise=True, scale=scale) - else: - assert loo(centered_eight, scale=scale) is not None - loo_pointwise = loo(centered_eight, pointwise=True, scale=scale) - assert loo_pointwise is not None - assert "loo_i" in loo_pointwise - assert "pareto_k" in loo_pointwise - assert "scale" in loo_pointwise - - -def test_loo_one_chain(centered_eight): - centered_eight = deepcopy(centered_eight) - centered_eight.posterior = centered_eight.posterior.drop([1, 2, 3], "chain") - centered_eight.sample_stats = centered_eight.sample_stats.drop([1, 2, 3], "chain") - assert loo(centered_eight) is not None - - -def test_loo_bad(centered_eight): - with pytest.raises(TypeError): - loo(np.random.randn(2, 10)) - - centered_eight = deepcopy(centered_eight) - delattr(centered_eight, "log_likelihood") - with pytest.raises(TypeError): - loo(centered_eight) - - -def test_loo_bad_scale(centered_eight): - """Test loo with bad scale value.""" - with pytest.raises(TypeError): - loo(centered_eight, scale="bad_scale") - - -def test_loo_bad_no_posterior_reff(centered_eight): - loo(centered_eight, reff=None) - centered_eight = deepcopy(centered_eight) - del centered_eight.posterior - with pytest.raises(TypeError): - loo(centered_eight, reff=None) - loo(centered_eight, reff=0.7) - - -def test_loo_warning(centered_eight): - centered_eight = deepcopy(centered_eight) - # make one of the khats infinity - centered_eight.log_likelihood["obs"][:, :, 1] = 10 - with pytest.warns(UserWarning) as records: - assert loo(centered_eight, pointwise=True) is not None - assert any("Estimated shape parameter" in str(record.message) for record in records) - - # make all of the khats infinity - centered_eight.log_likelihood["obs"][:, :, :] = 1 - with pytest.warns(UserWarning) as records: - assert loo(centered_eight, pointwise=True) is not None - assert any("Estimated shape parameter" in str(record.message) for record in records) - - -@pytest.mark.parametrize("scale", ["log", "negative_log", "deviance"]) -def test_loo_print(centered_eight, scale): - loo_data = repr(loo(centered_eight, scale=scale, pointwise=False)) - loo_pointwise = repr(loo(centered_eight, scale=scale, pointwise=True)) - assert loo_data is not None - assert loo_pointwise is not None - assert len(loo_data) < len(loo_pointwise) - - -def test_psislw(centered_eight): - pareto_k = loo(centered_eight, pointwise=True, reff=0.7)["pareto_k"] - log_likelihood = get_log_likelihood(centered_eight) - log_likelihood = log_likelihood.stack(__sample__=("chain", "draw")) - assert_allclose(pareto_k, psislw(-log_likelihood, 0.7)[1]) - - -def test_psislw_smooths_for_low_k(): - # check that log-weights are smoothed even when k < 1/3 - # https://github.com/arviz-devs/arviz/issues/2010 - rng = np.random.default_rng(44) - x = rng.normal(size=100) - x_smoothed, k = psislw(x.copy()) - assert k < 1 / 3 - assert not np.allclose(x - logsumexp(x), x_smoothed) - - -@pytest.mark.parametrize("probs", [True, False]) -@pytest.mark.parametrize("kappa", [-1, -0.5, 1e-30, 0.5, 1]) -@pytest.mark.parametrize("sigma", [0, 2]) -def test_gpinv(probs, kappa, sigma): - if probs: - probs = np.array([0.1, 0.1, 0.1, 0.2, 0.3]) - else: - probs = np.array([-0.1, 0.1, 0.1, 0.2, 0.3]) - assert len(_gpinv(probs, kappa, sigma)) == len(probs) - - -@pytest.mark.parametrize("func", [loo, waic]) -def test_multidimensional_log_likelihood(func): - llm = np.random.rand(4, 23, 15, 2) - ll1 = llm.reshape(4, 23, 15 * 2) - statsm = Dataset(dict(log_likelihood=DataArray(llm, dims=["chain", "draw", "a", "b"]))) - - stats1 = Dataset(dict(log_likelihood=DataArray(ll1, dims=["chain", "draw", "v"]))) - - post = Dataset(dict(mu=DataArray(np.random.rand(4, 23, 2), dims=["chain", "draw", "v"]))) - - dsm = convert_to_inference_data(statsm, group="sample_stats") - ds1 = convert_to_inference_data(stats1, group="sample_stats") - dsp = convert_to_inference_data(post, group="posterior") - - dsm = concat(dsp, dsm) - ds1 = concat(dsp, ds1) - - frm = func(dsm) - fr1 = func(ds1) - - assert all( - fr1[key] == frm[key] for key in fr1.index if key not in {"loo_i", "waic_i", "pareto_k"} - ) - assert_array_almost_equal(frm[:4], fr1[:4]) - - -@pytest.mark.parametrize( - "args", - [ - {"y": "obs"}, - {"y": "obs", "y_hat": "obs"}, - {"y": "arr", "y_hat": "obs"}, - {"y": "obs", "y_hat": "arr"}, - {"y": "arr", "y_hat": "arr"}, - {"y": "obs", "y_hat": "obs", "log_weights": "arr"}, - {"y": "arr", "y_hat": "obs", "log_weights": "arr"}, - {"y": "obs", "y_hat": "arr", "log_weights": "arr"}, - {"idata": False}, - ], -) -def test_loo_pit(centered_eight, args): - y = args.get("y", None) - y_hat = args.get("y_hat", None) - log_weights = args.get("log_weights", None) - y_arr = centered_eight.observed_data.obs - y_hat_arr = centered_eight.posterior_predictive.obs.stack(__sample__=("chain", "draw")) - log_like = get_log_likelihood(centered_eight).stack(__sample__=("chain", "draw")) - n_samples = len(log_like.__sample__) - ess_p = ess(centered_eight.posterior, method="mean") - reff = np.hstack([ess_p[v].values.flatten() for v in ess_p.data_vars]).mean() / n_samples - log_weights_arr = psislw(-log_like, reff=reff)[0] - - if args.get("idata", True): - if y == "arr": - y = y_arr - if y_hat == "arr": - y_hat = y_hat_arr - if log_weights == "arr": - log_weights = log_weights_arr - loo_pit_data = loo_pit(idata=centered_eight, y=y, y_hat=y_hat, log_weights=log_weights) - else: - loo_pit_data = loo_pit(idata=None, y=y_arr, y_hat=y_hat_arr, log_weights=log_weights_arr) - assert np.all((loo_pit_data >= 0) & (loo_pit_data <= 1)) - - -@pytest.mark.parametrize( - "args", - [ - {"y": "y"}, - {"y": "y", "y_hat": "y"}, - {"y": "arr", "y_hat": "y"}, - {"y": "y", "y_hat": "arr"}, - {"y": "arr", "y_hat": "arr"}, - {"y": "y", "y_hat": "y", "log_weights": "arr"}, - {"y": "arr", "y_hat": "y", "log_weights": "arr"}, - {"y": "y", "y_hat": "arr", "log_weights": "arr"}, - {"idata": False}, - ], -) -def test_loo_pit_multidim(multidim_models, args): - y = args.get("y", None) - y_hat = args.get("y_hat", None) - log_weights = args.get("log_weights", None) - idata = multidim_models.model_1 - y_arr = idata.observed_data.y - y_hat_arr = idata.posterior_predictive.y.stack(__sample__=("chain", "draw")) - log_like = get_log_likelihood(idata).stack(__sample__=("chain", "draw")) - n_samples = len(log_like.__sample__) - ess_p = ess(idata.posterior, method="mean") - reff = np.hstack([ess_p[v].values.flatten() for v in ess_p.data_vars]).mean() / n_samples - log_weights_arr = psislw(-log_like, reff=reff)[0] - - if args.get("idata", True): - if y == "arr": - y = y_arr - if y_hat == "arr": - y_hat = y_hat_arr - if log_weights == "arr": - log_weights = log_weights_arr - loo_pit_data = loo_pit(idata=idata, y=y, y_hat=y_hat, log_weights=log_weights) - else: - loo_pit_data = loo_pit(idata=None, y=y_arr, y_hat=y_hat_arr, log_weights=log_weights_arr) - assert np.all((loo_pit_data >= 0) & (loo_pit_data <= 1)) - - -def test_loo_pit_multi_lik(): - rng = np.random.default_rng(0) - post_pred = rng.standard_normal(size=(4, 100, 10)) - obs = np.quantile(post_pred, np.linspace(0, 1, 10)) - obs[0] *= 0.9 - obs[-1] *= 1.1 - idata = from_dict( - posterior={"a": np.random.randn(4, 100)}, - posterior_predictive={"y": post_pred}, - observed_data={"y": obs}, - log_likelihood={"y": -(post_pred**2), "decoy": np.zeros_like(post_pred)}, - ) - loo_pit_data = loo_pit(idata, y="y") - assert np.all((loo_pit_data >= 0) & (loo_pit_data <= 1)) - - -@pytest.mark.parametrize("input_type", ["idataarray", "idatanone_ystr", "yarr_yhatnone"]) -def test_loo_pit_bad_input(centered_eight, input_type): - """Test incompatible input combinations.""" - arr = np.random.random((8, 200)) - if input_type == "idataarray": - with pytest.raises(ValueError, match=r"type InferenceData or None"): - loo_pit(idata=arr, y="obs") - elif input_type == "idatanone_ystr": - with pytest.raises(ValueError, match=r"all 3.+must be array or DataArray"): - loo_pit(idata=None, y="obs") - elif input_type == "yarr_yhatnone": - with pytest.raises(ValueError, match=r"y_hat.+None.+y.+str"): - loo_pit(idata=centered_eight, y=arr, y_hat=None) - - -@pytest.mark.parametrize("arg", ["y", "y_hat", "log_weights"]) -def test_loo_pit_bad_input_type(centered_eight, arg): - """Test wrong input type (not None, str not DataArray.""" - kwargs = {"y": "obs", "y_hat": "obs", "log_weights": None} - kwargs[arg] = 2 # use int instead of array-like - with pytest.raises(ValueError, match=f"not {type(2)}"): - loo_pit(idata=centered_eight, **kwargs) - - -@pytest.mark.parametrize("incompatibility", ["y-y_hat1", "y-y_hat2", "y_hat-log_weights"]) -def test_loo_pit_bad_input_shape(incompatibility): - """Test shape incompatibilities.""" - y = np.random.random(8) - y_hat = np.random.random((8, 200)) - log_weights = np.random.random((8, 200)) - if incompatibility == "y-y_hat1": - with pytest.raises(ValueError, match="1 more dimension"): - loo_pit(y=y, y_hat=y_hat[None, :], log_weights=log_weights) - elif incompatibility == "y-y_hat2": - with pytest.raises(ValueError, match="y has shape"): - loo_pit(y=y, y_hat=y_hat[1:3, :], log_weights=log_weights) - elif incompatibility == "y_hat-log_weights": - with pytest.raises(ValueError, match="must have the same shape"): - loo_pit(y=y, y_hat=y_hat[:, :100], log_weights=log_weights) - - -@pytest.mark.parametrize("pointwise", [True, False]) -@pytest.mark.parametrize("inplace", [True, False]) -@pytest.mark.parametrize( - "kwargs", - [ - {}, - {"group": "posterior_predictive", "var_names": {"posterior_predictive": "obs"}}, - {"group": "observed_data", "var_names": {"both": "obs"}, "out_data_shape": "shape"}, - {"var_names": {"both": "obs", "posterior": ["theta", "mu"]}}, - {"group": "observed_data", "out_name_data": "T_name"}, - ], -) -def test_apply_test_function(centered_eight, pointwise, inplace, kwargs): - """Test some usual call cases of apply_test_function""" - centered_eight = deepcopy(centered_eight) - group = kwargs.get("group", "both") - var_names = kwargs.get("var_names", None) - out_data_shape = kwargs.get("out_data_shape", None) - out_pp_shape = kwargs.get("out_pp_shape", None) - out_name_data = kwargs.get("out_name_data", "T") - if out_data_shape == "shape": - out_data_shape = (8,) if pointwise else () - if out_pp_shape == "shape": - out_pp_shape = (4, 500, 8) if pointwise else (4, 500) - idata = deepcopy(centered_eight) - idata_out = apply_test_function( - idata, - lambda y, theta: np.mean(y), - group=group, - var_names=var_names, - pointwise=pointwise, - out_name_data=out_name_data, - out_data_shape=out_data_shape, - out_pp_shape=out_pp_shape, - ) - if inplace: - assert idata is idata_out - - if group == "both": - test_dict = {"observed_data": ["T"], "posterior_predictive": ["T"]} - else: - test_dict = {group: [kwargs.get("out_name_data", "T")]} - - fails = check_multiple_attrs(test_dict, idata_out) - assert not fails - - -def test_apply_test_function_bad_group(centered_eight): - """Test error when group is an invalid name.""" - with pytest.raises(ValueError, match="Invalid group argument"): - apply_test_function(centered_eight, lambda y, theta: y, group="bad_group") - - -def test_apply_test_function_missing_group(): - """Test error when InferenceData object is missing a required group. - - The function cannot work if group="both" but InferenceData object has no - posterior_predictive group. - """ - idata = from_dict( - posterior={"a": np.random.random((4, 500, 30))}, observed_data={"y": np.random.random(30)} - ) - with pytest.raises(ValueError, match="must have posterior_predictive"): - apply_test_function(idata, lambda y, theta: np.mean, group="both") - - -def test_apply_test_function_should_overwrite_error(centered_eight): - """Test error when overwrite=False but out_name is already a present variable.""" - with pytest.raises(ValueError, match="Should overwrite"): - apply_test_function(centered_eight, lambda y, theta: y, out_name_data="obs") - - -def test_weight_predictions(): - idata0 = from_dict( - posterior_predictive={"a": np.random.normal(-1, 1, 1000)}, observed_data={"a": [1]} - ) - idata1 = from_dict( - posterior_predictive={"a": np.random.normal(1, 1, 1000)}, observed_data={"a": [1]} - ) - - new = weight_predictions([idata0, idata1]) - assert ( - idata1.posterior_predictive.mean() - > new.posterior_predictive.mean() - > idata0.posterior_predictive.mean() - ) - assert "posterior_predictive" in new - assert "observed_data" in new - - new = weight_predictions([idata0, idata1], weights=[0.5, 0.5]) - assert_almost_equal(new.posterior_predictive["a"].mean(), 0, decimal=1) - new = weight_predictions([idata0, idata1], weights=[0.9, 0.1]) - assert_almost_equal(new.posterior_predictive["a"].mean(), -0.8, decimal=1) - - -@pytest.fixture(scope="module") -def psens_data(): - non_centered_eight = load_arviz_data("non_centered_eight") - post = non_centered_eight.posterior - log_prior = { - "mu": XrContinuousRV(norm, 0, 5).logpdf(post["mu"]), - "tau": XrContinuousRV(halfcauchy, scale=5).logpdf(post["tau"]), - "theta_t": XrContinuousRV(norm, 0, 1).logpdf(post["theta_t"]), - } - non_centered_eight.add_groups({"log_prior": log_prior}) - return non_centered_eight - - -@pytest.mark.parametrize("component", ("prior", "likelihood")) -def test_priorsens_global(psens_data, component): - result = psens(psens_data, component=component) - assert "mu" in result - assert "theta" in result - assert "school" in result.theta_t.dims - - -def test_priorsens_var_names(psens_data): - result1 = psens( - psens_data, component="prior", component_var_names=["mu", "tau"], var_names=["mu", "tau"] - ) - result2 = psens(psens_data, component="prior", var_names=["mu", "tau"]) - for result in (result1, result2): - assert "theta" not in result - assert "mu" in result - assert "tau" in result - assert not np.isclose(result1.mu, result2.mu) - - -def test_priorsens_coords(psens_data): - result = psens(psens_data, component="likelihood", component_coords={"school": "Choate"}) - assert "mu" in result - assert "theta" in result - assert "school" in result.theta_t.dims - - -def test_bayes_factor(): - idata = from_dict( - posterior={"a": np.random.normal(1, 0.5, 5000)}, prior={"a": np.random.normal(0, 1, 5000)} - ) - bf_dict0 = bayes_factor(idata, var_name="a", ref_val=0) - bf_dict1 = bayes_factor(idata, prior=np.random.normal(0, 10, 5000), var_name="a", ref_val=0) - assert bf_dict0["BF10"] > bf_dict0["BF01"] - assert bf_dict1["BF10"] < bf_dict1["BF01"] - - -def test_compare_sorting_consistency(): - chains, draws = 4, 1000 - - # Model 1 - good fit - log_lik1 = np.random.normal(-2, 1, size=(chains, draws)) - posterior1 = Dataset( - {"theta": (("chain", "draw"), np.random.normal(0, 1, size=(chains, draws)))}, - coords={"chain": range(chains), "draw": range(draws)}, - ) - log_like1 = Dataset( - {"y": (("chain", "draw"), log_lik1)}, - coords={"chain": range(chains), "draw": range(draws)}, - ) - data1 = InferenceData(posterior=posterior1, log_likelihood=log_like1) - - # Model 2 - poor fit (higher variance) - log_lik2 = np.random.normal(-5, 2, size=(chains, draws)) - posterior2 = Dataset( - {"theta": (("chain", "draw"), np.random.normal(0, 1, size=(chains, draws)))}, - coords={"chain": range(chains), "draw": range(draws)}, - ) - log_like2 = Dataset( - {"y": (("chain", "draw"), log_lik2)}, - coords={"chain": range(chains), "draw": range(draws)}, - ) - data2 = InferenceData(posterior=posterior2, log_likelihood=log_like2) - - # Compare models in different orders - comp_dict1 = {"M1": data1, "M2": data2} - comp_dict2 = {"M2": data2, "M1": data1} - - comparison1 = compare(comp_dict1, method="bb-pseudo-bma") - comparison2 = compare(comp_dict2, method="bb-pseudo-bma") - - assert comparison1.index.tolist() == comparison2.index.tolist() - - se1 = comparison1["se"].values - se2 = comparison2["se"].values - np.testing.assert_array_almost_equal(se1, se2) diff --git a/arviz/tests/base_tests/test_stats_ecdf_utils.py b/arviz/tests/base_tests/test_stats_ecdf_utils.py deleted file mode 100644 index aaf7d95501..0000000000 --- a/arviz/tests/base_tests/test_stats_ecdf_utils.py +++ /dev/null @@ -1,166 +0,0 @@ -import pytest - -import numpy as np -import scipy.stats -from ...stats.ecdf_utils import ( - compute_ecdf, - ecdf_confidence_band, - _get_ecdf_points, - _simulate_ecdf, - _get_pointwise_confidence_band, -) - -try: - import numba # pylint: disable=unused-import - - numba_options = [True, False] # pylint: disable=invalid-name -except ImportError: - numba_options = [False] # pylint: disable=invalid-name - - -def test_compute_ecdf(): - """Test compute_ecdf function.""" - sample = np.array([1, 2, 3, 3, 4, 5]) - eval_points = np.arange(0, 7, 0.1) - ecdf_expected = (sample[:, None] <= eval_points).mean(axis=0) - assert np.allclose(compute_ecdf(sample, eval_points), ecdf_expected) - assert np.allclose(compute_ecdf(sample / 2 + 10, eval_points / 2 + 10), ecdf_expected) - - -@pytest.mark.parametrize("difference", [True, False]) -def test_get_ecdf_points(difference): - """Test _get_ecdf_points.""" - # if first point already outside support, no need to insert it - sample = np.array([1, 2, 3, 3, 4, 5, 5]) - eval_points = np.arange(-1, 7, 0.1) - x, y = _get_ecdf_points(sample, eval_points, difference) - assert np.array_equal(x, eval_points) - assert np.array_equal(y, compute_ecdf(sample, eval_points)) - - # if first point is inside support, insert it if not in difference mode - eval_points = np.arange(1, 6, 0.1) - x, y = _get_ecdf_points(sample, eval_points, difference) - assert len(x) == len(eval_points) + 1 - difference - assert len(y) == len(eval_points) + 1 - difference - - # if not in difference mode, first point should be (eval_points[0], 0) - if not difference: - assert x[0] == eval_points[0] - assert y[0] == 0 - assert np.allclose(x[1:], eval_points) - assert np.allclose(y[1:], compute_ecdf(sample, eval_points)) - assert x[-1] == eval_points[-1] - assert y[-1] == 1 - - -@pytest.mark.parametrize( - "dist", [scipy.stats.norm(3, 10), scipy.stats.binom(10, 0.5)], ids=["continuous", "discrete"] -) -@pytest.mark.parametrize("seed", [32, 87]) -def test_simulate_ecdf(dist, seed): - """Test _simulate_ecdf.""" - ndraws = 1000 - eval_points = np.arange(0, 1, 0.1) - - rvs = dist.rvs - - random_state = np.random.default_rng(seed) - ecdf = _simulate_ecdf(ndraws, eval_points, rvs, random_state=random_state) - random_state = np.random.default_rng(seed) - ecdf_expected = compute_ecdf(np.sort(rvs(ndraws, random_state=random_state)), eval_points) - - assert np.allclose(ecdf, ecdf_expected) - - -@pytest.mark.parametrize("prob", [0.8, 0.9]) -@pytest.mark.parametrize( - "dist", [scipy.stats.norm(3, 10), scipy.stats.poisson(100)], ids=["continuous", "discrete"] -) -@pytest.mark.parametrize("ndraws", [10_000]) -def test_get_pointwise_confidence_band(dist, prob, ndraws, num_trials=1_000, seed=57): - """Test _get_pointwise_confidence_band.""" - eval_points = np.linspace(*dist.interval(0.99), 10) - cdf_at_eval_points = dist.cdf(eval_points) - - ecdf_lower, ecdf_upper = _get_pointwise_confidence_band(prob, ndraws, cdf_at_eval_points) - - # check basic properties - assert np.all(ecdf_lower >= 0) - assert np.all(ecdf_upper <= 1) - assert np.all(ecdf_lower <= ecdf_upper) - - # use simulation to estimate lower and upper bounds on pointwise probability - in_interval = [] - random_state = np.random.default_rng(seed) - for _ in range(num_trials): - ecdf = _simulate_ecdf(ndraws, eval_points, dist.rvs, random_state=random_state) - in_interval.append((ecdf_lower <= ecdf) & (ecdf < ecdf_upper)) - asymptotic_dist = scipy.stats.norm( - np.mean(in_interval, axis=0), scipy.stats.sem(in_interval, axis=0) - ) - prob_lower, prob_upper = asymptotic_dist.interval(0.999) - - # check target probability within all bounds - assert np.all(prob_lower <= prob) - assert np.all(prob <= prob_upper) - - -@pytest.mark.parametrize("prob", [0.8, 0.9]) -@pytest.mark.parametrize( - "dist, rvs", - [ - (scipy.stats.norm(3, 10), scipy.stats.norm(3, 10).rvs), - (scipy.stats.norm(3, 10), None), - (scipy.stats.poisson(100), scipy.stats.poisson(100).rvs), - ], - ids=["continuous", "continuous default rvs", "discrete"], -) -@pytest.mark.parametrize("ndraws", [10_000]) -@pytest.mark.parametrize("method", ["pointwise", "optimized", "simulated"]) -@pytest.mark.parametrize("use_numba", numba_options) -def test_ecdf_confidence_band( - dist, rvs, prob, ndraws, method, use_numba, num_trials=1_000, seed=57 -): - """Test test_ecdf_confidence_band.""" - if use_numba and method != "optimized": - pytest.skip("Numba only used in optimized method") - - eval_points = np.linspace(*dist.interval(0.99), 10) - cdf_at_eval_points = dist.cdf(eval_points) - random_state = np.random.default_rng(seed) - - ecdf_lower, ecdf_upper = ecdf_confidence_band( - ndraws, - eval_points, - cdf_at_eval_points, - prob=prob, - rvs=rvs, - random_state=random_state, - method=method, - ) - - if method == "pointwise": - # these values tested elsewhere, we just make sure they're the same - ecdf_lower_pointwise, ecdf_upper_pointwise = _get_pointwise_confidence_band( - prob, ndraws, cdf_at_eval_points - ) - assert np.array_equal(ecdf_lower, ecdf_lower_pointwise) - assert np.array_equal(ecdf_upper, ecdf_upper_pointwise) - return - - # check basic properties - assert np.all(ecdf_lower >= 0) - assert np.all(ecdf_upper <= 1) - assert np.all(ecdf_lower <= ecdf_upper) - - # use simulation to estimate lower and upper bounds on simultaneous probability - in_envelope = [] - random_state = np.random.default_rng(seed) - for _ in range(num_trials): - ecdf = _simulate_ecdf(ndraws, eval_points, dist.rvs, random_state=random_state) - in_envelope.append(np.all(ecdf_lower <= ecdf) & np.all(ecdf < ecdf_upper)) - asymptotic_dist = scipy.stats.norm(np.mean(in_envelope), scipy.stats.sem(in_envelope)) - prob_lower, prob_upper = asymptotic_dist.interval(0.999) - - # check target probability within bounds - assert prob_lower <= prob <= prob_upper diff --git a/arviz/tests/base_tests/test_stats_numba.py b/arviz/tests/base_tests/test_stats_numba.py deleted file mode 100644 index 8b76bde113..0000000000 --- a/arviz/tests/base_tests/test_stats_numba.py +++ /dev/null @@ -1,45 +0,0 @@ -# pylint: disable=redefined-outer-name, no-member -import numpy as np -import pytest - -from ...rcparams import rcParams -from ...stats import r2_score, summary -from ...utils import Numba -from ..helpers import ( # pylint: disable=unused-import - check_multiple_attrs, - importorskip, - multidim_models, -) -from .test_stats import centered_eight, non_centered_eight # pylint: disable=unused-import - -numba = importorskip("numba") - -rcParams["data.load"] = "eager" - - -@pytest.mark.parametrize("circ_var_names", [["mu"], None]) -def test_summary_circ_vars(centered_eight, circ_var_names): - assert summary(centered_eight, circ_var_names=circ_var_names) is not None - state = Numba.numba_flag - Numba.disable_numba() - assert summary(centered_eight, circ_var_names=circ_var_names) is not NotImplementedError - Numba.enable_numba() - assert state == Numba.numba_flag - - -def test_numba_stats(): - """Numba test for r2_score""" - state = Numba.numba_flag # Store the current state of Numba - set_1 = np.random.randn(100, 100) - set_2 = np.random.randn(100, 100) - set_3 = np.random.rand(100) - set_4 = np.random.rand(100) - Numba.disable_numba() - non_numba = r2_score(set_1, set_2) - non_numba_one_dimensional = r2_score(set_3, set_4) - Numba.enable_numba() - with_numba = r2_score(set_1, set_2) - with_numba_one_dimensional = r2_score(set_3, set_4) - assert state == Numba.numba_flag # Ensure that initial state = final state - assert np.allclose(non_numba, with_numba) - assert np.allclose(non_numba_one_dimensional, with_numba_one_dimensional) diff --git a/arviz/tests/base_tests/test_stats_utils.py b/arviz/tests/base_tests/test_stats_utils.py deleted file mode 100644 index b16169c55b..0000000000 --- a/arviz/tests/base_tests/test_stats_utils.py +++ /dev/null @@ -1,384 +0,0 @@ -"""Tests for stats_utils.""" - -# pylint: disable=no-member -import numpy as np -import pytest -from numpy.testing import assert_array_almost_equal -from scipy.special import logsumexp -from scipy.stats import circstd - -from ...data import from_dict, load_arviz_data -from ...stats.density_utils import histogram -from ...stats.stats_utils import ( - ELPDData, - _angle, - _circfunc, - _circular_standard_deviation, - _sqrt, - get_log_likelihood, -) -from ...stats.stats_utils import logsumexp as _logsumexp -from ...stats.stats_utils import make_ufunc, not_valid, stats_variance_2d, wrap_xarray_ufunc - - -@pytest.mark.parametrize("ary_dtype", [np.float64, np.float32, np.int32, np.int64]) -@pytest.mark.parametrize("axis", [None, 0, 1, (-2, -1)]) -@pytest.mark.parametrize("b", [None, 0, 1 / 100, 1 / 101]) -@pytest.mark.parametrize("keepdims", [True, False]) -def test_logsumexp_b(ary_dtype, axis, b, keepdims): - """Test ArviZ implementation of logsumexp. - - Test also compares against Scipy implementation. - Case where b=None, they are equal. (N=len(ary)) - Second case where b=x, and x is 1/(number of elements), they are almost equal. - - Test tests against b parameter. - """ - ary = np.random.randn(100, 101).astype(ary_dtype) # pylint: disable=no-member - assert _logsumexp(ary=ary, axis=axis, b=b, keepdims=keepdims, copy=True) is not None - ary = ary.copy() - assert _logsumexp(ary=ary, axis=axis, b=b, keepdims=keepdims, copy=False) is not None - out = np.empty(5) - assert _logsumexp(ary=np.random.randn(10, 5), axis=0, out=out) is not None - - # Scipy implementation - scipy_results = logsumexp(ary, b=b, axis=axis, keepdims=keepdims) - arviz_results = _logsumexp(ary, b=b, axis=axis, keepdims=keepdims) - - assert_array_almost_equal(scipy_results, arviz_results) - - -@pytest.mark.parametrize("ary_dtype", [np.float64, np.float32, np.int32, np.int64]) -@pytest.mark.parametrize("axis", [None, 0, 1, (-2, -1)]) -@pytest.mark.parametrize("b_inv", [None, 0, 100, 101]) -@pytest.mark.parametrize("keepdims", [True, False]) -def test_logsumexp_b_inv(ary_dtype, axis, b_inv, keepdims): - """Test ArviZ implementation of logsumexp. - - Test also compares against Scipy implementation. - Case where b=None, they are equal. (N=len(ary)) - Second case where b=x, and x is 1/(number of elements), they are almost equal. - - Test tests against b_inv parameter. - """ - ary = np.random.randn(100, 101).astype(ary_dtype) # pylint: disable=no-member - assert _logsumexp(ary=ary, axis=axis, b_inv=b_inv, keepdims=keepdims, copy=True) is not None - ary = ary.copy() - assert _logsumexp(ary=ary, axis=axis, b_inv=b_inv, keepdims=keepdims, copy=False) is not None - out = np.empty(5) - assert _logsumexp(ary=np.random.randn(10, 5), axis=0, out=out) is not None - - if b_inv != 0: - # Scipy implementation when b_inv != 0 - b_scipy = 1 / b_inv if b_inv is not None else None - scipy_results = logsumexp(ary, b=b_scipy, axis=axis, keepdims=keepdims) - arviz_results = _logsumexp(ary, b_inv=b_inv, axis=axis, keepdims=keepdims) - - assert_array_almost_equal(scipy_results, arviz_results) - - -@pytest.mark.parametrize("quantile", ((0.5,), (0.5, 0.1))) -@pytest.mark.parametrize("arg", (True, False)) -def test_wrap_ufunc_output(quantile, arg): - ary = np.random.randn(4, 100) - n_output = len(quantile) - if arg: - res = wrap_xarray_ufunc( - np.quantile, ary, ufunc_kwargs={"n_output": n_output}, func_args=(quantile,) - ) - elif n_output == 1: - res = wrap_xarray_ufunc(np.quantile, ary, func_kwargs={"q": quantile}) - else: - res = wrap_xarray_ufunc( - np.quantile, ary, ufunc_kwargs={"n_output": n_output}, func_kwargs={"q": quantile} - ) - if n_output == 1: - assert not isinstance(res, tuple) - else: - assert isinstance(res, tuple) - assert len(res) == n_output - - -@pytest.mark.parametrize("out_shape", ((1, 2), (1, 2, 3), (2, 3, 4, 5))) -@pytest.mark.parametrize("input_dim", ((4, 100), (4, 100, 3), (4, 100, 4, 5))) -def test_wrap_ufunc_out_shape(out_shape, input_dim): - func = lambda x: np.random.rand(*out_shape) - ary = np.ones(input_dim) - res = wrap_xarray_ufunc( - func, ary, func_kwargs={"out_shape": out_shape}, ufunc_kwargs={"n_dims": 1} - ) - assert res.shape == (*ary.shape[:-1], *out_shape) - - -def test_wrap_ufunc_out_shape_multi_input(): - out_shape = (2, 4) - func = lambda x, y: np.random.rand(*out_shape) - ary1 = np.ones((4, 100)) - ary2 = np.ones((4, 5)) - res = wrap_xarray_ufunc( - func, ary1, ary2, func_kwargs={"out_shape": out_shape}, ufunc_kwargs={"n_dims": 1} - ) - assert res.shape == (*ary1.shape[:-1], *out_shape) - - -def test_wrap_ufunc_out_shape_multi_output_same(): - func = lambda x: (np.random.rand(1, 2), np.random.rand(1, 2)) - ary = np.ones((4, 100)) - res1, res2 = wrap_xarray_ufunc( - func, - ary, - func_kwargs={"out_shape": ((1, 2), (1, 2))}, - ufunc_kwargs={"n_dims": 1, "n_output": 2}, - ) - assert res1.shape == (*ary.shape[:-1], 1, 2) - assert res2.shape == (*ary.shape[:-1], 1, 2) - - -def test_wrap_ufunc_out_shape_multi_output_diff(): - func = lambda x: (np.random.rand(5, 3), np.random.rand(10, 4)) - ary = np.ones((4, 100)) - res1, res2 = wrap_xarray_ufunc( - func, - ary, - func_kwargs={"out_shape": ((5, 3), (10, 4))}, - ufunc_kwargs={"n_dims": 1, "n_output": 2}, - ) - assert res1.shape == (*ary.shape[:-1], 5, 3) - assert res2.shape == (*ary.shape[:-1], 10, 4) - - -@pytest.mark.parametrize("n_output", (1, 2, 3)) -def test_make_ufunc(n_output): - if n_output == 3: - func = lambda x: (np.mean(x), np.mean(x), np.mean(x)) - elif n_output == 2: - func = lambda x: (np.mean(x), np.mean(x)) - else: - func = np.mean - ufunc = make_ufunc(func, n_dims=1, n_output=n_output) - ary = np.ones((4, 100)) - res = ufunc(ary) - if n_output > 1: - assert all(len(res_i) == 4 for res_i in res) - assert all((res_i == 1).all() for res_i in res) - else: - assert len(res) == 4 - assert (res == 1).all() - - -@pytest.mark.parametrize("n_output", (1, 2, 3)) -def test_make_ufunc_out(n_output): - if n_output == 3: - func = lambda x: (np.mean(x), np.mean(x), np.mean(x)) - res = (np.empty((4,)), np.empty((4,)), np.empty((4,))) - elif n_output == 2: - func = lambda x: (np.mean(x), np.mean(x)) - res = (np.empty((4,)), np.empty((4,))) - else: - func = np.mean - res = np.empty((4,)) - ufunc = make_ufunc(func, n_dims=1, n_output=n_output) - ary = np.ones((4, 100)) - ufunc(ary, out=res) - if n_output > 1: - assert all(len(res_i) == 4 for res_i in res) - assert all((res_i == 1).all() for res_i in res) - else: - assert len(res) == 4 - assert (res == 1).all() - - -def test_make_ufunc_bad_ndim(): - with pytest.raises(TypeError): - make_ufunc(np.mean, n_dims=0) - - -@pytest.mark.parametrize("n_output", (1, 2, 3)) -def test_make_ufunc_out_bad(n_output): - if n_output == 3: - func = lambda x: (np.mean(x), np.mean(x), np.mean(x)) - res = (np.empty((100,)), np.empty((100,))) - elif n_output == 2: - func = lambda x: (np.mean(x), np.mean(x)) - res = np.empty((100,)) - else: - func = np.mean - res = np.empty((100,)) - ufunc = make_ufunc(func, n_dims=1, n_output=n_output) - ary = np.ones((4, 100)) - with pytest.raises(TypeError): - ufunc(ary, out=res) - - -@pytest.mark.parametrize("how", ("all", "any")) -def test_nan(how): - assert not not_valid(np.ones(10), check_shape=False, nan_kwargs=dict(how=how)) - if how == "any": - assert not_valid( - np.concatenate((np.random.randn(100), np.full(2, np.nan))), - check_shape=False, - nan_kwargs=dict(how=how), - ) - else: - assert not not_valid( - np.concatenate((np.random.randn(100), np.full(2, np.nan))), - check_shape=False, - nan_kwargs=dict(how=how), - ) - assert not_valid(np.full(10, np.nan), check_shape=False, nan_kwargs=dict(how=how)) - - -@pytest.mark.parametrize("axis", (-1, 0, 1)) -def test_nan_axis(axis): - data = np.random.randn(4, 100) - data[0, 0] = np.nan # pylint: disable=unsupported-assignment-operation - axis_ = (len(data.shape) + axis) if axis < 0 else axis - assert not_valid(data, check_shape=False, nan_kwargs=dict(how="any")) - assert not_valid(data, check_shape=False, nan_kwargs=dict(how="any", axis=axis)).any() - assert not not_valid(data, check_shape=False, nan_kwargs=dict(how="any", axis=axis)).all() - assert not_valid(data, check_shape=False, nan_kwargs=dict(how="any", axis=axis)).shape == tuple( - dim for ax, dim in enumerate(data.shape) if ax != axis_ - ) - - -def test_valid_shape(): - assert not not_valid( - np.ones((2, 200)), check_nan=False, shape_kwargs=dict(min_chains=2, min_draws=100) - ) - assert not not_valid( - np.ones((200, 2)), check_nan=False, shape_kwargs=dict(min_chains=100, min_draws=2) - ) - assert not_valid( - np.ones((10, 10)), check_nan=False, shape_kwargs=dict(min_chains=2, min_draws=100) - ) - assert not_valid( - np.ones((10, 10)), check_nan=False, shape_kwargs=dict(min_chains=100, min_draws=2) - ) - - -def test_get_log_likelihood(): - idata = from_dict( - log_likelihood={ - "y1": np.random.normal(size=(4, 100, 6)), - "y2": np.random.normal(size=(4, 100, 8)), - } - ) - lik1 = get_log_likelihood(idata, "y1") - lik2 = get_log_likelihood(idata, "y2") - assert lik1.shape == (4, 100, 6) - assert lik2.shape == (4, 100, 8) - - -def test_get_log_likelihood_warning(): - idata = from_dict( - sample_stats={ - "log_likelihood": np.random.normal(size=(4, 100, 6)), - } - ) - with pytest.warns(DeprecationWarning): - get_log_likelihood(idata) - - -def test_get_log_likelihood_no_var_name(): - idata = from_dict( - log_likelihood={ - "y1": np.random.normal(size=(4, 100, 6)), - "y2": np.random.normal(size=(4, 100, 8)), - } - ) - with pytest.raises(TypeError, match="Found several"): - get_log_likelihood(idata) - - -def test_get_log_likelihood_no_group(): - idata = from_dict( - posterior={ - "a": np.random.normal(size=(4, 100)), - "b": np.random.normal(size=(4, 100)), - } - ) - with pytest.raises(TypeError, match="log likelihood not found"): - get_log_likelihood(idata) - - -def test_elpd_data_error(): - with pytest.raises(IndexError): - repr(ELPDData(data=[0, 1, 2], index=["not IC", "se", "p"])) - - -def test_stats_variance_1d(): - """Test for stats_variance_1d.""" - data = np.random.rand(1000000) - assert np.allclose(np.var(data), stats_variance_2d(data)) - assert np.allclose(np.var(data, ddof=1), stats_variance_2d(data, ddof=1)) - - -def test_stats_variance_2d(): - """Test for stats_variance_2d.""" - data_1 = np.random.randn(1000, 1000) - data_2 = np.random.randn(1000000) - school = load_arviz_data("centered_eight").posterior["mu"].values - n_school = load_arviz_data("non_centered_eight").posterior["mu"].values - assert np.allclose(np.var(school, ddof=1, axis=1), stats_variance_2d(school, ddof=1, axis=1)) - assert np.allclose(np.var(school, ddof=1, axis=0), stats_variance_2d(school, ddof=1, axis=0)) - assert np.allclose( - np.var(n_school, ddof=1, axis=1), stats_variance_2d(n_school, ddof=1, axis=1) - ) - assert np.allclose( - np.var(n_school, ddof=1, axis=0), stats_variance_2d(n_school, ddof=1, axis=0) - ) - assert np.allclose(np.var(data_2), stats_variance_2d(data_2)) - assert np.allclose(np.var(data_2, ddof=1), stats_variance_2d(data_2, ddof=1)) - assert np.allclose(np.var(data_1, axis=0), stats_variance_2d(data_1, axis=0)) - assert np.allclose(np.var(data_1, axis=1), stats_variance_2d(data_1, axis=1)) - assert np.allclose(np.var(data_1, axis=0, ddof=1), stats_variance_2d(data_1, axis=0, ddof=1)) - assert np.allclose(np.var(data_1, axis=1, ddof=1), stats_variance_2d(data_1, axis=1, ddof=1)) - - -def test_variance_bad_data(): - """Test for variance when the data range is extremely wide.""" - data = np.array([1e20, 200e-10, 1e-17, 432e9, 2500432, 23e5, 16e-7]) - assert np.allclose(stats_variance_2d(data), np.var(data)) - assert np.allclose(stats_variance_2d(data, ddof=1), np.var(data, ddof=1)) - assert not np.allclose(stats_variance_2d(data), np.var(data, ddof=1)) - - -def test_histogram(): - school = load_arviz_data("non_centered_eight").posterior["mu"].values - k_count_az, k_dens_az, _ = histogram(school, bins=np.asarray([-np.inf, 0.5, 0.7, 1, np.inf])) - k_dens_np, *_ = np.histogram(school, bins=[-np.inf, 0.5, 0.7, 1, np.inf], density=True) - k_count_np, *_ = np.histogram(school, bins=[-np.inf, 0.5, 0.7, 1, np.inf], density=False) - assert np.allclose(k_count_az, k_count_np) - assert np.allclose(k_dens_az, k_dens_np) - - -def test_sqrt(): - x = np.random.rand(100) - y = np.random.rand(100) - assert np.allclose(_sqrt(x, y), np.sqrt(x + y)) - - -def test_angle(): - x = np.random.randn(100) - high = 8 - low = 4 - res = (x - low) * 2 * np.pi / (high - low) - assert np.allclose(_angle(x, low, high, np.pi), res) - - -def test_circfunc(): - school = load_arviz_data("centered_eight").posterior["mu"].values - a_a = _circfunc(school, 8, 4, skipna=False) - assert np.allclose(a_a, _angle(school, 4, 8, np.pi)) - - -@pytest.mark.parametrize( - "data", (np.random.randn(100), np.random.randn(100, 100), np.random.randn(100, 100, 100)) -) -def test_circular_standard_deviation_1d(data): - high = 8 - low = 4 - assert np.allclose( - _circular_standard_deviation(data, high=high, low=low), - circstd(data, high=high, low=low), - ) diff --git a/arviz/tests/base_tests/test_utils.py b/arviz/tests/base_tests/test_utils.py deleted file mode 100644 index 4f03b43afe..0000000000 --- a/arviz/tests/base_tests/test_utils.py +++ /dev/null @@ -1,376 +0,0 @@ -"""Tests for arviz.utils.""" - -# pylint: disable=redefined-outer-name, no-member -from unittest.mock import Mock - -import numpy as np -import pytest -import scipy.stats as st - -from ...data import dict_to_dataset, from_dict, load_arviz_data -from ...stats.density_utils import _circular_mean, _normalize_angle, _find_hdi_contours -from ...utils import ( - _stack, - _subset_list, - _var_names, - expand_dims, - flatten_inference_data_to_dict, - one_de, - two_de, -) -from ..helpers import RandomVariableTestClass - - -@pytest.fixture(scope="session") -def inference_data(): - centered_eight = load_arviz_data("centered_eight") - return centered_eight - - -@pytest.fixture(scope="session") -def data(): - centered_eight = load_arviz_data("centered_eight") - return centered_eight.posterior - - -@pytest.mark.parametrize( - "var_names_expected", - [ - ("mu", ["mu"]), - (None, None), - (["mu", "tau"], ["mu", "tau"]), - ("~mu", ["theta", "tau"]), - (["~mu"], ["theta", "tau"]), - ], -) -def test_var_names(var_names_expected, data): - """Test var_name handling""" - var_names, expected = var_names_expected - assert _var_names(var_names, data) == expected - - -def test_var_names_warning(): - """Test confusing var_name handling""" - data = from_dict( - posterior={ - "~mu": np.random.randn(2, 10), - "mu": -np.random.randn(2, 10), # pylint: disable=invalid-unary-operand-type - "theta": np.random.randn(2, 10, 8), - } - ).posterior - var_names = expected = ["~mu"] - with pytest.warns(UserWarning): - assert _var_names(var_names, data) == expected - - -def test_var_names_key_error(data): - with pytest.raises(KeyError, match="bad_var_name"): - _var_names(("theta", "tau", "bad_var_name"), data) - - -@pytest.mark.parametrize( - "var_args", - [ - (["ta"], ["beta1", "beta2", "theta"], "like"), - (["~beta"], ["phi", "theta"], "like"), - (["beta[0-9]+"], ["beta1", "beta2"], "regex"), - (["^p"], ["phi"], "regex"), - (["~^t"], ["beta1", "beta2", "phi"], "regex"), - ], -) -def test_var_names_filter_multiple_input(var_args): - samples = np.random.randn(10) - data1 = dict_to_dataset({"beta1": samples, "beta2": samples, "phi": samples}) - data2 = dict_to_dataset({"beta1": samples, "beta2": samples, "theta": samples}) - data = [data1, data2] - var_names, expected, filter_vars = var_args - assert _var_names(var_names, data, filter_vars) == expected - - -@pytest.mark.parametrize( - "var_args", - [ - (["alpha", "beta"], ["alpha", "beta1", "beta2"], "like"), - (["~beta"], ["alpha", "p1", "p2", "phi", "theta", "theta_t"], "like"), - (["theta"], ["theta", "theta_t"], "like"), - (["~theta"], ["alpha", "beta1", "beta2", "p1", "p2", "phi"], "like"), - (["p"], ["alpha", "p1", "p2", "phi"], "like"), - (["~p"], ["beta1", "beta2", "theta", "theta_t"], "like"), - (["^bet"], ["beta1", "beta2"], "regex"), - (["^p"], ["p1", "p2", "phi"], "regex"), - (["~^p"], ["alpha", "beta1", "beta2", "theta", "theta_t"], "regex"), - (["p[0-9]+"], ["p1", "p2"], "regex"), - (["~p[0-9]+"], ["alpha", "beta1", "beta2", "phi", "theta", "theta_t"], "regex"), - ], -) -def test_var_names_filter(var_args): - """Test var_names filter with partial naming or regular expressions.""" - samples = np.random.randn(10) - data = dict_to_dataset( - { - "alpha": samples, - "beta1": samples, - "beta2": samples, - "p1": samples, - "p2": samples, - "phi": samples, - "theta": samples, - "theta_t": samples, - } - ) - var_names, expected, filter_vars = var_args - assert _var_names(var_names, data, filter_vars) == expected - - -def test_nonstring_var_names(): - """Check that non-string variables are preserved""" - mu = RandomVariableTestClass("mu") - samples = np.random.randn(10) - data = dict_to_dataset({mu: samples}) - assert _var_names([mu], data) == [mu] - - -def test_var_names_filter_invalid_argument(): - """Check invalid argument raises.""" - samples = np.random.randn(10) - data = dict_to_dataset({"alpha": samples}) - msg = r"^\'filter_vars\' can only be None, \'like\', or \'regex\', got: 'foo'$" - with pytest.raises(ValueError, match=msg): - assert _var_names(["alpha"], data, filter_vars="foo") - - -def test_subset_list_negation_not_found(): - """Check there is a warning if negation pattern is ignored""" - names = ["mu", "theta"] - with pytest.warns(UserWarning, match=".+not.+found.+"): - assert _subset_list("~tau", names) == names - - -@pytest.fixture(scope="function") -def utils_with_numba_import_fail(monkeypatch): - """Patch numba in utils so when its imported it raises ImportError""" - failed_import = Mock() - failed_import.side_effect = ImportError - - from ... import utils - - monkeypatch.setattr(utils.importlib, "import_module", failed_import) - return utils - - -def test_conditional_jit_decorator_no_numba(utils_with_numba_import_fail): - """Tests to see if Numba jit code block is skipped with Import Failure - - Test can be distinguished from test_conditional_jit__numba_decorator - by use of debugger or coverage tool - """ - - @utils_with_numba_import_fail.conditional_jit - def func(): - return "Numba not used" - - assert func() == "Numba not used" - - -def test_conditional_vect_decorator_no_numba(utils_with_numba_import_fail): - """Tests to see if Numba vectorize code block is skipped with Import Failure - - Test can be distinguished from test_conditional_vect__numba_decorator - by use of debugger or coverage tool - """ - - @utils_with_numba_import_fail.conditional_vect - def func(): - return "Numba not used" - - assert func() == "Numba not used" - - -def test_conditional_jit_numba_decorator(): - """Tests to see if Numba is used. - - Test can be distinguished from test_conditional_jit_decorator_no_numba - by use of debugger or coverage tool - """ - from ... import utils - - @utils.conditional_jit - def func(): - return True - - assert func() - - -def test_conditional_vect_numba_decorator(): - """Tests to see if Numba is used. - - Test can be distinguished from test_conditional_jit_decorator_no_numba - by use of debugger or coverage tool - """ - from ... import utils - - @utils.conditional_vect - def func(a_a, b_b): - return a_a + b_b - - value_one = np.random.randn(10) - value_two = np.random.randn(10) - assert np.allclose(func(value_one, value_two), value_one + value_two) - - -def test_conditional_vect_numba_decorator_keyword(monkeypatch): - """Checks else statement and vect keyword argument""" - from ... import utils - - # Mock import lib to return numba with hit method which returns a function that returns kwargs - numba_mock = Mock() - monkeypatch.setattr(utils.importlib, "import_module", lambda x: numba_mock) - - def vectorize(**kwargs): - """overwrite numba.vectorize function""" - return lambda x: (x(), kwargs) - - numba_mock.vectorize = vectorize - - @utils.conditional_vect(keyword_argument="A keyword argument") - def placeholder_func(): - """This function does nothing""" - return "output" - - # pylint: disable=unpacking-non-sequence - function_results, wrapper_result = placeholder_func - assert wrapper_result == {"keyword_argument": "A keyword argument"} - assert function_results == "output" - - -def test_stack(): - x = np.random.randn(10, 4, 6) - y = np.random.randn(100, 4, 6) - assert x.shape[1:] == y.shape[1:] - assert np.allclose(np.vstack((x, y)), _stack(x, y)) - assert _stack - - -@pytest.mark.parametrize("data", [np.random.randn(1000), np.random.randn(1000).tolist()]) -def test_two_de(data): - """Test to check for custom atleast_2d. List added to test for a non ndarray case.""" - assert np.allclose(two_de(data), np.atleast_2d(data)) - - -@pytest.mark.parametrize("data", [np.random.randn(100), np.random.randn(100).tolist()]) -def test_one_de(data): - """Test to check for custom atleast_1d. List added to test for a non ndarray case.""" - assert np.allclose(one_de(data), np.atleast_1d(data)) - - -@pytest.mark.parametrize("data", [np.random.randn(100), np.random.randn(100).tolist()]) -def test_expand_dims(data): - """Test to check for custom expand_dims. List added to test for a non ndarray case.""" - assert np.allclose(expand_dims(data), np.expand_dims(data, 0)) - - -@pytest.mark.parametrize("var_names", [None, "mu", ["mu", "tau"]]) -@pytest.mark.parametrize( - "groups", [None, "posterior_groups", "prior_groups", ["posterior", "sample_stats"]] -) -@pytest.mark.parametrize("dimensions", [None, "draw", ["chain", "draw"]]) -@pytest.mark.parametrize("group_info", [True, False]) -@pytest.mark.parametrize( - "var_name_format", [None, "brackets", "underscore", "cds", ((",", "[", "]"), ("_", ""))] -) -@pytest.mark.parametrize("index_origin", [None, 0, 1]) -def test_flatten_inference_data_to_dict( - inference_data, var_names, groups, dimensions, group_info, var_name_format, index_origin -): - """Test flattening (stacking) inference data (subgroups) for dictionary.""" - res_dict = flatten_inference_data_to_dict( - data=inference_data, - var_names=var_names, - groups=groups, - dimensions=dimensions, - group_info=group_info, - var_name_format=var_name_format, - index_origin=index_origin, - ) - assert res_dict - assert "draw" in res_dict - assert any("mu" in item for item in res_dict) - if group_info: - if groups != "prior_groups": - assert any("posterior" in item for item in res_dict) - if var_names is None: - assert any("sample_stats" in item for item in res_dict) - else: - assert any("prior" in item for item in res_dict) - elif groups == "prior_groups": - assert all("prior" not in item for item in res_dict) - - else: - assert all("posterior" not in item for item in res_dict) - if var_names is None: - assert all("sample_stats" not in item for item in res_dict) - - -@pytest.mark.parametrize("mean", [0, np.pi, 4 * np.pi, -2 * np.pi, -10 * np.pi]) -def test_circular_mean_scipy(mean): - """Test our `_circular_mean()` function gives same result than Scipy version.""" - rvs = st.vonmises.rvs(loc=mean, kappa=1, size=1000) - mean_az = _circular_mean(rvs) - mean_sp = st.circmean(rvs, low=-np.pi, high=np.pi) - np.testing.assert_almost_equal(mean_az, mean_sp) - - -@pytest.mark.parametrize("mean", [0, np.pi, 4 * np.pi, -2 * np.pi, -10 * np.pi]) -def test_normalize_angle(mean): - """Testing _normalize_angles() return values between expected bounds""" - rvs = st.vonmises.rvs(loc=mean, kappa=1, size=1000) - values = _normalize_angle(rvs, zero_centered=True) - assert ((-np.pi <= values) & (values <= np.pi)).all() - - values = _normalize_angle(rvs, zero_centered=False) - assert ((values >= 0) & (values <= 2 * np.pi)).all() - - -@pytest.mark.parametrize("mean", [[0, 0], [1, 1]]) -@pytest.mark.parametrize( - "cov", - [ - np.diag([1, 1]), - np.diag([0.5, 0.5]), - np.diag([0.25, 1]), - np.array([[0.4, 0.2], [0.2, 0.8]]), - ], -) -@pytest.mark.parametrize("contour_sigma", [np.array([1, 2, 3])]) -def test_find_hdi_contours(mean, cov, contour_sigma): - """Test `_find_hdi_contours()` against SciPy's multivariate normal distribution.""" - # Set up scipy distribution - prob_dist = st.multivariate_normal(mean, cov) - - # Find standard deviations and eigenvectors - eigenvals, eigenvecs = np.linalg.eig(cov) - eigenvecs = eigenvecs.T - stdevs = np.sqrt(eigenvals) - - # Find min and max for grid at 7-sigma contour - extremes = np.empty((4, 2)) - for i in range(4): - extremes[i] = mean + (-1) ** i * 7 * stdevs[i // 2] * eigenvecs[i // 2] - x_min, y_min = np.amin(extremes, axis=0) - x_max, y_max = np.amax(extremes, axis=0) - - # Create 256x256 grid - x = np.linspace(x_min, x_max, 256) - y = np.linspace(y_min, y_max, 256) - grid = np.dstack(np.meshgrid(x, y)) - - density = prob_dist.pdf(grid) - - contour_sp = np.empty(contour_sigma.shape) - for idx, sigma in enumerate(contour_sigma): - contour_sp[idx] = prob_dist.pdf(mean + sigma * stdevs[0] * eigenvecs[0]) - - hdi_probs = 1 - np.exp(-0.5 * contour_sigma**2) - contour_az = _find_hdi_contours(density, hdi_probs) - - np.testing.assert_allclose(contour_sp, contour_az, rtol=1e-2, atol=1e-4) diff --git a/arviz/tests/base_tests/test_utils_numba.py b/arviz/tests/base_tests/test_utils_numba.py deleted file mode 100644 index b2bdfe291d..0000000000 --- a/arviz/tests/base_tests/test_utils_numba.py +++ /dev/null @@ -1,87 +0,0 @@ -# pylint: disable=redefined-outer-name, no-member -"""Tests for arviz.utils.""" -import importlib -from unittest.mock import Mock - -import numpy as np -import pytest - -from ...stats.stats_utils import stats_variance_2d as svar -from ...utils import Numba, _numba_var, numba_check -from ..helpers import importorskip -from .test_utils import utils_with_numba_import_fail # pylint: disable=unused-import - -importorskip("numba") - - -def test_utils_fixture(utils_with_numba_import_fail): - """Test of utils fixture to ensure mock is applied correctly""" - - # If Numba doesn't exist in dev environment this will raise an ImportError - import numba # pylint: disable=unused-import,W0612 - - with pytest.raises(ImportError): - utils_with_numba_import_fail.importlib.import_module("numba") - - -def test_conditional_jit_numba_decorator_keyword(monkeypatch): - """Checks else statement and JIT keyword argument""" - from ... import utils - - # Mock import lib to return numba with hit method which returns a function that returns kwargs - numba_mock = Mock() - monkeypatch.setattr(utils.importlib, "import_module", lambda x: numba_mock) - - def jit(**kwargs): - """overwrite numba.jit function""" - return lambda fn: lambda: (fn(), kwargs) - - numba_mock.jit = jit - - @utils.conditional_jit(keyword_argument="A keyword argument") - def placeholder_func(): - """This function does nothing""" - return "output" - - # pylint: disable=unpacking-non-sequence - function_results, wrapper_result = placeholder_func() - assert wrapper_result == {"keyword_argument": "A keyword argument", "nopython": True} - assert function_results == "output" - - -def test_numba_check(): - """Test for numba_check""" - numba = importlib.util.find_spec("numba") - flag = numba is not None - assert flag == numba_check() - - -def test_numba_utils(): - """Test for class Numba.""" - flag = Numba.numba_flag - assert flag == numba_check() - Numba.disable_numba() - val = Numba.numba_flag - assert not val - Numba.enable_numba() - val = Numba.numba_flag - assert val - assert flag == Numba.numba_flag - - -@pytest.mark.parametrize("axis", (0, 1)) -@pytest.mark.parametrize("ddof", (0, 1)) -def test_numba_var(axis, ddof): - """Method to test numba_var.""" - flag = Numba.numba_flag - data_1 = np.random.randn(100, 100) - data_2 = np.random.rand(100) - with_numba_1 = _numba_var(svar, np.var, data_1, axis=axis, ddof=ddof) - with_numba_2 = _numba_var(svar, np.var, data_2, ddof=ddof) - Numba.disable_numba() - non_numba_1 = _numba_var(svar, np.var, data_1, axis=axis, ddof=ddof) - non_numba_2 = _numba_var(svar, np.var, data_2, ddof=ddof) - Numba.enable_numba() - assert flag == Numba.numba_flag - assert np.allclose(with_numba_1, non_numba_1) - assert np.allclose(with_numba_2, non_numba_2) diff --git a/arviz/tests/conftest.py b/arviz/tests/conftest.py deleted file mode 100644 index a4710f7abd..0000000000 --- a/arviz/tests/conftest.py +++ /dev/null @@ -1,46 +0,0 @@ -# pylint: disable=redefined-outer-name -"""Configuration for test suite.""" -import logging -import os - -import numpy as np -import pytest - -_log = logging.getLogger(__name__) - - -@pytest.fixture(autouse=True) -def random_seed(): - """Reset numpy random seed generator.""" - np.random.seed(0) - - -def pytest_addoption(parser): - """Definition for command line option to save figures from tests.""" - parser.addoption("--save", nargs="?", const="test_images", help="Save images rendered by plot") - - -@pytest.fixture(scope="session") -def save_figs(request): - """Enable command line switch for saving generation figures upon testing.""" - fig_dir = request.config.getoption("--save") - - if fig_dir is not None: - # Try creating directory if it doesn't exist - _log.info("Saving generated images in %s", fig_dir) - - os.makedirs(fig_dir, exist_ok=True) - _log.info("Directory %s created", fig_dir) - - # Clear all files from the directory - # Does not alter or delete directories - for file in os.listdir(fig_dir): - full_path = os.path.join(fig_dir, file) - - try: - os.remove(full_path) - - except OSError: - _log.info("Failed to remove %s", full_path) - - return fig_dir diff --git a/arviz/tests/external_tests/__init__.py b/arviz/tests/external_tests/__init__.py deleted file mode 100644 index 43bd40001c..0000000000 --- a/arviz/tests/external_tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Backend test suite.""" diff --git a/arviz/tests/external_tests/test_data_beanmachine.py b/arviz/tests/external_tests/test_data_beanmachine.py deleted file mode 100644 index 5f2ab86b6d..0000000000 --- a/arviz/tests/external_tests/test_data_beanmachine.py +++ /dev/null @@ -1,78 +0,0 @@ -# pylint: disable=no-member, invalid-name, redefined-outer-name -import numpy as np -import pytest - -from ...data.io_beanmachine import from_beanmachine # pylint: disable=wrong-import-position -from ..helpers import ( # pylint: disable=unused-import, wrong-import-position - chains, - draws, - eight_schools_params, - importorskip, - load_cached_models, -) - -pytest.skip("Ignore beanmachine tests until it supports pytorch 2", allow_module_level=True) - -# Skip all tests if beanmachine or pytorch not installed -torch = importorskip("torch") -bm = importorskip("beanmachine.ppl") -dist = torch.distributions - - -class TestDataBeanMachine: - @pytest.fixture(scope="class") - def data(self, eight_schools_params, draws, chains): - class Data: - model, prior, obj = load_cached_models( - eight_schools_params, - draws, - chains, - "beanmachine", - )["beanmachine"] - - return Data - - @pytest.fixture(scope="class") - def predictions_data(self, data): - """Generate predictions for predictions_params""" - posterior_samples = data.obj - model = data.model - predictions = bm.inference.predictive.simulate([model.obs()], posterior_samples) - return predictions - - def get_inference_data(self, eight_schools_params, predictions_data): - predictions = predictions_data - return from_beanmachine( - sampler=predictions, - coords={ - "school": np.arange(eight_schools_params["J"]), - "school_pred": np.arange(eight_schools_params["J"]), - }, - ) - - def test_inference_data(self, data, eight_schools_params, predictions_data): - inference_data = self.get_inference_data(eight_schools_params, predictions_data) - model = data.model - mu = model.mu() - tau = model.tau() - eta = model.eta() - obs = model.obs() - - assert mu in inference_data.posterior - assert tau in inference_data.posterior - assert eta in inference_data.posterior - assert obs in inference_data.posterior_predictive - - def test_inference_data_has_log_likelihood_and_observed_data(self, data): - idata = from_beanmachine(data.obj) - obs = data.model.obs() - - assert obs in idata.log_likelihood - assert obs in idata.observed_data - - def test_inference_data_no_posterior(self, data): - model = data.model - # only prior - inference_data = from_beanmachine(data.prior) - assert not model.obs() in inference_data.posterior - assert "observed_data" not in inference_data diff --git a/arviz/tests/external_tests/test_data_cmdstan.py b/arviz/tests/external_tests/test_data_cmdstan.py deleted file mode 100644 index 1ce6e95a36..0000000000 --- a/arviz/tests/external_tests/test_data_cmdstan.py +++ /dev/null @@ -1,398 +0,0 @@ -# pylint: disable=no-member, invalid-name, redefined-outer-name -# pylint: disable=too-many-lines -import os - -import numpy as np -import pytest - -from ... import from_cmdstan - -from ..helpers import check_multiple_attrs - - -class TestDataCmdStan: - @pytest.fixture(scope="session") - def data_directory(self): - here = os.path.dirname(os.path.abspath(__file__)) - data_directory = os.path.join(here, "..", "saved_models") - return data_directory - - @pytest.fixture(scope="class") - def paths(self, data_directory): - paths = { - "no_warmup": [ - os.path.join(data_directory, "cmdstan/output_no_warmup1.csv"), - os.path.join(data_directory, "cmdstan/output_no_warmup2.csv"), - os.path.join(data_directory, "cmdstan/output_no_warmup3.csv"), - os.path.join(data_directory, "cmdstan/output_no_warmup4.csv"), - ], - "warmup": [ - os.path.join(data_directory, "cmdstan/output_warmup1.csv"), - os.path.join(data_directory, "cmdstan/output_warmup2.csv"), - os.path.join(data_directory, "cmdstan/output_warmup3.csv"), - os.path.join(data_directory, "cmdstan/output_warmup4.csv"), - ], - "no_warmup_glob": os.path.join(data_directory, "cmdstan/output_no_warmup[0-9].csv"), - "warmup_glob": os.path.join(data_directory, "cmdstan/output_warmup[0-9].csv"), - "eight_schools_glob": os.path.join( - data_directory, "cmdstan/eight_schools_output[0-9].csv" - ), - "eight_schools": [ - os.path.join(data_directory, "cmdstan/eight_schools_output1.csv"), - os.path.join(data_directory, "cmdstan/eight_schools_output2.csv"), - os.path.join(data_directory, "cmdstan/eight_schools_output3.csv"), - os.path.join(data_directory, "cmdstan/eight_schools_output4.csv"), - ], - } - return paths - - @pytest.fixture(scope="class") - def observed_data_paths(self, data_directory): - observed_data_paths = [ - os.path.join(data_directory, "cmdstan/eight_schools.data.R"), - os.path.join(data_directory, "cmdstan/example_stan.data.R"), - os.path.join(data_directory, "cmdstan/example_stan.json"), - ] - - return observed_data_paths - - def get_inference_data(self, posterior, **kwargs): - return from_cmdstan(posterior=posterior, **kwargs) - - def test_sample_stats(self, paths): - for key, path in paths.items(): - if "missing" in key: - continue - inference_data = self.get_inference_data(path) - assert hasattr(inference_data, "sample_stats") - assert "step_size" in inference_data.sample_stats.attrs - assert inference_data.sample_stats.attrs["step_size"] == "stepsize" - - def test_inference_data_shapes(self, paths): - """Assert that shapes are transformed correctly""" - for key, path in paths.items(): - if "eight" in key or "missing" in key: - continue - inference_data = self.get_inference_data(path) - test_dict = {"posterior": ["x", "y", "Z"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - assert inference_data.posterior["y"].shape == (4, 100) - assert inference_data.posterior["x"].shape == (4, 100, 3) - assert inference_data.posterior["Z"].shape == (4, 100, 4, 6) - dims = ["chain", "draw"] - y_mean_true = 0 - y_mean = inference_data.posterior["y"].mean(dim=dims) - assert np.isclose(y_mean, y_mean_true, atol=1e-1) - x_mean_true = np.array([1, 2, 3]) - x_mean = inference_data.posterior["x"].mean(dim=dims) - assert np.isclose(x_mean, x_mean_true, atol=1e-1).all() - Z_mean_true = np.array([1, 2, 3, 4]) - Z_mean = inference_data.posterior["Z"].mean(dim=dims).mean(axis=1) - assert np.isclose(Z_mean, Z_mean_true, atol=7e-1).all() - assert "comments" in inference_data.posterior.attrs - - def test_inference_data_input_types1(self, paths, observed_data_paths): - """Check input types - - posterior --> str, list of str - prior --> str, list of str - posterior_predictive --> str, variable in posterior - observed_data --> Rdump format - observed_data_var --> str, variable - log_likelihood --> str - coords --> one to many - dims --> one to many - """ - for key, path in paths.items(): - if "eight" not in key: - continue - inference_data = self.get_inference_data( - posterior=path, - posterior_predictive="y_hat", - predictions="y_hat", - prior=path, - prior_predictive="y_hat", - observed_data=observed_data_paths[0], - observed_data_var="y", - constant_data=observed_data_paths[0], - constant_data_var="y", - predictions_constant_data=observed_data_paths[0], - predictions_constant_data_var="y", - log_likelihood="log_lik", - coords={"school": np.arange(8)}, - dims={ - "theta": ["school"], - "y": ["school"], - "log_lik": ["school"], - "y_hat": ["school"], - "eta": ["school"], - }, - ) - test_dict = { - "posterior": ["mu", "tau", "theta_tilde", "theta"], - "posterior_predictive": ["y_hat"], - "predictions": ["y_hat"], - "prior": ["mu", "tau", "theta_tilde", "theta"], - "prior_predictive": ["y_hat"], - "sample_stats": ["diverging"], - "observed_data": ["y"], - "constant_data": ["y"], - "predictions_constant_data": ["y"], - "log_likelihood": ["log_lik"], - } - if "output_warmup" in path: - test_dict.update({"warmup_posterior": ["mu", "tau", "theta_tilde", "theta"]}) - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - def test_inference_data_input_types2(self, paths, observed_data_paths): - """Check input types (change, see earlier) - - posterior_predictive --> List[str], variable in posterior - observed_data_var --> List[str], variable - """ - for key, path in paths.items(): - if "eight" not in key: - continue - inference_data = self.get_inference_data( - posterior=path, - posterior_predictive=["y_hat"], - predictions=["y_hat"], - prior=path, - prior_predictive=["y_hat"], - observed_data=observed_data_paths[0], - observed_data_var=["y"], - constant_data=observed_data_paths[0], - constant_data_var=["y"], - predictions_constant_data=observed_data_paths[0], - predictions_constant_data_var=["y"], - coords={"school": np.arange(8)}, - dims={ - "theta": ["school"], - "y": ["school"], - "log_lik": ["school"], - "y_hat": ["school"], - "eta": ["school"], - }, - dtypes={"theta": np.int64}, - ) - test_dict = { - "posterior": ["mu", "tau", "theta_tilde", "theta"], - "posterior_predictive": ["y_hat"], - "predictions": ["y_hat"], - "prior": ["mu", "tau", "theta_tilde", "theta"], - "prior_predictive": ["y_hat"], - "sample_stats": ["diverging"], - "observed_data": ["y"], - "constant_data": ["y"], - "predictions_constant_data": ["y"], - "log_likelihood": ["log_lik"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - assert isinstance(inference_data.posterior.theta.data.flat[0], np.integer) - - def test_inference_data_input_types3(self, paths, observed_data_paths): - """Check input types (change, see earlier) - - posterior_predictive --> str, csv file - coords --> one to many + one to one (default dim) - dims --> one to many - """ - for key, path in paths.items(): - if "eight" not in key: - continue - post_pred = paths["eight_schools_glob"] - inference_data = self.get_inference_data( - posterior=path, - posterior_predictive=post_pred, - prior=path, - prior_predictive=post_pred, - observed_data=observed_data_paths[0], - observed_data_var=["y"], - log_likelihood=["log_lik", "y_hat"], - coords={ - "school": np.arange(8), - "log_lik_dim_0": np.arange(8), - "y_hat": np.arange(8), - }, - dims={"theta": ["school"], "y": ["school"], "y_hat": ["school"], "eta": ["school"]}, - ) - test_dict = { - "posterior": ["mu", "tau", "theta_tilde", "theta"], - "sample_stats": ["diverging"], - "prior": ["mu", "tau", "theta_tilde", "theta"], - "prior_predictive": ["y_hat"], - "observed_data": ["y"], - "posterior_predictive": ["y_hat"], - "log_likelihood": ["log_lik", "y_hat"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - def test_inference_data_input_types4(self, paths): - """Check input types (change, see earlier) - - coords --> one to many + one to one (non-default dim) - dims --> one to many + one to one - """ - - paths_ = paths["no_warmup"] - for path in [paths_, paths_[0]]: - inference_data = self.get_inference_data( - posterior=path, - posterior_predictive=path, - prior=path, - prior_predictive=path, - observed_data=None, - observed_data_var=None, - log_likelihood=False, - coords={"rand": np.arange(3)}, - dims={"x": ["rand"]}, - ) - test_dict = { - "posterior": ["x", "y", "Z"], - "prior": ["x", "y", "Z"], - "prior_predictive": ["x", "y", "Z"], - "sample_stats": ["lp"], - "sample_stats_prior": ["lp"], - "posterior_predictive": ["x", "y", "Z"], - "~log_likelihood": [""], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - def test_inference_data_input_types5(self, paths, observed_data_paths): - """Check input types (change, see earlier) - - posterior_predictive is None - prior_predictive is None - """ - for key, path in paths.items(): - if "eight" not in key: - continue - inference_data = self.get_inference_data( - posterior=path, - posterior_predictive=None, - prior=path, - prior_predictive=None, - observed_data=observed_data_paths[0], - observed_data_var=["y"], - log_likelihood=["y_hat"], - coords={"school": np.arange(8), "log_lik_dim": np.arange(8)}, - dims={ - "theta": ["school"], - "y": ["school"], - "log_lik": ["log_lik_dim"], - "y_hat": ["school"], - "eta": ["school"], - }, - ) - test_dict = { - "posterior": ["mu", "tau", "theta_tilde", "theta", "log_lik"], - "prior": ["mu", "tau", "theta_tilde", "theta"], - "log_likelihood": ["y_hat", "~log_lik"], - "observed_data": ["y"], - "sample_stats_prior": ["lp"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - def test_inference_data_input_types6(self, paths, observed_data_paths): - """Check input types (change, see earlier) - - log_likelihood --> dict - """ - for key, path in paths.items(): - if "eight" not in key: - continue - post_pred = paths["eight_schools_glob"] - inference_data = self.get_inference_data( - posterior=path, - posterior_predictive=post_pred, - prior=path, - prior_predictive=post_pred, - observed_data=observed_data_paths[0], - observed_data_var=["y"], - log_likelihood={"y": "log_lik"}, - coords={ - "school": np.arange(8), - "log_lik_dim_0": np.arange(8), - "y_hat": np.arange(8), - }, - dims={"theta": ["school"], "y": ["school"], "y_hat": ["school"], "eta": ["school"]}, - ) - test_dict = { - "posterior": ["mu", "tau", "theta_tilde", "theta"], - "sample_stats": ["diverging"], - "prior": ["mu", "tau", "theta_tilde", "theta"], - "prior_predictive": ["y_hat"], - "observed_data": ["y"], - "posterior_predictive": ["y_hat"], - "log_likelihood": ["y", "~log_lik"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - def test_inference_data_observed_data1(self, observed_data_paths): - """Read Rdump/JSON, check shapes are correct - - All variables - """ - # Check the Rdump (idx=1) and equivalent JSON data file (idx=2) - for data_idx in (1, 2): - path = observed_data_paths[data_idx] - inference_data = self.get_inference_data(posterior=None, observed_data=path) - assert hasattr(inference_data, "observed_data") - assert len(inference_data.observed_data.data_vars) == 3 - assert inference_data.observed_data["x"].shape == (1,) - assert inference_data.observed_data["x"][0] == 1 - assert inference_data.observed_data["y"].shape == (3,) - assert inference_data.observed_data["Z"].shape == (4, 5) - - def test_inference_data_observed_data2(self, observed_data_paths): - """Read Rdump/JSON, check shapes are correct - - One variable as str - """ - # Check the Rdump (idx=1) and equivalent JSON data file (idx=2) - for data_idx in (1, 2): - path = observed_data_paths[data_idx] - inference_data = self.get_inference_data( - posterior=None, observed_data=path, observed_data_var="x" - ) - assert hasattr(inference_data, "observed_data") - assert len(inference_data.observed_data.data_vars) == 1 - assert inference_data.observed_data["x"].shape == (1,) - - def test_inference_data_observed_data3(self, observed_data_paths): - """Read Rdump/JSON, check shapes are correct - - One variable as a list - """ - # Check the Rdump (idx=1) and equivalent JSON data file (idx=2) - for data_idx in (1, 2): - path = observed_data_paths[data_idx] - inference_data = self.get_inference_data( - posterior=None, observed_data=path, observed_data_var=["x"] - ) - assert hasattr(inference_data, "observed_data") - assert len(inference_data.observed_data.data_vars) == 1 - assert inference_data.observed_data["x"].shape == (1,) - - def test_inference_data_observed_data4(self, observed_data_paths): - """Read Rdump/JSON, check shapes are correct - - Many variables as list - """ - # Check the Rdump (idx=1) and equivalent JSON data file (idx=2) - for data_idx in (1, 2): - path = observed_data_paths[data_idx] - inference_data = self.get_inference_data( - posterior=None, observed_data=path, observed_data_var=["y", "Z"] - ) - assert hasattr(inference_data, "observed_data") - assert len(inference_data.observed_data.data_vars) == 2 - assert inference_data.observed_data["y"].shape == (3,) - assert inference_data.observed_data["Z"].shape == (4, 5) diff --git a/arviz/tests/external_tests/test_data_cmdstanpy.py b/arviz/tests/external_tests/test_data_cmdstanpy.py deleted file mode 100644 index dd2f63ce7b..0000000000 --- a/arviz/tests/external_tests/test_data_cmdstanpy.py +++ /dev/null @@ -1,496 +0,0 @@ -# pylint: disable=redefined-outer-name -import os -import sys -import tempfile -from glob import glob - -import numpy as np -import pytest - -from ... import from_cmdstanpy - -from ..helpers import ( # pylint: disable=unused-import - chains, - check_multiple_attrs, - draws, - eight_schools_params, - importorskip, - load_cached_models, - pystan_version, -) - - -def _create_test_data(): - """Create test data to local folder. - - This function is needed when test data needs to be updated. - """ - import platform - import shutil - from pathlib import Path - - import cmdstanpy - - model_code = """ - data { - int J; - real y[J]; - real sigma[J]; - } - - parameters { - real mu; - real tau; - real eta[2, J / 2]; - } - - transformed parameters { - real theta[J]; - for (j in 1:J/2) { - theta[j] = mu + tau * eta[1, j]; - theta[j + 4] = mu + tau * eta[2, j]; - } - } - - model { - mu ~ normal(0, 5); - tau ~ cauchy(0, 5); - eta[1] ~ normal(0, 1); - eta[2] ~ normal(0, 1); - y ~ normal(theta, sigma); - } - - generated quantities { - vector[J] log_lik; - vector[J] y_hat; - for (j in 1:J) { - log_lik[j] = normal_lpdf(y[j] | theta[j], sigma[j]); - y_hat[j] = normal_rng(theta[j], sigma[j]); - } - } - """ - stan_file = "stan_test_data.stan" - with open(stan_file, "w", encoding="utf8") as file_handle: - print(model_code, file=file_handle) - model = cmdstanpy.CmdStanModel(stan_file=stan_file) - os.remove(stan_file) - stan_data = { - "J": 8, - "y": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]), - "sigma": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]), - } - fit_no_warmup = model.sample( - data=stan_data, iter_sampling=100, iter_warmup=1000, save_warmup=False - ) - fit_no_warmup.save_csvfiles(dir=".") - fit_files = { - "cmdstanpy_eight_schools_nowarmup": [], - "cmdstanpy_eight_schools_warmup": [], - } - for path in fit_no_warmup.runset.csv_files: - path = Path(path) - _, num = path.stem.rsplit("-", 1) - new_path = path.parent / ("cmdstanpy_eight_schools_nowarmup-" + num + path.suffix) - shutil.move(path, new_path) - fit_files["cmdstanpy_eight_schools_nowarmup"].append(new_path) - fit_warmup = model.sample(data=stan_data, iter_sampling=100, iter_warmup=500, save_warmup=True) - fit_warmup.save_csvfiles(dir=".") - for path in fit_no_warmup.runset.csv_files: - path = Path(path) - _, num = path.stem.rsplit("-", 1) - new_path = path.parent / ("cmdstanpy_eight_schools_warmup-" + num + path.suffix) - shutil.move(path, new_path) - fit_files["cmdstanpy_eight_schools_warmup"].append(new_path) - path = Path(stan_file) - os.remove(str(path.parent / (path.stem + (".exe" if platform.system() == "Windows" else "")))) - os.remove(str(path.parent / (path.stem + ".hpp"))) - return fit_files - - -@pytest.mark.skipif(sys.version_info < (3, 6), reason="CmdStanPy is supported only Python 3.6+") -class TestDataCmdStanPy: - @pytest.fixture(scope="session") - def data_directory(self): - here = os.path.dirname(os.path.abspath(__file__)) - data_directory = os.path.join(here, "..", "saved_models") - return data_directory - - @pytest.fixture(scope="class") - def filepaths(self, data_directory): - files = { - "nowarmup": glob( - os.path.join( - data_directory, "cmdstanpy", "cmdstanpy_eight_schools_nowarmup-[1-4].csv" - ) - ), - "warmup": glob( - os.path.join( - data_directory, "cmdstanpy", "cmdstanpy_eight_schools_warmup-[1-4].csv" - ) - ), - } - return files - - @pytest.fixture(scope="class") - def data(self, filepaths): - # Skip tests if cmdstanpy not installed - cmdstanpy = importorskip("cmdstanpy") - CmdStanModel = cmdstanpy.CmdStanModel # pylint: disable=invalid-name - CmdStanMCMC = cmdstanpy.CmdStanMCMC # pylint: disable=invalid-name - RunSet = cmdstanpy.stanfit.RunSet # pylint: disable=invalid-name - CmdStanArgs = cmdstanpy.model.CmdStanArgs # pylint: disable=invalid-name - SamplerArgs = cmdstanpy.model.SamplerArgs # pylint: disable=invalid-name - - class Data: - args = CmdStanArgs( - "dummy.stan", - "dummy.exe", - list(range(1, 5)), - method_args=SamplerArgs(iter_sampling=100), - ) - runset_obj = RunSet(args) - runset_obj._csv_files = filepaths["nowarmup"] # pylint: disable=protected-access - obj = CmdStanMCMC(runset_obj) - obj._assemble_draws() # pylint: disable=protected-access - - args_warmup = CmdStanArgs( - "dummy.stan", - "dummy.exe", - list(range(1, 5)), - method_args=SamplerArgs(iter_sampling=100, iter_warmup=500, save_warmup=True), - ) - runset_obj_warmup = RunSet(args_warmup) - runset_obj_warmup._csv_files = filepaths["warmup"] # pylint: disable=protected-access - obj_warmup = CmdStanMCMC(runset_obj_warmup) - obj_warmup._assemble_draws() # pylint: disable=protected-access - - _model_code = """model { real y; } generated quantities { int eta; int theta[N]; }""" - _tmp_dir = tempfile.TemporaryDirectory(prefix="arviz_tests_") - _stan_file = os.path.join(_tmp_dir.name, "stan_model_test.stan") - with open(_stan_file, "w", encoding="utf8") as f: - f.write(_model_code) - model = CmdStanModel(stan_file=_stan_file, compile=False) - - return Data - - def get_inference_data(self, data, eight_schools_params): - """vars as str.""" - return from_cmdstanpy( - posterior=data.obj, - posterior_predictive="y_hat", - predictions="y_hat", - prior=data.obj, - prior_predictive="y_hat", - observed_data={"y": eight_schools_params["y"]}, - constant_data={"y": eight_schools_params["y"]}, - predictions_constant_data={"y": eight_schools_params["y"]}, - log_likelihood={"y": "log_lik"}, - coords={"school": np.arange(eight_schools_params["J"])}, - dims={ - "y": ["school"], - "log_lik": ["school"], - "y_hat": ["school"], - "theta": ["school"], - }, - ) - - def get_inference_data2(self, data, eight_schools_params): - """vars as lists.""" - return from_cmdstanpy( - posterior=data.obj, - posterior_predictive=["y_hat"], - predictions=["y_hat", "log_lik"], - prior=data.obj, - prior_predictive=["y_hat"], - observed_data={"y": eight_schools_params["y"]}, - constant_data=eight_schools_params, - predictions_constant_data=eight_schools_params, - log_likelihood=["log_lik", "y_hat"], - coords={ - "school": np.arange(eight_schools_params["J"]), - "log_lik_dim": np.arange(eight_schools_params["J"]), - }, - dims={ - "eta": ["extra_dim", "half school"], - "y": ["school"], - "y_hat": ["school"], - "theta": ["school"], - "log_lik": ["log_lik_dim"], - }, - ) - - def get_inference_data3(self, data, eight_schools_params): - """multiple vars as lists.""" - return from_cmdstanpy( - posterior=data.obj, - posterior_predictive=["y_hat", "log_lik"], - prior=data.obj, - prior_predictive=["y_hat", "log_lik"], - observed_data={"y": eight_schools_params["y"]}, - coords={ - "school": np.arange(eight_schools_params["J"]), - "half school": ["a", "b", "c", "d"], - "extra_dim": ["x", "y"], - }, - dims={ - "eta": ["extra_dim", "half school"], - "y": ["school"], - "y_hat": ["school"], - "theta": ["school"], - "log_lik": ["log_lik_dim"], - }, - dtypes=data.model, - ) - - def get_inference_data4(self, data, eight_schools_params): - """multiple vars as lists.""" - return from_cmdstanpy( - posterior=data.obj, - posterior_predictive=None, - prior=data.obj, - prior_predictive=None, - log_likelihood=False, - observed_data={"y": eight_schools_params["y"]}, - coords=None, - dims=None, - dtypes={"eta": int, "theta": int}, - ) - - def get_inference_data5(self, data, eight_schools_params): - """multiple vars as lists.""" - return from_cmdstanpy( - posterior=data.obj, - posterior_predictive=None, - prior=data.obj, - prior_predictive=None, - log_likelihood="log_lik", - observed_data={"y": eight_schools_params["y"]}, - coords=None, - dims=None, - dtypes=data.model.code(), - ) - - def get_inference_data_warmup_true_is_true(self, data, eight_schools_params): - """vars as str.""" - return from_cmdstanpy( - posterior=data.obj_warmup, - posterior_predictive="y_hat", - predictions="y_hat", - prior=data.obj_warmup, - prior_predictive="y_hat", - observed_data={"y": eight_schools_params["y"]}, - constant_data={"y": eight_schools_params["y"]}, - predictions_constant_data={"y": eight_schools_params["y"]}, - log_likelihood="log_lik", - coords={"school": np.arange(eight_schools_params["J"])}, - dims={ - "eta": ["extra_dim", "half school"], - "y": ["school"], - "log_lik": ["school"], - "y_hat": ["school"], - "theta": ["school"], - }, - save_warmup=True, - ) - - def get_inference_data_warmup_false_is_true(self, data, eight_schools_params): - """vars as str.""" - return from_cmdstanpy( - posterior=data.obj, - posterior_predictive="y_hat", - predictions="y_hat", - prior=data.obj, - prior_predictive="y_hat", - observed_data={"y": eight_schools_params["y"]}, - constant_data={"y": eight_schools_params["y"]}, - predictions_constant_data={"y": eight_schools_params["y"]}, - log_likelihood="log_lik", - coords={"school": np.arange(eight_schools_params["J"])}, - dims={ - "eta": ["extra_dim", "half school"], - "y": ["school"], - "log_lik": ["school"], - "y_hat": ["school"], - "theta": ["school"], - }, - save_warmup=True, - ) - - def get_inference_data_warmup_true_is_false(self, data, eight_schools_params): - """vars as str.""" - return from_cmdstanpy( - posterior=data.obj_warmup, - posterior_predictive="y_hat", - predictions="y_hat", - prior=data.obj_warmup, - prior_predictive="y_hat", - observed_data={"y": eight_schools_params["y"]}, - constant_data={"y": eight_schools_params["y"]}, - predictions_constant_data={"y": eight_schools_params["y"]}, - log_likelihood="log_lik", - coords={"school": np.arange(eight_schools_params["J"])}, - dims={ - "eta": ["extra_dim", "half school"], - "y": ["school"], - "log_lik": ["school"], - "y_hat": ["school"], - "theta": ["school"], - }, - save_warmup=False, - ) - - def test_sampler_stats(self, data, eight_schools_params): - inference_data = self.get_inference_data(data, eight_schools_params) - test_dict = {"sample_stats": ["lp", "diverging"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - assert len(inference_data.sample_stats.lp.shape) == 2 # pylint: disable=no-member - - def test_inference_data(self, data, eight_schools_params): - inference_data1 = self.get_inference_data(data, eight_schools_params) - inference_data2 = self.get_inference_data2(data, eight_schools_params) - inference_data3 = self.get_inference_data3(data, eight_schools_params) - inference_data4 = self.get_inference_data4(data, eight_schools_params) - inference_data5 = self.get_inference_data5(data, eight_schools_params) - - # inference_data 1 - test_dict = { - "posterior": ["theta"], - "predictions": ["y_hat"], - "observed_data": ["y"], - "constant_data": ["y"], - "predictions_constant_data": ["y"], - "log_likelihood": ["y", "~log_lik"], - "prior": ["theta"], - } - fails = check_multiple_attrs(test_dict, inference_data1) - assert not fails - - # inference_data 2 - test_dict = { - "posterior_predictive": ["y_hat"], - "predictions": ["y_hat", "log_lik"], - "observed_data": ["y"], - "sample_stats_prior": ["lp"], - "sample_stats": ["lp"], - "constant_data": list(eight_schools_params), - "predictions_constant_data": list(eight_schools_params), - "prior_predictive": ["y_hat"], - "log_likelihood": ["log_lik", "y_hat"], - } - fails = check_multiple_attrs(test_dict, inference_data2) - assert not fails - - # inference_data 3 - test_dict = { - "posterior_predictive": ["y_hat"], - "observed_data": ["y"], - "sample_stats_prior": ["lp"], - "sample_stats": ["lp"], - "prior_predictive": ["y_hat"], - "log_likelihood": ["log_lik"], - } - fails = check_multiple_attrs(test_dict, inference_data3) - assert not fails - assert inference_data3.posterior.eta.dtype.kind == "i" # pylint: disable=no-member - assert inference_data3.posterior.theta.dtype.kind == "i" # pylint: disable=no-member - - # inference_data 4 - test_dict = { - "posterior": ["eta", "mu", "theta"], - "prior": ["theta"], - "~log_likelihood": [""], - } - fails = check_multiple_attrs(test_dict, inference_data4) - assert not fails - assert len(inference_data4.posterior.theta.shape) == 3 # pylint: disable=no-member - assert len(inference_data4.posterior.eta.shape) == 4 # pylint: disable=no-member - assert len(inference_data4.posterior.mu.shape) == 2 # pylint: disable=no-member - assert inference_data4.posterior.eta.dtype.kind == "i" # pylint: disable=no-member - assert inference_data4.posterior.theta.dtype.kind == "i" # pylint: disable=no-member - - # inference_data 5 - test_dict = { - "posterior": ["eta", "mu", "theta"], - "prior": ["theta"], - "log_likelihood": ["log_lik"], - } - fails = check_multiple_attrs(test_dict, inference_data5) - assert inference_data5.posterior.eta.dtype.kind == "i" # pylint: disable=no-member - assert inference_data5.posterior.theta.dtype.kind == "i" # pylint: disable=no-member - - def test_inference_data_warmup(self, data, eight_schools_params): - inference_data_true_is_true = self.get_inference_data_warmup_true_is_true( - data, eight_schools_params - ) - inference_data_false_is_true = self.get_inference_data_warmup_false_is_true( - data, eight_schools_params - ) - inference_data_true_is_false = self.get_inference_data_warmup_true_is_false( - data, eight_schools_params - ) - inference_data_false_is_false = self.get_inference_data(data, eight_schools_params) - # inference_data warmup - test_dict = { - "posterior": ["theta"], - "predictions": ["y_hat"], - "observed_data": ["y"], - "constant_data": ["y"], - "predictions_constant_data": ["y"], - "log_likelihood": ["log_lik"], - "prior": ["theta"], - "warmup_posterior": ["theta"], - "warmup_predictions": ["y_hat"], - "warmup_log_likelihood": ["log_lik"], - "warmup_prior": ["theta"], - } - fails = check_multiple_attrs(test_dict, inference_data_true_is_true) - assert not fails - # inference_data no warmup - test_dict = { - "posterior": ["theta"], - "predictions": ["y_hat"], - "observed_data": ["y"], - "constant_data": ["y"], - "predictions_constant_data": ["y"], - "log_likelihood": ["log_lik"], - "prior": ["theta"], - "~warmup_posterior": [""], - "~warmup_predictions": [""], - "~warmup_log_likelihood": [""], - "~warmup_prior": [""], - } - fails = check_multiple_attrs(test_dict, inference_data_false_is_true) - assert not fails - # inference_data no warmup - test_dict = { - "posterior": ["theta"], - "predictions": ["y_hat"], - "observed_data": ["y"], - "constant_data": ["y"], - "predictions_constant_data": ["y"], - "log_likelihood": ["log_lik"], - "prior": ["theta"], - "~warmup_posterior": [""], - "~warmup_predictions": [""], - "~warmup_log_likelihood": [""], - "~warmup_prior": [""], - } - fails = check_multiple_attrs(test_dict, inference_data_true_is_false) - assert not fails - # inference_data no warmup - test_dict = { - "posterior": ["theta"], - "predictions": ["y_hat"], - "observed_data": ["y"], - "constant_data": ["y"], - "predictions_constant_data": ["y"], - "log_likelihood": ["y"], - "prior": ["theta"], - "~warmup_posterior": [""], - "~warmup_predictions": [""], - "~warmup_log_likelihood": [""], - "~warmup_prior": [""], - } - fails = check_multiple_attrs(test_dict, inference_data_false_is_false) - assert not fails diff --git a/arviz/tests/external_tests/test_data_emcee.py b/arviz/tests/external_tests/test_data_emcee.py deleted file mode 100644 index d1735ce89f..0000000000 --- a/arviz/tests/external_tests/test_data_emcee.py +++ /dev/null @@ -1,166 +0,0 @@ -# pylint: disable=no-member, invalid-name, redefined-outer-name -import os - -import numpy as np -import pytest - -from ... import from_emcee - -from ..helpers import _emcee_lnprior as emcee_lnprior -from ..helpers import _emcee_lnprob as emcee_lnprob -from ..helpers import ( # pylint: disable=unused-import - chains, - check_multiple_attrs, - draws, - eight_schools_params, - importorskip, - load_cached_models, - needs_emcee3_func, -) - -# Skip all tests if emcee not installed -emcee = importorskip("emcee") - -needs_emcee3 = needs_emcee3_func() - - -class TestDataEmcee: - arg_list = [ - ({}, {"posterior": ["var_0", "var_1", "var_7"], "observed_data": ["arg_0", "arg_1"]}), - ( - {"var_names": ["mu", "tau", "eta"], "slices": [0, 1, slice(2, None)]}, - { - "posterior": ["mu", "tau", "eta"], - "observed_data": ["arg_0", "arg_1"], - "sample_stats": ["lp"], - }, - ), - ( - { - "arg_groups": ["observed_data", "constant_data"], - "blob_names": ["y", "y"], - "blob_groups": ["log_likelihood", "posterior_predictive"], - }, - { - "posterior": ["var_0", "var_1", "var_7"], - "observed_data": ["arg_0"], - "constant_data": ["arg_1"], - "log_likelihood": ["y"], - "posterior_predictive": ["y"], - "sample_stats": ["lp"], - }, - ), - ( - { - "blob_names": ["log_likelihood", "y"], - "dims": {"eta": ["school"], "log_likelihood": ["school"], "y": ["school"]}, - "var_names": ["mu", "tau", "eta"], - "slices": [0, 1, slice(2, None)], - "arg_names": ["y", "sigma"], - "arg_groups": ["observed_data", "constant_data"], - "coords": {"school": range(8)}, - }, - { - "posterior": ["mu", "tau", "eta"], - "observed_data": ["y"], - "constant_data": ["sigma"], - "log_likelihood": ["log_likelihood", "y"], - "sample_stats": ["lp"], - }, - ), - ] - - @pytest.fixture(scope="class") - def data(self, eight_schools_params, draws, chains): - class Data: - # chains are not used - # emcee uses lots of walkers - obj = load_cached_models(eight_schools_params, draws, chains, "emcee")["emcee"] - - return Data - - def get_inference_data_reader(self, **kwargs): - from emcee import backends # pylint: disable=no-name-in-module - - here = os.path.dirname(os.path.abspath(__file__)) - data_directory = os.path.join(here, "..", "saved_models") - filepath = os.path.join(data_directory, "reader_testfile.h5") - assert os.path.exists(filepath) - assert os.path.getsize(filepath) - reader = backends.HDFBackend(filepath, read_only=True) - return from_emcee(reader, **kwargs) - - @pytest.mark.parametrize("test_args", arg_list) - def test_inference_data(self, data, test_args): - kwargs, test_dict = test_args - inference_data = from_emcee(data.obj, **kwargs) - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - @needs_emcee3 - @pytest.mark.parametrize("test_args", arg_list) - def test_inference_data_reader(self, test_args): - kwargs, test_dict = test_args - kwargs = {k: i for k, i in kwargs.items() if k not in ("arg_names", "arg_groups")} - inference_data = self.get_inference_data_reader(**kwargs) - test_dict.pop("observed_data") - if "constant_data" in test_dict: - test_dict.pop("constant_data") - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - def test_verify_var_names(self, data): - with pytest.raises(ValueError): - from_emcee(data.obj, var_names=["not", "enough"]) - - def test_verify_arg_names(self, data): - with pytest.raises(ValueError): - from_emcee(data.obj, arg_names=["not enough"]) - - @pytest.mark.parametrize("slices", [[0, 0, slice(2, None)], [0, 1, slice(1, None)]]) - def test_slices_warning(self, data, slices): - with pytest.warns(UserWarning): - from_emcee(data.obj, slices=slices) - - def test_no_blobs_error(self): - sampler = emcee.EnsembleSampler(6, 1, lambda x: -(x**2)) - sampler.run_mcmc(np.random.normal(size=(6, 1)), 20) - with pytest.raises(ValueError): - from_emcee(sampler, blob_names=["inexistent"]) - - def test_peculiar_blobs(self, data): - sampler = emcee.EnsembleSampler(6, 1, lambda x: (-(x**2), (np.random.normal(x), 3))) - sampler.run_mcmc(np.random.normal(size=(6, 1)), 20) - inference_data = from_emcee(sampler, blob_names=["normal", "threes"]) - fails = check_multiple_attrs({"log_likelihood": ["normal", "threes"]}, inference_data) - assert not fails - inference_data = from_emcee(data.obj, blob_names=["mix"]) - fails = check_multiple_attrs({"log_likelihood": ["mix"]}, inference_data) - assert not fails - - def test_single_blob(self): - sampler = emcee.EnsembleSampler(6, 1, lambda x: (-(x**2), 3)) - sampler.run_mcmc(np.random.normal(size=(6, 1)), 20) - inference_data = from_emcee(sampler, blob_names=["blob"], blob_groups=["blob_group"]) - fails = check_multiple_attrs({"blob_group": ["blob"]}, inference_data) - assert not fails - - @pytest.mark.parametrize( - "blob_args", - [ - (ValueError, ["a", "b"], ["prior"]), - (ValueError, ["too", "many", "names"], None), - (SyntaxError, ["a", "b"], ["posterior", "observed_data"]), - ], - ) - def test_bad_blobs(self, data, blob_args): - error, names, groups = blob_args - with pytest.raises(error): - from_emcee(data.obj, blob_names=names, blob_groups=groups) - - def test_ln_funcs_for_infinity(self): - # after dropping Python 3.5 support use underscore 1_000_000 - ary = np.ones(10) - ary[1] = -1 - assert np.isinf(emcee_lnprior(ary)) - assert np.isinf(emcee_lnprob(ary, ary[2:], ary[2:])[0]) diff --git a/arviz/tests/external_tests/test_data_numpyro.py b/arviz/tests/external_tests/test_data_numpyro.py deleted file mode 100644 index bbbe194e06..0000000000 --- a/arviz/tests/external_tests/test_data_numpyro.py +++ /dev/null @@ -1,434 +0,0 @@ -# pylint: disable=no-member, invalid-name, redefined-outer-name, too-many-public-methods -from collections import namedtuple -import numpy as np -import pytest - -from ...data.io_numpyro import from_numpyro # pylint: disable=wrong-import-position -from ..helpers import ( # pylint: disable=unused-import, wrong-import-position - chains, - check_multiple_attrs, - draws, - eight_schools_params, - importorskip, - load_cached_models, -) - -# Skip all tests if jax or numpyro not installed -jax = importorskip("jax") -PRNGKey = jax.random.PRNGKey -numpyro = importorskip("numpyro") -Predictive = numpyro.infer.Predictive - - -class TestDataNumPyro: - @pytest.fixture(scope="class") - def data(self, eight_schools_params, draws, chains): - class Data: - obj = load_cached_models(eight_schools_params, draws, chains, "numpyro")["numpyro"] - - return Data - - @pytest.fixture(scope="class") - def predictions_params(self): - """Predictions data for eight schools.""" - return { - "J": 8, - "sigma": np.array([5.0, 7.0, 12.0, 4.0, 6.0, 10.0, 3.0, 9.0]), - } - - @pytest.fixture(scope="class") - def predictions_data(self, data, predictions_params): - """Generate predictions for predictions_params""" - posterior_samples = data.obj.get_samples() - model = data.obj.sampler.model - predictions = Predictive(model, posterior_samples)( - PRNGKey(2), predictions_params["J"], predictions_params["sigma"] - ) - return predictions - - def get_inference_data( - self, data, eight_schools_params, predictions_data, predictions_params, infer_dims=False - ): - posterior_samples = data.obj.get_samples() - model = data.obj.sampler.model - posterior_predictive = Predictive(model, posterior_samples)( - PRNGKey(1), eight_schools_params["J"], eight_schools_params["sigma"] - ) - prior = Predictive(model, num_samples=500)( - PRNGKey(2), eight_schools_params["J"], eight_schools_params["sigma"] - ) - dims = {"theta": ["school"], "eta": ["school"], "obs": ["school"]} - pred_dims = {"theta": ["school_pred"], "eta": ["school_pred"], "obs": ["school_pred"]} - if infer_dims: - dims = None - pred_dims = None - - predictions = predictions_data - return from_numpyro( - posterior=data.obj, - prior=prior, - posterior_predictive=posterior_predictive, - predictions=predictions, - coords={ - "school": np.arange(eight_schools_params["J"]), - "school_pred": np.arange(predictions_params["J"]), - }, - dims=dims, - pred_dims=pred_dims, - ) - - def test_inference_data_namedtuple(self, data): - samples = data.obj.get_samples() - Samples = namedtuple("Samples", samples) - data_namedtuple = Samples(**samples) - _old_fn = data.obj.get_samples - data.obj.get_samples = lambda *args, **kwargs: data_namedtuple - inference_data = from_numpyro( - posterior=data.obj, - dims={}, # This mock test needs to turn off autodims like so or mock group_by_chain - ) - assert isinstance(data.obj.get_samples(), Samples) - data.obj.get_samples = _old_fn - for key in samples: - assert key in inference_data.posterior - - def test_inference_data(self, data, eight_schools_params, predictions_data, predictions_params): - inference_data = self.get_inference_data( - data, eight_schools_params, predictions_data, predictions_params - ) - test_dict = { - "posterior": ["mu", "tau", "eta"], - "sample_stats": ["diverging"], - "log_likelihood": ["obs"], - "posterior_predictive": ["obs"], - "predictions": ["obs"], - "prior": ["mu", "tau", "eta"], - "prior_predictive": ["obs"], - "observed_data": ["obs"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - # test dims - dims = inference_data.posterior_predictive.sizes["school"] - pred_dims = inference_data.predictions.sizes["school_pred"] - assert dims == 8 - assert pred_dims == 8 - - def test_inference_data_no_posterior( - self, data, eight_schools_params, predictions_data, predictions_params - ): - posterior_samples = data.obj.get_samples() - model = data.obj.sampler.model - posterior_predictive = Predictive(model, posterior_samples)( - PRNGKey(1), eight_schools_params["J"], eight_schools_params["sigma"] - ) - prior = Predictive(model, num_samples=500)( - PRNGKey(2), eight_schools_params["J"], eight_schools_params["sigma"] - ) - predictions = predictions_data - constant_data = {"J": 8, "sigma": eight_schools_params["sigma"]} - predictions_constant_data = predictions_params - # only prior - inference_data = from_numpyro(prior=prior) - test_dict = {"prior": ["mu", "tau", "eta"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails, f"only prior: {fails}" - # only posterior_predictive - inference_data = from_numpyro(posterior_predictive=posterior_predictive) - test_dict = {"posterior_predictive": ["obs"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails, f"only posterior_predictive: {fails}" - # only predictions - inference_data = from_numpyro(predictions=predictions) - test_dict = {"predictions": ["obs"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails, f"only predictions: {fails}" - # only constant_data - inference_data = from_numpyro(constant_data=constant_data) - test_dict = {"constant_data": ["J", "sigma"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails, f"only constant_data: {fails}" - # only predictions_constant_data - inference_data = from_numpyro(predictions_constant_data=predictions_constant_data) - test_dict = {"predictions_constant_data": ["J", "sigma"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails, f"only predictions_constant_data: {fails}" - # prior and posterior_predictive - idata = from_numpyro( - prior=prior, - posterior_predictive=posterior_predictive, - coords={"school": np.arange(eight_schools_params["J"])}, - dims={"theta": ["school"], "eta": ["school"]}, - ) - test_dict = {"posterior_predictive": ["obs"], "prior": ["mu", "tau", "eta", "obs"]} - fails = check_multiple_attrs(test_dict, idata) - assert not fails, f"prior and posterior_predictive: {fails}" - - def test_inference_data_only_posterior(self, data): - idata = from_numpyro(data.obj) - test_dict = { - "posterior": ["mu", "tau", "eta"], - "sample_stats": ["diverging"], - "log_likelihood": ["obs"], - } - fails = check_multiple_attrs(test_dict, idata) - assert not fails - - def test_multiple_observed_rv(self): - import numpyro - import numpyro.distributions as dist - from numpyro.infer import MCMC, NUTS - - y1 = np.random.randn(10) - y2 = np.random.randn(100) - - def model_example_multiple_obs(y1=None, y2=None): - x = numpyro.sample("x", dist.Normal(1, 3)) - numpyro.sample("y1", dist.Normal(x, 1), obs=y1) - numpyro.sample("y2", dist.Normal(x, 1), obs=y2) - - nuts_kernel = NUTS(model_example_multiple_obs) - mcmc = MCMC(nuts_kernel, num_samples=10, num_warmup=2) - mcmc.run(PRNGKey(0), y1=y1, y2=y2) - inference_data = from_numpyro(mcmc) - test_dict = { - "posterior": ["x"], - "sample_stats": ["diverging"], - "log_likelihood": ["y1", "y2"], - "observed_data": ["y1", "y2"], - } - fails = check_multiple_attrs(test_dict, inference_data) - # from ..stats import waic - # waic_results = waic(inference_data) - # print(waic_results) - # print(waic_results.keys()) - # print(waic_results.waic, waic_results.waic_se) - assert not fails - assert not hasattr(inference_data.sample_stats, "log_likelihood") - - def test_inference_data_constant_data(self): - import numpyro - import numpyro.distributions as dist - from numpyro.infer import MCMC, NUTS - - x1 = 10 - x2 = 12 - y1 = np.random.randn(10) - - def model_constant_data(x, y1=None): - _x = numpyro.sample("x", dist.Normal(1, 3)) - numpyro.sample("y1", dist.Normal(x * _x, 1), obs=y1) - - nuts_kernel = NUTS(model_constant_data) - mcmc = MCMC(nuts_kernel, num_samples=10, num_warmup=2) - mcmc.run(PRNGKey(0), x=x1, y1=y1) - posterior = mcmc.get_samples() - posterior_predictive = Predictive(model_constant_data, posterior)(PRNGKey(1), x1) - predictions = Predictive(model_constant_data, posterior)(PRNGKey(2), x2) - inference_data = from_numpyro( - mcmc, - posterior_predictive=posterior_predictive, - predictions=predictions, - constant_data={"x1": x1}, - predictions_constant_data={"x2": x2}, - ) - test_dict = { - "posterior": ["x"], - "posterior_predictive": ["y1"], - "sample_stats": ["diverging"], - "log_likelihood": ["y1"], - "predictions": ["y1"], - "observed_data": ["y1"], - "constant_data": ["x1"], - "predictions_constant_data": ["x2"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - def test_inference_data_num_chains(self, predictions_data, chains): - predictions = predictions_data - inference_data = from_numpyro(predictions=predictions, num_chains=chains) - nchains = inference_data.predictions.sizes["chain"] - assert nchains == chains - - @pytest.mark.parametrize("nchains", [1, 2]) - @pytest.mark.parametrize("thin", [1, 2, 3, 5, 10]) - def test_mcmc_with_thinning(self, nchains, thin): - import numpyro - import numpyro.distributions as dist - from numpyro.infer import MCMC, NUTS - - x = np.random.normal(10, 3, size=100) - - def model(x): - numpyro.sample( - "x", - dist.Normal( - numpyro.sample("loc", dist.Uniform(0, 20)), - numpyro.sample("scale", dist.Uniform(0, 20)), - ), - obs=x, - ) - - nuts_kernel = NUTS(model) - mcmc = MCMC(nuts_kernel, num_warmup=100, num_samples=400, num_chains=nchains, thinning=thin) - mcmc.run(PRNGKey(0), x=x) - - inference_data = from_numpyro(mcmc) - assert inference_data.posterior["loc"].shape == (nchains, 400 // thin) - - def test_mcmc_improper_uniform(self): - import numpyro - import numpyro.distributions as dist - from numpyro.infer import MCMC, NUTS - - def model(): - x = numpyro.sample("x", dist.ImproperUniform(dist.constraints.positive, (), ())) - return numpyro.sample("y", dist.Normal(x, 1), obs=1.0) - - mcmc = MCMC(NUTS(model), num_warmup=10, num_samples=10) - mcmc.run(PRNGKey(0)) - inference_data = from_numpyro(mcmc) - assert inference_data.observed_data - - def test_mcmc_infer_dims(self): - import numpyro - import numpyro.distributions as dist - from numpyro.infer import MCMC, NUTS - - def model(): - # note: group2 gets assigned dim=-1 and group1 is assigned dim=-2 - with numpyro.plate("group2", 5), numpyro.plate("group1", 10): - _ = numpyro.sample("param", dist.Normal(0, 1)) - - mcmc = MCMC(NUTS(model), num_warmup=10, num_samples=10) - mcmc.run(PRNGKey(0)) - inference_data = from_numpyro( - mcmc, coords={"group1": np.arange(10), "group2": np.arange(5)} - ) - assert inference_data.posterior.param.dims == ("chain", "draw", "group1", "group2") - assert all(dim in inference_data.posterior.param.coords for dim in ("group1", "group2")) - - def test_mcmc_infer_unsorted_dims(self): - import numpyro - import numpyro.distributions as dist - from numpyro.infer import MCMC, NUTS - - def model(): - group1_plate = numpyro.plate("group1", 10, dim=-1) - group2_plate = numpyro.plate("group2", 5, dim=-2) - - # the plate contexts are entered in a different order than the pre-defined dims - # we should make sure this still works because the trace has all of the info it needs - with group2_plate, group1_plate: - _ = numpyro.sample("param", dist.Normal(0, 1)) - - mcmc = MCMC(NUTS(model), num_warmup=10, num_samples=10) - mcmc.run(PRNGKey(0)) - inference_data = from_numpyro( - mcmc, coords={"group1": np.arange(10), "group2": np.arange(5)} - ) - assert inference_data.posterior.param.dims == ("chain", "draw", "group2", "group1") - assert all(dim in inference_data.posterior.param.coords for dim in ("group1", "group2")) - - def test_mcmc_infer_dims_no_coords(self): - import numpyro - import numpyro.distributions as dist - from numpyro.infer import MCMC, NUTS - - def model(): - with numpyro.plate("group", 5): - _ = numpyro.sample("param", dist.Normal(0, 1)) - - mcmc = MCMC(NUTS(model), num_warmup=10, num_samples=10) - mcmc.run(PRNGKey(0)) - inference_data = from_numpyro(mcmc) - assert inference_data.posterior.param.dims == ("chain", "draw", "group") - - def test_mcmc_event_dims(self): - import numpyro - import numpyro.distributions as dist - from numpyro.infer import MCMC, NUTS - - def model(): - _ = numpyro.sample( - "gamma", dist.ZeroSumNormal(1, event_shape=(10,)), infer={"event_dims": ["groups"]} - ) - - mcmc = MCMC(NUTS(model), num_warmup=10, num_samples=10) - mcmc.run(PRNGKey(0)) - inference_data = from_numpyro(mcmc, coords={"groups": np.arange(10)}) - assert inference_data.posterior.gamma.dims == ("chain", "draw", "groups") - assert "groups" in inference_data.posterior.gamma.coords - - @pytest.mark.xfail - def test_mcmc_inferred_dims_univariate(self): - import numpyro - import numpyro.distributions as dist - from numpyro.infer import MCMC, NUTS - import jax.numpy as jnp - - def model(): - alpha = numpyro.sample("alpha", dist.Normal(0, 1)) - sigma = numpyro.sample("sigma", dist.HalfNormal(1)) - with numpyro.plate("obs_idx", 3): - # mu is plated by obs_idx, but isnt broadcasted to the plate shape - # the expected behavior is that this should cause a failure - mu = numpyro.deterministic("mu", alpha) - return numpyro.sample("y", dist.Normal(mu, sigma), obs=jnp.array([-1, 0, 1])) - - mcmc = MCMC(NUTS(model), num_warmup=10, num_samples=10) - mcmc.run(PRNGKey(0)) - inference_data = from_numpyro(mcmc, coords={"obs_idx": np.arange(3)}) - assert inference_data.posterior.mu.dims == ("chain", "draw", "obs_idx") - assert "obs_idx" in inference_data.posterior.mu.coords - - def test_mcmc_extra_event_dims(self): - import numpyro - import numpyro.distributions as dist - from numpyro.infer import MCMC, NUTS - - def model(): - gamma = numpyro.sample("gamma", dist.ZeroSumNormal(1, event_shape=(10,))) - _ = numpyro.deterministic("gamma_plus1", gamma + 1) - - mcmc = MCMC(NUTS(model), num_warmup=10, num_samples=10) - mcmc.run(PRNGKey(0)) - inference_data = from_numpyro( - mcmc, coords={"groups": np.arange(10)}, extra_event_dims={"gamma_plus1": ["groups"]} - ) - assert inference_data.posterior.gamma_plus1.dims == ("chain", "draw", "groups") - assert "groups" in inference_data.posterior.gamma_plus1.coords - - def test_mcmc_predictions_infer_dims( - self, data, eight_schools_params, predictions_data, predictions_params - ): - inference_data = self.get_inference_data( - data, eight_schools_params, predictions_data, predictions_params, infer_dims=True - ) - assert inference_data.predictions.obs.dims == ("chain", "draw", "J") - assert "J" in inference_data.predictions.obs.coords - - def test_potential_energy_sign_conversion(self): - """Test that potential energy is converted to log probability (lp) with correct sign.""" - import numpyro - import numpyro.distributions as dist - from numpyro.infer import MCMC, NUTS - - num_samples = 10 - - def simple_model(): - numpyro.sample("x", dist.Normal(0, 1)) - - nuts_kernel = NUTS(simple_model) - mcmc = MCMC(nuts_kernel, num_samples=num_samples, num_warmup=5) - mcmc.run(PRNGKey(0), extra_fields=["potential_energy"]) - - # Get the raw extra fields from NumPyro - extra_fields = mcmc.get_extra_fields(group_by_chain=True) - # Convert to ArviZ InferenceData - inference_data = from_numpyro(mcmc) - arviz_lp = inference_data["sample_stats"]["lp"].values - - np.testing.assert_array_equal(arviz_lp, -extra_fields["potential_energy"]) diff --git a/arviz/tests/external_tests/test_data_pyjags.py b/arviz/tests/external_tests/test_data_pyjags.py deleted file mode 100644 index 986546b741..0000000000 --- a/arviz/tests/external_tests/test_data_pyjags.py +++ /dev/null @@ -1,119 +0,0 @@ -# pylint: disable=no-member, invalid-name, redefined-outer-name, unused-import -import sys -import typing as tp - -import numpy as np -import pytest - -from ... import InferenceData, from_pyjags, waic -from ...data.io_pyjags import ( - _convert_arviz_dict_to_pyjags_dict, - _convert_pyjags_dict_to_arviz_dict, - _extract_arviz_dict_from_inference_data, -) -from ..helpers import check_multiple_attrs, eight_schools_params - -pytest.skip("Uses deprecated numpy C-api", allow_module_level=True) - -PYJAGS_POSTERIOR_DICT = { - "b": np.random.randn(3, 10, 3), - "int": np.random.randn(1, 10, 3), - "log_like": np.random.randn(1, 10, 3), -} -PYJAGS_PRIOR_DICT = {"b": np.random.randn(3, 10, 3), "int": np.random.randn(1, 10, 3)} - - -PARAMETERS = ("mu", "tau", "theta_tilde") -VARIABLES = tuple(list(PARAMETERS) + ["log_like"]) - -NUMBER_OF_WARMUP_SAMPLES = 1000 -NUMBER_OF_POST_WARMUP_SAMPLES = 5000 - - -def verify_equality_of_numpy_values_dictionaries( - dict_1: tp.Mapping[tp.Any, np.ndarray], dict_2: tp.Mapping[tp.Any, np.ndarray] -) -> bool: - if dict_1.keys() != dict_2.keys(): - return False - - for key in dict_1.keys(): - if not np.all(dict_1[key] == dict_2[key]): - return False - - return True - - -class TestDataPyJAGSWithoutEstimation: - def test_convert_pyjags_samples_dictionary_to_arviz_samples_dictionary(self): - arviz_samples_dict_from_pyjags_samples_dict = _convert_pyjags_dict_to_arviz_dict( - PYJAGS_POSTERIOR_DICT - ) - - pyjags_dict_from_arviz_dict_from_pyjags_dict = _convert_arviz_dict_to_pyjags_dict( - arviz_samples_dict_from_pyjags_samples_dict - ) - - assert verify_equality_of_numpy_values_dictionaries( - PYJAGS_POSTERIOR_DICT, - pyjags_dict_from_arviz_dict_from_pyjags_dict, - ) - - def test_extract_samples_dictionary_from_arviz_inference_data(self): - arviz_samples_dict_from_pyjags_samples_dict = _convert_pyjags_dict_to_arviz_dict( - PYJAGS_POSTERIOR_DICT - ) - - arviz_inference_data_from_pyjags_samples_dict = from_pyjags(PYJAGS_POSTERIOR_DICT) - arviz_dict_from_idata_from_pyjags_dict = _extract_arviz_dict_from_inference_data( - arviz_inference_data_from_pyjags_samples_dict - ) - - assert verify_equality_of_numpy_values_dictionaries( - arviz_samples_dict_from_pyjags_samples_dict, - arviz_dict_from_idata_from_pyjags_dict, - ) - - def test_roundtrip_from_pyjags_via_arviz_to_pyjags(self): - arviz_inference_data_from_pyjags_samples_dict = from_pyjags(PYJAGS_POSTERIOR_DICT) - arviz_dict_from_idata_from_pyjags_dict = _extract_arviz_dict_from_inference_data( - arviz_inference_data_from_pyjags_samples_dict - ) - - pyjags_dict_from_arviz_idata = _convert_arviz_dict_to_pyjags_dict( - arviz_dict_from_idata_from_pyjags_dict - ) - - assert verify_equality_of_numpy_values_dictionaries( - PYJAGS_POSTERIOR_DICT, pyjags_dict_from_arviz_idata - ) - - @pytest.mark.parametrize("posterior", [None, PYJAGS_POSTERIOR_DICT]) - @pytest.mark.parametrize("prior", [None, PYJAGS_PRIOR_DICT]) - @pytest.mark.parametrize("save_warmup", [True, False]) - @pytest.mark.parametrize("warmup_iterations", [0, 5]) - def test_inference_data_attrs(self, posterior, prior, save_warmup, warmup_iterations: int): - arviz_inference_data_from_pyjags_samples_dict = from_pyjags( - posterior=posterior, - prior=prior, - log_likelihood={"y": "log_like"}, - save_warmup=save_warmup, - warmup_iterations=warmup_iterations, - ) - posterior_warmup_prefix = ( - "" if save_warmup and warmup_iterations > 0 and posterior is not None else "~" - ) - prior_warmup_prefix = ( - "" if save_warmup and warmup_iterations > 0 and prior is not None else "~" - ) - print(f'posterior_warmup_prefix="{posterior_warmup_prefix}"') - test_dict = { - f'{"~" if posterior is None else ""}posterior': ["b", "int"], - f'{"~" if prior is None else ""}prior': ["b", "int"], - f'{"~" if posterior is None else ""}log_likelihood': ["y"], - f"{posterior_warmup_prefix}warmup_posterior": ["b", "int"], - f"{prior_warmup_prefix}warmup_prior": ["b", "int"], - f"{posterior_warmup_prefix}warmup_log_likelihood": ["y"], - } - - fails = check_multiple_attrs(test_dict, arviz_inference_data_from_pyjags_samples_dict) - assert not fails diff --git a/arviz/tests/external_tests/test_data_pyro.py b/arviz/tests/external_tests/test_data_pyro.py deleted file mode 100644 index 73b3c1c05c..0000000000 --- a/arviz/tests/external_tests/test_data_pyro.py +++ /dev/null @@ -1,260 +0,0 @@ -# pylint: disable=no-member, invalid-name, redefined-outer-name -import numpy as np -import packaging -import pytest - -from ...data.io_pyro import from_pyro # pylint: disable=wrong-import-position -from ..helpers import ( # pylint: disable=unused-import, wrong-import-position - chains, - check_multiple_attrs, - draws, - eight_schools_params, - importorskip, - load_cached_models, -) - -# Skip all tests if pyro or pytorch not installed -torch = importorskip("torch") -pyro = importorskip("pyro") -Predictive = pyro.infer.Predictive -dist = pyro.distributions - - -class TestDataPyro: - @pytest.fixture(scope="class") - def data(self, eight_schools_params, draws, chains): - class Data: - obj = load_cached_models(eight_schools_params, draws, chains, "pyro")["pyro"] - - return Data - - @pytest.fixture(scope="class") - def predictions_params(self): - """Predictions data for eight schools.""" - return { - "J": 8, - "sigma": np.array([5.0, 7.0, 12.0, 4.0, 6.0, 10.0, 3.0, 9.0]), - } - - @pytest.fixture(scope="class") - def predictions_data(self, data, predictions_params): - """Generate predictions for predictions_params""" - posterior_samples = data.obj.get_samples() - model = data.obj.kernel.model - predictions = Predictive(model, posterior_samples)( - predictions_params["J"], torch.from_numpy(predictions_params["sigma"]).float() - ) - return predictions - - def get_inference_data(self, data, eight_schools_params, predictions_data): - posterior_samples = data.obj.get_samples() - model = data.obj.kernel.model - posterior_predictive = Predictive(model, posterior_samples)( - eight_schools_params["J"], torch.from_numpy(eight_schools_params["sigma"]).float() - ) - prior = Predictive(model, num_samples=500)( - eight_schools_params["J"], torch.from_numpy(eight_schools_params["sigma"]).float() - ) - predictions = predictions_data - return from_pyro( - posterior=data.obj, - prior=prior, - posterior_predictive=posterior_predictive, - predictions=predictions, - coords={ - "school": np.arange(eight_schools_params["J"]), - "school_pred": np.arange(eight_schools_params["J"]), - }, - dims={"theta": ["school"], "eta": ["school"], "obs": ["school"]}, - pred_dims={"theta": ["school_pred"], "eta": ["school_pred"], "obs": ["school_pred"]}, - ) - - def test_inference_data(self, data, eight_schools_params, predictions_data): - inference_data = self.get_inference_data(data, eight_schools_params, predictions_data) - test_dict = { - "posterior": ["mu", "tau", "eta"], - "sample_stats": ["diverging"], - "posterior_predictive": ["obs"], - "predictions": ["obs"], - "prior": ["mu", "tau", "eta"], - "prior_predictive": ["obs"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - # test dims - dims = inference_data.posterior_predictive.sizes["school"] - pred_dims = inference_data.predictions.sizes["school_pred"] - assert dims == 8 - assert pred_dims == 8 - - @pytest.mark.skipif( - packaging.version.parse(pyro.__version__) < packaging.version.parse("1.0.0"), - reason="requires pyro 1.0.0 or higher", - ) - def test_inference_data_has_log_likelihood_and_observed_data(self, data): - idata = from_pyro(data.obj) - test_dict = {"log_likelihood": ["obs"], "observed_data": ["obs"]} - fails = check_multiple_attrs(test_dict, idata) - assert not fails - - def test_inference_data_no_posterior( - self, data, eight_schools_params, predictions_data, predictions_params - ): - posterior_samples = data.obj.get_samples() - model = data.obj.kernel.model - posterior_predictive = Predictive(model, posterior_samples)( - eight_schools_params["J"], torch.from_numpy(eight_schools_params["sigma"]).float() - ) - prior = Predictive(model, num_samples=500)( - eight_schools_params["J"], torch.from_numpy(eight_schools_params["sigma"]).float() - ) - predictions = predictions_data - constant_data = {"J": 8, "sigma": eight_schools_params["sigma"]} - predictions_constant_data = predictions_params - # only prior - inference_data = from_pyro(prior=prior) - test_dict = {"prior": ["mu", "tau", "eta"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails, f"only prior: {fails}" - # only posterior_predictive - inference_data = from_pyro(posterior_predictive=posterior_predictive) - test_dict = {"posterior_predictive": ["obs"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails, f"only posterior_predictive: {fails}" - # only predictions - inference_data = from_pyro(predictions=predictions) - test_dict = {"predictions": ["obs"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails, f"only predictions: {fails}" - # only constant_data - inference_data = from_pyro(constant_data=constant_data) - test_dict = {"constant_data": ["J", "sigma"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails, f"only constant_data: {fails}" - # only predictions_constant_data - inference_data = from_pyro(predictions_constant_data=predictions_constant_data) - test_dict = {"predictions_constant_data": ["J", "sigma"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails, f"only predictions_constant_data: {fails}" - # prior and posterior_predictive - idata = from_pyro( - prior=prior, - posterior_predictive=posterior_predictive, - coords={"school": np.arange(eight_schools_params["J"])}, - dims={"theta": ["school"], "eta": ["school"]}, - ) - test_dict = {"posterior_predictive": ["obs"], "prior": ["mu", "tau", "eta", "obs"]} - fails = check_multiple_attrs(test_dict, idata) - assert not fails, f"prior and posterior_predictive: {fails}" - - def test_inference_data_only_posterior(self, data): - idata = from_pyro(data.obj) - test_dict = {"posterior": ["mu", "tau", "eta"], "sample_stats": ["diverging"]} - fails = check_multiple_attrs(test_dict, idata) - assert not fails - - @pytest.mark.skipif( - packaging.version.parse(pyro.__version__) < packaging.version.parse("1.0.0"), - reason="requires pyro 1.0.0 or higher", - ) - def test_inference_data_only_posterior_has_log_likelihood(self, data): - idata = from_pyro(data.obj) - test_dict = {"log_likelihood": ["obs"]} - fails = check_multiple_attrs(test_dict, idata) - assert not fails - - def test_multiple_observed_rv(self): - y1 = torch.randn(10) - y2 = torch.randn(10) - - def model_example_multiple_obs(y1=None, y2=None): - x = pyro.sample("x", dist.Normal(1, 3)) - pyro.sample("y1", dist.Normal(x, 1), obs=y1) - pyro.sample("y2", dist.Normal(x, 1), obs=y2) - - nuts_kernel = pyro.infer.NUTS(model_example_multiple_obs) - mcmc = pyro.infer.MCMC(nuts_kernel, num_samples=10) - mcmc.run(y1=y1, y2=y2) - inference_data = from_pyro(mcmc) - test_dict = { - "posterior": ["x"], - "sample_stats": ["diverging"], - "log_likelihood": ["y1", "y2"], - "observed_data": ["y1", "y2"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - assert not hasattr(inference_data.sample_stats, "log_likelihood") - - def test_inference_data_constant_data(self): - x1 = 10 - x2 = 12 - y1 = torch.randn(10) - - def model_constant_data(x, y1=None): - _x = pyro.sample("x", dist.Normal(1, 3)) - pyro.sample("y1", dist.Normal(x * _x, 1), obs=y1) - - nuts_kernel = pyro.infer.NUTS(model_constant_data) - mcmc = pyro.infer.MCMC(nuts_kernel, num_samples=10) - mcmc.run(x=x1, y1=y1) - posterior = mcmc.get_samples() - posterior_predictive = Predictive(model_constant_data, posterior)(x1) - predictions = Predictive(model_constant_data, posterior)(x2) - inference_data = from_pyro( - mcmc, - posterior_predictive=posterior_predictive, - predictions=predictions, - constant_data={"x1": x1}, - predictions_constant_data={"x2": x2}, - ) - test_dict = { - "posterior": ["x"], - "posterior_predictive": ["y1"], - "sample_stats": ["diverging"], - "log_likelihood": ["y1"], - "predictions": ["y1"], - "observed_data": ["y1"], - "constant_data": ["x1"], - "predictions_constant_data": ["x2"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - def test_inference_data_num_chains(self, predictions_data, chains): - predictions = predictions_data - inference_data = from_pyro(predictions=predictions, num_chains=chains) - nchains = inference_data.predictions.sizes["chain"] - assert nchains == chains - - @pytest.mark.parametrize("log_likelihood", [True, False]) - def test_log_likelihood(self, log_likelihood): - """Test behaviour when log likelihood cannot be retrieved. - - If log_likelihood=True there is a warning to say log_likelihood group is skipped, - if log_likelihood=False there is no warning and log_likelihood is skipped. - """ - x = torch.randn((10, 2)) - y = torch.randn(10) - - def model_constant_data(x, y=None): - beta = pyro.sample("beta", dist.Normal(torch.ones(2), 3)) - pyro.sample("y", dist.Normal(x.matmul(beta), 1), obs=y) - - nuts_kernel = pyro.infer.NUTS(model_constant_data) - mcmc = pyro.infer.MCMC(nuts_kernel, num_samples=10) - mcmc.run(x=x, y=y) - if log_likelihood: - with pytest.warns(UserWarning, match="Could not get vectorized trace"): - inference_data = from_pyro(mcmc, log_likelihood=log_likelihood) - else: - inference_data = from_pyro(mcmc, log_likelihood=log_likelihood) - test_dict = { - "posterior": ["beta"], - "sample_stats": ["diverging"], - "~log_likelihood": [""], - "observed_data": ["y"], - } - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails diff --git a/arviz/tests/external_tests/test_data_pystan.py b/arviz/tests/external_tests/test_data_pystan.py deleted file mode 100644 index 69f214f76b..0000000000 --- a/arviz/tests/external_tests/test_data_pystan.py +++ /dev/null @@ -1,307 +0,0 @@ -# pylint: disable=no-member, invalid-name, redefined-outer-name, too-many-function-args -import importlib -from collections import OrderedDict -import os - -import numpy as np -import pytest - -from ... import from_pystan - -from ...data.io_pystan import get_draws, get_draws_stan3 # pylint: disable=unused-import -from ..helpers import ( # pylint: disable=unused-import - chains, - check_multiple_attrs, - draws, - eight_schools_params, - importorskip, - load_cached_models, - pystan_version, -) - -# Check if either pystan or pystan3 is installed -pystan_installed = (importlib.util.find_spec("pystan") is not None) or ( - importlib.util.find_spec("stan") is not None -) - - -@pytest.mark.skipif( - not (pystan_installed or "ARVIZ_REQUIRE_ALL_DEPS" in os.environ), - reason="test requires pystan/pystan3 which is not installed", -) -class TestDataPyStan: - @pytest.fixture(scope="class") - def data(self, eight_schools_params, draws, chains): - class Data: - model, obj = load_cached_models(eight_schools_params, draws, chains, "pystan")["pystan"] - - return Data - - def get_inference_data(self, data, eight_schools_params): - """vars as str.""" - return from_pystan( - posterior=data.obj, - posterior_predictive="y_hat", - predictions="y_hat", # wrong, but fine for testing - prior=data.obj, - prior_predictive="y_hat", - observed_data="y", - constant_data="sigma", - predictions_constant_data="sigma", # wrong, but fine for testing - log_likelihood={"y": "log_lik"}, - coords={"school": np.arange(eight_schools_params["J"])}, - dims={ - "theta": ["school"], - "y": ["school"], - "sigma": ["school"], - "y_hat": ["school"], - "eta": ["school"], - }, - posterior_model=data.model, - prior_model=data.model, - ) - - def get_inference_data2(self, data, eight_schools_params): - """vars as lists.""" - return from_pystan( - posterior=data.obj, - posterior_predictive=["y_hat"], - predictions=["y_hat"], # wrong, but fine for testing - prior=data.obj, - prior_predictive=["y_hat"], - observed_data=["y"], - log_likelihood="log_lik", - coords={ - "school": np.arange(eight_schools_params["J"]), - "log_likelihood_dim": np.arange(eight_schools_params["J"]), - }, - dims={ - "theta": ["school"], - "y": ["school"], - "y_hat": ["school"], - "eta": ["school"], - "log_lik": ["log_likelihood_dim"], - }, - posterior_model=data.model, - prior_model=data.model, - ) - - def get_inference_data3(self, data, eight_schools_params): - """multiple vars as lists.""" - return from_pystan( - posterior=data.obj, - posterior_predictive=["y_hat", "log_lik"], # wrong, but fine for testing - predictions=["y_hat", "log_lik"], # wrong, but fine for testing - prior=data.obj, - prior_predictive=["y_hat", "log_lik"], # wrong, but fine for testing - constant_data=["sigma", "y"], # wrong, but fine for testing - predictions_constant_data=["sigma", "y"], # wrong, but fine for testing - coords={"school": np.arange(eight_schools_params["J"])}, - dims={ - "theta": ["school"], - "y": ["school"], - "sigma": ["school"], - "y_hat": ["school"], - "eta": ["school"], - }, - posterior_model=data.model, - prior_model=data.model, - ) - - def get_inference_data4(self, data): - """minimal input.""" - return from_pystan( - posterior=data.obj, - posterior_predictive=None, - prior=data.obj, - prior_predictive=None, - coords=None, - dims=None, - posterior_model=data.model, - log_likelihood=[], - prior_model=data.model, - save_warmup=True, - ) - - def get_inference_data5(self, data): - """minimal input.""" - return from_pystan( - posterior=data.obj, - posterior_predictive=None, - prior=data.obj, - prior_predictive=None, - coords=None, - dims=None, - posterior_model=data.model, - log_likelihood=False, - prior_model=data.model, - save_warmup=True, - dtypes={"eta": int}, - ) - - def test_sampler_stats(self, data, eight_schools_params): - inference_data = self.get_inference_data(data, eight_schools_params) - test_dict = {"sample_stats": ["diverging"]} - fails = check_multiple_attrs(test_dict, inference_data) - assert not fails - - def test_inference_data(self, data, eight_schools_params): - inference_data1 = self.get_inference_data(data, eight_schools_params) - inference_data2 = self.get_inference_data2(data, eight_schools_params) - inference_data3 = self.get_inference_data3(data, eight_schools_params) - inference_data4 = self.get_inference_data4(data) - inference_data5 = self.get_inference_data5(data) - # inference_data 1 - test_dict = { - "posterior": ["theta", "~log_lik"], - "posterior_predictive": ["y_hat"], - "predictions": ["y_hat"], - "observed_data": ["y"], - "constant_data": ["sigma"], - "predictions_constant_data": ["sigma"], - "sample_stats": ["diverging", "lp"], - "log_likelihood": ["y", "~log_lik"], - "prior": ["theta"], - } - fails = check_multiple_attrs(test_dict, inference_data1) - assert not fails - # inference_data 2 - test_dict = { - "posterior_predictive": ["y_hat"], - "predictions": ["y_hat"], - "observed_data": ["y"], - "sample_stats_prior": ["diverging"], - "sample_stats": ["diverging", "lp"], - "log_likelihood": ["log_lik"], - "prior_predictive": ["y_hat"], - } - fails = check_multiple_attrs(test_dict, inference_data2) - assert not fails - assert any( - item in inference_data2.posterior.attrs for item in ["stan_code", "program_code"] - ) - assert any( - item in inference_data2.sample_stats.attrs for item in ["stan_code", "program_code"] - ) - # inference_data 3 - test_dict = { - "posterior_predictive": ["y_hat", "log_lik"], - "predictions": ["y_hat", "log_lik"], - "constant_data": ["sigma", "y"], - "predictions_constant_data": ["sigma", "y"], - "sample_stats_prior": ["diverging"], - "sample_stats": ["diverging", "lp"], - "log_likelihood": ["log_lik"], - "prior_predictive": ["y_hat", "log_lik"], - } - fails = check_multiple_attrs(test_dict, inference_data3) - assert not fails - # inference_data 4 - test_dict = { - "posterior": ["theta"], - "prior": ["theta"], - "sample_stats": ["diverging", "lp"], - "~log_likelihood": [""], - "warmup_posterior": ["theta"], - "warmup_sample_stats": ["diverging", "lp"], - } - fails = check_multiple_attrs(test_dict, inference_data4) - assert not fails - # inference_data 5 - test_dict = { - "posterior": ["theta"], - "prior": ["theta"], - "sample_stats": ["diverging", "lp"], - "~log_likelihood": [""], - "warmup_posterior": ["theta"], - "warmup_sample_stats": ["diverging", "lp"], - } - fails = check_multiple_attrs(test_dict, inference_data5) - assert not fails - assert inference_data5.posterior.eta.dtype.kind == "i" - - def test_invalid_fit(self, data): - if pystan_version() == 2: - model = data.model - model_data = { - "J": 8, - "y": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]), - "sigma": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]), - } - fit_test_grad = model.sampling( - data=model_data, test_grad=True, check_hmc_diagnostics=False - ) - with pytest.raises(AttributeError): - _ = from_pystan(posterior=fit_test_grad) - fit = model.sampling(data=model_data, iter=100, chains=1, check_hmc_diagnostics=False) - del fit.sim["samples"] - with pytest.raises(AttributeError): - _ = from_pystan(posterior=fit) - - def test_empty_parameter(self): - model_code = """ - parameters { - real y; - vector[3] x; - vector[0] a; - vector[2] z; - } - model { - y ~ normal(0,1); - } - """ - if pystan_version() == 2: - from pystan import StanModel # pylint: disable=import-error - - model = StanModel(model_code=model_code) - fit = model.sampling(iter=500, chains=2, check_hmc_diagnostics=False) - else: - import stan # pylint: disable=import-error - - model = stan.build(model_code) - fit = model.sample(num_samples=500, num_chains=2) - - posterior = from_pystan(posterior=fit) - test_dict = {"posterior": ["y", "x", "z", "~a"], "sample_stats": ["diverging"]} - fails = check_multiple_attrs(test_dict, posterior) - assert not fails - - def test_get_draws(self, data): - fit = data.obj - if pystan_version() == 2: - draws, _ = get_draws(fit, variables=["theta", "theta"]) - else: - draws, _ = get_draws_stan3(fit, variables=["theta", "theta"]) - assert draws.get("theta") is not None - - @pytest.mark.skipif(pystan_version() != 2, reason="PyStan 2.x required") - def test_index_order(self, data, eight_schools_params): - """Test 0-indexed data.""" - # Skip test if pystan not installed - pystan = importorskip("pystan") # pylint: disable=import-error - - fit = data.model.sampling(data=eight_schools_params) - if pystan.__version__ >= "2.18": - # make 1-indexed to 0-indexed - for holder in fit.sim["samples"]: - new_chains = OrderedDict() - for i, (key, values) in enumerate(holder.chains.items()): - if "[" in key: - name, *shape = key.replace("]", "").split("[") - shape = [str(int(item) - 1) for items in shape for item in items.split(",")] - key = f"{name}[{','.join(shape)}]" - new_chains[key] = np.full_like(values, fill_value=float(i)) - setattr(holder, "chains", new_chains) - fit.sim["fnames_oi"] = list(fit.sim["samples"][0].chains.keys()) - idata = from_pystan(posterior=fit) - assert idata is not None - for j, fpar in enumerate(fit.sim["fnames_oi"]): - par, *shape = fpar.replace("]", "").split("[") - if par in {"lp__", "log_lik"}: - continue - assert hasattr(idata.posterior, par), (par, list(idata.posterior.data_vars)) - if shape: - shape = [slice(None), slice(None)] + list(map(int, shape)) - assert idata.posterior[par][tuple(shape)].values.mean() == float(j) - else: - assert idata.posterior[par].values.mean() == float(j) diff --git a/arviz/tests/helpers.py b/arviz/tests/helpers.py deleted file mode 100644 index a51a20d0c9..0000000000 --- a/arviz/tests/helpers.py +++ /dev/null @@ -1,677 +0,0 @@ -# pylint: disable=redefined-outer-name, comparison-with-callable, protected-access -"""Test helper functions.""" -import gzip -import importlib -import logging -import os -import sys -from typing import Any, Dict, List, Optional, Tuple, Union -import warnings -from contextlib import contextmanager - -import cloudpickle -import numpy as np -import pytest -from _pytest.outcomes import Skipped -from packaging.version import Version - -from ..data import InferenceData, from_dict - -_log = logging.getLogger(__name__) - - -class RandomVariableTestClass: - """Example class for random variables.""" - - def __init__(self, name): - self.name = name - - def __repr__(self): - """Return argument to constructor as string representation.""" - return self.name - - -@contextmanager -def does_not_warn(warning=Warning): - with warnings.catch_warnings(record=True) as caught_warnings: - warnings.simplefilter("always") - yield - for w in caught_warnings: - if issubclass(w.category, warning): - raise AssertionError( - f"Expected no {warning.__name__} but caught warning with message: {w.message}" - ) - - -@pytest.fixture(scope="module") -def eight_schools_params(): - """Share setup for eight schools.""" - return { - "J": 8, - "y": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]), - "sigma": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]), - } - - -@pytest.fixture(scope="module") -def draws(): - """Share default draw count.""" - return 500 - - -@pytest.fixture(scope="module") -def chains(): - """Share default chain count.""" - return 2 - - -def create_model(seed=10, transpose=False): - """Create model with fake data.""" - np.random.seed(seed) - nchains = 4 - ndraws = 500 - data = { - "J": 8, - "y": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]), - "sigma": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]), - } - posterior = { - "mu": np.random.randn(nchains, ndraws), - "tau": abs(np.random.randn(nchains, ndraws)), - "eta": np.random.randn(nchains, ndraws, data["J"]), - "theta": np.random.randn(nchains, ndraws, data["J"]), - } - posterior_predictive = {"y": np.random.randn(nchains, ndraws, len(data["y"]))} - sample_stats = { - "energy": np.random.randn(nchains, ndraws), - "diverging": np.random.randn(nchains, ndraws) > 0.90, - "max_depth": np.random.randn(nchains, ndraws) > 0.90, - } - log_likelihood = { - "y": np.random.randn(nchains, ndraws, data["J"]), - } - prior = { - "mu": np.random.randn(nchains, ndraws) / 2, - "tau": abs(np.random.randn(nchains, ndraws)) / 2, - "eta": np.random.randn(nchains, ndraws, data["J"]) / 2, - "theta": np.random.randn(nchains, ndraws, data["J"]) / 2, - } - prior_predictive = {"y": np.random.randn(nchains, ndraws, len(data["y"])) / 2} - sample_stats_prior = { - "energy": np.random.randn(nchains, ndraws), - "diverging": (np.random.randn(nchains, ndraws) > 0.95).astype(int), - } - model = from_dict( - posterior=posterior, - posterior_predictive=posterior_predictive, - sample_stats=sample_stats, - log_likelihood=log_likelihood, - prior=prior, - prior_predictive=prior_predictive, - sample_stats_prior=sample_stats_prior, - observed_data={"y": data["y"]}, - dims={ - "y": ["obs_dim"], - "log_likelihood": ["obs_dim"], - "theta": ["school"], - "eta": ["school"], - }, - coords={"obs_dim": range(data["J"])}, - ) - if transpose: - for group in model._groups: - group_dataset = getattr(model, group) - if all(dim in group_dataset.dims for dim in ("draw", "chain")): - setattr(model, group, group_dataset.transpose(*["draw", "chain"], ...)) - return model - - -def create_multidimensional_model(seed=10, transpose=False): - """Create model with fake data.""" - np.random.seed(seed) - nchains = 4 - ndraws = 500 - ndim1 = 5 - ndim2 = 7 - data = { - "y": np.random.normal(size=(ndim1, ndim2)), - "sigma": np.random.normal(size=(ndim1, ndim2)), - } - posterior = { - "mu": np.random.randn(nchains, ndraws), - "tau": abs(np.random.randn(nchains, ndraws)), - "eta": np.random.randn(nchains, ndraws, ndim1, ndim2), - "theta": np.random.randn(nchains, ndraws, ndim1, ndim2), - } - posterior_predictive = {"y": np.random.randn(nchains, ndraws, ndim1, ndim2)} - sample_stats = { - "energy": np.random.randn(nchains, ndraws), - "diverging": np.random.randn(nchains, ndraws) > 0.90, - } - log_likelihood = { - "y": np.random.randn(nchains, ndraws, ndim1, ndim2), - } - prior = { - "mu": np.random.randn(nchains, ndraws) / 2, - "tau": abs(np.random.randn(nchains, ndraws)) / 2, - "eta": np.random.randn(nchains, ndraws, ndim1, ndim2) / 2, - "theta": np.random.randn(nchains, ndraws, ndim1, ndim2) / 2, - } - prior_predictive = {"y": np.random.randn(nchains, ndraws, ndim1, ndim2) / 2} - sample_stats_prior = { - "energy": np.random.randn(nchains, ndraws), - "diverging": (np.random.randn(nchains, ndraws) > 0.95).astype(int), - } - model = from_dict( - posterior=posterior, - posterior_predictive=posterior_predictive, - sample_stats=sample_stats, - log_likelihood=log_likelihood, - prior=prior, - prior_predictive=prior_predictive, - sample_stats_prior=sample_stats_prior, - observed_data={"y": data["y"]}, - dims={"y": ["dim1", "dim2"], "log_likelihood": ["dim1", "dim2"]}, - coords={"dim1": range(ndim1), "dim2": range(ndim2)}, - ) - if transpose: - for group in model._groups: - group_dataset = getattr(model, group) - if all(dim in group_dataset.dims for dim in ("draw", "chain")): - setattr(model, group, group_dataset.transpose(*["draw", "chain"], ...)) - return model - - -def create_data_random(groups=None, seed=10): - """Create InferenceData object using random data.""" - if groups is None: - groups = ["posterior", "sample_stats", "observed_data", "posterior_predictive"] - rng = np.random.default_rng(seed) - data = rng.normal(size=(4, 500, 8)) - idata_dict = dict( - posterior={"a": data[..., 0], "b": data}, - sample_stats={"a": data[..., 0], "b": data}, - observed_data={"b": data[0, 0, :]}, - posterior_predictive={"a": data[..., 0], "b": data}, - prior={"a": data[..., 0], "b": data}, - prior_predictive={"a": data[..., 0], "b": data}, - warmup_posterior={"a": data[..., 0], "b": data}, - warmup_posterior_predictive={"a": data[..., 0], "b": data}, - warmup_prior={"a": data[..., 0], "b": data}, - ) - idata = from_dict( - **{group: ary for group, ary in idata_dict.items() if group in groups}, save_warmup=True - ) - return idata - - -@pytest.fixture() -def data_random(): - """Fixture containing InferenceData object using random data.""" - idata = create_data_random() - return idata - - -@pytest.fixture(scope="module") -def models(): - """Fixture containing 2 mock inference data instances for testing.""" - # blank line to keep black and pydocstyle happy - - class Models: - model_1 = create_model(seed=10) - model_2 = create_model(seed=11, transpose=True) - - return Models() - - -@pytest.fixture(scope="module") -def multidim_models(): - """Fixture containing 2 mock inference data instances with multidimensional data for testing.""" - # blank line to keep black and pydocstyle happy - - class Models: - model_1 = create_multidimensional_model(seed=10) - model_2 = create_multidimensional_model(seed=11, transpose=True) - - return Models() - - -def check_multiple_attrs( - test_dict: Dict[str, List[str]], parent: InferenceData -) -> List[Union[str, Tuple[str, str]]]: - """Perform multiple hasattr checks on InferenceData objects. - - It is thought to first check if the parent object contains a given dataset, - and then (if present) check the attributes of the dataset. - - Given the output of the function, all mismatches between expectation and reality can - be retrieved: a single string indicates a group mismatch and a tuple of strings - ``(group, var)`` indicates a mismatch in the variable ``var`` of ``group``. - - Parameters - ---------- - test_dict: dict of {str : list of str} - Its structure should be `{dataset1_name: [var1, var2], dataset2_name: [var]}`. - A ``~`` at the beginning of a dataset or variable name indicates the name NOT - being present must be asserted. - parent: InferenceData - InferenceData object on which to check the attributes. - - Returns - ------- - list - List containing the failed checks. It will contain either the dataset_name or a - tuple (dataset_name, var) for all non present attributes. - - Examples - -------- - The output below indicates that ``posterior`` group was expected but not found, and - variables ``a`` and ``b``: - - ["posterior", ("prior", "a"), ("prior", "b")] - - Another example could be the following: - - [("posterior", "a"), "~observed_data", ("sample_stats", "~log_likelihood")] - - In this case, the output indicates that variable ``a`` was not found in ``posterior`` - as it was expected, however, in the other two cases, the preceding ``~`` (kept from the - input negation notation) indicates that ``observed_data`` group should not be present - but was found in the InferenceData and that ``log_likelihood`` variable was found - in ``sample_stats``, also against what was expected. - - """ - failed_attrs: List[Union[str, Tuple[str, str]]] = [] - for dataset_name, attributes in test_dict.items(): - if dataset_name.startswith("~"): - if hasattr(parent, dataset_name[1:]): - failed_attrs.append(dataset_name) - elif hasattr(parent, dataset_name): - dataset = getattr(parent, dataset_name) - for attribute in attributes: - if attribute.startswith("~"): - if hasattr(dataset, attribute[1:]): - failed_attrs.append((dataset_name, attribute)) - elif not hasattr(dataset, attribute): - failed_attrs.append((dataset_name, attribute)) - else: - failed_attrs.append(dataset_name) - return failed_attrs - - -def emcee_version(): - """Check emcee version. - - Returns - ------- - int - Major version number - - """ - import emcee - - return int(emcee.__version__[0]) - - -def needs_emcee3_func(): - """Check if emcee3 is required.""" - # pylint: disable=invalid-name - needs_emcee3 = pytest.mark.skipif(emcee_version() < 3, reason="emcee3 required") - return needs_emcee3 - - -def _emcee_lnprior(theta): - """Proper function to allow pickling.""" - mu, tau, eta = theta[0], theta[1], theta[2:] - # Half-cauchy prior, hwhm=25 - if tau < 0: - return -np.inf - prior_tau = -np.log(tau**2 + 25**2) - prior_mu = -((mu / 10) ** 2) # normal prior, loc=0, scale=10 - prior_eta = -np.sum(eta**2) # normal prior, loc=0, scale=1 - return prior_mu + prior_tau + prior_eta - - -def _emcee_lnprob(theta, y, sigma): - """Proper function to allow pickling.""" - mu, tau, eta = theta[0], theta[1], theta[2:] - prior = _emcee_lnprior(theta) - like_vect = -(((mu + tau * eta - y) / sigma) ** 2) - like = np.sum(like_vect) - return like + prior, (like_vect, np.random.normal((mu + tau * eta), sigma)) - - -def emcee_schools_model(data, draws, chains): - """Schools model in emcee.""" - import emcee - - chains = 10 * chains # emcee is sad with too few walkers - y = data["y"] - sigma = data["sigma"] - J = data["J"] # pylint: disable=invalid-name - ndim = J + 2 - - pos = np.random.normal(size=(chains, ndim)) - pos[:, 1] = np.absolute(pos[:, 1]) # pylint: disable=unsupported-assignment-operation - - if emcee_version() < 3: - sampler = emcee.EnsembleSampler(chains, ndim, _emcee_lnprob, args=(y, sigma)) - # pylint: enable=unexpected-keyword-arg - sampler.run_mcmc(pos, draws) - else: - here = os.path.dirname(os.path.abspath(__file__)) - data_directory = os.path.join(here, "saved_models") - filepath = os.path.join(data_directory, "reader_testfile.h5") - backend = emcee.backends.HDFBackend(filepath) # pylint: disable=no-member - backend.reset(chains, ndim) - # pylint: disable=unexpected-keyword-arg - sampler = emcee.EnsembleSampler( - chains, ndim, _emcee_lnprob, args=(y, sigma), backend=backend - ) - # pylint: enable=unexpected-keyword-arg - sampler.run_mcmc(pos, draws, store=True) - return sampler - - -# pylint:disable=no-member,no-value-for-parameter,invalid-name -def _pyro_noncentered_model(J, sigma, y=None): - import pyro - import pyro.distributions as dist - - mu = pyro.sample("mu", dist.Normal(0, 5)) - tau = pyro.sample("tau", dist.HalfCauchy(5)) - with pyro.plate("J", J): - eta = pyro.sample("eta", dist.Normal(0, 1)) - theta = mu + tau * eta - return pyro.sample("obs", dist.Normal(theta, sigma), obs=y) - - -def pyro_noncentered_schools(data, draws, chains): - """Non-centered eight schools implementation in Pyro.""" - import torch - from pyro.infer import MCMC, NUTS - - y = torch.from_numpy(data["y"]).float() - sigma = torch.from_numpy(data["sigma"]).float() - - nuts_kernel = NUTS(_pyro_noncentered_model, jit_compile=True, ignore_jit_warnings=True) - posterior = MCMC(nuts_kernel, num_samples=draws, warmup_steps=draws, num_chains=chains) - posterior.run(data["J"], sigma, y) - - # This block lets the posterior be pickled - posterior.sampler = None - posterior.kernel.potential_fn = None - return posterior - - -# pylint:disable=no-member,no-value-for-parameter,invalid-name -def _numpyro_noncentered_model(J, sigma, y=None): - import numpyro - import numpyro.distributions as dist - - mu = numpyro.sample("mu", dist.Normal(0, 5)) - tau = numpyro.sample("tau", dist.HalfCauchy(5)) - with numpyro.plate("J", J): - eta = numpyro.sample("eta", dist.Normal(0, 1)) - theta = mu + tau * eta - return numpyro.sample("obs", dist.Normal(theta, sigma), obs=y) - - -def numpyro_schools_model(data, draws, chains): - """Centered eight schools implementation in NumPyro.""" - from jax.random import PRNGKey - from numpyro.infer import MCMC, NUTS - - mcmc = MCMC( - NUTS(_numpyro_noncentered_model), - num_warmup=draws, - num_samples=draws, - num_chains=chains, - chain_method="sequential", - ) - mcmc.run(PRNGKey(0), extra_fields=("num_steps", "energy"), **data) - - # This block lets the posterior be pickled - mcmc.sampler._sample_fn = None # pylint: disable=protected-access - mcmc.sampler._init_fn = None # pylint: disable=protected-access - mcmc.sampler._postprocess_fn = None # pylint: disable=protected-access - mcmc.sampler._potential_fn = None # pylint: disable=protected-access - mcmc.sampler._potential_fn_gen = None # pylint: disable=protected-access - mcmc._cache = {} # pylint: disable=protected-access - return mcmc - - -def pystan_noncentered_schools(data, draws, chains): - """Non-centered eight schools implementation for pystan.""" - schools_code = """ - data { - int J; - array[J] real y; - array[J] real sigma; - } - - parameters { - real mu; - real tau; - array[J] real eta; - } - - transformed parameters { - array[J] real theta; - for (j in 1:J) - theta[j] = mu + tau * eta[j]; - } - - model { - mu ~ normal(0, 5); - tau ~ cauchy(0, 5); - eta ~ normal(0, 1); - y ~ normal(theta, sigma); - } - - generated quantities { - array[J] real log_lik; - array[J] real y_hat; - for (j in 1:J) { - log_lik[j] = normal_lpdf(y[j] | theta[j], sigma[j]); - y_hat[j] = normal_rng(theta[j], sigma[j]); - } - } - """ - if pystan_version() == 2: - import pystan # pylint: disable=import-error - - stan_model = pystan.StanModel(model_code=schools_code) - fit = stan_model.sampling( - data=data, - iter=draws + 500, - warmup=500, - chains=chains, - check_hmc_diagnostics=False, - control=dict(adapt_engaged=False), - ) - else: - import stan # pylint: disable=import-error - - stan_model = stan.build(schools_code, data=data) - fit = stan_model.sample( - num_chains=chains, num_samples=draws, num_warmup=500, save_warmup=True - ) - return stan_model, fit - - -def bm_schools_model(data, draws, chains): - import beanmachine.ppl as bm # pylint: disable=import-error - import torch - import torch.distributions as dist - - class EightSchools: - @bm.random_variable - def mu(self): - return dist.Normal(0, 5) - - @bm.random_variable - def tau(self): - return dist.HalfCauchy(5) - - @bm.random_variable - def eta(self): - return dist.Normal(0, 1).expand((data["J"],)) - - @bm.functional - def theta(self): - return self.mu() + self.tau() * self.eta() - - @bm.random_variable - def obs(self): - return dist.Normal(self.theta(), torch.from_numpy(data["sigma"]).float()) - - model = EightSchools() - - prior = bm.GlobalNoUTurnSampler().infer( - queries=[model.mu(), model.tau(), model.eta()], - observations={}, - num_samples=draws, - num_adaptive_samples=500, - num_chains=chains, - ) - - posterior = bm.GlobalNoUTurnSampler().infer( - queries=[model.mu(), model.tau(), model.eta()], - observations={model.obs(): torch.from_numpy(data["y"]).float()}, - num_samples=draws, - num_adaptive_samples=500, - num_chains=chains, - ) - return model, prior, posterior - - -def library_handle(library): - """Import a library and return the handle.""" - if library == "pystan": - try: - module = importlib.import_module("pystan") - except ImportError: - module = importlib.import_module("stan") - else: - module = importlib.import_module(library) - return module - - -def load_cached_models(eight_schools_data, draws, chains, libs=None): - """Load pystan, emcee, and pyro models from pickle.""" - here = os.path.dirname(os.path.abspath(__file__)) - supported = ( - ("pystan", pystan_noncentered_schools), - ("emcee", emcee_schools_model), - ("pyro", pyro_noncentered_schools), - ("numpyro", numpyro_schools_model), - # ("beanmachine", bm_schools_model), # ignore beanmachine until it supports torch>=2 - ) - data_directory = os.path.join(here, "saved_models") - models = {} - - if isinstance(libs, str): - libs = [libs] - - for library_name, func in supported: - if libs is not None and library_name not in libs: - continue - library = library_handle(library_name) - if library.__name__ == "stan": - # PyStan3 does not support pickling - # httpstan caches models automatically - _log.info("Generating and loading stan model") - models["pystan"] = func(eight_schools_data, draws, chains) - continue - - py_version = sys.version_info - fname = "{0.major}.{0.minor}_{1.__name__}_{1.__version__}_{2}_{3}_{4}.pkl.gzip".format( - py_version, library, sys.platform, draws, chains - ) - - path = os.path.join(data_directory, fname) - if not os.path.exists(path): - with gzip.open(path, "wb") as buff: - try: - _log.info("Generating and caching %s", fname) - cloudpickle.dump(func(eight_schools_data, draws, chains), buff) - except AttributeError as err: - raise AttributeError(f"Failed caching {library_name}") from err - - with gzip.open(path, "rb") as buff: - _log.info("Loading %s from cache", fname) - models[library.__name__] = cloudpickle.load(buff) - - return models - - -def pystan_version(): - """Check PyStan version. - - Returns - ------- - int - Major version number - - """ - try: - import pystan # pylint: disable=import-error - - version = int(pystan.__version__[0]) - except ImportError: - try: - import stan # pylint: disable=import-error - - version = int(stan.__version__[0]) - except ImportError: - version = None - return version - - -def test_precompile_models(eight_schools_params, draws, chains): - """Precompile model files.""" - load_cached_models(eight_schools_params, draws, chains) - - -def importorskip( - modname: str, minversion: Optional[str] = None, reason: Optional[str] = None -) -> Any: - """Import and return the requested module ``modname``. - - Doesn't allow skips on CI machine. - Borrowed and modified from ``pytest.importorskip``. - :param str modname: the name of the module to import - :param str minversion: if given, the imported module's ``__version__`` - attribute must be at least this minimal version, otherwise the test is - still skipped. - :param str reason: if given, this reason is shown as the message when the - module cannot be imported. - :returns: The imported module. This should be assigned to its canonical - name. - Example:: - docutils = pytest.importorskip("docutils") - """ - # Unless ARVIZ_REQUIRE_ALL_DEPS is defined, tests that require a missing dependency are skipped - # if set, missing optional dependencies trigger failed tests. - if "ARVIZ_REQUIRE_ALL_DEPS" not in os.environ: - return pytest.importorskip(modname=modname, minversion=minversion, reason=reason) - - compile(modname, "", "eval") # to catch syntaxerrors - - with warnings.catch_warnings(): - # make sure to ignore ImportWarnings that might happen because - # of existing directories with the same name we're trying to - # import but without a __init__.py file - warnings.simplefilter("ignore") - __import__(modname) - mod = sys.modules[modname] - if minversion is None: - return mod - verattr = getattr(mod, "__version__", None) - if verattr is None or Version(verattr) < Version(minversion): - raise Skipped( - "module %r has __version__ %r, required is: %r" % (modname, verattr, minversion), - allow_module_level=True, - ) - return mod diff --git a/arviz/tests/saved_models/.gitignore b/arviz/tests/saved_models/.gitignore deleted file mode 100644 index 86d0cb2726..0000000000 --- a/arviz/tests/saved_models/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -# Ignore everything in this directory -* -# Except this file -!.gitignore \ No newline at end of file diff --git a/arviz/tests/saved_models/cmdstan/eight_schools.data.R b/arviz/tests/saved_models/cmdstan/eight_schools.data.R deleted file mode 100644 index 189a950f88..0000000000 --- a/arviz/tests/saved_models/cmdstan/eight_schools.data.R +++ /dev/null @@ -1,5 +0,0 @@ -J <- 8 -y <- -c(28, 8, -3, 7, -1, 1, 18, 12) -sigma <- -c(15, 10, 16, 11, 9, 11, 10, 18) diff --git a/arviz/tests/saved_models/cmdstan/eight_schools_output1.csv b/arviz/tests/saved_models/cmdstan/eight_schools_output1.csv deleted file mode 100644 index 34c1d0cb05..0000000000 --- a/arviz/tests/saved_models/cmdstan/eight_schools_output1.csv +++ /dev/null @@ -1,148 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 18 -# stan_version_patch = 0 -# model = eight_schools_nc_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 1000 (Default) -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 0 (Default) -# data -# file = eight_schools.data.R -# init = 2 (Default) -# random -# seed = 779839997 -# output -# file = eight_schools_output1.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,theta_tilde.1,theta_tilde.2,theta_tilde.3,theta_tilde.4,theta_tilde.5,theta_tilde.6,theta_tilde.7,theta_tilde.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 -# Adaptation terminated -# Step size = 0.457932 -# Diagonal elements of inverse mass matrix: -# 9.90327, 1.6423, 0.896773, 0.818821, 0.863037, 1.01397, 0.923057, 0.843567, 0.9547, 0.785471 --6.80141,0.941564,0.457932,3,7,0,10.3077,8.22396,3.64491,-0.606567,0.890522,-2.30096,0.240332,-0.0384109,-0.121282,-0.264219,-0.987772,6.01308,11.4698,-0.162842,9.09995,8.08396,7.7819,7.2609,4.62362,-4.70127,-3.28172,-3.70725,-3.33506,-3.62553,-3.50689,-3.79816,-3.89328,20.7317,20.731,14.2208,34.2177,8.33889,9.67195,4.40839,41.2007 --8.50201,0.944826,0.457932,3,7,0,12.3851,13.1883,2.98982,-1.05137,-0.86088,-1.12852,1.09828,0.375517,-0.314971,0.00270363,0.0146909,10.0449,10.6145,9.81429,16.472,14.3111,12.2466,13.1964,13.2323,-4.3434,-3.2557,-4.01224,-3.68757,-4.56326,-3.83951,-3.33689,-3.81165,1.81016,11.0417,23.2999,20.7194,17.8092,18.2309,13.6693,8.1957 --9.76892,0.86231,0.457932,3,7,0,13.3638,-0.382747,0.979588,1.57676,0.901973,1.09696,-1.52133,-1.37546,-0.456012,-0.53385,-1.25118,1.16182,0.500815,0.691823,-1.87303,-1.73013,-0.829451,-0.905701,-1.60839,-5.22763,-3.50271,-3.71815,-3.64217,-3.11945,-3.33066,-5.00865,-4.09509,-6.83132,-5.75867,-20.9807,-13.7756,-12.4971,13.0095,-4.00945,3.62144 --7.85511,0.981983,0.457932,3,7,0,15.314,8.28647,0.964356,-1.07422,1.38666,0.905918,-0.466892,-1.60639,0.802725,0.379857,-0.773919,7.25054,9.6237,9.1601,7.83622,6.73735,9.06059,8.65279,7.54014,-4.58374,-3.23471,-3.98033,-3.31972,-3.48571,-3.58532,-3.65838,-3.84001,21.3886,18.2094,16.873,-1.0727,-5.52039,13.5899,24.013,24.7132 --8.16306,0.938818,0.457932,3,7,0,17.8718,-2.60588,3.5403,1.87432,-0.460782,-0.29393,0.778302,1.388,-1.23958,1.11029,-0.211361,4.02978,-4.23719,-3.64648,0.149544,2.30807,-6.99437,1.32488,-3.35416,-4.90381,-3.97027,-3.69234,-3.51075,-3.18371,-3.58092,-4.61182,-4.17312,18.7915,-13.1667,-22.8278,-14.0363,13.2386,-17.3464,25.9182,-3.22994 --9.00993,0.520802,0.457932,3,7,0,15.2625,-1.02061,0.702033,0.239642,0.154519,-0.73469,-1.38135,1.20244,-1.40908,1.30205,-0.693137,-0.852374,-0.912133,-1.53639,-1.99036,-0.176454,-2.00983,-0.10653,-1.50722,-5.4769,-3.61865,-3.69571,-3.65083,-3.12035,-3.35427,-4.86076,-4.09086,8.64413,22.1012,-33.269,-1.91822,23.7092,5.61891,14.8084,-14.7471 --7.35586,0.933757,0.457932,3,7,0,17.5779,10.1532,2.30242,0.289398,0.399781,0.692642,1.46035,-1.1973,1.19329,-0.30011,0.831504,10.8195,11.0737,11.748,13.5156,7.39654,12.9007,9.46224,12.0677,-4.28292,-3.26876,-4.11634,-3.49226,-3.55136,-3.90207,-3.58599,-3.80932,33.7331,20.188,2.36825,1.57983,-15.6932,-7.02372,3.4057,24.1595 --9.45684,0.96495,0.457932,3,7,0,12.3199,8.25661,4.22507,0.327862,-0.831692,1.49228,1.23425,-0.548235,1.625,-1.25303,1.12614,9.64185,4.74265,14.5616,13.4714,5.94028,15.1224,2.96246,13.0146,-4.37593,-3.27458,-4.29389,-3.48989,-3.41349,-4.14097,-4.35216,-3.8109,9.30864,-3.06009,32.6279,25.286,14.7522,0.0164033,8.89058,34.2544 --10.0728,0.555362,0.457932,3,7,0,16.2945,5.54122,0.598762,1.9965,-0.796498,0.974125,-0.648374,-0.37144,1.65826,-0.281126,2.00791,6.73665,5.0643,6.12449,5.153,5.31881,6.53412,5.37289,6.74348,-4.63172,-3.26462,-3.85414,-3.33093,-3.36263,-3.44339,-4.01874,-3.85195,-16.442,12.5684,2.87363,2.22922,-5.96582,5.13946,4.18324,32.5952 --9.5475,1,0.457932,3,7,0,15.463,-1.81038,0.384059,-0.1073,-0.362897,0.964029,1.5409,-0.812223,0.479804,-0.858745,1.33078,-1.85159,-1.94975,-1.44014,-1.21858,-2.12232,-1.62611,-2.14019,-1.29928,-5.60725,-3.71651,-3.69628,-3.59595,-3.12394,-3.34533,-5.24966,-4.08226,5.98475,-7.90046,12.865,-3.83532,-6.43626,0.917775,-9.73741,-4.26039 --7.16027,0.977553,0.457932,3,7,0,14.1361,5.5965,3.61135,1.21671,-0.040642,-0.859558,-1.26011,0.628525,2.19799,0.607011,0.269931,9.99048,5.44973,2.49233,1.04581,7.86632,13.5342,7.78863,6.57132,-4.34775,-3.25404,-3.75044,-3.46333,-3.60142,-3.96603,-3.74288,-3.85479,17.8214,28.4382,-45.9267,-10.5305,25.3337,-7.51767,14.9478,-15.9449 --5.96269,1,0.457932,3,7,0,8.82798,6.40297,1.93993,1.43035,-0.231534,-0.819455,0.175408,0.978138,1.54062,0.664454,0.33681,9.17775,5.95381,4.81328,6.74325,8.30049,9.39167,7.69196,7.05635,-4.41427,-3.24246,-3.81076,-3.31711,-3.65011,-3.60783,-3.7528,-3.84703,35.9887,-8.37005,13.8282,13.0024,17.6976,29.9996,0.25004,2.2186 --6.54023,0.970673,0.457932,3,7,0,9.82889,-0.569571,9.00661,1.03271,0.410692,0.349933,-0.813878,-0.810189,1.17756,-0.0109353,-0.827873,8.73165,3.12938,2.58214,-7.89985,-7.86663,10.0363,-0.668061,-8.0259,-4.45203,-3.34014,-3.75239,-4.23421,-3.40722,-3.65425,-4.96401,-4.42819,5.32756,-3.86262,4.85711,6.24052,-10.8839,10.4669,9.65297,-35.0521 --6.58352,1,0.457932,3,7,0,9.25297,8.60298,2.25839,-0.00306011,1.02604,0.517685,1.00847,0.740999,-1.32616,1.4314,-0.273356,8.59607,10.9202,9.77211,10.8805,10.2764,5.60801,11.8356,7.98564,-4.46368,-3.26416,-4.01013,-3.37906,-3.90109,-3.40458,-3.41152,-3.83418,2.483,17.3108,26.9636,20.3942,11.7508,18.3978,6.59917,0.947764 --7.67637,0.96069,0.457932,3,7,0,12.4766,1.44079,0.366654,0.559335,-0.993499,-0.602716,-0.501448,-1.2714,1.24976,-0.710444,0.731824,1.64587,1.07652,1.2198,1.25693,0.974623,1.89902,1.1803,1.70911,-5.17041,-3.4612,-3.72631,-3.45313,-3.14023,-3.32017,-4.63604,-3.97274,-35.9471,13.2574,14.0368,-14.0739,-6.99636,5.0284,-8.24823,0.534078 --5.03952,0.992047,0.457932,3,7,0,11.1654,2.38935,2.27816,0.184001,0.713683,0.21186,-0.159446,-0.686194,1.35073,-1.01934,-0.319439,2.80854,4.01524,2.872,2.02611,0.826097,5.46653,0.0671464,1.66162,-5.03723,-3.30092,-3.75887,-3.41906,-3.13675,-3.39927,-4.82946,-3.97425,11.3345,1.20053,9.52706,5.98259,0.418347,14.075,-4.82853,-27.9265 --4.96814,0.984984,0.457932,3,7,0,8.21449,5.15002,4.44377,-0.29175,-0.40364,0.613613,0.276819,0.742657,-1.13197,1.75214,0.634165,3.85355,3.35634,7.87678,6.38014,8.45022,0.119785,12.9361,7.96811,-4.92266,-3.32934,-3.92259,-3.31842,-3.66744,-3.32004,-3.34974,-3.8344,5.36556,2.47323,2.61832,-0.17437,-1.96875,16.0546,16.2285,-6.34674 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--7.19042,0.472766,0.457932,3,7,0,15.4291,-1.53703,2.94907,1.65276,-0.223136,1.12779,-1.32603,-0.0549356,0.825946,0.655875,0.510775,3.33708,-2.19507,1.78891,-5.44759,-1.69904,0.898743,0.397194,-0.0307165,-4.97868,-3.74122,-3.73632,-3.95709,-3.11918,-3.31688,-4.77082,-4.03267,41.1445,8.50801,-2.77218,-25.2126,-6.27745,14.7638,4.55621,3.21786 --10.5479,0.667624,0.457932,3,7,0,12.5138,9.89787,0.562044,-0.963275,1.11552,-1.64382,2.12237,-0.201089,-1.11407,0.542274,-0.335501,9.35646,10.5248,8.97397,11.0907,9.78485,9.27171,10.2027,9.7093,-4.39939,-3.2534,-3.97156,-3.38598,-3.83414,-3.59957,-3.52552,-3.81741,-5.70605,19.387,3.8474,-7.0134,19.0316,0.086484,5.76302,17.9037 --2.92194,1,0.457932,3,7,0,12.7558,4.29585,10.5161,0.611914,-0.713049,-0.235614,-0.723464,-0.42108,0.0900804,0.741332,0.358071,10.7308,-3.20267,1.8181,-3.3122,-0.13229,5.24315,12.0918,8.06137,-4.28971,-3.84902,-3.73687,-3.75626,-3.12081,-3.39123,-3.39606,-3.83325,13.9585,9.7562,-17.0404,5.91912,-6.96988,-8.33308,14.9622,33.3132 --6.19853,0.817549,0.457932,3,7,0,7.46739,2.26643,3.5666,1.72322,1.57004,0.382306,1.31362,-0.498713,-0.67181,1.02122,-0.973517,8.41246,7.86614,3.62996,6.95158,0.48772,-0.129648,5.9087,-1.20572,-4.47959,-3.22161,-3.77738,-3.31684,-3.12983,-3.32211,-3.95252,-4.07843,-23.0347,24.1374,0.117233,0.363753,4.3479,-3.79096,4.55906,1.72285 --8.26246,0.927872,0.457932,3,7,0,11.6172,9.92307,1.52588,-0.943019,-1.81567,-1.0447,-1.28957,-0.348415,0.556206,-0.140635,0.917825,8.48413,7.15256,8.32898,7.95534,9.39143,10.7718,9.70848,11.3236,-4.47336,-3.22511,-3.9422,-3.32061,-3.78272,-3.71141,-3.56527,-3.81002,0.213002,-10.3836,10.7299,-2.11898,18.9142,11.7219,6.01522,31.7438 --7.58882,0.992907,0.457932,3,7,0,12.1989,-1.44882,6.46016,1.99337,1.99557,0.215248,1.39763,-0.547448,-1.02804,1.1354,0.0413888,11.4287,11.4429,-0.0582871,7.5801,-4.98543,-8.09016,5.88604,-1.18145,-4.23723,-3.28079,-3.70843,-3.31822,-3.21421,-3.65828,-3.95526,-4.07744,15.5186,7.16742,0.0959979,5.30586,3.59992,-2.21673,3.09204,20.9682 --7.63223,0.2721,0.457932,3,7,0,15.2999,1.24819,2.35015,2.12978,1.77483,0.0372755,1.24677,0.541055,-1.03395,0.707907,0.60125,6.25347,5.41931,1.33579,4.17827,2.51974,-1.18175,2.91187,2.66121,-4.6779,-3.25482,-3.72824,-3.34974,-3.19264,-3.3365,-4.35978,-3.9439,-4.99021,6.88843,-7.90346,-7.27038,-2.16681,1.66296,5.94941,35.9872 --3.98561,1,0.457932,3,7,0,9.95639,7.38847,4.29756,-0.500203,0.881604,-0.331805,-0.0390795,0.0942519,0.725288,0.0491885,-0.524292,5.23882,11.1772,5.96252,7.22052,7.79353,10.5054,7.59986,5.13529,-4.77826,-3.272,-3.84842,-3.31703,-3.59348,-3.6902,-3.76234,-3.88203,6.28307,-0.278637,-0.372263,-1.68321,3.16955,-1.98539,13.4965,2.81886 --7.59491,0.865731,0.457932,3,7,0,10.5281,2.38245,0.255692,-1.55673,-0.77487,-0.417289,0.702093,-0.434534,-0.825307,-1.03885,-0.135581,1.98441,2.18433,2.27576,2.56197,2.27135,2.17143,2.11683,2.34779,-5.13101,-3.39063,-3.74589,-3.39822,-3.18222,-3.3225,-4.4829,-3.95308,-18.7847,9.07744,-25.6834,-2.33496,0.0778909,-3.36405,-2.5908,-4.78985 --5.10392,0.987989,0.457932,3,7,0,11.389,6.73945,2.90606,1.67108,0.610377,0.307429,-0.884503,0.362046,0.812367,1.07818,0.0270375,11.5957,8.51324,7.63286,4.16904,7.79158,9.10024,9.87271,6.81803,-4.22499,-3.22284,-3.91234,-3.34995,-3.59327,-3.58797,-3.55179,-3.85075,20.1337,0.243415,23.256,12.3727,-4.89314,22.1045,20.364,12.9695 --6.10215,0.978953,0.457932,3,7,0,9.39413,1.57702,6.12371,-0.215099,2.14161,0.746355,-0.201173,-0.0991538,-1.0757,1.07315,-0.213189,0.259819,14.6916,6.14748,0.345098,0.969832,-5.01027,8.14867,0.271512,-5.33703,-3.44541,-3.85496,-3.49984,-3.14012,-3.4661,-3.70677,-4.02159,20.2231,12.4163,-10.6008,-6.90027,14.7229,-7.82024,10.0107,11.421 --6.35951,0.921357,0.457932,3,7,0,10.703,8.36129,8.00534,0.306054,-1.76941,-0.965945,0.449896,-0.864969,0.461195,-0.0128534,-0.678847,10.8114,-5.80343,0.628569,11.9629,1.43691,12.0533,8.25839,2.92689,-4.28354,-4.1742,-3.71724,-3.41861,-3.15282,-3.82169,-3.69602,-3.93635,30.5092,-2.23083,-9.70763,15.2018,-7.03222,5.77655,1.84327,35.6767 --6.35951,0.0482676,0.457932,2,3,0,20.407,8.36129,8.00534,0.306054,-1.76941,-0.965945,0.449896,-0.864969,0.461195,-0.0128534,-0.678847,10.8114,-5.80343,0.628569,11.9629,1.43691,12.0533,8.25839,2.92689,-4.28354,-4.1742,-3.71724,-3.41861,-3.15282,-3.82169,-3.69602,-3.93635,19.9871,-0.418202,-21.5897,-8.93988,16.5488,21.3699,17.4663,-50.8091 --9.14901,0.982546,0.457932,3,7,0,12.1714,3.46146,0.516365,-1.12581,2.77387,-0.808457,-0.518544,0.581601,-0.27739,-0.0726788,0.591016,2.88013,4.89379,3.04401,3.19371,3.76178,3.31823,3.42394,3.76664,-5.02923,-3.26977,-3.76287,-3.3767,-3.25613,-3.33904,-4.28383,-3.91392,5.80732,3.85287,15.5041,8.36677,6.92832,12.7851,11.5041,-1.29664 --12.7348,0.91032,0.457932,3,7,0,19.1449,2.95116,0.121929,0.340363,-0.345669,0.958259,-1.94547,2.13452,0.0748966,2.18148,-0.789884,2.99266,2.90901,3.068,2.71395,3.21142,2.96029,3.21714,2.85485,-5.01669,-3.35111,-3.76344,-3.39274,-3.22564,-3.33271,-4.31419,-3.93837,-11.0401,-8.11541,12.1556,12.1345,-9.55205,-3.4784,16.0606,-10.1147 --8.74133,0.568371,0.457932,3,15,0,20.5316,5.23857,1.40136,-0.134337,0.935525,-0.200827,-2.09017,1.05722,-1.85642,0.352422,1.33807,5.05032,6.54958,4.95714,2.30949,6.72012,2.63706,5.73244,7.11369,-4.79741,-3.23204,-3.81519,-3.40775,-3.48407,-3.32791,-3.97399,-3.84616,18.2435,-6.99812,-6.39445,-8.86034,-1.33963,10.0085,7.62953,8.53056 --6.01421,1,0.457932,3,7,0,10.2642,6.67327,5.11107,0.21475,-1.36336,-0.387868,1.35138,-1.32353,1.03009,0.354747,-0.328373,7.77087,-0.294957,4.69085,13.5803,-0.0913663,11.9381,8.48641,4.99493,-4.53636,-3.56556,-3.80705,-3.49576,-3.12126,-3.81122,-3.67407,-3.88504,15.0222,-24.7713,1.5032,11.1773,3.94371,22.9815,13.973,-9.5749 --6.70467,0.950984,0.457932,3,7,0,12.4799,-0.642817,3.64649,0.512288,1.68338,0.159233,-1.00566,0.584623,-1.39181,0.0363376,0.290918,1.22523,5.49561,-0.0621757,-4.30994,1.489,-5.71805,-0.510313,0.418013,-5.22007,-3.25288,-3.70838,-3.84541,-3.1544,-3.50333,-4.93468,-4.01632,7.66451,11.3029,-0.541259,3.83412,24.4263,-21.5938,5.52504,13.2051 --5.95249,0.967378,0.457932,3,7,0,10.5642,5.66237,2.78774,1.06692,1.84257,-0.324874,1.00668,1.15002,0.0817144,0.393365,-0.402939,8.63668,10.799,4.75671,8.46873,8.86834,5.89017,6.75897,4.53908,-4.46019,-3.2607,-3.80904,-3.32575,-3.7173,-3.41565,-3.85333,-3.89521,4.19821,21.3591,-6.92152,-6.13052,8.04629,-8.19951,8.27462,24.7413 --5.26416,0.974163,0.457932,3,7,0,9.96375,5.38575,6.53859,1.63789,-0.905845,1.33449,-0.457659,0.339313,-0.343984,0.439811,0.790355,16.0952,-0.537199,14.1114,2.3933,7.60438,3.13658,8.26149,10.5536,-3.94193,-3.58594,-4.2634,-3.40453,-3.57317,-3.3357,-3.69572,-3.81254,15.6028,6.39303,-4.8087,-3.78802,4.01157,6.7666,-14.0756,8.97454 --8.52101,0.940019,0.457932,3,7,0,11.4712,2.95109,0.749397,-0.153079,-2.01214,1.54349,0.941358,1.25302,0.203119,0.546905,-0.726896,2.83638,1.4432,4.10779,3.65655,3.89011,3.10331,3.36094,2.40636,-5.03412,-3.43648,-3.7902,-3.36303,-3.26378,-3.33511,-4.29303,-3.95134,-9.2996,-13.294,15.9848,-3.20711,-3.37382,7.409,2.68015,33.6563 --10.7281,0.951872,0.457932,3,7,0,16.1392,3.00157,0.470817,-0.928573,-1.45009,-1.41627,-0.525659,1.26115,-2.32161,1.02293,0.476258,2.56438,2.31884,2.33477,2.75408,3.59534,1.90852,3.48318,3.2258,-5.0647,-3.3829,-3.74711,-3.39133,-3.24652,-3.32024,-4.27521,-3.92812,12.0971,8.61969,12.4909,-27.2918,-1.29466,-6.09426,-2.39591,-29.4002 -# -# Elapsed Time: 0.062 seconds (Warm-up) -# 0.016 seconds (Sampling) -# 0.078 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstan/eight_schools_output2.csv b/arviz/tests/saved_models/cmdstan/eight_schools_output2.csv deleted file mode 100644 index d1882fe937..0000000000 --- a/arviz/tests/saved_models/cmdstan/eight_schools_output2.csv +++ /dev/null @@ -1,148 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 18 -# stan_version_patch = 0 -# model = eight_schools_nc_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 1000 (Default) -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 0 (Default) -# data -# file = eight_schools.data.R -# init = 2 (Default) -# random -# seed = 779846429 -# output -# file = eight_schools_output2.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,theta_tilde.1,theta_tilde.2,theta_tilde.3,theta_tilde.4,theta_tilde.5,theta_tilde.6,theta_tilde.7,theta_tilde.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 -# Adaptation terminated -# Step size = 0.354157 -# Diagonal elements of inverse mass matrix: -# 8.92369, 2.20199, 0.877628, 0.794746, 0.950111, 1.06785, 0.731149, 0.860933, 0.908968, 1.05407 --7.47394,0.95594,0.354157,4,15,0,11.5466,2.51683,0.655844,-0.313525,0.967759,0.662646,-0.785828,-1.97007,0.880318,-0.114292,-0.845168,2.3112,3.15153,2.95142,2.00145,1.22477,3.09418,2.44187,1.96253,-5.09346,-3.33906,-3.76071,-3.42008,-3.14672,-3.33496,-4.4318,-3.96479,-0.846175,10.2964,9.04263,22.9827,5.72963,-13.7602,0.605285,17.9853 --7.83688,0.986055,0.354157,4,15,0,12.0163,6.05813,0.804563,0.398913,-0.0505343,-2.10448,-0.569168,0.98696,-0.837938,1.4046,0.797109,6.37909,6.01748,4.36495,5.6002,6.85221,5.38396,7.18822,6.69946,-4.6658,-3.24118,-3.79747,-3.32493,-3.49676,-3.39625,-3.806,-3.85267,4.55598,7.17996,-29.1305,0.367047,11.6246,-5.15454,21.1637,14.9065 --9.69888,0.397004,0.354157,4,15,0,15.0281,5.70221,0.242001,0.533076,0.541915,1.23401,-0.488709,-1.72239,1.74132,-1.33748,-0.373935,5.83122,5.83336,6.00084,5.58394,5.28539,6.12361,5.37854,5.61172,-4.71911,-3.245,-3.84976,-3.32512,-3.36003,-3.42531,-4.01803,-3.87229,2.55417,20.2813,11.4121,-9.27873,3.77824,-6.27415,11.3639,-3.32206 --6.65928,0.974765,0.354157,3,7,0,13.9792,4.69706,1.08584,-0.431396,1.61088,1.96133,-0.264636,-0.204511,0.529295,-0.0227719,-0.217631,4.22863,6.44623,6.82676,4.40971,4.47499,5.27179,4.67233,4.46075,-4.88272,-3.23359,-3.88013,-3.34456,-3.3012,-3.39224,-4.10966,-3.89703,19.7913,4.16107,-14.19,11.529,4.69365,-2.25331,-1.51772,-15.143 --11.3516,0.917934,0.354157,3,7,0,13.7536,4.67067,2.98187,-1.00984,2.18466,2.56224,0.0499781,-0.599229,1.36766,1.60948,-0.642735,1.65946,11.185,12.3109,4.8197,2.88385,8.74885,9.46993,2.75412,-5.16882,-3.27225,-4.14939,-3.33648,-3.20928,-3.56495,-3.58533,-3.94123,6.41165,17.3863,-5.03145,-2.69374,3.09182,12.5522,-1.49999,-17.9405 --7.66731,0.926301,0.354157,4,15,0,19.5077,2.30707,2.59919,-1.6149,-0.214639,1.81891,0.483574,1.1637,-0.172235,0.0551978,1.02297,-1.89037,1.74918,7.03478,3.56397,5.33176,1.85939,2.45053,4.96596,-5.6124,-3.41689,-3.8882,-3.36562,-3.36364,-3.31989,-4.43045,-3.88566,-16.1578,-7.61453,-1.26256,0.562578,-4.53509,-4.15443,-4.08806,8.87469 --5.7603,0.994014,0.354157,4,15,0,11.5342,5.17134,7.62507,2.11924,0.503784,-1.75614,-0.688471,-1.01946,0.0658293,0.739653,-0.598824,21.3307,9.01272,-8.21934,-0.0782982,-2.6021,5.67329,10.8112,0.605261,-3.72583,-3.22665,-3.74473,-3.52387,-3.13201,-3.40708,-3.47992,-4.00968,4.65435,-0.350809,7.24804,6.25718,-2.83264,-1.58109,14.4606,0.414963 --7.75819,0.415072,0.354157,3,15,0,10.842,5.06983,3.33504,2.41519,0.55573,-2.0705,-0.521909,-0.732313,0.276091,1.28468,-0.824718,13.1246,6.92321,-1.83538,3.32925,2.62754,5.99061,9.35429,2.31937,-4.11872,-3.22732,-3.69418,-3.37251,-3.19739,-3.41975,-3.59527,-3.95393,8.11512,-6.11876,10.2838,11.5554,13.3597,10.147,2.05732,-13.7773 --2.46401,1,0.354157,3,15,0,8.7687,3.38879,12.7817,0.347131,0.540657,0.564453,-0.156422,-0.268726,-0.482472,1.1363,0.403242,7.82571,10.2993,10.6035,1.38945,-0.0459752,-2.77802,17.9126,8.5429,-4.53144,-3.24796,-4.05296,-3.44691,-3.12178,-3.37581,-3.22156,-3.82775,8.75372,6.02843,-8.0415,-18.6447,-10.8288,7.5835,16.4411,2.29692 --6.07374,0.506162,0.354157,3,15,0,12.7409,3.69075,2.42461,-0.516867,-0.810823,0.675061,-1.07628,1.29137,-1.01785,1.19088,0.461287,2.43755,1.72482,5.32751,1.0812,6.82181,1.22285,6.57817,4.80919,-5.07908,-3.41841,-3.82697,-3.4616,-3.49382,-3.31704,-3.87381,-3.88911,-17.8037,-7.34243,-18.7236,6.97621,-0.791315,11.8612,-9.16229,-3.3342 --7.40373,0.941745,0.354157,4,15,0,9.9062,0.86336,0.598839,0.664659,-1.49478,0.235926,0.15878,1.38934,-1.02301,0.530773,0.484771,1.26138,-0.0317699,1.00464,0.958444,1.69535,0.250741,1.18121,1.15366,-5.21577,-3.54407,-3.72285,-3.46766,-3.16101,-3.31915,-4.63588,-3.99086,-4.43464,-3.22643,45.0467,0.943717,4.55817,-12.5541,-16.4415,0.868407 --8.05769,0.984372,0.354157,4,15,0,10.7262,7.00054,1.61109,0.137052,1.75031,-0.764951,0.513133,-2.36062,0.69913,-0.138915,0.839013,7.22134,9.82044,5.76813,7.82724,3.19737,8.1269,6.77673,8.35226,-4.58644,-3.23809,-3.84168,-3.31966,-3.22492,-3.52672,-3.85133,-3.82984,5.8461,-2.38596,-12.7082,-13.8672,10.1484,5.27165,0.682601,19.1674 --10.6281,0.606092,0.354157,3,7,0,13.372,7.68885,0.254019,0.185477,1.59538,0.0603681,1.42807,-2.56688,0.4295,0.107127,0.40595,7.73597,8.09411,7.70419,8.05161,7.03682,7.79795,7.71606,7.79197,-4.5395,-3.22157,-3.91532,-3.3214,-3.51487,-3.50779,-3.75032,-3.83664,-3.23276,-1.83843,7.11306,24.9587,12.5551,-7.61233,-15.4264,38.0715 --8.03887,0.793878,0.354157,4,15,0,14.28,3.20946,0.286602,0.893881,-0.961573,0.215412,0.23361,2.13354,-0.356433,0.662745,0.575997,3.46565,2.93387,3.2712,3.27641,3.82094,3.1073,3.3994,3.37454,-4.96462,-3.34985,-3.76834,-3.37413,-3.25963,-3.33518,-4.28741,-3.92412,5.66096,-0.748347,-11.9565,-1.31746,8.41179,2.46166,18.3775,-6.38735 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--7.76763,1,0.354157,3,7,0,10.6705,8.91226,2.81343,0.622786,1.39396,0.71206,-1.3326,-0.570731,-2.1956,-0.0460867,-0.0488291,10.6644,12.8341,10.9156,5.1631,7.30655,2.73509,8.7826,8.77488,-4.29482,-3.33836,-4.06974,-3.33078,-3.54208,-3.32927,-3.64633,-3.82536,-1.82178,27.6898,8.5424,4.39618,11.7042,-7.90767,27.4571,-6.85491 --6.32619,0.945217,0.354157,3,7,0,16.1525,8.47274,1.06905,1.32834,1.12637,-1.18686,-0.161213,0.389699,-0.818563,-0.210227,0.180659,9.8928,9.67689,7.20392,8.30039,8.88935,7.59765,8.24799,8.66587,-4.35559,-3.23558,-3.89489,-3.32382,-3.71986,-3.49671,-3.69703,-3.82647,8.67471,1.97696,2.23957,19.9607,15.2778,6.92549,10.631,-28.9431 --5.37305,0.320428,0.354157,4,15,0,15.6349,9.8423,3.1651,0.651283,0.37514,-1.77582,0.0495328,0.191011,-0.662691,-0.132231,-0.129535,11.9037,11.0296,4.22165,9.99907,10.4469,7.74481,9.42377,9.4323,-4.20275,-3.26742,-3.79339,-3.354,-3.92499,-3.50482,-3.58928,-3.81948,-2.86623,27.8699,11.997,11.8829,-1.82443,-6.17509,-5.31896,38.4775 --4.60172,0.919619,0.354157,3,7,0,9.28063,8.98368,7.38539,0.464747,0.355758,-1.36252,-0.550998,0.145656,-0.448277,-0.532348,0.0552733,12.416,11.6111,-1.07905,4.91435,10.0594,5.67298,5.05208,9.3919,-4.16668,-3.28672,-3.69873,-3.33481,-3.87117,-3.40707,-4.05977,-3.81981,7.38805,36.8051,-5.55002,16.0613,5.16515,-5.97807,6.76359,9.2952 --6.35569,0.531608,0.354157,4,23,0,10.3595,-0.842647,2.03864,0.229123,-0.553828,1.76399,0.787985,-0.190292,-0.149841,1.13807,0.321833,-0.375549,-1.9717,2.7535,0.763771,-1.23058,-1.14812,1.47746,-0.186545,-5.41626,-3.7187,-3.75618,-3.47754,-3.11649,-3.3359,-4.58649,-4.0385,-5.23463,-18.4974,45.2283,3.49943,1.94054,-21.0715,-5.63003,5.58009 --3.24387,0.99767,0.354157,3,15,0,8.03947,6.66535,5.51539,0.30521,0.431879,-1.36355,0.0156842,-0.316734,-0.513214,0.54688,-0.725325,8.34871,9.04733,-0.855171,6.75186,4.91844,3.83477,9.68161,2.6649,-4.48515,-3.22701,-3.70051,-3.31709,-3.33238,-3.35004,-3.5675,-3.94379,25.3382,25.1169,7.04115,6.2756,-3.43961,20.1908,1.64963,13.9535 --5.97808,0.913587,0.354157,3,15,0,9.77412,7.33101,6.19576,-0.61075,0.511471,1.06545,-0.912906,0.199947,0.728069,0.864698,1.09543,3.54695,10.5,13.9323,1.67487,8.56984,11.842,12.6885,14.118,-4.95577,-3.25277,-4.25149,-3.43401,-3.68148,-3.80257,-3.36258,-3.81623,1.73015,-7.38185,16.8031,4.77477,2.15936,12.9944,22.9391,10.6425 --6.95105,0.79765,0.354157,3,7,0,10.553,7.09926,3.4358,-1.27727,-0.0886323,1.81056,-0.977447,-0.0372782,0.910615,0.299828,0.559296,2.71081,6.79474,13.32,3.74095,6.97118,10.228,8.12941,9.02089,-5.0482,-3.22879,-4.21173,-3.36072,-3.50838,-3.66871,-3.70867,-3.82301,-0.840264,7.14057,5.90665,-9.15454,0.742178,16.6644,8.54787,10.5743 --7.98867,0.963605,0.354157,4,15,0,14.3633,9.1364,3.13533,1.52441,-1.56734,0.888697,-1.27231,0.667386,0.096607,1.20067,0.746397,13.9159,4.22227,11.9228,5.1473,11.2289,9.4393,12.9009,11.4766,-4.06779,-3.29288,-4.12647,-3.33102,-4.03928,-3.61114,-3.35153,-3.80973,18.5278,-12.3407,13.2789,-10.7816,4.6782,0.960266,24.8065,-2.52557 --5.04989,0.999579,0.354157,3,7,0,9.65005,2.19027,8.28882,0.193608,-0.0185777,-0.848997,1.86863,-0.464811,-0.117067,-0.02808,-0.268725,3.79505,2.03628,-4.84692,17.679,-1.66247,1.21992,1.95752,-0.0371478,-4.92894,-3.39935,-3.69819,-3.78808,-3.11887,-3.31703,-4.50833,-4.03291,23.6952,6.77119,-34.2569,0.221394,-9.61064,-0.678367,10.8271,-1.86142 --3.55845,1,0.354157,3,15,0,6.80717,4.43546,4.66234,0.528122,-0.0723997,-1.04681,0.8707,-0.531628,0.372383,-0.358003,-0.568411,6.89775,4.09791,-0.445122,8.49496,1.95683,6.17164,2.76633,1.78533,-4.61656,-3.29766,-3.70428,-3.32607,-3.17013,-3.42735,-4.38185,-3.97033,-25.9465,9.0618,1.07422,2.25513,-6.85527,-25.578,23.7393,-33.3081 --4.16343,0.980154,0.354157,4,15,0,6.31965,4.44426,2.96508,0.824258,-0.280971,0.165136,-0.676195,1.38469,-0.540542,0.539064,-0.066181,6.88825,3.61115,4.9339,2.43928,8.54997,2.8415,6.04263,4.24802,-4.61745,-3.31783,-3.81447,-3.40278,-3.67914,-3.33085,-3.93642,-3.90205,1.97566,13.9186,23.7,27.6075,0.815118,11.086,-1.5331,-11.175 --3.6067,0.936585,0.354157,4,15,0,8.00182,6.03772,4.37465,-0.386648,0.565163,0.0940498,0.235393,-1.38345,0.521954,0.79009,-0.240549,4.34627,8.51011,6.44916,7.06748,-0.0143716,8.32109,9.49409,4.98541,-4.87032,-3.22282,-3.86592,-3.31685,-3.12216,-3.53831,-3.58328,-3.88524,-5.62047,-3.9804,28.9678,14.8138,-2.60025,7.84138,-7.45938,-8.12666 --3.77026,0.900183,0.354157,4,15,0,7.80166,2.92712,4.76636,0.869327,0.428785,-1.30284,0.530322,0.641925,-0.201373,-0.251462,-0.0685885,7.07065,4.97087,-3.28268,5.45483,5.98677,1.96731,1.72857,2.60021,-4.60041,-3.2674,-3.69168,-3.3267,-3.41749,-3.3207,-4.54532,-3.94566,1.43269,7.44407,4.85872,-2.03509,0.133287,10.9837,8.03978,-2.64918 --9.55923,0.801666,0.354157,3,7,0,13.1579,8.15493,0.417492,-0.186416,-1.10191,1.48459,-0.545452,-1.79137,-0.398637,0.92646,1.48463,8.07711,7.69489,8.77474,7.92721,7.40705,7.9885,8.54172,8.77475,-4.50904,-3.22199,-3.96232,-3.32039,-3.55245,-3.51865,-3.66882,-3.82536,3.31401,13.2007,19.9087,-1.67816,5.03936,-5.14503,26.7311,19.0948 --8.42643,0.974032,0.354157,4,15,0,12.3493,0.0754629,0.806333,0.713878,1.55245,-1.97758,0.0849246,0.969246,0.302004,0.388854,-0.743353,0.651086,1.32726,-1.51913,0.14394,0.856998,0.318979,0.389009,-0.523928,-5.28913,-3.44415,-3.69581,-3.51107,-3.13745,-3.31875,-4.77226,-4.05136,6.12098,1.06409,29.5662,-14.4291,8.69335,-12.8054,16.8205,21.282 --5.9065,0.969742,0.354157,4,31,0,11.8489,-2.88608,1.89973,0.6754,0.41077,0.149956,-0.39986,-0.350713,-0.561561,0.848145,0.108551,-1.603,-2.10573,-2.6012,-3.6457,-3.55234,-3.95289,-1.27484,-2.67986,-5.57441,-3.73215,-3.69184,-3.78514,-3.15638,-3.4182,-5.07912,-4.14187,39.5658,-24.4255,9.65745,-17.1756,-3.00332,-21.2299,-13.0688,0.917936 --4.30531,0.966806,0.354157,3,7,0,9.54016,-0.993275,2.31244,0.210702,0.219541,0.0981948,-0.146457,-0.188087,-0.643453,0.572508,0.632073,-0.506039,-0.485599,-0.766205,-1.33195,-1.42821,-2.48122,0.330616,0.468356,-5.43275,-3.58155,-3.70127,-3.6037,-3.1173,-3.36691,-4.78256,-4.01452,30.6309,-3.75508,2.58391,-8.78291,-10.2157,-18.9887,-1.94144,-35.5629 --5.18772,0.926039,0.354157,3,7,0,10.2096,-0.329832,1.80661,0.524287,0.685652,0.00897306,-0.291768,-0.527856,-0.290122,0.572281,1.44998,0.617353,0.908877,-0.313621,-0.856945,-1.28346,-0.853971,0.70406,2.28973,-5.29323,-3.47294,-3.70562,-3.57192,-3.11666,-3.33104,-4.71727,-3.95482,5.89531,9.32472,11.4997,-9.59099,4.40914,-19.9701,-0.999336,15.7914 --6.95991,0.719992,0.354157,3,7,0,10.3047,-0.141701,0.367664,0.063391,-1.19533,-0.0344811,-0.112002,-0.255466,-0.47804,1.36773,0.0163573,-0.118394,-0.581181,-0.154378,-0.18288,-0.235626,-0.317459,0.361165,-0.135687,-5.38398,-3.58971,-3.70734,-3.53003,-3.11977,-3.32401,-4.77717,-4.03659,-7.52536,-6.99631,14.6977,-8.22898,6.97905,-6.11502,-16.239,5.64991 --5.1125,0.843785,0.354157,3,7,0,9.58193,0.691982,2.69368,0.222018,-0.312634,0.189221,-0.196863,0.17066,0.715987,2.11178,0.67668,1.29003,-0.150154,1.20168,0.161696,1.15168,2.62062,6.38044,2.51474,-5.21237,-3.55365,-3.72601,-3.51007,-3.14474,-3.32769,-3.89659,-3.94815,0.207283,-14.5794,-2.56741,13.8953,-10.7208,7.41178,-2.8011,7.44487 --5.06755,0.986765,0.354157,4,15,0,9.81284,5.92303,1.44263,0.942297,0.495934,-0.135528,1.33106,-0.142596,-0.985964,-0.76358,0.285706,7.28241,6.63848,5.72751,7.84325,5.71732,4.50065,4.82147,6.3352,-4.58081,-3.23079,-3.8403,-3.31977,-3.3947,-3.36747,-4.08989,-3.85883,16.8223,2.68592,20.7242,13.699,17.4032,15.5372,9.01115,3.89254 -# -# Elapsed Time: 0.084 seconds (Warm-up) -# 0.02 seconds (Sampling) -# 0.104 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstan/eight_schools_output3.csv b/arviz/tests/saved_models/cmdstan/eight_schools_output3.csv deleted file mode 100644 index f151a78bdb..0000000000 --- a/arviz/tests/saved_models/cmdstan/eight_schools_output3.csv +++ /dev/null @@ -1,148 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 18 -# stan_version_patch = 0 -# model = eight_schools_nc_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 1000 (Default) -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 0 (Default) -# data -# file = eight_schools.data.R -# init = 2 (Default) -# random -# seed = 779850357 -# output -# file = eight_schools_output3.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,theta_tilde.1,theta_tilde.2,theta_tilde.3,theta_tilde.4,theta_tilde.5,theta_tilde.6,theta_tilde.7,theta_tilde.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 -# Adaptation terminated -# Step size = 0.46217 -# Diagonal elements of inverse mass matrix: -# 10.2211, 1.04006, 0.837051, 0.867313, 0.896655, 0.845892, 0.751893, 0.777127, 0.970237, 0.989225 --4.54924,0.964931,0.46217,3,7,0,8.33891,1.37664,5.55848,1.33076,1.06894,-0.144721,0.102173,0.90316,-0.732836,0.474602,-0.943875,8.77366,7.31833,0.572216,1.94457,6.39684,-2.69681,4.01471,-3.86987,-4.44844,-3.22385,-3.71645,-3.42244,-3.4539,-3.37331,-4.19947,-4.19797,17.0892,6.88432,-17.4988,-0.929933,12.1068,17.0414,3.49897,-18.537 --6.51214,0.938057,0.46217,3,7,0,8.76924,8.73398,1.84289,-0.185979,-1.18792,-0.77943,-0.0506853,-1.42907,0.691048,-0.841192,1.01103,8.39124,6.54478,7.29758,8.64058,6.10036,10.0075,7.18376,10.5972,-4.48144,-3.23211,-3.89864,-3.32796,-3.42737,-3.6521,-3.80648,-3.81235,-1.25812,7.83355,-4.0309,22.1396,-3.62405,24.8154,11.1841,26.7111 --8.24786,0.944448,0.46217,3,7,0,13.1753,3.00857,5.67234,-1.40455,-1.00473,-0.0831752,1.09734,0.180775,-2.092,0.617633,0.538814,-4.95849,-2.69059,2.53678,9.23308,4.03399,-8.85793,6.512,6.06491,-6.04091,-3.79297,-3.7514,-3.33744,-3.27259,-3.7184,-3.88139,-3.86367,-29.609,-1.37327,-26.1646,15.5131,-6.16955,-28.3195,-0.945076,5.07752 --5.09479,1,0.46217,3,7,0,10.2288,6.931,0.628256,0.640968,0.70962,0.315611,-0.318589,-0.624961,0.535039,-0.709337,0.53566,7.33369,7.37682,7.12928,6.73084,6.53836,7.26714,6.48535,7.26753,-4.57609,-3.22347,-3.89192,-3.31713,-3.46695,-3.47914,-3.88446,-3.84387,-6.6734,12.3924,-20.6259,6.2794,6.42416,8.05041,-1.8584,10.4693 --3.53225,0.666672,0.46217,2,3,0,10.4816,3.05679,8.72987,0.199424,-0.788381,0.00676703,-0.579385,-0.181618,0.271412,0.494511,0.951217,4.79773,-3.82568,3.11586,-2.00117,1.47128,5.42618,7.37381,11.3608,-4.82331,-3.92076,-3.76458,-3.65163,-3.15386,-3.39779,-3.7861,-3.80994,6.94364,-5.14496,-5.57875,-0.0800579,0.902464,-1.48243,-11.7417,21.5411 --3.9508,0.827432,0.46217,3,7,0,7.89686,6.77145,2.11424,0.219054,1.10251,-0.058967,0.652808,-0.322667,-0.126193,-0.0311045,-0.902736,7.23458,9.10241,6.64678,8.15164,6.08926,6.50465,6.70569,4.86286,-4.58522,-3.2276,-3.87329,-3.32231,-3.4264,-3.44205,-3.85933,-3.88792,14.1418,14.7469,-9.69701,10.2679,9.31372,4.7583,25.6301,33.6286 --3.90758,0.988445,0.46217,3,7,0,6.26912,4.1016,4.98799,0.175051,-0.82782,0.169127,-1.14999,-0.294706,0.0652591,0.348292,1.0344,4.97475,-0.0275558,4.9452,-1.63453,2.63161,4.42711,5.83888,9.26119,-4.80513,-3.54373,-3.81482,-3.62491,-3.19757,-3.36537,-3.96099,-3.82089,23.0909,-2.96398,19.551,15.8445,-5.14437,8.85883,-1.32927,10.3594 --3.64186,0.879996,0.46217,3,7,0,9.7135,3.86282,5.49303,0.189639,-0.577668,-1.10926,-1.02965,-0.141624,0.181295,0.928773,0.352504,4.90452,0.689679,-2.23038,-1.79309,3.08488,4.85868,8.9646,5.79914,-4.81233,-3.48873,-3.69268,-3.63633,-3.21916,-3.37836,-3.62972,-3.86865,14.4903,-2.50334,4.97732,-5.21066,-8.09146,-8.26108,-0.0805677,-23.0003 --6.4681,0.95197,0.46217,3,7,0,7.84097,7.33811,2.18114,1.29857,-1.86969,-0.655395,-0.987013,-1.06411,0.0981364,0.0422256,0.57848,10.1705,3.26007,5.9086,5.1853,5.01713,7.55216,7.43021,8.59986,-4.33342,-3.33386,-3.84653,-3.33044,-3.33966,-3.49423,-3.78013,-3.82715,18.1726,15.7322,47.1296,20.2899,8.84922,1.49333,-9.4901,-33.3483 --12.7112,0.775787,0.46217,3,7,0,17.5652,8.00397,6.68198,3.19289,1.18848,1.11871,-0.0890833,-0.0493019,-1.0204,2.35681,-1.20846,29.3388,15.9453,15.4792,7.40872,7.67454,1.18568,23.7521,-0.0709549,-3.63097,-3.53717,-4.35848,-3.31752,-3.58065,-3.31698,-3.38696,-4.03417,26.5511,13.466,6.81425,9.31315,14.9668,-4.64829,15.0185,-11.3753 --12.7112,0.0462655,0.46217,3,7,0,20.9095,8.00397,6.68198,3.19289,1.18848,1.11871,-0.0890833,-0.0493019,-1.0204,2.35681,-1.20846,29.3388,15.9453,15.4792,7.40872,7.67454,1.18568,23.7521,-0.0709549,-3.63097,-3.53717,-4.35848,-3.31752,-3.58065,-3.31698,-3.38696,-4.03417,-3.30604,6.66462,22.1059,10.3164,-4.70922,2.92869,14.3735,-27.4616 --5.26783,0.817869,0.46217,3,7,0,16.6055,7.82135,1.86713,1.49843,0.170588,0.330897,1.13726,-0.167642,-0.780007,0.765105,0.569574,10.6191,8.13986,8.43918,9.94477,7.50834,6.36497,9.2499,8.88482,-4.29831,-3.22162,-3.9471,-3.35267,-3.56303,-3.43577,-3.60434,-3.82429,23.8095,-5.32286,14.9167,-21.4609,3.86464,-2.31077,17.1725,3.26112 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--5.9883,0.805345,0.46217,3,7,0,8.96639,2.69375,0.222428,0.0611092,-0.172632,-0.666769,-0.210205,0.952143,0.457146,-0.351134,0.818952,2.70734,2.65535,2.54544,2.64699,2.90553,2.79543,2.61565,2.87591,-5.04859,-3.36435,-3.75159,-3.39513,-3.21032,-3.33015,-4.40492,-3.93778,16.8107,-13.8669,-10.2094,1.77502,-1.42796,-13.6397,-13.0592,18.3522 --5.48794,0.973295,0.46217,3,7,0,9.2088,6.94442,0.402529,0.079291,0.271418,0.722941,0.201757,-0.966989,-0.574453,0.330947,-0.74665,6.97634,7.05367,7.23542,7.02563,6.55518,6.71319,7.07764,6.64387,-4.6092,-3.226,-3.89614,-3.31684,-3.46851,-3.45171,-3.81801,-3.85358,18.2957,0.455361,12.8543,-2.40166,15.3663,21.3904,12.3843,-8.14306 --5.65612,0.935427,0.46217,3,7,0,9.30442,8.36493,4.98074,0.976294,-0.561127,-0.966889,1.04611,0.182572,0.847937,0.111914,-1.11067,13.2276,5.5701,3.54911,13.5754,9.27427,12.5883,8.92235,2.83296,-4.11193,-3.25105,-3.7753,-3.49549,-3.76777,-3.87174,-3.63354,-3.93899,-17.0029,17.2451,14.5529,2.12725,11.4853,10.5225,-4.19776,-6.18032 --10.7952,0.805282,0.46217,3,7,0,13.4372,1.14879,0.287348,0.711027,0.184917,2.66259,-1.00111,1.37446,-0.0798743,0.914589,0.683825,1.35311,1.20193,1.91388,0.861129,1.54374,1.12584,1.4116,1.34529,-5.20489,-3.45259,-3.73869,-3.47256,-3.15611,-3.3169,-4.5974,-3.9845,-8.49392,-16.2927,-5.07667,4.59389,-3.97989,7.01754,9.34203,-19.5914 --6.32011,0.999079,0.46217,3,7,0,14.5542,5.53761,1.56401,-1.91056,-0.0210893,0.624783,0.00580571,-0.119362,0.673737,1.01131,1.12643,2.54947,5.50462,6.51477,5.54669,5.35092,6.59134,7.11931,7.29936,-5.06639,-3.25266,-3.86835,-3.32556,-3.36514,-3.44602,-3.81347,-3.84341,-2.62341,2.9465,17.8381,-0.857481,2.88807,5.97503,11.6332,-1.55929 --8.53852,0.904992,0.46217,3,7,0,13.261,4.29683,1.06624,-2.6201,0.679078,-0.124937,0.555027,-1.37307,0.19606,-0.237685,-0.91549,1.50318,5.02088,4.16361,4.88861,2.83281,4.50587,4.0434,3.3207,-5.18717,-3.2659,-3.79176,-3.33526,-3.20684,-3.36762,-4.19546,-3.92556,8.23124,9.078,9.31249,7.35691,9.35974,17.2432,0.704378,3.85897 --4.40555,0.862882,0.46217,3,7,0,12.584,3.0746,10.3847,0.971565,-0.83172,-0.736302,1.25934,-0.578285,-0.679113,1.01161,0.0412502,13.164,-5.56257,-4.57168,16.1524,-2.93072,-3.97779,13.5799,3.50297,-4.11611,-4.14124,-3.69635,-3.66298,-3.13917,-3.41922,-3.31921,-3.92073,24.6964,-25.5358,27.0782,5.88183,-19.4171,-13.4726,0.442733,-10.0769 --5.62128,0.259111,0.46217,3,7,0,10.6738,4.90781,2.9982,1.32892,-1.67942,-0.499953,0.76405,-1.05738,-0.102326,1.12096,-0.488645,8.8922,-0.127441,3.40885,7.19859,1.73758,4.60102,8.26867,3.44276,-4.43834,-3.5518,-3.77175,-3.317,-3.16242,-3.37042,-3.69502,-3.92231,-13.6739,-12.0385,12.9631,7.09306,14.9653,17.5455,7.79751,-21.0316 --6.53545,0.973673,0.46217,3,7,0,9.40682,6.41411,4.40845,1.61826,-1.16495,-0.344243,1.43154,0.99724,0.300717,1.24926,0.0933536,13.5481,1.27849,4.89653,12.725,10.8104,7.73981,11.9214,6.82565,-4.09111,-3.44742,-3.81331,-3.45227,-3.97718,-3.50454,-3.40627,-3.85063,10.4204,-6.22751,-9.84192,11.3283,-1.11966,-0.960868,17.2021,-36.564 --5.2089,0.900637,0.46217,3,7,0,10.5281,5.13565,2.39314,1.68133,-0.987582,-0.110971,1.21732,0.682139,-0.101388,0.275844,0.538358,9.15931,2.77222,4.87008,8.04886,6.7681,4.89301,5.79578,6.42401,-4.41581,-3.35817,-3.8125,-3.32138,-3.48865,-3.37946,-3.96624,-3.85729,43.4582,7.30419,7.95091,-20.7749,1.46219,13.9813,-9.16407,27.3731 --4.54572,0.915197,0.46217,3,7,0,9.77963,4.69327,6.48908,-0.189,1.0204,0.459298,-0.379173,-1.43594,-0.620029,0.0455571,-0.644034,3.46684,11.3147,7.67369,2.23279,-4.62463,0.669856,4.98889,0.514084,-4.96449,-3.27646,-3.91404,-3.41074,-3.19726,-3.31728,-4.06797,-4.0129,11.9852,10.8632,-4.09185,-13.0061,-18.2204,16.9695,0.607818,27.6236 --9.88416,0.592495,0.46217,3,7,0,12.0366,4.64976,0.5805,1.47341,1.05849,-0.750004,0.373884,-1.41962,0.700752,2.28699,1.22017,5.50507,5.26421,4.21438,4.8668,3.82567,5.05655,5.97736,5.35807,-4.75148,-3.25895,-3.79318,-3.33564,-3.25991,-3.38483,-3.94424,-3.87739,0.0240778,6.0942,5.95611,1.91201,5.14696,22.5652,-1.06706,21.8846 --4.94821,0.987908,0.46217,3,7,0,12.0512,2.81165,4.66869,1.05508,1.43722,-1.28931,0.833914,-0.946629,-0.254413,1.10439,0.863679,7.73746,9.52159,-3.20776,6.70493,-1.60787,1.62387,7.9677,6.84389,-4.53937,-3.2331,-3.69161,-3.31719,-3.11844,-3.31844,-3.72476,-3.85034,9.96262,18.1838,-6.58098,12.9699,-3.91269,5.43532,12.6471,25.1148 --9.4574,1,0.46217,3,7,0,13.5529,4.52493,0.182446,-0.598855,-2.10002,1.11638,-1.31291,0.522403,-0.118613,-0.0217656,-1.12199,4.41567,4.14179,4.72861,4.28539,4.62024,4.50329,4.52096,4.32023,-4.86303,-3.29595,-3.80819,-3.34728,-3.31115,-3.36755,-4.12995,-3.90033,18.3734,0.724843,32.0409,-10.2312,2.02477,11.6806,-7.77274,-6.36391 --11.0485,0.964993,0.46217,3,7,0,15.5843,2.8773,7.23662,0.0863799,3.27298,-0.781858,-0.706072,-0.354567,-1.32938,0.0496954,0.238897,3.5024,26.5626,-2.7807,-2.23227,0.311438,-6.74294,3.23693,4.60611,-4.96062,-4.94438,-3.69162,-3.66904,-3.12678,-3.56457,-4.31126,-3.89368,25.1066,13.6888,5.45835,12.9153,15.4178,-11.7431,9.18342,-14.7722 --6.8601,0.651986,0.46217,3,7,0,13.5946,5.49526,2.23174,0.22219,2.45933,-0.888119,-0.153948,-0.296518,-1.28379,0.748142,0.646239,5.99113,10.9838,3.51321,5.15169,4.83351,2.63017,7.16492,6.9375,-4.70341,-3.26604,-3.77438,-3.33095,-3.32622,-3.32782,-3.80852,-3.84886,5.20312,1.91847,0.597056,3.9036,20.583,7.86132,3.69793,1.65579 --6.21279,0.979432,0.46217,3,7,0,11.9171,2.31566,2.95882,0.0596616,-0.919733,1.14434,0.761931,0.747305,1.39063,-0.365664,-0.772997,2.49219,-0.405667,5.70156,4.57008,4.52681,6.43031,1.23373,0.0285002,-5.07287,-3.5748,-3.83941,-3.34123,-3.30472,-3.43869,-4.62706,-4.03048,-9.79439,12.7486,17.0574,20.1992,10.2852,-2.59044,3.94728,29.242 --6.8071,0.754298,0.46217,3,7,0,19.1811,6.84017,0.278445,-0.285074,1.74735,-0.176201,-0.262214,0.175479,0.454829,0.843529,0.527123,6.76079,7.32671,6.79111,6.76716,6.88903,6.96682,7.07505,6.98695,-4.62944,-3.22379,-3.87877,-3.31706,-3.50034,-3.46395,-3.8183,-3.84809,10.3061,18.8311,21.0057,2.99174,-1.98615,23.7373,13.2917,-9.06463 --5.16832,0.924665,0.46217,3,7,0,14.6396,2.3879,0.608939,0.718618,-1.30711,0.118229,0.507662,0.0154379,-0.348057,0.30192,-0.230781,2.82549,1.59194,2.45989,2.69703,2.3973,2.17595,2.57175,2.24736,-5.03534,-3.42684,-3.74975,-3.39334,-3.18741,-3.32255,-4.41168,-3.95609,-4.27104,-4.17259,20.2535,8.56759,21.923,-1.9241,19.7861,-4.63613 --7.70471,0.958654,0.46217,3,7,0,9.13689,0.376566,0.391139,-0.0675597,-1.56897,0.876679,-0.519354,0.076215,1.24529,0.475617,-0.464777,0.350141,-0.237118,0.71947,0.173426,0.406377,0.863649,0.562599,0.194773,-5.32591,-3.56077,-3.71855,-3.5094,-3.12837,-3.31691,-4.74184,-4.02438,2.85496,15.3325,-22.5383,10.6176,-2.18148,-3.18684,-0.165636,31.7357 --7.43935,0.988148,0.46217,3,15,0,11.9173,1.98559,0.727436,-2.11951,0.4383,1.04245,0.526567,-0.649333,-0.0780418,-0.630411,-0.213856,0.443785,2.30443,2.74391,2.36864,1.51325,1.92882,1.52701,1.83003,-5.31442,-3.38372,-3.75597,-3.40547,-3.15515,-3.3204,-4.57832,-3.96892,-20.0803,-3.58776,3.74268,10.3285,20.7162,-2.75634,21.8197,-14.53 --9.75641,0.873275,0.46217,3,7,0,14.6774,8.63225,10.0619,2.81465,0.21959,-1.06699,-0.108116,0.432249,0.231833,1.93952,0.284587,36.9529,10.8417,-2.10363,7.5444,12.9815,10.9649,28.1475,11.4957,-3.80511,-3.2619,-3.6931,-3.31806,-4.32284,-3.72716,-3.73638,-3.8097,37.9281,21.7472,22.7434,-6.14951,12.3143,-6.3092,44.7001,22.3025 --9.75641,0.0330755,0.46217,3,7,0,17.0946,8.63225,10.0619,2.81465,0.21959,-1.06699,-0.108116,0.432249,0.231833,1.93952,0.284587,36.9529,10.8417,-2.10363,7.5444,12.9815,10.9649,28.1475,11.4957,-3.80511,-3.2619,-3.6931,-3.31806,-4.32284,-3.72716,-3.73638,-3.8097,47.3834,20.6809,-5.56789,-0.444512,10.3695,16.3009,29.5563,-0.851426 --6.53858,0.749103,0.46217,3,7,0,13.7473,4.87875,2.29593,1.83634,0.192651,-1.41935,0.526646,-0.767188,-0.324352,0.626055,1.78423,9.09488,5.32107,1.62001,6.0879,3.11734,4.13406,6.31613,8.97522,-4.42122,-3.25741,-3.73322,-3.32027,-3.22081,-3.35742,-3.90409,-3.82343,13.8197,18.3015,-7.69709,-3.95872,-2.68137,1.92275,4.56709,-15.0912 --4.98629,0.999141,0.46217,3,7,0,8.64404,3.83546,7.51862,0.193637,-1.20917,-0.182116,-0.176505,0.113866,-0.399223,-0.355878,-0.949053,5.29134,-5.2558,2.4662,2.50838,4.69157,0.833854,1.15975,-3.30011,-4.77295,-4.1001,-3.74989,-3.4002,-3.31613,-3.31695,-4.63949,-4.17057,-3.8968,-27.3179,23.3439,2.14408,-4.67837,-11.4248,4.19165,1.90724 --7.62911,0.630306,0.46217,3,7,0,12.4205,3.02303,1.13253,1.32969,-0.685084,0.332909,-0.875794,-1.51883,-0.593475,-0.654073,1.73002,4.52895,2.24716,3.40006,2.03117,1.30291,2.35091,2.28228,4.98233,-4.85119,-3.387,-3.77153,-3.41886,-3.1489,-3.32437,-4.45676,-3.88531,-20.1021,1.09477,-5.67644,-2.79772,13.4696,11.0857,11.9171,25.2412 --9.63933,0.97145,0.46217,3,15,0,13.296,11.8549,0.175385,-0.43626,-0.765014,-0.85647,0.506706,-0.115913,0.213847,0.64408,-1.48327,11.7784,11.7207,11.7047,11.9438,11.8346,11.8924,11.9679,11.5947,-4.21175,-3.29074,-4.11385,-3.41783,-4.13299,-3.8071,-3.40346,-3.80956,27.8671,10.3545,30.2549,-12.9802,21.8548,-10.4223,5.04628,-14.9722 --8.70272,0.988436,0.46217,3,7,0,12.7169,9.66706,0.0697988,0.285259,0.804722,-0.463068,-0.00680156,0.00860104,-0.218511,0.974603,-1.21423,9.68697,9.72323,9.63474,9.66659,9.66766,9.65181,9.73509,9.58231,-4.37225,-3.23637,-4.00332,-3.34622,-3.81863,-3.62615,-3.56307,-3.81833,6.34493,-3.89035,-5.45454,-8.58035,-4.98234,11.9311,17.3581,4.34894 -# -# Elapsed Time: 0.062 seconds (Warm-up) -# 0.032 seconds (Sampling) -# 0.094 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstan/eight_schools_output4.csv b/arviz/tests/saved_models/cmdstan/eight_schools_output4.csv deleted file mode 100644 index 2b7fc6e8ab..0000000000 --- a/arviz/tests/saved_models/cmdstan/eight_schools_output4.csv +++ /dev/null @@ -1,148 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 18 -# stan_version_patch = 0 -# model = eight_schools_nc_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 1000 (Default) -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 0 (Default) -# data -# file = eight_schools.data.R -# init = 2 (Default) -# random -# seed = 779853166 -# output -# file = eight_schools_output4.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,theta_tilde.1,theta_tilde.2,theta_tilde.3,theta_tilde.4,theta_tilde.5,theta_tilde.6,theta_tilde.7,theta_tilde.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 -# Adaptation terminated -# Step size = 0.456636 -# Diagonal elements of inverse mass matrix: -# 10.1102, 1.42981, 1.03112, 0.70849, 0.910414, 0.922521, 0.649671, 0.763596, 1.19991, 0.953491 --6.21317,0.913029,0.456636,3,15,0,10.3671,1.08613,1.78064,0.334339,1.50267,0.782869,-1.34781,0.449765,0.371238,0.564256,-0.877969,1.68147,3.76184,2.48014,-1.31385,1.887,1.74717,2.09087,-0.47722,-5.16624,-3.31133,-3.75018,-3.60245,-3.16761,-3.31914,-4.48703,-4.04956,-12.8345,-18.1999,3.53722,-6.34161,5.69343,-1.1517,8.85626,-2.71599 --5.7865,0.992573,0.456636,3,15,0,10.3899,6.45462,2.09506,0.019801,-1.94259,0.86102,0.0932077,0.304059,0.699157,-0.133078,0.645031,6.4961,2.38478,8.25851,6.6499,7.09164,7.9194,6.17581,7.806,-4.65458,-3.37918,-3.93909,-3.31734,-3.52033,-3.51468,-3.92058,-3.83645,0.9365,6.73732,0.619372,0.861924,12.2414,8.05304,-19.9803,-11.8405 --3.37893,0.9726,0.456636,3,7,0,8.77356,2.85023,3.11002,0.39489,0.486493,-0.537914,0.463873,-0.942135,-0.585809,0.866742,-0.646922,4.07835,4.36324,1.17731,4.29289,-0.0798206,1.02836,5.54582,0.838297,-4.89865,-3.28765,-3.72561,-3.34712,-3.12139,-3.31684,-3.99706,-4.00157,-8.37139,1.16273,-30.0728,10.9724,-5.24862,-8.4415,6.84324,-5.79433 --4.73967,0.925921,0.456636,3,7,0,8.41528,8.72754,2.63888,0.010251,0.101557,-0.954992,-1.24924,0.352527,0.307916,0.165984,-0.53937,8.75459,8.99554,6.20743,5.43094,9.65781,9.54009,9.16555,7.30421,-4.45007,-3.22648,-3.85711,-3.32701,-3.81733,-3.61821,-3.61176,-3.84334,-0.72591,-1.63592,19.786,-5.69232,8.36524,19.9039,17.866,-5.93515 --6.22336,0.982781,0.456636,3,7,0,8.43994,2.80061,1.04443,-0.615193,1.04975,-0.536905,0.900958,1.40663,0.279291,0.220996,1.14004,2.15808,3.897,2.23985,3.74159,4.26974,3.09231,3.03142,3.9913,-5.111,-3.3057,-3.74515,-3.36071,-3.28758,-3.33492,-4.34182,-3.90829,-16.1068,-0.646218,13.4301,12.9246,-2.58308,6.19708,2.28717,33.8309 --4.21866,0.987395,0.456636,3,7,0,8.19288,3.7749,1.3864,-0.910513,0.148894,-0.242634,-0.366672,0.430598,0.548708,0.66131,0.83277,2.51257,3.98133,3.43851,3.26655,4.37189,4.53563,4.69175,4.92946,-5.07057,-3.30227,-3.77249,-3.37443,-3.29429,-3.36849,-4.10707,-3.88646,14.7245,12.9862,-14.2581,-2.89379,1.04154,9.89016,-8.77971,30.0359 --2.22674,0.998269,0.456636,3,7,0,4.75535,5.30467,3.8962,0.383526,0.399796,-0.312839,-0.23741,-0.191051,0.176619,0.603417,-0.304745,6.79896,6.86235,4.08578,4.37967,4.56029,5.99281,7.6557,4.11732,-4.62584,-3.22799,-3.78959,-3.34521,-3.30701,-3.41984,-3.75655,-3.9052,7.28605,6.46538,-19.4529,3.37909,10.5553,3.54753,-1.06851,42.6761 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--3.53463,0.933254,0.456636,3,7,0,6.75733,3.60168,1.873,0.348912,-0.538827,-0.799144,-0.166174,0.056514,-0.796537,-0.182002,0.37544,4.25519,2.59245,2.10488,3.29043,3.70753,2.10976,3.26079,4.30487,-4.87991,-3.36773,-3.74243,-3.3737,-3.25296,-3.32192,-4.30775,-3.90069,-15.0764,11.994,20.009,-24.0051,24.287,12.8133,6.51667,13.7098 --8.76323,0.735739,0.456636,3,7,0,9.8247,0.570233,0.191266,-0.718878,-0.505457,-1.49827,-0.518552,0.282783,0.679376,0.276978,1.58895,0.432737,0.473557,0.283665,0.471052,0.62432,0.700175,0.62321,0.874145,-5.31578,-3.50476,-3.71259,-3.49298,-3.13245,-3.31721,-4.73129,-4.00034,-16.7054,-12.2694,-19.4633,0.895927,9.81076,11.0796,-14.2077,21.399 --9.17407,0.93901,0.456636,3,7,0,13.8104,8.63009,1.09676,1.80267,0.262699,-0.476132,2.26025,0.182519,0.229967,-0.561362,-1.28676,10.6072,8.9182,8.10788,11.109,8.83027,8.8823,8.0144,7.21881,-4.29923,-3.22574,-3.93251,-3.3866,-3.71267,-3.57357,-3.72008,-3.84459,13.7866,-7.34167,-3.54853,28.3781,28.2311,21.3781,5.72484,19.6222 --8.52039,0.55082,0.456636,3,15,0,13.7584,7.80101,1.14966,1.23707,-0.500967,-0.214204,2.5112,-0.892138,-0.610697,1.14674,-0.36919,9.22322,7.22507,7.55475,10.688,6.77535,7.09891,9.11938,7.37657,-4.41047,-3.22453,-3.90911,-3.37304,-3.48935,-3.47054,-3.61585,-3.8423,-4.97158,6.35572,9.21205,-3.80278,14.5494,-1.32404,7.32849,23.3437 --5.39747,1,0.456636,3,7,0,11.9926,3.70906,5.26965,1.92633,0.576358,-0.808907,1.78367,-0.928264,-0.102675,0.62981,0.606236,13.8601,6.74626,-0.553599,13.1084,-1.18257,3.168,7.02793,6.9037,-4.07129,-3.22938,-3.70322,-3.47102,-3.11637,-3.33626,-3.82345,-3.84939,8.80116,7.90575,0.980599,-4.89419,-4.66255,-1.30471,-8.08031,-0.416586 --7.68036,0.919442,0.456636,3,7,0,10.6638,0.0147584,3.85022,-0.430911,0.980768,1.06693,-1.3988,1.23861,-0.75846,1.26251,-0.816181,-1.64434,3.79093,4.12268,-5.37095,4.78369,-2.90548,4.87569,-3.12772,-5.57985,-3.3101,-3.79061,-3.94923,-3.32265,-3.37986,-4.08276,-4.16247,-24.2315,2.86864,5.91369,25.7866,-12.1672,-8.39157,-0.834947,1.22998 --7.87415,0.9172,0.456636,3,7,0,10.9807,8.99854,3.81395,0.778183,-1.13583,-1.16654,1.48899,-1.25906,0.809547,-0.953574,0.857885,11.9665,4.66653,4.54941,14.6775,4.19655,12.0861,5.36166,12.2705,-4.19826,-3.27708,-3.80284,-3.5604,-3.28285,-3.82469,-4.02016,-3.80942,8.176,-13.2255,8.96874,28.0282,15.9648,17.1541,13.7299,11.6123 --10,0.654789,0.456636,3,7,0,16.7913,4.63606,0.117985,1.45396,-1.00841,0.398622,-0.915896,-0.807188,-1.32591,-1.70541,-0.369533,4.8076,4.51708,4.68309,4.52799,4.54082,4.47962,4.43484,4.59246,-4.82229,-3.28218,-3.80682,-3.34209,-3.30567,-3.36687,-4.14159,-3.89399,0.997634,14.5893,-0.966485,10.9208,8.55068,3.65196,-2.10363,22.3954 --8.69808,0.989341,0.456636,3,7,0,15.6077,4.79755,1.49789,1.91943,0.0746713,0.317086,-0.378123,-1.64724,-0.380267,-2.12577,-0.651292,7.67264,4.9094,5.2725,4.23116,2.33016,4.22795,1.61338,3.82198,-4.54521,-3.26928,-3.82519,-3.34851,-3.18462,-3.35989,-4.56413,-3.91252,-9.8749,-22.5486,43.5537,3.5726,20.2953,6.57813,9.85063,14.7536 --19.3547,0.851667,0.456636,3,7,0,22.3933,1.37225,0.0588145,-1.55283,-1.28285,0.090167,1.04371,3.65079,-0.293269,2.46201,-1.10918,1.28092,1.2968,1.37756,1.43364,1.58697,1.355,1.51705,1.30702,-5.21345,-3.44619,-3.72895,-3.44487,-3.15747,-3.31735,-4.57996,-3.98576,33.2955,-12.233,10.6841,-10.964,11.5115,-3.09199,6.5773,-27.2162 --15.184,1,0.456636,3,7,0,26.1681,7.88787,0.195162,-1.43388,-2.61636,0.812126,1.58133,2.16763,-0.65696,1.19978,-1.07484,7.60803,7.37726,8.04637,8.19649,8.31091,7.75966,8.12202,7.67811,-4.55106,-3.22346,-3.92985,-3.32275,-3.65131,-3.50565,-3.7094,-3.83814,15.2404,3.2121,-4.24999,-1.07346,15.5198,13.803,3.71114,14.3736 --9.3068,0.632117,0.456636,3,7,0,20.4685,10.3856,1.2664,-0.931946,-2.39866,-0.222212,1.05974,-0.570108,0.731045,0.545294,0.708938,9.20537,7.34792,10.1042,11.7276,9.6636,11.3114,11.0761,11.2834,-4.41196,-3.22365,-4.02692,-3.40919,-3.81809,-3.75619,-3.46122,-3.8101,-18.334,23.0286,0.314579,17.3643,4.96592,3.1414,22.0671,39.6881 --8.41679,0.598174,0.456636,3,7,0,14.8797,7.92035,5.25239,-1.20234,-2.06676,-0.1094,1.59553,-0.612796,0.137746,0.284103,0.595678,1.60521,-2.9351,7.34574,16.3007,4.70171,8.64385,9.41257,11.0491,-5.17518,-3.81941,-3.90058,-3.67429,-3.31684,-3.55827,-3.59024,-3.81071,2.4721,-6.94898,5.07353,13.9369,-5.1672,15.2115,17.8662,10.5192 --8.04308,0.968218,0.456636,3,7,0,15.6819,1.85366,8.00396,1.73945,1.73267,-0.10442,-1.63646,-0.290075,-0.799328,-0.0474536,-0.606594,15.7761,15.7219,1.01789,-11.2445,-0.468083,-4.54412,1.47385,-3.00149,-3.95904,-3.51966,-3.72306,-4.6923,-3.11791,-3.44385,-4.58709,-4.1566,10.6643,17.1301,5.74765,-1.96395,3.33127,0.34014,10.5155,30.1142 --5.0631,0.985383,0.456636,3,7,0,11.9244,3.26472,2.724,1.29836,0.290813,-0.621501,-0.462964,-1.06954,0.361787,1.58544,-0.992701,6.80145,4.05689,1.57175,2.00361,0.351302,4.25023,7.58346,0.560607,-4.62561,-3.29926,-3.73235,-3.41999,-3.12743,-3.36049,-3.76405,-4.01125,-14.9346,3.80959,2.16134,13.4013,17.5563,-8.33069,-25.3285,35.5943 --4.82036,0.559449,0.456636,3,7,0,9.41413,1.46279,1.68806,0.553086,0.345583,0.244183,0.374308,0.707533,-0.606425,0.610119,1.47955,2.39644,2.04616,1.87499,2.09465,2.65716,0.439112,2.49271,3.96037,-5.08375,-3.39876,-3.73794,-3.41627,-3.19872,-3.31813,-4.4239,-3.90906,-11.5744,-8.51386,16.3732,8.51401,14.8403,6.63093,5.2821,9.07625 --7.11557,0.282693,0.456636,3,7,0,19.5553,2.0116,6.35886,1.1714,-0.253885,-0.304658,1.25066,1.39539,0.691653,0.874385,2.0598,9.46036,0.39718,0.074325,9.9644,10.8847,6.40973,7.5717,15.1096,-4.39081,-3.51054,-3.70999,-3.35315,-3.98805,-3.43776,-3.76527,-3.82423,20.3617,12.4143,3.25783,9.36904,5.01012,7.19301,8.79747,23.5595 --9.24263,0.819839,0.456636,3,7,0,15.9428,1.03137,6.61281,1.71645,-0.643188,0.0826253,2.32728,0.65736,0.941175,0.118221,1.8636,12.3819,-3.22191,1.57775,16.4212,5.37837,7.25518,1.81314,13.355,-4.16904,-3.85118,-3.73246,-3.68361,-3.3673,-3.47852,-4.5316,-3.81214,-13.6695,7.90995,18.4461,18.8852,-1.97711,12.1494,-16.9661,21.649 --8.28281,0.994395,0.456636,3,7,0,14.0062,3.16764,1.41888,-0.542727,-1.46034,-1.23176,-1.35195,0.680827,-0.950954,0.831004,-1.60106,2.39757,1.0956,1.41991,1.24939,4.13365,1.81835,4.34673,0.895935,-5.08362,-3.45988,-3.72968,-3.45348,-3.27884,-3.3196,-4.15358,-3.99959,1.02986,5.16401,8.8941,1.84146,10.6254,6.52817,-2.64624,-9.37393 --6.18576,1,0.456636,3,7,0,10.5795,8.04229,2.86071,-0.107384,-1.86819,-1.16425,-0.237767,0.435116,-0.264695,-0.746476,-0.289979,7.7351,2.69795,4.71171,7.36211,9.28704,7.28508,5.90684,7.21275,-4.53958,-3.36208,-3.80768,-3.31738,-3.76939,-3.48007,-3.95275,-3.84468,-12.1062,0.371752,-0.0838938,-6.4132,-2.77364,25.9152,5.52046,-4.34697 --3.94984,1,0.456636,3,7,0,7.12251,4.0251,1.93653,-0.415212,-1.0825,-0.295398,0.272581,-0.353525,-0.556248,0.287663,-0.7865,3.22103,1.9288,3.45305,4.55296,3.34048,2.9479,4.58216,2.50202,-4.99143,-3.40582,-3.77286,-3.34158,-3.23246,-3.33251,-4.12172,-3.94853,9.74538,-14.1261,-13.7981,-13.6946,6.50245,5.26954,1.60372,13.1057 --5.51361,0.913016,0.456636,3,7,0,7.75331,5.21244,7.80309,2.11546,0.45566,0.178956,-1.22537,-0.133385,-0.278654,0.952388,-1.15501,21.7195,8.768,6.60885,-4.34923,4.17162,3.03807,12.644,-3.80019,-3.71464,-3.22447,-3.87186,-3.84909,-3.28126,-3.334,-3.36496,-4.19457,12.2334,0.479043,-4.62609,-11.3866,-4.08598,17.9223,11.5994,0.931242 --5.61048,0.272994,0.456636,3,7,0,10.9662,6.1941,2.21931,1.90257,-0.220373,0.413102,-1.08084,-0.14942,-0.802611,0.708126,-0.956908,10.4165,5.70502,7.1109,3.79538,5.86249,4.41286,7.76565,4.07043,-4.31405,-3.24786,-3.8912,-3.35927,-3.40687,-3.36496,-3.74523,-3.90634,-17.9723,3.73756,11.864,3.72134,25.3694,4.39429,7.76107,3.50982 --5.99431,0.960834,0.456636,3,7,0,8.83096,3.96196,1.24205,-0.499911,0.575344,-0.903622,1.97756,-0.774647,0.351415,0.302762,0.523624,3.34104,4.67656,2.83961,6.41818,2.99981,4.39843,4.338,4.61232,-4.97824,-3.27675,-3.75813,-3.31823,-3.21492,-3.36456,-4.15477,-3.89354,-1.22626,8.27719,23.7718,-1.29935,-1.9264,7.03,3.75501,39.2377 --5.88871,0.735842,0.456636,3,7,0,8.5961,4.04413,1.46469,1.42632,-0.0991244,0.903524,-0.201334,1.62234,-0.823736,0.178883,0.251972,6.13325,3.89894,5.36751,3.74924,6.42036,2.83761,4.30614,4.41319,-4.68955,-3.30562,-3.82828,-3.3605,-3.45605,-3.33079,-4.15913,-3.89814,25.3364,-3.36644,23.2094,-4.82175,9.50837,-0.846458,14.1567,11.0904 --3.76652,0.963501,0.456636,3,7,0,8.54169,6.17589,5.59941,1.11587,-0.153489,-0.188068,-0.862426,0.348404,0.444235,1.38278,-0.211118,12.4241,5.31644,5.12282,1.34681,8.12675,8.66334,13.9186,4.99375,-4.16612,-3.25753,-3.82039,-3.44889,-3.63035,-3.55951,-3.30481,-3.88506,6.10559,2.00395,-24.7829,7.80325,15.0044,17.6383,26.5593,2.55317 --6.29374,0.946541,0.456636,3,7,0,8.48955,-2.70817,5.3729,1.00215,-0.616489,0.39938,1.5215,-0.750408,-0.260069,0.581115,-0.0906709,2.67627,-6.0205,-0.562339,5.46668,-6.74003,-4.10549,0.414104,-3.19533,-5.05208,-4.2044,-3.70313,-3.32655,-3.31955,-3.42454,-4.76784,-4.16563,13.3994,-4.27908,11.9853,9.11077,-5.50407,-15.0782,7.67873,45.564 --6.69199,0.966011,0.456636,3,7,0,11.7277,7.61767,1.6636,0.421147,1.46604,0.124305,0.285286,1.06044,-0.473043,1.1399,1.49174,8.31829,10.0566,7.82446,8.09227,9.38182,6.83072,9.514,10.0993,-4.48781,-3.24267,-3.92037,-3.32176,-3.78148,-3.45732,-3.58158,-3.81489,24.0784,7.85531,-5.42193,13.8659,-2.03162,-11.4128,5.32541,16.7252 --7.61837,0.989549,0.456636,3,7,0,10.7848,2.94111,0.616903,-0.0547077,-0.792452,-0.106303,0.07022,0.865291,0.871011,-2.23923,-0.29498,2.90736,2.45224,2.87553,2.98442,3.47491,3.47844,1.55972,2.75913,-5.02619,-3.37541,-3.75895,-3.38347,-3.23977,-3.34222,-4.57294,-3.94109,0.922503,10.1129,12.8701,10.5815,-1.84459,-3.27487,9.99472,-8.96778 --10.4898,0.928561,0.456636,3,7,0,14.834,7.17444,0.860605,0.380523,0.597563,-0.472078,0.262992,-1.97751,0.281814,3.13682,0.338923,7.50192,7.68871,6.76817,7.40078,5.47259,7.41698,9.87401,7.46612,-4.5607,-3.22201,-3.87789,-3.3175,-3.37477,-3.48699,-3.55168,-3.84103,9.93766,20.0539,-18.1849,-18.7109,18.3421,14.7341,34.4021,-27.4504 --4.96802,0.751848,0.456636,3,7,0,13.7765,1.36663,13.7656,0.314383,-0.811901,-0.195564,0.357073,-0.227997,-0.870184,0.902755,-0.635047,5.6943,-9.80971,-1.32544,6.28196,-1.77189,-10.612,13.7936,-7.37519,-4.73264,-4.80745,-3.697,-3.31896,-3.11984,-3.87402,-3.30999,-4.38863,-0.707354,-18.302,-7.88081,-5.19032,-2.94251,-6.10625,23.7176,-35.1472 --6.759,0.140364,0.456636,3,7,0,11.1921,2.64906,14.9643,1.10544,-0.806051,-0.157357,0.303455,0.194004,-0.808928,1.06477,-1.50178,19.1912,-9.41292,0.294334,7.19006,5.5522,-9.45597,18.5826,-19.824,-3.79942,-4.73757,-3.71272,-3.31698,-3.38117,-3.7686,-3.22322,-5.37222,51.3866,-9.20718,-6.0839,17.7674,20.9351,-2.37889,27.9422,-14.0375 --4.26066,0.935881,0.456636,3,7,0,11.3337,7.73187,2.41431,0.726519,0.43441,0.627479,0.385172,0.45748,-0.990835,-0.388877,0.299532,9.48591,8.78067,9.2468,8.66179,8.83637,5.33968,6.793,8.45503,-4.3887,-3.22457,-3.98446,-3.32825,-3.71341,-3.39466,-3.84951,-3.8287,17.7971,15.9544,10.129,4.67265,6.97781,-10.2945,19.2056,13.3472 -# -# Elapsed Time: 0.062 seconds (Warm-up) -# 0.032 seconds (Sampling) -# 0.094 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstan/example_stan.data.R b/arviz/tests/saved_models/cmdstan/example_stan.data.R deleted file mode 100644 index 8a49555192..0000000000 --- a/arviz/tests/saved_models/cmdstan/example_stan.data.R +++ /dev/null @@ -1,5 +0,0 @@ -x <- 1 -y <- -c(1.0, 1.0, 1.0) -Z <- -structure(c(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0), .Dim = c(4, 5)) diff --git a/arviz/tests/saved_models/cmdstan/example_stan.json b/arviz/tests/saved_models/cmdstan/example_stan.json deleted file mode 100644 index 5cde52b6e9..0000000000 --- a/arviz/tests/saved_models/cmdstan/example_stan.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "x": 1.0, - "y": [1.0, 1.0, 1.0], - "Z": [ - [1.0, 1.0, 1.0, 1.0, 1.0], - [1.0, 1.0, 1.0, 1.0, 1.0], - [1.0, 1.0, 1.0, 1.0, 1.0], - [1.0, 1.0, 1.0, 1.0, 1.0]] -} diff --git a/arviz/tests/saved_models/cmdstan/output_no_warmup1.csv b/arviz/tests/saved_models/cmdstan/output_no_warmup1.csv deleted file mode 100644 index 29b2ece46f..0000000000 --- a/arviz/tests/saved_models/cmdstan/output_no_warmup1.csv +++ /dev/null @@ -1,148 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 18 -# stan_version_patch = 0 -# model = test_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 100 -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 0 (Default) -# data -# file = (Default) -# init = 2 (Default) -# random -# seed = 721268069 -# output -# file = output_no_warmup1.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,y,x.1,x.2,x.3,Z.1.1,Z.2.1,Z.3.1,Z.4.1,Z.1.2,Z.2.2,Z.3.2,Z.4.2,Z.1.3,Z.2.3,Z.3.3,Z.4.3,Z.1.4,Z.2.4,Z.3.4,Z.4.4,Z.1.5,Z.2.5,Z.3.5,Z.4.5,Z.1.6,Z.2.6,Z.3.6,Z.4.6 -# Adaptation terminated -# Step size = 0.830775 -# Diagonal elements of inverse mass matrix: -# 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 --12.6543,0.921464,0.830775,5,63,0,30.0749,-0.202441,0.355694,3.64272,1.64093,0.657728,4.0682,3.29224,4.77658,0.166118,5.11531,4.4304,4.59065,3.17088,1.61273,7.16316,4.72769,-1.96664,2.63084,-0.269298,-0.288923,1.01699,-2.46566,9.66223,-0.731973,-8.60955,-2.02439,-0.569771,7.11574 --14.4638,0.959078,0.830775,5,31,0,26.6517,0.36505,1.45164,3.00225,2.46308,0.289539,3.95774,2.90628,4.88464,-0.126912,5.94728,3.93982,4.32201,-4.28735,0.451037,1.40599,6.55043,2.79853,1.47174,5.73713,8.36895,-0.867325,16.0004,-0.700953,4.50305,7.22986,3.23297,3.64034,-4.00706 --19.5055,0.83882,0.830775,4,15,0,33.5825,-0.398026,0.649165,1.14661,3.80484,-1.04483,3.08361,3.92331,2.4103,2.08862,-1.47099,2.41584,4.70044,5.78117,-0.845554,4.87709,-0.912593,10.0662,-2.44255,5.29723,11.1314,3.64763,10.8811,7.1553,-2.49963,6.49419,8.40469,2.92164,-1.74328 --16.8644,0.985081,0.830775,4,31,0,30.2341,-0.982152,1.83059,1.47164,2.79069,0.354715,1.10512,2.72882,5.34341,1.52161,-0.0588455,1.6706,4.755,-5.53742,5.14139,3.57363,8.1366,-9.13183,8.10353,-0.77879,-4.45486,8.202,-1.98101,3.71351,7.67789,3.25209,-3.56204,5.84266,8.49597 --17.0369,0.997334,0.830775,4,15,0,30.5312,-1.03315,1.49407,1.18574,2.92264,0.558984,1.45691,2.91209,5.638,0.938924,3.86282,3.79415,2.83357,7.56175,0.0937066,5.12945,-3.28429,0.476702,-0.372456,1.26892,3.0204,0.777128,0.504444,1.40758,9.52618,-8.77247,-14.6904,6.16875,14.832 --18.7317,0.787258,0.830775,4,31,0,31.9786,-1.5823,1.35681,2.95131,1.88889,2.83238,2.01155,2.27857,2.57099,-0.402054,4.60194,4.22643,2.48244,-4.77762,2.29851,1.06431,10.1427,-1.69807,5.11908,6.32601,2.9171,5.15083,-0.654684,4.05568,-4.0171,14.3492,4.97161,3.92196,-8.97418 --11.7516,0.968982,0.830775,4,15,0,27.628,1.28912,1.42105,2.29831,4.00473,-0.52216,2.52388,3.10678,3.31002,0.101044,3.39942,0.14276,5.31971,6.85782,2.07244,5.01603,-1.61699,-0.660924,-3.21271,0.55289,4.30831,-5.11945,0.635269,2.9085,6.56983,0.828646,-6.3084,1.27302,8.06587 --9.58707,0.859772,0.830775,4,31,0,21.0557,0.225737,1.26125,2.69119,3.52391,-0.187477,1.17655,3.14525,3.52491,1.9956,0.902305,5.8365,2.95969,-5.28646,2.77894,3.51964,5.57199,3.71484,7.82313,-0.0995374,7.44283,-4.67055,5.02628,-4.5106,3.41734,3.54768,2.38032,0.456646,6.44395 --9.92852,0.604553,0.830775,5,31,0,25.6319,0.226505,1.25884,2.68869,3.52198,-0.188628,1.17453,3.14272,3.52608,-0.109219,3.16396,0.0346542,4.96138,-5.8028,2.13611,3.28835,6.16423,-1.63896,0.343438,1.9393,11.8003,8.41997,-0.169797,5.52494,2.75381,-2.62885,1.09911,5.47389,1.6191 --15.1815,0.845885,0.830775,4,15,0,25.5114,0.279758,0.514875,1.96521,2.8619,0.979001,3.04881,1.23122,4.80309,1.77921,-0.513595,3.62133,3.48772,6.85586,1.6223,-1.22689,3.30254,-1.44007,0.268064,-2.76367,-3.6386,3.88676,7.62681,6.96847,3.55354,3.8923,-11.0495,10.8213,-4.92824 --15.7586,0.959146,0.830775,5,31,0,25.1224,-0.786752,1.69886,2.03381,2.91159,1.18266,0.988533,4.92612,2.71396,1.13218,5.11241,3.47911,5.74643,7.25515,-1.45405,-0.654683,4.53346,5.16359,-2.52948,2.81178,1.11686,2.44702,-5.15617,-0.710616,8.52101,-0.613079,11.9974,-3.12916,14.8429 --10.768,0.983092,0.830775,4,31,0,28.085,-0.888667,1.9086,2.58078,3.78251,1.38683,0.983878,4.35497,3.33599,0.624616,-1.54559,3.35939,2.87709,-0.547951,0.185173,5.98085,2.24985,5.09427,-2.63249,-1.72487,4.64708,5.21946,1.55035,4.77623,8.1911,8.35054,3.54015,-2.71514,12.8858 --16.2931,0.451804,0.830775,4,15,0,35.3426,-0.0374679,0.702123,0.328449,3.6308,1.09554,0.39793,3.37299,3.87092,1.12733,5.71507,2.24029,4.93611,8.62606,1.10986,5.58246,7.65433,3.07551,-6.30955,1.44253,0.905619,9.62129,-0.858923,1.78973,9.10456,2.05236,-3.97868,-2.20622,13.4253 --20.1836,0.798076,0.830775,5,31,0,34.5489,0.822143,0.607108,3.41411,2.94774,2.14042,4.26237,3.32631,5.6311,0.846761,1.39977,2.60475,4.42512,-8.46109,1.64699,-0.742475,0.939109,3.13315,5.65744,2.20171,1.3551,3.51339,7.97141,10.6416,-5.30368,-12.9431,3.04696,0.9918,-1.98239 --13.4955,0.995212,0.830775,4,15,0,30.7881,-0.109136,0.518507,3.46617,2.40483,1.91298,3.40973,2.92047,6.23005,0.942597,2.5332,3.79457,2.82648,6.56365,-2.54475,5.89769,7.58102,2.50523,1.22086,0.256246,9.5756,3.89716,4.50479,9.6172,4.39681,-1.63683,0.852096,-4.75228,-1.81788 --12.1892,0.868022,0.830775,4,31,0,25.9883,-0.133587,-0.0373425,2.97284,2.72167,1.39986,2.16217,3.71787,5.12493,0.869472,1.46485,2.02913,4.82416,-4.80723,6.44405,-2.46796,1.90099,4.96027,-0.367595,-5.09235,5.33312,6.1284,-2.53363,0.803001,1.6363,-0.286466,-5.96334,5.14887,7.27624 --15.371,0.737581,0.830775,5,31,0,30.2875,1.08471,-0.0192562,1.36807,3.69131,1.76609,0.689265,3.98349,3.5081,4.41432,0.225631,6.01725,0.513399,-0.108181,-0.944371,-0.188382,8.14704,1.81539,-0.422886,-2.75473,2.04255,5.39851,14.269,3.79456,9.23577,1.30675,4.27573,0.646374,2.74813 --11.067,0.999344,0.830775,5,31,0,27.9605,-0.598354,2.44849,1.24275,2.10657,1.31249,2.04805,1.7821,3.62584,4.19755,2.61763,2.97602,6.9868,-0.56609,-0.0892443,4.16245,2.66287,-0.852321,-1.89853,-5.03523,4.63821,5.76348,-2.38244,7.91765,2.83768,4.17591,4.19389,11.4539,7.13771 --13.6961,0.962994,0.830775,5,31,0,21.0431,-0.125092,2.58605,2.61672,2.55344,2.15519,2.96055,2.08332,3.25707,3.94664,2.64275,1.43431,7.25318,1.51399,1.16971,2.60827,5.73262,2.70138,6.57649,11.8815,2.82834,-4.65526,7.67747,-0.770921,5.8539,2.66504,2.27089,1.29981,-9.01155 --10.872,0.906331,0.830775,5,31,0,25.322,0.173962,0.390416,2.39235,3.36282,-1.36387,1.50706,3.61654,3.10883,1.88473,0.556877,5.52197,6.33406,1.08283,2.32614,3.25055,2.65928,1.5714,1.52338,-4.52723,1.65527,7.15121,2.37397,7.05682,2.32138,12.4153,-0.981518,1.2333,4.17515 --12.1743,0.752142,0.830775,4,31,0,29.6356,-0.664505,0.63998,2.93072,4.03259,2.24624,0.30587,1.86177,1.89138,1.81913,0.609708,2.03564,3.12151,1.07867,4.66461,0.0848452,4.75218,1.24749,5.18133,10.2421,4.39081,-2.37231,1.42593,7.57021,2.67548,8.03341,4.93169,7.39398,10.997 --14.0484,0.99777,0.830775,4,15,0,23.2644,-0.224719,0.570075,1.64644,3.89344,0.795152,1.39303,4.76574,5.95635,-1.39452,2.23424,3.13548,3.46718,2.27847,4.49509,1.4073,7.4165,-3.26429,5.22973,9.79593,6.52165,-7.60208,4.55204,7.27486,-0.434076,13.3898,3.45224,2.61616,11.277 --16.1353,0.738832,0.830775,4,15,0,27.6523,-2.77861,2.42628,1.21913,2.10322,-0.33577,2.88498,1.47138,3.76749,-1.9081,2.01628,3.1857,2.92659,0.235501,3.81539,3.44673,8.7807,-3.25171,4.46836,8.698,7.65307,-6.91947,2.98168,6.23164,0.321281,9.57962,2.42346,1.61954,9.21998 --12.4082,0.997089,0.830775,4,31,0,23.9114,-2.13805,2.54799,1.09348,3.36901,-0.0875968,2.43085,1.78225,4.27353,3.97636,2.24878,2.83463,4.55887,3.24044,1.70594,2.03101,1.9358,4.34391,1.13293,-3.2651,1.7903,-3.59651,3.14233,2.41678,4.80195,3.79378,8.22097,1.39769,17.0084 --13.7243,0.902802,0.830775,4,31,0,27.163,-0.185213,2.69184,3.19867,1.56918,-0.357953,3.68323,2.65937,4.90805,4.58001,0.283259,2.65308,4.95787,-0.479485,1.00657,6.91604,4.99062,-3.67564,2.02049,7.29188,8.1087,6.63668,-1.1017,-2.12252,4.73662,7.95743,4.07676,4.9334,10.6158 --10.5284,0.904395,0.830775,5,31,0,28.0957,0.052351,1.6827,3.30344,2.76799,0.503408,3.5252,2.88397,4.24631,4.23454,-1.12749,3.38854,5.16333,2.17799,5.59695,0.472824,2.88307,6.73225,4.24396,-0.0117753,-0.0648311,-2.21393,1.9685,1.72937,0.459778,8.94767,2.96575,-3.63977,0.404325 --12.0407,0.795161,0.830775,5,31,0,24.0375,-0.825942,0.569848,1.79793,2.72252,2.11337,-0.385734,4.1238,2.97438,-1.73999,6.50283,2.85259,5.37252,-0.896935,0.100338,3.11997,4.26874,-1.63066,3.00215,7.24504,2.49681,3.51682,0.660525,2.28505,-2.31254,1.05571,-3.30481,7.34193,-0.685666 --14.457,0.994869,0.830775,4,15,0,27.7305,0.0542246,0.821793,1.53021,2.47078,-0.383496,3.44251,3.54352,5.97176,1.76372,0.603165,2.14207,4.65411,1.34044,4.38962,2.32572,4.42517,0.775611,-6.13438,0.979359,0.333268,-4.27121,-2.27038,-7.76114,11.263,3.51617,1.58932,-8.8997,8.74971 --10.1473,0.929423,0.830775,5,31,0,25.0298,-0.0203686,1.34332,1.20012,1.87671,1.35836,0.698969,3.9822,3.20643,0.733559,3.90516,2.13099,4.55691,-0.0548212,0.181199,5.78551,1.70981,-5.32422,4.80172,3.37145,10.8912,3.71628,4.52341,-2.89179,3.74114,-2.09272,11.5858,-3.01381,3.83065 --10.6927,0.767781,0.830775,5,31,0,23.1824,-0.050331,1.08607,1.93764,1.93703,1.58646,0.335012,4.1155,3.46754,-0.5475,3.49396,3.1627,5.2036,1.8121,3.7698,6.9559,6.85574,-5.52799,5.2728,1.70766,10.6869,7.52584,-0.631533,6.8877,-2.52165,-1.4704,-0.965977,4.20268,5.10012 --12.6549,0.335041,0.830775,5,31,0,36.7442,0.366747,1.06735,2.75248,1.49671,0.543772,0.192902,4.05169,2.65473,-0.518977,3.49162,3.13069,5.19319,-0.77042,2.76312,-0.89818,4.79445,7.49725,-1.25844,4.37584,-2.76661,-2.45235,7.13043,-4.04162,6.72344,-2.34585,4.44141,10.8549,4.54861 --11.9125,0.947381,0.830775,4,31,0,21.4925,-1.10204,2.56964,2.52556,1.93426,0.131786,0.180194,2.72337,2.92718,3.23967,-0.0251855,3.8217,4.19647,2.58275,3.56439,3.5015,2.8189,-0.642609,6.53177,8.04846,-0.02284,-1.02898,5.26524,-4.47797,10.2095,-5.03891,4.02644,7.80211,5.42319 --10.0522,0.85808,0.830775,4,15,0,27.8183,0.649915,1.04302,2.71041,3.08622,0.643931,2.44328,2.10463,5.57218,-0.455661,0.757712,2.65015,3.12612,-1.45457,0.0459728,3.70931,4.93244,-2.94583,2.90052,6.10512,-1.63008,1.16571,-0.786031,-3.00354,13.7361,-7.33213,5.34784,-4.81477,5.89141 --13.4177,0.689725,0.830775,5,31,0,25.2084,-0.772737,0.656955,2.07579,2.0135,1.5155,2.49198,5.69357,3.07389,4.60418,6.35685,0.650686,4.38054,3.73393,2.67817,4.11016,3.5105,2.06626,0.719054,-2.06013,4.88305,-3.62516,-2.78646,3.20524,3.62233,2.19325,7.8121,6.26241,-2.54813 --14.1879,0.988901,0.830775,6,127,0,26.7206,-0.504191,1.52887,0.855463,1.07763,1.02466,2.35182,5.21488,4.01882,4.54294,6.40789,0.68362,4.36231,3.88867,2.84757,4.30794,3.77423,-0.317922,2.83658,7.81034,3.20582,2.73988,-2.70067,-4.30208,3.39133,2.59933,7.65087,6.11241,-2.6162 --9.92634,0.994071,0.830775,5,31,0,20.4597,-0.376273,1.03504,0.770423,1.52277,0.739455,2.57535,4.29925,4.74414,4.70287,5.49478,1.3056,3.14442,-2.28382,3.33229,3.01758,5.15819,1.11929,1.15618,-1.98189,4.49891,-0.433768,1.76278,9.75665,-0.376161,0.643118,2.04843,4.95314,5.34682 --11.7438,0.70661,0.830775,5,63,0,25.5208,0.751843,0.932658,3.11203,4.7936,1.36854,0.771767,1.54083,2.62336,2.12003,4.00186,3.40626,3.99769,-2.40867,0.6615,-2.3827,1.46949,-0.816111,2.37033,6.06114,7.73117,2.83654,3.09564,-3.29789,9.12226,-1.43126,-0.663509,-0.906205,9.17285 --11.1685,0.996459,0.830775,5,31,0,22.6699,0.642208,0.388703,3.1688,4.64196,1.39673,1.0264,1.22917,2.77764,2.11084,4.00386,3.38436,3.99201,1.90169,1.15036,4.82996,2.44972,2.7059,1.6306,-0.036651,0.245024,-3.5403,-3.45303,7.4376,-1.51907,5.1604,8.88605,-5.66778,5.22186 --4.20629,0.846146,0.830775,4,15,0,22.2374,-0.649665,0.554896,2.05125,2.44561,1.11494,2.79646,3.47868,2.98184,0.731208,1.96196,1.37678,4.23311,-0.991961,3.21845,0.73369,5.54273,3.38126,1.59903,2.52035,5.64051,1.08356,-0.188457,7.38963,3.52954,-1.8956,-0.511423,-2.07181,-1.35406 --12.2398,0.785686,0.830775,4,15,0,17.7755,-0.790449,0.397901,2.54166,4.69852,0.81705,1.96698,3.63276,4.04843,1.67973,3.8634,0.767821,1.05738,2.1252,-1.3133,-2.00999,8.75507,4.09401,3.07266,5.73025,5.5107,1.8139,-3.25109,9.76682,-1.96001,-0.468209,-5.91433,-2.96975,0.347951 --13.3915,0.880267,0.830775,5,31,0,24.2429,0.790232,1.60254,1.45898,1.30033,1.1821,2.03356,2.36712,3.95213,3.03957,-0.482147,4.33807,7.48033,-0.18394,5.59739,7.7486,-0.625926,-4.12154,1.30886,-0.450444,7.02535,0.174763,-4.93411,4.44517,11.4263,-3.23834,-1.98359,8.86715,8.33718 --9.95091,0.80265,0.830775,5,31,0,25.8426,-0.54145,0.971476,2.72113,4.75911,0.90864,1.45809,3.87591,3.64631,0.423227,1.19085,5.13878,6.76362,-0.176489,1.86375,0.531464,0.329316,4.88779,2.55954,4.69082,-1.5958,2.04082,9.94058,2.63359,-3.52168,2.87926,7.96448,2.51932,6.26586 --9.98757,0.971307,0.830775,5,31,0,21.2768,-0.552428,1.99031,1.90652,3.70192,1.7493,2.16059,4.32714,3.89795,1.47044,2.86023,1.1193,1.30844,-0.756715,2.21382,0.612661,0.246772,0.913248,1.27207,-4.3666,1.38185,-0.546799,-1.86774,-1.08528,13.7604,-1.35991,-4.05435,3.8725,1.86391 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--13.213,0.750849,0.830775,5,31,0,30.8197,0.118784,1.20627,2.47101,1.56048,2.34818,2.29841,4.52455,2.87012,3.62978,0.949181,0.512799,5.81496,4.86654,5.35996,0.222771,3.39832,-3.80769,6.13477,3.31654,8.91505,8.11524,2.10088,1.98658,5.74795,-7.36328,-4.35711,0.434925,9.73552 --14.5758,0.922593,0.830775,4,15,0,29.8802,-0.631337,0.845736,2.59674,4.75664,0.0567897,2.63881,3.70381,3.30353,0.222358,0.391675,3.69917,7.52999,-4.11104,-2.82818,4.35542,4.05333,6.47393,-3.22753,5.36991,-1.97918,2.16264,12.8564,4.61461,4.54018,-3.12762,5.94814,-3.70538,5.59738 --17.2916,0.733694,0.830775,5,47,0,26.1922,0.457461,1.07811,2.85958,5.46045,2.49506,2.5691,2.50191,2.97639,0.321502,0.0548744,3.6859,8.16575,-3.81846,-2.73103,2.45639,3.41825,6.99385,-3.61868,5.02046,-1.98497,1.87784,12.0897,4.57389,4.48248,-3.25194,6.39445,-2.9949,5.94975 --10.4265,0.791852,0.830775,4,15,0,28.1453,0.963944,2.14755,1.6544,2.37289,-0.0974021,2.40092,2.31926,3.30444,-0.212036,0.139884,0.61579,5.10037,5.81769,6.8973,2.81866,4.4541,-5.03372,3.12759,-0.553342,6.25126,0.974098,-0.917582,0.624622,5.77134,-0.939273,7.56155,10.2638,-1.92115 --7.99024,0.795285,0.830775,5,63,0,21.0839,-1.36087,0.0635608,2.47401,3.12202,2.17889,1.4599,3.13177,4.31763,1.63857,2.92304,4.9604,3.60836,4.84216,1.70463,1.35148,6.10604,-4.03841,3.3432,-0.0436677,1.06526,-3.51094,-1.64018,1.92631,0.982607,5.12907,-5.65624,-0.0522684,8.33461 --15.4207,0.946451,0.830775,4,15,0,24.3491,0.949622,0.96312,1.13479,2.8411,0.144392,0.867196,2.06963,3.9347,0.25387,2.13062,1.46411,6.30657,-6.95228,-0.496373,0.726241,-0.266489,1.70522,9.10952,-1.16046,-0.920255,5.54001,-1.71002,0.793242,4.19443,9.82545,-9.39159,-1.71358,6.38615 --15.8002,0.611292,0.830775,4,15,0,33.6414,0.918166,0.485088,1.7403,2.63299,0.754055,1.41477,2.13837,4.28248,1.80883,1.40811,4.47138,2.07282,7.51475,0.941723,3.63735,4.65374,-6.40699,0.117473,-0.109165,-6.49619,3.10465,-5.24532,-4.46568,-4.64145,7.34825,-5.22756,-2.3803,0.191318 --12.6404,0.789472,0.830775,4,15,0,31.697,0.316058,0.145617,1.15477,1.00093,1.45742,1.49005,1.99612,3.78307,3.47026,2.8567,2.57311,5.00501,-2.50444,4.76345,0.791845,1.03733,-4.7653,0.618407,6.95906,6.24016,8.26601,-2.89065,2.89676,8.56451,-2.29569,-5.52016,-7.67277,5.33951 --12.8321,0.790245,0.830775,4,31,0,27.2017,-0.0756377,0.358871,1.11373,1.74171,1.55245,1.70932,1.95561,3.3321,-1.69497,0.658263,3.7508,3.12891,3.88851,6.41542,3.43514,1.84765,2.14579,3.52866,-0.694799,3.3569,2.55643,3.85035,1.15678,10.9482,-14.3403,-5.29975,-7.86233,3.06608 --11.1321,0.770841,0.830775,4,15,0,30.5303,0.562544,1.87878,1.00732,4.12398,2.1165,1.32292,2.17448,2.17088,0.864163,-0.159776,3.06439,-0.217648,-0.809994,-1.88536,0.820383,5.07808,-2.74067,-0.252804,1.70502,3.88947,2.47654,4.45123,-1.50297,5.21915,-2.41973,8.77506,6.77192,7.73198 --10.5214,0.853718,0.830775,6,63,0,21.0363,-0.560851,0.118323,2.99126,1.87855,-0.115643,2.67516,3.8223,5.82629,3.69104,1.13396,2.81193,2.71516,3.12828,4.85464,4.71863,3.05155,-2.97885,-0.307497,0.336536,6.28791,0.885892,3.61875,-0.352711,9.58188,10.2681,0.76285,-1.4332,9.60879 --10.2718,0.91839,0.830775,4,15,0,27.0805,-1.19376,-0.380665,2.41943,1.59336,0.877546,2.57683,3.94865,5.86951,2.36708,1.92315,2.80547,4.82645,2.26287,4.60871,2.85934,4.50893,-4.03075,0.361504,1.58191,5.90167,1.3978,2.34442,-0.463019,11.2528,10.5337,0.425365,-1.43199,9.5846 --12.1511,0.981111,0.830775,4,15,0,22.5239,-0.307224,1.30187,1.80994,1.56375,2.01139,2.04239,1.68329,5.5607,0.243274,3.63017,4.27563,3.2785,0.822182,-1.09093,0.0498756,2.55951,4.88255,3.19593,1.66169,0.84257,-3.09067,-0.514988,-7.0635,1.74314,13.5091,2.4395,1.81849,12.2316 --15.7763,0.724133,0.830775,4,15,0,28.3273,1.36917,0.765262,3.55799,2.90022,2.16188,2.27246,5.19047,5.50345,0.148963,2.69229,3.43255,4.39679,1.04071,4.59617,5.99378,4.03299,-5.29287,9.63659,4.84546,0.631681,1.35802,5.22422,2.53864,-0.609615,11.1758,3.57572,6.63595,17.3319 --13.441,0.987793,0.830775,5,47,0,32.1889,0.0218252,1.94897,2.7734,4.0439,0.318264,0.572228,4.05923,6.26143,-0.012675,2.61756,3.77535,3.80691,-0.787418,7.76404,7.41647,5.40592,6.1774,-4.99707,-0.158096,7.96184,3.11471,-1.49744,6.19614,5.08359,-3.63424,-2.14062,4.61285,4.13558 --17.671,0.811765,0.830775,4,15,0,31.7751,-0.971577,0.75333,1.76869,3.20708,1.01007,1.00559,3.96626,5.99211,0.112718,1.18053,3.46511,5.09758,6.9009,-6.54741,-1.57282,3.05096,-5.20568,10.3803,7.13173,0.126658,2.33551,3.71007,4.93034,3.60236,4.37815,2.01327,-7.13624,9.70535 --14.2579,0.85056,0.830775,4,15,0,31.0122,0.311392,1.32019,2.10975,4.36414,1.65066,2.58579,0.628917,3.36002,-1.29267,0.151313,2.38955,2.69767,-4.78957,9.19001,5.71254,6.84381,2.67762,1.88939,0.835176,4.29246,4.39095,6.79792,0.240174,6.97434,2.60699,-0.0106917,7.10466,13.691 --12.6715,0.870801,0.830775,4,15,0,29.0746,-0.440296,0.377727,1.98087,1.76883,0.47062,1.51392,4.82142,4.00326,1.6467,3.47601,3.01733,5.29643,-0.833692,1.86987,5.18654,12.2924,0.0621044,-0.163009,-2.19347,8.3427,3.74156,7.36279,0.0978851,-0.921917,-1.30179,2.38521,4.55349,15.3968 --7.21416,0.997544,0.830775,5,31,0,22.4834,-0.939091,0.899855,2.74067,2.0939,0.961696,1.71176,3.90845,3.82235,0.64077,4.388,4.45122,6.57628,2.37245,3.15336,3.98198,4.19657,1.09761,1.71428,-3.27931,4.96475,-1.74747,-4.39245,5.56825,6.77579,8.52975,6.14435,2.17327,4.44091 --14.8726,0.629299,0.830775,4,15,0,23.5305,1.64953,2.8374,2.51983,2.14203,-1.25232,1.84991,2.47643,3.82294,2.57613,3.06485,4.60554,7.24694,2.37402,1.37659,0.396707,3.30713,-1.64729,3.66832,8.41829,3.50586,3.88914,7.88367,-6.4591,-3.14255,5.97921,5.93684,8.28527,0.704821 --17.8162,0.925698,0.830775,5,31,0,29.5339,2.01172,1.57557,3.45175,3.6111,-0.575184,1.94042,1.71043,3.92246,5.33515,3.58944,4.96895,7.56728,0.301971,-1.44117,7.37294,4.21087,3.00228,-0.0759889,-1.94876,4.41584,-1.27565,-2.0368,12.9715,13.2268,3.62518,-3.74531,1.64753,1.4742 --12.8464,1,0.830775,5,31,0,25.7987,0.1923,2.0807,0.654374,2.51816,1.88552,1.75706,3.78138,3.93358,4.44004,2.80843,4.55067,5.25102,5.34568,1.8392,4.71592,5.37797,-0.903798,4.31221,6.97827,4.13553,3.33378,8.14747,-6.49624,-3.65208,-7.97646,9.78525,-1.38525,-0.439758 --13.3816,0.928589,0.830775,5,31,0,24.1076,0.416173,1.13206,1.60589,1.85413,1.20711,2.19678,4.24757,3.97381,-1.79776,-0.81427,2.73609,6.50253,4.22809,0.264215,6.77034,3.91988,7.51475,2.65985,-2.75821,5.47937,-1.76302,-3.40353,12.1478,10.112,10.9845,-3.08139,5.22562,5.27032 --13.0048,0.836193,0.830775,5,31,0,29.3466,-0.303438,2.06344,0.602846,2.21379,2.18293,1.92452,3.97372,4.38872,2.92614,4.26297,2.90518,1.34432,4.168,0.343033,5.81792,4.13847,7.55287,3.16483,1.42458,2.75672,5.35715,5.37981,-7.3324,-0.553859,-9.61981,7.25374,0.641922,2.74269 -# -# Elapsed Time: 0.031 seconds (Warm-up) -# 0.031 seconds (Sampling) -# 0.062 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstan/output_no_warmup2.csv b/arviz/tests/saved_models/cmdstan/output_no_warmup2.csv deleted file mode 100644 index a1e72c3945..0000000000 --- a/arviz/tests/saved_models/cmdstan/output_no_warmup2.csv +++ /dev/null @@ -1,148 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 18 -# stan_version_patch = 0 -# model = test_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 100 -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 0 (Default) -# data -# file = (Default) -# init = 2 (Default) -# random -# seed = 721270991 -# output -# file = output_no_warmup2.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,y,x.1,x.2,x.3,Z.1.1,Z.2.1,Z.3.1,Z.4.1,Z.1.2,Z.2.2,Z.3.2,Z.4.2,Z.1.3,Z.2.3,Z.3.3,Z.4.3,Z.1.4,Z.2.4,Z.3.4,Z.4.4,Z.1.5,Z.2.5,Z.3.5,Z.4.5,Z.1.6,Z.2.6,Z.3.6,Z.4.6 -# Adaptation terminated -# Step size = 0.844171 -# Diagonal elements of inverse mass matrix: -# 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 --13.3909,0.674861,0.844171,6,63,0,27.9908,0.474392,-0.563751,1.36737,3.64664,0.33028,0.560926,3.86748,4.15233,-0.311046,3.28398,5.26612,3.57651,2.74314,-2.72328,1.3694,4.97468,5.65451,-8.40634,8.33407,8.49446,7.21058,-1.06257,2.43277,5.6806,-4.50462,0.952263,-0.184883,1.00549 --11.703,0.714189,0.844171,4,31,0,28.2504,0.657232,-1.14858,1.94455,2.17346,2.12125,0.998684,2.9452,3.23678,0.166365,2.13431,-0.612292,5.72454,3.94232,-0.5957,3.4856,4.22571,6.6618,1.7447,2.67607,-0.543392,5.13481,-5.57966,1.82514,6.35119,1.36147,2.02359,-2.58933,-3.2643 --13.4343,0.962894,0.844171,5,63,0,22.6299,-0.377308,2.20475,2.74325,4.81869,-0.426657,2.40457,2.33111,5.28354,-0.0328328,-0.767731,0.529411,6.67746,2.58169,-0.0983652,1.01763,5.68028,-5.63463,5.11665,5.5542,7.35959,-1.26758,8.61367,5.2876,1.24468,2.24355,0.831714,3.29848,14.5303 --14.5649,0.999194,0.844171,4,31,0,23.8969,-1.29809,1.35038,2.06607,5.1597,-0.074918,2.3831,3.63984,5.20397,2.37637,4.622,6.32532,1.56596,0.241691,3.61176,4.93417,1.56396,3.16792,-2.57979,0.872351,-2.13634,3.90372,7.60086,9.02549,-5.33542,1.83057,2.13092,0.506524,8.88566 --17.0574,0.800709,0.844171,5,63,0,33.4565,0.376468,0.234526,0.938151,0.402459,1.73188,0.805984,2.76008,2.28808,5.2863,4.62177,3.74649,1.99058,1.68932,6.59304,4.45555,3.58864,-3.05296,0.767774,4.16573,-2.05519,3.30981,10.6604,7.56338,-1.49103,-1.82048,3.89251,-2.71189,8.13169 --15.5468,0.989167,0.844171,6,63,0,28.1758,0.615362,1.36993,3.03314,3.84114,0.260887,3.39861,1.36096,6.6465,1.82324,5.93898,3.04213,0.994337,-2.31309,0.903254,2.35498,2.75567,5.12135,0.873727,2.94772,8.37942,0.444343,-7.50328,-1.17895,8.42302,1.69715,-0.726329,8.91213,2.15181 --16.2414,0.942558,0.844171,4,31,0,33.5732,0.354726,2.57804,2.85503,3.62597,0.728034,2.87334,1.58235,5.50767,-0.909693,5.88553,6.69592,1.9128,2.86063,5.56908,3.81786,8.52969,-3.14312,-0.0170392,0.829327,2.26417,2.63426,11.4787,10.0684,3.07923,1.21925,8.60023,8.95643,-0.409192 --16.3971,0.969498,0.844171,5,47,0,29.718,0.115806,1.81942,1.63914,3.68248,1.38378,2.34654,1.27421,5.63025,2.88663,-2.32352,-0.650427,6.09083,1.49414,4.01862,3.71018,9.65764,-7.22812,4.12368,-1.47431,4.71957,-1.02133,-6.40229,-0.755153,5.73439,2.37943,-4.07286,-1.4504,7.95339 --13.1078,0.99671,0.844171,5,31,0,30.9133,1.29164,-0.200619,2.90341,2.9732,1.37836,1.47167,4.75986,3.8107,2.81857,-0.36797,1.21296,2.27495,3.19661,0.592223,1.84734,3.30276,11.3151,1.87023,7.23413,2.28256,1.458,7.45334,6.73883,1.73322,7.57149,6.76966,9.5831,10.1509 --10.8732,0.867972,0.844171,5,31,0,25.2306,1.54179,0.487756,3.43839,1.99281,0.844981,0.805353,4.88978,3.70037,-0.298159,3.37825,4.92294,6.6285,1.70859,1.34242,2.92076,3.22264,-2.75259,0.344142,3.64389,5.64798,0.345914,-3.96901,1.72694,6.03162,-3.39436,-2.76486,-3.37604,-4.43411 --12.6324,0.987016,0.844171,4,15,0,19.7275,1.52156,0.369789,3.05173,2.20651,0.880105,0.62134,4.88494,3.48614,2.38075,0.668833,0.655822,1.72244,1.07787,4.01123,2.3941,2.83032,-0.21891,0.021967,0.698629,2.59991,-8.18118,-6.09323,1.58975,4.51475,-4.9437,-2.79656,-4.3784,-1.51852 --19.8146,0.568472,0.844171,4,31,0,37.3762,-1.88935,1.1254,0.810084,5.39844,1.0211,3.7715,2.72716,3.52596,1.3223,-1.0899,1.79635,1.50574,0.166968,3.56594,5.37739,6.48638,3.86364,3.84393,5.66395,6.40884,16.527,1.79257,-1.24579,7.27936,6.33072,-0.627544,14.973,-4.8018 --10.9752,0.976584,0.844171,4,31,0,27.0144,-0.532625,1.68117,1.40145,4.83459,0.352956,2.64911,2.85243,3.45584,0.843761,5.33358,4.15654,6.46637,2.00554,-0.156844,1.1499,2.31259,3.34428,8.06987,-2.97033,1.50899,7.07461,-2.83915,6.34215,9.56688,-2.4419,3.80353,3.37625,-0.534427 --16.367,0.808274,0.844171,4,31,0,27.4454,-0.796935,2.13198,2.31145,3.9143,1.53673,3.28859,5.0216,3.85575,0.962788,0.287573,3.19376,2.20392,0.218329,-6.73715,10.5172,4.2026,-2.10534,9.09983,2.31477,6.21009,7.35141,0.73558,7.03518,2.07985,-0.959772,-0.173263,-1.30716,1.84871 --11.0833,0.909004,0.844171,4,15,0,31.5454,0.702434,-0.427586,2.14613,2.19083,1.1489,2.01618,1.4436,4.30216,4.94298,0.222701,2.24145,4.75342,2.83077,-0.280528,4.64312,2.87108,-6.90601,7.78136,-1.94472,6.84758,4.82297,3.18542,4.99604,3.07343,0.420901,-1.50624,-2.42628,5.12465 --12.0993,0.751391,0.844171,4,31,0,28.152,-1.34135,0.112457,2.28799,3.13338,0.628899,2.23786,1.71882,4.74253,-2.91536,3.13739,4.03657,4.20171,-1.5138,-0.488248,2.75006,10.3776,-3.72258,5.61656,4.13345,3.69356,-7.29086,-1.33985,8.47869,2.25517,0.260792,6.653,-2.58375,-0.132752 --10.9165,0.766631,0.844171,4,15,0,24.0441,-1.02694,0.151263,2.46967,3.09469,0.961199,2.24452,1.37049,4.61203,4.9264,1.11657,2.21737,3.52837,4.76998,1.03292,2.75476,-1.24521,3.21856,0.242801,6.88003,3.81171,2.55641,12.6699,5.25221,5.6626,-2.45394,7.10425,2.1996,0.989803 --10.5415,0.582497,0.844171,5,63,0,29.2673,1.03583,1.8643,1.46097,2.93236,0.998496,1.75223,4.60641,3.4092,3.15094,3.95846,2.69958,0.960862,0.362269,0.997566,-1.72135,0.56565,1.37522,-0.380609,1.03898,0.677773,-0.94149,-9.04167,-0.00226802,3.36281,3.74236,-2.72841,4.41554,3.70131 --12.7709,0.99867,0.844171,4,15,0,22.2694,-1.30815,1.14705,1.82208,3.11291,1.1793,1.45073,1.96038,4.3123,3.42885,2.24273,1.96138,2.88268,1.99071,1.30411,7.78455,7.21025,1.62642,6.6842,7.80027,9.13956,0.725074,8.79677,1.2639,1.37055,9.96278,11.5324,14.6065,-2.28461 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--9.94873,0.966694,0.844171,5,31,0,31.5477,-0.899142,2.8235,2.56815,3.60931,0.668322,1.28238,3.01981,2.90741,-0.224277,-0.896799,3.35299,3.19795,2.11716,-0.637016,6.14661,1.34555,6.67195,4.3975,2.21731,0.99982,8.63426,2.50583,2.87487,9.47393,7.10283,-0.817618,3.557,3.22755 --10.6984,0.97101,0.844171,5,63,0,19.382,1.01859,-0.662955,1.51639,2.63813,1.3297,2.86064,3.03328,5.2313,-4.22162,2.75674,1.30242,3.34867,-0.156965,4.56783,-0.200733,6.80913,-1.03827,0.114539,2.88885,4.55804,-3.37489,1.69326,-1.71158,0.716529,0.918476,3.30371,10.3742,5.06467 --11.8913,0.586913,0.844171,4,15,0,27.5843,-1.2404,2.64702,2.7592,3.21847,0.790891,1.55573,3.30515,2.9732,4.48719,0.975067,1.25877,2.81707,2.30209,-0.686633,6.27586,1.12821,3.44488,4.38009,7.82181,5.54342,1.93323,1.99078,1.58443,15.5866,2.64533,2.38953,-3.71288,9.57479 --11.4806,0.9874,0.844171,4,31,0,24.2429,-1.41355,2.29311,3.0175,2.51816,1.82177,2.43321,3.72783,3.34116,-1.82743,3.33558,4.72446,5.27625,1.46573,3.1561,-0.297014,1.81955,6.73805,1.54235,1.05816,9.3673,9.40591,-1.11971,0.754985,2.31278,-1.23332,-4.50853,3.22544,-3.90991 --11.8147,0.836065,0.844171,5,31,0,30.3343,-0.91348,1.47155,2.2968,2.94862,2.06174,3.45567,3.24121,3.89373,4.2969,0.794628,1.33254,2.20281,0.824637,4.32091,2.02664,4.05411,-0.0433369,4.37313,0.719519,6.40311,-11.2095,5.79554,6.95996,11.5774,2.44396,7.91891,2.58113,13.0405 --14.0337,0.73187,0.844171,5,31,0,25.3548,1.08267,1.7434,1.65118,2.24881,-0.924059,-0.770808,3.18049,3.57587,0.0835103,3.5891,0.28852,4.04931,1.15529,0.024807,3.69694,4.85546,2.95674,2.41808,4.11061,-0.677878,4.5637,2.84894,3.78812,-3.14299,1.22527,6.11547,-8.86968,-4.75737 --18.5837,0.970664,0.844171,5,31,0,30.4991,-1.32,0.0831128,2.52753,3.83755,2.76989,4.55473,2.95457,4.3001,1.93207,4.06664,2.00493,9.59384,0.777961,3.93475,2.12598,3.15288,-0.869573,3.79069,-0.225023,5.9899,-7.13236,8.60624,7.0444,6.09814,-9.5784,3.64838,-5.43138,-4.93085 --15.3508,0.997211,0.844171,5,31,0,33.2661,0.79593,1.01404,0.504183,2.21068,-0.556904,1.60073,3.20305,5.67864,1.91563,2.92893,3.42073,6.03574,1.59455,-0.848079,3.74367,3.92876,3.07345,-1.8637,2.91959,0.30784,9.08841,1.37779,-5.77746,-4.59627,-13.102,-8.12595,5.31247,3.34805 --14.3057,0.832836,0.844171,5,31,0,26.5825,-0.557611,1.16026,3.93298,3.72569,2.61642,2.43208,3.20126,2.21769,0.762787,-1.11648,2.60943,2.06207,1.25285,0.564134,6.17987,2.52074,0.685323,-2.37334,5.48904,4.39882,-2.79369,-6.17768,6.90093,5.03043,15.6519,9.30724,2.35716,4.72172 --9.86483,0.99809,0.844171,5,31,0,24.0617,0.379766,1.06401,0.12788,2.73787,-0.378014,0.391936,2.46218,4.29855,0.946478,2.76991,4.34294,1.64764,-0.159915,3.20975,0.368481,4.50029,-2.52857,2.9142,-3.04508,3.60786,-3.29532,0.482629,4.28422,-0.853608,0.462176,12.2663,0.692186,3.52964 --10.1412,0.886394,0.844171,4,31,0,26.6743,1.90388,1.75204,1.49221,3.3329,0.927816,2.72408,4.00339,3.69458,4.32276,1.36671,2.0449,3.06858,1.87358,-4.99267,2.08122,3.44738,4.99249,3.54306,3.72013,5.67639,3.14664,1.46558,3.95706,4.94934,-8.99123,5.89884,6.35226,4.70879 --13.172,0.930259,0.844171,4,15,0,22.1654,0.851022,1.6233,2.05234,3.72122,-0.801303,2.31897,3.36054,3.06983,-0.806413,3.08703,3.42533,4.78091,2.02315,9.27818,1.77245,3.85574,3.087,8.18453,8.00432,0.195468,6.51639,-4.74466,12.2987,0.6904,4.00797,2.88325,-1.09218,2.28616 --22.0046,0.721987,0.844171,4,15,0,35.5266,1.27187,2.02787,1.39853,3.70369,0.249259,-0.131343,3.93954,4.06998,1.68835,0.901839,1.07634,5.86814,3.33005,-9.63914,4.81704,2.81703,1.00768,3.73211,7.48436,-6.40276,-1.13982,-6.54884,11.717,3.0177,6.434,0.440009,5.76618,8.63217 --24.5883,0.977107,0.844171,4,15,0,35.5845,1.93773,0.506812,2.6117,2.88943,1.68813,0.108005,3.04741,3.11637,-0.380658,0.709147,0.0524012,7.09228,-2.99534,13.2502,-0.123076,3.41287,1.9176,2.17796,-0.934387,15.8349,7.80932,9.33291,1.6022,7.78862,8.42092,7.21528,-0.45236,8.20494 --15.7174,0.996509,0.844171,4,15,0,37.1702,0.612152,-0.301057,2.67833,3.96213,0.214074,0.986607,3.96263,4.47922,2.51522,4.40928,4.3231,1.6982,2.64057,3.76027,6.34819,8.4137,2.1163,2.42055,3.34158,15.7225,1.27394,8.51195,2.9311,9.50719,11.2513,-0.821409,-6.55951,2.48691 --15.2729,0.718156,0.844171,4,31,0,35.8481,-0.308537,2.37592,1.14271,2.23241,2.10814,2.97309,2.27431,3.45235,-0.132863,1.66578,7.57442,5.95981,-0.333208,0.269069,-0.28458,-0.478635,-0.0241709,5.98551,3.44164,-3.42877,-3.0604,8.19852,-5.33651,13.7337,-2.31071,3.69602,1.6973,3.23005 --12.607,0.896918,0.844171,4,31,0,26.1597,-0.842561,0.691641,1.81322,1.81089,1.25056,3.89509,2.62674,5.51071,2.25533,1.8136,-0.350533,2.80741,-5.2463,-0.761904,2.50969,1.24594,-3.13583,7.27677,2.05374,4.89,-3.14602,2.36529,-1.28767,9.62826,-2.41359,-2.46844,-2.21782,3.09375 --12.2353,0.942005,0.844171,4,15,0,28.0929,-0.00770032,1.22693,2.45631,3.21531,1.3943,0.300564,1.84364,2.49048,2.51751,1.11241,5.87685,5.22643,4.99861,2.86424,2.6333,7.28688,0.474024,0.906479,-1.68417,2.856,-5.2014,-0.770237,-1.62801,3.53849,-10.4893,4.40182,4.25373,-6.5405 -# -# Elapsed Time: 0.016 seconds (Warm-up) -# 0.031 seconds (Sampling) -# 0.047 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstan/output_no_warmup3.csv b/arviz/tests/saved_models/cmdstan/output_no_warmup3.csv deleted file mode 100644 index e995f93c5a..0000000000 --- a/arviz/tests/saved_models/cmdstan/output_no_warmup3.csv +++ /dev/null @@ -1,148 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 18 -# stan_version_patch = 0 -# model = test_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 100 -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 0 (Default) -# data -# file = (Default) -# init = 2 (Default) -# random -# seed = 721273429 -# output -# file = output_no_warmup3.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,y,x.1,x.2,x.3,Z.1.1,Z.2.1,Z.3.1,Z.4.1,Z.1.2,Z.2.2,Z.3.2,Z.4.2,Z.1.3,Z.2.3,Z.3.3,Z.4.3,Z.1.4,Z.2.4,Z.3.4,Z.4.4,Z.1.5,Z.2.5,Z.3.5,Z.4.5,Z.1.6,Z.2.6,Z.3.6,Z.4.6 -# Adaptation terminated -# Step size = 0.865839 -# Diagonal elements of inverse mass matrix: -# 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 --7.99324,0.999747,0.865839,4,15,0,27.6343,-0.844196,0.922784,1.11596,3.33289,1.26162,2.63801,2.62082,4.81579,-0.113506,3.37881,2.72398,4.9507,-4.15259,0.409396,7.19858,4.4907,3.84388,0.188849,5.44927,1.12953,9.21771,3.87542,3.51038,-1.11744,2.83948,2.25042,9.57476,2.6958 --7.27794,0.787339,0.865839,4,31,0,17.9656,-0.177788,0.553513,2.57808,3.26861,1.46652,2.25006,3.62863,6.0655,1.29781,1.858,4.01405,4.04753,1.94077,0.297402,7.03119,2.42021,1.04903,0.917541,4.01647,0.28534,4.54928,-5.92583,6.15784,-0.811678,-2.97067,1.30415,6.28849,5.84893 --12.6199,0.89174,0.865839,4,15,0,21.7022,-0.171442,0.577617,2.5742,3.27303,1.47002,2.26874,3.64082,6.06042,0.378713,2.30591,2.24993,3.78692,-0.349151,-0.655644,1.6953,10.0951,5.29987,6.88419,3.85508,-1.21277,-2.50261,-9.06099,9.67236,-2.82613,-2.79365,-0.848853,3.63162,2.68581 --5.84582,0.987989,0.865839,4,15,0,21.0716,0.0566283,0.523975,2.29981,3.65304,1.5442,2.33579,3.41114,5.31892,1.36622,3.49859,3.40588,4.08015,3.18621,4.32752,5.1632,3.12048,3.02642,1.1747,2.54788,-2.49743,0.976737,-1.71909,-0.474999,1.99858,-3.15515,-0.0240975,-0.284267,10.6034 --7.22337,0.850255,0.865839,4,31,0,15.9429,-0.282573,0.872757,2.20886,2.46174,0.951999,1.97062,2.32487,2.51922,-0.266081,2.12637,-0.388608,3.92384,1.18699,2.31361,8.51638,1.80135,1.75958,-0.310884,2.72,0.30332,-3.61284,-0.656564,1.86656,1.84,1.98524,-0.176411,7.07668,9.80269 --10.9027,0.629366,0.865839,4,15,0,20.3633,-0.267822,0.86781,2.22225,2.47075,0.986891,1.97651,2.32298,2.55526,1.98188,1.9004,6.37901,4.1605,3.76463,2.46728,-3.42439,1.27804,3.0038,1.54017,-3.68265,4.39045,-4.01741,-2.99049,-1.29949,-1.52809,3.59167,-3.18263,5.72538,10.8005 --13.6529,0.485448,0.865839,4,15,0,37.2489,-0.304477,0.884764,2.23611,2.4443,0.999686,1.98382,2.31985,2.56786,0.606615,2.58643,-0.399411,3.62874,-0.0990902,0.86497,8.76022,-3.10849,5.39632,2.5384,-3.73664,8.16854,-4.6134,6.01937,5.49111,4.1775,-5.19809,-1.20448,6.77595,13.752 --16.1529,0.742143,0.865839,4,15,0,26.3979,0.685866,1.3344,1.52711,3.7443,1.08553,2.00639,4.69817,5.71526,-0.250819,4.40221,3.21389,2.42591,1.27585,3.98006,-3.47957,11.9815,-6.14861,1.21054,8.59525,2.75425,5.38849,2.0046,7.62004,-1.08816,6.33458,-1.38757,4.95418,-2.83878 --11.8451,0.903298,0.865839,5,47,0,28.405,0.542597,1.029,1.94745,2.47994,1.32574,3.03457,3.28534,4.31514,-0.938232,4.59922,4.11986,2.25136,1.53947,2.00002,3.16024,-2.35844,8.059,2.54331,-3.14169,5.96624,-6.4629,-0.456707,-0.992526,8.80419,-0.57704,4.67939,-8.04636,2.00115 --10.9875,0.812963,0.865839,4,31,0,22.2055,-0.482701,1.54112,2.54665,2.71581,1.44313,2.00495,2.1686,5.38665,3.04565,0.359664,2.43395,6.06075,-1.08732,3.07119,-1.04471,9.85066,4.74879,-2.61278,1.69013,5.12853,-2.24694,7.02258,-0.666805,7.95246,-4.37686,4.64755,-6.88761,8.08631 --13.0434,0.979197,0.865839,4,31,0,22.5717,-0.28054,0.782154,1.83513,3.49819,0.84054,2.43592,2.78484,5.10321,-1.47375,3.54508,4.10588,3.36072,2.15372,1.3401,8.10827,1.42736,4.5914,5.57409,-0.367588,1.19562,5.26484,7.77056,6.59344,-0.214634,-3.24266,12.4537,-11.7781,12.7925 --11.5127,0.727392,0.865839,4,31,0,32.9491,-0.30323,0.788135,1.84558,3.50252,0.810453,2.44972,2.78223,5.09386,3.23986,0.33077,1.78959,4.48338,2.40365,7.04372,4.29813,4.31678,1.97204,8.85181,1.64427,6.95323,-6.0013,-1.10681,-2.09259,3.25829,-13.3011,6.27541,2.167,-4.98625 --11.4206,0.412407,0.865839,5,31,0,31.9094,0.287928,-0.276135,2.70389,2.51593,0.720539,2.41993,2.57323,4.00277,0.913996,1.35948,2.50986,3.59495,-0.173889,3.16531,0.455747,4.2488,3.24512,-5.15689,5.0326,0.0926552,8.04189,5.04049,8.06164,4.66657,19.606,1.14812,-1.97762,7.16684 --18.5445,0.698432,0.865839,4,15,0,32.3379,-0.195709,1.42223,1.76309,2.91519,1.90046,1.56617,3.82203,6.41629,2.9139,-0.17021,5.77294,4.59101,1.67686,3.11779,-0.6432,6.64,0.985195,-4.41138,6.96971,2.50154,12.4163,2.47543,4.72897,5.91658,21.7661,2.62818,-4.02403,6.75413 --19.5386,0.778045,0.865839,4,31,0,33.1235,-9.201e-005,0.0345739,1.27053,3.56901,2.92652,0.746443,4.57169,3.88272,-2.94227,3.29373,2.85077,2.96293,-1.36667,2.49282,8.85221,1.92427,-1.05609,0.824308,5.67164,10.2465,8.67593,8.87729,6.59172,8.76937,19.4917,2.47129,-4.39933,5.64623 --14.2337,0.732577,0.865839,5,47,0,35.2073,-1.88233,0.422604,1.82597,4.28268,1.82123,1.82141,5.528,1.92671,-1.12753,3.31424,3.26225,2.7166,4.00374,1.98584,-0.0724984,5.88411,2.18578,4.19563,2.33336,-4.55359,-2.98077,0.936078,5.13166,2.28577,5.19381,5.6343,5.68543,5.43888 --13.3854,0.967825,0.865839,4,15,0,27.3718,-0.784124,-0.045389,2.86341,4.93726,2.18635,1.36174,5.26973,2.79665,0.55412,2.83941,1.8182,1.69024,-1.03212,-1.66407,5.41537,2.09138,-3.38988,-0.455854,1.8202,12.1835,-3.65169,2.57416,2.89412,5.28103,2.85142,2.69144,6.15429,2.60226 --10.9126,0.986732,0.865839,5,63,0,23.786,-0.0508802,1.32195,2.68889,2.2444,0.988746,3.97638,2.87963,5.88221,-0.484539,4.4722,3.31742,2.86885,5.40471,2.79554,8.09863,4.54905,4.39842,3.85288,4.74176,-2.97651,3.33657,2.81967,2.36874,-0.655696,-1.68437,2.54913,0.809201,3.96263 --13.0597,0.77673,0.865839,4,15,0,23.1052,-0.380191,-0.0666404,1.64446,3.78051,0.595879,0.398033,1.99554,2.99433,3.13955,1.37998,2.15302,7.49866,-2.98216,0.791184,-2.19898,4.30614,-2.20672,-4.21831,0.675818,8.32453,1.66911,6.78373,-2.06015,4.32887,1.55471,-6.97757,-1.32032,-0.80013 --18.234,0.999279,0.865839,4,15,0,24.306,-0.180058,2.32419,2.24696,2.32678,1.24073,3.59927,4.119,4.96652,0.0995068,3.99243,3.402,2.73747,5.06726,3.32207,8.10584,4.34618,-0.919445,11.2266,3.66883,-2.09471,2.54517,-5.97989,4.09117,7.37347,9.23094,-8.59351,-8.38407,-6.6398 --15.2468,0.670213,0.865839,4,15,0,34.2639,-0.483395,-0.393328,2.43692,3.04355,1.91128,0.212574,1.80522,2.35912,1.71199,4.64224,3.38895,5.95728,-3.32466,1.77977,-1.75756,3.71033,3.78751,-3.61031,-1.13157,7.13568,1.54918,3.12944,7.01561,5.0153,-4.24312,9.47355,6.52506,17.3642 --12.4711,0.994658,0.865839,5,31,0,26.2335,-1.70997,1.38,2.17082,3.45186,1.25887,1.39603,2.23852,2.56236,2.85763,3.72215,3.23681,3.9267,3.31769,1.81491,7.86655,5.57173,-2.3588,7.03285,5.31571,1.07307,-3.60501,2.87167,-6.5295,3.05158,12.2364,-2.27426,-3.1358,8.90992 --9.52736,0.788663,0.865839,4,15,0,21.7905,1.58988,1.23726,2.27779,2.57713,1.73251,2.34443,3.24186,4.85945,-2.72175,3.55173,3.07978,1.73822,2.39307,1.8944,5.48778,1.51159,-3.34657,4.20848,1.78057,4.09386,-1.02517,4.71112,-1.04735,2.76031,12.5711,-2.45084,-0.923106,7.66764 --16.1163,0.522269,0.865839,4,15,0,35.6792,-0.388671,0.782361,2.89327,3.71894,-0.326894,2.12024,2.80995,4.07558,5.14939,5.3403,0.575264,7.43281,1.56251,2.12346,-0.935442,6.53239,-0.705856,1.71375,1.68033,1.8283,8.51187,-7.4524,2.95817,-4.21944,7.5815,10.7911,-6.23577,2.31297 --7.4209,0.947874,0.865839,4,15,0,23.5097,0.906248,1.66757,1.40813,1.63978,2.14045,2.13754,2.95365,4.03719,-1.48836,-0.527213,4.19204,3.38834,1.06491,1.93223,5.92342,1.97789,1.78431,5.74882,1.05386,6.39312,2.07957,-4.01288,2.00497,2.547,7.55418,4.5349,7.3672,3.08826 --24.1444,0.410757,0.865839,5,31,0,39.2614,-0.550419,0.331801,-0.819905,4.03975,-0.112096,0.31366,2.52377,4.26636,-4.11858,-2.17151,4.34266,4.0349,0.222849,2.77119,3.68769,-1.67594,-2.42781,6.86285,2.73881,5.7261,-13.9368,10.6537,-2.56037,11.8632,-2.56758,3.33611,6.72625,1.38359 --18.8506,0.936031,0.865839,5,31,0,39.7054,0.793633,1.89499,3.6806,2.54423,1.58217,3.78005,2.3512,3.43464,2.24202,3.78177,2.8186,0.47528,0.785381,3.16376,2.36332,-3.38423,2.20986,12.7327,-0.372989,3.6369,5.58293,-6.1896,14.5662,1.98535,3.72872,2.6252,-0.952081,9.15931 --17.1691,0.807496,0.865839,4,15,0,36.7711,-1.54745,1.16565,0.403734,3.11639,0.314292,-0.051242,3.61355,4.20209,4.96117,4.45808,-0.22851,1.19596,1.04959,0.699441,3.59337,11.2985,0.817995,0.180149,5.62509,0.773958,6.13717,9.30082,2.19799,-2.66785,-3.15095,1.4262,8.49368,-0.245023 --23.2782,0.836056,0.865839,4,31,0,39.3777,-0.600887,1.9123,0.597484,4.81967,1.6782,-0.706969,3.96932,3.47573,-2.86343,-1.18312,5.50252,5.30331,3.35973,-0.734182,-1.41227,1.53176,-2.73559,3.914,2.80287,6.56357,-11.44,15.4007,4.23682,-0.588034,4.99889,9.73497,7.25198,1.7603 --13.6762,0.999487,0.865839,5,47,0,33.3629,0.551371,1.10619,1.47059,3.73416,0.840287,-0.874297,4.07401,4.0411,-1.9691,-0.891578,1.92969,3.10761,0.471326,4.0641,7.05081,7.30802,4.12427,1.45891,3.56466,2.31781,5.02421,-6.40846,4.5849,11.8563,0.552284,-4.64787,6.12843,5.66446 --8.90221,1,0.865839,5,31,0,19.868,-0.568486,0.601738,2.84238,2.04533,1.32128,4.62268,2.65525,3.5681,0.764297,2.86088,2.52479,6.11335,1.53289,3.98998,7.3258,5.96203,3.06991,3.7064,4.72187,3.87516,0.884342,10.3038,0.673562,2.45272,3.17664,4.41302,2.02248,5.1552 --15.9697,0.844075,0.865839,4,15,0,25.0821,-0.578812,1.8712,2.70724,1.25524,2.334,-0.0586253,2.22241,4.63227,0.112001,2.68351,5.36318,7.10187,-0.321064,1.82127,-0.292898,1.7701,-1.66131,1.18107,-0.0866701,5.06597,1.68207,-6.52266,-4.08285,1.06652,-7.07441,8.70721,15.5413,7.56597 --11.5928,0.991048,0.865839,5,31,0,25.5334,-0.52368,1.86897,2.74116,1.30895,2.2801,-0.030225,2.14944,4.64403,-0.168656,2.55925,4.04024,6.02334,-0.998793,0.472602,0.0749483,3.14521,-0.952103,0.332597,-0.347932,4.81511,1.23491,0.527275,0.6478,3.50623,-3.39642,-4.31734,-10.7331,5.22233 --12.8071,0.84876,0.865839,4,15,0,26.5956,0.13117,0.654756,1.48086,3.92058,0.1847,3.39627,3.02815,3.08076,2.26354,5.14916,1.25517,5.13175,1.70239,3.51529,5.60348,3.57468,2.20963,3.12754,4.18617,2.11029,12.9654,-4.00346,7.30674,4.68884,-6.08942,0.934295,-10.4436,9.99552 --16.3072,0.876348,0.865839,5,31,0,26.2893,0.504099,-1.37287,1.98128,3.05252,0.882885,1.97143,2.96455,5.10251,0.636149,3.61952,1.39995,4.74194,-1.22156,7.41084,2.5498,2.726,-1.62096,2.51246,2.43002,5.29033,-11.4036,5.81244,-1.74915,6.31297,5.77693,6.42582,14.8909,-11.294 --15.2139,0.796552,0.865839,4,15,0,24.3016,-1.00777,3.46378,1.90932,2.36661,0.0428634,2.55001,3.33021,3.34047,0.660505,4.1182,3.32167,2.70855,-0.631685,5.2738,1.54927,4.58521,-2.34084,3.80095,0.716179,3.83054,-10.2314,3.84495,0.841417,7.13859,6.76065,6.29919,15.7105,-9.28789 --12.7402,0.910726,0.865839,4,31,0,30.7826,-0.7987,1.88261,0.812926,3.28284,0.626572,1.91653,2.81078,5.48968,1.23217,-1.14634,1.7066,6.03124,0.103017,-4.83731,2.67539,0.751304,5.31807,-3.50857,6.58234,4.40908,-4.92369,5.87902,1.61508,6.36236,-4.2549,11.1439,-1.23114,4.06497 --15.632,0.767634,0.865839,5,31,0,27.412,0.940908,1.23882,3.43963,3.34063,1.07106,1.78622,3.63198,2.43155,0.138026,-0.880337,3.38954,5.14596,2.07859,8.72536,3.41288,7.39098,-1.28238,8.37298,1.03911,1.03336,8.6916,-4.5765,-5.01069,-4.14013,-2.95904,5.52941,7.22566,-4.26995 --14.1406,0.844224,0.865839,4,15,0,32.1552,-0.799724,2.94176,1.93038,3.37905,0.924236,0.595391,3.87235,4.76174,2.32551,5.57661,-0.678339,3.4336,2.91122,-0.400259,0.702185,0.604985,3.45181,-1.45119,5.70019,-0.6917,5.70272,0.100431,-3.76414,-0.510148,0.0732873,11.0473,4.29446,-3.15233 --16.8898,0.917825,0.865839,5,31,0,32.6309,-0.810167,2.35132,2.98372,2.51541,-0.541998,1.40373,3.0586,3.16982,4.02656,3.2737,-1.28702,3.36764,-1.07471,4.03279,3.64104,6.21245,-2.09986,6.40557,-0.919098,7.60901,-5.3335,-10.6219,7.59799,4.67024,5.50955,11.5353,5.00264,12.7388 --12.191,0.985691,0.865839,4,15,0,25.4425,0.820613,1.80003,0.684336,3.51735,1.30617,1.36916,1.08554,4.33675,-1.42662,2.09321,2.88773,3.83883,1.20647,2.05501,8.05123,4.12234,0.124174,2.30029,-2.25421,10.9215,-1.45934,-6.42769,4.71087,0.196218,5.987,10.8299,6.1071,9.35735 --15.1967,0.962194,0.865839,5,31,0,25.4814,0.837966,1.79668,2.86319,3.91166,1.47979,1.90026,3.37557,3.46514,-3.48343,0.133393,4.27801,0.974912,0.755541,1.39805,-2.22305,3.67639,0.701833,-0.513921,6.17896,-4.22256,5.37191,1.89408,-9.10914,7.6749,-0.859741,9.53868,3.41645,10.0687 --9.84907,0.58602,0.865839,4,15,0,29.0162,-0.920999,0.184589,2.26248,2.24369,0.898419,2.15343,2.1098,3.46079,5.10008,3.34931,2.41391,7.06503,1.07335,2.58576,8.21922,4.38745,4.74591,3.25845,-0.280273,8.46934,1.28189,-1.39892,5.72385,2.37285,3.63273,5.14297,11.3607,5.68777 --17.5189,0.659589,0.865839,4,15,0,27.8766,-0.800055,1.29065,1.14439,3.00281,2.38791,0.835476,1.25995,4.24985,-2.72121,-0.0266913,3.75331,1.49152,-1.95849,3.79795,-2.86012,6.2694,7.80828,6.86664,5.97251,4.28067,7.37215,-2.99713,-1.25149,11.905,-6.63814,-5.36002,5.73728,-2.49498 --11.7818,1,0.865839,4,15,0,25.2243,0.915068,1.14703,2.2551,3.29753,0.134746,2.02198,3.95761,3.70221,0.99168,1.10622,5.34864,1.00794,3.14696,-1.33302,9.47601,0.601114,-3.20016,-1.1551,-0.58257,5.56216,-0.356853,7.20602,3.74288,-4.06279,-6.3056,8.74417,2.19571,5.53836 --12.0085,0.844426,0.865839,5,31,0,26.0179,1.25372,1.09933,2.97718,3.22148,-0.559928,2.84354,3.41433,4.77099,0.762462,0.203707,5.27813,0.540284,0.237482,4.59302,0.498182,6.42982,5.19302,5.11171,5.98777,2.5948,10.3178,-0.521552,-0.816239,11.3215,-2.80088,0.236084,-0.896378,-0.233894 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--13.259,0.851915,0.865839,4,15,0,24.8529,0.405191,0.40103,2.24187,3.56155,-0.977646,3.19907,2.67594,3.98411,1.75952,2.16474,6.32751,2.81195,2.11988,-0.380261,4.39573,-0.851708,2.11376,-4.4396,4.25464,-2.41816,-4.98963,6.86746,-5.9472,6.79275,-5.8708,3.20149,7.07415,1.04281 --11.7965,0.976346,0.865839,5,31,0,22.6269,0.138729,0.0193784,3.09018,3.02935,0.575404,3.39756,3.87137,3.01234,1.85857,2.43678,-0.452733,4.40459,2.41784,-0.402104,2.82857,-2.56878,-1.88587,1.38282,-0.0197944,0.992818,6.26608,-2.93956,11.5416,-0.0462801,7.38025,1.68635,0.797638,5.09254 --11.5463,0.643893,0.865839,5,63,0,29.5511,-1.79613,0.665199,2.05916,3.12682,1.90496,1.45759,4.20655,4.76394,-0.0877365,2.4327,-0.469344,3.2387,1.28198,0.389096,2.11717,-2.02626,-2.30134,0.142526,0.838751,2.1876,6.02637,-4.10473,10.388,-0.207154,9.0002,1.43628,1.50148,5.0596 --12.2665,0.870283,0.865839,4,31,0,28.8345,1.35121,1.27297,2.2008,2.90702,0.993883,3.13225,1.93111,3.89575,0.268447,4.21584,4.90494,1.42157,1.72241,4.18087,5.36284,1.31868,-1.01766,-1.21298,1.03709,3.66895,10.9667,-9.40612,6.50834,-4.4801,4.37032,3.02874,2.01471,6.29762 --10.5623,0.939576,0.865839,4,31,0,22.6652,-0.934029,-0.16083,3.10178,3.37089,1.44721,2.99516,3.73436,4.6926,0.369617,1.47618,3.35168,3.53876,-2.56009,-3.57939,1.87734,4.10599,6.86879,2.19457,4.32636,6.28081,6.54683,-5.47229,2.48481,-3.10442,7.59812,3.28946,-2.06099,5.08647 --15.4352,0.947249,0.865839,5,31,0,23.5531,1.67477,1.91999,1.33478,2.84557,0.673681,2.64274,3.13944,3.4783,-3.85568,2.81866,1.82853,3.99234,3.36981,7.41172,5.11696,3.16416,-4.43138,1.85508,0.392629,0.298295,-2.69219,5.45058,-7.18983,8.58691,-6.11416,4.34331,12.5553,12.1506 --13.7558,0.970976,0.865839,5,31,0,26.5007,1.63495,1.90479,1.28086,2.8835,0.662713,2.67017,3.19138,3.51754,5.08838,1.49894,3.88386,4.27064,3.6429,8.01642,5.99368,3.43592,-5.15758,0.763745,3.69279,5.69247,3.83877,-3.15504,8.08423,-1.35153,8.76701,-0.819184,-7.05947,-3.1653 --12.9089,0.767147,0.865839,5,47,0,27.974,-1.32498,0.201346,2.81096,2.19158,1.43731,0.4468,2.20965,4.54595,3.39828,3.67276,2.12787,4.54117,4.0879,2.27239,3.84068,0.442348,-1.82438,3.07705,-1.93066,5.02438,0.957106,-4.23946,9.51343,-0.542906,10.0241,-2.85152,-4.84986,-5.98563 --13.3602,0.811285,0.865839,4,31,0,23.0831,-0.504695,-0.118017,3.93358,2.51664,-0.406382,0.578441,1.8359,3.04978,4.61661,4.06104,2.01201,4.44137,-2.77463,1.58805,3.36876,5.93025,4.79033,1.80482,6.64477,3.46126,-0.000894093,10.0893,1.55891,6.01465,-10.3351,2.64398,4.03539,5.57383 --11.9114,1,0.865839,4,15,0,23.0004,1.13168,1.75984,1.43081,3.36955,1.12449,1.95476,3.07818,4.82388,-0.19947,4.44524,0.205149,4.17627,4.44414,3.20832,3.0832,1.68783,-2.43264,1.76801,-1.42861,2.23245,-1.88621,-8.60795,7.3816,-6.3206,-0.625755,-7.4179,5.76392,1.76741 --6.73131,0.991863,0.865839,4,15,0,18.2736,0.140827,0.824986,2.30359,2.39081,1.71806,1.90003,4.04789,3.31036,0.0688277,3.55725,4.04855,0.724011,-2.31251,1.11051,1.96403,6.15387,4.31458,2.14899,6.15933,6.11791,-1.89299,4.78035,1.1294,8.36327,-1.14462,-1.88356,8.95606,-0.0563538 --10.6589,0.787603,0.865839,4,31,0,18.5138,0.244793,1.91298,2.19723,3.94265,1.89903,0.969526,4.21433,2.98175,1.78104,0.321724,3.54037,7.55631,0.755057,4.31484,8.36285,-0.863514,-0.738317,7.00322,-0.348939,1.35922,3.10713,3.48382,4.28284,3.49783,5.77097,-3.11423,3.51461,4.00529 --17.2638,0.920745,0.865839,4,15,0,25.5458,0.00626759,0.456964,1.38365,2.63296,0.630415,3.26318,2.16423,5.31946,0.781345,-1.9716,4.6596,7.05532,1.06803,-0.30164,-2.48063,8.81593,2.47442,-0.430727,3.59914,6.66482,0.0754487,-3.9487,7.1394,4.17655,8.441,1.12703,2.18681,-16.2403 --15.1776,0.943402,0.865839,4,15,0,28.4463,0.22728,0.915835,1.47696,3.13999,1.03887,4.24526,2.30548,5.30382,2.33372,-2.09594,4.53037,6.52016,1.31595,1.66773,7.71053,-3.2777,-0.545744,4.80374,4.01769,2.81057,0.0744456,8.06504,9.3662,2.10504,-7.82404,4.36941,7.34277,8.96702 --18.7495,0.970623,0.865839,5,31,0,29.7172,0.233502,0.692681,2.62581,3.08135,0.79424,-0.541972,3.86131,2.97382,2.52239,2.11576,-0.185371,-0.383744,3.67145,-1.40288,5.33555,-1.68175,-0.953859,7.59358,0.617239,2.83702,2.01593,-10.3749,4.30039,11.923,11.0789,2.88227,0.123054,3.6013 --17.8757,0.989978,0.865839,4,15,0,29.9701,-0.616747,-0.344091,2.39225,2.16372,1.22484,0.40497,4.73184,4.01706,0.169365,1.43619,6.98009,9.44064,-4.46668,5.4735,1.73085,9.38835,2.02625,-1.49216,3.48034,1.28557,5.21917,1.27785,-0.471974,3.33344,10.3653,9.05425,-2.99605,5.75178 --16.4179,0.992418,0.865839,5,63,0,27.5448,-0.639253,-0.361403,2.37086,2.16291,1.23899,0.417747,4.7141,4.01507,1.98947,2.76955,-0.979462,-1.43756,0.596024,0.295883,7.48758,3.93106,2.88843,3.98906,2.04182,8.65503,4.08252,3.57994,-2.255,2.4974,8.08881,13.7455,-3.12733,2.23674 --10.8491,0.967371,0.865839,4,31,0,24.7654,0.0504549,3.25594,2.29329,2.64129,2.07182,2.75236,1.80632,4.03105,-0.0561385,1.42342,-0.578033,2.49782,1.99342,2.50832,-2.48786,4.14643,1.32878,0.648395,5.12332,2.56927,-2.43768,1.98894,6.01445,4.62018,-8.01919,-6.10385,1.10016,5.59807 --16.6656,0.875643,0.865839,4,15,0,29.3224,1.26773,0.968347,1.03149,4.2229,1.63662,0.772572,1.67162,3.00793,3.02118,1.19837,2.2556,4.00599,1.0904,2.72252,9.43036,3.49315,-6.46512,-8.58905,0.854746,5.04169,0.00102119,2.73105,0.518799,4.42446,-12.6278,4.85783,-5.5028,2.39641 --17.601,0.93024,0.865839,5,31,0,32.7627,1.49435,-0.445541,0.471767,3.89255,0.983118,1.36992,1.28416,4.51918,3.40915,1.27151,2.45759,4.36092,-0.118551,4.71724,4.75335,3.04436,9.18273,12.3631,4.60045,3.09361,7.5241,-2.22641,6.47034,0.377241,11.983,-7.3462,0.945585,-0.714589 --18.011,0.99826,0.865839,4,15,0,27.3134,0.533435,2.80677,1.33841,2.89245,-0.421687,2.01912,3.44621,4.74277,2.93374,-0.466209,1.41963,3.67076,0.164383,3.66256,3.09196,3.57613,10.5671,10.9787,6.07726,4.61743,10.4366,-3.46226,7.35537,0.435594,14.1865,-8.05412,0.691445,-0.472733 --18.44,0.929872,0.865839,5,31,0,38.4049,0.892812,1.52836,1.49351,4.71864,-0.439898,2.12137,3.7397,4.72385,-0.928394,4.22104,4.75728,4.79002,0.192425,3.62313,2.4814,3.68155,1.34971,3.06097,9.9993,-4.31983,-13.6256,6.35204,7.62664,6.77532,-12.0105,11.6985,4.87827,8.56848 --8.46771,0.917067,0.865839,4,15,0,28.1203,-0.432794,0.00426552,2.25792,2.07267,1.95017,1.86409,2.24747,3.39356,1.64569,4.01319,0.40591,5.28546,1.73484,0.34025,3.47897,4.51818,-3.26052,1.85891,-2.14667,7.36241,4.19128,7.6789,4.60197,-2.57532,5.25066,-2.10497,9.94394,3.62829 --8.94991,0.80347,0.865839,5,31,0,18.6935,0.375477,0.348753,1.86348,3.40688,0.85611,0.665444,3.76127,2.83418,1.21924,3.66113,0.843074,5.05807,2.49651,2.52654,4.69286,2.74322,4.49009,0.0905414,8.32528,-0.282046,-3.91457,-1.7219,7.26822,6.70315,-10.0271,6.13899,5.36049,2.88347 --8.34858,0.728646,0.865839,4,15,0,23.4839,0.359545,0.324223,1.86877,3.32479,0.857892,0.673869,3.80489,2.83122,1.87945,3.69187,0.950362,5.0378,-2.22468,-0.294786,0.396676,3.59832,-2.01301,3.33749,-1.98405,7.30219,8.17915,2.60526,0.136476,-0.389913,-0.749358,6.31893,1.82743,-1.11268 --13.1455,0.775968,0.865839,4,31,0,24.6358,1.54143,0.791554,3.44187,3.43646,0.507081,3.21067,3.4684,5.80148,0.891764,2.48472,-1.18804,4.19186,-2.59367,0.596778,0.133819,4.82079,-2.2395,3.18046,-2.99745,4.86223,7.70212,0.886227,5.13741,-1.93364,-0.827483,7.57935,-1.79912,-2.73335 --16.6126,0.963195,0.865839,4,31,0,27.1171,-0.169668,0.250883,1.44567,4.51685,1.46504,2.04821,0.366126,3.35962,1.35989,-0.0737264,6.35276,3.90277,-4.51004,5.30161,-0.352774,1.65118,0.800625,7.87873,4.30136,5.98248,10.2668,-5.0439,5.17346,3.90829,-3.37147,4.10636,-1.10772,-6.12715 --13.9085,0.958979,0.865839,4,31,0,28.8863,1.66679,1.89016,2.44499,3.61035,-0.693041,1.61888,2.39653,3.68025,-1.6377,3.87633,0.858217,3.02716,4.88763,1.62108,6.04599,6.51624,0.244226,-7.42272,-4.46411,4.36648,0.817814,0.454897,0.84875,1.75461,3.10533,-7.72444,-1.05247,3.91899 --10.3485,0.861063,0.865839,5,31,0,22.9173,1.59958,1.87596,2.45298,3.57034,-0.714912,1.58431,2.4587,3.78075,4.00812,0.367217,4.69677,4.97202,5.23884,2.37443,6.0697,6.45821,-1.46218,3.9578,5.40025,2.13457,2.71769,1.91523,7.34058,7.84895,-0.0852068,11.7638,6.91792,3.6685 --13.6929,0.989588,0.865839,4,31,0,22.4751,0.494077,1.06097,2.62294,1.93366,1.19953,2.08861,4.48215,5.70244,-2.22536,2.41954,1.03888,2.24248,-1.61899,0.583178,0.930719,2.49568,-2.78709,1.4487,-1.80238,5.7093,1.80629,15.8256,-4.07376,3.45328,-4.87977,-3.01638,2.69091,-0.210919 --14.3617,0.949735,0.865839,5,31,0,23.4696,-0.0313415,1.07303,1.29883,4.29325,0.485625,2.12991,2.65839,2.40754,-2.69905,1.24081,5.71097,2.80844,0.922683,2.9617,4.55817,2.56432,4.41194,2.35039,8.65686,2.59649,1.1746,-12.8512,9.46953,5.70976,3.76736,10.3669,6.08707,11.0088 --9.34654,0.750829,0.865839,4,15,0,27.5137,-0.0328678,1.09661,1.29703,4.26421,0.497428,2.10625,2.67804,2.41366,4.7858,2.54787,0.0216558,5.09419,2.26928,2.56129,5.48907,3.1745,-3.10345,2.42213,-0.952368,0.354423,-5.44642,0.288328,6.53408,1.93113,-3.05721,-0.187248,6.32519,0.526837 --12.4056,0.90735,0.865839,5,31,0,18.2585,-0.978996,0.00690375,1.41141,1.86007,0.51447,1.02493,1.9319,5.37076,5.54068,2.47167,0.679438,5.33147,-1.4708,0.961583,-0.721238,2.95015,-2.11095,2.74109,-1.07748,-0.0104726,-3.10683,4.16342,1.70519,7.98919,-6.29834,6.92657,1.10481,10.1567 --14.3828,0.941507,0.865839,5,31,0,20.1382,-0.00672402,1.61605,4.36607,2.02562,1.28405,2.19659,3.52132,2.71924,-4.30606,1.93849,5.83617,2.25769,-0.159121,0.0283844,-0.126287,2.52407,0.578712,2.02847,-2.59565,2.1823,5.26377,0.043613,6.90589,-0.3196,9.21973,-2.93732,5.21102,-1.96092 --13.7194,0.999776,0.865839,5,63,0,21.0899,-1.17045,2.06663,3.13037,2.18267,2.2352,1.67341,2.44201,3.31915,-5.0253,1.62782,5.56193,2.9926,0.711248,0.264753,0.424243,1.87671,0.661252,1.32603,-2.60536,3.3386,5.78726,0.0761449,6.28588,-0.188437,9.58905,-3.01468,4.66678,-1.81989 --12.7529,0.997347,0.865839,4,15,0,23.0897,0.607837,1.81155,1.02855,3.32901,1.23051,2.75226,3.04686,2.88393,4.3591,2.43018,1.39737,2.15521,-6.55385,2.30716,3.03137,2.33425,1.72829,3.28702,2.29772,2.96076,2.49212,1.20766,7.8574,-4.01907,13.1353,-3.98748,9.4927,0.124772 --11.5814,0.630604,0.865839,4,15,0,30.7951,1.43443,1.01579,0.568572,4.1202,2.66128,1.00705,3.51414,3.08582,4.71888,2.09385,0.0818523,1.35673,3.95681,3.18076,3.42843,3.2317,-0.0315153,-0.063473,4.7275,5.45006,-1.72694,5.11176,-2.27861,10.6081,0.951715,4.63525,2.31586,4.42389 --13.8856,0.996831,0.865839,4,31,0,23.1736,0.165278,0.95938,0.448315,3.69175,1.98476,1.43208,2.5957,2.14745,-2.48227,2.08165,5.79841,5.38045,0.998264,-1.05722,0.474262,5.73322,-2.42856,-1.8501,8.4341,1.96696,-1.03188,5.58373,5.05242,7.87883,8.7162,5.58385,-5.20054,-6.94391 --19.18,0.447455,0.865839,5,31,0,37.3489,0.592354,2.28471,3.95372,2.02488,-0.405467,3.74893,4.67849,4.89619,2.07418,4.28186,-0.531685,-0.00238162,-1.36822,-0.587818,0.977197,8.15588,-6.06356,3.66755,3.16506,1.40737,0.00143513,0.462973,2.82483,11.0907,-5.44575,-0.410612,8.50751,13.4168 --20.3354,0.992498,0.865839,5,31,0,33.4253,-1.01123,1.3018,3.98425,2.59648,-0.694918,1.32415,4.36559,4.42935,-1.31922,-0.79749,7.58135,8.78599,0.922406,1.2445,1.84801,6.72976,8.53429,-0.15796,7.33282,8.03413,2.57488,2.2403,6.10433,-5.29639,7.77296,3.15498,-2.67509,-5.64663 --25.0746,0.716257,0.865839,5,31,0,43.5928,1.12229,0.718229,3.13461,2.49461,-1.19697,0.5579,3.18327,2.10908,-1.33836,-0.917245,7.54097,8.80154,0.753881,5.70911,4.14734,5.44375,8.56038,-0.192641,7.33421,8.1166,6.76237,1.87966,5.24511,1.99138,-4.05888,14.7939,17.6857,11.0458 --17.1876,0.99983,0.865839,5,31,0,35.8868,0.454657,-0.199325,2.27296,1.36523,0.409303,1.34886,3.37512,2.46054,-0.37604,-0.0870231,5.44118,8.77376,0.92339,-0.716425,1.6693,4.45294,-5.02243,3.03965,0.115195,1.08269,3.43743,3.86233,2.96427,5.09515,-6.91654,-6.93059,-8.03607,-10.3997 --10.8028,0.999356,0.865839,5,31,0,23.2442,-0.570247,1.44764,2.36432,1.96155,2.56582,2.30689,3.11871,4.14345,1.03781,4.43651,3.77623,0.485184,1.3382,-0.850446,4.76083,3.57445,4.24137,1.63472,-0.122975,5.03148,-0.903792,0.143048,3.10452,2.36848,8.69338,7.57922,12.1533,17.0319 --15.054,0.763012,0.865839,5,31,0,23.2784,-1.49357,0.0806679,3.34929,1.79277,3.00865,1.84873,2.39673,3.72955,1.08461,-0.181087,2.32481,7.30052,1.77151,-1.07237,4.43703,3.22121,3.0044,-0.595539,-2.05357,6.90922,2.47116,5.64603,4.21159,-0.316235,-6.55707,-3.70955,-6.33269,-8.99167 --14.4651,0.972092,0.865839,4,31,0,25.214,-1.00767,-0.249485,2.32778,2.2765,3.03044,1.5968,1.29745,3.53143,-0.39877,4.90226,5.26916,1.29193,5.97646,1.74976,2.27734,7.67332,-2.90448,0.428399,-2.94759,5.15684,-1.4786,3.86064,8.52369,3.85953,-3.07611,3.32942,-3.48423,-3.59438 --21.4268,0.866597,0.865839,4,31,0,32.3452,-2.52158,0.131932,0.646933,2.65534,2.14149,1.6367,3.4117,3.62634,2.19458,-0.674677,1.15662,7.06146,-1.18394,-0.94759,5.11188,-1.09286,4.04269,11.056,11.7921,0.571518,9.3094,3.73453,4.96644,5.18273,-3.41688,8.55017,-11.3718,3.66098 --11.531,0.934526,0.865839,5,31,0,32.9183,-1.70719,0.701024,1.48878,3.04133,0.929906,2.40339,4.59259,5.79287,0.71755,3.99839,4.41476,0.652855,2.73787,3.8637,-1.03015,3.48397,0.621716,0.111376,-2.10476,1.57372,-4.5311,-0.783511,3.46738,-1.092,0.708118,7.53128,-1.27442,9.47838 --14.0544,0.968093,0.865839,4,15,0,25.699,1.76891,1.15436,2.84753,3.13794,1.16223,0.827416,2.32664,2.53994,3.19499,2.27923,4.62315,2.72355,-1.48439,4.151,5.55261,1.80742,3.7352,-0.0272048,-1.41041,4.87927,-3.65468,-4.79562,3.67322,-7.26507,3.83321,12.9485,-1.86531,11.2928 --14.1101,0.508562,0.865839,5,31,0,35.7331,1.75882,1.2685,2.82568,3.20115,1.14385,0.809612,2.27746,2.51694,3.28094,3.17389,4.75502,2.53877,1.87119,1.53271,3.48146,5.26554,-2.22567,3.83773,6.73944,3.29282,2.62255,7.92074,6.80584,14.6531,-8.17598,-1.8243,15.6526,10.9116 --13.5423,0.51509,0.865839,5,31,0,32.0258,-0.560323,1.54158,2.48505,2.35843,-0.222804,3.19825,4.21516,5.6957,2.49806,3.91757,2.75141,6.67588,-1.04653,3.02497,2.25281,2.10814,-2.2835,4.89104,5.71768,6.93677,3.96255,7.09775,5.44909,15.9834,-8.29843,0.419144,11.2253,7.2753 --14.7933,0.745696,0.865839,5,31,0,29.2532,1.21723,1.48665,0.373629,1.33037,1.52955,2.74904,4.19162,5.36418,2.66945,1.18077,3.76132,8.06554,4.78367,1.41284,2.11997,3.43092,4.09721,-2.38541,0.72479,0.892277,-1.82409,0.0496155,3.88417,-9.02399,-0.188268,7.03978,-1.87201,3.98573 -# -# Elapsed Time: 0.016 seconds (Warm-up) -# 0.031 seconds (Sampling) -# 0.047 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstan/output_no_warmup4.csv b/arviz/tests/saved_models/cmdstan/output_no_warmup4.csv deleted file mode 100644 index a13e35c4b8..0000000000 --- a/arviz/tests/saved_models/cmdstan/output_no_warmup4.csv +++ /dev/null @@ -1,148 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 18 -# stan_version_patch = 0 -# model = test_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 100 -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 0 (Default) -# data -# file = (Default) -# init = 2 (Default) -# random -# seed = 721276882 -# output -# file = output_no_warmup4.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,y,x.1,x.2,x.3,Z.1.1,Z.2.1,Z.3.1,Z.4.1,Z.1.2,Z.2.2,Z.3.2,Z.4.2,Z.1.3,Z.2.3,Z.3.3,Z.4.3,Z.1.4,Z.2.4,Z.3.4,Z.4.4,Z.1.5,Z.2.5,Z.3.5,Z.4.5,Z.1.6,Z.2.6,Z.3.6,Z.4.6 -# Adaptation terminated -# Step size = 0.859916 -# Diagonal elements of inverse mass matrix: -# 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 --22.6129,0.706157,0.859916,4,31,0,35.4383,0.20984,0.868052,1.77171,2.25843,2.88443,2.10095,5.07928,1.96094,5.20439,3.28477,3.17734,4.38809,3.98845,4.16185,7.0861,4.15191,7.18923,2.8777,9.84361,4.76784,-0.0251297,1.79367,0.0766986,2.95719,-1.91299,-8.47633,4.8799,-19.2805 --17.345,1,0.859916,5,31,0,33.7438,0.438482,0.78066,0.950382,3.93083,1.91415,2.19516,1.73318,3.54684,3.97597,2.86229,4.01245,2.98937,-1.36901,-0.30979,7.51517,3.1774,-4.36991,-0.349798,-4.0119,2.36416,3.31206,1.45411,4.02143,5.00804,1.60524,18.4178,-9.10834,18.7744 --12.1039,0.928066,0.859916,4,15,0,31.1023,-0.334011,0.896415,0.643149,3.49349,1.99164,0.882241,3.32229,3.48341,-2.07425,1.21849,0.0682538,4.56512,-3.44863,0.234755,-4.50474,5.92573,-1.6641,1.8972,1.88236,4.54557,1.20363,4.64947,1.70027,-2.88791,-4.01365,7.08669,-3.46178,5.61932 --12.1325,0.883287,0.859916,4,31,0,24.8862,-0.249282,1.25008,1.1697,3.98037,1.21104,1.97656,4.3602,4.40324,1.06115,1.63377,3.89736,0.399892,0.720508,3.16575,-4.45909,3.03681,-5.14731,-4.75019,-2.77423,5.28736,2.60286,-2.073,2.17533,-0.842004,-0.211157,6.40108,-1.22855,7.32571 --12.2418,0.793393,0.859916,4,15,0,22.6583,0.169925,0.101154,2.39624,2.83441,1.28948,2.9912,1.84652,4.23081,-1.56307,3.52895,0.859702,7.40449,0.199534,0.830515,9.67304,4.93661,4.30855,4.23126,10.0113,5.31586,-3.55699,8.88778,0.17476,11.2181,-1.42491,2.04108,3.34213,3.91865 --9.93588,0.787908,0.859916,4,31,0,22.67,0.110784,0.155392,2.49015,2.8335,1.26962,3.11409,1.7891,4.21834,3.41161,0.467302,5.54523,0.607312,5.18247,4.21631,0.49848,0.855527,3.10858,2.5218,7.1354,6.81532,2.16263,-0.0793505,3.41773,9.18214,-1.38252,-0.771249,6.89591,-2.75794 --14.1028,0.639733,0.859916,4,31,0,30.9443,-0.824559,1.46005,1.93618,3.70319,0.328259,0.771838,4.6462,3.63184,-0.98269,2.83883,1.41579,-0.36811,-3.13837,-0.1277,5.4939,7.33196,6.78952,1.96956,-3.31184,1.07979,0.597921,10.8573,-1.94303,6.68218,-3.25892,2.79099,9.13953,4.99961 --19.8965,0.60592,0.859916,5,31,0,32.6491,-3.37388,1.53421,2.91843,3.43959,0.200899,1.55248,2.86243,4.7197,1.53471,6.4264,2.36718,2.36435,4.63604,3.65627,0.0148699,0.150457,-4.5164,3.05494,9.81127,8.80833,5.30601,-3.9785,-1.86466,8.85851,-1.78671,9.18042,0.600912,15.0258 --12.8719,0.924056,0.859916,5,31,0,36.1185,-2.26655,0.206168,3.464,3.21936,1.53981,1.57247,1.28443,4.01605,1.56677,4.89665,2.18079,2.97479,-0.769546,4.72551,2.76777,5.34093,6.38977,0.817219,-3.23425,-0.648219,-0.705241,6.95594,11.5285,1.30318,4.42826,2.85232,1.95335,3.12735 --13.2411,0.973397,0.859916,5,31,0,23.532,2.57179,1.70618,0.989554,3.05235,0.822884,0.969754,2.79828,4.59806,0.815894,-0.145193,0.573526,5.45152,-1.57353,4.86258,1.86017,6.10495,-5.40615,-2.22208,-0.265008,9.79517,2.85032,-2.67779,-5.48882,6.06956,-2.01864,3.09372,6.12499,7.52951 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--14.4533,0.716086,0.859916,5,31,0,33.5658,-0.798513,-0.434289,3.09002,5.88697,1.08846,1.92933,2.9434,4.87561,2.16508,4.42303,4.28087,2.68978,-0.938734,3.38581,4.47323,1.8563,1.89053,-3.1804,6.6922,3.35414,5.94121,3.88409,6.67241,-1.68251,2.60528,-12.8665,2.63339,5.97042 --14.487,0.994226,0.859916,5,31,0,28.9788,1.91957,0.756809,3.35314,2.65196,1.75523,1.01067,1.44528,3.39142,1.34609,2.04347,2.99469,2.70495,-3.19666,-0.589419,5.25028,3.15863,-1.19344,6.98245,0.739985,5.36202,-4.19424,-3.09074,-1.17021,8.47934,-5.70037,13.0861,-0.649617,-8.31966 --12.9522,0.842468,0.859916,4,15,0,24.4321,-2.34751,-1.03058,2.48455,3.35519,-0.23159,2.46451,3.43989,2.78486,-0.302154,2.02639,4.65095,3.89692,5.27343,2.84334,0.521282,6.14708,3.89414,-0.508593,7.2349,0.831378,2.83679,-2.43915,0.447403,7.08253,-0.456571,8.56641,3.1306,-5.692 --12.468,0.992801,0.859916,5,31,0,29.7588,1.10611,2.23753,1.15655,1.75313,1.28809,1.74718,2.88797,2.10366,0.50453,3.82025,1.25923,4.06435,0.290583,3.64497,2.08436,7.12472,-4.7796,2.70839,-1.04639,6.96657,-1.91323,7.09642,5.05144,0.463682,2.73778,-9.84326,-4.47171,11.8372 --12.0247,0.994863,0.859916,5,31,0,22.0047,1.6873,0.99575,1.83594,2.17571,2.39988,1.44041,2.6121,2.5823,2.12371,-0.571,4.97879,3.36724,2.3192,1.01971,0.276582,0.416147,4.22952,-1.49074,6.25187,6.72713,5.15169,11.6288,3.76846,1.72781,-1.11349,-3.39912,-0.567403,12.5056 --12.5479,0.966968,0.859916,5,31,0,26.941,1.70222,1.14094,1.88426,2.10995,2.20222,1.55169,2.69694,2.50681,0.983012,-0.251243,4.87492,3.12776,-1.75082,1.44361,7.98198,3.56893,-1.70332,4.70252,0.129009,1.18043,2.96152,-4.74589,5.19736,3.09276,-0.53806,-2.49347,10.6197,-9.48383 --15.985,0.732589,0.859916,5,31,0,30.4477,-2.23566,0.955657,2.37895,3.73267,-0.422091,2.25239,3.7626,5.88226,-1.18549,6.24582,2.53816,1.21965,-2.00737,-1.42229,3.77988,1.97648,5.37283,2.41237,5.46785,5.32616,-1.03419,9.21077,0.399215,5.30513,3.15586,2.93666,-3.83258,15.7467 --15.6056,0.998741,0.859916,5,31,0,30.0413,-1.18092,2.00087,1.34282,1.65007,2.62929,1.22045,2.14842,4.91422,3.08789,-0.882334,4.57686,7.73441,-2.96105,-2.64661,1.9881,1.98378,-0.493348,4.31226,-1.7167,1.22209,6.63384,-2.81252,2.8214,7.14881,-0.105956,2.85037,9.6797,-7.25884 --15.0548,0.95164,0.859916,4,15,0,29.8659,-0.381875,2.09573,0.344683,3.46224,1.17454,1.00775,2.62415,5.39271,-1.0954,4.89433,1.4539,-0.0127885,0.451592,5.05772,3.33897,2.77994,0.0949254,2.09753,-1.89133,4.58845,-5.99037,9.9192,0.315388,2.9266,13.1128,6.70631,-2.35708,-4.03528 --14.1084,0.983591,0.859916,5,31,0,29.8754,-0.37986,0.180945,3.55694,2.3923,0.710948,2.66257,3.62158,2.55732,-0.281238,4.15458,0.849699,6.94482,1.61898,-1.03068,2.59599,5.10109,1.32713,1.57773,9.79102,3.61021,0.459191,2.64305,5.95104,8.73907,17.5498,-4.14569,-1.20054,12.8489 --12.4979,0.641345,0.859916,4,31,0,33.353,0.30848,0.517997,0.865585,4.15061,1.85943,1.68196,2.39379,4.65753,1.32756,2.05725,2.77447,3.86762,-1.38827,2.92953,5.92781,8.98072,7.35378,5.4103,11.1812,-1.7929,3.67488,1.64305,9.4045,6.61692,10.8356,1.82764,-3.39583,7.66785 --12.9707,0.540314,0.859916,4,15,0,33.1484,0.971203,0.936526,3.03421,1.6992,-0.0798932,2.57981,3.36116,3.30059,1.89725,1.57333,3.3411,9.15068,3.31012,0.960759,-0.0166559,-0.982499,2.94934,-1.89396,-0.738281,6.5111,-1.48505,-6.07174,2.4941,12.7052,2.34976,4.08506,3.72671,6.59062 --15.1537,0.979191,0.859916,5,47,0,27.2248,-1.17899,0.549146,3.16534,3.59716,1.65565,1.90947,1.49682,4.76984,3.4486,1.0477,1.25229,0.959171,3.17559,-0.477733,3.39507,-1.18711,-5.06752,2.83005,12.5726,7.12429,2.54643,9.91824,4.29581,-4.96938,0.422108,3.1731,1.71128,5.0355 --13.875,0.948876,0.859916,5,47,0,28.801,1.08311,2.11659,1.2349,2.18659,0.346867,1.52696,4.25054,3.28484,1.47108,1.60793,1.41771,2.9668,-1.18911,4.44067,2.44301,9.2082,4.85274,-5.30916,-5.4173,-2.20795,-0.169994,-0.00941975,11.9882,4.55249,-4.72622,3.38392,-1.47972,5.24275 --12.6096,0.737351,0.859916,5,47,0,26.7235,-1.4977,-0.179994,3.54054,4.39658,1.6465,0.793788,1.61542,4.12734,1.42444,0.164471,1.06863,6.03793,-0.918801,1.35127,2.62466,9.01591,-1.99594,2.91893,-1.45778,-0.891072,5.33707,-0.248807,-1.00965,0.410353,7.14707,-0.275293,7.08074,1.26036 --17.5993,0.894903,0.859916,4,15,0,29.0053,0.845771,2.55205,0.897319,1.45712,0.367155,3.34427,3.04441,3.46645,6.27214,3.9719,3.09585,6.23672,2.11878,-0.672223,3.82622,8.65957,-4.96956,1.85899,-2.91044,-2.81103,7.11268,-0.663002,-1.36878,2.89349,10.8205,3.59566,8.05534,3.0012 --16.863,0.997303,0.859916,4,15,0,30.2209,0.519697,0.736352,2.27721,2.14823,2.32194,1.41633,4.31963,5.06851,6.22584,2.29236,3.46019,6.48318,4.56636,3.57745,2.98567,7.17984,-6.83301,0.701455,-3.05114,-1.80767,7.60513,-2.89524,-2.38484,5.89535,11.1638,2.37995,9.05456,1.91641 --15.3284,0.997692,0.859916,4,15,0,28.6176,-0.464641,0.32277,1.51142,4.48319,1.21889,2.70384,3.73507,4.65015,-1.65376,2.04902,1.63194,2.88174,-2.72499,1.52382,4.91349,-0.246619,7.28998,6.06397,4.03434,19.1252,2.34459,4.0931,1.6657,5.11913,-1.02519,1.75354,9.4718,3.94054 --11.1202,0.583939,0.859916,4,31,0,35.8327,-0.349195,0.250315,1.53941,4.49202,1.19671,2.73724,3.53591,4.6433,3.68261,2.28329,4.38443,4.59987,-0.118217,-0.686493,-3.11714,1.47298,-7.76856,1.30395,7.37658,6.96213,4.35683,2.27839,-2.45195,4.42632,0.428508,3.63962,-1.66603,-1.08657 --13.0665,0.677305,0.859916,4,31,0,30.0481,-0.446176,-0.84333,2.58256,2.7335,0.691083,1.1633,3.70003,5.01264,-2.5177,0.521508,1.66881,2.91779,2.00363,4.95855,2.89021,4.69984,-5.93264,4.34422,-1.46454,9.98786,-11.4998,0.854588,4.68598,6.86755,-1.29161,-2.00813,1.36612,5.38702 --15.3053,0.976156,0.859916,5,31,0,28.5101,-0.132308,1.3724,2.82199,3.08736,1.03777,3.78568,3.7156,3.0099,4.20863,4.89585,1.81606,4.00651,-2.13622,-0.662736,1.76209,4.07952,7.03016,2.62998,-2.99369,4.2505,8.24089,2.35263,-0.73254,13.4695,3.5804,0.942343,1.37127,19.7991 --15.1535,0.7791,0.859916,5,31,0,32.7076,0.802769,0.655822,1.29774,3.28429,0.975132,0.415698,2.15115,4.42791,5.25744,3.55852,1.92718,5.39163,2.58369,3.82997,5.17453,3.68436,-1.75842,3.6172,8.9534,7.018,-6.25917,1.70391,7.46698,-5.46997,0.985211,1.58911,4.37327,-13.4731 --14.5814,0.837771,0.859916,4,31,0,27.2337,0.795052,0.609005,1.27719,3.18713,1.0392,0.464898,2.11831,4.55357,-3.24392,0.181565,4.03275,2.69498,3.13203,-0.762795,4.70285,7.22659,2.43308,4.605,1.88254,7.51878,-0.696357,4.83883,6.16808,4.78878,11.9633,2.01398,6.30707,-14.6037 --10.0217,0.784883,0.859916,4,31,0,28.9914,-0.349518,1.88446,2.88101,3.1462,1.11177,2.1828,4.64518,3.783,1.60199,3.15833,0.624725,2.7916,-0.493227,3.94351,0.00859464,1.64678,-7.2455,-2.04794,3.14796,8.08623,3.12603,5.60073,5.14381,4.15951,7.37008,11.0267,0.0778553,1.10496 --12.9025,0.764461,0.859916,4,31,0,23.0833,-2.0546,1.99819,2.25055,2.48696,1.91125,1.7947,4.55415,4.4222,0.452955,2.32179,5.52723,5.69567,1.54692,-0.357058,6.01465,8.22773,0.238719,1.10937,4.42714,4.88497,1.18123,8.73238,10.6677,13.2696,4.06453,10.2058,2.49576,9.12288 --10.1037,0.90306,0.859916,4,31,0,21.3521,0.33944,1.60804,2.4164,1.41871,0.442898,1.31127,4.29246,2.94986,0.852911,3.90886,5.27758,5.54774,0.685806,5.3968,1.36195,2.00889,1.03017,2.57475,0.898095,1.93658,-0.343915,-2.162,-2.09553,-2.30155,7.70605,-5.28324,-1.11602,-3.34527 --11.1872,0.955636,0.859916,5,31,0,21.835,-0.551872,0.243941,1.71684,3.95994,2.16659,3.2216,1.96912,4.92982,2.45496,5.101,2.46289,5.65786,2.45714,1.68171,2.55626,4.59021,3.39243,1.43318,5.74644,2.13027,-9.15137,-3.24644,-2.19047,0.311814,5.77497,-6.47713,-1.73941,-0.449245 --7.69685,0.88349,0.859916,4,15,0,22.4297,-1.3106,1.20812,3.3218,4.12328,1.17012,2.17097,3.46997,2.88912,0.822214,3.09229,4.27638,8.00522,0.493638,2.75447,2.35581,2.67437,-1.48384,4.18027,2.84662,2.70166,1.87193,5.87905,2.13097,1.12173,-5.01375,-4.49037,3.90682,2.38222 --11.2206,0.940679,0.859916,5,47,0,17.9685,-0.603051,0.377541,2.63365,3.49716,2.15977,2.20988,2.76167,3.17611,1.18634,0.982062,1.76055,0.218854,0.552595,2.78453,2.2191,2.60608,-3.4995,-0.338945,-2.85538,-6.1979,0.565614,-3.16837,4.36067,8.00141,7.07408,8.45855,2.0054,5.52786 --15.4403,0.649962,0.859916,5,47,0,27.1916,-1.48841,-0.0443652,1.9652,2.80373,2.8185,0.880316,2.59652,5.49742,-1.36368,2.40521,3.81938,7.7125,3.17783,2.141,2.19932,10.2585,6.14244,1.30234,1.27565,1.77112,1.91094,-2.77418,2.86939,11.0644,10.2695,4.93419,5.82036,13.669 --12.3163,0.964236,0.859916,5,63,0,26.8408,-1.12576,0.969584,2.16126,2.74569,0.899515,1.8721,3.80224,2.6802,1.566,2.5124,6.84416,3.71964,-0.927903,2.80924,2.43177,11.423,3.32134,1.50043,7.68027,5.86831,0.254419,7.56355,3.10835,-3.13358,-10.0143,-2.99925,0.0663054,-1.43013 --16.8868,0.729955,0.859916,5,31,0,31.4964,0.67261,0.881497,1.61705,3.27868,1.77475,3.19051,2.57196,4.02322,5.8619,2.00099,5.55335,2.9118,-5.73792,5.71567,-2.03406,4.82805,-3.385,2.95265,-3.92703,4.17229,3.09305,-3.76229,0.530877,9.79443,9.7466,10.9843,6.17728,11.5381 --17.0515,0.780401,0.859916,4,15,0,32.1637,0.778273,0.981826,1.70217,3.12764,1.56582,3.03311,2.66961,4.1543,5.78992,1.85874,5.80564,2.77223,6.01926,-0.703788,0.514517,-0.455022,5.91791,1.58318,9.74513,3.86067,-1.31356,4.2358,11.1458,-6.52518,-4.92145,-3.37125,9.29991,-5.55916 --10.5603,0.694157,0.859916,4,15,0,28.435,0.956502,0.888365,1.72265,3.09225,1.52977,3.0756,2.66181,4.14796,-3.46753,2.24733,0.556158,5.21868,-0.754862,2.21276,0.143714,7.91022,2.54112,0.598458,-2.72602,7.56164,-5.86223,3.83605,8.25721,0.910887,-3.19378,-3.01029,7.1662,0.0882806 --12.6052,0.686405,0.859916,4,31,0,25.6356,-0.314642,1.70269,1.24235,2.78149,0.422298,0.525956,3.93127,3.25105,0.972829,-3.03621,1.91761,2.64859,3.34131,2.63789,4.76711,0.548983,1.41677,1.71746,8.97784,2.83261,-1.05645,5.03369,-0.227953,8.55392,3.26599,-7.31715,14.9358,3.22604 --12.6098,0.901794,0.859916,5,31,0,23.3988,0.234858,1.40717,0.358212,4.13506,0.802967,1.22651,4.31684,4.69177,1.01585,-2.90658,2.53826,2.96208,2.72848,2.06229,4.90019,0.500111,1.59342,1.49161,8.84837,2.47604,0.0893811,4.38203,-2.34742,8.20092,3.16686,-5.8013,14.1004,2.59756 --20.0964,0.518079,0.859916,5,31,0,37.2994,2.06278,1.00223,2.40722,4.28723,0.970464,1.11353,5.09271,1.35747,-0.531953,-2.20665,3.23871,2.3119,0.951088,2.2429,-1.73433,8.10341,0.795995,0.181154,-2.4527,8.81787,1.96481,1.30791,-0.542284,1.38051,-12.8591,5.68413,6.65336,-3.16504 --22.6313,0.985902,0.859916,5,31,0,37.5681,-2.01651,1.27265,2.04456,1.41971,0.562496,2.47808,0.744188,6.43482,-4.62633,4.66233,5.49219,6.06614,5.16734,2.5503,1.67489,6.96024,-0.492431,5.52042,4.56353,-0.677001,-0.256587,3.02575,6.77831,6.38589,13.5147,-3.32378,-5.14598,11.1723 --15.0751,0.999925,0.859916,5,31,0,31.8333,-0.780468,0.677488,2.51719,2.84249,2.80039,2.48124,2.56611,2.57919,-2.59445,5.59523,3.36369,5.92006,1.21263,1.73102,6.59091,7.40217,4.49287,-1.87282,1.10872,9.50271,3.31891,0.0854731,-2.56657,-0.00985531,-11.5034,9.01627,10.2056,2.06355 --19.914,0.845289,0.859916,5,31,0,32.1395,-0.300443,1.13498,1.60257,2.79829,3.24673,2.76682,4.71512,3.27557,-1.89456,6.02976,3.41023,4.98275,-0.642796,3.4109,2.89839,-4.26017,5.21984,-2.47666,2.37945,10.0397,-3.99377,3.63731,-0.513337,-2.88656,7.60719,-1.1529,-9.86194,-3.65649 --21.1509,0.602111,0.859916,5,31,0,38.5764,-0.142924,0.946068,2.82701,2.75789,-1.12258,1.10227,0.620755,4.8328,-3.09598,-0.707412,0.869208,-1.24071,0.52419,3.24099,1.24957,-0.581173,6.07841,-2.18822,4.32392,9.3514,-2.76999,3.45691,1.02142,4.0615,-5.13439,3.27406,16.4918,11.1223 --22.1389,0.899823,0.859916,5,31,0,33.6966,-0.339193,0.931213,2.886,2.71804,-1.08924,1.09015,0.35164,4.97077,-2.83107,-0.830384,0.167681,-0.968036,6.55468,1.83697,3.55073,6.93599,-4.08308,6.51143,1.47794,-1.36632,9.90189,-1.42554,6.7934,7.12883,1.5445,9.13768,-1.75081,0.273884 --21.9804,0.967555,0.859916,4,15,0,40.1419,-0.540194,2.49695,2.99304,4.73491,0.173906,-0.480248,2.43778,4.49678,-2.02474,-1.90418,1.8689,-0.549195,-1.88555,1.95886,6.02981,2.09325,5.81386,-5.35707,4.08564,10.0766,-10.7442,-4.63033,0.712831,5.61362,5.62986,2.79374,6.95122,6.97647 --17.9912,0.96243,0.859916,4,31,0,36.9457,-0.527543,1.53903,1.5487,3.78224,0.506439,2.70253,4.40531,4.58875,3.46156,-2.66367,-0.0335695,0.647077,5.60124,1.93144,-0.89419,4.84778,-5.23454,8.0743,4.77393,-0.202629,7.21011,2.96783,6.42635,-0.505514,6.28818,-7.33191,-1.73906,-5.75029 --16.0253,0.778139,0.859916,5,31,0,32.287,0.76432,0.990039,2.55989,2.42681,1.361,1.75439,1.65682,2.84688,1.41667,-3.21304,4.3265,0.412717,5.77492,-1.89685,-0.744725,3.2843,6.16646,-2.5749,2.33507,5.12095,-5.4122,1.04321,-0.0364264,8.4328,-5.1995,8.96167,3.92101,14.0647 --16.4071,0.943162,0.859916,5,31,0,26.5687,-0.381833,2.07298,2.31868,2.06177,0.798281,2.45168,1.49451,3.6366,1.24822,-3.02469,4.2308,0.968445,-2.2413,7.69846,5.27872,6.69935,-4.27754,6.61837,3.07875,3.15348,3.97141,3.70027,6.23754,2.367,3.67751,-12.1563,-2.43528,-4.54102 --20.9305,0.862584,0.859916,5,47,0,34.4424,-0.709966,2.29291,2.16541,3.6833,0.639102,2.58386,1.74468,5.22968,0.595554,5.60459,2.35658,6.09126,3.40885,-5.45312,5.99079,-0.13053,4.61676,-3.59461,4.68933,6.85849,3.7477,-4.12346,0.290602,8.0503,-4.95875,-11.4845,-8.71178,-9.91394 --12.6123,0.808348,0.859916,4,15,0,32.6953,2.03431,-0.481515,1.85304,2.47349,1.07405,2.39354,4.14501,3.16814,-0.895454,-0.507796,4.10996,2.89084,-0.852421,8.54531,-0.779684,6.56818,2.32818,5.39589,5.94996,1.88772,-4.47533,5.36001,-2.03881,4.79507,-5.6791,1.93471,0.243327,5.75681 --12.3193,0.951801,0.859916,4,15,0,27.8022,2.07125,-0.497827,1.87295,2.47938,1.11321,2.36415,4.05436,3.05141,2.88669,4.7789,2.16574,5.57081,-1.20814,2.77729,4.32468,1.72462,-4.77712,1.98091,3.27797,1.55318,-2.8041,-5.49294,8.13332,5.5027,-1.74276,-6.71111,-2.87057,-1.88625 --15.847,0.817789,0.859916,5,31,0,26.9584,1.96685,-0.422424,1.84139,2.35552,1.10967,2.38798,4.05333,2.92283,-0.792415,-0.713089,3.8871,2.496,2.17455,-0.681791,-1.5091,0.122629,9.82555,-1.26646,5.39707,6.10618,1.583,-1.4231,6.72846,6.88418,-5.30276,-7.57486,-8.12539,4.63158 --16.4312,0.729381,0.859916,5,47,0,35.8245,-0.950554,1.2654,0.886089,3.70523,0.139229,2.57563,2.14612,5.5892,-3.61447,0.663131,0.298157,4.28795,7.04931,-1.67338,-0.590302,1.23336,-7.82902,6.50824,0.22734,3.4992,2.01079,6.11353,0.678041,-0.105617,7.89532,0.256027,8.04907,4.00376 -# -# Elapsed Time: 0.016 seconds (Warm-up) -# 0.031 seconds (Sampling) -# 0.047 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstan/output_warmup1.csv b/arviz/tests/saved_models/cmdstan/output_warmup1.csv deleted file mode 100644 index 8b5203850c..0000000000 --- a/arviz/tests/saved_models/cmdstan/output_warmup1.csv +++ /dev/null @@ -1,248 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 18 -# stan_version_patch = 0 -# model = test_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 100 -# save_warmup = 1 -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 0 (Default) -# data -# file = (Default) -# init = 2 (Default) -# random -# seed = 721290931 -# output -# file = output_warmup1.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,y,x.1,x.2,x.3,Z.1.1,Z.2.1,Z.3.1,Z.4.1,Z.1.2,Z.2.2,Z.3.2,Z.4.2,Z.1.3,Z.2.3,Z.3.3,Z.4.3,Z.1.4,Z.2.4,Z.3.4,Z.4.4,Z.1.5,Z.2.5,Z.3.5,Z.4.5,Z.1.6,Z.2.6,Z.3.6,Z.4.6 --20.472,0.0612995,2,4,17,1,33.5509,-1.665,1.77729,0.980561,3.19839,1.2752,3.55305,6.99586,5.62094,-1.53951,2.90827,-0.644988,3.25774,0.494528,0.728339,1.86767,-0.44014,0.709119,5.0961,1.18393,-1.43364,2.68804,2.13608,2.59464,-5.22838,0.620564,-2.12552,-1.60487,0.508207 --20.472,2.78641e-030,2.61037,1,3,1,35.2726,-1.665,1.77729,0.980561,3.19839,1.2752,3.55305,6.99586,5.62094,-1.53951,2.90827,-0.644988,3.25774,0.494528,0.728339,1.86767,-0.44014,0.709119,5.0961,1.18393,-1.43364,2.68804,2.13608,2.59464,-5.22838,0.620564,-2.12552,-1.60487,0.508207 --11.5641,0.999717,0.266025,6,63,0,32.0677,-0.150239,1.38278,0.111106,2.1883,1.34722,2.03249,2.94661,2.18558,3.55802,2.66723,6.81927,3.18785,-0.319093,3.08166,4.59568,3.96313,-3.87696,4.72113,3.64804,-5.74482,2.80635,1.08725,4.04576,0.685053,3.13889,1.9219,5.79532,6.99064 --13.962,0.966922,0.282128,5,31,0,27.6704,-0.495203,3.14582,2.52203,2.56736,1.1801,1.90261,1.50922,3.7084,1.52787,2.48182,-0.394936,0.427627,2.81197,5.91215,1.94411,5.27218,-2.24588,-1.98639,4.01621,-7.82423,1.5856,-0.838006,6.15004,3.14336,0.313544,-2.00931,2.55586,3.2706 --15.9284,0.948005,0.351283,5,31,0,31.9452,-0.727586,3.14592,1.65807,2.12012,0.821523,4.8391,2.91056,3.28978,1.97757,1.06237,6.01335,7.88875,-2.37065,1.91518,-1.24824,0.596331,5.26392,1.72435,-1.74502,3.7761,-5.25973,1.58801,4.22104,8.77233,-1.36814,3.63841,7.48352,0.844924 --13.7651,0.998045,0.472101,5,63,0,30.9015,-0.145667,3.31926,2.94225,2.39482,1.57918,1.30802,4.6478,5.09696,0.754277,2.84592,1.24548,3.03707,3.63639,2.65573,4.56664,8.9909,2.05955,7.09782,1.82665,9.63438,2.5378,0.32562,-2.13452,5.55337,-6.10568,-8.54325,8.88333,8.29782 --11.8865,0.87431,0.797279,5,31,0,26.7937,-1.21642,-0.0257434,0.504229,2.90333,-0.751753,2.12802,3.13982,5.6551,0.141455,1.43272,1.86416,3.45911,2.17346,-0.619825,0.349178,1.08907,-0.404669,-4.27143,2.71488,-2.2783,-1.83734,3.95878,4.02865,3.46846,0.6154,13.4269,-1.47274,5.76033 --8.52197,0.90024,0.963484,4,15,0,20.9426,-1.22327,0.0228495,0.477555,3.1649,-0.363089,1.92912,3.22842,5.12011,2.44617,2.79401,4.14095,4.93582,1.506,4.02935,4.01759,3.40947,3.71105,3.91344,5.72248,5.65048,3.70945,-1.70162,11.0543,0.952832,-6.03162,6.06967,4.62103,6.52429 --5.70169,0.490193,1.29077,4,15,0,17.1567,0.847654,2.53951,3.43114,3.42478,1.39975,1.33723,2.4396,3.61782,-0.129472,1.04747,3.28643,4.61481,0.806614,-0.0676757,1.5477,4.70024,0.0174547,-2.09489,2.64642,5.70738,-1.26494,3.41588,5.44757,6.22638,-3.59054,4.16218,8.50235,3.1443 --10.2617,0.925018,0.480465,6,63,0,17.7641,-1.3474,-1.54959,3.04015,3.15706,1.39395,3.0469,3.90322,4.35564,2.61287,1.39143,0.776445,2.38125,-0.634134,1.29492,-0.818315,5.02337,0.621745,2.52602,-0.783233,3.32609,6.14565,3.87538,0.267525,1.32907,6.71649,2.48797,-0.726444,6.33942 --14.1896,0.951307,0.710439,4,15,0,26.0108,-1.70637,-1.00889,2.36982,3.05968,0.43831,2.1011,0.96996,4.41546,-0.433041,4.85162,2.76153,6.53905,0.28723,0.754818,4.0409,1.9457,0.260192,4.76722,1.8932,1.15773,2.19223,8.58848,9.13449,-7.29909,7.83213,4.90976,5.81574,7.93619 --12.1885,0.848456,1.14919,4,15,0,23.4384,1.12531,0.363927,1.67162,3.31068,2.32671,1.9654,2.65753,4.37906,0.906417,3.86205,2.89909,5.06216,5.68223,1.39875,1.75006,3.6019,2.7262,0.0503329,4.65283,6.15732,-1.32802,-5.80529,-5.09634,14.6994,-11.7322,-0.793659,9.52717,4.73608 --15.2699,0.39342,1.34739,4,15,0,27.494,-0.444246,1.95266,1.10515,4.47338,0.327919,1.44643,2.07413,5.13565,0.0681344,0.163354,5.88596,3.15452,-3.76213,2.68972,4.33687,4.33263,-3.12761,6.09847,4.88722,2.55411,-0.145599,16.3731,12.041,6.07643,2.34663,2.32885,0.0568579,14.2765 --15.4279,0.998214,0.379953,6,63,0,33.5133,-0.00648124,2.30747,1.47509,4.76354,1.2709,1.71622,1.93493,4.7658,1.94391,3.69848,-0.055974,4.45696,-1.97464,4.15917,4.37688,1.01943,-2.2293,0.422564,0.1714,1.26342,2.39786,9.2681,-5.93255,-5.72621,6.58127,-2.48128,15.6913,10.0502 --11.2653,0.949202,0.717696,5,31,0,27.6551,-0.209218,1.20502,2.40055,2.94022,-0.238716,2.39013,3.83003,2.32487,1.62991,3.29042,5.79872,4.95836,4.28122,0.476561,1.17503,7.03832,5.67924,5.76333,6.47223,5.30126,0.234378,-2.25569,6.09144,9.07281,10.8216,1.45053,-8.27812,5.37131 --20.7756,0.238258,1.15857,3,7,0,34.5097,-0.0471344,1.42103,2.62988,2.59096,-0.122192,2.4923,3.8245,2.16762,0.056172,0.213482,0.0399874,3.39957,-3.72879,1.31816,5.50711,1.54188,-0.31648,3.61302,3.48983,5.09074,1.67512,3.0018,1.99498,16.6743,20.4776,9.4369,-14.0864,9.97778 --9.6611,0.999993,0.208812,6,63,0,28.2359,-0.496759,1.38117,1.78708,2.93606,0.246256,2.68361,3.65165,2.35301,2.47326,3.94,5.48965,5.15494,4.30855,4.36421,-1.53758,7.73424,-1.2404,9.15523,1.80375,2.25764,1.54802,6.78125,3.9008,3.73229,3.59415,3.70455,-1.75017,4.80764 --8.63027,0.998348,0.395787,5,31,0,22.6542,-0.867252,0.882392,1.4265,2.22236,0.892426,2.36185,1.68772,4.05918,0.751176,1.9978,2.86819,3.7858,2.12619,6.93312,3.26637,5.66571,1.24926,2.00743,5.59096,8.19519,7.62532,5.81997,3.59387,6.69683,-1.31732,-4.30085,-9.43601,-1.31493 --11.5213,0.865878,0.74027,4,15,0,18.5533,0.868625,1.04913,2.45025,3.99479,1.14543,1.56742,4.27661,3.98657,3.02483,3.51846,3.37389,2.5164,6.75674,4.90726,1.90373,3.04162,4.73985,2.02266,4.00783,9.47732,7.25915,6.14692,-0.423591,4.31882,0.649006,-2.35339,-10.3305,-2.58595 --11.1317,0.846478,0.921448,4,15,0,24.0968,0.484665,-0.527295,1.05656,3.592,2.41934,1.5966,3.06427,4.55714,-0.152013,0.172038,5.96696,6.74348,2.0901,2.31528,0.418159,6.95424,2.28055,3.2286,11.226,4.52252,9.50819,0.871161,0.862863,4.06512,0.699645,6.05203,-2.16863,2.22647 --7.18811,0.917134,1.07936,4,15,0,23.3027,0.58829,1.61124,2.31772,2.74239,0.73323,1.61092,3.00389,4.23779,1.49861,2.64947,1.82349,6.19889,1.30586,0.741551,1.53641,8.1701,4.60029,7.12496,11.2891,3.37085,5.72676,5.7332,1.07189,1.1645,1.94855,2.62098,0.152858,1.46874 --7.70507,0.310058,1.5547,4,15,0,15.5552,-0.398436,0.587802,1.66251,3.69188,0.550371,2.1499,3.49595,3.55291,1.12665,1.93965,3.63801,7.44135,2.29821,1.52658,-2.49384,3.8682,-2.23016,-1.6745,-3.88822,3.4246,-2.05397,-2.66761,3.05132,7.76094,-2.39433,6.27609,1.77143,4.8665 --7.89651,0.951198,0.375591,5,31,0,19.5192,-0.297202,1.56038,3.2713,4.49142,0.0814248,1.80106,2.7169,2.9128,1.24108,3.00981,0.784529,0.924342,0.487675,1.29061,2.87702,7.94175,3.13192,5.77946,5.52172,2.82383,4.90081,1.6637,7.84202,0.884559,0.326636,3.71391,2.51017,2.97432 --12.2789,0.966925,0.599379,4,31,0,20.9731,0.394742,0.456865,0.484218,1.88214,1.73488,1.98737,3.67032,4.71232,-0.467137,6.30977,2.46754,5.35372,1.20695,2.32995,2.53874,0.153817,-3.41129,2.41662,-1.11869,5.4385,0.385867,-3.04889,-1.39905,12.0911,2.44523,3.2077,-9.86664,1.86425 --13.5591,0.67046,0.993223,4,15,0,27.8084,-0.425208,1.52587,3.56137,4.14923,0.287816,2.01713,2.35181,3.25156,2.14932,2.60345,0.433392,2.39919,-4.8582,-0.703752,3.90565,6.43136,-2.64814,4.84804,1.8615,7.8124,-0.967769,-7.66741,0.902055,14.0379,0.979047,5.13109,-6.70191,3.20967 --13.2504,0.934124,0.699638,4,31,0,27.2111,0.891312,0.631216,-0.124733,2.24608,1.44733,1.40267,3.71026,4.52485,3.56431,1.83918,4.10957,6.95443,-5.81813,-3.0585,4.07174,5.19274,-0.725651,1.66379,7.02169,5.01766,0.180041,0.549777,5.66874,12.4835,-4.48906,3.99741,-3.30547,6.08394 --10.1999,0.569159,1.04647,4,15,0,28.1707,1.14708,1.47634,0.891113,2.4159,1.21172,1.40807,3.73784,4.58476,-1.55882,2.14533,1.77394,1.13545,6.67825,2.44445,5.96985,5.08044,-0.746387,2.21447,6.85827,-1.56827,3.49389,-1.62204,4.28578,-2.51853,5.05041,-1.84907,3.35569,9.19414 --10.0519,0.984224,0.557788,5,31,0,19.8791,0.880947,2.0879,1.0816,1.46077,1.19245,1.7564,2.32836,4.34192,2.83231,1.56962,4.57105,7.01397,-1.46174,5.11564,2.30655,0.503129,-3.50557,-2.44476,-0.170995,8.43728,0.804548,1.82709,5.16273,7.72271,1.11262,6.93413,10.3569,5.23787 --10.0062,0.49712,0.95473,4,31,0,29.8092,1.26243,2.06538,1.81971,1.82773,1.14029,2.99519,2.67231,4.62858,2.7955,1.66759,4.5272,7.02001,0.426252,3.80486,4.70359,5.40236,5.32236,6.41362,5.96763,-0.648629,2.02938,4.80384,2.26098,-2.16029,5.92364,-5.55271,-0.464468,-1.8597 --11.6051,0.985886,0.421874,5,31,0,23.5007,-0.092207,1.02787,0.895078,2.14324,2.08193,1.97295,2.46524,4.59051,0.42007,-0.462069,2.19631,4.57127,3.66304,6.56777,6.49166,7.6551,-3.6102,9.20413,6.52742,6.76733,-2.84885,7.56709,2.53682,-1.25227,6.23084,-5.89301,3.87372,0.361606 --14.7448,0.899919,0.720651,4,31,0,26.4627,0.0447493,0.966879,3.0854,3.84422,-0.0297273,1.72951,3.76668,3.32079,4.87811,0.729796,3.32339,2.84167,-1.61816,-2.58759,-0.419885,0.367263,10.1832,-5.14568,5.36241,-1.55256,-1.60376,-6.07678,4.59441,7.50946,0.638035,-1.78264,6.69535,2.97167 --13.4112,0.894868,0.966101,4,15,0,26.3762,-0.106427,0.984479,0.714255,2.02688,2.02305,2.30729,2.22329,4.66238,3.7096,2.38489,4.07156,6.10161,-2.06337,0.308543,1.19535,-1.50064,8.87346,-7.76739,0.222325,3.29885,0.784696,-2.42582,7.58931,4.60654,-3.80557,-1.15499,7.14519,0.759784 --15.2952,0.323799,1.27151,4,15,0,25.3192,-0.469705,0.991648,0.58845,1.97203,2.09866,2.56809,2.81824,4.77486,4.33241,1.99148,4.16303,6.54531,2.4072,5.01684,4.2408,9.89428,-6.32275,11.9705,4.78407,3.74648,1.62651,4.14808,-5.99213,1.43879,-2.20994,0.0944527,-2.9059,0.50108 --18.0986,0.981899,0.362248,6,63,0,30.7857,-0.903548,1.53545,0.67115,1.93017,2.61134,3.22842,2.68428,4.6543,4.22265,1.38006,4.06337,6.04797,-2.47264,-0.44645,-2.22748,-0.6565,7.87184,-7.63542,1.44056,6.04461,3.8036,0.427467,3.45679,7.08229,7.70309,1.99276,15.2821,8.90763 --16.5581,0.94741,0.602499,5,31,0,32.7758,-0.252355,0.95962,0.564272,1.73973,2.70291,2.98161,2.76922,4.4301,4.25253,1.49043,4.26568,6.04867,3.73913,3.1148,3.35873,2.44367,-5.93659,11.4884,4.54856,1.92886,2.33079,6.96807,-1.73557,2.77026,-9.54309,7.26644,-10.3168,5.09006 --15.0513,0.870335,0.907784,5,31,0,33.6705,0.140675,0.719471,1.03405,2.81217,1.27626,2.96654,2.61395,4.0166,2.71906,-0.632229,3.41892,6.02761,5.52042,3.50841,3.01915,2.37445,-6.87706,9.89135,4.78531,0.414206,3.98666,5.86325,0.128264,-7.58722,-6.39342,3.68692,15.3764,1.0792 --21.5142,0.345276,1.11137,4,15,0,36.5215,1.64241,1.68078,-0.0828444,3.42043,1.4617,1.07692,3.68308,4.57034,4.27898,1.55915,3.23252,7.26999,-4.68599,-1.06683,2.23927,2.74719,6.88134,-7.01048,-1.60742,7.87045,-1.4787,-6.14369,6.65423,15.7508,-1.24309,-4.00312,1.70381,-7.3105 --17.1418,0.979414,0.348426,5,31,0,34.2015,2.25699,2.04335,-0.261735,3.60684,2.02725,1.58643,3.86811,4.99702,-1.00121,2.9151,2.82912,-0.386243,4.99704,2.42416,1.27875,4.76637,-0.680664,4.70681,3.57898,11.9621,-2.68674,-3.2496,4.14491,2.94488,-6.73561,-2.91062,10.7153,-2.2353 --24.3482,0.913392,0.566833,5,31,0,36.1646,3.35291,-0.377668,1.28259,3.29024,3.01974,2.1435,4.58551,4.47175,3.24711,-0.63933,2.35314,8.59352,3.75986,2.25457,6.92854,4.74203,6.43195,11.2953,6.39698,9.40319,-2.06075,3.81631,4.84416,-2.03818,-0.196195,-5.55409,6.90023,-6.68164 --14.521,0.997671,0.773495,5,31,0,35.4288,0.523516,0.804662,2.2205,1.03984,1.85627,2.66214,2.65653,2.24432,-0.907762,3.15577,2.46599,2.21374,-1.95664,0.984742,-2.56934,3.5354,2.31397,0.107888,-0.856845,-0.522223,0.117864,0.79357,-0.33417,14.6199,10.6014,-7.44158,4.26338,-2.38308 --14.1988,0.791441,1.30014,4,15,0,27.5111,-1.42545,0.370088,2.04535,1.92529,0.546639,1.98297,3.93265,4.81914,-1.97668,1.26971,-1.45692,-0.407011,2.96911,3.5677,7.85168,3.48204,0.862,3.08413,3.36677,8.47381,6.10273,-3.37128,0.317578,-2.09902,2.17261,-3.81337,-1.92665,8.60729 --19.26,0.657747,1.29193,3,7,0,25.9843,-1.36037,-0.198176,1.1256,2.52108,0.811775,1.42514,3.0353,3.06398,3.89217,2.6305,7.32247,8.11309,1.36323,1.18103,-2.03739,4.29239,1.74614,7.60571,4.46418,12.0146,10.4294,-1.05824,-2.28991,-5.42427,5.25208,-0.667784,-4.31308,12.595 --15.6269,0.906843,0.919966,5,31,0,32.7118,1.54281,2.21175,2.82574,3.43494,0.955554,2.42837,3.25378,5.47683,-1.58664,0.803708,2.50509,2.47934,3.71121,-0.686192,-0.546245,1.94494,1.94953,6.66058,3.55897,11.6127,5.28803,1.68697,-5.25376,-2.84109,5.65369,1.87719,-11.0192,11.3366 --10.0413,0.813076,1.2193,4,15,0,25.769,-1.48638,-0.180878,1.17704,2.44907,1.42709,1.71547,2.16646,2.49466,0.705448,4.03959,6.03881,3.03185,-0.104299,4.70428,6.16697,5.43294,-1.19351,2.98949,-1.70006,0.768301,-3.05054,-2.8656,6.36915,9.34644,0.24333,5.2346,5.73864,0.895776 --15.6272,0.576874,1.27825,4,15,0,27.2448,-0.302402,-0.535042,2.13405,1.57716,1.4813,2.35798,1.28354,1.73707,1.56332,3.04339,-0.411602,3.87436,3.05699,0.519311,-1.41488,6.94513,-6.78795,5.61533,7.05649,4.08744,1.43791,4.73175,-3.74375,10.4661,0.0724539,7.08248,6.54444,-1.03495 --14.1,0.999665,0.75261,4,15,0,24.7523,1.54956,1.18707,0.784448,4.33375,0.774294,1.92044,4.29059,6.09919,1.68274,1.44239,1.6865,7.47603,0.806277,-0.489771,1.52525,8.05263,-5.20989,3.65508,6.86148,1.81885,-0.191442,3.13198,-3.91612,10.8212,-0.0582525,9.58289,6.4985,0.656011 --10.7022,0.970291,1.24303,3,7,0,29.9613,0.415291,3.002,2.0185,2.72921,1.99951,1.7232,3.04337,4.48178,0.362423,3.11235,2.39965,7.82146,1.90147,0.150708,-1.83962,5.9168,-2.41486,3.38477,7.88911,-0.0959325,1.26459,2.8303,-1.63896,11.912,1.36934,8.25514,5.87413,2.09068 --10.1366,0.17973,1.89955,3,15,0,25.1451,0.594424,3.20481,1.82348,2.87396,1.96386,1.50259,2.93588,4.53311,2.5505,2.88051,5.68023,2.71298,-0.301798,4.00125,7.83546,2.12141,0.713444,-2.42174,-0.46455,2.36452,1.64234,0.818754,-3.79181,-0.356724,7.38287,0.409759,0.144816,8.90745 --13.9129,0.987903,0.436545,5,31,0,24.3129,0.0332079,-1.08343,1.61071,3.24715,0.32704,3.13853,3.56187,3.6972,3.80362,6.57402,2.7876,4.80736,4.18011,4.59034,8.66479,2.12043,5.7788,-2.12282,2.61528,4.61351,0.901327,0.229455,-3.3299,1.34491,11.4498,2.09033,0.454117,6.48319 --13.7554,0.906077,0.696408,4,31,0,28.8738,0.0612807,-1.00952,2.36763,3.50525,0.0740774,2.0054,4.05438,4.82192,-0.652116,-2.22516,2.17278,4.33521,1.71913,-0.828183,0.0385288,5.91564,-2.89834,-3.10602,3.5681,7.87164,-4.97649,-3.6266,-1.93054,6.87197,11.0579,10.3741,4.90258,0.357037 --23.6754,0.636169,0.910295,4,15,0,35.9446,1.25789,3.61495,2.84926,2.48325,0.736282,1.56171,1.42689,3.16716,2.09794,2.37156,5.27955,5.43956,-0.553813,4.73696,5.74553,3.16732,-4.41515,-9.67524,3.18136,6.6313,-13.2447,-2.42537,2.73612,8.61091,13.604,15.1137,1.365,-0.410115 --12.2677,1,0.630175,4,15,0,29.8362,0.983799,3.70566,2.52783,2.92011,0.856921,1.50971,2.18296,3.1834,0.290278,1.00321,1.0826,2.822,0.148159,1.02672,5.76216,8.32669,1.15068,3.45278,3.03828,1.40698,-10.9952,1.17319,6.85786,4.54218,5.67027,10.4318,1.90484,2.59211 --15.5193,0.890129,1.02176,4,15,0,29.6788,-0.441576,-1.79355,1.44481,3.21152,1.07077,2.10957,4.06474,4.64908,0.14605,0.00476633,2.04689,3.607,1.77506,2.81616,0.289329,-0.684142,-0.819841,2.61467,-7.14683,8.98887,5.11799,6.64005,4.26589,-1.57207,-2.20413,15.3121,2.93833,3.73297 --12.8886,0.60889,1.27705,4,15,0,27.9055,-0.647658,0.35699,2.28888,2.69082,0.286329,2.03617,1.70294,4.06506,0.448489,5.49147,5.62141,2.4272,0.656007,2.5661,-2.6391,-0.274798,-4.1126,4.99474,7.52163,5.13703,-3.30745,-2.26327,3.36345,9.88476,6.52783,-10.0747,3.96377,1.61174 --15.0533,0.860687,0.834288,4,15,0,24.8458,0.145137,0.501819,1.6716,0.949432,2.2763,2.98448,2.77478,3.86827,1.01989,4.50205,4.01916,1.11395,-1.5857,0.112474,-2.63219,0.504059,-3.38889,7.16984,5.95773,5.39906,-2.18601,-5.94678,4.17892,7.97311,7.1196,-10.6727,4.25609,1.17029 --11.0141,0.828099,0.97334,4,15,0,24.9485,-0.411281,1.58398,2.63034,3.70914,1.29141,2.53006,3.12325,4.07358,2.56917,3.63312,2.98979,1.55089,-1.595,0.488934,-2.59862,1.31584,-1.73178,6.51101,5.8077,7.10324,-2.76669,-4.57597,4.71137,7.20017,6.71118,-11.6145,4.06411,0.105984 --11.3823,0.5385,1.05257,4,15,0,27.5028,0.241309,0.449926,1.77404,2.22641,0.730957,1.353,2.56091,4.09021,-1.94207,0.552384,-2.22321,4.44961,-2.29639,0.752047,3.79691,3.55708,2.2523,-4.37253,1.67857,-2.99018,4.47556,8.43208,1.29114,0.410393,3.48698,7.7888,4.94075,7.02436 --11.8066,0.876409,0.592018,5,31,0,23.0474,0.596411,0.691573,1.94543,2.19736,0.564996,1.35984,2.18351,3.58388,4.19197,3.02979,8.48452,3.50878,1.22412,-1.33015,2.94526,4.16916,2.49103,1.71276,6.56543,-4.24927,4.33607,0.402911,0.149639,10.8129,1.98147,4.11816,5.61211,10.52 --16.4699,0.855849,0.71475,4,15,0,27.6853,0.569857,1.12809,1.04082,2.07493,0.926074,1.11778,2.45949,3.85553,-3.68525,0.607261,-2.2169,3.90449,-1.20799,6.63897,1.23162,1.95191,-0.51521,3.51963,3.59317,5.39359,6.21598,4.35392,-11.0279,11.7308,0.169296,1.5878,4.36315,10.3555 --12.4651,0.990712,0.822374,4,15,0,24.5677,-0.0582714,0.811219,2.23155,3.16024,1.59047,0.559964,2.89533,2.73234,4.71846,1.43494,6.74543,4.19802,1.16012,5.84848,1.15003,1.98802,6.59257,1.06749,-1.35653,10.3335,3.03938,4.3185,-3.64382,11.5601,1.7332,2.00833,6.64276,9.09264 --15.7371,0.371129,1.27305,4,15,0,30.2983,0.316507,1.28982,1.53156,2.15576,-0.117911,3.29355,3.28871,5.53885,0.108614,2.85252,0.239152,5.36323,0.908048,-1.59666,4.10274,6.61427,-7.32039,2.61304,4.22225,1.77028,6.91879,3.93719,9.51139,1.7562,17.8314,3.18005,-3.05425,13.9876 --12.5051,0.920731,0.501595,5,63,0,26.1618,-0.263087,1.67313,3.02779,2.81665,0.522882,0.753232,1.09634,2.54868,-0.344022,3.66943,2.51869,4.5727,1.26432,3.45819,0.557406,2.35918,9.365,0.320408,1.09664,6.42316,-5.97714,-1.09149,-4.58966,10.86,-3.23,-3.9811,0.751541,2.0925 --11.1977,0.907504,0.664697,5,31,0,28.7464,-0.214408,1.23127,3.00973,2.98908,0.499969,0.964989,1.10185,2.80194,2.53916,0.421591,2.92286,3.82208,1.65189,1.39595,0.564798,6.12847,6.84525,3.54676,3.63892,-0.82686,9.22075,-5.7441,7.16494,9.50689,5.8991,-2.22347,3.54558,-0.225526 --9.74287,0.868186,0.853007,5,31,0,22.5578,-0.353796,0.24233,2.12111,3.42216,1.70691,1.71939,3.38079,4.53782,3.40036,3.49312,1.76863,6.69588,2.89296,0.562867,0.810752,6.52035,2.44721,-2.18801,8.95761,-0.993298,-4.45276,6.48941,-3.10116,1.01003,-5.114,7.7698,4.68612,8.7747 --20.4489,0.593738,1.00251,4,15,0,27.565,-0.365727,-1.06728,1.31638,3.81649,3.04427,1.50311,3.44103,3.11551,-1.71411,1.35162,4.5367,1.84746,0.413245,0.838297,3.88116,-2.21786,-2.58924,9.64828,-7.75175,6.91238,-10.7779,1.14143,2.37562,-0.351267,3.93237,8.64283,7.97136,9.90724 --10.4774,0.957068,0.651855,5,31,0,30.1328,-0.642583,1.92868,2.4484,2.03208,1.21164,2.07361,3.35207,4.85179,3.81556,3.02028,1.81507,5.41265,0.606269,3.06682,2.04902,1.34889,-0.385264,1.16408,-1.85299,3.73166,-8.04016,-3.71727,1.68232,-2.30939,10.7586,10.7351,8.41108,5.56806 --21.1062,0.779429,0.926022,4,15,0,30.5578,0.481364,1.09509,2.48471,2.67433,0.407221,1.3836,4.50929,3.61035,3.80971,2.41576,1.67526,5.41444,3.8033,-2.25108,4.16933,-0.689472,2.15103,2.90365,8.48838,4.94602,15.9995,11.7403,3.78223,11.4082,-8.85109,-8.13897,-12.5277,-1.02787 --8.34276,0.963774,0.898151,4,15,0,28.0525,0.427764,0.91683,2.61283,2.99674,0.224651,0.858106,3.29689,3.81631,-0.166761,2.51736,2.87728,1.90888,-1.55116,2.43754,1.8369,4.98774,4.76162,-1.29288,4.85441,7.34887,7.994,3.74844,2.19799,9.94465,-3.10307,-6.10403,-7.89227,3.72899 --9.78274,0.390048,1.28669,4,15,0,23.9123,-0.383559,1.25353,1.79959,3.67895,1.31766,3.5818,2.49004,4.20344,-0.130072,-2.30848,2.53353,3.56858,0.358383,8.64019,1.95413,2.77214,-1.00261,2.00934,1.84348,2.23629,-3.9104,1.21129,3.08339,-2.98833,1.62442,0.244064,11.4687,5.83156 --11.6068,0.990943,0.549413,5,31,0,20.2832,0.760359,0.406018,1.9059,2.05093,0.541495,0.393775,3.18227,3.5228,2.8038,4.91075,3.29232,2.85365,0.808896,2.9619,-0.541274,-1.42024,-3.49047,-5.66097,-0.367385,6.32789,-0.655474,3.28685,3.57539,-1.77053,0.58991,-6.36458,8.28417,5.40924 --14.0474,0.706326,0.831988,4,15,0,24.2321,-1.08415,0.586189,2.12414,3.70627,0.955894,4.72369,1.30337,5.46926,-0.444727,3.10833,3.90968,2.88702,-1.55607,0.630098,5.32009,6.66796,2.34668,-4.56226,-2.37352,4.59083,0.354392,9.35816,0.964591,-2.75935,2.18059,-3.32732,7.09551,4.5199 --14.2394,0.97618,0.693774,5,31,0,31.5634,-0.220481,1.08346,2.83026,3.3044,0.734259,4.52291,1.24946,5.16962,0.482356,0.691933,3.00418,2.71787,0.95279,1.28196,2.64163,0.988614,0.360302,8.02483,7.93626,2.62971,-5.59248,-4.29056,-2.47285,12.4483,-3.94865,11.1432,0.964507,7.60871 --15.1246,0.973381,1.01314,4,15,0,26.0121,0.127628,2.11067,1.82355,3.59792,-0.197423,3.19677,0.305946,4.15557,0.0272063,2.87853,2.74377,6.53796,1.54225,3.28908,-1.68835,3.77148,3.48554,10.2259,3.50129,8.37589,-1.62798,-5.36088,-2.1392,6.49979,-2.60175,7.22905,-7.46537,3.81097 --14.0552,0.827423,1.46512,4,15,0,22.9929,-0.568474,1.35539,0.798589,3.74385,1.64459,0.881672,1.75214,3.67755,-0.356762,3.28732,2.96104,7.66034,0.931282,-0.819338,7.96874,3.79429,-0.634155,-6.03147,1.58699,0.0234608,3.17662,7.01766,5.35573,-1.25577,-5.53087,-5.649,-8.14789,0.28281 --11.8005,0.42482,1.56521,4,15,0,26.7738,-0.625407,0.965273,3.74259,2.63643,1.01722,2.85712,3.59453,4.8273,3.62959,1.58215,3.92451,7.57432,-1.27355,1.69242,6.89382,4.7335,2.27187,9.28938,2.51893,6.5603,-1.78369,-2.52244,-0.142083,10.1908,11.3514,-3.06302,3.89988,6.28208 --13.8673,0.888692,0.735641,5,31,0,38.2969,-0.738824,1.07544,3.25364,2.66962,0.743929,2.93297,3.49361,4.8944,3.63594,1.60022,3.91465,7.60503,-2.46186,1.76231,5.27284,1.40426,-0.204812,-5.26593,3.38966,1.4932,-6.65539,6.42616,1.97716,-0.305837,-10.7352,-8.70914,-1.76737,7.17826 --11.439,0.99121,0.892264,4,15,0,25.3622,0.164156,2.53657,2.24078,2.34709,0.939618,2.98323,2.51416,3.27892,3.5233,2.18283,4.14713,4.56792,5.4692,2.72277,-1.71943,6.26122,-0.139805,10.2532,1.94454,4.26003,1.57488,-0.151054,2.07642,10.5778,-0.448746,-0.240718,-9.12217,7.25493 --22.7824,0.803448,1.32821,3,15,0,32.0256,-0.178382,2.68255,2.34751,1.96273,1.01204,2.96323,3.31422,3.71195,-1.93417,2.45976,1.41308,3.0346,4.36478,0.875226,5.55699,9.48575,-8.38547,12.4296,0.996863,0.802938,8.40178,-13.2189,-1.99224,5.03541,3.86659,6.826,-10.7806,4.83688 --14.5923,0.445052,1.35091,4,15,0,33.2184,1.0353,0.800506,1.71812,1.43777,0.854231,1.98896,2.24966,5.00745,-0.629444,2.72327,3.50872,2.21702,-0.559703,2.71685,-1.49444,-1.42347,8.02734,-7.04796,4.9222,8.66217,-0.851574,10.9924,7.69874,8.00104,-6.49966,0.910913,0.799778,5.23212 --17.2783,0.828484,0.67144,4,15,0,28.3349,0.360315,1.9777,1.06757,0.581805,2.618,1.4106,3.20149,4.96484,3.02767,1.04856,4.3327,5.17847,-2.18219,2.59312,6.22231,-1.25434,3.34443,-5.45978,4.74584,6.8358,-0.417131,11.923,7.52287,-1.18897,-11.5874,-0.796765,-0.466891,3.57995 --13.4609,0.992663,0.719962,5,31,0,26.9992,1.00418,-0.0145473,2.57994,4.70254,-1.24534,2.3398,2.28956,3.8302,2.26516,1.14573,4.77038,3.70039,0.120046,3.02187,4.45224,0.0594489,5.46064,-4.31267,2.30155,8.30334,-1.24247,6.72558,4.65882,-1.23182,-12.4564,-0.057969,3.01466,5.19054 --21.9926,0.669151,1.06741,4,15,0,31.9416,-0.880943,1.69032,1.99276,1.05733,3.26778,1.12457,3.82243,4.16894,-0.924008,3.14229,3.15894,-0.29129,1.79551,4.64129,3.85087,-2.66134,-0.274146,-7.37684,-0.909224,8.79929,-2.03052,8.18811,4.1094,-4.35723,-14.8521,0.615662,5.20807,-1.10513 --14.8089,0.97096,0.833893,4,31,0,35.8577,0.887931,0.690301,2.40281,4.34465,0.118744,2.80159,2.47178,4.78434,0.929167,3.01796,4.47905,2.10855,-3.62987,3.38148,2.79814,0.741195,-0.0277827,-6.22929,1.90988,6.84748,-3.72363,7.20183,3.90756,-5.41184,-14.66,3.07979,7.38563,-2.46319 --12.2208,0.597325,1.17948,4,15,0,30.5602,-0.261161,0.876798,2.69681,2.54567,0.683572,1.69295,2.31062,3.22328,2.61213,3.41677,4.96605,5.76491,1.51835,-4.78187,2.21677,4.07865,0.168816,-1.03944,5.30506,12.658,-6.02422,7.05337,3.82222,0.835027,-12.1043,1.89507,-0.482545,1.9224 --17.3709,0.555064,0.80243,5,31,0,32.3857,-0.345692,1.90263,3.44139,1.94215,1.35064,2.00607,0.428583,3.9532,0.597167,0.790079,1.34499,1.05333,1.42048,-2.46706,2.33406,2.7537,7.12953,-4.97531,2.42914,-4.1225,4.55506,-3.53958,-0.17597,7.79198,14.9449,4.16838,6.01486,6.10785 --12.7787,0.960651,0.504705,5,31,0,33.7914,1.89687,1.31436,3.64031,3.42935,2.29719,1.03299,3.29487,3.87157,2.11183,3.00594,5.42084,4.54035,-4.27181,3.91638,2.57624,-1.17438,8.31137,1.89121,1.86432,5.95746,2.69617,4.72153,2.74588,0.651041,4.81356,4.59946,12.5912,4.26284 --11.8508,0.909852,0.697936,4,15,0,21.2908,1.80975,2.45322,4.03496,3.2127,2.23759,0.899302,3.37035,4.92636,-0.591516,0.76767,0.338112,3.92647,4.52956,1.63327,0.112156,5.69004,-0.665554,-0.254943,6.94983,3.20535,6.89077,3.0654,2.20353,6.02216,2.36457,3.80091,10.5369,6.01864 --17.082,0.956083,0.872676,4,15,0,28.6675,0.0714876,1.07011,2.85451,3.43751,0.229996,1.88586,3.90201,4.60575,2.2181,4.92182,6.33493,6.02644,2.08853,-2.62375,-1.92368,-0.719747,2.48678,-3.54161,7.16245,10.8155,13.2587,6.22945,2.54623,0.208147,6.90963,-4.59106,9.71252,8.93249 --7.16046,0.675408,1.18967,3,7,0,26.3139,0.258378,1.17774,2.90405,3.36708,0.220635,1.93658,3.82371,4.68425,-0.348089,-0.72812,0.265408,1.97511,0.128391,3.77363,4.07162,4.36151,0.583823,5.49713,5.53189,1.54341,1.6594,2.36775,4.84872,6.68072,11.8749,1.55385,2.90387,7.96144 --7.46965,0.983882,0.947228,5,31,0,16.6718,0.0396727,1.63512,1.63339,2.86176,1.04307,2.02447,2.63816,4.8143,2.34152,5.11443,3.70678,3.98507,0.303454,4.22226,5.12454,5.03838,4.00778,5.85265,9.45994,5.13362,3.37806,-2.79944,4.4517,1.8989,-9.54145,-0.0532048,3.43656,-0.495772 --9.33655,0.324943,1.35686,4,15,0,21.1401,0.100751,0.76244,2.07071,3.11102,1.11182,1.82367,2.98605,3.45281,3.17438,3.48874,1.20968,1.81432,-1.38652,6.3381,4.12089,5.56565,4.65764,3.63569,9.77755,6.64546,7.14557,-3.1941,3.87929,7.72892,-7.39442,-1.35099,1.43071,-3.47494 --15.0352,0.812758,0.558006,5,31,0,21.1361,-0.865991,0.525455,3.55492,3.82779,-0.438459,1.22237,3.03555,1.85307,1.22279,3.4621,0.555835,2.28962,2.66425,-1.91321,-1.6767,1.5724,-3.14305,0.265567,-4.22073,1.90852,-3.12778,7.64141,-0.269326,0.0236785,4.5627,1.89788,6.23219,13.2784 --16.6096,0.994805,0.578824,5,31,0,30.7165,0.369473,1.32674,2.30643,2.38341,0.114898,1.47581,2.83912,1.51545,1.91045,3.95718,0.955541,2.29124,-0.0347432,0.208766,7.249,4.98199,4.54418,3.96808,9.16981,7.12883,3.97587,-4.90776,16.6561,7.55584,-0.0969058,-5.21663,4.90543,-7.83237 --11.8235,1,0.843999,5,31,0,24.4078,0.118835,0.415542,2.0881,2.55391,0.871085,0.876136,2.81539,2.07409,1.16877,2.54324,0.687689,2.28828,1.85509,4.96613,3.53913,4.01695,4.57767,6.77479,9.05569,6.81044,4.71273,6.59276,5.52393,0.470939,-3.01756,4.65872,1.45128,19.8182 --21.1232,0.341056,1.23854,4,15,0,30.0765,2.95072,2.60757,0.748767,3.79609,1.79213,3.48745,2.86818,4.60734,2.9337,3.04573,0.655681,2.93769,3.58196,4.18536,8.24225,3.91312,-3.06876,-3.72429,-5.09601,0.0251389,-0.767416,-3.87498,1.88841,4.89487,9.4097,-1.25304,6.60394,-10.6563 --20.852,0.982419,0.533023,5,31,0,32.897,3.23202,2.63202,0.805858,3.60457,1.86524,3.61508,2.98317,4.79253,-0.878558,1.01244,5.48601,4.99715,-1.43767,4.64244,-0.704753,1.80348,1.9765,1.01468,-1.33363,-0.161785,-1.22068,-2.32709,9.25485,10.6059,17.5438,-1.29719,5.65054,-3.74828 --17.9003,0.945731,0.756127,4,31,0,39.8364,1.39837,1.43471,1.56197,3.56698,1.56766,2.3133,2.35672,4.65737,4.28406,4.68824,-0.422709,0.734859,4.32526,-6.94185,5.84382,8.5868,0.997052,7.18166,4.58804,4.0459,-0.212873,-1.95211,5.60494,7.29659,4.96695,3.75899,15.131,-0.625194 --15.1836,0.604299,0.999575,4,15,0,29.0458,-1.40747,0.567389,2.4335,2.427,0.43124,1.67916,3.63633,3.34845,0.798431,2.0401,6.53739,2.42821,-2.33456,10.9617,-0.0747876,-0.584041,0.38141,-1.56897,-0.663539,2.26741,9.6738,5.55176,-1.02467,0.901197,-8.56203,0.140837,2.08842,2.35976 --18.3177,0.851124,0.70497,4,31,0,28.7249,2.85206,1.37755,2.10498,4.04526,1.12091,2.92158,2.84714,4.85302,0.500609,1.09046,2.04686,3.09912,3.55689,-6.57325,7.80232,7.68261,-1.20624,8.60938,9.21962,8.79498,5.05999,6.02882,2.45006,3.18798,2.26687,13.191,4.79431,6.15835 --13.6358,0.998755,0.782466,4,15,0,27.3737,2.69277,1.34731,2.10613,3.91155,1.15639,2.87434,2.89231,4.89337,1.50569,2.89474,3.94179,4.91569,1.63645,10.3064,5.1821,2.54363,-1.80527,5.89834,2.16047,12.7339,-1.7401,4.38807,-0.216765,5.7318,3.42851,4.77884,-1.26504,5.78455 -# Adaptation terminated -# Step size = 0.860161 -# Diagonal elements of inverse mass matrix: -# 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 --19.8045,0.784555,0.860161,4,15,0,31.0277,1.42314,3.24308,1.2917,4.04247,0.341963,1.07628,4.02629,3.51623,-1.35967,-0.213643,3.09031,3.44637,1.54016,-5.83262,3.07372,3.55726,4.09832,4.20379,11.8131,-4.54901,4.1573,-8.43337,-0.555849,9.08006,3.79805,3.52034,0.246193,-3.85523 --14.7322,0.723346,0.860161,4,15,0,31.7687,1.16223,1.46836,0.69435,4.77263,2.83719,1.28777,4.81356,2.36917,2.65261,3.37119,3.82962,4.65885,1.96259,10.4225,2.84646,5.03555,-0.630156,1.06086,2.79671,3.03748,1.08858,-0.16819,5.6711,4.54249,8.35036,6.77732,7.30719,0.948396 --13.9542,0.988624,0.860161,4,31,0,27.6292,-1.78629,0.467201,3.59377,0.740073,-0.124989,2.73847,1.16444,4.77279,3.46308,1.52216,2.42487,5.91396,-0.862453,3.09935,1.25765,2.49237,-4.48003,6.9563,4.06792,3.74916,2.36881,-2.60901,4.50377,6.11856,10.3275,4.17098,1.54635,2.82217 --15.5389,0.982577,0.860161,5,31,0,26.1478,-0.885832,0.662304,3.99114,1.67956,-0.505076,2.42935,0.669802,4.76531,3.27478,1.31962,1.94326,6.3824,2.15687,6.50455,-0.421532,3.3905,6.50365,-2.75401,1.69232,3.55955,3.33833,4.41853,1.12512,5.20733,-8.25145,-5.18204,2.75203,-2.74715 --11.1068,0.99652,0.860161,4,15,0,30.8247,0.507654,0.614184,1.44271,2.87975,1.36267,0.117397,3.04462,5.132,2.68584,-0.261838,3.30381,5.05267,1.67125,5.18549,-1.23074,7.87929,4.98108,-1.66889,1.86772,1.31748,-0.783023,8.67809,-0.0393086,2.49872,-7.40151,-5.11304,2.47221,-2.81082 --9.19391,0.99506,0.860161,5,31,0,17.1506,-0.184065,1.31144,1.74748,3.87493,1.23435,1.69764,3.45968,4.85408,-0.923823,5.60435,2.64785,3.40306,1.73878,5.98797,-0.929465,7.43764,-1.58452,3.12587,1.92961,6.94447,0.113709,-2.86648,-0.391712,3.57223,10.2451,8.73783,4.22667,9.87115 --11.277,0.74854,0.860161,5,31,0,19.7999,-0.256496,2.51849,1.74768,2.71752,1.36776,2.56106,2.48215,4.5472,3.1567,-1.68999,3.42849,4.53781,1.87474,6.0572,-0.832841,7.47709,2.40555,1.92321,2.53121,1.37118,6.31242,9.64383,6.96572,2.30089,-8.18477,-4.74342,1.67094,-2.03261 --11.7741,0.732793,0.860161,5,31,0,22.2239,0.431236,-1.23718,2.26473,3.65982,0.703821,1.5545,4.13435,3.71009,2.25786,0.921197,5.24686,2.1179,0.0477161,-2.1286,6.8615,0.528117,-1.83317,3.6574,3.04737,4.29218,-1.55061,-5.79991,3.01777,8.1025,2.73711,9.62103,9.66895,-3.07734 --9.53011,0.809277,0.860161,5,31,0,23.3929,-0.909102,-0.551032,0.838413,2.29149,-0.510233,1.23192,3.17679,4.23651,0.50004,2.27262,1.11793,4.98463,2.74231,0.788292,4.56888,7.27211,1.75542,6.35394,2.38509,6.22079,5.8871,1.68454,10.6108,5.16371,1.841,11.2936,6.89559,1.89482 --15.6318,0.867634,0.860161,5,31,0,25.4085,-0.539457,1.19295,1.26426,2.91414,2.37511,4.17069,3.85663,4.22561,-1.43011,3.14933,6.0507,6.47374,0.059116,-3.8395,6.51253,7.78941,2.70192,0.546467,6.92001,-2.58946,-4.23794,3.14667,-3.85138,3.23937,-3.0133,-7.3636,-1.4891,4.24323 --19.6111,0.708572,0.860161,5,31,0,32.1515,-0.645484,1.4593,1.68009,3.78379,3.88937,3.93755,2.51316,2.91456,3.64705,1.03893,0.155457,1.7953,-0.0222461,-3.95801,6.61011,7.84981,-3.38362,1.42656,1.68359,2.38817,4.93761,4.49583,14.6899,8.4134,4.87501,11.3466,7.3772,3.76911 --23.7182,0.94165,0.860161,4,15,0,39.6492,-0.0710519,2.37767,2.08082,5.21635,3.33296,4.0488,2.72007,3.83466,3.89807,0.300216,0.664843,1.06574,-0.0982979,4.26552,-0.731478,1.38002,5.29161,3.45587,4.23147,5.91468,-2.3133,-0.828304,-10.1604,-7.96188,-1.71833,-10.1775,0.95589,12.6082 --20.5415,0.947762,0.860161,5,31,0,37.0236,1.38747,3.04783,3.68479,4.95453,1.70161,3.72335,2.81238,4.13023,2.21624,1.63975,1.00802,0.162197,0.980312,4.45047,6.16535,1.24784,4.52581,1.08439,4.26188,6.18625,2.43874,15.2107,-0.44712,8.36275,5.02379,15.2568,-1.57278,-3.71284 --17.9654,0.916593,0.860161,5,31,0,36.0983,1.06114,2.84022,3.81075,5.72632,1.37324,3.68787,4.35986,4.34996,2.22929,1.62622,1.01792,0.161521,-1.81955,0.280419,7.0558,4.34641,4.49317,1.19264,4.35392,6.22536,-0.608062,-1.34722,3.53503,6.20395,-1.81037,1.33121,16.097,8.77793 --9.52757,1,0.860161,5,31,0,24.6315,1.18497,0.59497,1.88868,2.27682,-0.746597,0.243439,1.72679,3.14074,1.18037,1.25662,1.68681,1.12444,4.27682,2.22099,-1.32326,3.83868,-1.82169,4.96595,3.23721,3.68422,2.17808,-0.390674,1.70515,-0.300281,2.29149,1.36487,5.49263,6.36309 --9.77282,0.899257,0.860161,4,15,0,19.2688,1.14394,2.0986,1.08534,3.67866,1.48118,0.757117,2.26519,4.71438,1.2112,2.73611,2.2129,5.77954,5.57074,-1.22205,7.06883,1.76756,-3.10528,7.02285,4.10736,6.29634,2.52693,-2.74673,-2.59431,3.62268,4.96135,2.5747,5.75367,3.80917 --8.74747,0.781106,0.860161,4,15,0,25.5551,0.457058,0.757244,1.21565,4.10256,0.379015,2.32027,3.01163,3.07884,-0.0501144,0.416171,3.6245,2.08362,-0.418398,1.47666,3.46769,0.866342,-0.889791,5.36102,5.20401,2.62941,-2.19749,-5.03285,2.85653,-1.7026,0.375225,-3.34699,15.3261,-1.04486 --10.1565,0.869405,0.860161,5,31,0,19.3143,0.563632,0.591317,2.47207,4.29476,1.17929,1.10882,2.31904,4.38846,0.11198,0.0418487,2.50256,1.51795,2.09136,1.29648,2.59082,3.16586,2.83705,3.14147,-1.71314,6.1339,3.49208,8.94285,2.93173,10.8583,4.48935,9.56003,-10.13,9.05171 --9.97454,0.617886,0.860161,4,31,0,24.0239,0.633627,0.768211,2.34529,4.1351,1.36602,1.06781,2.09737,4.60805,-0.0632031,-0.247857,2.55488,1.5033,0.389759,0.743143,7.55595,3.82813,0.038678,1.264,7.215,1.81436,1.90984,-1.871,-2.41919,-5.18312,-3.28446,-5.57349,9.16333,0.305028 --12.5092,0.630084,0.860161,5,31,0,23.4135,-0.35291,0.865377,2.28712,1.34467,1.54421,2.44524,5.13422,3.90833,4.49079,3.97661,1.55523,5.40419,1.9546,3.97505,-2.22822,4.60045,0.288336,3.37546,-4.35029,3.26562,2.07135,0.44565,-1.84383,3.42559,-9.4524,-1.77648,5.80034,2.76602 --13.3596,0.826095,0.860161,5,31,0,28.1374,-0.397553,1.47077,2.60824,5.22796,0.652394,1.97739,2.44411,4.10534,1.84772,4.62578,1.82189,4.39518,-1.09823,2.49458,8.52225,10.9212,3.13516,-3.21565,8.60554,6.35034,0.277631,2.15531,8.49442,5.30086,11.7316,3.58374,4.79084,2.11601 --15.3739,0.792174,0.860161,4,31,0,29.9792,-0.0856389,1.0395,1.85414,0.338774,1.71409,2.50411,3.52393,4.85451,0.167698,1.05894,4.07617,3.55729,-6.30222,5.59306,5.68927,12.2215,1.33923,3.0802,5.63681,7.92106,-0.981619,3.41889,4.61486,5.44237,10.9701,5.34119,2.64888,-0.179001 --8.04987,0.903022,0.860161,4,31,0,30.1022,-0.196803,0.781156,2.73582,5.01763,0.309354,2.03756,2.24806,3.85197,1.50154,0.0307024,2.94036,5.65359,-2.50948,4.70125,-0.431406,2.15556,-2.02698,-1.21463,4.09358,1.8208,-1.00044,4.54858,0.440711,8.79437,5.69962,4.55385,-1.09605,7.28943 --8.96553,0.817163,0.860161,5,63,0,22.8569,-0.177539,0.557866,2.52455,5.07666,0.411983,2.14272,2.31709,3.86555,1.54814,-0.596161,3.62345,5.92458,2.5095,-1.8332,2.54081,6.60517,4.96126,6.1377,1.84243,6.22957,-1.92741,-2.98544,3.22467,1.99533,5.27841,8.9899,4.82817,-1.74178 --9.45393,0.670389,0.860161,4,31,0,26.6202,-0.0728854,0.326291,2.44134,4.94076,0.565235,2.32911,2.15403,3.73805,1.94576,-0.783988,2.97112,6.0337,-2.80321,2.63751,-1.65297,5.29261,-2.33496,-3.13075,4.39251,2.62871,1.29869,2.67064,4.70609,2.00335,0.412632,-6.05685,1.86362,10.6855 --12.9406,0.549837,0.860161,5,63,0,27.0614,0.28727,-0.401275,3.20091,4.13757,2.82288,2.62206,3.92522,3.44591,0.271789,4.65416,2.91354,1.86158,-2.70717,2.5824,-1.79929,5.39847,4.70284,1.38699,-2.87595,8.98659,4.86712,1.55675,3.76518,7.27136,1.48005,10.2142,4.18182,-2.77883 --8.62807,0.911568,0.860161,5,31,0,20.5171,0.556003,0.186491,1.45906,3.34555,1.93998,1.60054,3.39106,3.47379,0.00641074,4.46256,2.49695,2.32702,3.41574,5.24398,2.47876,0.883334,-2.63037,2.90374,9.10594,-0.822928,-6.09628,1.50389,-0.309368,2.07851,5.18092,-3.60629,1.64898,7.11342 --8.70411,0.812027,0.860161,4,31,0,17.7452,-0.569261,2.02902,2.03994,3.20495,0.547021,2.01601,2.24829,4.05771,-0.0103493,2.08415,1.8928,1.50897,1.40788,7.21178,0.24904,0.800434,2.00976,5.49062,6.51576,-0.229384,-2.66952,-0.944931,-1.4792,-0.413878,6.41946,-6.55437,2.95839,5.29358 --11.8975,0.780483,0.860161,4,15,0,26.4104,0.504944,0.564509,2.06611,3.21925,1.66058,1.89468,3.28442,3.31242,0.937788,2.45786,-1.30501,5.33725,5.69166,3.41433,3.41778,7.54613,3.46354,10.3324,8.13131,0.72175,-2.19183,-1.88318,3.53665,2.6346,5.64012,-10.38,4.42433,5.10611 --19.8828,0.630847,0.860161,4,15,0,33.797,-0.250429,1.40302,1.98581,1.65777,-0.301405,2.95721,2.32624,4.09783,4.06058,4.81126,3.76885,1.4313,-3.09539,2.27313,3.08191,0.39357,1.27661,-3.62335,-1.59017,9.26119,0.810427,12.4781,4.3736,-4.00285,1.07622,0.0862737,-18.2965,8.13019 --18.4393,0.791497,0.860161,4,31,0,33.1763,-0.0938752,1.60416,2.83108,2.25217,-0.200278,0.427915,1.28706,3.6763,0.793961,-0.495615,4.03644,4.80951,1.24642,-0.787255,6.6188,-0.333061,5.49005,-0.839077,1.11633,4.2419,-1.30289,8.42967,11.6832,-3.00606,7.65662,-0.510158,-16.2146,11.0911 --8.43402,0.839995,0.860161,4,15,0,28.7467,-0.219327,0.716214,1.15858,3.67467,1.51167,2.79394,4.69671,3.94805,-0.678437,3.45216,2.39455,5.71097,0.799094,4.74844,-0.521852,8.35624,0.179381,3.86772,4.1558,-1.30347,5.21444,1.6078,5.68671,2.50385,-3.79774,5.51249,-3.67519,3.8277 --7.31031,0.625592,0.860161,5,63,0,30.3577,-0.0642832,0.62445,1.01779,3.55564,1.201,2.69008,4.22211,4.26781,1.95055,0.371611,3.02402,3.12913,1.26844,6.20912,1.2733,7.67132,-0.0518513,-2.44874,1.79679,7.15186,0.50035,-3.64895,3.92281,5.93732,6.22249,-3.15915,9.71099,3.98595 --12.4951,0.578134,0.860161,4,15,0,22.6212,-0.429454,-0.00704628,0.341456,1.94889,0.343389,3.44671,3.62092,2.23527,0.935523,2.21528,2.06163,3.59814,2.09355,0.712938,7.7124,0.242232,2.28213,-0.772936,9.75672,7.60821,1.28062,8.27179,-0.274437,4.9779,8.06648,-1.69613,9.13867,6.53351 --17.344,0.740007,0.860161,5,31,0,27.9594,-2.07354,0.447229,0.161841,2.16303,1.80637,4.44019,2.69342,1.99508,0.708004,2.74816,2.18285,3.32803,0.274574,5.60365,2.00456,4.91895,-0.481554,4.95036,-3.86827,0.478142,0.157354,-0.722499,9.27908,4.15721,6.21503,13.8626,-5.8201,3.54719 --19.3946,0.731826,0.860161,5,31,0,33.8298,-2.12113,0.514613,-0.0680413,1.94275,2.00121,4.68682,2.58656,1.9205,0.170864,2.70988,2.44895,3.46576,2.63764,0.259325,4.88845,-2.46382,2.3487,-1.41938,10.1543,6.969,2.94677,5.74263,1.61178,0.607267,6.6529,-0.861601,11.89,-0.913847 --21.7224,0.826074,0.860161,5,31,0,41.1622,3.70294,0.743928,1.55994,4.05203,2.34433,2.37972,0.278004,3.00857,1.05258,1.71259,4.41686,4.68128,0.557769,1.78125,5.87847,-2.32416,1.32806,2.38044,9.59831,7.76989,5.51799,7.91897,-0.802,1.26936,4.51403,-1.15695,14.1598,-2.55562 --15.1159,0.918078,0.860161,5,31,0,34.672,2.19159,0.114454,1.66403,4.37977,1.89464,2.05576,2.83508,2.58104,0.75564,2.15073,1.29523,3.28562,0.617526,1.86971,6.04074,-2.28262,-6.79954,-2.07974,-0.550841,6.72517,3.29747,-2.79504,5.69658,8.09072,-2.3716,5.16373,-8.18296,10.6395 --19.3339,0.618113,0.860161,5,31,0,35.6708,1.5917,-0.40218,1.5456,2.47432,3.10366,1.66058,1.31578,3.93985,-0.31036,3.0242,1.85009,-0.659806,0.397643,3.72607,1.26043,10.3724,9.37309,6.91997,5.152,2.51248,9.34628,6.78529,-0.498126,9.68069,-3.19979,4.14351,10.6606,8.57623 --16.0704,0.901646,0.860161,5,31,0,32.7545,0.996388,-1.14991,1.05317,2.99372,2.78733,0.639611,0.976372,4.19797,2.48456,2.12412,2.0124,0.625223,1.6668,1.76474,2.96644,0.861406,-4.4252,-2.09528,3.84689,5.23745,-5.38072,-3.29064,7.58423,-0.507928,0.131471,-8.48878,-3.39661,3.39162 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--18.3954,0.684805,0.860161,5,31,0,28.5617,0.696733,-0.57608,2.19451,2.2634,-1.89913,1.88493,1.71683,5.13573,-1.70294,0.819443,3.1915,6.16234,3.64645,4.97284,-0.591489,7.11019,-3.99784,3.80958,9.91014,7.05112,3.62426,-6.73845,0.0750765,0.45336,10.6804,8.10877,7.10162,8.84098 --13.3217,1,0.860161,4,15,0,28.1192,-0.495684,1.71607,2.65677,3.90954,2.98255,2.25948,4.9079,1.88095,0.508449,1.97171,4.52115,4.38444,-1.78218,-0.743927,5.71292,0.691213,6.22394,4.11344,-1.3591,3.68851,1.67447,-1.15277,8.9472,2.79441,7.23469,8.86082,0.950356,8.32681 --9.60937,0.609719,0.860161,5,63,0,30.9344,1.13225,-0.14572,1.71394,3.54364,0.783788,3.58454,4.88378,3.82415,0.97786,1.90636,4.25177,4.17521,2.42848,0.532961,4.32969,1.32662,-3.54724,-0.533146,6.97881,4.77709,-0.524565,5.81543,-1.35475,7.65876,-8.01449,-1.79823,8.03482,1.31983 --9.63063,0.98381,0.860161,5,31,0,22.6885,0.585748,0.920064,2.18891,2.64539,-0.396606,3.12795,2.28931,3.85065,1.52782,2.32635,2.29546,3.03757,3.20822,1.08432,4.86114,-0.94135,7.80145,3.77788,3.2853,9.6236,4.75869,-2.22046,8.91638,5.72866,8.29346,5.14115,-3.30923,5.80263 --15.1239,0.802703,0.860161,4,15,0,22.7574,-0.312174,0.834929,2.08819,3.33138,2.94225,1.1939,3.09799,4.2314,-1.57183,-1.49892,2.81379,3.38944,-1.16576,2.94949,1.12468,9.01994,-3.39807,0.811035,0.748035,-5.02208,-5.71863,3.28132,-5.97355,8.99827,7.78913,10.6963,3.05376,9.08331 --10.3791,0.720263,0.860161,5,47,0,33.5646,-0.601611,1.77251,2.24487,2.7955,1.17255,2.48093,4.04297,4.33445,3.18582,5.3764,3.25639,4.93486,-0.911479,2.99223,1.31406,9.0083,-0.428543,3.75101,3.96422,-3.97733,3.88206,2.77614,3.60308,-2.36084,-5.59919,-6.71723,2.93275,-0.928943 --8.18564,0.8608,0.860161,5,31,0,20.9394,0.389945,1.31247,1.96925,2.78033,0.710454,0.396883,3.22585,4.10191,3.1971,3.05693,2.721,5.54695,0.522881,2.57321,3.64235,0.381491,3.17562,-2.08669,1.59912,10.2124,-1.78625,-2.57643,2.01712,7.25234,0.442403,5.37701,3.53046,-7.80824 --8.28366,0.808909,0.860161,4,31,0,18.8758,-0.0185936,1.10417,1.52161,2.92178,0.578218,2.99766,3.61487,3.99134,2.20822,1.86431,1.92758,4.77313,0.797828,2.44698,1.71409,6.76083,-0.560119,4.57238,7.53057,-8.33291,2.28088,3.3145,4.44164,-0.250339,0.127216,-0.300822,6.01234,6.61719 --8.21436,0.37558,0.860161,4,15,0,28.8,-0.00442925,1.14925,1.67314,2.91421,0.669679,3.14311,3.47453,4.00639,-0.23451,2.39601,4.04011,3.72817,-4.92388,9.12714,3.46034,4.99355,0.308943,-4.55018,2.28729,3.52271,-1.02315,1.40635,1.46019,0.434386,4.75563,0.759275,5.29107,6.2981 --10.7232,0.752643,0.860161,4,15,0,23.8665,-0.0197261,1.21653,1.62161,2.90739,0.729189,3.21549,3.54177,3.97123,2.04274,1.38045,1.56883,3.90847,5.60476,-1.67941,2.83652,0.103631,3.5451,-2.82989,5.09449,13.2584,0.837433,5.28519,4.76293,12.3129,-4.82351,1.93577,9.40939,2.39663 --13.4985,0.613638,0.860161,4,15,0,25.2295,-0.0889772,0.804059,1.82614,4.16984,1.77178,0.436037,1.93002,4.44932,3.35651,3.9819,1.31074,3.19818,7.70993,4.79742,3.68221,1.94676,3.51786,1.04123,2.63831,14.2622,-0.946087,1.14419,3.28483,10.456,-4.46484,1.05849,10.985,3.51703 --14.0961,0.989603,0.860161,4,15,0,24.9168,-0.53897,1.4819,1.12363,3.79454,1.45956,1.48419,3.1164,4.12228,-2.73809,0.672987,3.27672,4.76684,-3.4019,-0.349877,4.47482,-1.30833,-0.471887,-1.1728,0.432583,10.5259,-4.86023,3.70041,12.3919,12.6732,-2.14511,0.180609,13.7203,2.08803 --17.7516,0.746492,0.860161,4,15,0,30.7237,0.709823,-0.0805363,1.72807,4.3355,0.840025,1.50464,3.12114,4.9115,5.31744,3.20439,2.99475,2.74566,3.74957,5.66954,1.21243,5.25744,-6.60502,1.74799,-6.88061,3.89149,-0.399157,1.05531,-1.00895,17.2516,-10.4806,-0.526832,9.37027,2.03879 --17.631,0.817312,0.860161,5,31,0,32.2216,0.809384,-0.0269166,1.69364,4.03495,0.781735,1.55723,3.04783,5.01795,5.31363,3.38061,2.88188,1.69993,-1.46306,-2.31427,4.03671,7.94355,8.21865,2.77757,13.0606,3.99813,-0.759139,0.777636,0.760145,-8.88024,0.0655978,-3.46654,-6.45877,2.29535 --19.4841,0.840457,0.860161,4,15,0,33.0148,-0.430515,0.0463381,1.4538,2.6783,0.752094,4.17453,4.32689,3.65912,-0.808562,1.96169,4.52467,3.80386,-2.6542,6.42989,-4.66555,9.80105,5.23404,3.77337,11.2442,9.82,-3.48129,-2.06107,2.16491,-4.29833,-4.55313,-1.86328,-6.0042,4.6857 --10.8598,0.995652,0.860161,4,15,0,27.1151,1.16817,0.1344,1.12809,3.24868,0.0945902,3.84951,4.01012,3.34988,2.96192,2.03783,1.76587,4.42443,2.63814,0.892271,6.87473,1.11875,-1.49435,9.90036,1.22227,-0.0140542,5.39243,-0.925155,2.1814,4.30836,5.56547,-0.102283,-3.93407,3.39302 --9.43665,0.759701,0.860161,5,31,0,23.6189,-1.51397,1.72075,2.72583,3.05429,1.23405,0.89344,2.18649,4.48467,0.824332,0.962245,2.23892,4.50642,5.67314,4.87234,2.56031,6.4281,7.14082,-4.91695,3.47556,7.22459,-3.46407,4.98533,3.53776,3.56696,1.95092,7.05757,8.75105,4.66221 --13.407,0.942936,0.860161,4,15,0,21.4307,-0.366133,0.77257,1.58293,2.55401,-0.685115,2.55513,2.67993,2.29127,1.65432,2.16071,3.83786,2.75297,-4.45762,0.229301,3.58611,2.68081,4.39495,4.48879,3.34102,6.73079,-3.48768,-1.66258,5.97449,15.5188,-6.69684,10.6436,12.9375,-0.113143 --22.5904,0.513345,0.860161,5,31,0,35.1617,1.11355,0.830244,2.35149,3.36942,2.03464,2.88836,4.45714,7.14839,1.07784,3.19302,4.64928,2.43245,-6.53332,-4.26313,4.09943,10.1695,-2.04883,-0.615791,2.92892,2.62615,5.26364,4.48945,-1.07983,-8.32693,-1.23957,-6.3423,-5.3755,6.75186 --10.9229,0.996343,0.860161,5,31,0,31.0264,0.107438,1.72981,2.14936,2.79507,0.867267,3.29443,5.27874,4.74249,1.25263,0.0804895,1.94188,5.01581,1.8528,4.1499,3.97024,6.2781,2.63012,1.95844,-1.62707,2.09308,3.09211,10.1802,-6.79233,0.455456,-0.814438,-1.00885,-4.93144,2.83118 --13.6288,0.552344,0.860161,4,31,0,32.0091,0.287139,1.65643,2.18941,2.84338,0.724906,3.31185,5.22486,4.71536,0.742124,4.19457,4.61409,2.77634,-5.24137,-5.55015,5.6856,1.22124,0.510136,5.88726,-1.28806,3.07136,-1.31508,4.2807,2.11412,2.66238,4.51492,5.66451,-3.65645,6.83198 --10.009,0.866846,0.860161,4,31,0,22.6276,-0.0422607,0.429896,1.94854,3.44859,1.50486,1.44779,1.98256,2.81076,1.321,0.228988,4.0812,2.68053,-4.29686,-4.56497,4.35972,3.78649,0.552617,5.56139,-1.42327,6.1645,-2.43043,3.32902,1.96709,-3.24986,5.25532,4.04,-2.75529,7.16297 --15.132,0.503381,0.860161,4,15,0,32.6064,1.52984,0.000663133,3.37597,2.71727,0.957702,4.28721,2.41987,4.17804,1.399,0.867309,2.83461,4.69523,-1.14723,-3.30278,3.09208,3.07061,2.6864,2.98463,3.54531,8.00303,-0.435721,8.94809,6.38867,-11.4523,0.429564,9.7004,1.44049,1.89921 --11.1053,1,0.860161,4,15,0,23.099,-0.243354,1.23866,1.34539,3.01015,0.906882,1.68793,4.08651,5.23574,-1.13665,2.67701,1.05442,5.67828,3.26009,7.4049,3.79403,3.45196,-1.4025,2.93493,4.68502,-2.76416,4.73609,-3.81046,3.58142,15.4464,5.57147,-2.31507,1.92227,7.19468 --10.3082,0.874731,0.860161,5,31,0,20.9063,-0.375407,2.26094,1.73437,1.95901,0.890111,1.80237,4.69891,3.70502,-1.22224,2.8394,2.04379,6.88277,3.35867,-3.02564,1.49623,4.08529,2.94023,1.13952,1.54192,10.5654,-0.696751,7.55392,8.88063,1.80707,-2.81614,5.71471,7.11459,4.89149 --7.85628,0.700211,0.860161,4,15,0,21.11,-0.394476,2.2401,1.77678,1.94079,0.823571,1.67828,4.6015,3.84045,2.94916,1.05866,3.80606,1.32151,3.82827,3.85764,0.321519,3.1517,0.786519,1.92696,3.62927,7.74596,0.913734,3.60886,4.26259,6.22864,-8.19283,5.50963,8.43057,5.53878 --9.7938,0.873603,0.860161,5,31,0,22.3144,0.805893,2.13702,2.49539,3.7947,1.44066,0.770104,2.73621,3.36077,1.01292,4.48467,2.94527,1.37948,2.22044,0.135645,1.96588,0.0379794,-0.347908,7.12809,5.97505,-1.22242,1.1972,-1.12778,2.39086,2.65557,11.1565,-1.9119,-2.69542,1.17805 -# -# Elapsed Time: 0.016 seconds (Warm-up) -# 0.031 seconds (Sampling) -# 0.047 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstan/output_warmup2.csv b/arviz/tests/saved_models/cmdstan/output_warmup2.csv deleted file mode 100644 index 093478747d..0000000000 --- a/arviz/tests/saved_models/cmdstan/output_warmup2.csv +++ /dev/null @@ -1,248 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 18 -# stan_version_patch = 0 -# model = test_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 100 -# save_warmup = 1 -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 0 (Default) -# data -# file = (Default) -# init = 2 (Default) -# random -# seed = 721293306 -# output -# file = output_warmup2.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,y,x.1,x.2,x.3,Z.1.1,Z.2.1,Z.3.1,Z.4.1,Z.1.2,Z.2.2,Z.3.2,Z.4.2,Z.1.3,Z.2.3,Z.3.3,Z.4.3,Z.1.4,Z.2.4,Z.3.4,Z.4.4,Z.1.5,Z.2.5,Z.3.5,Z.4.5,Z.1.6,Z.2.6,Z.3.6,Z.4.6 --23.5491,7.18461e-009,2,3,10,1,33.584,-0.906349,1.26373,1.0243,1.46622,0.155039,-0.304288,1.60213,0.723284,1.19786,-1.98896,-1.45335,-1.80017,1.27008,1.45962,1.09203,-1.1192,-1.33588,0.932891,1.31403,-0.857182,0.402807,1.58501,1.01801,1.78023,1.86262,1.15535,0.032169,-1.19646 --23.5491,3.48647e-058,2.33506,1,2,1,42.9371,-0.906349,1.26373,1.0243,1.46622,0.155039,-0.304288,1.60213,0.723284,1.19786,-1.98896,-1.45335,-1.80017,1.27008,1.45962,1.09203,-1.1192,-1.33588,0.932891,1.31403,-0.857182,0.402807,1.58501,1.01801,1.78023,1.86262,1.15535,0.032169,-1.19646 --16.7669,0.999475,0.230236,7,159,0,33.2025,0.615777,0.856091,2.90583,4.83593,2.3122,4.25136,4.26438,7.39727,1.11922,-0.193063,1.32504,3.17652,2.83451,5.3926,2.27078,3.41373,5.86447,7.36499,2.79653,8.67806,1.78713,2.21527,5.10261,6.08009,2.03543,1.33785,4.03035,5.79061 --21.6891,0.994505,0.239456,6,63,0,33.5151,1.69249,0.677024,0.616013,2.8584,-0.0529588,0.390276,0.708075,1.02573,-1.89066,1.15333,4.99607,7.44445,-2.16824,-1.16109,4.1195,4.59156,-5.41453,0.0930025,5.85434,1.91067,-3.1493,-3.52969,2.33816,-6.47868,-0.353028,2.20936,7.0009,11.6875 --20.0564,0.999849,0.318802,5,63,0,37.945,-2.26599,1.09148,3.47496,3.06619,1.97238,3.7917,5.50166,6.28354,1.18588,-0.802846,5.02131,5.06922,1.08567,-1.39243,-1.0091,3.11655,-5.41679,-1.81573,4.03071,-1.29014,-2.38536,4.19566,3.75726,-5.23729,0.659035,0.390946,7.00171,9.44359 --22.4792,0.932045,0.49796,5,31,0,37.5886,-2.27342,1.11215,3.50395,3.08317,1.96618,3.80119,5.50127,6.32664,1.26247,-0.85883,4.97543,5.03467,-0.835819,5.17292,7.00972,3.11276,7.33087,5.76123,1.76246,9.25645,2.32844,-5.04027,2.37205,7.97512,11.6485,4.10824,-8.64768,2.8631 --13.5155,0.936941,0.688068,4,31,0,36.1084,-1.45149,1.69391,1.5265,3.72688,1.94773,2.79464,4.14314,5.95008,1.43912,5.9321,1.85128,3.26247,3.23448,0.0761344,4.36373,6.7795,9.42818,7.49491,2.84311,5.93714,-6.27115,-1.33146,1.89459,8.67113,0.994174,5.12232,-0.306281,3.09395 --12.6929,0.975713,1.00802,4,31,0,25.5137,-1.21474,0.757368,1.57335,4.13994,0.834958,2.74057,1.32648,5.07394,1.81827,5.03799,1.39025,2.51644,2.2453,0.362747,5.37649,8.08399,9.37075,7.99591,1.89262,7.43467,-6.73392,-0.354598,4.0539,8.47959,2.03848,4.23559,0.883416,3.11698 --10.9679,0.120452,1.71267,5,39,0,27.9142,-0.910013,0.353699,1.39158,4.3681,0.78959,2.45259,1.28193,4.75395,0.329707,-0.714338,2.77075,3.98128,3.79268,0.00958809,4.17974,5.56401,2.21137,-6.60787,7.23365,8.93317,-3.63284,2.15657,4.18948,2.13531,7.34659,5.05265,-0.322123,9.61173 --6.97565,0.999986,0.198608,7,127,0,17.3244,-0.42555,1.19605,2.63098,4.72193,0.690583,1.97225,2.15273,4.53861,1.5488,3.95119,3.47349,4.13327,3.72094,5.61929,0.980418,5.16023,0.346551,2.43613,4.75901,4.03408,0.0655499,7.11611,8.64687,1.36262,8.96157,0.426995,-1.63989,4.14042 --10.5939,0.976806,0.371781,5,63,0,22.5581,0.841567,0.751758,1.74588,1.6052,0.689219,2.48444,3.31449,3.68184,0.824379,0.436794,2.77393,4.82571,-3.02026,-1.84861,4.50828,2.2281,0.68242,1.05699,1.09667,1.64062,5.17466,10.7522,0.496022,1.14016,-12.4626,-2.56062,12.333,3.06287 --15.1309,0.786991,0.652307,4,15,0,24.5258,0.117861,2.01087,2.66291,4.64687,1.93536,1.4934,2.20363,4.81429,-1.07863,0.149642,4.44361,6.23431,-2.7168,-2.76272,7.23305,2.50556,1.10238,0.310591,-1.37866,3.45995,5.63384,10.3768,0.963377,1.87965,-12.512,-2.31656,12.6791,2.69178 --18.2108,0.843357,0.631289,5,31,0,29.8404,-0.100425,2.46875,2.41102,4.43389,1.88818,1.1069,1.84277,4.80822,2.92228,3.50823,0.898346,1.91969,-2.96139,-2.63446,7.32368,3.16063,-0.274823,-7.92171,7.89575,2.75035,-4.99625,-2.91228,3.18716,2.76788,14.5598,5.83824,-6.71585,5.07642 --19.912,0.926988,0.732095,4,31,0,35.0384,-0.984351,0.214278,1.94214,1.68385,0.0646418,2.39325,3.49348,5.86337,2.91856,2.84494,0.824175,2.31227,-2.25064,-1.82337,10.0208,3.03953,-0.555277,-9.78831,7.45038,3.62292,-7.18823,-0.806891,4.46405,2.01505,14.9227,4.91867,-3.61624,1.15836 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--13.7159,0.990085,0.734216,4,15,0,24.1107,0.83801,0.271689,2.93186,3.04892,2.28253,3.62346,3.20603,4.35626,-1.52007,2.02935,2.25633,4.08821,2.84619,2.44141,2.73302,-0.587503,-1.45076,2.07886,1.71581,0.521731,7.546,2.8262,8.20129,8.43578,14.693,-7.50896,4.14103,-7.69941 --11.7784,0.389965,1.31636,4,15,0,26.0278,-0.045724,-0.567376,3.62158,2.28703,0.601831,1.45887,4.30005,6.03182,3.54618,1.88299,3.46319,4.55012,0.567072,-0.197329,2.55289,8.82202,-2.50175,8.65583,7.28893,5.16879,3.27937,0.405499,5.89672,5.09788,6.65885,5.18939,5.06081,3.14504 --8.11742,0.996999,0.402518,5,63,0,18.9275,0.456599,-0.00292575,3.47988,2.33384,1.28407,0.860801,3.30112,5.35086,-0.802362,2.18428,3.05793,3.61218,-0.400425,3.03256,5.60617,0.0307613,0.323605,1.74314,9.61695,5.86072,2.48389,0.349301,5.38533,7.31343,-0.231969,3.12906,8.6808,-0.524597 --14.5333,0.785482,0.733383,4,15,0,22.5867,-0.795149,3.06206,1.45465,3.84538,2.81269,3.82732,2.28572,2.88122,1.24475,2.80555,4.04669,4.80197,-1.04568,10.2505,2.23138,3.58298,2.38521,-1.39649,0.483637,9.36029,3.95054,-1.08398,5.27414,7.32954,2.06095,5.87721,2.7736,-2.56257 --10.8579,0.999361,0.719195,4,15,0,21.0675,1.46614,0.182029,1.56367,2.79563,0.129158,3.30601,3.89056,3.76514,-0.0206611,2.8325,0.735144,2.86004,3.97044,-5.9546,3.3009,5.06088,4.76222,3.07328,4.47779,2.85128,-1.7768,4.85898,2.23491,-0.129925,7.87446,2.51643,1.50088,-2.75185 --11.3018,0.551005,1.30012,4,31,0,22.8679,-0.863167,1.88269,2.28091,1.27727,0.277884,4.44566,1.74879,3.3345,-0.608357,2.44238,0.0499443,3.52153,2.81411,5.0534,0.395459,5.76847,-2.81236,0.231623,1.11706,4.40395,-0.450836,0.471609,3.68707,7.12555,-2.82472,-2.10989,1.96678,10.7403 --12.9888,0.983911,0.653442,4,15,0,22.3461,-0.359216,-0.0511966,1.49718,3.32155,2.86159,0.496964,3.83004,3.87639,1.70679,2.53729,3.22622,5.05028,-0.831588,-1.53663,5.99674,2.09527,5.54722,7.58575,6.55303,6.01868,-4.85443,-6.50116,3.65453,0.729568,-7.7129,4.05426,-6.19291,9.17246 --12.9878,0.585581,1.121,4,15,0,26.0398,-0.619504,0.336149,1.37523,3.31239,2.68107,0.0864144,2.78131,4.22148,0.420192,1.25591,1.78357,3.32953,4.59101,3.87932,2.24966,4.44268,7.26607,1.28539,6.29165,13.1935,-2.01463,-5.70898,3.78742,-5.22211,-2.75661,3.50965,0.537753,6.86226 --10.5633,0.963596,0.628484,5,31,0,22.4814,-1.42203,-0.531825,1.8046,3.64191,2.27255,0.0472242,2.54977,4.36672,1.40408,2.15132,4.10392,4.05124,0.427621,-2.51216,1.26425,5.71529,-5.16331,5.65452,6.35352,1.52121,7.55548,0.29049,4.35128,5.63132,1.67531,0.722724,-2.18305,2.00711 --11.4089,0.560117,1.01063,4,15,0,26.1808,1.45185,2.41742,2.04252,2.58569,-0.379348,3.99252,3.51766,3.98391,1.88657,2.32292,-0.342671,2.04642,1.44669,5.80418,4.81122,1.94245,3.48752,-1.65117,0.677882,1.91065,0.489479,2.56924,1.58371,8.96817,8.66862,9.02187,2.4343,3.18245 --16.7016,0.896962,0.534045,5,31,0,28.793,-0.499616,0.252474,2.43591,2.60933,3.87673,0.658522,1.52366,3.93285,0.591786,-0.0932628,4.52386,6.65296,7.19001,4.07119,6.56463,-1.28949,5.13681,-3.67128,4.01146,1.02965,-2.05948,-1.12732,-3.31292,3.1905,5.42749,6.12401,0.853539,2.87601 --9.73807,0.977043,0.711971,4,15,0,26.4692,-0.431356,0.943745,3.21981,2.55141,1.06223,1.13771,4.1303,4.32251,0.599016,3.78895,2.0188,1.43192,-1.81257,0.264656,-4.13593,0.663373,2.90947,3.89637,-1.0826,6.01061,0.105015,-2.44773,-2.5451,6.0676,-2.89275,1.79837,0.670323,5.76375 --10.8023,0.543928,1.17214,4,15,0,22.9189,-0.36334,0.944266,3.22981,2.55652,1.15249,1.18547,4.02835,4.35336,1.39596,3.23195,2.24378,1.83142,4.99696,3.09889,7.23133,4.79647,-0.812892,-0.290334,6.49863,2.34127,5.57847,10.0517,2.34094,3.59182,2.59396,-12.5732,5.53712,-2.32159 --5.88034,0.964107,0.601711,5,31,0,16.9883,-0.331364,1.3154,2.64873,1.97099,0.961838,1.44807,3.72107,4.07306,0.743033,0.854148,3.40529,6.35337,-3.15412,3.82362,1.06517,3.62948,3.61023,1.94633,-1.86145,3.36511,5.98481,3.55897,4.59786,1.01084,6.28099,0.434977,6.88999,3.40781 --12.6729,0.616409,0.9508,4,15,0,21.4015,0.676954,1.61082,1.41553,4.99129,1.13639,1.86561,2.10425,3.8806,-0.879625,0.659104,2.93895,3.15531,4.3378,0.277234,6.47535,4.70497,1.37322,2.16537,5.78706,0.903019,-2.1174,6.4038,12.8243,9.18244,4.53699,3.1175,3.76808,20.1933 --13.3321,0.977125,0.597812,5,31,0,27.3324,-1.12889,1.20286,2.42556,3.80381,1.40687,2.46449,1.80716,4.12149,0.592316,3.27057,4.0591,3.34733,-0.0319428,1.98379,0.292021,5.28655,7.38996,-3.78578,7.31407,9.00019,5.23797,1.99189,7.49772,-1.19207,-6.44602,2.44849,0.949126,22.1146 --14.9226,0.532204,0.970188,4,15,0,31.3724,0.0280091,-0.804829,0.264394,3.20144,3.27006,2.71884,2.66732,4.60525,1.9042,2.10498,-0.0112303,3.59721,5.05103,-2.97426,0.596581,0.737265,1.55984,0.0277828,-0.0397546,10.5085,8.7258,-1.31738,2.35453,5.44967,-4.44274,-1.06615,-1.85166,7.4004 --21.1786,0.976504,0.493965,5,31,0,35.6081,0.481134,2.17343,2.00526,3.25768,0.488621,2.16377,2.30267,2.49201,1.46264,2.11851,1.91889,2.11002,-2.59649,7.6724,6.82062,8.59424,-6.0666,0.422116,9.59957,-9.55618,-0.0920229,-2.99639,6.40763,-1.46241,-1.11111,15.3334,5.77499,12.3726 --16.5492,0.962729,0.795479,4,15,0,34.8784,0.429135,1.74873,1.04789,2.84269,1.28095,0.872038,3.38053,3.81293,0.550874,2.47563,4.10885,5.36366,6.83508,-3.04098,-5.78799,-1.76392,2.44296,-0.102508,2.05046,4.85589,-0.489809,0.808246,-11.1154,6.66179,-1.94028,7.56153,1.01379,3.22342 --17.3852,0.378972,1.2278,5,63,0,33.6404,-0.201683,0.241773,2.78431,2.79733,0.776494,2.72971,2.48094,3.73355,-0.338654,0.816204,1.22359,5.92448,-5.62013,5.17427,13.063,9.47328,-2.58371,5.55188,6.37164,7.66806,-7.00944,2.07767,10.0159,6.92343,0.677969,8.4998,0.18314,5.66198 --17.1183,0.962689,0.429929,5,31,0,27.5656,1.35597,2.37619,1.16395,2.52682,1.03834,1.40356,2.07057,4.44812,0.465529,4.82462,3.75731,0.840556,5.62062,-1.29154,-6.2184,-1.13479,7.04417,2.20913,3.53459,-0.5151,-1.86479,1.36989,2.85629,4.75907,6.14408,-7.53428,5.77833,5.75995 --17.6506,0.896313,0.662164,4,15,0,31.4208,-0.192684,2.20656,-0.510031,3.74922,1.27205,1.83496,3.42204,5.28368,0.849377,1.07078,4.30669,6.52548,-2.86128,4.31887,12.6166,6.34849,-3.24164,3.83609,5.71198,7.63154,6.74407,-6.97297,5.45823,-2.21232,1.07312,5.28214,5.87212,1.59358 --14.4188,0.974921,0.858782,4,15,0,31.5894,-0.474069,1.89397,4.3394,3.0298,2.5609,1.99873,2.76175,3.62308,4.87081,2.37489,1.73372,0.626554,-2.22509,-0.222796,0.785192,5.91933,8.45864,2.17134,2.16467,8.81637,3.35727,-3.12251,5.33448,-0.340052,5.96381,5.72199,10.6223,1.91953 --9.35573,0.923289,1.34772,4,15,0,22.7852,-0.536174,1.77017,0.655577,2.47826,0.203311,2.9983,2.94556,3.41693,2.10392,2.64928,6.38388,4.92894,4.22711,4.07578,5.24293,2.26556,-4.09844,3.60442,3.26635,1.8837,-1.88123,4.44472,-3.12145,5.69629,-0.150233,5.92017,10.1083,-3.67708 --9.35573,0.133776,1.85092,3,15,0,27.4589,-0.536174,1.77017,0.655577,2.47826,0.203311,2.9983,2.94556,3.41693,2.10392,2.64928,6.38388,4.92894,4.22711,4.07578,5.24293,2.26556,-4.09844,3.60442,3.26635,1.8837,-1.88123,4.44472,-3.12145,5.69629,-0.150233,5.92017,10.1083,-3.67708 --17.3478,0.960482,0.368795,5,31,0,28.5786,-0.815759,1.18992,3.9581,3.15392,2.51898,0.863553,2.76448,5.22145,-2.96339,0.779398,1.48085,1.80471,-2.77818,4.6575,6.40201,7.83504,-2.36083,-0.440824,9.75744,4.19821,5.76118,6.2471,0.690995,5.31497,-10.0311,11.5909,8.41742,-2.06534 --19.072,0.973054,0.556732,5,31,0,32.2542,-0.81954,2.19161,3.23664,1.76109,0.209264,1.05503,3.10609,5.92652,-1.65701,0.577262,1.40879,-0.929546,4.5127,-0.00104345,1.94746,-0.318561,6.03136,3.80779,-5.56911,3.7119,-8.52567,-2.13719,5.96433,2.46436,4.58914,12.5317,0.875859,2.63355 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--10.6994,0.985385,0.845777,5,47,0,19.0331,0.553171,0.15134,1.36673,3.94166,1.70352,2.06865,3.70252,4.21007,-1.13869,0.484645,4.08665,2.37922,-1.13196,4.77589,2.98222,10.912,8.35784,-0.206937,6.97426,5.26829,2.31972,1.92001,1.71831,-1.39085,6.50728,7.54179,6.8286,8.375 --13.222,0.718584,1.32696,4,15,0,25.9741,0.0185238,1.03945,2.01255,2.81312,-0.487939,1.37825,2.26239,4.50254,2.67552,1.76786,0.246274,4.61868,3.11186,-0.922557,2.93758,-2.88096,0.707526,2.95492,2.87906,2.72534,3.20698,5.76315,13.9886,8.46722,9.23217,10.0841,13.2074,-0.0285599 --15.1833,0.59741,1.11259,4,15,0,25.8215,-0.428956,-0.0159566,0.343164,2.52411,2.73537,1.51641,3.53167,3.44805,1.28525,-1.5753,5.27622,4.56096,3.21504,-1.63384,4.91217,2.09253,-2.96893,-0.675096,6.88894,8.58955,-1.10419,-1.87691,-7.92574,-0.55343,-5.89391,-3.26596,-6.80422,5.86603 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--11.5592,0.745556,0.845991,4,31,0,28.2928,0.0050825,1.0995,1.45396,3.32413,1.68662,3.65396,1.70623,2.98902,1.19113,0.456394,3.69104,1.82046,4.96556,-1.51482,3.24531,2.50801,-3.97743,-1.78637,7.64219,5.11584,-4.69837,5.55073,-1.09902,-4.21916,-7.37952,2.01945,4.21865,-0.149323 --13.5277,0.876805,0.759792,5,31,0,24.9159,-0.764929,1.8911,1.76684,2.18402,0.917667,1.56771,2.20171,3.23215,0.753556,1.92547,4.17406,2.73474,-2.42243,9.32633,0.372894,4.97905,7.81654,5.30608,-2.23066,2.98849,11.7149,3.94506,8.5682,12.3043,2.40325,-2.144,2.93228,3.95146 --15.5991,0.789992,0.916723,4,15,0,28.0731,0.911314,0.100941,2.20733,3.89736,1.20896,2.32869,3.79298,4.81142,2.92374,4.53235,1.02709,0.550389,4.92645,-5.61878,5.81135,3.1249,-9.13311,0.159531,9.76047,5.08434,6.55671,2.96563,5.17574,5.30057,1.65815,3.82428,6.30564,1.50226 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--20.3145,0.97702,0.838116,4,31,0,35.2638,1.75979,2.37024,2.94854,2.25599,2.91276,1.6606,2.54675,3.32256,0.416566,5.82775,9.94335,7.10059,-2.84868,-0.410425,3.19945,2.59173,0.71373,-5.97174,-1.48326,1.2889,5.73703,4.01327,1.16871,9.03124,3.59914,6.73301,0.765641,1.34792 --9.48429,0.515033,1.23258,4,15,0,31.6004,-0.700828,0.434343,2.03333,4.03278,0.25777,1.35524,3.75614,3.80789,0.809977,2.24942,6.26198,4.0275,-0.897399,1.3995,7.24518,4.06653,-0.935021,-6.69866,-0.418132,4.01846,5.53375,2.07261,2.50646,11.7513,2.15846,4.48616,2.20142,-2.65691 --13.6662,0.784459,0.68681,4,31,0,24.835,-0.261162,0.431256,4.16651,2.57279,2.39782,3.09448,1.80574,4.34152,-0.0287853,-0.337641,2.13393,3.98704,3.83281,0.486288,1.78251,3.67793,-3.4189,-5.66839,1.48869,-3.48186,1.32903,4.25944,8.98499,12.1144,2.99351,7.68144,-0.239876,6.06387 --11.6718,0.782137,0.674494,4,31,0,26.0991,2.16967,2.28991,2.63123,0.60061,0.379648,0.615193,3.56105,4.92322,2.10416,3.62716,3.41701,4.17443,-0.700807,1.72358,2.31689,5.94714,5.38254,4.86952,1.29729,4.2074,-0.673906,7.2784,2.00808,1.51276,8.0734,6.88914,4.44675,4.27472 --15.2043,0.992227,0.659305,4,15,0,22.3958,2.73021,1.9453,2.46839,1.16992,-0.00574847,0.732707,3.13288,5.3886,0.247157,0.568974,2.55707,3.55847,-0.14097,5.53448,3.54678,2.33287,0.657108,5.89718,12.175,5.973,-1.43013,9.53649,2.36608,6.19573,6.65811,7.0985,3.5059,-0.267663 --12.2012,0.969415,0.993379,4,15,0,25.7169,-1.46314,-0.0421132,0.896051,1.93564,3.15174,1.57134,3.0694,2.92892,-0.642198,-0.327241,3.25299,3.24086,1.85347,-1.30975,1.78598,5.35341,-0.000299614,-1.56711,-1.22431,3.04348,-0.10208,-2.87038,7.31635,3.70603,1.44519,-4.82382,13.9554,-0.629691 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--11.9956,0.739969,1.09326,4,15,0,21.1713,0.215764,0.391491,3.21471,0.506982,0.075185,2.10448,3.83395,4.28188,2.78104,3.10546,3.32666,3.91059,5.51977,0.191141,-0.532296,4.02191,-0.300703,10.0858,-2.13443,7.46916,1.08273,5.51985,8.01142,0.171667,3.61351,2.74766,3.65254,6.68302 --13.3214,0.876639,0.979864,4,15,0,23.8364,-1.63083,0.512866,2.7997,2.41415,1.4162,4.50889,2.69153,5.04701,2.73247,1.43634,3.36881,2.22035,-2.84996,2.37334,7.39848,3.35135,4.42347,-4.92749,5.29984,-0.980654,-2.81757,7.96752,-1.43871,7.15079,-0.222947,4.88826,7.20118,3.14018 --18.084,0.959117,1.15506,4,15,0,29.1157,1.90128,1.51111,1.25305,3.12378,0.971387,0.693947,2.65602,2.94904,0.766919,2.1698,-0.522263,5.05354,-3.75058,2.54947,5.7464,1.0686,8.27514,3.59056,1.7155,-4.5024,-9.65972,7.6349,-6.35677,10.8652,0.970247,8.79903,7.15415,4.26648 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--11.4584,0.731627,1.14312,4,15,0,26.835,1.16535,1.92015,2.12183,3.99299,1.56085,3.48634,2.28848,4.64048,3.0432,1.61659,0.566092,4.67654,3.05871,5.36425,2.33636,6.97128,5.97525,1.59014,0.853119,7.24768,-2.0398,-0.909717,0.645166,11.4507,6.81815,-4.71557,10.2247,-3.4419 --13.4826,0.625421,1.01067,5,31,0,30.4594,0.732071,2.36432,2.14215,3.32313,0.81293,2.70622,2.59484,4.51275,-0.857582,2.3932,3.34901,3.46841,2.43733,-4.87902,5.61251,3.00748,6.56421,-0.813371,9.66124,4.12053,-1.44634,-0.252111,0.0270116,10.7051,0.496913,-10.5947,13.0262,10.353 --13.8462,0.964539,0.727916,4,31,0,29.4009,-0.607513,0.920459,1.69777,2.09758,0.770218,1.01119,2.22038,4.12659,0.125823,-0.197054,2.31301,2.13097,-0.843344,8.0902,0.572572,3.3363,-8.34401,2.70352,-2.63089,7.35826,9.2894,3.88907,-6.64355,9.79381,-1.83354,-0.116713,-0.332476,0.295914 --10.5556,0.485561,1.01248,4,15,0,27.479,-0.779485,0.984462,1.65298,1.89031,0.721129,0.823972,2.21117,4.04201,2.03088,4.34294,3.71565,5.90766,-0.81522,-1.23824,2.61954,4.93234,0.421226,3.22719,-2.04669,10.4705,8.27926,7.14627,-5.45872,7.24954,8.39216,3.40698,-0.421194,5.49631 --8.68569,0.938766,0.558867,5,31,0,20.0525,0.769895,1.15121,1.96317,3.95093,1.29286,3.01934,3.52556,3.91036,0.350861,3.48534,4.86432,4.51873,2.72465,5.24573,3.33485,3.10731,2.30308,1.07376,1.02103,-3.81423,-2.81847,-2.28462,10.1489,3.00983,8.47933,4.4843,8.15012,-2.46755 --15.5438,0.954645,0.738517,4,15,0,27.2753,0.431095,1.51151,2.68913,3.22977,1.31052,2.77279,3.06332,3.35303,0.617161,-1.32653,2.01531,2.44438,6.44761,0.0260205,-0.621469,10.3558,0.852493,1.40031,4.15371,11.5445,-9.83315,6.89209,11.8589,4.42857,-0.397058,0.088919,13.1555,1.40924 --11.5784,0.59084,1.00333,3,15,0,31.048,-0.998959,0.286978,2.14577,2.36215,-0.629478,-0.474466,3.32509,4.14876,0.193321,0.00020305,7.28139,3.62901,0.889415,3.9188,1.38151,0.243537,0.193878,2.05057,5.2043,2.3011,-5.58854,3.28538,5.06446,3.16084,-4.8672,-0.165163,7.3776,6.10297 --8.05821,0.997942,0.681616,4,31,0,23.0272,-1.56236,0.834001,1.61509,2.63693,0.266846,2.14081,2.47856,4.22923,-1.34599,1.28829,7.07279,4.86102,2.2337,5.67012,1.46557,0.570461,3.03135,1.90963,5.13584,1.07143,-2.51268,3.10935,4.51942,4.83592,-5.87285,0.00032457,5.18898,6.06275 --9.30562,0.68614,1.00249,4,15,0,19.6743,-1.51729,0.855736,1.5465,2.68318,0.278947,2.14949,2.46867,4.27799,-1.22824,1.31686,6.76505,5.44335,1.38448,-0.930065,8.26368,5.05054,-1.45579,2.47953,0.545777,7.36046,2.97477,0.625721,-0.580449,-1.95338,-4.06804,6.31823,5.87953,7.74185 --18.3458,0.66433,0.817433,4,15,0,28.157,-1.034,1.48627,3.31742,3.33424,2.76901,0.581948,1.48698,5.27407,4.62034,0.860327,0.655217,3.38653,-0.633279,6.15134,1.01553,5.15885,4.71574,5.54386,7.35897,4.18148,-0.731249,14.5647,4.06153,-2.23787,-4.20522,7.76545,14.8459,1.52435 --19.6951,0.957136,0.640976,5,31,0,37.622,0.984467,0.735274,1.96068,3.28713,2.73538,0.0770318,1.17203,3.35219,-3.05152,3.19217,3.74304,5.11721,4.81435,1.28701,4.47455,3.98566,-0.941346,-6.21606,8.90904,8.28378,-2.7194,17.1181,3.6123,9.02841,6.46968,5.48964,9.24344,6.78058 --17.1898,0.986493,0.869396,4,15,0,39.4116,1.59671,1.41654,2.60759,3.95616,1.17614,1.53747,1.3126,3.46866,-3.53102,3.08536,2.82462,4.90803,-3.11684,1.36436,1.97522,8.768,0.301151,10.3703,1.36884,-0.583788,0.41133,-6.86618,-4.5682,5.6257,-1.48681,-10.8257,0.287336,0.217241 --14.8695,0.305933,1.24197,4,15,0,33.1949,0.897664,0.0495986,3.49298,4.58842,1.14159,1.4718,2.22435,4.18557,5.53737,0.920879,3.16701,3.07165,6.05292,0.406893,-0.301078,1.59637,4.7044,-0.216011,5.13582,-1.41306,-2.21397,5.08391,4.58687,1.18055,-7.89784,-8.02667,2.01572,-4.89651 --11.7504,0.973087,0.502664,6,63,0,26.53,-1.05337,1.87044,1.20223,1.78593,0.30963,2.49916,3.66377,4.14122,4.06352,2.2235,1.57829,7.617,2.58487,-0.0313113,4.1904,2.40906,7.62197,1.76177,4.05019,0.427776,-2.09314,6.09359,-2.33082,7.7159,9.70406,4.84457,1.21707,13.0113 --12.177,0.986876,0.700026,4,15,0,23.8392,0.596517,1.57648,3.26344,2.5601,0.553111,1.82441,3.79655,2.51847,-1.35744,4.27417,4.08851,1.06702,-2.10389,2.17033,1.92918,5.05751,3.54641,10.4793,5.74355,1.71189,9.80105,7.50215,1.81757,9.26125,4.75623,3.89216,1.61249,8.82866 --10.6345,0.449776,0.997032,5,63,0,31.879,0.654165,1.4779,3.1988,2.44775,0.374964,1.71444,3.7223,2.56866,3.01974,-1.16349,1.00006,5.6074,-3.49301,1.72384,2.42279,5.8487,-4.14766,0.698715,5.74132,0.217977,-4.4396,-1.81902,3.12894,-3.23494,-2.51658,1.66142,2.97255,-0.868097 --16.7127,0.865063,0.531083,5,31,0,30.7231,1.05993,1.39368,3.14993,2.19427,0.302784,1.62926,3.77378,2.91091,-1.00887,5.06907,4.84485,2.42404,3.7643,-1.36903,-3.05317,2.97748,8.73029,3.569,4.63195,6.69706,-5.67227,-7.94566,-3.19242,2.97584,8.92447,-5.16223,-0.495005,-2.84747 -# Adaptation terminated -# Step size = 0.83483 -# Diagonal elements of inverse mass matrix: -# 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 --10.8118,0.903483,0.83483,4,15,0,27.4531,1.30684,1.18695,2.89328,2.71496,0.512828,1.76801,3.54462,2.89904,3.11497,-0.962928,0.513172,5.57458,0.410598,3.18495,7.25059,8.27672,-0.728479,2.96385,0.536494,7.8231,2.19201,-3.03059,7.76866,9.13768,-2.10544,-1.10009,-3.83527,-2.24569 --10.85,0.947954,0.83483,4,15,0,19.0036,0.24329,1.50928,1.22245,4.27076,-0.307876,2.37807,1.76085,4.00918,0.143432,4.29228,5.43051,2.29196,-0.719144,0.509173,-0.253295,0.397325,-2.46928,2.40818,-0.945662,5.15032,-7.5408,-2.36507,7.78431,7.11559,-4.18619,-1.03694,-1.90535,0.194602 --9.19889,0.918833,0.83483,4,31,0,20.1491,-0.318261,0.523354,2.75815,1.68363,2.31251,1.67231,4.30389,4.01718,0.487233,2.26311,1.36839,2.67311,2.42995,3.40731,6.30134,7.65966,8.05384,4.80066,2.1079,3.70507,-0.1085,9.52673,5.48753,5.26623,-0.246121,-1.36984,3.82387,-2.67224 --7.47001,0.474007,0.83483,5,47,0,25.4212,0.40585,1.94773,2.06828,4.16533,-0.104244,1.5412,2.85398,5.31592,0.960084,1.16265,4.16444,5.17187,2.76374,0.583729,4.62138,9.15207,3.35916,2.21535,4.72107,4.14607,7.20573,1.33221,5.71698,0.731926,5.58023,4.8409,2.8179,10.1126 --8.29322,0.995989,0.83483,5,31,0,14.1156,-0.34096,0.296963,1.68963,1.98041,2.30234,2.46622,3.1588,2.82529,0.136487,0.415403,1.41015,1.15395,1.64534,1.10025,2.02698,10.352,1.62296,-1.2783,6.18761,2.34386,1.95798,1.86878,1.33336,4.18422,-3.71758,-2.50925,0.806055,0.415608 --10.3768,0.925702,0.83483,4,15,0,20.5692,0.72442,0.962242,1.5206,3.94695,0.885197,0.72794,2.8467,2.84049,0.649141,1.09884,-2.11315,2.02938,2.36165,1.57387,2.64056,-0.719334,-2.58199,6.75713,-1.26497,6.83596,4.57706,1.97738,-1.4957,2.54949,3.54681,-0.494983,2.75616,5.05101 --9.29825,0.787775,0.83483,5,47,0,19.1258,-1.00375,1.96501,2.1274,4.09137,0.484656,1.01178,1.70539,4.35854,-0.348001,0.413892,-0.366398,3.46897,0.0346729,-1.35617,1.83134,2.57426,4.16666,-4.33078,7.12039,1.90177,-0.9021,2.40779,7.98852,4.58838,1.89688,6.40309,0.224705,7.17924 --9.29359,0.835642,0.83483,4,15,0,21.0906,-0.730081,1.43468,3.56079,4.26262,2.56663,1.02023,3.41193,4.50329,1.56703,3.93126,3.5142,3.51401,-4.59911,0.933832,1.0978,4.26654,-1.64901,-1.80076,-2.2324,5.83062,1.1993,3.54876,2.05805,3.3818,-1.66441,7.01944,3.2918,8.36962 --13.4056,0.984764,0.83483,5,31,0,26.03,-0.472207,1.84002,3.43737,3.46825,2.24806,2.22566,2.23146,3.08654,0.359253,2.73375,5.26601,5.8967,2.01464,5.15206,2.60941,6.04146,-0.833389,2.36248,-0.215557,4.54448,10.192,-4.80883,10.9135,-9.33497,1.40997,2.60774,1.09626,-0.625476 --14.9422,0.716375,0.83483,5,31,0,33.7182,-0.333752,1.46956,2.83897,2.99494,1.43878,2.04759,2.76075,2.81178,0.347737,2.66824,5.25311,5.95135,-0.388141,-6.81385,-0.924502,-0.408635,2.80711,1.65457,6.2075,3.42366,-0.225619,3.15525,-6.95157,14.6037,2.30662,3.14013,-5.03214,9.04084 --7.80812,0.905479,0.83483,4,31,0,26.4589,-0.106511,1.19013,3.26199,3.31634,1.52136,1.95282,3.67723,3.68411,2.18849,1.74612,1.31435,1.90146,1.9039,5.27175,2.1486,4.05483,-5.1812,-0.870323,2.34367,5.28976,5.60364,-1.37889,2.99087,7.10427,-8.76137,0.496294,-2.72102,-2.46107 --10.3869,0.641586,0.83483,5,31,0,22.127,0.152646,0.498574,4.22013,2.26157,2.29774,0.936892,1.46331,2.96051,1.35272,4.16151,2.40772,1.98558,-0.258961,1.06213,4.56221,6.45547,6.69628,4.37833,2.7508,2.40946,-3.97728,3.50845,2.91814,3.56872,3.93528,3.15632,8.79346,0.156626 --9.67588,0.989126,0.83483,4,31,0,19.4524,1.29714,1.65757,3.68743,2.11579,2.38379,0.605008,2.26785,3.25166,1.05096,3.56815,2.73795,2.00609,0.770895,0.568979,4.35369,8.14048,5.8878,3.76169,2.47386,3.57792,-3.76768,3.52333,2.28455,2.76708,4.29009,2.67729,9.66027,0.51315 --9.03912,0.785537,0.83483,5,63,0,23.5064,-0.108631,1.69251,4.50693,3.11333,-0.312665,1.61179,3.54345,4.25794,0.277078,0.683315,2.36945,5.42756,0.751022,-0.0747109,4.14504,7.82268,5.57527,-0.540444,0.988144,1.23542,3.33156,-2.06507,2.26034,7.49851,-2.58348,1.18099,-3.49764,6.97527 --11.4965,0.959964,0.83483,4,31,0,23.4407,0.527295,1.54339,4.46298,3.14975,-0.127277,1.55776,3.58555,4.39885,1.79804,4.07837,3.5342,3.41096,1.26141,4.16661,5.79536,3.11826,7.97481,4.64669,-2.5611,7.25299,11.5927,4.25469,0.928557,1.26491,1.53386,4.80473,3.35962,4.77792 --15.9332,0.981517,0.83483,4,31,0,29.0746,0.0202344,1.97129,3.67417,3.35401,0.386662,1.49422,3.86972,5.29289,0.740423,-0.497854,2.96175,4.5052,2.19653,-4.90042,4.24964,7.35135,-1.54897,4.12959,4.05704,4.79472,2.47789,13.5225,1.7479,0.573378,-16.0418,2.34251,9.43647,-0.121188 --11.9389,0.809329,0.83483,5,31,0,28.8491,-2.58475,-0.214628,1.65744,2.12806,0.785739,1.9463,2.5074,2.63639,0.296572,-0.0439075,4.30234,4.83872,4.51415,6.70063,5.56921,1.924,-1.64182,3.4046,3.45714,4.61946,3.65957,4.67672,7.43632,2.78809,10.7023,-0.996721,8.61415,1.80565 --21.4553,0.813824,0.83483,4,15,0,34.2135,3.1229,2.00062,1.70537,4.07254,1.34042,1.58738,3.37675,6.19392,0.216038,1.99216,7.49101,10.0023,3.71952,3.16334,4.25034,-1.77519,-1.40899,0.161753,2.89859,5.90498,-0.438603,-1.76059,4.38021,1.81241,9.30621,-0.365851,13.0975,4.28702 --17.8383,0.99519,0.83483,5,47,0,38.8249,-2.70468,1.20465,3.21203,2.88288,-0.122581,0.973055,2.60059,2.20141,4.50839,-0.734655,-0.479037,5.7255,-2.15542,1.3124,8.69155,-1.50638,3.79823,1.01666,5.42401,3.66354,2.51484,5.58922,1.47463,5.99071,-1.86506,5.451,-5.35539,3.70241 --18.3114,0.929721,0.83483,4,31,0,29.9374,-3.39542,0.924035,3.51688,3.51244,0.643457,0.960774,2.54984,2.05659,-2.92979,4.24132,5.86515,2.9987,-0.288278,3.24841,1.3102,5.45708,-5.07762,6.62062,-1.65099,2.42569,-4.56242,2.01725,-1.03688,6.3846,1.13996,4.17237,-3.8149,0.61017 --13.5562,0.803051,0.83483,5,63,0,35.4449,-1.90835,-0.0513946,0.638928,1.62255,0.198443,3.35926,4.01968,2.59602,2.33688,1.92464,-0.059979,4.72447,-0.584373,2.92707,1.17339,6.85178,-2.37283,0.995599,4.50052,13.881,4.59895,1.35983,3.58823,2.405,0.16325,0.446389,7.05974,7.46729 --21.0051,0.974597,0.83483,4,15,0,33.179,-1.33185,1.32837,2.778,3.1257,0.149198,2.21901,4.16366,3.0505,-1.40292,1.91984,7.81834,5.01777,4.12088,0.0384752,2.99382,3.06311,-2.8693,17.7317,-0.707477,-5.05339,-3.91213,-2.60847,0.0421006,-2.35944,3.18508,1.48848,-2.46687,1.14271 --18.0865,0.747436,0.83483,4,15,0,37.2851,-0.340567,0.198996,2.03386,4.20787,0.151401,3.38111,1.09423,2.53669,4.09248,1.74787,-0.609673,2.12543,6.42785,-0.399284,5.15379,2.46692,-9.46906,1.97251,-1.62726,-0.0776925,-4.16429,0.335865,-0.340774,0.501024,9.77337,5.10034,-0.76072,6.56574 --13.9449,0.95336,0.83483,5,63,0,33.7777,0.958931,1.96791,1.9904,1.73691,1.70748,0.814271,4.75238,5.58728,-1.5659,2.17069,3.5208,1.98139,1.82987,5.88624,5.23437,3.50729,8.6946,4.04013,7.77024,9.98028,4.93552,3.04506,5.22549,7.77034,-5.29937,-0.299542,7.91733,9.68064 --11.1698,1,0.83483,4,31,0,28.6202,-1.14028,0.662864,1.6605,4.12948,0.292669,3.10307,1.09469,2.55162,1.95822,2.268,3.34643,3.70184,5.01549,4.26592,2.90824,4.37317,3.7223,4.54968,9.88362,5.99222,-6.20838,1.1616,1.68628,11.0018,-1.8686,-3.73784,4.37057,3.87167 --14.7338,0.798087,0.83483,4,15,0,27.297,0.454955,2.23795,2.54471,2.33325,1.75017,-0.291665,4.25224,4.38334,-0.0575036,3.05243,1.35619,5.28488,2.73889,-0.266487,-0.759825,4.72769,6.07201,3.54431,9.45137,2.24811,-10.2975,3.43966,1.13636,13.193,-0.55072,-5.71268,5.97327,5.39284 --15.5928,0.888822,0.83483,5,31,0,28.4642,0.852452,0.733989,1.29851,1.95409,0.763028,-0.743476,2.74197,4.01927,2.40969,-0.312787,5.12866,3.8006,-3.09351,2.86039,6.22226,5.30562,1.84676,-0.850126,-4.50709,2.56264,-5.06556,0.28793,2.37673,7.99897,-5.98311,-1.96412,-5.68375,16.805 --12.4088,0.904971,0.83483,4,15,0,25.4413,1.2761,0.307509,-0.134376,2.09676,2.6887,1.72587,3.81198,3.27608,-0.0614721,3.0157,-0.00423774,5.1392,2.56093,0.0466323,2.64434,-0.940056,2.23719,0.292915,-3.98691,3.76301,1.16928,-2.13466,1.10669,4.36414,-3.54433,0.0119594,-2.98196,9.51162 --15.2338,0.910584,0.83483,5,31,0,29.353,-0.332246,1.72282,0.694009,3.07999,3.02149,0.74277,5.19719,2.56537,0.256905,2.84852,2.32058,4.68247,1.4682,-0.787886,6.5061,3.30343,2.06854,-0.519096,-1.34343,4.91119,1.84001,8.51995,5.09496,-7.76132,-4.34453,11.6677,2.10342,1.57033 --15.8188,0.994945,0.83483,5,31,0,25.9837,0.615746,1.41775,1.00788,2.51371,3.34784,1.4047,4.88712,2.0327,0.511853,3.08503,2.83768,4.01104,2.56988,5.24136,0.130565,4.10243,0.919252,4.34201,7.5629,2.75478,0.582998,-3.784,8.31304,11.9962,12.6371,-1.88002,0.603062,13.9219 --16.2821,0.768207,0.83483,5,31,0,33.7424,-1.43444,0.862667,2.85207,4.0402,0.044387,3.3097,2.6348,4.14178,2.60555,-2.44364,2.032,5.42637,2.74352,1.30558,-1.8375,3.55626,4.63605,-3.94637,-1.77232,6.55706,1.42831,7.30581,-2.28068,-3.80113,-9.82447,7.4054,8.00741,-4.9923 --11.4661,0.915915,0.83483,4,15,0,27.2589,0.0640601,2.28507,2.965,4.22213,1.48752,1.00268,3.22179,4.39339,-0.617984,4.32974,3.62397,3.31667,6.02253,-0.309625,8.14872,4.42177,1.82965,-1.62854,0.968422,9.68138,0.756488,6.71307,5.53864,-0.705569,-5.0546,7.13774,-0.480284,-3.50774 --15.7272,0.576738,0.83483,5,31,0,29.4495,0.0436042,-0.92821,2.13853,3.0225,-0.689307,0.895166,1.61584,2.87794,3.13991,5.8156,1.30143,4.7673,-4.50276,3.42695,-2.16267,3.84924,-0.81005,2.83785,6.56743,5.01451,4.41728,-6.93262,6.89184,4.18354,10.5855,1.26059,1.89896,0.812231 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--18.8873,0.997785,0.83483,5,31,0,32.1903,0.333258,1.95994,0.709042,3.60027,1.26845,2.04101,2.18611,2.50975,-2.17685,2.98268,1.41123,-0.534398,6.15491,-1.5234,-1.63031,3.63759,3.33771,0.143283,-4.87368,5.71351,-1.12335,10.2895,0.174343,5.1525,12.329,-0.237048,-10.2062,2.99182 --18.2656,0.796267,0.83483,5,31,0,35.8195,-0.764853,0.241086,1.30602,2.1897,1.75367,1.42465,1.51721,2.91435,4.16251,1.86096,5.06341,7.87251,4.33212,-0.70757,-1.91331,3.94759,-0.791844,5.22286,8.9611,8.96627,3.76687,-6.62339,11.79,2.34656,-8.48153,6.04213,14.6582,5.76854 --14.3318,0.829971,0.83483,4,31,0,35.0809,0.654234,-0.128177,1.70823,4.14618,0.342035,3.88528,3.19135,5.0284,3.24402,2.82401,3.38397,7.6781,0.581628,4.16649,5.51077,1.3806,4.2882,-2.27067,-2.28244,2.35233,5.74778,3.00256,-0.689958,10.2316,15.3274,6.33998,2.42288,-0.752875 --14.1795,0.989002,0.83483,5,31,0,27.7833,-1.10065,0.500012,0.639522,1.99458,1.15447,2.96104,3.00359,5.25747,2.41669,3.78312,4.3065,8.05638,-3.26201,2.00971,1.44393,8.3228,-1.02932,6.21338,7.14774,5.38219,-2.1337,-1.80424,1.01892,-1.32506,-12.3865,-3.47908,7.66373,5.54319 --15.3663,0.892112,0.83483,4,31,0,27.6151,1.77079,1.04284,2.84369,3.76692,0.213198,-0.0234226,3.71984,3.15203,-4.18686,3.40662,1.03041,2.93438,3.95947,2.67737,5.23837,-1.23734,3.64324,4.32435,2.76448,7.18904,4.01682,1.09661,1.88083,-1.15351,-9.30453,-4.10888,5.61266,1.72779 --9.13077,0.979973,0.83483,4,15,0,25.8327,-1.03807,2.02649,2.11163,2.70579,0.936736,2.59342,1.93758,4.67064,3.65316,2.2146,5.09264,4.1201,0.531782,0.954153,0.503004,7.47951,-0.265091,6.00626,-0.816836,1.98715,-0.59512,-0.939779,9.30924,-2.6216,-2.47754,-4.69615,7.2358,-1.7007 --12.4058,0.800882,0.83483,4,31,0,26.5269,0.217712,-0.358241,2.35709,3.11452,1.11803,1.95347,4.61052,2.54288,4.03046,1.92677,6.21681,1.46893,2.20643,3.14126,0.490956,7.02947,1.70507,7.02671,-1.71246,4.93485,2.24418,-2.40127,10.3574,1.82124,0.805215,-8.54407,5.00858,5.7804 --7.87295,0.992761,0.83483,5,31,0,21.0999,-0.801069,1.3605,2.30668,3.12099,1.83222,2.63524,3.34717,4.78003,2.47161,1.15116,4.58988,1.54683,4.48649,-0.556293,5.41725,1.44106,1.37807,6.7837,-1.65435,4.76682,5.82927,7.77189,4.10778,2.34467,-4.97965,2.35431,-0.992956,5.12786 --13.3291,0.806239,0.83483,4,15,0,21.3277,1.98173,0.429314,1.59654,3.10347,0.30195,1.38339,2.57479,3.42838,3.41259,0.685106,6.32579,5.4924,5.39482,-1.3069,4.5004,5.54893,-2.07817,5.33827,-4.55951,6.48559,8.80724,9.23753,3.57459,2.30803,-1.73084,3.1728,-3.55732,7.89801 --8.04945,0.790551,0.83483,4,15,0,21.7109,-0.974301,0.419418,2.06771,3.98268,1.49291,2.60003,3.98311,4.67452,0.366953,1.24232,2.74376,3.85225,0.704734,-2.75134,5.9203,5.97778,-2.02342,4.67175,-1.10886,5.37335,9.47304,6.60238,4.38352,1.65518,-1.62118,2.12335,-2.50092,7.86144 --14.342,0.545446,0.83483,4,15,0,27.8072,-1.10141,0.61495,1.46364,3.6632,1.05899,3.02919,4.01666,4.24086,2.2382,2.81969,3.19992,4.04999,-4.30352,5.11545,6.19058,-1.42586,-4.60732,2.16661,2.26862,7.43626,4.60851,8.65252,16.6655,9.31816,0.260952,1.14188,-1.60063,10.0562 --16.6672,0.58199,0.83483,4,31,0,34.7734,-0.277906,-0.771058,0.710021,3.15021,0.106369,3.67519,4.22077,3.83953,-0.979957,0.529907,3.12862,3.45643,0.747671,-0.0648186,0.860889,13.41,2.89486,2.27485,1.21116,10.6984,3.06716,3.00073,10.6979,1.35507,6.77373,5.00293,-7.30752,-0.891549 --13.2812,0.843544,0.83483,4,31,0,33.4922,0.623787,0.712441,1.98313,3.83719,1.97396,1.78893,2.46774,3.19702,3.59572,1.40046,5.99848,7.38103,1.78238,-2.9999,5.77607,3.39853,-7.0148,3.17669,4.32399,11.4006,3.36696,2.90925,6.10421,3.44419,5.82663,5.85641,-5.4899,-3.33579 --17.9868,0.809952,0.83483,4,31,0,37.7239,0.33107,0.857534,1.98205,3.70193,1.69871,1.53032,2.02471,3.47103,-1.64239,2.96373,-0.651324,0.725892,1.57276,1.77753,1.0449,-0.728743,-6.78519,3.76581,0.883017,9.92789,10.9593,0.17367,4.4489,-6.09206,1.82447,-9.76237,14.4734,6.88975 --12.9436,0.815776,0.83483,5,31,0,32.246,-0.0554511,0.733737,2.11026,2.35141,0.453305,2.19923,3.99324,4.41404,1.77193,-0.0882201,3.79194,0.589041,0.111116,-0.533839,-0.754245,5.22744,4.19795,-3.69736,6.36425,-1.51841,-8.49411,3.96571,1.03904,15.4698,-1.64595,8.43895,-1.78086,-0.681859 --20.3667,0.562274,0.83483,4,31,0,32.248,0.506867,1.94817,3.76351,4.10936,1.46747,4.46344,1.91362,5.36454,2.08974,3.2121,5.93628,6.99316,-1.71321,3.04508,3.44025,7.7467,11.4514,-5.03129,8.81317,3.79801,-3.56433,4.53439,3.85577,9.22199,-3.43831,3.126,-6.43819,-2.08633 --15.1081,0.986029,0.83483,4,15,0,34.3843,0.0202581,2.02611,3.83014,4.03572,1.51533,4.87625,2.41076,5.14263,0.661307,1.17961,-0.0567783,0.908297,0.935017,1.78574,-4.10084,4.69281,1.57316,-2.62149,2.30937,4.30498,3.74099,1.75727,7.29919,4.01511,6.92903,4.75465,2.65665,0.491487 --17.5213,0.995157,0.83483,4,15,0,33.0182,-0.0882513,-0.150307,1.32359,4.80248,0.525807,2.11932,1.17709,5.17601,0.859917,1.9682,3.78358,4.41043,-1.22363,1.01514,10.8521,3.8793,-0.358804,-3.91808,6.83336,5.92712,3.88565,1.92925,9.57317,11.6653,1.66751,-14.4467,8.10204,-2.6648 --16.3469,0.981162,0.83483,5,31,0,32.2769,-0.194272,0.119955,1.33306,4.90697,0.243144,1.81147,1.15914,4.9849,0.138497,1.57337,2.63842,3.75972,-2.0606,0.731789,-1.50205,3.3052,1.19482,-5.90678,6.30536,3.61453,-0.929199,-9.3374,7.1132,3.71833,0.285466,-9.83788,14.4635,-1.69331 --14.3793,0.956884,0.83483,5,31,0,26.2904,1.73549,0.402796,3.7394,3.84128,1.97494,2.85487,3.65101,3.90864,3.4006,2.38725,3.03798,3.61746,-3.65219,3.73669,2.55458,0.695151,2.23122,-5.43343,5.41061,2.22629,5.23311,0.272792,6.26529,7.92423,5.25143,9.95863,-10.1687,9.96236 --12.6757,0.928289,0.83483,5,31,0,25.8388,1.38384,0.208902,3.56354,3.3872,1.74356,2.74962,3.64868,3.89715,-1.20789,0.992838,2.71725,4.07512,1.37843,-1.1139,3.604,7.73779,8.38658,7.87932,8.90374,0.445377,4.29931,-0.562025,10.0762,7.72248,-3.83816,6.23906,-4.35023,5.28923 --13.4354,0.823167,0.83483,5,31,0,26.7762,0.554565,-0.246356,2.58226,2.60648,1.21155,3.67855,4.4744,5.72157,-3.2838,2.67886,3.20482,3.80729,0.552602,1.60852,3.62583,5.48106,-5.72767,-3.60482,-2.50196,6.94669,-1.22112,-0.820907,-1.78662,2.94731,2.54021,-3.1248,10.6819,0.559574 --15.3332,0.963142,0.83483,4,31,0,28.4467,-0.26168,-0.480551,1.6162,2.64708,-0.141524,4.1591,2.36011,5.22704,4.20182,-0.478539,3.14621,3.55536,-3.17406,5.74114,8.68387,10.1048,1.78366,2.85297,-1.9963,-1.09748,3.62595,1.50883,4.568,5.9887,-2.52205,-2.01162,5.99927,4.99629 --15.3778,0.946259,0.83483,4,31,0,32.01,-1.56214,-0.796007,0.164024,2.62667,0.696078,2.87316,2.90142,5.55965,-2.17226,3.29698,3.73987,4.79547,2.71769,0.737867,5.80112,0.985987,1.30753,5.20185,9.03804,-0.483702,-6.12864,-0.335334,1.88466,-4.27106,6.05599,5.05053,7.28618,12.416 --14.0244,0.994367,0.83483,4,15,0,27.0885,-1.62268,-0.464141,1.45023,3.41784,1.07936,3.02482,1.5747,5.30268,-0.902192,2.61135,3.02582,5.66632,2.54706,2.70504,6.39188,0.928956,1.63507,3.9373,9.94272,-0.356737,-6.68371,0.639644,2.74417,-4.68262,6.04772,4.32931,7.26832,13.2825 --16.5836,0.922613,0.83483,4,15,0,30.9394,0.191144,2.07233,2.09655,4.54387,0.0309058,1.03668,3.08189,5.49916,-0.159054,0.340813,0.248422,7.08412,1.20423,2.81703,-1.59406,7.71647,0.480008,-1.49273,-7.45798,8.05733,2.3979,3.12119,-4.09515,12.5444,1.92461,3.68287,11.0236,8.7107 --13.6497,0.97112,0.83483,5,31,0,27.2968,-0.302296,-0.0829458,1.10522,1.17663,1.43624,3.78145,3.64556,3.31326,0.356629,1.69662,1.2408,4.6353,0.829139,1.32821,8.33253,-0.177674,4.72723,3.79907,8.85145,1.97727,4.67062,5.85317,4.59425,5.30164,11.0484,13.8131,3.33908,5.36394 --14.6866,0.950907,0.83483,4,31,0,35.5999,0.640176,1.42647,2.45437,3.66658,1.4075,0.765389,2.43505,3.6809,0.481427,-0.0396887,1.63704,0.394027,-1.60201,3.68426,-0.859408,5.95755,7.5221,-0.18364,-5.06449,-3.27202,-4.02633,9.91592,10.5611,5.22265,-3.57167,2.42221,8.41064,-0.0685303 --15.6619,0.891186,0.83483,4,15,0,24.6287,0.736417,1.10281,2.40404,3.67398,1.01041,1.50427,2.1841,3.73231,0.485877,-0.0272711,1.70664,0.424976,5.9599,0.0769082,3.39522,4.44343,-5.52895,4.17967,11.0897,11.2831,7.08965,-5.17066,-2.0447,4.30583,13.5165,10.417,2.05862,3.49557 --15.739,0.802124,0.83483,5,31,0,30.4257,-0.41433,1.17469,1.42823,2.2043,0.942398,2.63697,3.63735,4.04622,-0.788289,-0.195085,-0.753101,2.02933,-1.74141,0.781675,3.44909,5.33927,9.57062,-1.09838,-0.2087,-2.31385,-5.43725,9.67399,7.27126,5.27379,-13.1026,-7.5675,-1.17203,5.85751 --17.2184,0.554375,0.83483,4,15,0,33.6153,0.332136,0.225981,2.32744,3.49729,1.36171,1.71554,3.2227,4.30314,0.0565614,2.56371,5.26488,5.79616,3.13931,2.08372,0.780676,0.398571,3.46416,-5.67763,3.03407,-3.1639,-4.90828,10.5409,10.9598,9.00137,-17.2989,-8.48035,-0.607664,7.26786 --13.9394,0.518918,0.83483,4,31,0,43.7154,-0.436462,1.66307,1.68845,2.57025,0.631327,2.36912,2.71284,3.76288,1.57394,2.13648,5.09356,4.79834,-0.888621,2.01377,4.99492,7.17745,5.1774,8.75254,12.5121,3.57339,-5.66478,-11.0096,-1.15642,8.33421,-7.44327,-1.67513,7.53604,9.1284 --12.363,0.783327,0.83483,4,15,0,24.6958,-1.14744,1.65554,1.54696,3.07057,2.22143,1.24656,2.45122,3.58253,1.76834,3.39747,4.98435,6.40201,-0.00371636,2.66224,0.97089,-1.44826,2.19071,1.08529,7.43077,-0.386727,-3.29753,-9.71437,-0.649837,9.60191,-7.24374,-1.05774,6.05328,7.07458 --18.0015,0.817657,0.83483,4,15,0,26.6881,1.83803,0.618081,2.44814,2.55915,-1.14914,2.70067,3.23367,4.05813,-0.994006,2.74898,5.97161,1.79175,4.33684,5.31696,1.1864,-0.450594,4.73126,-0.798072,5.51602,0.0640753,-1.42807,-11.9854,2.24233,10.6112,-10.4452,-0.0187378,4.84158,9.78279 -# -# Elapsed Time: 0.031 seconds (Warm-up) -# 0.016 seconds (Sampling) -# 0.047 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstan/output_warmup3.csv b/arviz/tests/saved_models/cmdstan/output_warmup3.csv deleted file mode 100644 index e847415e30..0000000000 --- a/arviz/tests/saved_models/cmdstan/output_warmup3.csv +++ /dev/null @@ -1,248 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 18 -# stan_version_patch = 0 -# model = test_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 100 -# save_warmup = 1 -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 0 (Default) -# data -# file = (Default) -# init = 2 (Default) -# random -# seed = 721295322 -# output -# file = output_warmup3.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,y,x.1,x.2,x.3,Z.1.1,Z.2.1,Z.3.1,Z.4.1,Z.1.2,Z.2.2,Z.3.2,Z.4.2,Z.1.3,Z.2.3,Z.3.3,Z.4.3,Z.1.4,Z.2.4,Z.3.4,Z.4.4,Z.1.5,Z.2.5,Z.3.5,Z.4.5,Z.1.6,Z.2.6,Z.3.6,Z.4.6 --40.7575,4.52778e-010,2,3,12,1,61.0848,-0.908895,-0.210423,-1.66602,-0.532084,-1.92435,-0.260259,1.29952,-0.474823,-0.646627,-0.82624,0.122843,0.972666,0.168488,-1.66123,0.398169,-0.55107,-1.95338,0.00963683,1.67538,-0.375565,-0.683834,-0.0544662,-1.31405,-1.12647,-0.104102,0.484509,-0.421776,-0.144484 --40.7575,2.32657e-021,2.33506,1,3,1,55.0363,-0.908895,-0.210423,-1.66602,-0.532084,-1.92435,-0.260259,1.29952,-0.474823,-0.646627,-0.82624,0.122843,0.972666,0.168488,-1.66123,0.398169,-0.55107,-1.95338,0.00963683,1.67538,-0.375565,-0.683834,-0.0544662,-1.31405,-1.12647,-0.104102,0.484509,-0.421776,-0.144484 --42.5217,1,0.230236,5,47,0,56.7699,-1.00804,-0.235228,-1.75775,-0.502045,-1.97058,-0.194034,1.34734,-0.398566,2.66641,4.83795,5.8657,7.05385,3.56349,2.72995,0.913605,2.68964,-0.776338,-2.21541,-3.49379,2.70912,-5.74495,7.12823,9.93726,3.71123,2.55124,2.30148,-2.76768,12.4905 --28.3309,1,0.239791,4,31,0,53.117,1.11744,1.99449,4.69772,5.00823,2.96696,3.18049,4.55111,7.91758,0.93408,4.18273,4.38436,5.80121,3.84199,5.29953,-3.00861,7.78927,0.169459,-2.71343,-2.2563,3.58558,-6.06727,5.15189,9.53853,6.26427,1.13689,-0.439613,1.59541,12.9017 --12.9546,0.996539,0.324332,5,63,0,39.6049,-0.864532,2.63046,1.69397,1.11266,1.16094,2.60879,3.40057,3.26931,0.712506,2.09477,2.13356,5.33439,2.93542,4.311,-2.33211,8.16268,3.05704,-4.13598,-0.916373,1.0573,-3.54373,5.31283,10.6841,5.73665,-1.50751,-2.1664,0.589966,13.1472 --11.3434,0.996835,0.501998,5,31,0,26.0646,0.278763,0.225685,0.774697,4.30371,1.02722,1.73702,2.50414,5.63785,-0.219882,5.76346,3.47832,4.0143,3.11214,-2.93843,1.90496,7.50052,4.97332,0.120791,1.26732,-0.811996,3.69227,4.8467,8.7847,10.2217,2.8091,4.89376,3.52435,8.09293 --13.3521,0.902608,0.846037,4,31,0,23.7941,0.174717,0.287707,1.36389,4.68897,0.191149,2.65674,2.3574,5.95755,-0.403444,5.61174,3.29622,3.98594,3.46579,9.71105,2.51425,3.24427,-3.03878,3.87153,4.73137,8.64601,0.21346,4.82217,-3.64307,3.45425,-5.03432,0.979215,6.34784,1.42638 --13.6705,0.997679,1.11764,4,15,0,26.5585,-0.135786,1.43744,2.83178,1.55407,2.05566,1.48773,3.20297,1.8531,-1.35601,1.6183,0.69966,2.1202,-1.31026,-0.37209,4.37289,-1.6874,3.69743,3.10077,1.81823,-2.79975,1.71868,-0.56815,10.6812,5.0776,3.72681,5.77855,-6.16069,-0.394769 --13.6705,0.00100456,2.03666,3,7,0,30.4711,-0.135786,1.43744,2.83178,1.55407,2.05566,1.48773,3.20297,1.8531,-1.35601,1.6183,0.69966,2.1202,-1.31026,-0.37209,4.37289,-1.6874,3.69743,3.10077,1.81823,-2.79975,1.71868,-0.56815,10.6812,5.0776,3.72681,5.77855,-6.16069,-0.394769 --12.3841,0.999672,0.162103,6,127,0,28.3913,0.451685,2.24481,0.832238,1.9074,1.77233,1.15669,2.39578,5.57261,2.11891,2.24523,1.8802,5.21682,-1.61092,1.8277,2.10765,5.79967,8.23116,-6.61218,2.5612,-3.55669,1.24772,1.51299,9.81041,0.240902,3.26797,2.47161,3.60893,4.71353 --15.9217,0.985545,0.303059,6,63,0,26.4453,-0.420782,0.0225412,3.35477,4.6611,0.337796,2.49552,3.49239,2.04925,4.10119,2.73727,3.00449,3.57058,3.5462,2.58051,3.71889,2.61619,-4.11808,5.79694,5.95965,13.4631,-2.29712,-0.945851,-1.37442,6.80029,-4.19748,16.014,-2.61382,6.28696 --10.953,0.992237,0.546739,5,31,0,26.2398,-1.3184,0.898324,1.3356,3.14773,0.047731,2.04305,2.89215,5.48675,-1.11451,2.88596,3.16211,3.46647,0.445489,2.12027,2.51103,6.05797,12.3034,-4.23255,3.40209,-1.17731,-0.614662,2.69478,4.34922,-0.198407,-1.2098,-0.033775,2.40848,11.0448 --7.89356,0.986054,1.01041,4,15,0,17.6344,-1.40581,0.874601,1.36515,3.21445,0.0698834,2.06735,2.95208,5.50104,3.0569,1.32188,2.80287,4.64056,2.30919,-2.4462,2.53128,0.142838,0.12968,1.07553,1.23256,0.214573,1.98136,5.65454,8.094,2.82536,-4.01401,-4.08759,4.56891,6.80127 --6.67256,0.0886246,1.82908,3,15,0,15.8571,1.24355,0.757626,3.43658,2.72016,1.48183,1.51902,2.42048,2.37446,-0.977734,3.10864,1.90332,3.68924,-0.316009,2.80859,2.10447,6.02448,1.66374,2.93964,1.66467,5.91823,-4.34067,1.0508,2.93518,9.7263,4.16035,-1.51483,2.39751,6.63648 --12.0713,0.996546,0.200897,6,63,0,24.3458,0.937147,1.4051,3.64249,2.78421,0.75792,1.68391,3.46822,3.17474,2.54887,1.17596,4.30984,1.26085,-2.02499,4.4273,8.30409,1.23067,0.829249,2.97932,2.08129,3.4772,-1.93515,0.0968298,10.6961,8.53248,3.04389,4.38361,-1.02062,18.7441 --12.0184,0.999337,0.378573,6,63,0,21.216,0.580893,1.73497,2.88283,4.20456,1.94062,1.96791,3.51321,3.53876,0.123257,2.06702,1.61622,6.93092,-1.59443,4.9095,8.1497,2.23193,0.562628,3.11889,5.67344,-1.46803,-1.97445,-1.92105,-2.44691,6.21089,-1.35373,-1.28081,6.72658,-10.903 --12.9585,0.912118,0.715108,5,31,0,21.4726,-0.532489,-0.160313,2.49919,2.31963,1.27157,0.781201,4.28328,2.3818,2.46087,2.40656,3.60522,1.88591,-1.23775,4.43893,6.84677,2.41945,-4.58331,-0.411434,4.44398,-3.88952,2.11828,5.26558,4.25807,2.90225,1.42258,3.78184,-0.512051,18.0344 --19.9617,0.442986,1.02698,4,15,0,37.7448,0.582903,-0.37097,3.51793,1.32219,2.05358,0.567999,3.29253,1.1857,2.75564,5.98067,0.92669,2.67453,6.05826,-1.78212,3.89374,2.73299,6.04925,3.44847,1.19687,11.4879,-6.27262,0.177186,1.92518,1.19331,3.02593,-3.2194,7.66814,-1.86354 --15.2755,0.999409,0.354292,5,31,0,33.6362,0.15479,2.64176,0.362301,3.89953,0.090774,2.42089,2.9094,6.48635,-0.376248,3.8142,3.14327,4.47965,-4.77731,6.87178,2.35363,6.00321,0.546916,0.368802,2.71339,0.267121,0.783419,1.44726,-8.02149,-0.543943,-7.49901,4.13435,2.49791,2.58918 --16.303,0.992796,0.662737,5,31,0,28.492,-0.145738,-0.66672,3.49253,2.24147,2.29325,1.68552,2.92251,1.64675,3.17242,-1.62542,1.86386,4.8084,0.20032,8.48766,1.89521,1.76049,1.45913,1.97716,7.42147,7.3415,1.44777,2.65732,13.8355,9.1979,4.6264,-4.17527,5.20046,3.74129 --11.3856,0.999521,1.20411,4,15,0,22.8433,-1.61185,0.439896,1.76914,1.9305,0.986018,1.75939,2.19901,4.31276,0.619381,4.59793,4.41458,3.48741,-0.485958,-6.45967,5.18605,5.53291,1.8919,4.83845,0.525161,8.72075,-0.990612,5.74136,10.9367,4.97034,6.09658,-0.0640048,1.78246,5.9023 --11.3856,2.36807e-019,2.21072,1,3,1,25.9594,-1.61185,0.439896,1.76914,1.9305,0.986018,1.75939,2.19901,4.31276,0.619381,4.59793,4.41458,3.48741,-0.485958,-6.45967,5.18605,5.53291,1.8919,4.83845,0.525161,8.72075,-0.990612,5.74136,10.9367,4.97034,6.09658,-0.0640048,1.78246,5.9023 --13.4235,0.987467,0.214568,6,63,0,26.4792,0.55357,0.338447,2.57795,4.63294,0.595543,1.8441,3.64273,3.93001,4.3707,1.74997,4.5834,3.4594,4.17482,10.8124,0.116025,2.1296,-2.76985,0.569728,5.21757,1.66366,-4.52738,-0.758396,1.20239,1.72753,6.02938,-9.08322,5.31722,5.78307 --11.6267,0.981864,0.382298,5,31,0,25.3119,-0.575738,0.343063,1.57363,1.75436,1.39808,1.96704,2.71709,4.13446,1.98368,4.13716,4.68108,5.77326,-3.85151,-6.23784,5.13427,6.11951,7.46675,3.43153,0.407071,4.81647,1.88161,9.84083,3.6958,2.05753,2.96253,-1.08548,5.83665,4.33007 --11.5502,0.741834,0.663913,4,15,0,29.379,-0.407792,1.19262,2.45766,3.95793,0.348376,2.21539,2.4039,4.07035,1.99199,8.83875,0.940501,5.89454,1.63811,0.569871,5.3815,3.77141,7.29116,-1.00774,1.74302,3.66179,3.83498,9.2718,6.0896,3.16561,1.61409,2.70694,4.77303,5.06324 --9.44307,0.971261,0.575434,5,31,0,24.4434,0.362226,1.01115,1.52108,2.31244,1.10325,1.96886,3.33778,3.72021,-2.58529,-1.09785,3.19433,2.67891,-1.85092,0.609477,5.72013,2.95857,8.69394,3.21653,3.92103,6.50703,3.52421,4.12423,11.2325,-1.20093,-4.52603,-0.526511,0.131063,3.43035 --14.0271,0.479952,0.957772,4,15,0,26.6806,0.00270772,1.05565,2.20246,2.82194,0.95191,2.41462,3.01671,4.21697,4.76006,4.80724,2.57761,4.93098,3.38114,2.30091,0.852279,3.7134,-7.09418,4.42532,8.2951,-6.36212,-4.05652,0.253565,12.448,-0.419755,-5.04975,-5.02915,3.30492,4.73688 --11.8232,0.861744,0.397662,5,31,0,28.8177,1.30605,2.16767,3.18853,3.41646,1.5673,3.66681,3.23459,4.25984,-2.54324,-0.388975,2.69671,3.13028,0.00471664,3.7587,-0.258148,6.94265,-4.60107,5.33737,3.82,0.949018,-5.66509,-3.94254,1.55815,6.20324,-6.27207,4.28517,3.51666,3.01976 --12.2058,0.994605,0.485318,5,31,0,26.6584,-1.14529,1.56376,1.71793,3.37367,2.46151,2.38892,1.81728,3.14136,-0.960468,0.714126,0.049822,2.864,-2.30762,3.84424,-1.62562,7.30716,-2.74068,4.71962,6.36823,-1.16238,-3.89797,-5.81788,1.0968,6.90245,-4.98418,4.95194,2.11422,1.1577 --15.3987,0.980415,0.852036,5,31,0,23.6574,-0.690236,1.7542,1.15005,4.35208,1.16748,2.54622,3.2513,3.68415,-3.41723,0.948107,-1.10062,1.60117,3.98281,1.16965,7.03724,2.38032,2.39121,-1.06447,-1.55302,9.14665,7.75647,9.88057,-0.827901,4.29314,3.18141,-3.71925,11.8346,-1.9401 --10.1167,0.453409,1.42544,3,15,0,32.8683,0.619742,0.308085,2.86125,1.65404,0.856728,1.44597,2.77769,4.41177,2.61529,3.29537,1.9758,1.56651,-0.376746,0.134333,2.19261,3.48561,-0.195021,2.27611,-8.19465,1.58501,5.2132,7.62671,4.19973,4.43135,3.26061,-0.810595,8.12488,-2.92896 --12.2246,0.862519,0.565098,5,31,0,24.3345,-0.132558,1.36474,3.35147,3.92868,-0.358566,2.0785,3.30363,4.17437,2.97237,4.07672,0.759727,4.03194,-2.57727,0.880916,-0.0337475,1.51586,-1.25206,0.364824,-7.71484,-1.8632,1.58532,9.02559,3.81826,4.04827,-0.39408,0.341574,5.16317,-1.92962 --13.7125,0.978811,0.684673,5,31,0,24.1878,-0.00424073,0.88354,1.59168,1.12864,1.04413,2.62264,2.26824,3.32154,0.441721,-2.13481,7.0037,4.78189,-0.22523,2.66686,-1.39642,4.78434,6.50651,2.72782,11.7054,7.11858,0.492374,-5.83238,1.4626,3.92878,5.22326,-1.07843,1.34653,9.18487 --15.0045,0.586495,1.12829,4,15,0,25.6974,1.22577,1.25382,0.516768,3.89969,1.1819,2.93057,4.25278,6.21319,0.863924,2.90171,-0.405051,4.60368,2.96067,0.190764,6.64818,1.48847,-5.32786,3.34251,-5.01689,5.47825,-1.31977,-0.840009,5.73911,6.67636,11.9601,-2.17737,3.18174,6.56143 --12.8081,0.985797,0.652033,5,31,0,31.5814,0.259444,1.67233,3.01401,3.16333,1.33175,1.44162,1.07082,4.0869,4.08198,4.2035,2.67911,2.13495,5.79599,0.860326,1.34719,4.52594,-4.72589,2.42293,-4.41313,7.75435,-4.31313,2.09663,3.98434,7.53825,11.2308,-4.776,4.44534,5.76792 --10.6963,0.765136,1.08607,4,15,0,26.5875,0.0363619,0.616986,1.48172,2.75895,0.785455,2.77035,4.90171,4.0885,-1.79328,5.64881,2.69224,4.12446,-1.87911,2.56269,3.93079,3.01861,5.70439,-1.64948,9.09269,0.415188,-5.38312,9.22938,4.62675,3.73512,0.691134,6.28909,-0.112118,6.92363 --14.429,0.499868,1.00911,4,15,0,26.9969,0.106111,0.69963,1.55812,2.73412,0.676505,2.93274,4.86165,3.9978,3.76728,-1.70993,3.22205,3.94486,5.66967,-3.46713,3.89573,8.32957,-3.09426,1.10557,5.7446,9.74646,5.97459,5.40945,5.61105,0.94549,1.68095,6.69124,4.5459,16.5027 --12.1638,0.988941,0.472386,5,63,0,25.9357,0.382088,1.73005,2.10714,2.96979,1.63912,0.573031,1.09528,3.57352,2.58186,4.34099,3.16415,4.6106,5.70619,0.884496,8.18583,2.3764,-1.26098,1.33029,8.46286,10.2733,3.36373,3.61121,5.70773,6.23439,-2.49838,12.2933,5.0713,8.28936 --11.3548,0.992297,0.785693,5,31,0,23.372,0.282795,0.535141,1.94541,2.99709,0.240419,3.48179,4.62102,4.10191,3.65541,-2.31158,2.03072,3.09616,1.81043,3.33852,6.02013,6.18754,6.76612,1.49418,1.87317,-2.53271,-1.40268,0.382204,0.29375,1.78842,4.35512,-4.45463,5.05762,-2.2375 --13.8176,0.255029,1.30775,4,15,0,28.7194,1.00253,1.07376,1.86246,4.71864,0.667264,2.80667,4.24229,5.54325,0.827641,6.83479,2.97907,6.18368,2.01899,8.88907,0.943925,2.39594,2.24485,3.78357,3.13382,4.25361,-8.47762,-1.20263,1.50556,5.76449,2.9204,-3.87677,0.329911,4.07283 --12.7063,0.999978,0.334512,6,63,0,24.2856,-1.09939,1.0505,2.86341,3.65237,1.40702,1.85983,3.54881,4.86065,1.52398,7.75382,4.20814,6.3037,3.39544,8.24166,1.85228,4.46019,4.60245,4.96339,3.57336,5.44472,-7.93798,-2.36749,1.16781,4.19281,1.32399,-2.89521,-0.567672,4.30873 --16.8872,0.999037,0.566832,5,31,0,25.833,-0.634905,0.82453,2.88243,3.47336,1.27835,2.1624,4.05478,4.44473,-0.302007,-4.0883,2.44866,1.1337,-4.18499,-2.8821,-1.37207,1.89497,0.160173,3.90489,-0.44225,10.7434,-3.18806,1.69153,5.13151,9.86356,2.06543,-3.18748,-5.51625,12.361 --22.1704,0.825296,0.950755,4,31,0,34.0098,-0.098066,1.44039,2.64031,4.48054,2.20697,1.25131,2.7447,2.51681,5.02835,8.21792,3.08747,6.50321,5.3968,4.16407,4.0564,3.50507,4.25904,-3.79281,2.08634,-3.94428,-0.694634,9.50402,1.84511,1.81188,5.42748,-11.4373,3.3483,16.3095 --15.332,0.974637,1.0296,4,15,0,33.7806,-0.514641,1.10272,1.88519,3.88165,-0.143289,1.75047,4.13232,4.29353,-2.83458,-3.79153,0.286044,1.6643,0.760189,2.87398,2.39825,5.49518,2.00695,5.12525,2.0768,0.913486,4.21604,8.39347,-2.14928,10.725,9.09265,-8.2899,1.56386,6.36389 --13.5228,0.200554,1.60724,4,31,0,33.3226,0.943553,0.632086,2.2299,2.16515,2.06366,2.28819,1.89687,3.58299,2.57539,5.32899,4.96127,6.77196,0.86135,2.24134,1.04112,0.16579,-6.09185,4.07581,9.41824,0.431247,6.30558,-2.06655,4.60689,0.0416915,0.333199,-8.42096,11.2698,2.1654 --12.8411,0.999786,0.377274,5,31,0,22.3277,0.807187,0.254122,2.23915,2.48865,1.72593,1.74007,2.19279,3.98522,-0.32183,-0.637312,0.36422,0.338587,-1.59888,2.60591,2.53278,8.90539,4.34473,4.5413,3.49187,3.03771,11.6261,-0.467605,7.72569,1.03025,-2.397,-9.87137,8.09168,3.18144 --12.3151,0.870487,0.62632,4,31,0,31.2023,0.271449,3.0324,3.10868,3.92545,1.70845,1.5256,4.22842,3.72232,2.94309,-0.655142,-0.434208,2.30062,-0.648573,3.03409,0.106073,8.09465,2.15921,2.85396,5.63619,4.59,4.42036,2.56495,8.14715,-1.00574,-2.39977,-7.24643,7.39891,2.36499 --18.447,0.954047,0.756419,4,15,0,26.7651,-0.0266538,3.46657,3.28561,4.17845,0.52877,2.36741,3.69297,3.01521,-1.6966,3.8146,5.27818,4.91904,-1.29376,4.88474,-1.16278,-2.07584,1.51733,0.397181,7.96683,-1.85945,0.108635,-2.03444,13.2633,2.45636,0.298058,-11.4521,2.88564,7.68567 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--8.66363,0.907843,0.565244,5,31,0,32.429,0.676798,1.4386,2.12601,1.51599,0.198801,1.942,1.56447,2.53114,-2.12902,1.25939,3.36476,5.30297,5.23351,1.67644,2.91842,2.16109,1.52595,2.8609,-2.78281,4.8346,3.47633,-0.0731105,3.59278,1.64051,4.56633,-0.279897,1.65244,9.27794 --12.5449,0.80344,0.736179,4,31,0,26.3132,2.05534,1.4879,2.51129,0.431521,1.02835,2.18406,2.06047,2.42308,-1.64137,2.35246,4.92978,5.19984,5.70145,2.80162,2.89301,1.35073,1.14831,2.52569,-2.1567,5.93891,1.98198,-1.22677,4.03618,1.90086,5.21403,0.725267,1.58358,9.38681 --11.9754,0.999839,0.754119,5,31,0,19.657,1.72622,2.63103,3.05259,0.979525,1.30882,2.41702,2.52925,2.6658,3.32171,1.58735,0.80123,1.92591,-3.01178,0.27957,2.77359,5.44289,-0.759298,1.33938,1.45902,4.16372,6.1715,-2.52265,2.57671,6.7877,5.27161,5.97578,-2.55689,0.0176018 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--12.255,0.946358,0.70725,5,31,0,31.0416,-0.588241,0.543138,1.6696,3.85439,1.37976,1.22283,2.95735,5.07288,0.603005,2.78341,3.4643,7.60594,6.48507,5.50649,1.14509,1.30012,0.0143376,3.7606,3.96577,12.8026,4.15957,-5.15588,3.41165,10.0808,7.03603,3.08346,-2.1575,9.82885 --19.183,0.580303,0.991301,4,15,0,34.26,-0.690733,1.26106,1.2975,2.23193,1.15686,0.420609,1.11464,2.42879,2.20777,4.26964,3.12118,4.03487,-3.43769,0.419024,4.98276,8.99418,-8.46189,6.91118,2.17381,-0.844298,-3.72274,-5.54167,-2.53928,3.08938,-2.59582,-5.38129,9.05517,17.3267 --19.0273,0.8865,0.621407,5,31,0,34.2081,0.667135,0.68971,2.73854,3.81109,0.724702,3.61096,4.99596,5.56134,2.14827,3.34698,4.71372,1.05254,5.33844,3.57258,1.10072,-0.979315,8.62154,-0.688539,2.45846,5.70665,9.95861,8.73364,2.19666,9.3887,10.478,14.8217,3.16956,-0.022935 --17.0291,0.944009,0.762146,4,15,0,31.6141,0.763176,0.453264,2.49347,3.47653,1.24257,3.83082,4.75931,6.39741,-0.547486,2.27121,2.36016,6.10841,4.83958,-4.83119,1.08585,2.5948,2.51602,-5.08772,4.75809,8.63038,6.39144,6.5459,9.05869,6.20944,8.54571,7.48566,5.55727,2.06624 --19.6339,0.515936,1.05597,4,15,0,39.5155,0.291487,0.621539,1.8144,2.14537,1.28681,0.526293,0.862905,4.10612,1.06091,-0.309296,2.16099,8.74608,-2.96779,8.53407,4.75506,5.51362,1.83375,6.48774,-0.611682,0.322232,-0.710566,2.08873,2.62107,2.59098,20.6167,-5.48361,10.6473,6.51892 --14.0118,1,0.580688,5,31,0,27.1653,-0.0679874,-0.643431,1.27936,2.26619,1.79419,1.84235,2.65813,4.15404,0.423006,4.54322,3.28648,0.790811,-2.4164,7.90523,5.20447,3.68216,0.392612,-0.37452,5.23647,5.97365,0.234062,4.1463,1.03395,4.67821,-17.1717,9.2079,-3.04468,-0.255347 --17.2364,0.812549,0.904806,5,31,0,31.0135,-1.31019,2.26584,1.68824,2.24534,0.33233,1.63968,1.781,2.84669,0.972164,-1.65072,3.97577,-0.730535,-4.50195,4.29307,7.70165,3.89168,3.48077,0.115358,2.07266,5.27692,-2.56333,1.00441,2.06038,15.4005,11.1213,-3.91593,3.21204,10.4697 --14.7675,0.972836,0.941717,5,31,0,31.0176,1.23218,-0.0290456,1.93333,3.80279,1.33005,2.24836,4.39646,5.83417,1.04039,3.1758,3.29422,2.34068,6.50534,-0.264992,-1.73741,4.03777,0.230044,4.0705,5.21257,-3.30094,12.4907,0.0215445,3.11373,2.9597,3.14036,2.86135,13.122,9.74984 --23.0925,0.400633,1.37485,4,15,0,37.8183,0.478177,0.558651,2.06255,3.53651,2.13909,2.2829,3.39027,6.30832,2.93173,4.02035,1.49195,6.38276,-4.04209,7.22133,7.92845,4.08388,3.24708,-1.24802,1.45542,12.5689,-12.148,-4.45668,-1.64058,0.432177,16.3308,-3.02612,-3.23822,-4.7758 --14.7513,0.947659,0.600053,5,31,0,44.4832,1.21662,1.64766,0.373254,3.61371,2.46428,2.66908,2.94345,5.90459,2.84078,4.19646,1.84911,6.53249,-2.39622,3.29429,1.16606,2.92231,-3.1154,5.24142,4.61711,-4.14945,5.73274,5.20829,3.06082,10.2467,-7.73537,-4.63186,0.607785,4.17856 --14.4465,0.89661,0.829626,5,31,0,26.1823,1.36216,1.43024,0.648288,3.73814,1.68693,3.17382,2.83962,5.39801,2.93687,3.05497,1.14071,6.62107,1.67766,2.95279,5.86028,5.63105,-2.82804,5.00506,2.72428,-3.99359,3.81898,7.10485,6.58179,10.2397,8.495,12.8959,6.01393,11.2901 --19.7289,0.619599,1.02783,4,15,0,28.1296,0.900098,2.07931,3.73269,2.95023,2.05903,1.85895,3.55271,0.95896,3.82808,3.41536,1.81991,5.46416,0.957492,-1.80928,3.19443,5.37841,5.64065,-0.505074,3.44327,11.7747,0.519535,-5.59262,-1.92187,-4.48277,-5.95806,-7.62252,0.0728154,-5.90518 --29.5732,0.852446,0.716106,4,15,0,41.2486,-0.563653,-0.926885,-0.31785,2.81336,-1.17442,2.50888,3.30742,6.0124,-4.67417,4.82507,0.882392,2.24164,2.4904,3.6278,2.15988,4.57921,4.00328,-0.0490614,0.833886,14.894,-3.29329,-3.8677,-1.88176,-10.5407,-8.80539,-6.39264,-1.19465,-6.04745 --17.7728,0.845495,0.808793,5,31,0,50.0487,1.02685,0.0703207,1.43211,3.31288,0.877981,0.492769,3.44496,3.17192,1.60501,1.60524,6.64653,4.01285,0.566122,3.25347,3.25478,7.97368,-3.89192,8.66735,1.13491,11.3012,-0.435072,2.9913,-3.13959,-0.558184,-6.56125,-2.68701,-6.07727,-14.834 --17.4324,0.847332,0.899376,5,31,0,29.9216,0.557843,1.66483,0.704451,4.53157,0.814351,1.48307,3.68351,4.75011,1.82463,1.82171,6.96488,1.71215,0.421935,0.950676,4.3532,6.44412,-2.99298,6.77633,2.82882,12.6281,-0.0163348,6.20307,-1.95612,-0.484969,-9.14093,0.412463,-5.41816,-12.7632 --17.4527,0.896712,1.00271,5,31,0,30.324,0.589881,0.72085,1.01813,3.87949,2.85716,2.00359,3.7323,2.93301,1.86819,1.14582,6.38686,2.16261,-3.20534,2.63345,2.96425,1.54867,5.30715,-2.82023,3.0571,-3.74766,-0.164217,-6.8653,12.247,6.16558,8.16125,0.595971,3.5355,18.9418 --15.4735,0.953573,1.23419,4,15,0,31.8795,-1.58318,0.579714,1.33036,3.44047,-0.2097,3.13539,4.02329,3.51686,-0.618306,0.834637,0.139055,6.36875,2.6379,-1.05764,7.08323,6.46245,-9.84189,4.78912,1.21665,2.60505,4.45185,-3.92877,-0.2313,2.25172,10.2907,-3.2852,9.80446,6.97904 --11.4158,0.24128,1.70012,3,15,0,27.84,-1.04268,0.805983,1.19823,3.62343,-0.316428,3.26556,4.52408,3.55472,1.74205,3.08204,3.70384,3.6372,-0.395798,0.805845,-2.36329,6.01783,-4.74241,9.00141,-4.36731,2.45727,1.55264,3.50262,2.68465,4.20283,7.81146,2.93705,1.02354,4.6976 --15.95,0.981187,0.558791,5,31,0,26.4147,1.96042,2.17026,1.86324,3.17981,2.3745,1.94537,3.19513,4.16835,0.387932,0.411123,2.69473,3.76736,-1.14221,-3.19575,-0.103345,5.6143,1.75758,4.53093,-10.4715,0.768731,4.84109,-3.88496,2.13154,-0.417053,9.9073,8.8218,6.10126,2.46321 --17.4114,0.634552,0.813913,4,15,0,35.1549,0.965,0.397493,-0.867183,1.42696,1.34944,1.89846,4.57422,4.39751,2.47291,1.73816,3.19364,0.306495,-2.93724,1.33277,7.88194,8.31643,8.15525,2.54153,-1.61995,4.84025,1.3012,2.92112,-0.638026,-0.556894,8.59791,9.49143,4.27512,0.235582 --13.0428,0.999163,0.59302,5,31,0,26.6352,0.870499,0.93974,3.2059,3.26646,0.763665,2.50718,1.76334,2.82396,0.834642,-0.0337284,5.56441,2.40483,-1.98805,4.0731,8.53792,9.26871,6.89632,1.90771,-2.19144,5.9516,2.71776,3.91229,1.55709,-0.723145,8.7716,9.27044,7.42761,0.914553 --17.55,0.995714,0.891418,4,15,0,26.6419,1.00202,0.766797,2.97609,3.24851,0.7784,2.48983,1.64707,2.97058,1.15368,4.03991,0.448853,5.58976,2.67188,0.772165,-1.05127,-1.44034,0.603649,10.1478,-0.146136,5.70839,-0.569851,-3.19512,0.0544128,-11.6559,8.95188,3.68885,13.078,3.53539 --23.539,0.298151,1.32564,4,15,0,36.1305,-0.39294,1.0062,2.28907,5.42405,-1.25996,-0.166693,3.42053,4.74062,1.17465,2.30049,5.58779,7.15266,3.98082,3.24665,0.883407,3.904,6.27676,9.65302,-8.1996,9.84114,-0.423462,-0.429744,0.990726,-6.32136,11.8105,2.21717,9.63115,3.49375 --21.9981,0.90964,0.500691,5,31,0,45.7649,-0.899092,0.243619,0.664149,3.55458,1.08804,1.11723,3.59312,0.553705,3.02928,-0.0207037,2.31278,-0.277573,-0.652005,4.90762,1.94458,9.64549,8.47235,2.07308,-6.5652,4.69527,1.39066,-0.0731588,1.98476,2.97062,8.7589,0.392818,14.1058,10.5374 --16.7574,1,0.628825,5,31,0,31.7551,0.444882,0.469993,3.35854,2.30888,0.0516264,1.4188,4.12829,5.83826,0.525266,1.38507,1.70134,1.05983,-2.46529,5.91263,2.10924,8.65525,6.314,0.340374,-5.77657,5.62798,3.73193,1.02407,0.799907,2.59898,8.97942,2.98563,17.6253,10.829 --12.7254,0.627784,0.939097,4,15,0,31.4082,0.112148,0.753366,3.47075,2.17266,0.112267,1.6816,4.12305,5.55314,0.576979,1.3349,1.45664,0.988885,3.09669,-3.96132,4.22427,3.39602,-4.02935,2.98318,11.7089,2.27265,-1.49624,-3.16342,5.48877,7.67625,5.01449,4.12546,-2.80655,1.89287 --11.394,0.873708,0.680691,5,31,0,30.8682,0.453937,1.84551,3.9915,2.42895,1.25478,1.08867,4.2346,4.72925,1.37296,1.88661,1.67623,1.90447,-1.87646,2.70902,1.44321,-0.231019,6.28377,0.156443,-5.1044,6.07687,3.26331,5.9717,-3.12222,3.76464,0.440328,4.61027,1.37671,9.61391 --14.9224,0.915297,0.79405,4,15,0,28.4563,-0.281933,0.0940854,-0.0131987,3.45332,0.766517,2.61905,1.72746,3.35936,-0.244504,2.41663,2.42658,3.45108,-3.12217,-4.10694,-0.87984,1.80452,3.32076,9.8556,-3.89921,6.63665,0.572123,7.95071,-1.26217,7.84331,1.97974,8.98624,-0.381008,10.9619 --12.8854,0.975029,1.0016,4,15,0,27.8212,0.786566,0.196143,0.583364,3.753,0.56542,1.96081,1.4508,3.82885,-0.188265,2.9836,4.00886,3.52702,0.353939,0.0865787,1.17989,4.33751,-0.870766,-6.08748,9.53715,1.73045,-0.581658,-1.80316,13.0986,-1.5692,-3.03788,-8.82783,4.71796,-0.172014 --10.5463,0.312546,1.41272,4,15,0,33.2678,0.806119,0.190775,0.595486,3.78283,0.580987,1.94754,1.4365,3.81895,1.38394,1.9607,2.86648,3.60526,-5.53625,1.7664,4.48219,5.99379,3.37548,-4.73809,-0.802234,6.19272,7.56064,-3.72724,5.48249,3.80849,-4.65883,0.653804,5.91839,1.49069 --14.5144,0.919239,0.565162,5,31,0,27.5916,0.737763,-0.650749,2.87468,2.20698,1.58766,1.34618,5.24289,3.89873,1.15863,2.34456,3.02726,3.71912,6.1522,0.175315,0.0217992,2.77614,-1.11345,6.93168,7.58288,-3.90715,-5.22668,0.593202,-3.07049,11.9273,1.28849,-1.5369,5.31119,9.65801 --27.0736,0.672688,0.716975,4,15,0,39.6973,0.426757,-1.43728,3.786,2.31437,1.67213,0.500095,5.61744,4.02269,1.04867,2.41523,3.66132,4.65729,-3.66368,7.38238,11.4993,8.19386,-6.25368,-2.7883,11.0371,1.45645,-1.97025,5.92305,-2.01182,11.7891,5.68295,-2.45491,1.03393,16.1546 --19.0425,0.971509,0.570858,5,31,0,39.1965,-0.848926,0.256282,1.99896,3.43998,2.68604,0.745336,3.19902,2.68225,1.11462,1.8268,4.41368,4.6176,6.73,-2.79846,-4.44959,-0.221565,9.71927,6.77595,-4.62834,5.44276,-2.20625,6.36817,11.4561,-0.722772,-1.83743,-1.21673,4.70008,3.64437 --17.4598,0.908987,0.796901,4,15,0,30.0082,2.67548,0.790473,1.49572,3.90044,1.41174,1.73817,4.60218,3.91837,-0.0341142,2.87958,4.2358,4.63781,5.32545,-0.770354,-1.61378,0.877115,10.3844,6.1314,-6.24463,3.70573,-2.72846,4.27025,10.6597,0.432423,-0.810136,-3.05409,4.78139,2.1866 --19.678,0.981385,0.987153,4,15,0,33.2505,1.09893,1.38644,1.9726,4.34693,1.83425,2.21611,5.10229,2.50543,3.33871,1.74176,2.34072,3.31394,-3.19516,7.66679,6.06143,8.04772,0.485271,1.57641,12.3157,12.5117,0.226576,1.30135,14.1011,4.34662,8.97555,0.434616,5.50787,-4.10595 --18.2396,0.975108,1.39613,4,15,0,30.4861,0.0789429,-1.25702,2.02002,0.70779,0.584211,2.05473,2.6676,3.43475,2.62211,3.601,0.21929,2.25537,5.14236,-4.09067,0.316168,-0.0124051,3.62052,-1.46029,-2.13967,-1.22919,1.74886,-0.823272,-2.0223,1.66101,-2.81166,15.7655,7.56537,-0.471688 --13.9956,0.0669312,1.94596,4,15,0,28.5648,0.353346,-0.891728,2.26236,1.47622,0.327218,2.64066,3.44845,2.90071,3.86605,1.47496,1.64248,0.723302,0.801525,-1.65888,1.52735,6.4474,-0.505482,5.33074,8.65585,9.04066,-2.20904,1.11918,6.10682,5.10425,-0.343636,-11.9428,-3.59484,4.07085 --11.5527,0.924075,0.508199,5,31,0,28.6497,-1.08664,-0.703038,2.17123,2.20532,1.80166,2.30273,2.34727,4.54681,-1.19703,1.9393,5.63331,5.39062,2.02453,7.43231,3.23452,4.26585,2.02506,6.66967,-2.05739,2.93361,2.77633,5.28591,-0.870032,7.98258,-1.40659,-7.71565,10.0034,10.1034 --11.9393,0.995732,0.645964,5,31,0,20.686,0.86026,2.58457,1.71216,3.95579,0.158216,1.75375,3.50233,3.45848,4.43341,3.70759,3.10866,1.07875,-0.637206,-3.52595,2.76563,4.10481,-1.71972,1.52223,8.65851,4.35898,0.912033,2.11968,8.15322,0.592783,-0.833548,-6.03329,11.0915,10.2589 --14.8148,0.804923,0.933932,4,15,0,26.6163,0.433633,1.96954,3.52334,2.96625,1.8092,3.78506,2.54309,5.06702,0.477544,2.35133,0.293402,1.18266,1.56908,6.7682,2.51589,3.60503,3.69151,4.30569,-3.31166,8.41474,1.57971,5.21918,2.05081,9.51078,-4.45976,14.1185,-1.27477,-6.18969 -# Adaptation terminated -# Step size = 0.839587 -# Diagonal elements of inverse mass matrix: -# 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 --17.3096,0.912766,0.839587,4,15,0,31.3288,0.620694,-0.117153,0.649115,3.58074,0.604721,0.878353,2.57891,2.57568,0.445331,3.12371,1.80518,1.41915,6.67667,7.43463,0.493523,5.8499,9.67562,2.40474,-2.0302,3.59475,0.0873912,8.74857,2.11082,10.6929,-5.0085,11.3629,0.861308,-6.89749 --10.1464,0.93777,0.839587,4,15,0,32.5462,-0.0706865,1.89177,0.822557,3.78565,1.11996,0.271527,2.68288,4.58357,0.904646,1.95541,4.56706,3.35728,0.894116,4.84047,5.96082,0.339995,-7.20997,3.46178,-0.171759,-2.74108,-0.7398,4.81157,3.09794,2.33757,-0.79112,8.34376,4.9156,0.749627 --12.7185,0.67664,0.839587,4,15,0,26.593,2.46045,-0.200071,2.06519,3.68712,0.913244,0.323938,2.21937,5.06525,1.74249,3.44541,1.95462,4.36932,3.30741,-1.75309,5.88666,7.98417,8.133,-2.13736,-0.727913,4.73638,1.72721,7.42687,3.5612,4.26402,4.29116,0.50127,-0.515827,3.18435 --21.9121,0.72534,0.839587,4,15,0,36.6168,-0.786572,2.09051,2.34394,2.5584,0.181011,2.71656,5.01596,5.15574,-0.572694,2.80129,6.2904,1.208,-2.02525,3.11356,-0.378917,0.485841,-12.4355,6.45374,3.37432,2.03893,7.30125,-9.25653,-1.81816,13.784,7.12022,4.63904,-1.00055,4.95936 --24.4295,0.746601,0.839587,4,31,0,46.3406,-1.14167,2.79567,3.5432,4.12733,-0.00733446,2.81835,5.1191,5.35262,2.94389,1.2298,0.078781,7.36294,2.68443,0.209894,4.1239,1.2356,6.23048,-1.35764,-6.45932,-0.12044,12.8427,2.53744,10.0367,-6.84839,2.10369,-6.95595,-3.87116,3.43937 --13.2944,0.998404,0.839587,4,15,0,32.2507,0.804589,1.13809,2.34351,1.78514,-0.157056,2.82529,1.3086,4.49866,-0.370975,4.00897,4.8129,0.204384,-0.64387,4.1213,2.07277,6.38609,-0.605417,6.26881,7.12493,4.66748,5.25367,-3.37893,10.1439,-4.61976,-0.936205,-7.21492,3.70253,3.53242 --19.0885,0.888344,0.839587,5,31,0,31.2566,-0.837124,0.342509,3.83456,2.37576,0.295952,2.44202,4.04857,3.56506,3.35508,0.694786,-0.846428,7.1997,0.868326,3.62227,2.38035,6.18395,3.01541,7.51933,1.16376,9.58604,-11.7958,9.26949,-10.3073,8.57619,4.40736,8.33156,2.8132,5.05767 --8.62617,0.987043,0.839587,5,47,0,26.0448,-0.0936385,1.4308,1.83786,3.68863,2.54515,2.77176,2.04696,3.57736,2.89679,-0.649151,0.844028,4.92749,1.25083,-0.303797,2.6546,2.06077,0.604308,0.122222,5.56491,-1.74444,3.11483,-2.87937,11.1858,6.78608,-1.49336,0.584578,1.21377,0.792087 --8.52597,0.796877,0.839587,5,31,0,22.7574,0.339932,0.197974,2.6214,1.97827,0.451773,0.908706,2.85267,4.0519,-0.144752,1.95108,1.33122,7.17966,1.88323,4.16695,-0.766786,4.95791,1.11973,4.40812,4.37393,-2.03716,0.0825178,0.0283813,9.29351,6.82287,-5.50786,-1.95985,1.18118,-1.85705 --7.70699,0.89241,0.839587,5,31,0,20.0046,-0.340792,1.68263,1.35757,3.98361,1.57934,3.29848,3.26915,3.91878,0.0412287,1.03885,5.52703,5.19055,0.387039,-0.281986,1.31643,2.15592,2.54522,-3.0874,2.55457,9.16929,1.90159,4.40138,-2.50582,1.32714,8.16222,2.39822,-2.54623,1.82385 --12.4207,0.773922,0.839587,5,63,0,19.6556,-0.561464,1.57532,1.31138,5.31039,1.22413,2.33539,3.48554,4.73979,2.3069,2.70082,0.8387,2.97959,2.54738,4.57001,3.5543,7.45151,6.04733,1.59773,-2.7553,-3.06654,7.67498,-1.512,-3.00375,3.65874,8.11145,-3.56572,4.04976,0.767614 --12.9715,0.610576,0.839587,6,95,0,31.2829,-0.386329,1.71925,1.25332,4.67971,1.53708,1.70224,3.17861,4.47618,0.667757,0.912153,4.56426,5.39174,-0.654437,2.81933,4.33467,2.16686,2.48615,-10.6278,-2.10427,0.531979,8.46743,-0.6198,-1.63101,4.77259,8.97246,-3.90837,0.716181,1.74823 --13.0471,0.967988,0.839587,5,31,0,25.5084,-0.639785,1.63089,1.32274,4.55734,1.40384,1.91472,3.41352,4.44684,1.09145,2.77372,1.18174,3.17264,2.13924,-4.17214,1.20095,3.52675,-0.718907,1.22355,-0.414132,2.72482,7.75053,-4.52771,8.43421,3.91267,11.0313,-10.5979,-2.27844,-4.92371 --13.1735,0.898703,0.839587,5,31,0,25.5453,-0.097206,1.34625,1.45127,3.09346,1.00043,2.82708,2.35782,3.91663,1.31681,0.926529,1.58738,2.81127,6.24864,9.11583,3.32742,2.28727,3.26812,1.46327,6.68648,4.92116,-5.75581,7.18683,-2.30296,4.64474,-12.0409,12.5966,8.82565,-0.422146 --15.1591,0.684304,0.839587,5,63,0,36.2606,0.137492,1.00695,1.41877,2.95638,1.22675,2.77068,2.65291,3.89321,1.05745,2.97534,3.75947,4.61976,6.72713,9.01321,3.56831,1.83398,4.77586,-0.529831,-5.93748,6.80883,4.30255,1.8687,9.02919,7.95081,13.7416,-9.07929,-3.42625,8.9413 --10.9287,0.737963,0.839587,4,15,0,25.2851,0.763648,2.05306,1.49747,3.03758,1.90681,2.56352,1.92092,5.56961,-0.739389,2.34507,2.06751,1.98144,3.82522,2.53652,4.69211,4.34537,-1.95374,-1.14574,1.27397,3.59185,5.92761,5.04008,6.42002,10.7121,13.909,-0.74457,-4.60359,8.05024 --8.68719,0.83075,0.839587,5,31,0,33.2282,0.683377,1.69505,0.986322,2.76238,2.35391,2.64438,2.30276,4.84615,-0.86957,2.23643,2.49013,2.17132,3.07624,1.6315,5.60353,5.03855,3.90328,5.07831,4.70499,4.32699,2.58499,2.90347,3.13813,-1.35229,3.26482,-6.47623,14.6331,1.00313 --13.4517,0.974788,0.839587,3,7,0,18.6582,-0.649643,0.705904,3.13312,3.76864,0.0485429,1.91852,3.96983,2.90434,0.711563,4.95029,0.847246,5.77943,1.2458,-0.791746,7.59116,2.36555,5.57181,7.56216,3.76,1.75669,3.43614,4.93033,5.00063,-2.59653,2.41233,-7.33752,16.4364,1.03505 --15.9974,0.597566,0.839587,4,15,0,33.5937,-0.798714,1.06442,3.17977,3.4199,0.727283,1.95569,3.73964,2.9718,0.651077,-0.602661,4.72679,2.54062,5.05275,-1.7748,-2.59561,5.30553,-0.917059,4.54552,5.24336,-1.26387,-11.6242,5.97448,1.5603,-1.12482,-0.175326,-11.3679,11.9026,3.85879 --17.357,0.510138,0.839587,4,15,0,33.1909,1.04072,-1.37751,0.5661,3.3649,2.52419,2.61341,2.92581,4.06879,1.25537,6.56055,5.08485,5.65803,-3.77112,4.08932,9.35582,1.94746,2.5451,-2.0194,-0.364408,12.2678,0.0684011,4.27779,4.37888,10.1103,1.67675,0.724226,3.01663,5.99314 --10.5927,0.839298,0.839587,4,31,0,28.9447,-0.722382,1.58811,1.49007,2.49326,0.233424,1.97388,4.06286,4.2701,0.798924,-1.86205,1.39558,3.42292,-1.30322,-4.99685,5.71959,3.31252,3.63356,1.16002,4.30272,10.1573,4.98504,-3.13419,0.372922,5.78558,0.583882,-5.32652,1.23631,3.33481 --13.1073,0.805055,0.839587,5,47,0,20.9952,-0.961627,1.03578,0.989994,3.09693,1.75642,3.24183,2.56953,2.94329,0.911559,-1.6509,2.4866,3.05581,2.86,8.91354,0.956719,5.20456,-1.93756,-0.807305,4.10386,-3.40469,-1.28939,12.7038,6.51234,8.24303,0.0384413,2.51629,4.33887,0.850135 --13.5358,0.847575,0.839587,4,31,0,23.9156,1.39847,1.29908,3.17488,3.27804,0.0628525,0.560358,3.49251,5.3348,-0.0632379,-0.30321,2.83001,2.58786,0.695048,6.54491,1.58258,6.15935,1.02749,-1.97183,8.7059,0.698722,-2.94368,15.9431,7.16235,4.5326,1.26321,6.29868,1.1734,3.16097 --11.0594,0.995768,0.839587,5,31,0,22.9132,-0.0916147,0.245756,3.0063,3.03994,1.09343,2.52,3.25565,4.54127,-1.00931,-0.0175774,4.36815,2.68898,2.58446,0.809274,-0.47888,3.90007,0.261546,5.49225,-1.42073,7.31058,2.89709,-11.0275,-0.206173,4.86572,0.75235,-2.32602,-8.06718,10.5246 --12.4876,0.563082,0.839587,5,31,0,27.0213,-0.54262,1.97386,1.39456,2.4562,-0.0054816,1.77301,3.09033,3.37214,3.12551,1.83821,2.57088,3.77368,0.632555,0.0171089,-0.945418,3.62895,-6.23188,10.3852,-6.46794,4.89609,-2.68449,6.62983,0.248013,5.06709,1.9153,5.12701,11.7496,-1.11702 --8.47872,0.942789,0.839587,4,31,0,21.1727,-0.312476,1.81325,1.5059,2.26061,-0.0712842,1.83063,2.88143,3.12766,-1.20777,2.42387,3.683,4.14863,3.45766,4.69702,8.99429,2.89146,0.408017,3.742,-2.38192,6.46004,-1.20238,1.00698,7.68446,1.50061,-0.384465,-3.2186,10.2742,7.89559 --12.5492,0.708415,0.839587,5,31,0,23.0386,-1.26713,1.20392,1.43936,2.53921,-1.15302,2.20217,1.5432,2.42772,1.43976,0.948959,3.70618,5.25005,1.82421,-1.35646,4.255,0.266225,-0.981041,2.0704,-0.367048,6.23244,-9.6378,-1.98457,3.7523,6.56921,8.61286,8.0882,0.669484,0.949631 --17.552,0.956892,0.839587,4,31,0,29.3007,-0.891631,0.407458,1.72807,1.60116,-1.38921,2.03581,2.13731,2.31103,-0.318616,2.7192,2.55517,2.98475,-4.60271,2.91793,-0.388338,6.51073,-5.44578,12.0917,6.32398,0.36629,6.57481,-1.62122,4.80969,8.55822,5.17781,6.31623,11.33,5.4052 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--13.1559,0.953234,0.839587,4,15,0,26.9956,-0.959197,-0.839656,2.90101,1.95591,2.4536,0.710967,4.52669,2.75314,3.08713,4.65491,5.82527,5.54419,2.92364,3.85286,2.2137,5.76758,-0.0339377,4.37828,-0.441215,5.48658,2.64583,-5.03391,7.09527,8.20393,-0.154063,-2.59844,6.63673,5.17994 --10.1383,0.98993,0.839587,5,63,0,23.3133,-0.59619,2.61473,2.22621,3.29978,-0.669655,2.68893,1.41107,4.61798,1.86162,1.57834,5.49472,7.28105,3.46984,2.65799,4.76779,4.5294,1.20375,-1.23353,4.09371,5.75766,-0.141535,9.30561,-0.90471,0.0503751,1.88701,7.80687,0.932596,3.90359 --13.9429,0.949759,0.839587,4,15,0,24.7337,-1.09646,0.981817,2.96765,2.8242,2.27475,2.44538,4.68343,2.15879,-0.641329,0.96908,-0.264953,1.48194,-1.24116,1.08414,2.18191,2.9568,6.94567,1.02602,7.47721,-0.176526,3.2249,3.76364,5.75031,-0.961909,0.613356,13.7365,4.34455,-3.42319 --12.2777,0.997885,0.839587,5,31,0,25.5276,-1.29447,0.0298463,3.08856,2.06029,0.838589,3.56816,2.26929,2.55166,1.69261,2.4801,5.31458,6.51059,-1.15871,3.57184,2.25505,7.14624,1.63848,3.93721,2.79518,8.99562,-4.71211,4.84634,7.7613,-1.52233,4.78508,8.82646,12.3452,3.37705 --13.1631,0.830468,0.839587,5,47,0,33.3939,-1.28857,0.0149313,3.07803,2.05868,0.843455,3.57393,2.26934,2.54977,2.24719,1.69255,4.12226,5.00626,1.23819,3.06681,7.36186,0.776179,2.58824,4.60223,3.03799,10.3795,8.97626,11.0994,8.09303,0.98819,1.73186,7.86289,-2.07982,2.55329 --11.7624,0.893207,0.839587,5,31,0,26.1499,0.122823,2.43582,2.29937,1.44439,0.140129,2.16075,2.36065,5.95497,1.08421,-0.684741,2.30397,4.51282,0.240989,2.57093,8.81279,0.40222,2.48823,1.3078,0.472313,7.33396,-3.58128,-4.76031,1.6572,4.88652,-1.62834,-5.63601,8.38121,3.40404 --12.4852,0.963898,0.839587,4,15,0,22.9072,-0.850633,1.30385,2.79011,1.74734,0.632455,2.14141,2.50706,5.90802,-0.041289,4.79472,4.60113,3.98089,-0.540904,2.01709,-2.82197,8.55459,5.3804,3.38959,5.47335,3.98919,-0.551,0.69578,-1.53666,0.901183,-3.27147,-10.5934,5.6443,-0.419529 --11.5357,0.980598,0.839587,5,31,0,23.2738,0.470042,1.26038,3.4296,3.6751,1.64318,3.29307,2.77029,4.63768,-1.80335,2.23848,-0.169526,2.64642,0.569158,-0.402602,-2.5288,6.87108,2.19959,0.164577,1.33085,6.46036,2.99195,2.95637,9.48543,7.00139,4.66349,14.2736,0.867393,5.21077 --9.5214,0.991126,0.839587,4,31,0,19.5143,-0.39047,0.733434,0.4168,2.38529,0.444734,0.715389,3.29581,3.3612,2.28374,2.9025,4.42913,4.69721,1.48104,4.42581,8.3798,1.08627,6.81994,4.12154,0.395068,1.35964,-5.41782,-1.98468,0.379273,7.12563,0.794038,7.024,7.18121,4.07545 --16.827,0.917836,0.839587,4,15,0,22.6134,0.0041343,1.80118,3.63479,3.97546,1.66715,3.86664,3.24303,4.62535,-0.561764,2.35108,2.65735,6.71957,-0.0592426,-0.972254,-1.1477,6.56734,-7.53558,2.06124,4.9019,11.0878,-2.42764,-4.66167,-1.06904,-0.622654,5.77566,5.81208,5.03898,-10.58 --11.449,0.960716,0.839587,5,31,0,25.8211,0.440658,1.5497,3.0784,3.50993,1.54913,3.42139,3.25325,4.77018,-0.511883,2.33927,2.77568,6.89315,3.08512,-0.173029,0.845151,5.71013,9.53615,1.98218,1.08653,-3.1165,5.8306,7.69216,4.04229,6.14448,6.6349,-3.13566,-3.00456,7.90753 --11.9478,0.993381,0.839587,4,31,0,20.6856,-0.394524,0.235335,1.46911,3.18325,1.42072,1.65127,2.38229,3.4616,1.18419,2.64995,1.78437,7.30475,-0.719777,4.22073,6.50996,1.6416,-6.67504,0.76576,4.38914,10.0892,-1.46969,-4.34725,6.33606,-3.3281,5.41896,-7.14875,2.90945,13.0226 --5.60775,0.837055,0.839587,4,15,0,17.7582,-0.39709,0.572305,1.35198,2.92728,1.45452,1.71314,2.76124,3.62159,0.796341,1.11952,4.09051,0.73756,1.86546,0.313083,1.40277,4.6,4.34437,1.04362,1.72291,9.3857,-2.35104,-3.12972,1.40545,-0.930727,2.41251,-1.65875,-0.724094,4.75671 --10.9976,0.724742,0.839587,4,15,0,23.5131,1.07651,1.52118,2.48203,2.69755,0.481617,1.85634,3.53955,3.45761,-2.5925,3.87654,3.61608,2.91348,5.57623,1.47133,2.15077,3.3178,2.87187,-5.18915,-1.62389,10.5693,1.22403,-7.94447,0.515944,2.74631,4.76864,-0.416121,2.75248,5.59898 --12.8265,0.768437,0.839587,5,47,0,27.3685,0.148403,1.75725,2.35434,2.44025,0.93856,2.21868,3.1515,3.56341,-2.73208,5.46737,2.50654,2.0681,1.72196,6.69235,3.90636,5.95882,-0.287287,8.88946,8.62843,-2.67124,3.75038,6.62349,0.539519,6.80937,-2.86,9.53164,-4.65993,-0.95938 --12.1054,0.658855,0.839587,4,31,0,25.9918,0.0638667,-0.411417,2.15268,3.86874,1.50258,1.84439,3.38857,4.37325,3.39944,1.41605,5.00712,3.3367,-1.36102,5.17056,2.37073,3.85027,0.466777,9.25274,10.8621,-1.9264,5.21127,10.807,3.37141,3.28037,-6.33676,8.52028,-1.492,4.48058 --15.6794,0.624807,0.839587,6,79,0,26.6358,0.720903,2.7416,2.63025,2.76937,0.140203,1.26715,2.82356,2.62023,6.1429,2.39885,3.05897,2.6697,-1.42692,6.24865,4.17491,1.42512,3.5056,5.1008,9.97072,-0.909194,-8.20481,-4.85772,0.6829,2.53012,4.91894,-4.15551,8.62669,5.07623 --12.8221,0.818058,0.839587,4,15,0,26.7841,-0.66036,3.39223,2.34756,3.24031,-0.984237,2.17495,2.11448,4.5443,-1.9941,0.680422,4.58887,4.98827,2.27201,-2.99026,-0.0530216,8.21618,-3.4348,1.7421,4.31714,0.874036,-3.07488,-3.42962,-0.0114133,6.16074,-0.103463,1.45869,4.3269,7.35635 --9.66506,0.98878,0.839587,4,31,0,23.0841,0.958879,0.449569,2.23258,3.47564,-0.23272,3.09313,2.37379,3.60199,-1.25445,1.81566,5.68917,4.40784,2.0228,-2.96263,-0.570615,8.90756,-1.40428,1.69564,4.18016,2.67183,-4.38519,-3.24443,-0.352755,2.72445,2.17291,2.5328,8.09458,7.70886 --10.1011,0.784169,0.839587,4,15,0,20.7998,-0.872598,1.57565,1.96097,2.74095,2.18411,0.857584,3.54162,4.25852,1.64682,1.24585,4.87223,5.46179,0.0261911,6.7867,6.66798,-1.16932,1.4377,6.21872,-0.283288,4.98612,6.59663,2.07198,4.32004,4.40872,3.46903,8.72993,5.07007,13.4223 --10.2585,0.909639,0.839587,5,31,0,20.6888,0.223678,0.802335,1.5913,3.15724,0.272139,-0.365751,3.41519,3.59399,1.46927,1.78079,4.69071,4.72701,-0.559306,5.27499,-0.324078,6.85765,1.31892,-3.06603,5.76575,3.66978,-2.72679,-1.51272,4.46457,5.26846,-7.76544,-4.59136,-2.89061,-3.73507 --19.1242,0.685198,0.839587,4,15,0,27.5,-0.873185,2.33614,1.65821,1.34717,-0.732394,4.72221,3.24133,4.82181,1.31545,2.06649,1.62662,5.75933,1.90038,3.20975,4.92871,-0.369919,2.55857,-7.66145,8.12999,10.5573,-3.22993,4.37219,8.96895,-0.619409,-6.61122,-3.09394,-1.53597,-2.59674 --17.531,0.996254,0.839587,4,15,0,34.7953,1.57797,1.04554,2.26534,3.88856,2.63649,-0.00426793,1.84081,4.6263,0.533331,1.67311,5.31314,3.77901,2.54798,1.58857,0.036305,9.36536,3.93606,9.14144,2.23669,0.763566,-0.597701,8.00659,11.4914,12.1255,-8.62622,-0.788683,0.758768,-6.34185 --20.2375,0.942329,0.839587,4,31,0,37.6691,1.79432,0.611979,2.38007,4.00509,2.39464,0.043687,1.54838,4.706,1.91674,2.41195,0.813214,3.86149,-2.85461,3.86689,1.38886,8.6947,-8.27016,-0.587169,2.69507,12.1409,-2.31575,1.66072,10.9933,11.7707,-6.11939,-9.40393,8.89033,4.50475 --20.8409,0.980919,0.839587,5,31,0,39.1992,0.899762,0.639018,1.66498,2.61254,3.38112,0.792454,4.16646,4.97781,-0.710188,1.72206,6.27497,4.22826,-2.70798,4.49836,1.784,9.69085,3.43746,4.09156,-0.34458,11.9992,8.12673,-0.456892,-2.63158,-8.62695,9.24148,13.3536,-2.6059,3.64091 --15.4453,0.865795,0.839587,5,31,0,34.8023,1.23311,1.14976,0.77969,2.37836,-0.0139242,2.32558,1.28026,4.78853,-1.35272,-1.47581,5.98159,4.23763,5.07209,2.6252,3.62464,-0.834095,0.662598,2.54594,6.78636,-4.02755,-3.84727,6.50394,7.66763,9.81885,-2.79928,9.41072,-0.313684,0.0459655 --12.2472,0.998809,0.839587,5,31,0,30.6259,-1.2693,0.656456,3.0972,3.70948,2.0694,1.6481,4.66793,3.26848,-0.271712,2.52419,2.17094,4.25046,-1.81608,-0.28699,3.38235,3.09034,-4.91575,3.97995,0.259465,6.50339,5.11832,-2.30367,-1.03555,-2.11798,1.49026,-13.8742,4.07949,7.5114 --14.068,0.876102,0.839587,5,31,0,26.6644,-1.54207,2.41098,3.12626,4.66668,1.5592,2.47784,3.94193,5.00873,1.79692,1.13938,3.6445,4.15341,-0.681745,0.710242,3.36777,3.16589,1.85254,8.00448,2.0599,2.15824,-6.63885,9.22439,-0.486619,10.2734,0.870627,17.5864,1.7446,0.256944 --13.3234,0.996358,0.839587,4,15,0,23.0199,1.48837,0.385745,2.1822,3.06303,0.430663,0.755869,2.74709,3.42828,0.723028,2.3384,4.04148,3.55847,4.90311,-3.52598,3.1303,5.44044,5.26557,10.8505,-0.648511,8.29837,-6.07438,3.52927,1.58001,11.1934,3.21138,13.452,3.89866,3.75656 --15.9762,0.680905,0.839587,5,31,0,30.7479,-1.3222,1.71359,1.7831,2.9557,1.48096,3.05135,3.07601,4.37111,1.38948,4.91319,4.28789,0.464787,-2.86441,7.34626,2.86411,2.41941,-0.388583,-5.93528,7.40427,7.13985,7.59566,-5.09539,0.328369,5.27282,-6.90309,2.61963,0.629162,-10.0222 --18.3702,0.750309,0.839587,4,31,0,30.9226,0.0665266,0.852099,4.51707,2.40746,1.051,4.158,3.14396,4.79819,0.436068,-0.34362,2.03146,6.53567,7.10945,-2.34688,2.98796,8.06018,10.544,1.56109,2.13247,0.205336,9.59703,-2.24944,2.80602,7.1171,2.80268,0.577638,10.576,-1.61392 --11.3445,0.993681,0.839587,4,31,0,32.9205,0.150612,0.741511,0.29845,4.29596,0.223671,1.34816,2.81992,3.78735,2.12839,1.14438,0.405555,4.85056,0.0388847,1.09908,-0.246072,7.77793,1.87692,-4.18915,2.69987,6.04082,1.64833,-7.6365,4.36644,8.66667,7.12336,-6.44058,9.23727,6.06697 --11.5595,0.807041,0.839587,5,31,0,23.4633,1.80323,0.618497,0.645638,4.20749,0.443443,1.44432,2.63673,4.76914,-0.341328,3.47961,5.94039,2.98207,3.26079,1.46979,2.54461,0.119772,5.59157,-2.46694,2.72516,5.76211,-1.14447,-5.59174,-0.109341,11.1115,5.70071,4.31359,2.44397,-1.82036 --12.1114,0.992216,0.839587,5,31,0,24.0437,1.28199,0.737472,0.941712,4.72224,-0.134786,1.50033,2.97878,4.2107,-0.170063,3.68388,4.90823,3.01279,-0.466946,1.05727,0.0534928,9.16081,-3.69622,7.6515,2.13841,2.93883,1.9255,3.58381,0.781605,-4.37978,-5.22424,11.694,3.00469,2.56769 --10.0655,0.88089,0.839587,5,31,0,26.964,1.14843,-0.0362419,1.07881,3.79814,1.46804,1.35988,2.53834,3.80731,1.68738,-0.605384,1.16263,4.58353,0.961763,2.96189,1.65136,9.16149,3.66625,0.361581,-1.63098,2.82459,-2.26378,-3.37956,4.35479,11.8092,8.61928,-5.4933,4.34774,3.52824 --9.10161,0.992752,0.839587,4,15,0,22.3397,-1.0329,1.73044,2.67476,1.92101,0.588666,2.6232,3.80226,4.29621,3.59645,2.81311,4.07685,5.25085,5.27459,3.83651,6.5588,4.56025,9.30316,0.535716,-0.359556,2.59298,-0.779866,4.63156,5.69678,8.96805,3.46612,0.359956,4.78293,2.96754 --10.9654,0.483129,0.839587,4,31,0,29.2231,0.92541,1.07506,1.08831,4.14461,1.32892,2.69406,1.93565,3.64022,-1.18565,2.06555,3.60115,8.58682,-0.197838,4.14698,6.53251,1.29266,7.41866,-0.237846,1.19754,4.83418,-0.879056,-1.0742,7.63841,10.8343,-4.07089,-1.62331,4.13194,1.5988 --14.81,0.695267,0.839587,5,63,0,25.5705,-1.08218,-0.764141,3.20637,1.1026,1.26531,0.0699188,3.96562,4.27187,3.63076,3.07694,-0.235184,2.58238,2.41008,-1.92547,8.35825e-005,5.64272,-0.369475,6.82397,5.1267,1.9764,2.88429,-0.596777,1.37886,7.0371,4.67739,1.67943,13.9789,0.280176 --14.7315,0.884882,0.839587,4,31,0,30.409,-0.00431764,1.3361,0.0309576,3.19817,0.778952,1.70673,3.24208,2.84074,3.16218,1.89055,-0.678347,-1.17322,-1.75536,6.61175,5.333,1.07055,1.78012,-2.33097,-1.25162,7.09718,-6.44256,1.31088,0.294922,0.293916,0.966222,0.555536,-4.74941,6.27538 -# -# Elapsed Time: 0.032 seconds (Warm-up) -# 0.015 seconds (Sampling) -# 0.047 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstan/output_warmup4.csv b/arviz/tests/saved_models/cmdstan/output_warmup4.csv deleted file mode 100644 index 69cfc9319a..0000000000 --- a/arviz/tests/saved_models/cmdstan/output_warmup4.csv +++ /dev/null @@ -1,248 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 18 -# stan_version_patch = 0 -# model = test_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 100 -# save_warmup = 1 -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 0 (Default) -# data -# file = (Default) -# init = 2 (Default) -# random -# seed = 721297525 -# output -# file = output_warmup4.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,y,x.1,x.2,x.3,Z.1.1,Z.2.1,Z.3.1,Z.4.1,Z.1.2,Z.2.2,Z.3.2,Z.4.2,Z.1.3,Z.2.3,Z.3.3,Z.4.3,Z.1.4,Z.2.4,Z.3.4,Z.4.4,Z.1.5,Z.2.5,Z.3.5,Z.4.5,Z.1.6,Z.2.6,Z.3.6,Z.4.6 --40.2408,0,4,0,1,1,52.9811,-0.911677,-0.74393,-0.0384146,-1.38241,-0.369228,0.744203,-1.84863,1.66237,-1.79712,1.48203,-0.392856,0.0999244,-0.192028,-1.21838,-0.62791,-1.42826,0.897633,1.89757,-1.28889,-0.942528,0.0751593,-1.29244,-1.64417,-1.01905,1.34992,1.79509,-1.5587,-0.141753 --40.2408,1.48915e-032,2.33506,1,3,1,49.9456,-0.911677,-0.74393,-0.0384146,-1.38241,-0.369228,0.744203,-1.84863,1.66237,-1.79712,1.48203,-0.392856,0.0999244,-0.192028,-1.21838,-0.62791,-1.42826,0.897633,1.89757,-1.28889,-0.942528,0.0751593,-1.29244,-1.64417,-1.01905,1.34992,1.79509,-1.5587,-0.141753 --25.399,1,0.230236,5,47,0,53.9983,1.57898,2.28379,3.30425,5.94291,2.38524,3.27444,6.62202,6.46026,4.20733,0.382482,1.903,4.45174,-1.06886,-0.272011,2.51159,1.0869,-1.82513,0.086033,-1.40807,1.29392,3.94344,0.702045,-0.56303,-1.36999,3.18112,8.2887,-4.83899,-0.533591 --15.6181,0.997544,0.239791,6,63,0,39.0915,0.0862465,1.11554,1.82589,0.0841481,0.46299,0.765856,1.08473,4.99742,3.62206,-0.35318,3.12988,5.54209,3.37724,4.88646,1.87208,5.42626,7.36652,4.72044,9.56036,5.22155,-5.79676,0.944533,9.90731,5.05998,4.31046,-0.454906,6.8344,2.2996 --21.9736,0.962173,0.322064,5,63,0,38.5565,-1.59723,1.52003,3.00505,5.77275,1.91303,2.19672,4.81247,2.31504,3.66196,4.5402,5.5989,6.68521,0.845974,10.7172,1.92604,4.40579,5.04492,6.23279,5.8395,3.82478,-8.23555,5.18408,10.751,4.30083,5.39787,-1.00244,4.95042,6.66348 --16.3335,0.999801,0.449805,6,63,0,35.5037,0.354986,2.40505,2.91494,4.57335,1.8807,1.0821,3.16101,4.16678,-0.872214,-1.74013,-0.888819,1.72585,0.86683,9.37379,2.20581,4.81375,-0.442069,-0.0947791,0.430547,7.9752,3.96395,-5.93703,-7.42494,5.59159,-1.60648,4.55076,1.9524,0.0933505 --17.33,0.888127,0.762725,5,31,0,32.1658,-0.559084,2.18396,1.42759,0.816487,2.29026,3.40065,3.13364,3.13385,-0.601609,5.84713,3.83875,5.61518,3.58413,6.86763,1.90607,7.27112,3.21876,6.15072,11.3215,1.04428,-1.3005,8.53553,13.5735,0.595448,2.33052,-1.84471,3.70451,4.25114 --17.9167,0.635632,0.961524,4,15,0,35.3348,2.03634,1.54554,2.01104,1.57264,3.53873,3.06708,1.96708,1.08564,2.56001,-0.045237,1.58705,2.63583,-1.59004,-0.114216,-1.47261,6.9255,-2.02982,3.04756,1.92754,-1.74485,-3.84086,-0.969539,3.43965,8.82781,2.48496,2.1002,1.05964,4.03292 --16.323,0.994608,0.560795,5,31,0,29.5874,1.27433,1.12311,3.43678,2.25155,3.0686,3.34087,2.82411,1.97942,1.80108,0.540837,1.52007,2.86568,2.80565,5.8199,5.54229,-3.14958,4.26849,1.58867,5.22889,9.10933,7.07045,7.10558,0.711972,-0.0959076,-3.97181,-2.65851,8.02519,1.06084 --18.9128,0.798286,1.02247,4,15,0,31.6423,-0.360134,0.523928,2.65763,3.39675,-0.886653,2.25896,4.97218,5.06827,-0.613758,4.57536,5.91951,5.11712,-2.80592,-1.67539,-0.0755035,11.1914,2.46492,1.26354,4.10691,-0.132682,-0.164608,7.25193,0.195309,11.733,13.3877,-9.34168,6.1109,0.816674 --14.2688,0.614223,1.01373,4,15,0,35.1322,0.46192,1.35219,1.36461,1.77559,2.58053,1.73891,1.39428,2.53922,-1.86266,-0.388615,2.02474,2.7209,4.02696,-0.813568,0.989843,4.45253,2.05501,-2.43325,0.020331,9.50817,2.70853,-3.13171,5.82797,-4.12322,-5.73697,10.6743,-2.59425,7.92908 --14.2538,0.998415,0.565197,5,31,0,30.6475,0.895439,1.67452,2.17746,1.70662,3.22106,1.43179,2.24602,3.46006,-1.78879,-0.758425,1.87484,2.75856,2.05436,-2.80701,2.43147,3.29599,0.353759,6.29181,5.85331,-0.885124,-0.0390908,10.7281,0.996347,9.01109,-9.42951,7.47653,8.40777,3.9964 --13.1167,0.607201,1.06493,4,31,0,31.8217,0.179317,2.25604,2.38575,2.83737,2.69978,2.27207,2.99377,3.44459,4.14407,4.42704,4.39889,5.28683,1.80626,7.82818,-0.52391,7.67755,-3.77671,-1.49648,3.9712,2.60551,0.923913,1.54317,-2.70478,2.00222,0.211406,9.02664,8.64181,-7.20396 --10.0526,0.896022,0.587628,5,31,0,24.1543,0.0296381,1.06993,0.719692,4.00706,1.25108,1.41644,2.14729,3.02184,-2.57888,-0.954049,3.22416,2.28333,4.83428,1.11624,1.13137,1.41874,-1.42192,2.26385,3.97024,3.72231,7.02,2.15205,5.12831,-0.310198,-1.28998,1.51511,7.25891,-6.2377 --12.3202,0.88588,0.805174,4,15,0,21.0055,1.20498,0.902226,3.37609,1.7904,-0.0452538,2.60652,4.40638,4.13102,-3.01437,-0.949227,3.74332,4.70496,-2.21158,3.01369,4.44798,7.01097,6.01236,-1.02268,2.35602,4.02303,2.4029,5.43918,0.0640483,2.00824,-9.23406,0.902433,0.372015,-2.12836 --9.74258,0.769986,1.06746,4,15,0,22.2942,1.23203,1.26289,3.50562,2.09837,0.0889444,2.46919,4.13898,4.02265,-3.5045,-1.28601,2.46036,3.45326,4.20352,1.43269,1.67682,3.45929,-3.7637,5.49346,3.91387,4.0538,3.17523,0.0985123,1.1771,2.26005,1.49497,4.22738,1.23312,5.8492 --12.9124,0.736863,0.988492,5,47,0,21.1159,-1.24117,0.0147336,1.36139,1.95741,-0.151039,3.89271,1.47925,2.92126,-2.66947,-1.44233,3.60709,3.79509,-1.55191,3.54394,2.40341,7.57142,4.0702,-1.55775,2.05373,5.20877,-0.663296,2.57134,3.21607,7.47778,-0.582713,-3.71815,-0.910806,-2.05662 --12.5223,0.887943,0.829211,5,31,0,26.7509,0.115805,0.309562,0.722145,2.13564,2.08307,-0.203246,1.78542,4.36241,2.10576,3.86426,5.02725,6.35093,-2.52531,1.81779,2.13715,4.90245,0.074769,4.25885,0.0905843,6.78038,1.34997,5.47824,-3.21487,12.6983,0.0692012,-7.79357,0.452673,2.24305 --18.345,0.350444,1.10356,4,15,0,34.7399,0.00715556,3.08309,1.43847,3.3567,0.820645,-0.414117,0.594254,5.68284,-2.43687,3.64937,2.63192,4.18228,4.33262,2.58285,5.64652,2.19397,1.98901,-0.757062,6.12832,-0.946109,9.17454,-0.70389,7.59338,-5.60084,5.95409,4.94573,2.44021,1.08584 --14.1497,0.989367,0.290778,6,63,0,29.2998,0.0587484,0.132006,2.14998,2.98867,0.714783,3.85072,4.88447,2.82618,-4.05659,0.823444,6.15316,5.51857,-2.29036,1.27806,0.855475,5.67894,2.26793,3.91873,2.16582,6.74813,-3.27796,0.628768,-1.23637,10.3737,-4.3691,4.34677,-4.13527,-1.48184 --13.7992,0.998677,0.526487,5,31,0,25.242,-0.243022,1.99457,1.87709,3.3651,1.45135,-0.0215868,1.22811,4.94986,-0.818825,1.2747,3.24181,5.15295,2.71229,-3.38383,-1.17886,1.2233,2.44824,4.21409,2.39001,0.924905,4.80287,2.93372,7.39222,-2.36114,10.6974,-0.0603038,13.4297,8.69976 --10.3886,0.975941,0.970903,4,15,0,23.5419,-0.364051,0.295084,1.89111,2.71466,1.01169,0.0743849,2.0245,4.05491,2.64231,3.05245,2.57209,2.71309,-1.07318,5.17778,-0.0951594,8.71467,-5.41365,5.66184,6.5501,7.83416,4.47218,1.41484,8.94756,0.858467,5.81977,-2.46107,5.43264,2.49182 --13.5353,0.334839,1.65857,3,15,0,23.8009,-0.341401,1.65311,-0.249532,3.29096,0.803403,3.18091,2.71206,4.2172,-0.339456,4.2195,3.02625,6.22141,3.14503,0.366842,-0.0397112,7.98294,-0.431756,5.21674,3.79701,14.2123,7.8996,2.70708,7.12521,-1.59648,4.16699,-3.72914,5.18105,9.35168 --15.1609,0.997831,0.435711,5,63,0,26.7033,-0.188392,1.90692,-0.234803,3.14154,0.744583,3.1767,2.71877,4.08583,2.266,-0.243552,2.94156,1.86191,-3.2443,1.55741,3.17568,3.16034,-1.4029,7.18649,4.07568,4.41492,17.3781,-3.43186,1.68917,0.0400794,-4.6565,-1.98502,-1.49781,11.0547 --16.183,0.787961,0.791419,5,31,0,30.6313,1.86769,0.631495,0.146983,1.95741,-0.545782,3.49912,1.6703,4.38647,3.23247,1.88566,2.68976,0.741264,3.56354,2.45518,3.75027,0.992404,6.04021,-3.31554,2.27686,2.52015,-10.6256,8.50319,1.6149,0.100161,-0.554924,0.564316,5.42193,0.581767 --11.7735,0.895856,0.781403,4,31,0,27.6281,-1.82663,1.46401,3.8284,4.02293,2.37831,0.469634,4.17699,3.55404,1.98511,1.93246,2.67776,4.14778,0.731512,1.3234,3.19243,-0.188723,4.93351,-4.88919,4.39009,5.33964,-4.93211,2.7128,4.56675,-2.67045,0.328196,2.23041,3.05428,3.54222 --13.694,0.632448,1.04771,4,31,0,30.987,1.61895,1.401,4.16321,2.67326,0.499167,3.32265,2.14676,4.72843,1.38252,1.58207,-3.24322,3.91069,1.61202,1.31167,4.29689,-0.267938,3.90317,-1.57061,3.75405,5.01896,-5.69147,0.714764,1.64473,0.856795,3.0618,-1.44829,5.16377,2.09097 --17.0463,0.995089,0.667081,5,31,0,30.4549,1.88259,-0.285653,3.44285,3.67396,2.40957,2.0987,2.16069,4.86556,2.15942,1.94068,-2.2446,2.64172,-1.11129,-0.576231,2.98839,9.88384,-0.960497,6.684,0.767134,3.0613,8.3808,-0.0490733,5.06059,6.75412,6.79882,12.2372,-4.37501,3.8116 --16.5576,0.913797,1.1751,4,15,0,31.2573,-1.24138,1.99465,0.9781,2.83093,0.439084,1.739,3.66107,4.13577,1.567,-2.5954,2.54942,-0.0454085,-1.74776,2.40859,4.44705,6.49592,-1.27678,12.5788,-1.12626,-1.03488,3.02464,1.7221,0.914537,9.5147,4.62776,14.6659,-0.931772,8.70087 --13.5181,0.300648,1.63819,4,15,0,31.9855,-1.15847,1.63878,0.674036,3.15052,0.435214,1.81144,4.10719,4.03422,-1.43438,0.576227,4.30077,6.07163,-0.110902,6.1405,1.29358,2.50454,2.25658,8.53215,-0.259109,0.326233,-9.85799,-0.386114,-1.8615,5.70419,-3.74161,-11.0857,0.557949,3.83087 --9.83773,0.994799,0.423642,5,63,0,22.7861,1.35682,0.977052,3.74066,2.72572,1.54994,2.30921,2.58343,4.18426,-0.691737,4.85903,3.48857,4.60382,2.08898,-1.79157,4.96439,5.33387,-2.47923,-3.24125,1.86718,9.25486,-6.9453,3.06165,4.23678,-0.581884,-5.17787,1.42196,3.69191,3.0601 --19.8866,0.775166,0.738102,5,31,0,30.6766,1.8247,0.456069,3.20295,3.00195,0.930117,3.86986,3.51458,5.83925,4.36142,1.02459,1.79509,3.5813,-1.58523,4.75271,-4.92046,1.76443,4.28507,1.31448,9.95675,3.51726,-4.9978,7.29359,8.18567,-3.76859,-6.44642,13.0136,-0.0581424,4.59651 --15.4295,0.998781,0.705234,5,31,0,28.4131,-1.20424,1.56796,0.352787,3.01308,0.255681,0.355819,2.64094,2.90057,1.44668,2.90501,2.68288,4.01006,-2.7601,2.18828,-2.33037,0.990493,6.99256,3.65099,9.93561,2.14729,-5.60252,7.46808,4.14472,-2.59211,-3.73558,14.2905,-0.234611,7.19804 --18.5072,0.66135,1.22557,4,15,0,32.6658,1.05572,0.0147449,2.84572,3.94076,0.302357,1.14132,3.19564,5.14969,1.45026,0.779685,2.49826,4.50288,0.626386,1.1376,-1.83641,1.74072,3.98107,2.53409,16.9145,10.0881,-4.0921,4.43587,12.3543,-0.382624,-7.30726,11.5114,-5.33637,5.1615 --11.8007,0.881218,0.863106,4,15,0,30.1714,-1.00559,-0.827656,0.0299341,2.73085,1.34806,1.78492,1.96383,3.72465,1.88109,2.78841,3.36908,2.93893,1.77072,5.23794,6.74896,5.64723,6.42938,3.99819,1.54639,4.20548,-4.28102,4.86857,-1.53323,-0.00195841,-1.90514,4.30731,-5.80258,13.5842 --15.1028,0.381785,1.08959,4,15,0,34.7327,-0.641775,-0.791003,-0.0702397,2.55077,1.06124,1.75524,2.03546,3.85389,2.45794,2.1984,3.84997,2.47116,-2.94867,0.625829,-2.71747,5.05772,-3.97988,0.263061,4.81563,3.56219,1.37901,6.3006,8.70666,9.02803,-11.3521,3.0221,15.128,9.05239 --14.3124,0.996071,0.372409,6,63,0,30.1675,0.0956265,-0.577004,0.684231,2.59352,2.0117,0.703174,2.46777,5.41775,-0.725505,2.61993,2.29284,6.25215,2.44116,0.227235,4.30307,1.51134,6.59939,1.55147,8.24839,3.55079,-1.93087,7.39294,-4.26368,11.0056,0.0548432,1.33853,7.69074,18.2091 --9.39268,0.988121,0.634662,5,31,0,27.619,0.133695,-0.710822,1.65136,3.55947,1.178,0.858134,3.01514,4.56729,2.44714,2.14897,2.23088,1.0091,2.17641,0.332403,4.48543,4.67899,10.2734,2.55479,0.477372,8.36495,-4.56516,2.45725,5.26358,0.563238,-3.84833,6.26794,3.5941,4.04096 --19.5905,0.593039,1.05099,4,15,0,28.9662,1.12632,0.184068,1.98098,4.29264,3.32019,0.314579,2.49034,3.98879,0.323943,0.63388,4.46065,-0.483574,0.457457,3.61295,2.07519,1.43286,-7.18641,-1.10402,9.34188,-3.73686,11.731,-0.52881,10.1256,2.99683,-0.514546,-2.72561,6.54741,9.3342 --15.0992,0.988871,0.630852,5,31,0,33.6556,0.136204,-1.24123,1.83164,2.49637,1.82973,0.407945,1.98808,4.93564,-0.129573,0.867912,4.48264,0.921021,-1.51414,3.45218,4.84116,3.23997,8.56568,4.61555,-3.39188,9.73433,-7.31066,5.36829,-1.32054,5.14902,2.35144,7.74044,9.61435,3.58751 --7.84343,0.998191,1.03862,4,15,0,23.0939,0.937532,0.883612,3.06109,1.84016,-0.0789411,2.18296,1.67693,4.6846,1.49513,2.7829,2.3491,4.92015,5.09552,-1.04121,3.36769,4.94204,0.795133,1.37384,5.68883,3.42178,5.41386,3.62351,-5.06647,5.72393,6.62796,1.98696,-0.365604,5.57218 --11.2867,0.1729,1.73738,4,15,0,19.3768,1.0509,0.803819,3.13406,2.04086,-0.255371,2.29588,2.10103,4.95318,3.78255,-2.05051,1.52294,6.22533,3.09941,-3.53184,1.45259,4.93344,2.71847,2.45234,6.79799,3.76146,5.4954,0.1173,5.18819,6.94004,-5.40213,2.61477,4.01791,4.3368 --12.4058,0.970499,0.368662,5,31,0,24.689,-0.538102,1.78679,2.31864,2.68038,1.29058,1.38343,2.4424,3.43626,-2.99747,6.53405,5.4737,7.49317,-2.14538,6.42525,5.19513,3.23764,-1.42349,1.09024,2.44067,5.16339,4.04417,0.876947,6.63595,10.3789,2.59257,-2.88133,9.40391,5.26035 --10.907,0.977697,0.575782,5,31,0,24.3312,0.628638,0.413863,1.71539,3.56042,0.889886,2.75356,3.56495,4.61455,3.4073,-1.40742,4.59276,5.54154,2.26684,8.72686,7.74806,6.347,-3.06464,1.43624,0.898345,5.71328,6.90786,-0.596785,5.35465,7.96827,6.37141,-1.67419,6.08409,5.04645 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--7.12381,0.227553,1.50999,4,15,0,19.1682,1.22201,1.8983,0.978272,2.59533,0.862331,1.85371,4.02454,4.16832,3.20666,3.96117,2.31736,4.99722,-0.516581,1.66929,-1.11221,1.89471,1.70272,1.59016,2.0789,2.57763,-6.89322,4.61743,-0.045507,6.23037,2.11413,0.327741,5.24435,8.75916 --13.4824,0.737172,0.483097,5,31,0,31.0777,-0.208804,1.55919,1.09705,4.19035,0.731376,0.848699,2.88337,5.10954,-1.4492,0.294316,3.34283,3.59351,-3.14089,1.304,6.62418,7.54931,4.45868,5.62141,-4.65276,3.58139,-3.67519,-6.53129,3.00867,-7.42526,1.46175,-0.645424,3.25993,6.47507 --16.3939,0.993806,0.432521,6,63,0,27.0209,-0.347541,0.870493,1.88469,3.44509,-0.315005,1.7563,1.69083,4.99743,-1.99657,0.201932,3.81869,4.43661,3.13826,4.01442,2.08453,-1.64534,-2.2423,-2.03856,11.1889,5.16273,11.2289,8.92537,-1.8252,16.5933,2.14507,0.274591,1.62511,-0.560331 --15.1059,0.866381,0.645577,4,31,0,38.3199,0.163266,1.40806,1.8342,3.71643,-0.339344,1.79149,2.15986,5.00282,4.27815,4.10758,2.34123,3.30544,3.4225,2.60246,4.63438,10.3276,-4.6883,5.13921,3.89387,8.87094,-6.85371,8.74374,10.2272,-4.27368,-7.1154,7.17146,7.04578,1.65168 --12.9052,0.830074,0.745962,5,31,0,28.1133,0.0127012,2.56167,0.853587,4.35459,1.67407,0.989148,1.01626,3.96321,-2.81302,1.39839,3.80611,4.99024,-2.26923,4.02149,3.51053,0.531476,4.27658,4.96254,3.29534,7.36474,-1.92641,2.25838,-3.44637,-1.72777,9.14737,2.09198,7.46167,3.15351 --18.9892,0.796265,0.801345,4,15,0,35.1759,-0.586681,0.2092,2.63349,1.41911,0.617818,2.37142,4.78641,3.67143,3.46967,2.18027,3.69848,8.15233,3.45353,-0.766886,1.65509,7.57338,1.39123,-2.43174,10.1808,-0.344507,15.6527,0.25583,7.91852,-2.2492,0.974788,-5.57748,13.2759,4.69101 --14.5444,0.994846,0.805075,5,31,0,29.5143,-0.484863,1.40356,3.15594,1.81768,1.27456,3.20383,4.35105,3.47907,3.6394,1.95746,3.26046,8.87286,2.16664,2.26303,3.66249,0.336141,0.402537,6.2668,-4.12384,8.36233,-4.9745,4.57408,-0.891758,10.8185,2.21821,4.10898,-5.62702,-2.08671 --15.7263,0.640534,1.19121,4,15,0,31.5144,0.11097,1.34976,0.929085,3.83438,2.53773,0.528541,3.09354,4.54319,1.48094,5.36958,2.69581,3.70778,2.35857,4.31282,1.46005,2.71381,-3.89936,-4.97067,-5.99357,8.30389,-10.5812,4.23966,-3.29956,4.78109,3.96812,2.56319,-4.52379,-1.021 --11.6147,0.834396,0.882809,5,31,0,30.3841,-0.144435,1.26875,2.38815,3.43668,-0.467009,3.06513,3.37588,2.33143,-1.04442,4.95047,1.29086,1.57889,-1.13458,-0.50773,4.5117,5.05501,4.98777,7.88982,3.57005,-1.98597,5.98206,-0.571612,4.32038,4.84762,5.0725,5.64547,-0.510913,11.6685 --15.4618,0.749762,0.95409,4,15,0,27.7802,-0.0274438,1.22489,3.05659,3.78742,-0.978589,2.71237,3.20094,2.59203,3.01941,-1.3723,4.36235,5.65259,-1.32237,0.988031,7.25336,6.88579,0.0576271,-7.54048,-0.114478,-1.5803,1.96235,-1.18317,2.81321,0.815822,12.4917,7.28803,-0.739417,5.38641 --13.5724,0.995554,0.875579,5,31,0,26.4958,0.911407,0.777234,2.33083,3.09754,1.24353,1.64578,1.1206,4.49334,1.85481,6.32986,1.92319,4.82471,4.16997,1.31345,0.121098,0.75215,3.8924,9.90124,6.60178,6.40009,4.03684,6.34686,7.87154,2.05594,0.529063,1.42603,-8.68055,-4.41729 --9.0371,0.753569,1.2873,3,7,0,20.0312,0.66848,1.76472,2.33529,3.65101,2.45005,1.90103,2.19485,4.60319,-0.542785,-0.848676,4.03472,3.56962,0.534189,3.37187,6.81552,5.57382,-1.25956,0.602217,2.86902,2.18791,3.75696,9.97632,2.86016,0.30121,-2.03359,3.5341,-4.56529,-5.2227 --14.5839,0.556529,1.18918,4,15,0,29.0271,-0.806902,1.22878,3.0587,3.09929,0.107625,1.73879,3.91996,4.28373,-0.636191,2.8677,6.91966,5.88809,-1.90676,-2.10107,7.65953,4.70397,4.07973,-1.75129,10.8923,7.6908,0.685194,8.0969,4.10218,-4.27375,-6.97064,7.47415,-4.4784,2.91709 --13.1044,0.797669,0.756385,4,15,0,29.5972,0.778516,0.729489,0.929475,2.93615,1.90877,2.29803,2.10247,3.81076,0.959734,-1.26706,5.91915,3.86115,6.57481,-0.867344,7.82999,2.17278,3.63175,-3.9545,5.90523,6.52001,2.85785,6.78616,8.37433,-0.20186,-3.96777,9.11352,-5.243,3.11505 --12.3166,0.730357,0.761696,5,31,0,27.2837,-0.782635,1.31716,3.28331,3.24396,0.0945582,1.75213,4.19874,4.20331,0.679404,3.85633,2.90635,4.26253,4.29998,-1.97073,9.09541,2.26556,0.79354,0.409925,1.28124,5.65708,2.33399,-2.80897,-3.17852,8.83268,5.28662,-3.64077,17.8841,4.16761 --8.83996,0.988904,0.675762,5,31,0,24.3859,-0.837865,1.38969,3.30192,3.18759,0.0732604,1.68627,4.08883,4.2142,1.26093,-0.0575198,3.16715,3.60829,3.48307,1.78593,0.584472,9.37536,-2.20408,-0.0877793,8.30647,2.4841,5.2341,3.94443,0.433221,4.81253,-9.24623,3.42927,5.30042,6.25772 --16.9398,0.594026,0.973865,4,15,0,27.9589,1.01912,0.717182,0.651853,3.78321,2.1954,2.68801,3.07372,3.67464,2.279,0.170965,0.274305,0.600512,-0.981782,-0.242413,3.02799,8.79058,5.88535,6.32823,-3.2698,3.55634,-2.46625,-0.816868,4.67913,1.77113,21.7632,-0.529179,1.06019,0.820935 --17.476,0.858633,0.66999,5,31,0,33.0671,-1.02876,1.35283,3.20709,2.14147,-0.276601,1.58334,2.63217,4.44075,4.05121,7.09534,1.59196,3.87008,3.01249,4.2483,2.98392,-0.795397,-2.64437,-2.80983,10.7276,5.4877,1.6727,9.55445,-5.30059,4.08972,8.03351,-3.82217,-3.67662,-4.04901 --13.4662,0.86523,0.755709,5,31,0,31.7553,-1.08499,-0.280776,0.886096,1.65188,1.31035,2.20501,2.09916,4.51269,-1.6218,-1.83603,4.44592,4.22282,3.23791,2.41291,4.28647,1.35906,0.0784515,7.18499,-0.262252,-0.375228,0.159479,-0.588849,13.5147,3.61323,-6.69314,6.30792,8.22987,10.4017 --17.2161,0.844388,0.861936,4,15,0,28.7431,1.77337,1.71689,3.96828,4.55764,0.844125,1.66893,4.02086,3.50632,2.56619,1.45421,4.19521,3.25193,5.84813,-1.34235,0.95127,0.956831,-2.04014,3.9154,-5.40385,-0.551959,-2.68278,1.32384,15.887,5.75556,-3.5795,4.91184,7.52885,10.8129 --15.8637,0.980149,0.945003,4,15,0,29.976,2.1192,1.13943,3.60195,4.40416,0.33997,2.76581,4.18282,3.3001,-0.139911,2.08383,1.64223,4.8568,-0.348271,6.74381,-1.04796,7.24192,9.10331,4.91386,4.35117,1.15403,-0.180753,-0.0453957,10.7702,5.23275,-5.00183,10.3399,11.6819,1.87083 --11.3767,0.822259,1.32779,4,15,0,21.9881,-0.724814,-0.980689,2.90638,2.80639,1.8499,0.648759,4.00498,3.3148,-0.0356002,1.87653,1.88496,4.47731,2.23353,0.478871,1.00196,0.201602,-6.30849,-0.514079,1.32463,6.75658,0.698743,5.95953,-5.37294,5.86941,4.60216,-2.37891,-4.19325,4.77658 --9.2447,0.757247,1.39444,4,15,0,19.2372,-0.704172,-1.08672,2.76074,3.06906,1.78198,1.24258,4.11819,3.67016,0.349452,2.41828,2.2857,5.44824,0.740398,3.27072,3.97689,5.20461,8.75818,2.92298,2.58472,1.7037,-2.01239,0.0892322,10.7579,1.00799,2.93599,1.04793,-2.41191,-1.63781 -# Adaptation terminated -# Step size = 0.893525 -# Diagonal elements of inverse mass matrix: -# 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 --14.5637,0.923055,0.893525,5,31,0,25.3697,-0.140089,3.02032,1.30538,3.4861,-0.535523,2.54442,2.06237,4.905,3.9262,2.76354,7.19636,2.92295,-0.52672,-1.44163,5.81469,6.1275,4.60826,2.64725,1.71697,-3.38758,-3.80328,1.41592,9.96794,9.09879,-1.24637,6.81069,-2.86019,4.55396 --18.6055,0.996181,0.893525,4,31,0,28.3434,-0.247938,2.57013,1.19418,4.27355,0.39661,1.74405,2.95021,5.70274,-0.642005,1.31775,-1.17257,5.45012,7.71595,-4.26276,1.96711,3.01332,-0.82055,5.61446,5.81436,-0.830599,-5.70842,-0.00568549,6.22567,13.9746,-5.88785,6.48792,-6.56154,2.46304 --15.8491,0.952444,0.893525,4,15,0,29.3963,-0.189515,0.525713,3.05651,1.60925,1.65299,3.1408,2.23476,2.47286,4.46502,4.25816,6.88929,1.767,5.24958,-3.01773,3.40555,4.58247,5.97197,6.04483,6.57843,0.182499,-3.99278,0.679445,6.13907,7.72295,-5.1653,5.43287,-4.92316,1.63365 --18.6719,0.996453,0.893525,4,15,0,29.3722,-1.54034,0.10261,2.20841,2.43012,0.83601,2.2333,2.06847,2.76487,-2.26622,1.22012,-2.54749,5.1348,-5.06622,7.44375,1.48177,4.1113,-4.81097,-1.49472,-2.56726,8.71056,1.36498,-0.350277,1.55641,5.60506,-3.69569,-7.35731,6.61025,-6.09752 --17.8565,1,0.893525,4,15,0,28.6654,0.399863,1.19623,1.6881,2.24395,1.11218,2.45183,3.3732,5.20652,1.20835,4.26506,8.78891,2.51742,6.05038,-3.96739,6.17659,4.14321,1.36524,8.87419,9.04344,-3.47944,-3.19909,3.64642,10.1948,10.2808,-0.858082,-2.43222,-0.494491,-1.07433 --14.4184,0.965725,0.893525,4,15,0,25.593,0.223243,0.0381863,1.81138,4.10629,1.76782,1.5937,3.26093,2.99951,-2.35052,3.26931,5.85013,4.23414,-4.09332,7.80311,0.00108596,3.74043,2.84484,-1.27402,-3.07015,8.27638,9.18176,-2.8219,-3.4108,1.31402,5.66062,0.112084,-1.21591,9.46317 --19.7427,0.637693,0.893525,4,15,0,34.5674,-1.28384,1.23429,1.06547,3.123,2.24939,2.42668,2.76753,5.2716,1.16028,5.63443,4.30686,4.84847,6.65858,-3.78725,6.49866,4.49138,4.84492,2.43551,9.78792,-1.62206,-4.79697,7.13343,1.26594,-1.57627,-4.85737,8.6497,21.1229,5.20203 --16.8677,0.693689,0.893525,4,15,0,37.1781,1.40231,0.733874,2.74417,2.50454,-0.0324282,1.27122,3.41188,2.843,-0.351031,1.52035,6.86092,3.93268,-4.00678,7.55,-0.511843,4.20007,-3.35425,2.19141,3.07901,13.8161,-3.87458,-3.06855,0.238381,-0.0342969,-9.87794,5.38384,11.7317,2.08058 --12.7024,0.566165,0.893525,4,15,0,30.5352,1.52284,1.40542,2.85839,2.65916,-0.160572,1.3584,3.21789,3.42063,2.33184,2.48699,-0.84918,4.06537,5.0943,-4.57162,8.66017,4.95958,2.50871,0.0117415,2.26324,-0.0605425,-1.94565,-0.196514,-2.15917,3.62639,0.847416,6.76118,10.7297,2.77624 --13.8145,0.760097,0.893525,4,31,0,27.1565,1.6247,1.08864,2.38602,3.68414,1.72205,0.192893,3.90927,5.03117,2.34365,3.49836,-0.958407,3.15255,5.47203,-3.57418,8.44477,5.15532,3.93294,0.67117,2.93024,-0.419003,-1.66562,-1.06561,-0.212455,4.21906,0.0711818,5.51796,9.35271,2.98187 --16.853,0.998678,0.893525,4,15,0,25.958,-1.51019,0.799463,1.61333,2.22313,-0.0235371,3.63522,2.04937,2.99182,1.03918,3.25926,6.23113,3.96767,-3.08019,8.03554,-2.7525,3.13001,-2.29084,-2.19063,9.47251,7.11951,-0.0630567,0.00599027,0.682764,2.89754,1.59222,8.61656,-5.72232,-7.25265 --11.4006,0.962657,0.893525,4,15,0,31.7437,1.80956,0.658122,2.73139,3.90103,1.4594,0.691091,3.92058,5.44943,-0.584093,0.514167,2.50044,5.38627,-1.89763,8.2207,2.85793,4.38652,-1.66178,3.91773,2.95845,6.22016,-0.771454,0.214931,1.14487,8.18381,4.75991,8.87118,-1.02369,-4.46687 --8.86267,0.962587,0.893525,5,31,0,23.222,-0.614601,0.15744,2.88792,2.79931,1.35163,1.6982,2.98566,4.53288,-1.42169,3.85668,6.58621,3.96246,-0.623683,4.90876,3.35526,4.34451,4.57731,0.912742,2.91822,8.98448,1.90845,2.6858,4.15792,-1.36621,-1.60151,-2.21022,7.96851,15.1542 --12.2506,0.76877,0.893525,4,31,0,23.5483,-0.501397,0.242336,2.96442,2.74597,1.56762,1.44997,3.27158,4.74234,3.40228,0.123343,-0.568112,3.99457,-4.24986,-1.69944,2.75098,-1.49107,-0.495327,4.27861,0.615069,0.836494,1.37223,-8.4925,-0.798024,1.25035,-3.34555,4.88966,2.67872,8.20961 --10.9482,0.89249,0.893525,4,15,0,19.78,0.133869,1.28405,2.59083,2.91704,1.06517,3.7949,4.65211,3.79464,-3.38317,2.93632,5.94596,4.61623,1.80591,2.57827,3.82374,2.18467,-1.36024,7.28976,3.30056,2.42599,2.28778,-4.718,2.47811,4.27702,-4.28055,9.20592,5.38937,10.2586 --9.65537,0.999192,0.893525,4,15,0,19.1104,0.627371,-0.0870634,0.99786,2.05359,0.365053,1.60624,2.15725,4.60467,1.74461,2.73768,0.624291,4.43225,-0.502032,2.04663,2.72803,5.48917,2.78984,-0.776285,4.073,10.9063,2.3856,-1.00221,-4.0998,0.714025,-4.03788,12.6828,8.8956,8.25287 --10.9573,0.872665,0.893525,4,31,0,24.9626,1.44196,1.27282,3.3475,2.59916,2.20386,2.00973,3.45067,3.84709,0.3344,2.07475,4.08673,3.5037,-0.471927,2.67844,2.17367,-0.255667,3.089,6.05882,-3.28304,0.679542,-5.19167,1.72028,-3.59777,10.826,8.71748,0.759107,-5.33391,5.08552 --10.7904,0.524321,0.893525,5,31,0,28.3929,0.405475,2.47907,2.7374,2.5281,1.37975,0.917753,4.62755,2.9779,1.68784,1.79738,1.77912,4.4052,-0.462273,2.69048,2.17175,-0.267531,0.680014,4.9587,0.702168,11.2309,7.97867,5.06539,4.86783,1.54698,-6.75266,3.27354,11.3006,2.85205 --11.2621,0.994478,0.893525,4,31,0,20.7327,0.213212,-0.618528,1.14462,2.57341,0.245507,2.52245,2.4565,3.89948,0.822596,3.72169,4.48521,4.45195,0.520677,-0.94318,3.13862,9.49379,-3.28289,-1.27214,6.34319,2.06072,-0.902651,3.53141,8.23783,10.9697,-9.19241,6.81726,12.5107,5.06184 --11.0362,0.729295,0.893525,5,47,0,22.7971,0.501273,0.284518,2.45251,3.70188,0.331549,0.883099,2.67813,3.98175,1.56133,7.33188,2.9901,2.80088,1.40668,3.59709,2.52817,5.47925,6.08621,5.84475,0.0473105,3.04972,3.30036,-0.408242,-2.35021,-3.01115,11.0345,-3.81775,0.827957,6.19048 --15.9562,0.535029,0.893525,4,15,0,28.7109,-1.79597,2.20845,0.784405,2.19225,0.553156,3.85868,3.09588,3.20711,0.805961,1.30889,2.05724,2.68049,0.349541,5.31234,-0.868492,3.7428,3.65463,5.02906,-4.37003,0.28793,0.261584,-3.61087,-3.8417,3.30039,14.3753,-6.00989,-1.51803,10.8935 --13.5933,0.983168,0.893525,5,31,0,31.0776,-1.66901,2.45703,0.527752,2.53295,0.545329,3.95896,2.91738,3.72048,1.58356,2.9641,3.68964,3.5151,0.626009,4.00002,-0.524809,3.53263,1.25975,5.19593,4.27023,4.00876,6.35008,2.67948,7.19221,7.54364,-12.9751,12.0702,7.27632,-3.44857 --14.0162,0.911472,0.893525,4,31,0,26.9153,-1.44036,1.29668,2.71874,2.50025,1.97251,1.79272,3.85874,4.29564,-2.21554,5.4997,3.25072,4.20781,2.9135,3.96785,-0.317563,3.92671,0.503123,2.97421,3.89688,2.00355,8.38079,5.19054,7.62715,6.78858,-14.4346,9.79602,6.42135,-6.0346 --20.3376,0.948143,0.893525,5,31,0,32.5283,-1.31535,1.37301,2.72728,2.8169,0.828161,3.12593,1.66253,4.05151,-2.02791,5.12339,4.94543,3.48522,1.89053,-0.712976,-0.487831,1.67047,1.36196,0.583201,2.69726,6.07016,-3.40537,3.6665,0.256506,1.76361,17.1715,-16.5718,-12.5854,10.469 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--15.6104,0.935218,0.893525,5,31,0,30.9534,-0.667353,2.41421,2.72602,2.33561,0.0278979,2.44078,1.38261,3.99267,-1.23275,3.28697,-1.69144,3.89278,4.8476,2.33739,6.79447,8.9984,0.179798,1.14939,3.2612,7.87079,-5.34921,-4.95588,-1.09327,11.347,-2.69056,6.41763,7.99384,12.1265 --15.2412,0.993027,0.893525,5,31,0,30.3587,-1.13794,2.60377,2.81437,1.94279,0.641674,2.25765,1.84922,3.45356,-2.63113,3.67566,-1.55406,4.34584,-1.39997,2.44595,1.25762,0.125323,1.86584,3.81639,1.86713,1.39863,-6.55387,11.248,2.54488,-0.672876,11.3907,-0.622815,-0.472842,7.11634 --12.7048,0.884285,0.893525,5,31,0,29.3975,1.38451,-0.55402,0.70087,2.35347,1.89637,2.29023,3.80475,4.73614,3.56731,2.69368,1.18642,0.336252,-1.26129,2.54572,3.4767,0.722511,3.30443,2.3962,5.00623,8.2649,8.36771,-0.308716,0.0719292,9.93858,-8.97299,2.39494,6.10643,1.15275 --12.9674,0.870062,0.893525,5,63,0,23.9643,0.691647,-1.27876,1.39217,2.11962,1.8028,2.16345,3.23375,4.62266,3.51476,2.67558,1.25694,0.30502,3.17973,2.94573,-1.86883,8.27121,-1.35615,1.58477,0.992528,-0.259079,-3.78076,1.831,5.15982,-2.47424,7.13036,6.79822,0.669578,8.21358 --11.0905,0.808797,0.893525,4,15,0,26.5302,-1.87008,2.75653,1.53599,3.50526,0.731473,1.19673,3.17746,3.34983,1.92448,-0.70812,4.08998,3.46714,-2.19773,1.97579,4.48006,2.60783,1.06533,3.81559,-2.54053,4.50512,-8.05484,3.16453,9.15859,-3.36171,4.3046,4.31926,4.30486,7.21183 --18.6163,0.822415,0.893525,4,31,0,29.0261,-1.69582,2.97556,0.457221,3.66735,0.147014,1.61885,2.6252,2.96017,2.46368,-1.79539,3.70982,3.97938,0.728232,6.30809,-2.28003,2.40999,-0.357895,1.44815,8.81877,3.31709,10.6774,1.86516,-5.89781,10.8486,6.30259,-7.37813,1.98607,-3.34849 --9.97511,0.834869,0.893525,4,15,0,27.7061,0.0615782,1.00688,0.459941,3.93729,-0.243322,1.69366,2.70517,3.95226,-0.288748,5.85691,1.3427,4.4299,0.960491,0.686784,1.41679,1.18954,4.19855,1.57115,3.28895,-1.09977,6.13438,-1.89717,0.704287,10.9517,10.0832,-0.591991,1.7594,1.72586 --9.83299,0.829111,0.893525,5,31,0,21.1519,0.146134,1.01922,0.428459,3.70944,-0.228163,1.82793,2.88154,4.12183,0.448478,5.10379,1.54242,4.94035,1.09265,-3.77181,1.39589,5.47202,3.70428,1.06941,3.76553,-2.65655,6.44265,7.06205,7.18521,-1.05895,-1.86388,0.604438,4.913,4.35909 --12.9326,0.659234,0.893525,5,31,0,23.9274,-1.03335,-0.0835177,0.838892,3.98664,-0.0849942,2.37122,2.58561,4.34393,0.606191,6.52534,2.94713,4.21026,2.78587,5.56174,5.8441,-0.735993,-2.61464,3.05679,2.71477,10.3835,-2.20857,0.260316,-2.04555,7.53118,6.11097,-2.73114,-4.26587,10.2053 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--14.7382,0.981244,0.893525,4,31,0,27.8518,0.221452,1.44734,3.4083,3.58044,1.8256,0.325705,1.47645,3.53799,-4.4609,1.60953,1.54446,5.47559,-1.16217,5.20736,-2.60179,2.88886,-3.47757,0.26129,2.10885,2.84762,-1.70476,-1.2392,11.2996,1.0342,-0.100175,7.18758,0.784752,9.09854 --14.1127,0.99317,0.893525,5,31,0,25.1642,0.0910495,0.445328,0.287113,3.67187,-0.34647,2.87078,4.28935,3.67538,1.97141,3.05899,4.1818,0.0658519,-3.40228,4.43583,-2.07962,6.17371,3.70539,2.56198,3.75147,9.74056,3.20798,5.56788,-5.63776,7.24731,5.1949,-5.01104,7.45599,1.62046 --16.7701,0.962794,0.893525,4,15,0,32.8172,0.313095,0.198439,-0.159747,3.35443,0.224124,2.82625,3.72358,3.52053,1.92217,2.94886,4.19263,0.0789012,-5.12267,-6.02312,5.40881,5.94469,-1.68661,1.46157,2.26602,-1.71202,-1.00461,-2.25575,9.26548,4.53435,-6.02601,6.20124,3.58439,-4.63645 --11.4676,0.718122,0.893525,5,31,0,32.4295,-0.402702,1.78131,4.12274,2.72437,1.71437,1.1761,2.2879,4.42173,0.84139,1.95051,6.48818,0.849379,0.962123,1.18278,9.36166,5.56017,5.10952,0.210448,1.78487,4.04406,3.36183,6.0021,-3.16439,3.74451,3.9915,-4.73468,3.38392,5.77487 --17.4567,0.715945,0.893525,4,15,0,30.2942,0.327845,-0.597239,0.6117,4.45618,0.659218,3.32753,4.36862,3.68447,4.15197,2.48064,5.07109,3.57328,-1.38507,-1.34265,4.49529,-2.90137,7.39879,4.24214,2.37748,3.87356,6.28221,2.51491,-9.54684,0.401942,0.920186,-6.75728,2.05637,7.21533 --20.8948,0.812858,0.893525,5,31,0,30.2476,0.860063,-0.675164,0.431288,3.8817,0.810197,3.65688,5.67996,4.02347,3.76429,3.39936,4.25083,3.67414,3.79053,5.31663,2.11716,8.87725,-5.32251,0.244528,2.84066,4.87653,-2.4167,5.13699,18.4303,7.38041,0.566384,0.847786,-7.43209,9.22917 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--10.4257,1,0.893525,5,47,0,24.8873,-0.823511,2.11173,1.86189,2.87001,1.56875,2.09909,1.73135,5.01481,2.63344,0.319314,2.08495,6.44108,3.33428,1.53895,1.11678,6.5577,-0.659915,2.56771,-3.45956,3.15209,-1.41768,10.764,2.94505,2.3127,1.23383,1.34582,-1.42226,-8.41951 --10.5064,0.818295,0.893525,5,31,0,19.6218,-0.613329,1.32569,2.01984,3.20288,2.05271,1.79516,1.10069,5.73935,3.04691,0.0987931,1.87501,6.28189,0.107174,3.72913,5.78839,3.26342,2.67274,2.64569,9.67823,4.68354,0.104263,-2.08596,1.28924,6.71771,-5.35545,5.01992,11.1368,-0.0139219 --13.5831,0.6029,0.893525,5,31,0,36.3918,-0.410967,1.10858,3.3804,1.85906,1.65054,4.88003,5.19748,2.88154,0.633538,2.28434,3.44688,3.47789,-2.22045,3.04194,7.06382,4.44957,-0.799165,4.13555,2.12904,6.95311,0.37069,0.806666,5.59835,1.77463,7.05124,-1.51109,-6.7265,5.58806 --11.3715,0.889154,0.893525,5,31,0,26.4578,-1.59737,1.23191,1.35571,2.69046,1.75345,0.767002,2.44379,3.96158,0.024992,1.7638,3.78066,4.66015,7.24988,1.15881,1.10007,4.11834,3.09683,0.155308,4.5399,0.748533,3.79118,4.57199,0.423099,2.37601,-14.4222,0.770578,10.4252,11.2735 --15.8499,0.857474,0.893525,4,15,0,29.5171,2.43476,1.50713,1.92884,2.77349,1.01335,1.87034,2.08585,3.88198,-2.2753,1.58595,2.17638,6.64192,3.92944,3.17769,2.30277,4.83923,5.81069,-0.873489,8.31586,-1.91456,5.7738,1.1774,3.07942,1.64359,-15.5008,-1.27889,11.8396,11.5992 --18.7437,0.746723,0.893525,5,31,0,33.2984,-0.0870014,0.463049,1.49283,1.46958,2.9706,1.71548,4.43361,5.2709,-1.37717,1.60744,0.543283,7.44622,3.86872,3.91731,7.53448,10.2785,-2.23312,5.49854,-3.15497,9.86478,-2.38461,4.43433,2.50616,7.05429,5.06312,9.99993,-3.90686,-5.4211 --12.9633,1,0.893525,4,31,0,27.9833,0.598913,0.073102,1.90851,2.71358,1.78495,-0.111175,3.87791,4.05016,4.04309,2.28673,4.65292,0.811728,-3.14448,-1.96329,1.2727,-0.27311,3.56203,3.3262,-0.161473,2.07859,-6.08753,-1.84635,8.61342,-1.15744,2.46266,-0.420354,7.60601,2.24773 --16.3484,0.928541,0.893525,4,15,0,24.8196,-0.0972204,0.289936,2.20677,2.47257,2.67806,-0.127307,3.44603,3.32298,3.92502,1.61124,4.96354,1.82862,3.44622,5.92583,5.9689,8.87851,-1.181,2.20407,5.47847,6.68817,11.9138,7.77563,-0.754437,10.7743,5.0184,-5.1187,1.49706,12.4652 --15.5447,0.966041,0.893525,5,31,0,28.2391,0.540935,0.316937,0.0285717,2.90575,1.3846,0.126011,2.15429,4.38899,4.92827,2.57078,4.28986,2.89014,0.166011,-0.213577,3.20951,6.6438,2.59747,1.11799,-0.0324971,0.351183,-11.03,-7.50684,6.51788,-2.62339,-6.64103,2.46612,-1.84733,0.753983 --15.7924,1,0.893525,5,31,0,30.186,0.753111,0.840836,0.77223,3.58915,0.495886,0.00484069,2.37796,4.86974,-3.00532,1.4235,1.72819,5.01187,0.170603,-0.223511,3.22319,6.61569,6.66289,6.92654,3.80019,-3.06603,7.4781,12.1766,2.01822,11.9524,8.4944,1.57376,7.94936,7.27151 --13.4628,0.800836,0.893525,4,31,0,33.6137,-0.727197,0.915884,3.06762,2.75732,1.51989,3.83303,3.87213,3.14831,0.82732,-0.448496,5.431,6.94119,2.0435,3.40761,1.8972,2.19808,-6.25449,-4.09948,-2.67722,5.73768,2.21376,-6.7487,3.3023,8.87249,-0.287449,-5.7276,1.03712,0.262014 --8.22278,0.999749,0.893525,4,15,0,20.0451,-0.716648,0.933328,1.94049,4.23399,2.26334,2.11027,3.71213,3.56476,1.01466,1.95095,3.12511,5.83831,1.49864,-1.33817,2.78888,5.72285,7.57997,7.57973,7.46099,2.84711,-3.32764,5.82832,-2.71219,4.94732,5.0637,3.26952,7.10042,3.13314 --8.16379,0.767972,0.893525,4,31,0,20.5903,-0.347524,1.5841,2.77636,4.0691,0.684796,2.56303,4.6983,3.57013,0.470959,2.44415,2.34423,2.59702,3.06205,-0.147759,2.52342,6.86539,-3.17912,5.80023,9.61975,2.04597,8.33287,0.23502,4.46233,2.9372,2.36335,-1.14946,2.16361,7.11068 --14.1161,0.972367,0.893525,4,15,0,23.3676,0.308133,-0.116716,2.68651,3.12328,1.83991,1.41655,4.19716,3.87655,0.861021,3.90794,4.05192,0.68456,-0.147068,4.26839,3.26178,2.00569,5.32326,-0.256171,-2.50252,9.17464,0.377479,0.413871,10.4493,15.2196,-2.62369,10.006,7.23544,15.5968 --9.60701,0.516898,0.893525,4,31,0,33.6061,-0.565387,1.88187,1.58939,2.37464,0.0913128,2.38257,1.95752,4.15604,-0.532841,2.93603,4.80566,3.73026,1.75503,-0.740831,2.30428,5.54643,0.759396,7.38268,2.67528,6.61428,-3.82882,12.5164,-2.42155,7.04644,7.54739,5.16704,9.82216,-1.02947 --10.5119,0.7665,0.893525,5,31,0,22.8433,-0.22338,2.04303,0.943128,2.27952,1.56485,1.14917,2.09582,3.88878,1.60857,2.73294,1.6255,4.53522,2.13018,3.49857,4.04832,1.86817,-2.83503,-1.11475,6.87794,2.91604,1.57005,11.6571,-3.67621,10.5968,12.3834,5.45053,-2.52627,4.3462 --14.5432,0.701431,0.893525,5,31,0,26.4554,-1.3341,2.38334,3.33419,2.4197,0.374063,0.414773,3.73768,3.40433,1.07397,3.53628,3.56208,0.147749,4.74572,2.88865,4.46057,3.67558,5.23679,0.569216,0.362408,8.30778,0.722099,-7.24753,8.80226,-3.20274,-9.10909,-2.88411,3.10874,4.42274 --14.3902,0.902876,0.893525,5,63,0,25.2239,1.36358,-0.421276,0.677088,3.64865,1.65595,3.6072,2.24531,4.51797,-0.323074,1.39386,2.55452,1.51899,1.36846,-3.83534,1.3798,0.746713,-3.39494,0.921575,2.99683,2.32983,0.78782,11.1121,-2.82641,10.5862,10.9035,7.15664,4.82191,2.42039 --19.5423,0.410372,0.893525,5,31,0,40.5689,-0.656555,2.69075,0.194768,4.33557,2.71516,4.26339,1.3089,3.04198,-0.278191,1.38866,2.51994,1.52839,-2.72912,5.94102,0.184703,6.61148,-3.47793,0.94303,2.94342,2.42747,12.2563,7.14134,11.3153,1.22686,1.05222,4.38296,-3.18195,4.80171 --11.8877,0.981855,0.893525,5,31,0,31.2957,-0.20238,1.53877,2.10697,2.0408,-1.91848,2.21422,2.87418,3.66362,-0.291215,1.00581,1.83068,2.43185,-1.82146,2.23535,3.61768,10.4307,-4.44258,-2.29354,2.10672,2.95996,-2.49234,3.86532,6.63267,-0.974331,1.68979,5.67392,7.1435,8.41327 --14.973,0.95684,0.893525,4,15,0,31.6797,0.575013,1.00461,2.2856,2.82048,0.661137,-0.75182,3.47931,3.64592,3.32249,-0.300945,2.72721,5.58035,-1.37789,8.84242,5.53139,4.25109,0.0890137,-1.2573,2.16138,10.803,-0.901223,-1.76158,7.87689,9.1543,-6.72383,0.490737,-0.17059,14.7648 --12.1264,0.973743,0.893525,4,15,0,24.8798,0.525241,0.718613,1.37143,2.75803,1.40708,4.96739,2.39334,4.05925,1.48561,3.0448,3.69556,4.14657,-3.38767,4.45218,4.62678,5.18415,-6.54676,-2.38215,5.74129,5.1363,2.63655,1.11019,4.66473,10.509,-4.961,-3.27584,2.76521,11.0226 --11.4281,0.993588,0.893525,5,31,0,20.1226,0.261637,-0.0936127,1.42669,2.29963,1.74083,4.68062,2.80811,4.81874,0.486878,3.1752,3.29531,4.73228,4.02885,1.09273,-0.0428454,2.2766,7.34885,6.29397,0.873935,2.5077,-1.57991,4.48831,-0.857293,1.91288,8.34134,0.775334,12.066,2.14327 --13.2834,0.92017,0.893525,4,15,0,26.9755,-0.110918,2.3192,0.60869,1.94811,1.5501,1.98603,3.88644,3.83929,2.93328,2.28835,6.98891,6.21588,0.925887,1.1366,2.2087,1.96116,10.1938,6.71307,-2.47877,4.82819,0.306275,2.15262,-1.32181,1.2226,9.35031,0.161184,11.7557,2.5839 --17.3543,0.813258,0.893525,4,31,0,28.9077,0.0995882,-0.61489,4.21843,4.3885,0.546887,1.76988,3.18754,4.03555,-3.48162,3.98316,1.14112,0.00905213,-2.70441,4.09159,4.74723,6.29746,8.57389,5.83351,-4.24051,6.80179,-2.91162,6.64972,-1.02542,1.34013,3.11015,1.34268,4.66757,0.892395 --17.0597,0.664637,0.893525,5,31,0,31.1521,0.501819,-0.478273,4.0853,4.08763,0.669078,1.92712,3.13974,4.00349,-3.45465,3.78695,0.872742,0.864679,3.96227,-0.568575,2.23715,5.0573,9.0873,5.02007,-4.99889,6.41471,-2.23232,-5.58452,9.07098,3.30405,-5.03934,5.1364,0.202684,2.8957 --10.9863,0.964602,0.893525,4,15,0,25.7316,-0.70149,2.64124,0.0235415,2.13395,1.09315,1.88298,3.06093,3.97376,4.01068,0.623916,3.89958,0.87079,-1.67026,4.68024,4.02814,2.78739,-5.57176,1.35553,5.59385,2.5526,2.61444,1.72871,-2.93259,3.88191,-5.36364,-3.58446,1.98353,2.95125 --21.4667,0.645293,0.893525,4,15,0,32.8557,1.12247,3.97507,1.10746,2.485,0.87984,1.91876,3.61987,3.51378,-2.61342,4.64565,3.60121,7.61697,9.15455,-1.65464,0.355653,2.08391,10.1771,3.79972,4.69807,2.50111,5.89906,2.8902,4.61814,6.24979,-8.56209,-3.81923,-1.97165,-3.1841 --15.4172,0.929132,0.893525,5,63,0,31.7813,1.0772,2.88625,0.310453,2.93548,0.815666,1.75336,2.59251,4.2675,-2.22707,5.5239,3.42781,7.99967,-4.16985,2.82917,6.53844,5.55484,-8.63338,0.258797,2.89693,4.82539,0.344772,0.145851,5.62336,3.19072,5.28475,1.53057,7.31204,-1.83064 --15.9158,0.993213,0.893525,4,15,0,30.6295,-0.98606,0.974237,2.64128,3.50116,1.95136,1.86185,2.89247,5.06572,2.25936,1.03344,0.875431,-0.412529,6.24271,1.01984,2.63408,4.19259,-6.96394,-3.19594,8.67486,1.98242,-9.0392,9.03634,-1.49999,5.67064,8.32133,-2.96241,8.47937,-0.0686124 --13.5022,0.617236,0.893525,4,31,0,31.4789,-0.772851,0.663792,2.75894,3.64684,1.57597,2.10361,2.39282,5.18761,-0.247648,2.97274,5.11592,8.39684,1.6676,4.17048,4.093,-0.813395,9.58205,0.258444,-3.63758,4.25567,-0.564469,5.35201,7.77694,6.28437,3.82919,1.30368,9.68441,-5.7191 --10.1837,0.982855,0.893525,4,15,0,24.8148,-0.0813405,1.48675,1.59444,3.20333,0.138064,2.37394,2.21443,4.97182,1.56947,1.07592,2.40034,-0.0292575,3.95817,4.24088,4.06281,9.32499,3.39054,-2.12006,0.887821,1.77208,2.85528,2.88062,3.79955,-2.36749,3.25021,-0.288394,-4.25373,-5.77331 --15.2424,0.634658,0.893525,4,15,0,29.3555,0.26192,1.06184,1.32857,3.24646,0.105676,2.10103,2.2316,4.7272,0.397005,2.94057,3.61797,8.0502,2.05767,0.0946534,-2.54421,4.68743,0.227441,-0.527785,0.973414,7.77589,-2.18593,-9.66458,8.72383,3.12217,15.715,1.6949,-8.03492,-2.82362 --21.7031,0.512295,0.893525,5,31,0,35.6522,1.53774,0.564724,3.1462,3.01728,1.33731,-0.629154,2.70888,4.28114,2.13029,1.39936,6.69758,5.67412,0.357838,3.76323,0.129607,4.71674,0.431047,2.67697,3.31256,0.813414,5.44879,14.9263,-1.48969,6.1293,-22.753,-0.654993,9.77894,6.39091 --17.002,0.708165,0.893525,4,15,0,38.2451,1.66775,-0.0235217,3.20881,2.71261,0.990574,-0.768468,2.81723,4.40748,-0.0944332,2.54826,-0.707318,2.33946,-0.497456,1.02717,5.16429,1.48354,1.87775,-7.75441,4.56153,2.37241,-4.83378,3.83945,6.40429,2.9772,-10.6761,1.67171,10.3947,10.1916 --23.0368,0.87009,0.893525,4,15,0,33.9832,1.83167,2.01864,1.93584,4.5996,2.61348,4.14364,3.13296,5.13595,2.64525,1.33777,5.43077,6.61306,2.11652,-1.60444,1.78383,6.04533,0.584077,-4.62404,-0.630664,-2.03172,-2.84429,1.74386,10.4971,-0.370851,-19.3028,-1.2909,6.69696,14.1151 --16.9184,1,0.893525,4,15,0,33.9474,-1.84112,-0.113636,2.06362,1.50179,-0.845997,0.0688089,3.06622,2.64436,2.26826,2.43111,2.50971,1.5926,0.571359,5.55928,4.34064,2.01372,6.0988,9.11372,6.5976,6.85665,6.80288,-8.30991,1.31011,6.18976,2.70739,7.30028,-2.21959,0.575756 --13.4497,0.964031,0.893525,4,15,0,28.2657,2.06759,1.88517,2.03153,4.55671,2.68962,3.88013,2.97665,4.94323,-1.1051,0.461124,0.821261,3.88711,1.5784,-1.09376,2.04646,5.44215,-2.40918,-0.376104,3.03831,4.50095,3.40338,2.18021,6.44679,-3.86522,-4.53872,8.8569,-2.86883,0.101805 --13.3097,0.985689,0.893525,5,63,0,23.9978,-0.458641,1.81688,1.13067,4.55108,-0.587807,2.57821,4.3395,3.59766,1.80005,4.38142,5.78002,7.34884,2.61074,1.62711,5.34154,-1.13147,-6.19384,1.43466,3.87577,6.57833,0.80797,0.35862,6.48445,-1.30164,-3.47383,9.08207,0.444976,5.32321 --16.7548,0.992864,0.893525,4,15,0,25.9435,-0.611885,1.70722,1.91256,4.08753,-0.558718,2.26147,4.67506,2.724,1.42638,3.7584,6.13557,7.74223,-1.93648,1.70896,-1.53221,11.4993,6.4725,3.41124,1.63341,1.08779,-1.627,1.87967,-1.20276,4.74747,2.94572,2.71493,-6.95711,11.8567 --13.6977,1,0.893525,5,31,0,23.7709,-0.0348727,2.3753,1.91407,4.24065,-0.183338,2.48153,4.59809,2.66369,1.47017,3.75191,6.17902,7.7481,2.8035,4.73861,4.93427,-1.13856,-4.43991,0.557245,4.37004,6.92477,1.69042,2.56632,7.2931,0.636308,-4.63603,4.21434,3.18431,-3.83695 --12.9904,0.829105,0.893525,5,31,0,24.7757,0.954512,2.32357,2.24258,4.46564,-0.175143,2.48001,4.42127,2.25393,0.606598,0.516433,-0.0972527,1.0992,2.75168,3.51613,5.74296,-0.589358,3.52465,5.54552,1.34431,-0.528801,2.23151,-1.22841,0.833627,5.63139,6.32234,0.830982,2.23831,10.8867 --12.7779,0.809684,0.893525,4,31,0,30.9311,-1.59846,0.551151,1.69222,2.01716,1.90067,1.39215,1.68216,5.07921,0.539343,3.87064,6.0261,1.46666,4.39794,4.01495,3.05614,4.42868,2.16376,8.91362,2.91122,2.61688,1.75577,-4.57003,3.88422,8.54643,5.72136,-4.11807,-1.00082,14.6943 --14.7469,0.686421,0.893525,5,31,0,31.2114,-1.19389,-0.189501,1.1353,2.28576,1.53594,1.57361,1.23782,4.75839,1.39719,-0.785606,0.917998,7.69384,3.32751,3.75673,1.60609,3.90543,0.104839,5.53187,-4.8396,7.30008,1.23704,10.5911,2.03966,4.13257,-4.32783,7.34504,8.42392,-6.15992 --15.2475,0.986114,0.893525,4,31,0,27.8445,-1.08349,1.35892,1.39036,2.95446,0.24891,2.91195,2.26254,2.75983,-0.727421,2.11583,3.41033,0.589622,-8.07355,1.32732,4.86093,5.28108,0.468481,3.57796,0.0377441,7.45135,5.24168,2.42214,-2.72864,0.154655,0.209238,10.774,9.20091,-8.80637 --12.8691,0.902218,0.893525,4,15,0,28.0952,1.0945,0.315962,2.55991,3.02734,1.8883,1.14326,3.49762,5.12975,0.866947,-0.80464,1.99675,3.32733,9.47022,2.80686,1.82135,3.13032,6.65696,-1.78161,-0.546003,-0.834536,4.79197,1.67196,4.52639,-0.973493,6.70168,4.44846,-5.79521,2.38978 -# -# Elapsed Time: 0.031 seconds (Warm-up) -# 0.015 seconds (Sampling) -# 0.046 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-1.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-1.csv deleted file mode 100644 index 1c4eafe7bf..0000000000 --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-1.csv +++ /dev/null @@ -1,148 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 20 -# stan_version_patch = 0 -# model = stan_test_data_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 1000 (Default) -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 1 -# data -# file = /tmp/tmp2ipdytqf/_pz629gf.json -# init = 2 (Default) -# random -# seed = 81267 -# output -# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-1-wo553htr.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1.1,eta.2.1,eta.1.2,eta.2.2,eta.1.3,eta.2.3,eta.1.4,eta.2.4,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 -# Adaptation terminated -# Step size = 0.385518 -# Diagonal elements of inverse mass matrix: -# 10.1549, 1.29488, 0.830867, 0.877862, 0.947812, 0.829839, 0.697341, 0.945683, 0.979058, 0.880175 --2.53762,0.966042,0.385518,3,7,0,9.94669,4.93861,7.59555,0.687798,0.0208477,0.581306,-0.354748,0.728817,0.423159,-0.110977,-0.379925,10.1628,9.35395,10.4744,4.09568,5.09696,2.2441,8.15274,2.05287,-4.33402,-3.23069,-4.04613,-3.35169,-3.34563,-3.32323,-3.70637,-3.962,24.9445,6.84183,32.7516,-1.26242,8.78332,8.23955,22.3938,-30.9551 --3.33816,0.959191,0.385518,3,7,0,4.78594,4.2105,3.4602,0.404119,-0.463639,-0.146885,-0.760172,-0.955706,0.283386,0.405943,1.09034,5.60883,3.70225,0.903566,5.61514,2.60621,1.58015,5.19107,7.9833,-4.74113,-3.31388,-3.72129,-3.32476,-3.19644,-3.31822,-4.04187,-3.83421,-6.36696,1.9282,-3.29464,12.8701,19.3994,-4.26254,-5.29595,8.83474 --6.66471,0.902778,0.385518,3,7,0,10.9397,9.41289,3.87017,0.291728,-1.27416,0.106004,-0.379673,-0.121491,-1.31763,-1.75334,0.336399,10.5419,9.82315,8.9427,2.62717,4.48166,7.94349,4.31343,10.7148,-4.30429,-3.23814,-3.9701,-3.39585,-3.30165,-3.51606,-4.15814,-3.81186,-6.48702,10.8039,10.3472,6.79224,19.854,15.3535,-1.11585,9.35172 --7.1506,0.895685,0.385518,3,7,0,15.35,3.37853,3.25133,-1.31991,-0.130096,-1.22281,1.29657,0.937701,1.45588,-0.221657,-0.79866,-0.912935,-0.597229,6.42731,2.65785,2.95555,7.5941,8.11207,0.781829,-5.48467,-3.59109,-3.86511,-3.39474,-3.21275,-3.49651,-3.71038,-4.00352,-3.47308,8.96633,18.6681,3.47742,21.3402,10.06,32.3956,-0.313593 --7.24426,0.985366,0.385518,4,15,0,12.356,7.61691,5.77209,1.8573,-0.653496,1.08093,-1.66562,-1.2252,-0.917503,0.0637779,0.403957,18.3374,13.8562,0.544958,7.98504,3.84487,-1.99722,2.321,9.94858,-3.83447,-3.393,-3.71607,-3.32084,-3.26106,-3.35396,-4.45068,-3.8158,30.4123,11.0991,21.9865,-1.31762,-9.74822,-9.8988,0.463525,25.319 --9.93085,0.839441,0.385518,3,7,0,14.9675,13.1323,0.738575,1.16694,1.14407,1.57696,-0.332227,-0.302995,0.0847276,-0.493177,0.0149621,13.9941,14.297,12.9085,12.768,13.9773,12.8869,13.1948,13.1433,-4.06291,-3.41978,-4.18582,-3.45431,-4.50084,-3.90071,-3.33697,-3.81133,4.5756,15.161,2.69951,13.9869,15.2501,20.4692,12.9592,-20.7697 --7.81605,0.927431,0.385518,3,7,0,13.4071,10.6243,1.7915,1.78277,0.756987,0.901341,-0.484762,-0.40294,1.04253,-0.142778,-1.05151,13.8182,12.2391,9.90247,10.3685,11.9805,9.75589,12.492,8.74057,-4.07393,-3.31137,-4.01667,-3.36372,-4.15624,-3.63363,-3.37321,-3.82571,0.157246,31.882,17.4461,15.4002,9.78053,8.17528,16.6432,-13.8994 --6.45841,0.995063,0.385518,4,15,0,11.6149,5.06787,6.16347,0.303549,0.173719,2.06496,-0.799385,-0.231615,-0.262808,0.803892,-1.15831,6.93878,17.7952,3.64032,10.0226,6.13858,0.140886,3.44806,-2.07134,-4.61271,-3.70125,-3.77765,-3.35459,-3.43073,-3.31988,-4.28032,-4.11487,20.7176,21.6496,2.07108,7.76042,4.25999,-3.1521,6.67988,9.80551 --8.43013,0.932174,0.385518,4,15,0,15.74,4.92484,0.410107,-0.0186004,-0.467561,-2.03042,1.10922,-0.202476,0.874985,-0.658854,1.47117,4.91721,4.09215,4.8418,4.65464,4.73309,5.37974,5.28368,5.52818,-4.81102,-3.29788,-3.81163,-3.33956,-3.31905,-3.3961,-4.03005,-3.87395,8.4989,3.63888,1.91457,-2.53732,14.6687,4.152,14.6282,1.1654 --4.22323,0.99988,0.385518,3,7,0,10.1219,5.55073,2.52881,0.341519,-0.340181,0.336258,0.152026,0.373228,0.0587043,-1.67025,-0.543497,6.41437,6.40106,6.49455,1.32698,4.69047,5.93517,5.69918,4.17633,-4.66241,-3.23431,-3.86759,-3.44982,-3.31605,-3.41748,-3.97807,-3.90377,15.742,4.66187,13.4889,-2.85989,3.75199,20.3686,-12.4496,-14.8644 --3.73383,0.987907,0.385518,4,15,0,7.03525,1.63385,8.04543,1.07466,-0.849183,0.784832,0.178971,-0.418864,1.41385,1.33693,-0.0250151,10.2799,7.94817,-1.7361,12.3901,-5.1982,3.07375,13.0088,1.43259,-4.32477,-3.22154,-3.69465,-3.43689,-3.22496,-3.3346,-3.34608,-3.98164,22.133,-10.2838,23.849,12.992,1.73386,18.8017,13.6438,-16.3598 --9.78357,0.748074,0.385518,4,15,0,13.7284,7.52032,1.09036,0.024407,0.895515,-0.53958,1.54634,-1.09424,0.892892,2.55757,0.668077,7.54693,6.93198,6.3272,10.309,8.49676,9.2064,8.4939,8.24877,-4.55661,-3.22723,-3.86144,-3.36208,-3.67288,-3.59512,-3.67335,-3.83103,-7.59594,-6.2725,4.04512,20.0134,21.5596,0.0967351,21.449,27.6762 --5.35381,0.975301,0.385518,4,31,0,13.567,1.57827,2.37307,0.589367,-0.684788,-0.919043,0.343651,0.0212604,-0.490333,1.57821,-0.503307,2.97688,-0.602686,1.62872,5.32349,-0.046781,2.39378,0.414674,0.383886,-5.01845,-3.59155,-3.73337,-3.32845,-3.12177,-3.32486,-4.76774,-4.01754,18.4008,2.36735,9.39143,-6.67736,0.372087,-8.18885,2.57163,31.4124 --6.13778,0.979817,0.385518,4,15,0,9.37806,4.02661,4.62906,-0.160315,-1.67963,1.24366,-1.28472,-0.246801,1.58669,-0.498222,-0.693775,3.28451,9.78358,2.88415,1.72032,-3.74848,-1.92041,11.3715,0.815087,-4.98445,-3.23743,-3.75915,-3.43202,-3.16279,-3.35208,-3.44121,-4.00237,-22.1287,-0.717723,23.2681,-11.0546,-3.1639,-9.02577,15.3855,-22.5293 --4.989,0.980516,0.385518,3,7,0,9.56005,4.91252,3.60577,-0.162747,-1.87309,0.626013,-0.706611,-0.0581316,1.21442,-0.280867,-0.912667,4.32569,7.16978,4.70291,3.89978,-1.84142,2.36464,9.29145,1.62165,-4.87248,-3.22497,-3.80742,-3.35655,-3.12053,-3.32453,-3.60072,-3.97553,-13.7668,7.23587,-8.99417,-17.7399,-2.39799,13.0608,13.9166,12.3939 --5.16183,0.982078,0.385518,3,7,0,7.5108,3.52963,3.29719,0.087827,0.727202,-0.89012,-0.322912,-0.0302405,-0.698453,1.36293,0.937955,3.81921,0.594735,3.42992,8.02348,5.92736,2.46493,1.2267,6.62225,-4.92635,-3.49571,-3.77228,-3.32116,-3.41239,-3.3257,-4.62824,-3.85394,1.72866,2.85665,4.30748,8.72765,-4.54318,14.6248,-2.09226,27.0661 --4.8336,0.986445,0.385518,3,15,0,7.3657,4.52102,0.643939,-0.52025,0.825182,0.0245879,1.0294,0.0737327,0.547645,-0.139447,0.449,4.18601,4.53685,4.5685,4.43122,5.05238,5.18389,4.87367,4.81014,-4.88722,-3.28149,-3.80341,-3.3441,-3.34228,-3.38917,-4.08303,-3.88909,4.15671,14.9379,-36.7371,8.96178,-1.34474,1.89921,14.5412,-20.4028 --9.42971,0.914575,0.385518,3,7,0,12.7583,4.6659,0.228477,-2.28693,0.517775,0.0331685,1.5803,0.23563,0.741719,1.02357,0.167314,4.14339,4.67348,4.71974,4.89976,4.7842,5.02696,4.83537,4.70413,-4.89174,-3.27685,-3.80792,-3.33506,-3.32269,-3.38384,-4.08806,-3.89145,-1.24883,2.01653,-8.6517,-12.1697,-3.07992,0.201557,-11.883,1.9274 --8.90459,0.670036,0.385518,3,15,0,14.9381,2.2788,0.347543,2.23557,0.0661138,-0.120015,-0.733511,-0.517189,0.269947,0.187428,1.82469,3.05576,2.23709,2.09906,2.34394,2.30178,2.02388,2.37262,2.91296,-5.00969,-3.38758,-3.74231,-3.40642,-3.18346,-3.32117,-4.4426,-3.93674,1.00353,-17.9763,23.9538,4.61803,-5.17028,8.2363,-7.07416,14.6875 --5.47899,1,0.385518,3,7,0,12.3047,5.76814,3.59837,-1.63909,0.168679,0.600025,0.0788644,-0.125076,0.574417,0.0953859,-1.352,-0.129903,7.92725,5.31807,6.11137,6.37511,6.05192,7.8351,0.90313,-5.38541,-3.22155,-3.82666,-3.3201,-3.45192,-3.4223,-3.73815,-3.99934,4.2739,1.13124,31.368,17.8209,9.78776,9.29246,23.5049,-19.024 --4.97416,0.970693,0.385518,4,15,0,9.73992,2.70012,4.91874,2.09348,-0.29377,-0.249925,-0.877311,0.20345,0.176898,0.0358771,1.46853,12.9974,1.4708,3.70084,2.87659,1.25514,-1.61514,3.57023,9.92341,-4.12716,-3.43468,-3.77922,-3.38709,-3.14756,-3.34509,-4.26261,-3.81596,15.6166,-3.64339,15.3275,7.44224,-0.769405,-13.742,-2.83815,-2.03859 --6.36068,0.987685,0.385518,4,15,0,8.07034,7.73223,3.83768,-1.63378,-0.50892,0.371623,0.602279,-0.45643,0.594068,0.0718048,-1.55744,1.46229,9.1584,5.9806,8.0078,5.77916,10.0436,10.0121,1.75528,-5.19199,-3.22823,-3.84905,-3.32103,-3.39985,-3.65479,-3.54056,-3.97128,-6.44324,-3.17129,-7.58395,-1.79422,11.652,30.3915,14.7857,11.0128 --9.31261,0.78922,0.385518,4,15,0,15.8795,5.78452,0.897225,-0.105096,-1.38567,2.0229,0.184502,-0.830359,0.36436,0.36061,-2.30594,5.69022,7.59952,5.0395,6.10807,4.54126,5.95006,6.11143,3.71557,-4.73305,-3.22233,-3.81776,-3.32012,-3.3057,-3.41809,-3.92821,-3.91522,16.4746,-0.713578,21.0388,12.8278,3.99817,-6.96953,4.98131,14.0204 --2.83946,0.973989,0.385518,3,7,0,13.7,0.855519,4.44649,0.2473,-0.142386,0.330927,-0.502948,0.0147535,0.255781,0.0846591,0.668065,1.95514,2.32698,0.92112,1.23195,0.222401,-1.38084,1.99285,3.82606,-5.1344,-3.38244,-3.72156,-3.45431,-3.12539,-3.34026,-4.50267,-3.91242,2.30554,8.14551,16.3042,0.118723,10.5168,-1.65256,12.2299,16.4283 --4.55654,0.932255,0.385518,4,15,0,7.74168,7.5806,2.5593,0.978325,-0.282863,0.0189342,0.261092,-0.38687,0.757029,0.499252,-1.48681,10.0844,7.62905,6.59048,8.85833,6.85666,8.24881,9.51806,3.7754,-4.34025,-3.22221,-3.87117,-3.3311,-3.4972,-3.53396,-3.58124,-3.9137,-28.486,9.89071,9.88196,14.4325,2.20966,14.5645,0.795969,27.3376 --5.8619,0.829295,0.385518,4,15,0,8.53659,0.309172,6.71926,1.21486,0.613581,0.640656,0.676749,0.248878,1.04544,-0.149178,2.3446,8.47212,4.61391,1.98145,-0.693192,4.43199,4.85643,7.33375,16.0631,-4.47441,-3.27885,-3.73999,-3.5614,-3.2983,-3.37829,-3.79037,-3.83479,0.498617,-0.271637,-22.1082,-3.87071,11.3729,-9.11295,2.41018,27.9464 --6.88446,0.947382,0.385518,3,7,0,10.7029,2.34919,4.50793,0.289766,0.888612,0.918965,0.483983,1.31171,2.11393,1.38663,0.947284,3.65544,6.49182,8.26229,8.6,6.35499,4.53095,11.8786,6.61948,-4.94401,-3.2329,-3.93926,-3.32741,-3.45009,-3.36835,-3.40888,-3.85399,-2.64741,19.5078,6.97917,10.8551,-10.5744,-6.83709,4.09572,16.1418 --6.73486,0.932114,0.385518,4,15,0,12.0298,3.37034,0.624562,0.0667308,-1.37556,-0.37608,-0.628736,-0.703525,-1.47888,-0.612337,-0.707795,3.41201,3.13545,2.93094,2.98789,2.51121,2.97765,2.44668,2.92827,-4.97048,-3.33984,-3.76023,-3.38335,-3.19227,-3.333,-4.43105,-3.93631,-28.0447,-7.00503,-14.4767,0.972224,12.8499,2.66021,-1.40068,6.99214 --5.11411,0.993281,0.385518,3,7,0,8.81416,6.46842,0.823389,-0.50248,-1.398,-0.451216,-0.832872,-0.0958422,-0.474771,-0.199448,-0.0927507,6.05468,6.09689,6.38951,6.3042,5.31732,5.78264,6.0775,6.39205,-4.6972,-3.23963,-3.86372,-3.31883,-3.36251,-3.41135,-3.93225,-3.85784,-11.9775,11.6983,23.8158,19.2096,2.50373,8.05658,5.48446,28.6585 --4.88538,0.97977,0.385518,4,15,0,8.46029,5.49517,7.59626,1.08384,-0.143279,-0.389317,1.59932,-0.0636603,0.508335,0.610142,-0.609044,13.7283,2.53782,5.01159,10.13,4.40679,17.644,9.35661,0.868712,-4.07961,-3.3707,-3.81689,-3.35732,-3.29662,-4.46155,-3.59506,-4.00052,16.7155,6.60621,3.52444,-8.94163,2.81148,2.73293,11.235,-54.189 --4.07941,0.995915,0.385518,3,7,0,5.87984,5.85725,4.99906,0.996943,-0.0827081,-0.298859,1.3079,0.0335994,0.944327,0.630089,-0.587473,10.841,4.36324,6.02521,9.0071,5.44379,12.3955,10.578,2.92044,-4.28128,-3.28765,-3.85062,-3.33348,-3.37247,-3.85344,-3.49695,-3.93653,17.1138,14.0845,7.39536,5.42921,-9.0669,17.6145,13.4487,9.60113 --9.09916,0.894537,0.385518,3,15,0,12.3263,0.875234,2.64948,-1.06029,1.33421,0.444047,-0.738284,2.58363,0.439281,-0.129683,0.880781,-1.93399,2.05173,7.72052,0.531641,4.41021,-1.08084,2.0391,3.20885,-5.6182,-3.39843,-3.916,-3.48973,-3.29684,-3.33473,-4.49528,-3.92858,1.5259,-8.06266,13.8231,2.95262,13.1982,-3.55382,13.361,1.57095 --4.58408,0.970131,0.385518,3,7,0,14.3263,6.94076,3.97256,1.86501,-0.231184,0.590157,-0.786658,-0.523912,0.674406,-1.04914,-0.0222608,14.3496,9.2852,4.85949,2.77299,6.02237,3.81571,9.61988,6.85233,-4.04106,-3.22978,-3.81217,-3.39067,-3.42057,-3.3496,-3.57266,-3.8502,15.874,31.6159,-17.9676,-3.58194,14.9283,5.62656,-5.25238,17.0129 --6.98133,0.953514,0.385518,3,7,0,10.5324,7.66985,2.10586,-1.7456,-0.350845,-1.50503,0.568525,-0.441514,-0.583586,-0.848883,0.135877,3.99387,4.50046,6.74008,5.88222,6.93102,8.86709,6.4409,7.95599,-4.90764,-3.28276,-3.87682,-3.322,-3.50444,-3.57258,-3.88959,-3.83455,8.73913,18.1782,15.0726,19.0434,7.23404,13.9996,-1.53636,-11.1231 --7.6502,0.978475,0.385518,4,15,0,12.4554,2.32073,5.93354,3.06404,-0.477109,1.76305,-0.991932,0.105832,0.872031,0.0673789,0.454563,20.5013,12.7818,2.94869,2.72053,-0.510213,-3.56494,7.49497,5.0179,-3.75194,-3.33585,-3.76064,-3.39251,-3.11764,-3.40294,-3.7733,-3.88454,5.90315,19.3902,-19.8535,-3.99973,-5.47118,7.36528,9.15742,-7.94343 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--6.61141,0.884788,0.385518,3,7,0,8.56737,11.3726,2.18342,0.852096,-0.0032377,-0.614963,-0.155772,-0.928771,1.01338,-0.717312,-1.05718,13.2331,10.0299,9.34474,9.80645,11.3656,11.0325,13.5853,9.06438,-4.11157,-3.24213,-3.98917,-3.34938,-4.06004,-3.73275,-3.31897,-3.82261,43.0482,18.2437,6.97157,-16.3146,18.0178,0.0984541,1.05673,42.0531 --7.13137,0.992683,0.385518,3,7,0,10.8179,9.10119,0.598424,1.78245,-0.263949,0.44481,-0.25318,-0.866109,-0.473168,-0.88948,0.183839,10.1678,9.36737,8.58289,8.5689,8.94323,8.94968,8.81803,9.2112,-4.33362,-3.23087,-3.95356,-3.32701,-3.72646,-3.57798,-3.64307,-3.82131,3.31454,8.49421,12.9164,7.86452,2.09311,21.2439,8.96421,-34.4188 --7.43029,0.809492,0.385518,3,15,0,10.6048,-0.452777,0.165835,-0.899755,0.827939,-0.247912,-0.173668,0.585277,0.602609,0.433573,-0.259451,-0.601988,-0.493889,-0.355718,-0.380876,-0.315476,-0.481577,-0.352843,-0.495803,-5.44493,-3.58225,-3.70518,-3.54195,-3.11906,-3.3259,-4.90566,-4.05027,17.613,4.91262,11.0759,-16.2914,3.42483,-18.9102,-4.21484,-22.6242 --11.8765,0.938242,0.385518,3,7,0,15.9133,7.12823,0.0114346,0.0944311,-1.36368,1.18507,0.925949,1.05798,-0.375035,-1.01414,-1.27822,7.12931,7.14178,7.14032,7.11663,7.11263,7.13881,7.12394,7.11361,-4.59496,-3.22521,-3.89236,-3.31689,-3.52243,-3.47256,-3.81297,-3.84616,37.2655,-3.31172,1.75943,25.6705,6.42084,22.6965,-1.52051,15.8952 --6.45893,0.422954,0.385518,5,47,0,19.8282,2.74592,3.15347,0.0100505,1.33385,-1.0078,-0.969309,-1.08235,0.734091,0.607119,1.51464,2.77762,-0.43214,-0.667246,4.66045,6.95219,-0.310763,5.06086,7.52228,-5.0407,-3.57703,-3.70216,-3.33945,-3.50652,-3.32393,-4.05863,-3.84025,6.08449,17.2055,-18.606,-1.33401,9.93126,0.194647,13.6128,28.3762 --3.39994,0.978062,0.385518,3,7,0,9.33878,5.35524,5.16323,0.860308,-0.36495,0.170295,0.375614,0.258907,-0.419493,-0.33692,1.16184,9.79721,6.23451,6.69204,3.61564,3.47092,7.29462,3.1893,11.3541,-4.3633,-3.23711,-3.875,-3.36416,-3.23955,-3.48056,-4.31831,-3.80995,28.7938,17.8456,21.2896,13.4998,-6.76528,2.79078,12.7141,7.65988 --5.19032,0.923005,0.385518,3,15,0,7.24146,5.05587,2.6856,1.59438,-0.753316,1.02218,0.202382,0.668781,1.00043,1.05041,-0.692418,9.33773,7.80104,6.85195,7.87685,3.03276,5.59939,7.74262,3.19631,-4.40095,-3.22172,-3.8811,-3.32001,-3.21655,-3.40425,-3.74759,-3.92892,11.51,13.4141,29.0068,11.5291,-0.921777,-10.1248,4.90951,5.71657 --5.99085,0.989085,0.385518,3,7,0,8.4588,3.91595,1.4376,-1.16195,1.05633,-1.11985,-0.485434,-1.13225,-0.426478,-0.0985508,-0.318818,2.24554,2.30606,2.28822,3.77427,5.43454,3.21809,3.30284,3.45762,-5.10097,-3.38363,-3.74615,-3.35983,-3.37174,-3.33716,-4.30156,-3.92192,-15.3399,-12.8706,5.78411,22.3647,28.7085,-3.57269,17.8785,-22.1274 --2.84648,0.999031,0.385518,4,15,0,6.79868,6.00893,4.43225,-0.229454,-0.502596,-0.44891,0.309437,-0.151576,0.00995399,0.687626,0.0631853,4.99194,4.01926,5.33711,9.05666,3.78131,7.38044,6.05305,6.28899,-4.80337,-3.30076,-3.82728,-3.33431,-3.25728,-3.48506,-3.93517,-3.85964,-18.6793,6.92018,15.7639,6.75345,0.935022,8.45768,15.98,-21.4242 --7.69056,0.861858,0.385518,3,7,0,9.81185,0.0908445,2.12135,0.569711,-2.12496,-0.279677,-0.562721,-0.724869,-0.380865,0.507924,1.51362,1.2994,-0.50245,-1.44686,1.16833,-4.41694,-1.10289,-0.717104,3.30177,-5.21126,-3.58298,-3.69624,-3.45736,-3.18823,-3.33511,-4.97317,-3.92607,-16.6256,9.50781,7.47295,6.53216,-3.46672,-11.0671,4.14522,-2.26332 --7.48261,0.894017,0.385518,4,15,0,12.3517,9.32873,1.67715,0.716405,-1.8965,-1.66118,-0.787243,-0.207085,-0.123369,-0.53347,-0.801939,10.5302,6.54268,8.98141,8.43402,6.14801,8.0084,9.12182,7.98375,-4.30519,-3.23214,-3.97191,-3.32533,-3.43156,-3.5198,-3.61563,-3.8342,22.8742,-2.62145,39.006,1.10374,19.9056,-4.8616,1.12719,-0.733504 --9.22949,0.921329,0.385518,4,15,0,13.4376,9.27776,2.98877,1.40342,-1.24037,-2.02276,-1.54628,-0.593498,-1.17663,-1.00213,0.334477,13.4723,3.2322,7.50393,6.28262,5.57059,4.65628,5.76108,10.2774,-4.096,-3.33518,-3.90702,-3.31896,-3.38266,-3.37208,-3.97048,-3.81389,6.16841,4.68479,3.0224,13.5982,-0.628349,3.75179,-4.42745,4.74081 --9.08173,0.999816,0.385518,4,15,0,12.6664,-1.71684,7.55436,-0.231044,1.06641,2.11997,1.3375,0.406914,1.93662,1.1411,-0.217432,-3.46222,14.2982,1.35714,6.90341,6.33922,8.38709,12.9131,-3.35939,-5.8267,-3.41986,-3.72861,-3.31687,-3.44866,-3.54233,-3.35091,-4.17337,-13.6403,25.4306,-2.81355,0.713725,7.89312,2.10029,14.786,-36.7942 --5.31777,1,0.385518,3,7,0,12.0189,1.25481,3.13418,-0.207995,-0.025385,1.23349,0.393754,-0.657462,1.72563,-1.06629,-0.209152,0.602919,5.12078,-0.805789,-2.08714,1.17525,2.48891,6.66325,0.599293,-5.29499,-3.26297,-3.70093,-3.65806,-3.14537,-3.32599,-3.86413,-4.00989,28.864,-7.72614,-25.4577,1.07506,7.07261,3.3137,6.7585,-9.07713 --6.67756,0.948836,0.385518,4,15,0,10.8329,5.3372,5.40225,1.09492,1.11317,0.76539,-0.422847,-0.231826,1.3674,1.94298,-0.704917,11.2523,9.47202,4.08481,15.8336,11.3508,3.05287,12.7242,1.52906,-4.25029,-3.23236,-3.78956,-3.63929,-4.05779,-3.33425,-3.36069,-3.97851,30.9359,-0.709008,-18.508,37.189,1.57302,-2.45999,23.1431,12.2765 --4.63328,0.502802,0.385518,3,7,0,14.2524,4.46666,2.7145,1.10384,0.77756,0.0445637,-0.406849,-0.613284,0.431987,1.6638,-0.0729021,7.46302,4.58762,2.8019,8.98303,6.57734,3.36226,5.63928,4.26876,-4.56425,-3.27975,-3.75727,-3.33308,-3.47058,-3.33989,-3.98546,-3.90155,19.7577,24.3393,-25.7799,4.29961,-6.11452,-3.24167,2.74489,-8.0288 --5.95354,0.987937,0.385518,4,15,0,8.5348,8.5474,2.16916,0.980981,0.552071,0.916475,-0.646044,0.263546,-0.644018,-0.717999,-1.28739,10.6753,10.5354,9.11907,6.98994,9.74493,7.14602,7.15042,5.75484,-4.29398,-3.25366,-3.97839,-3.31683,-3.82884,-3.47292,-3.81009,-3.8695,8.67922,-14.5279,18.4182,13.1639,21.7016,5.46882,11.7413,30.5505 --6.79119,0.948723,0.385518,3,7,0,11.5018,-3.80016,9.19239,1.70764,-0.140204,-0.162779,1.24489,0.277499,0.657447,0.0889743,1.62674,11.8972,-5.29648,-1.24928,-2.98227,-5.08896,7.64332,2.24336,11.1535,-4.20321,-4.10551,-3.69751,-3.72859,-3.21937,-3.4992,-4.46288,-3.81042,23.6282,12.4845,8.04634,-21.1603,-2.90931,6.60463,-9.96798,7.47584 --8.71176,0.927027,0.385518,4,15,0,12.9485,9.5789,0.362442,-0.739364,0.636169,1.27867,-1.41868,-0.0129649,0.683711,0.294421,-1.27071,9.31092,10.0423,9.5742,9.68561,9.80948,9.06471,9.82671,9.11834,-4.40317,-3.24238,-4.00034,-3.34664,-3.83743,-3.58559,-3.55554,-3.82213,-2.25571,-3.68516,21.2738,12.7244,4.18746,19.7507,14.4496,33.6258 --11.6747,0.949255,0.385518,3,7,0,15.2193,11.7755,1.40175,-0.936796,0.28643,-0.368251,-2.26653,-0.17526,0.515795,1.36256,-2.07853,10.4623,11.2593,11.5298,13.6854,12.177,8.59838,12.4985,8.86191,-4.31048,-3.27464,-4.10386,-3.50152,-4.18797,-3.55541,-3.37286,-3.82451,-26.1818,23.316,22.71,14.7738,12.405,6.27311,18.5344,4.5741 --5.25837,1,0.385518,3,7,0,14.1094,4.03108,3.52987,-0.55712,0.0667916,0.208229,-1.09862,0.0414127,0.0650344,1.95483,0.65588,2.06452,4.7661,4.17727,10.9314,4.26685,0.153114,4.26065,6.34625,-5.12176,-3.27381,-3.79214,-3.3807,-3.2874,-3.3198,-4.16537,-3.85864,12.9265,21.4266,5.63689,20.3503,19.6323,-5.91351,20.5531,-0.653214 --6.7852,0.957863,0.385518,3,7,0,9.59571,4.3292,2.0529,0.111339,-0.408673,-1.23092,0.489579,-0.568368,-1.00532,1.89947,1.05395,4.55777,1.80223,3.1624,8.22863,3.49024,5.33426,2.26538,6.49285,-4.84818,-3.41359,-3.7657,-3.32307,-3.24062,-3.39446,-4.45942,-3.85611,-5.10722,14.6516,-17.9333,8.03771,2.12784,13.7282,-0.269446,3.80958 --5.82935,0.993256,0.385518,4,23,0,9.10878,3.70409,6.3368,0.478429,0.168131,1.17412,-0.927152,0.0277601,1.37548,-1.61393,-0.842643,6.73581,11.1442,3.88,-6.52306,4.76951,-2.17108,12.4203,-1.63557,-4.6318,-3.27096,-3.78398,-4.07251,-3.32164,-3.35839,-3.37719,-4.09624,3.40067,-3.4507,6.18547,3.66561,16.4297,9.43895,-16.4244,32.4056 --5.87073,0.998805,0.385518,4,15,0,9.43112,1.26684,4.97602,0.439216,0.0799682,0.995273,1.14454,-0.63901,-0.0536627,1.86973,0.94864,3.45239,6.21934,-1.91289,10.5707,1.66477,6.9621,0.999816,5.98729,-4.96607,-3.23738,-3.69384,-3.36952,-3.16,-3.46372,-4.66655,-3.8651,-1.23489,2.24928,20.99,16.296,7.62997,17.9602,19.5511,8.37895 --5.43445,0.948683,0.385518,3,7,0,10.6629,3.46045,2.1568,0.173865,-0.124277,-0.462582,-1.26378,0.199516,1.62262,-1.0657,-0.882442,3.83544,2.46276,3.89077,1.16195,3.19241,0.734723,6.96012,1.5572,-4.9246,-3.37483,-3.78427,-3.45767,-3.22466,-3.31712,-3.83092,-3.9776,17.987,17.8291,6.6775,8.15938,1.81346,-6.54815,-11.1127,-39.8142 --6.36096,0.978403,0.385518,4,15,0,9.98232,5.65839,0.703171,0.904646,-0.187387,0.764581,0.879412,-0.63838,-0.528209,1.01618,1.38311,6.29451,6.19602,5.2095,6.37294,5.52663,6.27677,5.28697,6.63096,-4.67394,-3.2378,-3.82316,-3.31846,-3.37911,-3.43189,-4.02963,-3.8538,-18.8796,15.3175,3.82447,5.06548,-3.53049,12.1711,-8.56723,-7.41359 --7.18312,0.925488,0.385518,3,15,0,13.3844,7.0525,0.536485,-0.785607,0.392956,1.23824,-0.54759,-1.41229,0.637637,-0.88706,-0.784666,6.63103,7.7168,6.29483,6.5766,7.26331,6.75872,7.39458,6.63154,-4.64173,-3.22192,-3.86027,-3.31757,-3.53766,-3.45387,-3.7839,-3.85379,14.4199,17.6212,14.4838,12.0996,2.43148,-15.3593,3.96947,-13.146 --3.57012,0.978728,0.385518,4,15,0,11.5075,2.16724,4.28741,0.848686,0.784048,0.0122719,-0.0704019,0.535136,0.57842,-0.910977,0.150393,5.8059,2.21985,4.46159,-1.73849,5.52877,1.8654,4.64716,2.81204,-4.72161,-3.38857,-3.80027,-3.63238,-3.37928,-3.31993,-4.11302,-3.93959,10.1515,0.942317,-11.4717,-2.87173,-2.15255,-1.96229,-6.18639,-11.5867 --5.71552,0.953764,0.385518,3,7,0,7.5567,5.18188,4.79297,0.197623,0.264471,1.24283,-0.177861,-1.70205,0.694792,1.02248,1.44822,6.12909,11.1387,-2.97597,10.0826,6.44949,4.3294,8.512,12.1232,-4.68996,-3.27078,-3.69153,-3.3561,-3.45872,-3.36264,-3.67163,-3.80933,22.3644,-9.31615,4.77942,27.0775,7.94338,6.78692,22.9146,-14.4558 --7.48523,0.950119,0.385518,5,39,0,11.7693,1.50404,0.738203,1.2646,-1.14504,-0.881476,-0.529194,0.194484,-1.04242,1.17359,0.877372,2.43758,0.853334,1.64761,2.37039,0.658769,1.11339,0.734522,2.15172,-5.07907,-3.4769,-3.73372,-3.4054,-3.13315,-3.31689,-4.71201,-3.95898,-1.73658,10.8703,23.8903,5.29939,2.35226,-14.346,28.4556,-3.65272 --7.29664,0.986551,0.385518,3,15,0,11.7876,8.83,1.12759,1.06794,-0.171176,-0.204102,-0.482476,-0.554099,1.24103,-0.375854,2.01153,10.0342,8.59985,8.2052,8.40619,8.63698,8.28596,10.2294,11.0982,-4.34425,-3.22332,-3.93675,-3.325,-3.68944,-3.53619,-3.52344,-3.81057,17.9722,9.05458,-2.48611,9.22641,1.6735,-0.947614,5.29048,0.862101 --5.96692,0.989533,0.385518,3,7,0,10.9597,9.34948,13.6781,-0.494972,-0.728989,-0.815078,0.241453,-0.581316,-0.375488,-0.480268,0.310744,2.57923,-1.7992,1.3982,2.78035,-0.621676,12.6521,4.21353,13.5999,-5.06302,-3.70165,-3.72931,-3.39041,-3.11705,-3.87787,-4.17186,-3.81326,11.0313,3.22052,-1.97456,3.48576,-5.52664,10.76,3.19276,4.31117 --6.04693,1,0.385518,3,7,0,8.05874,10.8004,2.2575,0.362243,-1.50873,-0.089532,-0.685658,-0.382761,0.942418,0.671715,-0.673368,11.6182,10.5983,9.93631,12.3168,7.39443,9.25252,12.9279,9.28027,-4.22335,-3.25528,-4.01838,-3.43364,-3.55114,-3.59826,-3.35015,-3.82073,-2.5582,20.6438,26.3398,25.0129,-1.3185,-2.72148,12.6062,15.5287 --6.41578,0.776287,0.385518,4,23,0,10.9607,9.06737,0.671532,0.355793,-1.07882,-0.0371514,-0.0953811,-0.758793,-0.0389723,1.06442,-1.04848,9.3063,9.04242,8.55782,9.78216,8.34291,9.00332,9.0412,8.36328,-4.40355,-3.22696,-3.95243,-3.34882,-3.65499,-3.58152,-3.62282,-3.82972,-12.2664,1.43154,8.89884,4.41676,5.20958,0.968425,-5.4337,-4.03021 --2.50112,0.841596,0.385518,3,7,0,7.24577,1.05021,12.6362,1.00451,0.209848,-0.306386,-0.0617685,0.419931,0.533195,0.180316,0.728383,13.7434,-2.82135,6.35655,3.32872,3.7019,0.269693,7.78778,10.2542,-4.07866,-3.80703,-3.86251,-3.37253,-3.25263,-3.31904,-3.74297,-3.81401,28.3318,-9.2508,-1.5294,-8.34022,-2.82224,6.25377,-2.8107,-1.27704 -# -# Elapsed Time: 0.02366 seconds (Warm-up) -# 0.00441 seconds (Sampling) -# 0.02807 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-2.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-2.csv deleted file mode 100644 index 1efb8e7f2d..0000000000 --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-2.csv +++ /dev/null @@ -1,148 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 20 -# stan_version_patch = 0 -# model = stan_test_data_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 1000 (Default) -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 2 -# data -# file = /tmp/tmp2ipdytqf/_pz629gf.json -# init = 2 (Default) -# random -# seed = 81267 -# output -# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-2-gjhvdtyv.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1.1,eta.2.1,eta.1.2,eta.2.2,eta.1.3,eta.2.3,eta.1.4,eta.2.4,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 -# Adaptation terminated -# Step size = 0.40918 -# Diagonal elements of inverse mass matrix: -# 11.5948, 1.56488, 1.05158, 0.780774, 1.03901, 0.756746, 0.876817, 1.08772, 0.899484, 0.839705 --8.67722,0.974355,0.40918,3,7,0,17.3887,0.464724,3.58185,-0.167717,-0.283528,-1.25549,-1.63701,-1.39541,-0.584162,0.0377451,1.67728,-0.136014,-4.03225,-4.53341,0.599921,-0.55083,-5.3988,-1.62766,6.47247,-5.38618,-3.9454,-3.69612,-3.48609,-3.11741,-3.48603,-5.14775,-3.85646,10.1112,-1.28152,16.3638,16.9716,-1.8239,11.2806,13.2108,8.37732 --9.51664,0.985884,0.40918,3,7,0,14.4674,4.05812,1.44524,0.104786,-0.030639,0.173932,-2.67857,-0.507921,-0.654223,0.581434,2.295,4.20956,4.30949,3.32405,4.89843,4.01384,0.186923,3.11261,7.37496,-4.88473,-3.28962,-3.76964,-3.33508,-3.27134,-3.31957,-4.3297,-3.84232,24.9347,-11.1068,10.9492,-4.90384,13.6504,-0.0767166,-5.8205,19.4711 --8.77949,1,0.40918,3,7,0,13.6599,-0.406779,2.44777,0.127763,0.664987,0.129205,-1.05902,-1.1611,-0.619788,0.291593,2.5864,-0.0940455,-0.0905155,-3.24888,0.306972,1.22095,-2.99901,-1.92388,5.92411,-5.38093,-3.54881,-3.69165,-3.50194,-3.14661,-3.38292,-5.20633,-3.86628,6.93933,1.68346,7.478,-0.914628,-12.3128,0.902323,5.9146,8.28699 --10.8457,0.961075,0.40918,3,7,0,16.2024,6.21218,2.4341,0.947002,1.31152,2.67668,0.182283,0.935768,-0.916512,0.594774,1.90194,8.51727,12.7275,8.48993,7.65992,9.40456,6.65587,3.98129,10.8417,-4.47049,-3.33327,-3.94938,-3.31863,-3.7844,-3.44902,-4.20414,-3.81138,8.93921,25.0498,0.276982,-13.4558,-2.02797,11.2997,2.16293,17.3901 --9.38828,0.905272,0.40918,3,7,0,17.2085,6.35584,0.959567,0.189701,-0.508533,2.32867,1.35769,0.363399,-1.73893,0.343385,0.981145,6.53787,8.59036,6.70455,6.68534,5.86787,7.65864,4.68722,7.29731,-4.6506,-3.22327,-3.87547,-3.31724,-3.40732,-3.50005,-4.10767,-3.84344,6.97638,17.9778,14.2998,3.96929,10.2317,18.963,12.5707,-0.169651 --5.64407,0.984707,0.40918,3,7,0,13.5117,5.16124,2.58232,0.675462,-0.222148,-0.72931,-1.60694,-0.912962,1.07686,-0.775194,-1.1347,6.9055,3.27792,2.80367,3.15944,4.58758,1.01161,7.94204,2.23107,-4.61583,-3.33301,-3.75731,-3.37778,-3.30889,-3.31683,-3.72734,-3.95658,7.71718,-6.86141,16.8173,-14.6203,-3.40512,2.88952,3.11039,-3.85306 --9.47463,0.189962,0.40918,4,23,0,14.9024,0.938447,0.0587804,-0.570854,-0.157308,0.243966,1.11978,-0.744701,-0.776549,1.61965,0.583234,0.904892,0.952787,0.894673,1.03365,0.9292,1.00427,0.892801,0.97273,-5.25842,-3.46984,-3.72115,-3.46393,-3.13914,-3.31683,-4.6848,-3.99697,-1.12314,12.2076,-8.44677,2.57089,10.4649,-10.8864,-6.408,-18.9148 --8.35385,0.992533,0.40918,3,15,0,14.2131,4.48394,0.800681,1.67124,-0.180386,-0.389671,-0.362157,-1.18044,-0.954632,1.69583,1.41672,5.82207,4.17194,3.53879,5.84176,4.33951,4.19397,3.71959,5.61828,-4.72001,-3.29479,-3.77503,-3.32238,-3.29215,-3.35899,-4.24117,-3.87216,23.592,-5.6186,11.6706,24.6936,9.24028,-2.5778,3.87511,-55.2082 --8.12521,0.996604,0.40918,4,23,0,11.3024,5.0345,2.33392,-0.189017,-0.258359,1.24432,1.25097,1.30902,2.22557,-1.05599,-0.915833,4.59335,7.93864,8.08964,2.56991,4.43151,7.95416,10.2288,2.89702,-4.84448,-3.22154,-3.93172,-3.39793,-3.29827,-3.51667,-3.52348,-3.93719,-9.60781,0.253746,10.48,-9.50309,8.21325,1.30332,15.4534,-21.1317 --6.63385,0.981375,0.40918,3,7,0,12.6946,1.4494,0.686209,1.62996,0.943447,-0.0671362,0.593576,1.03933,-0.264726,0.656818,-0.0859316,2.56789,1.40333,2.1626,1.90011,2.0968,1.85672,1.26774,1.39043,-5.0643,-3.4391,-3.74358,-3.42431,-3.17536,-3.31987,-4.62137,-3.98302,12.2268,-3.46796,-2.8746,-13.556,4.95906,3.26693,0.653091,-7.91704 --3.54197,1,0.40918,3,7,0,9.45582,5.03716,4.74639,-0.247764,-0.592026,-0.123187,0.382299,-0.436669,0.961686,-1.25666,0.0784759,3.86117,4.45246,2.96455,-0.927425,2.22717,6.8517,9.6017,5.40963,-4.92184,-3.28445,-3.76101,-3.57652,-3.18045,-3.45833,-3.57418,-3.87634,12.8029,14.2777,-0.776172,12.1001,-2.17168,10.6467,28.2655,53.994 --4.42822,0.972633,0.40918,3,7,0,6.0017,7.0566,3.73611,-0.305653,0.0991266,0.491996,-0.496478,-0.068094,1.07094,-0.838101,1.42597,5.91465,8.89475,6.80219,3.92536,7.42695,5.2017,11.0577,12.3842,-4.71091,-3.22553,-3.87919,-3.3559,-3.55452,-3.38979,-3.4625,-3.80954,6.92731,4.76772,11.2244,4.19712,2.32093,-12.4702,7.58231,27.5687 --3.82914,0.992995,0.40918,4,23,0,7.13908,-0.149817,6.91626,1.74751,-0.234132,0.513696,0.307251,-0.172964,-0.0237439,0.658983,-0.253909,11.9364,3.40304,-1.34608,4.40788,-1.76914,1.97521,-0.314036,-1.90592,-4.20041,-3.32718,-3.69687,-3.3446,-3.11981,-3.32076,-4.89854,-4.10773,-12.2938,6.50801,-3.23722,20.8523,6.09619,7.87187,14.5767,1.7687 --6.61624,0.737066,0.40918,3,7,0,11.1671,0.161254,3.47579,-0.0810591,-1.29491,1.5687,0.471104,0.0640465,0.757445,1.61155,-0.805158,-0.120491,5.61374,0.383866,5.76267,-4.33959,1.79871,2.79398,-2.63731,-5.38424,-3.24999,-3.71389,-3.32316,-3.18501,-3.31947,-4.37764,-4.13994,4.98443,0.298666,16.0802,-8.25106,-5.67471,-2.86823,-4.53392,-27.4468 --5.97086,0.960998,0.40918,3,7,0,12.1621,6.11127,1.50397,0.0145014,1.45439,-0.913201,-0.317255,-1.14191,0.34442,-0.13776,1.24763,6.13307,4.73784,4.39387,5.90408,8.29863,5.63412,6.62926,7.98766,-4.68957,-3.27473,-3.7983,-3.3218,-3.64989,-3.40557,-3.86799,-3.83415,-11.0347,7.7298,4.48564,25.8046,10.6858,-5.92757,5.31065,-0.60662 --6.2798,0.981937,0.40918,3,15,0,9.5194,4.17872,3.46873,-0.575314,-1.48276,0.423789,-0.442561,-0.374988,-1.36417,-1.20485,0.0600114,2.18312,5.64873,2.87799,-0.00059112,-0.964579,2.6436,-0.553218,4.38688,-5.10813,-3.24917,-3.75901,-3.51935,-3.11617,-3.328,-4.94263,-3.89875,13.1042,-0.638476,-4.22771,-12.9903,3.90358,7.36404,9.49249,23.422 --5.34199,0.966724,0.40918,4,23,0,12.1825,1.77412,6.36556,0.684142,0.549267,-0.402652,-0.359899,1.06005,1.50492,1.59626,-0.477095,6.12906,-0.788987,8.52195,11.9352,5.27051,-0.516838,11.3538,-1.26286,-4.68996,-3.60776,-3.95081,-3.41748,-3.35887,-3.32634,-3.44238,-4.08077,-13.9358,-14.286,-13.7044,26.1835,6.80091,-9.85138,-3.54038,-19.8011 --5.34199,0.014673,0.40918,2,3,0,9.79513,1.77412,6.36556,0.684142,0.549267,-0.402652,-0.359899,1.06005,1.50492,1.59626,-0.477095,6.12906,-0.788987,8.52195,11.9352,5.27051,-0.516838,11.3538,-1.26286,-4.68996,-3.60776,-3.95081,-3.41748,-3.35887,-3.32634,-3.44238,-4.08077,-19.6181,11.4038,23.8724,-5.45414,8.35562,-30.6925,0.296784,1.68544 --9.48727,0.876846,0.40918,3,15,0,12.8271,4.1908,1.16216,-0.154372,0.326603,2.42023,1.31942,0.0996147,-1.81268,0.175318,1.16209,4.0114,7.0035,4.30657,4.39455,4.57037,5.72418,2.08418,5.54134,-4.90577,-3.22649,-3.7958,-3.34488,-3.3077,-3.40906,-4.48809,-3.87368,-5.06328,-1.0213,35.0926,10.7259,5.64012,-1.43366,20.5792,16.1901 --10.2462,0.996622,0.40918,3,7,0,11.9751,3.97367,1.20497,-0.0667577,1.03467,2.56695,1.1929,0.244168,-1.8366,0.281754,1.11828,3.89323,7.06677,4.26789,4.31318,5.22042,5.41107,1.76063,5.32117,-4.9184,-3.22588,-3.7947,-3.34666,-3.35501,-3.39724,-4.54011,-3.87815,-23.1775,12.4679,25.8645,6.15831,16.0666,-1.43488,-2.50246,2.73286 --7.80048,0.973615,0.40918,3,7,0,16.7675,6.31126,1.10223,-0.898287,-0.184952,-1.46287,-2.03994,-0.600149,0.934085,0.150675,-1.0196,5.32114,4.69884,5.64975,6.47734,6.1074,4.06276,7.34084,5.18742,-4.76995,-3.27601,-3.83766,-3.31796,-3.42798,-3.3556,-3.78961,-3.88093,-26.289,-0.434591,13.2906,7.0609,17.4938,8.46261,17.9908,-12.2461 --6.79955,0.628231,0.40918,3,15,0,14.1693,4.08609,1.57665,-0.518747,0.642735,-1.7228,-1.35936,-0.144077,0.62886,0.433907,-1.31247,3.26821,1.36985,3.85893,4.77021,5.09946,1.94286,5.07758,2.01679,-4.98624,-3.44132,-3.78341,-3.33738,-3.34581,-3.32051,-4.05647,-3.96311,-22.0533,-1.43731,-3.04913,16.142,7.65652,-6.62829,26.3255,9.02787 --8.50053,0.973226,0.40918,3,7,0,11.0728,3.87115,3.53339,-1.31015,0.370059,-2.26308,-0.649435,1.0482,-0.0923588,0.737093,-0.749461,-0.758111,-4.1252,7.57483,6.47558,5.17871,1.57644,3.54481,1.22301,-5.46483,-3.95663,-3.90994,-3.31797,-3.35182,-3.31821,-4.26629,-3.98854,0.853478,3.44838,32.5558,-14.7296,1.79979,-12.176,-0.810901,31.7481 --8.50053,0.011583,0.40918,3,7,0,15.6594,3.87115,3.53339,-1.31015,0.370059,-2.26308,-0.649435,1.0482,-0.0923588,0.737093,-0.749461,-0.758111,-4.1252,7.57483,6.47558,5.17871,1.57644,3.54481,1.22301,-5.46483,-3.95663,-3.90994,-3.31797,-3.35182,-3.31821,-4.26629,-3.98854,-1.7194,-5.05695,11.7425,19.4998,16.1259,-1.27447,-12.3906,9.58329 --7.92902,0.877808,0.40918,3,15,0,13.8572,4.37834,2.79085,-1.21193,-0.748894,-2.24052,-0.832075,1.30826,0.154479,0.472456,-0.303034,0.996024,-1.87462,8.0295,5.6969,2.28829,2.05614,4.80947,3.53262,-5.24747,-3.70906,-3.92912,-3.32385,-3.18291,-3.32144,-4.09147,-3.91995,0.00257836,3.24054,8.903,0.945129,3.48747,6.57951,-3.38627,-8.30685 --9.10005,0.97888,0.40918,5,47,0,13.9283,8.16345,1.01503,-1.87108,-1.26111,-0.75035,0.657524,0.427295,0.705373,-1.39464,-1.39689,6.26425,7.40182,8.59716,6.74785,6.88338,8.83085,8.87942,6.74557,-4.67686,-3.22331,-3.95421,-3.3171,-3.49979,-3.57023,-3.63745,-3.85192,12.2969,1.02018,16.0041,6.95244,14.8255,-7.41705,13.3799,6.35545 --6.47006,0.96541,0.40918,3,15,0,14.1821,1.93367,4.83864,1.48712,0.283711,1.15006,-0.0523042,1.40453,-1.13522,0.517243,-0.202952,9.12929,7.4984,8.72969,4.43642,3.30645,1.68059,-3.55926,0.951664,-4.41833,-3.22278,-3.96025,-3.34399,-3.23064,-3.31875,-5.54553,-3.99768,11.2782,20.6315,-0.880911,8.67593,7.18518,-14.4088,-1.1564,3.8611 --8.26714,0.789053,0.40918,3,15,0,14.2364,3.45491,9.551,1.27568,1.14033,0.852451,-0.0209774,1.08667,-0.795083,0.519103,-0.988923,15.639,11.5967,13.8337,8.41286,14.3462,3.25456,-4.13893,-5.99029,-3.96653,-3.2862,-4.24499,-3.32508,-4.5699,-3.33784,-5.67218,-4.30877,-2.09907,22.1224,22.8839,-3.60725,5.31429,9.24277,19.4434,5.40604 --6.45987,0.95626,0.40918,3,15,0,12.3544,4.18494,1.78611,1.2497,-0.649092,1.36007,-0.852055,0.527309,-1.1498,-0.295078,-1.1864,6.41703,6.61418,5.12677,3.6579,3.02559,2.66308,2.13126,2.06591,-4.66215,-3.23113,-3.82052,-3.36299,-3.2162,-3.32826,-4.48061,-3.9616,-4.342,12.6625,33.4266,16.1697,3.74821,3.81467,-17.1304,18.6747 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--9.06894,0.992986,0.40918,3,7,0,14.0528,0.803413,3.07497,1.78127,-0.508637,-0.549039,-1.72911,-1.50982,-1.27762,-0.300838,-0.656001,6.28075,-0.884862,-3.83922,-0.121654,-0.76063,-4.51356,-3.12521,-1.21377,-4.67527,-3.61623,-3.6929,-3.52641,-3.11652,-3.44245,-5.4529,-4.07876,17.6617,15.3541,-17.2417,-0.271557,1.6877,-5.43971,-5.30483,7.41874 --10.9172,0.835891,0.40918,3,7,0,19.9368,-1.51089,2.44901,3.5964,1.24931,0.182371,0.0556694,0.700229,0.477447,-0.491079,-0.0504435,7.29673,-1.06426,0.203978,-2.71355,1.54869,-1.37456,-0.341619,-1.63443,-4.57949,-3.63233,-3.71158,-3.70672,-3.15626,-3.34013,-4.9036,-4.09619,8.14366,-4.35851,-2.88072,-15.4579,-17.6238,-12.6634,8.22483,11.4969 --3.6768,1,0.40918,3,7,0,12.283,1.13073,5.53923,0.328385,0.64082,-0.469835,0.671062,0.51837,0.697084,0.336291,0.853058,2.94973,-1.47179,4.0021,2.99353,4.68038,4.8479,4.99204,5.85602,-5.02147,-3.6701,-3.78729,-3.38316,-3.31534,-3.37802,-4.06756,-3.86756,-5.67493,-13.4211,5.97341,12.3197,-7.62128,8.20342,-8.23879,-1.47231 --7.80206,0.87508,0.40918,3,7,0,11.6757,6.85945,1.51186,2.23852,-1.57595,-0.304999,-0.493015,-0.701794,0.511363,0.944353,-1.16028,10.2438,6.39833,5.79843,8.28718,4.47684,6.11408,7.63256,5.10527,-4.32762,-3.23435,-3.84272,-3.32368,-3.30132,-3.42491,-3.75894,-3.88267,10.0692,7.81277,18.1137,10.0559,-3.93033,-16.3213,7.7291,6.98908 --9.22561,0.987435,0.40918,3,7,0,14.0874,8.50886,1.25851,1.42731,-0.97389,1.0616,0.692872,-0.115919,1.03309,1.96049,-1.45893,10.3052,9.8449,8.36298,10.9762,7.28321,9.38085,9.80902,6.67278,-4.32278,-3.23854,-3.94371,-3.38216,-3.53969,-3.60708,-3.55698,-3.85311,-1.51975,0.29492,-19.9698,4.3819,9.96499,33.3827,14.4447,-24.4061 --7.81545,0.983302,0.40918,3,7,0,13.5669,1.49408,4.05984,0.0802005,0.813069,0.121547,-0.204916,1.06719,-0.266261,-2.13831,1.3655,1.81969,1.98754,5.82673,-7.18714,4.79502,0.662157,0.413104,7.0378,-5.15012,-3.40227,-3.8437,-4.14855,-3.32346,-3.31731,-4.76802,-3.84731,1.71091,2.845,2.10508,-7.82955,-2.7051,5.97097,-0.781618,-17.3565 --3.28922,0.989191,0.40918,3,7,0,10.1604,5.42494,4.04912,1.24673,0.475804,0.112289,0.200897,-0.605035,0.746686,-0.421602,-0.603776,10.4731,5.87961,2.97508,3.71783,7.35153,6.2384,8.44836,2.98018,-4.30964,-3.244,-3.76126,-3.36135,-3.54671,-3.43023,-3.67769,-3.93486,34.8882,13.4196,-4.49789,5.39389,-12.6354,-2.39598,4.53749,-5.3363 --3.49835,0.740245,0.40918,3,7,0,13.3451,3.01889,2.78308,0.403926,0.593536,0.139366,-0.81581,0.117718,1.32442,-0.382293,0.0591398,4.14305,3.40676,3.34651,1.95494,4.67075,0.748426,6.70486,3.18348,-4.89178,-3.32701,-3.7702,-3.42201,-3.31467,-3.3171,-3.85942,-3.92927,-2.8887,2.13477,12.5471,-0.478719,16.7502,16.4691,3.94038,-43.1455 --3.25941,0.945533,0.40918,2,7,0,7.6126,4.33167,2.06276,0.104007,0.339692,-0.228015,0.531398,0.0749742,-0.625663,-0.250814,0.232637,4.54621,3.86133,4.48632,3.8143,5.03237,5.42782,3.04107,4.81155,-4.84939,-3.30717,-3.80099,-3.35877,-3.34079,-3.39785,-4.34037,-3.88905,8.76361,1.66913,14.1943,4.63906,11.6337,-9.34158,-2.38076,-43.5403 --7.37085,1,0.40918,3,7,0,17.4557,6.63587,0.47852,0.708936,-0.746048,0.594384,-0.167577,-0.316476,1.98978,0.854391,-0.912786,6.97511,6.92029,6.48443,7.04471,6.27887,6.55568,7.58802,6.19908,-4.60931,-3.22735,-3.86722,-3.31684,-3.44321,-3.44438,-3.76357,-3.86124,18.6118,11.0277,7.53928,31.4361,-1.90019,28.4797,11.7023,-15.474 --4.83851,0.753922,0.40918,3,7,0,9.14212,6.71558,17.2724,1.07482,-0.58323,-0.192807,-0.333437,0.73431,-0.082162,0.603892,-0.614714,25.2804,3.38534,19.3989,17.1463,-3.35821,0.956321,5.29645,-3.90201,-3.64343,-3.328,-4.67143,-3.74223,-3.15049,-3.31684,-4.02843,-4.19955,21.1701,11.6254,11.7337,8.30855,8.15352,0.617957,5.1847,-7.97438 --6.4048,0.894469,0.40918,3,7,0,9.15763,4.46406,1.48497,1.33415,-0.128147,0.439078,0.891714,-0.801811,1.7176,-0.243961,1.41251,6.44523,5.11608,3.27339,4.10178,4.27376,5.78823,7.01465,6.56159,-4.65945,-3.26311,-3.76839,-3.35154,-3.28785,-3.41157,-3.82491,-3.85495,-16.3357,22.6853,1.98666,7.65058,8.36099,2.66517,12.3029,24.2817 --6.03793,1,0.40918,3,7,0,9.77277,5.78797,2.11276,1.12847,0.277226,0.84197,0.456969,-0.677387,1.60283,0.205399,1.68944,8.17216,7.56685,4.35681,6.22192,6.37368,6.75343,9.17437,9.35735,-4.50064,-3.22246,-3.79724,-3.31934,-3.45179,-3.45362,-3.61098,-3.82009,17.0441,16.8945,30.4,2.92398,13.9294,12.2679,6.56133,4.13314 --4.4536,0.995846,0.40918,3,15,0,9.58568,1.08501,4.04858,0.31935,1.09645,0.446008,0.00870564,1.08815,1.42298,0.387977,-0.0877993,2.37792,2.89071,5.49047,2.65577,5.5241,1.12025,6.84604,0.729545,-5.08586,-3.35205,-3.83232,-3.39482,-3.3789,-3.31689,-3.84358,-4.00533,12.9036,14.2521,3.5629,-5.44971,7.08818,0.0231727,-1.94113,-21.3987 --4.55551,0.964113,0.40918,3,7,0,8.76436,3.29671,4.91053,1.05823,-1.61635,0.971432,-0.0365319,0.0763876,1.33083,-0.520885,-0.565038,8.49318,8.06695,3.67181,0.738888,-4.64044,3.11732,9.83177,0.522073,-4.47258,-3.22155,-3.77847,-3.47882,-3.19797,-3.33536,-3.55512,-4.01262,20.2966,10.4317,-0.0889452,-22.5516,-1.31691,0.802587,-6.40379,0.681534 --6.39359,0.857115,0.40918,3,7,0,11.4513,2.21358,4.91512,2.10286,-1.92087,0.719988,-0.800607,-0.0509664,0.929312,-0.270368,-0.623,12.5494,5.75242,1.96308,0.884694,-7.22772,-1.7215,6.78127,-0.848536,-4.15748,-3.24678,-3.73964,-3.47137,-3.35557,-3.34744,-3.85082,-4.06407,-6.77492,14.1562,17.0533,-3.96444,-0.0512073,5.7273,6.63223,12.0327 --5.73607,0.967933,0.40918,3,7,0,9.56961,6.58902,2.37052,-1.40717,1.25836,-0.451736,0.498144,-0.240181,0.226667,-0.0582759,0.922649,3.25331,5.51817,6.01967,6.45088,9.57198,7.76988,7.12634,8.77618,-4.98788,-3.25232,-3.85042,-3.31808,-3.80608,-3.50622,-3.81271,-3.82535,-4.10449,-4.73873,-1.12456,-14.5099,12.8753,-2.20484,0.516357,11.073 --7.1453,0.879156,0.40918,3,7,0,12.0315,4.45475,1.55047,1.40921,-0.654768,0.202146,-2.17945,0.805022,-0.735015,0.173603,0.881943,6.63969,4.76817,5.70291,4.72391,3.43955,1.07557,3.31513,5.82218,-4.64091,-3.27375,-3.83946,-3.33824,-3.23783,-3.31686,-4.29975,-3.86821,19.0097,10.8369,12.1982,18.9896,9.04921,9.31055,9.86962,-3.51491 --5.90557,0.999237,0.40918,3,7,0,10.1921,7.99665,4.32893,-0.598449,-0.229756,-1.35885,1.07814,0.331981,-0.639893,0.450641,-0.140086,5.40601,2.11429,9.43378,9.94745,7.00206,12.6638,5.2266,7.39023,-4.76141,-3.39473,-3.99348,-3.35273,-3.51143,-3.879,-4.03732,-3.8421,19.0719,3.82759,-14.8975,-2.39075,26.7415,34.2604,-0.322707,18.8028 --3.53944,1,0.40918,3,7,0,7.04876,3.75078,1.98809,0.444071,0.0994205,-0.110646,1.21456,0.307006,0.0726814,0.324999,-0.237255,4.63363,3.5308,4.36113,4.3969,3.94843,6.16543,3.89527,3.27909,-4.84029,-3.32139,-3.79736,-3.34483,-3.26732,-3.42709,-4.21624,-3.92668,22.2908,3.07991,27.4411,21.5133,1.69041,7.42463,-1.3415,5.09801 --7.53037,0.918866,0.40918,3,7,0,8.93583,7.89879,2.97965,2.12093,0.477895,0.483224,-0.011942,-0.51375,-1.80116,-0.560152,0.0664408,14.2184,9.33863,6.368,6.22973,9.32275,7.86321,2.53195,8.09676,-4.04906,-3.23048,-3.86293,-3.31929,-3.77394,-3.51148,-4.41783,-3.83282,18.2535,8.00521,31.2664,-4.97141,9.80841,32.8428,-6.33419,-24.802 --7.12689,0.613436,0.40918,3,15,0,15.5345,0.435662,2.42628,0.608702,0.568455,2.26809,-0.650137,-1.38159,0.17503,-0.189908,0.716559,1.91254,5.93868,-2.91645,-0.0251066,1.81489,-1.14175,0.860335,2.17423,-5.13933,-3.24277,-3.69154,-3.52077,-3.16507,-3.33579,-4.69036,-3.9583,10.8138,15.1797,-11.8906,-0.0858161,1.8446,7.56047,-0.640922,35.4717 --5.3239,1,0.40918,3,7,0,9.83947,1.12057,3.31747,-0.124627,0.348896,0.985752,-0.965423,-0.937521,-0.0329732,0.60948,1.48289,0.707126,4.39077,-1.98962,3.1425,2.27802,-2.08219,1.01118,6.04002,-5.28232,-3.28666,-3.69352,-3.37832,-3.18249,-3.35609,-4.66462,-3.86413,10.0292,-13.8072,1.16042,-1.37605,17.5921,-0.247484,8.71211,-4.07371 --7.84376,0.715088,0.40918,3,7,0,14.824,2.76748,2.44888,-1.09819,0.236645,-1.63302,-0.596781,-1.84438,0.721742,1.11751,0.860422,0.0781427,-1.23159,-1.74919,5.50414,3.347,1.30603,4.53494,4.87455,-5.3595,-3.64763,-3.69458,-3.32608,-3.23281,-3.31722,-4.12806,-3.88766,-15.1778,-0.636884,-0.0153786,-4.26945,-6.56351,5.18625,6.26607,-3.09575 --7.74158,0.942815,0.40918,3,15,0,12.9616,3.89074,5.78835,2.49924,-0.657511,1.2274,0.781437,1.67867,0.991056,-0.678869,0.642726,18.3572,10.9954,13.6075,-0.0387863,0.0848373,8.41398,9.62733,7.61107,-3.83362,-3.26638,-4.23022,-3.52156,-3.12343,-3.54397,-3.57203,-3.83904,9.7839,-4.37356,5.6142,1.81191,5.40329,8.02306,9.68771,1.75541 --7.32493,0.92734,0.40918,3,15,0,14.8671,7.80039,3.14772,-0.24747,-0.105179,0.830078,0.670625,0.141643,2.21761,0.235374,-1.77973,7.02142,10.4132,8.24624,8.54128,7.46931,9.91132,14.7808,2.1983,-4.60499,-3.25064,-3.93855,-3.32665,-3.55894,-3.64498,-3.27334,-3.95757,14.9172,2.73246,11.8188,13.1149,18.1001,23.5716,6.32425,-16.1218 --5.32733,1,0.40918,3,7,0,8.71125,1.07495,5.98377,0.76678,-0.390565,-0.986514,-0.459895,0.145537,-0.80732,-0.215864,0.656294,5.66318,-4.82812,1.94581,-0.216735,-1.2621,-1.67696,-3.75587,5.00205,-4.73573,-4.04433,-3.7393,-3.53205,-3.11659,-3.34645,-5.58811,-3.88488,38.7359,9.67313,-6.51124,7.26424,-2.0588,0.874946,3.53167,-9.92451 --6.98048,0.77354,0.40918,3,7,0,10.3414,8.72275,0.934299,2.02528,0.441109,0.489192,-0.964439,-0.48888,0.153655,-0.48201,-0.594611,10.615,9.17981,8.26599,8.27241,9.13488,7.82168,8.86631,8.16721,-4.29863,-3.22848,-3.93942,-3.32352,-3.75021,-3.50913,-3.63864,-3.83198,11.2521,-2.58098,39.6077,9.50556,-2.15642,-5.99085,0.201799,23.783 --5.59856,0.933874,0.40918,3,15,0,10.5568,9.80176,2.35539,-0.540534,-1.36267,-0.466656,0.169534,0.86711,-0.227721,0.697902,-0.260203,8.52859,8.70261,11.8441,11.4456,6.59215,10.2011,9.26539,9.18888,-4.46951,-3.22399,-4.1219,-3.3985,-3.47197,-3.66667,-3.60299,-3.82151,23.5932,21.4193,20.9,10.0173,-4.06105,5.0608,-1.59769,0.308632 --6.76316,0.855917,0.40918,3,7,0,16.2496,2.9936,1.03823,0.0031507,0.695785,0.658496,0.830082,-1.12388,-1.1981,0.790949,-1.33101,2.99687,3.67727,1.82675,3.81479,3.71598,3.85541,1.74968,1.6117,-5.01623,-3.31495,-3.73703,-3.35876,-3.25345,-3.35053,-4.54189,-3.97585,1.98399,-6.65863,22.2665,13.4734,-7.78923,-9.66676,8.55688,10.2225 --6.05135,0.89268,0.40918,3,7,0,14.5205,5.10547,0.455265,-0.0295606,0.0392158,0.0836845,0.622315,-1.58674,-0.515271,0.742919,-0.763768,5.09201,5.14357,4.38308,5.4437,5.12333,5.38879,4.87089,4.75776,-4.79316,-3.26232,-3.79799,-3.32684,-3.34761,-3.39643,-4.08339,-3.89025,4.48915,-12.6192,8.92806,5.40728,4.6231,-17.8177,14.1507,-15.3132 --7.07183,0.976088,0.40918,3,15,0,10.2783,4.29125,6.8153,-1.34054,-0.243969,-0.464196,-1.11754,1.13508,-0.467312,0.345144,0.93361,-4.84493,1.12762,12.0271,6.64351,2.62853,-3.32511,1.10638,10.6541,-6.0243,-3.45767,-4.13257,-3.31736,-3.19744,-3.39413,-4.6485,-3.81211,4.73095,-2.98624,19.1795,1.03106,-6.04577,-3.7636,-2.62356,12.9275 --10.0368,0.820975,0.40918,3,7,0,13.3282,4.88167,0.421502,1.3449,-0.532117,-1.231,-0.442666,-0.367974,2.7552,0.93214,-0.475818,5.44855,4.3628,4.72657,5.27457,4.65738,4.69509,6.04299,4.68111,-4.75714,-3.28767,-3.80813,-3.32914,-3.31373,-3.37325,-3.93637,-3.89197,11.0563,-5.60207,12.019,22.5203,4.78757,9.19113,7.58015,4.91578 --5.879,1,0.40918,3,7,0,13.174,9.64592,7.30351,0.162132,-0.368014,-1.59928,0.752016,0.17479,-0.00648439,-0.149038,-0.303708,10.8301,-2.0344,10.9225,8.55742,6.95813,15.1383,9.59856,7.42779,-4.28212,-3.72497,-4.07011,-3.32686,-3.5071,-4.14283,-3.57444,-3.84157,6.69957,-1.29268,25.7062,6.33501,4.11189,28.7527,-10.4756,11.9959 --4.86273,1,0.40918,3,7,0,8.18144,4.67487,0.716522,0.00565509,0.9166,0.0638742,-0.534166,0.83944,-0.368249,0.705838,0.525396,4.67892,4.72064,5.27635,5.18062,5.33163,4.29213,4.41101,5.05133,-4.83559,-3.27529,-3.82531,-3.33051,-3.36363,-3.36162,-4.14483,-3.88382,15.3587,12.5415,31.584,-19.4061,34.2822,2.55665,-1.17017,22.3613 --12.9882,0.855619,0.40918,3,7,0,14.623,3.4773,2.88631,-0.089531,1.90787,-1.27192,0.911883,-0.67347,-1.33091,2.049,2.48278,3.21889,-0.19384,1.53346,9.39136,8.984,6.10927,-0.36412,10.6434,-4.99166,-3.55722,-3.73167,-3.34046,-3.73147,-3.4247,-4.90773,-3.81215,-29.6327,-11.012,4.40883,5.6878,3.56145,-5.91092,6.40165,47.3433 -# -# Elapsed Time: 0.032638 seconds (Warm-up) -# 0.00743 seconds (Sampling) -# 0.040068 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-3.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-3.csv deleted file mode 100644 index f5dcc44632..0000000000 --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-3.csv +++ /dev/null @@ -1,148 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 20 -# stan_version_patch = 0 -# model = stan_test_data_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 1000 (Default) -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 3 -# data -# file = /tmp/tmp2ipdytqf/_pz629gf.json -# init = 2 (Default) -# random -# seed = 81267 -# output -# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-3-llkr9wpb.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1.1,eta.2.1,eta.1.2,eta.2.2,eta.1.3,eta.2.3,eta.1.4,eta.2.4,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 -# Adaptation terminated -# Step size = 0.427277 -# Diagonal elements of inverse mass matrix: -# 12.6931, 1.16115, 0.989902, 0.910845, 0.958385, 0.911528, 0.76311, 0.950557, 0.805897, 0.992369 --5.79095,0.967314,0.427277,3,7,0,10.365,3.01052,2.6873,-0.473404,-1.45263,0.31315,-0.18336,-1.63014,1.64636,0.167379,0.380245,1.73834,3.85205,-1.37015,3.46032,-0.893121,2.51778,7.43478,4.03235,-5.1596,-3.30755,-3.69672,-3.36861,-3.11623,-3.32635,-3.77964,-3.90728,0.0422749,2.26619,-6.12752,10.0996,-15.0136,-11.386,25.1375,21.1875 --8.07588,0.946646,0.427277,3,15,0,11.606,8.55464,4.47064,0.723242,0.452029,-0.510986,2.2942,0.406132,0.639866,0.215814,-1.13542,11.788,6.27021,10.3703,9.51947,10.5755,18.8112,11.4153,3.47858,-4.21105,-3.23648,-4.04068,-3.34306,-3.94328,-4.62774,-3.43832,-3.92137,14.4822,10.1253,36.7617,22.6547,30.469,23.8266,21.8378,19.5739 --4.98864,0.978877,0.427277,3,15,0,13.6811,7.11007,7.16199,1.07826,-0.947594,-0.275759,0.847614,-1.78481,0.423834,0.207942,-0.60672,14.8326,5.13509,-5.67275,8.59935,0.323419,13.1807,10.1456,2.76475,-4.01228,-3.26256,-3.70548,-3.3274,-3.12697,-3.92993,-3.52998,-3.94093,12.6788,12.6516,-12.1518,7.58184,8.57212,11.6951,-12.7672,-14.7221 --4.67212,0.999443,0.427277,3,7,0,7.59585,3.24958,5.59917,0.284411,-0.805205,0.216358,-1.08105,1.1924,1.40357,0.605027,1.12364,4.84204,4.461,9.92602,6.63722,-1.2589,-2.80339,11.1084,9.54101,-4.81875,-3.28415,-4.01786,-3.31738,-3.11658,-3.37661,-3.459,-3.81864,-14.3316,-14.6096,17.6746,2.86421,8.79528,-5.86463,17.1408,-7.40981 --4.50208,0.981408,0.427277,3,7,0,8.17045,0.508995,5.55888,0.262235,0.876289,0.783007,0.4043,-0.155355,-0.232564,-0.687559,0.613249,1.96673,4.86164,-0.354604,-3.31307,5.38019,2.75645,-0.783801,3.91798,-5.13306,-3.27077,-3.7052,-3.75634,-3.36744,-3.32958,-4.98568,-3.91011,-7.03528,10.5341,-7.86156,-21.8781,2.73323,16.8201,-2.311,-15.115 --4.44091,0.862133,0.427277,3,7,0,11.8488,5.80564,1.84191,0.156455,-1.10973,-1.12321,-0.746936,-0.108845,0.874935,0.496397,-0.390372,6.09382,3.7368,5.60516,6.71996,3.76161,4.42985,7.4172,5.08661,-4.69339,-3.3124,-3.83615,-3.31716,-3.25612,-3.36544,-3.7815,-3.88307,-8.03861,-14.8288,-21.0963,13.6328,13.8217,-2.48648,-16.9,-18.6294 --5.58378,0.955063,0.427277,3,7,0,10.1909,2.05839,1.51051,0.477381,0.741143,2.10778,0.245345,-0.201056,-0.0971007,0.0931374,-0.0185124,2.77948,5.24222,1.7547,2.19908,3.1779,2.42899,1.91172,2.03043,-5.04049,-3.25955,-3.73568,-3.41208,-3.22391,-3.32527,-4.51569,-3.96269,20.3405,-4.09254,-4.34628,3.02329,11.1568,2.50622,8.90532,-14.6975 --5.9575,0.944683,0.427277,3,7,0,11.1314,5.5821,2.38442,-0.488822,0.710989,-2.18504,0.350608,-0.62047,0.404595,0.140501,0.0743951,4.41654,0.372032,4.10264,5.91711,7.2774,6.4181,6.54683,5.75949,-4.86294,-3.51245,-3.79006,-3.32168,-3.5391,-3.43814,-3.8774,-3.86941,-14.2204,9.73889,-28.8332,17.1468,14.3814,-0.445429,11.9812,9.85768 --4.51882,0.978899,0.427277,3,7,0,9.06162,2.93187,0.833376,-0.411022,-0.60528,0.241282,0.462756,-0.265925,-0.233405,0.936239,0.630502,2.58933,3.13295,2.71025,3.71211,2.42744,3.31752,2.73735,3.45731,-5.06188,-3.33996,-3.75521,-3.3615,-3.18868,-3.33903,-4.38627,-3.92193,0.613841,13.4854,-10.2327,7.88901,4.82029,23.9929,18.7687,-10.7736 --2.6833,0.972535,0.427277,3,7,0,6.74386,5.73082,14.1062,0.800466,-0.349681,-0.205786,-0.00362346,-0.0566165,0.88178,-0.818961,0.174667,17.0224,2.82795,4.93217,-5.82164,0.798134,5.6797,18.1694,8.19471,-3.89478,-3.35527,-3.81442,-3.99615,-3.13612,-3.40733,-3.22167,-3.83166,18.4595,0.788126,-10.9877,-12.5882,-14.344,-0.528336,17.7638,-2.23533 --4.04749,0.889552,0.427277,3,7,0,5.51155,0.699194,4.35284,1.4553,-0.3658,0.951651,0.47443,-0.103688,0.374417,1.09709,0.560293,7.0339,4.84158,0.247856,5.47466,-0.893078,2.76431,2.32897,3.13806,-4.60383,-3.2714,-3.71213,-3.32645,-3.11623,-3.3297,-4.44943,-3.9305,17.0896,23.6224,-0.0809572,18.7562,1.45291,13.3826,19.5796,26.2246 --3.84476,0.617753,0.427277,3,7,0,6.79858,1.41419,2.38985,0.863576,-0.28029,0.32534,-0.221685,-0.462732,0.29596,-0.198284,1.23496,3.478,2.1917,0.308329,0.940319,0.744338,0.884394,2.12149,4.36555,-4.96327,-3.39021,-3.7129,-3.46857,-3.13495,-3.31689,-4.48216,-3.89926,8.21512,1.41171,-0.0794572,9.66776,0.302412,-19.6378,-13.1964,6.45377 --6.44506,0.964131,0.427277,3,7,0,7.86909,7.69789,1.62127,0.135732,-0.875059,-0.490448,1.87074,0.110924,0.680432,1.1768,-0.465985,7.91795,6.90275,7.87773,9.6058,6.27919,10.7309,8.80106,6.94241,-4.52319,-3.22754,-3.92263,-3.34489,-3.44324,-3.70811,-3.64463,-3.84878,13.1448,2.95008,11.9271,23.4681,3.60921,8.54682,0.246466,-3.34083 --6.823,0.899726,0.427277,3,7,0,14.0497,2.96765,3.36903,0.0208052,-1.43633,-0.497866,-0.543906,0.905771,-0.738031,1.94879,-0.440355,3.03774,1.29032,6.01922,9.53319,-1.8714,1.13521,0.481198,1.48408,-5.01169,-3.44662,-3.85041,-3.34335,-3.12085,-3.31691,-4.75607,-3.97997,27.0863,-11.2204,0.437573,6.13205,-15.7291,8.27031,-1.26459,7.72182 --6.13788,0.833499,0.427277,3,7,0,14.8143,5.44042,1.64335,0.55459,0.40272,0.0118594,1.74881,1.41357,-0.626337,0.235581,-0.466412,6.35181,5.45991,7.76341,5.82757,6.10223,8.31434,4.41113,4.67395,-4.66842,-3.25378,-3.9178,-3.32251,-3.42753,-3.53791,-4.14481,-3.89214,40.5997,18.9005,38.0638,20.6416,16.4854,22.9209,-11.1122,-8.38134 --6.24101,0.982481,0.427277,3,7,0,9.41308,3.04073,5.75963,0.0449293,-0.959203,0.0816919,-1.95529,-1.59282,1.15443,-0.329565,0.678393,3.29951,3.51125,-6.13335,1.14255,-2.48393,-8.22104,9.68982,6.94802,-4.9828,-3.32227,-3.7107,-3.45861,-3.12976,-3.66819,-3.56682,-3.8487,-8.72501,4.23811,23.985,12.729,10.5934,-12.5816,8.87108,32.0065 --7.99241,0.983221,0.427277,3,7,0,11.5531,7.99594,3.23202,1.20296,-1.83961,-0.0944298,-0.491554,-1.33733,0.139108,-1.85639,1.39041,11.8839,7.69074,3.67364,1.99606,2.05027,6.40722,8.44554,12.4898,-4.20416,-3.222,-3.77851,-3.4203,-3.1736,-3.43765,-3.67796,-3.80968,5.31875,17.0794,4.91002,-6.12496,10.8327,3.59355,3.7895,-18.336 --9.88207,0.955115,0.427277,3,7,0,12.775,1.32546,3.70849,-0.870636,1.5473,0.277073,0.278349,1.57568,0.281286,2.1713,-1.43545,-1.90328,2.35298,7.16886,9.37772,7.06362,2.35771,2.36861,-3.99789,-5.61411,-3.38097,-3.89349,-3.3402,-3.51753,-3.32445,-4.44323,-4.20427,5.13072,-1.6165,23.4027,15.1884,4.13878,3.29577,-4.81719,-24.7485 --8.38299,0.861866,0.427277,3,7,0,16.3933,6.22116,0.525899,0.536235,-0.781979,-1.6257,-0.868934,-0.284183,1.26366,-1.82675,-0.166423,6.50316,5.36621,6.07171,5.26047,5.80992,5.76419,6.88571,6.13364,-4.65391,-3.25621,-3.85226,-3.32934,-3.40243,-3.41063,-3.83916,-3.86242,0.2532,1.33809,-27.7887,-1.3753,12.6691,4.37615,5.93705,17.9338 --8.47709,0.984283,0.427277,3,7,0,13.4482,3.15106,6.59852,0.553053,-0.957865,-1.58903,-0.22129,1.10119,-0.431944,1.31058,-1.39795,6.80039,-7.33422,10.4173,11.799,-3.16944,1.69087,0.300867,-6.07333,-4.62571,-4.39722,-4.04313,-3.412,-3.14522,-3.31881,-4.78782,-4.31339,46.295,-7.97982,-3.57704,15.1103,8.4028,9.24298,-1.92837,-1.50341 --8.47709,0.103857,0.427277,3,7,0,16.3524,3.15106,6.59852,0.553053,-0.957865,-1.58903,-0.22129,1.10119,-0.431944,1.31058,-1.39795,6.80039,-7.33422,10.4173,11.799,-3.16944,1.69087,0.300867,-6.07333,-4.62571,-4.39722,-4.04313,-3.412,-3.14522,-3.31881,-4.78782,-4.31339,-0.0839041,-1.58207,13.0508,11.0933,-13.8052,18.3756,14.3476,-21.9579 --8.12451,0.390264,0.427277,3,15,0,19.0466,4.5323,1.75985,0.735207,-0.384923,-0.352729,1.34872,-1.46501,1.71972,-1.72481,1.07205,5.82615,3.91155,1.95411,1.4969,3.8549,6.90584,7.55875,6.41894,-4.71961,-3.3051,-3.73946,-3.44197,-3.26166,-3.46096,-3.76662,-3.85738,-7.02392,-4.19828,5.91172,-0.713662,4.20597,-17.8411,20.3284,16.0921 --4.37828,0.995389,0.427277,3,15,0,10.8234,1.75018,8.38417,0.282976,-0.0766654,1.22827,0.600437,-0.319544,1.7973,-0.72278,0.0989791,4.1227,12.0482,-0.928929,-4.30973,1.1074,6.78434,16.8191,2.58004,-4.89393,-3.30346,-3.6999,-3.84539,-3.14358,-3.45509,-3.2285,-3.94625,-34.9997,25.1992,-24.5345,-9.6837,-11.2961,1.55115,13.3154,-35.7499 --3.61639,1,0.427277,3,7,0,6.24327,1.79977,6.07624,1.22872,1.00228,0.785761,-0.0267704,0.00948983,0.3642,0.847744,0.299046,9.26577,6.57424,1.85743,6.95086,7.88984,1.63711,4.01273,3.61685,-4.40692,-3.23169,-3.73761,-3.31684,-3.604,-3.31851,-4.19974,-3.91776,9.57906,16.2977,-5.4291,6.36077,-1.04067,15.0805,10.2666,26.8349 --4.28248,0.897576,0.427277,3,7,0,8.09795,4.42423,3.17823,-0.519162,-1.54205,-0.692866,-0.230953,-0.192289,0.759224,-0.755182,-0.0752014,2.77421,2.22214,3.81309,2.02409,-0.476772,3.6902,6.83721,4.18522,-5.04108,-3.38844,-3.78219,-3.41915,-3.11785,-3.34674,-3.84456,-3.90356,17.5869,-9.81317,28.3888,5.72169,-3.615,-0.921389,0.364992,-10.2826 --4.48304,0.97619,0.427277,3,7,0,6.99485,5.28713,7.29948,0.20246,-0.940065,1.44704,0.767989,0.44661,0.448907,0.0589771,-0.557508,6.76498,15.8498,8.54716,5.71763,-1.57485,10.8931,8.56392,1.21761,-4.62905,-3.52962,-3.95195,-3.32363,-3.1182,-3.72127,-3.66672,-3.98872,-5.16192,26.5557,39.1283,24.6143,-0.756144,14.1427,-7.50858,-17.3251 --6.14086,0.976052,0.427277,3,7,0,8.21701,6.18903,2.79315,0.573305,0.329433,-2.05859,-1.32396,-0.595069,0.827796,0.546967,0.26443,7.79036,0.43907,4.52691,7.71679,7.10919,2.49099,8.50119,6.92762,-4.53461,-3.50736,-3.80218,-3.31896,-3.52208,-3.32602,-3.67266,-3.84902,-6.41623,-1.52512,2.02933,-8.57768,8.34886,4.2034,35.4649,-6.23497 --8.30488,0.940314,0.427277,3,7,0,12.4418,2.12333,2.09958,-1.74661,-1.63393,0.452232,0.784436,1.54472,0.946346,-0.430298,-0.718011,-1.54381,3.07283,5.3666,1.21988,-1.30724,3.77031,4.11026,0.61581,-5.56663,-3.34291,-3.82825,-3.45489,-3.11675,-3.34855,-4.18615,-4.00931,-1.1202,-12.9661,28.5635,-15.2976,5.64586,-7.95285,12.5692,-18.7591 --9.00417,0.855853,0.427277,3,7,0,23.4685,-0.884482,3.0086,0.662631,-1.68453,0.0267744,1.31538,0.0550476,2.55992,-0.939829,0.377136,1.10911,-0.803928,-0.718865,-3.71205,-5.95257,3.07296,6.81731,0.25017,-5.23392,-3.60907,-3.70169,-3.791,-3.26757,-3.33459,-3.84679,-4.02236,6.42492,-5.70163,-16.1444,-1.90891,-5.4314,-5.96029,13.0148,-11.4644 --4.27746,1,0.427277,3,7,0,11.1705,3.336,7.28652,-0.0910245,0.728262,0.984374,-1.12661,0.158913,1.01447,0.769837,-0.0487915,2.67275,10.5087,4.49393,8.94544,8.6425,-4.87308,10.728,2.98048,-5.05248,-3.25299,-3.80121,-3.33247,-3.6901,-3.45937,-3.48593,-3.93485,-2.00027,10.7673,15.2215,13.9671,-1.04253,7.80014,10.2122,1.85631 --5.83238,0.830997,0.427277,3,7,0,10.9994,-1.35199,9.06899,-0.19609,0.496157,0.868968,-1.24869,-0.2477,0.747704,1.02745,-0.448383,-3.13033,6.52867,-3.59838,7.96599,3.14765,-12.6763,5.42893,-5.41837,-5.78054,-3.23235,-3.69223,-3.32069,-3.22235,-4.08973,-4.01168,-4.27752,0.454498,11.1517,8.24755,28.0323,7.83198,-31.4204,-0.902692,20.399 --9.12072,0.810368,0.427277,3,7,0,13.859,5.11432,1.21256,1.46251,-0.0585328,0.929484,-0.33965,0.178413,0.287668,-3.0279,-0.465284,6.88771,6.24137,5.33065,1.4428,5.04334,4.70247,5.46313,4.55013,-4.6175,-3.23699,-3.82707,-3.44445,-3.34161,-3.37348,-4.00739,-3.89496,9.77817,2.0331,-2.41977,-8.39848,0.783586,-8.18717,15.595,-1.44915 --9.18627,0.999024,0.427277,3,7,0,13.6373,3.01741,4.99714,-1.19393,-2.20951,-0.999991,0.477322,-0.352528,1.3755,1.76402,0.0765238,-2.9488,-1.97968,1.25578,11.8325,-8.02382,5.40266,9.89099,3.39981,-5.7555,-3.71949,-3.7269,-3.41333,-3.42069,-3.39693,-3.5503,-3.92345,2.36628,9.22219,22.4445,-3.56508,-0.921132,6.05284,-20.2506,9.60355 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--3.78841,0.997254,0.427277,3,7,0,5.38336,5.58952,2.97896,0.257302,0.215626,0.691061,0.48611,0.737393,0.0568021,0.880376,-0.729008,6.35601,7.64816,7.78618,8.21213,6.23186,7.03762,5.75873,3.41783,-4.66802,-3.22214,-3.91876,-3.32291,-3.439,-3.46747,-3.97077,-3.92297,-34.7022,10.7275,-64.5604,3.99456,3.65765,-0.351237,-8.1502,-11.5486 --9.0509,0.835481,0.427277,3,7,0,13.6894,3.19403,2.31315,-0.85797,1.09371,1.03806,1.73685,-0.522107,-1.48836,1.54362,0.60322,1.20942,5.59522,1.98632,6.76465,5.72396,7.21163,-0.24877,4.58937,-5.22196,-3.25044,-3.74009,-3.31706,-3.39525,-3.47627,-4.88661,-3.89406,14.1259,-1.50286,-0.285217,15.8464,10.6637,2.72337,-3.90415,37.8198 --4.70736,0.98051,0.427277,4,23,0,11.4904,6.50607,2.61598,0.274863,-0.166767,0.737456,-1.282,0.259321,0.378717,1.00099,1.23311,7.22511,8.43524,7.18445,9.12465,6.06981,3.15238,7.49679,9.73186,-4.58609,-3.22247,-3.89411,-3.33549,-3.4247,-3.33598,-3.77311,-3.81725,4.30437,-0.636067,3.10861,25.0684,15.0816,-10.9691,14.9075,-0.535254 --8.89562,0.923965,0.427277,3,7,0,13.2933,10.7635,3.70394,1.32762,-1.57439,-0.93422,-1.77054,-0.581834,-1.51651,-0.450492,0.627468,15.6809,7.30319,8.60841,9.0949,4.93206,4.20551,5.14642,13.0876,-3.96423,-3.22395,-3.95472,-3.33497,-3.33338,-3.35929,-4.0476,-3.81114,-0.609323,22.8864,20.7572,-0.517415,6.9482,-10.1874,2.96813,-8.33733 --7.88821,0.96861,0.427277,3,7,0,14.7822,-0.0468016,1.92297,0.337018,0.679705,1.09077,1.79625,-0.000734018,1.78028,0.752301,-1.10799,0.601274,2.05073,-0.0482131,1.39985,1.26025,3.40733,3.37662,-2.17744,-5.29519,-3.39849,-3.70854,-3.44643,-3.1477,-3.34078,-4.29074,-4.1195,-19.8587,14.109,-12.3057,2.55378,-9.92512,-34.4946,11.616,-9.92399 --8.72679,0.996417,0.427277,3,15,0,13.4383,-0.0678777,1.05013,0.989483,1.07928,0.333816,2.10148,-0.954816,0.740954,-0.85711,-0.80549,0.971212,0.282673,-1.07056,-0.967958,1.06551,2.13896,0.710224,-0.91375,-5.25045,-3.51931,-3.6988,-3.57918,-3.1425,-3.32219,-4.71621,-4.06666,31.2978,0.419011,-10.1034,-8.68861,-1.11263,25.9552,-18.8494,4.47284 --5.69548,0.998359,0.427277,3,7,0,13.1922,8.67836,4.3187,0.164577,-1.8038,-0.0472701,-1.14408,0.854186,-0.185266,0.500559,0.930164,9.38911,8.47421,12.3673,10.8401,0.888281,3.73742,7.87825,12.6955,-4.39669,-3.22265,-4.15277,-3.37777,-3.13817,-3.3478,-3.73377,-3.81006,31.0412,7.48737,19.6642,8.31501,-5.13223,-10.4863,8.22843,22.4205 --4.50564,0.974229,0.427277,3,7,0,9.69395,3.49508,1.11443,0.761254,-0.0802561,-0.707826,1.18806,0.131898,0.6631,-0.337249,-0.147205,4.34345,2.70626,3.64207,3.11924,3.40564,4.81909,4.23406,3.33103,-4.87062,-3.36164,-3.77769,-3.37907,-3.23598,-3.3771,-4.16903,-3.92528,-2.0898,10.2164,-24.2462,6.72074,17.5106,-4.00453,18.6251,21.7112 --6.65537,0.947258,0.427277,3,7,0,10.3144,5.83548,0.599591,-0.882159,0.758511,0.442537,-0.658463,0.179641,-1.53007,-1.10365,-0.0891757,5.30654,6.10082,5.94319,5.17374,6.29027,5.44067,4.91806,5.78201,-4.77142,-3.23956,-3.84774,-3.33062,-3.44424,-3.39832,-4.07721,-3.86898,28.5121,10.9787,-3.98986,25.1128,12.1113,6.61928,1.40557,24.7367 --4.45514,1,0.427277,3,7,0,8.94237,4.74066,3.86323,-1.28871,-0.874498,0.289875,0.896388,-0.663696,0.484708,-0.00223617,-0.230869,-0.237928,5.86052,2.17665,4.73202,1.36228,8.20362,6.6132,3.84876,-5.39895,-3.24441,-3.74387,-3.33809,-3.15061,-3.53126,-3.86982,-3.91185,5.87074,-9.71857,1.11157,30.9475,3.20683,6.99919,6.98624,-0.980122 --5.36202,0.950306,0.427277,3,15,0,8.52502,2.28182,2.89426,0.484119,-0.449143,-0.533742,1.25648,1.44854,1.27952,-0.166364,0.620535,3.68299,0.737033,6.47428,1.80032,0.981883,5.91839,5.98509,4.07781,-4.94103,-3.48528,-3.86684,-3.42856,-3.14041,-3.41679,-3.94331,-3.90616,4.35428,10.5624,-11.9286,6.44032,-8.07084,6.26075,2.96309,-8.79287 --4.68517,0.900394,0.427277,3,7,0,11.3253,8.58588,9.77794,0.145621,-0.181553,0.458059,-1.23658,-1.24562,-0.276395,0.07389,-0.360508,10.0098,13.0647,-3.59376,9.30837,6.81066,-3.50535,5.88331,5.06085,-4.34621,-3.34978,-3.69222,-3.33885,-3.49275,-3.40071,-3.9556,-3.88362,-17.4706,17.32,-7.21888,13.7155,14.0095,2.28515,-1.11052,21.1939 --5.48304,0.990686,0.427277,3,7,0,6.72874,-1.51732,7.28059,1.29433,-0.657717,-0.356307,0.968388,0.88483,1.62605,0.0109271,0.75344,7.90615,-4.11144,4.92477,-1.43776,-6.30589,5.53312,10.3213,3.96817,-4.52424,-3.95496,-3.81419,-3.61103,-3.28994,-3.40175,-3.51634,-3.90886,3.3685,-2.32347,5.3409,2.46274,1.07362,-3.76473,-3.99886,34.4639 --5.48304,0.0768333,0.427277,2,3,0,9.3581,-1.51732,7.28059,1.29433,-0.657717,-0.356307,0.968388,0.88483,1.62605,0.0109271,0.75344,7.90615,-4.11144,4.92477,-1.43776,-6.30589,5.53312,10.3213,3.96817,-4.52424,-3.95496,-3.81419,-3.61103,-3.28994,-3.40175,-3.51634,-3.90886,7.87168,-10.3671,18.3377,-13.259,-11.8502,-4.40466,-4.08537,-6.96647 --6.07549,0.978291,0.427277,5,47,0,8.837,6.02116,1.41531,-1.43631,-0.341598,-0.725885,-1.16944,0.0974304,0.579771,-1.34004,-0.00449627,3.98834,4.99381,6.15906,4.1246,5.5377,4.36605,6.84172,6.0148,-4.90823,-3.26671,-3.85537,-3.351,-3.38,-3.36365,-3.84406,-3.86459,4.30883,-4.41645,8.06295,7.4703,9.2925,7.04586,12.8477,-8.15797 --5.48481,0.916015,0.427277,3,7,0,9.51876,1.97619,17.8389,1.81811,-0.315858,0.724618,0.759278,-0.227412,0.494465,1.14648,0.0713062,34.4091,14.9025,-2.08057,22.4281,-3.65835,15.5208,10.7969,3.24821,-3.71827,-3.45975,-3.69318,-4.30042,-3.15979,-4.18813,-3.48095,-3.92751,48.7575,2.9313,8.0869,24.2656,24.2387,-8.89572,1.5214,-18.6056 --5.48481,0.0699794,0.427277,2,3,0,11.2917,1.97619,17.8389,1.81811,-0.315858,0.724618,0.759278,-0.227412,0.494465,1.14648,0.0713062,34.4091,14.9025,-2.08057,22.4281,-3.65835,15.5208,10.7969,3.24821,-3.71827,-3.45975,-3.69318,-4.30042,-3.15979,-4.18813,-3.48095,-3.92751,15.7053,13.9996,-26.1156,39.6862,-3.64651,31.8967,31.3683,-16.6465 --3.17225,0.598496,0.427277,3,7,0,6.49185,2.25659,13.6822,1.70014,-0.523955,0.0874953,0.551085,0.185503,0.657866,0.870675,0.396532,25.5183,3.45372,4.79468,14.1694,-4.91228,9.79666,11.2577,7.68203,-3.64068,-3.32487,-3.81019,-3.52923,-3.21064,-3.63659,-3.44882,-3.83808,8.32997,18.9917,-0.419898,33.2635,12.2622,23.9771,12.5365,13.7391 --4.39198,0.903324,0.427277,3,7,0,6.91561,-0.677262,8.29764,0.215854,-0.173452,-0.023072,-0.752329,0.327243,0.740624,-0.483833,1.21043,1.11382,-0.868705,2.03808,-4.69194,-2.1165,-6.91981,5.46817,9.36647,-5.23336,-3.61479,-3.7411,-3.88172,-3.12386,-3.57602,-4.00676,-3.82001,-5.59912,5.85455,21.8312,-8.06681,-9.80431,-11.4752,9.34559,6.65841 --2.94514,0.821851,0.427277,3,7,0,7.67007,-0.789231,14.144,0.309604,-0.000179215,-0.251902,-0.312336,0.168085,0.983472,0.288634,0.691958,3.5898,-4.35213,1.58816,3.2932,-0.791766,-5.2069,13.121,8.99781,-4.95112,-3.9844,-3.73264,-3.37361,-3.11643,-3.47603,-3.34055,-3.82322,17.0451,-7.64838,-8.9602,19.3132,-6.53436,-18.213,6.3352,-6.95488 --4.44812,0.793547,0.427277,3,7,0,7.05275,7.88032,2.87665,1.18548,-0.401131,1.26037,0.651529,-0.1098,0.545363,0.275094,-0.0796838,11.2905,11.506,7.56447,8.67167,6.72641,9.75454,9.44914,7.6511,-4.24745,-3.28298,-3.90951,-3.32838,-3.48467,-3.63354,-3.58711,-3.8385,19.3627,-1.26641,-10.8079,13.4352,22.4515,-1.18284,5.03079,14.2039 --4.11839,0.854024,0.427277,3,7,0,13.8538,2.87033,2.99471,-0.475556,-0.460351,-1.06816,-0.901252,-0.345883,0.149367,-0.466215,0.357201,1.44618,-0.328484,1.83451,1.47415,1.49172,0.171344,3.31764,3.94005,-5.19389,-3.56834,-3.73718,-3.44301,-3.15449,-3.31967,-4.29938,-3.90956,-8.19196,8.85886,-8.89302,0.557452,10.3935,14.1534,5.54258,-9.22965 --4.79478,0.962158,0.427277,3,15,0,7.35888,1.15094,5.49511,1.02235,-0.0911424,-1.13924,-0.471704,0.332017,0.183043,-0.735113,0.849636,6.76884,-5.10933,2.97541,-2.88859,0.650098,-1.44113,2.15678,5.81978,-4.62868,-4.0808,-3.76126,-3.7209,-3.13297,-3.34146,-4.47656,-3.86825,13.4208,-9.11184,-7.83934,-24.4347,-2.8605,1.31959,-9.71236,-37.2346 --6.29139,0.940579,0.427277,3,15,0,8.74212,7.30089,0.717152,-0.965773,0.189059,1.70805,-0.352182,-0.22206,0.00683397,0.88964,-0.225824,6.60829,8.52583,7.14164,7.9389,7.43648,7.04833,7.3058,7.13894,-4.64389,-3.22291,-3.89241,-3.32048,-3.55551,-3.468,-3.79335,-3.84578,-25.0449,3.44078,2.49996,21.2094,-4.5069,11.9993,24.1867,4.98922 --3.08872,0.994922,0.427277,3,7,0,7.95388,0.568037,8.95969,0.721435,-0.589397,0.0161783,-0.177219,-0.36419,0.731054,1.24766,-0.113973,7.03187,0.71299,-2.69499,11.7467,-4.71278,-1.01979,7.11805,-0.453123,-4.60402,-3.48703,-3.69171,-3.40994,-3.20125,-3.33369,-3.81361,-4.04863,-7.86425,-12.1965,-6.07824,-2.90389,-15.623,-4.19968,5.40672,-9.73602 --4.6242,0.868841,0.427277,3,7,0,5.63684,5.25779,3.96688,0.255375,0.852532,0.870593,0.624216,0.496438,1.38154,-0.68176,-0.614541,6.27083,8.71132,7.2271,2.55333,8.63968,7.73398,10.7382,2.81998,-4.67623,-3.22405,-3.89581,-3.39854,-3.68976,-3.50422,-3.48519,-3.93936,-0.042251,-7.18887,14.7745,-8.94365,18.7364,18.3103,14.6269,-6.022 --4.02043,0.999002,0.427277,3,7,0,6.15484,2.98583,3.16965,-0.15083,0.66865,-0.312305,-1.36479,-0.346809,0.0139446,-0.0440691,0.615111,2.50775,1.99594,1.88657,2.84615,5.10522,-1.34008,3.03003,4.93551,-5.07111,-3.40177,-3.73817,-3.38813,-3.34625,-3.33946,-4.34202,-3.88633,19.8319,0.329542,17.5858,-9.05984,0.504468,13.407,4.43766,-15.8262 --12.9403,0.807359,0.427277,3,7,0,14.6633,7.67044,1.78743,0.449019,-0.11185,2.35666,0.0237058,2.54268,-1.10244,-2.19859,-0.679469,8.47303,11.8828,12.2153,3.74063,7.47052,7.71281,5.69991,6.45594,-4.47433,-3.2969,-4.14369,-3.36073,-3.55906,-3.50304,-3.97798,-3.85674,7.17656,12.0865,20.0681,1.75167,4.98578,20.4366,-8.30639,15.2081 --7.82363,0.962776,0.427277,3,7,0,19.0131,4.35316,1.80602,0.928154,-0.0433897,-0.462048,0.898717,-0.998941,2.54959,1.21768,-0.926662,6.02943,3.51869,2.54905,6.55232,4.2748,5.97627,8.95777,2.67959,-4.69967,-3.32193,-3.75167,-3.31766,-3.28791,-3.41916,-3.63033,-3.94337,22.9256,-21.9586,9.65868,8.29689,-12.2344,23.0648,24.2466,-12.3451 --8.75307,0.425556,0.427277,3,7,0,14.3572,5.94359,0.692801,1.66726,0.0783984,-0.44265,1.06842,-1.10335,1.96523,0.797601,-1.03163,7.09866,5.63692,5.17919,6.49617,5.9979,6.68379,7.3051,5.22888,-4.5978,-3.24944,-3.82219,-3.31788,-3.41845,-3.45033,-3.79343,-3.88006,14.5255,-2.26847,-1.39855,-5.01492,9.93978,-17.6999,11.3,35.3382 --6.40235,0.9864,0.427277,3,7,0,10.8932,6.11895,2.39391,1.12311,0.623062,-0.348431,1.32121,-0.917843,0.890979,1.27942,-1.11996,8.80757,5.28484,3.92171,9.18177,7.6105,9.28181,8.25187,3.43785,-4.44554,-3.25838,-3.7851,-3.3365,-3.57382,-3.60026,-3.69665,-3.92244,22.0144,24.0537,7.44165,19.8931,14.0976,5.93846,9.57742,0.393786 --5.70956,0.996705,0.427277,3,7,0,8.8117,1.64589,8.01624,0.577923,0.233696,1.53043,-0.946013,0.385203,0.617561,-1.27605,0.652598,6.27866,13.9142,4.73377,-8.58325,3.51925,-5.93758,6.5964,6.87727,-4.67547,-3.39641,-3.80835,-4.3203,-3.24224,-3.51572,-3.87173,-3.84981,0.538523,29.3714,-4.88568,-10.8225,11.9627,10.1641,12.7226,3.42322 --5.71715,0.8533,0.427277,3,7,0,10.149,1.64021,3.36414,-0.0914467,-0.425009,-0.707049,1.03612,-0.482345,2.25718,0.229707,-0.578774,1.33257,-0.738404,0.0175309,2.41298,0.210419,5.12587,9.23369,-0.306869,-5.20733,-3.60332,-3.70931,-3.40378,-3.12521,-3.38718,-3.60576,-4.04304,20.2996,-9.80301,-12.559,-2.83557,3.94986,12.4174,14.1874,-8.58131 --5.43334,0.975626,0.427277,3,7,0,9.71174,6.58683,5.0604,1.59441,-0.486602,1.74761,-0.584236,-0.157756,-0.43303,0.209085,0.804114,14.6552,15.4304,5.78852,7.64488,4.12443,3.63036,4.39553,10.656,-4.02273,-3.49758,-3.84238,-3.31855,-3.27826,-3.34542,-4.14693,-3.8121,37.3641,13.5893,26.2486,28.8347,10.1804,0.271595,-23.2863,16.9546 --7.063,0.894759,0.427277,3,7,0,12.6227,5.07936,1.80732,-0.538163,-0.889065,-0.918783,1.81753,-1.72836,0.231252,-0.205443,0.690958,4.10673,3.41883,1.95566,4.70806,3.47254,8.36423,5.49731,6.32815,-4.89563,-3.32646,-3.73949,-3.33854,-3.23964,-3.54093,-4.00311,-3.85896,12.2228,19.448,6.83914,1.23396,17.908,10.3777,10.2191,-12.9826 --7.16514,0.925436,0.427277,5,39,0,14.3806,3.1861,1.05063,-1.4443,-1.77498,0.640387,0.972553,0.00715867,0.872698,-0.785647,-0.221477,1.66868,3.85891,3.19362,2.36068,1.32126,4.20789,4.10298,2.95341,-5.16774,-3.30727,-3.76645,-3.40577,-3.14942,-3.35936,-4.18716,-3.93561,-0.822112,6.56459,-26.8935,4.29027,0.869777,3.47466,1.61115,-19.873 -# -# Elapsed Time: 0.020332 seconds (Warm-up) -# 0.004139 seconds (Sampling) -# 0.024471 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-4.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-4.csv deleted file mode 100644 index 91369bc6f1..0000000000 --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-4.csv +++ /dev/null @@ -1,148 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 20 -# stan_version_patch = 0 -# model = stan_test_data_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 1000 (Default) -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 4 -# data -# file = /tmp/tmp2ipdytqf/_pz629gf.json -# init = 2 (Default) -# random -# seed = 81267 -# output -# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-4-_7_vglm2.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1.1,eta.2.1,eta.1.2,eta.2.2,eta.1.3,eta.2.3,eta.1.4,eta.2.4,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 -# Adaptation terminated -# Step size = 0.369164 -# Diagonal elements of inverse mass matrix: -# 11.9057, 1.02604, 0.984918, 0.805498, 0.940485, 0.825545, 1.07806, 1.02634, 0.799015, 0.796004 --12.9054,0.860945,0.369164,4,15,0,20.0226,2.27967,0.053022,1.06348,1.56898,-0.382589,-0.242891,-0.951297,1.62889,-1.89014,-1.53313,2.33606,2.25939,2.22923,2.17945,2.36286,2.26679,2.36604,2.19838,-5.09063,-3.3863,-3.74494,-3.41286,-3.18597,-3.32347,-4.44363,-3.95757,21.1433,8.50276,3.38737,30.0161,1.9272,11.5612,-3.18932,28.4895 --8.72747,0.990053,0.369164,4,15,0,16.06,3.32534,5.00513,-0.487143,-1.05593,0.693422,0.540779,0.417943,-1.58726,1.68983,1.44191,0.887127,6.796,5.4172,11.7831,-1.95973,6.03201,-4.61912,10.5423,-5.26056,-3.22877,-3.8299,-3.41137,-3.12185,-3.42147,-5.77965,-3.81259,20.83,9.4722,18.5255,6.88461,-11.1075,-1.2777,0.292344,4.77507 --6.09541,1,0.369164,4,15,0,11.9207,1.59609,1.21145,-0.650409,-1.46728,0.234131,-1.05427,0.473004,1.0605,0.715038,0.47437,0.808152,1.87973,2.16911,2.46233,-0.181446,0.318889,2.88084,2.17077,-5.27009,-3.40881,-3.74371,-3.40192,-3.1203,-3.31875,-4.36447,-3.95841,8.26745,-0.541297,12.4046,1.16677,5.0671,4.42632,7.59085,34.2168 --4.73033,0.985952,0.369164,3,15,0,8.97097,3.72425,2.89107,0.486321,-1.6402,-0.0575241,-0.334589,-1.2242,0.468439,-0.812286,0.620091,5.13024,3.55795,0.185001,1.37587,-1.0177,2.75693,5.07855,5.51698,-4.78927,-3.32018,-3.71134,-3.44754,-3.11617,-3.32959,-4.05634,-3.87417,-19.8264,6.19799,19.4294,-17.979,2.77588,-6.96988,3.1006,19.7729 --7.69667,0.942321,0.369164,3,7,0,10.6613,1.66344,2.80933,-0.493246,-2.08877,-1.01563,0.635585,-0.700588,-0.209337,-1.31814,0.481958,0.277746,-1.18979,-0.304745,-2.03964,-4.2046,3.44901,1.07534,3.01742,-5.33482,-3.64378,-3.70572,-3.6545,-3.17955,-3.34162,-4.65374,-3.93383,0.0130492,-9.55911,7.65741,-17.6039,-9.86163,-2.79762,-4.79594,4.11473 --7.04807,0.913396,0.369164,3,7,0,17.7883,2.52702,2.17947,0.0493197,-1.30374,-0.616712,-1.98139,-1.37062,-0.156313,-0.0610101,-0.806692,2.63452,1.18292,-0.460197,2.39405,-0.314439,-1.79136,2.18634,0.768861,-5.05678,-3.45389,-3.70413,-3.4045,-3.11906,-3.34903,-4.47188,-4.00397,-1.64059,4.34103,9.47042,12.9743,-0.9841,5.90439,2.59948,23.8349 --9.79665,0.820138,0.369164,4,15,0,16.7525,6.28973,0.0748328,0.157021,1.23954,0.818306,2.21716,0.687759,0.232578,-0.0573601,0.546257,6.30148,6.35097,6.3412,6.28544,6.38249,6.45565,6.30714,6.33061,-4.67327,-3.23512,-3.86195,-3.31894,-3.45259,-3.43983,-3.90514,-3.85891,7.68665,21.7141,-24.6959,21.1827,10.2912,-13.1694,-6.13266,11.8143 --9.50704,0.993841,0.369164,3,15,0,11.9349,4.43343,0.100144,0.21599,-1.47277,-1.33604,-1.85078,-0.231004,0.363685,0.198423,-0.81332,4.45506,4.29963,4.41029,4.4533,4.28594,4.24808,4.46985,4.35198,-4.85891,-3.28999,-3.79878,-3.34363,-3.28864,-3.36043,-4.13685,-3.89958,23.7571,-2.91383,-3.32822,9.30826,-4.86939,-3.42643,0.945846,27.1927 --11.2376,0.993163,0.369164,3,7,0,13.4658,3.66377,0.053052,-0.153293,-1.28352,-2.08761,-1.48996,0.518738,-0.262809,0.604646,-1.1927,3.65564,3.55302,3.69129,3.69585,3.59568,3.58472,3.64983,3.6005,-4.94398,-3.3204,-3.77898,-3.36195,-3.24654,-3.34444,-4.25116,-3.91819,12.648,-5.21923,-7.67659,6.57911,8.81458,3.39762,10.4949,2.57031 --9.94991,0.980126,0.369164,3,7,0,14.968,6.28639,0.0467822,-0.415947,-0.996083,-1.83703,-1.29516,0.574408,-0.167482,0.152307,-0.878865,6.26693,6.20045,6.31326,6.29352,6.23979,6.2258,6.27856,6.24528,-4.6766,-3.23772,-3.86094,-3.3189,-3.43971,-3.42968,-3.90848,-3.86042,18.0913,15.8012,24.9399,7.95037,1.65147,6.59925,-10.348,-31.8122 --9.72246,0.961506,0.369164,4,15,0,15.878,2.89351,0.0281101,-0.126041,0.610678,-0.134099,-1.17266,0.6055,-0.874873,-0.702131,1.56804,2.88997,2.88975,2.91054,2.87378,2.91068,2.86055,2.86892,2.93759,-5.02813,-3.3521,-3.75976,-3.38719,-3.21057,-3.33114,-4.36627,-3.93605,0.419655,4.96175,23.3892,13.9701,3.47026,6.2037,1.52962,28.2795 --10.2498,0.811272,0.369164,3,15,0,18.8683,6.59561,0.0282955,-0.918466,0.368426,-0.603587,-0.0987948,-1.19476,0.956866,0.551581,-1.68821,6.56962,6.57853,6.56181,6.61122,6.60604,6.59282,6.62269,6.54784,-4.64757,-3.23163,-3.8701,-3.31746,-3.47327,-3.44609,-3.86874,-3.85518,15.6056,-1.35406,1.14689,-0.0848492,9.17115,-9.4619,-10.9997,-12.6872 --13.2042,0.9455,0.369164,4,15,0,18.3185,4.7825,0.0102133,-0.986589,0.310845,-1.14861,1.2328,1.09396,0.365088,-0.84518,2.27013,4.77242,4.77077,4.79367,4.77386,4.78567,4.79509,4.78622,4.80568,-4.82592,-3.27366,-3.81016,-3.33731,-3.32279,-3.37635,-4.09454,-3.88918,21.6474,12.8904,30.4098,42.2129,1.45013,9.51473,2.83056,-40.6091 --8.68417,0.996963,0.369164,4,15,0,16.328,5.78421,0.0716085,-0.389029,0.390189,0.579485,-0.667476,-0.0391415,-0.548554,0.590601,-2.01902,5.75635,5.82571,5.78141,5.8265,5.81215,5.73641,5.74493,5.63963,-4.7265,-3.24516,-3.84214,-3.32252,-3.40262,-3.40953,-3.97246,-3.87174,-10.0862,9.12099,0.0203859,1.99426,20.0915,14.961,5.76999,3.78737 --6.43224,0.97576,0.369164,3,15,0,12.8897,4.58953,1.74044,-2.0312,1.22188,-0.0284622,-0.355102,-0.573429,0.632483,0.0360471,-0.568017,1.05434,4.53999,3.5915,4.65226,6.71613,3.97149,5.69033,3.60092,-5.24048,-3.28138,-3.77639,-3.33961,-3.48369,-3.35332,-3.97916,-3.91818,-4.49671,11.8326,-9.52446,-10.6358,-1.48396,5.48699,10.2302,1.49976 --3.20871,0.999653,0.369164,4,15,0,8.46287,4.54028,7.55803,0.966719,0.100959,0.60312,0.296893,0.357553,0.630265,0.918265,1.10511,11.8468,9.09867,7.24268,11.4806,5.30333,6.78421,9.30384,12.8927,-4.20683,-3.22756,-3.89643,-3.39979,-3.36142,-3.45509,-3.59964,-3.81054,6.60168,3.80953,27.1626,4.39842,7.00704,9.38122,20.5143,10.731 --3.62345,0.94502,0.369164,4,15,0,7.31557,3.37751,2.95545,-0.155337,-0.515797,-0.392011,-0.472961,-0.240662,0.052398,-1.08318,-0.541033,2.91842,2.21894,2.66624,0.176213,1.85309,1.97969,3.53237,1.77851,-5.02496,-3.38863,-3.75423,-3.50925,-3.16641,-3.3208,-4.26809,-3.97054,3.32318,12.9735,28.0018,5.86049,-6.39992,7.27719,9.18536,-26.3279 --3.96978,0.987009,0.369164,3,15,0,5.78173,2.35712,2.8107,0.572298,-1.05186,-0.610806,-0.166227,-0.166521,0.553433,0.456552,-1.0401,3.96568,0.640331,1.88908,3.64035,-0.599331,1.88991,3.91265,-0.566283,-4.91065,-3.49235,-3.73821,-3.36348,-3.11715,-3.32011,-4.21379,-4.053,-6.49598,-3.66731,11.2339,30.462,-1.00439,7.76778,-7.29682,22.7642 --8.44796,0.929471,0.369164,4,15,0,10.3588,0.323463,5.38655,2.68809,0.598981,0.255604,1.5372,1.05882,0.236484,0.902494,-0.950424,14.803,1.70029,6.02684,5.18479,3.5499,8.60367,1.5973,-4.79604,-4.01401,-3.41996,-3.85068,-3.33045,-3.24395,-3.55574,-4.56677,-4.24466,5.39635,2.58653,-16.0575,16.0431,-13.492,-21.5536,4.08662,-3.17975 --2.50745,0.991726,0.369164,3,7,0,10.292,2.69344,9.04188,0.308699,-0.389555,0.456466,0.05602,-0.013394,0.291071,1.06699,0.424188,5.48466,6.82075,2.57233,12.3411,-0.828875,3.19996,5.32526,6.52889,-4.75352,-3.22848,-3.75217,-3.43471,-3.11634,-3.33683,-4.02477,-3.8555,-1.47513,-1.51004,7.03349,10.0477,-1.57952,2.99485,-19.0673,-10.2885 --7.58407,0.840587,0.369164,3,7,0,10.5152,4.91414,0.387979,-0.133834,-0.529999,-0.850624,0.437156,1.70989,-1.411,-0.82848,0.319947,4.86221,4.58411,5.57754,4.5927,4.70851,5.08374,4.3667,5.03827,-4.81667,-3.27987,-3.83523,-3.34078,-3.31732,-3.38575,-4.15086,-3.8841,-14.2905,5.10185,-12.7831,-8.73892,8.22714,13.6576,8.62656,1.33565 --4.58731,0.459771,0.369164,4,15,0,10.6689,0.903789,14.9859,1.46157,-0.308138,0.0897934,0.193127,-0.806867,1.51464,0.945081,-0.754921,22.8068,2.24943,-11.1879,15.0667,-3.71394,3.79797,23.6021,-10.4094,-3.68692,-3.38687,-3.82247,-3.58573,-3.16163,-3.34918,-3.37844,-4.58428,4.45643,2.43886,-7.2669,13.3737,-17.8638,5.32908,27.9029,0.347441 --5.74956,0.821989,0.369164,4,15,0,8.85825,6.27707,4.00406,-1.16644,0.11468,-0.465143,-0.473584,0.604461,-1.09548,-0.759536,0.644064,1.60658,4.41462,8.69737,3.23585,6.73626,4.38082,1.89069,8.85594,-5.17502,-3.2858,-3.95877,-3.37538,-3.48561,-3.36406,-4.51907,-3.82457,7.36687,7.63063,-12.8583,4.72891,-7.02284,10.3871,-2.19763,18.7605 --8.22487,0.947894,0.369164,3,7,0,11.8534,5.29202,1.27792,-0.316891,-0.890263,0.565174,-0.0203839,1.40457,0.939029,-0.552215,-2.52086,4.88706,6.01427,7.08695,4.58634,4.15434,5.26597,6.49202,2.07057,-4.81412,-3.24124,-3.89025,-3.34091,-3.28016,-3.39203,-3.88369,-3.96146,5.32005,0.108669,-6.10714,0.934793,8.58582,34.3693,12.4395,-5.83057 --4.34853,0.986169,0.369164,4,15,0,11.8752,4.18357,3.70167,-0.0795378,0.632774,0.440806,0.110721,-0.971211,0.313215,1.3089,1.09793,3.88915,5.81529,0.588474,9.0287,6.52589,4.59343,5.34299,8.24774,-4.91884,-3.24539,-3.71668,-3.33384,-3.46579,-3.37019,-4.02252,-3.83104,12.7733,10.3119,21.9672,14.4581,8.84301,15.649,-21.9358,0.910929 --3.75272,0.981982,0.369164,4,15,0,7.74081,4.17823,9.51547,0.905162,-0.809968,0.170193,-0.911738,-1.59166,1.18707,0.276421,0.337684,12.7913,5.7977,-10.9672,6.80851,-3.52899,-4.49739,15.4737,7.39145,-4.141,-3.24577,-3.8155,-3.31699,-3.15564,-3.44171,-3.25343,-3.84209,-3.03311,15.6115,-2.70421,2.69292,-7.99053,-1.24444,22.8443,24.5912 --8.38336,0.799758,0.369164,3,7,0,12.9255,9.80665,1.48404,-0.311261,-0.071659,-0.695627,0.568979,-1.44002,0.537718,0.621188,2.24404,9.34472,8.77431,7.66961,10.7285,9.7003,10.651,10.6046,13.1369,-4.40037,-3.22452,-3.91387,-3.37428,-3.82293,-3.70172,-3.49498,-3.8113,32.5013,18.1343,-28.977,6.67366,10.0533,1.1321,32.8676,24.6314 --5.55493,0.984249,0.369164,4,15,0,12.6359,5.42438,1.3804,0.102332,0.3567,0.720596,1.42698,-1.19077,-0.216368,0.593945,0.894271,5.56564,6.41908,3.78064,6.24426,5.91676,7.39417,5.1257,6.65883,-4.74543,-3.23402,-3.78133,-3.31919,-3.41148,-3.48578,-4.05026,-3.85334,-8.99509,-2.64176,14.0332,-4.60057,21.9891,2.44222,15.0907,-0.292302 --5.75834,0.986507,0.369164,3,7,0,8.31963,4.40294,2.24043,-0.761394,0.526751,1.78166,0.483323,0.0693736,-0.104028,1.03701,0.923753,2.69709,8.39462,4.55837,6.72627,5.58309,5.48579,4.16987,6.47254,-5.04974,-3.2223,-3.80311,-3.31714,-3.38368,-3.39998,-4.17789,-3.85646,-8.0468,16.2515,1.95236,14.767,-6.09238,-22.8633,14.9896,48.2131 --4.8074,0.945839,0.369164,4,15,0,11.8301,4.08249,5.8214,1.64736,-1.02472,-1.28843,-0.418215,-0.495422,0.996569,-0.623367,-0.0744746,13.6724,-3.41798,1.19844,0.453616,-1.88285,1.64789,9.88391,3.64894,-4.08317,-3.87338,-3.72595,-3.49392,-3.12097,-3.31857,-3.55088,-3.91693,12.9995,-11.1617,2.07246,6.07997,-4.05111,9.47495,9.21533,-9.16555 --5.95112,0.902424,0.369164,3,15,0,10.121,11.5845,2.32498,-0.243431,-0.269078,-0.662445,-0.432959,-0.802615,0.821485,0.645351,0.588009,11.0186,10.0444,9.71848,13.085,10.9589,10.5779,13.4945,12.9516,-4.26781,-3.24242,-4.00746,-3.46984,-3.99898,-3.69591,-3.32302,-3.81071,-10.409,4.10819,16.9263,19.088,2.37908,13.2011,27.5225,24.1841 --8.30533,0.992204,0.369164,3,7,0,11.3806,-2.52293,0.591364,-0.456028,1.05748,0.806313,-0.655051,0.0330699,-0.183143,1.18033,-0.703392,-2.79261,-2.0461,-2.50337,-1.82492,-1.89757,-2.9103,-2.63123,-2.93889,-5.73407,-3.72614,-3.69201,-3.63865,-3.12114,-3.38002,-5.34976,-4.15371,-13.4365,-0.492067,8.68478,-5.78454,10.5409,-5.32895,10.6371,-21.4842 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--6.61273,0.982354,0.369164,4,15,0,8.91119,3.32938,2.75258,-1.40656,-0.623801,1.15502,-0.360431,-1.70888,0.143827,-0.782802,-0.690681,-0.542299,6.50867,-1.37446,1.17466,1.61232,2.33727,3.72528,1.42823,-5.43735,-3.23264,-3.69669,-3.45706,-3.15829,-3.32422,-4.24036,-3.98178,0.923627,-3.44807,11.8956,-0.929054,4.49227,3.08135,6.43551,5.24493 --6.18916,0.998471,0.369164,4,15,0,9.0678,-1.74871,6.24544,-0.548507,0.193862,1.33863,-0.214297,-1.46805,0.91504,0.84525,0.727889,-5.17437,6.61164,-10.9173,3.53025,-0.537952,-3.08708,3.96612,2.79728,-6.07263,-3.23116,-3.81396,-3.36658,-3.11748,-3.38586,-4.20627,-3.94,7.55299,16.6043,-24.3647,-23.1535,-8.78035,3.15656,-1.87263,-8.24326 --4.68298,0.98598,0.369164,4,15,0,9.70959,7.5976,1.50564,0.272899,-0.261688,0.345295,-0.344451,1.31827,0.112462,0.708977,-0.615683,8.00849,8.1175,9.58245,8.66507,7.2036,7.07898,7.76693,6.6706,-4.51512,-3.22159,-4.00074,-3.32829,-3.53159,-3.46954,-3.7451,-3.85314,13.9833,-2.12235,0.443941,3.86215,28.6067,10.5141,-12.9147,-19.3741 --6.79532,0.971286,0.369164,3,15,0,8.72776,5.5532,0.557037,0.742434,0.506261,0.128628,0.275452,-1.4741,0.43049,1.0299,-1.40645,5.96676,5.62485,4.73207,6.12689,5.8352,5.70663,5.79299,4.76975,-4.7058,-3.24973,-3.80829,-3.31998,-3.40456,-3.40837,-3.96658,-3.88998,15.8572,5.14221,-3.92714,4.22422,10.8104,5.9531,-13.4322,23.9567 --8.06085,0.961263,0.369164,4,31,0,13.0487,1.38208,7.57786,-1.14308,0.0764265,-0.46044,-0.941203,0.756388,-0.195578,-0.759973,1.52484,-7.28,-2.10707,7.11388,-4.37689,1.96123,-5.75023,-0.0999876,12.9371,-6.39294,-3.73229,-3.89131,-3.85168,-3.17029,-3.50512,-4.85957,-3.81067,0.282092,5.88132,9.93179,-10.1748,10.8594,-7.46618,14.3356,22.3298 --9.09486,1,0.369164,4,15,0,13.2554,8.68795,1.1199,1.99205,0.166981,0.405333,0.381605,-1.13053,2.05678,-0.166874,1.20533,10.9188,9.14188,7.42186,8.50106,8.87495,9.11531,10.9913,10.0378,-4.27536,-3.22804,-3.90367,-3.32614,-3.71811,-3.58898,-3.46713,-3.81525,17.5458,11.0213,4.55633,22.5742,14.7722,23.46,15.8452,23.3452 --7.42742,0.785065,0.369164,3,7,0,16.8152,2.31395,4.85722,-0.285841,-0.456992,0.315303,0.17735,0.471773,2.84284,-0.323867,1.88988,0.925557,3.84545,4.60545,0.740852,0.0942351,3.17538,16.1223,11.4935,-5.25593,-3.30783,-3.8045,-3.47872,-3.12355,-3.33639,-3.23915,-3.80971,2.46643,25.1686,-7.8952,8.3731,-6.94453,-3.72484,16.8416,25.7602 --6.55608,0.974874,0.369164,4,15,0,11.9114,5.98045,3.79699,1.14211,0.0982562,-0.229688,-0.176111,-0.218254,-1.52952,0.489627,-1.78111,10.317,5.10833,5.15174,7.83956,6.35353,5.31176,0.172884,-0.782391,-4.32185,-3.26333,-3.82131,-3.31975,-3.44996,-3.39366,-4.81055,-4.06145,22.026,13.0501,-5.65214,21.8729,18.9369,-13.0429,12.3154,-25.2142 --6.15123,0.989405,0.369164,4,15,0,9.98023,2.99169,1.43926,-0.234684,-0.899988,0.145628,-0.714004,1.76729,1.37977,0.29356,-0.654942,2.65392,3.20129,5.53528,3.4142,1.69638,1.96406,4.97755,2.04906,-5.0546,-3.33666,-3.83381,-3.36997,-3.16104,-3.32067,-4.06944,-3.96212,16.5157,-7.36556,-26.3927,-13.683,-17.8605,16.1897,-2.37835,23.8416 --5.06457,0.998825,0.369164,3,7,0,7.75031,6.58845,7.41195,0.525868,0.129318,-0.293072,0.620155,-1.64926,-0.648982,-0.334939,0.0743386,10.4862,4.41622,-5.6358,4.1059,7.54695,11.185,1.77823,7.13944,-4.30862,-3.28574,-3.7051,-3.35144,-3.56709,-3.74549,-4.53725,-3.84577,22.0579,7.91164,-13.5923,15.0659,8.04317,16.4743,-13.2526,8.11284 --4.53318,0.944794,0.369164,3,7,0,9.01344,3.78099,2.17042,0.467221,-0.524317,-0.36443,0.522137,-1.48049,-0.196585,0.76599,-0.732717,4.79505,2.99002,0.567704,5.4435,2.643,4.91424,3.35432,2.19069,-4.82359,-3.34702,-3.71639,-3.32684,-3.19809,-3.38014,-4.294,-3.9578,-9.33944,1.66291,-11.2367,-11.4805,11.0019,3.67766,8.63082,9.11945 --4.03183,0.992795,0.369164,4,23,0,7.06323,4.7102,7.18073,-0.0244274,-0.150536,0.514413,-0.25932,1.39287,0.694824,-0.700868,0.704887,4.53479,8.40406,14.712,-0.322541,3.62924,2.8481,9.69954,9.7718,-4.85058,-3.22234,-4.30425,-3.5384,-3.24845,-3.33095,-3.56601,-3.81697,-6.30265,9.18303,11.3182,8.50608,9.68055,-0.666977,10.8572,28.998 --2.79848,0.997185,0.369164,3,15,0,4.69372,4.51331,5.90898,0.50341,-0.288426,-0.450727,0.164178,-1.25278,0.739394,0.438066,-0.220799,7.48795,1.84997,-2.88935,7.10184,2.80901,5.48344,8.88238,3.20861,-4.56198,-3.41064,-3.69155,-3.31688,-3.20572,-3.3999,-3.63718,-3.92858,6.77882,8.3374,10.6862,-5.59573,9.39738,1.05048,1.60216,-10.5705 --2.99519,0.990484,0.369164,3,7,0,4.04025,7.92209,9.59725,0.177841,-0.442775,-0.269961,0.474098,-0.663971,0.910305,0.0896045,-0.0248051,9.62887,5.33121,1.5498,8.78205,3.67267,12.4721,16.6585,7.68403,-4.37698,-3.25714,-3.73196,-3.32996,-3.25094,-3.86068,-3.23052,-3.83806,-4.76123,1.34131,-20.2646,0.656598,-3.83857,7.14228,20.5175,5.87823 --5.11724,0.886708,0.369164,4,31,0,7.36054,2.86258,0.85422,0.157002,0.369027,-0.0843364,-1.16596,0.43706,-0.77008,-0.888735,0.266822,2.99669,2.79053,3.23592,2.1034,3.17781,1.86659,2.20476,3.0905,-5.01625,-3.35722,-3.76748,-3.41591,-3.2239,-3.31994,-4.46897,-3.93181,-19.1073,5.43989,-17.1249,24.5628,-3.994,10.796,1.58728,-16.6902 --4.99541,0.992122,0.369164,4,15,0,8.02921,3.17344,3.93026,1.44344,-0.579371,-0.0414923,0.916899,-0.619136,1.18221,1.65273,0.200196,8.84652,3.01037,0.740076,9.66909,0.896362,6.77709,7.81982,3.96026,-4.44222,-3.34601,-3.71885,-3.34627,-3.13836,-3.45475,-3.7397,-3.90906,21.2784,8.35424,13.4206,13.0206,-3.36791,9.98088,0.276357,13.9721 --5.78753,0.982177,0.369164,3,15,0,7.80424,0.0067736,9.48905,-0.197141,0.314576,0.303681,0.233311,0.767237,0.0607429,-1.09401,-0.145365,-1.86391,2.88842,7.28713,-10.3744,2.9918,2.22068,0.583166,-1.3726,-5.60888,-3.35216,-3.89822,-4.56423,-3.21452,-3.32299,-4.73825,-4.08528,-52.1556,14.2821,15.8672,-36.1323,2.07933,14.6305,0.258808,13.8304 --3.15998,0.989645,0.369164,3,7,0,7.05153,3.00892,2.09872,0.165386,-0.794495,0.0512145,0.385383,-0.662593,0.656833,-0.163106,0.0370856,3.35602,3.1164,1.61832,2.6666,1.3415,3.81773,4.38742,3.08675,-4.9766,-3.34077,-3.73319,-3.39443,-3.15001,-3.34964,-4.14803,-3.93191,-4.10092,-5.35014,11.9173,-16.0517,-2.4847,19.3641,11.9349,-15.6901 --3.48009,0.938194,0.369164,4,15,0,6.84712,5.91392,1.96524,0.965342,-0.820857,0.680351,0.334692,-0.358383,0.216462,-0.0186799,-0.280905,7.81105,7.25097,5.20961,5.87721,4.30073,6.57167,6.33932,5.36187,-4.53275,-3.22433,-3.82316,-3.32204,-3.28961,-3.44511,-3.90138,-3.87731,7.45218,7.30877,20.1848,-5.17219,6.44504,17.0381,8.3868,10.0077 --3.8653,0.970446,0.369164,4,15,0,7.37172,7.25908,3.29685,0.327098,0.293181,-0.468046,-1.38851,-0.246063,0.910881,0.0588252,0.477935,8.33747,5.716,6.44784,7.45302,8.22565,2.68136,10.2621,8.83476,-4.48613,-3.24761,-3.86587,-3.31768,-3.64155,-3.32852,-3.5209,-3.82477,-11.88,7.70015,30.3551,8.85384,11.855,18.473,30.1105,25.338 --8.34962,0.930414,0.369164,3,7,0,9.44166,9.25376,4.87321,-0.755058,0.409122,-0.574664,-2.3829,-0.907006,0.784179,1.17055,-0.508045,5.57421,6.4533,4.83373,14.9581,11.2475,-2.35863,13.0752,6.77795,-4.74458,-3.23348,-3.81139,-3.57853,-4.0421,-3.36345,-3.34279,-3.85139,-21.0584,5.19874,-10.8232,8.89772,24.8612,-4.51728,5.0089,3.15279 --5.57653,0.800702,0.369164,3,7,0,15.3286,6.4131,0.967993,-0.634413,-0.269267,0.363772,-0.986324,0.277539,1.01045,-1.09479,0.953782,5.79899,6.76523,6.68176,5.35336,6.15245,5.45835,7.39121,7.33635,-4.72229,-3.22915,-3.87461,-3.32804,-3.43195,-3.39897,-3.78426,-3.84287,19.2316,11.5183,7.98162,-20.1991,-6.07484,18.6265,18.2215,-12.6417 --6.71761,0.967312,0.369164,3,7,0,10.6376,2.11573,0.200697,-0.0421767,-0.00371396,-0.160334,0.892812,-0.690464,0.496336,-0.794879,1.14536,2.10726,2.08355,1.97715,1.9562,2.11498,2.29491,2.21534,2.3456,-5.11684,-3.39655,-3.73991,-3.42196,-3.17606,-3.32376,-4.4673,-3.95315,-15.0795,-3.90645,6.06748,-20.2038,3.23166,-16.9818,-0.464655,-13.8763 --11.3805,0.920223,0.369164,3,7,0,14.5313,10.0334,0.0580724,-0.697239,-1.06028,-1.25013,-0.558169,-1.00541,0.233174,0.548736,-1.7817,9.99291,9.9608,9.97501,10.0653,9.97182,10.001,10.0469,9.92993,-4.34756,-3.24075,-4.02034,-3.35566,-3.85926,-3.65162,-3.53778,-3.81592,-4.50666,8.81291,25.6592,9.81901,23.7309,9.235,8.27836,20.8535 --15.4822,0.971944,0.369164,3,7,0,18.6457,10.8565,0.0130103,-0.979334,-0.905573,-0.728073,-0.20969,-2.20911,0.317032,1.21348,-1.85769,10.8437,10.847,10.8277,10.8722,10.8447,10.8537,10.8606,10.8323,-4.28107,-3.26205,-4.06498,-3.37879,-3.98219,-3.71806,-3.47638,-3.81141,4.8798,12.863,24.6007,12.0423,14.7231,10.5678,2.45621,5.67474 --11.9581,0.992954,0.369164,3,7,0,20.5768,9.21139,0.0429352,0.729296,1.00982,-2.48475,0.471202,-0.102803,-0.118155,0.240747,-1.15788,9.2427,9.10471,9.20698,9.22173,9.25475,9.23162,9.20632,9.16168,-4.40885,-3.22763,-3.98256,-3.33723,-3.7653,-3.59683,-3.60817,-3.82174,25.0768,12.9549,21.5169,18.0085,5.46124,5.31182,-0.134686,19.1052 --10.1633,0.93777,0.369164,3,15,0,14.8789,5.69027,0.0768514,-0.253482,0.597941,-1.63552,0.106869,-0.168771,-1.49064,1.20845,-1.44398,5.67079,5.56458,5.6773,5.78314,5.73623,5.69849,5.57571,5.5793,-4.73497,-3.25118,-3.83859,-3.32295,-3.39627,-3.40806,-3.99334,-3.87293,25.9584,7.47085,16.2784,10.8111,-11.1892,0.89964,2.45489,-22.4096 --11.5487,0.966963,0.369164,4,15,0,15.574,2.74915,0.201511,0.662124,-0.412949,2.51725,0.0194159,-0.937281,1.73162,-0.734125,1.50248,2.88258,3.25641,2.56028,2.60122,2.66594,2.75307,3.0981,3.05192,-5.02895,-3.33403,-3.75191,-3.39679,-3.19912,-3.32953,-4.33186,-3.93287,0.000809418,-10.8504,20.9864,-14.1546,-6.6259,-20.6254,5.23079,-3.28123 --7.77253,0.941499,0.369164,4,15,0,20.401,6.61674,2.75698,-0.793575,0.0003745,-2.02221,-0.192798,0.377567,-1.36432,0.357472,-1.24095,4.42886,1.04153,7.65768,7.60228,6.61777,6.0852,2.85534,3.19546,-4.86165,-3.46363,-3.91338,-3.31833,-3.47438,-3.42369,-4.36833,-3.92894,-3.78415,-2.71286,-14.8503,26.8556,5.71236,18.2021,8.00909,9.86687 --5.12619,0.988886,0.369164,3,15,0,10.6825,4.27521,1.14884,-0.571456,-0.466946,0.0477136,0.567276,-0.61037,-0.539591,-1.50088,-0.366833,3.6187,4.33003,3.574,2.55094,3.73877,4.92692,3.65531,3.85378,-4.94798,-3.28887,-3.77594,-3.39863,-3.25478,-3.38056,-4.25037,-3.91172,0.824411,5.38386,-14.6761,-0.106592,7.66441,9.52876,9.32237,6.5858 --3.50537,0.995637,0.369164,4,15,0,6.8989,1.73855,7.58974,1.1382,-0.628591,0.21975,-0.923567,0.0523461,1.56521,0.898335,0.55285,10.3772,3.4064,2.13585,8.55669,-3.03229,-5.27109,13.6181,5.93455,-4.31713,-3.32703,-3.74304,-3.32685,-3.14166,-3.47934,-3.31753,-3.86608,2.87919,-0.113072,-5.29197,0.491614,-1.99548,-7.21118,24.1082,44.8652 --4.83846,0.933485,0.369164,4,15,0,6.09819,6.88596,2.75276,-0.624966,0.259124,-0.229295,0.786199,-0.00429061,-0.934789,-1.02887,-0.0740179,5.16558,6.25477,6.87415,4.05373,7.59927,9.05018,4.31272,6.68221,-4.78568,-3.23675,-3.88195,-3.3527,-3.57263,-3.58462,-4.15823,-3.85295,17.5386,5.23708,11.6551,11.7318,-0.716813,16.3302,-10.849,-4.54687 --9.24879,0.793122,0.369164,3,15,0,11.934,6.9483,2.36001,0.355073,-0.332595,3.06556,-1.25799,-0.138166,0.604937,0.766475,1.2077,7.78628,14.1831,6.62222,8.75719,6.16337,3.97943,8.37596,9.79849,-4.53498,-3.41267,-3.87236,-3.32959,-3.43292,-3.35352,-3.68463,-3.81679,3.61199,14.3008,10.3082,24.7793,3.62602,19.8556,0.826582,21.1578 --7.05936,0.966872,0.369164,3,7,0,13.06,7.41992,3.16871,0.133504,-0.0207399,1.91467,-0.753423,-0.551502,-0.475157,1.21224,1.51295,7.84296,13.487,5.67238,11.2612,7.3542,5.03255,5.91429,12.214,-4.52989,-3.37206,-3.83842,-3.39186,-3.54698,-3.38403,-3.95185,-3.80938,-3.59444,21.6925,12.6345,7.2172,-1.75594,4.10417,-0.878026,14.7639 --5.75873,0.974934,0.369164,3,7,0,11.9014,3.46091,3.4507,1.54104,-0.490229,1.42125,-1.20618,-0.327352,0.325712,-1.38777,-0.119216,8.77856,8.36521,2.33131,-1.32787,1.76928,-0.701265,4.58484,3.04953,-4.44802,-3.22219,-3.74704,-3.60342,-3.1635,-3.32879,-4.12136,-3.93294,-14.4331,17.6602,30.2322,-0.330285,8.77282,3.68602,6.89152,11.3052 --5.17056,0.989611,0.369164,3,15,0,9.43447,4.00089,3.85198,0.601921,-0.443723,1.30706,-1.38316,-0.0132967,0.183312,-1.44891,-0.249168,6.31947,9.03564,3.94967,-1.58028,2.29168,-1.32702,4.707,3.0411,-4.67153,-3.22689,-3.78586,-3.62105,-3.18305,-3.33921,-4.10504,-3.93317,20.0935,8.03936,-3.08813,14.0591,10.9219,8.98946,5.33822,-13.8562 --5.67346,0.982425,0.369164,3,15,0,8.92791,4.71261,2.46567,0.791174,0.700934,-1.10685,0.817554,0.820516,-0.597322,-0.429301,-1.14297,6.66338,1.98349,6.73573,3.65409,6.44088,6.72842,3.23981,1.89442,-4.63866,-3.40252,-3.87665,-3.36309,-3.45793,-3.45243,-4.31084,-3.96691,-7.49043,-5.54055,-0.193541,4.11438,19.1878,5.54598,23.6038,2.84796 -# -# Elapsed Time: 0.021656 seconds (Warm-up) -# 0.0047 seconds (Sampling) -# 0.026356 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-1.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-1.csv deleted file mode 100644 index ddfc050f3d..0000000000 --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-1.csv +++ /dev/null @@ -1,648 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 20 -# stan_version_patch = 0 -# model = stan_test_data_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 500 -# save_warmup = 1 -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 1 -# data -# file = /tmp/tmp2ipdytqf/x8rvpwpf.json -# init = 2 (Default) -# random -# seed = 31709 -# output -# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-1-lu7otosq.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1.1,eta.2.1,eta.1.2,eta.2.2,eta.1.3,eta.2.3,eta.1.4,eta.2.4,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 --4.80703,0.749647,0.5,3,7,0,9.44134,-2.17477,9.61676,0.766957,-0.853735,0.251743,-0.660749,0.507976,1.11998,-0.295372,0.272729,5.20087,0.246182,2.71032,-5.01529,-10.3849,-8.52903,8.59579,0.448001,-4.7821,-3.52213,-3.75521,-3.91339,-3.65985,-3.69205,-3.66372,-4.01525,-3.67582,3.84024,18.2507,6.00108,-15.8241,-5.59099,1.55725,38.0562 --4.80703,0,9.12515,0,1,1,11.5047,-2.17477,9.61676,0.766957,-0.853735,0.251743,-0.660749,0.507976,1.11998,-0.295372,0.272729,5.20087,0.246182,2.71032,-5.01529,-10.3849,-8.52903,8.59579,0.448001,-4.7821,-3.52213,-3.75521,-3.91339,-3.65985,-3.69205,-3.66372,-4.01525,22.4505,-1.73883,23.5022,1.06256,5.63565,-11.2577,16.8225,-33.3708 --4.80703,5.87081e-171,1.34755,1,1,0,7.70774,-2.17477,9.61676,0.766957,-0.853735,0.251743,-0.660749,0.507976,1.11998,-0.295372,0.272729,5.20087,0.246182,2.71032,-5.01529,-10.3849,-8.52903,8.59579,0.448001,-4.7821,-3.52213,-3.75521,-3.91339,-3.65985,-3.69205,-3.66372,-4.01525,4.08946,9.36857,8.03954,-17.5175,-10.0589,-1.80532,7.44362,-2.71864 --6.91452,0.986501,0.123058,5,31,0,13.6175,-2.73577,4.43247,-0.278325,-1.12206,0.261722,-0.912603,1.04024,1.05503,0.771632,-0.210872,-3.96944,-1.5757,1.87508,0.684463,-7.70928,-6.78086,1.94061,-3.67046,-5.8982,-3.67999,-3.73795,-3.48165,-3.39403,-3.56701,-4.51104,-4.18827,14.0146,6.16736,18.9239,8.56157,-4.32486,-10.5457,4.40152,-13.2114 --5.76513,0.998715,0.152613,5,47,0,11.5222,0.010128,4.10414,0.91448,0.455095,-0.341084,-0.911001,1.27188,1.83153,-0.354759,0.427351,3.76328,-1.38973,5.23008,-1.44585,1.8779,-3.72874,7.52699,1.76404,-4.93236,-3.66236,-3.82382,-3.6116,-3.16729,-3.40923,-3.76994,-3.971,-18.3847,27.7396,28.6303,-10.4047,1.15247,-6.10554,6.82012,14.2478 --5.58764,0.988312,0.230081,4,31,0,12.5677,2.27235,2.4009,0.707326,-0.585418,0.990767,1.21584,-1.38345,-0.210076,0.944624,0.612765,3.97057,4.65108,-1.04917,4.54029,0.866816,5.19145,1.76798,3.74353,-4.91013,-3.2776,-3.69896,-3.34183,-3.13768,-3.38943,-4.53892,-3.91451,-4.46518,13.5917,-15.5612,13.8122,17.1237,15.5177,-0.616516,13.552 --6.01635,0.974945,0.369907,4,31,0,12.2265,8.21028,2.38798,-0.077072,-0.0703838,-0.690142,-1.67491,0.652561,1.2533,-0.910436,-0.680404,8.02623,6.56223,9.76858,6.03617,8.0422,4.21063,11.2031,6.58548,-4.51355,-3.23186,-4.00996,-3.32067,-3.62086,-3.35943,-3.45251,-3.85455,6.44637,2.58935,16.3075,3.4781,0.216914,4.64432,12.8936,22.0458 --5.35047,0.937392,0.603718,3,7,0,10.7065,5.04253,3.4725,0.766765,-0.768986,0.471989,1.29735,-0.956046,-0.823817,0.911984,0.79291,7.70512,6.68151,1.72265,8.2094,2.37222,9.5476,2.18182,7.79591,-4.54228,-3.23022,-3.73509,-3.32288,-3.18636,-3.61874,-4.4726,-3.83659,12.4895,11.1629,28.7118,7.70884,2.6551,-12.5466,-0.974793,39.4774 --3.76791,0.845456,0.904868,2,3,0,7.98011,4.76195,3.15076,0.430938,0.991915,0.00317065,-0.240165,-0.531147,0.535616,1.21016,-0.165793,6.11974,4.77194,3.08843,8.57488,7.88724,4.00525,6.44955,4.23958,-4.69087,-3.27363,-3.76393,-3.32708,-3.60371,-3.35415,-3.88859,-3.90225,11.435,5.94707,6.38769,15.8923,-18.491,6.85994,0.730843,15.1984 --3.76791,0.245228,1.03248,2,3,0,10.2892,4.76195,3.15076,0.430938,0.991915,0.00317065,-0.240165,-0.531147,0.535616,1.21016,-0.165793,6.11974,4.77194,3.08843,8.57488,7.88724,4.00525,6.44955,4.23958,-4.69087,-3.27363,-3.76393,-3.32708,-3.60371,-3.35415,-3.88859,-3.90225,14.4644,-18.0268,31.783,19.3325,9.69936,1.26953,3.33534,25.6402 --5.11185,0.996094,0.178078,4,15,0,8.90438,4.82839,10.8689,1.60096,-1.09065,-0.0457278,-0.232334,-1.60606,1.44369,0.268672,0.911323,22.229,4.33138,-12.6277,7.74855,-7.02576,2.30318,20.5197,14.7335,-3.701,-3.28882,-3.87257,-3.31915,-3.3403,-3.32385,-3.25327,-3.82084,13.0314,11.9505,-21.1651,20.6884,-10.3245,-3.89996,17.739,-0.872244 --6.97083,0.960545,0.332351,4,15,0,9.77599,4.90083,1.20195,-1.8334,0.444866,0.492326,-0.117845,1.20576,0.142436,-1.03486,1.06283,2.69718,5.49258,6.35009,3.65699,5.43554,4.75919,5.07203,6.17829,-5.04973,-3.25296,-3.86228,-3.36301,-3.37182,-3.37523,-4.05719,-3.86161,-1.12391,-14.1708,11.0894,-6.0662,3.34754,9.26496,24.1299,23.7254 --3.82928,0.883855,0.556698,3,7,0,13.1232,3.55262,2.38358,1.29446,0.573255,0.0267012,-0.105605,0.318493,1.06444,0.572305,0.62794,6.63808,3.61626,4.31177,4.91675,4.91902,3.3009,6.08979,5.04936,-4.64106,-3.31761,-3.79595,-3.33477,-3.33243,-3.33871,-3.93079,-3.88386,-17.2327,6.23676,10.3471,1.80862,7.91152,11.0824,3.485,8.08966 --4.61894,0.759289,0.733406,2,3,0,9.70674,3.22115,1.09577,1.05485,0.334572,-0.408131,0.142248,0.163441,-0.450635,-0.844808,-0.733661,4.37703,2.77393,3.40025,2.29543,3.58777,3.37702,2.72736,2.41723,-4.86709,-3.35808,-3.77153,-3.40829,-3.24609,-3.34018,-4.38779,-3.95102,21.122,-5.26345,-9.36575,-10.3337,-9.4839,18.1247,25.7701,2.03426 --5.28992,0.733309,0.655308,3,7,0,11.1181,0.995581,2.8535,-0.76669,-0.855327,0.881083,0.262676,0.130623,1.54247,0.904037,0.843208,-1.19217,3.50975,1.36831,3.57525,-1.4451,1.74513,5.39702,3.40168,-5.52073,-3.32234,-3.7288,-3.3653,-3.11739,-3.31913,-4.0157,-3.9234,-26.6084,-21.8248,10.3794,11.6251,10.0421,4.28903,5.63639,-23.5128 --5.23642,0.9753,0.542212,3,15,0,7.868,5.45554,5.10989,1.95444,-0.994517,1.09405,-0.330339,-0.0580689,1.62834,-0.303117,-0.727896,15.4425,11.046,5.15881,3.90664,0.37366,3.76754,13.7762,1.73607,-3.97741,-3.26792,-3.82154,-3.35637,-3.12781,-3.34848,-3.31073,-3.97188,-13.9428,5.84075,-7.29793,2.01307,-2.65456,-0.537143,7.74489,5.81758 --4.76548,0.561002,0.948832,2,3,0,8.65236,4.70215,4.34997,0.512705,0.424181,-0.477827,0.761284,-0.609055,1.03349,-1.08908,1.38708,6.9324,2.62362,2.05278,-0.0353318,6.54733,8.01372,9.19781,10.7359,-4.61331,-3.36605,-3.74139,-3.52136,-3.46778,-3.52011,-3.60892,-3.81178,6.84054,4.25188,2.54871,-12.8135,12.9834,23.6616,2.44724,-9.50257 --5.89314,0.850819,0.46532,4,23,0,12.539,5.52736,7.53772,0.797317,0.467868,-1.21353,-0.23639,-1.41816,1.08138,-0.684577,1.31541,11.5373,-3.61987,-5.16233,0.367211,9.05401,3.74552,13.6785,15.4425,-4.22926,-3.89663,-3.70066,-3.49863,-3.74013,-3.34798,-3.3149,-3.8276,22.3818,-1.7091,-13.8434,-20.8403,-0.572071,14.7,19.7869,11.434 --8.98194,0.637898,0.555867,3,7,0,15.2812,5.83396,2.32974,0.0678385,-0.673757,1.11374,-0.577256,1.21612,-1.64331,-2.29671,-0.463018,5.99201,8.42867,8.66721,0.483232,4.26428,4.4891,2.00548,4.75525,-4.70333,-3.22244,-3.95739,-3.49232,-3.28723,-3.36714,-4.50065,-3.89031,24.4891,13.5443,-0.926535,-10.2753,9.63381,15.0172,-0.393136,12.7255 --15.0691,0.952876,0.349471,4,31,0,19.8566,7.8965,1.53741,0.0763566,0.0320233,0.0124677,-1.26441,1.74374,-0.225379,-4.30148,-0.1342,8.0139,7.91567,10.5773,1.28335,7.94574,5.95259,7.55,7.69018,-4.51464,-3.22156,-4.05157,-3.45188,-3.61015,-3.41819,-3.76754,-3.83797,24.2803,18.7171,1.31249,-2.23404,1.91671,2.2338,-10.2412,26.9519 --7.88361,1,0.566673,3,7,0,20.7041,6.39484,5.63439,2.31425,-0.00694911,-0.592468,-1.89875,0.723356,1.31161,-1.20302,-0.126111,19.4342,3.05665,10.4705,-0.383434,6.35569,-4.30344,13.785,5.68428,-3.79004,-3.34371,-4.04593,-3.5421,-3.45015,-3.43306,-3.31036,-3.87087,3.85393,-11.9651,27.6388,3.95583,7.75675,18.0244,16.1025,-3.04924 --9.56239,0.611632,1.04846,2,3,0,12.9145,7.37124,0.571434,-1.46095,-0.0794964,1.0051,2.20867,-1.15363,-0.673449,0.806732,0.559012,6.53641,7.94559,6.71202,7.83224,7.32582,8.63335,6.98641,7.69068,-4.65073,-3.22154,-3.87575,-3.3197,-3.54406,-3.55761,-3.82802,-3.83797,7.95776,16.8716,-2.82562,7.77325,11.8454,25.4126,-0.840414,-6.83637 --8.71921,0.98745,0.615195,3,7,0,12.2333,8.02728,7.66321,1.69751,-0.242418,-0.986892,-2.34172,1.01246,0.97361,-0.7923,-0.51784,21.0356,0.464521,15.786,1.95572,6.16958,-9.9178,15.4883,4.05896,-3.73477,-3.50544,-4.38081,-3.42198,-3.43347,-3.80939,-3.25307,-3.90663,22.5027,12.4756,9.90719,-15.57,11.7418,-20.4906,18.7464,3.50054 --10.312,0.362434,1.08627,2,3,0,14.1226,8.93428,2.16555,0.979096,1.24621,0.436146,-1.53441,0.808654,0.81558,-0.0563452,-2.69452,11.0546,9.87878,10.6855,8.81226,11.633,5.61143,10.7005,3.09916,-4.26509,-3.23917,-4.05733,-3.33041,-4.1013,-3.40471,-3.48794,-3.93157,11.964,13.7393,12.7498,18.061,22.0961,3.91029,27.9921,3.79485 --6.84796,0.99803,0.313719,4,15,0,12.5447,8.65586,5.92019,-0.349435,-1.33748,-0.937667,0.535829,-1.35938,-0.40855,-0.23568,1.5885,6.58714,3.10469,0.608049,7.26059,0.73769,11.8281,6.23716,18.0601,-4.6459,-3.34134,-3.71695,-3.31711,-3.1348,-3.80133,-3.91335,-3.86598,39.9564,-1.60552,-3.25577,22.2557,10.5195,15.6768,3.41399,17.4758 --9.3072,0.823128,0.568989,3,7,0,13.6288,6.99565,0.578693,1.91105,-0.506452,-0.991065,-0.06415,1.36595,0.428937,-0.342931,-1.98363,8.10156,6.42213,7.78612,6.7972,6.70257,6.95853,7.24387,5.84773,-4.50687,-3.23397,-3.91875,-3.317,-3.4824,-3.46354,-3.8,-3.86772,18.5915,-5.24939,29.1724,23.8262,3.9189,-3.3968,-2.36619,13.5618 --9.92484,0.898746,0.622542,3,7,0,17.6835,4.9769,0.284097,-1.92106,0.456773,1.01605,0.0600765,-1.41936,-0.337994,0.375549,1.9724,4.43113,5.26555,4.57366,5.08359,5.10667,4.99396,4.88087,5.53725,-4.86141,-3.25891,-3.80356,-3.33201,-3.34636,-3.38275,-4.08208,-3.87377,-0.920748,-2.34954,11.8291,10.3029,11.8328,19.0318,-4.30267,1.51271 --9.00494,0.506759,0.841127,2,7,0,14.151,3.23058,0.104047,-0.516262,-1.38031,-1.90389,0.0465323,-0.691621,0.295006,-0.886287,0.0495924,3.17686,3.03249,3.15862,3.13836,3.08696,3.23542,3.26127,3.23574,-4.9963,-3.3449,-3.76561,-3.37845,-3.21927,-3.33748,-4.30767,-3.92785,27.7986,9.71048,-3.76052,16.1958,18.1592,-8.19547,-2.23185,12.9503 --7.04281,0.988488,0.379548,4,31,0,12.5337,2.0987,2.86566,1.61124,-2.55201,0.0837418,0.0431643,0.0235156,0.880324,0.765459,-0.300205,6.71596,2.33868,2.16609,4.29224,-5.21448,2.2224,4.62141,1.23842,-4.63368,-3.38178,-3.74365,-3.34713,-3.2258,-3.32301,-4.11646,-3.98803,-22.5584,-10.5035,1.3019,12.8802,4.302,1.28135,2.66978,-2.2904 --6.2372,0.963047,0.656534,3,7,0,11.3569,4.70472,3.03805,-0.497332,2.22219,0.33273,-0.1033,0.269507,0.383311,-0.493941,0.818092,3.1938,5.71557,5.5235,3.2041,11.4559,4.39089,5.86924,7.19012,-4.99443,-3.24762,-3.83342,-3.37637,-4.07387,-3.36435,-3.9573,-3.84501,15.6442,-3.0339,-9.47562,11.9604,17.1667,-5.15391,10.1726,25.3382 --6.2372,0.00469946,1.04963,3,15,0,19.8152,4.70472,3.03805,-0.497332,2.22219,0.33273,-0.1033,0.269507,0.383311,-0.493941,0.818092,3.1938,5.71557,5.5235,3.2041,11.4559,4.39089,5.86924,7.19012,-4.99443,-3.24762,-3.83342,-3.37637,-4.07387,-3.36435,-3.9573,-3.84501,3.96878,9.7667,25.7805,12.2353,21.2821,-3.08017,5.61371,0.0324957 --7.75741,0.993906,0.123322,5,31,0,12.7791,4.57735,1.09482,1.24857,1.16208,-1.55626,0.156286,-0.913082,-0.669784,-1.34167,-0.832754,5.94431,2.87352,3.57769,3.10846,5.84962,4.74845,3.84406,3.66563,-4.708,-3.35293,-3.77603,-3.37941,-3.40578,-3.3749,-4.22348,-3.9165,0.177423,3.84939,-29.4927,8.85279,18.8027,1.69859,-12.092,-5.46126 --7.31003,0.997812,0.215535,5,47,0,15.0568,-0.98809,3.31021,0.73969,-0.261523,1.6516,-0.643443,1.47675,0.868337,1.67351,0.00346353,1.46044,4.47904,3.90025,4.55158,-1.85379,-3.11802,1.88629,-0.976625,-5.19221,-3.28351,-3.78452,-3.34161,-3.12066,-3.38691,-4.51978,-4.06918,-6.51912,13.629,14.3731,12.5124,-2.11606,-5.31439,2.11267,-6.67965 --5.00536,1,0.377203,3,7,0,10.1816,-0.62323,14.03,0.821024,-0.236845,-0.160415,-0.123095,-1.06225,0.80537,0.911354,1.74533,10.8957,-2.87385,-15.5266,12.163,-3.94616,-2.35025,10.6761,23.8636,-4.27711,-3.81273,-3.998,-3.42699,-3.16974,-3.36321,-3.48972,-4.02651,23.4784,-3.28558,-14.0709,-15.8754,-7.33419,-22.6413,6.8462,11.4028 --3.97893,0.554586,0.65799,4,15,0,8.94406,8.6481,5.26953,1.06221,-0.42895,0.711919,-0.48304,0.673193,0.275968,-0.766247,0.352085,14.2455,12.3996,12.1955,4.61034,6.38774,6.10271,10.1023,10.5034,-4.0474,-3.31831,-4.14251,-3.34043,-3.45307,-3.42443,-3.53339,-3.81277,16.8114,19.0247,2.58647,-4.42368,5.37872,0.0266943,-6.7996,-12.2561 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--7.0477,0.994685,0.195373,4,15,0,10.9791,10.2427,1.86139,-0.0365651,-1.81321,0.007168,0.093616,-1.68828,0.861062,-0.0758013,-0.388155,10.1747,10.2561,7.10018,10.1016,6.86762,10.417,11.8455,9.52021,-4.33308,-3.24697,-3.89077,-3.35659,-3.49826,-3.68328,-3.41091,-3.8188,-6.20209,11.073,22.8751,14.0662,-4.16889,-2.00494,9.51006,13.9595 --8.43706,1,0.349143,4,15,0,11.8463,1.80069,0.132451,0.741161,1.69883,-0.0406002,-0.19151,1.11644,0.103423,0.447171,0.995862,1.89885,1.79531,1.94856,1.85992,2.0257,1.77532,1.81439,1.93259,-5.14092,-3.41401,-3.73936,-3.42601,-3.17267,-3.31932,-4.53139,-3.96572,-21.0281,-2.92592,22.1328,5.74341,-2.21513,-3.52252,-6.74198,26.6643 --11.2045,0.423248,0.627131,3,7,0,16.0971,4.3762,3.68449,-1.27623,0.32178,0.513804,0.439323,2.51483,-1.81379,0.667766,-1.08527,-0.326061,6.26931,13.6421,6.83658,5.5618,5.99488,-2.30672,0.377537,-5.41002,-3.2365,-4.23246,-3.31694,-3.38195,-3.41993,-5.28334,-4.01777,-27.1736,3.20202,16.6769,6.80419,9.28107,-2.84836,-5.3153,-4.54865 --9.21942,1,0.226849,3,7,0,12.6068,3.29074,2.52735,-1.24311,0.634221,0.0975326,0.278204,2.16819,-1.60789,0.873981,-0.850193,0.148966,3.53724,8.77052,5.4996,4.89364,3.99386,-0.772961,1.14201,-5.35072,-3.3211,-3.96212,-3.32614,-3.33058,-3.35387,-4.98364,-3.99125,0.548499,-5.78983,19.2869,2.72075,-0.834196,27.8275,22.8897,-6.45429 --5.47212,0.981521,0.404996,3,7,0,12.1173,4.32698,0.524931,-0.256605,-1.22642,0.512519,-0.224162,0.222497,1.09982,0.765243,0.114,4.19228,4.59602,4.44378,4.72868,3.68319,4.20931,4.90431,4.38682,-4.88656,-3.27946,-3.79975,-3.33815,-3.25155,-3.35939,-4.07901,-3.89876,1.19594,18.5553,-12.1199,-5.28642,-1.67175,0.85067,-15.5238,30.5151 --8.87901,0.531472,0.681097,2,7,0,14.7338,2.587,0.912931,0.294084,-2.33027,1.73965,0.954745,0.668117,0.162513,1.19129,-0.111963,2.85548,4.17518,3.19694,3.67456,0.459623,3.45862,2.73536,2.48479,-5.03198,-3.29467,-3.76653,-3.36253,-3.12931,-3.34181,-4.38657,-3.94903,-16.3303,8.79336,-9.43226,0.608446,4.1717,-1.89645,-5.58435,-5.99351 --10.71,0.961828,0.337877,4,15,0,18.2466,4.29429,4.43084,-1.83419,0.844567,-1.58189,-0.892166,0.818481,1.84691,-0.973522,-1.30157,-3.83272,-2.71481,7.92085,-0.0192334,8.03643,0.341241,12.4776,-1.47278,-5.87882,-3.79556,-3.92447,-3.52043,-3.62022,-3.31863,-3.37401,-4.08943,19.8148,-4.26303,-16.4664,7.81707,-14.1146,-8.30608,1.23421,2.44464 --10.71,0.00429409,0.535319,2,3,0,14.4116,4.29429,4.43084,-1.83419,0.844567,-1.58189,-0.892166,0.818481,1.84691,-0.973522,-1.30157,-3.83272,-2.71481,7.92085,-0.0192334,8.03643,0.341241,12.4776,-1.47278,-5.87882,-3.79556,-3.92447,-3.52043,-3.62022,-3.31863,-3.37401,-4.08943,-0.655417,2.33819,15.8949,11.8837,6.66555,-11.8212,19.3932,-8.81638 --9.02932,0.999855,0.0665345,6,63,0,14.8624,6.21995,3.29596,2.54445,-2.05164,0.294322,-0.208964,-0.987829,-0.922273,1.27365,0.682794,14.6063,7.19003,2.96411,10.4178,-0.542169,5.53122,3.18018,8.47041,-4.02563,-3.2248,-3.761,-3.36511,-3.11746,-3.40168,-4.31966,-3.82854,-1.94261,11.1267,-2.23907,6.00518,4.12347,9.13871,-1.55478,15.5947 --12.7248,0.991627,0.117139,5,31,0,19.2249,1.46842,3.28948,0.955072,0.868397,-2.12866,0.534431,-0.507094,1.04631,-3.19369,0.46605,4.61011,-5.53377,-0.19966,-9.03716,4.32499,3.22642,4.91025,3.00148,-4.84274,-4.13734,-3.70684,-4.3796,-3.2912,-3.33732,-4.07823,-3.93427,-6.79426,-13.6215,-28.7434,-17.8664,1.55908,6.2357,-17.9615,-17.5719 --6.66557,0.998644,0.200043,4,15,0,15.6432,1.94004,1.70139,-0.877981,-0.616828,1.80802,-0.46792,-0.448169,0.351073,1.48422,-0.671562,0.446254,5.01618,1.17753,4.46528,0.890577,1.14393,2.53735,0.797453,-5.31412,-3.26604,-3.72561,-3.34338,-3.13823,-3.31692,-4.41699,-4.00298,12.0404,33.211,-8.07559,-13.9926,-2.5547,12.8057,17.6319,34.8065 --6.86859,0.970641,0.344959,4,15,0,13.3441,2.25531,0.647759,0.678266,1.28101,-0.844213,-0.0925989,1.03994,1.2999,-0.235705,0.839938,2.69466,1.70846,2.92894,2.10263,3.08509,2.19533,3.09733,2.79939,-5.05001,-3.41944,-3.76018,-3.41594,-3.21918,-3.32274,-4.33197,-3.93995,-6.29572,5.30872,-6.91156,5.22784,6.40557,9.84435,1.63553,11.9142 --3.68642,0.285714,0.548896,3,7,0,11.0139,5.82576,4.98991,0.445869,0.0970939,1.13354,-0.550254,-1.18751,0.929544,-0.545573,-0.145753,8.05061,11.482,-0.0998016,3.1034,6.31025,3.08004,10.4641,5.09847,-4.51138,-3.28215,-3.70796,-3.37958,-3.44604,-3.33471,-3.50547,-3.88282,-1.32638,10.211,11.5134,-13.7949,1.74556,-1.28031,-1.8315,9.41607 --4.14183,0.999472,0.151316,5,31,0,5.81549,1.49199,7.13326,0.531115,-0.52021,-0.552958,0.825878,1.14086,0.680745,0.704046,-0.0683649,5.28057,-2.45241,9.63001,6.51413,-2.21881,7.38319,6.34792,1.00432,-4.77404,-3.76779,-4.00308,-3.31781,-3.12533,-3.4852,-3.90038,-3.99589,5.78424,-11.3719,25.5667,2.47503,-1.33939,8.22757,-7.37216,-7.99606 --5.77077,0.921404,0.258731,4,15,0,11.3052,8.55359,2.12609,0.577639,0.0759579,1.35116,-0.759581,-1.55255,-0.000154278,0.252871,-0.516888,9.78171,11.4263,5.25273,9.09122,8.71509,6.93865,8.55326,7.45464,-4.36456,-3.28022,-3.82455,-3.3349,-3.69877,-3.46257,-3.66773,-3.84119,25.7673,20.7197,-9.34194,7.32844,-4.78184,6.4464,5.78728,21.6669 --6.90939,0.952564,0.36071,4,15,0,12.6592,0.299984,1.71253,0.142937,-0.25867,-1.58081,1.5046,1.07428,0.0518215,0.423454,0.365075,0.544769,-2.4072,2.13973,1.02516,-0.142996,2.87665,0.38873,0.925186,-5.30208,-3.76307,-3.74312,-3.46435,-3.1207,-3.33139,-4.77231,-3.99859,-14.7239,-3.86053,24.826,-1.90496,7.64847,-20.8901,-6.31629,7.1269 --7.04083,0.8782,0.540792,3,7,0,12.5217,8.371,2.586,0.377972,-1.2575,1.1122,-2.04065,-1.20799,0.486643,-0.779544,-0.308765,9.34843,11.2471,5.24713,6.3551,5.11909,3.09389,9.62945,7.57253,-4.40006,-3.27424,-3.82437,-3.31855,-3.34729,-3.33495,-3.57185,-3.83956,-8.30877,24.1395,26.8213,-10.5416,-13.9236,15.8417,6.85201,3.59022 --10.4712,0.0312093,0.670479,2,3,0,13.2953,6.05398,9.11541,-0.301858,-1.37684,0.785565,-2.76908,-0.588073,0.672782,-1.04376,-0.614443,3.30242,13.2147,0.693454,-3.46027,-6.49645,-19.1873,12.1867,0.453086,-4.98248,-3.35749,-3.71817,-3.76897,-3.30265,-5.00083,-3.3905,-4.01507,0.237704,9.71519,-16.151,-5.2462,10.7161,-11.7518,20.335,1.62097 --8.86044,1,0.103433,5,31,0,13.0495,3.65475,0.657329,1.06857,0.993681,-0.267731,2.05595,0.343947,-0.932964,1.24969,1.23092,4.35716,3.47876,3.88084,4.47621,4.30793,5.00619,3.04149,4.46387,-4.86918,-3.32373,-3.784,-3.34315,-3.29008,-3.38315,-4.34031,-3.89695,15.2773,-11.3488,14.2502,25.6859,13.5729,-2.89279,-7.15154,5.12575 --7.73282,0.998097,0.174071,4,31,0,12.3297,-0.317946,0.30493,-0.0700247,0.950302,0.433853,1.00579,0.687474,-0.383946,-0.685886,1.13098,-0.339298,-0.185651,-0.108314,-0.527093,-0.0281698,-0.0112491,-0.435023,0.0269245,-5.41169,-3.55655,-3.70786,-3.55095,-3.12199,-3.32106,-4.92077,-4.03054,-5.62772,-0.310577,5.49102,20.5928,2.89931,-1.87682,-7.87233,-7.72369 --7.663,0.992628,0.289442,4,15,0,12.812,5.67211,11.9747,2.15922,0.293588,-1.06717,-0.430143,0.620461,1.4876,0.788698,-0.318339,31.5281,-7.10688,13.1019,15.1165,9.18773,0.521286,23.4856,1.86011,-3.65465,-4.36261,-4.19792,-3.58906,-3.75684,-3.31778,-3.37198,-3.96798,32.3548,-21.6825,13.2219,5.99426,16.5924,7.45847,20.273,-26.1621 --5.80392,0.378065,0.471592,3,7,0,11.1395,3.78751,8.93039,1.56751,0.408403,-0.59347,-0.0394752,0.493958,1.80032,1.47838,-0.0821701,17.786,-1.5124,8.19875,16.9901,7.43471,3.43498,19.8651,3.0537,-3.85882,-3.67395,-3.93647,-3.72924,-3.55533,-3.34133,-3.23892,-3.93282,41.8075,-1.8949,10.0772,-3.79049,3.61079,-12.5323,20.7032,18.1358 --5.62999,0.998615,0.175784,5,31,0,9.73086,10.6273,1.79749,0.095776,-0.458993,0.170023,0.731531,0.0393832,-0.320807,-1.22988,-0.221065,10.7995,10.9329,10.6981,8.41662,9.80229,11.9422,10.0507,10.23,-4.28445,-3.26453,-4.05801,-3.32513,-3.83647,-3.8116,-3.53748,-3.81415,15.863,20.1191,-22.3033,-22.0062,-3.90934,13.4274,32.7167,12.7025 --4.94753,0.979901,0.289495,4,15,0,9.18783,5.34352,10.1522,0.209323,-0.512977,-0.484104,-1.42263,-0.507954,0.893691,-0.0626177,1.63493,7.46861,0.428813,0.186687,4.70782,0.135695,-9.09926,14.4164,21.9416,-4.56374,-3.50814,-3.71136,-3.33855,-3.12412,-3.7383,-3.28573,-3.96183,19.27,-7.44073,31.0883,6.18554,-10.2342,-17.6043,17.7583,31.8866 -# Adaptation terminated -# Step size = 0.298701 -# Diagonal elements of inverse mass matrix: -# 10.9788, 1.6107, 0.905631, 0.857679, 0.874675, 0.918614, 0.905908, 0.656822, 0.685638, 0.897676 --7.7547,0.983555,0.298701,3,15,0,10.6146,2.23865,2.41786,1.04403,0.909048,-0.0265812,1.48909,-0.429918,0.608725,-0.0612811,-2.36912,4.76296,2.17438,1.19917,2.09048,4.4366,5.83906,3.71046,-3.48956,-4.8269,-3.39121,-3.72597,-3.41643,-3.29861,-3.4136,-4.24248,-4.17957,3.72461,7.9298,-1.0375,6.80849,2.91272,11.9743,-1.4385,1.23895 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--7.65198,0.998051,0.298701,4,15,0,12.7769,4.97388,2.88866,-0.659473,0.959279,1.01116,-0.15623,1.91197,-0.826574,0.91863,1.15465,3.06888,7.89478,10.4969,7.62749,7.74491,4.52258,2.58618,8.30927,-5.00823,-3.22158,-4.04732,-3.31846,-3.58822,-3.36811,-4.40945,-3.83033,0.409908,3.49176,-6.7268,-4.21527,24.6098,15.2952,-20.5894,-10.5283 --9.79994,0.967053,0.298701,4,15,0,16.4015,4.74259,4.6989,0.657162,0.378126,2.24293,0.478135,-2.98579,0.263563,-0.192299,0.78736,7.83053,15.2819,-9.28733,3.839,6.51937,6.9893,5.98105,8.44232,-4.53101,-3.48665,-3.76874,-3.35812,-3.46518,-3.46506,-3.9438,-3.82884,28.1131,18.4655,3.46922,4.9463,4.53446,-12.6707,8.38205,28.5231 --11.536,0.991846,0.298701,4,15,0,13.0291,6.44264,3.57191,1.1982,0.459822,2.303,0.031992,-3.25874,0.418819,-0.119934,1.13144,10.7225,14.6688,-5.19727,6.01425,8.08508,6.55691,7.93862,10.484,-4.29035,-3.44388,-3.70096,-3.32085,-3.62566,-3.44443,-3.72768,-3.81286,19.2309,6.67346,38.2817,-16.3204,8.16474,-6.06792,-13.4363,-1.22659 --7.71117,1,0.298701,3,7,0,13.4837,3.76927,4.69068,1.5821,-0.421343,1.5528,-0.360139,-2.35836,0.62461,0.571681,1.54033,11.1904,11.053,-7.29302,6.45084,1.79288,2.07997,6.69911,10.9944,-4.25491,-3.26813,-3.72752,-3.31808,-3.16431,-3.32165,-3.86007,-3.81087,-1.18129,22.6731,-10.2054,9.55395,-1.66702,-0.175264,5.21618,18.7898 --7.04552,0.977633,0.298701,3,15,0,10.8338,6.08321,7.87228,1.50663,0.478446,0.972316,-0.249903,-2.57387,0.31716,0.164781,0.535772,17.9438,13.7376,-14.179,7.38041,9.84967,4.1159,8.57998,10.301,-3.85172,-3.38612,-3.93561,-3.31743,-3.8428,-3.35695,-3.66521,-3.81377,29.2845,21.1286,-38.0767,7.02865,9.09457,-12.0085,3.03407,-13.3828 --8.44185,0.977869,0.298701,4,15,0,14.4195,2.06072,1.46068,-1.10298,-1.52475,1.14362,0.716771,0.363998,-0.264495,-0.907198,2.01383,0.449616,3.73119,2.59241,0.735592,-0.166456,3.1077,1.67438,5.00229,-5.31371,-3.31264,-3.75261,-3.47899,-3.12045,-3.33519,-4.55415,-3.88488,2.9638,-15.7322,-13.557,20.4864,6.76004,2.93683,6.17591,14.3519 --5.89179,0.993682,0.298701,4,15,0,11.419,7.22596,1.65763,1.37642,0.573053,-0.695517,0.374799,-0.936165,0.756524,1.07229,-0.846428,9.50755,6.07305,5.67415,9.00342,8.17587,7.84724,8.47999,5.8229,-4.38692,-3.24009,-3.83848,-3.33342,-3.63589,-3.51057,-3.67468,-3.86819,-4.30397,0.307004,-23.6491,0.800266,22.2032,4.67345,13.534,20.3497 --6.42829,0.958027,0.298701,3,7,0,11.3715,8.35335,5.64841,1.38897,-0.499211,-1.88888,0.290497,-0.868312,-0.445582,0.436039,-0.401148,16.1988,-2.31583,3.44878,10.8163,5.53361,9.9942,5.83653,6.08751,-3.93647,-3.75361,-3.77275,-3.37702,-3.37967,-3.65111,-3.96127,-3.86326,22.806,1.57089,19.1772,35.6852,2.73848,14.5994,-5.26563,35.1148 --6.70917,0.92399,0.298701,4,15,0,10.3616,0.964292,5.68471,-0.763265,0.0837252,2.20145,-0.907512,0.157686,1.15495,-0.412832,0.72039,-3.37465,13.4789,1.86069,-1.38254,1.44025,-4.19465,7.52986,5.0595,-5.81447,-3.37161,-3.73767,-3.60719,-3.15292,-3.42834,-3.76964,-3.88365,-10.8894,11.4991,12.4648,3.11086,-4.36554,-9.51282,19.4245,-26.6995 --8.07182,0.932484,0.298701,4,15,0,11.965,6.58453,0.565167,0.932467,0.675198,-1.40979,-1.50649,0.386524,-1.19206,-0.897147,-0.617895,7.11153,5.78776,6.80298,6.07749,6.96613,5.73311,5.91082,6.23532,-4.59661,-3.24599,-3.87922,-3.32035,-3.50789,-3.40941,-3.95227,-3.86059,14.9031,15.6823,13.8807,20.5017,-2.5233,16.4535,23.364,37.3848 --9.34368,0.987934,0.298701,4,15,0,12.0976,5.05989,10.9536,-0.632695,-0.418266,1.20014,1.30246,-0.0206269,1.54394,1.58195,-0.0443749,-1.87043,18.2058,4.83395,22.388,0.478353,19.3266,21.9716,4.57382,-5.60975,-3.74232,-3.81139,-4.2953,-3.12965,-4.7047,-3.30039,-3.89442,-2.42027,27.1111,20.4591,5.08907,2.44289,22.4985,18.6108,4.72864 --13.4763,0.638371,0.298701,4,15,0,19.8775,3.45791,0.321062,0.908882,0.0626734,-1.76325,0.488497,0.992153,1.73603,-1.40506,2.90163,3.74971,2.89179,3.77645,3.00679,3.47803,3.61474,4.01528,4.38951,-4.93383,-3.35199,-3.78122,-3.38273,-3.23995,-3.34509,-4.19939,-3.89869,11.0776,4.66976,-7.93489,3.77905,17.8276,5.63014,-8.13977,-1.76928 --7.59638,0.992856,0.298701,4,15,0,18.8002,6.92617,7.23473,-0.401902,0.791686,-0.127074,-0.935524,0.432093,0.0676352,-0.265101,-2.22054,4.01852,6.00682,10.0522,5.00824,12.6538,0.157907,7.41549,-9.13886,-4.90501,-3.24139,-4.02426,-3.33323,-4.26694,-3.31976,-3.78168,-4.4989,-22.7997,20.1504,23.7621,7.23361,0.756727,7.90479,2.9497,-17.4855 --8.40607,0.952983,0.298701,4,15,0,12.5103,3.46321,0.148193,-0.386786,-0.413436,-0.630136,-0.55122,-1.23558,1.03611,1.23319,1.28459,3.40589,3.36983,3.28011,3.64596,3.40195,3.38153,3.61676,3.65358,-4.97114,-3.32872,-3.76856,-3.36332,-3.23577,-3.34027,-4.25591,-3.91681,35.804,-2.66732,1.34268,4.34317,-8.86874,5.52081,10.7575,-6.36439 --7.37081,0.679953,0.298701,4,15,0,11.7747,4.20873,0.0614311,-0.904761,-0.695665,-1.10325,-0.0707244,-0.0749196,0.677274,0.166401,0.035701,4.15315,4.14096,4.20413,4.21896,4.166,4.20439,4.25034,4.21093,-4.8907,-3.29598,-3.79289,-3.34879,-3.2809,-3.35926,-4.16679,-3.90294,2.9678,-3.28146,-5.65412,-1.49574,9.7583,-9.33236,-5.20433,8.96869 --6.31458,0.951231,0.298701,4,15,0,11.3749,6.79178,9.84204,0.835074,0.607501,1.07691,0.0627506,-0.14195,-0.540045,1.08076,-0.268782,15.0106,17.3908,5.39471,17.4286,12.7708,7.40938,1.47664,4.14642,-4.00193,-3.66246,-3.82917,-3.76624,-4.28675,-3.48659,-4.58663,-3.90449,37.0519,14.3189,13.8179,24.6264,11.4931,10.7639,9.05976,50.8866 --7.15322,1,0.298701,4,15,0,8.93942,2.19761,0.216423,-0.691992,-0.659126,-1.2081,-0.178952,0.253867,0.649532,-1.248,0.212706,2.04784,1.93615,2.25255,1.92751,2.05496,2.15888,2.33818,2.24364,-5.12369,-3.40538,-3.74541,-3.42316,-3.17377,-3.32238,-4.44799,-3.9562,-18.7635,5.19047,20.669,0.497807,20.3789,16.1536,10.2686,1.3874 --6.05396,0.994141,0.298701,4,15,0,10.1986,9.62099,6.60056,0.762039,-0.28662,1.43602,-0.577523,0.733611,0.656053,0.582272,0.945151,14.6509,19.0995,14.4632,13.4643,7.72914,5.80902,13.9513,15.8595,-4.02299,-3.83752,-4.28716,-3.48951,-3.58652,-3.4124,-3.30348,-3.8323,-3.53227,14.0812,13.1713,1.44334,3.7727,-1.87988,7.1337,4.07812 --3.678,0.98216,0.298701,4,15,0,10.4908,4.92666,2.50902,0.395408,0.699052,-1.01188,0.503663,-0.455034,0.389979,0.115298,-0.418459,5.91875,2.38782,3.78497,5.21594,6.6806,6.19036,5.90513,3.87673,-4.7105,-3.37901,-3.78144,-3.32999,-3.48031,-3.42816,-3.95295,-3.91114,-8.33957,9.19656,-5.72847,3.53603,8.28603,14.4595,2.0116,-10.8579 --8.44917,0.947535,0.298701,3,15,0,10.195,7.86539,3.16488,0.493629,1.03516,-1.50507,-0.177981,0.0465552,0.827363,1.76594,1.82328,9.42767,3.10201,8.01273,13.4544,11.1415,7.3021,10.4839,13.6359,-4.3935,-3.34148,-3.9284,-3.48898,-4.02614,-3.48095,-3.50398,-3.81344,16.836,-1.31298,25.0069,13.3362,18.4449,12.5245,6.43021,13.5906 --5.97597,0.972934,0.298701,3,7,0,13.3326,2.44747,1.89381,1.95352,-0.331594,-0.558668,-0.975416,-0.767408,-0.549727,0.841294,0.380742,6.14706,1.38946,0.994149,4.04072,1.8195,0.600222,1.40639,3.16852,-4.68821,-3.44002,-3.72269,-3.35302,-3.16523,-3.31749,-4.59826,-3.92967,24.4446,-10.3695,6.66254,-0.56871,-3.08656,9.62354,19.6181,34.7841 --4.39673,0.99646,0.298701,4,15,0,8.86893,5.85649,5.22616,0.29136,0.81846,0.464994,-1.50011,-0.601651,-0.26843,0.0916813,-0.0449602,7.37918,8.28662,2.71217,6.33563,10.1339,-1.98332,4.45363,5.62152,-4.57192,-3.22193,-3.75526,-3.31866,-3.88137,-3.35361,-4.13904,-3.8721,24.587,1.83164,4.89885,4.89924,6.33123,7.72337,7.54479,47.3436 --8.86343,0.836314,0.298701,4,15,0,12.4514,4.61578,0.961777,0.976378,-1.8264,-0.977469,-0.391516,-1.24556,1.69565,-0.898635,1.28421,5.55484,3.67568,3.41783,3.7515,2.8592,4.23923,6.24662,5.85091,-4.74651,-3.31502,-3.77197,-3.36044,-3.2081,-3.36019,-3.91223,-3.86766,6.98382,-8.4932,-13.1622,10.5682,22.2829,4.76992,37.7407,-13.5179 --11.0203,0.98643,0.298701,4,15,0,14.0085,8.18337,0.582851,0.10697,1.57793,0.987385,0.367349,0.882117,-0.646052,2.61438,-1.20992,8.24572,8.75887,8.69752,9.70717,9.10307,8.39748,7.80682,7.47817,-4.49417,-3.2244,-3.95878,-3.34712,-3.74624,-3.54296,-3.74103,-3.84086,30.0099,-1.26557,-9.88182,10.8707,-8.99998,26.3214,7.0102,-5.24199 --17.7022,0.937688,0.298701,3,7,0,19.8158,7.95742,0.44804,-0.468725,2.94314,2.24603,-0.44634,0.96232,-0.378093,2.6986,-1.89665,7.74742,8.96373,8.38858,9.1665,9.27607,7.75744,7.78802,7.10765,-4.53847,-3.22617,-3.94485,-3.33623,-3.768,-3.50552,-3.74295,-3.84625,23.0647,-3.67956,-7.03349,-27.9092,20.3599,2.66393,-0.98903,22.013 --11.63,0.992818,0.298701,3,7,0,21.1328,8.60126,2.12502,-0.821593,2.07055,1.18963,0.41072,1.71115,0.0315378,1.81081,-1.22039,6.85536,11.1292,12.2375,12.4493,13.0012,9.47405,8.66828,6.00791,-4.62053,-3.27048,-4.14501,-3.43954,-4.32625,-3.61357,-3.65693,-3.86472,-8.93307,8.45686,14.8708,23.1382,9.33991,19.0214,31.1974,18.0889 --4.77201,0.941716,0.298701,3,7,0,16.1014,3.98163,1.72608,0.511906,-1.68323,0.552363,0.960583,0.364086,0.534098,0.241591,0.322667,4.86522,4.93505,4.61007,4.39863,1.07624,5.63967,4.90352,4.53858,-4.81636,-3.26849,-3.80464,-3.3448,-3.14277,-3.40579,-4.07911,-3.89523,-7.38956,15.3499,-10.1588,14.4254,8.27827,23.6033,4.03859,-11.2164 --2.87819,0.932391,0.298701,4,23,0,5.87175,6.39457,5.97546,1.23326,-0.995589,-0.0478442,0.333601,-0.159633,0.50998,-0.36202,0.616917,13.7638,6.10868,5.44068,4.23133,0.445458,8.38799,9.44193,10.0809,-4.07736,-3.23941,-3.83068,-3.34851,-3.12906,-3.54238,-3.58773,-3.81499,28.4692,18.3268,5.05752,2.50892,-9.96816,11.4711,17.8379,35.1381 --5.41563,0.876657,0.298701,3,15,0,8.98142,7.5158,2.06891,0.359093,0.0262081,-0.796236,-1.07159,-1.18087,0.363046,0.202727,1.47581,8.25873,5.86846,5.07268,7.93523,7.57002,5.29877,8.26691,10.5691,-4.49303,-3.24424,-3.81881,-3.32045,-3.56953,-3.3932,-3.69519,-3.81247,30.7627,19.6985,15.138,4.5736,7.78217,2.58912,4.64287,-11.4374 --4.29673,0.973607,0.298701,4,15,0,11.8066,2.33458,5.06701,0.936671,-0.114264,0.154244,-0.636432,1.15673,0.0170147,0.692202,-1.06583,7.0807,3.11614,8.19576,5.84197,1.75561,-0.890222,2.4208,-3.06596,-4.59947,-3.34078,-3.93634,-3.32238,-3.16304,-3.3316,-4.43508,-4.15959,-15.9001,0.851256,26.9712,8.42796,8.3954,-12.4332,10.0616,-8.11024 --7.00182,0.883983,0.298701,4,15,0,12.0094,-1.0203,4.32209,0.788839,0.31646,0.857686,0.408764,-2.29977,1.61322,0.629391,0.614493,2.38914,2.6867,-10.9601,1.69999,0.34747,0.746419,5.95218,1.6356,-5.08458,-3.36268,-3.81528,-3.43291,-3.12737,-3.3171,-3.94727,-3.97508,21.6381,2.95945,-8.71215,5.85179,-4.75696,-1.97892,-9.22513,-0.800425 -# -# Elapsed Time: 0.042168 seconds (Warm-up) -# 0.010171 seconds (Sampling) -# 0.052339 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-2.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-2.csv deleted file mode 100644 index b2dba3f3a2..0000000000 --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-2.csv +++ /dev/null @@ -1,648 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 20 -# stan_version_patch = 0 -# model = stan_test_data_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 500 -# save_warmup = 1 -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 2 -# data -# file = /tmp/tmp2ipdytqf/x8rvpwpf.json -# init = 2 (Default) -# random -# seed = 31709 -# output -# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-2-6qogngy4.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1.1,eta.2.1,eta.1.2,eta.2.2,eta.1.3,eta.2.3,eta.1.4,eta.2.4,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 --12.1371,0.916626,1,2,3,0,17.6316,0.521388,0.0578072,-1.90026,-0.571792,-0.718746,0.452926,-0.0452018,-1.85238,0.430521,-1.5517,0.411539,0.47984,0.518775,0.546276,0.488335,0.547571,0.414307,0.431689,-5.31837,-3.50429,-3.71571,-3.48894,-3.12984,-3.31768,-4.76781,-4.01583,2.77667,-1.09237,5.80619,-7.12762,-1.11964,-2.51896,-4.76215,1.34525 --12.1371,0,12.3621,0,1,1,16.2689,0.521388,0.0578072,-1.90026,-0.571792,-0.718746,0.452926,-0.0452018,-1.85238,0.430521,-1.5517,0.411539,0.47984,0.518775,0.546276,0.488335,0.547571,0.414307,0.431689,-5.31837,-3.50429,-3.71571,-3.48894,-3.12984,-3.31768,-4.76781,-4.01583,3.11911,-3.68003,-5.77452,11.5363,5.94629,21.8511,1.86974,-6.62708 --12.1371,0,1.99742,0,1,1,28.0182,0.521388,0.0578072,-1.90026,-0.571792,-0.718746,0.452926,-0.0452018,-1.85238,0.430521,-1.5517,0.411539,0.47984,0.518775,0.546276,0.488335,0.547571,0.414307,0.431689,-5.31837,-3.50429,-3.71571,-3.48894,-3.12984,-3.31768,-4.76781,-4.01583,7.0329,-1.40253,2.34109,-1.47163,-0.0806743,-1.16375,-6.32676,0.433227 --12.1954,0.990822,0.192021,4,31,0,16.3336,0.711382,0.0541624,1.93316,0.906728,0.98561,0.968741,0.00155065,1.03809,0.527877,1.77004,0.816086,0.764765,0.711466,0.739973,0.760493,0.763851,0.767607,0.807251,-5.26913,-3.48327,-3.71843,-3.47877,-3.13529,-3.31706,-4.7063,-4.00264,5.07469,9.87297,25.6999,21.4353,3.77813,-7.7505,-18.4207,16.7559 --16.028,0.972906,0.24897,4,15,0,20.3975,-1.27918,1.27217,-3.42495,-1.7414,-0.751138,-0.742876,0.362139,-0.494373,-1.57909,-1.34524,-5.63628,-2.23476,-0.818484,-3.28804,-3.49453,-2.22425,-1.90811,-2.99056,-6.14121,-3.74527,-3.70082,-3.75421,-3.15457,-3.35979,-5.20319,-4.1561,-5.24078,0.175639,-5.64661,14.4888,-18.3641,-20.1902,-9.68876,12.9837 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--9.01821,0.866866,0.769858,2,7,0,11.7651,2.71302,2.15343,1.06605,1.26724,-0.0454357,-0.607767,-0.604172,0.325766,2.5484,-1.72248,5.00868,2.61518,1.41198,8.20082,5.44193,1.40424,3.41453,-0.99622,-4.80166,-3.36651,-3.72955,-3.32279,-3.37233,-3.31751,-4.2852,-4.06996,24.3598,14.9511,-19.8979,2.42352,9.51528,-14.6631,15.1074,-8.58273 --9.833,0.26792,0.928168,3,11,0,12.5753,0.47102,4.02421,0.660939,0.713057,-0.234424,-0.381909,0.852925,0.429158,3.30148,-0.976205,3.13078,-0.472354,3.90337,13.7569,3.34051,-1.06586,2.19804,-3.45744,-5.00138,-3.58043,-3.78461,-3.50549,-3.23246,-3.33447,-4.47003,-4.17803,23.3036,-8.3799,36.691,-4.11686,-18.036,-0.393224,2.57356,7.75073 --8.53305,0.808924,0.247993,4,15,0,15.7002,11.7455,5.11434,0.25912,-1.6056,-0.0165903,-0.0626547,-1.70006,0.393035,-2.1685,0.220696,13.0707,11.6607,3.05081,0.655071,3.53392,11.4251,13.7556,12.8742,-4.12228,-3.28853,-3.76304,-3.48319,-3.24305,-3.76593,-3.3116,-3.81049,-11.7231,19.1941,-21.5097,22.2197,-0.307206,9.69775,38.956,-2.60776 --4.9706,0.909518,0.260187,4,15,0,12.1795,5.95128,0.701246,-0.117135,-0.959264,0.234583,-0.800751,0.348871,-0.167572,-0.707969,-0.81899,5.86914,6.11578,6.19593,5.45482,5.2786,5.38976,5.83377,5.37697,-4.71538,-3.23928,-3.85669,-3.3267,-3.3595,-3.39646,-3.96161,-3.877,-4.21884,-7.97024,16.1821,-20.7682,7.70494,-1.87408,-1.29072,36.6638 --5.89382,0.926166,0.349851,3,15,0,7.99444,5.31393,0.488607,0.211477,-0.782328,0.96318,-1.23572,0.68776,-0.416046,0.239524,-0.634761,5.41726,5.78454,5.64997,5.43096,4.93168,4.71015,5.11064,5.00378,-4.76028,-3.24606,-3.83766,-3.32701,-3.33335,-3.37371,-4.0522,-3.88485,7.51938,8.15017,-4.01702,20.7974,2.45949,-5.82045,-1.61168,-17.3443 --7.49734,0.584703,0.487922,3,7,0,12.3098,3.50017,3.45592,0.252117,0.567104,1.30903,-1.15928,1.36655,0.0966106,-1.67182,1.3485,4.37147,8.02407,8.22285,-2.27749,5.46004,-0.506197,3.83405,8.16049,-4.86767,-3.22153,-3.93753,-3.6725,-3.37377,-3.32621,-4.22489,-3.83206,17.766,5.34673,15.7029,-8.92491,8.24988,-22.3564,4.96425,13.228 --6.97752,0.994391,0.294429,4,15,0,11.983,4.40713,4.63609,1.68936,-1.23498,-1.27049,1.64582,-0.502768,1.43558,0.0543794,-0.310088,12.2391,-1.48298,2.07625,4.65924,-1.31836,12.0373,11.0626,2.96953,-4.179,-3.67116,-3.74186,-3.33948,-3.11679,-3.82023,-3.46216,-3.93516,-18.5037,-13.2815,-9.3866,-3.86884,11.6343,11.0952,1.95328,-26.5462 --9.22926,0.777865,0.482639,3,7,0,10.6075,4.00732,1.76005,-1.07305,2.07104,1.51813,-1.1928,0.421457,-1.32436,0.217783,0.739512,2.11869,6.6793,4.7491,4.39062,7.65243,1.90793,1.67638,5.30889,-5.11553,-3.23024,-3.80881,-3.34497,-3.57829,-3.32024,-4.55383,-3.8784,27.519,6.72892,36.3969,-3.31846,8.83864,8.94087,-4.23739,58.8636 --8.36422,0.970557,0.466705,3,7,0,14.156,4.16698,1.56426,0.0928929,2.33384,-1.27713,-1.02101,0.957121,-0.977198,-0.110303,0.0265707,4.31229,2.16922,5.66417,3.99444,7.81771,2.56986,2.63839,4.20855,-4.87389,-3.39151,-3.83814,-3.35416,-3.59611,-3.32702,-4.40142,-3.90299,16.7879,-6.15576,22.439,15.2614,10.8246,7.95488,-7.70073,-6.86893 --6.92298,0.368702,0.715396,2,7,0,11.7834,1.16309,0.444134,-0.507681,1.39049,-1.01701,-1.02569,0.0371941,-0.269253,-0.199834,0.546858,0.937614,0.711402,1.17961,1.07434,1.78065,0.707549,1.04351,1.40597,-5.25448,-3.48714,-3.72565,-3.46193,-3.16389,-3.31719,-4.65914,-3.98251,-24.2935,-8.35139,-12.2758,1.13072,5.03007,-0.39863,-3.82081,-9.83258 --6.49962,0.988048,0.261379,4,15,0,12.0534,11.1491,7.15407,1.24266,-1.98121,0.814627,0.104468,-0.545557,0.644678,-0.537078,-0.0560875,20.0392,16.977,7.24617,7.30683,-3.02462,11.8965,15.7612,10.7479,-3.76782,-3.62446,-3.89657,-3.31722,-3.14147,-3.80747,-3.24658,-3.81173,30.7599,28.4978,15.1501,8.53135,-4.88899,32.7254,12.0107,-2.21285 -# Adaptation terminated -# Step size = 0.404623 -# Diagonal elements of inverse mass matrix: -# 11.3295, 2.44986, 1.21625, 1.0489, 0.857588, 0.732574, 0.69433, 0.832315, 0.889247, 0.879256 --6.4805,0.188521,0.404623,2,3,0,10.6733,10.1366,1.97273,1.06151,-1.61665,1.21924,0.116057,-0.398322,0.756643,-0.164763,0.177397,12.2307,12.5418,9.35083,9.81158,6.9474,10.3656,11.6293,10.4866,-4.17959,-3.32467,-3.98946,-3.3495,-3.50605,-3.67929,-3.42446,-3.81284,23.1478,7.60409,20.2644,-3.18861,5.37553,15.0963,3.7169,4.88755 --6.96441,0.914334,0.404623,5,39,0,12.3714,5.5209,10.2315,0.60474,-1.73996,0.227752,-1.08049,-0.941034,0.543333,0.167188,2.01128,11.7083,7.85116,-4.10732,7.2315,-12.2815,-5.53422,11.08,26.0994,-4.21681,-3.22163,-3.69392,-3.31706,-3.9018,-3.49326,-3.46095,-4.11609,1.6834,15.3099,-11.356,4.10313,-11.7825,-17.8175,17.413,47.9221 --5.79961,0.449793,0.404623,3,7,0,11.9772,1.98893,0.578353,0.739975,0.964887,-1.09722,-0.776031,-0.319883,0.0341439,-0.461382,0.170651,2.4169,1.35435,1.80393,1.72209,2.54698,1.54011,2.00868,2.08763,-5.08142,-3.44235,-3.7366,-3.43194,-3.19382,-3.31804,-4.50014,-3.96094,32.7216,8.21896,44.8171,2.01234,2.30076,4.35294,-5.92428,30.6023 --2.91561,0.855852,0.404623,3,7,0,7.42574,6.16494,2.31841,0.198306,-0.0517854,0.603496,0.335619,-0.0236528,0.505868,0.00591257,0.602733,6.62469,7.56409,6.1101,6.17865,6.04488,6.94304,7.33775,7.56232,-4.64233,-3.22247,-3.85362,-3.31962,-3.42252,-3.46278,-3.78994,-3.8397,3.07911,9.8195,25.8454,-2.21643,-1.07875,-7.23167,11.9669,29.9976 --2.86296,0.477973,0.404623,3,15,0,6.83197,4.58112,3.78535,-0.477446,0.314256,0.632404,0.19674,0.239139,0.117854,-0.187694,0.142323,2.77382,6.97498,5.48634,3.87063,5.77069,5.32585,5.02723,5.11986,-5.04112,-3.22678,-3.83219,-3.3573,-3.39914,-3.39416,-4.06299,-3.88236,18.3294,-1.52121,-6.36842,-4.81219,0.870432,-5.1764,-6.56447,-14.0818 --5.37162,0.76529,0.404623,3,7,0,9.10022,2.03683,6.94559,-1.09448,0.240585,-0.608264,-0.317496,0.692065,1.14612,-0.613804,0.46388,-5.56496,-2.18792,6.84363,-2.2264,3.70783,-0.168369,9.99733,5.25874,-6.13056,-3.74049,-3.88078,-3.6686,-3.25298,-3.32247,-3.54174,-3.87944,3.45563,0.825276,9.95002,11.6909,0.541719,-7.1307,4.11195,-14.8494 --5.88226,0.9473,0.404623,3,7,0,8.34743,9.68342,1.62216,0.25997,-0.300854,0.139916,-0.692324,0.306077,1.62873,0.841029,0.55938,10.1051,9.91038,10.1799,11.0477,9.19538,8.56035,12.3255,10.5908,-4.3386,-3.23977,-4.03081,-3.38454,-3.7578,-3.55303,-3.38252,-3.81237,18.4918,3.14141,7.73636,9.10958,-0.179432,2.98282,15.1923,37.9861 --9.02567,0.760429,0.404623,3,7,0,12.3082,7.30331,10.6834,0.345971,-0.94074,1.04766,-0.311416,-0.228818,0.503646,-1.24217,-2.42382,10.9994,18.4959,4.85876,-5.96721,-2.74695,3.97634,12.6839,-18.5912,-4.26925,-3.77234,-3.81215,-4.01166,-3.135,-3.35344,-3.36283,-5.25348,12.9258,22.0551,-8.25793,-3.78447,-9.31208,-10.4486,18.7487,-23.5237 --11.4293,1,0.404623,3,7,0,14.9924,7.21651,2.16891,-0.0137545,-0.123751,-2.15093,-1.89,-0.553044,0.704564,0.488552,2.91373,7.18668,2.55133,6.01701,8.27614,6.94811,3.11726,8.74465,13.5361,-4.58964,-3.36996,-3.85033,-3.32356,-3.50612,-3.33536,-3.64983,-3.81295,11.4197,-13.5589,2.05122,-8.26959,8.06633,5.58947,3.05923,41.6005 --5.83385,0.993661,0.404623,3,7,0,15.2604,5.15515,2.21411,0.491124,-0.260122,1.6492,0.374865,1.01571,0.208292,0.229661,1.68464,6.24255,8.80665,7.40403,5.66364,4.57921,5.98514,5.61633,8.88512,-4.67896,-3.22478,-3.90294,-3.32421,-3.30831,-3.41953,-3.9883,-3.82428,2.72522,-1.84465,-1.74311,-10.126,-9.22509,-6.75462,3.43645,1.40013 --7.30526,0.98341,0.404623,3,7,0,9.90378,4.86141,3.45495,-0.0973797,0.31216,-1.47429,-0.727291,-0.758278,0.109512,-1.00238,-2.14003,4.52497,-0.232186,2.2416,1.39823,5.93991,2.34865,5.23977,-2.53228,-4.8516,-3.56037,-3.74519,-3.4465,-3.41346,-3.32435,-4.03564,-4.13522,6.40547,11.8272,-17.3152,-8.4042,15.5638,7.30214,10.9268,-4.5205 --9.50418,0.783363,0.404623,4,15,0,14.1482,2.90947,0.435225,0.848519,0.589481,-1.62907,-2.27073,-0.146633,0.929361,0.185146,-1.10191,3.27876,2.20046,2.84565,2.99005,3.16602,1.92119,3.31395,2.42989,-4.98508,-3.3897,-3.75827,-3.38328,-3.2233,-3.32034,-4.29992,-3.95065,-30.0133,8.56386,6.64322,-7.12761,27.4735,-13.4382,9.51995,-9.62674 --10.6168,0.649757,0.404623,3,15,0,15.521,-4.00391,2.6395,0.378352,0.849103,-1.84703,-1.65674,-0.386514,0.226274,0.112548,-0.659577,-3.00525,-8.87915,-5.02411,-3.70684,-1.7627,-8.37688,-3.40666,-5.74486,-5.76327,-4.64605,-3.69953,-3.79054,-3.11975,-3.68016,-5.51275,-4.29524,15.1625,-2.79738,9.19112,-19.9612,6.63475,-8.04769,7.5761,-12.7037 --8.57796,0.372743,0.404623,3,15,0,16.4632,5.00154,0.0528931,0.476781,-0.710837,-0.190049,1.15348,-0.901,-0.048113,-1.48164,-0.176695,5.02675,4.99148,4.95388,4.92317,4.96394,5.06255,4.99899,4.99219,-4.79981,-3.26678,-3.81509,-3.33466,-3.33572,-3.38503,-4.06665,-3.8851,10.0408,-20.3565,16.1825,-4.86692,-6.64484,35.153,-15.509,15.5971 --11.8631,0.828044,0.404623,3,7,0,14.6199,5.68963,0.195582,-0.779904,-1.47102,-1.13297,0.102551,-0.0549948,-0.500595,-3.07005,-0.656892,5.5371,5.46804,5.67888,5.08918,5.40193,5.70969,5.59173,5.56116,-4.74828,-3.25358,-3.83864,-3.33192,-3.36915,-3.40849,-3.99135,-3.87329,-3.94334,0.849955,-12.3827,-2.60483,-18.7976,10.7788,14.3967,-4.06251 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--4.47898,1,0.404623,3,7,0,10.0259,8.34938,8.95719,1.47135,-0.61232,-0.699749,0.270649,0.42019,0.918941,0.100932,-0.835778,21.5285,2.0816,12.1131,9.25345,2.86472,10.7736,16.5805,0.863166,-3.72006,-3.39666,-4.13763,-3.33782,-3.20836,-3.71156,-3.2316,-4.00071,40.191,19.4975,32.2271,-3.44785,-11.7166,-5.42171,21.3956,-19.7561 --3.85685,0.977138,0.404623,3,15,0,6.42387,6.56672,4.62352,1.65738,-0.99852,0.0499028,-0.120833,0.128507,1.4401,-0.234964,0.101126,14.2296,6.79745,7.16088,5.48036,1.95004,6.00805,13.225,7.03428,-4.04837,-3.22875,-3.89317,-3.32638,-3.16988,-3.42047,-3.33552,-3.84736,8.23539,9.85691,11.1725,-1.33346,-15.9132,2.26579,6.26499,42.8667 --5.87632,0.893979,0.404623,3,7,0,8.15582,7.20221,4.07921,-0.674616,-0.530156,0.92983,-0.0203748,1.69114,1.34282,0.181326,-0.525628,4.45031,10.9952,14.1007,7.94188,5.03959,7.1191,12.6798,5.05807,-4.85941,-3.26638,-4.26269,-3.3205,-3.34133,-3.47156,-3.36304,-3.88368,1.22971,26.3482,-1.09097,-11.9612,7.84585,28.6309,15.5511,-13.5221 --6.62492,0.989531,0.404623,4,31,0,9.94198,5.33998,2.17143,-1.07809,0.275264,0.40348,-0.35031,1.80637,1.65341,0.330223,-0.739574,2.99898,6.21611,9.26238,6.05704,5.9377,4.57931,8.93024,3.73405,-5.01599,-3.23743,-3.98521,-3.32051,-3.41327,-3.36977,-3.63283,-3.91475,19.557,-3.06633,-23.4788,3.0426,12.5181,7.17811,12.3388,8.01709 --7.19566,0.981266,0.404623,3,15,0,11.6733,5.17339,4.04117,1.14384,-0.187852,1.30932,-0.886327,0.132981,-1.49087,1.6476,0.550435,9.79585,10.4646,5.71079,11.8316,4.41425,1.59159,-0.851469,7.39779,-4.36341,-3.25189,-3.83973,-3.4133,-3.29711,-3.31828,-4.99841,-3.842,27.9116,-2.90033,-3.44525,10.8422,3.33578,-31.6596,10.3758,3.40218 --8.21911,0.317803,0.404623,4,15,0,13.3666,6.88429,3.70795,0.103917,0.243417,1.13418,-1.00704,0.449293,-1.89505,1.70022,0.22324,7.26961,11.0898,8.55025,13.1886,7.78687,3.15024,-0.14245,7.71206,-4.58199,-3.26926,-3.95209,-3.4751,-3.59276,-3.33594,-4.86727,-3.83768,11.3442,7.79975,28.6886,35.7497,-6.52368,-14.6056,-16.9917,5.56779 --8.21911,6.88214e-05,0.404623,2,3,0,14.0084,6.88429,3.70795,0.103917,0.243417,1.13418,-1.00704,0.449293,-1.89505,1.70022,0.22324,7.26961,11.0898,8.55025,13.1886,7.78687,3.15024,-0.14245,7.71206,-4.58199,-3.26926,-3.95209,-3.4751,-3.59276,-3.33594,-4.86727,-3.83768,-7.57545,4.87334,11.2422,10.089,30.0084,23.342,24.443,6.92805 --6.47931,0.998696,0.404623,3,7,0,12.1225,8.40172,1.67607,-0.0808645,0.531463,-0.262424,-0.510307,-0.332924,2.31215,0.630156,0.478563,8.26619,7.96188,7.84372,9.45791,9.29249,7.54641,12.2771,9.20383,-4.49237,-3.22153,-3.92119,-3.3418,-3.77009,-3.49392,-3.38528,-3.82138,2.66569,12.184,9.1177,22.6323,3.34182,17.0284,14.7101,11.4766 --8.41711,0.971404,0.404623,3,7,0,11.3616,7.14775,1.54288,0.0423064,-0.285105,-0.867581,-0.947431,0.682167,3.04354,-0.172594,0.421047,7.21303,5.80918,8.20026,6.88146,6.70787,5.68598,11.8436,7.79738,-4.58721,-3.24552,-3.93654,-3.31689,-3.4829,-3.40757,-3.41103,-3.83657,9.23861,3.17006,5.60593,-8.86457,6.67294,4.32632,21.6189,15.5888 --10.6479,0.542833,0.404623,5,47,0,17.4446,8.75962,1.28807,-1.81155,-0.671823,1.59633,-1.0838,0.885811,2.37198,-0.448799,0.0329129,6.42622,10.8158,9.90061,8.18154,7.89427,7.36361,11.8149,8.80202,-4.66127,-3.26117,-4.01658,-3.3226,-3.60448,-3.48417,-3.4128,-3.82509,-18.2283,0.956567,58.6602,17.6869,-7.55352,7.31562,20.5042,-6.60139 --4.23181,1,0.404623,3,7,0,12.0106,4.84069,6.995,0.556811,0.693018,-0.32401,0.379838,0.470203,-0.303856,0.617731,-0.706851,8.73558,2.57424,8.12976,9.16172,9.68835,7.49766,2.71522,-0.10373,-4.4517,-3.36872,-3.93346,-3.33614,-3.82135,-3.49129,-4.38965,-4.03539,8.74038,-1.83183,-25.9114,13.4438,3.24101,13.8907,10.5203,-8.16016 --8.0549,0.775604,0.404623,3,7,0,10.3726,8.77038,0.97741,0.210408,-1.06477,-0.878612,0.747944,-2.26782,0.900081,0.16159,0.409594,8.97604,7.91162,6.55379,8.92832,7.72966,9.50143,9.65013,9.17072,-4.43124,-3.22156,-3.8698,-3.3322,-3.58658,-3.61549,-3.57013,-3.82166,-1.52133,4.155,-17.4092,8.5937,-2.43069,12.8119,0.314872,13.0803 --6.47147,0.754545,0.404623,3,7,0,18.1299,0.781066,2.35573,-0.216085,0.516815,1.08889,-0.270405,-0.147925,1.55252,-1.56989,-0.876688,0.272028,3.3462,0.432594,-2.91716,1.99854,0.144065,4.43838,-1.28417,-5.33552,-3.32981,-3.71454,-3.72324,-3.17166,-3.31986,-4.14111,-4.08164,-30.9101,-0.987351,11.3298,13.2603,7.19626,0.950377,5.35274,-2.9031 --2.79756,0.956047,0.404623,3,7,0,9.04508,6.8404,4.36995,0.58353,-0.192618,0.530755,-0.186937,0.523426,0.147738,-0.531708,-0.227056,9.3904,9.15977,9.12775,4.51686,5.99867,6.0235,7.48601,5.84818,-4.39658,-3.22825,-3.9788,-3.34231,-3.41852,-3.42111,-3.77424,-3.86771,2.78546,-0.560833,13.2936,-5.63263,-4.47663,8.8246,15.9521,-5.61671 --4.05708,0.565008,0.404623,3,7,0,6.587,6.74274,1.28664,0.666944,-0.431897,0.646043,-0.184375,0.320133,0.398808,-0.955611,-0.0980765,7.60085,7.57396,7.15463,5.51321,6.18704,6.50551,7.25586,6.61655,-4.55171,-3.22243,-3.89293,-3.32597,-3.43501,-3.44208,-3.79871,-3.85403,22.3624,6.64819,19.4835,6.01702,18.6667,11.3409,6.66054,7.30597 --7.10276,0.85934,0.404623,3,7,0,8.84306,2.2129,6.53203,-1.35485,-0.988984,0.555304,0.115552,-0.480266,-1.08358,0.0110199,0.155788,-6.63706,5.84016,-0.924216,2.28488,-4.24718,2.96768,-4.86508,3.23051,-6.29305,-3.24485,-3.69994,-3.4087,-3.18125,-3.33283,-5.83558,-3.92799,-14.1509,-8.31662,-28.5804,1.48308,1.93046,3.45483,7.87207,-18.4928 --9.63333,0.952515,0.404623,3,7,0,12.5219,7.33307,0.187511,0.530101,1.47137,-1.44195,-0.565557,1.39845,-0.595014,0.239224,1.32932,7.43247,7.06269,7.5953,7.37793,7.60897,7.22702,7.2215,7.58233,-4.56704,-3.22592,-3.91079,-3.31742,-3.57366,-3.47706,-3.8024,-3.83943,-15.4096,21.297,-16.3349,9.51875,12.0108,1.17058,-10.8054,22.0126 --9.47001,0.972126,0.404623,3,7,0,14.8514,4.95371,3.71188,-0.779143,1.12309,-0.900559,1.26192,1.91961,-0.508963,1.00048,1.43378,2.06163,1.61095,12.0791,8.66739,9.12248,9.63781,3.0645,10.2757,-5.1221,-3.42562,-4.13563,-3.32832,-3.74866,-3.62515,-4.33687,-3.8139,15.936,-0.139161,28.7263,6.69072,13.2012,17.755,8.21879,6.73271 --10.0231,0.998731,0.404623,3,7,0,15.5634,0.0147585,0.756786,-0.166526,-0.904897,1.44473,1.58429,2.26949,0.151043,0.841247,-0.241884,-0.111266,1.10811,1.73227,0.651402,-0.670055,1.21373,0.129066,-0.168296,-5.38308,-3.45901,-3.73527,-3.48338,-3.11684,-3.31702,-4.81838,-4.03781,-13.9955,-4.97614,14.3811,-0.903628,8.85237,-3.36246,-4.96789,-8.01378 -# -# Elapsed Time: 0.023495 seconds (Warm-up) -# 0.004659 seconds (Sampling) -# 0.028154 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-3.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-3.csv deleted file mode 100644 index 76fd479175..0000000000 --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-3.csv +++ /dev/null @@ -1,648 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 20 -# stan_version_patch = 0 -# model = stan_test_data_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 500 -# save_warmup = 1 -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 3 -# data -# file = /tmp/tmp2ipdytqf/x8rvpwpf.json -# init = 2 (Default) -# random -# seed = 31709 -# output -# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-3-rh2l2pkd.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1.1,eta.2.1,eta.1.2,eta.2.2,eta.1.3,eta.2.3,eta.1.4,eta.2.4,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 --9.69072,1.84804e-13,2,1,1,0,14.9553,1.6499,0.203791,-0.536572,0.523297,0.118934,0.175504,-1.9843,-1.70525,1.27199,0.07893,1.54056,1.67414,1.24552,1.90913,1.75655,1.68567,1.30239,1.66599,-5.18277,-3.42161,-3.72673,-3.42393,-3.16307,-3.31878,-4.61557,-3.97411,13.4694,-21.2925,-4.12945,3.79621,14.2354,-20.8889,4.40736,3.46646 --9.69072,3.26461e-34,2.33506,1,1,0,14.4284,1.6499,0.203791,-0.536572,0.523297,0.118934,0.175504,-1.9843,-1.70525,1.27199,0.07893,1.54056,1.67414,1.24552,1.90913,1.75655,1.68567,1.30239,1.66599,-5.18277,-3.42161,-3.72673,-3.42393,-3.16307,-3.31878,-4.61557,-3.97411,1.08706,-3.12027,6.03691,-12.5055,-5.32172,-22.3008,6.63884,9.13731 --9.11559,0.993168,0.230236,4,31,0,13.9838,4.64278,0.0629857,0.772733,1.22283,0.454491,-0.491118,1.05547,1.34281,-0.830319,-0.666555,4.69145,4.67141,4.70926,4.59048,4.7198,4.61185,4.72736,4.6008,-4.8343,-3.27692,-3.80761,-3.34082,-3.31811,-3.37074,-4.10234,-3.8938,33.9296,-4.88605,28.1339,8.14029,5.19936,-13.8322,2.29479,-13.3486 --8.34147,0.995418,0.235466,4,31,0,13.6655,-0.217878,0.551173,-0.840547,-1.2629,-0.556233,0.284764,-0.860767,-1.43081,0.908144,0.570164,-0.681165,-0.524458,-0.692309,0.282666,-0.913953,-0.060924,-1.0065,0.0963809,-5.45501,-3.58486,-3.70193,-3.50329,-3.11621,-3.32148,-5.02776,-4.02798,-23.0125,18.3147,30.7099,-1.77747,3.44171,9.11989,-8.2306,-1.16107 --8.98992,0.982266,0.313926,4,15,0,13.0315,2.45071,2.68701,3.44678,1.37144,0.677356,-0.127343,-0.324484,0.485461,-0.0363097,-0.248848,11.7122,4.27078,1.57882,2.35315,6.13578,2.10854,3.75515,1.78206,-4.21652,-3.29106,-3.73248,-3.40606,-3.43048,-3.32191,-4.2361,-3.97043,5.90238,-2.97616,32.3125,13.2996,11.5024,22.5604,4.89491,28.493 --13.3678,0.814525,0.46499,3,7,0,16.2347,2.00313,1.84462,3.27535,2.58451,1.12184,0.0248472,0.606644,-1.29807,-0.163008,-0.173904,8.0449,4.0725,3.12216,1.70244,6.77057,2.04897,-0.391307,1.68235,-4.51189,-3.29865,-3.76473,-3.4328,-3.48889,-3.32138,-4.91272,-3.97359,-25.199,14.4277,17.6019,9.0484,2.66432,8.58527,-4.79471,33.8942 --6.43411,0.993713,0.447514,3,7,0,15.8618,1.05475,6.06683,-0.855955,-1.14255,-0.709084,-0.0150803,-1.23252,1.10032,1.17829,0.757552,-4.13818,-3.24714,-6.42273,8.20324,-5.8769,0.963265,7.7302,5.65069,-5.92224,-3.85401,-3.71441,-3.32282,-3.26298,-3.31684,-3.74887,-3.87152,1.12165,-3.45332,-29.8432,-7.51286,-11.2663,9.99822,12.1055,18.1449 --9.77478,0.565636,0.776767,2,3,0,12.3781,0.856063,4.37467,-0.546908,-1.47767,-2.27539,-0.299753,-1.15169,1.50238,-0.113867,1.14189,-1.53648,-9.098,-4.18219,0.357935,-5.60825,-0.455255,7.42849,5.85144,-5.56566,-4.68323,-3.69426,-3.49914,-3.24725,-3.32558,-3.78031,-3.86765,1.71584,-0.97172,-5.67895,3.67206,0.063225,3.29117,14.007,-19.3012 --6.40919,1,0.362832,4,15,0,12.8212,0.975263,1.41956,-1.06589,0.423469,-0.327538,-0.133909,0.382999,1.39946,-1.49896,0.131866,-0.537823,0.510304,1.51895,-1.15259,1.5764,0.785172,2.96187,1.16245,-5.43678,-3.502,-3.73141,-3.59148,-3.15714,-3.31702,-4.35225,-3.99056,0.896996,-14.7201,7.3668,-5.27145,0.471389,-5.08173,-2.30738,23.8762 --5.69692,0.971888,0.671478,3,7,0,8.62038,-0.163838,4.52898,0.347273,0.617647,0.0985786,-0.279117,-0.688254,2.42507,-0.437923,0.840578,1.40895,0.282622,-3.28093,-2.14718,2.63348,-1.42795,10.8193,3.64313,-5.19829,-3.51931,-3.69168,-3.66258,-3.19766,-3.34119,-3.47934,-3.91708,41.3348,-8.00531,-27.0547,-10.2033,-3.33724,3.83071,16.1757,11.4939 --5.69692,0.000424931,1.15204,2,7,0,9.27444,-0.163838,4.52898,0.347273,0.617647,0.0985786,-0.279117,-0.688254,2.42507,-0.437923,0.840578,1.40895,0.282622,-3.28093,-2.14718,2.63348,-1.42795,10.8193,3.64313,-5.19829,-3.51931,-3.69168,-3.66258,-3.19766,-3.34119,-3.47934,-3.91708,-7.05324,8.39841,5.69437,1.83605,5.35618,-7.52349,3.21624,18.3863 --11.12,0.98487,0.0924002,5,47,0,15.6592,2.5103,0.0164366,-0.0220643,-0.843658,-0.179379,-0.180067,0.644719,-2.4845,0.00310937,-0.266681,2.50994,2.50735,2.5209,2.51035,2.49643,2.50734,2.46946,2.50592,-5.07086,-3.37237,-3.75106,-3.40013,-3.19163,-3.32622,-4.42751,-3.94841,9.87036,-13.2991,-11.7573,-3.16541,4.74593,5.44142,-1.20197,22.9461 --10.0422,0.995863,0.16774,4,31,0,14.507,3.70103,0.0144212,-0.271738,-0.853817,-0.555536,-0.789274,0.511747,0.127443,1.81362,0.306775,3.69711,3.69301,3.70841,3.72718,3.68871,3.68964,3.70286,3.70545,-4.9395,-3.31427,-3.77942,-3.3611,-3.25187,-3.34673,-4.24356,-3.91548,46.7307,5.80122,-6.00021,0.829266,-5.34904,2.7876,-14.2602,-10.3227 --6.17545,0.961286,0.31563,3,15,0,15.6782,4.75357,0.553388,0.102245,-0.529393,-2.03925,0.333612,-0.310714,0.0648779,-0.326367,-0.449341,4.81015,3.62508,4.58163,4.57297,4.46061,4.93819,4.78948,4.50491,-4.82203,-3.31722,-3.80379,-3.34117,-3.30023,-3.38092,-4.09411,-3.896,4.35903,-16.671,-6.97175,0.912668,12.6381,-2.67451,1.44347,-12.0848 --12.7043,0.847179,0.531893,3,7,0,15.2578,5.58824,0.248467,1.67246,-2.65811,0.58585,0.325665,0.464695,0.0484529,-2.44327,-0.240413,6.00379,5.7338,5.7037,4.98117,4.92779,5.66916,5.60028,5.5285,-4.70217,-3.2472,-3.83949,-3.33368,-3.33307,-3.40692,-3.99029,-3.87394,-6.02912,11.8718,22.7095,10.6912,12.5316,7.22766,-0.348571,-1.60434 --14.418,0.532538,0.62711,2,7,0,20.7842,4.58157,0.103017,0.100355,-1.91168,-1.64658,1.72099,0.0911791,-0.413297,-2.26672,1.87082,4.59191,4.41195,4.59097,4.34806,4.38464,4.75886,4.539,4.7743,-4.84463,-3.28589,-3.80407,-3.34589,-3.29514,-3.37522,-4.12752,-3.88988,-22.3621,9.10234,-17.925,-4.30149,19.344,2.51361,3.20974,12.3856 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--5.48403,0.934929,0.487377,3,7,0,11.8117,5.39354,8.88938,0.98283,-0.693654,-0.0129001,-1.05112,1.20644,1.01626,-0.249414,1.77537,14.1303,5.27887,16.118,3.17641,-0.77261,-3.95024,14.4274,21.1755,-4.05448,-3.25855,-4.40539,-3.37725,-3.11648,-3.41809,-3.28534,-3.93923,14.2199,1.32258,28.7531,16.4695,1.50985,-11.5843,27.967,36.9389 --3.89102,0.82705,0.692249,3,7,0,8.8088,6.65052,7.50849,1.09357,-1.32356,-0.831657,-0.441604,-0.13332,1.16368,-0.167913,0.783395,14.8616,0.406043,5.6495,5.38976,-3.28739,3.33475,15.388,12.5326,-4.01058,-3.50986,-3.83765,-3.32755,-3.14846,-3.33936,-3.25564,-3.80975,39.6294,5.46288,32.3546,1.21834,-7.41397,18.136,3.0468,37.4265 --3.89102,0.192268,0.756119,3,7,0,7.87928,6.65052,7.50849,1.09357,-1.32356,-0.831657,-0.441604,-0.13332,1.16368,-0.167913,0.783395,14.8616,0.406043,5.6495,5.38976,-3.28739,3.33475,15.388,12.5326,-4.01058,-3.50986,-3.83765,-3.32755,-3.14846,-3.33936,-3.25564,-3.80975,-25.5218,-0.789958,-1.26312,-4.33436,5.5409,3.73308,23.6477,16.2457 --3.66799,0.984883,0.182889,5,47,0,8.4571,1.06396,11.613,0.820501,0.436766,0.0239397,-1.00216,-0.62947,0.938451,-0.299449,-0.201558,10.5924,1.34197,-6.24606,-2.41353,6.13611,-10.5741,11.9622,-1.27673,-4.30038,-3.44317,-3.71211,-3.68301,-3.43051,-3.87039,-3.4038,-4.08133,5.66043,10.6641,12.558,-30.5739,-4.91084,-16.4988,-7.1387,17.2244 -# Adaptation terminated -# Step size = 0.339982 -# Diagonal elements of inverse mass matrix: -# 10.8626, 2.5466, 0.829996, 0.90972, 0.858617, 1.26414, 0.894053, 0.867558, 0.966067, 0.934681 --10.893,0.630439,0.339982,3,7,0,13.2306,-0.443486,0.563427,-1.61248,0.087617,-0.323382,-0.039501,2.71912,-0.603932,0.626723,0.832743,-1.352,-0.625688,1.08854,-0.0903732,-0.39412,-0.465742,-0.783758,0.0257041,-5.54152,-3.59354,-3.72418,-3.52458,-3.11843,-3.32571,-4.98567,-4.03058,-10.5446,6.47487,-7.22809,2.80695,3.87292,0.953728,-19.0504,5.48563 --9.6909,0.972425,0.339982,4,15,0,20.7655,7.2946,1.35519,0.0333484,-2.73045,1.26831,0.289276,-0.227443,1.14811,-1.04253,-1.48333,7.33979,9.0134,6.98637,5.88177,3.59432,7.68662,8.85051,5.28441,-4.57553,-3.22666,-3.88631,-3.322,-3.24646,-3.50159,-3.64009,-3.87891,-4.10673,18.0149,2.10526,0.766913,22.769,-13.6289,24.784,8.8182 --8.39916,0.98858,0.339982,3,15,0,13.3573,0.169798,1.73541,0.873704,1.60531,-0.623022,0.00834419,-0.291286,0.2071,1.01971,2.28544,1.68603,-0.911399,-0.335702,1.93941,2.95566,0.184278,0.5292,4.13596,-5.16571,-3.61859,-3.70539,-3.42266,-3.21275,-3.31958,-4.74767,-3.90475,-24.6469,11.9125,-14.0822,2.59167,-7.57046,1.87238,0.368315,-9.91349 --7.86684,0.994248,0.339982,4,15,0,13.6136,3.37474,1.78445,-1.59712,0.155423,0.56911,-1.25221,0.742003,-0.436657,-1.85895,-0.69087,0.524754,4.39029,4.69881,0.0575288,3.65209,1.14024,2.59555,2.14191,-5.30452,-3.28667,-3.80729,-3.516,-3.24976,-3.31692,-4.40801,-3.95928,-3.21431,5.46837,0.0195119,-2.70968,-4.2296,-16.2759,-4.01492,29.3184 --5.24501,0.988787,0.339982,3,7,0,10.2393,4.35339,3.15563,-0.794361,0.0880965,-0.0470737,-0.574955,-0.0666585,-0.795369,-0.351698,-1.61405,1.84668,4.20484,4.14304,3.24356,4.63139,2.53904,1.84349,-0.739961,-5.14698,-3.29354,-3.79118,-3.37514,-3.31192,-3.32662,-4.52669,-4.05978,18.1743,0.489176,0.0899429,6.78765,29.89,-3.87386,16.2825,6.16541 --3.80714,0.970377,0.339982,3,15,0,8.40223,4.07582,1.874,0.352072,-1.08422,-0.609261,0.536449,-0.412546,0.787147,-0.0738472,0.591004,4.73561,2.93407,3.30272,3.93744,2.04401,5.08113,5.55093,5.18336,-4.82973,-3.34984,-3.76911,-3.35559,-3.17336,-3.38566,-3.99642,-3.88102,-1.17245,-29.6634,31.9264,-5.75544,13.1499,-8.05923,23.3595,9.72728 --4.43902,0.998413,0.339982,3,7,0,6.28226,8.22157,14.4469,1.4319,-0.00283884,0.388795,0.510628,-0.265201,0.147379,0.2573,0.493266,28.908,13.8384,4.39025,11.9387,8.18056,15.5985,10.3507,15.3477,-3.62882,-3.39196,-3.7982,-3.41762,-3.63643,-4.19748,-3.51408,-3.82661,24.7367,12.0244,19.7761,6.98994,14.5575,-8.2612,-11.1923,28.3985 --4.27976,0.725663,0.339982,3,7,0,7.87488,9.34215,2.03098,0.703803,0.416763,0.573834,-0.245057,0.0752525,0.574924,0.273264,0.129446,10.7716,10.5076,9.49499,9.89714,10.1886,8.84444,10.5098,9.60505,-4.28659,-3.25296,-3.99646,-3.35152,-3.88891,-3.57111,-3.50204,-3.81816,8.96435,22.6815,2.83365,40.2352,-3.78151,-1.56609,-5.80569,8.25197 --9.11288,0.920884,0.339982,3,7,0,11.763,1.5787,5.0846,0.0187109,-1.09081,0.757832,1.25085,0.374964,0.263893,2.49057,-1.61807,1.67384,5.43197,3.48524,14.2422,-3.96763,7.93876,2.92049,-6.64853,-5.16714,-3.2545,-3.77367,-3.53357,-3.17053,-3.51579,-4.35848,-4.34599,-20.0218,20.6398,-11.0531,-3.36375,7.79892,30.1608,14.739,-31.4417 --9.40454,0.221618,0.339982,4,15,0,16.2585,5.13786,0.288743,0.704809,2.08401,-1.15405,-1.66664,0.915622,0.522782,0.265352,0.116819,5.34136,4.80463,5.40224,5.21447,5.7396,4.65662,5.28881,5.17159,-4.76791,-3.27258,-3.82941,-3.33001,-3.39655,-3.37209,-4.0294,-3.88127,13.0161,-7.00097,10.3812,12.1123,18.3562,2.76877,3.2462,-14.4871 --9.74313,0.724316,0.339982,3,15,0,15.672,4.41809,4.23529,0.381029,1.94616,0.671367,-0.0164131,0.00268556,-1.46303,0.289549,-2.09249,6.03186,7.26152,4.42946,5.64441,12.6606,4.34858,-1.77827,-4.4442,-4.69943,-3.22425,-3.79933,-3.32443,-4.2681,-3.36317,-5.17742,-4.22661,16.0317,14.9369,25.0828,-1.68161,15.834,-15.2576,-1.42459,16.303 --7.5546,1,0.339982,3,7,0,12.741,6.63363,1.91721,0.421803,1.70536,0.682187,-0.253852,0.129923,-1.33113,0.311875,-1.66266,7.44231,7.94152,6.88272,7.23156,9.90316,6.14694,4.08157,3.44596,-4.56614,-3.22154,-3.88229,-3.31706,-3.84998,-3.4263,-4.19014,-3.92223,6.83712,18.5713,5.09209,-7.63293,6.80287,3.86453,3.96638,1.35261 --12.7082,0.614089,0.339982,3,15,0,15.7042,8.22301,0.0449961,1.03288,2.22327,0.729735,-0.506272,0.422881,-0.167916,-1.72294,-1.33988,8.26949,8.25585,8.24204,8.14548,8.32305,8.20023,8.21545,8.16272,-4.49208,-3.22185,-3.93837,-3.32226,-3.6527,-3.53106,-3.70021,-3.83203,13.1645,17.8999,0.951622,-3.58207,1.61284,-1.51556,21.669,-19.9442 --8.73368,0.71584,0.339982,3,7,0,16.424,4.04135,5.81335,-1.10054,0.0733683,-1.78002,0.519911,-0.10458,0.73235,1.11959,2.02501,-2.35647,-6.30653,3.4334,10.5499,4.46787,7.06378,8.29876,15.8135,-5.6748,-4.24491,-3.77236,-3.36891,-3.30072,-3.46877,-3.69209,-3.83175,-1.36634,-5.48617,7.86028,11.2067,11.4382,-5.6825,5.23408,21.3401 --8.21948,0.947273,0.339982,4,15,0,19.3354,-1.96501,6.59462,0.100128,-0.63492,-0.226091,-0.409803,-0.479806,1.2874,-0.857267,-1.93805,-1.3047,-3.45599,-5.12914,-7.61836,-6.15207,-4.6675,6.52488,-14.7457,-5.53536,-3.87772,-3.70038,-4.19988,-3.28001,-3.44956,-3.87992,-4.91322,-28.2584,7.38764,-18.8014,-15.4098,-11.9464,-9.04635,9.43584,-34.775 --7.07805,0.982768,0.339982,3,15,0,12.2613,4.4617,1.49074,-0.07461,-0.761805,0.281494,-0.973466,-2.09977,0.961263,-0.646561,-1.36164,4.35047,4.88133,1.33148,3.49784,3.32604,3.01051,5.89469,2.43185,-4.86988,-3.27015,-3.72817,-3.36752,-3.23169,-3.33354,-3.95422,-3.95059,34.3183,6.24184,-0.619383,22.5341,-0.975033,28.1959,3.43794,-13.8039 --6.69225,0.98876,0.339982,4,15,0,11.1671,2.54324,2.52187,1.31629,-1.47182,-0.47068,1.07452,0.93185,-0.563115,0.995228,-0.871795,5.86276,1.35624,4.89325,5.05308,-1.16851,5.25304,1.12313,0.34468,-4.71601,-3.44222,-3.81321,-3.3325,-3.11634,-3.39158,-4.64567,-4.01895,11.1526,-12.8216,-31.6736,8.07273,10.4924,12.1334,1.43993,5.41562 --7.24162,0.969439,0.339982,4,15,0,11.9248,8.72287,0.953537,-0.700652,1.15495,-0.218979,-0.123132,-0.426439,1.37671,0.437852,1.49604,8.05478,8.51407,8.31625,9.14038,9.82416,8.60546,10.0356,10.1494,-4.51102,-3.22284,-3.94164,-3.33576,-3.83939,-3.55585,-3.53868,-3.8146,-18.7751,20.1493,19.1113,-5.82669,14.9759,29.9037,13.194,-9.04318 --6.47445,0.976609,0.339982,4,15,0,12.2221,2.21483,0.975596,0.945738,-1.71084,0.384486,-0.97147,-0.317069,-0.513099,-0.389945,-0.901014,3.13749,2.58993,1.9055,1.8344,0.54574,1.26707,1.71425,1.33581,-5.00064,-3.36787,-3.73853,-3.4271,-3.13091,-3.31713,-4.54765,-3.98481,-16.0922,9.91142,-8.98097,-14.2368,-5.19733,-13.793,5.15794,17.4777 --13.3264,0.824538,0.339982,3,7,0,16.2715,5.38766,0.0528513,0.0271361,-2.30717,1.77784,-0.100761,-1.292,0.379719,-1.57159,1.35725,5.3891,5.48162,5.31938,5.3046,5.26572,5.38234,5.40773,5.45939,-4.76311,-3.25323,-3.82671,-3.32871,-3.3585,-3.39619,-4.01435,-3.87533,8.82435,7.30183,-3.36939,-17.2057,13.9502,12.392,-1.27158,7.87776 --12.3627,0.99801,0.339982,4,15,0,18.9017,0.481179,0.25442,0.187121,1.08241,-2.33739,-0.277852,1.03877,0.956621,1.15785,-2.008,0.528787,-0.113499,0.745462,0.77576,0.756567,0.410488,0.724563,-0.0296942,-5.30403,-3.55067,-3.71893,-3.47692,-3.13521,-3.31827,-4.71373,-4.03263,18.273,0.131917,-24.1154,1.17147,-1.31011,9.45455,-8.84627,-5.3061 --8.74156,0.998614,0.339982,3,15,0,16.7489,2.82295,5.56854,1.26939,-0.297232,-2.29598,-0.817827,0.625272,1.09651,-1.07971,-1.03633,9.89161,-9.96231,6.3048,-3.18946,1.1678,-1.73115,8.92889,-2.9479,-4.35569,-4.83475,-3.86063,-3.74586,-3.14517,-3.34766,-3.63295,-4.15412,42.5421,-37.9621,11.2154,2.46152,-7.89018,10.6417,-9.72838,1.81521 --7.00138,0.980007,0.339982,4,15,0,13.9778,7.03521,1.349,0.339418,1.29382,0.265749,1.42505,-0.399421,-0.594193,0.929867,1.36223,7.49308,7.3937,6.49639,8.2896,8.78058,8.95761,6.23364,8.87286,-4.56151,-3.22336,-3.86766,-3.32371,-3.70665,-3.5785,-3.91376,-3.8244,11.9753,1.09613,-1.71676,1.24566,4.259,7.46973,0.942616,-7.18081 --3.75211,0.999925,0.339982,3,7,0,8.16957,6.38848,2.68234,0.279916,0.37552,-0.915553,0.727823,-0.631499,-0.00616812,0.470977,0.228265,7.13931,3.93266,4.69458,7.6518,7.39575,8.34074,6.37193,7.00076,-4.59403,-3.30424,-3.80717,-3.31859,-3.55128,-3.53951,-3.89758,-3.84788,12.0089,12.6649,22.3034,0.0666966,7.33295,-15.9339,-13.5456,-25.5135 --7.14944,0.900953,0.339982,3,15,0,12.2724,1.51238,10.237,-0.629896,0.306349,1.02215,-0.384997,-0.559397,0.682709,-0.476533,2.26145,-4.93587,11.9761,-4.21417,-3.36589,4.64848,-2.42884,8.50127,24.6629,-6.03759,-3.30057,-3.69441,-3.76085,-3.31311,-3.36542,-3.67265,-4.05676,20.8747,26.8345,-12.3134,-5.73255,5.60684,-20.9014,19.3901,21.3615 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--9.03212,0.946514,0.339982,3,7,0,11.1039,-0.869433,0.398167,-1.0013,0.128054,2.06384,-0.0431922,-0.375424,0.382653,-0.977789,0.775827,-1.26812,-0.0476777,-1.01891,-1.25876,-0.818446,-0.886631,-0.717073,-0.560524,-5.53059,-3.54535,-3.69919,-3.59868,-3.11637,-3.33154,-4.97317,-4.05278,2.52906,22.8681,-10.9436,3.33716,10.4131,4.73562,-10.6136,39.5003 --6.11181,1,0.339982,3,7,0,9.98708,0.346473,0.76484,-0.918743,0.50009,1.16499,-0.475955,0.0635154,0.286067,-0.790265,0.509886,-0.356219,1.2375,0.395052,-0.257954,0.728962,-0.0175566,0.565268,0.736454,-5.41382,-3.45018,-3.71404,-3.53451,-3.13462,-3.32111,-4.74137,-4.00509,1.19141,4.80758,-7.0614,-7.4226,-3.56803,-15.28,-1.01936,-36.0832 --5.56054,0.869735,0.339982,4,15,0,9.81823,7.48722,3.42373,0.479786,0.972044,-1.66431,0.123808,-0.593005,0.408759,0.849546,-0.0858158,9.12987,1.78906,5.45693,10.3958,10.8152,7.9111,8.8867,7.19341,-4.41828,-3.4144,-3.83121,-3.36449,-3.97789,-3.5142,-3.63678,-3.84496,7.98756,-20.5902,-21.0232,4.78475,9.18614,-0.207916,13.1947,30.2204 --6.30561,0.9476,0.339982,3,15,0,12.5392,1.22003,7.59806,2.05946,-1.47285,0.869274,0.0369718,-1.40592,0.979333,-0.507283,-0.197309,16.8679,7.82482,-9.46224,-2.63433,-9.97074,1.50094,8.66105,-0.279135,-3.90237,-3.22168,-3.77309,-3.70039,-3.61292,-3.31787,-3.6576,-4.04199,27.4187,1.98953,2.1891,-6.46639,-2.56308,2.80804,26.5199,11.9164 --7.31851,0.83151,0.339982,4,15,0,15.1824,7.24188,1.78433,0.287162,2.10035,-0.955953,-0.22028,0.885023,-0.611844,0.924395,0.0478374,7.75427,5.53614,8.82106,8.89131,10.9896,6.84883,6.15015,7.32724,-4.53785,-3.25188,-3.96445,-3.33161,-4.00351,-3.45819,-3.92362,-3.84301,-4.64711,23.385,9.36954,45.8526,1.16713,-17.25,-5.2994,-2.72355 -# -# Elapsed Time: 0.024466 seconds (Warm-up) -# 0.004564 seconds (Sampling) -# 0.02903 seconds (Total) -# diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-4.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-4.csv deleted file mode 100644 index 871b1e6ca5..0000000000 --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-4.csv +++ /dev/null @@ -1,648 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 20 -# stan_version_patch = 0 -# model = stan_test_data_model -# method = sample (Default) -# sample -# num_samples = 100 -# num_warmup = 500 -# save_warmup = 1 -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.80000000000000004 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 4 -# data -# file = /tmp/tmp2ipdytqf/x8rvpwpf.json -# init = 2 (Default) -# random -# seed = 31709 -# output -# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-4-h3yaihdn.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1.1,eta.2.1,eta.1.2,eta.2.2,eta.1.3,eta.2.3,eta.1.4,eta.2.4,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 --13.3095,1.2231e-11,2,1,1,0,19.9737,-0.991455,0.2459,-1.83948,1.22721,-1.8175,1.38095,-0.81022,-0.496013,-1.25183,1.30426,-1.44378,-1.43838,-1.19069,-1.29928,-0.689684,-0.651879,-1.11342,-0.670738,-5.55351,-3.66694,-3.69792,-3.60145,-3.11676,-3.32811,-5.04814,-4.05707,-31.1955,0.926049,3.55825,14.2731,21.3712,13.5828,2.81909,3.55566 --13.3095,1.07237e-23,2.33506,1,1,0,19.052,-0.991455,0.2459,-1.83948,1.22721,-1.8175,1.38095,-0.81022,-0.496013,-1.25183,1.30426,-1.44378,-1.43838,-1.19069,-1.29928,-0.689684,-0.651879,-1.11342,-0.670738,-5.55351,-3.66694,-3.69792,-3.60145,-3.11676,-3.32811,-5.04814,-4.05707,-11.2309,-29.4419,-15.3354,30.4119,4.37933,10.5536,-15.6524,-12.8072 --4.70631,0.981481,0.230236,4,15,0,16.0246,2.29954,1.20002,0.825039,-0.895977,1.09128,0.0129517,0.0741672,0.042978,0.283974,-0.7995,3.28961,3.6091,2.38854,2.64032,1.22435,2.31508,2.35112,1.34012,-4.98389,-3.31792,-3.74824,-3.39537,-3.1467,-3.32398,-4.44596,-3.98467,-15.9577,-1.78724,-13.73,-0.590091,-6.11513,-11.0355,9.81625,-22.5053 --6.70599,0.990144,0.228246,4,15,0,8.55757,2.71693,6.71199,-0.0930505,-1.87486,-0.463229,0.45503,-0.868786,0.0451027,1.31929,-0.732033,2.09237,-0.392259,-3.11436,11.572,-9.86713,5.77108,3.01966,-2.19647,-5.11856,-3.57367,-3.69155,-3.40321,-3.60151,-3.4109,-4.34358,-4.12033,-19.4183,-13.75,-45.2564,3.76314,-2.43983,9.53533,-11.6085,13.2503 --5.25887,1,0.299077,4,15,0,9.3785,3.42016,1.29592,0.946511,0.699508,-1.61694,-0.342709,0.316561,0.350759,0.542198,-0.24782,4.64676,1.32473,3.8304,4.1228,4.32667,2.97604,3.87471,3.099,-4.83893,-3.44432,-3.78265,-3.35104,-3.29131,-3.33297,-4.21914,-3.93158,27.5238,8.60309,21.5645,9.07009,8.57919,-2.58547,-0.609818,-2.15092 --3.49041,0.983995,0.466063,3,7,0,8.64938,3.87693,7.36848,0.581786,-0.947805,1.33806,0.527924,0.30724,0.924859,-0.24226,0.229683,8.1638,13.7364,6.14082,2.09184,-3.10696,7.76693,10.6917,5.56934,-4.50138,-3.38606,-3.85472,-3.41638,-3.14357,-3.50605,-3.48858,-3.87313,26.0609,23.3712,0.867238,-13.8136,8.66174,11.3972,-1.94608,8.53651 --5.95905,0.373526,0.75368,3,11,0,7.85584,2.42335,2.21532,1.05428,-0.523611,-0.116758,0.708857,-1.51957,1.9616,0.429094,0.420681,4.75893,2.16469,-0.942976,3.37393,1.26338,3.9937,6.76892,3.35529,-4.82732,-3.39178,-3.69979,-3.37117,-3.14779,-3.35387,-3.85221,-3.92464,-22.6966,19.0709,15.4823,-7.99164,2.43963,15.8976,11.1289,-19.4064 --4.26796,0.996637,0.191449,4,31,0,9.84285,3.36509,3.87117,0.474031,-0.0324501,-0.749309,1.00254,-1.19267,1.30597,0.518951,-0.154921,5.20014,0.464378,-1.25195,5.37403,3.23947,7.24608,8.42072,2.76536,-4.78217,-3.50545,-3.6975,-3.32776,-3.22711,-3.47805,-3.68034,-3.94091,-2.8098,-6.26248,8.91735,21.4209,6.4275,0.274966,3.73599,26.685 --4.89679,0.941209,0.341861,4,15,0,9.66664,5.56411,1.5858,0.131111,-0.495167,0.818787,-0.940215,1.35048,-0.316573,-0.659656,0.363368,5.77203,6.86254,7.70571,4.51803,4.77888,4.07312,5.06209,6.14034,-4.72495,-3.22799,-3.91538,-3.34229,-3.32231,-3.35586,-4.05847,-3.8623,-12.7297,10.4034,-6.69688,15.201,-9.02066,15.5946,5.06255,16.0556 --3.75181,0.991934,0.525317,3,15,0,7.66732,2.76049,3.82307,0.769664,0.327691,-0.598101,0.505133,-1.23295,0.0276041,0.516299,-0.158099,5.70297,0.47391,-1.95315,4.73434,4.01327,4.69165,2.86602,2.15607,-4.73178,-3.50473,-3.69367,-3.33805,-3.2713,-3.37315,-4.36671,-3.95885,19.0033,-5.2083,-1.29738,-3.90056,-8.354,5.15185,9.18527,5.76527 --5.72518,0.432662,0.959932,2,3,0,7.72256,3.26458,2.5293,0.964736,1.06684,0.771077,-1.12848,-0.683559,0.365552,1.08425,-1.18407,5.70469,5.21487,1.53565,6.00699,5.96295,0.410306,4.18917,0.269715,-4.73161,-3.26031,-3.73171,-3.32091,-3.41544,-3.31827,-4.17522,-4.02166,23.0333,6.91079,-15.8489,13.6394,4.87308,-6.14778,12.4661,12.0892 --4.71936,0.998664,0.301634,4,15,0,8.73753,3.53037,2.60536,0.157219,-1.14507,-0.218043,0.911947,0.489313,0.971247,-0.820917,1.1084,3.93998,2.96229,4.80521,1.39158,0.547054,5.90632,6.06082,6.41815,-4.9134,-3.34842,-3.81051,-3.44681,-3.13094,-3.4163,-3.93424,-3.85739,12.7269,-5.05436,4.50286,12.0242,3.7155,2.14265,-2.44414,-6.74653 --7.62485,0.332584,0.569854,3,7,0,11.675,3.10489,12.1517,-0.661271,-1.26656,1.06809,-0.402356,-0.817368,-0.337884,0.800363,0.254475,-4.9307,16.0841,-6.82756,12.8307,-12.286,-1.78444,-1.001,6.1972,-6.03684,-3.54828,-3.72014,-3.45732,-3.90243,-3.34887,-5.02671,-3.86127,-27.6537,37.9786,-5.82014,11.3283,-24.4619,-5.0091,9.6852,26.7408 --7.22843,0.976358,0.133296,5,31,0,12.3932,0.53429,0.644191,1.03948,1.29131,-0.991361,0.368765,0.816193,0.892859,-0.741826,-0.0953312,1.20391,-0.104336,1.06007,0.0564121,1.36614,0.771845,1.10946,0.472878,-5.22261,-3.54992,-3.72372,-3.51606,-3.15072,-3.31705,-4.64798,-4.01436,21.1903,-16.9127,-3.35807,3.33179,-1.20526,-9.11316,-4.09514,4.43465 --9.71994,0.978983,0.236554,4,15,0,15.0266,2.11943,0.174527,0.118787,-1.04238,1.96144,-0.134181,-0.841696,-1.71562,0.73955,0.0690717,2.14016,2.46175,1.97253,2.2485,1.9375,2.09601,1.82001,2.13148,-5.11306,-3.37488,-3.73982,-3.41013,-3.16943,-3.3218,-4.53048,-3.9596,10.7278,15.379,0.660873,13.8052,-6.55345,-16.2407,-0.340588,1.19119 --7.17778,0.896894,0.42172,3,15,0,18.087,3.40452,0.144431,0.246052,0.213878,0.555816,0.228307,-1.43876,1.31841,-0.0724618,0.0517688,3.44005,3.48479,3.19671,3.39405,3.43541,3.43749,3.59494,3.41199,-4.96741,-3.32346,-3.76653,-3.37056,-3.2376,-3.34138,-4.25905,-3.92313,-13.5881,19.3503,-10.3779,6.66592,23.6482,-1.54013,12.6953,-28.9802 --6.76966,0.873777,0.580802,3,7,0,11.4315,2.17098,0.8057,-1.5782,0.833481,0.705974,-0.989374,-0.521312,0.214393,-0.999231,-0.230236,0.899418,2.73978,1.75096,1.3659,2.84251,1.37384,2.34371,1.98548,-5.25908,-3.35987,-3.73561,-3.448,-3.2073,-3.31741,-4.44712,-3.96408,26.8725,-0.537906,0.248087,7.93561,-0.422728,10.9977,16.1534,36.7826 --7.3321,0.376129,0.743075,4,15,0,12.5452,5.53073,0.61785,-1.35408,-0.683172,0.293619,-1.27706,0.630547,0.698809,-1.28829,-0.941209,4.69411,5.71214,5.92031,4.73476,5.10863,4.7417,5.96249,4.9492,-4.83402,-3.2477,-3.84694,-3.33804,-3.34651,-3.37469,-3.94603,-3.88603,24.4199,19.6817,-0.428209,12.5123,-9.5253,21.739,-7.58089,19.4695 --13.8727,0.959364,0.20986,4,15,0,18.9945,6.57293,4.25652,2.29537,-0.737326,-0.536731,-2.65053,-1.09061,-1.19131,-2.59259,0.412405,16.3432,4.28832,1.93072,-4.46247,3.43449,-4.7091,1.50209,8.32834,-3.92895,-3.29041,-3.73901,-3.85976,-3.23755,-3.45152,-4.58243,-3.83011,6.35801,25.6012,37.9622,-7.03094,12.6272,8.77988,-12.9322,-6.26998 --6.58064,0.97565,0.349506,3,7,0,18.869,5.5623,0.912552,-0.155902,-0.244067,0.806106,1.02734,1.48,-0.514313,-0.590502,-1.33356,5.42003,6.29792,6.91288,5.02344,5.33958,6.49981,5.09296,4.34536,-4.76,-3.23601,-3.88345,-3.33298,-3.36425,-3.44183,-4.05448,-3.89973,4.15062,4.79138,-13.3799,-9.53441,37.4429,23.3573,0.427019,15.2386 --5.38425,0.510751,0.606545,3,15,0,14.3811,2.66926,2.46296,-0.568501,1.1141,-0.296711,-0.446086,-0.987704,0.56479,1.53603,-0.134404,1.26906,1.93847,0.236584,6.45244,5.41324,1.57057,4.06031,2.33823,-5.21486,-3.40523,-3.71199,-3.31807,-3.37005,-3.31818,-4.1931,-3.95337,6.79303,5.6725,29.537,-13.8571,-20.9941,11.9428,-10.0751,23.4316 --4.87775,0.998789,0.264025,4,15,0,6.76878,1.96951,8.0832,1.43129,-1.13872,0.632041,0.353561,0.759322,0.500334,-1.09563,0.264745,13.5389,7.07843,8.10726,-6.88665,-7.23496,4.82742,6.01382,4.1095,-4.09171,-3.22577,-3.93249,-4.11369,-3.35613,-3.37737,-3.93987,-3.90539,11.0256,8.1759,-21.3404,-17.1317,-6.84935,5.88192,10.4394,19.9188 --6.61941,0.818144,0.48761,3,7,0,11.0498,1.96837,4.11817,-0.645083,0.137679,1.62841,0.247008,1.37607,1.09063,0.156299,1.67676,-0.688186,8.67445,7.63528,2.61204,2.53536,2.9856,6.45978,8.87355,-5.4559,-3.2238,-3.91244,-3.3964,-3.19332,-3.33313,-3.88741,-3.82439,24.4967,21.7116,-2.95461,18.1274,9.01679,11.0319,-1.10793,-10.9538 --7.01993,0.854164,0.527399,3,7,0,14.2102,-0.487439,1.93091,1.372,0.136325,-0.764091,-0.382415,0.752674,0.111742,-1.73323,0.0876963,2.16176,-1.96283,0.965904,-3.83415,-0.224208,-1.22585,-0.271675,-0.318106,-5.11058,-3.71781,-3.72225,-3.80187,-3.11988,-3.33731,-4.89079,-4.04347,9.09216,-8.2331,23.2818,20.5523,21.2204,3.31326,-3.49428,1.62337 --7.61684,0.306734,0.632172,3,7,0,10.7778,0.208567,16.8623,0.970342,0.727432,0.691165,-0.412961,-0.742816,0.883602,1.8241,0.657423,16.5708,11.8632,-12.317,30.9671,12.4748,-6.75492,15.1082,11.2943,-3.91727,-3.29615,-3.86107,-5.69048,-4.23696,-3.56534,-3.26334,-3.81008,11.8857,1.91541,-11.5341,12.1174,16.5753,0.754594,24.3863,6.52397 --4.09171,0.964799,0.158129,4,15,0,10.1062,-0.189609,4.99827,0.749323,-0.6632,0.372133,0.0641683,-0.4585,0.96778,-1.02969,0.403001,3.55571,1.67041,-2.48132,-5.33629,-3.50446,0.131122,4.64762,1.8247,-4.95482,-3.42184,-3.69205,-3.94569,-3.15488,-3.31995,-4.11295,-3.96909,19.3565,10.0377,10.2581,-23.1585,-2.18952,-13.3513,-0.819113,-4.31713 --4.54179,0.975708,0.261282,4,15,0,8.18417,0.231766,4.78936,1.34209,0.6249,0.830935,-0.527742,0.738662,0.571379,0.982222,-0.563292,6.65954,4.21141,3.76948,4.93598,3.22463,-2.29578,2.96831,-2.46604,-4.63902,-3.29329,-3.78103,-3.33444,-3.22633,-3.36172,-4.35128,-4.13225,-7.55974,17.6478,28.3628,-2.57374,6.05934,-4.10361,-5.1238,-8.83084 --4.20156,0.999665,0.441452,3,7,0,6.41404,1.00083,4.62432,0.333527,0.578055,0.1274,0.927192,-0.0316349,0.349254,-1.06312,-0.218393,2.54317,1.58997,0.85454,-3.91538,3.67394,5.28846,2.61589,-0.00908814,-5.0671,-3.42697,-3.72055,-3.80917,-3.25101,-3.39283,-4.40488,-4.03187,17.5115,-3.90404,-27.049,-22.5749,9.50928,19.5062,1.9758,34.691 --5.06532,0.596873,0.790367,3,15,0,7.64219,2.1517,2.93865,-0.448862,1.36193,0.275714,0.478856,-0.575061,0.993518,-0.672149,-0.7745,0.832656,2.96193,0.461802,0.176497,6.15392,3.55889,5.07131,-0.124279,-5.26713,-3.34843,-3.71493,-3.50923,-3.43208,-3.34389,-4.05728,-4.03616,-20.6798,10.4231,-6.03962,-0.857465,-1.4683,23.5507,8.6614,-1.06589 --8.29869,0.924687,0.460743,3,7,0,11.6871,3.19792,5.8322,1.69417,-0.76879,1.07754,-1.74626,-2.19094,-0.0733272,1.09055,-0.0264199,13.0787,9.48234,-9.58008,9.55822,-1.28582,-6.9866,2.77026,3.04383,-4.12176,-3.23251,-3.77609,-3.34388,-3.11667,-3.58041,-4.38125,-3.9331,-17.0196,18.3106,7.88646,14.2123,0.698766,4.94707,1.34505,-14.8528 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--11.3356,0.562718,0.593944,3,7,0,13.7419,9.38103,1.49884,0.386309,-0.0298448,1.57996,-0.199761,0.741988,-2.1751,-0.963511,-2.2813,9.96004,11.7491,10.4931,7.93688,9.3363,9.08162,6.12091,5.96173,-4.35019,-3.2918,-4.04712,-3.32046,-3.77566,-3.58672,-3.92709,-3.86558,6.56414,-4.75965,10.6803,28.6121,14.0697,9.38943,30.435,-10.6115 --9.37307,0.99683,0.316908,4,15,0,14.0585,0.450273,2.74728,-0.168728,-0.275646,-0.356439,-0.0314581,-0.968673,2.72413,0.994837,2.28114,-0.0132708,-0.528964,-2.21094,3.18337,-0.307004,0.363848,7.93422,6.71719,-5.37086,-3.58524,-3.69274,-3.37703,-3.11913,-3.31851,-3.72812,-3.85238,18.8311,8.20844,-0.192149,9.41146,5.83757,9.99264,9.34085,7.12859 --6.37889,0.995332,0.561043,3,7,0,13.0195,2.90946,7.27153,0.756814,0.838825,0.371539,-0.171376,-1.97147,-0.578587,-0.694121,0.391208,8.41266,5.61112,-11.4262,-2.13786,9.00901,1.6633,-1.29775,5.75415,-4.47958,-3.25006,-3.8302,-3.66188,-3.73456,-3.31865,-5.08354,-3.86951,30.1747,4.05712,2.05583,1.10547,17.2811,-1.79953,-5.86744,31.9141 --4.17363,0.933913,0.979924,2,7,0,8.11831,5.80236,2.82093,0.510644,1.07664,-0.250488,-1.14785,-0.35089,0.282863,0.703818,0.434952,7.24285,5.09575,4.81253,7.78778,8.83949,2.56436,6.6003,7.02933,-4.58445,-3.2637,-3.81074,-3.3194,-3.71379,-3.32695,-3.87129,-3.84744,17.0653,14.8737,6.65002,-3.15069,-8.25203,2.22084,7.48425,-1.19219 --4.17363,0.10008,1.43734,3,8,1,12.4165,5.80236,2.82093,0.510644,1.07664,-0.250488,-1.14785,-0.35089,0.282863,0.703818,0.434952,7.24285,5.09575,4.81253,7.78778,8.83949,2.56436,6.6003,7.02933,-4.58445,-3.2637,-3.81074,-3.3194,-3.71379,-3.32695,-3.87129,-3.84744,19.2331,-17.9312,-1.51014,23.8307,-2.08559,10.9438,7.03627,39.1537 --9.98796,0.959218,0.22576,4,15,0,11.7896,0.907275,0.179496,-1.47322,-0.543558,0.00940428,0.167737,0.0793804,0.876337,2.34556,0.546389,0.642838,0.908963,0.921524,1.32829,0.809709,0.937383,1.06457,1.00535,-5.29013,-3.47294,-3.72156,-3.44976,-3.13638,-3.31685,-4.65557,-3.99586,16.621,10.197,4.04587,-3.99452,-8.31155,14.0963,-7.19981,27.176 --3.99311,0.967892,0.356086,3,7,0,17.3761,6.38252,3.1096,1.09035,-0.728188,0.359752,-0.673826,-0.665463,0.304515,-1.22026,-0.329164,9.77305,7.5012,4.31319,2.58801,4.11814,4.28719,7.32943,5.35895,-4.36526,-3.22277,-3.79599,-3.39727,-3.27786,-3.36149,-3.79083,-3.87737,12.4301,20.9102,3.67695,0.108946,8.1797,0.368555,5.48587,4.38621 --4.82418,0.949311,0.570408,3,7,0,7.28339,1.89506,6.86167,-0.832748,0.369588,-0.0554061,0.675816,0.462027,0.325855,1.1999,0.508376,-3.81898,1.51488,5.06534,10.1284,4.43105,6.53229,4.13097,5.38337,-5.87687,-3.43181,-3.81858,-3.35727,-3.29824,-3.44331,-4.18327,-3.87687,6.22113,-5.53396,25.4678,2.14096,4.59182,7.76868,13.1378,6.70075 --4.82418,0.244869,0.863997,2,3,0,12.0289,1.89506,6.86167,-0.832748,0.369588,-0.0554061,0.675816,0.462027,0.325855,1.1999,0.508376,-3.81898,1.51488,5.06534,10.1284,4.43105,6.53229,4.13097,5.38337,-5.87687,-3.43181,-3.81858,-3.35727,-3.29824,-3.44331,-4.18327,-3.87687,-6.85627,24.0286,6.3239,4.59213,-9.22813,4.55128,0.784916,-0.45609 --7.96345,0.951332,0.209949,4,15,0,13.0768,8.08236,0.591805,1.18857,0.296738,0.967604,-0.435931,0.21705,1.95459,-0.981572,-0.611249,8.78576,8.65499,8.21081,7.50146,8.25797,7.82437,9.23909,7.72062,-4.4474,-3.22367,-3.937,-3.31787,-3.64524,-3.50928,-3.60529,-3.83757,24.3753,13.4164,-2.89751,31.4023,20.7288,-10.6121,0.943594,4.94872 --7.26023,0.980474,0.320023,4,15,0,12.734,-2.62591,7.54484,-0.857507,0.23887,0.321459,0.548091,-1.01539,0.0844181,-0.298921,1.1483,-9.09566,-0.200553,-10.2868,-4.88122,-0.823677,1.50934,-1.98899,6.03782,-6.68496,-3.55777,-3.79523,-3.90015,-3.11636,-3.31791,-5.21932,-3.86417,-14.6402,13.5613,7.86996,-25.0944,-11.0671,14.5743,8.19552,8.54997 --6.43157,0.988487,0.522002,3,7,0,10.7497,-0.694482,3.32573,-1.0585,-0.611891,1.13321,0.317629,-0.915854,1.33253,-0.836718,0.109437,-4.21476,3.07426,-3.74037,-3.47718,-2.72947,0.361869,3.73715,-0.330522,-5.93319,-3.34284,-3.6926,-3.77043,-3.13463,-3.31852,-4.23867,-4.04394,-3.72097,13.3151,10.0639,-3.89799,-6.02677,1.04877,0.666197,-17.3958 --6.79908,0.317747,0.862429,2,3,0,10.3678,1.41696,3.81896,-0.180051,-0.742721,1.4149,0.156367,-0.451063,2.50416,0.201391,1.42453,0.729352,6.8204,-0.305632,2.18607,-1.41946,2.01412,10.9802,6.85719,-5.27963,-3.22848,-3.70571,-3.41259,-3.11725,-3.32108,-3.46791,-3.85013,20.5634,6.53921,-11.8879,-5.98546,-0.458243,8.30767,8.21848,6.42336 --6.02392,0.997976,0.26244,4,15,0,11.5752,2.76592,5.65889,0.269961,-0.619783,-0.351081,-1.14945,-0.466436,2.39061,0.120404,1.52718,4.2936,0.779194,0.12641,3.44727,-0.741361,-3.73871,16.2941,11.4081,-4.87586,-3.48222,-3.71062,-3.36899,-3.11658,-3.40962,-3.23607,-3.80985,15.0622,-8.73528,-0.379044,-5.4656,-0.5572,-2.32316,30.2564,9.23969 --7.12765,0.924505,0.442838,3,15,0,9.79347,5.34446,0.557421,0.588151,-0.0980776,0.0153352,0.962812,0.106961,-1.69768,-0.146219,-1.55717,5.67231,5.35301,5.40409,5.26296,5.28979,5.88116,4.39814,4.47647,-4.73482,-3.25656,-3.82947,-3.3293,-3.36037,-3.41529,-4.14658,-3.89666,4.50419,16.7229,-3.57569,3.48371,2.29822,3.24219,3.15623,23.6065 --7.76049,0.852472,0.618229,3,7,0,13.9178,4.78942,0.474197,0.524858,0.0799262,0.811577,2.32898,0.56347,-0.744069,-0.123477,-0.700401,5.0383,5.17427,5.05661,4.73087,4.82732,5.89381,4.43658,4.45729,-4.79863,-3.26145,-3.8183,-3.33811,-3.32578,-3.4158,-4.14135,-3.89711,28.7515,2.372,-9.70119,4.6775,5.73365,-2.7664,13.0404,14.9878 --9.35059,0.463484,0.719563,2,7,0,13.8643,10.1456,0.861007,-1.50484,-0.57572,1.1976,-0.00842597,-1.51873,0.389287,-0.218763,-1.62813,8.84994,11.1768,8.83799,9.95726,9.64992,10.1384,10.4808,8.74378,-4.44193,-3.27198,-3.96523,-3.35297,-3.81629,-3.66192,-3.50422,-3.82567,2.35104,9.2777,-7.09802,15.3534,13.2595,24.7164,-14.2178,-12.7084 --7.7343,0.980981,0.323531,4,15,0,14.1507,5.90573,0.420652,1.13952,1.72613,0.408416,0.782748,0.583886,-0.261059,1.02549,0.910736,6.38507,6.07753,6.15134,6.3371,6.63183,6.23499,5.79591,6.28883,-4.66522,-3.24,-3.8551,-3.31865,-3.4757,-3.43008,-3.96622,-3.85965,14.1011,1.52228,18.6674,14.9983,-1.54324,-2.89088,2.60725,-14.9607 --5.46676,0.854159,0.5152,3,7,0,10.912,9.58009,2.20707,-0.314878,-0.402162,1.06118,-0.453308,-0.970252,0.20413,1.00581,0.483059,8.88514,11.9222,7.43868,11.8,8.69249,8.57961,10.0306,10.6462,-4.43894,-3.29844,-3.90435,-3.41204,-3.69607,-3.55423,-3.53908,-3.81214,18.957,27.2057,26.1589,-12.324,3.7666,0.685077,11.8511,19.0874 --4.08374,1,0.600748,3,7,0,7.83901,0.0104924,7.11243,1.94868,0.0380325,-0.305578,0.371236,0.723505,0.7635,0.394666,-0.244497,13.8704,-2.16291,5.15637,2.81753,0.280996,2.65088,5.44083,-1.72847,-4.07065,-3.73795,-3.82146,-3.38912,-3.12629,-3.3281,-4.01019,-4.10016,16.9482,9.21238,23.6655,-0.438905,-9.90762,-7.54524,-2.26621,-7.74847 --4.08374,0.00482967,0.990292,2,7,0,12.0637,0.0104924,7.11243,1.94868,0.0380325,-0.305578,0.371236,0.723505,0.7635,0.394666,-0.244497,13.8704,-2.16291,5.15637,2.81753,0.280996,2.65088,5.44083,-1.72847,-4.07065,-3.73795,-3.82146,-3.38912,-3.12629,-3.3281,-4.01019,-4.10016,47.5189,-5.7527,33.1614,-16.7015,-0.526181,-1.62922,-8.17529,13.6866 --7.39284,0.988832,0.152862,4,15,0,9.40933,0.467311,5.29418,2.44886,0.139144,-1.52021,0.0985774,0.584874,0.791412,0.521561,-0.857122,13.432,-7.58098,3.56374,3.22855,1.20397,0.989198,4.65719,-4.07045,-4.0986,-4.43536,-3.77567,-3.37561,-3.14615,-3.31683,-4.11168,-4.20786,-3.05281,-13.505,-13.6225,4.96518,6.23859,-0.431019,-0.581437,16.6038 -# Adaptation terminated -# Step size = 0.436713 -# Diagonal elements of inverse mass matrix: -# 10.4634, 1.33203, 1.08422, 0.887361, 0.979304, 0.736565, 0.685875, 0.729361, 0.796152, 0.916666 --3.96798,1,0.436713,3,7,0,9.42343,2.58808,6.51356,1.86627,-0.862259,-0.730425,-0.205932,0.287435,1.16766,-0.0843556,0.34937,14.7442,-2.16959,4.46031,2.03863,-3.02829,1.24673,10.1937,4.86373,-4.01747,-3.73863,-3.80023,-3.41855,-3.14156,-3.31709,-3.52621,-3.8879,18.4242,-10.681,9.69277,9.44694,2.8361,8.83929,19.1675,-0.399984 --5.69167,0.873969,0.436713,3,7,0,10.2911,-0.34126,2.70165,1.65556,0.187138,-0.186241,-0.608195,0.862927,-0.477933,-0.503536,0.172601,4.13148,-0.844418,1.99007,-1.70164,0.164322,-1.98439,-1.63247,0.125047,-4.893,-3.61264,-3.74016,-3.62972,-3.12453,-3.35364,-5.14869,-4.02693,-7.79377,-1.92683,4.51147,-3.38587,-6.41704,-15.943,5.05281,-31.593 --6.18838,0.955452,0.436713,3,15,0,11.1231,-1.52177,4.69945,0.294962,0.169135,1.29322,1.34776,0.0657089,1.6886,1.40727,0.15206,-0.135613,4.55566,-1.21297,5.09162,-0.726931,4.81196,6.41371,-0.80717,-5.38613,-3.28084,-3.69776,-3.33188,-3.11662,-3.37688,-3.89273,-4.06243,12.1786,3.997,12.8206,3.03726,-8.32866,12.731,13.0922,-20.635 --5.47981,0.938909,0.436713,3,15,0,12.5305,3.39813,3.07643,0.372491,0.108266,-0.681923,1.71644,-0.671446,1.1821,-0.279478,-1.04251,4.54407,1.30024,1.33248,2.53834,3.7312,8.67864,7.03477,0.190922,-4.84961,-3.44596,-3.72819,-3.39909,-3.25434,-3.56048,-3.8227,-4.02452,14.6397,14.3096,7.01371,14.6859,2.52803,32.4815,8.74598,12.1148 --6.58728,0.971347,0.436713,3,7,0,10.5251,4.6942,5.60387,0.0547252,-0.787636,-0.31638,-1.76158,-1.83837,0.692558,-1.07099,0.914013,5.00087,2.92125,-5.60777,-1.30751,0.280388,-5.17749,8.5752,9.81621,-4.80246,-3.35049,-3.70481,-3.60202,-3.12628,-3.47453,-3.66566,-3.81667,-3.63755,2.8184,-31.5707,-6.29619,-6.91025,-8.31978,7.97653,26.2879 --5.08232,1,0.436713,3,7,0,8.43049,3.96614,4.24413,1.09092,-0.0912902,0.423883,1.24591,1.43458,0.425733,1.08454,-0.176263,8.59614,5.76516,10.0547,8.56909,3.5787,9.25393,5.77301,3.21806,-4.46368,-3.2465,-4.02439,-3.32701,-3.24557,-3.59835,-3.96902,-3.92833,18.114,5.13757,-4.0792,12.0623,14.8888,3.76665,-1.93895,24.7885 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--10.7328,0.973277,0.436713,3,7,0,17.3455,6.88596,0.64576,-2.09145,-0.258078,-0.274628,-1.8309,2.11834,0.0535718,-0.733628,-0.872685,5.53538,6.70861,8.2539,6.41221,6.7193,5.70363,6.92055,6.32241,-4.74845,-3.22986,-3.93889,-3.31826,-3.48399,-3.40826,-3.83529,-3.85906,3.16206,-3.74465,-11.8472,11.7633,6.49949,-4.80124,-15.6294,-4.50657 --7.73708,0.999989,0.436713,3,7,0,13.6257,7.32248,0.645433,-0.991848,-0.119106,-0.890996,-1.88729,0.089228,0.109966,-1.36949,-0.672443,6.68231,6.7474,7.38007,6.43857,7.24561,6.10436,7.39346,6.88847,-4.63686,-3.22937,-3.90197,-3.31814,-3.53585,-3.4245,-3.78402,-3.84963,15.8791,8.54845,-6.54538,-6.18191,-2.26458,-1.88524,12.2134,-40.8453 --8.3398,0.961375,0.436713,3,7,0,13.6985,13.0173,4.23672,-0.901317,-0.0347557,-0.0620415,-1.01798,0.588817,0.4846,-1.77585,0.079815,9.19866,12.7544,15.5119,5.49351,12.87,8.70439,15.0704,13.3554,-4.41252,-3.33455,-4.36085,-3.32621,-4.30368,-3.56211,-3.26444,-3.81215,5.802,0.769711,29.2097,-9.30439,26.8017,8.88946,22.8001,3.83454 --8.95874,0.805589,0.436713,3,7,0,16.621,-2.86101,0.689997,1.06997,-1.5762,1.17527,-0.219903,-0.381507,0.426489,-0.119444,0.662545,-2.12273,-2.05007,-3.12425,-2.94342,-3.94858,-3.01274,-2.56673,-2.40385,-5.64339,-3.72654,-3.69156,-3.72539,-3.16983,-3.38337,-5.33648,-4.12948,-7.31364,1.47335,-6.76722,9.66494,-4.56128,0.340468,-18.7502,-12.9659 --7.44875,0.99965,0.436713,3,7,0,12.9611,8.13996,0.240027,0.240566,0.183649,1.08133,0.805984,-0.7019,0.494303,0.312108,1.39419,8.1977,8.39951,7.97148,8.21487,8.18404,8.33342,8.2586,8.4746,-4.49839,-3.22232,-3.92663,-3.32293,-3.63682,-3.53906,-3.696,-3.82849,5.93655,13.0529,3.07212,29.7372,8.8691,-1.56584,7.30361,20.4849 --9.99239,0.514648,0.436713,3,15,0,15.2075,4.14125,1.80862,0.534866,1.10933,0.770587,1.1427,-1.61112,2.59747,-1.67575,0.247417,5.10862,5.53494,1.22734,1.11045,6.1476,6.20796,8.83908,4.58873,-4.79147,-3.25191,-3.72643,-3.46017,-3.43152,-3.42891,-3.64114,-3.89407,12.5374,8.37908,32.6165,9.558,12.7576,20.6373,10.2134,11.5913 --8.83237,0.771044,0.436713,3,7,0,15.8984,5.72946,8.8068,0.215148,1.53911,-0.82654,0.427672,-0.793049,1.57672,-1.25386,-0.130579,7.62422,-1.54971,-1.25476,-5.31299,19.2841,9.49587,19.6153,4.57948,-4.54959,-3.67751,-3.69748,-3.94332,-5.65594,-3.6151,-3.23457,-3.89429,4.70683,4.53732,13.7293,-10.9841,27.8131,23.0928,42.2558,19.7108 --7.34563,0.41623,0.436713,3,15,0,15.5561,8.39164,1.25469,1.76286,-0.4301,0.486364,-1.02077,0.288363,0.210598,-0.197347,1.84625,10.6035,9.00187,8.75344,8.14403,7.852,7.11089,8.65587,10.7081,-4.29952,-3.22654,-3.96134,-3.32224,-3.59985,-3.47114,-3.65809,-3.81189,14.2723,29.2819,26.5802,-2.11108,13.3075,11.2426,-0.822869,-19.3998 --4.67328,0.970912,0.436713,3,7,0,12.2476,7.32298,3.28686,0.7881,-0.557255,0.657952,-1.17059,-0.382421,-0.190908,-0.023768,1.51604,9.91335,9.48557,6.06601,7.24486,5.49136,3.47541,6.69549,12.306,-4.35394,-3.23256,-3.85206,-3.31708,-3.37627,-3.34215,-3.86048,-3.80945,5.79263,10.0898,13.0171,10.9241,10.7102,19.3002,20.7043,20.5471 --7.70177,0.969428,0.436713,3,7,0,10.7288,11.2988,12.0496,0.409291,-0.316493,-0.286765,0.579243,-0.403184,1.14443,-0.742999,1.62214,16.2306,7.84346,6.44066,2.34604,7.48525,18.2785,25.0887,30.845,-3.93481,-3.22165,-3.8656,-3.40634,-3.5606,-4.55049,-3.47277,-4.35735,2.90389,10.3727,17.3625,8.98652,5.99578,21.5258,11.3825,32.8672 --9.74217,1,0.436713,3,7,0,12.3912,12.1271,2.214,0.432241,-0.675334,-1.4433,-0.163001,-0.664546,1.62008,-1.0227,1.85443,13.084,8.93161,10.6558,9.8628,10.6319,11.7662,15.7139,16.2328,-4.1214,-3.22586,-4.05575,-3.3507,-3.95135,-3.7958,-3.24765,-3.83696,9.23664,6.08415,21.088,5.63498,-3.11354,2.32819,35.5726,37.6786 --4.80683,0.97387,0.436713,3,7,0,14.4118,6.78744,1.6116,0.969249,0.0217708,-0.227822,-0.029828,0.68705,-0.0197328,-0.259571,1.57254,8.34949,6.42028,7.89469,6.36912,6.82253,6.73937,6.75564,9.32175,-4.48508,-3.234,-3.92335,-3.31848,-3.49389,-3.45295,-3.8537,-3.82038,12.5125,15.8205,14.1382,-9.50575,1.86056,23.7584,13.9871,13.6368 --6.74231,0.895184,0.436713,3,7,0,12.9315,6.56512,2.52668,0.474792,0.742022,0.107085,0.548706,1.95443,1.7633,0.527086,-0.310399,7.76477,6.83569,11.5033,7.8969,8.43997,7.95153,11.0204,5.78084,-4.53691,-3.2283,-4.10236,-3.32016,-3.66624,-3.51652,-3.4651,-3.869,36.3676,16.9093,-1.98345,5.89438,19.4191,6.92546,16.7967,18.2748 --10.5644,0.905333,0.436713,4,23,0,16.2861,3.08253,0.183479,0.0975531,-0.761217,0.0146863,-0.592732,-2.14501,-1.53696,-1.80634,0.373207,3.10043,3.08523,2.68897,2.75111,2.94287,2.97378,2.80053,3.15101,-5.00474,-3.3423,-3.75474,-3.39143,-3.21213,-3.33293,-4.37664,-3.93015,7.94219,3.14051,8.44912,8.71178,5.90938,-20.0752,18.3311,-3.26441 --13.559,0.927975,0.436713,3,7,0,18.9605,1.53781,0.206195,-0.457543,-0.522666,-1.3657,-1.12633,-3.20847,0.472935,-1.37576,0.914846,1.44347,1.25621,0.876239,1.25413,1.43004,1.30557,1.63533,1.72645,-5.19421,-3.44892,-3.72087,-3.45326,-3.15261,-3.31722,-4.56054,-3.97219,1.00782,3.76191,15.1692,6.30554,8.56473,12.4443,12.3973,17.2268 --11.9749,0.989823,0.436713,3,15,0,15.1142,7.66924,1.64104,0.496044,-0.227249,1.42815,1.08221,3.22743,-0.409397,1.37911,-0.913265,8.48327,10.0129,12.9656,9.93242,7.29631,9.44519,6.9974,6.17053,-4.47344,-3.24178,-4.18938,-3.35237,-3.54103,-3.61155,-3.82681,-3.86175,4.14053,11.6107,27.2579,-18.7393,-5.63018,-0.13168,14.1069,-1.2595 --7.69639,0.978933,0.436713,3,7,0,18.3894,-2.14259,1.69165,-0.194959,1.17808,-0.514867,0.316354,-0.527261,-1.45166,0.382354,0.10355,-2.47239,-3.01356,-3.03453,-1.49578,-0.149701,-1.60743,-4.59828,-1.96742,-5.69047,-3.82802,-3.69153,-3.61509,-3.12063,-3.34493,-5.77494,-4.11037,12.8658,-3.24202,-36.7471,3.04906,-9.59883,17.7043,-14.326,22.7767 --6.72512,0.932974,0.436713,3,15,0,11.2417,5.2188,3.77619,1.3459,-0.852952,1.21014,0.206174,0.333875,2.63559,0.580244,0.667209,10.3012,9.78852,6.47957,7.40991,1.99789,5.99735,15.1713,7.73831,-4.3231,-3.23752,-3.86704,-3.31753,-3.17164,-3.42003,-3.26153,-3.83734,-29.8734,-0.681237,20.9198,8.62003,-0.693048,14.7425,7.48588,17.1929 --8.78781,0.939713,0.436713,3,7,0,14.0557,7.16821,4.38902,-0.525051,-0.843532,-1.5068,0.539268,-1.52679,2.18386,-0.0626235,1.67902,4.86375,0.554807,0.467086,6.89335,3.46592,9.53507,16.7532,14.5374,-4.81651,-3.49868,-3.71501,-3.31688,-3.23928,-3.61786,-3.2293,-3.81925,6.97256,-2.32261,14.3272,4.8157,-6.55892,10.1342,14.9615,17.2824 --4.90252,1,0.436713,3,7,0,11.1868,8.58925,6.60234,1.25539,-0.925871,1.31644,0.046576,0.120696,0.107203,-0.865333,-0.173341,16.8778,17.2808,9.38613,2.87603,2.47634,8.89676,9.29704,7.4448,-3.90189,-3.65219,-3.99117,-3.38711,-3.19076,-3.57451,-3.60023,-3.84133,-25.8184,15.9418,17.0387,-3.58259,15.5283,12.2942,19.9725,-13.2686 --7.7406,0.970133,0.436713,3,7,0,10.6304,-2.31031,2.86949,-0.163729,1.00896,-0.720878,-0.180005,-0.437933,1.54325,1.70591,0.84247,-2.78013,-4.37887,-3.56696,2.58479,0.584895,-2.82683,2.11802,0.107151,-5.73236,-3.98771,-3.69216,-3.39739,-3.13167,-3.37735,-4.48271,-4.02758,8.72359,2.58623,28.1673,0.892796,-9.51284,3.02114,7.80321,-3.03994 --6.24377,0.923775,0.436713,3,7,0,13.6827,3.33142,9.22052,0.793958,-0.849003,0.971866,0.224636,0.285373,-0.710093,-0.350898,1.79805,10.6521,12.2925,5.9627,0.0959552,-4.49683,5.40268,-3.21601,19.9103,-4.29576,-3.31365,-3.84842,-3.5138,-3.19164,-3.39693,-5.47212,-3.90587,27.8098,27.4832,16.3894,10.7169,-11.1983,-1.14117,-5.66651,-10.0507 --5.02565,1,0.436713,3,7,0,8.66406,8.67861,1.88381,0.297162,0.17331,-0.867329,-1.05028,0.462701,0.684949,0.953253,-0.0303146,9.23841,7.04473,9.55025,10.4744,9.00509,6.70008,9.96892,8.6215,-4.40921,-3.22609,-3.99916,-3.36671,-3.73408,-3.45109,-3.54401,-3.82692,0.476372,3.22476,22.9414,18.8303,11.227,13.7054,16.9439,3.92959 -# -# Elapsed Time: 0.042753 seconds (Warm-up) -# 0.00453 seconds (Sampling) -# 0.047283 seconds (Total) -# diff --git a/arviz/tests/saved_models/stan_diagnostics/blocker.1.csv b/arviz/tests/saved_models/stan_diagnostics/blocker.1.csv deleted file mode 100644 index 19f9bdecdf..0000000000 --- a/arviz/tests/saved_models/stan_diagnostics/blocker.1.csv +++ /dev/null @@ -1,1046 +0,0 @@ -# stan_version_major = 1 -# stan_version_minor = 3 -# stan_version_patch = 0 -# model = blocker_model -# method = sample (Default) -# sample -# num_samples = 1000 (Default) -# num_warmup = 1000 (Default) -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.65000000000000002 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 1 -# data = src/models/bugs_examples/vol1/blocker/blocker.data.R -# init = src/models/bugs_examples/vol1/blocker/blocker.init.R -# random -# seed = 1434717929 -# output -# sample = samples.csv (Default) -# append_sample = 0 (Default) -# diagnostic = (Default) -# append_diagnostic = 0 (Default) -# refresh = 100 (Default) -# save_warmup = 0 (Default) -lp__,accept_stat__,stepsize__,treedepth__,d,sigmasq_delta,mu.1,mu.2,mu.3,mu.4,mu.5,mu.6,mu.7,mu.8,mu.9,mu.10,mu.11,mu.12,mu.13,mu.14,mu.15,mu.16,mu.17,mu.18,mu.19,mu.20,mu.21,mu.22,delta.1,delta.2,delta.3,delta.4,delta.5,delta.6,delta.7,delta.8,delta.9,delta.10,delta.11,delta.12,delta.13,delta.14,delta.15,delta.16,delta.17,delta.18,delta.19,delta.20,delta.21,delta.22,delta_new,sigma_delta -# Adaptation terminated -# Step size = 0.350484 -# Diagonal elements of inverse mass matrix: -# 0.00353462, 0.935044, 0.207488, 0.057726, 0.0832164, 0.00725564, 0.0211464, 0.132239, 0.00629474, 0.015251, 0.0197042, 0.00475182, 0.0121151, 0.0166284, 0.0441517, 0.0187878, 0.0191823, 0.0188328, 0.0371399, 0.0956852, 0.106922, 0.0165145, 0.0231892, 0.0176588, 0.0290442, 0.0224101, 0.018034, 0.00933002, 0.0173806, 0.0260654, 0.0113518, 0.0125966, 0.0132406, 0.00643862, 0.00999104, 0.0133324, 0.0193036, 0.0283918, 0.0150186, 0.0120746, 0.0197684, 0.0258469, 0.023299, 0.0124972, 0.0209571, 0.0176207, 0.0276856 --5922.44,0.784353,0.350484,3,-0.233883,0.0351674,-2.26717,-2.18819,-2.30188,-2.44328,-2.5678,-2.401,-1.72203,-2.11902,-1.81217,-2.32678,-2.2435,-1.55042,-3.02961,-2.74758,-1.56368,-1.54294,-2.30471,-2.9833,-2.80307,-1.41886,-2.03942,-2.87719,-0.440641,-0.204123,-0.346597,-0.431105,-0.0834789,-0.163559,-0.467268,-0.253734,-0.216699,-0.301357,-0.281817,-0.0640587,-0.263632,0.00142648,-0.390133,-0.207506,0.0652445,0.0320738,-0.487441,-0.306619,-0.477649,-0.5027,-0.117267,0.18753 --5923.39,0.974627,0.350484,2,-0.189516,0.0171881,-2.66688,-2.0593,-1.9125,-2.26705,-2.47121,-2.82718,-1.66179,-2.13879,-2.1367,-2.22678,-2.38648,-1.69212,-3.04741,-2.91928,-1.30114,-1.36538,-1.93611,-2.85462,-3.07431,-1.42184,-2.22869,-2.72604,-0.0647202,-0.255459,-0.197714,-0.290459,-0.0141306,0.0171178,-0.300294,0.0335036,-0.268703,-0.20407,-0.100682,-0.0731371,-0.353475,0.127748,-0.322846,-0.254493,-0.20447,-0.237309,-0.642206,-0.0606385,-0.0977055,-0.346635,-0.0406729,0.131104 --5920.56,0.987544,0.350484,2,-0.192369,0.0190425,-1.9621,-1.75502,-2.50503,-2.33585,-2.42642,-2.80894,-1.79992,-2.15396,-1.94577,-2.31232,-2.29971,-1.52165,-2.68166,-2.89991,-1.53145,-1.36584,-2.08769,-3.17537,-3.38881,-1.57224,-2.04449,-2.99396,-0.12969,-0.301535,-0.141812,-0.327239,-0.11772,0.0526682,-0.312569,-0.1357,-0.23536,-0.296251,-0.197137,-0.0438254,-0.374016,0.18937,-0.259717,-0.462538,-0.195768,-0.334735,-0.216376,-0.226915,-0.305009,-0.147411,0.145772,0.137995 --5924.28,0.654378,0.350484,3,-0.326973,0.0140364,-2.95661,-2.68618,-1.71925,-2.46756,-2.38362,-1.57195,-1.66512,-2.03524,-1.85676,-2.18353,-2.34168,-1.32851,-3.26592,-2.6173,-1.25359,-1.49569,-1.72008,-2.71063,-3.69519,-1.42077,-2.24279,-2.90699,-0.525608,-0.390053,-0.394079,-0.239183,-0.245416,-0.561497,-0.468253,-0.195203,-0.330481,-0.403728,-0.36402,-0.361285,-0.241842,-0.295722,-0.259919,-0.0693857,-0.201931,-0.161922,-0.332239,-0.376229,-0.363975,-0.43692,-0.875785,0.118475 --5919.79,0.65234,0.350484,2,-0.26668,0.0191611,-2.36048,-2.22265,-2.408,-2.46987,-2.40688,-1.8984,-1.61404,-2.17069,-1.78915,-2.09168,-2.30773,-1.22091,-3.16179,-2.57429,-1.58849,-1.45629,-1.85631,-2.72012,-3.71654,-1.54804,-1.92541,-2.90197,-0.0300726,-0.204275,-0.260622,-0.220558,-0.306588,-0.367781,-0.496329,-0.133509,-0.39685,-0.406125,-0.248903,-0.253586,-0.162485,-0.180462,-0.322044,-0.317002,-0.251057,-0.41058,-0.204486,-0.405652,-0.384079,-0.352638,-0.577029,0.138424 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--5932.2,1,0.350484,3,-0.0596346,0.053237,-2.87091,-2.19272,-2.54451,-2.52509,-2.43117,-2.35771,-1.8508,-2.18937,-1.92378,-2.18458,-2.54226,-1.62932,-2.7022,-3.06052,-1.34246,-1.43936,-2.19818,-3.51183,-3.92121,-1.37341,-2.25936,-2.8019,-0.212305,-0.0512713,-0.123745,-0.206313,0.179029,-0.24434,-0.399407,0.00804065,-0.49633,-0.291814,-0.0189445,-0.0210197,-0.314149,0.500322,-0.318117,-0.0688799,-0.0103507,0.0271386,0.0236404,-0.279335,-0.197343,-0.539125,-0.38948,0.230731 --5932.67,0.863166,0.350484,2,-0.0849929,0.0200181,-2.19592,-2.19191,-2.1859,-2.29973,-2.62031,-2.12736,-1.68064,-2.4492,-1.82579,-2.28147,-2.32032,-1.46313,-2.89382,-3.13059,-1.48684,-1.62705,-2.20136,-2.48128,-3.22575,-1.4359,-2.14574,-2.71429,-0.0151353,-0.144837,0.119021,-0.359559,-0.246657,-0.18989,-0.256309,0.159791,-0.194971,-0.293788,-0.161084,-0.0115968,-0.367978,0.430176,0.021672,0.0126375,-0.0794464,0.166347,-0.394856,-0.152521,-0.29947,-0.483452,0.0957875,0.141485 --5937.67,0.966627,0.350484,3,-0.150331,0.024233,-2.38965,-2.25616,-2.31493,-2.38446,-1.93477,-2.43788,-1.83303,-2.21413,-2.22013,-2.24066,-2.28597,-1.88571,-2.9653,-2.78285,-1.08411,-1.81928,-1.96108,-3.60878,-3.61048,-1.36015,-2.11955,-3.08311,-0.178999,-0.314814,-0.488112,-0.289142,-0.351261,-0.287341,-0.366858,0.0462175,-0.108006,-0.212693,-0.279353,0.262163,-0.494849,0.047692,-0.343064,0.154117,0.0417568,-0.151729,-0.0547022,-0.328555,-0.31663,-0.189104,0.290255,0.15567 --5935.34,0.54221,0.350484,3,-0.269335,0.0530168,-1.83476,-2.42099,-2.11945,-2.41138,-2.48883,-2.28487,-1.65926,-2.28657,-1.96,-2.20703,-2.40999,-1.46832,-2.91187,-2.77586,-1.44642,-1.86173,-1.97961,-2.46475,-3.27443,-1.784,-1.79596,-3.04694,-0.215388,0.053962,-0.237775,-0.168359,-0.22839,-0.453282,-0.171552,-0.178568,-0.251071,-0.409753,-0.0964413,-0.0539777,-0.296024,-0.0597935,-0.222316,0.132155,-0.30664,-0.202166,-0.308631,0.021776,-0.613924,-0.275578,0.439289,0.230254 --5932.62,0.937387,0.350484,3,-0.104827,0.0690368,-3.05449,-2.34465,-2.08179,-2.4502,-2.30957,-2.11545,-1.84401,-2.0954,-2.02986,-2.3084,-2.23206,-1.46673,-3.09443,-2.8694,-1.57561,-1.56938,-1.73972,-3.16606,-4.45638,-1.35661,-2.11211,-2.87514,-0.10995,-0.364036,-0.113427,-0.213721,-0.0706475,-0.28905,-0.464212,-0.027091,-0.169623,-0.12331,-0.322205,-0.246085,-0.122203,0.150344,-0.0679014,0.0180877,-0.213304,-0.240342,0.24665,-0.220636,-0.512093,-0.217441,0.498282,0.262749 - -# Elapsed Time: 0.189802 seconds (Warm-up) -# 0.180837 seconds (Sampling) -# 0.370639 seconds (Total) - diff --git a/arviz/tests/saved_models/stan_diagnostics/blocker.2.csv b/arviz/tests/saved_models/stan_diagnostics/blocker.2.csv deleted file mode 100644 index d4b8043075..0000000000 --- a/arviz/tests/saved_models/stan_diagnostics/blocker.2.csv +++ /dev/null @@ -1,1046 +0,0 @@ -# stan_version_major = 1 -# stan_version_minor = 3 -# stan_version_patch = 0 -# model = blocker_model -# method = sample (Default) -# sample -# num_samples = 1000 (Default) -# num_warmup = 1000 (Default) -# save_warmup = 0 (Default) -# thin = 1 (Default) -# adapt -# engaged = 1 (Default) -# gamma = 0.050000000000000003 (Default) -# delta = 0.65000000000000002 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# id = 2 -# data = src/models/bugs_examples/vol1/blocker/blocker.data.R -# init = src/models/bugs_examples/vol1/blocker/blocker.init.R -# random -# seed = 1434732768 -# output -# sample = samples.csv (Default) -# append_sample = 0 (Default) -# diagnostic = (Default) -# append_diagnostic = 0 (Default) -# refresh = 100 (Default) -# save_warmup = 0 (Default) -lp__,accept_stat__,stepsize__,treedepth__,d,sigmasq_delta,mu.1,mu.2,mu.3,mu.4,mu.5,mu.6,mu.7,mu.8,mu.9,mu.10,mu.11,mu.12,mu.13,mu.14,mu.15,mu.16,mu.17,mu.18,mu.19,mu.20,mu.21,mu.22,delta.1,delta.2,delta.3,delta.4,delta.5,delta.6,delta.7,delta.8,delta.9,delta.10,delta.11,delta.12,delta.13,delta.14,delta.15,delta.16,delta.17,delta.18,delta.19,delta.20,delta.21,delta.22,delta_new,sigma_delta -# Adaptation terminated -# Step size = 0.394006 -# Diagonal elements of inverse mass matrix: -# 0.00322863, 0.807456, 0.197456, 0.057008, 0.0568689, 0.00658556, 0.0245002, 0.140821, 0.00633985, 0.0154381, 0.0227073, 0.00514662, 0.0106679, 0.0159943, 0.0524667, 0.0180388, 0.0261897, 0.0206052, 0.0286902, 0.0842937, 0.12626, 0.0161488, 0.019305, 0.019815, 0.0227948, 0.0226141, 0.0219908, 0.00837366, 0.0195498, 0.0276844, 0.0110352, 0.017097, 0.0176254, 0.00791707, 0.0105336, 0.0152164, 0.0236931, 0.026425, 0.0168275, 0.0155721, 0.0222602, 0.0295932, 0.0266263, 0.0133522, 0.0213881, 0.0193669, 0.0282044 --5920.06,0.944906,0.394006,2,-0.276974,0.00698287,-2.23739,-2.21334,-2.05925,-2.47399,-2.41292,-2.35844,-1.62829,-1.8695,-2.01179,-2.11007,-2.12297,-1.34989,-2.7458,-2.74806,-1.20604,-1.29538,-2.07298,-2.84211,-3.43846,-1.38036,-2.259,-3.0108,-0.223517,-0.345224,-0.163577,-0.221923,-0.184189,-0.0723447,-0.413496,-0.17643,-0.433357,-0.374598,-0.294718,-0.213375,-0.308022,0.167087,-0.306168,-0.241594,-0.272264,-0.265095,-0.321432,-0.339087,-0.278745,-0.575254,-0.531282,0.0835636 --5917.44,0.671807,0.394006,2,-0.344137,0.0126025,-2.52455,-1.93874,-2.08888,-2.39717,-2.39638,-2.04962,-1.70191,-2.17563,-1.8456,-2.29173,-2.3289,-1.35021,-2.87549,-2.93081,-1.21805,-1.51178,-2.28474,-3.50208,-3.56655,-1.37007,-2.31733,-2.75564,-0.548792,-0.23423,-0.479447,-0.338692,-0.310713,-0.34672,-0.426283,-0.336209,-0.24475,-0.296641,-0.231255,-0.254477,-0.387691,-0.0610687,-0.510707,-0.361057,-0.237379,-0.316267,-0.30089,-0.351853,-0.27341,-0.457794,-0.0945774,0.112261 --5926.15,0.340292,0.394006,2,-0.304378,0.020668,-1.72091,-2.30777,-1.73107,-2.51424,-2.30365,-2.05909,-1.56437,-1.95899,-1.84788,-2.22173,-2.52369,-1.35507,-3.06967,-2.9196,-1.35252,-1.42984,-1.73738,-2.62132,-3.00057,-1.45912,-2.27021,-2.85245,-0.0944224,-0.428497,-0.296265,-0.0462034,-0.2053,-0.372324,-0.49651,-0.153522,-0.457103,-0.28902,-0.0222778,-0.257379,-0.365907,0.449935,-0.223847,-0.223592,-0.154201,-0.117066,-0.220239,-0.237823,-0.227183,-0.539932,-0.48562,0.143764 --5935.98,0.426438,0.394006,2,-0.209506,0.0587488,-3.0916,-2.20599,-2.37811,-2.51981,-2.51717,-2.34206,-1.72623,-1.92897,-1.77862,-2.3439,-2.51954,-1.35229,-2.71466,-3.04462,-1.22782,-1.45819,-1.92973,-3.38742,-3.42173,-1.82031,-2.03604,-2.95642,-0.319037,-0.312097,-0.326303,-0.214735,-0.307383,-0.0374532,-0.425509,-0.482996,-0.147137,-0.176322,-0.0560504,-0.29956,-0.107503,0.220582,-0.257533,-0.227431,-0.325191,-0.207543,-0.197388,-0.325624,-0.302159,-0.396417,0.235017,0.242381 --5935.62,0.952999,0.394006,3,-0.114865,0.0453928,-1.83415,-2.2597,-1.94742,-2.43854,-2.4601,-1.96194,-1.75763,-2.17929,-2.20157,-2.23312,-2.27728,-1.6098,-3.53496,-3.07053,-1.53633,-1.51549,-2.21837,-3.06522,-3.36957,-1.04354,-2.20737,-2.86122,-0.096135,-0.174357,0.0302226,-0.191519,0.00786599,-0.12103,-0.263588,0.0763438,-0.222911,-0.390095,-0.276728,0.0660285,-0.282595,0.176826,-0.147859,-0.0437559,-0.158226,0.415384,0.0423299,-0.240262,-0.317187,-0.273653,-0.582085,0.213056 --5927.61,0.987393,0.394006,3,-0.272865,0.0112325,-2.66352,-2.14788,-2.13352,-2.39186,-2.51353,-2.05731,-1.59513,-1.93535,-2.15159,-2.31435,-2.40895,-1.35454,-3.48338,-2.68772,-1.44706,-1.55398,-2.24853,-2.95778,-3.37319,-1.42513,-2.2449,-2.9807,-0.360323,-0.188151,-0.183886,-0.30463,-0.131545,-0.444793,-0.445012,-0.342836,-0.275241,-0.383813,-0.159719,-0.369755,-0.474818,-0.224491,-0.391199,-0.1477,-0.273196,-0.170335,-0.314983,0.105146,-0.238287,-0.209836,0.188416,0.105983 --5921.66,0.98483,0.394006,2,-0.304855,0.0115173,-2.47041,-2.07954,-2.10003,-2.35217,-2.56796,-2.12264,-1.55604,-1.95495,-1.5148,-2.23318,-2.33247,-1.41098,-3.31666,-2.69961,-1.2571,-1.51237,-2.03092,-3.03189,-2.70372,-1.5156,-2.08567,-2.91376,-0.0911935,-0.394811,-0.184736,-0.348436,-0.187512,-0.317453,-0.517262,-0.475246,-0.49021,-0.275005,-0.388496,-0.302941,-0.215994,-0.171974,-0.173202,-0.115658,-0.256229,-0.384111,-0.293565,-0.0721072,-0.304349,-0.411123,-0.442522,0.107319 --5924.08,0.824202,0.394006,3,-0.129915,0.0197248,-2.21512,-2.21024,-2.00463,-2.42766,-2.25768,-2.49663,-1.81297,-2.07297,-1.87622,-2.22391,-2.26393,-1.56049,-3.3619,-2.83234,-1.25022,-1.60966,-2.03937,-3.08636,-3.02996,-1.3137,-2.17819,-2.65293,-0.138003,-0.362817,-0.182045,-0.252211,-0.334601,-0.391961,-0.271913,-0.197439,-0.364729,-0.366425,-0.0424159,-0.285062,-0.0684897,0.0970762,-0.162415,-0.08455,-0.106394,-0.153678,-0.0465626,-0.277596,-0.48811,-0.288933,0.0384193,0.140445 --5917.86,0.765326,0.394006,2,-0.233643,0.0143883,-2.30233,-1.99876,-1.97859,-2.39453,-2.31897,-1.89364,-1.77257,-1.80812,-1.88395,-2.32868,-2.37174,-1.51827,-3.19629,-2.85539,-1.30857,-1.4706,-2.21033,-2.93805,-3.62946,-1.41748,-2.27169,-2.82758,-0.279417,-0.257838,-0.169471,-0.246301,-0.214701,-0.289783,-0.207601,-0.248224,-0.23519,-0.36024,-0.344305,-0.137737,-0.210402,0.0520599,-0.148968,-0.114805,0.150383,-0.0677617,-0.118328,-0.273412,-0.439413,-0.305038,-0.153416,0.119951 --5920.22,0.820091,0.394006,2,-0.254796,0.0108544,-2.14963,-2.11049,-2.06287,-2.36087,-2.12873,-2.35213,-1.72251,-1.91015,-2.0526,-2.18057,-2.27843,-1.51412,-3.2172,-2.9605,-1.51978,-1.43225,-2.35889,-2.90516,-3.35751,-1.39105,-1.9527,-2.814,-0.292179,-0.324215,-0.324736,-0.294178,-0.331952,-0.369967,-0.363211,-0.370209,-0.0935574,-0.217467,-0.372836,-0.0339553,-0.0674899,0.290458,-0.155827,-0.236514,0.175702,-0.302772,-0.0914077,-0.155995,-0.405914,-0.270458,-0.309365,0.104184 --5921.16,0.909733,0.394006,3,-0.3035,0.0163175,-2.4731,-2.42248,-2.16669,-2.4495,-2.57829,-1.80212,-1.68174,-2.11734,-1.85836,-2.12773,-2.52813,-1.41278,-3.00474,-2.53787,-1.30528,-1.49521,-1.88901,-2.806,-3.33008,-1.54168,-2.34701,-2.95521,-0.302435,-0.41159,-0.297893,-0.197353,-0.0229936,-0.154209,-0.411999,-0.0148207,-0.387748,-0.257771,-0.29302,-0.284895,-0.5348,-0.349702,-0.392413,-0.244866,-0.385228,-0.0462578,-0.322166,-0.303068,-0.271515,-0.287463,-0.237358,0.12774 --5913.84,0.141854,0.394006,2,-0.292001,0.00798156,-2.49933,-2.12834,-2.30204,-2.49057,-2.49262,-2.70587,-1.64986,-2.05685,-1.82787,-2.13747,-2.547,-1.48182,-3.11062,-2.43117,-1.25289,-1.45445,-2.15604,-2.65194,-2.84816,-1.56763,-2.17629,-3.05095,-0.310763,-0.445604,-0.306534,-0.280508,-0.160456,-0.256347,-0.335874,-0.202691,-0.286895,-0.298816,-0.204537,-0.246169,-0.345203,-0.246763,-0.425165,-0.275942,-0.265088,-0.308852,-0.0980418,-0.288036,-0.361871,-0.319563,-0.371723,0.0893396 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--5919.66,0.991394,0.394006,3,-0.295321,0.0062722,-1.37713,-1.9616,-2.32638,-2.51208,-2.45346,-2.09705,-1.71057,-2.26981,-1.96733,-2.13398,-2.54845,-1.37951,-3.03345,-2.5901,-1.24607,-1.41629,-1.98384,-3.20738,-3.42422,-1.49774,-2.12225,-2.8022,-0.418638,-0.234687,-0.36101,-0.12443,-0.215907,-0.34897,-0.228002,-0.137575,-0.261011,-0.399273,-0.0360202,-0.28228,-0.242112,-0.153515,-0.362711,-0.425624,-0.441519,-0.376733,-0.143192,-0.247473,-0.229104,-0.330613,-0.345682,0.0791972 --5925.88,0.556114,0.394006,3,-0.183419,0.0200494,-3.10389,-2.06817,-1.95405,-2.26556,-2.20617,-2.16154,-1.76563,-2.38776,-2.04816,-2.3336,-2.22574,-1.60213,-3.03213,-2.78655,-1.39762,-1.68635,-2.05717,-2.86591,-3.78074,-1.48078,-2.44525,-3.04286,0.0492701,-0.293592,-0.113731,-0.358594,-0.157251,-0.0310618,-0.39061,0.0132912,-0.197852,-0.232503,-0.308969,-0.0592641,-0.210353,-0.0295012,-0.0580319,0.105089,0.18688,0.206632,-0.205824,-0.270502,-0.235702,-0.164202,-0.0713622,0.141596 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--5923.99,0.250714,0.394006,2,-0.301479,0.0127942,-1.8846,-2.03717,-1.67301,-2.31915,-2.43521,-1.9557,-1.74861,-1.96509,-1.9367,-2.23619,-2.12482,-1.52571,-2.81405,-2.60605,-1.27126,-1.35031,-1.78827,-2.6576,-3.28896,-1.25257,-2.30729,-2.97011,-0.54756,-0.356929,-0.509573,-0.326488,-0.197569,-0.384851,-0.460436,-0.215854,-0.309598,-0.386038,-0.345617,-0.407581,-0.330738,0.0798398,-0.204607,-0.192035,-0.33252,-0.233615,-0.587396,-0.529599,-0.349868,-0.303887,-0.546068,0.113112 --5926.42,0.807175,0.394006,2,-0.327262,0.0148402,-1.55551,-1.72494,-1.42335,-2.31711,-2.24013,-2.07505,-1.64891,-2.02456,-1.81189,-2.20705,-2.30761,-1.30164,-2.96291,-2.79939,-1.44587,-1.21232,-1.79381,-3.37064,-3.08206,-1.41629,-2.11185,-2.93025,-0.286642,-0.325534,-0.339848,-0.371709,-0.0405648,-0.152859,-0.358109,-0.249833,-0.252542,-0.222903,-0.291377,-0.222333,-0.287232,-0.0688645,-0.300109,-0.340369,-0.178883,-0.270468,-0.196204,-0.576001,-0.315391,-0.608241,-0.357147,0.12182 --5928,0.342424,0.394006,2,-0.218577,0.0128824,-2.93399,-2.12871,-2.17133,-2.39477,-2.50269,-2.05862,-1.68226,-1.79548,-2.36742,-2.35168,-2.44907,-1.6194,-2.72929,-2.78688,-1.40251,-1.32783,-2.2282,-3.20364,-3.65699,-1.45631,-2.01634,-2.46867,-0.24914,-0.422198,-0.25599,-0.333838,-0.22537,-0.228259,-0.523292,-0.378849,-0.162331,-0.264939,-0.147203,-0.255049,-0.229365,0.0895914,-0.111572,-0.419731,-0.242614,-0.161719,-0.173661,-0.370579,-0.287761,-0.521935,-0.336472,0.113501 --5925.82,0.285647,0.394006,2,-0.338902,0.00972867,-2.87118,-2.29964,-1.45477,-2.44099,-2.39072,-1.88884,-1.79147,-2.20894,-1.87493,-2.18191,-2.3565,-1.4091,-3.14098,-2.89755,-1.07689,-1.3272,-1.8089,-2.90632,-3.05068,-1.43757,-2.33333,-2.9569,-0.344233,-0.210034,-0.57933,-0.224139,-0.2803,-0.417409,-0.315575,-0.179994,-0.144497,-0.487402,-0.11987,-0.290393,-0.308984,0.208358,-0.418706,-0.389628,-0.164391,-0.155752,-0.203618,-0.370283,-0.158905,-0.388824,-0.10665,0.098634 --5918.48,0.54007,0.394006,2,-0.274459,0.0101253,-2.12092,-2.24084,-1.69682,-2.51452,-2.17176,-1.59455,-1.6779,-2.09063,-2.06463,-2.10679,-2.34741,-1.40457,-3.10185,-3.01107,-1.06673,-1.23075,-2.001,-3.02213,-3.15004,-1.46347,-2.30311,-3.0226,-0.219408,-0.296782,-0.530301,-0.207036,-0.334321,-0.264481,-0.36659,-0.248118,-0.217662,-0.472862,-0.133915,-0.349531,-0.273905,0.348962,-0.357007,-0.368522,-0.31452,-0.258293,-0.374076,-0.262841,-0.397313,-0.379085,-0.318284,0.100624 --5910.98,0.632625,0.394006,2,-0.12934,0.00473423,-2.09393,-2.28594,-2.23098,-2.48037,-2.53287,-2.68241,-1.78012,-1.95311,-1.97026,-2.27822,-2.56103,-1.37949,-2.85621,-2.75915,-1.44262,-1.79765,-1.88488,-3.06805,-3.49353,-1.64286,-2.14479,-2.81321,-0.268059,-0.196494,-0.0537084,-0.0943827,-0.101956,-0.258668,-0.316248,-0.324521,-0.193499,-0.216894,-0.138516,-0.133958,-0.250409,-0.0959429,-0.220508,-0.137888,-0.120172,-0.163779,-0.050384,-0.148727,-0.108157,-0.210316,-0.15199,0.0688058 --5908.03,0.158498,0.394006,3,-0.178018,0.00445031,-1.92224,-2.29512,-2.15405,-2.46519,-2.41829,-2.58258,-1.91427,-2.18887,-2.09545,-2.14936,-2.41168,-1.29643,-3.01044,-2.5586,-1.27603,-1.62881,-2.1347,-3.07831,-3.20539,-1.52986,-2.04223,-2.98792,-0.269723,-0.174848,-0.30569,-0.179021,-0.217483,-0.208728,-0.101457,-0.161089,-0.185646,-0.229148,-0.191378,-0.244462,-0.0683009,-0.216937,-0.123887,-0.177736,-0.219465,-0.1693,-0.239651,-0.143358,-0.317836,-0.127913,-0.204033,0.0667106 --5908.03,0.181003,0.394006,2,-0.178018,0.00445031,-1.92224,-2.29512,-2.15405,-2.46519,-2.41829,-2.58258,-1.91427,-2.18887,-2.09545,-2.14936,-2.41168,-1.29643,-3.01044,-2.5586,-1.27603,-1.62881,-2.1347,-3.07831,-3.20539,-1.52986,-2.04223,-2.98792,-0.269723,-0.174848,-0.30569,-0.179021,-0.217483,-0.208728,-0.101457,-0.161089,-0.185646,-0.229148,-0.191378,-0.244462,-0.0683009,-0.216937,-0.123887,-0.177736,-0.219465,-0.1693,-0.239651,-0.143358,-0.317836,-0.127913,-0.204033,0.0667106 --5914.74,0.00402167,0.394006,3,-0.231178,0.00602674,-2.00964,-2.11745,-2.26902,-2.40742,-2.32923,-2.32428,-1.83231,-2.0318,-1.94871,-2.17126,-2.46471,-1.30464,-2.89418,-2.70198,-1.25426,-1.50135,-2.27707,-3.02056,-3.49447,-1.6359,-2.04597,-2.82156,-0.256294,-0.415606,-0.0792214,-0.172117,0.0281725,-0.222134,-0.165808,-0.205257,-0.223938,-0.253304,-0.137047,-0.302748,-0.129897,-0.173396,-0.387075,-0.260497,-0.207097,-0.347425,-0.15973,-0.237417,-0.34013,-0.142841,-0.320874,0.0776321 --5916.73,1,0.394006,2,-0.15064,0.017351,-2.26322,-2.10594,-2.00695,-2.47251,-2.39427,-2.05474,-1.74523,-2.19338,-2.06613,-2.34334,-2.31845,-1.51244,-3.03847,-2.63928,-1.32123,-1.51492,-1.94681,-2.54953,-3.22812,-1.53347,-2.37431,-3.01232,-0.177777,-0.0244224,-0.332735,-0.220878,-0.45631,-0.15119,-0.35972,-0.115708,-0.241912,-0.234761,-0.208734,-0.103837,-0.249174,-0.0960714,-0.432873,-0.082274,-0.112334,-0.108847,-0.187637,-0.119933,-0.110995,-0.408513,-0.0820288,0.131723 --5924.26,0.65056,0.394006,2,-0.279739,0.0166843,-1.86503,-1.99288,-1.92919,-2.31439,-2.33975,-2.16693,-1.70094,-1.95803,-1.90497,-2.16541,-2.27916,-1.41969,-2.55331,-2.8304,-1.27111,-1.51044,-2.38748,-2.94386,-3.40097,-1.58504,-2.2292,-2.96227,-0.273465,-0.339588,0.0534483,-0.308446,-0.246551,-0.168877,-0.167758,-0.287819,-0.0990285,-0.191222,-0.273382,-0.163446,-0.302433,-0.046853,-0.135663,-0.247304,-0.110615,-0.218238,-0.0798249,-0.20417,-0.244006,-0.352001,-0.121557,0.129168 --5923.29,0.965501,0.394006,3,-0.255606,0.0235855,-2.67252,-2.44209,-2.29394,-2.46875,-2.56942,-1.68649,-1.50948,-2.27225,-1.66529,-2.14944,-2.35983,-1.45635,-3.15964,-2.76938,-1.35647,-1.42623,-1.79256,-3.04984,-3.66905,-1.40797,-2.07139,-2.98638,-0.0636753,-0.199701,-0.473038,-0.137245,-0.0575955,-0.295419,-0.444335,0.0298729,-0.233311,-0.228439,-0.157783,-0.163367,-0.0935531,-0.124048,-0.329778,-0.171263,-0.197807,-0.195595,-0.394586,-0.282765,-0.398495,-0.387386,-0.168911,0.153576 --5920.16,0.971327,0.394006,2,-0.229639,0.00633133,-2.09276,-2.21845,-1.8426,-2.4303,-2.42516,-2.68929,-1.912,-2.16619,-2.12847,-2.34136,-2.47673,-1.58717,-3.04774,-2.55181,-1.25383,-1.73265,-2.01335,-3.33698,-3.70988,-1.65018,-1.97145,-2.72551,-0.329504,-0.269306,-0.0841147,-0.236126,-0.237421,-0.234292,-0.244686,-0.239176,-0.316206,-0.324111,-0.113477,-0.185894,-0.324344,-0.217102,-0.189961,-0.201164,-0.219273,-0.539629,-0.0573529,-0.165802,-0.32912,-0.416612,-0.268131,0.0795696 --5911.63,0.760756,0.394006,2,-0.207285,0.00530494,-2.63007,-2.03179,-2.11033,-2.51515,-2.6039,-2.14535,-1.80691,-2.0984,-1.91525,-2.17858,-2.5178,-1.333,-3.01526,-2.7001,-1.20213,-1.69535,-2.04087,-3.39977,-3.07685,-1.56804,-2.20731,-2.84091,-0.190583,-0.328625,-0.462132,-0.123713,-0.228273,-0.227756,-0.221922,-0.219288,-0.228047,-0.230803,-0.154313,-0.256161,-0.17906,-0.229638,-0.349023,-0.143907,-0.287782,-0.322035,-0.16188,-0.306931,-0.236934,-0.165562,-0.260876,0.072835 - -# Elapsed Time: 0.176053 seconds (Warm-up) -# 0.165157 seconds (Sampling) -# 0.34121 seconds (Total) - diff --git a/arviz/tests/saved_models/stan_diagnostics/reference_posterior.csv b/arviz/tests/saved_models/stan_diagnostics/reference_posterior.csv deleted file mode 100644 index 86eca35f29..0000000000 --- a/arviz/tests/saved_models/stan_diagnostics/reference_posterior.csv +++ /dev/null @@ -1,49 +0,0 @@ -"","rhat_rank","rhat_raw","ess_bulk","ess_tail","ess_mean","ess_sd","ess_median","ess_raw","ess_quantile01","ess_quantile10","ess_quantile30","mcse_mean","mcse_sd","mcse_median","mcse_quantile01","mcse_quantile10","mcse_quantile30" -"d",1.00780272388603,1.0004168232094,465.136513484671,349.135270438316,467.84472286415,488.89443945697,311.806319792458,467.367576858138,1500.2193689464,754.211496074801,354.746981701936,0.00265985849234941,0.00206270285278761,0.005242,0.00812199999999999,0.003997,0.00375200000000001 -"sigmasq_delta",1.01090196780102,1.00035874354121,73.249623982678,48.5806371372426,134.497570910906,801.332161569803,132.049677080929,138.627800274732,33.1982442776445,63.6980562732759,98.6290985074029,0.00114848510125919,0.0010201024173082,0.00095392,0.0003001,0.00044662,0.00058668 -"mu.1",1.00731450966132,0.999545827128633,1231.30913527034,1119.63456276453,1189.5912192317,425.063968137942,811.795584886631,1171.62891354699,1030.60309993778,465.95223908163,635.768730056618,0.0125124382036462,0.0162666049948541,0.022465,0.02589,0.0285949999999999,0.0175350000000001 -"mu.2",1.00332752771857,1.00046552033653,558.783738495551,920.913340029631,569.193418116839,782.034728874135,560.847913565582,543.893011358525,1250.00943663727,715.745937098207,603.702320409943,0.0100753323647403,0.00677123534676899,0.012035,0.0344149999999999,0.0169700000000002,0.0116750000000001 -"mu.3",1.00400884522485,1.00119273844036,545.006145537413,721.191572843273,525.001599973821,487.131645340556,409.25009203697,519.896707667091,980.123379207967,597.010589383933,776.855700473939,0.012221584083923,0.00875247281355774,0.0182549999999999,0.0148200000000001,0.0200100000000001,0.015765 -"mu.4",1.00992230528499,1.00088653210741,582.054899011997,475.118727680651,572.691571673006,819.783260947192,453.404375352865,590.532677591646,1150.49950736189,1061.12497686928,297.464682466101,0.00338150237488137,0.00188911998992983,0.00423499999999999,0.00789000000000017,0.0046250000000001,0.00692500000000007 -"mu.5",1.00733922911401,1.00017537414794,754.786642573516,1073.96515350853,763.910100475586,540.466147265881,762.522622136922,764.757297566194,1601.95897581453,823.748224208616,602.971885735321,0.00535619470645841,0.00480446457947108,0.00728499999999999,0.01271,0.0105449999999998,0.00721499999999997 -"mu.6",1.0063264651477,1.00019046380718,681.625145574341,841.509646646211,710.977179062471,551.470250858593,469.426815292446,690.219361038973,496.664104993923,854.423772463052,799.11107396604,0.0128934361623842,0.011011524371348,0.02196,0.0800150000000002,0.0284499999999999,0.015415 -"mu.7",1.0009508247325,1.00226228815332,338.501572250893,798.174111097497,338.298033186,923.852334364149,271.172024542225,326.217452597384,925.997070900362,842.297594532647,322.156375656936,0.00461535071570277,0.00196184477437271,0.00647500000000001,0.00655499999999998,0.00410999999999995,0.00627499999999992 -"mu.8",1.0090643975564,0.999538833472528,500.370209497559,578.884594887594,493.348188663339,748.922281715359,313.922618265292,505.50985231336,1386.39640838638,640.804896232492,697.131902186387,0.00532227304880204,0.00302056504657623,0.00929000000000002,0.0093049999999999,0.0109100000000002,0.00605499999999992 -"mu.9",1.01019493017765,0.999603164368487,340.342755003858,946.607791310505,333.492896973282,747.532519745591,484.326477329234,356.445106502136,1562.99962092251,56.5180669671026,220.186644019022,0.00778879135503874,0.00373806209510348,0.00825999999999993,0.0133449999999999,0.0254349999999999,0.014645 -"mu.10",1.00075487572724,0.999511473112951,583.991419920615,471.41183217468,588.28304374764,544.526707462149,477.481294770334,590.14928532837,799.772115109561,906.418393458689,311.515150979097,0.00276260856418602,0.00217633000850717,0.00405000000000011,0.011425,0.00260499999999997,0.00502000000000002 -"mu.11",1.00595414640273,1.00237422670742,635.150979523831,800.479933058152,665.620410175752,649.717620197507,519.624529420413,655.71371952012,945.861386301236,586.200764916719,408.369416259054,0.00431255652753725,0.00329002222006113,0.00705500000000003,0.0158449999999999,0.01007,0.0102799999999998 -"mu.12",1.0047268539977,1.00514527883668,496.929163495085,531.044721860628,504.262711368638,610.093515741603,319.158390701,480.727694997398,1853.86082845226,572.398861171123,317.880247530293,0.0057037756397616,0.00363873911388514,0.00858499999999995,0.0151549999999999,0.01154,0.00814000000000004 -"mu.13",1.008945349242,1.0056573986565,183.23984965215,350.35095755959,187.049324363394,270.940571239581,475.166782427048,178.745879678592,719.678538769825,371.680213864137,532.208029969556,0.0173131687638012,0.00933778226030946,0.00905,0.0248349999999999,0.0225550000000001,0.013755 -"mu.14",1.01304235232272,0.999571875876497,146.455619305836,905.20577806428,156.913168025411,323.907305569586,326.757781606977,184.871406789517,1501.15708995927,275.031545588726,379.47949263356,0.0112582926739193,0.00525249835026592,0.00992499999999996,0.00940499999999989,0.012775,0.00852999999999993 -"mu.15",1.00166475852766,1.00098644134086,647.052787106276,631.753828191276,650.018161664313,711.883895013457,658.62198403898,643.855640478448,836.543429989515,760.283671777858,375.926738510082,0.0060179931852768,0.00400129337763143,0.00807999999999998,0.0173800000000001,0.0116149999999999,0.00492999999999999 -"mu.16",1.00740317948224,1.00853464974225,499.036519568152,746.05300433263,501.454892472435,585.824943822467,485.39675231805,472.13048627122,1341.97413184728,277.102847934913,609.721215668554,0.00640986388102947,0.00436871165434051,0.00609499999999996,0.014005,0.0172800000000001,0.00823499999999999 -"mu.17",1.00235785729279,1.00079863526808,568.525190685684,577.67526711659,570.160744519766,488.43648728312,549.959891120855,563.848255834083,1431.6908440639,747.739233140895,581.929203983128,0.00753372166353053,0.0057425523697688,0.00883,0.02302,0.0112099999999999,0.011695 -"mu.18",1.0058815147689,0.999605048059416,542.537401212283,553.368100868642,550.366452404361,493.121927080476,433.167272775349,584.744508829451,1341.05872361933,720.8954493708,422.716253311392,0.0122763089002585,0.00966254096644109,0.01478,0.0384899999999999,0.0162549999999999,0.02017 -"mu.19",1.00414368524188,1.00060208238245,449.416857063488,739.485184107347,446.219468477619,587.037945065159,345.725868771874,449.137074366437,1561.55711641924,544.69861487889,419.996836681418,0.0160809134566196,0.0102521331347103,0.02644,0.0399750000000001,0.0198449999999999,0.02633 -"mu.20",1.00303319392542,1.00045657842583,429.352356666043,667.698241600053,408.218014375817,644.200142740266,416.536296772658,400.23475139616,1563.70741553559,133.612813080646,279.606683576796,0.00693974335849805,0.00373913385852037,0.00812000000000002,0.015245,0.012025,0.010345 -"mu.21",1.00975536911073,1.01022808966214,374.47529790908,470.815645795366,364.204306828113,718.497561467271,336.118912328004,339.216837731465,1055.53653994431,585.531004662949,181.680640103669,0.00699623341543813,0.00347672764264547,0.00904500000000019,0.0112399999999999,0.00910499999999992,0.0165649999999999 -"mu.22",1.00294755213814,0.999600478698527,674.597258384371,600.678471867714,678.699385312522,684.513700576372,324.316517855016,680.605387522155,908.437133488286,764.541352392027,383.852018821601,0.00533742583176886,0.00385706039033489,0.0127699999999999,0.010505,0.007185,0.00717499999999993 -"delta.1",1.00193273557651,1.00110042421068,1172.74856393641,974.786547170994,1419.23404652728,595.145865508099,649.830820193222,1410.38271694321,977.529406338044,782.300515662222,668.284695026047,0.0038676938490309,0.00691910271515977,0.0048955,0.0203195,0.00914400000000001,0.00684599999999999 -"delta.2",1.00439696727112,0.999671764127513,757.273755729388,1041.21641448256,841.741917392082,708.716112634396,440.437505212779,836.017025076283,910.440957531096,819.510207777999,444.090052381873,0.004732095521024,0.00650851949720249,0.0056435,0.0389715,0.013379,0.00763449999999999 -"delta.3",1.0048779262348,0.999733784057189,854.667318035899,601.21846326955,881.923285826474,465.25904775017,667.795651854425,871.389790934645,1030.78267333512,610.820032214856,543.056438051934,0.00483645532470361,0.00690951517315717,0.00615350000000001,0.0424825,0.0080035,0.00698349999999998 -"delta.4",1.00178244152549,0.999578735499239,933.614899067375,750.167856320663,960.420142222955,710.318520349167,466.804294420144,952.265093307421,1562.99962092251,808.248888293026,848.385826906282,0.00286776827314079,0.00263559492868887,0.00450449999999999,0.016153,0.00641250000000002,0.00339400000000001 -"delta.5",1.01081774716755,1.00241813072529,586.411691467462,943.409653768766,610.921485388949,555.703140734042,493.500353919725,620.944209858451,1396.11137524087,958.339509742365,773.928892663521,0.00509922346905432,0.00489061156273221,0.008062,0.020229,0.007526,0.00504300000000002 -"delta.6",1.00190020811646,1.00213136770008,933.591422341377,680.772965157488,917.641844958605,491.499151214083,466.105251245502,869.978957461908,1019.34979665739,615.944560171407,464.449517690915,0.00467404005100357,0.00799313228494064,0.00564049999999999,0.042385,0.0136695,0.00843949999999999 -"delta.7",1.00412724855361,1.00244365868817,229.195505938696,203.70063395052,239.599032907495,499.306704483073,258.226892482532,235.167900306928,1298.99528753311,469.228350866314,335.674635373719,0.00738452488138673,0.00357892714561876,0.00690850000000001,0.00962150000000001,0.011208,0.00955349999999999 -"delta.8",1.01302686669844,0.999978104471402,767.371699778806,1069.01390989025,773.726493231427,675.028325538577,524.725055273804,788.520229384148,1617.62037766705,1228.32563592794,321.895527707361,0.00418005595173392,0.00394234473694659,0.0074535,0.0110275,0.00659949999999998,0.0072275 -"delta.9",1.00240486051678,0.999686448177722,967.147813834465,805.14293480976,921.332318713367,650.980891315601,714.971441907894,911.348066015493,1181.60609792208,988.399569707275,453.575392453051,0.00390437482860046,0.00435205963702307,0.00506200000000001,0.0135795,0.00882050000000001,0.00756899999999999 -"delta.10",1.01148346905793,1.00079092738233,221.58398760916,512.070682182092,227.340028184212,493.504192166091,321.202034626446,234.227618555189,1552.55702598724,1158.43349854518,502.709517349779,0.00558111846776516,0.00288399911648472,0.006744,0.010841,0.00319900000000001,0.00472249999999999 -"delta.11",1.00436479181684,0.999546010444357,865.224447923887,936.150395054657,900.81898633316,573.877464367464,420.212037985204,909.208813980858,1856.30870029447,998.186312144878,432.595765534901,0.00353200967338314,0.00383235362887953,0.005305,0.0114425,0.00684750000000001,0.00582550000000001 -"delta.12",1.0051455786984,1.00090228472677,746.592500353552,819.359175324269,748.477550571627,782.874695457754,464.352491232898,748.709658864382,1622.75285471028,1154.40505296934,436.170801499081,0.00438862230801511,0.00469488123015017,0.005106,0.00893000000000002,0.00667599999999999,0.003271 -"delta.13",1.00712064755481,1.00136237270726,783.642427498242,702.762955320348,727.365240511935,605.035938547086,590.519739539686,722.362255781916,891.110531194617,594.203121864699,517.190497501427,0.00513748338041085,0.0072270599204413,0.005607,0.068738,0.0169845,0.00620400000000002 -"delta.14",1.00889674194067,1.00288110313765,235.105098167191,295.2486556867,184.948807956383,323.287038146436,261.769361538788,196.761686493461,1563.56576720135,508.292867142042,241.656603953637,0.0136025602147262,0.00768487365672108,0.0116275,0.0037885,0.00726100000000002,0.014438 -"delta.15",1.002224994901,1.00035977797783,866.084170529215,924.630560015452,948.425424421912,624.75693151017,585.465279369756,945.741384752513,1304.47140508689,986.025789385593,547.648223875118,0.00373367946357549,0.00460707662574424,0.00571149999999998,0.0226165,0.005799,0.00572149999999999 -"delta.16",1.00118003375548,0.999889392269564,755.774599660567,582.770014668784,776.030216190205,539.95182923561,483.546077965929,768.797014600277,1695.47768547515,739.326289777271,748.420238399519,0.00430530869948785,0.00454091674268505,0.00664550000000001,0.0131295,0.0092825,0.00629549999999998 -"delta.17",1.00380769102248,1.00076796399963,741.233121810086,962.832809052428,735.27919044424,596.147437540295,459.696957867442,725.527316156038,1299.209055217,1095.53838527224,626.271526768023,0.00469314819989086,0.00506173088067739,0.007065,0.0208985,0.0060105,0.00402949999999999 -"delta.18",1.00282616625804,0.999971769662763,947.860153722703,1023.23760607047,1077.17739932405,641.888764191894,487.717830067959,1078.46726259807,1497.18404611291,1201.49370767278,421.965090431785,0.00447069886378408,0.00784038874200639,0.007466,0.024036,0.00412000000000001,0.0032065 -"delta.19",1.00187580387435,1.001941742794,735.893087934427,371.764051856208,475.25192234922,178.319541709711,750.537309072572,471.569878282543,1696.8877681804,951.551381547666,536.673613756547,0.0071125986704165,0.0138440988308067,0.00451449999999999,0.011247,0.00719049999999999,0.00651800000000002 -"delta.20",1.00225051797168,0.999717822530123,915.344392247887,1001.50462372747,955.28139954216,760.352893165514,494.374876093356,956.356734742678,1079.60736139358,1042.06407315138,476.08812586776,0.0036538376539592,0.0036982307535789,0.004577,0.018725,0.00770850000000001,0.00630150000000002 -"delta.21",1.00334854286192,1.00257449161737,486.571699178618,1107.14145806569,503.045495460339,906.655544279501,307.419511508529,498.194977586284,1554.67632672808,722.008102896109,430.97692940852,0.00574577160547241,0.00422215239803544,0.0082565,0.024096,0.0126775,0.0101385 -"delta.22",1.00133416957847,1.00108924163034,610.268446055575,549.42853808316,591.91289033177,691.360100340346,476.866844366228,582.663245138487,1296.27868963479,862.34812726394,400.797188229444,0.00514852849282923,0.00434132985764044,0.00678100000000001,0.0247445,0.010288,0.00797699999999998 -"delta_new",1.00208610605722,1.00004179219059,917.51606421515,591.868599290854,715.969590769736,391.495722708247,690.543958491751,696.850690503065,411.457471234125,461.972146449706,317.003585824348,0.00625781442705714,0.0146806979189298,0.00437649999999998,0.075263,0.0152875,0.0082045 -"sigma_delta",1.01090190553445,0.999554581904689,73.2498471698009,48.5806371372426,95.5938079021702,419.161051351765,132.049677080929,99.783539352302,33.1982442776445,63.6980562732759,98.6290985074029,0.00508027514881167,0.00239560824916483,0.0054414,0.0041236,0.00481375,0.0043662 diff --git a/arviz/tests/test.rcparams b/arviz/tests/test.rcparams deleted file mode 100644 index 54c0b89844..0000000000 --- a/arviz/tests/test.rcparams +++ /dev/null @@ -1,6 +0,0 @@ -data.http_protocol : http -data.load : eager -plot.max_subplots : 30 -plot.point_estimate : median -plot.backend : bokeh -plot.max_subplots : 20 diff --git a/arviz/utils.py b/arviz/utils.py deleted file mode 100644 index 9182fe25fc..0000000000 --- a/arviz/utils.py +++ /dev/null @@ -1,773 +0,0 @@ -# pylint: disable=too-many-nested-blocks -"""General utilities.""" -import functools -import importlib -import importlib.resources -import re -import warnings -from functools import lru_cache - -import matplotlib.pyplot as plt -import numpy as np -from numpy import newaxis - -from .rcparams import rcParams - - -STATIC_FILES = ("static/html/icons-svg-inline.html", "static/css/style.css") - - -class BehaviourChangeWarning(Warning): - """Custom warning to ease filtering it.""" - - -def _check_tilde_start(x): - return bool(isinstance(x, str) and x.startswith("~")) - - -def _var_names(var_names, data, filter_vars=None, errors="raise"): - """Handle var_names input across arviz. - - Parameters - ---------- - var_names: str, list, or None - data : xarray.Dataset - Posterior data in an xarray - filter_vars: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - `pandas.filter`. - errors: {"raise", "ignore"}, optional, default="raise" - Select either to raise or ignore the invalid names. - - Returns - ------- - var_name: list or None - """ - if filter_vars not in {None, "like", "regex"}: - raise ValueError( - f"'filter_vars' can only be None, 'like', or 'regex', got: '{filter_vars}'" - ) - - if errors not in {"raise", "ignore"}: - raise ValueError(f"'errors' can only be 'raise', or 'ignore', got: '{errors}'") - - if var_names is not None: - if isinstance(data, (list, tuple)): - all_vars = [] - for dataset in data: - dataset_vars = list(dataset.data_vars) - for var in dataset_vars: - if var not in all_vars: - all_vars.append(var) - else: - all_vars = list(data.data_vars) - - all_vars_tilde = [var for var in all_vars if _check_tilde_start(var)] - if all_vars_tilde: - warnings.warn( - """ArviZ treats '~' as a negation character for variable selection. - Your model has variables names starting with '~', {0}. Please double check - your results to ensure all variables are included""".format( - ", ".join(all_vars_tilde) - ) - ) - - try: - var_names = _subset_list( - var_names, all_vars, filter_items=filter_vars, warn=False, errors=errors - ) - except KeyError as err: - msg = " ".join(("var names:", f"{err}", "in dataset")) - raise KeyError(msg) from err - return var_names - - -def _subset_list(subset, whole_list, filter_items=None, warn=True, errors="raise"): - """Handle list subsetting (var_names, groups...) across arviz. - - Parameters - ---------- - subset : str, list, or None - whole_list : list - List from which to select a subset according to subset elements and - filter_items value. - filter_items : {None, "like", "regex"}, optional - If `None` (default), interpret `subset` as the exact elements in `whole_list` - names. If "like", interpret `subset` as substrings of the elements in - `whole_list`. If "regex", interpret `subset` as regular expressions to match - elements in `whole_list`. A la `pandas.filter`. - errors: {"raise", "ignore"}, optional, default="raise" - Select either to raise or ignore the invalid names. - - Returns - ------- - list or None - A subset of ``whole_list`` fulfilling the requests imposed by ``subset`` - and ``filter_items``. - """ - if subset is not None: - if isinstance(subset, str): - subset = [subset] - - whole_list_tilde = [item for item in whole_list if _check_tilde_start(item)] - if whole_list_tilde and warn: - warnings.warn( - "ArviZ treats '~' as a negation character for selection. There are " - "elements in `whole_list` starting with '~', {0}. Please double check" - "your results to ensure all elements are included".format( - ", ".join(whole_list_tilde) - ) - ) - - excluded_items = [ - item[1:] for item in subset if _check_tilde_start(item) and item not in whole_list - ] - filter_items = str(filter_items).lower() - if excluded_items: - not_found = [] - - if filter_items in {"like", "regex"}: - for pattern in excluded_items[:]: - excluded_items.remove(pattern) - if filter_items == "like": - real_items = [real_item for real_item in whole_list if pattern in real_item] - else: - # i.e filter_items == "regex" - real_items = [ - real_item for real_item in whole_list if re.search(pattern, real_item) - ] - if not real_items: - not_found.append(pattern) - excluded_items.extend(real_items) - not_found.extend([item for item in excluded_items if item not in whole_list]) - if not_found: - warnings.warn( - f"Items starting with ~: {not_found} have not been found and will be ignored" - ) - subset = [item for item in whole_list if item not in excluded_items] - - elif filter_items == "like": - subset = [item for item in whole_list for name in subset if name in item] - elif filter_items == "regex": - subset = [item for item in whole_list for name in subset if re.search(name, item)] - - existing_items = np.isin(subset, whole_list) - if not np.all(existing_items) and (errors == "raise"): - raise KeyError(f"{np.array(subset)[~existing_items]} are not present") - - return subset - - -class lazy_property: # pylint: disable=invalid-name - """Used to load numba first time it is needed.""" - - def __init__(self, fget): - """Lazy load a property with `fget`.""" - self.fget = fget - - # copy the getter function's docstring and other attributes - functools.update_wrapper(self, fget) - - def __get__(self, obj, cls): - """Call the function, set the attribute.""" - if obj is None: - return self - - value = self.fget(obj) - setattr(obj, self.fget.__name__, value) - return value - - -class maybe_numba_fn: # pylint: disable=invalid-name - """Wrap a function to (maybe) use a (lazy) jit-compiled version.""" - - def __init__(self, function, **kwargs): - """Wrap a function and save compilation keywords.""" - self.function = function - kwargs.setdefault("nopython", True) - self.kwargs = kwargs - - @lazy_property - def numba_fn(self): - """Memoized compiled function.""" - try: - numba = importlib.import_module("numba") - numba_fn = numba.jit(**self.kwargs)(self.function) - except ImportError: - numba_fn = self.function - return numba_fn - - def __call__(self, *args, **kwargs): - """Call the jitted function or normal, depending on flag.""" - if Numba.numba_flag: - return self.numba_fn(*args, **kwargs) - else: - return self.function(*args, **kwargs) - - -class interactive_backend: # pylint: disable=invalid-name - """Context manager to change backend temporarily in ipython sesson. - - It uses ipython magic to change temporarily from the ipython inline backend to - an interactive backend of choice. It cannot be used outside ipython sessions nor - to change backends different than inline -> interactive. - - Notes - ----- - The first time ``interactive_backend`` context manager is called, any of the available - interactive backends can be chosen. The following times, this same backend must be used - unless the kernel is restarted. - - Parameters - ---------- - backend : str, optional - Interactive backend to use. It will be passed to ``%matplotlib`` magic, refer to - its docs to see available options. - - Examples - -------- - Inside an ipython session (i.e. a jupyter notebook) with the inline backend set: - - .. code:: - - >>> import arviz as az - >>> idata = az.load_arviz_data("centered_eight") - >>> az.plot_posterior(idata) # inline - >>> with az.interactive_backend(): - ... az.plot_density(idata) # interactive - >>> az.plot_trace(idata) # inline - - """ - - # based on matplotlib.rc_context - def __init__(self, backend=""): - """Initialize context manager.""" - try: - from IPython import get_ipython - except ImportError as err: - raise ImportError( - "The exception below was risen while importing Ipython, this " - f"context manager can only be used inside ipython sessions:\n{err}" - ) from err - self.ipython = get_ipython() - if self.ipython is None: - raise EnvironmentError("This context manager can only be used inside ipython sessions") - self.ipython.magic(f"matplotlib {backend}") - - def __enter__(self): - """Enter context manager.""" - return self - - def __exit__(self, exc_type, exc_value, exc_tb): - """Exit context manager.""" - plt.show(block=True) - self.ipython.magic("matplotlib inline") - - -def conditional_jit(_func=None, **kwargs): - """Use numba's jit decorator if numba is installed. - - Notes - ----- - If called without arguments then return wrapped function. - - @conditional_jit - def my_func(): - return - - else called with arguments - - @conditional_jit(nopython=True) - def my_func(): - return - - """ - if _func is None: - return lambda fn: functools.wraps(fn)(maybe_numba_fn(fn, **kwargs)) - lazy_numba = maybe_numba_fn(_func, **kwargs) - return functools.wraps(_func)(lazy_numba) - - -def conditional_vect(function=None, **kwargs): # noqa: D202 - """Use numba's vectorize decorator if numba is installed. - - Notes - ----- - If called without arguments then return wrapped function. - @conditional_vect - def my_func(): - return - else called with arguments - @conditional_vect(nopython=True) - def my_func(): - return - - """ - - def wrapper(function): - try: - numba = importlib.import_module("numba") - return numba.vectorize(**kwargs)(function) - - except ImportError: - return function - - if function: - return wrapper(function) - else: - return wrapper - - -def numba_check(): - """Check if numba is installed.""" - numba = importlib.util.find_spec("numba") - return numba is not None - - -class Numba: - """A class to toggle numba states.""" - - numba_flag = numba_check() - """bool: Indicates whether Numba optimizations are enabled. Defaults to False.""" - - @classmethod - def disable_numba(cls): - """To disable numba.""" - cls.numba_flag = False - - @classmethod - def enable_numba(cls): - """To enable numba.""" - if numba_check(): - cls.numba_flag = True - else: - raise ValueError("Numba is not installed") - - -def _numba_var(numba_function, standard_numpy_func, data, axis=None, ddof=0): - """Replace the numpy methods used to calculate variance. - - Parameters - ---------- - numba_function : function() - Custom numba function included in stats/stats_utils.py. - - standard_numpy_func: function() - Standard function included in the numpy library. - - data : array. - axis : axis along which the variance is calculated. - ddof : degrees of freedom allowed while calculating variance. - - Returns - ------- - array: - variance values calculate by appropriate function for numba speedup - if Numba is installed or enabled. - - """ - if Numba.numba_flag: - return numba_function(data, axis=axis, ddof=ddof) - else: - return standard_numpy_func(data, axis=axis, ddof=ddof) - - -def _stack(x, y): - assert x.shape[1:] == y.shape[1:] - return np.vstack((x, y)) - - -def arange(x): - """Jitting numpy arange.""" - return np.arange(x) - - -def one_de(x): - """Jitting numpy atleast_1d.""" - if not isinstance(x, np.ndarray): - return np.atleast_1d(x) - result = x.reshape(1) if x.ndim == 0 else x - return result - - -def two_de(x): - """Jitting numpy at_least_2d.""" - if not isinstance(x, np.ndarray): - return np.atleast_2d(x) - if x.ndim == 0: - result = x.reshape(1, 1) - elif x.ndim == 1: - result = x[newaxis, :] - else: - result = x - return result - - -def expand_dims(x): - """Jitting numpy expand_dims.""" - if not isinstance(x, np.ndarray): - return np.expand_dims(x, 0) - shape = x.shape - return x.reshape(shape[:0] + (1,) + shape[0:]) - - -@conditional_jit(cache=True, nopython=True) -def _dot(x, y): - return np.dot(x, y) - - -@conditional_jit(cache=True, nopython=True) -def _cov_1d(x): - x = x - x.mean() - ddof = x.shape[0] - 1 - return np.dot(x.T, x.conj()) / ddof - - -# @conditional_jit(cache=True) -def _cov(data): - if data.ndim == 1: - return _cov_1d(data) - elif data.ndim == 2: - x = data.astype(float) - avg, _ = np.average(x, axis=1, weights=None, returned=True) - ddof = x.shape[1] - 1 - if ddof <= 0: - warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning, stacklevel=2) - ddof = 0.0 - x -= avg[:, None] - prod = _dot(x, x.T.conj()) - prod *= np.true_divide(1, ddof) - prod = prod.squeeze() - prod += 1e-6 * np.eye(prod.shape[0]) - return prod - else: - raise ValueError(f"{data.ndim} dimension arrays are not supported") - - -def flatten_inference_data_to_dict( - data, - var_names=None, - groups=None, - dimensions=None, - group_info=False, - var_name_format=None, - index_origin=None, -): - """Transform data to dictionary. - - Parameters - ---------- - data : obj - Any object that can be converted to an az.InferenceData object - Refer to documentation of az.convert_to_inference_data for details - var_names : str or list of str, optional - Variables to be processed, if None all variables are processed. - groups : str or list of str, optional - Select groups for CDS. Default groups are - {"posterior_groups", "prior_groups", "posterior_groups_warmup"} - - posterior_groups: posterior, posterior_predictive, sample_stats - - prior_groups: prior, prior_predictive, sample_stats_prior - - posterior_groups_warmup: warmup_posterior, warmup_posterior_predictive, - warmup_sample_stats - ignore_groups : str or list of str, optional - Ignore specific groups from CDS. - dimension : str, or list of str, optional - Select dimensions along to slice the data. By default uses ("chain", "draw"). - group_info : bool - Add group info for `var_name_format` - var_name_format : str or tuple of tuple of string, optional - Select column name format for non-scalar input. - Predefined options are {"brackets", "underscore", "cds"} - "brackets": - - add_group_info == False: theta[0,0] - - add_group_info == True: theta_posterior[0,0] - "underscore": - - add_group_info == False: theta_0_0 - - add_group_info == True: theta_posterior_0_0_ - "cds": - - add_group_info == False: theta_ARVIZ_CDS_SELECTION_0_0 - - add_group_info == True: theta_ARVIZ_GROUP_posterior__ARVIZ_CDS_SELECTION_0_0 - tuple: - Structure: - tuple: (dim_info, group_info) - dim_info: (str: `.join` separator, - str: dim_separator_start, - str: dim_separator_end) - group_info: (str: group separator start, str: group separator end) - Example: ((",", "[", "]"), ("_", "")) - - add_group_info == False: theta[0,0] - - add_group_info == True: theta_posterior[0,0] - index_origin : int, optional - Start parameter indices from `index_origin`. Either 0 or 1. - - Returns - ------- - dict - """ - from .data import convert_to_inference_data - - data = convert_to_inference_data(data) - - if groups is None: - groups = ["posterior", "posterior_predictive", "sample_stats"] - elif isinstance(groups, str): - if groups.lower() == "posterior_groups": - groups = ["posterior", "posterior_predictive", "sample_stats"] - elif groups.lower() == "prior_groups": - groups = ["prior", "prior_predictive", "sample_stats_prior"] - elif groups.lower() == "posterior_groups_warmup": - groups = ["warmup_posterior", "warmup_posterior_predictive", "warmup_sample_stats"] - else: - raise TypeError( - ( - "Valid predefined groups are " - "{posterior_groups, prior_groups, posterior_groups_warmup}" - ) - ) - - if dimensions is None: - dimensions = "chain", "draw" - elif isinstance(dimensions, str): - dimensions = (dimensions,) - - if var_name_format is None: - var_name_format = "brackets" - - if isinstance(var_name_format, str): - var_name_format = var_name_format.lower() - - if var_name_format == "brackets": - dim_join_separator, dim_separator_start, dim_separator_end = ",", "[", "]" - group_separator_start, group_separator_end = "_", "" - elif var_name_format == "underscore": - dim_join_separator, dim_separator_start, dim_separator_end = "_", "_", "" - group_separator_start, group_separator_end = "_", "" - elif var_name_format == "cds": - dim_join_separator, dim_separator_start, dim_separator_end = ( - "_", - "_ARVIZ_CDS_SELECTION_", - "", - ) - group_separator_start, group_separator_end = "_ARVIZ_GROUP_", "" - elif isinstance(var_name_format, str): - msg = 'Invalid predefined format. Select one {"brackets", "underscore", "cds"}' - raise TypeError(msg) - else: - ( - (dim_join_separator, dim_separator_start, dim_separator_end), - (group_separator_start, group_separator_end), - ) = var_name_format - - if index_origin is None: - index_origin = rcParams["data.index_origin"] - - data_dict = {} - for group in groups: - if hasattr(data, group): - group_data = getattr(data, group).stack(stack_dimension=dimensions) - for var_name, var in group_data.data_vars.items(): - var_values = var.values - if var_names is not None and var_name not in var_names: - continue - for dim_name in dimensions: - if dim_name not in data_dict: - data_dict[dim_name] = var.coords.get(dim_name).values - if len(var.shape) == 1: - if group_info: - var_name_dim = ( - "{var_name}" "{group_separator_start}{group}{group_separator_end}" - ).format( - var_name=var_name, - group_separator_start=group_separator_start, - group=group, - group_separator_end=group_separator_end, - ) - else: - var_name_dim = f"{var_name}" - data_dict[var_name_dim] = var.values - else: - for loc in np.ndindex(var.shape[:-1]): - if group_info: - var_name_dim = ( - "{var_name}" - "{group_separator_start}{group}{group_separator_end}" - "{dim_separator_start}{dim_join}{dim_separator_end}" - ).format( - var_name=var_name, - group_separator_start=group_separator_start, - group=group, - group_separator_end=group_separator_end, - dim_separator_start=dim_separator_start, - dim_join=dim_join_separator.join( - (str(item + index_origin) for item in loc) - ), - dim_separator_end=dim_separator_end, - ) - else: - var_name_dim = ( - "{var_name}" "{dim_separator_start}{dim_join}{dim_separator_end}" - ).format( - var_name=var_name, - dim_separator_start=dim_separator_start, - dim_join=dim_join_separator.join( - (str(item + index_origin) for item in loc) - ), - dim_separator_end=dim_separator_end, - ) - - data_dict[var_name_dim] = var_values[loc] - return data_dict - - -def get_coords(data, coords): - """Subselects xarray DataSet or DataArray object to provided coords. Raises exception if fails. - - Raises - ------ - ValueError - If coords name are not available in data - - KeyError - If coords dims are not available in data - - Returns - ------- - data: xarray - xarray.DataSet or xarray.DataArray object, same type as input - """ - if not isinstance(data, (list, tuple)): - try: - return data.sel(**coords) - - except ValueError as err: - invalid_coords = set(coords.keys()) - set(data.coords.keys()) - raise ValueError(f"Coords {invalid_coords} are invalid coordinate keys") from err - - except KeyError as err: - raise KeyError( - ( - "Coords should follow mapping format {{coord_name:[dim1, dim2]}}. " - "Check that coords structure is correct and" - " dimensions are valid. {}" - ).format(err) - ) from err - if not isinstance(coords, (list, tuple)): - coords = [coords] * len(data) - data_subset = [] - for idx, (datum, coords_dict) in enumerate(zip(data, coords)): - try: - data_subset.append(get_coords(datum, coords_dict)) - except ValueError as err: - raise ValueError(f"Error in data[{idx}]: {err}") from err - except KeyError as err: - raise KeyError(f"Error in data[{idx}]: {err}") from err - return data_subset - - -@lru_cache(None) -def _load_static_files(): - """Lazily load the resource files into memory the first time they are needed. - - Clone from xarray.core.formatted_html_template. - """ - return [ - importlib.resources.files("arviz").joinpath(fname).read_text(encoding="utf-8") - for fname in STATIC_FILES - ] - - -class HtmlTemplate: - """Contain html templates for InferenceData repr.""" - - html_template = """ -
-
-
arviz.InferenceData
-
-
    - {} -
-
- """ - element_template = """ -
  • - - -
    -
    -
      -
      {xr_data}
      -
    -
    -
    • - """ - _, css_style = _load_static_files() # pylint: disable=protected-access - specific_style = ".xr-wrap{width:700px!important;}" - css_template = f"" - - -def either_dict_or_kwargs( - pos_kwargs, - kw_kwargs, - func_name, -): - """Clone from xarray.core.utils.""" - if pos_kwargs is None: - return kw_kwargs - if not hasattr(pos_kwargs, "keys") and hasattr(pos_kwargs, "__getitem__"): - raise ValueError(f"the first argument to .{func_name} must be a dictionary") - if kw_kwargs: - raise ValueError(f"cannot specify both keyword and positional arguments to .{func_name}") - return pos_kwargs - - -class Dask: - """Class to toggle Dask states. - - Warnings - -------- - Dask integration is an experimental feature still in progress. It can already be used - but it doesn't work with all stats nor diagnostics yet. - """ - - dask_flag = False - """bool: Enables Dask parallelization when set to True. Defaults to False.""" - dask_kwargs = None - """dict: Additional keyword arguments for Dask configuration. - Defaults to an empty dictionary.""" - - @classmethod - def enable_dask(cls, dask_kwargs=None): - """To enable Dask. - - Parameters - ---------- - dask_kwargs : dict - Dask related kwargs passed to :func:`~arviz.wrap_xarray_ufunc`. - """ - cls.dask_flag = True - cls.dask_kwargs = dask_kwargs - - @classmethod - def disable_dask(cls): - """To disable Dask.""" - cls.dask_flag = False - cls.dask_kwargs = None - - -def conditional_dask(func): - """Conditionally pass dask kwargs to `wrap_xarray_ufunc`.""" - - @functools.wraps(func) - def wrapper(*args, **kwargs): - if not Dask.dask_flag: - return func(*args, **kwargs) - user_kwargs = kwargs.pop("dask_kwargs", None) - if user_kwargs is None: - user_kwargs = {} - default_kwargs = Dask.dask_kwargs - return func(dask_kwargs={**default_kwargs, **user_kwargs}, *args, **kwargs) - - return wrapper diff --git a/arviz/wrappers/__init__.py b/arviz/wrappers/__init__.py deleted file mode 100644 index d6de945430..0000000000 --- a/arviz/wrappers/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Sampling wrappers.""" - -from .base import SamplingWrapper -from .wrap_stan import PyStan2SamplingWrapper, PyStanSamplingWrapper, CmdStanPySamplingWrapper -from .wrap_pymc import PyMCSamplingWrapper - -__all__ = [ - "CmdStanPySamplingWrapper", - "PyMCSamplingWrapper", - "PyStan2SamplingWrapper", - "PyStanSamplingWrapper", - "SamplingWrapper", -] diff --git a/arviz/wrappers/base.py b/arviz/wrappers/base.py deleted file mode 100644 index ee9963d7ae..0000000000 --- a/arviz/wrappers/base.py +++ /dev/null @@ -1,236 +0,0 @@ -# pylint: disable=too-many-instance-attributes,too-many-arguments -"""Base class for sampling wrappers.""" -from xarray import apply_ufunc - -# from ..data import InferenceData -from ..stats import wrap_xarray_ufunc as _wrap_xarray_ufunc - - -class SamplingWrapper: - """Class wrapping sampling routines for its usage via ArviZ. - - Using a common class, all inference backends can be supported in ArviZ. Hence, statistical - functions requiring refitting like Leave Future Out or Simulation Based Calibration can be - performed from ArviZ. - - For usage examples see user guide pages on :ref:`wrapper_guide`.See other - SamplingWrapper classes at :ref:`wrappers api section `. - - Parameters - ---------- - model - The model object used for sampling. - idata_orig : InferenceData, optional - Original InferenceData object. - log_lik_fun : callable, optional - For simple cases where the pointwise log likelihood is a Python function, this - function will be used to calculate the log likelihood. Otherwise, - ``point_log_likelihood`` method must be implemented. It's callback must be - ``log_lik_fun(*args, **log_lik_kwargs)`` and will be called using - :func:`wrap_xarray_ufunc` or :func:`xarray:xarray.apply_ufunc` depending - on the value of `is_ufunc`. - - For more details on ``args`` or ``log_lik_kwargs`` see the notes and - parameters ``posterior_vars`` and ``log_lik_kwargs``. - is_ufunc : bool, default True - If True, call ``log_lik_fun`` using :func:`xarray:xarray.apply_ufunc` otherwise - use :func:`wrap_xarray_ufunc`. - posterior_vars : list of str, optional - List of variable names to unpack as ``args`` for ``log_lik_fun``. Each string in - the list will be used to retrieve a DataArray from the Dataset in the posterior - group and passed to ``log_lik_fun``. - sample_kwargs : dict, optional - Sampling kwargs are stored as class attributes for their usage in the ``sample`` - method. - idata_kwargs : dict, optional - kwargs are stored as class attributes to be used in the ``get_inference_data`` method. - log_lik_kwargs : dict, optional - Keyword arguments passed to ``log_lik_fun``. - apply_ufunc_kwargs : dict, optional - Passed to :func:`xarray:xarray.apply_ufunc` or :func:`wrap_xarray_ufunc`. - - - Warnings - -------- - Sampling wrappers are an experimental feature in a very early stage. Please use them - with caution. - - Notes - ----- - Example of ``log_like_fun`` usage. - """ - - def __init__( - self, - model, - idata_orig=None, - log_lik_fun=None, - is_ufunc=True, - posterior_vars=None, - sample_kwargs=None, - idata_kwargs=None, - log_lik_kwargs=None, - apply_ufunc_kwargs=None, - ): - self.model = model - - # if not isinstance(idata_orig, InferenceData) or idata_orig is not None: - # raise TypeError("idata_orig must be of InferenceData type or None") - self.idata_orig = idata_orig - - if log_lik_fun is None or callable(log_lik_fun): - self.log_lik_fun = log_lik_fun - self.is_ufunc = is_ufunc - self.posterior_vars = posterior_vars - else: - raise TypeError("log_like_fun must be a callable object or None") - - self.sample_kwargs = {} if sample_kwargs is None else sample_kwargs - self.idata_kwargs = {} if idata_kwargs is None else idata_kwargs - self.log_lik_kwargs = {} if log_lik_kwargs is None else log_lik_kwargs - self.apply_ufunc_kwargs = {} if apply_ufunc_kwargs is None else apply_ufunc_kwargs - - def sel_observations(self, idx): - """Select a subset of the observations in idata_orig. - - **Not implemented**: This method must be implemented by the SamplingWrapper subclasses. - It is documented here to show its format and call signature. - - Parameters - ---------- - idx - Indexes to separate from the rest of the observed data. - - Returns - ------- - modified_observed_data - Observed data whose index is *not* ``idx`` - excluded_observed_data - Observed data whose index is ``idx`` - """ - raise NotImplementedError("sel_observations method must be implemented for each subclass") - - def sample(self, modified_observed_data): - """Sample ``self.model`` on the ``modified_observed_data`` subset. - - **Not implemented**: This method must be implemented by the SamplingWrapper subclasses. - It is documented here to show its format and call signature. - - Parameters - ---------- - modified_observed_data - Data to fit the model on. - - Returns - ------- - fitted_model - Result of the fit. - """ - raise NotImplementedError("sample method must be implemented for each subclass") - - def get_inference_data(self, fitted_model): - """Convert the ``fitted_model`` to an InferenceData object. - - **Not implemented**: This method must be implemented by the SamplingWrapper subclasses. - It is documented here to show its format and call signature. - - Parameters - ---------- - fitted_model - Result of the current fit. - - Returns - ------- - idata_current: InferenceData - InferenceData object containing the samples in ``fitted_model`` - """ - raise NotImplementedError("get_inference_data method must be implemented for each subclass") - - def log_likelihood__i(self, excluded_obs, idata__i): - r"""Get the log likelilhood samples :math:`\log p_{post(-i)}(y_i)`. - - Calculate the log likelihood of the data contained in excluded_obs using the - model fitted with this data excluded, the results of which are stored in ``idata__i``. - - Parameters - ---------- - excluded_obs - Observations for which to calculate their log likelihood. The second item from - the tuple returned by `sel_observations` is passed as this argument. - idata__i: InferenceData - Inference results of refitting the data excluding some observations. The - result of `get_inference_data` is used as this argument. - - Returns - ------- - log_likelihood: xr.Dataarray - Log likelihood of ``excluded_obs`` evaluated at each of the posterior samples - stored in ``idata__i``. - """ - if self.log_lik_fun is None: - raise NotImplementedError( - "When `log_like_fun` is not set during class initialization " - "log_likelihood__i method must be overwritten" - ) - posterior = idata__i.posterior - arys = (*excluded_obs, *[posterior[var_name] for var_name in self.posterior_vars]) - ufunc_applier = apply_ufunc if self.is_ufunc else _wrap_xarray_ufunc - log_lik_idx = ufunc_applier( - self.log_lik_fun, - *arys, - kwargs=self.log_lik_kwargs, - **self.apply_ufunc_kwargs, - ) - return log_lik_idx - - def _check_method_is_implemented(self, method, *args): - """Check a given method is implemented.""" - try: - getattr(self, method)(*args) - except NotImplementedError: - return False - except: # pylint: disable=bare-except - return True - return True - - def check_implemented_methods(self, methods): - """Check that all methods listed are implemented. - - Not all functions that require refitting need to have all the methods implemented in - order to work properly. This function should be used before using the SamplingWrapper and - its subclasses to get informative error messages. - - Parameters - ---------- - methods: list - Check all elements in methods are implemented. - - Returns - ------- - List with all non implemented methods - """ - supported_methods_1arg = ( - "sel_observations", - "sample", - "get_inference_data", - ) - supported_methods_2args = ("log_likelihood__i",) - supported_methods = [*supported_methods_1arg, *supported_methods_2args] - bad_methods = [method for method in methods if method not in supported_methods] - if bad_methods: - raise ValueError( - f"Not all method(s) in {bad_methods} supported. " - f"Supported methods in SamplingWrapper subclasses are:{supported_methods}" - ) - - not_implemented = [] - for method in methods: - if method in supported_methods_1arg: - if self._check_method_is_implemented(method, 1): - continue - not_implemented.append(method) - elif method in supported_methods_2args: - if self._check_method_is_implemented(method, 1, 1): - continue - not_implemented.append(method) - return not_implemented diff --git a/arviz/wrappers/wrap_pymc.py b/arviz/wrappers/wrap_pymc.py deleted file mode 100644 index 28f40de4ec..0000000000 --- a/arviz/wrappers/wrap_pymc.py +++ /dev/null @@ -1,36 +0,0 @@ -# pylint: disable=arguments-differ -"""Base class for PyMC interface wrappers.""" -from .base import SamplingWrapper - - -# pylint: disable=abstract-method -class PyMCSamplingWrapper(SamplingWrapper): - """PyMC (4.0+) sampling wrapper base class. - - See the documentation on :class:`~arviz.SamplingWrapper` for a more detailed - description. An example of ``PyMCSamplingWrapper`` usage can be found - in the :ref:`pymc_refitting` notebook. - - Warnings - -------- - Sampling wrappers are an experimental feature in a very early stage. Please use them - with caution. - """ - - def sample(self, modified_observed_data): - """Update data and sample model on modified_observed_data.""" - import pymc # pylint: disable=import-error - - with self.model: - pymc.set_data(modified_observed_data) - idata = pymc.sample( - **self.sample_kwargs, - ) - return idata - - def get_inference_data(self, fitted_model): - """Return sampling result without modifying. - - PyMC sampling already returns and InferenceData object. - """ - return fitted_model diff --git a/arviz/wrappers/wrap_stan.py b/arviz/wrappers/wrap_stan.py deleted file mode 100644 index 08f6961a6f..0000000000 --- a/arviz/wrappers/wrap_stan.py +++ /dev/null @@ -1,148 +0,0 @@ -# pylint: disable=arguments-differ -"""Base class for Stan interface wrappers.""" -from typing import Union - -from ..data import from_cmdstanpy, from_pystan -from .base import SamplingWrapper - - -# pylint: disable=abstract-method -class StanSamplingWrapper(SamplingWrapper): - """Stan sampling wrapper base class. - - See the documentation on :class:`~arviz.SamplingWrapper` for a more detailed - description. An example of ``PyStanSamplingWrapper`` usage can be found - in the :ref:`pystan_refitting` notebook. For usage examples of other wrappers - see the user guide pages on :ref:`wrapper_guide`. - - Warnings - -------- - Sampling wrappers are an experimental feature in a very early stage. Please use them - with caution. - - See Also - -------- - SamplingWrapper - """ - - def sel_observations(self, idx): - """Select a subset of the observations in idata_orig. - - **Not implemented**: This method must be implemented on a model basis. - It is documented here to show its format and call signature. - - Parameters - ---------- - idx - Indexes to separate from the rest of the observed data. - - Returns - ------- - modified_observed_data : dict - Dictionary containing both excluded and included data but properly divided - in the different keys. Passed to ``data`` argument of ``model.sampling``. - excluded_observed_data : str - Variable name containing the pointwise log likelihood data of the excluded - data. As PyStan cannot call C++ functions and log_likelihood__i is already - calculated *during* the simulation, instead of the value on which to evaluate - the likelihood, ``log_likelihood__i`` expects a string so it can extract the - corresponding data from the InferenceData object. - """ - raise NotImplementedError("sel_observations must be implemented on a model basis") - - def get_inference_data(self, fitted_model): # pylint: disable=arguments-renamed - """Convert the fit object returned by ``self.sample`` to InferenceData.""" - if fitted_model.__class__.__name__ == "CmdStanMCMC": - idata = from_cmdstanpy(posterior=fitted_model, **self.idata_kwargs) - else: - idata = from_pystan(posterior=fitted_model, **self.idata_kwargs) - return idata - - def log_likelihood__i(self, excluded_obs, idata__i): - """Retrieve the log likelihood of the excluded observations from ``idata__i``.""" - return idata__i.log_likelihood[excluded_obs] - - -class PyStan2SamplingWrapper(StanSamplingWrapper): - """PyStan (2.x) sampling wrapper base class. - - See the documentation on :class:`~arviz.SamplingWrapper` for a more detailed - description. An example of ``PyStanSamplingWrapper`` usage can be found - in the :ref:`pystan_refitting` notebook. For usage examples of other wrappers - see the user guide pages on :ref:`wrapper_guide`. - - Warnings - -------- - Sampling wrappers are an experimental feature in a very early stage. Please use them - with caution. - - See Also - -------- - SamplingWrapper - """ - - def sample(self, modified_observed_data): - """Resample the PyStan model stored in self.model on modified_observed_data.""" - fit = self.model.sampling(data=modified_observed_data, **self.sample_kwargs) - return fit - - -class PyStanSamplingWrapper(StanSamplingWrapper): - """PyStan (3.0+) sampling wrapper base class. - - See the documentation on :class:`~arviz.SamplingWrapper` for a more detailed - description. An example of ``PyStanSamplingWrapper`` usage can be found - in the :ref:`pystan_refitting` notebook. - - Warnings - -------- - Sampling wrappers are an experimental feature in a very early stage. Please use them - with caution. - """ - - def sample(self, modified_observed_data): - """Rebuild and resample the PyStan model on modified_observed_data.""" - import stan # pylint: disable=import-error,import-outside-toplevel - - self.model: Union[str, stan.Model] - if isinstance(self.model, str): - program_code = self.model - else: - program_code = self.model.program_code - self.model = stan.build(program_code, data=modified_observed_data) - fit = self.model.sample(**self.sample_kwargs) - return fit - - -class CmdStanPySamplingWrapper(StanSamplingWrapper): - """CmdStanPy sampling wrapper base class. - - See the documentation on :class:`~arviz.SamplingWrapper` for a more detailed - description. An example of ``CmdStanPySamplingWrapper`` usage can be found - in the :ref:`cmdstanpy_refitting` notebook. - - Warnings - -------- - Sampling wrappers are an experimental feature in a very early stage. Please use them - with caution. - """ - - def __init__(self, data_file, **kwargs): - """Initialize the CmdStanPySamplingWrapper. - - Parameters - ---------- - data_file : str - Filename on which to store the data for every refit. - It's contents will be overwritten. - """ - super().__init__(**kwargs) - self.data_file = data_file - - def sample(self, modified_observed_data): - """Resample cmdstanpy model on modified_observed_data.""" - from cmdstanpy import write_stan_json - - write_stan_json(self.data_file, modified_observed_data) - fit = self.model.sample(**{**self.sample_kwargs, "data": self.data_file}) - return fit diff --git a/arvizrc.template b/arvizrc.template deleted file mode 100644 index 47ff2e5ab6..0000000000 --- a/arvizrc.template +++ /dev/null @@ -1,55 +0,0 @@ -#### ArviZ rcParams template #### -### DATA ### -# rcParams related with data loading related functions -data.http_protocol : https # protocol for remote dataset load, - # must be either http or https -data.index_origin : 0 # index origin, must be either 0 or 1 -data.load : lazy # Sets the default data loading mode. - # "lazy" stands for xarray lazy loading, - # "eager" loads all datasets into memory -data.log_likelihood : true # save pointwise log likelihood values, one of "true", "false" -data.metagroups : { - posterior_groups: posterior, posterior_predictive, sample_stats, log_likelihood - prior_groups: prior, prior_predictive, sample_stats_prior - posterior_groups_warmup: _warmup_posterior, _warmup_posterior_predictive, _warmup_sample_stats - latent_vars: posterior, prior - observed_vars: posterior_predictive, observed_data, prior_predictive -} -data.save_warmup : false # save warmup iterations, one of "true", "false" - -### PLOT ### -# rcParams related with plotting functions -plot.backend : matplotlib # One of "bokeh", "matplotlib" -plot.density_kind : kde # One of "kde", "hist" -plot.max_subplots : 40 # Maximum number of subplots. -plot.point_estimate : mean # One of "mean", "median", "mode" or "None" - -# bokeh specific rcParams -plot.bokeh.bounds_x_range : auto # either "auto", "None" or tuple of size 2 -plot.bokeh.bounds_y_range : auto # either "auto", "None" or tuple of size 2 -plot.bokeh.figure.dpi : 60 # dots (pixels) per inch -plot.bokeh.figure.height : 500 # num of pixels -plot.bokeh.figure.width : 500 # num of pixels -plot.bokeh.layout.order : default # Select subplot structure for bokeh. One of "default", "column", "row", "square", "square_trimmed" - # or "Ncolumn" ("Nrow") where N is an integer number of columns (rows), e.g. "4row" means 4 rows -plot.bokeh.layout.sizing_mode: fixed # Responsive layout. One of "fixed", - # "stretch_width", "stretch_height", "stretch_both", - # "scale_width", "scale_height", "scale_both" -plot.bokeh.layout.toolbar_location : above # Location for toolbar on layouts. "None" will hide the toolbar. - # One of "above", "below", "left", "right". -plot.bokeh.marker : cross # specify the marker type used to plot -plot.bokeh.output_backend : webgl # one of "canvas", "svg", "webgl" -plot.bokeh.show : true # call bokeh.plotting.show for figure or grid of figures. One of "true", "false" -plot.bokeh.tools : reset,pan,box_zoom,wheel_zoom,lasso_select,undo,save,hover - # pan,box_zoom,wheel_zoom,box_select,lasso_select,undo,redo,reset,save,hover - -# matplotlib specific rcParams -plot.matplotlib.show : false # call plt.show. One of "true", "false" - -### STATS ### -# rcParams related with statistical and diagnostic functions -stats.ci_prob : 0.94 # Friendly reminder of the arbitrary nature of commonly used values like 95% -stats.information_criterion : loo # One of "loo", "waic" -stats.ic_compare_method : stacking # One of "stacking", "bb-pseudo-bma", "pseudo-bma" -stats.ic_pointwise : true # One of "true", "false" -stats.ic_scale : log # One of "deviance", "log", "negative_log" diff --git a/asv_benchmarks/asv.conf.json b/asv_benchmarks/asv.conf.json deleted file mode 100644 index 4d9ea11d71..0000000000 --- a/asv_benchmarks/asv.conf.json +++ /dev/null @@ -1,133 +0,0 @@ -{ - // The version of the config file format. Do not change, unless - // you know what you are doing. - "version": 1, - - // The name of the project being benchmarked - "project": "arviz", - - // The project's homepage - "project_url": "https://python.arviz.org/", - - // The URL or local path of the source code repository for the - // project being benchmarked - "repo": "..", - - // The Python project's subdirectory in your repo. If missing or - // the empty string, the project is assumed to be located at the root - // of the repository. - // "repo_subdir": "", - - - - // List of branches to benchmark. - // (for git) or "default" (for mercurial). - "branches": ["main"], - - // The DVCS being used. If not set, it will be automatically - // determined from "repo" by looking at the protocol in the URL - // (if remote), or by looking for special directories, such as - // ".git" (if local). - "dvcs": "git", - - // The tool to use to create environments. May be "conda", - // "virtualenv" or other value depending on the plugins in use. - // If missing or the empty string, the tool will be automatically - // determined by looking for tools on the PATH environment - // variable. - "environment_type": "virtualenv", - - // The command to run to build the project. - // copied from https://github.com/sympy/sympy/issues/26239 to fix the libmamba issue - "build_command": [ - "python -m pip install build", - "python -m build --wheel -o {build_cache_dir} {build_dir}" - ], - - // timeout in seconds for installing any dependencies in environment - // defaults to 10 min - //"install_timeout": 600, - - // the base URL to show a commit for the project. - "show_commit_url": "https://github.com/arviz-devs/arviz/commit/", - - // The Pythons you'd like to test against. If not provided, defaults - // to the current version of Python used to run `asv`. - // "pythons": ["3.10"], - - // The list of conda channel names to be searched for benchmark - // dependency packages in the specified order - // "conda_channels": ["conda-forge", "defaults",], - - // The matrix of dependencies to test. Each key is the name of a - // package (in PyPI) and the values are version numbers. An empty - // list or empty string indicates to just test against the default - // (latest) version. null indicates that the package is to not be - // installed. If the package to be tested is only available from - // PyPi, and the 'environment_type' is conda, then you can preface - // the package name by 'pip+', and the package will be installed via - // pip (with all the conda available packages installed first, - // followed by the pip installed packages). - // - "matrix": { - "numba": [], - }, - - // Combinations of libraries/python versions can be excluded/included - // from the set to test. Each entry is a dictionary containing additional - // key-value pairs to include/exclude. - // - // An exclude entry excludes entries where all values match. The - // values are regexps that should match the whole string. - // - // An include entry adds an environment. Only the packages listed - // are installed. The 'python' key is required. The exclude rules - // do not apply to includes. - // - // In addition to package names, the following keys are available: - // - // - python - // Python version, as in the *pythons* variable above. - // - environment_type - // Environment type, as above. - // - sys_platform - // Platform, as in sys.platform. Possible values for the common - // cases: 'linux2', 'win32', 'cygwin', 'darwin'. - // - // "exclude": [ - // {"python": "3.2", "sys_platform": "win32"}, // skip py3.2 on windows - // {"environment_type": "conda", "six": null}, // don't run without six on conda - // ], - // - // "include": [ - // // additional env for python2.7 - // {"python": "2.7", "numpy": "1.8"}, - // // additional env if run on windows+conda - // {"platform": "win32", "environment_type": "conda", "python": "2.7", "libpython": ""}, - // ], - - // The directory (relative to the current directory) that benchmarks are - // stored in. If not provided, defaults to "benchmarks" - // "benchmark_dir": "benchmarks", - - // The directory (relative to the current directory) to cache the Python - // environments in. If not provided, defaults to "env" - "env_dir": ".asv/env", - - // The directory (relative to the current directory) that raw benchmark - // results are stored in. If not provided, defaults to "results". - "results_dir": ".asv/results", - - // The directory (relative to the current directory) that the html tree - // should be written to. If not provided, defaults to "html". - "html_dir": ".asv/html", - - // The number of characters to retain in the commit hashes. - "hash_length": 8, - - // `asv` will cache results of the recent builds in each - // environment, making them faster to install next time. This is - // the number of builds to keep, per environment. - "build_cache_size": 2, - -} diff --git a/asv_benchmarks/benchmarks/__init__.py b/asv_benchmarks/benchmarks/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/asv_benchmarks/benchmarks/benchmarks.py b/asv_benchmarks/benchmarks/benchmarks.py deleted file mode 100644 index f8a82242b5..0000000000 --- a/asv_benchmarks/benchmarks/benchmarks.py +++ /dev/null @@ -1,106 +0,0 @@ -# Write the benchmarking functions here. -# See "Writing benchmarks" in the airspeed velocity docs for more information. -# https://asv.readthedocs.io/en/stable/ -import numpy as np -from scipy.stats import circstd - -import arviz as az -from arviz.stats.density_utils import _fast_kde_2d, histogram, kde -from arviz.stats.stats_utils import _circular_standard_deviation, stats_variance_2d - - -class Hist: - params = (True, False) - param_names = ("Numba",) - - def setup(self, numba_flag): - self.data = np.random.rand(10000, 1000) - if numba_flag: - az.Numba.enable_numba() - else: - az.Numba.disable_numba() - - def time_histogram(self, numba_flag): - histogram(self.data, bins=100) - - -class Variance: - params = (True, False) - param_names = ("Numba",) - - def setup(self, numba_flag): - self.data = np.random.rand(10000, 1000) - if numba_flag: - az.Numba.enable_numba() - else: - az.Numba.disable_numba() - - def time_variance_2d(self, numba_flag): - stats_variance_2d(self.data) - - -class CircStd: - params = (True, False) - param_names = ("Numba",) - - def setup(self, numba_flag): - self.data = np.random.randn(10000, 1000) - if numba_flag: - self.circstd = _circular_standard_deviation - else: - self.circstd = circstd - - def time_circ_std(self, numba_flag): - self.circstd(self.data) - - -class Kde_1d: - params = [(True, False), (10**5, 10**6, 10**7)] - param_names = ("Numba", "n") - - def setup(self, numba_flag, n): - self.x = np.random.randn(n // 10, 10) - if numba_flag: - az.Numba.enable_numba() - else: - az.Numba.disable_numba() - - def time_fast_kde_normal(self, numba_flag, n): - kde(self.x) - - -class Fast_KDE_2d: - params = [(True, False), ((100, 10**4), (10**4, 100), (1000, 1000))] - param_names = ("Numba", "shape") - - def setup(self, numba_flag, shape): - self.x = np.random.randn(*shape) - self.y = np.random.randn(*shape) - if numba_flag: - az.Numba.enable_numba() - else: - az.Numba.disable_numba() - - def time_fast_kde_2d(self, numba_flag, shape): - _fast_kde_2d(self.x, self.y) - - -class Atleast_Nd: - params = ("az.utils", "numpy") - param_names = ("source",) - - def setup(self, source): - self.data = np.random.randn(100000) - self.x = np.random.randn(100000).tolist() - if source == "az.utils": - self.atleast_2d = az.utils.two_de - self.atleast_1d = az.utils.one_de - else: - self.atleast_2d = np.atleast_2d - self.atleast_1d = np.atleast_1d - - def time_atleast_2d_array(self, source): - self.atleast_2d(self.data) - - def time_atleast_1d(self, source): - self.atleast_1d(self.x) diff --git a/azure-pipelines.yml b/azure-pipelines.yml deleted file mode 100644 index 94bb16a5ad..0000000000 --- a/azure-pipelines.yml +++ /dev/null @@ -1,23 +0,0 @@ -# Python package -# Create and test a Python package on multiple Python versions. -# Add steps that analyze code, save the dist with the build record, publish to a PyPI-compatible index, and more: -# https://docs.microsoft.com/azure/devops/pipelines/languages/python -# -*- mode: yaml -*- -trigger: - branches: - include: - - main - tags: - include: - - '*' - -pr: - branches: - include: - - main - -jobs: - - template: .azure-pipelines/azure-pipelines-base.yml - - template: .azure-pipelines/azure-pipelines-external.yml - - template: .azure-pipelines/azure-pipelines-benchmarks.yml - - template: .azure-pipelines/azure-pipelines-wheel.yml diff --git a/codecov.yml b/codecov.yml deleted file mode 100644 index 0e7e8464fe..0000000000 --- a/codecov.yml +++ /dev/null @@ -1,22 +0,0 @@ -ignore: - - arviz/tests/ - -codecov: - notify: - after_n_builds: 6 - -comment: - behavior: default - branches: - - "main" - -coverage: - status: - project: - default: - target: auto - threshold: 2% - - patch: - default: - target: 75% diff --git a/doc/_templates/autosummary/class.rst b/doc/_templates/autosummary/class.rst deleted file mode 100644 index 7f6f73e56b..0000000000 --- a/doc/_templates/autosummary/class.rst +++ /dev/null @@ -1,30 +0,0 @@ -{{ fullname | escape | underline}} - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - - {% block methods %} - {% if methods %} - - .. rubric:: Methods - - .. autosummary:: - :toctree: - - {% for item in methods %} - {{ objname }}.{{ item }} - {%- endfor %} - {% endif %} - {% endblock %} - - {% block attributes %} - {% if attributes %} - .. rubric:: Attributes - - .. autosummary:: - {% for item in attributes %} - ~{{ name }}.{{ item }} - {%- endfor %} - {% endif %} - {% endblock %} diff --git a/doc/_templates/class_no_members.rst b/doc/_templates/class_no_members.rst deleted file mode 100644 index 6440e7018e..0000000000 --- a/doc/_templates/class_no_members.rst +++ /dev/null @@ -1,30 +0,0 @@ -{{ fullname | escape | underline}} - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - - {% block methods %} - {% if methods %} - - .. rubric:: Methods - - .. autosummary:: - {% for item in methods %} - {% if item != "__init__" %} - {{ item }} - {% endif %} - {%- endfor %} - {% endif %} - {% endblock %} - - {% block attributes %} - {% if attributes %} - .. rubric:: Attributes - - .. autosummary:: - {% for item in attributes %} - ~{{ name }}.{{ item }} - {%- endfor %} - {% endif %} - {% endblock %} diff --git a/doc/source/api/data.rst b/doc/source/api/data.rst deleted file mode 100644 index c27650cb0d..0000000000 --- a/doc/source/api/data.rst +++ /dev/null @@ -1,60 +0,0 @@ -.. currentmodule:: arviz - -.. _data_api: - -Data ----- - -Inference library converters -............................ - -.. autosummary:: - :toctree: generated/ - - from_beanmachine - from_cmdstan - from_cmdstanpy - from_emcee - from_numpyro - from_pyjags - from_pyro - from_pystan - - -IO / General conversion -....................... - -.. autosummary:: - :toctree: generated/ - - convert_to_inference_data - convert_to_dataset - dict_to_dataset - from_datatree - from_dict - from_json - from_netcdf - to_datatree - to_json - to_netcdf - from_zarr - to_zarr - - -General functions -................. - -.. autosummary:: - :toctree: generated/ - - concat - extract - -Data examples -............. - -.. autosummary:: - :toctree: generated/ - - list_datasets - load_arviz_data diff --git a/doc/source/api/diagnostics.rst b/doc/source/api/diagnostics.rst deleted file mode 100644 index 7b05191ce7..0000000000 --- a/doc/source/api/diagnostics.rst +++ /dev/null @@ -1,15 +0,0 @@ -.. currentmodule:: arviz - -.. _diagnostics_api: - -Diagnostics ------------ - -.. autosummary:: - :toctree: generated/ - - bfmi - ess - rhat - mcse - psens diff --git a/doc/source/api/index.rst b/doc/source/api/index.rst deleted file mode 100644 index f33090ba4a..0000000000 --- a/doc/source/api/index.rst +++ /dev/null @@ -1,23 +0,0 @@ -.. currentmodule:: arviz - -.. _api: - -API Reference -============= - - -.. toctree:: - :maxdepth: 2 - - plots - stats - diagnostics - stats_utils - data - inference_data - plot_utils - utils - rcparams - preview - wrappers - stats_refitting diff --git a/doc/source/api/inference_data.rst b/doc/source/api/inference_data.rst deleted file mode 100644 index 74a8480c21..0000000000 --- a/doc/source/api/inference_data.rst +++ /dev/null @@ -1,102 +0,0 @@ -.. currentmodule:: arviz - -.. _idata_api: - -InferenceData -------------- - -Constructor -........... - -.. autosummary:: - :toctree: generated/ - - InferenceData - -Attributes -.......... -``InferenceData`` objects store :class:`xarray:xarray.Dataset` as attributes. -The :ref:`schema` contains the guidance related to these attributes. - -.. autosummary:: - :toctree: generated/ - - InferenceData.get_index - -IO / Conversion -............... - -.. autosummary:: - :toctree: generated/ - - InferenceData.to_dataframe - InferenceData.to_json - InferenceData.from_netcdf - InferenceData.to_netcdf - InferenceData.from_zarr - InferenceData.to_zarr - InferenceData.chunk - InferenceData.close - InferenceData.compute - InferenceData.load - InferenceData.persist - InferenceData.unify_chunks - -Dictionary interface -.................... - -.. autosummary:: - :toctree: generated/ - - InferenceData.groups - InferenceData.items - InferenceData.values - -InferenceData contents -...................... - -.. autosummary:: - :toctree: generated/ - - InferenceData.add_groups - InferenceData.extend - InferenceData.assign - InferenceData.assign_coords - InferenceData.rename - InferenceData.rename_dims - InferenceData.set_coords - InferenceData.set_index - -Indexing -........ - -.. autosummary:: - :toctree: generated/ - - InferenceData.isel - InferenceData.sel - InferenceData.reset_index - -Computation -........... - -.. autosummary:: - :toctree: generated/ - - InferenceData.map - InferenceData.cumsum - InferenceData.max - InferenceData.mean - InferenceData.median - InferenceData.min - InferenceData.quantile - InferenceData.sum - -Reshaping and reorganizing -.......................... - -.. autosummary:: - :toctree: generated/ - - InferenceData.stack - InferenceData.unstack diff --git a/doc/source/api/plot_utils.md b/doc/source/api/plot_utils.md deleted file mode 100644 index d12a8f5601..0000000000 --- a/doc/source/api/plot_utils.md +++ /dev/null @@ -1,44 +0,0 @@ -```{eval-rst} -.. currentmodule:: arviz.labels -``` -# Plot utils - -(labeller_api)= -## Labellers -See also the {ref}`label_guide` - -```{eval-rst} -.. autosummary:: - :toctree: generated/ - - BaseLabeller - DimCoordLabeller - IdxLabeller - DimIdxLabeller - MapLabeller - NoModelLabeller - NoVarLabeller -``` - -## Labeling utils - -```{eval-rst} -.. autosummary:: - :toctree: generated/ - - mix_labellers -``` - -## Xarray utils -Low level functions to iterate over xarray objects. - -```{eval-rst} -.. currentmodule:: arviz.sel_utils - -.. autosummary:: - :toctree: generated/ - - xarray_sel_iter - xarray_var_iter - xarray_to_ndarray -``` diff --git a/doc/source/api/plots.rst b/doc/source/api/plots.rst deleted file mode 100644 index 0f9bb6af86..0000000000 --- a/doc/source/api/plots.rst +++ /dev/null @@ -1,38 +0,0 @@ -.. currentmodule:: arviz - -.. _plot_api: - -Plots ------ - -.. autosummary:: - :toctree: generated/ - - plot_autocorr - plot_bf - plot_bpv - plot_compare - plot_density - plot_dist - plot_dist_comparison - plot_dot - plot_ecdf - plot_elpd - plot_energy - plot_ess - plot_forest - plot_hdi - plot_kde - plot_khat - plot_loo_pit - plot_lm - plot_mcse - plot_pair - plot_parallel - plot_posterior - plot_ppc - plot_rank - plot_separation - plot_trace - plot_ts - plot_violin diff --git a/doc/source/api/preview.md b/doc/source/api/preview.md deleted file mode 100644 index 7dc62384c5..0000000000 --- a/doc/source/api/preview.md +++ /dev/null @@ -1,23 +0,0 @@ -(preview_api)= - -# Preview - -:::{py:module} arviz.preview -This module gives access to upcoming refactored features by exposing -all objects in [arviz-base](https://arviz-base.readthedocs.io/en/latest/), -[arviz-stats](https://arviz-stats.readthedocs.io/en/latest/) and -[arviz-plots](https://arviz-plots.readthedocs.io/en/latest/) -under a single namespace: `arviz.preview`. - -In addition, there is also an `arviz.preview.info` to check availability -of the different sub-libraries. -::: - - -:::{seealso} -* Introduction to Exploratory Analysis of Bayesian Models and Bayesian workflow - through [arviz.preview](https://arviz-devs.github.io/EABM/) -* The {ref}`migration_guide` introduces the new arviz-xyz libraries through - ``arviz.preview`` focusing on the differences with current ArviZ, - the reasons behind them and what new features can be unlocked through these changes. -::: diff --git a/doc/source/api/rcparams.rst b/doc/source/api/rcparams.rst deleted file mode 100644 index f486dfb39e..0000000000 --- a/doc/source/api/rcparams.rst +++ /dev/null @@ -1,10 +0,0 @@ -.. currentmodule:: arviz - -rcParams --------- - -.. autosummary:: - :toctree: generated/ - :template: class_no_members.rst - - rc_context diff --git a/doc/source/api/stats.rst b/doc/source/api/stats.rst deleted file mode 100644 index e08dd31cda..0000000000 --- a/doc/source/api/stats.rst +++ /dev/null @@ -1,20 +0,0 @@ -.. currentmodule:: arviz - -.. _stats_api: - -Stats ------ - -.. autosummary:: - :toctree: generated/ - - apply_test_function - compare - hdi - kde - loo - loo_pit - psislw - r2_score - summary - waic diff --git a/doc/source/api/stats_refitting.rst b/doc/source/api/stats_refitting.rst deleted file mode 100644 index 0905632042..0000000000 --- a/doc/source/api/stats_refitting.rst +++ /dev/null @@ -1,12 +0,0 @@ -.. currentmodule:: arviz - -.. _stats_refit_api: - -Stats (requiring refitting) ---------------------------- -Experimental feature - -.. autosummary:: - :toctree: generated/ - - reloo diff --git a/doc/source/api/stats_utils.rst b/doc/source/api/stats_utils.rst deleted file mode 100644 index 0fca50384a..0000000000 --- a/doc/source/api/stats_utils.rst +++ /dev/null @@ -1,15 +0,0 @@ -.. currentmodule:: arviz - -.. _stats_utils_api: - -Stats utils ------------ - -.. autosummary:: - :toctree: generated/ - - autocov - autocorr - make_ufunc - smooth_data - wrap_xarray_ufunc diff --git a/doc/source/api/utils.rst b/doc/source/api/utils.rst deleted file mode 100644 index 31327669e0..0000000000 --- a/doc/source/api/utils.rst +++ /dev/null @@ -1,36 +0,0 @@ -.. currentmodule:: arviz - -Utils ------ - -Computing utils -............... - -.. autosummary:: - :toctree: generated/ - :template: class_no_members.rst - - Numba - Dask - -Bokeh utils -........... - -.. autosummary:: - :toctree: generated/ - - ColumnDataSource - create_layout - output_notebook - output_file - show_layout - to_cds - -Matplotlib utils -................ - -.. autosummary:: - :toctree: generated/ - :template: class_no_members.rst - - interactive_backend diff --git a/doc/source/api/wrappers.rst b/doc/source/api/wrappers.rst deleted file mode 100644 index 6015265efd..0000000000 --- a/doc/source/api/wrappers.rst +++ /dev/null @@ -1,16 +0,0 @@ -.. currentmodule:: arviz - -.. _wrappers_api: - -Wrappers --------- -Experimental feature - -.. autosummary:: - :toctree: generated/ - - SamplingWrapper - CmdStanPySamplingWrapper - PyMCSamplingWrapper - PyStanSamplingWrapper - PyStan2SamplingWrapper diff --git a/doc/source/getting_started/ConversionGuideEmcee.ipynb b/doc/source/getting_started/ConversionGuideEmcee.ipynb deleted file mode 100644 index 929a4b2fd6..0000000000 --- a/doc/source/getting_started/ConversionGuideEmcee.ipynb +++ /dev/null @@ -1,10095 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a641832b", - "metadata": {}, - "source": [ - "(emcee_conversion)=\n", - "# Converting emcee objects to InferenceData\n", - "\n", - "{class}`~arviz.InferenceData` is the central data format for ArviZ. `InferenceData` itself is just a container that maintains references to one or more {class}`xarray.Dataset`. \n", - "\n", - "Below are various ways to generate an `InferenceData` from emcee objects." - ] - }, - { - "cell_type": "markdown", - "id": "279a434d", - "metadata": {}, - "source": [ - "```{seealso}\n", - "\n", - "- Conversion from Python, numpy or pandas objects\n", - "- {ref}`xarray_for_arviz` for an overview of `InferenceData` and its role within ArviZ. \n", - "- {ref}`schema` describes the structure of `InferenceData` objects and the assumptions made by ArviZ to ease your exploratory analysis of Bayesian models.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "b702e7fd", - "metadata": {}, - "source": [ - "We will start by importing the required packages and defining the model. The famous 8 school model." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "87f7958f", - "metadata": {}, - "outputs": [], - "source": [ - "import arviz as az\n", - "import numpy as np\n", - "import emcee" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9bdd7bbc", - "metadata": {}, - "outputs": [], - "source": [ - "az.style.use(\"arviz-darkgrid\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7e9a05da", - "metadata": {}, - "outputs": [], - "source": [ - "J = 8\n", - "y_obs = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])\n", - "sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1cd28960", - "metadata": {}, - "outputs": [], - "source": [ - "def log_prior_8school(theta):\n", - " mu, tau, eta = theta[0], theta[1], theta[2:]\n", - " # Half-cauchy prior, hwhm=25\n", - " if tau < 0:\n", - " return -np.inf\n", - " prior_tau = -np.log(tau**2 + 25**2)\n", - " prior_mu = -((mu / 10) ** 2) # normal prior, loc=0, scale=10\n", - " prior_eta = -np.sum(eta**2) # normal prior, loc=0, scale=1\n", - " return prior_mu + prior_tau + prior_eta\n", - "\n", - "\n", - "def log_likelihood_8school(theta, y, s):\n", - " mu, tau, eta = theta[0], theta[1], theta[2:]\n", - " return -(((mu + tau * eta - y) / s) ** 2)\n", - "\n", - "\n", - "def lnprob_8school(theta, y, s):\n", - " prior = log_prior_8school(theta)\n", - " like_vect = log_likelihood_8school(theta, y, s)\n", - " like = np.sum(like_vect)\n", - " return like + prior" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "cac78e4f", - "metadata": {}, - "outputs": [], - "source": [ - "nwalkers = 40 # called chains in ArviZ\n", - "ndim = J + 2\n", - "draws = 1500\n", - "pos = np.random.normal(size=(nwalkers, ndim))\n", - "pos[:, 1] = np.absolute(pos[:, 1])\n", - "sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob_8school, args=(y_obs, sigma))\n", - "sampler.run_mcmc(pos, draws);" - ] - }, - { - "cell_type": "markdown", - "id": "cf6af8a4", - "metadata": {}, - "source": [ - "## Manually set variable names\n", - "This first example will show how to convert manually setting the variable names only, leaving everything else to ArviZ defaults." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "95f696a0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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      • \n", - " \n", - "
    • \n", - " \n", - " \n", - "
      \n", - "
      \n", - "
        \n", - "
        \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        <xarray.Dataset>\n",
-       "Dimensions:      (arg_0_dim_0: 8, arg_1_dim_0: 8)\n",
-       "Coordinates:\n",
-       "  * arg_0_dim_0  (arg_0_dim_0) int64 0 1 2 3 4 5 6 7\n",
-       "  * arg_1_dim_0  (arg_1_dim_0) int64 0 1 2 3 4 5 6 7\n",
-       "Data variables:\n",
-       "    arg_0        (arg_0_dim_0) float64 28.0 8.0 -3.0 7.0 -1.0 1.0 18.0 12.0\n",
-       "    arg_1        (arg_1_dim_0) float64 15.0 10.0 16.0 11.0 9.0 11.0 10.0 18.0\n",
-       "Attributes:\n",
-       "    created_at:                 2021-08-30T18:14:53.853598\n",
-       "    arviz_version:              0.11.2\n",
-       "    inference_library:          emcee\n",
-       "    inference_library_version:  3.1.1

        \n", - "
      \n", - "
      \n", - "
      • \n", - " \n", - "
    \n", - "
    \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> sample_stats\n", - "\t> observed_data" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idata1.sel(draw=slice(100, None))" - ] - }, - { - "cell_type": "markdown", - "id": "62846712", - "metadata": {}, - "source": [ - "From an InferenceData object, ArviZ's native data structure, the {func}`posterior plot ` of a few variables can be done in one line:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "86aa367a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([,\n", - " ,\n", - " ], dtype=object)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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-       "  * arg_1_dim_0  (arg_1_dim_0) int64 0 1 2 3 4 5 6 7\n",
-       "Data variables:\n",
-       "    arg_0        (arg_0_dim_0) float64 28.0 8.0 -3.0 7.0 -1.0 1.0 18.0 12.0\n",
-       "    arg_1        (arg_1_dim_0) float64 15.0 10.0 16.0 11.0 9.0 11.0 10.0 18.0\n",
-       "Attributes:\n",
-       "    created_at:                 2021-08-30T18:14:54.506402\n",
-       "    arviz_version:              0.11.2\n",
-       "    inference_library:          emcee\n",
-       "    inference_library_version:  3.1.1

        \n", - "
      \n", - "
      \n", - "
      • \n", - " \n", - "
    \n", - "
    \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> sample_stats\n", - "\t> observed_data" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idata2 = az.from_emcee(sampler, slices=[0, 1, slice(2, None)])\n", - "idata2" - ] - }, - { - "cell_type": "markdown", - "id": "43a9f608", - "metadata": {}, - "source": [ - "After checking the default variable names, the trace of one dimension of eta can be plotted using ArviZ syntax:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "783e8c25", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "az.plot_trace(idata2, var_names=[\"var_2\"], coords={\"var_2_dim_0\": 4});" - ] - }, - { - "cell_type": "markdown", - "id": "96731bb8", - "metadata": {}, - "source": [ - "## `blobs`: unlock sample stats, posterior predictive and miscellanea" - ] - }, - { - "cell_type": "markdown", - "id": "32c2d203", - "metadata": {}, - "source": [ - "Emcee does not store per-draw sample stats, however, it has a functionality called\n", - "blobs that allows to store any variable on a per-draw basis. It can be used\n", - "to store some sample_stats or even posterior_predictive data. \n", - "\n", - "You can modify the probability function to use this ``blobs`` functionality and store the pointwise log likelihood,\n", - "then rerun the sampler using the new function:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c02124d6", - "metadata": {}, - "outputs": [], - "source": [ - "def lnprob_8school_blobs(theta, y, s):\n", - " prior = log_prior_8school(theta)\n", - " like_vect = log_likelihood_8school(theta, y, s)\n", - " like = np.sum(like_vect)\n", - " return like + prior, like_vect\n", - "\n", - "\n", - "sampler_blobs = emcee.EnsembleSampler(\n", - " nwalkers,\n", - " ndim,\n", - " lnprob_8school_blobs,\n", - " args=(y_obs, sigma),\n", - ")\n", - "sampler_blobs.run_mcmc(pos, draws);" - ] - }, - { - "cell_type": "markdown", - "id": "928030fc", - "metadata": {}, - "source": [ - "You can now use the `blob_names` argument to indicate how to store this blob-defined variable. As the group is not specified, it will go to sample_stats.\n", - "Note that the argument blob_names is added to the arguments covered in the previous examples and we are also introducing `coords` and `dims` arguments to show the power and flexibility of the converter. For more on `coords` and `dims` see `page_in_construction`." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0bac089b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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-       "Dimensions:  (chain: 40, draw: 1500, school: 8)\n",
-       "Coordinates:\n",
-       "  * chain    (chain) int64 0 1 2 3 4 5 6 7 8 9 ... 30 31 32 33 34 35 36 37 38 39\n",
-       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 ... 1493 1494 1495 1496 1497 1498 1499\n",
-       "  * school   (school) int64 0 1 2 3 4 5 6 7\n",
-       "Data variables:\n",
-       "    mu       (chain, draw) float64 0.6982 0.7962 0.8433 ... 5.763 5.763 5.029\n",
-       "    tau      (chain, draw) float64 0.6679 0.7259 0.8075 ... 2.051 2.051 3.239\n",
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    \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> observed_data\n", - "\t> constant_data" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def lnprob_8school_blobs(theta, y, sigma):\n", - " mu, tau, eta = theta[0], theta[1], theta[2:]\n", - " prior = log_prior_8school(theta)\n", - " like_vect = log_likelihood_8school(theta, y, sigma)\n", - " like = np.sum(like_vect)\n", - " # store pointwise log likelihood, useful for model comparison with az.loo or az.waic\n", - " # and posterior predictive samples as blobs\n", - " return like + prior, (like_vect, np.random.normal((mu + tau * eta), sigma))\n", - "\n", - "\n", - "sampler_blobs = emcee.EnsembleSampler(\n", - " nwalkers,\n", - " ndim,\n", - " lnprob_8school_blobs,\n", - " args=(y_obs, sigma),\n", - ")\n", - "sampler_blobs.run_mcmc(pos, draws)\n", - "\n", - "dims = {\"eta\": [\"school\"], \"log_likelihood\": [\"school\"], \"y\": [\"school\"]}\n", - "idata4 = az.from_emcee(\n", - " sampler_blobs,\n", - " var_names=[\"mu\", \"tau\", \"eta\"],\n", - " slices=[0, 1, slice(2, None)],\n", - " arg_names=[\"y\", \"sigma\"],\n", - " arg_groups=[\"observed_data\", \"constant_data\"],\n", - " blob_names=[\"log_likelihood\", \"y\"],\n", - " blob_groups=[\"log_likelihood\", \"posterior_predictive\"],\n", - " dims=dims,\n", - " coords={\"school\": range(8)},\n", - ")\n", - "idata4" - ] - }, - { - "cell_type": "markdown", - "id": "50b59d48", - "metadata": {}, - "source": [ - "This last version, which contains both observed data and posterior predictive can be used to plot posterior predictive checks:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "c9f059a2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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-       "         5.92827468e+00,  6.21724225e+00,  2.51280189e+00,\n",
-       "         9.57306721e+00,  7.59353227e+00,  2.09202476e+00,\n",
-       "         9.88059803e+00,  4.54680518e+00, -4.30483067e+00,\n",
-       "         8.92024468e+00,  1.26309327e+00,  9.46959872e+00,\n",
-       "        -1.17827288e+00, -3.66655282e-01, -9.16874577e-01,\n",
-       "         6.03922031e+00,  1.88068890e+00,  5.80097792e+00,\n",
-       "         4.10859484e+00,  7.52813003e+00, -1.11027182e-01,\n",
-       "         1.37308819e+00,  1.19388966e+00,  8.58912431e-03,\n",
-       "         2.22575099e+00,  7.20720195e+00,  5.48023988e+00,\n",
-       "         1.28400000e+00, -2.20449686e-01,  5.18856371e+00,\n",
-       "         6.01845550e+00,  4.77155051e+00,  4.18297145e+00,\n",
-       "         5.43830940e+00,  2.46429298e+00,  7.28660900e+00,\n",
-       "         7.21764793e+00,  5.36153164e+00,  5.04585974e+00,\n",
-       "         1.40262626e+00,  1.40262626e+00,  7.26603051e+00,\n",
-       "         2.59531892e+00,  6.01218155e+00,  6.23284317e+00,\n",
-       "         2.91248685e+00,  5.84996530e+00,  7.94833070e+00,\n",
-       "         5.81831499e+00,  2.29860647e+00]])
          • theta_tilde
            (chain, draw, school)
            float64
            -0.3253 -0.6165 ... 1.222 0.2978
            array([[[-0.32529772, -0.61651454, -0.1667071 , ...,  1.03328716,\n",
-       "          2.1823746 ,  0.47316842],\n",
-       "        [ 2.02958114,  1.38140157,  0.75320748, ..., -0.76415401,\n",
-       "         -0.26376613,  2.59527651],\n",
-       "        [-1.29974382, -0.93838206, -1.66930843, ...,  0.45610521,\n",
-       "          1.16510883, -1.90862339],\n",
-       "        ...,\n",
-       "        [ 0.76405243,  1.57497136, -0.19382101, ..., -0.90719718,\n",
-       "          0.13818502, -0.53287742],\n",
-       "        [-0.01956823, -0.44742285,  0.22391893, ...,  1.26672305,\n",
-       "          1.48674961,  0.59971362],\n",
-       "        [-1.31647166,  0.85301164,  0.48705457, ...,  1.10464535,\n",
-       "         -0.20971009,  0.16073751]],\n",
-       "\n",
-       "       [[ 0.39528795,  2.42188047, -0.63226669, ..., -1.08800486,\n",
-       "         -1.18916527, -1.15738863],\n",
-       "        [-1.14188208,  1.13112039,  0.07025925, ..., -0.71775304,\n",
-       "          0.42484354,  0.43962524],\n",
-       "        [ 0.71622022, -1.36901772,  0.34158932, ...,  1.14499937,\n",
-       "         -0.08757469, -1.25347347],\n",
-       "        ...,\n",
-       "        [ 0.18628521,  1.50088211, -0.65171658, ...,  0.48396343,\n",
-       "          0.77830976, -0.36442883],\n",
-       "        [ 0.86143906, -1.5081053 ,  0.16205563, ..., -1.43705565,\n",
-       "         -0.382151  , -0.01870404],\n",
-       "        [ 0.70965545,  1.59856161, -0.47552168, ...,  0.91627404,\n",
-       "          1.22218512,  0.29782471]]])
          • tau
            (chain, draw)
            float64
            3.1 1.632 1.529 ... 2.741 4.724
            array([[3.09988894e+00, 1.63207672e+00, 1.52902197e+00, 2.30099852e+00,\n",
-       "        8.31268078e-01, 1.88898817e+00, 5.85874168e+00, 4.71824548e+00,\n",
-       "        2.36699580e+00, 2.34732021e+00, 2.05737377e+00, 2.22388599e+00,\n",
-       "        1.26633038e+00, 3.10758565e+00, 2.43266266e+00, 2.49894565e+00,\n",
-       "        3.62801031e+00, 2.66434647e+00, 6.03401501e+00, 4.20103917e+00,\n",
-       "        2.87235863e+00, 9.61176630e+00, 9.61176630e+00, 4.47341001e+00,\n",
-       "        5.66040767e+00, 9.82792942e-01, 1.62994744e+00, 6.81143684e+00,\n",
-       "        1.42101541e+00, 3.99436157e+00, 1.06113735e+00, 6.66151684e+00,\n",
-       "        1.44678348e+00, 9.29213043e-01, 6.00298896e-01, 8.65854134e+00,\n",
-       "        8.65854134e+00, 7.49195541e+00, 3.92008212e-01, 4.67262667e-01,\n",
-       "        1.14031719e+01, 1.91770387e-01, 1.13407014e-01, 2.31341743e-01,\n",
-       "        1.87226273e+01, 3.07049024e-01, 3.79160657e+00, 2.29027053e+00,\n",
-       "        4.54164481e+00, 3.06000216e+00, 1.61702924e+00, 1.42514668e+00,\n",
-       "        2.69966316e+00, 7.76593714e+00, 9.35329781e-01, 4.80484917e+00,\n",
-       "        4.60629543e-02, 3.78059536e-02, 1.46668064e-01, 7.85845041e-02,\n",
-       "        2.45290474e+00, 3.99644466e+00, 2.81231960e+00, 3.45085186e+00,\n",
-       "        1.00796319e+00, 2.72362547e+00, 1.61928447e+00, 2.86957381e+00,\n",
-       "        3.01123031e+00, 7.49851917e-01, 9.57235307e-01, 2.35480165e+00,\n",
-       "        9.53584743e+00, 1.50321215e+00, 8.66088994e+00, 3.95555017e+00,\n",
-       "        8.28477536e-01, 1.16039960e+00, 5.29813451e+00, 1.82651411e-01,\n",
-       "        1.50293359e+01, 1.50293359e+01, 1.70908463e+00, 4.49037167e+00,\n",
-       "        3.72729985e+00, 4.20267362e-02, 2.15497907e-01, 1.44651372e-01,\n",
-       "        8.97409661e+00, 3.95659504e+00, 1.74384378e+00, 3.01505721e+00,\n",
-       "        8.89669163e+00, 6.33618449e+00, 3.36126815e+00, 3.36126815e+00,\n",
-       "        1.03556605e+01, 8.41171251e-01, 3.08236370e+00, 7.71365841e+00,\n",
-       "        3.40615951e+00, 5.29949043e+00, 3.89052725e+00, 2.97310920e-01,\n",
-       "        3.79674976e-01, 5.40723457e+00, 3.21220675e-01, 3.39422976e+00,\n",
-       "        5.62340485e+00, 7.33074407e+00, 2.03764109e+00, 5.68137917e+00,\n",
-       "        5.68137917e+00, 2.83686052e+00, 4.91676473e+00, 6.62090533e+00,\n",
-       "        6.64313421e+00, 3.60767832e+00, 2.64339476e+00, 5.23419542e+00,\n",
-       "        6.54119359e-01, 9.90659523e-01, 1.26385767e+00, 9.46167070e-01,\n",
-       "        3.42942204e+00, 1.17157162e+01, 1.51882166e+00, 7.75894868e+00,\n",
-       "        1.86186982e+00, 1.05676321e+01, 8.81156578e+00, 2.97112722e+00,\n",
-       "        4.97560335e+00, 4.97560335e+00, 2.37615802e+00, 4.85582620e-01,\n",
-       "        1.75115034e-01, 2.24492144e-02, 5.19194864e-03, 2.44152982e-03,\n",
-       "        4.96016077e-04, 9.81960781e-04, 2.61456184e-01, 3.04503815e+00,\n",
-       "        1.49740455e+00, 3.09210525e+00, 2.38372922e+00, 1.07710929e+00,\n",
-       "        7.37178789e-01, 1.44477609e+00, 4.44958349e-01, 7.56283868e+00,\n",
-       "        1.30693621e+01, 6.00919393e+00, 3.27627074e+00, 3.27627074e+00,\n",
-       "        3.61464843e+00, 1.42101331e+00, 4.23921964e-01, 8.45466943e+00,\n",
-       "        4.63349584e+00, 4.46047362e+00, 8.78396465e+00, 3.68844510e+00,\n",
-       "        6.55355892e+00, 2.16129532e+00, 2.77333940e+00, 3.76898084e+00,\n",
-       "        9.47836834e-01, 1.15779865e+00, 1.08542059e+01, 7.84930686e-01,\n",
-       "        2.53342726e+00, 9.22488199e-01, 6.24873580e+00, 6.24873580e+00,\n",
-       "        5.37298831e+00, 2.12673184e+00, 1.43190109e+00, 3.72911093e+00,\n",
-       "        1.67789722e+00, 4.10229845e+00, 2.91020442e+00, 4.75219278e+00,\n",
-       "        2.05454858e+00, 7.02387879e+00, 1.92291069e+00, 3.57246023e-01,\n",
-       "        2.83701174e+00, 7.95061579e+00, 3.87302365e+00, 6.27845736e+00,\n",
-       "        7.65529077e-01, 2.85486857e+00, 4.27274083e+00, 1.63762758e+00,\n",
-       "        7.98990100e-01, 1.20889927e+00, 3.59308318e+00, 3.23508196e+00,\n",
-       "        5.48955086e+00, 3.47140380e+00, 3.47140380e+00, 2.14376772e+00,\n",
-       "        5.27427110e-01, 3.29333098e+00, 1.75016375e+00, 1.22541172e+00,\n",
-       "        1.15733505e+01, 1.15733505e+01, 2.77463974e+00, 3.07338819e+00,\n",
-       "        3.07338819e+00, 1.70827704e+00, 4.18041312e+00, 7.70963663e-01,\n",
-       "        8.35031192e-01, 6.53935957e+00, 3.63566215e-01, 9.72246066e+00,\n",
-       "        1.40166669e+00, 3.38578921e+00, 3.07436946e+00, 9.76477570e-01,\n",
-       "        2.11253628e+00, 2.11351930e+00, 2.46627791e-01, 1.65123263e+00,\n",
-       "        1.29966369e+00, 2.56888919e+00, 1.00424894e+01, 3.52674417e+00,\n",
-       "        6.82369555e+00, 2.63738500e+00, 7.83608447e+00, 2.70102342e+00,\n",
-       "        4.45264351e+00, 3.08078679e-01, 4.49708000e+00, 9.99135072e+00,\n",
-       "        3.55835310e+00, 1.16907517e+00, 2.02513622e+00, 1.02567962e-01,\n",
-       "        5.16058441e+00, 6.94456578e-01, 3.04556772e+00, 8.53439537e+00,\n",
-       "        4.36448122e+00, 4.36448122e+00, 2.58496665e+00, 1.00182772e+00,\n",
-       "        3.81383379e+00, 3.74173366e+00, 4.48486880e+00, 2.82288845e+00,\n",
-       "        7.22636068e-01, 3.56734285e+00, 1.67506117e+00, 2.02941736e+00,\n",
-       "        4.12335849e+00, 7.57508195e+00, 3.25739237e+00, 3.14910194e+00,\n",
-       "        4.83292307e+00, 3.78441995e+00, 1.01299820e+00, 1.98906741e+00,\n",
-       "        3.41574334e+00, 2.06843286e+00, 1.55608621e+00, 1.92423040e-01,\n",
-       "        5.08025259e+00, 6.16125429e+00, 7.36498515e-01, 1.24789864e+00,\n",
-       "        6.27956089e+00, 6.27956089e+00, 7.49902395e-01, 2.11452399e+00,\n",
-       "        2.24293487e-01, 3.35055904e-01, 1.68753300e-02, 4.18807137e-01,\n",
-       "        4.58889059e+00, 1.54259092e+00, 1.03803681e+00, 5.31534450e+00,\n",
-       "        2.60984305e+00, 2.99648210e+00, 4.91208069e+00, 9.90204033e+00,\n",
-       "        1.35299652e+00, 8.37925328e+00, 3.34839656e+00, 5.28114427e+00,\n",
-       "        2.21231385e+00, 7.81986140e-01, 2.68384539e+00, 1.82893404e+00,\n",
-       "        2.51582305e+00, 2.22615720e+00, 7.25987045e+00, 8.99140943e+00,\n",
-       "        8.99140943e+00, 7.81139000e+00, 4.75357076e+00, 9.80665484e-01,\n",
-       "        6.26918311e-01, 4.63030324e-02, 7.68659875e-01, 2.10636907e+00,\n",
-       "        5.41121322e+00, 9.55072350e-01, 3.78895489e+00, 1.53556405e+00,\n",
-       "        1.36718670e+00, 5.32069899e+00, 5.32069899e+00, 2.10949104e+00,\n",
-       "        2.10949104e+00, 4.48217733e+00, 3.45878976e+00, 5.23224937e+00,\n",
-       "        7.23491941e-01, 1.09630159e+01, 1.90895106e+00, 1.94941130e+00,\n",
-       "        8.02391448e+00, 2.85057302e+00, 5.51741597e+00, 2.33330862e+00,\n",
-       "        5.63950929e+00, 2.00352029e+00, 4.18123136e+00, 7.96439656e+00,\n",
-       "        1.25523482e+00, 4.27480930e+00, 8.66754251e-01, 1.33945808e+00,\n",
-       "        1.96201914e+00, 1.33772085e+00, 1.78255793e+00, 2.23659524e+00,\n",
-       "        1.94135128e+00, 2.79944847e+00, 1.10391683e-01, 1.56316513e+00,\n",
-       "        3.72869234e+00, 9.55436772e-01, 2.13150808e+00, 4.47963252e+00,\n",
-       "        3.78356295e+00, 5.10899353e-01, 4.26619097e+00, 1.87941457e+01,\n",
-       "        1.87941457e+01, 3.77009222e+00, 6.41488833e-01, 6.80390052e-01,\n",
-       "        1.99263496e+00, 5.13747821e+00, 5.13747821e+00, 3.14078111e+00,\n",
-       "        5.88111221e+00, 3.29812183e+00, 5.75164624e+00, 2.69121554e+00,\n",
-       "        1.56347605e+00, 5.27496678e+00, 2.87100899e+00, 2.31302906e+00,\n",
-       "        1.68828031e+00, 1.09680422e+00, 2.91573241e-01, 1.42346712e+00,\n",
-       "        9.79412468e-01, 3.49815706e+00, 4.95950372e+00, 7.14087628e+00,\n",
-       "        1.62857484e+01, 4.72245450e-01, 5.43036556e+00, 3.80175888e-01,\n",
-       "        2.74008719e-01, 5.03512295e-01, 2.38120067e+00, 3.01954806e-01,\n",
-       "        2.33082112e-01, 6.65830660e-02, 3.33221349e+00, 1.93945827e+00,\n",
-       "        3.12383685e+00, 8.70943321e+00, 1.52682644e+00, 3.68808675e+00,\n",
-       "        5.24348750e+00, 1.95509466e+00, 3.66047354e+00, 5.97797944e+00,\n",
-       "        5.97797944e+00, 7.97725643e-01, 1.44672287e+00, 1.86453076e+00,\n",
-       "        2.15691321e+00, 1.40205795e+00, 1.28605987e+00, 6.35787524e+00,\n",
-       "        3.15948844e+00, 1.94095239e+00, 8.69195133e+00, 8.38406149e-01,\n",
-       "        5.38691304e+00, 1.15395625e+00, 9.96413349e-01, 4.18266814e+00,\n",
-       "        3.76753069e+00, 1.38748987e+00, 4.47522270e+00, 1.59775810e+00,\n",
-       "        2.60969544e+00, 2.33253345e-01, 2.34764683e+00, 1.44320625e+00,\n",
-       "        3.33586202e+00, 4.94987324e+00, 4.97271936e+00, 2.10739974e+00,\n",
-       "        2.33071428e+00, 6.63438674e-01, 4.57847467e+00, 2.01055839e+00,\n",
-       "        3.69642715e+00, 1.17259406e+01, 1.17259406e+01, 3.66305533e+00,\n",
-       "        8.64030696e-01, 2.50151607e+00, 9.32171449e+00, 4.08032473e+00,\n",
-       "        4.08032473e+00, 1.77127432e+00, 1.16114849e+01, 1.31348481e+00,\n",
-       "        9.71958059e-01, 3.26610629e+00, 9.46118073e+00, 5.62409928e+00,\n",
-       "        6.30274536e+00, 5.42199958e+00, 1.34460486e+01, 1.34460486e+01,\n",
-       "        2.41417679e+00, 3.57253639e+00, 3.57253639e+00, 9.10126440e+00,\n",
-       "        6.56418646e+00, 1.31892758e+00, 1.71368899e+00, 3.49349047e+00,\n",
-       "        3.22679402e+00, 3.22679402e+00, 5.40345503e+00, 1.01589129e+01,\n",
-       "        4.15664338e-02, 2.18942477e-02, 2.86514992e-02, 2.21497457e-02,\n",
-       "        1.79285582e+01, 1.52018745e-01, 2.01387018e+00, 5.44963070e-02,\n",
-       "        4.47199619e-02, 8.83507675e-01, 1.80266425e+00, 1.90345940e+00,\n",
-       "        2.63387518e-01, 2.40564145e+00, 9.87268294e-01, 3.78376592e+00,\n",
-       "        6.91571313e-01, 7.74075349e+00, 7.85526840e-01, 5.25342514e+00,\n",
-       "        2.59837379e+00, 3.78014832e+00, 1.65762665e+00, 2.03766737e+00,\n",
-       "        9.48164296e+00, 3.18067452e+00, 8.12671270e+00, 4.65211529e+00,\n",
-       "        1.35087314e+01, 7.04136640e+00, 1.79561310e+00, 1.30703893e+00,\n",
-       "        5.40688975e+00, 2.92931379e+00, 1.50542743e+00, 5.34863668e+00],\n",
-       "       [1.24679522e+00, 2.69730666e+00, 3.06708473e+00, 6.92341103e+00,\n",
-       "        7.26717041e+00, 9.72414460e+00, 1.35577385e+00, 1.82326047e+00,\n",
-       "        3.96073369e+00, 1.81744113e+00, 3.72269348e+00, 4.77067056e+00,\n",
-       "        1.56675436e+00, 1.02068900e+00, 4.81841901e+00, 9.12498146e+00,\n",
-       "        7.25951080e-02, 1.10271479e+01, 7.45875275e+00, 2.88800003e-01,\n",
-       "        1.81995149e-01, 5.12095187e+00, 9.87610764e-01, 3.69307794e+00,\n",
-       "        4.84798220e+00, 4.77871157e+00, 9.68037949e-01, 3.06196230e+00,\n",
-       "        1.15477283e+00, 6.61843048e+00, 5.59888635e+00, 4.14011897e+00,\n",
-       "        2.86431779e+00, 2.42811202e+00, 4.20164971e+00, 1.70820078e+00,\n",
-       "        3.75174586e+00, 3.37788148e+00, 5.25038077e+00, 2.60211647e+00,\n",
-       "        8.50346222e-01, 1.60489849e+00, 6.17231562e+00, 2.05356030e+00,\n",
-       "        8.92248648e+00, 1.06028349e+00, 2.38133402e+00, 2.00470276e+00,\n",
-       "        4.26467659e+00, 9.47934506e-01, 2.76079372e+00, 1.41926392e+00,\n",
-       "        4.82552494e+00, 4.41810884e+00, 2.49454359e+00, 5.94035090e+00,\n",
-       "        3.60946912e+00, 2.36784597e+00, 6.04802347e+00, 3.26922634e+00,\n",
-       "        1.84098669e+00, 2.75330362e+00, 3.40776199e-01, 4.93352194e+00,\n",
-       "        8.42721389e-01, 1.88560352e+00, 8.80146862e-01, 1.19693262e+01,\n",
-       "        3.23015199e+00, 6.10118786e+00, 6.10118786e+00, 6.10118786e+00,\n",
-       "        1.63457029e+00, 2.44627539e+00, 2.15039182e+00, 9.54263677e-01,\n",
-       "        4.59581704e+00, 1.27970621e+00, 8.90999077e-01, 2.59444082e+00,\n",
-       "        8.07765671e+00, 2.06489348e+00, 7.30716624e+00, 1.55705724e+00,\n",
-       "        1.95116251e-01, 6.26895788e+00, 3.16500180e+00, 2.64899286e+00,\n",
-       "        6.64882542e-01, 1.01369373e+01, 1.01369373e+01, 5.09688647e-01,\n",
-       "        8.00733988e+00, 8.00733988e+00, 5.51367313e+00, 1.75578277e+00,\n",
-       "        2.88077781e+00, 9.73438615e-01, 1.61544464e+00, 1.03220369e+01,\n",
-       "        3.08368988e+00, 1.61839634e+00, 6.21381470e+00, 2.73901378e+00,\n",
-       "        2.77239718e+00, 3.12358931e+00, 1.55532528e+00, 6.42751955e+00,\n",
-       "        2.50515300e+00, 2.07590623e+00, 9.79913098e-01, 9.64721831e-01,\n",
-       "        8.65572921e+00, 8.86250923e-01, 2.25136603e+00, 7.90066660e+00,\n",
-       "        1.85265675e-01, 6.44594794e+00, 1.18805200e+00, 2.82900436e+00,\n",
-       "        1.23349105e+00, 1.66764318e+00, 2.13600705e+00, 8.04333702e+00,\n",
-       "        3.09176790e+00, 3.63177716e+00, 1.98310717e+00, 1.10368125e+00,\n",
-       "        7.33181299e+00, 1.97166437e+00, 2.63493410e+00, 1.32731395e+00,\n",
-       "        2.85142135e+00, 3.22263124e+00, 6.69244299e+00, 3.98443734e+00,\n",
-       "        4.57490957e+00, 4.57490957e+00, 4.74879850e+00, 2.49749062e+00,\n",
-       "        5.08948541e+00, 4.79488230e+00, 5.33025905e+00, 2.13044289e+00,\n",
-       "        3.87433140e+00, 4.08152956e+00, 4.09627289e+00, 4.85782329e+00,\n",
-       "        4.85782329e+00, 1.89622359e+00, 3.55417008e+00, 4.20390028e+00,\n",
-       "        4.86505593e+00, 1.31719623e+00, 4.25118287e-01, 5.83370979e+00,\n",
-       "        1.60922560e+00, 1.74464914e+01, 1.15075290e+00, 2.30082422e+00,\n",
-       "        4.95258190e+00, 6.13898545e+00, 6.38753560e+00, 5.17571061e+00,\n",
-       "        7.47838700e-01, 6.50878033e+00, 2.91724933e+00, 3.36413301e+00,\n",
-       "        3.36413301e+00, 7.35672099e-01, 1.25595543e+00, 6.12928584e+00,\n",
-       "        3.70693918e-01, 3.27598876e-01, 4.28171982e+00, 1.35918814e+00,\n",
-       "        1.11209190e+01, 9.21687292e-02, 3.52776547e-01, 1.99177769e+00,\n",
-       "        2.73369093e+00, 2.09983715e+00, 2.05467612e+00, 2.98848085e+00,\n",
-       "        2.25755942e+00, 6.90530023e+00, 4.64255983e-01, 5.19906252e+00,\n",
-       "        6.09129927e-01, 9.18894282e+00, 5.10508085e-01, 5.11227240e+00,\n",
-       "        1.14972647e+00, 3.23907639e+00, 3.80036903e+00, 3.68816283e+00,\n",
-       "        1.91118207e+00, 2.60459123e+00, 1.63845804e+00, 5.97985216e+00,\n",
-       "        1.12298067e+00, 4.83394632e+00, 4.46988058e+00, 1.57787079e+00,\n",
-       "        4.71094053e+00, 3.00208086e+00, 3.81412747e+00, 3.08637257e-01,\n",
-       "        2.09300990e+00, 3.32846560e+00, 6.73214087e+00, 3.39545994e+00,\n",
-       "        7.46830145e+00, 2.53471019e+00, 7.43741906e-01, 6.87311468e-01,\n",
-       "        2.14268092e+00, 3.17597248e+00, 7.54868597e+00, 9.06028264e-01,\n",
-       "        8.03780173e+00, 3.49124570e+00, 2.02696755e+01, 2.24750423e+00,\n",
-       "        3.12782239e+00, 4.00393556e+00, 1.67990775e+00, 2.97917781e+00,\n",
-       "        5.67224459e+00, 1.05155587e+00, 8.34811215e-01, 5.59192368e+00,\n",
-       "        6.33305409e-01, 8.08629911e-01, 9.28525167e+00, 1.47331217e+00,\n",
-       "        7.35836287e+00, 4.28213077e+00, 4.41875045e+00, 5.43760749e+00,\n",
-       "        2.68760635e+00, 4.00252254e+00, 5.82620396e+00, 9.36370466e-01,\n",
-       "        8.44636664e+00, 2.70484604e+00, 4.75798008e+00, 1.13899875e+01,\n",
-       "        6.73646121e+00, 8.61142964e+00, 8.00575364e+00, 2.61346423e+00,\n",
-       "        2.64181585e+00, 1.47889937e+00, 8.39047969e+00, 1.64261744e-01,\n",
-       "        1.26094910e+00, 4.73942146e+00, 9.42158659e-01, 5.09953245e+00,\n",
-       "        3.04515105e+00, 6.92398732e+00, 9.15475583e+00, 9.34055603e+00,\n",
-       "        9.34055603e+00, 1.50525552e+01, 3.52253592e+00, 8.30393840e+00,\n",
-       "        3.20415695e-01, 5.56540289e-01, 8.12289162e-01, 1.08367839e+01,\n",
-       "        2.52925478e-01, 2.17691858e+00, 6.60660551e-01, 8.07436041e+00,\n",
-       "        8.14170694e-01, 9.81400007e-02, 8.16631769e-02, 9.50320682e-03,\n",
-       "        4.38901154e-02, 4.62650342e-01, 2.86580085e+00, 9.98134771e-01,\n",
-       "        3.46308983e+00, 2.47491866e+00, 1.73991775e+00, 1.26750078e+00,\n",
-       "        2.22276355e-01, 2.84272161e+00, 3.89759502e+00, 3.57384324e+00,\n",
-       "        2.58747694e+00, 7.87197187e+00, 2.39479038e+00, 3.28423496e+00,\n",
-       "        1.57064813e+00, 5.07859491e+00, 1.00456784e+01, 5.60688281e+00,\n",
-       "        1.76784621e+00, 5.34790323e+00, 1.54471064e+00, 1.37536857e+01,\n",
-       "        1.96878732e+00, 5.53015775e+00, 6.99402595e+00, 8.09870971e+00,\n",
-       "        1.59615648e+00, 2.85893262e+00, 6.26624606e+00, 1.42370624e+00,\n",
-       "        1.86458494e+00, 1.06274620e+00, 5.17332159e-01, 1.58439718e+00,\n",
-       "        5.01274221e+00, 2.48228633e+00, 2.48804834e+00, 7.78192623e-01,\n",
-       "        6.39927238e-01, 8.47501078e-01, 1.57846587e+00, 1.54192860e+00,\n",
-       "        6.12448298e+00, 3.41504117e+00, 1.04669325e+00, 8.26794693e-01,\n",
-       "        3.26448594e-01, 2.31055130e+00, 3.55396018e+00, 2.48894303e+00,\n",
-       "        5.73693319e+00, 5.97791264e-01, 3.49929145e+00, 3.40165053e+00,\n",
-       "        4.41449827e+00, 2.21977683e+00, 1.30474581e+01, 8.96360503e-01,\n",
-       "        7.85495457e+00, 3.68786391e-01, 7.10484820e-01, 4.20037716e-01,\n",
-       "        8.80345690e-01, 1.54460120e+00, 1.74275065e+00, 2.74753209e+00,\n",
-       "        1.26034447e+00, 2.50638706e+00, 2.65608510e+00, 5.90198610e+00,\n",
-       "        4.19635997e+00, 4.62376682e+00, 2.14554513e+00, 2.45664128e+00,\n",
-       "        1.85087527e+00, 4.07232553e+00, 3.20432446e+00, 2.92843027e+00,\n",
-       "        3.54979312e+00, 4.72110607e+00, 1.68868475e+00, 1.59983370e+00,\n",
-       "        1.52801921e+00, 5.57339445e+00, 4.15557352e+00, 1.78640308e+00,\n",
-       "        6.72885234e+00, 5.36081450e+00, 3.29838460e+00, 6.83324743e-01,\n",
-       "        1.99402329e+00, 4.12356458e+00, 3.86292350e-01, 1.03426798e-01,\n",
-       "        2.98910688e-01, 1.96901631e-01, 5.44922239e+00, 1.03903400e+00,\n",
-       "        5.23759149e+00, 2.79896505e+00, 6.97503607e+00, 1.49622930e+00,\n",
-       "        3.51469836e+00, 1.58002305e+00, 3.98545532e+00, 2.70836152e+00,\n",
-       "        1.62561548e+00, 4.32428599e+00, 3.07241158e+00, 9.00408379e+00,\n",
-       "        4.07247876e+00, 4.69332833e+00, 2.34669589e+00, 1.96065646e+00,\n",
-       "        5.39356713e+00, 2.08974930e+00, 3.44272039e+00, 8.86059996e-01,\n",
-       "        9.16080016e+00, 1.77550946e+00, 7.15100329e+00, 2.02439882e+00,\n",
-       "        2.43408258e+00, 3.38167253e+00, 3.93604714e+00, 3.22583456e+00,\n",
-       "        6.46415433e+00, 3.84153415e+00, 2.00919880e+00, 1.18775367e+00,\n",
-       "        1.37186338e+00, 1.99775159e+00, 1.80550083e+00, 2.83230389e+00,\n",
-       "        1.70997091e+00, 1.58043313e+00, 2.02102196e+00, 1.63450468e+00,\n",
-       "        7.69180995e+00, 6.62055891e+00, 1.27277253e+00, 1.79103936e+00,\n",
-       "        1.91416103e+00, 6.76462938e-01, 9.26518218e-01, 2.13629891e+00,\n",
-       "        1.65068060e+00, 1.74428204e+00, 4.69424515e+00, 2.53000972e+00,\n",
-       "        3.84178230e+00, 6.70391502e+00, 5.18767493e+00, 2.42109655e+00,\n",
-       "        3.21665057e+00, 5.25180278e+00, 1.76820664e+00, 1.01972651e+01,\n",
-       "        4.47006003e+00, 4.08581466e+00, 1.16629333e+01, 1.50425335e-01,\n",
-       "        1.19086084e+00, 5.36757088e+00, 3.57395144e+00, 3.16027541e+00,\n",
-       "        5.57001512e+00, 1.67214931e+00, 1.44469516e+00, 2.67965652e+00,\n",
-       "        3.50672052e+00, 4.17309155e+00, 2.62906183e+00, 1.89809515e+00,\n",
-       "        1.57168608e+00, 5.59775810e+00, 1.15598330e+01, 3.75898940e+00,\n",
-       "        2.35645221e+00, 4.35924984e+00, 9.95338301e+00, 1.05207855e+01,\n",
-       "        2.93462299e+00, 9.01546173e+00, 2.71570686e+00, 8.66364803e-01,\n",
-       "        9.96241715e+00, 5.42643053e-01, 1.37701461e+01, 3.24010841e+00,\n",
-       "        6.73262365e+00, 2.02728617e+00, 5.19188908e+00, 1.63399175e+00,\n",
-       "        3.99096094e+00, 3.26935682e+00, 2.47952116e+00, 1.61216331e+00,\n",
-       "        5.71464923e+00, 8.81879098e+00, 3.65560747e+00, 5.33226726e+00,\n",
-       "        6.11604399e-01, 5.65137882e+00, 2.69282071e+00, 2.52869261e+00,\n",
-       "        4.08528793e+00, 3.98212294e+00, 3.98212294e+00, 1.09339140e+00,\n",
-       "        6.84031531e+00, 1.65611617e+00, 3.83101526e+00, 5.54784515e-01,\n",
-       "        1.90193871e+00, 1.83026382e+00, 2.74058114e+00, 4.72413170e+00]])
          • theta
            (chain, draw, school)
            float64
            5.63 4.727 6.121 ... 8.072 3.706
            array([[[ 5.62961003,  4.72687023,  6.12122332, ...,  9.84107226,\n",
-       "         13.40311572,  8.10476638],\n",
-       "        [ 8.10377285,  7.04589407,  6.02063312, ...,  3.54418276,\n",
-       "          4.36085417,  9.02703109],\n",
-       "        [ 3.57690532,  4.12943539,  3.0118329 , ...,  6.26163706,\n",
-       "          7.34571917,  2.64591509],\n",
-       "        ...,\n",
-       "        [ 3.97800088,  6.35343688,  1.17208901, ..., -0.91761363,\n",
-       "          2.14463885,  0.17888639],\n",
-       "        [ 5.98201937,  5.33791529,  6.34857162, ...,  7.91843753,\n",
-       "          8.24967156,  6.91430325],\n",
-       "        [-3.59497067,  8.00880728,  6.05143585, ...,  9.35470458,\n",
-       "          2.32469486,  4.30608446]],\n",
-       "\n",
-       "       [[ 5.32205148,  7.84879735,  4.04090126, ...,  3.47268908,\n",
-       "          3.34656278,  3.38618173],\n",
-       "        [ 5.83158394, 11.96256866,  9.10110082, ...,  6.97559004,\n",
-       "         10.0575234 , 10.09739417],\n",
-       "        [ 3.56793512, -2.82766633,  2.4189104 , ...,  4.8830371 ,\n",
-       "          1.10262802, -2.47328233],\n",
-       "        ...,\n",
-       "        [ 8.28928178, 10.69534092,  6.75551742, ...,  8.83411145,\n",
-       "          9.3728429 ,  7.28132981],\n",
-       "        [ 8.17915864,  1.68523006,  6.26244159, ...,  1.87994739,\n",
-       "          4.77099916,  5.76705506],\n",
-       "        [ 5.6511123 ,  9.85042207,  0.05217945, ...,  6.62720573,\n",
-       "          8.07236995,  3.70556965]]])
        • created_at :
          2020-06-10T18:19:23.327079
          arviz_version :
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          inference_library :
          pymc3
          inference_library_version :
          3.8

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      • \n", - " \n", - "
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        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • obs_dim_0: 8
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (chain, draw, obs_dim_0)
            float64
            15.57 12.1 3.026 ... 18.62 -10.4
            array([[[ 15.56955881,  12.10362895,   3.0259356 , ...,   5.25677454,\n",
-       "          -8.13085903,   8.92486203],\n",
-       "        [  2.01095742,  23.8437438 ,  -0.74217338, ...,   3.79923879,\n",
-       "           2.69217957,  14.48449805],\n",
-       "        [ 20.29470416,   4.96292701,  -0.2854632 , ...,  34.46851646,\n",
-       "          16.74562782, -22.1119676 ],\n",
-       "        ...,\n",
-       "        [-11.94803447,   0.0896506 ,  28.35944418, ...,  -1.47988496,\n",
-       "           4.03098823,   8.01120592],\n",
-       "        [  4.86526649,  10.98289441,   1.4086219 , ...,  16.58321832,\n",
-       "          50.8494954 ,  22.64208451],\n",
-       "        [-13.88113504,   6.71350581,  18.17528719, ..., -18.6932786 ,\n",
-       "           9.77879534,  42.17198861]],\n",
-       "\n",
-       "       [[ -0.71864835, -14.63818199, -27.80108664, ...,   3.88081011,\n",
-       "           9.8554434 ,  -1.74781512],\n",
-       "        [  6.95980651,   3.50331516,  -3.28409145, ...,  10.57124477,\n",
-       "          19.94187807, -14.3233147 ],\n",
-       "        [  1.66781587, -10.98676567,  -4.72720481, ...,  11.92457337,\n",
-       "         -10.67383229,   6.63788789],\n",
-       "        ...,\n",
-       "        [ 29.8775133 ,   5.52945089,   3.75273982, ...,   7.82224107,\n",
-       "          -0.76100621,  23.18560467],\n",
-       "        [  5.19149401,   0.90691355, -28.91017224, ...,  -0.97321204,\n",
-       "          22.94379602,  18.21314178],\n",
-       "        [ 41.27000137,  15.79815357, -16.03026608, ...,  15.09251082,\n",
-       "          18.6168739 , -10.40009236]]])
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          arviz_version :
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      • \n", - " \n", - "
    • \n", - " \n", - " \n", - "
      \n", - "
      \n", - "
        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • obs_dim_0: 8
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (chain, draw, obs_dim_0)
            float64
            -4.739 -3.275 ... -3.714 -3.915
            array([[[-4.73906506, -3.27509052, -3.85402084, ..., -3.63982785,\n",
-       "         -3.32718035, -3.83272518],\n",
-       "        [-4.5066773 , -3.22607522, -3.8504566 , ..., -3.34358119,\n",
-       "         -4.15165512, -3.82295002],\n",
-       "        [-4.95251663, -3.29642998, -3.76211736, ..., -3.43123391,\n",
-       "         -3.78909213, -3.94433946],\n",
-       "        ...,\n",
-       "        [-4.90933638, -3.23507948, -3.72552399, ..., -3.33202902,\n",
-       "         -4.47848601, -4.02495647],\n",
-       "        [-4.70430311, -3.2569571 , -3.86222216, ..., -3.51462214,\n",
-       "         -3.69686815, -3.84922435],\n",
-       "        [-5.84530467, -3.22152401, -3.85154384, ..., -3.60526806,\n",
-       "         -4.45009958, -3.90066266]],\n",
-       "\n",
-       "       [[-4.76985395, -3.22163794, -3.78835204, ..., -3.34209906,\n",
-       "         -4.29513974, -3.92381317],\n",
-       "        [-4.71907467, -3.30003338, -3.97753632, ..., -3.46438619,\n",
-       "         -3.5369383 , -3.81489657],\n",
-       "        [-4.9534905 , -3.80771542, -3.74887997, ..., -3.3791395 ,\n",
-       "         -4.64912952, -4.13257557],\n",
-       "        ...,\n",
-       "        [-4.49034965, -3.25784794, -3.8774064 , ..., -3.57044249,\n",
-       "         -3.59366282, -3.84367117],\n",
-       "        [-4.50002374, -3.42090522, -3.85909137, ..., -3.32003342,\n",
-       "         -4.09655594, -3.86926338],\n",
-       "        [-4.73692825, -3.23864394, -3.70972218, ..., -3.44768275,\n",
-       "         -3.71431282, -3.91547939]]])
        • created_at :
          2020-06-10T18:19:23.474654
          arviz_version :
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          inference_library :
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        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • energy
            (chain, draw)
            float64
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-       "        51.2957646 , 54.69912894, 52.91242272, 47.38469588, 50.60785358,\n",
-       "        47.95949667, 48.71518855, 48.1594468 , 46.50873061, 51.51031032,\n",
-       "        51.30049846, 50.44937873, 54.15149263, 52.56008691, 54.29206958,\n",
-       "        54.81863257, 53.21781717, 49.41075476, 48.87790719, 48.7661371 ,\n",
-       "        54.6411877 , 54.67886209, 50.13460669, 50.58819395, 46.52766041]])
          • step_size_bar
            (chain, draw)
            float64
            0.6188 0.6188 ... 0.4424 0.4424
            array([[0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ,\n",
-       "        0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 , 0.6188256 ],\n",
-       "       [0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
-       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193]])
          • mean_tree_accept
            (chain, draw)
            float64
            1.0 0.4507 1.0 ... 0.9712 0.9935
            array([[1.00000000e+000, 4.50671723e-001, 1.00000000e+000,\n",
-       "        9.84083312e-001, 8.84419505e-001, 9.96869804e-001,\n",
-       "        9.89628920e-001, 8.62568351e-001, 9.85472261e-001,\n",
-       "        4.22377986e-001, 9.60298065e-001, 9.87207800e-001,\n",
-       "        8.80383510e-001, 8.87996630e-001, 7.75787976e-001,\n",
-       "        1.00000000e+000, 9.87113252e-001, 9.80601135e-001,\n",
-       "        8.47726582e-001, 7.19778758e-001, 8.97619585e-001,\n",
-       "        1.44855769e-001, 2.98298347e-001, 8.13732846e-001,\n",
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-       "        1.44676916e-001, 9.29609197e-001, 9.77275547e-001,\n",
-       "        2.71011611e-001, 9.67923108e-001, 9.29981363e-001,\n",
-       "        8.88573398e-001, 9.94708921e-001, 9.49523011e-001,\n",
-       "        9.89263407e-001, 9.82159915e-001, 9.21548082e-001,\n",
-       "        6.11092334e-001, 9.97523067e-001, 8.84511095e-001,\n",
-       "        9.70288964e-001, 9.62247950e-001, 8.13831477e-001,\n",
-       "        9.79740680e-001, 9.60187791e-001, 7.39476447e-001,\n",
-       "        8.16858344e-001, 9.62282760e-001, 9.64015648e-001,\n",
-       "        9.26618007e-001, 8.03640939e-001, 9.89798229e-001,\n",
-       "        9.20630601e-001, 9.73831343e-001, 9.39612786e-001,\n",
-       "        9.89602013e-001, 8.31983694e-001, 9.98690219e-001,\n",
-       "        9.77639073e-001, 1.00000000e+000, 9.80649004e-001,\n",
-       "        7.69267181e-001, 8.92907972e-001, 9.94293952e-001,\n",
-       "        9.01223037e-001, 9.72418989e-001, 9.81164809e-001,\n",
-       "        9.03453967e-001, 9.96054730e-001, 5.59525351e-001,\n",
-       "        9.95348273e-001, 7.98633617e-001, 8.77325269e-001,\n",
-       "        9.83331317e-001, 3.90902716e-001, 9.79332640e-001,\n",
-       "        9.74127534e-001, 4.50074264e-001, 8.38049960e-001,\n",
-       "        7.68579732e-002, 9.66325610e-001, 9.93313629e-001,\n",
-       "        6.47304242e-001, 8.69869120e-001, 8.37211897e-001,\n",
-       "        9.83199430e-001, 4.37429024e-001, 9.37301515e-001,\n",
-       "        1.60795702e-002, 1.80639407e-001, 4.31620574e-001,\n",
-       "        9.76523772e-001, 4.30740143e-001, 9.06878162e-001,\n",
-       "        5.74473577e-001, 9.73795204e-001, 1.00000000e+000,\n",
-       "        6.50207891e-001, 9.93753202e-001, 9.52524180e-001,\n",
-       "        9.92896342e-001, 7.31238657e-001, 9.49123785e-001,\n",
-       "        6.71207423e-001, 9.77894909e-001, 3.74868040e-001,\n",
-       "        9.44448423e-001, 9.66477679e-001, 9.87138720e-001,\n",
-       "        9.09704105e-001, 9.83395464e-001, 8.01274007e-001,\n",
-       "        7.89448924e-001, 8.21409164e-001, 9.42463389e-001,\n",
-       "        9.79780242e-001, 7.49125807e-001, 9.92424146e-001,\n",
-       "        6.99023733e-001, 9.09972014e-001, 9.12205907e-001,\n",
-       "        8.90706226e-001, 5.40540598e-001, 9.92769685e-001,\n",
-       "        8.13835268e-001, 9.99086032e-001, 9.95546042e-001,\n",
-       "        1.00000000e+000, 9.38014644e-001, 1.90608413e-001,\n",
-       "        1.00000000e+000, 9.55855640e-001, 8.15048232e-001,\n",
-       "        9.37276439e-001, 9.84767085e-001, 7.58388823e-001,\n",
-       "        9.90774947e-001, 9.94336592e-001, 6.80002580e-001,\n",
-       "        6.55607632e-001, 8.79611368e-001, 9.92928091e-001,\n",
-       "        9.32765535e-001, 9.74458631e-001, 9.97860374e-001,\n",
-       "        8.51225575e-001, 9.37751930e-001, 9.10183081e-001,\n",
-       "        9.17020768e-001, 1.00000000e+000, 9.86404911e-001,\n",
-       "        8.62356279e-001, 9.60048557e-001, 5.31342359e-001,\n",
-       "        8.12732517e-001, 9.92717382e-001, 9.97553965e-001,\n",
-       "        3.01832200e-001, 9.96128596e-001, 8.77590631e-001,\n",
-       "        1.00000000e+000, 9.86484943e-001, 7.48789338e-001,\n",
-       "        7.60629596e-001, 9.02519643e-001, 1.00000000e+000,\n",
-       "        8.76007973e-001, 8.65114983e-001, 9.96436417e-001,\n",
-       "        9.46368712e-001, 9.62792077e-001, 9.52414419e-001,\n",
-       "        8.47955480e-001, 9.91874114e-001, 9.86230256e-001,\n",
-       "        9.94163413e-001, 9.99313914e-001, 8.61302289e-001,\n",
-       "        9.14317420e-001, 9.50097933e-001, 7.76309186e-001,\n",
-       "        9.76887202e-001, 9.57457100e-001, 9.54560375e-001,\n",
-       "        9.68189396e-001, 9.58733692e-001, 9.92666973e-001,\n",
-       "        6.12553202e-001, 9.88046020e-001, 9.67017050e-001,\n",
-       "        9.38095092e-001, 9.44329823e-001, 9.48251262e-001,\n",
-       "        9.12011432e-001, 8.05815147e-001, 3.01454697e-001,\n",
-       "        9.88776102e-001, 9.32445038e-001, 8.25879760e-001,\n",
-       "        5.56368140e-001, 1.00000000e+000, 9.87556731e-001,\n",
-       "        9.18449703e-001, 8.75782460e-001, 9.97836835e-001,\n",
-       "        8.99619522e-001, 9.54744730e-001, 9.99854772e-001,\n",
-       "        9.98885645e-001, 7.01280823e-001, 9.90628011e-001,\n",
-       "        9.14458462e-001, 9.63638478e-001, 9.37876978e-001,\n",
-       "        9.77900327e-001, 5.22784738e-001, 4.03676032e-001,\n",
-       "        9.37313500e-001, 8.97972642e-001, 9.99783799e-001,\n",
-       "        8.86523265e-001, 8.74092337e-001, 1.00000000e+000,\n",
-       "        7.15666285e-001, 4.36484787e-001, 9.47444772e-001,\n",
-       "        9.86001975e-001, 9.21410448e-001, 9.59581517e-001,\n",
-       "        9.41665390e-001, 8.65504066e-001, 9.28833560e-001,\n",
-       "        9.71127158e-001, 8.49226397e-001, 9.85351190e-001,\n",
-       "        9.59521856e-001, 9.44883522e-001, 9.62561802e-001,\n",
-       "        8.91407891e-001, 9.98951233e-001, 9.91725091e-001,\n",
-       "        9.41195576e-001, 9.54785312e-001, 9.89622944e-001,\n",
-       "        9.72211466e-001, 7.68030455e-001, 9.18510998e-001,\n",
-       "        9.77385875e-001, 9.34273126e-001, 8.13575755e-001,\n",
-       "        1.00000000e+000, 9.99165677e-001, 9.39979846e-001,\n",
-       "        9.04483454e-001, 9.12474278e-001, 9.64830930e-001,\n",
-       "        9.82811048e-001, 9.81284951e-001, 7.66248589e-001,\n",
-       "        1.00000000e+000, 8.33995819e-001, 9.77581930e-001,\n",
-       "        6.43567368e-001, 9.96575975e-001, 8.98904567e-001,\n",
-       "        5.74750264e-001, 3.17044175e-001, 1.00000000e+000,\n",
-       "        8.95102867e-001, 7.81600476e-001, 1.00000000e+000,\n",
-       "        9.19912251e-001, 9.96642046e-001, 7.92473741e-001,\n",
-       "        9.82915546e-001, 9.95027400e-001, 9.96178055e-001,\n",
-       "        8.56028297e-001, 9.47404325e-001, 9.36733098e-001,\n",
-       "        9.63971987e-001, 9.96684608e-001, 8.90387146e-001,\n",
-       "        7.34300724e-002, 9.69701948e-001, 9.44124130e-001,\n",
-       "        9.83594936e-001, 9.25784233e-001, 5.80448001e-001,\n",
-       "        1.00000000e+000, 9.92149129e-001, 9.40362213e-001,\n",
-       "        1.00000000e+000, 7.80406441e-001, 8.81126100e-001,\n",
-       "        6.64381931e-001, 9.86171098e-001, 4.61282937e-001,\n",
-       "        1.00000000e+000, 7.58499002e-001, 8.83415421e-001,\n",
-       "        9.64025159e-001, 9.60763067e-001, 9.48179446e-001,\n",
-       "        5.90266116e-001, 9.24629732e-001, 2.00058914e-001,\n",
-       "        6.05169585e-001, 8.18267303e-001, 9.97805436e-001,\n",
-       "        1.00000000e+000, 7.73787636e-001, 1.00000000e+000,\n",
-       "        9.83944219e-001, 1.20935597e-030, 8.86723710e-001,\n",
-       "        8.48147942e-001, 9.59844921e-001, 9.66619335e-001,\n",
-       "        9.33360236e-001, 9.39173966e-001, 9.64370008e-001,\n",
-       "        9.71246723e-001, 9.93454895e-001]])
          • step_size
            (chain, draw)
            float64
            0.5415 0.5415 ... 0.594 0.594
            array([[0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
-       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714],\n",
-       "       [0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
-       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896]])
          • max_energy_error
            (chain, draw)
            float64
            -0.149 2.22 ... 0.09263 -0.2259
            array([[-1.48989577e-01,  2.21957240e+00, -3.67390751e-01,\n",
-       "        -2.42733853e-01,  2.69058870e-01, -5.13461197e-01,\n",
-       "        -2.28594235e-01,  3.57237391e-01, -2.03883774e-01,\n",
-       "         7.00655278e+00,  1.86070210e-01, -8.55723457e-02,\n",
-       "         2.47567187e-01,  3.11260747e-01,  4.13640303e-01,\n",
-       "        -3.72090705e-01,  5.86569842e-02, -9.12109435e-02,\n",
-       "         3.36031533e-01,  6.74652403e-01,  2.65541267e-01,\n",
-       "         1.64492801e+01,  6.02296059e+00,  2.52295139e-01,\n",
-       "         2.93858142e-01,  2.64872629e-01,  5.14895274e+00,\n",
-       "        -5.04986109e-01,  5.81983258e+00, -7.97687495e-01,\n",
-       "         5.46892690e-01, -3.61385293e-01,  6.37158670e-01,\n",
-       "         1.67737325e+00,  1.01204856e+02,  2.31968101e-01,\n",
-       "         1.21123897e+00, -8.18909645e-01,  2.28325467e+00,\n",
-       "         1.22623819e+01,  6.61811222e-01,  4.04787278e-01,\n",
-       "         1.14418034e+01,  1.88960250e+00,  1.43463992e+00,\n",
-       "        -6.32821622e-01,  1.98704854e+00,  5.28814110e-01,\n",
-       "         3.57580789e-01,  1.28331070e-01, -4.49235034e-01,\n",
-       "        -3.11985230e-01, -7.58022294e-01, -4.40860802e-01,\n",
-       "         1.70592283e+00,  1.41383337e+00,  5.14964398e+01,\n",
-       "         1.45865826e+00,  1.14919658e+01,  1.98279240e+01,\n",
-       "        -5.46685951e-01, -2.17966784e-01,  7.03257474e-01,\n",
-       "         1.71235943e+00,  1.21280013e+00,  2.25584324e-01,\n",
-       "        -2.69255963e-01,  2.18114649e-01, -4.96155630e-01,\n",
-       "        -4.14788274e-01,  1.77062167e+00,  7.33345982e-01,\n",
-       "        -1.07290354e+00, -7.84985005e-01, -5.16348796e-01,\n",
-       "         4.72573828e-01, -1.30984149e+00,  6.57052576e-01,\n",
-       "        -1.63409342e-01,  1.26034872e+00,  6.68367528e-01,\n",
-       "         1.85802588e+00,  3.64494566e+00,  9.07110740e+00,\n",
-       "         4.98040972e-01,  4.02776412e+00, -3.39673281e-01,\n",
-       "         1.73712466e+00, -1.08201292e+00, -4.30169424e-01,\n",
-       "         7.34991875e-01, -5.77166110e-01,  1.46681901e-01,\n",
-       "         4.91008427e-01,  2.95193166e-01,  7.16615151e+00,\n",
-       "         2.18303783e+00, -3.83362144e-01, -2.75120474e-01,\n",
-       "        -5.71472468e-01,  1.80064981e-01, -2.62243047e-01,\n",
-       "         6.65210297e-01,  5.75609791e-01, -1.14095749e+00,\n",
-       "         5.04804542e-01,  2.36973366e+01,  3.29838403e+00,\n",
-       "        -3.16063846e-01,  3.17368085e-01,  5.67899139e-01,\n",
-       "        -4.94755757e-01,  2.87739332e+01,  7.05179551e-01,\n",
-       "        -7.41398631e-01,  1.87150300e-01,  1.91368316e+00,\n",
-       "        -5.08268391e-01,  7.45951887e-01, -2.95089806e-01,\n",
-       "        -2.21708949e-01,  7.05258237e-01, -6.39449462e-01,\n",
-       "         8.28030896e-01,  1.74208446e+03,  2.87961006e+00,\n",
-       "         8.38346277e-01,  6.35664661e+01,  5.80461825e-01,\n",
-       "        -2.88162356e-01,  1.79674838e+00,  4.86242572e-01,\n",
-       "         7.05205767e-01,  2.82358739e+01,  4.22338355e-01,\n",
-       "         2.46046513e+00,  8.49414253e+01, -1.10868668e+00,\n",
-       "         1.98576704e+02, -1.03968034e-01,  2.10113673e-01,\n",
-       "        -4.14198288e-01,  5.17722728e+01,  4.06884611e-01,\n",
-       "        -5.30661695e-01, -5.60473748e-01,  6.52740607e-01,\n",
-       "        -2.04112035e-01, -2.83061481e-01,  6.61583365e-01,\n",
-       "         1.88905710e+00,  4.14786135e-01, -4.05742820e-01,\n",
-       "         7.31111412e+00,  5.01512871e-01,  2.50704132e+01,\n",
-       "         2.60671872e-01, -3.99993869e-01,  1.27117934e+02,\n",
-       "         6.77470381e+00,  1.73685306e-01,  6.85047034e-01,\n",
-       "         6.08168108e-01, -6.56196928e-01, -2.58025141e-01,\n",
-       "         5.66260436e-01,  5.55733196e-01, -5.72221896e-01,\n",
-       "         1.24852467e+00,  6.42314797e+00,  6.79112866e-01,\n",
-       "        -1.42316454e+00,  4.40984152e-01,  1.07572682e+00,\n",
-       "        -9.27740875e-01,  3.46925423e+01,  2.21524100e+00,\n",
-       "         5.56759106e+00, -3.00875940e-01, -1.27277580e-01,\n",
-       "         2.37932777e-01,  1.48797536e+01,  3.10175124e-01,\n",
-       "         4.83622036e-01,  5.50455369e-01,  4.08848761e-01,\n",
-       "        -4.70396826e-01,  2.54984009e+00,  3.19921115e-01,\n",
-       "         2.64054234e+00,  3.26065225e-01,  3.53056240e-01,\n",
-       "         1.58652334e+00, -2.88955356e-01, -1.99639497e-01,\n",
-       "         3.16041284e-01,  1.64063085e+00,  7.94948383e-01,\n",
-       "         2.13079198e-01,  2.33312676e-01, -1.14414675e+00,\n",
-       "        -3.19251327e-01,  9.43190676e-01, -6.45817103e-01,\n",
-       "         2.28286226e+01, -3.89923866e-01,  1.95051022e-01,\n",
-       "         3.46248427e-01,  5.67462762e-01,  4.69301902e+00,\n",
-       "         3.69625592e-01,  2.66265109e-01,  6.71798713e+01,\n",
-       "         6.37080303e+02, -7.65995759e-01,  4.21636945e-01,\n",
-       "         7.50343565e-01,  2.16898261e+00, -2.16226961e-01,\n",
-       "         3.09291919e+00,  4.31248270e-01, -5.29311053e-01,\n",
-       "         4.01934006e-01,  2.82501672e-01, -4.80919275e-01,\n",
-       "         7.80887653e-01,  5.14600718e-01,  9.00451605e-01,\n",
-       "         8.81063154e-01, -8.69692271e-01, -2.01723119e-01,\n",
-       "         6.86322715e-01,  1.12848542e-01, -6.02128795e-01,\n",
-       "         2.05284784e+00,  1.54511327e-01,  4.64045994e-01,\n",
-       "         9.25281857e-01,  2.49531712e+01, -4.94261432e-01,\n",
-       "         8.86540404e-01,  8.63264530e+00,  4.36901678e-01,\n",
-       "         8.94647907e-01,  1.82269749e+00, -4.70260051e-01,\n",
-       "        -2.00879424e-01,  6.72242567e+00,  2.16827039e+01,\n",
-       "         2.58767852e+02, -7.13851883e-01,  5.72961979e+01,\n",
-       "        -3.27427728e-01, -2.52932722e-01,  4.90883692e-01,\n",
-       "         7.11797118e-01,  2.63851039e-01,  1.16424698e+00,\n",
-       "        -3.67628683e-01,  6.75686593e+00,  3.74028937e-01,\n",
-       "         4.60345854e-01, -4.12159817e-01, -2.21925605e-01,\n",
-       "        -4.40007043e-01,  2.19682325e+00,  3.47183714e+00,\n",
-       "        -2.51749619e-01,  5.33898648e-01,  1.51211845e-01,\n",
-       "        -2.52947158e-01,  2.04169947e+00, -1.31612922e+00,\n",
-       "        -4.27896830e-01, -1.92626852e-01,  5.27890699e-01,\n",
-       "         1.01254747e+01,  3.30858892e+02, -2.47709987e-01,\n",
-       "         3.93215011e-01,  1.30176114e-01,  7.51969700e+00,\n",
-       "        -3.43158986e-01,  9.41831896e+01, -4.65959296e-01,\n",
-       "        -5.70299897e-01,  4.32314205e-01, -6.76974063e-01,\n",
-       "        -3.93088899e-01,  7.69348793e+01,  1.40311193e+00,\n",
-       "        -1.87306939e-01,  1.25850502e+00, -5.63314481e-01,\n",
-       "        -1.86475349e-01,  4.82023167e-01,  1.21987609e+00,\n",
-       "         6.48737608e+00,  8.93480168e-01, -3.48220177e-01,\n",
-       "        -1.77346832e-01,  2.10277970e-01,  7.31778319e+00,\n",
-       "         3.70933606e-01,  1.54940270e+03,  5.46264961e-01,\n",
-       "         2.60128258e-01,  6.66258751e-01,  8.12350725e-01,\n",
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-       "        -5.67237728e-01,  2.50222433e-01, -3.83524211e-01,\n",
-       "        -2.48871791e-01,  5.56381462e-02,  3.59204501e-01,\n",
-       "        -1.42082094e-01,  4.78843019e-01, -1.40871993e-01,\n",
-       "         1.66546635e+00, -2.61810891e-01, -5.19252400e-01,\n",
-       "         1.53687134e+01,  3.72049407e+00, -1.22635133e+00,\n",
-       "         1.79981186e-01,  1.96649255e+00, -7.70618309e-02,\n",
-       "         2.16772080e-01, -6.36733593e-02,  3.72045282e-01,\n",
-       "        -1.09774573e-01, -1.35848603e-01, -1.89825359e-01,\n",
-       "         3.03534925e-01,  1.55306550e-01,  1.47716265e-01,\n",
-       "         1.18696244e-01, -2.85207166e-01,  4.19819584e-01,\n",
-       "         5.18986892e+00,  1.00885783e-01, -2.04425122e-01,\n",
-       "        -1.40849261e-01,  1.79701174e-01,  1.07057396e+00,\n",
-       "        -4.11514293e-01, -1.26332174e-01, -8.15107271e-01,\n",
-       "        -2.77782962e-01,  3.25116993e-01,  1.20757101e+00,\n",
-       "         1.66572145e+00, -4.32564272e-01,  1.25543147e+00,\n",
-       "        -2.43918883e-01,  5.09511785e-01,  5.96079844e-01,\n",
-       "        -1.93127301e-01, -1.37330386e-01, -2.52257750e-01,\n",
-       "         1.42156714e+00,  1.56195761e-01,  8.36186033e+00,\n",
-       "         9.15127220e-01, -5.65338705e-01, -7.87366471e-01,\n",
-       "        -3.90881221e-01,  5.18319151e-01, -3.92801976e-01,\n",
-       "        -3.37473071e-01,  6.88874648e+01,  2.24396299e-01,\n",
-       "         4.08363766e+00, -8.83842396e-02,  1.67991437e-01,\n",
-       "         1.68452977e-01, -2.73194417e-01,  1.09057868e-01,\n",
-       "         9.26317764e-02, -2.25943052e-01]])
          • tree_size
            (chain, draw)
            float64
            7.0 7.0 7.0 7.0 ... 7.0 7.0 7.0 7.0
            array([[ 7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  5.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         3.,  7.,  7.,  3.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  1.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
-       "         3.,  7.,  7.,  7.,  3.,  7.,  7.,  6.,  7.,  7.,  3.,  7.,  7.,\n",
-       "         3.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  3.,  3.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  1.,\n",
-       "         7.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  3.,  7.,  7.,  7.,  7.,  3.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  3.,  3.,  7.,  7.,  7.,\n",
-       "         7.,  3.,  7.,  7.,  1.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  3.,  1.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  3.,  1.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  1.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  3.,  7.,  7.,  7.,  3.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,  3.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  1.,  3.,\n",
-       "        15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  3.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
-       "         3.,  7.,  7.,  7.,  7.,  7.,  1.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.],\n",
-       "       [ 7.,  7., 15.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
-       "         7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7., 15.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7., 15.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7., 15.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7., 15., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "        15.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7., 15.,\n",
-       "         7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         3.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  1.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.]])
          • energy_error
            (chain, draw)
            float64
            -0.1261 0.5558 ... -0.05267
            array([[-1.26073671e-01,  5.55780576e-01, -7.27591431e-02,\n",
-       "        -2.42733853e-01,  2.69058870e-01, -3.10309436e-01,\n",
-       "        -2.28594235e-01,  2.88516746e-01, -1.34207283e-01,\n",
-       "         7.56506289e-02,  1.41018753e-02, -8.55723457e-02,\n",
-       "         8.71437172e-02, -8.31500456e-03,  2.72671949e-01,\n",
-       "        -3.72090705e-01,  5.62033095e-04, -9.12109435e-02,\n",
-       "        -1.53104812e-01,  1.07862839e-01,  2.65541267e-01,\n",
-       "        -6.42682763e-01,  0.00000000e+00,  2.52295139e-01,\n",
-       "        -1.10616634e-01,  2.12548259e-01, -1.03926361e-02,\n",
-       "        -5.04986109e-01,  4.13848460e-01, -7.97687495e-01,\n",
-       "         3.54780172e-01, -1.12787935e-01,  1.66906991e-01,\n",
-       "         2.45362775e-01, -8.93673703e-02,  2.31968101e-01,\n",
-       "         0.00000000e+00, -8.18909645e-01,  4.89476357e-01,\n",
-       "        -2.91927233e-02,  6.61811222e-01,  4.04787278e-01,\n",
-       "        -1.36523121e-01, -2.35368597e-02,  1.43463992e+00,\n",
-       "         4.27003572e-02, -8.29382867e-01,  1.40416069e-01,\n",
-       "         9.54788225e-02,  7.24462871e-02, -1.24093852e-01,\n",
-       "         1.64837114e-03, -4.93491567e-01, -3.91735572e-01,\n",
-       "         4.62170795e-01, -5.74977164e-01,  1.40822480e+00,\n",
-       "        -1.95469025e-01,  5.54528049e-02,  2.63869249e-01,\n",
-       "        -5.46685951e-01, -1.99327188e-01, -6.49196643e-02,\n",
-       "         1.50740585e+00,  6.24992410e-01,  1.82133321e-01,\n",
-       "        -6.62691220e-02,  1.91084119e-02,  1.39688603e-01,\n",
-       "        -2.74597344e-01, -1.03443030e-01,  5.52638788e-01,\n",
-       "        -7.64666643e-01, -4.72640496e-01, -5.16348796e-01,\n",
-       "         2.48894407e-01, -1.04486589e+00,  2.61986693e-01,\n",
-       "         1.03015516e-01,  1.11157560e+00,  1.64573671e-01,\n",
-       "         0.00000000e+00,  5.72809685e-01, -7.39786129e-01,\n",
-       "         1.49448987e-01,  3.96423978e+00, -1.95557069e-01,\n",
-       "        -1.30865850e-01, -7.96888433e-01, -5.66029114e-02,\n",
-       "         5.00899554e-01, -3.01556925e-01, -7.51908574e-02,\n",
-       "         3.44335390e-02,  2.95193166e-01,  0.00000000e+00,\n",
-       "        -1.67040389e+00, -1.89375136e-01, -6.69257331e-02,\n",
-       "        -5.71472468e-01, -1.01412010e-01, -4.74222427e-02,\n",
-       "         6.65210297e-01,  4.81809560e-01, -6.45115769e-01,\n",
-       "        -4.98056614e-02,  8.04357034e-01, -1.79670162e-02,\n",
-       "        -3.16063846e-01,  3.17368085e-01,  4.62968258e-01,\n",
-       "         9.53648784e-02,  0.00000000e+00,  1.62234592e-01,\n",
-       "        -9.11974481e-02, -7.09183807e-02, -4.63608619e-01,\n",
-       "        -5.08268391e-01,  3.79762165e-01,  2.35583734e-01,\n",
-       "        -1.35074674e-01,  5.02728294e-02,  1.27296760e-01,\n",
-       "         1.55638078e-01,  1.29441092e-01, -3.17342085e-01,\n",
-       "         4.50042704e-01, -1.47885639e+00,  1.58885491e-01,\n",
-       "        -3.22279272e-02, -9.46533193e-01,  3.06130021e-01,\n",
-       "         1.12662757e-01,  0.00000000e+00,  2.23305166e-01,\n",
-       "         3.29358548e-01, -3.27353951e-02, -2.07844522e-01,\n",
-       "         1.70926031e-01, -3.97526443e-02,  2.10113673e-01,\n",
-       "        -3.18129947e-01,  2.45979519e-01, -3.16914460e-01,\n",
-       "        -1.34170878e-02, -5.60473748e-01,  6.52740607e-01,\n",
-       "         8.98127201e-02, -2.83061481e-01,  3.28089369e-02,\n",
-       "         1.58036364e+00, -1.47108682e-01, -4.05742820e-01,\n",
-       "         6.93008169e-01,  1.84777480e-01,  0.00000000e+00,\n",
-       "         2.39780962e-03,  2.29156188e-01,  6.08625233e-02,\n",
-       "        -5.06520435e-01, -1.78900224e-02,  1.49135233e-01,\n",
-       "         4.83391765e-01, -6.56196928e-01,  3.13548792e-02,\n",
-       "         5.66260436e-01,  5.55733196e-01, -2.77489363e-02,\n",
-       "         9.10028126e-01,  1.37733199e-01,  6.79112866e-01,\n",
-       "        -9.24288392e-01,  5.21517674e-02,  2.25598651e-01,\n",
-       "        -9.27740875e-01,  0.00000000e+00, -1.80135496e-02,\n",
-       "         2.79401240e-01, -4.97018126e-02, -1.27277580e-01,\n",
-       "         6.64363613e-02, -1.03533066e-01,  1.72033431e-01,\n",
-       "        -2.46607279e-01,  1.13620069e-01,  4.08848761e-01,\n",
-       "        -1.74226499e-01,  1.04834108e-01, -1.69977861e-01,\n",
-       "        -2.85688974e-01, -1.78935065e-01,  3.01749981e-01,\n",
-       "         7.37011214e-01, -2.04104061e-01, -1.80767132e-01,\n",
-       "        -1.42189086e-02,  1.00102585e-01,  1.82258868e-02,\n",
-       "         1.82522066e-01,  2.33312676e-01, -1.14414675e+00,\n",
-       "         1.18106320e-01,  0.00000000e+00, -2.59611150e-01,\n",
-       "        -1.12621457e-01, -4.06804553e-02,  9.57649002e-02,\n",
-       "         2.72782296e-02, -6.23834342e-02,  0.00000000e+00,\n",
-       "         1.82267424e-01,  1.70987404e-01,  0.00000000e+00,\n",
-       "         3.55827242e-01, -7.65995759e-01,  7.77433489e-02,\n",
-       "         7.50343565e-01, -1.03408389e+00,  1.82977832e-01,\n",
-       "        -2.28379635e-01,  3.26791036e-01, -3.05040041e-01,\n",
-       "        -1.35141652e-02,  2.49914567e-01, -4.80919275e-01,\n",
-       "         5.62281466e-01,  1.59071643e-01,  2.36130934e-01,\n",
-       "         2.05860564e-01, -8.69692271e-01, -2.01723119e-01,\n",
-       "        -2.93409508e-01, -2.12195478e-02, -4.52063253e-01,\n",
-       "        -1.12095243e-01,  4.38773330e-04,  1.25615569e-02,\n",
-       "         3.64299410e-02, -4.61375526e-02, -4.94261432e-01,\n",
-       "         2.88048115e-01,  1.87130219e-01,  7.78605244e-03,\n",
-       "         3.31375905e-01, -3.35956569e-01, -3.07223577e-01,\n",
-       "        -9.14639855e-02, -7.69447548e-03,  4.84629780e-01,\n",
-       "         0.00000000e+00,  1.05387938e-01, -1.75258989e-01,\n",
-       "         7.44165307e-02, -3.42050471e-02,  1.96873259e-01,\n",
-       "         4.64836213e-01,  2.72885885e-02,  1.66827008e-01,\n",
-       "        -2.68812261e-01, -3.16695338e-02,  1.50610694e-01,\n",
-       "         1.47372219e-01, -1.65573668e-01, -2.21925605e-01,\n",
-       "         2.60113836e-02,  2.57389333e-01,  5.50416990e-01,\n",
-       "        -1.87877422e-01, -2.08650276e-01,  1.33193376e-01,\n",
-       "        -8.76507356e-02,  1.50573827e+00, -1.31612922e+00,\n",
-       "         1.17930702e-02,  5.62168525e-02, -1.63709187e-03,\n",
-       "        -8.50364270e-01,  0.00000000e+00, -1.43382513e-02,\n",
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-       "        -1.07777351e-01,  1.35277912e-01, -5.15831896e-02,\n",
-       "        -5.92949869e-02,  1.02332147e-01, -2.68604960e-01,\n",
-       "         2.60997297e-02,  1.05468807e-01, -1.02704371e-01,\n",
-       "         4.07934174e-02,  9.08677725e-02, -1.07935688e-02,\n",
-       "        -7.66598561e-02, -7.64374908e-02, -1.80865631e-02,\n",
-       "         1.29231657e-01,  8.19795018e-02, -7.80995912e-02,\n",
-       "        -4.83313855e-02,  2.86503502e-01,  5.85783770e-02,\n",
-       "         2.82562605e-02,  1.00920858e-01,  3.69698673e-01,\n",
-       "        -4.73500733e-02, -1.69456228e-02,  5.83700051e-02,\n",
-       "        -5.67237728e-01,  3.39246482e-03, -3.83524211e-01,\n",
-       "        -1.97197811e-01, -2.20187477e-02,  2.36504155e-01,\n",
-       "        -1.42082094e-01,  1.97459601e-01,  2.51442444e-02,\n",
-       "         1.84367419e-01, -2.81883161e-02, -2.06375345e-01,\n",
-       "        -2.43116434e-01,  1.15457241e+00, -1.10687269e+00,\n",
-       "         1.28006962e-01, -4.11063803e-01, -7.08002334e-02,\n",
-       "         1.24827437e-01,  5.36999089e-04,  3.18077350e-01,\n",
-       "         3.28907079e-02, -1.35848603e-01, -1.25174574e-01,\n",
-       "        -1.31244089e-02, -5.17082317e-02,  1.47716265e-01,\n",
-       "         4.72589777e-02, -2.85207166e-01,  2.13554642e-01,\n",
-       "         2.46899722e+00,  9.08697403e-02, -1.22188092e-01,\n",
-       "        -1.16681805e-01, -1.67340235e-01,  1.03013138e+00,\n",
-       "        -4.11514293e-01, -1.10904917e-01,  1.54591957e-01,\n",
-       "        -2.77782962e-01,  2.50101322e-01,  1.20757101e+00,\n",
-       "        -1.46138476e+00,  1.01813827e-01,  4.64576551e-01,\n",
-       "        -1.90363952e-01,  2.04483444e-01,  3.39260526e-02,\n",
-       "        -1.52502519e-01,  1.24602164e-01, -3.67980742e-02,\n",
-       "        -3.34569286e-01, -6.75133245e-02,  3.38762479e-01,\n",
-       "         6.73356119e-01,  4.02884018e-01, -7.87366471e-01,\n",
-       "        -3.69373999e-01,  3.92678291e-01, -6.33274013e-02,\n",
-       "        -2.41038055e-01,  0.00000000e+00,  6.16684390e-02,\n",
-       "        -4.17457001e-01,  3.56791214e-02,  1.67991437e-01,\n",
-       "         4.53770958e-02, -2.00923236e-01, -3.14299356e-02,\n",
-       "        -2.45375679e-02, -5.26711313e-02]])
          • diverging
            (chain, draw)
            bool
            False False False ... False False
            array([[False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False,  True, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False,  True, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False,  True,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False],\n",
-       "       [False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False]])
          • depth
            (chain, draw)
            int64
            3 3 3 3 3 3 3 3 ... 3 3 3 3 3 3 3 3
            array([[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 2, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3,\n",
-       "        3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 3, 3, 3, 3,\n",
-       "        3, 1, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2,\n",
-       "        3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3, 2, 3, 3, 1, 3, 3, 3, 2, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 2, 1, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 3, 3, 3, 3, 3, 3, 3, 2,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3,\n",
-       "        2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3,\n",
-       "        3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 1, 2, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 1,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3],\n",
-       "       [3, 3, 4, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2,\n",
-       "        3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 4, 3, 4, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 4, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 4, 3, 3,\n",
-       "        3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 2, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3]])
          • lp
            (chain, draw)
            float64
            -45.78 -49.19 ... -45.28 -43.83
            array([[-45.78048101, -49.18878918, -47.81124046, -45.88878259,\n",
-       "        -48.31089296, -45.14855162, -44.49296796, -47.06173477,\n",
-       "        -46.00333193, -46.74945341, -45.31134603, -44.57091151,\n",
-       "        -45.21360856, -45.08678676, -48.93014192, -44.3319601 ,\n",
-       "        -44.10683405, -43.35268252, -43.03524428, -43.93322003,\n",
-       "        -46.08079228, -45.6846219 , -45.6846219 , -46.15356626,\n",
-       "        -46.04213007, -49.38770053, -48.34053206, -43.80699611,\n",
-       "        -45.51102992, -45.96717108, -48.69233529, -46.14925774,\n",
-       "        -44.29064411, -46.93019508, -46.63176458, -45.86788665,\n",
-       "        -45.86788665, -43.88184986, -47.85223431, -46.89128814,\n",
-       "        -45.37435027, -49.80568336, -47.92276381, -46.58699925,\n",
-       "        -46.93876301, -49.68452069, -44.46531184, -44.22245826,\n",
-       "        -46.62746534, -45.97081079, -44.73635903, -44.82874639,\n",
-       "        -50.01955523, -44.55905088, -47.35901965, -47.73677562,\n",
-       "        -50.42675194, -48.81429299, -47.71982842, -51.4284502 ,\n",
-       "        -47.47346232, -46.0599324 , -44.66857095, -42.01852763,\n",
-       "        -45.68232031, -46.65687534, -45.86968195, -47.0690995 ,\n",
-       "        -48.19324334, -46.3852222 , -44.90130478, -50.16581678,\n",
-       "        -45.36505614, -44.05814881, -42.63294644, -42.26222755,\n",
-       "        -44.621209  , -44.01706864, -49.5334657 , -51.81656903,\n",
-       "        -48.8597275 , -48.8597275 , -51.53180885, -47.33636019,\n",
-       "        -44.96535688, -51.38637552, -47.45798166, -46.01224865,\n",
-       "        -44.86837616, -44.42754385, -46.86798685, -44.3426595 ,\n",
-       "        -44.05714857, -43.53446044, -45.43752874, -45.43752874,\n",
-       "        -45.52642893, -47.19017679, -45.83775196, -44.18596385,\n",
-       "        -42.8669047 , -42.28571587, -47.96598513, -52.21374203,\n",
-       "        -45.0182377 , -42.05238676, -48.19013005, -48.54712061,\n",
-       "        -45.53565943, -47.38524796, -49.99698174, -49.38402006,\n",
-       "        -49.38402006, -50.29769779, -49.58451027, -48.9940765 ,\n",
-       "        -47.40922232, -42.6843339 , -45.19096453, -46.3147075 ,\n",
-       "        -43.65597466, -44.35146644, -44.52390399, -46.17249626,\n",
-       "        -46.99344625, -46.80208718, -48.65544225, -48.21334325,\n",
-       "        -47.32004813, -47.21866465, -45.32097994, -44.32968141,\n",
-       "        -46.91120509, -46.91120509, -48.43712029, -48.16994442,\n",
-       "        -48.90512804, -48.9750313 , -51.97941409, -52.66710777,\n",
-       "        -56.32411594, -52.43433435, -50.34598173, -47.8481962 ,\n",
-       "        -48.07973757, -41.93572432, -48.05793509, -47.15547856,\n",
-       "        -44.74877076, -44.89475534, -44.80333795, -43.51894208,\n",
-       "        -41.89061071, -43.65392569, -43.37718169, -43.37718169,\n",
-       "        -43.2104204 , -45.13167633, -46.98790503, -45.22230073,\n",
-       "        -43.84019779, -45.00268433, -46.00206013, -43.9482027 ,\n",
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-       "        -47.23045916, -47.35385276, -48.23889624, -48.2232962 ,\n",
-       "        -44.0562932 , -46.04453334, -49.75695745, -47.20113   ,\n",
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-       "        -43.39302247, -45.64042874, -44.5591906 , -45.7779977 ,\n",
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-       "        -43.93935447, -43.71260976, -45.38079766, -42.6213089 ,\n",
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-       "        -44.96072829, -46.87699193, -49.74543491, -48.58769776,\n",
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-       "        -49.68844758, -43.94238036, -45.99360426, -44.37610803,\n",
-       "        -43.46705962, -42.96344732, -42.8280763 , -41.921416  ,\n",
-       "        -48.37948115, -48.09864335, -43.78688144, -51.10977642,\n",
-       "        -48.96647002, -45.69115941, -45.69115941, -46.46577761,\n",
-       "        -43.60706445, -45.49722656, -46.58448303, -47.87843883,\n",
-       "        -45.50485557, -44.7802904 , -45.27924419, -43.83013398]])
        • created_at :
          2020-06-10T18:19:23.335024
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

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        xarray.Dataset
          • chain: 1
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • tau_log__
            (chain, draw)
            float64
            1.706 2.102 -0.9025 ... 3.191 3.0
            array([[ 1.70599509,  2.1023019 , -0.90248537,  2.56846292,  2.80035484,\n",
-       "         0.99311443,  3.00205734, -0.10636405,  0.62927854,  1.48301388,\n",
-       "         2.16985359,  0.88887636,  0.365956  ,  2.87258132, -2.71912399,\n",
-       "         2.63115465,  1.08852395, -0.15118725,  1.51487157,  1.12506038,\n",
-       "         2.10230483,  2.26720996,  2.05902668,  1.32215738,  1.382099  ,\n",
-       "         2.00485569, -0.04846091,  0.20013412,  0.09002789,  3.61645892,\n",
-       "        -0.98540514,  2.14471514,  4.54864374,  0.56399077,  2.08710895,\n",
-       "         1.81525651,  1.94972113,  1.94021807,  3.35868241,  3.02310503,\n",
-       "         1.32780055,  2.17443604,  0.52584454,  0.22766409,  2.10078604,\n",
-       "         2.90568936,  1.38453723,  1.33557796,  0.8604188 ,  2.90941416,\n",
-       "         3.91051141,  2.64583023,  2.59454355,  1.55461194, -0.19360908,\n",
-       "         0.61859906,  0.06131972,  1.11867599,  3.22237575,  2.8379133 ,\n",
-       "         4.0855867 , -0.94483429,  6.65039162,  2.26065082,  0.95464065,\n",
-       "         0.9435127 ,  1.53873811,  1.27423168,  0.03564208,  2.30234039,\n",
-       "         1.06483034,  2.63324965,  2.50508407,  0.45062262,  0.56089467,\n",
-       "         1.49177783,  2.82825203,  2.34121197,  1.69663461,  1.78877646,\n",
-       "         1.51950935,  1.22613645,  2.8868101 ,  1.73708598,  2.14715963,\n",
-       "         0.59152464,  0.90457465,  2.5158034 , -0.84755572,  2.35830151,\n",
-       "         1.33125608,  0.51518908,  2.53967354,  2.46873829,  2.20646945,\n",
-       "         0.11905285,  1.98203441,  1.24922572,  1.38711176,  2.27494119,\n",
-       "        -1.03321614,  1.77610568,  1.20474088,  2.53645718, -0.22378872,\n",
-       "         2.05238601,  1.37310813,  3.29017421,  1.29962925,  2.0130666 ,\n",
-       "         1.85922107,  2.52251249,  4.10067265,  2.64205754, -1.13808027,\n",
-       "         1.59080514,  3.27661494,  0.65264271,  2.28492573,  6.74870305,\n",
-       "         2.8758106 ,  2.92309975,  2.47741094,  2.34805628,  1.80015544,\n",
-       "         0.90205299,  0.64674031,  2.68653822,  2.59522977,  1.96274769,\n",
-       "         1.44877023,  1.97737637,  2.25395178,  0.27912863,  2.38677663,\n",
-       "         3.19065025,  0.96032573,  1.81979586,  0.03921373,  1.26702002,\n",
-       "         0.95537382,  1.35183552,  1.18545532,  1.25089938,  2.99997771,\n",
-       "         1.48068604,  2.28282277,  2.77664837,  2.57037212,  0.95504334,\n",
-       "         1.69486963,  1.37795916,  1.71540583,  1.28841831,  1.45745271,\n",
-       "         0.63274979,  0.89237036, -0.01351588,  4.29981174,  1.85461141,\n",
-       "        -3.12646604,  2.58317152,  4.42056351,  2.84004013,  3.74631516,\n",
-       "         2.19433234,  0.12803876,  1.87609798,  2.70373684,  0.85187241,\n",
-       "         0.66515593,  1.98560837,  1.75530964,  1.07881318,  0.9227799 ,\n",
-       "         2.72529773,  1.22990801,  1.63303327,  1.76057628,  1.14271821,\n",
-       "         0.24628423,  2.09863363,  2.45560525,  1.74689688,  2.22823845,\n",
-       "         2.14311367,  1.44538971,  1.25540438,  1.98040585,  1.46287083,\n",
-       "         2.8902565 ,  3.25237336,  1.07074438,  4.15041042,  1.08263665,\n",
-       "        -0.41342869, -0.21777453,  1.62728738,  0.06832458,  0.69369886,\n",
-       "         2.44087783,  1.85360789,  1.21200545, -2.87543828,  1.18588517,\n",
-       "         1.8493917 , -3.37669212,  1.22158871,  1.77583107,  2.36826343,\n",
-       "         3.07289327,  1.73923901,  3.23570516,  2.57518197, -0.74485098,\n",
-       "         1.1565447 ,  4.04021711,  0.06042958,  2.8397559 ,  0.87684921,\n",
-       "         4.03005226, -2.00286885,  6.21005584,  0.77883157, -0.50032742,\n",
-       "         2.63371528,  1.19401374,  2.50900826, -1.43283972,  2.44164446,\n",
-       "         2.06966697,  1.60198989,  4.78241693,  1.35488597,  3.00561363,\n",
-       "         2.8111901 ,  4.34814582,  1.39114041,  3.34679992,  2.39048077,\n",
-       "         3.29673684,  3.22296724, -0.03406225, -0.00755681,  1.36874808,\n",
-       "         2.91221   , -2.44972579,  3.57249281,  2.9723942 ,  3.75687405,\n",
-       "         5.63509009, -0.21011503,  0.46972604,  1.54744965,  3.04382219,\n",
-       "         0.2551527 ,  2.33588406,  1.71926505,  2.61660087,  0.72918814,\n",
-       "         5.45802459, -1.17878783,  2.09894824,  2.02709688,  1.03242723,\n",
-       "         3.71406849,  2.91980202, -0.87215908,  1.84922519,  2.06389569,\n",
-       "         1.51474805,  1.82651549, -0.98556942,  1.88015217,  1.86588563,\n",
-       "         2.19522154,  0.90610739,  0.12818577,  2.6526049 , -0.34089079,\n",
-       "         0.5623638 ,  1.17314998,  1.07607117,  3.45167634,  2.07764134,\n",
-       "        -2.25318032,  1.57445238,  5.17363495,  1.67180101,  1.51730549,\n",
-       "         3.60385835,  1.21391414,  4.88465051,  1.34819836,  2.07995028,\n",
-       "         1.9411707 ,  0.8344764 ,  0.53314648,  0.95237139,  2.70993294,\n",
-       "         0.25197114,  0.68954274,  2.10559803,  1.46707996,  0.38024107,\n",
-       "         3.6718587 , -0.27665213,  1.42949829, -1.62354754,  1.42518566,\n",
-       "        -1.06680294,  0.79928264,  0.51339205,  1.66846884,  3.79669344,\n",
-       "         1.39648667,  1.48479347, -0.15476104,  1.9127241 ,  1.2560361 ,\n",
-       "         0.58643927,  1.6832115 ,  2.0844959 ,  0.27473111,  1.98730803,\n",
-       "         1.73073339,  2.39902852,  1.25603917,  2.13876395,  0.70809256,\n",
-       "         0.38677292,  1.12534926,  3.49333425,  5.51480037,  2.27937884,\n",
-       "         2.19669492,  1.07241914,  1.29741512,  3.6515715 ,  2.20619041,\n",
-       "        -1.01632301, -1.00972668, -0.13866914,  1.58748499,  4.16389488,\n",
-       "         1.47806225,  2.18470271,  2.17287451,  0.35138308, -0.08737636,\n",
-       "         3.75533117,  2.12592275,  1.61577535, -0.08719834, -3.74146033,\n",
-       "        -1.36428267, -5.40154713,  1.85700736,  0.32336021,  1.9426761 ,\n",
-       "         3.56852419,  1.55354747,  0.97723259, -0.13230045,  1.5765091 ,\n",
-       "         1.28410518,  1.00776594,  2.02259487,  1.21227949,  0.62992772,\n",
-       "         3.05016949,  0.35337658,  0.40199874,  1.48527419,  2.64238791,\n",
-       "         0.43196549,  0.10784032,  1.70192077,  2.28243656,  2.03935257,\n",
-       "         0.61773816,  0.86907984, -0.76778866,  1.94899872, -0.02152823,\n",
-       "        -2.29418832,  2.37588713,  1.63970504,  1.19798191,  4.58844073,\n",
-       "         2.6773426 ,  4.52417613,  0.32385163,  1.97589555,  0.5072283 ,\n",
-       "        -1.26472421,  2.06191531,  0.84805248,  3.71899798,  3.85586477,\n",
-       "         1.27418696, -1.66429176,  2.89621868,  0.5836731 ,  2.42453699,\n",
-       "         2.83891664, -0.10785864,  4.878018  ,  7.05836481,  4.95169144,\n",
-       "         0.66509645,  0.88350777,  2.27091977, -0.70442498,  0.61656012,\n",
-       "         1.18896414,  2.97898156,  2.52738653,  1.25978368,  3.6986964 ,\n",
-       "         3.52576019,  3.52752708,  4.35818753, -0.09883578,  1.86856041,\n",
-       "        -1.38208987,  3.10093326,  1.75405802,  5.07287701,  4.1858028 ,\n",
-       "         3.15839962,  1.25920842,  2.07146588,  1.11480582,  2.82629916,\n",
-       "         1.09979417,  0.40300613, -0.99749685,  1.75980059,  2.72177195,\n",
-       "         2.58293073,  1.05452379,  0.26683768, -0.08899415,  1.8233168 ,\n",
-       "         1.25087499,  0.51544631,  1.71713821, -0.07991098,  1.04087024,\n",
-       "         1.92818799, -0.35251242,  2.77931855,  1.26470085,  1.39402269,\n",
-       "         1.69576834,  2.69417672,  1.35947815,  1.28346478,  0.97858565,\n",
-       "         2.01678674,  2.77387222,  1.22566557,  2.8065254 ,  2.39601751,\n",
-       "         2.79108204,  3.22977667,  2.34095033,  1.21336144,  2.65564027,\n",
-       "         0.85456707,  2.84012414,  1.32798643,  0.71154812,  3.49958034,\n",
-       "        -0.45660104,  1.81603681,  1.96863298,  3.10477242,  0.02468627,\n",
-       "         0.6817256 ,  3.54044187,  3.26327222,  1.65833051,  1.42266403,\n",
-       "         1.18580882,  3.200757  ,  2.84417962,  2.09119768,  0.01526257,\n",
-       "         1.78042628,  1.67860247,  0.53106916,  2.20371117,  2.16164611,\n",
-       "         0.75287014,  1.77576973,  2.20152037,  3.19102252,  2.9998979 ]])
          • tau
            (chain, draw)
            float64
            5.507 8.185 0.4056 ... 24.31 20.08
            array([[5.50686275e+00, 8.18498929e+00, 4.05560437e-01, 1.30457567e+01,\n",
-       "        1.64504830e+01, 2.69962919e+00, 2.01269022e+01, 8.99097276e-01,\n",
-       "        1.87625645e+00, 4.40620548e+00, 8.75700183e+00, 2.43239497e+00,\n",
-       "        1.44189180e+00, 1.76826038e+01, 6.59324864e-02, 1.38897985e+01,\n",
-       "        2.96988714e+00, 8.59686709e-01, 4.54883689e+00, 3.08040285e+00,\n",
-       "        8.18501327e+00, 9.65243249e+00, 7.83833691e+00, 3.75150607e+00,\n",
-       "        3.98325370e+00, 7.42502229e+00, 9.52694584e-01, 1.22156659e+00,\n",
-       "        1.09420480e+00, 3.72055864e+01, 3.73287963e-01, 8.53960826e+00,\n",
-       "        9.45041492e+01, 1.75767299e+00, 8.06157503e+00, 6.14265162e+00,\n",
-       "        7.02672777e+00, 6.96026860e+00, 2.87512835e+01, 2.05550166e+01,\n",
-       "        3.77273632e+00, 8.79722240e+00, 1.69188711e+00, 1.25566346e+00,\n",
-       "        8.17259137e+00, 1.82778393e+01, 3.99297764e+00, 3.80219282e+00,\n",
-       "        2.36415060e+00, 1.83460476e+01, 4.99244772e+01, 1.40951424e+01,\n",
-       "        1.33904739e+01, 4.73324937e+00, 8.23979948e-01, 1.85632561e+00,\n",
-       "        1.06323880e+00, 3.06079899e+00, 2.50876515e+01, 1.70800873e+01,\n",
-       "        5.94768225e+01, 3.88743982e-01, 7.73087020e+02, 9.58932810e+00,\n",
-       "        2.59773692e+00, 2.56898968e+00, 4.65870778e+00, 3.57595288e+00,\n",
-       "        1.03628487e+00, 9.99755332e+00, 2.90034686e+00, 1.39189281e+01,\n",
-       "        1.22445884e+01, 1.56928895e+00, 1.75223948e+00, 4.44499093e+00,\n",
-       "        1.69158665e+01, 1.03938259e+01, 5.45555640e+00, 5.98212863e+00,\n",
-       "        4.56998241e+00, 3.40803695e+00, 1.79360042e+01, 5.68076540e+00,\n",
-       "        8.56050879e+00, 1.80674094e+00, 2.47088071e+00, 1.23765481e+01,\n",
-       "        4.28460931e-01, 1.05729781e+01, 3.78579568e+00, 1.67395498e+00,\n",
-       "        1.26755323e+01, 1.18075397e+01, 9.08358961e+00, 1.12642945e+00,\n",
-       "        7.25749272e+00, 3.48764152e+00, 4.00327093e+00, 9.72734690e+00,\n",
-       "        3.55860620e-01, 5.90680857e+00, 3.33589457e+00, 1.26348286e+01,\n",
-       "        7.99484035e-01, 7.78645752e+00, 3.94760131e+00, 2.68475403e+01,\n",
-       "        3.66793653e+00, 7.48623952e+00, 6.41873506e+00, 1.24598627e+01,\n",
-       "        6.03808891e+01, 1.40420660e+01, 3.20433577e-01, 4.90769871e+00,\n",
-       "        2.64859643e+01, 1.92060974e+00, 9.82495650e+00, 8.52951810e+02,\n",
-       "        1.77397981e+01, 1.85988499e+01, 1.19103878e+01, 1.04652085e+01,\n",
-       "        6.05058789e+00, 2.46465783e+00, 1.90930692e+00, 1.46807663e+01,\n",
-       "        1.33996659e+01, 7.11886064e+00, 4.25787509e+00, 7.22376564e+00,\n",
-       "        9.52530346e+00, 1.32197738e+00, 1.08783723e+01, 2.43042260e+01,\n",
-       "        2.61254733e+00, 6.17059868e+00, 1.03999273e+00, 3.55025707e+00,\n",
-       "        2.59964220e+00, 3.86451242e+00, 3.27217637e+00, 3.49348353e+00,\n",
-       "        2.00850892e+01, 4.39596044e+00, 9.80431674e+00, 1.60650863e+01,\n",
-       "        1.30706874e+01, 2.59878321e+00, 5.44593592e+00, 3.96679777e+00,\n",
-       "        5.55893105e+00, 3.62704517e+00, 4.29500494e+00, 1.88278071e+00,\n",
-       "        2.44090864e+00, 9.86575053e-01, 7.36859206e+01, 6.38921495e+00,\n",
-       "        4.38725677e-02, 1.32390595e+01, 8.31431243e+01, 1.71164523e+01,\n",
-       "        4.23646868e+01, 8.97400746e+00, 1.13659706e+00, 6.52798276e+00,\n",
-       "        1.49354388e+01, 2.34403173e+00, 1.94479374e+00, 7.28347712e+00,\n",
-       "        5.78523878e+00, 2.94118681e+00, 2.51627568e+00, 1.52609569e+01,\n",
-       "        3.42091484e+00, 5.11937967e+00, 5.81578794e+00, 3.13527913e+00,\n",
-       "        1.27926313e+00, 8.15501952e+00, 1.16534847e+01, 5.73677315e+00,\n",
-       "        9.28349836e+00, 8.52594333e+00, 4.24350554e+00, 3.50925716e+00,\n",
-       "        7.24568306e+00, 4.31833896e+00, 1.79979255e+01, 2.58516223e+01,\n",
-       "        2.91755047e+00, 6.34600402e+01, 2.95245388e+00, 6.61378693e-01,\n",
-       "        8.04306771e-01, 5.09004862e+00, 1.07071279e+00, 2.00110367e+00,\n",
-       "        1.14831165e+01, 6.38280647e+00, 3.36021664e+00, 5.63914191e-02,\n",
-       "        3.27358321e+00, 6.35595200e+00, 3.41602660e-02, 3.39257327e+00,\n",
-       "        5.90518674e+00, 1.06788316e+01, 2.16043196e+01, 5.69300946e+00,\n",
-       "        2.54242936e+01, 1.31337069e+01, 4.74805052e-01, 3.17893012e+00,\n",
-       "        5.68386817e+01, 1.06229279e+00, 1.71115880e+01, 2.40331543e+00,\n",
-       "        5.62638518e+01, 1.34947583e-01, 4.97729044e+02, 2.17892486e+00,\n",
-       "        6.06332101e-01, 1.39254107e+01, 3.30030122e+00, 1.22927328e+01,\n",
-       "        2.38630316e-01, 1.14919233e+01, 7.92218432e+00, 4.96289825e+00,\n",
-       "        1.19392565e+02, 3.87631891e+00, 2.01986067e+01, 1.66296974e+01,\n",
-       "        7.73349370e+01, 4.01943124e+00, 2.84116683e+01, 1.09187421e+01,\n",
-       "        2.70243104e+01, 2.51024948e+01, 9.66511340e-01, 9.92471669e-01,\n",
-       "        3.93042704e+00, 1.83974120e+01, 8.63172526e-02, 3.56052398e+01,\n",
-       "        1.95386430e+01, 4.28143808e+01, 2.80084149e+02, 8.10491012e-01,\n",
-       "        1.59955592e+00, 4.69946958e+00, 2.09853000e+01, 1.29065869e+00,\n",
-       "        1.03385958e+01, 5.58042565e+00, 1.36891134e+01, 2.07339662e+00,\n",
-       "        2.34633470e+02, 3.07651440e-01, 8.15758555e+00, 7.59201379e+00,\n",
-       "        2.80787293e+00, 4.10203586e+01, 1.85376170e+01, 4.18047975e-01,\n",
-       "        6.35489380e+00, 7.87659490e+00, 4.54827503e+00, 6.21220244e+00,\n",
-       "        3.73226642e-01, 6.55450216e+00, 6.46165601e+00, 8.98199070e+00,\n",
-       "        2.47467082e+00, 1.13676416e+00, 1.41909565e+01, 7.11136568e-01,\n",
-       "        1.75481564e+00, 3.23215785e+00, 2.93313312e+00, 3.15532418e+01,\n",
-       "        7.98561131e+00, 1.05064553e-01, 4.82809693e+00, 1.76555443e+02,\n",
-       "        5.32174371e+00, 4.55992185e+00, 3.67397160e+01, 3.36663638e+00,\n",
-       "        1.32244240e+02, 3.85048211e+00, 8.00407098e+00, 6.96690236e+00,\n",
-       "        2.30360757e+00, 1.70428638e+00, 2.59184867e+00, 1.50282676e+01,\n",
-       "        1.28655891e+00, 1.99280409e+00, 8.21201260e+00, 4.33655373e+00,\n",
-       "        1.46263714e+00, 3.93249313e+01, 7.58318251e-01, 4.17660322e+00,\n",
-       "        1.97197890e-01, 4.15862987e+00, 3.44106890e-01, 2.22394499e+00,\n",
-       "        1.67094954e+00, 5.30404024e+00, 4.45536215e+01, 4.04097771e+00,\n",
-       "        4.41405367e+00, 8.56619848e-01, 6.77150997e+00, 3.51147472e+00,\n",
-       "        1.79757632e+00, 5.38281513e+00, 8.04053720e+00, 1.31617672e+00,\n",
-       "        7.29586706e+00, 5.64479222e+00, 1.10124728e+01, 3.51148550e+00,\n",
-       "        8.48893842e+00, 2.03011523e+00, 1.47222214e+00, 3.08129283e+00,\n",
-       "        3.28954468e+01, 2.48340395e+02, 9.77060941e+00, 8.99523436e+00,\n",
-       "        2.92244076e+00, 3.65982422e+00, 3.85351765e+01, 9.08105536e+00,\n",
-       "        3.61923284e-01, 3.64318543e-01, 8.70516003e-01, 4.89143145e+00,\n",
-       "        6.43215603e+01, 4.38444148e+00, 8.88800587e+00, 8.78349600e+00,\n",
-       "        1.42103159e+00, 9.16332164e-01, 4.27483744e+01, 8.38062714e+00,\n",
-       "        5.03178779e+00, 9.16495297e-01, 2.37194396e-02, 2.55563934e-01,\n",
-       "        4.50959860e-03, 6.40454158e+00, 1.38176299e+00, 6.97739824e+00,\n",
-       "        3.54642159e+01, 4.72821367e+00, 2.65709280e+00, 8.76077741e-01,\n",
-       "        4.83803718e+00, 3.61143493e+00, 2.73947401e+00, 7.55791129e+00,\n",
-       "        3.36113760e+00, 1.87747487e+00, 2.11189235e+01, 1.42386725e+00,\n",
-       "        1.49480946e+00, 4.41617610e+00, 1.40467059e+01, 1.54028196e+00,\n",
-       "        1.11386987e+00, 5.48447166e+00, 9.80053089e+00, 7.68563169e+00,\n",
-       "        1.85472819e+00, 2.38471552e+00, 4.64038081e-01, 7.02165340e+00,\n",
-       "        9.78701852e-01, 1.00843213e-01, 1.07605549e+01, 5.15364914e+00,\n",
-       "        3.31342339e+00, 9.83409701e+01, 1.45463863e+01, 9.22199170e+01,\n",
-       "        1.38244218e+00, 7.21307641e+00, 1.66068190e+00, 2.82317146e-01,\n",
-       "        7.86101169e+00, 2.33509479e+00, 4.12230672e+01, 4.72694765e+01,\n",
-       "        3.57579298e+00, 1.89324697e-01, 1.81055529e+01, 1.79261079e+00,\n",
-       "        1.12969976e+01, 1.70972330e+01, 8.97754492e-01, 1.31370031e+02,\n",
-       "        1.16254263e+03, 1.41413955e+02, 1.94467808e+00, 2.41937145e+00,\n",
-       "        9.68830770e+00, 4.94392776e-01, 1.85254454e+00, 3.28367802e+00,\n",
-       "        1.96677760e+01, 1.25207408e+01, 3.52465895e+00, 4.03946115e+01,\n",
-       "        3.39795947e+01, 3.40396859e+01, 7.81154245e+01, 9.05891458e-01,\n",
-       "        6.47896261e+00, 2.51053336e-01, 2.22186775e+01, 5.77800241e+00,\n",
-       "        1.59632932e+02, 6.57462608e+01, 2.35329041e+01, 3.52263195e+00,\n",
-       "        7.93644844e+00, 3.04897607e+00, 1.68828643e+01, 3.00354774e+00,\n",
-       "        1.49631606e+00, 3.68801452e-01, 5.81127847e+00, 1.52072448e+01,\n",
-       "        1.32358722e+01, 2.87060781e+00, 1.30582847e+00, 9.14850928e-01,\n",
-       "        6.19236323e+00, 3.49339832e+00, 1.67438563e+00, 5.56856956e+00,\n",
-       "        9.23198526e-01, 2.83168019e+00, 6.87703770e+00, 7.02919842e-01,\n",
-       "        1.61080403e+01, 3.54203298e+00, 4.03103308e+00, 5.45083242e+00,\n",
-       "        1.47933347e+01, 3.89416062e+00, 3.60912292e+00, 2.66069043e+00,\n",
-       "        7.51414121e+00, 1.60205492e+01, 3.40643254e+00, 1.65523055e+01,\n",
-       "        1.09793640e+01, 1.62986461e+01, 2.52740118e+01, 1.03911068e+01,\n",
-       "        3.36477616e+00, 1.42340968e+01, 2.35035662e+00, 1.71178905e+01,\n",
-       "        3.77343764e+00, 2.03714255e+00, 3.31015576e+01, 6.33433007e-01,\n",
-       "        6.14744658e+00, 7.16088069e+00, 2.23041424e+01, 1.02499349e+00,\n",
-       "        1.97728679e+00, 3.44821524e+01, 2.61349166e+01, 5.25053778e+00,\n",
-       "        4.14815656e+00, 3.27333328e+00, 2.45511084e+01, 1.71874526e+01,\n",
-       "        8.09460414e+00, 1.01537964e+00, 5.93238476e+00, 5.35806269e+00,\n",
-       "        1.70074971e+00, 9.05856906e+00, 8.68542308e+00, 2.12308484e+00,\n",
-       "        5.90482450e+00, 9.03874533e+00, 2.43132755e+01, 2.00834863e+01]])
          • mu
            (chain, draw)
            float64
            -0.1979 3.452 ... -15.29 -7.206
            array([[-1.97924571e-01,  3.45176103e+00,  3.66898018e+00,\n",
-       "        -1.02743940e+01,  4.25360730e+00, -2.02111574e+00,\n",
-       "         4.99512893e+00, -4.89951797e+00,  2.93259999e+00,\n",
-       "         3.14718356e+00,  1.27127831e-02, -5.89109961e+00,\n",
-       "        -3.95595294e+00,  7.20764616e+00, -1.13762985e+01,\n",
-       "        -2.64993320e+00,  4.63740279e+00, -2.61164777e+00,\n",
-       "        -4.92404586e+00, -5.32221786e-01, -7.24192440e+00,\n",
-       "        -2.84180113e+00, -5.93344391e-01, -9.75138109e+00,\n",
-       "         1.48560255e-01, -4.42832119e-01, -1.52289735e+00,\n",
-       "         6.52718424e+00, -4.55815233e+00,  8.03345649e+00,\n",
-       "        -1.25949421e+00,  4.09944512e+00, -2.85912239e+00,\n",
-       "        -3.12320344e-01, -1.25621514e+00,  2.68736028e+00,\n",
-       "        -2.19802764e+00,  4.40315543e+00,  9.20349072e+00,\n",
-       "        -3.84608588e+00, -2.21098734e+00,  7.98896705e+00,\n",
-       "        -2.56405874e+00, -7.55191977e+00, -3.66256916e+00,\n",
-       "        -1.93900442e+00,  1.22065841e+00,  1.71740829e+00,\n",
-       "         8.21454272e+00,  1.92083009e+00, -4.55360166e+00,\n",
-       "         2.24412248e+00, -2.13959890e+00,  9.43831952e+00,\n",
-       "         9.61207333e+00, -3.95567894e+00, -4.81381220e+00,\n",
-       "        -4.39614559e+00, -1.56718278e+00, -5.36653851e+00,\n",
-       "         1.92902328e+00, -5.46437773e+00, -8.28278477e-01,\n",
-       "        -2.15915746e+00, -5.48922909e+00,  1.13661423e+00,\n",
-       "         2.09137433e+00, -2.54786877e+00, -2.65724057e+00,\n",
-       "         2.05729726e+00, -1.61402241e-01, -2.83105628e+00,\n",
-       "         5.37481879e+00, -7.79670411e+00,  1.23781801e+00,\n",
-       "        -1.05330598e+01, -4.50752694e+00, -8.14796386e+00,\n",
-       "        -4.70073053e+00,  3.80565742e+00,  5.50567267e+00,\n",
-       "         4.16868852e-01, -2.35527277e+00, -8.36613464e+00,\n",
-       "        -7.13115096e-01,  1.43720177e+00, -6.10898476e+00,\n",
-       "        -8.81174187e-01, -7.00294828e+00, -6.25292261e+00,\n",
-       "        -3.70662453e-01,  5.97140466e-01,  1.69662795e+00,\n",
-       "         6.36365353e+00,  1.95191457e+00, -4.14199908e-01,\n",
-       "        -9.02638518e+00,  5.15984968e+00, -4.09147662e+00,\n",
-       "        -5.23406237e+00, -6.09587492e+00, -4.65743036e+00,\n",
-       "         4.33676745e+00, -9.91340056e+00,  2.83535062e+00,\n",
-       "         4.70713637e+00, -6.33033245e+00, -2.19378021e+00,\n",
-       "         5.86431705e+00,  3.46673670e+00, -2.43746040e+00,\n",
-       "         1.68730344e+00,  3.60685353e+00,  2.88654746e+00,\n",
-       "        -3.15878724e-01,  1.62471781e+00,  3.35119982e+00,\n",
-       "         4.63666452e-01,  7.87306500e-01, -9.24725008e-01,\n",
-       "        -3.38194013e+00,  4.52134311e+00, -2.26543848e-01,\n",
-       "        -3.44352266e+00,  1.72719800e+00,  2.39885986e+00,\n",
-       "        -6.50678946e+00, -7.56880421e+00, -5.30732847e+00,\n",
-       "        -5.17096376e+00, -1.94421141e+00, -6.93298928e-01,\n",
-       "        -1.52396072e+00, -5.60458995e-02,  4.94155671e+00,\n",
-       "        -1.07948739e+00, -1.69029823e+00,  1.84554371e+00,\n",
-       "         6.38458750e-01, -8.40430815e+00, -3.73774329e+00,\n",
-       "         3.07278534e+00,  2.05623948e-01, -5.27314592e-01,\n",
-       "         6.15851493e-01, -6.35782747e+00,  1.05558401e+00,\n",
-       "        -4.61172554e-01,  2.74970370e+00, -1.11008096e+00,\n",
-       "        -1.06719737e+00, -1.25335382e+00, -1.05161094e+01,\n",
-       "        -2.79222678e+00, -2.61743694e+00,  1.06859928e+01,\n",
-       "         4.17430844e+00, -8.61940068e+00,  6.62649786e+00,\n",
-       "        -3.77297366e+00, -4.79928278e+00,  2.11312406e+00,\n",
-       "         3.82841892e+00,  1.65150718e+01,  1.28827753e+00,\n",
-       "         7.69045233e+00,  4.32595573e+00,  5.67468866e+00,\n",
-       "        -4.26950865e+00,  1.42134184e+01, -3.02203250e+00,\n",
-       "         7.88690400e+00,  4.70002602e+00,  1.22227407e+01,\n",
-       "        -9.17601028e+00, -4.89870159e+00,  3.69229719e+00,\n",
-       "        -7.05443646e+00,  3.16827030e+00,  5.92829164e+00,\n",
-       "         6.32853189e-01, -4.97531523e+00,  5.35110233e+00,\n",
-       "         4.84398026e+00, -1.30950889e+00,  3.10966847e+00,\n",
-       "         5.19944573e+00, -4.09236633e+00, -1.51532902e+00,\n",
-       "         2.07513611e+00,  1.17597066e+00, -1.32898956e+00,\n",
-       "        -6.41614907e+00, -7.89735147e+00,  1.50822033e+00,\n",
-       "         4.09212007e+00,  2.47951153e+00, -1.63800121e+00,\n",
-       "        -1.86176988e+00, -2.52168837e+00, -6.64242059e+00,\n",
-       "        -1.02397782e+01, -4.54425137e+00,  2.62436848e+00,\n",
-       "         1.22193671e+00, -9.38962033e+00, -1.10203324e+00,\n",
-       "        -7.05103435e+00, -4.46289833e+00,  5.25312798e+00,\n",
-       "        -3.56515665e+00,  1.26120037e+00,  4.75885172e+00,\n",
-       "        -2.50167965e+00,  3.80542944e+00,  3.01167210e+00,\n",
-       "         2.70345411e+00,  5.73690638e+00, -4.54935671e+00,\n",
-       "        -6.43236445e+00,  7.41097905e+00,  1.35900623e+00,\n",
-       "         5.04012320e-01, -3.15086965e-01,  1.64858689e+00,\n",
-       "        -2.06980294e+00, -8.59024295e+00, -4.97349172e-01,\n",
-       "        -1.80382620e+00,  1.17371261e+01,  2.15827083e+00,\n",
-       "         2.22745215e+00, -1.20855349e+00, -2.44807351e+00,\n",
-       "        -9.87085690e+00, -1.34467216e+00, -5.81848641e+00,\n",
-       "         3.96936369e+00,  6.03001020e+00,  2.48189062e+00,\n",
-       "        -3.39454396e+00,  8.02506367e+00, -1.08853432e+01,\n",
-       "        -5.95905160e+00,  5.62999973e+00, -1.33903618e+00,\n",
-       "         2.43456328e-01,  7.46115585e+00,  7.25904503e+00,\n",
-       "         5.91408176e+00, -8.78687142e+00, -6.51018263e+00,\n",
-       "        -4.41948337e+00, -2.48947673e+00, -1.64088207e+00,\n",
-       "         6.20255356e+00, -9.17992329e+00,  3.82492982e+00,\n",
-       "         2.63842026e+00, -4.17331044e+00,  1.75196415e+00,\n",
-       "        -5.04693689e+00,  1.35289027e+00,  7.57024645e+00,\n",
-       "         7.42244523e-01, -8.30060584e+00, -4.41346052e+00,\n",
-       "         2.16165760e+00, -5.26439002e+00, -6.91396918e+00,\n",
-       "         5.37676522e-01,  4.51113679e+00, -8.02857560e-01,\n",
-       "        -2.33763106e+00, -3.14059882e+00, -3.39669533e+00,\n",
-       "         9.30930118e-01, -5.37274114e+00,  1.77406778e+00,\n",
-       "         4.51427847e+00, -4.11358649e+00,  3.99759043e+00,\n",
-       "         1.53790689e+00,  3.08286549e+00,  1.96950901e+00,\n",
-       "        -2.33147685e+00,  8.35301298e+00,  3.64283230e+00,\n",
-       "         3.93723672e+00, -1.55395905e+00, -4.08348595e+00,\n",
-       "        -1.12488433e+00, -4.94433054e+00,  1.09220685e+00,\n",
-       "        -2.96159007e+00, -3.82612120e+00,  5.71952703e+00,\n",
-       "         2.96089980e+00,  6.57383263e+00,  3.43877847e+00,\n",
-       "         3.23617754e+00, -3.26402642e-01, -1.83070903e+00,\n",
-       "         2.65271223e+00,  4.20683702e+00,  6.46751652e+00,\n",
-       "         3.60872783e-01, -8.27036082e+00, -9.63407531e+00,\n",
-       "         3.26333096e-01,  2.43822818e+00, -7.43272899e+00,\n",
-       "        -2.69406922e+00,  3.71935806e+00, -5.18549573e+00,\n",
-       "        -4.51118195e+00, -2.09199225e+00,  5.78108487e+00,\n",
-       "        -5.89811771e+00,  3.49169444e+00, -5.05185406e+00,\n",
-       "        -1.20226878e+00,  6.53307013e+00,  4.81768620e+00,\n",
-       "         4.17257863e+00, -3.86026668e+00,  5.58392228e+00,\n",
-       "        -4.41510369e+00,  5.57459816e-01,  2.72842246e+00,\n",
-       "        -7.78855364e+00,  5.68483356e+00,  3.29852882e+00,\n",
-       "         9.55117660e+00, -6.27973606e+00,  1.78814651e+00,\n",
-       "         1.39547434e+00,  7.55131835e-01,  3.65522852e-01,\n",
-       "         4.38405961e+00, -1.68567975e+00, -8.80176186e+00,\n",
-       "        -5.19047572e+00,  4.07829465e+00,  4.29162615e+00,\n",
-       "        -1.20809055e+00, -2.34765354e+00, -6.46386071e+00,\n",
-       "        -6.03662219e-01, -4.66821357e+00, -1.43937731e-01,\n",
-       "         1.09606189e+00,  6.30828939e+00, -1.30803069e+00,\n",
-       "        -3.03474141e+00,  3.35965967e-01, -6.33265044e+00,\n",
-       "         3.28254564e+00,  1.37978799e+00, -2.48652438e+00,\n",
-       "        -1.84211152e+00, -8.65571630e+00, -2.90481357e+00,\n",
-       "        -5.34261854e+00,  1.34312306e+00, -2.08973719e+00,\n",
-       "         6.18252186e+00,  4.09215122e+00, -1.48227522e+00,\n",
-       "        -9.72847583e+00, -1.06198133e+00,  8.99839205e-01,\n",
-       "        -9.42087378e-01, -1.78560508e+00, -4.11869577e+00,\n",
-       "        -1.94607602e+00, -6.34726065e+00,  5.24379982e+00,\n",
-       "        -2.23697781e+00,  3.40115386e-01,  3.73908939e+00,\n",
-       "         4.53282894e+00, -2.28989784e+00, -6.71804450e+00,\n",
-       "        -4.63620303e-01,  1.13621798e+01, -6.37943935e+00,\n",
-       "        -2.25709706e+00,  1.65004215e+00,  4.45505702e-01,\n",
-       "        -4.29752873e+00,  3.41480439e+00, -4.48702607e+00,\n",
-       "         5.83413204e+00,  9.06419203e+00, -2.52112835e+00,\n",
-       "         5.00974231e+00, -7.78393797e-01,  3.36146623e+00,\n",
-       "         4.83698962e+00, -1.05169071e+00,  3.66616293e+00,\n",
-       "        -1.67252497e+00, -3.62323547e+00,  7.80121165e+00,\n",
-       "        -3.08831078e+00, -5.43366444e+00, -3.69190337e+00,\n",
-       "         3.04940823e+00,  4.94988152e-01, -1.56235549e+00,\n",
-       "        -1.31380093e+01, -2.68698571e-01, -7.08968310e+00,\n",
-       "         7.25984399e+00, -7.94048679e+00, -9.01143028e+00,\n",
-       "         2.15010800e+00, -1.39225759e+01, -1.92367776e+00,\n",
-       "        -1.05580966e+01, -5.65026775e-01,  5.79205213e+00,\n",
-       "        -1.55147165e+00, -6.97345557e+00,  3.81667571e+00,\n",
-       "        -1.17125097e+00, -8.23705763e+00,  2.03794735e+00,\n",
-       "        -1.28032492e+00, -1.10053320e+01,  6.43417469e+00,\n",
-       "        -3.93257893e+00,  5.37706754e-01,  2.38022617e+00,\n",
-       "         6.33201935e-01,  6.87942708e+00, -4.85099290e+00,\n",
-       "         3.03039778e+00,  4.28465279e+00, -3.74243269e+00,\n",
-       "         5.41243454e+00,  4.19264965e+00,  3.94209299e+00,\n",
-       "         4.19019105e-01,  9.03729243e-01, -8.47680165e-01,\n",
-       "         5.44887897e+00, -6.64587581e+00,  2.24505549e+00,\n",
-       "        -1.62886915e+00,  6.52696615e+00,  4.65538080e+00,\n",
-       "         2.16153384e+00,  8.01812241e+00,  8.87701558e+00,\n",
-       "        -8.82977615e-01,  7.44374430e+00, -7.26633089e-01,\n",
-       "         5.07979280e+00, -7.77941048e+00,  5.93222518e+00,\n",
-       "        -1.22668195e+01,  1.15109144e+00, -1.05951734e+01,\n",
-       "         1.09823498e+00,  8.38289171e+00, -5.66352195e+00,\n",
-       "         9.02288722e+00,  6.64762146e+00, -3.59210701e+00,\n",
-       "         5.01362305e+00, -2.18552082e+00,  8.10170049e+00,\n",
-       "         3.35098197e+00, -3.67603554e+00,  9.50009626e-01,\n",
-       "        -2.69213521e+00,  5.97375478e+00,  9.19377263e+00,\n",
-       "         4.00684514e+00, -5.14242881e+00, -5.04404481e+00,\n",
-       "         6.80877591e+00,  2.08913991e+00, -3.28593673e+00,\n",
-       "         6.94325245e+00, -2.76457961e+00, -1.44369907e-01,\n",
-       "        -3.11771191e+00,  2.59296320e+00, -1.89779512e+00,\n",
-       "        -3.00382367e+00, -1.49473069e+00, -6.88520684e+00,\n",
-       "         5.65785904e+00, -4.92529741e-01,  4.68942995e+00,\n",
-       "        -1.52910780e+01, -7.20569740e+00]])
          • theta
            (chain, draw, school)
            float64
            -9.888 -1.89 2.237 ... 13.34 -21.99
            array([[[ -9.88782452,  -1.89045271,   2.23745711, ...,   3.05783633,\n",
-       "           4.50876933, -12.02277258],\n",
-       "        [ 12.52324972,  -0.10257462,  17.90235743, ...,  -6.03914366,\n",
-       "           5.39459901,   7.07427163],\n",
-       "        [  3.22949408,   4.20144679,   3.28459753, ...,   3.70696941,\n",
-       "           3.39716626,   4.14062422],\n",
-       "        ...,\n",
-       "        [  2.88020715,   6.85078948,  10.60362223, ...,  -5.47622351,\n",
-       "           2.56413578,  -9.94645561],\n",
-       "        [ 10.70735593, -35.010802  , -49.8329035 , ..., -20.31738401,\n",
-       "         -42.62663544,  -8.45896299],\n",
-       "        [-55.3145697 , -16.82455975,  -1.67088179, ...,  18.1635401 ,\n",
-       "          13.33866153, -21.98739496]]])
          • theta_tilde
            (chain, draw, school)
            float64
            -1.76 -0.3073 ... 1.023 -0.736
            array([[[-1.75960441, -0.30734889,  0.44224485, ...,  0.59121882,\n",
-       "          0.85469606, -2.14729303],\n",
-       "        [ 1.10830795, -0.43425049,  1.76549973, ..., -1.15955004,\n",
-       "          0.23736598,  0.44257976],\n",
-       "        [-1.0836513 ,  1.31291559, -0.94778141, ...,  0.09367097,\n",
-       "         -0.67021802,  1.16294391],\n",
-       "        ...,\n",
-       "        [-0.20016305,  0.23912163,  0.65431562, ..., -1.12467528,\n",
-       "         -0.23513155, -1.61923862],\n",
-       "        [ 1.06931022, -0.81106818, -1.42069815, ..., -0.20673093,\n",
-       "         -1.12430583,  0.28100348],\n",
-       "        [-2.39544427, -0.47894385,  0.27559038, ...,  1.26318892,\n",
-       "          1.02294784, -0.73601253]]])
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          2020-06-10T18:19:23.480401
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

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        xarray.Dataset
          • chain: 1
          • draw: 500
          • obs_dim_0: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (chain, draw, obs_dim_0)
            float64
            -25.31 -0.8959 ... 3.422 -20.64
            array([[[-25.31078195,  -0.89588955,  22.6413552 , ...,  24.13451408,\n",
-       "          16.67999654, -30.23034528],\n",
-       "        [  4.86375172,  11.18885402,  22.53588377, ..., -13.80804677,\n",
-       "          -5.73731196,  -4.12169577],\n",
-       "        [ -1.79113884,   2.53093031,  15.8191744 , ...,   7.61230557,\n",
-       "          -4.66752285,  23.29882677],\n",
-       "        ...,\n",
-       "        [ 28.47079674,  -2.79239653,  14.3904422 , ..., -12.63998944,\n",
-       "          20.70652864,  -3.05241501],\n",
-       "        [ 46.65347282, -46.95008753, -41.11688486, ..., -19.96545045,\n",
-       "         -34.31368027,  -7.26684645],\n",
-       "        [-45.06736361,  -5.81423829,   1.34788684, ...,  19.91178202,\n",
-       "           3.42152391, -20.64388589]]])
        • created_at :
          2020-06-10T18:19:23.484023
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

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        xarray.Dataset
          • obs_dim_0: 8
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (obs_dim_0)
            float64
            28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
            array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
        • created_at :
          2020-06-10T18:19:23.485040
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

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      • \n", - " \n", - "
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    \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> prior\n", - "\t> prior_predictive\n", - "\t> observed_data" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with pm.Model() as model:\n", - " mu = pm.Normal(\"mu\", mu=0, sigma=5)\n", - " tau = pm.HalfCauchy(\"tau\", beta=5)\n", - " theta_tilde = pm.Normal(\"theta_tilde\", mu=0, sigma=1, shape=eight_school_data[\"J\"])\n", - " \n", - " theta = pm.Deterministic(\"theta\", mu + tau * theta_tilde)\n", - " \n", - " pm.Normal(\"obs\", mu=theta, sigma=eight_school_data[\"sigma\"], observed=eight_school_data[\"y\"])\n", - "\n", - " idata = pm.sample(draws=draws, chains=chains)\n", - " idata.extend(pm.sample_prior_predictive())\n", - " idata.extend(pm.sample_posterior_predictive(idata))\n", - "\n", - "idata" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## From PyStan" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:47:57.347055Z", - "start_time": "2020-06-05T06:47:16.997392Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_9d743830ec4a29fb58eb4660d4b9417f NOW.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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        xarray.Dataset
          • chain: 4
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float64
            8.801 4.356 5.887 ... 5.811 6.163
            array([[ 8.80085977,  4.35621311,  5.88747459, ...,  7.56510323,\n",
-       "         3.75692215,  4.24241574],\n",
-       "       [ 3.88430209,  1.25503637,  8.12161043, ...,  0.77999781,\n",
-       "         8.88447765,  2.55867698],\n",
-       "       [ 3.69774758,  5.56187344,  7.18886091, ...,  2.35104484,\n",
-       "         7.36896537,  7.30112058],\n",
-       "       [ 4.56370867, -1.9814485 ,  8.93919361, ..., -0.45859494,\n",
-       "         5.81131473,  6.16295074]])
          • tau
            (chain, draw)
            float64
            1.079 2.76 3.322 ... 3.54 0.5714
            array([[1.07873807, 2.75983947, 3.32167212, ..., 4.65835454, 5.67923053,\n",
-       "        1.15391559],\n",
-       "       [0.68602682, 1.9898873 , 1.71348775, ..., 6.32124664, 6.49443423,\n",
-       "        7.88054963],\n",
-       "       [6.09793056, 2.08870225, 3.01847687, ..., 0.19977493, 2.0959731 ,\n",
-       "        2.08265721],\n",
-       "       [4.80268382, 8.63940768, 7.49279334, ..., 4.94150802, 3.54035862,\n",
-       "        0.57140337]])
          • theta_tilde
            (chain, draw, school)
            float64
            0.7707 3.4 ... 0.9492 -0.1983
            array([[[ 0.77065058,  3.3999605 ,  0.896277  , ...,  0.85548385,\n",
-       "          0.56312311, -0.02336804],\n",
-       "        [-0.23856364,  1.88839417,  0.11615247, ...,  0.20239506,\n",
-       "         -0.65088377, -0.59082016],\n",
-       "        [-0.44993014,  0.3660292 , -0.85342255, ..., -0.05060912,\n",
-       "          1.89747793, -0.29915642],\n",
-       "        ...,\n",
-       "        [-1.53738528,  0.34600749,  0.66613118, ..., -0.31358001,\n",
-       "         -0.13278535, -0.13050197],\n",
-       "        [ 0.10392075,  1.2781256 ,  0.17946725, ...,  0.73474013,\n",
-       "          1.59901667, -0.22310606],\n",
-       "        [ 0.40039   , -0.36919646, -0.41812356, ..., -0.82578604,\n",
-       "         -0.95433683, -0.70055211]],\n",
-       "\n",
-       "       [[ 0.28550956, -1.75918566,  1.08585615, ..., -0.17260057,\n",
-       "         -0.31717858, -0.15889076],\n",
-       "        [ 1.12616284,  2.12250411, -1.22377048, ..., -0.50452776,\n",
-       "          1.09665063,  0.62770833],\n",
-       "        [-0.82113198,  0.60950151,  0.12629451, ...,  1.18250354,\n",
-       "          1.03982808,  0.4350411 ],\n",
-       "        ...,\n",
-       "        [-0.14425363,  0.18750333,  2.30758557, ...,  0.19605514,\n",
-       "          0.04511098,  1.31371653],\n",
-       "        [ 0.70622565,  0.07230275, -1.80811131, ..., -0.30374127,\n",
-       "          0.73420799, -0.89225914],\n",
-       "        [ 1.13667967, -0.62298503,  0.70894404, ...,  0.93673866,\n",
-       "          0.83697246,  1.22364797]],\n",
-       "\n",
-       "       [[ 1.71586288,  0.2645208 ,  1.38154111, ...,  1.34611706,\n",
-       "          0.67924247,  0.6426671 ],\n",
-       "        [-0.63375751, -0.25986361, -1.85613258, ..., -1.1958948 ,\n",
-       "          0.65731631, -0.33917202],\n",
-       "        [-0.25273764, -0.31382369, -0.34433588, ...,  1.56951567,\n",
-       "         -0.42550589,  0.13231013],\n",
-       "        ...,\n",
-       "        [-1.3109662 ,  1.0167199 ,  0.29096078, ...,  1.2091756 ,\n",
-       "         -0.35967787,  0.28249554],\n",
-       "        [ 2.40744126, -0.73846363, -0.98632421, ..., -0.79229741,\n",
-       "          2.01420262,  1.00171644],\n",
-       "        [ 2.25146098,  0.08915173, -0.37030215, ..., -0.75901238,\n",
-       "          0.46540678,  0.5400871 ]],\n",
-       "\n",
-       "       [[ 0.43838309,  0.06907241,  0.44621424, ..., -0.26691355,\n",
-       "          1.45697414,  0.66782308],\n",
-       "        [ 1.38057301,  0.71432475, -0.92534916, ...,  0.23614769,\n",
-       "          0.48711266,  1.27270678],\n",
-       "        [ 0.38952426, -0.18324781,  0.28926883, ..., -0.11207212,\n",
-       "          1.06242534, -1.2021079 ],\n",
-       "        ...,\n",
-       "        [-0.57243771,  1.20324745, -2.03671137, ..., -0.31232212,\n",
-       "          0.68695275,  0.59062609],\n",
-       "        [ 0.83778081, -0.58047982,  1.78149714, ..., -0.62379571,\n",
-       "          1.44340525, -0.0475484 ],\n",
-       "        [ 1.5197586 , -0.90722855,  1.74266398, ..., -1.08440477,\n",
-       "          0.94915432, -0.19832845]]])
          • theta
            (chain, draw, school)
            float64
            9.632 12.47 9.768 ... 6.705 6.05
            array([[[  9.6321899 ,  12.4685266 ,   9.7677079 , ...,   9.72370277,\n",
-       "           9.40832211,   8.77565177],\n",
-       "        [  3.69781575,   9.56787788,   4.67677528, ...,   4.91479099,\n",
-       "           2.55987838,   2.72564431],\n",
-       "        [  4.39295419,   7.10330357,   3.05268471, ...,   5.71936768,\n",
-       "          12.19027412,   4.89377505],\n",
-       "        ...,\n",
-       "        [  0.40341755,   9.17692878,  10.66817842, ...,   6.10433636,\n",
-       "           6.94654197,   6.9571788 ],\n",
-       "        [  4.34711206,  11.01569209,   4.77615802, ...,   7.92968074,\n",
-       "          12.83810642,   2.4898514 ],\n",
-       "        [  4.704432  ,   3.81639419,   3.75993645, ...,   3.28952836,\n",
-       "           3.1411916 ,   3.43403774]],\n",
-       "\n",
-       "       [[  4.08016931,   2.67745355,   4.62922853, ...,   3.76589348,\n",
-       "           3.66670908,   3.77529877],\n",
-       "        [  3.4959735 ,   5.47858035,  -1.18012896, ...,   0.251083  ,\n",
-       "           3.43724753,   2.5041052 ],\n",
-       "        [  6.71461084,   9.16598381,   8.33801454, ...,  10.14781577,\n",
-       "           9.90334312,   8.86704803],\n",
-       "        ...,\n",
-       "        [ -0.13186495,   1.96525264,  15.36681533, ...,   2.01931073,\n",
-       "           1.06515544,   9.08432401],\n",
-       "        [ 13.47101369,   9.3540431 ,  -2.85818235, ...,   6.91184994,\n",
-       "          13.65274315,   3.08975938],\n",
-       "        [ 11.51633751,  -2.35078743,   8.14554566, ...,   9.94069244,\n",
-       "           9.15448   ,  12.20169557]],\n",
-       "\n",
-       "       [[ 14.16096028,   5.31077703,  12.12228932, ...,  11.90627594,\n",
-       "           7.83972096,   7.61668693],\n",
-       "        [  4.23814271,   5.01909573,   1.68496516, ...,   3.06400529,\n",
-       "           6.9348115 ,   4.85344408],\n",
-       "        [  6.42597819,   6.24159135,   6.14949101, ...,  11.92640765,\n",
-       "           5.90448123,   7.58823597],\n",
-       "        ...,\n",
-       "        [  2.08914666,   2.55415999,   2.40917151, ...,   2.59260781,\n",
-       "           2.27919022,   2.40748037],\n",
-       "        [ 12.41489748,   5.82116547,   5.30165636, ...,   5.70833131,\n",
-       "          11.59067987,   9.46853607],\n",
-       "        [ 11.99014202,   7.48679308,   6.52990814, ...,   5.72035797,\n",
-       "           8.27040336,   8.42593687]],\n",
-       "\n",
-       "       [[  6.66912406,   4.8954416 ,   6.70673459, ...,   3.28180729,\n",
-       "          11.56109482,   7.77105178],\n",
-       "        [  9.94588456,   4.18989425,  -9.97591714, ...,   0.05872764,\n",
-       "           2.22691637,   9.01398426],\n",
-       "        [ 11.85781836,   7.5661556 ,  11.10662516, ...,   8.0994604 ,\n",
-       "          16.89972711,  -0.06795243],\n",
-       "        ...,\n",
-       "        [ -3.28730045,   5.48726201, -10.52302049, ...,  -2.0019372 ,\n",
-       "           2.93598756,   2.45998863],\n",
-       "        [  8.77735924,   3.75620799,  12.11845349, ...,   3.6028542 ,\n",
-       "          10.92148696,   5.64297636],\n",
-       "        [  7.03134593,   5.64455729,   7.15871482, ...,   5.5433182 ,\n",
-       "           6.70530072,   6.0496252 ]]])
        • created_at :
          2020-06-10T18:20:55.257774
          arviz_version :
          0.8.3
          inference_library :
          pystan
          inference_library_version :
          2.19.1.1

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        xarray.Dataset
          • chain: 4
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y_hat
            (chain, draw, school)
            float64
            16.82 11.42 17.62 ... -11.89 1.282
            array([[[ 16.81606234,  11.42314561,  17.62390159, ...,   9.79825611,\n",
-       "          17.93449592,  20.74301303],\n",
-       "        [ -2.87371409,   3.26558897,  23.16168449, ...,   5.60727737,\n",
-       "           9.07428464,  -0.29335618],\n",
-       "        [ 29.69759628,  27.78756772,  15.23144444, ...,  11.23869389,\n",
-       "          19.64442795,  16.32327954],\n",
-       "        ...,\n",
-       "        [  0.22658945,  14.21873048,  41.77431262, ...,  27.79849965,\n",
-       "           8.97700113, -10.97758778],\n",
-       "        [-25.71679369,   7.54290441,  22.1185727 , ...,  -1.9379867 ,\n",
-       "          24.08319264,  11.10296328],\n",
-       "        [ 29.8534142 ,  19.97743567,  -2.22962611, ...,  -1.57819509,\n",
-       "           2.04335776,  -4.69118197]],\n",
-       "\n",
-       "       [[ 19.34365445,  -4.00444521, -16.23523472, ...,  13.94222586,\n",
-       "           8.86119588,  -2.0744115 ],\n",
-       "        [  4.70865213,  23.04754692,  21.70862296, ...,   0.26881933,\n",
-       "           8.3109079 , -24.0917072 ],\n",
-       "        [  8.61267321,   5.41437255, -12.84437828, ...,  30.43257723,\n",
-       "           1.84297616,   8.86262284],\n",
-       "        ...,\n",
-       "        [  3.59843013,   6.09765236,  25.76124843, ..., -10.2115549 ,\n",
-       "          25.59755363,  22.36298176],\n",
-       "        [ -5.29619527,  -2.95926353, -29.68418582, ..., -15.42170733,\n",
-       "          16.63492822,  24.59769265],\n",
-       "        [ 19.98940386,   3.80606888,  42.7843385 , ...,  34.96627185,\n",
-       "          14.25740899,  -1.3367825 ]],\n",
-       "\n",
-       "       [[  2.14679791,   8.14117178,  21.25441028, ...,  11.52465386,\n",
-       "           5.80097177, -10.65373129],\n",
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-       "           8.08712236,  -6.39852626],\n",
-       "        [ 32.71773469,   8.39087771,  15.51443141, ...,  14.32087597,\n",
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-       "        ...,\n",
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-       "        [ 16.35385465,  23.58801269, -21.75232011, ...,   1.94421295,\n",
-       "           0.10598705,  -6.81390168],\n",
-       "        [ 16.65187106,   5.47807306,  16.66239838, ..., -10.24095255,\n",
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-       "           8.22006103, -10.21126023],\n",
-       "        [ 11.9101745 ,   6.81738971,  33.3703771 , ...,  -3.27619529,\n",
-       "          24.69391859, -16.79777889],\n",
-       "        [  9.5301372 ,  21.03087493,  15.88465758, ...,   1.06856218,\n",
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          2020-06-10T18:20:55.276063
          arviz_version :
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        xarray.Dataset
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          • draw: 1000
          • school: 8
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          • draw
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          • school
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-       "         -3.59060827, -3.82535415],\n",
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-       "         -4.4135104 , -3.94204744],\n",
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-       "         -3.3902882 , -3.88723997],\n",
-       "        ...,\n",
-       "        [-5.31936954, -3.22844943, -4.05640831, ..., -3.42449599,\n",
-       "         -3.8324183 , -3.84855419],\n",
-       "        [-4.8702312 , -3.26699562, -3.80963006, ..., -3.51526552,\n",
-       "         -3.35474935, -3.94888271],\n",
-       "        [-4.83295204, -3.30903641, -3.7807787 , ..., -3.33849472,\n",
-       "         -4.32544456, -3.92254441]],\n",
-       "\n",
-       "       [[-4.89845162, -3.36317113, -3.80520915, ..., -3.34844607,\n",
-       "         -4.24873977, -3.91370182],\n",
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-       "         -4.28189242, -3.94846464],\n",
-       "        [-4.63380605, -3.22832122, -3.94260259, ..., -3.6626294 ,\n",
-       "         -3.54930289, -3.82445749],\n",
-       "        ...,\n",
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-       "         -4.65546843, -3.82242938],\n",
-       "        [-4.09608083, -3.23069079, -3.69156654, ..., -3.46125517,\n",
-       "         -3.31601684, -3.93182941],\n",
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-       "         -3.83371561, -3.88812705],\n",
-       "        [-4.66129633, -3.23698363, -3.85502957, ..., -3.81016597,\n",
-       "         -3.9530315 , -3.83934681],\n",
-       "        ...,\n",
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-       "         -4.45724293, -3.95131096],\n",
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-       "         -3.42692055, -3.81919966],\n",
-       "        [-4.19657885, -3.22284053, -3.86890841, ..., -3.40890727,\n",
-       "         -3.69484888, -3.82902314]],\n",
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-       "       [[-4.63811378, -3.26971504, -3.87555205, ..., -3.33834887,\n",
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-       "         -3.22757663, -4.0340564 ],\n",
-       "        ...,\n",
-       "        [-5.80231133, -3.25309289, -3.802066  , ..., -3.35407193,\n",
-       "         -4.35614598, -3.94976063],\n",
-       "        [-4.44812188, -3.31157248, -4.13794842, ..., -3.34482905,\n",
-       "         -3.47205036, -3.8716741 ],\n",
-       "        [-4.6040653 , -3.24926418, -3.89308875, ..., -3.40213025,\n",
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-       "        0.27638304],\n",
-       "       [0.30603051, 0.30603051, 0.30603051, ..., 0.30603051, 0.30603051,\n",
-       "        0.30603051],\n",
-       "       [0.40097617, 0.40097617, 0.40097617, ..., 0.40097617, 0.40097617,\n",
-       "        0.40097617],\n",
-       "       [0.27312733, 0.27312733, 0.27312733, ..., 0.27312733, 0.27312733,\n",
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-       "       [3, 4, 3, ..., 3, 4, 3],\n",
-       "       [4, 4, 4, ..., 4, 4, 4]])
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-       "       [15, 15, 15, ..., 15, 15, 15],\n",
-       "       [ 7, 23,  7, ...,  7, 15,  7],\n",
-       "       [15, 15, 15, ..., 15, 15, 15]])
          • diverging
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            array([[False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])
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-       "        12.29961118,  7.19678138],\n",
-       "       [ 8.84426238,  9.84895256, 14.77525803, ..., 12.35370489,\n",
-       "         9.67205678,  7.66728697],\n",
-       "       [11.52204836,  9.48080195, 11.1699379 , ..., 18.41838234,\n",
-       "        14.78772817, 11.62941587],\n",
-       "       [ 7.71092746,  9.24555017,  6.98821981, ..., 10.58081723,\n",
-       "        12.37474199, 14.71998465]])
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            float64
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            array([[-12.88137893,  -8.8319153 ,  -6.48855593, ...,  -5.43677471,\n",
-       "         -4.0330844 ,  -4.6496315 ],\n",
-       "       [ -5.97351824,  -6.9490221 ,  -6.14793864, ...,  -6.89176892,\n",
-       "         -4.88146623,  -4.37360644],\n",
-       "       [ -6.16694264,  -5.66030842,  -4.64701238, ...,  -8.85576565,\n",
-       "         -9.41927215,  -6.90323691],\n",
-       "       [ -3.13060803,  -4.68668125,  -3.99299316, ...,  -6.48974414,\n",
-       "         -5.58723251,  -9.34270347]])
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        xarray.Dataset
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            (school)
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          • y
            (school)
            float64
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-       "        -0.51718354,  6.5104423 ,  2.9149487 ,  1.4453427 ,  4.3771286 ,\n",
-       "         4.527439  ,  1.4286865 ,  3.5170612 ,  7.5207043 ,  6.6093435 ,\n",
-       "         0.9016738 ,  8.855167  ,  4.44285   ,  6.2064247 , 11.12649   ,\n",
-       "        10.901761  , 10.992046  ,  1.6697562 ,  9.535236  , -1.4199173 ,\n",
-       "         0.04521966,  0.24071217,  1.0354244 ,  3.8727565 ,  6.546321  ,\n",
-       "         7.4994783 ,  5.1963906 ,  2.2472491 , -1.4566408 ,  3.2481778 ,\n",
-       "         1.5054731 ,  5.427044  ,  0.7663537 , 12.421476  , 13.937447  ,\n",
-       "         9.55212   ,  8.892584  ,  4.5370684 ,  6.597247  ,  3.521164  ,\n",
-       "         6.200644  , -0.6878598 , -0.5076442 , -1.1443609 , -2.024046  ,\n",
-       "         7.497966  ,  3.9299464 ,  2.1074817 ,  7.6489606 , -0.5676765 ,\n",
-       "         8.88996   ,  1.7325966 ,  3.6853108 ,  9.022504  ,  3.554699  ,\n",
-       "         6.0461593 , 10.99902   , -4.534336  , -1.3497578 ,  3.4103231 ,\n",
-       "         3.4103231 ,  5.511337  ,  1.5036378 ,  6.0223374 ,  5.0781465 ,\n",
-       "         7.4995213 ,  2.2854679 ,  5.5526514 ,  4.916196  ,  2.576561  ,\n",
-       "         5.576095  ,  4.371717  , 13.234244  , 13.360312  , -0.45095968,\n",
-       "         2.989245  ,  0.6203029 ,  7.7407403 ,  9.276141  ,  4.255246  ,\n",
-       "         6.0797963 ,  7.8773675 ,  2.5528507 ,  2.1499953 ,  6.2999554 ,\n",
-       "         2.9320016 ,  5.145282  ,  4.0042167 ,  4.7150197 ,  2.9001827 ,\n",
-       "         3.637968  ,  3.0349693 ,  3.6715143 ,  1.2648342 ,  2.0227237 ,\n",
-       "         1.0630711 ,  5.546765  ,  3.8676581 ,  3.0002105 ,  2.6321206 ,\n",
-       "         4.190834  ,  5.7021546 ,  5.8634467 ,  0.2976514 , -0.47097135,\n",
-       "        -1.0752056 ,  4.7460985 , -0.6505427 ,  5.6235523 , -0.9203634 ,\n",
-       "        -1.8839953 , 11.195079  , -2.0718043 , 11.193365  ,  6.1056333 ,\n",
-       "         8.325111  ,  9.075426  ,  2.9479647 ,  7.128322  ,  4.3343573 ,\n",
-       "         3.1772745 ,  5.592222  ,  7.5314674 , -3.0730538 ,  3.9833744 ,\n",
-       "         9.110682  ,  4.5988865 ,  2.8390033 ,  3.4741888 ,  4.0355644 ,\n",
-       "         3.4970965 ,  0.71459496,  7.2869844 ,  3.6135454 ,  9.620527  ,\n",
-       "         1.0082316 ,  7.475091  ,  1.0405822 ,  4.5348783 ,  3.8841453 ,\n",
-       "         4.7387238 ,  4.521943  ,  4.521943  ,  3.539055  ,  6.8544626 ,\n",
-       "        11.370565  ,  4.756797  ,  4.6440043 ,  6.966434  ,  7.002037  ,\n",
-       "         9.16461   ,  5.50093   ,  6.000969  ,  1.3224025 ,  6.746578  ,\n",
-       "         3.1069808 ,  4.3904467 ,  5.1858315 , -0.6927078 ,  7.3248353 ,\n",
-       "         0.03497177,  2.7320523 ,  4.0992765 ,  1.4401541 ,  3.696537  ,\n",
-       "         6.4522524 ,  4.343199  , -1.7796711 ,  9.775927  ,  8.519873  ,\n",
-       "         7.5724382 ,  2.1690316 ,  0.6314293 ,  2.6643667 ,  7.1365185 ,\n",
-       "         8.02526   ,  0.5847881 ,  6.0416284 ,  1.4256818 ,  8.6619425 ,\n",
-       "         2.8416598 ,  3.5279734 ,  5.7481217 ,  5.685895  ,  3.817007  ,\n",
-       "         3.817007  ,  5.1601176 ,  4.495685  ,  2.047713  ,  2.5250795 ,\n",
-       "         6.463599  ,  7.9371533 , -3.1092994 ,  1.6143876 ,  1.0753083 ,\n",
-       "         8.186201  ,  5.4840565 ,  3.3782108 ,  6.6359487 ,  0.73560786,\n",
-       "        -2.5476358 ,  2.1959705 ,  0.595925  , -0.6245699 ,  8.363325  ,\n",
-       "         7.2956586 ,  1.3452858 ,  7.595389  ,  1.9837761 , -2.1932921 ,\n",
-       "         5.130442  ,  3.8775177 ,  3.8775177 ,  3.9545794 ,  3.3309066 ,\n",
-       "         4.0000944 ,  7.8284636 ,  9.910145  ,  0.29044223,  0.4511162 ,\n",
-       "         4.23416   ,  6.625504  ,  6.7739162 ,  5.979492  ,  2.0962138 ,\n",
-       "         7.1208596 ,  1.2915809 ,  2.0792012 ,  6.7928405 ,  4.659316  ,\n",
-       "         3.5058026 ,  2.5944214 ,  7.936224  ,  7.7682953 ,  7.1779675 ,\n",
-       "         6.921358  ,  0.8594191 , -0.21582747,  0.5738251 ,  2.7562642 ,\n",
-       "         3.674602  ,  0.5121915 ,  1.0169942 ,  7.307401  ,  6.4692693 ,\n",
-       "         8.035346  ,  6.3669305 ,  5.8459167 ,  4.03193   ,  7.025049  ,\n",
-       "         6.2521224 ,  1.1102605 ,  3.404097  , -1.5265621 ,  9.016952  ,\n",
-       "        11.493106  , -0.57146204, -0.26515374,  7.4198837 ,  6.505427  ,\n",
-       "         7.2936172 ,  9.671284  ,  2.9167275 ,  2.2982335 ,  7.3781447 ,\n",
-       "         2.1675684 ,  5.190227  ,  5.3436894 ,  3.0521636 ,  6.544801  ,\n",
-       "         4.743841  ,  5.7023606 ,  6.8151307 ,  5.6628094 ,  3.9610717 ,\n",
-       "         5.13286   ,  1.6667626 ,  6.377818  ,  2.9189663 ,  5.914687  ,\n",
-       "         5.556989  , 12.396165  ,  0.9704962 ,  7.037617  ,  8.90337   ,\n",
-       "         4.53032   ,  5.2621    ,  6.3007636 ,  1.6994746 , -4.1169577 ,\n",
-       "         0.44233608,  8.0539665 ,  4.4645185 ,  1.6229515 ,  6.553206  ,\n",
-       "         7.863461  ,  2.2408123 ,  6.6904135 ,  3.169836  ,  5.7995133 ,\n",
-       "         5.9354954 ,  4.294053  ,  3.3903077 ,  8.167379  ,  0.7955775 ,\n",
-       "         4.042009  ,  7.1713643 ,  2.5641007 ,  9.766011  ,  1.7654216 ,\n",
-       "         6.607266  ,  5.0007186 ,  9.228579  ,  0.11804408,  4.6933355 ,\n",
-       "         8.756182  ,  7.6457686 , 11.668673  , -2.2822227 ,  5.744774  ,\n",
-       "        -0.12102944,  5.706559  ,  6.738437  ,  3.9768252 ,  0.6605184 ,\n",
-       "         1.1702331 ,  7.049793  ,  4.7348275 ,  3.3859143 ,  5.335453  ,\n",
-       "         5.6728067 ,  8.422111  ,  0.6059617 ,  8.701169  ,  1.3406711 ,\n",
-       "         0.9726557 ,  2.9391463 ,  8.847219  ,  0.65883195, -1.1129549 ,\n",
-       "         4.2620173 ,  4.603801  ,  6.1635494 ,  4.814609  ,  4.5336676 ,\n",
-       "        10.157522  ,  2.3478928 ,  8.260862  ,  4.5819983 ,  5.691238  ,\n",
-       "         5.3385615 ,  3.9145231 ,  3.366712  ,  4.9146166 ,  4.2341347 ,\n",
-       "         4.2132525 ,  0.06960184,  5.7912407 ,  7.7237253 ,  8.238167  ,\n",
-       "        -1.5925429 , -1.8350666 ,  2.7601335 ,  5.2639394 ,  1.3687866 ,\n",
-       "         1.4656156 ,  4.4150705 ,  0.8827308 ,  1.5304792 ,  7.2156863 ,\n",
-       "         7.9426675 ,  4.7031536 ,  2.7878287 ,  3.0513043 ,  1.1879033 ,\n",
-       "         3.9434485 ,  0.61285603, -1.3296754 ,  2.951462  , -0.25293034],\n",
-       "       [ 5.01953   ,  6.86166   ,  6.6249776 ,  2.2893884 ,  2.501866  ,\n",
-       "         8.285073  ,  3.7216325 ,  4.7185307 ,  5.471057  ,  2.2488348 ,\n",
-       "         5.539174  ,  6.0150375 ,  2.0376778 ,  3.660337  ,  1.6074322 ,\n",
-       "         2.8387644 ,  4.8960958 ,  3.3996067 ,  7.467748  ,  4.651307  ,\n",
-       "         6.6853914 ,  8.318576  ,  7.023429  ,  6.5320916 ,  7.9992576 ,\n",
-       "         7.2837024 ,  4.562793  ,  6.547352  , -0.11292058,  7.4648447 ,\n",
-       "         0.98815346,  4.535407  ,  5.8668675 ,  4.8986874 ,  2.8843725 ,\n",
-       "         3.232449  ,  2.6490922 , -2.0015569 , -2.0015569 , -0.9957659 ,\n",
-       "        -0.9957659 ,  0.3136965 ,  3.6559486 ,  3.5627646 ,  1.8010818 ,\n",
-       "         6.344411  ,  4.7709913 ,  5.2192707 ,  3.2569563 ,  3.880964  ,\n",
-       "         4.206388  ,  3.2617676 ,  4.079238  ,  2.3552814 ,  3.5103083 ,\n",
-       "         4.422537  ,  7.6496224 ,  1.7483644 ,  5.4506264 ,  3.694887  ,\n",
-       "         2.9585373 ,  3.606896  ,  2.6271925 ,  2.4373152 ,  3.0032928 ,\n",
-       "         2.8940313 ,  7.236042  ,  5.4723234 ,  5.602808  ,  4.8854675 ,\n",
-       "         3.133181  , -1.9972157 , -5.426025  ,  0.74136853,  2.1391473 ,\n",
-       "         7.446571  ,  3.438631  ,  4.4135585 ,  2.515184  ,  7.4213567 ,\n",
-       "         1.4862064 ,  9.58293   ,  2.3712258 ,  9.991231  , 11.811281  ,\n",
-       "        -3.8299305 , 10.42727   ,  4.5649595 ,  4.0661554 , 12.225602  ,\n",
-       "         5.1727004 ,  5.384991  ,  2.5262818 ,  2.6618812 ,  4.545309  ,\n",
-       "         5.200153  ,  4.7776184 ,  0.37785053,  2.9929814 ,  5.422091  ,\n",
-       "         6.1752453 ,  7.7365675 ,  1.2166401 ,  3.3863912 ,  6.6130266 ,\n",
-       "         8.373728  ,  7.0688004 ,  5.1593804 ,  8.469555  ,  4.330571  ,\n",
-       "        -1.7626251 , 12.750014  ,  9.7615    ,  6.4595337 ,  8.6939125 ,\n",
-       "         2.2231188 ,  5.390884  ,  0.97161454,  4.505579  ,  6.609976  ,\n",
-       "         4.9074917 ,  4.2184486 ,  6.618556  ,  2.004937  ,  5.4137464 ,\n",
-       "         9.630217  ,  9.768252  ,  0.30616486,  3.3912468 ,  7.838382  ,\n",
-       "         9.576023  ,  7.655735  ,  0.18133971, -3.5502794 , 12.7816515 ,\n",
-       "         6.2193003 ,  6.8971252 ,  2.8146944 ,  6.2655287 ,  2.7793994 ,\n",
-       "         6.990551  ,  4.122216  ,  7.902957  ,  2.0129812 ,  3.2262692 ,\n",
-       "         2.7485383 ,  9.244148  ,  7.6056643 ,  5.710777  ,  2.5418003 ,\n",
-       "         3.4473598 ,  4.393044  ,  3.6912231 ,  6.887758  ,  7.915879  ,\n",
-       "         8.873672  ,  5.941959  ,  2.1800964 ,  0.72718906,  6.024666  ,\n",
-       "         2.1498783 ,  1.6291099 ,  9.088835  , -0.6355481 ,  8.986781  ,\n",
-       "        13.685892  , -4.462116  , 12.183017  , -2.3599992 ,  6.3404007 ,\n",
-       "         2.0727506 ,  4.6059237 ,  2.2072875 ,  4.4966164 ,  8.327364  ,\n",
-       "         9.140227  ,  4.9690924 ,  1.7394838 ,  0.86819786,  5.5950966 ,\n",
-       "         2.6679792 ,  2.9878035 ,  1.293262  ,  5.688295  ,  2.998148  ,\n",
-       "         7.8103237 ,  2.1600375 ,  0.8984237 ,  3.8629336 ,  7.250372  ,\n",
-       "         5.4487286 ,  9.825451  ,  9.6626625 ,  3.5370805 ,  2.28778   ,\n",
-       "         0.7985786 ,  6.5033493 ,  5.7602005 ,  2.1282008 ,  0.7583219 ,\n",
-       "        -0.2883258 ,  4.04663   ,  4.0531526 ,  7.637555  ,  4.522324  ,\n",
-       "         1.8140192 ,  6.2982025 ,  0.8498249 ,  4.9770107 , 10.625928  ,\n",
-       "         2.4482574 ,  1.7317593 ,  2.473702  ,  3.3864214 ,  5.2274213 ,\n",
-       "         7.1590877 ,  4.082163  ,  4.655895  ,  4.341129  ,  0.72216916,\n",
-       "         7.632001  ,  5.273179  ,  7.0966406 ,  3.9848332 ,  6.7652764 ,\n",
-       "        -1.8530495 ,  4.807021  ,  3.7115476 ,  7.7180634 ,  5.495058  ,\n",
-       "         3.5686898 ,  8.432459  , -3.9393754 ,  3.5627987 ,  1.3835332 ,\n",
-       "         3.4305634 ,  5.2384458 ,  4.813102  ,  3.7107148 ,  5.8590145 ,\n",
-       "         7.921546  ,  2.628134  ,  2.35359   ,  4.0655026 ,  3.800044  ,\n",
-       "         3.1461186 ,  5.714904  ,  5.1543646 ,  3.2590818 ,  3.7851493 ,\n",
-       "         4.40026   ,  2.7656972 ,  1.0225668 ,  7.9855137 ,  2.9293344 ,\n",
-       "         4.256064  ,  5.404146  ,  7.5405574 ,  9.850468  , 10.078182  ,\n",
-       "         6.9839487 ,  8.5010395 , 11.371491  ,  3.3040867 ,  3.5877137 ,\n",
-       "         1.6272161 ,  1.6272161 ,  5.909365  ,  5.780271  ,  5.5829563 ,\n",
-       "         4.555645  ,  4.3025413 ,  4.9703617 ,  3.4080243 ,  0.05362892,\n",
-       "         5.641428  ,  1.9199136 ,  0.4426855 ,  9.342329  ,  2.4715855 ,\n",
-       "         0.20299262,  7.053833  ,  3.064495  ,  3.7879605 ,  3.0975132 ,\n",
-       "         4.8171186 ,  4.1754603 ,  1.8580515 ,  3.4130657 ,  1.8592646 ,\n",
-       "         5.972606  ,  2.600781  ,  5.1370234 ,  3.590805  ,  1.6839093 ,\n",
-       "         1.1044543 , 11.325514  , 11.263687  ,  3.0054102 ,  4.2464194 ,\n",
-       "        -1.2235668 ,  0.78690815,  8.217627  ,  8.289513  ,  1.818387  ,\n",
-       "         7.193574  ,  5.6828175 ,  9.229635  ,  1.8125782 ,  6.0123167 ,\n",
-       "         3.310267  ,  3.2328024 ,  8.0518055 ,  0.5462723 ,  5.6560984 ,\n",
-       "         7.22637   ,  8.11613   ,  3.7949753 ,  7.096421  , 10.765428  ,\n",
-       "         0.62853765,  6.8455167 ,  1.8135126 ,  4.342347  ,  5.897304  ,\n",
-       "         3.0075347 ,  1.4480636 ,  6.296116  ,  7.323115  ,  2.1111772 ,\n",
-       "         3.2788057 ,  4.3963056 ,  2.1767483 ,  4.343244  ,  1.7362591 ,\n",
-       "         8.032246  ,  7.7730284 ,  2.500092  ,  7.870913  , -3.3032596 ,\n",
-       "         4.2814445 ,  3.4301393 , 10.24017   , -0.06418872,  5.7352066 ,\n",
-       "         5.756984  ,  5.756984  ,  2.188118  ,  5.0771995 ,  1.7392151 ,\n",
-       "        10.904758  ,  9.412046  ,  7.786703  ,  8.769625  , 10.503181  ,\n",
-       "         8.57885   ,  6.9199967 ,  5.4401836 , -0.05953297,  8.981047  ,\n",
-       "         9.556315  ,  3.1879342 ,  6.2667456 ,  7.8490686 ,  7.600093  ,\n",
-       "         7.9399824 ,  0.6109712 ,  6.932536  ,  2.7567782 ,  1.5435878 ,\n",
-       "         4.8659782 ,  2.2375255 ,  8.436639  ,  9.785013  ,  8.592904  ,\n",
-       "         8.077721  , -0.98542786,  0.47609437,  4.7859526 , -1.187131  ,\n",
-       "         7.878492  ,  9.416552  ,  1.2292858 ,  5.719812  ,  7.2965436 ,\n",
-       "         1.962739  , -0.49165836,  5.951705  ,  1.9709172 ,  0.49937028,\n",
-       "         1.8703003 ,  5.446128  ,  4.893553  ,  4.086196  ,  6.650732  ,\n",
-       "         2.2123806 ,  2.0845377 ,  7.724782  ,  7.0939484 ,  9.294254  ,\n",
-       "         0.98116875, 11.221153  , -0.876735  ,  1.3594122 ,  7.842224  ,\n",
-       "         8.8228245 ,  8.8228245 , 10.224634  ,  7.027872  ,  6.7001348 ,\n",
-       "         7.376293  ,  9.412687  , -2.8747458 ,  1.0567542 ,  7.480833  ,\n",
-       "        10.921535  ,  6.2435284 ,  5.193436  ,  6.8472857 ,  3.895755  ,\n",
-       "         6.2523193 ,  3.9274454 ,  2.3286366 ,  4.988546  ,  4.2868986 ,\n",
-       "         7.0591617 ,  2.0112956 ,  6.0083036 ,  0.5485797 ,  2.927389  ,\n",
-       "         8.296615  ,  0.8524151 ,  8.771065  ,  0.8508061 ,  7.5617332 ,\n",
-       "         4.0908194 ,  6.449942  ,  5.5285096 , -1.5314658 ,  4.6953344 ,\n",
-       "         4.931533  ,  6.3189793 ,  4.447262  ,  4.547436  ,  6.949565  ,\n",
-       "         2.027462  ,  4.97022   ,  4.3807526 , 10.430753  ,  3.2305171 ,\n",
-       "         0.36104214, 10.798279  , -1.6254423 ,  4.7359886 ,  2.0681913 ,\n",
-       "         3.7673833 ,  1.9625635 ,  4.2347145 ,  7.492998  ,  6.4289722 ,\n",
-       "         4.327282  , -0.13930517,  2.281067  , -1.5606796 , -0.6715053 ,\n",
-       "        -2.6402383 ,  6.4653087 ,  1.9133025 ,  5.9235144 ,  8.07356   ,\n",
-       "         5.0766296 ,  5.6559167 , 10.879023  , -0.92014146,  2.5594158 ,\n",
-       "         5.5791693 ,  2.8021357 ,  1.2819654 , -2.5959618 , -0.8181571 ,\n",
-       "         1.8510228 ,  4.257959  ,  9.037647  ,  1.8694059 ,  9.915788  ,\n",
-       "         5.596039  ,  2.743896  ,  7.2139883 ,  4.4983315 ,  3.314092  ,\n",
-       "        10.760844  , -1.5691249 ,  7.673089  ,  2.6870704 ,  2.4335454 ,\n",
-       "        -0.2063955 , -0.75785524, -1.10085   ,  8.008105  ,  0.92949176]],\n",
-       "      dtype=float32)
          • tau
            (chain, draw)
            float32
            2.8592596 7.631564 ... 4.101541
            array([[ 2.8592596 ,  7.631564  ,  2.2398646 ,  2.2167501 ,  3.1840062 ,\n",
-       "         7.012798  ,  3.243187  ,  6.973709  ,  5.5824986 ,  0.7016568 ,\n",
-       "         5.86172   , 19.530476  ,  0.25236988,  3.668568  ,  1.5498195 ,\n",
-       "         0.8107503 ,  1.0509298 ,  7.2098413 ,  1.8983476 ,  5.2955256 ,\n",
-       "         7.7060432 ,  0.43670726,  3.6594412 ,  6.322126  ,  0.20263568,\n",
-       "         3.1838627 ,  2.1906252 ,  3.6247058 ,  3.6476884 ,  8.569651  ,\n",
-       "         0.8154066 ,  0.52646595,  8.347277  ,  5.3391485 ,  5.3391485 ,\n",
-       "         4.3533516 ,  2.2721968 ,  4.044897  , 12.670698  ,  3.6306577 ,\n",
-       "         1.7800876 ,  0.8172901 , 10.408458  , 10.408458  ,  8.752845  ,\n",
-       "         2.7025979 ,  1.8058288 ,  0.77591455,  4.6208324 ,  1.3864799 ,\n",
-       "         4.8308764 ,  5.2640085 ,  2.2747922 ,  2.1527889 ,  0.6709348 ,\n",
-       "         0.40966344,  0.76452506,  1.7523602 ,  9.632993  ,  0.71305096,\n",
-       "         1.2565391 ,  7.3918915 ,  1.9825102 ,  4.3520317 ,  4.191029  ,\n",
-       "         4.46019   ,  6.5468745 ,  1.7655234 ,  2.4890428 ,  0.13036194,\n",
-       "         4.1576633 ,  3.6138592 ,  2.5137105 ,  7.9432316 ,  1.4701631 ,\n",
-       "         4.3312316 ,  3.2743263 ,  6.1689467 ,  0.81546783,  2.3457444 ,\n",
-       "        14.765036  ,  2.7969723 ,  0.6444865 ,  5.056386  ,  6.6326447 ,\n",
-       "         3.3019314 ,  3.4695005 , 14.173468  ,  1.0817696 ,  1.9358197 ,\n",
-       "         6.57442   ,  0.21757841,  1.6629736 ,  2.837197  ,  8.087209  ,\n",
-       "         0.923576  ,  1.5801088 ,  3.2785625 ,  1.1708685 ,  5.65828   ,\n",
-       "         2.544188  ,  0.97468114,  0.85679036,  0.4686036 ,  1.2682633 ,\n",
-       "         1.0238816 ,  1.0111494 ,  3.2353783 ,  5.850848  ,  2.6748486 ,\n",
-       "         6.8615456 ,  1.0207653 ,  5.1171284 ,  4.7231007 ,  0.9388937 ,\n",
-       "         1.1121522 ,  0.19136462,  2.5782864 ,  6.664866  ,  1.035229  ,\n",
-       "         1.2018548 ,  3.5486271 ,  0.26484078,  0.5413486 , 10.383554  ,\n",
-       "         6.841315  ,  1.663987  ,  1.40886   ,  1.4120247 ,  6.9056168 ,\n",
-       "         1.4446397 ,  2.328224  ,  2.377081  ,  3.0902762 ,  1.1421281 ,\n",
-       "         1.1183867 ,  0.9663997 ,  0.72247726,  0.4770038 ,  9.449483  ,\n",
-       "         3.4366267 ,  1.8293209 ,  5.91921   ,  1.5030367 ,  1.8295302 ,\n",
-       "         1.9259436 ,  1.0454125 ,  1.1833336 ,  1.635451  ,  3.1804576 ,\n",
-       "         7.3813477 ,  0.15226473,  9.872404  ,  0.6245333 ,  2.2530851 ,\n",
-       "         3.5460145 ,  4.784728  ,  3.6828964 ,  1.7373091 ,  1.3222244 ,\n",
-       "         1.2164129 ,  3.2084877 ,  5.9633555 ,  8.61425   ,  4.7127013 ,\n",
-       "         1.0490998 ,  2.031085  ,  0.84674907,  1.4061344 ,  0.20833626,\n",
-       "         1.1506217 ,  8.719571  ,  0.5209326 ,  3.2316248 ,  4.3853264 ,\n",
-       "         4.2003636 ,  2.0493677 , 10.459623  , 10.027447  ,  5.8418946 ,\n",
-       "         5.8418946 ,  3.1374278 ,  3.4132574 ,  3.4001408 ,  6.9404526 ,\n",
-       "         0.6055962 ,  2.6735137 ,  5.0053363 ,  2.393575  ,  4.3127356 ,\n",
-       "         7.9282527 ,  4.0160623 ,  6.3807807 ,  4.9094033 ,  5.3358846 ,\n",
-       "         1.5355688 ,  2.814463  ,  0.20154344,  0.16425201,  2.7684138 ,\n",
-       "         0.79762113,  0.29586637,  8.217145  ,  2.5801165 ,  0.62110734,\n",
-       "         5.5564575 ,  0.03571094,  9.594197  ,  1.6323905 ,  6.1980906 ,\n",
-       "         5.9835644 , 10.406311  ,  1.9029803 ,  4.983942  ,  1.1461568 ,\n",
-       "         8.874212  ,  2.818826  ,  1.3390121 ,  1.8619214 ,  6.0745525 ,\n",
-       "         5.101248  ,  1.0643479 ,  1.7463428 , 13.184822  ,  6.307802  ,\n",
-       "         6.2804346 ,  1.8698411 ,  1.3450637 ,  5.679282  ,  3.7104957 ,\n",
-       "         1.4926912 ,  1.3450874 ,  1.6143674 ,  3.5736926 ,  4.833857  ,\n",
-       "         0.38555366,  1.7992418 ,  7.924101  ,  0.5311662 ,  1.0728621 ,\n",
-       "         6.104986  ,  0.41169518,  9.873746  ,  0.6301795 ,  0.6971753 ,\n",
-       "         0.433702  ,  1.6750252 ,  4.7702184 ,  1.1450185 ,  9.231825  ,\n",
-       "         0.65097904,  0.07083836,  7.7014275 ,  1.1763483 ,  3.9216068 ,\n",
-       "         0.7965546 ,  7.643338  ,  0.3307356 ,  1.5628933 ,  8.430889  ,\n",
-       "         2.0809162 ,  9.241798  ,  9.241798  ,  4.945677  ,  1.0078614 ,\n",
-       "         1.0361626 , 10.885583  , 12.564192  ,  0.33511108,  4.6200314 ,\n",
-       "         4.940873  ,  0.5206075 ,  0.07736349,  0.08582618,  1.9981915 ,\n",
-       "         1.7438189 ,  1.5340894 ,  0.7772387 ,  6.601632  ,  2.115645  ,\n",
-       "         8.213202  ,  5.2723393 , 11.72069   ,  0.9106035 ,  0.85000837,\n",
-       "        10.724954  ,  3.2172287 ,  2.224444  ,  4.3728848 ,  6.769486  ,\n",
-       "        12.659085  ,  6.2303095 ,  1.7800417 ,  0.41352934,  4.2141705 ,\n",
-       "         2.3804212 ,  1.9563856 ,  2.9715116 ,  1.8923243 ,  3.1873477 ,\n",
-       "         3.2488613 , 11.12932   ,  1.2553946 ,  0.72405314,  8.201361  ,\n",
-       "         8.201361  ,  2.594084  ,  4.2437234 ,  1.8843505 ,  1.5556631 ,\n",
-       "         9.879009  , 13.189886  ,  0.6889656 ,  4.8971453 ,  2.709153  ,\n",
-       "         5.6291595 ,  3.035018  ,  8.095402  ,  2.2264924 ,  6.9823604 ,\n",
-       "         8.809837  ,  2.8444104 ,  7.2972403 ,  4.9063    ,  8.416786  ,\n",
-       "         6.3838606 ,  3.103317  ,  3.3709526 ,  3.7451754 ,  1.5255952 ,\n",
-       "         6.1873636 , 18.791533  , 18.791533  ,  0.5587105 ,  4.3095994 ,\n",
-       "         6.199546  ,  0.7963446 ,  3.250815  ,  4.6577425 ,  3.8594632 ,\n",
-       "         0.39367265,  0.94611126,  0.6230297 ,  0.35022599,  3.6903803 ,\n",
-       "         2.337941  ,  5.383348  , 12.14523   ,  1.4398934 ,  2.1742728 ,\n",
-       "         1.6072241 ,  5.467194  ,  1.2596793 ,  2.1560652 ,  1.1517068 ,\n",
-       "         5.9963036 ,  1.9717857 ,  1.7716382 ,  1.9160262 ,  2.8552551 ,\n",
-       "         4.745581  ,  7.348962  ,  3.2277074 ,  2.984302  ,  2.2140386 ,\n",
-       "         1.8019764 ,  0.59824824,  1.8449849 ,  3.216473  ,  6.51493   ,\n",
-       "         3.411695  ,  7.4430933 ,  6.659884  ,  5.054224  ,  1.388572  ,\n",
-       "         4.7616067 ,  3.9080114 ,  4.5838494 , 20.273996  ,  3.7882788 ,\n",
-       "         6.1818533 ,  1.1102964 ,  1.5883152 ,  0.45917422,  8.478489  ,\n",
-       "         0.09718665,  2.6336462 ,  7.2664666 ,  2.434935  ,  2.8858519 ,\n",
-       "         0.71751714,  3.0329962 ,  6.130552  ,  0.860823  ,  0.25771728,\n",
-       "         0.42584717,  0.5142102 ,  0.8449509 ,  4.968134  ,  3.1147995 ,\n",
-       "         2.4746912 ,  2.0156538 ,  3.6191094 ,  1.2793202 ,  0.7103939 ,\n",
-       "         3.7820547 ,  6.9056473 ,  4.0495677 ,  1.7817774 ,  2.3426752 ,\n",
-       "         4.633561  ,  2.070319  ,  1.5363076 ,  0.95820147,  3.6127422 ,\n",
-       "         0.3730284 ,  0.28459108,  1.723181  ,  4.1297984 ,  2.3784215 ,\n",
-       "         2.6407814 ,  4.4255576 ,  3.8354557 ,  3.4539526 ,  0.68732715,\n",
-       "        12.028891  ,  2.09528   ,  3.4303656 ,  3.6795819 ,  2.233653  ,\n",
-       "         9.531067  ,  2.4739041 ,  1.3033855 ,  1.8244535 ,  2.7561784 ,\n",
-       "         3.35038   ,  2.4797575 ,  1.2056069 ,  1.42102   ,  3.5945454 ,\n",
-       "         1.5078974 ,  1.030959  ,  2.032822  ,  1.9084902 ,  0.71502614,\n",
-       "         0.24140915,  1.3988683 ,  2.2474515 ,  5.052353  ,  0.88052756,\n",
-       "         3.1064644 ,  5.4538717 ,  0.08182631,  2.2336638 ,  5.490027  ,\n",
-       "         3.8521888 ,  1.7020229 ,  0.31675732,  0.54662824,  0.5233591 ,\n",
-       "         7.528719  ,  0.5788549 ,  2.3658106 ,  7.823274  ,  9.688509  ,\n",
-       "         5.4737873 ,  2.0360384 ,  8.556991  ,  1.9897192 ,  1.9756216 ,\n",
-       "         4.789227  ,  2.9638944 ,  3.9432104 ,  4.5745883 ,  2.8481593 ,\n",
-       "         2.703546  ,  0.4868355 ,  0.81143165, 11.803428  ,  2.3433266 ,\n",
-       "         7.462632  ,  3.5330765 ,  1.769893  ,  3.4632206 ,  6.0739894 ,\n",
-       "         2.1918657 ,  3.4950104 ,  3.3037906 ,  1.5210805 ,  3.043605  ,\n",
-       "         4.5550523 ,  1.3057604 ,  2.3426685 ,  0.89967585,  1.4859152 ,\n",
-       "         0.45666224,  0.29178444,  7.5672245 ,  1.3531511 ,  1.6870949 ],\n",
-       "       [ 0.9909044 ,  1.5977436 ,  1.5915296 ,  2.5393262 ,  2.087789  ,\n",
-       "         4.243172  ,  7.001409  ,  9.290381  ,  0.91922265,  7.900055  ,\n",
-       "         7.7129283 ,  0.6525277 ,  6.4433904 ,  8.98685   ,  3.415953  ,\n",
-       "         4.779433  ,  4.0793686 ,  7.4854875 ,  1.1678308 ,  4.129944  ,\n",
-       "         3.0361595 ,  2.4230134 ,  1.9914376 ,  2.428689  ,  2.7493334 ,\n",
-       "         4.6773024 ,  3.6110651 ,  1.139311  ,  1.1700116 ,  1.7576461 ,\n",
-       "         4.4287186 ,  1.3367376 ,  1.6895905 ,  6.6807804 ,  3.783243  ,\n",
-       "         6.1245394 ,  3.9788265 , 13.16905   , 13.16905   , 15.664255  ,\n",
-       "        15.664255  ,  2.3494203 ,  7.6555758 ,  2.4532216 ,  0.5315145 ,\n",
-       "         8.000637  ,  3.0699728 ,  1.4240437 ,  3.2925067 ,  2.53016   ,\n",
-       "         5.606144  ,  1.4063253 ,  4.0349874 ,  5.277519  ,  9.044592  ,\n",
-       "         3.3025165 ,  6.7250423 ,  4.524832  ,  1.3821722 ,  6.007044  ,\n",
-       "         2.626573  ,  0.5769744 ,  8.237151  ,  8.070817  ,  1.7475601 ,\n",
-       "         1.1638703 ,  3.0113087 ,  2.7498362 ,  5.914818  ,  6.1232486 ,\n",
-       "         0.29810682,  5.1201816 ,  0.563086  ,  1.8486384 ,  4.001296  ,\n",
-       "         1.2235026 ,  1.8340818 ,  7.513959  ,  0.7324949 ,  2.7174852 ,\n",
-       "         5.1975183 ,  2.0456045 ,  4.820118  ,  5.1033993 ,  5.1648126 ,\n",
-       "         3.173502  ,  2.104412  ,  1.9142842 , 11.029547  ,  0.78433573,\n",
-       "         1.357436  ,  3.3496656 ,  1.1371782 ,  5.2250524 ,  2.2853363 ,\n",
-       "         1.2432598 ,  2.9474394 ,  2.2762651 , 11.131979  ,  0.03542077,\n",
-       "         0.09264842,  0.6235244 ,  3.4893827 ,  2.3070836 ,  0.25985214,\n",
-       "         0.22785841,  0.39597985,  1.5273579 ,  1.6996378 ,  2.936499  ,\n",
-       "         4.2797337 ,  1.6371739 ,  4.6433635 ,  0.43203235,  0.7139715 ,\n",
-       "         4.613933  ,  0.9789104 ,  0.0981968 ,  0.53859735,  0.4859575 ,\n",
-       "         6.3171396 ,  1.0921121 ,  1.5136721 ,  1.112587  ,  2.716591  ,\n",
-       "         2.309645  ,  0.57045245,  6.38959   ,  4.577489  ,  1.3076562 ,\n",
-       "         1.4818728 , 13.748591  ,  1.4261547 ,  4.716801  ,  2.5566208 ,\n",
-       "         3.2431536 ,  2.4402661 ,  0.07903078,  1.96319   ,  3.2732992 ,\n",
-       "         1.5368414 ,  0.45010495,  0.19129893, 16.928976  ,  0.5482865 ,\n",
-       "         8.09678   ,  2.225882  ,  3.1512907 ,  4.057416  ,  2.610217  ,\n",
-       "         2.1332395 ,  9.60669   ,  2.2393262 ,  2.651483  ,  3.3116841 ,\n",
-       "         1.6829383 ,  2.4401853 ,  1.5279691 ,  3.227448  ,  1.6379778 ,\n",
-       "         2.4299464 ,  2.075777  ,  3.1862235 ,  3.2390447 ,  2.329407  ,\n",
-       "         2.201795  ,  5.703568  ,  0.40667385,  0.87402445,  3.3419642 ,\n",
-       "         4.641601  ,  1.6191708 ,  4.8605385 ,  6.7923694 ,  4.4612546 ,\n",
-       "         0.7880722 ,  3.642695  ,  5.7544723 ,  2.404113  ,  6.683872  ,\n",
-       "         0.81219465,  1.7379894 ,  1.7376539 ,  5.8059955 ,  4.278271  ,\n",
-       "         1.2685912 ,  4.5402994 ,  5.0245523 ,  2.5448208 ,  1.2269168 ,\n",
-       "         1.2570435 ,  0.09639393,  0.41742703,  2.19767   , 11.426054  ,\n",
-       "         6.1925087 ,  1.6211317 ,  2.6454413 ,  4.8318114 ,  7.5347095 ,\n",
-       "         4.0129    ,  4.6609755 ,  3.0834131 ,  1.8212253 ,  0.15747103,\n",
-       "         0.34305257,  0.50059736,  0.20459786,  0.02735537,  0.47397   ,\n",
-       "         0.05234714,  0.190357  ,  0.5601471 ,  1.4498736 ,  5.3912153 ,\n",
-       "         3.1650052 ,  2.2476134 ,  1.4172924 ,  3.296812  ,  4.6089897 ,\n",
-       "         2.8611212 ,  5.402524  ,  4.947583  ,  1.0548209 ,  0.7901154 ,\n",
-       "         0.9943615 ,  3.0772898 ,  1.2716869 ,  2.432948  ,  3.8804119 ,\n",
-       "         1.2244211 ,  0.8237403 ,  4.9685516 ,  1.316479  ,  1.0661899 ,\n",
-       "         0.16147617,  6.15918   ,  2.6841154 ,  1.4046829 ,  1.5815251 ,\n",
-       "         4.5101395 ,  4.211768  ,  3.2184887 ,  2.2674942 , 11.533861  ,\n",
-       "         1.5308465 ,  1.5567675 ,  3.4679332 ,  0.9563234 ,  2.6597273 ,\n",
-       "         4.113006  ,  5.94164   ,  4.1099057 ,  0.7565336 ,  9.429806  ,\n",
-       "         2.4552286 ,  0.53369445,  3.3575032 ,  4.5981975 ,  0.05409329,\n",
-       "         2.4364839 ,  1.5215359 ,  2.9032164 ,  1.1249074 ,  1.1532617 ,\n",
-       "        19.157143  , 19.157143  ,  1.3512957 ,  0.65963423,  1.5522751 ,\n",
-       "         0.5923748 , 14.26093   ,  2.619348  ,  7.663063  ,  1.520335  ,\n",
-       "         1.7601604 ,  4.5279403 ,  5.6468053 ,  2.8692524 ,  1.6483278 ,\n",
-       "         5.082613  ,  3.348174  ,  7.963322  ,  5.7132883 ,  7.863322  ,\n",
-       "         1.2458255 ,  2.605769  , 11.989439  ,  0.9381594 ,  1.8971261 ,\n",
-       "         2.5842643 ,  2.3395038 ,  4.5372963 ,  2.8475537 ,  2.8704324 ,\n",
-       "         0.7754769 ,  1.4568441 ,  1.3712978 ,  2.1131537 ,  1.5345919 ,\n",
-       "         0.9531036 ,  0.7739705 ,  1.5121455 ,  9.828634  ,  7.4287105 ,\n",
-       "         2.9388795 ,  0.2816141 ,  2.3579755 ,  0.14984295,  0.24800135,\n",
-       "         0.15856981,  1.0296572 ,  2.5968904 ,  7.6706834 ,  0.50782156,\n",
-       "         8.095074  ,  8.709511  ,  0.73007673,  3.476412  ,  3.0747168 ,\n",
-       "         1.3265907 ,  0.31783798,  5.4608517 ,  1.749261  ,  1.7294059 ,\n",
-       "         1.438313  ,  3.280551  ,  0.24643764,  0.66403604,  1.1227971 ,\n",
-       "         3.8661366 ,  2.7161083 ,  3.560833  ,  4.763618  ,  1.1688248 ,\n",
-       "         2.1935544 ,  3.8671257 ,  2.203893  ,  1.477727  ,  2.7158074 ,\n",
-       "         6.5069942 ,  0.456299  ,  1.5456046 ,  4.563259  ,  4.4469385 ,\n",
-       "         5.397097  ,  5.397097  ,  2.1064    ,  5.4984164 ,  1.3417419 ,\n",
-       "         1.709095  ,  7.2107835 ,  1.4702858 ,  1.2010643 ,  3.3940632 ,\n",
-       "         1.6871938 ,  8.092438  ,  1.875149  ,  9.6689    ,  4.6120315 ,\n",
-       "         3.9952703 ,  1.4235352 ,  8.582107  ,  6.6970954 ,  7.282295  ,\n",
-       "        14.207127  ,  2.9826918 ,  1.5618287 ,  1.7103666 ,  1.6702276 ,\n",
-       "         3.8603537 ,  2.7703676 ,  2.6307878 ,  2.504591  , 11.882615  ,\n",
-       "         3.8029606 , 10.591273  ,  4.6216316 ,  3.2560668 ,  3.2578232 ,\n",
-       "         3.8841403 ,  8.381626  ,  6.4172707 ,  4.9437313 ,  2.496393  ,\n",
-       "         2.5616014 ,  5.484207  ,  3.86267   ,  8.52895   ,  1.520958  ,\n",
-       "         3.0663316 ,  5.64784   ,  0.93371737,  1.988599  ,  3.3644938 ,\n",
-       "         3.9448767 ,  8.905218  ,  1.6713282 ,  1.7212286 ,  2.0422182 ,\n",
-       "         3.4921372 ,  3.8627925 ,  0.26233172,  0.43868166,  3.3021283 ,\n",
-       "         3.651146  ,  3.651146  ,  2.4456723 ,  9.569137  ,  2.3459058 ,\n",
-       "         0.2361946 ,  2.2865353 , 16.966942  ,  9.5944605 ,  3.9430783 ,\n",
-       "         7.300621  ,  8.237884  ,  1.1408961 ,  3.106974  ,  5.3557086 ,\n",
-       "         4.113908  ,  0.73947203,  0.8736652 ,  2.8615105 ,  4.674794  ,\n",
-       "         1.346915  ,  0.6225802 ,  0.19296533,  6.3390613 ,  2.802343  ,\n",
-       "         3.9072082 ,  1.5721794 ,  4.805414  ,  0.9118719 ,  6.6454163 ,\n",
-       "         0.35046232,  8.147577  ,  5.0826526 ,  3.0514736 ,  1.8530624 ,\n",
-       "         1.573253  ,  2.814517  ,  3.2968137 ,  3.1585622 ,  2.970489  ,\n",
-       "         4.581018  ,  1.5554717 ,  0.6137824 ,  2.1317117 ,  5.246378  ,\n",
-       "         4.361018  ,  2.8805962 ,  2.4948945 ,  6.1967874 ,  0.5377769 ,\n",
-       "         0.37031844,  0.7325885 ,  1.9997089 ,  2.398744  ,  2.0033321 ,\n",
-       "         1.6943733 ,  5.4950047 ,  5.6807523 ,  2.5063794 ,  3.8039002 ,\n",
-       "        11.003864  ,  0.7521229 ,  7.85719   ,  1.0913641 ,  2.1949823 ,\n",
-       "         8.510656  ,  3.865408  ,  2.2440512 ,  4.94407   , 13.895223  ,\n",
-       "         0.26907384,  1.5775707 ,  2.8049283 ,  1.0583861 ,  1.3993121 ,\n",
-       "         2.1294248 ,  3.9265387 ,  3.4271214 ,  1.6736029 ,  2.3646955 ,\n",
-       "         0.47015515,  4.9279428 ,  3.7552726 ,  3.7249236 ,  1.6924706 ,\n",
-       "         0.331155  ,  2.1292431 ,  0.959191  ,  2.2490025 ,  9.622374  ,\n",
-       "         5.5982914 ,  4.0080676 ,  2.5344353 ,  2.7411196 ,  4.101541  ]],\n",
-       "      dtype=float32)
          • theta_tilde
            (chain, draw, theta_tilde_dim_0)
            float32
            0.6061655 1.8185308 ... -1.4317532
            array([[[ 0.6061655 ,  1.8185308 , -0.09561205, ..., -1.3342378 ,\n",
-       "          0.70666367,  0.04662064],\n",
-       "        [ 1.0919316 ,  0.7878127 ,  0.51768357, ..., -0.86000085,\n",
-       "          1.1659694 ,  1.070951  ],\n",
-       "        [-0.44649038, -1.1334856 , -0.08350085, ..., -0.79331404,\n",
-       "          0.80516696,  0.05294776],\n",
-       "        ...,\n",
-       "        [ 0.27960604, -0.6067694 ,  0.63655376, ...,  1.3932375 ,\n",
-       "         -0.6413138 , -0.09322856],\n",
-       "        [ 0.65896815,  1.2729853 , -0.16130266, ...,  0.538792  ,\n",
-       "          2.0496454 ,  0.28260455],\n",
-       "        [ 0.6073655 ,  0.49289384,  0.3575558 , ..., -0.46220857,\n",
-       "          0.6597414 ,  1.7932637 ]],\n",
-       "\n",
-       "       [[-0.05533975,  1.5325787 , -0.02127528, ..., -0.8900326 ,\n",
-       "         -0.39435595, -0.55493706],\n",
-       "        [ 1.0245105 ,  1.0371798 , -0.12512936, ...,  1.3573611 ,\n",
-       "          0.12283397,  0.33178735],\n",
-       "        [ 1.2065461 ,  0.14588034, -0.02969379, ...,  0.61200786,\n",
-       "         -0.50436723,  0.29426408],\n",
-       "        ...,\n",
-       "        [ 0.5336305 , -0.36422867,  0.607937  , ..., -0.33750185,\n",
-       "          0.75881875, -0.354092  ],\n",
-       "        [ 0.11554712, -0.7284259 ,  1.029543  , ..., -0.9233731 ,\n",
-       "          0.17342997,  0.47577518],\n",
-       "        [-0.5292633 ,  0.8391605 , -1.8189237 , ..., -0.04487276,\n",
-       "          1.7539048 , -1.4317532 ]]], dtype=float32)
        • created_at :
          2020-06-10T18:21:33.589496
          arviz_version :
          0.8.3
          inference_library :
          pyro
          inference_library_version :
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      \n", - "
      \n", - "
      • \n", - " \n", - "
    • \n", - " \n", - " \n", - "
      \n", - "
      \n", - "
        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • obs_dim_0: 8
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (chain, draw, obs_dim_0)
            float32
            17.056637 13.765715 ... -33.684814
            array([[[ 17.056637  ,  13.765715  ,  28.1891    , ...,  11.842781  ,\n",
-       "          11.978273  ,   0.3044815 ],\n",
-       "        [ 21.057343  ,  -7.351166  ,  11.830295  , ...,  -5.1788282 ,\n",
-       "          11.68743   ,  22.859253  ],\n",
-       "        [  7.56715   ,   6.8423553 ,   5.7562637 , ...,   3.304874  ,\n",
-       "           5.348268  ,  16.123114  ],\n",
-       "        ...,\n",
-       "        [ 32.053898  ,  -9.957651  ,  -0.8588064 , ...,   7.980326  ,\n",
-       "           3.5410204 , -14.456598  ],\n",
-       "        [ 20.119667  ,  -7.6601057 ,  -2.9862726 , ...,  -0.10599685,\n",
-       "           1.5778246 , -16.948988  ],\n",
-       "        [ 25.463974  , -14.382548  ,  19.056568  , ..., -14.948351  ,\n",
-       "         -15.824716  ,   7.1326447 ]],\n",
-       "\n",
-       "       [[ -4.4520526 ,   3.186278  ,   4.188992  , ...,   1.6511054 ,\n",
-       "           8.9940605 ,   1.2203226 ],\n",
-       "        [ 23.332848  ,  13.669164  ,  -0.15677023, ...,  -6.408621  ,\n",
-       "          22.05013   ,   1.6746373 ],\n",
-       "        [ 29.850473  ,   0.35601377,  31.621748  , ...,  24.21464   ,\n",
-       "           8.574121  ,  -4.5073214 ],\n",
-       "        ...,\n",
-       "        [ 10.046104  ,  -9.08149   ,  24.914795  , ...,   0.8853679 ,\n",
-       "          -3.557653  ,   1.4098097 ],\n",
-       "        [-16.446758  ,  21.567833  ,  17.841633  , ...,  11.072165  ,\n",
-       "          19.724937  ,  -5.2535686 ],\n",
-       "        [ -4.726616  ,   4.298817  ,  10.874061  , ...,   0.40810192,\n",
-       "           5.026056  , -33.684814  ]]], dtype=float32)
        • created_at :
          2020-06-10T18:21:33.925982
          arviz_version :
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          inference_library :
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          inference_library_version :
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      \n", - "
      • \n", - " \n", - "
    • \n", - " \n", - " \n", - "
      \n", - "
      \n", - "
        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • obs_dim_0: 8
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (chain, draw, obs_dim_0)
            float32
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            array([[[-4.4705133, -3.3008652, -3.8681855, ..., -3.3328528,\n",
-       "         -3.6443205, -3.8491797],\n",
-       "        [-4.2400713, -3.22724  , -3.8871422, ..., -3.4007401,\n",
-       "         -3.404223 , -3.810225 ],\n",
-       "        [-5.572689 , -3.8407664, -3.70118  , ..., -3.3636737,\n",
-       "         -4.6304407, -4.0493298],\n",
-       "        ...,\n",
-       "        [-5.2727504, -4.190528 , -3.773724 , ..., -3.5955849,\n",
-       "         -6.145524 , -4.1133003],\n",
-       "        [-4.9237747, -3.276835 , -3.7557254, ..., -3.3465247,\n",
-       "         -3.9749088, -3.925208 ],\n",
-       "        [-5.274494 , -3.4969072, -3.71345  , ..., -3.333908 ,\n",
-       "         -4.6904016, -3.9407105]],\n",
-       "\n",
-       "       [[-4.806156 , -3.2322083, -3.8164787, ..., -3.3575134,\n",
-       "         -4.1154737, -3.89682  ],\n",
-       "        [-4.472113 , -3.2228694, -3.8738499, ..., -3.5833087,\n",
-       "         -3.8201694, -3.8420815],\n",
-       "        [-4.4680734, -3.228054 , -3.8706927, ..., -3.4967794,\n",
-       "         -3.9630103, -3.8464642],\n",
-       "        ...,\n",
-       "        [-5.3380413, -3.7239227, -3.7146387, ..., -3.3529468,\n",
-       "         -4.696886 , -4.111705 ],\n",
-       "        [-4.487238 , -3.2412963, -4.0651107, ..., -3.3996596,\n",
-       "         -3.6743426, -3.8204584],\n",
-       "        [-5.527108 , -3.2873592, -3.7158775, ..., -3.3171015,\n",
-       "         -3.709279 , -4.252307 ]]], dtype=float32)
        • created_at :
          2020-06-10T18:21:33.876187
          arviz_version :
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          inference_library :
          pyro
          inference_library_version :
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      • \n", - " \n", - "
    • \n", - " \n", - " \n", - "
      \n", - "
      \n", - "
        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
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          • draw: 500
          • chain
            (chain)
            int64
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            array([0, 1])
          • draw
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            int64
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            array([  0,   1,   2, ..., 497, 498, 499])
          • diverging
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-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
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-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
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-       "        False, False, False, False, False, False, False, False, False,\n",
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-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False],\n",
-       "       [False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
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-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
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-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
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-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False]])
        • created_at :
          2020-06-10T18:21:33.823431
          arviz_version :
          0.8.3
          inference_library :
          pyro
          inference_library_version :
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      • \n", - " \n", - "
    • \n", - " \n", - " \n", - "
      \n", - "
      \n", - "
        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 1
          • draw: 500
          • theta_tilde_dim_0: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • theta_tilde_dim_0
            (theta_tilde_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float32
            1.8310521 7.369206 ... -0.07544144
            array([[ 1.83105206e+00,  7.36920595e+00, -1.19266405e+01,\n",
-       "        -8.94674808e-02,  6.69769812e+00,  2.36118245e+00,\n",
-       "        -5.29538059e+00, -5.94624472e+00,  2.89671206e+00,\n",
-       "        -1.86073518e+00,  6.28713250e-01,  6.81693649e+00,\n",
-       "         7.71915436e+00,  7.51500702e+00,  6.99194527e+00,\n",
-       "         6.81107426e+00, -6.58409548e+00, -8.57430363e+00,\n",
-       "         8.83453548e-01,  6.32625198e+00, -2.42169404e+00,\n",
-       "         1.10239995e+00,  2.38961601e+00, -3.36049986e+00,\n",
-       "        -2.06537867e+00,  4.25744951e-01, -6.23024511e+00,\n",
-       "        -5.16256762e+00, -9.30563509e-01,  1.11181331e+00,\n",
-       "        -1.45188928e+00,  1.81846488e+00,  4.33571339e+00,\n",
-       "        -1.40784955e+00, -3.79042912e+00,  5.96538925e+00,\n",
-       "        -2.45449328e+00,  6.48085117e+00,  1.84746668e-01,\n",
-       "         9.50277996e+00,  1.59419513e+00, -7.20801449e+00,\n",
-       "        -5.50850153e+00, -2.64637923e+00, -1.01556320e+01,\n",
-       "         4.47114372e+00, -3.56763458e+00, -4.17484492e-01,\n",
-       "         2.15950990e+00,  1.10670459e+00, -4.33139133e+00,\n",
-       "         1.62531209e+00,  3.99615616e-01,  3.86136270e+00,\n",
-       "        -1.66640115e+00,  8.03124046e+00,  6.59306240e+00,\n",
-       "        -1.21368408e+01, -1.73939645e+00, -1.23819363e+00,\n",
-       "         7.29799569e-01, -2.25639677e+00, -2.72025943e+00,\n",
-       "         1.38648021e+00, -9.59429359e+00, -7.46370316e-01,\n",
-       "        -3.87315184e-01,  1.04348350e+00,  2.42776895e+00,\n",
-       "        -1.55527663e+00, -9.87819314e-01,  1.33469507e-01,\n",
-       "        -1.15047801e+00, -2.34605980e+00, -9.89660168e+00,\n",
-       "        -6.90545464e+00,  6.10778809e+00,  7.69460106e+00,\n",
-       "        -1.39867568e+00,  7.19076538e+00, -4.95351934e+00,\n",
-       "        -1.97004724e+00,  3.65625173e-01, -2.02283001e+00,\n",
-       "         9.23406219e+00,  8.91490579e-01, -2.46193695e+00,\n",
-       "        -1.28320193e+00,  3.88940901e-01,  6.97067678e-01,\n",
-       "         3.08503330e-01,  2.66331816e+00, -2.39172482e+00,\n",
-       "         1.02955704e+01,  4.05393171e+00,  4.36696005e+00,\n",
-       "        -2.88689470e+00, -2.59753019e-01,  1.83168995e+00,\n",
-       "        -5.59702826e+00, -5.45363486e-01,  4.74622154e+00,\n",
-       "         7.84159184e+00,  7.90888882e+00, -1.89434886e+00,\n",
-       "        -4.59797335e+00, -5.81234455e+00, -1.44287062e+00,\n",
-       "        -1.03176842e+01,  1.72845376e+00, -4.21865988e+00,\n",
-       "        -2.41528332e-01, -3.51904678e+00, -3.86472702e+00,\n",
-       "        -8.33917618e+00, -2.86969948e+00,  9.17288303e+00,\n",
-       "         9.08611679e+00, -7.91800928e+00,  6.45593882e+00,\n",
-       "        -5.40709019e+00,  1.03506794e+01, -3.90159106e+00,\n",
-       "        -5.60358238e+00,  3.02379179e+00,  5.49144506e+00,\n",
-       "         1.07119732e+01,  4.13225114e-01, -5.58654308e+00,\n",
-       "         2.38638973e+00, -2.92863822e+00, -9.32064176e-01,\n",
-       "        -5.47271252e+00,  1.19561327e+00,  1.26454878e+01,\n",
-       "        -7.34495687e+00,  8.26337337e+00, -1.26257706e+00,\n",
-       "         4.92097187e+00,  2.12870312e+00, -2.17358923e+00,\n",
-       "        -3.96695042e+00,  6.61581874e-01,  8.85077190e+00,\n",
-       "        -1.16093235e+01,  8.39181244e-02, -1.67453897e+00,\n",
-       "        -7.13899088e+00,  8.08746433e+00,  2.99629211e+00,\n",
-       "         3.96617866e+00, -4.35207939e+00,  3.00484705e+00,\n",
-       "         9.48771238e-01,  4.28298569e+00, -1.19457293e+00,\n",
-       "         2.45752597e+00, -2.69014406e+00, -5.79377270e+00,\n",
-       "        -8.44127178e+00, -2.69214272e+00,  6.68628025e+00,\n",
-       "         2.48715353e+00, -1.07650578e+00,  2.27392578e+00,\n",
-       "        -1.51223516e+00,  3.04859757e+00, -1.00483437e+01,\n",
-       "        -1.69506609e+00,  1.46986410e-01, -1.91448200e+00,\n",
-       "        -9.90330100e-01,  1.42848074e+00, -1.89951634e+00,\n",
-       "        -5.19370317e+00,  9.64916134e+00,  1.89337349e+00,\n",
-       "        -1.90529120e+00,  5.24329305e-01,  3.37787271e+00,\n",
-       "         1.24935162e+00, -3.98928595e+00,  1.06664634e+00,\n",
-       "        -2.32655501e+00,  3.89265680e+00, -5.16304207e+00,\n",
-       "         1.64964437e+00,  2.15085730e-01, -5.77951670e+00,\n",
-       "         5.68300152e+00, -4.04687309e+00, -1.45483112e+00,\n",
-       "         1.94204617e+00, -3.63496733e+00,  4.26030397e+00,\n",
-       "        -7.30518246e+00, -7.07409573e+00, -2.52731180e+00,\n",
-       "         5.80309927e-01, -3.93980503e+00,  5.90484381e+00,\n",
-       "        -7.15272367e-01, -1.15752685e+00,  4.38277149e+00,\n",
-       "        -2.20936552e-01,  2.76147127e+00, -2.84579486e-01,\n",
-       "        -2.42135954e+00,  5.01637459e+00, -1.76355731e+00,\n",
-       "        -8.02004623e+00,  2.04684105e+01,  9.89901161e+00,\n",
-       "        -3.71212363e+00, -8.04001808e-01,  1.52333260e+01,\n",
-       "        -2.71955824e+00, -3.53012061e+00,  5.94824219e+00,\n",
-       "         2.87859440e+00, -6.98200035e+00, -1.31001401e+00,\n",
-       "         3.54433417e-01,  7.41048288e+00, -8.93629551e+00,\n",
-       "        -6.54948139e+00,  1.54468322e+00, -5.30149841e+00,\n",
-       "         6.27821970e+00,  8.57540369e-01, -2.00245172e-01,\n",
-       "         8.83830130e-01, -3.87067080e-01, -5.35265970e+00,\n",
-       "         1.17853045e+00,  4.24543810e+00, -9.52724099e-01,\n",
-       "         1.05860481e+01,  2.68119621e+00,  3.16103578e-01,\n",
-       "         1.02651558e+01,  8.70150447e-01,  5.10061455e+00,\n",
-       "        -8.74845922e-01,  1.06127477e+00, -1.07085204e+00,\n",
-       "        -1.34933448e+00,  6.79564047e+00,  4.95760679e+00,\n",
-       "        -3.85755599e-01,  1.58938322e+01,  3.82075429e+00,\n",
-       "         8.43453884e+00, -1.87526774e+00,  2.94839168e+00,\n",
-       "        -5.34259796e+00, -1.37222636e+00,  1.56460536e+00,\n",
-       "        -1.82807553e+00, -3.93960208e-01, -2.65401840e+00,\n",
-       "        -8.08656883e+00, -2.96218753e+00, -8.40376091e+00,\n",
-       "         6.10560703e+00, -4.93199682e+00,  5.52653193e-01,\n",
-       "        -1.50517826e+01, -5.32551575e+00,  2.01356936e+00,\n",
-       "         8.41543484e+00,  1.65440190e+00, -2.26092815e+00,\n",
-       "        -2.43353653e+00, -5.91755152e+00, -4.91763067e+00,\n",
-       "         4.21300697e+00, -6.90289450e+00, -9.91271877e+00,\n",
-       "         3.00915432e+00,  1.23764837e+00, -2.11345482e+00,\n",
-       "        -1.16648540e-01,  4.33283758e+00, -2.26752186e+00,\n",
-       "        -1.16003406e+00,  3.81865025e+00, -3.34965992e+00,\n",
-       "        -8.85756016e+00,  5.39200974e+00,  1.10907221e+01,\n",
-       "         7.02498484e+00,  2.91726619e-01,  2.56532478e+00,\n",
-       "        -1.08591070e+01, -6.92411947e+00, -7.71028900e+00,\n",
-       "        -2.64707375e+00, -8.07448292e+00, -1.52650061e+01,\n",
-       "         5.43098688e+00, -3.78360510e+00,  5.23834610e+00,\n",
-       "        -9.35549498e-01,  7.61654472e+00, -1.33892345e+00,\n",
-       "         6.66223812e+00,  5.61331797e+00, -9.75874043e+00,\n",
-       "        -1.04858646e+01,  1.77814394e-01,  9.24438357e-01,\n",
-       "         2.80017948e+00, -4.24891996e+00,  7.73771095e+00,\n",
-       "        -9.24943149e-01, -1.84135449e+00, -6.52403879e+00,\n",
-       "        -1.71091104e+00, -3.77612948e+00,  6.72239494e+00,\n",
-       "        -2.76906681e+00, -2.94944644e+00,  1.83637130e+00,\n",
-       "         6.14798260e+00, -4.28675127e+00, -4.43767452e+00,\n",
-       "        -2.81541675e-01, -1.00980639e+00,  1.40076780e+00,\n",
-       "         2.90011930e+00, -2.16282582e+00, -1.81636465e+00,\n",
-       "         1.30194211e+00, -2.62355518e+00,  6.56885815e+00,\n",
-       "        -3.61158371e+00, -7.70886707e+00,  4.06852394e-01,\n",
-       "        -9.12503910e+00, -3.99473369e-01, -2.63025022e+00,\n",
-       "         6.63814926e+00, -6.40740514e-01, -9.53052616e+00,\n",
-       "         1.57841671e+00, -4.32275355e-01, -8.56765866e-01,\n",
-       "        -3.21094418e+00, -1.37355804e+01,  2.75833344e+00,\n",
-       "        -4.30860519e+00,  7.86440325e+00,  1.00282073e+00,\n",
-       "         2.19119835e+00, -1.11399639e+00, -6.83745909e+00,\n",
-       "         1.79168391e+00,  4.85345078e+00,  1.77308023e+00,\n",
-       "         3.60162592e+00,  4.91117096e+00,  3.42620850e+00,\n",
-       "         1.86812174e+00,  4.39406633e-01,  4.64404249e+00,\n",
-       "         5.62924576e+00,  7.49305201e+00, -5.01564980e-01,\n",
-       "         7.47700691e-01, -2.45133638e+00, -4.43825531e+00,\n",
-       "         2.54469180e+00, -4.52198124e+00,  1.93659878e+00,\n",
-       "         5.14554024e+00, -1.54210615e+00,  1.13418598e+01,\n",
-       "        -2.53370434e-01,  9.94487667e+00, -7.87288380e+00,\n",
-       "         1.02964675e+00, -3.00911784e+00, -5.36752272e+00,\n",
-       "        -2.14944553e+00,  1.41393316e+00,  2.45506263e+00,\n",
-       "        -1.77195096e+00, -2.79614496e+00, -1.34936082e+00,\n",
-       "        -1.16544704e+01, -1.95253313e+00,  8.18402481e+00,\n",
-       "         7.21612549e+00, -4.91288090e+00,  2.19248319e+00,\n",
-       "         9.97444451e-01,  2.07281208e+00, -3.99678826e+00,\n",
-       "         3.54919732e-01,  4.24775451e-01, -9.11628842e-01,\n",
-       "         6.72282791e+00,  4.29300356e+00,  3.48318577e+00,\n",
-       "        -1.90434575e+00, -3.69519830e+00, -5.08857298e+00,\n",
-       "         6.64628863e-01,  1.08672094e+00, -4.15021467e+00,\n",
-       "         5.20118999e+00, -4.28416538e+00,  6.84449625e+00,\n",
-       "         9.84103584e+00,  9.43293095e+00, -6.22304058e+00,\n",
-       "        -8.38033295e+00,  2.60023832e+00,  2.90164185e+00,\n",
-       "        -9.26851559e+00, -5.69589674e-01,  4.89514112e+00,\n",
-       "         2.62224793e+00,  6.37408924e+00, -9.53890514e+00,\n",
-       "         3.70149899e+00,  7.25612068e+00,  4.77210331e+00,\n",
-       "        -5.49839735e+00,  3.61553311e-01,  1.88503504e+00,\n",
-       "         1.35543537e+00,  5.75249720e+00,  6.40857279e-01,\n",
-       "         2.82977223e+00,  1.47245991e+00, -2.95132542e+00,\n",
-       "         6.06460047e+00,  5.75195503e+00, -4.99901915e+00,\n",
-       "        -3.38064694e+00, -8.64464951e+00, -1.02621114e+00,\n",
-       "        -4.99272537e+00,  8.79884422e-01, -2.89354038e+00,\n",
-       "         7.75743103e+00, -2.95966125e+00,  4.15148163e+00,\n",
-       "        -4.49703503e+00,  2.09148836e+00,  1.21093464e+00,\n",
-       "        -2.56159282e+00, -4.02910739e-01,  3.59068131e+00,\n",
-       "        -1.49127707e-01, -1.03146482e+00, -4.02073812e+00,\n",
-       "         6.10921812e+00, -1.11715519e+00, -1.03993597e+01,\n",
-       "         6.32133198e+00,  9.38441157e-02, -5.48374295e-01,\n",
-       "         3.23091269e+00,  2.65178537e+00,  3.63729024e+00,\n",
-       "         5.70872593e+00,  3.31697512e+00,  8.44650936e+00,\n",
-       "         7.31386691e-02,  3.26802731e+00,  2.44168830e+00,\n",
-       "        -7.86440313e-01, -8.20612526e+00, -4.75589561e+00,\n",
-       "        -2.73538065e+00,  2.09568596e+00,  4.75485802e+00,\n",
-       "         1.26565361e+00,  5.04449546e-01, -4.07291603e+00,\n",
-       "         5.41869581e-01,  4.77750123e-01, -1.77831125e+00,\n",
-       "        -2.42855740e+00,  5.80212212e+00,  2.78317261e+00,\n",
-       "        -8.38098049e-01, -1.13215065e+01,  1.04781361e+01,\n",
-       "        -1.98796690e-02, -8.12773609e+00,  3.65236521e+00,\n",
-       "         4.78217268e+00,  5.68206692e+00,  1.17952776e+00,\n",
-       "         3.48964882e+00, -7.54414424e-02]], dtype=float32)
          • tau
            (chain, draw)
            float32
            233.58215 994.4366 ... 27.182827
            array([[2.33582153e+02, 9.94436584e+02, 2.88996196e+00, 4.46266115e-01,\n",
-       "        4.96314669e+00, 5.80443611e+01, 6.78751850e+00, 1.49578667e+00,\n",
-       "        7.38433151e+01, 4.20371771e+00, 3.13775492e+00, 8.71614647e+00,\n",
-       "        3.26564932e+00, 4.71219480e-01, 3.88621855e+00, 5.17096996e+00,\n",
-       "        6.44069195e+00, 2.11158514e+00, 8.10319036e-02, 4.32135868e+00,\n",
-       "        3.56079245e+00, 7.56645775e+00, 3.47996688e+00, 8.96701574e-01,\n",
-       "        8.81987810e-01, 6.37050915e+00, 1.29537487e+00, 2.42891556e+02,\n",
-       "        1.45000434e+00, 3.44764090e+00, 4.36339569e+00, 6.93003559e+00,\n",
-       "        1.21201019e+01, 1.22789011e+01, 6.61703014e+00, 3.75274944e+00,\n",
-       "        4.36765718e+00, 1.34682770e+01, 4.79594326e+00, 6.00994720e+01,\n",
-       "        2.38157487e+00, 8.64460068e+01, 1.70366192e+00, 3.13302803e+00,\n",
-       "        1.08464298e+01, 1.89743690e+01, 1.00153017e+01, 4.93525600e+00,\n",
-       "        2.10727382e+00, 2.61057854e+00, 4.94686413e+00, 2.40696449e+01,\n",
-       "        2.96104956e+00, 1.18571396e+01, 2.97007084e+01, 6.49375916e+00,\n",
-       "        5.76199579e+00, 9.28328037e+00, 2.05416012e+00, 4.67196894e+00,\n",
-       "        1.94539279e-01, 1.69849586e+00, 4.11854172e+00, 9.30481796e+01,\n",
-       "        1.30114019e+00, 6.92282200e+00, 2.10049458e+01, 1.03697910e+01,\n",
-       "        1.64222889e+01, 7.37519503e-01, 8.64608002e+00, 1.58246374e+00,\n",
-       "        1.15422897e+01, 1.25697498e+01, 2.78301773e+01, 2.34611492e+01,\n",
-       "        4.61381073e+01, 8.49647427e+00, 2.38395095e+00, 3.06760292e+01,\n",
-       "        8.38161111e-01, 8.70039749e+00, 3.20347100e-01, 2.86008606e+01,\n",
-       "        6.00329304e+00, 1.99586225e+00, 1.01666679e+01, 8.67040217e-01,\n",
-       "        1.96274054e+00, 8.22034546e+02, 3.25686569e+01, 7.47367191e+00,\n",
-       "        5.82090683e+01, 6.80117130e-01, 1.32837164e+00, 2.87984238e+01,\n",
-       "        2.78351212e+00, 6.56047773e+00, 7.62254834e-01, 1.19701965e+02,\n",
-       "        3.16393661e+00, 9.53789806e+00, 1.14697514e+01, 9.03164291e+00,\n",
-       "        1.99453759e+00, 5.68666697e+00, 1.65687599e+01, 1.67806506e-01,\n",
-       "        7.41473317e-01, 3.13937008e-01, 2.65185714e+00, 3.46124039e+01,\n",
-       "        1.43749255e+03, 4.64936829e+00, 1.42269874e+00, 3.32906990e+01,\n",
-       "        1.39783907e+00, 1.89604580e+00, 6.28229370e+01, 4.18284950e+01,\n",
-       "        7.72986352e-01, 2.69628406e+00, 2.78242207e+00, 2.73950160e-01,\n",
-       "        5.61358929e+00, 3.10696411e+00, 4.68212414e+00, 1.69084892e-01,\n",
-       "        1.76831853e+00, 3.58425570e+00, 1.66552944e+01, 6.66814518e+00,\n",
-       "        2.22552757e+01, 2.18585186e+01, 4.84984636e+00, 2.61942062e+01,\n",
-       "        6.71889210e+00, 3.07793331e+01, 7.65414143e+00, 6.03538275e+00,\n",
-       "        1.08152752e+01, 1.53035221e+01, 1.82176857e+01, 5.10415316e+00,\n",
-       "        2.33483219e+01, 9.00594652e-01, 1.42888498e+01, 1.11383905e+01,\n",
-       "        2.80002928e+00, 1.97191668e+00, 9.47514629e+00, 1.59550037e+01,\n",
-       "        1.24129760e+00, 3.00389910e+00, 7.33918142e+00, 2.03434730e+00,\n",
-       "        1.40971959e+00, 2.60195613e+00, 5.19892263e+00, 4.85553789e+00,\n",
-       "        4.01490164e+00, 5.50108337e+00, 1.95370235e+01, 3.57205272e+00,\n",
-       "        3.22123230e-01, 3.18224182e+01, 5.46338511e+00, 3.92666698e+00,\n",
-       "        1.19204788e+01, 3.34672689e+00, 1.28777447e+01, 7.79884398e-01,\n",
-       "        6.61139846e-01, 1.53919148e+00, 2.90023565e+00, 2.00248814e+00,\n",
-       "        5.53505516e+00, 6.83246183e+00, 4.38924694e+00, 8.34967804e+00,\n",
-       "        5.10579491e+00, 1.58182859e+01, 1.78390675e+01, 5.87143898e+00,\n",
-       "        8.79881096e+00, 2.89170361e+00, 1.12620888e+01, 1.57768428e+00,\n",
-       "        2.32329521e+01, 5.88128757e+00, 3.41677904e+00, 7.61884537e+01,\n",
-       "        5.26621222e-01, 2.92264342e+00, 1.46880138e+00, 1.58016682e+00,\n",
-       "        3.81652985e+01, 2.46611953e+00, 4.79778290e+00, 1.88320708e+00,\n",
-       "        1.90092564e+00, 7.12588310e-01, 9.07944775e+00, 7.06309557e+00,\n",
-       "        2.61442447e+00, 1.78211820e+00, 3.95346403e+00, 2.53750610e+00,\n",
-       "        8.14840674e-01, 2.00210166e+00, 2.62024078e+01, 5.12925744e-01,\n",
-       "        6.95824099e+00, 2.35959935e+00, 1.71262836e+00, 5.10069351e+01,\n",
-       "        3.19263029e+00, 1.15136087e-01, 1.57397881e+01, 9.10933018e-01,\n",
-       "        2.54393845e+01, 1.22716546e+00, 6.51532364e+00, 2.91248016e+01,\n",
-       "        6.27013743e-02, 7.57493925e+00, 1.00060427e+00, 1.94976521e+00,\n",
-       "        1.05164957e+01, 1.20389929e+01, 7.51705074e+00, 1.34320679e+02,\n",
-       "        4.60176182e+00, 5.06012535e+00, 3.81312418e+00, 3.13340473e+00,\n",
-       "        1.12241850e+01, 6.68661475e-01, 9.63961029e+00, 1.80393448e+01,\n",
-       "        1.27726388e+00, 8.14500600e-02, 1.34037886e+01, 4.43923569e+00,\n",
-       "        9.86487961e+00, 4.39872217e+00, 4.16832018e+00, 1.09472620e+00,\n",
-       "        2.91746311e+01, 1.49736700e+01, 9.17174101e-01, 1.25601950e+01,\n",
-       "        5.54890394e-01, 5.42279053e+00, 1.89136982e+00, 3.33140159e+00,\n",
-       "        5.18533659e+00, 8.20474243e+00, 3.74159360e+00, 6.61919546e+00,\n",
-       "        9.13305569e+00, 1.16696537e+00, 6.32780876e+01, 4.78538799e+00,\n",
-       "        9.15983295e+00, 1.19233716e+00, 4.15419197e+00, 3.01625061e+00,\n",
-       "        1.33225393e+01, 8.89955699e-01, 3.94315934e+00, 1.25022098e-01,\n",
-       "        1.56112921e+00, 2.40924430e+00, 5.45164061e+00, 3.19068031e+01,\n",
-       "        2.74245787e+00, 5.39334259e+01, 2.68976002e+01, 3.05201454e+01,\n",
-       "        1.01681805e+00, 3.24505115e+00, 3.51270258e-01, 3.39455485e+00,\n",
-       "        1.03448999e+00, 6.52295971e+00, 8.54770851e+00, 3.17444348e+00,\n",
-       "        1.41029816e+01, 8.06216145e+00, 4.02719927e+00, 8.40551949e+00,\n",
-       "        7.55664349e+00, 5.41498232e+00, 2.03295345e+01, 1.33403766e+00,\n",
-       "        3.10369968e+00, 9.22239971e+00, 1.33640230e+00, 3.04194498e+00,\n",
-       "        1.49802998e-01, 7.67462845e+01, 1.00229049e+00, 9.41823196e+00,\n",
-       "        2.40813103e+01, 5.96192837e-01, 1.24422464e+01, 6.29803009e+01,\n",
-       "        9.61070156e+00, 3.14717960e+00, 3.35008736e+01, 1.62203751e+02,\n",
-       "        4.78771782e+00, 9.57639408e+00, 2.18938899e+00, 6.93462944e+00,\n",
-       "        1.47582645e+01, 1.59039507e+01, 3.55825844e+01, 1.57970297e+00,\n",
-       "        6.86061192e+00, 1.46212173e+00, 1.21917558e+00, 5.43149567e+00,\n",
-       "        1.26152265e+00, 2.14529729e+00, 3.60484076e+00, 3.20548248e+00,\n",
-       "        8.68775845e+00, 4.69007778e+00, 6.97661924e+00, 2.86727786e+00,\n",
-       "        6.55940008e+00, 3.04591227e+00, 6.40091038e+00, 7.69913483e+01,\n",
-       "        1.76605213e+00, 1.69695389e+00, 1.49314392e+02, 1.74520826e+00,\n",
-       "        2.10937786e+00, 1.85648233e-01, 7.94631481e+00, 4.20290565e+00,\n",
-       "        1.58578515e+00, 2.86422396e+00, 2.78503656e+00, 1.90926838e+01,\n",
-       "        2.03964019e+00, 2.16318626e+01, 9.85763626e+01, 8.25113773e+00,\n",
-       "        7.35772908e-01, 4.36113882e+00, 1.37380188e+02, 4.84867334e+00,\n",
-       "        1.83547437e+00, 7.29557495e+01, 1.18845024e+01, 6.21232643e+01,\n",
-       "        8.89026070e+00, 8.67310905e+00, 9.10755172e-02, 4.66671085e+00,\n",
-       "        7.80379639e+01, 4.44808617e+01, 1.78106499e+01, 1.36136341e+01,\n",
-       "        1.11453638e+01, 6.47962141e+00, 3.14819312e+00, 3.80757451e+00,\n",
-       "        6.99266315e-01, 5.32106857e+01, 2.26561832e+00, 1.17912173e+00,\n",
-       "        1.61539803e+01, 1.64006214e+01, 6.56036139e+00, 2.02290916e+01,\n",
-       "        1.35110483e+01, 1.61717999e+00, 4.25558233e+00, 1.24771166e+00,\n",
-       "        8.42467189e-01, 3.71366239e+00, 1.12391367e+01, 7.85699415e+00,\n",
-       "        1.17313776e+01, 2.07943416e+00, 1.07938728e+01, 1.71249676e+01,\n",
-       "        4.25358963e+00, 3.37078810e+00, 2.89949465e+00, 6.30591965e+01,\n",
-       "        8.41671944e-01, 2.24199390e+00, 2.29184461e+00, 1.12583256e+01,\n",
-       "        1.59082878e+00, 1.61483455e+00, 8.51412296e+00, 2.94279718e+00,\n",
-       "        1.55814886e+00, 1.94127309e+00, 3.83025789e+00, 7.02682590e+00,\n",
-       "        2.06390142e+00, 1.34827960e+00, 2.81168457e+02, 6.22805953e-01,\n",
-       "        8.24851453e-01, 2.11877942e-01, 3.92178574e+01, 9.66570312e+02,\n",
-       "        3.94462729e+00, 7.99840927e-01, 1.70189846e+00, 1.00781441e+01,\n",
-       "        2.87433743e-01, 1.20128322e+00, 2.29061928e+01, 3.59614491e+00,\n",
-       "        4.05278158e+00, 1.58789263e+01, 1.11763752e+00, 1.74107971e+01,\n",
-       "        1.57513487e+00, 1.86496060e-02, 7.02734985e+01, 3.25848503e+01,\n",
-       "        3.71071362e+00, 1.89383469e+01, 4.06730556e+00, 1.25093555e+00,\n",
-       "        1.31658208e+00, 6.87853718e+00, 4.67559671e+00, 2.59621410e+01,\n",
-       "        2.93781424e+00, 8.28132534e+00, 2.93957710e+00, 2.71064987e+01,\n",
-       "        1.96016669e-01, 7.85356760e-01, 3.60274577e+00, 2.52142549e+00,\n",
-       "        6.00418663e+00, 1.30085669e+01, 4.41197300e+00, 4.23312283e+00,\n",
-       "        1.43678570e+00, 9.44298446e-01, 1.23984262e-01, 4.08745956e+01,\n",
-       "        3.59652996e-01, 4.51054305e-01, 2.02281017e+01, 5.19320641e+01,\n",
-       "        2.71144605e+00, 2.10441933e+01, 2.76381893e+01, 2.75218344e+00,\n",
-       "        1.07174101e+01, 1.65728331e+00, 1.40117636e+01, 5.70923004e+01,\n",
-       "        2.04354548e+00, 7.14082837e-01, 1.98984873e+00, 2.52286911e+01,\n",
-       "        2.85708785e+00, 8.33650780e+00, 3.08708382e+00, 4.20598221e+00,\n",
-       "        3.83560791e+01, 6.60561275e+00, 3.31780434e+00, 1.28404593e+00,\n",
-       "        1.66767464e+01, 6.89008808e+00, 5.83668041e+00, 1.42359662e+00,\n",
-       "        6.85395598e-02, 9.49191093e-01, 1.09593658e+01, 1.75060701e+00,\n",
-       "        1.01909657e+01, 4.41580963e+00, 1.00245590e+01, 8.76809508e-02,\n",
-       "        5.11244965e+00, 3.84632111e+00, 7.63232660e+00, 4.04119635e+00,\n",
-       "        2.86264753e+00, 5.07223177e+00, 9.76756382e+00, 2.71828270e+01]],\n",
-       "      dtype=float32)
          • theta_tilde
            (chain, draw, theta_tilde_dim_0)
            float32
            0.6022393 ... -0.31073767
            array([[[ 0.6022393 , -0.39827886,  0.19862896, ..., -0.95424646,\n",
-       "         -1.1647658 ,  0.3821102 ],\n",
-       "        [-1.497994  ,  0.74775034, -0.78757095, ..., -1.6560527 ,\n",
-       "         -1.613713  , -0.46137986],\n",
-       "        [-0.16146933,  0.2763805 , -0.8783446 , ...,  0.56389153,\n",
-       "         -0.01581344, -0.8957549 ],\n",
-       "        ...,\n",
-       "        [-1.1428025 , -1.1540538 , -1.0932158 , ...,  1.6082281 ,\n",
-       "         -2.009276  , -0.5227252 ],\n",
-       "        [ 1.8399812 ,  0.33280653,  0.45785332, ...,  1.834717  ,\n",
-       "          0.8924277 ,  1.2369446 ],\n",
-       "        [-0.14975677,  0.8303282 , -0.00824529, ..., -1.6754906 ,\n",
-       "          0.26279444, -0.31073767]]], dtype=float32)
        • created_at :
          2020-06-10T18:21:33.976125
          arviz_version :
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          inference_library :
          pyro
          inference_library_version :
          1.1.0

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        xarray.Dataset
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            (draw)
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          • obs_dim_0
            (obs_dim_0)
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            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (chain, draw, obs_dim_0)
            float32
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            array([[[ 1.5241260e+02, -1.0443863e+02,  5.3149067e+01, ...,\n",
-       "         -2.1432968e+02, -2.7346640e+02,  9.8980553e+01],\n",
-       "        [-1.4595604e+03,  7.5740137e+02, -7.9331067e+02, ...,\n",
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-       "        [-1.2688635e+01, -1.5242083e+01,  6.5625114e+00, ...,\n",
-       "          7.7777863e-01,  1.3528487e+01, -4.4868240e+01],\n",
-       "        ...,\n",
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-       "         -1.4138508e-01, -2.4801119e+01, -1.1125523e+00],\n",
-       "        [ 1.1234647e+01,  9.1552639e+00,  1.3359819e+01, ...,\n",
-       "          6.3331509e+00, -6.9926414e+00,  2.9034739e+01],\n",
-       "        [ 3.9082799e+00,  2.4938337e+01,  1.8535004e+01, ...,\n",
-       "         -4.8968292e+01,  1.1919480e+01, -1.9538307e+01]]], dtype=float32)
        • created_at :
          2020-06-10T18:21:34.025492
          arviz_version :
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          inference_library :
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          inference_library_version :
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        xarray.Dataset
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          • obs_dim_0
            (obs_dim_0)
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            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
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            (obs_dim_0)
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        • created_at :
          2020-06-10T18:21:34.070465
          arviz_version :
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          inference_library :
          pyro
          inference_library_version :
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        \n", - "
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    \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> prior\n", - "\t> prior_predictive\n", - "\t> observed_data" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pyro\n", - "import pyro.distributions as dist\n", - "import torch\n", - "from pyro.infer import MCMC, NUTS, Predictive\n", - "\n", - "pyro.enable_validation(True)\n", - "pyro.set_rng_seed(0)\n", - "\n", - "draws = 500\n", - "chains = 2\n", - "eight_school_data = {\n", - " \"J\": 8,\n", - " \"y\": torch.tensor([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", - " \"sigma\": torch.tensor([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", - "}\n", - "\n", - "\n", - "def model(J, sigma, y=None):\n", - " mu = pyro.sample(\"mu\", dist.Normal(0, 5))\n", - " tau = pyro.sample(\"tau\", dist.HalfCauchy(5))\n", - " with pyro.plate(\"J\", J):\n", - " theta_tilde = pyro.sample(\"theta_tilde\", dist.Normal(0, 1))\n", - " theta = mu + tau * theta_tilde\n", - " return pyro.sample(\"obs\", dist.Normal(theta, sigma), obs=y)\n", - "\n", - "\n", - "nuts_kernel = NUTS(model, jit_compile=True, ignore_jit_warnings=True)\n", - "mcmc = MCMC(\n", - " nuts_kernel,\n", - " num_samples=draws,\n", - " warmup_steps=draws,\n", - " num_chains=chains,\n", - " disable_progbar=True,\n", - ")\n", - "mcmc.run(**eight_school_data)\n", - "posterior_samples = mcmc.get_samples()\n", - "posterior_predictive = Predictive(model, posterior_samples)(\n", - " eight_school_data[\"J\"], eight_school_data[\"sigma\"]\n", - ")\n", - "prior = Predictive(model, num_samples=500)(eight_school_data[\"J\"], eight_school_data[\"sigma\"])\n", - "\n", - "pyro_data = az.from_pyro(\n", - " mcmc,\n", - " prior=prior,\n", - " posterior_predictive=posterior_predictive,\n", - " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", - " dims={\"theta\": [\"school\"]},\n", - ")\n", - "pyro_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## From emcee" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Please refer to {ref}`emcee_conversion` for details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## From CmdStanPy" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:48:14.965999Z", - "start_time": "2020-06-05T06:48:11.404455Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:cmdstanpy:compiling c++\n", - "INFO:cmdstanpy:compiled model file: /Users/percy/PycharmProjects/arviz/doc/notebooks/eight_school\n", - "INFO:cmdstanpy:found newer exe file, not recompiling\n", - "INFO:cmdstanpy:compiled model file: /Users/percy/PycharmProjects/arviz/doc/notebooks/eight_school\n", - "INFO:cmdstanpy:start chain 1\n", - "INFO:cmdstanpy:start chain 2\n", - "INFO:cmdstanpy:finish chain 2\n", - "INFO:cmdstanpy:start chain 3\n", - "INFO:cmdstanpy:finish chain 1\n", - "INFO:cmdstanpy:start chain 4\n", - "INFO:cmdstanpy:finish chain 3\n", - "INFO:cmdstanpy:finish chain 4\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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-       "       [ 7.2998 ,  7.03214,  1.5383 , ...,  3.01368,  2.67249, 10.056  ]])
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-       "         1.27931  ],\n",
-       "       [ 3.75274  ,  1.13823  ,  1.8422   , ...,  8.23264  ,  3.29958  ,\n",
-       "         9.06202  ],\n",
-       "       [ 0.0756374, 14.1684   ,  2.65082  , ...,  1.91313  ,  7.97043  ,\n",
-       "         4.03989  ],\n",
-       "       [ 0.0525536,  0.213029 ,  2.08631  , ...,  0.51228  ,  0.541425 ,\n",
-       "         5.80725  ]])
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            (chain, draw, school)
            float64
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-       "          0.930506 , -0.765005 ],\n",
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-       "        [-0.65252  , -0.933794 ,  1.11489  , ..., -0.606474 ,\n",
-       "          0.323287 , -2.09671  ],\n",
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-       "          0.131326 , -2.29018  ],\n",
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-       "       [[ 0.409093 ,  0.464062 ,  0.978    , ..., -0.780695 ,\n",
-       "         -0.585484 ,  1.81793  ],\n",
-       "        [ 0.076946 , -0.649097 , -0.768641 , ..., -0.375243 ,\n",
-       "          0.651451 , -2.41116  ],\n",
-       "        [-0.474311 , -0.847334 ,  0.663324 , ...,  0.276397 ,\n",
-       "         -0.65441  ,  2.32434  ],\n",
-       "        ...,\n",
-       "        [-1.25161  ,  0.453957 ,  2.11518  , ..., -0.90933  ,\n",
-       "          2.48317  , -0.782639 ],\n",
-       "        [-0.137756 , -0.0470846, -1.45337  , ..., -2.4094   ,\n",
-       "         -1.58137  ,  0.137089 ],\n",
-       "        [ 1.41579  , -0.0828588,  1.27436  , ...,  0.247186 ,\n",
-       "          1.75653  ,  0.501351 ]]])
          • theta
            (chain, draw, school)
            float64
            9.721 4.33 2.172 ... 20.26 12.97
            array([[[ 9.72074e+00,  4.33032e+00,  2.17239e+00, ..., -1.06433e+00,\n",
-       "          9.53118e+00, -2.01152e+00],\n",
-       "        [ 6.95047e+00,  7.65896e+00,  7.59727e+00, ...,  9.15466e+00,\n",
-       "          7.49564e+00,  9.08194e+00],\n",
-       "        [ 4.66734e+00,  2.43171e+00,  3.19099e+00, ...,  1.73805e+00,\n",
-       "          3.98085e+00,  2.26076e+00],\n",
-       "        ...,\n",
-       "        [ 3.29955e+00,  3.21639e+00,  3.82208e+00, ...,  3.31316e+00,\n",
-       "          3.58805e+00,  2.87257e+00],\n",
-       "        [ 2.58818e+00,  2.63187e+00,  2.82664e+00, ...,  2.58155e+00,\n",
-       "          2.71143e+00,  2.34270e+00],\n",
-       "        [ 2.82542e+00,  2.06533e+00,  3.74569e+00, ...,  4.05377e+00,\n",
-       "          3.93464e+00,  2.38430e+00]],\n",
-       "\n",
-       "       [[ 6.90821e+00,  9.02878e+00, -3.30420e+00, ..., -1.05892e+00,\n",
-       "          8.27142e+00,  1.61287e+00],\n",
-       "        [ 3.79295e+00,  1.90747e+00,  5.97250e+00, ...,  3.11672e+00,\n",
-       "          4.16882e+00,  3.69857e+00],\n",
-       "        [ 5.31266e+00,  8.29796e+00,  1.70476e+00, ...,  6.32960e+00,\n",
-       "          5.34024e+00,  5.63497e+00],\n",
-       "        ...,\n",
-       "        [-4.86998e+00,  1.72888e+01,  1.58377e+01, ...,  1.12062e+00,\n",
-       "          1.41624e+01,  2.67495e+00],\n",
-       "        [ 6.71302e-01, -8.13246e-04,  4.57819e+00, ...,  1.40907e+00,\n",
-       "          5.20222e+00,  4.20272e+00],\n",
-       "        [ 1.46570e+01,  1.10534e-02,  4.84970e+00, ..., -1.12774e+00,\n",
-       "          1.61376e+01,  2.73417e+01]],\n",
-       "\n",
-       "       [[ 4.49277e+00,  4.44936e+00,  4.49306e+00, ...,  4.46195e+00,\n",
-       "          4.50075e+00,  4.63898e+00],\n",
-       "        [ 1.66506e+01,  2.65883e+01,  1.35256e+01, ...,  2.09433e+01,\n",
-       "          1.65890e+01, -6.80072e+00],\n",
-       "        [ 1.16275e+01,  1.02782e+01,  8.03598e+00, ...,  8.90421e+00,\n",
-       "          9.65709e+00,  3.50097e+00],\n",
-       "        ...,\n",
-       "        [ 3.23128e-01, -7.48028e-01,  3.62423e+00, ...,  2.23544e+00,\n",
-       "          4.72425e+00,  1.52349e+00],\n",
-       "        [ 1.65762e+01,  6.55194e+00,  3.74994e+00, ...,  6.41426e+00,\n",
-       "          1.25621e+01,  1.02751e+01],\n",
-       "        [ 3.53185e+00, -1.09566e+00, -2.55514e+00, ..., -1.47657e+00,\n",
-       "          4.04147e+00, -9.46361e+00]],\n",
-       "\n",
-       "       [[ 7.32130e+00,  7.32419e+00,  7.35120e+00, ...,  7.25878e+00,\n",
-       "          7.26903e+00,  7.39534e+00],\n",
-       "        [ 7.04853e+00,  6.89386e+00,  6.86839e+00, ...,  6.95220e+00,\n",
-       "          7.17091e+00,  6.51849e+00],\n",
-       "        [ 5.48734e-01, -2.29508e-01,  2.92220e+00, ...,  2.11494e+00,\n",
-       "          1.72992e-01,  6.38759e+00],\n",
-       "        ...,\n",
-       "        [ 2.37250e+00,  3.24623e+00,  4.09725e+00, ...,  2.54785e+00,\n",
-       "          4.28576e+00,  2.61275e+00],\n",
-       "        [ 2.59791e+00,  2.64700e+00,  1.88560e+00, ...,  1.36798e+00,\n",
-       "          1.81630e+00,  2.74672e+00],\n",
-       "        [ 1.82778e+01,  9.57482e+00,  1.74565e+01, ...,  1.14915e+01,\n",
-       "          2.02566e+01,  1.29675e+01]]])
        • created_at :
          2020-06-10T18:21:56.176067
          arviz_version :
          0.8.3
          inference_library :
          cmdstanpy
          inference_library_version :
          0.8.0

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        \n", - "
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        xarray.Dataset
          • chain: 4
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y_hat
            (chain, draw, school)
            float64
            27.32 16.59 19.23 ... 15.28 -14.33
            array([[[ 27.316   ,  16.5918  ,  19.2331  , ...,  -2.68653 ,\n",
-       "          12.2412  ,  27.0189  ],\n",
-       "        [ 13.7773  ,  -4.34203 ,   3.61368 , ...,   2.72659 ,\n",
-       "          13.737   ,   2.31227 ],\n",
-       "        [ -3.79392 ,  -0.568726, -16.99    , ...,   8.77864 ,\n",
-       "           6.83554 , -24.1435  ],\n",
-       "        ...,\n",
-       "        [ 16.0011  ,  16.3268  ,   0.235298, ..., -14.9994  ,\n",
-       "           6.60276 ,  -0.33642 ],\n",
-       "        [ -3.59346 ,  -7.50548 ,  -0.90007 , ...,  -0.173541,\n",
-       "           3.61428 ,  25.8661  ],\n",
-       "        [  5.06708 ,  -4.07957 , -23.1559  , ...,   7.18739 ,\n",
-       "           9.59156 ,   5.01793 ]],\n",
-       "\n",
-       "       [[ -0.843751,  -4.98264 , -18.7942  , ...,  -6.34356 ,\n",
-       "         -11.4954  , -30.0613  ],\n",
-       "        [-32.9141  ,  -6.91807 ,  -6.90439 , ...,  10.1167  ,\n",
-       "           3.70618 ,   8.15487 ],\n",
-       "        [  2.88749 ,   0.686142,  24.0905  , ...,   9.77811 ,\n",
-       "          31.1765  , -18.8176  ],\n",
-       "        ...,\n",
-       "        [-10.4054  ,  38.8779  ,  -1.72784 , ...,   2.10858 ,\n",
-       "           1.71828 , -41.049   ],\n",
-       "        [-34.6393  ,  -8.05134 ,   0.299065, ...,  -5.08346 ,\n",
-       "          14.0737  ,  18.2229  ],\n",
-       "        [ 20.5415  ,  -7.11264 ,  16.6271  , ...,  -6.10246 ,\n",
-       "          16.3215  ,  28.9235  ]],\n",
-       "\n",
-       "       [[ -0.601871, -11.7324  ,  20.1891  , ...,  14.0161  ,\n",
-       "          -1.10264 ,  -0.811892],\n",
-       "        [ 16.1775  ,  19.1952  ,   7.63344 , ...,  15.3313  ,\n",
-       "          20.5808  ,  -0.475661],\n",
-       "        [ -6.71155 ,  -3.46014 ,  15.4774  , ...,  22.6551  ,\n",
-       "          -6.43601 ,  23.9684  ],\n",
-       "        ...,\n",
-       "        [ 19.7332  , -20.677   ,  24.276   , ...,  12.3559  ,\n",
-       "          11.5496  ,  17.943   ],\n",
-       "        [ 13.6132  ,   5.89891 ,  -6.70882 , ...,   5.5949  ,\n",
-       "          16.5097  , -13.9093  ],\n",
-       "        [ 26.0067  ,  -2.92887 ,  28.0343  , ..., -11.2195  ,\n",
-       "           3.37155 ,  -8.39441 ]],\n",
-       "\n",
-       "       [[ -1.77315 ,  -0.850641,  31.9772  , ...,  12.9403  ,\n",
-       "          19.1804  , -11.9612  ],\n",
-       "        [ 12.9344  ,  -7.44461 ,   0.504742, ..., -11.9935  ,\n",
-       "          -5.35724 ,  20.562   ],\n",
-       "        [-11.582   ,  -9.02636 ,  18.8871  , ...,   2.61293 ,\n",
-       "         -10.874   ,  33.0402  ],\n",
-       "        ...,\n",
-       "        [ 15.5324  ,  14.9454  ,   7.0026  , ...,  -0.143009,\n",
-       "           3.01618 ,  -5.04935 ],\n",
-       "        [-14.324   ,  -4.90844 , -12.5231  , ..., -15.5133  ,\n",
-       "           7.05567 , -27.792   ],\n",
-       "        [ 15.8131  ,  14.6397  ,  33.993   , ...,   1.04319 ,\n",
-       "          15.2797  , -14.3346  ]]])
        • created_at :
          2020-06-10T18:21:56.187926
          arviz_version :
          0.8.3
          inference_library :
          cmdstanpy
          inference_library_version :
          0.8.0

        \n", - "
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        xarray.Dataset
          • chain: 4
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • log_lik
            (chain, draw, school)
            float64
            -4.37 -3.289 ... -3.247 -3.811
            array([[[-4.3695 , -3.28886, -3.74378, ..., -3.33444, -3.58013,\n",
-       "         -4.11228],\n",
-       "        [-4.61162, -3.22211, -3.91087, ..., -3.59162, -3.77323,\n",
-       "         -3.82245],\n",
-       "        [-4.8368 , -3.37655, -3.76639, ..., -3.31908, -4.20421,\n",
-       "         -3.95569],\n",
-       "        ...,\n",
-       "        [-4.98279, -3.33594, -3.78243, ..., -3.33894, -4.26005,\n",
-       "         -3.93788],\n",
-       "        [-5.06201, -3.36561, -3.75784, ..., -3.32717, -4.39022,\n",
-       "         -3.95324],\n",
-       "        [-5.03534, -3.39763, -3.7804 , ..., -3.35537, -4.21069,\n",
-       "         -3.952  ]],\n",
-       "\n",
-       "       [[-4.61557, -3.22682, -3.69171, ..., -3.33435, -3.69475,\n",
-       "         -3.97581],\n",
-       "        [-4.92917, -3.40712, -3.84876, ..., -3.33535, -4.17803,\n",
-       "         -3.91566],\n",
-       "        [-4.7708 , -3.22197, -3.73476, ..., -3.43421, -4.02287,\n",
-       "         -3.87183],\n",
-       "        ...,\n",
-       "        [-6.02796, -3.65293, -4.38461, ..., -3.31689, -3.29516,\n",
-       "         -3.9435 ],\n",
-       "        [-5.28667, -3.54159, -3.80369, ..., -3.31753, -4.04044,\n",
-       "         -3.90313],\n",
-       "        [-4.02262, -3.54064, -3.81187, ..., -3.33554, -3.23887,\n",
-       "         -4.17253]],\n",
-       "\n",
-       "       [[-4.85497, -3.28456, -3.80119, ..., -3.36636, -4.13267,\n",
-       "         -3.89293],\n",
-       "        [-3.91323, -4.94915, -4.22492, ..., -4.96036, -3.23148,\n",
-       "         -4.35478],\n",
-       "        [-4.22267, -3.24747, -3.9294 , ..., -3.575  , -3.56954,\n",
-       "         -3.92078],\n",
-       "        ...,\n",
-       "        [-5.32923, -3.60416, -3.77723, ..., -3.32314, -4.10275,\n",
-       "         -3.97869],\n",
-       "        [-3.91699, -3.23201, -3.78051, ..., -3.43797, -3.36938,\n",
-       "         -3.8139 ],\n",
-       "        [-4.95741, -3.63518, -3.69191, ..., -3.34218, -4.19573,\n",
-       "         -4.52025]],\n",
-       "\n",
-       "       [[-4.57723, -3.22381, -3.9008 , ..., -3.4787 , -3.79729,\n",
-       "         -3.84203],\n",
-       "        [-4.60246, -3.22764, -3.88173, ..., -3.46323, -3.80787,\n",
-       "         -3.85568],\n",
-       "        [-5.30159, -3.56015, -3.76003, ..., -3.32197, -4.81053,\n",
-       "         -3.85792],\n",
-       "        ...,\n",
-       "        [-5.08647, -3.33452, -3.78991, ..., -3.32673, -4.16193,\n",
-       "         -3.9453 ],\n",
-       "        [-5.06091, -3.3648 , -3.73815, ..., -3.31739, -4.53108,\n",
-       "         -3.94144],\n",
-       "        [-3.83703, -3.23392, -4.50885, ..., -3.77167, -3.24699,\n",
-       "         -3.81075]]])
        • created_at :
          2020-06-10T18:21:56.190772
          arviz_version :
          0.8.3
          inference_library :
          cmdstanpy
          inference_library_version :
          0.8.0

        \n", - "
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        \n", - "
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        xarray.Dataset
          • chain: 4
          • draw: 1000
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • lp
            (chain, draw)
            float64
            -7.117 -8.525 ... -11.38 -11.14
            array([[ -7.11711,  -8.52527,  -8.34257, ...,  -8.99953, -10.3924 ,\n",
-       "         -5.12677],\n",
-       "       [ -7.40979,  -8.6973 ,  -8.36134, ...,  -9.30419,  -4.65713,\n",
-       "         -9.11054],\n",
-       "       [ -9.35431,  -9.76551,  -7.86183, ...,  -7.19184,  -5.21445,\n",
-       "         -7.71352],\n",
-       "       [-10.2665 , -13.9631 ,  -9.83918, ..., -13.5776 , -11.3765 ,\n",
-       "        -11.145  ]])
          • accept_stat
            (chain, draw)
            float64
            0.9616 0.9694 ... 0.9886 0.429
            array([[0.96156 , 0.969437, 0.984633, ..., 0.966525, 0.988819, 0.919061],\n",
-       "       [0.892384, 0.952504, 0.999717, ..., 0.981023, 0.945826, 0.627332],\n",
-       "       [1.      , 0.997771, 0.14691 , ..., 1.      , 1.      , 0.459213],\n",
-       "       [0.714025, 0.911832, 1.      , ..., 0.823447, 0.988555, 0.428968]])
          • stepsize
            (chain, draw)
            float64
            0.4064 0.4064 ... 0.4323 0.4323
            array([[0.406405, 0.406405, 0.406405, ..., 0.406405, 0.406405, 0.406405],\n",
-       "       [0.466926, 0.466926, 0.466926, ..., 0.466926, 0.466926, 0.466926],\n",
-       "       [0.50536 , 0.50536 , 0.50536 , ..., 0.50536 , 0.50536 , 0.50536 ],\n",
-       "       [0.432263, 0.432263, 0.432263, ..., 0.432263, 0.432263, 0.432263]])
          • treedepth
            (chain, draw)
            int64
            3 4 3 3 4 3 4 3 ... 3 3 3 3 3 3 3 3
            array([[3, 4, 3, ..., 3, 3, 3],\n",
-       "       [3, 3, 3, ..., 3, 3, 3],\n",
-       "       [3, 3, 3, ..., 3, 3, 3],\n",
-       "       [3, 3, 3, ..., 3, 3, 3]])
          • n_leapfrog
            (chain, draw)
            int64
            7 15 7 7 15 7 15 ... 7 7 15 7 15 7
            array([[ 7, 15,  7, ...,  7,  7,  7],\n",
-       "       [ 7,  7,  7, ...,  7,  7,  7],\n",
-       "       [ 7,  7,  7, ...,  7,  7,  7],\n",
-       "       [15, 15,  7, ...,  7, 15,  7]])
          • diverging
            (chain, draw)
            bool
            False False False ... False False
            array([[False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])
          • energy
            (chain, draw)
            float64
            13.56 12.9 13.65 ... 20.86 22.16
            array([[13.556  , 12.8971 , 13.6523 , ..., 11.4512 , 14.1995 , 12.5183 ],\n",
-       "       [12.3025 , 16.388  , 10.6192 , ..., 19.1564 , 12.9302 , 13.1066 ],\n",
-       "       [14.6137 , 11.6894 , 14.2455 , ...,  9.98162,  9.17208, 15.7546 ],\n",
-       "       [19.118  , 19.03   , 16.7125 , ..., 18.6839 , 20.8571 , 22.1571 ]])
        • created_at :
          2020-06-10T18:21:56.182646
          arviz_version :
          0.8.3
          inference_library :
          cmdstanpy
          inference_library_version :
          0.8.0

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        \n", - "
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        xarray.Dataset
          • school: 8
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y
            (school)
            float64
            28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
            array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
        • created_at :
          2020-06-10T18:21:56.189062
          arviz_version :
          0.8.3
          inference_library :
          cmdstanpy
          inference_library_version :
          0.8.0

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      • \n", - " \n", - "
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    \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> observed_data" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cmdstanpy import CmdStanModel\n", - "\n", - "schools_code = \"\"\"\n", - "data {\n", - " int J;\n", - " real y[J];\n", - " real sigma[J];\n", - "}\n", - "\n", - "parameters {\n", - " real mu;\n", - " real tau;\n", - " real theta_tilde[J];\n", - "}\n", - "\n", - "transformed parameters {\n", - " real theta[J];\n", - " for (j in 1:J)\n", - " theta[j] = mu + tau * theta_tilde[j];\n", - "}\n", - "\n", - "model {\n", - " mu ~ normal(0, 5);\n", - " tau ~ cauchy(0, 5);\n", - " theta_tilde ~ normal(0, 1);\n", - " y ~ normal(theta, sigma);\n", - "}\n", - "\n", - "generated quantities {\n", - " vector[J] log_lik;\n", - " vector[J] y_hat;\n", - " for (j in 1:J) {\n", - " log_lik[j] = normal_lpdf(y[j] | theta[j], sigma[j]);\n", - " y_hat[j] = normal_rng(theta[j], sigma[j]);\n", - " }\n", - "}\n", - "\"\"\"\n", - "\n", - "with open(\"./eight_school.stan\", \"w\") as f:\n", - " print(schools_code, file=f)\n", - "\n", - "stan_file = \"./eight_school.stan\"\n", - "stan_model = CmdStanModel(stan_file=stan_file)\n", - "stan_model.compile()\n", - "\n", - "eight_school_data = {\n", - " \"J\": 8,\n", - " \"y\": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", - " \"sigma\": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", - "}\n", - "\n", - "stan_fit = stan_model.sample(data=eight_school_data)\n", - "\n", - "cmdstanpy_data = az.from_cmdstanpy(\n", - " posterior=stan_fit,\n", - " posterior_predictive=\"y_hat\",\n", - " observed_data={\"y\": eight_school_data[\"y\"]},\n", - " log_likelihood=\"log_lik\",\n", - " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", - " dims={\n", - " \"theta\": [\"school\"],\n", - " \"y\": [\"school\"],\n", - " \"log_lik\": [\"school\"],\n", - " \"y_hat\": [\"school\"],\n", - " \"theta_tilde\": [\"school\"],\n", - " },\n", - ")\n", - "cmdstanpy_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## From CmdStan\n", - "\n", - "See {func}`~arviz.from_cmdstan` for details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CmdStan helpers" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:48:14.969410Z", - "start_time": "2020-06-05T06:47:05.603Z" - } - }, - "outputs": [], - "source": [ - "# save for CmdStan example (needs CmdStanPy run)\n", - "stan_fit.save_csvfiles(dir=\"sample_data\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:48:14.970867Z", - "start_time": "2020-06-05T06:47:05.605Z" - } - }, - "outputs": [], - "source": [ - "schools_code = \"\"\"\n", - "data {\n", - " int J;\n", - " real y[J];\n", - " real sigma[J];\n", - "}\n", - "\n", - "parameters {\n", - " real mu;\n", - " real tau;\n", - " real theta_tilde[J];\n", - "}\n", - "\n", - "transformed parameters {\n", - " real theta[J];\n", - " for (j in 1:J)\n", - " theta[j] = mu + tau * theta_tilde[j];\n", - "}\n", - "\n", - "model {\n", - " mu ~ normal(0, 5);\n", - " tau ~ cauchy(0, 5);\n", - " theta_tilde ~ normal(0, 1);\n", - " y ~ normal(theta, sigma);\n", - "}\n", - "\n", - "generated quantities {\n", - " vector[J] log_lik;\n", - " vector[J] y_hat;\n", - " for (j in 1:J) {\n", - " log_lik[j] = normal_lpdf(y[j] | theta[j], sigma[j]);\n", - " y_hat[j] = normal_rng(theta[j], sigma[j]);\n", - " }\n", - "}\n", - "\"\"\"\n", - "\n", - "with open(\"./eight_school.stan\", \"w\") as f:\n", - " print(schools_code, file=f)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:48:14.972663Z", - "start_time": "2020-06-05T06:47:05.607Z" - } - }, - "outputs": [], - "source": [ - "eight_school_data = {\n", - " \"J\": 8,\n", - " \"y\": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", - " \"sigma\": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:48:14.974211Z", - "start_time": "2020-06-05T06:47:05.609Z" - } - }, - "outputs": [], - "source": [ - "import pystan\n", - "\n", - "pystan.stan_rdump(eight_school_data, \"./eight_school.data.R\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:48:14.975638Z", - "start_time": "2020-06-05T06:47:05.611Z" - } - }, - "outputs": [], - "source": [ - "# Bash shell\n", - "#\n", - "# $ cd cmdstan\n", - "# $ make build\n", - "# $ make path/to/eight_school\n", - "# $ cd path/to\n", - "# $ for i in {1..4}\n", - "# do\n", - "# ./eight_school sample random seed=12345 \\\n", - "# id=$i data file=eight_school.data.R \\\n", - "# output file=sample_data/eight_school_samples-$i.csv &\n", - "# done\n", - "# $" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:48:14.977219Z", - "start_time": "2020-06-05T06:47:05.614Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "arviz.data.io_cmdstan - INFO - glob found 12 files for 'posterior':\n", - "1: sample_data/eight_school-202006100113-1.csv\n", - "2: sample_data/eight_school-202006100113-2.csv\n", - "3: sample_data/eight_school-202006100113-3.csv\n", - "4: sample_data/eight_school-202006100113-4.csv\n", - "5: sample_data/eight_school-202006102306-1.csv\n", - "6: sample_data/eight_school-202006102306-2.csv\n", - "7: sample_data/eight_school-202006102306-3.csv\n", - "8: sample_data/eight_school-202006102306-4.csv\n", - "9: sample_data/eight_school-202006102351-1.csv\n", - "10: sample_data/eight_school-202006102351-2.csv\n", - "11: sample_data/eight_school-202006102351-3.csv\n", - "12: sample_data/eight_school-202006102351-4.csv\n", - "INFO:arviz.data.io_cmdstan:glob found 12 files for 'posterior':\n", - "1: sample_data/eight_school-202006100113-1.csv\n", - "2: sample_data/eight_school-202006100113-2.csv\n", - "3: sample_data/eight_school-202006100113-3.csv\n", - "4: sample_data/eight_school-202006100113-4.csv\n", - "5: sample_data/eight_school-202006102306-1.csv\n", - "6: sample_data/eight_school-202006102306-2.csv\n", - "7: sample_data/eight_school-202006102306-3.csv\n", - "8: sample_data/eight_school-202006102306-4.csv\n", - "9: sample_data/eight_school-202006102351-1.csv\n", - "10: sample_data/eight_school-202006102351-2.csv\n", - "11: sample_data/eight_school-202006102351-3.csv\n", - "12: sample_data/eight_school-202006102351-4.csv\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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    arviz.InferenceData
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        xarray.Dataset
          • chain: 12
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3 4 5 6 7 8 9 10 11
            array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float64
            3.692 16.11 2.111 ... 2.672 10.06
            array([[ 3.69234  , 16.1054   ,  2.11087  , ...,  8.19067  ,  2.92031  ,\n",
-       "         1.03508  ],\n",
-       "       [-1.71907  , 10.6737   ,  5.45426  , ..., 11.9617   , -0.0648748,\n",
-       "         2.95623  ],\n",
-       "       [ 6.44788  ,  5.00457  ,  9.41369  , ..., -0.588549 ,  3.43133  ,\n",
-       "         4.04414  ],\n",
-       "       ...,\n",
-       "       [ 1.20098  ,  3.74153  ,  5.71236  , ...,  5.38302  ,  1.55543  ,\n",
-       "         6.85928  ],\n",
-       "       [ 4.5479   ,  4.21596  ,  6.53758  , ...,  1.8295   ,  6.26736  ,\n",
-       "        -2.83281  ],\n",
-       "       [ 7.2998   ,  7.03214  ,  1.5383   , ...,  3.01368  ,  2.67249  ,\n",
-       "        10.056    ]])
          • tau
            (chain, draw)
            float64
            6.108 3.582 4.862 ... 0.5414 5.807
            array([[ 6.1083   ,  3.58214  ,  4.86183  , ...,  4.47256  ,  1.48153  ,\n",
-       "        12.0573   ],\n",
-       "       [ 6.89254  ,  2.58141  ,  0.803485 , ...,  2.4602   ,  0.599099 ,\n",
-       "         0.97436  ],\n",
-       "       [ 7.54792  ,  3.12201  ,  0.912562 , ...,  1.33408  ,  5.39912  ,\n",
-       "         1.91634  ],\n",
-       "       ...,\n",
-       "       [ 3.75274  ,  1.13823  ,  1.8422   , ...,  8.23264  ,  3.29958  ,\n",
-       "         9.06202  ],\n",
-       "       [ 0.0756374, 14.1684   ,  2.65082  , ...,  1.91313  ,  7.97043  ,\n",
-       "         4.03989  ],\n",
-       "       [ 0.0525536,  0.213029 ,  2.08631  , ...,  0.51228  ,  0.541425 ,\n",
-       "         5.80725  ]])
          • theta_tilde
            (chain, draw, school)
            float64
            0.1507 -0.07589 ... 1.757 0.5014
            array([[[ 0.150693  , -0.0758865 ,  0.222117  , ..., -2.20267   ,\n",
-       "          0.438651  ,  0.0774884 ],\n",
-       "        [-0.690449  , -0.61755   , -0.285046  , ...,  0.905517  ,\n",
-       "         -0.121017  , -0.0131619 ],\n",
-       "        [-0.89236   ,  0.30326   , -0.0355057 , ..., -1.07422   ,\n",
-       "         -0.238142  ,  0.145528  ],\n",
-       "        ...,\n",
-       "        [ 1.87184   , -1.41064   ,  0.60382   , ..., -0.52979   ,\n",
-       "         -0.753925  ,  1.01887   ],\n",
-       "        [ 0.00838069, -0.665387  , -0.0369778 , ..., -0.283951  ,\n",
-       "          0.982918  ,  0.749047  ],\n",
-       "        [ 1.60212   ,  1.30792   ,  0.0776785 , ...,  0.312027  ,\n",
-       "          0.669589  ,  0.215141  ]],\n",
-       "\n",
-       "       [[ 1.8168    , -0.0393204 ,  0.134036  , ...,  0.511469  ,\n",
-       "          0.916789  ,  0.526759  ],\n",
-       "        [-1.9468    , -0.116474  , -0.809836  , ..., -0.355235  ,\n",
-       "          0.339156  , -0.180418  ],\n",
-       "        [ 1.29277   , -0.441339  , -2.04181   , ..., -0.40026   ,\n",
-       "          0.39932   ,  0.263459  ],\n",
-       "        ...,\n",
-       "        [ 0.131124  ,  0.901146  , -0.759669  , ..., -0.87047   ,\n",
-       "         -0.700857  , -0.868739  ],\n",
-       "        [-0.815558  , -1.32762   ,  0.935707  , ...,  0.348515  ,\n",
-       "         -0.542335  ,  0.49039   ],\n",
-       "        [-1.59891   , -1.05952   , -0.0429794 , ...,  0.353703  ,\n",
-       "          0.096935  , -0.824714  ]],\n",
-       "\n",
-       "       [[ 0.919008  ,  0.427259  , -1.39858   , ..., -0.803076  ,\n",
-       "          1.4394    , -1.0618    ],\n",
-       "        [-0.666333  ,  0.134364  , -1.69212   , ..., -1.66072   ,\n",
-       "          0.569682  , -1.16606   ],\n",
-       "        [-0.558085  , -0.793793  ,  0.566323  , ..., -0.395261  ,\n",
-       "          0.948139  , -0.380621  ],\n",
-       "        ...,\n",
-       "        [ 0.666746  ,  0.252036  ,  0.0992795 , ..., -1.08194   ,\n",
-       "         -1.23669   ,  1.74524   ],\n",
-       "        [ 0.91375   ,  0.651899  ,  0.0450728 , ...,  0.794356  ,\n",
-       "          2.65045   , -0.710101  ],\n",
-       "        [ 0.231504  ,  0.297168  ,  0.0153996 , ...,  1.25694   ,\n",
-       "          2.37274   , -0.873043  ]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[ 1.52082   ,  2.08589   , -1.2005    , ..., -0.602199  ,\n",
-       "          1.88407   ,  0.109757  ],\n",
-       "        [ 0.0451789 , -1.61133   ,  1.96004   , ..., -0.548927  ,\n",
-       "          0.375405  , -0.0377435 ],\n",
-       "        [-0.216968  ,  1.40354   , -2.17544   , ...,  0.335058  ,\n",
-       "         -0.201998  , -0.0420111 ],\n",
-       "        ...,\n",
-       "        [-1.24541   ,  1.44617   ,  1.26991   , ..., -0.517744  ,\n",
-       "          1.06641   , -0.328943  ],\n",
-       "        [-0.26795   , -0.471647  ,  0.916106  , ..., -0.0443553 ,\n",
-       "          1.10523   ,  0.802313  ],\n",
-       "        [ 0.860489  , -0.755706  , -0.221758  , ..., -0.881374  ,\n",
-       "          1.02386   ,  2.26025   ]],\n",
-       "\n",
-       "       [[-0.728935  , -1.30284   , -0.725011  , ..., -1.13638   ,\n",
-       "         -0.623346  ,  1.20411   ],\n",
-       "        [ 0.877633  ,  1.57903   ,  0.657069  , ...,  1.18061   ,\n",
-       "          0.873288  , -0.777553  ],\n",
-       "        [ 1.92013   ,  1.41113   ,  0.565262  , ...,  0.892794  ,\n",
-       "          1.17681   , -1.14554   ],\n",
-       "        ...,\n",
-       "        [-0.787386  , -1.34728   ,  0.938111  , ...,  0.212187  ,\n",
-       "          1.5131    , -0.159953  ],\n",
-       "        [ 1.29339   ,  0.0357047 , -0.315845  , ...,  0.0184302 ,\n",
-       "          0.789759  ,  0.502829  ],\n",
-       "        [ 1.57545   ,  0.430001  ,  0.0687333 , ...,  0.335712  ,\n",
-       "          1.7016    , -1.64133   ]],\n",
-       "\n",
-       "       [[ 0.409093  ,  0.464062  ,  0.978     , ..., -0.780695  ,\n",
-       "         -0.585484  ,  1.81793   ],\n",
-       "        [ 0.076946  , -0.649097  , -0.768641  , ..., -0.375243  ,\n",
-       "          0.651451  , -2.41116   ],\n",
-       "        [-0.474311  , -0.847334  ,  0.663324  , ...,  0.276397  ,\n",
-       "         -0.65441   ,  2.32434   ],\n",
-       "        ...,\n",
-       "        [-1.25161   ,  0.453957  ,  2.11518   , ..., -0.90933   ,\n",
-       "          2.48317   , -0.782639  ],\n",
-       "        [-0.137756  , -0.0470846 , -1.45337   , ..., -2.4094    ,\n",
-       "         -1.58137   ,  0.137089  ],\n",
-       "        [ 1.41579   , -0.0828588 ,  1.27436   , ...,  0.247186  ,\n",
-       "          1.75653   ,  0.501351  ]]])
          • theta
            (chain, draw, school)
            float64
            4.613 3.229 5.049 ... 20.26 12.97
            array([[[ 4.61281e+00,  3.22880e+00,  5.04910e+00, ..., -9.76221e+00,\n",
-       "          6.37175e+00,  4.16566e+00],\n",
-       "        [ 1.36322e+01,  1.38933e+01,  1.50844e+01, ...,  1.93491e+01,\n",
-       "          1.56719e+01,  1.60583e+01],\n",
-       "        [-2.22763e+00,  3.58527e+00,  1.93825e+00, ..., -3.11180e+00,\n",
-       "          9.53064e-01,  2.81840e+00],\n",
-       "        ...,\n",
-       "        [ 1.65626e+01,  1.88149e+00,  1.08913e+01, ...,  5.82115e+00,\n",
-       "          4.81870e+00,  1.27476e+01],\n",
-       "        [ 2.93273e+00,  1.93452e+00,  2.86553e+00, ...,  2.49963e+00,\n",
-       "          4.37654e+00,  4.03005e+00],\n",
-       "        [ 2.03524e+01,  1.68052e+01,  1.97168e+00, ...,  4.79731e+00,\n",
-       "          9.10855e+00,  3.62912e+00]],\n",
-       "\n",
-       "       [[ 1.08033e+01, -1.99009e+00, -7.95223e-01, ...,  1.80625e+00,\n",
-       "          4.59993e+00,  1.91163e+00],\n",
-       "        [ 5.64823e+00,  1.03731e+01,  8.58322e+00, ...,  9.75673e+00,\n",
-       "          1.15492e+01,  1.02080e+01],\n",
-       "        [ 6.49298e+00,  5.09965e+00,  3.81370e+00, ...,  5.13266e+00,\n",
-       "          5.77511e+00,  5.66595e+00],\n",
-       "        ...,\n",
-       "        [ 1.22843e+01,  1.41787e+01,  1.00928e+01, ...,  9.82016e+00,\n",
-       "          1.02374e+01,  9.82442e+00],\n",
-       "        [-5.53475e-01, -8.60252e-01,  4.95706e-01, ...,  1.43920e-01,\n",
-       "         -3.89787e-01,  2.28918e-01],\n",
-       "        [ 1.39832e+00,  1.92387e+00,  2.91435e+00, ...,  3.30086e+00,\n",
-       "          3.05068e+00,  2.15266e+00]],\n",
-       "\n",
-       "       [[ 1.33845e+01,  9.67280e+00, -4.10847e+00, ...,  3.86324e-01,\n",
-       "          1.73124e+01, -1.56653e+00],\n",
-       "        [ 2.92428e+00,  5.42406e+00, -2.78235e-01, ..., -1.80204e-01,\n",
-       "          6.78313e+00,  1.36413e+00],\n",
-       "        [ 8.90440e+00,  8.68930e+00,  9.93049e+00, ...,  9.05299e+00,\n",
-       "          1.02789e+01,  9.06635e+00],\n",
-       "        ...,\n",
-       "        [ 3.00946e-01, -2.52311e-01, -4.56102e-01, ..., -2.03194e+00,\n",
-       "         -2.23840e+00,  1.73975e+00],\n",
-       "        [ 8.36477e+00,  6.95101e+00,  3.67468e+00, ...,  7.72015e+00,\n",
-       "          1.77414e+01, -4.02591e-01],\n",
-       "        [ 4.48778e+00,  4.61362e+00,  4.07365e+00, ...,  6.45287e+00,\n",
-       "          8.59112e+00,  2.37110e+00]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[ 6.90821e+00,  9.02878e+00, -3.30420e+00, ..., -1.05892e+00,\n",
-       "          8.27142e+00,  1.61287e+00],\n",
-       "        [ 3.79295e+00,  1.90747e+00,  5.97250e+00, ...,  3.11672e+00,\n",
-       "          4.16882e+00,  3.69857e+00],\n",
-       "        [ 5.31266e+00,  8.29796e+00,  1.70476e+00, ...,  6.32960e+00,\n",
-       "          5.34024e+00,  5.63497e+00],\n",
-       "        ...,\n",
-       "        [-4.86998e+00,  1.72888e+01,  1.58377e+01, ...,  1.12062e+00,\n",
-       "          1.41624e+01,  2.67495e+00],\n",
-       "        [ 6.71302e-01, -8.13246e-04,  4.57819e+00, ...,  1.40907e+00,\n",
-       "          5.20222e+00,  4.20272e+00],\n",
-       "        [ 1.46570e+01,  1.10534e-02,  4.84970e+00, ..., -1.12774e+00,\n",
-       "          1.61376e+01,  2.73417e+01]],\n",
-       "\n",
-       "       [[ 4.49277e+00,  4.44936e+00,  4.49306e+00, ...,  4.46195e+00,\n",
-       "          4.50075e+00,  4.63898e+00],\n",
-       "        [ 1.66506e+01,  2.65883e+01,  1.35256e+01, ...,  2.09433e+01,\n",
-       "          1.65890e+01, -6.80072e+00],\n",
-       "        [ 1.16275e+01,  1.02782e+01,  8.03598e+00, ...,  8.90421e+00,\n",
-       "          9.65709e+00,  3.50097e+00],\n",
-       "        ...,\n",
-       "        [ 3.23128e-01, -7.48028e-01,  3.62423e+00, ...,  2.23544e+00,\n",
-       "          4.72425e+00,  1.52349e+00],\n",
-       "        [ 1.65762e+01,  6.55194e+00,  3.74994e+00, ...,  6.41426e+00,\n",
-       "          1.25621e+01,  1.02751e+01],\n",
-       "        [ 3.53185e+00, -1.09566e+00, -2.55514e+00, ..., -1.47657e+00,\n",
-       "          4.04147e+00, -9.46361e+00]],\n",
-       "\n",
-       "       [[ 7.32130e+00,  7.32419e+00,  7.35120e+00, ...,  7.25878e+00,\n",
-       "          7.26903e+00,  7.39534e+00],\n",
-       "        [ 7.04853e+00,  6.89386e+00,  6.86839e+00, ...,  6.95220e+00,\n",
-       "          7.17091e+00,  6.51849e+00],\n",
-       "        [ 5.48734e-01, -2.29508e-01,  2.92220e+00, ...,  2.11494e+00,\n",
-       "          1.72992e-01,  6.38759e+00],\n",
-       "        ...,\n",
-       "        [ 2.37250e+00,  3.24623e+00,  4.09725e+00, ...,  2.54785e+00,\n",
-       "          4.28576e+00,  2.61275e+00],\n",
-       "        [ 2.59791e+00,  2.64700e+00,  1.88560e+00, ...,  1.36798e+00,\n",
-       "          1.81630e+00,  2.74672e+00],\n",
-       "        [ 1.82778e+01,  9.57482e+00,  1.74565e+01, ...,  1.14915e+01,\n",
-       "          2.02566e+01,  1.29675e+01]]])
        • created_at :
          2020-06-10T18:21:57.128552
          arviz_version :
          0.8.3

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        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 12
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3 4 5 6 7 8 9 10 11
            array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y_hat
            (chain, draw, school)
            float64
            -10.93 11.6 11.14 ... 15.28 -14.33
            array([[[-10.9346  ,  11.6037  ,  11.1382  , ...,  -4.97421 ,\n",
-       "          -6.09248 ,  15.0512  ],\n",
-       "        [ -3.69064 ,  14.6682  ,  22.4837  , ...,  23.5671  ,\n",
-       "          14.8374  ,   9.00593 ],\n",
-       "        [  0.408807,  -7.37329 ,  -1.04588 , ...,  -6.87661 ,\n",
-       "           3.75974 , -11.8118  ],\n",
-       "        ...,\n",
-       "        [ 20.0649  ,   4.67947 ,  34.8539  , ...,  19.3071  ,\n",
-       "          -1.81022 ,  10.6627  ],\n",
-       "        [ 20.023   ,  -3.45228 , -16.6117  , ...,   4.34399 ,\n",
-       "          19.4159  ,   4.02906 ],\n",
-       "        [ 12.6708  ,   9.25176 ,  18.6282  , ...,  -0.56782 ,\n",
-       "          -5.90848 , -24.1042  ]],\n",
-       "\n",
-       "       [[ 25.585   ,   2.60382 , -22.731   , ...,  12.1856  ,\n",
-       "         -24.8332  ,  -7.97341 ],\n",
-       "        [  3.05847 ,  19.6072  ,   9.67468 , ...,  -9.57937 ,\n",
-       "          -5.97203 ,   3.83901 ],\n",
-       "        [ -2.96353 ,   1.59361 ,  34.3077  , ..., -16.9225  ,\n",
-       "           5.94989 ,  12.7261  ],\n",
-       "        ...,\n",
-       "        [-30.3258  ,  25.8939  ,  11.0092  , ...,  10.8265  ,\n",
-       "           5.90875 , -27.5675  ],\n",
-       "        [ 12.7005  ,  -4.70354 , -11.1986  , ...,  -7.05527 ,\n",
-       "          -6.65953 ,  35.0723  ],\n",
-       "        [ 10.4867  ,   5.49925 ,  -5.00058 , ...,  16.8872  ,\n",
-       "          -1.90614 ,   5.35349 ]],\n",
-       "\n",
-       "       [[ 26.4483  ,   5.39621 , -18.0307  , ..., -16.5188  ,\n",
-       "          13.9216  ,  -3.97292 ],\n",
-       "        [ -7.85832 ,   4.44009 ,   5.35032 , ..., -10.6668  ,\n",
-       "          20.5908  , -12.8477  ],\n",
-       "        [ -1.72751 ,  15.6356  ,  -7.37621 , ...,  36.3976  ,\n",
-       "           9.78589 ,   8.58118 ],\n",
-       "        ...,\n",
-       "        [ -2.08977 ,  13.1182  ,  -8.81588 , ...,  -9.16025 ,\n",
-       "         -11.6879  , -28.6293  ],\n",
-       "        [ -1.44321 ,   3.20826 ,  -4.6157  , ...,  23.3955  ,\n",
-       "          18.8133  , -21.3375  ],\n",
-       "        [ 10.1546  ,  16.7743  , -13.0238  , ...,  -5.73191 ,\n",
-       "          12.5278  ,   9.80171 ]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[ -0.843751,  -4.98264 , -18.7942  , ...,  -6.34356 ,\n",
-       "         -11.4954  , -30.0613  ],\n",
-       "        [-32.9141  ,  -6.91807 ,  -6.90439 , ...,  10.1167  ,\n",
-       "           3.70618 ,   8.15487 ],\n",
-       "        [  2.88749 ,   0.686142,  24.0905  , ...,   9.77811 ,\n",
-       "          31.1765  , -18.8176  ],\n",
-       "        ...,\n",
-       "        [-10.4054  ,  38.8779  ,  -1.72784 , ...,   2.10858 ,\n",
-       "           1.71828 , -41.049   ],\n",
-       "        [-34.6393  ,  -8.05134 ,   0.299065, ...,  -5.08346 ,\n",
-       "          14.0737  ,  18.2229  ],\n",
-       "        [ 20.5415  ,  -7.11264 ,  16.6271  , ...,  -6.10246 ,\n",
-       "          16.3215  ,  28.9235  ]],\n",
-       "\n",
-       "       [[ -0.601871, -11.7324  ,  20.1891  , ...,  14.0161  ,\n",
-       "          -1.10264 ,  -0.811892],\n",
-       "        [ 16.1775  ,  19.1952  ,   7.63344 , ...,  15.3313  ,\n",
-       "          20.5808  ,  -0.475661],\n",
-       "        [ -6.71155 ,  -3.46014 ,  15.4774  , ...,  22.6551  ,\n",
-       "          -6.43601 ,  23.9684  ],\n",
-       "        ...,\n",
-       "        [ 19.7332  , -20.677   ,  24.276   , ...,  12.3559  ,\n",
-       "          11.5496  ,  17.943   ],\n",
-       "        [ 13.6132  ,   5.89891 ,  -6.70882 , ...,   5.5949  ,\n",
-       "          16.5097  , -13.9093  ],\n",
-       "        [ 26.0067  ,  -2.92887 ,  28.0343  , ..., -11.2195  ,\n",
-       "           3.37155 ,  -8.39441 ]],\n",
-       "\n",
-       "       [[ -1.77315 ,  -0.850641,  31.9772  , ...,  12.9403  ,\n",
-       "          19.1804  , -11.9612  ],\n",
-       "        [ 12.9344  ,  -7.44461 ,   0.504742, ..., -11.9935  ,\n",
-       "          -5.35724 ,  20.562   ],\n",
-       "        [-11.582   ,  -9.02636 ,  18.8871  , ...,   2.61293 ,\n",
-       "         -10.874   ,  33.0402  ],\n",
-       "        ...,\n",
-       "        [ 15.5324  ,  14.9454  ,   7.0026  , ...,  -0.143009,\n",
-       "           3.01618 ,  -5.04935 ],\n",
-       "        [-14.324   ,  -4.90844 , -12.5231  , ..., -15.5133  ,\n",
-       "           7.05567 , -27.792   ],\n",
-       "        [ 15.8131  ,  14.6397  ,  33.993   , ...,   1.04319 ,\n",
-       "          15.2797  , -14.3346  ]]])
        • created_at :
          2020-06-10T18:21:57.962326
          arviz_version :
          0.8.3

        \n", - "
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        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 12
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3 4 5 6 7 8 9 10 11
            array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • log_lik
            (chain, draw, school)
            float64
            -4.842 -3.335 ... -3.247 -3.811
            array([[[-4.84246, -3.33535, -3.81807, ..., -3.79545, -3.8976 ,\n",
-       "         -3.90403],\n",
-       "        [-4.08573, -3.39518, -4.33029, ..., -4.70812, -3.24862,\n",
-       "         -3.83473],\n",
-       "        [-5.65746, -3.31897, -3.73916, ..., -3.3867 , -4.67451,\n",
-       "         -3.93941],\n",
-       "        ...,\n",
-       "        [-3.91769, -3.4087 , -4.06842, ..., -3.41288, -4.09026,\n",
-       "         -3.81017],\n",
-       "        [-5.02336, -3.40547, -3.75872, ..., -3.32613, -4.14952,\n",
-       "         -3.90734],\n",
-       "        [-3.75696, -3.60918, -3.7398 , ..., -3.37642, -3.61681,\n",
-       "         -3.91745]],\n",
-       "\n",
-       "       [[-4.28416, -3.72053, -3.70102, ..., -3.31952, -4.11933,\n",
-       "         -3.96637],\n",
-       "        [-4.73721, -3.24968, -3.95358, ..., -3.63369, -3.42959,\n",
-       "         -3.81427],\n",
-       "        [-4.65488, -3.26358, -3.7822 , ..., -3.38741, -3.96876,\n",
-       "         -3.87122],\n",
-       "        ...,\n",
-       "        [-4.17584, -3.4124 , -4.02633, ..., -3.6383 , -3.52281,\n",
-       "         -3.81661],\n",
-       "        [-5.43877, -3.61404, -3.71539, ..., -3.31986, -4.91245,\n",
-       "         -4.02313],\n",
-       "        [-5.19954, -3.40612, -3.75985, ..., -3.33871, -4.33893,\n",
-       "         -3.95896]],\n",
-       "\n",
-       "       [[-4.10169, -3.23551, -3.69393, ..., -3.31839, -3.22389,\n",
-       "         -4.09334],\n",
-       "        [-5.0243 , -3.2547 , -3.706  , ..., -3.32259, -3.85061,\n",
-       "         -3.98388],\n",
-       "        [-4.4373 , -3.2239 , -4.01809, ..., -3.58481, -3.5196 ,\n",
-       "         -3.82259],\n",
-       "        ...,\n",
-       "        [-5.33196, -3.56203, -3.70417, ..., -3.35482, -5.26949,\n",
-       "         -3.97177],\n",
-       "        [-4.48375, -3.22703, -3.77854, ..., -3.50345, -3.22186,\n",
-       "         -4.04669],\n",
-       "        [-4.85549, -3.27886, -3.78925, ..., -3.4397 , -3.66416,\n",
-       "         -3.95239]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[-4.61557, -3.22682, -3.69171, ..., -3.33435, -3.69475,\n",
-       "         -3.97581],\n",
-       "        [-4.92917, -3.40712, -3.84876, ..., -3.33535, -4.17803,\n",
-       "         -3.91566],\n",
-       "        [-4.7708 , -3.22197, -3.73476, ..., -3.43421, -4.02287,\n",
-       "         -3.87183],\n",
-       "        ...,\n",
-       "        [-6.02796, -3.65293, -4.38461, ..., -3.31689, -3.29516,\n",
-       "         -3.9435 ],\n",
-       "        [-5.28667, -3.54159, -3.80369, ..., -3.31753, -4.04044,\n",
-       "         -3.90313],\n",
-       "        [-4.02262, -3.54064, -3.81187, ..., -3.33554, -3.23887,\n",
-       "         -4.17253]],\n",
-       "\n",
-       "       [[-4.85497, -3.28456, -3.80119, ..., -3.36636, -4.13267,\n",
-       "         -3.89293],\n",
-       "        [-3.91323, -4.94915, -4.22492, ..., -4.96036, -3.23148,\n",
-       "         -4.35478],\n",
-       "        [-4.22267, -3.24747, -3.9294 , ..., -3.575  , -3.56954,\n",
-       "         -3.92078],\n",
-       "        ...,\n",
-       "        [-5.32923, -3.60416, -3.77723, ..., -3.32314, -4.10275,\n",
-       "         -3.97869],\n",
-       "        [-3.91699, -3.23201, -3.78051, ..., -3.43797, -3.36938,\n",
-       "         -3.8139 ],\n",
-       "        [-4.95741, -3.63518, -3.69191, ..., -3.34218, -4.19573,\n",
-       "         -4.52025]],\n",
-       "\n",
-       "       [[-4.57723, -3.22381, -3.9008 , ..., -3.4787 , -3.79729,\n",
-       "         -3.84203],\n",
-       "        [-4.60246, -3.22764, -3.88173, ..., -3.46323, -3.80787,\n",
-       "         -3.85568],\n",
-       "        [-5.30159, -3.56015, -3.76003, ..., -3.32197, -4.81053,\n",
-       "         -3.85792],\n",
-       "        ...,\n",
-       "        [-5.08647, -3.33452, -3.78991, ..., -3.32673, -4.16193,\n",
-       "         -3.9453 ],\n",
-       "        [-5.06091, -3.3648 , -3.73815, ..., -3.31739, -4.53108,\n",
-       "         -3.94144],\n",
-       "        [-3.83703, -3.23392, -4.50885, ..., -3.77167, -3.24699,\n",
-       "         -3.81075]]])
        • created_at :
          2020-06-10T18:21:57.929618
          arviz_version :
          0.8.3

        \n", - "
      \n", - "
      \n", - "
      • \n", - " \n", - "
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        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 12
          • draw: 1000
          • chain
            (chain)
            int64
            0 1 2 3 4 5 6 7 8 9 10 11
            array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • lp
            (chain, draw)
            float64
            -5.144 -10.34 ... -11.38 -11.14
            array([[ -5.14448, -10.3377 ,  -4.36231, ..., -11.5328 ,  -4.41412,\n",
-       "         -4.53645],\n",
-       "       [ -6.16728,  -7.89382,  -8.32759, ...,  -6.95914,  -8.01642,\n",
-       "         -6.47187],\n",
-       "       [ -4.61618,  -9.6411 ,  -6.57635, ...,  -7.53224,  -5.69699,\n",
-       "         -6.2396 ],\n",
-       "       ...,\n",
-       "       [ -7.40979,  -8.6973 ,  -8.36134, ...,  -9.30419,  -4.65713,\n",
-       "         -9.11054],\n",
-       "       [ -9.35431,  -9.76551,  -7.86183, ...,  -7.19184,  -5.21445,\n",
-       "         -7.71352],\n",
-       "       [-10.2665 , -13.9631 ,  -9.83918, ..., -13.5776 , -11.3765 ,\n",
-       "        -11.145  ]])
          • accept_stat
            (chain, draw)
            float64
            1.0 0.4431 0.9951 ... 0.9886 0.429
            array([[1.      , 0.44315 , 0.995054, ..., 0.847876, 0.887551, 0.924468],\n",
-       "       [0.714286, 0.977521, 0.724435, ..., 0.934922, 0.946572, 1.      ],\n",
-       "       [1.      , 0.426264, 0.992927, ..., 0.860845, 0.996249, 0.129366],\n",
-       "       ...,\n",
-       "       [0.892384, 0.952504, 0.999717, ..., 0.981023, 0.945826, 0.627332],\n",
-       "       [1.      , 0.997771, 0.14691 , ..., 1.      , 1.      , 0.459213],\n",
-       "       [0.714025, 0.911832, 1.      , ..., 0.823447, 0.988555, 0.428968]])
          • stepsize
            (chain, draw)
            float64
            0.5095 0.5095 ... 0.4323 0.4323
            array([[0.509501, 0.509501, 0.509501, ..., 0.509501, 0.509501, 0.509501],\n",
-       "       [0.531567, 0.531567, 0.531567, ..., 0.531567, 0.531567, 0.531567],\n",
-       "       [0.449446, 0.449446, 0.449446, ..., 0.449446, 0.449446, 0.449446],\n",
-       "       ...,\n",
-       "       [0.466926, 0.466926, 0.466926, ..., 0.466926, 0.466926, 0.466926],\n",
-       "       [0.50536 , 0.50536 , 0.50536 , ..., 0.50536 , 0.50536 , 0.50536 ],\n",
-       "       [0.432263, 0.432263, 0.432263, ..., 0.432263, 0.432263, 0.432263]])
          • treedepth
            (chain, draw)
            int64
            3 3 3 3 3 3 3 3 ... 3 3 3 3 3 3 3 3
            array([[3, 3, 3, ..., 3, 3, 3],\n",
-       "       [3, 3, 3, ..., 3, 3, 3],\n",
-       "       [3, 3, 3, ..., 3, 3, 3],\n",
-       "       ...,\n",
-       "       [3, 3, 3, ..., 3, 3, 3],\n",
-       "       [3, 3, 3, ..., 3, 3, 3],\n",
-       "       [3, 3, 3, ..., 3, 3, 3]])
          • n_leapfrog
            (chain, draw)
            int64
            7 7 7 7 7 7 7 ... 15 7 7 15 7 15 7
            array([[ 7,  7,  7, ...,  7,  7,  7],\n",
-       "       [ 7,  7,  7, ...,  7,  7,  7],\n",
-       "       [ 7,  7,  7, ...,  7,  7,  7],\n",
-       "       ...,\n",
-       "       [ 7,  7,  7, ...,  7,  7,  7],\n",
-       "       [ 7,  7,  7, ...,  7,  7,  7],\n",
-       "       [15, 15,  7, ...,  7, 15,  7]])
          • diverging
            (chain, draw)
            bool
            False False False ... False False
            array([[False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       ...,\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])
          • energy
            (chain, draw)
            float64
            9.305 15.49 12.04 ... 20.86 22.16
            array([[ 9.30492, 15.4901 , 12.0411 , ..., 14.2758 , 13.6856 ,  8.80269],\n",
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    \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> observed_data" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's use .stan and .csv files created/saved by the CmdStanPy procedure\n", - "\n", - "# glob string\n", - "posterior_glob = \"sample_data/eight_school-*-[0-9].csv\"\n", - "# list of paths\n", - "# posterior_list = [\n", - "# \"sample_data/eight_school-*-1.csv\",\n", - "# \"sample_data/eight_school-*-2.csv\",\n", - "# \"sample_data/eight_school-*-3.csv\",\n", - "# \"sample_data/eight_school-*-4.csv\",\n", - "# ]\n", - "\n", - "obs_data_path = \"./eight_school.data.R\"\n", - "\n", - "cmdstan_data = az.from_cmdstan(\n", - " posterior=posterior_glob,\n", - " posterior_predictive=\"y_hat\",\n", - " observed_data=obs_data_path,\n", - " observed_data_var=\"y\",\n", - " log_likelihood=\"log_lik\",\n", - " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", - " dims={\n", - " \"theta\": [\"school\"],\n", - " \"y\": [\"school\"],\n", - " \"log_lik\": [\"school\"],\n", - " \"y_hat\": [\"school\"],\n", - " \"theta_tilde\": [\"school\"],\n", - " },\n", - ")\n", - "cmdstan_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## From NumPyro" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:48:56.834305Z", - "start_time": "2020-06-05T06:48:46.413022Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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            (chain, draw)
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            54.622334 53.479084 ... 49.01058
            array([[54.622334, 53.479084, 53.991596, ..., 53.81039 , 59.236557,\n",
-       "        56.882545],\n",
-       "       [49.65902 , 48.335026, 56.860115, ..., 49.79985 , 48.931656,\n",
-       "        50.302807],\n",
-       "       [51.492855, 51.79956 , 55.82659 , ..., 51.77163 , 50.36808 ,\n",
-       "        51.65256 ],\n",
-       "       [49.366512, 50.623386, 55.419903, ..., 51.691166, 47.321953,\n",
-       "        49.01058 ]], dtype=float32)
          • tree_size
            (chain, draw)
            int32
            7 15 7 7 7 15 7 7 ... 7 7 7 7 7 7 7
            array([[ 7, 15,  7, ...,  7, 15,  7],\n",
-       "       [ 7, 15, 15, ...,  7,  7,  7],\n",
-       "       [ 7,  7,  7, ...,  3,  7,  7],\n",
-       "       [ 7,  7,  7, ...,  7,  7,  7]], dtype=int32)
          • depth
            (chain, draw)
            int64
            3 4 3 3 3 4 3 3 ... 3 3 3 3 3 3 3 3
            array([[3, 4, 3, ..., 3, 4, 3],\n",
-       "       [3, 4, 4, ..., 3, 3, 3],\n",
-       "       [3, 3, 3, ..., 2, 3, 3],\n",
-       "       [3, 3, 3, ..., 3, 3, 3]])
        • created_at :
          2020-06-10T18:22:23.951922
          arviz_version :
          0.8.3
          inference_library :
          numpyro
          inference_library_version :
          0.2.3

        \n", - "
      \n", - "
      \n", - "
      • \n", - " \n", - "
    • \n", - " \n", - " \n", - "
      \n", - "
      \n", - "
        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 1
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float32
            1.8776392 3.322776 ... -0.5562178
            array([[ 1.87763917e+00,  3.32277608e+00, -9.43409824e+00,\n",
-       "        -6.36535287e-01,  4.97456253e-01, -7.59930372e+00,\n",
-       "        -1.73702741e+00, -6.81911755e+00, -5.64398336e+00,\n",
-       "        -2.23989189e-01,  5.17267179e+00,  8.54710960e+00,\n",
-       "         3.31850290e+00,  1.21073082e-01,  7.83620691e+00,\n",
-       "         4.69881475e-01, -1.08328164e+00,  8.50991058e+00,\n",
-       "        -8.41802776e-01, -4.04251289e+00,  3.32294011e+00,\n",
-       "         5.99611139e+00, -6.17558670e+00,  6.03334332e+00,\n",
-       "        -5.70485020e+00, -2.03827691e+00, -1.12319574e+01,\n",
-       "         4.22157764e+00,  2.38109422e+00, -4.61673450e+00,\n",
-       "         5.93315411e+00,  4.55151653e+00, -7.10970700e-01,\n",
-       "        -6.31284094e+00, -4.34699202e+00,  6.09261799e+00,\n",
-       "        -6.14266682e+00,  9.26569998e-01,  1.61548471e+00,\n",
-       "        -4.15261239e-01, -7.68356943e+00, -2.56281686e+00,\n",
-       "        -4.79618311e+00,  7.81891727e+00, -3.24380898e+00,\n",
-       "         1.65736473e+00, -5.14156342e+00,  1.10072966e+01,\n",
-       "        -1.44031830e+01,  5.42257833e+00,  5.59339571e+00,\n",
-       "         1.49750960e+00, -3.32892269e-01, -4.46871185e+00,\n",
-       "         2.56567287e+00,  2.47131181e+00, -1.37173009e+00,\n",
-       "        -1.49122763e+00, -1.84207225e+00,  9.69244099e+00,\n",
-       "        -3.78764582e+00, -6.92075205e+00, -2.02182364e+00,\n",
-       "        -2.73029757e+00,  9.31008577e-01,  7.28544283e+00,\n",
-       "         1.54594302e+00,  5.64335871e+00, -3.37919688e+00,\n",
-       "         4.13902712e+00,  2.63738465e+00,  1.40324092e+00,\n",
-       "         1.34920418e+00,  4.90401697e+00,  3.16824293e+00,\n",
-       "         1.47920895e+00,  2.66602039e-01,  2.51417804e+00,\n",
-       "        -5.36913395e+00, -6.41616249e+00,  4.33436871e+00,\n",
-       "        -3.89514852e+00,  5.18284702e+00, -3.32038426e+00,\n",
-       "         1.16524143e+01, -6.82530260e+00, -3.53052795e-01,\n",
-       "         1.58044920e+01, -2.88200164e+00,  7.18814087e+00,\n",
-       "         2.98873329e+00, -2.26602387e+00,  4.08212948e+00,\n",
-       "         3.42252636e+00,  8.58223617e-01,  6.26113534e-01,\n",
-       "        -7.87266970e+00,  2.15487385e+00, -6.07770061e+00,\n",
-       "        -2.48635459e+00,  2.68985540e-01,  3.20517206e+00,\n",
-       "        -7.47953987e+00,  4.08663481e-01,  8.99567795e+00,\n",
-       "         7.04715443e+00,  7.23012495e+00, -7.46469927e+00,\n",
-       "        -1.33649683e+00,  1.10470068e+00, -2.52437449e+00,\n",
-       "        -3.95289946e+00,  2.47145557e+00, -1.09172354e+01,\n",
-       "         4.06590843e+00,  1.29628773e+01,  4.18381977e+00,\n",
-       "        -2.29971838e+00,  8.35384464e+00, -1.05598001e+01,\n",
-       "        -3.01278502e-01,  5.95889282e+00, -9.29296911e-01,\n",
-       "        -7.44931984e+00,  1.46392691e+00, -5.03668404e+00,\n",
-       "         5.68537998e+00, -2.34223104e+00, -3.17319202e+00,\n",
-       "        -1.99956322e+00, -2.91206074e+00,  3.08447218e+00,\n",
-       "         6.02147937e-01, -2.49044871e+00, -3.42613077e+00,\n",
-       "        -3.59730273e-01,  4.51586843e-01, -9.63892519e-01,\n",
-       "         2.33508396e+00, -2.13027024e+00,  3.09376287e+00,\n",
-       "        -7.35646665e-01, -8.27977657e+00,  2.87267494e+00,\n",
-       "        -2.67699122e+00, -8.89165998e-02, -2.52230263e+00,\n",
-       "        -5.46618128e+00, -8.53099227e-01,  1.64677703e+00,\n",
-       "         3.64264667e-01, -6.47672355e-01,  1.26347458e+00,\n",
-       "        -6.80529928e+00, -3.41742945e+00,  3.55985022e+00,\n",
-       "        -2.55946088e+00,  5.54295111e+00,  9.42449033e-01,\n",
-       "         1.64480090e-01, -8.02109167e-02,  3.24023986e+00,\n",
-       "        -4.53600079e-01,  1.37038574e+01,  5.71575737e+00,\n",
-       "         5.76857448e-01,  2.17014074e+00,  8.14787924e-01,\n",
-       "        -2.32475162e+00, -1.25709087e-01,  5.28375340e+00,\n",
-       "         9.62919891e-01,  6.91391659e+00,  2.62722158e+00,\n",
-       "        -2.74987268e+00,  2.25159264e+00,  1.91202760e+00,\n",
-       "         3.22131705e+00, -5.74864435e+00, -6.94986010e+00,\n",
-       "         5.20943463e-01, -5.00131655e+00, -3.07738566e+00,\n",
-       "         3.08097601e+00,  2.67554283e+00,  3.52074385e+00,\n",
-       "         6.54691935e+00, -9.24593830e+00, -3.36103296e+00,\n",
-       "         4.60509825e+00, -2.21546078e+00,  2.92350554e+00,\n",
-       "        -5.45947027e+00, -2.05733395e+00,  4.57703733e+00,\n",
-       "        -1.06910706e+00,  7.19312000e+00, -2.47113132e+00,\n",
-       "         3.12669230e+00, -1.83465838e+00,  4.28496313e+00,\n",
-       "         5.37801313e+00,  5.02111244e+00,  7.15357685e+00,\n",
-       "        -3.89565319e-01,  3.89020848e+00, -3.05091119e+00,\n",
-       "        -4.23250914e+00, -2.70682287e+00,  5.63752937e+00,\n",
-       "         1.95974433e+00,  1.16094184e+00,  5.58682775e+00,\n",
-       "        -1.27820683e+00,  1.79363227e+00,  4.05184221e+00,\n",
-       "        -7.00801432e-01, -8.40434170e+00, -8.00249863e+00,\n",
-       "         3.10724616e+00,  1.68522644e+00, -7.73088264e+00,\n",
-       "        -6.81490719e-01,  4.33881950e+00, -2.85863638e+00,\n",
-       "        -6.09480000e+00,  6.74300861e+00, -4.61606646e+00,\n",
-       "        -5.85084915e+00,  6.28509665e+00,  1.95886517e+00,\n",
-       "         1.37964690e+00, -1.37427435e+01,  2.82988310e-01,\n",
-       "         5.19354916e+00, -2.49803233e+00,  4.12895918e+00,\n",
-       "         3.44766212e+00,  2.19018960e+00, -3.72761774e+00,\n",
-       "        -2.44066030e-01, -1.30070686e+00, -7.02205515e+00,\n",
-       "         5.58520174e+00, -3.75585365e+00,  5.79257107e+00,\n",
-       "        -5.56449747e+00, -2.05371952e+00,  5.01734138e-01,\n",
-       "        -2.25151634e+00, -7.47076082e+00, -2.79890943e+00,\n",
-       "         4.27494675e-01, -7.40755653e+00,  1.63190055e+00,\n",
-       "         5.20384073e+00,  2.63987112e+00, -5.63552999e+00,\n",
-       "        -1.90767086e+00,  2.34675384e+00,  7.09238386e+00,\n",
-       "        -1.90808758e-01, -3.74278712e+00, -4.35007524e+00,\n",
-       "         4.41096497e+00,  5.99206209e+00,  9.79538822e+00,\n",
-       "         3.85714984e+00, -7.91006833e-02, -6.17025375e-01,\n",
-       "         9.57570493e-01, -2.43118548e+00,  3.27787709e+00,\n",
-       "         1.25472581e+00, -6.39819193e+00,  5.21416187e+00,\n",
-       "        -8.07223260e-01,  5.50955153e+00,  3.49549317e+00,\n",
-       "        -1.64368260e+00, -4.73124313e+00,  2.40179896e-01,\n",
-       "         5.83935118e+00,  4.42518854e+00, -6.38878107e+00,\n",
-       "        -4.94022989e+00,  1.35768807e+00,  3.12588358e+00,\n",
-       "        -9.07020473e+00,  4.31778097e+00, -4.70105934e+00,\n",
-       "         8.42939377e-01,  4.88915968e+00, -2.72294712e+00,\n",
-       "        -8.45176220e-01,  4.62164545e+00, -2.80933213e+00,\n",
-       "         4.71438694e+00, -2.46584034e+00, -7.28257990e+00,\n",
-       "         4.51298285e+00,  2.38831258e+00,  7.25005722e+00,\n",
-       "         1.79590631e+00,  5.94742823e+00,  7.22948265e+00,\n",
-       "         3.35202265e+00, -4.28335810e+00, -1.37254620e+00,\n",
-       "        -1.73780191e+00,  2.67699456e+00, -5.62134886e+00,\n",
-       "         9.09809971e+00,  5.02816010e+00,  9.05102825e+00,\n",
-       "         7.36941040e-01,  1.68737400e+00,  8.23375225e+00,\n",
-       "         8.10452104e-01, -6.44127703e+00,  1.16090810e+00,\n",
-       "        -1.34760213e+00, -4.49384308e+00, -3.41933179e+00,\n",
-       "        -4.81171131e+00, -1.84972250e+00,  1.02269201e+01,\n",
-       "        -1.26457906e+00, -2.97555542e+00,  5.53493214e+00,\n",
-       "        -1.07800531e+00,  9.11830544e-01,  1.52422190e+00,\n",
-       "         1.16673994e+00, -1.45918512e+00, -8.07522106e+00,\n",
-       "         7.53456783e+00,  8.10562551e-01,  1.59508491e+00,\n",
-       "        -1.25905542e+01, -2.68738222e+00, -5.06097794e+00,\n",
-       "        -5.67802286e+00, -2.19069934e+00,  4.38740063e+00,\n",
-       "         1.46200097e+00, -5.58933640e+00,  6.96885443e+00,\n",
-       "         9.52366066e+00, -5.29678154e+00,  3.33105922e+00,\n",
-       "         1.38898647e+00, -3.12741876e+00,  2.34334993e+00,\n",
-       "        -2.98348856e+00, -1.02783978e+00,  3.65240884e+00,\n",
-       "        -3.50431395e+00, -2.64991903e+00,  5.53679609e+00,\n",
-       "         5.90298462e+00,  3.79234552e+00, -4.90194845e+00,\n",
-       "        -2.69498777e+00,  4.49044991e+00,  5.99002552e+00,\n",
-       "         6.21761417e+00, -1.55535388e+00, -1.58084214e+00,\n",
-       "        -5.09701395e+00,  4.06920147e+00,  2.25836396e+00,\n",
-       "         1.25704491e+00,  6.84968662e+00,  7.44565535e+00,\n",
-       "        -1.87951326e+00, -9.00892019e-01,  4.42775679e+00,\n",
-       "         6.54110003e+00,  3.14750051e+00,  1.95613086e+00,\n",
-       "        -3.91129422e+00,  1.61477637e+00,  1.50569409e-01,\n",
-       "         9.52131367e+00,  2.35001040e+00, -6.31063700e+00,\n",
-       "        -6.33711386e+00,  4.53157854e+00,  2.09618174e-02,\n",
-       "        -2.14906359e+00, -9.78138298e-02, -1.86061585e+00,\n",
-       "         4.90669966e+00,  1.24480343e+00,  7.89691955e-02,\n",
-       "        -5.58514953e-01, -2.23632979e+00,  1.30819321e+00,\n",
-       "        -3.82723236e+00, -5.53356695e+00, -2.46174788e+00,\n",
-       "        -3.10814881e+00,  3.28143239e+00, -8.99871159e+00,\n",
-       "        -3.80512625e-02, -1.40937150e+00, -1.05609441e+00,\n",
-       "        -1.55699253e+00, -1.06937952e+01,  7.26015615e+00,\n",
-       "         7.25563622e+00, -8.90508771e-01,  3.33858395e+00,\n",
-       "        -4.63448620e+00,  2.72201848e+00,  2.69444180e+00,\n",
-       "         7.78187931e-01,  7.55690718e+00,  2.75735259e-01,\n",
-       "        -2.91637230e+00, -6.81530666e+00, -1.57308030e+00,\n",
-       "        -7.58241272e+00,  1.43748093e+00, -7.55627155e+00,\n",
-       "         3.19303679e+00, -5.36505556e+00, -1.15430202e+01,\n",
-       "         4.40225661e-01,  5.46767759e+00, -8.20215797e+00,\n",
-       "         3.56679022e-01,  4.81598663e+00,  1.69988811e-01,\n",
-       "         1.18402758e+01,  8.49781418e+00, -5.40500975e+00,\n",
-       "         2.09457254e+00, -1.63413680e+00,  2.38487005e+00,\n",
-       "        -5.24984980e+00, -7.08062840e+00, -4.49847698e+00,\n",
-       "         4.75647640e+00, -6.63694525e+00, -4.31203794e+00,\n",
-       "         3.76967698e-01, -4.72881651e+00, -3.61989594e+00,\n",
-       "         7.30160332e+00, -2.82294011e+00,  5.45915794e+00,\n",
-       "         5.97896147e+00,  1.42480135e+00,  1.71769476e+00,\n",
-       "         1.83458173e+00, -2.03421044e+00, -1.21695495e+00,\n",
-       "        -2.88737923e-01,  1.16490376e+00,  2.53098369e+00,\n",
-       "         3.88871217e+00, -2.18504024e+00,  3.27239442e+00,\n",
-       "         7.90604830e+00,  1.32355189e+00, -1.46008480e+00,\n",
-       "        -1.72825086e+00,  1.21700764e-01,  5.27798367e+00,\n",
-       "         8.14821243e+00,  3.93104291e+00, -6.05948031e-01,\n",
-       "         3.23243809e+00,  3.96863151e+00, -6.16936684e+00,\n",
-       "         4.38937044e+00,  3.05740547e+00,  3.85804129e+00,\n",
-       "         1.32996821e+00,  3.84759259e+00,  6.66850865e-01,\n",
-       "         9.07183945e-01,  1.48809273e-02, -3.71322846e+00,\n",
-       "         1.65257967e+00,  2.42728853e+00, -9.26358759e-01,\n",
-       "         2.15981793e+00, -1.40861428e+00, -3.81361461e+00,\n",
-       "        -1.52566981e+00, -4.59913254e+00,  6.24776423e-01,\n",
-       "         9.58626366e+00,  3.67797637e+00, -4.09784746e+00,\n",
-       "         1.54376483e+00, -5.56217790e-01]], dtype=float32)
          • tau
            (chain, draw)
            float32
            1.142396 0.82828474 ... 27.683926
            array([[1.14239597e+00, 8.28284740e-01, 3.78205299e+00, 9.73977280e+00,\n",
-       "        8.45564842e+00, 4.66784382e+00, 5.73158979e+00, 1.75484061e+00,\n",
-       "        9.52065066e-02, 8.36029129e+01, 3.42491865e-01, 2.32345676e+00,\n",
-       "        5.29000092e+00, 1.06360044e+01, 2.88606501e+00, 1.34007835e+01,\n",
-       "        2.24463615e+01, 2.03689718e+00, 6.03032410e-02, 4.65588999e+00,\n",
-       "        6.22869492e-01, 9.02841854e+00, 1.86696701e+01, 4.86623240e+00,\n",
-       "        3.72949719e+00, 3.86954117e+00, 6.17830658e+00, 1.61912191e+00,\n",
-       "        1.82664406e+00, 2.62900055e+02, 2.35055580e+01, 6.12994671e+00,\n",
-       "        3.77730465e+00, 7.43117332e+00, 4.83659286e+01, 6.05498195e-01,\n",
-       "        3.65242934e+00, 5.45612240e+00, 4.06896925e+00, 3.69497752e+00,\n",
-       "        2.64012794e+01, 1.59782636e+00, 3.76606345e+00, 3.25247431e+00,\n",
-       "        9.63840961e+00, 1.81573849e+01, 3.03462267e+00, 4.15390015e+00,\n",
-       "        3.40319290e+01, 3.98135281e+00, 3.63751745e+00, 1.45697870e+01,\n",
-       "        3.92402229e+01, 1.08645380e+00, 5.30041790e+00, 1.26662433e+00,\n",
-       "        1.36301699e+01, 6.51986897e-01, 3.13314438e-01, 1.57919002e+00,\n",
-       "        4.63338089e+00, 4.35344458e-01, 1.36403275e+01, 2.81532745e+01,\n",
-       "        3.47195888e+00, 3.76934004e+00, 1.01469460e+01, 1.81859837e+01,\n",
-       "        4.16979980e+00, 3.12015228e-02, 3.77084970e+00, 6.29084778e+00,\n",
-       "        7.24869251e-01, 9.34037704e+01, 1.16617656e+00, 9.01426554e-01,\n",
-       "        5.80987453e+00, 3.58328152e+00, 1.74854606e-01, 2.08160877e+01,\n",
-       "        1.23795118e+01, 2.25147705e+01, 4.57677698e+00, 1.01074677e+01,\n",
-       "        1.59020786e+01, 6.53844118e+00, 1.44939363e+00, 4.70238876e+00,\n",
-       "        2.56741619e+01, 7.94446766e-01, 4.58743191e+00, 3.10524321e+00,\n",
-       "        2.20093393e+00, 7.25855780e+00, 2.31834793e+00, 3.17246890e+00,\n",
-       "        1.19931831e+01, 3.59134459e+00, 2.02834821e+00, 2.18186355e+00,\n",
-       "        1.52328781e+02, 7.97296047e-01, 6.14041443e+01, 1.57116842e+00,\n",
-       "        6.10496521e+00, 5.53718758e+00, 3.43557501e+00, 9.17702332e+01,\n",
-       "        1.94792724e+00, 4.70797110e+00, 1.12761383e+01, 4.50403643e+00,\n",
-       "        2.67536659e+01, 9.36010265e+00, 1.45561023e+03, 5.11155081e+00,\n",
-       "        6.21278000e+00, 3.19992809e+01, 5.87134266e+00, 1.35119987e+00,\n",
-       "        5.08365059e+00, 7.79440212e+00, 2.13237810e+00, 3.27075863e+00,\n",
-       "        2.53759527e+00, 5.20478606e-01, 1.32904005e+00, 4.55831623e+00,\n",
-       "        1.50372925e+01, 2.66347218e+01, 2.40739489e+00, 3.38194370e-02,\n",
-       "        6.79631591e-01, 1.20179987e+00, 2.96884084e+00, 6.56440830e+00,\n",
-       "        1.18999090e+01, 6.73027086e+00, 3.88416678e-01, 6.16611481e+00,\n",
-       "        1.67719424e+00, 2.62265873e+00, 1.72751961e+01, 4.79238605e+00,\n",
-       "        7.29303420e-01, 4.98975849e+00, 1.09518318e+02, 5.20093842e+01,\n",
-       "        3.32636619e+00, 1.53864784e+01, 9.14391899e+00, 2.47286773e+00,\n",
-       "        3.43405056e+00, 4.72796440e-01, 2.21970673e+02, 1.43445349e+00,\n",
-       "        2.41525345e+01, 6.49913073e-01, 7.70250626e+01, 8.21220779e+00,\n",
-       "        1.21312666e+01, 1.81146443e+00, 6.71422672e+00, 1.33674118e+02,\n",
-       "        2.32109451e+00, 1.55294342e+01, 2.90738316e+01, 9.29705524e+00,\n",
-       "        7.08691120e+00, 5.02321815e+00, 3.89524698e+00, 1.09841394e+01,\n",
-       "        2.99719925e+01, 5.36942035e-02, 1.37703109e+00, 1.68658018e+00,\n",
-       "        3.99150515e+00, 1.12473798e+00, 9.02250171e-01, 2.73737478e+00,\n",
-       "        8.98117161e+00, 4.47370958e+00, 3.10310822e+01, 1.22979450e+01,\n",
-       "        3.94588351e+00, 3.07086945e+00, 1.74434197e+00, 8.75386715e+00,\n",
-       "        4.26136182e+03, 2.31220555e+00, 3.54982197e-01, 2.05507069e+01,\n",
-       "        1.00697021e+01, 7.91926908e+00, 2.93408508e+01, 9.00583839e+00,\n",
-       "        5.90174198e+00, 6.23531723e+01, 1.52723479e+00, 3.05835623e-02,\n",
-       "        7.82072830e+00, 1.01906290e+01, 6.25540161e+01, 1.68074262e+00,\n",
-       "        7.66741705e+00, 1.23928988e+00, 1.25125512e-01, 8.52419281e+00,\n",
-       "        5.03511047e+00, 4.05304337e+01, 7.11188889e+00, 3.62678981e+00,\n",
-       "        1.42272607e+03, 1.86320722e+00, 2.36487246e+00, 4.01817417e+00,\n",
-       "        4.12784233e+01, 3.10363436e+00, 7.90520620e+00, 5.11676550e+00,\n",
-       "        4.15123844e+00, 1.36709328e+01, 4.25427437e+00, 2.52757645e+00,\n",
-       "        2.46149611e+00, 3.67261581e+01, 2.45107508e+00, 1.50059676e+00,\n",
-       "        4.93538475e+01, 5.50493927e+01, 1.70877190e+01, 2.86450338e+00,\n",
-       "        5.81457764e-02, 8.81091595e+00, 2.62867808e+00, 2.81487350e+01,\n",
-       "        1.07280159e+02, 6.49172425e-01, 2.99201131e+00, 5.41220284e+00,\n",
-       "        5.72809410e+00, 3.19130039e+01, 8.89182472e+00, 1.60706460e+00,\n",
-       "        3.11241322e+01, 5.25158691e+00, 4.20387451e+02, 3.32977676e+00,\n",
-       "        1.12320185e+01, 5.67404976e+01, 1.25112247e+00, 2.04379535e+00,\n",
-       "        1.57966375e+00, 1.11591601e+00, 1.01193643e+00, 1.76513803e+00,\n",
-       "        2.18080544e+00, 1.79301441e+00, 1.71908607e+01, 3.09946777e+02,\n",
-       "        3.46127319e+00, 6.00123405e+00, 2.99632111e+01, 1.82455597e+01,\n",
-       "        1.78776817e+01, 2.79887152e+00, 2.63987331e+01, 6.75481272e+00,\n",
-       "        1.35175915e+01, 2.23905325e+00, 1.06601028e+01, 1.53739376e+01,\n",
-       "        3.96356249e+00, 9.25004542e-01, 5.00293446e+00, 1.35800183e+00,\n",
-       "        5.93189776e-01, 2.26463776e+01, 4.93218708e+00, 3.72727633e+00,\n",
-       "        7.28081894e+00, 2.83549607e-01, 4.21679544e+00, 1.81901665e+01,\n",
-       "        1.21280432e+00, 4.17735249e-01, 1.08885217e+00, 3.38204895e+02,\n",
-       "        5.14020615e+01, 4.05303240e+00, 3.31265783e+00, 2.31162834e+00,\n",
-       "        7.68731642e+00, 2.76660309e+01, 2.93328056e+01, 2.63904119e+00,\n",
-       "        6.05144310e+00, 1.70312095e+00, 2.34002411e+02, 2.32598419e+01,\n",
-       "        3.39389496e+01, 3.65918040e+00, 9.16184068e-01, 4.22204465e-01,\n",
-       "        1.32080593e+01, 7.56184769e+00, 5.85528135e+00, 5.01661110e+00,\n",
-       "        4.70766020e+00, 5.80670738e+00, 6.71747208e+00, 1.51498508e+01,\n",
-       "        1.18939209e+01, 8.12217522e+00, 1.05186157e+02, 1.21349227e+00,\n",
-       "        4.31718903e+01, 2.86553049e+00, 4.41276360e+00, 2.33716488e+00,\n",
-       "        2.49567938e+00, 2.59115744e+00, 1.09836912e+01, 6.47885036e+00,\n",
-       "        9.83264732e+00, 1.60747280e+01, 1.71704245e+00, 7.54926252e+00,\n",
-       "        1.11699696e+01, 2.35523796e+00, 1.51282215e+01, 2.24561167e+00,\n",
-       "        5.48041821e+00, 1.02768269e+01, 6.11299992e+00, 1.04072390e+01,\n",
-       "        1.15984237e+00, 3.18276482e+01, 3.74301491e+01, 5.90000725e+00,\n",
-       "        1.38564692e+01, 6.41441870e+00, 2.22659164e+02, 3.50071564e+01,\n",
-       "        3.66892767e+00, 3.70496893e+00, 1.72718258e+01, 5.02200723e-01,\n",
-       "        2.67812119e+01, 4.20239973e+00, 2.01739979e+01, 4.26734686e-01,\n",
-       "        5.02637024e+01, 2.45705509e+01, 1.05007780e+00, 6.42584753e+00,\n",
-       "        3.04220700e+00, 6.73474884e+00, 6.40137005e+00, 9.73407059e+01,\n",
-       "        6.12944651e+00, 5.05982876e+00, 2.88817406e-01, 3.43511552e-01,\n",
-       "        1.27810249e+01, 5.02977324e+00, 4.91624737e+00, 2.71376348e+00,\n",
-       "        3.26131096e+01, 1.79950600e+01, 2.51596594e+00, 8.24217129e+00,\n",
-       "        1.70410385e+01, 5.17923450e+00, 1.21298075e+00, 8.87988853e+00,\n",
-       "        5.11569262e+00, 9.66544056e+00, 1.56618862e+01, 1.24250488e+01,\n",
-       "        1.28370821e-01, 7.94004345e+00, 1.31174731e+00, 5.52232218e+00,\n",
-       "        3.21314087e+01, 1.15538960e+01, 8.34687996e+00, 7.65170991e-01,\n",
-       "        7.75093746e+00, 2.88230634e+00, 7.61907697e-01, 4.07307373e+03,\n",
-       "        3.73773336e+00, 4.47626781e+00, 4.07136250e+00, 3.17902966e+01,\n",
-       "        4.30423450e+00, 3.98057222e+00, 5.95249653e+00, 7.50840083e-02,\n",
-       "        1.62638676e+00, 2.41606688e+00, 5.14008570e+00, 4.28319126e-01,\n",
-       "        6.68958855e+00, 2.72210503e+00, 4.58135903e-01, 8.02352548e-01,\n",
-       "        4.14515066e+00, 1.52046785e+01, 1.11286020e+01, 1.22764349e+01,\n",
-       "        3.71391237e-01, 7.82304049e+00, 1.60837650e+00, 4.45431185e+00,\n",
-       "        2.36078128e-01, 1.93308210e+00, 6.05777788e+00, 5.88573837e+00,\n",
-       "        2.07663870e+00, 1.57697382e+01, 2.48631897e+01, 9.37386990e-01,\n",
-       "        2.68251038e+00, 4.97329235e+00, 5.82806396e+00, 1.17972302e+00,\n",
-       "        7.03372908e+00, 5.78110352e+01, 4.18201790e+01, 2.00440907e+00,\n",
-       "        5.15675128e-01, 6.46189737e+00, 4.04254258e-01, 1.52897298e+00,\n",
-       "        2.78337193e+00, 1.67558727e+01, 1.09970245e+01, 6.27413988e+00,\n",
-       "        3.16200686e+00, 3.73198390e+00, 3.54864359e+00, 4.43692398e+01,\n",
-       "        1.20014963e+01, 1.45458870e+01, 1.65152054e+01, 6.97461590e-02,\n",
-       "        2.96747017e+00, 8.71357918e+00, 1.01789260e+00, 2.15747108e+01,\n",
-       "        1.78598366e+01, 3.58743811e+00, 1.00511723e+01, 1.46134722e+00,\n",
-       "        4.21542892e+01, 3.06898892e-01, 4.91473770e+00, 8.65565777e-01,\n",
-       "        4.15286713e+01, 4.55233002e+00, 8.57823486e+01, 1.15855193e+00,\n",
-       "        5.96807480e-01, 1.66686249e+01, 4.96415526e-01, 2.34539962e+00,\n",
-       "        8.30045283e-01, 5.00013924e+00, 1.27071619e+00, 1.05359161e+00,\n",
-       "        8.96452725e-01, 1.43233871e+01, 1.01140821e+00, 3.89470482e+00,\n",
-       "        5.53279400e+00, 6.70534909e-01, 2.58556634e-01, 6.23838997e+00,\n",
-       "        3.65321350e+00, 1.51140106e+00, 6.32374535e+01, 4.83192253e+00,\n",
-       "        5.79800606e+00, 1.87330265e+01, 1.41712914e+01, 8.54477787e+00,\n",
-       "        1.19185162e+01, 1.38340578e+01, 4.10225754e+01, 2.93181000e+01,\n",
-       "        1.13749208e+01, 6.11714888e+00, 7.03764915e-01, 1.31945190e+01,\n",
-       "        1.42294288e+00, 3.44097424e+00, 1.10320222e+00, 2.76839256e+01]],\n",
-       "      dtype=float32)
          • theta
            (chain, draw, school)
            float32
            0.61433244 1.9731792 ... -21.27477
            array([[[  0.61433244,   1.9731792 ,   2.8793137 , ...,   3.1135025 ,\n",
-       "           0.36174998,   2.3965962 ],\n",
-       "        [  2.855525  ,   1.7712725 ,   4.221959  , ...,   1.8995284 ,\n",
-       "           2.4188023 ,   4.186133  ],\n",
-       "        [-10.665205  ,  -3.991663  ,  -3.7031555 , ..., -13.287186  ,\n",
-       "         -10.481701  ,  -3.5811882 ],\n",
-       "        ...,\n",
-       "        [ -3.4689064 ,  -2.3196993 ,  -1.73383   , ...,  -7.962269  ,\n",
-       "          -9.47191   ,  -6.03683   ],\n",
-       "        [  0.13607061,  -0.9721953 ,   2.647256  , ...,   0.971618  ,\n",
-       "           2.8553798 ,   2.584463  ],\n",
-       "        [ -4.5051947 ,   3.6043565 , -46.242397  , ..., -29.500881  ,\n",
-       "          39.389797  , -21.27477   ]]], dtype=float32)
        • created_at :
          2020-06-10T18:22:24.053369
          arviz_version :
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        xarray.Dataset
          • chain: 1
          • draw: 500
          • y_dim_0: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • y_dim_0
            (y_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y
            (chain, draw, y_dim_0)
            float32
            19.690388 -0.3481581 ... -34.692043
            array([[[ 19.690388  ,  -0.3481581 , -22.367306  , ...,  16.622826  ,\n",
-       "         -15.768808  ,  -6.8451705 ],\n",
-       "        [  9.477534  ,  -9.470677  ,   8.480469  , ...,  -3.1395423 ,\n",
-       "           8.926895  ,  21.148169  ],\n",
-       "        [-12.141539  ,  20.385231  ,  14.313728  , ...,  -8.920135  ,\n",
-       "          -8.540663  ,  10.570219  ],\n",
-       "        ...,\n",
-       "        [  6.1951447 , -16.812447  , -21.583776  , ...,  -6.104438  ,\n",
-       "         -19.764782  , -15.566424  ],\n",
-       "        [ 23.94809   , -19.047083  ,  -6.228318  , ...,   9.324339  ,\n",
-       "          15.134213  ,   0.15730432],\n",
-       "        [ 18.83905   ,  -3.9360547 , -30.911896  , ..., -25.75918   ,\n",
-       "          44.763027  , -34.692043  ]]], dtype=float32)
        • created_at :
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        xarray.Dataset
          • y_dim_0: 8
          • y_dim_0
            (y_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y
            (y_dim_0)
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            28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
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        \n", - "
      \n", - "
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      • \n", - " \n", - "
    \n", - "
    \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> prior\n", - "\t> prior_predictive\n", - "\t> observed_data" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpyro\n", - "import numpyro.distributions as dist\n", - "\n", - "from jax.random import PRNGKey\n", - "from numpyro.distributions.transforms import AffineTransform\n", - "from numpyro.infer import MCMC, NUTS, Predictive\n", - "\n", - "numpyro.set_host_device_count(4)\n", - "\n", - "eight_school_data = {\n", - " \"J\": 8,\n", - " \"y\": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", - " \"sigma\": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", - "}\n", - "\n", - "\n", - "def model(J, sigma, y=None):\n", - " mu = numpyro.sample(\"mu\", dist.Normal(0, 5))\n", - " tau = numpyro.sample(\"tau\", dist.HalfCauchy(5))\n", - " # use non-centered reparameterization\n", - " theta = numpyro.sample(\n", - " \"theta\",\n", - " dist.TransformedDistribution(dist.Normal(np.zeros(J), 1), AffineTransform(mu, tau)),\n", - " )\n", - " numpyro.sample(\"y\", dist.Normal(theta, sigma), obs=y)\n", - "\n", - "\n", - "kernel = NUTS(model)\n", - "mcmc = MCMC(kernel, num_warmup=500, num_samples=500, num_chains=4, chain_method=\"parallel\")\n", - "mcmc.run(PRNGKey(0), **eight_school_data, extra_fields=[\"num_steps\", \"energy\"])\n", - "posterior_samples = mcmc.get_samples()\n", - "posterior_predictive = Predictive(model, posterior_samples)(\n", - " PRNGKey(1), eight_school_data[\"J\"], eight_school_data[\"sigma\"]\n", - ")\n", - "prior = Predictive(model, num_samples=500)(\n", - " PRNGKey(2), eight_school_data[\"J\"], eight_school_data[\"sigma\"]\n", - ")\n", - "\n", - "numpyro_data = az.from_numpyro(\n", - " mcmc,\n", - " prior=prior,\n", - " posterior_predictive=posterior_predictive,\n", - " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", - " dims={\"theta\": [\"school\"]},\n", - ")\n", - "numpyro_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## From PyJAGS" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import Package" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-19T06:23:50.353834Z", - "start_time": "2020-06-19T06:23:50.196427Z" - } - }, - "outputs": [], - "source": [ - "import pyjags" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### JAGS Model Code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Prior Model" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-19T06:23:50.982666Z", - "start_time": "2020-06-19T06:23:50.979758Z" - } - }, - "outputs": [], - "source": [ - "eight_school_prior_model_code = \"\"\" \n", - "model {\n", - " mu ~ dnorm(0.0, 1.0/25)\n", - " tau ~ dt(0.0, 1.0/25, 1.0) T(0, )\n", - " for (j in 1:J) {\n", - " theta_tilde[j] ~ dnorm(0.0, 1.0)\n", - " }\n", - "}\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Posterior Model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-19T06:23:51.265216Z", - "start_time": "2020-06-19T06:23:51.262860Z" - } - }, - "outputs": [], - "source": [ - "eight_school_posterior_model_code = \"\"\" \n", - "model {\n", - " mu ~ dnorm(0.0, 1.0/25)\n", - " tau ~ dt(0.0, 1.0/25, 1.0) T(0, )\n", - " for (j in 1:J) {\n", - " theta_tilde[j] ~ dnorm(0.0, 1.0)\n", - " y[j] ~ dnorm(mu + tau * theta_tilde[j], 1.0/(sigma[j]^2))\n", - " log_like[j] = logdensity.norm(y[j], mu + tau * theta_tilde[j], 1.0/(sigma[j]^2))\n", - " }\n", - "}\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-19T06:23:51.407328Z", - "start_time": "2020-06-19T06:23:51.404376Z" - } - }, - "outputs": [], - "source": [ - "parameters = [\"mu\", \"tau\", \"theta_tilde\"]\n", - "variables = parameters + [\"log_like\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Construct JAGS Model and Run Adaptation Steps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Prior Model" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-19T06:23:51.842644Z", - "start_time": "2020-06-19T06:23:51.830153Z" - } - }, - "outputs": [], - "source": [ - "jags_prior_model = pyjags.Model(\n", - " code=eight_school_prior_model_code, data={\"J\": 8}, chains=4, threads=4, chains_per_thread=1\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Posterior Model" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-19T06:23:52.134872Z", - "start_time": "2020-06-19T06:23:52.114138Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "adapting: iterations 4000 of 4000, elapsed 0:00:00, remaining 0:00:00\n" - ] - } - ], - "source": [ - "jags_posterior_model = pyjags.Model(\n", - " code=eight_school_posterior_model_code,\n", - " data=eight_school_data,\n", - " chains=4,\n", - " threads=4,\n", - " chains_per_thread=1,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Draw 1000 Burn-In Samples and 5000 Actual Samples per Chain" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-19T06:23:52.521467Z", - "start_time": "2020-06-19T06:23:52.429977Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampling: iterations 24000 of 24000, elapsed 0:00:00, remaining 0:00:00\n", - "sampling: iterations 24000 of 24000, elapsed 0:00:00, remaining 0:00:00\n" - ] - } - ], - "source": [ - "jags_prior_samples = jags_prior_model.sample(5000 + 1000, vars=parameters)\n", - "jags_posterior_samples = jags_posterior_model.sample(5000 + 1000, vars=variables)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convert PyJAGS Samples Dictionary to ArviZ Inference Data Object" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-19T06:23:52.881918Z", - "start_time": "2020-06-19T06:23:52.827507Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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            (theta_tilde_dim_0)
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-       "        [ 0.49964184, -0.74533837, -1.70633152, ...,  0.55752691,\n",
-       "          0.61285682,  1.02246266],\n",
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-       "       [7.48864072, 8.89845878, 3.85519405, ..., 0.35845809, 0.80083886,\n",
-       "        4.63863181],\n",
-       "       [3.45441525, 0.77457384, 1.72308523, ..., 0.77028832, 0.6131635 ,\n",
-       "        0.37541366]])
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        xarray.Dataset
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-       "         -0.23516297,  0.19975542],\n",
-       "        ...,\n",
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-       "          0.61477986,  1.71560174],\n",
-       "        [-0.34456317, -0.94928476, -0.22545099, ...,  2.40872479,\n",
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-       "        [-0.61380659, -0.29968026, -1.71916073, ...,  0.43929196,\n",
-       "          0.37558874,  0.75680886],\n",
-       "        ...,\n",
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-       "          0.50188358,  1.24763116],\n",
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-       "          1.09690801, -0.35646888],\n",
-       "        ...,\n",
-       "        [-0.74162647, -0.17192853,  2.14474553, ..., -1.2676518 ,\n",
-       "          0.40364072, -0.26263897],\n",
-       "        [-0.54642829, -0.40111569, -1.66033768, ..., -0.15261003,\n",
-       "          0.18717593, -2.04277684],\n",
-       "        [ 0.38928439, -0.31714689, -0.13634358, ...,  1.44254261,\n",
-       "          0.45682044, -1.71722348]],\n",
-       "\n",
-       "       [[-0.51944123, -1.0597853 ,  0.9618371 , ...,  1.47061286,\n",
-       "         -0.13483646,  0.48622419],\n",
-       "        [ 1.42526465, -1.01343223, -0.14500896, ..., -0.10968334,\n",
-       "          0.37813614, -1.79126724],\n",
-       "        [ 0.31486789, -0.77521119,  0.06163777, ...,  0.15416909,\n",
-       "         -1.618313  , -0.33723388],\n",
-       "        ...,\n",
-       "        [-0.7315915 ,  1.33776777,  0.37092305, ...,  0.74510867,\n",
-       "          1.03921363,  0.19720606],\n",
-       "        [-0.71781538,  1.1953773 ,  0.54485766, ..., -1.24106341,\n",
-       "         -0.64573595, -0.68705139],\n",
-       "        [-0.4341049 ,  1.57701704, -0.88924713, ..., -0.28156494,\n",
-       "         -0.86547147,  0.53826645]]])
          • mu
            (chain, draw)
            float64
            4.654 -1.541 3.409 ... -3.332 0.196
            array([[ 4.65431193, -1.54134706,  3.40852221, ...,  3.08275307,\n",
-       "        -5.14290226,  0.92978784],\n",
-       "       [-0.29611118,  3.08889133,  2.86584901, ..., -4.10944146,\n",
-       "         9.75974653, -1.39819553],\n",
-       "       [-0.58853395,  4.14783675,  4.62984626, ..., -2.42436949,\n",
-       "        -3.31313771,  9.50384399],\n",
-       "       [-3.68876703,  0.54715647,  7.39943174, ..., -2.92143299,\n",
-       "        -3.33214817,  0.19597932]])
          • tau
            (chain, draw)
            float64
            12.77 88.9 31.67 ... 4.114 26.26
            array([[12.76642906, 88.89765381, 31.66746203, ..., 13.09897376,\n",
-       "        88.31134139,  4.12752449],\n",
-       "       [ 0.50596232, 12.51825709,  4.12346651, ...,  7.30589409,\n",
-       "         1.35717493,  0.50805488],\n",
-       "       [ 2.08673563,  4.73909692, 75.18967022, ...,  8.31522156,\n",
-       "         1.3542986 , 11.60239688],\n",
-       "       [ 1.91847522,  0.77704296,  0.64164736, ...,  1.23623608,\n",
-       "         4.11407372, 26.26295554]])
        • created_at :
          2020-06-19T06:23:52.835882
          arviz_version :
          0.8.3
          inference_library :
          pyjags
          inference_library_version :
          1.3.5

        \n", - "
      \n", - "
      \n", - "
      • \n", - " \n", - "
    • \n", - " \n", - " \n", - "
      \n", - "
      \n", - "
        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 4
          • draw: 1000
          • theta_tilde_dim_0: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • theta_tilde_dim_0
            (theta_tilde_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • theta_tilde
            (chain, draw, theta_tilde_dim_0)
            float64
            0.6259 -0.05823 ... 0.933 -1.011
            array([[[ 0.62591075, -0.05822593, -1.34667486, ...,  0.5220049 ,\n",
-       "          1.49657226, -0.09110078],\n",
-       "        [-1.63058644,  0.75384376, -0.07248947, ..., -0.51070233,\n",
-       "          2.01420918, -0.63685846],\n",
-       "        [ 0.56332043,  0.15225759, -0.43446575, ...,  0.75181526,\n",
-       "          0.91067242,  1.04358195],\n",
-       "        ...,\n",
-       "        [-1.29342749, -0.38839071,  1.27008443, ...,  0.04984877,\n",
-       "          0.83977179, -1.00655611],\n",
-       "        [-0.23436997, -0.0567252 , -1.29407051, ...,  0.59762695,\n",
-       "          0.31896628, -0.60612625],\n",
-       "        [ 0.44615354,  1.27849237, -0.48100964, ..., -0.43710145,\n",
-       "         -0.53003482, -0.16956221]],\n",
-       "\n",
-       "       [[ 0.77292342,  0.41728015,  1.30116281, ...,  0.60598961,\n",
-       "          1.75784329, -2.23638839],\n",
-       "        [ 2.25214026,  1.91018121,  0.13392948, ..., -1.83242983,\n",
-       "         -1.10470279,  0.31154014],\n",
-       "        [-0.18460234,  0.53429385, -0.35811612, ...,  0.93326105,\n",
-       "          2.23085295, -0.6855231 ],\n",
-       "        ...,\n",
-       "        [ 0.56559632,  0.25455658, -0.84359086, ...,  0.24238882,\n",
-       "          1.43498743, -0.13700032],\n",
-       "        [ 0.5926399 ,  1.41338219, -1.40345751, ...,  0.75477275,\n",
-       "          1.52278466,  0.2743515 ],\n",
-       "        [ 2.13964096,  0.46433539,  0.53753241, ..., -0.62288982,\n",
-       "          2.39142079,  0.6179569 ]],\n",
-       "\n",
-       "       [[-0.62357813,  1.21312606,  0.02332749, ..., -1.85795028,\n",
-       "          1.40440496,  0.13550857],\n",
-       "        [ 0.40808923,  0.07567456,  1.17306305, ...,  1.25301819,\n",
-       "         -0.3578069 , -2.49587944],\n",
-       "        [ 0.44775523, -0.04736244, -1.11623165, ...,  0.67507359,\n",
-       "          2.07246375,  1.78842195],\n",
-       "        ...,\n",
-       "        [ 2.2371135 ,  1.11187675,  0.27148003, ..., -0.17553131,\n",
-       "          1.29880533,  0.19596854],\n",
-       "        [ 2.2936293 ,  0.92676865,  0.87846553, ..., -0.17362181,\n",
-       "          1.18790425,  0.82398055],\n",
-       "        [ 0.1900121 ,  2.19559171, -1.5187373 , ..., -1.89672507,\n",
-       "          1.26775356,  1.15303549]],\n",
-       "\n",
-       "       [[ 0.15815182,  1.26705501, -0.3804165 , ...,  2.3467277 ,\n",
-       "         -1.48880315,  0.32744594],\n",
-       "        [-0.82407985, -1.3273879 ,  1.04150429, ...,  0.00393478,\n",
-       "         -0.29381189, -1.9999881 ],\n",
-       "        [ 1.53612173, -0.79165433, -0.61818957, ..., -0.21186912,\n",
-       "          1.41784152, -0.10586098],\n",
-       "        ...,\n",
-       "        [-0.49140065, -0.87372717, -0.85024702, ...,  1.46580137,\n",
-       "         -1.09679543,  0.18858381],\n",
-       "        [ 0.66825142, -0.285897  , -0.90993501, ...,  0.2777644 ,\n",
-       "          0.08081879,  0.04777291],\n",
-       "        [-0.09543308, -1.14055406, -0.18135868, ...,  0.92126407,\n",
-       "          0.93304363, -1.01143416]]])
          • mu
            (chain, draw)
            float64
            0.1665 6.307 ... 0.3566 0.9405
            array([[ 0.16648025,  6.30650492,  5.58715254, ...,  3.40540871,\n",
-       "         2.96770844,  3.48107398],\n",
-       "       [ 7.9186115 ,  5.73733931,  3.63883791, ...,  1.27584629,\n",
-       "         0.26084865,  7.28096009],\n",
-       "       [ 8.02057312, -2.61108692,  4.11972209, ..., -0.75871149,\n",
-       "         3.5817588 ,  2.08628888],\n",
-       "       [ 3.23880708,  9.33523099,  3.94528447, ...,  4.84944117,\n",
-       "         0.35656092,  0.9404811 ]])
          • tau
            (chain, draw)
            float64
            6.621 5.507 8.88 ... 5.691 11.58
            array([[ 6.62127708,  5.50691977,  8.88018874, ...,  4.32440909,\n",
-       "         2.74299773,  1.72296131],\n",
-       "       [ 1.2860264 ,  5.92674038,  5.87171746, ...,  6.90253812,\n",
-       "         5.20568187,  9.63475622],\n",
-       "       [ 0.48961787,  4.15905179,  5.70170925, ...,  6.27740286,\n",
-       "         5.46124308,  8.38574835],\n",
-       "       [ 0.11407355,  0.36389414,  2.92227942, ...,  5.86402037,\n",
-       "         5.69135206, 11.58006309]])
        • created_at :
          2020-06-19T06:23:52.833729
          arviz_version :
          0.8.3
          inference_library :
          pyjags
          inference_library_version :
          1.3.5

        \n", - "
      \n", - "
      \n", - "
      • \n", - " \n", - "
    • \n", - " \n", - " \n", - "
      \n", - "
      \n", - "
        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 4
          • draw: 1000
          • y_dim_0: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • y_dim_0
            (y_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y
            (chain, draw, y_dim_0)
            float64
            -4.874 -3.559 ... -3.417 -4.61
            array([[[-4.87405047, -3.55928752, -3.75610755, ..., -3.34526017,\n",
-       "         -3.53549629, -4.04800178],\n",
-       "        [-5.71772909, -3.25172905, -3.84648857, ..., -3.34253866,\n",
-       "         -3.22333208, -3.93994528],\n",
-       "        [-4.30059756, -3.22714981, -3.73520612, ..., -3.84106725,\n",
-       "         -3.31509087, -3.82188337],\n",
-       "        ...,\n",
-       "        [-5.65212065, -3.41834852, -3.96800579, ..., -3.34522021,\n",
-       "         -3.82246864, -4.06800461],\n",
-       "        [-5.09190928, -3.35609457, -3.70294736, ..., -3.37059593,\n",
-       "         -4.22367894, -3.98582382],\n",
-       "        [-4.88048426, -3.24834599, -3.75392695, ..., -3.32917208,\n",
-       "         -4.41228074, -3.92911746]],\n",
-       "\n",
-       "       [[-4.43660741, -3.22255987, -4.00120886, ..., -3.56170211,\n",
-       "         -3.52734472, -3.88401093],\n",
-       "        [-3.80359726, -3.6318046 , -3.86895295, ..., -3.47175565,\n",
-       "         -4.99059421, -3.83940783],\n",
-       "        [-5.06577285, -3.22901377, -3.73171482, ..., -3.58920164,\n",
-       "         -3.22948967, -4.04607251],\n",
-       "        ...,\n",
-       "        [-4.78422679, -3.34488241, -3.69620193, ..., -3.33252961,\n",
-       "         -3.45402413, -4.01947127],\n",
-       "        [-4.97770541, -3.22225146, -3.72348599, ..., -3.35888263,\n",
-       "         -3.7029022 , -3.97337818],\n",
-       "        [-3.62701283, -3.29201318, -4.158344  , ..., -3.31715678,\n",
-       "         -3.98064711, -3.81166336]],\n",
-       "\n",
-       "       [[-4.54136819, -3.22341193, -3.92923215, ..., -3.4711435 ,\n",
-       "         -3.65321184, -3.83294022],\n",
-       "        [-5.48478683, -3.751598  , -3.74572475, ..., -3.32741603,\n",
-       "         -5.66340221, -4.77316679],\n",
-       "        [-4.63777553, -3.30764961, -3.69264145, ..., -3.51751195,\n",
-       "         -3.24281775, -3.81759345],\n",
-       "        ...,\n",
-       "        [-4.10819858, -3.23734807, -3.72193116, ..., -3.35064781,\n",
-       "         -3.78391602, -4.01441398],\n",
-       "        [-3.94126385, -3.22359131, -3.9444332 , ..., -3.32786085,\n",
-       "         -3.53601215, -3.8330031 ],\n",
-       "        [-4.94138394, -4.00251971, -3.80581287, ..., -4.22430408,\n",
-       "         -3.36105552, -3.80940265]],\n",
-       "\n",
-       "       [[-4.98748556, -3.32809116, -3.7664943 , ..., -3.34279486,\n",
-       "         -4.33620131, -3.92675687],\n",
-       "        [-4.42622836, -3.22515487, -4.00725304, ..., -3.60402367,\n",
-       "         -3.60623595, -3.82707173],\n",
-       "        [-4.47769568, -3.42429034, -3.74310318, ..., -3.33919308,\n",
-       "         -3.71270153, -3.91726968],\n",
-       "        ...,\n",
-       "        [-5.13292746, -3.56382834, -3.707543  , ..., -3.95681847,\n",
-       "         -5.13883436, -3.8656967 ],\n",
-       "        [-4.88999855, -3.65124185, -3.69801243, ..., -3.320465  ,\n",
-       "         -4.69788199, -4.00886596],\n",
-       "        [-5.38975973, -5.27532201, -3.69814217, ..., -3.78190055,\n",
-       "         -3.41713716, -4.60956267]]])
        • created_at :
          2020-06-19T06:23:52.840708
          arviz_version :
          0.8.3
          inference_library :
          pyjags
          inference_library_version :
          1.3.5

        \n", - "
      \n", - "
      \n", - "
      • \n", - " \n", - "
    • \n", - " \n", - " \n", - "
      \n", - "
      \n", - "
        \n", - "
        \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
        xarray.Dataset
          • chain: 4
          • draw: 1000
          • theta_tilde_dim_0: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • theta_tilde_dim_0
            (theta_tilde_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • theta_tilde
            (chain, draw, theta_tilde_dim_0)
            float64
            -0.7112 -0.4008 ... -0.7134 -0.2611
            array([[[-0.71123136, -0.40076082,  1.86888641, ...,  0.8115354 ,\n",
-       "         -0.24211776,  0.54841302],\n",
-       "        [ 0.73392039,  0.47663744, -0.61609627, ..., -0.80964506,\n",
-       "          0.49762657, -2.47327004],\n",
-       "        [-0.08231233,  0.28062108,  2.20405144, ...,  0.154088  ,\n",
-       "         -0.36351211,  0.36013468],\n",
-       "        ...,\n",
-       "        [ 0.79194157, -0.40861463, -0.85875295, ..., -0.82237095,\n",
-       "          1.47173464, -1.20540583],\n",
-       "        [-0.45090569, -1.01004541,  0.08397328, ...,  1.07454531,\n",
-       "          1.13587261,  0.53209085],\n",
-       "        [-1.05886621,  1.66164192, -1.4250031 , ..., -1.11740703,\n",
-       "         -1.84886498,  0.71930243]],\n",
-       "\n",
-       "       [[ 1.4125556 , -2.34874433,  1.20299551, ..., -0.25531169,\n",
-       "         -0.26411039,  1.13222391],\n",
-       "        [ 2.2267223 ,  0.02884086,  1.13865955, ..., -0.28934517,\n",
-       "          1.05906399,  0.55190591],\n",
-       "        [-0.23439601, -0.36784748,  0.47789977, ...,  0.74236118,\n",
-       "         -1.1212007 , -0.6610916 ],\n",
-       "        ...,\n",
-       "        [-0.77174629, -0.97774201, -0.73963299, ...,  0.20137311,\n",
-       "         -0.56425354, -1.75796363],\n",
-       "        [ 0.66127056, -0.20695707,  0.53286237, ...,  1.5300409 ,\n",
-       "          0.01747712,  0.54043691],\n",
-       "        [ 0.25492446,  0.42637825,  1.19142405, ...,  0.51370606,\n",
-       "          0.1491775 ,  1.21679596]],\n",
-       "\n",
-       "       [[ 2.99620537, -0.58296821,  0.72585938, ..., -0.75381363,\n",
-       "          0.27957383, -1.30275569],\n",
-       "        [-0.4803946 ,  0.40327934, -0.76396762, ..., -0.40640857,\n",
-       "         -0.75089157, -1.39522304],\n",
-       "        [-0.72583985,  2.36262907,  0.96360172, ...,  0.61588395,\n",
-       "          0.05045338, -0.43853064],\n",
-       "        ...,\n",
-       "        [-0.2673493 , -1.00842011, -0.56629967, ..., -0.89029002,\n",
-       "          0.00340085,  0.18408985],\n",
-       "        [ 0.07223788,  0.86688325, -0.0114382 , ..., -0.80392428,\n",
-       "          0.25731739, -0.1457207 ],\n",
-       "        [-1.97737722, -2.78400193,  1.61941847, ..., -0.87962511,\n",
-       "          0.23703341,  1.24248917]],\n",
-       "\n",
-       "       [[-0.02165655,  0.82049726, -0.58873389, ..., -0.56971679,\n",
-       "         -0.59921277, -0.33330181],\n",
-       "        [-0.15985731,  0.32461587, -0.20852091, ...,  1.7119704 ,\n",
-       "         -0.2676015 , -1.50186474],\n",
-       "        [-1.77468599, -0.9329657 ,  0.12679348, ..., -1.27709638,\n",
-       "         -0.078269  ,  0.90316797],\n",
-       "        ...,\n",
-       "        [-0.40739721,  1.38197559,  2.08072841, ...,  0.52669903,\n",
-       "         -0.68106617, -2.93601848],\n",
-       "        [-0.48058994, -0.00859444,  0.68029096, ...,  0.721311  ,\n",
-       "         -0.89124688,  1.21101712],\n",
-       "        [-1.6756115 ,  0.1954323 ,  0.5773097 , ..., -1.97671135,\n",
-       "         -0.71340016, -0.26109113]]])
          • mu
            (chain, draw)
            float64
            -0.5426 -10.87 ... 2.281 -4.709
            array([[ -0.54256157, -10.87124355,   1.96438294, ...,  -8.11403829,\n",
-       "          7.60765481,  -0.48159106],\n",
-       "       [  0.03072765,  -1.82026456,   1.38368157, ...,   0.96176535,\n",
-       "          3.89488577,   3.99766504],\n",
-       "       [ -2.62546345,   2.2632889 ,   2.55843055, ...,   4.86710568,\n",
-       "          2.21386016,  -4.73052159],\n",
-       "       [  0.32067926,  -7.23833533,  -2.82193459, ...,  -5.77218015,\n",
-       "          2.28055004,  -4.70868173]])
          • tau
            (chain, draw)
            float64
            16.53 3.872 9.523 ... 2.702 8.149
            array([[16.53391268,  3.87249575,  9.52339625, ...,  6.50219713,\n",
-       "         1.35029408,  8.34708719],\n",
-       "       [ 9.1205841 ,  9.21082813, 15.29874336, ..., 10.20594881,\n",
-       "        26.05528672,  0.70675771],\n",
-       "       [ 0.43585432, 20.06679265, 17.90884847, ...,  5.9273813 ,\n",
-       "         2.68746482,  5.18154874],\n",
-       "       [ 1.08752471,  3.25281493,  4.26600184, ..., 14.58456325,\n",
-       "         2.70201427,  8.14868071]])
        • created_at :
          2020-06-19T06:23:52.837977
          arviz_version :
          0.8.3
          inference_library :
          pyjags
          inference_library_version :
          1.3.5

        \n", - "
      \n", - "
      \n", - "
      • \n", - " \n", - "
    \n", - "
    \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> log_likelihood\n", - "\t> prior\n", - "\n", - "Warmup iterations saved (warmup_*)." - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pyjags_data = az.from_pyjags(\n", - " posterior=jags_posterior_samples,\n", - " prior=jags_prior_samples,\n", - " log_likelihood={\"y\": \"log_like\"},\n", - " save_warmup=True,\n", - " warmup_iterations=1000,\n", - ")\n", - "pyjags_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{toctree}\n", - ":hidden:\n", - "\n", - "ConversionGuideEmcee\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Raw Cell Format", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/doc/source/getting_started/InferenceDataStructure.png b/doc/source/getting_started/InferenceDataStructure.png deleted file mode 100644 index f854a71891..0000000000 Binary files a/doc/source/getting_started/InferenceDataStructure.png and /dev/null differ diff --git a/doc/source/getting_started/Installation.rst b/doc/source/getting_started/Installation.rst deleted file mode 100644 index 9e4986369f..0000000000 --- a/doc/source/getting_started/Installation.rst +++ /dev/null @@ -1,122 +0,0 @@ -################## -Installation guide -################## - -This section provides detailed information about installing ArviZ. Most ArviZ -functionality is available with the basic requirements, but ArviZ also has optional -dependencies to further enhance the library. This guide will cover both basic and fully-fledged ArviZ installs and several installation methods. - - -.. tip:: - - If you are installing ArviZ for the first time, consider installing with - - .. code:: bash - - pip install "arviz[preview]" - - And using the library through the {mod}`arviz.preview` module. - While some features in ``arviz.`` are not available - in ``arviz.preview.``, most have already been included. - Additionally, ``arviz.preview.`` include many new ones that - are not available in ``arviz.`` and never will be. - - -****** -Stable -****** - -ArviZ can be installed either using pip or conda-forge. - -Using pip -========= - -.. code:: bash - - pip install arviz - -Use the below pip command to install ArviZ with all of its :ref:`Optional-dependencies`. - -.. code:: bash - - pip install "arviz[all]" - -It is also possible to use ``pip install "arviz[preview]"`` to access -:ref:`upcoming refactored features `. - -Using conda-forge -================= - -.. code:: bash - - conda install -c conda-forge arviz - -.. _dev-version: - -*********** -Development -*********** - -If you want to install the latest development version of ArviZ, use the following command: - -.. code:: bash - - pip install git+https://github.com/arviz-devs/arviz - -**Note**: It can take sometime to execute depending upon your internet connection. - -.. _arviz-dependencies: - -************ -Dependencies -************ - -Required dependencies -===================== - -The required dependencies for installing ArviZ are: - -.. literalinclude:: ../../../requirements.txt - -and - -.. code:: - - python>=3.10 - -ArviZ follows `NEP 29 `_ -and `SPEC 0 `_ to choose the minimum -supported versions. - -.. _Optional-dependencies: - -Optional dependencies -===================== - -The list of optional dependencies to further enhance ArviZ is given below. - -.. literalinclude:: ../../../requirements-optional.txt - - -- Numba - - Necessary to speed up the code computation. The installation details can be found - `here `_. Further details on enhanced functionality provided in ArviZ by Numba can be - :ref:`found here `. - -- Bokeh - - Necessary for creating advanced interactive visualisations. The Bokeh installation guide can be found `over here `_. - -- UltraJSON - - If available, ArviZ makes use of faster ujson when :func:`arviz.from_json` is - invoked. UltraJSON can be either installed via `pip `_ or `conda `_. - -- Dask - - Necessary to scale the packages and the surrounding ecosystem. The installation details can be found `at this link `_. - - - - diff --git a/doc/source/getting_started/Introduction.ipynb b/doc/source/getting_started/Introduction.ipynb deleted file mode 100644 index 9f0b78652f..0000000000 --- a/doc/source/getting_started/Introduction.ipynb +++ /dev/null @@ -1,3569 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(quickstart)=" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ArviZ Quickstart" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import arviz as az\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "J = 8\n", - "y = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])\n", - "sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])\n", - "schools = np.array(\n", - " [\n", - " \"Choate\",\n", - " \"Deerfield\",\n", - " \"Phillips Andover\",\n", - " \"Phillips Exeter\",\n", - " \"Hotchkiss\",\n", - " \"Lawrenceville\",\n", - " \"St. Paul's\",\n", - " \"Mt. Hermon\",\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ArviZ style sheets\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# ArviZ ships with style sheets!\n", - "az.style.use(\"arviz-darkgrid\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Feel free to check the examples of style sheets {ref}` here `." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get started with plotting\n", - "\n", - "ArviZ is designed to be used with libraries like [PyStan](https://pystan.readthedocs.io) and [PyMC3](https://docs.pymc.io), but works fine with raw NumPy arrays." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rng = np.random.default_rng()\n", - "az.plot_posterior(rng.normal(size=100_000));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting a dictionary of arrays, ArviZ will interpret each key as the name of a different random variable. Each row of an array is treated as an independent series of draws from the variable, called a _chain_. Below, we have 10 chains of 50 draws, each for four different distributions." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "size = (10, 50)\n", - "az.plot_forest(\n", - " {\n", - " \"normal\": rng.normal(size=size),\n", - " \"gumbel\": rng.gumbel(size=size),\n", - " \"student t\": rng.standard_t(df=6, size=size),\n", - " \"exponential\": rng.exponential(size=size),\n", - " }\n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ArviZ rcParams\n", - "\n", - "You may have noticed that for both {func}`~arviz.plot_posterior` and {func}`~arviz.plot_forest`, the Highest Density Interval (HDI) is 94%, which you may find weird at first. This particular value is a friendly reminder of the arbitrary nature of choosing any single value without further justification, including common values like 95%, 50% and even our own default, 94%. ArviZ includes default values for a few parameters, you can access them with `az.rcParams`. To change the default confidence interval (CI) value (including HDI) to let's say 90% you can do:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "az.rcParams[\"stats.ci_prob\"] = 0.90" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## PyMC integration\n", - "ArviZ integrates with PyMC. In fact, the object returned by default by most PyMC sampling methods is the {class}`arviz.InferenceData` object.\n", - "\n", - "Therefore, we only need to define a model, sample from it and we can use the result with ArviZ straight away." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import pymc as pm" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [mu, obs, tau, theta]\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [mu, tau, theta]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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    \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "with pm.Model(coords={\"school\": schools}) as centered_eight:\n", - " mu = pm.Normal(\"mu\", mu=0, sigma=5)\n", - " tau = pm.HalfCauchy(\"tau\", beta=5)\n", - " theta = pm.Normal(\"theta\", mu=mu, sigma=tau, dims=\"school\")\n", - " pm.Normal(\"obs\", mu=theta, sigma=sigma, observed=y, dims=\"school\")\n", - "\n", - " # This pattern can be useful in PyMC\n", - " idata = pm.sample_prior_predictive()\n", - " idata.extend(pm.sample())\n", - " pm.sample_posterior_predictive(idata, extend_inferencedata=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we have combined the outputs of prior sampling, MCMC sampling to obtain the posterior samples and posterior predictive samples into a single `InferenceData`, the main ArviZ data structure.\n", - "\n", - "The more *groups* it has contains the more powerful analyses it can perform. You can check the `InferenceData` structure specification {ref}`here `.\n", - "\n", - ":::{tip}\n", - "By default, PyMC does not compute the pointwise log likelihood values, which are needed for model comparison with WAIC or PSIS-LOO-CV.\n", - "Use `idata_kwargs={\"log_likelihood\": True}` to have it computed right after sampling for you. Alternatively, you can also use\n", - "{func}`pymc.compute_log_likelihood` before calling {func}`~arviz.compare`, {func}`~arviz.loo`, {func}`~arviz.waic` or {func}`~arviz.loo_pit`\n", - ":::" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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    arviz.InferenceData
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        <xarray.Dataset>\n",
-       "Dimensions:  (chain: 4, draw: 1000, school: 8)\n",
-       "Coordinates:\n",
-       "  * chain    (chain) int64 0 1 2 3\n",
-       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999\n",
-       "  * school   (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
-       "Data variables:\n",
-       "    mu       (chain, draw) float64 10.02 7.399 6.104 1.803 ... 1.041 12.6 10.12\n",
-       "    theta    (chain, draw, school) float64 8.304 4.128 12.45 ... 12.92 10.8\n",
-       "    tau      (chain, draw) float64 3.041 3.737 3.529 1.581 ... 3.22 1.696 2.607\n",
-       "Attributes:\n",
-       "    created_at:                 2023-12-21T18:42:25.932752\n",
-       "    arviz_version:              0.17.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.10.2\n",
-       "    sampling_time:              5.5613462924957275\n",
-       "    tuning_steps:               1000

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-       "  * school   (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
-       "Data variables:\n",
-       "    obs      (chain, draw, school) float64 -4.287 -7.086 3.44 ... 11.22 39.41\n",
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-       "Data variables: (12/17)\n",
-       "    energy                 (chain, draw) float64 57.32 60.94 ... 61.22 58.55\n",
-       "    step_size              (chain, draw) float64 0.264 0.264 ... 0.2667 0.2667\n",
-       "    index_in_trajectory    (chain, draw) int64 2 -4 -5 5 3 -4 ... 1 0 -8 2 -10 6\n",
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-       "    process_time_diff      (chain, draw) float64 0.001172 0.001172 ... 0.001192\n",
-       "    ...                     ...\n",
-       "    diverging              (chain, draw) bool False False False ... False False\n",
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-       "    n_steps                (chain, draw) float64 7.0 7.0 15.0 ... 7.0 23.0 7.0\n",
-       "    lp                     (chain, draw) float64 -55.39 -55.34 ... -55.68 -51.57\n",
-       "    step_size_bar          (chain, draw) float64 0.2845 0.2845 ... 0.2817 0.2817\n",
-       "    perf_counter_start     (chain, draw) float64 1.286e+04 ... 1.286e+04\n",
-       "Attributes:\n",
-       "    created_at:                 2023-12-21T18:42:25.944471\n",
-       "    arviz_version:              0.17.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.10.2\n",
-       "    sampling_time:              5.5613462924957275\n",
-       "    tuning_steps:               1000

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-       "  * school   (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
-       "Data variables:\n",
-       "    theta    (chain, draw, school) float64 52.13 -71.41 148.5 ... 1.115 6.39\n",
-       "    tau      (chain, draw) float64 120.4 7.113 1.983 2.866 ... 8.423 6.926 12.31\n",
-       "    mu       (chain, draw) float64 -2.798 1.822 -4.905 ... -1.888 -4.516 1.978\n",
-       "Attributes:\n",
-       "    created_at:                 2023-12-21T18:42:18.246297\n",
-       "    arviz_version:              0.17.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.10.2

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-       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
-       "  * school   (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
-       "Data variables:\n",
-       "    obs      (chain, draw, school) float64 78.06 -75.96 125.8 ... 2.925 19.95\n",
-       "Attributes:\n",
-       "    created_at:                 2023-12-21T18:42:18.248182\n",
-       "    arviz_version:              0.17.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.10.2

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        <xarray.Dataset>\n",
-       "Dimensions:  (school: 8)\n",
-       "Coordinates:\n",
-       "  * school   (school) <U16 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'\n",
-       "Data variables:\n",
-       "    obs      (school) float64 28.0 8.0 -3.0 7.0 -1.0 1.0 18.0 12.0\n",
-       "Attributes:\n",
-       "    created_at:                 2023-12-21T18:42:18.248964\n",
-       "    arviz_version:              0.17.0.dev0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.10.2

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    \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> sample_stats\n", - "\t> prior\n", - "\t> prior_predictive\n", - "\t> observed_data" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idata" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is a \"trace plot\", a common visualization to check MCMC output and assess convergence. Note that the labeling information we included in the PyMC model via the `coords` and `dims` arguments is kept and added to the plot (it is also available in the InferenceData HTML representation above):" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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PJD+8rXaYk1tn1Ym1iwlTcv+LXZf33mU9fa/892EL+cmcXR/N4lVEePHqH3Bu6817Jq60fcXCw5KFq94GqnNzy0vinV2fNrHLRxffvXMoH9224/PjQHdWjvxbttwnW76zMKk7+uydq+jOMnr3KvHlP5n4WxHqW/Sr2rvuRDkf5tt/eY18Ywj50dfIjSVh4DW04R1GsFaFo1uJSNHlF4jP/dvb2++dNeO2WOtd5731n7LSvfox7P3uyLyY3Yhdid1P7CHNbYlLlW0+hvlwMkz9No/bf+dVm5+u9DG7rh8nvks/RFcGsSsQCHwuEBFGZ3Zvmq+roPnlYySX2thByL4aCAQCR2HlJjfNt3EDPkz7LLev8MHGz1jpXCHWMdOX/gS9fclvUYQxfjyLhrdXf8LF7fcOvH6YiLe3537e7gJ/f5tNNysdTJHy6sXnwNm10rvIdnaBYrVy2KksRED46BaIxcJzo78yubi7B06QwxK5R0uvEa28deR70lS4dPnja9OdUqTP+fPf/vBjNbrxKtH1V8DeJJHbJ4wVyzDrY63BWgMmg+HBh542ybHJ3YsFo8SFQ6epHBiL+01Vuu2SGqlRZ3K7m4zhs+fcz5npu8jHV93tLZxdap8r7TA+bDjmzUhzp+Ql+aenmFTiq8o24qZ74RMOY7z5Np+A2FWm7Lqz465uPnhtg2xtcPAP+8fjHcxBQewKBAKfC/KtEWYnofHcrcWu1pePgcDo7N7H37BAIBAAXnvtNb7zne/w7W9/m2effZbvfW8yv5GI8Du/8zt8+9vf5qtf/Sp/42/8DS5cuLeZf28qdt3iDtyK5Xr7Ap1kl/ZwCyOGB2YendjmXoUxGsndA5KR+6/Xk4pL5zZ3sv/m3ErpYBKvdWkOEbtEiM/9G9Te9bs/gE8R+xe8xgjkk6Xjs2rOro9ogViEDNZ0Hb117iPZ551S5Lm6m6qFo0rE48ddROB2MDnUa9BoqEMdGXdCKZRknx6h4mfLP+Tl63/Mmc03OLP1JtHyz4iv/eRAyNfW/3SJzh/e/bW5teVyse3uuX93OsJ3vy90u+POLLQIqU+7X0btyeviiH4vtmm14Pjxu9A0JsSFO3zvLXb3cXDtuvDWW5+eMEbj5/eadmGMny5nV9Ul9TGIXQbuNEH9rZgY8jL53aDuoHxxELsCgcDnguSSi0FpfmH+ltu2vuwEsVCRMRAIfFIMBgOeffZZfuu3fuvQv/83/81/wz/9p/+U3/qt3+Kf//N/zokTJ/hbf+tv0buNWuL5zmj8tPQjxN4svPAWoYejfEBuMh6cfYw//eiv80uP/TrPLD4/sY36mJ1dR7YtyXnlp/DiS+6/Iu/S6VPuNv6wsLQDHBqu51QPqbvjqlZjLELDXC4cQW+c+RBHcGuKPs0l4QeXfo+17sckru3vquEu8cXvoiqhZMZ+9C7qQuyKdIwa7t2kQTdHb11A9Tfvqg3FOb0bsct405Pi43XIHEVyqT1RaMHYcThlsc78sO1S2d3NSf2+3P2ccMj7jDV0k72JbdRwh4EZ0hltszfc+lBz0GAwFrMin66vEDC3fFL6Ijk9HNKvgw4yurU7sRhnjz/mxt5dO7vUR1ON8eOm050UhQuMNewNtw7+4WOmmN+LnIyfnNh1O9vI4b9/RBgDqvjO/xDOrgmqY9DKZH9GQewKBAKBCZKL7rF84zbErviBKaL5OqOzIW9XIBD4ZPjlX/5l/t7f+3v8+q//+oG/iQj/3X/33/Gd73yHX//1X+eZZ57hv/gv/gtGoxH/+l//61vuu/fiKv1X1++4TRcvCZ3u0Teu5naeEB9xJ5vkzt1Ri+rjbfaJHsXT249j0ZAfIbCsrAg/esnQ68FTT8KXn3f/ff2rsOiNwfsXkWvrzp2x128z8In2DywWRcYOppp/yRyiFtxGfjLV2yC6/vItt7sZ1gJpTvT2ZUiE1VuIXb2ecPas3J7QV/2cImdxEcboQ7LUYPwwqRo2muRDMjPp/LoT1M5lVHeV1BTFAGpQDXW6w4WY3jpPdOOnd9UW7cWh7C6i9YocOPX6rUUl1d90uZ28U8oYYXPTCUJLS0Knc/CYX7j8+/x06QdH7nP43s5EoYU8H4tdhQvxw16V0dJr6JuEch7arqHwk1fg4qVbb3so+UF1xMghJ0iEn+2d4fWlF3hj5UW2igqqd8FLL8MrP4ULF4WOd4gWQz7SrhedWJDRyDbH59tfB50f7dL54xuVth3+OYXYVas5sUu4wwcFflMV6Y9EEPkknlEc9t1wfutt3lh5kWH28eVeO4zC2RVHboL/NCXPn+BjyIHpdnlnzq6KmdnvY78buvoBkwK3CmJXIBAITJJcaqOnY+LTrVtuq5Si+fximTg5EAgE7iVLS0tsbm7y7W9/u3ytXq/zp/7Un+LNN9888n07O3d/UysiXLkKr7129DYfJmdX/13nlol1fXxvbCrhbEYgK/I83f1x7O4Kb7wpDAZOkNrZcfsc5eOcM8VNdJYL27tw4oThmz8PTz+leOhB99/Jk+rIxNw3/Dr07NoZVntONNrpr9NJxt8h1srY2bUQISJEN/aK+I9KHqRbLxT0yptOLPoQuVeshXijixqkRCsWdYtETMsrcGMZrl67ww/ar/kdpu9VNnpj5UVevHp4pbzbYXDpA9KLPyMZbKGGO9RUjDIp4hegH0ai6aUdru3efkhkIQ5ld6HdVcWuwzTRKnrbJfdSvsjD1Wvw1jvQ3nPhcq8ecg1bsfSS9m23x5iK2FW6EG/77UeiO8t3tPgeed2y07n5dkdySJ4wc8hr2+keqc0oDnKUf3hn7NVrsLud+890r0WR2/9gAMf6b3Oy91Ns5gQ55edDGe1rX2VuNUa4sSRcuyas++cZ9RrEUfH3WzTqsKT0kQL58GLNvXAkAnT9vHvow5h9Y01EkOyjaWhehjHW3b7v4AJpt4Usu7v+vq3zNHHcd39eRYRstX/gc10xxkK1uktn174HKRMPg6T8PwBU/fardsR31JpAIBD4jJJcatN4ev6WN/QFzecW2f1nlxCR235PIBAIfBxsbjph6Pjx4xOvnzhxgpWVoyuLtTvCk08skk07h8bi4q1zFhZYK0xPpyhgcdHnIBGXd2p62uWSmZubZXHq8H3KKEKmpyFuoA/53OXllEajwfzsAgsLC+hahMSZew+w89IQiXOmvz3N3Pwci7O33/YqV69ljBLLu+/B9DTs7mmefrqGGtrxcSzMEeuYem2KRsPwhS9O89Sjxw7sazgyTE/nzM7WmJkZPy+em8vIcktzqknUrDM9Pc0b7Tcg1fy7X/oPAOjs5jTqdeyXaix8/TTTP6zTaOTEI5DT08zNaRYXa0g2cn2g44l+q547Oz0NNkctLKCiu7uVTxKh1UpI6jGtVouFuYWbjo+pVsb0tCWKXDtvl2arRaNhUSgEoTZVZ9pOw9xseXwz/Rn6Mj3xvjsZqwV26U0ur7mx2qy/RWuwxswjX2c6n4bWAgz3fJ/dfvutHyN6cZGzV1+nnezwzPRXmKrP3PK9c7MpWgutqQhSmJ6eLo+rut/D6HTcWDt+XDMYCAsLNS5tf8Dji89Q29d+u9UANY2an0fNLtJq5UxPG6y4vo+mp1lYqE/cyxRj/6h+3j9nTE9n/t81cmOZmkoZ6jUeX3zilv1QtnP+OCCoxceRtO+Ep+46qm5RM8dv+X6Abs/1y8LCnY3DgnJeAtTCAgBxosr+iBpOBD8zukCj0eDPffmv8/LSi9RbNRYXF9lgY+I83g7T02M32ZSt0aBBpBXT0zHHFmeZnnYPWefiIVo3mJ2eZXFxBrvZAD3NoCGo6fH1MT+3QDzrBJXVNcONpbzyWXDyZI2MDtPTLebm6i7P2hHYd34PFh5FzT+InT+FmZ4mmqphBhmL80dXL7/V+AWo1913CIy/Q26FWLmtfEy7dp5Go0Gr0TpwLqZ3p5HEsLiwyExjjtH1DrXjLXQtQ87+EerRnwcWWVxcpP/+Fv0Ptln8tcepLTZvq41Ht2mK6eE0xxaPMz2cZn5+npnG3L7jMyB2Yg4aDIT3z6TMzSn+3J+tTx6LHzs36z810yftjc/TYWNThppGw+1jYX4edcT39q0YXtqj+36f5tQsjcfmyvM7OxszPfDX1fwcaubW+5+ZSYkTYWGhzvS0wo5y8unpQ7edmpmlsbBQXqczc7Pok7cneAWxKxAIfC5ILnWY/uap296++aVFbC8jW+pTf/TWN7WBQCDwcbNfeL/VE12TC7u7u/T77kns7u7tu1WNEfp9F2qwu+tcDZe23+fa3vlym53dHUiOuJUcdYj7fSTKMId8brffJktzRsMRuzu76HqE6mwS+bb2dxOG0yOSV9rs/doe9fzwm+BbkSTuOAouX4GZGajPbrO11WOvDV+a2WRupslee0CSwGC4y+5u/cC++j23r+1tyLLxueh23et5t00zGdHv9xn0BohVZZ/v7KyTJAnJlKHdbaO7UyQJZL0+Ziai2YDdXQXZyPWbjst+W1xcnDh3UX+Ashn57jZEB9t5O7y38hbb/VlqyQiGQ3q9/k3Hx8amO8ZOx7fzNun3hySVyLHvX/q3/EpzkanaCeyU+7xOt12O0YI7GasAmIz4xvskibsmttprJCahs71KP1NYPYvu98l3d+6oz2Lfrnx3l3xk6ff7XFo5xyPzT93yvd2uMBjCzg4wA/3+uI+r+z1wKEZ48y1IM5ibdX1+cXmNd9ZeZWN7jS+d+vmJ7aP2LiobYHY2kbxejvndPdf3o36f7e0+USX051Zzwv6/7+wIrZY79522cL19np0r7wPf4vTMI7fsC4Co10VmH8Q2H4Imbo5Yu4zZ2UKyWy9Jd3aEn3kj62BwZ+OwxM9LAObGOaLl19l+4Mvl8TYSJ+rlScJCbZasn5Enhs3ddU7XXV9Uz+Pt0O+P5+nGsEuUJoiFfl/RVrv0+3X6fXh41EOJZXd3l9ZcRtTehWzEaKiwletD7+4S5W4Mb/nr8vQpWPdRp+u7u/z4ygvke19je/spWq2j+ynu96F/FpbPki18iX5foVUN28+It3dRtcPFrpuN306ySz1qYJJWOfcW3yE3Q3JL+w+u0XxugeazNxdL9tq7JEnC0A4PnItur8sg7dNut0njnPYPrqJizcIvt4j6fezSOeYXH3ffjcvbZP0+5tzKLT/zVuzu7TAYDOh1+vT7ffb29sjqFXeZyYgv/DHSWsA8/mcBF5b745+4P/f7B8d0v+fcTTfrv16nS94fO5UPHZujNomfiPf2duGQXGdHsb0tHD/u2jVc3yXp9zEbO8TTpnJ+Ybq4rvb2but67vWEJIXd3T5pqjD9zF2HYl3oeW2q3DZvx8S7trxOe90uOotZuI32hzDGQCBw32NHOdlSj8bTc7fe2NP8kvvSC6GMgUDgXnPy5EkAtrYmk+5ub29z4sSJI9+X3ySEZWewMZEraT+H6Wjr/aWJf988jLEIQThckMttVibyPSyMEUAhRBuCfIjSYIclB3/nXbhyzbC5BVvbcGPZHYfJ3W1xXDs8yVJ0RGhQoUEaMeO2locvYM1EHioRW/avSvd/1m0c6/5kJ3fA1rZ7z432ZVbTN1yojYB6LyFbP3pBVYSP3elHVreP7Ii4fYOhSfZtc/ROr+9dYLV7G7GTlWTnqSQMfc4u8aF9xN61cZcRPHr9fRrKjdeJZOZH0PFCF9x5n12/4YQugDieDAc7NNdccd0UYW/+84pzhsjBkLJ8hNq5fKAy5n6WV4S1daHXB28MQSkwkoBAZu5g1YxU43VBaYZDYW3VcO2aCzW+GcPxer68Fu+cSijUwM2n0lub3MT341fmvggiNOOpjySMEUD7/GDF3FwdG0WCb/ETjDIZkuHyjFWLJFTeU4yTVsWUJDpDKejmKzcNJbTGYit5klQRPulF0TtJUi8+p5KI8PrSC7y+9MKdzxU+nDC97oquZOuDsrjUfjLfgbaab00sevkNSIdlm8h9KHxe7YjxGFRxcax31tbDyG3mCmKow3NNqsTnK6wUzKgm2D9Mkmy8s0zj9VsUD7mtMMa7S1C/siq88Rasru4LUdz/4K3Sf9GNV4ku/8kt913m/tsXxtjQV2lyAZLueGMrk+kM7mBsBbErEAjc9yRXuiDQePrWyekLms8sgCIkqQ8EAvecRx55hJMnT/LSSy+Vr6VpymuvvcY3vvGNI993VL6WXtrhrdWXuLD9Nt1kjx9c+j36XhTYHW7yg0u/R3d0MCmO2nc7fmhi55JbuM5s7pKHVzfdn6Def579EMlfqom1q1y55pLQA2USfpNFKAVRfPjnHSV2FRhrKkKU+6FX3yI+/4eY4k3aLYKa3zzt/tlPIc3HN/y3tRApVgm+ndnowPtGZ3fJNocTr62tOcfQ8rK4Uu4+tBABtZLRf+XoIgYfhdjVSlcrBbYquYJuMlYubr/HmY03bvk5RWW/rZlv0o1ny3NaJMSX0iVwd2qX3r0KPWeduR3h49VKTvuj+ixJpBQfq1QjxyLt8jtp5Qbf4QKz20eR46m4XGxFcK1eQmrvBtHGGfSojeouH3kMw6HwwRl49z337yce9+9X4KToO8tLtH/T5RWXaP7iJeH8Rbh+izV9XIlaLK/pPIH05onIJ0Sbao6qmsvhajtLIJYnFp+FPEF3llFK0dA1EEszbpHkw7urUJj2eWjvj4iNa6Pyc6a1LlS8mpeoOCbJrVvoi8V6AV63Kw8aKs3Ic5efq9o3caTRGjIZHMj3lm0OsYOMPBf+5AXL+QuVPxZ9oysqhElvqQS9vfoT/uTyvyz/A1cN9Y67a9+F0n91neF7O5je5PdCP+1yZcc5jMuHCyLo1bfR3VVoX/d/m+xfOeR7RKoPJe6oqcJ2d4cf/SQpnXvGGmJdG39Pirh2rb/vxugtPkMdosqoYYa6SdK+PBfOnROuXb9V+yuiZuV8Rtd+gl7+2ZHv6vvvyLTQxCtaV/Vw9p9rld6+OLw/QX3z+IBoWk3eD8i+uUaYeMBxM4LYFQgE7nuSi+7JUOMLt+/s0q2Y+pNzwdkVCAQ+Efr9PmfOnOHMmTOAS0p/5swZVlZWUErxN//m3+S//q//a7773e9y/vx5/v7f//s0m03+6l/9q0fu0+RHuKq8y2iQ9VnruezqO0MndBQumr3R9i3bnFZcHRu9ZS7vuLZjDWb5NQZmdMQ7XTLfqHB2FYt17zSTfYlq7S0SCGfrA8ze4Q6TLINWUZfEWMj8Z2DKheBO2/D2O8LurqLR4EjHW7EY3e+YK55Q2wlnlzsG3Vnxx+SFQeWSg+uTU5jFKXR7SOP91TsTu1RF7DIZ8aXvuwVVhdG5Pfo/mXSsFC6CYUUbEwzKgjpspeUxRsrFzJ3qjpOH4wWoAwLJnSpoFrVzBb15Fr15Dr15HrV3A2uFJD7GRutLXEuexaY1bLLnktPr27cCdbvC9Rsu8XcV6/Oj3anL56hTeuEivPnWwddrFeFCR64OwdgtMnkC0pU+7R8PnIvFpAyzPqu9K/saMCnQqmHlviYeW4Ikt3S+v0S+7as6Zjnajq+rol1KgUI70+IdWWImnV1nLxbqjtvH6AiTWKcrXL0mE5pLqctsfEB8+QX05vlD32sHGe1/dZX0WuESqZ4M1xaT9tE7l3lw9nEeajoXbU0VrlOhphukJtnnDro9VDZAS07NtEEMsXVjJ4tmMGbyWnjyCffTWIPuuOqPpvHgwZ1WBlSWufNSFg/Azy8aUukfEOb7P1mj870lhkOXPyqb1BPcPqJyQiO+8F3nlroJvbTDbGOBp489X742VZ8Z71AEKtVXb4kfI5HPS5ZvjEX7S5eF91ffKWtzWLwLrrMynmuLa31f8nlJzcT+i2MEwAijfHDb1/b2cJ0fnn+B8zs/5cpV91puMyI1nmeyjSH5+i569yrR0uuHiobVr5rbSFV2gJ0d6PVuo2DDxMRdEb6Gu+ju2sHtPcX4iSLI1gbY4fgBV3WXt5oGVGflyI3K1hTf+8VXUeUDRGTiWhGTE62/ffMP9YScXYFA4L4nudQGrag/fvtiF0DrS4vB2RUIBD4R3nvvPf7m3/yb5b//8//8Pwfgr//1v84/+kf/iL/9t/82SZLwD//hP6TdbvO1r32Nf/JP/gkzM0fnFMzSWy/OitAA5e8wi3C79BbhTdVtAd5bd1aWp459CdVZ4ZXVl8kzw1949NuHvtdITkN5t80+t4/1u9XxeNF18MNHIAbq06UjaeGvPXlgszyHqSm3IKi/t8rp6ZTH/saT/MGP3HE3mzDjc49EseaBByfdM0k+pJPscnL6oVs6u6w1SLFg2df11venUgrVWYb4C2RPneAJtcP62z0v8CkQixkKo+Wc6OSI+NCkyZVVUXH+OsvwwFcObJne6JV5J8fRj044iE0PZfpgvYvuiNVWvs/Ad/acsLwMv/oXbr062y/0HCb82OLFfIhuL2OPVfJh2Qy9fZlIVcJirEXlI2+FkHKnWX2Ortmhb7cxuklk61hr/JHvj5k5mlcqrqwnH6603a9MR/ngzorXTBiLxu87qin75RgBfvYzhZwSzD61cfTuFiTC5e1liAxLo+tc7Qw5LU9M7LD6NlvNWVZZgJpOiu1lDN9zQndt630eap1nafGvAJTVSJ3Y5Sr23U0o7Rjtj9G1Idkndm1tC7u74wqgzz1bOQbfbJU7YU7vXMKefObAJ5ieG7zpSp/647MTf1O+ep4Rg0q6ROhynR2rsRBXjxuuOEeeHgg1K8Q+fZRY7MdMJCmtbI3IJuzM/yLkCXn+1oTbyOXW8ifLJEjcIO9XluqDbZg6PhH2m2Uu1DWqnBtj8/JSPlS3FxcSWvS7tYLWauzaid3OTMcpYbq3ftPgahHLXHORxxef5dTMI7x8/Y9pxlPl0JhJrhBfP4t59BeR6aPD7g9QhFP6BxRJIly+AvHpeqmLyCFOR/GOYYudfHBS/n5Q7BIrvHztjwD4C0//9Vs2LTcpUQSp9Mr50YhzKxcPDgavbLDeW+G5X8yJswGIkO1aaovaXTdKTTw40XdhQWq3YWLGOHJKurswxqJ9ZmdI/0rF+btv7rt8BR5ZFOK42rfGqfXDXaKVN7ELj2MP+Y4qmibGzeVK++PYF7Y4Ee5+B09dgtgVCATue5JLbeqPzqCbd5bkofmlRdr/9jp2mKNbYboMBAIfH7/4i7/IuXPnjvy7Uoq/83f+Dn/n7/yd296nWe7C0/vmveEe+vILEGcoVGWxFoEIw5F7Aj/IegfbsO/f6U2cW/JGnfoogn25q8+cFY4dK0I+fNuSPvG1H2NnXBER8SXYaydq0AN7SPKx+NL3Acif+ytHtgFcTpsFv7bXw5QHnlZMTSlQbp/zc/C1r1kWWgq5pkhyNfEE+fXlH5LkQ37lqX+P2H8NHJYHDCAXi6jSMjWB9SEZsY5h5zJxvwG1X6O+2KTe6JFnBmMtEUK6bkiWQJ/bY+ZPP3D0wYkdi102dwJgbVIcG7yxeaDIivU5w6bT68wkKda4ypMqOnylVBX3rIUbS4dudii3syYpBCDVXkYlXVQlLE1lIxeiWJt2Dq1iv1PHkYXHqjshSeD6B7/n36ih9QTCRZSpCrd3L84UIqiIkJoRjbh1i3cc/EQrdsL9cej2vs+mp2DeZ18YDBRRdtDZpeounPDa9hqm0YXjX8Ba59gb71Am9GJr0+o/qo2bbLBvSPOVK+QPLzB619L68jEv1ikfCnv7/anEIhVRSPyC+cFTluE07EtJyIUL0KtEKFZrGIybrSfaeksmche59+RTi9C7SmRSijNTq7hOa9pNIGma0Ch348bsD6/8T9SjBn/28b90xOe586DtCKXcHKCmj2Hba87Z5fv8l/+sgatO8NjaMMzanNa5jHTYK+fd2ugGWXNxYkBlGdTrlbBO5cZI0c1HXX/DoaC92Fd1lartS6iFh4Em/VfWaD13+PurWDGlTNiqTbPQOoFUxlzNeFddPv6+yPcSlFJE82Ph9YAw7p104h/alC4jqU84U4vjLt9Xisn7nF2Flbci1NyqyMtRWJEyjLf4PshMSi2ql2GMu8MN2oM1rg8Tnpp+hHR1yPCtjOlnY6Iv5hDVJh+c7Jt+V1bHbbuxJJw9B7/yy0wISp0u5XiwVtBHzOFra3cndhXty1YPOt6qu0lSWFmBxypTMiYF3So3LHKWFRzM2WVdhVbtpm8ldjzUDzzwuv1jCGGMgUDgvie51Lmj5PQFzecWwQqj83sffaMCgUDgYybvH1Rloo0PUCZDJW2fd8PfiKKQtTPIyptgM4bZIXlw9j3Nzczh7q9e37K7oknS8Q3p2+8I3/u+cG0p5Qc/O0eW54yGMcZImQtJ+5/Fw3rdKOwJdxA+NNxDL71W3kHn3vkwsUnWZy1zIRBxPF6wF31RXQAluQuhyWxKHCvqdRd+9qMXhXxfmKgxeSlGqGpS8a7F+BitWCm3fy9+Ra0IraDb3+SHV/4VO4N1l5QasB3Xv5KnROf/ELV90f1BVRb41fwrN171L/tjz8X17z4EvxBVYJHS8SCZpf1vrmEHk+Mmn9RNJji7+SZnN2+dU2tq1aITdcQ6S4hUVFm+HIyPMSefxT70jfK/CaELQKmJYaKJyWaeIZ8+QXb6q8j+ZGrVAxrsoJd/Rnz2DybP6b7GVkP2zM2KM+w/ukNCOSf/vv9z3M9f+DnDQpTy819350wsyGCb6PIPy51qr23GVxsoY5zIYCdFMZXkmGT8b5snlZXm+DgO6EUCcs0JIrXlPdIrbfKtwUTOLnuXYoELSxo7uxoNlxdo4tpLnJBTUIhdrVZ1SjgktvEmqGr/e7En9yJ7lI1Qfh+FIKnEUo9cJ2dpRdw347kiyY8W/Utnl02IbIpVNRpNjaDJDWWC+Frk2qIVDIeGK2dysh1xTs8HE+wzCVPPxJD1J4ZQnntnVxHGqMBYWzq7Ll+BK1el/G9j0/239f01amfWJ7pNdTdQSQfdveH78/bmXSu2zCnnjkE5YXifo7G8lvsZvR+u0H/16PA5d3AW8qx0dhXzkFaNss2lC7diYRuPIam4udxDE7tfNCkO0b9eVHO9NeM8eFnm3HS9tM1sfcGJweITqls7/n7xxUhMT8r2Vl2z+421738w/v36NbePIneWHWSIFfdv32RjOPAd7d4jnL8wIbnf5jGOP88M98fuHyJO7n9z7q2aZQWXw58SlcKlEbA5Fy/DtetMjD+xk2GMiD3sUA8liF2BQOC+Rqx4sWv+jt8bKjIGAoHPMnZ4eOU2QZwLiPGi+Ow5+MFL75DazOXcukmlxoIiZ9f+xXqR1LbqgNrYdDfDq8nbbGQfsNeG1dWIixc5eJfs73EjH5tjDxFsjiJafduJZmm/zDW1X+x6Y+VFZr3Zqdk4KFwc5lYpquA1/OI7ScdJ293axrqn/YdoKp3XM/LzfVAK7WQLLM5J1xhcRklONnIr+bfWf0pn6H63Q4MdGdi7gbIG3duXQH6f2FU6J/zC7cxZJ8wVjFN92VIQs1iUcT/BCV77n+IX6caiaHKBszvcZKVzlZXO0dUSrQVlhNllw/z5WiVitbIIFetCf0oRb/z+69cNe20BfWt3dVWUi1QNEU0+/zAvvvcIbxXpXfaNVbVzmfj6y2Xemj/54SHHsPC4f+thC+pD2rFPBC3FhME26vorB7Y/sGj0p7T//Rt0f7jiw5tcUQG2L6PSXinUFNYLNdKofhGWBucGf1AWkGi8t8Lw5dXx8dh07JKrjp/C+VJdU26PnTN6/X30mR/4MMai3XcidnmFtWz2WLSt191f19ddAYU8F7IcHqoYGwuxK46qyfdvkTjoZgtiv30a1YmUpvv9G6UDs64rObuiwtk1FrXEuMqDIy+OpPmI3eEmo3xAvy/s7ro8Y722OweRjNCSYHTDzUdenFX+QJQU1QVBi0ENM0Ax/a3jvHP8DG/Xz0AEpIOJRPllGONEzi5ThsR1ukURAPff+rr7b7A6QndGvJe9w9WBy3WlUjdxS714OHwb51bE5wgbSwpaRc49aoVHdv+A6XSJ7R2c0wfK/IqyP8y+OK5inhpl6I33YMvNL6YcihEq6RCbfpmzqyraFufVip1wdr35ZiEgHRbG6Nxu5y/C9Rvutd3hJrvDShXMiabacvxnuctxKSIcm3LCKbkXgitFXFTNF1zJpLx+J3Lp3USV0eUDGTf3dL67xOD1DdIU4sjP5ZbyAdZEYn7ZJ/LeQhQejcZ/H/ivAjM6JLn/vt3sv9R0fwO9dX6cEH//w7HiPBf/9mJXatz3683DGOXm13aFEJcTCATua7K1ATLM78rZVXtkGj1TC2JXIBD4TGL3P40FilvLolpS4VZZ3tojM5d5CkAO5gY6jMyLXXujyfgjO/ILcChzgundAXahhSVj7qrBGGgSk2auSZ2OWzQcP6bGN7V+cWDyI+IGD6NYxNusXEhUxa4kH5HkQx57VJW5amyRGL9MHuJ+GjteqBRiV7Vb8hwY7jHV38Ty+NhtYG0p2IkVrBW63QzdVCgMaWZZ29U0ZYNGcpXGaAadPYLas0SrlpWVEQ9HU0SA6aYw7z5batOUHesbs7JqeKTIudOcm/hMODzk0uIXW4WzzzC5rvUWgzNnhelpaHn3UC2ePP43V358cOeHUaxhc3WoqUAQl/OofFRfhC0Jw4FFp9BJO8zWpm6aJ6u6cHSV2CLS1CAp9I5IQaduWsmvaHhRFdSM8wjdRAgo3BBfeR4uXRm7d/TedYgO5iyydjJfj7GghinKCmg1Ee5jxSBGSK93qT95rHSLSBSDzwvn0wGR2h5zzQXnPGmPO0DyFHQNSFE2H0ctemFg7HCs9JMCbI5NXMXSIi/RHYWByVjsyjK3U0FxbFEYeRH5XV9nYdWbfmZn4df+AvzgBbcA1soJO50OZJkQTYQlGmBfiKjs/1ldMBsSm7HUv0FTaWwvZTaaYrE+zxemT/vtLfWo4ducAlPlfq5ccWLyF58y/PjKvwId04hb5Nd/oxwDayPDPM7ZpZTFqjpxhHO1iRNEtKJ0vbhxYFEjN9b0E09js8chG6BSn2eresi4PqmOHytmwvXyq79SHIpMJDJf3WxziSGX+jf4wtzDB6riIYIZCrrBkRTOKl3xzyi0H6fjyafXheOdFazSIF4Q2m9l2i/6pokbLR13D15c39YK8d4l5kawE00zzPpMVZ1dlUmmKvpkI3HDoxrGWFbeECewME72Xsxvh+fw8k5LhHRgSyf0dG2WQdZDpf6ArHWuaSNkawN/XIxdhTdxdk3GHsu4D/zL6UqfdBYi3aFn2lh7GiLF6MwuyYU2U986Ra8xxdQUTHbu0dfs1rar2Pv1rwpzcy4NAIBJDFSjtpMOensN+GL50n6xTm+5Up/mtBtAyh7h7LJO4F7MrHM8lwnqJy3Fsk/8ul2CsysQCNzXlJUY78LZpZSi+dxCSFIfCAQ+k5hRPrEYdb97sSsfuRALfwPZzm8wftRqDr2XVJVHqUopMn/zemX37MR2EyKbdQ8d6ufWiVbcfNzasu4ptF+YiriwhZUVMERjZ5dPlHxt+3wZTngrCseKMplbSGQGtdHjz/1ZePYZ6Cbj+VzrQrjwi/zi6bn/mVduznO/cPvy83D8WPEaxNdfZrp3DmsTJwT5nDllB1pXMWtzM8fkCi2G7V2h29doyYka/ml8YogvWvRVy+5aznrPu9q6GYjQP5ORLBXOEtfurW3hwgVh02uNUlTWsy4BdcHwJx8QXflRJUe7RfJC4LNgxqKFyYWXXoGtLWFpGc6dHy8y49rBNYbesOj1w4VRK8V5rpyfwzYU8WFQk0lcXPJlt+/XV3/MRv/oZGG9nkyIXY+2vonYiL22X4zryX2PD2C/OFIViL1o6LeRw0LmRGDUnthFsYCt1cbV8QpGieXceZm4LvfryuZah8bbyyjfZu10Ee/qMAyvGAZvbpGuD5AkhQhERSif4yjetsxp4eGH4PHHDn6GNamrWKeYSJAuhSNt8geIRSvjBJ7dFUZ5nyJnV+kINJb0WnfCdXQo/jRkvo8ee1yxMC809gkqu3vuZ6Ph5ppTJ8fv19oJXz99DczNYmwPfa2qFFmGZgQoHmk96MahWB5qnqSha+X7Y5+zK6sU7RAjtDvu73r1bfSWy7eY5MOJNXrh2IpswtDscC4/g9ZuohC/n0a+SbTiQoGfeByaNYsaZez2NLpRQ1rHkLlHnBriBsHk4ajJMEYrhihyDrgvPeeEcK0VWoHSCqUVuaQs9y9M9oj/pf5ATLTYABHar6T0z+yrUFHBetFGV66jSEfue8XPmfWaEzdV0iXaOHNI/qVDTk0+dq2KqboBD4bObg/Wx5V8dVx+p+3P2TU+MdUEX2Oxq7gUon1TwmFYsc5clFmar1+j++4eg6EQR3UnyGf+W8Sf/8GFnHTdh8Tv2fIphKl07QFnV/Wi9aJdnlP2XzHPbObvsT67hDw0BUph9tw4vfp+ws/ehL02Y3dV9ZgPYW/P/ez1Jys87nd2xStvEG9XKqCKHB1WWHyPHvG5GxvwwVlYvjF+CGOi+gFn12Bo2d2rzLshjDEQCAR8JUag8YU7F7sAml8+xvD9nVvfwAUCgcCnDDvMDz4dFrBeEIqWXitD+HJJ/CaCElMugo+a+SIdl86nsqJj4YBJxqFPNh8vOHq9a+TiBBtjx9tXE0/3h5XcL96RpYwLEwEXChZdfqHcJl3Z58wpFql5gjFQP7+OeW+TurLU62pCeCg4ysWWV5xdmU/qPT+vyqpweQZF7pDIunaI8uJGEV1j3SJNJAc0CiGKxIkTktFsKVSzBsMclYOSGplk2LpC1bVzdoklWbMMPigULOU/34KYg+4tkdJZEkWQvX0WlXTRWbfYAPHHZhGUGQt8SQqiFVeujnf30tvLWDGc7f1rrvYqpQqB6LolulERTJKE4e9/H7Ozx/KKZTgcV4rzTTuA4CsUFqslvyjd2naLtEYdULoMm93PaCS8/KoLxY1UnV/++lOcnjuJNYpuz43PZvOInF37Vpi6InapInTQL5dsRcUoRB69dY746o8hGRd0yDLQNiWOvfhQGV9r68L1G646aNEh+/vE3OhMLByrYYypzbiyu8K5rbf54U+v8Oqbm1xbM3R6sQsDEsvMOcPiOzlzc2PHz6ShKQUVIUpPJKuXzEJ/ww/sShdhWey/Q2IzNvf2eP3iH2Elc/v0O85WBwze2mL0/s7Ee+0w39eAwq3p/hXHEVhzQOwqaHk3yUkvdlk73tVgCB98sN/ZtY/9924THZFTyPrHGsc4EBbsNiorLZozlaIdxi3uY3/dKy+ExTqmWXHAFGKXlpTl7D060iejhxQJ/sUyP7rAdn+Fs90rtFqKU7Mpqp+z040mc0ypQuyaPBylwJKS2oEXuyxKKb70nOLBB6tiT+XXGHQlhM/94j9GC81nFsoX0o2jXb6F2KX2ObtMVexqULqmAKRQrQ7okJUHMxNiVxHO7j/TuNnqa/HP+zZYsDmi3QVXfB9Z7ETOLlWGSVbDGN0PY0z58aVLzualA2vv96/Qf22j0lQn5Ec+5O/cq0MuX45IRgpQqMx/L/rr244mjy+94cZSVas9oN3Yg23/4CxsblSEL6BRc1+U/bzvRLuR+8Og7bfL4MCgOYKyCEA0ro46PeW+01VjUgWccH6SH1XMF5UfPm8XDPzXmkmsu/fQCqubEwKdWOGNt9wDGME/LLjNpF1B7AoEAvc1yaUO0Xyd6NhNfNg3YeqrJ7CdjPRq99YbBwKBwKcIm5rJvCjuUTPGh7sp1ISzw9uSiHKN3Z9PhUlnV6xrPm+JHBSQKonpR9czTDshkxE7yQVG1j2AsMYlMobJHEejRJWLryLn8XAg/NGPNul0BL3+/kTo2WBiASKI8gqZSciNS85dfVLvQnwmb5LLJMdF+JZfGBQ5jxCL6S6X2xf7y3PnJABQtl9GaVmxKHHv+5P11+hJD8SgiNBSJCxWfPnZ3DmOpuqoQQaZEFMnxyevn6lhexlpNqKfDydWFy7xvEyGNRXtN8IwG7CVnZ9wI82s/pB2eg5jLcpmiIKc3C8IveOvyCXsu2Rk9lhKfsrZwf8EOqOdLZPY8aJfZUwsos3yMtlKm/SPXuDs+30XZjXR2RzApSov6s2PN3LV+fwTfKUnnHZVilCb4dCNgSjSrvCAxAyHYDFYe9RKbHIppKT6GW6hKn71K9hy7JSFHUbO/qAqScrfvPovGGz/P6klmyjtBOQyEXnd/Vzp7dG2e4CQGztR7EFyOSB2FUUFRCxbyS65Sbm0c4ad5BJ70R6DJEYZ5Rb9Mg6JKhbuE5q3SbySrEuxAkCGPXR7GbV12R+je71mOn4fFt2JaL6as51dKnrIt9mdsyJUC1zFvc4f3yC9Pi4ZV1TKKwRaHWkQmUhEH5k+X3l4iV/6RUqRslH5ezX0y+S2kuvNfYa1QpoWQs7RC3slltRPNpGKvdh1UBxTSqGUwvYrH+z3q8QwGo77oRY1JrRCVamKaSXHqhpxFLk2C4hPJv9e5xLLow0GZoR5bRvVz0HrMmcSFONw0tlVrPnf3niBi8M/cq4YqX7mpGBQoOdrLhl/EW5dEdFym3G+8+5kvqrRZCW98f69mFdNUK+1E3rycZ5DkUry9+xw8aNsnfLCq7XohsLud3aZDOw4dNIUIqV2/VqGu4mU49L9+6Czq8xdaPLye6e4ZqK1d4nW3i23zSoPVsTPDTpzod55zRKrOkniH+LkXsRHUKJ8dcFKqOUonTimieMvWmmqE6v7fTh0uRhhfB1MN6YQBb2064aHd1YXD5mU3rf3mxQeyEsRejxn1COLyQTdqnyRls8OCjE3P9ppdUQhm2LzPAdtE7Yut8nEIFEDq6JJ8Xrimnb/DgnqA4FAABfG2PjC/E3zfNyM1tePAzB4a+sWWwYCgcCnC2sh3RkvLMStlhEVYecfcSFW/kZU2YzYOnGi9WNF/cVDwgYr82jsRSUrZiJsBECS8U3q8HpGcqmDlcwJQBXiImGvqSw+RI8dBr7E+qALuYy8+EFl28kdDl7fZO+7u+693tmF+MVLEa2CpabrE+8rFmxWLGSC3fYhJoVrob3E7vWXynC1mjePZTlliJu2vVLsclW4cAKXVXRtByQHpVDWuDw9kWZxzi86pmJ0N0WlEJ+cIidHmkAjQlLLW6tvcHWwPF64KsUHZ+DCebsv8bAtDoj1/lUS2yaTAcWyYjPbYX30OquDc6VNwu4LYwRQ4kJ6hmaXRMYPeooE9ZeG3x0LhKlzho0X1X5h2re0UicQVp/4H7rOEnFhUJVcZACDgaAx5T6rTrsqZsLQI0RaucWaiclznAtpX4gkOJG1050cQ7qSULp0Xamxs0uX4tikeFM9sMFwgz3ZJU53UMBe2/L2uzmr665fR2aPt9rnuGQuAMJbqz/ixat/QD91fS3eNUSeQMXwhoDqafR2zBeOPc8TjT/PVLJNGqfEtagcc6XbpxJaVK7/TU6eerFLa6QaR+WtHDLoT3TVTHLNu4fGx2jJfA4xf+37ao+9oeKqrxxn/NxTzRdWnEtfnJRaXYHYMsw02urx9blXeJR3mJken5uqGDaR1BvLpWsR/b6UY/qNN+GHL5YNnWRfjq/ci1E11QCx7Oxa2ntyYPtIRVhliefdg1Pxi22NoVsJ+apF9TJEEyadggaDqBitCxkENz+RMxu7XGDb6R5aO/FZ4oh+RewaC63j11Iz4lr3TRLvMJuemsxtlHdH2HTSSRWfbBGdbgCCsk7USCUv99vLuqyny6x0r5b7ia++yGEUYmlUTVCPdmPFuwanpmBQf5j1or7GEWJXoa5Ea++h1i8CBl3nQBijywWmSrHLWovpp7RfTjEjxs6uYh7GzW/Ku7yWBsvltVaGBJq8FHeifepINe/XuKnuWo5zw1NPKh77okGrGnnuHwrZYk4VJ0JHlCdO1yC53mPv968gmaUW+wjVA6bCQ1xpOLc0jOtJNGvuOygzOZLbUuCzXuzSalKHitbfg+HugWOCSWdX8RysrpyLTTcryS+95trKNqiZDlqymzi7blKtFPew4qH291jcfYuVdYNVdURVq1C4OXFcNZnJi+AWBLErEAjc1yQX2jS+OH/X7288PY+erTF86/CKLIFAIPBpJtmuLCz86mpkC2cHiF+QLA5dZmgxEKEn7iWz9QHZ6mS4YL9XQ0QwNi8XwdGSZfdfXi4T1ANecetjJS8XGwU1v6qo3uRbUeUCRUXw2MIXqFFHXFr1Sfa9kK30xzfBJiVPc7RNvRNr7PTQ+/I0lTfR2YDaBwbzahvJbemQUPmQrXSPkU9C7HLgFE/B3R2+sv0yZ5fFEF80xO9lkMPADhjaNjLqOGeXCJEy46TUC3HZ7Hi+xd6Xe6SncozSSGZo95zIJvsEJYUP+wAvyBSKnpTCkMYlfpfcOwwkpZftlp1ufDXG4r3Wul+zHK6MXmA5eb3spyKKCsBIClaoSQ2MlK6rIu+TzQDvklITYtfBcEKLuDCoMg+W8Y5Bt6KyPrebOUrs8i+fPAHHjrlw1SiCdOSqPxqyyrpp/LnvvgcfvG+8S67o2WoYo++PUmQwpYNlLA4eFNGsL8kZaSeIJN4lmWWwvWsZ2XaZ70ohtH0euVdvfI9h1kdyi3ogYqp5DrobXqz1lUXPNdFK04gaNEyEloi0lqGiGKwCOxa7xIfYVpt37v0hZy9YiOuAxlbEPSnKi+b7+tkLbqZwQRVzh1QEbi+oXLjo/jNGynBmVY/IM8vlK8J7PgH9aOQW1HEtKsdis92ndnETveKF9kr4UyEww2ToFwidXszlK5QhZ0W+L38y3E+ToTrLUM39Zw15IWbh7EfdjtCpGvmLAhsqwlpbCvBixs6u4njcfmqTYtxyD7tRXGsGq2royKniIk6o0RhqPvw6UYLGosQikebcOcaCrNbsd5918hU2R1eJtOKJx+G550djIRrov7xOcqHIKefeV394Gqutq/AnFSG5uPzEuBJ2xUMHjqYI7a2GMbq+MqWzq9WC+PiDbMz8ktugELv2CRbdTmEtG6I2L4G1qEh5t5ZMOrtgwmVpexl2pDC9sfOtDHfD6aBFXq9ze+d56fIfTrTBmrHYp/Rk2w5UjcRdW2IhKoo61HIi6i43GZU5TgSMn0KKcP+6Qvrue9f2Uqan4fTpgxG3UTZA+etTVYWfYpovvie1gIJs3/xYOLusjD+7QO9cOXBMUAmN3LuAJO5aqUXidOS4Go/u2nG8/wanOy+iJJ8wyUq1eu4txK6k+PNIsLHB6IYruFGMY1UctIyPB6E/uD3BK4hdgUDgvsW0U/LN4V3n6wJQWtH66nEGb21/hC0LBAKBTwCtSHYqMTUCG6NNLnQugY64fAWWrif+RtjdONbenSIq3Eme/ivr9H86Dhfs94Url2M2Nl3ITLGgiK44K5Xt+ht0FxWJ3jqPJUdV7rcjM6Lpo8tNxdllRY0XHTXFbGORhm6VYSOTzgxXLv7KVZmoIGYzAbE0l3/C4uBdJxYU4SpYov23vyLsbr6P3jiDaruVrullY3HF90Uugtq9AnlCHDuRZdBNXTiL7Y+1JutCC6M10DsxSkHNthl1l9D9LYwVl/Dbu+riEzXylmtTHE8jNSEhwyiNTWz5hN6KofP9JQbnC6eMAJa9PUiyyIUlAVkq9Ac+J5dXPgpNQ0vOyPSYCBERJpM+WyGpuPPmZuHpJ/2aw782tLuQQiNqOXGsCFWpCEfai13V3i7dfWJLMUPEV2MsxS7r85wZInEJxOFwsevdtVe4snMGgGe+CA895BbbcQRKnIBQb2TOMeg+rHxvt+vErWok74SzCxc2lKbjRXUpdo0THBWdNu4+/2ukLI10A20HNBJna3k/f5f19F0nXBGzl1+bGNIXt9/FGqGmU1SED9mV8fiP8O4yQeWGCM0griNEnIqPoQqxS7uxXoYx+jZtrAyxWIyqg9LYSmiopJPhRuUU4C0bxRhpxu7CXV1zbhIAm1o2esvspBfLvi0EMFXTvPAjod+HTlcxGAg3bvgqn2o8qp55yvLgA9AsFtVHiV3VpN5inQsEVywiGYzfY+14XlD9bbj0Jmrpg8oBOmeXVholR4UxeoFCaVflVMNK9yp5kaxeDDpR5LsRe21xybCA556Frz22TOPGBrJkkNWcmbMtrIpRyiI+jFFnfbTKS2FdlPYJ7AWJIpIUdgsTjnYT6jAbMMhcKPHQ7JWC5uyMwupkInTRJmYcylfMkVohGlDO2ZXNPcm1rSmWloo5MveVcIvtwRwhLJRhjJUHCEppd31Uw9fqU+TxjPu9UjW0YHPT8t1XL7K1Xcy5ru9d3QrXV4XYZdKUfr8Q/LULyzYGtGZzDa5cqSQhE1CxdgKp2LJvSkdimbMrL68RESZE0VLsqliXBMFYiIz/rlQZkaqVVUaVOBGfIn+jHn+YqkelMFu62SImChtYK5ze+zELQzdepRLqb3M3rsuwS6wXuyoTmaJMRSCWCQfwB2eEUW90cKzj+riW71HbPU9r9120gjgScgM77erxjwVfGM/14xeqYleCiHDmjLC2ejCvnsr7yHUXTl+PeggReTSFSRKwOSrS3tlV9I0rpFJUbL0VQewKBAL3LaOiEuOHELsApr5+gtEHO+Oky4FAIPAZQBo10r2q2CVsjHYAVT553d5JJxclqUIPO9hDKlJtbcG774tLwE1MOhLeW/kxsnXeiSdaubxZ3Qzb8DfaXgQwko8dWzZlljUWsqtum2qIhmGsG2j3urYu6fCBe3OBpWWX7DstD9O6iEFrYORymBSGCNcFLuG0SrrjhPpY3rr+Pd9nTiywnRTjVx/PzTzltkvaROsfEK28RRxBu5vw/zv7KtfbO/TyJe+wGh8zCKo/Ds8bDGHUNxgrxMqgyuTOFttwG9WUy26dyAjjEj6hXx+33fYyRje8s0GkFLiuLY0dMj95WRh5ocF6u5kYwVhxTgGRilNAJn46Z5eQytjJV6/D1JSLhSn6MbFtyMS5fKxzJcHkAkiJG1e6+sS/WKB1Vogvfq8UWp2A4100/W2yQZvp5CI10y0Xhofl7Ooku3QS913vwiydmyqKnMsGoFbPKku9SvuUCzMbL3LlQM6uG0vw7vtuYyuGaH/1xqp1ygt4hbNLa1jsvMbJ7k9oJeNKkkYSsG7crYzemBjXw8Tnfot1OW7cGtvnD4sF7WJlUblBEzFoNLEo6lLDetuTUjBI3RgvHGrLy0JsB1gsmdSdCFpZYat0MrysbFbERBhjM2qWC+WtbS/SJIa1zjKDfIOh2WXv+rAMY3TXdyEOKl562a3xkwQvZLj9Lh7XnDihytDgamLraiqKyTW9wfqQ6twI5z4Ynz9jqNhlLO1XUjqvVFwm1pCJoRbVQZRPiD05yYi1iBUipRFr2BltsDvY5Pquq2SoxTB3ZpqHLpxm0G2S+/Ffq0HTbPk2WmTVEI00Ko9QWly+RGCqc4ZIUvLWPPbYE/RshjUjFJbpac3xYxVxz18jby69yCvXv4uxhoHZmXBeJfmwnLcANx7GGnLRaVjlHkwoq+hk8yy15+h6R5tUchgaaxAL7VfTiXxs43Ph8wtWc3Yp/z1QhMhrkLiFkaJS7kHR+tL6ElvZOTZ7K76trlAKEYxsyms3fkDqE731uhlZWohdyrXROEVpY9V9dpL6XJJWIFauKqwPhSufi2S2DLk31kyIXSrzYpeF7gsuHFvFCrV7FazBDjOaVwyRce4xa3NiVSctnF3VBzPGVcAsxa5WvZzTJHPuy6prFuDGDVwRBDEoMZhs8iFPbtyh1S5soJSgNOSV866b8Tic0fo3eYyB7bWkMiDG5GYsXFnr5rDIv/fi9bFyPjif031pfN+gK2GwYoT+GYMZeqHZGtLU7fvSRUOSCEkyDrHW6QjZNjCj4YRmWDtNGh9nZRVIuhAVbm/v2LOwsX57ri5wJsVAIBC4L0ku7gHQ/JBiV+vrJ5DUMjq7y9TXTnwELQsEAoFPgEZM2umBnwLFCgMzAN0EHaNQNLN1jHG5CR/QD3Ja11HKQuIXNpU78NUVH/JiYO5CQjzK6c2dQQ92UPUWDIdIsw9iyeYM9HTpgLGkKCtE+QglQhyDzp07oVpxzKUV8zfJEaCgfjWnvqDY2wNZLLMvuZwhyTK5WcTaqXIfkglURAytZOzsEkvU2eZX1Dz25Ld4YeOVycVNUyMDwfQy7HyO6q1TZ9q1Z/VtqM+BGOIYBsM2BsMlcxFrY/q78zwskwn71UCPF6NKyDOFxTu7isUPhnxGQQIqcsmJcnJSFNvdG+U5KN0IhW7AeHEuOgKEYZJyufsC3kOBEZf+XXJxTh/xwk+5MHJCRCl5+UVakQMIxjlsquGImQxRiRDr2IdBVvqeIhQocyFw1XWahdfXrvKrD8/SiGukb74PSYZ+Uk+oGD+69M85kT1daeNk4u2C1CTkxqKBKPKfjSKKQSu3uI7rOcYeXPIUSaN7g5joi99CX/3JRI6l4nNHI4X4xNfR/jDGMjm6RS//DN3bwFonMBVuCiWTIpLyC27rz58ItGrTDLM+w1GPlndmqSJ8Z5iNnV3x2AWnspzIV74zShFLPCF2tUc7TNfnUfo47Q7cOAtzNiEV6/pjFHFx5RI/99RfcIdQdXZVxWXtzmEx/hq6Ri3rYqOYQqCU1F1vysL10Us8/L3naT2iiGo517bPspE0eHDf4vqhh5gQu0oLhkSAgUOqb2oFDz8MNwrtUASD5lJ+gd3tIRcGMMyc86x5HeorGXo7h8Yeuucc+nZniFKKR6YeITEZsa4h4sMUi+MRl5i+/cebMK/RX3GhdEUYo7HGiRQ40SKOFdmwQeYdOLUYdH2K/aik5jpJF9UY3bnKowbSnGFreJmdzlmOyxNEsaZeH4dtSnEh+R8vXPl9EoFHZ7+CjS6QmZQkH43F9iLPUalCjJ1dRlu0WLCKzW3FiYr/xYWruvnkbO8KX577gnu9nVB7YPKYyrkaRbY+IN9J0A/6sOM8JT8nDH7+z6F68XiOsfvaAwyTzEerFY4nfBgjbA436Y3qJKN14BGouC+jYr61OaI0Ne2ur6Gv5yECKtJOMBTj+sPPM7t/cKXMwSfGltOPtSCZE0X1doyPokbnbeyZa+T1XTj3CrNXDMP4JPZRi036nE7fxSQ/54RZKWZV8eN5fLy6VUdtu/BzSYwTlCRhuncVvRVhjz/N+YuKh/15Xxi8R39Ug/nZcj957toZbfdh1o7FrmKubkbl9WzMvtyOuITw+faQ3o8T5v/0OCGeySm/Iww16hfWYdELc4Xj0n93VMU5LVk5LrNt6yp4pjkzX3ZzcBHm22wKP/qxu46LSqt6lIKAejCCOGLQeBQd+SIoeeJCWa0t3ebbO7CwIyzMclsEsSsQCNy3JBfbqGZE7ZGZW298EwqBa/jWVhC7AoHAZwZpxuQjYZxXCIZmBPWWC6MB4nyPi2dnmQFadopj0Sx7tR1I/KKg4tQpEuJaA422YBqU+1Y2R+c99M4VrJwim8uRbm2cMwVDK1mH/iaKp53Y5d9rK58hosiOP49cfMctJmsuDfHMpSF7D8PyAB49VWwMzWwDlWxg7TdQNScCSOYaaV10h3NsDNuQ9hGxxNYQ12JMPvKOEfFJpoqDFCS15DZDd1epz3/JH0NhTYvQ2oVSAFhdd4uHzLCzDY8uegGqKejOOBzo0egJVrbXGIig5035enNwnbmpFdKfOwamjkYjWLaSDXqDNeqFoFWIXZWqhcVvvUHM5oalP7VFVhGqRCz9gWLviiU/7sUuF4NSGSiVfYtzjLnE9h7/IQuN02yLC8fLZIikPn9TxcUgxcpcVZxdFZFs0INOlvJWtMy3Hnic0asX0DpFPxW5xa6OnCsvHY4dJp79zi5jc0SEzBialIYgFIo4gsiLXVEtQ6RVdIhLziyCVosoDFfWZ3l8NMtxQElOvQ5pOharokjhKheaUuwaJ2v3B2cNuudCfa0UYp/xobz7XAgik9G4Ao8tfNFVWFx53znw4kLtgt6fLEOt5hbvUVG5UlDGObusBqs1NYkRO5knLbMJWlVD/yy5MuRWUftZg67tw7f9n7JJF6gUolOkIBfyRx5G1raItGJ2dMmJAPpxt9fEFxIQlyOtPUgYnK+zuPgq262YrSzijfxd+sNVkvwkX3galrRiZf28C6dMltCbltq6QepLmIU97Og6MvtA2ST7iOvJlVhx1QqDgbCWvUVqF6jLHg/JMRqpZl53SDPFlJygoWKIDUqPUJETwXSs6Zsh57oXkLjFfNRERLlE5KUw5A/HWMisy0MlaRmuJz73XpGzq1aDPLPlfKcj0Nq5XTuPPId5cJb8lTWm15ygprRmK9uhZqbRNCgjuaM6tWjIwrxl/kHFWn187mwhflbMmIqIh2a+yJNPPMOPr/4b2qPtsbPLOoGy1BiL6UuBaFeR1V05uM+XcoRQKjyMhb/R2T1GZ/dY+GtPln8zfrxpHdF/xc0N+iGfOD43MIDRO3vw1Lx3vqrJPvb0Bxascx3lRlhdFaaNQWnl8pkJDLOhF5FtmRdLI9g8gSwF3SLyztXR0IVYrq5YsjYMthWqbiecXVbGBSfE2nECfBk71lS3hsz7ipzakq4Z0rTnij/mfVTtpHN2dddpNRvYwR6KaXf9uPhwxEZ+znUuWxo1SDZR7SWYWQCgMVxmZngRWYdX3z8GHINyKrWodEC97sLJz77p+qlwXokIkZ7M2aWbkUt+v73JXO2n7KXHqaIkZ3RmF5tB3h1/oxgDNS9cWVUj2h2gixDiyAu+ctCZp+zYPWsTgGgih1dS6Na2kkeseG9WZMVnXPBDawTtvrsjhR1ZxD/McEUAgrMrEAgEXHL6p+ecffhDUHtgitpDU/R/usHx33zuI2pdIBAIfLxErRqmmldeXEJ5pyKNcw8VokRkfB6ZJtihE7pUWr2xLVwNbpEF+BUYxKIQJS7fsRXyKf/0t/LZtWzIUBTTU5Z6jA/tkLEDS4OxilffO87pzSaLCM0vLaIuavIpRb2zSaeTghe7TH8cCmGtf5otlu47GfFyjyQ1ztUlhujaK+itDNuoEWu36FV55sKJihA0KBeEkvv8Ljom9n1VJOgW7cSuzN+kG1VHk6Akp9+P4ClN+nQEKUQbRc8p6tolBRfEhfaZIozRLVhkVmDHJe63GEYTSWTGAotYkFSI1jrYx6f93yJu3MixJzb3Vb20bG0r4mnBHrNluIlUY8Eq+aZE3AJ2YMalL5WCX3z013hrsA5e7MrtEFLQ5Tjw78/GbVSS08w2aWk4eXqB9jJYt2JHELKdcSilHmyjki7SmIGkh+5tEJuHnFDmj2d/zq7MF1fI8pyZuOIyUdo5u3wYo5BhpAh7EqJrL/vt/rJP8K9J0sgLfWZcjc3vr15XZXhqkbNrbK9zG0fr74370Iprtz1C7AKUF5KUd2xFKoKojrIu5FdHDX+5uYW2Nta3T5XJrvXOwInWSpOJQiV1JBuU5wx81VQ1NvIpseRYJ6AoDWQk2YhGrYlUywj6xbrRLbQekkqKjqZRD+REG2uoWa9x4kSBPEmwIsypB9lki63sHDUzIO6v8nD/CXqjn+Ohk7vcSL5AL36ILz3o2hglKSCYY89hTYqZ6RKrlHhqGpl5GHvsaQ6jvwrdzHJab5E0TrOQdHgyOkYrtczONuiO4AvdDnQN/fmvEuez2JZb9h6ba2DEsp3uIY05pk5+A9l6D2ukdHaVUZf+7GmJ3Kj1F5e1FlFSOgG1d/MYv4rXCiJyLDGj5gOk03MMpk7S6AjsJsA050YXWbSneDx6AlOMp6iBYsjcnCKKI+p1/8zBCtYLRaXBS3zIXJKz9/tLLD5znB02mKrNoJX2yeMPcXYphdWCsinWi1155ZhdhcXGeN6pOBrd+LZ0sh5ztZlSJNcqGkefqwgSYfTjPVCq4lJ0PUqluqFYYXUden0hMkMGvQV2BIYpkBumTkHknYyjfEgL/53lv4sWRueJlm5A9gRrW7MMJUctCknqckuuLkGUgM4Uc1OWgQwqzrLxeLL7nF2mCO23qhT7VE0hw+KtxVzj85aJ0Ig1wyLVlxTzkcVe1ySJQdUtKEWknM1J9TchGqL1AnHuwpd7PUiGBmLjHjT5fpPUcOI4LCzgvl+9MdiJXRb2hTHWHpxGpE9jaxM5Bc38YN7hospkVZSydhzGaMTlmyweIkis/RxU/b7w85NkWGLAYHoW0RGSw/BqTjStSFP3IfnmDeq1k+S1hXIfulBzYydlzs1Cbw9fkdFgtSVPLbT2PSG4zaVdELsCgcB9S3KxTesbH40Ta/qXHqD3w5VyYg8EAoFPO6oZT6TlyPOM4g7fCnRaz6IGr5VPaiPjF/wNXAiJse6pq79b1KkFLRij0D5PDrl/wi4CRiGRhtO18U26OIuEiKDFhUPNzVjUSOH+Z8ubbq1d+rBorUPuc0c1npjDTscw6nLitRddW36xielbum9lE0+kdTMuF1z5Xk6y58INkXHeGisWTdMteEyKUgq7kaD6FlqMF2K+wmAU1cqn/4XYhY5c6FGRd8UvBJUXQ2wkyDENRQ5rGDsRlBe2BMpwHFU0z70ea41YS7eX06qe0NbYfSXXcnQyhMXIrUtVDJKxNdicWMTlpGSSERuwF4XpRLHaAjs0TFcXe2XiZlgbvU3X9MbjCKhHDR6YeopHm7OsJW+7MMZMmK7POrdLsc7yAqBY55abypaoi6ZWW6AWV0IabeEAAImFeOtC9UhdOCtOzBQVkWXCqJ/BY+NtiqT4eW6JY8rwLaVcNUalFJoYQ+oW+PvLnSknblndBOVCFTV5JceY275WV+xkXTce9Fgk9h8GwF7WJbUZpxrHEGO9SFeE++xfmAkNaZHRc3uyziGnRLvoPWuIKzm7wLUzHqy7fousFz0sERGiFDpqsr0s/MLPf4vvcaF002nvQqySiyFmirnmMXqdG4zO/IDGV/9yWT3PN5phPmDTdujKKs1M8TAzRDpG5yOcqi0kZoCIkKYJoqDONCfj4zT0KrEoFuI5FvfmWFjVPPLQQzz/1S8waj3G/LxrYNTrgs0wi18iSwb0p9ep513mphexUw9jF79EttonPtFyzk3PVlNojXIeiq5gpp8gyq6y5hNWNxsuQX4RVglg00mXYKQ0pxrHWO8usLk6xUNWlyGgk3jxCpc30BYxu9mQVnaeyA6BFnPRDNqCsWMBQdsMRCNakcuIrZ+LOPmOwS73sM05F1YuA6f/FOc6diFlRWGJhi/ikee+yxk7u7zcAl13HbRWYrInUrIopRbVSc3QVzQtTmmp4CE2Acmx4iIfElLiCMjG11HxRoMtXZIAazvXOL/3Pl+de6as5qkriolSimjdspvtEDOL0k6gtdZdyxNCuxXe/0BRTzeYG10myqYZDEBNQTZ0YYwi7vsiHfRwdTy8QwqIbF4eY7sfU2+4cZlmMBg6oe/Z5xQNNMvtNc6bs5ySY/7oXDs6HSHzuRpdP8FS95pvn0LNx8S1GgykrGEgiXOI6eJ7QBQzUxE7fZfTsghjVAgyjHx4oW/3zAzgE6SlqRN9TR+rYnKTo8QwN7roNo8U+FyLkYZanoEIeea+qnTsv1sjyKyh5/MXzp18FNOsEZnkQJXH4sx+8G7OIyNh1o6nGgG0dUVX8tzn7Cp0zki7+4nie8+p3cwvwGCYYlUdGGJHgIpItyz4ZyaJ1kiumF08x1z9PBun/8q4MfnY2QXQbEJPKVCabtLmysarzA2PI61n3SmxYCUjpRJ2fRNCgvpAIHBfYkc56Y0ejS8sfCT7m/kzD5JvjUjOtz+S/QUCgcDHjWrFEze6o3w4froOGNVEEKayVQAiE5M1T6GaANaFpPkQQxHh2NsjpjYEvWkpSs2rosofFnJF7ZEa+smpUsARXEzXVHrdbWgVhQ1IAwpTJjWPIsgvpyhjJxLSEmu0KZJdu5dMf7xwAyd2qbpGF+qQWDKjndhlxwsZi0X9JKf3dg7WObvM623qb06j12P0pvNySG4x1hCpelm9sQxd07FLhu4VHuvVwCKkqRCOiuTxUiZSwYcoik+eXYgz7j3Kh7popRFl2bCapDXuh/q3IlpfPe4SoBshtkPKJZXS5DJiuHGBqZ5bYYwWNZ38BkvmKgjYTWitFQ6nyoKzmpjbQmr2fc8pJ5oorZiJThGpmquumbrXj7dOo7xzqghjdA6AbHyKTIqOxoMxN2DT8dhSkV9Mm/FCykhO0nqI9tTznLsAF69OOruKsMbcWKKqswvlzjuufeuDi5gi8XwlflArP/6UdtUfVYwSl0MHMS6xu1h27RJv7n0Awrga4z63zM/2PuDdzgXfndbnzNpmKbvhr4HJLm1WZEwrEOsaNV0HKxjJ0LGaeEsz26Q2WAGrsE2LtS70aE7NMxOfZr7xBGIVsTixpOrWKJ7P1c6toxAMBmXrPDDjlMPB1oZzcmV5WUUtSQyr/V060sFo51Dr50O0UuPCDqpGblPyJCE1I6SmiFSNk9FzHIuf4lj8BA+3TlFTMeAcSc2mLoUuwIUtyb7VeFk9MMcmhv5PN+i+uMIB/HVWa0x6N2Z9Lh9rOeA4nHi7CNfPWC68NgDRWFMRu6TcCHDXbWJGXN49517fOEOrew6FYbb2RVpRE20mnV3a5FilIXLXJlqRN0Dlltzn+uqbASAY71IU7XJiZTaj9uAUcXFZGBDl8xZVuqulF1A1L6T7Bw+ZScvCEUIlNHNsvcJmeygF09emaG1aNvKrtM2e+6xiXjvu3pA35ybO0aDX9m0flm7LqtillUb1YCfZxCnKlJV5/UU17mb/eyN3FtiocCcJpGmOihRWLPXXctRb6/TNlhN6/ftdon/nerTUXAg7lix1lXqVCI2GcxHvjDarpxQRSBLh2nVYWamEN1q42vbiu3WO5SLHGn7elsQdkKsh4oqvzLQ0Sgzdjhp/gDC+GK0w+60GF6cUybR7EK/SjFbvArW8QxbNumhMyZ3YZcQJQEqBMaiNLsMfLlPvDBmOYG8PVCNyTlLtROxre+e5tv0uevN9jg1fJ06HBy4vgOG2JR9mtNtMFBURgcg7vUnddV+I5RJr10f7QuBPnYSWHmC9Y9pmAlqjFDQecG/O1gyynIMvaBFF43lJZRnzC7jcXEDDpxCzRPSSHkSQ9ncpHwkJrKTvc8GcO3hghxDErkAgcF+SXO6AfPjk9AXTf9bljegddsMVCAQCn0La0crEjW6aVcqpW7AqGofUAbvNb5FNPUDU8AtQY5zwRKnLUO8K02fMOIwxdz8jK6hcQaxKp1ZpKlAKbd1nKzsWWpRSzllTcXYVAohTy8ZikfI2ICvu5rxI31Q4WFZW3Vpu/s9MEc8qJM/IU+vcCna8QHTOLkW2Z1G5c/yorfMAxJeagMvrIkZc9b0oLhdyZeJ5pX3qGdeI0tnl8+PYcjFQaX9FuhB86KbfrojwUcr1Xaw0i8cthozRw7rsByNmHJZvoJ7vsNDcI675TDb5NlNv7LF4o+2cBUX4CYrMpl63HFf48lYRTGeHbjsr+1dLNCG0KAWRHjuEIuVXI6lf5CrGomghVAhQVjZ0cXTN+thlYnwoJgC5Dw/MQa/6t4tL0m9rs4iKiWj4Re14QKdeAE2ynHpt7LZSSpduhJZeBAWJHfjF7Pj9yju7hAiTu/OoxNCQDo/s/SGtbJU92eXq8H2/WrelwFY6uw6paIY1KA0/23qL5XwFiyWPxtmUFULTKcou94wX0WpRvXR2EakJgSwyuWu6daJHdKIOOGH0ocZXmGo+gKSK4Y92y2Mrjrb4fWY0wGVkyslHNZZXGy48bmTBOquIqrtzm6XuOmioaZ6cfxozaxg9nFGPYjeaBaxy2eTOrr7JUvsyUnNjQ5nxuHbn0l1/qtqYsjMmSqW6H8X7bV6KVLZ7sBJnWQAgGo/NxcWxE8VWNNzil+qnj0agLoyon11HRE84jsphVgioEjlhp8zRnZY2RasaKAXN7bx0D2rtw8FU5EQucaFrErnjs8onARfDiGFFnYwRpYmeFuoPT5chtWJhKCk7yXjRH6mYR5rfKtuss7HYFeuaS1BfFcfKMF+QbA9QWFUnHghaa9rGuY0Kt1b+JYs5lWGKGMTi/V5Uu9S/wbJ3QOlKmGOSj1DDYu7bF8ZYPd+AHRSu4i6Lc3DiuKVW89v666jMJzjMuDZ6sQxjHNQfRintnF0WRNUgd24qY6HdtdTrEMWqdEi5vvTnTSxtr+sniZkchsUDC6sQLaVa5xyr7vtBSmeXF3AuR0RXdxiNlHNgetGwyEOl6sKgNWI1W+GDzU2yVIiSHjP98yglDGsPkmYQ+bGCgaEeMlB9tDWogbsGWht77O2BnapjM8Fag458O0yC7qwSdW6gIpgaLpXH1W086dqTCv03M3Qycs7BfYbGyA5d96cuzFolba+Cjasx4k8Rvmti28NoN3dIDvVHplj8dxpMP1+n+XA0rj2S+u9DY4l/fIV4eY9otU9zSnHapyeo13Ff/ypimPXB9tGdJVqj6wfOz+0QxK5AIHBfklx032CNL340Ylf9oWmazy/S+eMbH8n+AoFA4ONmVOtPuABGaZF03C0TrYqYXxDq3j0QmQgrGtVyoRG95ffGT+H9j1rPLSS0iqjrFipTkCrilyOnNtX0RLiYy/xTud30N6rmkbQido0XiOJzgEn1Q5UiKpxdAm9tXWKps17dAoBO1wkSc9+sE51y7Yki0LuXyw3Fi10A2Axt9qW6LRbcqcUagxafM4ZKGGPRVu/OEIowRvfvcfLysdhVrykefQwefSjC4vPDlOKZF0+kWEBpemqHnfySTxztnEHWGoiVe683OdVtFyFC0M611tGc0g9wPH4EKbpdwXJ+oxTrtOQosVgfitrdHtHf2ChbHEmtdEbNzcKxRbejMjTO58IicZUBNW5xJyLYYaVSV7FgVO73eh0eOq1o1MEaVQqWWFBSI77SpHYxRuR0KXYRN5hTT7IYPwUIeT4+W0UYY5IYWq3xorhIUA9wrPZF55zDL/orYplCiGyC0Q2yQuzCEOdu0d/MNjAYxCqemX0KgFFeXENegDSWvfakdUKscSNeXHxQSorRrQm5s64a5b+suDDGWNdQxi1aVTwpdmlrSrOj0RaenCnbIDpCGjGSjIVm7UPe1OZZ6vkeAPUafPMbArGhux27CneJW8Dbft81d8qH0RnrheqI2nSD4bND7ImIqbjlhFtv01lehtVt9xDQ1pQbG3nhbPSL4gnV6RCxKxuVYx8oxTJMNlEgQ/ZZVArRXMdxJWxz7ESx1osT2Qg1dCJgJe86aerEGN+J++aByi9JF11cTP5HZk0lLDBGAVPLWRkKqTWo3GCJyjBGAIkU5AbjC0WIwOrME0hz0TcaZPoksvAgMn1ifCwCb+19wNpwrXRoLTZPEak62s+3Khv3baxjMJN9Vk5fWmGlT/YLFtNQKOOS9hfzWyHSoAXqgi0nT/d63hc2twRjhdyMOLaziNoeKyanpx7x50exbTcYZr0yjNFbodyG/Q16/+IlVC9x4XMaphuWhx4CU+uiTIpoS+Ln/nJWFZegPtdN58x1JXyxKnICvt9wo71FI3ZqjG5ERNtCfSdGRNhrWz44K7Q7bts8FWzuhOfVNVdsAHCCoa8KjIiLiLeWwjCntM8laBVxqonTjOGouD6K8eFPorGgNHkk7OzB5iboZIhSkJz8efr1h0lTiPyDHTFwg2usyDJx1uP08GVUd82Jge4kkyc+pLlIUD9yB5SqHCKFyvPyAVG7+Yx7n3/IMDSb7MgWl7vLXNh8mwtb77A2eofV9H220h3syFKXPq3eZWLbdc4uC1Tcd0VfR2TkUidvWyQXVHMc9qoa44dlkonLM7jt5tH4xi4KizLCqVOKr/6cRkfuuhQ0w3wAkmJEiHL3wOxAOPotCGJXIBC4L0kutCFS1J+4zdq0t8Hcrz9K/6cb5Lujj2yfgUAg8HGR60E1YoQk67sQpNL1oVFKSieEsjGIQjecaJRsXkMZy8qKlKmstHGupNFXTvPw9FeYYQbV0+hMMA9mxKcMjcH4oYD43DOCRRTE2q16JHbLTIUp74SjqBLhosaOJtAor4zs2m2u7myz1PXiTOX4dKzIM8PGprgEx5ZS9DD9Qkyq5F20BpXJ5E6808b2M+TFrrsRb8y5zctH/9a50GScs0trmJmy49Ah3zjzWIqIk9eiSNFqOFHKGHFVBynMLgJorLFESqO1a1M2zuyMtTnKV8QqRICIvBQTBSFvWeoqZpbmWOwCBulgnE9IMhe+V4Rfol1xAety0mjRPPAAxKrJo49AVCgJ/kdTu/5YjE4StxruD8b1jwzGoYal+FQ6S1zuIx35qmtD6xcugrYafGiQTD3AaO5Z9uqPouOIKXManeHGWGVA5zZFREhyS7PJ2G2FGo9pVJkk/kA4T56gsBjdIsucaKnElMepxJTnfLG2AAjNuDUxFm7cEG7cgDQd71ysqeTogRGWLJ4rhYspvcC0KqpEu/EWeWdXfMlirAvfmhC7jMs7l46gsxdx7ryU4wKlkWYN0GWfu3AxAzZjenS12MxdY8pQMzWMbiLKuS46/8Y7dKYb42MAULHLJ6YAXWO61nJhtjYChCyH9WU/PmPv1iwTkI+vl+JQ92td0lpA2Qy9dd5dh3ZcPVCybOwUBMhdeHGRf6tMlB3F43OmQXsHytIy2FzQm2fKPEPV8E7XTD8+rDo03EslHfT2RaJeWWmCPBf6A4vJrRNPlKs8p4RSuHfuqdw3aCx22QjEWHIKhyC043GnKAPUWtiTT0HcGIeQ2eKc5qXYsN99VoQxAsS67vY12GbU2yTJh+M3aIXkI2QhdufMgopj0sI9W4oZFrQro6AA8gSSDktXc4YjGCXwcyd+nidWHqf/8nr52XXd4NT0w4hYunaPS7vvs5culS7fwkGn28sw3EW3hy6blbjjW0+2uJpeoZcOeGP7Er3cVVkZR8/lgHJhxygSmlwZPIvVTawoWr4b9tJ1ErZBK1TdzcFT15tsbsKVqy55/2hUdKEw/6bh+AfuQ5aWxhUt0eIctdY5yLBu3hJ/nq3kKHHzZqOWk4wUWSpsblusCKIUeSb+uBUjo5AoAgUqGaGA5kwdUTHDIT4PHF5oE0QLamOL7vYWw94a88Mzzn0Va+dAtkI9rrs+9A8AznQv01M9bG6wmSBvp8TLPk9Y4r4Hd/Nr7OUdNoe7rPaus9q7Tie7zLa5wZXkBpLmKMlRWvHoqYGvxkgZrl8Mv+R6jr2eYy6M6LyRIQZUfSx26bqqOLsELTnsjKv9KmTC8KmV+7+EnNxkoHH9WDjKjPt02TeXHEUQuwKBwH1JcrFN44lZdD269ca3yfxffhys0P7X1z6yfd4rltqX+L+/9J/wf/7B3+ala//2wBPTQCDw2Se1fdK4zoVr2iX4Tgc0db0MybDKehHGv0FFLoxtOsZMe6Gin7C9A+2tATXjnhpbC7YRQz1CbSnUQKOtxTyaoOmNc6JQiAzOPRVpOHnMOzJcETkXRlYs1jS+Wp+jWDi6vD6uimCbXWxecTFUFnzRznnaG33W12FzG7BjIW9wMS8dCqWzS+w4OS7j/TDchbSPDA3aWohqREpjKot3l2i+CFOJqcXQqDn3UJHzBhHMIynpgnUigIKGV98aU5OfWzi7EO+WUoZGtllGmIgWjBi3UK0IPrXiKbhyIhqZItaaWOW0pv021Ghs1xj6fEBaXAU86x1aGo2NnKBpBcRmfPmxp/hrf/45tFaliFRoXifqz/KlqX+Ph6eeQjciJx5awYwyxFiipjqgH+IrkyEQazDbimvnFTt7Ajanv4fLH1NronLIoyk6jQeJE2H2zU3m3trj5Ns5gwu75W4zk5JmIBimWmMXi1aVnF1EpbNLKs6u69eFQcctKnM95ROAR8715hUR5WRJBJiuTfPNY1/ji8e/Vg6T5RVhc9ONiULfcbnX3HWFdXm/tpqPYqLpckF3ovb0OKxVpHR2RcSovg9LqrnxUlwN2ri8c8ftKU7JY+TGMtV0x7J4XCGtGqIUkg45Jg0enXvKibvlCXCXfTdtY1ROM3UOLdM87pNOpxhrMFM+J5lxYYzKqy0L8TxKR8zVplybfB4udyJ8OGyxvi2cXWM7JVBUEZxcocrik9i5h9A73n0pTjBOVgx7f9LFdMdJqCXL6f5ohc6/9fn/inDoSoitUqBq9fI929vjc15uULzbuH8r5aI4q5tZIySJsL0+csLGyJe1jRTtDmxtC50PDFPXmxR2sZlkCTouS77WQJ47t1GkSW3PicoRkFlEOWdXgzrDao680n3lP64IyRQQrVDWoHeuFgdT6V/KcQuFs8tCd5WzF/8VL137w7E2Cth8BFEN0e4BRqRqpKuPI9Mnx84uERgqDFA7rtFb59Hblxh0feihhqZush+xQiRelEehtOJy9zX/+XrSrWdBWaHRMDQaCmszBjZBiSIl5/KmK+JQOUxyUncMqoZC0RtqlvZcDFy9Bsdnq+c7caJSIyqdgHlW+YIq2iGGKBPioQuVtuUFDdY/eMDkZXutESLcuOunXbCCsop6ZBmOFJsbMBqKn1c0q2uwvGRBKUYj6DzbpPZwhM4SsgsZzZm6E61RRJIUB+oeConihr3Gje0NVnrXqDGila178cl9Z5zUDTC5C/1Viu10j2vqCpvZOukoZ2vqK7DTJpccM8oZMkTbEQ/GD/DN2S/z5x//y/z5J/4qv5xPcUqfJrNOaNZ+LEQyKoVK1Vv3o0hjqDNariObpnRrAajGWOyS6nOrDKRviTu746mgqZn71nj74vWe9MBa5loLfl51OxkX87y9dUuoxhgIBO5LRhfbND6ifF0FzecWaX7lGLv/w0WO/41nP9J9f5K8tfpj/v4f/y+JVcxMY4EfXf1X/K++/vf4X//8f3qvmxYIBD5CMhmw98zDqO6I3c1V0vU+zffm6P855RZPMg65ARBxN+VPokmeTGELNrZfo2VmeKK9QyvbpF8/BQjHkrOoR55BXVFEWQ1dF4grugYUphVEuaTsrRZEPn8TPlm5wmCHLmwy0qpalR5jfICgUu4mHpf4W4wqq0BagTh2OUP0YIM8K1UOhnbAVp7wQM1X3yqTTRftM6jMSWC2PoUyuRObehtoA7bVQosgviLjhLMrctmP3GFqrCgijK8iOA5jTFMhNRrtkpUR65gHTsFDJw14salwHilRxMk2sdZopTid7THiBIir8HXmvJPbhnvjPoojfIJ8J82QKrRWfPEpQ/9hRd4Gs1pnoFoMI6d+ae+ScwmFE+rUSJRPmi8g5NR0fSIPT9HO8e8KcusWkdai0gQ7yFzftBR0x/19qnaSjPECU9dAdd1iaWVqh3R7i43rxxhk08zMnXJ5XWIhl4R65gZR+tA0dlmRtcfiR2Yz+r5oZLNlS1FOVcWuSO9zdrlt2h04xU/cOPNiF40YJUl5oLVIqDUsIhFKR8zHUwx17KwFNufsWVgU6xxS/vDO9q6W7rDCeZTYEag6g6knqXev0IiZyOH2+MzPUY8a2MSAcudRueoNY3xY4RyzDCIXNjc/JyxMw/SXIuaGdfbe0Eg64GEDc80F1rrWOxl9XyjYHG2hlGZheAJ0z4cRGlSecK5/jWTQ5rFUM9JDNyYjl5Ptmdk/zUNP/hnU6h8wVAMssXNlQhkWNT3tQvFUKXZZdraF7lCg6fv+kGrW0piFzopP5CaYkdC/AFiL2fPnu79FdO597O4zENdcfrGyOqpGOhZ7Nkf9ag1dawDOstNpQ6sBFf2rxKXy8/PIUCbSAImFa8ua4QBaTRiZBGiCqhjVkmKiK8RRodG+jp0DrQRXlq+FKEUqfep6BtG72NySJV263dOo2oNMJQkYQQ2h9p5PDq/HDwDAhVzmKCyC8lUzyyhK/0tUuV5jXYd05NyW+0QxlJBlA2g0kUhxMn+CYW8DoxNGNiMryqQiyKzFdC31hzTpth/P/QTm3OmsRTWMSdjehpMn8A4oiCQqnVxYQeficrGpSgeWJ0JYXLAM1ou/uRaPVCXHZCG4GyG3KQ0UVrn8cbkRav7YmlOKWkUDqUcapV0YY6EkHnurQft5U87T7eYOeXdU+Sw7FhAtZYJ6fL4ulJvna3GdVm2eQdYBLWiraNYMg15xwfk5x495k1my3CXPz2ZidKpQNsXuWvSuqwIrSjvXlgjkgo3FtcHbIuu65opxoJA4YmpKSGzO7PAGx7MtrNU8NPc4Dy/G/Ki2SZ8Ob7XfJk1TsppiKX+XqWGTuo2poWio065bxLprymbE1BjgHJSxt+Vq8eci6ZS9NFr8Cuszj1HLXuOqvYaklo4sAZDvZajoEjL3EHnWoWsMUUNYzE7SOjfFVHyVwZNfJ6u3qDUT4tlqgQMw5PTtDk/qKcTUSXMpv09M7qNKb9PZFcSuQCBw3yG5Jb3SYe7XHvnI933sf/FFVv4Pr9J/dZ3pXzx96DbJ1S7b//QM+eYQ3YpZ/A++wPS3Dt/2k2a9d4Pf+t7f4uG5J/m//bv/jLnGIv/3n/yn/L/f+q945sTX+TOP/ca9bmIgEPiIsKRl2J7WkF3q0cprqCHQdLlZ8tmHiLtF4Q1397i2EWPqztFi0wF9GUA99jfcBq1GzOWbKNVCWqC6mv6MsL4hsDgOFxMB25hxoUne2aGsW9CIX8wpEezlAdJPif5infEK34V+RMB0+z061rnBsCA5Ew91q+6HIgIHpVi1K2SZ4oHWMd8fLoykrBxmDTr1i576HIx2GfuY/N8NoF2SeiMW0RF4AaGoxvhc61dZ711F4yoqGjsWu9Y3IFbaxwpBrCJqzQjR4wWfLsSYZEQt2yPC5Q9aiOdYUq5i2I4xNGzOxauKk5m7gTe6SS02bOZDltLXedy6nDLRyRqNh2uAItKWiIiBUqQ2pw5oH+pifd6oWMWIcnnEcmOcYNUGc6kNj4tL7sxkCFi02kaagmpE6J3L6OnLmEecPUY3C5OagIIakRe7vKAXuXAtqUVcn73O7NoUc0ZhqdPpKqbezplNIHuwj572Y+FEC7sJJhk74trdESurMNWC6RlDWjpGnOD35BPC7ILmJ9cPil0Fs/Mxg2NN8hwsLmdXUQRgYd6w3bZoYie4ikuMHa2+gx6OiGsuj5jRTYx3JK6ONsHWURHlOEntiJpeKPtvqqlKd6ECYu+4c04KTW6Feguo5GSPjCEZufBP6qCUpVazaFEoHVGbcuXNiqEXeafgerJNwyi3GNeK3GQsvjFNvdHHzDQRo7EjgTx1wkizxuponb6PgY2UZmPuzxCdnPVVQp0z0oXA+pBV46pJPnLyT3Gd7bH1Qiz9AW7sUwln3U/RMc5i5i+KGGyO6TjhRQ93nWiy8R72xDOopTZRMkR6BruV0Nl7lqZ9DxkJRDWeeNw5BbdtJTQaJuxb1nohXiDdzKGmYFZB17kAR1kNGDIa+Uq2NCs5v6QiHmmvZ6hyIa7znuuHWgu0IrV9ZqIHyKM9lM5RP73Gcfs8uR0xPT+ktmndFOGrzMo+sWt9A0w6wy9lJ5ibHvEOsNg8zQ6gipyHRT4srajpGnGakIsFXUf11tE7b8GwhWnn7GQdmJrjxCk4vr5IvjxgSYZcaF/E5D13rsViHjQMf85SJ2LuFxTdNzPw84f4c7a5CZtbUKu5AgFO/o+KLVAJzP80p2auw9MaMdVQZ8AYbFnMQsisC9lNGJWDpujqZgwmcfkeraqj0K6gQ3kqFCpV7mGKUtR97jv3/kKEhfqeJTI97GPHWc+vMt0uREblKs2KxVihZvy5UIAYF0KnXXhiPmtpxE1SOigrKKuZahoERZpC3TfaojGSEhGR+xyRQoTNXf5EVIRpZ0y1ACIimyC5c8jZmuASNzq6WZ9c1hB1AlXTPP64cH4tIY401LWLPK1N09SKU/XH6et16leFjfhxSGBmp4sWhVYQq5qrDpy7flc+31dn+hfQepPcJDSpo+uKaJSgJB8n3/JhpChFJ22TNB5gujZLK/IVL6fmUY/+OgCDGeHaZo+p1g+RzTrHBs8wtbGMamnMyTnUcLV0DaPcdJAzQlA07SyXlkENnJhYGXj7TaJHEsIYA4HAfUd6rYuklsYXFz7yfS/+z79A/MAUa//ojTIRbIHppKz+9s+48Ku/z87/9yKjc3t0vrfE5X//j1j5h69NhJ7cC0SE/+ql/wQQfvsv/vcstk4S6Zi/+2f+EU8f+wr/+JX/Y1ndKhAIfPZxSdQNddOm1rtGnnaI0GUZdcFipk5zvDHP49GTIIq5Oej2auRGXDJlQDFeiGh3h05MzDCNWHpQkX6rx/ZxS5K63Bql2ONFnDSrhDH5UCWJdZlEW3w1rdr7KeIf1wrKJSP3YRoR8MCjNbSC+modxOeI8g/dtU/4XOS2PuwO1xZP6wuhweboIieQVhQlwwq5y5oMlWqIG0R4Z5eKQCyRBmszYmIi1cKgUT7U0vhcXmWye6XAOodM5C07Riyroy2WhutlpbIrF1NGCaUQ1oxqGFH0nx3Re2LoxDoXP0kazVM/doKZaeFadomh7ZGZFCua2qkmU881QENtsMKUF8+suJDF2C9WTZFoHo3VTuzaHF1FA3FHITs5ajTur3JBYiy1azuu75sRyhpXtcz6ZMlxcQ79MBCFHux655wXJw0QRTz4uHBsEZ54EFQe0evDxibOUbM5ZPqCzxgUufxCZjReKO91nIT2yCNgxYydXb69X3haMT9/eBgjuPDF9WN/EaUjN8x8NcZyDSWCVQZrIy5chDS1Y2dSnlKLKbw2DEea0ajYdyVvFi7sqq6nShGt2QDlB+hUep0o8WFsPvl+58lWWZEMEciGqPYqVqDfdSmqnn5amG75j4u0c/g9P+vEFoVzEnoHzZbddHmHmhG5yYi0V2ym6ohW2AHYzIUgiXY+CDPvr1mlyaI5VFwrj0mhnNut6MtUiKMY7fP0lNdUpa+LqpOHr1D9eLFjRw1FO3Z9aJTSTsidVujtS6jre8QXt5GrOXJmD3yuOCUgUZ3ZWcWxY+5U7HdsFbhbOO0/MoIaqOPuvGQp5P76yA18ofUY9bjJNx79Ng09ByLjezpfkVQB4s+hHu0hRnjkqWlynWLJaehZHngYrEpALOrxR+g91SR73KK7UgpdAOKdaFHFXDmt5hiOFHO1Gb79+F/igZnHi63dZ/qKpowElULNRORiUGh0ZwU12EbvXmW09rqby6KY2UXFM1/QxFHkxXvvdLO5s9BEMW09QESI5zRWAcYJN9+c+RrgQvXce3xzrBe7ZOzk0zp2Dz8SQVULmIibhw2Zn9OFxKZoUYxU4tw7MnbdtSJ3PTmxJUIzTlDvBz61izVOrrs31GI9vmbV+CFEoztgNrnMwvBN4noxX0PN7oF1ufweqJ1yriovnInx+eRsjogg00L0a6ewJxQYi7aKVi134cE5KCV0e7DS3eCaveLGHP5YlCKfq8GUIq3BzuY6cYwLewXIhUzXXRjjPiNcz/YAjW5oFBm6t0kcg65rN8fXZ5wBbSZibnqeWeaZiU4xE53i2PICCyzSmnmU6afnoebD4kXKIg4SnwQUWZ6hlUE33NdezfTKL1h7+stulrNCZhNa8QIn6k/xSOsBHmk9wHMPfI3nT32T5099k2eOf5MHjn+L03PT1B/rEz/xICBMFbkEsWg9nhdchcshojTDXoTWUnlYUY4cbpcgdgUCgfuO4Qduwm49v/iR71s3Ix74+z/P4GebrP5ffuYS+maWnf/Pec798r9k63ffZ+Hff5pnf/zXeeb7f43nXv33Of4ffont/9cZVv/hax95e+6EV258l9eX/4T/8Bf+AadnHi1fj3WN73zr/8Ra7zp/cO6/v4ctDAQCHyWR6WPJmUmvoJNNRlsbbG/EbnEiIH5h//jCMZ4/dYpf+Ibi1CkFKiZLLNb7/5VYEpsyYoT2wpdLdK9cmfV4HEJTvSm1sQu9WF8fp6JBfNiNdvlJlNjKolGhKtX29toC+ciHMYLxSX2jvRg10ORiyjVOTIoVyHOIjz84cYeb2pTl4QZ5EbZZ6SOVFgF2rj3YQpEDkg4q00jruAtjxDqXV2+dRv8qgltIugT8EbrICVNkUhaXM0yTYa3rs0hFoN0i9IPuJc5m25j5xxCEyL+vyPnVjOvYLMNO56gIFuZzROuyfadPaRQKrVy+pjQfIaKJGvUyIbcWw8MnnYPLiEahiYw7h6frP8fD+hEnYimDMS40UAss1k9R1w1IhWdOfM13lv+RjRNk65YLqcMobO6cOeP1mvucmqoEkogLO1VWUZuK0A3QWhGlmvm5JgsLbrMsh0Y/czmYlIJY8/9n773jLUnO+u5vVXU4Odw8907OYdNsVFjFVUDBICETjLB5ZcDptTEGjAk2MsHYxsYm2fgFE4wBIQwYyUgECQmlXYXV5p3NuzOzk2duvid2d9X7R1X36XPvnbArCQWf57M7995zuqurqquq+/nV7/k9WgniHNjVjfoIBGEgHPDphK9zFDTJIIwxb0JYVk+cSKcVB0Z4SJMgRMo6TNBo4kixvCo5d05nQJrB4Pt2bhgknY7gwiV3TQd2CoeAGARKhFliBCVEVo6vO4Tn7iNJnPh6ItEKAn+QSEG0FwZj1krIMT1JNg6ElHgemFIwABxMMsxiqgQIbYgfj7Psoib0MEiiFnzu9GMYocCz4LXe69tsfE5gPYs+LDURQjIxJuy9RiEjWOooLs6nIPIgjNH+zLGgNgO7soQRg7lnlAPXVi8h5p+B3gomsWLXwhPoG7cQ3TCJOJyigraM3qMRSVsMijUMO8nu9/kFw8IClqmJY3jl6tbvW02o1MZFmX3j11MrN/FlyY6nrFyJv8Wm7NOx3WQQURujJWG5wL5DVu8rlBX8otWaM4CeaRJNhIj9kni3JN4liQ8oohsUsuIyvLqx6IsSs94eh6hB4A1CZ1PqmhQS7xlN+KkY/WALH2V1/mLHxNKAUFm22EJQs9qJQqBkblUUBoxmtjDB/sYBYuVzqjbOk9E83cQyu4qiSNFROCO3F5L+fPxxw9KlYYghfT7EK4NwtLTq3f4FluIFy+bSfcsCBoxjw1oQDDCGeusZPN1CIDE23QXaxqNiUmDLwDat2TID5QIgBeGe2mAgGJC9xF2nR7X/BAiQukegF6i1H7ZjN7EZfY0wCN2Dvmug0dl6HoaBrWOcZqY1eEq4saFZ29NhZdatGcbK2aWtTzwfcTDgmeJxnjv5CEpZwB0giQNaIkH7Azaa574T0q4pk3MeJrYb1J4nUVIRB00IylZbrCARB3JaWFmnQ6c6i2hIq6eV2DaJziKRqiJlCbkmMKc7SJkgyx4qFDQ6j6JWT7khb8FLllcxxASyMLzCBoM1Xye2XRWviCp3uLTtPMfn1jhX+Qxnuvdyunc/x1aftf+vPMNaNE8kOo4f6FGKL2IwBMe7jIWDeTcKYxzZyEb2f611H1tEBJJwT/1LUn7z7bvpPDzP/H9/jJU/PYHua5L5LuWXTrPlx15H8chYdqwsKGbffRtCCS79yjEKt0xw8razPHzu07T6K0xXtvGyHW8cAp++FJbohF/7/E+zvb6Ptx78Oxu+Pzp7J0embud9x36Dbzj0d4echZGNbGRfnabiNbRJECYmIaHfg6rwiJPYOltYbSGFZGZGsJqAHwqMDhD9VZAR4CFMwvH+WcBK0hKn2f/kgCWVcxR70maaa+9tQ1MgTpqBI5mGIkhhRduxukJZwr/YOCkrwdqqQfRbFuzCkoGEsJvLrWXoeX2MsZniPGEBpSQBERbZdvNu5ucfB+C51mlik+BLD/CH6ip6ti+E8pgoTLDanR90YL+FiH1MoYYSisToLOSqtPwo9fYztLBMrd3ebcwVezzQfZykNwcUAZu1sefV0doyVKzTJ6wTChi/gK7OOrArAgF9v0yRFQLpQxJhJHhSoXwDZtDjFOsYfwVV2wbLzxHFfcDHK4U2K1dsdWh8d81eQ1Jc8CiKVbpAQU0RimfomBaIBJ1AomPqUZPoyRUKfomXzbyBsFZNb5m1aBBKKAq2bGIQ889CZwnhKTSSnrFglC9zDpcBTwnqVcHslOC8ABNqEjNDubqGwbDQEqBByBTYsqF62hsOY+zHfULP6o6dWnmWqcKsOzzHEpCO2WU0q6vw/H09bhg32IR9grXeKl25CknDMruIKRYMHaAQaIwYhDGCRjpATRvjdMEMrH9euvDNDNISEk/4qY47XgrgYVlaK8uCz94Lt+7UGC2IaOF3jme4kPECpqcMx5VjyXkg559CLjsmtnBgl1DEiQt9y7OkAK0ipBbosxrpWFM69NBCsrxo6PUN8qYYOaNQ5yHQfsbzMQzC6ZJtt2Cmn6dYOA1dCyYSwULbwywLQkA61rs+UEG05jEXBCSOWLOJZtegoRZkUQVBrAL8hiRaOjs4zMo5oaqScLJAty8QsbDoaTorBPQvaryGQS9riAwmp6GeMoQuuOSKRkqMpzBImxDDVaXXBW08y7bRYPo919UCjLDZ4dLMi0gLwgmBid080Qk6sZ3WkVbvKJBVjA+xjtBS4KkiMlEEoSDZJe35aehbPqsm0PR24vVXc/0w2FRIGXZCSNQZd17X4EvPMru01fKLJ/aixxKMeBCAw9O3o1Y84uc7CC/drBAgYf/49exYvojxizxvlnisew7hQb17CaHHkTZQluPHNWtOuz8Fu86cNnBODjG7PGkFHU0sbIemdCUDnegCJYdgtJMuBkORIl3Zc8PCtnWHf5COeYalpAuiRjrDEpPYPhAC4ca28AwT4wLO21BiGSj6+9pw0QbhprCqlgnGs+uBcRkhhG7ZJBOJ6xNlUCfuJWlpej1DEidEsR0rge9jBDaU1BOgE4olYVnMwhBXE+I5hX8/GDT0FjHGrqeJUQgNUdkgeqCMRukusYlZPV7iYvIMxk/HGATSJ04SpBDs2WmoHFYsnLTroacklaRCLAooFWAM9LwxlisHqZSezyKO3YXRKhU1tGNWPf2X6Nij583iiQLhsSIL8Xni6BKt2Zh+tcPpS8/Q74YUjEAvlVhchZVFTWEOPGk3VPBLELURxWJ2uVQbbzJo0tErXErmmd/iM54sQ+d+ougCi25xjDDML9zNuShi2oFdImWYdxImziYsVl9Y4rER2DWykY3sa866xxYI99UR/pcOsJn9sdso3zrF8p+eQIaK+t/YSeWVs4OU9uts+oeOcvYTj/PkD/8F/+Yf/Xu6xS5Fv0w7WuOXP/tuvvX6f8y33/TPCLyNmW2+GPapEx/k+OLj/NhrfhUlN1/6v+HQu/jpj/1D7j/zCW6Ze9WXpB4jG9nI/vpMigRNjCQm1hbUUUjG/AkupswuYR1oALTB82FN7CTp3Y0nOkCVuJyQaEN7qkfpdMeBKDNYZ8OKsINlQpw9p2kHVqMwLhjEtgETSRtQmeRHLrwk55TLDAQQLoSrjRCC2K8TFxsW7NKQ9ODk0gq1uEQYgiJCGwt2hTKmWPHtDjsDplVfW4ZDlo0Ry+wi5RIIabehpcSrCEwrQkZlQNhsjCbJheVYBpanLNgVqinqnESYhGTtPKI3hlw+g5KQeGU65RkQfbs7n5Zla4BUto+UiTBCslbZQVgyhC0foyM0Gk94WFqPoFoxNCYFwvNJ5m7jwLn7OPkZj+54j8AUUKFnQ5YWngVClBRIJIkHvgiphIZubBl8q4U90F3EiNj2q46tY5pab3BvhADRjZCrg3B3GUikEjaM8dJJwINSmf7kLqL4BKEKUSb3LHZgZzkA3/lHJjToXhVYI77BY2lBUHoiQUZp/kxhmRweJH0XRtONESe6VLslVJhwxjxBZVXgFRPEOGjlwERtEJGBKGZp3uD3HuFCz/raRgtOrH4cr9dDRZPcPDWNMJpGXbNlF5RKGr2oEcIyoISOIRmE9IbxPLGJ6Of0idLhLAajyo4fqQYAlzTs3AHHF2Flxc6/tTUyZlcSH+OR+UXuDG/MxoinBMU0klCte89Q0oVAeSSJw35Mii5ZS0SEEAHaaJRwnCtfYaTKNK305EDPzmiXrQDLpsyyHUppQxqNtICAULZ/PZWhoSKxen8iBBEoCAXiOJe3vGaXMZQOevRknTBZIFrSmGIT0VnEJJYVJEg4fAgeXkxgDQgUw7Fekt5pTfvxyCY7WBfxlHQMrGn0EzEogQkVGoUZ4Kj0+waDj++5hK1p1lYlB6HWWcHShkC6JljypcFEWPCaDgJJIMrEFUFvMmapqaiLkN07FUkWLj5QDEzLTtmKQkjLNkpvvY7QPYlc7CEKG0O6pK/wYo+uSRBrkuDeMt2XFEH1MmaX8ooIF5amDjfheXuTjlT3MlHdCQunoFBnMpzj5LnHiNqSqgGjIwtyGjhzWltGkhkQn2yPyCxsPF0zLTlLZCLvtn0QxWuW/acHIF9ZFFh0mRSNgYKo4+kOW8NJ1rzniVYFkarS98dZ8QtMGmNZaulAdaHgxmmYgQW2QBKXtWUt9l0iAGWQ0uFBBoTuABoiC6rouIOQgvmLhpYD9tYSg5iQFAPLgBSRgRALdhWwtCPHyouR+Nj1QOsc89kxXnVZQh9kt480Mcc6j6L1DnwF2jeUKdMRrSH/QkqNKnoZg9fzBNPxFnj1EaYPbKUX72LhQhmEIq7VYGEgwI/OMScLgu6iRj2V0D3dpzs1gV8sUpY1IhIwMXFon6sJCdpotBHup21INagTqYoNUx3fZ295qTy4nLFA/HhYZ2vxRp4pvpFnF1ts7/4V/T7gz3L9mG1bm4TPsIWTnKRYlog1B6gDxsQEqwZZ3pQfelkbgV0jG9nIvuas+9gi5Zdv+ZJfp/7mHdTfvOOqx51ZOcEv3PNDnH7VY/zAr3wv/+rYT3PDz38DBa/E+bVT/PYD/5nffejn+eTJD/KTr/stmlbh84tmxhh+7+FfYlt9L3fufMtlj3vFzrdQ/8wYf/7Ue0dg18hG9jVgMukRdh5DmIjYhXpIFAcaN/JQcIFnOG8DgDLQCZe5qgbLYFRsM8t5MWv7W/RbguqpbpqDDYNAa8G5CzAj7QvtwqIhqgzKe+akYdIGN1odIgd2mICUgmIzKqZvrynDS2C9HPcyH/v1jNmV2nK7T83hDIqIJBHEMVRFBzbZOOi5LIR55qro9LPQEeEXrbtZMNRuC9AfjCG2rB4lFH0dZZ6z5floQs8yHBJZRArLdNMAqf6J66fZLTnNLid2nzbUSIPGoEyMlgFT5a3EwQKh9DGxZUH4MqBUTqhWJI2nQSqRRsoQPNGnKATLsdVYUiXryFxX3UtUBhKQeOgAAlVAxBItFMJYPabEH0OISySJIdExhRzYpXNMKiEhfOBU9rcxIDxpwxYTgUlShp7Vx+qbCF8EmzsmCbSN04kqaYQDxEwg2FY9zCX/sQFokoYreTZUsvfMMt2Tq4TPJdQo4EU2JWO/3kNd1HRXz7MSdFwdDeXnY9T8MrHpYyJoFe2w6sUL7JnrsbIiifrztJfLmK7GzK8SxoZkBcyqxrdCRWg/Irm0hliVJAlULt7NWgtExcNrGUAiVgWqZVCJ7Ru5Bp42+L7GiwSmJZGrEcWCoRwr2i2rySOCLvGlLloLjIzpYwHFhf4ip9snmGsMgOD1RDKhFEoNEg7YuZMMHH4gkX2EDEhMjJSBTeZQCjKBdgBTqAxuj04Q0rbJgnXuC5m/mzbQSPeAssIUPJACtdIlBoRNI0cqyW3ZLpuFMbpshosngACvKpA7p5AnjrvxMW7BLg2oAtDDUzA5lrB6AQh8DP2seklXohcG4Nd6KGj1vojkuIbEhr7ZcE4Hdrn6RTEgldU9jLFxjZ0FW1WTxtal9RcI34VSdsF78gJ6e4yOBCJU9JIWvighUIhA0D1s0E/4SOHheSpbT6VU6CS9Z2bop0V9dAb+kER0H1gheKIDzcF9A9A1gTQSz0hit34aIenP9/ClRwohSy9EJ3buSD9ldkmUFKBjOqt9Tl0KuFSc5szZx5Ba0e6BET08PEceTJiYgF4P2u20AsYmInH1lkJmwI9J7Pf+WExQUvSeT4jnW+wsbmFJrlgpP6FQQmGkbf2+yj7mzV765iQFX1KvCi6uWkblSvEIHf0sie7Z+bEO7EqRZ200Ip0PwkBgoWgtNPhmAOYawHRtKHJs++T8ypM0tKTVGkw8A5QrgkrJR84/RSqylqxFhJ2ufa6m2QONwngGM2O1GtP5lsgiJCskJQ8WgU6XZb1E1O8j8VndFbNVbWVm9wzi+ZiYActR69hpb7rQfN9H9gUdMYeqBsSrfvYs7R+cQX72eUSc0Fd1C3A7ZlffE5w5aRhbjSlXBPFSgGhIpoOtlDzDVNhjbnoHvVaf6pnzzL3GII9FxGOHKPYNQQd2NAXPLRsbQu06stOFYtGGYtukMfZz3dyJ6Fm9xATLys1bURXZV72VVukQ06WnmC49S29KcibusuidY3s0huqJwY7CNdgI7BrZyEb2NWXxYpfobPsL1+syBnnpKdSpzyEWnkEuHkd0lkBHICSmPIGpzKCnDpHM3YyeOABymFrbjdv8wSP/jd998BfwpM93vv1HmDTXMf//HcP8oy4cKTFd2cr33/mzvGb32/jpv/oHfM+fvIVf/Za/ZEx98cC6+89+kicvPcj33/mfrhie6KuAV+38ev7i6ffSiVoU/fJljx3ZyEb2lW9+f4HzrQ8SJWMUHDPBOikCISQGPcTsMk7tPYqt8+eHCYX+8/SIrGOgPPySx1x1uz1epHuusFQ8BJ277e61Ky/RsNrRTCEolBVKxlmiNlSXym0lxJ84UXP3cSriLaQN6RFJjHBsKI3JfGUPj4g+p5NTTEdjlJTPSq/EWmkvW3ZPItbOb+iP1QNLqLMzWZ2TlkYuJ5BqSvkVG3i2w7EOQo3oKwd2WTZW/1yEXzVgDOoCeFsl5bJk+0QBsThgjQnHADJAoWhD94QULjOjHOh6Ibjn3J+yZBYpUyYRIXPVA+yeneX86Y9jdIyWLhRRGK47Ili5e+ACG22ccL5ARgKNQPgW7Kp6ZdoqJtYWBIuLAl+GyJ5AF/MugAUdI52gtcH3B9+ZHNil1j8+TIJYO4nwBPKSR1JwGmVxB2OgJYuE4Swy6QxOKU6gx4uQ9DjWehzGwVQScIeYECaY4vTkU8RJF3XWMl4CXxIX4PQJDR+eJ9EJK3sU47NbOXzL9dx/5hNE1ZDoSYU/UadYGMgJxCWP7umQuOv0xQqWRXXae4qx3bsJlma4dOksUUdgnoro9S6w4oDR4FxA3SQI0yYq92ivnMM/USIKBaxEBAb6fpvqss2o6M8LKouGMACvIii1DA0N5eoKMga9XILWEitBhL9SorxiUNoQVs7SF8IypqRzvgWc6ZwDr0iizQZAeHD7LLMrcaFEWRhjDuaJZQ8pKmijkSh6N8yBr9BC0FdlIrp4pUlbllToJMZLSxA5ofTs2jYcTCCRfQMNH0mMqXrQisBoSvFpl/jBBY0Zs0nlGbAll88iumMW0FHBgMEW2HcRXZhAlwt47ZMWrXSpV0XggYiz+vXP9ZHLegAKrmPC6b7JtM0MAhN4aMQgkyu2eFkQCAmJlsQ9g1w8geEldn0zA9YVKKenL/DP+HiNFtG5LiYRyEARmz5eqtkm0iq5TKhSZu7+YDUdlG0ycF0MmF1OTFyncyanYyeFJCljNfFMbo5Ln8RoPClZdSHVAkGwvUp8voNXT8POJAqFiNoszGsu9ENaxQazwS2c7X6GdhKBgkCELhpRI4VAhYpk1T1jLqxS7Z7goo6x+VBUBtrpxCaykKsniPf1OH78HKpXJTxVRBgbpqnWlGPAOjapVnS6gmqYoFw6zLTn+6aF1nCp9wQz4uAg5K1rCD8SoauJBVzyGnbCWPAJWFOzCPkYVg7Sfn8pOWMZyx0LYLWieZ5otagHuy0TyY2cQkniZCntuq8CSBL8R88iDAShIQLiRLJ8XYvEg8TkmV2+ZUgVFcYDVtqclF1EPwQBcQAeCjGu8EqC5EJufGqXNdZzzL+Z61i8MMnaUkC7PSzmniAyMG+tsNuCZGnCiaoDp5QkjAW2QTaDpDCWaWaCMrp+kPPPtJnjU9myos0AW8zbwqLhmXvghuthesotRUIQ7fs6/LJCnLZ/d/wJgv7pDecbA0oV8FWZwBMEoaQ/HqO7Cf7SKWqeAJpshLE3txHYNbKRjexryrpOnL5w6MWBXfLCMbyH/wDvqQ8h21Zt1hQa6OZOTG2WthQ80b/IYvs44cIDTDz++0xoSaMwjjj4VuJDX89CfYaPHf8//N5Dv8TF1mlevesb+Ad3/DgTpRmS7+mz9PvPcP4/3M/O37wru+7Ns6/g59/6J3z/B7+Rf/QHb+YX3/oBJsuzX3iHAL/30C8yUdrCXXvecdVj79rzDt7/+G9y98k/u6bjRzaykX0Fm3OsFllghjEgsULriRloZ4kBQIOVPaFREywv+oyVE9pAUtLgHCdxgyCIitkFPDy08In86Qyw0MawXDyE1BFFowGJEBodlNC1GdTpcwijUUVlmVS5XVoBRLvGobqI0Ms88UREJ7KZG8+3z+bALp+YmI7QrMUtxooluqpKML2TxqQg6Vy0To6vEX2rGxXRcew2a8ufjRAtSMqOWeKHmNkIMenCSwINLZvOXQlFvGZonehC3KfdNvgXFEFVcttbJCIqsrIIwkiMEIQGdlZ2EM+vcl4JpBAYaVkO0mnppC3OAD4EWlgNMqF8K3StE8e4k9kufjDj0UulxRzYJYDC+YBYCgg3vt4LJNoXVGoBl3oSXR5olxnnDszPd+n1QZVyzK68Iy2xA8R5UnLtHN7CsnXEI0F0XhPUfJKpA5iThsVglrHCQVh7ICtDPecTb/cxcd+GEgG6kqBTVkgA+7cLlgMfvxARvc9qZnmeoN+QrE3McEqNE9OjIx4mCDzKQRWEoEsbPSsJZiuExdqgDR2fThQgOg6xCaEfRyShRuwIqdS30fbPw+5xhDlF+YBHxYmTd2sd2qaEpoJfg8qROlG5gyommIv2HvXDImsX7OCvzXmsnYnRRQgagu6yYSlRNMbHWGyDWuxQPlSnquaJ5jusXTRMGkVcqwMrFnxxEb73Lj0KlMDY8Ccp7BjpqzIm8MlYEVKilEDL9eJUA3ZTLPqZdpnREqNS3T1oB7N0jE8BxWxtF92Lz7CkIxuGaeRQGCO5sQqGWvcE1fY8XW87s8sfwuiYZW6goC8NxrWAcv95O3c3ZXblwHZjdQRTpKq836M/1aCnr0dPTMCl445WmTA5rmlJGJ/2WTrZdfWC4pE66sJZumdiq8Okhp1i40LJwhDCCUnLV4hGCbXbAxemlmhQjgmUGI+nP95n2zaBMD38eNUSuzLwRNloT+kYYWABlT6I0IJdShRc99mQZQ8LjuaF4aVUpDSvzZhdwmhwguJi5Qz4W+0x7QhRlphIozyfSEYILQjMQCvPKI9Ex1zsneFs9yxIu6YFswWCb9jFhZMXAQtyeEpBf41eHyqNkEod9LntrCXn6Iin8UWFQJQs2GUShPDwyh7J+QRjDOrCKkG8gsauW0rIDLzTCdDvIJTgROCzpFdRUQX/bIAs2jStUkhkLC0DS8CZs9ApQENppHbj1g2johxnLTlPlLSsDmQxsCypxPWVGxQ6iTAFW4d+I0bXNVxQJAUFnkG5tUgiWDSLSAmt+RCdGIwX0XeZdrP+xBAEIgPlLNgVQrJKGFrsNUmcZqOWDjjWJEINwpyFR0yEUAI9ITFn1tDd7SSdGh4GHYByGzE2ie9grGgdWdDKhWsLr8jO66d55JglIeZHvBnGve0zw4UxioKAmwJ6UvDsk01E6NhoSKTuWgAwqCC8gMgLiP0qnnv85yTZLKPSXWNxAdhm2X5AtsElPbtxJB3Lb9XfSdnvUYgusak5AoFKwWrfPf+6PcAwqaY3P2+djcCukY1sZF9T1n3MgV0vhNmlY7zH/gT/gd9GnX8U4xWI976O/s6Xk2x7CaY6Qz/p8bsP/hx/9Oh/px05kVAFVNNClqk/84t4z/wi8+6heXjyVn701b/MddO3Z5dS1YDJ//c6zv3U52l99jzl2weL9VxtF//+697L9/zJW/nXf/l3+c9v/uMvWMPryUsPct+Zj/P3b3s3gQqvevzhqVuZLM/xyRMfHIFdIxvZV7ll0YkeaKmYU1utLop2ITfuX5HCP9q+vd5wncAYhTzmsgOWEoSx3pwY0sYxbPF2U03atNUWFjBoFySTyAKJLKDjGGP0wMf1SqQXFkqCiYaciEYTzkxWCYsF9PwllhdjRCZwLkiKk9C5iIdH15tGC0Wltkyh1EfPPkNlewM4ilAupHAqQp0OKaoQm6TNDNqbtmLgybtucSBMI0Zc8uifaqG6CrOWfZVpMvk4wXlRoftohIoVxhg8o9lenOFxWg4kklmfeyogjgdMlEHadYERnmVQCYWQ0Ewk2gMpfbSxDn3poE/Ba7D0EERnWmhjwxz7gOdLpqYDSMEwoRAiJhQhRkJ9MkAuCDRB1n5tFMJAP+lhAD/HUo4vdIgXu3jNgm2Hp2nGjyKmBcV6ggx8SJOUYTD1LZj6HHHyPGDwRWlIIw0Eck3ZfgzcfS0a5A1ldCcEqQkDSaOuaPcF8c6QuD+JrxxTzgOUQmsriO17CuXGR5RY2sV67UwlZQ40gHO9C5xJziCKPtdN3c5zLYXRsFKIMFVQ1S7nk4uc7l6gU+ugCcEE+JUOhegxTDNBFkH3JMoDGSbEHccGHAuIVxPiisLUExIM/URCs8ialpSrCWO7ffxViUkS4p4BIykXzoEu2QHhQK2O6TpQxKCNQbl2LZWvI9k9A/xRNmzBgrXgyFSZ5pa7j6LLUu88iU5o1hT17ZLlFqza1G42KxsWjJFCoZNoKIwxHRIZgGUEZVFG9Bfc52kdBHTIsgi6W47SXTAehuF7M9QAwyA8SUiMFxLsKSB3jNF7dgWTCIxwc1RHFFniyGHBat8nnVuVW3wKu+uoUBEva5aKh9GyTbL0DPWGyC5DUVAeA12UIAWFl87S+eT9yI4mAeIIpNBYPXo1mPc6ptg7g+UBDcAuKQXGt0BUvw+mr9GxQASSREdIUXVj054SSgvGep7KxMPz80RnzK40G5/T7PIEURQg4p7VKgPoJ4ixwGYcpAzPLiFihsAuvIDl7jyxjgYbCznwxEs1nKRESYleW6TdhnB7GafTjicKaCkISQhwmT+1QUgoTGmaTz6COR8C+xBI2pN9vJZBCkWm0ZWk6AgkYQUjDQJNKAMHbliwS6XMrhxU4SmNTFnIDsidDA5g+l3U0pMkYxHhyyYx7zMMxr5lKxsdQ2hIXrpGv1fEFDX9o4K4YIXzpTQIY6iWobpV0GoZxBmPRIApRxjlsVI+glo9TZAsWTU7TxBIn8Dz2FKYxHgBog+FUON1NC4/AStrijF3T1uTt2BOPoVRbdZijQvOJ9kq0UsKuRzjiSLtiS5BKNg947F8ys6v/MwxvRXUcx9DZGCXR8k9WuNkONrYZP/kPvMGY0NIQSILtArbs8ye7S4EGJQAE1QGzLDpV1I7dwK0sfp0ObZphv2mySxS4DYFu+Tw9z3ZoFO5g62LHxhUTIgMoBwkN7A/fbmEFFUK5wL6iqy9V7MR2DWykY3sa8o6xxbxZkp4zWsAiYzGe+z9BPf8MnL5JMnkQbp3/RjxwbdCmKFYXGqf48c+/B08eelBXrPrbbxh37cwXdlKL+my2L7AfOc8i52LzK+eQi88xfYLz/GStTa7SzV6pohed9nxv3OA+V97jHM/cz+7/9cbh17MdzT281Nv/k2+74/fwc/f8y/4gTt/7rKi99div/fQL1EJ6rzl4N++puOFELx02+v50NP/i37SuyaAbGQjG9lXpqVLR0VUqZdnCHtWH8a+fMrsmO6ZKZZ7Icxax8DzJML3SXZ3aLdjkqIm9rYgogVEjiUhjEYimVJbOZHfdXYaP4mGJFOtcvEeCejaHMm0JA2FMo4nUtiqqN3QZG6H5PjnfHpJhBAxRnpIp2zfOVxDfvIinvBIVJFaOWatscLTi22iJCRZPcHByaOoqoe+sUdS7FM6W8nStltVGqwej6cRbdChgL7rLznIiBbP9BDzks4jS+hnPCgojLAsKlGR6AsaL7Y0nHjZZn8TCFqrCdUUTNTSsj70ACjwZMi53qnBTUq1mBA2I6C09RMCDukZxoo7eLjztA3FcafIgnUG46Ue2iR47pU+LElIHVcDujaDWDhFU04zW9xP1TzG+KUxns4xtFLNMm06GCUGjq8rY+3jZ6m+bivx06sE/WVkRSPqHoU9zo1IBs8o4VlmWl/3EAY8WRyGN4QA6aNrc+jmCrBsP57zkGshxB23++/qtKtAslglVEu2OmkWSxLiGHxPoqRCCkmUuIx568L1S0VFuWwy5uGZ6BxdepQICL0iFb9EIKs8tfQQy/GDnL5owxArXokxv4mn5zBO2yi9TxqTMRuEHnyXeXLSA6cjZXWQPLbOQXIGigUBbs8s8LHMQ71K8YZtRCtqAAynP42xTCP3oRYDMNK2140ff51m1wCFZEmf5mI3pITB9xV7r1Pc+3mDES6zoMsjIYREa0Xc71lMCXs/sxBWd60bLt3A2dNneWbrs4DBc5kzhcNQpUsGEakKnljN6nElZlfqlQs3J5I9dw3aJywjC2PHvTr/KKI9767p+qMi8arSprhMb4X0WItD1CrUG+4yGijb+a+lnb+JdkkCfR+I0Al4LmzQ5MaTOfuglc4yFnCJZdmCVBJ008BZQ6X7HN2nbNimHygSE6FsilkHYglCYTdkvRywnB+3WRgjgzBGMAgFa+clheMdhDdYw4USFA40Cc4W6EhI4oTAae8ZIcAv0+qvUPDL6IndGKGyOTZcD4knPE49vQJAqVGi7eaNEj7aU3iJHoQxkiDxKfZOsLYDzNIapf5pyyL0DHhW7zAFp/RASg4tFZ70mVY78eQyQjsmj5AoJEZCKMMsvNACUjnA0tn0s2UWhGB17Th+5Xr0WMxY1GDR9LPQfO3CypvjgoYHxy2RzS5dBsfsGpQahCC6HnEpQWmDUT5aBhngbNAEgUAaw02Ng3RXErRXQAC+0vi9NtXaPBc12Zqg0STCs0lHVZeH+k+xv7CTpPg8pi44fuB5FsYCfFFkLTnPgSlB4OKHcxGwtt4mQUYttAOVpfKzUOMkIV0sLm9yeB5GqmrRKAd2pcxPIQAvzK6tDYNjYB0CZ3+kH3U6cPHiIGQ4k1MTubKAhdINJNMGdf4RTGMHpm8PSpeFNKFCw2syVipzaWEJgKmpa/ONRmDXyEY2sq8p6z66cE16XfLcI4Qf+UnUuYdIpq+j85pfJtn1qg0vYoudi3zfB97GUvcSP/m63+Kl298wXND4JoUnEd5j70d86ucp/e43Ex1+G71X/SAUbb1kwWPyH1/PmR/9DK1PnaNy57A+1yv3vIXvuPmf85v3/Qz7xm/gbYe/8wX1QWqnlp/hkyc+wLfe8D2U/MrVT3D20u1v5P2P/yYPnr2b27a+5kVde2QjG9mX39Kd1oPeYYLKJP15C7CYhEEciIB4ZYooqOJnkjoCpIfylulPGNr+HIk3RZGFIcaGSEEs4ZhiBoywDrTv2YyJGg1GI0wEhDayyvPB8y0QlHI9BFbfJVRowEgrgKx0Fy0UyoFd4YwgEmTgjlQDL6BZnORiRhMTiKqBxKZs93KOnQCQHn4zQszEJFMK7rcOpSO6DLRyAgWJsYyXVYWuJCjggcbjxJ5AOdqDbsfEjT3IC6fotTsoM2Cj2ceKdCLWlkXRyfR+BnoqKdglBRaME9bRN0YipD8Im0r7XAhOLj1JP+mhnKMohXRAS2ouDE1IKv4WkCeY9afZX30tLKcxV/b4WLcxHoSbMIrbn71APN/D7y+jlU+iahizPOhPnGPu21CVKLGxNL4oDbJ9gq13AjooYVQ7OzcBPOnRc/2g3P3ypUVPpJTMbYG9Y5rxItz3iAW9fCes7auAyAEsguHnuBCSqakEctEyBuuDhl4BpSTbgpexpXaaS94qu0o9ygXL1rjYEzzSG8cknQFzwUG02mXeU7rNUvEwwiRs5dQQUzEN3ZNSUSpJVF2k0ZsANJtQXAuJ+13CXTU6FwPEyfVaNBZYS1kt1cY6Jy8dP2GRdriVsjxjRaeSCKN8fGGyNKi6kVC83oXUSQtMa6Mts8vY0LZW20Mna4RFSStRKCE2aHZJoRCrYNbcvHTrQjrGpe7S9cbphTPUeo/kT91oQ+9dudDA3OdCSYg1ll0KorMw+C5QpONc5MEph+dmIZvaOC1AexmpsNn7pNXrulB/FShNhQ/b44MaQkAsi3hJy2X2XHVRlCUWCwcw6hQHDth1Q8/ASq3FzqeWwFTxpwOOFR4m7vdRDgVMWxQK+07m5YTw8uN2fVZG0jBGT9CJPDpPtjEHc+PEIQjb6/t5WJ0jFAVEAkaFmNoWAhWi0UhPYsK6O2Vwbd+xIw0CYQRRq4UMQ7bMekQRtNpw6ayya7GJ8Z1AvUgShAQfyxiLEyhEVlzKODKrTBdVwMT2OSEkGOmjjKKMBXilGaxhBgPCUJTVbEwozyAdXUrnJlnYVoyVGxQqS1TrPkgIpQfdJdsigWW05Xp3905B8wIsOX0rfVjQfxBUYtciKUHECu3ZxCFGBnYOKpGlbfSVAJfRGC8crLtG89qdUzwen+Vi7qoaDTrBGJu8FCF4lktMuXHeihfpaQjVIATbS/UkJUOM5CRj/HUxwmpweu7QON5srg2vKUZJztRfTxhfYrx1PwYFSiDSmMM0aYS0myfS9fdTT8OuS4aJCWHXv2w9cGTdHVUS9zw5fcb+v3OHOyYDzIdr1g63YZqCuGkPnOgZhJSMNUEXbkRVnoX5p2nKJmJ/C0659lwjD2ATWbGRjWxkI/vqtKQV0X1iieLRycsfFHUIPvrTFH/3mxGr5+i++T/S+bbfJ9n96g1AVz/p8a8+/B0sdM7zM1/3+xuBrsuZ8omvewftd/0p/Tv+Pt7jH6D0m2/Fe/wDmffZ/Ja9+HNlzv/sA7mXmYF9243fy8u3v4lf/sy7eejcPdfaBUP2Px/4TwSqwDce/q4XdN6NW15G0Stzz8k/f1HXHdnIRvaVYZkYPLC4kGQ7rBvCGLMtVOPYRwbkIIueEdLt4uNCm1KzukCGQXnYs7MX78QGt9mXYiEQLhX8ddO3I5SyoTmY3PLrfnHsIk93iESMklANG4ShsD6FO8zzBMY3VLwStXLTtTuxAIN7zbVgl+foGLnQo4KACW1Dmdy1rV9mBhnLAm9Qlh6EF8Vb5tC+oSQlCIluReiwRqKcI+kqKI0iDWBMWS+eGoQQwiCMMWPSrAv3SBJQ0nfAYdoGq/W11l9Ob6rrtwGzJS1EAE54DDOxl/7Y9cRePXeMdTJj3UIqn1C3B7En6T2NLLikdJcoqJLIYtaVKhceJqzSswWeDPhiHbMLgYics5rzQrTReM7hTkycsVzyYNfYmGB6GrbMgHaxX42q55rt0b8Ms0sKifINHdPhVPI8CQOGnCd9pABPlNhS2ce0t50dxVm2FqdRQrroUzGUtVA4FCWNuIuURMuQRJVS3uDGUEoXlmXPz9dNUG5WkEkPYyyAllbfCMNMOIkNY7SBbLNbYO/+dbSNlDGooOtNov0SoreKGevSPypgSti5DRAYpmdn7PEexIkV7zbKRRAKQRR5SKmtQ+4YSYMQpLQR9mcUWTZZwR2Qgl3K9ElkEeXlKCl6fetJC3UNzv4ZfDboQKu1lYYa596bROCuneJj6841LuFCJkpv7JomRA7s0tAXFQrlASsyrmyn1zhELMuD86UFhGRfobHrUuB52ThJiqlAkqG0v8iyWnSAj5/vNhQFd882p+Dk3wv377P3pe81wINY+xY0iXN94O7LWGmKO3a8Dl8FEJnscylseDVBDlzLjdHAC12zJU8/qej1oDw9hZSCMBTcdKOwGV2VhzRWxj7RbsNDgPIswpKGrtkLWM6uEB6dQxJdDTFaZHkKjFQkqkaYLNiOSQF7BGVZYlJNscXbPhgC3oDZNT1tBdBTK/gVhNenRwTKIDvLyLULg6y4Og0bt8+3wDcoX2TMqLnrtnDwxobVhzJuvBuPRBhEEmGk50A6aROnSIOSCqFtu3V9W7aOYhJ86bOvPKg72Geh1jYbo5R2qY5Nd8OMkMKjqrYgpCAQPtqFyeaPSzLmX8eu8UnumRsPWFPZOevdDCXQMsi+MEI5xMr9nY4bP2WWDa5+/gI89bSh3c5NtQMTRC/dSemmCcxc7tnCQLtLZmORK1oYCl53l6BYFCAkwda7uM67kUk1iVEQBFAusulSspmNwK6RjWxkXzPWeWgetKF0dGLT7+WZByj99jcS3P8/iY5+O+13fZD44Fs2gFyp/ernfpLHL97HD7/qv3Jw8uYXXqGgTP/l30vn2/8Q09hG4YM/QPjBH4DeKjJQTH3PDbQ/f5G1j5/ZWFch+Rev+kXm6rv4iY98FxfWNmYsuZKdWHqSjz77v3n74e+iUdy8Py5bbRVy69ZXc8/zH9oUiBvZyEb21WFaG3x8tPDp60bu8xxjQgwYBbqXODaEAW8AyKRgF0CeliKMdunVRVZGChQFAQiU0/DKKSZpGCtPMlWZs2/7RmdhKlmFABzwoXSXomqiFEyWZ5moztIch4KoUlZTHJy5nj37t7Drhmm8rVbEIzEupMoVOebXUZnoidVmQQhE6JRwUi8hh9llWjlhjjEFGFd2oekxPmModRUrHz5D//gqUkLkNR2oZgvyTYCKQJsYh9vYsKJBg63Djd25N0JlIWMyA7sMQvn0kj7HFx8nY3a573VD4JV9fAKqt0wPM7vEIBwEKcD3bdYw2xWuPg7sMm0CISmuPW0FsHNmEpshUJk+iVdCMBCuL1xnnUaDQfgeBjh7zoIrUijkUHslxHaUZF6PgG2NfWyt77blqWLW3ykwmAKXxmikFIPQlvoAFMtnrsubRCKkZkkvck6foU+fIiV2lfbY49N+TKyzn6+udY0FBsXaGpw5Y517A/RUg7XJ21keu27QT0gX3iPQtVm0kwIQTtsNNkYYyaCENBGdVpwlGwAL8BW9AjaM0YABzxd46xIQpI5oNoyVT+UgJNv7mMkicodEGoOvQm5o7idwfTq7BWJtWWpG2lkokUSRDfEzQqa8y5xAffrTfp707Fl+KnjthpaQGi38IealqyzrLb2GMXZN2ew4IYXz4NeJF+GYXWmoYz62Kp3LrvJaW8F7f6uCuQCpbB8bIehH0O1BpSLoFLbR8Wcsm80vZACmMQ7QRSAiSUIMQqCk56qVq5gxCJfoQUAWxphh+Wk2xsuAXSmoro0mDOz6ulC6gcXKUSLjAwbjFmUpQeSyq4qUyRYlBBlYbMEu4W/u+gsB3k03UzgyReKeD2Zyz9AxEoWWFrynbYHZUvd5/NUTeCIiUaUhsMu4W6+kJKpYGqSJTbapojF09jSR210/9dO1ViGEYMKbIgi8TNpP5QTqC0VopJiKgILTgmxFaxhlnGYdmKiNibUVdCfH+nNaXummxuHxG9nSmM6+EkagjEJLt7mgpM0+qCzrTEo7dnAZJxEymyTy4hPQX6Xql9m5xd7ntXAXFyu3kegke9plexJuTJQyOUvB1vB2bh6/DU94GOHbRSM3J2L3HELZjR3RNxn7Mk4GexXe8FJhmxcodM2qzEuTWIanUBbgSlGy9JmU25gZH7M/tYZu1zHIMuzbZM81PbxPQr+fWz8Y/v1aTCooCKsZabyEWlVSrUFJFa9+MiOwa2QjG9nXkLXvuwgCSjetA3eMxr/nv1B87zshieh80/+g/5ofydJZb2YPnr2b/33sv/OOI3+fl+940xdULz2xj863/A69O78P76m/oPQ/34488wDNv7kHf1uF8z/74KagUsmv8BN3/SaRjvjXH/m79OLOJqVvbr99/3+i4JX4m9f9gxdV55duewMXW6d5ZuHRF3X+yEY2si+/XV84wgH/Zs403kBn/3ZEcxDOY2NMgBzYZbr27dVgMM5J3zoHBuWcPAtoRMqJLTtWlsnALuPALvA98ETgQAnjABwBCQgHrkilHNC1CaieYycFXp1Dk7dR9MscmL6R7fteTzx2Ew1vB5VimbnKFI1tJTzfgV06xvhlDIaZwsQgjFGkoJXBn/IJp6XN2JgDm1InPnWChAMWUsAm0S6FfagwWyO8aYE3WcwcFq3KGLws5MQjQEaGSEdIJ4GY5JubhoA6M0INWDTu4yQB5ZyOZxeOZd2lHXXPFARBo8xtt72ZO17zFstCAPwxgfXCUwBRurDI9GL2l/Y+y4jTpodSNqRIRMPPG9PXSJMgTEzsF1kpHCCuzNKevp3Hnqtyac3QahmMpxyjUNtMfrZRQ+0VMc4bs5/fUj9M6BWZrmzjtXveTuAVBmGMKcPPc/fFeXHXX58wPQkFd3886ecusQ4oERLlmYzRBdCUY2wr2Wx2KbioNWixzjtMARMhiGOYX7Di5QbDqr+DpDhJkvPe8gLspjJNt3HYdn1eZ2v98z6wIW2fubuH1gOWo688C/IZK1AvUpDaVVhPXYeePJAVMwC7PPwJiS7UMGGNcsEDo6kWaoR+mDWq2bQgkjYJRsKZM/DIoxDFPtJPrJeZsvcySSfhusUBd32PalWybdYBFp4g2d5E7pUY4VlAIHWIL7d3ltPsEuk9kutAICUwibHhVuvurwxkprdny1s3gVxZBgjnFMEOD3zL2pOerd+iJQBRrYEpT7BUug4lzVBYZEqqFIAXS7SJBtny1KBPkr2G0n6V1UOIARNx0GT7nRoaO2bD7/lsjAiF8MskjgGlu5EF1XrLqEpu3Lp+MJFmsmAjHXwZYtDIcHNwTUio1/dy8KZXZ5+F5eF3ZCkUiWN2mSci4kWNMBqvv4CSEbFXGgY6hL3nErum9UUXnVik2CgLviWFGrKR9pMDizIEBeoNiXI6kULqbA6kGypKAQaUVOgJYdf+wM9AU6Njl5QlzQKghm5kus6r7spg3TBADEp5RHJ4AkglbTioBKUkJDFCCQd2retbAWFgy+x7DSIZWn08zZAOXnrZek1wYF8K9klqYRNjIBGBLT7dlCrU6DW3Y5SP8Vy248iue1I6Ztc6nay86WohGyOxLBGG0PfGSJnXYrULBsbGoHhwMAZuPioYaw6XlduvyAC29ctbv5/T/GN4+grgtls21hGsLycrPp7LxigVloEKTBUmmSxupiOz0UZg18hGNrKvGevcf4lwbx1Vy4WI9FsU3v89hPf8EvGhr6f9d95Hsu32yxcCdOM2P/vJ72Outpu/e8sPfXEqJxXR7d9N51t+B4Sk+N5vJ7jvV5n6nuvpPHCJ1Y9sztzaWt/DD7/qv/DUpYf4+bv/xTUxrZ6ef4S/eu59vP3wd1MvjL2o6t6x7XVIIfn083/xos4f2chG9uW3IzvH2DbthJEbZdhmHaIkFjx3PM/uci/RnQQhhQWEvDSzm2Ai2EPosokliWG5sJ+JuSr79riQHSG58fo82GXwfNi53WfX7oS5nTZ9uLReHyr1nKVC6S5e0sqJcqcO/QC8UKaXOfKeCmjUZ9izR3H40HB7lQPoEh1DoUY0fSTbmQ5lQMUrsaO2FzBUbiwjPAs06dQpdZc3mAz0kI5WkDKL1O4CpZdKkB6mnqD2a0o3TdD4+l2Ee+tIFCanr+ITQEEQ6whZdM7wkNefuw/YbIwFJ5mVD/eQKfiXyyN/dvE5+1EBQq+I8lN1cMeKK0uab52ldqsPc2m2NWwoqbuyCTzisapjM9jMauktqNy5hdKtA1kAYawmVuyV6HsNlqpH+dh9E6wFuzDC2J1+30Mby7ySJu1XN74qloVA7ECrFGRaFzKZ75dAFdxfcujYalUzNTUQsh8Cu9Yzu4RCKI0mIXSAo0INsmCmoKImY/FkSQOEPcDk+Fi9niDeehutYCtSQmx6jrmVOz9lRKV4y1Bo3XB7ZWhBWmmGwS7PJSkQGJso1Tn6wkvT4/ngD1KS1ar2snHsyg9rICSzEyEzNxpmr9tGYccAhFFKgBQYbBhjrw8rq4Y4scwuhMzaMwBg07611xhPtlMtFCi4fjUGzFwFURBo4dnwK3dO73hMdKHHBsuHMWbMrmEXVShhUWLpQWFYg1SEim1brQB9ocC6ezcAq7LscAbWwh1I5QALITKwq1IegIaesOHXJnThtXgwvtuyuZAkpo8wbp3IhZld2vMw4UxCxuERUJD19Nchu3wY43rkwJ7p131iB3aZTp9idBZ/6dmhJAlCDUCbelDnyPRtBCqwGxbBZcAu9zOKLcun3CxvEAAXeBipsjVC94wDKF1InipvCGPsbY1sNtQATkX3YrS7xUrZdVaVMgXxdBNEIV1GWghCyVjDAluFgs6Ac2NsIoPQs/3j7W0S3ejZtT/0BmtAEmG0QTs9P9xmBxiMfRwhEHjnH0GkbCkDxALp+XRiQ69vMMpzml0DZpcSltllqy0GjNmhTh30oSYh1oMQ6r3127PfUwsCGy4Kds4ZA1oGQ9NBzwgSBKbYxFRtTKQ3bllOnmfHeUrQSseyKQ3WR5Njhvb8Cda2vIp2MAdSIDoR4aNnYU3jj0nC3YNkXZADvYfa6J4lBo6fMKyuDR/S6w9P5zwAt3UOGo31s8L1xY4qtbvsGnuh+lLLQFN2263gFTaA3pezEdg1spGN7GvCjDG0779I6ebci/nSSYrv+VbUs39F7zX/kt4bf/qKbK7U/sd9P8OZ1eN8/50/S+hdG032Wk1vuYH2t/8R8YE3E37q59gi/h3B9hIX/tPm7C6Al2x7Pd9x8w/yF0//Pn/82K9duXyj+bm7f5BGcYJvepGsLoB6YZyDkzfzmef/8kWXMbKRjezLa75vNY4yc++G58+B0ekuOhu2WuuFcSaqW7OPtoTXsbVwu31JlRojFMWyJPAMaTbFUiktw6CtD82WyQKaLqWXCaLDHetsaRtSA4OdfIaq4JyZnOMwua1GGDoHVgpEYIEKpUT+FKRvgZE0jBEvtA7RrKJ2KOTm5iFKfpksjZ47Ved35N1Ovg6ciHfq7LuLxFsP0991NDs/0gM1YOEJB8pIMDaro28CurslnaOG9HFS8nMOhBOaT+tikBkgk78tcsiRMkQmZrE7j6d8Du+8jZlKTh8mH8YoFV5VsuWmgFuO4kJHh581QlgRNGESG45iwBv38cYLCD/XN7EFKhL3HD2TRTpaBzjRBuEp52iZDKBKsb34+o5tZWwsaOXaV/Y25pBPxeYLnr3WgAFjC0uc0zgId7w8s0sKCcIwtSVmYtxmQFRCWT0pBoDUiROA0+kyOQAUxFBGvqgvOD/v2wQKEmLTx3PxexnYtQ5Ik0JxdMvL2VmaRSyeHKpfP3GhjibJD008qdy4M/STaCCjdhmAZOtWC3ilIt341iHcVpzmFbtewvQd+ywOmusf6aVhjLmChIfy7DxPj1x/yTRMrJZMsLu8E89zTBpDBhoY4WV9nFr3idWNFR/S7Eo2d2KlcIxU0NWp4dN9SRgKJqcdsLbu/DS86tI8mS7aamEf0daXIj2FNIZuzzrhQVFSLtmGVCu2asmUna9aC7S0QIr0BImIMDrPBHTjSdowutjdsD3N6ykpG3Gwvmm+t/m91Jkm03Borr+jSn/WAmdq/nmK0XkAvEqunAzsGgjACSHAGORlwK50evV6cKb+evxDr9x4jPDQfi5LntN8t9pnAuMPmF1WGksQTxoKb5mx90+BiQENQkkL6OlBgo44ZVkJRbEkmJ2DiUlJWIAD+4wNO3XPrZQsumXGnjM9neoOxuiCP8CZdIyJNd3uCgDNwmQGqmokCSZb47NxiweJx/i4j/EMnQ60krIFuzy7lnjKAZw6yvTahhODpPd6sNae7z/MpfbzVpdPwFhxODlVdl5O98+GGPoEucTo8SFD/3UBpjyJKGt6ryoQbrOi9p6yYYxuepKS8/oHpjFFl4RgXRh0eayCp8jCfQGkiSxRzR/2gdYzxbJlrmTBwKee3tie9cyuDYjvVUwIyzxTElBOgoCNmxqXsxHYNbKRjexrwqJTLeKL3UyvS534FKXf+WZk6xLdd/wa0dF3XtMuwLELn+cPH/0VvuHQu7hh5qVfmsqGFXpv/hm6b/xpvHP3se2mD9J5eJ7l9z132VO+7cZ/yp073swvf+bdfOrEn132uN954D/z+MX7+Ie3/wSVsH7Z467Fbt96F49fvI/l7vwXVM7IRjayL5+JPGvGLYHtzuCFGsGwA+TeXrfWBnotQvkIbMhhuWK46UZFvS7xvXSbXmShJzZCxG73W0dN82TrpPsby+xKnQLHXDFC0pnaR2FrLkRJFbhUvoWz9ddS37c3Vz/AW8f6cH8ql/E2BUKs/pGkfMAjnE3baENYTAYwiUw3WwjhsrUZEseYUROgagG+C7XRviIqOAqNgLnidNaxlhUXYBAkTohYCY96aZLDB+7IQKaDUzczU8iFYAgbHiKQaKEIHK4VzdyUHaLNAMw53T7LmfY5MIbt9X00J6ZtuFhquRDQNKxubhbGxqwAvzBON2et55ohiWQJX68hVJ6aQSb+bTvWerdxYPsmZaAhJMqzrD98j0SnEGg+dsX9L1OB+gQk3NI4TJhjZaXWd2H7oXTaMq59ke7z1PzDxA4MS8dSPkxwI7NLoo3BDzVKCiYmYNc2Rb2WVR+wmk2GFOCR2XeWBzIov9WG507o7PtY91AiwBMhOgvZ3MguaxYn2VPehhjKDAfTW5xYuYhsuKI7NZSB1cWLumQ0FBgwu9aZUoIj75yldsd+9LbbMYEdp1JIqi5UMlMHdxZPeXTGodvMA8+eZYRImd3DDeFQKRgjPJAS6ZuseOlAAy08PD9fLhButoGY6yujh4DF7IiU2WWGQXLbHxJZdAknbG2Hi81VPopyIV4OFEq7MwztKVNTgle+XNB0r1Da97O2aeFZEo8SJCLJwC4d1unTH7QTMrArn910z9h1HJ4axG1V3XtaPb8e2Kvx5KUHs/ev9D4EoSCqljDaINsLiJqkctTHGxuA4VkSAfKgvlu/4uH5nZ3jTun1AKHww433oFRU9OoS6XQAdddA32SR2CIo0BV2DZYpgQrtdPtAe+DyVoBwWUCNyMZzV3cBKDo2Z6UqbcILB04JKRDJ8PzauR2OHIZyxXd9HkFxsJ5c7F6gF3Vo95dQQnF0+nZeO3lHBoYZ9CCktLds+2hiH0cOfgeVqodRhpVVOHU+IEkMUkqmpjVB6Fh5OnZ6WptAKmIQJpmaNhCIChPFbYRBbsNp6F6o7KaUSoLde322zA4X3E96mMYO4ukjmMZkBvorZbW00uQ0c3PuFE8RbbeRHqleV2qB78T+cxXpq6rVNGvuHDp2/TogBBQONlCTpaGEOOttSLMr395rwKtStuDq1MuHwd9rRLFGYNfIRjayrwlr328T/JaOTuDf+xsU/ujvoasztN/5ByTb77imMrpxm//4ie9lsjzLd976o1/K6gIQH3k7nW/7fcZvvkB55gzn/vXH0a3+psdKIfnBV/4ChyZv5ic+8l386ZO/u+GYP3n8t/it+/8jb9z3rbxm99u+4PrdsfUuDIZ7T3/sCy5rZCMb2ZfLBi/bqRNkmTY5J1SLDYfn2TFGKgQye5+vNT0QEs/TGbPLcwBUGsYoBIyXpkEnLEer7qLWYVWZuJBzwESAuHECGaaIiP3RDWZIZBEvHAbj5DpHLN55J/Ge1w7CGNMdeqOHxNEt28BphAlpd8aHfCeBUNBKOtzbOQGAt2ua6mvmCKZUVmaU2HX69sZ1FqhJtWY8gUcIRtCO+1koyZGJVzBZHngryi9zpLqXveXtpJ5ipWxZKUZ4Wd+b2lx2TqkyYDY9sfYcT688DUJQ8IrIyjqwaJ1Avav40N/e8Xlku4/o28yHsapgA7V6zjGxx8uCN7glcQ8jPHTROkupEyKlREiXSdD30Mb2kzK2z3p5AXdPZmGMRgwYc+stzazoKwc6Om/p+aWneX7paU4uPWU/T/Xf8uN5E7DLGE1v6y3oYhNPCaplL+uLPOvAZI5m/scwswsH5qXnxqaPEiG7i6/NwMX0Hg66fxhkyltYKjI+Dp6IbRhj+rnykbEDxpIkp9l1eQ/RGytQvGESU3Hsp4w1ZQY/c3MiKSqWd0riihtzgMCBXbk2q/w1BZjKFD1/nJ43BlKiHEiqNUgHRJp1YFfS2LFR0CfXSRYjdykP15tjdhlthmhmqTNefe1WwrkU5HLA0JQLX6wPmIO9HpxyqhHSsc5STagwYHi+GHuXjQNjtAaEsmFvaRW1nXu9LbfTlWkyAsGJ9lnunb/P1iPHytze2MdMdXsW9hl6RV6x8y1cPzP8nholEaeWn+XJSw8N9VEYgi4Fti7GdpVXl4PwT9dXWRvSjwJpCafTlwljdKcct8teJgqft9uOemzbLYmOdBACknMJ5ukI45ftpkno05J23ElLgEKiiXVkgR9PZHqCgtjOISMzFq9GYAJDQWWxxrmQTGN10daDyQbH8nUh+jrGFAJIs0gYOHnhGN3eEkW/CF6INIm7t4LEJHgOyBZ9G39X8erUgjE7zlVeR82K7AtpVSqVE8gSUgzGcKGR69R03g/MEwF7S3exu3brQDx+3XSWaX3cmjc+rtBH3oou2rKV9KyGrxAkxYbdpEnXdRfGmCT2HpRzpFndLNF9ya6M4ZWa8hwYlQe7vHF6W1+WyRlkx65neEpQ9ZBCOJyJc3oKXvWK4eM2/f1ayFnuGK/WYM/cnZRVkZIahTGObGQj+7/MWp8+j6z61J79d4Qf/xnifW+g87d+F1Ofu/rJzn7lsz/B88tP889f8XOU/MrVT7iCGWPo9QzLy4ZLl+zPbteg9fCDT0/so/Pt72Xbd3SJFiQL/+RnMGsXNy2z5Ff4t298D7fMvZKf/eT38cN//rf4q2ffx90n/5wf/8h38nN3/yAv2f4Gvuel/3ZDGMeLsT3j1zFWnOKzp0ahjCMb2VetrXcuBWBEthMsBRb7Sb23TM9JoMs2LFxIb7A7b2NQAInqLODpFogcs8uFMQoBM5VtbEtDNXJkg4zZ5YAFm/Y8fSV1vJrcG6pXzLNDBOH+BsHOXChgsQ5+MQM+Hjn/WRuuZPRlQh2sS59svQ294+UARIcU/v4qNAyRjolUQDJ7FFm0u+GFOUW8uwdzQRZi56Vi5qnmGQJPFuh603SkR3/L7Vwq37Ix1bpz7ooqFQu3B0wWqxQKOYaSO69QENSbadVNBkgGKkQIedkMa9bxynU8A+dRrXRyHwub8QuYLC1SLovMeZYFRe3126i+Zg6R9ElkmN2c2EWL3nartMLIxoDyXLZPnTUgCeoDj8MDEafjaL2W1cCaRSesjQXWVJr9z1iPKr0HKaNrOCR2uEwpbFbQJBWpBpuwID1+COxyoG4mcg4IgZCK3btwWUZF1p++D/2kiycKeKJAxWsAUPftz4nCHBU1Taiqm7M/AKF8q88TxwT9RRQW6Cu4+wsgogjXZS/s+Z6CbmlcE8Ngl9ZiSDy9VoW9e3zbRjfPNy1TeqzMHbahjkJSEQMGuNC2/joXxmgQmOIYJtkM7MoBcoJNaGRu3FoUdbj+00fs954c9EvqHNcltb+xi8pMyMw0oASruShK5Q8AArD3NsNQDIOwSc/pHmpIjAUWpGJImw8VMBEcoejG69Otk8RurHo5pmVaxdtvg1sdwctXAYEK2T12GCUVoVeklwwniEjXsSCAyK/hb1fp1HUVHiANJtagY1QQUdhl0Q5R8ui9xkePbz4G13d5GG48pljwKJUlpp5ASQ4eLcW6BVzCMrEZMAGNssBtlPTtjPHBuOyQwsROl2wAdu2s7WGuOpW11e4DSIja0F6w4FcioC9y7XbrmpBIIZ1eoze06idzCZ3+CkW/ipG+Dec0VrMrESZ7bgymp92UQQgXLml/GmxYqHbXVEoONLvSJAZju9CTh3LFDNOdxsJtGGPXS9+HXYVXc3jiJUPHpPp/2XiWNjtlmsgiKE6gjQURtdFDa6iUbpzGdlz7m4CW661WdX297kG12XK1GbMLKQYsX2dKWf2x7O888fgFgl3TU7B/L+zeBeWwxq7iFiQSVdscuF1vmySkHNnIRjayrz5b+/jz1LedxH/iT+jd+c+Ibvvua0b9AT79/Id4/+O/yTdf/4+4acud13SOMYaLF+HpZ2yc+nPHDRcuwMWLVhsiijY/r1E3TE3ZBXx2FvbuLbH/636M5ud/j3MfmWPiR78Z9Xf+Ccnu12w4t+RX+MnX/U/e99iv87sP/hyfO/1RAIpeme+85Uf55uv/4cCR/AJNCsltW1/DPSc/RKIT1PpMMyMb2cj+2u13fud3+LVf+zUuXrzIvn37+JEf+RFuvfXWay9ApD7lgCkktUAEEhNr6+BhX7RNfY6kMoW65CP6iRPMNc6TsQ7yji09vGYdTykCEbBqljN/VAhJuTwFlyCLG8kJ1OvKJGqHhy44igJkTkx+9VbrdGZkoCjdOEH/+OrQwenaZ4yhn3Qt5ywfzi1SECerYAYm6BlJMFOFp+oQLWfPD985Ykoq9EyEEZrE9dEAMLHHFoqW2ZWIImrqDuZ7k3SDjbvhKdgl0/gfZ0f3NlG7But3el6zITi1QcR9cN5m4Ee857VW5L/fwnWK6zynkRMNyrOhnNYrKpcErGK9JWey6EERSHpoMQiBiZ1cWeB7SCFIEoPwJdpYMKvg9L484aELFWDNejqRQWsrUC8v85w+PHUb/aTLpfPO8ZVuTJo0XG4wTu3PXM+sK1MIiTYajWaqOEVVlKh55SyUNe/jDcIYHYCpLVBTqykLAjqgNL1+o2GITnYIRCk7H6CoirQA3/fYXrjdauRd5pVEKN+yQ3TEDYliXtSYLdWZDSdZEY6lKD3kBQM5p7J06yTJ4iaC78Olu4alyMQww8yYLN4MgPExqBQ8zq8AQjI3C2ZYIguhLMsqnqpifEXxYJnA77N9my3n2EqO2eW0vMSMskyqTUOdBiydDOBYb9JmY8RgQ9uu2OTB+TfeIEB79Nc8zp+XRLmkiMUjTSJfovplWHYAT8okSmyorzFgHM1Jb2tk00IKq4c0W06BCfBFyC5vL2s8MlSPcBPEIQzFBkBpsjzLq3Z9PZ95/sObZN+29RofA+kHPFd6DY3iQ/Q7PYTskk+DqEoe8uITFPYrEreJka1zZvMwxrzddAOZRmLepFCDhAUhJClwWKgS7zmMPFvO6hkc8FAmIAmqdg21twHjwrGFtOxPo0W2Hla8Ip5skMTuIZUDneXqGWRN0e9ognvL9F7txmz6vJKg8Ih17AA1e6P6L1sj8M6QRB28sJmxXkUSobHgecakSmf+qqDz3AIg8H3ooVCeC3mUEp2krE6JSKKNYYx+AVOaQI/ViKf7sJC/i5b5KoTNWFxUTcYKhlO5aZyCV4NlzAFpQQnd3EmxOEa7c4le3EWbZAjcVy7RRJzY8FybxXYTgBmYnIDrjthj4sRg1oNdmzBIN9PsEpKMqXi542Zy2qGlkj2+e7WlK62HEOzYkf4hKcxJdDlA+vEVz0ttBHaNbGQj+6q3+N576J/sMPu6J+i+7b+S7H71Czr/+eWn+fcf/x72jF3H/3Pzv7jsccYYnjsODzwIDz5oeOAhmHebmULA7BaYmYEbb7QPkWrV7nb4HvQjS5/v9WBh0YJiZ8/BvZ+HTtc+iGryG3l3+Mc8/sd3cbT4PXSOvA3e8EMbRPWVVHzjke/mGw69i2cXHyPq99ny+Dj9v1ziwvsfINxXp/rqOfyZjaK/L9Ru33oXf/7Ue3ni0gNDOhMjG9nI/vrtgx/8IP/23/5b3v3ud3PzzTfze7/3e3z3d383H/jAB5idnb3seS9/KXzqHveHC+/IwC5tHSERKmjHA+fB0ltABZZsIRSzwQw316uABBfK12wKdEWgfckNWye57+RxAiJ8YUPMvLG9mHOfQbTattwEZArIF5sku7ayFM/QzF60NwEvgk2c35yl4IYSg9faSEcYYzBTB4lLluErHv8U4JhRmYOQB40keuoASaGUfR46/ZjUAXny0kNW5B6XjStXWc8THNgv6C7eRr03yyPH7NcbfN1UoJ8BAPKS5g2E0ifOhTxVq4I9B4tM1hUnU60dB3qV/TI73niUSnkdEpFaJiy8DuxIwaFkGOwy69kNeqMjUdgBcdjM/k4S61xZVoW9RD+Ch56w18sYE0gO7HgdD808iDwFsmOs0y28y4YxKqkoynIOfLXjKTabOzjDYYzrmV02jDExCUFxnN2qYbskPX4dsyv/YSEE2oJduyUsOucud7b0exgMngMB059Vr0YLTbkQ0gF6vY1hjHp8N8Zpw0nloUwP0yuws7adHWUxxIIylRnk8ilMDuwK5iowd3kW+ku3v8GGg574DCnKJNZpYhk9XKdKoYoUaeZORa0q2HH9unuUzlVPEm8fo7CtCGuL1OsOPFy2a4NG4XnS4sqzPt5cBZYsiLP2ybN4MyUKe+t0n1yh//Ee4fRw3+fNanalYltXCUxaf76QlLZ5yNX0PrjqFzy8G8ZJPmc/KxRyDr42mMRQq0JprMDCnXei55r0HRAupWFiUlBLaSoCBIaQgFxeRFtuTl38WvZghZD04u7wZ+5nGNqN0oUnFOXSHCuxQvDw0HxVjZDGHSBDRXvR3RPXrmSTeQ2DLhXA5ORl5qTwsgNFRcKlTPwM4QV4gSDe0kD4Pv6EZLL/7UzMjrOtXuLp4Hm0t8TJkz6H3CPEABhJu7ofvEuYoAw5jE9IMDnAJdVkmw7HmWzYNT3bA5DCgV0RKI9gX0wUucI6CyQSpFfMsvyKuI8fCjQJlZJbp1INt0cizLgteHLakHiKk8+nzC6VJQ9QUoGOEGrjeDSNbVCvYNRx8givQGFsNCReOs3Wg0ypPqD73Kwb74EL8+wnXbTR2ToLdj3Wid2IuComLFIwzF1qA7Nr4zgYFpp34dxSUFzH7ErPvON2GxJbKIjcaYKJCcOp05fF/y9rRihK+z1gASOuzccZgV0jG9nIvqrNe+i9tH75/cAbCf7x95PsPvKCzl/sXOSH//zb8KTHj9/169lDJDWtDcceg49/wvCxj8Npl31qZtpS0K87Itjn6LWDbGRXs8FxSWI4fQaeegqefCrgo594JW/6+If50B9+D6+TP8/8I/fw4fJPMXX7Hdx4/fA1JIqJj5a48EvPcOrZR0EJZEGhW3a7vfGOPcz84NEvCPS6ZfZVSKH47KkPj8CukY3sy2y/8Ru/wTve8Q6+6Zu+CYAf/dEf5ZOf/CTvec97+P7v//7LnlcqCexrqQNXcmCXNPblMwWUUgc7H/6XvmvPTd1FzT9GrHyIc/qCzrlsFu36GdGn5JhdSvrWoRft7Nr5sJ7uxM10FmDCt9vfmXD8kMbHta2tefZp5DSfpPSh2ITO4kCza32WsvQ6CIT0hjJQBQ7sUrkKtaOWDWVa71Q71lQgSnQ7A4TrcqEkFuixZfgpALgufX1w6BWIWgX9yPuHPt9X309zblv2d+FAAzW2zuMACMoY6aEnD9g2bhaVhsyyfXmegLCG0GtWkD7Ncmg0pe2QRIPNl9iBXdKBXRrD08ctSKrReJl2kANPpQcqHqBM8vJhjKnlMTobsjhw1m+eHYjC5NkN68uUQpKYhETHqPputJbI+WdIn8UbmF05wl2lInjNDSATBYvp54JazfDal8FKf40wBONArj3lAyRqianCLHNze5BxjUvPpNGi68axAZw4v1AK6cL/sqPS7lMBSA9daiIqtSv2V96KfpmiX7ZAZkpJWqcdlp/n28KXMF3ZQtJ1KdWE3BAS6j627Uk1ABlmCwXC0trDos/EZJ9l1xapBEbbjcN4vks833Vg1zISF4knAG+TcSwFJnasvquyzNf1s2tDVNmOWTmx4eiUhT8zTXaPjDaYWKM8wUteprj3vin6CfT7tsekcm3KAF0AMwS4Z+xQGaz/6Cq133jQIEMfNBpwDsnCIoMo03XMT5kys9IfDvlIw4APTN606bUbjcvXS7lG1/0Kspx7PljEG09BtGMMsSwtCK7KBKqEEILJ0hyPeov0+i6rpkgzTkqSoI6eOmJZqPnYTAHC3WtTHIPZMTh+komwSU3WnP79gLEaR326URukhzcrMCsJujYL7SVM1EX55QHYJSAQHQ4cShhbXD+eBhshyhPIQNoQTKMRUlnQC6cXqKNBHOx6Exs1u6TxM25lmojE82DX2EGeW3jc1c3pEGYI5PAcTDdgenGHxc7FDcyuxIFdl6tWVr3cMJOSdUgWCL0pDROASgVWO4K1NYOQAs8XSGGy25Hewlp18wGfPRNfKNo19GJwbaeMwK6RjWxkX50W9wk/+m/wH/59Tsx/N/5MkeDo4RdUxKXWWX70Q3+bpe5FfvbN/5uZqk3dHseGBx6Ej33C8PFPWPaW78Ntt8Dffqfg1ltgZuaFrtCbm1KC7dtg+za467UC/v4sq7/9So7/yMf52N3/jiMv+SXeGb2LD773Lfydn/gBZvZOcestglvGFyn+98/Q+fxFitePs/2XX0X1rq2IUNJ7apmlP3yGS7/+GCt/dpK5f/cSGl+/60XVrxLWuW76Nj576iNXZL2NbGQj+9Jav9/n0Ucf5e/9vb839PnLX/5y7r///msqw4gU7BoI2ob3JDBuWQDAOmYX7nf7M67vJN5n1xKRZwg4PaAUsIlN34ETVkcl2wF2b6cqB3alL72F4sDBGPz7wt+Fb9v6Gj536qOZwHnWjqztrr4OVMo7lkKIDY7m5fSR8rvpmUnXV+uiRtI2GhUgkgFIKERGExownNQ6ZEwFiKDM9touLnA3orNoj18Xrl442GRTk4pk/xtzf4sNDrcQkokmBHIGdeQg/to45fBxTHcZU7YZjtNMjFoMHPc4tswnIVUaaMPCinChdnoAEBoGiNIGktCVwa6S26upVuFSz4NkEPsSegNQcgi03CSMMdExxhiU8lyWwtz3uSpMT0ukFEO3UEoJegA2CCFYTc7xuTMfpt1fo1YV7Bwr8cxTYMIpxtQs/cZBJgpV4jhf0nqwawASCc9DuWx0A8aZoLhbkcxr5BoQ1im+9dpkFoYuU2wg1s6BObKhGnv3CJ4+bTNMFmQd34ekl4r0q03nn2oW0J1WxjgZ6IFZO7inQ+us4NDLAuh2sjkhlAWRdHs9u8iNntjWzXgbBaOEElaLCi7L7Ip3vMyChylglQOO4/1fR2dFUN0E7LrxBlhrWfaJid25iWV2WcBFEASGCxfhrLbaYEJKq9k1FHLmwDi/hIjaGdtK5BaFa9Fb23xOOGBWCpoNg/EUnQ5QcPP5cuGJjgkpXUhxomNCr8hcbfidsFAQHD5oJTauZLdvu4tKcg/P1SRip4c5HltASlqgxTiwz1MWOM6ip6WyYYwk9Co7KG6fw5j7MVpYYKygEEGAWWGwISMH/Wb8Imw9AMJmFzBpVsYcGB5nz6TcOi4VidFW2yp0ml04JmjcQXpmEI6eEXVFVuhsaZLTukVUEzb1ivAyzS4ppF0XvcusYZts0pw/79FwOU2aDcGtNxtqNYHqb83Aroyluo5lmlrgwODnFh+nE7WGLyltRGscs0FHa72tB7uMv+6Ztnn0I2CF7yMhmG4wvLZn9/vK1/bd4+sKeNqmJqJ2/q9rOmcEdo1sZCP7qjOxdoHCn3wv6sz99G77hyz/1jS1181mD6he3KEdrZHomFJQpeiVh14w+nGXDz/zh/za5/8NURLx43f9JjurN/LJTxk+9nHDp+6BlRUoFuClL4VX3il46Utweh1fetv+T47SvrDEhZ97iIul7yP4+qd5o/wV7pr7KO9/5h/w/H84gF4+wQUv5KE7X87EO3Zx63WCWmgf0oX9DWZ++BbG/vYBnv+nn+T5f/wJWvecZ8u7b7WZtV6g3TZ3F7/2+X/DYudiJho8spGN7K/XFhcXSZKE8fHhFPUTExNcvLh5UguAcrmMbDYplXuE3RI6iCgUCpRLHiEhBaUol8uUxhu05w2y4NFsNvF7ivKCZfFUehW0MYw1Fc2mXUN0ORdeHUpks0lcq+J7PoRQq1RpNpvIgqFYLEIh5MDUYbqFCrV6nWaz6eoXUS5rJqeg3Coj6nVEs8nqWkK5HCMFNJshUfkCQHYesOlnhSigvFimWA4pl8s06g2azSamq4jLJbqFAMKAWnMC0WxS69cpx7YtjUaDZV2lzUpWXlq2LpcJV0KEa7eUirLL0ibdMd2Wh660KK6GhKUKLu0lU1MBvi8wt74DjEYoH10uE0eGouohwjJV37PgYK4teZub3kX/4gGeWDkOYUijWKBxmWOvZL22R6/cIgwHgEOpVKZQLhCYkNrEFra85BDm2ClgFdncB4DprmDKZW6/Y5wlU+PJp61zXasJms2EMAwQnqRUqWAKHi3fo1wqUyqXqcguRZnYsVjqIdrWw/EqITU/tCy5y7Sl2YSZGU2tJpl/tobM6bxMT2zJ2HyrNCh3yu6c4bKacZOFyKJmzXqTut/ALJURbgwIoSmXIwIfbrpxDHO2bOUDnIC5aDYhLmEulikWY/rqKMVigvANzeIYu8YOMhFs49yZiNJEnWfntrN90qPZtHUrl22lG80xTG7e7A72cs70aDabJPUGpWAVpUOCQFAu23lWPgzBsQIqsgBQc3wMr76JevgVzHAI8/znEUWJKZcR9YZtE7BtW42g1uKhR2IqlTpjY2Va4TTh+RKqWKJUqmzoz8ZddaKFLsfute2rlAsgBu0qhzCxbwo5NobpKAqFkFgr6o06/UVDLShn60ez2aRfqUAY4nkWQKqNz2T1S81vxrRLdsyVmyWK2s3D/HG5382hV0OpicjJQDTHItR523fveHszyzCZL8IYQ1y+RLlSRfcS/HpCs9m061RbczEqUWzsIqifh6Ki4pUpF8sEzQbFQoFQhBTqY7CagO8hymV3fs9d6+r3rrZSI1bDgkaiXCdUZZrNkGYTDh7pcuaJSxB4lCsV/Ep5qM/S/pXdAFEuo5qBXefDIgUTbLin6/vhctaoVTDzxygUY8wWQ3KmQ7lapdFo0kw8Ls4nhJ2QcllR8MaoVMo0mx7NlTHCYoEg9BGVrTS2bKPcepJisUytVmXnq25Ed1a49N+fxigPdESpVCCoVOiGIZTKjG+dRBcL0NeUimXCZpPIdNHlJerNBuX8GGxsI1ww4AV4/UXCJKQ+PkNjfApzqYxoCwrFAmGxQKVUplwus1bs4fk+hWKRcrmMUQm3zlzPXj/gufYS5uY5xvbXWf3Q51A1Sa1SBQTGK9LPPw+dFWs1ysUyYRg5Zp6mUqpS9ss0GnZ9SPs86HvZ89aUaxRVmXq9jlkpIxpNRLNJsVgkKBYZb4xzoV9D64RyMLzmNZsxi4sJxaKg0RA0m3429jaMs5qk2XTZRHuGYsPHCxez76ul8oZxUq3FeLftobEN9izMozsxjbEmXjWgUu5lkca12uBdYTNbayWUz8aUioM6XIsZPYvpu/ed6lWQWWcjsGtkIxvZV5XJMw9Q+D//FNFfo/M3fp7V1s0kCx+k8LJJ3vvwf+FDT/8vTiw+kaUFByh4RZrFKZrFSbTRHF98jG7c4dDEbdwZ/Cx//Kt7+aFPGzpdqNXgzpfDK18huO2WzUU6/zps+vtuwp8pcfYn7+WJT1Twt/wwZm2Nras+2+QzBK/UfObI6/nYsSme/0UAw8QE3HqL4dZbBLfeDBNbK+x+7xs4/58f5OIvPUzr3gts/6VXUjjQeEF1uWObBbs+d+ojvGHft3wpmjuykY3sGm09M8AYc0W2QKvVIl5cpNUydHoRMo7otDt02j7mYodeT9NqtUi6Ad1WCxFJ1OIi7WiNVsvuGuvVNVotaLdhMdWA8SeQSyfSixAvLhJ3I6I4YrWzTKdjWFxcpNVfo9PpIqMe9A3FYo21tTVYtC/Vy8uGVgu6XXu9ZGUV4y2ysmI/lxIWF9tZXRYXBy/jvTAmXugNfRbriFarxaXFi7RaLVZX11gUixB1kO02ndUlRK/HcqvrrrOclb2yvMLq6qDdM9VtWdleq0Wv1yNx3wkhaPXtS3rsjolW23Q6LTodaC+ukcpsra3ld6PJyuvEHdqqB7GkEwVDZeWt2Wyy1Inpdrr0etZxabe7mE2OvZpFK23a7Ra9nP/TaXcR/S6mZ8fL0moLqerI548Rh3PgBYj2PKrVwh/vU2IF1w34HiwtryASQy/uEXc7GK3oRz1qok+r1aIfrKJbLVqtFn4vRjrdpLJo0m6vXrbdeVtchHarQ6s3YDKsLA9AydWVtU3HCMDq6mr23erqGku1SURlF6YwB4uLLCzYsRY0YHl1DdVqYXoJwukmxUtLkPTxWi3acYm+HkP0jtNqCcJSiYacZnl5iVbL1rPVshtm6VxptQzlcpmlpSU8Vw9TGmP75B1sF4LFxUU6vR5RZ4UYg5LQag3m9HW163ngxFkAllsrSP0C3bdOgtdqkVw8g0rnmG/7qLXWysZDp9VldTWiRYF2cx9hP6Lb6WzoTwA82y6AteVFRD/HMGm10OVJ9OIi9FYpl3ucv6RYa6/SX11Dn5mn7fph4dI87XYb2euhhAAFy+3+hrHdXVujm65HLY3W9vfLj5sytPr2/7SMrsHr9RACVlaWLttdrXaLeN4jWeyRdCO8xUUWF+0YKZfLdLwmpv88ui+IxgVRVdBZWqLbaSOiHjfNvI5+5Tz3to9Dq2XXQddXi4sb14L1trbWotUZZuz0OzGxaGXnJ1LT6/WIJ4u0O13E8iI6B1Sk46yzami1mvR6Ca1WC923fJhN7+m1mNFuLTS0oxAvtuutXFlmreXTakGv16PXg9V2zMrKCouLgnZrjV6vR6fXYWnJ0FhZZrW9QrvVpNVatXOl36LX6aK1BzrGb2v6Kyt0ez10p8Py2gr+dYrVe3qsLq3RXlwkXuzaub2yks1xgJUtN9CWzyO6q/Q7LYwxtHqwtLqG12rhCcVibRcrrWX63T4t1aLd7RBHEb2uXbfo2DWlE0Kv12XV69MuGJJDbToFk13PFL2ha6cWrypa2vaVwkcT0aWP57W4eNEyu1LrRK2sjE67i5YtlldW3Hxdw4hFui8LWF2NWF5ZIe4mQ6yu9H6urRlW16w+cBjYNSgde+ttdXWwRkWRoRVpCrkHw9rq6oZxsrJiWFOwVoBCu4Vux8jlJVTs02obkmRj2ZtZu2Xn08LilY/bYKKBLGxBzj+NVj3qVz9jBHaNbGQj+yoxo/Hv/XWCT/08pjpL5x2/ip7Yz/JP3QuB4F92/wFPf+4YN868jO+4+Z9TL4yjhEc7WmWhc4GF9gUWOxfRieBw+E7WTnwdj77npdwfCSYn4M1vsgDXjTcMBBu/3Db2bfupvWEbS3/8HN3HFkEJKnt7jBXeR+nSn3NU/QL/5NveyIUt38DdZ27ls/f5fOaz8Gd/bh9sO3cabr0FbrztJnb/wjTtn/gUT7/1A2z5l5b1da06OLuah5gsz/GpE382ArtGNrIvkzWbTZRSXLp0aejz+fl5JiYmrqkMI1y4yaU2cnud5pMJbHXslVQE3u3M5sP5UrmfXPQheuY69PQRvCc+mH2mZo8SP3yMyPQI/GFhd0Q+PGRQTq1ms9cWigJWBt9dy/JUftkMJhp+kVfCQwhBP+kOtyMND4y79hP3d6zzaXNz4Z1egcNTl89yuV6LJd9GJQzdqyQ90xP7EUvHwSxd+cDUvCLShQiZoAQTB67tvPW2SccKRCbXIlz8ialMw8oZ5NJx9MR+iK0TZFRAXqJJKUAIAqnomMQpuNuw1elJwUythXy8jSm5cnPhLVtqO+HSwy+g6lfSarr8gMmfl45H09yRfVarQb0GB/YDOj02d3+HQiTBFyXSKKoDE0eHDsk01HPVuf1WmJjwiaLBh7q5K+trsJpKafBksi60R6RK1jDIFvhCLNWg6zugJR+izCCsVQplw4+0ACSmeG0xRnp8H+rsA8MfpowqIZmZFkzPCFpOlbz37ACkXPvk2ex3ExukL4Y08zLLt/uK4+Dy5nv2rm6iJz5kQgp6Ty8DIIv2WocOwj2fsd8HgaAHIKB42wSl4iQrq4PxUvQrFLbthmfOvah6brZ5IRlmv/ihoHvHTjvwpLp8PJhLPylcqF2iY7z1odIvqHKDsM3z1TuZHk/13YZD16zunRgkAc0gB22fJ1KgjcYYOThv6Hiy44FB4e79PMtTkcvGeOvWV3Pvqb9y1XHHCc8mKpEK5RUyjbxidYbzWM0wWdtKMn0ULn7EXirtfzfvRHkSIRcwJkF4PsnBm6B1OpMPNJtpzLk+SU0Kj8RE2VrUWZdsUw7NyTSMWA61XdisL4DVklwfwgiDsd2PBs/r64/Aw49urF7+fknJhnBMoTcHycDd33ROpqGklyl7M0v1xJKrPCc3u7ApNOxli41rOmUEdo1sZCP7ijfRukj4Zz+Ed+JuogNvove6H4ewitGGc398jMd3P8qat8q/f+N7uWXuVRvOX1oyfPJT8FcfN9z7eRvLvmUG3vGN8KpXCg4f2pgN5SvFvIkiE981rEWmeQ2t+WfwH3ov/rH3MffY/+FvBhXedvQVxG97OSfi67n7yd187j7JBz4Af/CHBphhy/RbeJd3D+ZffZaTv/EMEz92O1tfM3FVDQkhBK/Y+Rb+z+P/g3a0Rsm/fPankY1sZF8aC4KAI0eO8KlPfYrXv/712ed33303d91112XP0/Wt2e8GiWgqxKLGX4npbnK8cZ52/uW74w6sVdcdLAR6Yl/28unVt7NSPGjr616crbaVA9SEGBK2BpvcY3ICykUF58kdC/lDK6+axUTDDp1QcoPfK4TAk35OsystyLN6VXHP6gI5PZ9+TgMqr9m1AVgRAuMNi8dvMHetSsls2rd50xP7CMb3wLPvyz4zwcZQmHzZ0oEAplBHbaJrdC0m5GaOSNpuk4kjp2CgvPQUJqxm2TfZDOwCAumRkGQZzQzagifHbMiJDL2sHVkmQylJ5m6BqwqOu2u5ipf8MtfPvHToO3mF51h+LG8GmHme4Pbb3B+rtp55bTUQ4Pq7Xd5Nda3GsjjG0dk7KaaZOVPJu00wh3pdUKlIhkgS69oscn+vdwBVMmADicvpA13JvBCEHLCvhrpKuP/s9ZUC6RDJ/ksU5bm5qxZv6nOY5VOI9gCIT7NMDi4jssmcrPQpHBmDWNN9YmlwTgoehBtF+EUe7JISXqiTjMUuTtVeQTG8CognBbhEHakofqUi2DpnWFyCUlnS6+ASLOQF6oft4OTN1Ao2DGx8DOYXrq2eaVIMIUQGqislhsaW75GtN0LJDZpdRiqETojHDsFqhHQiSYmJCXhxa0fe+n3QYYFS061ZUgxl/0unYwZeORBZG02ibXKLJ5/WlBORexyIYYF6yIT3/Qm3eZImUsnnW3AXzL+b5jW7APBCe6+EIN79akr6HJ3TD9lDpo9gShMYsU5d3S8R73612xh+Am0SOvEyF80KQuYAQ69AuL+BXukTncsx93JzVTrIRSCZnoK9e4b7M6+R+ZLbFN12vh82zvl8Mq2jswMdv/w9SAGlmRnBw49uBK42CNSvt802dHJWvm2K3jMrSBdyPZRB+YpnDjJRxpsnB72imeo0ybbbMaVr2+QbgV0jG9nIvnLNGLzH3k/4sX8PUZfuG36K+Mg3Zivq//mD/8auC2WWvz7iV9/+V9lLpzGGp5+Bez4Nd99jePSYXbO3boW/9S0W4Dqw/9qEQr9SzYzvof+aH6H/iu9Hnfw03jMfQT37Efwn/pSDwAG/xHccPUR01wEusI/Hl/Zz78m9/OHjr6bGSd5+4vN4/8+f8qHmbh655SYmD5bYtVOwcwfs3GlTW+f759W7vp4/evRX+PTJD/HaPW//srV7ZCP7v9ne9a538YM/+INcd911HD16lPe+972cPXuWb/3Wb930eHH929BLS9nfa+EumuPL6CWfG3fHHDufO9a9mIsgdd4G8//G6y1BoLpJZiU9sX9QhhAofBKiTEtJSmXffKUZ7ADnwC4hBLUakFhHJGNgrbuU17h2B83PgV1DYs/Kh7hlfQcHXOWZXevF6vMW738T+tkrQ1jppaoVQ+r2v/yllz0cKSSN4gRLnUvE+15/dcZKeQIWAOltLpB/LbYZsysPBqXp7vNZIbVG9FsY6YEXInJsOsvskigUCTEFX9pbaAwyx2SSgXM5htgEHqY6c81VTzPdlYM65fUi81cQut+M2XU5MyWniWfWMbuEIj74FvoPGgod2D/1NppFOXQIDICqq71emHX3L9G5euUubYpNkLPgRtS1MrI3XM8vIpefd3/l622BTpllgMsDBcJm2rsWW9/gFBjO+lsM1V0WFcFcnf7JNXTHerwZzuFvwpTJs+uUgmjjIVcz34NI1fCuRmxSIis/E8XHalotLlmdusWOrauSA0dfZLRYW9fZ2oA9eNONg6yPV7OSe5etBHVWe0uAXUe6ueUnn+FVSIVZlyRA6AQ9vg9zpgBEOYH65OqD8xosBSlKqZatGGbM6aAKrcE0mhhTTE2CpzTdfp/VTkyna6j4cjBEpEflRp+Vp8YQa+eRBcAYGncG6B0O2HAXybC9lH2kNiYXScsEMCrM7hVBmaIZrB9KeiAEFc8CtI1iThczKCOTHgLQJJxpPUXktxzY5ABRL6R4qEnv+MoQ2JUHaIWQ7N4Jt201zDQ3Z9emVq95jDcErKbz0LZ5rrabC2unmSjNsNa3zMOpytyQlm4eQw+uMs7ztUiTKPQPTiN6Mf5z85vuQWy1kd/MbgEVBpRuugzgdJUh5uQuXxTYBWDK164fPAK7RjaykX1Fmlg6Sfjhf4138h6SuVvovv4nMGO7s+//4qn3cvIP7mO7/3Le9b0/Rb9b5BOfMXz6M4Z7Pg0XLtrn+aGD8J3vEtz5ctiz+6sb4NrUvJBk96tIdr8KzLsRSydR5x5GnnsYdeEY4ZN/wvbeCtuBNwjQd8wRv2k/F+L9nPiTOW75xHPc9NGTfOyxw/x//mEi9zJQLMDsnGF2C8zNwuzsUereLH/22Pt45Y63fcWEeo5sZP832Zvf/GYWFxf5r//1v3LhwgX279/Pr/zKrzB3GfbF+vWuHW6lPTlH8fgJgiihXHIhe7tr+HNlSolBuR30vEM0NXXt892TRRIdZeCDBSgEKBDeFRI8qYB416uyEKZcQqwXbEr69OJO7vpp5Xxsmi+ybIxRjsET6yjrs82YXVd9g3fOfDGHy5VKVz7n6JY70SbJnLIrmRjfj155ElNoXCWk7wplXCaMMfs99TzzoU7SQ/RWIKy6YwZfFYu2BK8mqTUSrr+jy7OLj6JnEni6B45sIfw0JCd33RcYkpe22d8kDGtTRzf77srMriFTPsn0EQsonn0wKyErK/M/h6+XMiMyQOJqTVsHulVqinLZgodTOT8u2fEyxLmN4Uov1ExlGrHwLKbYwNS2DKqRMhnJgV3rwhyvqXzWNTkN7cq3Mw9yO/Cl+to5krWI9ntsRtnLgZ/Cz9NQXtzYT0GuawljzNap3II1My3YuyfgxB8NxnIKOuezMW5286UUhNeI16dZRv0c4FwoiKHsenmwy6a5HIBd6tmP2qpLlTHTZKAyNtzVAN8XYoWiZcEJKbI5cLrxBsZmJSwOwC4lPaanBNKPOLn2BJ1Fm1xEMDgP5SNufxO12yXygQ+iSgJjNNIXGKXsGHPvnp0nuvi363UhdJtsVghpWYaF6tAGQbo5DuBJH6QiFAFHanvRYXOojUoohLCZJBPdd+VLENo2MMsKO3zfhSez+s2Ft7Jt4hhTjbFN+3FT9mmaVdT9XQlqvGLnW4bOGy9ND5eTu7VXHW+bTG3dsICfiBIKN62nclsd41tvuUxxYvPfN7OUdXaFSMkvmo3ArpGNbGRfWdZdIfjsr+Df/z/BC+m+7seJr/+bQy9Mj124j1/42A/zrx75YRb2bOf//ecFnnzKoDWUy3DbrfDdLxW85A5obrKD8jVrQmKaO4mbO+HQ37CfGYNYO4e8+CTy0pPIS0+hLj3B3MIn2XpTRHdXnZN3v5bXPRHzpskHEP/kep6cuJnjJw1nTsPp0/C5e6HbFXi3voWlA7/Fa9+8yvRYhdlZmJuDuVnBXPb71Z27kY1sZC/e3vnOd/LOd77zRZ9/4w0CL1LEqwOQJ9xXRwhBsGPwcvtiHaJQVNDeyiC8x1IeQIA37tN3jIhNLRfGlx7yYsAuX/kZI8LLh5s4kERVVQYuVYJapn1iw2DsBV8Mc0oW7DnFmQIsXds5VuPr2l7HhfIx5ensvBdlUgz16b69ENwiefTP3TUyVs86QKm3iqlucdcefFwu2Q8KhyTx5BrHlj9Nq79C6InhOmaiYLmqXCtrKKu6Pd6TG8NJrzRelZTXdFxqprkTAN26hFw5PfTdAOxadw3XlAsXh4+7rK0DbJTvsXvX5iepylXCZ6/B9MR+jF/C1OeGgNUM3HUuoVrHkLkSY27I1ifOSMOs8oysPO7lwtGEJ5ElD93cDX4BOba55HQKjtmTX9zYT6syPX3l466ki+b7g94xYnid6HnjFKJLmM00x16AFRzDKA/IbKyH/SkAXMhiaiLVZpMKf6ZIdGoNr1GA+fScL+wdLZk6RHeqAJG9fyZJ7H6G6wojfOoN+/sWh6umbVnTZ9EaViPLTBJCDjPepGf3ItL3SBfGaNx8yXTuhEB3YkyKlkhx2TVRT1p9Q5mbc3kgUQll2alVgfQzlbBBlcQgBDM2PSR2HTFCIkyUhTivv7xQItP1CmWNG2Zectk6pvNMCDHQFfSLdtJsMp52NPZT9qtsqe4Y+tzLTZPgKmDXldaoeGsTVbr891ezq03RIBDs2WWuPhe/CDYCu0Y2spF9ZVh3Bf/B9xB8/jegu0J85O307/zejKq6smrDET/3yFk+GL+LG+67k9JayK+vHaBQgHd9h+CWmy2Ty/e/sAf515QJgaluIaluseyv1JI+cvE48uKT7Hzdk0x98jM89559dN/9MC979f/mbf/sKMmRt4BfwBjDwgLc/eTX8/OP/Sqv/lsfxj/7Ns6cgU9+EhYWh7dmmk2TA79yQNgcNOpfg+y6kY3sq8gKRZAVj+iCfaWPDipkYePr4It1iO44WuF0az2oYMvyGop+h4GDcgUbiBa/8Dr4cvCW7+XD8YKQ6k0+slHMOBiHp26lHa1RDRvucpdhdq2zyfIWWJofrnPJp/q6rehAwcdfeL2vZl8URoYcvrOFgqDWyIX5pR2fZ0/pGJFE6E2crlIJQFDwA0xF0+qvghT0b/WQT+QYQpuQXuQLZOikjw5/PRDHlUGZYWbXtfeh3nIjeurwMGBzGcahDQc0V+D2WDPSR+hoA7Nrw985SzVxviCTakiUP7ssqZM9HNo4+P5aJ+BlwhjzX+fDuoLc9XwJxfqmxWx2PFJglI8e27P5wZexmWlLxJndcuXjrhYqmgdc8mGMq+Ee2sEch4IvACUAmsVJDkzexHTF6i1Wg+aGY3wfSkUbsWBi32oRbmLBXAV/tkwv6QzAri9wHTFju7nxToMx0PpLx4Izw/htoSB4fU5K0pM+5aBKy8yjNRS8GrZCYoNY+/DFUuqWLdwIQWmfR+sUVsMxD3ZdZazmNxX8HGCebogIKShf57N8fvi8FIAyJCRESHKbOFxBoN6XGdiVlnM5S9elofUprBIfeNOmxxe8ElvrG8f/ELPr2iQmM5uegvMXNi/rmuwFMLsAdu/+6/EFRmDXyEY2si+ribUL+Pf/Nv6D70H014h3v5rey7+XE939PPJxePhRzcOPwPHjgOwSfN134TXW+IbP/w30toCf+/AWisUvggPwf5upAD2xP9Pb8V4Be77zLOd+6MOc+fB+Ws88zN5v+i+YV3wH0fXfwvh4gbe85Bbec3KOfuN/8RPv+sasqHbbcOYsnD5jmWCnTxtOn4GHHoYPfdgMiaqWSjA3a9i2DfbvExw8APv3Q20TLaCRjWxkX3yTAkTJH2Sralx5p/mFWqNS5VxHkJiNCtIydNfsX0OWNzH04wVZPtuYlw8PlD5+U2LCMNO3VtLLgC7IMXc2aX+gQvpJj1fueqsFw5b+dMMxquy7gLAvfnzGFwPsEpKMaTcIAboKkyd2sXlqo/dULAJGUFjHtjJ1QXjrOGGnRu+5lcxpzbIxihcOdulUrFptpCxcydG9mkD9ZU2IjaBNWs4m3SREzje/XHUKdWhf2gBurdfwGi73S/d8zMZ7ziXM9+U1j7n1dUzHSgpSVLcgzMYwRnuqsAuTNpftuCwM1l0q2feGa6tXzqS0m29XP3BQh9LNG7WBMu0nuW48CUEivjBWV2pztV0AHJi4adPvhRC8/GXuj7MBdFfs70lOgzDu2tA/IZB5nbYvkNkFgzDecH+dzoPzCF+irqK/VAvHuKgs2BVplyUSmYW0bWom1UEbLBxe1S5gJh7oQCKuDtKq3HqTf0ao3DNCBpv3jZQKbWIS3ccH259S2NBQx+xar+eehmlfLWw2O16IF7Y+bVrPwe/BCwS7rr8O9rTh7k9/QVWwZX8FuWUjsGtkIxvZX7/pGPXcJ/Af+QPUsx8DDBcn38RH4+/iY5/Zz7HfgKVl+9So1+C66+ANr4Nj1R/m3vn7+En+G6ULK2z7pVtHQNcX0eTUFmZ//W9T/F9Pc+oH4djvTnFo6b9Q+tyv03/lDxAffCtvPvBO/sd9/4EzKycy8dVSSbB3Tz67zOAJGkWGc+cdCHZmAIQ9+RR85KODN4O5WcPBg3D0JsFtt8Ds7IgBNrKRfSlMSlCNIJtf5jJvgi/WIZosb6Ed7WOsOLWxTMe6Nb2rp1L7gsIYc8BL/vdM+HoTzaf1tpnT8ZLtrhGXAQABAABJREFUr0frZDg08jI2NQnVL3Li2pSZkAfnXrC5RATxbAPv9BLg1tqMILHxmSoysMsxIHL3xPcFRILCJkBYYbZKqBpEFzqEu+twkWGB+hfoESUuVGuz/r8yayIPrHxx3hk2I/+s17TfzJK5o4i1ixtDk64C/MV75GZJ2b4IZisqxDrwaZPfr8VMoT7ceKmI977Ogl8XBhSePHgFIEOF7sQbPt/0+C/xu0EKUpRunSSY2ziJhRTsn7iB/oTJ+ufL+rqigkH20JwGoW4MmHyXY+59oRburBHutNkzlbwywO+rAKnsPFmNlgDBnt2CQzuvcFIKdqXzQwjsMigwUZIxu64lcUN+3cj/nge7xGWWdiUUmpjYRAx2YqRN2nGZ50ma9GXPHrh+c6mu4eP5wsGuFOBS6ur3eTN2arkMg2y5L+za4jK/f7ltBHaNbGQj++sxo5FnHsB76i8Qxz6I373IClN86OJ38hsPvYNTra0IATv/f/buOz6O6lz4+G9mi1ZadUvuveNugzEuGDA99N4DXAIhoeSmUd4kJLRLSL0J3BCHBAKEUGK66WDAYOMGBuPeiyyrSytppW0z5/1ji3a1q77qz/fzsSXNzM6cnbZznn3OOaPg+EUwbZrG9GkwYnjwBvzKlr+zYe0L/NfkO8n6YQDLtFyyzh7d3e+qT8q5ZDy2IU4O3Pwx37z+30y6+kMy3r4DY9MLfGvBzTyt6Szf8RQ3zb2nxXXZbBojhgePY1DDR2BtrWLnLti+A7bvUGzeAh+uCH7IDh4Exx2nOOF4jdmzkA7xhUgSXQdLVkOwq6knwfD8cFOa1rJZ7IwfMK3x2oLbTgn1RdKKzK6O1MeaqsiocB84ZnPBtvC38fGVjmBHxi0HuiDYN1qyOe2ZTM6fzcD0xAMStIZut5B15kgCqy2RYFe06CBnYPTxWPd/2lCB1uODXeFXpejx2VY2ix3dYSHzlOEopYLBrnDTH502B1QNFcwGSRTsai5wFl3pa/coliHhgFaLFcGm3prFHuw3K2755stljO5YuZvS0GdX4oBgW5sxmgPGRfp2iwhlvjTXkU/64iGY7gCWzMTpKLHBrlYWqb1C5UzUvDvMZknBmdbQx2F3BruUxQ7KRKspigRejJHHxQRUbZbgPV8plZTMrkQirTubmG/TbaFAouJwMWCBgXk69iayqYCoVMmoUT2twSaEyq9oSE+NXUfjQI9FtzQZ7Ir+XbdppIzPwrvb1ajsKdSYNbEJu5ql4dxOQLNoDMsaQ423isGDWt7nuqZj6WAwPi1NY96xClsrIjwtnbMdOad70nfVEuwSQnQew49+aB31G98n9eCHpBll+Ew7nxYfzysHL2STexGTjrJy6sUwfZrGUZMTD23/ZeGnPLbulywadRYnvr+Q8qLtjFx6YruH4BYtS180hHEvncG+qz9g2xOnMu6XZ5JT9r+MWPZdlgwbz/LtT3PlzP8m3Z7Z/m2ka8yZDXNmAwQfwg4fhg1fwoYNinffg1dfU2RkwMIFiiUnahw7VwJfQnSEpoElJ6WhwtNMZ8yLRp0Z2+dVOylHJrhA5Y8AqlCBVgS7GrrFabNEHZgDqHAH+KY/4fzg9kIV3VZUOgLjloDZzrHT20HTNIZmju7wevSUZprMxbSDCY3MFcrsUgkyu0IvSlhJiz4OkcpnBzK7VKjimzD7rLk+r6ICSckaiS7RamLqwW1dYZs7yEmO8H1Aiw52daSD+sZtuaIXaeaZTXdYmw0uxQQvOjuzK1ROzdHEdaLFLtcFRWpe6B5tOfwFxtDZQNQAAVFSrKl4/HVJHY0xphgtxGOtFjuBkRqUK8ItWi0tjsjaqBljdGZXwGw4Bs28pZlDFpBhjx34IPp8arw/Uqfm4t3twjqwIVhotzjwmuWxp7fFBlozvcBbdSZlz2p6fmNJaMYIre8WpKWl2poBGLN4D3pMl2CXECK5Al6qv1pF/cb3GVK9gjStGhVIY2XxCXzpORXfyEVMOiGdG6cGs7gaD9/d2CHXbu7/6CZGZk/gVt9dHHlyDbnXTCJtVl4XvaH+yzEph3GvnMm+qz9g18/qGPXIkwywPMe13zzDB5m1vLH6l1xx4h+Ttj1N0xg+HIYPh/PP1fB6FRu+gE8+VXy2Ct55V5GXB2eeoTj7TI1hw3rQp6kQvYSug2bRSZ8/BGPj180ua2+q4902mjjoWCxD5qNn5NDaYQq1uF9az9ZUM8VwsKsVFYpWVfI7OOpad2muDhPzvsO/hzu/brL5Z3CFx+ZMQxt1OqsPBId2tCUKlGrtCKSEhEep60ifXR3unLuhP+xmtTn4kYRKbnskGpAhJtjV5guw6WBXeGAKS04Lw8S1oLlgbVJYwpldTQW7wgGWHvIMEn2dhbMwE1yrKRYHHn8dnRWJ0Fv4gsKq2zBGWyiOioqZNP3FA4DWuBkjWjA7TNcgYAY7gSdRYEYj1eak3u9mQFrbh/zLOnd0zN92PRWTQOTsViiMITOI3Zex577Wxi9mdfQ292PYWsF8ugQT+wEJdgkhOsz01FLy+UqMze8z3PMJGVY3Dm8Wn1adTFHOKaRPX8jMa1NYlCCN1/QaGJVezPoAeqoVS6YNPS34IV3mPsKd71xGdm0Wd26/hyPPrCFtdh6D75zd1W+x37IPT2fcS2ew/9sfsv/mNRh/uJphl5zNie9eyXN7nuNst0nGkl9Bak7St52SEuyAdeECjUBAsWYdLH9T8e9/wzP/Uhw9R3HJRRoL5rccNBVCBIXrBCmD0wlM7JoK9vCssZHfU2cOwJLdcmU3MupdO7aX7WjiyxBrCsbAKaj0+P7EItsNjxzZVOctfUCwYhhb9VFpGtQodEszwa6mmjGG/s6wpROwNoxEZ0uUYReuEKu2Z1lNzJtBfvpQnPaMuHmtzezqaDPGyPZaKHqbg12hSq6ypWKMPRHrjvjBDzpFC80YW3+MwpldTS9hybKjp9tInTGgraUEwDbMiQqYWLI6nm3aHM2ioVl1tJZ6Fu8pzx3RAeRwB/UJgl32UJC4s/pDDcewBjRxeK26LS6B0RNobijGYL9jeuX+uFRfzaoFR2MMZ4Y1HtxU05g34pRINmhbNd5HNkvoi5/o1dmaH3WzxfMnbpt6UjK7Wr+9Tlxf8sdnaTcJdgkh2qeuguJPV1Cz5R0OW1eywe5hY4qiNM3A0Ew0qsketZypA4uZPeQAqVnno1QOnm2VuFcXUf9NBfVbKvDucYERe1fU06xoA2wc1g9yU801DKjIxaMfJveqCQz52dGRYJjoGtYBDsa8cBoHbvyIQ7d/RuCeY7j+wtdZ/dpJ/LngVR54agP+k35GYOKZnZbLb7VqLFoAixZolJUr3nkXXntdcdfPFMOHw6UXw5mnQ2pqD3n4FKKHalzZyHK0r+LZXuHOjFvSkWaMNoud2UMXJZyncsc0+1oVekpP1GdXXxY4SsccomFJbxRI0HS0cFPNppoxNgwnEDs1wcGLGpCvzRU7i24lL21wwnnNZSDZozLBOrOD+kYFapPIaIy6teVIWhKFR+mzaAmanAb/at2KIq9pupar2y1knty2PgCjOY9pOkidTLahzmafMyOB+K7rM79ZKi3qHm74ULol4TkUztTtrGaMmqaxcL4ipYnvMqy6LW4chiEZoxIvHGIOmoo5cErURkKZXFYd02s0xLgaXZDBzt71pGUvjRrmYEcxpIbiW6o10Zy2ZnZpeqcdm8TbS/IKe+jjtwS7hBCtprkKqNnwIYEtHxIw1rHMUcfbmQE8ukm6ls3MYYtZMmAs2Y4BuH3VFNcWsPXgelxvH6R81yam75uGoyr4QGUbmoZjai5ZZ44MPlikWjA9BobLR/mhAr7ashJLjcakCXMYsmAyGUuGkzK2/f1DiY6xpNsY/c+TKfjvzzhy3wayNo3hv67+OX/dfC//scLlb/6YwLbleE++B5WRuDKSLHkDNK6+Eq64DD5bBc+/qPjD/yoe/wdcclHwX6K+34QQsVmQx408lRRLcpoqJltHr+Cc1Px2vc40g81mWjPiYl9itdkIDAjEVbaUpqMpE9Xc/gi9RoV+js09ivK64sTLhlav9ORWuptbV3RzyqT12dVSM8b2rjCc4aXb0JrpWy5ZhmaOJtXmxL9vGM5QRT46cNjq/RVu8qz3/qqlbVAatkFNZ+1EkoVimuR2cqGao1swBk7BUrI1mIXZxLXqsASbXZuq5T4T2ystrekdYbPYiU52Gmif2rp+X6N3bqjsWooN/2F31CJtPwA2ix1/1OiVzcnPSWX61Ba2ETovLNl2rLmOtvd5RfIyT1u1vX7ymNz770hCiM6jFHrpdtjxAb5NH5Lt3UG1ZvCIJYWVmS6smp0TxlzKeVO/zaT8WZGHItNnUPN+Aa7l+6leMRdVH8CXEWDr2G1sOWkL3qN1Tj72Mo4bOSXmW9rK+lLe2PoP/uN4jMxpOdx78hNMzp/TXe9eNKKnWBjxf4tJnbGFot9sZPo3Qzjnisv4Ey/C1PO4dMfnpD11Nr5FP8Q/47JOf+i1WDROWAwnLNbYuk3x7+cVT/xT8eJ/4JKLFZderEnQS4hmpNnSu7sITYpkT3TxJRwe8U/X+v4jcmZUPdNqsRNI1Nl+ONhhadgf4WMyYljsBJUVzNoZnTOZ0TmTE280fDyTXKfrrKZZ7dXW4oT7JlKhwJwx4ZRkFymhVJuTVJuTwUsaMlW0mCBO02/EmQa+UKzAzJuAsqV1+pddPUpP6aAeGjKeAvVN9q3nCI1G622h6WBnsWrWmMwuvT1hiFBwKnXWAALmAOq/Lk+4WGv6mlsw8oxWbza871rDmuMgdXrbM6bznENJbcN2OizZzRijfm9n69FO0fc/yYUQbWMGsBR8gWXPh7DjQ+x1hRhK56uKWTyVNYNv8lZh0X1cPOW7XDb9VrJTG/pG8R2ooeLfO6n8zx4CZR6sA1PJvWwcWWeNJu2YfKaY9QzY+yqvbXuS/139U1gNQzNGk5OaT42vikNVu1EoThl3Md+bd2+XN68RLdN0jfzvTSPt6HwO3fYpSx44mnHHDeXFmc/z6cyRXF6rmLviflK/fh7f4jswxhzfJeWacpTGA/dq7Nmr+OfTiiefgheXKS69WHHJxVqrR6cRoq+adyxUVXZ3Kdqhiy9dUxkAWPtAhkpzTjoBaqob/s525FHkPxgf3Ii0J43u00ljyYmqoUmsphOYeEbrmt+FF2lxFLa20Zsbjq0TtJjZ1dZmjKm5mDmjMXPHhVYQ+36mDTq2bStso+iMz9Z2Sr9gftRymo7KHpHsYvVMoY72uz3AFS18v/J7IqOoNhb+cqPe7044v7NZLfaYZvRaO65ZLRTssuamY0nPxDYoDaPK267yxDVV17QmozSp1kZBqOaiOe08L8YPmNa+F7ZTWu8cX6XN+vYnuRCidfx1WPavwrrnQyx7P0b3uPCaDlYVL+STklsonWph/1EPUuE7yJkTr+Ta2T8lzzkk8nLPbhclf/gK15sHQNfIPG0EuVdNJH3RkJihmVMtTr416SrOnHgl+yq3sfHIZ2wt2UCt10VO6kBOHnsRJ4w5N6YzY9EzOY8dxMSPzqP0sS1of7Pw0zU/5MjQYj4dtYOXB2jYD/sYsu435PiewekbjdOVR1qVA6uyYU93kD45n4wTh5N5+gh0ewe+4vfWoFcXorlLIeBhkuHnf65N59B5efzz9SH88+nMSNBLMr1Ef5aZoZEZ3693j9fVV6xhBoNdlj4e7LJaNdIXDkGzByuck/NnMyxzdGTEwwg9PrMLgpm1scu18j4e7sc82a11Wog8jMqeSGldYYc30+qMhTaPxqhhDpra5OyB6cOanJdsbR+BsX9R4X5mo9rkdXvgK3T9aQEPpiMr4SLhYJfPaF9wqKN0TeeU8RdQuGsjVYH97Qp2EQgGu1S4/7FUK3pqgnt1Ow5IYMJpTc5rVR+OPSibqbFEcbyMJD8PjBoJ23Ykd53J0Lc/yYUQTaurwLr3I6y7P8RyYDWa4cWjZ/NxyRKW71nCAet8TviWm9r8X/JlwSuMSpvIL055jemD50VW4S+tp/g3G6n8zx70NCsDb59B7tUTm+3rAILfCo/NncLY3ClcNPWmzn6nopPoaTYG/XgWed+ZQuVLe0h77wCDNwxG88V+orrTaqnILuVgpouAJYDTncbQt4aS8Z+9+DID+G/IYMb3ziTN0UyTKqXQXAXoJVuxlGxDL9ka/FeXOIV9IvA/GXDPFWP4umImL753Ale/cjwXXpLGJRc136+EEKL7hR/Mu6sZY1f2ndJdbAMbvtrXNb2JbOpwZleSqgyRzK7krC6y2hayysYNmMq4AU0Hk9os2X129SDJ6sS/zwoFuzRbDzrK0cesicCMRbeSYk3t1i+Uo0eCtbYnuzPcx5a1E/qabOELDk3T2j26Y0+UkpLc83f4cI1at+JQQVJX22ES7BKiH9GqDmLdswLr7g/QCzeiKZNA+nA2Wi/jn1+czKpDs5h8lJUrvq+oGfhv/vHFA3iPeLh+zl1cOv37kU5eVcCk/OkdFP/+K5TXIO/GKeR/byrW3J7Z0bHoXJYsO3n/dRR5/3UUKmASKPdguHzoaVYsGZBy6F0sX76Hq3wbhy2KA7nD2ZmaSfXOTEa8N5bxfxzLxy8+wq4flXDuCdcw1p6P5i5BL9+DXrYLvWwnltLtaN5gmxul2zDzxmOMPRH/gPGYmUNRzoFgS0VZbGjeGjR3KXrFPqxFmzjW+IR5c1+lzszg5TXn89/Lr+DEC0ZzwXkyeqMQPVV3VSmMUL9V/a2D+iaFO5+3JLnKkOTVtStLpB1aW9ft9kyfDuhp/Z/1NMoIdZLegzK7VFSgJtIUNoGFo1rfT1VnUZg4HMGm9W1+bdoAtNriJvslC+uM7MTFo8+m1ufii8MrE34+2YY58R2oIWVczx9Ma+H8zl1/TwoJSrBLiL5MKfSynVh3vot/30c4S7YDYAycQtmUW/jPliX8c9kE/H6N4xfBo3doZA7byR9X38HmPWuZPWQRP1jwm5hvgeo3l1Pw49V4tlWSftIwhv5qLiljev6NXXQNzarHjWQUyLqIwLSLSC3fzVE73mbagdXoBVvQUvwEzl7H13vHM/j9sxnwsxx+d/F/M3zkPn5Un8YApaNSMjAHTMA/+WzMQVMwBx6FOWA8RI2u1Vj4Q9YA/ACmgX74C2ybl3Gl5TmuHPsvlq85l9tfvZVTLxrGeecm/xsuIUTHOELD1w8Z0vxyyWao/tGMsdX0+D67OsSAnNQ8qlOS229QVwVoIrsjwbxBA6G4JFyeLilOp+iqwGGvFc7ssvagDuqjs7kcPfuZ/IR52eytPIQzpe2dsZtDZ2Ma3hZ3eGfcDyy6Fave9LOnnmIh46Sua27cXrreea0buv06SEA+yYXoa5RCL9uFdefbWHe+i165D6VZ0MYswDP1YjYHTuSp14fw6T/AZoNvnQGXXqIxeIiXZ7/+Ey+89ihOewZ3Ln6EU8ZdHPnAUKai/IltFP36Syy5Dkb9/UQyTh0h3wCKVlMDxuNbcBssuA0CPvTyXWjVh5lWcwTvGT52/9bCd5+9gXdOfo8rTljLD+b+P0446tsd//TULZgjjsU74li0xXdg+/IpzrY8w5kj3uLpD6/jmv/czOVXpnL2t8Bul/NZiJ7Abtc4ZYnq8s8Ys5/02dVq4eZRydofARiaOYbRw5LbYUxLzRiTZfIkcDggNzd+3rSp4HKBp3u6REoaea5rnooEu6Izuxqa53WLXtTsenTuePLTB5KekrhvsWbpFtCb7yqlM3XVfaY7zZ4JHk/7Xpse6o2kJ3V+L5/kQvQRWuV+bFtfx7rrXfSKvSjNgjFyHr5jrsc/9mQ27hzB4/+o4ZvNkJ0F11+rccH5kJOtsbHwM37x6h0crt7L6RMu46a598T03eEvrafgR6uo/aSQzDNHMuzh+VizU7rvzYrez2oPdsY7aCoGoM+B8acGOHznGs585XTGV0/iIc/P2F67j+8c84vWdQ7aCsqZh+/4H+OfdTX21X/mBu1xzvG/wy+f/QXPPreQa68JBoCtVnnYF6K7dUelW9ctYIAlWX1U9XbhSnSSmjGagzRsKWk4JmcnZX3RUm1ORmVPSvp6o6WkaEyamHiermvYbAqPF0yzU4vRqaSD+lay9qDAR5KekbqCpmntC3S1ZRuddA5HEgB6VEO9tlEt3Jvy8tq/74YN1cjKVKSn95x7iHySC9Gb+euw7nwP2+ZlWA5/EQxwjTgW35xrCUw4Fa+ezbvvw/O/URw8VMOwofDjH2qceTo4HBo13ip+++mveHfX8wzPHMfvznyJWUMWxmyi/pty9t/wEYbLx7CH55Nz+Xj51k90Ct1hZfj/LiRlYhb8Bn5ReQ9/qv0jB6p28ouTHifV1vaU96aojEF4T38Q/9TzGfDBr3hs/k18VnMud//v3fzr35lc/2049RQJegnR38waspCKuuKkBdh7O2VNCVYbk5Q5kuXMwzlxUFLW1dj8kU2PptZVws0ce29VWDK7WkvrScGu0PWp+sh9KzBqQWyn+z1EXwgEd/a9qScFukCCXUL0SlrVQWwb/4Vty8toPjdm7li8x/+EwJRzUc58qmsUr74Ey15SVFTCUZPhD79L5+jZ7shw4Z8ffI//XfVTqjzlXD3rh1w54wfYG41u4nrrAIf++zOs+amMf+1MHJNzuuPtin5E0zQG3jIdx4QsDv1oNT97/G7+c+oyflp3MQ+e8a8mRgtrP3P4XOqvfgXb+sdZuPavvHfBen67+yEe/PVcnn4Wrr8WTj6JyHUjhOjb0mzppGU1MzJsf2MLNRkK9WXWEYvHnI3ei5pbtUdWFriqwdq336Ygts+ubhfKvFQ5o7u3HMmS2jPrG+GRSlMsPaidnmiWBLuE6C2UQi9Yj/3Lp7HsWQEWK4FJZ+GfcSnmkFmgaRQVKV580uSN5VDvgQXHwRWXa8yaCbm5KVRW1lHtreQva37BB3uWMS53Gg+e9izjB0xrtClFyZ+/oeT3X5E2dyCj/nYi1gEy0qLoOpmnjWTCu7kU/GgVl712MYc2FPDnr2/nxv/+NYOzRiR3Y1Y7/vm3YIw5Acfbd/CLEddz9bHX87MVt3HfA3aefAquvAxOP0369BJC9C/KHsqoDfg6vK7+MMLlhPEwZHDndQAtepCe9CWYphOYeEaPzIbqDp2VnWi3pDB10DHkpA7slPWL5JNglxC9gF60Gfunv8N6aC1mai7+476Pf+ZlKGc+AJu3KP7zksnHHwc/5047BS6/TGPsmNib/Wf73+JPn99JjbeK6+bcweUzbot7+DQ9AQp+shrX6/vJuXQcQx88Dj1FvqIUXc8+LJ0xz5+G6/X98D+fM+Ifw9mz7HWqThnHkNOn4Dx2INbc5AVhzcHTqLv6Jewrf8vYr5/gX8ev4rOLf8NfXxnPw79T/P1JuPRiOP9ccDp70EOuEEJ0EpUxGDNnNGbuuO4uSq+g6xqZPXswPJEkPa65Zx9pwpgMndnccFB6kr9w7QLTp8G2HeDr+HcWvY4Eu4TowbSqg9g/+yO2ne9gpuXhWfILAtMuAmsKgYDiow8V/1mm2LotOALGZZfBJRdq5OfH3uSr6sv4zarbeG/Hf5g4YAYPn/4iY3OPituev6SeA9/5iPqvyxj886PJu3FKz/swF/2KpmlknzeGrLNHsfvVNWx94i1sb9rwvxQc3902Ip3U6QNImzGA1BkDSJ0+AEtW00NDt8iWiu/kezDGnkjKez9j8fZLmPedH7BO/zb/ek7nsaWKp/8F55+ruPEGE6t8igoh+jJNDw4mIoQQolcaOFAjK0ux8rPuLknXk8d0IXogra4c25rHsG16ASx2vPNvxX/0dWB3UlWleH254pVXFaVlMGok/OSHGqefBqmp8YGpT/a9ziOf343bX8MNR/+MS6d/L+GQ6p7tley/fgVGpZdR/1hC5inDu+CdCtE6mkVnwkULyDhtOHe/dSVpO3VucPyIrIO51G8qp/qtA5Fl7aMzSJuTT9a5o8lYPLRdncgaYxZT9+3Xcbz/Sxwrf8vCoR9y9P/7H3aWjeTZ5xXPvQAvLqvkjNPh0os1xoyWoLAQQgjR11kHphIoqU84z973W+r2Cn2hI/lk669fzvbTty1ED+Wvw/bFP7Gv/wcYPnzTLqXaeQWuz2oo/+1qqne78dcGyLSkcfnIAYy9cSxzLh+QsPPsyvpS/vz5XXy6/00m58/h/rOeIEcfnHCzNR8f5uD3V2LJsDH25TNInZLb2e9UiHYZnDGSP5z7Cj977yruLP8eFy2+ieuOvhOb20r95nLqNwX/1XxSSNXLe7ENTSP/e9PIuXxC25vjpubgOedPWLe/ScqKB0h75gKOWvwTfvXzy7npOxqvvGrn5Ve9vLFccfQcxcUXaiyYL53ZCyGEEH1V+vzEz9LHHA1p0m+56KGCz6a9eZzY9pFglxA9geHHuuVl7J8/iu4uozrjPEqLz6Dy3kr8B1ZjahqHbDmUOXIZNMnCxJRa1PY9mHfvYN9/8hj6y7mkzQ7232Uqk7d3/pu/r38Ar+Hhprn3cNHU75I3II/KysqYzaqAScmfNlHy5004puYy+okl2AandcceEKLVclLz+f23XuHvGx5k2ZalfHbgbS6d/n3mzzqN/IXBwRaU36Tmo8OU/m0zhb9Yx6G/buDAd6rYMm4rhTX7qfPV4DO8ZKRkk5Oaz/jcaUweOIfZQxaRanM2bEzTCBx1NsaIY0l5/xc4VtxPYPf7DDv5V/z8/83i6it9LH8LXnlVcffPFUMGwwXnw9lnQWaGBL2EEEKI/iAnWz7zewzpgkWEaEqpdoX4Gleaky0nJ6fTtyE6To5TBymFZff7pHz2R4zCYkqKzqR0+yzqd9SjNI09mYP5zD6GojHDOevilJgKtOH2U/XKPkr+vIlAUR05l4yj/uY0Ht1xD1tK1jN7yCJ+sOBhhmcFO5VtfKx8hW4KfvAZ7rXF5Fw+nqG/moueJvnXPYFcV633TdEalq67l+1lGwEYlD6czJRcLLqVirpiyuuKGbd7NBe8fR6Dywbx1fzNbL/qMGkZGdgsdmq8VZTWHWF/5XYCpp8USypzhy/htAmXMm/4ybFNfpXCuvklUlb+BgJeLCf8gOppV4PVjmEoVq2Gl15RfPElpKQEB4o49xyNyZN6YEe2/Uxbr6mcnJ457Hl7yL2k95PPhN6vtx3DFXteAWDJuAu6uSQ9S287jv1N+Lw9fvRZ2CxN99/aX4/j+x8Gwz6nntw3nklb86wmmV1CdBO9YAP2lb/Dvb6SgzsWU7ljBMoPlQPT+HDgNNY5RnLU/FQuvEBj/rz4plEWp40BV08k64LRbP3Nu5Q9swvf616GnZbNObc/wskTL05YwTa9BhXP7KD491+BpjHikePJPm9MF71rIZJr+uDjeOSctzhYtZMvCleys+xr3P4aDNPPqOyJ5KUNYeTiCYy4bRxp//Qy6zGYV7aQkf+3GMfkhg9Jn+FlR+lGPt3/Jp/se53PDrzJgNRBnDrhUs6ceCXDMscEs7ymX4wx9gTsn/wWbcVvSfvyRbyLfwrjlrD4eI3Fx2vs3at46RXFe+/DG28qxo2Fs74Fp58KWVl94wFDCCGEEEKInkwyu0SHyHFqO71sJ7UfPczeFWXUf7kQx5HB1Dt9fDH5AJ9O3ElJfhUDspyMHpLDkJwBZDkGkJ06gGxHHtmOPHRNxxuo50jNAfZUbGVdwYeU1R1hTO04rv/ov8j4wkbKuExyr5pIxknDsI/KQAUUKSUmBa9so+JfOwgU15NxynCG3n8s9mHp3b1LRCNyXXWe2lVHOPTfn2FU+Rj6q7nkXDkhLihsmAHWF6zg7Z3PsebQ+xgqwMzBCzhz4pUcP/osUqzBTjmyKjbje+MuLOV7MIYdjXfxTzGHzIysp65O8eEKWP6WYstWsNng+EVwzlkaR88JDlEvuoZkdoneTD4Ter/edgwlsyux3nYc+5vwebt4zNlY9aZbq/TX49gfM7sk2CU6RI5T85RSFNceYk/FVvYUrmbfjvdwfj6UuasXk+vKpSivmI/mbWD96BJs1gEMGehgYL6G16yl2ltJtacCl6cCQwUSrj/Lkcv0QfM5ccy5HDfyVFIsqdS8X0DpXzdTt6E04WvSFw8h/3vTcC4Y3GTTqkAgwGuvvQbAeeedBxDztzU0pEfj5axWa8JprdlGW17b2vU15/333+eyyy4D4JZbbuGGG25g0aJFGIbBL3/5S77zne9E3rdhGABYLBbOOuss3nzzzbh94/P5+PLLLwGYM2cOdrs9phxVVVWceeaZHDp0iBEjRnDrrbdy0UUXRdYVXq9hGDgcDlauXImmacydO5cLL7wwsp3wdj0eD9deey0ATz31FA6Hg5dffpkNGzZwzDHHcOGFF8bsg+j9c8IJJ3D++ecD8K9//Yurr74agOXLl5Oent7qY93RY9BaHo+He+65B4D77rsPh8PR5LbC0+vr63nxxRfZsmUL06ZN49lnnyU9PRjYDZR7OPTDz6j9uJCsc0cz7KHjsGQkTnevqCvh/d0v8tbOf3O4ei9OeyYnj72QMydeydzxi6mqKMO65RXsqx9Bd5cSmHA6vuO+j5k/MWY9e/cp3nxb8e67UOWCgQPh5JPglJM1Jk6QZo6drb8Gu5577jncbndSr8e+JBAI8J///Idly5YxduxY7r//fhwOR4fX2ZH7YKLXd/ezViAQiHy+zJ49G4vFgsVi6bTzKtEzSPTn27nnnsubb76Jz+djw4YN7N27l7FjxzJv3ryYz8vw/P3793PxxRdzzjnncN9992GaJkcffXTM53T0Ns866yxeeeUVXnzxRZRSjBkTzH4/cOAA48aN4/777wfgnnvuwTRNZs2axZdffsn+/fsZN24cv/zlL3nrrbdYt25d5HP82muv5V//+lfkPfX063HlvjfIdOQwa8iiNr2urc920fNOP/107r33Xvbs2cOYMWMix7Pxvop+Jrjnnnt4991347bX+HwFMAyDjRs3JnxGaq2WrsXw+4l+bkzGM27j9bdmHa19lu9L3t+1jC82fMFY22wuPL/pY9wZ99T2HBufz8f69es5cOAAF198MZdccknC5+6zzjqL119/vcln/Ka28fLLL8fch6ZMuZAUh4UJ41tX/2vNOZLoGT16XeFrAYLX4BdffBFTv+nIeSjNGIXoQr6Ah31V29lbsZXd5ZvZW7GVvZVbcfuqSfGksGjDcZz++TU43RkcHuznL0MGs41zWJj/Yx4+TWP2rMQVXqUUtT4XVZ4yymuL+fSzT7Fg46IzL2NgxrC412SeNoLM00bg3eOi7utyfAdq0B0Wssfno83OxJonQ8WI/s06wMHof55M2dItFP1mI/VflTH0/nlknDQsbtnctIFcNuNWLp1+C5uL1/LWzmd5d9cLvL79n+SnD2XawHlMHDCD0Wf8P4buW8Ogza+RuesdzLFL8B17I+bQ2QCMHaNx2/c1br4x2LfXu+8plr0Mz72gGDECTlmiOHmJxuhREvQSQgjRvRaPOae7iyBEu8mTVGKTJ4PV2r/2jgS7hGiHGm8Veyo2s7t8C7vLv2F3xWYOVu3CVMHodZotg3EZY7moaAaTVg0ibfMM8KayLyuPl4fMoXb0QM45W+P+MyEvr/mbjqZpZKRkk5GSzRDnaPZYiwAYkNZ0ZhZAyrgsUsZlRf7u7m+GhehJNF0j/3vTSDt2EIfvWM3+az8MZj3ePA3nwvhrS9M0pg8+jumDj+OW4x7k0/3L2Vq+ng0HV/LR3lcaFkwDS5pOVvlrZL/5Klm2LDJzxpExYDJZqflkOXIZNX4iP58/B9ObzsrP4MMViqefgSefUowbpzjpBI35xyEZX0IIIYQQbSbPTiJIgl1CNMNUJkU1ByNZWsGsrW8oqj0UWSbfOYzxA6Zx/KizGJ82iVGfV2F7+yDVm9PxVOWiLIqvnUN4e+BsRp88gO+drTH3GOmvR4iewHl0PhPeOYfyp7ZT+tct7LvyfWxD0sg4eTipMweQOjWXlPHZ6A5L5DXp9kzOnHglV+bcQmVlJdXeSg5U7qSivpjK+lIq68tw1ZdQU/INNZV7KCj5kqqyjVRpCgMTAA2NowYezfGjzuLuX52H1TeEjz6BDz5U/ONJxd+fgAEDYP48xfz5GnOPhrQ0uWcIIYQQQgjRGhLsEv1era+aopqDwX+1hyiuPRT5/UjNATyBOgB0zcLwrLFMGTiXc4+6nnG50xg/YCqOQiu1y7+k9v3d1G4pozJgRbMOpSpX5/1BM9gzZjKnn5fCn8+AAQOksipET6PZdPK+M4XcayZR/c5BXG8doOr1fVT8a2doAbANTydlXCYpY7NInZpL+qLBEOorIDMlh+mD5yVeuTKxHFiN7evn0feuoFpT7Bg6ha9yB7LKvYel6+/l8Q0PMH/EaZwz91oePW8x1S6Ntetg9RrFxyuDHdxbrTB9mmLO7GCT5ylHgd0u9xMhhBBCiGjydCTCJNgl+o2KuhJ2ln/N7vJv2FuxjaKaAxypPUSNN7ZpX7o9i8EZIxmWOYZjhp3A6JzJjM2dyqisCditDvwl9bg/K8D9wTaK1r6OrzSY8eHIrSEwwc+HahYf6iey8EQbZ58io64J0VvoKRayzxtD9nljUErhP1hL/eYKPLtd+Pa68O6ppnLDbspr/QAcmJBD+qnDyDp7FI6puYmbHGo6xuhFGKMXodUcIW3Tf5i79VXmFWzlppQMDo69gtdTDd4sXMGqg28zLHMs5x11PaedeBmnn5ZJIBAcyXH154ovvgw2dfzHk5CSAtOmNgS/Jk+S4JcQQgghhIS7RJgEu0Sf5QnUsenI56w//BHrCz6ioHoPALqmMyxzLEMyRnHUwGMYkjGSwekjGZwxksHpI0hPyUKZikC5B/9hN/4dtXj3VVH05bt4tlbhPRK8gVoddWSOOEjVOJ0Vagbv117P9LlZnHqyxi3HQUqK3GiF6K00TcM+KgP7qAyyoqYrpfDudFG76gieVSWU/m0LpX/ZTMq4TLIvHkfOReOwDU5LuE6VMQTfwtvxLbgVS8EGrFtfY+TOd7jNX8dNWcNYMex4Xvbs4S9rf8ETXzzEKeMv5ryjrmfmjKOYOSN4P6mpUWz6Br78SrFxI/z9CYVSYLPB+PGKKZNhylEaU6bA8GHS55cQQgghhOifJNgl+gylFAddu1hf8BHrD3/EpqLPsdRqjKgazgJzARMCN5DHILIC2Wj1YLr9mHWB4E93AMO9k0N1WzHcflS9Ebf+lKxK0gcW4VxQx3bHQD6qP5atniuZMT2dRQs0rjsWMjKkYilEX6ZpGo5J2TgmZZPz4wWU7iui+t2DVL28l+KHN1L8269IXzyEnIvHkXnayJi+vhpWomOMOBZjxLF4l/wc6+4PsW57nTO2reRMZbI9ayLLUlN4f+cLLN/+NJPz57Bg5BksHHk6I7MnsnCBxsIFwXtNdY1i0ybYslWxdRu8/S689IoCICMDJoxXjBsL48drjBsLY0ZLIF4IIYQQfZd80SfCJNglerXK+lI2FX3Ol4Wfsr5gBZVVxUzaM5FjDx7HhYdOIe2QPWZ5zV5JbXotljQd3WFisQewWL3YU+uxpNVgVeVYtFosdh9WhwcjA0qtuXxVN5YvXTPY4rqKQTm5zJ4FF8zV+NWM/jeEqxCigTU7hdzLJpB72QR8B2uoXLaHymV7OHTrp+iZdjJPG0H6oiGkzhhAyugMNKsOBIPzhsuH74Ab38Fp+ItG4zt8Jf5dBfiP1HKW18MZxtnUOvyUZLnZnfc1jw59i9oJHgZNHMuk/NmMzT2KIRmjOHbeCBYtdABgGIqDh2DrNti2TbF7Dyx/C+rrgwEwXYcRwxVjx8LIETB8uMaI4cEssKwseUAUQgghhBB9gwS7RK+hlKK8roitpV/w9ZFVfH1kNfurdmD32Zi9bw7X7LmSoZvy0T1gybTgnGon9cQAjoEuUtKLcVgPYPMWYHMfQVOByHpNdFzmII54hrOlchxby2ezu3oCeyrGYUnPZtJEmDgNzp2u8fPpMiKaECIx+8gMBv1oFgP/eybuNcVU/mc3NR8dpmpZsAk1uoYlwwZWHbMugKoPxLzemu/ANiIX+5wR6DaFXleMs7SY3CKTkXsXoAwbADWZNewcuZs3R33I7tF7KckrIc85hCEZoxiUPpyB6cMZOGYoi6cN4+L0YeSlDcNVns6ePbBnL+zZEwyCrfw0GBwLS08PBsKGD4fhAw2G2OsZYNaRkxogK1sjfYidlOHpWPNSu2yfCiGEEEII0R4S7BJdQilFfT3U1gb/1dRCXR14POD1BX/6vODxQp0ngMt3hKrAAWrUAWq0XdRYtlBn30rAUgFARm0OC7Ys4JId32LwgYHohgU9rZ7cCV8xcOIWMocdQtODlTgzoFFaNJAddcMorJtFYd23OFw3nML6oRTWDaPCGEzuABvDhsHwETD8OI25w2HiBBgwQDIdhBBto+ka6QsGk75gMMpUeHdVUb+1Et++agyXD2UqdIcV2+A07CPTsY/MwD7CiZ5mS7xCw4d26GsCn6+lbu0Barfq5Owfx9GbZwMQSPdSPr6C/eOOcDC7gE9T1lCZVoXP5sNvC3amn27LZGDacAblDWPwiGHMOHEo2b4B+Iqt+AossF/Dctgk5YsAGSsCpHtM9NDmvUBJ6B9ArQMOD7VQPCqLmknDSR80nOzUXOw2DasNrFawWWHSRBg7Vu6fQgghhBCi60mwSzRNKbTqw2AagMI0FHWhgJXbHfynzCMUlJZR5D5CpaeSGl8ttf5a6gI1eAJevIYHn+HDZ/pQuhdl8aF0H1i86NZ6NIsHLF6wBOeZug+frQ7TYUaKkaI0JtSnMmffMMYeOprcveMxCkaB0rGl1+CcvhN9fC2eoSmUWgdz0DKTOusgPPbBeOwDCaTkYU2xYbMGO3EekQYzciAnB3KyITVVAlpCiM6h6RqOSTk4JuW0fyUWO2r0XCyj55JxBWR4qhl2aC3GVxtxry2ldquGc98wBn01h3nMacOKA6F/QZWZlZTlVnBoaDkV2ZVUZlVSleXCa/eiKQ1nnZO8ylyGHRnGxL0TmLTXgI8q2Df8db6evI1No4sod6Sg6vNQnnzyv8znyvMGkp2ah9OWQZo9gzRbBmk2Z+j3dKx6EwG+fuaxxx7jk08+Ydu2bdhsNjZs2BC3TGFhIffddx9r1qwhJSWFc845hzvuuAO73Z5gjUIIIYQQ/VuPDnaV1RXhN7w4rGnYLQ4c1lQseo8ucrdTShEIgN9P5Kc/AIHQT58PPHur8W0swadZ8ehW3I40ahxOarFR79Goqw9mXc00XuL63HtwoyjSTY7oBoW6yZHQv0LdpEg3qQ5lUJES+hfFqsBOMGCVgoZdEflpRyNFBec5AlbSa9NIqxtAlmc0+fX5ZLuG4qgaCOVZ1BekooxgQMo2zkbeDQPJOHMsjqNHoek6QgjRbzgyMSecijbhVNIvgXTDz9Dy3Rg7txLYeQj/oTKMoipUrRvTF7w/apqC0D9NV5ipUJ9hQppBINOLOaCOdLtipG5D063oFgu6noVGLrqmo2s66FZ0TUPXAgTYSkWhldpvshj61UDGfHAm5wMVQ0vYOWUH30xcx+4BB/i/tYFm34rd4iDN5sRmScGiWbHqNqy6lVSfk6zqDDKqM0ivdVKf76NqYi3XzbmT8QOmdf4+7mJ+v58zzjiDWbNmsWzZsrj5hmHw3e9+l5ycHP79739TVVXFnXfeiVKKX/ziF91QYiGEEEKInq3HRo4KqvZyxfOzUaiY6Q5rGun2LNJTssiwZ5OekkW6PTPye4Y9G6c9k4yU7NBymdh0O7pmQdM0dM2CRbOghR7eda2hs+DwtkxlBn8PTQvOMyPLKEXkb4haJvS68DKmMgiYPvyGH0P58Rt+AmbDP7/hw1AB/IYvMq2o1MeKT4J/K80PmCjTDoYdZaSAkRL6Pfi3CqSAacf02wn4UzB89uAyZmg50waaiaYZoBmgG1xSsJeTSktxAJlR+9ZrUVQ5DarT66nOdHEku4y7smdSk+rCb/PjtwYwLAapAQc5KpdpZHOiJYdsI4Ns0kkLOLB57Vh9Viw+C3h0lAdMj8KsV5hehQooVACUEfoZABVQYBLH1DUCw53Yx2SSd+4A0o7OJ21OPtbslPiFhRCiv7LYMAcehTbwKGyLIJIrpUy0ugq02uLgv7oKNF8tmrcWfDXBn4YPzACa4QczAKYfjACaGQh+Y6L8oEwwzeD6VPAnymC0TcFsA2YpvJWpVGwfScb20eR+sIjjPjge3ebDMagElVOBmV1L/dHH48lIxWP14q+vx1/nI+D2QlUAW5mFlHIrKeU2HJU2bJ7Yx5PKwdW88ssPCJi+Lt+9XeH2228H4OWXX044/7PPPmP37t18/PHHDBo0CIC77rqLu+66ix/+8Iekp6d3WVmFEEIIIXqDHhvsGpY1hl+f/jwV9SV4A/V4DQ/eQD21Phc1Xhfu0M8y9xH2VWzD7aum1ueKC471SiM1dOxoyoaGjqn5MPEGv5VvhpXWHdA3TI1PajKx+204vA6yajLJcWWT7coh15VNrjuP8WXZpO8eiiUwq9XFVlaNQJoVM82GnmZt+JdlxZpmQ0+1oNn00D8Lmk1Dswb/1lOtWHJSsOSkYM1JwZqXim24E91uafX2hRBCRNF0lDMP5cyDQVM7fXNZoX/+4jrca4up21hG/aZy/AdrCGyuR//UII1a0oCGT6w0sGjYBqViG+LEdowT25A0bIPTsA5KwzYoFevAVGyD0licemunv4ee6quvvmLChAmRQBfAokWL8Pl8bN68meOOO64bSyeEEEJ0v1Srs7uLIHqYHhvs0jSNo4ed0KbXmMqkzldDja+KWq+LWl81Nb4qDDOAqQxMZUb9NFHKxFBGcHtoaJqGhh76qUX6cdI1HQ0tuJSmhbLBmlgm/Fo0dN2CVbdh0+3BnxYbFt2GTbdh1e1YdStWix2bHjvdoscHeJRSkSwwn+EJ/fThN734DC9+w4ff8OI3fTHL+E0fFs2CRbdGstqif0+zZ+C0Z+C0ZeK0Z5Jqc8ZkuxlVXsxqP6bHwPQEUAGFbtfR7Ba0FJ3s/Fxq/O5gUEsCU0II0e/ZBqWRfe4Yss8dE5lmeg2MSm/wc8RjoNktwc8NhwU9w4ZmkebozSkrKyMvLy9mWlZWFjabjbKysmZf63Q6ycnJwWrtsY983SYQCOB0OrHZbKSkpJCTk4PD4UjKOoF27femXp+T04F+/zooXCaHw4HT6cRisWCxWDrtvGq8D4CY7efk5MQcN7vdTkpKSmReePnwfJvNFpnncDgwTROn04ndbo+8h8bbDL9eKUVKSrBFQfR5AsSsy+FwxMwPTwuXJfpnX74eE52/zV0TjeeFj1f08Wy8rzweT2Tfhvd1ou1Fn68QbA4efQ619xg0dy2Gt20Ywfpl9HXS0XtD9Ppbs46mrqOObL+nOzn1PEq3uUnVWz7Gyb6ntufY2Gy2yL2j8XmZ6J7UlvM3+jqA1j0LtOccbXw9hn9vfC1AwzXY2vIkQ586y3VNDzZrTMmCjO4uTXJpmoZVs2HVbaTauiZqrWka1hwH5DT94OfIyaC+svk+WYQQQvRveooFfXBadxejSz3yyCM8+uijzS6zbNkypk+f3qr1NTWQSksDrLjdbiorK/tkxaajAoEAbrcbv9+P1+ulsrIyKcEut9sN0K79nuj1OTk5VFZWdqhcHREuk8fjwe12R4JdnXVeNd4HQMz2Kysrcbvd+Hw+vF5v5Gd4Xnj58HS/3x+Z5/F4ME0zctzD76HxNsPzlVJ4vV6AmPMEiFmXx+OJmR+epmlaZL0dOS96i0Tnb3PXRON54eMVfTwTBbs8Hk/kNU1tL/p8hWBFO/ocas8xaOlaDG87OtjVmv3QWm1ZR1PXUUe239MFAgHMeg03zR/jzrintufY+Hy+yL2j8XmZ6J7UlvM3+joI34faes60NtgVfT1GB7uirwVouAZbW56WtCZg2ffOciGEEEKIbnbVVVfxrW99q9llhg8f3qp15eXl8fXXX8dMc7lc+P1+BgwY0O4yCiGEEEL0VRLsEkIIIYRIstzcXHJzc5OyrlmzZvHXv/6VkpISBg4cCMCqVauw2+1Mm9b3RqcUQgghhOgoCXYJIYQQQnSjwsJCXC4XhYWFGIbBtm3bABg5ciROp5NFixYxfvx47rjjDu644w5cLhcPP/wwl156qYzEKIQQQgiRgAS7hBBCCCG60Z///GdeeeWVyN/nn38+AE8//TTz5s3DYrGwdOlS7r33Xq644gocDgdnn302d955ZzeVWAghhBCiZ5NglxBCCCFEN/r1r3/Nr3/962aXGTp0KEuXLu2iEgkhhBBC9G4y1rcQQgghhBBCCCGE6DMk2CWEEEIIIYQQQggh+gwJdgkhhBBCCCGEEEKIPkOCXUIIIYQQQgghhBCiz5BglxBCCCGEEEIIIYToM9o9GmNOTk4yy9Ft2xAdJ8ep6wQCAZxOJxDc71Zr2y7h1h6rxtsBEm43UXlaW8aOvLa162tORkZG5HeHw0FOTg6apkXea/T7NgwDAIvFQk5OTsJ9Y7PZSElJifxtt9tjyqFpGhaLJfJ7eBvR6wpvyzAMHA5HwrKEl/V4PNhstsjfDocDp9MZ+dl4HzTeP+GyNP49PT291ce6o8egtTweT2R/hN9rU9sKT9d1HZvNFtnv4feWbHL/6z3667FKdD8QQeH7Rfj+Hb6/JGOd0L77YFOv787zN1ym8OeLxWKJ3Fc747xq6hkk+vMt+rjZ7XZSUlLiPi/D8202W2Sew+HANM24z+nG2wy/XikV+WyPPk+AmHU5HI6Y+eFp4bJE/+zL12Nbn+0azwsfr+jj2XhfNX4maGp70ecrEHm26ug9sblrMbztxs+NHX3Gbbz+1qyjtc/yfUlb9k+y76ntOTY2my1y72h8Xia6J7Xl/I2+DqB1zwLtOUcTPaNHryt8LQAJ6zedfR5qSinVqVsQQgghhBBCCCGEEKKLSDNGIYQQQgghhBBCCNFnSLBLCCGEEEIIIYQQQvQZEuwSQgghhBBCCCGEEH2GBLuEEEIIIYQQQgghRJ8hwS4hhBBCCCGEEEII0Wf0qGDXY489xuWXX87MmTM55phjEi5TWFjIzTffzKxZs5g3bx4PPPAAPp+vi0sqElmyZAmTJk2K+fe73/2uu4vV7z377LMsWbKE6dOnc+GFF7Jhw4buLpJo5JFHHom7dhYuXNjdxRLA+vXrufnmm1m0aBGTJk3igw8+iJmvlOKRRx5h0aJFzJgxg2uuuYZdu3Z1U2n7t5aO1V133RV3nV166aXdVNrOJff9nmvp0qVcdNFFzJ49m/nz5/P973+fvXv3xizTmvuKz+fj/vvvZ968ecyaNYubb76ZoqKirnwrIsrSpUuZNGkSDz74YGSaHMfeobi4mJ/85CfMmzePmTNnct5557F58+bIfDmOPV8gEOCPf/wjS5YsYcaMGZx88sk8+uijmKYZWUaOY//Uo4Jdfr+fM844gyuuuCLhfMMw+O53v0tdXR3//ve/+eMf/8i7777Lww8/3MUlFU25/fbb+eyzzyL/vve973V3kfq1t956i4ceeojvfe97vPrqqxx99NHceOONFBYWdnfRRCMTJkyIuXbeeOON7i6SAOrq6pg0aRL33HNPwvmPP/44Tz75JPfccw/Lli0jLy+P66+/ntra2i4uqWjpWAEcf/zxMdfZ3/72ty4sYdeQ+37Ptm7dOq666ipefPFFnnzySQzD4IYbbqCuri6yTGvuKw8++CDvv/8+f/zjH/n3v/9NXV0d3/3udzEMozveVr+2adMmXnjhBSZNmhQzXY5jz+dyubjiiiuw2Ww8/vjjvPnmm9x1111kZmZGlpHj2PM9/vjjPP/889xzzz289dZb/PSnP+Uf//gHzzzzTMwychz7IdUDvfTSS+roo4+Om/7xxx+ryZMnq6Kiosi05cuXq2nTpqmampquLKJI4KSTTlJPPvlkdxdDRLn44ovVPffcEzPtjDPOUL/73e+6qUQikT//+c/q3HPP7e5iiBZMnDhRvf/++5G/TdNUCxcuVEuXLo1M83q96uijj1bPPfdcdxRRhDQ+Vkopdeedd6rvfe973VSiriP3/d6lvLxcTZw4Ua1bt04p1br7SnV1tZo6dap68803I8sUFRWpyZMnq5UrV3btG+jnamtr1WmnnaZWrVqlrr76avXAAw8opeQ49ha//e1v1RVXXNHkfDmOvcNNN92k7r777phpt956q/rJT36ilJLj2J/1qMyulnz11VdMmDCBQYMGRaYtWrQIn88Xk24qus/f//535s2bx3nnncdjjz0mTUy7kc/nY8uWLSxatChm+sKFC9m4cWM3lUo05cCBAyxatIglS5bwwx/+kEOHDnV3kUQLCgoKKC0tjbnG7HY7c+fOlWush1q3bh3z58/n9NNP5+c//znl5eXdXaSkkvt+71NTUwNAVlYW0Lr7yubNm/H7/THN3QcNGsSECRPkOHex++67jxNOOIEFCxbETJfj2DusWLGCadOmcfvttzN//nzOP/98Xnzxxch8OY69w9FHH82aNWvYt28fANu3b+eLL77ghBNOAOQ49mfW7i5AW5SVlZGXlxczLSsrC5vNRllZWTeVSoR9+9vfZsqUKWRmZvLNN9/w+9//noKCgpj+C0TXqaysxDAMBgwYEDM9Ly+P0tLSbiqVSGTGjBk8/PDDjB49mvLy8kj/hcuXLycnJ6e7iyeaEL6OEl1j0mSs51m8eDFnnHEGQ4cOpaCggD/96U9ce+21vPzyy9jt9u4uXlLIfb93UUrx0EMPcfTRRzNx4kSgdfeVsrIybDZbJEAWvYw8D3edN998k61bt7Js2bK4eXIce4dDhw7x3HPPcf3113PzzTezadMmHnjgAex2O+eff74cx17ixhtvpKamhjPPPBOLxYJhGPzwhz/k7LPPBuR67M86Pdj1yCOP8Oijjza7zLJly5g+fXqr1qdpWpumi45py/G77rrrItMmT55MZmYmt99+Oz/5yU+kwt6NGl8bSim5XnqY8DdPYbNmzeLUU0/l1Vdf5frrr++mUonWSnSNiZ7nW9/6VuT3iRMnMm3aNJYsWcLHH3/Maaed1o0lSz657/cO9913Hzt37uTf//533Lz23Ffk3tN1jhw5woMPPsgTTzxBSkpKk8vJcezZlFJMmzaNH/3oRwBMmTKF3bt389xzz3H++edHlpPj2LO99dZbvP766/z+979n/PjxbNu2jYceeoiBAwdywQUXRJaT49j/dHqw66qrrop5wExk+PDhrVpXXl4eX3/9dcw0l8uF3++Pi9SK5OjI8Zs1axYABw8elGBXN8jJycFiscR9G1FeXh6XISl6lrS0NCZOnMj+/fu7uyiiGfn5+UDw28CBAwdGpss11jsMHDiQoUOH9qnrTO77vcf999/PihUr+Ne//sXgwYMj01tzX8nLy8Pv9+NyuWKyEMrLy5k9e3YXvYP+bcuWLZSXl3PhhRdGphmGwfr163n22Wd55513ADmOPV1+fj7jxo2LmTZ27FjefffdyHyQ49jT/eY3v+Gmm27irLPOAmDSpEkUFhaydOlSLrjgAjmO/Vin99mVm5vLuHHjmv3X3Dci0WbNmsWuXbsoKSmJTFu1ahV2u51p06Z11lvo1zpy/LZu3Qo0fFCIrmW325k6dSqrVq2Kmb569Wq5afdwPp+PPXv2yLXTww0fPpz8/PyYa8zn87F+/Xq5xnqByspKjhw5EvPg29vJfb/nU0px33338d577/HUU08xYsSImPmtua9MmzYNm80Ws0xJSQm7du2S49xFjjvuON544w1effXVyL9p06Zxzjnn8OqrrzJixAg5jr3AnDlzIv08he3fv59hw4YBcj32Fh6PJy5ry2KxRLKy5Dj2Xz2qz67CwkJcLheFhYUYhsG2bdsAGDlyJE6nk0WLFjF+/HjuuOMO7rjjDlwuFw8//DCXXnop6enp3Vz6/m3jxo18/fXXzJs3j/T0dL755hseeughlixZwtChQ7u7eP3W9ddfzx133MG0adOYPXs2L7zwAkeOHOHyyy/v7qKJKA8//DAnnXQSQ4YMoaKigscee4za2tqY1GvRPdxuNwcPHoz8XVBQwLZt28jKymLo0KF8+9vfZunSpYwePZpRo0axdOlSHA5HpJ8I0XWaO1ZZWVk8+uijnHbaaeTn53P48GH++Mc/kpOTwymnnNKNpU4+ue/3bPfeey/Lly/nL3/5C06nM9KXTEZGBg6HA03TWryvZGRkcNFFF/Hwww+Tk5NDVlYWDz/8MBMnTozrKF10jvT09Eg/a2FpaWlkZ2dHpstx7PmuvfZarrjiCv76179y5plnsmnTJl588UXuu+8+ALkee4mTTjqJv/71rwwdOjTSjPHJJ5/koosuAuQ49mea6kENUe+66y5eeeWVuOlPP/008+bNA4IBsXvvvZc1a9ZETtA777yzz3Qu21tt2bKFe++9l7179+Lz+Rg6dChnnXUW3/nOd0hNTe3u4vVrzz77LP/4xz8oKSlh4sSJ3H333cydO7e7iyWi/PCHP2T9+vVUVVWRk5PDrFmz+MEPfsD48eO7u2j93tq1a/n2t78dN/2CCy7g17/+NUopHn30UV544QVcLhczZ87knnvuiasEic7X3LH61a9+xS233MLWrVupqakhPz+fefPm8YMf/IAhQ4Z0Q2k7l9z3e65JkyYlnP7QQw9FmsS15r7i9Xr5zW9+w/Lly/F4PMyfP59f/vKXffJ87i2uueYaJk+ezM9+9jNAjmNv8dFHH/GHP/yB/fv3M3z4cK6//nouvfTSyHw5jj1fbW0tf/rTn/jggw8oLy9n4MCBnHXWWdxyyy2RGIEcx/6pRwW7hBBCCCGEEEIIIYToiE7vs0sIIYQQQgghhBBCiK4iwS4hhBBCCCGEEEII0WdIsEsIIYQQQgghhBBC9BkS7BJCCCGEEEIIIYQQfYYEu4QQQgghhBBCCCFEnyHBLiGEEEIIIYQQQgjRZ0iwSwghhBBCCCGEEEL0GRLsEkIIIYQQQgghhBB9hgS7hBBCCCGEEEIIIUSfIcEuIYQQQgghhBBCCNFnSLBLCJF0H3zwAf/85z9jpr388stMmjSJgoKCpGxj7dq1PPLII0lZlxBCCCFEfyPPa0KIvkyCXUKIpPvggw94+umnO3Ub69at49FHH+3UbQghhBBC9FXyvCaE6Msk2CWEEEIIIYQQQggh+gxrdxdACNG33HXXXbzyyisATJo0CYBjjz2WCy64AIDy8nJ++9vfsnLlSjIzM7ngggu47bbbsFgskXVUVFTwv//7v6xYsYKqqipGjBjBf/3Xf3HJJZcA8Mgjj0S+JQxvY9iwYaxYsQKv18tvf/tbVq9eTWFhIRkZGcyZM4e77rqLIUOGdNl+EEIIIYToqeR5TQjR10mwSwiRVN///vepqKhg69atkQec9PR0Nm3aBMAdd9zBeeedx2WXXcbKlSt57LHHGDZsWOTBqLa2liuuuIJAIMAPfvADhg0bxieffMIvfvEL/H4/V155JZdccglFRUUsW7aMF154AQC73Q6Az+ejvr6eW265hfz8fFwuF8899xxXXnklb7/9Ng6Hoxv2ihBCCCFEzyHPa0KIvk6CXUKIpBo5ciS5ubnY7XZmzZoVmR5+eLrgggu4+eabAViwYAGrVq3i7bffjjw8PfXUUxQVFbF8+XJGjBgRWa6mpoZHHnmEyy67jMGDBzN48GCAmG0AZGRk8OCDD0b+NgyD448/nvnz57Ny5UpOO+20znrrQgghhBC9gjyvCSH6OumzSwjRpRYvXhzz94QJEygsLIz8/emnnzJr1iyGDBlCIBCI/Dv++OOpqKhg3759LW5j+fLlXHzxxRxzzDFMmTKFmTNnUldXx/79+5P9doQQQggh+hx5XhNC9HaS2SWE6FKZmZkxf9vtdnw+X+TviooKDhw4wNSpUxO+vrKystn1f/DBB/z4xz+O9C2Rk5ODpmncdNNNeL3ejr8BIYQQQog+Tp7XhBC9nQS7hBA9SnZ2Nvn5+dx1110J548ZM6bZ17/99tuMHDmSX//615Fpfr8fl8uV1HIKIYQQQvRX8rwmhOjpJNglhEg6u93e7m/ljj/+eJ599lmGDRtGbm5us9sA8Hq9pKSkRKZ7PB6s1thb2/LlyzEMo13lEUIIIYToi+R5TQjRl0mwSwiRdGPHjuWFF17gP//5D5MmTSI9Pb3Vr73uuut46623uOqqq7juuusYPXo0brebvXv3snHjRv7v//4PgHHjxgHwxBNPsHDhQlJSUpg0aRLHH388H3zwAf/zP//DSSedxJYtW3jmmWfi0vGFEEIIIfozeV4TQvRlEuwSQiTdxRdfzJdffsmvf/1ramtrOfbYY7ngggta9dqMjAyef/55/u///o+lS5dSUlJCRkYGY8eO5Ywzzogsd9JJJ3HZZZfxz3/+kz/96U8MHTqUFStWcOmll3LkyBFeeuklXnjhBaZPn85jjz3Grbfe2llvVwghhBCi15HnNSFEX6YppVR3F0IIIYQQQgghhBBCiGTQu7sAQgghhBBCCCGEEEIkiwS7hBBCCCGEEEIIIUSfIcEuIYQQQgghhBBCCNFnSLBLCCGEEEIIIYQQQvQZEuwSQnTYunXruOWWW1i4cCHTpk1j/vz5fO973+Pzzz8H4JFHHmHSpEldXq5HHnmEtWvXdvl2hRBCCCF6EnlWE0L0NxLsEkJ0yN/+9jeuueYaPB4Pd911F//85z+59957yc7O5jvf+Q7bt2/vtrI9+uijrFu3rtu2L4QQQgjR3eRZTQjRH1m7uwBCiN5rzZo1/OEPf+C6667j7rvvjpl32mmncfXVV5OZmdlNpRNCCCGE6N/kWU0I0V9JZpcQot0ef/xxcnJy+PGPf5xw/tSpUxk6dGjk7wMHDnDDDTcwa9YsTjnlFJ555pm412zatInrrruO2bNnM3v2bK677jo2bdoUt8ytt97K8ccfz4wZM/jWt77F3/72NwKBQGSZcCr+o48+yqRJk5g0aRIvv/xyZP57773HpZdeysyZMznmmGP4wQ9+QFFRUYf2hxBCCCFETyLPakKI/kqCXUKIdjEMgw0bNrBgwQLsdnurXnPrrbdywgkn8Je//IXZs2fzwAMPRPqKANi+fTtXX301brebhx56iIceeoiamhquvvrqmBT7I0eOMGXKFO677z7+/ve/c9VVV/H000/zhz/8IbLMCy+8AMDFF1/MCy+8wAsvvMCJJ54IwHPPPcftt9/O5MmT+fOf/8y9994bs20hhBBCiN5OntWEEP2ZNGMUQrRLZWUlHo8n5tvAltx0002cc845ABxzzDGsXLmSt956i/nz5wPwl7/8hdTUVJ588knS09MBWLRoEUuWLOEvf/kLf/7znwE4/fTTI+tUSjFnzhzS09O5//77+elPf4qmacyaNQuAwYMHR34HcLvd/O53v+PSSy/lvvvui0yfMWMGZ555Ji+//DLXXHNNu/aJEEIIIURPIc9qQoj+TIJdQogO0TSt1csuXrw48rvdbmf06NEUFhZGpq1fv54lS5ZEHp4A0tPTWbJkCStXroxMq66u5rHHHuO9996juLgYv98fmVdWVkZ+fn6TZfjqq6+ora3l7LPPjkmlHzJkCGPGjGHDhg3yACWEEEKIPkOe1YQQ/ZEEu4QQ7ZKdnY3D4Yh5AGpJVlZWzN92ux2fzxf52+VykZeXF/e6vLw8XC5X5O+7776bNWvWcPvtt3PUUUeRmprKpk2buO+++/B6vc2Woby8HKDJh6ScnJxWvx8hhBBCiJ5KntWEEP2ZBLuEEO1itVo55phjWL16NT6fr9V9QTQnOzubsrKyuOllZWWRhy+v18tHH33ErbfeyrXXXhtZZufOna3eBsBvfvMbxo4dGzff6XS2o+RCCCGEED2LPKsJIfoz6aBeCNFu3/nOd6ioqOCPf/xjwvnbtm1r07eJc+fOZcWKFdTW1kam1dbW8tFHH3HssccC4PP5MAwDqzU2Vv/qq6/Grc9ms8V9ezhnzhycTieHDh1i+vTpcf8SPVQJIYQQQvRG8qwmhOivJLNLCNFu8+fP50c/+hG///3v2b17N+eddx6DBw+msrKSTz75hFdffZVly5a1en233HILH3/8Mddffz033ngjSikef/xxPB4P3//+9wHIyMhg1qxZPPnkkwwcOJCcnBxefvnlhENRjxs3jo8++oiFCxeSnp7O8OHDycnJ4Y477uD++++ntLSUxYsXk56eTnFxMWvXrmXhwoV861vfSto+EkIIIYToLvKsJoTorzSllOruQggherd169bx1FNPsXHjRlwuF5mZmcycOZNLLrmEk08+mUceeYRHH32UHTt2xLwu3BfDM888E5m2adMm/vCHP/D1118DMHPmTH70ox8xY8aMyDIFBQX86le/4osvvsDhcHDmmWeyePFivvvd7/Lhhx8yfPhwINiJ6v3338/evXvx+/089NBDXHjhhQB88skn/P3vf2fLli0YhsGgQYM45phjuOGGGxg3blyn7i8hhBBCiK4kz2pCiP5Ggl1CCCGEEEIIIYQQos+QPruEEEIIIYQQQgghRJ8hwS4hhBBCCCGEEEII0WdIsEsIIYQQQgghhBBC9BkS7BJCCCGEEEIIIYQQfYYEu4QQQgghhBBCCCFEn2Ft7wsrKyuTWY4+JysrC5fL1d3F6BdkX3cd2dddR/Z115F93XV6y77Oycnp7iIkhWmavWJ/i+b1lutGNE2OYd8gx7FvkOPYN7TmWU0yuzqJrsuu7Sqyr7uO7OuuI/u668i+7jqyr7uW7O++QY5j7yfHsG+Q49g3yHHsP+RICyGEEEIIIYQQQog+Q4JdQgghhBBCCCGEEKLPkGCXEEIIIYQQQgghhOgzJNglRBcwqn0ceWADdV+WdndRhBBCCCFEV6uvQqvY192lEEKIfkOCXUJ0MqUUey9/j7K/beXwz9eilOruIgkhhOjBli5dyqRJk3jwwQcj05RSPPLIIyxatIgZM2ZwzTXXsGvXrm4spRCiLawHVmEp2drdxRBCiH5Dgl1CdDLvnmo8mytwzhuEZ3MF7lVF3V0kIYQQPdSmTZt44YUXmDRpUsz0xx9/nCeffJJ77rmHZcuWkZeXx/XXX09tbW03lVQIIYQQoueSYJcQnaz200IAhv12PnqGDdc7B7u5REIIIXoit9vNT3/6Ux544AGysrIi05VSPP3009x8882cdtppTJw4kYcffhiPx8Py5cu7scRCCCGEED2TBLuE6GS1nx7BPiaDlNGZpE4fQP035d1dJCGEED3QfffdxwknnMCCBQtiphcUFFBaWsqiRYsi0+x2O3PnzmXjxo1dXUwhhBACAGUq6aJF9FjW7i6AEH2ZUoq69SVknT0agNQZAyh/chvKb6LZJNYshBAi6M0332Tr1q0sW7Ysbl5paXBwkwEDBsRMz8vLo7CwsNn15uTkJK+QotvIcez9nE4nALocy15NrsVYJf/ZgTXHQc4po7q7KG0ix7F/kGCXEJ0oUFKP4fLhOCobgLSZeZR5TTw7KkmdNqD5FwshhOgXjhw5woMPPsgTTzxBSkpKk8tpmhbzd2u+Ta+srOxw+UT3ysnJkePYy+Xk5OB2uwEIyLHsteRajOd2u8HtRqvM7O6itJocx76hNQFLCXYJ0Ym8u10ApIzPBoKZXQD1m8ol2CWEEAKALVu2UF5ezoUXXhiZZhgG69ev59lnn+Wdd94BoKysjIEDB0aWKS8vJy8vr8vLK4QQQgjR00mwS4hO1BDsCnY0bBvuRE+34dnl6s5iCSGE6EGOO+443njjjZhpd999N2PHjuXGG29kxIgR5Ofns2rVKqZMmQKAz+dj/fr1/OQnP+mOIgshhBBC9GgS7BKiE3l2u9Az7VjzHUCwCUrKmEx8+6q7uWRCCCF6ivT0dCZOnBgzLS0tjezs7Mj0b3/72yxdupTRo0czatQoli5disPh4Oyzz+6OIgshhBBC9GgS7BKiE3l3uXBMyIrpZ8U+NoP6r2VERiGEEK1344034vV6uffee3G5XMycOZMnnniC9PT07i5a9/HXgTUVGvVlJoQQQgghwS4hOpFvXzXpxw+JmZYyJhPX8gOYPgPdbummkgkhhOjJnnnmmZi/NU3jtttu47bbbuumEvUw3hqs+1ZiDJyCyh3T3aURQgghRA+jd3cBhOirTJ+Bv6gO24iMmOkpYzPBUPgP1XZTyYQQQojeTfNUh35WdW9BhBBCCNEjSbBLiE7iL6wDBfZhzpjp9jHBoXm9e6XfLiGEEKJdAp7gT6uje8shRFsp1d0lEKJTmD4D/xF3dxdDiAgJdgnRSfyHg5lbtuGxwa6UcLBLOqkXQggh2kULBbuUBLuEEKJHqFtXgntdCabP6O6iCAFIsEuITuM7HPxmwz48tvNgS6YdS04KPmnGKIQQQrSP4Qv+lM7pRa8jmV2ibzKqfd1dBCFiSLBLiE7iL6gFi4ZtcFrcPPuIdHwHJdglhBBCtIcW8AZ/kSZhQgjRI6iAGfqle8shRJgEu4ToJP7DbmyD09Cs8ZeZfWS6dFAvhBBCtJtq9FO0h1KK+i0VmPWB7i5K/yEBWtFXyaktehgJdgnRSXyH3dgadU4fZh+Zge9QDcqUTwUhhBCi7ULNF3th4MC6/U304i3dXQwADJcP724XdV+WdndRhBB9Re+7LYs+SoJdQnQSf0Et9mHpCefZR6ajvCaBkvouLpUQQgjRd2jK7O4itIteub+7ixBDGVI7FUIki9xPRM8gwS4hOoEyTHyF7riRGMPsIzMA8B2o6cpiCSGEEH1EuGP63lOpCph+Pt3/JlV++ezvb3wBLx+WrqXYW05vOmeFaBc5xUUPIcEuITpBoKQeAipuJMYw+4jgdOmkXgghRJ9RX4Ve9E3XbrMXVapqvFX4DS/76g53d1HiKK8hXSt0olqfC4DDnpJe2fRWiDaRc1z0EBLsEqIT+ArcAE322WUb6gSLhu+QfLsrhBCib9DdpehVB8E0umBryeugvtpbyaf738RneDu8rlpfNWazTSt7WCUw1HzRrAtQt6GkmwvTd6lQ5V+LZCS2jWHKAAKi95BYl+gpJNglRCfwHw5mbDWV2aXZdGxDnZLZJYQQou9QXTlCYvK2dbBqF37Dh8tT3qH11PvdrDv0IXsqekbn860Rnc3lP1LXjSXp2xTRwa62nbPFtQV8su8Nar2uTiiZEMnh3R/1Bb4Eu0QPIcEuITpBJLNraOLMLgh2Uu87KJldQggheo+A6Wdf5bZIpkqsUEZTV36tn4QO6hO/l7YLZ4ZVeyqa2VhSNpU80nSxS6jQedqevK7K+mDGXbW3KnkFEiLJpE4jeiIJdgnRCfyH3VjzHegOS5PL2EekS2aXEEKIXmVfxTb2VWynxJ2g36lI4KkLAijhAFUSA2vtbWLWNj0suCTBri4Racaotecc64rzUoi2i/6iIKbOI+0YRQ8hwS4hOoHvcC22YYmbMIbZR2YQKKnH9Eg/DEIIIXqHQKjvIJUooyoSgOrCAiVFsgvcTHCih1UCpVP6rhHTjLGNu7whQCbHSvQwUaekWR9IOF2I7iTBLiE6gb/AjX14000YIdiMEcB3SLK7hBBC9A4q1FQxYYZKpGP6UMW+Yi/4O6sfqHBgrePNGPs12X1doiMd1IdfozohgtDygApCtI5R5Wv4Q4JdooeQYJcQSaaUwlfQisyuURkA0pRRCCFEr9HQbCVBpV1F9dkV8GIp2Ybl0PpOKkikQMlbZ7uamLVeTwwqKKOTaqVKgeFrebl+oiFIHPyrLSLBriRnBfbGARVED9PUOdnDMlhF/yXBLiGSzKjwojxGy5ld4WDXvuquKJYQQgjRYeHsEl1L9AgZruBEBXXMzmqqn7zRGJOXMdP8ehI2/exu0ZXSJMb69OLNWHe93+sz77TK/VBf2eH1NGR2taPq1UnNGD2BYNaljPIo2k1iWqKHk2CXEEnmKwhmarWU2WXNTsGSm4J3rwS7hBBC9A7hSnvCLBPVHaMx9p4O6jujGVpHdVZml151MLSBnvee28JSvAXrgdUdXk9Dn120eZ90VjNGQwWbHVv0pgdTEqJZjU9JLfFkIbqLBLuESDL/YTdAi5ldACljMyXYJYQQohcJBbsSVWcSZvF0YjO5zlx/O7TUTDE4v+eUF+iC0Rh72PvtJuFzo0N9diU5cGiYfgCsui2p6xX9l2YJhRZ6eZBb9B0S7BIiyXyhYJdtWCuCXeOy8O6R9HEhhBBdRynFtpIvqfO3vc/IcJDLVEbcPC0yGqPquspOErbTkc7DE0nYeT89tBljdLCrM/osk0pvUHg/aBpt7rNL65zMrvDIqhbdmtT1iv4jLgBrldSuvs6o9hGo9HR3MVpNgl1CJJm/oBZLlh1Lhr3FZVPGZhIorseo9XdByYQQQgio9lZypOYA20q+bPNrwxkqzTZjRNH5w/z1vD67WsrsUgSDgD2pOaOSzK6IykpFINA55Y30ddeugGo4gJDszK5QsEuTYJdop0anZCd1Lyd6kJqPDlO78kh3F6PVJNglRJL5CmqxtaIJI0DKuMzga6QpoxBCiC4S7lxetSsgFc7sSvDa6D67orO8OkMnrL+jQaiWMrcaBwi1ir3dn/kU3WdXd5elG3m9ig1fwqefwfsfKurrk7svGrIH206LJMt0TmaXtY2ZXYGAoqKi/54rohX68b1E9CwS7BIiyfyH3dhb6JweYF/lNnZn7ALAs6uqk0slhBBCBGmhYFdLmUiJRGJMiQJlMZldXSWJwa4ONjNsKRjReH9bSrah1RR2aJsdFZPZpZLfL1RvyfCoCw5MSCDUOtdVTVIr7JFj355mjJ3WQX0w2KVrbeugfstW+GJjMEAo+rnQKWDJSenecgjRBAl2CZFESil8B2qwj8podrnXtj7BTa8s4c4tV+O3B3BvLu2iEgohhOjvGjq8bkewK5zZZcb32dWQ2WV2wTf7oUyZHpRB0HLwUBEX6OjG4peVK2prFHqqBcfU3ODEpI/OGLu+XWWb2FuxNcnb6Lhad+zfwUOZzH0R1Yyxnedssk/1cAf1bQ2i1dR0Tnl6s4ICxaef9ccdEroPW2NDCv393HD7avAbvu4uRtJ1frP35JNglxBJZJR7MN0B7KOazuzaW7GNv6y9h+NGnMrN8++lYGABB9Z+04WlFEIIIdoZ7ArVYsxmM7ugy6I4SalVNTPCZDvW01RH92aiIKDetqyaZNr4FWzdokDT0EIdS6sO9Fmlucuwbn8TfFGRo0bv95BrD/srd8S9trB6Pz7D2+5td5S70VgNpiLJmV3tHwSh4TUdK4/Ho6itbViHEQ5Yt/F9xvS1LwDYtgM83Xf6dr/GEYXeFxNJqrWHPuDLwpXdXYykU55AdxehzSTYJUQSeQ8En5bsI5vO7Hp6429Js6Vzx+I/c9HUm7Ac5cS222RH6VddVEohhBCiffWR8CiMiTuoTzQaY2fVepK//o424Wuxg/pE87VufhRXCixawwiSHdgHmqsg+LOuvE2vq/e72V66kc3F69q97Y4yGh8aRaPgbcd0KJAaHo2xg+fnqs/h87UNfycaUbU1wskdZg8cXDRpDB+au6zNL0t6M+CeLhL4bBT57Mb9UFx7iBV7XokMwBAsjmr3sfEG6imuPdTm17l9Ne3aXmcyzfbvBwDT0757RneSYJcQSeQ7ELyxNdWMsbB6P58deIsLpn6HjJRsAOacdCqpnlSWvfdoVxVTCCFEPxZpitiOyny4n5/mOqjXUEkNFDQrCdtp6Iesox3UtzA/nEEWvWB3B7tMhaZroIcCKh1pppIwYNb69fkC9XHT6v1ufIHOH+a+8bEL7oae0R9csvrsCgenzEbHuM3r7eSxJ3oCS8EGLIfWQqLm2s3oy/skoUiaX+PpXV6SiD2hZtLRmaIf7X2VDYc/btf6vj6ymi3FG2KCZ8lgmopDBR0LPrXVxyth9eftf71KejP3zifBLiGSyHegBnQN27DEozG+t/sFdE3nrEnXRKZlzxoGQNWGQxys2tUl5RRCCNGPqXDQpf3NGBO+Nno0xs6u7ai4X5K50na+uvn9GW7K1l3VBdNU7NuvYoIdmmEG+9uxhGqryajMJGjOGgg0XamLBAETzPv84Ht8duDtjpcpsjGFVlvS8mImndNBfTuEs2aS1UG9JxQ7bNjvbVuvGX2Z91XecMdkbTtufXqfJBB5v3o42tX9bVubCo7XeKvatT6PER+Eb05rg1cFBbB9BxQcbk+p2scwoK5tbyeW9NklRP/mO1CDbZgT3Z64D46P9r7GnKEnMCBtUGSa46gc9Cwbk/dP5uWtj3dVUYUQQvRzCfvdauk1ocpf4j67EjQt7LRnYxXzIylr7GBNtaXXBwOEjQOBXVd5KDgMu/fAgYNRE81QM8ZwZTUJlRktumNmpQgEFB99Anv3JV4+PNhBRwJCrS5bxR4sBevjAl4JM7s6IdilUO1fb3ubYXkVO3cprKFH03Blt73ne79oxhjWzv7M+rz6qmBz5dD7tQ1MxT46g9QZA7q3XER9RiXrftLGg5qoeXCgwhPXBDAQShTz+5tYj6ko6OLMrxb1oKK0lgS7hEgi34Ea7CMTd05f4NrL4eq9LBh5esx0zaKTsWgoM/ZP5/2d/2n3Nw9CCCFEazQ0p2tPZaDp12oqqsPrzn5Aj7Q9TEIzxiR1UN9iZhfhSlh0MLDrag/h4IQRXecyVDCzK9ICsQPlCR+LRqOQhStztXt2oHld8eWK7LfO3xeary74S6MyRr9ti1FH5oE30erb1vdYc1Qk2NWBdbTz1Tt2BgOcgdBx94SDXZFrufnXx2XlJT/O3AO17/7Sk+ISncl6YBWWg2saJlg00mbmoadag3/3gB3RkabDCdfXyjM+UZCt9tMj1K4sbGrFCe0/EBz44EhRa0vYPCMpWbvdf1zbSoJdQiSJUgrPriocE7ITzl9fsAKAY4cviZuXfsJQHOV2soozeG/3i51ZTCGEEP1cR/rsavZb86hmjFp4ZELTj+bqzHYaHXj4Nvzgb2jy0tHn+HBAoMlR6pQClbzmaMmgmSaaVUPraDNGZaJ5QoGsuMyu4K+Z3t3o5XvjXhrO7OqMDIbi2kMccu1uct07dioOH1Yxxz4lEAxy6VVt75S6KWaCpp1t1d7zRm9U22tohti6QR4++gS2bI16ffLizD1fGzvx74WxgI5p/Iajuu2rrlEUFXVgh0SP6toO7clcTiRhX4vNLd/EhWHWN+rzq4UWn+H7ZiBB5pe/tB7T1/pz0+tVHG4i1tYWSgG1JZHBSHoDCXYJkSSBojrMaj8pE7MTzl9b8CGjsicyOGNk3LyME4eBReNbu8/h9W3/7JJUfiGEED3D0qVLueiii5g9ezbz58/n+9//Pnv3xgYFlFI88sgjLFq0iBkzZnDNNdewa1f7+nnsSFChyUBZ407Jo/62HPmq3dtrriTx220by/7PsO75MH6d7S1RC5/dwcqXit13Sa4dN/f8kLBuZSg0ix7VQX37tquX7UILVU61gDdmXrjSpjdRuYtp4pdkW4o3sKvsG2p98RllAAcPwdbtDWVsKBNJjeaEzw3DMCkpaWvTuI7tF0ujnjXaMyBDouySXth9DyiFXrYrLrOvueXbuPoO8Qbq8be2bD1B+P2GI/zha1wp1q6Db7a0c731lVj3fozWgYBzsjK7Iv1UtvJ6MRoFSFsa9KOtp4xSCvfqItyrW5/y9eXGYIZnc4qKFe9/qPB6mymRqdCrD6O5S1u97e4mwS4hksSzswoAx8SsuHn1fjdfF63m2OEnJ3ytbXAamaePZPrnkykpP8jGwk87s6hCCCF6kHXr1nHVVVfx4osv8uSTT2IYBjfccAN1dXWRZR5//HGefPJJ7rnnHpYtW0ZeXh7XX389tbW17dhi276pjn1pE80Y4zJXuqom3P7taP7g/o2uzPgCHsrritu1vpYyCVTCoE7y9lONt4qP977WpvJrpgnWhtEY2x3B8Decq7GBBBVpxtjUwJPhPm46NePtyNdYt7/Z5OyamkYTVOS/pAgH9I4Uw9ZtCo+n9etua2ZJY9ZGwa5IZpZqeb3NbrMXBru0unL0sp3oxc1HYbR2NpPuaLBr1YF3WHPo/Y6tpCuFR+CN9E8fTu3q2Gq1QPAeotW2vw1fshMHyssUZeUtv7G4+1gT2bJNZgCH5zf6O1Duwaj1R/at4Wp9UDTqUaJJhaHML3dzCXW98JqXYJcQSeLZGfzW0JEgs+vrI6vwG17mjUgc7ALIu+Eo9GrFOZ+fw2vbnuysYgohhOhh/vGPf3DhhRcyYcIEJk+ezEMPPURhYSFbtgQrZEopnn76aW6++WZOO+00Jk6cyMMPP4zH42H58uWdWjbTjO2vp8lmjI2zlbqqz672Pn2b0d++N1T6NxWv4esjq9tVUYrUj5soUp3fDUphdlKwy+UJNr+raCHY1bDrFJjBzC4tktnVzvJYUxp+j963SuEPBH+2lNnVmeeMVnOk2fn+mMwuLb6D+g5WnMPv0edtYcFEOniuW6yNVhd5Ky1nrDR3SHplB/WhCEPj7MMmdUIzRqUUO8u+pt6fOKrQozK7/B608t1NzzcatbFL1mCM4ePkazpKU1+vqKmJ3eHR9+1EgdpqbyUFrvim1M0JXx9ffaPY+FXLy5tR97/tpRtRLTQNb+1tr/azI9R8WNBp98lwc+dmPwJ6YTqnteVFhBCt4d1ZhXVgKpbslLh5aws+JM2WztSBxzb5eufcgeRcPp5FLyqezX2ewmP3MzRzdCeWWAghRE9UE0ozycoKZgoXFBRQWlrKokWLIsvY7Xbmzp3Lxo0bufzyy5tcV05OTvzEugDOKmfT86O88MYh8rIdnHx8PgBOpxNTmTidzpjXqoAX5QyuU8vKBN2KqnRG5us5OezbbzB4kE5qasdrRKbTCYYNbGnoLbyHRJSnOlJeZ4pOwOLEtPnQ7eC0OHFmpOGwpbZpnRn+dJw+J860tLj9WlJzGL9eT5ozjZS6FNLHH4c68g1aZiZadsvlb+k4AVSrTJz1TjIyMxMuX1ll4HQGyMy0kJVlwemox5qSQlZuNvZcJ6azisyMTBw5ma1/0yGmOw2MAZCWA+5yMILpRFp2FpWeDNKdHtL8DlJSUtAanTv1ugtnrROLbo0rt9PZuvO0KeHXO7HgtDshMwMCzuA5mp2N0xkfWEi1pOJISSXdmQaWYBRMy8pEs9jaVQaAvbUGKSkp+Kw2HGlpZFr8pOkBtKxhLb620szC6XWSnpHRrv2QVRHA6WyogKdnWMjJseJ0OQlYvGQ0s95AQEX2UU5O8PnW6QwGirKybOTk9K68CWU3UOVOSGv+vmE6ncF+6LIy0ZyxyyXaV5F9km3Hmdb8/a3GU0VlcTFmnZ/5o09ttJ6One/JZu5bDXXFaEPHo6U1lMkMlTPFmYbpdJKVnU1KTjqGzYfprCQzMwunM3i+hM+btlB6Xej+rNCsPrSMQXHLrF4T3Odnndmw/oAZiOzDzKwMctKDZQ5P21a5HoDpHN3qfex0OlFKYUlLw6antvh+9HqFM/TZ5zJKyc7MwnA6QYs9rhWVwesyMzN4PTaWmRWcn5FpJTtbxx96D9lZ2QScZUDrzxOn0xsTp0r0HjIz/dTVm2RkWMnJscTNB6iv1CAlfFx7xjnaEgl2CZEk9VsqcEzOjpuulGJ9wQrmDF2MzWJvdh1DfjkX974Krl52BRsqXuTspT9GT0l8wxFCCNH3KKV46KGHOProo5k4cSIApaXB/jEGDIgd1j0vL4/CwuZ7na2srIyfVl+JO9RWIdH8aNsq3oMKmDPtQgBq3bUopbAartjX+j1YQ+s0qlygW7BEtYdwF1WwfkMw1jDv2I4HuyzuWjTDj7IaGC28h4Q8rkh5PYe34M4fz273tsjs0vJi0lPiuyVoTlV1FW63G6uRErdfS6qLg/vcV4fhqcNVU43F7cZwVaGUs4k1BuXk5LR4nACqq6txu93UWKuptMcvX1OtcLuhuhrKy8FdE8Dh9VLtrsFW7cftdqMqq6jPaFs2C4DuqkTz1KNMO3qtK5I+FKiqorzcpK7Wh0d58GpeDLc75v1UVgfPR13T495na8/TpoRf767zYvNbMK016G43hqsaf6AyYZMd5a2nzqintsYa6YcsUFEB1uaf4ZrjNTx4vV48Xi+m20391s8wUzQCk8+KWc5UJhoaWlQbJ5creF5V69VUOtq+H1wuFfM+XVVQWalRU1OD2+um2pb4fFmzVuGtqsBnyQZNp7IymGXjdgdrzZWVYLUmK5Wna2juCixuNyqgN3vfsLprQSmMykqUr+E5vKlrMbJPKtz4vM3vk3q/G7fbTcBrJv18TzZLTQ2a241RUYaKSoYL3zu9lWW43XXgcmFL9WPWhe4jVVW43cHR6cPnTVtorsrI54dRehgViL/2Gs7DhvX7DV/DPqyqxOpPDS0bf6G3dh+73e7gIGTuWmy6mfD9uNcWYx2URsroDFyeipjtVZaH/tYatqmUoqY62GTQ5Qpej41Vh+/XLqgoNiPrtFS0/vO74T2omGBXovdQVxfcXmUlpNgTn8Peqmq8Xm+btt1qAR+apwqVPrDVL2lNwK13heOF6KEMtx/PtkrS5uTHzTvo2kVR7aEm++uKZnHamPDsmRSfX8uYFYPZdv3rKH98nnidv5bd5ZubTIEWQgjRO913333s3LmTP/zhD3HztEadfHTG6HUtCW/TjGveE92MMdgRe+zrgj+97WnGlbAgcb+08fWxn61aXUXM336zHU2JIvsm/nPbboJevgvNDBAwDSo8oYpCZxzCljqDCbUy1ULNa7SoPrva3VrPNECzBjvmatSkNeAHrd5L4EjiIFprOqhv/UhoKuGyiaYZTcX0NC10XEy+2aw4XKja3JytMV/AE/N3U00AP977GrvKNzWxlhb2QROj1zV+6w19djXffLTeVcPAms/Jqt8OwPsfKurqops0N1+cHim8481WHs8EF4Thjm26Z0btiFY1YwwdR5/haWHJ7qf0UF6MmWBIQGgYKrCZPrva9TllRrUrbsPLo/uS7MrBvvxFddR/XZZ4u+H7bOgeu3274oMVUddPa84Zb3TT8IZf2zIiY0sih6653daJ17zlyFdYCta3fvCIVpJglxBJUP91ORiKtGPio9HrDgVHejp2+JJWrUu3W1jw+2/zxoXvYn5WQ8FDayPzytxH+P1nP+LS56Zz82uncOG/p/DK1r8n500IIYToVvfffz8rVqzgqaeeYvDgwZHp+fnBL1LKyspili8vLycvL6/Lyhfbd5fReGb0H3G1PrP5enUHCtXOCk1UQRQKveogWjgABfiMtkflVDN9IGmuA2jeWqgP9qtV5XNR5VIUFSdvh7Slg3fTJHJQNKve4Q7qNdMA3ZKgF3qFuw5s+8vxHwigueOrHvGB0wTlbeVxXrn/Db4o/CRueqJ901yfU8H+6YOvqaig3edZZWVwdDNfTB9Rifdx+D0ert7XqCzB5YtqDmGYgbjXAVBX0eTodY3fZyTG1cz56it0Y6kODn5hNxpGsoy+BXV1rD0QUGzfoQgE2rbhmC+Nw+daa4OXjY67r8RNzQcF+AoaBgYxY2O7La+yFQMD9BjhYJcRe94p3ZZwejjoFf3W2tW3W9yAJ61T4j7c8KpOGo2xpePW+F4V6QcxdI89FCpiONiuCGZeNXVeK8AMB7t0LWZ3GOXJC5i2qs+u6M/N5vaDtwatuvms8zjh/t+aCNq3lwS7hEiCui9KQIO0WfGVjnUFHzIudyp5ziGtXl+aLZ1Tf/IdVs5bRdXfd7Hyqed5fP0DXPfSAj7c8zKnjr+UX5z0N44ZegL/t+bnfLjnpWS+HSGEEF1IKcV9993He++9x1NPPcWIESNi5g8fPpz8/HxWrVoVmebz+Vi/fj2zZ89u1/ba8xqzuW/NG3dQ36iCEq7wGB2sf2hVh0IPw+H0lLa9XpmKiroSPjqwnPpGAS2tpgStLhiMak8n0c11tG6Gpo1OH4Wmaeyv2cPKfXvYvbsLe/lunHQRCG3bonW8g3oVCFaM9diuF4yAoqoKSNFRCvTK+B5UIqMxNnNeGqqJIE/j5UyD6qigZaR4CZZtshKuVCg5MWqB1mYCNbLhS/h8nS/uekm07fD715rp5Xt/5Y6E0zVfMPiieaoSrDf270hmVzMXT936Emxby+KmR1+/XZg4A0DBYThUAAcPtv41htuP660DePdVByeEj2NLxzPBaIwqYOIvqw++vLYh08lInHTT9Kp705B24dENGl9/4VM0vH8ahmMMz4gs2q6YXnQwspUrqPW62FnWkBVpNvO6xveazw++x86yr1u1nSYzQiPbjV0g0kF9aNeER0f1hT5iTBNWryGu8/voBF3lCa5TT9GJ3reBstYFu1qzC8Pba/ZLgOjPh2YWtO5biaVwY6vKFlm3PS1YjmYGJVh98F32VwYzTeMGR2iCBLuESAL358U4JmVjyYxtU+721fBN8dpWNWFsbO7wk5jz6ws5POIIjgdcvL36aRaNOosnLlzJDxY8zAljzuVXJz/BrCEL+dPqu6isL03W2xFCCNGF7r33Xl5//XV+//vf43Q6KS0tpbS0FI8n+CCraRrf/va3Wbp0Ke+//z47d+7k7rvvxuFwcPbZZ7d5e+2pbAWb3DS8Li7DpPE38U1kdrUzZkAgoPD7FZaiTVj2fxYpi9ZU85oElKlwvbGfI1/uBAXrqzbjM/2Romr+OvSqYE060J5mjKEymTRdCRjqGBQKZmiUqzI0FLW1LR+PdQUr2FuxrcXloPlgSUTlAYa6PkBTgWCgKxTsMirb2c7UNFC6Ja4JZX19qK8YPVT59MWXrdkgamR6204cn+Gl1lfd7DJNBV41TFzVsGVrdFus9jcXqvV4UHXlBAyFQjV5dFTovIlvrhxVtiabqCZIqUnw+pi/G2WsxK8xvIMa5kc3Q+7qZoyR7NC2vMYdvE/5i0IV6NBx1NqR2eV68wDuLcFguGZLXIVuVQCwB2d0+Uvq8ZfWY9aH7u+hzC6tcQZXmIoN5CQ6uRu/XX9RHVWv7YtrDhpNMxt9nrRC46bnqpn7cOP7TL3f3eIojeHrJNBC3D3uHtYos8saih+Gr6XwW61ykZBSUdmJVj3mwjN9yYs4hzO7Wt2MMerDvKio7RmXcawOADSfO9I80/QZeHZUoXzBILPHXxf8HDQDWPasaNVqJdglRAcFyj3Ufl5Exmkj4uZtLPyUgOlvV7ALYP6401n8rxtJNdP43f6/ctcJjzI4Y2RkvkW38sOFv8NvePnXV/H9uwghhOj5nnvuOWpqarjmmmtYtGhR5N9bb70VWebGG2/k2muv5d577+Wiiy6iuLiYJ554gvT09DZvL7py+03R2lYFUXyGN/IQr2lagmaMzQe7DBPcRikB5cfvb/qhWHOXgT/+2+rPVsHHn4QDXKHaRrji39osrHDfKRs2oVfswW8G2OU+EL+cMtuc2VVdrairay6zK7zvQsEoTcMSegoPDb7ZrFqvq+Eb7STQqw+DAt30gdbQn4y/0I3vYCsK1JhpgGYJ/osSPi0sGKGEv/jasBF1LjUV1Gpr/zv1/tpINxIAZb7K0DqiKorhlkFxtaHgMkZU8zetqeaDzQhnj1QHCgkU76HwSMP6EyVFNP0eG8pstzjaXo7Gde9WBo20BNl0vujLoptiNm3qEr9xolFkZ7RyLQmOSXGJ4pvtDa+PvtwTxbEqKhRFRVHnXTNBmO7m/rwI9+oiqt9r1By2qS8VmgrcNZMAFG4CalQ1E1hXgWDwHNBaGRxs/AVMc80YEwZ4Ax7wNnfvSxzsapwl1ni74cyucKDaGmoBGp3Z1RxlxmZUxWyunanSZoJIdfg+2OwqozceWtDlUnyzBXbuSrShNnxJENo/gYpaqt8+iO9wLfVfleH5+hDaxg/QqhuaqGIarb4ny2iMQnSQ660DYCiyzxkTN2/1wXfISMlhysCj273+tDG55N84ldK/bMZzcyWOSbEjTwzLHMMp4y/h3V3Pc92cO8lIyW73toQQQnS9HTsSN02Kpmkat912G7fddlvHNxj1wFrqLqTUXcjY3KMSLBZVQVNGpIJg0a1xFQtNmRwpUmRngV0BWkw6Ch6fhwOez8iwDOGrr49j7jGJi+bbvxKbLRUmnhkz3R8AjYYHZ00plNWB5q8PNmdoYbRjiKowmA37IGAa8RUfwxcTgAHQj2wCiw1zYPx+Ali7Hgo8JqMmJ44BhLehKy30TB/+qVpsFtNaLfclY1Dk3cIwc0psQoYeW/H3HazFPjKjbRs3/cEskEZ9doXLZMEINSvSItPDlb/WdCpttjEl0N+ocl5QX4yOxrishi8mw5VMq7Wh4mk13FjMcCU8uhlj8Hyvq1Ps2QtZmeDzw/hxTQdNwuuvNg5DbSZ2XDHnmtutKNyvGDM6Niurucw8XWv7CN2Nz4pIC70W+iDSEgRlujOzqz3BtcaJcFroPFJxfcs1tc34866kBHxZDa+PCXYlWMUXodZc4W4Yo/e3y1POF4dXctzIU0mztf2Li04XLmpTTcbMcCAn9Hd8K8b4eFj47+YG0jDNUODcoLUHvvE9u7kAefQ9xwidE5aSbVhVZtwIqVGvAoKfRRBsImtx2uIuhCYzu0Jv12YNngPe0KidTd3/w/vNVERftLG7o53X4DebYeaM+Ok2oxrlsQBNnIsxmV3B9xneH75E3w+Zgbim7U0KvUejJrjCQEl9MDMz4Al+IVNfFbdsa0hmlxCtVOY+wq8/uZWfvn0Jnx98Dwh2GFj+5HYcR+XgmJQds3zA9PP5wfdYMPI0LHrH4sr5N09FT7dR8sg3CedfOPU7eAL1vLXz2Q5tRwghRP+luUux7Pko8m1sXPOy0AOmVbfFjXrn9xmUlcH+AxCX2aUUevFXaGYAr1lDbUPfznFWV3zFVxWbGyb43OjFW0AptMbZY5aU4K+BVja9S/CA7M8YEhfs0gw/noAbb6A+Mm3H2oPsW7+HvXsVHk9TD9qhZowJKlmR/pg0LZLZRZKDXZHyh2pVRpWXqtf2EQj1M1RSv5eKwB6K63YHG9OZwe03rnJGmjC1ZZumARZrJBsj7FDNHsr9u7HoKiazK3qfm63I7GpckW1JIJSZp5SiyhXcVr3pJaYpbmiVlqgiD67+mExPMEUhJqMkdE0UFQf/7dgF+/Y3X4aGQRlM6musaGiUmaW4AkcwTdi7D3bviVq+iYyf2GBUw+/bdyg+/az5Sp/ymxiu2OsjUm9uaM8YOz+UsZGoqV90U6tua43XptSukAR9cEXzG77EQb9mOpNXSuGrqWH/m29iD1Q2t/rGr4z8Fh6MoLK+tId2WB+6b8XdYxtFtUKBq0gz26j3YgQSj5Da/GaNSBPKyrfLqPsq2H9cWXnTzeXaktllxgS7/LGjPyYqTsz7Ae8eFzUfFGBU+2h82cZ1UB8ub1Qzxm11r7K/JhgFber+H7l/mMRuI7qTeKN1+7XxUiUJer5RCgZVf4ra+TGHChpeUVSsOLA7QKDCE5thFg4cR3fb1vhLiTZlxIaD/WZkveGO+YPZbe0btECCXUK0gttXwx3vXsrqvW9TUXyE+9+6gfdXP0fhL9bi3e1iyM/jM7c2Fa2hxlfFolFNfUPQepbsFAZcNxnXG/sTNi8Yk3MUc4Yu5tWtTzQ9Uo8QQghBE004AL14C5q/LvhNKrGVB6VUQ2ZXKLskutNwIxCuFBJ6+o3dhl5zmFR/sB2X06k4XL0vwahVwQdbl7/hc85y5Gv0yv3YDBeNaxXKGszm0lrb5DBBKoofI74iZvqoqCtl1YF3Is0ZAwGoq4c9++DrTbGLe72h9x7Vx1HA9EcyBqAhkKGrcDAq/H/LHR43xahtvr8yf6jzYn9xqFPt0P5WqNhAReMMi7bWuZUKHnNNj1vXXtc2in3f4DeqKTPKaMjsSpzNlaw+uwKhzK7y8mCn5u66+Ap3uLmOtcnEg+hgV/BcT2k5gbDhJZHmgmZMttYh3xeJs/9UbOAgcYkaXnmoADzehjlxZQbca4pQG2JHRYsOwiXcRig4EQ4uNw56mSp4zXR1B/XRDh5UVNe0IzgUOo5ao3Pu0/1vsrM8QQfl4eUS3Dt8Pqg4GAzCpPqC97b4LCZFqu9wbIAi6vdgX4jBe2pXdVx/oGpnw4ifUV9iJBycIvz+mxghL67MCbqOq/u0kJoVh9sW8DKNqPuJwnegBq9XsfEr2LK1URkiWbqx98PmmotGlyWgAmj+5kcANFXDezVM+Pzdeg4dUph1gfhmjI23G/6io1GfXZWB/cH1NZPZpZk+MgvfB48rvPJG2VXJO2fCb8Png+1RyebfbIYDrx2h9tMjCTO7wq9Lc+/CuvOd2G4F2lInjdz/GlLaVKjvLoxGX5B4Axj1rXvvEuwSohWe3vA7Ziwbz4P/8yv++56bePh/HmTw5X4qn99N/i3TSD9+aNxrPtv/Jg5rGkcPXZyUMgz49iSwaJT/a2fC+ecddT2l7sN8cTh+yG0hhBAirLWVDtOMrhAa7CoPZhdbQ8PORzctC/gbHny1BKMxRr79NcGjH2JH6VccqIptvhkIJBhZKpwxgIqtdKuozK5WBrsSfQteG3BTZzTabtQD+qf736TaWxkz29soySH8d7iSYyqTlfuWs67gQxrTtHD2gxbcQ6rlzK5EASDPbhc1HxY03+9NZKOxfyoFSmnhyGT8/EAboxjhIFqCPrvCCr2bqTGrQQWrHrGZXVHnWRPNFdvah1q4s+rwKRzpZydmW8GfliaCXVpMsCs8YmTsMkYzmRUNLY9M9FCVKxwsSxQoSnScd+1WuOsapjd57YauDe8hL6an4fwNVHhDmSHRwZZwuVTM3wCeHVVUv3Mw2IoMAxVQ6HU+CBjY9paBYbK7/j2qAvu7vBlj9OZ27IK165rf/wlfHNnH8ZmFR6pD/fdF7ZBwR+nhe4dSClfgIKbpT9hsq3FpNNchBri/wulr6BtQKRPNVQCGF18oY8qiW5vNREqmPeVb2FH6VXC7+1aiVYQ6Zk94QEOBGn9dEydt6GczHdQb1T7MWj9mTWwwqnFM11dQS/3WitBmDdAtMQG48PVaVt6ohKFFGge7mvuciw5IBUx/w8qbaN6qQk3fLWY9mqcKTalglqNGXGZXXIZZo9EYdT0qS0z5mg122Y1qNMOHXhk6f0wVey37Teq+Kou55tvCcmA1esGGyPaiFRUp3v8wdPw9/obtN7yxmEkOTyioHtXktT19HYYzu6pdZsOXaEbslxU1HxTiWtO6zwQJdgnRgjL3EfhDCSd/dhLZZ41hyK/mkn3nFN68+AOe/9Eb5P10etxrDDPAZwfe4tjhS7Bb296ZaCK2wWlknTGSyhd2J7ypzRtxCtmOPN7Z9VxStieEEKJvam0GQSBUAUg/ZODZUkFJbbCDWIsezuwKPuyWuY9Q4i4KrjsYwYmr9ZkKMCH/Kw/anuAoedHNBAECRkPg5kDlDj7b/xaV4RH1lBnfh5A1GOyKNLFRZvPDkSesS7Y8fNju8s0xfzeunNSHY2VaQ+YUBEf4irxGmeilVlCgo6FCNT1D+QkYwb6gmuJz1Uc614+sryK40egmh3HHtYnKXnExVEaP/JWkYFcwE0NPOEtXJtEx0NhsrvgMOIitrDYeaa0lRcU+DhWoSBaFaRAJLka2laAZY1M0M7ZiFylXotPNX4dl9wcob/D4R2d2pTshwfgNoeVUpFy7diu8XsX+A7B1e1TltqlsFdPE9CnqttZR/VkRBw+qUPlCa00QNIgEu6LOG8/2YGA3GLQ2oNhA2+nGetiFpaQGrdTFgHwPXrM6NkjmUfh8XRP9im7N9Pna5peNi8+ED3r0edC42XbMi2IzuyoDZdRRTLl/Nz4fkUbA4cBo4+2F702W6PO3vgLdXYrmOowvdM/TuzCzK6Z8/no0X7BdeeLMrqhpvroEa4gN5EQiWImCq+FpTdyX/MX1+EOd12tmKNhlaJHlIyP6mrH3y/B0v+lD81SiVQaDd+HPp0RBr9hmjIHYgH0T71KhGOJaQerhVbGJlNFNHE0jfsAXQ4G/HlQAX0Et5sGGL09MFcDbxK3NNEFpeuizM/xlUtRZogUDib4DNVS/e4hARYIvi6Kk+EuxGLEZbFp9JXptcWjdscsfKohfR8xnQ6ORLiLBywTNv1slfEwMAyOg2LlDsXVbcAAXZYIZ1WdnwGditjLQLcEuIVrw8fPPs2jdfFKvG8GI/11E3n8dxYhbjuHE/76atZmf8tLmpXGvWXPoAyrqSzhtwmVJK4ffr3AtnoRR6eWF2/Zx989NvneryRXXmFx9rclN37VgO3wRn+57l3+/UsbefSrhaBtCCCH6F+U3I0N5Ny/4tGo5/AUY/kglwVls4tvT0NGWRQtldoUeTjcVrWFL+RcNqwn4sJTEtjUxTJOU6mC3JdaKUAe0/kBMsCY62FV8cD8+w0uZp5LaLX4sxTWN+uwCNA1ldYA/WAmzFGzAuuu9mEUKqw+wYs8rmMqMrfSEVxU/FF9coC7N5ox9L412ZcAfXlW4QhIfkDAKAlh3OfAd8oNXBzRQsMWzgq17t/LNip0Js1RUwKTinX1Yd8S/96Y09JsTu2z02y8u0eLmN2wUKmtLWXfow5immE2Kbn7XODMiNEtXZijYFN/OKSbYFbW96Mp/oJnMrpLawxRW74+ZtnNvgCpXQ3EMM7y++EpyOCCmN71LCY9GFz604aaPvgTBLt11GC3gRasOZjpYLCoS7NL0YOZC9JEOn5fh86a2Ntj33RdfhhcgqjP/BM91hi8qg0JxeG+AHbuCowZu3wHV1cTs74auuqJr7I3erhlq6hcA/GakzaeJgdMJBv6Y82nDF/DJp3RJwCt6NLz6+qaXAxqCLs302RVonH0SvYwywV8fyc6xjUsPnifKDAW7IgvGbCYytSEC0DAxfC5rWiTYFSxqN7QLjf6CoHHru4AZ84Y0I0EWaSQjqomLx4xqMt34/tb4NWawaay/tB6jzg+aBdPQCe+76FEC9+wNF9JoyOwy/OgV+9Hrg5H88PWUKIgYva/9hi/4ZYqmxZSpzH0kmPBAbIA1OmiuDDMmSOhrnCVMMMinl25HL9mOf28x2r6GkS4ViTt2V0qFznM91Eo8Kuga3rauUVbWkPHkXl0Uv6IouXVfk+HdG/k7fhTJZl8eXCZmhFoT5Tfx7agIHpxE53r4vqmCWWIHDjS9kfJykx07FWbACAX4gsseKSLYjDGqgHv3Kg4XJl5PYxLsEqIZZsAg969QPdjN2J/FNkc8buRpLB59Nk9v/D1HaqLTkxUvbfkr+c5hzB22pEPbLypWvLhM8cOfmJx5juJ7j+VTYM8me+UOioogNRUmT4Jx42DgQLAdvhyl+Vn67st8+3rF+Rcp/vRI8OYhhBCif6r+4BDVbx+MmhJd8VWUlcV+OaJ5a9BcBRiGiRaqoET3zxVuxhjbZ1dUM8bQN8XRDKWw1OvoGpgZKVh3GpgbaiJD3JteIxLQ0CotGGuqsBw0cRd68JWYWIpqE4wOp4HdGclM0Nyl4TcVWWJPqLN7wwyEKgqx30YnbrYS+5lp1x3NzI2uT8d37B95jalAA89OL5YNDix7gxW7NH8hWs27wU7Rqw7Hv84XHC1Sr2z95/j+yh2U1B6OG0QgJiWgofSRP9OPH0LKuEwA9pZsodZXTa0vOgUsnr+4Dn9RKFsgQWZXOKxjCSd8mFpouomvoJaq1/Zh1AUi2YJfHVnFjrKv2FuxFU+gIZPE10xm1+bidWwv3RgsQiRjLtxBfXCZSEW5oXf2yLRwZldTA/SZmgXT8LPqwDuUe4KV31kzg/MCzSQSqsj6zUi5wiNwxsRTwkVqOCkBcEfefkOwLFHF3brrffTy3ZEsE9MXXHl0c1vbvvLIhqqrg0Gp5jKJlAo2Y8RUaJho/lCWDGbwGla+mMpxOLuxucEnOipc328cHPB6Fd9sDt7H4sRFn2KDt5u3KD5b3WhE1ugAT10F1j0r0CpD16ZFi8TH/f6oIEQkS66x2ICOaSqKDrnx+lRMk19lBiBRMKkzhfuSCgffVCiQEjqpg1k8ifdLmNZ4WlS8wx6oIL1+X8P57fc12fdXZP2Gwr26iOpPa1GajvJHZU1FHbrdhSV4/MUMr3oHyvcB8f36RfoobGbgAQgHO00wNXbv0SJZY5uK1rCpaE3k/UT2RXTMPKAiv5e6CzlQtROtyowN7IVHrPTVoR/+BkdldHc0ia/BffvDncibwXtF+GYVdUiKy3WOFIErnPxsqGa/1NJUAF013LAaf2mjFNhskJUVHDEy9sWhZaKCXZgG/iNuAvtcWA9VRr4s8EW3Pgp9eREOUO/d1zDrUEEwABZ+9ti/X+HzgeEzgtd69O42Y49voi8ZmtKxIeKE6ON2P7ea/OIBlP/Sgp4Sn9r6/eMeYMPhT/jdpz/k4TNewKrbWHvofTYVreH2+b+OPLy1RXm54u134ZOVim3bg9PGjYVzz4ZZM3VGbp5EzUNr+b/bKkibmdfo1Udx6xtzqDv5eS654Dus/hxeex3+85Ji5gzFVVdozD8u6ltfIYQQfZ7yxQZfoh/+yyvgSDEUFMBIZVLnryXTlg7KxFAm1lDmRHQ/JOHPtugsnJYyiZVSaIZC00AvqQe3iWn3Q07wm3z36iK8E0M1ZQWmGUAv0qmzeIBMlMMCoeys6M8wZU+PZNBEGL5IE8dwPy6KYEUqKrIQ1EywK83mpM7vJmDEB69qahQZGaEARCR+1kzlyqoI1hiC5bce1MgodOKaWEedtQpDN7Ac+QotxY5KHxh5mekzg5W2Fh4nrNvfRLM17Je9lVvxbTgGZwlMnRwqX0yxGjK7Ih0n5zow6wPBY+m1gh38CSrg32xWFBXDqSdruNcUQ8BH6tTQOhvtT7trL9n1Kdht+XgBvy+0F5TCuzdYS1NeA6vTjmHWYyqTw65gjaisriFToa19dhnhSl3oPYZP1einn0gwKlRkvYmK5/9n773DbcvSst7fGGPOufLO++RUdULl6gzdTepuRBtQFBRQELgCopIUDFzUey9e71VQ0YsgYiLZiKBIaKChCd10rq7uynVyPjuntdZecYYxvvvHmHOttc851d0kJezvearO3nvNNeaYY44Zvne87/uJCkntkDgbsN5+ngaHiXLvrU+UdBU4iDYOhUaQvAjnXmaXc55gOGai3BVK0Ern7MRXZv94XyE3IlhMMqDMTg/baeCmKjjxrDE5ULCRXoHZhRuxfXSae6Bph9JgSe5r4XS3n909YRPUsI1U5uC3+Y5cdPNu6WivNaC51GVtfZHP+9z7f2dEPpxkcYmwugaxdUgXGk649TMfJ3i8zMl8EzVsc6u/yvL2KmflrWgTFp7pxAlUhjlA/wrMrrvZLq0WrNzpszmEI9MTLJmNl+msd3lxQ3js0Vd+R7fOstq5ydGpB3/37/LFCcyG6PWXsZUH0JsXiWYShu4xD2zsAcvvc8ILwKw4jonjPdD5MNvDLVL3RqCEuf1RglYM8vpX6A+jSqAgiFV0PpZA1f9lkmR6e/hBjBtyhilM+zYcevAu8HYS6Pf/npp9iEpY48LGMzkQ5vuauhicIxkaun1F7wY8/tjerk0a1MvEWIiVEZi10V0m3ggofdwihwN2TwvlkmKldZ1Zl2EkBCtIJih/N3hFwHkzX7dR4jwZtpAxTnh2uZwlOzmlex9co/HWo/dtU4nbA3a5u06nB7g94PVK81jSCeatdajIgAOz0wel2N0Vnr5p+fQFoV5X94Bdk1P2ek4ys9bf/4qPbCq+b/kCkYJ7PLvUK4zb/WKf2bUf+/EKISI0f+QKq4trPPmXPu++2yxUD/E33/zdPL/2If7Je/8GH7z1Lv7FB76dB2cf5fPPfcVva1/PPCv8H9/l+JIvE37o3wtawzf+dcVP/YTix35Y863frPnsz1Ic/6oH0bWAnZ+4v1H928/+JW53znPmdS/xj79L8ws/q/i2b1VsbMDf+07hG79FuHDxU79J7Md+7Md+7McfzpDOOsHFX/K10if/fp8XxcEQLnWu8XTrZWKboMThnEOnxYv1+CU30CFLy8J73j/x4vtJwC7rxFciVGq04l60aZs+Q053+kUHAWEmmSHtZDhnUeJQ6Vg6FS9b+lf7SFRDudQDXMWb9AQwskci5iaW5F3xaq2JcqbaxMEAMF89RKCDvWBX3udJCcV40f0TABE6f+meGCadKMJ2gCDcstf9saV7dVkyzCvffaLl6VyGpNpjk5V+0mUnXqKfD6nqbqDsXZovyZMG5cep1xNUoLl1Cy685NGcSZlVs+m3WbuHuJcflNb3AhgC2sVExqGANBkzlAoQ1okj0Pce4OScy34bnl2+UmAf4wZj1l2+z0k5m0juuJRPBaXvf/6cClCZ3791dpQQwit4dhX9yPtijKDznfik7v7MrrFn1L0ghlL3GvtPHm+xQyUylm7erc6bgPq6vcnkfaLN3jZ661IuY7QjIpRKM5SklC4vEzQdjmQ0tpPy21fyHypCr7+MufNRVPv2J97wPlGAHXczu/SND7LY/ej93PfuNVnbUwRDcn/xjMyC6gjLtz7EzV//1T1fuT1YpZ/0aQ93RhRFwYO16Qc3keG4eMbkeU0SYWW5AIP8v9aCLublBEqhOuusbwiB65N+Ak/vncE6l7deoFd4Gf5uQiydrE8Wd9DNm+j1C5ANUSHgLPHVNrsfnmB23t9oLj+AidHPwUAnjlvpDdpx7ih/t+dX3t7autDp5GDGaBfeg27ypN4NzhQfir2XUatkYvEhbzQ0ESVTuWfb3c5O3heFQu7r4TfyosSf4wI0Tdf6JJeWIb/3qrwyb3tNceWqZx0uN69xZ7A++q5DGBd1vf9zc9wHGZnjFx0ZnYagmIvjsLuvcAGKh9b0BBv7fmBXAS6RxXvOt9wH7AIPhjrJ7w9KESceJO90cyZrvth0N9i1vCKjhYKxEja/RkbMruLa8aD7K7EvP1nsg137sR+vEIPntqheDbj6J5aYqx54xe0+9/Sf5xs//R/zgVu/zP/1G3+FUlDm/3zbfyA0n7w+tYjwgQ8Jf+0bhW/9NuHZZ+HLvwx+6icU/+4HNV/xFxVHj+59fJt6yMyfe5DWz9+8703tLQ/+WUqmMjKqr9cVf/5LFD/5DsV3fodiZRX+6l8X/tn3uk9oiLsf+7Ef+7Eff7hDtvOl07vKqk++JxYv1WkKu6lnVmViASGztvBcx06YwxplaLa8uW4RRcIraOL43meLdYJ2CqUmmFB3SQptDigop5gOalSyKqqvGLoEHWdEz6zSv+370bucMbzaB5OjDi5DcsCkMIXe6W+M9u/Ee6vcvi2kE49OURpzFxvJ5K/HSmmU0qTZ+NiLZEEEGLT8z1magyevDHa5IpMYxYgLAsBABvcVQklsfdL2iUgwuTRIJgCjJBG20ovjY1p6msrWM6PfJWd2qbxfl6/Ahz4CqVN0exBYv8PhRBGBjz3jt7knRhNKIXcb1Of/aufQegzAiMgocXJZRqA/8TvTVm+N5H7VOu/pipCmUB+8xPTg4l5iyoRDvll/iek7v4zSY9u2enKvjBRgGCwi/S3IhlixKD0Bdn0CcMdPB588jzy7lGdYuE+QtznnaKW3RyCWUl7GqJS6P94gkz9MMLvuVjRNmJIV7Lq7Q7dvQ9LH2RzAsXm/05TZ/ktMDS8QLjlEjWWMk3KoOIYsk/u2DaDya0bd1+y8GID7S7EKUPluNl3S99d7IL17K7Pe3Y1sgnqWm32XkmWCzjIqFW/Yzt426kEVxBug61CPwPh0YwA4SARV3Bcmvnf+Aty8PcFCzaWzWvw8LgCyNBUkB5wDe68O1Do78s5Lc2D7fsD65Jg7J5+8UqUIH22+yMdaL/vfO55OFDQ8zaZ74SbNpRsTCxl7mVO+ifvf80T8M0NNghQj0tZY8g7w4kvwkY9OfA7ev8sW3oY5CHLPrhzPpE/7PoiDzpgJaiYWVUbXUX4N+d6PmVL9d69irhb3aLm/jaMbH70AKu9Mtjkg/dizpLvLHlDK729x5u/FmfUH6gqWpPXjYez9xnQcRR88cAQb65adHSlQe79NoPeMY/6F+4bKn9/3Y3alaXEux7aLh1q/TrV/9Z5294Bd6RCxbrSGpJS/L2pJSQvvvhwUnLwXJYk3ni9CBFTzJtX8/utS59muA2+Kp5VfY0rTu+TGn2Lsg137sR+vECv/8TkGpQHzX3z2k277JY/9VX78L3yE7/5T/5X/8MXv5dj06U/6nac/JnzdNwj/+98X2m34jr+j+B//TfGNf03fA3DdHXNfeRYZZLR+9vo9n9WjKT7r1Bfym9d+ds+LYRAovvDzFT/5nxVf8ZfgF38JvubrhOdf2Ae89mM/9mM//kiG8VK+PToHcajdO8DehGNjs3jZz32WRHDICOzqDsZvq6EpUU43memOkY+iMpIow2CyUlbjkP83Z9EoFN2+o70rE6vr+X7tWHpW7bToXV2i01TELkUNUxRCujWZ8agxk8hZKBhaORvpudUPjg8bwabCbkc8+6RoRqkREFFEKQdetDJoZbATFBklGVEIprtCcOuDqN0VppbfzVz/OTJn0a9k/CRj3yVKY1hLp377kiqPkujJcIlPdPeQPVw2ArguXhI++kGfILtcugm5nOyu5FBNHEcqaT7+3rNrp+n/nhWpQeZwTkYVM9P0E70r5J/drxpjse8sy4HOMZtoxOzKHKEJvcSts3LPGBSy2c3+6ifoA1zfOY+IZ55Z5xkprkBiRrnleFAEn0gpQLmEmf6L97TplKEfHUVwqCzGOuu/oxRhwCdk4TiBKF2n0XkplzEykjF2Jog5dzO7Upewknyc7TRn8BfMM9Q4sR992dH+aEL/Sua9xm06YkjcwzrLd2TcgPn4ZVptt8fgGibIkWmGwuKsv6Z0VoA5glbeoL6QLk/KJYdDeM9vwaX7iQ+yGJUXk+CVgEubElz+FdT21Xs+KrpZMLve8DrvLVSwyY503ou59p69X5o0qHcZylkkv070yrMomzDdfYFgsIEqrKsQnmq+yMda5/lY6zyJS1E2Z/WZ/DyKeGurHDEoQIRJ4GHP+Ocg7OqqZzoCiMvodITLF2M6HYsTQUsuD3UZpH6MPnjrl/mtG7+QH04Btu+9uK2zvOf6z3Gr5Qf+Yx8Xfvhd76M52NyzXfuXb2Gu5iBz/lzo56bqKvfqisOY8/ZZrt15D0uDNUb4yJ55Nz4u/2XuDeVQVo3l7sV3xoj33u33/O6w3b2A0OhSHp0oDyKJzVCt2+jmjdG3jQr2SO9HXSrYYCJI5kjbQzKbYrYVLj+XnwqzS+djtb3lO3W1dztHZPz2B2sP5/sDbM6pFLyvljh07nGp9b331X5f2NnJ+5tliBVaLW/InqYyRv2Cewddl/cyZDc3ha1tGRV30W6vZ1ev54tYbGzISMZYtFpKJubOfZhdev0ievPyBDbtR1dLOp77+Yc2g/rwOsb17pE6Owdm/eVxv1LnD7EA9ZQjS4VnX3BsbBR7+tRjH+zaj/24T6QbA/rvWuWp1zzNGx/6k5/Sdw41TvD6o2+hGtY/4Xa3bgl/7zsd3/Z3hF4P/s9/oPiJH1f8mT+tiKJP7fKtPD5P5dUL7PzE5fuunr393F+kk7T48NPfS/iRH6T0S99O5cf/HNUf+iwW/+Nr+bvZm/nQl76d7zzzt3nfv/oJ3vEftj75CtB+7Md+7Md+/KEKVYAfk2BN6zZ66wqqt7WHKWItLOfEFisWchljAXYtrY/bKAUVqskSKttG3Li8exCAMpp+bzLJyA3DnVCoxARhd3eiClreh0FhXuwURgUEKiBNNbEkuTm97H1zVQoRQ5IKWZxCwWyaYHfEsbC2JlhrsakQ2xattDOBwtz7KlwyBdil0EqTTbiQK5xfvU7aOCe89GyPfh+qySrOOUIxcJ+KdOKKhEdwx1JsThhX6Zhp8MHms1y6q2y9xJaVFcutO34MAfTKc5i158H5l38XewBBJhjlaeqlPFqD3EPxgZeSD3M1mwBTipw1HJcvdG7sefbe993TxOTBee+a+4Bdo3azJAe7imRzAnTKHIEOUbtL6M46Kt7Lbjk18xBwrwH13XGzeYntnbFhM+DNtnPpz93MOXFgVJZLd+S+BeWcKiHK5ECwQ2TMugjDV5IxjpPiqcEFjEr3AKoiwnAy4Rt2MJffjeRAUHFNtu1YlqpUwexyBJfehV7zhRdUmtJqd/n4+Ut04gEXdq+wM/QX8j1V3vJ2pwcXKCWXWLvZYXV975gU3XRZ6hloOcOvAHMcMkqIU5cgIntYG738El6+D0muYHWJDlHpYA+4kWXC8opQVG/TrTv3fP9uQ+1GA0oliJOCienlZWpnvBA8KfEcsboCL2VTnTWmnnoe/XzOF8nNt5UoulmfdtqhnXboZn1wCicWMRPNWUZFM0aMT1eMg9BqF1v6eOb5hOtrN8dSssySpFBJ1+n0FI4ALalP/u88RXDtN/xmLgMruGE29iC8692/EzfZ2hJ+7n0vMUyHPL32K/TdNhc3n0UyR//ZTVxskdQR3MpB1bsBx7zJoSRk0YQfY7bXd6z42aWC697HDE95NpbFPz8KEMnFYPsyXny5m6m1p3nBDT3rdEQMK7Y3hU9aQTu24LLRYg1AqAP/HGOCWYYayYGdOHbffYfOe/NrrOSwIii5P7NrkonpBEzWR4lldXWvxL3A4aJwmtngAT8uzuVA9ZghplLLww/Bp3+ab/hC7+fZSi4B8KEP+22idJvZpz6KPJ+gijF0jCqDqlxSm2QJg5y9rcp7kbrnXoBnn4NisNWkjNHKqJppq+XGMkZdHOekNLWgi04MhEB/c4fNLf9r/PKAwYsp2iVj9mV+rmV3nZnBBab6l+/BOG083LML71M5/lwrv1AleHLIaOefYuwb1O/Hftwndn7yCljhyltvc2Lm3O9Jm8NezM/88B0+/r4tDjW2+YGv6fGaVzuMFrgQQlhBoiqENaQ8hZRnkPIMhOX7tjf3l8+x/Hc+RP9ja9TP2Vxv/xJm7SXeuP4iR5Tm3S/8W76gN4XMHMfNn8EdfQ0S1sAmmP42nxG+yGfP/gpp+3t45rvfzqmv/VvUDt/f2HA/9mM/9mM//pBFLvFTLhu/GuZeRbjMs7ySHlADxpXlCrDLOocaMXHGL/VGGQwBKQl2kEvonIARqhEM+5YxHaUAu5z37NLj5MOJ5U7rCnq7irRj2lM7zEw1KAVVqqpCF8AFnoEwKg2/9xBtDJcvQbyb8bpjedKbpT5ZMCVWm0O2tuHmLccJFDvZTRoTq8aT4IwTYbitmWsZeNTLGLXSDJMhNXFopdFive9SkhHH0OmCLpR+ElN6703ozxB/wfyo3c0LP8P6zlVKGKoPhaAtybzGvuioJAbpGfq1AVWXsNy7zeRbh2RCt+erYo58T4at/MNCmpJLRa5aTEmhm4LMqJEEBJtBcG96kBDjJEHFbZydBjSqkMbYzCdWOLLs/onFZMItlrEGZs9GeZ+tRWuFZGNm1/i7FqMnOngXa8XkjD13H3nbpIk9SY+oswlyghGCZ1OmhrfHQNfeXA0jCVoHvuLgfcAuq0uMjKRz0MyRAhFhmANKznqfq4VzY8C1OHRJMZpcPlUAapOdsOj1F723TV5N9O5kMI7hhZeEc2dBBt7/SLfvAI9hOkN2hh0AWrsdQOgkOxjnSJK950Llsj3B8EL6HFqfvPeAjQFnkZxV5PBjb2wOxJFLKjV0egm//psljh/zXw0DRsmzE+4pJqGGLQ9Q1w+gd5cJLv0y2em3QVjh0mVYWYVGCebgnjkAez2GFGCMolQS4lSjcSOQwmxcIJt7MP9S/rc7H8WsLPpjCMuooSfIlJImg3w2BgUAKcVQlNBiSV02BrsKJk7O1MJZ2rbFWnqH0L0aYQrwnn7V+A4zgzF4/fG1X0fsLnOS4HQJZxMC4326+uo0mCo6ycgycN0mxiiIu5CllN4HuwfuEPc34fVCP+0xUxkXqWrHO6znRJyLyzfICqkkkNzpktzujq7tov/xXb5fxZBbLGIUZVNiaGPSRAhq6q6JKXSeTcnowfTsnnYKA3/JKxwOhr7h/rUMroF5NMPfkMasQhFho7PMjJtF+2oNuMFEY4BNHKBROge7Jr7rmUvj/gXKjJldIzmwGjFvRRySOmTYBgQTCda7ohN8MmaXQGNwnUxV6JTPYPN7WkSZLF/oUIHxALfLMDu3gHrO7PL7DjIIA4WYmPO9nwVgIz0PPDw6ikq6PqrqWAB7zjGu9FgsRHUu0VQxTz7ymfd61BXnJH9WKBzVsqU/NDz9Mcvry8XnGeJCJh+xvYGGgr+h83GeRCgF2vGY4OFalsw49Fw83qo4B53c6FEH98hRVb7INTKoty6Xw4/Zb777E/f/3wZdax/s2o/9uCskdWy/4yIXz17h4de+8XdW7cRl6M1LmKWnMcsfJ125ymzvNn9NOXhTvk0TeM8naiTvT1D2wFepAUHkZSku41AvZq30p9n9h/+Kg3/qnX5bFDL3IPb4G3k7p/iR1Xdz/Wt/gYMzZ16xfbVznTs//9M8ufnTqHf8Os1XfRONz/va+65278d+7Md+7Mcfnhh5rUzKOUbLtg7TXiZqbaDdo5w6CTvbPpnNxIKzWG1R+fulF9IVvkMKYxqkaYrrd/LmBDFQqWma6/6FlckKS07QE+bgAC6eoq+HuGFKkGZEBBz8+BGqQYW4eOUXzU7bEuFA3Vsvb3vL76G7m42TsbTH9vVfI3F9VP5W3Ok5krbX9hVlzZ3gk++8rWYT7I2AbuLgIcEog1Ka5mCDpl3iXPAwSlKMwft0TUTLNT0jbSdC6R7InE/U4g63W1dQWYqTEuEBDc2cGVKzTPVDqpsVVmsD3/1CSppY4mu7SOYwRlDpBItoUroJ6K0e7oRgb1uCnJlV3hA6KKxkPH37PZw7Nhj5/IyASwfadjG3Pkht8BB9dRZX+Dpl3iTYib2HUVPEevMO3d4KZQezqdzz3nCxe3MCZFVe/mYhTmQPs8tmjkCFE98fmbr541MapRT2PgDIC6sfHv1sti6jY8G4RZzyLBnJUgLXm4TWxj85CEyCouorld0X7CpPMLs82HVz8H7gc0dgl2rfRu/cABXgFs8hAts7gp0FyHJT+gnPromEcWZwHj1oQkmNmCm6I+gEKBc9FsQp0tWb6JqB+ilczQM3ahCzswPRrHBzdwXjqv4InQdj9pyRvP2uDLwUU5JR+6Mo3nlH1RdDsGByHyvJqSkK2G4lVM24WEOpDN0JUt4HPugT1c94c37swzZSmkJKPkEeDIRkp0PtYGVUSEEKROs+LL5JrCXMSYyHD8H2DQOS3Res9OikQzJI7+wQLRoI/MA6C4Htjq6HMFFeLlwAtDqgocpsxS1ULj9Tegx8eK8iR9PtsOs6RNkqIh7sShKoJRPMPBw6XaUe38Lpo2SqwrFwHmY+nY+65+nbCiUdoSXh1h04n8ATj4N6+b2U3r8BFX++rc3AwsXNZ3BiOTbtQb1+0h3da1c2xyfBOjViAk1G+HRCt/8BCmTdTphSiRIIoWrKDG2MTRyJc/zW7Xfx5OkvYr56ECWC7QlLTVhfanL0VbsESYDKhtSabaJTr8LhZYy7u444FiqjThVg1xjA3LVLLLdukNkeB8qLfnyHe2XutvCWKuhT+d/jzNJdG+6Zx4EKSO9idvV6wJDx35Ie7FxDd28RRDqX5ebXSJxTiJwUPuwjcM1lgjhhwAaZO0KSCnEiNFdCygdTAiYwJ5vlt7PxeRAg6Fmw8okLDUwCxflAWTt+rhenNXExNSAJEsK+h3bEyajKrt/72KPssXMJT79QgUnvwPsU7JC9NOq9GH1tEWih1BgZLB5fxsYjj8nCyN/lKzUeVNt7mC71KLNtO2TV4hYULnckMAaPStsxYOd3JqhDn1o1132waz/2467Yffcd7PqQ93/uB/i6E//Pp/7FYZvg+nsIrvwa5s5TI6R6m1N8bPlhttQX8Po/+QAnHjmAq85DqQHa+DUym0LW94adaR81bKMGLf/vsOWp30nXm+5mQ9A11FSN+c/ssvlbj3HwOz+d4MxJ3OLDkL9EfG53iR/9b7/GL179ab7u9X//Fbstcw9y7K/871x85mto//fv4S0vfS/bK09T+vLvgcrM72Ik92M/9mM/9uN/ZWz+4grVx0ClPe9RIoJS/qW+ubTB3LTxL6dYrnc/wFAGiMsN6sV6k+oR7pBBzvJQSoOtIC/ssn7oKpwCnMMZqFQNOwJPbV9goLq8dfo44F/MjVNoo7DFW/PuOeZrcOf2NHO9NufOdklXr+b785topeinjlLoDeALyxGVa6kGeU5Sr9oRiKZ3V3hp8ynU7AmC3gBjZ9m+MaCxlgC+rPnONnSbPhc5a4ROX9gdlnlInSCJhaWbQmdJ0Uw1g0Soi9+RcTFaQ2c35eo2qLJ/ib9qL0P4kDe7Fwv5cD1/69e8J44zeb/HGcPgWEzaE6prFV8AwIG5pUgfGpJe7JLc6oACE3g50G4h4VD567tYVJJhrnfplbI9bBjnrbgYpm2iLOPi1hpRPOM/kwK4VES2hVDxSYkBUIjRKOfBLuvcHk+myXh59WnKXQvpkGN2lj0aGGB5sL7X0idnDVy+Am86NZE5WfFsjiK5u8eewTMyPqGMsUiqLEwNr42qhw1aHSLyRCxnZxUh5PI8hTdwfsW1TZ1/10t9htLGiSMMFb0+E4w034fNLdhcgTvtG6wlH+NgDnZJzuxShTkOENndUZckP77qsxkH2imd14e0szu0spsEqkyYthDm9/RM2cwvdOYm6uBIXUyabGHbQc7ZLMbI7+hK8jQzBoLc226PJE4bIEPyBNTlc22UtOLACRqLlbFOUnWGVF9epffAUaTqkajhXd48uBRMhEwdg81LXL8B26t9Xve5E1LI8WDQbHpz/6mpnB06cfpLJkG1Njh06Bhbo4IS95y43EvQYYdC96WM6hkIF8t72ivqFgSJh/SngwZb9EEbIhWysQHJBjTKCR+7cZ40nZhGNkGco3G5xPChDpcuw+JCzvrCMxnlZoaa7VGa8UZMFkdq6hyKFuhkHnnvJGWUDvZUywMY3MhQSYwa3qIdzXswOt93a7jFbPAAgyE028mIMbO2PQa7xN3NyPKhmwnJhP9S4lICyRmUSiBQmBzIsLHQyXqQ7LLUvsp89aAfMBGGsbDNbWzLEToD6y9T3gx5LDmLqLzAiThu3oKHq/ntYUL6XoBdhReZa99Bdzx70Q4FFSiqj1fpLoO9vIOuLpDpYt75Ly+tWAYqJp4Rspxxt6gN1u1dkHj+eUXFaKoP5v54Lhv55BnRI/ByZRWe3vwNZmcU4dNrmJUhvOXkmJGcQZ8+m26dLLtJaoUkgZKKkG6WSxf9+ffaxfxelMRo5e8DlQ1H+LzF/YlXvqdNVk8tpoW1jECzguga96os9Xoce7TDrJ1BMkf7l25RfnQWmAbGQJG8lBLr2zD7ECqXZQNosR5oVpPX0cT+lfLyzYJNF9WQIBlVwpwMkw3BgOiQ4dU2uvMckuRMQ5fcQz6TxN8osiven8xOacL0DoqjNOpAi1z+mb855AD2p8ru2ge79mM/7ortH7tI/0DM0qOrPH7w0z/xxumQ4Mq7CS7+Iub2h1Euw00dIX3ki7huX8//847X8fKdRb70z8M3fL2iXFbc77Ymd/37qcbc63psftbPsvprRzn6ltfv+exg/Rifc+qL+IWLP8pffPJbqEWNT9jW2dceZv3ov+QHv+en+Xr3T4h/+CvRX/2fkNxceD/2Yz/2Yz/+sIX3VSFL/Cp2GqM3LzGMxSfpVqFD70HUSTeRnK1gxYFNsXrs2bWH/IFCtfzTbOPGJTha98yuUDC5VKZ3MSGSCpxRdHcd/YFjjPMIxZtquw3MQK+918NHcvlKKevSi3rMJrepqAou97gq8B5nc4P39h1U3Ln78DH9DaaHm2A+h1YLigpsSQxSUex2FN0QOj1oVx5hKmqwHK/Q2YHjpzU2g3AolC2QwXRlQKrHRr9Gkj1ggdbaZyE52LXTvUOWWIZtzVzu+wRjPxQJBK0UWNA7AWZV0zm/SRSH4zaNZxdsN6HTEaacIWsJ1DNU5kAsO71dNvstnD5IEChc5hPLQe+3KDdj1oeKqkqgCi5/E1ECHbfD9QFYyX2xHGAUYj85s2tctM4xjBPKKq+exl0ACqCc8kBf3pYThyb383EyKgaQn9SJb3oGoulsgNVo8T50UppCavP5eOqR15NzcCxaoF9bo9mEKNsZtzTB3ICc2UWCybqE2V6GxaOPas6fz9kQo8pu/rsKGGZ9wrDmCVAjS6O9x3yjd41qLq8bMbvGmTAHD8CtZjjGd+w4Oa8my+j+LdZzWaSTDMGM9lEYTqt8rFxYRwUhSjx41rzyfsotx3TwEFbXEGXGjCWsr5yWtVDh0b3MrsLbJ/cAcio3qKcwqHeofpOZaAlXe43/SpZQ3VkiDA1mo0N2ai8gB97DauWC5czDESosk536LNxL78O4Ac3W2FawML1X4vjYM/5vp06Kt2WaYITMD1/ErK0j5Wl/bLDXa8mmbLUCXvyI8HDgRqBftosvhkDufyTQkh0Pdo3kaPm1pwMiApIUStua4cE+V+7k8EFxLWcpDIWwp5GbbViAzU3PwgyVgr7ArkP1eshs7heFxakqYjXlrecASEyDNJ9jYdbBqQO41JJsuNE5Xll/hvrcmdE1JCI8+7yXjt4ejq+ZXjIGu5xVuP5eb0RsjO6skZXG8kPBV6tFGw9oBmpUpdYmjtRl6EGbqLUEh/3xjxhuWA5UTvGqB1/HzmDIRbnJaut67tnl53uSQBIpSpGMrlWkqOKbA8ECOulDuer7nvjiWuGBkNBVyZ7pEhypk+QLDCXbBGCQWcrxGsnlEusH+thIeAA9kjFOzu+C6euc9aze/HoKJPC4lPjqh0s9mJoSKktdz2TcHbfjXFGdWGElIcsl5mHgeMC8mvq843rsJbztliXcFeoKrvW8D10ocyiMl5vfB4gcxwR6mwgEOUBbMMTy6yFQEanrEbsBYqewnQQGO9iPvgQHvxBfhCXfOBPU0I+/Eju+plwGkssYizWHSabuSE0qoz8MhorN9b2yRvALQiJAWGFwaRMuXUc/5AFwyZJ7Fcpp7N8f8l/tQDB6k0aSUm/v0nGzHjT2pSzxhS0dN9x1PuMTjF4R+zql/diPiRhebNL7yDofeMOHeMOJzyXQ4X230xsXiH7jH1P7959N+Ve+A928Rfq6v0L/K/8721/xa/yz8/+Qv/hP3k7LLvID36f41m/WlMu/AznkJ4nwcI3Zv3SW5n+9SrJ8b7niL3/ym+klu/zipR//lNo7eFDzxf/oy/lnGz8C3U340a9ANW/9Xnd7P/ZjP/ZjP/5nhIAUZuk2wSw/j7LJKLmOY8++UDiCbNd7+ghsxDu4uOMN6i0ozEjCAT5hPx6f9D+bPmbzomeNaEEbjcSC3gjRLcPwesy1n0v838RLIAWHEsdc71my3R2iwTYH7EVoTfgv5Y9Mg0aMsOKWfEpUVAXMqV0200jLoT+2ys57Yrovp1hxDOOcPdPVVJZKiPKVzrR4UyFrFY2GolRSJIlPVg4seiYZCAenhUce1hw/Lhw+LCzMKx59BJ4410frcfIfZzukOepTynY82AWjEvQqi9ndhUFf5WwPlyf0xQEKBo2yClK/74tXB3SaExXMlGBiaNyydHcd3ediXCKQxeg0RuHYylo0tzN2b93ExkN0L2Z2fRmwqI2AJB6yI54xIa4/2n1b2tzsL6NyA3JrAaNHMsZ+0ufXLr2Tnt3aM7WcZHuUZrvDHpOeXSNzaPGgGgKiAnQCZjiuxCh4iYpRhjcdejMPVI/uYaipuIfZvkLQXUOaN9Bbl9FblzHLHyO4/KveqN1lKJuQZsJGdpxqULq/JLHQ2TAeWiMxU+u/xVz/+T3MIFWps1N9klblMQQzYnY5B4Hr4vo7hKE3KS+6q5s3CS7+0qiaWyADQPL5Mtb0Li44Tp6ASgWc8kng8nCD27vX9vRPdcEkhYm4wukSy8MN/19v2TM1ncs9sBW4kf6I8q4HQaeG16jF/j1udkr4zDdDaGxeec0RuB68v0d8rU22M0TiwnDcFxTItIeJApuDXwg67nkP8vxvc71nmEnOE5oM3R+zvQLb5Vjzl1C9LW7chMHA0drNB6c8RWrqBK7PrVswKORlBT1pYg7cvAV3lqDbG5+fqGD3uEl59eTJTli5k9DoXyEdjkHEZFsT3/L3AOcgZkiqDAKEnRpJKjSMP2bRgWc3WQj6OYiri7MhGBfjBDLRgJDl94H2LrlBt5ow9LYjk//d6DiiAo/pWscwWMAFVQaBlyo24qtoscTL96LMbtCcqKoq2Py+deZMxumTEZWy95Qb4a+3M5KbfhFgBKzkoGo6ASq7ooKDDnDKIYG/LwGkHUuSU4vCbEzXGy3Ui6PgzzRMDRS8cPEWw2d6BD0DyqEkHRue5+d48MI2u++8OW7tbhBEKXTNoAyUz8344hAiiMoopdtEWSvfzsKOxQ4h3MnBWVEjsMvl+1NKo8SPw2/+VszGuq84DKCdHrGHitBbV3HT/neznVFNfNUFyXwbggG8hNE6MAgu1QQmzK8vTbvl6A98ldIitrNtz7TS3Ldi5MQAUClsm1PPpNvdhXiQL5jkoFfvWMTWEwFDGYIVbDtFt5YwkSUX5o7AcQCNR44VMr7MxI2B9NEZGcsEC78/753pz83apkGJRUnGbP95dNrzC0w5UC7hSLg6Yh0qm9zj2SVZTGdincoN/dwquSZa+Rusx4HHhQ0cMT3uzXvvF/tg137sx0RsfP+LUNO89/H38qYTd1VhFIe59h4qP/3VVN/xJYQv/w+y02+j/2X/mf7X/grJZ307z6w9yv/29fAz/wO+7C/Aj/4nxaue/L0HuSbjwDc9ARo2v//ektln5h/nDUffyn9/6YdG5cM/WTQair/+f7+Wf9n8UeJujLzj61Hdjd/rbu/HfuzHfuzH73s4XJGbuOwenymFB2BC+pQ619DiV2TbaYenN57CdVdRkssWJxeglaJqy9Rp0M0rKTknoEEbDbGMcvbB1fzZM/RgF/gV8iBP/ErZDtFm02M68YARM0gVfdRIMN75SMZYWI9lIPEYI0g2HCtbQzY2odd1VK/VKG0FqGHKdP88pZzpowRMoIhKamSqXR04TCsDhJk6aGVwYlFFlTzt0HbomTqSkUrK5eQpLmXehLqcbWIKP60iyc1iX6VSisKAjjtLcCtfR1KBH19tFcT+fLS3brD7/MdHhuXOObQTqpuO7pVdst08QdtdZnH3KS9HUYLK695vr+wigxZacpmag+odnzVpFzPXfconipNsvRycSjOQQEPO7Lp8LWZpNWM7vbxn7mSSoEaJvJC41J+UwvJrj7+K/6dc8wnxwQsZ/Xev+o/ES2W1MpRNmUAHFChaP+2NT/jBJ0lPvJHsoS8ge+jzsYdfRTZ7CuUsKumRDBM6HUjNFG138BX8m2AyoxbnE7/7baqCMv3ScQYkXB++l+fal0CEw5VHafSvoG9/mPm1X6WcrpNme1soktAiGdcKNON6jNONsTRPlGZ7By52btCL22BzgBA4uFrm8MUxwpNJgBPhpfYNLrYvs5NdGzPaBMhZjuIcRnlABiBwfYIAHj1tKZchMo4sNwTTkqD6jsFLO3TfvwriKB3SbJ9sshsEOFXac2zaWEKlUQq6mWeqXIrfz27WJDAC2Xh8K6k/x6q/hTE+4b52U7Pb8YyWJJilkq7TbY+RAOuEwUC4edP3/egRePihfFwnkuTCm+5uY/JxQyk0l6gmK0TDiXdYsfTP+yTZMxYFpzw4kewELPTPUNH+mOcqi2g05Q0PSKaPmNH8VpkQdjpspFsM83Ys/npLUwh3b3lwa0SqyjAuZhgs0o0Oeh84FKDJTIMDB4Tl9DzDYGw6b7vChCWSH4Oki16/mLcpZFaoVqBSzTgwX6WcgyO1nMqmtu4zNvnNczfrjlhi5zvXyTLrmV3KeYA933nWdgzTmJEpEyA2B0rEAQ6Vg13lvCKsWmtjM0vQMYS2yezwJVyhVZ2gisrEM+HuWO2tcK18E4+gKK9cU57hU4DzMJr2dLq5XyFlxGlcXlG4+4tLBJcsU4MLHG2/m85Wl434Au2WjHwMtdPEKay4Ja7334N2McQdnHJs7QjxrSZTgwsolXt24ZDQ97s/8H03LhtLvjNHuJN6ebSoSQzb708yJBgvCtwvjAZTaPCcH6VOF668x+vZnTFEEZRmwJYUg3SIOMHduQZi/bNlwpi+CCUZapjtYXZNb32IaLi6t8bI5E1UJnB47T+rlBWldIdKukqgi8WL8bYyoVcsTrmSlBdfAuIMteuv+14rZm3dH2upBC4VRKnx7sV6TFvsiFApKhsBz58s9sGu/diPPIaXWrR/8SYrn98iqaa84ehb/QcuI3jpZ6j+2J+h8vPfiNpdIX7Ld9L7ht8ifvt34469nsEQ/r9/7fjmvykoze8rm+vuCA9Vmf/qh9n5ySv0nlq/5/Oves3fpjnY5J0XPzV2F0C5rPjmf/QIP9j5d6jBDsmPfQPcLQ/Zj/3Yj/3Yjz/QsXTzCjbVoDTKJiPPnnGJdv+MMrmsCcYvtH075GbrRZSVnNkF0QdSVMtBz6KcI1QRWTo2vnXaoY3JV6FBTDDahyCYrI8R75ul85dwp0LvOSQe44ijA2xtjms/CoIzE2ycPZUBc0+cKNiTCLd7edIZ2xFqZtMBS/bmnqQqMP6FvYjpC5bFRc3CnBA65VloYtEtn9THSkE6yMEuS1taaDJixmwHz+wSVAZq0PSenP5AGCbQ2fU+NSM/o6ComKigZyAJiVauEKQpeteDD85mVLMNStJhsD4cZyMiYMUnLRPHpcVXoyznltDO+dNQXo2orcejBI+cYOYxIH8+kgSvB7OOnSb0B8VIK7aSS3TFm25biccyRhFiW4BdORtkZA49ZgqoIKBcyeVjCBvdJTpJy5sPK+9vo1F7ZYwu81XUgrKX1+bZmEwfI50/DUrhmkusXdmg21M4FXLs5JhRANCqPEq3dIo0FbYnkn8BDCn3Q7ukPA3AwO4Qu3a+jaByqY9DMDpjenDpHk8zl94FKmsPrI5zxzzBzufR2k5MZgVcho2F4cDvLioLVSOYuJBPOba7NW6uTyHOsT34MMvdX6GjOjjRqGx8rWkFoj2otDDvOHMahueb9J++Qu1mQioRoNBZihMhTWU07rqkuGFvsSnLfryD8QDNzDmMeLBrYLfo221iGbJi79BO1yiu3OjFZcLrW2zZTV5qXckd4iyiNE99FNbWoBudQImjlDVH7fe7ws2bvsopeNbS3FhtN5IqBmGOAonj1PH8HiT4ipiAcimD2Es3C7DaD+LYm83mcigh8JLu3INMi+aNs0/yyPwTqB1Ded2PFQaCrI2xQyprQ3QyZDNugfVsoozU+5jFHaa6L3mwM79fmayNcpbUNLyMEVPgRGS6wrHjKUdOdInDvGKkFZJtR3ioSvqqPunDflJYcaicGpPYmAudX2J1cAErlnJQGd2/q14JSNppEXd6DIdCv+sYDv25zqyQOcuc9vO8k/VYHmxgRViON6DqWa5uPsNZYfBhIfxojexOhrUZ11vLOGTso2fHD5CD5XlC2yPNwfd6ue2v01Htgb3egvlfUb2dcVEwEXbiHbarOyDC8xsfZq131bOnJENN3O9SP7mwOmSGGR4PXwU29xsbJv7Zs+QopS0UiiyOSVyPsDSWoA/1EeLBYXZ2txi6JoHr58CK70/U7Izm327lIYZBA2ccjpQVt4RWEGgv+d7aEpI7XWpXu4RdOyHfz49ZRR4INQrbTPZ81mpNbOxkL0M19LLL7d4q1mVkx2YIH5tn/lF/rL2cEqU2bpImwua6jDzgJs3dw+5tjnc+gMLtYVnVOxfy67Q4JRN9KWSrIvktXuWyajhU20JProcVbcb5sSmd3/OVr2bsEsIb25TOr6J6MdsbQ5IEoggkrNGuPERcPjSWVGrn1z+KY3CATrlnYF8h9sGu/dgP/Mriynd9FDMd8c7XvpMnD72JejSFufabVH/8z1F+9z9EwgrDL/xe+l/7K6Sv/WooTwHw7HPC13yd8DM/C1/+pfCj//H3n811dxz8268iOtXgzre+n3Stv+ezRw+8ntcffSs/9eIPMMz6r9DCvVEqKb7xux7j33e+n/rgGvF//mYku9ttdD/2Yz/2Yz/+oEa7myAJYELPdChW0UXoRSdGL7V6AuwySxHhpSoHZQFaK5gs8RIsQMUQfdzS/811lHVMqak80/BtXoovsp1tQ5qDWFPHxqvD1ptzl/t3fDKORc3pXMalRyvBK9s1BkOIc7zA4XCBIdX+mWtVuMfnxK+ymz3v5QUAZzPvqwJg+rdHyZfvsPfinp+HuXlYmIcwVBijiSIgFW8qHk9UNgsrqGyAOKFlt7hpr3sGwESoIhu3ntECoNermIH/++3be4FGAkGhaVytMFwKSXshcRrjRGgnbfTWFaLWywTSp+K2SVox3Z6wtCR0e17ZocSOFGzAKMEpm8LvCVSqKK9HTN3O6MjuZA/8z7mcJk3xBvWZ93ULc2ZBx65h6+e503+a5bX3YJZ2RpXNAFKb7pExZi7DidDrMuY0BdqznOwA6W2xtrvC8vZFcKCzBFzq2SQ56LK8ImyspYhodBDdY1DvEFz9IJkLECKGwSIzM4q5xYjXTz1BHMzRLZ3E6RJORaytw9KSI8sBCBEIXXc0RSeJDK5+MD+NHjhstjUuc2jCEegQGBBlyCbYTP5L9zIoA6XHyZkIN/rLXOvfRonlkr2KCxsosSzf8ufAAdl8QmpTwp7XgTrn6PW9L5qzjseShHLaRYx4iZjzRvrihOnSAWzFYbGUI2+mD5A8c55oI8SlGU5FlJM2zaZw8RKksa9oap3DKZ/Yx26Xfu3Y+NDEEaJomDpaMlLJ3yszxc5wid2c7aV7CcHGLlvXVmj+yjrDxOUsLD8/Xj4PLmdQKcmY8ZgLN2/5CnHlnFBWLnu5ZxFHj/h/tSlAEcfsjKNez38Nq+zuCtcuxzjrPX5UYcRfmcXNn6GY+a4Au5TmmD7lAYG83VpQQZsS5dwjzqgAAqj3r1HO1sgYoFwKKILdiDIlUDB0bZIJKedYFSdgAzJpIDbzPmNOIaIQXeJ2+zKNumJUAW/NcmNtlvXKSdKy5VY3I0kUmWQjMKE93MGSsjW8ReZSSkGFA4tQrcDMtEOJJd1aY+v8Ja5chZdecFy5CpubQqsNrSZ01mo0m95oXgRW+qskkiKBo/GWAHskQXC4TFBWEV9JeeHWZT54cYWtwS6Cw9g+1ZWnYNAEhIOlOZQoMlJKEUxPe6riCNiaQFgKAAUgaTXpdv3PAxv7C7Lix66VbNNNmwS6ANjGN/yhEzquQ4JQGRjkRko68CzBtDME8uIssWdYFsxLp8QDvTMpzWaN6csh9asVPKg99ocC0JnzxvBtX9nTiUICv43DocOASFmyDFZHanyFSWQPK8qeSBAdoiRFDQT3kQ5RazweT398PHXI3J6btJQN/VlLM7nDcvsWTmn00Sl0znweZgnOOSTxYHG76a8t35O996hDpkNwdZu0OcnI2mtQPwkoTnq0eQROIflKSaNRvAPg5/O6RVYznKrm/Z7GWkhrRwGoxzdHzZqd/ghkDgJ/z8lUFTvXmPAP82NfSEyVBetSKnuqb7xy7INd+7EfQPvnb9D74Brlbz7BxeR5PmP2SSo//VVUfv6bAGHwZ/8Ng6/4b2QPfQHkZqGDgfCvvs/xLX/L+zH8m3+t+JZv+p/D5ro7dDXkxA+9BdtJufGVv+YrOE3EV7/m73h214Uf+221G0WKv/z338S/Xf9u5nc/yuaP/b29SP9+7Md+7Md+/IGNJHPs3LSgQ5TLeHrrOW71Vz2jQYUjEEKTjVaQ5zcPMHfnNOpyhWZLqA7XKF4XrfWSDaVA9TKMMpSykk+wxaGV4lr/OsRCp6dY3xAuXy2McfNOaS8BMWSw6FekSyUZMbuK6m8CqOMBWSXz5Kz8DbzbNUjQGHmNSOZGCWIhmyjWZbLEokRTKcNhfWM0LmEARlQObikadUW1okb7JU+MxAmuKEEPpEHkP3MxiUxaA6h7flQpqJbDhdOEy1OUOgGNaZibEWZnxpvrYOLbgcJo72m2UnPcSVbpDjZHFcNAEbdTmk3/KN5t4w2Hcb6C2ij89qXDXlLkBMwwN/LPtsebiUJUThDLQbs0AYzCDDN0IkxNjTcPAj8+5UubBEtN1HAM3sQ2Jde3+LEXS7vl991XC7ipw3jbKqGcrpNsLbN1u0lvZRU17BPdfgq9u4JRGuUysnhAb2OLTjtlkIS5pHRvwrbd30AahxlOnaNbfZR+6SiNBqAj6qZKSJkkmBuNneRl9womlgiU0k3ujq36G0bVqF3hs7SraDYFJSb/u8MYz1bI4r0gXCEXVhPzwhg9ZriJcL23xFKyipKMTCl2WhpxQjocn7/shNfnmqEgCNZZwpZh9sUyM8/MsXhpjkjKHuxyoKyi34c0FUpBFVt2XD1+jaEbz1UXVrw5fTxES4yxMf1tP8fTToetpM0L21fI8MUmttJLLLtNYhmOzqvB8FDlNEoygsi3rXL2YDdZG/sMiSNsB4Bwp3WBJXuD5ewa68mLrCUvspJe5I69zVryMvVjL/q/py8Tqzuk7SWmg3exsvU815ovES68yPTJFzHzL1I/+iLN0g2udG9x7dovcrV1mU27iXPQGlS4dRvWVxLPnpkAGqS26Cuh52GtBypQmkWdo2hmNPygDTNhgyP6GNN6ATGj6Y0jppXdHvkaVZRH5AZ2hzSZmA85sOoQOuFZDjxvmTrfQ2lFZ+a1tA++bY+xfmG23006ZCZgKWuwsgpoxc7BnIkkY7AOCiJmRqBCymXN6QcVld3LnC69wPSMY2EeTh6JOXuoxYnjMDcrNGq5RK5XYTU7yc3WUcQ5Wrspw9T7NxmlkbLzcsB8EHvZgA889zLKKYaZRbAYF9PvGrbvNL2ZvlIYG+xhGJpg3N9Jdq4IYH2l3c4u9PK1hdilyLQHVXaT3RFYFmjBSrrHQzJxjlvuBk6FRKlCmo6bLzhav25JW4XMHqLz1gNVOdhmbcry4grtgZBOFARpDK9T325R+nAd1Z3wrWqCXE+xd4Z+ccE4ojBn7oUBocrIUkG7YX5gynvqyfgeL1qwJmd2ieCcxezFxsdhM6xkZPk9SCnh5En/UWfY9zaZ2lfrBei2uly6/DOc37nufQOtMDW8Qi2+hRK7Z5EoW7PMLL9AtmY9Ozq37Jo0qCfNmLr5AtX4zviaEIeOwJv7m3tIsVlpHroOWbVk9TO4hXNIaQpnIWjMEAezvmhI8ZwcphgXY3XJi3rz+i72+CJKQSopVnt/SDVZ2EBSRAufSuyDXfvxxz7S1R7L//CjVN9wgOff8CwAb/3IT6Batxj+iX9E/6t/Hnv6bXuW/J551rO5/sfPwZd/mWdzPfnE/3yQazIqj8xy6kfeRrY15Mrn/yJbP3wBN/BvdI8eeB1vyNldg7T3SVq6q92K4kv+wRfwX1a/idmbP8Xue37y96P7+7Ef+7Ef+/F7HD21S2czRkyIZEP6rSE3P7qJszJmEODBrkIZp3p1bKoZ7OS/ix4xuzpbPZqbiZe6jbxbFDJQzJyPqL1QIbgZItueUZUkUKzAS+YBrUQnDLMtjAz9KjKOcuT8ZnvALoVaNCw8diR/MfZ96A9VLmnwyYRLBXH+s+iU/+6wnydm3Qy0Qhuo5ElJVrdMTykOH1KYYCz7GocCBLPsiH9zE7EJqqcJX6iSkPvRpHtZ0r3oOLrsk+jYOhpBjfp6hfD5ELMe4cRL2MIIjh1TzMxMvC+EjIDG3uNDVN3hcHRCAQM7aWfkf5XNKnYGS6R50QHr8CCiuKIo+57jaMxVWNQHCWRvsR2V3AvOSQ52JSleFoqjuuFGZuvgmXBF7NplzJJjZmqRqWiKxGZ7MqVWN6WTv26IRMw2jnmwS+VVPLctRgYkGZDEGKXJdh3Ji0CWYTduU0vuUMp26AxKaKVxYklT4cWXvBTr4uYzDGPh5m3QGB46CzPTChXVfdKko1F/Jf9PiSNNIc6ZLPd7cytM48EnVQCiVA7oht4QHkcQQClr4gZ7jZKLqoqHDiimcmwlMGoi65LR0Ktc1Jb1a/S6QqnlUDg65/pQFurTwtEDEIWCs47SRojKIEgMDIWSi3AmN6i3anRtKlsB5ejPDrh97va4b8r7Tg2TkwzCQyirRibWWb9DooRhpkEzOvf9rE1Xcp8rcRg0oTEoLFHYzL2pFGEglMuW6MhTo8klePblrbVLDEprbNt1Wukt/1+2zLZs0srusN4v/rZE90KP5koXeeo8279+jdXOLfT0bYbBbdY2nsMmT7GRrrMSb7LaW2ZpuMGN9AaDgfDMCyGiNEaGnjEiQKionjYEB2ujfkFOIFSe2UWWUxj1xJquDrl9WyirMlqZPWCXUtBz23nFOoXCYJQhkyHajZldngXkWJIt5i7kVSMHGUbDMDpCpqt7GIWFF9lSehsJAzLt2TGZrpGV846NNd4oDKkMPPiqzchnS6VD6jVFqQSVqmImucxs7wLTU0K5JJRKiukpRSOMkGEdlzZoNqHbL7GxhQc10BBCPxuOpc+Il7ZbL6stKrsO+porV+xobAIKsynYdT0GYTBSJ8sEE9I5wArRcM0X6QB2hrt0zrRJX+t//9jWM6PzokhzZte4jd1wAWMgqlWo5IsW1atD7JpjcH7Hz8GclaR2od3OmVBJx3tmadA2GN27Qteh3E78VTqBW6phvkkvxgKNaWGq4TAaonJIYDQMO8wMLlBLc4ajc35RoZg4WrH7+Nhw0uGLwIzmyySgv9zlenKb2+6WnxtGKNd935uthMz6a9QYzzgddDo4EeIkHT1Py9trzKy/kK8kcU9kWw55ISFruXw+jdnJ+naP+rWrzG4+B+LodsGmjuiwyaWJspcVK4DSdMqn6ZYeoPfUFkQ1CCtkAyFyGnsFwvNbmKZ/huph4ekZ0qh68sjwgUWSXIJ/M7nFmlzGCYjNn3KpkLpkz/PoE0XwyTfZj/34oxvihDvf/kGwjpN/t8Z/eOZf8qAzLD75VfQ/429Cqb5n+92O8IP/VvjFX4Zjxzyb6381yDUZtU8/yJlf/kKWv+PDrH7X02x8/4ssfO3DzH/VQ3zNa/8u3/zOL+AXLvwIX/7kN/+22p2ZUXzat/8NPvhvLvDpz/xTNhbPMvXEG36fjmI/9mM/9mM/fi9i0Bhys3ebhj0GvS2CywaVCq4niBpbZts0G720llwJC0js/6BsiIQKnBAkm1RSGPQFnbtEqVQRvFQlHMboqIJZLt5AcyCpYBzlq/mpztBuSMaQRgOmDwqDxBE7SBMZMSVs4T2jqizqA+yQYQxk6D2WTjIB3KmqIlF1smGbstakWJQKKEVg8+p//WMxbASjF39z02KerfL4nzhBb+YM9FpgYxg0kdghicXcLKG6HowhxFedmghRChMF6AyGsXB26ihNDrDiXkLvhuPhmExqc6BNGVBKkU5lKDRB0c+aZrrn6Ay77M44dh/qESXTJJc2QMYm1lifRrU7965yhxVFQzXIlKPNmMFkYk0W2QL98SvpuZQkSSA7Mg3XBZ34lfbFeWi22OMfYyVmONTMcZK2btF12ShZlExY2/TJVdQMUPMNDpaPsTO4OMJ7LNZXxwRUainpiN2Pp0imULuWmAA90OiaY2Mnwm5pGjMZt27D2jpUah5QajaL4TUjM2dVmWZ45E0Mlj8KFKZjRecdN2+Nj2NqKj8JE0b1MnGiHBmBKlNk20oC79klYyBQ7S7jFsRL/HSAZAlaQ7mkCaOc+aEneF4TCW2U7mARKrqKcztUVz34VRRCMyHUwox6fBshxlarhB1FKBECRJRACxkKMs3R6DBd8wRpGkA5r0LXEBhKzsawiAqp3amxNTONuKY30QZsnGArxksiFWitKOlpdk5nxHd2kJY/bwGGUAVoSWl0P0JzqKl0AvQMHF3QdN0KWuZGqFGvK0ylC7ztxGs4v/0InfLZ0fG/9YEIs3AMdfgx0htCebDKI40PeyA6qOAWH6YxexTdCFFaEVz8JYjATR9Htz2gsDLcZLW9QUfqZLqKVWVq8RJakgITRx00LN0KOSgJCkWSOLbWYFBawLkUl/rrVIKATielt+Q4fNQw6Bfza0SoGk0nJZkH55VCEIJAMcSNZFkAZEIapgw4NPqT4DwbKwd3JsGu08fBNiNiq5DIUF+cpTl4HFEB1fRCMSn9PwJ1cwClVvM5FhCYiGyiwp9kAsrQTtrMRnXUxgV0f2f0ubkKc1ZABbx0qEo9qFP40+l8BWSjsokpCdJT0Bd05lmEVhxKhoDDiBmZreuyGi2QAIgRbrNMwx3wv6d+u9ZgizvtNQaNMgeeukxLICPjdq+JSmNQM+N7Zm5Qf233A0ilzkzWxagaQxnSC+YIpx7hUEkwZgFp9dGZB07WVjOsDtnehmOoUbVd03eIKNCCCkGr8p7lAjEyQsglEFSm6OvDLKgOpBmCoEKhLEJ1RuFqVYLMoPsbYBRh1kVFs95frUDaATGQ1Mukcxm4FOcsOh3v2ZFh8kWVO2vPMV3K/dkk4dxZx6BiadSh07WUYkWtBj0tTFVqSKiwGYRKeXAvFuSqv0/q0l1yewU7bps0ryi62c7oT1kioBw9RF/1OZpXMZVrGfPZM8Rlw9rRReanFTQVWDdSZitVHKYmNVNIKWCEFIYVds4/wNzAYfsByqaoKEVUSLDbRW6mzEYZpqwonymTBXViHFluOzBUw9w/zc/rpCOkLkOi+6B394l9Ztd+/LGO7R+9SO+Daxz/yhZ8+K/yrOrxprNfSvK2f7AH6BIRfuM9wl/+auFdvwpf9Zfhx/7THyygq4joaJ0H3vF5PPgzb6f6mgXW//lzXHzTzzD97xyf3fh8fvqlH/xts7sADh02HPkb/4Y7gxPU3vW36K+t/D70fj/2Yz/2Yz9+ryJbSMmc48ZyhfVNgSwly4RuNgA1lpw1qhk1U+asPocSBYFCrEKl3gdIhWXAr2ADxIMUEKgqpCy4sqN/NEaq+MqNgaL5WEL7ATNOIDK/CixaMG6IKEcYKeo1QedvzMtLkLm8AtgEoKWLvgbCzfQSV1q3RniBTd3IRJgA+nFIb/UhDt2qcvCliDlt0EphY+9HciTI/YfyjgXXHKiAhQNvYKo0O8o89TCXL7ZyI/2oRpp7deqsj2kpgpHERYNSRCFEqWePjGQ2icGhC5sTv2sR7wuGBxR4bUrv1NAf5yFHspDSnTdkBSsuJ0y5+gSlg/GPsQwZ2L3L3IHrEZYU6kwIbu9nhZF50cz2DuBidrMlmoMtnBbShhCmBqPhidNHec0TNVBQzg2twQNgF9Si98Fx4lf7Bxmt9yeEQ0tltUT1TpnD3QcwOvAMijyBtmIJjK+klvZSJAly6ZRB9xzmlqZxdQ7TPEW8fJid34i5+t+HLP3qOuGldewLq6j+uOqnUhozicZV5/aiCDljb1L+JGiuxtd4qv8yy/bOOLEuGB6BBwGNinx1MBxqwrNLa8/YcxYudm/yvu2PsxHvsDNcR6u9u2/UxsBX0WmlyZkxirKqjwBh5QRVeFIZUP0eUdZGcokYaMKcr9CggWiIM8Xp2UeYDms+uRONUm58OAUTEzee47FQXquAyyiXvbG+nVK0D+WyKa1QaFy9ztqJdXZkEwQMBqUUCkvLbXO4W6LeAJMNeWCQoHotKrXnGYYeVWy4wzzWeIK5mWAEZo/maRRgisp6cUb08bVxNbicutF57zL9Z8Zgre0LrrU7+r2iSzhTYbX2mJckHi6NAaecvnfzFqxvaC9hVXDrwxZ5KWGgptHKYGO/UxdEtFrQvOHYfPf2CHUPXIrrjas6erDLAiZP8h0nj2sOHMwwMgEsWJAxUZAwhErVg13OeQBndLyJUP+wpX6rStA19FSHxx8D0SEo5UFI8PdoPCtqOjhGJfD3HKMCwpzN+MTUWeaiaVwmdF3MnaTJbtwDG2MPpsyfbXC6dpxJBD7TVTJJcnRgDHZljw9JTg/ITsbe1in377JOcGf7ZIe20JFBS4a1QjCrGIl28/uelOvjIoz5XOzETQZpn2G8MgLKunTpdGEwFJZXNLdu+75Awc5KCeJVSqrEI8HjVFQVSzKq9KtD77Hmvblga9vRGSjiQq+dwcHWIrMvplibeQwb0KrK7GzRPw92FXcKF3jENFYLJNEBBnoBp0AZYWSarjRBuYq2w3z8/HVmMosQ4Eyu7dUKpyKmgynP7MqGhMOx+aHL5Ya91k8RuP749JSHBFFMKgnTxS1YQ6PumcmPPVQlK9s9hvP5pACg1N0CgWG4AEEJi6UlTYYYL5EWL1cV5RhkXTbt6nheFrJTDVo1/e1DqT3MrlH/8w6nD8yPbAWsFTJTxTjH3OEaqqEwubRakyE7Dh1FTH96RDI94HL/l3mh+atslec86G68wT3OkqXCncuClQSJhE8l9sGu/fhjG/H1Xdb+6ceZeXSdw5Xv570PvBoLfPYTf3XPdlevCX/rbwv/1z8SDh2CH/4Pir/29ZpS6Q8e0DUZtTcc4NQPv42zv/ZFTL39BFs/fIEv/r//BIdfXODnzv/w76jNc4/Psv25P4CWlP6PfivZcPDJv7Qf+7Ef+7Ef/0vCTTvSRLCqjODo9aC1Cxu7g5E3zMI8zDQstbBCXS0iAv2pI+zIyZHfjo5KKNzYZBcvtVIzmviJmOG5AclCCiVAaWQWbFljD2q2sxWW7B1KoaNc9kmEkaGXhYXlXC5TVFkS0hTa5YcYlA+OjsMonW/vV3aXuquIE65eg/bOmNlllWIwEIJIow9a5qYSKtU8ibXCzBy87oGcFZXLPADczHHs3LnR/sJcSqmVoSYVJChBbRGbg3K9eIP29e3czDhnAmnNW+Y+HZ2VvDdKvgqtksAzCO6KME+AS1TQFS8b0yj0tMGejsm0obCCKsAuQqF5pvA5K4bMcUe2GKp7xRqlkkZNadzofSVPlnNwUOX/syokkV2W4qc5v/N+enYDSlB102jjGSPgWV5hbjoMUNIN7HwtB7scgsL1vKE7A9CFXDI/joenH+F09YRvK7RUyp41sbsmXL9o2O1A2ZQ4tn6U6I6ggohop45uKoIUVJyhkgyVWOxWh+yaZZC/hug9YhXlC0PeR6S416hZaEuLoYKO7I4rkubz6Q2vByeeaeHNmHNmF2PpqzEemN1JPTj64u4VLvbPM6Q/2r+YkEMHQ+bnRmjnqAdZLiGMVGU0Tul0hgr8eRYt6A1N7XoZ60CJRhSE4o83lJAFs8i0eZhQV0dSzdMPamSPPmpUCo8i/Zu+6TD9CI1DK0Fshn1Is5Ovh2qNZxvmiXpb/DFGOvSSp7zNaEowWmGGu1R0mSlVRaW3iXWPgACdlWlYT5F64vG9qaeOwtG1YtoDwIOIBKU9VTnT5R5iHcmOov1UQuf9GyPvp7KKMGnGwO4gpouuWYYyZChDkjQhdil9OyR2QxI7pN2Naa/HHvtwFqMDstu+DzYHS+S25crLMirCUHJDVG+8yOsxnJzZhWJazaCdRim7R8ZIJtiqJakr+guaQwcVc4f9MVrrAasReWk1ByfzIVqR63tyDTtCX4pzClpFHKgf97tyKaHxDDWjDBVTRjIYOi9rS3UVN/sg9khC9URAfbqcg1KKMATlyGWZeOC5uF8U8zXIvRlTKG2FxC6lHbWID6QYDFpi0hSCmbH0HcDNHkaXSmyWXw1BeTT/M5dhBlvQehcdPHgpUR0nsNsVup3SCBB0TnBaEXiHSXQOmgYEWIkn9qY4fAgOLOSAoDjOPgzzf0ERaAeZMJf6Z4B1KQ4h1Y56tUS5lDOE87om3nAdwvxmrTIYVo7iJPAeZsbfCVzdYhcVYRh4ADT3JgzskCjpYVUVW0ijtUJUwFR0ABV3oHWLRu/OGPxxQ+LkKp3kZcCSHU6QQMhcyktXf56l2+9GUdz/FfW6lz5W43XKaotO7E33rXOICO3pNruqzfDOFdbjDpumzPbiLNtZk7Sm0Kf+JEfMUc4EZ3gkfJQnyo9ysHaM1CbIUJiZYSTF3osa+Yq9CDSTXZayJe6kSywNbrCRXOBa+FGupZe4sXOB6zsX2EgusNK/SHnRgVEEboCdq6Ee8PM1mK6iS4q+dMgkZpgO6bkWCjDGkAQNsEI1WUEnkAbJuEjFJ4l9GeN+/LEMccLyt/86Rvd58K2/wPDP/H/8xo0f5ejUAzw49yjgS8f+yI8J7/wlf6H/7W9TfNGfBvMpXlx/UKL80AzH/9VncuBbnuT2N7+Pr/+vX8t/k5+j/+hfoRrWP3kDd8XDb36AZ5f/GZ9x+xt58Qe/iwf/1j8dV5/aj/3Yj/3Yjz8woXKfKqcjHEIc+9X7LTdFoZMqWB+B1qxX34Qpb2On6mT9G5SXpwFBhyWUc4jO2THVvPqhViCKobffIgoV2mlkBhwBx47BxgNt5q/EhJEizRRiBC0xmSoxt2AYroDKQaR+6Ks1WVMlCzIOHtxhfR2UNoDGBj4hFasYJnnZdCeUIm+0PrQwHDiqc0K2kKG3g1EimYnFhDDVyJklokaVJGvhFFIsiTvLwdICVVOhFjV4pHKa7lSXy+3YV27TISvDi6Mxnn6hTilU1BoRwRu89CVLHWaY+zg5gxOL0tCYAIoOH1J0d+FAo0qSe9h48Y/2Vf4CoRcLNedhlbqpM9SCmqqjyGjMpcSroe8Tij3lGPMoVRS0QMQ/o7ulE9TjW6hC5SS5NCuocGNwBR0+htMRS/FHmS8FlDt1Ut3GaINCoTVE2rNIynqG2eABEuWlQUoUye4QafnGXQvKncBLYvOzUA7LOBUwOBozvV0iKCkaU45KLNQiodOBqYYwr2bB7ODKkffHakR0zk2za7ssVP0cSVodtp9xNHA0gJIaEHYyskcNUs5wEmP6jiBPrA/OwZEymB03YVAvuNRhTAnpdbGxRVqCbrcZnE/RwJF+j3azhOpHlNtCpXSHUjcisUK/nMGqEG8JWQVMTuEJ2+LlSIMI5zQSlhn2M/R6iBJBjEHZCKyQNhMqYggj6PZC72s366hjgBRPWXGUuoadFIJMYVCYHOzCwYJZYEvNkaYDBKiUHLWKoaC7KKVGYFdwRMGqb9okHig2boBuv59r2SZhNjZw11pRior3O8WsmudsvU5JRQxRKLEjwBTARAocvHbh0+g0nqOnhZ0hxKUypc4AEaExbZif82zCMABlQsiNp8VMvEvqEOwEaATYVkK6njPULHReSNGhIm0ppldDVmsvcuOxl8m6y2xnmwQdQ9Q5zE7aYu1gl2HsKG0LG8sDgmiVkosYupOUdRlLwewqFYeLKIPEhb+aHssYFRwoHWancxNRAQvBKUzaRDtBlEVLljP+BLGwY3ZonjmOAmzo0KsOoxVZBru7Y3BL7QpiclA5gLkD7FFiOIUHWXL/KSdgxHAymGXHHudQ/Tj1aJqd/iZ1U2HbtXCJEJ8YIBuZxya0B9aVVky9IUKtp9DO99kTXDQGZPWI2iNkVjjVOMCOpBy4cxMdawbRkC26GAwBIeKGpClEjZzhK2DDBlKqYSJBRGGdQosDl5E1b6DTLpVrY+pbraEhDuhaqJhZLIo0FXBQMlMoPBhiJsCu1PVHOIzSmqiiKOf3Q60tQcVQ3n6R2cWzDDMwqgDCMrZ2QOqLBENFgc0r6zV53a4f6mDOQ+QmsegKJLaPxTFbmqIeL7BR2SA7B6Ud48F0o1CS0Yhv0jMBqZpBclBbjEIE0tIx1O4tMrysfHpwkTiYYzH+Na5kl3IwGTD+Hr3jmtQBZRPU7hILc4d5+HWWUinEiSNo3eZAZZpeOcFZg11w7Bzpciu6ycymz/Wym4YYwx00hoRB4wT1B0/B7ee9pVfPISVFFJSIthQmczSmQ+JBRrNXRumAgKLqrl/g0EAr7ZBKSokSSgVoZTAmQr05Qt1K4HyMVpC4Dl3dwQYlAjdEQkMwHeNePc3C5x6GzXVEJxTwVCoxJQVDHXNxeJumXaGcOVxyABfKXV6brxz7YNd+/LGM9vf/Ar1nujz4RR/Dft1/ot04wDMf/Dq+9Im/wc4OvOO/OH7+nX7F5Uv/AvxvX6VGpVX/sEbpwSke/Kk/yYWveidf9pN/jvef/K/8qb/x9b+jtl7zpW/hQ//mW/nM+Pv4yI8+xuNf+9W/x73dj/3Yj/3Yj99tTBq4dqIjZMkWcXAAqwwyWtP3YZRhbhbagKtGgEaUJnRD6vY2zm5RN3WOmoNIxbO8lMHL6GKISuCOWKSjKWuhKhqtIZ4BFwlx7Fffc9yFqZmAclUzdD7NdCpiGB4YdzgwBPlbqsmNrlvKGzR1bhueW7/FdPQAyvkqkBIIH9u6ynDx9Zx80xTtTlFy3YcTh9Kgtc6Tf6hkZU7NP06oS2MJmTiUUkyHdZxNMSlUoymM2iFLU/o6RVc3ccUrtBEGC4pGVyOrRSn6lEaccrZ+ktVKjQ02mDINTpXGvj1BoJifVRytLHKz4+VRnq/gXbCPnnAMzwtxF1BCqAKmw1nWUth5YpXBjU0a8iCCeImdKLQqpD4+SmXtpaN50upCDQkjI+gie0+oUot2MW6I0xGC48jU6xmsL2GSLmEO3FUrkOkZ5qInCFV5tB+dJ7ad960QSODPc4sJplTRKUW5DFP1iNLQkpWmieot6tYwXRa6DrYXalTLCcsHpqnHU1TuONLFGlple8yb+8dKdLf8fqen4ORUyGBFiJ7KSGZ2UDhOXJqjlXnGyOHDQtStIp0UycGurnSphVVUrUKwk5DVMySGoNujd2VAL9lFL/epdkJkGFJqD5mSF2kRkVqISxa1KWy4JqYW4WaOo1t3iDqC0oIeRhinkTAi7jv0tmdEiTaeXecE106pYAgDYT5dYC0ElMUUQIMWyOdu0PGZ76HGIwTHm9DOPBssZ4tYvBG21uLneU4VS1zKRneZxelTRGc1XZVgP6YwqeCUQVTGhl0nyeDmnYAC39KB4thRxdQjMU+/9GqOVQ4S6XXEgbXlMUvOgUSCPizINpB4GZ82CuoKORgSugFuCCgz8joLQy/RU2lBz/NgOoDoYPR3XTG4Th936UVIU4KGwlQVtic464GQw7XHoNcgKM9zeuEgS0vXkLREv3ya6eQ8JQU7jSc5orvYoEc5dCQMycwM9WAGcb6KeVYsAhsPdtWM0MuvzUlOYDn1vldOBagR8KJBWRQWo8GU21x0y7Qif62YACTwzCGtFe1ciWnz+Rj0NNm8w7Qjpqcs6XTIh2+/e7xTlct2nQcvxWlm45tUr60Q7TyIEDN3+gBvO/3FBBd/CbWrEQu9eg82vBdcUaBAKYVEdeZm+0RDoVKGuC0kDZUzEsdg12AAm1tw6LDy8ljnMIScCI5xKIg4c8oQBZqLNxOyzN9vinssKM/kKQqEZIpSIEjcJcv6ewq8VytQihRptEhmMkrBYfoKOh2hOg9GlzB0fBGAvG/1coAl9VVMcyaiqWtqiWctH512oP2Z06GCnozkvUJGIgGNQ18JO+MqtcoqlFOju1YjmKbJNio35F9PXoYoJTysWJybZzWMwWjCyNwzj5P0GDowOPE+X8Xzzym/aOCmLPHuIbKszdTGLkPVRztFFE48X0oHKMebXnKpBeUsOukyPZ0fRzpAo3jz0dO878INxKbctktsVfpUqHBSPwBAt3aSJJ3h1aenSdwuSXOGzPj5IJlgr2ekM4rokRJBDFZZGm+YI35fE6srpKVZGizT7+d3dOtQSkgkpaZrHDAHoHIS0jnq5Qd44gBIvMvmzW2GIajwJVKTkEYNQrtDZJtUkxUe+rSD6CgvTBMkGDWDlQSl/DNNjJBZQ+Nylcb0DKvSI1Lr1EyFTyX2wa79+GMX6jd/hJUfSJk606X2Pf8EV1vgfZfegZWMmx/4Qr707wvWwhe8Hb76LysOH/7DDXJNhmlEPPqTf5bf+NP/mgP/fIbO61dpvOHw76itJ//6N/Dcv7jA63f+Gc+86xyPfv4bf497ux/7sR/7sR+/myg8sgBiM8UwPECoqigX50gVIzRIK83cjKINSDmk96p59Ef8hw09ZFdSIma8dKNm/Qt9STGMFXECtSpIXaEOaE5YYSkzDApFjBYPJinP7JpRsxyffhhvApahlIzlM3mIURTv+5EuAzG7dIAa3AyJZUAaJmAlZ88k3JKI4clNnji1SPs8pI8M4Gq9sGtCGy9ve7RxmupsgGpU6PT9C3P3Q97keY/7vU0YXhug6jVfaS3LeKr/EpUgw1UDen2wJUf3uGJuVdB9L+uwebIT6RBlIqz2HjZ71qEVfNrsEzSCKlp7PyKNzpM4oVpVuLKQ7IDTXjpqFAiWjrSo4CVwkqfg01MwrwLWt8fm1GGoMBM+WSjoVo8itIE1BE0czDDQM9TdGsYNSZliNniAcnmGvtymtH2VMBjCwimCQHFkwWCVlyIVYEohydraSDm06E/apJ+LcUU1SM8IaMyWyAaH/ByMdlFKI0u+rZ3wALfKp+nyHmozlqwPeq6Mojuq/AbQR+gdNig0U4cc4VSVwYoHIEqvn6WiDhDqgyzyOJf77+LQGUewMyRohiSxr4J2I7tBrQrmwBTdm6tEh0LiTDOcPsHqEy+w2VuheQxc6xBx5+PYqItkJQalPsGRaWZrJVo3hfXBJo2FAfZIGbPaY3fDcTgEmR8wWDyHat/m8MEpPnarh4h4IMdlZFborXdo0acRTHHgmZSWrjDQ2yiMZ+Hk1dyMhvq1Ct05jTLBeC5Z8fIipfx1kAk6KuRwvnpZ6jI2+8tsJtucSrzvlwvBpB5kztglr5dAZkpEAvPzoLY0gdGcWjjF02wTqtpo/LM04mHzOFvqaQ+eGsEULP/Yg1TlcwY9MKihotK8Qvd5TXkh2wN2YUKIc9THyfgq0YGnb2UxtXMR/fdchyj14J7R1B7Nk+PKLPbo6wjftUvlhQ4z6xUOPXyCvm6ROYOYecpRg6qO6B87R+vKMpWwxXS4gxjHI488yPV2BVZfACCJZkZTFRQqR481uW9QHlHmJZ1OBdjDNVR3B+0USlmUWLSBXmWdTNfoVT3IHRjQoQFxI3YRQJYI4TOWk9E51g5tMti2uCRDah4ke/ABzwDLOjlBT/BtrEH9qSXkzb6tbCcmPGqJr7SoGyHohIDQq7cJtC884hF/z7W0J95EdPwqB+1L9PIKtllJEQLHg4fY2h5SsnU2ex4I7PY0Wil05jmooYSgIsqRZ4Zp543EnQpJnnDESylRonxlz5y8lTlNKengsv4eOe/cLGilcArq9QXmZ95OahR9FJ0OBAkoE/ilABEMkWeqNmLYJQd9/XVgagqTaioVRVRy2GwXQlAlkPao9gOJ6+G0RmGIX3eCh+dew4Uf/zhdNyiwKg7oQ1SCGjtmK/+WnwTt0wpjDI3Ha3AdqqUaKMX8PEgNKlPQWg4RFYzM25tN6DaBRSCvNigNizVV9HoNWnMMnaNi/WAJgAFnPNuwbCK6jRrgJ4J1GZgSkvZQSmHmzlKZOUvae4o+K0TGsGhOETxYIr7TIA2PYrKE8nQNZwaYUuD5ZlohuQ2nxILZNZSaGlexBIt1tGoiKKz2/bhxG3Zuag4cDNGJkElGqIL8dI5X2EQAo0ZM2lIYkeqEYfkYJVlntv8ywcEAffAM5BVsnU4pmTLD/H1Ba++ZljlFAMx35mhGq7gKvKb6BJ9K7GuP9uOPT4gQfegH2Pz+p7FJiUM/9FVIdZ6nPir80K+/A7fzKB9+1xN8/p+C//KfFd/xd/UfKaCrCF0JqH/fo+xMN7n+db9OcrvzO2vHaI5/4//LSvog557/Nm49v/R73NP92I/92I/9+N2EALZSrGJ7oKBs6ijrf29VH0Vq3r/EoAhN/oZvFJRLSOC9pcphheP6JNPVGeTJKkr10a+OUHVNmms/jAFfjtz6MvU6N2VH5ca2hX+REKmIIGx4A2zx/l+C3mN2K2b8e6RDFsNzEBRu5P6ftl0H53AO6jMZi4uaUyfhZiuvWhYWEqRc0jKYWOMVYbJomuRVEMunNPUn/HaquwFZjEsUWmk2d5dxSqGSsdGOBxM1roFnTWVCNnEcWgekFT06/nGMTZxN/jquc26XUx4ciireFsVZcPgEWcRhcsrezELK/KJw4ICiUdWcrBweDc50A0xg0Aa09mwx0VAqHSYqF6CFJgnnUGoWZczI1DsEAvoYFRG0AsrNMmrNEn0wI3rBs+vKdovZ/osEtodWiuaOZmXVeckRYEUIdMAhfYQyjT3njSAYga1S8kwwI6DqmrkDhtrcFILDTCuGjxkwBoWhUhbOnfXt77R9X2eCkwQB6Gk/Zm5BERyqEh2u4mb9f40HNW5BI3MWM6d5rvYsL9SeJ5222FmLPjjF2qFzbEfbbLlNmtkSO4N1DjVO8Kr5z2MxfARB088JSAaNy1lmRkN/aKll81TDGhLV8oQ/N/5RmiLdKhhujzXO8PjUGZSCDbfugQsT5FsIKIdWkf++y82DRhRFhWjDqICb5JtNlbHOkKmQkmv6Nt1gbAgvfs5f2b0GSlPJAZh4YYp4wY9lLzqODb3UViuwpwRzvMKRqZM8WvtitCpAS5COoZ6W/DXufdoxhQwxN1IrPHVU2VeutANhcLFHYPDVXY1AUEJluTGWTIJdvt9zZ65SGl5AOW+OLiIjwzuJqtiTb4aghD7pXbuDVp9sKTfYjy1SDoifPJrPP0W3UiN3JMcYvCfWbJV+dIxhuEgaTY3nqhrfg3SoR9iMKCbAKoM7MeOla04hWBTOA1upL6zhciTx6BEwYe6pNQSSDJKM8ragm0L52AyHHjkDQY4O5eNXqypvSq6EXh/SNQPOolcdocuwLefniBMGL2wRX98l3XHMxjOYska0g1zwLCqXMaIgiKh+2oO4OYU9ks/RABp6lp0bh1ldhemtR2HggZl+V7MdHkVlvnCB1HQxrDmG5sFVUQHaGGzFM39IBRP6Y7FOsZt2udS65MdBQj/Uo9ujUAoCqmEDTUCUT7kp9yhV3cC4BCWOkq7Sqj6BPvQ5HDsKhxYZn7eSGV8u4rjWfM7vqw70HEFsmZ1VZKRIzgZLxDAzNU9NVf3xjR41yjOfAmGsLQRrFEZpIqV5qH6K1x79LEBTrSpm5xTaKF9YIO9SfAA6J4f083Oq84qLru6fy6VdIS2XufrqPp1zfcwEc7gWHmYxWKRkIqRxADEldLyLbF2GQROat9Ao1KGHoXyYI7WHORud41VTn8aDb/wLDA8coVc5hsqrvuipCGUUc0d8/5qNJ2kH50YAuny8j8rAaovoYCzp1UHuHQkiil77ILvWX1uB9mDX5KKVL74wBrsqUUSsYobRIqlpgEASTENldjQBEptQK1cwKhzNK1uxbJ3VDI7GaKUJ0wwx4tmDn0LsM7v2449HiBB98PvgN3+CtRe+iakvOc2vXVngv/4T4Vb7ZUp/5jne2Ph/+Y6f1kxP/9EDuO6OT3/sT/Lt3/B9fOW//hJu/dX38uD/eDumFn7yL94VpUad+C99P+anv4zSz38L2wd/gvlD1U/+xf3Yj/3Yj/34/Q+BrJHR7ayTiU8oq1EN7fwqdWbqiClBV6HFEE7nCY/WKDTDhZTGFhBCqEIcMCgfoz68ec+ujM/rsC7FEgJexujby6VJSo2kJgQlMDEgiPOVq2ZmoF6HO3cAPU40FUKk6gwKMC53t4r1gErap590qQYpM7MKqY2f4RIWxsogoUNNudEyrzgQde/zShkhWjDMvdWw855W/kdNNWrQcduIMqhsjD2I8blzaVFBF4IgZGvu05ja/hBbm8L1DcVwQftDvXtfxfHlPwQqII5O0K9nzAFhRUiBDMHgCLTgcKNxVYEl0AoywCmC3NtsGCww9TnXveG99swrhU/SD009gptZ5kY+P1CgVYgKQnTiQYLPPXmd8PYdqsOUuY3TlFoVTBRjrRBmlgEQZH6hbLH7YTQOsRqHJct8nQInnulTVVVahWyyMLzWepxYVSI0GmXg8T8XIrOaI3OwbPxc2Nr2CafO5W9Hj1kuXVYkNkEToPBVMLUxxG8OIPLbT+ZBRw570EgkT1jzGISHmFJrlMJpRPe5kt5kYB3NuMacCzncOEGzV8/bGjc4Mz0BdgUeRtja0MyfDZHyDCKdsTm8UnCXr2ndlCBP8sEzVZSOxl0zGUqV/T6dA9RI0qtzL6XeYNwfG5TITs7hbrfph0eZU7fQq8+jVUIaTJEbt+UD7q+1Rvk41dIxlhp3SNUF7HAWEjOSWRkN9gFFcHSvh1dxSPW+w+z02ToGWM/YLOcMlOENL3vVRb5ukklNqweZnlsiPBpiD4VkGxmD5jJPPtrAdYEExPjx0SWF1BZxh2cR7oBro4IIGE6eSiiZUb+TG0PcszFBqJHFkO36Gxi5uhcKMxlX8iRQDJ58kGz9EBLm4KADFfh5pOY06lS4RyKslWIQHiGpK5SK/HWUQOniHcKNOfRRje5583oXwAMnoVpV6IHBAtWPpZTVHQCmYoucVlReN0883BoZn8uEDN34S5s0g3TFUN9OcZGXC9othzkspGt9dNUPevdFX3by8PQCN6RDaAw2r5g6SXUJpkukrwlIdx39NRjMe7mmQpia8oyr3W7ErgwItjXtsJrf3RVyOiiKDlIUQnXOM7sMGld2SBvUEArVs4iibbuj/ceP1BnurqJ2KqP5oVVAqaroSkC5osgSIeieYSb+b3R3b8FUjZKqMwTmp6bYDj0bzZ9ehS6FqPyZkrkU0XkBhIMKXoCKFlxDEQeObu4xtbgIphp4ZthAM9QHRjJ9pRUukNxb0s8jF2rPZHSWY5WDZEGZwejGnHcmdL4SrgKxQjqTASWsBXWwhmwa3EIVG3pJceWAomMMtupARSAZC+UZutFpZgZNhrqfSxmNX7DaukzQXkFcCvWDmNCAVpT0FEqVcpAqZLv+eo4cuMPOmh8HXTY0Pu84DxtF6SasPl8iTkMiv0ZFEAUohFZ5jY9s9aHfZz3bpW1X2ZBrHrxKhJYCpRyzs4cIBwpVUnBmlqQzl59rRswupSAKI1KVcCN+H9sMiLIb7KZTNG92UEkHvX2ZRBuqUZlOfwhKE6oAY2Bh+rWc6dQBwZB6mfanSNnaZ3btxx/9ECF6/78g+ui/4+aVr8G6gP/j0pN89z8XyiV401f8JJEp851f/uf/WABd4CUNn/vWv8gP//kfY3i5ydK3f3BUIva3G1OnTrL5Wf+CB2qXufNv/w8G/bvr3u7HfuzHfuzH/6oYHkzon13yFfaAchQyWaRNBQHRMzXcs+FIyoRRKKWJDyV0X9NhWPJeWs4p+uGhPe0XrKnTteNEuoSzqS8jrwuwyyfCohTMnqI/c5gkmMPZkje8F1/hS/BMsJnpggE2kekJXuoWjH6lRJkkjNlIXmape5m1ZJNJQAIYM7tqQvrqPvqcHQFLkkH/1r2vwUrf5xnmMuYqB1AOz/awanTcooUw1MyeqKNDT1pq9wKcEwZDCMMZ4llNWLZ3NapG/S3keREl2rXX4HK5SG32KOiaxzxwBMozRwrewk7a5lZ/hYIBFKggN4PP2QNKE8ee2RWEIDpnzDV9QqdEEAWBKoEJUM7SMIc4YsrMzimOH1fUojwJ7W6BTai1XybMWmg84mdcTNzqoq1GZGz+7pxglKJWg4XZ/DzkZlA6HJ9bCUJQimBKe1AuCCmVFCcPHvC/F3IyNMeOwk5/g4uDX6CdLREq3zcT5FhKReVzdy/YVYx34lLOp+dHfxkGi/TnX0Mp8Ey3ki7xZPBqzlbfzmee/HxmK4vjdvIfqlWIAk02c5y+HbKZbXoAEuOrVgZlz3DA4XAopXnjwTfnB5s3Jc57ck2wWTxrSjBuSOR2UCo/KIdnaWnFdAOUFbTKQdvpI/Sjw7TrTwIQn1qgf/wUhB5ZOHBQOHZ2rpjEHJs+nYNdegzAKcXu1KO4aHr0e05qYjKb/OzPhLMPhf5cVMtoY6iXxQOLTjFVqnG06uk16dqoRKYfV+nTeHVIUFeIy9mcSUZ5MKD9vl26L2WkSy2mqkKpJJQOa8/2iLx/lpTqUK4gQd3L+MwEcFccXY7SBQYqjy5y5FWGI6fAztdy6lHOJFT59eEYAYjKKGytTHZibgzK2fxrAGXfdk0VxRnKaK2xgaH5kPGVQLUHgKLLTWTVYs+nsBSQlcCWPFHtLQ/+2dH9Z25RcfhzFkgfXGD3pCF9dUCgA5w4slMaiUAmvIKDu+gpuj9E9XvjqZmPhetne7ZbKM3w2fOvoxyUyMShakVhErXnXxMqOqcMtqJIyVD4aqmNhmKmHhKFirkZjatEvtKp0qPCE8oYKNdHp+TFCwFKaWwp11wKqBCcWKxzo76KUvSnXgsLGe5oStVUAMFoQ7WSM10N1FTC4savYJTnwCpxlFSFh87CsSMRetmhWx4cDN82D5GXwGqlyWxKUZGjMuur087O+OOulhKclFiYMzx0DlQUoKqaoGewuo5MMhkDz64sZONiNIEyqGGb8UkoTg4QVhlUD9I8Z1AK7HC8UBMnwJEGwy9+AHf4zOg+EC16VpwfmxAh4PCB14wKNxilwYCuLeKqc/45C2SLDyMHHiUI/EKVcwpxoNUedHb0ozIaXTIEgebsGcWBw5okxx+NgXJU4dzhKRZnKtTqRynrCKXnUI1pGmGFiqoQqgraVSiZGrOHT1Kbr6JPB3B8BjfnrxNrYeg03Z6XLM+UFjiweIKaOUg5OIArv46w+mks1o+wUD3MYmmOo5WDHKo9OBrHhWiGx2uPMBOeytmvcKb0IGejh/hUY5/ZtR9/tEOE6Le+m+iZH+eZ+Gvo/9Y07586w6FX1fm2L1ecPLfFV//3n+StD/5ZGqWZ/9W9/Z8an3fmS/mRR76H57/8Kq/6L7Dx/S9y8G8++Ttq68CbPovrK9/GZ976Xn7he8/xOX//G/7QVa3cj/3Yj/34oxZSSJyOa7iQy/TuBrtcQCOsMxdOY6z/QHRROt6iAuhUHqBXPYDUQjJT27OP4k5fMxVMpcZm0sZa56UWugCE8m2VZjA3yzA65pNqE0DaJ3IKq+coT/rN6jEzbAQS5Awxg6ERRayVhCBxlJMq1LoTvSnaAPVEhos8rSooHPu1IU2nobaAmYmwrbGe0R17DW6qhd65jg7AZaBsilZmRA4hU7ioAr0+QQRf8TlvoZwOkYMvErUNDsWWO8buQg395KMcTzSHdi684nmKxTOqIhWR5Gyl0/OPMf/YUZ77+B2G+jc4Xp7DSYfG8BpOZYAhc+PE9njtOCoZMDUFcS4ZUvjKX0oropLC6Ih0qkLYhMMVg5uCfhc0IZYAzZCyWQSaqLszhLkmtGYJdJ2pyi1cG2IzT5k+rR3BTU2znV3muszy8JEu7rYQaMXcnEIfhKwPlUfqOBOg5oKd4lsAAQAASURBVDVczU/trEErS+XBHIwI/fx68tAb+a0b78wVfIoTxwyDULHdX+fMabhydUjJTHH4sEJHY4BvYvrcN3quu+d3BZSCXLoXzBCpEoGuEuXowoixUpiQB74/PRfz4ebLbA8d/WCRVvnVHFSekSSA0xYR4XDjJDVqMOxN7FMR5V4+ACEGo0qewZhuebmpi/014mTEyvIm9WAaEJWhcSDk8socKpelLrkGHAHVCoEhanqBsFKCDtTeMM3RM49x48I7fQVtYwAL1rMTC1xrIXyIuaSPUpfwCi7fyVJJMQgD3OFXQcUg22soILhYRnU1Zw+dIDCaGDsCM4o2KxVHOKuJq4pkN2Ox08QchZkZIM0BhbgLyxdQ4qieDRhcMUipAez4ETMKVz6KVFOkMQXcZpLalSu0MI2A8mMHqJsQN3MS+/KYmVac0EoV5hpQyu83SnnPOYAnn4BgHVbWhEoFVIynKgKPhY9zslxiYDpoBtjIM2mUiqCiUFlMIhlrdpUH9CLmrKPdc6AVbzv7+V6CNV8hrXcwrypx5rEpojvC7rb2fodKe5D0sCY5vHcCG604GzxGQ1tuuuvQ7/OwepKeao4q6t4vXCIEymCMoWMHvDx4Bqrj8zoalom1hVQylFhmZ/P5mRuBlyKNRCVEcn+t/H5oj7wWnbZR6jJx4gEbg0FCYXjSEj5hCO44VtNniLvrHK4Vsl6DyxY8SKNyj7/yFEYHlCoKMyhh9P/P3n3HyVHXjx9/fWZmZ3u7XnPpF9JDEhIgCR2pIkhREZWvqCh+7QLqT/wK+EXEDha+oihSBAFRARUQKYYuvQdCGum5vn135vfH7O7tXkkuySV3ubyfj0dyd7OzM5/pM+/5fN4fRYt3PKTA7XLhN6tAD6A0N8F8i1OtPX985JxaS8ptoqwERqKTbKILO+KcXw1dMeMYg1SHm7Wb1mC6LCJhD7Nn6sVrlebV0ds07PxuYzs9jjg1u3K96QBsNHSl9TbBdbq4LOxQWNWtpGNd5NwdAMTM8qq9it7qcMkKhX+jjbdVo6ZdZ8L6WbTrNltcUayaOdha3JmD5tSMdrm8pBTk8tcN23DnO0hU6IYia2m4KI+/UfpMZpRve49fobI5LM2NpqVQSqPeXYXLr+FtOZbNU9bR+SoEQxGm6lk6MrDVNQmAsBfGVXjo9ncTj/cG6wBWvgObN3tQkxqIVOtUTLeoDpu8uzqBaWwkG4qgeaO0VilItGP0dGObftKpCCmrB01pBA0fgYCPTahiOjCXrpHRDfq/0BiY1OwSY5dtYT54OeazN3Dbuo9x302HgaY44fpZfO8KjXlzFbe/8gvSuRQfnP35kS7tXucxfJw07SPcMPU63O+rZfMPnqfjz+/s8vTqT/s4bwdO5r3+H/PIz/86jCUVQghRcNNNN3HkkUcya9YsTjvtNJ555plBx7VtCKoQ0xoWkgo5d4YuQ0eVVF7yPqFTa0ZRmk7m9XZsQweX04zRUu58knun9o2tDCzlLn7XKuQVAgJ+JwdVzl9FumFesVYGShWbMWpo2C7nN2X0Phx4vdDQWk9F1JnWuGY4YHrpgjj/NbkboNqk2WilvspFw3jn4+pcE0HTT8gd7S1bpfN2WIVtVD7449e92LqJXT+DnH8iaAbe2ZXlK80Xwqo5AIDQQSZ6fS1WuMlJ8p6zweXD9YYXKz8vTYOgJ4ppeFAGhCIuTBO6Dl1Iz+zpoBRVgSm4CxmaB5CxnIcWFy6UUrxn2vtoiUxFdzvraLx7ImE96CSFznWRVmmirhCT/eOK6zioRwAwTfDXak5yfKVomjOBmnqY4GuizjOXTGUQJ7ylodLO5jOUSc4y0K0kkcQKZ5IlD0RmjYbdnEFlEk5gwO3GxiDtiji5VzQXOZyaQZurunitYgXJhkwxT1kh579y63iadZTqfbK2ohGM901Ar3ACTrbb2W90zcCXD3yhVDFQadk5PO78vqy81OcrGmqq/JFmsAchpUGNVts7kgK37sw7Ez6ATcFDi7WAyqfjTN9lOPPqyjhJ1S0L0nqYnFGBlXWBbuabyOaw8z3a2aWRhPw0Dc0oBhzqvIei8vmhLM2VP16cAJ7tT6DleyV0ymKTCTYzafF43NEo1VUwY2bfGo35B05XbxTLrDQxKjz55l0aVqWznjMVLgptwAzlJWqMx1TufLBwgMdEpfAeWEPhyVNrM3pXpUZZ1MTrh3AYmpryX9UBO4e9rpto1KmBh+YcF1rXu9C+CbLJYqJxY3KjEzgIN6E05QRHQuNQ+ZxdpTW7cjlILhiHd6mTQyg79T1YtTP6lx/n/BLyg24Uaib19mLqMp2AQU01tIzPr1evsx62tfVOQ0NzaqaCsz8HNIxEO24Tcu4YcXMb2ZoMWZcTKPLlI2uegJ/0IgMCer4silCwtyamZfWtAeqYXjOfoBZ2xlEm5mtpzK3t5AKFlw/OunBP8ENsa/F73gnOfNyaAUor1hbre3joJZs6p3I01uVwuRS2J4LR7HRA5XYpbJfT3NYo2Tc0XcM2TJRyEunbSivu25k6C7xOvkBNc3KuFXLXxRIausuP2w0+w0MmUAuaC0Nz4fWCpnSWVh1Onds5R+s61KSqaOoJlS2By9d7blVKOdWIrBwq2UkyvgWlslieELnmg9Cqa/BOdmHVbkGhCE51Y5qq8GUnj2RW4dlWUhPKygfTlY1d76FjopMD0Sg9rosHAL3HgFLk3IquWQZdNYWm/IX11jv9nkaNzXMNcCtcbgMNjaCrmTpzDi7TA5pG1ghA5SRsDVyac9193rRItSxxZpnfHrpLOTW78jUTC7uHP1SoMa31y3Xl8Tnf7fRMxY60gJFfnxagGwSOHEdqbhM2WrGGYfoA58SrNMpSCtolCeo7O511YAfczJpvYETcKKWwwl7SU2rINkYwC5uuuF5sdB2a3Aup9Dbh1t0Egoqc5iXlriISyY+qD36O70uCXWJssi2yf/4fzBdu5tcrzuOhns9wWOJtqj80mUkLnSO/PbGFv7x2PUdNej9N4YkjXOCRccoB56JpOv884zH8h9Sx9gv/pvPuVTv8nm3bvLLpaW5/+Vrufv0G1netBqWo/fjlvKMWc1zqGzx522N7fgGEEGI/cu+993LFFVfw6U9/mrvuuov58+fziU98gvXr1w84vteraDUOYHJ9M3W1S6kwJmEYCq0QfEChijfszpvm9PQ6J18WGjH3OJLByeQKXXwryu4wbeU056uMOvf3utLJ2Tly7t7aX8EghCL5PzQFSpGcW49nWVPvDa7pw+t3YY53XqeHw4pQxCirnZPTPLSEK6mfczLVkw1cml7o0ApDuZkUbKIlOL63bO4wudoZWFVTcOXfNgcMn5OQX3dhJfLJevvkq9TyuX+yk47CmnEM+rTp4PI6wRQLjLcqsEItxSTymm4Xl0O5FLZm4HXbtDsJTQBoaN5+Qwp300HOalBujjoC6uryTYu8uvPwYCjn7X1Js0fNG0UFe3tTVr3V58jVaiytPBCAUEMt0WqNgOHDsFzONtBA22yg9Ti5X3Rl4s/nOvNnNjmTKX2O08HrcaMyCXQNtlYuosM3g4wepsM3nZh3ClbamX/acmrJpKuz6IVqUdlC0fIP9aUPiZpCCzf3JngqBEnzywL5/VT1BrsKXMpb7NmvtKZK35xdpdMy0AkpJzBno3FI08nFb2qah4wR6fM950er5zCmG7MwDPDq7vwHNj6vcxxoyiAeywcUbKfZqWXbKKWVV5spnShQXaPT7Z1TbMbrFARiZouzLVu6sUKFXi/BbdqMn2Bg108HpVFXp/CHyh/ntMIurbuK+6ZHN0tqXCnwuckcOgHLZ+ALOcG3WnMmLs1HhaomagaLx3dfrmovgUXOeprkb6bJU0sukX/iLVlWzdAY16wwCzUNNQWuPnnyDBd2sA5Q2BkbZWdRmkb45PH4FjWTbT0B3EFnv7VsJ+WGUZJ0Py+bBQwdl7sQcDAGfhpWTpCtZDdCldR6KRTf7VZ4xgfxLTZRngGmkx/RbYKNQrkURpOFNSmNd16K7LQ4Fjl8FUEm1zUWv+YxBs9rq5TmdO4xyGeF/SOtO4F224ZEuM7ZxLZN6Lhx+Gs2Y8ScXGDhxSZmtVPOQEuA3AQvuSYNq2oyucYFZdM3DMU038louGjQJ6Dnq/9a4UZMT8Qpu6mTMrLYgE/rPccrXXc6CdDynYeWBDyKNc7ywYliJwNA1tIJBg2OapzLAcGJJC2n9pPb8OLNn9t1zS7GbrVc/nuvp7BLQhgN3vHOelng9M6qTBfYueJLBFTaORY8EacZq5WFoEXqMAtCpecbDU0rD0apfJNXLOd4tyMGqQoNW2m9vY/mxywGsgqLn99guYDCLiS2zycvK+uwRClsI3+saU5GNMv0oat8JwWaIhGYgvJXgqYwtEIHDUFe6ng5P4l8sCvqJmfpEHJ6Hg6HFUsPhapqraxMpbzhQhNfAxXI59tyF5o1a7gDOrant2dJwAlYTXaaedvJHCjyHXP0XutS+QrTiw/KdwRRwqr0M32GYt7c3nVfoOsQNOoJm871ze121tG20AICNflm2TsRwZJglxh7rBxbf/tNIiv/yPXvnE/ovV/kC42vg21T/emZxdGufvzr5GyLD8/90ggWdmRV+mo5cuKp3PPODQSvmYFvfjVrPvcoW3/7evFi1Nc77a/x6b8cy+fvOZlfPvUtfvzYhXzk9kVc+PczeafrbSo+9RM25Say6J3P89q/XtnLSySEEGPX9ddfz/vf/37OOOMMJk2axDe+8Q3q6uq45ZZbBhz/sJbpTGt1gl6GN4RXr6AyqlEbyjf7yNooXccGzEYX4ePGMXOhyYL5Tm0FW+nkXKGyagBNjZBy15J0VRWHFd7u6ujYtk0yEy9+Vl+n8PV2/ufwuFGe/EPYDAM7H+DwzanCM8O5mS0kWnbYTJxkYE4/jIOObqKiSnfypbjBo0ecN8tKoas+QQXdxPKEafY6b6FDRj6vTLb3gVIzy7+jh/Ovml0eMNzFv5XSMFZZaNtsbEORHOcszBTveOcpLpd2AkS6C7duF2uKLJwPzRPKa3XVuJ1lLAThpjYs4QBjBqYyi81pAMywSWpWI5snnUoq0IIq9Oqn2U5+KDP/4KwMNMv5zKrOkpvQe3tvKw3NXaix4DzARoI1qKSG3zTRMjB7hkEw6iboh7rqPg9sgFU1gQNajqLJW0fAa2C5e2v3TZpRQdaIFHPSJLOQzTi5bfTJTpTLztll01RKx4oUalM4NThyTQvI1c9xHkoL2wat2BubN7+/5OzeppuG8vb2AFjy8KiUKq7HaKQ4tPjDKGRxUQqf18DUnAfQoKuavopNDbUAPuXD74cp/hai3ioqXGH8PsW0Vg2vx2DTxny+HbukZhdabyCvWJLesnp8Bg31JuNb8jm87By2Moh7WsgngsLOB7sUikhNkKDX6tMsqfxxzl0MzhjFh8iw5kXb8EK+AE4w2+uBWTOgrqH3uz4vtE7RibrKa8/0pVU568qjuwmbQScg0Kc2mNIoHtu24SE3aWn59vXm11ewHqtyMnYGFJZTM0hT5TVQdOUEuqx8hnuAksBQocaJa0d9LSkn6GaXprYqmU+h10Cn/KosEFY6DZSLxiaYNKm3to45UZGdGMKqytJeq9jq9TN7WhMnHrSo+FW34URxMrn+PVZoShv03lvL53hTU1zYSsPyVZDSwiTCjc5Wsm0nUJ9NolwKpYPmAVszyDUvwrfwOLKtphM4N4Mob3iAeRhM859EjdGCYRea5ym8Lmcb+k0XUW8rFUaUgBboXXVKYevuAWOLhVpcPlcgX7PLIlvINaV5cbs1PBVTcBk6hf3No/sIRxRVlRDw28WXHv5WA6PS7aQBS/fOrMpVS7S2Djvs1FpSLhfkSrraVTnQzHxNQh2VdpoU24FqSvdxG+c8WeDXKvDhB1ORiWSdwE64Nximl54kVe/LjULgqXDsaRrodtJp9uvz4FbB3nH60l1sCS0j1jqVULMHw2s4wfTC6tFA13Ts/Cm4Lb45v+3yOevCJl1zmsn4enuk9HiUU5MaCFf0n2/owN7azYXjyK4YT65+nlMkXVFdBZbSy7ax7TacGHQyv25syoKQ4JxPgsHyHWP+PDhwLjQ2KDzFc1X+Z8nhXWgma+SPSbv0PKerwVru9iPBLjGmZNNZ3vnp1xnffid/6vwsR3zrcxxzUJq2m94ketpEzGbn5PzIqrt5ZNVf+di8r9IYmjDCpR5Z58z7Mjkrx41v/Jjxvz2K4BGNbLjkKVaf9y+Sb3WWjfuftY/whXtOoSOxlS8v+SF/Ovt1bjj9ST658BLe2vYyn7rraG58/ZfoH72GmB1mypPnseHFN0ZoyYQQYuxIp9O88sorLFmypGz4oYceynPPPTfgd2o9lbjyN4oNE5y7WF3TCehgxGwqcy2gwKV70P06yuXUFIlGFAH/IE2YvDDz+AUYkxahab21aGxsarxVKKVY3fFm2Xecbtt7k/uqfIp1lUniqtCw8wm1ATyTw4SOH4fm7X2LrPJNGyi5L3brbqZUtBLxTyUQwIkLDPQAoTQaPNUsqTyQoOEre4M8ENUncKBH3WV/W0FF+jCDYFjh9UBLddCZpjuI0sF2BzGN3ofwQIB+JvqaWDr+RFz5IIih6/jVACMCKuh2HqI0vXf5FJiaCZ4Ilq8SvGGwnZUzf9JMDmo+umQCCuXOL3Y+yNdUOZnpwUlUePyMq7eJhHVsfzWhqdOozD8MKaWKtbuUS8flr6Z2UhhPnc7iYzzFcni8oLTeB6t4Brq6wcLG3ZLf7vneGLVCT2a2TWaOTvogp/aNpjQw/djhpj4Lr4hEFLNm2TTWOYVJ5x9iJ02AKeN7a8mU1ezKr6fFB8HcOcURiryqNzmc24TaiiiTvEcztX5S//Wf/6mbBo2N4PcrvLqbeTWLaPQ6zSGVplEZ0dByfmxstvnmknF5ignq+9bsUn3+mj3dRUNjfn3bFrZSmCpQHDHXlMbye5kw6USaqmZgp3Nl+2nZ70Cg2tk+06pnFx/AI6kEWpdTA9TWTSdwqsDr1VCaVlxntbUlD6clD/AARrUXLeBEk1QohBVtAcA32cA/w+UECkprjiinZhCAHawrr0Hl1QkeU7K9NR0rYzuBiQEeT5WmIGc7gdP807AqedqdfgDMmA6BwMABuqmTCxNSgOoNwFJ+SigNdiiXRqx+afHvjB6gsrJQS83A5VL52ouF/Vv1Joszfdj+WnJWaVStt2aXZQ9cg2uw4U6PpDYqqGHrCssMsr5iDpUzg/kyF75no7kVRjDfXE1p2P4qAp6KYqCtMMW+Qvk8VVnNhyuf2872VRD2VHBwxRxCpp95c4NMq+0Njjh5IXWn9qvmBEQMA+pqi8UBwOUyiwGRnG1huQOkVRDTpWHVzSRXP4uw36nJo2kaLlMxb67CNHrbyIXqNTytTqKu0mCXlcqh3CXHgLtPk3HDxnYHnXOE0nqbv+p9IqMl+7tuaIRcdWhTXaganUw4x7sLE7QZ64vj6mU7Tj4SBb3RmpJ9KaOHCQRgQtUSJvmO7teUsFROD+Jv9DLrw/VUVGqMn2rQUA92Koftcpp4Zyf0bRqdP35rIJ51lj8ZLknirilmzoCJkwc4tnQN1/y6sqKjNCd9Qd6c2XDYMq2sowTbbeQD9JAzw04Qsk8AKhTqv3wVFap4HJUsQPHXwjysHODyYVVPdQboCjtQg234nFU9xGCXJKgXY0ZPV4ZVP7mYxf57ecj4Akd885MYhmLjd1/FTltUXzALgBXbXuL7j36BaVXzOH3m+SNc6pFXH2zhlAPO5c5Xf8X7pv8X4687gm2/fZ2N//ssK+7/M4Gl9YSOG8crk17lihWfpzE4gf899mZqAs4NTNAd4cxZn+H4qR/il0/9D79//oc8vvZ+PnXCN5h696VU/e3jdId+R3B8/5tIIYQQQ9Pe3k4ul6OysjzHVFVVFVu2bBn0e36/Hy0aZfFhOba2Oy8wPF1ualfkaPb5mDf3COz1L+EPefAXMhIDRx9hoq3w09kBvm4futuN7fXQ2BChulqjuhpe3OjFtExMjxuvz0tttIaatE1Pqqs4HZsEymOiuUwMl5spkw2atQiTJvmx39Cw/X4SoQqU6SdaMn+7O0d6ix+3O4vl0vD7/YRCIfzRKCoQIOXupKZ+CnWHe8it3Irbm8AfjuJnc9nyB4Jh/NkUfkBVT4XqKbiMGPHXt+FpCRGIRsn4N4OmCB/SgDvaP+hkV3ZhJbO43W5yh3gw3QoVyGJEPARCPgLRSpRWTdKqJPfEZmyPly35Hv6qq51gWTpfG8rvd4aHqmqxNkUhl0arqMCd/7x0HQCEwikyGQgGQ5geP65NLiorNcKJEN5AAHfIqWHn9fkg4SYUqcBTPxF7W755S7SCXDRA3JvFk3Bh+P0EtQhZj4cmdx2hmomMb5rFu4m3gQAhXzP+TqcpY9KvsFI2/mgEf2Ut9mynNkiitgoz2o41roKG+ihve7dhdLtwm4pOr4tc3OlRMRjw4/G4IRhF6RqhYA474cdId+H3BzAqXGRzGSKRKNFI+XIDBDuD2EaWiRMi2LaNv9OPyzDwG378fjhoYgPvdqZoy/iJhCNsyzjrNhqJEvJEiwm2/Vud4Xa4GrOnC5/m50BrIeuih1NXF8U0FWfURPF4FKvXpPLbwdkeXd05/P4s4yqjNHt69w0VrSC9KYg76YZAgJA3SibhwvSuwO3JopoW4g61EAlHCLsrsTsobmO/P4DH5cXd5QbTQ1VlDZqeIrAgSKajDcPl4agl43C1H8grG54Glwc1ZwKVKac2lWYYVFRWkPU7x320Msrx73Fq1vh8Cjt9JKlXVuCbP5Nx6W62ZDNUeFyg3LiDi/B06Pi7ggSDOqFQiG7LT8q0cOfchEIhAipAQ7aODW6bltqJRH3OioyeWL6NMpVNoLJUTnNyiqXbc2S8JoUeMAJBH1pVHTQ7TYGNN9vR/U7Tsopjx2OE3cVlsDMGWsoNhk4gGETrcxzokSQZK0HOyuCLePAZfkCVjVdT028Xwu93tuekSS7e3ZBB+XNOLrgut1Pxze8n4POTzgdAoxUeusfNgUyKQDRM2hchld9u7VXHYTdtxeX6F6bbj8cLpt8N/gDulJuqYAXeLHisFFU1rbyrsuiGKjumvQEPb3W9kN/Hovky+ot/T/FMZ1umf7P0SCRKt8eDrdl0L3DzquZjVmULk6cowrEgqC58AQ92wI9vvlMbV/doTvPC/HyODZ3GgyvucqYXjhANlc//6LlR/r08g6KaQKADf109Wp2TF9CqnQSpbg6YUcXmBzxs3ZbDcLuJRhWRaAXKEyTVfDzdMYOq7BvUh+oIb90Cphu/308kXIHb3Ykd1+jotoiOr0bv0amMholWhEk0aLRu6j2+ItEous+F1e7H2+InGrGpqNDIeRpwudoxNY+zDFENy2wnYAbx++OEQkHC7iBpt5tJ4XGsjm9A+X2Y0WqikSj+5CbI+TnYnMPjpo0y3MXtYLvB9JjomkHLODetwSDb3N0AuFIGaSNNRs8Uj+NQwIPf5wQvVbSSdMCPhgd3IIiW8uPzQzjkwe2GlL8Wo7GBgKuatO0nHFIk88393e5Mfju4sGwLTzxEZaWLykon2BQ6JsC2ezMkPDH8lQGqz5jEBN98/rP2keL6CgSd62c0CkplWPvumbTU60Sj+Zdc0RTJTnBFvf2uMQBHnR6hfZoX9VbvvYTh95Rfky2LiiY/waBNPOMnlbLxb/Hi9+skJh5LpytBdSCA398bsD3kYBPDGDywV5x22o3t94PLxwGTo2QyWerSflyuAwlPnoR/nXNO8atqOusqcLtfxOvdUTXO/HIMaSwhRrmezhRrf/JlFgf+yYv1X2XBB/8LgMzmBNt+9zrhk1twTwjx+pbnuPgfHyDojvA/R1/vNAMQfGjuF3jg7du56tEv8OMT/0LVuQcQOWUCbTevoP22t1j/jSeJAl884PPM+fyJVPsb+k0j6I7w1aU/ZmnLCfxw+Vf4+rMf5eS55/GR5+/Hc9vHiJ/1a3wtU/f+wgkhxBjS942wbdvbfUsci8XItrcDEM8m0XwGqVSKbNYmlUoRi6XRMllUPEk6Px5AOpskm4nj9ysi4ThbUylySR3L6qC93ZlfIpkgk86g0ini8TjdXd2k7SyxRAxDdwIZKpHAyKXQMxqZTIJ0WqdxQpyOjgx6KguJGPGUBZkY7SXzV7FO7HiMVArsVJZEIkZXl5t0u04iESeTSqG6uknbkE0msfUkVncPsVisbPl7VO+wbHMDdMegGvTqSjI4QUTjqGqUUsTJEC8pQ4G+pILuP79DKpUilbWcHFRRm4MnN2NXpenodIKI2WTCmZcrSyzlPLi1tztNOpOpVH57ONPMtrdDXb55U3s7qfzn7X3mn4jbGC4/sUQcnST1dU5NESuVIxaLFb8Xz9hoOQ0VGEeyoxOjsMydXRiZOOm0TVJZpGIx4mYCK/89l+Wlu6t3HXWTwpP/PZVOk0vZkEqS6UkUp9nT2U5XqxNkS6c7GDcuRXcqg9sDa5VNJpshreVIJb2kMily8ThKV3R1JdFiMTrSndh48JkB4ukYPV09tNv913u8J04sGaOtvQ1dM4jFYsTo3b7JnhRdXV3EYjG6zO7iMnR2dJIrqZBX3CfcVaQz20ilU2hoaOkgPT3txeMnkYBYzC7bbtu22cRikAwmiOV6553r7CSVSJBKOcvnsROkYz5S3c4+2x3vwK3H6fZ002FXEPZXkVAetGQn8XicrAtSqRS2pejq7EJ1dZNRaRJmHK8/i9uMYafy4+BCpZLF5QgcWE97e3vxb62nCz3fAUJnoUL+xEZS+X2pQqsgFoth+yqJZ9L09NiY8QQ+D/R0O+vNSuuoVIru7i56tB68WRcLGxZDyqA91X/bAOQaXVDdQiz2HwAycYtk0sYw0/imGcRTcXJd3dh2AEiT6ukmEYvhqvPRbcWhPd67bXJZ9O4kmlvRE9PJ9TkO4vEeMh1x7FSOXMLG1vL79wDHa6nC9uzpcY49LZEgnomj5fd/KxYjbnUT050H9J54inhWA+XFjvXQlVHFYywWT6AK+3fGIplMkollIZ4glErhyZlM9E7Cm/biMxp4s+s14ire75guLHNhePnfGuMD03ll09Nl3+nu6gY7SSqVImlkydnp/PdsrAqntmPX6/9GWTmUlcjPCGw9U1yXtm0X59XV1YU7Vz7/7u4OLMsmmUyTSMTRkjmsQtmr5jo/OzpIplJkMja5lEY4jHP+S2Tpzmh0Jy2CuTiZRJopdisrMmvz56kMuWwaO5vFIkt3T4ZU2iKV6qG93SKd6CFecu7WOzvRUjp6VycqGcdV3UBXZBx2ooNAIEMql8KyOti2IUfXlk7iEefc29nZRUjzkUyl8GBCaDzxyhzE4nR0dmJvXo3KZQiFK4nHN0Aq27t9kp2kM2lyVpZMJk1WN9mstRBOvEkmkyURT+I2Ib87kE5CrJB/rKMD87Aw+rsW8XfTJOIxWg9IY1UkWbnS+U46BbF0nHQsRne3TSzrLG9Tg01PDGKxNLmcTTyWIDghWbzOAqi5QWLpd4nFYvR443hVuOxat619K2GVj/Yq55zV3k5xGvFYN+lYDCNgOR8MxIiVTTMwL9Rv3zXicUwXJLpiJJKQSieIJTQSqTjtPWB2dhevcX4fdHfHGZJMEiMWwzZy5Ho6mDgBOl6Kk06D3tFBJq2RzkAsHiOZssm50sTimSFNWpoxin1eT3sPm6/+DAsD/+TV8ZcwMR/oAtj0vWexMxahz03h/56+lC/c815C7ig/POEuqnx1I1jq0SXkjvLFQ3/Am1tf4LsPX0A6l8Ko8BD41ATuveIxvvPfV/LK6e/Q0jOBzec/xZpPPkR2a2LAaS0edyy/OvUhlrScwJ0bf87nW0zesXL4bjuH+BvP7uUlE0KIsSEajaLrOlu3bi0bvm3bNqqqqgb5VrnwcePwzXPGrauDyee24GrwO3l13OXtDQpJxJWC2nxPUqg+iWZt8Cgvti+azz9lY+pOlEEvSVRb6I3Rcud7Zcy3j8s1zCfbcjBlmeiLSpKMexWaBlow/ya3mCXXKvYaqHkGyNnFIE0b+85pqN06ldIVwSnu8qZZASfg4B8fxDSdJiUFvlOW4T/r6PJpaPoAycv7j+KUUSv2uAW9TbhaIlOpD7Y4zT1qpoE3UtYkpJiz6SAPqRn5hPb5Zm+2z6klWLqOdMPZfrY7SHET6gYYvdGjvqsrGFT4fBrKragoVATQLTRUbxMxXfV2y1jIzZNvVjVY/praQHNxvL7b1tBd6JpB0B0BIGCW7L+DbM/G8ESmu1vLhu1o22fyz1OuQvKw4hc1jGI7Tw2X4TQFnF11JD6tCgvni5rK166ZtBQ74tSSUfnvHBCcSKW7UFPT2d+Vnc33zKjQ8i9kbU0vNuUyqr0YFb3NfoFib6ODsSPOerSi46kLNuPLt2abOqV3HH2ul/S0WsIhijudrvVZ5j680yvwzi7Jc2bn8x4ZCiMwwDYtHCsDFTef2B+dAZsaK01hZwrNz7Z/zAyk2PyqLLdb/mc+H5ymUZKgHJTubJM2/1y63RN6i67A1tzFp2g7v0C2y0udv56oGcKldJojk5lbf+hOl3Wwc1ZLCzQ0QF2totBSz+srKW+6h35tu/rksiv+XrIRZtQuZE79Ic4y2BA3G7C90WKPtv0orbgtXQYU2jsXVp2mq5IUTM42c+kudAPSYZtEY4o3KnTaWg1M0/mSq9GPd2YFroZCD6z5eeXPc3awHttXiXIHcQc0muucXFKplV1g26imQtNFu5gnDmBi5cyynHa239lf1QHHUxNootJXSy8FGpiYxXUUdzUWf7es8kuVXhpGUU5nL0pTZPIBZ73KRKEI6PXouND13l4vS7eFz6eoqS6sT8WxR2vFTkoKjCovLTWtRL3V1AaacOnlzevTuWTx98K+kS5NW5bfXn2b6ZfSg73NP/2LazEqPYOOm823zg0e2UjomKZibsXSee4wf95QKTjkYFi2BELHNmMc3OSc86QZo9gfxNu7af/FZ5jhfZYXJ13BxFPeV/ys598baL/tbXo+qPPJZ95De2ILx039EB+f/zUi3qE9GOxPDm05jgsWX87Pnvh/vLn1RWbULuT5DctpT2zmrCM+y5nzL6YiHOWtHz7OxiufY8V7/krLr47Ad2D/hK5hTwXfOOKXHNpyPD99/Gt8rjbGuW21fPivH6cz9SN8sw/f+wsohBD7MNM0mTFjBsuXL+eYY44pDn/sscc46qijhjwdPWiSXqCDDfUNOnZtFamgC2NqpGw8NdD70D4PqTZQrzVQWV9DRXIrOX8VZnqbM5+S7teVrvBpHoKV41nNut6HOcNEGZVAF33ZJb2PqahO5MhG9Ihzg18MMFk2KIXtjWJWdZD2V0PXa8VpjItMYVxkCrRtwu7bC9weoHl0Iqc4D8WHtZR/ZjTk7zs2s1OKq1zTiknoTc2Fsi1sIFDoNav4hd6NZJcEMI2IAe35AKahYTXMG3gZigEOlQ8k2s4TrW4OOH5hnn5XiG6jC8MFmXy5VaQFqyq/IjTVm9ClkExZ632oHEhTeCINofFoSiOVLX/B5ssnza4NNBF0R7bby11Ba9U8Nuhb6ACam2Daoh19o/fhzeXWoaSSgrN/9iardue7jHMRxqV5ydGZ/2SAwI2zdmjwVFPvjZADZz+2Qdk5lFZ8Is7/NHrz/wy0qvTBH2ABrKpWrGA9eMIcEKhlatUcjML0Es7PcKuH6RU+PB5FLjMXrWMteAZIuNNvYUoCCTnb6cm1NBZVksRHDdCZQKGXxUIUoZDwvB8NCr0+KEOHoVXqKOrNRVT8D3ezjr60EW2VC7bkA2Jlif8VpglxsxFMJ+iR89cTdzWieytAbQRgyjQP9voJ5JrGF/Oigc2UylkDlmXp+BPLehXtt6gDBO2zVgaXS1FZoajQFOEqWNI3xV0mjm24+313QCXruDbQOyHbBkvzkGo6BN8gQdTw0iDrXornJ6MoTcRe+Fn4ZqHXRl030XXo8deTaugmnY+OuF29OQLdk8LkuvMvc4rBrnyAM389sU0/mrs3Z5cVz6L5XKiQCzryCfHN3nOBrhvFbapQWPVzsGpnoJRiZu1BfdaJU/A6rZFQaAKapvf2sJgPdpXumkbfoGu+1wNXjZtsO9gVBmwF23K6otQN0HIl+/lO8hg+5jUsGfCzlkhvEL/Qf0i2JF1cIci1vWBX8WUSoIe3vx9l8tMOVJtoHoXLdI7NdBpM0/nZMm67kyi3nRWiNFXMPQoGyl/IATo0EuwS+6zklk3ErvsME803eWHKD5n63vcUP8tsSbD6S4/QXtfJ/068ktbgPC4/5vdMrZqznSmKU6efR0uklVtfvIZXNj3F1MrZfGjOFzig5kDAuVGpOm86gcMaWH3ev1h51n00/+hQwieNH3B6h088hdl1B/P9f3+BX9kPsrazkf93/wVs7LicyLJT9+KSCSHEvu/cc8/lwgsvZObMmcybN49bb72VDRs28IEPfGCnptPbo5RzXvdM65/Do/Ag7zF8aB4nKOFpLs9npWtgKUVldCLZwIGguzDbncTGhQCGrZksDM4g7Ve4GiYyadLCoRWyz81vIdBV+pltWXgOiDpvoJtnolvlD5CTK50emLOTjtphDaodCb2nmdQ7TkcAU6pmEXJXwKrHd3o62fFLUMnOfsNjZhMZo38PaTb5wJEnRC7txXIHCdgapT3RKb034Xbh2Sw78XAnQJXNt7kpCUqo7eRQ0UrSOxRrdhnljwv9nks0RVN4Im9br6NpvVEIu2ICVj7xvtI1yD/gRz1VtJVNbzvlKTxIlwQAagKNZYEEnytQ1ovdYMGzvjUIh1Kjr2UcdHRCtFIrC3aBKqvZVQh2pVKgYeRrdqkB5+HEW1TpX6A0cjnAzmIYTo2QYi+Omt7bFmeAIm9ve1KYlyec/1VhqP5VLjRV0jOay9ubFHpHlCI77USM1+/BCGmgFO7mQY61AZYhdEwzdjpH97/edWoIaVbJjleiNKCn73ywy9kOdiGa5gzTQA+ZFB6bDb08GKB0jepqResUmzdWOHE5pWkk3A0EVYbxFa1Em8cTMBU0zwDALmzX7TyJu7YXOGbg/bd8mJMY3+vt+/bBRsXbyocNciwMdowUDvXtVZ7T/B4mTEo4+ysU9+ViXvaS7zZUnUxDg0ViTTuGDjYmOV8NATckU1AR7XNMaqrsZzHYVTh/uXxoHo1sOh/wSGTRvHr58hTG1QyUy6klWWzur7TBA/emn8jkiWyJmUS9Zj7A3Fu+QrBLw8U0/0lo6p7y7+e7D/S2hjCrWkjG1+WHO+XTdVCWKl1lu8XU3aRzKZZNOAmjpBZmoUZVpuQY0Ss86BGzt+bcAJSmnPVlKDTP0K6X3nwt0dKaXR4PHLZ0JxewEMz0D1AZpc+kioeY1OwSY1l6zWuoP3yGaq2b1+dez9SjFxQ/y3WkeOXDd5Fti3HL+XfwlaN+zFGT3r9rzRT2Qwc2LOXAhqXbHcczJcKku45nzScfZs1nHqF2VTfVF8wccB1X+Gq47Ojf88unvsWfXr2ObMckLn/m66zZuIKK07+82w8gQgixvzjhhBNob2/n5z//OZs3b2bq1Kn83//9H42NjcM+r8Kb75AnisoZJBdPYGqfigoTxkN3B7hcGna+Z6tC8wqrpMcrs1rHyGjoB/QPqm2PpuUfUPt90FuzS3PrxZ6W9cGuJ67Bm2MMuSweA0xnvs3hyTsYezs8YWxP/6BWu3/gl3G5LGgmGIEAgeoTqVoXZFp3B0mjd73YVsk6KlyHTX/Z31blZFiZH5R/oPfNrSr2rqcpDcu2UPmyWVWTUca2/Aycx4XcuMXYurt/q1Pl9BAW8EbwebcQAwJ+ypoe+hZUQ9xJfjy3ZiG5YD2vbn4m//Ud35+VBrvC/XqWKw9cDXq/V1oLaTsPSsGSmG4wqFhyCGD7sA0PKpssTqu4fErhczvlSadBU0ZxFzUGagpYEnApDXrlchDRwuSMJIbmwioUUtMLnW0OvFg7qNm1PYV1NVBtop2lmYroiQ1oW1eUDB1gRZdsH82jg0cvRJJQmj3gfWFZL4n55R3oONpxIUum4yoPPOh6eeCw8HttLU6wqyRYZwMVvlr8Znntt0LTYNu3c+e67ZleM58qfz3wKuA0Xbb03XuEH+yYO2AaRKMQCg6+w9mG2dtjJxS3V2H96Fq++advNmFXBVGvIunuQjcgpznrrmWcs969fYMq/QKiznWkWMNKKezm6eQ25pvGJ3MYlZ7elyv5Y8Z/5lHE/tPmNAlGYTsNbHewUhTV0yfh63Y66Agd3YT9794XKDbO7jNYM1M71ICV7MSqmoLSteI1MJfTAIWhg5YdvmfR+Y2HYWiufucYT/5yV1qzylXjxVWz4/uE0DHNg8VHAbC9EWwzyLwq2LyFYvL5YoAtC31jsEOiGc4LGpe3/2d9pqf3nnaHRIJdYp8Rf34rHXeuJPXaKszu/6BVTWHDkZ/myNMOp6Or00m8uHwjb1/8INb6JA98Yjn/c/6NZdVzxfAxoh7G33g07174OJu+9xzp1d00fGcRmtn/JkXXdD6z6DIs2+LPr/0GY8th/M+661n9sxVE/usqdH9k7y+AEELsg84++2zOPvvsIY9ve3f9oavQ6y4GHDNAS0nThKpKRbbkbjTkqcBnBqgLNLOy7TXQXeg5HbNZI7szD+Xby7VVfOvf/0H68Imn8NDKPw99Pjvh4HHHkrV2skrJbspkwGU6eVh0zWBW3SKM+EN4lnqIZaIkX28vjyf0q3VlkJ12Yv4Pm0DAyftkJXOYLcHiaH4zRHeqA3TTGd+2MSIaqY0Wyu88PRUe5MmWr/diIMJUmKZiXFMhiNBbGD3oglih9pmRD0wWmjXu+KmlNJDpNQavnQDbCZ4pbYdhtcOWDlKrRWnkJh+Fvno5KtEBdg5VNQW2Pg0oXPnmWIWaXYXd11MalFO9zal6myj2PrnlctCkjWNcfS0u3SSTyTfdNLzFIMBwv7gtrKvhCHaBkxdMda2HbBrV91jZToBRczv565SZwx6o86jS5oUaZCcsA2NXg9j5ZfaWB7sMo08zr0LTr/znQ3o3642Snfqe3lp5Q+AzAyQysQE/U0pRFyxvDzapYnpZDr3tsQMDdFHJ4PuRaSrGNe9gon1rRvVtxqgDClTJBq9sbsI46DWyG9xMafCjaZsGLkfhGtH3HF+yf6qKGuwNbeQ6UliJLMpbkh8yP0+9MoweTmIlssVA/pAU5qspNK8ByvmelV9GpTnHN4BVOQm9Y2XJd3WsupnFPwvz9HkV3d0KTQddK9+ndofXNfB5UNPUgNfroVCu7V+jcy1ODroqoDRVaGl+rl1eNnOQ87pWPsHGBmirgqrKgUfvS4JdYtSzsxYbLn2Gbb99Hc1j4Q1tor1rMvarblyPvMTy772Gq8FPdluSXHuKjkg7yz/3HJ//zNX4zeCOZyB2mebWafrxoZgTgmz+4Quk13TTfM0yXNX9I/NKKS5YfDlbYut5QPsnVdu+xCeT19N2zQdInvJTqqZJT41CCDGc1LT3kOse+CFqeGfUezMaMEMsbnZyihmayYptL7LjEMPgPG6IVpQPMxvcpF8Bo2qg5lh7ru+lwR4u9qTC42IhDwv5GnS2O4Tuz+e8MjXsdCG3zeDr+ugjnZ9K+XDVlee4ml23mA3dq3uXUSnc9TpGSGHXljdfHahmFwB9mtOVJdTXNaya6aAZxQfwwoPpUGp2lfK6Atv9fHcCQqa5/e9awQb0RAfoJlb1NHJd83AbHoz8s3ixZld+0fvWQANnddm+KuxADVY0nwS88DCtFP582yAtH+yyXd4BHyBLm6/uruEKdqGb5CYejrb1TdTWFeWBkfKUbWUCyxpQb76DoXUNGOwq7QhC6Rq4dy0Hn10agPU64ZjSYNdA8ywEP5ub8tsOJ7/aoLvtTvb0vqjp6B2PBOSaD0LrWDNooMv2VZQ1Y7QqJmBVTysbp9Ckb3fYoUboXNdveGGfzwSaUfE1JF3VBPOzMnU3SxacRCHj1INv/2nAaes+A+XRe4/hPjm7AIwKNyjoftjJj6Z59QGPeXdrBLIWqqf32NqR0v0MwFYuOrwH0JXpzk8DVP5YsWumQWmwq49Cc9V5MwO8tVmVBbnHWlsjs+QwH654vNkUIL22p99203VFfd3QZyLBLjHqrf/WU7T9/k1qlq5m/Ow/cue7p+E75RwWT8qQeGEr1us99KxuIxewuVG7llWz1/KT998jga69RClF7Rfm4J4QYt1XH+OtE+5m3M+W4T+ott+4mtL46tIfc/6fj+ZB/61M0H/BQW98jbq/nsWTy7/O7HPej8uUTmKFEGI4KNMHWmpvzGnAoU3hiTSFJ6Jev2eX74CnTHG+V5JrF726kooj1pKLhoecpHZfV3yY0E2yLYeCO4BLMwgsqye7OenU8GL7D3Tb+8xteBkfndZvuO7Xyta9Mx3nZ2UhCJnfCHa+jLnqaahcKt+ktWQLuTxY9bPL5glgDDE4MK9hCV2p9h3e3203eLabD2J2xQSyoYZiwGFh0xG4DS86zsN+W5tT86NY2U0vrX3UW7PL1nSsmgNKPnJ6HLUs0K0EFqAKHQzoppPLqo/g0U6+q93RW7Nr9++9bG+k+LtVOQXbEyn2fgeF/Fhg1PQPAGoeHS3qQcW6Bw52lQZxtxPQ7cvnhWSyZEBJy2jNo8ixnWBXvmaXriuOPtLJ+dTZSe8+tBPl2J6+x6U92FnNHcSqnTHodHI10zFW/bt3Oi5fv8iipjRydm6nA8xl5fNXFfO0lfLn4+QxK0JswonkXtr5aZstQVzjSoLZfXN2AUaFh+CyBnKdadCVE7jv6l/b18jnedRiO7Fv9xn14EWQSEzkqcdfcD5WFDud0Haw/WsDTehKp9JXx6b4ShIZp4nnWKSUwmXYxcT1w8E7twrPjIodj7gDEuwSo1rnPato+/2b1C/6D3XzHuVbL1zOQR8/mcMOV4AH9/gg0Y9F2da2ja/fdzYvbHyMq99zT//eicQeFzllAp5pEVZ/6mFWnnUf9ZcsoOrcA/qNF3RH+Mbhv+QL95zC8gk3MfPsO9l6y8Uc1nkJD3/7cdqXfJsjjg2g62PzgiCEECOtLtg8aLOZXbKDQFZ28jHD+irbDjeSdQeH1lvcHpSdfDRYw3N33zIOAtupsGSU1poqCSoYUQ960CwGu/ZGlQGlFIcstou5Yax4vhcyf77GicuL7fJiGh6CRw3e1mRyxSwinkrCnqG1R4l6q4l6+/cA3a9821kJhaBh38DGTimpWRN0R4q/19XarN8AujJ6m74NEERSSmH3qUllK43Jk5yaYSrndAFpNS8il92Y/1L/YhTzXe0ONTzBruzU48rPA0r1a0Knh03CJ7QM3lQqX2txoPaCpR1UDDV5NsAhB/f+3twIPkvBG/maPnp58zuj72RLa5MVauSUboc9lAu4dFtEd6b3eL1PTdcBtqmmdHLkhqU5bK5+Dqp7Q/Hvulp4403nPBbOP4Y17UI6ybKyDdCMEZz9oXSfKATMtQHbmpY0H97RvPs0MwwEVNl52UlQP/STh5NrrffwLZZhDD7imKaTs2u4DgulKZR792ucSrBLjFqZV17l3S89TqBuM/HZXZzx0J8470vjOPLw/kfRLS/8lGfe/RdfOvQHxd6XxN7naY0y+a8nsO4rj7HhW0+Ta09R88U5/S6q02sW8LEDL+I3//lfFjUfxVEX/h9v/ek3HGr/hA3PvMyld/wvU4+az0knQDg8Bq8IQggxgqbXLNjxSDtjRw/KxvZ7H9sRKzq+/8ARDnQB+aDH0HLn7MjUKQNf6yaMh1hs+9dBZWjoIZNcVxqsvVPXze/vLZMVc3IzjW+ajkeFeWvbywC4dQ+6b4AE7Xm6plMb2FGCoKEr9E62PVUtVXg9WwkGVb8aa7srGAQ2gEJHNwaosVb6DD9AW1C3W+F2Q7be6axAGR4nX9cg0xgOdr7mzICJ9HfGEDsb2m5OoEKTxwHKood7zyGFGmJDml/J/ee0aYpcp0b3m+XjFIK22T6V5Mryd5V9QG/HjntA1FvNpMoZhN0VZcHUHerXy2D/AvYG0na/8Ha4CTvcmxfZNBXLlti4XLuXN6qUKjZj3P41pik8Cdu2aAxN7PdZYZmHlLdrkNmMa3byJlr2Ljb5LQSVi7nIdn4So10hb9do6w9O2guJ0Sfehuu+y3j343dBzuLFxeP58L+u47++OI6jjux/BD295iF+99z3OGbyGRw/9UN7v7yijB40GffzZUQ/MJnNP36Rjf/77ID5Ac6adQGz6w7mJ49dzMaeddSddh6pD/6eigq46oCP4Hn0Ks46K8kl37Z46GGbVGoMXhmEEGJM2HN3t7Ynst2mOwMJeyoZH23dQyXauyZPUhx+2I4f7r1zKtECrmLvintTIW+UK+RhXGRKcbhb3/1eMHdGS9TJ/alvp1mkNW4x3gPfQ3bKMcM+/0JeNUO50TXFxIrt7Ld9g0P5B3Lb9IHbaaapazqBPr39DfeTZHeqA4CQe9c7shg2+e3WPxDoCB7RSPDo3et0ys4n29L9CjufELsmX1kw1zfYNVCTM5X/T7FHa+e0RKYS8VZtd18uyDXMI9d4YP9cYQPWKhx6/qpd4XarHTbv2ynFZozbDzBpSqMl2jpoTUrYTvPQ0nELtfn6rJ9wSOHxOL1MqpLwSWkz3e1Olz7BrjGoUGt2tAW7pGaXGDVUz2Zc//kdrhf/wNpHF9K9/hAemn8gt78ynW99U3HUEf2Pnk09a/na3R9mXHgKnzv4u3vs5C12jtI1Gq88GM1jsPXaV9DDJjWfLe+zXtd0Ll52DZ+860iuePiz/PCEO9Eb5sIn7yL7yA/4qLqeEyY/zLee+w7/71+z8Xph4QKbgxYqFi2E+nrZ1kIIMSrsyWvvDt7oD2R+47I9UJDRzajwEDpqeHuftkINYG4/GTyAZ1qU9JruYrOwAidn197THJ5Mc3jyjkfs29xrmLjzD3tevYKDxx07QIcGpc38+jy8F2uwlAc2D2o+atBk3sOhUEslsjPN5faUQiDGtgYMS+xMja7B6AEXesSNZ8FCcnVOoMLvV8yYblMxhHhfU6PG2iqo9TJqnurtUMPAHwxQPm0nmvSNBrnqaehbXt+tdX1A9XzeaX8VjzGETg0GCUYVEvtryumAoli+5oOGVIbC+tYLQe2yWe5Eb5GjmGvvv2cZEgl2iRGnOtbg+s9vcb18B1g52rUzePepCbzUOJk7e6bznUsVSw7tf/KJpbv55v0fJWdn+fbRvx2RXpLE4JRS1P/PQnI9GTZ97zn0sEnlOeVv2msCjXz+kCv5zkPnc8NzP+Dc+ReBy0f6qG+Sm3I0lf/4Bj+b9yE2nHgmv1//OR56OsIjjzqXiOZmm0ULYdFBinlzwePZNy7cQgghhsAdwqqchBVpGemS7LeshnlDGs/TGsHTGin+Xe2vZ0tsw373AtJdEtsb6J7Ua/hJF/7oG+zK1/Sy/YPkLys8HQ/zKp1Rs4BYpnv3mzEOA7tQK2c3ewvcHmVoBA/vn0iqofQFakkS+750XTFunCLXrkZl3qXs1OMw3vy788f2anaNxsIPwK6cRLZy0m5NI+ypYG79kh2PCIMGuw4edyypbILH3nmYoF6384Uo5nzL1zIr2b8WNR9Nd6qDlzc9tfPTHUUKwa5deD+1R0mwS4wMK4e+6hFcz9+CvurfoBtkZ5xGfPJHWHH6s2zzGPw+sIAr/1dx0ML+J572xBa+dt+HWNO5gp+e9mcaQxNGYCHEjihN0fS9g7G60qz/f0+ih0wip5RvqyMmvo/n1v+bm174EbWBJk5oPRuA3LiDiX/0r5iP/5z6527gQs8/+PzXvsQ70dN46mmNJ5+2uedeuP1OG9MF8+bZHLxYsfggaGraNy7iQggxJgxDT279p6mwqvv3DihGv5m1i4bUZGisce+gItv8xsPIdnU4f/R9InQHybYcAp7I9icyzAFE0/BgGnu3uemgisGu3ethcneFjmnGSu24DKMymFvaPHaQBPVicAM2XQU8hg+P4WNpy4k8vt7c6YBOIbhY2GVKg11el98Jjm/alRKPHoWardnhToa4myTYJfaueBuul+/A9eIf0LrWYwXqSB/y36yftIRnt76M/vG/UbHNy2/Ovp2K6T/kzrZGnnpiIs2hSTSFJ+E3Q7y86Ulue+nnxDM9XH7M71nUchTt7e0jvWRiEMrQaL5mGas+9k/WfvHfaEEXoSPLm1p8/pDvsi2xkR8t/wrb4hv50JwvoGs6mH7Sh32V7Iz3YT54OZ4HLmFq3e00zf8qJ86cTO58g9e3+njiKXj8SfjxT52rR1OTzdJD4fDDFNMPGKU3JEIIMWbs3jnWt6B68GTQYp+jlNpnao4MJ01TNDbYVA/SItA0PPhc+WahAwUdvIO3o7OqFKYngOeAUZBbaw+xQ43Y3RuwoiP7AlvzGmjewR+Ri7eUo3wXtwdorltM1s6+32xuT1CGhjk+iNkSHPBzM9/Zir6zwa5izS7nOWUPVl4cMYWOHhKJkS1HXxLsEnuebaNteB7XC7dgvPl3VC5Dpnkxry84m0fsNh5dfQfvvHQZ59zxIeaumM3N73mOlkMm4vKk2Nyzjvvf+iOxdFfZJGfXLeazi69gYsUBI7RQYmdoHp2W647gnQ/ex5rzH2bCjUfjP6i2+LmuGXzziP/jp49dzO+eu4qH3/kLJ7R+mCmVswi4wxguF8aSb2Ff/zjJn24gtv5V4FUAfDVeTjt1Ip/6/nS2ZDw88RQ89rjN7XfCLbfa1FTDYctsDj9MMXOGUwVdCCHEMNrNFwpm447zQgmxqwzdRTaX2Svzmn7AEI+Fna0NqSl884aWDHufZZjkWg4Z6VIM3Wi/nRygaWqhGaNljWztudHMN2fw/HWFGl2RyM5Nsxj8V2MwypUXyF/Gk9vvEHdYZCceDrn0DscDCXaJPSmTxHjjXlzP3Yi+5TVsd5B1M07ibp/GA+v/ybvP/g2F4kBzGV/47XdoXqvz2NwFXHTVOUQivVcQ27bpSG5lXefbxNJdjI9Ooy44bgQXTOwKPeBi/O+OYuXp/2DVfz3IhJuOKbugeAwfFy77KYubj+EPL13Dz5/8JgDehJdlTyzhsCeW4E15WdeQYe17HqXWv5lZXV7MtkPZel2CthvfoOoT03nvJ2dw6ikuYjGb5Y/Dw4/Y/OVu+OMdNpUVsHSJzWHLnDxfhjHa71SEEGJfIOdSMXotaTlhwF6hR5LdtzfGHQh7KvZQScROKwT3R3urAb1/Qn+fK0BHYuuQennckyZVTB/9628AhqFYON8uBnaGrrCsg5+HFjYdMSpy5+0qr3cvzsz0A0PL1S3BLjHsVPcGXC/8AdeLt6GSHeSqp/Hmkgv4dfdzPLrmt9i2zfzGwzlr+gUEb56G56YVaJbF6jMX84nvTSzrljWbzfLnP/8ZgFNOOQXDMMhms9xxxx3FYX0N9J0djQNw55138swzzzBv3jx0vfcmJJfL8cwzz/D22287PXFoGmeeeSZnnHHGgNPuO/1FixYxe/bs4meHHnooy5cvB8AwDKqrq/nyl7+M1+vl2WefRSnFpZdeisfjKU4nl8uRyWT44x//yJYtW1i8eDFf/OIXOf744wH417/+RSQSKc7zPe95D5deeikAF154IR//+Md5+eWXmTlzJjfddBOBQc7SA62Xvn/vDqPCw4Sbjubt0//BytP/Qd1F86j86DSUq/cN57IJJ7N0/ElsfHMlm37zItpfelAxm8RSjXUfzLC+zubtrV3cufFZslaGRdnHOWdeLYFnP8DmH2fZ9rs3qPrkdCo/Oo1jj3Zx7NGKRMLmiSfhoUds/nE/3PUXm1AIlhxis2yZYuF8p7tkIYQYS2655RZisdig18Jhs4+ePrPZLH/84x+5/fbbmThxIpdddhkez+7lLxrKPcie/P6ekM1m+90j6bq+x8q3vXu0BQsW8N73vpd77rmHdDrNM888w8qVK5k4cSKLFi3itNNOA5x7l8Lnq1at4vTTT+fkk0/m0ksvxbIs5s+fj2maZfeWhXmeeOKJ/OlPf+K2227Dtm0mTHCa1a1evZpJkyZx2WWXAXDJJZdgWRZz587l2WefZdWqVUyaNIlvfetb3HvvvTz11FMopVi4cCEf/ehHueOOO5gQe4H58xfsVM2uwyacXKyRM9oNdt8+2D5d+tl73vMevv3tb/P2228zYcKE4vbsu48lk0kuueQSwNkG//jHP/rNb6B7+lwux3PPPceCBQsGnO6QbacZY+l9O1B2nAzHsb0z08jaij/3eV566eG3iNudLGsZ2dq0LdHWHY+0C/bG+bO0QsZQ590UnkBnclux44psNse7z7aTy1h86WdfYvXq1Zx++umcccYZA07vxBNP5C9/+UvxHDiU/bdwHJSeh3b0vV1Zf6XH40knX0Z9nVk2rcKxAM4x+J///GfI5RkOI38FFWODbaOtfxbXczdirLgfgNzko+mYdRr/t/5e7n7lCjyGlw/M/hwntp5N5799vPuhJ4i0vcqqynom/WARJx0ZGuGFEHuDq97P5L+ewLqvPMaGS59h669eJXzSeNwTQ1iJLJkNcXoe20DylXZ0QxE+aTw1n5mJZ1p5noqeVCf3vHkjd778Sz5rrGFu40/5+Mwm/E+fwqYrU2z5+ctEz5hM5TlT8U4Kc8ThcMThilTK5pn/wMOP2vx7Odz7dxuvFw5Z7AS+Dl4EPt8++uQmhBBCiNFvJxKFj3QtHDGwwZKZjxoD1JzSlEZAjd28b6NVbaCZ2kAzmzb35uzyaWHSamhN8fYVBy208XhG13EhZ0+xy6xUjrYbXqXjDy+QWp1G09MEm8JETzgf30ffx0vZtXzvkc+zObaO900/j7PnfJ51L/hZfsaLTHrlDUzDzdoPL+W4b7fgcu0bb6zE8DAqPLT8+gh6HlnPtutfZ9sNr2OnnGSZyq3hm19D3dcOJHLqRFx1vgGnEXCHOWvWBZw6/Tz+seIP3Pz8j/nvSW8xY9IvOW1lHeP+s5S2G1Js+81r+A+tpfKjBxA6ugm3W+PQQ+DQQxTZrM3zLzg1vh59FP75L6dnx4ULnZ4dD14MtTWj66QthBCjhVU7A23TywPmhhFCbMfOducmRo9iM8aRLYbY9xQbM46uVtVjmgS7xC5JPvs2az/zCMn1OsH6dYQXxsmEDqDzuUraf5Ig85v7eXT2ozQubOCzh/6UrU9M5k8Xr2LGyjeYZOfYtHAqi66eS7hhB/00izFLKUXwsEaChzViZyyy25JoPgMt6Nqp3hNN3c3J0z7Ke6Z8gL+/eQt/ee16Lpv4Bkx8m6buIIufW8DMZxcR++QmttRu4W9nPsjmCW0EzQg1gSamVM7miDNn86lPz+OdFUEefsSp8bX8MedKNGmizeJFcMjBihnTJc+XEEIU2KEGcqGGkS6GEPuMuJ7v5U1qa40Bcj8ods4+mKZsnydnWrFTtA0vkrrjdlb+pAndlWbqBevxfeh95JoWglJ0bXqOP173XaY82sRhjx+GvlzBj9/Ez5s0o4gd2MKUy2czZ2ZkpBdFjCLKpQ1ag2uoTN3New/4GCdP+yjvtL/Gcxv+zdq219ky4S0eOfo2mp+LMu0f7+Gcq8/gnUOf4T8nPsfK2NM8suqvgFO1e0L0AKbPW8B571mAkZjAm6+EeO7FHLc+GOeW5evxRNdTN2E9wdpNKO9GMvSgKw3T8FIXaKa1ei7zGw5jXGTKcKwWIYQQQowhm9zjybYcij5A8nCxjxmlgQurchKjtnD7OQl27X0S7BI7lurBeOMeXC/dQezZdlb8+SyMCo2JNx4FEyfy+jvw/J/j3Lv2KtZ6f4UdquWhyGcITljI0vBmZjYmOeAgDy0n1mJU7c2uGsT+SCnFxIrpTKyYXv7Bh7LYn3yGjVe9jLrvIGataGHKcXdhVUR4xe/jxWCIl5IxHlxxK399/Xe935sMxmTnVwtYn/VhbazDjtfj0RuJRGy8wTgbux7ngbdvB6C1ai5nzPw0yyacjLaPJJUVQgghxB6mNGxPeKRLIXZHoQ3aKA1cWNXTnF+y2ZEtiOgvv89IM8a9R4JdYmBWFn3tUxiv/QXjzX+gsknau5fw5l9PIlcd5Ikzj+GaX/h49fUM6fo/Ycz5PlpwLbVdH+XYuq8z7xshWqeC2z1+pJdECIdmoFoXU3/dYvwPrGPdV5bz4m3n03ieYu7415m/5TW0re9gZz2s0Uw2aBY9uoburcTlraQ62ER1aAK+0Dg2xut5aUUVbz1ssPF1m66UQcKMED0gSeXsv7NZu5HLH/oUE174CR9f8DUWNR29U00zhRBCCCHEKCb3dWInSc6uvU+CXfupZDbOqvbXWdPxFp3JbXSm2kikunDF23B3rsXbtppIOklY92FVH8LGl9/DhJuTbDYC/CQwj+Qzz1Ax/VE8778DtI1MCM/mc4dew6y6RSO9aELsUOjoJqb842TWfXk5a6/ZQPthh9PwP1/FPTGEim2hpmMNdR1r0DpWozrWoPVsRm1cgXprOekuD/bTh1D7ymyqs32SMnen6Xw3zYqqs3mhdjVvTbiX/9d+DtWZhRxZ+W0WT5vHxAng98sNkhBCCCHEPqdQsWu098YoRh0lNbv2Ogl2jTL3rbiNLbH1VPhqmFgxnckVM4ety+H1Xat5+J2/8Pjaf/D6lmexbKv4mYmG34asbZNWkDLs/N6RYO7Tazj7zi421mzil+f8iow/jg70KIODmo7kxGlXSc0Vsc9x1foYf8PRbLvhDTZ9/zlWHPtXomdMouqTM3BPXIDVtKBs/PTaHrb+3yu03fwm2FDxHi8VByfxVmzGbttG7JUMbc9E4fV6Fiqb41vT1JhB7m/q5DrPM9zadQKrH/Gx7O5mmhPj0T1hdG8QVzCINxIgUBnEXxEgY7rp0TRikSg5y4cvWI/hiYIuvZ0JIYQQQowkO5PvPVx6khdi1JNg1yhz16vX8ea2F4t/h9wVHDzuGI6adDrz6pfsdEDJtm1e2fw0t7/8S5av/hs2NtN94/iobwatXe1MjnVRaWlkXY28FF/GPSuWcv/Kg0nbBk3j2zkl+TIzHltHZrYLdek4PmR8Gb8rSENoAgdUH4jX5R/uVSDEXqM0RdXHphE5eTybf/ICbbe8RdstK/DOqSJ4WAPmhBBWd5qe5Rvpum8tSldEz5xC9QUzMZsCQPEFH/78v9q1PWz7zUts+4PJltdmMXWeyS9PTPKX8L38lad5wvMGXustmrNeKiyFns7SszXDtjaLNs0iPsAh3pDTmGiZTMLPFBVlilFLpbcK5QujhWtR4VrsQC12sM756asETd9bq1EIIYQQYr9gpXMAKLfcZ4mdI/VC9j4Jdo0yPz/lPrJWhi2x9azY+iJPrL2fx9bcxz9W3Mq4yBROOeC/OGbyGfhcge1OJ5eOsfyV6/njm7/ntdhqIrg4N+Xj1KRBdUc3MVeAt1KH8+d1B3L/WwexqmcClRWKBfPhK++zmZlaT+rXL5B8uY3IaRNp/O7BaB45qYuxyaj00HDpImo+P4e2P6yg+5/r2Hz1S2A5oSyj1kv1BTOpPKd1h71Gms0B6r91MDVfmk/7rW+x9Tevsf7yNIdO+hDHf+AbrJq3nqdzj7Cm4y02p9qwcgqXFSGYriAcC6HifrSEjpZUpLPt5PRtpPzredW/nuXeNmy1DXiLSLfJpDY3s9dazLIUrTmdStt5y5izddqytbRZDbTbDXRpDcSMRhJmA2lfA1l/Pd6Aic8Hfh9lP31+53fTVKSyCd7tWsWG7tX0pDuIZ3pwaSZuw0ulr47G0Hiq/Y2ShF8IIYQQ+wU75QS7NAl2iZ0lzRj3Ogl2jUKG5qI+2EJ9sIVlE04mnUvx6Kq7+dOrv+bqx7/Gr56+jCMmnspRE97HDHcL2adXkV35LtbWzXSk1/Cs9jr/9K9jVbCTGleaL2crmZedz6rOmVz/9jReXjWVVCJIlTvNlLo0545P0hh+gaCdJrMiRuymzXT2ZHA1Bxj3y8MIn9Ay0qtEiL3CqPRQc8Esai6YhRXPkNmcQPO7MKo8O12rUg+aVJ03ncqPTaPr72vYct2rbPnOC/iB42YeSvj4DxJ6zzjcU8IDTjsajdLe3k46bdPVBfE4dHQneKvtVd7peIE1sRd4J/UCz+ZWYONUqQ9aVTRkGhmXqqDVMpmVyzJRf4ZKYxO6ykEW6AKrU7ElWcOGRD0b4g28EW9kfaKB1TkPawMb6Iy8hV3zH1T4DVDbvyIbVohIbiGRzEFEM4sJZuaicGHlwLKceKGVg5zlXNytnDNs6RLFSSfse6+40rkUiUwPyWycZDZBKv8vkY2TzMbpSrbTkdxKR3Ir7YktzKtfwknTPjLSxRZCCCHEcCjk7HLLiz6xcyRB/d436oNdmY1x4v/ZglHlwVXvw9XoR+mj9OSSaMd34/tR6RgZlwefZoBuYutuMNzYLh+4vCU/vWD6nL8NL7bpA90EO5d/OsySzeTQ4jGW9HRyaGo+K1QFj6/aRsdD7/Lmqr+RXT0Bd8adL0AUiDKZOUzOD8kqRVoZrAH8VoaldLCUp3rLvCo/nkujK2xiVHmIvH8iwWUNBI9oRBmjdF0LsYdpPhfu8bufJ0sZGuGTxhM+aTzpNd10/mMtXX9fw6bvP8+mq57H1RwgcGgdgSX1+OZW4WoKlCU9NU1FVZXz+zh8zGYB0JtPLJGJ8XbbK7yx9Xne2PI8b2x9jvu7XuB+wFAupkXnMbPyDGb4pjDBDhCNpaBtM9n2Ddgda8D7Monwv9msLLqAipzOxJSHKav8NMePIJwI402EUYkAdtJHLm2QNHN0e1O0BWKsr9zEOzUrWV37I94KJ9GzHsLdMwh2H4i/cxFG7AAyth8tq+NK2fgzWQLpBGZHkg3PJZw3pIaGEXXjqvdjjg/inhzGiLgZCbZt05ncxvr177DlrXfoeGcTiXUdxLo66Ul0kkolSLqTxH1xenwxOoOddIQ7SXgSZd2Qh9wVRLyVTKqYOSLLMZJysQzp1d1k1vWg+V34D6mTnI5CCCHGFGVKzS6xayTYtfeM2mBXPG7zu9/bNNz3GhOffqU43NI1UtEAqaoQ6aog2ZoQuboghNxoHh3Do6N7dXQXuDQbQwdd2bh0G12z0dNZ9HQGPZFBJdPQmcLqSGF1JLE6UtjxLGgKZWgot4YKmmgBExUynd+Dzk8CJpbXxDJ0svkaDLm0osr3IQx9G6ZukUnEIZ3GzqVRmRRaNomW60C3NqLnkhgkMawkBjlsu1CvUWGjwFZYWZ1s3E8mHqC7rZquTY2ojYdzcNoEoDsc5/UpG3mteQPrw1mSVBFJHsDE1IE06h4qtBQRO0nYbxEM2LiiboxKD0aVB6PCjR5xo4dN9LCJcuvyMCLEHmaOC1L9ielUf2I6mc0Juu9fS/cj6+n62xra//AWAMpr4Kr14q7ykbVykLOxczZkLWwLsGxsy3aGWzZYNnrG4oBchGmZZZBdgpW1sLMWyiqde5YtdLAFgGD+31QiwPz8v8Eo3UL3ZjDcaTRXhmynSSQWpilnMJspwBIAbE+CeLCLbleKpLLxpl7Dl1yFL+FFz/UJHL4Im11pLFcOldPQU+XBrbQ/TrK6g0xNJ1Z1F6q6G5cvg+m28JgWKJuMZZHFImNBKqHIpHUySZ1cQsdKmhA3MRIe3Ekv7oQXI+sCl4XtymG7LGzDIo1FVrPJJRWqy43Z7SXcGcab8lIBVBAGwjveuG4LrSKHWWXjrdJwV4FZYePanCPzyrXYdn6bpSDbA9luRbZbkeuBbEyR61HYWUC3URpobhvDb6F7c+h+C8NnofssQMPKadhZjVxCJxfXyMU1sgmNXExhpTSUCbpXoXkVRhD0kIYR1pyfIQ09oDsvMozCT815iaTrgIZta2Br2Err/RuVv07pznXKVuS6LbLtGbJtGZLvJEi9kyC5MklmU7q4WjSvxoz7Z6HpWVQug7XVjd7RBlYGlU2DlYFcBpVLQy4DuTTKyuarAGZ7X/5YWUCROfAj2FGpbSyEEGJkSW+MYmfJo/beN2qDXYkEPP4EdFlzqJg2AV8qQSgVpyrZRWWim+qV3dS8sQG3nRvyNHP5f31ZKGKaSY/uIakZaLaNho1pZ/FaGXy5NAbWAN/sbxMmUD/kMg2VrSms5ggcUYW5oA7/wbU0NHqZ5wOPBwlUCbGPcdV4qTh7KhVnT8XOWSRfbSfxahupFZ1ktyRRsRx2OoPSNZShnCC8XvJTlfxtaCiXAl1zfjdUbxDDUFiaxdbURtozW4lZ3dg6KJci7K+iKlRHVbAR3TSK39P8LvSQiRZ0oQfNAfP12bZNrj1FZk0H6dVtZNZ0kV7XQ3ZzAiuRJZNN0KF3sFXfytuuNjZ4N7HZt42uYBddgW66At2k3U5QxETDlzWp6opS01ZF5dYqwlsrqdhSRdUrTQRjwQHXoZn/tz1JT4KEN0HcGyejZ9FjBq6sG1fGhZ7T8dkauq2RNdNkAz2oinayLWuwgl1UBruo87fjD3aju9Io3bmC5NJuskkvmbiPdCxAujtEqjtEujtIamuI2Kog7T1BsDVg+7UDNVcaw5PE8CRRuuUEknI6uYyLbMpNLumjrMrYAHQzie6OY7hTaEYGK6eTypj5cnqcIOkQr2G7SnOl8VZsI1KxFe/krXiibXhCHZihLly3J4vj5QDvDqZlKw00I/9PB6VjawboLnJTjiEnwS4hhBAjxFXvI7MhPtLFEPsgV/6W0Lf99L9iGI3aYFdlpeKG6xWgARX9Prdtm0zGJrU+QeKdbrIdabLxHNlEDiuRI2dBzlbkLIVlK+dvFFmXi5xpkHGZZFwucgE3lteEfHS+8EhnA6n8vw7bRmVyaLE0WiKNiqUxks4/3c6hOc+baMpG05zffT436VQSXQeXAS4TDJfCNEE3nAdVNJwHVqWcZxnN+akKP03dab5Z7cWo9UmCeCHGKKVreGdV4p1VWRxWyNk1XOqYM2zTAifAblR4MCrq8M6tG9J3LNsikYmRtdK4dDeG5sKlmdsN1tu2TXJbF91vbyHZ1kOqO0aqJ4myQdd0dN3AMFy4Q37c4QCesB8j6EaPutGDrkGbvdv5OuRKqe2va9smVVq7yMrmV4CGQuFWCjcQVE7tJ+e1ncLO2WS3pkivj5PrTDvjGwrl1jGi+Zq1UTfaDppB2JaN1Z0h15Um15UGTaGZGsrU0YMutMIy2jbYVr4mVP6nbWNbWazuNLm2JLn2lPOvJ+10nZ7LYWct7JwFmfz3NFDKBqz84lj5RXKSrinl1KpTykb3gx7RMSIGrkoNpU3Kr5vif2RRZJXKN+l3EYxU0h1PYusmaC7QXc7vustpxl8IcAkhhBCjkP+g2pEugthH+XyK+fNs/H5Y8eZIl2b/MGqDXTuilMI0FeZ4P8Hx/j09N5zIlAsY2ryG+0FVCCH2dZrS8JsD19IajFIKb1UYb9UQmhLu5HSHOCKofC0jhp5HTBnganTjagztWgEL09FUsbn5jsupA3rvWxucq5fuBb1mt4qxQ0OtN6ZFo1hybRRCCCHEfqiiQpHNjnQp9h+SfVwIIYQQQgghhBBCjBkS7BJCCCGEEEIIIYQQY4YEu4QQQgghhBBCCCHEmCHBLiGEEEIIIYQQQggxZkiwSwghhBBihKxbt46vf/3rHHnkkcyePZujjz6an/70p6TT6bLx1q9fz/nnn8/cuXNZtGgRl19+eb9xhBBCCCGEY5/tjVEIIYQQYl+3cuVKbNvm0ksvpaWlhTfffJNvfvObJBIJLrroIgByuRyf+tSniEaj3HzzzXR0dHDRRRdh2zbf/OY3R3gJhBBCCCFGHwl2CSGEEEKMkGXLlrFs2bLi383NzbzzzjvccsstxWDXv//9b9566y0eeughamtrAbj44ou5+OKL+eIXv0ggEBiRsgshhBBCjFbSjFEIIYQQYhTp7u4mHA4X/37++eeZMmVKMdAFsGTJEtLpNC+//PJIFFEIIYQQYlSTml1CCCGEEKPEmjVruPHGG7n44ouLw7Zu3UpVVVXZeOFwGJfLxdatW7c7Pb/fTzQaxTDklq+vbDaL3+/H5XLhdruJRqN4PJ5hmSawS+t9sO9Ho9HdKtfuKJTJ4/Hg9/vRdR1d1/fYftV3HQBl849Go2XbzTRN3G538bPC+IXPXS5X8TOPx4NlWfj9fkzTLC5D33kWvm/bNm63G6BsPwHKpuXxeMo+LwwrlKX051g+Hgfaf7d3TPT9rLC9Srdn33WVTCaL67awrgeaX+n+Ck5z8NJ9aFe3wfaOxcK8c7kcQNlxsrvnhtLpD2Uagx1HuzP/0W5n1s9wn1N3Zdu4XK7iuaPvfjnQOWln9t/S4wCGdi+wK/to3+Ox8HvfYwF6j8Ghlmc4jL29XAghhBBihF199dVcc8012x3n9ttvZ9asWcW/N23axHnnncdxxx3HGWecUTauUmrAaQw2vCAWi9He3j4mH2x2VzabJRaLkclkSKVStLe3D0uwKxaLAezSeh/o+9FolPb29t0q1+4olCmZTBKLxYrBrj21X/VdB0DZ/Nvb24nFYqTTaVKpVPFn4bPC+IXhmUym+FkymcSyrOJ2LyxD33kWPrdtm1QqBVC2nwBl00omk2WfF4YppYrT3Z39Yl8x0P67vWOi72eF7VW6PQcKdiWTyeJ3Bptf6f4KzoN26T60K9tgR8diYd6lwa6hrIeh2plpDHYc7c78R7uhrp89cU7dlW2TTqeL546+++VA56Sd2X9Lj4PCeWhn95mhBrtKj8fSYFfpsQC9x+BQy7MjQwlY7vLUR/IN075C1tHuG8obotKfg31nR9MFBnwLBM6BWXjTZNs2uq7vMBo90PQL+n5H0zT8fj9erxe3242macXIeGlUPJPJ4HK50HW9+OZQ07TiPCKRSNk8S6PsLpcLpVTxDdNg+V129BZooOURe46s671H1vXes7+s67PPPpsTTjhhu+M0NTUVf9+0aRMf+chHmDt3LpdddlnZeFVVVbzwwgtlwzo7O8lkMlRWVm53HlKza3BSs2vnyiQ1u6Rm145IzS6p2TWSpGZX/3ns7zW7lG3b9h6dgxBCCCGEGFQh0DVjxgyuuuqqshcuAA8//DDnn38+Dz/8MDU1NQDce++9XHTRRTz++OOSoF4IIYQQog8JdgkhhBBCjJBNmzZxzjnnUF9fz/e+971ijV2A6upqwHkb+r73vY/KykouvPBCOjs7ufjiizn66KP55je/OVJFF0IIIYQYtSTYJYQQQggxQu68806+9rWvDfjZG2+8Ufx9/fr1fPvb3+aJJ57A4/Fw0kkncdFFF2Ga5t4qqhBCCCHEPkOCXUIIIYQQQgghhBBizNB2PIoQQgghhBBCCCGEEPsGCXYJIYQQQgghhBBCiDFDgl3D7Be/+AUf+MAHmDNnDgsWLBhwnPXr13P++eczd+5cFi1axOWXX046nd7LJR0bbrrpJo488khmzZrFaaedxjPPPDPSRdrnPf3005x//vksWbKE1tZWHnjggbLPbdvm6quvZsmSJcyePZtzzjmHFStWjFBp913XXnst73//+5k3bx4HH3wwn/nMZ1i5cmXZOLKuh8/NN9/MySefzIEHHsiBBx7IWWedxcMPP1z8XNb1nnHttdfS2trKd77zneIwWdd7nlwbR6/hOven02kuu+wyFi1axNy5czn//PPZuHHj3lwUUWJXz3WyHUfepk2b+MpXvsKiRYuYM2cOp5xyCi+//HLxc9mOo182m+VHP/oRRx55JLNnz+aoo47immuuwbKs4jiyHfdPEuwaZplMhuOOO44PfvCDA36ey+X41Kc+RTwe5+abb+ZHP/oR//jHP7jyyiv3ckn3fffeey9XXHEFn/70p7nrrruYP38+n/jEJ1i/fv1IF22fFo/HaW1t5ZJLLhnw81/96ldcf/31XHLJJdx+++1UVVVx7rnn0tPTs5dLum976qmnOPvss7ntttu4/vrryeVyfPzjHycejxfHkXU9fOrq6vjKV77CHXfcwR133MHixYu54IILijc6sq6H34svvsitt95Ka2tr2XBZ13uWXBtHt+E693/nO9/h/vvv50c/+hE333wz8XicT33qU+RyuZFYrP3a7pzrZDuOrM7OTj74wQ/icrn41a9+xT333MPFF19MKBQqjiPbcfT71a9+xR/+8AcuueQS7r33Xr761a/y61//mt///vdl48h23A/ZYo+444477Pnz5/cb/tBDD9nTpk2zN27cWBx299132zNnzrS7u7v3ZhH3eaeffrp9ySWXlA077rjj7O9///sjVKKxZ+rUqfb9999f/NuyLPvQQw+1r7322uKwVCplz58/377llltGoohjxrZt2+ypU6faTz31lG3bsq73hoULF9q33XabrOs9oKenxz722GPt5cuX2x/+8Iftyy+/3LZt2a/3Brk27lt25dzf1dVlz5gxw77nnnuK42zcuNGeNm2a/cgjj+zdBdjP7c65TrbjyLvqqqvsD37wg4N+Lttx3/DJT37S/trXvlY27LOf/az9la98xbZt2Y77M6nZtZc9//zzTJkyhdra2uKwJUuWkE6ny6rMiu1Lp9O88sorLFmypGz4oYceynPPPTdCpRr71q1bx5YtW8rWu2maLFy4UNb7buru7gYgHA4Dsq73pFwuxz333EM8HmfevHmyrveASy+9lMMOO4xDDjmkbLis6z1Lro37nl0597/88stkMhkOPfTQ4ji1tbVMmTJFtvNetjvnOtmOI+/BBx9k5syZfO5zn+Pggw/mfe97H7fddlvxc9mO+4b58+fzxBNP8M477wDw+uuv85///IfDDjsMkO24PzNGugD7m61bt1JVVVU2LBwO43K52Lp16wiVat/T3t5OLpejsrKybHhVVRVbtmwZoVKNfYV1O9B6lyYyu862ba644grmz5/P1KlTAVnXe8Ibb7zBBz7wAVKpFD6fj5/97GdMnjyZZ599FpB1PVzuueceXn31VW6//fZ+n8l+vWfJtXHfsqvn/q1bt+JyuYoBstJx5F5y79ndc51sx5G3du1abrnlFs4991zOP/98XnzxRS6//HJM0+R973ufbMd9xCc+8Qm6u7s5/vjj0XWdXC7HF7/4RU466SRAjsf9mQS7huDqq6/mmmuu2e44t99+O7NmzRrS9JRSOzVcDK7vOrNtW9bjXjDQehe77tJLL+XNN9/k5ptv7veZrOvhM2HCBO666y66urq47777uOiii7jxxhuLn8u63n0bNmzgO9/5Dr/5zW9wu92Djifres+Sa+O+YbjP/XIc7T178lwn23HvsW2bmTNn8qUvfQmA6dOn89Zbb3HLLbfwvve9rziebMfR7d577+Uvf/kLP/jBD5g8eTKvvfYaV1xxBTU1NZx66qnF8WQ77n8k2DUEZ599NieccMJ2x2lqahrStKqqqnjhhRfKhnV2dpLJZPpFm8XgotEouq73i7Rv27atX805MXyqq6sB5+1HTU1Ncbis91132WWX8eCDD3LjjTdSV1dXHC7reviZpklLSwsAs2bN4qWXXuKGG27gE5/4BCDreji88sorbNu2jdNOO604LJfL8fTTT3PTTTfx97//HZB1vafItXHfsTvn/qqqKjKZDJ2dnWW1ELZt28a8efP20hLs34bjXCfbceRVV1czadKksmETJ07kH//4R/FzkO042n3ve9/jk5/8JCeeeCIAra2trF+/nmuvvZZTTz1VtuN+THJ2DUFFRQWTJk3a7r/tvdUpNXfuXFasWMHmzZuLw5YvX45pmsycOXNPLcKYY5omM2bMYPny5WXDH3vsMTkh7UFNTU1UV1eXrfd0Os3TTz8t630n2bbNpZdeyn333cfvfvc7mpubyz6Xdb3n2bZNOp2WdT2MFi9ezF//+lfuuuuu4r+ZM2dy8sknc9ddd9Hc3Czreg+Sa+PoNxzn/pkzZ+JyucrG2bx5MytWrJDtvJcMx7lOtuPIO/DAA4t5ngpWrVpFY2MjIMfjviKZTPartaXrerFWlmzH/ZfU7Bpm69evp7Ozk/Xr15PL5XjttdcAGDduHH6/nyVLljB58mQuvPBCLrzwQjo7O7nyyis588wzCQQCI1z6fcu5557LhRdeyMyZM5k3bx633norGzZs4AMf+MBIF22fFovFWLNmTfHvdevW8dprrxEOh2loaOAjH/kI1157LePHj6elpYVrr70Wj8dTbBcvhubb3/42d999Nz//+c/x+/3FfALBYBCPx4NSStb1MPrhD3/IsmXLqKurIxaLce+99/LUU09x3XXXyboeRoFAoJh7qMDn8xGJRIrDZV3vWXJtHN2G49wfDAZ5//vfz5VXXkk0GiUcDnPllVcyderUfonSxZ4xHOc62Y4j76Mf/Sgf/OAH+eUvf8nxxx/Piy++yG233call14KIMfjPuKII47gl7/8JQ0NDcVmjNdffz3vf//7AdmO+zNlS0PUYXXxxRfzpz/9qd/wG264gUWLFgFOQOzb3/42TzzxRPEgu+iiizBNc28Xd59300038etf/5rNmzczdepUvva1r7Fw4cKRLtY+7cknn+QjH/lIv+Gnnnoq3/3ud7Ftm2uuuYZbb72Vzs5O5syZwyWXXNLvpk9sX2tr64DDr7jiimKzCFnXw+frX/86TzzxBJs3byYYDNLa2sonPvGJYq87sq73nHPOOYdp06bxjW98A5B1vTfItXH0Gq5zfyqV4nvf+x533303yWSSgw8+mG9961vU19fvleUQ/e3KuU6248j717/+xQ9/+ENWrVpFU1MT5557LmeeeWbxc9mOo19PTw8/+clPeOCBB9i2bRs1NTWceOKJXHDBBcXna9mO+ycJdgkhhBBCCCGEEEKIMUNydgkhhBBCCCGEEEKIMUOCXUIIIYQQQgghhBBizJBglxBCCCGEEEIIIYQYMyTYJYQQQgghhBBCCCHGDAl2CSGEEEIIIYQQQogxQ4JdQgghhBBCCCGEEGLMkGCXEEIIIYQQQgghhBgzJNglhBBCCCGEEEIIIcYMCXYJIYQQQgghhBBCiDFDgl1CCCGEEEIIIYQQYsyQYJcQYtg98MAD/Pa3vy0bduedd9La2sq6deuGZR5PPvkkV1999bBMSwghhBBifyP3a0KIsUyCXUKIYffAAw9www037NF5PPXUU1xzzTV7dB5CCCGEEGOV3K8JIcYyCXYJIYQQQgghhBBCiDHDGOkCCCHGlosvvpg//elPALS2tgJw0EEHceqppwKwbds2rrrqKh555BFCoRCnnnoq//3f/42u68VptLW18eMf/5gHH3yQjo4Ompub+a//+i/OOOMMAK6++uriW8LCPBobG3nwwQdJpVJcddVVPPbYY6xfv55gMMiBBx7IxRdfTH19/V5bD0IIIYQQo5XcrwkhxjoJdgkhhtVnPvMZ2traePXVV4s3OIFAgBdffBGACy+8kFNOOYWzzjqLRx55hF/84hc0NjYWb4x6enr44Ac/SDab5fOf/zyNjY08/PDDfPOb3ySTyfChD32IM844g40bN3L77bdz6623AmCaJgDpdJpEIsEFF1xAdXU1nZ2d3HLLLXzoQx/ib3/7Gx6PZwTWihBCCCHE6CH3a0KIsU6CXUKIYTVu3DgqKiowTZO5c+cWhxdunk499VTOP/98AA455BCWL1/O3/72t+LN0+9+9zs2btzI3XffTXNzc3G87u5urr76as466yzq6uqoq6sDKJsHQDAY5Dvf+U7x71wux9KlSzn44IN55JFHOPbYY/fUogshhBBC7BPkfk0IMdZJzi4hxF61bNmysr+nTJnC+vXri38/+uijzJ07l/r6erLZbPHf0qVLaWtr45133tnhPO6++25OP/10FixYwPTp05kzZw7xeJxVq1YN9+IIIYQQQow5cr8mhNjXSc0uIcReFQqFyv42TZN0Ol38u62tjdWrVzNjxowBv9/e3r7d6T/wwAN8+ctfLuaWiEajKKX45Cc/SSqV2v0FEEIIIYQY4+R+TQixr5NglxBiVIlEIlRXV3PxxRcP+PmECRO2+/2//e1vjBs3ju9+97vFYZlMhs7OzmEtpxBCCCHE/kru14QQo50Eu4QQw840zV1+K7d06VJuuukmGhsbqaio2O48AFKpFG63uzg8mUxiGOWntrvvvptcLrdL5RFCCCGEGIvkfk0IMZZJsEsIMewmTpzIrbfeyh//+EdaW1sJBAJD/u7HPvYx7r33Xs4++2w+9rGPMX78eGKxGCtXruS5557jZz/7GQCTJk0C4De/+Q2HHnoobreb1tZWli5dygMPPMD//u//csQRR/DKK6/w+9//vl91fCGEEEKI/ZncrwkhxjIJdgkhht3pp5/Os88+y3e/+116eno46KCDOPXUU4f03WAwyB/+8Ad+9rOfce2117J582aCwSATJ07kuOOOK453xBFHcNZZZ/Hb3/6Wn/zkJzQ0NPDggw9y5plnsmHDBu644w5uvfVWZs2axS9+8Qs++9nP7qnFFUIIIYTY58j9mhBiLFO2bdsjXQghhBBCCCGEEEIIIYaDNtIFEEIIIYQQQgghhBBiuEiwSwghhBBCCCGEEEKMGRLsEkIIIYQQQgghhBBjhgS7hBBCCCGEEEIIIcSYIcEuIcSArr76alpbW4v/5s2bx7HHHsuXv/xlHn300RErVzKZ5MILL2Tx4sW0trZy9dVX8+STT9La2sqTTz6509M78sgjufjii3c43jnnnMM555yzK0UWQgghhBh2cq9WTu7VhBCljJEugBBidLv11lsBSCQSrFu3jr///e+cd955vPe97+XKK69E0/ZuzPyWW27h7rvv5oorrqClpYW6ujoCgQC33norkydP3qtlEUIIIYQYaXKvJoQQ/UmwSwixXXPnzi37+4wzzuC6667jqquu4oADDuC//uu/9ko50uk0pmny9ttvU1NTwymnnLLdcgohhBBC7A/kXk0IIfqTZoxCiJ123nnnMW3aNH73u98Vh7W1tXHJJZewZMkSZs6cyfHHH88f//jHft9du3YtX/7yl1m8eDGzZs3i1FNP5Z///GfZOIVq+W+88QbnnHMOc+bM4aqrrqK1tZU//vGPbNiwoVhlf926dYNWjb/vvvs488wzmTNnDgsWLODzn/88Gzdu3OHyPfroo5x88snF5fj73/++i2tKCCGEEGLvk3s1IcT+ToJdQohdcthhh7Fx40bWr19PT08PH/zgB1m+fDmf//zn+b//+z+WLVvGN7/5TW6++ebidzZs2MCZZ57JW2+9xTe+8Q1+/vOfM3XqVC644AIeeuihfvP47//+b4444gh++ctfcuKJJ3Lrrbdy2GGHUV1dza233sqtt95KTU3NgOW75ZZb+NznPse0adP46U9/yre//W1ef/11PvzhDxOLxQZdrjfffJNPf/rTRCIRfvKTn/CpT32K733ve6xcuXK315kQQgghxN4i92pCiP2ZNGMUQuyS+vp6ALZs2cKf/vQnNm7cyN13301zczMAhxxyCN3d3Vx99dWcddZZ6LrO1VdfjaZp/P73vycUCgGwdOlSNm7cyE9/+lMOP/zwsnl8/OMf56yzziobVlFRgWma260KH4vF+P73v8+ZZ57JpZdeWhw+e/Zsjj/+eO68885BE5j+8pe/JBQKcd111+F2uwGYOHEiZ5xxBhMnTtypdSSEEEIIMVLkXk0IsT+Tml1CiF1i2zYASikeffRR5s6dS319Pdlstvhv6dKltLW18c477wBOlfPDDjsMn89XNt6yZct49dVX+73FO/LII3epbM8//zw9PT2cdNJJZfOpr69nwoQJPPPMM9v97uGHH168eQLnxqupqWmXyiKEEEIIMRLkXk0IsT+Tml1CiF1SyKdQXV1NW1sbq1evZsaMGQOO297eDji5Iu644w7uuOOOAcfr7OzE7/cX/66qqtqlsm3btg1g0DeC0Wh00O9u2bKFysrKfsN3tSxCCCGEECNB7tWEEPszCXYJIXbJQw89RENDA/X19UQiEaqrq7n44osHHHfChAkARCIRDjrooEF7Bep7k6KU2qWyRSIRAL73ve8NWJ299Catr+rq6uINWKmtW7fS0NCwS+URQgghhNjb5F5NCLE/k2CXEGKnXXfddbzxxht87WtfA5xcDjfddBONjY1UVFQM+r2lS5fywgsvMHXq1LKq58PtwAMPxO/3s3bt2n7dXu/IvHnzePjhh0mlUsUyvvTSS6xbt05uoIQQQgixT5B7NSHE/k6CXUKI7Xr++ecBSCaTrF27lr///e/8+9//5tRTT+UjH/kIAB/72Me49957Ofvss/nYxz7G+PHjicVirFy5kueee46f/exnAHzuc5/jjDPO4MMf/jBnn302DQ0NdHV18cYbb7BhwwYuv/zyYSlzIBDgwgsv5LLLLmPLli0sW7aMQCDApk2bePLJJzn00EM54YQTBvzupz71Kf7+979z3nnnce6559Ld3c1PfvITqRovhBBCiFFJ7tXkXk0I0Z8Eu4QQ21XoYcfr9VJdXc3s2bO57rrrWLp0aXGcYDDIH/7wB372s59x7bXXsnnzZoLBIBMnTuS4444rjtfQ0MAdd9zB1VdfzQ9+8APa29uJRCJMnTqVU089dVjL/YEPfID6+nquu+46/vrXv5LL5aitrWXBggW0trYO+r2pU6fyi1/8gquuuorPfe5zNDc3c9FFF3HjjTcOa/mEEEIIIYaD3KvJvZoQoj9lF7rpEEIIIYQQQgghhBBiH6eNdAGEEEIIIYQQQgghhBguEuwSQgghhBBCCCGEEGOGBLuEEEIIIYQQQgghxJghwS4hhBBCCCGEEEIIMWZIsEsIIYQQQgghhBBCjBnGrn6xvb19OMsh9rJwOExnZ+dIF0PsBtmG+z7Zhvs+2Yb7voG2YTQaHaHSDC/LsmT/HAPkPLPvk204Nsh2HBtkO44NQ7lXk5pd+ylNk02/r5NtuO+Tbbjvk2247xvL23AsL9v+RLbjvk+24dgg23FskO24/5AtLYQQQgghhBBCCCHGDAl2CSGEEEIIIYQQQogxQ4JdQgghhBBCCCGEEGLMkGCXEGKXWekcW697lfX/8zTJtyTRoxBCCCGEEGJkrO54k+5Ux0gXQ4wSEuwSQuyyjZc/w4ZLn6Htpjd4+333kni1baSLJIQQQgghhNgPvb3tFZ5e96+RLoYYJSTYJYTYJalVXWy78U0qPtrK1IdPRQ+arDn/YaxEdqSLJoQQQgghhBBiPybBLiHELmm/9S0Aaj8/G7PBT/OPl5Be3c3GK58b4ZIJIYQQQgghhNifSbBLCLFLuh5YR2BxHUaVFwD/oloqzz2Abde/RuzJTSNcOiGEEEIIIYQQ+ysJdgkhdlp6dTepNzoIHtNUNrzuonmYLUHWfXk5VjwzQqUTQgghhBBCCLE/k2CXEGKn9TyxEYDg4Y1lwzWvQdMPDiW9toeN35XmjEIIIYQQQoxVua40HX9dJTl7xagkwS4hxE5LPLcVPWJijg/2+8y/sIbKjx/Att++XgyKCSGEEEIIIcaW9JpusGwy62MjXRQh+pFglxBip8Wf34p3bhVKqQE/r/vqPMwJQd79ymPypkcIIYQQQoixSHfCCbZlj3BBwLZHvgxidJFglxBip1jxDMnXO/DNrRp0HM1r0HTVIaTX9LDl5y/vxdIJIYQQQggh9gZViCaMgmCXEH1JsEsIsVMSr3WAZeOdPXiwC8B/UC2RUyey5Zcvk9mc2DuFE0IIIYQQQuwdmtPKw7ZGuByAzcgH3LbE1vPChsdGuhgiT4JdQoidknqzAwBPa2SH49Z+aQ521mbrr17ds4USQgghhBB7TVubzdZtIx9cECNL5YNd5EZBtGsH1netImvt2d7iX9r4JNvim/boPHZWNmvT1b1/HqsS7BJC7JTkig40n4Gr0b/Dcc2WIJH3jqftpjex4nv24iKEEELsKv2dR9DXPjXSxRBjVDzdQ0+qc6SLMaz+8xw89/xIl0KMOD1fsys3uoMpHYmtvL7lOd7c+uJemd9oyh/2wovw5FOjq0x7iwS7hBA7JfVmB+4p4d43OTtQ8ZFpWD0ZOv66as8WTAghxIhS3Zsgmx7pYuwSlepGxbaMdDHEGPXI2/fw1LoHR7oYQuw5oyCOsr1gjpVvZ5nJpfZOWUbDCsnrGFtx9p0iwS4hxE5JvtmJe0pkyOP7DqzC3Rqh/da39lyhhBBCjKxcBv3dZ9DffWakS7LzMpJXUgghdkk+prOnanblutL0PLYRezebSe7t4NNorEU1Cou0x0mwSwgxZLnuNNmNcTxTwkP+jlKKyCkTiD+zhczG+B4snRBCiBFTyE6c2ffO8yrVPdJFEEKIfVOhF8Y91Btj4qVtZLckyLUPpUbWjsug1NBapuy+/TCyNApJsEsIMWTp1c4DgTkxtFPfCx8/DoCu+9YMe5mEEEKI3WJlnZ9KbouFEGJnFGsL7aFgVzFmtNeCVMNjNDVjLJCaXUIIsR3pNT0AmOOCO/U996Qw7slhuu5ftyeKJYQQYqQV76L3rQcSx374BCCEELtIda5Dta10/sgHufZYgvr8tcUeQjBtNAWYpBnj6CDBLiHEkBVrdo0L7PR3A8saiD21GSudG+5iCSGEGHGjv9v5Qdn9fhFiv9GR2Eo6mxzpYoh9iL7hBfTNrzl/7OGaXcUAzSjv7RHKA1yjKfC2P5NglxBiyNJrejCqPOh+105/N3BIHXYiS+L5rXugZEIIse+69tpref/738+8efM4+OCD+cxnPsPKlSvLxrFtm6uvvpolS5Ywe/ZszjnnHFasWDFCJR5A4SZ/H2tqAvTmG9sfX3uL/ZplWzy7/lGe3/jYSBdF7Kt2oubVbk0/u+MXKiNdmyprZXr/kOvJqCDBLiHEkKVXd2O27FwTxgL/olrQFD2PbRzmUgkhxL7tqaee4uyzz+a2227j+uuvJ5fL8fGPf5x4vDfZ+69+9Suuv/56LrnkEm6//Xaqqqo499xz6enpGcGSl7D34Zpd8gZe7GGvv5Hl5VfsEX8Y7yuRiQEQT0snDWLX7K2cXUMJdo20VLa3Z9/RWLNrZ04/7663uf+fNqnU6FuOnSHBLiHEkKXXdO9SE0YAPWzinV1JTIJdQghR5te//jWnnXYaU6ZMYdq0aVxxxRWsX7+eV155BXDeVt9www2cf/75HHvssUydOpUrr7ySZDLJ3XffPcKlzysGu/bFml379s28GP0SSefR1xplQeF4xglyuQ3vCJdE7LOsPV2zK/8jO/rP04lsrPj7vh7sWr/e+ZlIbH+80U6CXUKIIbGzFul3YzudnL5U4JA64s9uwUpkh7FkQggxtnR3Ow+g4XAYgHXr1rFlyxaWLFlSHMc0TRYuXMhzzz03ImXsZ5Q9xO+c0fdQsifE486b+lhs/1je0cgeZbntCjW6TN09wiXZd2U7UmQ2xnc84lhVCHLtoV27GETLDaEZ43bO5Xsj+JTM9u4Htm2TzqV2+J3sPhDE25cZI10AIcS+IfNuDHL2bgW7/IfUseXnLxN7ejPBZQ3DWDohhBgbbNvmiiuuYP78+UydOhWALVu2AFBZWVk2blVVFesLr18HEY1G90xB+7DNHLbfD24/2l6a53Cxc23YPX6AsrJv7FpLT7qLyVUzRqpoRcOxHbe1ZfH7c8QTOk1N8giwt7ndbkLhEG7DM9JFKdqc8eJP+fF5vDu9j/n9zoN8NLp/BcpK15Od6mHzb+6BuulEzz5wBEu1d1l+53ypwiGMYBrDn0P3ufbI9SZnbsPym/i8AQI7mH46m8KfL1vfsmRdCfxdfkLB0ICfD4eNab04f8tM8+KG5cxrOpTaYNOA4ycSNg8+lGbmdIOWFn3Yy1MQ8KfIWRCJmLjdQ6t9HQplyGQtIhEX0ei+Wz9KrnRCiCFJr9n1nhgL/AtrUC6N2BObJNglhBADuPTSS3nzzTe5+eab+32m+iR/H0r+n/b29mEr2/aoeAd6LIadscntpXkOF9XZiR5zmp9kS8q+/O37AajUh/96tTW+EbfuIeiO7HDcaDQ6LNuxu9smFoPOTmhv3webm+7jUqkUbe3b8Bi+kS5KUWdXJ7FYDCu98+eKQg3B9vb9p1ZT32NRta0kmUphb32X9vYJI1iyvcsonC+3bSXe2UU6FkPldLQ9cO7v6erBzlpk2jUy7dvvICudTRLLl63v/twRd/b1broH/Hw4bGnbXJz/u1vWEIvFWL1pJWbWP+D4W7c55+S3V0IotOfOyT0xG8uC9vbYkINdhetFeweM1vQEQwlY7rthOiHEXpVa7SRB3tUE9QCa18Azs4L4fzYPV7GEEGLMuOyyy3jwwQf53e9+R11dXXF4dXU1AFu3lvdmu23bNqqqqvZqGQe1L+fsGoFmjC9ueJyn1/1rr893b8tmbV54cd9Pcjxclj+eY+PGUbQu8gHzjJUe4YLsm5SVy/+ynz5S29ne0+ee7o0xM7qaAA+ktNmiyl8L7e008c/mO2907Xwn92KI9tMjUwixs9JrulFuHaNm95KY+uZXE39+27BetF7Z9DQ/Wv5V/vzqb0ZdT0dCCLEjtm1z6aWXct999/G73/2O5ubmss+bmpqorq5m+fLlxWHpdJqnn36aefPm7da8V7a9xvqu1bs1DaAk8+0+GOwqvW6M4WvISGyZTZth8xZ45N9Ok539XSJpse7dkS5Fr0Ieo5wluVR3SeF8ofbB895wsHK9waisvUcCUr05u3bz/LEXzu0ZK9VbAzv/c3udUmTzh52x51owlhnDl7dBSTNGIcSQpFc7PTEqbfcu6P4FNWy77jUSr7bhm7P7NRJWtr3Kxf/4ADY292TjJHMJzpp1wW5PVwgh9pZvf/vb3H333fz85z/H7/cXc3QFg0E8Hg9KKT7ykY9w7bXXMn78eFpaWrj22mvxeDycdNJJuzXvVe2vA9AQatm9hRgNCeqTXahUN3a4cSe/WBrsskDtpSePETJSDzzPvwAHLx6ZeY8Wtm0RGLhF04goBLts2yZrZTC00VvFxLZtNm6Eurr+TbpHjnPes5XGy6/YVFdDbc3wl60n3YXfFRxFy51nZXtP/ZZN572r8R9Sh6t6+Hv3HEpvjyPdA2I6l8LUPaSyiZKaXYOXKZuvGKiPwojMaNvVdpXU7BJCDEl6bc9uJacv8B3oNMeJ/2fLbk/Ltm1+8thF+Mwgvzv9cZaOP5Ebnv0+Xal9K1+MEGL/dsstt9Dd3c3/Z++/gyTJ8vw+8POee0RkpK4s2dXVuntmukf2zOxgsDs7uyAAEgR2QYA4GIADFgABLg924PK4JIw845E0LoXBAANIGmnGI8/uIEiAIAkDV8/uyJ7W06qqRWmZmZVahQ6X7/3uj+fu4ZGiKqu6umdmN75t1ZkZ4eL58+fi933f3/f3C7/wC3zta18r/n3jG98olvnFX/xF/spf+Sv8yq/8Cn/mz/wZ1tfX+Qf/4B8wOXn/PooPFD8ksssGKeHVJiKCP/8y3uq797wNJbvIro8RcRp+rPv7uKGSHr7J/H1G4iEs5jBF5R4oxNhDqd5/1NVdKytw/iLcXvp492vFYvJ0xT1fDj5fXYP3Pzh4O+feFdbW752MaYXbvHn7uyy3b97zuh81lDV70hfNzgO+p8munz+iEBFSkxQFKHKyy8oBY4fBPXF+Aa5c/egP8L4mOn7E+/1u+BHkEUcYYYQfNYgI8UKHia+c/NDbqpwap/LIpCO7/tqzH2pb7629xoWNt/h3f/LvcnT8JH/1+f+Al+d/m29c+Sf8+c/90odu6wgjjDDCx4ErV67cdRmlFL/0S7/EL/3Sj+i97YeUzhNcbJAsdfGO1Lh/TcqDSWNcat0gSPs8c/Szd1zuezd+NdurcPVWn08+XqNS+ej77YcxUz+58n1OtWHpyJ/4+Hf+IwjBGUV/nGj91gKVU+NM/IG973BlEuxO6VY/Cogzf6P4Y7YXO7fyMq1wh3/pqT+998us/9Qh+m5r2/07dY+v0vkEbpD07m3FjxJKu4kBa/aSIQ+AHEnW+yhP4R0tVS49xL35h6nsSjK/rpqXkV2HSWNMBr8v3oZPfuLw+zOdmN4P1pn8+ml07XBq5N+PaYwjZdcII4xwV5hmhO0kH6oSYxkTXz5O/+0Pb1L/Ly78f5irn+BffubPAfDYkU/yxdNf5xtX/unIu2uEEUYY4R6hdm7hX/5tMMndF96NH1KgrMfcS77ZLqkJ7lWhIgf+cU+4uvU+t5vX77hMWT3T7cKNW5ar1+57l/eHj/PxmO1LN/r451cPvdpKe2HI7PnHDWkqfOe7wta2DAW7IgZzsNDj0Oi+tkb39bW7LnfjpvDBeSFZO6hi4o/Pu1KZrN3ZEV58WUjTw7U/vN7CdO6PJWuFOwd/ad29Uj5CBjNMAwBq/oNPDbxfSJ7qXTaoz797AEOq94N1uq+uDW/7w1p2fcRjPc6KPFS82p49H4T0Q9wLohttbD8lPfDa/nAYpTGOMMIIv28QL374SoxljH/pOMlqn3jl/meptvvrvHH7O/yxZ/4C1dKD5euP/zwrnXkWWx939DDCCCOM8OMN3Vx0v6T3QzL8cAzqle9eZU23RNCZe03H+vjSGMtV74yB8XgJuef2/vihem0D3Q4P5bsTJD0ub57l4sZbH0PLBlDbNxzZ+wCi9W7Xjapbt4arsQn2gSi70s2AdCO463I3b2X7PeCYhjJ4D0kGvLn0Pda7tw+17INEfmcRgWvXncKrf/cuQEQIL+zQffnwZOuhkZHXHyXZFWVk14+Un1rOhMidPak+NMr3iw+5n0E7P5pnlMmIz4quDu3P3CGNcTfxffXajw/5/OOCEdk1wggj3BXxQgfggSm7xr98AuBDqbu+ff3/wIrhj33iLwx9/tVH/ygAP7j97ftv4AgjjDDC70d8mKncH5Kya4g8Udlr7T17D92Z7HqQ6UNpWTUXdpnpX2Smd/GBbf+OGMSnHzuKfd5DwJorWj4u6O1skuwBjOX8MLUeTmMSMmVXGh+KVL50WWg2P/wZO6jbywTXYUgLEaEbtbi48c6HbtOHQZE1fZiFbV4t8MHfo1R+PX8MZNcP23x9X4jNPfpLnz3Adt6B61I7t9DrF3bt+uB9f9T9l+9bZc8hmxcvuEObdpNdC4v3sMP7eFzfz6n5cU+UGZFdI4wwwl1RKLseeTDKrrFPzqInK/Tfvn+T+hdu/jqfPvEVTk8/PvT5sfFTfOLo53jj9nc+ZCtHGGGEEX6/odBO3Puqh/DsWlsT4vgQ205CvFsv0WgvcX37/J2XzckuAdFZao29xzRMubN64PXFb93T5prB1oHfpaW2WWsRBF9+fNP17oa8N7Wkwx/cAbnXzcdtR3AY36XDIg9ildpFKCEYA/71b+Nfv/N7ijHC0jK8c/bDt+fgQxtu212380MkXFTp9lQmE++K/Njvkcy/vHmOd5ZfvPNCkhMadx47H2Ys58qgj+t6CNM+ry787uGLPe1u14Pkuu5wb/Y2LqIb8/eytQfSpoO37ravi/uXGxN38ux6EKf0Xrbx40Z2SWqJ5tsfahsjsmuEEUa4K+KFDv6p8cIb5cNCeZrx54/Ru09l11LrJjd2zvOzT/7Jfb//wumvcWnz7I+138cII4wwwg8N9/VGfOdgL4qEDy7Ae+/ffVO6uYiKOrx78zdZbN4lJd3kZJeAdnWX1EGV0w7E3dMY7yXQPLvy8oHfDZNd7qfWP+ZT54eAIk/3Opx6CO5cxexuuNW4xGuL37y3lYpzvCvADu892Eqy06zUrjRGsYf27MqXO0SX7QsbGlSQ3HEb5XF9N8LGbecBGI59SAj3Vg8jH3P3Klxdac8P+XXdUTV0l5P0YQgD9WEmIe4D3ahFlAZc3Hj7EEvL3sH1YdMNy+VKy0PygM3qtfOo3sETDINmfTzKLp35meUkl5QPQgS9dh5ipxa+X0GgDVLi+c79N/Ye8HE+nTovrRBebRZ/Bxd2CN7bJt26f5XviOwaYYQR7op4sfPAUhhzjH/pOOGlBqZ370bIL83/JgrFTz/+c/t+/+kTXyExEde371ADeoQRRhhhhGF8iDTGgSpm/23kFdRarcNszR66PUUAI0BumnzPBvV3d0F+UIqWsmeXFYUgHxnZtVtFV9js/BC4NS3Zs/4Q+877+k5eN3fDrZ3LhMl9GjeXOkhvXcWffxmi7j1tIsmG4N40xsNXY9yXFIt7h06zbH9rkbH3lu64zG7V2d3wQy3+Uxq/90QAPqBKsfsSffm273JSH0S3fVzVMnOyph/fYcyXbiZ7ju3DGsknB9yPDyK7mgvorbtXNP6oMVB2OXplQNqXzlvYQjcX8Fbfdd/d5yntfH95aM+HbuN9nJulpY/vujeNiPDSQFEoYaZq3FWIwjQjkkN4FsKI7BphhBEOgXi+Q+2J6Qe6zfEvnwAjBO9t3/O637v5q3z21B/g2Pipfb//9IkvA3B+/c0P1cYRRhhhhN9fyAOY+3gD308VU0JOdh3qldka4i2DXj3Ea6opbVHfmexqBJsstW7Q+u0F+u8doAR4AMouALOPusyG6ZCyS6wju+5XuXMnLN62vPSthH7/h6saa6Qt2rY1IEMPQ3blQeI9K/QeFAaNVJ2V7KN7uybS0jzebhLpfsiuXk/AGvyb30evvnfXdZvBFlvdtSLQPmifw8quw5BdH4JwSUL370GgJOi8K+52gVnjPNTKiHuo/vA9Yn/yNfMDE4uKDp68vVM7o+hw1+jHlUJ6bz5usvfgPizZVfZWGxJZ3kFZV5vJFj94GXvA+slaHxt++HtNfm3knl1SeHYdfM3c771f4sE244UOvXfu3xbmbtjcgtv3U4/CmvtSxe6H3crJzosr9A5RjRZGZNcII4xwF9gwJVntP7BKjDnGnz8G6t5N6m81LjHfuMwfevJPH7jMbP0Yj8w8PSK7RhhhhBHuBYXw4X7ewO8cfcbxvh8PoRu3WWrdBBHa7yf41zKjX7G02wcornLzaZES2bV/0Hlu5RWuLr2HpHY4BeQunl2wKxXlEDCS0o4aNAIXhCRrfdrfvE28OVAbGZsFZx/BrHnj3Ra195Zor+3t+I9TnHO+f5mr5jKDMXXwzlVrCcJ2EbA+iOD+fhQJr7wivPJalvoW5+fr3raTK7uM2d0GIT1kXF0mqH7wJpCRLap398D2wsZbrHdvE1s3zg/uhrurGpu/fovggkvns/tcB4ftY//Gd/FvfPdQy+6HsjAr75vD7LrgWY0lvNLc0169dQVv8bXhtt78Prp5m3Ie3b6kRbYtsZbauTur6HKozhoqq3y7uSm89Ao0Gne4LnZ5QB2ERrDJB2tvHKoNd0J5P7E5BDlpGRL0flgVUJnsGiI57rjZw+wzT2cdNFaMpffGOr031++tkftufVjZZQ/h53Yo7jhoDA303cSPacYkS4dTnt7LmSlfb/G9J+Gg1953qlhziIf/XVAm9+65HR967yOMMMLvacQLmTn9Aya7vKkqY586cs+zES/c+DU85fP1A1IYc3z6xE9wceOtH67kfoQRRhjhh4iNjXt9QfwQeW6F6uHuZJc9YDr7raXvceP9f4RuLtBL+4CGVFhbs7zxFqxv7LNe4dkFkqXfKHNAGqMVaq/u991gu2urZl+lxb0+S6wY3l76PudWXnHNbDkPybL3iCkC9gf/nPK6LkhN+wNm5cMlcd0n7iHFyVt9zwVHOYHwAPrFyL1W5oQoFoLdGTL3qGjKPbsc2bU3jfEwx1ZWdg0rs+58JuNYWFgOkOw/OPiSPkjF0/7WIr031ovAOrre2rPMYL07NueBQ6REnd6LsksgvNwguT1MDKioC2nIzZ1LbPZWwKaD1LPSDvZPI8zJroxI2WeJZlPYLL3qesvv4K05m41mltbdPoQA5m7k77urr7LZW8HcczXag/dz90q0goigvIMphbPnhPmFe0i1S/ZRdin23DsWFoUrV4cnWe75Pp2nyUX3qOxKQue9NeTHtzuNMVNV3kltdrfbStjCX3htOE3TfIgL7n6VZPexngqa7pdDKnSHvNp2fxffv/JuRHaNMMIId0S84J7AtccfLNkFMP7l4/Tf2TyUYS24B8kLt36NLz38dWbGjt5x2c+c/ArNcJvl9s0H0dQRRhhhhB87vPXOfUzHcp9V6QrvrP3XLc8MHxh4Z1+kYmgkLVCg+tDpum3297FgklIgW/qw+DVIehhrXKC6a79WLN+79s9Z7blUtSQRrl4T3t/H7jFJLZcuC8srh3te7Q6MVSUzLY5dIOpfMtTWXGBZXrTfF7a3Pzx74GUit+jCDp2XVj709u4XUg5W2atK2HedB5iuda+pkEly4OC8p+3kRJUxu9VQdyaf9tsGgN4n2B9unhAvdRErXLnqUo/CYNCXh0pjLO3ABoZkrT9Q2ai9ywy2cfdjeaCQklrrMMvnYy7uoVfOIeEuFUwaoqxhvnHJKaPE7kt23clDTvZZPsdb78D5i4dp6DDiW9s0/9nbxTm4Wz/n5/JOZFfnpRWiG3c2TiyPicjcxRdJcMouTw1/VsL2Dly7fufNDGEojTEjEbXa0wHtdmkSZZ/nzm7ia9+xm5FdqubdE1GmNy+hmwuo7iA7ZaDs2mVQf6c0xgO+6vWEi5cE0qzQVikdUA5BdiVrfewuAm8sWUfFXdJUDp06O9jpvS0+vNLhpln2O678sxHZNcIII3xkiLJUjwet7AIY/9IJbDsmunYox2Iub55jtbNwxxTGHJ85+RUAPlj/8JLuEUYYYYQfVxz2BV5ESqbDH51n13ZyjUZw58pZt3pLtKWHp3x0WwqyYF9/6YLsklIWpnsxTkzM64vf4vrOB06hsKtpQdTGW/uA2xtn3aYsKCzpPrFiu21ZWoaLl+7Y9AK7A07lu9aZxLXNW7HUb+VNHzTs1dfh7LvD25KdefzLv31P58XL99eOMY1dlYk/RnJioMAxJDY4eN+lY9tNFIoIUXp/1bgOY3K/uSlsbbmGXb4C+zXyXgng/JTuVnblOIxvVzndUetSu/a5EJLbXfrvbBLPdwa8c+n/B7bzbv5MWbCZK3c+LpP0/SB7OZBDjeVCdRU5wkB3d2UU5D5iOTEqsq8aZ3/SIluuUHbd28VV3HKBK1eFRrNENL3xAbq5iGQs/2H90u405k0jIji/c+D3ri2DNqQHEmelCpEijowqNvDhbjBDpEd+yN5esmvXWtn/9ydvXbP2rp8TQh1p8cLNX6MbHS4eSRJYWhZsSUV8kGfXna6Zgw7p/Q9geYVCYTp0xR9w8yi8DmND7411+m8N28Qc675NbfFF3noHXnrlwCYVGEobvodTqpffQS+fvfdxsM9OJHteDqn97hEjsmuEEUa4I+L5Dt7RMbzp6gPf9sSXjwOH9+365vX/jZpX5ycf/WN3Xfbh6SeZHTvKhfW3PlQbRxhhhBF+nHFgUG2GVV/uhfzDpDFmO7IG1d6rJMo3uR6f5+zyy3fcxmKwBlVhrn4Ub9EWL/H7zg+XlV27KqO1IhfU9eMuqY33BMZJ5kdTUT6AI7lEMmJhGNFBip8Mq6tCp1tOeRoEnCJSKB9sUg4eszSzu7zHy/pl94u5u1IvSHq0wp3iGO54/j8OSU62i610ntXoPHJQg4YUWMPturx5jlcXftelmN0jrFiu3xCuXjUH9t+778O5kuf7voTFPZA8i7eFzYzPTXd5dhVKq3tQdj1yxm0nTfdvw/qG0FkOs2YOxm/59EYhLC4KW9uZYqTUosITaj+CJ9unEbh6TUgSw42bQqdT8j36mMjT4h6yMSAC7yWNUXTF/W1K5K9NUbnHX0mdlRMUetmgV3LSYr9qjLvasbs9IkxEC8UCQ4TLUHEAWLwNb7/D3u/jXrbpw3X0h01jLB/EoQg2kQPFO/umvVohvn0Hj6mh4Zn9odVduK7DeMkdrOzq2CYAzfDOEzE55hcVjQY0d/Ze25qcGB4QcL0310lWe3vacGDmXvEo3tuxByq78ptKts39qt2LQDfr+ntRdx3mOlPtFfzLv43urKE7q+W1nUfdXVS2kgpihWar1KeZV5fEFmvvQ5HGiOwaYYQR7oJ4vvORpDACVB6ZxD9eP5RvVzdq8Z3r/5w//NSfYaJ69/YopXjuxE9wcePtB9HUEUYYYYQfS+z7kpqE+Ne+hWrcKj4aDuTuHuDs9d3KCKk0wls555RI8cDvRWQQfAhwdfU93rj2rWxjqfM/KQVpJ/yjVFUVFcpdDH7L0Wb+uzuWVuCq/U7VZl11xF1Nzs2Xq1kQbIxTdu1Hdt3NYP/8RZhfGPw9mM0X7PY1iJw6wyRpoZaxUnZVugPuUmWyjNcXv8U7yy8W2xyQAsP78a99C71y7q7be1AIrVPVWHNAwLOP9w3A92/+Oqsd17FRerhKfmkqBclnxXD5Zpf+xTfw8/F2N+x30dwDo3Pl6uB3Y3aTFNk1cAgOIT+G8XH3Mz4g0Hv/Azj/dhbYpnZY+KXcOsurcOUanHvXKUaK1kjJY+gOZNd2Q7GwCFevC/1geKx/XCiyEQ+REl1G0ddZx6jyOCpXhyzugbZQdlUuWyqXTLb/O3h2FSlsu+WjDY70z1NLt9hJbnCp/2tsNbKbSamQxs4OGIlpp8uDdT03yayv9MDe+T6Yw1phdT1x1/t9spDl47zr3UnEnYOy6XupmfupZMNLDfpnN0nW98lLdw1w9zprBhMdWu25f2/ZTVbNnaql7lJ27XMsNnQN1FX3DEgPKG6yd8uZP6S6s2eXilro5UWSa/P0z+4l0g46pXlvJlsRybYdHugHkF0FCZb32QEE5FjN/WzdRcQ2tP4Bw8C//Nt4C664g966tv92ept4ax+gN+4iizbC2rqwfqvB6jdXSbfDQQqvEc5fKCnS7sGAckR2jTDCCHdEtND5SFIYwRFSzrfr7squ37j8jwnTgD/13F8/9PafPf4lFlvXDi1LHmGEEUb4vYZ9hTSJCzJ0ezD7KkPKrjsHVf2+8N0XYG29rFLYu47qlLcPQs68wMY3rpC+0KQbtehvXqK58T6qOyglXsEvFCf2Ti/vQ1Y5+cu++zCxLqhUKFKbYKKYNBqoOuJ0L9k1lmxypLN3kiSK9idjDkKurlC9DWT9PLrlKrXZKIU8npJM27XrJE2F1yEqKR+0nx3r4RUbOTGQc0uS2D3EwPDs+53x7e8KN2/ee/BcpNSpnOA7oB9Lx5YHpUkqrG2UFHKHrIj5yqtw8ZILcZbbt7gRfJs4nr+XVu/z2QH7TuMDgzxwQ3E/8uBePLtqtfzvgrUZ3gEUEhEbmYGyy3JgkJqT1YIUaVf7NUrSLIDPhuB+RS/umVO5T+XRvhzkvaxY5JYO7gEqS48VkeJmmZr4UAb17e8u0bwcub7czzsQUNmxejaimTqF142l7AZg0+KettOA1ehdlqI36SfZtZ8pYdSqQa8Kdic+UBGVt63ZgguXLS+9At/53u5uONyJGros7+p5Vzr2fTawH7edm8EflJomAnrtA/T6+QGxs8uzy4pl3txk2d4GQO0+x+yfDr1nX5lyyM98tsyu4w2SXlFRtwxL+SLLfs0nfErVGPX2TbyVJrq9jDdbLaVD58+2/Y5finERvL1N5/1hAu5A30MrmF5C+1u3s13sn1qak+dXrrriCYfBnZZSQcP9kk/K7F4rv97NrnT63Utbi26vMBnNky5t0H1ldXDbtcJ6FiqKCKpyeAprRHaNMMIIB8LGhmS5R/UjUnYBjH/5BPGtzlCFqt1oBJv8b+//d/zUo/8qT849e+htP3viiwBc3vr4Zq9HGGGEEX6UsPv9PkkElSsYSi/DFjtsIHMH5JXDtu6W8VGdGGqHJUUngnfDoLImvLn0Pd5Ye4VzrctDzFxF/KIt0shTtCw2GA6Uh1I6ijTG4cqQghDFKWvvXaG7vFIEPXEW6FaUj2iP1MBEvMRYvL6HvIuTOwdwu1EoBNKERAySp7b0U7zVsrv2sN5AScJMcAVvqZSCnzEN6g4kgd68guqVgrIs/aXo0nRg0L/f6TXdhP57W/v72mTB1Y1be766K8pJPihYat5goXmVa9sf8MLNX0OMZbW1wM2dvQ7ey8suZS0IMqLskGbzSQoKTbstNLNANWR/Vdh+hvT7igYOIID1+gforavQ398HyUpOJOfbuXeyKy82sCeLTizq4jeYDq6i0oR21MAGAwLFysHKHFtwOVKkXe277C7PLrMfYXDPZNf9mU3fd7pkQUTtcw6TECuWWFJUVrnzpYXfYTUavrnpFYsJhuWdjeWY+dcSlpcHxTMUQpKU0q2yRnsSOmJRzGB87aoaa8Rtv/CnK06SRYWCebND/+zmHpIoioRON8FYIQzBSspy9zK3w9f374cSXlv8JguNQaW/ZDMg/u4mejtT1Bz0MMhVcm4WA+XtL7XJlV3V95Zpf3cpW/fg9mQHXBy3FONvWNnVDnddbzZ116Etk+P7b1+VrvC8L7W4iyyVYWLp9cVvFRV13QqCXn4HP2ln29pL3nsZcZZf9xJnfVXRw2RcTjiLxZT2ay2lVOSSN1r+2y5lVxQJ7bYgRkg3SrFU6fmu2HvNhZErnnAYHOraUwNaqUgZPszKYt0/IygGRLD7avg66pktgqRfFHw5DEZk1wgjjHAgkiUnnf6olF0AE1/KfLvO7p/KaMXy9175ZRIT82995T+9p21/8tgX0EpzaeOQd/MRRhhhhN9jKIuGbs0LL7/Qw97MC3eUyC4pWTKLgaCJauyfq5QLTIbS/e6Q+tXYSWi1wEjK1KJl6w2Lr8awVpjxHufTc5/lE2NP8RO1h4pVKzJQdsnZdVSQYM6uD2atdx9gKY0xnz0fTPZbNrYTtBisgMoUba0sYHJBkR4isawxJKkU//r9g8muVrjDzWBYRpGrykCcYXRehj6x+Dfz9rkUoPJMvbZ78yXjbUP7nRhJBwGR6SZ0X1kt0jz09nW8228O2r9L6CCJlHyFBoNCb1wCawjOOXNz09q7/8OQe/tBskC4jNvN69zYvsDtpivP1vzNW9z41TeYb1xxyytdKDLMQAjojumQyi5wyvGF2wOzbnVA3sv+6al7VSIHEsC5+samkMb7kl758Xg2GphI3+lQ4h7ezRdQ6xfROqvEyEDhiBjoboBYrIXp8BpjrZe5vfk+a9uDa3ZI7bhrp4NfZUiJ4jZfCuBLnl1u6cMru/p94dvfFTY3dy1gU0fMxj0IGns2oLob+/qr7bufQwThspvsKg0FlQaca13mle2zQ/eS1dC9k5p+h9tLgjmXErw4nIVQvi7KRuKv/aCUbpWNP12oTE3RALUrZc5TPr7pFenVA+LGYtoyIEpL94tGQ3jpFXj1B4abt3BkFymbySU6Zm1o+/tdPmHS50aJaDbn3sG2mni3c5+yu3WwU3b5x8cYe+4IaswbOid5H+kgxnaz473bpEr5sisru0rHvduEXwUN9NY1VGO+tPLwDvZLAy2qjWbn5K5+Z0kf3VmjEm9nGyhvc1jZVZBt+bBK7NDy+XBbit7kSv+39nwOcPu2sLGx+/oZ/vvqNVhYdJ/nSrXSIblfS8/Eg9Ib74R7Jbvw/KF9J4nsW00ZwLv2Hbzr30GMDO7Tu5R6+Z8L4cu8efsFVFUfOpVxRHaNMMIIByKadzMXH5VnF8DYZ+ZQNU3vrb1kV5D0+Puv/DJv3P4Of/Or/yUPTz9xT9uuVyZ4fPZTXNockV0jjDDC70+UX1K3tmA8Xin5qGRviybGRt1SECL4C6/irZ/fsz1rpXhfz4Pwrd4qm8EgEIxjIU6EtBnDzhJr3/smUztvcqz9IioLXpJY0dmZpf+/zuBdnqC5cJS4M5btBKq6iirEXYKKU6RZUueYBO/mi0jYyxcqvSCbYr3853ajlCbXWSVKejSyNguAHpBdInDunOXyFYp/jeYgiLi95NL6cvXG9e3zhDZLlzcCsRDnJeNFSGxaBChD3mi5sqt0knQ+w689xDrD3vYHAXHTItEgOA4v7JBuh6Rb+yuWZFeQkFe1AtjcsJjsPOidm6jO2uBk7mOCPvC/Su+pGp8VWwpcd8nKiv6wqNh98b2tN7ncXdijyCi0DYeIuEQEzwb4GZFwt+D1Tl5sw4qsAxi/PHVHBG/xdfzF1/csYoxF24iZ4CKTWTrlnQ5FRR3SXo9weQGlBjFkUVXQJOj2MgRN0kiQnsVagwoaREEwOF/l7gdqZ5eK3wteByk8higF5/2+G9/RQgfiHtXmZRBLPbrNVHjjQPJQRHj7HWeE33bFvFlb3318bbzbb+Lf/D7+wmvDisQkxFt6C736HrtxEJ+uGgt486/u2x5sil58C5Jg38IDNg64tdXBWBmcY7HumgVMr4Vn+vQDsLEdGoPDnEPBJA+PqULZFYGUVLUAJh3qxblwkenwKmHYgiREhe6eksSG5etCI3fkKO24n5Fs09OWOM7IrgPG6oHpbznCNmZlE9VeLqWHH3C9l831rYBWjD0zi656JYJC6P3WebyNxvC6B9RBaQSbewnyXNm1y6D+oGOkVLX1IGXXeneJa5vZsy0dEJzuz7t4duWKIwGJha1vbLJ8NcralE08KKfgkn6aN8T92OVGn1+DHbOabXNAhOePYmOFKHLVgF95Vdx9+6DzaASJS/0yXMLxzsd1F9wz2SVlnzHh+nW4cIBll7IJyiRDx2UNfHB+UCW3/F1iY7BC5eT4odru332REUYY4fcr4nn3pvJRpjHqqkf9c8fovenehkSES5tn+d2r/4zv3/o1+kmXv/rF/5A/8clfuK/tP3viS7w0/1tZDvx9TGeMMMIII/wYI00ESUoeF0PWJ1kgtn4BL1Ywm08ouBfjc63LPNT7IscmnOLq5k3hxi146slsU9kmF5rXUL1FTtYeBjIDbCtMnd+iOunImLFkk8R20GKACoIlSTzwIboZIEFKu+bBrNu9UgpdyY1+ZQ8Jo4IGKu6iWiky/eQw2VVMjQ8IlTBKKVL7jGB2BzVqQHZ1u9D0hLkjUK870/3P6Pcxc1/j1oJmIRPPRBHUohW8zcvAMQAqHxj0thD/qQhvyaJ2NOkTpog1dvs37fZVGpBdPu3fWQSgt6VoLMNUGDlbZGsgLklJ9iF0TNkbSgQJIwRHJsaxZWnZERrVmmGR7zO3+Tw7F8b48pftnuAg75fL/d/k6MocX374Z/fsbz9YMUWIVTx9BUiF2osp6ZN64HOT9ctSuM5s0By0vdQ9hyHaoggean2PreQCqxPPEcUJ1bSBYkBKlaUNDbcrKv4grSjfo0ktRTLOQdFewUQZVJz5KYkdCvyMCCo7r5V0cGxRJGyuCadO7X43EZIEUPDkExCbgKXwPM+bp4YXsym9awa5YtBkijg7MOfv9mDGG7RbpYNAuJzGWCgoc5LCCDduZgteC3h6YgHPj/FNj4ngIpumvX9f4MZKownd8/Bs2XWiTOhuXR9eKd1rEq/izp5tH0R2eRt7Sfkcqr+DCjuotoFKfc/3ra2QZq+GtSFHjuxPoOTXpGCHgm6RcuXOfByXDisVKtnxeDYEHVNP1kE9knmEDd+DfOOOOUw6eIuvE8fQ3hESBD8VAi9T55TakIs9p2cMdj1vyeA4jBG8PMXwIMIi7kLYQvU2saEgXhWyW4rFOpWd0pjAosf94XdpAWstkQ0h6RGZkDhMIB6nntbw1q4y112h6z0N3U2wj5TWdw26eUuw1XUWw9d5yv8UJ9OBwrcgiPZ4dpXGstgBYVtSBO7x7DIx3so57NGnubF1gWOnHi486fL97Pbs2g1VVhwmQmPL0no3oa+q9LwB2eUtWPR8CI+oYkxIYkAG6Xe7x7MlxaPCS6/A3BGGxtryCgSeM5WfKD0Ly2NBrAyluJbPU06yihx8K7sTDrXOkBK2/HtWKMWDbleYnDyAKDdSyMXzyZjtbTh21B1bfs7FQtAXJg7HdY2UXSOMMMLBiG608GaqeLO1j3Q/Uz97muDdLVbnb/CffOcv8+/81p/ghZu/ytcf/3n+u5/7bf7SF375vomqZ49/kU7UYLl96wG3eoQRRhjhRx/Bqyu0f/UD7G13D/TKaXKlF/fVcIuz2+/TSXukJsGKZSdu8cHqa4NlsqyYoJg8dy+fxiaYkv9MmkAYJ0RpQNIoBSgYJqNFyAJzTenFf8PQv2GoezUez0gz7WdBOIIytiBM4uXuIFCMI9TOreylP9tXGlKu5AVCYpMi6LACxqSkaVYNMSMncgJgx25zsfdrnDoFc0cUcxNdjtdXOToVFOuDq2x36VtnkaBZvNzrbfczMgH+FYM3r11ttyRATIKdhORzHqITlDXZS/ygj7zMtycNFJJaJLW0ulmKTRA7H6oL59Ar72akitrj/QMDfyuxTr2lr7+E33OVy1SmPklT2O5GpCbh4pobH42X1nn9/71Moyn0GwnN769gwkEAuLy1vzcVuKpbZWN9KSu7Sj9VpwEI3mpJ1ZYFpsoauP0Gqr89WKUgCu+eT/lyJvDJlUe2s8lkND9YYFcAvJp59CcpXLiYr+v2vDGUfmcdaZruysXZx9x9tz+OKSk6lB6oN65egw8u7HMQYh3BKDA7A4vti7TNEpv94YICSgQTZiSLoiAeivQ6Kf43BN90SRdf5Y3FbxGmfbTyUD0h/O11ko2AZLU3tHwQKtKmpfrmEuzjMT1saM6gPZGBOEUEbtw0LCwK1goq2kWWmdI9qdSfcSy8975wa35YpTi0770f7VrAkrbFbbdUIbXYdWKwVFxAbIXK+yn+xV3EuuRqHruLXNinBaVGhmGxEFpixqNbVE0TQVhdAxU0h9aWjGGKk4DuTo/1Dej1odczqHKTSp5Nzp8OKrUSwSWD/hxKQS61rfPiMmb+It7KObyta/jzr0ASFoso40gbEeuqmF58kc53loiu5vKywQW90LjCe2uv8frit7i0eZYbm+9y7fqvI0EfK+DrmAoddHvZpU3vsqG6cRPOfhCg+kLv5TXCS04JtrMtvPNWdl/wcg9Cpzgsk1JDpJYZEKd7lF35uOsNVMgDcuhwyq6gGw26MiM7lbHML8KlK+55opRGN7PfIz3oqmR4ZmN3ergt+XYpBRhb3IuqFffznXNw+dJgG1Hp0um/vYEt3auH0hhLyq47plAfgHxctL99m+4P1ui8tELa3HUzkHTXCrLn89ff2N8nEaDdsEMk/PC2HRkITs146fJexehBGJFdI4wwwoGIrraofWL2I1dETf/Lj4LA//Rf/we8t/oaf+Mrv8L/8Rfe52/99H/Dcye+9KG2/Wy2/iiVcYQRRvh9h25I2oxR2zfg1gcAeLZkYFsKMjfjBo2kyZuN85x94Qe03s08Zsr+Th7oVkD87VtIP+Ctjd9gvnGZVFLSkrrIWHiv/z7Xm1eKNC8rliQ3nxXJvH9yRUm2Yiz85NwXeOTRP+z27ZcUJ8bS6rgZ395bG5iOe9FWcYgKm9hOD9PLVCFpiLfwqgt2RNhaNfS2Yvwwq7hlhOWVlKvXhNtL0MqC4TLZdeqU4OWpfbnvzi4SI869dEWGggmAdn/HeROZ2JmA93Zg9T2SL3nY4xqZu0E13aH6gSK9EBbHqW2MbBlar+01GrZhwhtvwdK1hosjrMU0IvZLsTO5R4s1ELURA+Nb5/BNF8WgMmNGvZCQmWNHkO5E3LgBr3874oNXQ9bmkyL4aDb37KqAd/tN/Fsvltqwt10qaKGbixC0imPOGlosE9gIksHx5/zCnZRdnagJ7bVifKtMwmgyFYRCZSlSw4bSExs/YCrcVU0xa9Oli8L5C+KqjopwffsDXlv4Jsm+5IwpmXYPH3e53VodTNzkePHyt7nw8jySCpUKgEXbmPr62V1LCqIHvytlQIS07Lm1z/Zngkts7FwkWFrBrIVopdHrzrOv+coi/feGzdk9zwXI9TF47KTgezDk92elqGxaBPAKwhcWGTt7G7myxfyFkHYbwrSGjB9DqgNZhkpLQXOpCuLaOmxswvUbLjV639M/xBbtPVrTiYlWsnNTHmthG2yKNRZRKkvjtehNwdtldZWTl06NVSLvLXRsm570Bg2RQc9E0eB4PBuRyzsFYbl9jGhnc4h4yBVZ/V7CjdtjhWWXLRYqWN9inTSFSgU8bzDmIjtQxZUJlTJRZ5oxnW9cHFSGBVQzS6ezCaoveFtq4OPWcQRovNllp18ii6wlNAEz9aM8d+JLnJl5Eno7eJ11WF9wSiJfDQiHJDjQoF6vD5/gXh/M1Yxcz8jchUX4wRvD6cmmPDCSwVgqKlT++i3CK43iXpMksLmVHVd2f7Am97Q6+B7TaApn34nZaZRvnrYwsZRsEiFXmRUpmfmQToc9u/ZTdpWhUlMs5PuDhfvdEqFaunRMP8V0yvcmII3xbr5ANb1zVXrVmId0HyY7MZAOnhe2n5KuB5hGRPD+9vCyJkW0j4wfYyhtche7luzDJ8aJ8P77QreX9V2esq0yxZqxxWSHMY5kXFnbu539MEpjHGGEEfaFiBBebTLzxx/7yPd1fuIc7bktfuL6T/A3//Z/z/GJ0w9s24/MPM1EdZpLG+/wR5/+sw9suyOMMMIIP+rQrRJhEALjjuyylUlkfAwJGsWbYHkWPF7qkFTH4AxDL+eeBm+7R5pC3N7AzsB2fwNjB5XMwL2MFpWmlPOeumwv0pcek9RRyJCyq5jNzbkwbxIAlWeeIeh+TNAOWFkZo119h/q84en3S6pjA+3X+8z9tPtTRR2kXsNcNoSvvse0slSpY7BYgV7P4nuCAnaawtraIP1LoaiU0r+wBtGC7+1jcIx2AisxiBoo1dI4odJyHklWnILJVbwsDmqgjWiliBWMdQoQIoHakLuw206Up1NlJtBiCC83ULaCv2tOqqiaV0TM7kct3SbyjxW+bVZcQGWU+yDnbqwFlfl8XbvVoTOxXHy+G6YT401V93xuxQ4HdAJBJ2G8hhtXAoXheWnBVMxQPtiA/9hP2mPpBju8tfC7PNnrcKLzSbqqRUCAEoMdGNQVKZ054tj1Ry3dpjP2zHBDs58isLkJx8XSiVzAuNlb5fR09m40MNQij8hzskvrzDcr7KJzxUipUuJBULdSOt0e9bbF9wFl8W0PoYIRi5cH09awvpyvZDHaImmKCcv+N3t3ZFUVEaics1A1qEec+iQxIVe3bnF88jQweA8LQyeW1OMWz8/ZnMFAuLHzAZvRTX7miZ/HGK84zGKedLWDN21hAqLZZ6g++jh6+R3amz2Ugsnp/YzThPX1guPg5s39ibvykGi3DG+84/FTfxDGxzOiPMrPvyoGUv9CxCTfp/LoSUzqDOO1ViXF1C4SJld2IUNj0Qps2XXA8oSeQmt33VWrTnGz08iUirhUSEG7rhOIvSMErW3kWOm8ZPtZWYl5Qs9RIVfAyVCTZBfZ5fvg+4Pz0TbLxe+vvAZjvsFbbGCPTuBfGax7IgVvqYZ5ImKhv8oj/qmsq1w7qh8Y5NFhZuJ2+wbrqzt8LfUZRztlqkmZqk1zaupROpM+rdYkNu6xfDnARkBNoTJWVpVI4d0eYmpYUDiEhc41bCsibT5KnGjCqKRkK09CpCH4GrSPiC2UTuHlJvJF9/vtZUXSMcSfkILsyu8tWzuCOV1K/Syh04Z6suFITBhSduUfmFSh0KAHZFcxRlsbII9kB6v23EvNbmVXOlB2KTO0ILkoOirzxAIypOxSqP4WKu4zHQwqbu65hych3voFWL9A+sTPQC17/hrL2DuL2Okx5NRDez3fMvXl/AI89JhQ78ZEjRnqz82i+1vkyu/dafapYQ/6fQaVbyiRXTivr+pYgHwme/5lArnDFk4ZKbtGGGGEfZFuhZhGRO0TMx/pfprBFn/35f8Hi19Y5bGrZ5iT4w90+1ppPnX8i1za3D0jOsIII4zwexxhgvgeygMb5y+PFludQupzKGswWRpdYtMioNJxzxEOgM7e1PtJ1xnpivMTCmwTa2GqNkNqkyL9QyQjjQqzd4sspvRtr5jQdwGsJZ/vL4KBJFe9ZIGRP3hNraxvMxNcLiopdpqNXQdbSpsofRYuCaHdwTONwg9kpxuT2JATx2HuqMsCXF2TUnomVEoz6ViDIHh6OEqIbIeOdEhtSdmVHaQqxe9RYFna7BGaGGzMzo6w0EuJZ1OSh52ixvZTmr1tOsktekmPTtwlMZl6LTcrzgI8UTpTmaSu8+YvEm8aWm+nkHnQbEY3iWynpJxydOaR/nkmooUiULBkRsK7AqCc7BIR5oOXWIvfzzdToJ90effyizS/s0B0c6+Pk5QrfCrBGOi8rZyxf2FSvVfZZawpyDAY8Db7VQL0Fl4jvvJNlt+9wlqrj2f7LHiXAailO9hwkHboMl1LCrLsfPv+rgMbHAEAYzWncKxXnBppozcwei8XdcjJTk9i/O7LVJS7JvTym0wEzgQrVRFWDO92L/J+cm64D/JNJllzfIXvD477Uvca3996a9A6I5AIqaSsJZtst4R+L6H70lvMXR7KZRyCVZWCLAXQlxP0tpBkac29UpqhthFp1lFBP0tDZDgtar3nvOWMmCKQjmJolC5RlX2xuqa5eEmwc09xa7nG9eVJVOoulstXhPlbbrk0tjRb8NRTcOok3F7eP7gtd93yslu3vN9ipTzPE0As0ZJFBTvYVEBptKdRQT7ghoPzMtmFFcK0TydoDu87XWQzOY9NoZrxvvMLcO1qWbrl2mIRrKpgjQxVWM3TGDudBMmqc8T+EUDQEqHTLEW4RDqsdueJ1TbKH3TO5ES5f4S5TpPpTpsjcZfJSZicdGq9bk8h2T3tem9xIBD1HQGu0Ujup5btMsrSBMOs3Ukao5sLVLevF93s4RG+VGXnXIdeH/C8wbVr0gOVXXeSO27112iF23yw8RIiQrPbLL5Ld/tspQPDeBuUyDrJU8az6qOJS0vd2bF8cN7S7giLt+HaLks5a4VuV7BBl7FkE09nTbWZ8jkjSQXBWoVSClHZdVt6LKn2CrQ3ys0Z3k+JtNvaJiPRJOs29/P4scHvkKXKAomEQ+nSOWLjkxpxBRKyfe7t5sEH3sIrg4+3HPuo2yG2m9D6zfnhtVJ3jUYRLC1B952AcCHBRrvP6/D5SYYKOLg2BwF4rQDxNeqUhxzLLiIFykZMtC4w03NxnLWgbMqx/lscBiOya4QRRtgX0TU3gzn2idmPbB8iwt9/9d+nE7f42l/+s0hs6b648sD38+zx57mxc2Gv18YII4wwwu9hqDBB1QRdVcULqMIFd6KdOfjFi8LGRp7yUUqVyIKaXKXyg8VvM9/7ATvpDdJUSGXg7yKZP0cqhtQ4Q3wdaUC4uP0Ba5vL+F1v4NktEX5rnqrtldYfQBIXDfmFQb0tTKJ1fh/fHeDkKSPljxDSiipUa0crs4DiVn+ZVrrCeF2YHvd56KGy0XTmgVPZS3bVV1/HNwNPqhvBd7iSXqIV9wYGwHkdgP5g/ZXbwtubN7jVXUHZmK1tkIrQfzzETjj1k+0lXD7/D1kNX+N2PM9Cd4GrW++7tJrMV0biPKjKlV3WpUqmAd3zKaYHKgt+2tEWW8nlYhq/HG/4tk/PdrmUniclBbGFJ1DOBV1qfZtO/zYpURGbFkFehksbZ2lub9GNW5jW3hQYI2bPOfEioOG7XhYpKQoHgVoijsQr8XRue4VKzaCXz+ItvIoKW3SrD9OrnmGrWaNjOyjP2cqPJ8u0Nt35UiinwAt2IHbjLk8BqlaGfbYKNUXul1NzjcjTFxvB5iCVsVDqDRQrpvc6S/1vEwWvZ+0fpFLumA2WojdoJx3iLHV0T/CZSBbQZQoTZRkeoXm/WEiEbeuqGVoPwqhPI16hmhM3+/EHSjnlX0YE6dsJujPscOTZgLneOWaDi0W0+PDDQhTl6c2DpfPKhVbMECGVV2NEBmTXxrZmeQVeenuG1Zk/QqonISN1by/B/IJbrpOlap04DrOz2X5KvMVjj+49vLw6aqUy+EzylVRp8IrNjMmFZCfBb+tMBSOQRoyFA7IvSyAmry4nVnht4Zu8vfRCdmm5bcZEpARESbhLuTIY1yojHI4dy8guy5BvYpFCJwm+tiR6kthzBaLqySrVYNndi0qcxnz7HDf7LxVE/Onq8zzxuOLJx/P+sTw6FXLmYcWTn/A487DizMOKo3NuPsImJQVTTj5VXB+hVFE8QIygNy5RaS3S7QoXLqX0+0KcnbuqKV3/VtPq2qIog2hVYqwHlWklHR6cai9XM4Dn7ueN/g5byRWWWovFVya/x2nP+WRlFRlFLLZfUna23f0o32vQD7i4+Q7v376AkkEVzd0VWm8vweuvWaIbTUScArfXt7RMk9j2S2mMFmuUS6FW7npVooafb313UdjIEt4YTi20kuB5pQ9K3mzWwMQ4jI+DKXlepSmkErERf8B6d5kylHIeWZcuMXST2ZOqWfpbWcPWtrC8Mmx2L28usRtJZAtiOQgEG6aD+2G5Hbue1UmZS87uHeHNFN0OkaNV1GkfmfKKY1Danbdq4ia6bKak1jJMSh+EEdk1wggj7IvwsruD1T5Csutb1/93Xl/8Jv/WT/wnPPUzX8Y/PkbzN2498P08e/xLWDFc2/rggW97hBFGGOFHFX53h7HWOXRNIXEubrAICrRHYlzKVZy4EudHKjN8be55APom8z4q+w1paCVLGGIEp+Iogn6B3s2Yxvdi5IOY+s0xVwlSoI8jF/KwSkuMCEzGKyhJBu/hubAi22SEotPNZ+PzhTJfpD1Vs/ZG9WFkyS1MqlSZ8Kpov0K9Bg+ddOlGntIuZU4sUrwWK/yhNMbUKbs8xU88uTC8V6UxtuTTlCu7eqXg4l3N2FqVXgBi0nIxQGxVSGIhaoWEO138toff8Yvg5OLGO0RZlT8b58ouD2sGyq5uS7K0UVUouwryqgigMwGXWERibtsFgn6APRe6tuZKPOvIx9B2aQRXM0LNfef5wykwQdIFgwt4demg8mMTU5xbXw0YCPdZRiMU7vO7yK7S+RwcQkYa9bfRnVUQsNOnSY48SVQ5znbtc5z3AjjxyeF2qEqmugBun0VffwGAKCMGK5W9PltDO85+L6sX4yK4z8ejHai8rCNkA1xga4qUHIVS0E8WB0cnsietSEXu8L3c32uXom0t2KHTEWzqzk1BUykhTQNEXBXRubkDuC4xJMagsuPRAxY6+79QTcuyLEWlAmP1gLXzLSrbPlriknG7FD/3EZdkirq8f92+cmNto6tcuRgNTKuzbUXNlOrFVcaqtiCvyiTEw6eHFnffh9nOw+bgi3gfPzBg8ZawswPJ+YCp61UsGtVVYGK8sVIVu6y92kRIv1GWGbLYea90lILWjngIAtf/tWSLqcWryIbBno2o7bgD6Jz6KdAVt6mSIXqu7FE2YnzMgFWAI3DSSUP8RAJRp2hDahOMcSotnaUxHj3iFDE6z67FYrsZKVAiT2o1EHTOvbjJj/weUHH3W43CJgHxhiFtCKQhXhrR6brUwX4ASeaRVdFZPrxStJoekY1JJZuYkEEKKXZQmbbcnqwDDoTvVZw/GXVC2yCJB+tG+aCrZJI2M1Dilcmuhbci1jYgv2bfmP+mI7ElAJzytEwEXdx4G3CG6P7tBsmVBnItZfOthJY0WUxvsWpuEiUNGsktAtPAWu18jhWDCRhTujcG7l6ebKZEF3ZQwWBQW0mHya7Sc88mgueB74FNB4U9rIW+cR57ZdP+JBG2dgb3tZIAda9Echfbfu5duHgJdBJSj1eHJh7KWF40xTUZ9rN7o+eVnqP5gQ2TUkOeXdYSBEKwlT2gHnLWBHFzDSXpIB1aBumqxkJkmoMXirtgRHaNMMII+yK8sIN/sk7l+N5SzQ8CjWCT//HNX+Fzp77Kv/bsX0N5miN/9mna37pNsv5gFVifOv5FYGRSP8III/z+ghf0UDWFqg6nMYIG5RH0B6bryyvQ2FHUvCqe8gjyFDprnfrAplQbFp0IVlIES7cHH1yK6HbdS3m4nA6MbAW2tqDdyQJhLcULtyqULrjqZHl8YV3KiESu8uL1RZ0Zopde+gvTo+HIKI7snpf2S5clF0VRpYpWwpE5zcSEwtPgKw+lFNrL0hCVSxN54nE1rPQSU1RMnJ4wVIYcbxXWlNK6cmVXyUTYFlXWoN+1lKM6qbh1zr69g//uOJM36yjrUkNabad8ahlnBGwjg7/cRJrW9Zm1JIlhddEF7oFNULnvmc0ak6sesi68mJ7ng8iVLKy2fEDQ66Czqn5WQDAceSd157qkeMqVXTY2mE5MahOUhTANimpp+c78y78NzYUSRVkiVKS86C5JGdCI2xn5CMomVJtX0RuXUMvn8G6+iF59D9E+5rE/iD39PEkWNFs1RlA9ydhkhfoTn6ZZf45e9QydsaeKPrh4CW5lc2ph5EgBz2fIcw6xTIa3qPmpIwWs+yyxcVGwZxAUZ8qetE+QsbTV7PymKiYxXebD5awPFFrBbHCxNFT3CSRT5welBZZaNwiSTmlP8OryVS5tr3B2/SK3zA16nntnEl/AOKWgUi79cm9+qqAlJTEpKg2zgTGoejpA6XxqR6jkabWVRoXZ4AonO4OUJ71suf3yeVYbC2gbMhENlDdlZZeo4dDTqhpxENNqDqvpwmsx080FbDMurrcogloVvv61PAA2VBvX6PVcEYEktvimw9jyq+ita9DfQW3ddtuNe0UVSLGCCYTb76cZaeASM2lCrydopTlemyvOGUAlWido3SIIu6gdi3fbkhaVbRUenvMDzwokTE7AVHgdWUqRpcwDq6FJdR38GlSq7tLMUji1jQridyxeYuzGFoIi1WMAdJ8OsBMWlYZstJdYaS8QJD2sdedGRQnPHBM+/2lHFuiim6W4L5Y9l8bGwFYh6GbKV10p8ZE5XaGQdkj3fEK47L7M/eIMru+iTJnm5yyNAsT9vpLeHHyUtUGlUfF7dKtNWlKEhn13b4giYatUCVVEMMqQpo5A13iYYGDwdfV61nCvimifJEpYWRWstUhoUL7Gm64S7ljSFDfZA8jg9CE2VyUO9rvWuY1qLTHbOw+pccUuuhZpWEe8ZPdUayJW43fp262hAg2uo1S2nNtu/1JM94PECdwsqLDkdcmwsktZQZH5PSbW3as8wA4IcmvBkqBsjN/bcDerNGZ+HhYWBs+locdl/vyVJCOzhu8RlbQJwHjrMvVkDV1N900h1ukg7z/pZb9rH5FdLFSxP/ez24Vvf1fY2XETNtdvQNIX7PF64SHqETETXHb96LkxbPMCDxY2o0tDt6g7YUR2jTDCCPsiOL9D/TNHP7Lt/w9v/KeEaZ9f/qm/V8wqzv2FZ8AKO//06gPd18zYHA9PP1nM0owwwggj/H6AshYqCo6eQVL34qtEEBS3e0t0w2QoyG/suHvxmFcltgPWxLv6TbyVD5i6ahhvBEz3zxXqlVY34tYCgGA9u8eGpZxyMVA0iTO9lhSVGT27j93vNhJUTRUrdDpSvNfmxIAq7ajfF27cEMKwtHOlSBIpFE5aea6yUxFsO6NvhUJr9yIu6EFAkQdnwc5wlSpr+HK5SLBSrjpUFiluZgWqvI2cPBKSPJ1HQZKY3ZEH1KC33aPTBaGCVVU6XaHdzv1N3PLRUox/uwE3o0Itk8YWIqdKuNFbLBRaCGjlyCxEMDOPARASICUCy2LwFhR+3wVVQdJ33myRoFNIJSy84j0PlHh0vrdM6zu3nfmzEeI0GFZ25WNn43Lxkc4VUGIQJe6YLJh8/Illyp9Ar1TwL42hIkGbiKnoFnNSYbZ+DFMZQ2pTyPhR7PFPuXRcEaKcMFAeTpUHulrF6hpR5ThG12mOfaIYO/0sLgsDyVIYhyttTsRLzAYX+eKZW3hZxmgQWPpBTNUby/ptmOx6efl7vL7xRual5tqTqpS16M1C2VUMGRgiu/Lh0GzKwNTaApJydet9kiz9rfDytxDQxwRjaDTR6YjOM33Sccvx2smC7PI8xZkzZeIDsBYlKUmUVXrrbxUEnrWlqoO7GryarDHfujH0sW96RRdULhs2ry9wc/Edjnff5Ej/A7QNUZJgZZD2uFv1MTZRRSEs3My9oQzWCmkCE+kKSkEls++JY/d7LSveMBXdZPPiVW7eckUE0lTwTc/1bdRCZVVIAUiGJ1ETSdi8tcxauEJOdsUNodO29Fo+n516mp+a+wLliDowEa/f/DbVcwb/WuDSCrPzX6HqDOqz9O9ajcJ3CyCWGOPnEw2C9l0aYxi3uN7/FmJ2sBh8392nq6EBpbF6zBFkZGSkWK5tfsDlzbO8tfQCIuCJIfreJjPnDeM1t2x+zj1dqrxaShv0PMWRkx7KuPRevOqAF82MnbTSNBY2uNC5UaiGdJYOGkvEG92zXNxyXn7VLHUYpYrCI3nfeLkvYAbVXi9+D+cHlSPXVh2hubUlBOHgXG121+mGjuwSKxyxq/iNm8X3BQniVcCrsb6asL0Db75tOP+BBV+Bp5DivqvyQeDWqyisOEJHGC6qoXubLoVeQanWhSMmxU2K2DSmmjaY651DktSpFJW7jnQr6+NsvaRpibcswY3I7ScoV490yq6+2eJi71dpx0skNuSWvcFWuI3nZffgOEVQLCy61EulDRXTwQZNepuX0BsX0OE2iAwpsyUWwutpkTp7ovMaR3tnUVEHYwbPz5MdNxmi4xA8hXl6xpFdu56F0/2S6X0cOHWxrmC6BtMrPeMyZVdOvG1lxV6XlgExjoSNBal6g/YqV9RhsnsZpdw90ZaUXUpkRHaNMMII9w8bGsKrTeqfmbvrsv2kW5ScPixemv8tXrj5q/zFz/+7PDLzdPF59bEppv+VR9n+x1ew/X1q034IfO7UV3l/7Qd3LF0+wggjjPB7CxZ8UBUfMYLYhK5tsRM3uda8wvX+YuGrAgN/Ea00VoTUCFoskY0hdSlYvu3Qkx5J7Op+m2x6POgLjZ6UrLQUFdOhakq+JFk6ghJDksKUmgbskLJLrFOh6aouPL5SO5gVzoMGb2GQFhenbsNJAmbipFvOq+F5UpBijtAASsoSEacTcx+5QNQFiQrVXUft3EQ3FlCZZxeAsilaw3p8PmvPMNnV2/U41GgSnEIIBUlkMOngLV0Aagov6CO+0J84guAV5IAxzq/MisWa3BfMpRwpMS6wSmVAaIQRN6/0iEKnplJFRUSPODvXZXNxg2WgLhLW+7foGndudSw00/mi0z0PVF8hkcFIgrdq8akQmZDwyg5J0223/+4mrddjbOnc5oSKypVdkpNd7piOVCb5wswn8Zaq6IaPbmimuhfxbY8pNQlzT2KOPoV9+IvYh7+IHHHk3Qs3f40bjdyiQKGUq2C4O6vS6OqedMEosvh5jF5KY6zYLidPwsSkI43CED74wHL1RkLNz8iufKBnB6iDZpHukxuNp4QgJaI0b6UajGOV9X27I7z1Dly/IWCF1LggtqRlLNbJg+5pPUuVGsoXzIQFJRzxj6CoFuuMVWJ0qbIlRlAYYlvHVifQyaBwRBC6lCIrgsqOoZYVPI387lDKXRllasxEgjYhIsJ0eJ0j/fMurSwn0naFns9+popSMH77e6h+TPXNFYJNp8DxM86kWhnsp3xePRsOKrlaQc3v4G833WfKcyqiTPVXPV667oGOtOlKh2baQZRyaYzWfZtGTvE55pWqvebHl6aINuj2apEGCooKPlpDkim7tIbnnx80dsWuoVOFKA+lLCoju7bCeWLp0UluYrEcPwYPH3OptTnFb7Rrh+iMNCqN42qyxWz7MjYj8yqeYwbziYX6ZgOsYGxKHA0TfrpmUUbRaEKz4zvlEgoZy3gEpdCBGweJ5ISFRgQC6ZGScqp6jIfHTlL18jRGR4jnsGLp2+bQPaecWqpqOSEoKJOytMwu5SxcXV5kaVXR72k8zzBTiYeURhZXSAOvivUq2CxPrh8KvY4rhaJ8XaqamHVOBHEsRMr5ZaUpKFuqUlIiXkSR9U/2kW+KSQCTBkXar+kHfP8lWLytWFoG2fBQPV1UT8yR9Bu0lr+D6g88IFMJ8T1op04JGqcdInHft6IWXpriYVFhSsdUaGfWclpbdj4rNPwGC41L9NPAPXdFhrwIWUpJ1gyq4UjqSuY/qeKOK6awy5hf0hQ80H6KSS1qex69cx1Mil45hy5d+V4auOxR7dM716H1ZkL/akrvUoKkKRINqtTm504ETJyQWRdyIvnBQFGdk7U2AGVBwIh7QbDZ+4AakV0jjDDC/SK80gAj1D+7P9klInz3xr/gr/2fP82f/F+e5o//z4/z7/zWz/Hy/G/vnRHchZX2An/v5V/muRNf5s9/7t/e8/3x//tnMI2IrX94eZ+17x+fP/WTtKMd5htX7r7wCCOMMMLvCVioKqj6iIGV/gdcSc+zHW05VYxVTEYDDypPaVdhCkWnAyurAAojFpXmCT1CR9pUkw2UTUkzsmtjU9juyZCyS5GWTOkVSfVY9rlwTB3HU96wsgunBJPIosYUw+Fpno5XNh/JUJBVAnoMe+RxEEOlKoW/jUKjtGJgKJ4bjjuiTIkgKlN5oVBRFx22ICETR2X7izoohO3kWtYylb28D9IYc2LHPJS4/iPF98HzxSlqbPnILFQ1fi9ApYpgzqf9TEw8nSkpOivEaYN5uclWxRmRK5EsjTFTdgmonLVZvcHMlVvUWnm6pGUp3KA936a3HRT9nyMlRZmYStrBjnn0/QFbl8cSeVBX37TMvB/w2pVzvPTeDVQE02oGESE1Mb3zLsiM5zuYUDBSVknkW7NY68zplR2QRg/VTlDVlWIx3QyLVooo1jZqvH1u/1rzBeGBYXxcUErtCYQscPPW8GdRYJwflBpOY/Rs4Nb3quhM0VEPF4ijiGrQxls5hySBU0MEe995co8tIyGh2R5mg8jSee3g79RYGr0Wie3TbQomcdfETXPF9VPuv5RtJ01gyq8zzhhjqg4eJN6UqwZpjVOcaEAMaucGE/HtYu/KSkaSAtVJN3aSEGOE1ZWQnc2ItTXQmRrD9+Chh1PqYxmhV7Ra2DBrbMaXsBbiREhSYXPVmbUjFEUlWsE1dpKs83elMdYmxnjqSTh2DJ6Yc95Ji+9b4till4odNpzPz6vW4NkSkdi0+CttKrcbWAu6s4psXGN7Q8ADVRkeELFEVKkxqaYo0hiNuw94khfGkCKNcWCLJlDNFXDldmk0HpIX0lCgvSwZ8CGPcFpQic7IPgHPkV0qTwO0oVNTbp1BUkErsI85c/pEuyqgvUBotWTIyF0nXVCW9ba7j/vadZbvK04/BE/gJhuurr/FhfnvlRoMumJQRtHrw0ajyuvbmQfZKYM8lKkxgywNLp9sME4tFGUk7onKESaYLN0sGJpU3rBrrMQ30WkTsZlysXTDz8lIP20yFS7gp+2hqoKSFz1QkKYevm+p1GpDJuc2I5mDpMK7F6oljygLVri5oLh2SxU+j5IPokjY2ATrg7EJJgyZ7X9AvefGqrf2Xn5IWapjPuGiqVQluy+KU/Nlz5U4csskme1gXqzgQn+YSVoK1tlOGqT9zeKzVEI8r6QatZY062cvraHfvE3y4iI6SljYGjCCyhNQlmTOKaXV0WxCozRJlDXcLW/i4cEbden3IdXjQzGcSlPQoL14QCxbgbwAQP7I0+CZwJHwys97jHDZEK1ZgmsRciGGjQjEUg1dIbKNTXjzTVso7KioASGW+68BWmdVTMWiCrJLePIpDoUR2TXCCCPsQf+su/nWP3dsz3dxGvIr3/s3+dsv/k3G/HF+8Sf+E/7y83+LbtziV7731/lbv/NnuLlzad/thmmf//KFX8TTPv+vn/0fi4dyGeNfOMb0H3uUzf/hPOn2vSnG7oTPP/STALy3+uoD2+YII4wwwo88fIWqutn+KG6gEBpJC1Ea39aGZtx1qgiWLArlXtate2lOY4vqu3lcnb3hzk0ETJmVQUqcZNW9is25F95ciaEEyE2MsYOotcjZyv4yIIlF+YM0RsQWM9RWhoPPfFcuXBWihRDTs1lgI3gIFT9Tdg1kXOjeJqq9VKSU5f3gyK4MKVTOTqC3/IGyKw3xOwulXbsUQp1XY1Qu8AUwT0Skz0XYTJ3jeWD7bWaiAfkwMQGqRtGPtuqRTIHkBvkmJZWUztN9Ws91SR854jxbUlBRhySxxBKzYtdBC5LA2FoVjypWLMaGNOI2N1s3MJcHaqRuphgwYlBJQDXdJiGg9/iAFPBVfaivJ245w/l2NyGRLioSxpULyKO0PzjZMghO8vGgcXImT5LsdDuFV54elQsfVGbm7K1UUP1BwYDV9QqCzYz4HaJIWFgUlrMiziKG2dnMH6bEbTz26EAlk8MYlwJUrbhly8ouz2YVKK0puJmQkHq6weK7WTW1pMt778N77w+TXS6Wd59pJSDJMNeVtc2WVnhv7WXeW/8e14Pv0A03iyqRNZ9MnTHEjoHRPF5/GE9pZtUsZ07/FfTEzzDrnURsSjGCrSNCa+l2sepYtE4tbbgCB37m75RGrKwq6vES9WSV/lQfNS20pc1SusyV/vyA9MFdywbDol1gM7lMY0dYX4fGDugU5s31orlBNaSh1mgly3RsGyMGKylWUmaPpKTKozJmOX7SMjvZRsQQBoaxMcvkpMUkCSgDyq1jSTE2xbv6G1TjFYwYjKRsxus0zDzbyQ0udW9wdus6b63f4Pz6EhcaDa73brMUrLEUrLMUrBMSUqWKj3sPlZzswuDlo1Eovi8QysAAPU8PLByuVEH0OLLRz0hTBbVapuzSoAXP1xjxs/MFynSorNbxN6eRHQ+U4vNf9ZmdBVE+OxPPk3oVdnbSom/nxk8wp0841VV5eItF9TY5OmWwKsTYFGmvolprjinNoH2DjhUqVi7l0ijXcC3IyexcRylSccQ1ZOSUCBEhCthchrX1oiAhSilse8BEJaT4kcZLG9l1oIdM4G1sEbEEyU0QhWfDIfP5ejSPR4xo0Ph4CrzasEzKYkj7wu1fa2JDH2UHBvX1igWtaPdVoexSiCMmE1C+QrRC0oTaovMhq0ZrFObyeZcOM5uIMmhxydkmDREUvg9p7NRzShypKQLi15zv1GB10qx4hw4H951UIveMKAo/mGIySVmnxKpvnMVLesjYYFwqbRFtSU/HJJ/pU/204B1V6CDGf3cTez5GAkvuMaaTiNq7C0g76484V5eV/ANFXAEB7e7ZvgTuXu7XKDom+/HM0+AHffe95w3dfLtdoXUtO4ZWwlR0kyOdc5xufhOv1UKCblFUBR+UJMTeNGszXy/Sd5XKCDBJUVlhHCWC3qcoyn7w777ICCOM8PsN/Xc2qZyZoHJqfOhzYw3/+Qu/yBu3v8Pf+Mqv8K9/+hcLv62/+Plf5neu/q/8w7N/m7/x63+EP/XcX+cvfeGXma4dAaATNfkvXvi3uL5znv/8j/xjTk6eOXD/p/6fX+TqH/l11v/b93j4v/gDD+SYjk+c5uHpJzi7+jJ/+tP/5gPZ5ggjjDDCjzLmw/c4UzmJqrjgwM/eq3tpjwk1uyetq74IvU698FdC3Gx4/w2D3x7LXm4zUkjcrLGOBVt1wbi1g/dWN59qGdpF5uuixRbPDlXyLAKYV1/lSNCnooSpeIlassVM3MNUHyGUAOxstuQgeDBWCE2LRBKC6wHBjZiJT6ekVpiuZwSW0Y6LyV6QHxo7Tr0Ot4M1BGGsBt1EIx3r1GN1ULF25EukhggLbUppOCIkKWg/y8VQztemMHafsYQnYqZ6ztNHpzEVhHwLU5PCo88r1lczNZCv8FAoJShJsRbMlMFMWKxNEe2UaI7TCJE4pCc92rbHrE6RVDGjZvD0LB2zQj1ZpDeWabzK6hR3erGSySaAJO1jlSqIp7qeo2OWC2VXTgwCLvjcFMYrLq0vNhE2EsLFtGA8TakaI4Bn+676pgGVRqSpsLqe0oqFyUXD7JxTFsqYRQJFdccnOB27cZgFyUkySK27dBnaA8sfnn/e0Ail8KzKUfEh2hUXucIHlnoder1hz67iYG1acAihBFjlMa7GseK0W9s7MIsMBcLdNBiMenFm8HsE72rA74oY2lGTqjeGrxRLrfeYDC0VH3zRmJxkLfX9MXUy809zKrb62AwP8xgnKl0kMoUPXWEMXTr2me5FmAAThqRGocTPfHgqSBZk+0duof3j9Le6iBLGsag76CPSEKq4Cos6Fbq2jbWO4G2eaCLNKloUV8wlWqFw7FSdo0ehreDFJYOXV8rePstWdJp1b4sjVcVG4BHNfxsbam4mQhQajoQb9G+eRHbeY6Wk7qmZCr6dxqgurVaHlRYcPQqBqZKkhs2VkHp4gtkZhfR38PCZ0JPZuYoR0VSlhsIwQaYqEaiq4VBZn09Q3ZTdcGS7RtmEI/338eRTiFEcnYNtD6iOoeMuYjUKV1nP4KMy78M4XSGp1RivTpMs+zAFfr0yXLlVQdVuoII+MIunfDQ6WybT3YqgNm6hwzZ2uspi8xpVrwY6u5eFDZg84Y5l0iL4jG1WaU/VHAGqQLSA51RuKgQqwmKwykNyHPEciZSPlbjniOpr1+Hxn3DX0IQ+QjPvLzQkKqukV0PwwBi8KQ+JDDaxbCaX6JotpgTEU0QxjAPqkxX8y1tUgxVQTzGWdjDhBrryTKnfFQZLbzPiZusmWo6hjjs/OhHLJ54WKhXN1WVNuFxIQDkhT1NZeoeaB32lUGFCfVmBeOiTEY3W/NDJbSWLTBXaZo3SFiXO89GmEQqD74GXbLnr3opTQwlORe0LraTLTGWS+jM+9qy7T+k4ckUjdJVUgswjMLsXmRSbkV0ihiTZIFUpVdMimHx8MCa1dYSXApm2pJKiVU5kpU451ZGc60IvtvCiSWQ5RU1XUbFLa2zWn0PkHWJvxlVMtoBWaInQNsQY8PWgUrCozN8yEvyLO9hpz5GlpUHbaDK4D2ELtadKE2YvvouaVu5kA1QUvoQYVQWtCCsnGZf5LJ3bxaCKXAluhwneO2BEdo0wwgh70H97g/GvnNzz+T959+/zg9vf5t/9yb/Lz33qLw9952mPn/vUL/AzT/w8/+js3+HXLv5/+caVf8KXH/5DTFaneWPpO7TDBv/BT/93fPWRP3rH/deenOboX/oE2//LVeb+3NMPzCj/yw//LN+69n8Qm8g9/EcYYYQRfg8jeNiyaXaoZy+LOnWvuyJuBj/NmCnBQ2EyekoVaXiIB2mMLZYbpEUoFPUln7GdlMbnemhJMAbyMNBtK1sJRbf6FJVqBex2vpfMLN66KM5xOCyt+KxftMyNWxfw2S6i6qwnl/Ftn8fTGmHQxFrBpEKvD1thQCs6y21PmEwfotVRLH0rpT63RL27WuoRRa4hmqvOMDdWYylYR4BHH4UgVti3EmzoU00mSZ/MK1IqLnVucqJ6BF/7zjC3QEb+2YEJvS4RA0oU4jslmwcD1chgATQWO6UhxPWJUtiJLuPbS9jM6wug1YuZ8lywnMZC1Aed7iBYUjS90KJrCtD4YvFsQEI1O3EqPxnFTyXDn1gTYNVAZVVRjsianYG/+sf/OO8unqPTXQQUUvEAQzWpopXnTOqB/g0DR3Oya1CwQCmFzvqq1/OZDFJMuk03OkKiIXg7pfUTToFoTifIhkdtsUp43CnBfM9DrPNyysmupf57Q11psS7YV2roHI2NOUFOGdvbFg/DxIQjuwqz9fy0KFwaYHYq+9JHoxhXdfpSTtUaJmvfaL6P8DDglF1a4lIfu+pqSg0UkNvpdaas5YnpT7HtB8ThZZeG57q3CCxzuvUzU0+zxixKJRRjb8yDEKzyERO6NFyAinVeOUAnXUUrn4nNOnq5TwVhNYRaUuVEPQYqg3YqQZEQeZOM16f57DT0dcRKtEloomLQ5GP5yFtpQd6e1M+ywwWiMGQMny496nqOOe9RtLbUK5/lkdk6p0+UlWJj6N4GJgTRR6h2fU6MgzemUDOfxDs2htkW7O33mNU1Hh5/GDvexNOQeJNUbZcxVcNWnqUu69QqjwBwQgtr/QT/SBWjU1r2cZ45miAsstLKjlVgxqvxialPw1iTqcTDt116PWFjVaiw612xsQBZhcS9KbpQjdcZ32kw1qshE5b6uOL0GTi/MQlsMfdeDY528E5AKlWQFCWK0GxivYeYqE7RxHkn4rtr3WYEvWhAW3RzGZjF0S5l5yTACrVXBCWTRD/tvolNhNIeGFBJP7slKyqnLNHYJEH1aazqYGLlRqgmI9stNgYZ08gnA1avbDLBJI2GzfYOvs00rxrW11JOKKjqcY6oozRkm1BrqtrDWOO2LU5B562+g4iPmfoKke2QPwfE10SROLJrwk0aWOvGZEUSfNtFe+559qh+nBW7hMWwFm8R2ho22MnGlKsYjE2IELxqtVTURPDbGQntalrgV1JMJBlRFfHu0vfQgOppFIZ2tIDImew8eCht0aJd/9sUZQx+DbwooJZuMxbvMJZuI36mGtSwFKxR048QdnxSkymtVIoXQd1WSL0V6vEaVlKUGLykicmey9vxPGkck6oQT05gJwbjUvmumnCn44puROMpSpVM4T0FfaFXO8OcuY0KIsbTJoyrIcVr6k1gJ04CIZ44vzvlKXzTQ9nA+QjGlko9I+NygXYkWTXRwXOv1xXq48C4hsysXlmLVZkiLRYqaRvdTrG1KgrnD+aZAOPXwNOI8lxxSXrcNNcJbAIyRxiCvotlThkjsmuEEUYYQrzSI1npM/Gl40OfX9t6n3/63n/LH3vmL+whusqYqs3yS3/wb/MnP/VX+bVL/4CzKy8TJj2ePf4lfuHzv8wzM0+jGvOo3haqv4NK+5CErgR20kclIdiUM19VtH/jGEv/5q/z7H/VQE1OQGUCqU4g9SPY2UeQ6TOQzSofBl8584f59Uv/kPPrb/LF0z993300wggjjPDjgOiU4XJ4k6R/jMcAU1Qkc2RXkkXcQeUU48kyuuIqcxXuXKKJw4h2R7A6D7qzF1eBatMjAqb68+iKh1jBTGmISqbaua+JqlPVFpUpwiqqwtFjHqvNjCzwlJOFGXGpMp4q0gEFW6RPCoIOm2ztKFY88H0wlRQtMZGFWwsK0ZrxyHKksYMek9x63h13qTRd90KCMj48LvgGxrXQLc1K643sNTlLreubkKnKHLrk75SrpIY+UwophNEK8UArhYePJvPo8Z0XlJUsFXLWwCZ447MIK8RzKTqOqe5Ui1i60QswdpE5hGbbw6YGCBEsiVXYUKj5iirgZeqAVNKM61KFGXyRuip5Zos7BmNCygInPyO7FFD1x7Jqh+7zk/IkT/oreNpnLGiRtFdh/LRbMRtXaW7iblx6Wx6WazF0eh6e18VUUk7NtKlvThOs+pyqHWPRu016Ypx0aYyxjQQ5pvB8jSSWOIEJoNu1zG/fpIzUOBWEl5uA4yEYlFI8/gR0LnSoqzq+8kk++C4nn/6DLhVGCdPhNXZD2ZRYIm6mt2lLi0n/cfJCnZL08I3zbzO6hp04ju5tIpL5oGXsrWdDRA2nwqlBF7EVn6fajrmyMoGnUoxxZ2S2Oo1pdPAvp6SfdhULRUDjzNOVHpxHr5IRpbqCmB7gvpcxi3Td/trpbVCK451T9OMO4YyzqbCqAmkMUsMQI1RAh3gSYxVo7aGVUPfHeMo7g519lMsL79FkgbqqU4s28aOESLl3xpoZB4TW1U36qkqkAibUBBNMMu2dwlSf4ZGZMU7PlMmuKt7aB8Q1w6Y3Tc0KJ+ruHlUbf5TazDThESFdvMJk5RSP1E8h49v0PGiMf5paVVNfP0cLwS+RnEHD3U/USWALBB8YVtopBTP+STyrSMWisuIPG+tCpwuVeJpZlRLRpiAXo5KcEDIPKEcx+k2L7CTIQwHySH5+FJ2Zkyi/78zmb3XxHoKUCsokzNYCTN9Q4Ri+V8lIIQvVCuoPPszWzXcBCIloSWOQRSYKjXbnSXkcGT+JGGHKn6Sf9lCldECXX6tQcQ+J2lCdRHsG6yvGZZamCrEJzNsOY9qifKHTSmk0IbI++pEUr5owtSmox7JtWkd25YmRSWhcip8Mkl4NIP4YJjW4KRCNmBStnSbLxAa/nUDsbkbiDYhdt35GmCuLh6YaADrFx+eEd5I1u4LFYmLBiIdOfHSgmVlLkU8abmxeol/rM1f9w2TlFgFBZ7ZVzc/7VG5Z/L4jsCXFeX6lEaqn8d6tUxmPCY0U6ZeCRrTlxJxHGqZ0QvBMilerQS/Ct328LJXSFUtQTgoNBFFKrwPtjkV5UJUuY90uD91KiP116sd/gGcqTEa3mAiqhHoMDXihphJtU3l2jDNPVHjuOcW7F1yFUnwLIoQRhBE0g4AqIVPBVfoa7ERMg88hWnE0jbHxbZQ1xJ6P9o5iw8zpHp1NTgjaxmAhqsxQS3dQEtJoQNK2nBlLqRb3MEFi8EiwGTEcBIqdBoyHoCYVks8jBAabT6VEmaefVnS8x5n2r2aTbJaJSc2mGhBxPXFp2LHEbrItqxY6UnaNMMII94XuK24WfOKrA2VXahP+3iv/HnP1k/yNP/Ard92G6m/zZGOVv1X9JHq6im7MoxZWUBf/DVQa3HFd8Wrg+VRQPP1Hz3Dpf//TrP73Szzx9d/dd3k7eRI79wT21Ocxpz+POfMVqE7su+znH/pJKl6NN29/Z0R2jTDCCL/n4dkARJN6mdImCZhUUzzhfY1rrUtMGPc6mRv2as/NQufKrumrdTa6KeMdwUyAaIsGZtUckQzKx/vWQ6Gdsksr1KM+ZDzE0ASsUlTE57R3hgoVtNaOLBIhi0NReR5kyeh9R7ZR2SurFIFQKSDKPD3EQuoZ5KljqGgduSqDjAqVvxsPyK54w6J6zii61YipPTms4tDd7MU8IyYimzDl+UNm5nk76rWYJo4EpKKJv6jxtgFRiBY0ior20HjYLOUlTV0KptaQHjW0Kj3OHP1DNFrLpOxgq1k1Q5UrsYSN9DJPMUViq3gEWfMEURpTs8RNDwWMpw366TapmqXVchzfmLEu+sz8Y5TN/LHCaaCNNaGTQeE8XDxVzbvO9UemMKmoMaZPrDMRdbFpRC0N3ax70SWuw9a33M+HliaojQlhVwDF7AxEa843S3ttvLBB1ZumdTEFZlgZX8R//gniqwYv9onmH2Um7mLDkOjJPsxO8NobKSUfa8ARewCe9lEqwVM+aUbw3e5fpmMuMamm+JT/HFpinnrCwspwml+O3LOrZZus+B7H0jon9RNo23Nx1voFTrU36FXPkIiU/OhcUObSy8Sl5XjjQE40uzGZVySbiBaJl3vMTmtSaqhMzVUZr2AS8JYh/TRoLfT7ZOb+ufIsO4++27fVPpKmTEW38SIPlEs5cscnmIomiQM27CaKM7iLzMsqfrq+M7oCKoTMV0xrH1R2bpWCSt15+QBjapzJcAkvnYSKI7uOT9ToVeoEBAQSoD2PcT1ZqqaqiyqLu3obsXDimAyCY6UJPthGT/iMj4/RxlXGVElIbt1nVRWpTzgvIjH4ymCsQmshzooHWJWl5ynvgJOtsEaBFZTngvdqFRCwRjGnj7JOl/xG0Om6yq97xCWiITaAh+2kSJKfHzBViE+exNvqwJjrg1gqYF1aWqSqVNRJdNWR8gKoisab8JFKnj6n6NBhfJAli0oUtfUax+rHOT71KFh4bPw0vaTL1bjke5tVP8XE6O0b2KNPo5RgfaimGtEVwvFP0NHgW4OqgDFOVUTNp9+DqvUYM1nFDkDhFSnOKIijjOARKalb3aSFFUMvgDlc8Y9svgUSy9SVPhJ5jPmQKDtIkxNh4bFFqg2dHT3Ub1RRz2cVIr1JdOJo/UTPYtD4xmN8uUYSGYLOKmHUh3HFsROaFSTLfhO80GJrirSiqOrBHIhONMZakn5A2oekBSr2UDUBZWBKQ18jWMYqmrBiqQMnOEO/vgPEGcHtniXphCMAUZAkwkYTJk475Z4SqCXrzDbWqJrTmMTLDPwDKqZDaqfQhICitlVBjYOqKY7OhSQVxXjdkV1WWcrem8vhBibtccScYIMGiZcQBxpJoJos0kvdUzQ2KUFzh/hbfRamQjYrCbffXkLbAF8HnE4hnDzFGDepVSEIgSMaW6pKScPSWzWuwmz2PMmvsSgG6iVSO07Qa32YAbrZ/UB5ROo43YmEaVwVSq01Yl0VTafqy3SjAtPeUVJ9FE+1UKWJqzthRHaNMMIIQ+i+uIJ/apzaJ2aLz/7F+f+JG5nX1mR1es86qreFd+slvMXX8VbfQ7cG5rt24hhy5AnM6eeRiWPIxHG6HGOlfYzl5hwr2+M0e2M0+2N0ozF8X1OrQW0MJp+FT/5L78D3oPHnfplH/9UZ6l4f1dtGtxZRzdvo5iJ66xqVt/9/VG2K6ArmzJcwT/wM6dN/FJl5uGjLmD/Ol05/nVcWvsH/7Sv/2SBVZ4QRRhjh9yD8tIOtQOq59C6TBMz503SbkywlPk+LdZ4bOQ0kCmsV6tgJknWDn/apLviIFkcsKMHTMDej2ClVN/OMh0rdCyoeroJT5oIkJW5Kaw1WUcVHlGLyaxPIqz62naI8p4Txr2w5JYyHq56YQRUJklIYgN801zmtHirILmOgUVlneu5xZ6jvD8qTF55Du+77CpBlD8Yh3XEv4HY4MQiVunViSUBXoETsKCyCZqxqUUnqAuQZDyp5QKgzQZniSGWKnh5jy25SH3ez8F4W+GsbIVXwVIUnKs+jzRg31bVdPskW8bI+UP4gIVIEqzUyYag2fFCKU7MxYQBpz7W121MEiWV8pYatCOGpuCDxJKwDbcKZBF2dZevTIccupKhqDeKsj0SwWY14X03AVMKRz1XpM4m/6JNKv2glYglDodEyqBT8WFEZc8ouz/eoHT2NqTZIYoPKSKCKr4mybhUPul3wax5+Z5akN4OaSNBtS+dqh6s746S2TDg6pNl5ccouZ2hNlmCnPXcOu9Lh7eQN/uDs59mObvP65ht8Wr7M7ppdKvPsiomJvSM85n1p4L9vS9XpJGUl3AR/MK4SYjQeftojsRGpfwzouzRGJ50sqrS5wBhq4tFX1YJX8mYUbLlzDWBSdw1urg/IriLAzQaRaB8RQYvFi7sIs4NjwREbSRSAUsSJUwNZNNZadFb1zapKnnmMRVDKA0pEpq4SpW7ZKTXDtjg1GyKYqjCz+Q6fnfsMoZelAFcrBHqsKEQgymNud6HvPFfUwtQUrjgFrv/16nv0Xn6G8fE6rbbFO66RKMgIN4VRNSpVDQKT0Tx+DSKOUaVDzvXY7HhE+furQbTGcTiCUq6CaLJlkWsDmaOUrsI4VkN/g6tKOj6nWN7KDinso5otUB7h438Ie/EN+k9Oov0epJaoscn25ga+Tal4hlCNUZMJvMm87y3K8wtFkMr+L0pcrrR1fn6VlQSvW0E9mqlgCxN2UDGQ9NHdDbfN6iT2+KfQm1dQ/W0EwfqKSh+OXEnREwpmFZ044saa8JC2zq++7saErblrTEcGlSgqncrg5qTAb95CVcayftSkE4bEE+pGYzA0mpozoiAOoaocoRYM1KZaw/QRy+mqoDehSUI4ExHPAmGmFtYAEQqPVv1T6OgHxKpKMnYGYcUpdZVTD4e9Ddd/nuLIMc2RGUtro0pyQqhsRfSddRlHamcwehGn+HLVh9cXmng9zZQFHWt0RbAzPqqikb7zD9R4gOXoUfjEYzXe0Z67L0uIEkU6aTBHE+i5Z0AYwRjuGZldMkjV4nc8KhLT9ia5Gc0T2Ul8ILFJVtGzeIgh2qLSaEAWAuLJUNVFpy5N2LZb1Ko+p2pP04k+AUpxenqaRhzimw5hN6LRO8FUZZUjQZ3Qf5ipNCKSVTpmBewMcXUWgEoVelEFpQXJi7EIsGNhQqOUIddzF9WIDajxrO1THqYd0WpeouNtUlvLCHrr0exdo1lRNO0yWgwn7QS3o9eZTG6h04ht2XJpreIKnVgqIBbx9rDm+2JEdo0wwggFxFi6r6wy/UfPFDer5fYt/vG5v8fXH/95fvLRf2WwcNDEv/o7VC7+Bt7qu4BTWZnTz5M8/xcxpz6HPfoM1CbpdITXXoc3XxbOX6ConATgafdyUx+HsZor1xtHEEXuZVelX+A/rK4y+R+9wS/9Dz/H1OkpHn/8JI899hyPP6p44gl45mehpiP02vv486/g3XqR2ot/h9qLfwfz8JdInvvXSD/1J6Ayztcf/3l+cPvbXNl6l08df/7j69wRRhhhhB8CrIDxLIkkqNRQq4zlyWQu+NYACq+vqQQK6grvy08SXw3x4gDJwrreUwEm3ELdEMbrHjuWItCpRT71a3VSLS7oVwOPmZzsmrlpMI/6FJIMFH7dcxoGK87hOvvcNW+wnaHjQQhKvErbtkD7KBRGhK7f5MxYAH2wFUEr8CuOsPJ9vSfYLVeZMz2hckRDsEuykSu7TAzaR9J8plmYnRFUbRKSDlPdm1TS44idGHSOuGBEozlWn8N4Plt2k6kJRa0i1DMDfS0h+Wu5VlWm9DS+cqozk0KNGgEW35sEkczvbNAnojWmnhMKFcbHPLwIAgakUCIx1W2XUheeigtVRtXTxEcSth+PGO+PMe7VQTpINWsPgBFma8eZ8GKmvBOIXgcNY2f8zJC/NNsvlihy6acz5ydhMivSJRaLhuoMMlmFnRUq6SagqPqaiOyUV4VeD6bGNH4Axijsp2YILqyyfTPEbm3h00cfccURHnvEDaHUOFLGqZFAK784Dd4uEUDLNtloXnHnVWJglyWCUmANicRU1Diix9Cm7YIsBhUXV+P3aXRWqRw5lg8JYknQKKqmR4AirR4FNoq+2UWrIQKTyTKx1sRtwVQsaioju0SovBMxnq6AgihSVCgVvkQh+cEpjWTG8K7yqKGRdJmWaZcG6UMqIdZTroIoPpKTXTZXWlTyZiE4QmzomlGKaTuDlk1m1TQDIY6FeoiOq5i4uJid2kNZlLE8+gg8/UUP3999Eaqi7wp4VTAxiEW110BO8uiMZexhRes7m5AmyGwFUR5+RZcZYVIZp0oHmzryzst8mUR5RX/v3n8cKuKOdcodILqaQlkYVVrHwyMtXVdn/Keo2ARd0UyNG+iB7u9gEw/8Gnq8jojF0z6mosBall99gWCnwSl8pqoVxPPwJn28qlMJCpLd19yOZ2bAT7PbpwVMglY+Iu58+501mH6c8ELmWYVC9QSVp1zmDGalDtU6fjUgRhBfoyxUO0K7A6oOphNjE0MsO4hX5cjYSR73j3KxfhOLxQ9hfLlGLalhp0wxXqq922wsJcTJKRSK6FiCamqU55RdgiJaMJi+UKkp1sw2t1ZfIzaWKk4dNHvEcqQKnW0w5EoxOF55jDG15G7NNnZjHeeZRc8irShTAfno1FEifiAw5shzPIVWlrH1KqpbQVUV8YxLqfQ8v5hEUKkiNileT+OFWeXeRKGMIMoieXqdsnhSAXFp0jOVKhXPKQ09G6Ks6xObc1XiCG6LLSZxoqMJJjGonTF6tccJVZOOdwEtmaI2UwUXwxSKv5VJOHXSXed2PBpKoxeBk9UTBMoyWZnm+NEZks4kGKhMf4LYVJiK36beGcfbeZTTx1NsrJHq55k1VXZsRNN0Mck0NqvYWjmiSXvj1KSLCkLIb62hwFQFrQ0WTRgKSTY5hKdQ45rqV6p0maNx+X167QDRAWNSpT7mCLTIrtM77eHTQdkWSTRN14wziWLLbqFUBFmKpI/GlQ2xA7bvLhiRXSOMMEKB/jubmEbE1B/KTBhF+G9e/VtUvRr/9lf/KxBBL79D9dz/gnfjBZRNMMefJfrav4d54mewx54pXlqSRHjpZfjt37G8c9Yx/EePwmc/DX/6TymefgoePg3Hj7P3xSeDMUKj4bP2xtewv/QN/uPJN/j+Z3+a+UXFu+9BmDnOehqefLLKpz71ZZ791E/w7E/+Mk/OLTN243fxL/4GY9/+T5GX/h7JZ/4v/NRzP0dFV3nx1q+PyK4RRhjh9zQ+M/EcN7vrXF2wnDEx9c46Y9HTxJMapTyM0qTjp/F2FFNXx/G0ws4pdGUwY5orGBItKH8H1LgLpNLBfbueVtCi6T8EHNX4ofMdslVL71TM2PwYCph9K0UlOpvZ1s40WRJEBK9SFGt0uMOkbTAohohYhVEGnwpTMsnyIymPf/40Sy9cc2SXgP9wykOeYqwNKDAPJVD4kWeBdv0IykT4RxSysmuHJld2pYhXwXbXmOueo1H/NJUK1I5NEVzuU+sF1OMNNuc9xp8bK7Yvynl2+RWPJx6Hra6LV6tVpwtrJm02kyuIqg7t9rnKpwlUl2WW0Gjm1AxrSjExbukEg/4XBFEKW8uUabqCp12VtjRT5QiKLi7wnVEzpArEptSZxBiFqVnQFt+mPJ9EdPVJopo7CUqcj5qvfWb9x4p9iABp6Kp3Spa+pBRKLMY4g/qcePE9Z6JsxBETXtUnxQVmWimqlWxMfMLDm3AqJql4zpPNgKp5hHMKdhS6GeBFbSYiS+9RzfREpijYsVSuWHw/ph6kzK3GRJJSbSlULKTzJYuDCaG6baExTtcEdFsB6exJamdaAIRUHdlFjK/rGD1GPVlzvj7i+lwpSNMNej1FpaaYyjadEFOnjlIKq7yBTw0u1a48tkUpTqiHmIwW8E1AtynE0wb/iIJ5d+b00ibqOI6sShQVYcADK+X87gDRzsS/qmoIfXKWdjlZBv0Q1lckEiGe89vxlU+iPOIUVDZOrKoQEXDdXMEwi9pVjRAF44zzhWufR82CmspS7rCkdfBSiPtSLCtTVXTDYMXgVzSVyj7veyVlV35ItjqJyszGj3wxoP32VaonPHRXII6RVKAlmHoV35o8s5KpKWBKcav6r1LpbuHX26jJGvXGYn4G9uxei9DcUYwjKJ2lfGfZZ/v5YDtFTzr0iTtc5SrfARKD7QtKC3os847Dx1TAmhSloPGsMGMSJAjQt2fRdYXyNTJ+DDP1CDJ5ErXl9nNm8jmmuh7b6hITqs6p8dOcnP00q1xw+7YW1V4mWa1k8wQK1XfjZ3c/z/zhI+hgm24knDyj8Oehk3FinVhRJ2U83aB3RLD1Ezx8+hS13g7UK0CKHzgFlFYe0hiQ3MZA2HP7LCYRrGJsXOMHKb62RCvGpVJr6NPHa28xHjuSSCkQl8eLYpCW/EnvWUL9BF6W5mZSp+wC8NDQADPhrsvZOQ8dKtbahukti3pIgQc2NqjMkNALIH1qBntkg6mdVWr2KDabB1FWcbb/HhMLE8NdZwQ8g2Spc1YsSgZj2Y88vGntyFUboEWwikyJJ85830CTBlMyR/sLXXe8q4aZuQq9YAJUG+V5PHayS/fVMfyOR5qRiVaNoQgLsku3F/H9Cg8dD9neuoWuDB6KYuFU5SRdbVBVTW2ydP/59DGiziS8+Xa23dIzJyuOUqWCTtyTQyo+kT/H+GebmGNHYbELSewmtQRUKrTSHutmC4nqNC5ptAmYMGvYmhCmCWMV2DEP4dcazDZmObn5GPpIjWd+wnLhBZ/22KeIzpzhdP0y1ze/RTcJQY8Xnl2F771V+Gi0KEcy7s2H3hcjsmuEEUYo0PqdRVTNY/Jnncns7177Z7y7+ir//k/9fU4svkXlnX+Et34eqR8hef4XSJ/7k9jjnxzaxs6O8M//hfBb34BGA06fhv/rn4ef+WnFJz85kLceBp6nOHYMjv2JObZWv4j6z9/mb/7lm8z9x89grbCxATduwuUrwsVL8NJL8Ju/5R4EtdppPvHMX+NTn/g3+MmnPuCLyT9h+tz/zImz/4ifPHGKb1/73/lrX/qPqHjVu7RihBFGGOHHEyfqsyx1G7TCmNvhGrVWlZr1iU64Kkubk1/Cn1indq0BgI/H6obiaCHlcOk6XgUSz7pgLE953PYZ0xAZqFuPBE0wZ106ojg9mK0KyaQZaGYUpdlYhdKqMJzVvuLkCdhouO37NedX/4R+ig27Ri9jp7RvybLpAEhIUL7CQ2NOKOwY6PoYzfqz2OoFPOPUEGNHFbSz3A9/OII1R55AjjyO2rqGP6XhqxFmPsa7XUU8H/HGgIBoI6XfjvGOGiavjaNrMerL7pj0wlNMba9ga4KOdtDrTbdx0aAFTyvQHpOTQlXG6Xsz6N4mILzfu+R6RPKKjpkRvKeo4BjA6YkKsxOwumOp+MITTyi2O6cJ11dcaolyZAhojp+ooFF4vlMS9KvumW7GLF6oeWj8KMf9M4hOsL4itB0qY47U8sUpHeo2JvQ95ipPcXSy7lKTSjxBHvSpNHJKKnAGzspD18AaFxB6uJjRz5VdSoOFatUpuay4lFf98DN0H/eZ4SYT40AMtuZRqYDtK6BCMqXonzmJSWuYywHj65bxTUttTeF7VVITIz7oEz62pzDdqjPun1VIokk3B2lpyVRM7Ygz+W9EDVY7PeIdTXLsCuILa50KpxijY/tUVJ1UT6DE4inFzg6ks1kQKpZuB8JQc1wLMzPZ6FYaLzMuH/ZSHtQ/8zRUqPGwftSd79AnjWLSowbtadInIvQG6CQoBd4Zsafh+DGIUkWnCAo9rIUjao4jj0xw68gWLEG1ZiHO0hjFpU1GMxqPGjQ1cSJ4Nie73Lls0aLOHFoPAsqJP/uHEFVj/dz7EAmybmAya5VY0rqgemDz6muP+Vi/6ioIWos6MO0oa78VsJbZr1fpbYwTX3JkFyfOgL6FWCHpTaJsC1HQWnmc6vo6ajkpSJd6XXH0YcXCjkbER/k+/bFHiCanqWzvgIJub/fuHRnhxnhGGFtXRCLJU2tLt4yi2EVxUjQTE7jiFlnFP9uzmK5CjzvSw0qKpyqkE4p+0CP4rEfarWCTCFVLOBZ4nJi16GoVvAp2bAa0h9auARVd4Yg3S0NpxnSVZyYfo6dqTOgpDKuMe8PKRAWoQHaxdY7RUdUqKhREhFrV49RpxfKiwg/EXadALBHB7DOoI2NMTc4R93aoVuqI38YPLTpV+DJZkIzozIdOHKEruTQw8+zSvkUMBUurFKS+odPNVhd3U7fakF8wuXrOUz5KDCq7B5k45PhRzcljinbTI1EGYyztRz3O9BVaKU4et0gtBSqgwDtZQ+Xpd56QPjJDf71NFaiHW8QawFVINFahjAKbGUkCU/3bcPxU1ofOs0uL5pH6SYLJ0/QubMBXFFZV8W3fqWa1O/d4NTK7MCwxV80l4uyeIOOGiq1Qt5pWDOgKXrdPteGI+Ye8h9lkjdibwtPJoFBM4ybwSehvYT4RUmuCPzHFieCrPH1qinAlI3e1QlVKN+4xH7qKlv9JxiobiPKz6w5EFIKmqir4iSFBkIrH5uRXOfMJQbYvueunA0kqRaXdjbjh/PlshYQQj5CEhP7pCCOWiobQxIwf10zWpkFV4eg4tdN9nvnT0+hPPMpWy+OonmVpq04j2uSZz87RfsuNlRP+Sfq0UQKTFxOqcRuDsBZtM1xKbX+MyK4RRhgBcC8Z7d9dZOpnT+NNVNjpb/A/vfkrPD/zHP/6a/8rXuMmdu5Jwj/yK6TP/sk9VRC3toR/8s+E3/hNl3Lx01+Df+1PKr70xYGp7YfB0b/2LJ3vL7Pyn73FxB84Se3JaU6dglOn4Kd+MpfAC6trcOkyXLokXL4Cv/UNxT8PPwf8XR6b+1v8jS/8U37e+we8ONngjf/zL/LT/9LfRo4+/aGGDp1vAAEAAElEQVTbN8III4zwowbfczPfVlcJxUepFK20q7joOSKrWgXJ0zXQGKVI0jwozYitSR8DKD/3j3GfT09ZJmgwZnxSpUkqWbWyKadmCk9G5eQ2AIIzClZAlHYGsyJYK+DjyC/tyIFKTZFGmUIBTewdoWqaxCW/LKPH6ehxZiZWqbVm8VRmZOwlPPRwheayoBsaa5XzC8tTv8qpIaoQlGDFklRTVxEy89wSfxzGjiJMEXWniTZiZDPF72tqcYqfKFAe1XqVZvVRqkkDX4VY6wgul4qC8+7SThEi/hgy/RD0Np0hcYFsn5lSJK3OENVOcqZiOFYfI5ha5mjFMrMjKK0wEyeBFRegl5z4te+jVa6uELSewtgAWxWqMUxNwva1BDoWcuXO/5+98463o6j7/3tm2+nl9n5z703vnd47CCpif+zlsetj76goduyIFVGRn4qooCjYsIDSkd4SkkB6ufX03Z3fH7un3ZaEBBLCvl8vyD3n7M7Ozs7O7nzmW2KO5zLmi5AYEhUxCWuNNFhJlOMF0jftQa+N8Swp3NwY5emEl2tLwwpt8yyzHAcQaNI3GvAc47wsbjoU9GYsezua1Ah3JlCjDqYD8ZigzQBb15G+3pEZ9Y5R8oNmZ7vDjBpebK625f3gwqY1a3B6JLI/ijMyQs6MMeIMY88XOLYiW6j6peVSWUIzBU6qwNBonpwpaXpiBonHbNAgOgKONkh6zERXcRA53JzX98J2lMwugRgqESoZCCkwNYnuhDESEBuCsLCIpSS5YYk+bCN3hnAcB93w4t7Jgn9/Ogau7pUrRlyUAU7E8YQhTVHuncKbh3vXVHnWckZYEJKSkbLHrPDELiEELXMSrN3pJRyyLEFXk+JhA2xVQAnJ0CwNaccx7tyObSsEJVxTkWvSK5NDL1mFTmRAJ3N/CZlOVu6Zyv3jB6UWysUxvYo0xmArgCZw0REUPaFhqoDS5QL94OFCCiILkhQfeAI0E7dtEUI8hmulUcpAi48Qm6ljP9pISY+jZ7ZSslXVwq0Iq1fClrwX5HpDzaEyGS9ERi1SKGzHP7YfT8lxwDSqYlctrhbFVhLdzfjVlzQ0CLL+/aaUYntxF6FdSWKdJgVKOHhuh44l2DUvjypI7GwYhyxGzEUzNFIJDYSG0yohqeO4DmMl3y1RCqQUdFqdNI/oFNfuIjsaJj4WozUygKXJurFECOFZpNbEcvJiPlFJpuCiwLfQKsY8sSsqIpQAB4eW+NkkU4OYFCgCpojhGsNESjGanRbiIuH3E+mdedmgr5wBVuH58RkhxrQ8BdFEt29disATqWofEAJGnRHu23kHKcdmWHkXSkejOWVjNfpJPUolEkmD1hbBmockpVKB4fAIuRYdOxVCPWEjd4yhjQ7jxtoQxQiEJerwIvY/HEa6i0SFjnJKlBRYTgHdsCqWXQqBcMFFQ9RY8CnNrkjVLg4SSTocJx7thOFtiBGBEbYQeRfNtSnpOsOxuXTks3VWYmP+eWkaxFscMhkLITQ/OYuGzFRFYa0iNkt0oaFkCad5LnLbA5SG1vHY1jsQfdDaLLBNyZh2GKVECdHwBKotROPCOKiqyXTZNd1WETJmDyjF6CioEYfQLRuQpSGsmCC9Q2OnPsw29QA9HTrrx+CJ0hoS7jaMrIQsGH6xrluiUW8gbPRgiX50Z4Rkq4VI62gaNCXhobEFNBkP0dhjs3m7DiETIXLEWkzcuE5XHNxSGyERwiFDyBKUwhKKYGoWWUAWJGZWUNIlLg5rM4+zaOLtOYFA7AoICAAge+s2ShsztH1wOQDf+tubKRZH+fCOJyA9h9w538AZOHGCj7RtK37+S7j0Mi87zZmnw/+8TNDZue8CVy1CCrq+fBSPnHYNj7/9n/T/+nSkWb9KKISgox062uGkE7zjO45i/Xp44CF44MEWLr3//9h246uIP/coflW8mVN/dA73yOeyZcG7mL2shebm/VvvgICAgAOG8N08gC25ZpYYElUA4eAF2lUukQj4hhieEKYEDz06bmyNeZM/TYfRZWOIjS3l4jFKI5gihiENirKIq1ysmGRkSQYHF2HXVYfOuSYiZoAR8SwXlI1mjyFMHWyQfWkKJYkZF+QKvrgmdEp6EtMZZFDbWvEgcoSJrcVAg7AIYaMhhUIJh75+na1PKNxBoOCt9nuxVgRucz+uPscL1Bytmt08PLqWXGYLDaFk/fnnFcYDUeyozpCbZ9P69V57uXn0MQ3HNbEsQVOTZMeONBabScoEqXCErfmcl41RlC227DprCxc1MfRIOTum7gXyj2sJNOWJZy0dCvmgJ0QYM9Ns37KAHKA0b1Lsap6Fj8Tzw0rMK+KsC+OQo5SwCY1p3kR4tJwdTwNT4kZdQqWdCN2zMlKLYjjxOPrjg159HYUhS8QK6wAoiTYU8Ni9O9mYsFAjCsd1iczSQHoxoSKmIpb22l4C8ahLxNK8rHdCEIroxFyvj0pdY+FCRWwEnhiG5iZBvNNg8GGv50jf2q3kOFgSHFORbfMm2R2L57Azu4VSQznmmWeeIoT0REbUhEU3W9kU/ezQKT1JItYALd3EB73YWs2hKHEzyYijeDifxqlYqnjdxXUVmvJj8ChJOCwoz+MBGlISDIEquzqVL7mqxtvyk7RVyzUEdkjgmgpNGr53nPejMazjhFxEQ9Uypky5O7lCqwoOGijloITmW096yQ+g6hYkNdO7H/z7P9deYKRLp6HGxVcKHaNRkjrGwvZjgw1HF+HmhokVNqBc39UVFyfk1Suqe+6EY9KzFJOi4F1zWT+uVKhE2sZT9QAldaxWCVFvnHA6l0A6gsrm0Ia3oSclTksMR4ujaTu9/Q1fvC5CPC7Q2mHjTr9s3zXbVRMFN0eGKhkmkVDS4pS0ODG96s9cvnyuqyiafRSc7ejFHH7OSv+aSFwUI9FRxsydRIRLrCHEf574nV901QJSSnBECFc5yISLMyCQOQ1h6NgLNcz2NA9sv50Hdz0BgCYFUijCMoIpFdm7hyhF0oiig4zU9209LSlt9y3VVI1aJzwX8rK/uFIKp1/SUughEWugZVgn2auxvvAfFApLxmiJxtDyj3rlihBFy8XIKBIiCSHhxWzy3d/KDSWU4wmGlouuPIvWUqiRnSMbUDR4olIsTFHGgKGamkuUb1m3093ODnfMs5TCQLezREwHYQucYh4RthBSUCCPtHWcctdq0nB0G5kFcrrXGVTEa3OtxNhADkczEUJDqhIg0BzQTRclvQyJ4U2+JZbUK4kbvOazQeme1K8UUkmEUbWek0OC7tkm1t0uZjFL3iqnkKiOHbVEo6BMG/DCCgggfGsc3RGMoWMn88hGHXzBXxPSs+wyowghGBx8FMe3VpsZ7SESbeO2MRNbgTQF0flx2o49nA0P5PGzXeD6YpfTGEPfOgJCMDxSUykF5i4NUxjkVQmSWyEiGSoqRpwtwCiWH1PM8NdYklqcuBal4MiqsuT3h2gU38JYJ57Q0HXvfGPJcmaWqhQlDZNC8rm0JdbSkTB4iIf9fqfh4mIOGVgiRAZ/3NyzkF2B2BUQEOAx+Ks1yJhB8sgQt1z1Ev4+dCNvcVtpPvVT5OY+x1+Rrueu/yq+9BXFunVw3LHwtjcL2tufOrHIaI3Q9YUjWf/6v7Hty3fR9qEVu91H0wT9/dDfD2ed4dUtm23k+//4AFdv+jDfHzyNV+nXMvuu6/jRla/lurFXM3t+hEWLBIsXQd+M/WOZFhAQEPC0o/CtncBbH5denCTHszRSfgas5rRAL1EJyFws+pm9hBfLSbUqLwaNAx1WW81M23uDD5sG8U6Dh40iY4VRRvVtpJtLJEutDA0qStRElLdMREyiNE900Z2sN8c1oOEYC32sAWfYT27nVz1vtFDOqhcJefUo2RAScUpAgxmnI5FmWyhLKuHiKNubxIccQEMUPcuuct4vZYS8ANgAC0q4EQv1uEtJOSBhV3EYUp71sgpX3c5K2SIbGgqoVCd2fj1GZhQtr+E6vqWz5blJDkfmcGpMEQ4Ltg49BJKKGyPUCxWOclBmnFqlRPnxaKSmVy0JXNAQKM0lPGCgz0hRSqVwbh9Fx6iIHMPz82jLUvCPnbjpEgwUMdZHyDNIKWVTcAVaTawZuyGK6h7FdcG0d2EYngBUlyBAAa7CqgY68yweFBSyRcxIgS0FxWjGpdFXcDKyBWE/7FnUKc8mKRIW5HVZcX9JN0jkiEAKz+otkXDQcgJVFvuEhr1ap7C9BeFPWVw/js+YsxWADnM5YSNaEcO8/WRFUJJCB0p1aeqzRjtK7MKxc3SEmmmWfWwegc0zm5DDnhAQmhUjbpUo7VKUMm04wkQOefGRcqUipRYbfdBgtJTFFgZmq8DdmiPSDGPbgDYLrCKjj2voHTrGljy2bWOanjXl2JiXlEcv6ki9anmxU1ckCgqk5lkg+lkMw5u8AM2iAU/EKF/CmsmiqjlHoQGuDdLEM59xMWJJCo1Fsi1lccZrszGrCwoPVDL/VcoTsiJQuekZle9d3azcEy3uPFw2snVuESF1jJDAzfu9VgMlDIRwEK5E2QI3byND+oTjgGeEJPysCcK1ic43cJu6cQFhWKAECtPzJBOicp+YCf9eFiBMgVvOH1FykX5MWDfktbFL/fvccGgOuh7Bdf3BT4OinqKgN6HJqtjl+p22xWxiSMbJuNs9IU8VKWd6FUA44uKIMbRFWbRYgpw5hhBhrz6VoGRg6AaIEjaexaTTqxFvbccOKdgKjmszmNteMcySUnhtUxZK3RIZf1itDsdeHc0Wnfx2gcwNoiIuIlvuFxI0gapYdrmomEZ8eQdnF+EfIRfRlSG7ZYCRokYbngWmVnadEzqu6RIZHQFSiLBA5ZW/iFBtU4mN1m7gRF00JbzMqJWwbILEHB2jL0ZuuA8nrMg7uxDYRLVdCN+N0TC8xev1OxJIJLHso5UwKObtYeRJnsBWUiW0ksFgtiwCuyghURVXdYHdL3FdB00rWyYrmoZvZCsKW4a97K2ui6u7iJzA2mYBdqV/lVFaCeWYnpCnHHSlIQyIH9dO7ld3kS41oN2awl0/iixJXFkVu4QSjMzNkjObsfAsu6TwrDjdNijGGxnduJ50cwQyOxlrMjFiXhb58nnoUsPVFMrw+pMpdM91OdqIJQ2ajDhSgq1M8rGFuNEW0E2kVXMSfhvaMxpACvTNw4CXAGGXXbbmhG7RiW2mEC1Hs2SGRqmkuOeBWwnpm1CaNyYZutc/GpNNDI7uIFMEdC++GMKzjGxIe9Z4rtBxXEEiAU22oKVbhxx14xfAyac2IoSX7OOuzkeJDoNhaGjoaGMS0zQASWNa1Y1307GHmlhAQMChjJt3GP79elKri4z84gw+N/h3ZofaeO4rbsSe/9wJQtfgoOLTn3V52zsVxSJ88XOCz3xKPqVCV5nEqd00vGI22y+5j7GbtjypMiIRwRtPfgmpUCN3Ln2c/Gt/R67nRN4891tctvwsko9fy5e/4vKq1yrOPFvxnve7XPYTxR13qkpQ/ICAgICDHd8pwv8k0cprnErgvfI7SAlhSxCyqm5wyg/8qhCMzs3izoiR15uZy1J6zW5SXWHcmEOHFqPRStFsNhCPm6Bgh7udXc4uQpYvYox7LCjTE4YMd6zO0qOcoarsGlEO7zMYWYStp6sF1EycNGHQZi5hINJNNCro7tRoavQmiiDA8uPnNNnIsithpRzNz/wHqk0nU6yKTW68Hbe1ieLqMdz22iyDCmVI7MUhxmblcMIucpeO/ph/kv68REmdTONK7zB+Ri2B8BWI+jA6tnJwGwbq2ihjdjIYWUChdx4qGUJ26L57msR2itwYuoXH3Y00NQpmzRQkk07FWscxJCIVJ32MiT07j9MyF0OEvesqFW7YRQgo6mkyVg9FI40QAsPXC3RfElQIGsduI1LaDK5CuQqBQzkmsMTG1UJeBrW4Z5Vg2y47S0NszQ+RkyFU1saSZSFHef8Jqn6jCFTURROan66xflqiSUmoVdLUafiilec+VHJzjDiexUtC7/a3rRe7hBAopehqiVeOVbmMQsfBoWTnMKRBtvUINiVPqommReVaCVGOslUjJCHYusPFkSE/C6WL1KTn1ulfW0N4GUKRsmI5VU72UBYnpAST2qwMeDF2UEjdE3Vw7HE/+5N6vzqxFclKf1I1gp+m44tdXrtpQiNldZHtKeKULYF8Sy1Hhjz7t3GzQq/XSuy5Z+G2LqirY7mtos48OrRuNN2htwcicYGT96+zJnCFjsCBksvYvwuMXPc4E6hxYyxbdrmpbtz0DNyGfv+YgALlqPKlwS3HtoublXpJE5wRh8JjI7jZElL3hTRf7ELVD0jRuO6dybZizbn5bt1tsxgKz2c4NIeCSFAoKKJapOLGXa2/v72QIBTJARtdExScAiW3VOl6RdcTOQTQFGkFYCjUi0CgdAuzIYZuefV0lO1nMPSrJQVS+tXXTJxchl2Pbx/XkP7WmnfllKZQZn3m1vu238pfN/6VR8bWe26P/r1iWQLDkkgMSloCW/NiRuma35cAiYFjKgyV8cbgyjOjZpz3LbtCoTBztPmV36q6ucTq0EA3KEkXKQxaWo7yknMICbKa5lfGm3CjMwBo6JcYjTX3oCMRCPr1mRi2TrHcpXUvbmD5dnW6bc/C0ilSTj+ruTkSvqjvyjCa7+6ZalFE/XvDMqHdnEub7KgcU8ezhHJRCKHQ0BBSoCV0hCnoLvXSkZ6F0ryELAIXOwROOxQHcrghl0y8AxCkRBohwRYO9nyBSETItkpK/Qns+S2UWhew0Fji9Sm8hSddepaD+GKXQKBiLah4OyHNAlx0zbPCzli9le0w/TIMrTIWIQQq7PU1kZLoNc9/zyLQxsGoxPvSde+NoXydh8LzKYqYd566rBu6XWmyJXkcfX2ehWU05o256QYdXRd0dOpoRrlTTfTQKZPr0Vh/eCemZtApu+gx+nDnRb2cHFpdIMRpCcSugIAAxq66BXe0REP6Z3wglkNZCT529m/RrFjddo6j+PVvFS99heIvf4XXvAp+cqngiMOfXsun9o+uxBpI8vi7/oU9VNj9DpMQ0iO8ctn7uHPzv/jP2H2YL/kS2ZdcQayjjY/MeR83vfENfP696zn5ZNixE77/Q8U7/k9x2lmKN7/N5dLLFPfep7DtQPwKCAg4OFEuaEqSiONNJMvDVdELMKzw3Otaw530J7ro6vRjA5sm+bTECfkuRZpL1urC1uIIIHn4TI47aTH9x7Uwc0Ub0T6d5KwQoBiRGm6iOkGYIHZFvOeKUOVg9v5m/nZlscs0Jtm3LNZV3MA0NGGiG+WYYxrgYrslVDiNkori4WM4PUVPAClXSfNnXxIkgpH8II92r8GeX6qvsw5Ou0txmYZeFm10KpPbYrqECrcgxhRuXKDi1QrnrVaKHavZtdM/jqiZ8dX4tNjKqfq1lX+VJhlrBjIax56TRoQ8wUT6ExGANaNrAYjGvetYzjRmyxgYFkIXSMMEPUxIpijrNUr6WQRlmILeiGuYRMOSZMI/vZqYaOHSVsKlzZ5M5ShQLmUjJN3NUXRDKAWJmPQsClzF5uIO1uWGybrDhLeaJPXqe4Qo/79mwgVgSN1XMOvboWytFY0K/9p6ll1OTYYCUREatAn7rVyuOHvV0eOO71kbOcrGcYoYQkPpYVwZqj++rE7y6mbreLHtdrjbucV9tKYeos5Tqc1q8kU379rFZLxmW+/fGaEuerS++oxoXg0RmunF7PKvR/norWWrLCC2UEdvi1TFrlrxWKv/LmpEiBjNSAXNWi9NxmzCeqN/NBfbTJM1O/3P5ftMVCbbdbVrjVHU0oyEZlEqxEFozIrO45jeIysh4MpuRq4wEDgI3Lo2rMf73jsPv3MYEU9gK5+TEF4g7RqxqyxKaSGNeBza20FPSOwxl9zdO3GGiohy9sey6FVThZYW6OkRvpeZX3HNq4+uQyQdx5UWjhahpEUZXZxBOfXn4ISrYrh/FpQSXv/Mu0WKbsm7VlorTWY1oVPY9ISInJ7E6VgCeghNami+OOm4Do7rVMQJ3RdBPbHLIJuDaOEJahNHCP9/XpxDIKxAKJQZpS3UBLh+zC6NDbktFNwiph6u1l+CVGZVEAEMszo8SanjWi66obz7IlIVYsodVCmQygYBMRFjeKYni1YuvZ/hFc2gqGyGlyZx+1orLVh2YwRQ0WYG+iK0tEAkKgn31bi82RpCQlTE6Fb9WGaa1hbPsg5ZjneH58bo2rhOEYWiox3amkrMmWkQjYAjDE9sVw7hBIRDgnRblFgMhKy/L11NobtjuLhIQHOlp6S4jtcn/XMshNo8IUooXCWw50ncmn4Cij5tACGE54YoZHX8kgIz2U2TtbTu2J58KVCG64mdzXNwI41e8HtpkGhbitOxAik9K2Clqm2uWRp2d5ri/Pa6a+s0xygs7kT0G8SPMGuOoxCNGsXlbZUyhBCkFyxiLNTDrshSXD3EsN5HIdpFZHWb10fqsjhI3KbZAJiG4NjjDOKJckcVVcvqydKdltvbBUeLYGgSXehYXTE8T2w5qVvoVARiV0DAsxmnhPGfbzPy3T9gJsb46vFR1rqjfPSE79Ie763b9MGHFG96q+LLX1HMnQOX/VDwutdILGuql5enDhnW6f7GMTg782z8wL+9F6AnwVlz/ocZ6bl855ZPUHQKuB1Lyb3kZ+RPOp/w4H2cuuZ5fPiwi7nsOyWuvVrwpc8LXv5SbwC+9DKvPZ7zPMVHz3e5/s+KTCYQvgICAp5aLr/8ck488UQWLVrEueeey2233Tbt9mFhYRoaYZmqBAPW1o+QfqCAUi7GVhfLtoiFwhXrKiEkwwMao3O9ibY0PSFlV2wVmeQC3I5l3vchgdmsEe7TCadNJBrbQp0Q8yYvji0YCs8ia3ZV6iNMk1Grj6HIgvoYPv6jxPTFlHAYWlqrE1qArNmOijRWvilbapWL0X2XOdstgW5hdy6vvOkKbeKzSsjqSrLbInEb/Gx9tZNyDaymKF1Jz8JE+fOtVBIic0oUTkpTPNqgtFoH311ECbBLMDJak71SyKoFSI2oYrsOUkBJS5A1akRCysKft4/r+mVULKQETwx7gpdSLu3d3uShoDcgNXA6V7ByyRs5qvd0+s/rYqzDn5j64XVs6ZmhaZZJZ0fVYkhTZSGpPMPFM1dyFcK1y8ZAAOR8982Q6VnJ2crFUQ5jJS+TmqFLYhHvOBEtBCkvYH1louJPknXhTVwrbp6q7PzjHczQZcWyK+tux/XjEMW1tkpdasUuUTP7r7X4avS0HTSZ9ESBUh5DGnUujpXy8kNevSM1bVFpGe9zbezycriDPquX+foiGsykNz3VJEm9m9aWt5Az2uqatjfUiS50SlrNwqJSngWF7sXsEq5n2ZVOey5BzU2C+XP9JAIC0C3Cvl6h/DbyLB98axu/XRqtJgxhsMxYRbsxixZzQbWdgLbUsYQTq2jQB3ArrkWirl3LOD1p8it7KcXSiLyN1DVWz44S16MIszw7phIfSpZsL5veJO3s5h2cjH/fucp31ZvsXhWehaGjKvdy2ZJNmpJUShAyITJbJ3lqM3qj1zdlTSa6wvx2hod9sbBLR7S2IDTDs94TFfMllJCY/lzcsryxSADKUChXUjt9VpqqWLkJIXG6HYrC6xljdpZN0kXXdHpCRxKSXixA3YCQ7g0WSjl15Wl+PRzfXbcSg02CEAolBEoalMqGaFQzjPp+oAhNIYRE+QJLT/tqRLwDzLh3Tfx7YldphJiVquwuBdi2ZGjYE3mXLILWlhpLRExc0/XEdUF1vENUXco8ZdxvD8Gs5hfRcFxfRTDLa37FNZOi7SBMs+YAgpZQmq5YD3EjBgJSiTCtLf4YWjOGi4L0rPCUQqLRHl5Wub9r71dRGEJufwDlevEkdU1UrI6VGaWopZCu8P3EXS+FRjSNSvUghF535yvdsyZylYtQApHT0CICfc1fELqoHld6ypcynfJoXZcnoKglPIHNjGC7ZbFLr9R8eecxWJp3XRYtFHTILlJ6J3K5jVuOadc4Eyfqufsd3nMKbvNcCCXQfMsut0ZbFgLszhQqbNTVAyFQEa+j6zoUFnaA8BKKoIGj1ycimzEzwlio39tPeFabttWMMPWJkW6kwG2aVf2sGTXCtcRNebEh0ccL/VUSejsAbucCRufNQ+u1EJpLOfGKphtT7ltXlT3aKiAg4JBD7niY8BUvQf75ewytG2DzKRp/2XkTr1vxYVZ0HlfZbnRUcdFXXd7wJsW27fDJ8wVf+ZKgp/vpF7lqCS9ooO1Dyxn5wwY2nX/LuIxWe4Ymdd5y2AVsGl3Hr+/7nvel1LCXvITsq3+PPft0zP98i8iPzyG16yYOP0zwxtdLvnOx5He/FXz6k4ITT4D774dPfdoTvj7wYZc//UVRLAbCV0BAwP7l2muv5bOf/SxvfvOb+c1vfsOKFSt4wxvewKZNmybdvqtLo6MxxCmzz6HRmOWlhgdAoBVA4WA95CCQ/oopzOgVHLFa0NhQnXi5mkNrM7gyQt53K3GTXROOZ+FPMP132qZGQVFvqE4kATRBLjkfGmZ41jPKCwhdnneX3emk9OKIQFUwQmigh6pzCr9c6btGeiv0yndjxHM7lFWBqfzUkuXvIjUTFEAWfFdGPYIKp3HDSVS8FaFJNK3GsgtIxAXx6HiLLH/Sq7yYYtlcVRiRQlZjE9XMDLyVfRgNDZA3W+vK0zUqE2ld98UupTwxSAge3vFfRkvDuMol1djDYGQxjuaJSyreRjjagqWH6ZobYvlJnlul67vy2NITWBYfniBkVacDultkIr5/nmvXuasMjXntHg15YpXjujjCIWcrpGZjajoN3VFmxXpImyn8dIJ1bowAGl5Q++pqv/dPWajSNCqWXTtLj5J3RwBoMuZW6qLVuvBNItAAhKMa8RhILYWrWQg7jyH0Soyw2r7gWkn/2KLmd38rvxFc3/JDKBchYYW+mg6jjYjwroEUnijlOholI01RT9VXyC82morUfOWCcpHSQGkgnFKlLOn5VJLfUBUTlB5ioB+WLoa4H/RZ12vKlxq9kQ4iegQlvEh9mla2DitbVLkYCzppTi6hzVpMR/KNpDqaUOgkYpOEdxYCDA03YtLYAJ3dOmFnO9rWeykbQErTP3dpoop4wcAniX86+rcnGL3BSwqgfDdGNYk1Gb7Yhe1WZ6/+ddZ8ga0S38rQ0HyxS+jVY6pEiLGMf4+2aKjOWZ44hBdTyCvMc2Ns8oWT2bMEMwc8yz5XgdGvU+hL1rQFFVVBRjScPkXRLZEy4pipXsbizRhatQ3nzgZDF4T8jOZOnWRa7fOPD3lWgy3NnrDe2iy85vMtu8prvMK3HKtURxMIze8bIRc30oiUOkQafRW0KhLmnQLRWrFLwlbvUmDb0NIi0DRBY9qLX9vUaOAanrVYua28g0o2N5zMpuQpfpNUBUtbi6M1meSP9NrgBvt+cjmFkga24yDx7r82cwkDsbnMjHeR1KNENc/SUpc6bspbfLfMGnHD9kf0souwlGjSs5RSUqMz1FJtE8dG2QXc2ueBZlBMzfZceKUA1/bjfHkWpslwCxINgcCO+q7dvrWYI1xkXqK5Es235hVlC1AA6YlkTsS7topqnEKAMauPTcmTcMMN5YtWEfbLAr3UNNo9rYeQCBHT2tAMUXGF9sr1Tj6sRyvflcUuRY3YVetiOMXUREpQId2rgMCzahbmRN258hAVlbKFofvb1dRNiPrkq1Kv3K8IDaw4dv/xE1z4a+mJrGIgfArF9BxGO+YhpI6fKxNQaNokY9Nk57ZHWwUEBBw6uDbGzZcQ/ul5iLFtbLbOR7mCH7RdwrEzzuZFi94KgFKK665XvOyVit9cDS98Afzsx4KTThB1PtUHksbXzaP5zQvYddlDbD7/FpQ9Psn97lnecQxH957FT+66iO2Zmsw70SYKZ3ye3Hk/AqER/tXrsa77COSHAW+ic/xxgve/R3LlzwXfuVhw3rmwdi188gLFeS9WfP+HLtu3B6JXQEDA/uHSSy/lBS94AS984QsZGBjgIx/5CG1tbVxxxRWTbm9Zgo4OgWmC3RKv8RjwZk0KL34TQlQsMkxDYFreJEv3BYaR0oj3UousZn5rXzLheKbwRSf/ndYyxURrDg2OO9Zzf5fSK08JvfJSHY14L96a7lvLjH/eCEHIgrgeJSa9lW3Vuxo33ecH43cZLQxTcryV/PLbuagpS/ixqI2G+rq5iXaUbqKsOEgdle6nPHvX/JNS/uTZaZ6D0zKvbv/KIrWCYhFGcqHK81IIAVYcp3UBKlVvOT1VDhS9xmWyp7vsxuhl1uuIeC5na4fuByARSdHiu77UTojKNES8yZ8LnntYuV6WhTPr9OoxfbFL1ATTRoEq5r1g6UaIrNnOqNXHhuwMolHPsksCtuvi4FBUoGQRXXiWD43LowilvLmQpByWybMQQHmikBAoX+xanV7I0vajKi50hi4qwiZAwRe7pNCJ+UZRclzMrsqFqEGl+7D1OIZMoAxvR0PqFfGzdut889LK38cfKzhsVfU3Wba2q8nzJX0rwbL1pGe05vXxrB88u7J9jevunNkwa1ZNP1QuQrlecoJyn6uZ1BU3KIrbaq6NZiGloLlZILSq2KXKorBmenHTlOt3NC++mLd79bhGU9WKw9HjmG3ddHcLopEpMigCytI9qyepV2aUMuT3K/9ecDFobYWIVZrUsksVPeFWuV7/8PrdJJZdmreNchQq3lT3W8V6q2Z8M3timL1xjN543ba1cc3K8by84VAiBPQNwLx5Gsnk+HFHgQt6q4HTWrXEU7Jq2VXrZpw2EsxtWgIIHKrhNspCpGnUJ1wYT9Ep+NsLursEmu4FqFdArmigIhLRoCH8MTw6VwcU1qwkQvOtfyIuRE1AVK3hNEAzaTCThLUQTdH2anvUXGpT1IcxicUEul7NECp8QaTcgEqanvirhHdPSticPNFvF4mICLauNMiFbNatB0dqOI5vkSmF546uWUhhI8e2Ic0o6CGk0HDbFuI29KFZkt5V3rV3Cq5nBVg2xi3HmfTH/AYzSU+4nUbTFyaHn/BcQH23TalZ/rNPIuYncLvGUCEXBESMGF3Jfl+sEYwN5Bidk2UXO1gTnYWrHLSshiYkWqwq+tSLXQIn7C0kbdxExRKvJ3QUnaHVntt0tAmneS7KilOb5EAKyYknaDQ1euWNWX2AQOoSt2ZhX5Ut+WqekYYB23dALlfxxK67mypusTXXeig8D5Xq8mP4+RK4L1SPZ0avoLWFqtglQBgaE25tKeof3VKvqm7lH8wokyU/q5yfq2HJWCW0AZpEKD8eJ2pSq9PJCMSugIBnEXLHI4SveCnWjV/Dnn0a2Vddzda/KzZ3bMGaneR9x3wVIQTr1nvxqS64UNHZAT/4juAdb5NEoxNfQA4kQghaP7icpjctYOePHmLNeX9k7D9bprTyckaK5O7bxcj1jzN83QZG/7GJ4uNjvGnVJwDFF//5LhzXqd+n5zCyr/gNxcPejP7A1UQuOxttzd/qtpFSsGC+4K1vlvziCsHXLhIsmA8//imc9xLFhZ9z2bw5EL0CAgKePMVikfvuu4+jjz667vujjjqKO++8c9p9DQPsvkZY6mcY9K2lXMfxX5RFxSKjKg557mUe0n8/FXUxP8YT8wPill98pZAV4atCjSuK0r0U744wK2/kzc2C444Bvc7tsP7Zo2mCJR2tGFo5iLgA3UIIQSjcxObR9Tyw/XYUChVv989HIBGEjShzlqzE6ksQ6tbq3VRibbgtC5gMrTxL1WBey3K6mxZ5VmY1SNObmIYNxegobB2MMtY2m87eMJYwEZpEpWdUXvDDmjXZ6VXPWnrXKpUSGIY/qfGfb30JL+ZLeV+nxp1pMrHLNMrWSAph1ExiNFknQlTi85QtvASgFNraGxH5IaSmkTfaGI7MpyiiWJZ3nXWpsaO0C1s5lFwdV+bRpIYwhT/HUQglfAudyhmioq4nijVYFcuuuB6lIdJSsaDSNYmsEZZKwrPA6+vRWbHMP40aMaw8CRrfDspKkI/NxJBRlN/2htARvpjU2FAzQaveEOi6rIoUJvR16V6MMyHplN2e4CCr7qZewSEvZpeUlYlsrdCi41l/mKaoiE9e3V0ELlIz/JSk0BLtrPxeeKwmppGVQEXS1RP0z8PQwelejdO2wLsvkETTW5BRB6FcNH+mW3GdEpJajyCJXukHte06HhUyvBZWbmXCXZ78l6+xKwxaVhqeGDfN5NTNU83GONmCqgBV8q6nap+PPeuU6mn7Abir/UqhRQ0iS5swmqoxqcJh6qxMy66O5cMlF+hEYpMI7IAb8iy7XEdg1FiL+cqAV4+KmzHoUicdbq38Xb89lbFLsWeLtAJvjHMc2LBRoxCS0OOVYbRIrHaN9MkxQrNS6FFBbIGO22BjzxSEljV49xdULCg7Qy0c2bCEdLi5cozyNZ8bOZsXHX1i5XsV8gL6RZKeJdJYfw5zsYGQgmSPxB2oWiZKTZBOCxzNxJHVtq8IL8pLinLbPRrbtnt9rdwmSvruzG4J5VtWVgQN/7pZjZ6w6old1Fh2+YK58oJTCiWIG1HaQv75jW7GDqcqwrHUjUqdSvFW3NZhoOiLR/64E/YFY+nFZhtWwzxh3+8lpbAFmtCQRtmyq9pv5FE95BbanrCPZHhYMOzp88S0FpJ+Ug0pqQabF7VjQP19UtRTKCHRZb1ll6vcivBeZtbM6t/l61nbncvTo/nzYcF87++xUD926xJPdBa+JqWDK6wJt0Jnl/DcRcvnKgBdTlywkeMtu7SqRfNklpuTUK5rOZa9EGVx1z/2hJeLydkz+6+AgIBnNq6NcdsPMf/9TZSVIHfON3Bmnszoo9ux7x7hrjPv4ZMnXQp2hEsudbni5xCNwgffJzjzjGosioMRIQTtH15BZHEjmz5+C4+96HqM7hihOSm0hIkzUsTelqO4YRRnaDL3DNDSFh87/AJ+0PotftL8JV694gP1G+gmxaPegT3rFKzrPkL4t2+hNPdsCid8CMLpuk2FEKxYDiuWCzZvVlz5a8WvfwPX/1nxvHMUr3mVmLhiGBAQELAbBgcHcRyHxmpgEgCamprYvn18Vq4q0WiUSHuaaKyIVYjiGg5WyCKlW+wSYFkWkUiUSCKCGCwhIjH0VIr4UJSiGcdlB+FolJiMEHPiRKMa6bQ3M3ZjsboAs8sWSOxMlkRcogYtIuEIsXiIoj+TNkxBLBYlnfbGTZVaTmFeAvVIlkg6RzRqIP3f3M1RDENhWRZ6JIw+6pXR3tBGl5VECsH8+VEKRpREIoGWKhGZ08O8ziwPbbsLYSqiIkpP1yk0RdtJDOkMjW5mkWHRMjAHBsC9+x7CbphoNMp0RMwoLYt6WLvjXoyYSXNDC6GCxS67PiOwiDpIq0jKClMsRTFM6OtuobQ9zKzT27HmzkNLpYnuiiGKkI6mcAtDhKIxLKtUuV5lkkmdSCTrfx8jJjNERB4si6aGFhqMIYrWEIlkgrndS3i4OIglLFKJVKWNy5ScMIZugA5dz5nLI9u3IEZypJpSiLSJFYlh6HksI4SlWeiaRHctSoYiPOLibNUIWRqlko1lWWh+PaMiSjQqiFphStkCZshEjIWxQhoRM0wsGfUs3SyJiYmtWQjLAkfRHOugoU3Q3tSIsagb5bqoLV65Mp0mVUwzZG8jlUgRi8WwHG/CrlljHL4sxuFzWitCQqhkEt3l7TujbSYjagdLOo4gEUrXtWko7BBSDWixJJa9i1QihZtKEo3apBtcEtkQQ0OKZCpJtODtJ9INmHnBFsvCsgRmKs4uJ0t7aS4zcoPkrW0YqQaKowsxok9gWaNEUs2E3RDhkAZ+TC4rFMYsGCxKzaboxAmHQ0SjGiLdhGVtBiBs5jCKOolEilA2hGZodDXPZDDnWZ6HLE/URTOJLz4Fs6V6bnoYDMMgEpGk0o1EIlE0q0hYEzTMjtO95j8ULROrKcZoLkqjFWMwMpOI1ki6Mcmgl6AOpRQiFsW08sRisQl9afZsm0cedRANkshwllDRIBr1hMqQdLEfyeHoYFkmVjRNvCGG25WnKLsRVpRUIonwBb5SdBvKMQiJMK5ZAh1iIbMyDpSRiRylfBYXk3hjmnBTmmjUs35qaLEYCoWRpiIajSASCUR5HHFdolHv3mpIC0YsC6NooFsWzYt7CC1sZ9M3HsW2XeTi04l3b6Owy0QIgQzruDnP8kprMDEMA8cRpJIxcrqB7uig65iGSciysDObCYdDULSIR2KkGpo4tf1cNKnzt62+hWjU6wuNjQ1YlkUsKkgnYyxsW0k6np4wBpRJpVIM9R1P4eGHMArefSbDOnYMoimTaNSCcBSZTuNGo0T7wNpsQTxKy7wudg56962KarhRk7iCqBWta+dksojjKnQNZvZb1YOn06i2HmY7NvesMUgmNVJGlNIOl9TSMKPDyUqdQ6EQIcvGCoUq38UTcZKFKJZlY9kSLSRZX3wCy7KIhuNEtRi6ZRGNxonGs5SsPFYsQTQqSSW9sUzZDah8lKReQo9vRy+ZxKwYljDA0InEo+ixOCE9BCMWib4IRWaiHRHBenQt4UiYkBFCjbrg5pF6iFgsgmU56E3zOVY8zpAa41FjJ+Gw90yYszDCzh1zeURfg65BY5Nk7RYLaWiYwiQSihBNRJG6gHgR3AgiGiXR1IQVtTAcg1AojOsqLGEgivXX1o5FK3GPjXgca8QiGtVobGhCkxquv61lWQilEU1EMLNm5X4MhUKECdfdn+k0FIs2Dz3iEE9opNM6tl29B6JRSTTqkk7pRCKCdev9e6PBJBotYhoWpmsgIxbhWIpEovq8B8jtEshEnnBUopQkEpWkGtOMhi3MMQMzHUdkixiRCA0N6co5yHQadzQF7ijEkxPu78kIh72kAum0xvadDmE9Rk9ngV3Lulh7kyDaFtttGRCIXQEBhzxi56OE/vhhtK33UJpzFoUTPwLhNEop/vTN7zMg2jn+ja9g7d0zeMc3FFu3wnPOhDe90VtNfqaQfM4M4id3MfLHDQz/fj3FjRkKDw8hEyZ6c5jksibM7hhmTxyzMwq6xB0rUVgzTOY/W1F/fpy3j7yZtX99jFs/8gdWnXHGhGO4LfPIveznGLd+H/M/30bbcBOFEz+OM/vUSevU3i54+1sEL32R4kc/Ufz6t/Dnvyje+mY4/bSnukUCAgIORca7kSulpnUtz2Qy2CNDtLcpMptKiFKJfKFEurlEb2uWwlaXXD5PPpwjX3BQ+RKJvE5bqBfdtXAiD5PL5cnnDYxslhEDBge94+mZbH3kXdMiZOXIZEArFMjpOYrFHLbtvVCXijA2lmFwcLCyy3CqEXt5I22lu8lkitj+b3omQ6mkGJnZwC45iLithCnipPVWisWdAMiYA/ksw8MjFDIZnJxJUjSjOSbFXImx/BhZI49uhhjJ58kWbSjalePrmQyFfJ5MJjNtm7tFKHXpjPXbuBGHzGiWkdzwhP1kwcUoOCjDK3NGL/S2zqM4tglHlRjJjYISZMbGyJYyNBYVhUIBJ5OhUFCV61VmdAQyuTwiCmMlRc4W5Ic3IOwio9ki7VY/O8Vamsw28vkCVrGFuDuDBq29ro0Bshmbkn8dskaRfCGPLBQYyYzCkE42NYuMehS7aFOQBRYudlC2zYb7i4SMMfKDBaQBBZmkUCiQ9+uZN/NkMi5FPU7RdcjmRhmzdRocG9d2yZWyKFuRz2mUnBJFiqhsHlGAgl4gqizyKsdYzTUBsAcHyYxlyGQyZMQImYygUS1kU/F2hIBMRjA6PFo5v6JdvY6Z0SwL00fg5GAwN1jXprqusPMSmXAoFAoUs0WGh4fJZGB0zMHM5SkUYHhomFDjIsTwRtzhYbJZ71opBTJfougqinkXXS8QDRcZyWXZpjppzq6nUCgwlrfJ5fOUihJX2BDCO16phCiAW1Tk8jkyGYFtNDPiRLHsXdhujpJdIpvPk8+NIZa55LaCXfKuXV5GEPkh0BTDw8PoRnURb+dggaZSCceBoeERstksZsGhaGtkshlyOS8WUzyUZ/WCDMPDkHHS2I7LWHaUTKbGuiSbwVU2mUxmQl9qavTeFTcPu+SEjRMvksnUuOO12ghLUBgqMJYtkslkyBdL5HUBdgZt+y6K60aQEYNsJgNOCTaNYY+6CF2QyTiVcaBMJjNGaXAMAJUZJT9IJSnQSKZIvlBEOg6ZjMIZGUZp3v6jo4ry5Y9GvGtQKpVwCpKR4UGKIUGhkKNQgIwLw/Eustl1AIT7GsjdO4zRFsGa0U8u91+ULSiVsth2CduxsVWJQqlEvlDAdkvkshlkoUApX2R4NINyw9iUyGS866T5lRkZHqG3u4BuQFi1YdiRSjtPNh6NDI8wmo8w5CTR7WEcmhkJzaet7y+oVq/9la3h7NpVuYcKhQJuLsfw8AjZXM4ba4oSO1Oi4Fres6GmnTMZr61MEwYHsxPqULBzJDvTiOwuSiWvzfJ5yOWLZHTvmKVSiXyhSMEwyGQyLFoAIyMj5PNZCgVFuJTn0czDbBWtONIiT5GMzNKbLCD0ItlClnyhhJlIERU6jVqH1y4yhTSaKNqKolHA3VJk068fJH9/BltGyObzGJksRVmklM1QiOQJndTESGnUux8zGUYjIUp5b9wT0iCfy1IowFBO0hdvwLWHKZVsCvlC5RooHUp2ybvvhaBQLJAr6ZgFl6JdJJv3nsn5okM2lwMyjGVHyduCYdmMXSjiuEXy+RKOW39t3XwG2/bFrtEMhUKBTNYbe4QQdddRKIdioUA+n2fXrl0IIcjmMhSKxQn3Zz7vXcfBQe9ZPTxcvQc2b/ZiSY6MeC615e+HhzPec7tUoqRKFIRJJptlbKz6vAcoDI+Qy2TIF00oFLBtGB4dwVrgUnxAkEOhFQrYmTEGB+vHczmWQWYyuCKDO67OkzEyoigUYSzj1TNnFjAaRlFNDtmeUSynebdlQCB2BQQcurg2xm2XYv77G54119lfw5lVFWX+8NDlJG8wGZpvc/X/O5mbb1EMDMAnPiZYtPCZI3LVIkM6qef1k3pe/x7vE13VQsNLZuHmHXb8/AFGL8pgvmkrj77mb/R/+FikOc5MVjMoHf5mnJknYV33UcK/eyel2adTOOnjE6y8yjQ1Cd77f4IXPF/xpYsUn/mc4g/XwecvdCpZlAICAgKmI51Oo2kaO3bsqPt+586dNDU1TbEXKNPPuqeBQkPONbC1RmRpA0ap7Ocgq650QkMIwYz0XAaW7eCvjwscMYmbAl7WsnLmPBVpQk93wbZb6raRcnoXbl0H25nck08IUPEQaTWLgraFlNZb5xYZO6wJN9RCYe2wv4M38QjrUUYLg5Uyyr8BCL06obd7jsDZrIASrbEu0uFmHtxedQltj/eyeXS9H9JH4PoZDQ3NqvMNiZgxssWxyjGSMUX3IvwsYiHCZgIKIxPco6yW+biD9S4dixbCPfd6fzc1waMImho99ygRbUHsvAcAFWmgxYwSCru4WRthaQghaTHno01ysUyj/jgCP9OaLiv1cmQY4Qpm9IJwHYQUJOICVyjiSw3ErHmU/imw8uCHPUP6waqlpZMz23CjbTgZDU0reFkWDVCOF2MFF8+NtRKzSlQygE5GJd6Zv03K6CHr7qQo1k3YVqtxFZPTuMm0NsRYtaiBf6wxYZRxMWNE5aOUoCKNqIhnSVnOzpdKwUjnCpydT0BWrxg2ll12ykkghGF5wbIdF60m41jJSOIFD6rpDlJjJDST5rFbKtn1pKYhXNfLnqeHfBfIcS5+4/pT0fFjdpnU3dNld9eyi6WmSXRd1N2bE0PjCYpH6iT76+PLlX/TNQW6xDyyk6g+DIPrKr9HBnSKQwqGwPHdQZVWdflVJZf8g0N151EOum82T94f6rKpjsusKjThx/0qH6Dmt5quUIn9Uz1s3faaWd8fzRlxzBlxhCZp/0+UZnUYjiMwdEkqBcXtwr+UZZcuWWlkQ+h1iSjGI4VWserRpDHldrWVrbiFCcFIZDaODDOYWoY07qo5kXGuu8iKOy2AkpAONxNrP5Lx0cLK7aFPUW0pNFSyC6eURSsn0HQBKTF0LymHVomD5v3b1iYY3lGtky0sXOFWEgLovgt7QwPgaH5sRYmmh1jYOr/m4Dpu20L0jbehNIWLi9YRgfu9ihshz/3QVa7vVuu7RfruykVVwtWTlZMUullpT9cVKD2M8l1oBeP6mls1YO7uguhWMFyjLptvbYB6zZDsjB1G3l6DgUThxfKzqLGWgzo3v9r4eZMtXikv8pn/txeIQC0Jo+REj5VyP7fticfJ5srHKHvtqPptwgbCFriRehf9mooA4Po1tkwg0oDR085Qfh62CqE0CelJJjdP0o1R1oyTtTe5Ns39VXfYPdoqICDgGYXc/hDhK16G9a+LsGeeTPZV19QJXY/suJvfXHkxLbua+f32Y7n/AXjn2wU/+M4zV+jaV2RIo+VVC+m97lRuW30XuR8+zkMvugZneHLXR7dpNrmX/ozC0e9GX/MXL5bXI3+a9hh9MwTf+Krgg+8TPPQwnPuiYf7y1yCWV0BAwO4xTZMFCxZw44031n1/0003sWzZskn3ETOPx+k5CvBizyqhISISFdO99Ol5b/wRWnWypWrjyyhFb6QDEJX30/qYXd7zwh44EafnMAyrGgzaTXbixpqZMaM6xg0dNXFSF/LfqV0rMbH+lbhBJg16vx/LxJ9QRARCk8iQNmEHXTMoVgKt+4WUA+rWbh9pwIl6q8OJUJqEVV2wmN20hBnpORPqBKBLg65EP3E/k1k5rpGqxCoTvtA1PeGmeZWYYmUaatZMLEtw3LGSWMwvONbmnYPUveC+gCr44kh4+vVrw6ivz/KlXrY1L/mWH29KhBAI4vFxdVcKaYLQLaSp095W/cmNeO0Xjnii6pYdGghPXNVFefLqleEFhIHa2PfjVU4VacJN+jFt/Ng14VB1utJhLZs0pFNtnBsxzWTqiJ5T0TWNZV0nMLNtFU7Xqrrf29s8EXCctzC6Lpg/z0vcIKThxQeqiWE34f4wozjKxZEhNFHt99lwH5G5K8gbrSTi1ftt5kyNtjbQ/MlcOY4YAoQRQqW6UKnu+kpN0cV0U/gR8/06tSzAmX0aOyNLyeuNEG3w26m2rEkK00SdODwZSoHbMh8VrbeyKBenhIHTthi3dWHlN2eoULet2e2NG7GFOpE5U/Tj2uqNe20ShhdYuzZmV+UUasUuv+is2UFBb6jU0faFG8Mad65SVNwtyzqW7Xj3UjwmvADigkq2Tl1o4AecN6VRF/dtwunU9FFjD8QugfCE/nExabNmZ7VplFtJolRzIE/sqgym0JGY4Yuh9edbXkiYKhRSWVBWSlUECMeFlSs1jjsWBvqgual+vC1vXyZj9pJJzKvEr9NEiHJGTITmJewbt5hQi2GEcTqK2K0OjavTRCPQ2AArV3nZGZVyq33RjCL9OIAPjj6G7ZaDmwPSS26RiMOsAQFGmHQoQcJM0hqr3mdSL8eF9D7HEoJIEswxk9rQWrVil5esohxwvvrMWmjVx4Oc3VS9JyYbs5z2JTjdh9F+eAoroVezwCqvw7otEpmc2HfKwnypnOhzsvHSP9zhq+GoI6rnV1rQhLHEwPUzTEzYdZwCFbIAqSF6V2JrUdA17L7Gyn1Tx2TpIaehnOyjEu9N08B1vHcX5e6ZSEwgdgUEHFrYRcwbv0b48vMQo5vJPedrFM66qM7iaMfIIO+/5vUsuf0wikLS/ZIefn654IUvEOj6FG9OzyI62wY44Ttv5Jcv/y35/+7i/pf9FidTnxraVS4PbLuDn997CZdZOW494xO4sTbC17wD69r3Q25oyvKlFDznLMGPvi+YOaBx/qcUn/2CW3FhCQgICJiK17zmNVx55ZVceeWVrFmzhgsvvJDNmzfzkpe8ZNLtRSRdSRGoaVDUvKC/rmF4wW3HquOOHi+/UdZONhXd4TaoseyqS11eYw0GYNZkb1LRFpzmORiGZw0T17omraPlL3aXeo7GnlN1H3c6V+LUTJDLpJ7XT/pYk8QqY+oJkbSwnZJfRX81vOS9OU8UhfyVbaFVJnOWHqYrObWFsCFNNKmzvONYwkaUmY0LvQmlNlkjVY8xnskmOBNTvVczzclIA27jTIz2pdWSHV+wDE2/yj1+AissfxKnyYolVNuMBN3zO+oFT//gwhdQEqd3Ezm1au1TaFmCPfMU+loXe9YUeJqW1EQlwYEQgFP0szGKajZG5IR5j9NzGG77Yq/ONWLX0UdCf98UbYRnDdGV7Kc7OYCpWRM3GEc60kLnrLMh0lBXhqYJWlomzzqtad73QtMxTQiFREX4K29etuxyrTjF5jnkzQ40YdaUIog0t7DstDmeZY9vndDRqdHcJDDcjH8sAzfejtIthJeWE/DEFy0kyh/riCUErrAI92jePVn+XTdB08gYneyIH44Me8JynWVJjTjR3cluKfencjZXZdbHmaotW6W6a1KVgjNYL3aFlzWTOsrEbPYCfju1Fj2T1E9L1meJE5oEKSrzcBVtqfym13TlchD+vNHK9vgRlWtWFiiN0DiLsZo+oOle5ljHUXXCsRKKWssu4RfmiV3Vg7f4+suyjqNZ1XVCnfWhPi7r3fyWFRPPH0EiPuFr77dyUa6Nvv6mcT8KJLLSZiJXva8mlFPWw6a07PLGilmJ/so2juNVQAhBf793/0BVAPQoj38CJXWUlaj0D718bwi8LIYaKGMKqyJA6mEIK8y5OjKaIt3VRKxnBlZEViy7VLgBZ/YZoJnIcUlEKtaOmomUgt4eQTTqCX+aJulOzcLUq4lDdEOSTEBzc7WAEgp9XOIGL0C997ehVxcQys+fEGGioXpxpjc9m6gZ98aUSSQZlexCRZuYeVIDq9/RW7Hscn1B3FVOXRbaMhMsuyZ7TPrfxeOCSKQ2MYdEaqIidk1YjPAvZUen8LMm+4tIQjB/LnRNO3aUTQd3Pz4DLF3sLy5U3jMkwiniOEW8bIx75qAYuDEGBBwiyE13Err+Y8hdaygtOJfCce+HULLyu20rrrnW5pIH3oKb2srK+1YSOaGLt7936ofKs5X+hvm88YNf4Afmh3nuZafz9xdcQuGiBkqixEM77uSWJ/7KYK4+IPQxvWfy4f43k7rl+2iP/5vCyZ/CGThhymO0twt+9IMEX/36Ln70Y1i7VvGZT/kr7QEBAQGTcOaZZzI4OMjFF1/Mtm3bmD17Nt/97nfp7Nz97FTTYDCykKzZxRHHxbn31y5i0HtzNZenMUpPABBaXJ0oohSa5xtY1VxqLLvctiXI7Q+AbxlmjBMZajNHTRVWrDzRjI7LgqbirRCdRCSqFZVqhKDaYxg1lmplQUmL+wG0Z1efi1CdONRZP+yGSuY5qXFEj2c1fXz/cxnasY37brsBwnu2liwQLGk/kuH8LjKhNjRhTiJ2lb/wXVesOKaVHF9UvYXbNHUuE17ahN6SrRMOEimDaMdiXBe0x2vcUZXriRBC88Qxs8ZyxvCyBhqaQX9nIzvFMCgvm6bu+hM+/3KILge1syp2CQFuUx9OavL3kFoxMBwWaJp33HnJU1jU5UzYfnbTkmnbYLfs8fXXkFKwaLEgtlPQJzpxmpfw6CYo+MbgUkps3cRVhXFilz8BHicUl0Uvyx6kgGfZpeJtqHgbDNcKPTpiFLDFBA111WqJvfgkTDXsiw0151PzZ1kAmux0B/qhv0+gtvUSNiITN/Dp6/PKbC8bJo4TSE1zwi4VnKECCIisbEEYnoudNGvu/Vj7xJ38n63Zqcq9XD0hQdl+w+47Dqxq4GrPVdBrqAmC7zixywxNfd9KKXAcbx/TkFQiWgkqx9aEBD+jtyF17BohYtFC7zi67qkmuVI1dlPteAXQFu/h/m23j6urIJmkolyoGgWj0pXsehGx/KsQEqMnivqvwGkXTGX5WBagpktyd3z/c9HW34hikEQC2lqps9RRflvMmQ19s+v3Tek9DNnrcVwXATQ1glEwccveuWWBdrxAVYtmsTK1gFC0xRNZ0z3eWfqi/fgMrMK/Bm7UM9UUtn+DhuvHUBVpQGgC5c+booe1krt7B8KVJP1M9G44DQJst4QWTuOaDYB3HYVOpUOZpid2hUMw0K2zYYtAZKsLOwCHrfb+Xd11EgA3T51jpnouFXNJxYahR9iZ3UoilJ6wXVnsKh9vckvYcZ/Lj1IhkRIcOYUg5S/kdHQKmscV0tkpcB6feuFe5L2UlCrcMOU2tTQ0CBoaYMtWr8yKyLzzUVDssRtjIHYFBDzTKWYw//VVjLsuRyU7yb3gBzi9R1Z+dhzF9X+GSy9TbOu4AH3+33jj0EVE8jadL9vz2FbPNrqSA7zvoz/gj4nvMutrTfz9Ezfwm9OvIRlqYEXH8RzRcyqrOk9ASo3fPfhjfnDbhby3eTtffPFPSP7lk17GxvnPo3D8hyA00T0HPLeI171GMneO4lOfUbzufxUXXsCz1pU0ICBg97z85S/n5S9/+V7vJyUgNLREI+GwwIkphBfvGS1uoHIpGk4Ywp5V+/KsKhYIk1l2qXgrTry18nm64PmTrVyD50ayfYcXPHpinasT1UmpefmvxaixlKikKQ9ppJ7bN6EIRVns0tB9t4iW2B6YtkyCjBuUFmuE2usn4256BtqWeyadxDVGWmmMtGLOV0Qi01h2ASXfNdPUJpbjuY3suYWwNDWsGRNNRaSQqGgjSuoI1zcNUPW+erVVLMf3UQp6G7t40IlSdEcRcieaW7YeEzScYLFu2IVBoBzXColqno1KTK6MJEONNERasPRw5RgAMStG3Dpwz8mGSCsMPUxTrAN2PkF/tAu7aYDBBkVhPVh495vj2nR1gXqiVnz1+2TFHajsO1Z2G/KFVL26j5Y0USHhuR0L4Yu9omLVV0bXBXosjMKPl1PbRLVWSjVxydxECH0sz3jmtSyftg10XTBrZvXz+PhU+vgZZk1dnNESMqRhdkyRBVVOLTqJSTwQhBDoM9oIhZ6otONkjPesGm+NN6HOtdtKgW17QkJl0i28uHozGoByLKRSTWD32jhyUtSd1nSWXVCNF1hfX8Hi56TYdZdgnYzWfD91vctWV9LSKR6uTzh2LRVrq92qA8K3ivI+ubUV8OPRWSGB5btD96fne5bEDT0MbVtPySngut7x+vpgzdqy2KXjdK1APTw29aF1k6QR8+Jg1dZIF7639DixS0icjiWUO6Abb0dYMUSq3tJYRZtRC06Dx71FH70phDA0hB8HT0mJSs9A+NbQemoGrhaiInYZVXHZE3q9OF2NKYPHNnrHCIep9JNEvGoR5ddg6nP2KVt2FZw8j+70AjsW7In3rmkKli1VJBPlNphY1vgu4NVDofyjlC27suPzFJSrOZmbIhNFNKdtEfiiuRtvRcvuQEUmCnTT0doCzjxoS3fB+sdotR2e0Cw6YxPjCU5GIHYFBDyD0R77J9afz0eMbaW0/JUUj3pHZVCxbcVf/go//qli/QboPPpn6P3f5bwFb+Kw77eTbd5O/IQn90L/bCFixDj3Pe9mY+YWjvs+PO8Fb6XzRUsmTOhetOgtdCb6+MRfXsvFj13Ou176c4xbvot58yVo62+icOoFlNqPxN5VwGgO+/Elqhx1pOB734YPfkTxzncrPvExOPaYQPAKCAjYf5QnuGUrBjumqhYBhobTdJjnala3k+lNjISc1LJrMha2rmZndiubR9fjKpfZjYvJWDmy2RSwecL2fX3Q1MzEOFF7gJr4xg5QEUegKh5MhetPjjRf7Dp6xpkVsczwJ84dCU8kO6LnVGy3NHlB5fKapW9pUlPPVA92qmfCtrXPkq4ufzLmjgsYXClEkQ43kww1MtBYjf0SXtKEM+hNeObPnd4qw5EhHDGNyQ1Vayq3aRbatgf8kyr7w9THrxlfT1O3aDeWsTF/G4jBCSvvYpzvnRByCh8bj5iZYGn7UdVj+ZtO7yozNScOPH/yH/YyikAs1DBpWQ0NMOTrE4bhWQ1apiDeaFKqubUEAlRZ8fPbSLdwm2Zh6HeQd+rFo9gRbdi2xHjA8d0o/R+c6Ss+q2kRo5GNE76vtewqzm0dJxs8ScZda8+dbfL6qZKLjE3jyjTBjbbGXdeYfJIdOWU5uEsm1ANgwXwYGoJCAezOVEWxr8Tsaoyh7RirWMTET+zEzdaHb9f0any22mQPugWGIysiRqW+Up9WhaqNMadPEntoXsty5jYv429rf+PV1b9vGlt1kic1MHoPzJ0DkYhAPjjFcYR3klJodePg7sSu6cYQr9xxx6t1iyzHRpSicvVNPcT8lpV0J4Z55J9QdIoov5j+PkF/Hwz91rdYS7SBXDfloVVZzHTGjcPa5BZr3rlWv5/ZfhidiYmLHgDCqrkOmvCsDlX53OrPWZNa3cpPbcwuw/D+cmzQhEGh4P0WqRG7xtPdDTdvnZhEoa5+fvnbxqr3dNGZKHYBXjy58n6TiV2T7FMeX4UEx38GjoyM30pVtpm0juO+VzXPPZWegZ3q3WML2kqZQtDZAZDAaZ6Dtf0hjmxYimPGdrcrEIhdAQHPSMTIJuw/vpfw/b/HaZxF/iVfrcS4yOcVv7sW/t/PFVu2wsAA/O+H/83lmz7E6o4TeXXPe3jkz7+h+U0LpnxpCKin48MrKdw7yPAn7qf5sH6sGRMttY7qPYOXLH47V9z9dVZ1nsBRR7wVZ+AEtKs+zua3XcX2h9biFiV6c4i2Dy4n/cKZdfv39Ai+/U34wIcVHz1f8e53wfPOCQSvgICA/UN5Dlh+P7dTLuX3ahGS3gRz3CTT6VyBGN0Cu+5A+qLQhHBU42iJdVJ08mwe9daJTT1EU7ibxwcnT/YhxNSxaMpEI0w+HS9PfMbVKR1uJmJEyZYyTP5aX0WpqmUXUBfvSZdGnagRNqawRNmPSCnom6FoLXuTVmYPCl0arOg8tm57a0YcfAutzs7pz1W0zSMyvdaF9CeGqqEfB0HqqPvAfQIQFXGxdkIzfmLc0SEQmSihsbA32xuPBvjzVFmxUtozurshnYZE4sA8G91EB3Jk05QzvbqJqhCEjAj5UhZT1+vFLiEq7m614ozbNJuZ7QnWD+8gFDJJhhoYzu/ytyvvDGa7pDjkWRJOR3u8h1i8Gui+TN01kxI9Uhtga9oip2aKOFBTISPTmlFN/K4sUE1hUeIVOnkdOtoFHe1QKCiGlqTZ6TdpJSj3zGZKM5sr10+LmxNcJevEG4NKe7otJeSQDsPUDzW7cbGqFZyMKazRasXw2r91XbB82bTFA9ATauMxvOyCtULQvrgx+iWM+1gtL7o0SmlNBhnSJozZuqaj61Aoeu6WEWPiwL9bV3J/fBb+ooM5I05x3agf92pie8lx/VKTel3m1qkQQhBa2IAoGPBw7Q/+uUijahLIRDdGFxvH9Z4ZzU2QHksTCnnui/Yk6yWdHYJFC6Y/93Kf2Ty63kuQodQES7bJz2WSsibrAsJzQxWiatlVjpNYoXy4KRYpyrfnlNXaS6FrPCreDtsf2quyArErIOCZhF3EuONHmP+5BCUFhWPeQ2n5K0EzGR5W/OrX8KurFMMjsHQJvOfdgqaBe3nPH15NV3KADx9/CSOXPAauouGlsw702TxjELqk++vH8MipV/P42/5J/1WnI82JbwOvWv4+7tj0D770r3czr3kF0UwX6376Uoobx2hZcBfRnizbN57EE++5idLWHOmP1U9akknBV78Mn7hA8aWLFDt3Kl776skD5QYEBATsDRV3M986wfXnGU67nDp7nW6h0r2e2CXq958ef5W+ZmPFNC/A03DYKi9j4z8fb0GUxk2f/EC35ow4xY0ZjI6qL2TUTJAtZXY7fpZjdh1M4+zMgZq67GHmqj2hu2v351jXF6RWF0tpMjfG8RPjOXMEs5x5jOyKwZZ7JjlCtXwpprd+mbCnFCQmjwowLd2pmTi7scjbE9z2Jbgt9RnVnOY5qIgXD8gcpz2t6DiWTHGUjWvrvxcICKdwk124TfXvYgtblzAQ34ahh1nafnQl81rVDFNgNmmkT47jRHeTjWyCWFK2GvS+L1t4dXVWg1k/aSYREGKxamB2a0ac0sZqnKppxa5JqLhs7oU4Oh7L8kSiP/2lbNk1zlpnmrKlEJVkj7oBxVU6+sM26BA7vIGx3zwGwOr0IjQhKqLMVNTeZ3uSUGF3ov1kzIr1MqP/tAlCkJzCpTzqa/nJvbzHlFa9llrMwBjQxwWo93/zhSfHUVgywbLWk/buQFBNdOA/TCJLmggv9uNxTZbwY1y7TeVOP+mhUhZO0Rs3ZDjN7OZlPJi7A8DLBFijWtX2JdMER3m/hY0oz1t5FqH0DaiG/or74pPBjbfjihyOUyBmJRkrDO/RfpMn9Jj8O0eGEEJS0uJYprcQX4v0x5wJcfPKZeyxYPokMaMoM4IoZtnTPIuB2BUQ8AxBW38j1l8/jRxcR2n26YTP+QxjboQtWxQ//6XLNb+HfB6OORpe/lLBwgWCjSOP8a7fv4y4leRzp15BVIvxxBWPEDu2A7NnN0vpAXUYbRE6v3gkG95wA9su+i9tH5wYz0KXBh867mLe8JsT+MnvP8epXzwSbJeBX59FrG0F1nUfpHX7J3gk/Fa2fuFOmpZ3IY+s910PhQSf/iR8+auKSy+DHTsU7/k/gkyZAQEB+0Qle1Z5xiYFhWN10PdM6Klk2tsDwaq8Al0WkvTDO8g9kgfu3+spW9mKx22cxKrKn2BrcZPk6fVuguU4OLtzYywLcnuy2r+/2V3dylsBT04pHMeRvadNOdHVpY7t2vUuTuOtdWqzK/pMsBAQAqnJKdtTyeq0XQrxtOSFn9W4aP8UJGRdVkEA1Vi10jYMKGopLHsQNBNLD2PpYTbLcXGEECAkbvskAfV7jiAyugWlm2iARjnAVmVn/9896Du72cQ0BSedoJBSsPYxtUf7TIWaJB5d3wxB91xfWGsKkzx7BsPXrANAhsZZkbYuQNt639QHKLsxTuP2+mSJxWBsmjBRALopsIFYTBANg4oLnBkOabf+XTqu+6FEOqePeVYfs2s3oiVPXowvB2ivPd5kGfzAsy7y3MV2W+q4jzXXslz2ZGKX1JESbAdM1HSh2aY5tP9sSXZXvyrHhZzkmBMEzb08qJA6Tss8dDNKSK8upuhCBzM8buNqPRxVoqnRu7amZuHOPm2vjjsZke7DURtGAU8gXdC66klbG0/WDAKwtSgj3adhb5D0TuIubnbHkFEdGTXI3bNzwu9lC9aGPYtB/+TQTCC7xwtBgdgVEHCQI0a3YP798xgP/xE3PYPcC76P03sU6zfH+c73hvnrX737/dST4WUvFczo9Qbbx4cf5b1/OA+lXD532v+jKdrO0NWPUdqYoeOTqw7wWT0zSZ7WQ8P/zGb7t+8lemQb8WMnvhV0Jft5Zds7aX+fpKTyzL7yLEJz0rg0kXv5VZj//iYDXELusdfy8Fv/wKy/vQAtVb+qp+uC978Hmho9wWto2IvjZR3AgLwBAQHPbCrZlnyxa0Xncdy+8e/AnokuUgo6u6G9bffHao11sSu3jf70PG/fhIWMehVoiXVPt+t+Q/fFloplzBS45QD1+0F1iZsp2uLd9Kbm7HNZFfbjsF87WRuPoVnY7jjznvEzospkrvrVhAx3EpQ7eaUFos79RQptr9wYD3ZME4bDc8maHcwzq5PQyQTBKSlbU46nqhCO+2Ia9mCTspVXTzcUC96/T4pIA3b/8ehrb5i6OrUZBMfHtUvPgGnELuVOtOxaunj6GEd7yqoVdR5pk9LdJVFD0NSks8M/aGuomflGE85kDW3umQjRHJ0k8+QkTHfvTstkAtC+Dirjy9Rq5YSpy9akTspqZUhtxRHFqWKc7xZ7zplTnFeNq2ZNPY6ecSb/WnfthG0mI7yoEXtHTWAtIbzEIsLL9ljeXZM68VP6sI0ZNX1eVE5/5QqX7RmxR0LmnhIxYsxIz2Hd4EMY0qQ11rX7naZguiHINCXHH1uTiGEcekMIVZrcxLu5GbZuoy55xX6n3KaB2BUQ8AzHKWHc+VPMf38TlEvhqHdRXP5qbvuvyRXfdLnl1mGiUXjxi+CFLxA0N1cHpUd33suHrn8pAsGXz7iK7uRMlOOy7ev3YM1JET/56ZlsHIq0f3wl2Tu2s+Et/2DgV6cRmlNvmVV8fIxln+xlpLCTn771l3x65gurP+omxWPejZx5EgP257nn26ey8wPfpeUbr53wYiSE4HWvEaTTiq98TfGe9ys+9xlvVTEgICBgbymLEmUbk2Ro75de58zes/FHkzoLW1dXPgu8QL3Lm86hv+HpefXUNe+FeHcB5csxT6aydtgbhBDMb1m5V9vvwVbljZ9cpfaQOU1LeWjHXfWZHvfAsmtC5jYpprRCE5X/lTedPkD9Mw3DAISkpKfqvp+gGT4ZsaHcpOXGfxJFLF40SXY1H10XzJ2792XWsYcCj3fAvVM69IYQ9rZcnftj7XvvvqDrYrcZCHVDooe94xmayZG9pxHZ+F9kblc1NtVeVueYGWftVgyZ27yckB7eQ1fHqamz7NqPrtFA/Tghxpsg1tMZm8O6bVuxVfHJWXbBlGPhVOOpqVmTW65OgtWfwOqv+nHW3quWHq4ZjgVa1ACq1y9xaieEvKDps5oWY+oh0uHmPTmjPaY8Pu+Npd/euDGWmUroqjBFM1qWYMX0Ro37jNKMCc+S6QiiUwcEHITIJ24j/NMXYP3jCzg9RzD6P9dw7dgbed1bDP7vvYq1j8F7/i/Cr34ueMubZN0D/++PXc27fn82hjS56Mxf05v2VpgHf/4ohYeHaP2/JU+JGfizBRnS6f3BCWhRnbUvvp6x/2yp/Db27y2sPe+PuGMl9G/3cUfsZn5xz8UTynDbl8B7fkjL8SW2XR9F++bLkU/cOunxzn2e4BMfF9x7H7z9XYpdu/bdlSUgIODZh5xm3N/nlf49rcNeBrHeF8qTyNLusidWAtQfpK/EmoFomYvTffhTepiGSAtH9Jxal0FxgmuaPzutt+wa33fElO8YQoi6lPWh2amDIlbafvAQBaaeIE40kNuXc94by676bVpbBH0zDnx7gxcLdW+wZidJnNrtCwwHnpAeqV7HJ9mBDM3cbV/oSPTSEGmZdpsyTvsSlDZ5+9RnY9y3cXj82Sq5Z26MAKFKply1uxj+e42sjVE37v4oW8bt7blXXCQRhI0oM5sW0hhpndQiVYb0SjzfkB5hTtPS/f5cKSczcPcseCYw+aWYTmjcszWYAziOlAXiyRKgTEJg2RUQcBAhsjsx//EljPt/g5voZPiMi7nq/uP5xVsUm7coZsyAD39AcMrJ0NISZnCwmnI2V8rw/ds+zW8fuJSFrYdx/onfr6woFDeMsuWzdxA9vJXEGRPTnwfsHWZnjL6fn8b61/6Vx150PZFVLaiiQ+6/OzH74vT96CTC89Kc8Lfncfl/v8qRPafT3zCvvhAjRN+338z2JZfyxF+XMYtXUlz2CopH/x8Y9XEATjrBy1b24Y8q3vJ2xUVf8rILBQQEBOwN0YiX0a5MOaPTMw2na1U1Bf0UaMJ7Id5dYPLx2RifXvZsHBdt82Bw8CmuyySExkWqnsSyq0LF8mjq4qJamJ01k6zwrKcysMue09DgGZj1dAOb9q2sObMhlar/rqsTtu+oft4XcbkijuyHmF0HErE7y5Hx2wuBCO+faasUu88q+0xEJbtg12PgTBzzxP607Brf92rFLjG9GBuqeb+tdWM0OqMVASm6ugUZezKi5tR9KmREGCuO7NatfTzjn49dyQFG4sbkiRL2UQCa27wca5LYd7WUr93ePLfL1art99Nadu2FwfEBoew2u4dJRw7SZayAgGcZroP+3yuIXHom+kO/Z2z5m/ieeTVnf+A4vvYNRWsrfOGzgh//UHDmGWLC6uFtG2/gTb89masf+BEvXPhmvnj6L6tC1/pR1r3qLwB0fenIg2IV9VDAmhFn4JozaX3/MlAKGdZp//hKZv3xbMLzPNfGtx7+aRJWmk//7Y3kSpmJZXTGaXr9Anbc08dw62sx7vwpkZ88D7nxjgnbrlop+NpXBKOj8Oa3KdasPQTf1AICAp5SjjxC1GXkE+zZxDkZaiBqHjxJTVSsBcKpabcp1zesT+9apcoZ6g6AZdcz4Xlszzyl+kFMtOyaHFHzf1Cm59rTH+liVlM1WPyBSAowGZYlOOlEUUmGsC/0dIsJGdfCYcGRh9dO3vfFjXHPdzmQ3ctN9eK0L53y97217NqfHHUkrFyxP0ryG1i3cNN9OB1Psf/WFNi9R2L3HulXafKLbtQsDkyZfXePGS921boxapNvU97Ut/pMaB11VY2ubCGyzJu3GO3RKbP9TUd9co364zeEW/xt9m5RozxGdSb6/GK9cvWm8CRb79sN15HopTHSOu02DeFWmqMdzGxcuMflWpagpxuWL6t+N93YsEc6+gEcXJThx6/bQ9PAg+MpExDwLEZuewDrT+ejbb2HQucR/CLzMb7zpV7GxuDoo+AVLxcsmD/5oPLIjru5/L9f5eZH/sTqnUfw3qZP0HZXF0O3PYxbcimuHWb42g3IkEbvpScGGRj3M1rUoOVti2h52+SZnlLhJj5y/Ld57x9ewFdvej8fPPabEx4QTW+cz84fPcjGmw/HeP/xhK77COGf/w+lFa+meOQ7wKiu8syfJ/jWN+Dd71O89R2Kz18ISxYf/JOlgICAgxPhp1jcnaXJis7j9uk4lh9uJjLZ/OApIhlqYGXX8cTN1LTbtcV7eHzo0QNk2fUMoDb74CQB6suE5qTI3b0TYUiU7bvYSFDRZpzO5egPX4cQgngoVdlHPumgPU8dbqoHFX5qLc6ezERx6fxjyW7egjVDAxuejBvj04nbNv1kXBgTr709cNLTUudQSBCa3oBmrxCGgHAKozOx+42fCsI1sWMrln+ymo3EJxlqZDi/c/8L+7Vxx3ZjeWiZMC/yPIQQ+92Ncbrg813JAWJmklS4aa/KlEJyfP9zK20mDEn8uA5kfBLLs6dhwUSTGovaDtvr/ebMFpRK1UXyScMaPIlwgNbM5F7XZV9RyW4cIVGJSdJFTkIgdgUEHCicEsYt38W8+RLcUJpro1/mwh+fxlhGcNyx8OpXCGbNmjjkOK7NjRv+yHVXXY762yjLHlzK8x/7NLIogK1sYWtlW701TPqFA7S8fRFG+5NLTxuwbyxuO4JXL/8gP7z9QmJmkrcd/pm6F109HaLx1XPZ/u37yL3zHNxX/gbznxdh3n4p+tobyJ92IW7H0sr2M3oF3/4mvOd9iv97r+KCT8BRRwaCV0BAwN7jrfDvnVvHk6G5WbB8qdqndOSWCYXi3u2TsNK73WZW4yIGGhYcGMuug9nPbBomm8dafQmsvkTdBm7nMpyuGXU7CL3WCuQprOSTxG2bfPFqf/Jkrnsq2Uzq3Gbkjodhh1fKHhzooEVMlorP2I8K1NOB36+lqZE4vQdhSpzs6voYVk9/pQDf1TtaL+wsbT+KgpObbKe9PMS4jlWTjVGVx9Ep3Ozi8arYG9q3mPsT2N0YvrdC11Tljs+gXuUgvuHY/zpy6rl9+7fAPUUIz2V3DwnEroCAA4Dc8TDWHz+Etu1+7jPO4d3XfpBto0mOPw5e/UrBQL/ALThkbt1Jcf0ozlCBQjHHg9vuZN2j9xLfGOWF605FczT07ijJV/YQP76T0OwUMm4gDInQxOQvEwFPOy9d/HYyxRF+fs83sd0S7zjis3XuG01vmM/OSx9k2zfuofurR1M88aM4M0/Buv4jhH/+ckorX0vxiLeB7j1gW1sE3/o6vPeDig9/VPHBD8AZpx3cD9mAgICDj/Kk++lwSWhs3LdjHHXk/gskPp6DNjj9QYLTuhA5Vl1I2+PuoslJ4vtM7Wr0bGH/BKjfC57GpERuqhc5/PjuN9QPpWuvkJYn4qro/s2+t9eUx7JJ+pgmNSIytj8OMvkxocaNcfIA6tGadXfT3L99oNY694AsJBzk49nuDGkrIbsO7tPYawKxKyDg6cS1MW67FPOmb5AjwQX3fIM/rjuRk06EV/6PoL9P4BYdtn3zfnZ8736cwULd7i2YpK3FyK4QPW9fTPjUdkILGp4RcT+ezQgheP3Kj2BoBj+96ytsGnmMj57wHdJ4Vgd6Q4iGV85hx3fvp+Wdi7H6Ejg9h5F95W+x/vFFzFu/j7b2bxRO+2xl1TmZFHzty/DR8xWf+axieBhe8qKgHwQEBOw5FbHrIF+Rhsmy/j3zeSa0O4BK9+Kkeyufd/vKoQv01jDWwEQXF6EJGiNtZEujB7shxH5lfwm1aq/e9/z7+2kUlty2hbt1YYRnRry68Sg9hGieXfnstMxH23rvU+72unc8De063bUruz1PEiQfvHG8u0uRegq83w54nzrQx98Ne1y9g/s09ppA7AoIeJoQu9Zi/fFD6Fvu5oadZ/CJWz/CvOVpLvuYoL/fG1lKW7JsePPfyd6+ndBJLdy29Gauc65iNDLKsf1n8/wFr6e/YyFCCNLpNIMHIjtTwJNCCMGrl3+ArsQAF934Xt5y9Wlc9Lxf0Wp6E4jm/13Azsse8qy7LjrK28mMUjj5E9izTsG6/mOEr3gppdWvp3jYW0A3iUQEn78QPv1ZxTcvVgwNKf73DeLAP/ADAgKeEex7oOKAZyO7e8QIIYgd3jb5j1LSFu+e/LeA/YynsIlDUCg+EDgzT0Kk09XMqFYMp+fwA1up8ZRvTjW5ZdX+xOlYOtGSTfNd/OzCxB185s55avqjVmvZdSDegw/y56nXJlOr7nuT6PWZRCB2BQQ81bgOxp0/Qf/nV8kUI1xw50Wsj5/GJz8nWLG8OqLkHx3msZdcj5spsfWjDt+IvIOinefsua/iBQv/l9bYnvsnBxy8nDzzPHpTszn/L6/htf/veN591Jc5aeBc9MYQja+czY7vP0DLOxZjzagmE3B6j/KsvP7+Bcybv4O25m8UTrsQt3UBhiE4/6OQTCh++jPYuEnx4Q942Z8CAgICpiMQxg8wz9D2L1e7ZW88toTwzJtqfGmC/vckkNO7idVSDmVhdO0P17X9Q/yETtycfaCrcciihG8v+lT5fNchQKvPmqh0L+6acPYywOL+qM0BF5sOjfHs0DiLKge6VwQEHNKIwfWIn7wS6++f54aNx/CG//6GI157Ot+/ZBKh68XXYbslfvauX/M5/UPMbV7O955/A285/IJA6DrEmNW0mIvPuY5F7Yfx2b+/he/degFKKZreuABhSLZ+6c6JO1lxCqdeQO7530Hkhwlf8RLMm74JThEpBf/3TsHb3yr4xz/gTW9VbNz0dLzoBAQEPJORh9xrbcDTgRCCY46CRbv3VqugLC94/bM1lmg8LogaMVpb9rGgcuY7Z/eCkTAkiTN6CM3bfaKGpwstYWK0Rg50NQ5hno4xvXyMSd4z9f0cdX4vOFAZdd3Gmd4fz3DxPrDsCggI2HOUS/Gmy4nffBHZosknH/487ac8h4s/LbCs+lGksGaYtS/+I1l7jIte/lWcpMbHDvsux844O1j1PIRJhZv41nm/58I/vp2f3/MtpNB43coP0/K2RWz90l2MvWQWsaPbJ+zn9B1L9pVXY/39c5j/+Rbamr9SOP1C3Oa5vPiFMNAPH/+k4vX/q/jU+bBqZdCHAgICpiB4xhxQnikxuyYjFNq7ujtdqxGFYdj47BS7DEPwylOPo2DvYza8cnIbtWfWUdI8MAJAwAHi6XBjnO65oRlP3XF3g6zN+roHY+vyjmNQ07j17Slu8xzc5jn7XM4BR4z79xDh2fnECQh4CilsfYKhb76Whpsv5D/bDuOy6G95/VfO5mUvlROEruyju7j/Bb9lOLeTr7786xx93PP54bn/5Li+cwKh61mALnXedvhnOGfuq7ni7q9zxX+/QdP/LsDsi7PpYzfjFp3JdwwlKJx2IbnnXYzIbid8+Ysw/vNtcEqsXCH4/ncELS3w7vcpvvM9F9sOrLwCAgImUp4QuE9DfJeAGsTTlwXzoEE3UdHmZ3X8KFOziFupfSukLHa5U7wfBDyrcZvnoiKNqGjTU3+waV4t3di+mjDuPfWWXbsfZ1LhJtLhA5w9M+ApJ7DsCgjYTzi2y0P/7xcs3PxFTFdyhf1plr/t+by2a6KmrJTi1puuw37TY2C7/OV9t/DJ5/6MgcYFB6DmAQcSIQRvO+JCsqUxfnD7Z4iYMU684DTW/c+f2frFO2n/yMop93X6TyD7ymuwbrgQ66avo6/5C4XTLqSjfTbf+RZ841uKn1wOt92h+MRHobPz2TvJCAgImIjuu0Ttj9XtgIA9QgbPoX1BBWJXwHSY0achaP40boyAPefMA2I1LA94zK5Dg0NtDSYQuwIC9hGlFHf9fSOJv3+M1fH/cE/hSOwzLuDsFR0Tts0UR7lx/bX859dXc+r3j0HqAi6ZwUePfcuza4U3oA4pJO875qvk7Szf+PeHiBwbY8Fr5rLjO/cTmpMmfd7A1DuHUxTO+AL2rFOx/vwJwpefR/GIt8PK1/C+9+isXq34/BcVr3694p1vg7POfJZZEwQEBEzJgtZVbB5dT8xMHOiqPCt5JrsxPmmC58++4buJCTcI8h5wgKi4Sk6xSHKA7vEDFbPrUOEQ9WIMxK6AgH3h1nu3cMM1X6En9muGmxS/SR1B6/wFJMN/YvNjDRiaRcHOsWl0HfduvYW7n/g3R//jcM654VTcXo25P34Okd6DJ3BowIFDkzofPv7bfOxPr+SL/3wXH/2fS+he084T770JZ6RI42vmVkQqpRT/3XIjN67/I5tH1xMx4ixoXcVJL72cxn99HetfF6E/+DuKx32A4445kvlz4dOfVXzui4o//QXe927o6jrUHmcBAQF7S0iP0Jeed6CrEfAsIjC+2EdkMHULONBMb9l1oKhdyA0WdZ88h1rTBSNmQMCT4O/338L3b/gSW6x/oFq970JaGMf+O6U7r5+wvUBw+OAxfPiaDxJfGyL5vD46P3MYWtycsG3AsxdTs/jEST/kQ9e/jAv++UZe8dZ3c0xkBZs/cSvDv1tH5Lwu7ui6nd9s+xGPjTxISI/Qk5zJusKD/HXtVfzISvOOIz7LyXPOxPr7Fwj/6nXY/cfTcuz7+eqXZ/D7P8A3L1a88rWK174aXvIi0PVD7KkWEBAQEHDwErgx7hsHMAB4QMDBTGDZtW8E2RgDAp7luMrlzw9cz/dv/Ca7tNtImoJXFBKsXvRmeg97A2EzBoDj2owUBhnJ76LoFNAfcHAv3U72z1sw2iO0f2cVyTN6D/DZBByshI0onz/t//G1mz7Ajx/4Mlcf18g5becy8IcMiQ9upwfJW7VXoVp1kvPaiS5sIn5yFxs7N/Otmz/KZ254Ew8seCNvfMVVhO/+BebN3yby43MoLXkpzzn+TRxxWJqvfkNxyXcVf/gjvO2tcMRhh9iTLSAgICDg4CQQu/aNwDQu4ECzOzfGA0RtzK5npYv4/uIQa7pA7AoI2A1FO88fHryKy265mBEepVWZvC8b4biOcwif9hFUtD6ThyZ1UqEm9FtLbL94PWM3bUFLmbS+fxlNr5uHDAe3XcD0WHqY9x/7dU6b9RJ+99BPuCF0AzcuMzkqcwKrc0fTPJSkuGGUwkNDbLvhHrZ97W7MvjgffcsX+OXcn/Gr+77DhqFHOP/E7xOe/1zMf38L466fYdz7K8ylL+OC97+Gf5+R5hvfVLzvA4ojDle8/S2Cnp5D7AkXEBAQEHBQIQKxKyDgEOFgE7sCy659oWLZdWCrsd8JZt0BAZOglOKxwQe4/uFf8bsHfkZeDdKZS/EBN8oRoVmIc87H6TliwjCvHJfh369n+yX3kb93F0ZHhPbzV9Hw0pnISGB6HrB3LGk/kiXtR067jTNUYOQvT7Dz0gfZ/L6bOXHGUua/7Mtc+MQHePe1z+czp/yUhpM+Rmn5/2D+5xKM236IcdfPOG7py1j9zVfwq+ub+eGPFK94teLMMxWveaWgpeVQe9QFBAQEBBwUBGLXPuN0LEUZ0QNdjYCAg4ogG+PuCYfBeJapP8+y0w0ImBzbLfHYrgd4cMedPLj9Tu7e/B82j60DV2f2cC/v0F2WiBju4f9LafmrQK+PtWXvyDF45Rp2/fRhihvGsGYl6broKFLP7UMYux98bdvmt7/9LQDPfe5z0XUd27a56qqruO2221i5ciXnnntu5fvf/va3RKNRTj75ZHRdn7YsYELZu6vDWWedxe9///sp9/nTn/7Ei1/8YgB+/vOfc8opp9TVd86cOXzxi19k27ZthMNhzj//fFKpFJqmVepUu+1XvvIVhBBcc801vPOd7+Tee+9l4cKFXHbZZVx//fUT2qC2vplMhksuuYTR0VHe9KY3cfnllzM6Osp73vMeLMvizjvvZNmyZWiahqZpnHXWWVx99dWTlrknbTFde46NjfGqV70K27ZxXZd169Zx8skn89nPfpZQKLS7bvCk0FIW6RcMkDq3n9G/bmTrF++k4UKHL8z6Ij8+/Ee8I/8cPnXKZfQ3zKNwxucpHv6mquh1x2W8Ys5ZnPn1/+GHf5jHr38L112neP7zFf/zMkE6FUxKAgKeyVxxxRVkMplpx/5nM7Zt88tf/pIrr7yS/v5+Lrjggn0eqyd7nj+d+z8V1D7fa5+nT6Z+ezIfnew9pvZ96JxzzuH3v/89xWKR2267jbVr19Lf389hhx3GueeeC3jP6fLv69at47zzzuPss8/mU5/6FK7rsmLFCkzTrHvnqn3u//rXv+YXv/gFSin6+voAWL9+PQMDA1xwwQUAfPzjH8d1XZYuXcodd9zBunXrGBgY4Pzzz+faa6/llltuQQjBqlWreNWrXsWvfvWryjnty3VVic4nve9TzVTvs1P16drfTjvtND75yU+yZs0a+vr6KtdzfFvl83k+/vGPA941uO666yYcb3x/BXAchzvvvHPKd7/9ef6O4wDU3Sf7497emzKe7Hxgt1SCOh1sll0Sx3W4/bbbGbzLfdrHzydzbYrFIrfeeivr16/nvPPO44UvfOGEeQ6wR3OXyY5x1VVX1Y1D0+4nwHUcfn/tH4lGCnvcfrX346c+9anKM3T8vQDePXj77bfvWX32Ewf+CRoQcABwXIf/brmJOzf9k7u3/JtHdt5D0ckDEKIJsXU+p+Rn8I7kfTTKEewlr6Zw2Jsg7GVOdPMO+YcGydy8lZHrHyd723ZwFdEj22j/+EriJ3cHpvoBTytCCBIndRE/oZPh361j65fu4tU/eTmPz9jIt+58F0e+7AU8b+Hr0NJ9nuh1xNsw7vopxr2/ou3+3/CBrlW89tMv4jv/OJErrwxx9TWK816geNELBOl00JcDAgICAvYDwbtRQMAzGmWEvT+0gyvJlggsu/YL4iATMfeVQOwKeFaRt7P85v4f8LsHf8yWscfRpcHc5uWcOuO17HxkKTv+FeWF6es4q+sPiJDAWXweo7NeRfYxg/xlG8k/cC+5+wcpPDoMjjcYhBY20PKuxSSfM4PQzOQBPsOAZztCClLn9JE8o5fBXzyK9s3/8uqfdbLjDzv55nFvYearjuLoBWeRTHVTPP5DFI94O8Z9V2HceTltN76Pj4djvOPtp3P5fefww58t5xe/hOecqXjpiwVtbcEkJSAgICBgHwjEroCAZzQqNQNHD6PibQe6KnUEMbv2DTHhj0ODQOwKeNbwr3XXcvHNH2NbZiMrOo7jjavOJzx0An+8GnKP/oVX9V7B0sV34ehRRlOvZOeGIxj9yjC5e/5RsdQ1uqKE5qVJntFDeGED4cVNGG2RA3tiAQGTIAxJw8tnk37xTIavfxz72zdx4m8ayV+b5yfLPsj6U7fTPm8mMxsXMrNrIX0LryS67UGM+35D6pE/8LbIlbzxxd3cPHoKP/jnybzk6kWccrLk5S8TzOg9xJ6EAQEBAQFPCzISTD0CAp7RCHHQCV0QxOzaXxxqb/jBEyfgkCdTHOUb//4Qf15zJQMNC3nnqovZcvdS7vzazSwWn+L89j8TWZalEO1jc/7DbLk2Qf7+YYSxjsiKZlrfu5To6lZCc9NoyYPLZDcgYHcIXZI6s5dlZ/aSvXsHj13yb47549Ecews8NPdR/r7wcr4960Fs06Yz0c+spsWsOvldHF4QtKz5O0fnfsIxR/6QUZr50+MncvH7TiQ0ayXnnBtmxXLPfTIgICAgIGBPkGZgfREQELD/CcSufeNQfZ0PxK6AQ5p7t97C5//xdraOPcEpif9lzj3zETf9lOc1vYVoT5aCTODOfQ5bsyew8RtDFNaMEJor6PjMYaTO6QvErYBDisjiJhZcfDalbTl2/fQhjJ9FmPvLWaiwYGRpgfUzHuf2xpu5KHkNtmEzu2kJq458IStVhEXb1vM84xrO7f45Bcfijp8cznVfXEUq3ENHT4pwZ4To6lbCSxoDASwgICBgGhJWA9niGJoMXsMDAgIC9geGtA50FQ4JhAhidgUEPO3szG7lwe13MlLYRdxM0ZueTUe8D01OskKoFKODa/npbZ/nqieuodmJ8sXhdo4a/AVEYTjWQbb3ucilx5HTFrP5wv8y8ocNmP0Jer53PIlTguDyAYc2RkuY1ncvpeWdi8ncuo3ha9Zh/X0TyX+bLGYAJBRaHbY2bePRxMP8MrWTK3XJDE6ib3sr6UdjyG1hOgHERkb19QyXPGHYmmHR+NqFpF86F2kFK/gBAQEB45nTtJTu5ACm9uycnMVP7MTNO7vfMCAgIGAPmXROGLDHHKrr1IHYFXBQc9fmG7n8rq9w5+Z/TfgtqkdZlJrHskgvc4kSz4+wfccGbs+t4XfmMDngeUWT5+/qZ9RcwYMzl9F5xHK0pgGsgsPm793Ptm/+AYDWDyyj6fXzg8l5wLMKoUlih7cRO9yLvVDaliN7x3byD3pJGJKPNNF7ayeq6Fb2GUwOcm/7ozy2dD32AujpambOEzqzdmwgtsVg6z3L2PTxAtu//C/azi2RfuFM6JqPSnbXPUmVUuzIbsZVLlEjTtRMBBZhAQEBzwo0qRG3Uge6GgcMLW6ixQ90LQICAgICyhyqr+CB2BVwUDI8soHv3nw+1z3+B1rNNG9oPIZVKkpzdoShsc2sK27nXq3E7YU7+LZ2W92+0pD0Di/lcON1rFp9Ei0r0nTo3h2sXMXwtRvYfOHtlB4fI/mcXto+uhKzI3ogTjMg4KDCaAmTPL2H5Ok9le+U4+IMFlAKtKhO0SyR3H4XYtut3Pz4n/nXtl9BEuYOrGTAPYVkbztt962nZw08fmkDW698hM5VP6ZpyRqeaOvk72HJjaUtPJLdSM7JVY7THO1gcdsRrO46icO6TyZmJg5EEwQEBAQEBAQEBAQEHAIEYlfA04/rIDLbEaObkSObECObKn8zsok/59bwNX0XI0LxmkKIVw8pzJ0PUwy1MyzasfNzKe7sIP94O7HhDtKuRaFpiBlz8iyYmeLUVYvo66xfMnRGiwz/YQM7vnc/hYeGCC1I0/3L04ge1nqAGiEg4JmB0CR6U7jyOYTBkvYjWdJ+JC9b8k62jj3ODY9dzV/XXMXvd30Kvcdg/vIjCI0tIfLvJMv/ZVL86+ncddsw1x/zF25edjOzgefaOr1uBC3cwGg0zQOixB0b/sRf1vwKXRosaz+ao3rPYEHrKrqTM9GlgbJdsndsZ/SGTRQeG6a4I4sbAZp1zEUJYivaaJzXg9SCIKUBAQEBAQEBAQEBe4NSh5aJVyB2Bex/CmOIsa2ekDW6GTnqCVpyZDNidDNibCvCtet2UeE0j8cb+LJ4glv0XRy1cRXHP/p82NjI1YMaw3mLMWEypEUYNSNEusI0z4tw9mkR5h4RpqNbA0fh5h3crE3u7p0UN2UoPDJE5rbtZG7ajCq4hOal6f760STPnoEIJsQBAftMa6ybFy96Ky9e9FbW7rqfP6+5krs238gD+e9QWlLkhsWwaMMyTv3rabzw9+dy6p9fyr8bO9ncO4KZXEt/fC1HpNfywtA6dCl5QItzg1Hkrxv+xVc3/g2AWDbGsbcfzWG3riIxEsfWHHakdzAWHcMqWjT8J030F1HGeJg14Syb+3aQm28QXdFDz7LVdDT2oesCXQdNA8sCwzi0HuYBAQEBAQEBAc9mLC10oKsQcJARiF0Be45yEdldnljl/ydHa/4uf1/M1O8mDVS8DTfeTr5lFaNt7exy2tlWaGf9cDv/3eFyf+6HND1xI4vvO4bz71tJsiBwEAyFHMy4QU+yQNgeRR9+HIYd2A7cCfwMdgGDhkSV3Il1FhCak6LhFXNInT2D8NKmIC5QQMBTRH/DfN7Y8HEASk6R4cIuwnqUqBlHfUKRuWkLW7/yX06/ZS1q1GTssMNY0/xy/monGdriYGQ2kbLX0q6t4eWhxwi5mzAebSLy4FykbbCj/2HuP+kaxnrWU5Jh7EKKfKGRTK4Rd7CJyI4Ezbt0ejY1MPP+GFxZwhH/4O6Gq9kYd9loNPKEGuAJey75cAPxhCCegHgMEgmIxyEcH0NEn8ANbyCvrycrN1ASuygxhmlI4uEobfEeelOz6U3NZmnisAPc6gEBAQEBAQEBASs7j2eDvvNAVyPgIOKQFruUUty95d+4yiUZaiAVaiIVbkKKJ2/Rkx0ZYfNld1NQeVRK4qZBbw2T7Gol1dRC2Ige/GKKUqhSHpHZAcUxRGEMURyD/DAit8sTtLK7ELldFLM7GM5uY7iwi5HiMCM4SCCkBJYriI00YYz2ITJd5Ifnki/GKDgRcqUwYyrGsBNnsBRjOG8wlNfIK0HJzGBHdyLj99CiX8PsEZczHxsgmp+HY2rYy7swTu2l77kdxJrNcVVXuMNFSluzlLZkKW3NYW/J4hYcZFhHhjRESMdoi2B0RjG7omhxc/J2CAgIeMowNJOmSFvlsxCC2FHtRI9sI3vLNnb+5CHkHx9i6Z8fYHV/gtjhrRgLYwitmdKWGGM3tVB4cAgR0gg/dwaxs9N0p/rQRhagjz6OntmMkd2CkduMWbgf0eLCnOrxR/NtPLp9Bhu3p3G2JejbmmLFoAQeAx4jG8qxrWWQLfYwG7WdbI7sYI2zmVE24uQdzFETs2gSGWkmOtROZCyJkbewKbEueit3x//ErtQuBlNjCNlHyl1Mi76YptAMmmKtJCNhwiGJYZXQzIL3n1FA6gWEXkBqLqlQEw2RFhrCLRjabsYppcApglNE2IWav4vVv50C+J8dO8eu3Ha257ezLbuDYStONt5FyS1gmRqmZmFqFnErTTrcTDrcRDrcTEiP7PE1dvMOxfWjFB4bofTEGOaMBImTu/auo+wGpRTD+Z1sz2xiKL+Tha2rCRtBjMWAgICAgICAekwtRFjGDnQ1npEc7PLFk+WgFbvyecX/+wWMjSmkBpoEWflPVP7WtOq/df9JyLKFSza9EEU1vbEmTFJGJw1WN2mri8ZQJw1WO5YewtRNNE1SdMcouBkypUF25TexM7eJXflN7MpvJrxN493ffQeRfHVCYAM7eYRNRpGx2BjoAil17z9NQxgGmmEiDB0VMRAxk3TuFkxtBzJUQoSKyFARGbKR4RLSdOD/s3fncZaU9aH/P09VnaW36emenp1hWGfYVxGQxQQTr0aNAfcYDV6DIRpvlouKyUt+V9SLXO+N1wAmE2/EJUIwaFABEZB9Hxhg9r1nel/PvtX2PL8/6pzTfaZ7hplhmJ5pvm9ezXTXqVP1nKo6daq+5/t8HxvAjo48pTBYaEuhAa0stDKEWGhjYVAYo9BK4RtwtUvF+LjGpYxPxVR/CKrTA8pK4wEhEBoV/QuEBkIUAYqyAh9D3IvTVmyjtbiYOfk5LBifz8LRBSwYW4AfxCa2gx3gxgJC5aKVR7PO0x4Mc0JgYzHdO6gFOB13cUj7+5ax4J0raHv7Eqzk3g9LpRT23AT23ATJlR2v5xATQswApRQtFy6k5cKFBOMVsvftIv9IP9kHeghTLgBWi0PTOV103vAW5n7gBJyOvael+4Af+qjiCCo/VO8uncgPcWZpnLNKI6jSJlRpnP5ikW3peWTGFhCOLqBtZBFL1x/PRe6pr+s1lZvKjHaOMTzvJTLtv2Uk4dIbd7GMIubHiPlxkm6CpJsk6SZIuEmSXoIhS+PHPLyYR5jwUAkPJ+7TlPBojfvMibsk0SRDSIQK5SUJvDiBlyB04/heHN9PELgJQm1RTFbINxVJN5VJNRcpNJcoNhcpNpWoJCsETkBgBwR2iLZDDAaFAqNQRqGA5lgbnckF0U9iPvNYSGcwj7lBB4l0DKcPrJ4As9tFD1TATGyH+HkdhJckql8oKbQJoy8o0GgdYtBoE/0YowlNiBuUybsZcm6anJtmvDTMaLGf0eJA9WcQL6zU1/Hlt9/GO078wOvaX0IIIYQQYirz2rMcVY7YYFehCPf92pDNgtagQ9Cm+rve392wEJqfQ7X2oRIpVNMoQUs/Iy19jLb2oloeheYhlJp+ecYoKC/AFJdgSidiSpeQLS3iC2+bR1O5jTZX0eYp2oMK7TpPewDtYQzLKmPZebBcbMvF1hZ2aON4DslUguZyE2FlEU2V47A49HWjHKC1+nOoGaA0N0FpYYLBUyyKS33yC4sUF6QozB3Ds1OUwnGKQQZbOcTsODErQbNpZp6zgHn2AjqtBXQ5C1k+dyWdJyzGao695nqFELOPMy/JvE+ewrxPngKArgSgzYGfE+wYZs5SzJyl6KV7n21u4PLW0liUuermwC1AJYffX8TbVcZPuQTjAfgeluNi2R6xpjJOsoSTKBBL5rFw0RWNn0/gZttwM3OopNtZmGrn+F0nERZbIGz8aDUYSHgQ9yDho+IBJh4QaEVQbEX7DsqN4bgJ4t7UDC+v+lMTWiHlRAU3UaGScHETLtqG5vw8Fo4sY3klScI91B/vOQBKiTIj80YZ7RplZMUoo/PGGJk3SqojTSVRgTtf31ps5TC/ZTHzW5aysutcLl3+Hha0LGF+61IWthzDCZ2nHYLXIoQQQgghalpbYHwcLGt2hbuO2GBX1zzFf9w5fT6dMaYa9Ip+wnDi34YfDWF4TPVn6uNag+t7ZL1R3MDFCzzCUGObVmzTQsKag61iKKqpfWoixW/yNKq/16dRT8gCNCUzTj4YpGzy5EyZQJfxKWF0gFMBp2xwiganDE7JYJcM9UWb6H8KhYVq/E9Z0Xqs2u8Ky7KI2UlidpK4k4j+tZPEnWR1PoWyoLW1Gc8vYzvgxMC2owYrNfnFRL9bTTZOVxPOvCTOvCQqJoXdhRCH3r6yOg8JJ1EPijWs9yxIEv3si7/H3wlgUUcH6XS6Ybp2Q3TBR9kKlXRQCWu/u7cbX+PnKqSHh8iMjeLh4iuXiuUSNoU4bUmcZIwmJ0FnrIXOpgXMTXZN6QZpfE2QcQnT1Z+ch/F19ONpjB9Gny+1c77FxGcAk/5utakkK+TjeSpzPFRbhTnhUhJBmaVGYzDVTK3qv0S/G6OxLBtL2Vgq+vSq/64srOpPwk4yJ9nJnEQHbYm5tCU6XlepASGEEEIIcWBWrICOTnjqyT2vdo9uR2ywa1+UUvXuiq9fAji0NUYa2cCC6s+Ro2OaGzQhhBCvn5WwsRIH9wGlYhbxec0snHcCCznhoNugYhax+U3E5jcd9DImW/TaswghhBBCiKOQZSnmdc50Kw49+fpUCCGEEGKG9PX18Xd/93dcccUVnHXWWfze7/0e//iP/4jneQ3zDQwMcO2113LOOedw4YUX8vWvf33KPEIIIYQQInJUZnYJIYQQQswGO3fuxBjDjTfeyPLly9m6dStf+cpXKJfLfOlLXwIgDEP+/M//nI6ODu644w4ymQxf+tKXMMbwla98ZYZfgRBCCCHEkUeCXUIIIYQQM+Tyyy/n8ssvr/+9bNkyuru7ufPOO+vBrqeeeort27fz2GOPsXDhQgCuv/56rr/+ev7mb/6G1lYZal0IIYQQYjLpxiiEEEIIcQTJ5/O0t7fX/37llVc4+eST64EugEsvvRTP81i/fv1MNFEIIYQQ4ogmmV1CCCGEEEeInp4e/u3f/o3rr7++Pm1sbIyurq6G+drb24nFYoyNje1zeS0tLXR0dOA4csm3pyAIaGlpIRaLkUgk6OjoIJl8rXFZ92+ZwEFt9709v6Oj43W16/WotSmZTNLS0oJt29i2/YYdV3tuA6Bh/R0dHQ37LR6Pk0gk6o/V5q89HovF6o8lk0m01rS0tBCPx+uvYc911p5vjCGRSAA0HCdAw7KSyWTD47VptbZM/nc2vx+nO3739Z7Y87Ha/pq8P/fcVpVKpb5ta9t6uvVNPl4h6g4++Rg62H2wr/dibd1hGAI0vE9e77lh8vL3Zxl7ex+9nvUf6Q5k+xzqc+rB7JtYLFY/d+x5XE53TjqQ43fy+wD271rgYI7RPd+Ptd/3fC/AxHtwf9tzKMy+o1wIIYQQYobdcsst3Hrrrfuc5+677+bMM8+s/z08PMyf/dmf8a53vYsPfehDDfMqpaZdxt6m1xSLRdLp9Ky8sXm9giCgWCzi+z6u65JOpw9JsKtYLAIc1Haf7vkzPYJ2rU2VSoVisVgPdr1Rx9We2wBoWH86naZYLOJ5Hq7r1v+tPVabvzbd9/36Y5VKBa11fb/XXsOe66w9bozBdV2AhuMEaFhWpVJpeLw2TSlVX+7rOS6OFtMdv/t6T+z5WG1/Td6f0wW7KpVK/Tl7W9/k4xWiG+3Jx9DB7IPXei/W1j052LU/22F/Hcgy9vY+ej3rP9Lt7/Z5I86pB7NvPM+rnzv2PC6nOycdyPE7+X1QOw8d6DGzv8Guye/HycGuye8FmHgP7m97Xsv+BCxn31EuhBBCCDHDPv7xj/MHf/AH+5znmGOOqf8+PDzMJz/5Sc455xy+9rWvNczX1dXFq6++2jAtm83i+z7z5s07dI0WQgghhJglJNglhBBCCHGIdXZ20tnZuV/z1gJdp59+OjfddBOW1VhS9ZxzzuGf//mfGRkZYcGCBQA8/fTTxONxzjjjjEPediGEEEKIo50UqBdCCCGEmCHDw8N84hOfYNGiRXzpS18ilUoxOjrK6OhofZ5LL72Uk046iS9+8Yts3LiRZ599lptvvpkPf/jDMhKjEEIIIcQ0JLNLCCGEEGKGPP300+zevZvdu3dz+eWXNzy2ZcsWIKr7smrVKr761a/ysY99jGQyyXvf+16+9KUvzUSThRBCCCGOeAcd7JrJUWHEoSH78Mixr9Fr9hx547VGyjjY0VemG/Vjb89pa2tr+L2jo2NKe2vdcJRStLS01EfEmW5kJcuyUErR0dFBLBZDKVUvyru30Ucmt9e2bSzLqq+j9nsikZh29KjXGtFkX9tiX9szFovV2+/7PpZlveYIX/I+PPrJPjz6zeQ+vOqqq7jqqqtec74lS5awatWqA16+jMa4dzIa44G1SUZjlNEYX4uMxiijMc4kGY1x6jre7KMxKmOMeUPXIIQQQgghhBBCCCHEYSI1u4QQQgghhBBCCCHErCHBLiGEEEIIIYQQQggxa0iwSwghhBBCCCGEEELMGhLsEkIIIYQQQgghhBCzhgS7hBBCCCGEEEIIIcSsIcGuN5l/+qd/4qMf/Shnn302b3nLW6adZ2BggGuvvZZzzjmHCy+8kK9//et4nneYWyr25ic/+QlXXHEFZ555JldddRUvvvjiTDdJ7MXq1au59tprufTSS1m5ciUPP/xww+PGGG655RYuvfRSzjrrLD7xiU+wbdu2GWqtmM6qVav4wAc+wLnnnsvFF1/MZz/7WXbu3Nkwj+zHI9sdd9zB+973Ps477zzOO+88PvKRj/D444/XH5+N+08+J45ch+qc4nkeX/va17jwwgs555xzuPbaaxkaGjqcL0VMsmrVKlauXMk3vvGN+jTZj0eH4eFhrrvuOi688ELOPvts3v/+97N+/fr647Ifj3xBEPDtb3+bK664grPOOot3vOMd3HrrrWit6/PIfnxzkmDXm4zv+7zrXe/iYx/72LSPh2HIn//5n1Mqlbjjjjv49re/zW9+8xtuvvnmw9xSMZ3777+fm266ib/4i7/gnnvu4fzzz+eaa65hYGBgppsmplEqlVi5ciU33HDDtI9/73vf4/bbb+eGG27g7rvvpquri0996lMUCoXD3FKxNy+88AIf//jH+elPf8rtt99OGIZ8+tOfplQq1eeR/XhkW7RoEddddx0/+9nP+NnPfsZFF13E5z73ufpF7mzbf/I5cWQ7VOeUb3zjGzz00EN8+9vf5o477qBUKvHnf/7nhGE4Ey/rTW3t2rXcddddrFy5smG67McjXzab5WMf+xixWIzvfe973HfffVx//fXMmTOnPo/sxyPf9773Pf793/+dG264gfvvv58vfOEL/Ou//is//vGPG+aR/fgmZMSb0s9+9jNz/vnnT5n+2GOPmVNOOcUMDQ3Vp917773mjDPOMPl8/nA2UUzjgx/8oLnhhhsapr3rXe8y//t//+8ZapHYXytWrDAPPfRQ/W+ttbnkkkvMqlWr6tNc1zXnn3++ufPOO2eiiWI/jI+PmxUrVpgXXnjBGCP78Wh1wQUXmJ/+9Kezcv/J58TR5WDOKblczpx++unmvvvuq88zNDRkTjnlFPPEE08c3hfwJlcoFMw73/lO8/TTT5s/+ZM/MV//+teNMbIfjxbf+ta3zMc+9rG9Pi778ejwmc98xnz5y19umPaXf/mX5rrrrjPGyH58M5PMLtHglVde4eSTT2bhwoX1aZdeeime5zWk9IrDz/M8NmzYwKWXXtow/ZJLLuHll1+eoVaJg9XX18fo6GjD/ozH41xwwQWyP49g+XwegPb2dkD249EmDEPuu+8+SqUS55577qzbf/I5cfQ5mHPK+vXr8X2fSy65pD7PwoULOfnkk2U/H2Y33ngjb3/723nb297WMF3249HhkUce4YwzzuC//bf/xsUXX8wf/dEf8dOf/rT+uOzHo8P555/Pc889R3d3NwCbN2/mpZde4u1vfzsg+/HNzJnpBogjy9jYGF1dXQ3T2tvbicVijI2NzVCrBEA6nSYMQ+bNm9cwvauri9HR0RlqlThYtX023f6U7kZHJmMMN910E+effz4rVqwAZD8eLbZs2cJHP/pRXNelubmZ2267jZNOOok1a9YAs2f/yefE0eVgzyljY2PEYrF6gGzyPHKtdvjcd999bNy4kbvvvnvKY7Ifjw69vb3ceeedfOpTn+Laa69l7dq1fP3rXycej/NHf/RHsh+PEtdccw35fJ53v/vd2LZNGIb8zd/8De9973sBeT++mUmwaxa45ZZbuPXWW/c5z913382ZZ565X8tTSh3QdHF47bkfjDGyb45i0+1PcWS68cYb2bp1K3fccceUx2Q/HtmOP/547rnnHnK5HA8++CBf+tKX+Ld/+7f647Nt/8nnxNHhUJ9Tjvbj9mgyODjIN77xDb7//e+TSCT2Op/sxyObMYYzzjiDv/3bvwXgtNNOY/v27dx555380R/9UX0+2Y9Htvvvv59f/vKX/J//83846aST2LRpEzfddBMLFizgyiuvrM8n+/HNR4Jds8DHP/5x/uAP/mCf8xxzzDH7tayuri5effXVhmnZbBbf96dEw8Xh1dHRgW3bU75dGB8fn5KNJ4588+fPB6JvkhYsWFCfLvvzyPS1r32NRx55hH/7t39j0aJF9emyH48O8Xic5cuXA3DmmWeybt06fvSjH3HNNdcAs2f/yefE0eP1nFO6urrwfZ9sNtuQhTA+Ps655557mF7Bm9uGDRsYHx/nqquuqk8Lw5DVq1fzk5/8hAceeACQ/Xikmz9/PieeeGLDtBNOOIHf/OY39cdB9uOR7n/9r//FZz7zGd7znvcAsHLlSgYGBli1ahVXXnml7Mc3ManZNQt0dnZy4okn7vNnX986TXbOOeewbds2RkZG6tOefvpp4vE4Z5xxxhv1EsR+iMfjnH766Tz99NMN05955hk5CR+FjjnmGObPn9+wPz3PY/Xq1bI/jyDGGG688UYefPBBfvjDH7Js2bKGx2U/Hp2MMXieN+v2n3xOHPkOxTnljDPOIBaLNcwzMjLCtm3bZD8fJhdddBG/+tWvuOeee+o/Z5xxBu973/u45557WLZsmezHo8B5551Xr/NUs2vXLpYuXQrI+/FoUalUpmRt2bZdz8qS/fjmJZldbzIDAwNks1kGBgYIw5BNmzYBcOyxx9LS0sKll17KSSedxBe/+EW++MUvks1mufnmm/nwhz9Ma2vrDLdefOpTn+KLX/wiZ5xxBueeey533XUXg4ODfPSjH53ppolpFItFenp66n/39fWxadMm2tvbWbJkCZ/85CdZtWoVxx13HMuXL2fVqlUkk8l6jQEx87761a9y77338t3vfpeWlpZ63Ye2tjaSySRKKdmPR7h/+Id/4PLLL2fRokUUi0Xuv/9+XnjhBf7f//t/s3L/yefEke1QnFPa2tr4wAc+wM0330xHRwft7e3cfPPNrFixYkqhdPHGaG1trddZq2lubmbu3Ln16bIfj3x/+qd/ysc+9jH++Z//mXe/+92sXbuWn/70p9x4440A8n48Svzu7/4u//zP/8ySJUvq3Rhvv/12PvCBDwCyH9/MlJGOqG8q119/Pf/5n/85ZfqPfvQjLrzwQiAKiH31q1/lueeeq58EvvSlLxGPxw93c8U0fvKTn/Cv//qvjIyMsGLFCr785S9zwQUXzHSzxDSef/55PvnJT06ZfuWVV/LNb34TYwy33nord911F9lslrPPPpsbbrhhygW0mDkrV66cdvpNN91U774i+/HI9nd/93c899xzjIyM0NbWxsqVK7nmmmvqIy7Nxv0nnxNHrkN1TnFdl//1v/4X9957L5VKhYsvvpj/7//7/1i8ePFheR1iqk984hOccsop/P3f/z0g+/Fo8eijj/IP//AP7Nq1i2OOOYZPfepTfPjDH64/LvvxyFcoFPjOd77Dww8/zPj4OAsWLOA973kPn/vc5+r3r7If35wk2CWEEEIIIYQQQgghZg2p2SWEEEIIIYQQQgghZg0JdgkhhBBCCCGEEEKIWUOCXUIIIYQQQgghhBBi1pBglxBCCCGEEEIIIYSYNSTYJYQQQgghhBBCCCFmDQl2CSGEEEIIIYQQQohZQ4JdQgghhBBCCCGEEGLWkGCXEEIIIYQQQgghhJg1JNglhBBCCCGEEEIIIWYNCXYJIYQQQgghhBBCiFlDgl1CiEPu4Ycf5gc/+EHDtJ///OesXLmSvr6+Q7KO559/nltuueWQLEsIIYQQ4s1GrteEELOZBLuEEIfcww8/zI9+9KM3dB0vvPACt9566xu6DiGEEEKI2Uqu14QQs5kEu4QQQgghhBBCCCHErOHMdAOEELPL9ddfz3/+538CsHLlSgDe+ta3cuWVVwIwPj7Ot771LZ544gnmzJnDlVdeyec//3ls264vI5VK8X//7//lkUceIZPJsGzZMv7rf/2vfOhDHwLglltuqX9LWFvH0qVLeeSRR3Bdl29961s888wzDAwM0NbWxnnnncf111/P4sWLD9t2EEIIIYQ4Usn1mhBitpNglxDikPrsZz9LKpVi48aN9Quc1tZW1q5dC8AXv/hF3v/+9/ORj3yEJ554gn/6p39i6dKl9QujQqHAxz72MYIg4K/+6q9YunQpjz/+OF/5ylfwfZ8//uM/5kMf+hBDQ0Pcfffd3HXXXQDE43EAPM+jXC7zuc99jvnz55PNZrnzzjv54z/+Y37961+TTCZnYKsIIYQQQhw55HpNCDHbSbBLCHFIHXvssXR2dhKPxznnnHPq02sXT1deeSXXXnstAG9729t4+umn+fWvf12/ePrhD3/I0NAQ9957L8uWLavPl8/nueWWW/jIRz7CokWLWLRoEUDDOgDa2tr4xje+Uf87DEMuu+wyLr74Yp544gne+c53vlEvXQghhBDiqCDXa0KI2U5qdgkhDqvLL7+84e+TTz6ZgYGB+t9PPvkk55xzDosXLyYIgvrPZZddRiqVoru7+zXXce+99/LBD36Qt7zlLZx22mmcffbZlEoldu3adahfjhBCCCHErCPXa0KIo51kdgkhDqs5c+Y0/B2Px/E8r/53KpVi9+7dnH766dM+P51O73P5Dz/8MP/9v//3em2Jjo4OlFJ85jOfwXXd1/8ChBBCCCFmObleE0Ic7STYJYQ4osydO5f58+dz/fXXT/v48ccfv8/n//rXv+bYY4/lm9/8Zn2a7/tks9lD2k4hhBBCiDcruV4TQhzpJNglhDjk4vH4QX8rd9lll/GTn/yEpUuX0tnZuc91ALiuSyKRqE+vVCo4TuOp7d577yUMw4NqjxBCCCHEbCTXa0KI2UyCXUKIQ+6EE07grrvu4j/+4z9YuXIlra2t+/3cq6++mvvvv5+Pf/zjXH311Rx33HEUi0V27tzJyy+/zG233QbAiSeeCMD3v/99LrnkEhKJBCtXruSyyy7j4Ycf5n/+z//J7/7u77JhwwZ+/OMfT0nHF0IIIYR4M5PrNSHEbCbBLiHEIffBD36QNWvW8M1vfpNCocBb3/pWrrzyyv16bltbG//+7//ObbfdxqpVqxgZGaGtrY0TTjiBd73rXfX5fvd3f5ePfOQj/OAHP+A73/kOS5Ys4ZFHHuHDH/4wg4OD/OxnP+Ouu+7izDPP5J/+6Z/4y7/8yzfq5QohhBBCHHXkek0IMZspY4yZ6UYIIYQQQgghhBBCCHEoWDPdACGEEEIIIYQQQgghDhUJdgkhhBBCCCGEEEKIWUOCXUIIIYQQQgghhBBi1pBglxBCCCGEEEIIIYSYNSTYJcRR4pZbbmHlypX1n7e85S188IMf5Fe/+lXDfCtXruSWW255zeVdccUVXH/99fW/f/7zn7Ny5Ur6+voOaJ7D5brrrmPlypXceuutb8jy93ytQgghhBAHQq7V5FpNCHHkcGa6AUKIA3PXXXcBkM1m+elPf8p1112H67p88IMfPKDl3HrrrbS2th7Qc37nd36Hu+66iwULFhzQ816vYrHIww8/DMAvf/lLGZZaCCGEEEcsuVaTazUhxMyTYJcQR5lzzjmn/vsll1zCH/zBH/DDH/7wgC+gTjvttANed2dnJ52dnQf8vNfrN7/5DeVymbe//e08/vjjrFmzhvPOO++wt2MmeJ5HPB6f6WYIIYQQYj/JtZpcqwkhZp50YxTiKOY4Dqeeeio9PT1THvv+97/P7/zO73DeeefxZ3/2ZwwMDDQ8fjCp4PtKn7/jjju44oorOPPMM/noRz/K+vXrG5775JNP8tGPfpTzzz+fc889l//yX/7Lfqe5/+d//ifz58/n61//Oo7jcM8990yZ5/rrr+eKK65g7dq1fPSjH+Xss8/m3e9+N7/+9a+nzHvPPffwzne+kzPPPJOrrrqKF154Ydr1rl27lquvvppzzz2Xc889l6uvvpq1a9fWH//nf/5nzjzzTHK5XMPztNZcfvnl/N3f/V19WiqV4oYbbuDSSy/ljDPO4N3vfjf/8R//0fC82vZdvXo1f/EXf8G5557LX//1X+/XNhJCCCHEkUeu1SbItZoQ4nCSYJcQR7n+/n7a2toapv385z/nhRde4H/8j//BDTfcwPr16/nCF77whrXh2Wef5c477+SLX/wi3/rWtygWi3zqU58inU4D0Nvby1/8xV+wdOlSvv3tb/Pd736Xq6++mlKp9JrLHhgYYPXq1bzvfe9jwYIFXHLJJTzwwAN4njdl3lwux5e//GU++MEPctttt7F06VL++3//73R3d9fneeKJJ/jSl77EKaecwq233sqHPvQhvvjFL5LNZhuWtXnzZv7kT/6EYrHITTfdxE033UQ+n+dP/uRP2Lx5MwB/+Id/iO/7PPDAAw3Pff755xkeHub9738/AIVCgY997GM8/fTT/NVf/RX/8i//wuWXX85XvvIV7rjjjimv4wtf+AKnnHIK3/3ud/nkJz/5mttICCGEEEcuuVabINdqQojDRboxCnGUCYIAiC4W7rzzTtatWzflQzaZTHLbbbdh2zYA6XSab37zmwwPD7Nw4cJD3qZUKsV//Md/1OtDnHvuubzjHe/gBz/4AX/zN3/Dhg0b8H2fr371q/XaExdffPF+LfsXv/gFxpj6xcj73/9+Hn/8cR555BHe9a53Ncybz+f5l3/5l3ra/GmnncYll1zCb37zG6699loAbrvtNk455RS+853voJQCoKOjg7/6q79qWNZ3v/tdmpqauP322+ttvvTSS7niiiv47ne/yz/+4z+yZMkSLrjgAn75y1/y4Q9/uP7cX/7ylyxZsoS3vvWtAPzwhz9kaGiIe++9l2XLlgHwtre9jXw+zy233MJHPvKR+r6C6MJsz/YIIYQQ4ugg12pyrSaEmHmS2SXEUeb000/n9NNP5+KLL+af/umf+NM//VOuu+66hnkuueSShg/kk046CYDBwcE3pE3nnXdeQyHUhQsXct555/HKK68AcOqppxKLxfjrv/5rHnroIVKp1H4v+5577mHFihWccsopALzjHe+gtbV12vT41tbWhvoQnZ2dzJs3r94tIAxD1q9fzzvf+c76xRPA7//+7+M4jbH/1atXc8UVVzQUhm1tbeWKK67gxRdfrE/7wz/8Q1588cX6OiqVCg8++CDve9/76ut48sknOeecc1i8eDFBENR/LrvsMlKpVMO3mRB1NxBCCCHE0Umu1eRaTQgx8ySzS4ijzN133w1Ae3s7ixcvJhaLTZmnvb294e9a0UzXdd+QNs2bN2/KtK6uLjZt2gTA8uXL+X//7//xL//yL/zt3/4tvu9zzjnncN111/GWt7xlr8t95ZVX2LVrF3/5l3/ZUGvhd37nd3jggQdIpVINRVj3fN0QvfZaGn0qlSIIginttW2bjo6OhmnZbJaurq5pX9fkNPp3vetdfO1rX+Pee+/lM5/5DI888giFQqH+7WZtvbt37+b000+f9nXWuhDUTLc9hRBCCHF0kGs1uVYTQsw8CXYJcZQ588wzZ7oJU4yPj0+ZNjY21vAN4kUXXcRFF12E53m89NJLfOc73+Gaa67h0UcfZe7cudMut/aN4K233jptgdR77733gOokdHZ24jjOlPaGYTjlImbu3LmMjY1N+7omX6i1tbVxxRVX8Mtf/pLPfOYz/OIXv+D000/nxBNPbFjW/Pnz91pk9vjjj2/4e/I3mUIIIYQ4usi12gS5VhNCzBQJdgkhXrc1a9YwOjrK/PnzARgeHmbNmjV8+tOfnjJvPB7n4osvplQq8dnPfpaBgYFpL6A8z+PXv/41559//rQ1Eb7yla9wzz33HNAFlG3bnHnmmTz44IN89rOfrV+oPPTQQ/X6GjUXXHBB/Zu/Wnp8oVDg0UcfnVLD4v3vfz/XXnstzzzzDE8//fSUArOXXXYZP/nJT1i6dOmMDAcuhBBCiDc3uVaTazUh3mwk2CWEeN06Ozv59Kc/zec+9zmMMdx22200NTVx9dVXA3DnnXfy4osvcvnll7N48WJSqRSrVq1i0aJF9RoVe3r00UfJZDL88R//MRdeeOGUx6+66iq+/e1vs3379r0uYzqf+9zn+LM/+zP+6q/+ig984AMMDAywatWqhnoPtfkee+wxPvWpT3HNNddgjOF73/selUqFz372sw3zXnbZZXR2dnL99ddjjOE973lPw+NXX301999/Px//+Me5+uqrOe644ygWi+zcuZOXX36Z2267bb/bL4QQQghxoORaTa7VhHizkQL1QojX7eKLL+ajH/0oN998M9dddx3Nzc3cfvvt9doKp5xyCqVSiX/4h3/gv/7X/8rXv/51jj32WH7wgx/Ua1Ts6Z577mHOnDn8/u///rSPX3nllViWNW3x03257LLLuPnmm9m0aROf+9zn+OlPf8q3vvWtKTUkVqxYwY9//GNaWlr40pe+xPXXX09rays//vGPWbFiRcO8juPwnve8h+HhYd72trdNqR/R1tbGv//7v3PZZZexatUqPv3pT/P3f//3PProo1x00UUH1H4hhBBCiAMl12pyrSbEm40yxpiZboQQ4uh1xRVX8Na3vpVvfvObM90UIYQQQgixB7lWE0K8GUlmlxBCCCGEEEIIIYSYNSTYJYQQQgghhBBCCCFmDenGKIQQQgghhBBCCCFmDcnsEkIIIYQQQgghhBCzhgS7hBBCCCGEEEIIIcSs4RzsE9Pp9EGvtL29nWw2e9DPny1kO8g2qJHtEJHtEJHtEJHtEJHtEDmc26Gjo+OwrOeNprWWY2cWkHPA0U/24ewg+3F2kP04O+zPtdqMZHZZliSUgWwHkG1QI9shItshItshItshItshItvhwMk2mx1kPx79ZB/ODrIfZwfZj28esqeFEEIIIY4gq1atYuXKlXzjG9+oTzPGcMstt3DppZdy1lln8YlPfIJt27bNYCuFEEIIIY5cEuwSQgghhDhCrF27lrvuuouVK1c2TP/e977H7bffzg033MDdd99NV1cXn/rUpygUCjPUUiGEEEKII5cEu4QQQgghjgDFYpEvfOELfP3rX6e9vb0+3RjDj370I6699lre+c53smLFCm6++WYqlQr33nvvDLZYCCGEEOLIJMEuIcSsZkLN0M1rKD4/PNNNEUKIfbrxxht5+9vfztve9raG6X19fYyOjnLppZfWp8XjcS644AJefvnlw9pGP/TYOvYq2ujDul4hhBBCiANx0KMxCiHE0aDwxCCjt61n9Lb1rHz2KuJLW2e6SUIIMcV9993Hxo0bufvuu6c8Njo6CsC8efMapnd1dTEwMLDP5R7qkSXXDb5AOhjGd05k0Zxlh3TZYu9mywihb2ayD2cH2Y+zg+zHNwcJdgkhZrXUnRMFnIvPDBH/0Ekz2BohhJhqcHCQb3zjG3z/+98nkUjsdT6lVMPfxpjXXHY6nX7d7ZtsLD1CsVSkmC+RDg/tssX0Ojo6Dvl+FIeX7MPZQfbj7CD7cXbYn4CldGMUQsxaxhiKq0fo+OhJ2B0JSi+NznSThBBiig0bNjA+Ps5VV13FaaedxmmnncYLL7zAj3/8Y0477TS6uroAGBsba3je+Ph4/bHDJdA+ALZlH9b1CiGEEEIcCMnsEkLMWsFImXC8QtPpnQQjZYoS7BJCHIEuuugifvWrXzVM+/KXv8wJJ5zANddcw7Jly5g/fz5PP/00p512GgCe57F69Wquu+66w9pWP/SA/csqE0IIIYSYKRLsEkLMWpWNUYpy8rROgrEK+ScGMIFGOZLUKoQ4crS2trJixYqGac3NzcydO7c+/ZOf/CSrVq3iuOOOY/ny5axatYpkMsl73/vew9pWX1eDXUiwSwghhBBHLgl2CSFmrcqmarDr1A68XTkIDP5gifgyKVIvhDi6XHPNNbiuy1e/+lWy2Sxnn3023//+92ltPbznsyD0D+v6hBBCCCEOhgS7hBCzlrsrh7OgCbs1RvzYNgC8nrwEu4QQR7wf//jHDX8rpfj85z/P5z//+RlqUSPpxiiEEEKII5n05RFCzFr+QJHYMS0AxI+NAlxeT2EmmySEELOCdGMUQgghxJFMgl1CiFnLHygRXxIFu5yFzai4hdeTn+FWCSHE0U+CXUIIIYQ4kkmwSwgxKxlj8PqLxJZGwS5lKeLLWiWzSwghDgXpxiiEEEKII5gEu4QQs1KYcTHlgFg1swui7K5gpDyDrRJCiNlBMruEEEIIcSSTYJcQYlby+4sAxJdOCnYtaMIfKc1Uk4QQYtbQRs90E4QQQggh9kqCXUKIWckfiIJdkzO7YvObCEYrM9UkIYSYRSSzSwghhBBHLgl2CSFmJa+a2RXbI7NLF3x0yZ+pZgkhxKxgpGaXEEIIIY5gEuwSQsxK/kAR1eRgz03Upznzm6LHRiS7SwghXg+p2SWEEEKII5kEu4QQs5LfXyS+tAWlVH1abEEU7AqkbpcQQrwukth14CpBiZIvIwILIYQQh4MEu4QQs5I3UCS2pLlhWi2zS+p2CSHE6yXRrgP1zO7f8FzPQzPdDCGEEOJNQYJdQohZye8vNhSnB3AWJAFI9fazuu9RqTkjhBAHSboxCiGEEOJIJsEuIcSso72QYKRMbGlrw3R7bgJiiodf+ClffvBj/M/HPztDLRRCiKObfFkghBBCiCOZBLuEELNOMFQCA/E9MruUUnjtIWos5O3H/yGP7vxP1g49O0OtFEKIo5kEu4QQQghx5JJglxBi1vEGigDElrZMeSzTkmaxfwxfuuwfmd+ylNtfuvlwN08IIY560o1RCCGEmF7ZL/LIjv+k4GZnuilvahLsEkLMOn5/Ndi1R2ZXtpJiJDnE/NJ84k6Sj571l6wbfk6yu4QQ4gBpo2e6CUIIIcQRabw0BMBgfvcMt+TNTYJdQohZx69ldi1uHI3xlcGnyLXmacpFherfffLH6GxawE9e/c5hb6MQQhzVpGaXEPst0AHP9T5EtjI+000RQhwGlnIACE0wwy15c5NglxBi1vH7izjzm7ASdsP0bePrKLaVIB1iQk3cSfKhM/6Cl/ofY/PomhlqrRBCHH2kG6MQ+y9fSVPyCuxIbZzppgghDgPbiu5BJAt6ZkmwSwgx63gDxWnrdXWnN+EsaAJtCMZdAN57yieZm+zie6u/LqOLCSHEa1BKAbM72BXqkJFC/0w3QwghxFHKVlGwK9RHV2bX6Kiht2/2fL5LsEsIMev4/cUp9boAdqY20bZ4HgDBSAmAplgLV5/3RV4deoZnex88rO0UQoijTe1Lgdn83cDO9EbWD79Aujw6000RQghxFLKto7Mb4ytrYfOWmW7FoSPBLiHErGKMwR8oEt8js6vgZhkt9jPv2KUABGOV+mPvXvHHLJ+7gn954UYC7R/W9gohxNFp9ka7/DDK/PXCymvMKcSbRy5nSGdm7/teiDdCqKUb40ySYJcQYlbRWQ9dDKZkdnWnNwGwZPlJAASjEzcxtuXw6bf8PX25HTzf+/Dha6wQQgCrVq3iAx/4AOeeey4XX3wxn/3sZ9m5c2fDPMYYbrnlFi699FLOOussPvGJT7Bt27bD2s7JXb1nc7fv+jfyOpzhlghx5Hh+Nbz40ky3QoijQ+0z8mjL7JptJNglhJhVvNpIjHtkdu2sBruWn3gaAMFYueHxC4/5PeY1L+I32+46DK0UQogJL7zwAh//+Mf56U9/yu23304Yhnz605+mVCrV5/ne977H7bffzg033MDdd99NV1cXn/rUpygUCjPSZs3s/bbaro6idSgzfaVIsRBCvHnU6lrqo6xm12wjwS4hxKzi90fBrvg0mV1zEp10dS7BanEaujFCNGrK7534QZ7vfZiClzts7RVCiH/913/lqquu4uSTT+aUU07hpptuYmBggA0bNgDRN8Q/+tGPuPbaa3nnO9/JihUruPnmm6lUKtx7772HrZ0NRelnUWaXH3rsSm+ufxNfG0XrUH4jr41kiQkhxJtNKOf+GSXBLiHErOLvJbOrO7WJ4ztOQSmFM79pSmYXwEXLfp/QBKwbeu6wtFUIIaaTz+cBaG9vB6Cvr4/R0VEuvfTS+jzxeJwLLriAl19+eUbaOJtGY9w2vpadqU1kKmMAOFYMgOAQfiM/ObOrEpRmvItksWhYt96g9ezZjwetnI5+hBDiEKl9Rkqwa2Y5M90AIYQ4lLy+AippY3cm6tOMMXSnN/FfTv4oAE5XEn9karDrlPnnknSaeHngSS4+9p2Hrc1CCFFjjOGmm27i/PPPZ8WKFQCMjkajAs6bN69h3q6uLgYGBva5vI6OjkPWtlCHtLREXyS0tbUd0mXPJDuraNEtzHEC5s6dS4EOWsottLQ2H7LXWPZL9W336thTLGxbyrnHXPoaz5pwqLf1ps0e+YLBdmLMbZ/47lv7IRiw4vYhXd+RTPc+BoB11pVv2DrGi8O0tLTQ1tx61L5vWlqigRs6OhKvMefsdrTuP9Hojd6PvlOiJdeCpayj6piZbe9zCXYJIWYVr6dA/Ng2lFL1acOFXkp+geM7TgXAmd+Eu3NqV8WYHeeMhRfx8uBTh629Qggx2Y033sjWrVu54447pjw2+bwG+1ckPp0+dBkroQ4pFqPs2aydJZ2YHdkw6VyKcn6A3PAIzrIKWQeKxSIZK31QrzHUITk3RUfT/Pq0sl+sbzuA7tI2jms5fb+W19HRcUj3I0ChYCgWIZsBoyeOq+y9uzChYe77jz+k63stXugSs+JTjvHDwanul+AQb+PJTNxQLBaJ6abXty/9Mqo4hpm7bP/WawzGgGW9/u1aLEbnm3S69Bpzzl5vxHtRHH6HYz9mi9n6Of9oOmaOpvf5/gQRpRujEGJW8XryxJe3NkyrFac/oTMqTh91Y5x+SPkzFr6VXenNlP3itI8LIcQb5Wtf+xqPPPIIP/zhD1m0aFF9+vz5UdBkbGysYf7x8XG6uroOYwvNXn4/ulX8EgQegQlQ/sQFfniA3RhDHfDErnt5rvchXh54quFzZM8C9YrDH9TZHyY8/PvVDz2e2nU/3enNh33dh4s5RAMU2H2rsYfWQrh/gyds2Qq/ffSQrPrNJfBQhZGZboU4is2mrv5HMwl2CSFmDWMM3u4os2uynakNKBTHdawEom6MYaqCCaZefK6YdxYGw/bx9YelzUIIYYzhxhtv5MEHH+SHP/why5Y1Zm0cc8wxzJ8/n6effro+zfM8Vq9ezbnnnnv42jnp4n221KcfLvRVA1GGwISgVD1j7kBrdhW8LEHo4wbl6vMnAhJ7Fqi3VHQJPj5u2LFz5jbmkbAfS340omh/bucMt+SNsz9ZmPsl9GoL3K/Z+/qif6U224Gx+1dj960GGUnvoBljeOoZw9CwHHti5kg3RiHErBGmXXTBJ37sHpldqY0snXMCSacZAKerCQwE4xViC5sb5j256ywAto+v48xFFx6ehgsh3tS++tWvcu+99/Ld736XlpaWeo2utrY2kskkSik++clPsmrVKo477jiWL1/OqlWrSCaTvPe9752RNhsOTabKTCv5+epvBl8HoCyoFxY+sBvdfQU0pmR2VYNda16J/j7xhANa1axSCardCPX+ZSsdlMCLAkWJ1tee9w2w5/4/eNWMwP0seq1UFBfTGixJcdh/XjUrU2tJDZmOX4oOrHjLXmfRGspl2LgJFi08jG3bCz/0MEYTd5IH9fywmvVq2/uXlXvIAtzidZG3rxBi1vB6om+H48v3zOzaVO/CCODMjz7opuvK2NE0n3nNi9g2vvYNbKkQQky48847yefzfOITn+DSSy+t/9x///31ea655hr+9E//lK9+9at84AMfYHh4mO9///u0th6+m/fJF++v50I+0D5rh57FC6bvTr47vYVXBp/e6+OHkjEGpRS2squjZk0q1n6AIybuq9tKQ2aXMVjBgddfO1LpSkCQcQ/6+bXMLmPMGzZKpb3rCZzux6c+MDkI9QZm8ZjQxxpeB5Wp9UIPSDVIyv4Gz6qHWfg6Nmtvn6nX8YGj+1jdf7Wg4uzO7BrM76bg7eOYDD1Utm/KZGfHozg7H9vnsg/FYWJ8TZjzXv+CgCd33cdTu3990M9/7HF4/ElQpXHs3hemfYEjhX52p7cAE58HqjiMvfU3B73eQ2n9BsPmLW/c+1dlelHjOw5ZynCQcdGV1/celMwuIcSs4e2OvqGf3I2x7Bfpz+3k9076YH1abH4TAMHY1BEZAU6edybbxte9gS0VQogJW7Zsec15lFJ8/vOf5/Of//wb0gaV2omVHyRcfsl+zf966pEM5XsZKw6x29laz6atL9cYdqQ2ApCujLGw9ZiDXs/+CE2IpSxiysY3lagbY/W16QPMXttXEGDyY/ZOjdProY+dCK6FITiH8Kq8UjHs2gUrVkxfnFwpwA0oPTpI239ZgtV08CvPP9z3uoraV/yJz+JKUKIl3raPuQ+OCvYSjJsc4AoDsN6YWyNdTqHCACu379FTX1NtV+5nUFAdgmDX5i0w+RAyZmK5s1YtqKjDqD6aDiF2EBlBlRxY9j4zoGbSppE1AFxx4sRIpLVzlVIKa/BVrMIIQXLuAWdFal1b3sG3r/j8MMF45bAPmDEdbYAQrOENKDcPbg6S7Q3zrB9+AYDl1bIpAFZ2ABXfvwEl3miDQ9G/p6zc93wHyx6KEgWC5k5oev0jUBYeH0A5Fu3vWX7Qy5DMLiHErOH15EFB/JiJD+TdmS0YDCd2Tox65XRVM7tGp88aOKHjNHqz29/YLhVCCHEEsUc2ocqZfc7TWLNr33cw2g0Ji9OfQ2sj7k0XTCpnuut1icrVjJ9DoVIxPPRbQyrV2G5jNJaycbAJTIiZVLPrQDNY9nw9k7uuhSZAFQwqZ7BHDBYK4096/LWCEV4x6oq3n7bvgN5+GB3b+zzOaB5dCqLPztfh9Ra1n/xZeyj3+X6ZHOyq/l4qTX+sTCcIzH7VwzLVY9pYNpXg9YxyVss42r9AbD02djC9KMOJ423ySzyoZR0CxhiGR8wbnllW8HIT0TwdYnc/gbPjtwe1LGfXk6+ZATVT9ta1NnvvbgpPREFZVQ9EH/g2rx8nr2N3BePRdfp0+zwIDL5/+LMMTTWIo0rje50nOqdN07bQx9l836wf/GCvXy7sBxNqSi+PokvR58J09ZUPhAS7hBCzhtdTILaoGStp16fVMgRO6Dy1Ps15jcyu4ztPJdA+vdntb2BrhRDiCLSXG8lsJbVHAfF932Tkf9tH/uGp3V9gYiRC7Yb1m5n683Y/jjUajcq3M7WJp3bdP+X5ByOTjf4dGGycro3GUhaOVe3GqCZ1Y5zc9bCSjW5S9nGDs+eIe5P/DnVA/PmA+OpqcEWphghC8Bo9NZydj2HvemLfM00Sj1ebPeljznUNTz9jKJfNRBtg2l2Z+UX3fq2ndkPyemgT0hSLMl/KwWEeCTmcHOyKXksqFf05sh/3o48+Dq9OU/XA8/YIqlYDlZmgyDO7f1Pvunngon2m9lGza/S5cR77eRHXNRMxmwO8X1TZfpxtD2GmCYAf0LLcwiG7sR8YhLXroL//dS5Ih+BP/2VntpLihd7f0luOUmCUCVFvRHdqY/a/K+obZK9f6GpDmKkNhHDw6VmHNCg6zeqffhYe2/9T4qFjRydX5e79S4KKP01A2xjwove9NX7k318YYyiVDjKYuB/BrjBs7B5dn57x8HoK5J8YnPJY2S8ecCKCBLuEELOG15OfZiTGjbTE57CgZaIrjNXkYLU4+8jsOrX63E1vXGOFEOJIMTljaC91i17qf5zu1Ob636/VjbGWtWR3P4E1tq3hsVpml3mxQOGpSRe0xjBYGaNFxehMdALghQf/DXFjgyZ+9UK3XhsqqtlloUw102FSN8ZwUkBBlaMIiCoM730Ve9wQTt5Gk0d2VKVqZle4/8EuOLBvy5OJ6N/ypGDX4BCUytA/UGtf9Z7bTN/+/ZF7aCKgaabJcKp/S1+pbsvAnZKhpo0m4TThWM70N4mH0p6vcfI+DquZBPUJ+7fIsT3in6mU4fEnYWx8Yl35jEu5YqIubcB4aehAWj1hP2p2Da/JYm8aZmQ0imfG1/VT2ZE9oNVYIxuiX6YJCu3uYdqb1Ok43Y9HoxoeAl71sKm44PuGvr79P17DvEd5/ThGa5ytD2B3PzbtfLXBEnK1YKQOGU8Zeg9gXfvDGnwFZ8vB1486FML9qlFXOzkceD/YifPKAT91Km3o6TFs2jyxMO/QlPI6CNU2hHsPulSC0tTzqdGoWvdjZU990mEwr/ASc0v7N9p8d3cUUNxXwMvzDBs2GoLANOxotR+f2xs2wjPPTRT+r6llchl36jH3bM+DrBl4cr/aXyPBLiHErOHtzhNfPnUkxhM6TqvfXNU485vwR6fP7FrafgIxK053WoJdQojZZ8+Ly8ndlfa3FtD+BkaUm8ca28r2HYa166q1YGqZXdlqwKkaJDE6IBvkWZDopHVkUmBtL+sKMu60AZZp5510X/fUzl/yyprvYg2tR5sQC4VVD3Kp+kX75AL15XKUGbWvO7c9R6hs6MaY2/PiX8Gk7hn7E+w6ELVmlgu6HmhSkx5TCrBUNF9t5uD13ZVO193EHy7j9RQob4iChc72h3G2P9QwjzYhllLEneQhC26OFQfZMvYKm0dfpiczOdja+BrVpBtWK7UjmqP6MiwLcPPYPc8fUPH6XDXhI5OZmLZpUynqUloNdmUrqf1eXmODJ7rX7U2iGujcvAU8H1QlIMxOExnY27HsFevbRU9zYO7aDS++dECtPiTqiYgGhodh05bqe3I/VDamcXfkCIeqQeu9br9qIL72pw4YGIj2ZZCqMHrPNvQ0N+EHarrabcViVDx8b+c7XQ72+3xXz97cQ5Cu1M8HtQwZpRR4RayRTdVRFie9j2v1uw4gC62yJYM/VJqo2QX0D7zOiJeBVHoi63JG1bZJOH20LZU2PLV6ugzViWw+Y81MsKvJH6LV3V3/Oxiv7HVwkczWPMnnuiln9368b90WZVyOjdOYwbm3LveVXD2Ank5Hk6bEBP2px9rk477gHmDg/oDmFkKII5QuB/iDpYaRGEMdsiO1nhPnnT5l/tiiZoKh6b9BdqwYx849WYJdQoijWqEw9aJxbNzwyGOQy026wmwYKXA/g137U4hlUiChexcMT+nNVPuGPPrX9YtgIGknaFYThcL1NG0Ksx6FxweobE7vV3u9SV/CK69AoTyKldmNNjrK7AL0PjKz1rwSXdjvKZgUINrzJnVyu/3SxMW/ld6FVU5jJj03mPQSgz2CTiY88EhY7d7ArO4n95seYFJR8T13XT15Y896Zo1/j40b8vl97HdtMNpQ3phCl6M2lwshL7w6TnZk6s1Pee04/lCJ0ITYyiFuH7pg19qh5+jPdjOQ3cmOnkcmtXGPY6m6j0xTR1QXjYltp1RUcFmVxg5oFEVruq6DpjELxN/LjfJ0yuVJtYnqEZ+9Bx/2TEhT2qD36Fapcv04W+4Hd2p3SlWcKPSm9tJlaF/B2XJ5/+qYHajJr8uvrj8Mq+19jSC91RydT4LU9F9yNqzHzU8Kdk0st7I1g/E1uQd6Xtfoo65rWLfekEo3bqO1L1dIb99JZY9kulLJ8NBvQvp/1kN53d67Udfk8oYnng7Z1TO1jYUnBsk/GmVj1oJdtlvA2fkYVmon9vZHsAZfnTh3146z/f0SxNdUNqcpPj/cEMTYuAlGRw/+mDDa4PuHbJC/qQ5kFNjaNgmnZqhClDlbKJejz5PJQXKjJz5f1QyEYEpTI4WFpwYpPD79oBnWSHRuKKX9vQZPs9W4U8wBu//FiQf2ch53dj2JvfvphmnGTHzmlTek8HqmnpNMJTjoWn0S7BJCzAqVrZnoJmnlxOgfO1MbKPkFzljw1inzO4ua8fcS7AI4vuNUdqY2UdmaIf3znW+SobaFELPJ6NjU81ZftddZefIN1eQL/f3OYJlYdq34eyYTTSvd+zQqvQtrOOousWf2Re2mwKhqRlct2OVFKTFJK06TFcfZFkLZ1LsTDg6a+kVxLZii85Nuxr0iKj999zC/Opvre/U7JmM5GKIC9ZaJglthYNDGYDAEocb3dbUbR3SrXSrq+o285xkefRx27TYNr2vP1wmgSzmszO76DZLlZuuZUIuyj6CyUSEiY6Jlrl1nCHMeYc47oKyimvqq3UnPrQVhartDNWZ2FV/Yo4vmHofPy6/Acy/sY51B1GZ3W5bcQ70ADA1kSAU76O2bepPuducoPj+MNiFKWcSsOF44tdvcWGlor5/BZb/I2qFn9xo8UqVRrGz/xPP3WE4hF7JuvaHsxcBoikWDW71PsyzAi64Tapktr6419PRWj5+9tKk+kN+kw0Fprz5RuTl87YFfQhVGSGemr10DURbmU89EtaqqS6ouZ+I9G6QqZH7ZXc/Yqa03vq4f5UYRgtBrvJm3B16JluZNk4GifYoZzdpXNMX8Xo69vXTx1Dpq74aN0z8+nXw+Cv6k09NvA61NYw00MxFss3J92L3Po7I9+1yHikeZNNNmuE1eV3EUa3w7yi/haZ+yPynIOSkY7PcffG25YvWp2Sz1N6rWhqaxNcwtb0JXqjMYA4FHOg1WoUI2B2G6MYiQzpgpgYhiAXrGf8tvNt837fqNp/FDj2L1fGtPrklWO0xCLzo/1Wt27V9mV60WrtVkE+7xlAMaEdQY1Ph2JqeHBeGhHxzB+Br/5bXodfeiDyAADUwJdk2uJRWYCmFosEYmvREME+/b/Qx2BYHh2ef3/SWD11ugtGZ0ynTXNQ1fnDg9z+51GUNDhhdfalyHVW3ili3w1DPTP69UjR3rPXa2CtzomJkcTK92w9+zBt54Kqp9mEoZ3O1Zgml63YR5f9ovvfaHBLuEELNC7dv95Clz69PWDj8HwJmLLpoyf6wa7Nrbxerxnafi7AzZ9nu/pO+vn2Js1YZD32ghhHgDBdVsEJXtR41HXbSy1Xs3Z1IvisnFrp1dT0Xf7E9SfGGY2MuNN72TT5217gi1WlBB7zCqPJFxNTkLJAjMRBBojwBEbZS6uB8neNLB7tHEtoVoE5LLG9ZvjLpmwaRuDdbEXbe96yns/un7V9Xqu+weHsf1ovWXwzIVv4SlLBSGUGuefyJgYHWZnp4oG+GRxzW/eHwLhTB6Pes2wI4dE8ucU97KcH90cb5nsGtygXqT6a/2H6zdJFtRZpfROLpMfDSqcl67IRwegdxv+8g/2n9gWQdV9cyu2qYONcnMJlrdXRgDVlihxdtVDXZF8+x5Iz355n6/ChWHBlOrzVVbZvVuSId7Hj+TlreugpMbpKnvxSmZXcOFPtYOPkt/rnvaVb4y+DRjxSHybgaIbpge+q2hUnIbCkiXdfUGa9I+qVQMmzdF7Roec8AYnnkOenonll/vqlS9kR0e0azZFGU+7e3G25rU3W5C9HwrP4g1voPAL0XHa99qXnwpql0DkCmP4Ycevh8NJvDIY9H0QgE2jrzIb4efmvI63O1Z8pU0YwN99GV3sj27JlpX0cMejrZBODlY1FCHa+qLUDpk5LEAsykgPTZ9Zpe1l2BX7fgdmq60Xehh9zyH3f0kKrUTu+c58EsMj0Tzb98x/TLXrYfHn2wcETKsB7uqJ5297AxdCcn8oht/oAjFMazXGHE2rAZbDfB8eh3PDjxB2dTu6CfVJIpN3EJv2mx47vn9/EI09HCGo/OrUtTf25u3gGWiF+VX95W/Zi3q1d/glV2sXIVWdzdWrDFQ8OJLUwMRxSGXtg0ZYr17jww9vfvXbB2L2mFNilzWPwqUovTS6ETAe2+Bhj2zWUvRa7Ba4/VDNO13Uwz3MSzsNFRuAHt0C6owVF9PsI/MrkzGUChMPDhS6CdTjtYZ5ry9juhX2ZKmvLqHZzau46W+R/erbVprikGZF8dfpjezjXQmqiU2OeAe6Apa79ld1tTPI/1DFq+unciAfGG1YWg4+ntygGp8e4nyriw7dtY2g5nyOZN6fjej26YGe594Cl6olcvbW7BSB6j0Ljasc0lnGh9S1Te52lcWt9G0uLvR3kTygHES4BWwhtbhdD9eDwoqN7r4MFasYRG19329vmTgUsoUSU+qqxzm/YYac7oSRp+N+0GCXUKIWaGyOYPV4hA7ZqJm17qh51nSdhzzmhdOmT+2qBlTCff6Ld/xHady+XOXQpOi6bwuRm9bf0jqNAghxOFSCzLZg69gV0c4dPK9JP0RzOTuSdUL8rSXw9cBVrZxFEV/sISV2nsXvcndvva8qN7zCwXPm3huzk2zceRFntjxKx7deQ8bhqMr82DEYWQgTtx0YlQURAr8iecD9UCMsidu1FTtYniPQu6eZ6hUouBTr/scu7ohCA3Pjr9MwctVuzEqXM9gb8tQXlOkUjI0N0F2zi+x521kNFHromGi2kuA7eeJldfijf+6+ohGZTT29mqGzaTXrt3oSt40Vdtq2bi9+XqgsVZHzRgg0FhjhYmujdrHGIM/rgmLPoTea4wKGWWlQZQNsW69Yag3JJnfydzSBjDQll9PW2UX+CWMNri7cpTLjRk0k+uk5HKwo/QwQ+FEdsD4ht6GGzxdDiitGUald0Pos269YXSgMmVZAPX0KUANBDiZXuLKwQ8qDcdMbSCBgpeZ9rWW/Sh4WKvLmdmyiXhlJ3pwazTiWXVRXv1GaeL4XL8BPDf6u+Q6U+oSFQpMdMcLfXalN7Op9At2V54kV87t9ca7lhHR1w/jtSL1eyzbDyv1uliqFmAxhjUDT7J26DkKhYmsCYCWFhjK9wIq2j57LK8ns52NIy+ydfQVtA7rgVZTjWqbSZldYSE1kSkyXSBVB2BZ6MCGwCPhj7Eo+0hDwKPWm7K/v/HmfF+ZN6qUjo7boII9sglVGscefLWedZnJTqqLN6kL9Eg1aaV2PtvVA2PVHlm199jeanDVRgvVu7diZXtRmd4p85hSlnJ3mmxuHL+6nFJYxtNRdCWno75ak/e3ik/cQvdvcSn2NvYUGBo2rHtmYvCNwA8pFEz0mnPR+dWqni/Hxg39A2CqQafAi2pzlV/cRX6ND7kh4pkUMT+FNbqT11LKRTshNq4b3kumek4wRjcETBqCXfV9qaq9H16jG+OU8/3E77W3zqD3CrsrT+73gA8Ny61GNY02BMHej6/VL8Gzz090h1s//AJrBp7EhJr8o/0Unpma8RtmXPzh6psshPx+1tEz2pALiuT8Ij3pLWzeHL3XR8aic5pNDN+UCdMe8WdaUUWLoKApvtAHfnS+7O23GRmFfD76fMrmYMMGeGlNlOVUb+MTa1n46m+JuWPgFtg4+AyP7fxF/bwYhoY1OzezZXA7wTR9i4u1w3LSZ6KZnFXm5lHlNHPLUdmWycdL7Ty2Z6yrvDFF6dXoQzARjNNRWk9z/0TReNPUgQp9rFw1GKWj4/Dl53NksgbiLY3Lq+4CYwyuaxhdt4H0li2Udk5kxemc1zBgjLsjS5jfv0w8CXYJIWaFyuY0iZVz699EhDrk1cGnOXvx26adP7a4GWCvdbuOb17JeevOIfuOkIV/ew5h1tvvbxGEEOJI4E9TcLyztJauwmqaux/E3vobCpndPLL7Pjbld7Imu4k12U1Tvjnek1KKnJuuF4o1GgrBcPQNsFfCTk66q9GNNyhBMCnjSUHcTrCs9USWz13B0palLEp2UapEN+hubi6qDOV1qXowqHYBXq8vZSt0JcD4eqLor1/tSjPwMs7m+3jiCZ9MFsiXaBrVgGFwMCoIbTBYSmEZcH2DCjWWZdClKMDQNU/RNU+xaCHMbTfEbEOpFN2UGwObgvXsdrdE37jrkPhLIc5uTezlAH/jRBeo0I0CGrqpVhTaxhupENs8jMnoejBIa7BH88S3j9a322i+j9U7N5F/1aP04ggq0xtlxezlBnTLtpfY+MzPwZh6IGHnlnBSHSKPWJCJ2qED/KES5VfH2b4Dtmytbl9tKE6q+TYyCq7JU1ETXQo3P/0sW8fX1uepbEljcmlUOYXK9TM0DLncRPe9yezND9d/10ZjWTZxKwYmxK9md0VZDNEN3GvV8tJGQ+gxMvYkPbkfoGvR0eqxMF03RmNAES0/JDblxn1kFAaGqgdc6NObnUg9KpX1XoNdkyc/sybFi/2Pofeo2RWGPiOjITsK/Wwu/gxtAsLqa8276YZAVzT/xO+eCTC1G9tKrrHdOnpdutYfrfpW9CtRV0HXNQzsLrFrN+Tzmh3pzfWMSqMNJtQY1wPLRqGJFfuYX3geOywRT02q5aUgnTZs3NxYy26vXdWMQVXPF3rpeRPLKaUIKxPXYU88BdnNa2HTb6YsYvLoe/UskOpxpcqpKd3KovVW113LNN3z1FbOsO3eO1hz7928/MIjPP9KjtExQzGo3YGH5E31fTwpO0g5E7fQifUDxLc0prKtWw/OwEv189au1VvZ8NhmXn6+UM8cVBZgNC+/Ai1uD/EwWk/g+Rg/AB2iAzCZIeaOrY32q9U89TUCGzcZ+vsNubxhpJqNZ7nRNjPaMLxmB7oSHTOV6msLQ4OuFLEnjTBbjydUj6kgHZB9zsMf2Uutsz0zviYFtfe3pr3rRiP6NdR5q0daqgE6baJujK+RQPfo47CrZ+I4CDPR72HahTBEZXsx1Syk/OMDhHmP3bsNpSJg9uguuxc6rHat90FX3Pp5YGioDNrQZHcSmApBd4kgMKi8TX6tR/jqq+hq+pSqvq58fuJYTiSjgC9EGaoDgwa7Eh23VjmH0/04Yzuj98VYKcpozGQgX4wK9+cKY2gvRHvTvAmrwS7X6YiC+rVGVz8znTDqbtjQ9XryiCaACTT+YBF3WxZvV5QxalXPa5O7MZrk3IbnYULCEIJCnv7+iXVO2a6eZnh4+lE2dTlgbDwgrH1OFn3stvi0y9mTBLuEELOCuyXTUK9ry9gr5L0MFyz93Wnnjy2KLhj8wemDXcn1EA/i7Dyzh9ZLFmF3JMg/0jftvEIIcSTa84tebTRFE13UGhMFOgrdvwWjGahE6ROFoMSjY6t5tudBnnveMDQ09eK/OdaK1pp//c1v2b5Dk3XH6HGfYbC8hXAsS1hprK9Tu+at6CyuF6LRMFoiLFfoal7MgtgKdrx8Kkvjp3J624nk3ej8bEKDlTd4O3MEKRcrW0btrKd0AFFXi/F7e8g81At2NAydPbYVVRrHyg2gtWFp5kGUCVgyNMjpoWbRIoNlRbVChocBE+U2eL4BO0oaUz7EJvW2sCyYt8TjtFOjsvWDQ9USLERBj0D7hJOzJVKGsLsIoY9285SqBc5Na7VuibIwuWHmjG7D7Arq2yh0NVauGhirLm7t4GqKbpnQaHTenygaHroM5XvYNjYRcAIYe7EbZ0ijsqWJ5RYr9d/njDyNpavBIx1OdD2cZHAInn/eVIN6E9lsANnKpKwyY9idrvYtnTzqsYl2vKlmVGWyuqEr5ORi+BqNUg4xywEd4lXrW/Vmt7O1+trcYGotLwA1vh174GWsbQ/hbHuI7kq1+HZ1EymvgLEdgsVnVlc8sY/i8agWl8FCm+lvifKFaLrSPnErWZ/u+eH0WSahV8+gARh015IqpChXAxnDo1GAtVTU9A4FvNzfj2U8fFOpd9OxlEW5v0xs6zBOd7Stg2Aie23XyyOM/LqXcDyDs+tJVKbahanWg9RQHxlUVd8nIwMhQ8NRMMkvVTAo+nM5due2s20sKghWXjtO9t7dBKlKlO1owoli6aOa+OZRrFSx+vqjYGA26MWddHM9eaCFWoAxyGvK66Ii/55qwTTPQ7cvQ887Edc1qHLj6Gr9G/rZtBl2bPcojUbve5iouzeZqQW7SuPYfdU+W5VslF0IjXWn2KMbnDEov8TYYBYCl0oO1IsjxF6KMk9iloOVXUjB5MnpLIEbHZcVzyVVyKLygzib78My0fSyX6zXbbJU2NDmWHoHrYVthLmJ16oUmGrA2i48RSYZHbtjoy6pweg15/OGSt9I/TxqwumP0+7BMTZsMjz/AmiTo7MTbN9QTvsMb9zB9hdfond1lCkT6AC1LaD7Sc3Ahsbub7XYld0eQ8UsglRIWDa43dF+dzbft0cdqj0+H8zE9D0DU3sOgFGzdRv0D2he3LFuou6VshuWH1a/uDE6+jJ7KN+Dp4vVWSZlI2mPgdGJOlHBpK7ZKl/AKo7ByPqJx6ufkcU8uJWQx5+cOnKk0Ybs/buxB7PEto6QeSiNxhBf3Yr1aJlS9TYieLSP+HMhcdWKJiDd65PJQdnX1EoR5h8bxs+HE8GuwqRgVxyamsDXZV56GTas13jV9qmNaYKCZo4THZu1jNZsDuIqmpYZGyX3i03kft1Dac0oqjDptVcDwWEtWOqXGravE+Qm/1ndmI1FHotrRim+0DjKTC3Y1fC8ZHvDPBhNGEKseg7EhGzdZhoGjcFotKcnsrP34JU1L68N6zVHjTb7HcWSYJcQ4qgXjJUJxioN9bpW9z2CpWzOW3L5tM+JLY4+HPyB6YuMFp4cJHRCXlm4BmVbtFy4kOJz0xWhEEKII5NfHTGs0h9S3hmwI7WBTcEGxvRo/Z5keMhifCy68A7LCXT1ZqrkFcgXoq5ee2qOtWKyhmRKs313rl5U3NU58k9G58laT4nal8iBqbCz/Ahbx9aitUbf30e2b5BSroXMSIml6ftJ7Y5u7EtqQfTc1uiGp+TnKT87SHJDD1Zfprrc6BUUUlkGXtnCwK4A7OibXlUcjTKfmOgtp4zGiUWvs70NFi+CmBMFcda8DNu3GdJ5jzwZPBc6NwfM2RhdeKv8ALg5QhOSTCqSieib9Ep12aE2/Pq3BbaNVkfwNSG4WbTR2H2rGdr4U7IFDy80mJbopiOVsRjZNkBySR60QVcM+W0j5B/YjZ2ObkTqwRQdQqgwRKMd1jO6ApeNIy81ZBwBDOehVAKVmqhZZfWnKT7tYnyDFdRudCZuticLQp+e9C5KwThhWO16WuvNZCBdaSyGXPCim5ioS2k1OOQVQQcTRZ+NZs2zHjuz/ewuDWJC8EOXvuxOMAbbsnFUFOxKl0cYKw6SKldvrIIK+cIg4+ntUSZTJQduHkopLDePTs4hbF8aZYIF0Q1asRTV5CL0qLg2YT3LIHoh1vAGOrPPoUyIUTbaTN89UKladyqfWEOwqzGzK5MxjIwYnG0PkRhd07CMTVugFoly3ahA+eCgwUcThBrb+GgzqZuOUpTXp7BTJZzharZPEGVBog0jfRmGi4P466pd2qo1uFRAlNmlwdQiX7XAm5n4PTNaIbBbyJQroDWx6vvGHyhG2S29FYxtYQxsGe1md98OyoUiEKImZTdt2jVKv/si/aWJk0QYgqvzFIJhNu8eZjyTJf+yj7sjS2G8wqubmxgaNrhdZ/LS7uPYug2yaY9kYmJ71bpZ9e4KePFfB4lvGoJAT+75OmHS8auq9bisnU8S9kUBPOPr+uAPoKqZpqaacWXQJRdVidanK2D5BsePjqFWpxmed0jsjLE13ExfajsAT296lV88+ywq28eIm8IPRjHG8GzPg6wfjkZwiFvV+lvVG/pevYudr2wi8VJ/vaugpcAtR9tza7iZ/qCfkinydP8DvLImii6P5Nsxm6vZoE0K40VdELeNr6M/182ItwE6V8OCJ8mFUSRg3sIUsepgtvmH+yi+EnWnrFSjMtoEmG2aObtDCH1cL6SnN+qOqquDiVgJhXEDyrui7Wuleuojklqp7ontH1TYOb6Bl/of56X+x9k4sppMeYwg9DG62mVdG5pHSzRvv69hpM/JskEvffntdKc3o1LdqEo1KFh9P/q10whRPa6NIy/RW3mW0Hj1bWzrMp2ZX5HNTdTeCieNgqvcWjfOKJssDE09i8hC4VaD/r0j4/Wgmy75lF8dw/gaZziPnSpiwqhuVhAYBvsDnDDHnPJWnMwm7JGB+nkin6kOPuAr6lFAHVDeEVXaVxUf14VC9TYkkQBPF9lWfoBxfxvKDSaChwWXzIt+PXOylo2Zz4Oqpm+6v92JNbYV3Bxeb4HYjoltPbwrhetp/GL0Xnd2PhYF5WvdgAONcn20P00Ev/pZG+Qao81mrJuO0vr6Nq1PjzU1fvGhA8LAENPVIKTW7J4UY3XCAktT92Plh1Bm4ksNY8D3PfDLmFIRTUi5DAbD6LDBTF7HPjivPYsQQhzZKpszACRPmcjseqbnAU5fcAGtifZpn+MsaEIlLLye/LSPF58dJreywvbiBowxtFy4kNwDPfhDpXpWmBBCHMn8Z3djzuiitDW68UqfGnWJ2BXuZImrmMt88sUQF41uMWRTMWJ2grZFuYlBsMxETZ5MxtDbD+++uBlrp6atx1A+JkWoo5tFQwgmurRUdvU+pZqR4Ovo6+vx0ijuYLaeAZIaS9AVZilgsLPd0K4ITAIboFoXZyjfyzzdxNzyDmLNizHh8fUb93B4O44u4Ze9KTVhTGjqQ6ODwbIMaDih/UR2ZfuYP98Qb4MmDLo7ykQY0d00swKAeCHKTLLyUQAvTCwFY4jFoqyw0tBEBsG4v42OErQqoJzFquSgJQulEFd7ZFOKeNHQjIuzKEalmgzV1qUo9sPoy2myPY+xsOkkQtOCrWITwa4wBA2h0ViGKPtpLKT38W7SZ2g65it0GGDZDn4QUMwa4oAqTtw42Ok8JgFhKcAQo6iL7A4GOM2aR8nPU/LywGJQ0eiHhWCUvOews3sRg0PRDbJtRfs0WxlvKBYMEOoAO9AT+0D7dOVeoqAr0ZO0hmd7WGeNUzmtxFULFzKY203eywIGG4WjbJQJ6plGlrcIzy/SlI36VhaLeRa2HFNf5+Z8dNMdtCzlmZ0nckLCophIMc/aRqkMrhf9jBccxpcYWvIGx9XE7BJWehd22eDoGIkmi4Kxqt0ao0yviYOo+m+mh7CprT7Z3yOza91zw1ScBSw5Bpzi5C/GatXqJ7ZXKg1eQhMYDz8EpQIC4xFqu/oMRTkb4DjVzBNjCAJFkxUnKEc3ziW/SDCeh0XUs+cIDBiFNlDROUaCDXT4b5242Qs02BaWcQmtJGXjoVKQXNyEG5TRuW7sbA5vjkM6yJNJnkB+qJtkJqSoCiSaAnLBAElzAraKEVRvTMNJ9f92jG5jRzm6AR54znDG9l5OmrOEFgyFbIAhQT4XZd6NZeIcA1jGI5acCB4bZUfbS/ugFG2tYNYP4F04se/ruyfU0Fjvmu5dUbD31DOB/DiqXM1EdGIY47NrdxRwPPVUjSmVCcNqtSzXTNrhsCDeSSVnEwuqQfdKdL3oB1TPa4p1uW2M+gGddjsKyJTSrF1nCKopOb19cOophqFcnoSxQPuU1PG0MAgKyiPDQHRcWQo841W7faUIA4PnzCUeZglMQD4Jc7yQseIgvZntGGMY88F2YcEcxbxjAs48DrZvSGPbABZhbhzlbYNEjGr8jVCHeD7YYRFlAsbTFp0BJJPgVAdssGI0jAIbZDWZjX10Jg2WbU1kBO16iqFKD6bjeFribeTdccq5NKlEijleB7kgRcugprOnQGG8iZb2fpyzu+rL1Trqaq0JiAUlVHEcuxRFQtzBEGWiTNraYCsAlaBCJmtwTZ4tpftYOng6sAInLLApWE/CbSbBydG+8idlN/kaLNBK8fAjHmorLFwEGkPoQzGnabJCtuaeoLw5x7JSkQVj/wXtRvtftyawKz7aGAIrGm1SGZ+5hZdoochAUKFVa6zo04tKNXhmharWZzpq07jGcsokxvrw5xzDQC46gKPPySiIVQhHmO8dM6n7taZ/AAbmG1oXQdkv4XmGUjX4AxD4PtgWyiuiY21QHUTBqwSMb95OdjREjZQxx2nArgZddbRtQ0i83EcxmQCWRKusNriWHRoGBltXz/HKgkn17xq+I7BiGKcJ5U+MZBv4ftR9UjGl62uskMK84oHdh6XjmDYL3dJEemAXqZGXONdJRgH849+GAbIZ6NkdnROX89oks0sIcdTbcyTGvuxOdqQ2cPnx793rc5SliC9rw+spTHlMlwPKG1NY57RQ8vOMFPtouSgqcl98XrK7hBBHBx2CyU0E9EefnxipMBvkCUKDF4boUKONwsbGCmNUKmZSoMXU6zgND0cXvUmrg+XWKVgBmJE0/qYx4hld7VJU/aY4Fl0oV3aHeCmNs3scFRi6+4r0b6zgB9Cp5mGMqn8zXetSEujo22dr0lVqUM1escMCxjf17Iiw2v1MKUOluzwxvWwYf8xlZEt0Ya3QOLHosSY7qhLv2Iq5cxWLFhuOWRjQ2gLtnVDRaVqaIeZE3eBqoswbQzwOSW+I9kKUxWHrCjF/BM8HYzGR1ubmKG+PAlXKwHx7IZVSSGZhheIpGnV6nORchbLAq/bp2O1vZ8SPvmTRXiWqOeVHy9DVNDkTBlQGQoaHAka3GZwHRtn14L+SzXTjrr+PWLW7FYWJYJdlfCphhQ3eWvqzaTbkusnrAiODo2wd3sRwIcp+MZZF2Q0IAij5Y/QN+lTCDKHxSSSiG7JKUMYPGgureKGL8TQohd0c7c+EN0ZLaQstXg9OkEEZH521KJVhdMjUu9aoTC+W0VjGwhrfjvKiY3ZXt8OubVGQND5vBcV5xxMuPb/+09vcgtdxEpUggesZBitLcZ0OkgsW4ThRVk2xBFrZDI8Zdu2G9et1vfh5qGGeM0RTs4M2VpTAsGdmV/V4XpPZSDq7E1tFx6Y7qY9ws9dPV+FFWrzoJn1yjx4nLDCnvBVM4/ay8PFxq8e8ITRePYAYbglwi5rOpU7twEMbUDj1wTz9Vo9CvkRPdpzNY1G3snw6yvwzBnJBdCPq+RPvf1UNEDu6TKiSlCsesbUWwW/TPPvM/WwafB4vKLFxZB2j3ihlBTqYA0Q1wPxgBDf1n+TK0fB/tXpqlrKpbEmz+6ebeP7FdSgsjotdykljZ6A1uNoDA34lyqIbHauOCqsURsWwtE9T08S2WbbM4thlUddR0xyjsxNUxY/qKfkhyvVp8gawwyJm8o2zsrAGX613KyPVTWFwUiaRlSDIGvKv+PjGZ3d6M1t2vUw6C5VyiPKi/d2kmnn7vPM5pmkhsUkRdM/3WD8YveeVgYproY1BEQVeTKDZvnkuwyMT3btqmZFhaR4Au3U3OfsYRlvfCgaCgS00BWO0z4G5cyEW19huiFXIk8mCb0df2PbrXrarboLAx3ddrH6NtakaTKmebhYv8WlpUXiei2VDLGYYHy4zOKwZ6quwdROs32DYurWEP9xDPMyiQlUfnXbyCJtjapRseSKD03MN236ZZ9e9XhSMnCQoDLOodRnnLL6Ellh0vBS9HK8MP8R48QFiFRen2uXQH23sjrzpmRLjIyEGTSy1Bac6kApAcXM1VZGJbskAQRjQ2wexIAvG8My66Nxl6wohYT1IA+B57kS82dVoD/yMwVndTRBCpQygsVwLa61BB26UlFTO0l8eJjO2a2LFtdpVGsLAQllRV99AWTgEeNrHUQ4Ki3iQrg88oXJ2wznBWDFUX3ReK4x41E7XWkPcGyLhR131lR8ShD7aaEI/BIt6TbG+oSKPPxkNouHEq4NshD495UG0X0Gb6FwO4JejgF+2sgijHPChXDaEXoCq5MjlqQ/4EqQmgoP1bqjagBdlZ1lDr2JV91G4x8AU/rjGn7OS0s4Sm59UpKrnI68njz+Yw6RD/MBipK9AZ3Ei+1WNV7tY9pex0KjFNsXO5eRMLsp6dkJMYNAE1WzBqE1eIJldQog3ieKLo8SXt+F0RKnDT+z6FQrFZcftPdgFED+2FXf31Myu8tpxCAwdFyyHDHSnNnHhKb+HNSdO8blh5r7/+DfiZQghxCGlAx/v6Vfqf1uhXf+as6xdfC+6iY1VhtAtNjY2HVYn28fG6DoGFuWewteteH4MA1i1nlGeIelG2UeJHVn0XIv2tIb5RTBRf6Ta/VClP6RSMDjlLK1lTWz+WuxsM2DR6XQwZgyBG2LKulpD1yY01cKztqZcCImrMoEJiAHGWJhA14eS93uq3U20obytQuiFxJIK40VdX0wq5PjzbJYudCllDZSjmkgToiLonc0unUZhp2DO0pCJr+EnCjPXanLZNjT5w4RMBDxaKhspuCdXv7mubn/ts3N7hkrexQ4dEnaCSgXKFWhuClFKkWgxqDYLVYjWl40ptPFxdY54aieWq1DdHqpioaN+hxjXZ1c3tLi9tJYGCDNl+rMePRt/zJnWqcQqJQK7nRFvLYuc07DtFhQhZe3RsitJYXcBfXaIMuCHaXJjoJItzAVQip6+avdNYyiFY/S6z9HuHEu8BcIgjhe6URbBJF5YodlrA2NIHmtT3OSDoT7YQbPXz5xQM14NOI6NGNzxHmhvRRnN+Jhi5EmDtSKGiQ+iWwyW1iSCFOksLD9hOSUMpm1RtC90iEnOZeOGaHmLqzd8Bo01dxE6Y6hUCqCLaCtBsQzzgWJxooaT1lFc0iIgNIoo8cA03JgqZfBblpIdfR4TdOCoJKHxcD1dDwjbukTJlCgHUTey7nIvY8EWuuIrafYG8HWR0Ezccjk4WNrHM9XMCQwhLqGJrmH0Zo0NzDs2wVhvwNxWTaZi4QchqqTwfY220wzuGmR9vJvF6WbGcwsoutDFdsLyfLBbwMC8/Frs4FQ8pyPK7DIGS0eZXb4XZXaAJrY5hDDGtkJPLTkTg8F2q138jKY50UfzgIXdsgGOfzu6evzbKsb655+mlDO0VkLmnnU5c3oVTjaGxsYzAb2ZbWx1XyUXa6M42g+jUSaRP2cDI5mt2GM7yVYDeU2tG9GVCptLaVqLLWAZhlwYza5n0RafsAT61PVoZfGMbdGcDGhzWjhrzpmMbpnINklt2MDQhoB5vqGpSWHsePSuzmvG9BjjgynKY71AEzEnRrO1kCI5EiqBYzkUCganFvw0gDH4yxTWzuj3cik6BpQJqIRZYgE4ZUi+0g0r4vVTiNu8HJUqEcQqaN0EYUCrSqINuCVYxguEc6KZFy8LKD1m0+RmKZRjmGabUuIYym4K1T7CjqEdlF/NENseRl0xl9r1HmN+retdEKBQzO8yWMUsVqlMq2XTNsfQaYOTy1FQGgePtG7HCvN4yiXvBrQTw19WYWdxC07WpsNqq+5/wIR4ZUNYhK3BTjztszJ5IiGq3hX2uI5T2MQzqIqh9QkXv3WQuBtD4UTvL+0SpCoUnhwkcfwcck9nibc2YU7Q5IvgtgK1wfqMYaA0RJC1CdIljDkVpRR+JUdncS0QUowvQ9md0fuqmhWVzSnsnKG1FTZvCogXm1CVgH5b0z7cwvgpRaxygbnlTRTzK3Aw2GWLhG9Q41lUK+BZONuS9KhdnMGJ9fZA1IXer2gqno0iINzusXnOegLixFQ7jnZpdXfRXIlRAOysDRPJbBg7Ru1DIiiV2Bh7kGVNb4HxZTSnXqYl3I2XPBXtuWwv7QDtMN+dR9yJ3pPjKUOl5LGourxYUkMFim4BbYpUvBxxA7ZXoaW0ntJ41OvFs+dgV7Mxt++A8o6NLM+movNY9XQ+Ogp0NLxc2pyAyppezJJqvcxqsXvtTwSbQm3Ir/XR/UVcLPI7DUErtIaaYl+KcimH6Q7Qy5oZsVyaGSTVZLAKFezx6gAZFQ22jupmOgkMBh1qQltjQgujq9m0oU9HcR0mPIX9IcEuIcRRzRhDafUIrW9fUp/2ePcvOWPhhXQ1L9rHMyF+bBvFF0cwxtQLvwIUX4zqhCy/7Cz4FexMb+KiY99Jy1sXNGR2jRUHaU/Oq3/ICyHEkUSVR/F6y1EGhTbgGaiWHRoY9cg4RN+EG9DGohLron2wlaWjSyDuEzdZYuU0TRWf7WOG1lDjd1robHSTZxEj1D5jVhwrMMQ2lWBRdOOhnImojzEQ4hMvuxjADj0giW2DIiT0DGaTj2dBxg1hNAow2Rbkh8eImxLNHUuJUb3pCnQ9g8tUAwYt+c3oJti2uYW2XJE5HSFF10DZwc6HuLuHosL4RN0yVmROJOsXGFhiMH4FhcLZ2ISVzaGbHazk3ChQ404EdUITRplVJrqxCv2gfkNbMZWodoxN/XWPJnuwCg7+TkPMd7DjinI1scGg6eyEuXMVqdNi+NtCKq5h1LFQy21UeoSeyiArmxbC9hJ2KUaQDHFdTSk7UcslUckShnFiWmECj76BAGUMfrMimS+R8FdD0/loPZHhEKAJ0dgGCiaPKUMpbKE9rsFy0LU6SMZQ0VE/0HI4zhwHHNNMEGYpvtyY5eyHLibQUQ8Xu3qjpE19WRpQ2kMnqvstNFRKChWPlp9JW8z1LVRPHHVcEca2s6hYYFgXyOehZ1czLfMmiiOHOkAVDHa5VnPG45y3+GSe82kdtimXFpEfSdPkeVjJeZh8CVMOUfkchU02ybGAwthC2vQwOhZl0HnxiczEGteFF1+JEbQZ0BrbaJrdfnTxOLRZxoLckxDmWBuso8IwcAJ93gDj/hrOLPdSCNP4TBodEYgRQxHg1rKSDITGq9cJCkNFDGg5tonT0yWK7R6ZikMQapQPmbLBy1mUy2lUqChmAePjeAqTUySDcYp2SzTSZOjR4vXgOR2oUKOMh8KQMlmKlTIdng/ZPrCi12diSUxQxnNDCgygjIWFhUbjqTItLdCeChg6Ns2wF3VXDAIPt1KiEhhsT+GoZpQbfZHoEMMNPQpehmSiCdtaiuMcC0BzHE5ozxDmbNo7j2V3Ndi1uCmFsouMZOYTUy3MbQopWCGxoEKn00YlDlbQBXFwcjaJSpHN24qY5QGmMpdELANUB5/QUXZVNmOIx2LUEsjKpsw8PYdF9nFsI0VHcxM2C8moEdqtKPOlexdYtf1WK29kg2NDkzfC4NYmBnxoQeOZPCZwsIvRPnRGcliLISxq1j26FVUxjJy4hPbukETgY2jGmCjrq63NYGwHFQYYNI4u0t6Uo1heQtjZQiUFfjgHq2kUL+6ihidlH2rD8e1n4To7CKoDO9QC0batKDn9WIkybbbD/HaY36LQyhC2h1gpB3Qzmixrg1doH7b4Hc6mYEpRmpfRkDC4cyvQk8SqRkQKKZfB+CgGQ9ATMBZXzEvFsEuGTqeZec2LGC8Nof2QxHiM6BRt4TntqMDFH47O7253Lsomc8OoHmHaZnDU4ri3GnbvMrT7ASk/hak0MZbOgF5Isz0PM76dODYeIbZx65lctewxywT09MLSxdBSCJjbbuP6IfGcxgClTEjCG40G6Sin6+FtZQxBWEYpsLIW1qiDbjY4Jycp5HR9JE9jDOPdhopro5QmyORpqsTJA012M83BKIVJAx4aA6HWuBWL5mZF8qQkTk+RFq+PUn4xtEPZ3kmbWUYwqbtz2RvDVDSKCuNBL2VrhAplSgNZ7Ob2+hdXTtwQJBXuvJCmMfCCMrYxJMpjtLpD5B7YgR7VmC4Hz54T1boaDanEu9k8mCGZgELFUAwGyY8qcscHNFkdaBQxR3N8YiebzVys7G5ITApwTerJPpFgqdAaQiuJGYi+CMMYTFA7t8WIEQXL7OEcsd0pVOBjdVgYraMaipZFaFWL+PlFQsfGwkGHPgbID+ewgxhOZRA4l9ciwS4hxFHN210gGC3T8pb5APRld7AjtYHPXfT113xufHkbOucTZtx6VhhAafUIiZPbaZ3fweK25XSnoqLDLRcvIv9wH/5wiW1qI397/5Ws7DqHb7zzJ7TG57wxL1AIIQ5Swe0jX4CO07vwdo2iq9lQrjOPeJBnbCwK4EQjXCmyiRPwRgs4Xpywz4WlLeT9HHFjUy4rkqGmxYfta306hxSWciiFGbLNNsGpDvYWjd+pSQBqjzo62ni05fvIAYlqf5l4HJSOgl0QZdq4RdChAivqemhrDw0UCh5NgNZRZhfBRLFhiO7LfB+8dAuD9hIGCw+QLxqW2ydR2RLCfFOvf2KFipa+NlTgMHCmxpgylrKwstWaSZUsNHXgWHH8SogqW9g7EwRvjbKCtIZYOke4NcA5ziaYExIQRMWDVVSfxSQ0wbEu8TGHQEOsEmsYpKqgx7DaW1CqA6UModJQzkNzO978JAU3R6FYZMAdJcTF2A6DQwY/p2kZd4nlotcf1xalisPISAK/dS5N4zbxMEuqs53WbJGULtGlK/T5G2nTGQA8AgJ8bBMjbyeIh7moBhEaZalqxo6FMtGgAyceD8mYZjQNMVogzFDuy4JfpKN5MbmwhFfJQdNCsKNC9QaqXS/DapBNR8uv1XsPA3wf3KxHPGZIhhZKKYJQoQxEsbnobi6wminkkrjORBebypY0znM+XdXa4y1OCls5LMlonLzDgjDEG5yDppUWZeFYFYwXYCeybOq3WDkvJEhFXfGUDWVvkGLJQ53UMJxgdFyqGKOjMCcZEg+yWMEIifE1WKXlDHvrGdZRAXBFgKkGQ5u9PjoXxGGoWjzaTLTdVjYxY/CodW00jPlbKXqnQmgIQwUndOC0RLeF5tVh6FqO5xvagzitsSZi8QQVStiFJMq3sC2PRDrGorEFjFALRGp0qKNaOUTdGBN2dNPZ7W+ky7dQxoCbx3KjrA1tJ8mUminoJEa5US01YoSEuLZDYu5CguIYdnILVAO3peII84DQUlgBOCqBCqMgZgyHUhgVvl+g52PiK8kmTmVeJyxaBMeEHqfEhrETIzwQewfOQIaTTsphx3Ng+RTbF7Ow3SbUu0h6mpaYIVVKEN+6GOvsOCZUlJ9K06T7KTflaeqzMcdr1JxqJKC6O3N5KHpxTllQnTzq0/SoRfui+cxNHsP81hw516LLOQbLHyCfB63iUL0xV6Zad6m62HiQwQTHkhiJoUyIrRKo0KCtapfAat800xuSb8qSXTYHd250LCxZWCanOzA66rrtOGASc1ClFD3PjaO0haU8ArsFf3knwTFzGS++TBcKo0w0EAFR4N8KwbEctBXDr3YL1n6IrWoDLlS7cYcBxitBS3TOj8+3CFLQWuogtjNL4YQyyZ3N+It8eiqD0LYYTEh3uY98Is/S8klYJkQp+O3mTYTHGObOhcD4FCs227fFGXbg0kVg6ZAwM0wx34KTVNiuRWglCawYKsiiYopyOcq2syzQtiZdeYljtzcRUxajJ0Bvro/xjIaWagZUGAWxADwdYuMAXvW8Fb3GWDoDceqjY+by0BELOeFEC3++Re4VTakMnusT1irzmVp2Y/Q+DN0SlmuoD7RRyYEybNmusHSlvt11LxilUMYwFvbRaUGcOJSbsXWIClU0ymx1Fal0NI6EvfAEYsk8lnZpVqO05SvMcbfTmlyCTZFgUraw8V1wNARQ1CmI+XTNg7HxnRSD5ej2AEs52Gi8uZrywnHmeBYDuV6UFceudDCU6SfWb4GBtB7ACm1y+ThzhnYznshiG00sgLznEQYtoCyGvSEcleTY8HySwRhOZpCk7zX08M6OV8hVosBvXufJlTJ0ug661EolTDJuilQGRtgZAHMgXzKU9ThOMI+ENtATkGgaJekNQavCPs5BbfOxgzS+Y2OsKESVH80SLG3GMQkIAprGNXbZjr4gmG541mlIsEsIcVQrrY6+6W2+ILqCebx7/7owAiROjAJU7rYszlujYJfRhuKLo8x9b1T28ISO0+hOR8Gu1rdFmWL5Zwb4n/5n6Wiaz4aR1fxq0w/52NmfP7QvTAghXidlcgRBC7Fl8xjftpu8ytGm5tDinIAbvAxMZHaFxoIgJKAZlQDje2TdNN3+Drp0By1eBax2nFIn9qZRmt1B4raFqyCe7CQ+pxmt++jthZO6QFshgdY4ll2tT+5hV7s9JB1FWwc0xUEVoyHHa8oVhUFhLJtYkEFVb2SMqQBW9Fhg6sPY62pmVxBGIyQapQjsVpQVR2uXeuJtrVo+EOzwsIm6M9o7NOY4t143rLoyCFxOXvhW1lW2Y+9OYOVtgkyIWqhpaYFCJsuA7scuR8EuANsrokKD5ebQcwwkDZW32ZQe0LQ6Fk3NE+uY2+azsbSV9uTZKJXAW+hjD2dZ0XUZY/EkA0texNc+2WIBbRxwHMJqlzNrMLrp6uiANsshrxzKsSUUsEnl+jBNIXZiFMsEaDRFk0GZiW53PhpTzYeIbugNiWAMk9BgKTQhtooTK3p4yTSJJFhWBaXAUS2Y0OCFLlZhlPkmSbm5lTC9A0sr6FhUDwhoraPgT7WwvVFRbZy2zc00zQ9oboFCUeN70FzrEqMt/FcSFAbaiS06htySVsJYgvnYhFrT2xfSlLRQmQpBXJE5MVpZczle37/qHa14nmZkY5SzYVlgqTkcUy5gmtrxrDgdx/WxqX8ZC61RCiZH9/adzC9olB9Ws/OoL88oCz9QDPaWaTPDuIAXaIxfrge6IKrT5IYeBogZlz61c8p7MubA8cco1vdZeNUA2Fx7PiUU3alNqCDK7AoTOTy3QG1ABFXx2bo95G3lZhYm5qGSNs1zfOYMOYzorVi4LEqeQUvYA9Vgl9IT75PowNckrQqucdEqRujahGHY0HHTDy3yXtukwR4U+hgo+AVs52SaYxYBIyQmFf72y1FdO68JrFGDFWisXHSMtlqtZCmCCUkELahmm3nHwYknVFcwlMRxFIQeqlDG6cuQfbJE87KQBcMBQesoJowRC/OgoSmeIelFNQVNYFAOhPFjCbwcQ5ldzHFaiSULDPpR0KDJjdPuO5RCKAceVmGIsdBgxkNSrkK5DgmrExUvQDmoBzj9AAIriUOUoaZqMVBHYUy1iH5vQPNYkhanAyd+Fs1rttMfq3YB9nzmd8FAKSQzH4pd1UE8FCRjLoFt4WcNJhozAJwkeApVsNCtCwitJqyWTrAUJhEnNE5U39AykBmBUEFsHioAx3EwdgIvjOq+mdBgWzGC0Iu6PVs2SocYP8p8Ck70iTfZrBw6ETfXxbDxieW70RqyfhFsQyKeINRlipTJNJ+MlxnFskOStoVbsnBL0NICJUoYVL2WHcbg+EWCcolE4KEK0ZvJsxP4VhyUYai7QO8OOGlplkTg0Wu7zKlsAVqpmAqvZreSDXLMrTQxt9mhXDaMDEMy+DlL9Ur8jihgjIkCzBWdpX/3AyzaViJ2rIOOa1Tc4HqKZDwEW+E0OzQlDW4euos7MKqZE8wcmr1+PFMLmmicgQzJ4QBTTRhSOoRCL/GwlTnZLZTNQozWGG2IkawOLlHBsmGpdSylsqLV60dpoizKaoJzxY1OK77TxosDW5ljDDHLIVkZo4k88Uovc9QOUtVgV2j8qGajo7EDC2Wif5N29GXA3G0eJ1yRo5yYSykcp8vup1D08BbYJHPN+PntVIIlZIbGieko3JP2B4mHNvZYGWMyJMI4S60lJGMWnrZIx0+LanrFDWPZtQRD48RNCL7BNh5+UE8Mx929kXDFfBwbBkd6cEyA8eLocjNuGCerNaHJUhoHVbHws2k0GssbY04hjklpWq3tOE4Z6/g4TkKhkh7kDYGKMaJ76+va5Q1yrHciJgiYszskFkbHWRhM+mJiHyTYJYQ4qhVfHMHuSJA4KfrK/Ildv9qvLowwMXpjZUuGlrdGBejdbRl0zqO5+vfxnafwbO+DeKFL8tQO7Llxdj30MkNv6eGr77idu9bdxpO775VglxDiiDN8Qi+J8ZOxVAe7ywNYKqpIZKkoUwOiYJcOwX+xiXZVwdCCmWvA9Qk2GVr9pvq8sSBLrNRBnGp/I50DO0lHcjklChg0YahxK4a16e1YBThjzslE35379RiCpRWWUlgKlAkbgl1BMDEqXkIXqAUcMuEQC2Lz8YKQoX6PSmYXhQEbVVT1wdiMBicR3VSOZxyswMWZVPhaV29kw57om39LWTg7XHDBOnYiEKWMxs4NYnW5qEK1ujwQZA267HPc8R4PubuiZYTUu3l1FPtIlOJRZ59qd70iDoHl0dassBK1ulI27a1RXbBnU6/SGh6LFw+wTvFp8RVxqwWlNPl5Hm2FGNmsIYzHmWdr9AmtqE3V7BELWhyLrLLqGVPFsIX5i/Lk1CiWacHCoqRSKHS9h55tW9jGps3MIWPFSZAEXcAYjbKjYFecZlr7K9CfxT63OmCAAosmwlSK0bExrOo2jGMRmBD8EqioDlY6BWEyqttlrCjoYtkaQrArFoOjw7S1QWc8ii+cdKwisxPyahl6aATP6cDKNLMoKDO82MZKOgyNwoZigK3inO25VBIKtzXa32HMqQdtnHgCQoXlREXoY/EoW1BZijmVDYybMbK6mcFwmBPjCh2G+EmLUraMqngTd3TV5YXGYLCJB2ls1ULMUZRdDZMCPhAFu0qBGwVlgJzOUzRTiyhbloVjQtxq1tBCexkFO442fcT7NRXfJxs8wZZtg5xQuAyrqQtVcjGtmkJK0RkHK66YOxdGRxy0ilNYkWNxv0KpKGy7ZGFAUPRI58ZpM20oE1IY3ISX72eOU8CJJ2gaipPWZRY0V1+nNoyO25OCfVGgz046VLo8KktsjvEXk16zC3u8D8tqwjghlmvRHGvFjRVJBCMsXXcf45wNQIvVSnuThfYNecehowMWnzCxTUxiYoTLhZ0+VidgDKUd1eL3/iBWSzRCZUgzMRWAraL0GteAo1AqzhznWJTfQ4sdp9mBQjWyGzc2TVhoILTaaI7lSRISd5LM9ZKEeRsdi0XdrgMvGo3TQNFZTCk+lzmM0q7ayQclrMJwVFfIxFFuDJ2KgsZWPb3UQlWHlOxq9uhoM+wKXfJhdIy2tkCiRWG7ZZyNgxRGovNQaXwRzrwyKhhDJ+fiXTqHcO1clv//7J13vB1Hfbefme2nn9t7U6+WLMlykQsu2LQYTAmhBQgECAkJafRmCCTkTSC0BAIhoRdTQ4cACTbNBtu4V/V6pdtP3915/9g97TZd2ZIl2/vwMbpn6+zs7OzOd36lw2DGFpQ90ISO70uQClnIBc+V0RpY0rkCdVOZQluJSmsJPIUuNFzlISsFlOGgVAlVGAffxctWMCsaMpWEaR0lTaQXuJ8V/SJSE6xIbuTm3M+xk5JCSaLI4fsV4l4SEV5PqQyePw1CkiwfxhFToNIkjS5kfg+CekxcVzqUsUBA5dhRkpVRSkdGKeRnKMdGatvlyTHhzWAQZDsstJUZ3wtkoe3uaQ5zE+a6GBoa6/QNPCim0PeNkz10Dy6DqHKc5C4Pt8VjfI2OKT2EFIi4DspHSqi4Cozg3aejo6ru7UrhqgKlMhw9rOgNy3R0vILhT4OrcPxDVKYgX1IIodMqshSc3ehSQwYGVAgEwhMIv4yQhNlRS8TQGJ2c5L7EHkb8OJIkpVIZ5WSoSBPr6EH8koeuNKScQlUCMbfYUcY+Ioi7NuYtgu6VJdyyS2rvYZZfAb/+/hixFsWkD4eOQr+ncLsqjLOX5FSCHi24kpS+DWG5aF6etH9P6FseJlesBHa0vtSIyTStd7pMud9HIHGnFMdKexktgTEFqRQcK8J0YT9Cy2PsFfQ43SzrzOC3r2amZKNPghFbRXL6AdxSCl3LMuod5khljPuLk9ieCR54hk/BLWFJEAOgZmDMyzPFWgbC+p8olSkdG8Xr7Amt8VzG/XFSXr3vWIxI7IqIiHhUk7/xCLGt7QghTsiFEUDvdNDSZi2bI0Du14GlWDy0FBvOrsFXHnsn7mdZ6zri53Ux+cu7ab+4h3P7r+Dg9G7+7ddv59D0HrqSA/OeJyIiIuK00OJxp72Lgf1PCOKQuIKKKmMLLQj+qnzyeoZsXuLMdKI5/SDGUVlBceckuhtMCPjUxSiUj6SCWGti3pMGSiwr/47R8igHzQxUFEdG4aAq016uzvaDT7k2hq5ZSYhA1PKKwcBW16Ecxr0SEoRWDyQvlctebzeFylF23dFHUuzhyEHFqmo0XcCyoWtY4+5KHP+e4ONdq37pKr8+qAnH2naPhrz3EGJPG3Q1ixKB551HH+0cFAeRQlLZA1O5GfZn7sB0FSUEsiQxPYuiKCClXt0TPxZcZKEoMXSwTEkqZpNQCYq6Qop87Vxj/jiuL7F8DelXMGUCoTyKqFqmrpJropSPzAIrdLinFLjvFCEpW/FFD9P+ITQSqLhAacEAPE6cMXIIfHwFpqbhJB1aRZaYtCloGWKapOTtDTIGChGcRzNg1sS5kNBW3MvB/fuIF2IgBMLXOHxA4uYq9EqFI1w8pcgXwA2z/yEVHi6prAeHoaS3U1SjGF4geiJA5jXsBVBs5AABAABJREFUTJJx26VgaRSzgpacSeYQpHZ6aHGBXlDIyXE0YZMTZfZPAQmwLRjpF/g7wwQCs7LFGQb4nuKId5gZNc2YOoY/AXtLCmJxKqKMa2sUvCLT992Ma8VobxeIUpL84Um09H7iEzGkqhAXDtggS4KDuQdJ7K2rqQrBeKGIfdQh5XXgj48SPxoIZbom8Dw9iFM3bZI4JKj4DgqJaRXJ6GAccdErQYwp3fCYIYdXnOTeyfvIH2nDHC4jigLigv5B8CYFQuiU4ibCPoYcdOCu4DnScweQrg+e4Ih/mDge+andaPkJKnlIxmyE8gOrOxk8p74HvmgeGioEXug3t3z5NmLFAsfuTCPvGKfTd3GHduLkenHaO9Asi5R/CDXuQ0yFsQIhacRxK4qpXS6uP9EUZkc1+PauSdzJTEcrjBbBTKLMGKI4gehfwfgeianl0eX9KEdAJYxBGA+sORN6F4l8iXRG0mJIdC9oAz4V4tInpoGpDdEXlyitAJXA5CanJ/GlAZbAmn4AQQdKaDxQORtDC1wxW2U7QhngHUXMHEB4nThjRq0/kfjo3hRCgSxWg/b7qJwirwq4VlCnXZ2Q6hdUDuVRbiuiQwNDINctR969H1iJNyhQCYGnfHRdcN75kgcehIMHQU0Keo0kB9UxhmK93IeOdBXauIcqSrz78+S7JhAeaNKAQvht63tBp+eWkIduw2/Zi+bEYWAd7N8NgOkFkwwFr4SpGRzeb2PmO+lIt3PQD3rESrbEMc9FeBoxEcd3cxxTR1EySXvhPmxDB3UexUIgck77gdhV1tIoYVJBozzqY+V3UkYx7k4y7k1RckeRQDIRWLkZbWCYgjxwdKaCdPXA3bbaZjwXDw9HxOjER99zBKkkvvTRJhPAJOaMh1bU0HUfNIFwtJrYpXwV5hEJLTerFqH4VMIA7uW8wlQWk6Uie0d9EuRr3o75AoE7qdBxMDFNiy6nhakZcF2Ftk8gEiJw3HZ8VEnWLEC1aQ+VEUghOeDtD4VMG3Ewhnwgh+FJEjik5D3Y0qEYN+kcGMYr5aCcQ7iCzN44JSfI0qimplAoLFvQkoQx5VFwSoisiV/0iFWWQy5MslKpWqo2949KgXSDKSlNFUiWU0hSGATZOC0gGYNCHlQFJo8FApLlCyibJISNXkriVhRSyFpogdyqlSRu2onmBpNjIraFuPcgWrmAHn4NlO0WDEYxJUFflIQeN0vcOoe07jPp7qXgK1ymKI/tD+chFOPqGFnVwVKQx98kIiIi4szEHStSun+yJkx9+55Po0uDi4d+b0n7CyGwV2cp3j1RW5a/6Qh6VwyjL0gHs7x1AwB3jf4GAH17msTRGE9OPAdN6mzvvxyA3x36xcm6rIiIiIiTgpRQEoKSKqMMhahIypSp2kL5+HhSoFd0Mnpf4IoiJMISjI3kg3hLBDPgnu3j2T4CDyFchC1IDC8nu/lKdM1AIhlfqeGHbpEQxKNBqSANeuieodAQnoDQskvzC+iHgwxqug4VN/g0lcEUef1aVBkpgyDEM4UHmAwTQx3zDrPTe4BxfwxpCMxuG7MvhkLiGwq/GuAGharGLgrLFx8OBvlZM40qzwoyBshj99NVzrAluxYfxbHyBJXpKdSxA8jQ7c6Y1Gm5LUX69gRaRSc3DZVMBb8nDMirJBgKXUjsuMYzNqxjzXCm6TzTaooJbwKpNDRVRBcW/T1B3ee08B4IA99X6H4BoftMq2nuK+7EVwoNg6Tswh5TWCaoTKJWd1JIPFEOrBaw8c0uBtp3kCaLgUmrsRzXDGb+NVUgVt4JykXHRKgK8dJu1pnrMDULWVG03DtD5oF+Kq5kebyfUkngl1w8X1EqBZU7ORWc3POCOlAycHfaV9lPIg5xvSsYbNo+7khgCeM+CMmEz/A5Dn3bJEhBKQ3llZIufzWpW8dpvdNF3z2GsXuMB3b6VBLBeZYvg0SrqMdkCwdzQoImTExDw8dnj7+LMXUsKJMPCsmkk2I6NYRvCrxWnxzHyFdGmS4exHPuIJfcQzl2P/iSlJ+mS/VgSkHZLbPz0E2BO1n4n/CgUC6DJ9B9jRg2eAIPJxB4vdDiwxX0qM5gP19D+gLN13GnFJ4XuOhJR6Kk4p6pe5Ezh6B4G633jeFVfNAFWthchdCZ7g+yuxkiEBc6zXaWOb2BmOxDhSJHSkEweU34TKkpbC9O58jlTK2O4WeCiTrPB9ealXBHSEwtCBadtVvQTQOMOBXKJD1B+t4EsSMTGAmbgdR5dGk9QRymoQQQWLUcOKCYngnbozZr6GnV452KqaPII3cEwrSmo1I9+B1rIZUFIdGcTCDA2sF9Vwc8/HsrKCSWY4T33kcVBaoQuJrhK6qaqxKSQ2Oh2V448C9prfgYoIeDfr9M9eHxVL1PaHXbWOYMsqVtLVbpMJpQTKoJlDDQ/Qqp4gNohTG0vIdtQzrpUz7sUxA5XFvSkgXHEbhrNSpeKUi00aMhOjWsjUncQYm7XOKuC0U6P+i3TFOwcqWHL0p0aAN0Oa2sSgwR1x20iZ1oJdDGK5iGje97FI9OI6YUmtRrLplKaigni2/YKKcFT3hoQiLjBtIMhJA2lcFXMDpRxqsYTM14pEQCJTK0a2dhYKHMCnlVxvQd1ugbWKGtZkRbzgp9I7rQA9dSpXBdhUOcOHF8YdLal0XTgwyqvmvijwcZ98r9geWTUw5FFVOg6wLHEZyX3RA2GMWg1odVtpBI2mQHqlSgoILJgpgXJDnxTYWHjywF7Vfg03a7S6V4FCUBPQy43+LhxTx806ck8hRVEcsO+k3w0cr7QHkIT6Fm0uTyoEQRw5+hpGWY8icD12xfIdCQymfQ7iFuJtFCDUmOKoQfiGi5wSLlxkDuPiAU5V6XSm8wkxE/YiKPxHFbUsyszJMbKuL7Pnk3h8gITGsFLXoLWhjrUs8bCN/Dd0uomenAcrqll5gDrbKN7NA6ekvrWdl1Acnzz0f0BmKrKIchARrELrnORJkC4QYWzeniPXT8/KcMeh6tqptuo5uBWA/LU910tfbSqXXX/htmgOG7B0laG5BChs+4CPqRnjR+2oG1FrJPoISG1NvppJ8Bb7B2jLSzg+X6StbYK1lprGSZvpIerZfWZD8dg+ciZYKElUVJsMd9NGHRmvFIxEHIhkm4RYjEroiIiEct+ZuCF2RsaweFSo7v3Ps5Lh7+PVpiS1P7AewNLRRuO4Zf9FBKkfvVYeKhpRhAT3KInuQQN+z6Ebffofi2fycAHb/czl13K1rNYRJmmrtGf3vyLzAiIiJiFp/97Ge59NJL2bBhA9dccw033XTTotv7wuLB8i/xdYV0BR10YR4KMkv5eFhxge7qgETXAksOTWooQ5EfKdeOM706TynhIn0PqQXLRadJygxUJ4HAswUTqXEqVEBAoQi3Hd3HgekpvDBTmJI60hckzQxCQKx8AD8Mtq7rQaB8AA8HJHTqqxmSI6RFthYg1z56B2o8iUBnWk2gUMyoGfy2CvqmOFq7xky/ory8wIPn3c7o8iPgllGh+CIQ2AMahq2x/YLVtBsZxDxil+YI/IKiFmUYOFw6xrFf3wpAobtEobeE6hQIH5yCTWEGpgqKXid4D7kihtQC1zVpgq4JpNEgKASx+FEKJsrTmG5gTbK1NxjsFYcUuRWFQHSIaWxclUfoiqKfJxz7IBAYxIkd8dEN8IZ68EUgUEgkhj+Jpgr4SJTZhpBaIBqoehsBmC78lr2ln2J4M6STJoY7DSjM/xvj7MlzOOuuteiTJVrNtXS1PhHLSlMqgiXLIHxybpGZGZ9DwasZzwsHV5qqlRMgpfUFyy0b9Po6IaClLY7jCBJx8CgjO2OIVSMUR1qYHNbIb2yneM4gU1vaKaclw4PBxNW+0oN4YQBrTQaDubTexarYU7AMDZd6W4YgxhsI9sQtJg0TU0tw9lnr6N78RDq2PJ11T3oaG552MWddPMj6qzbTvm413WsGMFY7eOvLTK6YJrc6z8zKPBOrBUfXxplZmae8Lk9+dR6xWiO11WRmZZ7RtSkOnr2M0kqX3Oo8cpOLsyrGzMoCU6vKiNVJCuvaubNfciAMASZNQEL57AKVdQUcaWG6RwOXOQOMMCuaRKcS+upa4hgKSVZL4+g2mhQkExBPgBtmqnMHAnEoprWRTw0wHRthKicYG1cUi+CZzRaOCkGn1s06fT0xM4Fmm6AZuAJsvxBYxSiFnJrEM3QcR9LZBbHkYXQvT6EQWKrNhGKXrs9y65QalfazqYz7TQGwG5VuaUhWroC1mzJ4HevBEIFVlBlYBSpTR8ng+TXbJd6UQL+/grq9AnkVhgkPjnm4MsCMNdh0HqkJpts3gQJJpZbdz6feJ6i9LmOjBrbukBGtCOEzKkq4MoYQ4XBaSWTBI54sIYCJAzkKiRzxeCe6Di2xDrp7hik6JTzDQEgRZN8zc3jLNbxBjepDXdlsYK3MAPDLvT9ESgA9yAYZBu9WCpJ7K4jDZZyuFH5SMLN7FFEBvV9DeKGbbbwNzAReqheV7MITPprQ0DIWy3rb6em06NYCC9mKC5NjJsLWEIMaua4UvttBrz6EryuUBmbFxFcCYa4jpfeTlMlaeXDLeK5CShhItyItiaELNA0qrssDeyxyeUAT5Ptb8JIezngBfaYuwKRlAkPqDMghhs31mO4k2464DMoRUiJFx64kg/oKDqcuxPQspCpT7ChTaXVRQqPSWQ5cjr08lfIR9s7cjzESx+svUV5VYHplATfpM84ouzI7oV0hBegqj+5PY7nHoKIQXlDPevkwUnhMayZH1ShH/SATpZISgY8Q4Cd70Fp6qi0K4QnSaYXbYJEGgaAslUuxvYzXEbyP7FEdX5kcHTiPo61DVDIu+YEi+f4iZqtD0egM2mbYjLWwvfm5w6jDO1FSw4plMTSNEWsIPX0NR5LncyR1AWZ3mone9RSN+rhICR0/bSFGDIQdJoNRKnB1D5Vh6YeJGcJz6rZAjDS/I7MJn4LZTVFvC/aRMJ0T7N1LrbCa7UOrpNKRoWtrCuH5qGMeXqyLSXsVvhscUwGFQpBkpq0NLjhf0NUpWJFYTp82SFuyA0drY8VIGkvNYFg6M84QSyESuyIiIh615G48grAkzoZWfnj/l8mVp3jG2j8CQEwfRL/ja5j/+49Y3/1brG//JeaP34Xx208hD98RmHUDyQt7UEWP/E1HKN45TuVAnsQlvfi+4rbbFR/+V8X0vVfw6z3/xytfk+MLR77IaHqSiS+5vPyViqc9XaBPbuI3u35bm1WOiIiIOBV85zvf4T3veQ+vetWr+PrXv86WLVt4+ctfzoEDB+bdflNsExljBEdm6bA6WJFbRe/ubqwDHtZRAzvuYVpl9LJGa6tEN4LBbVWUKKXqAoEnbSq6QlChLVFk2QjELJ9MIZgAkOEH+KGePZRiZazOQHgYyxUZ84/iU824KOny2xlsX4awAgtaP+w6ExsMaA8GGLnESg5mL6Nir0QKSUw4tTDa9pQiKbtI2VlAYGBQocw9lfv41dHfkBdjFDoEdoePFIJ99gHM9t2I0XvCstYH0karRFRmcHfVB1vKVKhuD6tH4iaH8MxgoKCEZKIyhXbIQAioZFxK7RUSa11iDsT2BZnxfEAKG8d6BQXSCARx6SCM4LxC1gcNXVYbBNnZycpWdD9PunAXXU4b59rbWW+dxdmpbUhhgy6xZR6pKcpGexDUGhBCQ/qKjOhDH5BIR+ILg+mVedw11HQDodzA4kMIKPhQDV4eBpf2VBElwRExLJEmnVKkkoH1UHnXNFWfSl+L44skebuffAFiegUhYXdpH/myWxtdlNxgwKQkuDKw9Nm8ejlK2mjtI6h4BiWr1lgCFNh20CYGB2D1apdMRsdvjeN3JCm2StqHFBWKqHBWf0v/jrB9+uyP76WyScPUgnrp7ghFIFOjUgtCHeC6YGpJpioHma4cxZRJfBVYN6B84lO7yFppWs00rXY7Ca2jNglmSI0y5Vq7zZu9+GH60YJbRinQpYYlDdrbIJmU+JpNp+wK23vVfUqDwD4E0/fJln6Hmw1UofASSCfjdCVaSGY9Uqng6czFBxDJIAO1QODqFh0dEieh8DQH0TC6s8zAgrLYGvqkZioIzSChdeBbBmiCY8cUBa2do60dIKFFX95QUwIpNRwRQwgwQssvJXUMZM3i01wOvmlgbI6RPt8kUdkTBh2vo2lgWHOHnrnfjDN9SwVVbvyGahbFLEsEbpm6EQzO+3TkCgO5wqCytr3mfhlbqRPfFGPZJQa4YSyy8BhKSBAyCMINQeyv0MJ031iMXL5q2RU8kGIew5G77wljiQkfhUQha/2lUhrgs7t4G96aMuXhAvnhIhl9IMi4qBRxM0nhbEX+rCAu7P7Mb7jl4PVzzuO2+DhrAgGq5BbRNPBdLbD8Ezrj44qpadD8EpomsZMGyoHR3EFUqyQhHggyMGoGhJY8vvIxh+qWXdZIiu6r13LW0GZivkWSJAYmCS0F+PStU6zZaLLlqUkYdPBafZSmMDwTcV+ZfMWicsxE3jGFYyn8MY/y/aO4nkKT0NarkXhSGLBfQMl18UOXWaVJpuwBqiHtWvbU3YE1paFJ0IWOsIfRvRwi59eEl55jPSRkPxUthS+yWNj4usb+VWtRl0pat1ZIOzna7SPImSMUpnYFiTd6i+hGYPUFPlIDN+5TLpdAgJsIWkrGl7SWFUIm8IWNUhV8aeDKFCCYZooKFYRscPnVdDKtBul08EymHYFuBuJgUa/HEPZCSzQXFyMpKQ8XMSwNXylUzKCCR1zEKbe4lFtderV+fGEEVodVAUnouAMl1MwoM4U8ymmhKzPMedmzcCyNmRko61mU0DEtyCWGycX6m9qX1tZK20jgcqhkMNGgqcDKb8YaZDy2gbKWDkIQZIcQTpbZJC3F8pUGg8NBPfg+TIdh2pSsZ9IVUjCxbjvJdRlUv43o1em8vANPi6FiYb9ZqB/XtkAzg/2TqUDQzvS1khrajL78aFB+PArDe+aUaT6imF0RERGPWmZuOEhsczvClHz9rk+wum0z648dxPjh76Md+h0ASrdRiQ6QBiJ/NEgpDygrhbv8clj+DIQpmfrhXkRMByn44q4efvAcxehRME0YOefJTOv/zoqXPZe9hZuoXP4M1n7tEH//VwVu2unwvb2bGbc+xKtfm+c1r4yxevXcgLQRERERD5dPfvKTPPOZz+TZz342AG9605u4/vrr+fznP89f/dVfzdneETE67Y140iFWmCQnfJjykYaGcuNkW12SUpI1NdJpk+kJKE771aTseLjoaPiWT0UmMOLjZOUx4mYvsZigzyxzeCI4l0QilEe2V9GyfIaC20H5zhnKlWrMrzB4eEFHVjT04T4qu3Jg7cQPB6WxriAGCHeD1ERgrRGOcGwcBrIZkipNoSA4rKVJp+MU8nksJ8fh3AEcS1DRTHZP/jpwx2wYVxf9cj3rnKK2TgiBmVVwrL5tZUMeGTOC7IKFLN7MIfqdLvaUj1Ha1oN29H6KJR8/9Id00hVmenoQux0EUygBkxOKSdFDq38uaTlGUo8hq1Yzet2yy5Ym2+KbGC9oJKTBhJ4lWXwQvTDAsuVBWQ8dlAhhgKahe9NBbLN2m5jRCtMHSHWkmVKKizb2cqt/AFcEGS29mI/UtNqAUqoK0neQUqJ2hr41cUKxSyJwyWpZOlSMNnM/D6Dw+8qY7Q7lKYhZZUqALy3cCjywSyOjwNYqSBEIdhU3sOqbUNMcdY/gYJG3urDyisFYmraNNls3tnLzzwSurN8ggURaMNC6giPF/RQqOYRWwdEszr1M8OMbNCjC7uL1HCzUrcMy8TiEoYmKaxS+J8k4baSsLC1DgUXED+6UeLPErkoFesxtnNsfZzpXZs/RMr7/K0DhVA5hTd6HPJqvNpK6QAIYugwSOzS4x1VdgwphNkZdaOhCw7EFui+hGAzeATzl4ZhgeQnylInHFF1xj/snodxzjPG+DMNmAQpBNjjLtPFVAUMXZFsEWruGzHQA+4Pz6wLdTqCXwRMWGC4QxF+rWiTm+lzKgzqpjMfUMo3UtIWKm1CQVDwfV48ztsJgeHcHWrEuxqb0AZBj1WpAM6v+kxINnXLapTTsYnZqePtApmykEVpQrgrivqn7g9+OE7gxKs+vxeMr753BnfSD4NhFhRCQvcTi2C8blKaw/RbvDG90c8gh1m3UefABHYXEcARm1kYUi9htHsWjPp5sPpCqulJWs4aqoE1jCmS5gidtdKk4Z7ti1w3N50KIoK1KLxTOJMmYgLSG6pGIMRdr8hB7jaPYnRWsioaGFdwKFHEjBZog508GMZecDPPh+W7T75YsVA4X2X/IpjBqoXsVCoMl5L4yRuEYyclREDkgRvea5Vhjt9buUxX/CTHsgTa4vh7XTpoawpRoOrRrgQCnDIe88knFKlgpCyctaV1tIfYH9dee1lATCs8DbW8Z6RWxdJ3cThd6H8Rzu7F00HStpjTomqRc8mpWpEKHsgualkbXZkh0dWJWCpRL0+hKDyyZJFTMDDF2AoHFz9Gjwf1SleC6ClonvVofh8wxXM3GMQ1QYKwpoz8YnIvcAZQXCPWaDKyW/HQeOQZezOPI9BF0aePHXJiGdtmFUBKhK/ACcbpktaJE6IaPG2Rc1RJQyQevKKkjfI9USmDHBDO6hlKKdFaRl/UGWyqB9F0qCKxYkrZlU2h3B2+miuXjuj4xESencoHYBIHvo1+fpNF0G78lizeV4va9eYQRQ7NsrLKJpoXu5CGWFbRZPxtDG52pt694FtMKXnq+AF2C5hfD3xZK6OTNHixdoPd3IYxelifu4t7fhgeXgsp+D9mhk4jr5I1AyPOrD1UozB1Jno8vdKTUMBwo9vYRzx1B9HZQWmeiHJNSIcvKnnF2h9qV1r28Hq/TToEoIRyBm0nhWfVAkspo7tMXIrLsioiIeFRSOZSnePsYyUt7ufXQz9kzcR/POTqG/Z2/hkqO0kV/Q+7F3yH3Z78h/9Lvk3/xt8j9yS/JveJnFJ/yT7jLL0e/7/skvvFCMqse4Ngn7+boh2/nZruPL//IZu1aeMdbBd/6uuDj7zqPJ4w8g72Fm+iI93L+a54KwPI77uEvXiN5yyu3IKTLnpnbeMWrFZ/5nML31XGuICIiImLplMtl7rjjDnbs2NG0/IILLuDmm2+edx/bFngyhqslONre17BGo6D3Md27FfvmDGlnAJFopacHYrZbs1Rw8Zhcl2NqZR5P2sykukCAlgYlDWJ6Pci6QCCUF7jveDOodDeGIWYZaAi8Ygt+eghtqBdhmCCpiQa2LWofyVKC0kRNeBNC0BdvJ50wSTpBPJqMHMMWOqZmsqlzgHO2vbDu4jPLMqSk6mLX7I/f+AYdIcMYW90VsAILGs0J3Zk8SBlhDKI+B3dVLyrjMawto1f202pm8LY7VNoGg2tEERoskdS76TV7g0FyVUNosAjQhIajWcFsvas4mtgWLM8dxDQEpinCAbYATSJ8j1Uroe+qBKQs/OwQLZ0pzj4LlKtQDdPYhgFSGFQLM9OaoNiRBTmrBoSkpLcG55UaqaRPTE4F8dY6KsQ3JUle3EvcqtDXJ4glDCouNQHI0oLAz74PFS+w7DpotiA8gY+PMCUdnRIzvG5hVkVMiXCDOrY6JanzHQzNZEPn9uBe+C4yvFubz9JYNlLTblD2Qbq7wNAaLPJq91fSkehF13R0XWBoGi4lJpx1NZebUglMLUXctolbKYQwQqsLhVRuEO8qnBwD0RS43dLqlgzBeWVtfckvgwpcjariVlVw0EKVxlM+K5bDCmMza8xtDA/6QdsPzoRvCfTQ9VP6LqZeF59sS7BinV5znVNKMRC/kPP6L8V1Bb4wqWRX4WcG8TMD+Jn+MBGEhzIEmvBQKZvyhp5w5C9RyicVJjazYxKBrFlRxrV2imYHRxNbAdClgZ8SoGloQqeUESAkxtQBjOJhhKyLzCIuESmJWGGEbQsQAm+8xPRP91N6cIr8b0drgrZfrBkhkTjbwTkrbJMZq6m5Dg0LOjvrv1s7DTQJKmOGzSo4SO+AQNcJ4jWFtQuAUQ2uFN4/Bb4wketNRJ+OQhB3wNYhmQTR2qCuyaC4MsjDCEgSCQG2IJfMo3s5pHKZyB3iUPEYXVkbKTTiseBZzzhtWLrN0dJOdqXOh5YhAFa1b2q6xpJbZN/kAzWPAcsS9GRiuB41K8Kh7HKckTRW/ghmSkOlXfy0IDvQg9TmTrr60sP1K/gtw4iezfVLsrWai9xYfDNIjVXLXRzLR2mBMN8/oNPerkgISTqpI0WQIEBqgmzax3GC5wDloyqVIL6iHlRwa6oLXWr4uHhh2ZXmMTbhkfNHKDl9YMQgFbyjdKEHMfUUlI16TDcjFr6XXPArkuXLIGcMMh5bT8Xpw1XlWt/qd1Xw212UkPjCR4VWppodtCUVnyF/vofn+BQHixTWuAzHlzMoh4M2VFFkkwoMB0/5qHQvwSxJ1X1YkDDb681K6uBkUfGOYDJBZPGFTyot6GwXxBqyAgtcXCp4yT789k7KF6aZ2AT3Fr6Lj46NQ0Um6UysDncQjadGGjFUew/TdjxInEDwroSga28cfoSXi9tdTwQB4Dot1MLntWgIAaY7HUySSDtsLzYz9jISF/QQP6cTwxSIYT14nsPnoBo8UNMIY7ZVCxn8m22zQGikUsE2BbOb/ZkrwUqhkoG18mjyfOIr1pFOB1k7zb5V9YImO2pux0J5DMZD69hUz2zjzwWJLLsiIiIelUz/eB8AyYvb+d5PX0HKFzxBZihc/R68kUuaZrMa8WOt7HaexK9mnsTNe99A6+EfcPWqr5MYNfGExgUv83jWc7YTS8ea9nvtBf/IRUNP5eyei4ibSdJPG+TYp+6h7Y/Xsq4z+Gh47qtu5r5vn8O/fUxxx53w9rcEHygRERERD5fx8XE8z6O1tbVpeVtbG6Ojo/Pu09Orc9G2bn51owcD67DUMfz7y1imgyktjFgMy7KJt/STas/izVSY6ezBtQ5jlA2EUMSNOCVVQreTuFqK8pZRWjb3k7BiOJ7PASv4mrY9m6ScwrYtfJknnogjkgZ20mfqqGQwvokZfxzbc4i3ddDS0wapSUzTCIL9WoJ0Oo5t2xTSCeI9NsVkAt12MCrBB7VtaSCCD/hYrISVO0pR+phxk2wKEu2dJLUUvb0FDk63kk4dwQgHusKV6IaGZVk4VoxY3CMWr4sIsTX96DcdRrPBt8DWbZIdLXhuFtefgbKFNqyIx+MQj5Onl56dwSB47YoRVvVuYB9T/OJbowhuxbZs4mbgkmcMr8U+ejuJrINmSxLSIBfWWyqexCKGPm2Qt4eIxdOYxTgJRxDkwQqC+5uGhdHmYMccNCHJpNK41jR4ikQsQcKO4ZZ1Llj5RLQVDjdM/huyXMYczaLph5CaiZfswR7oJpErUzAMynoWy7LQlED4STR/HMtysDCxLckwWVp7ekgn4viJVjxTg1iCpB1jZkZh2j5GxSCTsjFLOp4HrhLEHRvH0skb3WhyHCvhEFM+MWERj8fRkxkc26FigdelIccl7evTmIPrENksZlknPh7UXdJJkc1msRMm7TMJhPCxJjw6OzXa2iStre3Ejwbb6pqB6xm0ZFvIxupuN8lkEt0sAUlMOYmBgY5FKpGho8NEN3xicRdLc4hhY7hFYrEYCQMw4ohMGivmYYX3I5lq4V49j64BCkw7hvQlhmdgGhLD8EnaCVJxHatsYcs4kwWIWTFMDAzLIJlM4MSzxJRHMmUxYPUyaQ1Q0RM8YFkkEzZaxSJm2aSdJLoRCG+WaZFuzSKsGCXLorPTopAdorvPorJvnGnLoqc3hWGkiXuHcb0KlpPFtnyUZZFwTNpS2yjFgzpzfAd3hYY54mJNKbKtCUrHLDTdQFbAsh2KXRvQdI3WVhN0G3NHinxqGieXAM3GsHwSsQRdpdtItqZJ9qxA7M1hVkJzSQvcXeA4kpjjYBxWKNdAO+wh43FURYMpC6kERhzi8RjxziRyZBB3WRd6yuLIPXVhPdURx8xqTEwEAnW6o5ftfz7I2C1HaW3NhwHti3ixEkKUMB0NVfAw7eC7TosJjJwBhsAyrUCrSGSwSja+VQHTwjiUJ7YiieraQtEUJKaqllKgGTqmoaNhAQLHsilbFgNt3UwZd6MbBnr4fHc5GZ60dZCdBYve9DCWbjNcXsld9+xCOhrJVIKLV1yJpTvsy9/X1H/vLzzAyr71xONxlrWtoz82xt7D+ziYczDEDHYsSTYh6F6uYScky/pS7F9dYrB3GYd3xtANAzQdUe2jEzY3j/4vsdZeMt3LyGaCZ8Rc7jF9r0Wm5JOTFqbj0JoIMrmK1nZENos/liaZNmGDIHEgRsX0ce7Ok0zbtKdsiNt4iSyGcQDNr+A4JrGEydNWPBn98vM48q4vMONK8irHWHEX+WQRYWwhpieRdhFncwL7qIHv9eEcVDi9DroOZqoDwwj66VhaxzBcSiWwpE1rS5pDlkKzEzjxNL4taO9IYo1ZxHSbQtJDzsTw5AyT5T1YlgVtI6TzB0laecrCwZhyMWyBaIkTz8cRZk3BJh6X9Lb1kF0/ROFAG6Y5Q6loIHyFpltYsThGwcC2JDKZDmIhJlbiVQaxCzej2Q8G/WvSBtdkoqtALhtDd/oQpcPEU2lioXvginbFbbe7gEUpfQ4rrVWsWm6w564DaFY3hnYjyjDwhIGTbGFkoIcjt90XXBNgJy3iTieG20l8Jl5rQx0dBvF4BeJxODeGtncMcXQGJ9lCZsUmRo/eBZ2Q6rfxfl7E1bKYTrw2q2AZkmw2KKOfSWF3lZlIbiGx53psR6Il0gjDImcblIpgOzGsoo+eiHP+FRmSCUGhoLAsQbGogrIA2axR+xsgnc6SWBmnTIxYewsAlfgRdF1haA5W5TCJuEN/Nku5Yy17pEcq2yzgLUQkdkVERDwqmfqffZh9NvzqJfyv9wBPb92G97SvNLmHVDlwUPHb38Jvb1b89pbADBpgaDBG9zlPZ/ycp3P2n/2G+G8/ir77k/hf+A8q57yCyoZngx68SGJGgguHnlI7ZudfnMXkt3Zz5AO30fP2bXQnB3lw8re8/S2Cjevh/R9U/PXrFH//dxCPR4JXRETEyaEaN6iKUmrOsirasgvRytPkcoqKWyLmlVGVChXhIUdLjN29j1KpRLlYpqBVqORyOAnF3tR6EoWbibWkyLursQ6XIJHg8GSeg9ZKZlpWYh+5F1GeoVQKPoorfgXN202p2Iahl/BzORy3grShnyEmy+C6FfyZCuWOCpNTk+SLJTyvgr/GoGy2k88fo1gUTK1px+mA3KE8drGM41SwbSh6BmZL6EZRmKBcLpNOKZw2j+K0wp+aIm/mUarI+evb2L27TCn0epioTKCXY5T0EiVVQCtVULl6TLK8bMOtVPDcMl6pjEqOMNVzLirtkX+giO6XKKTz+Lkg0HehqCiFmb+mUstgOk8sXSCR9MkXPaamyuScYNv8yn7kRV0UD/0IcjCju5RKQZyYjB+nouXZY16Bjwm5HPmyTy5XrJWtorVyZG0Ju/cQ3959CxtSK5jQdIrFEu1aF7gaU8cmUWUfx7XRSwauWySDSf+gz8zeClMTOrpKs2FlnPzPxzlirAksMUolPFWh4vqYvk+BFDk9i1M6QttAgpinMzUxhidbKE4FbmRlt8DMTDAecmIenlvBUx6upziSP4Rb1Cm6KbyJOLazkXF2Ui6XcYVGLpcjr01TKpWDuFd+CVaVyK+6jLwQMD5OyS2QC+tZ9yzGx8cpeyVyuRy2rWhrBduBXE4wNTFV21YKia98JienoFQf4pSLhaC+CTKSmVQwKllyuRwzMzmmpyGXL5KTBQqVGagcQ/l5crngufImp8kXKxjhMeyKCtpyGM+5WHLRlI/tViiWoFwW+NKjXChRKpUoiiKQwCv7VESFmUKOXC5HrpxF94rkZ4oUK5N0+t0cq5TpHiyBG7SRkipREWXKVhY5c4SKVmF6ZhpVdCmWSsRjJc7bnmOSFOlVy+g74FEu5ymXoT++Al95OP1xfn30VkqixLDeRWLQ5KDMsWcvFEslaDPJtBXJCEgYRabLOmXhoruKcrFMqVBg7TpBsZin4pXJlfIUEmX8HBQ9iV4uUcwXKRR0ioUZZvIG+ZKLqtT9qfxKBdeFfKFIYd9RVNGDXLjSLSNLJSiBFhfkcgrlG3jjodvieL52jwFMr0AlJ2p9z8RMEQSkWyrkcjl8LY/M5SiWKlQqHkL5qIpXawPS8rArFRCCkgqWFfN58mWFrJQpVzTU7lEOy6MUKooZy8KqBANzTVp4nocrXUqexDZc3HKZslUmPjZKj99N0hSUOcZYZRLbMJiYyZM1ushPF8hTwC345IqTPFj6OasKJUq5CiUqTddYZc/hneRyObyEolyaIpkqcZ8foyjasL0ieiVP0QUvJ+h1VtCW6WN6cppSqYBbqQQWUmFfU5qaoOQGwZFy03nGVVC/fhb8pEuy7BNTJZQo4k1NkdMEbsWA8XG0XJ5isQgx0PsE3s4KJb9MzPYolkr4FYdpP8HkVAm/UEKLQ7HkgcygJibwPZ+p4iGmlEfcdyk7RQqlPG7BotseYdWyQe7UbqS8V6eYb2fqwCSVike+WOSYNoRQLq1dHRTvuBXNL1KcKTM9M0l+OodZKlF2FWtWTpIv57F9g3X2Mq5PHkA/EkNNj3PPvpvxSyX8Yolkm4FTEcxMV9BcjVK5iDIUnqlQlboAo6SgvM5ktG0/h+48QqyUouIpNOWC61FwXYxKhVIJVKHeXyvlU5ooonklyqUSermMrFTIpwvMOBUEnSirnVQhj/Lr7+54TDE2DnkcpF+iUCwx5qbAzZEuV/CES9nQKRlx4lqy1p5bY52k9DYmW4aYmFTkDtfb0fQ0lIrVhByg2QKjVCJfzFFp6aVUuiV4BB1wMz4zUwmKfqWWudH3g4k2AG16hlKpxLQJVrlCuQSqWIKyi+cFfV8lX6BUCvpcz51gKszMW6lAoaCoNvGpKWhs7pPTM2iFPMpQtfPlcjkqFUVel9huifzMNLmZoxQrHiVKWN7SZKxI7IqIiHjU4Rddcj/bT8e6W/hBeTcVHa665P81CV27dit++CPF//wY9gVhLWhthbM3w9mbBFu3QHd34wBxG8XhbcgDN2P+/ANYP/k7jBs/QXn7K3DXXwNas4hmLUvT8twVjH36HtpesprV7Wdzx+FfI4TgmddAJgvv/LtA8PrnfwxSTkdEREQ8VLLZLJqmcbSq1occO3aMtra2efcR8VYoBx+OClFz5xFCw9cF3p2BtYgUGtIKrJRMU7DpLMlv7hD4msAVSeKZp7K8fT+HJ3+NNAyEVo+ltWZN4E3wqzuqvksKEJiOjTtQItMVh0N1jwOtBHrMQCYM9FYb+YRL2JdL4pQP0hg4S9MIfDJE4ALitEnKseX42SLa2AMQZlZ0YgLDEJTaV4EQtMY6OTC1m5iZaKqLYqpEqs+C8aork4G74mL0+36IMhNBenqoBUwXuglSQ0tq2CsyWJVDiPCcumYwklkJ3Ntc36bEMCQyJxivdCJC1xXdEOgZCw6FG4auNstj/UghsazACrgQjrMa40MBQTB66xC+5UMZ9hQOUfH7SG7ronv/MMKQeOOhmBPe4/OHnoJ57H4KVhLDFLS1C9bsWEGmX+eQbE4/L0J/rpw5APE1eEUXI3OU2LKwHMpDG7sfKnmUncHc2kP5rjJyuoic0pBITEsjn3eZFkc44o4jxXoSsgVDa0FoOwNXzNC1RWgCiaj75QSNsvanbCxb1QUwrBOB4JK15/G7g78I19f3q7kxzrLs1hrcNlcuc+ifHqJ8pJv94f5SKBSC8QnQYmXOXnEU3UpCeaZaQU1ujDHNJu8M4hR2A5DJCCYn6hnyTEykFDWX2qrbqI6OALywzK5w0EQBITw0EcRoajeSbFqznZmjd3HP0fvQhBa4sIbX5OwQQaB1wGyVlPINddA6jGbsbLhuHQ0d2ZFEGw0G44ZSZFoNJgrVSxNksi5SCjraQT8YBlwX1foPXItbQkM5vZpcQdcxhUlFpvD9EjNT4TWqMkLTqYeFr+NlhhDxdCB0NdIYu63656xnIHl5H9M/Ciz6RUsbFIK+oqI1POfVnatlbPBe7OiAsXBwrapujLMC0PvCCDLSCr3uMiZA2Q1Z6ISorevuMRCaiYr3UJbgupOYa4p0D2VIpVrwEx34nesCF70GYkYCAbiqhKHrgWuomlWYkGo7NzULlIfUBQqNotGFJfY0XbY+tguR7g+rT6AMVXNh1qVBzq1HAdcaXKmlrRHfMcjEtw/Q1yFoXVlGCIGKtYIRdGIqTIYBYAgZ1IEALexvfKcNJXSOTRhoooJpgTd8ASpMpJC0MhxiGk3GmFlWwLCr1Slx9CROIklFBZMPhm5TPhK+tzSBG95jLePgt9poo0VUKUgsIMLsgX19NhVvEgW0GGmM9AB+tw8786iYT0LoTFUry0pCZQrNm8ESKSp+EQ8DYQpYY6DuqtDWBtIH2WWRL88w47lUvCmM0KUQJITuzLVF1K9JuOC19OF16vgxgXFPfWJFCh1PlZk9R1V7R4YJQxo8tKHDQEwBTgvC0InF6u6dXckBTNOZu0/42zDBLwXCldeeRElJvCuGlALTgHIFCtl1ePJeSloWZWg1sUs1X1iwDIknbIQsoXUmcEcL6BkNNIUvJOAHLvezrk9veKRne9LXGvGsRF+aBip0SxZ4SLdI3MoSUzmG2lazFKKYXREREY86Cl/9AX5RkVk3zn+3ZFnXcQ6D2cDH+7bbFa/9a58X/KHiM5+F3l74q9cKPvcpwdevE7ztzZKnPVXMErrq+D2bKT7rk+Sf/V+odC/2/7yD2CefhH7bdbXBVZWOv9gIuuTQP93CmvazOZLbz1j+CACXPUHwjrcK7rgT3vRWRbms5jtdRERExJIwTZN169Zxww3NEZN//vOfs3nz5gX2CtiyOcxEVvvYlBxbo+G2+XgdAk3o6J31oCKOE4pnRhw/HLTqJgz2Q1819FcY8Fe2DkH/2czYK8IjB8FFTM1i07oVbOhYFu6ggig3lUDsElKQ2NFNaiTIGjXb9Vxr+Gm0ykAokTrEMsGooKE/VrFsbVC2sm0T5/Rfhi4Dq1w/DABd8IrQ4QUfvkpVo23jda7H6z+nXjfVV0PDYNA+exCjRdY+xC8aeiq9qRVz6lkYEsOEGW2ESWddbXn1I19ZyeDDXVTjkNUvMpZoCCRdjWtjxvF6tzAVW4dEQxrBIGjcTjBdmsBocUhd1oeWrk/GCCMUQtrXwPLL0ZxMOKgXaLaB0CTOtk78TP1+V8UuBEhpYDjNKebxXMTYAwhLR185jJ618DrCIE+ahhQSzdDJpKC9M8xSJ8N4YYBtpZrurzAkQoim629Eb8hWWQvK3BDkuS3WxWBmZZiNs041vlGjWAYQd3QyaVixHJ4wdCmrO7ro7VH09oTlkdTKFyvtRVNlvK4NDUcQKFEvU5udYSBzNWOxTYw7G4LBbnhOpaCQ3MRY+2Xo4bK2NknMkehhI6uKXb40wtpXmF4grBm6T8JM0el00ud0siIxGIh3VdFPD//WdBIbDTJPml/obkQmU2h+CeFXiGkWSur09EBfL6xZJenqahBapKCvR9SSGtQWNyR0WNG2gY0Dz+ZI8nzKepZSOUiiEOzugpBN4mDtGOlWzJFUWKbGNtbQNjSBstN4PZua9tUaXI79/m24yy5lf+aJHE42xjAMj1MVIaSgpQXa2glcTkNWrQt/zPos84WJkOALrak7Uo0KgoQgsptAZDWkEGhOK5odxJISpkCrhq8Q2hyhCyBmJlm+HHp7qIlcswXaRmwjRtLKgPIRUtTamlBeuG94Or+Ctu+mYJmUVLbk8MJEgKlYS9MxtVnPiN+zGbnm7OCHbgax1QbOrdeBXRdXrJSO6wb1oGtBGYQZXGfe7KGtNcwcKOuNqPtp20jrA7Qba2mPbUJv7yOThtyKJP5gFmlqlMIA6WaYsbG9DVauahDBDYkajiFSElUIPPPwFH29MDxiU/bC7K9DO/B7NqESgtL5Ok6/jjdVTebgoKzgWoSVwBI2mgSfsH1JwYSzFscJ7nG1L5IaeKqEaFBp/GqfNN+9EwJlmaCZqJTA6y/XXy3z2Bpt7qm345rYpcNF1cXtBrLfD+LvGRpxM9l8ujBmlz7r0EKAEeTeqt0OvzVem3iohgN000Mci50dXItRbxv+LA22O2xPU/ZK/PbVSCfM9LnxPI5u3EE5dKvEn2tx3iTEzR6CzWqPVaSsi/tG2EdqZpzh7GocJzHvPnOOsaStIiIiIs4QtPt/RO7zP0CaLode+Wp2Tt3PE0auZmJCce27fF71p4oHHoBXv0rw9a8I/um9kmdcLRgYEAu6+syH338Ohed8msIzP4GKt2P/8C3E/vMp6Hd8HcIsOUZnjPY/Xsvk13eyejoY2Nw1+tvaMS6+SPCG1wl+fSO8959U7UM8IiIi4qHwkpe8hOuuu47rrruOBx54gHe/+90cPHiQ5z73uYvu19IiCGaig98CDd8SlDZI3A06macOoXc0pH53Mqxd/wJUshM//NoVCFIpgaEHfakIxS4/1YNK9SBk9SPXo9fpYqRlLVkzhS51hp9ssvYJeRTBrLAeqw9cq6JWNaB8tZuufhgXtwwQX6/Xv1h1E2T9/EDtKz4wQpEkzBR2ZhA/0YFKB+pc2a+wt3QYP+y/qx/7KjsIhoMMR8Oi6lbSILigmSgzxqruc+nPLG8qdyN61kJLa0z3mQihkQy/xavZsbyhC/FWXImqBmpv2Hf1KklrC2zdAkPLqgMvA5Xs4uyzLTJpDWnbeB1rUMlgxBELg+Y3vdv0mioBRgxTs8ENzDA0Kziu2R2jvLy9tkvj/stGdDLZYDs/O4QyYuCVgkxjT99A/ILh+qBFiHAQJVBSQ9cF0vBrV6aFAtFI5hxWt51dP58WBEGfo6g0lKc6+BcNQ5WWWDtrO7YE5Wxdx9beS+bdf7ZwkHIy9PcJbEsEVolAawusXRO2G6glQrD8qWCgrjcERReCLVt0RoZh3dpgUdJsCwa0Uq8NIlMijefDjHLBsNFCaxhNk/zBjmeEohX0xvtrh1ZItPIE1kxgkWVqFXBLSGBVYghLGmhC1h4MTUiQOirRhdexFtXeEMx5FtaKNLEt7SgzgYNNW/FB4roDmoWuC9asFsRjGqrBxEnokEqxaPDn/vRyEmaKsl4VG/3afZKCWoD4RsQag/QT+9Bb7TnrqkqaFhfIDWfjDe1orv8522tgOIEA2TBIVjVFLnyWjSCMRGaoB9E+Ui+LGa7vbG4nvjBBgAot8CAY+6sGpay7OxBqq+Jnd3IAS3cwDDcUgBqMFBcQsGJGgrXda2nJiqbvwnMHrpizbVeyn3P7rwhEF6UQEmKxqlhqMdAfCkvVU5amkAduRiBQpomfDhRd24w3HVfOc4/i53aSvLwPb+QJuCue2LROxVrZmlnH8vgAZquGviIQahN2mHnTtMPjCuzQk0E0lMt2dBJaJ5rQ8dNXomLtgVgVM1H9mWCjsEim7oT7CAYG63UoDYFUFYgJNE/DmMixfVWJbIvAMuvtStPtet0nDHSpUfFdfCcbTmII6N2E1TPMQKdFOgPpFg1NgxUrYfVqid+2Er9jbT3hSaOpX71Wgv+fJ6OmRCKq8zHhvr291UPocw5l67HaM1ftN3W9HvdXCQ2hBVbT0pC1Pi5lZas7hdc+qxwyTFSiwY4LoJZMNTzXZNelHEhfFvTpYVtUjWKX0dxO2trCtmdZwXszEZY16VAy2sh1ZkEK/PjckDKN7VTO7l/mmHrVqVp26aHYVe0bZMxYaJcmIjfGiIiIRw3aruuxvvWXjO9+NYlLBvj5+C8BSExcxQveqJiZgZe+WPC851LLbPSwEAJv8HwKA+eh7foZ5s8/iP39N+D/+qOUz38N7sqraH3pakY/cjupH+kYfSZ3j/6WCwavqh3iSVcKjhyBf/+EYmQYnrf4mDQiIiJiQZ785CczPj7ORz7yEY4cOcLKlSv52Mc+Rm/1K3oRlAgHadQFBFeVQYBumgghMIeSmL1hUHW7hcCCIbQ8mD36rQ7S9GoQ9fCjF5/VqRV48W4gyBKZapO400cIUkqBkawPZGtWT2GZtOpHe/Ub29CQmqiPGzUt+DB26/GAxDwfyjEzybZ1L+LXe/+nvlBCLc3XrH1aEt0UrBbGGSNP1RWrjjfyBLqAruqh5pvM1yXigjgrpnKc1SPJWnDr76C7u7pBOHCpuWXU6zQWl5wdGujJvAFT1KzLWlsla1ZpHJgSOE4r6zq3cTR3kIHMSgDc8Xq8GKE33ycp9SDjoQDdserFaFDrSht7cW+/Cb2gSCd0GPPw+s/B7+xByx9DuOHxw3I3ztD7sTZkJQ/SBPIgvVpGLlOE7kdSoulmXQTQwsmnReZ/dGlQ9kpNwtWm7h0L79DA7Imt2oAQ5oQkgGAwWBVba/dVNJshOAkDrSH+pqZBTLaS94/V2mZGZMn7RTqNjWgaiOFL8L3A2tvUgrrfZm9jZHg7/sxh8mYvduVYIO6F7maGAXJiD/LovQ1nr7sxSmRwH4RAtQwvWg96i43RFSM/KVmjrwcIxN90vb8QQuCrBrdCIWrWdI3MXtSolQhAQ+JJGymLTSJP97phDt6xExwNM64hnVBUHElRuDVwRYxtaSe2Kqgfd/XAgteT2NGN0BceFFfVAhU+u/aghmobQt+4Fkb3UW1w0pDIs4PzdTtwMHQvVkJiWoGAadn1IypNQ7RqqGMe0gjO4REI8JYMhBldc6mGKKvV1TyCUpX+9AoeHLuryUKnKl5XCcTdrbXfXu8W/In7GF5mUiyBds7ZJGJHcTMD6Hd/u7adnDoQ9PXxdsS0DvjIWR3WfJOvQpeB5eB8WEniq55K8ui9kDvK8AbBmCfQQstfYdhAGa3RhE42Py+1xZqGrmm4vhdMvPjN22sNEw1Ck/T3BjH6DEuycU0J0asxsdehdHOQmMUaTlHSGjIDh+3v3IEr8H2PB3yNopdDGQ2CmGGDLNM2sprD+w+ztmsty5xRlAs5TYIWD+qjatFZtZ6rWcEKKlqKvNmNynQ0VZWM6UEfFMaUq2VwFAIpGyy7Gp4prSlD71w3RlfGsfXgWFXr3a1XXkX+5iCsQVVYnO3GKATYNhQKgXA2PKS4pyEPgmY7+DLs96qZbhsErtK6ntrfXuc6tMN3BK7D6+PEt7pBKAJTQ2TjMApe3MI7Z4jjMUcHXsSysbrO9KbCQodil73w89VIJHZFREQ8KpD7bsT+5p8xVdxGecKh40nD/HrvR2iTG7n2zd0MD8EH3y8YHjoJItdshMAbvojC0IVoD/40EL2+/Zd4t36e0hPeRPKyPqa/sZtlb1rH3Q2WXVVe9AJ4cCf860cVgwNwwfmnoIwRERGPC57//Ofz/Oc//4T30xsHMeHHo6cq6LI+Sxw7q+4SpYeiQGubS2YG2tsFo7VwYQ19WOg+KELXE4k/d2RM87eskaqLXY1WQlAXGxo/2oWsaw9CiDA+Y4Nb+QJWu5Y2y4pEKFa0rIOZo3P2kYakw2phyp/Ej7chFnCrqG2/4Le5QNcFmtQwTcG2rXO3qLkhLWRtrFXTyTfE1Qn3sXSHlJVtEnCsZWnyvwkGfrOFPyW10LIL9NA9UUqarl/FTDYMDbN794OkkzZlSvX1UodKof439Xvj9qSR9GENDODf9iPk3gmgglA+QtXFLj2uQ4H6gFEPXBjVXN2phi51yl5pQVfHxZjtxpi2W0nbrYy0rEGZqTnbx+OCoSFB5daGhU3nFTVrvCqaBv32eVRUnhn5YwCUdBgqbmKfE1i/CTNGW8tqupIDNW1YConSTVT3RtSdqmYxUmv3ht4kdHm9WxGTuynt2Id2IBdahx1/+GZ0x+rWmoaDa7Sge3n8znWBdVitrgReaO0IwaGllHOM7mY31eZBtUIgqUgnELsQrF4FThH0DpsWBa4vkJoATZC5OhDp3NEi7uE8WsZC5XVUpp/FmNcqrKmQVSGi7sZobjsLFcZ1GhkOxPVGkXn4uf3s+kld+ElnwezViFW7QkFwcwY0RK9GIi5ACLRkNnApqw7ERbnWI9UF04XbriY1zuo+n8Q87bFKe3zWJIadQnWtgn2j2DZkemIo5hcHawJpg/vpyraN3H/sdoazq0nbLfPutyhmPDR1UzhxiZ0U+HIAkT+KqyeBKTSj4ZobxK5UCnq64cgRKEqJoWso1wv6gbD6N/VdwNQ9e+dYFWaydUsxXVOIlCT1pGX45SAOoJYyKRbq1onVOq2Kh1qqBzWzv+mYCTPJNFPItpVcMnAhFCdhchShC4yeOFpLDLM/gT4dCLJi1ntKhfVbNLoQpoaq1M+vpUwEAlF7rOoXtHoljD3YT6E81mTdpEtjboyrhmfsWHwLPdY4HM3VYm9agykKtxxr3qehaxgaDCaPlo0oanH3Z53DqLoxutQnsKpu1gkLZTYc0MniDe2gO6coFMBoDy2FB5Oo0tI9V9rb5lp2qXmeFelo+AWvNjlkV44ElsazzdeOQyR2RUREnPHIg7/D+dor8dN9HBl/KcLYifWEdm772m8o3/kSLrkY3vC34tQHgRcCb9kTKIxcjH77VzGvfx/OZ66hdfiPmfpelnPyO/ji5McpuvnAJLm2m+CNr4MDBxTX/p3iEx+Fvr5I8IqIiHjk2LZNsOt7gTtROd2FYCdSeE3xkRoxwuW6oTh7s+BYviGujgji6ojiZG1Ap8LZaOH78w/ypMCJKaYrAivdMMNeC58TWq5UB2cNh/CGduAVZ2BfJRi8zeerERSsaXH12oZjvbSZGZSrqBwLXJRma1lGbxwvLRA9Hiq5+KAbwHHmX15zv1tMpJnHsquJmtDU4O4ZDuCrFkKNmH0JjK4YlQO5pvhd1WMNmN1MeBVEmIRgPqGut6WbwVgb2py61Rssu5rFLmUbxLf1EEsJ1h/ZwtiRn5O3p+nI60hjNcIVlFd1BvGZQqHUT/UgbR2x3qYSL6A151uon7bm6nli78qORO+cOjI0ky29Fy26XyKhM95cgPrfQjQ1GHfwfLS7A3cjU0/XkgqOJc5BijRQr6MNXfW4R7NZNgK5u2XT9tJJAJO1bVSiA+UkobQfbBW0r0Ushqo4m9rqCQEEjCa2o9BYPcd/SODNsuwSiDmBbhYXu4J270kHKcdB+aRSAqkEnpRIKTB0fU7I+vi2jlo2WW/llce9puNSe26CwvupnmrhgvNVLfMaLY7iBsqpDtQFlgUyIWrHEtStYgO3SEnXxtW4x3bhSzCyNnrMYUbbisjd2FSM492n1ljngusuGn7qvH2zmM9/eh4C6zytQdwQ9KWX0ZdetsheS0DIWqyw1MWtFI9lKd1nk4xDOgVdLTKwSqW5DxdC0NoqGD0axEs0dB3XLyMbLLtSTgvCzs8NFFVFE3h95yBzR5AxG9kQDi1tt+IYcaSQpO3Wpt10OVfuiJspYH9d6G3YJr69q1Z4PVdNMlFV6qvtoi7uCEM2iV3ClKAL3L6a6llbt6VvBz/fbc1ePG/MtqpwtXEDHDtmovItQA6MhfvERoFsxfJQFLOCdt1Q/Nq/tt3wu+rGGE6MKXP+9rt+3dzzG0vzKOSKy4J98/lZ4tg8k0uJHd24R4uYuwXMQE+Xj0oFArAwlvYcQBSzKyIi4gxHjt6N89WXo+JtFK75OJM/OEziwm7e+8U78EWJc4fP4x1vfQSErkaExN3wLPIv+S6Vjc+lVf0nCJ+zbuun5BX47YGfzdnFsgTvulaga/DWd6hayuyIiIiIRwInphFzQDgSZRmBO5Rodp9oRA8ttXw/dFOZJTp4/dtxl11W+y1CsStRjYkyC6EFAlFfL+iJuWJXdZ/ax3pDFyljaYQdjmykwOxsFjMWGgAKIbh02TMYifeRMhKkzEToxjhXkJOmRupsE2GHZTlOjEfLEowmzmEstrH5nFV3uEXdMpq3nY1yAqstPzvUcC1hkN553PAgcEEyB5LzrJG096UYSPQiq7GKGk/bIIhAfRBRjcXiN7i8qdDirFHoqP5tCIt2q4V1mWUMasPYMhB9lC4Dix7A69uK3xP4aopeC8xZI68GaoGhj2NhBzRZqQyGrp2L4betxOtY03w+U3AsvqlBG5h1/0IBRTkZcLK163ZsavdTSZuSEZgEzRaDaocV1HYYGRacdVbo8mlCNguptsScHRrrQBNzrczmPU9jHCcRZvhsyCRYuywhm13atHr55pa7YbNGISN8WD1hBdXUYClWHcSqTB/zcSKxVGeTiENnowdZtZ6Uwl1+BX73WbVzJC/pJX5e4IRsdDT3HxecF/zryhiGRZPwER44PA6IoXPRnVhtcfyibhLndeHHOygYXbXtgj8e+jB7oUmI5sD+iyGC53X+eYCHgQj6T4I+U8uELmUoBgYEpjVLJJ6FUoAUWLoZeOOi4VcNirSF+4NgvQQng9829xmXQnJu/xXzxvHTGiwhR1rWsKx1Has7N9OdHKwLjo39TMN902t93qxg9Erhtcbx4xb26rqVbbX8/qUxvOG5nYAUsubGeLxbUj1lZ4dg7RqB8MKKWkTsqopX9gIh72bv2dcLK5fDQD+17KQqZlJe3k5l5PjJL6pIKebG4ToR5hGGZczAHEhy3oUJNqwXtLUJ/FQP6ScPknri8SekqkSWXREREWcsYuxB7Ov+CGXGKTzrkxR26VT2znD/9o385M5vYmwSvOll2x/Wx9LDwk5RvuwtaCuuIPGtn6F+7JJY7XDD7u9w/kAwS1lyC9y47yfEzCRn91zIm98Ef/M6xQc/ovjr10bWXREREY8QYXDs8EeQHh0xJytXFU1q9KVH6EwEg9TGflYgAlc7rWHgJW1WaKvYkEgwXzAmoUM8bXNshWzKrFezEgoHEUND4ElINnj3CCGaYvVorTYtT7AY+0kQJMcacXAP2Bh9zUGY5yCBULxbcEAVCl1LsSgqGe3zLK3O/C+8f92lTeB1b0LkjjSvT3bjrnpyUxmrrme6WOpgN0RqxFbpOGaiFoa8arRUPLu/bqkQnkpLmsTP60Jvs2tlgVvCHZstu6Ah5lp48FrMsGoT0GQ9MPE8lgzNWQ8bjlu17FrCa3JL78X8+IGvAfNbvs3Gb5ubSTMQWee6+tYKYcTC5ABBALaa2OVA1TCos0NSGGs8Xp3qhFw6TfNFVWNxSUH31rMQlQIUm/dtFE6DmF1LGL7NErvm+xvmaadShAHY4dganXara979agNxcz2idBAgjNlFkCm1pp5quCuveljCz0Kcd+7si6m2PT90dW4ob9pEAzJXD1Oczjeti8UE55+rOHRoOZppw7Fmy6JaPDcBGE69L5T1e6NrUKYZtQShdj4sfWF3zcaslIshAMx60PMTtZBc+MCNx6mLp6pSVazmd2NsQgpSTpqZygSgan1HvbAL7Kctfg1CiCZhq7Zbg3CYddpJ261Yus2ajrMbyjR/+zTCxCu2E3hi67oILRR9MCTlDT1oqeZM7YiFhSwptVqAeiFhY9e5ZJz5RaXZmRWpVC2vFhO7BBftUMcR2xvLIxgcBN9XiAbR2w9F93PmccNfCCnrr9fjMad8tXYzvyGA174KUZgAK3HCLTkSuyIiIs5IxMRenC+/BKSk8KxPolI9jH/tRpQu+cCNvXQ87xe0tq4jaWdOd1HxBs4ldrXgyL/ezVXjOt+87zpeuPHPGS9NcO1PXsZo7gAAr97+Lp6x/WW84HmKz3wONp+luOzSSPCKiIh4BBCS/j44NgaHyrIWaHchCwKAlW1nLfnwSuikZQajybJDUP14FVKw8aIhcv1bm4Sz+kdvaLlkCFYMCyYnmz96ayKKr5pFNoIBYOKCbhZFSAR+WB614EBMiONbdVVZuxpKs0a4oiYeLT64V1qQUU+le1HpeRIMzCqDF44ItSW4sM0+jhCiaaAYiE8KGuKxaGkTla8gDIGRbvDRbBq8Btvr81h2UbPOaP6tNDnvyK9WP1JnY/d5c9ZXB60nGrPLWILYNR+6HooT8976YKFqGaktSaXqgc1bMzamVWTdkMHdt8JMbu5gzrYF69YGde/OFtIAPzMQBI/Pjc45e6Nl13HdGKuP3AJNePbk4Oz6zcTbEaIMQuDGBX7caSxmjer1tRorkJl+xitHKBid6Mbd+NlB5Pjuhgt4aKLPCVOzulnADa62WbPAC4GL47JlGt5EF3Cgvm3jhiI8RzVuk6jfm8D7t9rXzSrPCbBj8EnzZkpsRMtacwL1u4MXoO++oV5uMwEUQQR1YevHmQhYKrOV0+pvr6p2N7i7z9PHVttNNtbCoend+Lg1yy5pa9irM8i4QfHAnF3rll8nSKMb44L9yQICshG+a7LLNOIFSQkg8FatZ2icR3ya7zzKCixv65ZdAl0atXdwb3wN1kz9PsnZ9RdmyZVtiwQ7pJ7BcV4W0BOlFFT6sxhlDz8R9KGaBun00utcajDHV3mpHO992bp8sXwmixK5MUZERJxxiOlDOF95KcIrU3zmf6Cyg/hlj6NffJDf2v1sOF+Sj9/Exq65H8ini8QlQ+ALrlavRCqPv/zqZfzld56OqVm896ovs2PwyXzsxmsZL4zyspcKztoIf/+Pij17I3fGiIiIRwbTFHR3CUAiwjzvSxVPGi0D5rMS8EWDJc4Cg01NSGJGs6td9YNe1Y45dyAa7hxs56m5A5PjfCi7K56IO3Rh8EMFYtd8GRyDs4f1swTBq7dXMDI8SzxYghujQuF3bkB1rT/uOap4KhAQF3I7XZAlWpc4G1tJXNi9eDr3ebIx6uFAr2bZVVsXvts0UY9HZc4SbQgsLdpiXcymep1yiUOVhJVuOu6J0piRcQ7ztIVspr7f5p4L2dy3lYxdt9CYz7KiPnida9lVbdNVF9am/ZoCyi8udiUu6sFalmq2xFx07FtfuaX3YlLh+ecGqG9e0JSRU9rkrAF8aaHWPgWcLCoWxE1S5kkSWZZCrYyLf1fJxeJezSPQqFCcyGYAodXrQtQtY5sOGV57NXnHiWDq9qITEADJi3pInD/rmXEyqNAizB26ED/s75SAdZ3bFnR/PmFmJW6ootzm4ObAvCrD4ACsWgn9mSFWt28iq480udzZq7Jz4g6KMOveUuOVzabx2g25QD0s0G9U3fmFLZGbdVSiwQKpOrExT7nmuP13rcdvGQlj1DU8zw3Pcm98NWm9H8cOXHTnHLPdoHSJjkzX3wHWcApzaD739flZrC9QCYvyWb21xnyi0mJTwP3jvHbmvn6r1pInXxiPxK6IiIgzCpE/hnPdSxGFCQrP/HjN3WDXl/YhpkvsWr2MF/zp7yi5hTNK7HI2tyEcHTV5Ee9a9WqGyy5Pkx184IovcHbPhbx821tw/QrfuvtT6Lrg7W8JAka+5e1R/K6IiIhHFkV95llbolvc8cQfJQxKekvwMb2YL8NCx5ljcTJrdfVLWtEUuB2oZWtaEM2of0QrBQrUcQKMPFy3n6VYJJ2I1VI1kPJ8bjqLn2R+95D2NtjQoLVJXaK3HCfbnZwbs6uGqg78gn/dgTCOliaRloazsZX4ufWA3PVA/vPXc8ULXFQdIzHv+tls7t7B9v7Ll7TtfGhaYNmVmic53nwiWDIp2LgBVq6AuJmkKzkQWgXWj7cg811zVcSUOn7r8oXLiZzT/hvRMxbO+uYA3YuKXQ0rpZANcZMW3gfmsTqZhcr0BzH9nMziBzqJqDCFogqFz4VYtCtrsB4yeuPoW7tACDIZ6OwMdq71RbJu2RU059DNrG0Z7sorUcm5Iu4ppWZpZNfF/JM+0p8l1Fa7Za9u2ik0sDolZl/zsyvjBpYlGOgPrE0HW0fYvFFj4yxP5tlWa9IJhWD10L6VzYasvObsDL3HYY7wGJYtLVvqVlL67HeXmNu3SwOExDHibFhXF7MaxfxqEPmRkXlcdAHHiIMmmly1nY2tTVmUj8eJvNVONEJMte+88AK45OIT2xfdDGIp9p1zgjsen0jsioiIOGNQhQnsr7wMMX2IwjUfw+9cBwRZO373T/cxYcR4xYe6uWf8lwBs6Np+OovbhDQ14ts7mLnhIJt2vIW/f8In+JuxMl3feT2Uc/SmhtnWdynfu+/zKKVobxe8+Q2CBx6AD344ErsiIiJOParm/idqboxLtxSa5b4yD5N2GDhYNYtdfuOgb4F9ZwsKi1l2qTBjoN0XKgpLcV1rFH2UWrAc8mGLXEuw7AoHbUu1WoJ6HJ/GTL9LK9D859h0lqCrs9G1cSnHCravB06ur6oGqhZ6IABtfGKS1muGQ3EgsECoDlqDQ1WtB+av7770MnpSg/SkhpZQsMB6I24u3cJhNrouOHe7pHs+fWKBttLZITBmBYuu/poTb2chQmFYNWTB9NtXLbj5ibjZ1vdZZF1DGxRC1p6zzi5YNnxCp5mLcWLCwsNFJTpwV1wBsZZFt1tMqGt0lbNXZJAtgXWWJqt1L+tCkmh83qkF6heCpcVVO8l4fdsCoVQ3689V9XL8k/Sd2eTG2LDcr1t2SUMQX2vMEa2SF/fMCSze0SEwzVn3Y5a7YnxrO+ZAAi310KzTrAaB60TdwGdbxEkpGNGWk5bpel3Mdq8Uc/v/hJni0mXPwNQsuroEw0NVC+b6dv39sH4t8/dBwEB6BRu7zqUtfhyX/UWYnY1xKdsulXVrYfNZgcv28SbHdB2M8BGpvkf8thVwCixBI7ErIiLizKCcw/vU85BjD1J8+odrGZuUUnzwDRMMHztA8lnL6e7R+N2hXzCUXT0nvfDpJnFBN6V7JqgcKeCtfCLFp74Peeg27G+8GipFLhp6Kodn9rFz/G4Azt0ueMHz4OvfhP/5SSR4RUREnFq8wQvwM4N40kGeqBvj7AD186CEHoRxmW3ZtYDrS9MmsyyPquPJehKshtgwoVgUW6HT8gQL9KXEaWp0cVLzxpOBxut8aKJXVTxYimXYiSRXGcysYmP3ebTEOo6/cQNqidZjixXF69uG3xCvSgjBRTtg/br6NrHNbaS2GAhdoK27grbWevDq+YS0qtC3kCiYddpZ3X72Q3ZLfChYtraAELL0+1Qb/y7VsqsqDJ+oxd5JYvZzXRVy4nFBLPbwhN/TwhLc9aSmkU7D0OB8FnaLu+EhGlyc5+QzqIpdp2l4bSVrQmm1DDIRZsOsLB7HbOnM8W8Nju8d//jCkE2C94LbzbbsihnENrcv2Gcfj6UkrVgIPXwuq/2QrJZNqbpllxAgBXpHIIya/QmqK03NoiXWwerGYPjUJy8a+zchBN3dCwtFQoiHJXQFB6kea+6qzGyDyBOsbl0PMiYuqRhCcMnFgisuE2zccGr7mUjsioiIOP2UczhfeyXqwO8oPu39eAN198TPfA5SP7oLDI31r1uN57vcfvjXnHUGuTBWSewIXkK5n4eZiZZfRunJ70XbdyP2f7+G7d0XIxD8cu8Pavu87KWCDevhH/5RsW9fJHhFREScQsw4ftf6wNqmGqB+qW6MS/jy9UOxaw5NVg4LiV3zU539pcGNcbaYppYSj6aWNqwaoH6hT+DqjPtDFLuq4+BFB7zhoPgERhNSyHljWx2/QEsVuxYui0p04HesaVpmWaJJGBKmgZ4KzxWKj344/p1vjFoXBM4gQWUh4fcE2kLN6GfRXRrFrrCStOOLAH52CD87tOSy1M52Am6MzHJj3LQRensWP/6qucktz2xE4EqXSM6tmCahRYq5dSe1eZ8VKWDSWUNJz84bd+10IRMnmL31OKjZExfzWI49RG/D+lEfoqi1EA81aQUEMdT60iP1sCnh85HJNFsH6xkTozNG5uphtIzV4KYt2dR9ASmruU0MZVcH+x0nPttJZ5F7s3lT8+8zqGd+WERiV0RExOmlNI3z1ZcjD9yM9uyP4I08obbql79SfOHfcpyXe5C25y1Hb7G5f+x28pUZNnSeexoLPT/2mixai8XMDYdqy9xVT6b0xHeh7/oZXT97P6vaN/PrfT+urdd1wdvfKtB1eOu1inI5ErwiIiJOPSr86tW1Exe7FhJG1ELCmZhjAtHERTvg/B3BviqM8VMVSozwkKLRjdGa5eqgL0XsapiRRy34Jf9w3RjrYtlSYnY9AsOJpus+pSeas6R6yvmq4ngxu04LC5Zl6WWMh01zUcuuxuNVhdslJBJQTrYW3uFEWGqAeiFE3Y0vvGft7YK1axa//oEBQSIBA/2LbnYGEVyP175m7ppGAbfh77LdWV04bzwsIaGipRhNnv/IZZ9cBEOatMQ6WNdzkmMgzcokWptD8Br6F8N5SKLsqeLh9jEr284iaWUAkOHzIZRfz8YIJC7swRqpB/w73il7U8NcuuwZJy9xwCngTOqaHw6nx2Y2IiIiAqA4ifOVlyNH76b4tPeTWv97MD4OwN59ire/U/EceQ+aULT/cfCB97uDvwA4o4LTVxFSkDivi5nrD4YZV4I3hbvuGZRyR7Gu/2e2jqziC8d+Q9HN12KvdHYI3vwG+Ns3KD78r4pr3376riEiIuLxgU8FeHguHnOOuaBlV8Pgb571Qar0GO7QDgjTs8fj0N9XH0BraQtha9irMqiUjWsm0HddH65cutglCALUiwXUiFpMk4fqxriUmF2PpLvTIzVimec8I8NQKEDbPBEHqrHiHmrQ6VPCQoLTCdThqpXQ0RG4AS58ngZBJbTsUqcwxtNixZ8pTwGQtDLYegzfq4pvJ3aO87Y/ikbGQuCufsqStqsynd2MO1IKg4+Hwnujx+MZdvlCCDZ1XwCAd3nm5FlLiQV+NIhdXsda/M6Bk3O+k8RQdjUxY/F4UCrWOife5GyqmReFlIs+IzV39jNMMVqsuz3DinrSiCy7IiIiTg+FcZwvvwR59B6Kv/dBvOX1LEozM4rXv1ERF2XOOXIv6acOhj7w8LvDv6Q/vfyE45Y8UiR2dFPZn6O8e7ppeWXby6ic9Vy27bkd169wx+Ebm9aff57gD34fvvI1+MGPSo9kkSMiIh5nbD8HhoeD7H5LdvFYQsyuxuxcAF7vliBD2hJidgFgp+uilBCsXlWPGyQMSfrKAfRWu7ZtzX1xKUJBoxvjAgHqvd6t+JnBps1PlGrdiEU+sasCz8PN+HimE4sJtm0V6Prc66xO9pS8wiNdrIU5CZZdmiZoaz3O9vPG7GpuL+7wxbhDFy75vA+Vahs8q/v8sBiP7TZ5QjR1W1o9ePaCMbvOTLS4saRYWUtCzHJJrwp/JysA/ilipGUNXcnFBThv4Fy8wQsW3UYzDVS6D9k+QkuLoKN9/u1qlqtnaB8/X1c3NwPyI1OWU01k2RUREfGII3JHsa97KXJyL8Wnf6Tp5eJ5ine8S7H/APzLufeibq3Q/qogP7qvfG4/9CsuGn7a6Sr6cYlfEMTtmvnZQayhhhzmQlC86A30/azIlb/Zza6x73L2ay+szRIBvOLlgt/dpnjr23N84mPQ031mviQjIiIe3aSSAifuUvGWbtm11I/2xq1UogMv0YE4dn/DBievX/OGdiDKuaXvIARiJnQzN+Zet0p2IooDMLX7IZepWk9LCaz+iAyEquU4w9xlamKXeyaJXQ/fsmuJJ6r/WXVjnO36ZiVO3tmEYKFgPWs7tpArT9f7AX2ukLMQF+04OeU7U1k4UHj1j/qyx4tGqKyGjKdi/phdJwUrhToD3EFnI4Sgb3Uw2X722RJhLC6QP5qaxRyx6/QU46TzGNHsIiIiHi2I6cM4X3oRcmo/hWd8dM4sysc+rvjFL+GvX+VifOsukpf14awN0kg/OHYn0+UJNnadefG6qpiDCYz+BFM/2te03B0r8sCzfsSe/1jDVT+5ipUfWMvOp38Nd6xY20bXBW9/SzD8eds7FJXKmT1TFhER8ejF9U/MjXGp7hhe90bcwfNn7dzoxngSPz0NBxVvW/r2DT4cyk4usNE8I9kTIXRzWjTge1V4eCT8Rsw4Xuc6vJ5Np/5cJ4BTE7uKx9nyEeSRMmVouO+12EZhdraFsI1YLYPbycTSnSZLeREKGGu7tnLuwBWL72uJ0AX5MYom5u0GaoJ2Q5KLBfNdPNZYQOxSbsP36kn4dI09fQeJp59//A0facQCf8/ikcwieyJUb838ll2PzWf5zLwTERERj0nE9EGcL70IkRul8MyP4/c3B8784peKfPbz8MxnwLnj9+ONlej4sw219Tft/wkAZ/dc9IiW+0QQQpB91jJmfrqf8t4ZANxjRXY+9wcU7xqn/0MX8tvP7uRzT/8C+Tsm2PWC7+KX6zECursF77o2wV13w799LBK7IiIiTg1VVzpDnrhl16IfxZl+mJ2NbKE4L6eB2tmd1PzrH+YHvwj/tyiPsBujyg7VMiSeKZihcHMqBJyHzEL3/hRadqnMQBA/6jiJIs4fuJILBp90kssxP/bqLJmhbmLGybMue1Qi6okG9Kawg6HY1ZBB8zGqE8xltrVV9cJPsmWX3mKjZc6sPgua47QtZs5Xe4+cYQ3jREIknmFFf8hEYldERMQjgpjcj/PFFyKKExSe+Qn8ns1N6//3Z4p3vSfHhRfAq1/mM/rRO4if30Xs7LpD/I37fsKK1o1knQWc5M8QWv5gBcKUHHr3byjtmuLB3/8Bpd0zDP3nZWR+b5izll/GjZt+i3/1dyjcPs3o+5vjd112qcmznwlf/DJcf0MkeEVERJx82uJdAGgn2VVk/g9kebwNHjlE+H9mbIHVounfE6Uj0ctIy9qHWLjHF5t7drCl9+LTXYwlcJLb7Ol+Bo6DvTKDfgYKDY80Qgg6O2DlchgZqS/3K8EEpTTrfefjxrILULIqzNZ7yaZsjGdS0omTTcOju1h8u8ViNp4JLKULOsO7qSVzZt+JiIiIxwRiYg/Ol16IKM9QePYn8bs3Nq2/+RbFO65VbDpL5+1vFcx8/UHcw4Umq66Z8hS3H/412/oufaSLf8IYXTE6/3ITk9/ezb0XfR33cJ7hT11G4vxgcLm2fSuGZvGr3+umfd3tjH7kbgq3Hmw6xp+8UrB6Ffzd3ysOHXoMfzhEREScFtZ3bmfH0JOXvH2jxdNiQtC8llFnkkuHAD87sODotCZ2PcQv/ZSVZSCzYtFt6tkYHyOjiRCve9NcF9ZFyDrttdhdZzanMGZXxBmNEILBQYGmNfR/k4psrJ21K7Y3bHc6SneaMJzgX1EPUH/SY3adoSw1gcPDzep7yjiR23SGFf2hcgZ9fURERDwWEeM7A9dFt0jh2f+F39E8433Tb13+5j130D1yiA99IImpKUb/9Xacs9uIh+IQwPW7vo2nXC4YvOqRvoSHRNsr1zHw0Yvpev3ZLP/eU4lv76ytM3Wbte1buHn6PjrffQW6nefQn38V5bm1bQxD8I63CXwf3natwnUfHx8SERERjwxSyCXH64KH+dF+holdsHDaeHGGZ9E6k1Hp3rkurI8yVKwNb9Z3yklXMh5XyshjDyEEPckhUq2ttWWPK8uuqtjV8M16pmdjPBlcNPxUdgwubYJInEnvvHlYSg+UTZ/yYjwinNl3IiIi4lGNGHsQ50t/CL4XCF3tq5rWf/+GPbzux1fBlZczeu42Pn3r2xj/xoOU98zQ8acbm2a9f/zg1+hLLWNl61mP9GU8JIQQpJ80SPufrMfsnRv3YlP3Bdx/7DZy67bT/fI0Mw8myf/j+5vMv3t7BG/4W8EddwaB+yMiIiJOF00xu05UCDqDBvdCUAsiP+/6R0Dk0kM3oEhQO/PwBrajWoZnLY3uU0Sd5MU9xM/paLZ2fRw1kVpSENkwaeCd3AD1ZyK6NNB0/fgb8vDd4U8VS/UwXTYMK1ee2rI8UkRiV0RExClBTOzB+fKLASg857/w25rdOr75gzHee+Nz0ZL7+ZMt/8yVK36f//zlP3Lf//sp9tosyct6a9vumbiPmw/8jMuXP+sx4/axqfsCfOVz68EbSL32eThDLvs/Y6L930eatrvkYsE1T4fPfQF+/ovH6BdERETEmU9j33ui/fAZNMttdmpBeRYoUv0dc+reNes7z2FF2wYcI37KzrFULtoBF5x3uktxhvPY+OyIOEloGQuju/nZfVxZdmWHcAfPR8Xb69kYvcfH92nt/XAcd8aaZdcZNmbJhoa3nZ2Lb5dIgFyiy+aZzuPo0YyIiHikENMHca57SWjR9Z+olnpkT6UU//GfPu+74a+QiX383ZWf5pqznsdfXvBPvCb3BmL7TXY9c7RJ1Prsre/H0h2etvoPT8flnBLWdmwlZbXws13fRkhB1z88mdJUhrF/uwnv+g83bfvqVwlWLIe/e4/iyJHHxwdFRMTjhX379vHGN76RSy+9lI0bN3L55ZfzgQ98gHK53LTdgQMHeOUrX8mmTZvYvn0773rXu+Zscyp5rLgxOis0Ulf2ILQFYnaJhxezaylYukN/evkpO/6JYFmCWOyxMag5dUT183jCXp1Fbz2xLKFnmKZx6qm6K9didvn1dY/lz9QwdpuzvmXRzeQZKrHE44IrLhNks4+fBntm3omIiIhHLSI3ivPllyBKMxSf+fEmoWtqT55PvOgBfvLtz6INfpeXb3sj24a2BitdxeqvDTI+OM0/am/m+/d9AYAf3Pcl/ueBr/Cs9a8gbS/+cnk0oUmdHYNP5ud7vk/ZLZI4r4vUVf3s+81FFL/6Txg3f6a2rWUJrn27oFyBt78zit8VEfFY4sEHH0QpxbXXXsu3v/1t3vCGN/CFL3yB973vfbVtPM/jFa94Bfl8ns997nO8733v4/vf/z7/8A//cFrK/Oh2YxRIy1x4fXht6rGcUSzixDhJ7VfF2k7KcSJOLfaqDIkd3Se0z+PJsms+HjeWXVKQuXoYazi1+HbizHRjXCpn0Cv7YbM0x9OIiIiIpVAYx77ujxC5UQrP/AR+x5raqnt/NMbYK37E9kqR7cBdiVfyzBe9rLb+2H/dTfHBSdZ9/Co2q5/xjz/7Cz598z9xaGYvZ3Wdzws2/eVpuKBTy6XLns537v0MP935DZ644vfpeuNW7vvxfvbd/UJG4n+H0kzcjc8BoL9P8Lq/DsSuT3xS8YqXP4beRBERj2MuuugiLrrootrv/v5+du7cyec//3le97rXAXD99ddz//3389Of/pTO0P/g9a9/Pa9//et57WtfSyIxNy7gyeZhWTqdQZZdwKLlqcbT8pS74DYRjzdOzvvW69sKvndSjhVxZvFYEgdOiMaYXYLHtlXXCfBoD7nyKC9+E2fY10dERMSjltI0zldfjpzYQ+Hp/4rfswkIZse/9Q2XXa/8GZ6Az77y2/xyx02s+Z8R9r7sf/EmSuRuOsKh995M65NHaLlihL+74tP82XnvYWXbJv5oy5t4z5Wfrw1AHkuc1XUBy1s38IXffQjPd7GGkrS+ZDWHr29hynoK1o/ejn77V2rbX36Z4Oqnwac/G8Xvioh4LDM9PU06XU+FdMstt7BixYqa0AWwY8cOyuUyt99++yNSpqYA9Sf4JazOtNltqS28SgTzwF4kSkRU2/nJGvlJDfSFrQojHr08Xi27qo+G8hVCDyshsop91Fp01XiUF7+RyLIrIiLi4VPJ43z9VcjReyle/WH8/nMAKBQU//R+RelL9/Kc8iT//aqfcmv3L/j9F3+O3p90sP8tv+KurV9GlX3M4SQrPnQZOVFCFwZXr3kJV695yWm+sFOLEII/3Pw3vOVHL+JjN76TV5zzNjr+bCOTX9nJrv+7nDW/P439gzdTdEu4m54HwGv+VHDXPYp3vlvxHx+D7u7H0BspIiKCPXv28JnPfIbXv/71tWVHjx6lra3ZBSqdTmMYBkePHl30eNlqRNqHied7xI/Ew2O2oMvmT8grn+iTyymy2blCkrJAHQv2lSepPA8FPx6UQWRbEMb8MXlK2gzxfJyYbZ+0ujsZnEllebzgd6+Ayf1BezkJgtfpvIfxeCksg3XayvBYYb776LqKeLwcrn/81LFnVfDi4wBIW8cvughNPCr6q1NZxgk/Q7wUJ2HHHxV1UaXaT2QyBtnsY0PBjcSuiIiIh4dbwv7mnyEP3ELxqe/DG74QgF27FW9+m+LgLpd/KN3K3tUH+L/uH/L2Sz/B5p4d8HyIbW1n/LoHkHGD1hevxmyPkRsvneYLemQ5b+CJPH3tH/GVOz7KL/f+kO7kIOueupxN/7mCfz+vi2zvIJf/5B2kvDKVLS/GsgTvege89OVB/X7kg0FMr4iIiDOLD37wg3zoQx9adJvrrruODRs21H4fPnyYl73sZVx11VU8+9nPbtp2ocH28Qbh4+PjSyzx4vjKJ5fLATA9OT3vNo4N857O99DDfd2TVJ6HQq0Mk1OgFebdJpfPk8vlUOWTV3cPl2w2e8aU5XFFchk4fTAx8bAPdbrvYS4XWNuMj+dPWxkeCyx0H31fEXYvj6s69otu7b0gMfBzFZAC/Qzvr0718zg9PUMul0NUtEdV313tJyYnQNfO/LHFUoTESOyKiIh46HgV7G//JdruX1C66u/Z27mSn976AW67b5Qbf5HAaIvzvOQxrG8M871LfsS1l/0n5/RfVtvdXpWl+01bT+MFnBm8evu7WNN+Nv+787+ZKB7lV2ffSN/3Whn4VJb3vPI2PpJWvPKX7+BZbonK9lfQ0y1465vgb9+g+JcPKf72r878F1JExOON5z//+Tz5yU9edJu+vr7a34cPH+ZFL3oRmzZt4p3vfGfTdm1tbdx6661NyyYnJ6lUKrS2tp68Qi9C1S2jOzl44jtLDXfoQoR3hkxmLOLGqIXrPBW5MT7uERIM53SXIuJRgJSP14BVDe7tjwJx5JGilujkUdomFnlFPuqIxK6IiIiHhu9if+dv0B/4McXL38EPbZ//97WLKbklVDmFWDuNqGis+5c3Mr4pzzte+3myTvvpLvUZiRCCy5Y9k8uWPRMIZir2tNzBrhf8iI+Uv8Rnl/0HH9z9bfQb/55nuEXK57+G888TvOgFik99BjasUzzpqugjIyLiTKKlpYWWlqVlkK0KXevWreM973kPclYAmE2bNvFv//ZvHDlyhI6ODgBuuOEGTNNk/fr1J73s8yGE4OLhp6HJh/jpaKfOnM/+RQLUa9UA9X4UoD4iIiJiyWgCpMDZ8NjJnP5QEeE75tEqdiXip7sEJ4/HhjNmRETEI4vvYX3vDej3fZ/SJW/kV61dvOd/X402tZnidTfx1PJdfPuFe/m4+w3iuThbr31WJHSdIMmLekhe2kvx3/by+s0f4oKBq/hwrMyRGz+M+eN3gu/xRy8RbDkb/vGfFfc/8Oh8oUZEPN45fPgwL3zhC+nq6uJ1r3sdY2NjjI6OMjo6Wttmx44dLF++nL/927/lzjvv5Be/+AX/8A//wHOe85xHJBNjlYcsdD2KqMYiiyy7IiIiIo5DwzyrMCWZpw1hDaVOX3nOEGoB6h+lwfoN47EzgR6JXRERESeG8rF++BaMu79F6cK/Znz91bz7J6+BqZXMfPdTvOnPe3jtn0v0osfEv99H8tJeYpvajn/ciDl0vXkrfr7C0ff/jj8//73oRowP9/Rg3vp5rO/+DRoV3v4WQToFb36rYmbm0flSjYh4PHPDDTewe/dufvnLX3LRRRexY8eO2n9VNE3jox/9KJZl8Qd/8Af8xV/8BZdffjmve93rTmPJH5toYTZG9SgdpERERJw+entOdwkeYRo0EWk+hnzfHibVWJr+o9Cy67EjcwU89qfoIiIiTh5eBesHb8a465uUzn8NlW1/xNu+8HomikdI3v6f/MP7E6xaGXSTB991E95Umc6/2XyaC/3oxV6epvWFqzj2qXtY8cJVPH3tH/GF332Anef9NcO/+HdEcZLs0z7AO98R49WvUbz7HxR/d+3xA1ZHREScOVxzzTVcc801x92up6eHj370o49AiR67qFgLIj+26DZ66MYYERERcSJccdnj79ur8XtTGJENTRVDmgC4fuU0l+TEuPhCkI+x2/gYu5yIiIhTRqWA/c0/C4SuHa9laujFfPJl36f9u3u5+Jev5eN/uZxVKwVKKY58+DbGv3A/7a9ej7Mu8t1/OHS89iy0hMGBt/6ap695KZrQ+ao2RfGqv0fb8yuc617K+pEJ/vRPBP/3M/j8F093iSMiIiLOTLz+7bgrr1x0m2iyICIiImKJNLkxRpZdVQwtFLu8R5fYZZoCXX9svQMjy66IiIjjUxjH+cafIg/eQvGKa3nwV2dx9Pe/ylbPA4KBw8EffJXRrhhCQuVAnvTVQ3S+9qzTW+7HAHrGouuNW9j/ul+Q+dYU2/uv4McPfp2XPectKDuN/d9/QeyLL+DZz/gYt93ezUc/pli7Bjad9dh6WUVEREQ8bIRcNDh9I13JgVNcmIiIR47t2x57FhsRZwCzYnZFBBiadbqLEBEStcqIiIhFkaP3Evvcc5CHb6f41Pdx05dWM3ntL7m/Y4r3vPr/Ufy/PpZ//2n0/N12Eud1EdvaQf+HL6L/AxcitKiLORlkn7uc+PldHLz2Ji53fo9j+UPcdviXeCOXUHjWJxC5o8S++ALe9LIH6euDt71DcezYoy9OQERERMSZwKXLnsHaji2nuxgRESeNVEqQSESTYBGnjkjsqlN1Y4w4/UStMiIiYkG0+36A84U/AK9C/jmf5psfX472Xzdzy3CZf3/5tey45OlsG7oUZ02W1heuov9fdjDwoYvIPG0ocgU5iQgh6HvveQhd0PrXFVYeWsWPH/gaAH7vFgrP+RQoj5avP5/3/emvyOfhzW9TlEqR4BUREREREREREXEKaIzZFbkx1qi6MUacfiKxKyIiYi6VPNaP3o7z339OuW0FPzz/tbzhH/+P7i/+Hw8MHuAzz38blyy7mj/e9pbTXdLHDeZAkuFPXw4uvOrf/oiBax0mbtiLUgq/fRWFP/g8frKL4Z//MR/9469wx53w9msVrhsJXhERERERERERESeZRjfGKEB9xBlI1CojIiKakAduIfbpa9B/9yXuO+sZPMc/yAe+9wae9h3JeMsYv3jNTfzVE/6ZN1zykShr1SOMs6GVlT++GveVGXr2dLP3D37Czj/4IZVDeVSql8LvfxZv8Hw27norn37+/+OGGzze9y8KpSLBKyIiIiIiIiIi4uTRlI1RRh4dEWceUYD6iIgIAETuKOb170O/42uoVA93P/G9/PEvX4c+Hue1//VGErrO2V99CpcN/eXpLurjGi1hsPH1T+Z5vZu5+v5nc9ZXDB54xncZ+vTl2MvTFK/+MOb/vpd1N3+S6655gJf893tobc3y0hef7pJHREREREREREQ8JtEisauRTd0XoMnItfN0E4ldERGPdyp5jFs+h/mrj4JXorTlj/jm5Mt4/3dfRAKbv/nC35AslRn+zBOwhpJzdnddl2984xsAXH311ej68buVpe7TuN1TnvIUvvnNb3LTTTexefNmNC14gZTLZb72ta8xMjLC2972Nr7//e/jeR4zMzO8+93vZmxsDIA/+ZM/oVwu1/bVNK127qNHj3LJJZcwPT3N9PQ0AG9605u49957+epXv4qUkmuuuYZYLIYQgnPOOYeLLrqIyy+/nHw+z1Oe8hS2b9/OLbfcgpSSa6+9Ftu251xnsVjkD//wDwH4r//6L4Cm34lEYgk3DDSps33ZlXxBfoqnfvF69r/4Zzz4rO8x8oUnYq/OUn7CG/FblzP8k3fxjaueyZ99459R6ixe+mIRxVKLiHgc8fnPf55cLrfkvvnxhuu6fPnLX+a6665jZGSEd77zndi2/bCPeaLvxJO5/6nAdV2++tWvNr1/G9+hp+J8jXUA1M6/detWfu/3fo9vf/vblMtlbrrpJh588EFGRkbYvn0711xzDQDf+MY3aut37drFs571LJ72tKdx7bXX4vs+W7ZswTTN2jXM/t742te+xpe+9CWUUgwPDwOwe/duli1bxjvf+U4A3vrWt+L7Pps2beK3v/0tu3btYtmyZbztbW/jO9/5Dr/+9a8RQrBt2zb+8A//kK985Su1azoT7uupYL72u1ibblx35ZVX8o53vIMHHniA4eHh2v2cXVfFYpG3vvWtQHAPvv/978853+z2CuB5HjfffDNbt26d97gn8/o9zwNoek5OxrN9IseY7zk6lX2LOAPErtPZf84+d0us47jblstlbrzxRnbv3s2znvUsnv3sZ9fKvNAYaKntt/ocNPZDx9vvodRf4/NYHf80Hqv6LEDwDP7mN79ZcnlOBpEbY0TE45VyDuPX/07845dj/eyfcPu28auzv8kLP/1a3vu9T3HWgRJv/q8/J1V0Gf7M5cS3d57uEkc0cPHw75GvzHB7+lZGvnIV0tZ48Pd/QO7GIwC4G59D4Q++QCpj8J87XoT8+cf414+UI5fGiIiIiIiIiIiIk0vkxhhxBhKJXRERjzNEbhTjFx8ORK7r/xm3cyPXr/4CL/7Wv/DBv9K46M7v8u4fa7zoK88jtayNZV9/UiR0nYFs6j6ftN3C/+38b6yhJCNfvBItY/Lgc77P4X++BW+qjN+xlvwLvoK/+v+zd95xklTl/n6qOs70xN3ZnIBNhIVdWKIs+aoocpUMIihXUcScEO9PuRdQEb2KCogrKupVEETlSsZ1iUtawuacd3LqHKq7qs7vj+ru6e7pmZ3Zndkw8z58lp6uOnXq1KlTp8/51vu+5/184aif8aG2j3L/9zfIKo2CIAiCIAjCkHEwWHYJQikj04ZWEIRilEJvWYHn3T/h3vwc2CaxyWfzf8HP8MovxnJU8zY+lXwUv5XB8KfZdNRuzv3kNUy44Eg0l2jiByMu3c3p0z/IC9sfwzCT+GZUM+uJC2j+9hu0/3QVnb9dT/3ls2i47ii44H8w57yfGU/exhfMy3nm9qs55tM3MH563YG+DEEQBEEQBOEQR+YLwsGIiF2CMJJJBvGsfxz3mr/h6tyI6alhhe9qfrfmcqIPwoXBlXw8sxK7xseYyw/jpcOX8gf9F3z/goeYOOXoA116YQ+cN/Nintr0R17Z+TTnzbwYV7WXaT87g4ZPH0PH4rV0/34j3f+7kXE3zGPc589Fv+Fk2v76Ez6o/S+xhx5j24wbOfySy9E8+xajRhAEQRAEQRAE4WBCxC5BGGnYFq6dy+Ddv+LbuRRdmWzPHMcj227l71suYEYszEeT7zA+1I17dh2TvnQGNedP59kdD/PAK3dz2bzPsnDKmQf6KoQBcNzE05hcfRjPbHqQ82ZenN9eccwYpv/8DDL/uZDWO9+h/WeriCzZzbS7z6Tu6lvZufZqYn//Icc130H4Z/djn/RxPKdeCb6BBcgXBEEQBEEQBEE4mBGxSxAOcZRStLUp2t5ZiX/rU8xMPUuVq4NuYwx/aL6EF1sWMiU5iRNI88P4EjwtYTzTqpjw34uo+8jh2Fg8suYX/Oat73Py1HP51In/70BfkjBANE3jg3Ov5tdvfY/twfUcXn9U0X7PxEqm3bWIun8/nN1fXcaWC55k8n+fxLgrZjP2yPv5xx/fomHDr3jP2z8m/c4vsY/6IPb8S7EnHguyaqMgCIIgCIIgCIcoInYJwiFGZ6diw0bYsMEisW0dhyWe5eyxz3B6ZTNJ08er7WewLjIBd5vBlB01fCEYBIIkfSmaZnWSOl/H/ZEG1rifo/3NJl7d9Qytsd2cc/hH+MYZP8WlS7dwKHHB3Gv444q7+Mvq+7jpzJ+XTVN9zhRmP3shjV99haZvvkbHL9dQe8FhnHBkA+Gzv8dP/9XEjNSjnG8+QeW6v2DWH4E95/2Ys9+LPe5IEb4EQRAEQRAEQTikkFmtIBzEBINZYWsjbNioaNvWyWzX67xn/DKuHv8KwYZ2dofHsqzpBNwt76d6Wy2BUAUnAsmqFJn5LpILA0SOTrFzbCebgivZ1LmS6DshAPzuCuZNOJXPn/Z9Tpn6b2giahxyVPvquPDIa/nr2l9x+bE3clj9kWXTWWMUu29NEn46SsNTGYxfRtBM536fAtieuTw+diGRmhQLTvwXJwR/hfeN+7Brp2LNOB1z+nuwpp2M8teysvVVXtv1HJYyOXHyWZw87d/QNQlMKgiCIAiCIAjCwYGIXYIw1CgF6RhaJgmZBJqZAnSU7gJdB80Nugt051PpbmLdbjasS9G406B9d4yOpjjdTUEC6UamVDZxTM1mrhy7hnEnt7IzXMMLbYfxt1dOZfqOw6iL1lIJhGvC7J7djP+kBo55/xnMO2F+WfFKKUU0HcKte6hwB0TgGgF8dP6XeWbzw9y17CZ+8sG/FVnnKaV4fttj/OKN7xBKdUIlcCm4TBcN3WOpi9RRF65jQud4jtoyh2M2TSS24xx+EvgE9qxW3rfwBY6JPknFqocJaYo76l28aHfh173ouofH1v2Go8efyNdO/zEz6ucesDoQBEEQBEEQBEHIIWKXIOwNVhot0oQeakQP70IL7UYPN6KFdjmfZrLXIXEUr3kybHFZZFB40Jhk60yxdQ6zXCxUGieSFZ7qsv8ApblIqCPZsP0s3nmrnpqmeuYAqfo0nFKB9z3TGHfmTI6ZNQld37N1jaZp1Pjqh6omhIOAal8dXzztDr73wg38+JWv8pXT/wePy0tjeCv3vP5t3mp6nqPGLeSWc+/n6PEnoqGTsQ0yVpq0ZZCxDFJmgrebXuKpJ/7Eca8czWnrLPTlGk2rzuNt/cNUVrbi8TZzsg3/Vhvh2CM2MHbOep6tq+PujtV85rFzuXr2NVx5yi14PJW9ymgrm1CyE7fLQ8BTLe6ygiAIgiAIgiAMGzLbEASgKbKdde1vsSu0mUQmhobGlMqJzPWO5ygq8UZb0UO70MK70cO70aKtaMrOH688ldh101H1MzBmLCJkTaAjXElrdwWbgjFe52m21b2A5TLQbBe67cHWMyjdyudRrQWY6ZvM3NoZzKydSYM9ldQbPsx/GVSv8wEQnL6brk8qTr38w4w78jCxyhLynHPER2iO7OCBd37A280vMS4wmU2dK/C7A3zhtDu48MiPF7kauvRK/O5iUeqw+iN5/+wrWLzoNm559zbO2nwW81tOYGybTcIKEfZCums+3m0m2zcdyfo3PsK0927it9Nf5Nfenfx+0+94fsMfea93CrWByQT9AZq0DNuTreyK7cSwUgB4XX6OHLeABZMWcdKUc5nTMB+X7tqv9SUIgiAIgiAIwsjloBW77LRF6LHtaLqGb2YN/qPGoPtH72RoZ3Aja9qXc1jdXGaOPabXJHVvSSYVySS4XM4/Xe/52+Vij2KKMm1SG4KkNoWx4xlcdT7nfs2pQ3MPXwwfO5Eh9kor0aWNxF5vw+xIovtceGdUU3F8A4ETx1N54ng84ysKCmtDJomWjkMiyOb25SxrfoFXOt9ie6oVgPpoDbObpjO5aQod8RAdbOBflXHqKhKMqbKoqPdgj51CvOEIQpUVdJvVdKaqCMWqSDQGiKwO0N0ewLI0tKpd6JNfxDX5RTTczIpcxpmJC5mrxjPWZ1FRq5P0JAm5goRVJ7s6thBp6SDdmCa6S2dso4cKBc0Tuln/4Q4mXnQ05yz6IgFv9bDVq3Boc/WCL3P0+BN5fMPviRpBrp7/FT581HXUVTQMOI8qXy1fW/Rjzpt5EQ+tvJt7Ou+hxl/PeTMv4bJ5nyUVr2T1KsW6v+xk2nPvEHtkDs9UX8A7kycxb+7DdM75M7/VdqLCOyEME2yNIywXJ1suJjKGjKeSVhRr2tfz591v8Wrj3xmfqCOwIMyMilqmeOqZ4KmhTq9An3Me6vAPiKg7yklkYqxpe5Pwzg4WjjuHMZXjD3SRBEEQBEEQhIOcg1bsMjtTtPzXm9hxEwDNo+M/qp6KBQ1ULmjAN7sWPeDBFXCjeV1Qbi7U3/xIlX6qwo+y+yjcV+6YwoP7yD/3XSlIBjWMcGRAZXp13eM8se4PBBIB6uP1zLTmMiNzBA2JcfhCHtweD1qVG9cYH66JfuzxOpkGm3hNkpDVSZfZTle6jS6jjWgyiM9VSbW3ltdfHEc6PA5SDaBc6HqKcbWbmD7mXVLuBEk9DRr4bQ/VGT9j43WMjdQytmMcVW31eDtq0M3ezSjjSdM6sYXWKU20TGkkWhvGqEiQ8WRAs/FoUK106pWXcVYFY60K6lUlLuVBV14MpRHBJKKZBJVBWKUh6qe2awxTdkxnyvYZuE03GV+a0PRWMrPjKBP8bTXU/m487l97AEhVxLF8Bpo7g+42sV0WSbdJR0WcUEWSlF/xXutkZgTHEmiZioo5QpLSFJkKhYnCm9Jw272FO1uziVfGiQZixALt2c8Y8co4qcoUpmYxaedUjl17G2N21GI3p4DdwG4iQO7O60A9UM9hwGFolW70mX6s93upOH8SZx5/CRWeQK/zC0I5jp+8iOMnL9rnfBZMWsSCSb3zqaiDM8/U4MzDsRNTafvJSs78zXrO2Lmd6NgzaHVdSiNeGhMWsbCLOqORGncHHm87Lrqpy6SZHnbxnu5KVFcN2C4sl8mvx/2cJeO2oAr77eY/oT3vwkUlLipwU4Fbq8Cn1VLJZCrUZCqZiN+ejF9NxmOPRVk6lqWTyehYGY2UlSJtJTGsBGlCJLUW0noraXcrGXcLliuESwU4/ug6JtY3UOdvoNY/hoyVJpYOE0p2Ekx1EEx2opQi4K2iwlNFpacKt+4hmYkTT0dIZGIYZhKv20+NbwzjApMYH5jC+KopjA9MpdpXi1v34HH5cGku0pZB2kphmCkMK0Uo1UlnvIXORCtdiRY64i2kLYOAt5qJtdOo8zTk8xpTMQ6Py4/X5cWte9A0DZX7vSD3u6HI/Ufu7+wnBX87h6mi43Lfc2ks26Qr0UZnooWOeDNtsUY64s2Ydhpdc9FQOYnxVVOZWDWNhsqJ1PjrqfGNwa170HUXOjqapuUtC21lo5TCxkYpO/vdxlYKW5mEU920xRrZ3LWSNW3L2di5Als5VrC3nvc7Tp9x/j6374OVdDrNZZddxoYNG3jsscc46qij8vuam5u57bbbeP311/H5fFx44YXcdNNNeL3eA1hiQRAEQRCEg5ODVuzyTg5w1LuXk2lNYmwKkVjZSXJFJ6HHttH9h40Hunj7nXmMZx5fL9pmeA2C1Y1Eq6Loto4v7aM6Vk1NvMfyxwuMB8bng0AVB5DumTKkCrbOzP7rG9Nl0jipiZ0nrmXH1J00Tmom5UsyLlbHEW2TOaxxOuObp3D8Wydz0uvvGfT1eoFqYEqZfeG6blac8Cbr5q5j/YytpNw9roCVtpsq08eMlqlM330YVcE6yLjQMy40W8djuvFnKmjoms70ZBXelAel6SQCAbaPr6fzqAa6xo6le8wYLN3lTARthdfKMNYVp1rvpMY2qDVNaiyTWjPNhFQGFUyS6Uxi7TJQEbP4Wg6rpuKksVQc10DFsWPwz65Dr/agMjZ2NIMVTVNTVU0kEsFV58M9vkIsWYRDAr3Sw6Rvn0j9pTPp/O16XI/voCa+hTllU3uBiQDYHhfRyWMIzhpP+8QJtNaOpTr6CY7sSBGxdhFXzWT0bix3ENPVhaUlMbUkST2J0pPgC6IFVqJVPo3mTjmKMYAL8Ayg4ErDbTXgsydSYdejeeLsjjWxrrubiNGdT6ahUesfQ13FOOr9Dei6m4gRojW2m2QmQSYrRgU81VR4qqjx12OYKRojW1nR8gqxdHjQderS3IytnEBDYBI+VwXBRDvbg+vpiDXnBakDiYZGQ1bI87r8WCrD2vblPL/tMSxl7jmDQeBzVTC74Tg+Ov+LLJh0OicecQZm8sDXwXDywx/+kPHjx7Nhw4ai7ZZl8ZnPfIb6+noefPBBQqEQ3/zmN1FK8Z3vfOcAlVYQBEEQBOHg5aAVuwB0vxvfYdX4Dqum5n3TAFC2Ir09grEjip0wsRMZVNrufbDqY0Cs6LH4KhUUst+LNpem1cpv7zmm4OA+0ua+V1UFiMfjxScs1ThK8neN8eOZUIF7fAVWhc3O8Ga6E23E0hFsZRG1TSIZm4qgn8qgj4qwnyq9hiqtGt1ygWWDruXz1jRI22nimQhKU84Kfd4qvG6vc1KN/Kema1DnIloTIzQ2xDimMlFfxCKXhzp/AxOqpvWyQFIZm9SmEGZHEitkYBsW2ICtULai0l9BNBEhaoaJmREszcJWFi6Pl2p/LZX+Giq8VaBruMdXUDm7Dletl0J7k7RlkDZTVHiq+o37k7HS6Jq+l4GxXYAfGLvHlMpyRCw7beOu96F5+nDn9LpwBTx4JlYSqK8nHRy9brrCoY3/yHqm/vA9TLnjVIwtYTLNCcyQgUqY4NJA19D9Ltz1PtzjK/HNrOnDzbkSODL7D+rr6wkGg0UplFLYtuNyDRAxgnTGW+hINBNJdTtWQqi8tZDX5cfvrsDvrqTKV8vYyomMrZyAWy+vilm2ScQI4nH5qHAH9imWWCIToyPWRFu8iXg6QsZKk7HTWLaJ1+XD567A6/Ljc/up9Y2hITCJWv/YothquXro6Gp3rjPeTHeyPZ9XxkqjUGhoeZFcczpuNE3rtT23rff+4jSFf7s0N2MqxzO2ciJjKsbjcfW2JLJsi65EK93JdiJGkKgRxLQz2AWWW5ay0HAsvDRNdz57fdep8dczrnISU2tnFvXX1f46gslgr3OPFF588UWWLVvG3XffzUsvvVS075VXXmHLli288MILTJgwAYCbb76Zm2++ma985StUVVUdiCILgiAIo5zqs6egeYcvdI0g7AsHtdhVDieGVy2+mbUHuij7TLmJ3GBwAbPHHgtjjx26Qg2AOmDaANNqHp2KY8b0uT9XB/sSgcXr8uF1+faYrtwEbTjQXDquOh8iXQmjDc2l459bj3/u8K32qWkaroKHq9Y/hlr/GGaOPWZI8nfpbuorxg1JXpWeKmbUz2VG/dw9J94Dbt3DxOrpTKyePgQlG3pcustxs6wqZ48r7InOzk6+853vcO+99+L3+3vtX7FiBbNnz84LXQCLFi0inU6zZs0aTj311P1ZXEEQBEEAwFUrrvTCwcshJ3YJgiAIgiCMFJRS3HzzzVx55ZUce+yxNDY29krT2dlJQ0PxQhO1tbV4PB46Ozv7zT8QCFBfX4/bLUO+UkzTJBAI4PF48Pl81NfXlxUb9yZPYK/qva/j6+uH7yXCQMvk9/sJBAK4XC5cLtewtavSOgCKzl9fX19037xeLz6fL78vlz633+Px5Pf5/X5s2yYQCOD1evPXUHrO3PFKKXw+54VqYTsBivLy+/1F+3PbcmUp/BzJz2O59tvfM1G6L3e/Cu9naV2lUql83ebqutz5CtsrOO7ghW1ob+9Bf89i7tyW5YRYKXxO9rVvKMx/IHn09Rzty/kPdgZTP0Pdp+7NvfF4PPm+o7RdluuTBtN+C58DGNhYYG/aaOnzmPu79FmAnmdwoOUZCkZeKxcEQRAEQTjA3H333dxzzz39pnn00Ud59913icVifOYzn+k3bV+xHPcU4zEejxMMBkfkxGZfMU2TeDxOJpPBMAyCweCQiF3xeBxgr+q93PH76gmwr+TKlEqliMfjebFruNpVaR0ARecPBoPE43HS6TSGYeQ/c/ty6XPbM5lMfl8qlcK27fx9z11D6Tlz+5VSGIYBUNROgKK8UqlU0f7cNk3T8vnuS7s4VCjXfvt7Jkr35e5X4f0sJ3alUqn8MX2dr7C9gjPRLmxDe3MP9vQs5s5dKHYNpB4GymDy6Os52pfzH+wMtH6Go0/dm3uTTqfzfUdpuyzXJw2m/RY+B7l+aLBtZqBiV+HzWCh2FT4L0PMMDrQ8e2IgguXIa+WCIAiCIAgHmKuvvpoPfvCD/aaZOnUq9913HytXruTYY4tDElxyySVceOGF3HnnnTQ0NLBy5cqi/eFwmEwmw9ixe44lKQiCIAiCMNoQsUsQBEEQBGGIGTNmDGPG9B2zMse3v/1tvvzlL+e/t7e388lPfpK77rqL+fPnA7BgwQJ++ctf0t7ezvjxTpTLZcuW4fV6mTdv3rCUXxAEQRAE4VBGxC5BEARBEIQDxOTJk4u+V1ZWAjB9+nQmTpwIOMHoZ82axU033cRNN91EOBzmzjvv5PLLL5eVGAVBEARBEMog64QKgiAIgiAcxLhcLhYvXozP5+Oqq67iy1/+Mv/2b//GN7/5zQNdNEEQBEEQhIMSsewSBEEQBEE4SJg6dSobN27stX3y5MksXrz4AJRIEARBEATh0EMsuwRBEARBEARBEARBEIQRg4hdgiAIgiAIgiAIgiAIwohBxC5BEARBEARBEARBEARhxLDXMbvq6+v36cT7evxIQepB6iDHoVoPpmkSCAQA5xrc7j13K/0dU1gPpekCgQB+v59AIIDL5QLA4/Hg8Xjw+Xz5NJZloZRC07R8Xn6/H13X88e6XK78uS3LQtf1ovSBQACfzweApmn4fD58Pl8+j/r6enRdR9d1fD5fvmy6rlNfX4/f7+9V/lQqhcfjKbrOwu+Fq6odqu1hqJF6cJB6cJB6GDy5/mogffNoI9dHF/6G+P3+IckTBv6bOJDjD2Tbz5Wp8Pe38Dd0uM4HPdddeP7cb33uvnm93vzvcGH63H6Px5Pf5/f7sW2bQCCA1+vNX0O58YbH40EplR8LFLYToCgvv9/fayySa0u5fPelXRwqlGu//T0Tpfty96vwfpbWVSqVytdtrq7Lna90vGhZVlEb2tt70N+zmDu3ZVkARc/JvvYNhfkPJI++nqN9Of/BzmDqZ6j71L25Nx6PJ993lLbL/uZAA7l/hc8BDGwssDdttPR5zP1d+ixAzzM40PIMBZpSSg3rGQRBEARBEARBEARBEARhPyFujIIgCIIgCIIgCIIgCMKIQcQuQRAEQRAEQRAEQRAEYcQgYpcgCIIgCIIgCIIgCIIwYhCxSxAEQRAEQRAEQRAEQRgxiNglCIIgCIIgCIIgCIIgjBj2u9iVTqf58Ic/zNy5c1m/fn3RvubmZm644QYWLFjAKaecwne/+13S6fT+LuKwcsMNN3D22Wdz7LHHsmjRIr7xjW/Q1tZWlGak10NjYyP/+Z//ybnnnstxxx3Hv/3bv/Hzn/+81zWO9HoAuO+++7jyyiuZP38+J554Ytk0o6EeAP70pz9x7rnncuyxx3LxxRfz1ltvHegiDSvLly/nhhtuYNGiRcydO5clS5YU7VdKcffdd7No0SKOO+44rrnmGjZv3nyASjs8LF68mEsuuYTjjz+e0047jRtvvJFt27YVpRkN9fDggw9y4YUXcsIJJ3DCCSdwxRVX8OKLL+b3j4Y6KMfixYuZO3cu3/ve9/LbRmtdDJbR1p8eSgxVv5dOp7n99ts55ZRTWLBgATfccAOtra3781KEAva2v5L7eOBpa2vj61//Oqeccgrz58/nwx/+MGvWrMnvl/t48GOaJnfddVd+bnneeedxzz33YNt2Po3cx9HJfhe7fvjDHzJ+/Phe2y3L4jOf+QyJRIIHH3yQu+66i2effZY777xzfxdxWDn11FP56U9/yjPPPMPPf/5zdu/ezZe+9KX8/tFQD9u2bUMpxW233caTTz7Jt771Lf785z9z11135dOMhnoAyGQynH/++Vx11VVl94+Wenjqqae44447+OxnP8tjjz3GwoULuf7662lubj7QRRs2EokEc+fO5ZZbbim7//777+eBBx7glltu4dFHH6WhoYHrrruOWCy2n0s6fLz55ptcffXVPPLIIzzwwANYlsUnP/lJEolEPs1oqIeJEyfy9a9/nb/+9a/89a9/5dRTT+Vzn/tcfhA2GuqglFWrVvHwww8zd+7cou2jsS4Gy2jsTw8lhqrf+973vsc///lP7rrrLh588EESiQSf+cxnsCzrQFzWqGZf+iu5jweWcDjMVVddhcfj4f777+fJJ5/k5ptvpqamJp9G7uPBz/3338+f//xnbrnlFp566im+8Y1v8Jvf/Ib//d//LUoj93EUovYjL7zwgjr//PPV5s2b1Zw5c9S6deuK9h155JGqtbU1v+2JJ55Q8+bNU9FodH8Wc7+yZMkSNXfuXJVOp5VSo7ce7r//fnXuuefmv4+2evjrX/+qFi5c2Gv7aKmHSy+9VN1yyy1F284//3z1P//zPweoRPuXOXPmqH/+85/577Ztq9NPP10tXrw4v80wDLVw4UL10EMPHYgi7he6urrUnDlz1JtvvqmUGr31oJRSJ510knrkkUdGZR3EYjH1vve9Ty1btkx97GMfU9/97neVUqO7PQyG0d6fHmrsTb8XiUTUMccco5588sl8mtbWVnXkkUeql156af9ewChnX/oruY8Hnh/96Efqqquu6nO/3MdDg09/+tPqW9/6VtG2z3/+8+rrX/+6Ukru42hmv1l2dXZ28p3vfIcf/vCH+P3+XvtXrFjB7NmzmTBhQn7bokWLSKfTRaakI4lQKMTjjz/O8ccfj8fjAUZnPQBEo1Fqa2vz30drPZQyGuohnU6zdu1aFi1aVLT99NNP59133z1ApTqwNDY20tHRUVQnXq+Xk046aUTXSTQaBcj3BaOxHizL4sknnySRSHD88cePyjq47bbbOOuss3jPe95TtH001sVgkf700GNv+r01a9aQyWQ4/fTT82kmTJjA7Nmz5T7vZ/alv5L7eOBZunQp8+bN44tf/CKnnXYaH/nIR3jkkUfy++U+HhosXLiQ119/ne3btwOwYcMG3n77bc466yxA7uNoxr0/TqKU4uabb+bKK6/k2GOPpbGxsVeazs5OGhoairbV1tbi8Xjo7OzcH8Xcb/zoRz/iT3/6E8lkkgULFvDLX/4yv2801UOOXbt28cc//pGbb745v2001kM5RkM9BINBLMti7NixRdsbGhro6Og4QKU6sOSuu1ydjFRXJKUUd9xxBwsXLmTOnDnA6KqHjRs3cuWVV2IYBpWVldx7773MmjWLd955BxgddQDw5JNPsm7dOh599NFe+0ZTe9hbpD89tNjbfq+zsxOPx1P0kjCXZqSMDQ4F9rW/kvt44Nm9ezcPPfQQ1113HTfccAOrVq3iu9/9Ll6vl4985CNyHw8Rrr/+eqLRKB/4wAdwuVxYlsVXvvIVPvShDwHyPI5m9knsuvvuu7nnnnv6TfPoo4/y7rvvEovF+MxnPtNvWk3TBrX9YGGg9XDssccC8MlPfpJLL72U5uZm7rnnHr75zW+yePHi/HWOlnoAJyjkpz71Kc4//3wuu+yyorSjqR7641Cth8FSej1KqRF3jYOlXJ2MVG677TY2bdrEgw8+2GvfaKiHww8/nMcee4xIJMJzzz3HN7/5Tf74xz/m94+GOmhpaeF73/sev/3tb/H5fH2mGw11sa9If3poMNT9njwL+4/h7K/kPu4/lFLMmzePr371qwAcffTRbNmyhYceeoiPfOQj+XRyHw9unnrqKf7xj3/w4x//mFmzZrF+/XruuOMOxo8fz0UXXZRPJ/dx9LFPYtfVV1/NBz/4wX7TTJ06lfvuu4+VK1f2mtxfcsklXHjhhdx55500NDSwcuXKov3hcJhMJtNLhT3YGGg95BgzZgxjxozh8MMPZ+bMmZx11lmsWLGC448/flTVQ1tbG9deey0LFizg9ttvL0o3muqhPw7lehgo9fX1uFyuXm9Nurq6elm1jRbGjRsHOG+ZChf0GKl1cvvtt7N06VL++Mc/MnHixPz20VQPXq+XGTNmAHDssceyevVq/vCHP3D99dcDo6MO1q5dS1dXFxdffHF+m2VZLF++nD/96U8888wzwOioi71F+tNDh33p9xoaGshkMoTD4SIrhK6uLo4//vj9dAWjm6Hor+Q+HnjGjRvHzJkzi7YdccQRPPvss/n9IPfxYOeHP/whn/70p7ngggsAmDt3Ls3NzSxevJiLLrpI7uMoZp9ido0ZM4aZM2f2+8/n8/Htb3+b//u//+Oxxx7jscce41e/+hUAd911F1/5ylcAWLBgAZs3b6a9vT2f/7Jly/B6vcybN29fijnsDLQeypFTi9PpNDB66iEndB1zzDHccccd6HpxUxwt9bAnDuV6GCher5djjjmGZcuWFW1/9dVXR+2Py9SpUxk3blxRnaTTaZYvXz6i6kRlV2V97rnn+P3vf8+0adOK9o+WeiiHUop0Oj2q6uDUU0/l8ccfz48VHnvsMebNm8eFF17IY489xrRp00ZNXewt0p8e/AxFvzdv3jw8Hk9Rmvb2djZv3iz3eT8xFP2V3McDzwknnJCP85Rjx44dTJkyBZDn8VAhlUr1stpyuVz5ebbcx9HLfonZNXny5KLvlZWVAEyfPj3/NmvRokXMmjWLm266iZtuuolwOMydd97J5ZdfTlVV1f4o5rCzatUqVq1axcKFC6mpqWH37t38/Oc/Z/r06fmHaDTUQ1tbG9dccw2TJk3im9/8Jt3d3fl9OeV9NNQDQHNzM+FwmObmZizLYv369YDzbAQCgVFTD9dddx033XQT8+bN4/jjj+fhhx+mpaWFK6+88kAXbdiIx+Ps2rUr/72xsZH169dTW1vL5MmTufbaa1m8eDGHHXYYM2bMYPHixfj9/nz8gZHArbfeyhNPPMEvfvELAoFAPqZCdXU1fr8fTdNGRT385Cc/4cwzz2TixInE43Geeuop3nzzTX7961+PmjoAqKqqysctylFZWUldXV1++2ipi31hNPanhxJD0e9VV1dzySWXcOedd1JfX09tbS133nknc+bM6RUoXRgehqK/kvt44Pn4xz/OVVddxS9/+Us+8IEPsGrVKh555BFuu+02AHkeDxHOOeccfvnLXzJ58uS8G+MDDzzAJZdcAsh9HM1o6gA4ojY2NnLeeefx2GOPcdRRR+W3Nzc3c+utt/L666/nG983v/lNvF7v/i7isLBx40a+973vsXHjRhKJBOPGjeOMM87gxhtvLFptb6TXw9/+9je+9a1vld23cePG/N8jvR4Abr75Zv7+97/32v6HP/yBU045BRgd9QDwpz/9id/85je0t7czZ84cvvWtb3HSSScd6GING2+88QbXXnttr+0XXXQRP/jBD1BKcc899/Dwww8TDoeZP38+t9xyS6/B9aHM3Llzy26/44478q4ho6Ee/vM//5PXX3+d9vZ2qqurmTt3Ltdff31+RaDRUAd9cc0113DkkUfy//7f/wNGd10MhtHWnx5KDFW/ZxgGP/zhD3niiSdIpVKcdtpp/Nd//ReTJk3aL9ch9GZv+iu5jwee559/np/85Cfs2LGDqVOnct1113H55Zfn98t9PPiJxWL87Gc/Y8mSJXR1dTF+/HguuOACPve5z+XnS3IfRycHROwSBEEQBEEQBEEQBEEQhOFgn2J2CYIgCIIgCIIgCIIgCMLBhIhdgiAIgiAIgiAIgiAIwohBxC5BEARBEARBEARBEARhxCBilyAIgiAIgiAIgiAIgjBiELFLEARBEARBEARBEARBGDGI2CUIgiAIgiAIgiAIgiCMGETsEgRBEARBEARBEARBEEYMInYJgiAIgiAIgiAIgiAIIwYRuwRBEARBEARBEARBEIQRg4hdgiAIgiAIgiAIgiAIwohBxC5BEIacJUuW8Lvf/a5o29/+9jfmzp1LY2PjkJzjjTfe4O677x6SvARBEARBEEYbMl4TBGEkI2KXIAhDzpIlS/jDH/4wrOd48803ueeee4b1HIIgCIIgCCMVGa8JgjCSEbFLEARBEARBEARBEARBGDG4D3QBBEEYWdx88838/e9/B2Du3LkAnHzyyVx00UUAdHV18aMf/YiXXnqJmpoaLrroIr7whS/gcrnyeXR3d/PTn/6UpUuXEgqFmDZtGv/xH//BZZddBsDdd9+df0uYO8eUKVNYunQphmHwox/9iFdffZXm5maqq6s54YQTuPnmm5k0adJ+qwdBEARBEISDFRmvCYIw0hGxSxCEIeXGG2+ku7ubdevW5Qc4VVVVrFq1CoCbbrqJD3/4w1xxxRW89NJL3HfffUyZMiU/MIrFYlx11VWYpsmXvvQlpkyZwosvvsh3vvMdMpkMH/3oR7nssstobW3l0Ucf5eGHHwbA6/UCkE6nSSaTfO5zn2PcuHGEw2EeeughPvrRj/L000/j9/sPQK0IgiAIgiAcPMh4TRCEkY6IXYIgDCnTp09nzJgxeL1eFixYkN+eGzxddNFF3HDDDQC85z3vYdmyZTz99NP5wdPvf/97WltbeeKJJ5g2bVo+XTQa5e677+aKK65g4sSJTJw4EaDoHADV1dV873vfy3+3LIszzjiD0047jZdeeon3ve99w3XpgiAIgiAIhwQyXhMEYaQjMbsEQdivnHnmmUXfZ8+eTXNzc/77yy+/zIIFC5g0aRKmaeb/nXHGGXR3d7N9+/Y9nuOJJ57g0ksv5cQTT+Too49m/vz5JBIJduzYMdSXIwiCIAiCMOKQ8ZogCIc6YtklCMJ+paampui71+slnU7nv3d3d7Nz506OOeaYsscHg8F+81+yZAlf+9rX8rEl6uvr0TSNT3/60xiGse8XIAiCIAiCMMKR8ZogCIc6InYJgnBQUVdXx7hx47j55pvL7j/88MP7Pf7pp59m+vTp/OAHP8hvy2QyhMPhIS2nIAiCIAjCaEXGa4IgHOyI2CUIwpDj9Xr3+q3cGWecwZ/+9CemTJnCmDFj+j0HgGEY+Hy+/PZUKoXbXdy1PfHEE1iWtVflEQRBEARBGInIeE0QhJGMiF2CIAw5RxxxBA8//DB/+ctfmDt3LlVVVQM+9hOf+ARPPfUUV199NZ/4xCc47LDDiMfjbNu2jXfffZd7770XgJkzZwLw29/+ltNPPx2fz8fcuXM544wzWLJkCd///vc555xzWLt2Lf/7v//byxxfEARBEARhNCPjNUEQRjIidgmCMORceumlvPPOO/zgBz8gFotx8sknc9FFFw3o2Orqav785z9z7733snjxYtrb26muruaII47g/PPPz6c755xzuOKKK/jd737Hz372MyZPnszSpUu5/PLLaWlp4a9//SsPP/wwxx57LPfddx+f//znh+tyBUEQBEEQDjlkvCYIwkhGU0qpA10IQRAEQRAEQRAEQRAEQRgK9ANdAEEQBEEQBEEQBEEQBEEYKkTsEgRBEARBEARBEARBEEYMInYJgiAIgiAIgiAIgiAIIwYRuwRBEARBEARBEARBEIQRg4hdgjBCuPvuu5k7d27+34knnsill17K448/XpRu7ty53H333XvM79xzz+Xmm2/Of//b3/7G3LlzaWxsHFSa4aT0mgv/XXPNNcN2zjfeeGNY8hYEQRAEYeQiYzUZqwmCsP9wH+gCCIIwtDz88MMAhMNhHnnkEb7+9a9jGAaXXnrpoPK55557qKqqGtQxZ599Ng8//DDjx48f1HH7Su6aCxls2QfKPffcw+c//3lOOeWUYclfEARBEISRjYzVHGSsJgjCcCJilyCMMBYsWJD/+/TTT+eDH/wgv//97wc9gDr66KMHfe4xY8YwZsyYQR+3rxRe86GGbdvYto3bLd2xIAiCIIwGZKx2aCFjNUE4NBE3RkEYwbjdbo466ih27drVa99vf/tbzj77bE444QQ+9alP0dzcXLS/1Ox9IPRnPv/ggw9y7rnncuyxx3LllVeyZs2aomNffvllrrzyShYuXMjxxx/P+9//fu65555Bnb8c7e3tnHrqqXzta18r2v7rX/+ao48+mrfffju/7bnnnuPyyy9n/vz5nHjiiXzpS1+itbU1v3/u3LmA88YwZ4L/t7/9bcDHF9bHn/70J9773vcyb948Nm/evM/XKQiCIAjCoYeM1WSsJgjC8CBilyCMcJqamqiuri7a9re//Y0333yT//7v/+aWW25hzZo1fOMb3xi2Mrz22ms89NBD3HTTTfzoRz8iHo9z3XXXEQwGAdi9ezef/exnmTJlCnfddRe/+MUv+MQnPkEikRhQ/qZp9vpn2zYA48eP5/vf/z5PPPEEf//73wFYs2YNP/3pT/nsZz/LwoULAXjooYf44he/yJFHHsnPf/5zbr31VjZs2MDHPvYx4vE40GOCf+mll/Lwww/z8MMPc/bZZw/4+ByvvPIKf/3rX/na177Gfffdt99dCQRBEARBOHiQsZqM1QRBGHrEFlMQRhimaQIQiUR46KGHWL16Nddee21RGr/fz7333ovL5QIgGAzygx/8gLa2NiZMmDDkZeru7uYvf/lLfqBw/PHHc9555/G73/2Or3zlK6xdu5ZMJsOtt96aj99w2mmnDTj/Y445pte2G264ga985SuA84buqquu4vbbb+eoo47ia1/7Gsceeyw33ngjAPF4nP/5n//h8ssv57bbbsvncdxxx/GBD3yAv/3tb1xzzTV5E/yJEycWmeMP9PjC9L/97W+pq6sb8DUKgiAIgjAykLGag4zVBEEYTkTsEoQRRuFgwuPx8PGPf7yXWfjpp5+eHzwBzJo1C4CWlpZhGUCdcMIJRW/EJkyYwAknnMCKFSsAOOqoo/B4PHz5y1/miiuuYOHChYOKJ/Hoo4/22lb6Bu7mm29m+fLlXHbZZfh8Pn7zm9/k62DFihXEYjE+9KEP5QegAJMmTeLwww/nrbfe6nfFoMEef8IJJ8jgSRAEQRBGKTJWc5CxmiAIw4mIXYIwwsgNJmpra5k0aRIej6dXmtra2qLvXq8XAMMwhqVMY8eO7bWtoaGB9evXAzBjxgx+/etf86tf/YqvfvWrZDIZFixYwNe//nVOPPHEPeZ/7LHH7jGN3+/n/e9/P/feey8XXHABU6dOze/r6uoC6HOQVF9f32/egz2+oaFhj+UVBEEQBGFkImO18shYTRCEoUTELkEYYQxkMLG/yQ0wCuns7Cx6o3fqqady6qmnkk6nefvtt/nZz37G9ddfz/PPPz8kb9bWr1/Pr371K4455hj+7//+j4svvpiTTz4ZIJ//D3/4Q4444ohexwYCgX7zHuzxmqbtxRUIgiAIgjASkLFaeWSsJgjCUCJilyAIw84777xDR0cH48aNA6CtrY133nmHT37yk73Ser1eTjvtNBKJBDfeeCPNzc37PIBKJpN87Wtf45hjjuGPf/wjn/nMZ/jGN77BP/7xD2praznhhBMIBALs3r2bD3/4w/3m5fF4er1VHczxgiAIgiAIBxsyVhMEYaQhYpcgCMPOmDFj+OQnP8nnPvc5lFLce++9VFRU8IlPfAJwVsd56623OPPMM5k0aRLd3d0sXryYiRMn5mNU9EcunkQhXq+Xo48+GoA77riD1tZWFi9ejMfj4c477+Tf//3f+c53vsPPf/5zqqqquOmmm7j99tvp6OjgzDPPpKqqira2Nt544w1OP/10PvjBDwIwc+ZMnn/+eU4//XSqqqqYOnUq9fX1Az5eEARBEAThYEPGajJWE4SRhohdgiAMO6eddhrHHXccd955J+3t7RxzzDE88MAD+fgIRx55JC+99BI/+clP6Orqoq6ujoULF/KTn/wkH6OiP6644ope2yZMmMBLL73EkiVLePjhh/nBD37AtGnTABg3bhzf//73ueGGG/jLX/7CZZddxpVXXsmkSZP49a9/zeOPP45lWUyYMIETTzyRuXPn5vP99re/ze233871119PJpPhjjvu4OKLLx7w8YIgCIIgCAcbMlaTsZogjDQ0pZQ60IUQBGHkcu6553LyySfzgx/84EAXRRAEQRAEQShBxmqCIIxE9ANdAEEQBEEQBEEQBEEQBEEYKkTsEgRBEARBEARBEARBEEYM4sYoCIIgCIIgCIIgCIIgjBjEsksQBEEQBEEQBEEQBEEYMYjYJQiCIAiCIAiCIAiCIIwY3Ht7YDAYHHDa2tpawuHw3p5qxCH10Rupk95InRQj9dEbqZNipD56I3XSm4HUSX19/X4qzfBi27bc/xGAPMeHPnIPRwZyH0cGch9HBgMZq+0Xyy5dFwOyQqQ+eiN10hupk2KkPnojdVKM1EdvpE56M5rqZDRd60hG7uOhj9zDkYHcx5GB3MfRg9xpQRAEQRAEQRAEQRAEYcQgYpcgCIIgCIIgCIIgCIIwYhCxSxAEQRAEQRAEQRAEQRgxiNglCMKAiPyrkcabXiWypPFAF0UQBEEQBEEYBlKbQ1gh40AXQxAEYZ8RsUsQhD0Se62Vndc/T+jv29n5yaVElorgJQiCIAiCMNJIrQsSfbH5QBdDEA44aTN1oIsg7CMidgmCsEfafvgu3qlVHPnaxVTMG0vjV5dhxTMHuliCIAiCIAiCIAhDSswI88rOp2mO7DzQRRH2ARG7BEHol9TGEIm3Oxj78SNxN1Qw+funYHUbdP/vxgNdNEEQBEEQBGGIUEod6CIIwkFBPBMBIJTqOMAlEfYFEbsEQeiX4KNb0bw6dRcfDkDl/AaqzplC56/WYRvWAS6dIAiCIBwiJLoPdAkEoX9E6xKELBogAvChjohdgiD0S+yVZgKnTMBd789vG3f90ZidKcJPiWmvIAjCSMU0lQz0hwgtuBP3rtfQoq0HuiiC0De2PO+CAKBlxS7h0EbELkEQ+sQMpkitDRI4bWLR9sDpE/HNqqXrdxsOUMkEQRCE4eb5F2Hd+gNdipGBlok7f2SSB7YggtAfonUJQhFKHopDGhG7BEHok/jrbQBUnV4sdmmaxthPzCX5bieJlZ0HomiCIAjCMJKz6Gpu6b0vY6XJWOnBZWhE0eIS+0QQDmaUWHYJAuDMdYRDHxG7BEHok/jrbegBNxXHju21r+6SmejVHrHuEgRBGIH057348o4neXnHk4PKz739JVy739zHUgmCMKyI1iUIRYgr/6GNiF2CIPRJck03FfPGorl7dxWugIf6y2cR/scO0s3xA1A6QRCEkcHixYu55JJLOP744znttNO48cYb2bZtW1EapRR33303ixYt4rjjjuOaa65h8+bNw1YmGd8PE2IsIBzMyIMvCIDE7BopiNglCEJZlK1IrevGf8yYPtOM+/TRAHT+cs3+KpYgCMKI48033+Tqq6/mkUce4YEHHsCyLD75yU+SSCTyae6//34eeOABbrnlFh599FEaGhq47rrriMViw1ImmfMKwihEHvwDymu7nmNd+9sHuhgCQN6NUZ6JQxkRuwRBKEt6RxQ7blLRj9jlmRSg/spZdP1xE8nVXUX77JRJYkUnqY1BMQEWBEHoh9/85jdcfPHFzJ49myOPPJI77riD5uZm1q5dCzhWXX/4wx+44YYbeN/73secOXO48847SaVSPPHEEwe49D3onZtwb3gSlH2giyIIwl4gj+6BJZmJ0xrddaCLIdBj2SUB6g9tROwSBKEsyTWOeOWf17fYBTDxmyfgGVfBzhteJLGyk9TGEC3fe4v1Jz7K1n9/is3vfZytFz2N2SkrUAmCIAyEaDQKQG1tLQCNjY10dHSwaNGifBqv18tJJ53Eu+++Oyxl2Jt3FFpwh/OHbQ1pWQRB2E/Iy0lBEEYQ7gNdAEEQDk5S64JoXh3/rNp+07lqvEz/1dns/I+lbL3wKWejW6P2ghnUXXg4mbYELd99m+1XL2HmPz6I7nPth9ILgiAcmiiluOOOO1i4cCFz5swBoKPDWcVw7NjixUIaGhpobm7uN7/6+vq9Kkc6rQgE0tk8fEX7AoEAADW1Nbj0nj7drqoG00Crq0VzFx9jZ4/R97I8+wtb2RhmigpP5ZDmq5LVKCOAVlOLthd1UF9fT8yIoGkaAW/1ns9n2aTbEvgmV+1NcUc1XV02mgZjxgytTcDePov7E1MzsAMh4NAo74FgOOsl17dK3Q8/e6pj05MiEAlQVVUl9+MA0fXMdvzTqwkc3bDXeYjYJQhCWYwtYXxH1KB59jzYq5zfwOxnLyT6gjPpqjpjEp4JPRMF79Qqdnz8X7T/ZAUTv7Vw2MosCIJwqHPbbbexadMmHnzwwV77SpdCH4iLeDAY3KtyGIYiHs/lkSjaF8/u6Oxqx+v257e74gk0M4XZ3Q0ef9Ex7uwx5l6WZ3+xtm05bbFGzjniI0O69LweiaDH41iRMMo1uDqor68nGAyydOvfATh35kV7PCaxspP0jihVZ03GXefbY/rRSiSq8HrA7++51/963nmu3nve0N3/3D082LFCRv75PhTKu78Z7vsodb9/GMh9jCQixONxfCoyqPvREt3JztAmTp323n0t5qgn0hok0hqkblJ5Q4mBiJDixigIQlmMrWF8M/u36irE3VBB/aUzqb90ZpHQBVB9zhTqr5xF52/Wy8qNgiAIfXD77bezdOlSfv/73zNx4sT89nHjxgHQ2dlZlL6rq4uGhr1/47mvmMos3qBlh5XqEHFjTIXRoi1Fm7qSbQBYpdd2iGEnnPKrtARh6o/ly+HlZZDJHGTue2YaTGO/n/YgqwVBOGDs7cuO9e3vkEgPz8IxwuARsUsQhF6ojI2xM4pvDy6Mg2HCV+aDBh13rx6yPAVBEEYCSiluu+02nnvuOX7/+98zbdq0ov1Tp05l3LhxLFu2LL8tnU6zfPlyjj/++GEq057TmHameIOmEQorotFDQ2Bx73gFV9M7RdtcmvMG2T7E445penaiJjGY+sXOVk/8IHsP597yT9xbluz/E9vSXgShmL17JmxZ7eGgQMQuQRB6YeyMgqnwHlEzZHl6JgWov2wWwUe3YoX2/9tKQRCEg5Vbb72Vf/zjH/z4xz8mEAjQ0dFBR0cHqVQKcN4wX3vttSxevJh//vOfbNq0iW9961v4/X4+9KEPDUuZ7D7G6VaBCGTZpdZPGrt3w9tvHbpCkZ4Vu6xDxTptTxzk2kXaTJG2DvyYIJPZc5pRwWgUu5TCvfFptNDuA10S4SBiX1eSl5XoDw4kZpcgCL1IbwsD4B+EG+NAGPvxuXT/cRPBR7fS8KmjhzRvQRCEQ5WHHnoIgGuuuaZo+x133MHFF18MwPXXX49hGNx6662Ew2Hmz5/Pb3/7W6qq9m8AcrtABOpt2eW8Q9UOdoWlH/TsNQyf2NXjGrO2bTkZO82CSacP07kOfl7Z+TQwsDhkw0kmp9vaJiitxyV3BGPaGWxl43X1xHQrnZ+vW69oah7a+GUHHXYGlI3esR6rbtqe0x8CrG59g4548wF/rkYCpc+EnTIxO1N4p/b/26uwAVmU60AjYpcgCL0wtkQA8M4cOssuAP/ceipPHk/X/25k7H8c1eNmIQiCMIrZuHHjHtNomsYXvvAFvvCFL+yHEvXt/VYodvWy7MrFOBmAUGSnTFTGJrq0Ce9h1VTOP3CxxwrJW3b1slobetpijcOXee7nVawL+qTQ8iLjLDyKe9OzNMTG0Fl96gEq1f7jjd1LMMxUsSBS0lya+l/sdWSQawcjSODsiI+GGze8qOzDoEoeisg/G8FWeKYE+o3rJW6MBwcj56kWBGHIMLaG8UyqxBXwDHneY6+dS3p7lNgrLWX3x9NRHl59L7cs+QQ/XXYTq1tfH/IyCIIgCP3Tt9hll/0byItdGnse5Eee3U10aRMA6R3RvSvkMJATu+xhdGM0u1PE32gblBBlDTaGWPZe5E+RjqO3rCo6p7JsUhuCqNHoukaxq26mQNv0m137vzD7mdboLgwz1XvHaGwLuWddEyscoX+UUj3PyB4eFTUKxK5gUBGLHdx9hohdgiD0IjXIlRgHQ83503E3+On+Q7Elg1KKv6/7NVc/ciL3L7+d5sh2nt/+GF956iN87/kbyg/KBEEQhGFhIGJXrxUL826M5Qf50ajCNA/ugbFLz7oxDqNlV+KtdjKtCUgP/JhBrw5ZYtnlal2FHt4NyWA+ibEtQmpjCGNbZHB5H8qk47i2vwRWuljsGmUxu9a1v11+R8GDb6cs/K9vx9Vx8IjRQ0quL8s1hL1cfU84uLCVTcwI73M+ecuuwh/Dwt+vPQjDpRZhhzKmqUgkel/PW+/Aa28cgAINAhG7BEEoQilFemtkyF0Yc+heF2OunkPkn7sxtjsDbNPOcOdLX+De17/N0eNP5L4P/5NfX/wif7lyFf+x8Fu8sP3/+MY/LidjDWJmIAiCIOw1fQ3T+3VjzCosWpk32pal2LETWlqHqIDDxP4IUG8bdvYcis1bFJHonidFgxbf8i6l/RUkn/ng8t4HtHgnJLr32/lK0bu2ohlRtFj7qBa7CimczBfO6+24Uymujtj+LtKwo3dswr3x6WyMtpxl18ibFg/aInQvUUodNBaim7tW8WbjUlJmYt8y2pPl7R4tuw6O+hgK3n4Hlr12oEuxd4y8p1oQhH3C6kphhdPDZtkFTqB6zavT+Zv1JDNxblnycZZsfZRPnfj/+N57/8jssccC4HX7+ej8L/HVRT9m2fZn+eWb/zVsZRIEQRjtaLF2yGStaAvG6S0tBdYeBSJQbzfGvi27cuN+6yBf5LAnZtcwFjQ7KUzGIGXA9u17PmSwYld+3l463ypnvbIf52Su3W/g3nVwzJoKxS5z+EO0DR+mgd62tsdSaZAUWQ2OoAl6f2jh7MqLttVTbyNR7BqsReheEl3SSOTpXfvlXHsinHLEdHPIrHMLxeDyf5djJMXsihzCxp0j76kWBGGfMLY61lb+YbLsAnA3VFB30REEH9nMLX+9hreaXuBri37Clcd9oWywxw/M+SjXnvhV/m/9A7yxe8mwlUsQBGE042pcjisrRBSO49esg9Y2Z0ORG2PJZELlrYn6Frv2k6HBXrM/Ynbl6GsupFRvd8+9nrT2NyHT9pxkJFNkxVTmXjQ2Dk3FdMSbCSY6ira5dr2Oa9vzQ5K/3r4ePbgDLda2V8cXrao6cubne6Dn3mr2yLXs2h/9GICdMFHmQdJ49tChJTPxAb3M6CtAfUGCPRTjIKmPQ5ShsowbeU+1IAj7hLHF8XP3zRo+yy4A18fGoVI245+t4r/O/S0fmPPRftPfuOhWZtTN4Rdv3CLujIIgCMOElnFcP0rHmWZ2Plzo3tfb1S8XoL7vicRgPF3SlkE4tX+DhevZCe9+sYjooy62bIHnX3RcP3PsbYD6fidkozJEUUGdFlRpuXnV+lxo0X1UaFe3vsEbO5cWbdMSXWjpfXSzKqWcYjcAiixgDhJXtP2H1nN/R6DYtb/cGA8mcuKUVqaDa47s5PXd/2R3aMveZl7+737KsScs28Iwk3tXnpHMEHVFI++pFgRhnzC2RdAr3bgnVg7bOda3v8PX1l/J+iM38f7l7+PUunP3eIzH5eXGU26nKbKNx9b9ZtjKJgiCMCopme33GbPLLnRjLJlI5dwYyygHuS3KBpUpdX8s+DPRhWvnMrAt3mp6gbebXhpI6YeMvNi1HyaJWh/aRHN2seJC7SInvvW31H1x5s5Hvy/HtfKJDEMRCo100UPrXTdlLDG0cCPuTc9AOr5/irU36NlVBPfWjbHAsmvUWPnlL1SN7Jhd+8mN8VAglOxkQ8c7KKUG9NI8Z1lUZGFU9HwMjRvjytZXWbbzmQGlHSpaWxWvvX6QP+xi2SUIwnBgbA3jPaJm4APqPWDZJu2xJta0vck/t/yF/1pyHV984gJ0zcWC2z6MFrVpv2fVgPJaOOUsTpv2Pv53xU8IJjv2fIAgCIIwQFS/X3s2OwN4XdN7CUK5Q8pZduXG/ZYN4ad2Fu8s+L1x7X4DLRkCM0UqM8SWL4Ogv0miYagiq6vBUBTEOftn6c9tuXjxuYlTOWuF/jA7k6QbywcYz5+35FJefxOW97FY30giV8+6np1XlZlcaVFnRQXNOIiDtOdEmr10WSuObVRQBwVNLTfht21FJuP8fbCvrDpgss+W2stxbzCkaG09uOoiN4YfzlVlcxxsgdj7sqgqtEQeTJn7tNDa02qMfYhdqZSio6Pn2FCyc8Bl2ScySfTmd0HZrFkLsYNYvx9K3Ae6AIIgHFwYW8JUnDBun/LYEdzAH979H1a2vkokFSz6oaivGMfVC77MZfNuJOCtpukqk85fr6f2Q4dROb9hj3l/5uT/4j/+diaPrL6Xz5z83/tUTkEQBCFLUQAjC2VZgKdXspzo4nF5HUHINtFCu1D1hzMQc6JyuzRd651goJZVRhTcfnD1LitAU2Q7GztWcPYRH85bbfVH7veqv1g3L70C4xpgwfyBFbEQK2qSG373VU25Sy8yKMiJXQOckOfSZZriZJriVBzfT+KSgqSHMFKAUortO2D6NHC7Dy6/yVw9u105sWs/xNgxYuCpYOcuhZmBGUcOQZ55sWvvRIeBxOxashSmTVXEYhAMwYLjFCtWwSknKWpqDq77OjCydaXUPrsxvpUVhidOHIJiDREaGgo1rKvK5lDpgzM2VX9uhAOxuip/fPmVS8vl29f5166D7iCcuUjh8+2/Z0dvW4Mea0fVTAGceZ5SasiMG4acIdJQRewSBCGPnbJIN8apu2zWXufx6q5n+e7zn8HnruDMwz7EuMAkxlRMYELVVMYFJjOl5ghcOZN7YOJ/LiT6QhO7bnyJmY99AM+4in7zn1o7k/fOupR/rP89l827kTGV4/e6rIIgCEJvXFuXUhEygAt67cu9EXfpbmxlo3dsRA/uwPJU9lgq9Rezq9wco9wccyCTNKVwb38JVTkWa/qpZZNs614HOBYOusu75zz3QDrtXGTHXr6Mt8I9Yldfll15B6siI7C+49CUpTRZqZuqUqSMkhMOA23tsHUbZDIwd87wnWdvyFVJf5ZdQ0naTFG5/UWUt5JIJLsxFUHv3oo9aUH5lTIHwj5adhVZ/2TrwDAUnlRxfexu7Pm7O+h8hiNQM3zrGe0H1IhcjVHXXdiWvX8suwrc0pWtil9eHAiUKv4sl2QwKzH0lU+ZzYX13Zf1mDvb/be1wfTpAy/GcKDU3nc7w47E7BIEYahJ74yArfAdsXcjl12hzdzxwo3MHHMMD1z8Ml85/Ud8bMFX+eDcq1k45Sym180uEroAXDVeZvzqHKyuFNuvfA5jx57Xt716/lfI2GkeWX3vXpVTEARBKKHgjbTWTzyTvGWX7nUG9rm0ysoP7jVlYxe4eChbkdxmoiyFXRqvC8cyIN1c7FOhDcSyKzux0JLdZXcHkx19x2bpI/+ycVoKSKWcz72dz1nxgvMOar7VhzLWF3tItm07vP0ujkvaMGo8uSDw1sESJ7vgvubcGF2uXBX0roh3V0BLHy5qGzcp/vmvgVfeKzufYkeiuSgwvat1FXqkGYxIr/Rd3YrOrgHkn20T2l4GqC+yRsk+t5s2w2tv5jb2PuYg81wbPKrgj4KYXVpoN1po97Ce2jDKV146rQgOUaw8V3ZV2f0Re1ClC9wDB7Aio8rYpDaFht390S7pYFWRVdZAzl2u4Rf+XSY2ZcGzVHp+Z6NFhc+xpIzupWe0bVhYkZ7fNaUU4ad3kt7df4Zamd+Qwd6C/emyKqsxCoIw5BhbnMHW3qzEaNkWP3r5y3jdPv77vN9SV7Fnl8QcFceN5bDfn4fZkWTze/9B838vJ7Ux2Gf6yTWH8b5Zl/P4hj/QnWgfdFkFQRCEUvYQfyT/6Qzg3bqnxNVP6xG7sIssuDKNMYxdFrRY2Eb5yVdieUlfbg8gvkrW/UpprrK7321+pfxx0I/Lmir5zJYvoUilFInsolnu8l6Te6YwzlH2ulrboLFxT/U/WMuu/tMFg4DmWFyVI2I28/y2x4ZksmypA7eC8ovb/8GKlmW9d2g9TUDXcW53YZvI/m1Z0NmHFd+uwWoiStGdCRdvy1oblgrMhqFoboZ33y1pF5kEWnB7r3yB/PMwePoIwN1Pkzzkxa4cShWIhBqu1lW4WgcWR3Zv6OxSvPQKZUXM1WugsYl8TLR9Qc/2if25Yw8VRQuODCCWYXJdN6n1Qcy24VmBsMcytjQOZc/3gQaPd/Lr3WcXnaiAvMClFFbI6LXfte0F6lueA8DovXtARP/VSPT5pp4NpkKlbZKrB79y8UCe48J6HNBzb1u4dr3mhBjYF8SySxCEocbYGgYNfIdXD/rY57Y8zPqOt/n8qd9nbOWEQR8fOGUCs5/7d+r+/TC6/7iRze99nC3//hShx7YVB/TN8tH5X8a0M/x59T2DPpcgCILQP0qBZvcWKXKrMToxu0onUj1CUZElT26zVWwFUPakwLZ4I8tbXi44vI9Rr5Wd3A8iFlfPhkJho9wb+uLvy16Dl5dBImuU4xlkIJBoJsq/Ot4gnip4+15QhI2by5S5XMyuQQao7wtNc/6nVHkxsSOz3nF1NPdtkYB4JsTGxJN0G417TjwMWLZFd6LdsdYpEe6KLLsUFM6u+nPF3WuUjYZGOFwwecy51prFz1rPLSm+N67db+BqW9fT9rP5AgOPc9dvGQvqoJ+Zrd0zp++XTEfyoAtg7lD4cJnsTrbyr+algxJB9oZodv4fCvXel0wN3XlyvcT+ELsKA7UPyLIrG+Or3Nh+aOnHjXEgMbtyVr59xekqU/68m/9Wm9TLnVjR4udaM1P5PFIl97uzSxGJ7rlOSlczHvDzVeDeORjXxcFa5WqpEFqiG1fb2sEdmEXZitSG4IDa0kAQsUsQhDzG1gieqVXo/sGN4k07w59W3MVR4xZy9uEf3uvzeyZWMvXHp3PkW5cx6daTUCmT3V98hW2XPUtqZ7GJ/+SaGbxv9uU8seEPdCXa9vqcgiAIAr3mBUqBq8AiJz95yio0Lt2dF76cBFp+8K+hsEyFyi93p2VjIoFdIHYlM3HsItcrha1stieaiBqh/Na2WHkTGi0XH6XEPZ5MqlgMyF1Qlng6ypuNS8mUiWezp3lDbrI62HlaKHs9XYmO7HlUkfVbuclH4X57kAHq+44zo/o8Xzn2NXhxLBMCIJrZTyuOlUEzIo67YMcGChu6XSB2ASVCz8BneAOfbDonLLIIc2fFLmtgYheWiVKKTEeBVUxe7Nq7+EyqT8uuvq+ruWXP+WY6ksRfbcXYXGzNppTKx74bDKtWKzZvHiqBpCCuk2WyKbYTlMLM3vdYOsK27vVDdK4sySC1bS9Qk9yELxtLsKhEuds4BJe4a7fO+g1qr8SudHMcs3PgVldFzWQgq9QOsg8aLLn23N9z2V/w+nKYdqaXEFouh1waPeL84BUF7y95xkvFzXfeVbzxJoNnwJpQQZun1599Uih29ZX+n/9SdHZm613Pmj2bJaZryobMnttVpjlOamOI1Pq+PXwGg4hdgiDkMbaG8c0cfLyuF7c/TmtsN9cc/7UhWdXDXeej4bqjmPXMhUz9yemkNoZYcd7DpDaHitJdPf/LWLbJw6vEuksQBGFfaI7u4I3g6qJtut3bzyK3epOO7ghfhQPngnTxZS2En9iZzSi7T6m8G2Mo2cm27nWEUh2FmRPKZNWkgonF+vZ3iKbCrF2nSCQKzlJi2WVsDRN9sQn31n/h2v5icbkLSrcztJGYEaIrHSpT8t7pC8lZZJgD9BjTYu1gW/h1HwCpdArNrTv1WCh2KQute1ufs4me8gxU7OqjPNkdGTtBR3q9E1utL11sb/xISi0m8q6tGs3hbYQzMYJBVRTTbdixTUfYLBGDVKll116LXQNNWGZWmp0YalZ5nyatzEw2tcMi/morZneqKF9tb8Wuwme48L7s4z1SCac8dqK4XOs3wIsvD0IkzNLWDjt27VORemNn0KLNuS+YyilrR7yJHcENQxrzSg/txm3FqUltxhvd3mt/cyprCVNSLWkzxdKtfyeY7Oh1TF90B8G0wN4Lq7rE8nZiy1r7TqAUWrTgJXOhZddAxK4cw6R2lbPIKv0+qNUYleKtxud5afvjxS9n+ovZVc5iNh3P7wNHbB+sy6qdLPeMD7gDyn4UhgjoO/W27YpIRJVYGPedvkfEzyYqEff09vW4ty4FK41SCquvtpIrZoFQWGoFqJQasGWgiF2CIABOx+GIXbWDPvYf63/HtNpZnDTlnCEtk6Zr1F86k5mPfQA0jR2fWIpZ4AM/sXo658+5isc3/J6tXXtnLpujK9HG9uB6kpn4nhMLgiCMMDZ0vEvMTGAqC6V7UIBL9Z6A28pG13R0zVVs2YXWMxBWCitYZvKuyJvTxNKOtYdh9bzeTqQjbIk7I2afXrxyYmfIoLkF1hUaW5SIXck13VghZ4CtlbxVLp74aKU+gmXSlR9IpzPg9YJpDmCynorgalyOXuDOYaRTaB4Nhd2zGqCdYnJ4Ca729WiR5rJZ5SZRevZatXgn7g1PQiaF3rHB+bv4gou/WqqovB3tDxJPryVl9vOmfZDzZC3ainvj02A4rppaoovKpqVodgYNjQ0d7/BSyxoam2DHzsHlvU/YFinbAL3Yaj03V9LzYmzP5KoyU3ofHKtDo0x9DUZP6NMNtaS95ufUqvi+oWmYUeVsz7kz5U2C9i5mV5FLV+EEch/1yNxktHR1vtasTrKX8fSHhJyLppZJgJVd6MIyMbN9mpntW/bZDTCTwrXtBcgkaQlV05RtVuWuvSO1CejdnsKGswBHU6S3QNYXuXtazl3PTmSwU3u/SqMW2oGr6a2evmqQbox7aleuTc8U9ZkDRin09vWQFY77tewqqJfEyk7MYG8f0sLDE5k4trJJZgrc0MtkXxiUvlRsK1p0JduuzKLbUJw+nbJY/3Q3ZuFql2ViXg5YYCyzSmVfVdTerti6DbZsHXj/1vP7nxXfS8QuLZGNKWYavLsClr7QR0Y9Prg920qErdjLLYQf3zGgconYJQgCAGZbEjtuDlrs2h5cz9r2N7nwyI8PiVVXOfyzajnmzx/CbEuw+4sv97jGAP+x8FvU+Mfw/Rc/W3YQ2h+WbfGvrX/js//3Pq7483yu//s5XPLgMfxq+W2k+3jLKgiCcKjw5NMD78c82dhBSSvlvHFXzqqKOXp0LBtN09E0PTuY70M0Khyb5ifuYGeKXUzMggHxW00vEDXjoMFp409h5pij8/usrMVF0c9MzpJFc5Fp6f2ionT13xxaNjp5XzZUheUrR2WFk2qPsUyyExotE89PfDKZNLidustV77jYm+jZ66No6frCUpVMnEKOWqSlwuhdW7OJCu5XSflDz8eIrzMxDJtEQuHKdBFI787WRZlrVWpQIg44YheAZjhCphbcia3AbcfRNN0RQbNFTO/PmPXKdixcitpDjzir5YLVK5vNW5yNvkxpsGfF+va3WbbzmV51u0+WXTn6sCDSUL2EEWU6z53m1ovz7WcV1V75GhH0ri1gm8Vtq8jacAAX1l+SvJrYx6H70bivT2y7J/agncm7MZrZ59BWFm2x3Xsdy0sP70ZLx9HDu+loL1ipz6a362rus1So3quKco4pJ9ZF/tlI5LnyruGFYpUW3NkrlhyAlnNFy46Tg92KZDLbbw7KsquPzbaFHtzRUyZblXWrTKedRUPypEJEt2+lfYMjCvay7CrzUsOKZUjviJJ4t28366Jc9iAGK1UidhVdY88Beva+FBmKlWS47aUwne+EaHyrJ9B7WTExp2Fl7LJB8Xun37OA25Vd4LiycgDPaTaB2+jCtWNZ0bihCJdj3ayZRj7/smTrWJWs6FxI2ZdpfSBilyAIQDY4PeCbNTg3xiVb/opLc3PezEuGo1h5qhdOZPJ3TyH2QjPtP+1ZKafWP4abz7yHXaHN3PfGfw0oL8u2WLr171z/97O548UbMe0Mnz3lNm45537OOeIjPLL6F9y69JN9L1kvCIIwwvC7KwDYnmgmlIk6AerLuFDZ2Ojo6Jqz+mLPBKJHNNFQRStNKUtl3Tp6Bq25gX3aMlC2or1dkc75Bipw2Sa+na/mhRPL6m2JoOUtuzTib/ZemVcvWKWx94RR9fG3E2BX37msd8yRLH6/82mWFKlvtwy7J46MDSs7XnPqIzvncNs9Qp0eaaIusSZb5oIsSl1zys1A9uBylW63Wf3yLpa9qgqEF430rlhZsbA7CN1lVo0bOCrv9uNYNO1fdSMvUOTqv2Qhg7xlXX6zygeN1krLqhTt8aZsvntp7aNUmfl9/5aEUCo6aiiT7MNU0hZK49T1g961Fc2IOpZNRUUsOJlVoFIPko4ORSyS8xMtP90cjGWXaQ5128nVnUVe9rbNArHLGf81RXawtu0tmiM79u08aEX9qbIp6l8ct6xsMfq61LhNakNwgGfNil0lfXiPNWB2g20V9SXJNVkVwojialuTjXNXmnlBnw+sXavYktXbBxKzSxWqzP2Q6Uhi7IiSWttNbFkrVsS5J11diiX/Urz4srNoSA5N2ezaDbFY7hL7Lkuub8iLQ5U6O0ObSkRNVZS2zIX0mW/Z/UVu0lnLQRticUVLa8mLIwq66ELLroLnIF+PBQ0m+mJ5y+DsESUZ9y1kJfuwHSiXfkL0JSaGn6c6sgYtFepx1yw9Nhef0OyxolNKoYV2oYV6/JOVlXVzL7Lsgs1dq1m69e/lC9YPInYJggAUiF2DsOyylc3SbX/n5KnnUusfM1xFyzPmytnUXzWb9p+tIrKkZ2Wn4ycv4qr5X+KJjX/g4dX39vkWLGOleX7bY1z/2Nl8/8XP4nF5ufW8B/jVR5ZyyTGf5szDL+QbZ/yUry36CW/sXsKv3/resF+TIAjCcDJQqwC/y1FwOoxudiVb8lOl3vn1WHZB8cS/8Fy7GwuEElv1BKhXPfkApKwU7+5cxcrGlfkAtwBaKoIGaBHHWihn2VVE1m0rsb78yNyll19sRdOciVHazhA3kyUWaQXxaDLlVyL0ZsfshZP1eFyx9AVoaiqs7+xkThVMeS0FblDY+Y2asvNJtWSQKmNntiw9OfW6j3khxyxMVLa8uUIYhqIy3UKVsQOlO4WyLRO9eQXxl7f0OqKlFVas7r/9ZFoTZYWyXHkspXAm+vp+N+XJt82cCNirDp0PLafT5gXFHmFO5SbkSuVdSK3S2F8DvKyyFg99mPPk2lZ5yy7nwHx2ebehTJ+FMU3F2nWqTIwgVWLZ1cfffdBX/7JiFaxamRW/c7PNTKIoQPVgmkOpsGyaita2IWhPyu5Z/MG2e1l2dSacSPyFwvng8i//xVbOynw9e1RfTSG/R3s7SWpjKB/3sC/aYo1kVCKbV3HjscLFAr570zPoLSucL8kg6R3ZxQRyQly/glQZ6bZA7Cp+GVKYKJ+gn7wh/nobyZWdpJuc/iW3ku/2HX3IrznXzZ4CYKesfGy7cjG7zC5nXzcdbO1ay+5w736w8LenaGXGnEFlOI1KG2jBHUXX2xLdRabQS6SgfDlLXtt2rqezq4w4l3P/7ctNNC9YDvA5yNd7z+9Nr7BiaaePyIld7e3Q3Z8VFuCxYrjtBCrXHPq07HJ+OLWCPsA0wdW6GldrT7zQWMhi7ToIdRcLh29t2szqtX20qX4QsUsQBACMLWH0Gg/uBv+Aj1nf/jYd8SbOnXnxMJasmMm3nUzFsWNp/MarRWbNnzjhJs45/CPcv/x2bl36KTZ1rsSyLdJmild3PcsPXvw8lz40j++9cANuzcN/n/tb7vvwPzl9xgd6uV9+YM5HuejoT/G3tb9iTdveLI0iCIJwcDDwcWFPP5ixTUo9MMJGB0o5cYtcmo4rO/mzi94SF1upJLPj/LwLguqZDClA113U+cbic9Xg02sZ7zuioOBWUXwju5zYlT2fsdvotQ3Il9E5n7PdshTxhLNlS3wXrwdXUTh1Uiiw0yj6ECcAj6f4u2Wp/Op06woNIfK/LQVWODYoVzZwdDnto8jVJvvZsgbbiBbtLzsFVWUmQgW0Z43fXCqVzUfLTrgVerR4VeM9xS7LEX+jraxVXT4X5ZS2u1vDHKKl5AdKPqacbTrttMQiy8628Zy0lbPmUlq56ZHKt0erJJ+BP2N2mXAPva0tinfbRfkrC2xDOelzMbGKYm4VC16JhDM5bGpyVlDsFSutxF21MCvN6v9+pe04b7Q9RmvUscpoaVHFYm/Bcw/g3vo87q1LaYi+jtcMDkrsyhQYrb25XLF+A6xeA7HYwDOxU1besimZVDQ1K7DMIusnS1mg6WSyll2xrGWpPgRhOgqtBW0btHDPS9ui576vS8oJlX0UJZFQ7NypWNu2PL+tNMB+brEAzefK32w90gzpOO6dr6KFHevFnBCnPJW9T1RqmaXK3HNg1WpYsrRMQQfWteCqcjraXJyqPYl8PeVwFgBRKGIvNRF7uczSodkA55mskObOxogsXN091weaBRaTRUKLUphJkzfva6T9H2/haluLnegsurauZGHfWPAyp0Ds6rNl5UxOCwXEgj7U2BbB7EwOQvzJ9hd24Quq4hQbnnyRNU+9lbdwNdKwaVkMV1OoV3qlFLrdO9bZHleFLbBozJQxRk1mn+lQV3b1Rst0rL+z6zOUvmzYE+VfeQmCMOpIbQrjn103qLhby3Y9jUf3cvLU84axZMXoPhdTf7aILR94gqb/fIPpi89yVgbTdL519i+YNXYef1xxF6/sfDL7Js6ZnNX4xnDmYRdyxmEXcOKUs/NvaPvikyf+J8t2Ps0v3/gv7r7wqWGLRyYIgjCc2Hahm1bfqIIBcEaZzrA4b30VYn33K/jqZmOXWHblLQfy5jH0HkFb2WmDKkxiU+kOMKX2cHymwo7ApMB4mlKPc3jFFLAtZxKQ7XpzlhbFXXEZoaDg73JujGvWwtpGjSnT7PwbXz24A7uyASodC2XNMsGt8oGrSycT3qzYlVtNa/MW8oGntbiBsdUotpJWBXYblgYeDdu0nKK6eqqvV9UpwMqgOjajdWyE2vFlr7OH/lcKy6EpG6Wp7DVk73sfv4lF1hC2cqziBvp7qFR+NTjL0ti+XTHFVbh76C291q1XVAVg+nQtL2JoluFcRYmJlFI5K78MKE9Bnbp6C53Zdg97b9lFVmAs2eiUsY84YBrFseFSO8pY8hW2+dbV6NFWzCMvwDQVy16DqVOcOHOu1gh2KgOzGkryGKBll5W1RszGCjPsCAAd8WYmVk9nzTon2ZQp2bJnxbLSOE6+TBe11nps61TyD8AeKLTsCkd6npfBuEJGnt2Fq85L9VlT2LULjITC/24Ez4SeTDK2idJcWEYULdaKqpoIdgZb2Zghg46XNqOfUo/uG5yll8oHhst+t0GPtmBzfG5L0b6So53/m72SFvHuCkgkgek9CayCCrJCBsn1wZ4DCuNxWWmUrdDMpPMiwtXjIl7marKf/Vsetfe1eOQAHxjNW+J2bOzhZhdYTjlecDZ20sqesvictrKx45kCt05nfyIdZcAoyKRsTAtS7dmg+CV9QzRVUN85y1EFOk65LBvcbsiYuZIXkLXs0gruoSpwaUytdUyuqhZN6rOIhuH02V5vQfvL1YVpk94Ro/Lont8qjxXDY8WKHn3PFudGWlPqSsQuJ33+u50tb6k7dWk/VVBHZhn9UsuGM7AN23Gj1d1gz+g5ZpArpIrYJQgCAMbmENXnTR1weqUUy3Y+w/GTFxHwVg9jyXrjn1XLxG8eT8ttbxH6+3bqL3asAXRN54rjPs8H536MNxv/RWN4K6Axb8LJzJ/0Hty6p/+MC8/hruS6hTdz50tf4NVdz3D6jA8M09UIgiAME5mBDwoLrRsyucDq2W02zuA0aoTw6B5H7MpKRbnjLJuiQXQRSoGCmBntCUifjoOvOnuebBlsOK/hFOcQO+PsyWdZ5hWwsp0YJiVxUpRSaJpW5MaYE21yLhl2gdWK1rEJt7YZ88gLemJjKZWfXZZOqD1ZN0bDgNfecN7SV2Q6SHon4VvdTFJpjthVYOmmUGhR3dGjXLCtex3U969CFgtfPXnpHRt7VrYqm4Ze8ybnQnMfFo6JmYaybdAhF03KTllohRYUBXn+63mor4MTF/Zb7Py5NcCysxZkmu640O2lN9hAyYmO06dnrQ6V7cSlqlDFll3ZRRi8VpD61tdo49yeyahWJr6YKrTs2suV7Mq6MebO01uIy20vnWDm8srHv7MtrKiNu1pHzy4SgFJ5kaypCWbNAs+OLtQ4gAKxixK3oMJZblakqk5uJZbxwMoEmmmTOvXwwsP7JtcP5Kw5PRVomSQpTwM+oxNX+xo4fH4/GfRQzgIEBi405lYftEJprFgGXVfQZJEOdoJmoyp1tFywel3H7liPnklgeyrRu7aiKqdhtAbwphVmRxLv1KqBnbjQmqfEjRGlCHWbLH/XxcxZdvG+omvMCqK9syxJ53wWhjcstOyKvtRcfGxBXFpN5dyqs4JFrj2WtFnXrjcgFS7appVtoAWnsRQuVxnRbJBid18rSLo3PIldfxiqoj5bIC1b7AIRLmMXfbexi98NlLFiLHouTOX0k7bt1JBSFFpFWlmhJ//yJ7s9bZa3Os7H7LLAlRW7egXU13u/AVFlYteVBm8v5KVXnM/3nkdB/+eU0bOzCyMcw5zgwz124F49hectjDeZj0hYKHYp5axGamdQgfH5lDlybTUWU/iUQo80U9m8HN2eg235nd8L28Qq6ABKrRX3hLgxCoKAGUxhdqTwz6kb8DG7wptpimzjPdMPjAg09j+OInDKBJpveaNXvJBqXx3nzbyEj59wEx8/4RssnHLWoISuHOcecTGTqw/jkdX3DVWxBUEQyrJ8+XJuuOEGFi1axNy5c1myZEnRfqUUd999N4sWLeK4447jmmuuYfPmzf3mqXVEe1s+ZAPClk40iiy77ExRDCNdc/pP00pjKxtd03FlzcVs2yKVUrzyKgRzL7FLLDmUpcgok1azmcbEaiLtEYg2oyedA/LToIJBu2ZlimxgzDIB6lEqG7arZLKVveZyFrx2fg7Xc0ymL/Eib9lVvDk3BzGyc8W6xFrGxt/BbRWsmlVoMZOdFHlWVwIK5c6VoSfPvMOjouiYvFVHgVCjBQt80Ypc2Pp2Tyk8iVYQMN/OB4fSULYi8uwufO8WuFeViDDBUOGpS0+Su96eGFmOO1HWWTArevakHnrLrhyhthCWaTpxolT2XCUTdwV47ASaptCtVI/Yhat3gHoKLbuGIUB90YTWJr3Lcd0pjdnVY6zY086NHWl2vh6iqaNwRbkesavgUS5jqKPygnWmJU6mNdHTTrIn9pldjIu9iTZIN1TdNNCbV6CSkewGD3bVBLqrTiDlHosebuxzEYhSSsWuULh8ur4oXL0t+q/GvLWrSsazAeqdi04rE9CKRXlApULonqw1a2Yw9VBgBVVwjy3l5JXc/C5Vqe1s2txbqHbtfBUt2uOCp/Whdm3sXMGatjdxZdtGoW7T56IZOBaPAEp3gW05j23OYjfbeEotHLVEJ1ruxUOuf81Vn66VXY2xNN5aj0FuGeGmL+EV8u6MaTtBW3p19oVE1hUzuCNfv3b26S1ebKFE1FXF38tbmfZs871o4llh5dPpLSvQWtfkn822VAttRldRP+3kW8bqWBWsxqgK128oud5cf1RYp+Xa3kBjduXdunOrBFtOl2zlb8gesyhs+3r7euqzi6kUn6fnhrs3PoWWSWStvbLnKejQMhlH6Nq+w3Gx1mJtYCtcdqroN8zM9ORpDcacExG7BEEAjE3Z4PSDELuW7XwGDY33TH//MJWqfzRdY+qP3wOWYtcXXh7k4GNguHQXFx9zPWvb32R9+ztDnr8gCEKORCLB3LlzueWWW8ruv//++3nggQe45ZZbePTRR2loaOC6664jFouVTQ+gmXYv0UMLN+JqXY0W3F60vSjQPIqMbeYn/OMir+HrXkcynXHED6WxfbuGaTpuarlgtuHsBFQrnfBYPW/ATTtFU4tGOq3QjGzZe9Qu5/xZiyxN03rcGFU50w6FyqgSZafQjbFgmJubCOXH9T3pzMIJSmGQY7u8ZVephuaynQrQC8toF5ar2Kog68GCf4eFns6JTbkr6rmOAq0rLzqpnO9dvrxlym7a2LEyqwlnj9OU1SN2Fbqhlpt89iNIqXQfv7v5MmUD1DumEGiUxIcqaZzJTHzQ8VjK4bEitLz8Cs1rt+RjD9m6u9dqlUqBrmXdZZVNvv1pOqUTT1RPeyq17Mpdxrbu9f2vFtafZVdBXaQ2hjB2WhByLNOKY2oVWHtk75fRnWFjZwcbOndgKTu76EIfFmGl5SjYkY+9lnVTLG7DvcmoPpZsy+JNhwCFFnLiQO3cabF9p4bSPET9M9myRZGJ97G4QQlGH4tj23soYz5dovie5R4hy84+A5qOS9NJWEksZfYsTJBtM7am5d3q+hpv9uuWq2nErSCZbB9haT4AfEYbdcl1KJwJfsDYlV2JzkJLBnE1vdP7GSz52hTeTnusCVtz7kfhe4F+VxLMCXqay3lmlfOUam6dTCZNu9FdLOz2dX3ZPklzaY4FVJbcS4FMpmQ1zWw+yVVdRXG4lGkT/r/tJLOuuqViWKzborNT0ZhYQVdmCwm7k5bUm057B1y5QPtZj72iYPIZu+i7reziWFj9rSKZE7iCWYEs9z3alheUu9JdrIlsIWlmFzVRxccWbyywmrbJi5S9z5tNW+gmWs4Czc6uXrgnet704DHDvfsjs/8V6N2NQaLP7MROORetRxqL9uek/F5ujD0FBXLWvtlTWj2ujPG4cx8yHT11k6fgum3bdtraABGxSxAEUptCAIOy7Hp159McOe4ExlSO33PiYcI7vZopd55G4s12mm5+rdePgFIK03RWoNrbZavfP/tKqr11/GWNWHcJgjB8nHXWWXzlK1/hfe97X699Sin+8Ic/cMMNN/C+972POXPmcOedd5JKpXjiiSf6ztS0es9PshN2V/t6yFpWQbHYhVJsSW0noxyhwGNHCHcbrF6bIRS26exw0dys09IKhQP4nqXQe/fFdolVj2Ur7HSGri5VEK8qmyC7RLlW4MZYOPGxLMW2bQpl26gMJZN3uyA0SUGsk6KJhlY0ObCURXe3YuUqRTpt59NrtknECLKla20+7WHTywQUzl5ApjB+ial6rIMU+dhVuU2ZjMI0obKjxEooN8HBzopJKn8t7o0W+iazWG0rmrA4aeNvtmOFyolduQ8LG4WmANum0whi2nYfk/jc+csIYek+LJzy9z+3GmP2XhS68WgFF5vltV3Psbzp+eK8TAPXrtdwbX+56J/esYG+0G3HYiUT78xP6JW7Iuuq1SMuObFzco3Fyu9T5aZHys7f91JBzr37NfTmFewIbshm3dd4Q2U1KptWqxm7UJBSNt2JdrYH16PMrNBpO7eseNJXWH5nbLOrUZE0NIwUrIls5vXgKpRtF1n45C27oFj0K5i859DcuUmr3ePOliUXjyuY2U5remXZq2xrz/UDuU8nv+4gdHZlXUF1x20qGiwT4LoM6T4MwHy7X8G98ak9Hl8qZuQsu+ys2KUldGp21xE3k2SsTEFQMOdeN8aayOjOJL5vsav/jRuTb7PRdAKbmTjXrxVY9VSntuAzuxy1qjAoum057S/eAel4cV9YcC8NnP68MA5SpjDgW6lZn5WmKx3iXx2vk8kkna5E19E8Gk2xZlZHNtORbC28mOLj8/7nyhEHXVq+7XfGW0jgWKU1NcPzL0I0musLsrmlbVIben6DnHhfCqM5F9up+HTr19m8u7KnFEmrm4i5nXWxrUXpFFq2Wat8GVXGLrbkQhXVY+7lR2E8wvxvRqF2o+gR81XPs2lnlyLcHnKsrT0uT9G1Zk+Sz0LXsy8brAINqpe1tfO5Y5tNR4fKX0chmktj5UpnwYb+sG2Fmc7GvcwkmRB9xVmVt/CUVv9il6vT+X3LuZOqElErb7daLuQA5Psdq2A+ZpoFVs1mhkyHUwaN4p82u8Cq1LKtHkF+AEjMLkEQMDaH0as9uCdUDCh9Z6KVDZ3v8qkT/98wl6w8iYRi6zbnB7SpawZVJ0U4+i8rWfaKzaMzTiMS10kli025AbxeRWUFVFRAXT2Ma4Dx45xAtjOPgCMOh+rq4sFAhSfAhUd+nD+vvpu22G4mVE3bj1cqCIIAjY2NdHR0sGjRovw2r9fLSSedxLvvvsuVV15Z/kDT6u3GmLWaspWN3rUVe+qJgCNGaCEX7m0+0gvTdGa6SWU2Usdx+UG/hcmmLRYuzcUYjys/UYRcPJrc5LbndEqp/ARdIxffRRGJKtJAOAPTshPo/OTD5QMSRcXOiSZYNttXZ9je5aNurE1tusAyJhcEOrtJ370cxs5wdpe6iBS6MdomzS3QXgcel1VgaGayomUZ8WQaXc3luGPcTJqkEQyWTNrQidlRNqWe5wjrKGB8iVWMws5OUlSVwh7TRntLDUr3oqedSVhukpU7xNURI/HPELUfaCAYstm+w6a6PYbuq4GZPQP9wpW1HPdEMDv6sLjJuzFa5CopacaJWl3Eg0lqOo4trKGC/xcHSc+n6EPscizHyAsyTj7KEfDyt0thl4lOnEjHsiKQwqW70FJhtEQ3qnIMKhuOQDNT6F1bHWHTU4GqP7wnT2XRkdlMpYqxK7mDMW4D5faidD07SS2Y+qhs/DItW4+5smquvJtRzzUVuDGW7kt0o1tByC1coCxcmnOe5mYbI60IBMi2TY0mu5E2uwWf5i+Y4CpWtCwDYALjCnK3i55hpbSsK6MCG1auAq+lyNkvdGXCJJKKSCyNoid8Q6ErmSqycFO9no0iy66SffV1cMRhiufWbclVYS9Wrc7WSy5WV8GqkTkh0c5aNoW7DGqmKtzu/i01+rLs0owIeAdg5dGHAGlbTg141lXgtysJTuxmQ3QbufrMuVobKs2mrpUcx3xUxiZtpogYQRoCPcHBc6dIJhWRqBPTb5xp0xPV1iaFI+5llDf73OeOVXnLUKVUkSuYvmMZemwbWnJi1l1vLuCIrm2xpp660C2Usou0TLug8Wha8f3SzAw7Es1oVoZU05t47ICzw62TNtOgwEzHnXar6WXqUCP4j+24WxXK7ULTNZSpiKUjrGp9nca0YpbnIlqzelk8DtWlIX6LLJKyBcz1EaXWVrl+HZ3a5HrGJ9tJ0Y1WImfkxa7CFzGmjfIUvpixiwPr9+Map+Xanpb7Petxb897BuYPV4TCikD3CQT867GVwtgRwTMpgKugPG7dyh/XU60KzU7j3rCEJu9JdLXYjEmsIVR5LDt3wbhxvcWujK0RCSu8e3BuWbdO4dpgM+8YIO2IVjmL5Nzvgt61pf9MTNWrCdibM2gVGtpUNyor+CkzTXKriW+yC1dFz7OZi+1WKFyZBbrymLbnnPh5CqzDa6BAZy00ZrBsC49Xz7u17gkRuwRBILUphH9O3YBXWHpt17MA+y1oe2urYtlrBm+8YbNmLWzZ2vPDouswYfyxvO9ojVPXreCzepLt55+ImlaH263hcjkmwqbpDECSSecHtzvoBG19593ipasnTFAsmA8nLtQ4aSE0NGhceJQjdj2z6SE+fsJN++WaBUEQcnR0OKshjR07tmh7Q0MDzc3NfR6nmTZ1dfUEAgVvq/UEyS4Pr7QvZ97YcUyrd4L6VlZWoDVVgaWjKxcpjwbKRyAQwBfz4cGDz+elLtOCS28gUFlFUvdTEfCjKnUq0xVU4CeTSaEsH6bPR4Vfp76uHldlirDXh0t34/P48Hk96CkXbs2D1+fDV+HC57OoCgQI+ANQ2wB6moDLwI8fLRBAc1ViBgIEWjvQOyMEDp9OIBCgIl6B5UtDZQWa7kK5bQKVAXSfRoXhIRAIEInavLncz4XvrSMQSOPX/fi8Pny6M+HWTQ8ezUsgEED3+sHnw+fzUR2opMZVgyKBXemifkwt9fUulLIJBDJUV7kJVGaoND0klAuf20PC1U4gcDi11bW4UKjuAHgqqLTB7fGgz/Zip9uptlMkvIdTlbRwx13467wEAm5iKQPTkyQQtqgYE0DfmmbHq0m0aR7cyW58teOpqq4GI2t9U12NSjqTVK2mGq26nkygZ7n7aFRRXa2hon483gxunw/d48WLF9vlwp/pJOXz4HK5CTSthMB8fL4MPp8PTZlUVlZge9N0JDsIBGYDUF/v1JuRcKMC0ey2euxIAKwAWnU1Wn09dlcVbo8Hn+bFX+HDZ3vxer34bB/uphTWzhCB4wL54wMB5++VXS+TNg3OP+oKlBZDBQJoc89E8zlBwVUmhdryAqQ7wFBok2ej+avJpNJUG4/RmVnFFuLUaBp+jzPVqayqospXCZ5KsCNotTVURWugwk+lv4JK5aO2xu1ct7cSl4XTviw/Ps1HTU011e5qSNkEqiqz5XXMjQKBAD6fRlXXJuz6aShfBq/eQKBS4/lXu2iM+xhTDxV+P5W4SLvjeGwPFd4KOu0OxnuhOlBJwHaEkBqtmqTHh7vVpjreTc15R1CfXcwg5K/AHTCxK/xUV1Vjdgao8rrR3R48Hi8+n6K5xWTlGh+L3lNHIJDJPt86Pp+PigqduppqfD7nHlJRQXV1NfX1Pe1Gr6vA54ugPD50bxqP8uD2+/H5fFRVeaiYWI22JYMPJ7/c8bn6yOF1e/D7fHj8Aerr6/H7vWieSuyqAKYZwGtUEA3prFpdxbnneOjuVowd61xnPG6zdavFvHludF3D40kTCPQWrNyeSjZttph0WA2TJ/W9+kEsYOIK9FicpF1+PF4Dl63j9XvxeHyMd48l5OsiriXwV1RAuuB8lQG8lgfi4IvphLUg22JrmD316Px119d7cbk0Nm/J0NHpDFLDsQoWzQ2gqirx+byYKQ8+nw/LX0dFRYxApcLns6gMVBIJOeKk3++nrroSy1tJqtmEJg3/eA9ejwd/RSV1tbW4a3y80/gy7Ynm/HPTHNxCm7Wa6rQfn88pu7+igvpsH29Wd+bFEs2jU1Ol4/f78elpApWVVLoCGJWVeOtrcLs0fB4fFVaY2vAG9MNPR1kZVPZcAPh0kr5KfD4TvG6qa324Kr2sTawhEAhQX2FRr8VJe8fj8UJtrdvpPwMRzLTzXPqra6jJls9KmpiBSrSYj0p/BenKAFaBWZXf68ITCJBxBbB0C7fbTYVp4vdX5evA58vgVV4qKiupqammotYRiGoC1ST9ISq71sH4OdjKpra6BgIx0DVSWoLqzR68xwfy9RWyqwkkA5CycPnS4NOoqakBfxJ8PiorfJh+H16fDz3tZkrNBDorK9nY5GOyqsDv94Hyw+YEZijNmHnVqFgAv9+kSqvAcAUIVLnw+fyYlqIyECBgpQgEAqS2NjJG8+JzQ7UWw11RRX29F+UNYwZ62nlbSKOyogKXz0cg4Mm2w/r8/lzbTMRNGnw+Kiud58nv82H6AlRUBqirq8VbH8DeHcn3C7n6xLJx5/oKXaOy0k9dbR2eej/pygCeVBRS4J7pw+V3xgtmyk2qzY3RBuPeW9BeAgHQkmh6IJ9/ZaWLykAlPp+F36/ht2y8fh1/XQC6bPzZc3sqq6hwK2zdR1V1DRU1KfRxA1vtRMQuQRAwNoepPm/KgNMv2/kM02tnM6121rCURynF2nXw8iuKZa86QQshRiAAxxwNn7hW46ijHGuACRPA49GA4wj+JYB+65uM+80TVJ05ibHXHkn1OVPQPLm34GVCwypFVzds3Qpbt8GmzYrly+HZ55yBwrHzFOedO4kF48/lmc1/5mMLvoZLH+blpARBEMpQ+kKi3xgxgDIytG9swm+k8c2uRbOSaIkuQpFODMNgW+dWqsY7lgvRWBRlZdAyOumkQTqTxjIyxONxPEaKjJXBMAz0+A78WhtJ1wkkXSliUR0tbpJIxEmacczWBEbIg5GuI5mCYHc3iXCEtGFgWSZpwyCdTmNaJrZmYRgGiaiJL7yNeOhY4tVxbE8aPR4nlU7S3Z3C5YvhNiux43Eq20OkkwaJcJRUXRRXPEnKMLFjMWdZKyOOPx5HNzVC4STrumNEonCYK8KW37SQ9E8k4U9Q4UngVs5EoP2VOKmQi3htHDwRvIZBSqWIhrsx7DSxeAIz1k00ouH3aYTDingctm0HX8cKVLoJwzbI2AYp00M8HkfvCuJ2hXDF4yi3SSyYwMxkMOwEsbiBsryYZgbNzJDepJEaaxGP6yxte4tdCZPD4vNJ+BSuXWmIWKSTaTLpDHYqSSyeQDOcWEd2OIiejXtkBYMo00s8EQcF4bBi126YPAnG2SkyZoZ4ysBleki70pimx7kXegZTuUnEksQTcQxDkU6kMHSDuBbnhfX/xEgrtnWsZKxnNsGgI3qlQ3Hi2ZhxnmAQPRxCj8exQ93YviCuaJRkyiBtGey2VuAxUxgqjWEYpJrbqByfwXg3gjlbJxgMEs9eRxznMxgMogU7ccXjmJE4uAvcYyaeAqkIyS3P8PryxZw853J8nS0Yse3YdggbSKXAMDTsqnHEEkliRhjltdDjcaxwmEgkQDoZJaWSJBIxwmEwDIOkncFrJYnH4/iTSbSMQSQcJmbEiRtxukPd1OlB4vGs22hFHNPUSMcimKnNPBt7GtX8Xo6YWsWqzseyBfaQ6m4moVUTTUcwMTGUwcqWVXhS3Zwz6ez8dUeiUYy0QSYNRjxBW1uILVthzmww4kls0yATj2OHwkSjUJk2MC2bjJHGMGwypk08HqY76CYeVWDZdFe6nGtLwfZt7cQTKdwuDTsRJxwJE/T11L9WaWMYBkq3cRlJMloaM6VhGAY723eybf0KDMN5dpJJiEajtK3aTaLFharpWdXNlUyRMpyyasEgRipJ0koRi8WxLDBSKTyp1XTYE3jjTR+7dsNpp0BNfB073tnORu8FVFdDXZ1GV5ciVcaVcUujYwG6bmUbFX7HO0Hr3oaqmZp3hwZIhsIYBfHBojujpEMKj64gmSSTMbFtiwbq2N3VgZZxoWp7TmgnU2hx51ydba1Et6WJ6TG6u7udWENKsa25i2p/Fc3NftLZpupOOu0oEwqSNlJYptOPxjWLaDROPOFYgMXjcTKmc9DOnQleNTs5bFMIM6JIGGnUDjcZb4ZU2iIUDOGyvOxu345ZYAEWiSsMw8kvf53RKMHsyiHxeLzHMkjX8IYiJFMpjIxBPJFAS2okEzrpZBwjbWCt0omdFCfeuh2zYhpaJomroA7VlrdItI7BMCailEUsaaLZKToq2jGtDFXdGwlYFsH6CwAIR6CiQiMWjWLFHXOpTFTDCvrIZBQr3jSZ3h2lIpai8ekU9uRQ0b1OJT2k43EMV4iMmWGX6SzUkUoZ+bZrGApDGcRiCcLhMHbCjUrb2J0hIr5GDMPA7tiFv2oCoWCIRDyO5tGJhcKkQ0EyVQmCE5z6ikQixONx3NssXIaNcmtEImHcsQi6YeBKWIS7OjFSKTKmiUorkokE6ZQbM9ZN0megIiabW9YQ7Axy+tTj8MbjJJOKFDESepxwCBLJlNMG9DhKWcTjcYyERipVhZ7JkI6niIdiBIMa0WCYUJdNtd9G0zU62jSSY3x40km6wxF8bj8dW1uwoxm8M6rzfZRbpak2DKIxA7dLI2UoUplKEvE4oe4QHk8ad9zp+23Nm69P0ib+ggYVj0PXzjZ8qhIVi5PJrhxhGi5ceox4PI4Zs0kZmXz6HGnDQ6IzhrvOl8+/qxuqYwkMAxIJiKdM0pZNMp1AJSHlz4p1HR1UhFdguMfSFVxIXdRGt93UsWdE7BKEUY4ZMjA7kgOO15XIxFjZsoyLj/n0kJels1Px9LPw5NOKxkZwu+GE4+Gij2icdUYN9fWR8ssXZ6m/bCbV751K8MHNdP5uAzs/9TzuBj91Fx/BmKvn4Du8ptcxmqbRMBYaxsIpJwM4MQe2b4dXX4elzyt++nOFe/pHcZ/9H/zl1aVccfq/DdgKThAEYV8ZN85xa+rs7GT8+J44iV1dXTQ0NPR5XGPHy8x4YwFUaGheF4H0y0CPG5a7QCxLppzBeiXk3UVcZVYzdNzRdHSybozYbEtupyntY9xWC9VpgssEf/4AlJ11lVJaQTDwnnPryW7cVpxU03YyMxUuV26SqtHWDsm4yexp2Umdnl1W3lZoWsGKWrlVuaJt+Vgfjc2KSGX2MCPr0rl1N9Hp25nUqGPPUqRMCIZt/GbOxcKpG6UUmm3h1r0oy8S0k/n4Orl4Px2dMDXdlK8XlNUTI8ayQS+Ir5TJ+eBYRW6ekcOAzUDK2Z+bvGqWjfN7ZLM5vZmIEaFOZS+mj5hdjdHtKDtOVbYQuUDVhgF4IEGcDeYmZmjHobLvbOyCwhQua69ZOJ5c2frdvRtMlSJmtqLULF7Z+RRjw2MJtu+ixj+Geo5AUzZGi4UdT2EEFIkWG7PA/aTXCocKXLtt7LFaWeE2ElE0r0lz9FgFrjIrKvuqaEt3o5kRgk2vMynlxqBAoFCQDExne9sYZrg0qCqMVUU2Zld2ZTKsglj/hQHqez5zMeB2BDcwvW4WuWmUKnheTKWwTNCUSVOB0aWyDDTTQFWMx6Sjp+6zrp6GZUDunpTEYVuXjcczYQL4sx5llm2SyRiYKp2Nq6WBKow3ZGJb4NneiasjhjX+MOc6Ndj1yju0K0cERfV2VcxHFrey+xIKtTKN2xOhLbiRlFFDrqHnrj21Poi71UumQOzKu4lZToByx41Jz7t+5dqDz+xm927HHTCddlbWy3Tb+LdvI11dT/fMOlKGhscNmeJwaXkaOv8FfAiSQVzt67GTQewpCwvKUpze6rIhA6oy9wxoaGhU4MO7sgLl8pI5ueAATUO3daLtG9jSuQF7+kQY46wM157eQGdmI1oz+Nw+0pkP9hxWuBBEQTUrzYWtetq9uyDeH8qmuzPNlJCdj21n11poSQ3lcvd4iysbw1C4XOB2a71d1gGzL/e8vKtkzsU826+i0FwKCxs95Oop3/aXyuQBWna1SnTQXDrKtHH9MwHjNNDsXumLPgtob3fE+a4umFomHJPm1vNucG6zJM5buXiCOTdGPedaZ4M31/A0Z19ubQ63Bij0aAt6pKeBKRSYihmJw2nUtqE8WZfcnMConN8L5URAZNc2N/Ycm+rUVupTLtJUkrHSGEYKPBA3IngBNB0dO/scF9ZHYZxHq2e7aWNmbMBFtNtiR4ub8QGbiRPBTtt4tnUSNHewpaubo8adQOxlJ1aaZ0qA/HOaW2zFVQkks2dz9Tz+fSzyopUJiJ9c2YWGURzLDI3CWGY996HHRX/nDotUCCb7C9wYzZ5T20nF9ncs9ICG7lYULv6qsqKax4ph29lYpAOch4nYJQijHCMbnN43u3ZA6d9qeoGMnea06b2DKO8NSilWroKHHla89rrjnnjC8XDdtRqLTifvflNf7yYY3HPH5q7zMe7GeTR8+miiLzUTfGQrXQ9soPP+ddRfMYtJ/7kQV52v3zw0TeOII+CII+BjH9XYtUvxj6f/jceSE1i85I/860/ncf1/wEkn9ra0EARBGGqmTp3KuHHjWLZsGUcffTQA6XSa5cuX8/Wvf31gmRR0VaayUC4PbuUEe9d12LzFZnxUUekjP0l1qezqYwXZ2NhompaPX2Qri7idIGmHwMi60xUcEXulBSuc7gn/o8DGxCKnphSMs5VNYxNMn+IuKnJ1dDW+jjEY2lmgOb8Tmq3Yts3mMMuJAqRFW1G1UyEdRaU9UFHSN2tO3Kmu2DrqtlroOsSaXYRcFnVpmxofYJjYVgYqyMec8mguPJ0bCETTuLSrIBUDFSCvTBTWi7JRGoRT3QSsiSiXTWKLSWJSgu3hVrxKoVw9U2sAq8JZJUwlcrGtslgWSrmd2Go66BktX4cpQyPeqWho0IrErk3da1HJFo425+J3VYAOUasVIx5hah15ISipwtnr69Ejeia72e+WAg9ErRa8SUUyO7/MqASmncGMGnSv2o0GRIzu7P2ziG8wURVRNgYbqYykMNKK2nEK/GC/DXqdGxUxwVWwOppVHF8nx7sroSKUwaj14Cn3W6vpqMpxaJ0taMGd4JpIsmCiZtuQMLyk07CzEWbPKV04IRuzi2w8qVx7LxJ5e8TUwgUPDDMFVOXzgdx4wMZWTuvIpa9IN0PEEYGsmmnAbkBhYeXFhGgm6lgmAioZLwgo31MvzdGtuKJNjFU+NoXXYjY1sTGlU2NHUbiKBB3bzmCkndhvAGbKzl0GLjNK0ZQ+e4p4OoJpZai1ssHTjQy66jmuJrWVSKwDzer94lAphWYULgPYM4vvTgVRYeeaC6bzdAYW0hB/m/zKo2SFWU3DarLQsAitDNG4QYMpdXg8fYtd+XaciyFYGjy7ZLW6nkUsnODdCqdP82tep9yl7c2ZXZMM70JPhshYYzHSGqtWK1J2KJ8smjTwFh2YrT9lUiz2ati4Udk4Xf5MW3arc891O0NjE1RVgV2pMI9JorVYTijDgjh4m7Jhlo49pny9bNlq0WBkBa0dQE50VxaRLe1whAvG5sR6J2+l9SgQdpnnsrAOVW6hBJUmrWysjOmI2O0KStavKhf3ryj7fgyVNY/ec5N7xforiAul5Z7lYstnlbZQPiuf3omLlt3v1vPXqQpvu1Joaajx1XF4/ZFs1TZgWTY7NplMDSgqFSjTyka903DjJpZRuO149iUPpIwk7uzLnbARoh6wcaGhcOnFMbtUToHHEcu1gv4mE86QiEEmpVAeV94KMG0ZNBmrABhDNhZn9rfJ7EwBWWvHbDs0x87F27WiqPaUrfIPhELPp9U1+lyl0g7F2b6t+BZoysZOKxIbC3oXEyxdsXUrpNMRAJIF4TgLxa70NhPLAisFupZGK4g5aOdFNzsrHOvlnHXKImKXIIxyUpucteIHatn1+q7nqPGN4ahxC/ecuB9sW/Hq6/CnBxWr18CYerj2Y/CB8zWmTN53AUlz69ScO5Wac6didibpvH8dHfevI/qvRibfejI1F8wYsFA1fbrG5z/jxfvGFfxl7b0Ed7Ty1W9MZMF8uOHTMO8YEbwEQdg34vE4u3btyn9vbGxk/fr11NbWMnnyZK699loWL17MYYcdxowZM1i8eDF+v58PfehD/ebrrDqoodI9A1BTWaB7cCubpS/A2DHO1Ltw8JhfyKrE8sPGRkfHQsdWzuDaVspZuVHLxefIDtjtDFZrCPx1PdsUNKXX4KdwMa9s/ClsJ5B2VmzQCgqk7DT1xipqw9sIW0fREHwdfYxGdxc0AFqyC+V1JumRdzLUn+FMOWuTWwhXzHImZBYobNwpBX6FmdTQqpzAv+MrNIKrtxKxDNQZ+YvHnXbcO207jie8DXf7JtyBo4HDiuo5F4AdDRrDW5lsHUW6Mc62TS10B4OE4hncQR+BRFe+OmtVMxnVDdpxecsuACzQzJzYZTkTOKtHjNq4GVwpGDNGkW5NolpMKg53gwHetzMY/iR+PHiT/5+9947XJDnre79PVXe/6bwnz8yZPLszm5MySCAkJEAiGZOEDddYIppkY/tiLhcu2IBNMo6YZIyNQSbZBmMQGDBckIQQWqXdlTbv5HDOzIlv7u6q5/5R1W+Y2ZV0r80Fo/Poo52ZczpUV1dX9/Or3+/3XGevuEizhNK3Jv05ZpkIXj3OKUOnuK5OGHkxEdwuz5J2Jk3LtU/uRqQfdMggbGMqaf+4NFnJYueDuMj8a7egzMCvC43NOlo45PhknASC060MgjyHlhb0veeFluP8gbtxqcGt3E/P3M7Oc79MbbiNIzD5So0AEpZrlwvaR3wFUYUxruGeCW4CgGAmoAmRXaOKU09qMwqXPy84ZzAQTfg9gfEB0CjWoRPBLo3ACjrD9ukUHahHr50L72VCEdTQr8bwzNYjrAy3aWZLYJQT7TN8cHCWIX1U7Gz1MkqGU3UKykE0xHY69XCH41fXcm77SehdZ0WOAA+xOHx8XNlyOvGWOKdMQgLTcmqOqYpSeO84u32Ny08V3KfpDIhUJAu3HHuUh8stytD36xsgR8Lvswz6L1B7IV7cR/jdTWBXBTCocn5wBcMSBsikMX42nFcGFxLm5oAFMMwyEK9cmfD+hUsAAQAASURBVG27ajDcnga7RD27u0q6MCkKEbfGkY7BrlF5kfk2dHvhQCZKxzo9Q70Z9rta36C2vUbN5bz30jtn2vLsc0p/EFin02Su8HyHvxvPGBBt7DxJ34/gYgC7vHrKQsmHnnTsyB7ZY77gqe457p67jdRMQQcRqLlRPEnPdklvCAzjEobIePxXxUPM7nlM0kP1eZ5mYfyuGZRDzg0u464ZVhvHObiwHMAunRjDz8Qtj6JGIM5PxmGvxFfMTjHhevMw99nETI5x0zihAgrjeM/zwFDc68ABBV+4caeKGIRyvK2qBoZsfwtb7DB0DZAEJMwv1b2aNreX8XuynLkuGZY88j7LqgdNLTb+buSmdILxfSw1i44cbi9nDHZF1pUnobz9tfDh35+Ar17HrCyVBKPh/hsDPgLYmhhkimpVDG+qGKGhzd1HC7YuBcB/cVHAwWAY5vIqBv0w56ZJALuqLh977xdKe/QsXbfGxYvK3BzMTVXYKF0Eu/wLIN83xT7YtR/78XEewye2MfMpyVrzo27rvONPLv13XnHsdf/vfKuKAWb7LGbrLNJdZ/2Za5z/8CbpIOeblnOOfYlndS1Dsjp8uI2/cBCdO4RfOI4/cBfUPzbW2QtFstpg7dteysJfvp3L3/ouLnz9H9J+3VGOfO8nkB2b+5iP8zn3fBm/9KF/wV/6m7/I/MVv5t/9rPI3vkH5vM9V/sbXyC2VHPdjP/ZjPz7WeOyxx/jyL//y8b+/7/u+D4DP//zP5/u///v56q/+akajEf/gH/wDdnd3eeihh/jpn/5p5uY+8hxWfT/qMIdIai19idoEjQbMm1txVVkiaDNO+h1COZPYOxyCQQgJgsak3ZEHJGsq6nvP4soB9sSDQegR6UTVTCnxc3vCLorsgilmTeLDx7wqtEYXQWBu70PkzQRxFuemVvWnsjxfhLalcX9Ux0mftyFp1YGheZCKYMZ890NsuV26u7CwZAMoUwaWzRV/id7eEyzWzKSKFVBqSSKBgVUxuyCslu9c22S72MNbRzEMH93F1Er5crvkhhCq+EUiitmxLD7WQJoFUEP9BOyq1t5dbiJzCHrv7yJDR+O2hHRbkBFozTPaegrp9Ui0RDWhW/ankrAolVTwquzuQV2FYsvDkdiXU7hBf2olXlC6+c6sLGwMnExYUuP7gKeeQfZYSZdpKdfUWPEVq8Ejow5aX2CUKwnQdzf4o+0P8JLuAxyaO8YLRbenXLlkyE2dRWkytB3qNOi5wA5wYrlxPedKR3npmdB5qmBxYzaI97F/xE7aN0FFQpVFkwSw63nAOSMyPq7iKXTScT4eZ+TycYdVElLvleeu7aFzi4gIvtRxZTOJ9LsR3cBoVKGkREQ51DpGJlcY0AdZACdjBtPl0cPUdjo0I7TnIrPLlVFGW33CTYPZrsDkvTFoVgFdN4c8T47pnSKFDw+zEcR7BCi8Y2sbNq7DnTqiqsYIAYAM1xjGTZoEn7VrGxF8iEBk1bxsljI1E6qw+8ifwMEzrDzf729iqFRzzsAPwEukVAl1SWPboN8DfaxOUQd7WhE1k7lwahz7CijgVuaZ4LlwEW47djPYRQC7PPS1z6X8HKcPCHsdJe15TL+HYthuPsSB1iNAmPsu9S6R9q/TzXdnjlVUHmEW8mn5F8rCqUdITMrK5hw6DOOq+/iI3T2IuAaKcuki7F7xHNNpKbDnmd4FNkZbrGZLHK5PZPNaMcF8h5pd5MTyYS5efDK0w6TjMd8orrHSex+1FIwR0Iee5w5V3aMMfY5X5cLlERd4hje+9BXslDegf4XV7lXO2VlQU2+R22vAb1B6HY8rFLmSUyzHmyMGVc+7361kF+GhQ1Mgpp9aYHEFMiwBQTIDebV4FDFbDYBadVcDO3bqbekVPJjRHj5V8ryL2kVUBfUO2x3glhvhledL5obP0q5k7H52ghXv6e44iquga3byDE6z1whMax3luLykszMtIY9glxrIWnhTAzzqSsyl92PMXLz8CHapx1gD/Tywog+2Sa7sjk/nttZnelzwuPeMKE+22N0L79zFxeq5m82NhkMPDUjTWWZXJTmV0ykIZOUOAN0utIrqneWDDYMHe+VR4AUojVOxD3btx358nMfg0U0a9698TCynJ2+8n53h5keWMPoSc+NpzJX3Y698AHv1g8juxRn69lLRxtRXmFurMbdQQ4yB3gjZzWG4S9LfnNnetw9THrmfdPVe3LFX4NfuB/sRvnpeIBr3LHH6V97I5r9/kvUffD9Pf9qvsfo197L0pjMfE+h1ZP4kLz3yGn77mZ/n33/x3+KNbxD+7c8ov/CL8I53Kn/nm+E1n7IPeO3HfuzH//v4hE/4BJ588skX/L2I8E3f9E180zd908d8zHzJkD24ilnv4PvlGOxy6sCmDLvhAzJNQnKuEiVNTqNtkR+vCOPADIP0zCAYMYF0EpldmdvFSUgzBU8zv8ygGDDowJGjk+SkPXwOdQZrQuIe86UQVZYtFud0Jjd0GuQLIjFRHZd8N8EHC5DO1Zt6QCcADzr2sCKq/8wwwUyZnjsH1vcYnU/QZ2vsLnfRl5VjYOepzpMcrd2D+JAhljrkA+X7OGqOYrAhoYivgKs75xjuboZzO5CYRHmpiA5mAg6mAoWiTkmeqcckqgzJS2SLiasIWYp3ITl/7MIuab/DQRJUFZPKOOF5Zu85GBZYOQIaQDnGfVFOkhYNq/ATFl/soimvlm4PUmlwKHuQK8W76eVTVC+mGHjjhHFyvxXFOLDbkWExJhZN5HPiIyNh7wqmdx23eiedvSaL6um5DRyWq53zHxHsungRTA+cqXHbsUUOju6ku5fQnf903r79GxQupDzGT+73jGeXOkznOoWdi4BMxegYD05UPVZsvI7nAbswARTzUPgO5/N3jH9X5a4XrxWoGEQdniCN3OvAbmeb+d0zzC8WuCIyF+J58YoahzoHRclVuU5q1kAhlRoDHQCLeC9jgMFRcKX7DGfkReEQIxd4Z+XEC8jcSJBtgx4NPzB7l2duY/h7YH6ZClAQucWKCSZgUj7cwdcEyQvqvs/5zeFYdZbrCDPN1qwYnJHlUqsFGeP2jlBPFRnOnujo0QCaPV+owpVnNtm5fIhXn479Fn9hNp+BojF7TdOYlUKFUo9vq8gYBM+LyI/R2b6xfUWTMrBiY9ws1auA37JbkG4L5dSnqyPFK5RaYMlRWwcGLD0BNulG0HVqeUCqRkwdn8BM9R5ubIKxzHgpaVmy+/hVkttarDD5zo2KWUqaaNJl6HOKUWAWDaKhPQQZY/fDBTbJSO6/aZHbK4y61PMLFGaNA/OHuegj2CXJGFBc6b0vbD5eeXkBH7F4eR5POfYPDNd+rvsUu4PnOFDchzc3d/IEzhAJ7zIURrly8ZlwHLUlLPkxqOmj1NF7ECtonBem2XL2wh9hL69D7eWYegK9HOfD+03xnO9fZVg7hkY6rMEEsHm84OGZGhq4hwfsags9XkfO98l2r+GXj6E++NbNF0/TkCbjWWfmQWTCrEptYOkxu+agPjBKzbXH2VzPubC3OiYgT5hd1ZiyoIqUQ6S/jdkLEkMfwV7BYcRgersslx9gkykDu7LADTaDsXJ1bg2j1DeXgKkiBiWYm9AmH+eKLAsLKeOpzmnwF52vmN2TzgsMuupcHkqd+f1Hin2waz/24+M4tPQMP7zNypvv+pi2f9eF38ZKwsuOfurMz6V3HXvuHSTP/QH2/DuRPHhE+Lk13JEX0Tn1l3nbw6f5z++4jZ49zF/98haf97mQpMLw+U7kCqS3gdk+h7n+ZPj/1tNkT/5u+EBO6rgjL8Kd+hTK05+KLp36mK9ZrGH1Lfcw/4YTXPueh9n4Z4+w8c8eoXZmgfo9S2Qn5shOtMlOtmm+ZDW84Kbis+76Mr7n97+G9195Oy89+hq+7muFT3u98oP/WPn271Q+93OUv/WNQr2+D3rtx37sx59t7N2ZwqEGZqePjiazbRFljEVRgCppyoRFAZyuH+c9+VkUj4kfye2nm2R+SH5v+KgXlQnYhScrt/EMMGQkrkcyJa/odcrgxaMgqojmISlzRCbMRMaowF5HuPQ4LB6ezKP9/oAPdN/Lq/RFE0AOcN7AzQv7MM4JBYnMHWWshFDoDZRMUmwynGGuiULzQp3enHBxRzk3ykmeXaZ2uEPtVEwEysB8cJEWseW3WDYrJL6P0RFQ4+yNJ0g6O4GBlQewSw081LyPPxl9iL17+6weB66Bpgql4vqK5ELv1JDWsVXYKkPCRJQxVgl6ZOY8uvMcB3cXabUXWS4aIWljUoCgiAncYAjdxI/pLH6KjeCm2XDTSfRN+ejhpVVsv4E1MCh6M7+b4DIVAjkNrE27NE3dnOmoEDCXx3MXlCVsFB+idGdZps5Wf4OL13Y5uDRPrTYZF888q2yWSrIHS2kENY+9lFH/FJujPUwR5UfRkd9S6WkqMMshgNEChrv0szsRdTMgV2BxBEaBNZUp/ax8DSKzK4J4I783e4k+sEI2d4dUsMsVf4k1XRyDf4u1I+j2WVxvtvPFB6At3XoKcRYVcOICCKajONaFaeJcbctjRh68I/EDBqNVDIHZZeKNTp6q42uCfsLz3w+njov+PB7PqjnAvCyE68xz0GyGgalO2S7PcWlvi6JtSPrKsNwmSa4jPuxXMCKbWlgdM7vUYSPYtbUFB5xhcQ42hmE8ZYlSEuwuHrgPHv3QpKmlaZL4/vjChZJeT9ndVQ6eAEZ7mBtPIdebjDP/qcv1WoFHsV0e7po7RZE1eY/fmOoUxahMCjooLDxSUq9dw989MeZ2pYzlacaPaBTXAOi9Y4/m+YzBnVBZEZWkVOzYY2s5msyTJINwLl+MgYdyim0UKUtTrQott0awQ0/SmFyLeGHuUoluD1GSCASFqHCK0gmyd5C3rz/H8VFCnRqjnXXU1sDnePXk6x6rGeXtnnzXkR2MgMkYMIxgqBGMC+bviUlQZsdxNdV0dkqeexLuuhN2e1d49tl3cGf98yqqGF49O3vQah6i7+KCQTJpfHmTfG3aV6yQOgJcvAxnVsIJD59K2L5WcmW9hIYy3zb0BxP9oOlcCIAos2CXqiKFIA3BFJuYnQtoPzCJ+vTp9a4zvPgEhoeIb5ownqqO9iCDuDAg4DdH+Bb4PSXbWae0S4z6YbHBaD6+isHg5vkydravgDszUQNPd3Hew4+64AuKYnbBogK7nIaiA3qxQAYGIug3DpMEGb0GL8+sdx2pCXV3gxHz8RY5yoJZFMlHIC1rzza75Ba0qWLHjZldydS2U0XIqjZby7jaiuApnQv+ivsG9fuxH/vx0WL49C46cjQeeD7S963xxxd/hwfXXkkrayPb50iefBvJs7+PXX8MCOBWefdn446/AnfkxfTsGj/3H5Rf/OXwcf7FXxQM3z+q3M+m6PxR3PxR3MlPAqC5tMT21XPYy+/DXvoT7IV3U/vDH6T2hz+IXz5NefpTKU+/Dn/4oZkPsBeK7EiLEz/2Gkbn9uj83mW677zK8MNb7P23C2jkgJtWwspX3sPBb3wQUw8v91edeCOL9RV+48mf46VHXwPAHWeEH/sR+Ol/p/zsW+HRR5V/8F1w+vZ9wGs/9mM//mxDVZGaxXUmy/2FL8BkOO8xFHiNsq3IOlqwc1gCGiUaZIx2aGhymRwQNcyNLqASmCkVy8VRYriVdXvxYkk2HGdG45CIUlXyscpYuDcM8+3OOSXpWco5R/ORVigidTTmRDG/LF3CsBhSnzakt1mUc2lMQRTDRMYo6kn8kFQSskxmwK7q752+YaWuNDsOkxkOp/NkNr4bfAUcmvE+imL9gGZxCeX2IAOsinblkY5mlZaZCy2qK1KL76pE0CH0IyDpEyXoEQ0+uLcHGWMFZnjQUmmcTSmcpzMouNbZHCc3G93LOFdhFg4kodvzqFfqVzP8mh8XxHTTyd0E78RMVWa0Bu68A/LrGZcuwHr3EtlM/h06tvPeKO/0BZVzkapHJIwt0dC7FQA2bVAfGGyxP1xOc+v99MrALPQRqHrXB9d58el57jgzOfeNTbhRwKGsun+exFpoLtOtLZNVXlURYTB6M7MrXLTRIjAjJMVOeWkJgan15LsvUi49hr3jtfGadeY4QGQ/BbCr1NmlPPUhsXPqmUFn484ZNZZqa+ysP4cC6V0p8pxHusp8S+nlHlMOQVuoUZwvAxMQG9tqUBfYlu0nmmROMZmj4a9Tz9cpn4tyxtJjFDq1M6iNQMJ7euQPTaoB9soRgwcOkP3xYAzujPwQNfMBHN98lrlGnW79NBU1c1Tm9N0NFsrDNGpnsKMRC8nTbKcbSB4mlo7fI3EbdMuJbcaW36HrrlL4JlZhawSJ36RNzsBvUVJwx20jBieG3OgLWyNlqmAeNd1CKJkrhDotjC947iyMElhxigwKeh8u8Ht7EC3R9qaIiWWURC1mC6DQeaQgMQmJqfGi5gOsmxHrXAZVuqNditF2NdDC+BgWqDoa1z16UnDOUMsCwLwwCGU0dVT5synJwJLXRox8Hx+ZXYlV6k/WcYsNDh3ahWXFbOd4kzG67zDFyWegS2SC+vGYGQMzAsYLq4+VcMzQXQvPy8JzntqOxzUcyTMj/PKECZNEktb2tqfdswxG0Pc96rrCcHGEXknI1EUgKZxn+D5P15QsrRrEBHD16rVozy6WEmHeLrFbbIYqtuXsUK/A9J0bBZAyGMDl3bNspMpiIx/3qUej/51FqyIOFSCiincuzurVgkcAmDbFs8mNSXsjaNxYTOjcKJGiZLeEeYKHVOoja3awMT6W3kwWKgWpWSQfBmBy0CFC6kjXQDdUtLQjIX2/MGwqWsmh1ZOcn1ARXRIkzH4zMIAXh09wvnsyzEM6sQF45tkIRk6vPujUv80E+PMawHhVML3rgeWWzgUcaGr/sYwRiw5K9LoLYFIwGJycxiaRGRjYlnhC38/U7HC4ArZX7meB907aBzP513Cg1IYe6jcxAnUCdnmNFSkBnCJTCxkmMqidMmahA+zlW+BX6HafBxR8ntgHu/ZjPz6OY/Bo+ND5WMCu9e5Fzm4/zjcd+Rwab/0i7PqHUAR/9CWMPvnv4G57DX71DhChLJX/+hsB/Nnehjd8Onz1Vwpra/+D4E99AXf6U3GnA7NMOlexz/4+ybO/T/renyF7z08FyePdn01592fjV+/6qMh/7dQ8ta+YZ/Ur7gFAvVKu9xk+vcv2f3yW6//iUfrv3uDkv/lU7HxGajM+/cyb+JUP/xQ7gxssNoJ/QZIIX/NVwsteqnz3P1S+9uuV7/g2eO1r9gGv/diP/fgzCvUMc8/1S4ajU/KtImoLnBthTEER1XcqhI/b6A2k6m+VCijUnjQs+EdpvmidgWuPV9ZLU07VT5qE0YnvV+XTZUyQ5glTMrpoUO8xaN/jnlbm8gbDtXzCInAV2BWTE2+4fgMOHVSy6kNZLPgSMQEkEjzW6FhOZcsB4Ln90EHWk73xh/rqCmzuwnAEYGi3laLtwBlMW8kr415Xedr4CNv4MYtBK48hr0jscp8H6ZeaYGrdKYBExpIuTRW6yvueegpIUKss9z9AuruAXw2m/6aUiQzIKVwrSTqWocDT/essbA+xzSPjPt/cEhIPmjggCe7UI6W+nmEs6PHQ5159xQPhQ1uXyNJF4LYZz65GpCIFtsatUSXd5fYU26kqjFDd97GvVwV2Vf8I/auqk0QpDz5pLb/OnnqcWrxXcj98/opus43BGCGJ5xuNwl9cVT20KNjZUdqHokF99OyagGCGMEorBk/4My883d0Ra5HZNWbExWvfKTps53tgTDTGHzHfhr296MulsYicCFaywMia6rsDZjUk+A68eJIlgyzB7aue+QeVqw8HHyJ8kNfNZ20GfeVY9gC7+p4ASXiBQrBDA2lQk1pGGIkAiUiQMXrFR4kcqrBZsPueS+MufKp7lsut91DXAQZDjTo5BR32xvcwc3vVX8Erg7wLKCv+JDZZw0ifptlg04Z7nWjKlm5S6GN0RhPGVM89Q1bbpn1wk4s7Su+80p17CjPKMAslWdbiYsdSlpZr69DpKJeHDqMFztTpFo+jYsh8wUPJS7BTflPFs+/DfbiDW4/3aglG65e4cdlP6jAQgJOjzSPQ2wljMCLBNRpodgTyy6BMVYRjRp4mw4L5846sKfi1jLQewK5KhshI6ffDMZvPzfPIfR9gN825mxeF4TNQzE4KRRs9vI7NFKsFxdoK2q6TR7aLV6XIdTz2xtiHgdQbRkC6q7AWfl7bI8q+3S0eczYR0jSAcL6owJnwxI5cjrtdSZ62weOtwqRLhQz8QLEtQZ2OwVAjwsOPWl48f4rV1mF2BteZ+2ATfSDMbbJopuavAHYBWJOwvpEz18hpbRZsbysNCfckPJNKPx+ilQJTAc1pyRxd7cR7qJR3fibnr72Ly+WHqOCNPFaetE2LDbTGMfLh9zzJ5W3EWLx39N2QslSuXYHNTWVlKTJqvSDWQFIPU9VoBKSoMVAKZa7U4iKIAEd2Xk5WO8/4BongSs9oCFniAMFvO8RAkniKgYc6WM0pzURqOxlc8T+qE5DTmvE87CMDq5oX8/ieDz6P08yuuCDlDINHt+KBlUuXPAeOTs3qJrKXY2EUdUAmiJkG3pTimsOvTCOZYdHqyrXJz67fALZgYfHmSwptqceHsMhBO1HyOc3squbgRHj2qRJOACib/Wv0+022N+AMHz32wa792I+P4xh84AZmPiU72f4IG22TPP3bvPeRHwPgNU/8IRx8MaPXfhvlnW9E5ya1hVWVd75T+dEfVy5chJe+BL7+B4S77vzTAXy0fZjyRV9K+aIvhVGX5OwfkDz5tjHw5VZOU979OZR3fTa6ePxjOqYYIT3cIj3cov0pR9h9wwku/s23c+Hr/oBTP/N6JDF81l1fxi8/9mP89jO/xJse+PqZ/V/yYuGnfxK+/TuV7/gu5SveDG/+8mjKuR/7sR/78f9jZOU2Fy8qRcfS6DoO3KN4UQpfgrE4rxjJGZUxKROdMIecwMhhWrOV58SBjELZ7znXZGdjb8Lsep5qaKqQ+z0qZbdoyFeaTWgW0EHHchLRkNAPhxZ2gmG4GpBCxgbM5TAwbSqZXeVBMhhAFj3JMGbMUhIVjix7ZH0iYzy4rKQ5HDpwNxv+2fH1NZpC2yu7owDI1c0A+j1EIjAVjd1xeZR/eCYOLtPssNCHVOyoQoIUzUwAv3ESA7BgYBvs9ZQCRROloMQUHZxGA2MnYzN+c3nAhFoVOqJ4uM7woQ7t7W1c+/C4LVUyKjJJTP2U3s2pZ3B8ROviHNv9Ib0rNzjSPI1MMbvCNSnWpFOeRrP32XnHtBdP7JogcRpvXpm3B5Br++J1XPcCcuQ4lUE9gBTDwFQQjxWPcwbvLIUfzlSa2x5cp6xAI5RaBtpTrDFTCU7llVaZoXsuXoJ77tIJs4sAdnkfAAqtkDmdSDA1VlmsZIx+3JDw53t3PhyOr560d5V0tM2Ju9oMBrD9oZD3FmXgGi6YZXZ8RS8Kx08kRUSQEroDxXYCZpskYDTcv7GXvIFTrRNcvq5krZSaNuiLAW8wncrvJjxLCSP0oEJPQQXvIlNGbADHbBj9T914ZELeUOj7GxRakpKRSUpHO3R9lznCWJwO7z1lfPaTIiTiUgZDds0CmHomew0HiiFu/m7Wy5PjfQ8VlvvuO4Q/dC+X3tvjysYWy0XCsdYQ1ziKNldI1+aoHV8M93xLOfTcu8jcHlcWX8+hwrLLiKv5owwZYPyIWg3y0lFsXsXsFTNtle4G9akfOfXosxkcrSaoCHapJ0+WGGSHCcpXZeoRR4pqXACVn1ARQIc0Iv7OxGx+pKxf95QLDr9Zp+cewhnD+3bew+HcIYVFbQZJkDWKKHbeU54I1zyK7d3agWRXGeUFpJPHTQTs8y3sCiTxpk7PT1XMzQF9z+ULFo6Fiyn8kPP5WVbtIQwKU1XwKjZj2fV0SqHuGM+dxliwBhFDPWkgBJahPltCz7P3iQ+QZV3gKhPzRLAVwL25Q3K1xGvOtW4Hg2VheI6uhWc3H0WjdDJ3Q0rpTHwCmTC7PIrDUeni8oEjBZJWgrFgCheRD8VcdpGRBmf3LjJ0IwZDYF65ft2zeeEa+p5w3GDFJ9zYhPxizqJavChuIIxGnqSurK6AbAqpZLRMG+iBBtuV3c0+eam02j4cL/eQKmkCphix6B/DaEGZNcFNMUIjQD3wu6FQSaUXN5PKvJWPJQrewPpok7mkiclq4wUhs93H7nVgHvLLA5JrfVQkAmBKv6dUXEslQQcema/k25V3nSJaYnqbMNcITbs81dZuWPjZsG0OTI2x4ppj4BXtGGRxyocQqMf39WjHo0/HQf58tc8sDIudqX6BwaC85R30QrEPdu3HfnwcR+89G7RedjDQkacj75E8+3skT/xG8ODyJe9YdJysrbL05rcxWDxxy7E+9OEAcn3wETh1Cn7o+4VP/AQ+JuP7/ylRmxszuhjskDz926RP/DrZO/8FtXf+c9zhhwLwdecb0NaBj368GAuffRI/KLn0d97Jxr98lEN/+yGOL5zhwbVX8l+f+Bm+8L6vGX/8VrG8LPzzfwL/5J8pP/3vlIuX4P/8VkjTfcBrP/ZjP/7/C/ElpVMy2SXNb3B+9xrPlhcCCCFJALtMQelqgAbzbQWrlsbjDUxHmfuE8wymwS4/YSStPNYib+/h74xJ0POUAt/WTTaKTQ4ZiR5d4efWCLU6dPIowRA4vOa5dg26fUMyULQOvvSYUsasrKJXMbqqb93wdTyVk7Fx3ZLfCPKalZ2M+p5jb8VTllVCG/2V/IRpVoURodkyGBGyFLQIfjTiJwCHGLA6mrC5pphdYxng3g6SB1mfFhI8t+xUP6LR4wlogmt7KKCcO4baJ3jCfZgDvCgkJBoAhqKmgTE1dORz80x/7fcH0L/SoU0D6VzDaCUnrQCuCSBXUkxJhjzlYgkXJ31Q9xuYKQqVMVGSM8UouG3pbs71Hx9vc3nvORYhMAN8Ee51MuG+3RyNnQ9zvWfYLWGhDFLY9AnQuRTfGpFsJpjWEGOU7T3L1jMtLLNg1/uvvIOdCMqpKi9/Gdz4kKeWGaqCbSJCkkxkjOP+6ilD1+FKfpZVDiIRyAw8JKHZhNtOKf1Hw/FzcrxCzQb2hXPV/b7J80YhGVwjzSG5Jtile8OPPcHnRoRMapPNI60xIQnPlYPtjjI3Cv2OhsIFxmjATj0gynCgrO/C4U9w9BAUwW4nWF9jOrr+Opeaz1IvjrA4Wgtsia5i6gFATtOKXXYTuCnRmB5DShbGCcpc0mTo88l2Cur9+Nm3uQT1buERBZeFghat961z4GUNBo02O7358f51aTNn6/hsnnZDSGXAnKlTN57SZJA0yGyTZhb2KWrKgi9BmtTMPA1p4s0iV3mUi+4Clg6DWp+9Pgx2YbGfwiACnntnKfINKgLigiwGxl5u2dlRlutM2IWqlK5iPAnmWpSORanZqKfBhzBTlh4fhOdfElRN8Bga94+i1wN1dnhyl2SnTran5EvQUceCH1DL5ybUxxJMb4tivoWzgdU5kDlqSyfpnu+yRMFO7wYsTu6YCNgKTp66jcvLwth2Vm9+BgURZZgt46OPnZKwrRsByLeCEcHlk/0qsHzzirIZ7CCnwHSDpuHCnVO2tgM4Vis9zSVlcPB29mwBXMVUFQcFTDSXz/oXyMo6V8rzJHgMCYhQLPYY3jtPGpvxWPEohW1jEBZliR3dJteC9e7FKb+tOM+NdAx2WQumekl4T1X/RAS6eZ/SKdHCkkuXHFce+++cHu5iTIIYoT8IOxg/RGiEWa0I7GdFsUlgsoqGPwF6XSVrKIU0UBnh1SH1yNLqK3YJbDHAmII8WaRTO8ly/8nxJYh6+tLjerlF4paZ18hjMjJm9flY3CFve8qTQ9JzDXJfIFkNW/YQLcieXKc2WocHCQVdqvuPIvhp7BG3o+jjBdZ2MMtz4/lJjdAaXSDp7iJZKI7hp8qj6rkSJMXPTyTKAL6r5IVDex7mU0aNI9RHV1nqP0Kt/mBgiU0VfS3TFnute8jKXVo8wTV3FSeO7ig8C4fMGgNgNHyekrAvEB/d2GY/9mM//kKG280ZPbVD82WRmVXm2Gf+O7Vf/9u0fvyTqf/m38NsPkPx0rdw9Ut+hoelxyff/dfQm4CuS5eU/+vve77265VLl+Fb/3fh3/2U8MpPlP//gK6bo7FI+eCbGLzp39P/6t9j9Cl/D1xO7ff/Ic2ffC31X34zySO/BIPtj+lwS190msUvvJ3rP/IowyfDPl/ywDdwtXOe//7sf3refbJM+NZvEb7+bwi/87vwbd+hDIc3f2zsx37sx378aYbHOWU5f4Qk3+a5nSBVGg6VTi8yu7SICYtyyB7mdOMUiSbYvg0AR9mdPaSTsdeWiRXuJA//Lr1DFmY/LQvNEXUUJsorZqbBkGR7H6QY1Upvt2+DXNEKmugsk8RPdtUrJey48ZGq6A0Mu9vBCDe7HigQbmvyRW1kApiFS59mZSmNpmF+XhAjkcxlxx4xapJgCO0rsCv8fNq4Gg9mcwMpBmimkAtSysTInSifm4j5GKahfSvNw+Ov8y0/RNWFVfRS8InyhBqk9LGSnYzlKRAAsVGuqPOY6BlV9ZyLSRlAaVxorUbGh0wxNHxOy12iOZhUtpT4e5EJm6SVTQAL8fDM5mPhgEk2e59QRCsmm0aLmIrJFw3py3APzDVD9lSD5GyN2tk66Y4JRBsxpNLA6Wgs17m5GqIxjkZDWFoOifdUoTDSRCaSshjewfnue7lcXKDvBxNmV+ShLS7CmdvhwErol4EOUA9z0YDZxWyzUWzM4AiLaXucRAPYyOa7ccPT71d3ZAYNQT2kpOG2O8XHfTygvmSnd53MbYVx4AUsbN5wiCpH1kpEbQBpnFAb1qtuD8+IDtFUcYTxlbs9bly5xspTMBjFxHuKVQeBaCky8VQzse9K8RH4DReXlVt0u+D2ysjsMpgIjkheohZuq93Osl0jsXDiCBOpcXX5Y9kg1Jth4M81Ipj9PFX7bv6sFPUktFhwC4x0SM9dZ8/v0jW7XO7ssp136bh++P9ol67r02l12a3vsuu3ceoxGIrY7sFAGA7CQ1wBA4fNcZInTaDnxWfz8jlIy10ob5CVN2jlF1EtaVzymGt747ax49GRwWZdEPA1sDkYp/hynp47QKe/E8aFmPFc+qw8Qz9dI01glHu0sRyfec9mb2N8nwAWaivYqmFT/TPfFmwlgbwZcBZB24cokjaeBtuN+xmZRRK1eCEY9QH+kViQoemhHSW9Q9jzu2wOd8cgvzUGjfusb8DWluC8sss25/1zKCVjkfsM2BXGVnP4FHOj8/hYStIT7kvZduQrZjx3ioJoiUc5k9zJQXOI3Ds+tP4wnXwHP1VJtYzVPLWhDModsvMl8x9uURSw0w33WMoBOzvKzm6Uhari/ACmKtYGcEnjuys8LyqKlgbFsbgQ3p9h5tDADFUYDj1lUbHOTGBSthmv1IjAyu77ABjZ1RkZY+ki2EU/XreCVw4dgrQu8dnW6IkIu2cmU65XxdSEpc6jrO39AbVyk6zcZpAeGvuJxXLLY0/CKiqrQTPIwyKFKtgZDBhcsBXQdBZYB8ZjAEDuy7AnE4q1MzBQikdT7F44f2t0Mb7vNSwCxNhdupdBdnRcqbVPj67pYiI6mZAEtnjukedjgT1P7DO79mM/Pk6j/77roNA+vkHtv307yTO/i4z28M0Vivu/iPLuz8IffhGI8PYn34pXx2tu+9zx/ltbyr9/q/Kr/yWsDH7VVwhf8sXQaMgLn/TPILS9RvGyt1C87C3I1tkgc3zyN6n/7nehv/c9uBOvpLzrsyhPvw7q8y94nMPf9XI6v3eZq9/zMLf93KfzimOv586VB3nrB/85rz/9hbewuyCsKH/pX4GFBfiBH1L+9v+u/MD3hQ+Q/diP/diPP+0IlYuUuEgOeZh7Nq7Ddtcyj2J8jmqJAEYNxSjBdcLqtOKh9JhrU8f0k3xKMCEJiJ4vzpfjSmO7fpeRDuJ2LtJUCLKIcUSj8ggcWBvOO8wNC04hmseb0fOszeaKdjw+rbyHwh/eK16F4eMFycEkeL/sXSEb3QXuKurzycZqMGJn0sCY3s/8TAC5YQNz4XADKQuMH6I2SN5U/YRVoAGgkDKa17cdumuxQ4OvT61Ga2B21TLAK0Va4pyiOlkZH/gRlzuBciVOMDbBRxkOJXFlPqA//ewYBTt0ul0WZ15lnkx7eF+OQQUvkS0Wb8cEBgvtIhFMOcmARCA1tfEthChbmokIIJo0jI8pzy6ZARMDE847xmwqRqHyn3rHIK+T7wWzeDP2krOk0qSvW+PEzKsbV/UCsGlMbKNh81TOFcAuF5zCxrLEmCwKnj3XwWhINFUsOh7hGsz1gaEOUBVaSWAYlM5j/Ijl3gdgdapfxATGYAV2TZmfRRgoGtmH2N6BogcmsaHKacmYAagiXOpcZPPqOvO9PbpRrnR9E+ZKz+qqZ6HWI42yLa9CTVISfBwbZXiGE6XS/a6Xz1KXAWkqaD4lBotjQ9MGxckbUMaiC5hoFh689CxmnFXPjc6zZZfY+Z0BvU4d0gTJI5BROCQVWnYea5co8YgXsmz2WVYslSv4/KLhzjugLeBGkzZNh7llKlD0uZzTN24je0WNsoTjq7B5I7Ad/W7O8Xh//MqD5HueG2JZ716lS5CSZtQCk6tQrt2wJA6Oz008/jKpk2Wwe0PHk584yNw23cEfYWM6LUWX5uUm9Dfh7nnAo9vg04zyTISFjKPeT2k8MqLZgLy3jSsHpCthXKUfDM+/a81jjKXdhqu7GjASMaQkqPMQCxPcXv9U7jswz4d7vxPHANwV7HORP5nMtTd7dqEeu5Ch3TCXqEnBQxKN5zFBkuhiJ5S3jZC1AvOBgFU95Z5gJU84yO3h2rGQBs+5omAM5O+6XRrAsNylbg9UjZncz4hilXEMzOXXyIleavcLeeZDJdNqBy8YLSl1/DQxHMH5Rzz3XO/T7KaUZzxdoBiFvc4NPsTV4SVS38OUwrVrHo0Sv/nBU2yojtnKqDDyO4EBGW+32ArlF8R70MCSLQpB1bG07Fm/AQFAmvSzicwpxQQJogdtgDmS4C8V4R4V1Zw8Pe/EMeY9QwZAi4huYQSMNeMCtooPP5MCjXNVoQU2U3BhUaY1ukBp59hsvoTVSj5dudprYHaVHU9xw+MqIF59qJxcbSs+zo0gLlSO9DrbXmCimwVIwB5IwjvtsThKRg4iRpZlYGVyc+WuFBefA2MNR4/AxaswSsJ3gjiw1ob3ZV7SmuNjin2waz/24+MwZPMZBr/8O4ips/zkN2NaDcozn05592fhjn9CKD07FX9w9tc4vnCG25bu4cam8vO/oPzqrwVK/l/6S/AVf11YWvrzD+Do8m0Ur/wGik/8esyNp0meehvJE79J/b99G2oS3LGX4868nvL069H22sy+yWKNg9/8IFe/6z10/vAK7U85wv/24r/Ld/7uX+f3n/tVPu3MF73geT/7M4X2HPz971b+9t9V/tkP89ErUu7HfuzHfvwPhqindB6fZajmSDERlKlYyjGzqwwSDQzdLnQ/4IKnBwqlItcmyYl4GScyRgzOgckFMnDOjbP8TQ1VqFq0MOroS1wynkKRfPyAHwMUyaRtOBALKorpP898ORovu4djKZSF4jxjz6XaeorEBZjDq5c5d/5JMrOAROmMurBaX2rJlcEGh+sxGTPTBsA+QF9ikIsp+uomDPawOkS1ESsM6pjhEHYqIBqj+7ZDpY5QjHG+0H8ewXDgADQ6sLVXsL0LwzINq//AwPVoxH8YD3PJAtdiH0lkolgzqWaVy0GgO1PEy2pJrdwFW0enPvsDSWHCLJv2ZcOA5FPHMJY7Vh/gQu95th0fIMp3Kk+rxwvkxbXxtiJEpkDYf9rrO99Tdq9ukQ8LusNFfN7BlFCPoJF6i9UU9eUE7PJuhlGVJpOqoIabmF2phPFSJXgEUFRIEJRnBmeh7HCbniamt/G6lNKPuOgusOO3qNMYM2hKrzSKCfttOqZhheTGUwCs2FWulVdjQjtJCAcDTwLUqIWzOsbebkiogojPcOLRPICHJWASR6YFZvMZ7j1wiLfvZOT1K0i/FsEXRdSh4vHpZHxKZOgFPx5FigEUfVBPUSjcoeich12NElSZgF1iArNrimkpPjKPtIRaGpL30iOFQxOZutQgx8zaN4FdYpDK688rtZqg/ZgAFwPwnvxch3K9z/ynHbmJGRoAfdkN+x87rJhEaLWE7e0J+HzLnRFYMsvYWNUub74EYS94NiFBhqjBK67aYdAnPJfx0qerlaoYFljAuVC9sAI6xTu0A2a5xJtw7mbSwIwO0DWX6Z0O1fxq+ZDmgTp2IzIgFUw9IU1hvg39qwFMVxHq1Mn3rgHHAy5tGiTGkO5N2pNFP79pdYVOPyzxIUrnU/w1Hfsf7ozWKewuHphvLGNYH4NQJMqlwTpNt8DApeN7XzHGjDE4a5ALH8AOj91CwRuVu2TZwcAom2YROuXge4vACBRiFeAw7x86bnni8i6+dHFON2NmVxnPazFsbQs6UEYDJbueIPeMoJiAXSN6YZHAB3Zj9oTFN+O8rTpmKkJgjo3cDhgfwSQTNPYQx1JgcXX7noGPxu0yuR5xbgLua/TZCzpA1MO13T67mWGJyJaK40gKS3q9g/biAkBTGPoOQ4YgLVSD9FxEmRs9Ay5FfYLiEQPzw6dxtbD4tDHaYjfdwJZz9MoRc75BJz3N9uBdJMOC1Z2C9fIKqTM07QJaQu+xEjfUSXtQKH0A+QyRYRvfm8PupFjGTEgw7p/8M8x3WdwPIfFhJtJccZsD0iQBH5+ZRMaMrivlMzA4H86bKAYfis2IQfG40o+/Fz5a7MsY92M/Pl5iuEfywV+g8R++hNbPfC6dd3eZO9Mj/4J/Su9r387oDf8Qd/KTbgG6dgY3eP/Vd/DyQ5/LP/8R5U1/VfmP/xk+7fXwH35W+LvfbP6XALpmQgR/4E7yT/pm+l/xW/S/9JcpXvaVSG+D2u99L61//ak03vrFpH/8Y5jrT40/jpe/7E7Sw02u/+hjALzy+Gdwx8qD/Jv3fh+DovcRT/kprxb+0fcKz52Fv/Mt+jGXzN2P/diP/fj/HBGIwXo8DqblgCI4b8iSEq/BTEumVmrzJMo8irCiPd5tSsYIJlToyg2CBE+r5/myFBy5zYPMY2bqC9BJNcdWi8IqZlyZyVtFSpluwvPGaARXr8HGBpMV8lipCmCvqOhpgfVysLmMapBkdcs+28Ue/WgOrLdcRPApUxSSOmIE6/PQp5EXNbCBkSEKUuZjQEDnHHm2igBpberiI2vAiJBYKObKwOzJ6jNn3t2FPIcz2RnEpjFp82hM8BcWGCcV4qBNO1aTnPR9Ev3ZxiBV7HaA0s5x8wL9VX8Fhp76jZDEHW6eITEpItA0qzz75Dy9nrLcOMB8bSl2a0yKb/qG8ASzKtPbIHW7458FyWCI7csjnn74MoMBlMnyuO/EgxkJK48YFh/e5uD7ctzFPc6dVwpXzoJ66RS7TmTGNylLQ4I0fV/TGx/GFdtkktGyTYYMQnIbDeorGHDbbXGZ0O6TySlMVYyh9KQuvPencQQJNwO5aQgtmiXq1GEKPArbwx32LqxYjApSKoUpuOjO46QMz4sHb4FuE+/BtTySOFJf4AbK4lzC2sIx1MBwKNQlsNRES1QcJmHM+gjUDAn5e1WhtbfBoK/sdqA3tBGQDBpfmWmvicy10DulaUQvOw0V/2pBwip5GTy7UpmZD9QFNsd0qEyYXeNqc7kLzRztYbbPAuAHDvP071G7/EfjfVeWIlgeT9JIodWKIPOU3NnFBN5sPju5/yZj1RxgxRygZlcBTz4Cjx0DgdOA7PbWNODB2KA+9LOnJnVWdGEM3APIsAyysabjnHsOFI6khzEkuDkYrhpGS1Culvi5WXBu/oDlJS+G9nyQII9yKG2bmmlAWVXyhGQAeqNHdraY7kIAEjvlqTQDwXqyg4bGvfN44xEPK8sghcEnitWE+w+9ZGaPim14o9ymW8Zx7yGPBSJEDGId6j1Z/zLVhF1Jlod+L7CRxIyN01GHrD9BUg7wcf4wCK1Ibm3WDYLDdK5NcBWNjGUigzDeeztSbmyGebCSqZdxQcRkBjHFuHNrO0K2HeZtNdmYWVwd32kfohefGIOY6MWlFfs4wqYSFy0GG5hRFxM9u6RaPBp74UWA28N7r53ncXkktEsC6NO4WKOxYUiuDdAtj2469JJjvTgfrk37ZMOLiCrWDWgXz2GL2J8opioN2l1haztIrGtpDXFCv38XndqJMOeWI4p8RO5yRjpkRzdRysDoqmxWtsOxRAvMuy8SV8Hiiztsc/UajApB44ujq12uuatc81e4VLwn/N1d5VG7x8Op5en+Y1xzV9lwl8iLXgBunynov+sayU4PXCjEEW5lOOb18iKlD4xztYGBJk6wWNQrZVnOzPEfKfbBrv3Yj7/IoYq98MfU3vYttH7iU6j/938ArqD70LfRv36I1he+Fnfm0yC5VXddxX9++G14dfzyP/tcfuVX4Q2fAT//c8K3/T3D0aPygvv9LxMi+LX7yT/5mxn89V+n95bfZPQp3wI2I/ujf0nzZz+P5k9/Btnv/yPSa+9h5S130vujawwe3URE+KZXfh83elf4t+/9/o96qk/8BOEffY/wzLMB8Or19gGv/diP/fjTC8FzefhudrnOM8NzATSaCueFWuIiaKOYKS+hcsEHtKF0+GzimdI6X5+VMXrIrmS0LjfGnjayavG16WSwDIBKpZuLMZeErMbHBNGIRDP0kBBJ9OyaBme62uE598yYxXMrMAVgxhWqxkKtmEzN2VXuX3mAQ41lEGZW9a0YtOGDVqraLYhQEIlglwjYjJ5b59Lo3QjKIF1jU6IHlwq4iQmJNjzeBrBueUU4fmyqlRERUWPJ04LB3TnDew5NNlDG7wmftsAoPo0XNmbXCe0lpVYLbBNj2+TTrCwRmqaBUz9mYcgEX6SwrQiWTVLbrdYmANlexcqKHm0Cd1y+m7vO3sf6deFod5vVYaR7Re8zvUneqChm7xqmDN4zwbNLxwq1ZhMONraY61yg9CkqGSMOMUpW8NSxowCujA418YnQuzbi6Wfg0hU34zWTJJMiAeZmz66xCftkrIxcgfSfYDVZoZU0KbWMTI5ZZlfhR5SmxdrCX6dFeyyD9F4x0XNsGmAwIiFJZjYEIc00eihNA4I6HqPnzwoXziods8e6v8YlLmJEkGEf1QHSreEFfN0j1mFF2f3jnKLfZK5+kJpbpj5cZllWmEuWMFrGMcsU+8SDGHxDmKLJjeVquY9SJYnMLpmSMYrFmCjHwgKGNO+Ql0NGuhfQJkBGZajGOE5gg5RVHaQ3yRjPnLGMLo3wQzcGHbScjG9Gwf+KModRjoz2UFXsesmZwTV4JkcR5u99MWonuqZpsOtKJOAN93oUZTi0yKwkUoFRHu6UagAziyLOI1Oy1moestMyXzxu0YVqf95R751DfI70clYPCO3jE3DKmhLBoJlEwDcC+VO62/L0ELuSMjcXZM6KC1Iz2ySxB0KlXMJ0W3/kCsX7N8J9YTK9tmuLHGofC3NRbwMddScD1Xvm7ktJWineBGZXmoI4i0bQWJKEIoKh7ngO9TgXGOgVYVHAbqXsaQCCjRgswffOqxmP6XEFWJ/HvjNj5pMd7qDDktTtTTwPgVoNTh1sUUvjuPM6eW34CoaOQFaUnJupOc/Ep1jLYN5OIsytlbcAraA4yWbYf6LgdIiIm7BYjQQ2pMKJ4z6wSPGIFiS+x8ZmqMSaZvGZGoPu8SaJIC4Z/0yski8VmExgoNQ2U5ob4ZfyQIocDFK9er1kzszRBNL8Cmm+hXFdLvmLnOs9FseUH4NdSSwDqh7uWruDw/YIx248xGp2PwezeziVfjK3NV/G6ZX7WLRrsfpxgRtNOsBH9lbllyca3+nikQhKO1ddW7gXe36XEUOcOpwUuPi/3doRBiajiD8baJ+9wXXY8TAMrHLbH4ELi1L3PwDHj1dI8RQr0QY5qLgIvHuPc54k+dhy0H0Z437sx1/EKPokH/410g/8HHbzWbS+SPHgmyjv+3z8wXvY/o/PAu9k7rVHn3d355Q/ehf80n9UPnTwP2Mad/C5r76LL/0SYW3tLwDA9RFCl05RvOwrKF72FUh/E/vc/03y7O+TPvrLZO//WY6xwvXGV3Ljh36H4//6s7n34Ev5/Pu+mv/8oZ/kRUc+mVedeMNHPP4rP1H43n8A3/6dyt/9e8o/+SFoNv9i9+l+7Md+/NmE4CncFhfcOQ7Z+gyzqyZtBt7Rs5dRtwiAUaE0TZLaALekyKYiHYc6KBYLsq00Siki+IGlXPCYoae2kwUASkBOJPTmCtrPZjGPCGlDxZgAOFhbZs40YrXBuBK/E5KU5HJMcBO5xbZn22+jqZK7nDp1hr7LunuORVli0SzFswlLS3DhegAZFMYf6ok0whaJgBjE3wSWWWDKMBdVaK4gw92wIo1BbMLZ/L2QnSCkQ5GJNEbIYqMNkIacIBHATFgfOgXEeWMptMA0LDQbbMmLmR88NdMsYyTIOhM4557ihH1onPBlTbAdwTjo1m5HB9cRDRdRlwZGRnhVPAW1DAY+AjIaWg2wc3CP+kaGoAwPFNhtP2bhmSjjEQP2Rhfjc+RA7Le8C7UMnA8ASEVpkltN5KvwOJyHYbJCllwiSSQYmosNPkGRJRfuW0AmikN1yj2okLwnn3bky5NjJsmE2SXMMrtqqbDuNrjiL3NMglT17OAi4kuWa0uM7C6OnajEDDbTxLFaaI6VDEwt+Ep7h7n+JCYRjJ8CJsch3Fu7F28sN4JRTWhDJqwtwpWtWc8umQK7KpB3IMHr7oZeZ75MMJubbLZz8uIQpRkxbxaYX/TMEYpH+MYyVhOOpreRyDYAc0mbnl5mdGgYlXGuui3hXDHxL0vFWzdObn0EK4XABi3tAmb8jFiSTDB9QU1C4ntkw7NIfhTMArYRwCbJS6T0YDRKwMLz3/lAQet2T5bB0mKo3N2+Ydj7owEmP092e3x+c4e2VpHR3qTFox20CCCpPlpgrCKjMpDCjDDXAi3t2A9oYR6GQ2Y0pddvxD4/REjgp+5av+dJHKRNgx9At6t4Jxw4AHlPotQ75Xj9EOdGV2Z96BJPUSuRkUXKHST3tEdnkTIUTRjVKj85MLUGp05YNvPARrW5sFBnJiP3h8qxptvaMMN4H4EsSbgtO85ubZG0jEy6uFAQdg5/HG6fxJghDHcweR/yPnTXyRtLrO8+x7J6JAFnSrLKY64M8j6nCmlKZ5jTHSl72Yh2L4AcyyoM3ICFwQKLnSZdHImkWElBg3G5Uzse0xIbqL4CkyeNNPkuPg/svqEOuO7WKShYTGqcaBwmidJP9ZM5du65Bt0zfY7MHQ/HEMGTkYwBG0GKsGgiLnhciRX0jGfuOU9nC0Bp1IWVrIO/vclgd0TzQlXYQSj9ABuZXeo9pnMJiSzOatHkFik3sHZQMCccV3c0zq+TVRrjk7HSG5T+bYNQ2fW5iVk/QiiKYgM7tJE5VDJUPE4dy71HyZzj2uJVWnoygqUTsMsYw5o5wkpNkaZBjiXI9sQzEOfR+B60kgGK0xH9PtSWhfkaY6micRVzLgwOkVhVV1KgCM9PBKcLCprSYiU5TtJ8NUv1BKMeabyWQ/NwYOEqW1a51OgxGO4xWh+Fwi2Xr7E1uI3u1mWyDEa9gktmgYG7Z0aC67NwblMEsEud4ku3b1C/H/vx8Riye5n0A28lfew/IaM93NqDDD/zBynveMOkQhKw+7bzpEdb1O9enNl/c1N522/Br/26cvUqHLztHObBP+bLH/gOvvzlH+Os8hcotLlCef8XUt7/hVAMsRfeRfLs73HwRY9y9Q8e5NT3fybp/Xfxdac+hQ8t3cc/+r//Bj/4xl/m3oMv+4jH/aRXCd/73fDt/5fyf/5fyg99f/QU2Y/92I/9+J8Z6pG4ytrLDXZKemMkoUeXHf9HuLxJ4EgZfMNi7srwZUhS9bpDEXw2xdSKiYc1lnylJBkW2K1G8KWpwBwzuwI/ybJvAobiViLQeaSA3JB0d8I2Rsb7L87DTsTAXKrsFjvUzBpDBng8Ax2wYpdwLgAIrZYwnyv1DPoO8I5D5jBiVgPAYGMrXJWMB/aPWo/4EjUZIuCTOtTamGE4uR+BMWmQ4Iwz6WBMXJdGVHvEhKhWmQ/LeLMZk/f4c5UAdiWkM/3i2g4GMVmyJjC7komkaNx7SQR5SkAMqcxRagA9rIRKaaUGl5uWsYhGwoEJwGBL5ugd6VLfyMZ94RMwcbyYKWaX8UMWBk/Q7ABzUDrFxUpZGk2cbTOOnWnZZNXWyOwqpMEoWSHaC7HeuYKXeRIrjCoPsnSNogYqKWItPpWxAbrixoydNIEDB104f6nBcLvwvOxBpdWE33sK8mKPq6NLHE2CW/n2cI8Vt0Bb5yj9EF8Gw3vFTsNPFDoipY2KxStYVaToI/l1rC7N3IcKyZyTNj6BiK1w7CicXrN88Hpkicxo+3S839ruH7Kdd9F2hTLGw3ohS6GnKWscQ+08R45A/5Fz4/MaAzplhm+skM+PGB0fBlLXWD+siBHKZYO7EJ+phkfqFdiVRCDWoSjOtHCmjpaR2YXBeoMnwTBCPKgrwRhe+aL72NhcZ29YkhbbtPyV8YAfFwboel7z6inWxlYzyPLOfhCTtYHTYcCIRdMWkgdAz/Q28dIgzQcsNJXlOy3N16xhtlJKWQnX1BOSTDFpAKm8h3WtEvippH8MLMz+GKA1b+n3g/QxTYR6jWiYXgIJKRnCbBXUmq2x3FzF7O2iwHOjc7R5EHEeMYZRaxUvNbq1Y8jJh5jrbYALTLI6wdNVb2KpaBLAlyQJz1UlqVRJWZZ57jz2qdzYVB7nLMZMAOl6oxoSEVSd9s7au8zVfIvuzlm2i2WaRtEIWgSPJoumoTouxtLyC+z4LbyDnZ0wZ2x0hgxdziIHOWSXmIupwY4B4/MorYuAcfyvT+p0iutIb51hS4jVIZCii+bBW87h6MSCAam0MAipNTjTQH3JqAzrEOLhnit3U7+nHftjjr6ZY2kE3gpagOSKaXjqw6sYexixBlLFn85hK4zHVtJhPhlwMRkwWjIUi10WHmsRZIwj0sqgXj1m7wImL0GDQb3gcVYpzSGScn3yzBHk7fExo6pmWMxDvZMEAuz4veiRqcqkxob2jzsNEBfep0YshY7CMY0gJviRXboSwa7I2kwkoSlN5ky03lo1FEurSC8nWd9DXDTmT8CY4FeocVFkexcW1oKMFyAbbVEbdhigoRqjhAIn3iSQlPHGhgUGR0lKiiKoEfrZ8ck4VpCGRV6UcXixzcbuOp2r27iWY2lUg+ENCgoEGPohu26dXlEP79gYPlVEPVIYLJarVzzLeJJ9sGs/9uPjJ2T7HNmf/CTJh38NxFDe+UaKF38Z/vBDt2xb7ozo/sEVVr4yIOfeK+95OABc73hnWLl56UvgG79OeLb5y/zCo4bPvu+L/wyu6s9ZpHXc6U/Fnf5UFh7scu1Vv8KVK3+NU6f+K+0/+H7+qXi+dkH51t/4fL7lxd/Gpzz0dbMfGTfFJ79K+Pb/A777Hyrf+33Kd33H5GNlP/ZjP/bjf0YkrsdCcQMFOrXDlFdTDnXPwmoPEMRajIW8+7tIdixUjIqJM5JSHiyQ9bAc7RNleGZA/ZnGFNhlwsduw5NicFRMjgDgAGNpWSVDUa9xE5mYljNhPNH18dgxDzXKQpvA/kEpyPE1T6/fZcQAT4M8WSEtOyRJlFjESFOZYjw4atTIKw2fJaxY30w+sqC2ViluJl5H8ZpdUUIXajtK72DYeSE5zG55mYyMVVkMQJNAp/B0N3VMcFEb5BrONLF0JsCWSSm1pE4DM+43w+CeEbWDpzn1+FY4vwnASPf2IXp+AgRiw99C9ULheOsk1/Mu3VGBBZKp6pJr6SE29dzkiuRm4DF2VwqmADNSTD8YOZM7auVWbF+I3fUmV1M4UI5QLCIhGQuKrymwSyZ/1Sil0wg4aM2T7iSUWGzNoA5cKhgvOLsIsZqny4C9AHaZjQ4rZ8PfDx0As9thr32R2pWSYn6TvUaor9YHmpcKDq6XNPMWanKcOqSRspy3KGo5ppExf71FtztCT1coSGht7kck5mCU7TFm9LhpGWO1tY0yImWGmWBEsPGYIjJmRlbnqLaUDrQ9bDemBqUJZu21GoClHNRIxZDWp7VXMQlmouVKjWAoyKMnk45RHUVEcAeFjTsOsfrMLnnhkVrQ980lhzlaG3K+E7eX4OE1ODrEXLWkKwbZNsw9XYNmQdF2qBiMJGQ14ejtlu6FnMztBrDOTJ5xYLZMJuDbx4DHQyI/Ckb59aMWc8cCvfdsTbzgyiG+fhy/fo6FBaF9V4KrGJRpbONzJcNBjm0EZl650mavdiJU0OO5mfPeMuRVkRdl1A/M0792nbwI0r4KnC5tAdRIK0+6KZ+nw+lRkrmM4dUIkIwldx5JDf3VO9nZCKCdTeIzHo+bWEisUJHnfG0ePz8PNmNUDhCToZXHXWR2qQuS4Kqiqe2sk6TC2hrY+UBMrbzjppmWqor0rmOKPrDMSAQMzM8BT3i6YtDMB1l5mrAsK9xI98hrMAhkQwaa4x0ssjgj30UUq6PA4prUNsSlczAXPAvP9v+IJ59eJNX47HgXnqObEEcRwdcWkMMn0cefBVfS29xkyYR+mAaLh9kRDrz3CqCMFg2NIegIFmu7ZMNd6nqKEgHv0MShsXiD0YLSwHl3DjWNMF9FqaJnBH4irBRTMT0jSCXgrKf0zVkgRQwy6qCjwNDMfAenHtcE6STxBPE+o7B8iE69xsDtYqfHZARxxAURvTGxWmXAgIP9lyr9vszIGFOyydCMwJhbaSODAHbhFO98qJJIyvYDXeYuhPeZRAZhuH+CH12hGDjysk7BkFyVxDs6Tunc08c+BYN8k/VskaJYINGUw4eg05JpMmWspBl8z+675zAH925nW54A4FRR0txdwI1aNDK4c6nO9W6THe1hp5mTRtGkwORh7hQcHX8DPyVb/kixD3btx378Lxyy+QzZu3+C5Mm3ga1RvOSvUbz0LejcwRfcZ/fXz6OFh9ec4md+Vvn131CuXoOlJfgrb4LP/Wzh2DHBq+cnfumXeNnRT2Wl+XxU/Y/fSNfmWPi827jx3y6y+o//M0l5ldZzv8+PPv1bfNveO/ju9303L3nvD/PXTn4BD7z8b8HCsec9zmd8urC1DT/yo8rSkvK3vnH2A3k/9mM/9uN/JETL8d+dpChwxp6kpSl/EpyoxoWmUEf4jBSSBKRM8HM9WI/AlVEWb3Pkz4ZEKnjeSLComnO4VU/uC3QhQCcB7Aq+P8ODOb7uYS9heRHKwaTAFegMS6JnT4VKjAakYdAps/Xq89dn0SQcj1NPYdu4cpvEQmfxTg61duIOU0CCeg4eEPLIUJPohi+lhWh2rDbBHTgD5RCGnVC8j7CyXrW3yKH33hHt5xJGzQ2wYJKDUF6OlcQEXAmpsGsdowE0qyQnEi1UzIyMUY0J3mBikHGmA0veMF8OSMQGH5xYpW/xgEfPVcwpiV/zBlMGeWY9y2i4Ol0KLErDNMZ9WDfZ2KesArs8nkYD6jUYOqVRgzJVzAAOPFqSXtthOLdF8d5dEt/H2Sbqg0ZMBBbsYcSfQyOQMga2/CySWDY9Sd+MvcM0jiOd8yTbFi+W/KE5rg/3aJ/1iPdYH2Eaa/CZgPPU3ncBP9rBp1C0BH8Q/KLBtGqUdUO61qbRWpmM/bmEvZpDhwUDGSFiyJsjTJ6QNBOSdp0iLyk6DtUAzVweXePG+d+i53ukUgvMLs/Et0Z1hsUBRHP+KGGa0lH6xeNUXDeRIMU5aU9x3p2L9zom79ue+WyejYXz432F0KbECs2mhb5hOHcbyNmpnhWkKBEpx02xRoKUNakhrsRPV4YQIIVhq01uPcoO4gPYtdfJmPNNxAyqi0LwFPOO3aOG2kKCPCskvYRTvTOst6/TV4/BYhJBGwmm1wugaJIgVvC3N2k+dyF0lZtGSEB9VYI1atxUqZ+yuKV6zMAd+PB8ep9QjlJMq0SsUMnhNAmFJqp+VBfO47dLSjNH4jvBpN5IpDQyAfUnLcEmQnKwAW1D2fU0UomKZAM2VNOzxtLpSDQfrzobJJMACMVn9NAhx9XnOjzpz3K5Mz2Jhee8srZLKmpjlZE3l9G5AJq+8/xvcXrpwTHYFSJFnafc6JGfLWjll0kGWwiGNBG0BHveoctgXJjA3OECux7m/4op5w7dh88a3HGnIXla6W15uoct2WIsipDWcB5sYkgM3G3v5aq/TFHmiBdSn+EjK6jZhB0Cs8seNeiWnTD5TEZm6xgNEFx4vgLYhIK3GaKhTVkK7XYYcTq/RrJyBhFLvzuE3iXq9RaD/k3gvExUK3lb4LrAUDi01qBTXiepQ7Q5x1s/rvYnRnAmzvsymXPxAsUmqZqp5zqMF4lML8HjjVJnmQXZAvYmm6nC7hgmC8e3gUoYvO+UwrYBj7nnpYzevkHHtlnc8pPFgTgu1bng2ScpzjucuFD0IbLGvBUUh3F9QBjW72bhwWs0jkK3enwBjQCzOE8x9LiaYiTFWNjWdXJXUPiSfk9YzxN67jyD0YBGaSlK6GifshCWygb5sMlFNmhrk71RjUwVK3UyzajVhawmDJk6t0JZW2Wr+SD+4BFsZwI6SwYGR1QiA9BIWwz9VRpTx0CApMAUBosh7SqFDnlitM7dfPTYB7v2Yz/+FwzZu0z2jn9O8sSvQ9qgeNlXkr/0zdBc/oj7udJz6V89zvbKMt/4HYs4VV7xcviGrxM+6VWzUrr3X3kHG73LfO0rvutP+Wr+14zVr7qXnf/0HNu/+AyrX3UvxUvfTOOlb+aHe9f5jXd/L//h/H/hb1/4WU6dfSvfMPcSXvqSb8Tf8elwk3nvX3mTsLWl/IdfCNVw/tqX/Rld0H7sx378xYvpnMALmWlStzmpJqGEtzGRUao0i6sIhzlwwFBYgpyr8kwBfKIcbq5ytT7EjGIFLAmlzo2AO670ZTj5UK9ISw2P3paj1xKQSMIw4eNfBDK3iwClE/zqnZT9JjjI7kopMo8WOgFPDFhvOJ3ewWXO43BRXmTweMxRuG15DtndgapA7hSLpl4Tmu2K2RWMtqeZXarEMukRsIm58dhQBXC7BZhgspyMcsq5Bq964EEee3iX41d79MoObqWERWHkAhjiqs4QjQBaSKQkbeKNp5/cjV9/ZwARMdTMPLcl9/JAE65RsYdk3LdzCx43BV6oKBq/6JNEWF41bF2MgJYoDRskUW3aWDOVxMVwWga7cUMoDGDC/TaukjEKo+f2IhPP40lmvNQsDvwU+FbdrwoQiGyXYqkg6deoKiOqlAiCb3istVBacjUYK6gFnMN4KIyQiGW4JBS0SMjItcvOssVnQnEa/FJG48AK7oIlPTRHbW5+MvbLjEGvIB04HvePc4e9i2KuQHJDMmfJVurkV0qK3RLXG+LqJVeKazTNIQ5nB+iUJycJcZFzYzNUejsS5aSqgenlNQ3iTQUT3/Wa1tFsLrBVVKnXgYHlgDnEeXcuAL0Vm6/vkZbMPLeKQ0aCubCATVtstV9GMn8Cf8AAj8etDDIoZvZrJhliHN40MdJD8OyU56lodpIQi06EZ9j6MjLzKnPxaFAtFtRze3qGi+kdLMxvEDiWQdJY93UG5AiGJAXfSJjvPhMHRhyDiwnWDwEZm6uPr89NxkiwOIpSaGNQY8MlxYIP6gxKDbExk/dToEJqxv2YLguuDwuJI0vBlyZIXtN4WZPHeTJGXtzASA+TGGTJoB0f9o2KvEO1E6zMNRhu75Dn0ODqZN94nS5UaUAEDhzuY4/3uHClHLPq5u1RxEBiUkyaUDNtsnQj3sJwjPLkLPOt1GEAu1yci0yKd9B9+2XKToJoEZ5Ll0PZR2iRPOPx9S74FHcsRw+NMDcyrjaaDIsEn7UoGkvgRuE5i9E69WKK3Q+jwNPPWQqzwmptj77dpiENWjLHrtkkLVNwOuPDvjT8MDK0pEcT/NUUtuJtFQmVXCMwFua+Aol94kxKNSG5+jLCdmRuVQsBCS53WBOKcAwGQVadJ4v0sqMMTB04B0SwywAD4cjjNc7tCSu3lVyOY8WbcPMD1mkYSmDIzaer9F030LpUSN0mmS5Nrq9qTmSrrheXKWuO+VrKPUsH8HaeXdcNrKhdRTc88/NB+qkKPpPAfsTgraVTO8r20hlMPSG/7zDleaHVAqkqIoqgqhgf+i+VFAEKzTG2hgd6R0vmThxkcKFgsdZBMVhTD16UN8n7Tp4yrL8PcJ6dLWXvKrQQjpgjDNOExBjEhjk2lTpNm3Gw0SLzLXIPPjGQCcY4iu4C9yQHkNSzlTyISQtqtk82ujQzjqvw4ZGgXzuOWMaVMgFMKpiqqmYc9vVsDsVxc+EZn3nS3RTZhYVzytKCIrWcjyX2wa792I//lWLUIXv3T5C+/2dBDMUrviaAXI3Fj7jb3p7yG78Jj/3bS/yVy7v87u2fzJd9mfA5ny0cOfz8TKJfe/zfslBf5pUfxXD94zUa9y3TetUaN376cVbefPeYJWBbB/hLr/vnvLH8Af7vx3+GX/jgv+RbBg9z4g+/gs97xxqf85Jvxt7/Jkgn5eX/xtcIm1vKT/xrZXUVPvMN++yu/diP/fifEPG7crd+F8slLDVPMRw8Cc3xJ//M5seOgm0KW5Ygl0kqBk6oxnimdYJWs6Cfh/2sNWOiRCJpJGd4wOBNSmky8nqbZnObLlHKN2UijkC92CC3i+Rag6w1brRtAE6Cp0xsX+/UAG3Bye3gQl0ul5S7jurr/nx5gQd4yczlT1+hVCUaiQmqVp5dU3vITTtLtSYffpFvltjod2LzINE4ttbgRv8o5nowldeax50AIkGnKOLH/NizK6602xSdP4XvN0LlOwwiwunG61lw70HZoCorGcCueEMT8DrrQRTYA8raIcGmyZglbAnSyVPmdqRlsIZg+j7pFEpKEoGKlLWWHuRsmo/TjUoKGiqSOTAZWjCunme9jwb1OjkIRA8vxsinxvb76O217c4zB2g9mJb7UYIxNoJtwNBRv+IZ2gAC+ky4eOQ8DbuCpYmP4zCMK8/13pVJh0xFvSYcOwp7z4SfFxQzzDqb1HENx/mNS6w/9TsY22OtLhz2q9QulFzoKcNkE+0VdBsbmA/VsUZQExL4wa6yt16nly6yttqj3C7QhuVo6yjXyg66IPQpsVs1Ti075q4UaL+gVdYDwJQoSgFDRZZvRmFKzFYC+QrZSCe6unSqirYE1obayb7N4RUOzB/khvEYwImn5zawc55iJTKjjKJicF4pB2XwtvJhtFfSKCGwq5bSZfLsHpLWDdppMyTvakg1w+gAhyfJhHIuwcQqflXSbXVCM7mF2VVOzNsDqh7Bm7Q2Bq+IoLF6i0oysXyYqsgqRsaVQKXVwPgBxZ5n7RBcPieT9hRVh+lsXh69kGyajIdPlhrStjDIhCKp4bqr7O5s3YyTIfGeOFsGKTgwLEa4wqE2et2RcKz+CsxRT2PkuMN9Do+vP02aRmBQhNHrU26OwAZUXJQxeizFdU/9cIHXZMzeNf1NTNHFZ62wY9chLkF0iE8y/OJxdsoeurRM/vKEEoe6AurQyuZJl9uQ3cE6UNg2l65AzZxg5ba7eO3ye7k8gExq4VFWBTeLNSKQ2wW87mIY0cq3GMU+T0w61WcmMqTC+PJTyEzdNoBtpivDik1CVVFTzUMawVZDr3YKKYIznsuEsim4mnKokzC3UmNxQdBaEefCqhqtBMavhU0bELm12m08NzwX+ldSjA5JvJnINMNwAWDkc/p+AJIxN5dQswLUaUSPNd9Vjh+CUm2guwE+EUrTQpsH6WUZiMGZwEi78wxsdaFdCq1KlRclheLC8kdmUqQQRukIpIb3MDpY4huW4ZEhZXvEoH8Hd97eYPvDsSDktBK6IjIo0aQeVBIO2yPkaQ2xOUlWcKiV0tk5w+3NDguLwrWBQcQhSRbAehlhWjU2Fz+NbP4atdwipsCO30WCsbNPh+pE8RvY4KFhu427oX4Ow2hm+7lsMTZ19jjDtZxkt470AqutVvO4j9H6xXz0TfZjP/bjzzxUSR7/rzT/3WeRPvzTlHd/Dv23/Bb5J3/zRwS6nnpa+f4f9Hz+Fys/9qOON1x8P+WhNt/1Wyf5mq8yLwh0Xd47yx9d+C3+0t1vJrO1591mP2D1K++huNRj77cu3PK7LKnzGQ98LT/+V9/Lt776X7C8fBf/Uq7w5of/D977059I+q5/BcNdIEiBvu3vCa94OXz/DyrveVhvOd5+7Md+7Mf/25hrgzc1nG1inDIaGc7vHWW7fn9kEc3ONYFwZUIlO0kRo/HDWILfFEISq4RJBEc0qCqo00AVNvwGXd9BRRilqxRZbSyXAyaUr6nI3M7447ZSctt6aOEY4BFYOaBBNhjhp9J6vJY07AopGR3tUrpi5tgzYBcSsgCN1RhhpkKliky8xajAGQk+S/FI5VYBSXC2trmiYrFWGPhDlLZFbhdDqia3nBk1GlewTWT/xORWNHqvzO4zGk0uQDEhWY+ogJtxRgkeWxATQpkcqUolTWqxd9cwqdDYrGO7tiIrRCEJDM8MSO87wcnsOG7KE2osG9JQfl7FkI+EvKh+74JsLJqtV8mV15uAjYr1F6G9ajVf6x4jwtKBlDvuCh41gwOGcilsY0odg3cdd42N/EPolM+PCJS+4JnNx2bbO7XB0qKQxHa5mJyJhn5KbAOfKXbZsJjcxqo/wVE5zCJLaKFIoWipaKG43ghbWHzuQnXAQnG50uc43s2hueBzhVI4Yg6SlTXIBZ8LmoPNoWlBCw2ynAKkNIHBOGcwi6FTlpbg6JHApAuNtdFLLzLzZsAug719kc25FzNI1+I4CPLbMp0P+X08TtlyjE66ybMrBu8VFx3QQ5/IVLY8SRU1PguHG6scTu/BiCHzgS6leJJESNrpWCpWMbtS150cY6KsDlHGMeKJIEpgqGlanwB7FdjlDOrNpArbVJKNkckckmXYpqC5j8b9ZvZSZOr/DSG/Zw2JjCwz5SmWReZeqw3UDDduKKW7dXyJDcwdTSI9TaDMc3w+giSM4cwEEMpmhuZDq3T7CWBozui1bg2vDjGeMvij40wDt+eRwQ4m38Oom8aXJ/1cKLZzNVbUSMZ96RcN1ATnS3KXo6uW+7/odZx61SciYthu3A/NT0IbGfkdB0kfOEgaqTF16mCUYe0odP2E+RT7cqv1IkbZQRLfJ/Pxnpvos1QR+BQSt8vc6HyYe8yEd1OLY9ppRb+DM82XAVoVp4znM+N7aiI1TW6PxvM1wSZQswFMKrN8PJ59PYzh/vGc8lVQngxAS5LEZ0nAjFJsT6hpQp7DaKTRKy0C5a6Mz4ZgzEeAUaoGK2hKkEGny+P7cG3wBJd2n+XECTh4JC4GTMkYg9QzvGRTMpKu5cbmOt44vAfr+xi3i/gBRuCOexocPhzO6X3lkhcX4eO7TrwHr/j5OuWdSwCk0sOZBv36YZ7tT0SBvgRn6si9GRKltnJvhj1TfVPUb0I7q3sz2w3eTxUnNmBlAlCnh2oky5PfASw11jjT+Azuan4qtzWP0jZtEHANT1kzyNTr/aXLD75w/0/FPrNrP/bjz3nI5jPU/vv3kFz6k1Bd8S//OP7QfS+4vXPKH7wdfumXlcc+BI06fOYb4XOuP0Lxc7uc/JevI61/5BIWv/LhnyIxKX/pnrf8z76cv1DRfv0xstva3Pg3j7PwOaeed5vM1vj0O97Ep9/xJj5w9Z38+Du+hW/tPMebPvCDfMP7fgZe/tUUL/7fSNIG3/P34Rv+pvLt36n86L+EM6c/tlWL/diP/diP54vVZUtr0bKzAXlLMIVhmB4kTw4hbAAK+YTtZAyoMSQ2MDpGZshZ/wxz5jY0ialk/HKsvv9VlTq1MRhx2V+MZw+eSd7MJmK5KyLQNpsytrKQeIwLF8bXlEYbLEWozUHmJRKwDKXJcaRk1DhoDrJuujy79SHuzlZpP5Si71DI2qgbIN4FcKdilqUGhoJeuul9aKC4z6Li0afitmLG16cC5lgdPqi0rhpcYigudjDrA/bqd+JHT9IbQPvmhFjCsc1U4kRMyBCl9IoYwzTEtLvLuPO8CrfdDqNDCpvK4P6M/GwJpGCgPg/FjeB7o8aMTxGqsk01pAwCtOaVGivtJbLsDnJZx8ku5ZJDS0tqE9xU9U1SS3KgQePZd9JVqCoWblQKLDxFEfo2O9oizQMYuNl8CAaPTmSTRrHYCI1MFC8aDdkbzZRGK/ij+UwYnoS5ncCquBlg8BE1OVl/NQfbT7OXX5/cQplNQif7Tphd3oErA0owV1/h9uQMd71oiffnL6derHN8aUjrgYPQfoZye5m8dgyz8wjNBxN2XZddujRsm0WzxKaWbK0ZnE3hUI5cyagdaDO/6vCdgmI5o61Cca2PO5zQ3GrSPe/oFH2MEXzawMSMMCGHIqhsA3ssPndiMEJgMClIOvEqan3iAZJyibxTR9IBjeJaYNpka6jpRZaiCwNQfGBGmQA+apQAV3dyDCVrhWQmbL76k5nLL1CauTimDKkksb0poh6PI62BttPJoLXVbS8w92awU6DFrBl5xexCI4bgitCMrD6eBCQC2F5teADHYFdknopQv3uRwdUw92iSki4ZOO9iEl1NKpO5rvqnLFr8QgN2AzBmk8mElZmCEZAsWFiqwc4U1Wd6fFnBS8pq6yC+9MAefvM8mpdghZce/jQe3ajHvgv7LC7CM7t71Os6YZs9T3j1mAh2QQAaCmeR7jppJxS/qNinMzHwSDGAtgYPwurwleG+Lyh8TmoykrkMMwrXdt/S53FkLeXGdRiutKg1oR7VYjWph+73Bt3xYW5um7H0VDFcumyoTzVDJADZ47WOaS8sBT9l7ZHZGqVjDJoDLNYOMidtRqYz6b+p1RNRw/Iy7MbHIT/m4IqSxYWEwo6AemhjXdh6aIQYh6lNKgebCHapgBkmtJ9u0jyQMvBCp6dc2whFBASiND2wWK28MNglSZDDOw8+rcAsM15MEYGnbjzCkfnbJuM5UqCSFExd6REWw8UJy2aF636Tru+jQOIHvGLpD9nTHscXjnDs5BugvxsKoKhFtUQrdm8sxoKCOI9r19FVA9thPAyTFQaNE9i9IYYJC9PZOlKfoNNSF5KKiSUyVaQhnKdx563kiMosP+wi4/oUKoJdqFG7A7oLGc1oNW2skJkWiRY0kwaHkjrPRsmwywwMCL5oqtTS+i3ne77YZ3btx378OQ3N+2Rv/2GaP/v52BtPMvz072bwV3/+BYGu0Uj5L/9V+bIvV77z7yu7e/DNf1P4lf8ovOXAMxQ/9xjLX3Yn8697frP0KjqjHX7rqZ/n9ae/kKXGgT+NS/sLE2KE1a+8h/57r9N7eOOjbv+iw5/Ej3zhH/CmB76eX6qN+FvtPuU7fpjmT7+R5JFfotVw/OMfEObm4Fu+VVnf2Gd47cd+7Mf/YMRkYucOS34mzukuZEitcynZwy0qkkwlLbQJIBaijNHjUKPhw3uqPLqIcl/9xTzYeOBWnlgF0iSRdlAt7VdEFZGZVeCDB2bnO7GhPeNkHwPpRNxgMWyzw+CQYtuL1KROK2lQ+gIVwVTf3dPg0ljGqNi2xS5mzIagovg1g5+DPCb8QYZTJSxgFlPKtkPU0bqUMvzADaRK4sUyGsGVS5PrOV5/5RjskgjcKZMkC5Td+l3caH/ieB+VST8Trz+pxeIBqhw4Ce0Tk+Ix7WXD0aNQq0XvIpnIRetTOUEowGZRUdKFz6CVHuG0vSMkBDohvLwsPYONxsscn6N+58KEiXVT+pD6AfbZLoqSHm3ijr+Mbu0UuWlXO4TrNgTfmpgsju9/CqNXzZO/IonAYvixb1bXEBLH6evYKD4MwGtfscrB9qEojayu8QXArphpllrQ6QWAQARIUpbNCiZSz6rrmxROCAwLxaCj/vi4HQ0JuJ8d+TgPksyCqFJVOwAOHTCcOA5zLVhZVqZNdmxs47hvKr8gMzE1VwWdArvEBvaQTqGaAoitoZEbNpGG6Rg4qzy7qhimq4iGCqY2Vobwpo5L5tht3gsi+OXbo2ddBOfKDMGR0sAkBjuXTiRIU10gDSHYM83eGy0rqXRsmi/D3JDWIQIWksc+VxuYXfEQMsXsyo7OsfDaJbJVQ3aiRbpoMI3wvIxNye1481CTQgg+XjBmdtWbk/alkUaSHs9mMuZbYCkTZGHJyTqLS2sBFHlW8aVijdKutbESTlQl+/feA5/76js4UF+5+Wgz4dUjRlmwt3Gq8WqMgb6bA531zLI3s4w8kDbRNNzvm72USl+QlyPSCLhXv243a9xzl+W2U7AwD0uLEz/fVFJaZo615h3hFNPkUgnsJY3VOwUoTRMXZZUCpF2NQDnIIUvjxSlF25CtNahRZ6G+CEyYpgAmPkc32d2On9HXfYql1ZywRN0hIC0xg20SsYwYhmfJ++ADZ+PeoURpPEdFi9XxcU+u1VhYMNQy6E+ZvY/KYKwfqiTeDKNM9bG1CEJ/wKRGqjfjcVQ936NyMGZA+ggeLa8Kq6vV3BfAwUWzhCVh1+2NT1GrK6vtLRqNBeayedAA2BbJYtWI8N9qbojsTbUGlfHA5877miwuTL1zqkhvldVen3tF7CtBpiXJdaF+ssbNXaKxm6sjJ8nUnGrT8Nwlgo3ssXaUcpZ+cqAKICwzC/0KPVPk5kHxArEPdu3Hfvw5DHPlA5Q/+ulk7/kpynv/Mr23/CblA18Mz7OK0O8rb/155U1/VfmhH1ba8/APv0d4688IX/QFgnvnRa58x7uZ/4zjHPmeV3zUc//yYz/OsOzzhfd/zZ/Gpf2Fi6UvPkNyoM76D3/gY9o+MSlf8/Lv5Dte+xM86nb4plOH6Swcpv6730XjrW/iYP4B/vEPhKo+3/KtSqezD3jtx37sx//3kOifo4lAOyYe3iNiqG1GKUgpvCR5eShNP5Yx2nFyqBC+myUYW1dRy29g8hKcjZWmAIQ6dfx42ZyxZEqm/aLEwM0f1wTfsIV5aDaDZGrKOCqwyiJokJODKIufKBy5PSSMq1nQRJSLB5DVg7i1HHt7pIZVUZkDGaF+zwJMARVq02nSAYO4it1ImxNmF6D1jO6pATsP7rF1f8n8G45T3L6KW24FDxoPpZvKBCtgzwT/rHFrkkZsUhkqHJrm9J2b+cNXjJbY/pVDjtrKYtxGIas8bSRm9eFKUmuo1WU2DyM0Qn0oUGDFkkUPGdEgtUwlJZXgh2NTC344YWKlCd3aKQo7j4qlVu5ir3RQYHPX8N7H5xnZpRnfmAq4NFgCD0hnbr8/cghth7aMcb5EyEydtexBBMN8G+44PX1MG6pIJtP9NgVuTfV/uHfhzxt6PW4X5Z42jb+/KTSM4ioJVBE0n3jMiASPH8VTyQ01ghBVIla1ZbpNRiwLC8LyUgXGTJJKe7O7tKlA6CDvDHI9kGldl7EBRJkqpFCrAyZjrhUZHVQsxSAJlXisqm/yBUUl4ehxi2CiobyNQOMk/MF7cEdeHPy9AKdtDpvD3LZ0ChBsZtD7WsiDGTIFsHix+KMPUPYSio0pD6/ST/0d0AK1aZgfknr4M+/E22FQNRPAvWJ2VeDk4jJzD6TIYgCBxSi1mnDsyGQuGkcZx1+UeHVrJxGBJMtotwnsVhf9sCqjfVHkYDLuY1c9r0bwWMQK5mAAxstNjy8VkXIGAKjyc2uF1fklHlp9ER8pnC+xBoxr0UwO0GpCJz1BIa0pA3WhITX8ggN1aE3HoLVkYA7cM3n+K+DAl4HZFa1Kbk4vjh0TXvFyod0OO5w4Hn5+Mj3FQn0psnqZPDQSQMUxsIgwSlZRmzBfW6Z+wbP8RIms52G/uiANofuilGyxxh3tYxxYnKOdznG0dnDc0Ao0vlUxGJdAKvA09mvLZKTx2UhNQuEGcVtlghwHsGvyvCfcbs+wlCyPC1HUjCXJLNYGSV8lrCzchOl0y7M6zcu1U6L06FNVugmzq4p3XfjtcXERNICbj4+eZBCluyISCqkINGmwp50xYPrs4GJ4l88diScNc0SvdoJ+7TjX514e2hlfgeMFGROYiPEE2CTh2GEdPx+jxbvxK2dmAHUInmyj9MD4GLOmZlRrAgxffJzRiwKxwmvAGavxlYyL3hgwyWT4xN+3WjL5fYz7sk8AoL5kJvNp8AHgY4l9sGs/9uPPU5Q52dt/mMYvfhm4nMEX/wyjz/geaCzdsulopPzCLwWQ68d+QjlzBv7FPxV+8keF17w6fFjv/NpZLn7j22m+eJXjP/LqsYn6C8WN3lX+02M/waed/iJuW7rnT+sq/0KFaSQc+KYH6b3zGp0/vPLRd4jx2ts/j+983U/x1N4zfFNzm/U3/iNkuEPzF76Ue576Dn7oO7e5eAm+/TuVotgHvPZjP/bj/2OYqdXZKtPyGkCnKgd0EqRf8Ys+5BcW7OzcY5h8lAZrqOAy0ulOJfMmCX5S46XcWbbJtKTh+aJWE06ckNnvWBn/J5xn2bAiB7ALKQ8du5daFq4xNSnlnZbkVcvoym2423PsScOkMVB5diGGZKE2w8rxSZ2KEaXA0I8wYkhNbXx9HsU1GiG5MuAzg22muINt/EIdJEG8hI9xoNWC+QVDrUYE/oRWvWR1BSQyCiqz+Vkp0izYpUwn+VC4qep7AjpeqBcwhtKDz5Q0me3noGCzYR8m/TyWj2o5PqzG+28Sg3H9KcZVSmnn6NRPx6QpLNAUpee5c0J/JBTax1VSuKmcqGJ2efzNZBMA2rXFSQ8IjF7WpnjJyQDAGKjXZ0GjJHkesEtuvuZwkfVGTJDjNXfrp4OnVQT6JuBc1c9uZuyBZcaGTGGr9RC9dJUyejJV7AyTvLBTjEwN7uC3NH1NZvzz8AONTLUgAdJKCiSTRFusGYNXFaCXpfDgA3Xa82aqcASoKGIm0lYVGCSH2Thxiu4RQ3Z7YzLWJZlhyd15ZtLmYbJKt3Ybo/4ytx9ucvKojIFtqTPxxKu6ShKk0cRtj+i969rk5zNglwa21ljeK5BVrt2RWeenAPCK2VWBkXMHKc98Gtpei/0Sn8G52JYpVqq6yGaKt6lTP8Pm4c9GxXLkCBw+ImhZTPoXT/7yI9gTAczyUmflxGq8R4zBP3ElxhiGPscNFXsz2PU8n+Fr9RVm5qipcFpgLZRF2DHLwrmGcyfHjEmAesXQ2rkIciU0CoVGnbS2MOtXBpQ+p3CjsS+v/SiIwMJCqBiIMPa+k5Vp6h6RtTVNnwv+anO1BZb1EEKCbIzGvz47uoiqwVjL4kJ4Lk401qLZe3xWI0PK3jQ1VgC08ZVWNvxxPDnIXe2TQFhcHq4Mwy+8oyqraNQjowlDKjOOZbNCvdkcP1eiUwBzoXgX3pfVYs1oRWee45tDphscfaq6nedf4GFs76X0tMs2u1waTp6RipTZjDLiQ4dg7SBs5jvh0uN414VjbC+9ikF2mM78g2OGl4lAuFQLMFbwZsLsAqilk8HkkwbU2rgszKuj5NYcVGcmb5llDtYStB4XEGI1xvFlHzjNKFlikB4KYNf4e2Cy+wP3wwP3T36wN/fKALQtV/N80Dyr3gw2Pn/sg137sR9/TsKsf4jGW78osLke+GKSb/x93PFbmVhFofzKf1G+5MuUH/lR5a674Kd+XPjhHzS85MVhJby41ufcm3+Pi9/4dur3LHHy374OU//oFn3/9n0/gMfzlpd+65/GJf6FjeUvvYPa6XmufOef4Efuo+8Q45NOvpHvfv2/4+z2E3zL0z/Gxpf+B/JXfC3J4/+VT3z4s/jJr/pF3v9+z/f/oE5KuO/HfuzHx3W89a1v5XWvex0PPPAAX/AFX8DDDz/8whuv3YNfOjn+Z/UBbrf61K7n45XZtVXh7rviRkaeh9k15XOzNCUvmJqWxgm7GEqKyddrtQA8RlDijz+C30n4/c0/qRgDIA3D0ssOcu/cA5xYvHO8RSoJ7rihrJfjxkm13FwdNyIaYgTJZj+WtTE/BS4FGWMmafC9ir/Y6F/lBtuTfaavw4SEPOQ2MdFuwu23S/BfikDa0TVPsyGYmGiWbkqyMu6mmFBaQZYs5V1LUYUZ2n+ue34mgdUqARUJCVtLGa3k1KI8pDZlpxLYUyZY7ZjxIbAW2qOLZHHbCdiVIr4/TtQ1mxzMH6iTWCEvodtTEIvHsZF/iKvDD0w6M57EICie3DSfj6zOSnONe5dejxCkdrQStJaCBBbUdCTUsFZI7Kzc5hYD8fjv1VVh7dAEcBhlR9leeXUwySF41FR9X/V15IGEn4thMJzqx9ST20W2Wi8ajzFXkSeSm5ldk7jZUwy5VS402RgkAinGCEeOwIsegpsRlIkXXDUOIKnXCfJLqDJm0ShHru6lEbyt4ZOU3hFLktrxWPeSzPTlyZNTAKwIebKIL4S0Jiy8dhWTWWz3ylgCWbUj9J2dgLVTofkU2OUgZMaT59LPH8G3D+OXTgUze502qPfccQYeenCqL5JaGNRi0JVToXvs1CAnsLh69ngc9FNAYwUKVIm7q57B0K6xULsChs2kPyqmjGlDuZDTKXrsFh1YWAnzaXWOm7tA4b72mRfCunDexXEfrqF6BjwZTlIG9UC5qtkaBx+YpzwzBNvDbj2HFANMzdKuLY4ZRUcWTmGN5UbvGr28M5Yxjue9j/Cp6cpwAVkKUhPkZIIQmLjzi4Q+r4zjK9f8CpQSiT6AOn4fDLXEKyynKxGHulUr6iOgkSSwlM5zMDkY55N4nqrSa9w+9TntO2qYBJqfBMO0z7gYizG024LRERQjajVoLM5z2/1HaLVg5TZwEhmuLjzvIkAqbMy/kmF6aDyX5QtK9hnHX7CvZi9l8i56nvosY1baXAtGjGg0Jx1gTJAxqhha0mIubZJYIcumxm1kCCOCry+NTeGXl+Duu2BuLtw3qsqnxuCqOWfFYJdqzN01P75X1ZGL+hKXFj+Tnca94+NXoen0u1PHiwK3GNRr+H817m1jjuvtV6EmCwzOcR9NYu2QsHpgMtmXtkm3fjv5Ug1ZsqSS0bL1KWD1I8c+2LUf+/FnHb4kfde/ovHzfwUZ7TH4gn/N6NP+PlKbm9nMOeU3f0v50i9XfvifKseOwr/6FwHkuvvuKQT8dy/x1Kf9F/rvXufI97yC07/yRpLFj15R8Ynr7+e3n/5FvuDer+LQ3AtP4Ptxa5jMcuS7X0H+3B7Xvv99H3FbLT2dP7zCtR94H2f/t99h9Vsc/+g9/4T6Hyv/x++8ha1XfAX9L/8vuEP38eLL381vfOGbefyPzvOT/2Yf7NqP/fh4j7e97W183/d9H1/3dV/Hr/7qr/LSl76Ur/7qr+bKlednlUo2dxOzK35A7w6ondujiGW+E/f/sHfecXZUdf9/n6m3l+3ZZDeb3pNNgRCS0JuAItJFRawIKj8fFfDxEQULICoqCIIFUQFRBKRKlS6EXkMnvW/fu7fOzO+PuXX37maTbLKb5Lxfr012p5w5c+bMmTmf+RbFTTDokBcVhFDzL+zpbAAsRQj0WhUx00CI7MQ+P9/LzQBVHJxC/A+NMv5hhe0Nww3W7PjLx4gUZK3Oci/LvWeL2ex0aTWElnVjTFquIuHgZCddxfu4X4V7Ky12qB5HNYuEPYf2TFc2LovIx8TpycRZGf8gP7ktTpHuKIrrBmMLcm5tZbOj21lFJCt2pe2su5QotpQo7KhOMPCNzgasypkf9S4373Hiil0Bv0O0UsPILq+ugtoad9IYFhEausdAW7Jkwh7wCxobIBLOHipbHVPzIhJtBcsuXaF+FNg+g0w4RF2tQNcglQZbKFhuxG0ibyYIrC9YxbmORApdxhiSagBFgMcsfT8RCGZMDDF19FiCIfJWWtWVCuFsvaIR1xJl3ymuqZHSazrTV0jNTuIUBUMvlm9Utw+bQbYEFtBefWC+7fNxfor2d1DYsIHCJEvLuFnWnMJHroyTizGkFu9a/ItrWZevK2UnbXnLK6XUXWf0aIVAQJQIQiiFmF2RMNSPcgtW9aw5oQ257JXCcVC1IsuunOiVu780tUh0FXmhrZji28lBIzBTR/M6iJ4WtPUvo9pJus0mNoQOQmhZqzlU0IuF8qwEFy+4hTkZEMIpvTd1LwTrwBvBSbtZPPPVs208HkE43Hc6m5nyEagYnT0nhYwaoN07lU7PJLrNcXROn4lep0JR91MUStQeq9qd5CuqAo6DN7U+1ypUVBQ1RJFll6JapKbGSc2LkdxHwWluKuvGWNyC2Vbtcw7gxtbStIIQngsvZdmC7tBM0kYh5tek6Bjsmowb5zDdg0KCCeFxNEYm4TeDaIrO6NA46oNNhaMP4kOqVTsda/Q80hn3XA2TvDu7EK6bZ30+o2Cu/7v11bMZFoUQpPwOK9Nv0Wl10eGdSnflXJJdERQtN3j1shYCehJqthwIan7Caghsp2TcLdlVaOiVCpGlJh7DxIltwW79gFwGxepqQeOYggBXMXkiZjDE+HGCmiaV7tEaCoordoUE6elx7FqdlO11+2Uun4JSyExcjuJxP6FV9a1nSd0FmbEK3gMD2KM+xDAEtj+7ynK/FqlR11XRrJ7YZ/fieG2mCa2t0NnljvUNY0RBxM0ULLtyVlFCEwQPqEf16UXP8uw5ampWwHTPM1PkZm+NCpc9j7Ixuxz6iGAATmh02XcDtyy3oHCo8LsQoIzXmTF3HuN8o3Hsvvd9OaTYJZEMI6JjLd6/fwbzv1eRmXoMPZ+5E6tpSZ/t/vuMwxmfc/jxpQ6RMFzxM8GVvxTMmV06erTe+A4rv/AfzPFhJt3/USrPmFpqStsPiUwPlz72Var99Zw259whO7+9icDSeqq+OJ2WPyxny5/e6rM+055k87Vv8PYBt7PiUw+x5bo3sTpSOGkb8/E0Z974aY766WIu+csX6Q5UkTjhDySOuoxRyrv889BP4Dz9J+68s3fObolEsjdx/fXXc8IJJ3DSSScxYcIEvvvd71JXV8fNN988uAKK3jgzThxb9WJ7Imjveuh6JZ394p4Tu7R89nQblVb/XAhU0VM5P5+KvKzYJXrN5lTIGBE3s1Nugm3aCNXAjjZRW+vGhXHCpclTci/N8/V9qTGiOGZW9KgSjKor3tCN+9HlmUCqeiFCCOLpmHscchPn4tl5dpqkiMI22TNwy6PkbyUb50Utds9RBfVjdEYp9YzWZxadqwDUnNeKu7UC5viQ+0c2w6FdMQ7bX4ViugHcM5mcG2ORcFb0e8AvmDKlKDo5ZN/8C9V2ijy/cpMD01ewzNF0QU2NK+KoQsUjvDgZO68rKNmL4/MVAsQ7unsiQa+GkmzLW7wIXcHjAWHbpNUAquL2mVTKcbOPKe75hMJQk9LxKCa2GcD1dnGtbmzSCAEV+5Zm1BJCEIkIpk0MomsiH3g+6C8IX2NGC47YbwLzJo/L7tNb7BLl/+4V9F/kxC4BCb0WJx+fKtcAFqpacJnKXxvF3c4XyOCg0pJ+J7/OzqpHSq8A9cqUw7Bqp/epn2sQ0c+kWZDtj6J0GZSoJkJVC3N9RaDmXAizVjv+oMj282xf0Iotu0rL1bRCXBzHta/rW6+imbwjVLSIcLMoZrMmgmsVllH92Fk3REdopaE0soHp7Z4MSnY8cSxA9I3Fo1WYKF4NO+W6ouUsxJSObPZFtbxlXM6V0jOjgs37HkRSryKjZuvjN/FM1EquhaKAsDMgwDFDOIonu9y1DAomPyxsqwqKOhJ2zo0xmyUUj4MTUlFVvcSyq99X8n7cui3Hyo7HWfEo21UsKysiKALFFJi1SuE+yPYZoQtGBxoImVEmVc9mctVsEODV/fnyPTmroAFwouNwgqNI226gftMg/wzIG83l49opWVXbvb5aPgB+Lji8TYvdgqV6+HA1JOIatuYrzVLrVh+AtFUQu/LfLXocnER2HLQdGiMT8+N2T9U+hXPLuienO1aCbeMo5d0IFaFiR5uwq6eSNrP931JAVRAVFig6KStbv7xplkBTBvCYyVrxJrRaHEXH1tx9ylp2AdZElQ5vO1bNNKzqKWQmu/dSfYWgpgqqmqqpmTcXPVgQzuzKiVg100o+kkyeVJQkMVcVkR3Dss8ZR1GwLOj0TCoEnKdonMvVMXufZdQALf65tPkKz7qp00T+Q0rBsqsv2cSJ5bu37qV3spM8ijum6YZg0uTCx4ZZMwWNjQ441qDdGLfu1ySRSHYK2tv3Yj70AwASx/zC/QrVixUrHa78jcOzy2BsI1zyI8GSxX1f5AC2/GE56y96juChY2i8+gAU7+Bv7+uWXczazg/4+dG3uRk9JNtF3QXzSK3pZv2Fy4g9tZ7QkY04lk3Psk2037UCJ2Hh37+OUd/fh+AB9flr5GRs2u9agX3hU9RcVs0V8a/yzS9fjXf6x7DGLsJ46CK+xeW8suxBXvb/iOZDJ2ylJhKJZE8jlUrxxhtv8KUvlSYPWbx4MS+99NI2l2e7odTzZDocVJ/rwqPrglkzVT5cAzFyH18FduN+pFcU9qmsgEwltHYVLA/yk/D8pBzM0eNIbYrj9awBwAnYeOdosC6KE2/H8Wa/Eiui75s6oC2ySJFEybgp4KsqBOs3OPnjFYLcCkJmBava38XjjYHjus3ZIRu1JbtR8admIXpZNjhFcxmBiCsIXcnqW6Uv5bqhEVQqSeqNhYWKcN21HAE4VOnTaQh0YTaFSO3fXbSzDyfcgMhOUjI537dyMbtwg/VruuK6U7avzG5quOIRiptcUy96L1AU97wUtc8kQw24dXPPVqBkSl3vBdnEADjEa1OMmqJQM8OPeDedrZODZequHmE5xI1RCF7F0CGejGMLFSubvl7TQLEEU/1jeVV0gHDyVk2Wk8IJCHSvhpMw8Op+JlQUsk2rWfHAzooHvQUttUgg6u0WWFagcQshe+LZc3XFrlwTrV6bW53bziEQgBpFoEQh/krOIVCjOppE82QgLojZW/KHSNtFYlca8qKp7kUzAmSsdN9JfdFkVVFgnrYPFaE23k6tAMXBKRa2ypkKqop7Sr3ishpGAFKugBlusFi12aFKVOFoCeyi+zNfj+zxRX5kUBBl2rLEsktorvhkpUApCCd5tz7FtXFUVNW9l7L0vLiZ9MYecED1CeyU44ZIU2zodczA0np6XtpManW3K3blBJZ4G443Cp4yViaAEnRn46pfw/EVBdvOzel7nZqqAo6VdQVW8sKyUAVmprVkf5F1Dw2H3GDub2VFGcVJF8Vb01AVJWs96fRpO8DNbmmlcLoTlCNjp9E1sPJCpbt83XpXAFIVCDR70Hx2ISOpmg1Qb5DvO0IR7j0kChakVf66EjfwraFNXcoGTxcTZnhpy2jYuTbLtgkULNxMRQfLosI7Jn/e7tjquG52QsPGQhU6dWN02CAoyWiRZcYMlUQPWYGzSJh8oxMOAGyHoBnNj9uKHsbxViNim/NiV9xOuiOX4S8ryihCcTPdxzdja++7x8kAhorrRahi27k+4e5jI9B6mekpGmSi08DOoLK5ZJ2jgUgJN7sj5cWftJUCzYNH95EKrCQ9NU6oVsN+SWALga6795FVPQWRjuNkP5YUj4Ver6CxwWHV6l6asUKJZZftQKe317XvPbYUCfZxo770fHpfKpH/pwR7AMsuAFs1gTiJUfuRGRMsqovqundaFGKj5VxiAwrYNopvAPfv4tMY1FYSiWToSMUw//MT9Dduw6qfS+Loy11TziI6Ox2uuTbG325x8Pnh3K8Jjj/O/RpXjvY7P2T9Rc8RPmYsDVduPRB9MTe+/EvufOtPnDLrHGbXLdqhU9vbEbpC428OYMt1b7Lp6tfpvN/96qgEdaInTaDy01PwTI323U9TiB4/Hv++Nbx54h0cduUiLvN/ifM/dR1efzXJj11J/PV7Gf/vH2G8+Ak2Jb5G9COf3cVnJ5FIhpO2tjYsy6KysjRVfVVVFZs3b+5nL/D7/ZhmGp9aid/vR8u6jY3WZ6Ga3WjGq2DpeMLViMomTH8Hfn+cqM/P+s0m3VGwa7x4TIuKigpa2zJ0mCaKbhMMqlRO8rFijUWmLY2e0hEeL04mhq2aaJqO6dOJhE3CMwTquqxLkyEIBAM4fj/4C1+LFY+GnXDFDdPvwzRdIcYf8uJ1MtCTwqt58+cEEKmoYsxoha7lSUbXh9Ar6sh0JFhjbcI0Tfx1E9D224T6gO26zGkGqAaBUAgjEkXXdddbzePB5/Ph8/vQ/DYOcVSnC4/HS7ixknS7B9WjoNZ5MQJBrEQA0zAQviDRaBS/Pwm6Scb00RUETTcJj/k8+x5moCiC0aE6grofv9+P36ugZTQi4TD+uB88HkzTJOAL4s9aXXgcH2bWx8pxIByJ0un14knZBPxRuqO1KN0Cb9U44hWd6FE/qpnA5/XhM4LohoEZ0DENDx7dRPEI/H4f6hIvG1d1gwOmaeI1Tcz8j8Dn8+H3uu3rxUvTgZOJVkZx1geI10RItauo1RMIpDZg1PnQ/T48yni8ybW0xGOYXgvD0klqOh5TQbN1Ar4ApidJNGJQW2PSkTFQM6B5TULNh+HZ+ByV/hqmNBT6QkLtwt/jx0568GX8hIIWcaVgkRIOh4lG3eep7dj4NxXWRaJRgmZBAAl2BUkrcZyUDzLd6Hq2XwUCeL0BGhp03n4nRSprmKRlMvj8PsJBP04swL4zo4hwBe9t8tCxxWaefiiEnsMyujFQ0TKCKn0S7elVtHaYjDFNwuEQ/rgfr+Vx+zlwxMxP0J3sINi6Fiw/Zqd7fT0eP6bIBgrX3NhIfl8KjxFGmCkMXUfN3rfBcIhQ9rxT2WWRikriwsAfyhD62CxCL79DarNFpGo0J9R/juX/vZJVnUk0Tcejm6hBhbTPj2lm0AyBlnYwTAPbFIQjIRyPHz2ho+oaHo8PPVv/aDTr81ffjWl2YI2vBlXBH1gHugXtb0H23vT43XJC+KmsjhMaE8FKhOnZnJ0ld4Hhy/b10VOJv/q+6/rm8RAIh1GiUdL+TdnjRtEiKeItDo5q4wv68PizU9joKJRo3/cqAGdBhNToKsz6AP6VSRRvD4qZRPP5cPx+Al53jPFnzy8cVgmP8pJ8/03SNWPx+4Oo/jRev4FpmuQkK13V0XWBME0cr0G0wocZC2E6bvuMCzXQymbw+wmHIoXxAaio0IlGe72jV9XgX76+7DkoQiFkaSS7A/h1P8GEe92yK/Ga4A+F0Mw0juNgdpkIj4ppmvhCXoLhKEo0igjHSMVVfJEwNQ1NKB6HiVUz8zG7vF4Hvz/F2LEq0Wh5eWC/JVGcxW42zy3veFGSDh4veEyBL+DBr/gx1TC6YeDxqIxX51EdDuFX4/i8PvS0gaaqaI6Cxx/AMDVMNUBwylyUdx+HaA10u+/N+ANEo1EiAQ/r13ppTaao3M9P+kHVFct0E79uEg6ZOP4uPIYXU0BluIqAvgbwo/obMBPvF6xhQxWQiUPGvU6maSL8fioqXPd3x0xjeL1oqoZHNzH8PlSvien14TV8dBkGhuPGCTRMg8qKKkS4GhKdAGghBSviPp+VRA+apqFp7rWg1oux0cQf9mCl3UQbDdWjaO0pPLsNQ8Ov+plcM5u3W1+HUQ7h6gBx00Fk+2gqmMIfKXX5r4hWEPUV7oFoNENLq0UopBCN6iQSDh5fK6ZQGDVK4f1QEDx60fZue/h8PQgzic+v4vcr+E0/Kb+fcgRDGu0eD0I4aJkUhqETCAYJiSCdXQUlTADBgEIq5RCNun1t0X5W9rgqdkMlye7NjB4TxV9dVXIM/9IIqc0WIhrFNE08Xh2/309gVBWpdAhj6qiydeuNFLskkl2IsvENPPd+C9G+itR+55Da7yz3U0AW23a4999wzW8dumMJjvsYfP5MQTjcjyQOdD+5njXfeAr//nWM+eWSQQtd8XSMP734U/75xrUcOelUPjf/f3f4/CSucFV99kyqvjyd1KpuhKagj/aX/xrbC2N0gOl//zjLj7mdxVc1c1H0TL5/zPV4dT/KrGPoqFzI+1f/mAPe/jnxLQ/ifPIq0MvHuZFIJHsmvS17HcfpN7MhQCwWo0k9BgWVWCyGJ5kLHq0RDUdoSSRR0ml6bA0SGVIVFaBZdCkekskkHWOTWL4MyWSatrY2ursc1uhzqEs/QSKZoSfuUFsjWLMlTTqThnQGMmmSdgpPxiGZSRKLZRCJOJ60qybY6Qw9yRR2LFZSV1U1sGJuynU73kOuqmo8TpwESjKJbrnnkUy6E5j2zi40tZuJE5I4ThdV+hg2WOvoTMSgcjrdjod4IoEnnSaRTLruU2qGWEylp72ddDpNxk5jJ5PEYjFi8RipmI3o6UGd2onfkyFhWqSTSRLjusk0pZgTnMXKzWsRKYVETydtbW3EYm59umdF2JysJi38VMVidHT0ADDVHJu/HjFLIWkl6OzsJhaL0dHZSTKZJC4SKCm3TaaPSdDyTpJUChy8dHR1E48nSGbbwMqoJOMxvGqIuOpgJXswk2liPTHIqCgBQU9FEqXbJrEhiaoIYjGHRMIhnVV1hEiRiDsoySRpPQnJFPGeODHbbd8l0dnUBWbT3tqCFovRk/DSbVeTTCdRFlfQ9byK0tZDIpkik85gOw5ru9/F61SRyaRJpUFPp0nFUyRTGfz+NIZq09OVQnNiBKwUsVgPPbEePE43bW2FwP/dPTH3OqccnFiMVLqbWKbQXzqNLtq0wvaxor7U2dFJRi9MuDJxm1gshoi7fcjnc+jqhlisB0uB9nbB5MmuFT2AbvXQY/YgOtpRYjGsji4cuw2FHpLJJBlHw2+pbIx30xpfTzKZBMckmUrSQRXJZJJYKo0ai5GIu/1KIEh0p9Dw0tXVhRKLufsBSiKJnnJ/1yzIZCCe6CGOQLdSkIyT0d31Xd1dWG3u5DSR3b+jq5P2Dp1YDLq7IRFPkEradHR2onszxFM2iVgXmUwAO2WRSiRIiRjJJKSsDGQckmkHG0Gsu4Nk2iadSdM9OYU/VIWTbdu2NrcvW5M9dL0tsMPuhLk7kUbE3smLCsmkQ0xLEbdiJNQ4vkicVCZOT7ybRK97HsCZPJq43oIS24gAurp7sNva8te0ra2NeE83yVgMUnG0RA9WzLU6sfUYdlG/6YMXerL3pxaPoyWTpHt6sGIaKdUdY3LHicWgvVuQrt+fnpUbiL+8BsdyEIkOkskkqgqaZpKxM64wmkwitCRpI05PSxojez0maPVkMj1sTKSIdfeUjA9dXeW9M2Jl2iVHMuUQ70mgaTHi8Vh+XHRUBVXY9CQTqJlEtu2TiIxKKpkiaSXp6o5l27KbdKyHTJdOukOjzhhHd2cM13bXpXm2g9cLbW1bf19NJRMkk6BnUiSSYGTvsUzawpNKoWcskpkk3bEYfi1OMpkkaaewMhlUBLFEmjgxUD10py00/2joiaNkT86Oue2GlSKRSJBMJUmFE1iaSbrbIUWans4u2lst97hqgmRSYKVTdCkmSixGqnYf4oExKFveA8DKCEQyjZJMYpomyWQSKxbLjzsd8Q4S6RQxtY6E4ieZ2oKdTJHKWPSkEqQzNtZGm4hTRdwZQ6y7C8U3Gjy1KBtfx0DJ92/VV0NMrCdpJd0xo66JlohFSllHMgnJJHR3u88bn+6nJx0jGXfPvac7TiKeRGSSJHoSJJJgx2JoFSaJWtGnr3R1dkOyMJfs7naIxdy+1tbmWi8Hg0mioQSJBMRSCdcSM0vuvo4nE6jJJPEeiJmCWEolppeP+SyAZLYPVI1SCRuCDk89neu66N2V2zvceyvXrwL+3HFBC6hMnpQinWyjra3UUk7UjUPR3qWzy30+JhI2sZiC3daCYiSId/cQqd1qV5Vil0SyS3Bs9Bf/jPHEL3D8VcRPugF7zIKSTd573w08/9rrbqadH3wvTFVV14DFxl9rYeWXHsWYGKLtIp37Xvo+77e+zuqO94mnY/j1IEEzQtCMZv+PEDIjdCU7eHrV/XQmW/nE9C9y1sKL+mYHkuwQQlUwx227S6jREGDCdYcjTnmAhpui/K9+Oj8+4q/49ADh+iqqz/oFF37/CP5n8o/wXH04+qJzSC84s0Q0lUgkex7RaBRVVdmyZUvJ8paWFqqqqvrZy0UtyvZmRXyo7T3U1kB1NbSszK1xX0SFpmOPmoPo+CC/T7fdhqO6wWmFgGSxyN4rzIdQXPceRVXpHqfjqy7E8fGoJgkr6cbE8ZZaqAElbk4lnl4CioOd2BXjgfdzBwQKmdE8mo+JFTN5cd0T2Xr1mrg5thujXnFjs9hFEXKdon8BnKCNVqXhmRTGG9dxkjYIQYW3huq6Rbzx1jsoTq9YipqKLSqLwxf1pShDGcDG+Ht96lpRoaBUw9q1rqDpFI3xVs0MVKGQwQ0eDx1FRSsEDx1DKHkEVapDBa/TsaFQGUUFMV7HWJumrglWrinNMJerwYzpWdejNc9h180sqbfj0dH95GOG5d4fBJAkhqGEs+fn7qai5Pzj8O+n0PKWStUHKbye4mx/pddJzbpaRaI2UxvAE1RZs7pPE5Zv3l5lefVsYOVsHXxegWE4kBb57ImBIgOGghtjLhmAKDmmJQw8qoHjaKxKPAWAKdznfVKrYE3wIJqM0vs024nL1lehr/uWANA9OEpnr2LKuzGWHKY3ugdhp6iLdDGqVmU1hUDS+QD1uf2VgruY5RE4/r6TXcVQsaNumyoCNzZYJlmyTc6NsRAUTuv3Y6wadTMoOhYoVqLsSeQzOSp6aVbH3hGxtwGzNFxcoajc5c9mSVWMbEy9/D3iXks7KLCn9SA0gd1rSm0IDRS1xMWsuIxtoThmV+/9FYW+baC4/UQxgGzyi/z9PcDxfb5tr1xvN0Y76zqoKIANVnbcVhA4eXdOgSM0bDIoqCUxzfIUuYIW42Q1GgcFLAcn6/YeqQARgTGjFWwmYUfGoOg+VM1buOt0D2TKu4uC22bjJkKgxQfRJkT7KrcKqoIdMPNVMoVJjT45Gw5SFJ5BpsgHsEcz6KpsIC4+IKw1MKdpGj36W7y/pTBaq1k3yEr/KHra3yNjZ/Dq/vzYB7mx1S00sLSezs4V9PKQxKP5Sv7OeR/a+eFL0DhWIbNZkEGUjBeVFUXnn20pbVIUM2TjtAehoInlmT4NIhGRv0b+gIpn3D7gjboJJrLomvvty7YHcGOsme7Gx/P3fYdxouOwouMQ63vF1cw9XPtmeyiLnN1KJDsZ0dOC5/azMB+7DGvCwfR8+vYSoaunx+HK39h8/osOa9bC9/7XDT4/adLA4kXirTY+OP0B4v4EPzn+x5z/1Gnc985NOA7s33gUH5v2WRY1HkljZDKaorMptpaX1z/J3W/9hWfXPMT8+gO48th7OXu/H0qha4QR2K+OqjOnsfTZxfQ8v4nz/30K3Un38dgwRnD0/xzFyY//i2Wdh2I++Qu8N38S0fLeMNdaIpHsTAzDYMaMGTz11FMly59++mnmzp076HLSU2tJN1Xi9bovwWOmTGDi3FF4Z2RdObIvyMUZEMsFDs7Nr+y6GdlNSidjAoVUlQmegoqSCwZve0LY4VL3fehf7Co5sBnCrpk24DlqxRkoexeUi5ibDVbseEoFjd7amCLcwM9aqLhuAjs6jpgxhm6jsWR7gSg/eSveJi+euGVGIrl9e33ZLqoyojgIuZqPu+PVfQgHxldMY0x4Aj49gBrQUSqbiEbG9TkvQxdMXqAw7lAdVRV4slpG8cQIskG5hUCJbUbE2/M1CvghFCwWBgqTPSEgbSewREEAFEKAJVyBQIDp1dECgoyTxNQLAcJ7C1SqkovZZVNZKegt8oTNvmJp7/bNETT7urkpwhW1cl46Son1da5zW4VzBArBvgQe1XAzBWaZPc2b3dOtt2GUCmS9alhalyLB1DCKV+jYVaOw/TX97eqiqv1nNQOE5kVkUpjpVvxet9/nk3Bm23niJJg6BXAcGscKolHoE9QqV17RSR18EPlA+MXkYjfli1DUPtHZlaCOGjYQmpIXGN0EC2VuoJzApRswruj+L7ftINF7vWbnE2j2amNdzRCNQP383FgnSvqrG4+qUA/HcYV9VIO0XSoCbm1sKIeqCDQ1J+q7y1LT67JCM4heyRBy3VcYApFJlZ7Udoht5ciF+stf0uz/VtYdNy+m2rixExUFRwFLMXBjIyrYjoUiXLErdGQ2C3y+L+U7aOGYjfshJmSfU0JB2HY+xqNQBOFQdpwQArICt64XCUFCxdEKCqdVPaXXWQk8PkEwoCKEgujJfQDSXAWpaBiwVLMQ4koIPI1qnzwTsakhOsepaMJE1wVVVaKk/RvCE2kIT6AhXIjDq6tGduzL3tCj56M2F4LulxO79V73X77ti8eEnObsKfSVhtGucUWeXFtqCt5ZlUybq9O7a0GhDzc2QFVVdswq06/07GO4tbXfocSNuRkd28/KUvIeMjmrtEF+5JemABLJTkRd9QzmfechEp0kDruIzKyTCl9GHYdHHoUrf+PQssXhpKNSfPZLBqGKrQtP65a9xfrPP00PMX596tVMnjCfs6b8kHn1B2Bonq3uLxn51J3XTOeDqzn7wXP47uj/47x/n8SlR91CyIwyc4bg3AsqOevCy/naIYfz2a4f4fvrJ0gt+pq08pJI9mDOPPNMzjvvPGbOnMncuXO55ZZbWL9+Paeeeuq2FaQIyE5WGg48EDXTSqLFC6taIBvEtvSlunRiV4zjcycfqhB0eKdRIVry+4jiiaggHwzeCdZhK9D7Pbo4sYrtieDQQVoNUaFuBitevgJlUIvGwBIRRaiF2Ve2HGtmgkybjbIZcgHqSzO0FTJ4VRhhtuTe2lWdNn/xTMElHBLoAXjvgz6r+pD70FRTLYjHHUSm9PnvzWopoZCS/YpdUBoVxZ18qYrOvPoDCUSrSR/UU2r10g8ejyAdd7cLBqEbcOy+opMdGesGxM9+SQ8tqWROTSOKpzSwvxAKE/2NfBDr5B0lgUUKza0mCgp2qpAJ0K95aWqy0TbaJRaHvSdxOcHSylqmFNdravU8Knw1DJZKXy3jK6bxYdd63Lx6DooimDtXEIj03T5/Zk6pRUHO8iGthlzLLl/BwrGuMkSNPp2w1ojHBFXvZfJSTO9skWTyi8c2uu5Hac2dGNuRqDtRdbI3bJmQCEIIamsdtrTA+HFgdqlkUiZahfs+KDRPoetkxclcEgJd9QPteDwiLyhFIwpaa5GF2wAoisBR9T5b5rMT5kUWrc+tGzzYFbydVJHph6D8zDg7ETfGBlGCdsE8ZpAfa8udSa4NcuSt+/pcHzcIfaaujvZgGHgHRShYorCBIxRXgMl+LagzK3lHTRaCxmfZXkM0TbchDd5pERAGdtCLVRtCdLW7iQmK80xo7pVTdMDKim1KUWWHgExWn1U13CDiOcsuYYBHILLGN3bEAz09eaEiqVWRdtKkMm9g6xkUNDTVjdcIYFdOhnR3yTUQReOeWqciUjp86Fp25fpFf1kOdc1HCrBzSQz07BzJE8oLYjkcx3YfSr0SWaiaKyZ3TFOpf9+tjy1MhAKBQ0ZjJyy8LW8RX9l/xnQ3eW6vJCeqyaSq2W5g+twyxSixBlQ9EXz7BfJ1GYxxgtLLsss9vntOWpHYpem9RP682OUuCwQEzXMcnn+xV/m5705mITNyuVAt9fWwZg0kkkOmsbpjV96yS4pdEsnwYWcwnr4Kfdl12JUTSJzwe+yqQtaLDRsdfvYLh42PbeYM600mdm+Aq9OsvBrUsIF/YS3xoyeizItgNhWyU6xY8yYvXHEn42+roTvYxSv/t4qfHn4b46IDf+WW7H4oPp0xl+7Hh6c/xMVtv+T/9HP59n0nctmRtxDxVnHgUsF3zvPyk8uOoPXj+3DupJ9gPvkLtHcfIHnkT7CrJg33KUgkkiHm6KOPpq2tjauvvppNmzYxefJkrrvuOkaP7mslNRCOquQyw7uZ3Ly1iE43W6CTFbtKX6pFn98Kc6ecC5uCpXgKc1bhZnLLz1l6WYcFjAhxNuYXaZUezCkRkh+6bluZxsWsXQc1nU9Sb1bT421gZeZ5iGz9K3CJZVcxQsmKBk42w5PAMWzs6gzKZtfyzBEFYQZyeof799zwVDLjDhnw2M1z4KV1pZPoPtXIlmc7hRmqEG4bFnDweATTpzmEQgodxQKe0PKWXY7joGZn0Hpt6eSt10HJNC0Fx0Jb+XTWIEYQDAqiddCRhqTTW+xqcMUu251cqGETx1MsYmRTzguBqRoYwsBRHLrtzUSyx3RwcFK57RwUzUPIp5LW2tCUghtjH8uu3Pll27G4P3r1Ac4TemXYdKn2j+bDXiJGJKJA2cQ/ucluqWVXzkUnpYbRph6Pv/1xoD2/SZXhWorstxDIDH56l7Pscl1HXauqpB0Epw3HFES9lbT0bCypWm80TTBndvaP5sMJzNfyyoowCu2lFEnMQoChBqiqay8IN0XrBiskDWjZlXMO7uVypFV5CkKYJhDF7nblFKHstsZoP4hCZtPe5fZHf6dSP6mKNVmP02DudbuoLXz71EDnm+4fqgEigUCUWL66VoIKjpv7FV0HQzWZ27AYX68M59vjxggQNH0kM+CdWsHiyQ4ffAgfEkVURxDqppJtHZ+Nd3oQdVw9VtV4t47mzvHg0HVKhTYh8IxRqasWtJqNTJ6uEv93qysyK4BQsIVJp7WO3AeREms3TQctWnIMK9qE3bMBNJ/bn1SBIwTYTiErYD+nZ2geOmtnZFU5QKhYVZMhXAHxUqs7BwdU1+USIDPT/T+XWdX2FcYqS3HdGtWggRoEWkoF+95jkKLAmPAE6vw9bORDd1neIrawn6F6+jx7XWu13F+FdbWBMdQE+j77y1p2ZYVsLaBCss8ubtm5xixyN/aVGWrLCrb5535hkdcDlZWwdt3293soPQ9HKHkB11FlNkaJZFgQnWvx3Ptt1HUvkZ51MsmDLsibutu2w513wQ1X9XDiumXM7lqNWuUhfPw4zAlh7HiG9NoY3U+s470H/gOAPtqPVS/Y1LaG0AofkzP1bNq/i+k/+xgHjhk3nKcq2ckEltYTPGQ0PX/ezI/+8Se+9+yZfOu+E/jpUf+gwlfD6Z/0smp1D3+9KUriE5fzzWOPxHzkYrw3nkBq0VdJL/ictPKSSPYwTj/9dE4//fRBbTtj1AJIqPx3Ra8VSiFmTy6OjshaouTi1Lgv2e5vxe/MovdLbS5eSTaOTW65KfxYxbZbohAfa0bdPmi9XlTNyRGE0V/MLkGlr44Pq6aAEQBcCxjLoiwlll3FBSmqK9zkgoiIgpiS/cM9X8fJT9L7VKYftWHqFNedo0+MsCyZyUehvfPvkmUBI8Ko4FjWd60sI3ZlzyUfq0grlKwo+cm2k41B1i/FMwVFBSvnp1o4l0hYoLSKEgsN9+CuiJH/kl5uxpJ1G1L9gvCiIGraJGV3owoPikhg42Cn3O2s6ilYTQcSWPM6bbSVXKfe7Zpbl5s0FrfrQNYNdcEGvHrfDGJue/Xar78JWL7ju23l5AXd3N8qmqayz+iD+e+Lt2MIf95lZ8pk0HUBOe2m7CzPXaYKFcuxEE6mz7amonPI6I+Q8VXT9f6qvNg1mGQ3eeuVHEYw3889wdE4ppv5TxGgqDq1wTpiPuhItOarlxNwBkUZsStn2aXa8UKdcl3Zq+HftyiytCLy1oNCEeRdExWBks26aE4Mo1V5XGu14gjYg7XsKtNsmanHEAYqXnRobXMtVdyNC9sY9X7IhU1TVBwEASVIpcfL2qz4qSiCjBoAFJrGFlxYo76+0bO3x41xes18NsYCpLNNqSgCr8c9tuWIsv06PKECq2FefpHiczuok+pn0NxOehuVAUycPwbHG2VWNqtjHLdvO0XVVIVBmrRr2bW119RgLY7qjncCGxS3bwrLzsfImjdmKZqnb3w5XTH79k/Dn3XfLVV9LMcCVaA0egg0j8LeIFBaQNE1SFPoFwIsxewj+qhB4Z5sP6iKxrhQM6/woetJnx9XCh0u58ZoR8aidK0Ho1RtCnlcIXBW3UKq/fVlj9M7ZhcUxg3DVxC7et8Tws6bj+WXufdE6QOm/JBWujAacWOCWrYrdtl2mX22B92LSGbjWZcZd8ohZ0ESyRCivvsAnge+BzjEj70Ca/JR+XVr1zpcerlD6zNbOL/tcfxOktrz5lL1uan5h1AxnlbBG7c+yTsPP0NiXReKpmAfYzD7jEOYtWD8LjwryXBS9535vHvkXYy6NcAlX7iJ7z7wKb553ye4/Kh/EI1G+fIX3QfRX28Cyz6M//niAjyP/gTzySvQ3n2Q5JE/LrEqlEgkew8NkQnZTFO91BBF5CcYuZdgobv/O+liyy53fCmZ9PYjduWWm4rBRG0GCWMOb4mWUu8ZJ2elo9JHaBClk/0+olp+ubvAP3EGwkqVCe1dKoaUujEWBZLJBuL2qz467K78udpqL8suSv/ub3LdMMbdpiPRjxhRxgJFEQrTauaxvmtltq5FZReLVIa/ZH8hVPxGiA425ON+9YdjBIBstjpFywesdi13+m5f0l65Y+bFrv6OJRAKeBSTsZ4lbEy8ij8UAOVdcLKWXQgwvAjdJOgJ0YYr9uREmN6iUN/A3oVjKwPEaRpTFP+mdH9RZnJU/lr1CVDfa2sbNavxCZo8B2AofnRdcNghA2RGLWNttl90FnErSULJ4O3nlNSsOjKlqtltq+2wkFBylnBCoNXNgWwWSze4uaAu1EirN0FHojXf34UyeLHLMQI4ilawzgIccmJX1gpD96Fm3Sq9zVW94vMJ8IYg3krOUhAgfOzYQkw3ReTdMkuV8EEGqRb9a8LNc3oJ51sxQ/EJP5WeKGspJCGwFC+OcMdVR9Wx62aVr8d2GFjpqonfDz09hWW52G7pNDiir8Te2+pFyXYwO96/q912IQR2xTgyYxtyRo7Y9c2lm6gC4ShZscsNuC6U3EcSrSDo90PYU0lrz2YMNWs5nCunyLIr7KssmwCh90eVgfDrrmlf3cLJaH4POcNjNace5WK6Oa4g27ubaDV+vLWVxF9r6RMGoNyHmcI4Vip2KUIFw49dObHPmOvRfBwy4fgBz6OcZVfudz2gIdro/VRza2G7d7/Sy9p1+jRXX165qrR8+upgeUaNcj/U1Na4mSErouW3GxRFx7AjY1E3vp6tiLTskkh2HekE5uOXob/yN6y62SSO+TlOeAwAluXwz9vg2t87zI6v4fxNj2PU+xj7u6PxTit/9zuOw12df+PnxrfRjzE4ceZZnDjzLPxGsOz2kj0Xz5QI0ZMn0PKnt5j6meO47KhbuOD+0/ife4/nulMfxCOCfPmLoCgOf/4r2FaEb37jcjKTj8R8+GK8N55Iar9zSO/zeWnlJZFIANeNsfe8IGfZlbP8ESIXJ6p0cpQPYJ9/4XX304RGld7EtOBonM7NpIWBglLQl4omm8pWZnxGQyBffjT7mAyZEepFE2MjrnjvVIwb0KBJiFxcqRITsWxlrPwJzAlNpi3dyfKslZcTKKPCDfBnmSMDMHECLO7H+Dqv//US98oJV3bVZETTLOhOlGw/oWIGgSov3vaBg5NbjYuAe90/FLUweSrKute7dlbdLEQmWZhM2OXFrobR4K8B3nDL0TTwOGHG+paSnt1DcsyH6HeCnYIKTzXrRAyBwG+48XNUoZW13Cpum5yVVmlb9d9/esfFKV7uaCYCQcQIYjvOAKKG4k4O8wHqc+5GufhAhfhTNeFK6nNxawbrq5PdzqOaeFQTp9pG2OX2dY/nX1hL7NmNJftuC0JRQTPwZAP1a6pOxkq7p6WLEuEpR4O3ls7Y9EGV74TqsQI1aO8+UHRQt56KnQQEaF5Ur07kuH5uikgVTqIbobUWLOn6OdeSWGKDVI/EAGKXqpYmleh91Mz4g/LZJkWx4NhrQycrgjq+KpzgqLLHUgZjmdcLTdEZPw6aijy4c2JXxqLkvpwZmohP9fQRdkU2HuJQiV2TJ7nWOqlXAU8YPCFmzYSWlr7bCp+Wt+zShIkuPLQ6rnVesXAdPGwMTsKi+8n1Jfs3RaZSG2jApwdoz1l2CcV9VuXum37aVcu+9+qqURIbK8fU6oL1m1f3lwhJObE3F39PKGCbAZxsrMHirpcZdwCoJsoWt30dx8HQyWczzOQtPQv7lHNj1BUzn6Vxe8knjynW6nPhCUwV3YBUij79NzOuEkQbSrTUMnR0vSCVcvJi1wDGqn3QNMHkIYqqIgDHLJoHSzdGiWTXIFrew3PPN1G2vEtqny+S2v9r+Rtw5UqHS37q8PobcGrjag588gm8syto+tOhaJG+5rYAiUwPv3zqPB56/1b2bzyKbyy+nKi3uuy2kr2D2m820/6vFWy4/CWmX3kAl3/kH5z/71M49/aP84uj7sBvBPni50FVHa6/AZIphwu+fRjWGQsw//MTzKd+ifbegySP+Al2tbTykkj2ehTRZ3Kd+yruZLLWVyhZF/yuku1yL9De+QahCCVvvjXGDPzam3SzGVe+KIrHIkTBimeACV/4o015K6995jtEW4GUK3BMrZ476FNUFY1MziKpcJaFX7OB53VFo8IIAwlX4DAF+YyNQHEw+95lNDZAdzcl5EUaj8DnG3himxN6GsITWLvuvXwmv5JtVAOhe4FESR0UoVDhryFOmdll8ZZe933EHKVmhcmcpQz588o0LUG0vgKA3bQYJ9RYEAaKAwL3muVMnSrItCh0vylAgdpamOCD9z8ATXhQDAECnJRDXXUDkydMRQhB0AyjqQZezd+vZRfA/NEH4tV8uTPOL1cHsObpTwgTQgHVfe+aF3bjnGb6tezK7pO37HK3i9ftR2zjGhBKfkK5aOHgxAtnIEVS1QuCYhn0Oh9qyMDqTJVM6j1zRmGv3dTvfvljOzZ21QTMRCU4DoZikLHSKALSo7wEltbTkirO6CyYEmxiQ8dE+soDZRCiZOKZ0YriVOWqq5V/580R2L8OVrWjZzq3Hhh/Oyy7tkkj7L2x4Xd/AOE4RWMa2JXjsRr3g7WuACNEmf0BQ4dU/5d4QFRFQylyPYeC2GVlKBG7arNZSu1eQoAaMtDrfHimRLavEr3IuXwqB82CzX4Uv0ZdQFDX13Mzn3hE2CBQEULDFjbuOFaop+rXwd9XwBBC4NNd93VF2AhF4CBct7tcgtR+nik5C9Gwp4LZdYt45P3bS9bXh7YeA1LVcplhBfiq6PTNzNaraKOcCKMUTLgWzAflQ+je4FrguTsVdilnoWqoRh+r1m0l1z/tYouopFsv4VHdvljmxhY+ndSkmrIGvCUinVK0sJfF6o7E5uoPx4HxnoOpCSdLXbQHeTApdkkk20A8HeP91jdIZuJU+0YxbvXLeB69BMcIkDjhd1hjFwOQyTjcfAtc/ycHnw8uPXo14d8+jm9OFU1/PhQ1WN7PeF3nCn7w8OdY0f4W5yz5IcdN+vygMm9I9mz0Wh/VX5rOpl+9Ss8XpjNlTjM/OPQPnH//qfzksa9w8aE3oCoqnz9TYBgO1/7OobXV4UcXReDorJXXQxfhvfFE0gvOJLXvl/IvbhKJZC8k+5KoRgsT0Fy8LKcoG6MdHUe32YlT5C6QTznvFWhhQSb7FTr33qnkIt8Lkc+05f4NOeFINfr/Ils8aYlEBEr79r09q0IlQzovoqTmxQiODdH1cHf2OCooGnblBCx/Lda7DyP8ep/Awo2Bxt4VzP86ZXLfuvUXs2sgJlbOQmmczAfvqQNOivsru1xA9hyKoRI84yDU2CbsbHwtd0XRRp4w1pgFWLFNfTOUKWrBjbG/cxNuEGVVFYwfJ3j/Awc3QYHAMLWCeJS9toqiMaVqDopHJ14cM60XYU9F4TyKXVMHmOT0t658zK7+zwcomDHm9vNV0JbNPjrYSV1YD9GBa1li5aWj0p2txoVo7/9n4IJE3yob+y8aVB26U27QKU9WONRUA9Ixtywd1ICOaCs+lti2mF1ZHN0HdobNkaX5QE7x8DQc78atNpgaNFBqPCgtAtuxB7TaLGGwAep7HX7B/IE27n/VgQforH0fbLJCrhnG8VVS4hhW5r193336CuPF+I0gsVRX2XWG2lcozIldlZXlj9fbxUsoAv/CMkrUDqKOnYh/K3qRZ2oE591NZHwCRaglwvW0iQOLoL0RuYy+muq6MVoMeL1ysf+Kk4EAzG84gER3P5Ha8wfLPiezll058Sgnxm7NwkkRCorqJoXIuTHmh5Z+Y3aZA7ppD4ac2FXshZ0PT2Cq+fiCWxv++qM4xMCg79MdxKNGCJuAtu1HlGKXRDII1nR8wB9f+AlPr7qfTNHXt9GWwiGV4znuqN8TqXSz8Lz7nmvN9c47cOgh8KWpq2g9/wl886ppuuFQ1ED5l/xnVj/IpY99FUWoXHLEzRw28+PZWCsSCVSdNYPWm95hw49eYNzfj6B51BK+c+iV/OjBr3DDS5fzufkXAPDp0wVVVXDpTx3O+brD5ZdC1cTD6Bk9H/Pxn2Esuw7tzX+ROuDbZKYcvXM+w0gkkhGN49HwjIlgFGX7FYpAr/djjHWXKUIBRSPVKzOW3Wv+n/slp7fktCp36qeUWHbZ2eWqrm37W/I2jlU56x9BVuDxOKi+otfe7CTZrp6KcGwyM1S0iiC0FjaZFhyPR/OWxgUbijGzVxlCCCaM9VAVcQiHs+s2ltmvaHsAoWYnS1uJeSN8Fdi+itJlfXQfBQx/3w9sipa3OnL6/fhW/viLGo8gHbkPetwD5utdtHl/box9jlC0k6F6+t+un3JyFl/2qNlusOkB6p1fnhe7RMni4kX94gljR5sYHz2QGieNV/eTKGMn5ei+PgJjYWXhJsnHI9oON7hRwUY21q0i3F2J0Rhkqj6XZWseQYj+i1MUMcD1Lo81/iB333WFoOWp0HisseXjqPUh36iDHxycQQoDfj90ZH9XVIhGBmjHAVYpits25WIFOjm37TLt5vUKvN7+y50/+kAe//DukmVhTyVTqpvLil2qKjhgieMKF5v6Hs9RR84UX4t6EAdVY7/8HorQ3Yyg2TYeM2pwQcZz5F2Js8k27G67bNzjHLnngNUrQnp1YBRt6a3NsbJil5F9VuSGA3KifZk9im6o+tA4WqJxNmwq+PEJ3OD8FI0FpWNbwY2xwlezlfqVJ+/GWByzKyt2CY/Wv9hVRlAv1LFv+W5iiX4sc4dQBSspajsMQKTJiEQyALZjc+PLv+SLtx/EC+se5/jpX+DSud/nWquJ78QDNEYmcWPqQz51z1H8btmP+e0fO/jClx1atsBPfij4RvNKWs9/Av+CGteiq4zQZTs2N7x4Of/34KepD47lmuPuZ/7oA4fhbCUjGdWvU/vNZmLPbqTrwdUAHD/7cxwz5dPc/MqveGHt4/ltP3Kk4GeXCdaug89/yeGVVx3wRkke+WN6TvsbTqAWz73fwvv3T6NsWj5cpySRSHYRkXDp36Yp8EyNonhKJ0T+fWrQa9wZWX/uYL2/UPemsFt2QlBk2TXKrEIIBcXYni/X/c9AlWA2hoq3cD5aPmZNth5Zy578dLo44Ht2qd1rCquUPeZWRJlBWHZV+twgT1ov64u80AUFoWMAVUUf48czNYJnarTfbfpWMCea9LtBrz+LLLvK1SUXF63MKq/uxwiUmXTnglP7tSI3xq3WvLD7QDG7+lmnCAVN1ZlUFKOnv4M6uZhdvQLUKyUTvq2bP9i1M1CMACFzgOuTcyUdu7/rDtffZrlMpduhtYY9lRw47TgqPzoRNaATMMNMqJjuXoY+c9SChVJxRtJBHTZrORgKlS4aLE6x2cvAByr6dXBT2Vkzwe/ts3c/xQ+wRb5uffu8kxtftmNCril6nyyiqqIQMEL97OGO44oi+rHs2jYRaWcTNmqp0qcQURsQRQlKjK24t/YmnxFVVREZC6s9iRrp/1z7s+waFLqJ7asEf9Y1ND8cDGDZVXQpFKEwp2Emk8ZrTJ9WWD7OczDjgwvLHjIn5C9qPIJZtf2PBwPR2zAVwBznfsQSpoKnn28Fos8vA5e/PWPR9lBX6wa4z8WscxStNHbXVpBil0TSDykrySWPncP1L17K0qZj+dPH/8PXEn4O/M+VzNIqOPzEf/HDkx7j+hOeZHb0WG557Sr+EV/MzI/eyJ/+ZDO7fSWrv/4k/oW1NN1wiOuL3ov2+Ba++8Dp/OXln/ORyZ/kl8fcSW2gYRjOVrI7ED15IubkCBsueTH/lebshRczNjqFSx47m9aeQuyOfRYIrrtGEAjA1/+fw9//4eA4DvaoOcRPu5nEkT9BtK3Ae+OJmA/9ABHbPFynJZFIdjL7LCi8lR64FBbvv/V9+hNtvNkXZbPX/KK3ZRdAwu4ouDHiUGGEmRGaXDYg9mBq1B9mU4jA0lHodQULmWJriP1GHcj+Fc1u3KDcoYsi9Oe+SpfMsYWStW7rJcwN1sdjACZWzGTJ2I+gDzJ1eg6rcSFWzbT8tRFC4JkSLZuFrP/65YItlxcc+3yhV4uy7JWZVLtaV+nEv7qq8LunMXecomyKHhX/wlp886uL2nxwM6cK38AxTAcSGw9oOpb6UNMgjpKbLfYOUD+IXQeDKCPWeKOuO1xJGxeJTeaOuTaVQ1EK92vIrMj/7wRcixJLuP1zyiRYuO/gy509CyqzhoTbZIhWnC11wO2KVcfBtYuuCypqs9dxa/fL1uosCj8+I1Cywo3ZtYun1706puOvzl/DkYIQghpjOorQUFBxslXW1H5i7PXzjOjpyYpdmoba2oMdtwpZOsuQ+6CQE7vmjz6QyVWzB1lrBSfSiO5xnyU5scvvG8Cyq8wgMX68wOMpCGSG4iNi1Jc9Ys6qy6v7tztQfc5yq6mpsMwzJUrkuHEIUbAwTCT67DooSmJ25ehr/Dpk6Lpg/rxCG1qTDsdqWjro/UeOjaNEMoJIWym+99AZvLD2Ub68z/c5adRheG//Gurm5aTmnEbqgG+D7iWZdPjXzWN58pZfER33BWoOv4i3e77NtVc8zNF/OoTAwlqarj+k5GszuGb7y9Y8zBVPfZvOZBv/s/jnHD3l9GE6W8nugtAURn13PivOeJjWm9+h4txFmJqX7x18HefceSSXPHY2lx55S/4B2TRW8Lvfwk8udfj1bxxefBnO+yZUVChkZhxPZuJhGM9cg/7SX9GW30V63mdILfhcIdCmRCLZ4zCMwb2O9mfZ1dAAgQAEN/WaYOUNkQoTdK8SQVGzfoHZWa9jBtyJzFatN7aN3hMeXSmIXd5RcxCBGhzNA+Jtt569vve62RsLk2zHV4Fj1GFXblsqqcFYdgkhMLT+J2jZGuRLzO9nBGGr+20FzcBqWIjjCcNba/rWrY9lV9H7S7k+Ybtx2IrnPc1zRN49UTHL+8bkhclE/wHqe7Ok6eg+1nB96j8UQkOuLnlTjp04k+tVX0dREVZfsUdkrSGd1FaEoG3AMNx8DACVvlqWjP0IhubB9tZgRxqx17p9rbFx205c0wThsENLK9vWZrlru9VJ/rZbdgEo4yKkN2kolQPHLB24Kxbuy+l1+xCqL4gWhQD1u1rsKjqeEFgN26BM7iKqqyAYgLRwY1jlKNfWgQNG9Zk35YiEIR6DicdUoaXAF1HQR/XjBkwhCHxubA97KkpiAQ6GbHz6/HDQ2CiYsG8/Y9ZWP4Zs06G3C0URHH5o/+t92ebq6SldXsZbu886t3z6briLrLzcY23b/SXFLomkF7Zj89MnzuWFtY9y3tJfcUx3BuPGk3DMAPGPX5OPSfDqa25srtWr4ePHwVe+NBuf758su+VfmH9qY0X9Ch496TYOXLGOefVLiXiqaOnZwPutb3DXW3/m9Y3P0hiexE+OuJHxFYNL7yyRBA6qJ7BkFBt/8QpjP9UMCoyNTOZriy7h8if+H7e/+TtOnHlWfnufT/DDi+C22+Hqax0+c6bDt78FBy4VYAZJHXge6TmnYTz9a4xnf4v+yt9ILfwy6TmnbTV7kkQi2X1oGAOr+2ob/dKfO5gQgooKyPgPRKR7+q4v+q3WmMXisZN5euX9YOhuNljVg9AUhL6NX6230awmZ9mVtpMgFJzQaIi3FYLqqn2Pn3Opq/aPojW+iWD9/EFbjuxKdtTCyPFX9buut9jlGD5E7jKX6xOOu1cfjSwXnys30+inznk3xkHMlsrFLurNjiT1UURRnBsh+gSo345wWVunt9jlr0Z0ruu7WdbSxUlthztWP4yqEzRFCyeVF2CFyGZi3X5BehBeuH33CY/BsjM4kcaBN9yObIzgxrmyaoI75saoe0GAo5t4NF+JdaZD/9kYt4/BllPch3al6jB4VFUwa6bDiw8LDCVQ0I/LtJUW7V/Qr6pwqKoQZCZ4SzKA9nvcrBujtT1ujLn69FJLVFUQCvbTzlszGuxnt4mVM4l6B7ZaHSoCWa3X30vzLQ4835viZbn2GChA/RB/y9ohpNglkfTiumUX8Z8PbufLc77Jx157GG3FE2TGH0zyiB/i+CqJxdxsd7f/C0aNgl9fIZg31x0Fuh5fh/973ZgzqnEutIite4wr//udPseoDYzhG4t/xpGTTtnqV0qJpBghBKO+v4D3jr6H9857jLqfuX7/R0w8hf+ueoA/vnAp+445lMbIpJJ9TvgE7LMAfvgTh+9+z2HpEoevnSOoHyVwIg0kj76c9ILPYTx5BeZjl6G/+GdS+51NZvrHYBtdbSQSychj6hTB1CmD336r4oMZwDEDfZcrUFUFbT0QDCj5DHBAPhC3MBQUj0rw0DHEX95CpiUx5LkycpPQtFUUFFyoqAEFu93uI2IJCpZdIU8Fs+q2N17KEJ1IObVgJyYUEZSf6TjBOmhftdW9+03UmFeIys8CBxugfrDsiGWX6YF4PPu7WSx25crewcqVo1d97VFzEKkYItFRslzxjjzRdSDKuTVvFSFwKsYNZsOiXwd/vQd9/QbYTgRrsWqnQdLsX8AdZjfGXYEdrENktpLNsBci6/qpCYMGz2KEvgMyhDK4fXMB6u2tucYOQG+xa0C2ci36W1v8zr6z8XgE++/n9E2YMMhulI9XuIueTTuKFLskkiL+8do13PrGtZxUdxifffafkEmROOwiMrNOAiF48mmHn//CNc0++ST4wpkCr9e9wbuf2cDKL/wHc2KY8X89nEkRk+P4Aqs73uPtzS/TlWynwlfD6NB4xldM36Gvj5K9G8+UKDXfmMPGn76E99A6wh8ZixCCc/e/jM/fdiA/feJcfnXMnfkvWjkaGwXXXAV/vxWuv8HhU2c4nH6aw2mnCHw+gV0zjcQnrkNZvQzzyV/gefB72M9cTXrB50jPPCH7pVcikewNbPMzKje5xaGmTlBTJ3D6y3iWjZmjBvTBz4a317KrROxSCMzSyHQ5CE0r+SothMgHqN8R4WWoRJutHWVX4ZjF0cb79gkloIMQGFXbFwfJkxVA/QME4t4WdqT9F8yDlhaorgbPh0Vi10ABqbeHooKsulm91ik4ZtAVu4o6qF7vxzM9g9k0NO20s9key67tolzgpP4YdF224rZqBCCZLntyiiKwtsHabCgoFtecXSQ62KPnb/M+Qrj1E8LBr1WVta7dGo43ioi3Dbpj7UiAel01EGSTAAxResFB52DYyfj9/bffoL0zy8TsGomGhVLskkiyPPz+bVz73EUcqo/mG289j10/n8SRP8KJjqO11eGXV9o88h+YOAEu+ZFg6tTCHR17YTMrP/sIxtgg4248HDVSMLVvCE+kITxxOE5JsgdTfdYMeh5ex9rvPIN3RgVGY5Cot5pz97+UH/7nS/zj9Ws4dfbX+uynaYJPngpHHA7XXOtw/Q3wz9sdTjsFTjjedXu0G/YlfurNqKueRn/2Osz//Bj9mWtIzz/DdW+UMb0kkj2ebRUNir2/yqEqKnqdj/SGniEPUF+OSl8dPiNAQ6To+asoCE2gRwVWr0mya9k1+PhRu45ix9BhqJcycMwuNaATPrYR/cP3B56A9iOeVvnqWDDmoIEzFm4DO3LtPB7B6NHu744QCHsnBajPYtVMB2Pg+FE5hBB4JkWGtgJbYcJ4SKW2vl05nL4h54aOcgH+t3G3gTfcWjl9xc+xjRB7c2gVvkHf7yNqvOqf0mpuX52tMftAZvCR1QsB6rfdsmvJ2KNL/u6dlGVPQ/T5ZbA7MKL7oBS7JBLghTWPcvnjX2OBZXJhzCF98PdIN5+Gg+De+xyuutohmYAvf1Fw2imuYJAj/moLKz7zEPooH+NuOnzArCASyVAhNIUpvzuSlw69hQ9Pf4jxtx2FXu3lwHEf44kV93DDi5ezsOEwxkWnld2/wm/zreM7OWlsjH8/rHDLNUFuvsXPKSfBxz4K0YjAGrsYa+xilHUvYSy7DvPJKzCe+z3p5k+SmvsZ8G1bkE+JRLL7sK3uYH0tOQrPyQPHfRQQKI0CO2GVCBKeyWFibUnU8NDGCNRVg/0aDu+1tDiQc6/zEyL/9X+HLLuG6KXfCdRAx2ocT19LnqESvfTRfpQymaJLKHb37OfcxLZY15RhqISuoUUBcmLX0Fp2Od4o8OEAH45yBxpe84/x47b/hLfLjXF72Baxaxs3FP1VvowVy+RJAtUjILZtddqu+u2CUnYGOTfGHbqPVH1Qsbpy5CyURwXHbvOhisfyJfsDq4FBeHQD/Wf8HLmXZ+BvFaKMddsA24+gkF1S7JJI3v7gPn7w6BcYn4EfVxxI5vBLcMKj+eADh19e6fDiSzC3Gc77lqBhTOmdnXirjQ8/9SBa1GTczUegV0s3L8muwzcpStMNh/LhqQ+w4tMP0fSnQ9HrfHxt0U94ef1T/PTxc7nyo/fkv2xl2pN03L2S9js+oOf5zflIvIdmfzo6wtx7xRRO/dN4lhyqcfxxghnTwa6fS+Lj16Bsfgt92e/cnxduID3rJNLzP4sTKp9CWSKR7L5sqxtjXuwq85pb7FKt+kvL1aq8hI8ZxERkKJQGpdjdp9f5OQ4bulYDlMYZG0Ksxv3AHpw7jROsIzPl6LLnPVSCmn9BTXGhW9+hvz6RXz6CZ3LbSonVwtBadjnBUWQmHLL10ADD7es0AIsWgj6A5rBT3Ri3s9DB7pa/v7am1PVX4A66MY4Jj6cn3c2kqtmD3GP3uO+EgPSkGpQNHaDuujofOO5jOxw6xusVqIOos+rX8UyPYowpE8+SkePGOBCDvTLm2CDx191syyPYsEuKXZK9GCvNqqd/xv++82sqHZVL9/kRavMZdHTDH39tc/sdbnr1878lOPaYvi+X8eVtrDj9QRSf5gpddTvn5VgiGQhfcxVjf38wK7/0KO8dfTe132wmfNw4vrH4Z3z/4c9y62NXceSWj9Hx75V0P7oOJ21jTolQ87VZeKZG0et9OCmb+PI2vHd8wGkvLuMTPa9yy737ctYDjUyYAEceDocdAjU1U0ke83NS+38N47nfo7/yN/SXbyIz5SOk538Wu3bGcDeHRCIZInpbdo3dSoK0/H47KRylMxQTuuJJaK8JacbO5H/36INzLSt7iAHq6fgqt7GwYZhBDDQL61fscuu5q+IFlSNoRuhKtg9hiX2DMA/p6e3mMTADgYEbY6e6MW5voYPczSko9wOX0+/6HTvphvBEvNsyBo1goaEYIcAOe8lUeiGz9e2HCnUXZ9QdyN14JF+qgbIxlsOcEMbJOCTeautTxkhCil2SvRJlzfO8+9B5fDuzHI/m4dKj/om/ei533AW/+71Ddzcc/3H43GcFoVDfO7fnxc18+JmHUQOu0GU0lFfwJZJdQWDJKCb86yOsveAZ1n7nGdZ+71mqxgT4SecP8baarOEp9HoflZ+fRuT48Xin9XUZ8S+speqzU+l5cTPrvr+Mz7zyOCfMbeRv6kKu/q3JNddC8xyHIw4THHjgWEJH/IjUoq+iv/RX9FdvQX/rbjINC0nPPxNr3NKdN+OVSCS7hN5fwvtNtd6LghfjCHzrLZ70DDAB8u6AZdfOjKvlMLSZC4sZHRrH25tfxtQGEGG2Inb1N5XTAoLtDP00aObVLyVtD+FR8gJe4Zx30Ftzr8KT9UreOXGOtq//5wy1tmZUI3QFYap4Z/YTqmFrY9suf/8R/fw+shiJj4RdzYhug20Uu1xKheGaGli1GqKRIazXDiLFLslehehpwXj8Zzz07i381Jegyl/Hpcfeydsvj+G87zh88CHMnwfnflUwfnz5u73tn++z9n+fdWN03XgYxmgpdEmGH8+kCONvPZL4S1vo+s9akiu7MNQId8RvZNPMNi466yYMbesxcXzzqplwx0fY8vvlbLz8Jc7a3IL3B0v5z4ZqHnzI4bKfOfzsFzBnjsOSxbUsWfxN6heehf76regv/hnvHWdhV0wgNf8MMtM+BoM4pkQiGZksGHMQLe92ELfbmFw1cBa4ErelneGiMdSzhAEmpL0z2W5buTtxNrMTfV9Gh8YxOjRu+3bOWsk5FePLrg7O08lM3vaYOduCqmg7dt36kLuOw6Bw7YIZcdRXA61vUuGt2frG20FTE/gDUFW1E85lF7gxho/auilrv+7E8mNfWXLNVVUF6zcMb122F2eALIbbVM4IdmPcPtx2iUYEhx86zFXphRS7JHsHVgr9lb/R9syvuFrdwgP+JLNrF3JM9Hd891tVvPW2w9hG+PHFggOWln+ApdbF2Hj5S7T/8wP8+9fReNVStKrd2wxdsmchhMA3rxrfvOr8ssWrbf73wdO54aXL+eI+/ze4clSF6i/PILCojlXnPE7nWfdzzPnz+Mz103j3fcHjTzg8+TT8+iqHX18FE8b7WLz/Z1iy/+nMVB7AfPF6PA9eiP3Ur0g3n056zqngHYkBiCUSyUCEzCgRPUKEsYwJD9KySzhZsWskf8Km3wnp5EHHyZHkEYLM1GP6X62K7czAOYzk+keROdeussqwo02IzvU4/uqtb7ydhMwoh0w4fqeVryiC2p2jo20/Qx0vqXd/yCv+w2nZNXJRVcHSxQ6GsfuKXal9VEJNg/TpL8NI1kEHk40x1Dunxm4g2kmxS7JHY1kZ1r56A+++eA2vpDfxoCcDisYB4fNYc9c5/OWlVpqN1/n85CQNNRbq4xpbXvegVnnQKj0IXSGzKU73E+tov2sFQghqvjGHmq/PQqgjeMSSSLLs23AoH516Bre8dhWTKmdx0PjjBr2vd3YlE+89hrUXPMOGH79A7L8bmPCLxUz5vIcvfh7Wr3dFryefcrjxZvjzX1UqKz/C4v2P4pjpL9Dccz3m07/GWHYdmenHkZr3mX6//Eskkj2DnScI7BrLrir/qB0rdpdMPHePye1uTd4zcxjcGM0g1uQjdtHBdkd2rmXXoMsZ6ttweytYvN8IHxo8npGRaXS7UQWKsWtjgO0qttb99t8PzP6cNUZwv5Nil2SPIpHp4fWNy3h1wzO8teZR3mp5jR4sEBDyhZikHM/aB7/K5jcyfLrzbiKJGAhQ2nS6TA27J40d6xs1UY0YVH52KtVfnI4+avsD10okw8HZC3/Iira3ufyJ/0d9qInJVXMGva8aNGi4aimB/etY94PnePfIu2i86gD8C2sZNUpw0glw0gmCri6HZ5a5wtfDjwjuvGsBmraAw5s/4NMT/szUN/6F/9VbyDQtJT3vDKyx+4/w4AUSiWR72Gm39RAVbFVPQd38dr8xu5QdzKQ2VJkSy+EM0wTRjoxFaV85LMcePrYrgI1kV7CD8el3+JJuayTvnc5IqYdka+zOV8pfxoVzd3DHlGKXZI/grc0vccebf+CxFXeRtpKoCKZkFI5WglRXfpxX3v88Tz80ng2dHXwh8TxjtmzAM6OCmq8uIHDAKNRgIYKmnciQaUmQ2ZLAyThoURNjbEBackl2W3TV4PuH/J6v3vURzvv3yVxyxM1Mq5m31f0s2+L5tf/hhXWPs7bmAwIXeDn4d/vy/in3E/ziOBrP3Q814OYeDwZdP/3DDxVkMg5vvAnPLHNYtmw8p99/Mr7xgpqx99PTeg/OQ3fhUXRmV8xin2mfZunE4zFUGdtLIhmpKNvwhj5i5n/94FROJFM5sd/1qtixV+NdYdm1MwW1cth1M7HrZu7SYw4/fWN2yQD1I4Xt6/92dmJeXbWTqzGsg+AIH4D3ckby83G76ra1zKUjACl2SXZrNsfW8dtlP+CxD+/Er/k4Th3F0o4tzFCreUX9PL96+jTeWeGl0kxyrvk8TeveQQ0b1F2yH9FTJ5YVsBSPhjE6IAPPS/YoIt4qfn707Zz375M5798n8d2DrmG/xvJuEptj67jvnZu4752b2Bxbh0fz0RieSGeNzhVf/DWH/+sgFl4LL9y4nO5TVGZ87lBqG5ry+2uaoGbcSqLiHrSqf2K2voGDTndqFvFVi6lTNjC64lVesV/koade4Jqnv8vJM8/iE/P/H5qi75oGkUhGENdccw2PPfYYy5cvR9d1nn/++T7brFu3josvvphnnnkG0zT56Ec/ynnnnYdh7JR0Z3kWLwJ1G4yd8sLYCH75HYgdT1O/Ey27dofP6HsKuZlfUX8YyRPVvYrtvBBVlTCuCZp2NFfCkJmIDREjORBUP9TWjJzmGyxLmo4e7irsVPLXY3seMyP4YkqxS7Lb8vD7t/HLp76Nbaf5ojGR0zZtwREa/1j/v5z7wskkbR9zZ9r8eNG7VN37MnZHisrPTKH2f+agRqQViWTvozYwhiuOvoP/e/BT/N9Dn2HJ2GP4yOTTaAhPJJ6OsXzzCzyx8l5eWvc4juOwz5hD+Op+P2Fhw6F5EcpxHNae+AHPPfBvjN910fiHetZd/wj/nbGWzfM7WT1rIyt4h3VdKwCYVj2fc/e/jAPHfYyQGcW2Hd55F5591qHy1Reo9f+M5fVPc91rP+fuF//IId6LWLzoJCZMcAPcSiR7A+l0mqOOOorm5mZuvfXWPusty+LLX/4y0WiUm266ifb2ds4//3wcx+F73/veTq2bz7et96EUZHY2uyYu2N5OX1c1sdNSjUq2je3r/5ommDhhKA4viv/L4ygqbg/Ztfen4692BS/H3qXH3RFmz9r9xrCh8EAYwZpQnm35pqJVeEjSgRYdufNqKXZJdjsSmR5+89QF3Pf+35lt+7moy4OW1Lny7f/l9lUnMnWmh3POTDE//h7xG14ntaob73611F+8L56pQ5cRLpPJ8K9//QvLsgBQVZXjjjsOTRvcbZXbH9im/YaqzEQiwYUXXgjAxRdfjMfjKSmj+Lz8fj+f/OQnAbjllltYunQpF154IY7jMG/ePAzD4Mgjj+Tiiy/Ol6dpGtdffz0XX3wxuq7zne98h4qKiny9MpkM//jHP7j11lsZP3483//+97n//vuxLAvLsnjmmWd4+OGHCYVCnHXWWYRCIW644QYUReEPf/gDDzzwAM8//zwLFizg6KOPLjl27lza29s59thjAbj77ruJRCJkMhluueUWrr76agDOPvtsTjnlFDRNK9n+jjvu4LHHHttqWxa3+THHHMM999xTdp/i9j777LM59FA3N+9TTz1FXV3ddl3HTCbDP//5z61uV0yFr4ZfHXs3f3/tav7x+jU8ufKekvUN4Yl8cs65fGTyJ6kNNPTZXwjBmPAExpx0DpwEq19+k/f+9BR1j9Uw4bWxLBQzaRu/EHuRh0nHLGT0ftNLLCgVRTB1CkydIoB96Or6G5OfXcvM5Rdzp+cebkx/nbV3XQ5vXkii/ijmztNYMA8aGna9645Esqv4+te/DsBtt91Wdv2TTz7Je++9x6OPPkptbS0AF1xwARdccAHf+MY3CATKWyLffPPNxGKxIXvG9EemaSkbV7lvyLvTbVr8HIo0C04+6aQdLtOyLJYtWwbAAWM/us3tPtD4P1wxuzKZDLfddhvPP/88c+fORVXVbX7n2dbjFbcBkD/+ggUL+NjHPsY999xDKpXi+eef54MPPmD8+PEsXLiQT3ziEwD861//yq9fsWIFJ554Ih/96Ee5+OKLmWeuZvy48ax+I1HyTlL8LH/usUd56elHaUsptEddF86VK1cSjnyCk046mUTC5sILL8S2bZqbm3nxxRdZsWIFEyZM4Pvf/z733nsvy5YtQwjBPvvswxlnnLHNz+vdkXL9d6A+XbzuyCOP5KKLLuL9999n3Lhx+evZu60SySR33nQTAEefdyD3339/n+P17q/g3psvvfQSCxYsKFvuNtNrrLPrZpNp+YDb7/tP2bnBULz3ly1DM8hMOBTtvQdLBuBy99FQzztGGjtjbrUzjp3JWPzzn+4Y9dxzz7Fy5UpOPPFETjrppPx+vcekO++8Mz8GDqb/5u6D4nFoa/vZlsVdd92H15saVPvpdT7Mg2v43x9tfT4J7j34wgsvDLo+Q8Ge18sley6Ow9uv3sMlL3ybtU4bn096mLt2Ade+9Wkccxbzanq4du6riDWd9Hx/Ex1pG+/sSpp+uJDAQfVyoiyRZNFVg9Ob/x8nzfoKyze9wKbYWkzNy/joNMaEt+2zZ0PzdBp+OR3HcUi81U7nA6vxP7qWnpu20PbXF+mqWU7k4+OIfGI83ukVffYPBgX7HDaGfQ67juNim/j9/WfzL/EkYxd+ma93NPLGfafyres+QdI7hubZDnPmCJpnQ1OTtPyS7D28/PLLTJo0KS90ASxZsoRUKsXrr7/OfvvtN4y1Azwh0qorxBTiGg3V/Tly7nNH3brL6J4Ys2uHEAJnN3Szylnn2M5u1NaSXUyvvqEZ2JUTgTe2q7Sxkcks3/Ti9lkQaQZ2pBE70rhdx5ZItjsZ6AjPTinFLsmIxrYdPni9ha7/3s362O+4NvQBkz+cyKdePp3Apqmk2pMcmWwFXAucZNjAnBim8vPTiHy0Cc/Mit3rpVAi2YUYqsmcUfsPSVlCCLzToninRak9dzaZ9iTdT6yn4+4VtPzpLbZc9ybe5ipqvjqT4GENiDJClddfw9c+cStL1z7O5Y+ezXnqSs6Y8yvunPJbPrQW8Y8PTuCqxw4lZZuEQzB7tkPzHEHzHIbGNUEiGaFs2bKFqqrSqMrhcBhd19myZcuA+/r9fqLR6E7/eur3JwEIBoPoih8RDiMiO25NbW/xg5pGRCIIb2SHyysmk8ng9/vRdR1dE/j9AaLR8nV2Fp4GCMRWopRnMhnMbH727Wn3XJ167x+NRkmonfi7/QSDwX7ruTPI1cnj8eD3+/OWXYM5P2ffUxlMu5U7HpA/z+LjR6PR/HUzTRPDMDBNM78ut31uva7r+XUejwdDK90+Z3VTfEzT40VVVRRVy19PXdfR8mVpeDwebNvO1y13vFz9chYOuXLLXdc9jXL9t78+XW773PXqfX2KSWTbOrdPf8cr7q/gWpUU96HtvQbBYIBUj4I/HMLf6z7MHbvYsmsw7ZBbNr2xecBjD1hG9IABt4U9vw9urY2LGeoxdDDHdhwbvz9NIODkx6jc2NG7X/Yur/cYOBjLrt7j0ED7BYNpEok03T4ffp8+6D6SSCTyx8iNscXH723ZNdj6DBV7Xi+X7PZ0dzu88t9W4i8/SEPX/cwMP8s1Zg+r3m3mm/89hWhrBGEq+OYG8c5uwpwQxpwQwpwQRq0wpbglkYwAtIhJ5KNNRD7aRKY9ScddK9jy+zdZ+YVHMadEqDlnJuFjmxBa3wlQ8+gDuPakp7nm2Qv547t/46lIlO/HP+B/1W9z/owQHwQ+ykObjuWeV2fxxJPuPn4/zJvbyYzpDs1zYMpk0HU5FkiGjyuvvJKrrrpqwG1uvfVWZs2aNajy+nu2be2ZF4vFaGtr2+kvlLGYa9nV3d2J0RPD6ujAcfw7XK7a3Y1IxMi0t0NiaN34MpkMsViMdDpNOiOIxbppa2vb4TKTSVf42552z9WpeP9oNEpbWxsd3R3EYjG6lB2v5/bUKZFIEIvF8mLXzupXvdsAKDl+W1sbsViMVCpFMpnM/59bl9s+tzydTufXJRIJ3hJ+dLuaVNG90fuYiUQCy7JIZ8hfz3Q6TSZflk0ikcC27Xzd0uk0yWQyX79EIoEQIl9u7+u6J1Ku/5Zb1t/2uetVfD37iF3Zts7t09/xivsruBPt4j60PdcgGo3S1dVNJhbH6jRItZW+w+SOXSx2DaYdBsu2lNHffbQjxx/pDLZ9cmPqrj52e4dDLAaaauXHqFx/7t0ve5fXewwcjNjVexwaaD+P6RCLWaRTXcQca9B9JJFIkEgk8vUsFruK7wUo3IODqc9gGIxguef1csluh+M4vPtuhqf/vQbrrUeZZD3CoZXL0FSLZdFRfDM+miX/OJCF6xrxLqii6uJphI5oRPGMbLNJiUTiokVMKj89hYpPTqLjnpVs/s3rrP76k2z82ctUfXkG0ZMm9rmfA0aIby/9Jfs3HsUVT32TM7QWvrTvpzi5I8XE925lknYjXz6onq6Go3gp/REee3car79h88ST7mTYNGHmDNfya85smDEdTFOKX5Jdx+mnn87RRw+cvWnMmDGDKquqqopXXnmlZFlHRwfpdJrKysrtruPOIK+9yQ9PQ47Mxjg0tFo+urUIAzulZt0Ye7mqKWqK8ePkdZBQlHpWItk2RuJQPnasoLoa7rvP2vrGuxFS7JIMC+3tDi8+l2LTiy8S2vQE80OP8cngB1ABLcp41jR9ngcCrbT+fgOnPbMYIhqNVy8hdMxYabklkeymCFUh8rFxhD/aRNdDa9h89eus++6zbPrlK0RPnUT4mLF4pkVL7vHFY49ies18fvX0+fzmnT/wZN3+nPepWxi98W20d/5NaPlfOMj+IweEx6CdeQSbwvvw/JZ9eekNDy+/Atff4GDboOswbarDnDnQPFswa+b2ZJmTSAZPRUUFFRV949RtD83Nzfz2t79l06ZN1NTUAG5yC8MwmDlz5pAcY6gQqp77ZahKHKJyBsabiDI+OmOXHGtHkdkYdz6FmF2lyysiy2lqGoEzVcmuw9n9knFIJIPB2HpYyt0OKXZJdgmplMPbbyRY/ewrKKueYyzPcUT0VTx6knS9QVtkES1TTkOfsYQnWp7nyRv/zsG3LmZm50SCpzXR+J1FqOE98A6USPZChBCEDm8geNgYepZtYvNv32Dz1a+z+crXUKMmnikRtGovwlAQhori0zgr+HUOTxzG7R/8mbPXHMdRcz/FyUf9iDAa2vuPoL33IPZLf6Mq9UeOVE0OG7MP1n6L6YrO4+WNU3n5NY1XXoWb/wZ/+auDqsDkyQ5zZkNzs2D2LAgF5ZurZHhYt24dHR0drFu3DsuyWL58OQCNjY34/X6WLFnCxIkTOe+88zjvvPPo6Ojgsssu4+STT+43E+Nw4dROxfIEcAI1w12VbcKbitAQnjh05SnBISsrR5V/FBW+apqiU4a8bEkpOTlrJFpgSEYIUu2SbCOyx+x6pNgl6YPt2CQzcZKZOCkriaKoaEJDVTQ0Rc//rwyQXacnZvPei+tofeM11PWvMcp6hfnh11ikprEqVbbo02lv/CShWftiN+6LP+jh9hev58m/fY15t03jhLePxZ6gM+FPh+GfX70Lz14ikewqhBD4F9biX1hLpi1B54NriL+8hcTb7SSWt+GkLOyUjd2Txu5KE3LgDD6JIxzea3qfS/f9DLXHTueo6acyefqVVIQCdL3xMOqHT6CueBzzscswgUM1LwfVzcI+bS6Jytm82TqFZ9+q4+VXBbfdAX/7u4MQMH68w9w5MGe26/pYUSFfSyS7hl//+tfcfvvt+b8//vGPA/DnP/+ZhQsXoqoq1157LRdddBGnnXYaHo+HY489lvPPP3+Yatw/QtVxKsYNYYG7333YpM9G3Qmv2Jqi0zxqyZCXKylHeTdGycihyx6ej+BOygZAGLtfllGJZG9Dil0SetLdPLfmEZateYR3t7zK6s73SVvJre5nZAxGbRlFRVuUQJcfLaNhC5uMmUDxxtF8XRiBLszqBG+qNTwbmE39uNkERs8ko2q09GxkbcujvPHKzxDLepj76hw+/cYJCJ9K7XfmUv2F6QhdPkgkkr0BLeqh4uSJcHJ5ywrHcbC70sTfbCX2342ot3qYdMtEOu/p4o59f8ZbSz5g1oxFTAjPZMzEfame83HMZAx901uIDa/RufFlWl+6mk2k2ajYbNFUjGaDGQsMLCeAkh5NfMsM7nn+EG69Y1+wDRobHGbOgIkTBBMnwsSJg7f+suMZEm+3Y3WmcJIWRkMAoymI4pGPXUlfLr30Ui699NIBt6mvr+faa6/dRTUaOVj1c1HaVoAZGu6qDBpdSEv03Z7sUC8tu0YmryRqyTA8sXvtRAZAxg6WbDdyXNl1yLfuQRJLdeHV/QNaM+1urGp/l38t/yMPvPt34pkYIbOCGTUL2GfMIUS9VeiqiaGaOI5DJtVDank74pUU3ncFoZV+ghvDKPbW28NWbDoDnbSHOtjgayWlPwSAJ2UyvqOC/bac6JYT0aj64mSqz5mJFvXs7NOXSCS7EUII1JBBYL86AvvVUXPubLqfXM/G37/GMY8EOeoJi1fnv8mdM3/LqtGry9uK+9z/dKFRq3qotRX88U7SdivrlRWsq3wC56DfUuEozLfHMKGjGXvVPrzzWh1PJ6rZlKhBhKoZM87P2LHQMFrQ0ABjxkBlBWQ2xmn/14d03LOS+OstkOn1NqMJAvvV4TtyFOnDvXRpHQgBpuZlVHAsHs2309tRItnt0L3YNdOGuxaSvYxczK49K1TznkN6GKewecsu+fFKIhnxyLt0ENz2xu+4+tnvIRBU+euZUDGd6TX7sM+Yg5lQMWO3EsAymRRPvHcPd751A6+1PoMuDPbxLWVhYCmNqTrUNe0ob7egbdgA69Mom1XszV5SmyuxUrUAqEYSX80GaF5PvNpLZnQNxuTxVM6aQKRhNHaXhdWeJL05TmZDD+kNPVSsjxFf20G6tQe7x0IAWsDEMy6Ed1oFNQeNh9kBacklkUgGhVAEwQPqCR5QT+K9Dlr+uJz5/zSZ+8wsqNFIzhakJkFqrIM1WiEwtpJooIYa/2jCnspCEHzHQXStR2lfSbzlPV7Z8DRPt73Co4lVPFGximjkLg5NG3wyZTDLUhEIeiw/mzbWsGVlFW09lby+sh5zVRT/Fh3hCBKjTFKLa1GmV2OO9hELbKBz3Xuklm8m+lwrlU+uJ/HDBM/OfY4nFj5FS0UrAHWBRmbX7cecUfszu24RdYFGmZBDIpFIhhHHkekAJOVRvNKyS7JtyFe6Xc/eLXY5DuCAY2d/cP/PL3PXH1x/CGJenO5kO+u6V/Fe21s8s/pB/vjCT4gaUfatms+iynksiEzHL3Sw0wgrDVYarJT7u+3+Law0lq5g9HTl/8ZKub/buX2y+xX/7Vg4lo1t2Ti2hWM7kPvfscDOnYPlTt6wEDgIx0Jgs0lN87CR5DYzyQbFps5W+GrSy7FJA/2d1XSte4KuDfX0bK4h3lpF2s7GyVJtqEpizVBhvB9zbj2Vi8dT1VSFovYzyEeAhgDebbgU0WiUtra2HbiYEolkb8UzMczon+zH1EsPYfXtb9B5/yp6XtpC+qFYfhthbITGHtrHtRAfH8I7owJvczXG2ACE6rFC9RiNi9iHT7MPcLaV4oW1j/Lwe//g7tUPcqvZRZ1ZxaGhGSywaql604/39TDWGzXYaQ091I5n4at0z3iFlsotfKDavKlmeDdtYbUDPqho1miaHmTa6smMf2k+S59fzAHPLqFjQjvvLVzFq5Pe4unYv3ngvb8DENGqmRJZwPiK2YytmkRVsAKP4SVtJYlneoinu+nJ/qQySRQh8BlBKn11VPpqqfWPIRKJDMclkUgkkj0CG4bJWU4y0hGq/EAvkYx0RqzY5TgOz78APT2gaqCqoKnZ/zVoeu/nVK+7A4DNL02ldfkEFD2N4Y/hiXbgibThibbjCbei6mkgK145rpAlGLyzbAD4dK9lrSLMMi3Nf1Mx/pt4mPvXPYTmQHNGY4alMc1SmWCpVNkKnqJvQg6CtDAAAwudDDoZx/1J2+5Pysr9eElkwiQyGqm0hu0IbFRsRyn9QcFyBGkcbN0hbaaJebrpNrvZ5N3EKs8a1uvtAMzomczJ6w9h3MZJiNUKy1dkIO62hVJtYE6voGJGJb4ZFXimRjHHhaS1lUQi2S3QwiaR48YROc4Njp1pTZB8v5PUik6SH3SS/LCL1IeddD+xHifhOqeoFSa+5iq8zVX4mqswGoNotV5UXWXfmkOYbyyhO9zKG889xdply/G+pmKtHcMWW6Xb180rzc/xwsyX+LBxRYnrZED1M8Ecz7GMYmyqmrGpEJFkBiXVhV7RiXHwi2gLnyX+5liMN2YQuWkOSyINVEwaQ2Li27xZv4aXzQ5eSd3Ps1vug3f6nq+ZNKlsraC6tYqq1kqqWqugK0TKeo/1wDJvDz2BOPGKBJlqUOtMgjURajxV+E0ffsOD1zQxdAXdUDFNFUXVEEJBCAUF16JBAEr25FRAQyWg+VAVpfBcpff/2Q9IODih0djVfbPHOY5DZ7KNjd2r2dC9mo1dq9kcW4dH8/HJSV/HWZkg+WEnyfc7SK7owmpPUvvNZnxzqoaqy0gkEsmAWI6QYpekBHNSmMzm+HBXQ7IbIi27dj0jVuxatx6+eZ6DbZdff1BdM/tXd2Oj4NsUIBAPILoUtHUqWqL0sZTxWKSDNumghe0FdAfHBIFAsQWKraBkgKSCSIJICURSoKTc3wEcBWwFLE0howlSqkpCVZmoaIwSKh2+dtoiK+ioeo+noyt52NdNwkwCDiLtQ0kFUCwV1dbwJDx4Ex58CQ/+hIkv6cGfcH98cQ/+uBdv0kA42TtCQMKTJuFN0OOLE/fG6fF10+OJ0eXrotvTRY8Zw1JsHMUBBzw9Jmabh0gywjGJ42iMNRHZFML6sCc7AbEwpgTxn1CDf98afPvWYtT7d9LVlEgkkl2PVuFBq/Dg36emZLlj2STf7aDnpS30vLSZ+Mtb6Hp0Hdj9fwQJAxF9IsbsCInDHDZN7WDLFBsv4zhYncYxup+AEWZUcCz1oSYinqpBuSCGgOpEio673qHl7x+y7vkKeG4xdYbgmNEKx4QcLE+CHieGnUohuhX0LgOty0TpKY1tqPq7UQNdWFqaDA6ZrhDi/RBasmBnm9JSbKrazOqqNbRUtNAZ6KIz2EnM20NaT5PW06T0FJZquY8K4WALV8RyH0nu/8IR+BwI2SoBWyFgC/d/J/u7oxCwFTy2IK2ZdM8/k55UJ22xzbTFt9Des5nutna0LgV/jw9/3E9lWwX1LaOobYnybsc/i05MYIwJoNf7EIp8UxypKGLAW0gi2a3oUCtZ2WOwKaExZrgrIxlReKdXDHcVJBLJIBmxYtfoesE/b4GuLrAsyFiQyWR/z0AmcygZ61DSKUinYVPa/T+dgkwsjbKxG21TJ8bmTswtXXhaO/Gt78JMJBFlUiCkFZWkbuR/0rpOMmSQ1g2EAqptozoWRiaDJ5PCyKSoiKfRU0m0ZBrhADRlf7YNy7BJBTKkAxnSwQypugzt/h4cYePggO2g9Wh4ekzCsSDGFg09pqHFB/etSfFrGI1BjClBvCdW4p1diW92JWrE3Oa6SiQSye6OUBU8U6N4pkapOG0SAFYsTeL1VtLrY6Q3xd3g8gLUiIlW6cEYH8IcG9wplq6KxyB60kyiJ83Eak/S/dQGV4R7o5XMxjisMQg6QYSmoNV40Ru96KP8GE1BjKYgZlMQozGI4i16pNsZnEwSv+Fh/fK19LzdTuK9Dro/bKV6lZfadaPR3nQQ9q4Rj6rz/1b3u40S0fFMiGAuCGGOD2NODGGMC2GMDaIY0rZipLNkMSRTw10LiWRoSKh+Poh5cGTaNIlEMkRo2dc0n8xHtMsYsWIXQHW1oLr/9+IBMICK7E8pjuPgJC3s7jQoAmGqKKaK0LZ/AuM4DnYsg92VwupMYXWmsTpT2F3Ztz5VQWgCoQpQFSL1FfSoKdSwgRo2tzt1rZO2sTqSZNpT2J0pnEzObcQVuJSAgRrWUSOmDHIskUgkA6D6dfwLa4e7GqgRk/AxYwkfM3bHClI0hKFhRqOEp6mEpzX22cSxHazWBOnNCaz2JE7Cwo5nsBMWTsrKxrF0smEssw+X7N8oAoRwTfIVkfV1zP4tBDY2SauHlJ1C1wx0zURTDVRVJecbqfh1tKiJmvvx6zt2zpJhxTQFpvyGJpFIJBJJWXw+wfy5Dn4/vFsmPIVk6BnRYtfOQAiB8GgoQ5guVgiBGtBRAzr6qK27AoajUewhCMYudAWtyotWtS2h4CUSiUQicTNaymeIRCKRSCQSya6hokKQyQx3LfYeZORxiUQikUgkEolEIpFIJBLJHoMUuyQSiUQikUgkEolEIpFIJHsMUuySSCQSiUQikUgkEolEIpHsMUixSyKRSCQSiUQikUgkEolEsscgxS6JRCKRSCQSiUQikUgkEskegxS7JBKJRCKRSCQSiUQikUgkewxS7JJIJBKJRCKRSCQSiUQikewxSLFLIpFIJBKJRCKRSCQSiUSyxyDFLolEIpFIJBKJRCKRSCQSyR6DFLskEolEIpFIJBKJRCKRSCR7DNr27hiNRnfq9ns6sj36sru1SSaTwe/3Y1kWAKqqEo1G0bTB3Va5/YF+99vWNhlMmTkSiQQejye/be73cucVDAbz+wWDwfz2tm3j9/sxDKOkjNyxc3VRFAW/34/f78+vyx1H13VM0yQajeaPa1kWpmmiKAqqqubL0XUdRVHy23o8nnyZ5c5FCIGqqvnlkUgkf9zc8uI69d5+MG3Zu83726d3ewsh8r/3vs6DvY7bcr33Fna3cWRXINukL3tTmxSPcZJSyj2HcuP0jpYJ2zcu97f/cPbZXJ1yz1xVVbf5nWd7jgeF8+79zC++boZhYJpmfl1u+9x6XddL3hV6v7sUv5Pkjpnb33EcTNMEKOknQElZHo+nz/tMri/lyt0bntfl+u9A90TvdbnrVXw9e7dV7/ep/o5X3F8BLMsq6UPbew0GuhcHmhsMxTvbtpTR3320I8cf6WxL+wz1mLo910bX9fzY0btflhuTtqX/Ft8HMLh3ge3po4OdT0LhHhxsfYYC4TiOs1OPIJFIJBKJRCKRSCQSiUQikewipBujRCKRSCQSiUQikUgkEolkj0GKXRKJRCKRSCQSiUQikUgkkj0GKXZJJBKJRCKRSCQSiUQikUj2GKTYJZFIJBKJRCKRSCQSiUQi2WOQYpdEIpFIJBKJRCKRSCQSiWSPYaeKXddccw2nnnoqc+bMYcGCBWW3mTJlSp+fm2++eWdWa9gYTHusW7eOs846i+bmZhYuXMiPfvQjUqnULq7p8HHIIYf06Q8/+9nPhrtau5Qbb7yRQw45hFmzZvGJT3yC559/frirNGxceeWVffrD4sWLh7tau4znnnuOs846iyVLljBlyhQeeuihkvWO43DllVeyZMkSZs+ezac//WnefffdYartrmFrbXLBBRf06TMnn3zyMNV253PttddywgknMHfuXBYtWsTZZ5/NBx98ULLN3tZPBtMme3o/kc+RkctQ3bOpVIof/vCHLFy4kObmZs466yw2bNiwK09FUsS1117LlClT+PGPf5xfJq/j7sHGjRv51re+xcKFC5kzZw7HHXccr7/+en69vI4jn0wmwxVXXMEhhxzC7NmzOfTQQ7nqqquwbTu/jbyOeyc7VexKp9McddRRnHbaaQNud8kll/Dkk0/mf44//vidWa1hY2vtYVkWX/7yl+np6eGmm27iiiuu4P777+eyyy7bxTUdXr7+9a+X9IevfOUrw12lXca9997LJZdcwle+8hXuuOMO5s+fzxe/+EXWrVs33FUbNiZNmlTSH+66667hrtIuo6enhylTpnDhhReWXf+73/2O66+/ngsvvJBbb72VqqoqzjzzTLq7u3dxTXcdW2sTgKVLl5b0meuuu24X1nDXsmzZMk4//XT+/ve/c/3112NZFp///Ofp6enJb7O39ZPBtAnsuf1EPkdGNkN1z/74xz/mwQcf5IorruCmm26ip6eHL3/5y1iWNRyntVfz6quvcssttzBlypSS5fI6jnw6Ojo47bTT0HWd3/3ud9xzzz1ccMEFhEKh/DbyOo58fve73/G3v/2NCy+8kHvvvZdvf/vb/OEPf+Avf/lLyTbyOu6FOLuAf/7zn878+fPLrps8ebLz4IMP7opqjBj6a49HH33UmTp1qrNhw4b8srvvvtuZOXOm09XVtSurOGwcfPDBzvXXXz/c1Rg2TjzxROfCCy8sWXbUUUc5P/vZz4apRsPLr3/9a+djH/vYcFdjRNB7rLRt21m8eLFz7bXX5pclk0ln/vz5zs033zwcVdzllHt+nH/++c5XvvKVYarR8NPS0uJMnjzZWbZsmeM4sp84Tt82cZw9u5/I58juxfbcs52dnc6MGTOce+65J7/Nhg0bnKlTpzqPP/74rj2BvZzu7m7niCOOcJ566innU5/6lPOjH/3IcRx5HXcXLr/8cue0007rd728jrsHX/rSl5zvfOc7Jcu++tWvOt/61rccx5HXcW9mRMTsuvjii1m4cCEnnHACN998c4nJ4d7Eyy+/zKRJk6itrc0vW7JkCalUqsScdk/n97//PQsXLuS4447jmmuu2WvcOFOpFG+88QZLliwpWb548WJeeumlYarV8LNy5UqWLFnCIYccwje+8Q1Wr1493FUaEaxZs4bNmzeX9BfDMNhnn3326v4CruXEokWLOPLII/m///s/WlpahrtKu4yuri4AwuEwIPsJ9G2THHtiP5HPkd2P7blnX3/9ddLpdIlbf21tLZMmTZLXeRdz8cUXc+CBB7L//vuXLJfXcffgkUceYebMmXz9619n0aJFfPzjH+fvf/97fr28jrsH8+fP55lnnuHDDz8E4K233uKFF17gwAMPBOR13JvRhrsC5557LosWLcLj8fDf//6Xyy67jLa2Ns4+++zhrtouZ8uWLVRVVZUsC4fD6LrOli1bhqlWu5bPfOYzTJ8+nVAoxGuvvcbPf/5z1qxZUxIDYU+lra0Ny7KorKwsWV5VVcXmzZuHqVbDy+zZs7nssstoamqipaUlH/fu7rvvJhqNDnf1hpVcnyjXX/Zmd6UDDjiAo446ivr6etasWcOvfvUrzjjjDG677TYMwxju6u1UHMfhkksuYf78+UyePBmQ/aRcm8Ce20/kc2T3Ynvv2S1btqDreh8Bt6qqaq95XxwJ3HPPPbz55pvceuutfdbJ67h7sHr1am6++WbOPPNMzjrrLF599VV+9KMfYRgGH//4x+V13E344he/SFdXFx/5yEdQVRXLsvjGN77BscceC8j7cW9mm8WuK6+8kquuumrAbW699VZmzZo1qPKKRa1p06YB8Jvf/Ga3EbuGuj2EENu0fHdgW9ros5/9bH7Z1KlTCYVCfP3rX+db3/rWXiNu9L7WjuPs1td/R8h9kcnR3NzM4Ycfzh133MGZZ545TLUaWZTrL3szRx99dP73yZMnM3PmTA455BAeffRRjjjiiGGs2c7n4osv5p133uGmm27qs25v7Sf9tcme3k/kc2T3YKjv2b3lvh4JrF+/nh//+Mf88Y9/xDTNfreT13Fk4zgOM2fO5H/+538AmD59Ou+99x4333wzH//4x/Pbyes4srn33nu58847+fnPf87EiRNZvnw5l1xyCTU1NSWxwOV13PvYZrHr9NNPL3lJLMeYMWO2u0Jz5syhu7u7rJXTSGQo26OqqopXXnmlZFlHRwfpdLqPEr07sSNt1NzcDMCqVav2eLErGo2iqmqfrwctLS27xb2wK/D5fEyePJkVK1YMd1WGnerqasD9ElVTU5NfLvtLKTU1NdTX1+/xfeaHP/whjzzyCH/961+pq6vLL9+b+0l/bVKOPaWfyOfI7sOO3LNVVVWk02k6OjpKrBBaWlqYO3fuLjqDvZs33niDlpYWPvGJT+SXWZbFc889x4033si///1vQF7HkU51dTUTJkwoWTZ+/Hjuv//+/HqQ13Gk89Of/pQvfelLHHPMMQBMmTKFdevWce2113L88cfL67gXs80xuyoqKpgwYcKAPwN94dgay5cvxzTNkiwYI5mhbI/m5mbeffddNm3alF/21FNPYRgGM2fO3FmnsNPZkTZ68803gcLDZk/GMAxmzJjBU089VbL86aefloNsllQqxfvvv79X9IetMWbMGKqrq0v6SyqV4rnnnpP9pYi2tjbWr19f8nKzJ+E4DhdffDEPPPAAN9xwAw0NDSXr98Z+srU2Kcee0k/kc2TkMxT37MyZM9F1vWSbTZs28e6778rrvIvYb7/9uOuuu7jjjjvyPzNnzuSjH/0od9xxBw0NDfI67gbMmzcvH+cpx4oVKxg9ejQg78fdhUQi0cdqS1XVvFWWvI57Lzs1Zte6devo6Ohg3bp1WJbF8uXLAWhsbMTv9/PII4+wZcsWmpub8Xg8PPvss1xxxRWcfPLJu3XMjP7YWnssWbKEiRMnct5553HeeefR0dHBZZddxsknn0wgEBjm2u98XnrpJV555RUWLlxIIBDgtdde45JLLuGQQw6hvr5+uKu3SzjzzDM577zzmDlzJnPnzuWWW25h/fr1nHrqqcNdtWHhsssu4+CDD2bUqFG0trZyzTXX0N3dXWKSvCcTi8VYtWpV/u81a9awfPlywuEw9fX1fOYzn+Haa6+lqamJsWPHcu211+LxePIxCvZEBmqTcDjMVVddxRFHHEF1dTVr167liiuuIBqNcthhhw1jrXceF110EXfffTdXX301fr8/H5ciGAzi8XgQQux1/WRrbRKLxfbofiKfIyObobhng8EgJ5xwApdddhnRaJRwOMxll13G5MmT+wRKl+wcAoFASRxAcK3PI5FIfrm8jiOfM844g9NOO43f/va3fOQjH+HVV1/l73//OxdffDGAvB93Ew4++GB++9vfUl9fn3djvP766znhhBMAeR33ZoSzEx1RL7jgAm6//fY+y//85z+zcOFCHn/8cX7xi1+wcuVKHMehoaGBE088kdNPPx1NG/bY+UPO1toDXEHsoosu4plnnsnfgOeff/4eKf715o033uCiiy7igw8+IJVKUV9fzzHHHMMXvvAFvF7vcFdvl3HjjTfyhz/8gU2bNjF58mS+853vsM8++wx3tYaFb3zjGzz33HO0t7cTjUZpbm7m3HPPZeLEicNdtV3Cs88+y2c+85k+y48//nguvfRSHMfhqquu4pZbbqGjo4M5c+Zw4YUX9nkB35MYqE1+8IMfcM455/Dmm2/S1dVFdXU1Cxcu5Nxzz2XUqFHDUNudz5QpU8ouv+SSS/LuNXtbP9lamyQSiT2+n8jnyMhlqO7ZZDLJT3/6U+6++24SiQSLFi3i+9///h7Th3dHPv3pTzN16lS++93vAvI67i785z//4Re/+AUrVqxgzJgxnHnmmZx88sn59fI6jny6u7v51a9+xUMPPURLSws1NTUcc8wxnHPOOfk5tLyOeyc7VeySSCQSiUQikUgkEolEIpFIdiXbHLNLIpFIJBKJRCKRSCQSiUQiGalIsUsikUgkEolEIpFIJBKJRLLHIMUuiUQikUgkEolEIpFIJBLJHoMUuyQSiUQikUgkEolEIpFIJHsMUuySSCQSiUQikUgkEolEIpHsMUixSyKRSCQSiUQikUgkEolEsscgxS6JRCKRSCQSiUQikUgkEskegxS7JBKJRCKRSCQSiUQikUgkewxS7JJIJBKJRCKRSCQSiUQikewxSLFLIpFIJBKJRCKRSCQSiUSyxyDFLolEMuQ89NBD/OlPfypZdttttzFlyhTWrFkzJMd49tlnufLKK4ekLIlEIpFIJJK9Dfm+JpFI9mSk2CWRSIachx56iD//+c879RjLli3jqquu2qnHkEgkEolEItlTke9rEolkT0aKXRKJRCKRSCQSiUQikUgkkj0GbbgrIJFI9iwuuOACbr/9dgCmTJkCwL777svxxx8PQEtLC5dffjmPP/44oVCI448/nq997Wuoqpovo7W1lV/+8pc88sgjtLe309DQwOc+9zlOOukkAK688sr8V8LcMUaPHs0jjzxCMpnk8ssv5+mnn2bdunUEg0HmzZvHBRdcwKhRo3ZZO0gkEolEIpGMVOT7mkQi2dORYpdEIhlSzj77bFpbW3nzzTfzLziBQIBXX30VgPPOO4/jjjuOU045hccff5xrrrmG0aNH51+Muru7Oe2008hkMpx77rmMHj2axx57jO9973uk02k++clPctJJJ7FhwwZuvfVWbrnlFgAMwwAglUoRj8c555xzqK6upqOjg5tvvplPfvKT3HfffXg8nmFoFYlEIpFIJJKRg3xfk0gkezpS7JJIJENKY2MjFRUVGIZBc3Nzfnnu5en444/nrLPOAmD//ffnqaee4r777su/PN1www1s2LCBu+++m4aGhvx2XV1dXHnllZxyyinU1dVRV1cHUHIMgGAwyI9//OP835ZlsXTpUhYtWsTjjz/OEUccsbNOXSKRSCQSiWS3QL6vSSSSPR0Zs0sikexSDjjggJK/J02axLp16/J/P/HEEzQ3NzNq1CgymUz+Z+nSpbS2tvLhhx9u9Rh33303J554IgsWLGD69OnMmTOHnp4eVqxYMdSnI5FIJBKJRLLHId/XJBLJ7o607JJIJLuUUChU8rdhGKRSqfzfra2trFy5khkzZpTdv62tbcDyH3roIb75zW/mY0tEo1GEEHzpS18imUzu+AlIJBKJRCKR7OHI9zWJRLK7I8UuiUQyoohEIlRXV3PBBReUXT9u3LgB97/vvvtobGzk0ksvzS9Lp9N0dHQMaT0lEolEIpFI9lbk+5pEIhnpSLFLIpEMOYZhbPdXuaVLl3LjjTcyevRoKioqBjwGQDKZxDTN/PJEIoGmlQ5td999N5ZlbVd9JBKJRCKRSPZE5PuaRCLZk5Fil0QiGXLGjx/PLbfcwj/+8Q+mTJlCIBAY9L6f/exnuffeezn99NP57Gc/S1NTE7FYjA8++ICXXnqJ3/zmNwBMmDABgD/+8Y8sXrwY0zSZMmUKS5cu5aGHHuInP/kJBx98MG+88QZ/+ctf+pjjSyQSiUQikezNyPc1iUSyJyPFLolEMuSceOKJvPjii1x66aV0d3ez7777cvzxxw9q32AwyN/+9jd+85vfcO2117Jp0yaCwSDjx4/nqKOOym938MEHc8opp/CnP/2JX/3qV/x/9v48SpLkrNNGH3P32DJyra2reqnqbvUqqReBpBaiQUgwYhPDAB+aw3AkhgMCoSvdI91hmblzD/MhvjmC4c4wutKH0GFASMMgwbBLSAJJ3WotvW/VXfuamZV7ZmTsEb6Z2f3D3D0icqnKqs7q7qq255zurIjwcDc3N/dw+/nvfd9rr72WBx54gHe+853Mz8/z13/91/zFX/wFd911F5/4xCd4//vff7l212KxWCwWi+WKw96vWSyWqxmhtdYvdSMsFovFYrFYLBaLxWKxWCyW7cB5qRtgsVgsFovFYrFYLBaLxWKxbBdW7LJYLBaLxWKxWCwWi8VisVw1WLHLYrFYLBaLxWKxWCwWi8Vy1WDFLovFYrFYLBaLxWKxWCwWy1WDFbssFssAH/vYx7j99ts3/Oyxxx7j9ttv57HHHtvy+hqNBh/72Mc4evToRbdlZmaG22+/nb/5m7+54LK33347H/vYx867zL//9/+et73tbRfdDovFYrFYLJaXC/ZezWKxWC6MFbssFstlpdFo8PGPf/ySbqC2m/e97318/OMff6mbYbFYLBaLxfKywd6rWSyWqxHvpW6AxWKxvFjs37//pW6CxWKxWCwWi2UT7L2axWLZLqyzy2KxXDJaa/70T/+UH/zBH+S1r30t999/Px/+8IdptVqAsbZ///d/PwD/4T/8B26//fZ11vq/+Iu/4Md//Me5++67eeMb38jP/dzPceTIkYHtxHHMf/2v/5U3v/nN3HfffXzoQx+iVqudt22tVot3v/vdvO1tb+Ps2bPAemt8HMf89//+3/mBH/gB7rrrLu677z5+5md+hieffDJb5vOf/zz/6l/9K173utfxnd/5nfzYj/0Yn/vc515Qv1ksFovFYrG8GNh7NYvF8krFOrssFsuGxHG87j2l1MDr3//93+eTn/wk7373u3nLW97CqVOn+OhHP8qxY8f4sz/7M/bs2cPHP/5x3v/+9/Mrv/IrfN/3fR8At9xyCwC/8zu/w5/+6Z/yzne+kw996EMopXjmmWdYXFzk1a9+dbadT3ziE7zpTW/id3/3d5menub3fu/38DyP3/u939uw7ZVKhV/8xV8kjmM++9nPcs0112y43B/90R/x6U9/mg9+8IPceeedNJtNDh06RL1eB+DJJ5/k137t13jXu97Fr//6r6OU4vTp0zQajYvuT4vFYrFYLJbtxN6r2Xs1i8WyOVbsslgsG/Ka17zmvJ/XajU+9alP8c53vpP/+B//IwD3338/ExMT/Pqv/zrf+MY3+L7v+z7uvPNOwNjS77333uz7U1NTfPrTn+YXf/EX+dVf/dXs/Y2Skt5000185CMfyV6fPXuWz33uc/yX//JfEEIMLHvu3Dl+4Rd+gZ07d/KHf/iHjI2NbboPzz77LN/93d/Nz/3cz2XvpU83AQ4ePMjo6Gi2f+k+WiwWi8VisbzU2Hs1e69msVg2x4YxWiyWDfmrv/qrdf/91m/9Vvb5wYMHCcOQH/uxHxv43o/+6I/ieR5PPPHEedf/8MMPo5Tine985wXb8r3f+70Dr2+55RaiKGJlZWXg/RMnTvAzP/Mz3HzzzXzqU586780TwF133cVDDz3E7//+7/PMM88QhuG6z+v1Ov/u3/07HnroIZrN5gXbarFYLBaLxfJiYO/V7L2axWLZHOvsslgsG3LXXXete6/T6WT/TvMw7Nq1a2AZz/MYHx/P7OWbkX5/M9t6P6OjowOv8/k8AEEQDLz/+OOPU6vV+Omf/mmKxeIF1/vLv/zL5PN5/u7v/o4//MM/pFwu88M//MP82q/9GuPj47zxjW/kox/9KJ/+9Kd53/veB8Cb3vQmfuM3foPbbrvtguu3WCwWi8ViuVzYezV7r2axWDbHOrssFsslMT4+DrDuiV0cx9RqtQs+qZuYmABgcXFx29r0sz/7s7zzne/kgx/8IA899NAFl8/lcvzSL/0SX/ziF3n44Yf5jd/4Db70pS/x27/929kyP/RDP8RnP/tZnnzyST72sY8xNzfHe97zHrTW29Zui8VisVgslu3G3qvZezWL5ZWMFbssFsslcc8995DP5/n85z8/8P6XvvQl4jjmjW98I7D5k703v/nNOI7DX/7lX25bm4QQfPjDH+YnfuIneP/737+lm6iUnTt38q//9b/mzW9+MydPnlz3ealU4m1vexs/8zM/w8LCwgWfhlosFovFYrG8lNh7NXuvZrG8krFhjBaL5ZIYHx/nF37hF/jEJz5BqVTie7/3e7MKP2984xv5nu/5HsBY58fHx/niF7/IrbdDxndXAAEAAElEQVTeSqFQ4KabbmL//v3823/7b/kf/+N/0Gw2eetb34oQgqeffpp7772Xt771rZfULiFElq/i/e9/Px//+Md5y1vesuGyv/Irv8Idd9zBa17zGkZHRzl8+DDf/OY3+Tf/5t8A8NGPfpRKpcJ9993Hnj17mJub49Of/jSvfe1rs6elFovFYrFYLC9H7L3a+CW1z2KxXB1YsctisVwyH/zgBxkfH+ezn/0sf/7nf874+Dg/+ZM/yYc+9CEcxxhHHcfhwx/+MP/tv/033v3udyOl5DOf+Qz33Xcfv/Ebv8H111/P5z73Of76r/+acrnMnXfeyQ/90A+9oHalN1FCiPPeRL3hDW/gy1/+Mn/+539Ot9tl3759vOc97+G9730vYJ6I/s//+T/5yEc+Qq1WY+fOndx///188IMffEHts1gsFovFYnkxsPdqFovllYrQNpjZYrFYLBaLxWKxWCwWi8VylWBzdlksFovFYrFYLBaLxWKxWK4arNhlsVgsFovFYrFYLBaLxWK5arBil8VisVgsFovFYrFYLBaL5arBil0Wi8VisVgsFovFYrFYLJarBit2WSwWi8VisVgsFovFYrFYrhq8S/1itVrdzna86IyNjVGv11/qZryssH0yiO2PQWx/DGL7Yz22Twax/THIldQfExMTL3UTtgWl1BXT55bNuZLOHcvG2GN4dWCP49WBPY5XB1u5V3vFOrsc5xW765ti+2QQ2x+D2P4YxPbHemyfDGL7YxDbHy8+ts+vDuxxvPKxx/DqwB7HqwN7HF852CNtsVgsFovFYrFYLBaLxWK5arBil8VisVgsFovFYrFYLBaL5arBil0Wi8VisVgsFovFYrFYLJarBit2WSyWqwKtNfUvTTHz6w8TV/yXujkWi8VisVgsVx1BoFFKv9TNsFgsVznKl3QPVdD60q83VuyyWCxXBY1/nGL6lx+i+rlTTP8/voGW6qVuksVisWyJT37yk/zUT/0Ur3vd6/iu7/ou3ve+93HmzJmBZbTWfOxjH+P+++/n7rvv5l3vehcnT558iVpssVheqXzjW/D8oZe6FRaL5Wqn+9wKwekG8VL3ktdhxS6LxXLFo7Vm6Q8OUbh1jOs+8ibaDy/Q/PrcS90si8Vi2RKPP/44P/uzP8tf/uVf8qlPfQopJb/wC79Ap9PJlvmjP/ojPvWpT/Gbv/mb/NVf/RW7du3i53/+52m1Wi9hyy0WyyuJ1NG1tPwSN8RisVz9bIOB1IpdFovliqfz1DL+oVV2/fJrmHjnLbg7i9T+9syFv2ixWCwvA/74j/+Yn/zJn+TWW2/ljjvu4CMf+Qhzc3McPnwYMIL+Zz7zGd773vfy9re/ndtuu43f/d3fxfd9vvCFL7zErbdYLK8UlDXNWyyWKwgrdlksliue1rfmwRGM/dB+RM5h/MdupPFP55Ct6KVumsVisVw0zWYTgLGxMQBmZmZYXl7m/vvvz5bJ5/O84Q1v4JlnnnlJ2mixWF55vIDUORaLxfKi473UDbBYLJYXSvuRBUp37cAdzQMw9qMHqPzpMdqPLDD6L254iVtnsVgsW0drzUc+8hG+8zu/k9tuuw2A5WUTM7Rz586BZXft2sXc3PlDticmJi5PQy0vKvY4XvlcDccwCDTlcgjAxEThJW7NS8PVcBwt9jheCYjhDmFZMDY+TmGifEnrsGKXxWK5olG+pPP0Mjv/7Z3Ze6V7dyGKLi0rdlksliuMD3/4w5w4cYI///M/X/eZEGLg9VYqFFWr1W1r2ysBqWL8uEs5P/JSNyVjYmLCHscrnKvlGPq+pt02/65WO+df+CrkajmOr3TscbwyaDUbxO0u1GrkCuG6z7ciWNowRovFckXTPbiCDhTlN12TvecUXMpv2EP7kYWXsGUWi8Vycfz2b/82DzzwAJ/+9KfZu3dv9v7u3bsBWFlZGVi+Uqmwa9euF7WNVzuHl57gsXNffUGlzi2WqxWbs8tisbzorHnQdzFYsctisVzRdI+sAlC6ezC8p/xde/EPV4lrwUvRLIvFYtkyWms+/OEP88///M98+tOf5oYbBh2p119/Pbt37+bb3/529l4YhjzxxBO87nWve7Gbe1VT6xpBUWk7q7dY1mLFLovFciVhwxgtFssVjX+0ire7SG53aeD9odcbJ0T34Aojb7nupWiaxWKxbInf+q3f4gtf+AJ/8Ad/QLlcznJ0jYyMUCwWEULw7ne/m09+8pPceOONHDhwgE9+8pMUi0Xe8Y53vMStv7oQwjwHVlri4m7ruuNaAFLj7Sxu63otlhcLZQ2PFovlCsKKXRaL5YrGP1Kl+Ood694vvXYnCOg+V7Fil8VieVnz2c9+FoB3vetdA+9/5CMf4Sd/8icBeM973kMQBPzWb/0W9Xqde+65hz/5kz9heHj4RW/v1YyTiF36Mji7Wg+ZYgLjP37Ttq/bYrkYlFacq53khvFbszG/Fazh0WKxXElYsctisVyx6FjhH6+y8+fvXPeZO5yj8KoxugcrL0HLLBaLZescP378gssIIfjABz7ABz7wgRehRa9cUmeX1PIlbonFcvmYa5zl9OoRAA5M3L7l71lnl8ViuZKwObssFssVS3C2gQ4UpVdvXI2jdM9OOs9ZsctisVgsW8PBJMK1ObssVzNSxebvRY5ze1pYLJYXjW0Q163YZbFYrliCU3UACreOb/h56Z5dxAsdosVXXnlsi8VisVw84jKGMV6NdJ5Zpv3k0kvdjCuKr3xN8+RT22uREqtnENWz27rOjeh3dtmKpRaL5XKgAolsR9uyLit2WSyWK5bgdAOAwk0jG34+lFRo7Fp3l8VisVi2gEidXVyGnF1BfdvX+VITTreIZtsvdTOuOKq17V2fu3QUd/HIlpfXl2iZUH3RvdJG+lpeaQQtRHvlpW7FVU/jn8/R/OpM740XIKxbsctisVyxBGca5K4r4wzlNvy8+OoJ8IQVuywWi8WyJbJqjGp7Z/KzjbNM1U5Q91e3db0Wy4tJv7NLWfOj5RWGd/Yh3HOPvdTNuCjq9SvQgbk2OeAL2AUrdlksliuW4Eydws2jm37uFD2Kt4/TOWifwlgsFovlwjjCBbY/Z5cfmXD6SIbbul6L5YUgxMUt339a2ChGy9XE1RiWu7qqefxJmD539e3bVrFil8ViuSLRWhOcalB41eZiF0Dp7l10n6tclT9iFovFYtlehLg8YYwiUxWsHcZy5WKdXetRShPH9h7zSqbWXeHBM39HI6hu2zpb35x7yfMZdn3zt32lR5rbMEaLxfJKQ1Z8VCMkf/PYeZcbunsncjUgmrnSr/QWi8Viudw4qdi1zWGMcJEWGovlZYh1dq3n0cfgwYde6lZYXgit0ORTnGtsX5GHeDWw+QwvAR31X2SSPzaM0WKxvNIIJpvA5snpU0r3JEnqbSijxWKxWC5AGsaot93ZRbJey0ZUOovEanuqb1kuzCUnqFcb//uVTNsW/L5sxBUfHV/+gVbwSoBxeF1VvFx+cMKti37Kj9e/aZ1dFovllUY4ZcSu/IHzi13F2ycQBZfOQZuk3mKxWCznJ63GKLd5Jp+u91JFhqsZP+5wcP5hji49/VI35RWHuEjH4WpflNfaHNIWy3aiujGtb82/qPfvoQxetG1dLdRqF7gQ+A28M19HrG7NNTfg7AJEdRJn6filNs+KXRaL5coknG6BI8hdWz7vciLnUHzNBN3nrrKnNRaLxXIVM9eY5MzqkRd9u2k1xm13dqWiwisw9utCE8hYmSf5naj5YjTHcol0OprlvlupuTmYnX3ljWfLi4MKTCi5al9+x+crJq9v1N3W1U1NaZ54yiTC3wyRFGcR3a3lQ9Nr8t+JbhVRn12zkMKdenhL67Nil8ViuSIJp5vkrivj5N0LLjt09y66z6+i7WNIi8ViuSI4tvwMk9VLf5q7GeoCvwNOInYpvb05uzIR7WX4MyTbEdGCmZAoX1L7+7OE51rbsu5GUOVbk19ksXVu84WSTkn7yPLyRK45Jc7NwJFjW/++s3QEZ+nFF7A3JWjhnn4A4he/QqpoLuDMH3zRt3slkTp8hHf5rwtXu+NWAKI6hXf6AfAb27bexS3l37/IvpUbPGhaswpZbRPPbc3xZ39VLBbLFUk41SR/w/CWli3dsxPVighO1y9zqywWi8WynSi9fQ6rM2c1X3sQFhY3v/nOxK5tTlCfVmN8OU6qWt+Yo/3YItBzUYTT2+OyagVmYlXtLm+6jNTG2XWxIXVXEnp1Emfl5HmXkSomiC/BebGBgiovMH4vVnSNY82Roxf3nbU4q2dxthjK9GLgrJ5BRF1Ea3Fb19toXrhz3dmncOoz27rdq444Fbsuz3VBK03n4MrGOaKuQoRfH/i7HdQT3Uxs5RBtaSHQ8sLnT/Pr8zSf3Zrjz4pdFovliiScbpHfv1WxaxcAXZu3y2KxWF7WNIMazy88lr2+pMn/JqTl1zvnzZWbVGPc5jDGHi8/sUuHZl+31f3cWcWZfarvjc0nOqkwI7Y4GboS0TPP4KycOO8yT819g29PffkSVj44VucaUzx09h/oRhdOCr1WYIzm2xsmBJ+cgsbVFmV6Gcbb8rLmscdhfv7ld55faaThbJfL2RUvdQknm3Sfq/ByvC5vO17e/H2x85JdpLK+NozRvHmB1+fBil0Wi+WKQ3Vj4qXuBZPTpxRuHsUZydm8XRaLxaIkztJR2Gbn0nbx5OzXWW7PZa/9ePtKncXJA/ytaDrbncNlOx1qL4RqdYP9StOJRaqnSb3A3XdnnsBpLoC6sGui39nViVrbcsynqsd59NxXXvB6XkxawfkdF3W/wlxjcv0Ha8bWctvkt+lEFxeKKmsB7ceX6B5aPe9yO3dc1Gq3neVlTbzRhHgTwqlm5lzcmO0717u++du8yCjgKDp/G9Ym7X4loOMXKYzxIg9/p3Nx4++lJP0ZOzcLSxUPACFDtNYXDOm/6O1EPqK6Dc7NLYQxXgxW7LJYLFcc4bS5i8jv35rYJRxB6a6dtiKjxWJ5xSOqZ034znbclF4G1opMl0XsOu+80WxfbnPOLp0IEgNhjDIC+eLlC1pY1Dz5NMzOrUkA7Cb5xDZw9AAstWYvGBZH2B4I0xOZAJPm4zqfsysVxASPTn+Fh6f+6fzb2gKnV4/QCbcn79jLhadmv8Gx5WfWf7BG7ErH2MWGhSrfHGMdrD/WTt+M8dp9F7XabaXT0Tz7HBy9iFxhnWdXspx0g2y/s8tJVnkxxVy7Xc3XvwHT5zae0ctWRP2LU1kV8lcKmcB3mcIYB7a1wcMNP+6s+/2RUnP6DBx+GaWe2yrn0vEVhxw/Dl97cPvWrTW4c0/hLh6BaO25lvbtFsMYUyGxb/H1R2fr6pcVuywWyxVHmkuksEVnF0Dp7p34R1az6i4Wi8XySkSkycA3mo0pCZ3zuzouJ9EGws9Fi11Rd9MS53Fy+d+KqW27E9SnAkQY+9nEyjv5z3gnXzz3kZ+4TrprI0PdZFYR6z5nl2ljI6hyaPFxTlbOn0zbnX7EhOnJNI+KTlZz4Vl/Wo3RuQIS1HeiFrG6/NXhLop1fXxhgXHD6WOYhJPm1x+HfrHLy5m/Hfniu+XTc3jdGH4hJGN9sTVDI9haxbjNSIfwlo2hWmfn5fImae3SPHobC3ZXJyqQqG7i+HRevPDm/ocRx5ef5fjys4OfJx/XrsQUwMl1QsiQmaS44XY5mDVs/uAm07q2mrOr73q2afus2GWxWK5ies6ureXsAii/fg86UElsvsVisbwy0WLzODVn4Tm86Ucg8l/cRiWESS4Rz/Eo5crAxefscmefwl06smGJdZkYiDaKkgB4/pDmsSc2v4luNjVBsPnn/WFosYoG3FDppKIVNjhXP3W+XdgWVCei8+zKQB6uzaYaIhG7dKzWTUhS11V33dN6OFc/RTPZZxGvyQOzZpJyPpdRFsZ4BaTsenT6Kzw1+9D2rlSri8prsy4kdptCZLPqd7kNxK6+Y+O6sBqdYdL/Js144eI39HIJoV4z4A4vPsGTM18H4Njy05ckfF28s6snMG+aMy8tbvEKqSiulabx5WnCSfNge7siyv24s2keu40Kh8QqyoT4bLlkMWeuQVy5/L+TrYcXaD44uy3rEukDHK2yob+t0frZul7ghTx1dvW3zYYxWiyWVxLhVBNnNI87Xtjyd4beuAeA9uPbW3XHYrFYriw2F7v81iKn2udgm11NWyVKxK679r6J79r/dsaKOy7e2ZW6bjYQAC4UxriwaCY9SulMnFpqzRLJkEZT8+jj8ORT678nVcyRpSd5fOYBllpmYvKNs18YyBnVL1C0QiMQSamZnNR0Ots/ie0crBBONZGr6ydk6yY4qdjVlxeoUtF85WsaJZOE/Rv058mV55murUm6vmblW3EOpKLgNqdJu2y0w+0NJ3PPPYazvPW4vHXOsnV9bv72C4x6M4W3T/DpiV3u+sX6Zow5DwJlyrDFenMx+sRJzfETGxzUl4vYlTHYxkiGzDWmODj/MADOwvO4p766pTVdirMrPQK7l76Is7RBfFwqcm2T2KW13jZHz+UgXlozpraprQ9P/ROPTP/zhp9ttAkTyr7xueWeWaH1rfltadf5iJe7yMb2hLqLtOhK37V8u4aBWWW6ss2yyW/R2bXROLdil8VieSURTjcvKoQRwBsvULxjnPbjS5epVRaLxfLyQkUbTCqFQGvN0dUj655yP18/ylRnjk584Spul4PU2ZVzzYOMglfCv+hqjJvfUG8tZxcEIYAmjH0OLT7O4aXHaSfpnzobNOf4yrMsNM8B0I4avfX0tb0/nC/nmKpY1ZpJZD19Doi6uNOPXHQOr4WFTRINp+Xb+8SMzUx9taiCVLFxdiXrmkvmcVq5yVe2alXZeFYiECitMjFwsKnmwGx36Oh2c9mKDERdiLfuEknddlprlN/CqZwc+HytSyVa7lL/4jQqPH//plU5s7DWPvqdXZ7X/8nm59vUdDK21/ICwkAvl04m+xw85lzV6ORcdGrT652Lm7AVZ5do98I/m03JE0+RCRBOfwi2DBHNBXQQGhHM357rcv0LU7Qemrvwgi8R63IHXi5H2wWUno1CsNOvbFkkUgr37Degs4rqRFlevO1GS4Vsnv+3o+fs6nMTbquza5POuehqjP1hjIN/n3pab5rbbjOs2GWxWK44wunWRYUwpgzddw2dJ5ZekVVtLBbLK49gqrHBu4J63GKuM8uRpUGbUjaZfxGe+mutOT47yZETvUl+KnblEzGo4JUuOoxxs8l3HPckgM0mokMlgF4OnfQb7bCViVz5/Prv9YuGm3VdvwDhOSbpUZp3aKgEwq8jOqsQbt3JtrSkef4wTE5tsL1kgjgwcUgnOH3LtcIGU/UTzDenzLLJh3JNEcUtCz3rOqD3vdOVQxxafJxadzDXUyo0SC23LSSv1xxtFJItVIW8EHIb1rEhWwxjTHOape04cxamvv4ITmNQuAhDxcKiRiYDXbVjULonZtFz3PW7v1S0tfPfXW/8ujgusR8bTU37Beg9m7uZNJHqCQWRihD1GZh/Fq3kRYmcG4WHNZqa1VXzhr9wjiNfejQLh15cNH8dHROpeODcdBYO4c4+BdU5iAPk1NT23L8qjaxvTVT3jv0jzuLhF77Ni2EzY9B2bya5RqpOTHxifTELpdW6Y5/pOVttlPQRQRN36SiNr8zQ+KfpF9Tmzeg8vULzgdl1Ds7+VmZFQy6Hs0v3bW2zlW41Tl32fT9bl/m7WoXjJ9Z/5XxYsctisVxRaKUJzzUvSewavn8fqhXReXqTLKAWi8VyFaE2qq4nwEEk944b35RuJan4BbcdSGRrcwfHbOMMX37qaZ45bfJX1euapSQHSurs8hzvwlUAN2PNDbfsW81asUtrTSuoU0gi46MIajVTdWthUTM3LzmTGi4ucXLQP2kSye13GPY19SL7vPPMMp2p1sB6Bkj2/0KTY6licE3olo56okvaXzKZeBw/oZia2srOb/JUX4gsJDXWg2JH6ujSzQXc+YPbWqFSo3HPPIh34oVXeJT6MopdmwysOO697zrGUpWGMXa7EPqmTR3ZpSONejo/D8srJhQV6DljLjSzTa8XqeApNU89rWk2dbaKrqyy0D6TfeViKz6a9lzaOf3Y43DkIqowZmht+nitQ6hv8h3L3rVqsnoM0a2itebg/Lf51uozWw7701nfDbb7qWfMv2tL5hyopunAki4XOuZJ/ym+fe40zx7UxJ0WTjOxV2aOLk3zm1tzZM3OaU6eWt/mTcNZoy4EG1cvdaqTW9rmtrHOGbR+EWf5OKK2Rjg6zz5sSNIVshESn2pBMBjeqbRcJ2plH5/HbRbEXVbaybFLf2ycyyu5zDzbwvc1Otq8XYLLl7NLD6zs0tVKHamBQgybOukuouFW7LJYLFcU8VIXHSjy+y8ujBFg+Lv3gSdofn17kj1aLBbLdvHEE0/w3ve+l/vvv5/bb7+dr351MEeM1pqPfexj3H///dx99928613v4uTJk5usLflOMnmV7QhZS8NwRFalbb1jIRFILjAZ1dpMpCYrp+lErd6NfR/Nr87Q/NrMpuuoJu6eKMn58/iTcPBog6FcGSEEwWQDfcyIX1sSvOIA79g/IsJksrNm3+I+nWKt2DVdO8HjMw/QiWtorYljODutmZrSrFRgaUVt+t31bO4eyf6VrERXJfpsBIeP9X2+tZv4cLpF/Lx5cLPhPCrVLfoEz00nOOn7sd5gnqnQaFptxYlTg+JLb6H+J/FrnRB9LoK0SuAagSTL2dUxBWS2GjK2FbTW511fo6H52oPnLzyQcsnC64XQet1BUUpz+IjmwYdgZiZx/whjqUrFQq174/GR1ed4ZPU588Ixyx8+qoki3RsDF5FHCqDRNE6KEyd7zTvrf52Tlef6RICLF7vchech2N68Z+dDVCdx5g+e99Tsd3YttXv3iKvdJSIVsxpt5JLdcFXm7ybbyiWVLKM4XT7Jk5YIESdWVllegfjoN3pf6vbsbKoZbS5YJUipOXJ0Y8en6mws2HqnH8A7u6bwwkuV12udXr72DYVTOWXGUR8b7sP5WCd+rv14vePSnKrn75dn5r/FcwuPJssl13rxQu2QmxPHmqUlODu5QQhoH5mzi16euBfV2bWFa0W0tImzec0q22GTWri1c9KKXRaL5YoimDQXt/yNFy92ucM5ym/YY8Uui8XysqPT6XD77bfzm7/5mxt+/kd/9Ed86lOf4jd/8zf5q7/6K3bt2sXP//zP02pt/iQ7dfU0vzpDM8vRkj691hs8tTavH597iDD2abc3TjC9sgJnJiUPHTrIo9Nf4bmFR1ntDOZD3Oymu1YzwkLqkglUgzAOiHVAV9UYKUwA0D1YgUkzAd1KLifhr6kFv4nY5YhB1wVAPVgFIJQ+oGk0IAg0t96mee2rBa95teK7vwuu3bfxA/0NQ1q0QjTnMheL6ncMKGXcBKdjdE3hHDp6MeXb+jcMrI8Oabc1y0vJJCtcv971T8n7PtCm6mT6ppTaRNklk7bV1Y3aceEZk0BsGDrX3wBJbzLWDGoXXOdWGMg1toFYNX3OdH3mtDkPvaqRF560HTmqeeTRrc0kxTqBUNNq9/KmpeGurmMmzPONKVpBHaXYMNm45yWiNYq5Obac4DxLDL3xcB5ss0iOpRB0u70vNBqbJz/XSftFaFxLWxEYtwPh18z2+/dfSQh7IlLqlit4pcH2J/9e60a8EJudzqkwnYnv6TmhooF+l7EiXJIoX6O7RgDIBAp5/n6r1s7Trk3Ero15icSuCyC6WzhZ1xAEmtNn9IBYP3ic139no9+dnn61ed/4SeVapSUoZcadc/nErvT3TCl67syNlzR/dH8fbGNDst+RJP/cyknwGxe1kdSZVnjV6GAD16zibO0Es/7WcjBbsctisVxRhFPmaeCliF0AIz9wA/7hKsHki/dU0WKxWC7EW97yFj70oQ/x9re/fd1nWms+85nP8N73vpe3v/3t3Hbbbfzu7/4uvu/zhS98YdN16k1cOAojZoShysLUkg+zZZbaczx70IgB/ZPZ5GNAD0zo+p0R5+OJp+CRR3suGV/V+ebkP3Ki80Ui3WG0OJEtK4QDWl9a+NgmYlc+v/7eO3W4aWVui1PXxcR4sio0Q0OCYnH9pH8zRGsJp7mIqBl7hdaKgoxwghZKKbqHVtGuQOxyzXG6hHxpmbdmjfaysAgzUwqtNdFCh/CcEUQ3lmjW2SiY6XsepIz+1RO7NpxnrhcHei9T54rYNM+N1GsFQc0TMw9uuOzFooJ2b1IbnSf/W1/nRJFxRK1rZ5JrKhXr5uc1jzy28T7NzkGrfWEXCMDaMEaNJuwzo6WfuMKEMS6353hi9sGsq9cLK6nYpfG8njgyeKb3xKq1G9qoyeveEj0h4FsPG1G00dA89kTiMNlgJUJr1M5Xob0CldPTnP7Cl2jVL+ziW1jQPP3MNszK+1bhLDyH01pCthWyGRHJVOwqbvhVtWZ/Oh3N7Oz6hwUXSmCevt8Tu8zBc1UqvInkc03rcEzz2QiiGCcHQ9d3oFsd2I/UmfjVr/X6qLFG9x/Yj645biK/uQQgWxGqG19yuGkzqPHA6b+9ZMF6UzE+JclrqN0NEihuwvS0KS5S6++btaLhBr8LG4YxXmAoOsnvlnGGSbP8ZXR29T+82ZKz6xLCGI8eM9V5N0WveaE1zsoJ3OmH+xqwBReoVKxWNfXZOUR1ks3qomxYlGUTrNhlsViuKMLJJqLgkNtXvqTvj//YjSCg9vdnt7dhFovFcpmYmZlheXmZ+++/P3svn8/zhje8gWeeeWbT7+mNqjFqI4Kg4dAROPhc/2e9fwgh+tMtDSDEGsfMRRLFIFXEkLOTvfl7uHXnPezN38O1udeyL9cTuxwhEHV9SVXw+t0yormI6iau4DysjQLKJubaIe2EPbs1Q0ODnztOIvysE3Q2cgsMJgPWaJz2Mk53Fa0lshEixhzICyMoyVQUOv9NvPIlVM7hzD+XOVXWHh+lQEiFUiCrAf7x2uBy/QYXregzVIHG7Hc2aR90dkXR+n3uX6GIu3jH/jF7HQaa5w/rAbeYEKYyY1qQIH1fZRs9bxecl7OTmsWl3gqOfeFBzk4mbQu3ls/n698w/60lFbucxKVx6Aikxkp3+hG8419c953uheorpM6eNWMo6NOAUs0hzdmVLqMSjWyt2NWbCCojqsjsYJ6/LefJ7bUuzx2DAvTyMlmbm801E/CBXD4OeCW6dR+hFX79wsfk+cNQWb2UgMk19O2X6BiLYv3xiObXF5DV0xD7FLo5RGuD/V9zvXviqV7+sKFwltH28QEhZCNn15GjOnMQxn3pDD3ZZkf7WXP6JSdpmkJMdU0YqsgJhAdO/dzAfqxWzbbKIxEr1S4rFc38Qm/dco2gk/4mCHdzCaD5tRka/3yOSz0R674JRz5bPXqJ1qH1AvyGn1/Muje49g04/dBrNHvjULw0scs152UidmnFZXV2Rb7K2rbhA650uVAyN68vSeyamVKI7uYPtNaHMSbXNSW5mHGkY83sLKyePmPy5m3iNr2YAp3ehRexWCyWlw/hZJP8DSMI59Jue3J7hyh/115qf3OaPR+465LXY7FYLC8Wy8smN9POnTsH3t+1axdzc5snLF5dkdw0MUFUNnb/iYkJdDhMOFSkFBYoFPL4QZmJiQK6OkUp76GcAgyVGB8bp1wexnE1ExN5isXetTKKFKWhJiWnSLlsbiXHx8aZGO0JVf3b7Kc01KUtlymVi4wOjXHD0F3cNqZZOHSK0Ztm2FmZw7nuJ4jKS0gnYOSQx+j9I4wUx8/bR9r10dXeQxAxNooYMdtW575OWFGUy+9g506HRsPsU8pQvUTolImHRihGJYKowP79ZcbGxyhXytl+VGuScjlmbCyP6/b6Y7g+jPIiqjVFq1Vm4pYJSqUShAXGRkeJRoZoL9Qo5/II7TBcHmYoVyRXMhnxcznBaKmMKJc5Hk4z3znM22798YH9m5iYQHYiKl89g6ocpZj3KHkFyuUyY6MuExO9W/pyOaKQy1MqeeRyAqfgMjExQbNl2j/St7zMBxQLRYqyyOjICLmxIUrFOit+larToI3LULlMoSAol8uUyw4TEzmkkpTLZdxCl6GhIcpl00/CCdDl3nHQDFEoFGi2yuzbKQlEh/HxcWZrp5itT/JDd/5ryo0ygeigu0VQBRgqIkrldWNnKzz8qFFcyjeaNhQKEXGcp1zOgaziTNw5sPzwcES5rBgb85iYcJP+C5I+Lwws67sNys0yea/IxMQE5XKAI33G/Rm0CGBoCCdp89hYQByD43qUyw6nz8QcODDE8Pg45XKYrV8rafprZJiyn/bbKFPnNOUylIqCobJgYiLHaHOUyDHqWcErMqRGcIsFikVBoVAABdEDy0wcjqmNepRuKjFUHmU4DvHKkvHRcXITJbOFaJRyVGZ0dLTXz8NNIt9laGSU4YkJtFaUyxHDww6jI4JyWVKIC5TLOYolj4IoUCwVKOfLeJ7L6KigXI5pd2B+waFcVklfmPNFDQ0hxkbRBXMd8APN6HDpgsc5PR6O6E1yh4fFwDl8PsKk8sT42DjukEmapYaHIXLpFADZpN1aZDSoMnzmJoK2S/yaglEZh4agUKBQLGTtnJiYIJcLyOVgfDzPXv+fkc/65F9/K6PjE5TLMeVyr31p+2t1GO6UGE7aUyx6jDjDjBZq5At5cjKHdvKUy2VyUYlCwQhSoi3IlwWloRyyOLgfU9MRu3Zqlrx/YnquyujX/gW5m3Zw816HxSXFyEieQqF3rWqVYtxyiFvOZfuTjUHASX4vpJI09Ar7SgU8x8vG9UZEMsJPK9IKwbAqU+6WcfKCsZmHELtvQ+x7zZaOFUC3Ak65V6W3ODzC6MQEcaz5p6+EfOeBMnvKZYLIZbSvXap/HyJNLtfb77GxmEKhQLHkZPs9OjKKm5yLXV2iPDREW7mMj4+jtKJcLjOUH7wO6dIQhXxMoZCnXB7acOyOVkbp+i7FUoESecpDZdyxcYKyOe4Xc11reYsUCuK83wkXWxQKBYSA0aERihOj2WeNhrnmA5SCHK1WgeHyUDLGYGwsx/Dwhb1Po2dPIloBE+967cD76dgeHfUYVkOgJGJsFIbGszElxsbQrTJidBRxgX1vlWMKpQ75Qp6iUHjlMnGhQLGYY6Tv2inyebwod8F2gxW7LBbLFUYw1SR/4NJCGFN2/MytnPvAN2l+fZbRt12/TS2zWCyWy8vaXEEXCpHqNCXVapV220xEqtUqTr1Oq92mG3XwfZ+206Za7eAd+ya+7xPIANVu0Ww0abVa+AHUau2BCVO9oWm3u3Q9n3bbvN9oNCjIXoxb/zYBpmonqPsVJmtlKtEpclVB4A/R1m0WjtQJggA1M0/7NTnipM3dwCcIAirVFeLi+fdVNOu47V4OHlmtomMz2fTabdotTTtoE4xCowFPnDoMwC07X0uj2aAdtAmbLbrdDoEMkLK1ru+aTU27DZVKe2Ai1Ww2aQdtjp/QrOSaHHj2aUIC9I6AeqPBwcrXzHqCDo5wqB9bZqjlEsqQCHCCmFqlSq7d4mh9Gl0aYen4HO1HFhj5gevZdf0eqtUq8apPu93GqfmgYrpRh3a7TaMJ1Wrf8VmVBEFAsxlQKAiEL3CrVRoN0/5Go7f8yrkF/K6P13VpNBq4Xki73WG+e4hK06O85OJ5kq7v0nbb2XdjFdFutykEEZ12O+snla/j9B2HjtNM2tJhuNmk3W1Tq9U4OX8ErTWV1Yrpf7+N8H2cIEC1O2hVyMbOhse7chohA7RXRO+42bwZh7RbHgiRjYVioFBC0/IB/xxyfHCd6TGtVo2wZMauTo75YLLk1cYq7Xab2FPJ2NCMdw7RPDuVnZtx0mYpzXoXFmBmBpyGZvpck5Hr5mm3Tajc6dNtVisRd8g2WjdpJ4LB1HSVdttM5IQwbatWBc1mk47fMUn38y5xo8mQ79NqaYIggEDQbrfwg4DSZJfo+g6VSgN3pc7M0RZ33L1K3jV2xXqjbsZOo0FVmDY3G01kOyCuu0RVj1rd7EM+B64Lh1YeolAOaLdDOr5LEAR0dJtC1DZ5ogSkh/7Y8V6/ray0yefMeagaTURz0VxrAghPPkB16Huh2Jugp8fl2HF43b29dTpOzzHluYNj/nz4ieWsulrFDUy/uq0WIvZptXy8nGC1VUOoiMAPCIKAKAgQSiLbLdwgoN1pU61WjeidHHuA1dU2QTtARxErh+fpvNah3TaGzrR96bIAbrdLLmnPykpAs9AgbFXwA59QRURa0G63aQVddJDYuwLIFRy0H+GHXrYfoQw4Pl1h9/A+6p0likdjIjXHm37UZarSot0eZ2WlzdBQr586KzXCdhtHuTjp+RW28ZJODhdXaLfbTFWPUzlR4zVhl+uKe7JxvRFPzDy4YciiE7m02z60nyEuXrulYwUQNBp0221E0UUHkrAhkNUi3a4Zj0eONHB3tjhxyuH63auMjpj9S/fhf335j4lWb+Gn33YXhYJANkIa1WS8dsiOY6NWx0++0+l2WFluc3rB5YCosv+Aub6pkIHrULvdIeia863d1hteozqdLp1um2qjAqFPu2Uuuu22OffOd13rZ25ec/rZNjffBN7q6qa5AiuTVYIgQAC11SqF0Z6tMj2HAUb8LlIFtFqSdqdNGJprSxRtEuCudbbNsGKc0at97dC6t+5aHcZaLdAKWauifZ0dD1mv4bbbqEITdYF9767WCcMuYRjiI6HRMpbRbky0Ws22l/PDns34AtgwRovFcsWgtSacal5yvq6UsR85QO66MiufOLzlctIWi8XyUrF7924AVlZWBt6vVCrs2rVr0+/JDZKTk+YhUaoXirhBqff+MMa1HwtMWF7/++uTjg9yunKYlfYCgerlS3SFmXiGa+9ZtcYpuSZnF4NVI3Xk0w42SkqjkZ3+5NiD+56GLuZy5t/TtZNMrp6g0dBZ/jClNTt3wC03pyGLgzueGoHPm0s++dCddHFWPIRSREmOGYXCQSCPJa9ds1KtQbUipr9axTtithlOm36S9V7oSJaYOumXtGrmuhxkSSiL78Ohwxq/0wtd7Wf5UJP6gys49b5QEa2J+3L1SCWTvF1qYN8HQksHooEGOyeN3pGyL08UwuS1Abqnquhza8Nj1oeFKq2IZLKcX8ddPoZTncRdOgrdGsgI58Q/c33ti+SDKURzDmfxUG9NXvG8MTtrixZsuEySOy5tO0DsDG242vRs8P1el3x77gQPnurl2HvqGZia1Ml+9votX+gTR/oEHo1mvLiLfSMHiHW8ac4upcz2HdeEMU6e1dQbRuQEcGafQjQX1zd6k2TQZp0aX1U4sD+p5Jrk7EqP6flupVRfQm8tHNSuW9DJuY8Gp7m+muuJk2YC3T83vpQaDgP0tTFalawuK+YXYKnZpBLWKLlFxCZTY7lBZT7Y4NKZvO50odEc/HCsc4RC3LuGhyGAIqda6DQHonAQDZ94Zc15VEjCkNNYauDM6mFONR5FeTVc10RhC6BeW+TQ6oNUo7PrxrUOk+OWrD6UAdPVY73PuxKlJK2wgWir7PyLFjpZzty1hNJnx9Ae7tp7H3tHbujrC0mzaYqcVKsXca+dLDry1utwyrmsU9Pk/kpqoggEimiDyLpGAyrRKXPuaU3zwVn0c+vHu14TC5eGlrbbveT068IYFaB1Fm7un6ity5PlYI7RbOMsNX81+c6FB6/Siunayezamhi6TSjyeeL24naM9hxz+Y423o7oRrBoRNb+EP/znbdffQAOH9k8fnB9JPv6MMYLbqR/Ga3QUuE4fb95vYvfZs24IFbsslgsVwxyNUA1oxfs7BI5h92/8hrajy3S/MrMNrXOYrFYLg/XX389u3fv5tvf/nb2XhiGPPHEE7zuda/b9HubiV0ml0gvKXa7vrbc9+Cd5EaTTI264D2sH3U4s3p08Hv9ubSSAIN43QNaDY6TiQqx7C1w4tlP8vhz/4P22VX8k7XsRlo2I+qPhfjTacUpsx3f19Rq5gm05xqHStqE+QX40sNnWK70hBwvpymVjJyHjHGWjiICM8lLNY7z7bfoy5HjLOZwKifJzz+b7ZYQfYnaXQGuWd/MuWmmO/M4yyY5+tJCksOrP9Q+E7uS1clBASolCDp0ZY1a3ax7tWq+u1a8PPRUxNTZAKc6CVGHNGdXJGPc/n3tm8hobVw3Cwv9G+0TGddO6pLXUg6Koak42j1UQRxJQpbWJBVrNDRffQCOHdMcXnycb06aXGDCN6pNvP/NZv/nDvP0F57NqtDN1D9ligPICI2mnb8eNbRzXdXD/r5QEkT1rMk3llbgW5P/Jq3Y1++w0GLjIJl0vb7fy6FVV3Vm1t1yJMeQniqRTsL3XmPGaypYGKcFeI5HrKJE1OorFKFhvjlFILsIAa5rRIF0Mu46Zked5gLuygnWnue9NHPrB7h8YJIdJ/ramAh/mQgqN8+pJSW9cSEEemQf1X1vz/pJe8aBOTWlOXxEI6XOhkJ77aUp2/4mGzsfKh3DmtYzPq3nzD7MdU2OKaHEwPn2xom7eN3eN5kcc2QnwqbtWDvH7z/WruwwEpylEPfUO520xVFB9hqtKRyZJ5wczInmFITp4LCDWDVFL1wnh9ZQDWZwHZDJMu1qHccxlW7jdWJXplYDcHz5GU4vP0M9MrnTVDemGyedHvfy6LUfW6Tz7ODDlhSpYsq5EXaXr+W60Zt621KKZtOIeufWjHutNf7x2sbiTJrHToj0yUr/2yh1/kIAjuhdF3WQiFbV5BrTv3zftVrrvrReuq9oydr8jOn/tDlPph9apX1k0K3kOG62nXpQTZZPruf1WZzlE+sbDUzXTnCqcoj5pjm+YaL5eB7rErUHZxrU/v4sWipUjFECBZvm7CocnsE510FpkYh167tjI+bmGRDzVN/xWj/2+w7KgBCWvtj8gZh77jG8419C+wEj4ZneojLOGnqp3gQbxmixWK4Y0gqKhRfo7ALY8W9uo/I/TzD3nx5n6PW78XZsXIHHYrFYXgza7TbT09PZ65mZGY4ePcrY2BjXXnst7373u/nkJz/JjTfeyIEDB/jkJz9JsVjkHe94x6brjIONEtSbaoxaK4SKKAfTPP74Dt40MXjnKpXa8Ea40lmkHeQZbx9huRwBm+fNmaqfoDOr2V+6BmfpMGrHq4i1T3lO4g4Jk8iXwae0OnnCi1KZ2CX7nEYLfgUH6DyzRL4xz7DsEN/xo6gkSa+s9264ldJ889twfTIX8UYGk8x3u7AaHmRfd4jyaCJ2pe1Ao8MGIvahafKiecrHkzFKDa/r0xQhdfYoWfjmjt0VLqRCCSKb+GgX0gTjcbcn6C1/TTLU6DJ6ux6YH/QmHamzy7zOt6Yg2guOizN/kDOVReK4xW75ekBkiYvTKU5/vvBcbBJ1i6iTvSdVT+xaO8lQCh59HCKlKN5M30IkE9PBWZlKjl0se4KYNqpf0hbVS0yfqXjmT6MBQsfImWOstB4xbqW5ZxB+A+14UBpHjV5HuLRMXnaYq4zQKO4ljp8Fwmxbfm4Pjz/V4KZhza7bQXQqoDW6vCvbNynBWT6ZtEKi8QjDZKKJqdCoN5xhbzwDSwUo34cxOZUtla4jVB00iiEtBvoJzPngVNoc2K85HQ/3ReuYTvbcHLGM8JITJ+qruLDaWSLtXtdTrFQg39TpiBmoSCmqU7Dr7t6enGc2qTXkmprcszHRvR4qS1CvszZvVmxNKjhVOUSrdhgpWtA9x2RN48SHmVqEXRMdRGeap583MvDZrplkA0wd3lhsLwHerDCZ3LViuLyH23fdu2n7+5qaiRxOaP66Kk8M3JS/lhnd24kRb4i8HMfxXVRxY2dXP11/0KU6NGQEEXehQcldWjfrTvfLSGlJWxKBY504nBfZ6eEsHYX9t+AKD60h0h0j4ntmJ1U7ximDEC5nz0J9R6/d8axEtzS4GlXVhDKEOEC7wyAcwqlm9kBCxHrg4cRmSC2z4glFbyh7X2uFnxQtCNc4sKK5Nv6xKjqUlO4azEXZ2+lE8ErdnSrtG43SUFWrRI1T7Np1K2CqBpdKgrysATuQKinokazL9E6PWlUTNzUjI0ZRE/3XuWy/14hdfeJ/vQGLSxCflry6dxplCerNZpMLqFYQthHtJcRKDXbfli0fBJpOB6LkNyJ1lfm9tGVopQekouBUzbwfJRWVBWjXgU2qMXqBif+LnDEgeQqC2FTt6r8WzJzpCa/9wtfmp4Me+B0QfUL3ZohOhe5UTDx/GkcHpnqlpmfJXit2OVtXvqzYZbFYrhjCyaSa1jaIXcJzuOG/fjenf+rLTL/vG9z0P38AkbNmV4vF8tJw6NAh3v3ud2evP/KRjwDwEz/xE/zO7/wO73nPewiCgN/6rd+iXq9zzz338Cd/8icMDw9vtkpkuMENYersUopSNMdK9wRR8dX4Q/T5/bVxbGwwtz+2/Axua4hStIAjW8AOgI0rJmrIPxITF59CRDEi6hBrn/E5hecI8iMSdmu00hSjhd62tEZL3RO7Ygkywj39td6qNYhuFSj0NrZmP+NBcwSeByWTn5tOh2wi1vV7YYyi/yY6EQbSEvfDs19jbwOU+tH1+5oSq0z/E6FjJrtC4B05CpQQfX2lnN5kw+vLm+ItKigk7gWlCWKfxdYM4/FYsmKHZlPTiiRChZRWDyEa0ohdrUV0awrYSez0JjRaKrTu/ca1wgaSmKIykyDteInQqImkJNEhBx7SLwQHGVV3Y6Zw/a6I/pDGwXGQTpqkGrS9OKQhqhqCJqLV6E2G0v5RkI+rDMsplsMWCIHwa2aRcRMupa69F39IM1/pJTCvyTuQezXOwnOARgkPKR2WlxS7AHf6UXOo7vjRzDU1EO6lTRxgFIFoLxMcf4xvrXwf5ZticPqqRmb7yDr6xa6h9mHaa8bpqe4/AXB38V8M9GG7ozlyVjN8conAAff2YXwfTp/RzK2E7N8n8ZwkyXriror7JoTm/VTsSo5SqiarnqipAW+6Saxr8IaBpm08AdbgEOMsATgI2QWKWbt7Vf/WT2ilhLnGJAUVURQe2vHwHBC4hD5Ulx12j3lZmPDigjE9ep4J4XI3mCMLZZxq7vIxup0Kc3tu47aRW/CmHkYe+C4ojCBaS2ivV2QgFYdT55oWCqU1QagZikcoquK6kKfm1+YonCmj3jY4frN19r116pQm6CsUriREC3Vyk6vkwwX0qxRiyBn8rlIIrQjdESI9wlA9Ip+HuNtnaUp6NXOdZddlhdIQ6xDXTd+X6EgmRh+HyqqpZJlSmJaIEBCaqadiAucc5VINZ9iEw0dLXcSePNRAtBRy6Pyigkqq+7rJBSPv9h4cay0JEsFfLleQjVHcUXNx1FFyDDZwEWoZEUQdHjzzd9wZ3sno0I61S9COupyWJxmuFrmbW1lY1FRPw/4bNEPdKVadHShJryKxTl23vcF0/LjGXYK70pzrafdqUEGMe0qib1t7Pev1f3q5Cnwz/hvhCo5wcPpFHWUc0CIVvGCdS+uJJ41QeuO9aTPM9/0Aitk21/ZTbxsyNi91ziNubFwx0QiAEGLELiEU4G4aGtz//vHjOrt6qU2cY3rdqws/EFhLcE5C3iE1uXY7Gi+ISdPQDzx0cUDtiNetYyOs2GWxWK4YwqkmuIL8dZtP7i6G0t07uf73votz/89vMfvvH+Ha//wmnOLlKw9ssVgsm3Hfffdx/PjxTT8XQvCBD3yAD3zgA1tep+x3o5BOLEw5dRH7CBVQ13Xi4Dmgr8qSHnyi33+TGasQrXLk1jxq1Zhwwdk5ePWdvTYD1JZWWZz1KOQEuu8R//DJFl5Yw71ugaFwHnB7N/YKhJs4v1SMCBpJGfN0e2sTFW1N7Nq104giZyd7t+C+rwjCpF+yCY9Gx8mj9UTsSj9bNz/r1gDjchOx7pndNCgfXFxyx4zKJnAyZxlJeIpSIML1xQe0FpxdPkpVmMTob9Lfl3YstRrIgspkJ6EidJIgSygzoW7oCnlMvjdinc21tIbHz32NuaDIzWv7USfOLre3r+n3auoMfnwLUEajcBbSUJ+eMLre2WVeR5Gm1VZJ2KZmtQqFIdPntBZxGjVUeXevEUAswdERCFA7bwGviLz5rQPrn22cxe8Wgb0onbgihJPlQtOAErnBGKU+PH8ZT5aQcrjnyEv6NIpABCt0fRiK5qnVJbmJNeLegOzXt9/Jm3G8Ztpn5pgZAiMWyOQ4zMzCAdd8w4QiGnPDmbPgrzyHpwXe+KvMsp2DtOUk5XgnOCB0z7knBBQKGr+vMVprE64KyD2vxvva46hGFd4AZ1aPQKvNDnZtqN5pYCiYwWkIRK3ASGeFrndXtvf6wUmC20rg7V33XRlrpIrY5e5kder13PGm/RTGoTJ2gJ3tZxHidm7ZfQvLfQb7/TfAbbfCyorJ3TU5NbjOUhHu3SdwWx1mdY6jnVXi1gI5FeFUTqGufR3uzBNrdqIn+tWjJqe652ipJIn8Yg5dWF8EBDRCOEno9yZi1yYWl8rJM/inD+Ho23F0jD4WweuSypTJ6nSinignj3RKoAKKBXPIZKxxPYFUkiflMV4tXwXkeqJm8kBCEZB3Mc4koVBK4iZiF8Dbvq/XpkZHo2MTCnjKP0u9dY7h64JemKbWkBQj8c6C7ADnKaQolbnIps4uIUSS71ETRjGxzCEEDH/jIVbnrmXPT+xFT9zE+QQQb/LbdJd8IEfVX2Y0eaDSc6Rqnmg9R/+bnSRxue/3ruFKgfJN+5SGfEOj9gwKUcVoEdQuOl1NFPQ+6h6r4k0p9JiEXmTmhmJXvab52oMaff03KZ5TjIcTfd/R+B1NSUnQqSM3+URphCPoJj8zMnNorv0toJevkTWLaCOqgkCNl4iWG+hYITyHhx/Rpj90TCFaJnaGkJiTTCQXos2OwsCQ7nOO9odJZs5EHSNOfgu9N0lon6VJ6LVx/UrXI3LCOLoiDQJWKqAcyYHiBl9XoAvnX1+KtTFYLJYrhnCqSf668rY6sMb/1c3s+X/dQ/V/n+bUj36BzsGNcxJYLBbLlYaUeiAnykprnq/NPkCkJVprPGkSvTs6NI4jNEprqjWFVHJdXhStTTJ3LWPWJjzXWnHwH2us/u1ZpC8haOIGJg9MoxvjCY9uR+F1+m9QHUQn7D3dz/QzjZYK1/PQaJaaM4kA1+8mMv9uHoyI5lrrJwNarxO7SkWJ5wle8xrYtQuu2Q17dkMQK06cNN/pmSc0InF2iaiLe+Kf1hqPDFEHp3ICp2ZCUIVUA+1UvhF0lNZ0uqnooBj9sZsQrjBOIQXa77cWASjq3SaPHpokCEzHxAtpAiOR/F8hUGbSEfdERCc2ny/LKSLdMYJVvD4sNVQttM4SQmV/YiWzCcLMTG+u4ziD4T3eKfPdoyvPshqasSTWTWh6yx87YY5hEGpmZgTT03C6cgip5aDI0OfscnRkxlki5K0NtTu+/CyHlx7JXsfaz5bTWqBRSTL0NAnZoBg3UnmcvY2H+udziKTNUoHOlxFAPq5n2+53MW4WmJNNaEkno9kn6xbUGmSSm0b0LSMw7qXUdeboCCEE5fworuPSjJZYUossBCt9qzaOQEfA0FASjtbv7IqTGX2umGzerHyyepy52tnBJvaf36o3uRdJgQgT7qmyRbuLVQLVWNcXYRyjtaLb8ggjweys2bdu/jo0gtCXBMHgd/bsNsLJ7t2C0dF1q+wdr8IwOceDOCASRlStTK0wPb3BRDhzwGm6MkAgKCSTfxcX5WsjRrtkTiWzn2wqdk2f+zpL8QbJ/oFcdxECjaMiROLCIzbHpqcJaISWGNnaQShNoWA6tBWNoMu7ie/s4pd8nmue4GRrKhOQlZIm0ktH5vRQkC+a64/jGofQ6Ai4rsj+E1rjuALPE9y0t524w7RxaCkjnuls1zVydf1+DXRpVrSh5595683/imtHbyTwTX8NJ263pVNzuItH0lWbvhWC2t+fpfPMcvZ9rcGJfONWEpo4jpipn+4L+9R9+eX6FHwYeGARx33XVQ17j80zfLaXQzIX1RgK5xD1Wc7NmMqppl9758XaJxu9VJcaP+4yGzyBL2t0VIWVCnhnFbVTEX7HPPyJY8mZMzA30/dwSEG86lP//CSdgysm8b7WxJ0WxD4bsklG9m5LEUsjOKnhPEpqVMuEQ7Y7cM3qV7i29lUEmiDXC9sWaczmJnpRv9PVjXsnZ7+zK11XLq7TrLRZTE4D1ZWobu/HN44kR49pWs3zh8QKj+TE6L0XJmJlYjqmLZeJVGeLXjGDFbssFssVQzD5wisxbsQ1H7yHm/7XD6DaEaf/5ReZ+z+f2LSiicVisVwpKMlAEt7pVZOUelF2MFOhZDKsNatxDV8GNJuwtKRZWk7CL5o+z/3ZP/Ct579ApIygImWMQiMVNFu9SYYzYya6sitxKqdwOibxcxj1bnydvptoRzjoYg7VZ3UxT87NpMvxXGZm4OiZCqH0B0Pnkn9Hq4p4pbMuebuzcpJoTRjnUMFMAvZeI9h7jWDPHsHuXbBvbzphV4CCwDy+1zKpXhU0ESruqwbW2wnRmEvanQhQcd+jf6DR6qDQBIGp0OZ3jW8onSiUhkxdrzgwoXPxXkVcMMucWDyG34GFRYk7qYiXk7DKJLxToDKxJM0JBiBk4ohzkqakYtca8VLo2OTEUsYBYPZNobTM9lVj3DXmeJFVRNRaQZ+YON1JZoprnV1rEj0rDUGoAUFU0ZmI6eAMuq/CFoXqERwdmXleKnb19W2c5bjpba8anzHvKdBO3jgfhIveROzq5QGib/tq3aJ5WcuSyA+GbQ6KvilSQc4zn8uBTQ6KtmgjKvuyN8nNxF8BwtGZWKaTPDujTom33PQvub38I7i4+GmVyr7jKxwoldLJtcreRyvjAEycOOg1inDkQ3O5t3y6/51zvRdJ/iDRF66kFZytPc/pbi/UOFuljABNHLogHHO8UuFYuITherGrrAOUL+kcXCF6ZHbdOnuaqyIvcojYJ4y7zM3D/FzE8ZPrvtILX5SaSEd4To6iMAFarnCR7cSZUhB4mdhlwqkVRgxaK7ZOnXuAObm+0FHqjNS+Ji9rCJ2cn6FxrRjh2DisTM6uVOwSlErmvYXGMNrNo/Ia7TjErkuoIqJkQMhkfyQhriO4di9cd51CSYXrCO643eE7Xre2E0waDwBXdROXaeISU7BUP0t79XmzoO4bi5uQ5lMMuoOSghCCTleiY40Tb5ATN11tMg7C6RbN2fm+whc6icBWzNTPcGLlOar+SvZZdkqlx0T3XZOTdUpJlpBfKXBVB6cjs4HtqDT5eV9eqHQ8xxF0KuuuFxoyoa0dmt+7brRCpNqEIUSxZr5SZ/Jsjm67RKdj+lE1lxCxuX4rXyNr5pyNV3xyHpSiOcTsM7hLR9FhkyDuDjyAQJlQ5tlZzbFjmjCGWk3zyKOa2cU6udZDjIYnzeUoVtkcxtFR9juvEZA4GUVfEv+NSId5IVrhmupDeNLsa/vb8+uqT6aVW9JiEo2vzND68951oNU2D55m5zbe1kDnagX53iOEtNhA2qDZ1pc50/57cwzE+cdmto4tLWWxWCwvA8LJJvkbN3jEtw0Mf8+13PrVH2fnz99J5U+Ocuod/8jq/zqBbK8rE3bR+HGHr5z633z24MdMqIDFYrG8CMRq0NmFArSmLbtoJ49MJu8CyZHOcVRpzFRV05o4EY/chk8ofeScn4WsmFxBmjBQTE6ZSZfWGp3YotLQkZQoLbeoVU+ZiDoU4pqxrvS5KMz9biLCJZOyatVUZAwjTa2u8QPdP91BRzKrTJgiVATN+YEZe95dn+PDcQTDIz3BrnA8oPCt2IgwfRO9sE9MUnFMEJiqiUFQZ67aoRskAlQ8WKXy+cWTdH0jaFznXJ+EL6lsklEoAq4gDDXa00S3Qne3g0bjUkAYDQynqfuEnmTCojWuMMKZ6KtYWQpWETomdno+oXCyiWqZCdb8ggnbHPVPgY6p12G1YiZlcQyKKBO7oM9Z5PScHBqFyOaXGpk5xCQq1Mh2OpnqTV61lihlEjILHHYciTORzhUOwm9my7orJ8k3zuKqIBFDkxCgvs71k5C8/glbM17I2hxP3EyjcAPKKaLXVHoECEOdJX/vdzI49IS59GC6ykdl43/NBHiDOZdWkMuBIF6TD2ywDbH2acddgkRIFgJEUkLPEcCzC4lzwwhGAvBOfw0SV8/wTImgGfXWnTggBFAsSvZ3vkkumZRrqUErqmGTc0nFN1RvYowGZ/ko8tgxan9/FhUqRvzTCBWSC3qudxFLMwK16jm7Ntb8AAjiCLRGRSZsanaul7BcCxctFfXEEHbv3fC6e6HzzXmaD80STjbRzfV5iLLTXSnj7JIBkQxMZdekId2u5vlDmjjJD9V+eN7sq9LEKibneLhJRh+Bg2xpnOTccp1e0joHgdSKub85xFc++2eEfe4bIyAK4tSBmOA6ydGKNEW9jKcSofp4hJ6OyfKua52EzRr5UCjIexKTitFBSoFyUktTkv8qEZjSc0sn1/GhAkzWjmRi7I4dkMv1hIP0Gik8857j+6RVdWsNxcqKZmX5eZYaxxOn6Hpxb+1rpSW1uuaZZz2Wl3ufOTgEgUJMxhQbu4idErlcbx/63bzmYNWYeeQpJp86Q6WiCeN033RfkYvkYYs2scF58qAVUsdGMGwo4uWkLKjWRHF/UYze8VKtJtVnVxDp/b1I86P1PUypT+METZxTdRpfOYesh8lvQu+Ypf3vaEE+mkWH3WwVRWccBw+Z7Ec+pxFNc21qPB31zjmp8DzwVCfLezddPcZTx75O/ujCQL+fOQtHjsG5WahUIAhNW5aC51lmnlw8g1LQfnyJ+hen1l2YNE5P7EoFfQC/jmgtDR7XpHl5WQcFOdnK+k82w+zfA+tPX4eDJVSz6+kmwloYao4c1XQaGrcsELf3ws6jQNJu6+y6NhqcZKx71FywN893v2b7FovFcgUgawGyGpA/sP3OrhR3OMe1/+cb2P+HbwEHZv/Doxz/7r9h+ZOHByy5F8NcY5L3/cMP8rvf+AB//NR/5pf/7vv57MGPbXPLLRaLZT1KMiAC9SoBCrSb61syeYpe3oMGXF9DJzK3/zkzQRVdnTlppIzQTdVzkqjkiXgqdrUHRQF5wkwScuEKo/5J0CBvnGQ4nDMTz0ZftScFspo4qjzBLkbZ0TpIt93h2HFFo2mS+Q6ENIZyQNRrPBnSORETR+AlCdiLRdizM6bSWczcSSn5rCs0TsXso5m79tYZqLAnAMUR3/gWfOOb8M/HH6ZeJ3MkCDUoxInA4dysRnaLFEQRtPFxpDf+pRLgQRRJlNDszO2lu1sQ3LoDrR08ndz4D0wUTD/vnJAMlxNxrX+fYkExWiJyE8cGEC10EA8+SymcRaOYP5dO2E2Sa1OQQCNjI2Y6fROJ/jDGc52nUDpGy77fRA3dOEC2FfVvNql9O6T+eEStrul0zJdX4zMEukkUmSf9aRLmVNh0hWMqX0KSxMyE4XqyiSTXt6leR3SiVrZ4bzyYv0EAyinSKKVVz/qyTycsLJqXntcTu06d1llnm+Fr2qK1RsTNZAv9s7z1Eo/WZolhltnRPrhG7NID358Pn+PR2nPUE0EKASROSOEIwmXTJ67yzXmY7YdEh5LiShHnWCrMgOeYCn1CgPZ9dp9dZrgzmbUL4EznnNmQAMImOnVeZqFhSeNWVxg9d5jh1YMDwq8nPeNO0tIIK0qT1h8QUW+5ic5zTLQPEiZit1Ae6dRzcSltsovQklOnYaysKJ9bYcdw0v9JCJoQUHz0LAB33A4339TXRi3JOzmEjAjjLnFfX1dWQZ2IskIUACqUBKfrhCrGc7ysUIJGoXydJRfPJWF5WT4kpVjtLiOWJN+a+lIWLiuSI9KhSzFaJBfXescx7dP+Sy1AReIkl+JcZw5Pto2zS7gIJXCJKBZBC0EsHaQjTXhw0rZYps4ulfVP//EbcF9mOy5715DkIULwZCNzmU7PC+bn6Cmlve4dZE24eKwi4tiEMa5Wk2qpnQoIgdYSJ9Q4noDdtxIW9vTOv+Sikv0+Jb8tjgpYrUCzrUAIJHJdVV6lA+IY8qIASprfJa3QpyJaR+JkPzVBFPXy1SkAB6Qgnlzi8f+9gAjX3Nenv2fp9RAQTY2qLBOcrpCd+YlIKZN9Ecqh3D2G0+kThLUZGSoJDXXdgS31con5Es81oa6p2BXGPupoG6eRhmSzLowxnwMvKUrg4uHloUbNiHmhSVPgNJPvZwUZnJ6rN9tZ8Ca/tS6/XRYdKtzkEtj3exv0OUUxD1rU8QhZSc7XNQ8DnKToi1qbaiCh3THX36V58HY4qEKRpZE3A1AOpmk2zbaOHuv70kWIXTZBvcViuSLwT5kcEcVbxi77tsZ+5ACjP7yfztMrLH/sORb+81Os/I8jXPOr9zLxf7wK4W7tOUGls8ivfumnCGSX3/nBz3Hrzrv5xGO/yR8/9Z/ZM3wd3/+qn7zMe2KxWF7JaA3Nh+ayfEhCJVNlIUyI1xpETTB0sIzXauB2Kuhbk3w/GmhrKtUQKTXBSoP4pKZ0AwQ3pPf+Cu2Y9et54xrJHDCBwG076JEujnLRKGTRTGa82RpyrJ21IY6h880FRL6EcB2GZZtFwG+1stttJdOJu3kir6MY7fZuwOdOK0olBbcK9jYe4jWvNg6u0JMcnH+Ycr7voUnYSsIt9wMaIaRZp1QDwogvQ0bEkNlG3AufW6yE5AGRzgr77/MF6K4gHo4pJqGaSpoJUJrfxHPByQmiWLHS0lRP7US7s8QTRZyzHXJ6BzCY5Tz9p4PCEanY1XN2OUkYo8m9o3pROs0znK19gbGdH8STps99HST9ppI8VxrFGrErmRM6DvgqoBpPUlIjiR9FJOFGmripUd3e7PjcOZjLJ+6HUJNraU7jEBR6E3elTL72dEKb7qEWLkppcqqFpDdW+0XOUPYmg5IQj0I2SZ2cNsUIymlYUipq9CXQ6nbNJLRUhNVEJel2AS8RaVWvs0/J47T9cUrsHGjDYF1Ksn0C2Nl8nGYE0gXH7QvP6vtGSy2jNSwHFRgxibj7J+G7d8HKbA3nGvPdnkHNCEwuDpFUeMDdw7cx442hVAXPAx1I4xTLVCyMEwZMPQUNIuqiTz0HxfU2DVkN0NMx3WaDsNsTQcadUWapYLL86URtNJ+5fZqrJ9s4OiSMdxs3jHTZt08wudqbu2vhIBJn0k0jHcJTLSOatyuQH4JcaWCfHZFcY7J2qkSY0pysHWO+M4sGKu2YtnqaXL3MStnsnnIj4qcP4R2TaBUz7JaIhAPaiMJaarSMEK0lPK2pVjXHT2iKRai1mozEXci7EGtWo9Psyb+GvMiDjqmrKtcFkwy1q8yP/UAyOIQRCkYd9LCG5URAKQhEEsZY6M4SA0oIcHOmymRcN4UccOCenUQ6glhnYVvxGmfXWrErzq5PqcWthjf1baLd32GW9xxQEgdpxC4FijRXkoZEnHCEg1qbs0pq+tJzobTCcyG/7NON6zg3z0FzAWfXfvNd84MDjoum55ZLH06oLKdWqpwk7rXkotUMalny+yh92KIjfB/GKeCriFjFiCT5e7cLzTaIIU0UxybXnCsSAdpBxdBtREApG0U6GVEi2X+tQWfauMKpTqJPrsC9e/pydvX1f6zROkLFcXbuZ89FYsXzwTPkwpu4hl5xrXAycbFq8KZW0YWwL9xZo0qmT9Lf7v6HVu5sDRkbJ6FQChePQh4CJ6DmV6g0JhnyyjiMwQhZdUP6nV0XcFulbm2pNSpW6CTPp9IQd0JcmUOGEVoXjMWsrYhPKXijx7rQT63RFYk6UyV6dYfc3qGBz5UEvSBNBWZPoYWHcjxqukpd1Xj10E1EkWZ5pe+6u1nDN8CKXRaL5YogSMSuwq2XX+wCk2+g/J27Kf/p99N+comF//wUs7/2CLW/OsMNf/C9MDFx3u9rrfmv3/oQjWCV//6jn+eWnabS2a99z0dZbs/x0Yd/ne+49nuYKO0+73osFovlhdBnCMhu5hEC5azPo7JH76MoChxxz1I9V8e9NkJIhVQwNw3LzwTMRLC3KtF6FTc010GVOruSrMudo6eJuxFxIgBpQHsaoRVCuzgqpN3VTPQnhko4cxbiWc0NNwGueTo+erRMfG+cyQpGf0u3p00YYyJQdOZN/p8ggJEwRAgjdAGoRBBqh81se+6KSe7jci3oiHzjDFExD/HeTEAD4+wazWYwXVCjOKvdXt6zTFDoOXd0QSECQeEZN6uMpqSg2dI0TsH1mHQnbgFUR4EDjnBBw1AwSdw9x9DIKEoIRAB6KFlv6ihA4mRhjGE2yXBCB+1qtEgneYZqXAcJLbmUtbeLCTnJyRqq20K5O5A67E2gGXR2aQ2L4fPcoO4FGXPjrtfSbM5T0WfN5tdMQtKtj5+S5DqapbFEqkj2QUpjfHH7xS6tQDhIpXFVgKS8bn1AFjqplAkH9ESB4WFNremgUfg+DKXLi1Ts6rXP92HUM/sl+kLT0omgmdia5eu6Tk71JqsqcUiafenf4Z5rL3VzSAnaTWecyfjQJiV5mnYmdU2iNbmFJul0uVgUjM5WaU2Uk31PZ+EKHQtcXKLYCBsODmPe9ezJ74P8UbRSiRsxERZaEZETGpGyL/Kx/cgcuR2SND1ON/ZZqB5n19gdyRsKZeQQ0z+xSIxhMhG7NM1oFe3C0KLCi2rE140D5vh14xhcDcrDywlcF7J0SbjceaskfyvoFZf2KdArizj1aXRhDL3z5j4xx5zz6VDRWoMyxQ3uHL6ZenEPkYjI5QTKO8BuOYMSHmMeFPOgintwCsOoUhen06AoyvgkLlIEWkG8dBZBi4IzRLNuWtgOQubPCbqrXfbsHYaOxhG5Xhs0RETUVY1hZ1+vY5M2x07MwWsOsn/Xrew6PYwqlugO30ohOtanCwhKwx5ogecv4IwKVL1IODxKVI+N6yvpBz8L51sz4U+2GcqegA0gulXzQWsFGE7ELiNoa2kEjIqusUuNpE2BdASuq3KrTfGR6UeQN34vUhmBp3i2QTTvw82JgCkclJJZKQ1jTk0PnMwKiuhgbUimEd2yfRNp8QaPOLl+S0Ji5VJwCgQyMuH1eo19TiehkFIh8m724CX2BUcPmwIcPQ01Pad6u5hJ06k7udY1OdugJyrKNHl6jCJGqDi7VqbiW6RD45zzZ7mG27PmieY8esSMFXehgdofEqg+96kDGoVIKiZGIRBLbr1VcO7RKnovmfC2e3cMSxCJkEp7AVxNJ2ohwwrTQ5MmlFblaMkSfuhwrHmWWX+UUO5FcwtrkSrmkXNf5ngnpBBV2Bkt4EU7CFhELwMnXVR1BtmocLR9D0PtJm/UJdz0GKwdl0pDS6MCRbzqk9s7RLTYQdZC3N1FWv+0gK6acZC/BvSygxY5ak4bgSTWMaqTx0lCKnHJ3L9bwYYxWiyWK4LgZB1nyCN3bfnCC28z5dfv4ea/+SGu//3vpvNchVM/8gWaz2xcfSflgTN/w+MzD/BLb/hPmdAFJg/Eh777/0sQ+/z5wY9e7qZbLJZXOAN5NRJnBwh0aXxwQQF7y/txcOjeEqIAp+kjlKbZhFKzxvDjMwipCdKk9K75G/jQ7khwBJ5qsTylOJLGKSUhe1qp7Amzo0O6XbKQobVJkJuN1CaSBglB3Jc/Ucm0TLtAK02zEiBWzgIMhCx1jtVwXfjaymM81ziJ6su7tZbr9kn27grx8qYKo/aDgdDA1aiBr8zKC4vPcN3sFykfO04u6Yues6snfuiSQkcCSdwXtieQnRqeNIKbI8AtCGJpLE7p/XtONlBCkVcOBaeE6PaJKmmCZR1SiFayxOMitWD5griYTh5VdtxDHTF2eBhX5HuJfzM0zrwJV1PEA2JXiuP0tCzVfBhRn8HROtEcNTrS6yc6SaOdpOud2ITk5ZNKl2lOGwcHZ9lDdByczipBJ8qKH4T0OQH6BrRKxVQFKkkA7uUUu3calUnK3mRVr/86ga/xcomIF0f0C0lm/eb7Ldkyn/Ulc++FiOnBcywZ77m4jtfvgKHf+acZ7x5hovMcmhitYTw3wk077qBY0TiNgHjvaLYdSYhQaX69JN/S8jG0jHCEEbtWVswEO4occqJE3hPoKEY4IgsrCk/XaT3RgOdyJlF61jiJU9Xgm1DKGX+RVlgj6hr3nwjl4H7GcFv+1mS9GiE1K8FptIbSqsKdXoXkuCrVZbL+DEppXFxynjPQL1q4jJQVnifATc6RRhLSmYjh/WKXI7JoaTMWk327tribG0s3c727n1cN3cD13o1c5+xnh7OTPYWd7C3sYu/wdewfvYW9Izewt7iTvJNjiDITYgelkXfQaUNrqciImuDW8v5sOIQ6RkTCJBsHdFfjkicf15A+XHt2Ly4uFb1CRXeS40ammkTCXEfO5GeoFWpEuZ1Exb00GqZoRTosrrshx85R8HKC0bdej//G2+mKHQQ7bkRP3JS153TrHK2gjlIKl1wvvDoZYmlOMZUJ8OnfpD+9JIZSgIhM1GxDt1iRi5iHIWaMOjhrKokacUxXTvLs6vM8P/XPpmhJUxtRJruOi+SaoDInsUgdXkl7dKSoLT3P0ef/mlZQpxMFPBk9RlvVUFoTa8lQroyryzRWNc6CypycUkdo4eA6I7j+CjL2e9ffPqSUZnfyTtYFjhSINHdZKgLrwYud1ppQSpO3st9JerbR5+xStGMzToeKEeM7JII4ywForGHJgwjVe0CR25Ecg+YCRG2IA1OaIAoIdVo0QoMkc6miTU7F4pPT6KdmjAvX6Hi4QjM8FCMciERELEMKXhGNprl4kIq/TD2u09ZtuqpJO6qyHFapRbMshs9nobC6z23nx10iGTLm3cDe3C1co/cyntvDqHs9u4eu5/r8jdxS2MPNpRvYKyYQKiaiL2dd32+AVhpRmwJpHIS6UYeoS/sbZ/CfPsW3/6rG0okF6GoYccDVKOEREyJzZr4X6gjZVOTOLFFYzSXPSqzYZbFYrjL8kzUKt4whnC1e3bYZIQQTP/UqbvmHH8YpuDz/43+Lf7y64bLdqM0fPfF/cceu1/GOO9697vPrx27mR27/Wb5w7DOsdpY2WIPFYrG8cDpyFT9K1J9ulbCrWKmY4DPtFGgXDqAcU41sX24P5dyouWEtgHIdRDdCxxLZ9cnLKsVwjqFFRTkpjpbMnZg6B08/ayYGXhoW1zfR0iiGThQZfmbYTD50TKzATSe36YRiMJIF7YgsYXSluUxFmZwoQQgnTmqCQNDuwNKxWVYXk9CdPq2lNdlhYtysdzlYzQSRjL6b8rFhyc03ShxXAAL34SPoTi37fDlY5dH6s0l7gUhT8k/3Jk7JZEukLjdAF3oCVeaKwUF2ltnT/Ha2r14hcTY4ApFMvCQS6Wgc5XC9c9tAeGTaXW7UoNw63idCJO6kLsiSJq1mluvM48wfJNRh0j41kIMFTEAaCmSs1+XsSulPWk+4kO2XwDiLtBzsU9NY0y/lxBTlRLC7+W32REfNdtPFpUAcK+A9XwJgaVkTE7Oilpkr7O+trq/dtbrk2HFtnE1JB3me5obrXIoFk9utNwNb7+wKAkUuEbtUX7KnrHqZgvn6Ek+3DqOEZ4oepP21TtRLG6ihtcw1zW8N5OnpJcrWptplMhYFmmvyu/jOibsYzo+Ra2m0J4hv3En+xhEWWzPMdg+iYx+tNbOPCNqriuN/M8XIiRO4SWiYH5g8U1FoxpTrkeXiKq95RqgbAqfRf/w13a5G1pLqqamoFyjauk1HzhMSZuNMS8EOdwdFUTLneep46fWOEcq1Yl7PEEddduavxRMebs60LduycLMxkrqIVDsRmVOBr0/sEk7vte/DieOSMDbHNk7CP3M5uKbxzSxXWjaUNWipTAXVJMG/EDDh7MAVBaQ0wm3R38X8rEu3Y75UdoZwYpENJdk1Dsw9zW8jVh0EgjExDkBNLWbDIA0Zk5lD1GWBeSC3pr9MGKPj5ci7jhG98zlyIzlT4a80ji6MZKF1KAikj1QaT/QcullxujRHkx68NqUrELmes4vIIQxACZeqMve02jHXcidxhgYLfUKS1NQ7S6yGdVbCKseXnwXfCGNO/8VuOSa3qs15KdJQZ/NwAqVAajrNGWTHx4/b1BO3bU0uc1QeoilbuE6O1ZVhmnMa57mYsJ4I5Jhzp6g1yAhVOZldd3rXB02spNmeI5KxK3B8By0lLhFDgamiqVRfGKPWaKWYnNbU6mR5tAC6h1aJliV6Jqar6sSJg25iLKI4pBBaZoUXkr3N+t2tmEEf5noPUNzVE4xec4Lh2kGcapcw6lUAFXEv96PjV1l56knycY2zc48xHz6OMqcdjtDI5LrkOz6xihktTCBw8MIz7FnczWvduzng3si+/D3cnL+P79n5HVxXMK7NKBEQO0djk9QeiGSA0jDuHeAa70Z2sZvx3C5GvH3s1i57VYkDu+5if/FartMlhFbIJPejaX7v2th4MqL9eITumrBIMX0Q7/QDOCsncGrTlGfPMBSaGwrhYUIjhYvSYebGjXSc5VETkcjErnpr7Vm0MVbsslgsVwTByfqLFsJ4Poq3T3DT596OU8ox9QsPImvBumX+4difstKZ531v+u01eUh6/PRrf4VYRXz+2J9e5hZbLJZXKivBSWYaJyEOWF14ljOHnqLV1ITJQ9jA20EsjNiVhs+ZCaAgLjo43XAgEbnGZ3iudyMr1OCEGalxVAglQbxDM+SWKHpDAwIFWiCITYLhfBaPNNBuN3GMaamzierq6UVWZvqqU6EJIjMfFnGMnxSAyrQMR+DImNEhI+I4ix7BZAvR1rhn08lf377oGK2lmTEKgWg5yIbGPZenEKV9ZJaViiSNlsbJQgp7YYzprFjn+9aPg+saB1OsdJaQGSBXFMhYJXPCVOyK0Ghc5eA8YUS+0vfsoXH97l61SpEE0iWbaT5WoflMBDF4xVJ6VMgHpt/SsNLiqRpoZaqZ9fVovQ4n52YAvamza9y7iYIzShgP/valzi4nD6UDvZCr9NgPjSbrCE0bcq7ps1Rjqs65VOsD5inO6AWeyQ8x5T9KHOtEKOr16fyCJIqhXjeTn9tugZ07wXHcTOjIhMc0jDFNjK1NNdGe2NXbcDpJrdbgmed96nXBsLMboTZwdq2db2mFjs2E1usTu1I5SGCcc/3L50UOMzkWeB2NKplwIHckTzts4giI4xa5iocz53H6EUk4J3FqXYqilK0qLzyiyIg9jnDQibtqZDhts/mf1uCs6j4VCCorIfUm4OZBa5RUnDvbYVHNs6KWWVDzhCJRzWQ69pRxmUjjHpQ7HOKiQKFwV1rQlUS+z7AY4Yb83aZPPGegXzRuJmpl2kCcJroeFLuESs6R5LIxMwthoFitJUJCUkgonwzrdLWVVTh3zpyXulVHtNY783VfRdgoNGMqjEyjdsp9FMJCNpZUW2cuUZWElo6JCRwc3ERQUppMdIqdxIEoRJKLy0OqtfeGqWzsmf1z8+RyJodhFuIqeoPN5NNSuKJAfkXhLKm+2LvkMAUx0XI3u76qNEzOEb3OiQSdDgjpEvo+vQT1pgqlHlF0jicFE5rzRA99hTipgnrT2G3csvO1HBi6B0d4iMwtKpBPtigfT4WGNM+ak7RRocOegObHXeZ8Izw5Jn4PDXhOjlLeiHl+AHHsm6qkqbDlmhx3cdgEFdPQ9YHTUUqVCKTCRMA64PoOzmqXQrhkxqErer8ZGsZPK5xvT5lrNJi8Y30PY1QtcZvqNlqDKwo0uhWqahWHOBkzgE7FLoHQ4E7l8GXA890T+IlIhgK3ZELxnbMtON1zdumoJ3blGpPkoxrDwVm85lmGw3OZ2OU5oFSEcMgE6YJX4o677+Pa0RvYG+7LxoUWvZxdnmOuMWHiXg4WE9E6VkSdlWTfTLVLlAZHE96xh5ycw1k6CRgBcSRcYCiYzx42mLDe3jVatjUq1BCY6sbVJUW9rul2NDJec/H0BPVaSEM1aKqF7MSP0odUShrRGVCxpt2xYpfFYrlKkM2QaLZN4UVITr8V8tcP8+r/9aNECx3m/tPjA591ozZ/+fz/zRuv/35evef1m67j2tEbefP+H+Lzxz7Tu5GxWCyWbSbNR7IYrODGXRKJpm+SnoRFIfAD44YRQqCKCtGNKAazOEnlMZWFiiRiS7/bCGUmozsFzp15otEYR5hMVbovq6/Q4OgYBOS8JOfWGrGroJoQddGRznJdySUoLuXxmknydTTCccwkRqnM1KQkiEIZ8iCiCP/pkPxjw3ini3Sfb5B7VuKdUSbhcx9O5QwiaCdzTpN2XAfgnsuTO2kmXI5jwnGiEBOWge5LlGtu8oXuhXvpkuqrfiUolSDvuSCMawlMqF3+GodglyTcLbMbc6UlSoATZfX3aEqXk2cF7V4+f4Q2c5F2WxPONohqyuRvymVrwpchq2E9m7TlKl2EVniiv8qhplaD6UUjrLkxDM2r7Ds3l97G6/e8g32Fe8mLchKaA0LFOK2lxDUDImeGlNaaaTlJQyYOsERLyAUBGo2HS05EmaknrpsFIiFQI/sQQNcdQjs5Yu1z9LgJ1eunWEry6Sgo5BUHDghcV615yJQ6PhKxK9mfMDT/znkmzkirQQEKYLUKEpOTaNjbh5MsI7pVdCKYrHXIgc4KGAxWYOs5TlS/oocJ70uT67ldkKXki05vUiqjDm4nEQ06aeinpIAZmzvFLmSs8bvahAkKAdFgaJfWoIRGoXFWNfG1N6BKE2itcVVSwa9o7rOaLYiSmOBxMZG0NJmMJxNOJ3FbaGUm2cENDpXXeoR7y7irbcSRDrmjgvGHY449aCpnejkxGN4pPIgD07hUOE6FGCVxSv2KoQljVDNNnEqb8cUHknWYPuomoc5Dif6XCqm9y4tGyz6Fr++f/XP07PzVJs9gEAgK3QJxAcgLpE/fNdGoKEKAp92B0DOdOruSBGmhO2ZESKeITD4bGkr7IW2Jh3AFuDlyOVhegWeeNY7cXpIpkuThCke4FI9Jcs9Ldub2ZJ8DqGfatB9eQKdCru47L7REC4WIPbSEiecKlCeL5rtOX9dcZ5K8a6WpTs7TOesT1lvJR5L9xT3s7vSFbkLWH8noMB0tTLigf04i6wGy0bvvrfsVZCpKk6aLh4JXpFQ0ymUUQu7ZI1y7+k/moQSC2vCbkO4Q4WqD4w89z4paZsUzYW4CZZxdUoMr0AicMUXOAy1juiowoqIHKr2WA4WaubCpnlrf84ZWTqLa5vgGOZ+cM4RHAaGMUJ3zTDJ3ABGHFGJTwRFlQmBX6zG6qPDuToUhcIZEsr6dhIF5IiDaKxCqXr4wr5w1Im1WapBzUSiZVKxMftZzbp6861EaGkIEoi9BvSB19+2pP0ehU0OunEYmopFoLSLmjqLOPYLoruKSR5CEggqFNxzgFEWSb818x8VFKON0NYVSyE4klVZmzS59kuoKTJ+DlVWYXzDXMLGvd707fG6BU/EhKvGpbNxEOjIh5UXX5AvUSfi7DWO0WCxXC/6RKgCl1+zYtnVqrZltnOW5hUc4VTmUlTTeKqOv38vuD9xN7W/P0nhgJnv/H45+irq/yrtf9+8uuI4fvu1nqPkrPD37jYtuv8VisVwIXegg5FCWmN1RscmzIwSvv/YHk6WSJ73C5cyZJMxFOMQFjQhihpqnyMta8n4y2U9vvAciuRJnF2nyZNnLU6XNpCczXSVi19Bw4ohaI3YNxfM4y8dw95SydYRJjqHh06mTJX3yDkJr4o4mv8dBuQLh5dCFPE4cmZv7bPIa90SuDaLQVKUCvukTDSQpuriueA0AZa9ELmfCKElC5xwZk1YZK0WLSBnSGeoSfUeMnpBEO02fOUKQy8G+PS4jo3DNPvN+sQjekCC8XqHLvepbMTHa0TjSwessEu+P6UaJ8JdOqJL8RUqZxP5LCzGxiol0hJNUMNMozkUzzPvL2X7GeU1hWpDrDOF7ewGn51pRMQVnjLxbZGRWkmuZ/ig6Y+S8HPnDc+w8LEmGAu7qFI40VkEVaiNqCQjiiCW52AvfTAQOJ0omlhpyTmjy4gClKSNYRFqBV8BxwFuT7L7R6Dm1AOJ+gcqRhOda6I4RAA4cSEfJ4NhaXtK025pOB9pykce7T/FI+wnmgoOZw0P0DQ5FCDg4opTZzpzqJGL2yWyZ/uHrVE7jLR3Ojs9o4mirD5vcnSYnWl/IpE7EADRIcKRGJ45H0V3FS46jlJ2eYaKbCscmt9oB7w72Fu9j5hwEoSafS9a4RuxaXoHJRYdWOznWYx4k+X1Esm8rYT3bqdShVhIlhhnuuZ9ijXZyOGZmi4ojtIaxkjlP4oJGqRipI2IRk1MO7rRZby4/mLNrzy17ETJENOd6OZ+ysEYTvuQNJ19QGt2NkMdWKB2fQgRGZJlf9uh2Nd12TLGwVmQcJJrvbvyBFr3+7emS2VjItTyiYdAFgeqASMRBhclXBSBiB2+5V6mVZGxHTkw7v58gt5tG+dUgPGQiuu29BqSOjetSa0THIxIR2s31HGpaUq2RTe7dmTzhmQZKKXpZDWFv4YbB9ndiUDGqmwhLmeiFCeMUCmIXr21O2lRuQhjhx0FQFXWqYYPqmUnaHajVIU5CxnPtCt7kt8jXJ3FU0DvXhEDgmPMidcomObu6k5Lm1+fRcV/oq1ZZ1cehcCoTZIbzYziY64JSwGIEixKdWgvdIeLcGE8/fYrmzGkAJL3rfawkflfTakNw34107uySz8NcdILZ6KzZf0/0Kk4mf2a6B2nEK1nbsg6OfVRkNh3kuuREKbFvalDmn+l4KQeT5OOmebSUiMONNL/jaJ/jVwiC4i7a+f10EmFPRB1EYxmNItIdJoN5urKTdq3ZfmCuh4gYtGbIK5gCMHGDvCgAMRSFeShzJkp2z8lOLXfOZd+zdfinQ9QfM2NZtBZg6QyRihFRQK4rEFGciV35uIZ2QLnl3kVPFBHKQSNZHbp3QExWQbJMLlXoAlphk2qtQku1aKoWHXeFulOnpVvUZZ2KXMRNQnOFgILI01IdjrZO0c23cWIHR4vsN3Ar2GqMFovlZU/38CoAxW0Qu2bqZ3jwzN/y4Jm/Y7p+Mnt/Z+kafvbeD/GOO969aejhWnb/ymto/OMkc//vRyl/9ccJCyF/eegPuO+GH+COpMTz+Xj99W9ltLCDr57+a954w/df8j5ZLBbLRrRv8Vl4XrNvqIOU2uQTKRknQquWWAqS+9Cyfw1LdcEeYfJkRWmC805vwjw6FrPYAlConKZQE+w8HCMLgtHRU4y1izjJaqWQrCw7TIyL3iQoeyCsmBgHr2km7F5QyQK7tDIhdKWbXaKRvAlrgSzJdroeI52JZC4i0ZFmagbi0IQF7r/dxZ/td9CsybMkMXfBHYGz6qGuj4i+3UqamIh0yc16MZ9jT2EHbdnF84wriDSnihaM7ttBY75CTjbw5SrTzVX0PhBNiF7lo2fKEAs8F6RwKeQF5VHJ9bvMdtodjdZxMnFNBC0doVyN141xxRKyc5aur8FxkApTnQ2JEL05bCzh0elpUznP9ZJQS9WXL8occEc2KJzr4KldyFwRETuARGuBQuLQC0Ms1BXxiJnMey44zYCcdPFNZCeOlqYAwbxHHCtywybLfjPuJBs0W3WSrnfTSYoGVyhimR5f87fbElROe3gK8k5v7Lkij+OEAy5AqSQOORQRThTTeXoZz4/Z8QN7UFpyil7VzdTZdeq0omGMKXRLKwwXJWWvRNWvI9XupId621AqxhU5nDSkJ11f1o7BBPWiOd8TzQQc2C94/pAmdHuVHLtqtfcFdG+Mx8a9o1xBPq7hLT2H1w3Ne3E7EZl7k8k0zK9TOIDM76SYuM3i2DgJ09BMIQCt6XYFfnGOsAlupDk17zAeGaHTScSuBX8p9cChk3x/Dg573L2I/C5U8TjKzYGTw9HQlIt04nG0hpxnJqnn5GOMhZKanKPggqcdlNYMD8PQUM/ZNTEO+++8Bo6DCDugE/d+6nzTJreTk/eAGKE0ztIMXrCKpxS6bYRCVdWcm4sJDkjGS2tyy/WhlMZJxIilJY0oO6TjMzOW9btFNOSlSULuShdZ1IyP7GR2eQahQ+NmUWTnrKMcCtMasT9CF3OZoB47MUHiuoqckE5QIZLXmvW68Fz8LE1nH3ec24mKBc/XT/Ha6M6ei4cYoWC8MMJ4cZh5f5ngcA11t0LgZMulQnm2vy44C8/DvhyRhtapGowO4XktYr+BQhHrHXhhF7wc5jmFBgfUAQ0R6LzmbGOR3d2RrL2xb8adK1x0rIlX+0LY0gGXOLtSkUsAKnWWaQUy6mmKZY2qG9deqMPseBTcAkL0HmooklBNkUqMOaQ7hA4E+SRXJCKHUOYc7nQkR4446KIgug0cV6M9jfZjBHkQyuSJkorMFgUoHRMnuazQCjw9ECLqOOBPSMLrxonmI0R3kfyjw7h3Ns2YEH3uREBII7qb/GVGy8vvdMjvTfrjrh3wNP3BzdQWI4quoi7nKYsO7aU61+i9WQhvYqbD9yNcpSl5BQQSJ6jgqQh0DkYdwrGd5OuV7LiopGKi23YBJwttT+k0NK1ihOu7FJ+fxx1uJsUhQ3KyaVyH7Ta6vkBXDlE7dgMjzedRQhlHeN9OKD/5xSkZMb+tK1TlAvSmXlRvnMXranLOPhCz+DpkJHcXHb8FwqHoFGiqJhpoORUKsUhCXRVbnKpZsctisbz86R5axbumRG536cILb8Bye46vn/l7Hjzzt5yoPIcjHO7Z+9385Gt+ievHbqLaXeGLx/+M/98j/56nZh/iP37fJ8h7xQuu18m7XPd7b+b0v/wiyx9/nq//yLcTV9evbqldnpPjrTf/OF8+8Tk6UYuh3PCFv2SxWCxbRDgxGoUf+tTqICZMZbYwdDh9JluKA+5NdNtGhDGRAw5xKZ1k9LtcJIUCEGm0qyn4Ncp6H6vSZ2x5EU/sQidPZZUwoomKU7GlT/ASJq8QDox3j5CmHdK+RqwkriVHQNzNEt4PWLFU4tgRSS6WZHKsHBA3eegwT35cIRcCGBAi5qDoZhMijSZ/0ISIhPsidJaHKtn3ZL4jPGFy5GiF55mJho4TV4eGfDFH4O2kEK+Y9fZld9dA4I4jYmEcJ8IBRxP1hbKZeaEccGlIYuKSxO0YZ8LicsxI2ywsY43GRQi5LreWTFxDXs5BOYmrbsC4ICh3pvE1lJ1Rdng3UYpa1NRKEsoVI0ShT+zSdK43s4rUMeORQxhTD1LC0pKguZinsVOxa9w1Od+ySZTGlR0clUPoiLI/i2YfaPDwCUIPhkArQa5knGylJwsEr9pBp3gzO2jQ6IA7XMRxQp5+VjIxEtGJvk78dJsdDUGsY4aGq+i9E8Zh4Xjcu++7efyZL/dya4k0e1BfPhkdUMrlyMkySvt9uXvMPzzZQhHi4iGcJKQnG4J99p9+tEJKzYpa4UaxE3DXLKVZCg+TPrpziIGcmd2nQ8JNxrQAIh9XRuQ7y0mBw+TAFAVOFDM6CionaHRi9GwMJc3ICFSFQJ/shYoJFLGWVFhkVLhEEcQj0KwJdJlM7Or1E9TVAnnASfYhvLaMNzSBLu5ACYeOqgLjrPhHGAVyOaN0KweG3b2MOxFlLyKnSrS0YnjYrD8dRyINGXY8k8MuHahZDi+TR0gkzhAvbpI/9xxxUyL09YRHI1QLcCJiIYn2RhTHGJwEewIxItBVRRRBwTO5g4LAdHsPcy3JYuiSg5aPTWSBK8z55OVyxt2jjZNJC51t0MEhRuEkeeEysUv0znXtCCrBaQrSOP1c15yzGoE7b65p8oaAdtTKBA0vpxh3b+F1E+OseMvMY85rqTTgcP3QAUpJONz1Y69CjwqW8lUQSbGQUBMuq2xcF4LnKb5eEBx2kedG8N072e01IQntRQA3gRQKVoyYuyjn2eXswXFcAj8V2QT1J0Lk6qAl7sxZwWrFJKPXSXiuMVwlzuF2heJELVteFzVaanI5GF1WdKs5YmCiMME0xmGltAlnOyNP44n95lopHKRbwpXgqsT5hJs5w6RSCCU4cKNg+B7Nc0eNBpQ9dRHa5FBTPbcpmFPRyfTWxK3aF+IqhCk6EI8XKVd2QMeoN2NuGeHdyKKaS7raPOhRyuStTCsIajTDd/cNvnIBNVJEBmnItQmhl0MhKlrCpUWnCyvFacpCgKMRWqKFS9ePGRaKslvMjoBoV/Cq5xAFQXT7LvKPG7FL51108nDBdRwQDqtxjajdQnsaEQumTwR0xrrs0olLUCkj8OmIvKwhXNCxZmYqYLWTQwvXVO3UChey9QN0TyY595Kx6U+06O6IuXX5FkRdo/c4XFdykMNDtIYOMJ4/gnAES0M/wGLr70AIPPJ0X9VFjIBeVjiRwNEuAnVeB2c/VuyyWCwve/zDqxcdwtgIqnzj7Od54Mzf8vzCo2g0d+z+Dt5332/zlpv+JTuHrhlY/vtu+nH+/uif8H8/+v/htx/8Jf7P7/8UrnPhK+nQPbsY/4mbWfnUUb44/hne9Kp/we277t1yO7//Vf8Hf3/0U3x76kv8i1t++qL20WKxWM6HEEbsilQ3M9kIVJYvJlkKgUDjMD4G1756L6c4g8pLI6yg0A5JjiZpwubQWfqX64Xi7KuHuPZEBbQDo8YlJIXEQRD4prqVThwwAigUoVAQSeVD3ZtcNhW6qaBg5o+5mUcAM4nsd/QIJUBrlDahMsUijI5A6UbBucAhvGYYke8lMk5RWiPaVdzhfSBhR3EXXT2JKo6jVTcLn8oCetIn067AxUFqk/MFgFgbYUkJHM9BOgVEGtbYnwtLg/bGGSmUyeVChDbOpwGxS4BGIoSTuXyW5DTlIYWzkrTFgWrdJI2WUqOSkDInVQ/X4OUhKHlEToR7OBG8NIgceKa0FQ5Qcnf0pQIyDjMcF73PI2g5FGoKNwDKPceMI3LmGGCSXmcCnSILY1TJNorRCp5q4wQ3kY97oXhaCzzdxVPQbkNRObhDmmENdR+cheu4sQ5dHZNbhfiAwBlS1EdjVmqr7GrV0HWNo4co5WDvtZgZsUhHtBEXu6rKXPtp7szdlxxb0wDXhetvCMm1PfKuSyE8hw6N+2ZH5zkib4xrGt+kFc+aSowUk5DD1AmkBsZKpGK+UXmK14zfQaXjMynPckBJRtiX9W02IPoOl0jDfbXCWTyOoyVNlhkWNxGKGF92cGRsROe+r4qSwAljPA/27BY0k3xVe/fCjp1QWyUZ/0Z41FqhhAJHEe6BvAJZEkTSQaMSwdjpE7tSq1Pqy4F4tGDeqkikBDGc9GXigEzFLu3CqHsdY6JJ2QvoSIkQ2owf0UtQnwm1bs44fdLTVSUDSUt0fQl3141mTEdtVKRxigK1u4QnXEQVdMMl6oKII0pFBiuJaiCp+nrqtObOW9XGOXzSMEYhsv3u19fjpHjFUGkEHYOTiF2ma/rXKEFq7r4LGjWJ8oyzq+TswBMFYjGLcMCbrKE7MUEOSrMF5LBAOBp5q4seUQjhcPNNcPQYlAsxdDwTvuYn5//C8wQ3e7jBKDtbs+RCgRraw1hxB24pz4rTQKsOIEw4tqa3X47AKQh0SZBvK1RO4LrJmaFBC43QApkIrtqBNm2G8YEys+c0q8MO7Abp6z5HnFl/vQGVShoSb/4n+sWuoIFwjGuMCLSjkBF4Hgy1BJEH+9QBiiunEGrCHJ7k1PN1hxERZW66MM6R14Vs20K4iWiVhAtrh527obwTHEcTu6l3VyMcDY5gtaIzRUS5wuQ8lH3nuQcEiQCbVOw0rl6XkruT64ZvYFi4OJQ4lg7izMCqCcRuik4zye/GwG/ZYlBhMSgi3FeD0viBplWHvJbU4knK8Ty7irvoCEHXXQYE8ZBEKCN2lUsRdDT5pMSppz1Et2JOQlxkck4C6IKHkBp/OoamYszZQ1Esk3MEoqjRHUFeahzhsUfso4H5vaBrjm9OtoyOKKH9cIi+1xxPx2QOM2JXn7NL+kYoNM4uCHVAvpyn0CqgGxLhugjhEYoRytftpLhqnnoJx2VP/rWM5ScphktE4xI1DMFqlyENrnIA1atOegGs2GWxWF7WqG6Mf7LG6Ntv2NLy1e4yf/n8H/D5Y3+KH3f5/7P33nGWHOW5/7eqOpw850xOuzubg3alVUIoIAkJRAbbmOsADuCcEw44Xl8H7OvsizHYYLANxon0M5dokk1OEspZm/PuxBP6dHfV74/q7tNnZna1ksH2hfPqo52Zc6q7K3V1v0897/Nuqu/gu6/4eZ6+5RuYrs2d9zghBN+w53twpMcff+pn+evbf49XXPkLF3XNiZ+6jPl3P8I1H76c217yYxd1TGq7x65gujrHvz7y9gHYNbCBDewratLEaBNyumn1moQGZ1liJnvOmQFkKDHCUKkbhJpALku0iDGOIjIh2teojsxlkTOQZEzEAeNKgs0RUrkYYTINRIFELR/LhO2twyIyLZrUc1/FjbEMG0kiRGyd7VJJ006YDiK2O+Znzmna0SKjpRJ+waNQsTIlIxtBnl1Z0x/WyZCohElSl8O0AaSDoNgTpvYiTEdmml2CJPsZGjepeztsc0afsuQARxIL+4WMQ+u8J62adCaoeZczO3wMonnLkhFWb8pIhdBx4l8n+jv5zvAELql4ryFuryBlAx2BETZrW/q+HzjDuBzvjb0bIlCU/PsI0r4HjLIsCmNIwBsQmdegEXEbKcfR+zyay+0E7ErKJdNGCS/TaxNGZGwYmzRA2DAooxFG4Girpi+DRBcmASorRUXXiZDCaqCVFXhJa4oFm8XLHTK0kzGoHjbgxfjeacyQJtaGc0WB3FFlYktIcaSG+2gFcZdBCIkQkrExOH7iAAArOrkHTETbtJkoFxmqd6Ht4CmHiIiDh09y5ljSxuP/QkwZqaEoXFQhwo09VDlCnnbpLGnU2QVEp0UnjjnXWaHbdjlaPUN7UVJY8lhym7SrEXNFOH50Gf+0B75BSI3fTuaLBK0lneUO8bmzuN2zHNUHGNMT3L50HyYsAT5ax5kDLQTgCoQJrTC6J2HJ3iulgn2fEQiCqEMrhodbR5iPapSdGYyC9sYY6g7xoiCMUrg0wpBkFwCM0tkcTgHYalUgziZswdMat6hAG0qnNJ2GpOiNMO7uwS9aPa80zNZEIcLVREdjzFUqu4eyBJjSgl1WQd9SBo1XRgRLyKWjqJaLF4GJChaPc4ERD6fiIEpghnx4EEQY4QlDeEJjzgJDMqHp9G4pHRtkeo/lnGWTYu4S8stcajVZo+jWmKhvgPgunHgZg6DdhoqQCXhsc20KrZkYF4xuMBy4x2p2OcJHCsfWX0B5qYlZ1ERlmNIuOpBQhHh6FFwHUZlkpCa47jrN4c8YTCyJIjh7RrAEKHUG2g71u2ahTr8l97aJ2nS1i9vG0u2M1UdM72NTcqjXu1TlBkbqMQ8tnwIMWhgkVncNQEs7Q3TJIGoSc1KjuyrTWMuIcBk1MgXCe8lHBNAHM0pgf0C32YRmjIlsYoUoeXZIJHLpGKVOEtaYAx6l6GZg19IyCLURzz9AezKgcNLgn/RQjsYUYjzXQbnpZokBJ82SCEiNVonmVrLp4phl3CAgyvJ76D60RMeAMFbvCwFSUPfqeME8nQd6YbLkpL5kLJIr5P5zCpjyKHc//FkOBStsL+2FJcOp0yADgYehENr13BUuFVEk4Ay6GtMRIeJ0xOyMh78l4tG7DUpKLnH2MeLn+heIpZeMkcC4CtGJaD1i+3hIjeO1HUoCJmcVoY6ROAhHURHjFuxKQPZs6BLgWApgpTdWXbq0o6McWDmGDnqDpS6JWTwHK1oS6A5FUcnOkfWpdPH9fgi67mxiumYQ7Sb7vH2clQc4ouyzRIV2QETfw/L8NgC7Bjawgf23ttbtZyAylK4ae9yyXzj6MV798R9mOVjkGVtfzIsv+QG2DO9Zo2NwIXv+ru/g4XN38Xdf/mP2jF3BUzfe9rjHrIy1+eyVX+D6L17Hpvbmi74W2JfSp2/5Rt5255+y2DnHUOErJ8I/sIEN7OvbhkSVll4CHeLgUDpsQx327LuOR1tgX8cF6jEw7RATLSPGBZ7wCE0LlCE0XYxroANahhSah+hqTZh4jMKRiBzzShhtGU/CAkRokTiSKdqSXlZk3oBZ551VSHLUD4EUcRZqIRIKwrw+R9OcxjVDTDKOdAXbtwl0XdF1d8Hdd/WdU2NDjhxhwS6T7tYLMG6NjB0QaYwRpBnP0WkYo6GQOBPtsJmeFKTAJFnzZF+mvbRtAilTJ0EgpaSju5wO5tE6oigaWGllQd4Dv6x4BRkpQxmK4Uki0UAbk4mFP9R6hAfDc0zW/yfTvCs7tj4a4cxbZl9fHzuxZdqZHmMng7qMphwcQIit1jlNcLb6QxHeuWNo0WW4eTsLhZ2IGIqygN1eTwCSjFnVY3bZL4AwxKJgMN0SLB9VOJ6H07CdPFVQHJ4M4YBPY0QwVDcUJyVnXMXJrmBYjaJbC5QfjVGnVjgWQWuboJ4wGprdZYaLdUQA0eeWaBVPMXUipto0nD4NBdlEd0M66k5OBw9Rqs4RLxXxF4qo2KVyosQReYblHEbqyglcrZDSR3ldfO3hyBi54BOUNO7iWWTYZnkx4uS5mI72cCZcOvMB5cijHca0K0lY6cIyxQUPxxO4ImZ7uI0j+hCekOiOoH0uJooMAo0fn0F2HyN0QpxkzpqYfgfaFxgTI7Bgq1qwoIDnG+gsIs89SsvxeCywumVtfZYSkxhp2FzaxtGSgUWITO/eNUL2hsxJB87ex6MjMLHX5YEPmiTqUrD3oR18clcHoSEYEhw44DLq7UTEXRqt99i5JbCgnImIDkVEZyL8BOwK0/tLuYi4ay+XACamMIRpbEaeexSndZhKECFaNuRVKoE0PSZYmvzAbYW0vmzQgcYcNogJ1eeoiwTQWG+9yZhdeZ87V64oSmwoXoFyHNCG+eheymaCbiCyg0QCdnkPnKL5uRJ+N0YpQyxDlPCITRejLFCw+TLF0IhHsSiIH+qgDiSXrEhQI4iERqlNjJIW3J5fNLlAZ7JrrnnFNeDcfz8ciXgobuMuCi5xtiO6K5g4BAn3Lj/K0XpItTLL9pE6rbM2ZrvdhhPHYW7UEAYdBIaSU2HJKRBNesTGZuOTQvWF9iHIMtPaaD2Bjk0CqEsWuydZzlLmhoCyYaQlgz5+krL2Ua6fzRlplJ2XCaCmDRmTR5zrokQPwhBGYWTCmgL8sy5VQLgBnnESHyCpi5NnWGq0KmTglzAxvj6F1BLZtY3qBob5RUOjr9MN5fAQbmc3yBlEErbqulB0YxsOmsXEkmgiJmww0XsO6eEtAHSdOsWywJMxHcCLLFNNuB1koJBBCSMErnDRkx30SZCmS6FQspldtcHxFEVRYmWxi4wMY44gTTMAdv7gKETUxa1LOBVn07vVsnhzu0PWPyYMcOMlRByjhYtKMoomuK7NdJokuoj9zTTjk4TxCY4uncbp9qJiQtGkGfuEFCn4hoqso5WDsBlekn5xcFzB7l2WyQgwOwsjWnLuJJRkiWXhZaiVihQQDzS7BjawgX1tWPOzJ0BCrXYH7if+CbF4CLl8EtE8BVEnE4F8i9/mtRxnqxriNZtewYbRq9GRFZe1u+wXbz98zW/w4Jkv8zv/9mO89oUfZLq26YLl//aOP+CTN36Ep97xFE7/+d3M/NZTn9D1bpx7Pm/98h/xqUPv5zk7vv0JHTuwgQ1sYOczRUSpe5poGUbkFCf1cQQCDxvaYJ0zgQhtGKONyBF4+MQmxOiQkBCdvABHboQ0iQZRGtKmYpQJe2AW9MAuJCD6vgMbJoPy1rysNhrQaUMYYZ2nxLlJ4DS0Y1CRQMSC5fgEoTmHApb1MvctrzAcFNnNnD14ZAPQD3aZBGkbKU+yGD9GlRonss4aRhcFLK8QbW7iP1jr1VuDaAt0YCjUhQWLdI8VJaWk+oigqH1MbMM+U4864Qz0bbr40qUVdzi89AAAVztXWVYUFlRLLyukSFSABEYKHN0iknbUEBIpYCFcTM4qMEKhhcfwmI/vNRlu3Y2WQwRjIaaTPAelFUpOuBrJkdaaxiI9UijLOEq8hGpZIsIYHrCZhz3dZLPYyoaCsuFsKdhlByth5eXALg0iDqnVfJpdgVyw/ZNPBiMQ6EZEdzpEHrdldF0QD0k4BCVvjOPiUZpXTrISn+RMsruvkpDR48sHUUWBbgiEEVlUlchdAQMmSLO2RcQ6RAmFI62OmpyCTdXtANwb3YMWZ2ibNmWnQKG0kVJ8JyP7LuPogXtZjgxdZvA7HdzqAu2TCyzoFbztFRYOrRAR0BoX1EdchBB0TkyycHyFdnGEtnS4JJAsRCvEMsadGaIx7NH6aBuBpqCPc6R1lE1DKnH2tY0RNb3RGp2GhVNYlqSQuFGMUqAkiOYiEokwS1l5hWsZllJTUGV0MtaI/P1pOHZcMF2y95rIoUJCgFSOZTBJBULhdQJGFu/GCyep+ttwhAUrjOqNq6MsiCBNhFQSk2N2pmCXUS4iWLH3VAoWSwVSoke3obeU6X7ubnyznIDLoHRPY61dHMfnKOpYhJ50qeyTjCXZTM8kYJeYUERjDUyU3fH9lFLT616TCan3r1tCwEobdBjjEHBUH8Y1Dqkumw1HtVkVw+MtFpzTHFaPEjhdlHAJU10pCZQ0xSS8ywzFxFs1uq4y1ks7tOMT6dDeiyi6XYEQIgvTNNpgkqyQeTPGIL8cJ6AKtEtjtIKYIFimcu5hEMZmZy2NoKsCJRWyZO+nILB9cPgQdLsdRkbKTDduIO48wHJlhnDxDCbSKKMwGhYXE/2zomVxLS8byyIDvHgRQxWE4GxwiOU4AtJQtd4GiI5CpCnhu4melN3p4O57oN2MmfauQsePYThg59QjEbW4ALPJuMSGVmGcZsHJQCmjDJX7FlCNMqhkjhuDKaSMRQPCoJUDJNmK4272XBNJBsVu17DQhEa5NyWEsP3qdefRubBX5QhmpzUHjiYTid65TAKEano6ZsarEE9fTvyIsZtG2uDiMapHWWGB1kzApnMbiZfTjRJJLG29y8FBHKdO58xjiNZZlF+3wPKsYrltmKwLzLyTbYoYoTCOtOuIxCZnMBLmkzoqwZkFgUg6sH3iFNVOTLcygqYHdkVacPaUIY7BhLbdJX8PJfYAcKUo0/ZtxlPXEzRmPE5qyVEHdl8S4swUueeT2pKBkxD4WHogJY6TUISB4WGBOGzfSYwBhUK7ST+HEk3MkKqvmfvr2UViYgMb2MAG9p9o7QWce96F/96fpfOO91EeO075o6/E/cJfoU7dh3E84pkribfeSrjzefz91Ax/xnGe4c3yF2Y72+58N4X3/zylt34z5dc+lcLbvxf3c3+JOPvIRV3eUz6/dssbEELy6x/5HoLoPKmqgYfO3sX/feAt3PiUb2D4W7Yz/48PE55oPaHmbhnew0xtM//22L88oeMGNrCBDexC5mr7gtrugBy3zozBZGnvU2ZXKgAtEzaVL3yEMcSmQ0xMWA8xEqKNXUadYSblFCYNY1SC4eZdfYCWJk4IFYmuirZgmBWTTy6rPJL38MynLJdAOTa86bFD0E01sxO6UGPUUC5jmV19cS0abTTzaiE5QCD8VScnYRtJScWtcd3ss/Biv3eOWGGq08RbFabRn6EKA/EXHLwvlDFbr0NqstBMizsJ/EWBf8ZDmIiVVvJFenkh+oA93/Fop9m+EEhpd/1lAj/mLWWKxUphjGbzHD3HHFKIkpiQc6V9dAuzSGVDLj1HIqSmPRMQDSWhpLJXtywKJAHiTujjCIPVvRGweTtMTsDIiOC675nALWaxPfjaBwRLSzlmWJJJM2Uv9BNODIWiYLRu+0IXh7O6Z+1IhkyPCIwDetiyVrYVn2WZMRoCvcyZ8IHsOGF6+/ZL7iLhFQ7etQ0q108TXukQXO4wv9NhZVcBucNlZXuLlW1t5B7oXBbjXwbFyxQr29qwCVzl4SqPIWeIJbFIKLu40gPHZSTaxKMPVTh2Cs4tghSRbYtwaDsSJLhKEWqN4wrimf00TYcTokus7PeCGKRgbFwkIW0Rx04ITp5NwRLdu7ek7WthNKZLwoRIwK4pyfi4QDkC4alEB683X2tuGT0RZsMb0+Vc9ChGGhzp5jJGiozZFckKBsH8Aiy2NXYUHaIkk+RQcYTpp+1murqJTJQ9ELh6kQ1ej/sicnnllGcHVem2BbCgB3b1hTF2obPUE6eXOeZOsUDHGU2YbQapQJmASBY50ngeYaEOWGDNm/LxRhX1GUUxFbwXAjHjoKse3RCO9aJ9e6Z7DCWE7AdrU5OGL98dIyM77qTrWcrsErKXEAFYWlhGFQ1jYyDxKKghZGiZXUss9M4rIJ416IneIvHoufs4uPAgnzr4AYv7CcWZs7kDSOprOtmmgDNexJ0po1fCJGzQYJAYVeaB+Yc42DoOUUDTJO+0fgUcC6LIoh0bS0gyEAagO7RCH+3UcaQNbe9qq2nlGImJDUtLaY0MQsccOGiXZke3AU1I/0ZIr+LJegzExtBVU3gJ2CXTzrVfIoSg0DnSw85N0BfuLSLoejVCt5xdy13RuNFZC0zJ3Byf6bK0I2DxEo0pGsyo3wP9jU28EslKL0ybmF7cK4SqloXPpZGM2bNIKnuvJvi06A2VJS0aQ6st+MLxx1gOm4RxkH2pJcjYMC4nmDCTtumFGLm1t1kvE6KWEYCJcGQMS8cQRqCSh6kYUYhZhygWnKlcbTexphV6yMcoiYg10fBuO2XHe+fWGLv+Jk2JU9KzjvBcwUgSdHJu0bXgJiTZO/stPKM5cRJOnEyOFwJZ0LC3THGDwi24MGSvuzyznbPl/SwVdqBWhTam7yIkWKIwKlsXVWTXhoL0uRgbMLsGNrCB/fewYBnn4Q/jPPBe1KFPI3RE5E2xcuw7GXl+gdZ3vAs9vBmU13fYhx95O3/y8f/D7srzqcz/Oa98THHqZEQ1PMys/yB7q1/iyvnPs+XAH+F/4g85bTZzrHYr0c7nsOkpuykU1g9xnKhs4FU3/Rm/9MGX8qeffhWvvOGP1oRDduOAP/jET9MojPLdl/8c3ozi3N89xJm/vJepX7nqopsuhODGuRfwj3e9luVggapff8LdN7CBDWxgq00gCca7+Kc8nJpLWwVWDDmW2fdgmVKkKeylZOecx9GVFvHDbTSGuKBZuLRNedgyj0qijK7EcMbFdE0C0fQcgsi+nSZZmvLCOIC2YIdRXgZ15DPWpxaEsJKQTySWxaSK4LUEsiuzbHgCBy/RFwrdnJNddqle7toMV5+xn+mMySRg4UQGlEghER2rpYKbE8LKmF0C3ALam2LhQ+eYm6xxyo8gSB2eNOOeg9SaU2ehknlmAiNk3/Oj4Hic1oHtN5GyogwigbsAxkaBsyBMhBGCQllRq8Zs2Ch4AE0q+pziIg+0/i8b4wpS2vpHJmbDrIQo5PQRSzjR2gJz6VilrnUWzpiGeCXtKZclrmu/lQWHysYCpx9KBiw2LC6m7BxbJmXGaG04c1bDONl5k0AiCAW4oAs1jFuEc48kY99rTDyniOcUng+FZQ8hS0Sx3UQK9BL9lmeH9cLJ0v6ulGHDLGyoRHAnWZbISITEGlzpEE9eSfNwgKJ37o1yjnFpnU5HNlhMwqZUcrxGJ0CkIdIO5/xpiB5FG02kNXW3CAi+WJB0TUzHdDBIpLEeYqkomZqEYyc0Esnp0yQZGrUNyYJEKNyghCYOTdaPYMMYsx6o+ExPryDPJizCOGDYG2bJX6BZ6kIz9Rg1QhoUXgZ2GQzhyHaap6yYVJrZ1OZRsPVY9ucYry2AX2bz7n0snzqKScCu0hEbGq283ruZkr0skI7vAW2U7iBVP9jVK+Qh4i7qyJcQrVoymBJRUJhOjJDCAm7a2BA5N2F2JW6scZKxcUDUisAyzrCAo9nESE4paLdzIPkqZpfWgCMwKFgX7MIiDqHVmhv6chXQ+GUFnciyEwmJTBfw8ZYdK8KOZSCOOtsJpqfxO1/izOQ82ypbEK1z61TG2kLbZiK0YHgvNMxq7BmIwJguGdgmEzqssf0X6W4yplgwzzX404quCdDDWzCFIVxl9e1kOZdUQ4IbLaBFQLM9RDf0cKSDVhqtNBEhPm4GiNiDclpYupdX9kR4nFL7dNJCOw/vWX6E0XYVGsn9ri3z0HMUbUzC7Erbmm4a6Oz5EREh6GVnF1Yr3oJaSR3KrZMEnESFMwg53EsooUAWFtF+CXm5g5l3oa5gxWpc2urn4RGDcWxmSSEFbXeCgj5LOgSI3AaF4+PoCAOUqQFnk82FXhjjwpIgNvC5+S9zefTUtOsQKWupJaGdtNkxNuQeEua1BcxVrW6fF3FAlNzIStpkLUZIhNHcdWIz3VKDQngKMemg3SFoJc8pUyLyqoi6RMw6mCNRppNJIsyfPpSljhgZdfCTBPXNlpPNRLMO2GUAPAFd0/9ZwWbpNVIxt6eInGvz5bMl2t6MHRdpAViR3av2n5ShK4XMGObOskdI2873i7ABs2tgAxvYf6nJ0/fjf+hXKb/+RgofeBVy/iDhVd9D62Xv4NTON6NDSeUlN6HHdvYBXcePG/7oLV/k1R/9aeLjN3D76/4Pb/9nxZlzMLPBYfayzfiXPYsDO1/FW6rv4DfaH+Wvz/0aBxam2LP4Zq78wos58+oX8/ZffAtv/asFHnyoJ6SZ2lNmb+E7L38lH3jo7/mrL76673ttNH/6qV/g4bN38VM3/D4VfwhvQ4XGN23h7FseJDrX4YnYjXPPJzYRnz70gf9Yhw5sYAMbWGISSXuqS3Oug1P1iEr2ZT4OE3AjLWjAJOAJBmplj/qQobXFIxqK0J6xzq0SyORtVA/10ooL0Q92xQJKZWx4GCJzrMECHzW3wo2zt659C82F6CAgynbXE2chedktHk2ybxkYGp5gb30LE/4IkY7RRmesE7curS5MYjpxeGTzJPLEvUQPHcOGS0pkJzllzs8xYJ3V2CD9Gjg+Oo7RZyOMSMV5LZVpahJKFWUF6onRqXizgdXv5I50Eg/AUkmEMDZTZOYikmV9FCYG4eAXBdu2GhxPMq/P0E3Cl9xDPiROZyeah6EC0S7DTGEcT7osJeDQgokJIzAuibhyrtvzf6SC833fC4QvKe2p4JWtS09kMoHxVDfIJNnGlptp8/IMQmOZTLFIGCkGkXPgGd2+Zj5oE1MqSq6/FsZGU1BQs3VLvlAOBEjmZirQnv5eHxLESahSlPxs6RYYQ0F6+KqEFg6NXFiMBXVLlEQJJcjCgVKwC0Bq+5yPYghNGuYTE5mYimOd8a6QIB1iE6KlhzSWFpGy2kSm1ZbUX+tePyQ/y16bgjTEVNBFkBWJyAFGquraMZApCBJAcYjJ0ijhJS3CPe3MITdS02q6nD6THGws8EwGuib3t2MB5KpsYISLLo31zQuT0TITp1z1xuHKS3tesJMIdEkTIhyroWXDlXLnSllcBmR7Pmm7gyymIj0C5UvOFS8jqm0GKVC6Q6xTPSIL8EoJsuChG3N4owqVbmZmCGGOEOr1ws+AVLsdoUALibk3hHlbuKuGeuMhBcQR0vRAkUYjF8aI4WT3y7TCZXSowTPE5SmUcBFCUiiM4O8fJVRd9PhuTOn8Oq3pmmpJgamQOpj0vtIW7MqwIQEibEIcIknYp0JYto4GU48pPnUb0fhOTGGIfZPXsGf8atu0Ym7hSxCGDcM1Qu0SGUWxoDBSsyAXiIkZPzzK4qd741wKT1DqHM76MmVLIaATt5PpJVhsByyvGE4EZ3phjAY8p2xBLpM+NxKLbchbQRQsWB+JZB3vlZERoBSmFwGeWccsYWLTYwwmPeuHp4kTmrHY7GIweIsaZ0X15nZaBRmxkuiNaZNstAiDjEkW90SrzC3jRCGT7qWMCZtUq6clJrKw0rRjjiw+2quRa7/yHpSYUzGO44IEKRKUKX0+S8PYTJGpaYNcOEQcJYxgYRnCobQsTC3TB1/K2ixh3IS917bJUQqhXQQ67jiPnB5nubwNv/8whLZZWlMQKm6UbNdXJHT7nyOA1QZLQ0Fz5Opss0c61DZtZGjISiekJpPQ5zQCWgjRY3YB0thQcyPBCZJNqjVXX98GzK6BDWxg//mmI5yHPoR7x1tRR7+IcYpEe15EuO8l6PE92UvX0gc+iRryKF8zAdgX6dvvgH/8Z8Mnbz+J99xX4MkpvmPbX/KUFxfYthVc93xI/wTwrcC3cub0Aoufeh/1x97Od+nfIjj7e3zkz5/B3yx8O1NX7ee2Z0q2bbOL7cv2/zTz7dO87c4/5VTzKK+48hcI45Df+cQf8K8PvZ3vvPyVPHXDM7OrjP3wXub/+RHOvPE+Jn/28ovukm0j+5isbOTfDryH27Z/y5Pr14ENbGADy5kgYfnUY5RUIKEhGsSRZdcgLLNIaAvYyGQXWiJwlKE0ITgTdyxDKzmnLAhERYFn8G9qIpoqy47YoY1nfCJsOJuSAowgzgsZJ2waxy0hVC4cp1fp7Geq6ZNBQMnu93ADmk3DtD/H2P4q3tmFLASmq0O8PEUsF+8SG8uiQcfESxqz2ME4BtdziZLQQ9Pzu+1PX9usjCl7y2iihTjTJUvZBo4jiBwHpVeohI+htdVFi7VYE6Lh5bJhIqzGWabZlQBAI8WcEywkUgiM0XR1wKI+BzHsdEYRTUHhpAe1pDpjLRqbx5nVBYqqwHx3CSUNrYkunXpEselnDDcti71KJKaQFOUwc41R7jn5eRzpJo6WQMgYb4NCPhxBZLI5Iej51EJCuy3Q6F7XJ8550fMI2mRhQfmdeSGcNSCbNhohJKWSwEnnCjGlomBm2nD0GAmlI62HyH6KpM7pJlVsIrqmS5SE2J2MTlGlSlEVcIt1thVuYdK5i5O565vkPtDaZMwamWSoMxgK0RlboxgiE+ABQRTR8maRI9uBxexcsQmJhY8bW5BNKpWb6gIn75HlwRnlIuMQWSgSxMPoehO50e1jCqpGsXeMMZbGJx17z7vgDvdQAONodNxDgJcuqaB7RKzs4sY1gKEmh2lBBoQnjc/KFSlxasuZvpvYCeez36WbC8MyESay5XYsH6OyuUzwiCA+sEJ52LK2sntKKGTRIZ4PQBu2bpec6BZwCmEWVRZb+pmddFIgpUG4Cj1xiY0E+MKHgW5uTTE9rb2SwCRZRpcKOzJWo1TJ+boGZz6mNASHgkm8eNGOh5QWgNYOqbstZZ4pa4GUlWASozVxo0pcmYTlXg97jk8zCWHT9Y0Y6WCSUMy8nWudsucVPeDLtjsBVyKBk3flpUAduwN5ytYlDRMkxjJMpUGP7yFunYDmQQpOCSHaVgatktsVEJa5eOOGnTxl5nL0I5IH7nBomphQWoDLX/ExPXIVBbNIDFn4p8yFc2qtszvz0JkmgYZxp4I2XbQxdCMY9iqJHmRs1/I0867WuK7BEz5b3K18OX4wOVNv/k+MGFqFXRwU963pQ21CTKh7mX37ANseUArgrSRg4ip45CAHaDclu9hKpBVInSReMPb5mUxc45UQTYOnNVmykVT+jf4xBMF8xngDxoss71KMnRMI4TDlb8CRRRxRARYtK1TY8wm/iJAGog5xoJFuFQRs3QL3nyjBylJSJ+g44yyULmHF2wA5gNzk2HMGwRm1E8ddohgZOvd0IQGkZRwmfZSA4KUCcr+PORohzrAGSTLCSXTnDCiIp/ezpCuweLctIB1MbZrILdI6MpQdJ9Msj2ny4RT0gkT+wIK9zc1t6geT+60fkzyvDcCugQ1sYP95piOc+/4F7zOvQy4eQtc3Edz8KsI93wCFWn/RTsTSh45QvXUW4Uq+dLvhz//CcN99UB9tMfXNL6elVnjNC/+BuUbjCVWjOlan+qJvA76N1ukHiL/4dm713s1zZt/LPScu4a3/8zt5yLuNW57h88xnwI9d+2pGSpP8ze2/z4cfeTtgdT2+/+pf5SV7f6jv3P7WIYaet4mzb76fse+/BDW0mq+/vgkhuHHz83nnPW9gpbtExas9/kEDG9jAvu7srW99K2984xs5ffo027dv5xd/8Re56qr1w6ZTFpZKHPX93lUY2eXsWQmTqXMmkNoKoAOWcZMct7vyBT6OzrS3pADnComed9jt7WJkskVwWCDaEJgO98Z3Ms40Sg3Z0Dch+0NdgFiUwfUxpZHMyUj0eldVHro5sEuqHrNLJqEj3kiFbbs8RNsjCFxYgjuWHmBrbZIhozHKBdFjHyyES2hVx0iHuG1QRrPx8lEK89t58Oxj1nkrxRBbx8QYwDOYQCAKtg+0MWgMUSEkFuAYZalsQOwPo0UXwTK6a53ZMISyT9bA8k6HeETBEhlooInRxAnMKNhSeDqXNh7lwIHjiC1uku7esn6sVpgNuYzG9yKX7kcK2DBjGD8O8YRASAc01JwyujqJ4x4n1qALGpp21LvOCMuFbYlLlg4ENMQIQgomKrNMVGb5/Bfeg+ik4UlWBF3oKCXWkY6QHUcbXqlN4tilAEvCSlEJs0DULICU19uScq1LonWMSj53nNT7sZOiXErnTi7kKWN29bR/4sTJjQlpm1auLGAMRVUglhJP1foYiGD1eQrOImG3l/1S0QtjrAQHATgeLxLoCA841V6k427Crc+wGuzSwkfGixTCkxixNZv/KdhV2qsI8siTMJjyOCZYROsKNC2ANzrSX09ntNT/gTEgJK6T1FnIjGnjFhNgMRmbqOQgx8pwj/27VAIVWWBEYNsdbhpGFhczXaceKxEm5RQTpVk6me5RhHv2AcRmB1oGpENxYpr2yWNIbYErYwxDogsHurQBmiHRg10KG1QSSgVI1dORCmJKNcW2kiE600tsYbRlCxrscVKC8NPUjCVMYxZ4NBevKzLGZa8dEKlyxuxyHDLGydioFR03Z+zfsxsExw8IBBEy7r3byYyJlIRZ6SatPUcpLneRZcXG2i5OnprOytdKHueWDGHcxfXK6Ml9XMiU00vEYIfXMpLGxSRSTSM4lbXPImMxdbfKQpiE5aaa7IkOUnpPSKEyjFIVFFE1xllWLPtbMcFBHKEoVGdpq3M4yqPTXcZXARJJHIu+zKVC5q6TZBVNbbFzNkPEl50idC1TLcawvAxhbBBUSOFfN785oA1FPwk9DgXjaoIVzmRh7KTNkmm4n7UqVZZZRuu2BbvWCRONjdU0S4Gh9KeUhb5yXdnFmALHTwooJixmiUXLlLDroVTgVgBBIThOFFsA2kiT7ALYNSNURWJVJKpNoRPA02j7bAsbEqElQip8isyyga70EXUJi4miozAYJYlGduDNHyDuakRjI8ZxKRRiao0izRXLpEznxIo/Z393QJc8IEQIvXb9Vi6qayAAglw4o3Sze0inlFLRY4HmzTglUMvZwJjKJKEje0kV0nW+2OjbiJI2VpeMIJoHJU26BaeJqjEiRcTWvDSsb4MwxoENbGBffdMRzj3vpPSm51H4wC9iCjXaL3otrZe/l/CK71wDdAHM/9MjxPMB3Vu28XO/oPnxnzKcOwc//0rDFT/w4yyIO/mVW17PXGPXf6xqYzsRz/5Fuj/8MTrP+HV2zHX47St/nr/YfRv646/l+7/zLD/5M4aRUz/Bnz33Y/zkdb/Hz9zwh7zzFffwP/b98BodL4CxH7sUvRxy5g33PqG63Dj3AkLd5TOHPvgfatPABjawr01773vfy6tf/Wp+6Id+iHe9611ceeWVfN/3fR/Hjh1bt3wmEO9OEslSb5c5FZ/NoU3GUdluqk0rb7OKWXdAZtopTuJpDqkh5krTzM5CrSrp0EFgw8SiJFxDCUkc5V41DcQUiRs7bJhSv48IkAEOoVPLwhiHasKyxIBoW4dNpSlm3EsTB8q+PHtJ2EYzanPH2Tu4/di/E2++qbcjXZnoXUxKdGBBmw1DE/iFxLnxwHhp+I1laVinvwfGGKMJ4oDOtg7jzgRbnC1ZanmktELaAN0kxC0Ez+/vg7wmDULwmcU76NLFEz4CQ0HVLSsHEHWJmfEsC8poyyhLxk4Ky46amBLUq4lOlQCRhPy70gEEngvjcoJdak8mIB/JAmksXP4pJozAi+aRx+9EPfxhuLaAvjmhcOgI6YCj+8P0kwglDBJTm0zkjnJgl7BubNbqkiG6xs1AKUjm3CrTJs6esVnom0wc9bS4zp+jp9m1+pwGaJteshmRhGr50rKkjHDRBmLpc6T+bM5UrmKpuD1xvnQujDFJGJADHx4NH82017oJulv0+sWTY9Ol7VoRMy9e6nt3SH93RyXBTNDrUwkoF1MdxZ0p4DgwMwPj46veOxxB8ZKEJWFjREFIVKmenWvYsWFVwon7woeMhjvuzrm9UuIIKwZtmSsOul6ifOtGRC7LYsr1FELgem4PTItXLOjUUFC2YPrw3CSzcz66No2JNKazCgFPFgITWUaov8myCd1pmwLPafgIV1rQQkM0tjPp1IRllzBeLNiVm0dZTFTaWNMDu/JMNXuSFCPs65+kUwCoVm1MsjQW7Eqd/dT51vTateIGxKUIzyuwqb47m4vXXA2lgp0b4TqZFNezTSObuHLPCFkoKhB0DVOBSzU6lTAkRbJRYY8pO/2aVroyg64noXUpqCMkzmSSmbfssLK1zcK+FfyKj7dpF9HWp4MQyJrHSGmcJhFn5VncZLxWcmCXynfZmZBCwsINC+N9bWkmx8YYwslLke4sU2qaUrmIZ9ysXmmyBaE1Bd8yqcIQZJzqXyWmDUJasEs7dt0J6xFlWUX5HrHpQGwwOm1zry5GaIaGkrBskZ7VrgX2P7s+ak/bEPCEKS2EvaY0mkJ4EtE6g10YPUyxga9aiDSMXaVjZrMxGilZLmwlVv0AtSG283cVgKSlB5sdjJswuxQgJcaxfRV3RTK3kpDbsgVVW94s65qb3h/JXHWSujkS7ajeutqrGAg3m1c6XffOgyBF5QmWS9sAaHcF84siYWym6/f6PKv0uikLOkrfUWQCdhmVvYPo0bTwWgBz3XNfVKmBDWxgA3syZgzqwQ9QenMKcg3R/obX0f72fyTe+nQQ6y9BuhVy+nX3sLJhjO/90zHuvBt++AcFb/lrwyPDr+JTh9/Lj1zzm1yz4Rlfubq6RaJL/wfBy/+F9ovfQHX7Hn5k12v4wLOeyYvcV/OXf3iMH/ruLdz57pcy1f42Jiobznuq4u4GQy+Y48wb7iU6c/5Mjqtt5+h+xssz/NuB93wlWjSwgQ3sa8ze9KY38eIXv5iXvOQlbN26lV/6pV9icnKSt73tbeuWT8Gulr+Z09WnZm99crGLPNtEIDFGILSElDmTxvLoKE2UjkShpQ0zk6sEqMplQdGzL7BCgItrczEKUEnoHVgnIyOEKKtjlL7X58kWqbRKZf91HK/bNd7Nafzo8QjXcTNgI32JLqlCAu7YzxY756zGVuqFSYXxDFtqW5COQAcGdIRwQbjK9kWlp4uDkNkOvnUlJUhwL63xYOVhTFGjhIM0ImN2ReMSI1xUR6GjkOWmIYoEedzDAM5qYCdpXkH0GAWSPM0oEbg3mkhYUfS0fnpolnhsS8/7EwKRP78QVKtw1fRGKrJqQRCVXtReOHBGQVg/SCGpBg8jFw8jog4S0/Nk4xDpiEx3ikqOVQU86vqcKNfQAkyu/g5FwCAT0eLDzgLdYogczYW4uWudoNj09KxSsEsnYFPPuc7XIWV2rQ92aXSuDOxqXGJ/V9KG6pjUoVVQmcBgAWBhDJp+sCvMZQKzczZh9yU6ZgXP6QO0YhNgpEvbHcfRLSDqiTEj+1hljoKbts31mI81gRiSTE0p3OGpNf0kBBQ2V+3vK5blY4TE2/c04rGdzBYnqcg6BdnAd9y+Yw1kgvjpuZRQSNnBOtAque9zx6SAGtDyZ9FOKQtjdGPLJopkMaHc2P/NxCVQncTEBt3qJZKwloJdtmxpR4n6izbjDBcYev4mnNFiBnahDSJBQowOs9BrlF1jhJMLa3V6DJBKBQs35HG2vFa9NolmV+++yBhkGePIAuXSREjdY3aloG1sYlqzAcwpQmK6cQclnX74QoCn7H2eZeN7HKv6Q5TL/Z8tr8DKGbdvXDD9IGI83cWUtc0MKDxMwQIsqXahEorCtiFqz9qIU0rmhYJ9uyZ46u7LwLXl/bkqwzduZrmi0FLje2uB6ex1PrLPFzHj0JkI0Qmok/ZByieKteGhx1xOh5ehh57PzIzAD5JxBSbGBIUCEGuUiLMwXxWnwGLKxkoeJ1JhXMHClSWacx30fkGwUyG8Fn7jLKLTY1mmpgVs36EoFWK0sPe5ZccpNqhNDEtLoYwLmk5gQdVUe0soQ0E3GW5/2Z4sffZIxcSoZsOUXbuyzKokjLA0a6LJMfUAk6wrchU0o4WXCLv3kpcYlaz9YzuJyhtQWiGcZD3yShxpPI+uU1/T3uxYYxBCU6nAhkslenOVeGoIo1yc/keH1XoTTu9eyD13+4C5bK0TdJwxAE7Pwxe+JAi6vTV5LZqWfJxM5KGGLZdPHmYMLC6k1EGIt7ks72ghKuueau25L67YwAY2sIE9MZNHvkDxbd9K8T0/iXGKtL/hz2l/+z8Qb7kptyiuNRNq7v3RzxIcbvL66HJuebrgbW8RfOu3GF7/xVfxnvv/hpde9lO8aM8rvjoVF4J40/V0vvF1NF/+Xtj7fF4w9fe871nP5jXP+GUe+8Jj/OhPGJ7z/AX+6s2GY8fX31mYeOV+TFdz4ne+9AQuLbhx7vl8/uhHaYUrj3/AwAY2sK8b63a73HPPPdxwww19n19//fXcfvvt6x4jkTZMQ0jrrKfvqfMB3kOnsF5osh470mb9MjbEROgwcyikkHScMQQ9VlJ+GS+InOOPIQLLTEoBLgDlWaAn7lpxbsdHj21fU2cxqRC7XaojLjoBFFbvdkvP6qQI0QtOcaXDjSNXUlSFvo2UXqiFoHtph8ZNDaSTUo5ihAPCTRhRfg+c02UBVZOF/gipCG50CCZjoh2pJplCIBBRAnZNCbo1SXvcoDW0mvYy1XKeTkCWJj6tV9qXFuxKNIXQbNuaFlEgEmbA2fsThofAEINQiJaH80DiucsesysdKEcJRhq9cSv4gtFRwfiYvXDHm0YWi9Qba/ta0XOyhInR5QZLhZ0sF7akpDpEImAc6pBH5u9Ba8uwGBPjXOpcTkEOWXxuyGDcAvHWrYnD1RsnVVob8p8v4yVgV5yCXQnaNTmeH+uk75JjVM6xEtJwprSbWNXYrnayp7SD6XKaCUwQC5uhsO3aDIzVqmWkRZEdC5MwGpyEjROJfKiTHQ8g06fzPScLwQSSDH0QCwv8iUzHyPa5thGgGAOuCyXPhg51r3IIL1WY5BbTpQ3EG64h2nJz7/JJFr6hp7iIwDr1tZtG8Cplnr75RWwqTQGGEXcbl/uXr9LrMcRejmUGuELhmnmrsSVUwmbpH5uuP06wcZzl3TsQshcm6cQroJRlkiaUP1nIAZmRXgt2pcyuEHuhXL+lbDLhSXSQsPqSdI4mjhM9K4nY7FLcoJCV3LFOr53TU7a1Oh0gkV+cyCKqRHK/95v9WymT6M3FiNjLEJyMfXh5ie5oSG24DsawEq6gZD8gJbAyGGAzeq9OjLSeSaFQabNKgqiS9EMscuc2FnXNAFSI57qYirYZ9tIwRhLhenr3iSyoTD8RYP/0dWuiJgrjVSbGLLPIVWsBi6weyxoVt/CLh+gO55hr6elThq4xrDSNBeb9EkoJzJ1uUj87RkEACIPrxBkIo2JlwaH0Xk/umyyMMR0LV3Dd/gmC607T7nwZcfz2/npg2WXS9dmyKbTsMeJkE0X2F1UQx6BtzCzHwuPYx5skE47POl6ipKaYMNvS0HuwAJ1O1n6H3vphNGgRJ3rs/XMvSkIqzVQBV7pWt1JawAq3hBYCFUri8d3EM1cinceRTlGCdhuEiJDS6mrqsRJIwfCY7OMgpDp9RjhEE3tZ9jfnvuzpbAGZ+L0xoIeKiBGF2GjHZHkl15myH2zPTpfc542G4paboVjKbRTEyTtJMuZSKuKSvtgoxoFm18AGNrCvrImzj+B/4g9xHvkIujpF59mvJtr1gj40X7cj2neeJTzVJjpt4+md0QKUHB749bsoHzvHxzZcxo/8yQRXXyVY7Jzllz/043zuyIf5jv0/zXde/rP/KW0xjc0Et/0G3Wt/BPeLb+aKO/+Rv7v2XRwbeiZ/c+AHedNf7+Sv3gz7LzM899mCm2+CUrJA+5trjP7AJZx+zV3UX7SZytOmL3yxxG7c/EL++Z7X85lDH+KWrd/4VWzdwAY2sP+XbH5+njiOGRnpF+wZHR3l9OnT6x6z6+lXctenKxTdIqVyjUKnQOQavEIRLX0KhQKi61mmVLFIpVLGKzlUq1UKbQfPsd9F5b2gBIXCAlVdIfJ9KhVBuWxfI+ulIVznDI4LjlZErktBFPE8F9mVKO0gi0PoTgfj1ylXyjQaDZjYgOs+AlhnqeArNm5waFYcprY0ePjRLq7rgufTMS7SMxhfUiwV2H9pgUKjSLVWI1MzBoqtArpURpTtNZarRRzXBd9HVQqMjY+wPNKiu+AjPEmlUsR0hygWipQaFdqOB8bHucGlJn3O3u1SiHx0tUq5FqDdLsXp3TTDU/iej991KR8F4UOh4dK61FBpFlle1rguDFd8xms14rCAdAoMby0SiC5+K92JLxAag+toav4Q7WKBuFymVqvgOBUOHwlZQVAsFimVigTNE4wMu8TSp1Qp4q/4MG9PJQuKqCSp1hukRJCiU4aOT7VWxvcjlKdwXEVtqEjNL7G0HGN0gThWeK7Hpk0l6hQoJ6JYl87sRftlGpUG+kyBypBClRrgKlx9DOPGRDrCkQ6e61EdquCd0kgp8R2faqGKR2DFjzf4LF3SpjRht+TLpQoriYD50PgIfpiAmzkaS7VYpdFoMLwi8H0f13GoViyL6SlXxeybrnPPCVu+WC5Spkx9qE6j0qC2UMv0xqQuISvD+LrKuJxgw5BiqFbFLJUpjwxTriiCiW8iOqEoC8HUlKK7HFEOCxhjKFcquEGFipC4octJ5yTTZpiSKOEKD8/3cSMX5Uh84TMyPMypuEaQZHGLTdfWX5ZwtUupXMLXPq6jKXg+nudTLjm4rmv1u0ol/MBHTNu+EkGMPNSlVq9Rn7Ugcbc+Ae0FGo2GTRIxXiUo2FSYQ/VhnEYD0/Uxp8r4nodwfaTwKRbL+JHt64Lr4xbLxA/YcfB9gaxM47iLKCnxvAKlcpnh4TLFYsKuKy9QKCmWNl9CoeRRbD6EiX3K5TK1qEtlZJrCUYPX9CgWinjjdcKE5V4sV5Cu2zfGxtGw4uO4El00VOrDyFVarO5ISOusAQfckRpnjvooBdVqmTCEQrvAxKUOqj6MSI6VQ1Vc10U6LtVqgdJiEcdxcVwX4SmMsuCQ7/s4hRKu6+IXHVzPxxUuhYLEKUjcyijuUY9Go4Y0lq1VcCpoFaFwKBaKtN0VZsqzTJlpOqbDSmER2i5D1Tr1Rp1y2WooNRoufrFMZaFCoJrJd6toW6usUW9QdxucLJRQlxkmV2aR9xg85eK4gkISulkoFRClIvghwrH3i/Q9ZGhBdLfiUq/XWdBlyu0yI8OjGSDsxBI3Yf01GsN4jr+mHju2NeiqAt5BDzeX5EDOulQK0DxQQQgXzz3F0MQIlZM1in4V3w8JQ4PjaFxHoKSD47r4BZ+iKlIWJcpuB4plnMhFKInv+wwN+YhLS2zeUeLw+1yiBHBycIi0olwu4zd9/IIirlTxfR9P+piOi+v6zI1u4LHmw8SeoVQawl/xkcqutQBu0aM2MoFpH2WkYTixrHCkh+u6uICrXXQVXMdFKQfH83B8Bx26FAouBVXAlz6FrgTPQ5TLmKgC4QLKcXGUg3IdcF0cFNJIXK+I7/vUyqOcXIxxXPD9mHK5QKlVoOCDL30WqlcQusMUO4fxjQ8b61xywxxHteJgpcbx1gEua1yLt+JQ9srU6sPUNk4xFEaUy/GascvGqtLiyNGQyVFFsehRr0uKzRJXXD9E9Yxm6bSP66baZaCVoFgeojo5TeQMUWk+gK99tB8Rex5+Qls21QJiuUOx5FA0ZQo1O6fTuV0qFiiXy4h6Pbs/y+Ues7E2VKQsy6Bc5Ogw8UoXXZ6n4/vEvk+kIzzRxtUuhVIZd8XFL66do+vZAOwa2MAG9pWxzhLep1+De8ffgVcieNrPEO5/Gbi93YvgwDKnX3MnC/9yENNeTWO3pqXPZ551E9/zRxtxCx0+9PB7eMMXfoulYJ6fuO53ecGu7/rPalFmpjpJ9+ZfoHvND+Dd/hamb38rr2p8kJ/4/uv5UOf7eONHruK3fxf+8E/gmqcYrn2q4NprYPzH9rH0wcMc/slPsPXdz8WbXcu51Z2I5udOEZ1q428fYue+/YyVp/m3A+8ZgF0DG9jA1thqnUCTE5RfbaNTl6Dj4/hinGarQxAEaB0RBF20FARBl8qhmDAKQYe0Wk26RuG1WgStFk3XJYxCwrhEEC3QCQK6cUAQCLpdaDYTNks3tOcAgjigrdu4TkxL1Aj1KYyOCMKYthwhDgWdoMP8/DwrrRXCJOWiUIpOEFEyMY1GxPLyPM0maHc3Y9G/EemQKOiig5CgGyBkExW1WV5exlS3gltEnrybdqdD2+1gRJP5+Xk67RZRGKKDgDAMaK6s0InbdIMApQTNpqbZbhJ0AlbCJu2wRbcbEKkubQTduEsnCIhiTbPZ5GDwCEEU4BV3IaoniFciOlpjhqClI4JA4EZg4pAwgsjt0mw2MZ0Ote0xraBFpCOCwL7oT3gTbC5v5VHhEHUjOrJFkyYry0u4nSZBYDClCkE3YGVlmXZrGYioFDocPv1odh4AR3m0OzHNZkCzaWll+0auQLcU7VaLIDC04hZRWMDTDq12iyCAru4SiZAwDNFhl9gJWGlLhI6RgULKIvPz86jFedphjSAIMMIl1F0KSjMyFHH8bETQ7aK70A4CQh0Sd2OCIKAbaqIwJOi0aBcDTFK3VuATJePfMWHWljj5HsCJfebn52k3V+z8DQ3tNsQ6whhDc6WZtdWJl2h2miwvLeOEBVaWV2h2k3PpiJWWixtqAgJaLVhcmEc1m0SLyzSbLgvL0Exw0ziGjRsDxhY7nD0LzWaTdtegolOEUUhHau7sWraIFi7D1WnC6H46nYhABrRWVmi3OrRDe32DRxAExHGEF4W0W3aehRGEhLQjSavVpRuGaAPtVtuWT/uiDHKHpj1hmJ+36GarNA2laeYXFpDxIk6zacfGwNLKCmJ+HqIuTrNJt1sk0AFB0KYVdti6M2B+Ho6feACAWi2mUd3B0qZHUMerhFFIrF2CUNNstVhc7NLp2Pt9ZXmFdqdLq93CmJBOECBap3CDGBMcZSXcQKfTwYRdWgToQkQnaUd3XiCWBd3cGBMHyCBALVuG5XKzjU7amFqn3czOoQLftlM6tJpNhIAgCGi3u8TLKxjXHttO1hcRGFrtmHarhWh3KYQhItSYMAZtjw2bbcIwJIw0QRghwpBuV6ALguWpCrWioNVcQDFErCPiTkwca0ju5TAMiQNJqKqoKCBqtYmDgIXmMosLC6TNXVyEciQYUmM8dOweJtxN2fw9ny0vreD68xxx9jHe+QybGg3mdZfuShcdxnQC+z4dLS3gdNp0ghhpBEEQICMH2Uz0AzsRZ86d5sFj99IMmiwuLGbPjvbKSraGLy4u2iysq6zTDgi7AW6ks3UbQJQEsavphhGiqTFxhI4lO5xdRFuvZOnYx+mGhih57Vc6ptUO6HgtlKrQDjo0u010NyYKQyQ+nSBgcsIwNgVCPUAch5xy91EMHiTSEaEOaTabdDsB3SAm6IQEMqAt2qgopNPu0olcgm7AUnMZXw0TBAFRZAgjC2CdXDnF0YXTDDWbdNuGOOoSu8bOgzCkU1AsbViBCM7uW2B8SdI91bHPojh55oXQCUKIBbrZhMDOZbG0TGQiQhMShyEi7BLqkE5o18R2M+LhR+1iIwQsLs/T6XSIuiGBCDhbGraJUtod3CCgrUOCToe6V+W+dgs/iGg2mzTbLRxTYHllmXi+QHPFcKHp5AQdnCDgtLsVp3M/Wgsu29dBeEssnl0g7HYJQ7tBoBTEkaDViRAryzSbDrLdxg0CTBSjI5cgCWmPSwoVBDRXAlqqTCDt561mM9GYa9FsNolXmtn92Wz2WG8rS12arSZGecTz8+hWMr6R7a/IRHRFBycKCcIuYRTSDS4uDHgAdg1sYAP7j5nROPe+G+/f/wDRnie87NvoXvsjNtNGYjqIOf3nd3P6z+4CKWl88xaqt87ibazijPh85nbJa1/dphJ1+Maf8Nh52Sf448//Np85/CE6UYsdI5fy6tv+ji3De/4LGwoUG3Sv+zG6V72C2sP/QuHfX8uLmt/N85+7n4envp9/uP1GPvkZwcf/zS7gO3dIbr71Bi77mw/y8Dd/gLnX30TpMite3HlggXNve4iFtz9CvNijepevm+SW73oh7zzyZtphk6J74R2/gQ1sYF8f1mg0UEpx5syZvs/Pnj3L6OjousfUCg22Fm/N/k7Dl0wSfyYQFJNMY8aRWdgCCJSJOdY5TccZRckixAvJWdKQnt51KokuixGSgIAFc5ZhZBIeZkMj4pgkgxWJoDLI3Ena/ixwyIaMTO1PnDBD+6rNTK7chX9UcNgctTUQ2NiPpK4mEV7OUoslDtwjZ+9hnF5WKiscZpCJHkgvmlAk8UVANw0HdGy2Ny8V5rfhIUHUwXeKbGlcQ3v4/+JtUIQ7rBcnVgx1ZxNdcTRr13oSJfkwRikVnsi9jidtSDOD2do5PYH6NHOYjlju9uvQOEJZAfeco1pxq6gku2/Tm0WLLwAwVdtEazE9v82+R5r1Sgr05D7UsTsQOkq01gwibCMTwWnjSOiC54HnySxK5bHji3RkiaIzRs2kAuK2fdL0wv2gX5+muKsBX1zbV1kYo5uGCcYo4RGT9HnuHGmI49pzJCGgREhyTnzax8nUyGcOFcDklMJpCUQS46ZxcFJg2S1hur3wvVqxwhk0OgkLVlL2dOVyZnJjLTKVdCuOvrRkwNhMo6NenSG3yrm0/sagx2V27/S1TworQJS3VRnLzJBHXCwztwkqjuAe2WsXwNIWxWUb9rHnkh3c/YXD8OAnkuOdXKiWNX9rDR44k4UuCSUxQCU4lBQoAR1ERVK5rI7aVqfzwAIA4ZEVnGGfvEB86fJROu/HCtR7AtRaoEV4OXF8VzJf2kfgNFACnnI1mLuyzuiVK6Z6fWn0nLH3tO38/jDGXF4KQz78VWBw0MLBW3gIP7ahXAoHjcOoGMZzS4htS8SywMnajczMv5f9jb18cekorajd13fp70P+MEfNYywF/aDeeqaEwnEgUhXKW3aysXAX+r4CB8U2Rs3dvYI6pwPnwJbyLAfkPI5yieIQI+G+U19iJQl1FetUTErRd0/lzZEuXekgyzJPpgVhdeaMkIgIcKyOVscdw9syTu1sg2brHNALk19YUZghO/elKyAGRzpsLs1QUJax47oCFwto6OoUZsEB+sP0euOWhEAmo1cqCczEXpzOQeYXT7K4+KC9fnEIWEQJRUzMZ099ltsYQsahDY2XCmEE50r7CfxF4K7sWi0REOgW0oB0DbKbCwdO9M2yh0oYYYRguThHaQF62Snt91LnwqANGGHHbrG4l1Dkw9CTIsm8dmUBZM9niE1snye9KMoLWzLOoTdC7FSAJjK5f/0dddzjCg5HvaIaq+uZLif07h/XoXcP5cIY82vU0BAsLIIUqeL8+ppdxvHQjTn00Ia+eqbijEkuSlzcXubeQRjjwAY2sK+2yVP34n/4N1DH7yCeuZLOi9+AHuuP8+8eWubgD36czt3nqH/jFiZ/+UrcMZslJooMb3yT4W/fqtl09ZcYffrf8ZrT/0rw0TaN4hjP3PYSbpx7Afunrj8vc+G/xLwy6vofYmnHN+Hc+y68z7+BnV/6YX5pbBfdX/8+HnJv41OfVXz6M4Y3fKjOVP0Z/ODJjxO/4L0sDw/hS413Zhk8ydBzN9F48Ra8TTVW/v0YJ377izz113fynpdKPnfkw9y0+YX/1a0d2MAG9t/APM/jkksu4ZOf/CTPfOYzs88/9alPceutt17gyJ4dq91KwT+UZZabca5EyQVcDd082JWIdQO03SmqyQu6bmyGU/blMy9kW3FK7Heu5FH1MEvhIjDMkFsmeVcG7AszJBoricBLXlMHJO6IxN00hhma6X3sKpySYtirc9g7Yo9LnH+LnOWeDSanxwMcXHgQFeYAoUTkPc0s744lQF+cgDCKDGwywjoApJESWuIqjzDu4jsF69TRL9gvgGF3K8fE57PP+rKU5TR+pJBooxNwibUm8p641YZBR8SpCLVUtLaGTB6e4kzreFIXB+hmzoYpj2Xejyk2WC5sw4h+8C6tT+r8i1hYUNJJOilVTk/C8Wp1mxUw8BzoJlUDSPTezs6D9tp4qoxKPGKTiDELo/uUztN5EO5pI1OB7FXP+jUC9cRIoTKtIykku8Yu5/7Tt/e0iFZ5QY506cYabWLa/mYIbEidSNXKk0bkReJt1spElD5z9HpomHIELW+actfOyWLBsQL4WiEUSRKFdTJMJvdS2sxaFeqeoLUER44IwsgCiFJIdlc280nAlR7dTMx8nXeh9V6PskyEyfhvrxL5w/i+YHJccc/8mq5m82ZBwSta/SOR1jfNQNcr52+uIW6uwgn7txQ9Vx7A1KYBC264o16mo5RadC7AnSoRHrfzQ9WSUKjIhgOzTgidLOfC5pSg6W8EwBFWzNqpydTTzsp5+7ejv3gQp9FN1raesP5qsCs1oUQGLBTnlF0jFgWBM4o0JykFjwECaRSBO0xXjIFXtgL6qYC4kFQSvcGyV1tXW6joWqb/l49/au2Xq9suFEIIbrnZoJYlnIDSiI8+5dAn+aXD3hqjYHNphonRORa9TTx49k5QcLq5fubedH1WrrMuSJuVKzRwpiXkI+dlKrMmMJEGBd3qJs6YvUyZtP7pdSDw51gqliBJGCGUyJIDlnJZJPNWevomuvfWqdx5IDvX/kvBPAp8ubeWGAxTk7ChATgerldjPnoY0V2235dG6CpBldiusdK119YRwsS9v4VAJs+n4Qacm4dD8cM40Qqj0mLJEonwBP6WKu1WCtKk+nMRsVsmcgpAnIFdJoFeCnIEeChr36GVOxFA299ELPOb3Ln1Ugh8t8CGoU2c4j7ONE/YzZv0+UB/Iol1LR0HnWOEp8f6CneuCHIZtAWqzpzDJgpYNYk3bIAoAbyOHoNosoY6tWwz8UrBxCiMjsI90oJd2ebNecAuIUBPXLKmnjbTqcbBpSQqbFZ1wrSRYp0beB0bgF0DG9jAnrh1FvE++Se4d/4DpjhM59m/Q7T7hWsWw5VPHufQD34cgE1vvoXaLb1UuPPzhl/7Dc0dpz/AyEt/m5PqYYKFUZ6786XcOPd89oxf3Scu+9/SHI/o0v9BtPebcB54H+7nXk/xvT/D3sYcO67+Pl72LS+gEzrc/8AI93zh+Rz8lwfxHzlDJxA8NLKLL1TmkI/5zP0TbNoIGzfuYPMvjlD59Q/wine9nH/d+c8DsGtgAxtYZi9/+cv5uZ/7Ofbu3cvll1/OP/zDP3D8+HG+9Vu/9bzH7NwBD9hNbYz00J6HCK1n4ZsyQ84GRHwYo0WS8c8CSKkAsBEKmbJRvDJtfxNwrJ+xJARdfxPKHARA4bHRn2FeQkmWWTane8kCjcmYAyJPD5NQvdTFeJLViiPelERVBDpBmIRMhORXO5FGk1DHso/i5GyeU2S6vAEwuOOK6n4XNxFtNybxfQWZQL2RAhPF4PcANF8VLNilChmjoI9oIxKWVI69I1Mgif5HpErALpvVaq3l4QMjpAXVooDQRGivTKdQIx4XeGeKGctCCZuEQCKJtj8TpINYPpGdZ+uuIsEKcBbcPvaMZXaJ9D8JJmFXkLClRHclGYshdt+m+PzJWo70kHpQWNArJT9lboZKr9Kfen7VO4Me2YJRPjdseg6fOPi+pIgt47o5RlievSMU07U5Hpu/LwPAUjZYOodd6RLQJibElUUWinPMcJ8FBwCEQq4CuxBknqOUFtyVOeaY6xji3MiVCq5N6GAUc6WnUXTLmHXQFLPK2asPCSrSgl2WuyCBGF2bwZSG4ewXMZgM4JXrISdSIKJu/2cZQpemUdNrwODVp0r/FMrJxtA6p2JdwKZ3/d54HBt6Bjs8vy8xBIA7WUJ3IuIFW09ZcvA2VqwwdZJqzwCmNokeWZu4whntMWHyYuq9qZCgV6vA1NbURmqdhxOA2fQxU/osYXzZ4bF9508pywJdhIXSHuAkSIFBoQxoVxCZWm4zNgW7HJSOeEpjH+7YFXTXYXaV3ItMJUdvvislevdDzYfDBkeBPykJTmjk/MEMnE/7SCgriD9V2cghceS81yiVJENDUBqpnndzOYwDzNAM8d5LUfF9mLu7WbNFmuE1AhyBVL21dbIyy4n5c0CM1iCdMlpK7B0kMOrxEBrwRmvoRgktFBvqQxixgdFReEp4A2cffZTHUgYjxuqCJe33HJ+mdHq4iFWzRwmBkJFdj7TNsCnQCKGYmRbUARqSxQIUi8A8GCltG43FktOV29sxTvvOlEXYu9+MkBlWFTh1HK9Lp2IZYAV62psZ31WsBepN/m8pQCp8t0C4V3GEY7h303s+s2pzZR1LgWdLjF5LB4t33MZK8Uuobht/usjsXAmUyLG1DDu2g1gWLCsYHhY8NjSRsTzj2F6jO7QZ4RymnGCXOl1cz4PGnW95sX2ukUKyw7uKSnCA+TRpxYWbmtkA7BrYwAZ28WYMzoPvx/vobyHaC4SXv4zutT8KfnVN0cX3HODwT3wCf9sQm/7yZryNvTJ33W345d8+zcreH8Xb+2+M1Xfyk/tfxw2bnptlqfl/yqRDtPsFRLueh3rkI3iffT2FD/4S+tOvwb36e9h/yYvZf1kBvmcfxhgWFuCxA3DlQThwwHDgIHz6s/Ce9xpgmBtqV/HSh2I+/Z7P8LOfPsT2mVk2bRQJINYTwR/YwAb29WXPfe5zmZ+f57WvfS2nTp1ix44d/MVf/AUzMzPnPWbjBsHRo4aVRMeju2cK96FTyFYX74FTbN0iOHEfmERs3qShgRj7oilEX7jZ2Ijk0BGo5ny1YHw/55rT0PwkAEU5hD0LlFWdcbmFs2aWEyzR1guZIyTy9LDsGmsBAikF3niPTSDS+IpcFrz+Y3v1jRLQaLw8w5bKMMZoGw7R6JXxN1cQDwpMQ2HUNJx7LGF2xT1mFxLPKUB3CVf5GbNLSJhr7OTA/ANJTWRflirl0KO35arqCEVIZMGl5HOlyJxx0dcPNhtjqCOOdU4jC1uJTAw4+F6BNI2fEgkjRwhIn6Wi168bNyoO3JsCHTLXX4Ku3wBOInRC1UpBqcRJUYc/Z//2ShR2l+AcrPgbKXvgxw9lRBkjeu10um52/uzfHNilJpJMY4UEYPTtvFG5MMzU0e8Hu3JhZqLXnlhHfZ9lfa08jGliTIwQDiAol0CeewyjXDvHpckyKUI/s0tKcD0QCdgVyRLSL8BywhAzhlLRwWZVlFSdkaTr1gmrXIc1I/vCxlJW3iiiPAJn7ae+U6QTttYHIgQYvx88McJJHOi072NGm19Iyvezy7J6qPSnzMbQYOfUah81f6xMvlws7EBLf1VZW7B8zQTGGBb/vwP2mJKLv8WG1+pWD6gzw5vWD2OUAme0QHSmg3TycyFtr7TskVX9m888KSTo7RVEZx5WdF+5DOySuWNyp4plEV2bBnmakhy2890BwnVCVZEQh1SdErEqkIchsyhY5VH2qjQTxtGFTOZ3FmJ7NqdWYMOsZGip1wnCRL0LpNXKgHbBeaITkwoVqG+9hMhZP1sekLALBY47jvDuz1aPDRtBLFrwRIYGUYBiWcG8ZQc5ymWqMsfpszYZSbkoOStM8rARj4/QAF6CmhilmC2PY0oWlCs7NShN8phJAe602QkQpQp9k1cKW0+TsMoAYuVhZAhY5qnnCSaHBe2iYjhJyjDSMOAJlo4K5NgcO6dH6D5s2a4iP+Hza5MCL8FouxWfE7s3Z9/FkWJ78dmsxCc53r2dlD3piAKNOpTL4Ptw8r48s8tS6KSQ6AmJWOxlS77oMMbshjG9e3jVQhB4owgiRM7XSJsVOCP4/iOEK6uR8h6IhhR0h/cQb7iExrJBCqiWH5/ZteqTpL659pNsDp0HrD+fDcCugQ1sYBdlZvEohXf/DM6jHyOe2EfnxW9Ej+1ct+z8Pz3CkVd+kvJTJtj0xqejakmaaGN4+zvh//zdl/FveTleaZ4fuuZ3eN7Ol/Wl6P5/1oQk3vYM2ltvRR38FN5nX4f/kd/E/czrCK/8LsLLvg3hlWk0oNGAKy6HvPezsmI4dBgOHtjOwm/ez/P/9Tn80Te+hc+97ReI457jMztruOpKuPpKweWXQ606AL8GNrCvF3vpS1/KS1/60id0TF+kX9El2tjAu/8kshkgR0HMOJhKAo4koUAGA8o6GPmd42pFsm9v/5qTMmnSddwXZaQnULM+xALnsRUmhx1OnBIJYWadN/MnsIwJAJ28rPc1zlBxyqzkPotE3DtG2FCnvB4WgDPsEz+jiPAFujxOXKijWlYbTVicCX/HEFVPc651ivx+uxCwZXgPB+YfSHbmZZ9Q12rmV+/XBBqTvRAfS8Yxq4ta5pGUNOM2UaGGl7CuHOVQ8MoMF8c41zpl2dBiFXiyyvuROech7xjGboW4UUK2k0MyhkLUYwQBuCVE4r23Chsw3grekMKcNUSxPVvG7DoqMeMGJ0EMhRR9emJqU4nuNSspkYZLJ59K2auhpMqYTCkLMAW9Jsb6wa70c4HIwhjT9qfMKke6IAwxIVI4XPdUgTqVdrqd90mEa78lA1OtwPQklKpbaR98gKV4B2OuoN3H7FLsdvZyToQZqyQLOV1leU0oAG0UeZA3ndd5hlrVG6ITtiwbcE01BXhlol3Pg4+9w36m+q9R61igQQ9vwVSnMhAtOwc93TUhe2I8JgFQL+xYpmwp2avPOnTGPFAnSzntMi/3+wWuU37qBN3DK5kURn/59Hr97TYiN9+EgSGFKEhMs78fRQLqSgcmJgWcXccvd3yQ0PC20ZWn0DJGil74YubwC5WBUkjZB/7l23fJ+NV87shH1rRztDzJRGUDD5+9iyDq9IO7STixGilRXxbI1irW3aqltcfIEZjihRfZq7e8gKXg3Hm/V9IhikN8VSYP4zYakoWl3MaDI6hUJddfazdnF77Ue0aAvWcDs0CMtPe56q+XHt6CPPdo32dewfZBd+8EauU0URRa/bVUg03aOadXrZ+u8vvZpFJikPjCAZkI90uFLrWTbpLZ2pwHscplC8oNzdrvVH4vog+rlhnwv2GjQG6Bkw+s7cugI3FlEUcna6OAjZW9dDqS4QZs2WKvc+K+/HXssyWbD8nA56NgHhc3TCeLzm8UrUa9186T9N4N3FHi8T0w39OKMzkg9fBRYERkp6hWBbfeAuohY8NDc3VN+wmgVlt9waSaBbsJAyBMjEHmxmUQxjiwgQ3sK2E6xv3y24g++ccoYwhufhXh/peyruousPiBQxz52U9RuWmaTX9xM7Jgl5l22/C7v2/4yEPvofDsH2ekMspv3fbe/3rR+a+GCUE8dz3tueuRR7+I99nX4//7H+B97g2El7+McP+3YUojaw6rVAR7dsOe3YLm8FN59CWLXL30GX78vV3OnPI5dAgOHIS77zF88EPwrncbpIQrrzDc9kzBTU8bsL4GNrCBrbVVfm8SGpCatsLsqz0yAybRzhGsv95nxVMNrsSpkImD6W4tweEIKWGoLhCnbPkU7BJqHS/tYl5gRbLLu6qsMJo91S2cUe3MGYsbGzDifqv9IWQSv7EahLDevMkJ4BhjQQaBoHGzT7RthGkzxMGFB6n5I5gunKjdhDsW5s9iHaWc96mkyPRo+iNS0v6WpGL8fT5GHtRIdu4PtI5BaRjfKRBEHSarm3Bcl8nKRibKGzjVPIqR/aDCavTg6r0vY/nU8ayNJHW2jCxpwzOl6Gd2JdpW8fieBITJ9bsEp+FlnotJIn2UgpJSdMOePpg2elUYoyQ/tUbLU7l+s451bxdfsO+S1LnrD2NMv8/ApVVtdqWHAGLTRaJY7dulh6wWqE9tclIwukUA27k93gaH3onn9cI1wVDyBSVRwlVVlpI2pUwzR+UZbqaXKEIqhI5xZOqgi9y1RS+RgTHUCsOcbh6nHeaVwS9guZveKA8IMAj0uH3nunnLi/jg3f8K2PDUzcWbsyVASrVOGOOF3i0SAf91WRvrHyeLuXmg+m6M815FKIk/V0vOaqHMNeyUVeDu9u0SN2OE5sIYV1sCmtSGIDIOUgvWMKGEQkhBlGoqSVuT1aw3gwRtgRST06tabSlovdpGS9NMVGZ5+KwFFPJhu6YyDucexdk8CwfOYSqT6KoEjiVtJFe33k9ZVJjKhd8Pi275gkmRrpi+kaXOPOMlycG7oZO7njEJ6GGMvaelyt5HhRJ9bRBSsGULnJOgF+Ra2mBhqP9vkYYlGrqVcfyqIn60CXE+4YBAGMHEBBD01sCR4gRH8tcWdn0eVxP4pYgTQCAVZkMTDtAPxqymSeW/KzWA48kfvSLVp0/T+uf7sSSkGJEybHPTrliAoJve5b2QvOx6/Tsd9t8k67KVFVhbLm3v42l2xWMV5EoAM/Xz3jNGrg2t7yuSaPplYJVchYZLsZZhlsoDrEM9u+bq8/sucWmcxcIQQ50Hk2e+XFPfx7MB2DWwgQ3svCbPPIj/wV9BnbgTseNWmje+ClM7f7hM87MnOfyj/0bp8lE2vb4HdB06bPjlXzUcEu/Av/nHuWTian7tljdSL66fQexryfTMlXS+6S+QJ+/B+9xf4H7mtbif/wuinc8l3P8y9OTedY8rXzOBvrzAdR+9mk89+i88Y/dL2LgBbrgeQBBFhvsfgM9+zvChf4XferXh9/8Qbnya4cXfKNh7Sf+D4+TKET5z6IMsdM6yZXgP12x4xnlftAY2sIF9bdmaF+CcMLwAq0OTeS1AGn7lpcyuC4NdKbNBJz+VKIDo0EpCJwt+eqHU+UnjpaDjT9MVZbwL+GFrwq2EIk6FtvoqonGEolEc53R3AYCwMISpbyKlaAlj1lJ4hNWqMsl/tk2JM52jShSdMjdufj6OdDm6YohUBZ3XUxaJwyIU5RI0W1B2ffIJIXttSJyTjA2TvMOnzltfHUWPkZUbi1KhipAWiLCaRKKvn9N6Jw0CwG1sxFnDBrYQgDAghrehx/zsOHX6fsz8gb5zrfJrMBueQlhahkVSxI/RYcm4IzgSGpRvnzWhiTCq53p0ovMDN460GeR6zK21AJetS4/ZlWl2ZTF4CbNLuUnOBU2l5Pb3T3qMXAV2pcyCyoQFGFaZ58Lm2QILj3h0iVGFIkJJFos7M+wmBd98VQACJApNhAU4wfhVRHuB4YYVen70YE9TByGzdk5VNzFb20I7XGGmNnfePuuznGMeb72Fk48uY4Qi3V6UQmagoS9rFORQplmmVE+g3oj1wcG8ieyeWWedOI9jKor9oahCgfZH197s57tmgluvBrtWO9NjEy4qTkFSeoD2qsuIWOMXoFQWLC3lGWo908Nb0JPDuPUVAh7DKJu5sLd0pnVQfckP8psN+VM655HtWK3L1gd2lUaIdj0vuRvOYWpTUI1Jwa4sbDW5plMXuH4Zf07C0sF1r3exVvFqVJLMrjMz8Ai9Z4vrAUiUNFAQqHzyEVfm1jsXEws8T+ArLDtSXXjM49FeFEkoq8m9U7OJRZKohx07BStNQ622nHUFQL04inKKPaxJOpYdhGCiOMEJFgnRaAmtmYB6+zyDhQWBMhvaxFKhQq3zUN88UY0KQqXLd4TJdcPcRiv2fvIUdFIiWQ64Sn/PP69NHnGVaRhjljHDfpzP8Pp4OJCSqEvGEp3K7OZZVSY7cS68t/+Z0ncdi9T16izFGobZegL1KTa67vpietfVspfgxAiRbZZdHK9rAHYNbGADW8+iAO+zf477+TdiCkN0nvv7VJ76UszCwnkPCR5d4uD3fhRvY5W5N92S7dp9/N8Nv/07Bjn3Ttwrf5zLp2/gfz3jzRSc0n9SY/57mJ64hM4L/gQx/xjuHW/DvecduPe+m3jqMsL9LyPacVtPYyWxLT9zPQde9mHe+4b38LTffT5+LkuN4wj2XgJ7LxG84rsN990PH/yQ4QMfgg/9q2H3bsO3f4vguus7vPn2/807730DkQ4zh27j0HZ++emv/9pk1g1sYAPrszX+Y98HdifeKJWEGQhEN0GpkjVH0GPPmOokLB7uO13qYOrkBVcJFyE6VKrQmge/YM+SvtVmzpwQtN0JjAFfJNc0a19hww3XIksenP6s/UCqHBi09k05vyMfJyLkImt3j6mUO6IPLOk1LM9gsn3gJC/e6bt/HyEuOZMRgkYDrh3dw2y9ynx7TRV74E1Of8RiVSnYlWOZ5QEHKTNAruCWQK4Smlq1yW5W9Y8oKPzNNeKlLpxKK5Yr4zWgbh3alHmUhk6lfZAHGISwzCHLHjIYAdu2QWnBha6GCJwE7IqI+sIYHXF+fSAnceAy5kOuUfmNmh5YKNd8lr+OQLB5zrBnSva3Nw19FBCuB3bNXnXeOj5j6y3cf/DznGOJofIo94w/h04Hqim+mIyhfXYHto4JA0YIwCtDewG8MrVaTo8rASyFENy0+QVZNr5dY1ecty5rLO9tSkXoDK0p0rvcKidbqYwRZBK9uPMda48/P2vjfECZ9PqBMbHvOjgXoBoXtwknhZXCy+rci9tbVbDn6rquISQRDl9Vr3rVMOklYPGqczTqML8AKJerb9vBgdN3cO9Ry+wT6VjmrC8JgVTnBbvOl/UwCwu/SAaLTELQVbVXPgUnVFFS3jtOc+UoLF3U6S7KhACxx8NJEFK/INiyReBEhto+iXRUL0zakb364BHFAlckhKDVC1ZiurGZeHYGUx7tHw8hiLfdiFk+h+lESSZdmN0oefhoXw0B28c37vkuPnr7n9owVKeAK61el+NVIF60YYzpsivd3M2/6nmQA3yklMRJxs0+BqB00FN74dDdSMI+IM/zrfagowwm3QBJN4BEbq3r7+nsN1Mex5RqPdkXAZuHd9tkB0mxxwtjLBVXJeJIL77O37Kg0K1EB7EvIc2qNifl4+ES6lwLnHUyDKfP63WigtYjcwo3uQfmhmBhPrmEsUBlCmyv28K19sR4YAMb2MC+5k0e+Tylv/0GvM++nmj3C2l913uIdj3vghT2aL7DgZd/GOFK5t58K6ruE0WGP3+95pd+xTCx57Poq36S/VPX8RvP+OuvO6Arb6axme7Tf5Hm93+c4JZfRgTLFN73s5RffxPeh/8X8tjtmZNTedo0XFLgqg9fyt/f8ZrznlMIwZ7dgp/8cck7/lHwyp8WtFvwK79zim/882/in+7+c561/Vv525d8lvd/9xF+8xl/Qztq8jPvezGPnrvvvOcd2MAG9rVha8IYiy7dHSlbJQk7QVK4ZJjqjdO9co5V101fyEdLk5jKuNUGyp8vZXYl4W0qATFmpuGyS5Od2B7W1ROolyLDdKL4Arv7pWHwq+hCEsKkEloH5jyOeI7VkoAZ9hkmk3ibtcwuklDCHshkkix4MnGA5JpDYK0ml0jCLKQQbBiprXl2GrdEPHNlz4XJgV1SAsZQ6J5A5MLVBKqnc4Yg2mTrUnBL/Tvu+Yqt+TvNVCgoXjqCrPSAJpFCbMZePzvnakbfKmaXLnk2U2HJy7UHHLcHVhEaJG7ya5w5O1fN3sxcYxfns1SkvgfI9NpV83syAHnNrvOdI2XFVcqCgrcKYEvmghDrZGN8PJM21HBEjiGlwE1Ond5vY2V7L6UAaY+dZk++qbYFPX05emxHUt8c2JXp8TiPE0LYb+VLHJwhkYUUP0717TWEZN/eRJeIhNmVVkQIdq0vz7rG1mV2rerI8jUTFHY11pSqXDvJ0PM2Udy99rv1LIeXr/pgVUhWeRRTslEErmuw97WzZnxlrG3WuYxiaS2e3MeVV8CtT88VTkLqTLJurrnH8/0g+r9fPZROMk4zQ5tzp7fH75u8lg31bY/7zuyM+Aw9xaUwq3oIQNIEnbCizgesPVmTEkRB4I70WEYpm8upiP6QP1eSVsyTZXufZdFovTmvR7ahhzbav4t1y6jM1bs+ZOesM1kFKWjdeZb2l88kx68e0Nx9LgRmaCOmPAHA7AbB1KSiMLoJgKA2RXdsH4ulPThuD5hjdX6J3IPUUTJjXYnVCJNf7FUpjzknp/Vy+9p5gGu9MTK5dunJvZjGpqyckbmsnuucu7JOwk+ZPgJzx6xlsCXgdy7UuH+Or9aJs3+EOyboXLXJ6tSdb8laR6dzveVNKEn9RZtx0rBlAYLYHv84TPPVNmB2DWxgA7PWWcL/99/Hveuf0EMbaX/zm4g3PvVxD9PdmEPf/3HCY022/MOz8DZUOHPG8Ou/abj9DnjWNx7m9tHvYaYwx6/d8sY+dtLXtXllwv0vJbzs21GHPoVzz7tx73kX3pffhh6aJdp6K/GWp7PhB6+EH+vwnnf8Lc/c8RKmHyeEoVgUfMMLYf8ND/Ez//IyFoMzdD/yZh698zaWxwVT2wVP3Xgbc41d/NR7v4Ff+/B38+cv/CAVf+2u78AGNrCvDVtPx0MPlzGeY2EOKTAICtuSdSCRIiFh6xRknUnvMnaPb1z3/OlLv84AoGLirwscN+/gJzvGeac+se5oqhWzdr82CxMa3kysNZzOgV3rMbtyjn6kw16eRyEQ2qzD7EoBn14YI4DGYMpjxJtvXNvoDLjr/8g6L+d3LPXwHMYt9jRWhMowu/QzV6+kDQHiPocHIajuneDU3FGKbom2WhUKuPrS53Ny+5g5glqVBOwih+Spnt4YsDqLXzxZo7Tdxx11e0wgYck0FuDpQmhQwjIgfNULIaz5FwY1UhAgC1PM9cFQYTjX3LXfp87gvslrOLl8uO+9Q4p+plKqYSXEKiLHeWgDTkqqEL26pYVTsCs9/SUTVxPpkIfP3pXU1ek79db6TrbUN8JSv96SgPOP2+OYs30ztbFDRBdzfAp2OYLJiVz/OZb9loI2G2YfB2wzhkoFTrOa/sEaT9adLOFO/sc3PDMiVx5lzX+RKxhPXYbzyIdp1A3tk5q5zZL5FcFivlysMVM7iDZNwAkLoBivhKlvXB2lhVCWXWkkBPumqd4oOf7hhzMtLy3zaMaFHfMrpm+kE7cZKU7QCVucbZ3M5lXZq7J9ZN/j9wUaVU4AkDTsTEE8cQlmaCbppxzrSwgaXyEZEa+PiCeydT8/DsKVlNwqDWcLRTmMWztCJwNMRO+G8Ku57Ldr7aor0/tc4c/VCE/kNgRyDCo9PIfedG3fsbvGruDhs3cS6QjVmGR88/VoaUHz0ESMzZTonlukeFBAmu1zDQG4dw0lJS1vBjdeRo9sB47k6iKJh2aJR4eBnOB/Ckj5+Y9yYYzrPjfWAqXrApfJl4WC4KanWT1fxxF86MNmTTGzChBdfc/MzAo6gCw7WSKLPn1NIfp3efKHO4+z7qxzP1wIy0/niBAw0tAsnM1d+yKpXQNm18AG9vVuxqAefD+lv34ezt3voHv199H6zndfFNBltOHIKz9F87Mnmf2D6yldMcbnPm/47u813H8//PyrVji48bswGH7jmX8zAFTWMyGIN11P8Nz/TfMH/53Oc/43engr7pf/nuI/fzdTB74dVQ+48dPX8psf/h66aTjJBewLRz/GT773BSivw/954Tv5qZc8i4MH4Xt/wPBHf6JZXjZMVjfya7e8gdPNY7zmM7/0n9DQgQ1sYP9Vdj7R2ni0DAY2bYbtO3LOQm0G4/UcUikUw+6WjKGy1uyxGTFG1QGrSdRfKmExZUrYgs1zoIeKRJvq561/72XYapYgFej4vC/JeWcg0qmAvHXARGehF5aXr5lYJ4wRKwqMWzhvndYwu4RcPy6j7+Bcxq/cq7hQkj27YOsm25M6SWRiQYeeEPPusSt4yuwtlvWTu5YNCVpzsfWrkOu8iQlBtSYy/Zss9Ga1Y5ImIsid0mn4NpQrPVTZdkzt3w5eiRW5EQefudI0OyubLtAp/dZjQ62tf9nrzSuZoI35LIXpMQWnxKbGzj5H32Yus98bp4AZms037XFt10545p5ncOuu27KOEKvArt70lnjK74UJZv3Zz/DK6rYK1Hwypif2Em2/7aKOz3SUVrmDSgoLYF9kHYTQbJ4T/eF7pMDgk2vH414zu3b6ywUGMCk0NQF7dkOt7uCtvqUjDZW6DS3NaFFrM19Cz/E3Eij7qEaNM+UrOVe6FICmN9srvGpire7Sij/EaGnSrj/JCvqEWVgih8rko7vzgFNuHK7f+Gz2T93wxK6xyopFwcw0TKc5JVJEcD1tJkdiDJTUCNWKoFEXdilfb7m6gOXXrOKlI9Ru29D7MgfGmEID3H5Adbq2iStmbsrqJsujiW6doqu7IHrMxhSzVWbVOCRrjfGTJBlCsljaA2rVc1EJKI8hiuWE/Qex3xsBP4+Fpjpd4jzMrt5WTTZ3VMqa7Qsl7P3qeQLHWb9nU2ZX/qDVoe4zc4oNswJRyCcTyZfpD1M066wT8dr9pOTQfs2uNadeXTzFtQzMbdRcuq+3aWYuEu0aMLsGNrCvYxPLx/E//Bs4j36UeGIfnW96A3rsIvnqwInf+RKL73qMyV+8gspz53j9GzRveSts2QK//quav3zwhzm88DC/++x/YKa2+avYkq8R88pEu19AtPsFELZRhz+D8+jHmb78LuKPXsXbH3gX/2vhcv7X1lfgbLkZPbW/7yHbDpu84Qu/ybvvexObG7v5zWf+LROVWXa/CJ55K7zxTYa3vxM+8jHDD/8gPPu2y/nWS3+Ut9zxRzx7x7exf+r6/7q2D2xgA/uqWRplUSxCu21ftoMuRBsa1PZtxJ2fpzI9lL066un96PZjuTNIdu640BXsBeIE8ei646xMj1Bv1GD5+JrSGVtHChsZkn/ZXef9dY0mjuMj4qa97npvyjlmVyZaLuiJRgN6aAMy1R4TOYH6HOAVG7MGCMgukYJd627Gr9IjEqtywPWFckgbSapAKkWlYqi4xqYqU2koX79GmZJOb/OoD+yy/6wje7b2QwGNhtUiGm4ITgoyRzkD0BwPurlT5DIfpgMlpXXSNlb3Mr/sMtvYzA1bbf8vnfFoPeLh0KbslHCUw9rYoPWt5wQ+TibQZHxMDuySq3Rh8k6kZXZlDc1dr/+86/YhVnNnzzYbWqM77VXf2Z+rw4YzFt9qt2sVPSkN7605lfX1ry7GhFjrfJ/PUlBu1SSWQmCExFwkJ0KYtQL1+/aC+/h7c0/aepkj7U9TGUcEy+dpezq4hnJBg3bWEkxibcPRBKAccBz0+O71L67s/WwkpHqGHW8SgMv2Qak03CP6rAonvZBjn8+O+kTMVCcwSyVEt5XDmkQ/4NTHSrrI+fE4NjycW3skySJrch9Y87cPoR9qARqpyBJp5NmR/rYhgof6uHZPyFaHkq4X+pvv14xFq3yiOMTSa5MvE+BMmf6xMxVJPCuJ5mRfRtg11+nFB0NRsLBV0a0JZoRNNrHcXcixCpN1TtIXfp+r6Jpf18/aeHEmpWWw2uE6DyDmJ+t832NqFRAvyE3YJ1CRdda1Cx3eaFgwzhi7zihHUXMts7euLo5AMQC7Bjawr0fTMe6X34b3iT8CILj5VYT7X7qucOD57PRr7+bM6+5h5OW7aD1nD7/4o1Yk/UUvgB//UcGb7vgNPnv4X/np6/9gAKI8GXOLxFueTrzl6VSu6CCu+Wd+7O6X8cvjf8j3PfDH/Mgdr+Mpso6z4VrOzV7OZzzDX933ek6uHOEle3+Ql1/xC3hOb+uyUhH8xI8Jnvscwx/+seG3Xm143/vhx3/ix/hw9R386ad/gde/6MO458kONLCBDez/XUvfL0eGYfs2+3L5kY9hQZ6JWaJGI2E0rG/FomDjhvO/kXpFe4Gd08/krpXPoUQRsya9osicjUygXiY7zXlBrwtY1a+zHCzYLJHROWzMy9rjVoMd3ac4FDdNwfzdAMTTlyOCpb4ySjo0u0tooxMnQBCb+AIi0snPdb4e9ua4tGaBkHhsF16hSucwSF/YUDmxti82zILyk5Pq2IId2XosciDgKqfO7a+AJSNcxI63sMBNHsQUvTjU5Fxu/9XWaWzq8xXdMgsi7neQlABt1jAHLsa6sUVKxspT2WcTlVlGShP910+TIyRe/taRS9YwEMVqsOsCobJPxMSqcBrvPI9PmQF3aRhjksEvu2aP4rDT2cuWouRi7ofytZPo5e7jljufpUyy1U5vysi6ENjVf0gCduWcdb8gkaFYEwn2lbLVrBA9uhNd32RFyNcUTn6056GziCnWUaXV6CbgJKF1QmBmL8PUplefqf8QKZCrdMqGhsD3c+c+j9bfeqYz/biLZNQpgYkNQiniqf04Bz/VC71bNYXy65h6Au/6T8wEKenI5K7h1H0mX7KJw58IGT95hOVeokpIgIzinmGc4QLNz558QlcsXTVGeGxtVtf1AMM+7TSL1uBKj1AH/eWSUDyhwZGKKNkkMY4k2pkkKllHCd6d7dfPStfmoNEDp3aPXwHAhx5IM8cma4Pq/X4+y5hd643fRc4ZKe2+icnND7GKIyWS5BGmqxNQy5bducMma0CI/mnt9tdndgYm+pfpdesp1n60xnzfJuMqnsUmE5EuFW+YK5yrqT+eGn9iA7BrYAP7OjN5+kH8D/0K6sSdRJtvJLj1VzG1mSd0jtOvu5sTv/Mlhr5xM5++9Epe+/1QLMBv/S/BTTcK3vvAW/nne17Piy/5AZ6786VfpZZ8/ZgzXKD+TVvhnYLf++V/4A/ufCU/pw6jaFM9804Wzv4zAFsp8GeTL2b36M3EqwWYE9u+TfBnfwrvfR/82esM3/d9BZ7x0t/mo/qlvOPev+Rb9v3If2bTBjawgf0nmOy9a2fhDZfuM5xM/YoLAF17dsE1Gy/8Il2vS66+EoaGNnHyxKbsWvT9lmMWVC0qIISwdVvHcc5b+jJ8+fQNhHEXc+ARwCB0tO6b8mpHx1QFTslFnLROkSkMWRZI7gKb6ju4/dgn+o7TRp8/pChzPNZWfWP5Csb8E/ajka34I1CoTiFOLyVMD5l70ZeAoVgUGNexzDJjrCeoXDbPQbOuuXNpLRsJQA3lERYBT3C/wt9Sw982xKYvLFGIknHJUs+vDmNcJ5NWCnZN+HCoRSfI9b0SCK17Q3qeaeSuI6a+feRSFspnGCr0xOgvmbh6TbnUgU2BgrJbXVNG5UAYmc/ElwcDngSRajUoUUqkwYJgdTnJ5jmF21Xc9Ri5Odv/M8ahImv4bueinFd3vAjjT14HtRcW1d9415GW2SUk27ZezJlSZtd6db7YnGlPzNaEQAkB7oX7QibaaEZ5BDtvhU+8v/+cqscUXSM6njPdjii5FUbGmjzlqvXLxVP7EeuyWi80rikL9eImY+mqcToPLlgGUZQAFKlml2TVWvEk0NwnYgmzK7vVVs8pV3Dd0z0W36dYOkvG7OqzJ1FFb6aCN7OOEvs61vdcSNiprnIJ424fg1Q4IgMNPeES0YfOAaBWzY/6izZnv6cZItdkHM39XipCq90DIZXMbQKdr/55nUfoC2O8WLA+1Sa0+N36Bwkv2UDoapyGT3QuAENvw6sp+q594w024vdTn7E/7YbaxQ/m4xWt1yWqncgMCHuPPpFQ3wHYNbCBfb1Y2ML7zOtwv/gmTGGIzvP+gGjHc57QdqbRhpO/dzun/+xuvNvm+GN1LZ/7P4LrroWff6VgZETwxaMf548/9XM8ZfZWvv/qX/0qNujry0ZfsYv5tz3EzMdGePP3f5ovHvs37j31eRbaZ5h1G+wJYi4/cxTn4U8h7v8wRrnEM1cS7X4h0Y5n970ESil4/vPg+uvgNa81vO/NT6f2nNv42y/+Mc/a9i3Uv0LCpQMb2MD+e1j2Xp5b7ifGBRPj6xbvP1adX/+jZ5J6PS3TCxvss9zfha29ELwU7MrCti4Q5uBIF0e6NAv9O+jnPaDv8rnP3OKqMqJPMF0KiQZio88bRrdeGGOG6axTLzO8hVg6ViMqDnPOgCTzrNLPjM06ZZRLpSKoT2juXLRnv3KiX0/TGc6LDwkbYpWPwUvYYabY6DsuDVmUJQdZdBirTNM9t9KrUnq+vOXGxvOg2+1VeexpY9zXDjE5J1Ao0dMBW+d0ADePWgBrNSRS9qp92lyrzVM+3biHKukk66ZaBzjLj739vpt9k5V5MlhANvi29oVkKFaDXUoo6kOSmvS46zELCqUMInu4De3syAaCxSS090mGMZ7HbrxhvTYmzvMqpM9xBS13hqJSFIuP3zHCrINmPknNsYu1tHsuDqRcVRfHQ1WtppMQNlRqYjwBOdKiF1jzTCdm49A2Nl9Xz617ZOcDMEMzmTj8xVoK2J4vdHq19Yn9p3pSKEAn/ZNnNH5l59MaSxmrWSLP818vFfeXgkynDHh8rcMnYOuBLev1gSt9OuE8nlPsMZ1UD+xypQsJyzQ/py/Yn+mat4rdnD/kisvh4CE4eDjNGEsWEruqJedtR18bL7Lr8vdLti6uitlOGWmmG+NMlROwa20YfGopk/HGG2yZ9d4X9Mg2xOKRvs8uRrOr72ImhsdJALOeDcCugQ3sa92MwXnw/Xgf/9/IlROEe19M8LRXQrH+hE4TLQYc+qGPs/S+Q5x+6g5+++BVOJ7k514peMHz7KL76Ll7+fWPfC+bG7v45ae//qtIlf76s8LOBuXrJzn71w8w+r17eMrsLTxl9pa+MgEQRF3UsS+hDnwC59GPUvjAL2I+9mrC3S8ivOI7MPVeNrVGQ/ArvyR49rMMv/v6X2Z+5BZ+4q9+jz992e8wNPRV3gUc2MAG9p9mazOXPTFbT5heD80i05fXi3akEtZETt1dKQEIIlVFj25HD21Ye9TqensV8GtAf/hKtPFaRGcBadqsMSGIJy5BLp9MaQ999VLSwVUeYdy1+l1uidicQfm19VuyDvkp9dVK6yWbExLTmMsdnAca8swBY+NMpIQU7HGLmRZRrdSPUKohj9IVY7S+dHrdeuIWiDbfuIa9lwFiau2kyMLz1mS26zX2qivg6DErhgzgFRX1Wcn4WK64I0FHGeNnvfmXsoouTsmrZ1fN3kyru5JrTyruvV62r9VhjGupXefT6Lqw9TeoeF5m12pQadX1k/trxZmlymICTn9ln8F9oXWJGZPUa5Xz6CgInRpGhmuOWc/alc3AvVx2uc9qTe8n2bGPa/KiHeV1Ckm3b9rPzuTYXOl5L5BVThQUqgP1xtja7/4Dw5YFkz2pmFo77/0NLtHZCKchMTlk42LZYk/WLF4rEGqtZtc6pZMy9AHz4isIdq1nfRkpk4FOQXMvnyk+Nzm8/LMvH5p5AeafiRIAbw2zq3feYlGwayccPqKyU8s1N8/6lgrU10tPfGO6DxzOqtN/j8qSPb/0Ff7WGqri4kzk+keIbE1zRnubLa57/vHTYzvhPJrQFz3djbZi+E/w/vgqw7wDG9jA/itNnH2EwttfQeH//jSmPELr2/6e4Lbf7AO6unHAwYUHufvk53j47N0stM+sOc/yx47yhWvewuIHDvOv26/iV089hetvkLzlbwQvfL5d9B45ew+vfN83U/Zq/OYz/5aSe3G04oFdvI2+Yjfh0SZLHzx8/kKOR7zxqXRvfCWt73oPrW95K9HWW3Dv+kdKb3oO/nt+Cnnynr5Drr5K8NbXbGeH+C6O+G/h237kAd7/QbNOZrKBDWxg/y9a+l7+ZHyJmza/YH2wa+oyTKoptd55xapf8jpVaVa/Vb6/Ht2xbijSml16kWjC5NkxAKVhzPCWdR07gcA05og3XgOsn0HKTxweISR4ZcKpS5GV9cVHUjFyL9c1vi/Yf6nN2Je2eV0TuTDG1Sm1TAJ2ITHlUQvgjW7DVCfRw3NQWUvH8zZU+i63JkuVX13rfGbhTuugBudDEnKeUrks2LG9//urrhRs3Nh/nqkxzYaZr7y7UXBKDOeAv/gCzK4+vSKh1h2Xziox9Yt6/KUhb0l/p4DSyEh/sRRMykCl7PpJ+F9lnGjLzTSVFTl3HJ6wQ/dkzPfsLTS7anzcx2Vy9t/yndIc0a7nMTLmMDryn7NRloHNT4LZZZSHVGs+tmyuhJVzIbCretM01aevz9r6Dw1bmkzjyQCdSUc4jQL16z2kJzC5e+FJnfMiLNr1PABUWYCQFDY9Po/G5MXg+5hdX7l6rdfe/DqQPlMKbolIR4RxsPaZBZTUKuZsYqvDGPPWC2O8+AZZZtfFlRdCcOXMTeydumbduuXt5htXH9v7vV3fjSkNY4rDfWVU1aN8zQTFfSMIIXAnS+sy5YaeVqb81POJcz2+lc+vnrCq0slPo0FISmXbT/468nzr2YDZNbCBfS1aZwnvs6/Dvf1vwSvTecb/JNr7zdmuxGLnHB96+B/59wP/l/tOf7EvZTfAdHWOy8dv4OmHn0HlnzXtL51hsVHntVPPJhwf4fd+UnDtNb2F756Tn+dX/vW7KDhFfv8572CsfGFRz4E9OaveOos3V+X0n91N7dkbH38XTAj0zBUEM1fQfdorce94C+4db8N98P2EO55N9/ofxzQ2A/Yl/Xde+jN8xz++HXHlb/Cbv/23vO/98DM/xQWFqQc2sIH997c1hJKLsFQMfj3wIDPlQry+QPZ6IVMCQeSLXiarJ7uT/7jaJut9thowWyt4UnRKrASLmaMUX0AbpFIRXH+tobRK6HpszP4dbb2FtSnf1l5byNX1SDS7UhZBaTipu8QUGuufb5VdTEr27LG/HrMr/ewCzK6LMeEIJsegMPP/s3ffcXJV5ePHP/dO2dlespuyqaRDQgoBQiCAoNKRKlJE4GuhqfxsEP0qfgGVpqKCYkRFRGnSlADSCSSQ3nvdJJvtfafPvff8/piyM7uzu7MtW/K8X69kd+/cuffMbXPPc59zDvi3Jp/Hyh4V1xF/90Ufztg6yeyyxWd2xR0Tvu6MHJjk2Dj9tJZAaJv1R9aXpkVqefH3Xs7McPMutD5pxpiMzaYzc4ZGfk7iMdBRhkYyHY/U2jdSbwKVZCbdFn4AYNNQRlxmkU2P/a0lOS9ib3fZwdXuy0lNnRxuttaRMbmT2FG9njR7FxcOYHNiDj8WlTUC+74Pw9MSMhr79njSnRp5X5iArcoLnuo2o1DGU4SHA7TbwOG0mBo5frrSz1Nnkge7bG1eT7eHz0VPsBktei3Ps4MP9GwHE9JHUe6vJuBIS30Qr1hmlw5BGD8O6us7fouug2613UfTp0HN2rbz57oSA1Tt9ofoiHynxK0nynJkY45bkPx9I5OlJyfSXXpCs/WumjMbGpuSN3tMRlMWStMpKLSTPYmUmliDBLuEGFrMII6Nz+Fc8QfwN2HMuorAaXdApK8Ob8jNS1sW868tj+MNuZk6bBbXzr6DcblTyXUV4Dd8VFaW4H2pjLH/HYatvopDBfV8MC/AyvQpXHdBAV/6ohZ7eukJNvOvLX/g2Y2PMjJ7HA+c+yyjssf35xYY0jRdY/h3ZlN6xzIaXz9A3sUTUn6vyiwkeNr/I3jS13Gs/RvONX/FvvsdQnOuJXjqtyEti1xXATfM+y6Pr7ybq7+1lCVPnsmN/6P4yvVw7dUtzVWEEINL9B69K3WJE4pPJ2R1PNKbOXY+mrsyabAi2ap8YzJoyLG1VLy6VGNtLTKiXbJKTZIn5G0rP1qb3x22tEhxwn+HzGCHzfFbB7oSdNRZdlyWW7IyaZaVsE1SycpIm5KLw+4DKjqdF4hldrXsg5aXos1YlCsv8T1d7JpAs+ko0yJW3Uiyn63RJ3RpmZ1JFpy1x01LHKmzpTxW8jFdOtH287hcSYKHsQwwjfGu0xlBCVDZbvqYzdZX3bon1zoQYu9C7fDYack/c4u+7aC+W312KSsS7CKh/axm12L9FdnyuheAbe9SNn68xvhObo+LcyZQnDOhW+uFcL+AiYWJC+60M/Jmr3JmYBbPQfPWJx8VMyp26dGYM0cxIpoN2JvNGDvptzEagHbZW4I66QXZOM92Mmn8HFyz0tHsGvY9G5mdM5UVGV04Hhw6mGaso/ecbI2c7PADpHaLi5Y0I3l4kYZjJFR1JxifRPwm7u/GGw6HRuGwzueLnb9Khferpqcc6AIJdgkxNCiFbfdbpH38CHrjQYwJpxM8/ftYReHHJQHDx2s7nuLZTb+j0V/HGRMu5oa532d8fkv76eBhN7VP7yDv2Xqs5gxqxxTxp7F+tp3wMbbx72Bpv+a/jtGUfjqfDEcWNd5yNpQvx294OWfyVdx2ys/Icibv20T0nrxLjqFm8VYqH15P7nnj2gw73ylnJqEFtxOafQ3OFX/AseGf2Hf9l+Bnfogx9Ty+MP1G/rP9STbo9/CPp97lD4/r/PmvinfehR98D+bMloCXEION3iq2lAqbbu84qwvAkd7SD1V0XVo4jpIwSlpEsDgDM6TFAkjR7NTcXEhLkhA8cgRUJBmJPqFz7+RpXB2XG5LFunDozjYv9lVfNy1NDluVQynASlpR7Uj6cQXojfVQQ2q1mNbNGCMcozPRM8KpSSp3NEZaFvaSZW3enpJoB/WdjMbYm5I1uU2zxQ3Q0k5mV3o6+OK6enOmUrdN8YRScc3Tsu2F2LXySBJf8gibzaZ1uQ+z7mgJgOhJp/dw6b2wjPZ16ZrWaiaVWYjNhDb9gdt07MNcZJ46MqEvoq44Aq1PU6e3zWTq9JreE5oONicqu+OmbeFR9WJ/tLzQ5312tV1+uqOlLZ2GxgmTzwz/EXcZ6er5kHXaKIwaH269pR/FU8Z9Hqet43Z3eTk6h4GsVr3AFBZqFGQlNPhsK8VtFx/r716Av71RV48QTe9yhrEEu4QY5PSy9aQtfQhb+QbMoun4rvgz5vjTADCsEG/tfp6n1/+KGm85J40+i/+Z90OmFM6Kvd+o8VH1u83U/XMXlqU4MH4Cz+VOpzxzGBd/Hn5yzaVk53vZVLucN7c+z47qdfgNL7muYZwz+SrOn3YdU4Yd318f/6ij6Roj7zqBkhveo+7ZXQz7yvTuLSijgODZP8aYcRlp7/4frte/i7HlZbRz7uMbJ93NT9+7iY8qnuTuH3+d889T/PLXim/eoTj7LMVXb9QYP34g3dEJITrSpc6ceyraaqLNuhQzp6ezbp8rroP68I9p03Uyjm1buJkz4Nhkl7hIsCtcT0rWXCXZiI6dZ3ZFM4BUXBCivdEYey55U0ENwrUQm6PtvJ3ISw93mF2YOarTeWN9MraqJLVpvuXKRdkcaGZqnZUnLMuuRfqvOXLfF53tez0ydH3kldj0k0+EpR+Hf583lzaj7PVE9HjSNK1llLwkwa6RI0A1HfmBfVId/a9rC41+jr7Z913L7AJz1BxURj44wpk8ulKRpRoa0gABAABJREFUJrxxzRgjzakcRR1kZaZYrgEhybXLpvV/1T++zy4rPoTTm4ldnSwsPvg3Kns85c0HEq77UcrmBMPbZnpHbFkObFkOaGwJdqXSj/GI4TYK8jrLlGwl8RDufPa4RXc32HXEJYw6qad+0kf0/xEvhOgWvXIrzk8exb5/KVbWCPzn3o9x7MWg27CUxYf7/s1T6x/icNN+Zo6Yz48+8wdmjWxpm62UouHV/Rz+8SosT4j1I6fwL/sMyM3kCxfD5ZdqFBZGLzCZnHfsl5g/8pz++bAiQdZnisk6fRQVD6wn++wxOMd0fzAAa8QMfNc8h33T86R9/Csy/n4JZ5z9ExaMO5e/rv0FC8adw0knjufvT8I/n4XnXlB8uFRx7ucVN96gMbp4IN3ZCSGS6U4zxm6vSws/gU62ruMnTGDmhPGJM0O7lRxN05I3qYo0ZaCdYFeyik6baUn67LJHmmMaVktgJ1kfUL2h3WaMkQ7qVTcyyrKcOZw96bLUZo5UdFpalLbflMiceBYYgTbTOxVZlooNNtl3B2CWMwd3sKlrb4orjtOpMWG8ouQA5Oen+n6d0cUtozCmskJdI27fJtZSZ84ANflELHd5x81ge0n0GOxW9mInu9Iqmga6HZU9shsl60IxUjykVG5ih/K6Dpo9cQ901Cl96uUZQPdEcUGBaMbjuLzJvb4aK3csyplab+N6poPsplyqtEaApAGm3tDZfoh/vTBzJOXNBzCTlMWceCamrw4qPwXCfWWNyBqTaik6nWPSMVBxOPy7rtnaBLpUtDloWnbHa0rxsEvos2uwBLtaP5iSzC4hhja9ahvOT3+Pfe/7KFcegYXfJTT3y+HmJEqx8uDb/HXtA+yr38akgpn84vP/5KQxZydc2Ev3hSj5zqfkri9ht2s4/yg+hdEn53DbuRqfPTv5ENVi4NA0jdEPLWD3Oa9x8PaPmPj8OeEOU7tLt2HMuRZzwum4/ruI9Dd/wA8mn8WXNRuPLP8BD577PGlpGv9zI1x+KTzzvOKll+HtdxSf+6zimqs1Jk+SY0aIgcrWjWaM3dV6HQmVyVZ9VfWsYtj+e9sbjbH99ydmdlkJmV191Iwx8jOxGWPkMb0yE4JxfVKBtpJndiVlc7TKNEtNNHgQ6wRc01gwrm8ems0tPp2g2X5ALtc1jEZ/bawckV8S5pkyWWPyJNWF7a1RUBCet6Nmh/EDBmg6NKVNQuWGULljE5emaWiuLCzXlBTX3zv0JJ93yiTILO3BQm1OrOHH9mABqelikkeMzQYMs0F9XI2/g07pO5ORDl5f5/MdUXFBAZtuTz0QngJj8udjmYnWqFmdzN0ic/4IXPV5DCuYyqpD7zEya1xLcfshswtast2saFQ+ns0JzpZ+vQoyhjMmd1JqZUjhA02cqFGiokHnJPO7cjHGLYj1vdzBylIsU8vvPQ929UOnX5rW5b4jJdglxCChV+8IB7n2vItKyyWw8DuE5lwHkacpG8qX89e197Otag1jcibx488s5oxjLkbXdOobFJs2KzZtUmxZ5uPCNR8yPlDHR5Pnkn3jcfzhHJ2RIyVYMZg4R2cx9jcLOfC1Dzh4y1LGPX4menrPLukqbyy+q/6OY81fGfXJo3wrM4sHyz7ipa1/4sqZNwPhph233azxpSsVz72g+Pdr8NY7ivknK275RqiLFQUhxJEQ69/mCKxLa29d7fUjpXUjmBNpxtj+y0leS2E0RrvetqOmvmrGGC2jShJ0C3fEq7eZtzfZcp0YtX60tGjaX+RHL2ZfRZelIoE1pWsJfeT0JofNiaODUR3nFi+Mq8x2FCjtShOi1OaNBrs0LRxbVLoTY8QsbH3cR1Hq5WobMXK5tA5HSbNFD5u+HzQyqXZa4aZM0zSsvHT8p4xr6TA9xVHhkjlxHng83X573+jLezF79zrw19Ns6CMzcEDb4Ft3I5dJpZ7ZFX2g0V6WWcIDmv7oryqjoPN5UmTrjcwuVy4qLRuzqJvdqHRZq2aMXSTBLiEGOL10Dc5Vf8Je8jEqLYfAqd8mNPd6SAs3Xdtds4k/r/0Faw9/SFHmaL49/9eMV19k32Y7D72o2LTZ4uCh8LLG08DtFR+QqQLk/eoz3P7FsR2sWQx0OZ8fy+iHFnD4rhXsufgNiu85icxTR/Ys2KTbCJ38dcwJp3HxGz9glb+WJ1bdw7EFxzOj+NTYbMOGadx+q8ZXvhwOeP3rRcVNX2ti2lS4+ktw1pmpDycshOhb3emgvrtiOTNtfmlnfl3rVhROJf6XuMwk62yToZVknck6N1d99fQ6UkbVNip4RDK7XDMKcIzNwpbZ6jP3ZgAm+h0QaxXaf98JuqYnOQZ6WJ6UO6iPzI4WOxcHQhOiaOW+vezFwEI7OROSN9k6ZkK44jyqb1spdqonwbby3M+Ff3GXhJdl6/7C0tI00jruf1x0QnfZyJw/As/KJKOS9Lq4YFckU8hMltlF4vW3K8GuVOedXnQCle5DKS83cSWRpu8pFiv+fDGTf9zO6TbMY87o5pt7SIJdQgwRSmHbvxTnqiewla3DyhgWbq44++pYu22/4eVPnz7Ea7v/RBr5TGy8F89HX+aXi12RmyhFXi4cPxMuulBjllGG9ouP0LPtTHjyXNJnpjTeqxjgCr40BceoTA7f+Qn7r3kHx6iM8BD0IzKw5adhy3Fiy3Viy0sjfdYwnBOyU6o4WcOPw3/dS/xg6S/Yvf/P/Py/V/P7c54jf8ypCfNlZ2t8+Vq46kpYtjyDvz7l4Z77FI//Ea68Ai6+MDyPEKL/xIJdRyALo6uBNfvwdGz53aglxvrsShLsStpnV+sPn6wZY9tgV9KmLe3obLStZGtPSHjTdLBC4eZBXWyq0VWarmHPiytvtEC9mdkVCR7EEiYGStZvO80Yu7GglObKTy+kOniIPFch5ZHDMJUBM/taLLOrvQ7q07TYyJyt2e0aEyf2VclS15ux2Z5kdone4RiZ0flMKehKn13R7F3VzkmZ8H0S977MDHB0vXV3G8U54ynOGd/5jEloWrtdV3ZqIATcU6Il3/6pkmCXEAOJZWDf9RaOVU9gq9mJlTMa/2fvxjjuMmqa0ti1DnbuUqw5+Am7876PlbkfY8eN+Nb9CNewLKZMgc+dpTFlCkydDEVF4Qt63fO7OfyTFaRNz2PCk5/ttS8TMTBkn1HM1KWX0bikhOalZYQOuXF/WoHZEMRyJ46gZR+eTu6F48n/4qTOA54OF87P3cv/bZ7Ct1fdxY/evIrfzrkb54lfa1Nrdjo1rrjcxVmf8bJyFTz/L8Uf/qh48im46ALFF6/UKB4lN5JCJPP444+zdOlStm/fjsPhYM2aNW3mKSsr495772XFihWkpaVx8cUXc+edd+J0dt6c5Eg2Y6SLcYTMkzsepj7pKuJveJNUUJI1y7K1Dh4luYFOd2SSZnfhsmfG+ndqrwLU2hnHXNSlp/4jXIWU+irJdxWAzx1elyMdrbkhHB3qZjZBT/VqvzmOSLArGBmNUNf6o5eXDvQ0syu16HF+ehGfH3klTY1NHD8z3Al+6p3a9x0rbpTIwaonLd+mTobMLGBZZEIPMrvEwNK1PrtSf7AQ/75TF3R23hyJVOpIP4/dWNdACLh3VXcGbpFglxADQciHfdu/ca75K3rjIQJ5U9gy8UE+rD2Pnc/a2bkLausU2N045v0c27SncBnHcLbrZc68egFTfgw5OW0vdEopKh5eT/Wjm8k+ezRjf39G2yYLYkjQ02zkXzGJ/CsSO85UhoXpDmFU+fCuq8b9URl1z+yi9skdZC4YQdFtM8k6o7jDm90Jx1/PTzPz+dGHX+fudffy4IEVcN4DqKzhbebVNI1T5sMp8zX27FW88C/FK/+Gl15RnHG64uqrNGbOGLw31kL0hVAoxHnnncecOXN48cUX27xumiY333wz+fn5PPPMMzQ0NHDXXXehlOInP/lJp8t3OMBuA5erL0qfqO0Ai310vkefaKeY2dVW25tmXdM5bfz5KKXYXr2OiuaD7TZtaS1ZVlhHcp35fLZoPmbckPTKmYVuhh9QqLgK2BENRvTiumId1IciKQT93EdVTKxd4ZFpxggtwdasLI2ZM3q22t5iWuFu9bt67A4kPclWHT8+vP8aosuSzK6jRvw1tbNgV0Izxi6c8wMyhhz3ddntZoz9SYJdQgwumqcac8UzuLY+h9NoYG9wDn/edRf/3XcmCh2bLdwvwvz5kD7hQz4J/YDGUDlXzryNG+b+gDR7+48GTXeIw3d+QuOSAxRcN5Xi+07ulWGVxeCi2XXseWnY89JwTc2j4OopmI1B6l/cS80TWym5/j1cMwsounUmuReMa7fPinkTL+JO6zEe+Oib/LjuPR74+xewPn8f5pTPt7vuyZM0frRI4xtfV7z8iuLV/8CHSxUzjgsHvc44HWw9GP1IiKHi29/+NgAvv/xy0teXLVvGnj17+PDDDxkxIpwJtWjRIhYtWsR3vvMdsrKykr4vym7XOPMMhX4Egg3RG/zYjX50yHS9F285tdh/SR9PpzSCYkcd3Gsao3MmUNF8kDxXHzX513WwWpXDGdd5+5GuKUU3Y2/eJkSCB8qIBLvScnpx4T3RXpi0qwb391fICgJda347UPS0g/pketJnlxhYOm3GmKTPrlTm7Ys+u3qk9fdtJ+K/LlNICh8YpBmjEIOHYSj274fS9bsYWfIUc/Ql6JrJ++Wf5dmSG/AUzGHaDI3vXaoxbSpMPAaCNPLHVT/l9d3PMyFvGvedvoTpRSd0uB7f9noO3rqU4IFmRv7kRAq/duygTlMXvcuW66Twq8cy7CvTaHh1P9WPb+HQ7R9ROSGboltmkHfFJPS0tl/+n518BYYK8cuPv8MPnU088Nq3sM+8ksBnfphYSWulcJjGN74W7sz+zbfCTRx/8n+KCePh61+FM04f3M0ohOhrGzZsYMqUKbFAF8DChQsJBoNs2bKFU045pdNlHIlAF9Cm/m8VTsUaFujwGtHtFSmSBrvSI9lShZkjqfFUMDJ7XJt5OrtpznUNY+GEC/ouEBDLJmgph3LltvzeR6MWtiua7NSbfXZFM7tMG9awyZijpw+M8FA/ZHYNREYki3AwZ3b16gB+4qiRrM+ududNNmLuQJNqsCvy02FnwGSYdkk3rlUS7BKiD4VCim3bYd162LjRIL96KZePfpaLh3+CT0vnk9CXKB93PePPHcvDU8KjuURZyuKDfa+yeNX/0eiv4/o53+Wa2Xd0eOOtlKL+uT2U3b0KW34aE184l8yT2jY1EwLC/ankf3ESeVdMpOmdQ1T/fguHF62g8tcbKfz6cRRcNxVbVuIXy7lTrsZSFr9a9l3uGj2JX2x9mYxDq/Gf9wvI/1yH63O5NC67BC65GD5eDn/+i+J/71ZMmwo3fx1OOlGCXkIkU1NTQ2FhYcK03NxcHA4HNTU1Hb43Pz+/L4vWRk52EKUUeXkO8vPDNdGMi+eBrpGW3zsBHEduCE9GJumudDKys9BbfcZ88ikefiMaGkEzgNOW1ubaohwGqi5cntbvPxKsnFzwa2h5eSh3pBxFI7HKw79rY6ag2Vu+7zMzw9P7an/as4PYM02y83JJ76V1KEthZtaiOXRUQRr5RcMHxDVe2YOo+kzIzu7xvrcyUz+GjvS52JnoMVU0bATpjoykrw20MkdlZYUIBC3y8luuM92lxjRh1PtT/qwDdZtEqQlzQdPRBng52xPKrAK6vp0njzqW8qaDHb43ely3niezsv3jPWgEyKwJv56Xm5tyufy2JjI9fXsemVm1WEGTnLw8XPnJM2czMwOx34cNs1NWbjBtio0RIwZHGCj+u1orKELLyY9dd1MxOD6lEIOEZSl27YLVa2HtOsXmLZCharl8/MvcP/l5isaU43WMpHbm90hbcBXzXG0vTIYV4pODb/Hcxt+xq3YTUwtnc/85zzJpWMcheMsb4vCPVtLw8j6yzixm7G8WYh92BDpoEYOepmvknjuOnHPG4vm0kuo/bKbi52up+u0m8r4wgfxrppA+a1isknL+1GsB+PWy73HHhBn8si5EzvNfxtx7PZx8O8RlJySj6xpnng4LT4V334M/P6n47g8Uc+fAt26HqVP6vzIkRE89+uijPPbYYx3O8+KLL3L88centLz2ggSdBQ/q6+tTWn5v8XoVHg80NsVlk0Va3Hvrg72yDr/Pjd/nxRbwYTY1YnXyGb342kzTvI3YPB4AjCO8jQBsHh+a34PZ2JRQDq1gBvgbUc1ewAuEK0qeyDx9tT+9TU0EPR6s5ib8vbgKj9cTSyew19cPiGCX1lyPzePB0tydHjudsad4DOXn5x/xc7Ez0WPK0+TFrweSvjbQyhzV3By+zjQ3gb2n3SHMycJmZKb0WQfifmzDVRz+OdDL2Y5gLjhGpHd5O4/LmM6e8u1A+8dt9LhuPU9Hx3vIDMZeb2pqpp7UytXobuzz88jt9aACJqqpEV998k64PJ6W7GdXGkycAAUFUF/f/9fiVGiehpbvSLcXzPrYdTc7hfdLsEuIHnK7FavXwKcrFCtWQl092DSTS2et4Lvnv8J09S66CmGMPw3f7P/FmngmaXF9lyilqPaUsatmI2vLlvLJwbeo9VZQnD2BH535OJ+ZeEmn/Y/4d9Zz8NaPCOxrYsSdcym6bWavNkUQRwdN08g6dSRZp47Et6mW2r/voP6V/dQ9sxvX9Dzyr55C3mXHYM93cf7Ua8l0ZPOLpbdxa8EUfnXM1Yxc9wwZ294k+JlFGNMu6LSJh82mce45cPZZsOQN+OvfFF/9huKiCxRf/6pGQYEcw2Lwuu6667jgggs6nGfMmDEpLauwsJCNGzcmTGtsbCQUCjFsWB/1KdVDfXn2pk3ORfNk43TpKY+W2Jrq7+YosSEyW2WcZRZBZtGRL4+lIsXp3e2iOfSW0RgHQKArwUArTz9pM1LpYNILu1Cz6XRhQD7RxwZai5TE61ZXDriBd33RNBg5cuCVq0Px29/W9Y7GJNglRDdUVCg+/AiWf6LYtDk8okVODlww/xCXTvg3k92vYPdUoJx5hI67ltDsL6HyjwGgxlvB5ooV7NmyHs/GatgfwAyGqC6ooWZcA8fOmccF067lxDFndRrkUpai9skdVDywFltOGsc8+3myFow8EptADHHps4Yx5penMeqnJ9H4Wgl1z++h/P9WU/GLteScN46CL03m9NMu4uefz+Gn793E7aaPP1z/NwrfehjXG9/HXP8PAmfeiVU8t9N1ORzh5o2f/yz8/R+KF16E9z5Q3PgVuPJycDoH2RezEEBBQQEFBQW9sqw5c+bwxz/+kaqqKoYPD1cEli9fjtPpZObMmb2yjt4SvS/ty1HNNZuOa3I2WpmG6u6a+jvQEa1dK6t/yxEV66C+l4NdNh2FNcDqfdEP2zuFUhkDM+A8lMW6XevfYogBRtO0bj8AaW/Qr4QO6rtwwB3JDuqPmhMhEuwyJn4GIn0OdkaCXUKkqKJS8eFSeP+DcD9cAJMnwTe+VMm5o99iTOOb2Cs2oap0zAmn45vxQ8xJnwGbk8NN+3lv/S9ZufEthn+Sw7zNcznl8CRgEkpToGlo0cGKJuWQf+0orC+G0PPa758rWOrm8KJPcX9UTs45Yxn94AJptih6nS3bScG1Uym4dir+nfXUPbeHhpf30fifEhxjsxh343QeuuAF/vfD6/nyf7/M7Sffy7mBL5G2/LdkPHctoannETzt/6Hyx3e6rqwsjdtu0fjCRYrfP674wx8V/3kNvnk7nLZgAGYFCNFLysrKaGxspKysDNM02b49/CUzbtw4MjMzWbhwIZMnT+bOO+/kzjvvpLGxkQcffJCrrrqq05EYj7RYh9F9Ge2CXghW9e/1ROm2cAnUwBj/XfVu/CdGc+gkaUXav2yRvigd7Y9onSpj8ud6d6TRI6ggYziN/tqkr80aeQqObmRRCNGfTh7zWZoDXW8yeNr487C1ex53bzTGIyFtQg7+HfVojtRSEwfjbXRCFnb02t2FAW8G59VZiCMkGuD64EPF1m3hadOnwaKvHeSMrI/gg+34/+MlFHJSkjkTx9TzsM87AfvYUXhNH6vf/Qcbtn4EW3xM2zeVb5Rch27paMemM/ymY8leWEzaxFw0p06wpBnPmirqX9hDxX1rqHx4PXmXHsOwr0zDNaMgVtEPVfuo/ct2av6yDXSd0Q+cQv41UyQQIPqca1o+xT89iZGLTqDp7UPUPbOLivvW4Hw6m0fu/Ce/d/yCBz7+Nu+OPotvXPZrpu1biXP1X7Dvfgdj+oUE59+MKpjY6XrGjNG4/+caa9YqfveYYtGPFCefFO7P65gJcpyLoed3v/sdr7zySuzvSy+9FIC///3vzJ8/H5vNxuLFi7nnnnu45pprcLlcXHTRRdx11139VOLOWX2esBRNIevmivr7OzOaud33Gyo10WhXb2d22SPL6+/tHUdlDMMcfQIqa0TnM3fG3kejdR4Bc0ad1u5rhZmjjmBJum8AHVZiAMh0ZpPpTKUnp0TtZXVB4oPWrtS1ovP2Zf3MNS0P5/gsdFdqIZ1Beb70sNAS7BKilcqqcIDro48b2bgpfPM3Y1qQ+25cz2lFS8k6uJzDzx/Dwc1zUdax2LIVttx0rDoNY40PntkIhPtWGYudsZwNgH1aFvnfPIa8Syfimty2A++0iTmkTcyh4KrJ+Hc2UPv3HTS8tI/65/ZgH56OozgTqzlIYH8zKEX+lZMY8YO5OEZmtFmWEH1JT7ORd/EE8i6eQPNHZZTfu4bm27bw3cu/y5orz+evB37JzUsuYsG4cznv3LtYULaXrM0vYt/+Gsa08widcCPWqFmdrufEeRp/fQL+/Rr8+a+KG/9Hcdlliv+5USMnezB+YwuR3AMPPMADDzzQ4TzFxcUsXrz4CJWo+6KZXX2d2BULFnWzyQr2fs6EjmQRaAMksyu6w3q9MmTX+2a5PaSyB0cwR3RsoB1XYmCbWHAs5c0Hu/SexGyuLgS7IvPa+rhDuFQDXUcr2TpC0BLg+uBDxZatYNMMLpizlVuvW8HMrFVk1qxFq/NSv3EKm979AqHmNAquGk3g2mFsdKxhXfn7bKr4lKDHS0F9Accym5nOE5gx8mRGTZhE2pRcHEWpp8u7puUx+uenMPKuE2h84wDe1VWEqn04R2eSd9lEci+aQNrE5EPMJmMYBv/+978BuOSSS7Dbe/fUb718IOX1xb/3wgsv5PXXX8c0TUzTZP369Zx44olcfvnlSZdZWlrKggULALjrrru4++67Abjhhhu47777uPfeewG49957cbnCFRu/38+Pf/xj9u/fz5VXXsnFF1/Mvffei2VZzJo1i3//+99MmDCBE088EafTybnnnpuwHLvdzvPPP8+f/vQn8vPzufzyy9myZUusnMk+q9vt5oYbbgDgqaeeijU7amho4KKLLgJgyZIl5OXldWvbP//88zz++OMUFRXx9NNPt9usye12c80117BmzRoKCgp4/fXX+d73vtemXO2tJ9k+zT6jmKw3L6L6T1up/u0mxr7p4BsX/ZDyLx3mrX3P88nB/+LQnUwdP5PxwRAjS5cwfP+r5OUeQ/bUi8iaciEF2aNJdyRPSbbbNa64DD53drgD+1degXfeUXztq3DxheHXhRDJPfvss3g8nj657rdH62HCVdd1N9iVhmmarFy5kgf/969MnDiR++67L/Zd0V2pft9aeePQGw5iZRah1+zu8vt7nWo/2mUYBi+//DJr1qxh7ty52Gw2bDZbSuXT7Mk74u9IsnuK6PpPPPFEvvCFL/D6668TDAZZs2YN+/btY+LEicyfPz/hfiH6eklJSZvv+3nz5uF0OmOfofW9yCuvvMILL7yAUopjjgn3uXrgwAEmTZrEfffdB8Ddd9+NZVnMmTOHdevWUVJSwqRJk/jpT3/KG2+8wapVq9A0jZNOOokbbriBl156KfaZjth+PcKSHb8dHdPxr5177rncc8897N27l2OOOSa2P1tvK7/fz5///BTBUBYzZ1zCO++81WZ9rY9XoM19ZV/sg+jnMc1wEDv+POmNc7sry+jJvflg1dn2mZA/nQn507u9/I6aMSbb3kDS5pHReYPBIKtXr+bAgQNceeWVfPGLX4yVufU16T//+U/sGpjK8Rs9D954M4imwcRJk/jMGSehd9DsujvHqN/vj9W/4utbrc8FCJ+Da9eujV0Xu3IeKkf3kjuG3lEuRIrKysMBrg+XKnZsN5meu53zj13Nz7+4mjHWWmwhNzSD6ZxM8NhLqVg7m7JXGjAn2Fj5gy2863iY+nXVAIzJmcRZEy/lhFGnM3vUqeS6eqezUluOk4Krp1Bw9ZReWZ4QfUFz6Ay//XiKrzqOZVc/y5h/WUwpmcPV93+D7ZlbWF++jG1Vq1ntq6TW6cFymBDaClu3wtYHARiWNozxw2Zw3PB5zBp5KsePmJ/QX0hursZ37tC45GLFbx9T/OoRxav/gTu+CSfMlYCXEANFLNjV5312RTKG+nxFfcSVizH9QjAC/V2SsD7ajJojGuzqm+WLo1NW5kH8gWFkpd51jxDd0t1mjNEvQ72fh/rMz9tKIFiEwzF+UGZCakYQAJWe1633S7BLHFUOlYYDXEuXGlC5kxOHreJ7E1Yxc+pa0pQbADNrEuaYi3FOP5um/GMx9TwO3PUR7lcOsW32Lp668CnSbFnML/4sJxSfwZxRpzE8a3Q/fzIh+l/6xDwO3+oiZ7XB6Dcb2XfBmwy/eAJXnf8VMuZ/Dz3DjmkY1NVU0FhZhWf3Onz7NtIY3M6BnBo2+9fwbNky/sEjZDiyOHH0Zzhl7DmcPOZs8tILAZg4UeM3v4Jly+HRPyi+/R3FqacobrpR49jpg/BbXIghZtJE8PuhlwaibJdy5aIc6ZiF3X8YZIw/jYr1ZcCO3itYlw2M65bqo+HtNNvA+HxiaLHZQmRmVAzKyrsYvLrSQX10Xj3a5L6fhM+VciZOKO/XcnSXyizEyhuPVTi1W++XYJcY0pRS7NsPyz8OUrp6MyP8azlh2Dq+MmU9mdObAbAKJmGOvQj/mJMxx5yEygxXqjPz8yldtpKDt75MxgEnb3z2vzRcZfF/M//GvNFndjBqhxBHMU2j6WQHp3/3XGp/u4XGJSU0vLQv6ax2MshmAdksYAxwVq6PzOLd1M7cyMczalhe+hEflSxBQ+O44SdyythzmDXyFMbnT+P0hTnMPxlefBmeeVbx9VsUC04x+PL1QSZODmEpk+y0/H6/yRDiaJOZqXHySUdgRTYH5qSze7QI5crFY2vbh+ZA1aej40Uzu3q7g3rJ7BJCDBXdiK72d2bXoKfbsEbO7PbbpbYuhhy/X7FhtYey1Ruwla5lmmst/5O/CdfEcFOBYN4UtPEX4h99IubYk1GZRW2WsbNyA+vufY1JT49As5ms+X4JV137YyYNm3GkP44Qg5J9mIvRvziF4ntOxrO2Cv/2elTQAl3DluXAlufElpcWzvZqCBA86MazqpKmpTno22dxznterj/uU5pnrWPZCCfLmkr5y9qfx5Zv0+zYdQc23Y51eYh0M8R6TNavBFZGyqA7GJ45mqmFs5kx/CROGfd5RmWP758NIoQQyaRYd5o/9nNHJNjVVx3UYw7S5qZCCBHRncwuSY7oX7L1xZBQUVLHoeVrMfatpTi0ls/kbMeum5ijbTSkH0dw0rWoySdiFs+F9Pw271dKUdKwg3UHP2bvqyuZ+cYkZpaPp/F4H5N/ey6nTJ7YD59KiMFPc+hknTKSrFNGdjrvsOunYQVMmt85RO0/d3FwWQbaqs9y5twyrp76NoFCLzvT7OzNH0Vz7kiC2SMxnJnYdHu4Emg62bM7jc2bHDTUaziHVWOfvJ+txgY+3P9vfr/yx0zMP44zjrmIcyZ/SZofCyEGjUxndt+uoIMO6nsi2kG9kmCXEGKQ60qwK0paGPQvCXaJwUdZNO3fR/naTRgHNpAZXIWVuRePzWRfgca/bZlYDcPRvS7MkCKUVo/f8xrBklcgz44rPYNMRzZ23YFhGKiyIM7dFuN3j2XGzuOY6j6T0CjFxCfPIuPsMV3rjFAI0SN6mo3ciyaQe9EE/LsaqH1qB3Uv2alZeSOZM5yMnVXJHN9y0svWomlgZY/CHHcK5qhpWOPmYJ04GetqjZWr4aWXFav+AZYFx8woZdwpb9Gg3uSpdQ/z9/W/5MTRZ3HhtC8zf+znsOuO/v7oQoij0gC5x7D6qM8uGTFXCDFEdKVOaKnIiJzSjLFfSbBLDHi+hoPs2vI+B/etoq55Ox5Kqbb7KdMtyvMsvHr4Bq2oppDPffJZLt85g3RP+0OHBzJCBDID6CEdlzsNuxG+CFlZkH7qcEZeO5Psz4ymYFgB9fX1R+QzCiHack3NY/TPT2HknSdQ/6891L+4l9Jn8ynlIuwFl5M5xSRrxCGydq0nO/9NXC4/ypmFOWoWZ46aw8JvzKD2tqm8u6aYd98fw9K/fBX4KoXjSik6+Tm2lz/HqtKbKEgfznlTruG8qddSnCPNHIUQR5/YoJa9HuySrAYhxNHHjAS7pM+u/iXBLjFwWAbNlZvYvPtN1h1ayX7vfsqop9pmtMyTDmmWkwJ9BCNzJzKreCpjrPGMezof+3+9aC47eReNJ/O0UaRNyEZz2rA8IYy6AEaNH7POj1Hrx6gPoKfbsec5SZuci2t6Pq5j8+WmTIgByJbrpPBrx1H4teMIHmjGvawc74YavOuqaVxVDKoYuBDnKJ3Msc3kjtrNsJFPk57ezBjghrQcrj9zGu4LprCnfjxrS8bz/kdfpK7mW6hRy2g47p88432MZzb9lgmuM/j8xOu4aPZ5ZKan9fdHF0IMdQMte7y3yxMZjVHPluxZIcTg1pVmjKYVyezSJdjVnyTYJbpEKYUn1Eydt5JabyW13orIv0oCph8NjUxnNvnpReSnF1GQPoJhGSMYljEy3N+EUuBvQK8/QKhuD3sPbGJr1WZ2B/axx1bLvkhgy6FgjJHFmMB4jrdNobBgHpOnL+CEYyeRlxHuc0tZivrn91Dxi7VYPj+FN8+g8OYZ2Avaz+oSQgxuzvHZFIzPpuC68BDEZnMQ39Y6fBtr8W2swb2xlvpVmZTY5pI1L5OCBX4Kxu/F6d5BTskS5gWamJcB3zgFlGan2T6G8sA4Nh24lvf0w+ws2sgT227mz+vzyKy5mNHaF5haMJ8xIx2MGkX430hwuQZYBVUIMUgNkGtJJLWrt7tu0F3hip5r8uAZ9VIIIZLpTjNGyezqXxLsGuSChp+gGcDCItOR0+PosWGFKG8+wMGGPZQ17Y8Fsmp9FbHglt/wtXlfpiMLl82Fskw8hoeAFUx4PcObzjEVY5hQOYJMTxb2gAuvzSTgDFCbH6I5LxtX5jAWuKYzJuezTJ/yeWbPyiYvr+1FRSmF++NyKh9ej29jLZmnjmT0z+eTNklupIQ42tiynW06wPfvbKBxSQkNr5Vw8LcGh9InkXveWeReMI7ME9JxhErRGw6g1x8gveEAkxsOMiVrK1/01mIFFGvMLF5zelhW/DQ7tacpM+0cf3A4NZvHc7B2KoZ7LF7nMMguxJE3jPSiYeSMLKSoOIPCQigcBllZ/bhRhBCiizLmFuHf2YCe07sZWLZsJ7kXjEdzSOa8EGKwSz3Y5bCFr6Xpjsy+KoxIwVET7Gr+4DCHf7wSPcOOoyidulkjYEI6rmPzcU3Ljz156mtKKTwesNnAbg//i0WJzRCEfGhmAIwgmAE0IwBmELevjj2NO9nVsItdzXvY5yml3tOE8mg4Qg4cIQdokG53kJ+WSaEji8K0DIocGRTZMykKZTHMn4ndZyPkUTQ1h2jyhGgOhKh1NlGRUcfOwkNszS0lZDNj5c3ARiEOCpWdGcpGkXJRaLootKDQsCiyFIWWjgsNCL9PkYHbyKaiYjS1JZPxH5hIWvXw2DJN3SSYZqIrG46gDd1q2T724em4ZuSRXr0HGgvwT8kNp74rCB324FldReOSEvzb6nGMzWLMbxeSd+kx0om8ECLGNS0P17Q5DP/ubHwba6l/cS+N/95Pwyv7wK6RNiEH57hsbLnz0Fwno0IWym9g+UIoj480r58rPEEu9QXx+0IYAQVBG07DGVuHYfdiZG6DrCb0/Dqc+XXYc5rxZVjsSXMQVMMIaSMIOAqw0oehZw3DkZdPRkEumYW5ZBTkkJ2fQU6uhl06cBbi6DVA7l9sOU4yTxre+YzdIIEuIcRQ0JX65oissWjoFGUW92GJRGcGbLDLtExWHHobT7AZm25D12zYtPBPXY/7XbNhi/u7ZV47uq5j0+xkp+XhGpdJ9umjMBqChCo8VPx9K6Y7FF6ZXcM1NY/0mQW4pufjHJeNc0I2znFZ6K4UNpFSYAbRgm4INKMF3GjB5la/u9ECzezc3EzFgWayHW6y7M1kxf102QJYKCp0iz26yV6byR6byU7dxO3OYXTlKIorRjGlYiqfqTyTrLphaKR2A+GN/GstO/JvAnAKYNlMQsMasQ13k14UwF4YIpQXIqDs+EIOvH4HHr+DUsPBfstBSDnwm+k0BbJR7gyGe3SKG4MU1TRjNywsm46aMhzj3FEULBjGyPn5OItcsYuFMi1CZV4C+5sI7G7Ev70O37Z6av5UjgpZSUoMGScNZ/QDp5B35SR0p6SGCiGS0zSNjDmFZMwppPinJ+FdV417WTn+3Y0EDzYT2NeE5TfRHDq6y4busqO5HOi5LvSRdpzpNjLT7eHvAZdOI/VUGKVUeg4SrHKj6kyyGjIpOjgB55a5RHsXtAP+dA+mMwDOINgPYNr3YtoN/DaTBpsBaQFIC2A5DSynhXICTg1bug1Huh27y4mekYbNkYHTkYndkYnTkY4zzYbDYcPu1LA7dOymDj4NzQuWRwMPWJnHoOfnYMtyYMtxYi9Kxz7chS0vrcsPBpRSBE0/AdMfyyQOmD4Chp+QGUDTdI4bfmKfDK2tLIVR6SVY5sWo8GLUB8g9fxz2YdJUXQghhBBHVlf67AIYnjW6j0oiUjVgg11VnlLu++AbGFaoV5ZnQydvRhYF9myGOXIpSssnvz6PwrIickvzMQ4043+rGl5I3CRaTggtNwi5flS2BzOrGTO7kZDDTdDRTMDuxm93E7CF8GsmAV3h1ywCNgtLC/9D6WhKAxwozYEx3IGy7CjsWMqOsrLxBzLxGgbKr0j3uchrzKWwroiZNSP5bPVw0gPhjAIF1DldHHJlU1GUTaMtnYDmIIADExtYGigNm1LoSqHbPNic9QQyGvFl1eJLC+Gz2fBaeXgDxYR8Y0hvHkae6WdksIniUAOjm+sZV1uHfYufEGBpGg3ZuXjyc1C5Lux5aWS5NNKtIC4jiKO6GUoaUJ5aINynTvaXp5D1mdFkLRiJnt7+YabZdJxjs3COzSL7jJbItxU0CextIri/KRaUdBZnkjYlF8eIjF45JoQQRw/NoZM5fwSZ80d0exnFwLFxfyulcAcbaQ400NhQg3tfTTiAdsCHVW1HC9igOYjmc0DQgIACP2ghHUfAjsuXTnqg48CNL81HU7oPb3o9AWcFDsOOK5iGK+Aiw5uJw0jW5GhL0mUZusKTEcKdESKYFkLpJkqzQLPQsCJ99lhoSqGUIuD040l340334k334U334kv34XcGCDoDBJxBAs4Al4z/LWOy5qHpGkrTMC0wLQ1ThX9aJpiGIs1h4WnyYhkWVkhhGQrlN9DdATR3AJsngKPBg6uumfS6ZtIbmrEZZsJnsBe6yD1vXJf2mxADW6TyNEAyvIQQQiTKcGTiDXm6HOwS/W/ABrtGZY/nhWs24a/aiv2/i1AhL5ayMJSFhYWlTExlYSoLC4WlhRvRmYAFmCjMyLRGTVGnW9QG/NTqPmq1Ckp0RaNm4RsODAdOCK/X5XNRWD+MwrpCCuuGkd+YR05zDrm1OeTuzyfLMwYHOg6gr0Mu9pHppE3MJe2zuaQfm0/a9DwYn8cYzYHPB14veH2Ef4/8NAywLMKVCyvyuwW6Dg47OJzgcMT9boe0NMjMzKJ41CRMs5HMTHA6NUIVXnyba/FtqSN3cy3Bg02YZVUYWwNgKfRsJ7YcB84xWbiumoTruHwyTx5B2jE5Pf7sutNG+rH5pB+b3/MNKYQQfUDTNLLT8shOy6M4ZwKMAz7T8np+fj719fUdLiMQ8OGpbcBT24CvtolAvRt/nYdQvQ/VZEBTOvZGJ9mNWeR4FCG7SchhUJ8W4nB6NZ4MP54MH+4ML83pbprTPbgdJnpAYQ8o0rw6GW4HmZ40stwusptzyHFn4wymoZk6umVDKTsKHaU0LKWj0EHpuNw2hgVsZAR1MkIaers3eQcj/3oupOnUOrMpScuhtqCYWlc2jWmZXPDlDM66LBNbnoyQKYYYTQNNxxx+XH+XRAghRBIFGSPwNu5Dofq7KKKLBmywCyAnLZ+csQvh68s6nlEpUBYoMxzZIRLhURagIk+rVWxeDUVubi6NDfUEjQBNwQaaQh7cphcThQEYysCwIiMD2pzYdSdOmxO75cDeoOH02rH77dj9NuzecIUhHGVTKMsK/zQj69Y0NJsGeuSnpoGt5ffoa7rLhi0/DXteGvbh6e1mRPVVN3f5+Tbq61sqM46RGThGZpDz+bEJ8ykrMmKPLtFtIYToibS0dNKK0ykoHtXfRYkxDEUwCIEg4Z8BCAUUyhPCagqgvAb4QiifgfIbKJ8Blgp/ByqFril0FJoGOgpdA92ukZmbSSDoR3do2Bwaul3DnmHDUZCGvcCFLT8t3NRSvlvEUcaYdn5/F0EIIUQ7Jg87noKMEWSn5fV3UUQXDehgV8o0DTQbYIMUunBSgJaVjwrZcQDDGMOwrqzvKO9nTioiQggxdNntGnY7ZCSkL2tAWuRf96SS6SaEEEIIMZDomk5hxsjOZxQDjgyPIoQQQgghhBBCCCGGDAl2CSGEEEIIIYQQQoghQ4JdQgghhBBCCCGEEGLIkGCXEEIIIYQQQgghhBgyJNglhBBCCNFPSktL+dGPfsTZZ5/NrFmz+NznPsfvfvc7gsFgwnxlZWXccsstzJkzh/nz5/Ozn/2szTxCCCGEECJsaIzGKIQQQggxCO3btw+lFPfeey/jx49n165d/OQnP8Hn83HXXXcBYJomN998M/n5+TzzzDM0NDRw1113oZTiJz/5ST9/AiGEEEKIgUeCXUIIIYQQ/eSMM87gjDPOiP09duxY9u/fz7PPPhsLdi1btow9e/bw4YcfMmLECAAWLVrEokWL+M53vkNWVla/lF0IIYQQYqCSZoxCCCGEEANIc3Mzubm5sb83bNjAlClTYoEugIULFxIMBtmyZUt/FFEIIYQQYkCTzC4hhBBCiAHi4MGD/OMf/2DRokWxaTU1NRQWFibMl5ubi8PhoKampsPlZWZmkp+fj90ut3ytGYZBZmYmDoeDtLQ08vPzcblcvbJMoFvbvb335+fn96hcPREtk8vlIjMzE5vNhs1m67PjqvU2ABLWn5+fn7DfnE4naWlpsdei80dfdzgcsddcLheWZZGZmYnT6Yx9htbrjL5fKUVaWhpAwnECJCzL5XIlvB6dFi1L/M+hfD4mO347OidavxbdX/H7s/W28vv9sW0b3dbJ1hd/vEK4OXj8MdTdfdDRuRhdt2maAAnnSU+vDfHLT2UZ7Z1HPVn/QNeV7dPb19Tu7BuHwxG7drQ+LpNdk7py/MafB5DavUB3jtHW52P099bnArScg6mWpzcMvaNcCCGEEKKfPfroozz22GMdzvPiiy9y/PHHx/6urKzka1/7Gueddx5f/OIXE+bVNC3pMtqbHuXxeKivrx+SFZueMgwDj8dDKBQiEAhQX1/fK8Euj8cD0K3tnuz9+fn51NfX96hcPREtk9/vx+PxxIJdfXVctd4GQML66+vr8Xg8BINBAoFA7Gf0tej80emhUCj2mt/vx7Ks2H6PfobW64y+rpQiEAgAJBwnQMKy/H5/wuvRaZqmxZbbk+NisEh2/HZ0TrR+Lbq/4vdnsmCX3++Pvae99cUfrxCuaMcfQ93ZB52di9F1xwe7UtkOqerKMto7j3qy/oEu1e3TF9fU7uybYDAYu3a0Pi6TXZO6cvzGnwfR61BXj5lUg13x52N8sCv+XICWczDV8nQmlYBlt5fen0+YestQ+Ay9TbZJoqGyPXrjaVL0vaksH1J/epTsyYVpmm2ewCVbZvSCDJCdnR373eVyJTxdiP89+gSivSe98U8Uo09945djt9tj5Yj+3tmTFofDgcPhiC0j2r+Opmmxm7D8/Hzy8vLa3U6dbT+bzYbD4UhYfrJy6Hq49bqu6+Tn5yctV0frid8O3ZnnaDNUriG95WjaHtdddx0XXHBBh/OMGTMm9ntlZSVf+cpXmDNnDvfdd1/CfIWFhWzcuDFhWmNjI6FQiGHDhnW4Dsnsap9kdnWtTJLZJZldnZHMLsns6k+S2dV2HUd7ZpemlFJ9ugYhhBBCCNGuaKBrxowZPPzww7HKWdTSpUu55ZZbWLp0KcOHDwfgjTfe4K677uLTTz+VDuqFEEIIIVqRYJcQQgghRD+prKzk+uuvZ9SoUTz00EOx7EuAoqIiIPw09NJLL2XYsGHceeedNDY2smjRIj73uc/xk5/8pL+KLoQQQggxYEmwSwghhBCin7z88sv88Ic/TPrazp07Y7+XlZVxzz33sGLFClwuFxdddBF33XUXTqfzSBVVCCGEEGLQkGCXEEIIIYQQQgghhBgy9M5nEUIIIYQQQgghhBBicJBglxBCCCGEEEIIIYQYMo66YNfjjz/O1VdfzezZsznxxBOTzjNt2rQ2/5599tkjXNIjJ5VtUlZWxi233MKcOXOYP38+P/vZzwgGg0e4pP3j7LPPbnM8/PKXv+zvYh1R//znPzn77LM5/vjjufzyy1mzZk1/F6lfPProo22OhdNOO62/i3XErF69mltuuYWFCxcybdo03n333YTXlVI8+uijLFy4kFmzZnH99deze/fufipt3+tseyxatKjN8XLVVVf1U2n73uLFi7niiiuYO3cuCxYs4LbbbmPfvn0J8xxtx0h/kWv2wNVb50kwGOS+++5j/vz5zJkzh1tuuYWKiooj+VFEnMWLFzNt2jR+/vOfx6bJfhwcKisr+f73v8/8+fOZPXs2l1xyCVu2bIm9Lvtx4DMMg0ceeYSzzz6bWbNm8dnPfpbHHnsMy7Ji88h+PDoddcGuUCjEeeedxzXXXNPhfPfffz/Lli2L/bvsssuOUAmPvM62iWma3HzzzXi9Xp555hkeeeQR3nrrLR588MEjXNL+8+1vfzvheLj11lv7u0hHzBtvvMH999/Prbfeyquvvsq8efP4+te/TllZWX8XrV9MmTIl4Vh47bXX+rtIR4zX62XatGncfffdSV9/4oknePLJJ7n77rt58cUXKSws5KabbsLtdh/hkh4ZnW0PgNNPPz3hePnTn/50BEt4ZK1atYrrrruOF154gSeffBLTNPnqV7+K1+uNzXO0HSP9Qa7ZA1tvnSc///nPeeedd3jkkUd45pln8Hq93HzzzZim2R8f66i2adMmnn/+eaZNm5YwXfbjwNfY2Mg111yDw+HgiSee4PXXX2fRokXk5OTE5pH9OPA98cQTPPfcc9x999288cYb/OAHP+Avf/kLTz/9dMI8sh+PQuoo9dJLL6l58+YlfW3q1KnqnXfeOcIl6n/tbZMPP/xQTZ8+XVVUVMSmLVmyRM2cOVM1NzcfySL2i7POOks9+eST/V2MfnPllVequ+++O2Haeeedp375y1/2U4n6z+9+9zv1hS98ob+LMSC0vk5alqVOO+00tXjx4ti0QCCg5s2bp5599tn+KOIRlex746677lK33nprP5Wo/9XW1qqpU6eqVatWKaXkGDlS5Jo9uHTnPGlqalIzZsxQr7/+emyeiooKNX36dPXRRx8d2Q9wlHO73eqcc85Ry5cvV1/+8pfVz372M6WU7MfB4uGHH1bXXHNNu6/LfhwcvvGNb6gf/vCHCdO++c1vqu9///tKKdmPR7OjLrMrVffeey/z58/niiuu4Nlnn01IgzzabNiwgSlTpjBixIjYtIULFxIMBhPSfIeyP//5z8yfP59LLrmExx9//KhpwhkMBtm6dSsLFy5MmH7aaaexfv36fipV/zpw4AALFy7k7LPP5jvf+Q6HDh3q7yINCKWlpVRXVyccK06nk5NOOumoPVYgnMWxYMECzj33XH784x9TW1vb30U6YpqbmwHIzc0F5Bg5EuSaPfh05zzZsmULoVAooRn9iBEjmDJliuznI+zee+/lzDPP5NRTT02YLvtxcHj//feZOXMm3/72t1mwYAGXXnopL7zwQux12Y+Dw7x581ixYgX79+8HYMeOHaxdu5YzzzwTkP14NLP3dwEGojvuuIMFCxbgcrn49NNPefDBB6mvr+e2227r76L1i5qaGgoLCxOm5ebm4nA4qKmp6adSHTlf+cpXOO6448jJyWHz5s386le/orS0NKFfhqGqvr4e0zQZNmxYwvTCwkKqq6v7qVT9Z9asWTz44INMmDCB2traWH93S5YsIT8/v7+L16+ix0OyY+VobT51xhlncN5551FcXExpaSm//e1vueGGG3j55ZdxOp39Xbw+pZTi/vvvZ968eUydOhWQY+RIkGv24NLd86SmpgaHwxELkMXPczTclw0Ur7/+Otu2bePFF19s85rsx8Hh0KFDPPvss9x0003ccsstbNq0iZ/97Gc4nU4uvfRS2Y+DxNe//nWam5s5//zzsdlsmKbJd77zHS666CJAzsej2ZAIdj366KM89thjHc7z4osvcvzxx6e0vPig1rHHHgvA73//+0EV7OrtbaJpWpemD3Rd2T433nhjbNr06dPJycnh29/+Nt///vePmgBH6/2slBq0+74nok+IoubMmcPnP/95Xn31VW666aZ+KtXAkuxYOVpdcMEFsd+nTp3KzJkzOfvss/nwww8555xz+rFkfe/ee+9l165dPPPMM21ek2Ok78k1e3Do7fNEzqUjp7y8nJ///Of89a9/JS0trd35ZD8ObEopZs6cyXe/+10AjjvuOPbs2cOzzz7LpZdeGptP9uPA9sYbb/Cf//yHX/3qV0yePJnt27dz//33M3z48IR+t2U/Hn2GRLDruuuuS6hUJDNmzJhuL3/27Nm43e6kGU4DVW9uk8LCQjZu3JgwrbGxkVAo1CZCPlj0ZPvMmTMHgIMHDw75YFd+fj42m63NE43a2tpBcy70pYyMDKZOnUpJSUl/F6XfFRUVAeEnY8OHD49Nl2OlxfDhwykuLh7yx8t9993H+++/zz/+8Q9GjhwZmy7HSN+Ta/bg0ZPzpLCwkFAoRGNjY0IWQm1tLXPnzj1Cn+DotnXrVmpra7n88stj00zTZPXq1fzzn//kv//9LyD7caArKipi0qRJCdMmTpzIW2+9FXsdZD8OdA899BDf+MY3uPDCCwGYNm0aZWVlLF68mMsuu0z241FsSPTZVVBQwKRJkzr819FTl85s376dtLS0hJE5Brre3CZz5sxh9+7dVFVVxaYtX74cp9PJzJkz++oj9KmebJ9t27YBLV+AQ5nT6WTGjBksX748Yfonn3wiF37C/ePs3bv3qDgWOjNmzBiKiooSjpVgMMjq1avlWImor6+nvLw84UZrKFFKce+99/L222/z1FNPMXbs2ITX5Rjpe3LNHvh64zyZOXMmDocjYZ6qqip2794t+/kIOeWUU3jttdd49dVXY/9mzpzJxRdfzKuvvsrYsWNlPw4CJ5xwQqyfp6iSkhJGjx4NyPk4WPj9/jZZWzabLZaVJfvx6DUkMru6oqysjMbGRsrKyjBNk+3btwMwbtw4MjMzef/996mpqWHOnDm4XC5WrlzJI488wlVXXTVk+1jpbJssXLiQyZMnc+edd3LnnXfS2NjIgw8+yFVXXUVWVlY/l75vrV+/no0bNzJ//nyysrLYvHkz999/P2effTbFxcX9Xbwj4qabbuLOO+9k5syZzJ07l+eff57y8nKuvvrq/i7aEffggw9y1llnMWrUKOrq6nj88cdxu90JKdJDmcfj4eDBg7G/S0tL2b59O7m5uRQXF/OVr3yFxYsXM2HCBMaPH8/ixYtxuVyxPhOGmo62R25uLo899hjnnHMORUVFHD58mEceeYT8/Hw+97nP9WOp+84999zDkiVL+MMf/kBmZmasj4zs7GxcLheaph11x0h/kGv2wNYb50l2djZXXHEFDz74IPn5+eTm5vLggw8yderUNh2li76RlZUV62ctKiMjg7y8vNh02Y8D3w033MA111zDH//4R84//3w2bdrECy+8wL333gsg5+MgcdZZZ/HHP/6R4uLiWDPGJ598kiuuuAKQ/Xg009RR1hB10aJFvPLKK22m//3vf2f+/Pl89NFH/PrXv+bAgQMopRg7dixXXnkl1113HXb70IwNdrZNIBwQu+eee1ixYkXswnDXXXcN2QBg1NatW7nnnnvYt28fwWCQ4uJiLrzwQr72ta+Rnp7e38U7Yv75z3/yl7/8haqqKqZOncoPf/hDTjrppP4u1hH3ne98h9WrV9PQ0EB+fj5z5szhjjvuYPLkyf1dtCNi5cqVfOUrX2kz/bLLLuOBBx5AKcVjjz3G888/T2NjI7Nnz+buu+9uUyEYKjraHv/3f//H7bffzrZt22hubqaoqIj58+dzxx13MGrUqH4obd+bNm1a0un3339/rKnP0XaM9Be5Zg9cvXWeBAIBHnroIZYsWYLf72fBggX89Kc/HbLXl8Hg+uuvZ/r06fzv//4vIPtxsPjggw/49a9/TUlJCWPGjOGmm27iqquuir0u+3Hgc7vd/Pa3v+Xdd9+ltraW4cOHc+GFF3L77bfH6qqyH49OR12wSwghhBBCCCGEEEIMXUOizy4hhBBCCCGEEEIIIUCCXUIIIYQQQgghhBBiCJFglxBCCCGEEEIIIYQYMiTYJYQQQgghhBBCCCGGDAl2CSGEEEIIIYQQQoghQ4JdQgghhBBCCCGEEGLIkGCXEEIIIYQQQgghhBgyJNglhBBCCCGEEEIIIYYMCXYJIYQQQgghhBBCiCFDgl1CCCGEEEIIIYQQYsiQYJcQote9++67/O1vf0uY9vLLLzNt2jRKS0t7ZR0rV67k0Ucf7ZVlCSGEEEIcbeR+TQgxlEmwSwjR6959913+/ve/9+k6Vq1axWOPPdan6xBCCCGEGKrkfk0IMZRJsEsIIYQQQgghhBBCDBn2/i6AEGJoWbRoEa+88goA06ZNA+Dkk0/msssuA6C2tpaHH36Yjz76iJycHC677DK+9a1vYbPZYsuoq6vjN7/5De+//z4NDQ2MHTuW//mf/+GLX/wiAI8++mjsKWF0HaNHj+b9998nEAjw8MMP88knn1BWVkZ2djYnnHACixYtYtSoUUdsOwghhBBCDFRyvyaEGOok2CWE6FW33XYbdXV1bNu2LXaDk5WVxaZNmwC48847ueSSS/jSl77ERx99xOOPP87o0aNjN0Zut5trrrkGwzC44447GD16NEuXLuUnP/kJoVCIa6+9li9+8YtUVFTw4osv8vzzzwPgdDoBCAaD+Hw+br/9doqKimhsbOTZZ5/l2muv5c0338TlcvXDVhFCCCGEGDjkfk0IMdRJsEsI0avGjRtHQUEBTqeTOXPmxKZHb54uu+wybrnlFgBOPfVUli9fzptvvhm7eXrqqaeoqKhgyZIljB07NjZfc3Mzjz76KF/60pcYOXIkI0eOBEhYB0B2djY///nPY3+bpsnpp5/OggUL+OijjzjnnHP66qMLIYQQQgwKcr8mhBjqpM8uIcQRdcYZZyT8PWXKFMrKymJ/f/zxx8yZM4dRo0ZhGEbs3+mnn05dXR379+/vdB1Llizhyiuv5MQTT+S4445j9uzZeL1eSkpKevvjCCGEEEIMOXK/JoQY7CSzSwhxROXk5CT87XQ6CQaDsb/r6uo4cOAAM2bMSPr++vr6Dpf/7rvv8r3vfS/Wt0R+fj6apvGNb3yDQCDQ8w8ghBBCCDHEyf2aEGKwk2CXEGJAycvLo6ioiEWLFiV9/Zhjjunw/W+++Sbjxo3jgQceiE0LhUI0Njb2ajmFEEIIIY5Wcr8mhBjoJNglhOh1Tqez20/lTj/9dP75z38yevRoCgoKOlwHQCAQIC0tLTbd7/djtyde2pYsWYJpmt0qjxBCCCHEUCT3a0KIoUyCXUKIXjdx4kSef/55/vWvfzFt2jSysrJSfu+NN97IG2+8wXXXXceNN97IhAkT8Hg87Nu3j/Xr1/P73/8egEmTJgHw17/+ldNOO420tDSmTZvG6aefzrvvvssvfvELzjrrLLZu3crTTz/dJh1fCCGEEOJoJvdrQoihTIJdQohed+WVV7Ju3ToeeOAB3G43J598MpdddllK783Ozua5557j97//PYsXL6aqqors7GwmTpzIeeedF5vvrLPO4ktf+hJ/+9vf+O1vf0txcTHvv/8+V111FeXl5bz00ks8//zzHH/88Tz++ON885vf7KuPK4QQQggx6Mj9mhBiKNOUUqq/CyGEEEIIIYQQQgghRG/Q+7sAQgghhBBCCCGEEEL0Fgl2CSGEEEIIIYQQQoghQ4JdQgghhBBCCCGEEGLIkGCXEEIIIYQQQgghhBgyJNglxBDz6KOPMm3atP4uxpBy/fXXc/3118f+XrlyJdOmTWPlypXtziOEEEIIkYzcq/U+uVcTQrRm7+8CCCHEQPfTn/60v4sghBBCCCHaIfdqQojWJNglhOgzoVAIu92Opmn9XZQemTx5cn8XQQghhBCi18m9mhBiqJJmjEIcZZYtW8bXvvY1TjvtNObMmcMXvvAF/vWvfyXMc/PNN/O1r30t9ncwGGT27NmceOKJmKYZm37DDTdw++23x/6eNm0av/3tb3nsscc488wzOf7442lubgbg7bff5qqrroot54477qCioiJhvWeffTaLFi3i1Vdf5dxzz2Xu3Llcc8017Ny5s83neOutt7j66quZM2cO8+bN4+qrr+aTTz6JvW4YBosXL+bcc89l5syZnH766Tz44IMEg0EAAoEA8+bN4+GHH26z7Oeee47p06dTVlYGdD/tva6ujrvvvpuFCxcyc+ZMzj///DbbWgghhBAintyryb2aEKLnJLNLiKPMoUOHOPXUU7n++utJS0tjy5Yt/OIXvyAYDHLdddcBcPLJJ/PYY49hGAZ2u51NmzZhmiahUIht27Zx/PHHEwwG2bBhA9/97ncTlv+vf/2LqVOncvfdd2NZFi6Xi2effZZ77rmHq666ittvvx23283vfvc7vvzlL/Pvf/+bzMzM2PtXrlxJSUkJ3//+91FK8dBDD3Hbbbfx1ltvYbeHL1lPP/00P/vZzzjvvPP46le/SlpaGps3b+bw4cOx5fzgBz/gww8/5Oabb2bWrFns3r2b3/3ud5SXl/Ob3/yGtLQ0zj33XJYsWcL3vvc9dL0l9v+f//yHk046ieLi4m5vZ7fbzTXXXINhGNxxxx2MHj2apUuX8pOf/IRQKMS1117b7WULIYQQYuiSezW5VxNC9JwEu4Q4ylxzzTWx35VSnHjiiYRCIZ577rnYDdT8+fN56KGH2Lx5M3PnzmXlypXMmjWLQCDAypUrOf7449m4cSN+v5/58+cnLN/pdLJ48WIcDgcAHo+HX/7yl1x11VXce++9sflmzZrF+eefz8svv5zwJM7r9fLnP/+ZrKys2LRvfetbbNq0iRNOOAG3282vf/1rLrjgAh555JHYPGeccUbs9zVr1vDGG2/w61//mgsvvBCAU089ldzcXO666y5uvfVWpk2bxiWXXMJLL73EqlWrOOWUUwA4fPgw69at47777uvRdn7qqaeoqKhgyZIljB07NlaG5uZmHn30Ub70pS9hs9l6tA4hhBBCDD1yryb3akKInpNmjEIcZSorK/nxj3/MZz7zGWbMmMGMGTP4zW9+Q0lJSWye4447juzs7NgINitXrmT+/PnMnz8/YVpeXl6b0YQWLlwYu3kC2LBhA263m4suugjDMGL/Ro0axTHHHMOaNWsS3n/CCSck3DxF+2AoLy8HYN26dXi9Xq666qp2P+PHH39MWloan/3sZxPWefrppwPE1nnyySdTXFzMa6+9Fnvvf/7zH5xOJ+edd15qG7SDMsyZM4dRo0a1KUNdXR379+/v0fKFEEIIMTTJvZrcqwkhek4yu4Q4iliWxa233kp1dTXf+ta3mDhxImlpabz77rv88Y9/jM2n6zonnngiK1eu5H/+53/YuHEjt956K8FgkGeffRbDMFi5ciUnn3xymw5NCwsLE/6ura0FaLcfhfz8/IS/c3NzE/52Op1AuN8GgIaGBgCGDx/e7uesra0lEAgwe/bspK/X19cDoGkaF198Mc888ww//elPcTqdvPbaa5x99tlkZ2e3u/xU1NXVceDAAWbMmNFhGYQQQgghouReLUzu1YQQPSXBLiGOIgcPHmTr1q089NBDXHLJJbHpH3zwQZt558+fz29+8xtWr16NZVnMnTsX0zQJBoOsWbOGDRs2cOedd7Z5X+sbqry8PAAeeughJk6c2Gb++D4gUhG94aqqqmLSpElJ58nLyyM9PZ2nn3466evxN1+XXHIJixcv5v3332f06NHs3buXH/zgB10qU3tlKCoqYtGiRUlfP+aYY3q8DiGEEEIMLXKvFib3akKInpJglxBHEb/fD5CQum4YBkuWLGkz7/z58/H7/TzxxBPMnj0bl8sFwMyZM/njH/9IMBhs0wdEMieccAKZmZkcOnQo4aatu+bOnUtGRgYvvPACCxYsSDrP6aefzhNPPIHP5+Pkk0/ucHmTJk1ixowZvPbaaxQXF1NQUBBLoe+J008/nX/+85+MHj2agoKCHi9PCCGEEEOf3Ku1JfdqQojukGCXEEPUf//73zbTRo0axejRo3nkkUfQdR273c7f//53lFJt5p0+fTq5ubl8+umnfPOb34xNnz9/PosXLyY/P58pU6Z0Wo6srCzuvPNO7rvvPqqrqznjjDPIysqisrKSlStXctppp3HBBRek/LmysrL43ve+x3333YdlWVx88cW4XC62bt3KsGHDuPLKK5k/fz4XXXQR3/zmN7nxxhuZNWsWEO7QdOnSpSxatIhx48bFlnnJJZfw8MMPk52dzYUXXhgbSagnbrzxRt544w2uu+46brzxRiZMmIDH42Hfvn2sX7+e3//+9z1ehxBCCCEGL7lXk3s1IUTfkWCXEEPUHXfc0WbaxRdfzO9//3vuvfde7rrrLnJzc7niiisoLi7mxz/+ccK80b4g3nvvvYQnbtEbqGR9QLTn6quvZtSoUfz5z3/mtddewzRNRowYwYknntim09RUfPnLX6agoIC//vWvfPe738XhcDBlypSEz/zwww/z9NNP89JLL/H444/jdDoZM2YMCxcubPP07qKLLuKhhx6irq6OL3zhC10uTzLZ2dk899xz/P73v2fx4sVUVVWRnZ3NxIkTe9yhqhBCCCEGP7lXk3s1IUTf0VSyxwRCCCGEEEIIIYQQQgxCen8XQAghhBBCCCGEEEKI3iLBLiGEEEIIIYQQQggxZEiwSwghhBBCCCGEEEIMGRLsEkIIIYQQQgghhBBDhgS7hBBCCCGEEEIIIcSQYe/uG+vr63uzHKKV3NxcGhsb+7sYIo7sk4FH9snAJPtl4JF90jX5+fn9XYReYVmW7PchQM7fwU/24dAg+3FokP04NKRyryaZXQOUrsuuGWhknww8sk8GJtkvA4/sk6OT7PehQfbj4Cf7cGiQ/Tg0yH48esieFkIIIYQQQgghhBBDhgS7hBBCCCGEEEIIIcSQIcEuIYQQQgghhBBCCDFkSLBLCCE64P6kgkPfXU5gr3RkKYQQQgwWJfU7cAfku1sIIY5WEuwSQoh2mA0BDnz9Axpe3MvBb36EslR/F0kIIYQQKdhXt501hz/s72IIIYToJxLsEkKIdtQ9vwerOUTRbTPxb63Hs7Kyv4skhBBCiE4oFX44ZSmrn0sihBCiv0iwSwgh2tH8wWFcMwsY/v9moWc5aHh1f38XSQghhBCdUEgmthBCHO0k2CWEEElYPgPvmiqyFo5Cd9nJPms07qWHY0+LhRBCCDEwyXe1EEIICXYJIUQSvk21qKBF1oKRAGQuGEmozEvwgLufSyaEEEKIjkmwSwghjnYS7BJCiCR8W+sAcM0sACDr1HDQy7Oiot/KJIQQQojOdbcZo2EoSkslUCaE6Bm/4WVZyRt4Q/KQvD9JsEsIIZLwb63DPjwdR1E6AM5jsrHlOvFtqu3nkgkhhBCiI91txrhnD2zfCTW1EvASQnRfjaecoBngcOO+/i7KUU2CXUIIkYRvWx2uGQWxvzVNI/34Yfg2S7BLCCGEGMi6m9kVDIV/mmYvFkYI0XWBZjRPTX+Xott0zQaAqeRi0p8k2CWEEK0oSxHY24Rram7C9PRZw/Bvr8cKyheXEKL3LF68mCuuuIK5c+eyYMECbrvtNvbtS3warJTi0UcfZeHChcyaNYvrr7+e3bt391OJhRjgpIN6IQY1+/6PsB1a2d/F6DabHg52WRLs6lcS7BJCiFZCZR6U3yRtYttglwpaBHY29E/BhBBD0qpVq7juuut44YUXePLJJzFNk69+9at4vd7YPE888QRPPvkkd999Ny+++CKFhYXcdNNNuN3SH4gQrXU3s0vTerkgQoieUVZ/l6BbYpldlgS7+pMEu4QQopXAviYA0iblJExPnzUMQJoyCiF61V/+8hcuv/xypkyZwvTp07n//vspKytj69atQDir6+9//zu33HIL55xzDlOnTuXBBx/E7/ezZMmSfi69EANPd4NdsfdLYpgQA4Ph7+8SdEtLM0ajn0tydJNglxBCtBLY0whA2qTEzC7H6ExsBWnSSb0Qok81NzcDkJsbvgaVlpZSXV3NwoULY/M4nU5OOukk1q9f3y9lFGIg624H9UKIASY0OINdUdYgzUwbKuz9XQAhhBhoggea0bMd2ArSEqZHO6n3SrBLCNFHlFLcf//9zJs3j6lTpwJQXV0NwLBhwxLmLSwspKysrMPl5efn901BxREl+7Fr0oIOMmszga5tu+zsEM1ui9xcO/n5tl4tk+zDoUH245FhZYbPXy3TiZbX+9u8r/ej4fCR2ZRJerpLjpl+JMEuIYRoJVjqxjk2Cy1J5x3pMwuoeWIbVtBEd/bujbAQQtx7773s2rWLZ555ps1rra9JqWSv1NfX91rZRM/UeCswLYMRWWO69L78/HzZj13kDbnxeDxA184Bt1vh8UBjI6S7eq8Dr8G+Dw1DoWlgsx3dnZoN9v04mNi9XlAKs64apbJ6ddlHYj82eBrC16CQLsdMH0kliCjNGIUQopVQqQfnmORfrOnHRzqp39VwZAslhBjy7rvvPt5//32eeuopRo4cGZteVFQEQE1N4jDstbW1FBYWHtEyDnZGQwDv+up+Wfem8k/ZWrm6X9Z9tOlxM0ZpBZngg6WwclV/l0IcVaLn8CA9F6P9Bkozxv4lwS4hhGgleNiNo71g18wCAHxb6o5kkYQQQ5hSinvvvZe3336bp556irFjxya8PmbMGIqKili+fHlsWjAYZPXq1cydO/dIF3dQ86yoJHjQjQpJBeRI8vsV5eVHrtba0w7qRVseb+fziKOX0RAgsL+pdxY2hPrcMy3poL4/STNGIYSIYzYGsZpCOMdkJn3dMTYLPccZHpHx6ilHuHRCiKHonnvuYcmSJfzhD38gMzMz1kdXdnY2LpcLTdP4yle+wuLFi5kwYQLjx49n8eLFuFwuLrroon4u/eCklOLobpB1ZK1dB14fjBihMFQQpy2t8zf1xBCqLIuhz7QMgmaAdEfye8/BwLO8AmVYOMdkoTl6mE+TECAanOdyNLvUUmY/l+ToJsEuIYSIEyx1A+GgVjKappE+s0Ayu4QQvebZZ58F4Prrr0+Yfv/993P55ZcD8PWvf51AIMA999xDY2Mjs2fP5q9//StZWb3bl8lRY3DWnwYtfyD8s8Fbx4bKj5g54mSGZ43us/X1NLNLYmXiSFpX9jHNgQbOnnRZfxel2zSnjjIsvOurcR6Tg6MovfsLs+ICRAPpZPTWoQU9qLyxnc4avQaZEuzqVxLsEkKIONFgl3N0+0/X0o8fRu3fdqAMC83e9unVocY9NPrrOG74ieiatBYXQnRs586dnc6jaRrf+ta3+Na3vnUESnQUGEgVqKNAdGyF5kAjAA3+mgEZ7EoyLs2A5/UqHA5wOAZh4QUAzYGG/i5CgmpPGSEzRHHO+JTfY8t1YnkNQuVeQuVe8i45pvsFSAgQDZxrtf3gpwAYKQS7BlK5j2ZSCxNCiDihQ5FgVzt9dgFkzB6GCpj4t7UdXWXFwbf56stn8P9e/wJ/XPnTPiunEEKIHrCkInKkHD6siPbRbEa2u9bHjUh73EH9ILL8U1gp4x6IXmJYITZXrGRH9bquvVHvxXN6CPRzdTRdg46U0sa9NPq71rJGgl1CCBEnWOpBz3ag5zrbnSfjpOEAeFZVJkx3B5t48KNvM2XYLL4w/UZe3vYEmytW9Gl5hRBiMDI9IYyGQL+t/6gaIMsMgq+hX1ZdVaXYtqMlthirAPZ5ClUPmzH2UimOFJ/vyKwnuv8OHlLU1HawlZTqt2NO9Ey3s8x68wFC3AVaG6RBIxkko3s2bFRs3hLedkopjFp/7LVdNZtYe3hpl5YnwS4hhIgTKnWHO9fs4EbcMSID5/hsPKurEqa/svUJmoMNfPe0X3HLyf9HnquQFzb/oa+LLIQQg07zu6W4l5b1XwGOoswu26FV2A8s73zGPmC06q7GOkJBxp5mVQzS+nWPBUqasILt9zFkRBJudu6C9RvaX45WX4L9wHI0b23vFlAMXH12zgzOk3EoX0M0dxV65dbEib4G9NLVPf7g1TVQEckl8G+vx72sHLMHD8Yk2CWEEHGCh9042hmJMV7mycPxrKqK3VCblsFrO55iwdhzmDRsBk67i0uOvYlPD71NtacfK3RCCDEIKaXYXbsZX8jTNys4ioJdmj/cT9ZASGezWjVjtLyhPllPT/vs2rYd/P6j5xgBMBoC+DbW4tvYfoAqlOLu0gLN4V+C3l4omTiSrG5eJ1Sra6oV6EHH7PEBk6PkNKyqUjQ0DI4PaytdjV5fkjitfD26uwpCvXfOm/XhIJcyuv/dJcEuIYSIEzzk6bC/rqiM+SMwa/0E9zUBsPrwB9T5qjh/2nWxec6aeCkAKw690ydlFUKIgaKxUVFermK/NzX17Ka9KVDPoYY97KxZ3xvFa2NwVCl6SXSglH7oB6d1jrSpWoJd/j2NNL1TitkUPOLl6ojddDOm/nVKd1T3d1E6ZfVi0FYFwxXK1hXL+Cy51pl67Yplxx9VZ9qQ0O3md62Oxab/Hux2GbQhcdx07TNs3Ayr1/ZRUXpTkmCWZamWj9uDJupKKTR3IHahUWZkoUkGA0uVBLuEECLCbAxiNQVxju082JV5cqTfrhXhXNv39rxEnquQk8ecHZtnTO4kxuZO5tODb/VNgYUQYoBYtQa2bIv8vlqx5vXmNk/6u8KKjMbVZ01B+jGzK1jmoXFJCcrsm0yr1k34lGYL/3Kk2hB2IHpMaJqGGemLxfImD8KtXacoK+/efupJM0anER58Js1f3u1l9KaDB9vvH6tXd2nkeNRsLZXVYJkH9/KK2PlippysE1nGUG7L1Q3BoGLdekUgkHy7tHfcBgI9f4CQsu7us1bXVJ9PcbisN8o8AI+hFLbRgOuzy1sHoZ537mff+0HC336/4r0PoLY2ejHS0Cu2YN/xepeXbVT5SNtShnN35EGD2fNtKMEuIYSICB4Oj8ToSCGzyzk+G+f4bJreOUTQ8LPi0DssnHABdt2RMN/8MZ9lQ/knhMyB9eRaCCG6q6L5IBXN7T+11+u8OPbV4N/Z0O11GFa4vVRH/Sf2SHvBLqX6JAMqvhLr31KHMlUsk6Y3rS9bxgf7Xk2cqIdv93dUr2d//fZeX2dXmPGVRL3jgEhdPWzd1r31DLiKZg/s3N1+/1hW0N9rzVNjWRS2luphcH8TwSoftupws0RLMrt65HAZ1NbBwUMt01IZ9bDkAGzYmNo6fD7Voya4vdWMcc/ecHPg7i0sfll9fwwFzQDbq9ZhpnqAp7CNBtpojPaDn2Lf92GvL9cbSfRqjDXBVOgNB7q1LOUPb38tEP7+j2WZmqrb21OCXUIIEREqDQe7nKM777NL0zRyzh+He1k5a3Z+gM/wcMaEi9vMN3PEfIKmn711W3q9vEIIkQrTVKxekzwzoN5XjTvY1KXlbatay7aq9ttbaJEMEdWDPluiDwh0rW9uVZUFNZ5yDjTsSpiuV27FvuutXs9Iia9Atq4Ubt/ReV8tvpAnpYcm9b4kTe/0cGZXWdM+9tftSKG0fSi2GbRYsEtZCm/ITVlT9ypIyfSsz65IoKavB4zsIrN1loNl4Nj7Hnm+cETQ8hsJTUKV6loFURmRrDt7ywe3ZTtBgRYIB4BTziTro2DX4cOKkgMDK4iQzL59infea1tOZ+R5qBGywAxX6OOP+/aOW9NMvb+0ZZ/Axz0Yj6L7zRi7v85kpWj5te/3d6O/lvLmA3hCKX4XphQQPHLHqeUzCJQ0dz5jXLn1ml1onpqerVip2O7RtNiQu91fXOwap+HdWNOS9atUt49LCXYJIURE8FC4I2RHCs0YAfIuOQYVtDjw3Gpy0gqYPXJBm3lmjDgJgC2Vq3uvoEII0QVNzdDQCLt2t31tfdkyVh16L+VleUPuzmfqQpCgdeCn1luJ3/ASMgORRfXNrappGmyqWMHe2pYRpfTqnS1PpHu5M/dos0wAK2TgCTajzHAAsvRw8n0Tb2P5ckoadnZv5dFmjAOgg3ozVjFq6UoMC1Ydei+lDJeUxVW4upwRMECzkjytu8oxQ1gWuELhkaGb3jpE8weHW+ZfVk7jf0pSX4GlwAigB1o6qFcqUsVUYKtqJrC3McXtGQlkmlavNdf1eBTbdsDuPSQNJHWm6b1SfFuSdL6vFLYDn6C5q9q+1g3bdyj27o8uOrGcNnv4p6tqHfbdbye8FggqjCQdceuVW0lv3I6lOu+jLf51w1B4N9YQLOvqIB8ty0gly2vzlnAAstcymcwQet3+pOXpVMjXrWBLtOxWypldXWvG6NtWhxFptt0XvBtr8G2swWxs/4GIUgqfL1Imy0Cv2Y3t0Mour0sZioZlAUL1FvGfMva1H7dt7Dvf7NL3TiyTyzAJxgXvlNX9TDkJdgkhRETwsBs9y4Et15nS/OkzCnDNKmD4G1mcNvY8bLq9zTz56UUUZ09ga9WqHpVt7eGPeH3nPwgYPW9vL4Q4Orz+ZoAdOxRm5OGove0lqsuaAw2pz5zKzamZGJTYWP4Jnxx4i/LmcNApPkjUm3yBtkE7vXZPyx+t1rtlq2L37h48sY674S+t20tJ/Q6CQR81kbp3TnbH7w+aAQyzmyMXRoJdWh8Eu7paATHjAx/RZoyWilWqo8vracX5YGPLvuzpyIzxAgHFjh2qyx3DWwdWYStZFvv7wAHFtu2dLyNhPa1nVxZKxfXJ1opRF0j4+/BhxZ697a9TGRZ61XZstXHZfypyGiuFY18NoR11lKSSgBeJZLpXNtC4pJ03+BogOlJoJwxDsWlzSrMmpZTCcocI7E2SuaNMNF89ell4MAzTMgga/sjvJrtrNhE0A23f147Slnhj2z7OFLiCFdi8la0mK3btho2b2+4fvb4El3tf8uW14omLazU1QbCkGe/qrgXxVEKguPNrRkVlOADZW5ldeuVWNE/Hg0N4POHMuebmuO0V9GDf+z5a3d4urzN6jUi5CWfcfEa9v8OArtZgEdjdiHd9Db4ttT0aWbBdofAyQ+XtBDaVoro63LS0qUlBIIUsMECF2garDbfCCoG/xAxfHGK7QLX6SXg7GamfO7HMrtangaVQ3TzAJNglhBARoVIPzjGZXeojpvFaxfDqQj6z7TPtzjNjxElsrVzd7Zv3/2z/G3e9dRWPLP8+P3r7ugHXD4AQYuA6dBiMXgx2hVKo9GUFSnAFK1JaXnxml99oSV3xhsI37UYX+s/SvLXoNe2nSCml2LsvXEGy4tpjJa3gtGqvVV5BuPlUN6+/ZsjAvtsEU+Ezwp/NNIxY5dTWyb4xldn95kWRPrt6O7MrVOGl8T8lHY6m2DomZLYKarX5nWiwq3tl8vkUVU3VNPhamuekUmFvmbfj13fuCp9TdXVtX9Oay2MBkzYaD6PFBXZ27Qn33+R2d7zC+D6P2jQhtAyUApvlR7M6b+K6bQfsL+lgBksRbrMYDnwp02rZIJEfphWuMHcufB9l1LdfLvuB5djjAoAdKa8Ad6t6fNKAYzB5ZT/ZIAiapzrciXar96w9vJRlB94E4FDjbg417o0F37uqdXDKtKDQszb2ACI2PfJ3bV2rz2SEt5+yQG/00fRReZt7QNMyY31NVcfFiIIpdBWrearbdFpuxS2/S/13tdPGtcsjhrZurp3kpKyKfM7KuDieFtmPmq8h5VUZDYHI9owGuzqIJiaUI/xZrYCJ+6NyvOvaNgeM7ic9MriE5QkHWwN7UwvwdoUVCJfH9Bj4ttRiBVt9DmXhiySW+f0kXIsItZ9x1vzhYRpf7+jYj2vGGL1IdPPiffiwYsUnkT67Wh9LXWySHU+CXUIIEREsdafUOX28t0e9yaGxh8n4WxDLn7xSNmP4SdT5qqhwd30Y5qZAPU+ufYB5oz/DN0/5ORsrPmHp/v90eTlCiKNXLNiVPAGkS6IZDh09FMjy7ycjlMJIdkYooYIU30RyRNYYAEwr9Wwm28EV6DW7sO36b9KgQzAIbnc48yLaXEVzV2Amyy6Jq/Qo0wClyPVtb7fZR1Ozwutt/2bcv7cR20EL22ETZbUMqx6Ixg47uI+3lBXpfyn1imdCJbWPmjGGKsLBSbO+/QBo61Vakdq/pSy0WAf1La8bVgjTMrs9yuCyT2D5isQ3W8pCayxFr+i878z4+lSyIzwaNzSS1Ilth9ehN5V1uPxoUzlXWvjvxg66CAqFFOVxMeM2h0hkO+nKYGTzRx2uNxWxjBMFja8foPn9w6DCp6gW2TBGqrHnWLdnSQ5sX31CllsqPJ7wth87pmVa60CS1lyOfd+HSZsjWu7IgBc2DSvSl6DWFL5Gad7EyGV8H4YN/nAQI83mSlquNv2otXk98W8VCgdyWh8/LfMlLk8LNMWmOnZXEaz2o0KJx/fasqV8XLIEgEOlkJ8Xnh5M4dJpO7Qqyb6ID3alnlnb3uWlq+dyt7vKiwbJbI6O54vO3hDAvbQsHLCPBGXNDoNdcR9ExUV/IWnzwWjgXjMTP1FXLsN69S70yq0dzqMsheUN7+xQmYfA3ib8W1tF45UZy1RVgBafbdXBZ7a8Bigw6iIBsdYj/VoWFbEkxWiwq3sX7z17QbMUdrMJu5F4YVSW9NklhBA9FjrkxtmFYJcv5OGTQ29S9dUgRpmXyl8lHyon2m/Xtqo1XS7T27ufpznYwC0n/5QvHHsTE/Km8cq2P3d5OUKIo1e0YtVZ9hBGEK3hUNKXgmYAT7CZoBm+6VVKtT9yVVxrBstvJm/iYRp4Dqxl2bJ/s71qHVsr1vLq0i00NSvmFi9kxoiTGJU9HkN1fWREzTJjQYfDh8PNXQxDxSrqmha+Sccy0JvKUaUrknyGuDJv+y/DPGuxW752M0dWroLln7ZfpmigSq87iF5fEpkI/kDs13aZkey2rtzsx1dSVaRJmd9nEgj2QWZwB7XTpJldlhHeHpFgVyjUUlFcVvIGnx58q8dFamhMzBazlW9MaYSwhFY5SYRwc8i/glCoe81rbaXh/jujQbNkgQDTVBw8FBcIjZat1bxa9LgwFHqk2Z3WXA5BD3v3KWpqOvgkSqE1HU7s26xV4MbyGi0vR4NdHQRQyqv8HK6IZAkpsPwtmZCGoQhGjj2tuRLT3dD+gpLweiErM2GgyDbbTvNHmmYF2kYQo5ldylQ0/Tfy4LGjvtksA9My8QTDAXgrSVbJ4TLF+x8S6wdpzVpFaWnifIZJOHMm6OGDDxWH9zUlrjKyUw0TdCtAtm9HYiZUXJaTZkSCwK2K4g40YimLmqY6giEYPjw8vaR+OwGjg36iIuvWzCC23W9D0IPlDRFcUUfaeyE0d0vzYu/6atzLO3mA0U4GV6oJOUqpcKZv6zckWUDSZy2xYFdqXZFY/pZzOLCiPjyto2BX/PddKgGdaLlbb5cuZCjptbvR60uoqlJ8uDR582kVsuKOJ9UyrdU6Y8Eu1ar8qfQ/Fhk5WAUD+Pa0fCcfOqRiwa5YB/WtD9AUg182G2Ba5Pj3kuNvlT6qupahG0+CXUIIAZhNQczGYMqd0wN8cvAt/IaPEy88n4KvTKPmiW1417XtZ2B83jQynTlsqex6v13v73uF6YVzOSb/WHRN5/NTrmJr1epeHbVKCDF06VrqzRht5RuwVWxKGtBZXfoBKw+9m9B3jZkkEGVZKqHvlqa3DuJelqSSZIXYW9XIgcO7afDXUN5YhjvUSHWlg/z0onB5dTtGCiMQdiTaf47b3ZLpkGbUYfmaiN6Um3HNOOrLLXYvN6ivbRkFyjAgPVSJhsm2rSHq61tu5ivdpawv6zhLxb7jdYgE37SAL1YVqK+18EViAx1lP0SDirFmHCFvu0HJqOYPD8eayzQaHvZ5Sjl4wOq0I/yOGEarylYqlaTWdS5/E7aKzeCtiQW7Wu/joBnodmZXVHWkVdGBg4ryDdu6tYyWylsLj1lNs1mON9S6t/g4KWyXaMUzWR9MVVXh5pI7Wo1H0GaxloFlgdoURO0xwAigNVdgK13Nvv0kZIW1ptfsxla2Ac3dMlOsgpzQWkuF90Vkv4c6iD2/sOINXlz1ZvSNWEEVOwBWrISlH4f7PDuwP8T2HXHN29rZXpal2LdfUVOr8PogMzM2sCiQZNtFs8mSLK91IC/xxTbtQ7FVbMY69Gmsgp0sCFJZCaX+law7uI5AQFHfANtb7TPTBPve97Dv+xDDhJA/fBFyO8ckrNs0IDuwn2z/PkLe8LGllIqN2KgsKPBsiAS7kn+WmsbwdTs7G9C9HGrazqHGDk74uLaUmhlCbyzFt7kOqza8Tr3aigXYgwfdGDWddLDeTrCrs3O5qkrhdqvwgAqvldBxuDlRQiZm5DqiUszsSnxztBljB4VNEiDq6FRvyexKrTgd2bUnfO61DoADiX2AaS0j3CbOZMWeSSiLVsd8CtfxyDoCuxsw4vpJ8zYaOPbVhDPc2tsYKQb3wsGuduaVzC4hhOiZ4OHwTYJzdGbK73l/38uMyBrDccNPZOQPT8AxOpPS73+S8LQIQNd0phXOYWfNhi6VqazpALtqNnLWpMti086eGP79oxJpyiiE6Jyuk5DRhFJozRXJb0DNICHD4EDljjY3/dHBMeIzBcxW/WmlB8tRoUBLa4ZoS4+G5AGroAqRQTanjP08xxWdAoCTAvA3QchLdloehmXQ6E8yglorVsCk7oMAobrEcqenh3+63RBNIMoMHEAv2xB7Um+oSIVSKQ4vC+Hda7BpvcHqNYr164IcjrRMs1terJDB/v0t225r5WrqfS0POcyGAIF9cZkl0f5wGiPZJIYWu2nfsSKu8tjBfXxlTZD9JSpW4bYdWhkOSnYwclio3o9vS7gpy5ra9ez3HqbdHqSNALRqypXMB0thbbIBEzto0hqtc9lNT7iCFQgH4JS/ITYao9G6AyO62e1LyEtecFf4/ZGP2tQMNQdS76A7of6b5GMpLXwQ7dpjUVEZCf5ZZmJTo1bnjtvdktXUWrJgVzSgU9/Qat2ty2IamEZkqtuCSCA6EAjhMcPRvp27FIeWlOHe0bIwpRRaYzRYGlcVNOMq8KrlJLbX7iC9Mjx/ys0YlRVuHRVZji/SxLeyEqorI02uolli7Qy80NgIe/fB+g3g80FaWuI+aRtEaZWpZQTDzZnNULuBGIgfuCGSrRpZsNFcjhUJ6LeX8dNklrFm5wE+aife3aapZWR5BpHso0gT5bLy8GsKhRECz+qq8EiakWusURVp/muphMBdKC5Q7PaE58nKBN0RXk6yfo7qfdU0eGtZuzp8XWmhwv21xV0nUu2zy7m1vN2AYmfBro2b4dOV8QMqtDnS27wn6RUn9tAitYaQCZsmlvXUUbAr9cwuy2dgbHOHV9Imltr97Nr4Mje+fiD8gD0uiyt2frTeF8pq+YzRv+Nf62y9kWCXanUBMEubsVU1Y6toiq27zUAoXcjsatNXV9wiwiPDdn3bSbBLCCGA0KFw+rtzXGqZXTWectaUfsjZEy9H13RsmQ7GPLSAwJ5GKn+9oc380wrnsK9uW5dG9Flf/jEA88d8LjatKLOYSQUzWXu45/1zCCGGPt3WUkFVCrTmMmyH12Lb9yHEZadotXvR/I3sWF5K6WvbqK0uj7xHURfXabJhhbBHRp6ND3bpVpBhnnXYDq9tP1Lha2gJMFkGQQKkqyyskEWOcxgjnMczhknYSz7GVr6JosxiNE2jNn7kMjOY9OY52km6/2CkQhK583ZEHvI3NIYrVdGXLFMBimBIcfBQiO07FH5/+AF1Xh6MGRbJEjBCWJhomkKP9B+WmZEsOBP+zM1Ly/BtbgnORTtNVs5w5Vbztdx6Z9Q2k1O2hRFNSxNu4ZVSmI3B2NP5DRt9uD1x/QNF25J1UImwIjWsYFxwMtp/UkVzYv+RtpJl2A920AYzTkN892Yp1DuUBZoKMbLpQ/K9m7FiTTL12D4yQ22DHd0JdtlK15Ll2Y3N9LZUsJWF3XTHRgfsbMHhl9uvLJsqclwoi81b4MBB0BrimqZG1gnhLKam3TvYsaGesvLEqEe0fMmCXe3t1tbTt28zObg/fqHhhe2sK+WA/2OClodgEA5u9uPZ1BLMNE3QosdFfHNXq1XgI/xB0UI+sgLhbPL2gl2G0bpyHQnKRAsd2e47d4Me2Ya1dYRH02vnvigQBM0dQK8PX6fs9sRmjJ+uDG9jAK9XtfSDFe0YvKEEvaksvG+S7ve2fcb5qhrR6luuUZYRRKvbh7bvg+QfvBPR/RvrrDwS7DJjwS6or4/PmFMYIYtQWfi6YTUH8O41MPZHgm6GmVDegNlyfvuDBroGDoeG3WaiDJJGbNeXLePTkg/xbqqmuSax42/NaWvZVGbqTcf05vazvpQiEnBMMVLaSTPGKvdhVla8QsiK61g/4EZvig6D2c45boaw7VsaGwG0rkbFmjurSGZXh312tWrGGD8SpOauwvTUsqd2C/W+ampWlxDc34xWZ6BX7QJP2w7s4737XicXvCRJkMqwCB5yY8UFu6LnsDIV5eUqPPJipLyxZoytMrs0f2PSfu6iy6qoUOzYGpk/ug8j9wDRfDFnoB6NxHM9bkFJP5LbrRL6vLNpCs0b913QKpNu126LLZFnCqEKb8od/UuwSwghCKdoAzjHdTL+e8R/tv8NhcVF06+PTcs6bRQFX55KzZ+24V2f2JxxWuEcDCvEvrrUm1NsKF9OYcYoRucckzB93ugz2FK1KpZpIYQQ7bHp8RUuKGnYTWWgFi3kxb5vact81TsAaGj0AgrDG66AHjoEm56vIvRpS4aDwxbuXduI6zzetNx4lTc8elj0HjW+Jm8GsR9Yjl6xCV/Iw87azZiYZNVk0Pj6AUyvyTDHZDJMX2x+m27HptkSgmr23e+Es7Jaad3ZudLDUa5o5Ty+SVcoBM1uCywLrxfqmwxKD0NpKeDQSHfB+OEmJ52occIsg0M5awkV76YgL67C2YrCpC60lyrP4cQXon3+OCLBrkBLZhcaFFt7ydTdseCHFTDxb6kLN0P8eA165VYsop26t7wvvNIOmnxEKgrulRU4Voe3RfSJ++aKxP4jtY769YloE8xIWED7LynAMMOVkjSjLja6ZvwT+lCSpqrdCXapSBadpoKx7ekKVaMA5cqJzNRx5T2hMpmkENEmlyqyT8LHV/JMlBUr4fDGPeRWf9ImuyXajV20/mxYodh2SNbFHbQkhISqfTT8ez+N1QGIzxiLLKw5GBk4IJKxaFmJQSrLTPiQcYWKnjxxH8k0wn9GgoXtBbs8rVs+KwUmLds7LpAWDRrX1kLJgZbmZ60Fg5C2pQznznCw225PbMYI4YxNCPeXt2NXtFobqfBH+24yAm2b0yYE9iyUpTB9Fo0fH8K5NrwdgyqEZYXQ/Y2djgRnN91Jjy0rGA2OAspCVyEUGpbmCGcFKoumSFdjmrJQgBl3fWn6sLolgE8ksyau7PH3gSHDwB4J7uv2UHiftBf3aQZ7ZSNqfyguMKwi53Kk+V0wxQ7qk/UjFbe9TBPse95JOihB8pEaO97Wle5wlqHfaojNqgWbY69ryqK0cR81nsTm85qvHgLN6NXhtqbbtlocOgSN/lpCkWOy49EYW/bvgQMWK1aBJzKaqlFdwvq3X2d/3S42lC9nZ+V66r3VYPrDI2n66tv9eOGMpdSYFhi1/oTs4YZak6oaAxx6y7ItxZZtsHJ19G8z8Wsj7rPYKjbH+hJMKFckm6u6Buqqo/27RZrUu3LDP23hpRY0bYp/Z+slha9NZpCmJsV7H4QHc/l0Jbz/IbGgoa20Hi3+uzX+/sFSHDocOa9ReFZWxjKXOyPBLiGEAIIHm7HlObHldN6xZXOggf/s+BsLx1/AiKyxCa+N/NE8HCPTKfvxqoSbqamFswHYVZO8E/vWlFJsLF/OnFGntRn17ITiMwiZAbZUtv1yEkKIeLreUp9tCtSwr2EX25v3hSdEOgvX/A0AbG3eG55ZWYSC4QCI2wO2Og9EbnaVZYQzu3yKYE1LZthu71tsMzaHK8WR++iN7za1jFAYaaqkuSs5VObF57Mo1IvIbcoJt/TwRF6PBtAi/a7omg0LC625HC2SPaM3J+kDLBrrilbYIxXdpJkzCiqrLPx+C8MAu9MgIz3cWbxmB49y424OV05UJBBUZzQwdrTC5QIzmFjjb25WKCwqgpuodpdFtlOkwhh5om+pcAE1n95SHzFBt8JlVyqchdWw9ECsIqMqDqPXl7Q0pYr7TmlcFcS/o52bfbOl+VGowovmiwbKIs2zWgUsQpZBs9F+H1Tl5YrVScZXiR5X/p0NNH9wuO0MQI23lD2+d2m0GlBoUB/5LHHfa2ZPmjEqKxxgNYKxke4cmh97qAkaDpIRKqPeqqOCaMQ39Q50PG7YscOCuIpqyAqvI5o5l+nfj+ZraFN4wwqxreEt3FYzRkvLuNjr0bpmtG63pvQDlh14I/aRkn7UyDbxbqlBKYXe6IdQy4ZqbowGpKPbttWCIsetFTcgQHy2TeyeRcX+C49ECnTWNGxL6wHjlBXOLIprDhmlq8RMvobaQGSWxJ0eDCau1W6LdfOWME80O6SxKT51hZZR+Qx/m4CMClnEp7p4dxo0fRLACHpiGUI+MxALICbL+InFnq0QI5uWkufb3mYe0+9JyOrSlIHS7Cj0yDFhYZqQkW5RWBjufd4ImhBoQi9bj+ZLbMJtmYnBLl+oJcoYNCzsNjjctJ9K7x4sE5rdJGUGQY9sp8RsoZamYlpAtW3OXtKM2dxy/Cil0AJtMzNj/c0pheauDP9M0hdksutz61hJsDyA2RSMjaLpsDmxFPitRnxGJMhlBBPev6tmI5sqVuDbUkvDv8Ppjz7Dy/s1q3jn8FLW7d0eeSigKG3cR7W3LHzebnPH1tOaFncMNNRH+1pT+HzhZqj1QQ9+H2hxR61mWeHTMOiO9YUZ2NeE5W6OnXtJt0EruuWPzeteVp6QPbxpvcm6A+uoCLRk7LZpUtq6GWMHTeBj4pcRjcDHDno99r3VZt5W57GmLGylq7HvfocDB8PXwvq42N+KVbB3n0J5W30PxF0nVNw1qaICarydjbbTQoJdQghBOLMr1ayu5zY9iifYxPVzv9fmNVuWg5E/modvcy1Nb7Z88RRlFpOfXpRyv12lTXup81Uxe9SpbV6bOeJkHLqTdWXSlFEI0TFNb6loe40msAyceksHvraKzeh1+zEsgwp/pKmFsggEoxXQyHKi/XGUrcNVvYu0FQbe5eGMC3/Iixbr90prya4yTdyR+k2oMlwp0SyTbTuhrk7hjGvKE72XjlWEIyvWNB2lLNSBtWx7b0vCKHvxwjf3WkugIC4Txd4qG0QphdtyY7obMQzQbRazZ0FxMeSNClDqr2RveTjTLRhqqS2GLAObDYxI0EApxeEyRclBCFqtKnKmCncY3liB6VOtAjrh1BnTit6IK5SC5Qf/y/aSlqhSqM6ieX0ISxnYLB9WKIgyLJTSCDZaeLbVk5RFXMfa4Z+HDxNrZhJslUizpmErq+o3J18WsGUbsf3YZlWWyf5DW/DVNaM1V4b7W4tzuGE7wzaaWE0m7Arg2GygNetYqNhxYibpsymhvuSrR/NUJ/yL9oVmO7gC+553sO95J3acp1s1ZPr3oprDx/Mum8GWxvD+1HwNLf2oJRG/3qZmaNyzG33/8nATXFqy0ByO8MpcddvaBl+VxYadlQSVmwor2hw4YYaWzC4r3CG/NxQOiriDTbFYRvGoNoslWOpm9/71VHsOY9vXhNrfclxVVRiEgippPM+yQIusVIXiM/nighlJgl2YZqQpqklG8BB68DC2NbuxlEnI8saCLfHHVFVV+PhWZks2oWYpdCuIbgXaBLu2bDZZ+pHi/Q9I6NssGIwbQda0wpldrWqulVUtwVub3qpCbkWzUQLJg10RvpCXUE0kEBw3kmNloDbWx5bVQe6NjolSCqfREnzOjSQSBt/dR9PqEEqB3WxGVyEszYHStFhn81bQzzDvRtIc4TCTGTLRa1tGo1NKUWlUYSgDFQolPEj1hpqx63bsup2gEcThgJ3VG3DmlYGCYCB5kNIMgBbLGI18jrp94POgUKgMDS0QF4C0DDANfBtrCJa2XAyUAr0pMTO0wVeDN5LRilLYq+KuLa2CLMmb8SYG2Dyb3DR/cJim/x7EX+rGoaehlKImuIXNde8CrbMD4zLf9kYeHBgW7mD496pqjbdXr4t86bRky+o1CnO/F//Wdh4ixJU9NrIosG9f+Hev8oS/b+MDhFYIzQrvA71mV0u5Xn8PW+napNsgWRbh8Lr3WhchvG08CivSDLguFBcYjezUPO/WcBcFHTRjbE98x/f2iib8uxpamrVGvl+jgWZlxAWmkzRD1by1kfKb8W+P2bcf3E2B8LyF4S/s5vpAS5+EcdnKNbVQWudoG/luhwS7hBCCaLCr8/669tVt48Utizlv6jUck39s0nlyL5pA2qQcqh7bFPuy0DStS53UbyhfDsDskae1ec1lz2DGiJMk2CWE6JBtw0HsNW6UgvTg4fBNsWXiVQZN+eNpbFSx5ml+K/7JeDizK2QGqfMnZuwoBZmmBUojYPhRpsXHe19DV+EKT/ipcXheDSt23+v5JH5ouGigI3yzGgzBxkjSq67Cox0GyoIEDzSjazqWUngaFda6AFU7TUxPkht1S4XvoFW4ArB+g4nHEx5JMTe3ZbasDEWFVU6z1YTVWEUwBHaHIitLY1iBRsAR6b/RsGMpi09K34+9N2iFsNnAjAzruLN8D3WReFPASgzyKEuBGaJxRYjGFUHMuBqNlW1imZH8iWYLc20QtbcO+8qWjvKjQg0WFia5vh3oBzfS+PoBmlcHKSuHkpLkFXDNJNbJdLTipYhkJiiFxwNvb1rKv/5bQjCo8Eb7/Ylbb623Eo/fy779ietwxg101uSrZXv1OtzBJqo8pdgOr8FeEu5rMhhUHCpVVJf4sYUsbHtBucMZcIS0SK0t0gQoSdu4aH3XEaqjdPuLaAdXYDu0Ku7fynBWoq8eK3sk5ogZGNnhEe5cKlxZjVYMLd0Ri5LYSldj3xvep03Nim3bE0eYjM8gAIXDbE7o4ypkhdCAefPCzYLaCb2yu8SNpixcWnrC54muRA814zAaME0SvsvdgYZYPXTaVHA645cK/oOVBJorCBj+hEwTCHdwHgyBMmMh1MTtGflz1zZ/S39DcbXnaBAlGMnitCzFnu2h2CimerCMOv8nVHnWU2/sY7fvLT49+DZKxfWXRaRvPGVFAtAtbagLvBsobny3TbBLJ1xuS4E/Lm4SDLaMIKuFzKTNGGurggTWvY4rWNES7CLch1dJSTgIhRGk9eh00Xszt+Hlk8pP2NV8kJAVxPDWoGs6x2SMxm14Y9sneh4ZzUEa/r0foy5+cAkr0tS4pfI9cXz4ONH8XkxPeE8Md6/AYbmxNDtEM7uUhT1Qh65HsnBRBCu9CctyG14azSZq9Zpwpl3cR/GGmslwhh/UVnj2URMKB3WzsjQyXWAaSQICpsKx00RTJhYWFXHZY1rVPrQCB1aBgoCKZTDqlVvRKyNBq7htWXoYAhUlNBgHKG8+wKGG3Rxu2k+NER4F0h5qZv/uAEFnYfgNrUYxTdYsVsU1mY6PyZiG4tO3A1SX1JNRu4F87yaINNnFDKJ0B0q3JbxJizSzs7xGLDsvfIxpaFb4eqQ1lbY0N7esWFZUWdOBxJHP45YbCkbOEbMlOBdSQdI2GKS9H3d8G0abBMtQyIsyFZo3HIxvHezqqEP/1q/V1LZkaqpoZFgpKNnEiMalZAVKwl0UxI3GaKkknci3otWXoAKJQUz/pnK00kj2YiRa5fQexm42gUF8tCtxYfHrimSMJ8SplCLHt5NhTWvJtB2G9PCLNQfrOVjaMk/89UxTCltu5y1xQIJdQgiBshTBQ82dZnYdbNjND9++llxXAV8/6SftzqfpGiPuOgH/1npqnmjpo2ta4RwONuxKSDtvz4by5QzPHM2o7HFJXz+h+Ez21G5OaZQyIcTRSXMHsO2uRispZ5hnA2mN28NP6HUbK6s+5eChcGYERJrsRDh0O8GQn61Vq9jbsBJTBWM3pxYWuqbjsqXjNzxYPhNb5RYyg+G7UqW0WGXIafx/9v47zNIsv+sEP+ec110fPiMifZav6q6qrjbVlkZIQi2E3IIEA4sZjASzzDIPLGZgBwaxCwta9oFlZmGZFVoNaBBIGAlJtISkNpK6q12ZLpOVPjMyIzL8jbjudcfsH+e990ZkVbca88eiit/zVGXmva857j33Pd/z/X5/B9gHzYkA5wxiYm0rjpmeS1fSvW8ZvTZi9PIuwkH3QHPnir9meV1z+MXyLVlv/Ru8IE8dN2/5l/n9rl9I1OQAaX39rIaUEcIx8cqJ4iOgQLXQUVZ5f6YjMi+LRSlIhxqtHbcOXiXSB7TS6xiOL+CLWz3SN/awhcNoR3enpN939AeO0fm0ArTcxHPJ3N5DVgDEy+tfYnc4NQw2Y3ZJJfMwQ39cnn0tcye4dduyuzfeDa/6wxwyk11mOIQ7W3tspZ9jcGP/yNrEVU1peeX+5/jVq5/jxs1xGQp6en1i+A+w3rs5+bt4YEnx8iuVlEmOvYic7/P80MtfdjSudwC6QOu3YXZVfx6WV7g+XON2ew59/sPo8x/Gzj+MKIbIXb+gdp2zuNkLFIvPYEVEgF+4ToaeSHi7Jc/GOqxveN8oANG9xcLmzyGOgH5OTEEJgNLkKAVSOo+tTiyppn1htKV03jBJ4dGZo2QHay3Lvc9yqv/rGOOZXUElubPOTpk2ElaXj7SJhWzjJWR2gDH5MYnfurnHnr6LteCMqIA4e+zc8YXTg/60zs6SXTtAH+QTKZItPBidppD2NYfV86mZPgulHVX95PilXwExzFn+qkaOmVnOM7vyMmWQH4KD0PSq/gjYrz/NMPY2EO9/tuT9762ue2QoHAW7KA0qeCuZIzJd0hSaxdoU7LKG116HjXVLnleVf3D9XTG7clsg7wvysuDq8A666BEIxXw0U13L13kj22Gzv0ax6d/fyvXhpE9VYTGpnviaSZvTXPtFZvU1XOFZhE57SWBerFUyxorZZUrCfM+DXUIgBBQv7UEQT8qqln1/OOVwxhwD7q6v9Un7DYwzWAuHZm2SQCQOoSgF99Y9qPvGZcfrX8zY+5Kh2HMIp9mw93i9f30y/zsNlF3E4RVEXk6YOMcAiyP3v3IVDtNbDM0uh9k+/cIPFoUfz3HFdntzx/e1KEY46yjvDyl3UrIbxzcJrLWkR5iXrpx2eFnJYg+2+5PiTPwGTYE3LDsOQYvEt0W5NfKZNo8W31iEKxBGI7JdisJx/ZZjv+uqMr/ImzsvTgt3BFwelodcG/0CL7xxZXI/jUF1xfGxpksPdlWDpZd3ubrzEvu6h6v66UELyKOA1uiapv/S9KHIdXHMK7PIQYoMqwRmLF+2BnROcO/AXz9ocVTGaMdeeg/Sq8aRdlFbryM2rxz7WB7coaiygo7HejK6Szu7gbNywhwrcsOrr03N/70XXHWv8q1y+aTconX3Cq6EkP5kqs7MJmv919jddWxtVr+X43Bu6tP5G8QJ2HUSJ3ES7/jQ2ykut1+X2XVz/w3+zM9/DzjHD3/iJ2nHs1/3mp1PnKPzHefZ+n+8zOgVv3vz2MKzWGe5vvfa1z3X+3V9jmdWPvwWv65xvGflozgcX9184etX7iRO4iTe0SEEiJtd3B2N0ynCap9JSUif9c96P6c7WynaOM7WlmmqBv1hl/3RDg9gON6jBc8wzUxGcXfwgGph+rJfKzdRY0De6QljqZHdBBwC4X2cjkSnUTDqTdlN8lB4M/lqt3+cLc896OK9fxesZm93XAovv9IG5vc+w3ubn+KxR0FXKwtVvSjPdKDdPlK/6rJFmVaZzqaVM85LqYTT3Lrlv2nmtwltH8xxadzeq2v0Lns222EPdtYNB4c+w9z2oZvs5rc/FiObgmLMjHOOrS34ypXb04v1BkT7AeJIooGqFdD7GeXOPehtUJYOsZ/i0gGjzHL/PrjDNcinGywh+eTei+vXGPy7TyK3A9LM8SuftmjtyPQI5xwHwx47xWWujv4d10e/yL38i5SVd4x1ltHRbJ5CHFuEp+PmsOAhEYuyGdpaRCmwrxSUX34JuXsFMwa7TI44XPMnjetoR2xuQhm3cMkMuZzBzpwDISh3r7CWbuKSGUYjx42bMIqWUaqSawb+d9qIEG0gTY8jHko5pM05OKj+vV1JHSs2UmgG1IsNrIWt4QY39y+jXYkQYKylEmNyUPb51O6X2K8W+sOBRbuc2Y6dAE7HsIIj/jZaO4zVLFf+n9bZybFSCh5+GD7yIUiKTfZu3Wc0GOEihzmSlbRQJTkZI9v1YJf1INFRsMscWXBHuksmEy4PbqNNQfZGl8FnNt6SjVFKEEeAyDGzQikm4K7RfswGG4fUy31a+1s4Zzk4sGQjx7XeDW4fXPEyJBEwik6zMfNtMHOWbv1plk5JosBMQNQXX/b+PbYwjAaOOPbPqdobes+uB1aukemTpWBdgOpXgL0zldf6mFnq3iKtGnt2GWcJrqlxBRnoEbGKaAX1cWdNzrm+99oRzd903lr4qsVeLhmjCcpmCAEtt4UrCzJT4+CzBes7a1wxl8kwUIGo5nAXgUZWDKQ79gbr+9uUWlKOpXJJdSMlcNbgRj3WDq6xM9xgfStl/XbHz+UGQqUmCUTaDT/PDQY+GcDejqP/6Q247gHVWBWkLsW6qSeZtQI5WIPIgS2x6dsA0eOMf1WbWqtpxMs8vvgenlh8L/WwOQG/ZSUv3OjNc2/doda/TP7aGsMvbjP6uRewn5oyZ8l7bG+/yBc2PkdZgYzp+hHAtsI7zDTvH67qH6GLyqdRHOtrGftyZG900fmUvSzwmzJjLyxcgH3TVmP3LVWmqui0qPaQ0o24e7A2YVhaDIoKpBzPX8ZQZGCqNtsfbYM1aKEnGQ0ftCwc30b0t8juGc/urcbC9d2XWD+cbjJoDUlY4iQYG9DPDxkUhx5srhiag75hY0OzU+XN2t3z7eYepEmO2yavdoHs8YdNxEd+q8cyxnGmZyTOOnTfsvsz93G5Y6/66S9yw8uvBwyHDlekBGZI/ebPkZTeBkFuj3B3NQwtBJUXmJDsmg20S1m/b7h50x2bz3B8wzLGb9zd6yRO4iRO4jdpFHcq2crXYHa9ufMi//0v/D5qYYMf/vafekt2xK8Vq/+X50lf3eP2H/xlLv2Lb+Ox888CcGX3Zd69/PzXPG/t4CoH2S7PrrxVwjiORxbeTRLU+erm5/nYhe/4hspzEidxEu+sMGhC/ALGHRifacoanAqwTLMCFjlsdjNGsaAhGhz0ZtDFPhAgiiOL3FsGoSXUBEoGWKvJrx6QNBOgAkGMAQu5ahPpwwkoJYzBlWCVo5ndIDUHIJar73JW9n+J7p3HqT+W0dUeEBCA6oOr2YkaarLeeECWJLe9H8r4/Vc4R174xYBZ07QvWmbOCvZivyCe60B7BYKgOmGcZa+6QT895Ev3PuUNhsft6SzzcyAHmttrcB8IRIh0BTwgY7zdvQLliPetziAPHRQGKpaPBQ4Poe0cSoEIxWS33gGDFUnZFNieQ0pB/Oo29d0EMw/UwDiHdiVgOfyVNba5ihBw2z5L89pd0pbG8agvSHpAOsakLNR2Ig5bDpRAlpKdYh+RSw4OYb5jSFNFqUZcv+EN++FN5mbh1JLPaDnOpvjK/c8dW1QKIXzFHgAjhPOgnNdReqZdaAXkA6g1wWpcfxvR2wZTINMDbNKZ9LOymkIrrBBs3Ic3LsP735sw89C38OrGZ9kPS9o243OvbHJvf52z9Q/SaOX0U0VUa9JLFnEy5NoNyRkL737XtGzx3musHq5xT3yC9XXFeefBkbHEJzT+3cAYeH3vFVxtDmsdQnggSUmLM9Atfd93yx5zUYcscxinaSdu4vV0dLgW5VGGVIlzjlgl1XEG60AaiysFIpSEoWNh+BUYwvZWgWtbtM2JqmtnqkIWheHy6DqDTKM6i8fYZl665A25Y93llsxp5rtE/Tt0eHJ60LjfeusQn0XpqbS23XGMStDFAFceIETFSnEZB6OX6dhDEp1hMVy/7kjWnG9L5xtRoLFCsbjg23SUggxCsPoYY/DmLYheWsf1m8RtBXuaYLNHEMy/BexqBH3SAcj7A0RPYM5bRFMjpX9ei5GjeD3FLB2XZLnCQACl08fGsbaG+XAGKSRPtC4xbM6z1t+c9M1EYizFkdO8jHEz+yJSBYTRI378hILhSNPLmyzWR2RyBLOQBnOoitllRh6Its1lnNimUEN2i3XW11cwcpaLT69glrbxsw0E2S75rfvcWEpxsUfpY9lBHFrCbUe0EAJ+jms2BTNn4OK8YOajAr2fM+gKzJaf806VBTd2fPUncmcjcIHFRRXjNNNQHm87HNzuvklpNNY9jsNiZmsEp2rordQbyOPr5T3KFEEUkQ+h2DFweAV4BJF2vWRQWwK7zs21T/nNFCvYKbosiwXyNQMV81ZrP4aNs0gnAes95coc8h5F2iKqHQe7CKYDZms9o9evJiIhEMZOGL+UIVJXssavhaEcYXaNk4a4So7tnMNgCETE+LcQQOeW4aHgoIT5+SPXKQSjm5boIfdA0kHN69sv8vjyU+SfegGdV4Bv38KsoxilFHb8W+Xlw3HsDbOslaz1roLVvMvFYBxuQ7Pf0dwrHeMt+sNDy66zzLQV0YO7WYAYSzqln5P0sh9nMhYMth1SQSiEr/NEJispXszptUDWgJ5P/lI0BIPYYkXI7l6OkDmROcA6qBWbZOEpZHe6UVRrK9qLsHVHMWEa45MyHPvFn2QO/Y3jBOw6iZM4iXd8FGt+Yn87ZteV3Zf585/8PczWFvjbn/gXb8m++PUimE+4+OPfyo3f/Umuf9fP0/nEOf7w+h9i9l/mXE1/GhFJ2t9+nsUffBKZTKfjsV/X1wO7AhnyrlPP+wXHSZzESZzE20Sq90iCGabyNONftEXEUVSi1GBdgbQhO7tA0SUfOqLUUbttPNhlCuTrJUFWR3xcoKTyC6T0EBtWDASEBy8c3Gz1ifdu8IitXLadody1yEXPKJNunGpNEJQ9CutIDjYxt0YQOay1KEBeLZGny4nviXN4utqDCr4xOCIcWTBPmB6QDh3WCvobfcJhyPxvcSwsFQz7eFRMCeSYn1N5v4zX+2LCvrHMRR32i0OMM4SRYHFOMxh50/Z2BXYJU8kvjryAm/OWN+vXqbnziFyDUNjH/EIoyx2n7TWiOEQkAjfwss4Js6wjic6Dfr1iLQCkAmqwPtpize6x4B7DaguBb5eN3ie5qCFbD1D1SnanLWnu2yfZjkh2IuK2w5wWOOlITTZhIbiKEZPqYQV0+Vha8qCglI7SFBQ6o5vu0AhqaKbsriNKmUmIKgukRRCGoDOLE+64rPVgAznYx9bn/Ac6J77+b6kV78VQeM8t5yZ+Tmt3YebdEaVUIAMGA8PlnSPpIhszlEEbbUCrxrgkPBjB0EtvpdO88abi/OnxkccHlzFMFtDOq2VxznrmEFOGyzgL2ygtMBRE4ZTZVRSO4dBxoHZ4ee1nWXaKSMSkRUExckRLSXV9i8kNyVfWGCQJ5fsC8rLky+UXWBVn0KMhjSWLTUsPIgJGVm2pfP+V1lZJGaZtbCqLtMAOkK4kUw3iviQ1hs74oCPVFmmX8GCfegY9e8iu2+GxcIFYCOLRLp3hLv3mIxWzK0On91ESpBa4MfdmooAzaJshhMahPNt0DEoHwXGwq2rg21cMYrYgmZn2m5Ie/D0acaDRDvZGN5FlwFfv70Gwxe1RgSnuwvVNGnsRVh/39xH3Yi5dqFGkmvEDYEpF1o8IEt8Xq8kitnGWjauvIrqKfLVknMEvu7IP843JeCko2Ld7tL78y7TnZxGLEIWGkbNoWWNTPkm3pimCLfL6KWqFB+5NVvlztZaxjVMsmpcZJQ6RGaxQaBFjx8ZtAorckualz+xXgV2J7CBe0LQ2IXw6wLoKPLDHsynq/SMPNX6TQVjf5Lk2EIKzEieB0IGzbO/dY7H/MsdQbOe4df+LGK0x7iLCglQxzQ8u0/vFu6h+4AEKQBiNUwGdDrhDx+A1jUsCmJsUEVFohu42ydhk3kKvHLDEPEeRp7Ghv0OjCDijVtmwQ1x/HbOf0V9boX3hkBfftOwuORYWxLEsgXdvFwwloKpn1TmEy/3vW8W0c9gqg/EDulc4Rs+UduAza1oLTk+8zQIRc5SoZQqHsGNfrmqGsJqiK0mtQR3kaDOVrB7quwx31ukfRiy84K8ULe3xhtmhwXdgCoOt5s4xZq6CEqHAuDFD0dF6PoJ/VUDmMNpydC4QWO6tO+6tK547aj1cPXsi8yC/M963Ty46ROQQmWeFAZxelqRbFrddgaTIymutAhu1o7xuGIwEZdFDbKSUosQtZ4gkxjlwThDc6yIPp2CXCCUqFJWvnd9YsE4jxwzNI2U9kTGexEmcxEl8g1Gs9UEJwtXGsc9v7l/mL/7Cf8VMMs/f+R3/+j8I6BpHdL7Fwz/7Hcx81wWGL2zyyPWHMIc5yeMzBPMJ23/nZa5+888w+PzUvPnlzV9nuXnuN7zf08sf4lb3Mr28+x9crpM4iZP4zR817nvwxvpdUDvxbxL+5XTM7CqgpAStKEvP0gnLkvBNQ9jzi55sa4ODjR3K0sGhQKWHmCUBe9cw2ZGXTqvBOFIxBMTE/HtruMHrl2/7rHApNBrQbHgZoxN+FzfSPZQofRGtg2KITLsE14cTGSNAX4/oj47Pe7s7jo11x16+QDFqwKsZ3V/zKe839AbXBmsUWUpaHqKUQBRjxzDPXBOVJG8ijxzfzznmQr+oHC8c43C6G+5fykHY/uSzScYqZTkQPbbLPTCWUOwgl/WEfTa3khKFApEIrLOYZUl5UTI65V/P8zHbYpzpMq2yVGnPmnBozBHDF0k2ATXcRJY0Rgkh0ZIwAKtAmtQzrY74GQm8of9g0EcdkfyNe1cIKMyIz9/9RQDOdR6ZtoO1xwHIiRvyGPBy1JKKuTaUvi0nMh7rmWHC109kPaxztPJbYEdYEU4WkzA1tZaVlOb+tj4GZSlljh0HU4+ZozHua+mmB/o17nGwy1qQB2vI+y97donwsi8l3WT92z10bGz4v39+45NoegSBo3AF28b/vt+8Bd2ih7WOXbuDlhmDdI8bt4BqwXtj/w32Xn4NKUDvp3z1y7/K69tf8PUs173sN3JYqyd9bCp9q1Neuuusl/v5xAaXaRRrWAvR5U3iQy8dymWd9FMNuv9u1/sKAS4dMtIp27n3WRqPgKHz4y2rJF9S4rOL2pJSQ+lSlBnLHv1YxlmcBqccWIs2I6QzWBEeY2dJ5cEuIQRiVJB84TYfvHDAolojGu6TRI7ZmepY91aboZVThtxl7AcbaJejR4bS5BgKjMvRhw5jS4zTx/473LzOxvYNslcs0sETrUusBqssyVOMKvzWm50b5G6AMJWEUDtu7r/B1d2vIt/cQThDrPcY2SFCC6IsQh4MEQIUHrzRp9qkoo61klF8BhEaNsrXuZdtYbMRVkY+66QQSCeIQ1dJaSV5DlZ4xmHP9RjaEXfuGjAljy++h7PxhwhEzGb1Ctlpq+n8MwYIKsDoaAZKP1AKsIqigCvXLPfvV8+wcp7Z5Rz9Wy/wxf3j1hujfIjcu47s3kK7HByooxl+RYBzmtTsoWyBE4EHLcaPmZoe6xyIvCS678eWMY50BKkucMX0oDx3rN311bFWI5zEiQRsiTEFtnQQ1Cj6fq7Z9MOcrU3L1r5kc8th9XGGmsg1wmn29qushnY6dxunj/k16m6GG05lkLX8Nue/sM7sVcO+fJnVM9VcQnzsHunAz69CVk/TaA+sZnsXNu+Dywzu/jr1bA1pc1Lb5e49uH+3PrnGdraPLT2AvH7P0B/6co397SQ57fIGjPandQuBpsRpgTNmAoyDZ64KZ3CoY8k5/Jhw2MMuZmgpM8P6OiyOXmBx8EUIj8sYx781vouOZEJ2DnffTNi5+vYQsZFjdi1udwRUUu1tTXDvANGQ0PIPtgiEn7vGv6tYrNMEun/MoD4zB+ymW3wjccLsOomTOIl3fOQ3e0Rnm4gjdOfC5PzQr/wxkqDGD3/7T7JQX/46V/j6EZ6qc+aHPwzAP//q/8T/8uW/w7/5/Vdoxh0GL2yy8Ze+wO0/+Mtc+F+/mdrzi7y88et89MLv+A2v++zKh3E4Xt38Ah85/4n/6PKdxEmcxG/OiIpNiJ/wL/ACb9BbmcUeBbvKEpwrkGUyOTewAchK4jIRY/ndZLepUINtzPvamPUSlzrivRBhBMVsBiOHbXqa0ziz1U62iyAlP7TIqw7VgCQRDHL8TRxIlyOVYhSuQrpGmF5Dpl3sYht2pvXazHaJuteYWTmFzQ0ikoxGlS+REoidklBZZJoico2qNry//E9/Dqz2BKxyPN97OcbEOHds8jteSThLUGW5MhUIcmbF8Obh+OyxJ1MKtFi75zhVgWFOOrRwFKVCOEvgBh4QELDzHmic8veSi5JssUHwkKRw4LzvOreL+8wVLbCe2eHScZnH2fbMMbBL2XwC8IwZRWPsTjhBQ0lstfHeGryJE2Hlg1PVuT/i1o9tMVR7LOxqtt7nF6VjcEII6JbrNCtmQRxU46Uc4cwWzjQBge5myJ0SOk2ER1txSKLYl2vQt3RG4BaaiOwAqyEUAaZaXIq8j4n8OdIcYkRCVo7Q6T4wdwTEEhjruLdR0Gh4ph2ACqayp3E86A0HHsSq1+Gs/TImmJl8/mCmsnEb7u9bBkCSPMDswtHvQ6dCMW2espBdRenT7Ll9um6fObtAQECiYpx1bNh1dsw6O5kGGSFdxd5xjv31a8yL9+KcI3zTYFarhZ/xNVlszbK7b1nTVwhdibEWRYBWObZQWOt9tbA5ymY402dzUBC4SwS7PdySpJQhghKJRB6uYecfQu68ya2Rz74aiYCa89YOqhpvqclBQLMJB84QywKtQ5xLEdohQzwohEFYixs53IKFwrJfXEaE1xmUmsbwgLKEgww62XVaewZnDkg31xmWq6x95QAR7tKpt7nfX8U1LZ0GvH5/h5ER3Kswi6eegLv9y9wyuwhhWY1WeSRYoD4zg5AfRx++zmpwjVbosDOPTwey1dw9vMHwlR7xqEYoFVJIakFEKqAcYxoyJL933IsvG47ItJ8rAjk13R8x8mAXEcp5BlVcoTVBOyHPFeHQoVJHOXvAvr7L9fQ2K9EShggl/XjGCMLAIpzFCsXmFgSnpwN5024wV55CmIJOvEAraNBqgp31mVLbLcl+aglVhMn3cP09xNK7jg1ksyIRgwJyCTrGbqbI25adRcPpjsVJhwoEWIPI/fg6Gmk24nDoaDQgs4cI55CyGr+jPYLtq+BqDNMXOFOcp4xmEAL2Wh9mYeszx2SGzoEscuR6hq07Dg6hvwdhTWNrU3RzOJyeUBSWUEicrOGMweocZQApSfsCi8EJePV1h7rrECjksMAtl1DZemFBjgrGwPZw4JCVPNkY0IfrqM1XMQteDj747H1k2mXuWc/6sljPTgbC0HInW4c6BMSMh88odaSZowH45nGIdB+pUwwzFIWl3Oiy9lO/zOBsj7NLD3Fgb3EYLSD1FFgb9AElKiqgYXfPkq9otK42B1yKlKDSLYgW8bJKD3iZXGGNRYijcsVq7hKSbMMQRIJoqcpiWaYc/tqIwcBxd85RHML8oj9Li+Ng1zFj/WOgmTv6B2JMHwQotR/bFhgaTwR7tIFYE7g+qLpFSCbsOIlFmgPmh3cY5qdJii20bLCTvU7j1i2e5eP8RnHC7DqJkziJd3zk1w+JH+4c++xfvPo/c693gz/3sb/3H8Xo+lrxxJJPOfTa9hcBaH5wmUv/8tuILrS480d+hasvfol+ccBzq7/lN7zWowvPkAQ1Xtk8kTKexEmcxFvD4sEtp7zszzlLU9V4pPOwzzLHlAFTUhK46R6osgEuGb/cHl+cuAMIWxLXqARqGwH19YRkMyK9XHmZKAEIstzy6mvOm3I7ONzyKcQFR/xlnZmQaYSSWBfgHDTfFSKEQA+usFdM0S6DJatSmPc+ucboKzuYwXhBJoj6BSurgrn+y8Qv32Piwzt9+4aK2SWFwJiA7I19v4ipFnZigho5WFeIvpwwu5QwLFULADFhX/nyBGbA1nYFnAWW7W3QIgAMTvh6C+krH1WGv1IJdFtCKI4qZVgvttnItifMLvJxZr9pWq393er+A0uY5ZPd9FpxF4ByjNQ4kHn12m+OLkzw7KtCEGz2GI2g2z3uhD9m00gJ/aof1C2DGkrqQQM52MaN9ibNO/j0XYJr/jhnLcI5Wi03ZfS4yrB5nIFQW5RQiGExGQcWR2j6RG84amtN9kZbvL73GWAKYgnhzbezomRpkQowgCTxktDJArmqxMiNKMeOyqbAGl+nhAFxem9SNuGOruKmisvRZMHtWX5KeobCGAqetGp6SCBB5VOmha68cbSbZtOTcgqWCjxYIEYgrITZ2pTJMJb8GcEptcCp1hxYS0O0MBg0BiliUB7PcNob1Bf2AIA9u0u/2COzPSQGh2feTC7+QH0B1rNtMlNWR8hjdVBSUKtrpPPJDko95PSSf6xkKTzQOtQ4Api1YA1puc/QDRi5Iak5JLWH5PaQkT5kMNxmuP4lSreJNfuk3S6pycnyEeloQCZG5HbEID0gPbxJbg+QySE2PKRf9plf1HToEDmJLQBdYAxIV0z9kI4+WOk+NZMwynIOigGqauDx+DUGbtxw9EYh6WtHkjAcWF76wqcm/5ZuQDO/Pf3eVM+z9UkMZjuW5VPQnIvos0WQORZe19hiUMlfYaQLnAyp8HREKajbIcuPSBCK0QiubVXzdNv32a7e8kBUxVlZXYGFTp0oEhXjz7LSOs+yCXF5H5FWWTssiEiin1Tod+ck7xYYERPdStgq71NsDz0jTzmkkuAKhH5AMu4sGxuawx6kIxiY+35uqcAuOdzh/pZD2YLAjnDrBpf6sV7aBuubAaOBgfTQy0F1gchKXMX2cYCwkOoSm1fzT3g8MYHDkOcSIWrYskDrDOsCrHXcu+UQa0PCfvVcWcdTD++xar9KkI3ovNageSsg6lqkyVE2nVw1zTzYZQ3o1Ov1RLpP926OHXah633TPIn1CKNWOHJdEPYChAgniaWMgWBUjYkplo2rnmzhLP3LB2zbTXazgDRYRNkRgU2RxRRkNdaPC0sxMeS/fNVMQHhnR8gAn7SizPxvvgQCgTAKM3IEh1NPSeEMwllu6Wu88pWbvPSFG3xu/xVeuPvveWHtF7g2uMON/d2pX+XE1vLYhHrMGk0467M+coRNLMbls9NkEVoj8L6EYmQx802k0LAc4p6rEc4DcpoXR2LBpeAcgRlQLzdo59cQ1rxlU+JrxQmz6yRO4iTe0eGMJb95SOu3rk4+2+jd4cdf+Xt806Xv5bnVj32dkx3iYA3Z38RFDezCI8fSRb9dPL74HElQ46WNX+ODZ78VgGA24eI/+RaufeLf0v+zlwl/X/j171tFIEOeWvoAX938/DdW2ZM4iZN4R4VuawLtUNLvEhtrkAikDLBOTphK1kHReYRgbyoNDPcEedNW3hpH5S8AApUoEAKXONyewNb8brf1plqYoGLTVOdq7VAhdHf9RYScehw5ayYvxlJJsF50IUIQNZBlzq7eYdbNIoRn89hRysHP3AKgXB+id44aDvmLNWNHZrsUDzgOi9BBKTywgcDdUmTZiOiILE0UGrCIwmJuKoK8Rrbn6I5yOr+jnHoOTczEMpTNic0u0egWxQw4af3ut5Ioa2i3BLKB964SjrDdAesBsLEPS6yagPeRLAyVBco429e4D6p2wzAaGtqAu1rSMAonPKtnrtFjYB1FWQErWjC2dhLVlrxwY7mIpvVqg7DWw6nO5H7CgAum/SSEz8KX9xyrt1dwzT0eynPuBHUPBFbXN6XxrAM73tG3xDUvg3N4DxuLwwWRl5HmjqgIiL4ocM0a+t0p2lhEWeBSR2A8g2wMXIy9agSCLANJSa3GhMEnlaNW8wboceQzYILgDf0qQa/De2ef4P6NX2SgR8yGdaybAh0OmJ1xRD1/PlReQX64Mzv8KsPoaYwzE2bXxNx7/DwhEPK4PFI77aWqTk9AMalAVMcofB3FoPp2JsG5/qSevj8EOhXs3m/weGuV9Z0hd+0eulkgs5hzM6fZ6G1iHbRqUNqc1JUIZDWuvXxp6DLs7cPxYMLat/e+SU1G6srJwv7YY+Q8qGM0CDucIH2ilFinveeUjKDpfQIX5QUeS2t0Z97Lwvxp8hy2S3jvXIfZwAOjT1ww9MOzNMp1aBYQNbFzjyBrCpsa2nUD2WUC8RhnHznHubMCpWNEs8/VGyVmqHGlQ+cFevManWLDK9ECKkAvgDJDHq4zq9oMUsvBgWN5ZZ7aJUX/Fd8Xxvq+v3U35JwboYTCOIPsHfFskoo4v3esvaQWFePHzw/CCsJIQFTDjgbT47Z7hKVAFJJhkaGGIUoDEah7EVJIZs4puOsHtE4Mh08NsUCyGVGIHIhQFUAqJVycfYIb+69jtcE6ixQSpRLfLXkfm1fm+lJAmUJ2QLAwQ16PCQdgZI2e0Dir2TMHKKHQMgcj4Mj4yDLLYKih6UHwYvgGdTooUbFPgwQp5IT1RM8S3c6wz0A6tDSNYm/fcU7eZDPf427RJR3epu7ckeQjgsJoXOHBOVmX2MlwdVg0Uih0uIDIc7LeAZGVFAW4A4swKe2BZe8pibOWJM7QTUh6+2gtiLoKhWYmfYMszEBPVdfgwXRtSzAQfNHy2t46p9Q+yx0YXi5x0fSZb+S3CRotRCZp3a/hzkqUDDBocBB1FaAJ5iRje0MXjeEuS1F4FqDIG+zXHsJln0I6zezBV4ClcZVBC2aHX2Er3wTaWDRF7tvcmBFSea8+OdjEtpa9PDHwQLV5LSXhLrwnxBWO+Cv3KM8M2Y/3OEdITQWoUGKiDoiQFEhFn9hZIn0w8caqfkr8PCseALtwuAoUH4NQ4lzAsK9xDT9/5LnfRBLOeCAzN9hGhHAaI2s4oVBuiEwEWviLh+kGVnbA+d+7cXh57JECfJ04AbtO4iRO4h0dxd0BLrfED89MPvuJr/59lJD8iQ/8j29/kjUEr/0U0Zf/MfJgbfKxCxL0Y99O8aE/hWuvvu2pkYp516kP8uLGZ499Hi7XOfv3PsrtP/DL/L7P/346Pzj/tuc/GE8vf4j/74t/i35+QCue+Q2PP4mTOIl3ThgMUbZHLL2ZkNmEXi5YWZKe2VWBGiERsh6g9qapyCUSta2xSpAt1+AKPr156BAIglACDvWMZtQeMcoDtCmxFQHLBp7ZdcTIg0rzBc6/MPsd8EqeUYWQAmcqIEr6/0wJWEFOTkKCsRZ7kGONwVhLngUYUy3GpWc+qbpgFpjlDq8fTZTonM/uVQgoBXbuLO629zIzmcMNKzZBKRCHObInEFJBAGYPXAzC6qm0b8zMMT3aaReoYXUPaIAyCCkphcbaIQsNSYYgiuD0EtMVVuUBJYTg3Yu/lf7aIXeyX2U4BI3DuWn2rXHv+Cho5ju4I+bbzvmcjzKwzB58lb7y56jh1BhcVJmthBUkqkGRZxgLrfQq/cazOGeJRItHoo9w1f7ydExU5T148xzNrXmEvFV9LimdZnRV4zTcXh/RN3vcPHiJ2DqUdHTLHrvmcHJfr1+K/OLVOYKv1BFuBGkdW6+xWZbIfsgoWgLhQQhrgX6KEQLdFThnyTKYSUqkFDyyV3JqZUAQGFpNnylwYR7W709ljPtFj6FOeeHmKwxG8K2N54+Rfpyr2ARHVr9aw6ioxgWGeraLs5dQwstx9Ng7awJ8ChBTIAtg5Ia8qd/gbAZW+UVsID3wuBw97Y3UADFyOCdhqYHb354+F6WgeaOGlILCCtZulSgv9CRfKkhPxZyKOuyudRE2py4ch1j27QEK7+NksQhhWbN3aGxVC2nAOvG2Up+NYou+hRp1aqKO4IikzwmELZDZLsmgCzhs3RJsS1wvRRyUGAJ0ZDgVL6Dud6C/Di07kfIC5IvPYO0V5OFd6jXQNQvZ2Ki8ar9AAgbSPtlrmifdJrPnzlflMLggRrgSTMnetmNwBZrSv5vZMYvx2Dzk2FyLCFlkUUJsEqIFOwU8HZWXnwJreLhxjkPd507luySFb88r+lUAOmKWWETs6QNvcD5mYlqHq89DGFM0QeYRZVAS3+8z24VE1rnl7tFxs0TSUDyvkHuBv/nFR3FfUaAhU01c1JjIEJV3OZ8wPIX04FukYrS2PqOhEBWobGE0ov/PP4fM91GNOqr+ph+vzZgyapAGKzipsFazNriDXtBoawmEAi0wxtHtOtotuH7dMWx6sOu9jWdYPtT0o3f7JBJVYVq0qdH3GX+dY35xyJ4AtAW8TBMqlqODMtv347Nq/9pBSLaUorVFRhIhPUO1p+9TsxdR0iBQqNYSDCDt7tFyix4M1x7sLmtj9qtFJSGNlmBewNahHwOqYo2Zx0DfNBOQOYq8jLEoR4hMwqH/vKiOzzctRjvsrEWhfAbGsg+hQImAkBpGKHDa/yZhKdsaGsAIXGixNW92JbCkqR/rKpWU/ikisH2cnm6cWwdSS5TeJck2ac+2sQPD7m6lyCwzpHKMGDHjIhAO7QyFKtm3B1jtQfPBTkJg+5jU0L1/C85LlqNFWknMXCtGL74HkfW5Ea6xK3dxWtMs71GvxYxSyPV0UiwKjhK+PSOwogBPNscigdYgjyQjkUYjnSa/pr0fV6eGtKXfdBESgUXFgt5zTXjBP4tW5OCCY0wuZUv2D74xsOtExngSJ3ES7+jIr/ntovgRL2M8zPb4pRs/xbc+/P3M10+99YS0S+0n/zDJL/2PuPoC2bf8NUbf92Ok3/U/oZ/6HoIrn6T+Y99J+NI/PU6bPxLPrX6MW93L7A7vH/s8/Mgsn3v/F3jm008wemX3Gyr/08sfwuF4beuL/wG1PomTOIl3Qoxsykb/02zJaq65KtAvRPQPQspSYqqd0kQkhJHAVNQZsaJQlVm4VZpm8bo/X4ANPGwQRH4hWCqNrlmUkrjIIWb8oTYQOOH9e5yzHAwcVguctTQanq0yBk+c0RPPD9t3MPCLICRkuSBPBZY6Gu2lhg5Edsitjc/x61de4v51j7AFAbi2hwDCua/9iuuqVPYiF9Bcqkz8rQdrjhwXfcEg+n5xK5Lxbr7ApsWERTSe5wsKbyotKlYAgLJIKclk5hNUiSNysSNsonHGKSUUgpBEdiYyycI6ysoYfDBwvP6GI019w4XFPTKX4nKLqwx9HVDOapy0pG5AWZlSq0wyxhbH8hRHgEjPEnWnmzPSeiaPFIrQxtQ3LcHVMSXM/9HIfeZi4UrSkaPIBMZZ9MADhirfJS1vE6VDpNEszFeA4NiXqJLypGXIxpale+g42Bc++YED3TzLS7mkK56kkHUi0cJeewRxxRC/tk75ymvc+vkXKe5lDIcwkFfBOGojONPfwQnH0pLg0UeYyMPGYJd1jsKWDIZgRcxavs7l7E229RRYAnuMxbTXtezuTW1p6vkWbrCFqJgFZsK88+wZDzYcX2Td1xuT4TKRMSqQ+IX72P9GpGAjQT/YY3C+MqeyjmBYgWEIUKBc4b2S8NnzRKNJHAkkgtZGzPyLEQJDTsaIYSW3NGB9RsTpMLQY4w3lR/q4PxXOM34KIQmEJDpiQi6QKJsSDe9iR57hpOd8con6G1twqLHC4UKJtA5ReeA5K5ESHnsUzp6GucUIl7Qm92MsM2XqaC4CCUYju/fQPc8KnQC/1oKKwflMhb1u9WxXfZKmcHjgcL2qf10Falk3oUo6axFKHF+8AxbpNzeloi4Txo/vfP2UBwmlN3QPCWiKFnEaHmN2OQtORYhA0L8QcPjuFkVHIlWJbkD6SMrg4ZT0PIjMEX9GY5sr6I9dQpx+hLPnFFHoQdRe7RFa8zUWohmE8xfXvRK1fjCR7wohMZXXk0QiXeVHaArEcAenDRRHEmkoiQsFVsU4EWCBQdkHhR/HyiH3ArLrIXd7XQ4PQdsR6fDLCASJSKjR8Kyu6WSOENBG4XAchPe5kd7EUSKMB0RCdQR4BHTeBwxpNfziocKVUOYaEUu0Lbl9+Cp9s8Hc6l2ENAgkSbOFFSGFsYx0wSgvcDC5vpJw7rRGSIcIBaLCUF3l6YcUuGD8m1ZtCqgx2DX00v9qrpTHWEQCpx0BIUty6uc7Hyz4jRYRkB84+gNfRxs5erJKKlLiN1lwiNiR5wWmZsEpTFpiHUS6BxW4PvmNKAW1hmF+1if6sK70JXYG07uPUOBExUzWGW8MbnBTXSc7AlCH92KE9YC9soo5u0pcMfIOv1Bw8LN3KTaGhDL0vlk2I6kJFqvforz6WTMGrt+oJKcThrOZ+iKON69C4f9ayaGtCJltauQwwxz4DSZXj5CuwIoQVzFihYBMBTgBARFOpOQ2Z2inVgbKFhRH015+nThhdp3ESZzEOzry6xXY9VAbgJ+78k8pTMb3PvnH3nKs6N6m9q9/ENHfIvvE30I/8Z3HeP3m4W+meP8fJ/7lHyL+1P8Vuf4V8m/7mxAmx67zoXO/nX/0pR/i83d/ke98/A9NPv/q5gv8zLf8LB9a+zDrf/HzPPyz34FQX39P4rHFZ4lVja9ufo4Pnfvt/9HtcBIncRK/+WI8O42Uf+F1TiNR3LkquVWTLFRgV2ADKL13EgCJQM7FsJciRY4ylf5COGzgZYphKMGWFLb02d+E8iDGeM1TzY0D22Wz+AqRhCwV2LrFCXukhA5n7OQ8IQTysMS1qCSLgAXrQqwz2IpNE3a3yPqKLLtAXN4BoTwAdbGOLRJU8+2lWTiHq1lqqkZiJJkMJuXAl+bYsSIFIQKUk160JgOGr4+oDz5NrXb/rb4hwvt+ScApi1DS+5e5qVSzuvjknmMZoxASa0CJkFl5n30JpZ0yu2xl7Ds20O5HW6QZzGfLNKpFlxMhxfkhfWHp7aYEzjMExn5CAggyn7UvVysEIsboI2ZaeABEoBBaM3d7H0UH/ajifPtJ+LW7tEfKX8hodvegbyWqZdnZcSwuQGj6NMwW5aCPSiWC9vEmsgLrYG9f4rRFKTC2RpYOSBTceR2GQcjZooVFo0RE+cUBdW0plxNutu9Q6xmGmy2MgIUFOzF5yfSItBxWdRUE44XthHUALx5eRknQBNxK7xEEkBqNc0toDQGWPbPLTe1T3C2IhePlR+DSfVSVJW/M7LKuSuVwZEy0W35xWFR9dm8D7KIHwqTyBZJCTSSa+YFFBxF30s8Tl84v1KybygSRzC3Crisq2ZBH1YRoEIQ5sx1JXSkCGR4bm85ZCtfnavkqqdPUmMeKCBxYrcHoiTn90RGqZRNh+gQy5qH5p7m885XquwilhzgFgfbgS3Bao+8reuk95h1oJ3GBYm/XQfXMWxkgBcSx4PHHpy0KvtnEmAUSRKBzMCUirCF3r+BWqqONYfTlbUSkkHczSELIx+CA4uI5x801M6lEbwC9/h6n588fGeXTKArH4WAKLEzW7AQTwzYlJWNEUggxoX8Jy+Q9MBgpchzSFTgXeDBNSKQSGKkRSYyYDYhHOdmM4tH5Fa4P7lEuKkwhUVsWghg7nyCkoFYTnDnteLXqRxkoTkULbNptD06+sEF412JuVvOIkGjtTdiFkETVhoW2mkAG9MoBOwvz2NoMMj3wDKQKvwxDLzX3RnYVCFVAOXLYw4iu26OV1+iKW0QcUFDJDh0k5Q6lqNQIVVmD6pkYuRENoXC39ghvZ4CYGPiPm9tlKQOTIceEoEBhB4q9UQ6rO4z6A8q8hlIxVvVAGIQIiMMALevsFl3W93dID0acEi1UYBEWZmehWVoQjnBOIiq809QsgREQVvO9ODIXK+czjJb9Cnz187YSRzK2AtZ4kFoiJ56GSkoODqFZVz4rpKuOji1Xuc5p94w/MPOZX8RZRR4oRp2M4K6EUYpRyjOH9ytwHoONHTIX1GolkZTI/l1aySl0OYewQ/97I6ux3ocgO2BUDDDtGotnHqdx8xB33zCMztK0G4yic9R6kL25hJv1jWJGDhqG4UsDIhH4a5WbrHQGbBSO7Yo1F1lf2YHdoVseMrQwsiMyUkTW5kD0yNhiYAb07IilvI6yEUNTYGTC4+UsIpv1885jftNE2Yw8mEe5rGJnSwrhqCERJsCFQ7Z1lz2T09wJkaVAmgLxDaJYJ8yukziJk3hHR379kGC5jmpFaFvy05d/lA+c+W2cm3nk2HFisE3tp/4IFEPS7/8x9JPf9YCBhQ/XXiX7nn9A/vG/QHD1F6j95B9CjPaOHXO28zBnOw/zuTufPPb5r97+WWQj4PT/8AGy17t0/8WN37D8kYp56tT7+fL6Z/4jan8SJ3ESv5ljPEU5eRTEkN4AGzhM/OKvs96k/Qos3JibnKgi/yZpncFUC1Un/OJACIGqmF2F9bvRqmKLqIXKnLfumV2jsdmKtCClzwxVlU8KgaRK8W7GCzaBO2J+WzrnZW9WeWYXjl5P4MpqXeUsRccjHVI6bwLfkYgjpvS2M0YSBoDj7PwpHmqc5V3BIxOYa2o0f6QBnUN2BUiB1LLyvAnQ2xlyY5/GcNvLLhptZK0xOc24ahUYjMEuqLs6j7bOHbv2RO4hoDF8k6B7B1cMicsdgtEmUVj5pIw9tHCEIRPD8FrNn97PDjHVZ0aEfrEqHXE8NRX3i3J/fG3XEt6POBM9hxQhQvsFQbPtSGK/wBJC0n7lq8wbDfkQnGOldYmV9CHUmEVW7dhLJAd9Lyk01epZIJClQDiBMPmkv0GQRudJww6X916COKXdFNRPnaXPMlujBcyhI+w75P4Qeb4gfXKR9NFZuo8qzKNzFG2JlYK8r4mDkFqiGCcbu7n/Br2v3Ce4bAguGzp3LA8NDSt7lvrdmOGLMfJ6RPNeTO1uQvt+THInRt4WbF02aO2Boft6k9KVlK5k3R73ZsJJGGzT7r/qwa4KENl3exS29AbQVVvPdATRA1aeMl3mtDxTSWG9LHjt2iEuH7K1BgPnJU7074HVxJ8uUFVyAYkgarcQziBdOQFnpWwiJCSJZ/BJoTjqLO4cZLYLWKzPkEAarpKrDk5rVNtf38bN6TmMGXEOJSTiyMadU9HkuiqXhIHkyYVLLL0/4v67RtzVN9nQN7EyoJwTyLEJvp2CA5MYf2CZjPVxVgm59Rqqd8snFKifQiUeOC7vjyg3R5S7mnzd4So/LStCQgGnVy31elUJVYFpzgORQomJ5ApgNLTc3YDh2IveQRYu4OwUBFeo6RxVmfornYETE081lXkfRFH511VEUEQgMJQoEbL6XEz5XIBdSpDDWZ/dVgboRyswti6wkZuUWYbTRCJOeFacsyCMRq296Ot886Aql8To6pkUkprw/ZVZj7TeTTcZ6NGkbY0UnFn12TXjjqBwhb94NXcWfcEwj3DV78VacY/CDT2zjIrp6KBe3COwlb/cGBw8CjFIi71z4GWdSDCVLJuxUbv34QLQdUtDJahMcmNznzvFfa7e38P17hMIGGY9cjtAIIhChZF17ufbCAO6knkGgUVYRxz6jQeBJVqRE48nG48BVe+x5qszZlv6DYWt3SG9Q7DWEdgDAjdlSDknQDvvf4mc/J616r7OO9tjFMazmEfNBUwcop1l7FnocFAXyPpdbEP4/u4PEDZHlIJMn6IIZjEYbOjrZUoNxv9WdVqDqiy5n2OVwwkoqJHnvjaXgodZWDoDrfFvQ4JLHXI8niZeflU3Ha4TtAJiGSKRjOw2m3qHa2aXNXuHu2KNXbfDrtuha9bZLnfYdzukjGjQpK3mmY06zKgObdFhIVikLTt0ZIO26GBUSKr7hGnPs7MTwDmUzbxnFwqL5m65T89uExEToNByQKZTVO6orcfE2xHKeMbeNxInYNdJnMRJvKMju3ZIUkkYX1z/LHujTb7rif/6+EHFkOTf/AlE3iP7XT+CXXnm619UCMr3/mGy7/mfkbvXqP3z/z2id3zH9GPnv4OvbHyWnaGXNqTlkE/f/Gm+6dL3MvedD9N4/hRbP/wSpl+83R2OxQfPfiu3upfZGtz9xit+EidxEr/pY5K0b+xMXi2spfWARC86A0BYRrzr1HtpimqhKyEIK9BCW7bwMsiKvIUAwlARACObebBrzJyZE9hnFTr2rJOxabeTgJBYO2VxSQGBGRLowcTcHCGPgV3ap69COEnqUvatNyrprym6hyBdSXYhwzjDjfIm18ovIWyJCKoFLuDiihkx2uXxaIHTjUVUIrCZQxw4bPcos+soU8tBCUIGSCux0jCwOdZrQXA2AhynzjaRpx6dnGW8Vg1hNUiFlYKzsx1iFXEsxtkOBciyi0oPqN39NIuDL7KcLHCutuLFIRMDdMeZR6bXEAKiANIinYAeToQgwXYMjbogjq1fkBkxkTEGdoDqKc8+qb5TgWchzXQ8CCdRSAyL6hRJEEMJURAibcbM6DVmR6+gi0qe6HlslfQpmXwmjKgAjAfHZZtctLAOdtwWAJ12gA0SknrAo01HfctSuAHlkiPqLGJnaxRtSdKoFqwBZMOS1fYFTncuTd2TAblnj/03Uzg6hSDoBdANMDsBQT+gNgpo64BmvwkHcO2VDe6Ut7jRf5ORG7Eol1iWKxjMZEGrUAgkFkuut9HGoitvLofjhbs3oRwdI/Epp8ijxcm/AyJW1GmU9E+IQNHZ/jXSm1dQuaOICkLTQ5aHkHb9NVI5bevajAdyJiMchGxW0smKwSfkcWaXdRXY6byvkloGAUYGWK2R+S4Ox+EgZJS6yaWFiJiR88yoDk5M5Y+iDH0SVQdBqji/dIbTtUWeaT7B2dYHWJDzzCRneaz9FO1THQ9qA8YG0yysk4tNWXdj6aKLO8ioApHybQgizMITiFqITHdovr9O5xPnmP1wiIim8kot6wjrmJ8T1JKqeapiexzGeZneESnnmKVln4oQy/7z3foHsHa6TFZCIkqHK/vE77eIwTZY658rgET4RAAioHSlB5ctgEQEAus0UoQEKsTNSQIVEThf7oXFACJB8ZyieK/COoOQguSJWRofXcFhqMlZ3jXzPFLBBXmRZzuPT6ScYjKfCg+IAGrvBvW0j1MBeQV22ZnzY0M/3xdCESqYnRFQ9xl5nZtujphMEIQBc8utSZ8JN2UZ2iPPdmgHEzDR1/qIVDaZhyMAy9F2N01TSQirezYMUgUko4jMpoxKQDhiEi7UnqQZNpDCUbiSUIXYoO7HTakn2VWRntkVBRWoJK1n+lbz7Rg8EtX3PlHEGDT2nmebm0P6fUGhIT+/y5bYPlpsXOkqqGsK3rbqjtlonzxXFXDqiCMo6wEmaKKdxgWW/ImU/LSmK/bZ0weIIKBsC8ruJqQQHQQUsoFrBOTkFZsadKFxYbvq67wqjJcOjzMOa1mflDEhgVoHwgqcFCGkDlVtgDihjm/uFH1kTSCl4mJ4iUfl47yr8zDPn//dXGz+Xp6J38MFeYkL8hKnw6e5FF/iQnCJ9y5eYlEtMReusBwvshQtMCfnqc98N8vPneH8h04x/54VyuV5Slui8hykQKkCZUcIHFrWAMFmvsvNYh2NZU7MIUVAGfgsvXH3SFmxbwXNv0acgF0ncRIn8Y4NZyz5tQPiR2YA+Mztn6EVzfC+0791epDVJD/3Z5E7V8l+59/FLj72DV/fXPom0t/9I4hRl9pP/H7k7rXJd7/z8T8IwL954x8DXj6Z6iHf/ujvQwjByl95H3o3Y/cfvfEb3ueDZ7188fNrv/gNl+0kTuIkfvNHcbEgXyzJmwU916Ok9KonU7E43Jj1IwijI4s/ASoMfGZFbUEIsuAUQjgKA4NK8tMYHdAtel5WWDm2X0lvVDvoolqFjcEu/5kz1cLmyILXOiZgl8/SOA09YXYJcjIGDHAIdFUH6TSinlOQA47U9hlGZycSB7/YmV5POuulkonApI7gy9WGQplVxx8JZ73cRCqUUYx0zvXFddYfXQcc7asJshDH5eaiMoV2DnSGFTFhTbDYriPDozWbMrsEnnWihJwsPp5qPsTFxor3PZocZ2jEW5PqCATttmD+/CLLZ6o+FYFfBMQO/VhG/FRBSIS1NQhCL2O0Q+/z5HwjiWqRmJqc6/2XwGWEZjhpjQutS7y39lG4fpeZ9LL3JJoRVDZMFWPL19sEnuEmkQgreDBhlkOibEBZeuh1vIhu1TqsPCSYuyBo9iKSA8fG7Bp2XlGT8xP2zNlzvi32BpD3Da16SKySCbOr+GBA8dGQ4qMhq9/57snfsw8F9J4a0ntqyO6j/s/R+0qKb1lAfGiF0fmU/axHen9Ib2Of5n6N2W6Hc/k8T+aXuBh+iGg/IN6PiPYD0vuaNzcuc3dzjXITov2AaD9g74Yh2g8I9gMWDueROwHGfYCgm0yO6Wzt4PYN4V5AvOcI9wrcvkHe89kpi7bwHkHj4eIcKlW4wCGejHDNJeaXYlZXIF2uPJpUi1rNt/u4TxbnLZ3KDgtrmQnOIJ3FIZETjzcvoXSDTXYO6gzTcJKFEqAdnGFGLhCIYLqiBpIbEa1rdcKNJsFQkrQnGRuYFU1mnpwlfuZJFmvnCAKDsLpiiQVvw+ya6AYnskEXt3APfQB76t2Yhz6KXXrKZwUsS8I5SaxvT9pG1hU2bIKEIpzHpr5uUlbXrIo9HPrnsp9yHLjDzwkiEBNGlcg1zkrKwlEWzpdROxhtEw6ugsg88G+rSide0uZQ3LV36GY9P9cJQW5HlG6EJKAZ+c3VmmoREKCkoN6s+mJWQiQmz3vy6AxBJ8I5SygatOJFhBLUXJP5qDNh5ajRFvS3kEIi+o7wRY3YXyckIJARqcm4n+34Oggm4KLBTnzIiCskHO8TqLVjcDbDdY53kXBmMkm6KUZFoncq8MtfcDwOdTSHcK2JjHWcxW+8cRLUAhAWWVVmft4RRIJm6QeurnDMiJBOepVzmfdDG5ESBiEuqHnAVTuKSOCUQQqD0I449CxNISyoqMqdCmVHU84JWPS+Yt5/0SFc6YEgZ7CjDKcTrIE1+yLdZg89/wh28XFMPANmyuwahxKSM7U7hKLhs5TikEJgE4kDog9Z9PMSW7fsnz3Na3LIpt1ABpKyLShGe7ib5xFb57EBjGojduwWLqyAwNKAauCkwtkCaTPq+R3/cxpKEA6HQqG4mJxFCokhZNg8758noaB0yEqS6YSa+BBGSxLVEBPPPCEVoTYopYjiOjpaJZChz7QpJEKESCcJpKTRkMQxk/EzfoaJa7gkBCWwYQLJGUoBvYMDDmWPYf4movdJ9uwug3CfYbTDINijFrZ5rPHdpM3nQbVwoUMYCIbjlpbH/viN4gTsOomTOIl3bOTXe9ihpvbMPKUp+PU7n+Qj57+dYGzC6hzRp/4Gwa3PkH/zX8Fc+Oh/8D3s6ntIf88/AaD2z/8AcuNlAJaap/ltl76Xf/X6P+LfX/9JfvyVv8sHzvw2nlh6zh/77nk6332BnX/0BuXW6GtdHoDV9nkuzDx2AnadxEmcxPGIHenpHC01u9bvTBeumGR+sxi/2FVtVCem04bFVb8z3MjuYQqNtLby3xIIBbpp6PcFqiYIdcbGfUeeQxz6ax6UPUqqDFNyunBDOCwSawwjleJWNSIcy4uYuH/7RZX3QnLOVWAXCI4uTqdpzkMk7fmS+IMF4n0jwFGqFu6CZ+CGK+IYeiakZz7JusAcVgCcEJNFmsNh25bymdFkNSdQtGQTbTQkEbtmn+v5LQSaeCdCyBBZOho3E0QpfLsGBmE09WCVi8m7vGSzMS2IOLJSFBK0s0ghcBYOak8ilSSJFU45hDOYpkXXDO3OOoGCVqWaFEIgD5rcsLvkwSJG1qbYwbzGzRgaUeSlq0l92hTCgQt8OzoPOg1NhrQF9WKDZnF/Uj4lA9wrQ9zl+x7DnJXYpdo4OZxncTgPdlkCesmjELqK3fJWub84AtdRd6x+aIaZ77qA/khI+UwAl2KKBvTnC1ZaFwhljShUPPkENKo09iMtkBrOnY4JZDQBu9yU5MPKEdnoUYAlTaHU3sDa1eep1y+QJYZQxTy6+wiPrD3KYxuPEq+FqHuWR9IOC/da1NcS2msNandi9BVB7XaCvlXC1YD6WkJ9LaFR/Xnp3kXUtYTgWkL9iqV5S0yO6Vzfw93WxLcSZm4VLL3+Iu62Rt2NMJGgbAoCGUxXac4hC4GJLSpRqCjEBZVEbTlHSfj4x1uEsagynDqEkCgMQeyBlEjEtMMVhHHeJL6ybRZhgNGQdRQHc0uTNnI4P/yFZy9aC4Q1bG0G2zkNQqIySbhZg9BRPzs20IfQ9BA1iU7alKKGsjk8FPkkCsa+ldk18exyng0JIANEokAFiGpuccZNElc4FUxYRKIWkjUuMpi5iGt0KPYCsjVDebUE7RjWLwKwtmbp9SyHQ3EM7HqbohC/cg9rBN0D6B74zx6qn+fxzkVkhcgLS8XsEuwvPkceX6wkolDokkJrbvdu8vrhpwDvxddOZgFoxUsIIUiCAFR4rAj2aNY5xSRhhItqlTl/1UcW2ukVotEGsr+BFJLgikF2HXQlo2uauqrTDSN2g+oeFcMWvJeXflRhlgRmVgA+McjmrmN9U5F2NMWzlvI9mvMPnSMiQqBRmaRxK8G4YAJ2xWYf0gNcNSmEhFhRQzfalefiWAovPTgTCGzc9nNLYCYZLIMIWg3JWbXM6XCFJGjiBMSyRhhCzXhZ4oJc8Vk9g5AiWkRGTzPQYD8Y0Dkz4ry7Te1OH221B7viBtnH6/SeGGITy+jhENGW1BuOdscb1NeLdaLhOric5M2A+r0WxoB0OVYpXJhAWEO3zmFMiBCe2TUG+2Xlz9ZU8yzJZZpFJX+WEpykVBpbSygW380oWqYMZgBBWT9NekrQfVjRfaTG3qMNDh6P6Yl9AEzFTN4bdv18KgKMyTnf3mB+Ad+eniaKEwGn5ApnY590pDMjOWi+m5k/+q3YumcFqwkTVU36TwgPQKKrzLdOItPCZ/pUoWcMMwU9wfsuHnVzcQhf1wrskkphneftGRGj2nPYBLb1JmvNu9wyN7hXvuL/dFfQS1cowyEziTfnK84vIUUdqxyBDogPBKZmp8/uW39a3jZODOpP4iRO4h0bo5d9Zo/6exZ4ceOzDIpDPn7xuybfhy/+GNEr/4ziAz+Afvr7/6PvYxceIf29P07tX/4xaj/1R8i+8+9hLn6MP/n8D/Hq1gv8rc/+t8zVlviTz//QsfOW/9x76P38Gtt/96uc/psf/Lr3+NC5b+MnX/sHDIoezaj9dY89iZM4iXdGTIghR14KZ+UcqZHUNy3uVE6fd5NzDtUMaT70GFZG0H+TIApguImwEisVTngZXDFfsrf8FEHnPkFvmkUuDKZoQukMiNB744wPkA5rBHfLO7QTgXpYIl+tyufAOVkxLCocxvn3boNFaEESdbBsM9OGbm9au9EzEqsKOLWPyC0jcxpjDbIWM/PRCK00fOlIm8jAM9dq4/xbR1vKA2xYiat7jY5wXvbVDGps5eCS2O+gV0wFYaBWm0f1S+xoGSsG6LggfyYlyCAKFqk7j0yp5Dizy+JZcwKHsRas5LAHg+QiJPeolz1s6BvDJTA4M0IlgsUF6LkGNumQBHVMJXvSqoFwGnkETXDCsTqfoG3hWSvVV07CynLAcKviTFVfKeM9JqUQE7YFgKwpAqnBwc7888zJ1yZJt3zPVTVyoFUDE1QyxgeYXUoJlFWTj6WCWjtCjNGoWKAea9Dt+02eVtRhD0giiZKCIKi8mUKYARpdi+saRHecAcwDgJ1kvgJ+qh5+m4WREDBTW0CpOigQ74oRRLigTlQvSXtQm4eZU4LDYoXOlUVmxAxr9jbhSp9h11GkJSIoKCvz9AxNQU64IFEdQ5GNKBptuuu7hMMhOGjLeYSKKJeGDNeHSLWAkC26ZsheS4EQtGXTMyTFVGLrjei9n56Vynt0iYRcDYhCL2NMTYZwjmY+omt1xebTyOr5Fc4inESgGK4ELB8qdA5BXXDwWEjtixXDcbII9qCaq4zYk+YqmR5VPQ4GRfrkiDhwUDEEI90DQKsmWvjrxc2+Zx1a9/U9u6ym+a6ANGhCBXKJ8dxiHWbhSVRxFVGMpovqWgAyIIuXSToB5aFCb2v0oSMLF2nOKlyV4FoXDhFU5vNfI8aZTa2WGDNNLjdTSuIwJsNPUs6B0P75KZozRNF55vQuPXuTYtewnR4wREDs38kKN2Kl+RjNhmTz4BxwjSgYJ8iYxhgY2ujdoRXO4/DZBwk82IX1XlLOQWCnm6HiSMO6nr9mLahxGB4B9o60vXYaVxdc+qb3cnDZcZ0NP9QkOBkgrCFpBMx2ZpgZNLh/LWDM1F3sLVKoGXCVvE/AFz6X82g2NfEvT7egIyGdUncneQ9DhW0uQtrnDCGRXGHX7iBEiVSSMBBY0WA5mmdLlrRoEYSAgw/W3ke/AquFhDQ8w+7OKZzqU8oMFISBgNe3Kc9l3JY3OVUuYxZWSBs5oRmA0/TsIedXHaOD6TMmJMT5TQYYlGxhdR8hDO4Iq9E6gdZe6C2PtPmY5SUkJLbL/tkcvYr3qkQwKgru7gru64C6tBiZsN94ltkEls1TrM28Pu1L6Tg4NSTUBSuLp+lv7KPLglBJXCixJqNdSxkWgPbjsdGC2u4j1MX65PlNaopv/WbBcOhwFagqKTFA/v7ThNld2PIm/EIKbDllfUlb+M/DEFs9E0pVeJiYErh8f3uArPX+mOGnq82uUGEy7wtmRcjHv/lR2oMd3vjiJkEIlb0cWtaxi7+FZ+a/iuxvchi8m50umOU2YfthLt0agbRs2HuUbU2U1RGUCPXAj8vXiBOw6yRO4iTesZG+vIfqRETnW3zmV3+GVjzLe1Y9e0td/UWiz/xtysd+B8VH/vR/8r1c+zSj3/Pj1P71D5D89H9D/on/G53Hv4N/9D2f4vWtL/LY4rN0knmcc+zvw/oGrG80id7/KPZ/u8K/lo9x2OwgpP+BbzRgdlYwNwvnzsF7lr6Vf2b/n3zp3q/wTZe+5z+5vCdxEifxmygqD5bT0VliG5MPLK09Q5gOYWhQSngZRNSowB0IgoBYNBF2gJXeR2tM0nG1OmbhMR4pC76C330OxrJBB7ry55HKJyQHQFis84blCLCLj+HiBtCvZC0hZ5fzainl/1+WU/+YhlpkkMSo4DXAcUqt0qk57i9qDkpvjszsGexhTBhqEAIZjmWTR8I5721UmbtLIY/hMQ5wwVju48sRr9ZJr48rH3hApTpepYpwu0aynjNQTawIMa17iCInANrxw6AOoASVSCiO3MlVTAcsxlnurimiMIAYpJTEscAkvgydICSZH7dHJRNVMVHUJsu7EPgFmyNgrvUkDi+B/9D80xwGBWEx8L5p47srx8qS5PqYlSD94pfAEZGQzNcRPd+PycUG6sI8duMmVoTYoOZ9wSZgVyWhrMAuwPuaHWV1VRnsZucEpx9WvPml6kzpiGUIcspuUUdYN3Ho3d2T2DvNCOUTHOhYEGuLfW1IJBPUrsPVvXT2+bPfQj1skuupHu+tbCJ4svk0T6x8hFvbmzysHmUu6GCtoNF0yEAgMpAJyEQQBIrFmmc+Sa0gcZA4tCmxOC7VHmbT3id1I98LdUWr2cAFMa3llGEhGVW+YkJJhBSIBFYvOk5ZxXBPUJbOZ+4EEm/ANUaCfZ9VdQjCgNpvm0UeDvlo+hwE26ggAQGL0Ryb+S7tPEU4gx0z/ZwHVXEOZ5VnCsUCGSr0sJL8+dIBcHDgyZa1pvIG6wU03r/IhRcjjDW8uvvZ6vgAkTikLSftWis30bKOEQla+ActsCMKocBYHkwybTOH2arkcU4TLYbIR04xeu3QHzCuv7YQ1CA5hSj2psyuRE1AIPnEAp1TS8iDlIPPxYzMGVq11I8dZzHGQfC1mV17zWehVrHLMusX6haMdqjhIUIpRFjzzWRFleVU4Bp+nDblAn1uUdz10khzMZj4B7bUCkpKzjQvsVv5MIXq7ekp2pa8ufMiSdDAOusTDgQ1VCJw25Y3LzvaR1g54BMWjcNV/kZJkDBJVcrxjQ9jDSg407mEbI/Ig3mc82wkIyJOL+eoGlxqX6Js7aEfcpSHJfXdgDk5z14voja+mBDM9r5MmU3vFck2h2eAa4ah6QKzeCTNM7LQDuck8/EMZTG2q/dZDUXV7U3ZIQzO4oTxTEwHgR7gaCOVf65t7ojzCD0jMHI0+b0DyBo522adu91tmHuoYgQ7jN7hqrlOu5xn4mWHfw6UuYuSIIM2pd7ACfyGD47h0LG7r6EUEylwIBTamYlvpZSAUJRLJXZRQE8yHAluDksGhFUWSF8fYyFQoOxxP0frNIXImV+dZVGdYiZqsW+uQl8iOhFmNwNbJahw8HDzPMwU7PdnEZtTsGsssRcCUNLLyNHYCwFWRYxGIApHLAUoN5ExendPizMSGYST5yV+f4T+fOHfB+wDmwiiwq3HSQoChXXSe3oGAQvzApk3iEVC4CaKYXR0mnatQR2FUDHMJ0RbUJRgZY3ZVp3+/RQE5IsFwY7iG/Sm9/3zjR96EidxEifxmytGr+xSe3aB0hb8+ton+S0XfqfXpG+8TPLv/jz29HPk3/Y3+IZdEH+jqM+Rft+Pkfz0nyL++T+HGGwjn/xD2I3fxk/9kuP1NyxvvAGD4fSUpnkXf13cYOFnX+azT30cW+0y9geQptPZXshnqX/fCj/yy/+KZOe7efZpqNW+QY7vSZzESfwXEz/+4z/Oj/zIj7Czs8MjjzzCX/pLf4n3ve99b3vseIYYG9yq8x24k08kX5QaWYCoiwkaIMaLbCU4+/DjXOv9KsIqFsNHGeKTYDihyHRAdyvk2eC9bNtNZprLZH1Ly+1U+8ZjUkq1a6wczgrvvyXHu+jVItZVbApRIISXPDjrwa7JsSKkEz7BTLvH7v4GCRGrC4JgccR6tbB7aOZR9le7PP2omSyQd4ruA43izeVVszLFTiTuCcHeFw4Y6hHphQFmyS+2RNYFBMFsQvPZAH4dbAPc4jPYZB+G+6g0QL6pCfpVXRAYYQgP+zw78wEucwoeDahntwlaDsbJeSeGP97g2OIoonNstL+FaePB3GlL0hW0k4DDmsBJ573UqoiDBHImb/Qz4QUWlp9mZ8eDXUkYczg2JJZisjgZhWd8X+BlWEJ4Rp6MBGfUWW+2LSsQUfldFmk1VkY46ceIGZP2joJdxt/AqMoPaIwRCun9foAQxfKy718pBZGMkFO0BSWnyxPh/CIwjgUlnvUSBDBakegVQfuxc9TDJhd6AVcOX/bXl/4cwfQ38O2YXa2w6ZkZMmBGzhIEcPEiyMSxezynzJRdBJw7rdirqmYrYDYgICRgNF6wI1k89R7mzQUWOxdRZo++9gs41ZsCLVIKTs0r9jSwO71frKzHCseZPX1j+HPCABmHyFCwdPph3Ny3Vt9DK2zQCj2TMJIhmQxIT+fUt0FVmduwEolCKIkKPHupjDrHCjAGLQMlqZ8JWHkyRoQSJQOUDJC1JUw/o9V+H892VqbAU9XOw/gsCEExhUOQ0jvaCwnZtQOKtQHNj64w+LVd2NQkZ5SXnIGXMYbVMzpBUJ2XMjbq4Hag8C9LIgwmeEUYCWQ9QY6mGRfD2tiS22K0AwXmVANR7+OujTO5+ncrJxRibGSVMpHqagNRXkJDUaQOW0k7ZcXsctHYdytBbilKV2IeLzBn55ixp2jU3kMoaxNQUQif8dHMPMxxEaOPe4c+G7dxZcXsUqAU0aqEuxq9byfzuxAw85GI/Lr3ubIzArcpIISkHUF/CkChBAQeFMtVSFCxY1QgcCIgb5xjGA1YOl0jLK9iVeTB5xBkRyH65SQJghXhNOFn1e9jABwglDUIMuyiYHhrD2tqJGM7+iDAuhxhPZu3lzxKOVwDB5EK0BVzzVpHFEPShiTxwKjd07iGRFaqOXHXoVyAnWug2WB3lE3KMDh9GwrLND2vQLqSOL0zmU+ccMSRIwiqOaOzw6wTpGXbWzmOwS7nuHkLSqeRpspIXMJDjXOkNpsMfim9RHCaDTnEGiis9RspeBZsEPjElEEASh+HZAo3IKQkqJ4fFQTMjNoMQ4E9t8hoNOCy0sznGUJLotos852SrpR+HE/Ghh9wSuGBdCEQtoRZhRMhgz70t+DiWUeeOyI8bcu56rfCQFwLJ2zHIBpPpB7sCgL8JhkDD2I6PbEukIHCuKCSHk/nT88C83/fa7yHLFxiNgIqwDys1/j4bxG8+prj8G7M7CKMrgbohsErZyvG5ze4NDsBu07iJE7iHRk202SXuyz9qXfz4sZnGRY9Pn7xuxDdOyQ//X/AtZZJv+vvT14K/rNF1OD+N/1D7L/6i5z/7N/m9Z/8Cn/txb/OyHR46CH4lm+GS5cEp1fhzGlYWqrR/YdPkfzwy3znn92j8d5pRqc8d+zuwe3bcO16wCfv/27uz/8D/txf3aHGAr/1447f8e2CZ57mmKzlJE7iJP7LjJ//+Z/nb/7Nv8lf/at/leeee46f+Imf4I//8T/Oz/3cz7G6uvqW48c75Sg4eHbAxTiC9ZyKeIUwBmk90+noYl7IykT4XAN3S+P6MQI1YQUIFLfuBLgDCETAqjoDjVkQd3EOyuoGg8cMS1c1rVnLLhbXF8hCQDA2XJ+CXeMMYUKADmYoaisU5T6rwVl6oosSDR57HILBKuHTEcm6AixxBfy0ggb1sMXc7AFRYshzxxd3v4xxBuWm87hwFqSXMc58KILZOm4AO3kX4wx2ufTGMZqKtQGylhC0AooPDHBzCg9Q+fbqvitn9fk6/Gx/3HgIoN+TbIygPxvRnmuSzCufpfFo7xwxPSldgRDRsUUZQDzjqNUE4dK48Z0HFqSgfFwRrIc8PP9ubudXgYxAxMRRQigDztaWEZWsbzwOFJ4CI5xCCcE4RaMQPoukGi9mHJw+C27kFxUiEGAKLBEoyUHzKWr2i/5aE7DLoY3ANmPcyCLzSsoYAyoCm+IXm4owEIQBrIhFmqpGegSYCo6AXe1GzDqwMKcY5HCY7RME3nMr7giSZgMpA79YHBwBbMeDibf8dRJjVs24L4WAKBQ4aalVGE1eJT2TFUOiXgdXD9h7wEpTCYU6sqxSUuFWnubdgLGa20AUCbRxx0C4cXnVA0SjemgZSIewFmcqOmB1WhgHMBpTKY9qiY5f46HGWe4+GrLWvYvYglpxHxGCI0IIhVACKR2HtcepzT2E3Pr3b2kjKQTnzgnqHeHle5O2q3PvudOsRvOEwRbgcEGMqGSLuqJOaioZpJD06he9jPEgJbvsQWhzkGNLz7exhauGo/TMzPio/E54ZpcFEXtfQZF7uaSIVIU8esDCBWM2oH/eGh3FiCmzSwRgLswi0u0JKBAEUBQcX5SPKrPuMwF6qHHGkeWO27cso8w/ikILz6oUgux954nyDLUlOSwHFM0BtfgcZxsf5o2d4+2aptBvPk84mwIHb2n3m/uXq2orHDkChRQwOPdBRi/eoDHa9vNmLGg8oRAKQhVhlipAoxrwcS2E/pELC3BxB7P4OHmtTWR8f8lQ0FBLaLtNHs4jEzBzT4EKEUgPyKCoyy6dVgNGFdjlptcFGDYeIu/u0xT7KFkHMvRZCbcsPX0PZzXzKuHWmmBt4Ggtz/g5X9YmGxtR0vEgnwODoxZBu1OBRAb0DQ2nNVJWstl1CzNNRLtF4YbsD4dIFMnTgt7ohvdEq9pj9XSA3vftnwVLaJeBhFZzOra1NtRVndI1fcWEw0rFuLLWFchSIBMPdgVS0YpmQVfJIt4CdkWeJYXEijHY5abPvAAlHsjUOx6XYyhUCVZY5F6YsHj2ce7fuoI2BTv1NrbWwC09in2sg/jyIQ7IKrzv6NyGlBWzrUSI4Fj/be1A2ofVpdJ7R7oKxDUOUckfD2pPMBtcnpRtDGza1jJw3dfPOg5ihVgcg13K7y9VlbWzF4D1yblpuAxCEoZUDyCTddeTT0B5pknx+WpOV8637fg38sSg/iRO4iRO4mtH+soeGEftmQU+c6uSMDYfpvYv/6j//nv/31Cb/c92P2sdX/ii4y//Fcv3/J6Q7/7f/u/8w7X/no+d+gy/9N3fza/8/V/iH/8vkv/Tn5H8775H8PwHBKdPC8JQsPBHnyBYqrH5N77i/WSqiGPB6VXBRz4s+IO/1/LX3Yf5C//g/8jfPvx5fmDuDX7tVy3/7X/n+K//mOPTn3FY675OCU/iJE7i/9/jR3/0R/ldv+t38X3f93089NBD/OW//JdZXl7mn/2zf/b2JziIiFiVZ3hYPQp4Ro6sNvpFUe3ACoE4IqcRXumBsQopDJLk+DaqUGx3j7+cJ7F/kXYOdLUAN20BHx/CM7dxSWU6L0JsNF/dZzonuSMABwK0qlOWkAQRjy/Nc+mSIggEQkgee65Bp+XPXZB15qIO5+srE0bQF+/+MsNAUc6cfZs28UwCV5vFnX0cGXmJpnkio3wifevxgJiZ9QyggMmiKX8UBpdSdBRBQ7C4+jDt6IK3sxfiiAk7XvbHg1iEw2FBSLaLPUpKYtnk9Cq0mtP7IMF8ZER0zv/7Wn6HofZIizB+5z4OEpQat59ECPjow7+Li/XT3sB+DGApwcWnVn2mRKRnylTQi082p6dZOR1EsUAFnoknAgmm5OyFiEsPS7J4mYPaE9Udx2CX4X6rTfGuVYw0yEJQiHmKJ5dxRyRWyvl7BDLkVDLnFzJH0CglpmNhaSHmIx+CpSV/zu3umwQBhIH3/xonlDnmzzX2zuGBMV39e/ypnnuiajJ17HOcpe7xFL8I4/iG0ViuVFTP0ewsxEHgMxY+cIwv23FWg3xg+aWQKAmX1MMTRmItdN6jy+TIkZcKj8FmGYQTDyFxxDjnLYDe3EWixinG/D3v1ZNQqg4ChQgkQoGRNYaFxFJybuHU9HxRZdqszwEQLDWJH+kAMJOsYGJBUl9EjDcFj/oaCf+ZNoqNzrew3vkEJmyCsTTKKfPGdHPGLe9KPMO0AoWji22Sx2eIL7VRzRDTL3HG4iZgl0dxRBQgBQhrfX8pPze1WpZHHobOfMDKExJqyssYAVuBC+5UhFhUk34+6s3kxoBmItjvQpE6DKCcnYIJ+sjcGUicCsieMJTv9icbjhvyj7G08fn15Osvxcfj2mfAg7I279mVaeUZdjEgXvHAZeubz6CfUhBX/R2AelAzOv5nWKPQKaH0/SRDQUed43z7A74qwbgdBVJKanXPuGzWBFHV3W6cUhEmD0+hZiZgRCgroHNWkj7jn41BlRMxEy2MdeTRKoczz/Hkk5K5OX+RKIgnPzdai6PDaiJ3dkOLVLCSPIQqHXamQSBiRnqICy22ucTSylO+aKZEVBd57tyzPL28AsKP+4Oyj53TmLMTfTlCC5bmn2L1dEij4YgiMDKcJE3ZLa8gjPC+luNQybSI0m8YTZ5HUUkC3TSxinN2Mhac49jccaT3J5+LABIV8dDsU6zOXuCxhWdIgpon1gmFDCNEc96DQQ52KnB1DNJ7j3wv3+3MCJaXvacW8xIxI0nb3itwe8PTcW1l4uhcAEIQhTBILh2TOztXEcLjNsP2E+TBHDv7IFYDxNmAIJQYF3gW1xigqs1yf/47jlTRX7DVAteoNvOr5BtKCZJ2c5LRt96Ij4Fd4hvUMp4wu07iJE7iHRn9z25AIAjf2+HXf/aT/Nazv53mv/mTiOyQ9Pt/DDd7/j/PffqOf/Mz8DP/1nF/078U/97vh2/77ZKLF/4A+c77if/9/0DnF/40+vo3k3/TX8K1jzM0ZD3k1J95hvW/+AL9X7pH+1uPL+DMYcGdH/w02ec20U84DswWj//ai/y/PrHLle/9GP/0JwT/57/qeOgS/IkfhA89f8LyOomT+C8tiqLg9ddf5wd+4AeOff6Rj3yEl1566W3PeabxbkZlTFhlUjKqDvWMqEwIXEgoJXE74syZGu3ZDkHH4LQlISZOFLrW4sPt99PjNKPdgFYrZBRoTi01MaMGcelXPQ8/pMhW20TXYwIVIiJBHMfUAv8nWKJYIVWIqD1FVD8gadSo1yPCsJLY1Rok8RCihFotJonrhGFCvaWZiQKYbyLCBFfWaTYtWewX+YvtBsttD54dtGdoVGbwIrbUF87gRveRUYQI/StvEkc0mhHBox9G1Gdp3f0MZZrAauRldwR+Z1kJVLUCnj17Dnn3TeJ+DI0GH3v4u/jk8McQuSSpNWk2m6x8xzdR/Mo+9tUDVLiGC2PiKKbRaNCZlTRcA+ecvwZArUaDOs1mm1JZwiDkE+9/lpWnPLBgb7ZB5MQ9f3x79hR7+z26bkAY1CGKCC40aJkODRo0ZIMwyEnimNnZDp2LH8V+dRdw1DuWMAtxtZjOww0uvHmJZvNRmrUGUZSgdEgQhKhAkNRjwjBExZJaI6AsDK12g2iug4kCkk6bzrkZNnSII/HHOoUqA4w0NBp1aDQYRRIlJTJqUFvpINZ3wYSgAjqNDr14h0CFJImg0aiRzM7R2PF9Nzszz0OXNtjasizNn0IIwaiIaOz77y9esJxuPcHZUzPMdvymVKb6NAb++/k5b06vrZ5cUxvHfhhxSi6yzxZDtcTcqRVmZ0OacxHEMUlN0GgEfpEfBzz7jCWKBFIKZuodenFMHAuSepNk9aPU+i9gLZxdaPBMfY7BlRFd7YGp2eYMs7NHNsxuVc+CcNRGNWIZV88GNJtNipFkpbbKYtKmZ3eZ7YQcDqPJGFTSEgSOOI6ZnZ3FmTncqIFo1hHVfYr2LHl86O/XXEDMnaUzc0C4ExOGiiQAGTRJgohaUqdWr1F7dgn35lle6/176rWY2Zk6+1v+WRm2z5EkNRoXH6Px0IcQUZ2ZOcvOxjXmWjXev/Q8AI2ZORrJIcRNROmIY0PcnCMIGtQbilrLP6vL5xrUZ0M6jZhs1uK0g3sl9UYDBjFKSlzN0WjUkeO2m/fPQ3i+JLvbx9UbNBbmqdkORECjQdSao1YbEpeK+bk2nXmHO2iga44watKYCWm8v8OtL7SQmylRFBE3ZohNjHsoQDhNsynQ2lELGxB7RlcrdigiVBJiQhgNYXYpJIkThFQEfuVNWAtpNPw4S2SLRrtBtOApgTKJmJnt0Gj4a87ORiSJYG6uYDB0zC+0OEjzt52/AepRgzjWNJImc3MdHn5Y8PmfrRP2FTQUcSOh1QoRQtBcmWEmnaOM75HECbVWjaBem46zuEOvMcQ1KqNyqZhvLDA7O4sdZiTJLkHdj8tOOyCqQPJOs0Osezz3TIc3X+gSZCFhGJLUGsRR5B/rOELECh3XcWFILEOayQxJsk+jEZCtWPaeLmndjOjVnuC2vE0QQJzUqIUt2m1DFIW4MKLZalHEJWFoMWHsfw8qeWt4qkN3b59YxczNzvCupefZaC9gOk0atRaNSBE8bhFhnYvv+xbufu6l6vcggUaDmdYih4d+8yGME982MdABtVkhngZmVh6jbjK6OxG9Wogt/fMSxIbAhgQuIKnVUENBEiuYW4F93491WUOPQqI4JI4ViU0QYUCgFEEcEQcxoQw5d67OxoZldVUR7baJRzH1mqDjnuZ+9gqhjWiIBrGMsXXh2aatFjMLc7hGn9qojqs2rDozfr5pLHUYdWNUHLPf+SBzCwve+9E4as2UIIyZmRUsnG+w1u2Q9BOoMH+TlJihQAY1wsj3YRzXmZ2d5RPf5ugPHIPLj2IaPeI4ISxDoggajQZxM4HckSQ14qwC7OfaHKw3SOKERtiazIfNVk4/+DaUGdGIG7znmYDVVYWzHwWTe0+8I2HPNInP5bzn0XcxtAUvxT2UDWjPvj0b7sE4AbtO4iRO4h0Z/U+t03j/Eq/0P8+o7PPbb76MHGak3/sPsUtP/idfv9d3/ORPOX7yp7wH1/vfB//NnxB89CMQhlOwyS49Qfpf/QThi/+E6HN/n/qPfoLymd9H8fwPHmOWzX7/w+z+fy5z/699icYHTqE6fpIv7g64/Yd/meJOn7N//2PceexX+buf+/P8vf7/yuDvrPHc02/wrf/4XXz6M/AjP+r4c3/B8eEPOv67Py1YXTkBvU7iJP5LiW63izGG+fn5Y58vLCyws7Pztuc0ZYtBkZHjX8K1ClC2pCg0WpcUpmBhriAIMvrDAXkxwqaGIsvZ3xds7Q5YRJOHMbkpCC4MWWlZhMwZpCELIqfT8ayX4SjDmJK0KCnNiFzlzBKTVzowbSTzdhVtT1GMtjBDS5ZpytKDXaYoyfIcrKLIc9J6Cf0MIyGlhstLKAwizRgNB/5YGTIcTk0Oh/Fw8u+N3bsMh0NUnqMKUJU5eJaOGKVgDw8hh0F/gB4N0HlBbnxZnXEIXRCVIS6s0dcp4WhEnueY4YjeYY9hKSl1Sdhq0R/0aXLIKM+QponThrIsOQzaDIdDhgPozyzg4ibpwTXk4T3saMSBLXlj/xa3ers4HZBmI7pdL+9S/QFiNCTPc+I4pjTTtozKECs1eTHCPF1j+PkheZFT6pIsz+j3D+l2BZz+KHL/JiWvUJYlpszJswxcTKQ79A965EVBpA3WlOhS4FCUZUkSSLJCU+YWRgOC7hZZnmMLg8v6DEcJtaKgLEuEgLqr09X7uGyEtENKWzDn5om4wCPzF+jHb5CNMpKgRTEqfVsqSwYMh5bs4GDSd8NkSL2WcvECHBwc+H7To2N9PVMLqNnOpL16g97k+/E5xprJZ8Y6ytKQqBqNmRKNJlB9ul1Bf+RIS5AyYzgUOJlNGFOjIsSc+QDD3QF5nnvpVFYwHA5pdCS1Ws6imiFNR7S1B1Dm5QLZkb4cR57nlNpRFiW5yCf9mQ5H5IUizyFjyMKpEa7Q5GVOVAa0gjqnWSQP55mby+h2u4j+EDUcYqMetlZJAk+9h1Hn3lS2NRxSp0Ns6zSyPsZBM1hlO8sp8oLClIxUyqAV0OtnGA1FXnCa06zZO+Q4yiKnPxhQDhUMc5xzDIdD0tRN2nbU0sRmiNOSbDQiz6Gfapwc0u3CuNtaczkxI3r7MTrPUO2IcnMEZYrMc6QTCAW9mffiHmi7QhWMDjyTy/QjTKAh3ULonCIYMhym5EPFKO1xMEgJqjLm5RA7DJHpiCxLsWmOrBcMMk09z3EUCBxF4SWrQ5MTjJ8zZQgp0aXDlSUmBoI2ZVGirQHtEFpQJOV0bOYFS2aZS7VZvnLwBmY0pN87nLTBwcHw/8fefYfHUV0NHP7dmdm+K2nVZcmSXOVesLExtimmd0zvJQmBEAgphBpI6C2E5AOSOBAgofcOSXAIJfRO6Ab3XtTL1pnvj9ld7UorW7JlS5bP+zw8RruzM3fqzpw991xcLsXIERYtLbCkpYWWtha6EgnZ50qbFSYSaaC5WREIRGipjVJfDw5N0dqaSDF87xEiXgfR+oVEwsOJWAbR1laqc0ajULRFW1gTWk+8pT2LKaDyqaurozkaJ2TFqC/MIxwKY1guWlrs7d1MK7GWFsLRKNFwFBXVMaJRWlvacIYjRE0nDa5J4NDxN7VhRSNEtSixiEmOqqCl5TtCbRYtTnBHY6yMfUdDQSv+qElbJE4wFqKlJUKVuwLlbKYtFCISDRONQizcRjQWJhRODBYywUXsqzjhtghNTfW01NnnUdwMM2FslFULQoTjMeL5URraokRyh2DWLsIydayWFpqNKM2t9vY2zUjqHAT7ugp2l9toOEZrpJVYNEosHqUtCo2NLbSF7O9OPRancrBJVDNpzRkOyoNy5KGa1xCJtmBGwsRiUUJxjba2EI5IhIipESZCJB4mpixisQiDKxTRCMRbQ4TDYaoqYViOyYoPNSLRKHFMwiqMsmKEwiZmWwg9ca2LhKOEI3b7m5ua0erqiE/OJxaFWDhMXdRPQ0MdDofCsizaIiE8sTjhSJjmNoPWtmacifUPeSqoLFzM6hVxQm0OIm4HjuYooUgsdR0zdFjvGUEDiyAUxhuNEjWgpaWFcMiiLQTr3COJhD+1t2NrIy1tETa0hAjnRlLzqa+3sENQORBrIR7H/r5KCZEuWjiUNZU6pRX1BBqW4jMGM7igmQ1W1+dNOunGKITY4URXtxL6rJbAnApe/ep+ci2dKW0x2o66C7Ni5y2ad2OjxZ13mRx9nMXdf4OpU+Gevypu+a3GnnuojEBXimYQnXo6rae/SGzMXBwf3Yfvr/vgePuP7QVYDY2K3+5KdFUri056iZb311L/zCK+PeR5YmvbqL5vb/IOG8Jew44k4Mzj8fGPkntoNWt//wnRbxvYa47i73crfvJjxSf/g1O/Z/Hsc1ZGt0ghRP+nOvRVsiyr02vt79n/enM9qW5ZOOxOTaDZhY9VotuXpnCNzLOnMaC1FXQzjK5D3OmhdepgrID9wOFxaVjKwOOB4qJk3SO7yHE8DjErgkLDFRiWaoumLBROFBpaLFl0Pr0boxNXqUbOLl6i06tgWqk9MFQgB6tgKCiNwN4V+Kfa3ahypjoI7JLZ1VxL2w6N4brkBgOgzF3IcF9lqkB9st+NpjQsy+xY7gjL6cVyuLH8JSgjrR+N0tCVTk7Qh2/kZIrKfan1R1PoyomuoFUPsN63c+o9s3QcVrAa3PY21uqWUBuupynWimnZBc51R/ttuZWs7WI4Ibcc3V+a2cDEbtTd9gNa6ejh9q5T7vZuU5qOpRlormQ3Ri1t1TWsqJXqRpLcdDkeD0WFUFjkSPU8UZqFphIPyLoD3Z1ZYGrQIBhSam+HdeYigpXfYmomGhpuPZ+AP48cdz6l7kLyDJ9dJL2D9G6fRtrIjElah9HzYmYs8/NZzoH01zQNwo58cv06Xo+ipNge7TK5OVbn7E5d/kz777SugVZeNXjyUImNallpXRSLa9BLR1DmKkQpWJ8zi7LAXJy+3Wkpnd2xMal/NDLXRVNa+wiFVpyi8P/QY+HUU1q5uwSXYRDJnUjergfak+WWY+ZVYRaMaF+E4bTrzaWtt9PhZmTe3uSMyEdpkKdVUKPZBe3tUSHtczoWS4xEp2skzw3TsGv+pG/a5DY1RrSfe8pIZFio9mmtxCABsbTd5CjzYTZFia1vQzk0fNOTXSYT2zZiYRYMwQqkdaVMbiN/+zGhdAWGGxVLBCqchl0DyLLSut+BIjmCgm53LcO0d6Bq78aYrBuYPGecToUzsToFRTrKqaiubm+HlahhhWbhatHR2zSstN1poeFQDvIcAQZ7SplUtmuH7Wf/63YrCgpUp/ptHUUT9do0pafqyOl6+3VT7zCao0N3Y+n2yIZ6QAEWhuZA14z2wUfSOBPd73SHRninSto8XsYHDmN65ZzUNFqqyLnTHmE1+cUSt1jvnYpROpSII4+wo7B90IpE3+gK7/hEN+X2ZZpWnIhfsW6CQbTIneoi7Dbc5BoB0FTaNSyjjBrK4bavS/aAuzgT+1hz67gcDpIV84v8FbgNL9OHzcVdMQ0r8eOxMtxErRgVFTpDqrNvc5/hS7UzeZ01dYNo1CRu2Qd0qTEMw2FnfSYvlFaitpWmTHRM4o5czOLRybXGxO42npy3lnFcJPs02tvbqfwAGOgEg1BUmtYl20het9JeS466qEGbozhtvsl/FZau2XW6wD4n0pbvKK8h2RPeikex8uwhh43CzLrF7ceyyvw7cViE/RWpaQ3dniAWs+t3dUXbRDTKNWgIMw+sxD9iPJ4pBzBzZjX5rgCW6t7ziwS7hBA7nKZX7GGWfCNW8Oayf7G75Sd67H2YZRM3e54NDRbz7jQ56jiLv90L06fB3+9SXH2FxvBh3cugsgIlhPe5gtZTnyVWPRvXm7fivWs/HB/dD/EI3p2KqPzLHoQXNbHwiH+w7JzXMQo9DHv2QPy72A9CHoePQ0adyn+XPI/2y0FoAScrf/0uAIahOOZoxb13K8aOgRt+a3HhJVbiVxYhRH8WDAbRdZ3169dnvL5hwwYKCwuzfygZ7JqwO+uL97NvtA1SN9wWVsZDiKs6h7zDhth1mgDNDNlFgjVXRs0ul9vAVEZ7vRbsYIXu0IibECNCXo7OoNFDMPOH2svS7Yd8hZ5aZDLY1eIajGW40VwK3a+huTQspdkjoKWNKqv7HDiK7JtvI6ChF+dkrK6eFkRojTTbN/hKEa8M4y1y4dKd7cGu1PooLMvEJPM6aBbWEBszCDOgMoshKbv7T8AVJC/PLgRtWYktqil05aDVPZR6d1Xqcxk382nzqo/Yoz0m69ZrhtZpOiu3ApU7CJVTlijuC9HxrZilsURXS4O8w4ZQOm4Y5a6d0ZUjs8aNAiOxDVXcfjhTGmitUayYieXQU9MBBHI9uD0K57Ci1CZSpolKZL2hO9F9Bukf0jSFK1GAuNZcweKG/2EZFoZy4HIr+8k32SjLTIwCmTGLjMCUrjo/GGkq85HFZbgz/s4WNMh4GERRMWYQg0dnKVGgErXkdE+W9+x56Gn7JhnsyvUWM3PksTg0A6UgYgRpco+gyT0My53TcUb2emida3ZpSksdI3lt/7NfQ2EZRuqTpWOq2GmK1n7cKg2zdJwd3GqfUfqiUs1vzakhkl+A8mqYrQrHYntI0DhR6sLLqY8uIRZLBKzTDlbTUBi6srsbpsk7bAjG0Lz2Fxyu1MLiedX2tSEhnlY/3yiwpzPb4qkBMQK7D8Izzg5EWAA57YPwZGwjb1pnJE1hpe1/5XJimvZxquvYmW2QKgmhDD2xfU0UVqJQeOfOTSNHwOSdNIYPg1E1EAhoDB+t4fMru/C9mQx22RvY4QCPG/IK00bGTg1PCCP9VeR7SjLKHXZ8sO/id4pOFFoqsOXdOR812CBWE6TNmRkEz3EHsfLiuEdoeIa0F1W3l6VQkYzJcSZq6SXbFY6Ay6VlnG9W7mAszYGWMxjSR/qLWUS1HMxEXSewR680lcsOECXqge059PD2oIsFMSsKSmE6E5Gk1DbQUhupPUhjkRH7Nlx2UlDc/u5yJkbOjGs6huZAWSZKKcYVT0MphcvwUOBt30a64SZmxTG0zoFCgCpvGVU+O2Dj0r2Y/gJizny7rqMJZmJcT/s6mxbpSWu/rkwUMZRuYP+oBMqyEuGuRM0u4hnnaTKYbw9cqFHunkqFowancuF2Q25+58CWlnahT15TNA02+KayPO/AxPZLWzlNJb63LdCdmUFEXW9fHwtMpfBMceLbKfM61ul47bQJ21/w+9tfHVnTdcipu+eA/UuBjhbwoFCpAWQ2RYJdQogdTv3Ti3AWm3z1v7NpURYzd78Ws3DEpj+YbV71Fn/+ix3kuu9+2HUG/P1uxZW/1hg6tLtX8ExW/hDCB99C6wmPYhaOxPWfq/HecxDGl8+SM2cQNf+dS+W83RnyyL6M+OfBuKozv4wOH/N9dGXw5Mq/UvLTCbS8uZrm11em3i8uVvzuJsXPzlO8/wF874cWn38hAS8h+jOn08nYsWN54403Ml5/8803mTx5ctbPpB5KdD1x4w3eHMXIkXa5assuQ2s/vFkZkSsADCuEGbdH3UoPHGi6PWx52FmY9hENI/EAG7dilBYZOB3tD0GaBpqyswvMxMhayeepqOa3554cZEnZIz3FYmB4M6+jVvpDmNOb8V57kfbENtNd9so4wD06eSNv2stNZdooLCueNcs1NkInOq3jQ7GGpnQKve3ZJz5nAKWB5TLQlINAhUEwP5Z6mMh4qEhrf8yMg7IL/ZqYGOnFpBPTlfrKAbsbX3IXWAGT2LBErbMs7e44+K7mT/zqH0pkvynQEp8zHQYa7cWUAz4vOd/bB2PCxNRTghU3UZEme1tqjlQwNDyq/SHSqTkI+GHwYHtebe4gTgyGDtGzBLtSK9ke7Eo7vpIDDRh6+1NuembX9MF7U+DNzP5RqvMjTcdsL5dLw0im7WQbqTHbU1ek2V5+cqNaYCa2XY47H6fuIj5oErHyaR2W3XlW9nqoTsEuIDUym6HFEtNp7UEkzQHegoxBJLJLnldpAQSlUIaTdYFdadJGE9Pb7xciRhhNh7rYosQy7WBXcl+YOhi6hhWN00l6QC3ZzniMePFYVubtl3ovPbMr/cBUiSxGPc+Fc3DaU3EXG06lZRMqTYEjLdjpdGCaYDkMkgkksVEHYSZrr2q6PcCjZdqFwjU7MzWdBbjcGg63A11vz8LXvHawWykwLTB1JxqKuObG5QKfV1Fe1h4kTQ922TPQOp2PPWXooNIC+Ua+A1WkEwvmEiUz6DssfywT80ZSWJ1jHy8qPRCnoVoyrxdehz/ZzBRnh1JImitAfOS+aO4csNpH+Y2vtVfMlxy0EDu4GTKKCAWGETbau9yPK5xNpWtXAEwzhpWM2ZI+OIqyd4Selk2oLPy+9rZYul1PUSUOrGRmV1zp9nXDsq9FKu27wKGlZwU6iWkGFAwheRAP9bVnI/kNXyqI5DRcDK+YSdhXRdSCVautVGaXwkhleybPBUexG0e+hoaJZsXbg64mhByFFBaZxDV3al+mHxfuxMijSoGu6xjKTaFuX/sNw/46sHyJjK3k90radSSZLasnv9uUYnBFh4Cebgd5LZPEyKntbyldT4TlSBSUVyinQnU4GLq6VipI3XCEjSBR3U9eXvsPaw5n5+Byp3l2k57rsq/tZvc+KMEuIcQOJfT2F7S8sZqymv/wQlEBQXchk4Yd1m91eQ4AAQAASURBVOP51NZa/HGe3V3xgYdg9ky47x7Fby7TGFK9hXc2CWbpOEJH3UXbkX/FcufifvECPPfOxbX+v+TuX4l/l9LULzzp8r3F7DviWF745gHCB7twDPaz+oaPMh6KNE1x5FzFvNvt4d9//BOLx5+UgJcQ/dnpp5/OY489xmOPPcZ3333Htddey6pVqzjuuOOyTp885VNdi9DQDDvL036uSGR2KZWRpZW8CdfNNmJx+xf69GBEYaGG3wcFO03HTGZPaDqGIzkKnoWuO+wARdqogsmb/PDEzGwiO4ii7O4ZloXSYG2iDFmnrt/pQY20QraWJ4iWl5m1E46F2kfayy1uz4LJSCxSiVGyOl//lFKdMors7aNRmTeS0cU7MbFsV2oKJ9mb0O0gOrEKbZROSVmMRE38jJv5jvOr9lfZo/m5LdzejBQwAMbkj2f/0ceS5y7Ecgba305040sP6ETGlBIeN6hD9ojC4UsOAacwTYUVT9sGht1tLTkbQ9PBFQDd0f4wZ1qo1g3g9NoP74nZmXle8vMTn9N1XE6Fy2vPq9VdQlvuFHQj2XUyva9XeqBJZfwL7aMxOrT2B6307eZL3w6pteze925quvQujunHYbKJugPLcGHlJh6EtWRGBhQ5g4woHM/QoF3f08opx/JnZiR1CnCktT/5kGqkZ2Yk3k52UdOVjpUY9k4lAjtqU1GT5Nt6xgHevr/GDCJZ9z06tBAMleje154VoqUFuyzdfljumNkFmce0ciT2kxntlLmUHuzS0rrpKkf6eZwWyOoioKcyTyIso717ldINYqOKCY8tSwUNIX172d3i2gNRdrBr5Ai7Cy7Y18pYzYGdUq9UaTlWQRWaZgfgjUH2tbCoxMBhKCo8pbhc7cGUNqNDZprSNprZlSU1hjHFUzL+HjkSZu/avmK5BW6KCiGqBbCKarD0tPPEsih0BjvNE0Bz6MSrMhvgddrBrvTt5srsuZba9rrmTEQOLGoLJmOGXCjLIuCH3XdTBIOJTD6lMJ05GauW5y7Eb5Sg0LFovwSotGuPlfgeUppKbafcPHA4FZavyO6ya7hRBqlglyMWxXLqWFr7yKxuzQlpo/umd4t26C4clTNQvvYfasrd7d3+kq1K0nUDTbMTyUJhC5MICsO+HumZ07uG5uAq09BiLYCFK9D+/RQ18mgrm4KlGegqOYKsvW1L/BVMGKtTPghczvRguKKqEvJyFfgLsHLt4Ffy4pJxPUjEo9OPtcoOgxGbHrtovhUHEtmoqTXQNKywfZ5bJpgkjlu9c5dyAHcixho2PalNEInCipWwLrAra3J2R9cVQ6th6JCss2hfdg8fmZxDc/AM1zGLo92aXgrUCyF2DJaF8fkT1F3xHrpzOOrcA3jjq/9w+JjvpX5F7o5lyy0efNjiH/+AWBz22RtOPUlRWdk7Aa5s4lW70lY5A33BP3G98X94njmHeMl4IjPPI161a9ZvilMmn8+/v3ucOz+5lnN/fhHLf/YGjS8uJffAzIfBESMUd86Da6+3uOUPFsuXW5xztsqa3i2E6FsHHnggdXV1/PGPf2Tt2rWMHDmSv/zlL5SXl2f/QFqwy75MKJQr8UBBslZV4tE2LRi+pnAfclb8G2WZmCaJrg8xTHcuWqgBh9Ngxi6Ja0RjchlaaphzsB/WdWWkum0ozUIpDUvXUjVHkik+ltKIxOzi1JY7l8rB8EVTDiFHIY6qURges70bU/qlKS2AEi8ejaYZVOQOpdhXztqWFRiak8WxEKqtFqt8Cqh3gIj9wJaqVaVh+osxm9ek5mUonQgwvnQXCr0damUlF60UZYH262mq9o/XB4n31icz1TJiWJkPmznOXNwuhc9toTS9PeSWuq7br+R5CikPjmBFLIy+/huCDh+lxVMIetofrs0c+8EjoxsjCt2ZyLirVvayHBYFiRhOshtjMsChklnOmo67WseMWjhLDVRTPa7RZaiKwozh532+9gcvlTjQlAaWQ6ESdZFI1LXR3SrRi7R9J5qFI4kPzgwQJLtWVeQOpbu6qlvXUXvQLEtmV/qO0h3Eh+6Z+tNIHLNer72swbnDM+fbMYixkeYkH2an542nLdE9NBls8PqSwS4NDIPItDw0xhBvjG/6qTB5vusK4gor0dUrlWFYFqA8tB63C5Y4dTuLRGuvG6XrkJZ2h2WAboIV6ZzZlXEaJjK7VDzaqYmxtI9qRto2Tw98aTqakYjfbiSgp3kNzFa7+y56WkRGcxAY4qBuWYdATSrzRrO771omYKbq4el62uKSJ56ZGdhTwydiotDUN7SF4Jv6QnQ0cOVi5hWhwlGUAxJjgBDXfSwPHsR4XkjMILM2Usftky1I2/GeVNcUzrSAIN58CqftimrNIydXYTYNR1/7RaL9mfvKyq+GdYAC//4VmMu+ynjfbXhT2yIpmcyT7y2itrV98BPdcBMbEcLa4CDWmIO+ocUOhBt2za/0eaQSITvESXUMLCuW8UOHlfoj+cOIhsOpKC62KKlWRBYqu1uwy49lOCksVrQujeJ3m7S1RrC8TnJz7KCWpek4tcy6dT5nezaj1+FnVNFkcMV5fcNDdps6dptO+6yhGyjsc8G0orTGa3FqPiyrPUjXHmTWQUFhoJmYBmuC9vW4fROYiW3gIEYIpcHoop0oDVTa2ztoz8hptAeYcnISgeeM7GD7tVTtP6VSp2367996h9WauY+TUCPQBGg6ZWXQsLB9lT1VOnxiYlmJbF7ICKSm83mhvqUGh8tJCV2ftkVFCq1eEc/yY1LSpmp2daSUwjVYh9ruTS/BLiHEwNdai/ulyzE/eYcNX/2YghOreTl3EVEzwj7Dj+3WLL78yuL+Byxefd2u03DggXD8MYry8m0UFFKK+Mj9aR2+N8ZXz+F863Y8T/yAePkUItPPSgS92r8xCrwlnDDxPO764DoO3+903CPzWHPzx+TsN7hTNlggoLjmKvjzX+wstbXrLC6/lFTxXiFE/3HiiSdy4okndmvalsAY1obdlKr2+3fNq6DFfrwwMUEDR3UAZ1V7toylOYlrLgyzjfJBsLRZRykTK1hNPBZCy3IDrNDSsjLsosiaSqsxpIOd3tX+kKBSN8AKb8CBWbUzZmkJ5UrxxZca6/3TmVQIqij9WtS5q1/6/48stGsv5nnsX+4X132FFRhkZ3BlZBEl/h+F5fQRDxRD4yoAnJqTDqVtNin9IXb3IYeiKY3abxMtznjYVRm3/cnaU0WuvIwumilpT4uGcrRHskwz9ZDUUW56z3ZlB6Eis9qw/AE0S1GQA3F34iHK0DMeuK3ymtRfmlPhH+vATBQxd9fkYBUEMBMBAa3DjyIjfVWYxaP4MPItjrQHTBTtTzSKVJdDBVi+fCxfZrDL6wwk6vxkzn9syVQCruxZK92WbVCWLJldHaNVDqfGyBH29382Dgd4PNDWZv+9sSSsQWWK8IgDcde9iDsRtHG5FKNqLFqboTlm1+wCjXhlDlqtm3hjyyb74yiXhlHoxl2TR8u7a+10FFTqAdgw7ONP1y0sh04wL/G6Q0E4ka2hrNTxEHcpVBj0nCzne9r66YkMNCyzUzAnvWaX6iLYBQpvjUGsyUKryety/TSfA7M1Zi8j/RqkFCOGWwwqA48n/WRLNMtKHm9m4pqTvA5A3F0AbGg/JxP/o/kdOCv8aG4DMy3YF9NzWBM8CM/6pxKziW68e6lSKC2zblbm21mCXYnMRrfDSyjaCtgZSRm8+RQke3GnzyPefuUy84dihQthXVPqOtDVsu0sVgvTaj/Gx5fMIG62Z8/ouhOcFvGyCLRpqHjczsRVdhsi6RfNDquVPO00DDuYooHdy13LCPAbQUU8bO8vt0ehVLKuZGKGhhtvrmLwoDiRT9djNkUZN81DziRojeuYRTU43B3qmKVdM5RSGMpB0F+M5fRhObxQPBrWv5e+YVL/q+k6Q3J34mPjA6JWCxYxAnoZxJrTzkeV3ECgwIjU4chxMWPofjSENvDlV+/b+4NkF2VH4gcnlbX7tdtIBsnSrsuFQ2F55iiFWvp11EoO+tL+vtEhyuPxaMQH5eFoWIHlL2FQQOEtg1Wr7MuzNW4P1LsvY4bidhBXAzoMFmJZEJ5Ugb5yOcUVXooPrsBrmPDwSix3FxfHLjgMiMY2PV02HQcs2RgJdgkhBjR90Wu4/nkpKtzI0rqfY1kaBWdO5aWPbmBEwQSG5o/u8rMrV1nM/zfM/7fFwkUQCMApJ8FRRyiCwT4KBGkGsTGHE6s5COPzJ3G+/Sc8T5yBmVdJdMJxxGoOwArYX/RHjv0hL3x9H79/50Ju+sldrDznLRqeX0LeoYmc4nATKtICmoHmyePsswxKSix+/38W5/3c4vprIC9PAl5CbK/i+SOJ1DfZXZUU1HvHUuD6FEJunA5FW8TEzNHwTMhPFYwGiEUBpVE+KHENaNHsmiBKA4c39TCWQcu8cR8aHIuutUfZUvEljdSzQTLsY6FRPkihAnmpoNWY0RAOZ3kYTM96yBLs6or9UNU5xcKu2WWmPQJDwPDRDBjZ1rOr+af/op4ISGmJh9yMZna4Sdc1g5n5k3BqDjLGhEx9KG3kNU1Prb/ZoUA7QM0IeyUyu35m/r+GQgs4kr1esNKCDpFdm9GcWR4irGQfmeR6KYYOsSguAusrlWqjoRl4PEHKPUOI+UbgdK61P6cp2jPpsmezpMvWdRSgxD846+v2mvU0syvts8njUXW13QClbfQHIF1XzNoV3n7Hoqk5SxJW4oVB7iIKA4pYiQZ1mZOk7zdNaZ0iZpvqxqiUwj+zLKP5iZrOifnb/+blwtQZOh82JjJJEsEurxesXD314Yhf4ZhcjqsmS3ZH+qHqSOtGt7FujOnt7zAYg7NYx1kMsfzOx3Xq8z4D1oEZimMFM9ukaYpA596tacvQwIqjLCvVyFjNAYTWKfjuhfbpEkEDPceJOxF4U7qyC+AX6ljK7uaZjF8ppVBZnvHjg6ejmuzgeU/vopLHocfwpYJdydpaXa5basF2xMnSdMyCYbCqMTFJ5y7ZHbtLkjiVkxlBuqanrmUAelrAzTJ0O2ATjaM89mcbGiE5RVeHqkJLxmDt2JCppbqx+ib5cHraiKxU7auVujAng10unEUa0bUm4Q0hlFMnd4gXw6EwLAfoLpyBsoxl6pqBzxkg112Q8TqagVlUA+7cjJed5U5C4fYV8Rp+TAOilh1sMpQbaEnb7MlglyO17yx/KR6Hj1CstT2+niimr6cdMNmuR04j89iOl4xBxfOA1ekbMvOz8eQPEGnrnSUI658zAuLVqZEj84P292x+Neh6AIp1tGYLlZu4omb5YSsZ1CosUASKEoG78gARPbfTtKkGZQvquSGa7VrZDdawvTGN7v0kJcEuIcTAFIvg/O/NOD/8O/GiUbQc9BfWH/gJuQeWsdS7mK/Wf8SPp1/d6WO1tRYvvwIvzbf4PJEVPmkinP8zxb77gNe7GVflrUF3EJtwDLGxh2N8Ox/HJw/heu1GXK/dSLxgGGbxGBzBas4v2Z/zv7uDZ4MXMqviANZd9S9Ka19Eb1mJCjelZmfpLsyiGo4bshvllx3LJdfnc9Y5FjffwLbLXhNC9Krx4wx0DXJz7aeYiBGksWR3CsvqKfY7afTHieZ1/sU/FLZ/VXa5SLtJzcwC6EijvRujGzd+Vx661pIR7LJQWC4HAWcuAa8H1FoKC6BwsD2Nntv+MFU+qIvrTtrTtJV+Z99FgCQ5WqKmNOJKS0viaQ92mR2CXaNzhhMsHpnKDuuObK1NdgdKf+DvGJRRykhl95hp62MW1oAZw/K3F2LXNQM0g3jhSOK+zALtQPbu9On7yozj0p0Yu5RQ/2lqpl0Giix3LirUAPFop3kNSwzA0pi2GAswDAdjCydTqFloia+Y9CCN5Q60d5lE9TwS0IXudmNsX9e047l9Jukz7LiAnrWni9jr6EDXXTNNfzFmrZ1Fp6lkZDhNTyqdt6dPpv7XYbR3BcwtNFI7r6rKIrwUvB6o2GMMCxtraQy3YjoVBVXOrNs2o4SWI3tXJ7DrXGX/TPcD1UmuoTlEljZjFHtAz1I0v1Mbk6ldYGmGXTQcKxX01nSt0ya2kt0Y0w8FXYOx9jpaykjLDrPQxw9D1QyFdzvMx1eIlagLtbFuWtnOvfYsHcXgvOH4HB1H9uw4k7QuoYlgl1kyDvS0fadlLmtqxR4ZGU/2gu1/jC6SZrREgGSIt5wFye7PbVFULiinxrAhsPyj1IplmzWWZQ/nqGlONC2KMhXmkHzc+U4c3kb7mMwo/N6eOZZcV2XoeOeMxF2YWZbDZbgxdEfW7M/pg/fOvlJpjY2ObiNQ4ECr8OIsGUzTyysS9cO0RLCrDYVdc8vOeuqwrk5n+/+nBaxS6574iUHDwaBEPC5bhlLyeGlyD8MMhLByKqAx83hXTj0V7LIAo8TOBstSxrez9IE/NEX5IIglAu1rg7NZk69T2PK+HR/uomZX6ipq/3qBc1wh1qLO05kFI0BpqZFR002eBHX17V3Ee8Lh8jOlap9uTSvBLiHEgKNqF+F+/hfo674kstMpRGb9gtr7FxJviFB4xhhu+d/l+Jw57DvC7sLY1GTx2n/tANeHH9k3ZyNHwNlnKfaaAyXF/TjYozuJ1RxIrOZAVN0ijEWvoS95C33Fhzi+fJZZwKEeJw/WvsmU6To8fggbvptIcM40rJxBWK4AxKNoDcvQV32K863b2ct5D4/+5Ey+f+dJ/OgcJ7+/mc0eWVII0XecTkVVVea5qylw1wTRvS4cEfvn66wPXGi43GCpZE2nTWVOqVQX6THGeBTJWij2vHM9OcSH5BIpLma3ijJcLkVb8Zd41i/CdCWyfhzduFNPfzhQOpZmoMxY18EuVOLxNpla1v5O8n3TyqyHpCstaxfB6cHxtA2alXU5yYf6vLQft0cMhw3vduxWmNlOrauAncONWZ6ZeZHaB05f9i6PWaUFmlwBcBVA6RgC+Q5CX9UxZlic8DpozhI7iFfPQl/4H1SHzK7M2Ss7Gya5mMSDakmxornUYz+EJd6Ll04Cp4FqaMnWPKqDowj2IMDY1XpudKpNBG5S69Npop4Vlukcl9p0+8yKnbFWvgbxEKCoyhnKKpVW36gHATdlaFhh+7hOHpuGA/yzyojXhzO2g8MVpapSMbxgHC6Pl6GjFJ8vMRkzGoJdZHenv5p8OLcMV/fr72xGsEsPOMk7pNr+w9z0ZxzlPsKLGjEK3LDeQCOOy2ESTdYRVAojMSBAst1GkQfN78A9Mi9jXr6gIloPFjpKxVODHOrVFYls+q5rEm18t9lvVgdHsbjOrqfld9kXkaq8kRk1+bqeRefMrg6F+7C7MbZPlzU7N/leVz3ENJ29iqYDsCCRmae1RtA89ii7Q4fCMrcDFY51Ov6Tp1TEb6LXgVPPQdc3QEzD1DTcw3NRaxOBlVSRubRrS9pGjNUckLV5huZgt+qDu1yvrlhKY7ivEl+uG4dTw9R1NLdhfx9p9gilaIq4FcKh7L6jKqNbaOK7xHClToxkkMgeAMV+zaQ9s6sgPy3g3wVTc6W+A9K7wgL4dy2l9gW7zmRsuIaz3M78S2ZvdhxRs7uihn3sKSvWZWZXNl0FSNEdmEWjsr7lcilKO/9m022dgrVdkGCXEGLgsCyMzx7H9Z9rsRwe2g7/M/Ghu2NG4qy/80u8U4tYXbWOV59+mqPG/Jh33vQz/98mb70N0ShUVMCpJ8M+e23dgvNbixUcQjQ4hOhOp9ovxKMQC/G9UB2fv3waF45/l2s/OZzlr0/G++tDM7otJam6Rbheu5mqb27m6SNf49QXbuGcnwb53U0wqmb72yZCCFvaD+OZL9BF/QulYegKS0vWBdnU+a/QNHskKU3ZI/bZozHanx/vG8UXhV5w6KlFW/4izGK725Jvl+7d9Vrp3RiT3frMGF0FEzSlYVpmqnh6e3PTg10moBjiLc8yMlc7v+HF3cXDZ24uFBbAqJr21wIBxd5zrIzAgqY0LKcXFWlNNCM92LXxbZw+n2yjR2b/kP3P8MAQAoPnJGoU2c8wvukluFatodmrQUtXM9AgnkhNyxqU6NDmtGiHf5advmCGEsEyXUdpWipo5zI8Geu0sbICm9JV18dO06FR7Mon5knbz2mrYCktEdzbvO+79i6R3f9MvGLn1OiCuZ4i2sINGEpnWHA0Q3PLaVlndwftSbxNORP7VLUHYnUdNI+B5sl8/IsmAiT2NrQHHSgqUsQ3kkmWeSopYkP3AM2R6KqYeWzm5thd3DJGSuzQjbHHOgZzsjAK3OQdNsT+o9bBqBExIstgSTytvmkBhCbOpCi/2Z6tUydnr4pO8yofZAeyP45qQBxl2uuZ7T6qo+6sXrJOE9gDNMwZNnfTH0otIC2zK5bof5e8rqRd99PPkWxB32RQpmOtp5S0a++M2ToffGlndmme9q6nkRFFuD5bCSqzGFNyksjIXFqbIviNAOgbMs8yLTlKYXr9KSuxelvx/lMpqrztXR8tjx1EUU4NpamMzCMt2QVRpWUFpmVRpmaZDHYlU16xL43VVVDsctCW+mj7h0YUjsepu7MHjDt0SdRznDiG+OATu3h+ksejmD3T6jpg2U2alajZ1THYlTy1PQ6gfR8b2RPA+gUJdgkhBoZwE675v8bx9YvEqnYlvP/1qaK36+/4gsiSJoqvmMIFL/0EPZ7Ho9eeTVuDRWEhHDHXDnDVjMx+A7Dd0h2gO/C7Atx84JNc+tJJ3DP1r3z/odNY9ujHVB6/U6ePWMEhhA67DeOr5/H+81Ie3OM4fvjWXznv5xXcdD1MGD+Ato8QO5BUb5bUj+btd9RGhyK0O00Cx/L2p6TB5RCNKdYBRb7O3RHs+dmZXcmgmGEkut2l1WpKFohP1SpPPEwbxR4cJd7OM826oPQhvxyY+UPQ137Z9cNvauQqDc1qTSuGnMhYUxqWZWd+OTUDl+7sbhgpg9OpmDwp2+Izr5kKsHzFqMhi+++Mdm8iey49S8syNzJlxyVClW9wKtCV8a7TgwZYehdrrTSwEg812Z7CErENR6FGeJW56X40CgzdQXWwJjUSXG/obs0usBifM4J44bjUfk71mlKJBrb/kbaAnn339WRyy98eeBtZMI5K5cGptddVS+lBN8ZUlqRqLxLfcddMLNuVT1a9STQxImS2Ytldzr9jU5y+LqetquqcIa+cPcuU22KagU4rGiYW7eecUoqKEUFg41kiyu3DFW/BihlA1A6iY6K6kcrmdNr/ja7p/F7y+rBl955pnw012P9qeoe3VEZ2brZ6hMnzoctASbLbnLcAd2775zV32gcMnXiuF001Znw0N1cxY7rF8tXT+OK7WtrMutQIg8lgbHuXuVQ1+7Ss0d6/95w+eG/7Opo2Amesaldw59mL99uBnkCgfdnJbZiXlyX4rKn2Zurt2yf9OhPwKwYVOPluQ/K19pkkR3iNxTpfi7MNguCuCRLfoGGWZr7ndvfCtkpu9i66Meo7lxEoab9CdRkgzSIvN3OU1q1Ngl1CiO2etu4r3M+eh2pcSXi3XxKdclrqWyiyvJk1v/+U2rGVXDz/YULD30b/8P/YZ7dc9p6jmDghexHHgcbvyuXG/R/hwbL/Y/lrK1h3w3r+VvhH9qk5hinle+Do8OtNbNRBmHlVeJ44g7/M/AHff+s+fv7LQq6/BqZOGfjbS4iBSk89A9nXSCOtqG5SQYFCb9GhFVAao0bZXQBDsf1w6tkLSCvs2iZJDkfiRj4ZbEp7YOnQ+6NnzzHpmV26gZU/lFhwSJfRBZUWvMiZmQPNDcRG7p+R2ZWaNvGamT+sBw3qIaUy1jcjq26TD87tHzR7HJLrYnqnG7NsAixd0MUitbQh9bpun3ekgacazGwZw4l6MI7BAWLr7JwGX3K0xt76OulmsEDLsg4Z2VhdpWaldW3MyC7sYl4dd+Wsqr1pasp8wosN2Q0VygwK6A4fgVQQMFUFPfFvl4vt3I5ksMtq330d21TgLWFw7jCWNXyXmL3W7WX05OE2W/CkOxlRm2L6S8C1kcLtaSzNADMOlomZraL8JsQH74LVuAEWJIL3ZuK61o2u17qu2H129vcy6tcBg3Kqsk+4Ue3HptZsd22zUpldyQMys0B9p9Ed02xs38aG7gGGC03TMf0utOYwmjst8LWRa7rfrzCjLgJGGaFIA5phtzx1aqUCK/G0eVh2oGcr/BDtcyZGNEgGCMEuVp9YlnenQrvmY8jO+qsboZODj939oNrszC7NqUj+7JD2dZflS679pfQfl7JlVWdd1SyBbq8vQKxmC1O4urA2sCslheu6/l516uh57fvd0YPrwc5Tt+0zxDYOqwshRO8yPnsCzwPHQSxC2zF/Jzr1e6lA13cLLV467WMiEbhl0P8IDb+ZCf7jeOaWo7jgFxo7TVY7RKAryWV4OG3KhYy5ZD+KagvhH038av4pHPPgBH7/5gWsalqSMb1ZOo62ufMwQhu4c7czGT64hQsusnjzrc3JexBC9KVU3Z7EvXHywcehdVWTI/mA3X4z7Ta8G+kuptDSMru0Dk89dm+O9iyvRCMSf/fkKT5LjauNfD69vdbQaVhjZmdkgaVGz3L6USji5VOxgpvzwNk9HWuHZWR2bWI49TxPe2ZWVd7Ibi5wE9s2btkBxK4eVpRKdBNlo8E4pSm0LjIKlK6Re3AV7lF5W+OZ1V5GT2t2ZfTDy5xTVokHVMvpJz5ktx63z+8MEDA6ZD+5Ali55RkvJYua2390+K7twVdvclRNK2riTcTO3J7O0xnpxapV5rG5MZ4s8+pK1mBXd+rzbYJZMbXLekCdG+FIBbuGD9MY09Mesw4PKti+r1JZUt0uUrZxSil2H3IoNYWTN+PTWQ6MZIH0tMtw+nV2Y9fcjXaBc/pSPzjEC/1Yho5R1P4DSPKzds9D1ekQrk5dWlUi0zAtbJ/sxpie02ilZraRRm2hjO+0zttI1xWDyyFnqEbVTq5URrLSIHd6WuBUU5iB4ozreMY2T9aFSwt2ZcumzJpAmyXY1ZuZsUnJ8yJq5GLlD+/0fleXoJ4Ev7c1CXYJIbZP0RCxJ3+G+1+XEq+YSuvJT2AOsm8Sli61uPRyk4u/v4CKbxby3q5f0DzzGvYadiQ3HfVbXK4d+9JXechkPBMKOPrdY7l2zv3sUrkPLy14lDOe3IN/f/dExrRm2QRCh/4fjroF3HHQ5QwfbnHxryxefkUCXkJsT+LtZZOA9mf9jlmdSVZXKSpdsAv2dn7doTup9JZlZM1oWpaAQ3f18OFyQukMBuVU2UE9hxs8eZ3abTfUgzVsDlZgCyrmdkOnYBfpmV0bD3Z5HX7mDJvLnGFzKc8Z0u0lboyW48RR7iM6vI0qb5YuqkpDmfHU/2/u8pSu2ceIs3Ph7N6wJd3AMuJfqfOhw/wMJ7GhexAfMhscXUd6tnR1LG8BZl7nwRF6KtlN0IqYDBsKU3eCnEDnxqVnaiqldXMfb7wAdsfn8mw9WzsGuyxvAfGSsd1a9mZJ1vYz43h8jq5He93YLNJWTFmJa1mXlbm7J/241TV9847jDhGl2NA9wJEZCMkWLOlKdwMXuaNzCE+txFHWHsT1dd2bFbBHNPd57fXWdLv2YLJHdjJjMhnsStbs2lrdGNulB747L0dTGnl5irJSRVGJ0T6dplCGas9SVApz0CQ7UzYtY8/rIfX/kOjenzbvTq1JBdnSXswyYmF36xRujOXOw0zrRj2ovXTZRoOeHTdT8pjppdhvr+rHcTghhMhO1S3G/exPsTZ8Q3jGOUSnnwWazvraGL/7+3u8tfRl9Ip/8z1jZ9rcQ3hv/1f4ybTrOWTUqQOrJtdmUkpR/POJLDntZYa9PpZpJ9/K96deynWvnM11r57Nwtov+MHUS1PbKl41k8jsX+B57UbmnTKBcx86ld9caREOwwH7yfYUYnuQCnYl79WT3Ri1Lrr0pLKmunv3amW90Z1dfRBG6HkaGizMDred2eqQbNJGupBlE3DlMaqoc33CVBsyHnS2wZ16h26M3an5s4ULtP/JNsIg9kOwb2oxe9SeiuXtXNMrfZt0awTITRXZ75jR00W7eqqrzC5DM4iZ6cWys9TDSf/D4YFIc/b12Ehdqk3r/nqauYPR6pemsrw257ZFuVMnOpqmCAazT+dKC3b15OHZvj/Ivk4jhsPXab1iu5OpEq/cpdvL3iyaYQdtzXivPJFrqQzWLZ1Xb9xDpdWcqp6VeZxuRhfY7hY3nzwprYdzwsQJsKYJzGV0eW47HPb3j6bAwmyfLBXsT59pokD91rx339Q1K23jtXeDVu2XxuQKpF/a02ZZXW0PgtV5Hl1ft2pGQkF+ehOzT1fkG7RFzzXx6pkZf6ef13qWr9quLtfJYFe3S0luQxLsEkJsV/Rv5+P+x8WgO9BPeYhowQTWNa/ipufu4MMNT0DOaoyxDvZo2Y/xX4/Fdd5g7jzlvxLk6iCwZzn+2WWsvuFDcvarpLC4lBv3f4Q/vnMZD//vNnzOHE6Y+JPU9NEpp6Gt/hT/2zfz+59N5oLbJ3Dt9RaxKBxysGxbIfq75ENJ6hfYTXVjTAW7uvfkoysdTVMUayW0Oss7va9pYGqZ9b6SgQ8r3oOARy8HpNK7kfTGL+WbXF6HhxurG6PKbdkCu3d9tvKHZn9dM9Ie4DY/syv1bofC5FaWYsy9yaG7OgS7Ui1p/7+0JluGO5WTsSW2KIbnySM26qAsM+3+LJxVfoibOKsDG5/O6JjZteXf55WVirhp8a1dCmyLR4brFenHQA8D5tkka3axhd0xk9cDawsOGMtXhOXOJT5ocqeAbMcu49D1ICNJ3d1fmqY6xQ2dTkVBPqxb1vXnfD5Q65Prnd6NMZnZ1U5Zlv0VtFUTuzY1qEZ69l1aZleH3xGUptozopMfRaFrivQSaUopiv3lrG1ekZHlla5ycPdWeHzp9G5NtzmyJS0WF8GixfbIwxnTJjeL1v8CXhLsEkJsNxwf3IPz1RvtrnUH34KvfAS3PH0RL3x3B6ZlkhPZh5Onz2W/CXuw4bwPaQ6uZvjZsyXQlYVSikHXTGfBPs+w6qr3qLx1N3TN4JxdrqUl0sQ9H17PqKLJ7DRodvIDhPe5Cn3NZ+T8+5dcf/kT/OpaHzf81iIShSPnyjYWoj9L1uxq78aYCHZ10Y0xeYPfrWwe7EDRnkMPZ/5iqO1imriWWRQ5mdllxfru7lhPG5WsJ6PRbS47oLbxbjP9SvrxkWX75O7qRsXT0hY2lSXRoRtjb+37rr7nO2UuZgkqZLyUCP5YRtcFvLdUerehbtmczC6lcA3L3eR0zrSncDvjpPsLmzI5YyC7DOmJW+kBEf/MUuLN0c4f2MrsQSe+BdKKt2+J4S74orUXMrt6ge4kXj0r+3sdeozvMfSwrrOJRsCiJVvWJThz2Z1rdgGMHAEtmkYt0LLGag+uOX2YBcOI60H47I1EDUAr0fht1I0xi/QfQdq3jdY5s0tTnQtEZdmWumYwtnhnqvJGZpx//U22oGdOjmKfvTq/rmmKoUMsigohbvavgFc/OEOFEGITLBPnK9fhevUGYiP3o+3ov1FvODlu3kE8v+g21LLDOaviDZ746V85YuohONZrNPxjKfknjEDzSEy/K67qHIrOmUDD04tp/PdywP4i/+nMG6nMG8m1r5xFfdv6tA/4CR1wE6pxFYHXr+KaKxW7zYZb/mDx8KNSw0uI/qxjza6kroNdPe3GuPGHJE0Dq8Nw96kubT3J7Opl6aNhZRupr/d13EZbeZlb+uC6iWCX5u5YmL6Hwa5oLwW7uliuQ+/+yHsZGV7ZunR2w4jh4PV0XbvIcnoxy6f0bKapGneb1aSNyqzZpVIbwW94KQtsfKCG/HxFYUEXNdrSXk6/5hiFHlzVOZvf4M3lcGN5En05eyObcoiL8F6dR7L1+6GosIvPZJH8uNXj0VV7KDUqrtbldbqyUrH77K0ffDcMRXmZhsNQVFZaTJ7Y/p5ZNAozZl9zdL8Ca1t0Y9z4NTj92qIrw85OTQtsGUFXcsJO7cx2XXLpbpRSBFx5W9Lqra6nGZnDhipychTBPEV+fv/5EUeCXUKI/i0Wxv3cz3F++HciU04nfNDNrG0Kc+rfjmNR0zsMWnYr95/ze44+oDL1BV77968ByD+5pi9bvl0oOmss7nH5LP/pf4ksbQLsEV4u2/MvNEca+esH12ZMbw6aRGTXc3F89SyeBc9y5a8Ve+0Jt95ucd8DEvASor/qOBpjzIwA4NK7KLbdzWCXWTwGM7cCy18K2NkBUyZnmV22e19jM7ox9jI9y8iMW5PqmKWw1bPJtrA7npEWLOpORswmM7uSx1Vi/r2W2ZV9Ozo6ZBOa+UOwDBdWWnaVlfF+NWbhSKzcis1qR16eYuauCqNjQelE0NDyFvV4n7tHB3ENzcExaEtqhmWnddGNd3pwPKOLu651t0ldBLv6UvugG5v/I2hxkUZpSfu2SgYzyuzLHzOmKyZN7Mk5l+zOt9lN6tb8t2piVBaOxGXD38Uhm8y4LC9z4O4wiqvmtg8YR35yxEu27nVyE9umY1d3/+xBeMYGU3XnvNNL8O9WlggWpz7U5fzSA8w9oec4cQ3ZdoHi/nLebikJdgkh+q+2ejyPfx99wb8I73Exkd0v4H+fw6l//jlNxpccnPsAd//qKIqK2r9UzLYYtQ98Q+4BlTi3wo3hQKO5dKr+vDsoWHz6y8Q2hAB7WPujxp3Fi988wJdrP8z4THTnHxCrmIbr31fgaF7GZZcq9tsX/vwXi3v+LgEvIfqjWIfMrkgsDGTW7MmQvMHfVBaE4cIsm5iarrIy+6+62eokt9fs6lnAIz54GrHq2T36TFf09MyujTxQxYbuQWxYlv4bPaQyqhiz9YNdqYeuzbw2axvP7OqxxFO9npcIQm3lzK5O2UmuAPHhe0OWborJ0RjNwhG9XxvOnUO8chfMkjE9/qzm1PGML+jRiHqboze78aY/6/ebUhJaqg/3Zs9i56kOxo9T7cGuxLzGjoE9d9+Sxm2le6duBF+2Bq9XMX4cDBmS/f0i3yBGFI5nSHB0p/ec1QFypzkwcjXi5VPAn59lDr2oB5ldmtLR/Q5cQ9uDTppTxwhm/x7Nduxv7vkQ2LMcz4TNyzjdHP3mvN1CEuwSQvRLqmEF3odPRFv9P0IH/57QxJP5270W597yGNGS55g75FKuPOsg9A6jedU/tYh4fYSC0zt/gYrsnJUBqu7Yk8jyFhad+BLRNa0AnDjxpxR6y7jt7Usw0zvgazrhA24A3YH7hV9iqBiXXKg46EC48y6LP9zWukXFVoUQvS+V2ZVIaogmMrvcXWR2Wcmh67MW9+6Z2JDdiFbvAWQ+V6RGY+xhkXLLVwTu3vmFO71A8EYf9p0+cGzeL/LplAKrQ80uM6+SWOWMLZ53NtaWpnSkdwPM9uAW71n9JUexF0eZF+9Eu69Xb2X1dfVglu8tZkLZxrfttvq6srwF2yCTb/Mlu/FaTi/xknFbNK/++ZicbNWWB1g7ZnYplSWbrzst2lYBhT447HICqsv1U0oxOHd4RmZt+nu6L3EsBkqJV2+da2PaEjf+rkoPdnXzR6DUjzv2/7iT36dim+u/V1whxA5LW/M5noeOQ7VuoO2ou1mVtw8//YXFHffW4dnlSkYX7szZc87s9DnLslh/15e4x+XjnVrUBy3ffvmml1D91z2JLGniu0NfoPXDdXgcPn4w9VK+Xv8xry56JmN6K1BKaJ+r0Fd/ivOt29B1xYXnKw4/DP5yRxt//LMlAS8h+qHkDwSR+CYyu1x+AFS0dcsX6gqAy8601dODXZuZ2dWbMrpxbbOaXR2CXaXjwbuVsxc2k5Ves6s7tY42lSXh0PBNK0HL6X4tre7oKrMr/b1NBRYGSCJDjyUDDslgb3zonljBjdfr2pT+uC1TAXzVC6Mxdgh2bfZ8kt0Yt3Jm19bOCtyatnrbexCATtV47OozXQ3Y0E/DvzsCCXYJIfoVfdHreB45BXQXrcc9wH8WTubU71t8/Q3s8v0biBt1/GzWDVm7m7S8uZrw1/UUfm/0gEm/3Zb8s8oY+sQBYGh8d8Q/WH39h+xRcRjD8sdx9wfXE41HMqaPj9iH6IRjcbx7B/rSd9A0xS9+qjjpRDcPPgx/uE0CXkL0F1Mmw+C0MkTJUaAcWvYC9ZYrkTkVi2R9v6eSg5ZlZnYl/ujDy0R6gfptNxpjuq39ILeF808PhmbZPpavCMuRnh3YveUlv6NTgxRssW4Eu7qaZgf/mjIS14BevW9KlsfqT7diueWYwaFY/h5UkO9C6jzupW221e6VVH/cEf1MD/ahlgz497Tum1JMq5jDLpX79OxzfWDSBBg1gEoeS7BLCNFvGJ89jvupH2EGq6k74gFuvLuaSy+zqKiAa275lk9b72XumB8wND97zYsNd3+FUegm95DqbdvwAcQzOsiIfx5C/nHDWffHz1h4yIv8MHABK5sW8/zX93aaPrz7hVj5Q3H940Joq0MpxUW/9HLCcfDY4/Db31mYXY1NLoTYZvLzFaNq2m/qdxq0GxNKd+n6Addw2cPAD8pSbX4zZH3m2oxuP70tvRtjlyNT9qLOAZetvQ22cP6b6LoZHzyN+NA90xbX/eX5di0lsMegzW1Zhu4EajZWk21H5jbsjKeMcgVbKHW+96tNrsCT2ytzSh5LW3pMbWlm2Cbnn8zs0vv+WjsQ6JvI7FKJQVeMfPvHpGTGnkLhd+Xidfi3fiO3UFGRYnDFwDle+tUlSAixg7IsHG/djvtfvyJetSufTfkb3/9ZIc88ByefCH+6VfHPlTfjNrycMPG8rLOILGmi8aVl5J84Es01QIYQ6SO630H59TOovncvzMYIzh+s4fR3fsADH/ye1mhz5sQOD6GDfotqq8X9r8vsYaKV4kdnKk49GZ5+Fq6/ySLeh6OtCSE68zh8FPrKNjqNWTQKPHm9srxkrbChaQWLkwEKzd+7Xdp6Iv1htasst97V4SFiW2Uhb81LcMY6dH99HEUeNG/v7PuNdmNMZpF18YDqTfRuy+udOMh2Z1zJzlQHR+F39t5Ib8m90b+CXb2nt7oxbvEAEt2dv2R29YpN1ezS3DqBPcrxJGsSSu+GPrflnZaFEGJLxKP2qH6fPU5k7JHc13A5f/qJQV4u/OF3ip0mKxbWfsEri57m5Ek/J9edva7Jhr9/Dboi/6SR23gFBq7A7uWMeOlQVl39PhMesvB8OZcnBv+Jk/b4ZcZ0ZtEoIrN/ieuVazH+9wjsfhZKKc74vsLhsLjzLot4zOLiC9msAq5CiO2friv2yTKYYWCPcpS7736g0FV6gfqtf32yh6ffhtfBXlhWvHwKqq22m8vb4sVtli3J7MrJUcycYeH17pjfTy7Dw9D83h3Up39mdvWe9vp+vdSNcSv3pd2ea3b1J5us2QXouZ1/NNnaGXyiaxLsEkL0nXAz7ud/jrH4dWonnMNF/zyLd99T7L4bXHi+IifH/nK458Mb8TtzOXJs56L0APHmKLUPLyD34GocJTLiSW/Sc5xU3LgrvhmlxM83WXf+etY9sYyiysEZ00Unn4S+5L+4Xrkea/Se4CgG4LRTFA4H/GmeRTRmcfmlEvASQrTL9mCwLaXX7NoWVJYC9f2dFSjFCpR2c+q+W58SfwWlgcpOr7cXqO/6AXVHDXRtNYnN6fP1bTO2luR1Y0u7MSZrJ+pq62S3WskyEn3QjXE7uLT1WE+7um/tIKbYNAl2CSH6hGpYgfupH6HVLeLdsqv5+S1zicfhgvMVhxzU/ivtF2s/4M2l/+D7Uy7F78rex2DD377CbIxS9IPstbzElgvOHUpjbhOxH77Lt8e9SP4/TkPPSfvSV4rwftfi+fvhxB75ERz7ABj2TdyJxyscBvzf7RaxmMUVl4PDMQDvgoQQ2x29OyMM9iI7syvjlW205K380KU06MWaT5tjbMnOWV9PdZeV7IptpjUxiGsg0Lft2FpS3Ri3MKIzOHc4Ds1JWZYgba9IBLu2ZWaXe3QQAEfF5kc64yVje14Efisamj8aj8OXqm/XXV6Hn1x3AcMLxm6llolNGaDJpUKI/kxb+RGeB46BpjX8fsNf+OG8uYwcCX+7W3HowSp182BZFnd9cC1BTxGHj/le1nmZrVHW3/EFgTnleCYUbMvV2OFUzZnIVxevxb3K4Jsz/4EVy3ywsbwFhPe/HtZ8gWv+ryGtVsExRyt+/lPFa6/DRZdahELya5cQYsej0NjeMru6wwxW2f+zjYOH3bPpzC7Ru/LteAeDNl4WcLsV9BRSGhi86Qk3QVMag3Kqt14X6mRm1za8zmhOHe/EwvbRdjeDFazGym0fPtgo8eCs6rvIaXVwFCX+nu9vTWlMKd+NXLc8n/QVueoLIbYp44un8TxyKg2RHI57+SEefm86P/+p4vc3KwaVZX4Zf7jydT5e9QYnTfwZHkf2X4jW//VL4rVhis+bsC2av8M7+IQzefqw54m9Uc/aP3za6f149Uy0vS/G8cXTON/8v4z3jjhccfGFivfeh5//0qK5WQJeQogdS8dnTqu/j8bYTWbRaGIj9t1oLZu+sqkC9aL35eUp9tlL4fP1n2CusyoAil4pdxH0FDGmeGovtGrr6stujL3Jv0sp3kmFfd0MsR3qP/mBQoiBLRbB+eoNOD95gI8bd+HcN37H1Jm5XPcjRWlJ5y/hZFZXqX8wB9aclHWWbYsaWPt//yP34Cq8k4u29hoIIM9TyPDTZvH2onfZ5VbwzSzFv0tmLRdtt58QXrsQ5zt/xvLkEd3p1NR7Bx2g8HnhN1dZ/ORnFjffBMG87fsmTAixfRtXMg2vc1tlDfTRaIxbm1Kg992omt2xpfWVxPZNz3WSd+iQTU84kCRGwlbbebBra4tV7Qo9rMcltg9y1RdCbHWqcSXq7yfh/OQB7lrwfa5cNI+rrs/jqt9oWQNdAC998zAL1nzKKZN/mbUgpBU3WXDev1EOjbLLs9fpEFvHkWN/yCtHvEV9USPLfvJfYnWhjPeVUoTn/IroyP1xvXI9jg/uyXh/j90VN16nWLoMfnyuxZq1kuElhOg7xf5y/M6cbbKsbTHiY1bWjnudtRK1xJQ89ogdjNUH3Ri3S54gOAfoaAo7OLnqCyG2qvoP/wt3HIm5bjGX/O9WzD1/wV/vdDB1SucvXjMcZ/3dX/LlXk9QvF+Im66+jqofOll1zQe0fV6LlbhZt6ImKy5+m/r/LKPsNzvjKJURGLclj8PHaTMv4o65fyWyvoUVF72d2jcpmkH4wJuI1hyA69UbcL7xfxkPW9N2VtzyW0VtHZx9rsWy5Tvug5gQYsehUKkHz2G+wf2y299AYyaCXZLZJXY4pmR2bS3xip2JD57e180QmyDdGIUQW0VdXZzv/vZndovfznfNI3it7Pf85NZq/P7sX7ihbxtYdu5rhD6vY+3wWv63+6ccPOpUjK9jbLjrS9bP+xzX8Fw84wto/WQ9kYWNVF0ynZxjhm/jNRMAew87iv9Oe4EXFv+Tg1/cn/rHFhI8eljmRJpB+IAbwRnA+c6fUA3LCe9zJTjcAIwfp7j193b9rrPPtfjdTTBiuNyQCSEGLrtulGKwp5Rq7yBiMkLgVpf8MabPsuqE6COpAVIlztvrLH9xXzdBdIMEu4QQvaquzuKph+uYsvQi9ih8nY/VYQTPupxjuygIalkWdQ8uYOVv3kPzGXz4y4Xc6/szP595M8NrdgcgXh+m4cWlNDy7mJb31+KqClB26RSqjplAXV3dtlw9kaCU4qczb+SM1bszeeFktF+/g296Mc7KDnVvNIPw3r/BzK3A+d9b0DYsIHTwLVjBasAObv3xVvjpLyzOPc/iphvsIJgQQgxEDs2ua2WR7F60lZ9CJcCT2taS2SV2OPFEF94tGBlRiO2ZBLuEEL1i/XqLBx6y+PrVz7hu4s8oKljPqilXUD3rcFY0LuKrJQuJxiM4dBdBTxEF3mICTQHW/eZjGl9cCrt4eejIp3mz5SVOnfxLDqw5MTVvPc9F/vEjyD9+RB+uoego6CniJ7Nu5P/W/YJL/3Ixy376X4Y+ul/nCZUiOu0MzOLRuF+8AO99RxKe/QtiE48DpTG4wg54/ewXFj873+I3l8GsmfKAJoQYeAzNCUoRs+L2C1s7GJUI8FgOz9ZdTj8WcOWhazpVeSP7uilCbFPuUUHMUByjwN3XTRGiTyirU6GV7pFsiq0rGAzKNu5nZJ+0i6xsoenfywl9XU9LbYxF38ZZvlJR7lvCpMIPUW4nLcOm81V4Kd82fkYLzbR6Wmn0NxFyh8hpymHkwuHs9L9JaJbGi3v/i/9MfxW/O5czdr4sI9C1MbJP+offv3kBKx79hFMeP5Hin01k1JV7dLlfVNMaXC9dhrH4deLlUwnteyVW0B4dqa7e4qJLLL74En5yjuLoIyXg1VvkXOmZYDDY103oNbLf+5c1zcv4fNXblNYtZ0LOSGLD5sAmAlFbev6qpjVY3vx+P1riQCbX4IFB9uPAIPtxYOjOvZpkdgkhui3eEGHVle9R98RCiFtE3E4a404cCqZ7anE0t1HXPIawZRD7ZD1l8QCD47Ozzsv0Q/2eYRYctpTSQRO5rOBEdq6Yg8cho6Fsb348/WrOrz2S9xd9xNRboGDSIIw9i7JOawVKCM2dh/HlM7heuQ7v3w8jOulkIrucRTAvwP/dAldda/GHWy1WrLA498cKXQqrCiEGCEOzRxdOZXZtg5pdVqBkqy9DCCGE6G8k2CWE6Jbwdw0sOuXfRFe1smDcaO6vH0Gd289phyzl+4Gf4az7hg27nME1La/y1rKXmD54b34y4zqKfRVYUZN4XZjYujbiTVGMAjeuoTkoQ2P3vl4xscUcupPL59zJuXUHUFJfgvrhPxny0H74pmQPeKEUsTGHEa+ehfONP+D48B4cXzxJeNdzYfzRXPlrnXl3WNz/ICxfYXH5ryAnIAEvIcT2z9DsW++oGbNfkDpSQgghxFYh37BCiE2KLGnim6Nfoml9jBtK9uVP0Z3Y68QAz9/4Mj/SjsEZWsuX+13GmSsf5b0Vr/CTGddz9d73UuIfjFIKzanjKPHiGVeAf0Yp7pF5KEMuPwNJgbeES/edx53H3EVjsInFJ79E89urN/oZy1tAeJ8raTvpCeJFNbj/fSWee+fiWPoGPzpT46ILFB98CD840+Lb7zarx70QQvQrycyueHKYNCkgL4QQQmwV8rQphNioT96J8M5BL9NcG+e2ir3Z7fuFPPpAjJ+O+R1F//kJZv5QXtj9TM7+4FJCsVZ+d+CTHDr6NBniewc0tmRnztj7N/z2hFtoyGtk8UnzWX/PV2yqNKRZVEPoyLtoO+x2VDyK54kzcD95Jofssojb/08Ri8KZZ1v8a74EvIQQ2zeH5kgUqJfMLiGEEGJrkm6MQohOLMvi7Xfh3ntNdv73W0xuaWTxD/fmjz8L4o+twP3C+eirPmHD+CP5o6eV59++kPElu3DZnn8h31vc180XfeiAkSfQZjVxneN6zn/5QqzL36XxH0sZdOU03CPzuv6gUsSHzaG1ehaOTx7E+dYf8f7tUCZNOoG7b/0Rl16Xy5VXW7z3nsVPf6Lw+SSYKoTY/hi6k4w6XRLsEkIIIbYKCXYJIVLicYtXXoP77rdY8C0cZn7N1JYlFF6wE5POKcP4+kVcL11OnbJ4fOJePL7mKRrDdRw7/hxOn3IhhiYjPQn43vQLWVW3jKvcv+GXu1zJ4HtrWbD/s+QdPpTCH4zGMya/6w/rTqI7nUp09KE437odx8cPMOiLZ/jjyWfz10nHc8+9Oh9/YvGrS2DiBAl4ie3f8uXL+eMf/8jbb7/N+vXrKS4u5tBDD+Wss87C6XSmplu5ciVXXnklb7/9Ni6Xi0MOOYQLLrggYxrR/2lKY1jBOEpC0o1RCCGE2Jok2CWEIBq1+Oe/4L4HLZYvh+pquPLodRTd/AE5B1TiO8nPe0+dxCcrX+cDn4NvrRasJY8xrWIOP5j6K4bmj+nrVRD9iFKKH02/kvrQBm5Sl3PBXbcw7vlR1D20gPrHvsM7pYjgscPJPaQa3ddFgNQTJDLnV0QnHofrlRvwvnYdZxc/xZ7XXMFFt43lnPMsjjnK4vunK7xeeVgU26+FCxdiWRZXXnklVVVVfPPNN1x22WW0tbVx4YUXAhCPxznzzDMJBoM88MAD1NfXc+GFF2JZFpdddlkfr4Hoqaq8kRhrvu3rZgghhBADmrI2VUylC3V1db3dFpEmGAzKNu5nBuI+aWuzePY5eOgRi7XrYPQoOPlExc7Dmvn2oGdoc4W4/0d38UXsOywFLmUwtnQGE8tmMrv6ICrzRvRp+wfiPhkIkvslGo9w2fxT+XDlq5w17QoOrTiF+scWUvvQAsJf16N5DXIPrSZ47Ai8OxVutM6b/u18XC9fjWpZR9v4k/nD/37MI8/4KCyA885V7L4bUiduI+Rc6ZlgMNiny7/zzjt58MEH+fe//w3Aq6++yllnncUrr7xCSUkJAM8//zwXXXQRb731Fn6/v8t5yX7vhywL4+sXAIiNOmiTk8v5u/2TfTgwyH4cGGQ/DgzduVeTzC4hdkANDRZPPg2PPmbR0AhTdoKLLwRX+TvM/+pxmo7Np6SxiNvPuI2y0AZ+4B3K+Gm/YMTwQ3Do0mVGdI9Dd/LrOXdy4+s/4Y/vXMZX6z7i+8dewojvHULLR2tZ+vf3Wf/0Auoe+pZ1xRv4cvoCjEMKOXK3syn0lmbMKz58b1oH74Lzv7fg+eTvXBj4F0decTm/um83fvVri2k7w9lnwfBhEvAS27+mpiZyc3NTf3/88ceMGDEiFegCmDVrFpFIhM8++4xddtmlL5opNpcE5oUQQoitToJdQuxAFi22ePRxu8tiOAyzZ8Jxx0dZoj/In7+8iyWffMMpT59A5ZIKwoc9zD35OThmXEZs3BHbvIhuKBTi8ssvB+DKK6/E7XZnnS4Wi/H0008DcNhhh2EYm76sJT8Tj8dTr8XjcT766CMmTJjABx98wHvvvUd+fj7//e9/ATjnnHO45JJLeP755zOWFYvFuPvuu7nyyitxOBy89tprvP766zzwwAN89tlnKKU45JBDuOGGG3C73YRCIX71q1/xzTffsHDhQnJzc3n++efJy8vr1MaHH36YP/3pTxQVFXHrrbdy3HHHAfDQQw9x3nnnAfC3v/0tldVRX1/PQQcdRGNjI+effz4nnnhil9sjFovx6KOP8thjjzFkyBCuvvrq1DaOxWI88cQTvP/++0ydOpUjjjiCUCjEqaee2mmZ2bZ/c3MzRx55ZGray/a8g4c+vZV7P7qZlxc+QYGnhLZYC60TmnGOcrDPkgPY6YNJ7PbsLsSfj/PK8Nsp2GcoM44+AteQnPaMLZefyF6XERt9CK6XLmfk+z/ivsMO5vHWS/jj33M5/QcW++5j8YPTFWVl8jAptk9Lly7lvvvu46KLLkq9tn79egoLCzOmy83NxeFwsH79+i7n9eCDDwJw9NFHd+vauKOJxWLcf//9PPDAAwwfPpybb765y++anszz0UcfBTa+3U2fDwCtwy/TXX2+L7MNY7EYDz30EO+88w5Tp05F13V0Xd9qx1XHbQCklj99+nSOOuoonnzySSKRCG+//Tbfffcdw4YNY+bMmanvyUcffTT1/sKFCznhhBM48sgjufjiizFNk2nTpuF0OlPrkL7MuXPn8vDDD3PfffdhWRbDhg0DYNGiRYwYMYKbb74ZgF/+8peYpsmUKVN47733+O677xgxYgQ33HADTz31FG+99RYAM2bM4KijjuIf//hHap0G6vmY7fjd2DmR/t4hhxzChRdeyIIFCzL2Z8dtFQqF+OUvfwnAddddx7PPPttpeR2PV7Dv895//32mT5+edb7dtbFzMbk+yfvL9POku9eGjenJPLKdR1u6/P6uJ9unt6+pm7NvOl6j0u/bO16THnvssdQ1sDvHb/I8SL8Obepzm3OMpp+PN910U8azRPq5APY5+O6773a7Pb1h4B3lQogM8bjFu+/Bo4/b/7pccMB+cNSRig28xO/eupgVrSsY6yjkmlcOwvvxJCr2+pTi7x1PdPzRxAxXX6+C2M5pSuOEieex17AjeX3xcyyt/xa34WF08RQmlc0k6CkCILy4keX3fcigJ8MEbtdZcPvTGGVefFOL8U4pwjulCM+YfMxBk2g76TEc796B8515HOt+i/2v/hV3vbsvjzwGL//H4vBDLU48XlFYKEEv0TduvfVWbrvtto1O89hjjzF+/PjU32vWrOEHP/gB+++/f+rhJKmrbrqb6r7b0tJCXV3dgHyw2VKxWIyWlhai0SjhcJi6urpeCXa1tLQAbHS7G4lpYh260mT7fF93uUm2KRQK0dLSkgp2ba3jquM2ADKWX1dXR0tLC5FIhHA4nPo3+V5y+uTr0Wg09V4oFMI0zdR+T65Dx2Um37csi3A4DJBxnAAZ8wqFQhnvJ19TSqXm253jYnuX7fjd2DnR8b3k/krfn9mCXaFQKPWZrpaXfryC/aCdfgxtzj7Y1LmYXHZ6sKs726G7ejKPrs6jLVl+f9fd7bM1rqmbs28ikUjq2tHxuMx2TerJ8Zt+HiSvQz09Zrob7Eo/H9ODXennArSfg91tz6ZIN0YhdlTREPVLVvLuq+v56v11GKH17BncwM+OraW6qBYjsoG/v/Qld6q1VJsav2/zU/npbix5dReCB+SS96cbiWoyHLroXSX+Co4ad1aX77uqcxj2qz0YeunuPP6v2/n06flMWrUToz60aHh2MQDKreOdWIhv11L8s47Gf+xeuF++nOC/f8Z5w/fhmDt/xR2PFPLEk/DUMxYH7G9x4nGK8nIJeolt68QTT+TAAw/c6DQVFRWp/1+zZg2nnHIKkyZN4qqrrsqYrrCwkE8++STjtYaGBqLRKAUFBb3XaCGEEEKIAUKCXUJs72JhtNWfoi9/H7XiE6KrFuIJL8evLCoAEjXkLd2JpefT0JbDVdZi3tTWsn/uJM4bfS7rH/Cw5F/LyT2oivJbZ6Mk0CX6kFKKo/Y7h/LRI7n2lR/hdQb4zYS/MGhJCa0frKPlvbWs/cOnrL3lEzS/A98uZ5JbvZSCD/5G+dJDueSAizj5+EN58BF47gV47nmL3WZZzD1csdNkKWQvto38/Hzy8/O7NW0y0DV27Fiuu+46tA7X4EmTJvHnP/+ZtWvXUlxcDMAbb7yB0+lk3Lhxvd52IYQQQojtnQS7hNjeRFrQV36MvuI99OXvo636FGVGMS2N75qHs6BhAmvMwyipqWTCjGKClUVYviJw+llQ+xlX/Pv7bGit46e73sSc+IEsufgd2j5aTsH3R1N26RSUIYEu0T/MqNyXWw95nsvmn8rP3zuaQ0edxlE/O5PhvqnEGyI0v7Wa5tdX0vz6KpbP11nO93DmtVHw0nzK9v8X55/0U047ZQSPPWHxzHPwymsWVZVw+GGw/74QCEjQS/S9NWvWcPLJJ1NWVsaFF15IbW1t6r2iIruL76xZsxg+fDgXXHABF1xwAQ0NDdxwww0cc8wxGx2JUQghhBBiRyXBLiH6u7Z69JUfoS9PBLfWfoGy4pgYLI6O4/Vlp/Lu2ql8G57EtNkB9jlUMXMC6Lr9IG8BlmXx4jcPcNtblzI4VM0lrnvwXGPy3avPoxe4qfzjbuQeXN2nqylENtXBUdx2yIv85b0reeqLv/L45/MYlj+WkYWTKCurouj0Mgp+XEqwdhiuDyyib9Syer6HVR+bFD//J8rOKOSHJ57Naafk8cpr8ORTFn+41WLeHbDH7hb772tne2maBL5E33jjjTdYsmQJS5YsYbfddst47+uvvwbsui/z5s3jiiuu4Pjjj8ftdnPwwQdz4YUX9kWThRBCCCH6PQl2CdGfRFrQahehrf0CfdUn6Cs/QqtbBEBcuVhqTeTdtWcy/9sp/K9uIsFiDzN3haNOU0yaCA5H5gN7rDbEkjc+5a1/PovxZYzfrL4UT5OLOMsJV/gouWAyBSfXoOc6+2JtheiWXHc+v5z9e06Z/Av+s/ApPlr5X95dNp8NbWs6TRvYLY8Re45l9svTsV6ayNoLW6l6+UcUnDiHfWefwL57+/n2O4unnrH498vwj39aFBfD3ntZzNldUVMj3RzFtnXEEUdwxBFHbHK6QYMGMW/evG3QIiGEEEKI7Z8Eu4TYmiwLIs2otnpUqCHxXz0qVA/Jv9vqUa3r7SBX06rUR1u1Ar6KTOL9NXN5ddFkPq8fh+FyMmE8TD1Scc4uJgWltTSG62gI1fL+qgasxSG0jyLon0VRn4RwrbZHoBmvhhGrVhQfMBzvxEK8OxXhHhOUh3qxXSnxD+a4Cedy3IRzAQjH2tjQupYNraupbVvD+tbVrGxczNL6Bdw3534cQ+H4p4+BJw/kP19/Qsu+s5k5+WBGTP8p5/+sgJ/82OLNt+DFf1o88ig88KBFaYmd8bXH7ooxoyXjSwghhBBCiO2RBLuE2EKqZT3ahu9Qtd+h1S5Ea1iOal2PatmAat2AMqOpac24RrTVR6zNA5qF8riJedy0OT0sD+/MZ2uH8eGyIXwdKmGNESJYsZT8ysX4Jj7ETjkbiBu11EVqeSK0gb+9Uo9pmlSsKmf8l+OY+OU4StaXANDgb2RR5WIaprdSMWMsu+9/NMH84r7aREJsFS7Dw6CcKgblVGV9vzncwDcnfsLyeZ9S9cA4ot+O4ukpb9Py70M5YMRwJk77IbMnTGbWBI3mepN337P4z0cunnhS46FHLPKDsPNUi2k7K3aeCvn5EvgSQgghhBBieyDBLiF6IhZO62L4MdqqT9CaV6fetpx+zLxKLF8RZtEoTG8hLSuDrH/LydqPwqjFCs3s/MBsKotmTwR/oJlJgdcY4q+n3t1AfbSehg0NmJpGRHORZxQwJDaE0tUlFC4MEnzXg2OtwtJB3zmA48wCHDPzyCl3s5N/EF6HFC4WOy6/K5edKneDa3Yj/L0GVtzyPns850S9tTsAn/Ed8F1q+jLgRJfG6SOD1FYV84lRxr/eLeafL9lflcOHWUyeBOPHK8aPhaIiCX4JIYQQQgjRH0mwSwwYyxu+4+1l83lvxX9Y17yCtlgrxb5BVAdHMa1iL3YaNBuPw9f9GVoWqmEZ+qpP0VZ/SmzdZ/hWfpbK1DJzK4lX7Ey0dDxm4QjM/KG0qEKWrVCseK+Rtie/Iefjr8irt4jpbSwpX8rCmYvYEKyl2RPCsFy44258US95rQHyWnPJbytgUNNghq0YjvE/hYp33Tzl1gnsPoic/SoJ7F2Bkefawi0oxMDlGpbL0Nv2In5NhMZ3VvLJx//hg+9eptlqZTQGu0UVAaAtVEnzukr8/1nPrtEvmekAfVSA1eVlvBWp4j9P5/LoYy5QitISi1GjYPgwxfBhMHwYlJRIzS8hhBBCCCH6mgS7xHarLdrC52vf491l/+ad5fNZ0WgXch8SHM2w/LE4DTdrm1fw6qJneP7re3EbHnYZvC+7DzmUaRVzcBme9plF2+wuiLULsdYvYMOaD1m77jPWxhqJKlCak0DOCJyDD0Z3z2KdPo3ldQWsXWCx9k1Ytw5Cy1sZuupTpoU/p6opjqlMFgz5ln/NWkL9mEpGV81k5rBTmTi8HLdz0wXhrbhJbF2IyIoWoitbiK1vQ3PpaH4H7po8XENzUQ5ta21eIQYkPddJcN9q9tj3dKZFj+ahT27l7s/+zN+A44tmc4LZxuDaf8L65TQtK6VhSTX1S4dQ+L8mDuEbDgE0ZxQjN4RpxWitgw0faiz2enjLkUebHsRbmIdeEide3Iozvx7dtxaHt5G41kRrtImWSBMt0SZC0Racupscd5DK3BHUFE1iYumuGUF5MxInuqKFyNImYutDGEUeArsN6rPtJ4QQQgghxPZAgl0DVG3rWl745j7cho8SfwVD88dQFqhCU1svOBJd3cq6u77A8iko0rGqDOKVOhFPlFCsjUislVCkkUi4iXCkkWi0FY/S8WlOfMqJT3fhU078mgM/Brpl0hptojnaSkO0jbXLG6lfFqJ1ZZzQ6jhtzVGcYQf5cSeH6QfgNQL4HEEcLjemGyynwnRZxN2Hs8K7lK9zPuazxtf5/L03eaA5hyH1JQxqyCWn2YOz2YvRlIMWdhM2YkSMEbR4y6nLq2NDXi11eXXU56yiIfA1Idd9aC2l+NcOZ9Da4YxYV8heq1yUrLeDZ4vLl/D8bgvwH1TB7lMP44iSnTdruytdw1HqxVHqhSlFvb27hNjheR1+vjf1Yg4adRJ3vn8t9yx8ksddQWYNO4DJM09jmKuIYDzOkHAItaqets8aiKwIE1kdI7xWEVlvYK7wUhZxUgaMddWybtAnfBNbwnfaCurDDZgrTDRTw9vmIac1QKAlh7y2HCraKvCG/BgxnageIaxW8rm2jM/0ZymKl1PUWoS/wYNaFwWrvc1GoZvRHx7TZ9tsW7Isiw1ta1hS9w1L67+hMVzL/iNPoMRf0ddNE0IIIYQQ/Vy/DXap1g24nzkXIq2gG6A5QDOwDCcYbizDnfjXBYYHDBeWI/Fv6j03ONyZ0zvcoDsAlfgPSO9yojJft7JNk86yn0JU+tOIlfx/q8PfHd9Pm6bDtJZqRjU2pD3kdGf+7f+u2vAJD31yK6F4W+pjbt1NdWAolYEqKv2VlHjLCDpzyXXm4dCdGCgcpolumah4hEi0lVisjVi0jVisjZZoI/WRBurD9dRHG6mPNlIXbaY+3kJdvBXPsgJOuP9kcloCGavb4G9kTdEa1hauY03hWtYUraXF00LMiBPXY5iaiR43MGI6jpiDQIufQHOAnKYARRuKKF1XQsm6EnyxApL5DhFnCDxtOI0oHj0OMQdmVCcWCRMPp+3DhALKmUA5cBAdhZ1hmgLNtPjDhPIVRrwAV8xDXp2DUYtNHOGN9CUEokaUpUNXsGSPRozd85iw854cWHoxutZvTy8hRJoS/2Au3eNPzB3zA5796h5eW/wcL37zQMY0Tt2NK+jBKDDQJ9rndlO4nnCsjaINhVSuGMzQlUMZsXIYs98Yxu7mxgPcuqsNwx1Cd0SxTC9xs4CQqRO2NJpdbazI/Y6GQY3Uj64n5A/TGgjR5gsT9oWJ3vp7TBXH1ExMzQRl4rYc+C03Plz4cZNjucnBRQ4uAjjwGAaGaeJU4EShrDhYJsoywYqjEUMRRVlRNCuKZkXQrBiKGG85j+dLbToRq5mo2UKERsLUEbHqiKg6Yno9RYPqCFn1aGi4HV48ho+AK4+AK0iOK0iO2/43+bfb8BC34liWSdSM0hSupyG0gbXNy1na8C3LGr5laf0CmiMNqW3mNjyMLp4qwS4hhBBCCLFJ/fZp3NJdmIUjUW11EI+CGYN4FBUNQVs9WiwEsRDEwqhoG8RC9k37ABEDelBdqpPpwH9w0YqL5Xqcb7U43+pxvgt9wye1X/IvzdrkPDZGsyAXjXwMgjip0ZzkDY6y7NIHcJtefE1BPLUFuNbl41+XS+7qMkZ+MRRa9B4tJ1oYJ14N1iwHjpFBimoGkTe0AD3PYccfLQuwMoKClgnxZpN4Q4x4Y4x4Y5RYY4x4E1gYGEVejAI3RomdNaX7HRttQ7whQmRFM+5mjboFa4m3xlCGQs9z4R6Ri2t4Hju5e7ZeQoj+Z0zxFMYUTyFmRlnW8C2L676mMVxndzuMNBKJh4ibMWJmDAuLHFeQXHc+xb5yhhWMZVBgCLqmY7ZGCS9qIrqmFUxAAz3XhZHnRA+60D1xtPAGVMs6aFpLpHY98YZ10LzGHt013EB9rJn/qQ18o5po1NoIqzhx7O84L2BYCgfgALAUzcqiXrNYqpk0KpN4r5YN+3X2ly0NFc1Di+bhCedTlFuEZVmEYq2sb13NorovaQzX0Rpt7tHSCr1lDM4dxl7DjqQ6WENV3kiq8kaS6y7ohXURQgghhBA7gn4b7MLlJ7z3b7o/vWWBGbWDX7EQREP2v4n/VDTxbywM8QiZWVHZMrCszIyptAyuztle2bK/OjxpdGfatGl8Pi8tra0d5tUxA62r+bX/rQNVKKqAOWmfaYuHWBeupyHaSEOsmYgVJW5ZRBXElEJpDhyGG8NwYxheDMONx5VH0FtCnq+MgLeox13zLMsitj5EZGEj8cYIVjiOGTEhbqFcGsqhoZw6RqEbR7EHvcCN5uw6iLSxcJ0eBH1wj5rX9bxynXhy8wkGg6hpeb0zUyFEv2VoDoYERzMkOHqzPq95HXjG5uMZm9/lNJbHh5VXaS+Pzl/GhcCeif96yrIsmiMNNIQ2UB/aQGOoHsPloLaujnAsQtSM2LnNCjSlIPGvUnZx/fTXAFyGB4/Dh9fhx2148TtzCbjy8DlzuvU9EEtkbjWG62gM1dEYriMSD6EpDU3pGJqDgCuPXHc++Z4SfM7AJucpxPYuVj070dNACCGEEFtD/w129ZRSoDtBd2K57BvlLctd6ltaMEisrm6rzd8JlCf+21aUUjiKPDiKPJueWAghxGZRSiW6EOZRkTsMgGAwSN1W/E7ZGENzEPQUEfRI7UEhUtw5fd0CIYQQYkCTodyEEEIIIYQQQgghxIAhwS4hhBBCCCGEEEIIMWBIsEsIIYQQQgghhBBCDBgS7BJCCCGEEEIIIYQQA4YEu4QQQgghhBBCCCHEgCHBLiGEEEIIIYQQQggxYEiwSwghhBBCCCGEEEIMGBLsEkIIIYQQQgghhBADhgS7hBBCCCGEEEIIIcSAIcEuIYQQQgghhBBCCDFgGJv7wWAw2JvtEFnINu5/ZJ9sO6FQCLfbDdjbPfn/HQWDQXw+X+r/DWPTl7VYLIbP5yMej6dei8fjuN1ufD4fLpcLXdcz5uV2u7MuKzkvAE3TUtM4nU6UUiilcLlcqXVIrpfD4UApha7rBINB8vLysrZR13UcDgfBYBBd11PLdjgcqf/3+/0AqflpmobP59vo9kjO3+FwpNYtuY2T7yW3RzAYJBQKZV1m+vonl9fc3Jx1WtG35Pq1Y9rUtWBHln4dTL9O98Y8ofvfSd35fF+evx2/E3RdT313bY3jquM2ADp9J6XvN6fTicvlSr2XnD75vsPhSL3ndrsxTTP1PZ3tuzx9/pZl4XK5ADKOEyBjXsnv9eT7ydeSbUn/dyCfj9mO342dEx3fS+6v9P3ZcVt1vD/sannpxytk3udtyT7Y2LnY8f4y/TzZ0mtD+vy7M4+uzqMtWX5/15Pt09vX1M3ZN8l78PRrVPJz2a5JPTl+088D6N69wOYco109r23sWau77ekNyrIsa6suQQghhBBCCCGEEEKIbUS6MQohhBBCCCGEEEKIAUOCXUIIIYQQQgghhBBiwJBglxBCCCGEEEIIIYQYMCTYJYQQQgghhBBCCCEGDAl2CSGEEEIIIYQQQogBQ4Jd/cjy5cu55JJLmDNnDhMmTGDvvffm//7v/4hEIhnTrVy5krPOOotJkyYxffp0rr766k7TiN7zpz/9ieOOO46JEycyderUrNPIPtn27r//fubMmcP48eM54ogjeP/99/u6STuM9957j7POOotZs2ZRU1PD/PnzM963LItbb72VWbNmMWHCBE4++WQWLFjQR63dMcybN48jjzySyZMnM2PGDM4++2wWLlyYMY3slx2HXB/7r946VyORCFdddRXTp09n0qRJnHXWWaxevXpbropIM2/ePGpqarjmmmtSr8l+3D6sWbOG888/n+nTpzNx4kQOO+wwPvvss9T7sh/7v1gsxi233JJ6ht5rr7247bbbME0zNY3sxx2TBLv6kYULF2JZFldeeSXPP/88F198MQ899BC33HJLapp4PM6ZZ55Ja2srDzzwALfccgv//Oc/ueGGG/qw5QNbNBpl//335/jjj8/6vuyTbe+FF17guuuu40c/+hFPPfUUU6ZM4YwzzmDlypV93bQdQmtrKzU1NVx++eVZ37/jjju4++67ufzyy3nssccoLCzk9NNPp7m5eRu3dMfx7rvvcuKJJ/LII49w9913E4/H+f73v09ra2tqGtkvOwa5PvZvvXWuXnPNNbz00kvccsstPPDAA7S2tnLmmWcSj8f7YrV2aJ9++ikPP/wwNTU1Ga/Lfuz/GhoaOP7443E4HNxxxx08//zzXHTRReTk5KSmkf3Y/91xxx089NBDXH755bzwwgv88pe/5K9//Sv33ntvxjSyH3dAlujX7rjjDmvOnDmpv1955RVr1KhR1urVq1OvPffcc9a4ceOspqamvmjiDuPxxx+3pkyZ0ul12Sfb3lFHHWVdfvnlGa/tv//+1m9/+9s+atGOa+TIkdZLL72U+ts0TWvmzJnWvHnzUq+Fw2FrypQp1oMPPtgXTdwhbdiwwRo5cqT17rvvWpYl+2VHItfH7cvmnKuNjY3W2LFjreeffz41zerVq61Ro0ZZr7322rZdgR1cc3Ozte+++1pvvPGGddJJJ1lXX321ZVmyH7cXN910k3X88cd3+b7sx+3DD3/4Q+viiy/OeO2cc86xzj//fMuyZD/uyCSzq59ramoiNzc39ffHH3/MiBEjKCkpSb02a9YsIpFIRsqt2HZkn2xbkUiEzz//nFmzZmW8PnPmTD766KM+apVIWr58OevWrcvYP06nk5133ln2zzbU1NQEkPr+kP2yY5Dr4/Znc87Vzz77jGg0ysyZM1PTlJSUMGLECNnP29iVV17J7rvvzq677prxuuzH7cPLL7/MuHHj+MlPfsKMGTM4/PDDeeSRR1Lvy37cPkyZMoW3336bRYsWAfDVV1/xwQcfsPvuuwOyH3dkRl83QHRt6dKl3HfffVx00UWp19avX09hYWHGdLm5uTgcDtavX7+tmyiQfbKt1dXVEY/HKSgoyHi9sLCQdevW9VGrRFJyH2TbP9KNatuwLIvrrruOKVOmMHLkSED2y45Cro/bl809V9evX4/D4cj4MTQ5jdx3bDvPP/88X3zxBY899lin92Q/bh+WLVvGgw8+yOmnn85ZZ53Fp59+ytVXX43T6eTwww+X/bidOOOMM2hqauKAAw5A13Xi8Tg/+9nPOPjggwE5H3dkEuzaBm699VZuu+22jU7z2GOPMX78+NTfa9as4Qc/+AH7778/Rx99dMa0Sqms8+jqddHZ5uyTjZF9su113LaWZcn27key7R+xbVx55ZV88803PPDAA53ek/2yY5Dr4/aht89VOZ+3nVWrVnHNNddw11134XK5upxO9mP/ZlkW48aN4+c//zkAY8aM4dtvv+XBBx/k8MMPT00n+7F/e+GFF3jmmWe4+eabGT58OF9++SXXXXcdxcXFzJ07NzWd7McdjwS7toETTzyRAw88cKPTVFRUpP5/zZo1nHLKKUyaNImrrroqY7rCwkI++eSTjNcaGhqIRqOdotWiaz3dJxsj+2TbCgaD6Lre6VeWDRs2dMqwE9teUVERYP9CVlxcnHpd9s+2cdVVV/Hyyy9z3333UVpamnpd9suOQa6P248tOVcLCwuJRqM0NDRkZCFs2LCByZMnb6M12LF9/vnnbNiwgSOOOCL1Wjwe57333uP+++/nH//4ByD7sb8rKipi2LBhGa8NHTqUf/7zn6n3QfZjf3fjjTfywx/+kIMOOgiAmpoaVq5cybx585g7d67sxx2Y1OzaBvLz8xk2bNhG/0v+KpQMdI0dO5brrrsOTcvcRZMmTWLBggWsXbs29dobb7yB0+lk3Lhx23S9tmc92SebIvtk23I6nYwdO5Y33ngj4/U333xTvoz6gYqKCoqKijL2TyQS4b333pP9sxVZiZF8//Wvf/G3v/2NwYMHZ7wv+2XHINfH/q83ztVx48bhcDgyplm7di0LFiyQ/byN7LLLLjz77LM89dRTqf/GjRvHIYccwlNPPcXgwYNlP24Hdtppp1Sdp6TFixdTXl4OyPm4vQiFQp2ytnRdT2VlyX7ccUlmVz+yZs0aTj75ZMrKyrjwwgupra1NvZeMSM+aNYvhw4dzwQUXcMEFF9DQ0MANN9zAMcccg9/v76umD2grV66koaGBlStXEo/H+fLLLwGorKzE5/PJPukDp59+OhdccAHjxo1j8uTJPPzww6xatYrjjjuur5u2Q2hpaWHp0qWpv5cvX86XX35Jbm4ugwYN4pRTTmHevHlUV1dTVVXFvHnzcLvdqdoJovddccUVPPfcc/zxj3/E5/Ol6lMEAgHcbjdKKdkvOwi5PvZvvXGuBgIBjjzySG644QaCwSC5ubnccMMNjBw5slOhdLF1+P3+VJ21JK/XS15eXup12Y/936mnnsrxxx/Pn//8Zw444AA+/fRTHnnkEa688koAOR+3E3vuuSd//vOfGTRoUKob4913382RRx4JyH7ckSlLOqL2G0888QQXX3xx1ve+/vrr1P+vXLmSK664grfffjt1kl544YU4nc5t1dQdykUXXcSTTz7Z6fW///3vTJ8+HZB90hfuv/9+/vrXv7J27VpGjhzJxRdfzM4779zXzdohvPPOO5xyyimdXp87dy7XX389lmVx22238fDDD9PQ0MDEiRO5/PLLOz0YiN5TU1OT9fXrrrsu1c1G9suOQ66P/VdvnavhcJgbb7yR5557jlAoxIwZM/j1r39NWVnZNlkP0dnJJ5/MqFGjuPTSSwHZj9uL//znP/zud79j8eLFVFRUcPrpp3PMMcek3pf92P81Nzfzhz/8gfnz57NhwwaKi4s56KCD+PGPf5x6FpP9uGOSYJcQQgghhBBCCCGEGDCkZpcQQgghhBBCCCGEGDAk2CWEEEIIIYQQQgghBgwJdgkhhBBCCCGEEEKIAUOCXUIIIYQQQgghhBBiwJBglxBCCCGEEEIIIYQYMCTYJYQQQgghhBBCCCEGDAl2CSGEEEIIIYQQQogBQ4JdQgghhBBCCCGEEGLAkGCXEEIIIYQQQgghhBgwJNglhBBCCCGEEEIIIQYMCXYJIXrd/PnzueeeezJee+KJJ6ipqWH58uW9sox33nmHW2+9tVfmJYQQQgixo5H7NSHEQCbBLiFEr5s/fz5///vft+oy3n33XW677batugwhhBBCiIFK7teEEAOZBLuEEEIIIYQQQgghxIBh9HUDhBADy0UXXcSTTz4JQE1NDQDTpk1j7ty5AGzYsIGbbrqJ1157jZycHObOncu5556LruupedTW1vL73/+el19+mfr6egYPHsz3vvc9jj76aABuvfXW1K+EyWWUl5fz8ssvEw6Huemmm3jzzTdZuXIlgUCAnXbaiYsuuoiysrJtth2EEEIIIforuV8TQgx0EuwSQvSqs88+m9raWr744ovUDY7f7+fTTz8F4IILLuCwww7j2GOP5bXXXuNPf/oT5eXlqRuj5uZmjj/+eGKxGOeddx7l5eW8+uqrXHbZZUSjUU444QSOPvpoVq9ezWOPPcbDDz8MgNPpBCASidDW1saPf/xjioqKaGho4MEHH+SEE07gxRdfxO1298FWEUIIIYToP+R+TQgx0EmwSwjRqyorK8nPz8fpdDJp0qTU68mbp7lz53LWWWcBsOuuu/LGG2/w4osvpm6e/va3v7F69Wqee+45Bg8enJquqamJW2+9lWOPPZbS0lJKS0sBMpYBEAgEuOaaa1J/x+NxZs+ezYwZM3jttdfYd999t9aqCyGEEEJsF+R+TQgx0EnNLiHENrXbbrtl/D1ixAhWrlyZ+vv1119n0qRJlJWVEYvFUv/Nnj2b2tpaFi1atMllPPfccxx11FFMnTqVMWPGMHHiRFpbW1m8eHFvr44QQgghxIAj92tCiO2dZHYJIbapnJycjL+dTieRSCT1d21tLUuWLGHs2LFZP19XV7fR+c+fP59f/OIXqdoSwWAQpRQ//OEPCYfDW74CQgghhBADnNyvCSG2dxLsEkL0K3l5eRQVFXHRRRdlfX/IkCEb/fyLL75IZWUl119/feq1aDRKQ0NDr7ZTCCGEEGJHJfdrQoj+ToJdQohe53Q6N/tXudmzZ3P//fdTXl5Ofn7+RpcBEA6HcblcqddDoRCGkXlpe+6554jH45vVHiGEEEKIgUju14QQA5kEu4QQvW7o0KE8/PDDPProo9TU1OD3+7v92dNOO40XXniBE088kdNOO43q6mpaWlpYuHAhH330EbfffjsAw4YNA+Cuu+5i5syZuFwuampqmD17NvPnz+faa69lzz335PPPP+fee+/tlI4vhBBCCLEjk/s1IcRAJsEuIUSvO+qoo/jwww+5/vrraW5uZtq0acydO7dbnw0EAjz00EPcfvvtzJs3j7Vr1xIIBBg6dCj7779/aro999yTY489lnvuuYc//OEPDBo0iJdffpljjjmGVatW8fjjj/Pwww8zfvx4/vSnP3HOOedsrdUVQgghhNjuyP2aEGIgU5ZlWX3dCCGEEEIIIYQQQggheoPW1w0QQgghhBBCCCGEEKK3SLBLCCGEEEIIIYQQQgwYEuwSQgghhBBCCCGEEAOGBLuEEEIIIYQQQgghxIAhwS4hRJf++c9/ctxxxzF9+nQmTZrEXnvtxXnnncenn36ammb+/Pncc889m72Md955h5qamtR/EyZM4MADD+S2224jFAr1wlp0dtFFFzFnzpzU38uXL6empoZ33nlnqyxPCCGEEGJrkHs1IYTIzujrBggh+qd77rmH6667jmOOOYazzjoLp9PJ4sWLmT9/Pp988gkTJkwA7Buod999l9NOO22Llnf55ZczduxY2tra+O9//8vtt9/O0qVLufHGG3thbYQQQgghBha5VxNCiK5JsEsIkdXdd9/N/vvvz1VXXZV6bdddd+WEE07ANM1eX97w4cOZNGkSADNmzKC2tpYnnniCSy65hLy8vF5fnhBCCCHE9kzu1YQQomvSjVEIkVVDQwOFhYVZ39M0+9Jx0UUX8eSTT7JixYpUavvJJ5/cK8sfN24cAEuXLmXJkiWcf/75zJkzhwkTJrDvvvty44030tbWlvGZOXPmcNFFF2W8lkx7f+KJJ3q0/Ndff53jjjuOKVOmMHnyZPbbbz9uu+22LVspIYQQQoheIvdqcq8mhOiaZHYJIbIaP348jz/+OIMHD2bOnDlUVlZ2mubss8+mtraWL774InVz4ff7e2X5y5cvByAQCLB27VrKysq45JJLyM3NZdmyZcybN48VK1bwhz/8YYuXVVFRwddff536e9myZfzoRz9iv/324+yzz8bhcLB48WKWLVu2xcsSQgghhOgNcq8m92pCiK5JsEsIkdVvfvMbzj33XK677jquu+46CgsLmTVrFsceeyw77bQTAJWVleTn5+N0OlNp7ZvLNE1isRihUIg333yThx56iNGjRzNkyBCGDBnCzjvvnJp28uTJVFZWctJJJ1FXV0cwGNyiZXf0+eefE41GueKKK1I3hDNmzOjVZQghhBBCbAm5V5N7NSFE1yTYJYTIatiwYTzzzDN88MEHvPnmm3z44Yc899xzPPPMM1x99dUceeSRvbq8jkVT99xzTy677DIAIpEId955J08//TSrVq0iHA6npluyZEmv30CNHj0ah8PBT3/6U4499limTJlCfn5+ry5DCCGEEGJLyL2a3KsJIbomwS4hRJcMw2D69OlMnz4dgMWLF3PyySdz44039voN1BVXXMHYsWPxeDyUl5fj8XhS7/32t7/l/vvv58c//jGTJ0/G5/OxZs0azjnnnIybqd5SVVXFnXfeyV/+8hd+/vOfE41GmTRpEueffz5Tp07t9eUJIYQQQmwOuVeTezUhRHZSoF4I0W3V1dUceOCB1NfXs379+l6d95AhQxg/fjzDhw/PuHkCePHFFzn88MM5++yzmTFjBhMmTCAQCHSah9PpJBqNZrxWX1+/We3ZZZdduOuuu/jggw+4++67ATjjjDM2e35CCCGEEFub3KvJvZoQwibBLiFEVuvWrcv6+uLFi3G5XOTk5AD2TcvW+MUuXSgUwjAyE1GffvrpTtMNGjSIb7/9NuO1119/fYuW7XQ6mTFjBmeccQatra2sXLlyi+YnhBBCCNEb5F7NJvdqQohspBujECKrgw8+mN12243Zs2czaNAgGhoa+Mc//sErr7zC9773PZxOJwBDhw7l4Ycf5tFHH6Wmpga/38/QoUNZvnw5e+21F+eccw7nnnvuFrVl9uzZPPXUU4wcOZKqqirmz5/P+++/32m6Aw44gF/96lfccMMNzJ49m48//jjrjdamPPjgg7z//vvstttulJWVUVtby7x58ygtLWX48OFbtC5CCCGEEL1B7tXkXk0I0TUJdgkhsjrvvPN49dVX+d3vfsf69etxOBwMGzaMX//61xx33HGp6Y466ig+/PBDrr/+epqbm5k2bRr33ntvr7blV7/6FZZl8fvf/x6A3XbbjZtvvpmjjz46Y7ojjjiClStX8sQTT/DQQw8xc+ZMbrzxRo455pgeLW/UqFG89tpr/O53v2PDhg3k5eUxZcoUfve736VuHIUQQggh+pLcq8m9mhCia8qyLKuvGyGEEEIIIYQQQgghRG+Qml1CCCGEEEIIIYQQYsCQYJcQQgghhBBCCCGEGDAk2CWEEEIIIYQQQgghBgwJdgkhhBBCCCGEEEKIAUOCXUIIIYQQQgghhBBiwDA294N1dXW92Q7RDbm5uTQ0NPR1MwSyL/oT2Rf9i+yP/kP2xeYLBoN93YReYZqmHAMDgJzL2z/ZhwOD7MeBQfbjwNCdezXJ7NqOaJrsrv5C9kX/Ifuif5H90X/IvhByDAwMsh+3f7IPBwbZjwOD7Mcdh+xpIYQQQgghhBBCCDFgSLBLCCGEEEIIIYQQQgwYEuwSQgghhBBCCCGEEAOGBLuE2I6Fvq5nxSVvU/fkQizL6uvmCCGEEEKIfmb1aot16+Q+UQixY9ns0RiFEH0rurqV7458EastTu1936B0Rd6hQ/q6WUIIIYQQoh/53+f2v/vs1bftEEKIbUkyu4TYTq265n2IWYz496F4pxSx6sr3saJmXzdLCCGEEEIIIcR2Jh63eO99i8bGgZEJKsEuIbZDkZUtNDy3hIJTa3BV51B0znhia9toemVFXzdNCCGEEEIIIcR2pqkJ6hvg62/6uiW9Q4JdQmyH6h/7DkyL/FNHARDYfRBGkYf6Jxf2yvyl/pcQQgghhBBC7Dhicftfh6Nv29FbJNglxHao4R9L8e5cjHOQDwBlaAT2rqDptVVYsS3ryri8YSGnPz6LX754FGual/VGc4UQQgghhBBC9GOxqP2vMUAqu0uwS4jtTGRpE6HPasndvzLj9cAegzAbI7R+vH6z521aJpfPP5XmSD1fr/+YP759+ZY2VwghhBBCCCFEPxeN2f9KsEsI0Sca/2VnW+Xslxns8s8sA03R/MbqzZ73ByteZWnDAn68yzUcM/5s3lj6Iovrvtqi9gohhBBCCCGE6N9iEuwSQvSlhn8sxT0uH+dgf8breo4T96g8Wj9Yu9nzfv7reynwlDCr6kAOHX06hubg5e+e3NImCyGEEEIIIYTox5LBLl3v23b0Fgl2CbEdiTdEaH1/HTn7DM76vndKEa0frsMye15gPhqP8P6KV5g95GAcupMcV5DxJdN5Z/n8LW22EEIIIYQQQoh+LBnsGihjlUmwS4jtSMs7q8G08M8qy/q+d0oxZmOU8IKGHs/787XvEoq1MrV8j9Rr0yr24rvaz1nfsmpzmyyEEEIIIYQQop+LJgrUI8EuIcS21vzf1WheA++kwqzveyYUAND2eW2P5/3e8ldwaE4mlu6aem1qxR4AfLTqvz1vrBBCCCGEEEKI7UIyo2uAxLok2CXE9qT5zdV4p5egHNlPXdeQAMpjENqMYNf/1rzN6OKd8Dh8qdeq8mrwOgJ8te7DzW6zEEIIIexyAUIIIUR/J90YhRDbVHRtG+Fv6vHPLO1yGqVruEfn9TizKxIPs2D9p4wp3jnjdU1pjCqazJdrP9isNgshhBAC1res4vXFz1Pftr6vmyKEEEJslGX2dQt6hwS7hNhOtLxp183y79p1sAvAMzafts9qsXoQkv92w/+ImhHGFE/t9N7ooil8V/sF4VhbzxoshBBCCADqQxsAaIrU921DhBBCiC5IN0YhRJ9ofnM1etCFe0z+RqfzjM3HbIwQXdHS7Xl/sfZ9AEYX7dTpvdFFk4lbMb6r/aJnDRZCCCG2E+FYG/9Z+BRN4fqtMn+FAujRD1FCCCHEtpQKdg2QryoJdgmxnWh+YzW+GaUoTW10OvdYOxjWk66MX6/7mFL/YIKeok7vDckfA8Diuq960FohhBBi+7GhdQ2WZbGicVFfN0UIIYToExLsEkJsc5ElTUSXNW+0XleSuyYIuiL0WfeDXd/WfsbwgglZ3yv2leN1+CXYJYQQQmwmpRKZXQOmc4gQQogBa4B8VUmwS4jtQPObqwHwzyzb5LSaW8c1PLfbmV1t0RaWN3zH8IJxWd9XSlGVV8MiCXYJIYQQW2ag/FwuhBBiwBoo31QS7BJiO9D8xiocZV6cQwLdmt4zJp/Ql3XdmnZh7RdYWF0GuwCqg6NYXC/BLiGEEGLzbLwEgRBbi9SJE1tT/dOLaH5jVV83Q/QS6cYohNimLMui5c3V+HYtTXWD2BT3mCDRFS3E68ObnPbbDf8DYHjB+C6nGRIcRV3bOhoSo0kJIYTYccTNOHEz3tfN2K4lv76lG+OOq7Z1bZ+cRwPloVX0X7H1ob5uguglEuwSQmxT4W8biK0P4d910/W6ktyjgwC0fbXp7K5vaz8jz11Igaeky2mqg6MAWFz3dbfbIIQQYmB4Y8kLvLb42b5uxoAgwa4dU1O4gY9XvcGCDZ9s82UPlIdWsWOzLItvFli0tsoBvS0MlOuGBLuE6Oda310LgG9a18Gojjxj7GBX6ItuBLs2fMbwgvEbzRqrzqsBZETG/2fvv+MtybL6TvS7d8Qx19+bPrO87apqW21oGhoQTghJyKERQgiQxCD70HwGPQkhaaSHkARoHpqRGWZ4GoEQDzEjIaBRd9PelemqyjJZVen9zev98eeE2XvNHzsiTpxzb2bezLxlWh0/6Lp5zonYsWPHDrN+8Vu/VaBAgQJfj4htXKRC3SFUkcb4dY3YhAB0ovabvu3i1C3w3wK6XZi9Bq+9/lb35L9t7EbZFc42kci+OR26QxRkV4ECb3O0X1jBPzxC6d7xXa/jHxjBP1ildxNlV2wjrm6dvaFfF8DMyEEmK/u4Uvh2FShQoECBAtfF5c3TBHH3ur8XxEOBNxvFnCvwZuDNIj/s1wbH8jWP6103TC2gc2Kdzon1N7dDt4mC7CpQ4G2O9gurjH348K79ulJUH5+5qbJrdus8kQ15eN+NyS6lFPfPvKNIYyxQoECBAgWug0awxdWtc5xefWnbb31lV8E8FHhzcStkl1jBhoU/X4FbR/33Z9/Q9jPfw+IS+obiZsouse4HCb42rhMF2VWgwNsY4XyLaKHN2DccuuV1q4/vo3euhsTXfwVycfPm5vQpHph5jKtbZ4tUlgIFChT4OoKV4jX6biHJWBVjNoTuFqqx8Fb34usWt/LY1nlpjcbvX7vjbarmEvTqd9xOga8hvMHhQUF2vTnY9fh+jRyIguwqUOBtjHbq1/Xh3ft1pag+MYMEhuBK47rLXNw4yWhpnGOT99+0vXunH6UV1tnqrt1yXwoUKFCgwNcmUq+h//Zx555a6cugHZXYyXdfjy+M/Nln8RZPvNXdKLALRIvOU0zMnRG23sLL+Fef3osuFSgwgK/DS+hbguuO89eY/WRBdhUo8DZG54UVvKkylUemb3ndzKT+zPVTGS9unOTBfe9Eq5tfCu6bfgSAa/ULt9yXAgUKFCjwtYnQ7g3ZZQODaUV70tbbFWmlxVp3nfX20sBvX0sG9eFi+47JjgLDeOuO/62QA6rknge/VlKUCnz9IJ3HtiC73lDc1KA+e3FzZ9up1YSTp974g1mQXQUKvI3RfmGV0W84hNK3/pBUeXAKVdbX9e2yYrm0efKm5vQp7plyZNdc/eIt96VAgQIFCnxtIjLBnrTT+PQ1mp+f35O2vhbw2vJzA59TtZe8zT27TD2kc3yV7msbb3VXCtwKRFBbV8BuJ6luxdBblT23TkF2FXiLIMYSLXe2f59eOt/el9CveWTD/AaP81YNlpbBmDd2QwXZVaDA2xTxepfgYp2xb7j1FEZwb+cqj07TvY6ya6k5Sydq3dScPsW+kUOMliaYqxVkV4ECBQp8vcDYeG8a2qPnWVW7hn/2ExB/baZXvt3JrlTRZdt7dNwLvClQzSW8ldPojTtT36tyquyy9HpCp/P2nq8F3noo380ZPV7ak/aCi3Xaz68Qre5c1fbtVo1RRN60SpRvCnZzyscBqle7s80kQ/ZGk2oF2VWgwNsU7eO379eVYuTxGXqnN3f87fz6qwA8sv89u2pLKcW9Uw9zrVB2FShQoMDXDYw4hcetVgTeDUTkhkVUdoJuuPRAFVzfj/KmsMYZpu/xU/aN/LhuJY1R1ecdofdWGN1nRSMLkuNrCqmiy2xPFb6lNMZyP43xqWfgma/uRecKfF1gjy4Zkih9zNbOquK3+srUObFOmHjbAXRf3aD+yTe2EuVbgeuRigrQa2fRq+furP2bpUvuEQqyq0CBtynaL6yiRnxG3rnvttuoPj5DvNIl3uxt++3s2itU/RHun3nHrtu7Z/ph5grPrgIFChT4ukFaYXA33o63iu6JdeqfuLUgQfyK+0d8++mVqrGAt3gCtXX1ttvYCbtSbQ092c/NCy++1P9Or57GW3Ivo4i337vfaKg98mPZEV/XVSrfpBB9hwN3a55dRRpjgVvETU2ebg161CnEbHuQuM028xZeRiSyhLNNOsdXs5cb4bWm++2/kRcEuzqce3AQbKHsKlDg6xvt51cY+8DBzCz0dlB9PDGp38G36+zaKzyy/7142t91e/dMPcJKa55evD2XvkCBAgUK/LcHmzzUqjeA7AqvtXb+wUTo5ZM7qlQoVd3fuJ/iYmoBtnML5vfaBfS6di335Z0/cd+I7Ep/G17m7DnnXZJ1bfNK/8MdEHr9DQt69TSE7ZsvO7TenmMHP6kCe4QbKC9viexKTnPbiwsn8FuEyJ2lffbiDtdqX3svlPfaSyudysMFTfaYU7tzDJ8ft9ivtys59mZ1qyC7ChT4OoZpRfRObzH6DYfuqJ1qUpFx2LcrthEXNl7jsYNP3lJ79049DMB8/fId9atAgQIFCvTxy7/8y3z/938/Tz75JB/5yEf463/9r3P58uB1VkT41//6X/PRj36U97znPfzwD/8wFy688YGRTdIYPeW9YdvIP/Sr9hr+hc+ga7Oo+g6G9kk/VI4Ian55kcZnb8P83vR9v/bES+sGT+03CmzGe1dc2uIQGaT2wpcsbKE3r+AtvnLDxaK1Lo3PXOunlX6dK7tUaxW9vnfn15vn1baDsus2Vm+ebVB5bcF9dZ25e+as8PrJt2fA/lbgyhWX9nm7hNepleNc3DhJN7pFYvqtRjI/9oq8yUit2CJBiKkNkv5v5Yyz1mb7GfSEIMj15hb2f75+iS9e/l0i8/bznryZQb1s+8dtbudNOpAF2VWgwNsQ3RPrYIWxD94Z2eXPVPGPjG7z7bqyeYbQ9Hj84Ptvqb17ph3ZVVRkLFCgQIG9wwsvvMAP/dAP8Z/+03/iV3/1VzHG8GM/9mN0On0V7b/9t/+WX/3VX+Uf/sN/yG/91m9x4MAB/uJf/Iu0WtdRR+0R3khlV24j2T8HCIYdlcfJsnuR4pd72t6LQO12lF0AE8ElrBUYLgawl2mMNyGaeqc2sV3TV1N8nZNdevU0ev08qrW6J+25+SUgQrcrhOFeD/ANPOFuYVPpaRBFoHpuLly4CM9+VTh5arCh+QVYXul/3twUNja+fsmvWt397d3maRtbN95v9yIW10UsThG4VzAx3d/6BM1PnADeHoquV1+Fk6fcv0+fEs7mbatu4fI2WzsP9I/52xFv9HgXyq4CBb6O0Xl5DRSMvHf/Hbc18sQMvVODZNeZtZcBeMctKruOTdyPVt7XpMy6QIECBd6u+Hf/7t/xp/7Un+KRRx7hscce4+d+7udYXFzk1Cn3VC0i/If/8B/4q3/1r/IH/+Af5NFHH+UXfuEX6PV6fPzjH39D+5Yqu/QtGKwPI63wt77hAv3tG8n9O5+O5e1Q3St5MlZ38kZ8x1fTe0B23eip/Ubm9WIxO2T4KbMHaYy7RTrsWQDyBkQgX0NkF/6I+xs096Q5QVBbs+i553n6WXjq6T1pdocN3ZlnV1+l0/9q9hq0O7C0fONVX3oFXj5xC9t6C2BDQ+1jV4hW3jg7jrcDKfNWQGJL49Nze9ESACpsETcEnZyDezGsYdyj3tu5cFfruWVazyzdcP2N3KphAHFMds+6lWtmkHuR0WgK1+beRpPmZumi+WvEHUz2NysttSC7ChR4G6Lz8hrVd0zjTZTvuK2R9x2gd74+kPt+bv0V9o0c4tDYXbfUVskrc2zi/kLZVaBAgQJvIJpN93A/NTUFwPz8PGtra3z0ox/NlimXy3zoQx/ilVdunJ52p7B7QFBIJBgjLC3B08/u8PvA026O7NpRTZYqu+4k/SMJpnL79mapKXYKiBTGveUeHuu98Oy6RZJSUpXdG8FLfS2RXdkcubHPmGouuRTUmxwrEUF3+5YSe26HdSNh1y2RXYPr+Fc2INhDtc5bDOm4fem+trHnbb8BBWu/NrDXczltL3KEkPiVPSPfn5//PC8tfHnH3+KVLvH67mV5IjJ4bt1GFwXh+Rfg3PlbX/eNQkZCXXeB6364JbxZyq7dO1MXKFDgTYGI0Hllnck/dO+etDf6/oNghe5rG4x/0xHAKbseO/j+2yol7yoyFmRXgQIFCrwREBF+7ud+jg984AM8+uijAKytrQGwf/+g2vfAgQMsLi7esL2ZmZk76s9YOMZYOMZoeey224qbIcHoGJVKjDc2xvR0GaUU0ZhLEZuZmkZX3SOp3ZoA5QIONTWJmhzcpnTHkd4Y+GV00p+snV32T6SBNMZAqayNmp1grDvG5OTkbe9nz2sy1hrLPufb2TSTjAVjTExMMFmdIJhrMvroDJMTAdVOiZERj1FbR8b66zM+lvXvdvskPd+1We23teNyEw3iqMfYxATeWIA3VrrjuZPCJvukJidQo3vT5hsNuzECagw1Poa6wTjY2mkYG0NVNWri+suttRapVCpUy1XGcOMxM1PZs/6KaiH1MZiY2HacRSxjY9GutqnHOwRjEEWWSsVQqQUILcy77srWX14xTE9pxsbCgTbHxoLsc3rMbzTn3grEKsCOuXzD253f11tvcjKi27NMTZWYmbl1Pcn41jgElunpacbKE7fVt7cC6fU3xZ1eNzrroMd66GALU6nA2CQz0zNoT3Y9j6+H8nKJMqUd+7mb+8jYaBu/UmFkxGekMkZ1vMT4+BhihJnp/n3sZhhLzo+pqSnGxirJdvfuenAnGBsL8XxhfEwxM7NddBHGHWylglfVTE9Po/Sgn6d0ayAWNbrvhtsZH49otS3T02XGxlw8OjtrmJhQ7Nu3d3qsguwqUOBthvBqE7MVMPqBg3vS3uh7DwBOLTb+TUdoBXXmahf5rof+9G21d+/Uw7y08GWs2DekFH2BAgUKfD3jH//jf8z58+f5j//xP277bfgFxW7edm9tba/GeyuoNbZot9vY8PbbimsBrVabIIBeu83mZhutFe22M2L2trayIMGrbaAC972p1RBTHWhLN+rodhtoE6+vItrP2tlt/1S9hpesEyfr1Bt12u02Da/BVuX29rPeqmV9Ge5PveF+a3oNFp4/i6mFTI7HdLseQRDQbIK98tJAe9arY7e2mJmZuf3jGLTw220kUpgbtNFqtTDtAFMr0Wu30dZH3+HcSeGnY13bgjcxM/NO4NVrqLCNLdew1euPg2510O02praFxDuk3SYQXwiCgJ7t5ebr7lLpvnj5d5ms7KPe2+DJYx9lZmT786Fq1PHabazXwA4dt+VlIZ2WN9tmu9Ekandot4UgOVa2qwiTBjY323zlKRgdgU53sM12W7LP/tD59XaBqQW3fL3I40bnYqvlxrlWA9+/9ZfJrVaLdthma2uLsPzWq+lWWnPsHz2Cr28wr0UGrnlw5/ecoF6n227jdetIEGC7XbY2N2m01K7n8fVwvetz/rcb9b/diqj2ujQ3Qxpqhsgr0Wq1wcrAfczYmPXOMofH775hP7ZqW7TbE3e0T7tFrSaMjEClcuO52WwJYQgIbG1tXzaudekFAZ6vqG1ubLMb0PPHUXGIuf+bb9qfdhu2ttqEodvO88fdNeS7v3N3589uiNUiUi1Q4G2GzsvuDf7okwf2pD1vqkzl4Slneg+8uvwsgvCeIx+5rfbumXqY0PRYae1FXn6BAgUKFEjxsz/7s3zhC1/g137t1zhy5Ej2/cGDLrhdX18fWH5jY4MDB/bmXjGMldYcxhokST27kzQ/CQwqbFGOneGJHc5myzc9kA62wzZzqXD+hc+gNq7dRod2aveN9exKf7IiSJCkxinQyZP4tjF5iyB77NklIqyuCo2GcOr1+I3xAnsjkBYLsDdOY8xSbW+S7nhH548I9Z5Lu1tqXm++Xz84PHn6dra58/ept9ywCfvXynG9lV6q5vJtebbd7lCoO/BFHIaxhi9c+p3b9ththQ1OrbzIubUT111GtdfQK2dus4c3Rz/NXNLaDtuwuelS5N8sKITx4Cp69QxiLNbm0ldz3Ti//hqnVo7TDGo3bE9y9zO757nNgzj+Ejz9zO6Xv+48Nnkidoe0fGtgF8b7hWdXgQJfp+i8vIaeLFN5aGrP2hx9/wE6r6whIry8+BVGS+M8dovm9CnunX4EgLlakcpYoECBAnsBEeEf/+N/zGc+8xl+7dd+jXvuuWfg97vvvpuDBw/yzDP9J9UwDDl+/DhPPnl71/IbodZd59TKi1zcfH1Xnl0iwlz9ImaomqBqr4OJkNDibVxgPJhFN7rUPnYFG/TJAck95CsbY8cPpw3vtLHBz60be++8uvQsm53hino7mHjvgfGM3dVTu7DRWuHUynFMHJNmgFhLRpzE7/hepDz+1jhdm5Ttuv4i5y8IX/zS7vq2tgYrq87kfGNT2NzkTfPuurDx+nXNqG+KlOS6CYnVJ7tuvE97RQbpN8AYKrZR1r/+X/fb/v0MzIWskMJQN+K3Xoi0O9wCoeAtvIR/5Su3vIk7PdR7cS2KrHtpsNC4clvrx0kBkMBc38PKm3sBvXHZfdjDaZmNX1oJGHa8HnU6wkuvwJmz/e9ma+d56uon9q4zQ9BAybYQAemF6K/OIgnZlr+PdeNEnTxcYXcI+WP9Rl7uGw3X+K6m/008uyTdJ7neQnLzlwQU1RgLFPi6ReeVdUafPIDSe3fnGH3yIPFaj2i+zcuLT/HeI990Q1nyjXDP1MMAXCt8uwoUKFBgT/AzP/Mz/N7v/R6/+Iu/yNjYGGtra6ytrdFL5BNKKX7kR36EX/7lX+azn/0s58+f56d/+qepVqv80T/6R/e8P2FSBTA2ESZ5aL1RsL7eWeLC+utc3sq95Tch3tzzeIuvIFGfCPCWGoiA2copuPJP4GLJGKCbKLtcgyPX7Zexho3OCo1gmPB4Y8iuG+tG0mDDst5yFb/CqIen074CYpHRA45AUWqPSCEZ+nuTpVOVxA2O9+w1iG8eywCDpt2TvYtsLtbxz/0+qjVMQO4tRIS52sXrmlHfDEpuUdl1E2newPy6g+hOXdc+4vbb/MqVj3Nq9fhAM2kXK+EaSvoqjZTUGubcopsLOd4eeAMD63RMbmcTxsb0TJIXuocq09uNJNL5ulu12V7GLNn+m/61S2T7FTqdi51c9t+ljVNEd1Kp9ybQGgTtTvdWAHHuvE+7bWNaofOF2+l8zd9LRQQrhsh23zDSJ4qE54/3P8fxjTeUKa6ud0kbuNZd5x69C7Jr2Aj/jVK2FWRXgQJvI9hORO/MljOV30Ok/l9LT51lrn6R9x/71ttua6IyzczIwcKkvkCBAgX2CL/5m79Js9nkh3/4h/noRz+a/e+Tn/xktsyP//iP86M/+qP8zM/8DN///d/PysoKv/Irv8L4+Pie98ckgb5WHrKLsnyRcZFumCunnj3BBg0kNIhAiyZGpW+FZfuyKTK1zE5bEyTnEWJv8OLGJqqcbYqrgQpag0qWO6ouNRQdDAc12d+MUFJZgJzFD+kXSt9RX3IbHvhYq+3cZkZe2KEI5A6hc5GGjeY5Mf9JYhuj2mt7s4Hr4I7IS7HZuKmhY9pqCVGUazslZm+mAMv1RxEz2T13W2SmzoVuK6vChYtDx2voeN8sgEzn7GprYaAdEfBsl0pnnone5Wx508/AHcDXjLJrh3Nyr3AnoruXF58iTq6je6My3Rv15K4KWYmFPSW70r+m/3lIRfT8ccO1+gVE7I7j/kal1Xo5Ja6y8VA1RvfhWv1CdizT8bPdmO7JjYTcylUBFstc76tc6H7qllPZTSOk8Zlr2N6Nrz3D7T7z1f6/9dp51Oag+u9W0gtPnrRcvjx8fxXULtIYs36lJGFuN8LF9p4dw8KgvkCBtxE6r22AEUbfv7ceLJVHp/EPVFn+3Bn4KLz/2LfcUXv3TD182z4ABQoUKFBgEOfOnbvpMkopfuInfoKf+ImfeMP7kz6Me9ojyJ5Ar//g2Ys7gO2nNwA58ycktEQSs2pXaEWnsfbhvoLIbXBwHeUNfs5DBLwKJMEEw34tvQaURsAr5fxQbvDQLDbZ3jDpdTsYIhrE4Ck/+SWnsEr+qWQweBpsQe1Rfke/jZVV4bXX4V1PCEeP7hycSvrWP/kTxj3KfnXHZXeDfKC1YOaJGltsRe9gv/ZoBFtUvRHKfpXQBHTCJtMje/P8I7sN9kWgV4eR6Vyn8/N4MJB85QQcOQyPPJJ8kbGVN/HsypGdk72LTPQuo2pVZOb+3fUzRS6yf+119/eRh+x1ibPhQHd5WZibhw990LUTD6tg0n4CgkJp8FPFETmy60ZpjG9C+m27LVydhccfg80tmJkGz7s54TLQNSuwi3V2i0zZdRu7n/d22huy6zoHatfru4lz00JUwhtGdg2cw0NDcm1jDsVJwsgww2PbmrBi8ZS37fs7hacFlMZY502VP9am5s6lbtQ3wU/3ofPyGvF6j9KxMdR0n36p1YW2XUuWvbW+BJcb2K4hXulQvm/31TvD3CmvN1wsF+97YNty1+1O7mbVbArx8KHP7v22/+JqB2Q+lnbwr97q0Dm+SvWxabzJMu0XVpn8nnvRVY/IhHjav6UCaYWyq0CBtxE6LyXm9O/bW2WX0orxbzmG90LA/uoR7p1+9I7ae2DmMa5snfmaMSQtUKBAgQK7R+oz4ikfs4vXzd2ohVq7yPkvf4XZ2aG0ORFsaDJ1lVEJSZVTnPRFVenTrh7+pY/kAdocfEfycTA9zL/6FN6Cq2po5HopmPl1Bpe5IyPxoXVNjihJf7NxgI266QLbczkyZReovbjH9g1w6Cab7fS5C2w4RNIk4ylWWG0t8PTs72fm6LcDY7d/dryQ5sX5L3F84UsAnFh8mpcXn9q2/mJjlvX20i1vd7fKFlW7ij/7DLJyDrWVKBzyxNUQiRXHEO2gYlK79OYRAZUpVm79+O4UwPvnfh9v+bUdlzdDh/f1U1Cr9z9HdlCBkp221qnalAJt+9HxddMYB3b/jX82vHgJFpdgft4RkHnfphtigOzq/3Pj6jJBJ5dafRvKKC9qsr/14nVTWre6azwz+/tZavh1u7gH5316Db9d0/uUpLnZ+iKAlQFO4877P8SCJNRrvlmlNFpDKO3hld2qN1Va3h487UhgY0ANnVydl9dofmGBIKdwzq5DuftU/tp04rXc97c65dIBuc4hEpHMq+uWmt32jyHkOyqWOAZVX0AvvATdWr9f5sbXxOEXPNlwJjcN04oJLrqLlW2769RTVz/Ba8vPIiK8+tru9q0guwoUeBuh+8o6lYen8KbKe9726LceodIo8wfi79mdLPkGeGDmCVphnbX24h71rkCBAgUKvF2wWYtpNgWtdRY03CiACUwP22sRScjy5Q0I25xde4WnNl4GnLJLUkWTVlg7RFJdV9m10zYFlEL2Peg+5s2j0gCtu5ksmRBM+ajWRKhOzsNrG8m1l2mMuXQVa9GrFlWfQzcWIepgrc22N6Dogr1LY9zJnyz5Klrt0vj9a0Sr3Yy9yHt2bfXcC7h2eOsV6VLYHNuVjnFswCb/DmLHvLXCRrLZwf6eXXuZ15afu+Xt7pa0VEGbblc4+9QFWudPpZ3O7wCqtZqlXVoZtqxJA/MbBHYi2MZK0i9Qu0gNvkGPb/L7cBrjzkul6Y1R4s+XKSVyyi6FuGmRm8fXNah/kz27qonYsJ34NTUau1wxn8aYjEEcxqy++CLzT7+QW+7Wj9F443VGohVUsHNnLmy8ThD36EatHX/v9gRj5bq/3wrsLskq1VyCcJAwanxunuBELV3ixhsSADvIfu4V15k/D2XwdqApIYCRYNtqcHOy63YJOZ2QXXYHZVf6IYj7JmKRCQfT+9XgtSn/751Sjjc2hJXV2+vrxYvw/HFo78wHXhc3TWOUPksl4ghwb+kEurmMP/sMpPsrN3kBMHTLza5VqUrQWGyQvgDrr7fZWSMMYXWXmfAF2VWgwNsEIkLn5bU9T2FMMfeY82P44LUP3XFbD+57HIArW29cyeECBQoUKLA3EGMHTOJvhlNnDFevOc+u2TnD5uaNH7ZF3Ft3QTjQfA7/8pdYbM4S2ggQJLb9B+jMGyrfQNaQ+6uur+xSIklg1X8g7rczuI/W7qCgufAC4ZnFbeukQcdGZ4UvXf4YX7j0O5lR/24xnDo34M0yF1J63WCXTBIh9BArWYCTLZoFjYo3WiFjtnoDf92Hfp+t3WUq043Qy0da/UkQXicY3QuvoTDu8fTVT958QQDPz5RuaVAYdCOiKPGGE4s3fxxvLiFCcmRXqyUsL6XKjesH1731swRzx2m2xS12E0XGjXCr1RivT3a5v3Gi7NIJwZw3p9Zq+8qZsmugLcm+15obRMl3jhOvCl95SqhW3Of0mO2kthtGtNql/dxK/4ukn3FybTS9vOTx1udhWtRArpc+l/rAXed8ungJrl6F06svsdW9M0+73aYxegsv41/+Uq6Lgm1HxPOOsLnhuZ+pAYfSGO+8HKVrPl+ub4c2RUBHywP+UJlH1s2qo97mtdXderRTPubILrVxCb30KsQRvbg/jy5svMZrK0Nk/YBvXI5I3oHsevmES1dundzE1K5zP7rOMb427/7eumJsWzd3XgA3X4bVo5nK9SYKxuFqjMO+lWIESZXHQ3YFtzLFCrKrQIG3CaK5FvF6j9En9zaFMcWX6p9g4egSM6+O3XFb98+4/PjLmwXZVaBAgQJvdzQ/P0/9k7O7Xt5K30R+bd2ysOQqNJ5efRGAtfYiK6353BriPL1TJUiyLiTklJX+87HW25RdMqTskptVY0yrFUL/IVix7Qk4VXTllV3t51bpXIxz3lSDT9yRCbNAqXOLiqbhAOrlxa/wzOyn3G8piZRyP2J3kAmlO5L83dM0xu1ot4UgGCRe8l5qacCs78D7pjn7X9iw60Te+EB6THgdA+O9SD9qBFu7X1h5fa+YJCp67nnL2XOAV0JFuVJvIgOH7PiLcG3OOpXIDZRdp1Zf5OTmVba2YG3NKaZuF9evxtjvYx7dU5tUn7ty3cWiJEVx+BgLoJS4aZFrsh/YCm2TqN1sn2zaS+umnbC2DkHYF4Omlfh2oyyLlgYlLulcD8OE1B0gbG6jeEBKdl3n8KbXIa00RJ2BiqQp6Z0Rr3egpoR+CvWtpjHadjqPU2Lu+utL3ltxr4sxWpsbx/RlSm4ZLMQBY90zTDZf39bGzciu9nXUd3m8vPjUttRqd69yyi6dO+edmk8wQW1g20HcIzLhwJxI50G3J+T3yubuBZ/9vHApNX4XYePlGs2nbi2de9gLK8Xo9QsYAzDZPs1053UECALh+Iu5+wQMVk+2cv3KvDdJ7R4mu7JrS9q+kewlnQxVkLwVAq8guwoUeJugc2IdgJE9rsQILkj5ypWP0/pwTPf4OtFy5+Yr3QCjpXGOjN9bKLsKFChQ4GsAtrv9aXSn8uxq6yoErexhXBAs/QfW5eYcAK8vP8+plX4tcyvWEVgDZFfOpFZyRNAwSeU2lC07312hlRr87hg0umAjbat7zi2rtLqusmugKuJw2fUhZVcenr61Ok7DQW4Q97I0PUpuK81Og1gMiEWs7Xs5pSsplft7a6RIcKlOcPU6QZxsj0dfO6k4P1RrZkeyS+9Mdg2nAnnnP+3UDTmc617mirmE4PXTSkUykmU4mN61sfwNYIbTZ0TQq2cgHpzzavMyev38Nqu4rALjMAEUuPSygSBSJPl7fZIutlGmZHSBYTpuN/NDEp573tJo9sc5JS/E2F1FfKnnzf6Z7cHiWnsxuw54w2mMFjTbK92lpNZmdJnZ3tM04yUnVEzIpqHZ/IZhOEC+SdHJnWGFMBTihOwaONy3QbpmZNfQcYljV8Ez7wvoXfkKaxd/PzMz303wbjsRwaX6zReEm/qCXXe9hpsPkhS5VTeiCpQiNahXeaLwTk9hkey+kTXZiel+dhbVTc9hC2KJifGMG8MwFE6e1NRqclPS/Jkrn76pcrfWXafWXR/qGy6NURLlmTBw4Y9724/PAPGmVDYPLl5KSLsEZujad/lKck2ywvyCezmRIpxv7Xhf3wlR5NogMmkXro+gxXhwhfHgGmKdL16tDvML+R1K7pkyqOocxs18DIcL/6bvg1R2DRp8ITZY3fiGTQ+gILsKFHiboHNiHT3qU310as/bfnb2UzSCTR784Y+AFWq/c/mO23xw3xNc3jq9B70rUKBAgQJvJoK4y1NXP7Gtqq63cgpv9hmsuMi1290eNMQ7KHIEyR5c8zGPqntILEMPqiojCbJYP3vqFc61rvLC6vMANMM68/VLQxuzuTRHhWlG/Q1nfR1OZRFePym8/ppFon4zbiGT7cNO+3UryIiaruCfM4NP5EEDvTWL3kpIHhGskZwh/FBb6taVXd2Tm3RfdWbyphZgA7OjiuhGfv15EjIlja6nDhluR9kYXe8r/uJYsv1as6s0xRFxxgpRQgJ62h+YU/aOI2W2F1Vor6E3L6NXTg58rdcvum3myK6VVck8tVQ45J0UuheFKcGitfPfsvbGxQSsmL5yzsJuyaA4hkZTmJ/f/lv9E7NUXppL+j+oedkJ3tD3y805Xl9+ntna+cEFcwHogLdYciCj5FD1jBubWHqI9EkwGQr+t2GPUhzTZlJVyYHWC+j1XVQJD1oQONVUbUt46vc6XDnu9kUPpeJthDXmuys7tbIj1HX8DZ95Fr70lf71yFrLwrWYE1sXOb7wxYH9ydrKMRLtsEkrbBDOt+me3NxVSnpflXmDUH8HYlmCZB/Kals/tkHpjOza82qMQ2RXtNhGIou31koWEayo5HrhFux23bVqY3N3ClEzRMa0WkKrdeP56fgtp05WJnZbzm0r3MFvbbCq5PUvvja59ubPZ2sB6wj1y1fSJoTOS2vEa112g9Nnwb+2yeGnP81k++wNiWE1YK7fP6yD3Ong9UZMhDV2+4ukmxVi6N+eBxbXKdkV58YttqyuWa5cTX4ryK4CBb720H1lnZF370d5e39a/v75/8iR8Xt53wf/AGMfPszWf750x9VSHph5nLnaxR3VAQUKFChQ4K3HToa34AzlgcEiI6mhsY0zNdfLr9oBZRcwUGnKbcRAa9Vl5WEzHkqFltKpEZqnXc5RZseRaj8GlF3u31nwoRRbYYPjK89xfn2oypyII4IAKzn1k95ODmUKNYHlFdj84uJAKl36Dz1/HBrbC67Y3MN6HLvqT1euOjXI1aTqZBQJYmLEWlbXDCJC6ZTBm7eoXNyjuu6Nv26mUhSXxrjtXpwj8u6EGGh+eZHWlxdu2EYmqBP6Pim51+32xQ7eJXNdtdXNuvfFL0MjycZaWbuMjtPjJgRxALHgKY/nrn021+adkyHGxszNu+ME0EkMo9W255U0uHSflFa89nqftOiqwZePqWInI8c8pwLLyNsd0OsJl69YtjZTBWFOOXYTxLEL6nXusTA7FgLK9Of39ZCR0M/OQtA/l7tJemb6DGdy7ULKX/SVmgo3JqlHVt5fKDWpTte7LuF2+Ut4OX+oO8Ewn1mN1tDr553Z+g3SSvXGBfTGRTARdnOR8rkVeq+sIyLb0hhP1M9xrnU1K55wM2Tk4NB1N0wJ9mSMaw1hY8uyuQV24SXXl6H9yRPMz899jhfmPt9P6Urab4UNLm4MErgp7BDxv/NC28mI/nXZ/b0p2eV2bNCfPq90ioWt2i2e04mySwYuUP1ueR6ZTyTkEr8V2XXs3PqrzNUv3ngzQ/P0ylX3vxuuk0tjzFItc8fb5Ih776Kh9EqMDQ3ewkuo5rK7roplp+q/6TmY392J7gWm205YkN73trFVu5A1evUunu0xUb9w4wxdSVN63ccdN5m7R0gnovrSVerHI7aeGrq+JvNLL76CnnuBYeTecbnPQ8qu/IsXiWPWvvxxotr6wMu13aAguwoUeBtAIkv35CYj79u/520vNWd5afHLfO+jfw6tNNP/3UMEF+vZ29/bxYP7HsdIfNObSYECBQoUeGuwtLTzU61OHv9M/u13LvBJgzLn3TUUuJlBskuvn0NvXckePrOgJ3lQjVqWvMGwTtO+8g+ymUojSStRmpfrZ7JtDxAgYhnwtUr/daM0xiQI9Zq5N+ECrbjDfP0StrGI3hhSkDEYiDQarvrTxUvw6mtw4SJsbQlf+gpsPfsp2udf5eJlYW4+N2T5bhvB175TWigAM6B46y96+2mMw3BpLrfYRv64tGJ0W66rcNstL6ViGL/qM37VmcUYK0SnO1S+HANqIJ1oLwzqN2sxtbpLwQFYbCZ+dUNtp6ST2bZJ98XTV9+N+NXs2+y8SMmuxNDKfd653089A2Fk6QVpGwM9uOF+OLLLDpJdOyoQ8x8Gf5ekIoTSoHv9QHx4mE3mN9Wfj0rlZmWywlbNfe5fLlzgn5FdN9gfFbYHPdDuAAO7mXwIQ0G119ALL+Kf//QOK/X/qdfOUF573RGbYng1fpnZ6AqhCRDpKw9huwLoZrDXyUm0WOLYoltLmIQ8VNagerVdBe8p2bXeXmS1tcCrS89wrXYhIywXG7NZ2nRs45srU3c6gZOTIX1RcEPPL6USJd+QsivX7Jmz8OJLDHo+7QYDyixx/5808ejDIBLTWlwe3KDq97cV1Lmwvt3L63rodm/ev1bYSE4MPVSNsX+8Q9snfPxZi94UaBgwsat8ad31dCefq1TZlT8sU73zjHWH/DaHVcBWCBdamFZE73xtxxcGtprkpfZuTBRJSlBpQCyt1FEgv81c+95KCx1GxM0dGk2LXzQW0e3Bggt2iCyHvmK2bBoQdQfTGIMegjASuxdrt3JbK8iuAgXeBuid3UICw+j79t6v6/fP/yZaab7nkR8AYOoP34eeKLH+K3fmt/XgvicAuLxZpDIWKFCgwNsR3d5NiAMROlHLBUj5yoGZsXuaxtY3s0k9ZtzqArELDlPPrn45t+TBXfXN65NGXZaKscQmYrOzynJjlvn6JTa7iWFzVuUrWSUf+IhkigKxffWTKultEXy6H2INI+Ei+SdkEZjtLnJh8ySLwfrAs/Oly8LpM0IvyCu7+r/XEluWTtc11GyC11wAhHqDPoeRDzqMoFB4ykM8SZRdfd+lHasxJuvb0LA+Nz8w9rtF63j6YmswOugGwbbvgL6RPiDGgNml2uomJJUSwW9rMI7QipfcgNohw5d6b4NWsDtfouvBDikkuqkyxxrCUDh7Nq2COajsytQs6frKh3K/qI+YweXTNEZj2ObT5Jbbrt6AfHrgjcfVDc2QamZ4HZEbDv1O6cWwnZrrVy5N/linwkw5V8XwuTXQhSy90a0rdLvOp2o32NgQ4uEUqB2wttZfJhtuEWY6Tv3Z64HqbKJzxu/XhTXJIFggJiZmOV7m6auf5OTKC/TiPil3o5S49dWAz39B+OznJZequvO+LK9YLp/cRC+ewJJrM+xsU3Z1usLqajrfhLl5IWi5dU4uHefkygsD6qDQBJxde5nXll3lv9NnDCdP9a+BOyGODWa4yl2qHkuVvkrfgNVOlV3XJ7u6CWfY7Qhq8woSR9jOTaoJCGC2++6lf5WG0XCBcry1A6GnbksQ29qefTiAK5tneGHu8/RsM+tOSnZ1ElP/RtRiK6hR9iqDXc+l46VpviZL++3/Zrddj/Ir5T4Oza/uaxt0Xlyj+fl5eme2iFe2pzdKJfGf7DnisNHMGeAPtJ0ou5TzoFtIBM+DmdKpStX9n0pfYA3jBmmM+eXTptNbz77mS+i1s4MXGdO/9kmyvmrtrlpyQXYVKPA2QGZOv8fKrk7U4r+e+fd8073fy4GxowB44yX2//lHqf/XqwSXdyfN3gnHJh6g7FW5tHlqr7pboECBAgX2EOG1nSt6ZVUKbczzF36LZy7+dp+sUKpPdiUGV57qG7WfXXsl+3fqtZQ8vydtJ4RBSgzglF1Z0C2AdYbDG+0VlpqzXN44w/n11zizfiJpWaPqHunT7oACDZsjhPqBmKSmxjmk3k1Sn2d/+xXKppZrRrCJ4uxSe46NsIZqLiImptctYSxsbvXbi3YQd5gYFDE66UbfhN/98a7Zfv+sq2znKQ2+oMQmyi6yMRxYWSlMJ6b2sSusf+wi537/WV64+rntndgBecIqWnFKvM2wRmg72ca+cvUTLIeJ8iHvnyYuNbPZTD3FdlYTpav1ztfondvaMbCpx3PZPtnE7H/iwihGJFMNmSGy6+zaK7ww/4Vd7WeKWned9c5y9jmfSqSUIkxTb8Vw6TLMLcDKCn0y0YLRFcSCpyVL2RO8XFppPxDMpzEilitX4fz5wTHa2BBeTzLMZBtZMkgIXw9R5MZ+MEVseCnh4sU+ITmsw8nOu6GIb5ggy6dWpT3U2P50FJuZg49ONtmKryTbc+SCGeCjhYuX2Fb8IEPcD1KDQHj5BJzcxaPkiVxGc9rVsqkxFjpTs14P1A6m4+Fca9D/J4HzWrPZ8RHrdraxvk6Q815KlV0S2QGiQdUXWPvKZ/HioWfp6xzXzS2LwhJbMDkSSkXbya5XXlG8mpyezVZiEn51kJBMUwxFbNbHdO5v1d2yNjI7km9xLHzlK4ZziWWbXjkFJsS7/DSquYRNryEitP7dx+j816e371Dq2cWQQX3SwSiSjMAIN5bwVk/T+/KrND47P3CN2gYBbHocVf90Sb7RCpT0j3OWGm9vbKh/I9L+Rmqn9ktrbHz8oruP2b5aViWTfvaq25drvRWMNYyWJzKFbKsRsjaXI+8lUfhlh7K/YXO9tOS+BwA7L7AbJH0OHTF1/LjzABu2OsjILg1Kcl6KSR9MPaR9ojbYLwyN5va2lDX4Zz+Ra3y7miv9d6MpLCbEWiYoHUpjTMeq14PWSkDl5HbrgZ1QkF0FCrwN0D2xjn9whNKxsZsvfAv4+Nn/QDOs8YPv/YmB7w/85XeiR32W/smLt922pz0e2vdOzq+/evOFCxQoUKDAm464ubOnYvY2eesyeuMy3to5gtVFGg0h0aoAYJKHXc3OVQn7Ve/6b3azh9i81CNPmIiiZ3q8euop6sEGZa/Ch459O8cm78u8OtQalE6NoJfc0/1AGpHAYBpjPxIa9kJKSbte1/VFme6AD5cVC2KwYglthG6uELfqGbl36Yrhk09d4ennupxOxNCPPAzj48kuxs7faRvZlcBbFfRSjowDfOWRCuVsr41nhtK6csqu2laTTtTsG1vvsvrWgJgj6dKJ9RO8uvKp/iIG2nYwtSTFufNwdRYkdsqu6ymQRKB3Zove2doge5IYgC8EL2JVCaMqRHoSpcDraQylfkrgjYLeXeLlxad4bemrSacsUa7qYsUfIUxT0lJ/rSFYC63KfY60tbZfDUxprq2O9k2rh9IYPd0PtPOV0sCN32oyvOVS/jyUHDO8g2dSLgqMEmWXAKuraTqpDBE3LiUq4wxFeParwqXTEbYXZ5saJrt2Cu6tmEFll0q20xMqz89SeXWBI+UOjcpzg+tZtgWv6fc7Bua5VMZmwimtrd+YjBhGf9EhtddQxU1TC+i8vEb3tY0BRq7XFUyUrm+yllTdUv7SGt3n+h5DKdle/+Qstaf61QLSAgYj4RIqVwF0IEXLCt5CDV3rEMUWwSOOhwp/RF033jZyqp88odZcQoJkvBIiSw3NYSs266NOSkqm1z79hTbtry4zjE7HzV1jXH/11lW8uRcQI6juVl8dGTSwkSWeX6V3vobNqV0zInjopGp8eg4xluOf6dBquH0JmolH3HpyX7gR1yWCiiOiUNENfYavP6IMDdMvHJCeg1Yc2XW9WTScIi3iziXbjemELVbDU9sruYZC71qNoNcFAzoO8ay7niibpnoagkDY2NJYsVT9EUh2M1xbwKxe68/tJHW9z/ELM9PpT26ZznCmb/I2SdsQTHi9y3F+x3bY+aSvSRrjsF9WitSvUGnQOfVheojDhdbASgpBIXQ6UNsa3uaQgs+EYGPUxqUsZTPdv1df7NKr1Vyb6bNE/t6Qkl3iSLoLT7WQXXpcF2RXgQJvA3ROrDPyvv03NoK8RfTiDv/p9V/ig3d9O+848L6B3/z9VQ7/j++l+bl56p+Y3bmBXeCxg09yfv3V2y5xXKBAgQIF3jjErcGHzfSBOwtkwv5T9esvt5i9Bs24TWhdupxJnti12pnsil20iKX/Bj9VU+ktZ5hkdaJgSl7XahGuLjSxVohMSNmr4KsSZW+ENAJSoQIF+zslEEv72RW6pzaTnbD9NEaBgSf/Yc+u5MH9whW3/Fp8mTOtK9miFtlGOJggRKsS01PgjzRYiV9hMTiR/X7/fYpHHnb/jiKX6qE1CLqfMpSvSJl2KVUDKY34rs/NC+eYqJ0Y6Hq7A0tLAkrx2tZJrmycotPrIXGI6iYRUtRF1Xco0Zdgfc3m0pMSlZYVyrUznG7/Dp2lZfxX+xUCbTMiXu2nvvTJCosy1/fRGgiUcoSkf+UrA8tFenzgs1EKSQfmzrmuAfjnP4Vecy/h/LiBF2sWlrqOgLjOs4pLwU0YSLE5ZZfm9NZjvLz6ruSnRI2WS2PMFBPX2ZHx7gVGqWWfAwlZii8nBtuD61xYf40vXv7d7Dw1sTtnez1YWXO+cSIG6fTTWZWNkV4/eFaddbqtHou/M8fS750lilSur/md3t5XI/0Koul/M+o1+X5cRXgD5J3KW/IBw8Tf9sDbn30WlaQatnPpY2m1uZ0w7PlkLYwFVxkJ+0SORbuAegeYeoh3LSVEhbV1WF1xCktsL9tb1QPd3SJq9AdosXE1Owei1Vx6oyojIlSjVUdEZKdcf12z2aM0t0X53GpCGBiiWAaqjlobYY1hpnuS0XCBKIT05FRzL1FeP8eBV2Mk9RwcGlIjJnshoLWXjEXfgy1e7/ssighxLUjIrlTB635TvTpiQErjfaIhIa7xq/TObNFNMlFIuygujW04T9ZsBZgTK+gtN15xN/VjdPeSaKVDtHID/zYTsrBWZnU1eaEh/f2ebbxOy+Z9h3MvPIa0jca6oiGmHW1LRxWxNL+8SOMzc8w1zhFJh56tJ2OS3Mfq0i/kYmGm9iyeTZWiybkvhq26xYrGRFDyKjw2+d7chgZr4opYmqnoWlump5LmraXdFl58eWgsrCPUZron8S59KbsOibhrexTJwDm3U7XO7PoUinuJkTZtYX5BeObZVB2X3Kc1AwRudn5L/0N6/00JMiuDhLUK+id3FAnNrR567Rze2lnnX5Zr+1jzCxxuPpP1KdtWitgMvEyaaF5i0g5Vkr0Odn56KVCgwJsG0wwJLtaZ/pMP7mm7Hz/769R66/zIk38r+67TEV59DU6fEeYWHuWjM1fo/A/P85sfO0Q8UmF6Gg4dhCOHFQ89BA8/BFNT1yfgHjv4fn7n9P/JXP0C9888tqf9L1CgQIECd4Z4WAlkBTyV8zRyf2PjotW2bfF87Syl+G5XZa7sSIpU2bW+ZllaVbzrCZdCc279BB9Sky6YSJ6LTT+fEYBuYKkGNiO7xMaAptlyioLJgxUQoeSV+jGL71JXRkOL2pojWnsHQU0x8s59A2QXkpWKG3gIz3bXGqdaaGvs6YBQcpG1JNWvUgIkVNiSZWE+whspc+QwHJtuUm0rRpSllQvE07guilxA4GmwSSdUJKicWa+kXU024ynP5XIqWFvq0DqyxEF9GEnG+PWT0JCAKd3CW9GwdY3Xau8maC0wuew27M29gApbxBNHk1y6PqJIePWE5eAC3Hev+7w0C1sl8P0AZSOar5yn1JqlMu2Ob7S8c9C5vGLxJ1yq5eXNMxwcO8pEZXrHZbflxYU5QmZbcK4oZalAuZhGLCpsIpWpnbexG4gQdNdBphltX6J3qcdWLWJ9xHLwYK6CW+7RxlrA185bTgSNdTRPMs/a5bsx8jpfXPg0tei72M9DQBoQuv0ul/s72e0KcatGJYqoRkv4pb7QMSJkxcyxX92DNTKgOlhouEnWCLaYqu7LlF0AVT0N1FDrF/FaV8E+AShKlzeQayHRw2VGRtxOlZtfoGmmee7sPGM8wJh3IPHeyqmgdhB/GBsPBLL5aoxgGR+Ho0fgSk7xoZTKrOe0cu0efwmO5Y5H5mXllcAfQQUNVHcLGT9Eqw2+B7Hp+zvthOeGirkZAzOdwdxHo6ooM9hIupu22U8lywfuvrTxkjRIZ3ztErHzxEjj0me4Egn7uXug7dqnV2Ehpvxgg2q8mkXVqZchXrlf3TAjRixRLJlBPcBmdx16LhWybBsYA+XVLhyKMQZ03ceLBOhBt5M7YdLqpjmyK1F22YSoSNNTUxKid2aL3vka7QfvyuautWRqU7fbNkvplIzETj7HFlWbg1I1f+JuI7skTs9vt43N9YDlSCgn2+y85GSP03/8AQCuzQlawSHdIbzaRNsIq0p4hP3hS7YX2NYguZzbD992k3uQ609kA+RMl+Byg+p3HcpW2di0XGlY7m6EKK2yogIKMEYQlSic6kIjSCRLhoFzSBkLibozNDGCh1JuvK69fiQjtCprJSJl8LRH66uLrIyepdnpsO/AKB95D5xKuLTTZyylnUIp27/OxEGYnbwbG7CUE+29+139sR9WSWYqNIGZ2iuEYwdole/DWtjaguwOkHm1JSov645t3iMvo8KzYZZsHXJzSXX6xOj5C7CyEPAH3psQY3lvyDikWoUwhP37gB2uTRIZ8v5zo8HirrM5C7KrQIG3GN3XNtxD/Xv3zq8riLv8p9f/N95/7Ft57MAHePpZ4XMf32LrwhWOVWeZLtd5/74mI98WMfJ7B/iRV36Tze/ustEeZWF+hpeb03whnGapexRv6gCPPKx47B2Kd70TnngcxsbcTeSxg08CcGbt5YLsKlCgQIG3GUxnKCUjeRDNUsjSN/sGjIScN6e4RzzGA6f4rZddyKqVDyLYT8aMH9HE7/Ao+VDvbcLIpNuWcWRP9gCaPJcurwrRhrC/4n4ITJ1a3EY13cNyMF5xQacuZSt96J5vo3v6WayK0Y2tIWWR9JkKcab0rhKj9DeadcESRuBvuR1XEmMT35W59iamatDNZbwrFbylEu33tfAkAOXjl6AR1ACYGBkh71+ccm1h6JRdSoO1ino8x/7zBpW/nSuwURfVWkMphU59drTQNU0W7DwLdp53xQ9yH4dAKbytLpEY9KaPReiaLUwI5SCJIlJfImu2kV3GQCdaZ6l5lft4ABM7xUAYge/DTPckwj6whtHeGtoGQJWdEISWbk1hxXB16yyztXN8+4N/Ivu9E7a5snmae6cf2aaaUl03dhrL2KgmL3eb7c3xCHcD3sAhU501dH0RO3O/G6Nb9aYxoVM7xB13HIGqjfFtm8hEQGXH1axxBGsau/naDLJhaCIbgbUsh6+xr/SQI8V0XzFR8WK885/BHnsfTz9/kLu3nEqh441tU1UpDF3pbkv/mh45wGZnlVp3jVFvhstXyNQM+0sPU/Jfhe4qEh8iijvAGLrp9rPbtIxPgbKKS9GrVGMnP4y9rutnLuN3NFygcu0rcHQKvHK2fcmdv46jyvfPsm8m9YmyGUFF0qy14HlO4DegY5E8k2KRsQNu/ia+XUHg0oJjM+j7NYxwSLA1vKxSzncN8hVXpe/7025mhz/P0fi2m10HvbpHaSVwHlgCvvaIk3nd2ZzLyC6xkl1zpOZo7vL6OuZwMoOiAP/i57D7HwY7SJApHNl1zcyifZe6dmp+GRIi3lLCrluqV5tUOkt0ujHxNVdJr9y+gtaS5KyWsjatNRiJqdWFiZKbbFZisMLVOSFcgd6EW7Z8qotuQhjH+BO2f4jSIUlefIgF1RHES9XBffWUt/yaU0r6pcxwXQ3NcelLfQHwTYe1NbjrOirRxUV3DMeXkuIC1iBUctrCPjzToxJv5rfm/oRtpjunaDMOybGKTIgsOeJdXTuOrp3GHnqChQVDbCwTdQhL8zR7yTKmhokteAnZFbo2JirThLaFEemTJxZHdokhMjGiNCJQUSN0mja5J7rU7WbcZiFY497RB2nM1hg7Oo/vP+pSNtNsUCzndvK5k8RHUDliqnTQ7W94HZ9/icw2IigjuxSMtJYYVSu0yvdsS+tOqzEqJfjLddR8i96T9yBbEd1TPbAGnaiKU7IrvU4MH6n6V0PECFPfVHZkuLiTeHNTWM9Vv9RRm9jCyAjoMfoEcR7GOB9BCfBMB2tHWLDXVzfnUZBdBQq8xei8kpjTv/fAnrX5Wyd/mc3uKn/Z/7O8+o9+kg+MvMgfOrAOuU2I0lAaYfP7HuDCf/3DPPyFGt/3J34Z79jg1TOSCivBXVw6dS9nn32c36s/QXvqcY48eoR3PXEf46V9nFo5zvc++uf2rP8FChQoUODOYbtDaYxWUFEPm5Alknsjfip8lhKD4qhUHaDxM1JidNVibZ9gWewsEdgQsRpIq9y5oFsQaAt2zFBrDCoc0u14jCBWKOly9mVJVTC6RGAMqu4NpntJn+xKAy28xMR4OI3RWsIATMlQ8j1U4H5fi2ostRcR3z3Ye0sucDRdhZaIx95RooNyVSrjHl53ZaDdVMQQhmQpb3XTBhR+t68qAKdc0osvJebJCo1GtE7Ih9ScBK6F87xfDjAbnWY6nkI8MsVaYFtooBQnj+2Zsi0GErIi8UPxavMsTK5xKBbOnruPUiRUHpK8lz8QuGGMNNPd0xC+I6s6OFydTVmh1UnVFYO/zTcu0Y3a1HsbHJTBF3Y2SX+amnQikGZuTKwIm+Em+EcSlULegw1UZyNZzrKyKvg+1O/aYKOzyoP7Hud66NS7rC50MNZSiTYQDyqqjG/bBBINeLoZ61RvpZJKSBunXqjEG3ha+mmNAEoRSd+Y+nT7d/ho+EdQqpx9hwlRVpK0r35lbUEYtpZRIrRobktj1MlxNWJot/vrZ+sBohRLrQ0ur9eQ+CHGkpTYF1dfZ7IivPPkE1TDcm4d3SfbknNzJFxCRgUVdRCvjFbOa8haA3EPuk1EZlDYjPMbTMRKOFbjriFiXdOldDt59UtnAxk/DEq56nXKA6+Crs9RL93L1nqFA4erqKjvO6Zaq6jaNezdHwTYsVLjMNnlaYjVMGkr/cA5jglqQ+ROcv1Ii2r4mz6MuM8WQ0n5xMn5Hecupa2vLDLxB+5CYQmkh/RiShc2CFsxHCqhk+trvLnE8myPatSjV3JzQomlGQWEWEYA0ytTq8VsEVEGtERZJma1u8raKYNqelABsT2g0vdfSq+DCN0gYm4eTNPjA3cLltgpKgUOH4bSQ8kwrAsyCt7hGDVjCc72OdeoYwi2YvSIYENL+asxst8C5YFra7MpXJ2Fhx50fV5qL1BfraPX3QE8MnEf+oy7piorKInxe3V3/Rg2HEsQmyG+XKyLU9JrvCRngkCpPUhyZFX64gilFL7pq0qNjVFJVVBpraESklVrBe2QzZqlO7pEr+ehbMhIfAW1cpb46P2ugSz93MO/aGApN7ez67cltAbQHCod4EDpfi7aTiLr1YBlOdhEgF7UxXlY6iQL1OZrg+wMm6isFKysQi0UHmd7avLE99xD63PzmGZEXAsYIPetBZXcu5LxUAx6GIpINhnsrMFfqmH8/ehWgLQDgkaD6nRtsKdis3FQSUFk5TnCyvTS45KMYaKWXFiEzdF+99rNCNuDctnd2nfyVZQozpRdU71zBPZxOmp31YkLsqtAgbcY3VfXqTw8hTdZ3vH3p69+kk9d+E0awRbvOvwN/LHH/gJHJu7duTERls/8Fr/x8j/nO8Myf2Lu11kdO0rvyDfTfefjcOBB7L4HkJEZKI2CUlSAe753lmt/4yucOvELPPivn0CbBqqziWouousLHG4scGT9Mt+6+VT2YL/SO8JXP/0RRo7cx2drz9B9yvKNH1Z8wwdvnPpYoECBAgXeHJjQDhoKi+Bf+jw62IDxicwQ173MTyuS9ZfPTI5VaUCBIwKj5XE6YYsztXNOYITGYgfSGFPTdiVx9r0ayp8qqTHXL11iY0MY6Qrdrnufr43CW/Gw+y313gYTwT34SRAkInQvRIBCeY4VircC4gVD+Z7Ut8YpmtCW8miMCgwmhkbT+bhYKy7oSccrAs8GTIyVMpNgvXER642BfE9GsmXKrgh8idnaghVjoewh3tD9zwo26kGSFqTRrgElzu9SYPrVcUbu9Vm7d4utqIn0LiOjipTtsjZw6W5ZRlHqcN8/Vv6Fz6KXAwgsI5UVovgQvTBGGaEcWPK2a7GO3VvyyKW0nlx5kWP7H2ff6CGGiiOiBF58JWT8mEtpW17pHz8vUZcYOxypgsnSGK3z0cllnKYpn8nPfWSyoj4bkZq8v7TP+YClZJdqrzuCz+8HdK++1MVudYnKUI1XEQWVRLkUmZi88vD0GWhswROPJwEZCrGwv/0y3ugE4ZBUJR4yt+9FPawt4+cCXlDOZDwHyzbxHWCpyRZX53vce8gRbtk44siuICU8qgJdR5SKdXbQ9VYb7DiV3nmqScpn1LGEkcGIpbJRopcNuXLTNpfGaFXJkdIJq1LyygRxD4vFWz6FDQWRGWf1nUjClNgBsVs/QO8Xp+jvZz+tyVt4CXPwHci+hG1RKlOTzX35WY6JpXzwAzT9IwSJYNGbP+76maQs76T4stY1lTfgt3qI7BLp52vupCgSqMSbhOkkzJFf1kpGPgKEUb8TcS3g/OqrTMc9FuwCzaDDe+yTpAUC003OzQntq1cZDWMCfzpT6ITWp1W5hxGuUNIegkGnxUAkpNsWRjGMB0vJ/iUkaHqOZSl3ybwRQy9wJ+5ifZEvnv0CkQQo667hhw4qpu93yzbOW2xVUZmJCfdZTpgVDgbjjIyMc/qFORqtNu/0yrA1B6MzyHKy+TStM0fEdjpQrkAzaqLUOCOlCRrBBu2wzkTbzUsiQ7m+Dq8Hbla8c9g3y82fOIL17iyLjascm7wfJCFGBw+XGyNv8DinBVWs3Z6+JwjEll5PuHTGEt8jyakgKGMJQgujoJLT1kiMtDcwco8b49TDSmn0uoVWrv30xQ7i7qEKJrwJ57mWVNp1pJZ1RQe0ctUObXJeqeT+mL67wLIcvEYsPe6ufkN+B7PYCyDopn0a2FWsp6GkiRba9K61YOqB7DeFhXJy3U321bPhwDUtisBLLQ7WTV+xFcQYX6AMtjdYLKZkmoyGC2653GU97/OfTh1H2G2PD5cXI/bFUKkAmx2kI5Cv1xYH6PXLiNcf+9AGmfflzVCQXQUKvMXonFhn/FuObfteRPg3z/19PnbmVzg6cR+Hxu7id079n/z2qX/Ln3/fT/KD7/kJPJ0ZBOBd/hKlZ/4V/7p7HN+DY2d/nP/y4R/kO//0g4yW8jU1tmPqD9/H3f/im5n/H55m4RcXOfZz37izWX7UQ6+fR6+cZN/ccf7o7BcJWOZ/Ge3yh7d+gGd//Y/zb/75H+Twgwf4xg8rvvHD8Phj7NxWgQIFChR4Q9GLOth8RcbM2DZ5oE1oB/fAO6i4AqiGK/TKh9F4A6kF1kJJV0CakPAcCo3vg103dDYMcQSlJNjSYjC519empNCxoAQUPrYdE9Y16+uWydDjtU3FewEChepoZmcuoeoWf2OG+xJSwWz0iGvWlZrXGjGW5jMb6HpM+Z5ysrsGa8CzFqsc2VFvQp064Rp0WnBgv3sDrRWYQKF9y4ifV2ZFrJsasf1HvP89fwuYHlB2lcT56qT2YZKPzzqbqM0ucs9kNvYujVGDMsTWoGK34shKlc5xw755Hw4pF2yTZGGJS82yqcIlvaemfjqJWgHgSmcBNZmQGumd/2yMfKB/bI0XOtswo5x3kFiWmrPsG+172uSDRmtiel1nkj6a86f2xD2DGIm3q+oSc2LHs7j/HvQOsmrXEGd0g1beNs+ubJIAOxnjW7HoXh1v7vn+lzogsmUqQYNAgowcEYGyKhHrUTqqkilEKtE6ge+k7hm5p3RGUlSkScDowHYj7MA+miSYHR+1VDzopR7gJkLZ/jkn2G3xnRKLIMwut+mc63vtpJXgrDF0LzdBxh3ZlYyk8xFTqI5yZABOISiA6mrAsBU13HJOUAIoRkeS45Ajmg2SzRtflwh6XdqfW2QsTMbegtY2YXYNvm2h1EyWuqRtgLIegnXKOGvxV1owPtE/LEkQr8IOIpbNTSFAc6QSZuMAUNVduh602sm8S1ksG4NXzo5RPnXSGJc2mf6mNYk2KodeHe/80+hVH3Q/5M17dsUSsWZXct87lr4WNhhVHqqVEE3V/lN0ZALmV87TbLbccbE+2IjT4UXGzAHuFUcyiIkzMnemc5JqtOLSGB17BICHh2c7+NLJOhW0HPmVXOpQyX6ZtLGEoehG7eSjodvrk6wnLzp/Ms8m+5OD7SWEajOiGr/GNXuVTgO+b/obqYdNV83QxgQ2AhNkXoL9i0c0aBkm7pycHN3H0akjXNxosdFZSbzC7sVfblCdS8ZX+duuE+c+vs68TCBjFRbClznUihzZZa27TqDcOoIj2coCpaF0ZLFIZ5Pm1d9z5NaQWkkBtRpI2dLtwmi6O8YQxRYPSLhGYgwS9vqebZmyy8mWBuiV9AUOFqtcTqOyEHTT3zSiNEqcylhp5dLxRBAUOiW7cu1txpfYBmHALD6OYagnrqtWoUoa6aUniSWVlSprwUvisUTZpW1vgOyKY9DWYpUPBNmx0q0eZtzNA9vtr7CwCCOH+zeEnH0X+VoAUt0PbKCIWVvvE4QpUqJXKZATi0hP0tbc951NRMkQ2RWihnNnr4OC7CpQ4C1EtNQmXuky+r7tfl3/9+v/ho+d+RX+zLv/Oj/2gb+Ppz02Oiv82+M/y79/+Rd49tqn+Klv+VfcL2UqX/xn6Nmn+BejFZ4vxxy8+k/4jv/3j3HP3bsnmWb+5IMEl+qs/avXGXnyAPt+4JHtC5Wq2KPvwR59D/H7/hyI5fGLn4CnfpzOvUv89Pg/5afe83O83vkm/v2nf4Bf+7Vv5fBRn+/5buEP/UHFXXcVpFeBAgUKvFloh3XijQPMzbvS4B/8Tvd9+qAcBpbaplCtQP8p1T0fl0uggkVHdimfrQ1II2hroazLlJ+KUc0y4WOgUXhtD3vFcHndsDaiOGxhy65wQCJE9x9yRUPjAY93zL8La+DMFxrOE+kBYb8+gOmlbJL7G8QLNK5WmV7rcV8FUCp5Dk+iLRePk0YmyjoCR8RiLJTEEg298olCYMR5oEwkcWarDeWD4NsheRMwOqIY9ZeA6YH0ES0xVixNaQLT5DI80UED6YSItVmMp1Pjcx1jrEGn1fLwiLsWZZRLAZVktEpCpbGIwdkOWSt4Kg1gnI+JChoZOdW1PZR1iitJAiRBnPAqOcSRClzKVKSxGMT2U2nS4CdvBmxNwOaW+/fl7he4t/pN+KqKCV37m51Vnp6f59vUTLZOquxSOH+jUT3DJAGrag2RxPdJ+4QDUVGOpUr6rW3AVPcc3mKqMtD42ke0jz36XryFl1Bhh7WtEoeCy2x4ZTxbBpxqwZMyzerDNP0pIKLcmedg6zXaZefpkyr4pL9Zl44zFEhFSaXCCe8oTbOEMTbJqBVAWI5XeVgOgYm4q/7ZbL3T4QvcM6TsShUTIhFBAMvNazTDOnGitLKXOvReX8ef0ZQP9skuRCFaQTsJvCWnmmh5dDshS711FCOIUiiEkRHLXXe5So7pDnoSYEVQvQYyJfi6jLdoMe0+YRJHglcGleQrVqN1sEdJDecnO6epRj5U3++Ip/U2paUN/H0GjjlyRmuINizRZkD5kGFhEWo1zZFHBw24jFX4ZWdQ/8Jx+OaplEUxkCO0SmWIE0uudtty1MuTlalnVx/+ta8SWFxq5k6wENHfZzcvk7nnVfGUpfSaIz3N+9yGgkC4cmIWG53u+3GZEnWpYZRh2S5xb1qR1gbkOYly3MBqb4B+UqJpiCOnZvaXqG1E2MC66o5GiFUlOzfjVNFlB6kOwdINt1+zMsFa+nLD2Kw/8WKNcKEJBx2nsbDexVQNfk9jxLjrcT6nLL2A2SgVmuXGzaKTi6KnS0CPre4aMfspMU7JtDC6SkZc5bB5qklpLCJ84ig6EkwYoWrX3DVT6ey83NwSajXgEJnvYX7/l5aPc7Z7iR6gcx6Ephtn3bXZuIkTV8WW2Bq0CMpkvyBR6FJ6kzEUP6lyaS0MjXy/B5Ip7cJAUFYQUY6giV2VYh+flc4cOpUlqoSMU/1WtkFpEINng2zclQhRnDs0YokOVIljUBKiF09gph5ElWJktJys47apKwIJoX2o9RwynR1EYqMoi0WUh9I6I6O9zQ5xWIZJsL3U+sARUjLM5GfKrv44xfsfQ9RXUWK4fAUGa/OSqWEV/cqOUSh4GfEdZirxFMZG+KrEbrA7SqxAgQJvCNJqJKPvOzjw/dm1V/iVl36e73jwT/HjH/yf8BJt+P7Rw/zdb/s3/H++41dYbc3z1373O/nV//s7+b21Z/iz4zP8VnmFo62/yK/+vb90S0RXisM/+T7Gv+UoS//oOMHV5s1XUJr7H/4jTFcP8MwD76P9ox8n/sa/yrsPX+J/+dBP8NSf/B7+yjt+iU/+5xV+4IeEv/E3LV/6smzzBClQoECBAnuPyIPe2S1qtcTgOVeyHBzREwTQ67oHaEiDfoVfgukp8GyEDoX4czEqeXNdfT7Ej0pEbXGVn2LnRlVqe84TGmfNEqUxrURZNUaVqBV8pSjbGGuh04UoKPGOyjdzj77PpXqQpPwBgdQp2TotFxOy3F2j3k3fKItLY4wjdN2lU4gVvGvPJT5EzivEyFAwKGnqmCP3Sj6IlJmYgKrkFMm5W6nkFFQplBgW7Dxr1pXFEj95KFcgJQHrDN6dZ1eiEFAaqQg26Cu7LEKUeqT1YmzSPzvWJ4PqW3DhgvTzKNOALGiBdV5YImQV0Hq2juuNYCx0k+7H0nNqAQOrdgVEqDdc+qi1YCerA4FXK1pMKgO6NtvGPbuYqD+mYdzFdAUbuQDFmjgjRjQMpK9kWgalKOV9ZYbSGLe6q/i2i8Jgxw5gJ44Q73sQu/8R7LEnEy8ojWou429dQts2tdI+OuV3Zs3VDn8vSlXoxYb1dUHqTmVSNjUgXwFQD/rXDIVIcaL4mfKdjUSckl1Y1swas9FV5rrLKNMnT0IJ0Dsou/xEoVAN59FRi9OrLzFXu0gnamOMYNoxUQRlGztDchJ1nCh3yNuOhLBYSIJKU9dsrim63UTBlQzl2FiM56lMdTE9BdqGmI6lfLyEqjeySqjpMRcriLVu2znSr7p5im5vK/uscD46JqlSpzR4m203eokpfvO1iO7FXhaZC5rm9PswE3dRG3XHaWYqxkvkF41mmubqlFErqwnJAVRywi2FydZRyhFrhgphKNTrfe/AnfgDEcBXEPQJCoAxJigrtxGZOAwTR/vrJGmC8ZP34pkGQUNB0ELQVH2fljSxCibUJGIM7XaS3pkREgJWGC1bYqUQNA+VHsrOhbuOwfhUmdFR0LF1Zt4RRFLNGjGpN5IZ3CljbZbGmIe2ur9tQFKvpiTVOp2XnR58Zu5VWh2FaJ9oKswGSvInhbXbzdKSeZNVgcylM1sVUjINSqZB5E0kKqfBvisFtupIC78rSNBG2uvJpcAZF4pAp9NvdzhTXMTSbrcyQj5PSEUXGsmG6O+LdQbuFd8gYtwuRVBWlSSFNacqtYCv0Hggtp8VK0LHNoglICO7tIdYCHqCMs4XMavGm/ShG7XYjJ0noc7IrmS+D01Wz3YdMW4Fz/Y4fAjuv88dlzDsk111M8fJYy9w5mKH0kRCHHU2Ufk5kXp2VVWinOp/ne5oHJEo6jy0VuQLVEhoXJXKyKZDmCjOcm054Sy9OUN7oa/wxGoEHz18D06gbYisGQhAK4s64rFS9dnYMMk4Bc4fMHdcnTfY7uLcguwqUOAtRPvFNfSoT/WJ3NtQa/gXT/8t9o8e4W9+08/vmAL40cMf4ddL7+fbAsVvVrr88/Ims72QD9pf5N//jX9KtXp7p7bSirt/8ZtRJc38//h0v5rKDaCV5sP3fCcvzH+BeOZ+wm/6CTo/9lm6f/yXKN/zKH9s4pf41Hd/N//lz/wk051T/IN/JPzZHxJ++3eFKCpIrwIFChR4o9C7u4IxgrZO2RAmBu3p+20vUZwEkQvaS5Twz1YZPzmJDhQTE4oHSmfxW44lUUrjmQ7lpVn0tTYrq7C2Bt1W4mkCuOjHKZTSuKgSbyDabXti3BEgM8F5pnrnsrQkgKoaSe55GhPngoCqW3d82Xn5nNo6ycnlF5JAPKmM1qpl7YgB1XVVHPWWZWRpUMEBUJYkcrYuUJ6chKPHqoxUFeNqZw9N292EXiMbN3DVoVrSJA2wrFbMzycftUup6ZouCxsuGLNWgfKQEev8taIqJcqICGH6hrsbYsUdJ6kIrSc6RNMxuqtpfWE+IyB0bdYt36vRbAjt0AVYOgnUmmaRsBpSqQrljpepYIyJMl4pIkKspdGAi0kGjTkwPhBYDBMGJqmqZaKcQkcs9edCGscjpDqFNZEbBAVZCh5phpoClVSxS97Oe5cM3rV+W7GNOLl8PEtxkYmjyMRRov0PYA8+ioy7ypXticNO1RPHxJOHaesqse8UN1ZArI+nynSjkKVlMM1Nt70k1bCbiH5Sz660jwMsJxCJI7u8ZG6kaYzKOdUhAqHEYCNEhDW7wtiBLncf2/4cNVpO0j+JmV37jYHfTp+F2fMxtRpU4jBRjoFCY61CNixiBLsvcimoMZlqUvecv5UyFp1E5XEmV8P133PKrnhtH4Ql9FwNX5edkCr19ErSmhtmnYVgLdc7S+PF89Az2eishCfZatdQsSP1RClKPkyOCuZKX3ZV+8Rskr6laZh99A68l1blfo7dpZkaGwyCXz+pWVsXtjZjXnsdzl903/u5fKS8h1j616gyC4twbQ7qCTE+YOFUHs2+UxMKadisEiMWjnkPcKCaZlrowTTaFjDmYT3DgqwkBH+ypPWJibFaIVh6Padg6WbpbM4/0AsUlYrBJOmoZVXJ2BDl+0jpCAd7ZbzYoiRCYkUkFUgIU5OoC/Os7OUrwhefrbFwJWfWnaTqlmwJFLQj9/JaEt8xnXoEJ2bxqfl+0BVkXBNMpieFZdB8yYIJiWzM1fgyscTOc0ts5m82oAglomSaCIpO6ZgjkIfcx5UiS7Xzu2CNuPRz69SVLWmw1l3n7OYzxBIkLwwGYwfBsL6WUyXGsLLaV12Bu2ZF4rwKU1/KsjIZWev3pimrEVIPOmMjCFvuIuLjRAeuIgpaQ4cOa3aBzegSSiwuqdhHrBAGwqHGc1SClez8FXFzVgc1CIVSHbx6bv7l+priaP0LjnheMngS4OvE10rcPqYhWmQdY39+8Rr+w2OMPurSRfNkl0retnTveQ9b+p2Z8qqZlBlWWHd/EONISS/Lg04Opgz4Oabb9kyu+mmyC52LMVEwjT38LuzUvYgqYZWfqLZyfgYJ9FYHmYsx12KXrjmuwVNsNlaITURsu4RRNEh25RRxN0NBdhUo8Bai8+IqI+87gPL7p+Inz///ubx1mr/x4Z9lvDy5bR29eobR3/jTHFh8nZ/+yP/CBxfO0PvPL/GXj77Az//3P4Q3XPbnFlE6Msqxf/JhOi+tsfGrZ3e1zofv/i4awSanV19MOulhHvp2en/yl+n82GeJPviXeFC+yr984s/wuT//43zbvc/xL/5Xyw/9qPDpz+TewBUoUKBAgT1DVLbY1gbT3TP4pkE3SdlKg7hUNQJOKTESjzC2NgNxCb/uIktf6b5RLRrPdl3q3FYDz7gGu03l0hjFVWCUjqWyXsKnDCg808yecSfHhEqFTLGcppFV4jWmu2eSDiaeQSjQkgXzXha5KucVBqyuCq+/uM7m1X5QXnvalTy3Yhg9Z4noUjd9tfJIFR7aP0OlBIgjuxQK7VUghlH8TA0x5vW9m6SxiH/1qYEx9qx7s58pFpL9jO/TziPFKF7dPEuvC10TEfYSZdeIxRpLuVVGoejZgFbcSXbfJBkzgviCrQrWF5cy0o2RxOxetddYubzGwoUtrs7C4mrsFAtbSWqkB0ZFKAVTl0dyWTdDPj5pCo8I6/ccRSo+W9Hl/twYEnOE1gXWcZ6pNC4Qjycfw1T3ce5cRNnUnFdUEpWUSunhVaCcqsYTH0LBv2rxrqQyCMvc/GWWlwUlrghB6rlkhlJMn+nOYg8+zlb5McyRd2KJ0cqnUX2UTukuwlDhqwrtJPDTEiFop5yBzBQdpcg/iqQkW4pYYjz8TAkU21TZ5UhZcQMIJmLNrjJrrnI5uIqntyu7xqp+1mbLpCdlnD0LBTXX14O2hUrleI5JwrQiujZAJmKMWFpRg7mEJVRGEXvjA34446MRRElAagWFoG2I7WikNIZudfDmEplGzzFEKdm1Hq1Si+sDfe/GHSrPhAP7dHnzNETGnUciGAK6812CtV6SDqeJe+6EFaWJI5euCKDLJbCxU54mEKVYX4fN9TSVTCA22fwBUOJUoZ7nFDKODPIysWOUHr58lqxXxU7f50ZnTEPUr8SYqlQkNRcUhcFy3+gxJkrjqJbAmGFr9SlHJEQKxHkyeeKKc3jNEnpT0cupkPre98LMWUWZLqFXdeSvqP45WZ3GP+PhX61wz4Tl6IEeosvESZVbX7qICVxzOcKo3YHa1kXGT89zsG0ZH4OyqTMWzjEebqKAa40L1HsbSJxc9ysaVV/I1LapQFOJQnyfTuIRWO41USqifDA9Lw29oM5itMS6rLEUrTg1qRHOn1ecvyDcNfkgU9X9yfkUom3gUhiVvj7Zlcx7rysYYzHiyOMYy7pZZqW3htg+qbOd47DYMBj4vLoGYSjYhJWZbb7KQnTNVQ5NvhsJ1/HjTeoNqLYfRSXUtbVg26t46xdQjRWXxqgc+SPJ/cKUk+udBCgEg0XhCLGoE4E4ZRaqTyBb69pWPcXMhZiJlzYxLTfxH7gf7rmnf976cQsJBWkmY5O8sPI8p1AzBuw7D1OtwL13u+tJKC0kiqne5e6tKldUISW79MwYokqsVb4VQbn0ZnD9jul7pXle9tIEQBnpn1OkouK+gjRpos8uK8Arwdh+xAii9JCyK0d2hUnDPXHbLEGkQupbz7Cw9AJnWmc507iCILQq97m1rRCWprfNhJ1QkF0FCrxFsJ2I7qlNxj7UN4RtBjV+9aVf4MmjH+Wb7/vebev4536fkf/rz4EIrT/zG/zMx/8UX/78JD/xl47xA//dtnI/t42pP3Y/E999N8v/8wnC2ZunM37o7u+g6o/whcu/ve03mbqL8Ft+kvaPf4Hg236KfeYif/fIj/GVP/eDfPuRz/Oz/8zyV/66cOZsQXgVKFCgwF7CagvNDbQG3wa0WgHXusss9hwxJEmQsc+bYWLM4DU17bbCKo3XcPcUT3nO3BYyo3ER8OqbVOPVZEMujVFLGvS7r8V4gCYixKYKlSQzKjOXTUzDR8MkDTAW/OWmi4mUQqqSPa2alI1QjuwKTEi7LUwFl+jUB8uQ2xDmFwxhq0fJtkBpPNw+jYzA1ISH9l2ApzVUvQoVqXLP6w8RnG9l7dw3ele/zaw6petHKa7h2V6iiEnTpoRwTGEe9jKT8HYnBAFTMcSRwk710xPLWyUUijCJyn1VwUqcvTlPiT7RTjNhBZov9z2ILp3tUFvrgDjTXgHKtUT5paBHm4gQ34fxeBwf36VBCfiJda8kEjwpa1bDKhZDKP17f2psfqD0KBU9SSSJMXaQi36iEKM9KFXpbRpk0zAeOOWZpxXHjir27XOLtpo6SSlSeFY7QsJE6LCD2vBQ1rB25nO0l5fxJMLmlHZ5siurXmhcmuboaIyIQaMpjY3RKx8iDDWeKtPLyRJCf6rfRqo+LOssTuuN3EOz+iB5bDUNPh4qrYwX99MYdWL1JEDUC53iCujZAGW3k12psisiwqIhbOMtvw6dzaRTglR9RkfAftWdYwqFiGKzs0nPBlgMHVqs2hV6JTcfJs1kQqIIR8tPcmT/GLo5j147C8odR4+QDbtCvdsEv4w/76NOtVCtTaQ2y2J3lW4coZK0RIWikxC+CogaC5SiQSIwCC0qtmgl1MJZznU+y9nwHNeiWc63ZmlEDYIwTsZIE0bQS7gJv1QCEzE6UA9AEcewvuqOWenSGtUXr+F7+SWSFOXEw1wpqDc0nYTXSwkcse6CM/XhsmN/U0f/tKBpck4fVAcBIU69hqwmNsK4P8qkN4bqgA7nKLUvoaWHilV2XL2UIOt6lGY9VM4sPrssJNvxjaZdOgxKoUS764/CpeMmJHalK5R1gPVHsGia72gzEq0QYwlDiwn6JISn4eCYcGC/4r6m5YFjcPhgxOQElKzro2hnZp+mMVb2NVCBIzGnTo1R2kokcwbwFB0boJTCVxp5MqB8KLkA9+qcW3yGa2aDXukQgQ2wYlnbgCD0CAIYKY1x99SDlL0KRiKmRnqMTSUeWko70/4crMR0oyStzzji3UhC5qRDmNwjBEcweUNlCEUsEnayz+mLB71gMfNdJyiwFqyi0YRWw52f4+0LjNg5Z8xuVHJuJ9UhQ3f9U2EPSnmDeqfsslVHHAvummMRtPLo9mBrxV2PJ9Uh9ntOKdjuQDMRAKfqNz/YwvYc2TU+plxBiATV2TnkZAibqSmfu3cG9LjU+TS17iam5KMemMbel5Bdto1N1LaNrgyQXSTXITVdBQWlnkFM/xlAYel0SLzSPJciKBYlidpKxCkAE4Xr+kZ/fh86CPHoEbdv2fuoHMVkBKtKuIqSKt2dDJ5JlOPGpUbjK0LcfvSaK2zULfX2GFsjjxH6+wj9aUdO71LaVZBdBQq8Reic2AAjjH6w79f166/8Iq2wxl//xp/ddhKXXv51qp/4SeyRd9P5c/+Zf/mfH+fTn4W/9lcUP/Df3bo/142glOKuf/qNqJJi/qe+uq2U7zBGSmN89L4/wpcuf4zQbPc0AaA8RvSBv0Dnxz5L7w/+E8ZLTf723X+TL/7An+GB+Cv85b9m+YX/r6XVuvG2ChQoUKDA7qCkjo1j52kjAavNRS60ZumaHiCIdWqbRx4oMV6NMv+oaELQQWo4rJ1SRGCyd4WSbSBAKep7LalEhZUmsqTpBko0gmLJLhIoF5DrXhNPG7zE40Vh8G3HKTVeDvDPJClyFipHNE9848OuchzQrgkbX+yhFzR6SwiNcX1LLazy0hwrLCwZRkLn0eR5go+Thmil8NGMVqHkOTP++0aO8t7pD3FgZD/BtT5xNuqP8E373uf6lHrmxB1GwiUON5+hkvhipd4lSgCdjIIWMIo4FpRReNOW6GEfcx/YSWi8o0P0oKH7SEjvaET07g4yUiKSDs2w6Ui09PaepgMKmLYQbBjijsWP1nk5Os6m2cIQJ5mDqT8SbOhFluMlPA+OmKMcbh6mtOVIyZTsSsctNbKOGLyP75uCY0fh/pl3UNXTdMyGM//f6MtxVBggniOlWs81YDYlSN1/K1VF9bCHsSBSIugZlHIBpjKgWo7sLJ0bQTU03XaAlhBlo6Q6WHpY+wFhJ0nPSomT6ohLC9LKEUUAUaAdUZTbn9CfIY9y2dk4pKITfc97aCcKghTXliyNms5IWhOHEHcd4aEdURaGlnNnon4gl+NmUxw9ApWKI15DCYliD4mSYC/xnMMYzP5xvBGPjU6qWHREMyaZVxIxxhgePrbsOj5j9zGiZlAYFB6e9rBR6sDvIlFte6zbVTemnjuHyyZGt1p0TI+tqMFCbxVXQMCxSNdo0ZEOSkFoI0Z7zYFotdszxGGHrqnTjBYpeRXuH72XA/qwG7vmNU7Mv4o1LpBu9blkdKmEsjH33+eCZjdsGmlaeHmZu20db8OdjwNK1CTVzdOO1NWaLFUWcn6BFtAeuuKUXyIqLZSXDItr867pD6Gspd10bbSbcPa8pV6XxBBdsJ7BJKl/xCpjY5RJrnxKOe+mnK+VlaH0PplMKtMqxCrEuoqaTuiVkkot9NEAmZl0BFHJEk8ZrDXMrvRYWMjNZhOiVP+cKL1umJmC8XHwxKUxqhi6L6z30xgrMPqQj0XwjcJvuWuxGIVoS9f2KCnfZb5qsEfe4Rqvz2fbETSBDV2VQciu/lk/dJnYhojt0hR37bcqNXnvYyueZbX3GpHtUFbSJ7tsLolOcMRMqjAeSjO2kcWEgyplRCiddZUPVUknxI37vdU0yXED4xtia/FihVI6U3aZVA0p2im7ujWInKpZazDV/n7MxmexibIrisA3HbBQ9acZ0aNZX6M4Sa3udxS5diJr59Ahy4Eki9avJamnWxaZKqMOuWNUj1r4GlphA2MV6oEZ5B0JeS5tbBQRG6HRimEnZVfZx45W8Ns95LUIrjry8Vj985QvfgrdWQMUyvNQWGY6rzPdPQVAGONIr5SvTN/xKAirh915tYP7TTjbRHoMVJTMq4u9XhurSiDi7l0+RCqiG8DqVkyE0ArH6PqHkwOu3Xnr+ewGBdlVoMBbhM5Lq6Bg9El3d5/dOsfvnvkVvu+xH+WBmcf7C4pQfuZfUvnSPyN+5Hvo/qn/k9/8+D5+67fhh34QfugH95boSlE6MsrRf/BB2s8us/WbF266/Pc88mdphjW+dPljN17QLxO/6/vp/OjH6f2hX2Cq3OKfPfHX+MQf/yHWXniOH/lLwosvFYRXgQIFCtwxpIZNVBie7RFF/bffxAHWGhcoojJ1CgrCkRFExtx6ysMzFi19AkQs2Kgvs1DJW3FlVeJ5ItiSoCoqC+DSN7UqbOPrAC8xvlISJ298oVF9iOlHXCVga2Hs8RL7q/3n52hVqG2Bf1HjLVuMcW/X07gnLziRGJB+NTSl4OgBn5lE1OPhMzamOHJQ4U1MOUIk1KD8viwEp/jyE2LOJJWiPNOlHG+5f3sk5F4m4UgjM/AEZR2Joo3CH7FY32N5Bbo9jR2xyJSCcUXvqMWMG0ZLM4Bweu0am1tZlXg3xspiPQ+JLWc/EXHxEyG+WcZiWbBrGJV6cSWpM+/yiMfFkTkKdKioXPaJQksvyCm7+q7LbmoMkV1To7B/n2K06jHpHcMQ0bbrsJ7LPQtCTBJ8WNEZ4Xl39SgpUaM8XOVP64yclVg84zlFSS748c+MELwSUdr0XeXAXJW9Ti/i6WedmX47dDk47YQ8udx+BkmInvGk5FeczM2830voTSe76/yGSiVHdqWLTE7At3/bwBBglMWGfhbUl5ae49DGF1DpIceZqydHyy1TSj/1n9M8Dyq+h8YjJMSi6KWG0daAFazEiAcjj04SextJ+iGIVU6V5wGmx0HvMEf1MWwpSXWKNdPqXu7V9zlvI60zRZHCoiKDb3soLYh1FS2bLQgagupE2bJW3Hk14jmyxBDTto58DW1E1auS41cIY8NS+wVWgwsoC4+OfjP7/CnGVWpSLXS6Qmh7eJ52Chcc8Tc65qPaa6i4lwX6ojRyNWZMIkaXNrPt5I3Zh5VdIqny0WLFEkSWIIzZOBshGEQLF654XLgEq2uCVUKj+hCN6v20y/cyUvbBCK2mEBuIA3deBAGu+qkIK9EaW6aeEEh9+27f9MNpQdA59ZIkErK+N9hBJPEuSpWyKlF2pWVeddRFH7VETz5M9MR+pCR0H+xSf2eLZbuIjfvjMNU5RbXtzPa09lBt4a7FoxzrHmGU6WzmRSudTNkVqZDTnUv0TIDW7trUKd9Np3QPogSrLGXtFKdUx2DUWaqkZLh796EIbOReRIj0lbrpPO/VKYUrXIkusxQushVfZcvWwMZDhJfF6wnhyc8wGjWJIqHdSzy7kITAFJfanpAlQ5ZdmFcs6sLg9pXEfWKppJICAW40uh2D17VQF8yIBS9GR5K9rDFWMFFSTdYKeus8fmMZHbaRRMkppfxclETZ5a5/pbgOIlhdQnkQepPE2l2Q4pzRulYa6QWo+jXU5mX82kWOjbuCIVpF/bE+4NRYiBDa2HlGWg8jKvGxCx0JZbvEGxfdCw/P4AVtVOJNqKy7T2qtsOMV/FaPI0c05U5/P7rtROGrPNA5+wKxeB6Ybo+1q2v9QgDJbS+efiQZBfpMXu56Hm/04GKQVVoEUFGE6oR4tosOY2LtpJ0VM49SikDlnjc8wSo/UyDvO5FUmNS7i393R4kVKFBgz9F+cY3qYzN4k86Y9pee/4eMlSf50ff/nf5CIpS/+E8pn/gNovf8AMF3/E987oua/+1/F77rO+Gv/PgbQ3SlmPmBh6n/3lWW/ulLTHz7XZSOjl132fcd/WYe2vcu/tPr/xvf9fCfdhfxyLL1Xy7R+NQ1bDtm7COHOfCXHsebroD2iJ/4Y8Tv+F780x/jyHO/xP/xjT/G8ca38U/+4d/mA9/9AH/tryhGRt7YfSxQoECB/3bRxdqk0pXt4C+fJzyKexhuLiOJ582IV0FpmJAJBIMpa2JzmIqs4ylNKWqhoyXIlXQn7AcXyip0Uqkx0QjRmxihHN0FoUtl65dNhzIddLXEzHt87Jqls+mCGKsqlMYcsZF672il0xgF1cXZzKvUN0nQNnDpSYAZ3cdqA0bsJvOnhMr0GiqpoKVEqJRKeFaBgv3+FPeOHGEratFFu+2ECpQHuWC1rMtIasiepseYbubptCzzdImywGAkXMbTLSbK30TkA13HfWkBXbFoPBotRSweEOMpDyOGIFbML8B9991FqX2YlnmOyiWIygbwKGmoVCy27GcBbNAFMW10T+NvjiE6yMiukqoy/uAEo3Iav14ligQVqaTCpeurp0ogCpsEn5WSJQJMjuw6chgou73TWjHmHQIUQXsV246w0wrdtOjAYHVKdqmBgA5xgb3yFI/tP8JVIiQhuFQImCS3VVLFhUKsUO6FxJMhserPu6UVQ7cLC4tQ2tdlbl6o1V01Td9XHNgvPDCjWU99iJSiVFLkvfQD37EqS3aBRbvAd/ofIu/ZpZRrK6fBIBZD1cRUo4R8CbtYGQMFNlEchXE/3XdkBEZ3eH5Ryo3JPcc0oQnZ3HTE3bh2qg9tIJYui/Ii4n+QqSl47EEw65qe4IhBLZR1cl7nlF061hh8qspHYfBUzmRdBG+thVqPUEoQ4xEb6IU+9XVBj2tizw2aMeBpl8Y46U+wrqcIWGEjIXhHvWqWklgugwkDdASqCr4tUfad952biYKJBaxl5mCAHdFsJaKZBx8AlhPF59ZV/NJj2ThVPWF0v8XmKi663N6UJHeEiPZAGeHV4FUu2S5L8esAeB048ZUS5ZUK3rpiY6JJNXoQIk3VLtOSLvvlMEb5juDGY7RiaXSE2CriAI6qR4ijNTQaHbZZadSJpizi+WBLWG2d6Xjcl19qXzgwbakH6bD7NMpHWQpOEJWOcfBaGe4KnS+9aJcCniqVcp67dvoA0pnCjregIWhP0TEh45TRyfmaKjI9E6BaK/gTx7ARlOc1B/1jhPuqVJJ0xbXWIvGK5hhHaZo6ddOk3U0KYhiFGpvCJqSwAkrKJ5CIDdOgo93OmOSU6I3fD90mscSJsku2Kbu8zgYq7oIPuuTTCdcIbBuYBhOCdue0r8uUOoJXO8/0JGzoKr3AMuJDmCrCJHnBQpwUOXB28OlLkl5goaeoLpdRRtG9K3DefLbkaCjPNZAStDa2VAJncB4dNqj5npu/OJVdrWFY6HVgTMAK3Z6wuqyRSUFJ4oXopRS3u7dYJc6zC8N4Z9b50+n0Wt0v0JGXmGrARoJubyJeCepz6NYSvhlDpwVVPIWZGWG9dQm6lmi+i6+m6XR8oo5ifCrAWzzOo2WfufYIl1YmuWdiH9o37Ku/imnN0ag+wmg4B9WESB4p4XWcotUrq6x0S1r7QNCgPe69O2IxuX2PyQbRytqwMA+sIJVxx9MK2OoM+E0oDcWLoUJLTCQBgQTI6VU8i6vU2QsJyp4j8ioKkRLhANkFfsmjdK2JPuRmqGARvTv7nkLZVaDAWwCxQuelNUY/4FRdT89+kpcWv8xfePJvM1lJ5PUilL/yzymf+A3CD/33BN/5j3jlNc0//XnhyffB3/sphd4lq327UEpx1y98BIyw8Peev2E6o1KKH3zPT3C1do4vXv5dwoUWF7/vEyz8na8SXmshxrL6r17n/Hf9Ht1T/Td1eCXid/9pOn/xUwTf9lN84MDL/PZ3/gkevPjz/L/+So3XTxYqrwIFChS4HSjVwRrnUaTEYNYs41HyEBoHBLaC1jDqjfCR6fdxQB0EDcb3XEn4yQfRaLyo/+CpSfyEI8W9+j4qVBOvDYUSBSNg9ytq948QewcyYsqkLucCY+Uuh/f73PVuj4ee9Ng/neaPeSjtPLTCiEzxYq2LCwcqBOKqCmadAoznsdS7h4Xu3RALI4lnVLqan6VOOkP9R8bv470Tj/Lg9CMopTChcg/QQ0/02pkX5crRx8S2yavRKyzZBawuZRspmTYo4YGZd3Js/CCqpxl9cZzeyF3o0UpCNinCifvolO9C4yUKj2QnJqpw9yGCfUL9nW3CSkKiPRQhWuhNjTE/nwS7qkwsEdXFEcobZUQZtDh2QInlEX8anZRn1xp0DZxXkFu/hO/SR+IQxFJOFEL5NEatXZrqeGUqIWo8qnoKu7KKsQZ70Hm7EDuvNwDJKbu8NP0OharAZGkcT3yswflZbVqUgYqXI1KT0SwlqURGj/T747l5FEcQmoBW2xFdd7sCjBw9orjnLp98HHRwv+4bkQNWleiWDrNpE+OZSghKZ4d9JyuYGEPFRhxsv+RUFGnaqgKTrFBv9pUL/ujOLwcVCk8pSiXQpZiSL0SBZcwfQQSq4lQ0oW5wpXkehaIsCp+QsY2Xna+ThndPPYooz71YLDnVhmc8hPT4G0bKkkv7TAixVidJ1fJccKt9StaAgijxQzNaKJUsIhalPHw9RkRIM24z5o8w7o+ixdl5V70AaXfxIkHGFBU15kyms9TYvuKy3WnQ6SzRjJdomyVaZomViQOsBVustRdpRu63WrRFZapNrLfoxjW6pkZIjXqybjNeorN8ldrsJs35TTYWVwjDkJnSA5SnfoD9k9/OUe7hoD3KPrWfKbuPu/U9TPr3MOnfxT61H097BNLGYpwqSeDAtOtzo6nxWoeY0gdd2m3KtoszHhcNEvmYhOjRuVxjCxw+YDJloYhH158iLO1HdBml4PGxiPExRwI7mVgykXIT70q4iFUdlHXFAErlQ6SCLp2mKee8FGcoQdzL+iEGjh72KJX6ba5vLKJ8TUAv8ywLqnfR8h+mMlJG2USBpqGUENfieby48SwAm5sxK2tlrOdS86xY4uSthFaDxINGE+NS6McnPCplslQ1BuxOcueMr/BLXZr4rJb2c/m+dVJhlxgIbItrvWdZai8ObMsmXltq/ShsHWNkBHSi7ELcvUeUS2NUkWtQhxbEsuwtonUPFdnsMHR7lkaz6yqbxkKjLa4SanIJ0Rqs11fzlkowPiHcd7fPXcfcPBqpAplYr38MjGi6pSPuKCmNTdIvZfo+4rs+kIxdTLu7wmVzkcq9wpIssyZr1KMWnTMGvWKzNvdP9jBiuXuyzHi0ztXmCq+utul4LQgF21hntPUssWkTqJBW2KZXDYnpsthbZyFaYPbaRS4EZ7kQn+VE4yznzEU24g28XJGOSueaM9wfghiyNNz65PuIDz6JPfQYeL5LH02PkdJsdRc5FTzN4tQJFlunWQlfZSV4hVmuctWf5Wp4ldNynkU7T5ybF6IFwUOv1ahuOfm5sF1NeD0UZFeBAm8Bggs1bCNk9IOHaARb/Ouv/jSP7n8Pf/SxH8mWKT/7Lym/9O8J3/8jhB/9Sa7Nw0//A+Huu+Cf/ayiXH5zFE/le8Y5/FNP0vz8PFv/8cbpjN/6wPfx6IH38rH/+r9z4Y9/gnC+zX2/8h088vk/xkO//b08/Mk/giprrvz5zxEuDpoJ45edp9df+hT2vX+aH3rwN/j/vfMP88Vf/E3+j/8jIgwL0qtAgQIFbgVat+n2oItzBjfrgnlxhF4g1DZCjPhOTaIUo34lS2O0fqI2MD5avMygXAEHSwc55h/DhoKvSo66sQqtNMooTFWw9ynEV843yE/ILvpkl7Ihhw/4eJ5i9LER/OkkfSdJbfR96HbhwkW3irW4qoxIotBJfJNSEi65Hcb4oDSRnoScx0tWIEql79kVnUsG0xE88RgZ2Q8oJPLda3sb411NH7bdOh5elsaoTEQtWCAyYeIV5PpdDVeyMRQjzIxOZP1XXonSiOLeux3ZFTGKURU8NJ52IbPbmoenyoAgJUlM/kHGLZ0PtpynVyRE3gSRN04sMZMHPWIVEkkEtq98mfZH+MiB97rPGlQ7UeAl41JSJSJvAmssJdOi5LmBCnLm9NoDjPDEoQ+mWVZMdqdRixt0DkVUJsec+XGssMn4OmWXk/l5mYM4eFVH6/nWc35LjQX8pQt413qM6UmaLSFI7vVWyDzkYj2CtUIQCHFCyMQGgriHiWHfDDx533v6fVZe1leAqcnBNEaUYmPsA9TGP+iWL7mKlf15wjYYDF5iUD8aLmIlMahXZFXt0jlusciwsiEHjaaTzPWSb4ki4Z0TD/MtUx+kKsmc0bAaJB5esTAeLVKa1egNn0q5QkmV6ZYOs28fPHHkKPv9g5RsyfkiAfcei6n49Em+9Bz2BSMGm6k4fMrSA1HEyfzuHogoffNhl5KnFCU1jiAENqCik2IBSbMz3dOUE69Vc0BRUmNocWlnjCgOv2cKz5bxVYnGhTa1468wFzzHpv8cJ1ef57XGeV4NFnl99UUu1L/K6vKzXKufZa63zFztAovtC2zGF+iVL3B58zhzwXPMd75K8+JJVleW2IyFsLbByNIIh/W72Vd5J0cnHueId4wD/iGm9QzTeoYj3jFGDz3MhHeEaT1D2asQS+g83vAgBhVaRhllnz7ElHcXhG48osS/0HnxufRQUSViceoerbUjNxCnPIoMnnapldKTbGKZssJOKfx2THVUucqPol0KuddP1QXAK7EYnKBTOkqvfAiZeGd2nfMTiZI1MRrN+8fezV0j/WJXSiWphZI7CaIOenmNDb3E+dq5rPCFX1KObK44UnuzJnRDSyn1ydMeVHwCE9Jsu7cOIiBKEcaWTteRPpXKBiNqJdtcRhAqUNpjZARQZTcUJkjyRAUZKvWqSkItanAtWMGWkwYSZRfRPCO9K06ZmBIdFiIsdaljVRmjKzx8cIpxPc7mJmxsWqxypK/ueUydGmNkDbwwJlQhlCxKd1CxSV5EgBWDMkmRFHGcpD7mY6acEb1SIJ7Fw2PCuwutLZWqolzy0Z5iZqaMlMed8iy7lvRfBLQPjGBVFa1Un+zyfJc+iEvBbAQrTD4WUX7/Oot2zkmDE4wkc+tbv0Vx9LBL2y1pjxm1j6Y0OK/qzI7M0pUOl5bOcSp+jWvxVS7bC5xvf5GLfIUNe5LNqI61Fm9F4a0oDIYwNrSVYS1acymnCUzfW34QKQGroNVWNLeMuz9Fgnr3QVZXk+u5KqPOdtj/usfjZx/kgHeMfd493OWVGX/yQSYPP8Fh7zDehKZWmibWUZb6qcvuTt4yy6i4T3ahdqfsKtIYCxR4C9A+7gxHxz54kP/1+b9PvbfJz3/P/4WXvEkpPfe/U37+l4ne+2cJv+3v0mjC3/lpoVSC//nnFRMTb25q3/4ffYzWlxdZ+AfPUzo2xsS337Xjclpp/mbpH9L4pVPUqxvc9+vfweSTd2e/jzyxjwd+47u5+H2f4Npf/hIP/tYfQleHLlaj+wi+8x8RvecHGfnCz/H3Sj/La/Mf42f+5s/yIz/5CO94tEhrLFCgQIFdQdpEEcQjVZqVB4jsLNF6ldU1GBmJKG2OUzrQdaqsNLtNgy0nvkQvKfSkl6XNAOwrTTmfotAF+qMVoRUptLg0Rrw+yaRxCpt87TYlClGCTsgApWL8YyXknM5YhnKpv3wUCY/Iu9gcvcKYCKYnKZ+EjQIUVaZmoLYKkSTEkPKRRZOpmqqJMGjCH2VxJmam5ci/aN3iTymoTGCPvAu0D9rHHxOekPcxdu8RuOJUDZ7SWRojNmbs1YhQjxF9sM1+/z7q4WlGo0WUjDoewFp0SWGM0LVJmocnzMwoviH6Fp5pnEOhufuYx1pLs9hMA1MPjZ86oODZMh1/DDvuweY64nvYRHFjVRkTxpQqJUplDQEZ2TVi5tHyjszXRKvMIYiUqdBojKrQi0coSQPPF1bC16mbOQ6rakKMdcG6+3tKIB1ZPcxmZY3ooS6HRo6xJGsDyi4rKvNzYV3hxFgKVXHqFRX7hD1FWwmtTcvmyhYi48TWo2UmqLRrjoSNFUZXEV3i1Bm37aNVw4wHcQxB0ENw5Gg+jSrfV4ByWSPAkllkwc5xSP6QI8RICIZSiIpyyq4hKYAVg5vaPgpFNV7Fyn0Z2ZUW4zNZYQB3Iol2lSWHEdqI0WMfojP/VUq+0OtZKrpEhHYsHhCJgkSVoyIo2xaduRJ2queM/bXrqALuGj3GcinCsy5oBlwRiFLOK0pSpVUMsSaKYG3NEZ4AUaAQ31L2FKGJqVQhFKcgKemxTOtXSdIntXXzyPeh2ttkLNxiYuIjKO8QWpxaD1+xX45BeYrRuMz9Y5oJ9Riqej/vftz5wAHo0iF0c4mIIxy7eA3sfRw9mpDkMw/xwEwV34f1iX1MXLFMzF9kzARMfOA+Dn3Pt9H7D1+ivuWxeGaN+IH9+KqHbJi+4V2C4NGjjD53MdmPCi1CyuJ82Mz5DmoEfE+YlH3UlceoGDwPgkATaaHeEN556D66pTFWbBdjFyHxcHLcpXLjHRlH8q8YvHaAraZeSMCIQroGz3P6UhsruhMPIyOTZLmfAF7JKYh0iXDkLirl/bSSeeZJzJGJe5hrn2V8bhS1L5U/9V9KuHnTf15W3Tq6FdA4dNUtmxLXk1D6f9g77zg9jvr+v2dmy9Pb9aYrkk69S5ZtuRsDtsEU0wIhdJJAKAk14QeEEiAQCEnoJBBIgACmGDAlhGLABTfce1Hv1+/puzu/P3afckXyyZYt2ez79Trd6Xl2Z2dnZmd3PvstVY3e7zLmaiqeYGTMDzBPTVBQipJXQiNQJcn0Qx4iLpiY9tg57QCadPVBYj0J0O1MjFUZOSwadZEKP16U7ce8mjoAQTIG6dSuD986S9qaSkH4gd7Toj6NoKHTU+QxZsR+QsNhPeLvLfyXBom9SYaia9jNzTxwYCcdThwtXIyif9L2qEanXRzTwVNRFlm9VJza3BhcM3mNztcEO43sNHGqDpN6hJxr4EoPgcAQwRwigphpArQj8Rww9RSurahb7+G7EU4OKSq9Fu178+hy8IU08QIB3K6MohwbbM2hyhgeMTyjMZbb1BgtvWmsiECUXFztYkhJl+rEc10Mo49SajflWBUckwHVjSUknoKORVs5ZbHALDtE7rmNiaur5KtgSIVnGDAGbbHFePyK8eo0pcBSOJmEyQqIZqM8fGPHilcA/Jhbt9zkYe6EynA72cNR8gfhwEGN0ZGhOumAcmhLpRkdyRDzIL6kBeIrkelW2ocOMCUPUJyMMxFbQcKYwKhMI01NtSaqubV+BuGFll0hISct+d/vx+iMcXX1F/zv/d/ixeveyFBuJQDmDV/Gvvpfqa56LuXz3k3Vgf/3Xs3BA/DhDwo6Ox9/sUdIQd+nzyK6MseO1/6KsW/fP2cb7WlGvnI31dc/SLQnxedf+2Ved8clfPPWTzFRargt2oMp+j55JsVbRzjw8T8c8Zhe2zCVF3yJ0sUfZ3nHbv5pyaVc/4lP8+UvlRqpoUNCQkJCjoITvIU3KKskxYpGBW/sDQnxnTatD8R8cUBQF7vcWpwcT2DcayKcxiKsZgHjOZpIBDJpjT1iYj2k/AD1quY6JVCoYBHYQGgBQmNZCT+miVtBKReaBIpcrrH9yAjERJxYxPADa9tw4KAfcLgaLNRbgyxulVrAdaHIpgWpHTbp1gSxiGBRrAtLWqzNDdMV8XfwyroRRzd42SRsA7MFzLwkSoxGfCvVsE7zHIT2Y2AJARGZJSOydMjOwAxAg+OgTMFIKUvZbPUlpkC8MAzphx8SAkNI36KgbnUmfcuu4DZnubG6i5PAQ0vY7dzPtDeFJw1cx/EtjoSffEvWBD/TQ3mlRta5iAeGDOLKBMKHsNHCwpVRBA5mFsqen2mvw1xJu7XK39kFWc4jvQpUXYyJMtnuzWzqeSoRK+HHw3FEPbaa6/mLfuGBvk8iD0hfoPG1RGxtgobpEkSETdR2ySYU2Y4c0bgi1pWmlIphR1MkYj0YBY0lEggk1SBI8VjxMIenD/vtafoCV81yTwrFYG44sJCDaMS3BDjg7fPrp8scrNxBIg4tOXBlFUTDTWn2U5arfWVXCdVYEDdZO+qaZaF2GPNG2evt8cUoqWizs5y77E9mlDflFtjYcxYIScQbY7nY4Mfe8oDANTcTWQLVUZg+iNrt4RXzaA/Gx+HgQZgu+O5bQgSOYBJMpWdYh9S8aw+0HKA20D23QvLuGEnZTya1kniHHydrqgyjVY9KVVP2HLQ5iac1UiqMoB0RYKQH0PF2KGaRZYFUEC2OIJViMLmMXPEezOo4uJqJxEp2H4owOemLaBFl09ed5BkXZBjoypK0M/5PrI2kipKcGGWg06K/2yaWShNRNvGxnSRxiLoObYduJffAfrKTI8RMm+FlCVKxFiLKRqGQxSrWnfsxtk+gdzjoAzOFRi3MugWlpSK4ukzRm0Cg0BVfVvGGo+ge36VWlF1ME7QrmZiEqZJGu376Ry2tupDYEmsnpZIkRNIP9+F6vmXXtEZQxQvEGS1BR0EXHIxSEI/QFXhmEMi/SazUKhIkv/BAgGlEmYiuwFExcBxaYp2oXS7miIE3PlPQrAVwF5VAAMsfQgSugzpR8E36AitOqSTxqMDe45dRsDsoW61MldIcOKTJFyUgKXkVPHyLqtSO4EYBVINXGTVX7+kdD6H33NEQn4WiI9FLzEjVwrjPsBhSzsiMPlLq1ukzAAEAAElEQVS2xvV84bmlWzC5pMLkpEa7kJFZ4sShWqB2lQoNJbMNRyapxC1fqd6jMA43vEe2j90DeKiqIJ30rwlZcXANF23YGKZEaN+yyzdK9LDvNbBujtf7TeC/pNFo9lX309Htkoz71suB9IKUBsISeK7EtnyBy7BNOs1l9Jmrg9r47VIy29Et7bi15BRS+bGopMSqFHAReMkE006Bjk6J1dYYyyo/gTG1E7Qf7N/VHqZS2CLCkLGUiGpBo6mYZaq00yJaSZAkLdO0J/pY1NJHV/cgqXiqbsVae7lyOLEZ0+xHKMme0gH2ervZ6+1mRO1hr72j/v/azx5nFw+N30HR8ZPfiJp4qfz7uRezcNqTlBb3UOgv4/WUsFoUkeohItkxvGSUqchitJAIKbCVSRVNVRkQxGpUQtTvh8KBmm+rdMOYXSEhJyVaa/JX70dsifFPV/0NazpO5SXr/xoA8+avYf/mo1SXXUz5gvejEXzik5qb/gB/+07B6lUnzqpJxU0G/vt84qd0sPstV7Pjz3/N9G/3Urp7jImf7ODBS3/K3ndfR/LsHlb+8FI+/GeXsap9C1+84YO86Jsb+MiVf8WdB28AIPWUXnJ/tozDX7yL/I2HjnxQIXCWXUTl1T+iuuxi/mL401y48/l89K9v4YEHQ8ErJCQk5KhIj6IoUvAmmXB34bgecRFHaOHHI1EKEr77ixCA1uRkC8loP3EzDUIghUK5jQdtKQL3J+3/zYoqqUGHTlsh3Fp8DR28zDbqGelqeHaGSKqXVO/poPx4UUq5fvyh4Kk0nRb0b/UXfbUMdyYCKTXa1jgelMowbfdSpcIDxV0Uydctu3ItDpPmGMm0hzKDxX8ki1CQXGTXRRF3WtcfomsI20DFBXguEz/egTseZLNssuzSbhWvyV7NkAmWGMP0qX5/oembnaHMwIUPPwaZtn3RwAjERI3AVAp0LfKPQEgZZPXSmAZEiQMCKXyR0FEOHg4VKmgU8URgRRM80svAzSw/WEY50zM8PUQEEA2njkKkj3Z7FXGji6llBeJbonjaJaZaEU07Cldj7ryG1M0/xbrPd9X02pKYMhpkMZw/ZpfQkrSRJGElaY13+5qdLejMBPZlBnRmEqxr76U/FydimySzNrLLoBRN4tntJO6L0nKngyEiSFQ9I9sDo3cyMRGMDcN3Ua0JUUoaDLetZl32YlYsA8NQ/kI96Owpdz+Hq/dSkoeJxwR7SgcYqe6q90mzVVjFy7O3fCO+naJRP4braV9bEI0A9SWK7HR31FrN7w8k2pgZj6zLbsWQJhkzgyE1ueoI1Wrgruv448pQMeT0TmS1gBzxUFNFwA+i78o0Bw6CI+NI5QeZRgls2xe2AaR2UZZfrwOlUapuEOy7WvHj4qEwB4cwV56CWGJSWFYGBcUpCxAckL/H1Q4iaI9W0UbGTBJN96HTPXQdXELqLl8MkK6HNCTKmUDqMtE/3AYOaCVB+dZnRs1dTar69VdDB9Zi2tUIKUiuMEmc2Vb/Xk7sQI4+iKyUSRi7/T5uV0hLwKwg1dEoLHpue90qTkqYWrWCqkqCEHiBcNditBBXHcRVKynVDUBshUnHgEanA3FvT36GeysCnKoER6NFpP55yk7TbXdgChsQ5B90EHsdhCI4nj9vuBHhW3ZpTeqW4DNXNAQHF3SyBy/VBWaUkh7H1VWkgGpF0BHZhlYG0q2gpg7AtEtCxOv7N7vqahcoe1DNIyt5LGmihcRLFEC7dTfGehsHY94028i0PAfHSeAmXPKBZlTWDr5TrMCN0HAd9xwEGjvi9+HEgQnfBbkmMUhJ0s4RMWK+EDirv7Rbyxzh1yea0rS0CDo7IR4HN+qLkG7Vt4qS4wJZLDcqoIX/MsdsoRoLXMGFgZpqZKSoVsG1XZQlMS2BmQdr3MO1qmhpo5QRZAUmcO/1fCGz5gYq/Pkl32TVZCU0SokgVpkOxHADaYJ2BNmMIJ4ALIEpo9TdGOudLRHJGF6pdg8SvigqFKIqcISHiPjbJpOSlk5Q0cbEpLXG2H0dk2XfmMBomrSU8K/LolFCeCkOps8ib/VyOLllhou2aIrnJgFd1Tj7TWIPTTOoljEQ66Fb9tCtehiI9dAf6afLCD4Lfjp1FwClIL6iCJIeaFNh21BZ24Mz1IojYzg4GEIh7WCe1tV6PEaNb31rKoUjPBxRaYjsyXo1UdWGy3i1LXRjDAk5KSnfO4FzqMRPEz8hbiZ597lfwJAmxm2XYf/ygzhLnkL56R8GqfjmtzQ/vAJe8TK44PwT775nZCMMfPV8Dn32Dg599nYmf7Kz8V17lN5/3kbmuUMIIeimnw899WvsGL+XH939VX523zf5vwcu4+zBS3jT6f9I599uZOpXe9jz1qtY8pNnICNHmY6iWbxnfoTi9mfQ86P38MHki/nyP72Gq7a+nr98XSh6hYSEhMyHkpLD5jgTjp+aPomJLSKs1euxhOZuMQZWHOlohCnwtImUmu7YRjz9ayT+m1aV9wUGKYOQVsE6QkgQiRKi18G8X4BhopWuW0BZSiGBcmsVJ+kgxjogkqa9cyUimgFl+aKQHUTOCZ5drf4kqYjB/ns05WCBoVw/sPJ2625gkMMjEJMaIzLNVGWcKa9ItrKCyrIOvMqNTOyd8oUnKXE7BjBXryKbuHNG+1THNVZ77c26AE8jbAOhAkVPeJT3ecj+ISw9TXH8IX/bYgUvCN4rBLhmP1TH/e+CGC84LrZlYZoGxaq/cMP2RQ/LK9Qb0FQgtET7Od/8B37hm+SYJsQqMaDkW3YJ8JSLZ2pc16G7W5JPVjk8addfXydiNvtjMdJRUE6+4ZInoKdPkj9ksP9g7SOJYSWZXrUEw/09tllA49at3Oo4HhJBdYeD8qZxUwl01ESUJsAro10Xr+JbrYBv2aXRSC1RUtKfXlKPSSQMMLTANgEDIsmIb4GGpifZz0Eq7OtcR+XQr1Elr744azNMClrUg6gbxDl0eARLxDHMfNCFfvZBGZz0mWf4Oz8wIunq0hR3ebgOFL0x6p2Hv4TfW7yDrh0GoiopZfcjYwLrbk3J2YtZ3UfOyRHXERAuCSdK6UCVfHE/ViqNMAWZg0kcHEBgo4hMKgxpgmEyPVVgzY7VVKpllokIQgimK/tZsneArok2Dh+sMpkHxwOj6LuWLd8UZddDfr/hVlAjDTcrV2YY67qA4cQ+7EOCTCvoLlDToCd9CxFwMGqxXT0obtJ4P3WoFKdB+hYpWkq0tMi3DTBZGSFj7SEpkixub2G3LIHwg0DbNpTjp9KxSNXDbWStNg4F4104Ale5gfoNuuiL3V7ODuqi/fhFR8qeVvs80MOMjETEYo3vA1XJVPimPElJZnXN/FTita2gkM+jpUnP2SkimQqYQBUqrYuZ6lzBFCtm9LmtImTMRU1idx6lXPpaJrEivdwZXOpmh6Kys8kyyxFQ0njJGG5rFXaaSCkx2yVy1Ldu9FwPOQITkRV4ZYnnTTAxqOjuOBed+LV/jtJAO3472bVwXa5fv+p6/9xdXWa0eh+YUCxKMuYiVKyFicIYPHgH69xuDoh8Q+xKayj4hqXaw3drDgT6jkVnsX3qOtzKGNItQZPoAg2xCymJx7uZPquV0d33MlWKU9yliZc9kB54vpVNrdmqVEBrLAVlGUMy4U+ddWsc3yKSNhu93w3iGzZeFHheJZi7NHEjxrRwkaYEQ+B7YQZj3vXHgUAiD5loIZi2+jG0U9dC3EAcElKhyhVMZ4yqynB4BDyrSkwoNC5u1EM5mmq8gjZSKKGQ2q1b/e1PbsHgh0gtAbcek69QhHhQJQ8PQwSB+aWvesc2ZRG3jqCr/kuOTAYKRpCJMWfBXppiMwpk0vYtRMsmVDSudkAayGpgAWfX+iiwCN7iUrnbQYyn8ZRAlCa4YfJ2JL7lcQ0h/fFTMooo16AqEpSNNlwVmSV2qdoli+vFmXqwBQP/DYLZniROlL42jdu9hqgoED/wEGUbxqRv8Zst3IarDYqUqLgVTAHWZIFq1ELHLGrvyCwTKlVFBU0cs/G+RYNby7QrfKs6SxlU8XClL9o5sS6qfSYHogaZ+11U8OxR6C0TMUPLrpCQk5Lpq/wsItd3Xcf/O/cL5GLtGHf9EPvn78EZPIvSxR8HZfK7qzWf/qzm/HPhlS8/8UJXDaEk7X+1hhU3PJ+B/34Kiz53Nosvv5Dl11xK9tLFc97Y9WeGef2pH+SbL7qZV216F1ft+Amv/d553Dx+Db0fPY3yA5Mc+PgtCzq2O3AG7mt/SHH583jVki+w7b4/47V/chvXXBsKXiEhISGziYgIedN/OpzukhC8+VbaQjkWQvqrrPxVHs60R/+mpdA2gKviOIHrVjZpkLH8p1bT9Nezrm0GCxr8oM1pF3MFFDd0Um6NoNEkEtDdKRESSp2VwNUrcLOpPWErCzl9kIg4hBYGqvYiPcjiFA9ifAsB0vIzwrmxIFKuhmrkMF6L6a+HhOah8i3sj2/HPD+HMxQcw9JUzjSJrm2Z0z5e+yqmxpYAIGOBRUzEDF5ze1CaAFejzRiJSI5pt4CrPfaM7KCkgzfZAKqNicgwBavbV7oEyJG7UbbEEnFUWpBOCbykvyzLBwkDUmlJLFKz/pLUomp1dUmWLPYtwCxhsyZyOkPpZYFsUGFiZZGpJQ7aTlOIdYKVaVg12VnakxfQJ/tnWCghIBKXxOKK2hpBI9CWgRtLkc4KLArglfyA3UCiFmPd8erlC+1ix8dJlu4juvd3sP16Dh5wGdkjGQus8IpVjcZDeU1KW80VUwrQgkRc0NYJ0o7Wx4VlxuhNLyabTFJqCdxag27M3O6Qu93F2lMgYoN2HeL5OEuiOaxpjXuojHGPh5jw5mSGE0KSSgtacsGi1T0cfC5IGnFWJoYQBZfi4TxUQbgeuuqB6+FUCyjXoFsuxfDMQJCJMV6e5FDlEJOVSTxXE/VipMyI33KuQHgC4WiEK/G0ieVZWF7ET5zgaHTVQ1Y9bGGA68frdkY8IvsrxLKCXGsUrQwqp02jEwWSTX0ZsyRbtkpMU2Ak/MzcyXTDIk1HTaRTqovHwhE86NzNIedu9u/ejgwsNgli2o3H1mDHNuHFWkmIJO1GEkuYIDRSSPoXQUd3BDOwTPR7NBCAwE9MYbgkEr6IEY/73e1lo34gce2hvYcXu2qhmISC5sBp9tPPw2tbTiIG3R2aRf3Q0VHvXDAjVM0cleEO7MEUhiERQWIMmWmdc7hpe4ApexDdfIEIgZ8XwkNng2DvWqNSgimjh4rKgNC4jiS/ZAmTcYv9opOi1Y2hDOwBg8pGE7TvwqsMQTKZoKPLon+NpNQiMa0kZCTVVQqpQN9fhYcyZO5xUDs9cDVur8Rr9+tlGFDW07iubwEG0BrPYUwp3Bss5FQBEWlYhmkxM/MmJReCTJtK2TiDKcp2cFHX9MVehXdGDHdA4diCStb/ouRZ5JMDCCvC9LTBgZ4K7oBLOaeRVX8EAEx70whcDENQMTOAb5xYy1AppMCI28g1aUAz7TXMo5JrTYo9RdAe2VZNa08KnWuIpkr5ghGA8DS+bCQQQcKAgdSfUjZ9C8BsFqpxKLZIRFJhFw5juRPEjGmcKriZKoOt/fRuaGdieYWxzQ7l3iLCiPixtnDq1mi+WO9RKhh4WtfdlKftAcqGP55GqxNEbMFA2xSxiIuOQ6QzhrTAKYBX9IjGBKvXCpxTOvGW+CdSS2ai8ZCZDF6iDeP+AayrHdx8TezyLX21Xbsh+hWwpAkRX1jzYoHlo+sE5flzW9nIIaXfx1XTQQkL+8ZdQcaPmeszYaj6/OpVbCpk699VPN9SLxIVxNI22CkQgtJwnx8TUxhMRRYzGV2BISyqgZusZYGX8gWsSgWSCegJwjxXBBjCbKhPnl9z/19fVLeVgRNEalPCJNtqg2H5rsNKYAYesLkWzYrhhclYoWVXSMjjzD0/vYZSbpwXXfBm1nRuRd37M+yfvhN30VZKz/xXUBb3P6B53wc0y5fD371TzBGQTgZk1CB5VveCt4+acf5k3RvY0nsuH7ny9bzjZy/g0lV/zjP/9Okc/uKdpC9cRGxj28MXZMXRF7+f4tIzWPnTd/Mv6efwwX97D9+67Bm84mWCtWtOvrYKCQkJORFYwsKxPaiAawuqXRq1C0Yiq/HiGVz1K/y37AJn1ENYFk7cF2Rc7X9uK4tkvELM0CQFCA8cx8bQfga7mnBltUs806BaiGFWDxGJQDajOHiI+pvxGnWxqxZHyPMfdmuLc5WyIA+1tbUQ4EaSyPI4iYymuh8qDlRPVVQSSex97fRaccZEgZHq/VjGMnS6FdiNjvrmRlLMfTA2elI4B3zRSiVMvOkqImoGLp0ucmIPZWFgqhwpKw0appw8D+x8gCpBYG8HkCaJwaVEzAp7r/45CI2qFJDJONVNLUxaE7QMCMDEGbqI3oky2cof6OkyMUoOeLLh3oLwRT8NyvAFS09GUXLaFxa9KkJoSjEH6QoqO0yiVYMJw/Pf3sd6INuFFH5szdqi30u4SENgKINEAsYmAisDie86Y5jIyR1k83dTtbcwvNQXN+867Is/ohL0s54gnjiMKAbBoz0D052AgqJUdskXy9x1+DZSuhhYRvjnVP8l/TGENLBjAu1GEbWM9oHV0JqVNn09z2Ln//2UsYN5jEwbmfXrOHj3b1CFKkpBbH8BY7dDV2WaiQddKqkxVN7D3OdSrUyQNxuut97UNGpEI0cjxEbAiUwRBeIpUIaNiFnE91RxqKKXWsS2dROPCypVzWhpO47O4JJBlkapqDQT4gBajFAogNviUIxq3D0eHekUB6d34jgatShLxCtgxdIk1/ZxqLgDWYbUar8tnOXdqHtvx63AvrvB6gbnugpaO7h9Esuw65lBtZ0na2c4EPWzlBpSYWVs5Jld2PvvqPdz7boqmy2oyn7QgQ+xB55bxfGKCKXxtPADUjc9W3ZYa5CtA0yN70c4d2Aps25ZY5qC1tbGtelOVpCB2GXutogX08TjaSK2Q19fUGaL9AMDKoWginQqM1xoZxB83oifB7hVvGSXP04GUpTujON5Jpbhoey5z8Va+65TQvoibs2NMRKbdd0LQcXIUooNYquddWsy0EhToGOtiHg78BAajREXaKH8HwluVTDRv5bx0lXovRls4ccbVAZYIkIeKHtVJJKWxASpvnYOLi3D74KEHVLgtQtkl2Kkup5pdw9JZzdqh+u7MTbpgREbql6eqgsr1ghkESJ7F9N/sMzhAyXaOzREFdRdRHXzL/86076LeGQwg5eJ4dGLKoz4LwE25HEHJau61nOHfT2pLkl1bzCGKpDLaXJtggN7YkyWHeKWi5sXqLzGFgmiRNHawyaGpRRlI00EcL2GG6Np+O7FyvJPbHt+N+uTPbiO5pp9hykql6jlIKUgsjaGs19hGA2rvlRaUNQVDLcApPxXAhWBsg0MYaO0BTgIAT3LBS4StdcjtV8TB+wsZHvA7XZYcWYv+cIB5MQYQnsIQJkxhBII7aCEJBaDh/RNdMSyuBWLamVfPUuwY0bxRImawZmjXXLuHg4EYr4hwOpXlG4X9fiXUgqEdutB9esx9VAYCYtKLVGLC+6V45ijMaL7PBwlWNN/BrnRHVwpJtFSIoVE2x5C+O7MVc+px/irpHvoPftUbrk3iSz4wtNEayspOwGHa2OfGS9AdEsv4BtgYESargUwbRPq87J/fmPZUxhv7SDywA4AugZSTEyA0xXltuwk5btuJ2bAYdekUDTZfci/hxwYg31FsNEoFGZO0tUrUMOS5PpWyMHEmKT1IBQLRl3odjvT4BxEGoLlsUuoRu+jxRhh775xDAuikTBAfUjIScfNu36HebNDfp3Lc1a+GvXAr4j8+K143RsoPevTYNiMjGje8XeaZMIPSG/b4uELfgKxpGU1n7nkZzx35Wv4zh2f5/NbP43RFWX3W6/GK83NWnQk3OGnUnnF94kNreVDm97BC4138NY3T/Omv/G45VY//XNISEjIHzOWsKnGa+Yd+G9GTXC1RdmLNd6+B8G5tW4smKPtviWUlH78KGNLAbGx4gedrQYZ2WRdxvAXn1IEQYj9B3Ijbvqfz7qN1ayEKPumQJ4GZ2WubtmlMla9/Br5FWWcoTKpDGSy/hepVoHOSFztEpNRckYfQroYSqGjNl52AG03xC4vN4Q2IkQHFIk1BvZQulGnIDOwUMq3LClPgVfFS/WQv2kac49C7jeZKhcwHvLrpzDo95aA8C2xUhmLgtWDFo04P77lQPPJC5JJSW+PH59LCBksDP02icUES5bAusEL2ZpaC4BjpFBK+TFlqAIeBZFnx+QduMUq6UiOqJXFUXHk+ha81pR/qFgaaQuqawo4S1ysVomUZpPRjAx6QyIMG+1U8PCwvGls27cY0gbgeOjAfSTefch38wTQENUJOmU3rspRdVz2HqiihaBKBekGbjJNwoSQILVAxzvRLQO+olevjv+3bQs62g2kEVj4dQxjdiZRGRNZrKI19CcqtC82MU9NUtmgMLak8VbZYAv0hIM7Vq7/MO4iJ0HlFUZBYeY10YKJMaVhSiCmJNKFSs5BmH7Q9/1TO3mg+Avy7mEskWhYZAjfNdMLXLlc4VLRHkooslY7TVcEWkoU0l9IzoPwqnWBt1qFSpsi1tqL11PBRvlWOYC2C+BJ4jG/7GTU38nIRerXq27tx8sOAFC2c75VUnUKrUyEK9G6YfVj2QK3M8viNRatTQaPOuEHYzKTQdIEqZF2Y/BaA0nip3X6XRX3rx1rn0WH2UHciyGC+EtGUhBfZgRtIDDdKZRXbAp+1cAdL1M9FMT5cQPLTwW4ZUh2QtxX2YQh8aws2ojPyZYpY4ZftBHMaELQ2+/H77LjMyef0uZFlDb3Bzs2xTlySwgTvOxAwyJOexjRwA3N/wAdWCtWvTxagCGiWEYUJMREDE9a7CjsZXfpAA+O3sWkmGDnxH1AINAJD6SgvNqlvHoR+RUZ5BKJqOALJJFGfW0bKrqABtpaJatWCoSpKJp+H7gqRSnVWXdFq7WLCPpalqYRygEhMZO1MSjxYjnfSijqB15si3ezsn0TibhgcKDRR1L65cTtGGUXKrpCNOOfQmKXYqW7hhXGanpUL6YhcVS8sW8tZp0hwNMoofx5sSZkuDDuTOIYGhvpJ7jIDLJ89Uuw7EaQptr0EK34vtcCAZVaOHUwgoyqxvos2ZxFJiOQUYN0WhC1G/OU9BwwIyAUUmqEdkAqlBHzRSntoDCIRQRLk5DtTKJViqk8CEuQt/vwmk1N8eOV1V+ieB4KgYpLzBa7YcUKfrIIXaFVthFX/nhWwsCINQJaOhtMvEGFuwhKHRUKA5LWzg3IpU9DW0m8jjWI3GI/0UhwXRXcEsJzyFlpevrPI9mWoaNDYsgobvpZiPgqrL4hvEy0fpwZGnFLL4WWDUxGFuMsXdz4uAWSabNpn8CN0rJmWFzGYrBokaAjt4io3YEpokTNKIaZwBZJpJMkbvkxG22RJGV0k5UtSEvQfp5N+ryLkK2DSCnI5kx0sgOjd3O9fLe/ndI5BjJwF03F2mlrUbS1+hEQFmoIElp2hYQ8Thwu7Oe/vvoPvLz0J5z2/HMxdlxN5EdvwmtfSfE5nwczRqGgeds7NZOT8Jl/FbS2PLmErhqWEeF1p36AwdwKPnnV27GeDc/+9FM5+M830/m3mxZcjk52YbzyMvL/+zHOv/rf2PKs23nr9Z/g9W9cxrJheM6z4SnnQSTy5GzHkJCQkKNhCYupDhMZV5QzAj0GSoGcqCCLeWR9sRcsHTzqQsbQOWeTNQo437vS/87wf7QGLwgo3fysKZFBzCSNpzUISfy0Ljb0XciDN/5nsEYIjhP4hXitw6gDtxOLgpeM0zKYhgOTyEQgptXc7TR4bQJkFcoC24K+XkE5mNt7MouJxqaJxmPkskVGiwfrRi3JeIZJVcZSEbz2FdC2jKjzEwAq6Sa3rMDyQGsJSiCCgLuYUdzJCnpPFWOPzcQuf0GfTEO7kWSwO8euNtD3juLkRd2NsV6ujtQXzxu6zwjarbGBRCC1ROO7P/V0CwxDkMsNM7FqKQenRjGjvWSFH0B9yvHfxGspqHgFLCUwVQQZmEdJU0AF9qafwkCfSWTnT9FJD69zJSq9nKjpwC9v9uuBbFTXtP2g8soLIocFmAJR9fAqwWcmFM0ObGeEQ4cc3IIgLmPISILxvIsj/MUYgOHOVCWECL7yBAgT4jkY85ciscUG01MzlyVCSrQHngIlwc6ZiHsKjP/+ZjozVeJLUshWG21IZKuJMA0qbQaJRd1EzcbCe3y8SGX/g8hDeSZ3w2jcIG304qZ34qkSmYxNfr8idmAEX5SA/dO7cHWJpOogaw6iq+PkcrBvElxXUvTjveNqB0croigiZpI+s5+HnIdACrzcMBW7bY770Mw2EZimZmpaUrIhs6Sd08UoYue1iFrWOssPnq2Unz0ylZ7rDqjTHRD1Y585Rtw3ojMUWlmYnkNMxqgAaDAtyRmvbMeQgrExzeEgIZ6OmpTWLyI2cHeQrE8jhCJ9UT8YvlijHV8AyXYkiJYHMLJTRAuLKTg7sGIuZSA6aNTNKbSQJBKBS6xTbNTX9UWfqSv3QiVPZA3gBh7JUoBbIbquBREInhgCLzGEWxzB4r4Z5544sxtX+NelP2cIYmlJa6vHlDmr7VVjTGpVl919Bzkl0JEURhmqfVnYvgNV1yklyqTu4htX7ZTEFO3mKoQsIRQYwmD/2gyZ3EHGD5fRmU7ujdyJ6/luWX7NAhc96TuCmiboVKOOblvjb6VE0AeNOcOVqm4ddHDUwhuw/NhcgHamQEcbcqtbRkenEdUEpmWRNluYKI2Q7d7ChvFRfn34ev/MhCRm+QKTaep6RC2pamJXlAPCYlpPIToy8CBEDmn0/gpiwAjqSj34P0AqIcnr4Hw1KKXQgYUQ2ne68/CwbZtGE0vaEl1YOaee0GpNyypujd3si1556m6MrW1+k5rB3jE7iVaBANVtwn1BnMnCKHpsDKNTo+OtiKm92KZH1fVASqJ2BNOe9s0KAyHHuA8QgljEZKoM0laMxdaip+6nln8EAZWOEqYtGXJ6mTAFMt4O3Im5eoDqr+9B1mKI4aK8MjGRpqo6gAN+uosmod9osxlxxxEpBVRwai7gZiBUicCyK+7hdWr0cJSyNwlelaXJYQw18wVRj91YS7ktedRIvh48voaQ/mh0VArZFgMmAYhY1F88xLZ2UxgNngmW+DllJphJJt7GM4eHOXRwpx/gP9GOF/Hn3840ZDPgHYKMFydW3t7YsTk2pBB4vVto8Vy6rBQCQdLoQhsN92wzbuCVHJQBrvSfORZCaNkVEvI4oLXmn3771yy+vR8sQdvSUSI/+Cu8liUUn/tFsOI4jub/vVfzwIPwD+8XLF365BdoLhx+Mf/w1P/m+u5ruenUWzn0+Tso/OEo2RnnQUhFdeufU3zBV0hGinxh85/w2Vd/F8fVfOSjmmdfqvmnT3jcdXdo7RUSEvLHhc5to2pm8HIJEAIttS927Z3G3DVGwvBdxxXKDzrs0RQvxSCSioOQ9QUaAnKbTSqr28EOMjrW3voKhWlYjFptlFUMz0piRC3sJQMUzJ5ajfxfpSCrYbYfHWslGhWcus2i49QW0s8cqJepmp9Sa4KB0jQ/vm7pPZeentOI9g+zZFUbXZ2C0cLB+ve5dAdnDTyjyY2xSWiyG6KBkfUtBFRLDKFAOEF6+2ARIYTAEIpi7cG7wyVhW8RsWDqkKT80ifvAuB9rRYD2goWO68eeiltJstG2oAZNdRAyWED7Qd1105tz04SKkQMh/MUiMO5sRwRpAAXQlVxE24ZBWtauJ6l6yLT6Vh+etJHNDShNaB1GRk0/s2NTWwgk0rIZqYzT1umwelkFPAeKo+goiJKHroXaMQUj8Y0UrF5KZagqgZmTKGXiCZeqLjS6rJYavpHf3k9qUGsbGdQLiCxS9b9rtCY60YBhJZAKEkMW08kCI859FFIlZE+kvuDRaLpSA35fzipHCt8EUSDqVjAR6Vu/ecFzgSVtqoHrrhBQrOZJqA56I1uJq1a0MOjugg3robWlMW5GnDGk4fehHbPoCsa6ADCilKMZHg7ThEI5aIfqAd/1qdpoR23V4tr452BvSs4pQzQFa64s7sTuMYh0KzAsdJ/DitQGWs3lGEJS3eCgalkbZ60EddRCKelb5gjtWx+asiHQqoY1aDQi8Noy9P/ZMGvPNzAsj9y5NmYt3hoCpCKbAdMKrqWgmIkf7aBQy8YtJFprtAtezzq/X7KD2AMprN6Ev4kh/YDu2pyzepURRftSX/SwLUAIrA5JdFCRXJOdt83FrJMv9y/D7d0CZhSlwO3J4G7tQJkCTxhofEux2njrtNYyED8naBffChKgK3s6RDRebxVnWOHG/b5T86y4t2yCdWs1OiVw+yTOYomY5Zq1ZAiWLqnXGGVLqjJJwepmUnYzmV3mu3sCwi0hpg/OsLbVhvYDuwtBT3B9lJ0iSkg2Z1YxkF0WtEdQvjHXsithJ/CESYkSngHFnETX4oIFKAM8ZVEy/XhnsThYpsA2IkRWZjGk9AP84yeR0J4vdmkzTqvn4bU4pCI5P3lCkzjcFm0jGhEkLKteT1EVxOOw7TRQgd+n2ZT2V1omRpOAKFzfRVHH/CyzHW2a7k6NMgx6uy36BySBU3bT4ID2doWd1Mia4tls2CXAGyoRXWoTNSJ0R1r8tdzyizEHusk8JePPacC01U/FyACgg+yDpozPFLuC+wzKoLUVlgwyh6SRBAnOMo/D8REKbgncKoa069vMp6t72RhORwqnJ3NE3V2ajXFndPhupGrDVowNp9SvN8v2LZjr+9Tm0uE02ZygszP4wGi6hxnUX/a4siGGeq3D89ZDSUXWHCBj9iOERAroTA74xaZNHO0nAUHqBVt2hWJXSMjjwK8fupwbdv+KUx86jeTmBPH/fT1euo/ipf8BkRRaaz7yMc1118M73y44ZcuTX+iqsbnnHD558Q/4xUW/YSw1zn2v/Bmle8ePuRyvZxOFP/0ubu9mTjv4bv7nee/i858sctaZ8NP/hdf8hebPXqH5n29pxsZC0SskJOSJyde+9jXOO+881qxZw3Of+1xuuOGGI267dHgzi6MX0B85E4C00YaUDVeSpNnBqo4tKOmLXWhRX8yiQZgSbfoPvloq3N7NRLedy7rT44jlJpnTGg+vCEEqkmWaIiPx9Uhp+EKOkPUn8HrortLMhRLQyNYnG/e/2kOy1rCx6wzWp5f5Ao7w40Am7DRJO+NvZCfqmeIAdFbgDEgia3Izj3OEB2SjNULqqX1YfUnfjcqpgGGCEHVLM7NDUhDgRVJ4MRdDKqbvcBqL9voxGvcY7cX9xbNudtOfbdnVELvsxrqlfv4A1SAFmh/7xZc9pIRcrIPYcA7V3YJ1xjaMqFH3FpstZAghEYG5XDwGQ0MyWLEIsBPsLO5HCoHllpCjDyLHdqAjLlQ0zqTnx7axfMVqPLqSfenzALCyEsMwKOkJJpyddbev6M5gfHi1yOP4i9mg+4Wc5eI3q8LZWDvDuS0oFSESASsTwdpmED/PoLrewEjZLMospSvZT3dygP7MMOcMPQtTWTPKEaKxSq2tLy2R8I1MggWuaUSpUqFotvvBqZ0Ctkw0FeILPlJAS4siE3jAVj2HZAIWdStaO6z6cUTNSkT5Y8ey5o47bfnWD5EIuIFVjJ+ZchaWBmngaBe3t4KRjs3ZpFnswjawz1iDSndQfmqCWIeFLW1smWTA6mVN17r6QnGeUHZoqfxpQNBk/Vlry5nnoaMCYQVCg1Opf65U4GpmQKTPADuF17LEt+Ao+VY31T15apXQ1UBst02c5RejUzNjwgpToh0PLQy8njVz6jzQLzj3bILQHwJpCaIDBqkum22nzTC2BKCri/pcV1najl7dg060B3X3t5mMDuN0rsdRvjBqRRux/4SQKKPmHtYIEh83FtFqZWYcyzaibFqVY/3axmerO04hk/GtOBHCF8YG5lrsRaMC2xL147Z3CiIRQcnsQAuFF4lAyhe4tdTgVqg5+RlJv1LasEEKOhJ9tMY7GcguByBtJhjKrfTrGAg6Xame+rFrwpctM+hAHE3qFJ4FEn+8poIA5Ibhj+GC1UNVJYmZfuKQwedtwB5I+fcYASOjkumih9YwFh0mby8lszXDWdvWYptxZveUAJbEFzEU6/XPRRj+9FcRxGKCpOWLmbl4J8WqP55Sdrbhag2+ZZlQoCyElCipScZ9yy7btLDiCoFHXewKMA2FlfZQMhm0b61GPhvTKyAI+K/TvTPrHVw2bsdKtLSYsoc4uPFcqutW0mGtpsfehJKS8ukGla0GRk0IEgopBM2Xc81KtSfezZbMKtJ2BqTBA/ldCM9tCGXML6oiBM5gCzpmHVnsijQOqLI14c30573aPVmKGfekWlOopDVjXtBNlVcGJIJp1JUxDAXaSuC1Lp2/IrM4b/FzGExtAMDKWHhBwgUUTa7ZRycUu0JCHmOmyxN85vfv5lTvHMy9gtbE5ehEO6XnfQmi/iT9hf/Q/PRn8NpXCy582h+P0FVjKLeSTzz/cn76+t8wVR7njud8hz0/v+PYC4rlKD33C5RPfyPG3T9k8y0v5F2veYgffFfwzrcJEgn41Gc0z36e5m//n8fvrta4bih8hYSEPDH48Y9/zIc//GH+8i//ku9///ts2rSJ17zmNezdu3fe7QcGFOtXJLBkjGWxZ9Bm9iAldTe12Q++vidHsEDSIJRE12LmeC4IBXaSeNJg1WpBtqMhImghyURaKHkTjFTvRwaiihCCocX+QqcmAOhys/ATzMGzBApt2DMerNORFlqsTGBBIehK9nNK73kz9ml+U44Q5Nb20Zo5eiIVFVh0If1YL0jVWCipCNF1rSTP6SZ5WpbIiuCxOZrFXbKYSPdGnNQw7ngZlbEwh9KARLiCvfsEpb6zqFqdSAluzSWNmYKBO3AGU7H1fn2zkMs1vjOaTied7MGyfDFE4DUFtA/aqyZwNX08u3+lkGD4jRqNCKJR5btvCYmwGgKK8qpQ8ReOXtQD7VE56CHiMwt3ZZSDydMptq/HbOqsWqxRiYBoBppcColmwGmUkTirm9RmXyyJrW8leU5jsW0tSpDNwKr1Bn29ftyWjnZBNhMIV8LAVBYr2jfWhc75EhHUrVaEor3NXxAaIuJnDAwazpRxdkf6uUo/wG93XI7WGivIanbmNti8uZ7C0rf+CfrGcQDtkUkZYNhoIdhgbOL0zgtY23UaS3KrAVixQrFq5cx6uYtOR9tJbAtcGQnavjxjG23YvugUM/G0BxIsZTMbYcw8b50bxBg4k1Xd21idXFIXHJWQtCbb69vNuziWRnC9c0RxGMDtkrjdouGO5FVnbSEwvCKxpQbm8l5ELApa+3HUoJ51FaF8N1lXI+atkH9+7lg5aH9r3m0Mo2ZBOLPOsZiYcS2BL2hpKYOMsoJYk35Y27ZvkYJ0bTz6VibNrlPNi3r/kL7VqR0IF+lIjs2957Ct/+mcNngObU0uiu2Jxjhv5kgeCPVsqFKSTjcdv3YO4Fu7AEbKn6qjSwx0vMNfawh/3lnbeRodiV6cwbNxFp1WL98yIpw5cDH92WX1xCC27delvyeCYfuNMphsY0PPKWQNX9wRVe1bIkpZ9zvPtRjYMZNVPaeS7fZjMClpBDG7DMYmPByhKKoorpFGmBHfCk2oucqEdrGV5YtlgKhlWg0SZmRlP93WJjLxlrrInYhkZuZCMDWGUH4nCQXahdIUCIUhTYSSdHdpBhZBu52jdk/LWGl60210mv6FWxe7hH8dZc0koNF2Gm2n5u03avUVAiebRSdsWsylGCKCIQ2ICnRCoGqicjB/ySZRbVPP2ZzSdz5Ce6TMBI5uZDYVQqBUU3yth1F25lzOtUvGaopfV7+Ogg9qscfEzPcRzfea5n7Tpv8ywFCQTEIyKThzG/QNxRkaYoY788MhJdS09FynwopEg2N4eDNeIB2ZMGZXSMhjzNdu+SQTpRFeuvc5VCiTWT1O8Xn/WV9A/NfXNP/13/DsZ8FLX3KCK3sCaY138b5Xf53Lej9F63uLWK+6kRsvuILMW1ewbnAb6UjuqPtX3QpjxUNU3BLm6mfS2bWO6E/eQexrz0c95e95xsWX8IyLBTt3aq74iS8u/vZ3mt4eeNEL4cKn8aRLBhASEvLk4stf/jKXXnopz3/+8wF417vexe9+9zu+8Y1v8Ja3vGXefXp7BfGE5vbbTV/QESDFXLFLB6GWakKPV1tzNQsVtW0THciOYTwETAbxc4SgP7sUuAsPP+V5DZXMUTQcpJHF95dsegssDQTMcN8DcIfOxT24Bx64NfgkEFCExK9qc+WDLGRy5mPtms6t87ZJM4nTOnGnKk1vr5uswwwbe8B/q2+0pbFHTZzlCnVIcvrGZ2NtmSk6lO6c9hdCZcmk6uX62xMoUZ4jdjUjoxnaF1vc+QdBLNo4T2jE7AFIJ7u5oGsrKrOd3ff42+lWgdkZa26CIBtdbf/gt1CNRFuyaYEeZGP021Lgtg4jPAdZdogtNSjtdNEJDzzf7UjG5t4jK0YWYStEMKaiUWhtkYxOQcVsw8kMooK2dYafiienkLuvDuoqMHIRiGzDdUpYqZnuefZACmtRst43SqgZ3ys58/9HwrculBhS4WrXd+UVJgJwE2243RvoqiaYDKw0hnJgmgrP6OL++/24n6JaO5bGteLo9pUwcieuBygbQxvBc90BP/i0MkjFOpvq0HB1q2NY6GgW257EFTYaSTrViHkGoM0op6eWU93hZ3/TUmPOE/B+hmUXjWu7I7kIQ95GLjKF9GIkEh7CNOpjpHkOWDYMXZ3ALhPleQThluag0hbuRIVTzr/IdwX2g0rVA9TP2Nb1F7bRTT2I7S6lu8co3TfhHzui0FU/VpKu+BrEDIucGefXmB/EbOVq7tZz66GCzKlN/0f57nBLV0HbYOM7KQUXnO//7TiaVAq8uKDUJHa1t8HBcu0C8zO4TkaWojV+8HfAVBYpO3vUmtbErcHcch4avfsop9QQIGqCgw4EqNpXWvoui8qC7FnB3LTHJGXP8/xsJ+Z8VBOLNm/yj3HNXt8CKxpRrF+3iLEd+xletxVdjXP7/VN+rLCavql8K9hYDNIZQWqridveEPSkJHBjVLiywr3Tuyh7S7BUHE/ZQMUfQ8HJDOVWkuxbhDhwN41ZEAhiplFoGg9CIqVgS/e5uJ4LE3vrGpOXdHGGSijhXzNCGohKwY/+77mY0kIYklhUg4Q2O8e4YVFyCkg7RV/rMPHlaxi/ryZ21Y8aNLo7vyBcn5CPPEcZTSJVbXNtJ/BiLYhsY0BayvYFbu2/1Kp4Tv34pjDAbQzs2VPMw2Fs7qK8Q9STgeiI4ScVgLkXv5hp2SWkQNXiXtayMluAqehfBO3tjcpEIoJFS+IY94mGpe8ROOsM+M3vaocUZLOw9RRNKilwn72Vwj17mBjfhcvCLLtCsSsk5DFkpHCAy+/6Mk/tvgD1b3tJ9Lm4r/ocOtkBwDe/rfn8FzVPfxr8zZvmplL+Y8NUFn9y8d+wd+tD3PqeH9J/RQcjN97HW5/1DxxeOk5nchHZaBtRM07EiFKoTjNdneDg1B72Te2cofJnIi08dc0zednuHbT89B1U995E+dy/Y9Eii7/8c8FrXqW56mr4+v9o/ukTmi//J7ziZfCMi5veDoaEhIScJFQqFe644w5e+9rXzvh827Zt/OEPfzjqvtmMwNOayfgwXeoQnmHPSDMOgAta6BmZ9gAiq1soBRpE/R4lhB9zQ3uIB6l/ZhoGMdlKwTs8S4gQlKw24sGBYmc1LEu8jtX+wis6a1EoFSLbR0Vtx3L9wLlO/+lQPTCzLjQWCqaaGatpIQhT+oJL/YMmy426mxLoZAeqbytudifuEjDlXOsS1eKXc7BrGDuyCmsaFJbvgtckUMyM2aUYWGSyLC+R4+KIljRSSaSdQh4+SC6j2VsQOOsM4ov95wnPq5XnCxb79jfa6JTsKkbaN/rHNprErpoLYz0oS9z3Zm1bjJ0ZJdKbBz2Fs8QhqkzGoz2MTzcyWNawbEE1sOrRGxVORJDfPUBMtc04HaGMwJQosBQIkgIQy80rqjTqGLTvLDFzoWE4a+3QbC2hhAEC3FQPOtVNzNS0Wb5710A2eA7IwGB/vfL1g3q4RJI2yTgkkqDtJHRtQye7EdxRO+jCKofAssBRMTxpsqiv7AvANXE0kiEiLTzLaVh2yXksu4L4czUhZGYGUEmsso81rWXAzxJZb4emy9QywTQFKBOlXbTQzbp0nfipnVT35bGbzKG0MBCzLbuECNzDBJgxkH72VXes7A+BmmW9lHgV7QvuRxK7mlfxSs2dv2YddzaGQT2QO/jDsNYOudw8QmR9P0H/IkFkpI2d4v662LVqJRw8EDSeVJSXXkwxCPRvBuN0IXFio6YvOsXMueJTM7XjqqQ1R+xKnt6B+YCJHvF8YajJE3ZjxzYqgev3QonNErUlkvU9Z6G7z0QGgpi7dSmlX1SImi7J6q66OVzjFuFbe9VawNMOCPCkQZ7xetsIFK4VA6ZqBwvaJU7CSgViEiAMimYLyjkMgHGfiVd26xk8pSGIGMF4LE81Xtq0OWD4MSWBGRaxOtnpW1cpFUwmvnv4osxS8pVJlGHj9a6ha1GC2+/TM8SuejHahZpr5Qz0rA0hlYTJqcYW84v1Ep1ZhLLmGQ+B2161aa1jCDXjuptt2bV1C/z++qbSZ8e7y0bQwbgtr+5G2wbKmA6ON+ulmGTGdZI8pwcZq/W7/3lLDjLrmT/BWs16+2HmRt/wYGaDpgKXXGUZZJMtTIzjuzQugFDsCgl5DPn6Lf+C5zm87HeH2H+4hd73r0Qn/Td93/2+5t8+rTn/XHjn2458o/1jpLt1kO7PvJGp3+9F/vVveMN//iU7nz3KNRffyER5lIPTuyk6eWJmktZkB8Ot6zlv6Dm0J3qJGDFKToEb91zJZff+F7+ItvEPa57B+lu/iTx0D6Vn/gs60Y5hCM4+C846E269Df79S5p/+mfN/3wb/up1cMbpYX+EhIScPIyNjeG6Li0tLTM+b21t5dChIyf2yGZ9ESkWK1M1h7CWKmIPVClXNNGoJB5XaNvGNgyEFSMeSRI34iRTBtmsglOyuP3nYx9Ik0wk6uXViEaj2NhE40my2SyJWAa3OkUykaxvG4/HsewypqmxbYOW/g7SzeJWe9e8dfc8zfZIFNMpk8lkENE0ieIyyvZh4vF4vfxCUqPiZSKZDPEmS7TZda2XG/e3kUf5vmAD6ZYZZRTlEvbt9VcGudxcawkhPMa3+YuUeCKBJeMMDiiyPReQibbMyBAYP+D/ncvmcLwq0VgMijbpTAYRbxwzHi/Xz0XLLdiHfoudg5aBCC3tQ/X6JRJVpvMemYzB8LCk6vgBor14nDhxOhatB6A8MY0RBHOOJZJEkjF0PE4s3tRuHX0kF61D3/cros409E/R2tbJIe9s9A6X2bZ+6aygoz0C01VS66KICphmHlvZJBJxX0ABsi1ZCiMQiUbQFZdULn3EPpqPjJslXoljGREqTolo3F7Q/mU1TWwyQXQ6iueA7URIRrI48RhLupeTzWZJpx3Gxv3FU0uLNeeZTJsOeiwO0QRD8Q72TGwn3hkImPEE2bYe0tEskWgM4UImkyHdVDdvl38d1KiNPW/SxsvGscZasOQoiYTpxwEq57Enbci2kfSSiEUJjN0PIaWmraUTMxgjXiIBWhPvbceL51mzWlPdkCKXayzxPN9kkFJG4BU16UwOERy/UtHE475FVjbnX/PeRI5ExWbUNolEI/O3cVfrjP96qTRUi9BkhxOLxfHEUuLxfciWVgojChWvIKMGdm+S4v1jEPeFO/dBhaEMYtkUmXmOp2JljLjfP8nWHPb0ka9hXbHQs67xTKZSj88GkMkqDiaK2E6VTC6DlZ1rwVojcUmUzoM51k5GuOtul3g8TlubTTxZxrBt4nGTeC5bb8d0PIVdtYkn4nParjYGap9ns1m62rrJRFvZPn3XnGPbRoSyUyKTyZCNZ9FbM5SmoxweOYyXSTKwJEnHihxeNYltmzhVTduSONG4Lyx4lTTlkiaVyRDJzk1scDRigZiZy7XOeZGQ7S5zKN6BXRkhYttYUYt4PE4kEiUaiRKPC0Q6Wx9nKiK41jYxKgqNiyENbNsmbiUw+jaT6N2PyPWjPY0TP1xvG28kTjFiQ8sw3lSMSH4axzTBsklZCWI2YNvkWjIksv71qNVqzD3jGPebyISHtiWJmH/vKoxlsG0bEq3Ect20ZFtJVqcoRapgWOBEiKayZAjuXbkM8WyW8871eODnEQzDwzRNIrbfl25ZUYnZGLP62ptIgBdHZHP1Pl+3zuCmPzjksoLRMU1LtoV4MI4TsTgV6SelMJTJaYPnEzEbL1sAdDmHLh8kaiYQ8Ti6cymJ/CjxeLw+zotFl3i8YenV2mbVx6XflzPja42N+9snk4pYh399ZbIK72AFO5Hw57DkFNWyQSKTIZbN+u0HtA50zKiffarG6oxjZOaK8fVzWPM0MKOIeSwLm2nc9+aWZdvncGD8FpYuf8pRy6gRil0hIY8RB6f3cMXdX+WSapTqTacgE4rUpX50yst/qPnEJzVnngHvfpcILYmOQHJrN6v+71L2feBGxNfuZWPuTXS9Z/OMiTqbzTI2NjZn3wuHX8yDo3fyvl++mr/Z/z0+ctab2XLNl4l+7VJKz/gXvJ7gLbcQrFsL//rPcO118LnPa975d5rzz9W8+U2N2CAhISEhJwNzAkTro2clqs2P01Max/UzcVWqDuWynykpnxfIchlnpEIlladgRMiX80xMwNiYX+5UWVOogDU9PWe+LRVLlMtlCoUCY2NjOCWPslOmXCzVt83n8/Qv8rAPQrnsMjExgVda2Nw6XTVoNcqMT05BySOfL+CWy+TzhXr5pYkJSvk8zrRJJJNgpHBgxrnPxsj78aicI3wvEv0UrX3oqp5RRqlYIZ/PY0hj3rInJzXlsv+QXpAFqtL3MIm4SUrTFUo0Fh35oA7j476VQ7FYQpbLjE9MQkU1baeJx+PB8aIUE73IkQc43V6CSqyt1yMW1eTz4LowPu63bX6ec3WmC1ieouCWKOTz9A5aFLuL7Aq2A5ianGLctjDyeYrOFGLiEFOtUSYnJ2narFFHO09XvIdI5DB78ofA0VSdKo7nks/n62KXMT5OeWqSSqlCuVomXy4csY/m4+DoPvL5PD2tS3ANhxbVvaD9p/JTFPIFzHKFslOmVCxR9Iqs6jqPDtNkbGyM6WldP7fx8fzca6o4hZHPox1JT8t6Il6SRGyChwp7OFx2qBQcxkpjlEolKuUyk9NTeE11S9Poc2j0hxo/jCgUmCppZNlvL+0oRDlPuVzGLZaZGHwaVaOMc+UvcdwK09OFeswiI18A7eFMTdbLb8lN0dws0mpDjm3HXqvxhMW4Y1LbwHEa5z01iR8MPV9CFV2qThXX8RbUxqpURZTygR+tb4HS0VHANYaZ6FsGY2OUpycp5vNITKolSWm60R7CzKDas+jc/NdtfnScat4XA/SERax0lGu4Wpwz7ovFxnkKoFiEYrmEyheZmJjAMOe6YDYqByRdOFQAbPL5PGNjBfJ5TcItk89XcCfG6uVXk/48MDk1Oedcan3U/LnAZLw0PmN81LCiMfLFPBMTE8iKL2CVUn750ViV/kVTjI0J/1otlXDbXSpWCS/vW/tMTReo5j2YnKA4Nr8r9ZGo1WdyYnJOLLyK49ehQIRSuUylCvlCnmi5SLFU9OfpySm0bJznmctW8rtrbyavixgKyuUyZbfCdD7PuEjB2Bha6xltpCYnKFfKuMUi5bLCqrqUFk0hd5mMHRhhetrFLJeZnJqgatZiQUVg61PI691oawqZP0RVlv0XRuMjlMtl7JYE+Xye/HSR6XyBYtEB5SDKZbymfvAKESpjEs/1z7fqVHFVlXK5TD6fxy1BsZRCTOeRTX0qp6aQ+TzuxCT5fJB8BTj9VP+61VozPjldP1fDjZAPxvSytvUUp0sUmZWswmxDJIoURkchnweZIJLrZqJ9c/16npzSM+boifF8/f9Swvh4YUaRkxP+9pMTsHI53P8AVKpTlPN5KlMab2yM6akpnHwZb2qS8hj1e9yc67RDUNYFGJt5jJkYUK1C4ehzSj6vg2PMV5bilPV/48dLXABhgPqQkMeI7/3+A2jP4cXTw4zcuZjcS5ajkhbf+KbmYx/XnH4avO89odD1cMiYSfeHttL62pWM/MddHPrM7Qvedyi3kk9c9D1aY128/c5PcPNF7wcrQfTbL8O4+eszfCCEEJy2VfAfXxD8+WsEv/0d/Omfaa78bRjAPiQk5MSTzWZRSnH48OEZn4+MjNDa2nqEvRrU3dws2Yjr0XT7cYOHy2hgEWLP9dKbv9ycH1ukFnOrERtl5r3NMAS17PDiGHxqVpy3ht5tp0CQuS4TacHtlERXNllWNXmMrOrYsuCyj4TODqKTXXPcLcwgMLiSR3CXnOe0mgNfHwkhmt0Xj9422k7gRdKojlUzFqCdnYKnnOdnbzv6wRT9sW6WJvoBQXuvSX/vTHcaT+t67LIzc5s4I7MWHc3SHcT6P/3UmUXGUwLLsOlIBBnJFFQTvnvkjCYUzAxAbh7bMiRu+UGgc7F2+jPDM1xDj4Zoygi6IbOC4cQF/uENs5GVsOZ+xVxB2f+i4cYohKA13kXCiLEmtZQzBy7GqI+JhqvvQtCBtZ+rYni1OHfNvkZBFjmzK4m1FrzO6gxfJS+I/3q043ntK3EGz8Yd2oZecQHYDQufGQGna38rky4rw/LUIKlattOHO4+gL3RTjKL+RZKhwWY/1uBvyYyA2CKifFfSc5Zh9RzB4qNp3HjOw7Xt3O9nxBoSEIuCVhKnysL6Sog5LmBbt8CqizJE17TMLGLgHNzONQvOFucXH4iXsyyoYoE7W/OcKeImXiqC2R6dMVYXRX3PEdW8vK+NlUcRJmW+pA+1rLGuZ+B1rKTUtYk57T5rvygGluXH8itHuoNqqRm7zbn2ai57TVkwvYwLCD/RSRBcUplzz89rS9fjI9u1pA7lwI+wuV2bBNrZ51Bzh2xUSzT2gyCTy3zz2Ny1w4zLWogZrvDNbXzE+6NU6OzgjHucaadmxF+b7STU3Jznnzu33ObvMxnB5k2iEaC+tk3gbq6rCx/Pj5b2tuNXVih2hYQ8BhTv/xlXbL+cC2QLxbF3IKSg5ZXL+Y8ve3z6s5rzz4MPfUDMm4o6ZC5CCDrftYnMcwY58LGbmfrN/JnH5qMl1sE/XfgdUnaWv7/xvey79PO4g2cR+eUHsH/+bpgVUNUwBC99ieDL/y7o6YF3vVvzz//iUS6HoldISMiJw7IsVq1axVVXXTXj86uvvpoNGzY87P61gPOx1VlEry8aNN+Batp/32KDjetnBpc9ajzJVDfaiqGD0oxgwT4nU9IM0WPh975YwkClGzG+hlvXs/5p55Na0exCUVO7glhMx4Hk2d0kz52ZMS1qxJFCsrRlzbz7zFoisGnDzOyKR0PXxBLj4VRGic4NIWMtc75ZUNxPJZFC+kG0hUBEjTn94Wm3LnZZWhNRNiiTVFJwwfmCaJN3zVlnQCpdW4g2hJ6pFRGKq9uwlzZifAkh/JVHTWA6xqQwvanFbOt/+sPGN5qNDMQuKQQ5K4Mtawvdpm1mJkObS/2LeZ4FxIxVbLDZ/OemDRu3Y1X9/17HKpz+0znltCgrVtfU4PnH8Kb+ZZzRsmFGzb3ujTiDZ4OQJM/rIX5659wdhWjExZvV183umqJJ7AKIGxE/y95CqAmPR9m+rrtIgYzUstRB8uweUk/t84PdH4Ho6lw9GYORCeaY1BEyrc5zHcwMrA2RCCAFjq+bLIC54UZSKUFqTQ57KDXjtJUZAWnMiTEHsLR1Das6Ns97hOVtG9ncc86Mz2pjXTSPMUNSWdmFiM8UxobivZzftnXWPPDIl/pHSwBhW1BZ0kY1Eyfz3OUIM0I9eP6R4g5WBEJAmThTBX8SEcijx97T2i+ulsk3ZoLp7+CV3EZcqSPEeqsl5Gi1Mv52Nfd50x+vMSsJQiK0DoQrv5z41g5UxsLo8sdcvX+FxEt24aWa7w3zHFs3vYFhzp/ATGFzRRBT8YjlNdGR6KM75QcTrMcpa1Rv5v8fZmy3tflH62qOJFDPyOyfg70s438cf/wcAtet9e81x4PQjTEk5Dij7vtffvbz11OIaJ419GlGPrST9LMH+dx3onzrMnjmM+Ctfy2CLEshC0UIQc9HTqN0zzi73vBblv70GZhdR46x0Ewu1s7fn/8l3nTFJXzo6rfxoWf8N9Hffx7r2k8jx3ZQvORf5wRH7u8XfObf4Av/rvn6/8Btt2v+4f3Q1RX2W0hIyInhFa94BW9/+9tZvXo1GzZs4Jvf/Cb79u3jRS960YLLsLtiiJwGJud9EJYxk5b4/PPcfG+cTWmBkPWFXURmEChy0fY529aEgmOx7JpTPyGPkuFsYYlevNal6EjmqNuoeeKOKKk4Z+hZR9xn9pptoUIXwOLOLcR7zvIDeT+WBIs8aYLRl8BojeAy0x9E4zVS2zuBK01zFsfgtFJJP5iwk68JPI0ylsSeAhEbFZueEUtcCFHvf2Ec20JcCIFtRB9+w9n7BdkmBRIv0QFBiLv5NKojhk892rhqtspoEvzmw10yK86MVBDNkgBkwYJRP3C6AE7LrSPffUZ9U0MaGIDTXHGp6pYdKmmhji0s0wxqxlM6WIRH+hVOy8KERW3OY2U3uw1ko5GNzhixTW3ImNEQvo6CtBXxrR1ox0MYEif3NI6c6W5u2xtq5reRCI0TXkjM3COqoMHXTUUkrBTDrWtpj/fM2a4vveSIZdQEjGbipv9iwmiyJs1lIRGHocGHqzSotE31YAWxgDaezZbe85gqz+9ulkqB15ogPeyPj1wO+vqgZRLff3oehBAYyqBcdojKDDAJ6KO7o2kXYfjWVzpi4C1JEreitKS7fcsu7d93jqjLCcWiaCcxFcEBvJ6NuOXdCARnD17iC3pCkjk/TmXMpnDdOAAqZZI8uzmbZFMHm3ZdQ5SWH/Te7p95ncw3ombrwM2WXREjRnuih4PTe5ALuIeVHN9lM2HPTBii5hG7Fg/C2Pj85USjgqecP7uiM+dzI2OTvqh/RkbUJxKh2BUSchwx7rwc9bO/41uZKluyZ6LfN4HK2HwztYHvXwYvegG8/i/DrIuPFBk1WPS5s7n/oivY+YbfMvQ/T13wvsOt63jz6f/Ix377Zr5x27/xp6f/DV7LYuyf/i2xr7+Q4rM/g26Z+RBiGILX/YVgwwbN+z+oec1faP7hA/4bh5CQkJDHm4suuoixsTE+85nPcPDgQYaHh/nCF75AT8/cRdWRUIpGavGmqcxql+gl6SPcn4485w3lVhGfOkRrog8PiKksK+KXsHh2/Pajuao8WmoiywKL9VqHj+/x5+FYz7E/u+wxqskspMRrX4lMOMQ2+r4iwmssYuJWsr5I19IEpxz83VgyCCHYvFGTCNZ3s11WASwVpaqZKyQ0veiTR7EcOf4IdPdGvO716CCD6IxMkQ/XXYHYoOMN/xqvdRh5+N55N9ePwKKmnv0zsKiIqQhWdB5/nsfoGbK+GA8W4UZaItqiR8yUOYOg7sI9Suwr2XAJE0Jg9R6bhR40CaTzWE01NprHssuYvYlAS0E8vlDDroWLXQC96cULKfVhycXaOaX3POJWQ8U0TcFppx5lpybsFS0YnYmjBg0/EjEzcUQrynhccOY2XXdnFEKwbJlk4l7R1KAzR46Mm3TFhtlXPhy4u0+i8XCPllRPe6AI2t8XrU/NrmU8laa8fRJrh592QB7JolBIlib68bIDQSWMelbAuuWakMjSCHKqycxvnvIGImcSNXYixf11SzuhBNmLOtG5zKx6z70pzbYMnG35V38JcAzXd3KWm/HsXYWAoaFjmy/ELLELeMIKXRCKXSEhxw3zpq9g//oj/Lirn4mxu3jhT55P+YEJfnrOU7j8VxFe/UrBy176GDzk/5FhD6To+fCp7HrDbzn4L7eS++C5C973aUtfxM37ruKrf/gn1naextplF+Kle4lc/npi3/gTShd/HHfwrDn7nbZV8IXPwjv/TvOmv9G89a/hGReH/RgSEvL485KXvISXvOQlx7zf4AA8tN2/B8ngwbV5FkusMnGWz80w+HAoqegePJ9aNA/LgnJlpsbRkehFCsEYDwXHPb7z5+x1xfK2jcTMhVn+Ht+KNP/n+Jzj4iGoVB55WU7/6Q3rrBqGjY6m6v9tfi7Z2tdkeSQVIrAgmC0uZLNNdZrHUn39Oti9BwxTMkP+aHabO87j4EjUsvAJ0w4WzXp2VR7euMewcBaf13DXw7cQ9FqXztgsmfLDAlnH6KLpHyMIZO1VcWZbgDXzMMLLI6UhdjX19QJPQ9fiBh3FJ62+iJ5tfnK8eRg3xhpnPTNKdUcWmV5AgMKHeXZfqLfnsbCx+0xgrvXOsSCEwMgtLLbdsRKJzFZW5vwxg8SZXUSz7SRu6WbUPgDsQ2tvXsuuekw37SFNESikjdiQkeE4FZ1kaWmSfIF556AazrKL6v0n5hGhtQhmIkFju3nKi6lWbDmJbURZnFsK4/uCys4t08v2owqH0XYSy4RK9YjVa5zzMawPl7dtYKRwYI4YOXscPqIl55NsefPElelCQk4WtIf1649g//ojVJY+jSt3tPC3X3g76voyP1l5Gj/a3cnfvkPw8j8LLbqOF5lnDZJ90RIO/uutjF2565j2feNpH6EnNcSHfv06JkojeJ1rKL7423jZfiLf/0vMG/9z3oe1vl7B5z8j2LQRPvIxzb9+ysN1wzheISEhTwyWLG7EwBBGw8IifmoHseedj7P4yC8OjkWUMIN1Y/PtblXHFoZb1jbKO+6WXTPjo3Sn+slEHz5o/xOBoUHBaac2FuOLW1bREus4yh6ziGb9YPsBtTf0KtUo84j9a0YbbXsUS5r5FoaZjGD1KjFnpVETPLTx+L3884Lg07WFbiP+TnO9FlCQGX3Y1WNHh2D5MrAjj8SyKxAl3KovfBlHsMY5zu2Wm+UVrJuDpC9UWIvMI8jMMTOZ9fsxYx6hYh7vVzOiiC3LLmwcPsw2j8VYfqLNYUIIZMwgvipwxZ71LC1tRVmaOP053Ba/Q/Q8boyJs7tJnusnuxCe63urChXEhfTHo9VhEVvXim0Lcllx9DH1cH0jZvm4whHFMyEES1tX09Y8B89zjehkJ87yi8GwOWULrJ0/zOPMsoNz00cNYuYTMWL0pOb6sdrHbsA3l7pl1/z1aG2FdGrer05KQrErJOTR4FSwr3gL1k1fYTT5Gq7/0uk8+zNPwzaTfG74afxSLuYTHxNcfGEoch1vut93CvaSNHe/4ieUH5xc8H5RM867z/0CE+VRPvbbN6O1Ric7KL7gv3CWPhX7yn/E/vl75gSuB0gkBB/9sOCFL4BvXQbveo+mWAwFr5CQkCcWdTdGQ2J2xDDa0sctVpQVrJPnuKY0CwvHe7V7kkzDkSYDiuhsq4fjRH9mmHVdpz/i/VXcJHFGF9E1DSu+Iy3UvURT3LWjuY0dNSj53LhNFbcEtpgTXPmxQgWL2UgwxoeG/GxfmSZ9plbNR6tZCCExTd/dasbni7bMCEw/LzVxy30YM5DjbNm1dg2sWAaxWM3yqjnw+cIbxO1cg9ux+sj71jTphcTIejTM04kdHbD0yOGyFoDf5itXGJzxyC+/BbGt/+mc3v+0x/YgjxGpC/qwuo+suJR9r+h6IhEp1Jx7hZGx63Hc3N4tkMw2LLvqboazXAAXeOEedTvBgsao747YbBZ69OsxGhV0tD98/WpV8xYgdh0J2xacc9bCwtAdsR7zuDE209UpWLToibOuDcWukJBHSmmC6HdfjXfTVdx1/Tu55wOtlB4Y54fP+AXvyj2Xg9lWPvtpwaaNT5wJ4YmEjBr0f9G3RHjoT/6X8vaFC15DuZX85Snv49pdP+c7d3zB/9CMUr7441ROfT3m7ZcRveyVUBids69Sgje8TvLWvxZccw28/k2aw4dPkpVWSEhIyAKQtXTq6YW9Bj4Wq4X2QB8xzbnfNbwNHxvLrhNtPG1ZgjWrBEMDsHnjw25+wjBaIoh5XMm6kjMDZOvmjI9Hi6/VVNS2/qezrf/pjQ/m6ZSqW0Hbc+PNPFZkoq2s6tjMkpxvXtHdJVi3Vsxwwzpu+lH9fGc+F4hML7oWN+hI1Cy7rMdHBKxhmoLe3ubF+yMTu3RmETrbPIZmi101K8HH+kKdW74QYt45aeFF+gPEtgXR6GNbf9uIPmoh2O1ci7togYG9jjdHuAYA+nyDLbas7OG04VW0mSsYnGugVEcn2tE9gYAqQOkghmAgXFuLjhz3rS+9mLb4zIyd8957tNf4++GE5npBzdktjk/swUbGzUe3pjBNwdCQ//d87rsPX5FaNeavh9kVm2EZfLITxuwKCXkE6PFdRL/5p4zfYPDAL9+A5yiM17Xz99mXU7jtbaxZHuMD7xNkM6HQ9VhiD6VY+8PncvNFl3H/RVfQ9Z7NZC9dvKBAis9c/jL+sO+3/PsNH2RN51aWta4HIamc/le4rUuI/PRviX39+ZSe9Wm8tuVz9n/2swRdXfDuv9e89nWaj37YdxMKCQkJOdlRcZPqUCti9fGPa9XXK2hv09izYxY1//cxC1B/cszB8bgg8gjc2E4k5ww9a+5CcIHWfjU3RhFRc7MlzhI2tOMhhcSN8bhZdgF0JPqO+v3x018eRUFS4fadgn4UMZqOCzPcGI/fNaW9x0nsOkKdH5X13gL26eyAtnnyCTyeaGUi3CraTkI0c0Lq4LUsRlSm0cnOOd/19gp6ewEEi1gGcx+v51CL37V8GJK9fgxBbfn3ruj6VqLr5nf3XNq6ds5ntRc3zS9wRM3NWTSZdh0BpzuNypRmiV2PfK7f0H0GjucLbLX5Vx8HU+XBAcHgwKMuZl7ipxyDG/1JwBPrThwSchIg99yE87mnc/A37dx7+XOwBnN0ffcZvNv+DhUd5eKlL+OTHw+FrseL+KpWlv7kGUTXtrDn7ddw96nfYddf/45Dn7mdsW8/wNSv9lC45TDO4eKM/YQQvGXbJ2iNdfLBX/0505WGZZg7/HSKL/oaaE30Gy9G3fu/8x576ymCz37K7+fXvUFz7e9DC6+QkJCTH6nAbU/WA9U/HMdqiTVH6IIZK0x5nN2wrKEUMmlidp+AoPRH4PEKvn68kELOteA7UsyoWQgliW1sI3H63MXtHP2sM8aK005n7ZlnP8KaPjYcP8uu4PcjdEXS8TYwTrDVRLMV36MRu2btW3OPkvbjs/z0cjNNhh6VbreAAbJmtaCz48Re983ZQk8YZgx30Wn1rIePmiADZzQCyUiQbMPyLbqEEMfsFiuEYHn7+sYHzZZdD4OzKEfy7J7jJnZlo21167O62HUM9XlMqMVVfJIsaUKxKyTkGDDu+B7Ry17Ovj9sYfvPzyZ14SLkJ57GX3xilLHkD9iSfRlvf1MGw3hiPeQ+0TG74gx+/QIGvno+8VPayV+9n/0fuYndb7mK7S/7BQ8888fctfHb3HXKZex++9VM/XoPXsUlYad51zmf5+D0Hj7067+ov10B8NpXUnzxt/DalxP90Zswr/n0vDfExUOCL3xW0NcL7/hbzfcuf5LcHUJCQp601BZ9x3qnOl5GHsdbCFJxk9R5vfU4LycHf1zPAVZfAhmZ6zAyeyEqpCCzuptM8iRYlDdxvIyNau5VR3X7fELxqBSiGf8zOmNEV+eIrMgeYfvjh7P8Yrz2lTNrE6x6H5ll1xNjyezV4qadIKuuxwIRiF04Hl7HGt+q61EIwucOPZv+3HDjAx0EDTuGcaGbrm99vK51cfwsu0IahG6MISELwXOxfvsJrBu/xOGpZ7L9J6tJX9zP/c87g4+8WSC3fAZDKd72zNec6Jr+0SKkIHlOD8lzegDwClWckTLOaAnncInKjikKNx9m4sc7GPuf+1FZm/QlA/Rfupg3n/6PfPyqt/DJq97OW874RP3tto63Unzef2L/4n3Y13wKdfg+Sk//0BzXjtYWwaf+Bd7/Qc3H/1mze4/mdX8uUEdJhRwSEhJyoqjF013wI/XxULlmZGIL58YnCtqMLjyOzXw8MTSC4ybkep1rfPctO3l8CjzB6OMo8gghsBefOBdNL9A00o+kCie52OX2bEaUJkAas+KmPfGpWQLqqkane3DTPcf3AI/EhOk4WXY105noY9f4/eSiJ9ZNsD4VPklMu0KxKyTk4ShPE/nJ2zAe/DXjLa/kgS/2kNjYwveXnMa3PiBYtuk+dg18jUtWvPzY0oGHPKbImIkVM7H6Zgav9Cou07/bx8TlDzH2zfsZ/co9LF6S5u2nvZcvjv8L/ywUbzr9I6haphfDovzUD+K1Lce68iPEvnY/pWd8Aq91eEa50ajgg++Hz3xe881vwd69mve8i8c8kGlISEjIsVIXuxb4LHu8LbGOJeD9E5Unyzm6Q+c8qv2F5Vs9yOjJveR4mIRqx1CQgU52HafCZuK1LEZM7XtMyp6Ns+g01OH7wHoUrsEn2SVgBEOw+7HpnhOKTnagk0/ONUhtDtHOY+TeF6igbutivGxpgZU6/gHqk3aG8xY/57iU9ah4kty7apzcd56QkBOMPHwvkR+8ETG5h6lNf8/97zQgofm3lrO57ocGf/JC2Lv4g4wcTPDS9W850dUNWQDSUqTO6yV1Xi/dH6ww8ZOdjH/nAbr+K857+DtGMiP8eMXH2XTR02k5exB7IAVCUN34Ury2Zdg/fivRr7+Q8rnvwll96Yybgp+pUdDTrfnkv2re8GbNP34IWlqeXDeOkJCQJzb1zOKP44tb0WTZ9USLZ/VHzaO0WlBxk+R5Pcj4o0mF99hTd+09iYem17Yc5kmY85gQy+Eu2vrI9hXymOIgPV60tQm2naaJxU7iTg6ZQy1A/WNHMFaTnRCdOKJIu6iv+T7WLHY9ucaTTJlYA8kTaoV5PDm5bTJDQk4gxl0/Ivr1F0G1SP6SL/PgJ1NUDpf4RPoc7jpg8+EPCk695Hf8fvfPecm6N5OO5E50lUOOEZW0yL1gCUPffBrLrn4uXe/dQmRtC9nbY4y8+xbuPev7PPDSn1G8YxQAt+8Uii/9Hm7PJiI/fzeRH74JURiZU+5zny34xw8JduyEV/255uZbnhymwCEhIU8OxDFadh2fgwraYp1o+eSxejoaDyfobek9l9MWPfVxqs2JRSWtYw4i/XhzknupPaFouD6efH0eCl1PPGqWXY8VXjrI1FoLqH+E+9OyYcHw0poqfvwtu04WhBDE1rWiEif3C4qFElp2hYTMxq1gXflRrJu/htN7CqUL/4mH3nYn+RsP8fmOs9FDOb79z2m0sZPXfv+v6Usv4VkrX3miax3yKLF6E7S+agWtr1rBnQdu5Gs/+RiJKwVn//4Mpp65l4defJjKC2K0J3roOOPPWdm3lfg1nyL6lUson/9e3OGZi5bTThV8/tPw7r/XvPGvNa98Obz0JYRxvEJCQk446hG7MT6azGzQnuilI7vokZfxBOLhBL2knXl8KhKyIE5yLe4JRtiYIcePx9qyS7cswckthtGyf7yFPKc3i13HzQc65LEgFLtCQpoQow8R+cnbUQdup7L5VZS3vYk733oz3o938J2Wjax+dR+vfqUg2+Lxum+9lvHiIf71GT/CUgtLzx3yxGBlxyb+4eX/wx0XXs+1t/+U1n8ZZ8lX+/j1vb/hi0/7AAiIGnE2D2/hRYfH2PSjN+EMnkX57Heim1JdDw0Jvvg5+MS/aP79S5rrrod3vh0W9YUPgiEhISeOE2LZVT/4CThmSMjDEFp2HUciaSgcPtG1eGx4ksbFOpl5XCyBhWjcmxaifD+JLbuebIRiV0gIgNYYt/4P9pUfBTNK8ZJPUeo5h6te8Htarr+fa7qW8+zPrWTDBsnhwn7e+90Xc9Pe3/COs/6Npa1rT3TtQx4jVnVsYVXHFvR5mv0fvJFzvghPHXg+o6+HG/ZeyW+2/5DXVw+xtmeQN+y7ltVfvYTq+hdTPeW16FgL4JvM/7+/FZyyWfPJf9O8/FWaV70CXvh8MIxw1RcSEvL4U5t5FmzZdVwWG0+AoEghf7SEll3HD7dno58ZUD053KBqOMsuRGRzMD5+oqsS8hiyIOFbNglcoVJ+UhOKXSF/lLjjZaau3Etl5xTKKBPZfTmRwvVUh8+ieO4buPPyKca//H06JvPcdEqWxR8c4ZD6Nh//3XX86sHvofF451mf4ilLnneiTyXkcUAIQef/24SwJIc+fTsD6ZWc+nf/wGu3vJuf3fdNvnbLJ3lN5ADn5Rbzupu/Qs+t36K69oVUN78SnWgH4KkXCDZv8q28Pvt5zY9/An/1ejhta/iEHRIS8vhyrHqTPp4mYOGUF3ISIk/eMFNPPJSJjree6Focf4T8o4g3eDJiDSSRkcdYtqhbdi1AvHoSB6h/shGKXSF/VHhll90f+T3j/3k/wm3+Zjj4AT58NTYgcof4jxf+mNtX3AFX+1/FzATnDD6L1539XmJe5nGte8iJRQhBx9s34BVdDn/+TmTUoONv1nPJipdzwZLn8507Ps//3PopfpOucGm8lz+75b/I3fx1nKUXUF37QrzeLeRygg++T3DNtZpPfUbztndotmzWvPzPBOvWhjfLkJCQx4cT48boHyxcF4ScjITjMiTk5CW27nEQT2uTQGio9aQiFLtC/mh48A+3sO+N15DaEePqzddww4brkW37aaeNluhWijt6mLgzgSxESWy02fD8Rbww+Q5eYSaIWUniZpL2RA+GNMmms4yNjZ3oUwp5nBFC0PXezXhFh4OfvBUZNWj7y9VEzTh/uv5vuGj4T/nKHz7Gt+/9Gt9JSc6MDfHsXf/H5nuuQGQGcIafjjP8NE7bupwtmwXfuxz+6781r3+jZs1qzYteKNh2WujeGBIS8thyrG6Mjf0exdxUP1Y4v4WcfIQxpkNCQmCBAepDnjCEYlfIk558ZYqffPQzDHy5BWlpbnv1Lzg/ey2vN3LoMz7Fj3Zewn98BQ4cgK2nwOv+QrB4KJzoQuZHCEHPh7aiSw77P3wT1f0FOt+5ERk1yMXa+ettH+MFa17HFbd9lTt++1u+sr+L7+kE3fkKGye+wJbrPkck1YPbv40XrTmdZ/3nVq74dYavf0Pzrndrclm48OmaZ1ws6OsNx2FISMjxp/YC+3G17AoOKuPho2fIyUdo2RUSEgKEk8GTjPCJI+RJzT0P3shtb/oBy25ZzNjy/Zxy7vd5SmSS0vCL+f7UG/mvD6XZuxdWroB3vVOwccPCJzjHcbj88ssBeNaznoVhHNvl5DgO3/3ud7nhhhvYvHkzl1xyCT/4wQ+49tpr2b59O52dnfz4xz9menqa/v5+uru7GR0d5ZRTTuGUU05BKcVtt93Gpz/9aQDe+MY3smbNmhl1mV1HgMsvvxzX9X04XdflpptuQmvN6tWr+dznPseDDz6IaZo85znPYdu2bSjlB2EslUp8/vOfB+ClL30p//3f/017eztf+tKX+MUvfgHAxRdfzBVXXDGjTeZrp/k+m56e5mUvexkA//Ef/zGnzFKpxGWXXYaUkq985SskEokF91GlUuHGG29ESsn73/9+IpHIMfXVbISS9H58GyoXYeRLdzH5052kLurHyNo4IyWKtxzm9NsXc1plcMZ+RbvIv268ify5d3H6A99l223fIovkxS1LeOEr1nFfeR2X37SWb35riK99QzI8rDnrDMFZZ8Dg4OOUkSYkJORJwze+8Q3y+fyce9SJELtkRBE/pR3V8ujm3+OJ4zh8+9vf5rLLLmNoaIgPfOADj/r+4HouN95wI2M3e4/42eDRPFs8FjQ/r2zYsAGlFEqpx6x+8z27zH5euuKKK6hUKtxwww08+OCDDA0NsXXrVp773OcC1O/9N9xwA9u3b+d5z3sez3zmM3n/+9+P53ls2rQJy7KanktcrrvuRgzlsnXLRr73ve/xrW99C601g4P+vXzHjh0sXryYD3zgAwC85z3vwfM81q9fz0033cT27dtZvHgx733ve/nxj3/MddddhxCCLVu28LKXvYzvfOc79XM6Gfr1sWChz3zzbf+0pz2N973vfTzwwAMMDg7W+3N2W5VKJd7znvcAfh/87Gc/m3O82eMV/GfeP/zhD2zevHneco/n+dees5uvk+NxbR9LGUdaAzya45/sPKo2dgNX+0do2fVI+qZSqXD99dezY8cOnve85/H85z9/3jXcxRdfzA9+8IP6HLiQ8Vu7DprnoYfb75G0X/P12LzGmn0tgH8N3njjjQuuz/HgyTfKQ0Lwg+n+7qvfRnz0IEsKA5jnXcmFa65mrOciPnPgr/ifT/UzNQVr18AbXy/YdnooJIQcG8KQdL93C+mnL+LgZ25n9Gv3oksuMmYQXdtC66tWENvUjr00jTAlhXtG2P7NGzj956dRvnkTPzz/Cj6w+SY2JgY426tw9gM/Y1XpMlal4B3PSbFXrOXGA6v5xRVr+c5/ryHWmuOMbZpTtwrWrgHbDsdrSEjII+QYxS7N8VHFzK74cSnnZGZd5+ncL3ee6GqEHCO15GqxWPnEViQkJOSEoL3gPhemZn1SEYpdIU8q3HyV8csf4v7P/YrM9iijndMsueTbTHWu4S13XcYvf7gCy4Kzz4JLnyNYvSqc0EIeHfGtHQxu7ZiRrWw+4dTqTbD+/H7KD02y5++u5QU/upQL9l7INy75Nh/lZj4WEazqXsNp8SFOr5osPbybZ1W+yLO3+m9EDru93Lh9DTfeuIav5dcSHVjBxlOibN0CfX2hWBsSErJwEoHmtKjv2PYL55mHJxdtp93oP9HVCDlGDAWDA/sxDedEVyUkJOREEIhdIhS7nlSEYlfIk4LSPWOMfukWRr+/A10UjHaOsv3CG0lGV/OaW/+Tfdf3s34dvO0tgvPPhUQinMhCji8LXQTagykGv34Bo/91D+KDN/KG7a/FfM8irhv6PVfv/Clf3PV9vgi0xbs5ZfMFnJpYwuaqQfrQPTwlcytPm/oJAI42uPeWYW6/cg0/dteg+tczuHGQTZsF2Uw4vkNCQo6MaQouOP9E1yIk5OTCtkKhKyTkjxVh+FkqwriSTy7C3gx5wlIqaR76wV04n7se7hcIo8rdq27hJ1uuIV5eR2nfFxhanOEvLhVs2QSxWCgAhJwcCCFo+bPlJLZ1setNv6P45vs4+0Wn8KL3vp5JOc71u3/F73f/H7/ecQVXVCYxpcWazlNZtuE8huJ9LHY0/RMjDOy8g8UHf4LtfROAiRtS3PbzdVwt1kLfero2rWXVhmTo8hgSEhISEhISEhJyBIyWCLEt7ZidsRNdlZDjSCh2hTwh8DzNrl1w961TlO65juzotfTdO4J74xpMu8zYqXfxyTP+j6lEmT8Z/BTPPeXpxOPhAj/k5MZenGbx9y7kwL/cwqFP3U7+mgN0vG09F1z0PJ669AW4nsOdB2/g97t/wQ17fs23b/8sjlcFQApFb2qIRWtWMWC3s9SzGdxXZHnsXk6vfhZZ9vCuEjz0s8XsEevwutfTtm4NfesHkKZ9gs88JCQkJCQkJCQk5OTB6l54XEln6Jwwc+MTgFDsCjkp8TzNvfe6PPD7Byk/dCupqdtYFr+NZ6fvwSnEuO/KS5jatQG1VvOb1+7mspHLWJReysfP/xK96cUnuvohIQtGmJLOt24geU4Pe955Lbv+6rfsa7uB1AW9JJ/ax6pTN7Fm86m8evO7cLwquyce4KGxu9k5fi87gp9rJv4XV/vuF73di1nddh4rjU569kD73p2sq/yS9OR34LfgXGlwSA9QySwlPrCU+OBSvJYl6HQvyPCWEBISMpfjFaA+JCQkJCTkSYH15E+48mQgXNmEnHi0RuQPkd9+H/tuvY/K7ntJFu9lVfRBNhtFSEEhmWYiuYadI2/g0LeSeA6MvkXz713/xr7DO7hw+CW8buv7iZrhxBPyxCS+uZ2l//tMpn65h/HvPcj4D7Yz+vX7EKYktrGN+BldJM/ppn/tMgayy2fsW3UrPDB6O7cfuI7bD1zHtXt+yU9LIwBk2ltZnFtF3F0CYy56YhpVHiNV+DnJ+76LuM9fxGoktpGiO95JT3qA/tY1RHNL0akedLITHcuBkI97u4SEhISEhISEhISEhBwrodgV8vjgOYip/cjxnYjxncjxXTC2g+rBXVjTuzApEgfagT1uC7sS/exvewrmQDeya4BqJY33bwexf1xmdGicrz7na+xIPshwZB1vO+uTrO087USfYUjIo0ZIQeopvaSe0otXcSlcf5Dp3+1j+qr9HPznWzj48Zuxl6bJvnAJ2ecOYbRGATCVxfK2jSxv28jzVv8FWmv2TD7IbQd+z+0Hfs+O8fvYX97FpBjDSVYg6Sed8VyJ53m+4IyDIw9A+QAcvAV54HIWe4rTqyZnV02Waxsv3gnJDl/8SnSikx14iU50oh0da0FHM2idObGNGBISEhISEhISEhLyR08odoUcPyp55MQuX8ya2B38vcv/PbkXrywY3zHI2ANLmTrQQ3lyI563GdfQTOZK7Os7yIOLHuCu9lsZjf0Ot+jSdlUr6+9Yy7brTyNSjvCzs37FHy66k/V9Z/BXiz/K+q5tYSr0kCcl0lIktnWR2NYFgDteZuJ/dzH2zfvZ/8Eb2f+Rm0g9pY/s8xeTOLMLGWlM50IIetOL6U0v5sLhFy/oeIdHNHfcVebm+/dwz96HOFS+gV2x3/LVlpv5SqREvAxrRuCsPSU2m3fSbv+KiCzOKaeCgWGkcawsnp2GSAoZTaLiCWQsBXYSHfxgJ9FWEteLUZ00cUs2GoV2NEiBSpiolInK2MhoeLsKCTnRCML7bUhISEhISMgTg3D1ELJwtIeYPoSYCMSs8Z2I5t/F0ZmbR7PkvSXsufNsRu5oR2+3kB4UlMlD2QzOhv2Ukw/gTI3QdqiNxbf1sOr6QZ7JU2aWI0GcmSD6pgH+Ys1ziVupUOAK+aNDZWxyL1hC7gVLKD84ydg372PssgeZ/OlOhCWJLM9i9SVQORuVtn2RKGWhUhYyZWFkbMzuGKolMu/109oiOPuMCGefsRhYDDwFrd/Bzv0T/OKu/+O6Az/kOvuXXEuVuDNMavxPSe+/gNSURdzdj+2OE2OcjDlGxhgnK6dJukWijoNdHcGsjGJUFG4xQrUYo1qIUy3EqeTjeNWHD5hvJB3sdg+rU2L3mdhDSeyhLNaydmRrKzqSAWUe93YPCQkJCQkJCQkJCXniEYpdITMpTyEndvvi1cRuxMSuwEprN0zswZkyKE+lqZRtPEx0pBWdWIRObKRgtnF4spWDu5JMPKSIbR+jrTgBwGEzwa7lBgdPv597hn/G9sItAPRnhtm26EJW9j+dpbm1OLvyFG8doXqgiC67WL1x4qd2hmlgQ0KasIdSdP7tJjretoHpq/eTv3o/xTtHKd0zjjtexp2ooKvevPuKiMLqTWB2xpApC5UykQkTlTCRScv/nTBRSRMZNWipejzP3cpzjA3ki29gx0N3cWDXdqqHDxHPX07ayRDXSWzHQpdNdKkFvBwAGigEPwBaCCpRm0rEoGJJyilBMaMpiipEKpiRIhGjSNzOk46USMoKUV3FKGu8CYvqRJTC7SnGrk6DngQmgR1Io4pQDtLwEIZGmAJpClQC7E5NpAeiSySxZTYqEwEzAspCCwlC4nWuQyc7HoeeCwkJCQkJCQkJCQl5PDhpxS6tNXcevAGNJh3JkYm0krDSTwiLnur+Avlr96MyNkYuMsOa4vBhjeOCYYBpgGmCUmBZPDbnpjVUC4jSOKI0gSiO+38XRhHTB4Kfg8jgd7Ga56D0OCQ9qsUI4vBiOLgEsfsC9K4MlNTDHHCcDOPYpmBfj8MDKw5x/+qbuT11DWW3iBIGK5Obee2q93D6oqfTmx6asbcaSGEPpI5/O4SEPAkRhiR5VjfJs7pnfK61Rpdd3ImK/zNZwR0rU92Xp7IrT3X3NNX9BaqHinjTVdypKt501Q/k9TCkI4qW1pWQVYxmRtmndzHq3k3VqhJLpGlr6SIaT5HKtZDuaiPWkSXV2YLZGkckFa5wKVULFJ08heo0pWqeydI0Y2OQn4ixbyTBxKEso3tb2b/PZs8+yOeDg0tQrQ5DK8ZZl9rPkDxES3GChFsgosF0yyi3guuUqVarOFOK4s0p3N8m6/WPZPaT6NhHomMfVmIKaVXwejdSWvNqvILj/xQdvKILjocwpf8TNTBaIhhtEYyWCKo1gpeAilei7JYoO8Xgp0TZLVBxK6TsLO12N7HJGO7+ItV9BdCa1NMWIe2Hm0uPjtaaklNgsjzKRMn/qf09VR4jYWd4+elveVTHOFlwXU2l4t/OYrGT/xngSYkOszGGhISEhISEPLE4acWufVM7efMVl8xIdx014nQkemlP9NAe76U90UtHoofWWBcRM4albCwVwVI2ShhzxKNarInG5wLQ/sIwOE7tb42G2t9ao/Ea32n8/YLvxNhDGHf9EC3AQ1G4ooPi5Z0zju2ZLuVMiRGrwERymolEnqpRRQPKNTAcE9MxMV0L07GwXBPLMTA8hVQCDIEyJdIAwxBI08UwHQxZxXBdlOciHQ/leAhHIxzPX6ipEjKSR0eK6GiBcqRIwaowZZWZsBwOGwbjrkneUTilHpITKVpHW1i0p4+OkXYAXOGyp2sv29fexcHWQ4xlxshHC3jSQ2sTgQbhILTAMRxG02OUoiUAElaaJS2reUbuz1jbeSrru84gbiUJCQl57BBCICIGMmJgdizMKlJrjS46deHLnfZ/C0shIwqVtjBaI8jYXFfBHeP38tvtP+L2A9fzo9EfM1487H8xHfw8cIwnYAEDEB9O0RlpJWW2Yus2RLUVr9DG9JTg2nyBn5emKetRXHMUETmMiBwGewwhZ1q1RUoRevf2MLB7Cf17BujbN0D6nlWzDnrlnGp4QiP1kcUVV7jk43mmY3mm49MUI0WEFijXIJlPUJ1MU55OIGlksfSk5tp3lBBLWomqNDEjiZQK0EgJHlXK3hQld4KiO0nRHSfvjJJ3RpmuNn5PV0epeqV56yWQZO0eVkVfQ3na1yk8b+aP1uB6oGd97mn/sxnfafDcWd9pcN2ZZfuf6XnLrn3mulCp+D/l8hF+z/recRrn9sH3wTlnh4JXSEhISEhISEjI0Tlpxa7uVD9fvvQqDkzvZrI8ymjxIIem93Awv4cD0/+fvfcOk+Qq7/0/p6q7J8/sbF7trrK0AmUJAUJChOsL2ESRMRds7AvmYrCvfX1B9rXlS7KMzTX4J2zQg4k2YGERLRAIoRxXYbVabQ4zuzs59XTurnDO748KXT3TMzub0/t5Hmm2q6qrTp1TdbrOt77vewbYObmJbGX8eBezKepyxeKzeukod9BV6mRRbhG9uUX0TveyONfL2oElXFw8p+E7nu3ipTzctIuT8vBSLm6qgmv72K5NyktheYEolvJSpL0UKa8FCwvP9qimXDzbx0u5eCkfN+Xi2z4Zt5328lraK23YJnAStIT/LSXIzJNEYyh0WEwsaWffBV0Uz26hdm6aVO85ZNqu5IzWMme3l2hpL6OtChW3jG2laE210Zpqpy3dwZK2FSztOINlHatOGjeeIJzuKKVQ7elAzDrIiL6zFl3IWVf8KQC9vb3sG+ljML+HsdIQxVqOgjONr31sy8ZWNq2pDtrSHbSnO2lLt9OW7gSg4gZur1x1kunKJNnqONnKONOVCbLV3Uy7j5PzpzDthtbutuAFSGsv3emltFkXkDHXYrtLobYE5SzBqdmUazUqToVSzyTPdI/x6MVDuPaztLpF2muaFu2ilEMtU6OWcalZ4KTAMy1orxNV68aqddJS6aGz1ENnoYuucjtdlVY6Kxk6qx101rrpmrJY6ikMFj4W+VSafZ0+0ytLTPdMkls8TG5ZP8Vlu6i0DMH+hdWtMQpqvZjqYqgtxtTWYGqXQXUxprY4sbw3+FtdDG43FRT//avH3o2jFFhW4j8FKvE5cjK3ZMK/LcHfzk5YsqRxWX0bRSb895VXHPNTEghmfAXie1UQBEEQBOFE54QVuwDW9Jw7K8wtieNVGSsNMVEexvGqOH4Nx69R8yv42ofIrZVwbSU/YwwohUIFAz0UJP4diTSWsuZYN8d24TZt6Q460t10ZLroTHfRkWonY2VQxse4Hvge+D7K0ijlh6+/PYzv4bhlyn6Zsu9Q1lUqvoNqtRgvTFLwa2DZGGVhULh+GtdN43kG1wPPDf4CZOwMmVSGjJ2mpZam02mj23TTQxc9qXZaLEh1pLHaU9idaVKr2rEyhxdeIwjC6U1XyyIuWnYVFy276ojvO+jbwbaOXD/laReFwrZSaG1w3cCBBI1uJ20AU3cqJdcZ6tvEf02TZRrKXoGCM0nZy1Fy82gdONGMVihStFhdtFg9ZOiixe5GGbtpGbQOhaQZApOywLagu7uTUqnYuE4FglODKKXAsuv7UU2WRd+duaxhnXWUwvGF405P6xIuX/UyetuWHe+iCIIgCIIgLIgTWuw6EJlU6wEFsRMVlWn8PPP9eyb8b1FiWW9vL9ls9qiWSxAE4UTmSIpcESmrHpppWYqWA08OeZh0h/8dXXp7M2SzIj4JR4Yl7TKJgyAIgiAIJw/WgTcRBEEQBEEQBEEQBEEQhJMDEbsEQRAEQRCOEwMDA/zFX/wFr371q7nsssv4jd/4Df6//+//w3Gchu2Ghob48Ic/zBVXXMFLXvISPvOZz8zaRhAEQRAEQQg4qcMYBUEQBEEQTmb27NmDMYZPfepTnHXWWezYsYO/+qu/olKp8IlPfAIA3/f5gz/4A3p7e/nud7/L9PQ0n/jEJzDG8Fd/9VfH+QwEQRAEQRBOPETsEgRBEARBOE7ccMMN3HDDDfHntWvX0tfXx/e+971Y7Hr44YfZtWsX999/PytWBLmzbrrpJm666Sb+5E/+hM5OmSVREARBEAQhiYQxCoIgCIIgnEAUCgV6enriz88++ywXXHBBLHQBXH/99TiOw/PPP388iigIgiAIgnBCI84uQRAEQRCEE4R9+/bxb//2b9x0003xsomJCZYuXdqwXU9PD+l0momJiXn319HRQW9vL6mUPPLNxPM8Ojo6SKfTtLS00NvbS2tr6xHZJ3BI9T7X93t7ew+rXIdDVKbW1lY6OjqwbRvbto/adTWzDoCG4/f29ja0WyaToaWlJV4XbR+tT6fT8brW1la01nR0dJDJZOJzmHnM6PvGGFrCKXqT1wnQsK/W1taG9dGyqCzJv6fy/djs+p3vnpi5LmqvZHvOrKtqtRrXbVTXzY6XvF4hCAdPXkOH2gbz3YvRsX3fB2i4Tw63b0jufyH7mOs+Opzjn+gcTP0c6T71UNomnU7HfcfM67JZn3Qw12/yPoCFPQscyjU6836M/j3zXoD6PbjQ8hwJTr2rXBAEQRAE4Thz66238qUvfWnebe644w4uvfTS+PPo6Cj//b//d173utfxjne8o2FbpVTTfcy1PKJUKpHNZk/Jgc3h4nkepVIJ13Wp1Wpks9kjInaVSiWAQ6r3Zt/v7e0lm80eVrkOh6hM1WqVUqkUi11H67qaWQdAw/Gz2SylUgnHcajVavHfaF20fbTcdd14XbVaRWsdt3t0DjOPGa03xlCr1QAarhOgYV/VarVhfbRMKRXv93Cui5OFZtfvfPfEzHVReyXbs5nYVa1W4+/Mdbzk9QrBQDt5DR1KGxzoXoyOnRS7FlIPC+Vg9jHXfXQ4xz/RWWj9HI0+9VDaxnGcuO+YeV0265MO5vpN3gdRP3Sw18xCxa7k/ZgUu5L3AtTvwYWW50AsRLA89a5yQRAEQRCE48x73/tefuu3fmvebdasWRP/e3R0lPe///1cccUVfPrTn27YbunSpWzcuLFhWS6Xw3VdlixZcuQKLQiCIAiCcIogYpcgCIIgCMIRZvHixSxevHhB20ZC18UXX8wtt9yCZTWmVL3iiiv4yle+wtjYGMuXLwfgkUceIZPJcMkllxzxsguCIAiCIJzsSIJ6QRAEQRCE48To6Cjve9/7WLlyJZ/4xCeYmppifHyc8fHxeJvrr7+e888/n49//ONs2bKFxx57jM997nO8853vlJkYBUEQBEEQmnDIzq7jmSjzdEbq/cShWVLMg2FmAs1k8sEoOWeUi8W2bVKpFLZtx0k7bdtuyC3SLGnhXMkpo/hp3/fjxKvJJJ5KqYbjRGWI/h0tT6fTs+phIYlJmy1Lp9Ok0+mm+0kez7Isent7GwZ4c90XMxNARt893JwswvxIP3XiIG1x4vPII4+wd+9e9u7dyw033NCwbvv27UDQ/95222188pOf5D3veQ+tra284Q1v4BOf+MQB9y8J6udGEtQfXJkkQb0kqD8QkqBeEtQfTyRB/exjnO4J6pUxxhzVIwiCIAiCIAiCIAiCIAjCMULCGAVBEARBEARBEARBEIRTBhG7BEEQBEEQBEEQBEEQhFMGEbsEQRAEQRAEQRAEQRCEUwYRuwRBEARBEARBEARBEIRTBhG7BEEQBEEQBEEQBEEQhFMGEbtOcL785S/z7ne/m8svv5wXvehFTbcZGhriwx/+MFdccQUveclL+MxnPoPjOMe4pKcH3/nOd3j1q1/NpZdeylvf+laeeuqp412k04Inn3ySD3/4w1x//fWsW7eOe+65p2G9MYZbb72V66+/nssuu4z3ve997Ny58ziV9tTmtttu421vextXXnkl1157LR/5yEfYs2dPwzbSHseG7373u7zxjW/kqquu4qqrruJd73oXDzzwQLxe2uH0RX6rTlyOVB/qOA6f/vSneclLXsIVV1zBhz/8YUZGRo7lqQgJbrvtNtatW8dnP/vZeJm048nB6Ogof/Znf8ZLXvISLr/8ct785jfz/PPPx+ulHU98PM/jC1/4Aq9+9au57LLL+C//5b/wpS99Ca11vI204+mJiF0nOK7r8rrXvY73vOc9Tdf7vs8f/MEfUC6X+e53v8sXvvAFfvnLX/K5z33uGJf01OfnP/85t9xyC//jf/wPfvzjH3P11VfzwQ9+kKGhoeNdtFOecrnMunXruPnmm5uu/+pXv8o3vvENbr75Zu644w6WLl3KBz7wAYrF4jEu6anP+vXree9738v3v/99vvGNb+D7Pr//+79PuVyOt5H2ODasXLmSP/uzP+MHP/gBP/jBD3jpS1/KH/7hH8YPb9IOpyfyW3Vic6T60M9+9rP86le/4gtf+ALf/e53KZfL/MEf/AG+7x+P0zqtee6557j99ttZt25dw3JpxxOfXC7He97zHtLpNF/96lf52c9+xk033UR3d3e8jbTjic9Xv/pV/v3f/52bb76Zn//85/zv//2/+drXvsa//uu/Nmwj7XgaYoSTgh/84Afm6quvnrX8/vvvNxdddJEZGRmJl915553mkksuMYVC4VgW8ZTn7W9/u7n55psblr3uda8zn//8549TiU5PLrzwQvOrX/0q/qy1Ntddd5257bbb4mW1Ws1cffXV5nvf+97xKOJpxeTkpLnwwgvN+vXrjTHSHseba665xnz/+9+XdjiNkd+qk4tD6UPz+by5+OKLzc9+9rN4m5GREXPRRReZBx988NiewGlOsVg0r3nNa8wjjzxi/tt/+2/mM5/5jDFG2vFk4e///u/Ne97znjnXSzueHHzoQx8yf/7nf96w7KMf/aj5sz/7M2OMtOPpjDi7TnKeffZZLrjgAlasWBEvu/7663Ecp8GCKxwejuOwefNmrr/++obl1113HRs2bDhOpRIABgYGGB8fb2ibTCbDNddcI21zDCgUCgD09PQA0h7HC9/3+dnPfka5XObKK6+UdjhNkd+qk49D6UOff/55XNfluuuui7dZsWIFF1xwgbTzMeZTn/oUr3jFK3jZy17WsFza8eTg3nvv5ZJLLuGP/uiPuPbaa3nLW97C97///Xi9tOPJwdVXX83jjz9OX18fANu2bePpp5/mFa94BSDteDqTOt4FEA6PiYkJli5d2rCsp6eHdDrNxMTEcSrVqUc2m8X3fZYsWdKwfOnSpYyPjx+nUglAXP/N2kbCdo4uxhhuueUWrr76ai688EJA2uNYs337dt797ndTq9Vob2/nn/7pnzj//PN55plnAGmH0w35rTq5ONQ+dGJignQ6HQtkyW3k2e/Y8bOf/YwtW7Zwxx13zFon7XhysH//fr73ve/xgQ98gA9/+MM899xzfOYznyGTyfCWt7xF2vEk4YMf/CCFQoHf/M3fxLZtfN/nT/7kT3jDG94AyP14OiNi13Hg1ltv5Utf+tK829xxxx1ceumlC9qfUuqglguHzsw6NcZIPZ8gNGsb4ejyqU99ih07dvDd73531jppj2PDOeecw49//GPy+Tx33303n/jEJ/i3f/u3eL20w+mJ/FadHBzpPlTu72PH8PAwn/3sZ/n6179OS0vLnNtJO57YGGO45JJL+NM//VMAXvjCF7Jr1y6+973v8Za3vCXeTtrxxObnP/85P/3pT/l//+//cf7557N161ZuueUWli9fzo033hhvJ+14+iFi13Hgve99L7/1W7817zZr1qxZ0L6WLl3Kxo0bG5blcjlc152lXguHTm9vL7Ztz1L2JycnZznrhGPLsmXLgOCNzPLly+Pl0jZHl09/+tPce++9/Nu//RsrV66Ml0t7HFsymQxnnXUWAJdeeimbNm3i29/+Nh/84AcBaYfTDfmtOnk4nD506dKluK5LLpdrcCFMTk5y5ZVXHqMzOL3ZvHkzk5OTvPWtb42X+b7Pk08+yXe+8x1+8YtfANKOJzrLli3jvPPOa1h27rnn8stf/jJeD9KOJzp/93d/x4c+9CFe//rXA7Bu3TqGhoa47bbbuPHGG6UdT2MkZ9dxYPHixZx33nnz/jffW6IkV1xxBTt37mRsbCxe9sgjj5DJZLjkkkuO1imcdmQyGS6++GIeeeSRhuWPPvqodIDHmTVr1rBs2bKGtnEchyeffFLa5ihgjOFTn/oUd999N9/61rdYu3Ztw3ppj+OLMQbHcaQdTlPkt+rE50j0oZdccgnpdLphm7GxMXbu3CntfIx46Utfyn/+53/y4x//OP7vkksu4Y1vfCM//vGPWbt2rbTjScBVV10V53mK6O/vZ/Xq1YDcjycL1Wp1lmvLtu3YlSXtePoizq4TnKGhIXK5HENDQ/i+z9atWwE488wz6ejo4Prrr+f888/n4x//OB//+MfJ5XJ87nOf453vfCednZ3HufSnFh/4wAf4+Mc/ziWXXMKVV17J7bffzvDwMO9+97uPd9FOeUqlEvv27Ys/DwwMsHXrVnp6ejjjjDN4//vfz2233cbZZ5/NWWedxW233UZra2scqy8cOT75yU9y55138s///M90dHTEeRC6urpobW1FKSXtcYz4h3/4B2644QZWrlxJqVTi5z//OevXr+df/uVfpB1OY+S36sTmSPShXV1dvO1tb+Nzn/scvb299PT08LnPfY4LL7xwVqJ04ejQ2dkZ51mLaG9vZ9GiRfFyaccTn9/5nd/hPe95D1/5ylf4zd/8TZ577jm+//3v86lPfQpA7seThFe96lV85Stf4YwzzojDGL/xjW/wtre9DZB2PJ1RRgJRT2huuukmfvSjH81a/u1vf5uXvOQlQCCIffKTn+Txxx+Pb9pPfOITZDKZY13cU57vfOc7fO1rX2NsbIwLL7yQP//zP+eaa6453sU65XniiSd4//vfP2v5jTfeyN/+7d9ijOFLX/oSt99+O7lcjssvv5ybb7551oOocPisW7eu6fJbbrklDueQ9jg2/MVf/AWPP/44Y2NjdHV1sW7dOj74wQ/GMwlJO5y+yG/VicuR6kNrtRp/93d/x5133km1WuXaa6/lr//6r1m1atUxOQ9hNu973/u46KKL+D//5/8A0o4nC/fddx//8A//QH9/P2vWrOEDH/gA73znO+P10o4nPsVikX/8x3/knnvuYXJykuXLl/P617+eP/zDP4zHw9KOpycidgmCIAiCIAiCIAiCIAinDJKzSxAEQRAEQRAEQRAEQThlELFLEARBEARBEARBEARBOGUQsUsQBEEQBEEQBEEQBEE4ZRCxSxAEQRAEQRAEQRAEQThlELFLEARBEARBEARBEARBOGUQsUsQBEEQBEEQBEEQBEE4ZRCxSxAEQRAEQRAEQRAEQThlELFLEARBEARBEARBEARBOGUQsUsQBEEQBEEQBEEQBEE4ZRCxSxAEQRAEQRAEQRAEQThlELFLEIQjzj333MM3v/nNhmU//OEPWbduHQMDA0fkGE888QS33nrrEdmXIAiCIAjC6YY8rwmCcCojYpcgCEece+65h29/+9tH9Rjr16/nS1/60lE9hiAIgiAIwqmKPK8JgnAqI2KXIAiCIAiCIAiCIAiCcMqQOt4FEATh1OKmm27iRz/6EQDr1q0D4MUvfjE33ngjAJOTk/z93/89Dz74IN3d3dx444187GMfw7bteB9TU1N88Ytf5N5772V6epq1a9fye7/3e7zjHe8A4NZbb43fEkbHWL16Nffeey+1Wo2///u/59FHH2VoaIiuri6uuuoqbrrpJlatWnXM6kEQBEEQBOFERZ7XBEE41RGxSxCEI8pHPvIRpqam2LJlS/yA09nZyXPPPQfAxz/+cd785jfzrne9iwcffJAvf/nLrF69On4wKhaLvOc978HzPP74j/+Y1atX88ADD/BXf/VXuK7Lb//2b/OOd7yDkZER7rjjDm6//XYAMpkMAI7jUKlU+MM//EOWLVtGLpfje9/7Hr/927/NXXfdRWtr63GoFUEQBEEQhBMHeV4TBOFUR8QuQRCOKGeeeSaLFy8mk8lwxRVXxMujh6cbb7yRD3/4wwC87GUv45FHHuGuu+6KH56+9a1vMTIywp133snatWvj7QqFArfeeivvete7WLlyJStXrgRoOAZAV1cXn/3sZ+PPvu/z8pe/nGuvvZYHH3yQ17zmNUfr1AVBEARBEE4K5HlNEIRTHcnZJQjCMeWGG25o+HzBBRcwNDQUf37ooYe44oorWLVqFZ7nxf+9/OUvZ2pqir6+vgMe48477+Ttb387L3rRi3jhC1/I5ZdfTrlcpr+//0ifjiAIgiAIwimHPK8JgnCyI84uQRCOKd3d3Q2fM5kMjuPEn6empti7dy8XX3xx0+9ns9l593/PPffwv/7X/4pzS/T29qKU4kMf+hC1Wu3wT0AQBEEQBOEUR57XBEE42RGxSxCEE4pFixaxbNkybrrppqbrzznnnHm/f9ddd3HmmWfyt3/7t/Ey13XJ5XJHtJyCIAiCIAinK/K8JgjCiY6IXYIgHHEymcwhv5V7+ctfzne+8x1Wr17N4sWL5z0GQK1Wo6WlJV5erVZJpRq7tjvvvBPf9w+pPIIgCIIgCKci8rwmCMKpjIhdgiAccc4991xuv/12/uM//oN169bR2dm54O/+7u/+Lj//+c9573vfy+/+7u9y9tlnUyqV2LNnDxs2bOCf/umfADjvvPMA+PrXv851111HS0sL69at4+Uvfzn33HMPf/M3f8OrXvUqNm/ezL/+67/OsuMLgiAIgiCczsjzmiAIpzIidgmCcMR5+9vfzjPPPMPf/u3fUiwWefGLX8yNN964oO92dXXx7//+7/zTP/0Tt912G2NjY3R1dXHuuefyute9Lt7uVa96Fe9617v45je/yT/+4z9yxhlncO+99/LOd76T4eFhfvCDH3D77bdz6aWX8uUvf5mPfvSjR+t0BUEQBEEQTjrkeU0QhFMZZYwxx7sQgiAIgiAIgiAIgiAIgnAksI53AQRBEARBEARBEARBEAThSCFilyAIgiAIgiAIgiAIgnDKIGKXIAiCIAiCIAiCIAiCcMogYpcgCIIgCIIgCIIgCIJwyiBilyCcRtx6662sW7eOK664glKpNGv9j370I9atW8e6desYGBho+N4TTzxxyMe96aabePWrX9103Q9/+MNZxxMEQRAEQTgdkWc1QRCEI4OIXYJwGmJZFnffffes5T/+8Y/p6OiYtfxLX/oS69evPxZFEwRBEARBOO2RZzVBEITDQ8QuQTgNec1rXsOPf/zjhmXDw8OsX7+e1772tcenUEcA13UxxhzvYgiCIAiCIBwW8qwmCIJweIjYJQinIW9+85tZv349IyMj8bKf/vSnrFq1ihe96EUN265btw4I3hhGtvkf/vCHR72Mt99+O29605u49NJLeelLX8pf/uVfks/nZ5XtH//xH/nSl77EK17xCi699FIKhUJsxd+wYQPveMc7uOyyy3jta1/LAw88AMC//Mu/8KpXvYqrr76aj370o0xPTzfst1gs8qlPfYrrr7+eSy65hNe+9rV885vfbHg4e+KJJ1i3bh333XcfN998M9dccw0ve9nL+Ou//muq1epRrx9BEARBEE5d5FlNntUEQTg8Use7AIIgHHte+tKXsmLFCn7605/yoQ99CICf/OQnvOlNb0Ip1bDt7bffzrve9S7e/va38453vAOAM88885CO63nerGVa61nLPv/5z/PNb36T3/md3+HjH/84IyMjfPGLX2TXrl185zvfwbbteNv/+I//4MILL+Tmm29Ga01raysAuVyOv/zLv+T3f//3Wbp0KV/+8pf5oz/6I9797nezf/9+/vqv/5rx8XFuueUWPvOZz/D5z38+Ls+HPvQhtm7dyh//8R9z/vnnc//993PLLbcwNTXFn/7pnzaU9dOf/jSvec1r+OIXv8jzzz/PF7/4RXp7e/mf//N/HlIdCYIgCIIgyLOaPKsJgnB4iNglCKchSine9KY38ZOf/IQPfehDPPfcc+zevZs3v/nNbNiwoWHbK664AoCVK1fG/z4UBgcHufjiiw+43cDAAF/72tf4kz/5k/jhDuDss8/mve99Lw888EBDAtVMJsNtt91GOp1u2E+xWOSzn/1sXObly5fz5je/mYcffpj//M//xLICY2v0UPZ3f/d3WJbFAw88wNNPP83nP/953vjGNwJw/fXXU6lU+MY3vsHv/d7vsWjRovg41113HTfddFP87w0bNnDXXXfJA5QgCIIgCIeMPKvJs5ogCIeHhDEKwmnKm9/8Znbt2sXzzz/PT37yEy6//HLOOeeco3a8ZcuWcccdd8z676Mf/WjDdo8++ihaa17/+tfjeV783xVXXEFHRwdPPfVUw/bXX3/9rIcngM7OzoYHvnPPPReAa6+9Nn54ipa7rsvk5CQATz75JKlUit/8zd9s2N+b3vQmHMdh48aNDctvuOGGhs8XXHABQ0NDC6wVQRAEQRCE5sizWn25PKsJgnCwiLNLEE5TzjvvPC655BJ+8IMfcNddd/Gxj33sqB4vk8lw6aWXzlq+c+fOhs/Rg8xc019ns9mGz0uXLm26XU9Pz6zjN1sePXzVajUgsNT39vaSSjV2j9Fxcrlcw/Lu7u5Zx3Ecp2mZBEEQBEEQFoo8qwXIs5ogCIeCiF2CcBrzlre8hb/5m7/Btm1e//rXH+/iAMS2829+85t0dnbOWt/b29vweWbeiiNx/Gw2i+d5DQ9RExMTwOwHMEEQBEEQhKOFPKs1P748qwmCcCBE7BKE05jXv/71PPLII6xbt64ht8FM0ul0/DbtaHPddddhWRYjIyPceOONx+SYSV784hfzL//yL9x1111xHgiAO++8k0wmc1i5MARBEARBEA4GeVabjTyrCYKwEETsEoTTmMWLF/OVr3zlgNudd9553HfffVx33XV0dnayZs0aent7ufXWW/nSl77Er3/9a9asWXNEynTmmWfywQ9+kP/7f/8vu3fv5pprriGTyTA8PMwjjzzCe97znllTbh9JXv7yl/PiF7+Ym2++mcnJSc4//3wefPBBvv/97/ORj3xE3hYKgiAIgnDMkGe12cizmiAIC0HELkEQDshf/uVf8ulPf5oPfvCDuK7LLbfcwlvf+tajdrw//dM/5dxzz+W73/0u//Zv/4ZSilWrVnHttdeydu3ao3ZcAMuy+PKXv8wXvvAF/uVf/oXp6WlWr17Nn//5n/O7v/u7R/XYgiAIgiAIh4I8q8mzmiAIjShjjDnehRAEQRAEQRAEQRAEQRCEI4F14E0EQRAEQRAEQRAEQRAE4eRAxC5BEARBEARBEARBEAThlEHELkEQBEEQBEEQBEEQBOGUQcQuQRAEQRAEQRAEQRAE4ZRBxC5BEARBEARBEARBEAThlCF1qF/MZrNHshynFT09PeRyueNdjNMSqfvji9T/8UXq//gi9X/8ONi67+3tPYqlOXZoreWaOwWQvuPkR9rw1EDa8dRA2vHUYCHPauLsOg5YllT78ULq/vgi9X98kfo/vkj9Hz9O17o/Xc/7VEPa8eRH2vDUQNrx1EDa8fRBWloQBEEQBEEQBEEQBEE4ZRCxSxAEQRAEQRAEQRAEQThlELFLEARBEARBEARBEARBOGUQsUsQhFOKWl+eff/jAYY/+xRGm+NdHEEQBEEQhDq+izW6GbR/vEsiCIJwSnPIszEKgiCciIx/+XlyP9sLQNd/WUPnS1ce5xIJgiAIgiAEWFN7sLL9mHQ7ZvE5x7s4giAIpyzi7BIE4ZTBzztM/6SfRW87F6s7Tfb7u493kQRBEARBEGZj9PEugSAIwimNiF2CIJwyFO4fxFQ8lrx/HYveeA75n+/FuPIwKQiCIAjHg75+Q/9eSSnQiAr/Sr0IgiAcTUTsEgThlKGycRLVatN26RI6X74KXfaobJk63sUSBEEQhNOSiQkYGzvepTgxUUbELkEQhKOJiF2CIJwylDdO0HbJYlTKov2qZcGyp8ePW3l87XP/np/wzNBDx60MgiAIgnC88Hxw3eNdihOMyNglzi5BEISjiohdgiCcEhhfU31+irbLlwKQXtlOek0H5WeOn9j169138Jn7/4CP/+Id7JzcdNzKIQiCIJw4pLb9DGt82/EuxjFBi9jVhFDtOsG1rulpw8TkCV5IQRCEeRCxSxCEU4Larjy67NF++ZJ4WftVy46r2PWf275NV0svlrJ5qP9nx60cgiAIxwuV3QuV7PEuxgmHNXl6TKCiNbgeGAnZizHq5MjZ9eTTsOHZ412K44jvyCQCwilHqXRi9ztHGhG7BEE4JajtnAag9aLeeFnbCxfjDpTwi8f+tfL+3C62jj/N+6/8X1y56noeFrFLEITTEHv0eVJ7Hz3exThxOM1EHx1qBZ53fMtxYnFyiF2nO6mdv8Le+9jxLoYgHDGmpgyPPg6DQ6dP3yNilyAIpwS1vjwoyJzdFS9ruXBRsC4Uwo4lm0aeAOAla36Dl679r+zL7WSyPHrMyyEIgiCcSJw+gwyoi12HGspYqE1z7+4fka+dQu7A2NklnOio6vTxLoIgHDGKpfBv8fiW41giYpcgCKcEtT150qs7sFpT8bLWC3sAqO6YPubl2Tz2JL1ty1jVdRbnL7kEgL7s1mNeDkEQhBMBrQ2ed3oJPU05zZxdvh/8PVSxa7I80vD31CDK2SUhcoIgHEPCnx/rJNHb3ZEyfsE5rH2I2CUIwilBrS9PyzndDcvSazpRbSlqO3LHvDybR5/k4uXXoJTinN4XALBnassxL4cgCMKJwPon4b4HjncpTgQSYtcpLnwZY+KzdSWMsU4cxXhqt78gCCcWOupyThKxq/TEKIV7Bw9rHyJ2CYJwSuDsyZM5t1HsUpai9fyeY+7syteyDOR384LlVwPQ2dLDso7VInYJgnDaUjhBwyYqbgl9jBw26/f/mkf23lVf4J/a0xRGri4A9/Bezp9iHNxIUxvNaHHgKJXlyFCriXNTOLXYOvYMW8eeOd7FOKJEYeXWaaQAnUanKgjCqYqXreJPO7OcXQAtF/Yc85xduyc3A3DhksvjZecufoGEMQqCcNKhtWFgwJySs+n52uOxvXexbfDhY3K8opOn5lUTS069Ok2iExqiOLuSRGGMC2v//ux2No8+yURp+CiW6fB48GF4Yv3xLoUgHDmGC3sZLuw93sU4soRdzkli7JoXx1lY/ylilyAIJz3O3sAykExOH9F64SLcoYXFfLsjZUa/sJHsj/YcVnl2TwVi17mLXxgvO3vROvbldh0zB4EgCMKRoK8ftm6H0VNwfg1fe1hTfeT2PgBGo43G08fQbXWK/x40iF2ntont4DjIBPWOXwHA1cfOHuf79YGk4yxM7C5XjmaJBEE4XE62MMa58DzDQ48sbNvUgTcRBEE4sXH2h2LXms5Z6+ozMuZov2rZnPsw2rD3w/dTeWYiXtZ747mHVJ7dU8+zrOMMeloXx8tWdp2J69fIVsZZ0r7ikPYrCIJwrIlECu8YOXOsiZ3o9iXQvvjAGx8BVK2AsjMAPN33MwrG5dXnvfWIH6f+oiOZs0vErtOTOGnXQX3rWJorkyGoDzwEq1bCJRc33/Zkcn3qqtcwkdGcnETnJAgLpdnP0MmI7zf+vsyHOLsEQTjpcQcjsatj1rrWUOw6UN6u6Z/0UXlmgjX/7zo6XrKC4U89hXb8eb8zF3umtnDe4sanwhWdawFO+LwbgiAce2677Tbe9ra3ceWVV3LttdfykY98hD17Gh2mxhhuvfVWrr/+ei677DLe9773sXPnzuNU4qOHNbGD1L7HjsmxTOKJXxXHKQ8+iTW+A3vXr4/4sRw/DF80hqJXZluhD7PQp/WTlIacXSJ21VEHF8Z4PGwY/ozHn+F5JsM8VkL44eIMFMn/cj/eZPXAG5/iQrRwehLd1/5pdHmL2CUIwkmPM1DCXtyC1Z6etS69ugOrPXVAsWvy61tpWbeIRW8/l2UfuxR/skrh7v0HXxa/xt7pHZw7Q+xaGYtdB79PQRBObdavX8973/tevv/97/ONb3wD3/f5/d//fcrlcrzNV7/6Vb7xjW9w8803c8cdd7B06VI+8IEPUCwem8zvh/Qi+CRxRygUuEFdK7eM8qpHfLBb8+oxXpvyOxmsjlF1T9Cs/UeII+HsigRJdbLH3TRwaM6uY2nHmCl2JZmeNjz9jEGHMVHJtk2GP55oRCLXQtJanPTWF0Foghfe16f4e5YGROwSBOGkxx0oklk9O4QRghkZWy7ooTaP2FXZNEll4yRL3nchSik6r19Fem0nU/+x+6DLsje7HU+7XLDk0oblKzrXADBSELFLEIRGvva1r/HWt76VCy64gIsuuohbbrmFoaEhNm8O8v8ZY/j2t7/Nhz/8YV7zmtdw4YUX8rnPfY5qtcqdd955nEs/H4cxYNSH5qw9GExD+WaU9SCFumxlnHwtO+d6T0f2l8RRm+k3TglVmmiy4uQjGlCl7JPL2VUuG371a0M2e7QEj3C/C7zG1EHm+DoS+D6kvWla3NnX4uYtMJWFWi34nHR2RctOesTZJZwAFIuGfP7I9UORiH2yX94H8/MsYpcgCCc9zkCJdJMQxojWCxdR3ZGbc33uZ3tRaYtFbwlydClL0fNbZ1J6dBhdOTh//q6p5wE4f8klDctbUm30ti1jtCRilyAI81MoFADo6ekBYGBggPHxca6//vp4m0wmwzXXXMOGDRuOSxkXRJMnUq3neUpNbJ/a8Quo5o9qPqBo3woanv6D/FoHd9wNQw/z1MD98xxLR/9ILJw94rAmd2MNP3tQxz5RiUJlWlrmn41RG836/b8mWxk/NgU7ANnp4O98oXuHRXgNDI8YHnt84deZOcbOrhWFR1hWfKJJORpJCpm1Y5dD/+gixi7hMMlWxslVpw5rH489AU88eYQKxIkVxrhnT/BS4Wjn/JME9YIgnNQYY3AHi3S94ow5t2m5cBHZ/9iNn3OwezKz1hfuH6T9muXY3fV1Xa9czcRtWyg+NkL3q9csuDw7J56jM9PDys4zZ61b0bmWUXF2CYIwD8YYbrnlFq6++mouvPBCAMbHAxFgyZIlDdsuXbqUoaGheffX29t7WOXp6fGYyvr0dKfo7bUP6rvG9zAdwYuIjvBvT0+GVKq5U8VoHW8PoDpbGf/FOG3n9tB19cpDPIO5yTgpWlpaaEm10tPVRUtLCwBt7W2ke3pQqdm/F3MRnd/60V/x2oveOWu9kyrRUejAeDatpRa0Z+jt7qFtUWP76FwGdAvWjHY73HY8HtQcn0H9M3qsNKvtV9Pb29J0u7JTxKR9Bio7OfeMCxvWTfo9dNQ66O7uOWZ1UCz5dHR4dHVZ9PbOTo9wqETlNyaPKXQwsa8F090xZ70ATE5q2tu76fA66OnuPmZ14Lqa6fB+iK7tqJwdHQ6WZehZlKGjXVGpBvUF0Np68P3EsSLV7VDp0HT19NDWu2jebY1bifuiU+FeFGYzsx1LZcP9DzhcdWWKVSvtxHV/aO39xMjdALzuBe/Cr3hYGQtlH5zPqKOjFpZh7j7iYGhrc+joMHR2Htm+7VAYGavR0QHd3c2fCYwxuHO0Qbls6OhYmLIuYpcgCCc1/nQNXfIO6OwCqO6cpuNFyxvWuSNlqluyrPyLqxqWt1+zHKs9RfH+wYMSu3ZNPc/5Sy5pGnawonMNfVNbF7wvQRBOPz71qU+xY8cOvvvd785aN7NfWcgb0Wx27tC6hZDPG0olyOUhmz3IcCrfJVUqAVAK/05NlUin59iP9uLtAfxcjlKpRGlTCe/cI/Own6TkFKjValieIp/LUQtjsPLFAnY2CwchdpUS5W5W59liNtjGq2JVq9T8GrnpLFXTGIJvZydR1TxeYh+9vb2H3Y7Hg2zWkKuMozLQ6ZWYnCxhWbPbvuKWKJVK+I6ZdZ75fHAN5FtyZO2DqwNfe+RrWXrb5p6JuRnRNV8oHMI1PwfJNlS5aexSiWq1Qskukc2Wm35Ha8Ov74NSOk/36hLTuRydHJvrIJs18f1QKpVQEJezWDBUazA1WcKpKSYngvoCGB+DttYTM79aOZ/HKZXQuTzVA4WoupW4LzoV7sWTEW+ySmpJ61HZd7N2HB0LruOdO6G1RcV9enI7rQ0PPgQveAGsWD7/dZ78/vRP+kivbKfjJbNnY3ddQ7UKXV2z91cqmXAfzfuIgyXq26anj1zfdqjUz63UXOzSpmkbQCB2BasO/FwgYYyCIJzUuANBRzhXzi6AlguCUKDaztmhjMWHAldE1ytXNyy3MjYd162kcN/8rokkvvbZM7V5Vr6uiCXtK5msjC54f4IgnF58+tOf5t577+Vb3/oWK1fWnUzLlgWD9YmJxvw5k5OTLF269JiU7dAiDZqFMc63ebBSd59xOAddMHPl7PLxqbhF7t39Iwq16SNzrKZzvjc5Pz98W30SJVVxh0uYJuGp0SmkQwPBXHm7dNTuRzhP25axp9gw9DCOv8BEUk4JNdV3RMuQRE3uAq/uRlAHuL6j1eXKvJsdFeaLNp65TTKZveTsOjhy1UlKTuGYHOtkwh0pU3x4mFpf/qC/++RThpHRI/fbYYzBD/umajUIyd61a+Hf19XA9eiONBesnnwKHl9/2MU8IKntd9FRiPKAHv3jLZQ5nwmOUCFF7BIE4aTGGQhms0qvnVvsSq/uwOpoPiNj4YEhUivbaVm3aNa6rleuxtlbWPCP7UB+N1WvwvlLLmu6fknbckpOvmFWLkEQBGMMn/rUp7j77rv51re+xdq1axvWr1mzhmXLlvHII4/EyxzH4cknn+TKK6881sVdOHM8qxYeHKK0vonwH22vjtHjaTJnV1LsMoax4gAAY8XBI3OoOCl5/UimmbgTCTPHIEH/kcCbqFBaP0Z122y3SySGpMI4EmeOqJNICNQcWYEhHwmVCxw02fufwB7bAuFkAkdyQGjKU9jj27FGNiaXzv+dWTMZHLsR6kytJ2kqjcoVbRO1c2vLKZSz6xjx9OCDPLH/nuNdjBMKNbkLkw1C9/38wV1QnmeYzkE4t8tBEV3XMwMzdk09zwN9P0UbHc9mmJonNq7kFGIBH8AvBCq/yjT/XSvNYdoaHDrC97vRtJf7geM/G2Myd+fMfnZkxDA6Zo5YdydhjIIgnNS4g5Gza+4wRqUULesWUd3SmCjS+JriQ8N0/9c1TcMOu14VuL0K9w3Sck73Acuya3ITMDs5fcTi9sC+PFke44zusw64P0EQTg8++clPcuedd/LP//zPdHR0xDm6urq6aG1tRSnF+9//fm677TbOPvtszjrrLG677TZaW1t5wxvecJxLPx+zn1Z9H/xsjaZSTjRACMUuc5SfyBucXcnBifFxdTDIStsLD2Wc91jxE71JHHdG/WgfpU+u6bK0EwpV5dkZ6KNTiAaGczm7fBOcs2lyzocjODXb37z4wTmooyEqhWVR2ltwQubo8j8OkzEuaDAcbRNVc2tr4OzaucuwckXzsKwTgQXV50ly/52K2OPbsUd84NKFWQwTRC7I1kOJfpzjUMP5/mC10bhOkI9uLrHL8ao8sf8ezug+O16mS6HY1XJwuey2HIWsJ1HX4x/DdykquxfQmN5zZpVj5r8BNoVC5W/cMPc+ZTZGQRBOG5z9RazudENy+Wa0X7GMysZJTGIKksrzU/jZGp03NE9un1nTScv5PRQfXFgo467JTbSm2ljTfV7T9YvbArFrSkIZBUFI8L3vfY9CocD73vc+rr/++vi/n//85/E2H/zgB/md3/kdPvnJT/K2t72N0dFRvv71r9PZOber9UhwWAPtg1Yqolfr4ePpUR5v6tDBo5RqKGvwBj8Qu1LWkUniW3d2mfpbf6OhVkDlQ/eYn3AxmJPD2TUfxmgy3hTpdCD2VSpgjW+fFSqow3PVR1hgiJxix3IWw4NjYc4uxaHdhO5YhVr/oYXIzdIYks6uaJuwuXwNlgLbBseF/r3w9DOHdNgFk80aHnzI4HlHqW1PpDiv44nvHBfhL/oJONhDV0Oxq2oNHtbsrklBut5/qFiwT6WCbWp78g3jCs8EvymT5fpUrtGLADUjX6HvG3bvObbXWXRax9LZZeUHsfKN46j5xK768rnr5mBuT3F2CYJwUuMOlsisaRzsGWO45YE/ZOv40/ze1X/Oq859C+1XLWXy61up7sjR9oJgVo/iA0OgoPPlq+bcf+fLVzH177vQNR/rAG9ldkw8x7mLL8a2mm+3pD1Ijj9VFrFLEIQ627dvP+A2Sik+9rGP8bGPfewYlOhI0czZNc9T6gxn11EfZM0RKugbjRsKT0dKgEm6jHT4pG60JtX3IABe92rwEgmPjkOciTGmqcv5UGmZ3EJnbS9pnWFr+ceobTdy5hk7sZTCW5x8y28a/h4pojo/WLHrQLm0Dp8ofHb+48SXgIq+dXDlKj0WDLhbzu46qO/BAW69GQNmY8Cygv9iY+JRrsK+/iBkMpeDGZPUHiGOnQixd5/hiUnDS1584jnhUjt/he5aiV599bE9cGTHOUhnVyUUuwad9aghxfVn/SaZ1MJsXskwxmS/n3TlOqHYZdvB+KOyaRJd8+NxRfQ9TydsrKEga1wNbjV4kZHpYN9+2JPQ/Y90/9uMWOyao1qj8MKZE4kYV6PSh+iR8l2w5pacjnZfIc4uQRBOapyBIukZIYz37vkh9+75IY5f4/89/CeMl4ZovyJI4lzZUH/TU7h/kLbLl5LqnfuHsPOGMzAVj/Iz878hcrwqW8ef4eLl18y5TRzGKM4uQRBOIPzkCNWtHsE9z36K3Te+/cAJmZVdL89RxITOH4VqGN1rNK4OhCd9hBxW2mjwquFxI3Gncd/qODq73JEy+Z/vw3hHTmSzwpxZ6bQViCG62rRJj1Qdz95vKHYdosPw6Fx+yUHk/CLfTGdXyTn4ZN2Hij/jMmg2BtcJ0cuygm2OVXhUJjTzz5UHrikH057HwM0UXZ/5QvDfiYpVGDnwRkea6Ho7SLEruh6sUOEou8WDP7Sq/zZAor/G1J1dNphIxHLqF70filx+4kVK5Pwynia1+9ek9twfbDPjXjkWZsIDhTE+/Ag8+njjMl3xyP187yFNFhAcbLY7cCHOrvnuVwljFAThtMEZmO3s+vGWr3Pe4ov5x9f/FGPgOxu/SHptJ6llrRQfD4QmP+dQ3jBB1yuahzBGdLx0BSptUXxoeN7ttow/jeNXuXLV9XNu05VZRNrKMFUeW+DZCYIgHF2mymM80PdTctVJrPGtpHb/umHGuJhDeRBv8kTaP76F/uy2ObavJylyJnxqe0uHcNCF05AgPlFW32hc79DFrqpXZs/UjIQrxVHssa2oWr55eJ3RjWGMxzhBvS57GE83DNwOGze0WShYsQwyfvPBkj9vHdcHmgdL7Bhb6HdV9Odo5OxqMgunmV9Um5mgfii/l4Hc7oM+dK1m2LnLHJToNytBfZNymYSzS6nQ2XWMDIktByl2+QXn4ITcY6A8+Hp2njuhkWazvM5HvHk8H8js71fcUoMYFR+rYT/NnF3gOvXjNBOAm+436eyah0O95IwxTP9nP9Vds2ecn7lzrQNXWqUChcLsA9acYF3ynL1s8FvojR/a5FpKu8x8gEiea9M+w/Hwc7Nv7mxlnKpXPqgeWsQuQRBOWvy8g847pBNi10RpmK3jT/Oqc9/Cyq4zuf7s3+Kh/jvRxqf7v66lcM8Auuox/ZM94Bu6X7N27v1rj721XZhLW5i6r2/eB8Vnhx/GVikuWfmSObdRStHbtkxydgmCcMIQ5TXJVadQxTEc7eJ59emhjnhQhXPg17XGsilu8qhsP7p2h8bQQj/xb001rINDCWN8dO8v6c9uo5hw4phaOBDxKvXn/uS+tdcQxqiOsbMrGlSaI5gDyQqT/Kvwf8q4QRPPmG1zvkTy5gCC0EI4VGfXkaLo5PnFzh9RmjETszqAg2xmGCNAoTbPgHYOnnw6yKNVnmPWt6bHnlEmz4eJycbyRu4v3wfLPrbOrlSYSm8hsz8aYyjcOxhPaBTVpzNUwpuuNf3OURE8Z+CbRrGrWDzB8oQdwk1X3ZVj+id9DXmsDono6/OIXaowGiY/rxMLsHN8xxjDY/vu5rmhx5usC/erwg/Gb3DYGmPi2RgbBJrEwWa2KVAXWQ2YecL4k9U9Pl7/cMDfYN+ANlQ3T80j6Nbv3ZVBkAnZ6bl3WUwY4kwlOCer7RCyX2k/rMu5xa5mNdL6zP44DDvJhqGHWb//1wdVBBG7BEE4aXEGgt44s7Yudj2y7xcAXH/W6wG44ew3kqtO8ezwo/S86Rx00SX/y/1MfnM7bVcupe3S5skefrXrP3j79y7lQz9+FT9b9EPczXn+4Fuv5O6d35/14GyM4eH+n3PxihfRnp4/WfTi9hVMirNLEIQTlIcmn+GJwfsAmJ429O87jJ01GyzNNzhNOLuOBcm+XCdcFlXtxK6L+V1HdfSBHAjRsWqgSlEC/qTYpRvDGI91zq5I7DpIJ8WBdhm0ZJSjqu7Tatxu7nONXXCHEVZmCAdbC3DLaW0YHT2ydT9WHACjmXCyUYFI/iO6NPqyW3ls393x95rdPs1y+hhPU9szd4hRlMfoYC6pKDQxyYZnYXCwPuBPOrssFeU6CpcdYP+jY+awxJ2obhbi7DK15idefnIsyN0a4mufwXz4YvOYOLsar8fHnjjqhzxIDr4Oajumg2/Ol5vxIPCnHao7p5uuswefwh59Pv5sjGFvcSOOLjcsS+L6gbiZrUzMe1yNxh5+Dnv4uabrA9F+9vKGXF31hfGXmmhhMdVEBoFnE4edqybLGyeo9Rca+uzq1mzzjU3d2ZkOheL5utTkfaVDsUtlDkE2in/T5hH5DrK79bR3UJemiF2CIJy0uJHYtaaes+u5kcdY2Xkma3rOBeCa1a+kNdXO4/vvpuMly8mc283+P3qI2q4cyz74wqb7fbD/Tj734Mc4t/cF/J9XfoXXvu+/Y2Hxgv6L+LuH/og//Olr2Tz6ZLz91vFn6J/ezmsvePcBy7ykbYUkqBcE4QQleIJ0cvvBGJ58+tD2UiwafvVrw+Tk7KdYFT7sV9xmIYrRE6zV+HEuvOaujIViwpGHQsWiCkDZr8YHX0gY4/4Bw+at4M5wRSVn0dNGgw+Z9S2kN7aHx0/se4az65jPxhjbdY7gID8M9VEY7IoJbD+GWULCXCFDyXId1oyKxmDvX09qxy8OuOngEExOHuJxmoX/Ep6T0YzWppqGOUX0TW1ruC/qCeDr9dNM7KpsnqKyaRJ3bHaYUaOge8AzaNjWajKY35KIQE7m7FLWbHFsPp7bdJjizgFyDzVsWlvYvTSQ28X28WcZLuybcwReGy42redD4WjlqjtiHKTgpytePVTvUG/XOEY2/ONpqlvmEHCAsdpULDAVnGlGy3sYqj2V2E1jQZxQ7ErbTWbZjZxdgBrb2mS1IVPoZ3FpQ3AvRfdiMgS+WRhj1Kdqf15h57EngpDj5D0b3VPN3KlOf4HKxomGWYvnDJU0Oi6mHabEXOh7DV3xo100pVQy5JuERAJBcvrg2/GikRHDUCIzzAEvM2OCGXyTedBE7BIE4XTA2R88FCZzdm0bf4YXLK/PGpNJtXLJipfw7PAjKNvi3H9/DV2vWs3qv7uWnjecPWufueoU//jox7lo2VX87Wv/nVed+xaueOWrSC1r5a0Tv83/fvkXKTg5/uTnb+F7G2/F8Wt8e8PnaU93csPZbzxgmRe3L2eqIs4uQRBODJqJCFZpAj21/5D3mctpuivbmBoPHnSLm13SeyYwxsQPzHumtjCUbwxBiZ9gwxltq165YQr3BpwSqV33YA1vxBra0PTpt+jkGS8NNflysO/nRtfHn5POropfjcOYdKgQaG3YtdswOjb7OCNhEd0ZL/Ubc3IZqCXUA69x1r9U3wNY+UGyxQxDQwtzISXRRjN5GC9SYpfOEUxQ72kdjBw9Q+fTHnZf7oAJ6uvJ+w2edhuSQx8qBoMqz+/kiMjlgm8cNJ5DatevsMZ3zFEIQ8Ersb2wa9b+D5yzKzH4bTJ0M9Uotmr2jpKX0cGIXXM5Vxr2ret/I2fX0UZrw3ObDIViYxnm/c5MsWuOgtrhjHH52tScjZJ7eJDSYyON4oP2sXf8ElVocv/VCqS2/Qyqs513zXJ2LSTk1hhzxGaJPcCRDmrrhty2xlB6epzpn/Qd0jFnnl7SvZSdNjz0sKHgVNiU38m28Q1AcG8YAxqvLnbN2FEkdmXsltlHTp7uVPNyd+Q30+4MzXndzQxjtJQVhIYrQLvM/EmYiec1XtN2ExPwfAWf25lbF5hS0fwv8+yzYS/RPucITV3/g2me/n7zNwQqcroldrhpM+xKpB484CVfzWKPbcGa3LXw7yQQsUsQhJMWZ6CI1ZXG6gkylU6VxxgtDvDCZY1TJF+56jr6sluZrkyQXtnO2d/8Lyx+9wVN9/mTrV8nX83yZ9f/A2k72K+yLbp/6yyK9wzyX9e8ndve8mtecc6b+NrTn+Wt37mIpwbv40PX/DVt6Y6m+0yypH0FueqkJCYVBOGEQgVTUMWUCo0DxPrgwTBRmn/CjlR5mLbKNlTuWQCcMY09VghmuEo8Lxed6cYyzAhj3D29k5FCc9FN1YJ8XlZuACs/1NTltX7/r9k00tw+Ml2ZIHnCyWT12ujQlaRiISaXh77+wJGyUBpzgmmUqQ+yVc1qmPErIldKk8tz0M6uXZOb2Dj8KIVwBsS58ObKyRXbdOpikz7MZPXGmEAvieIZp5vP9Kln5MUB2DL2FA/23VkXuQ4jrGzhQpnCGOis7UXpg5nmD6L4JJUfnOP4QRlqYViPMSa+3meb2cyM5fUNlDq4oZvn13d0MGmU5nJ2AYw6m6j4UwnnWeBAORhn16FSLMLoGIyN1499IJpNutBMFIjqNpgtNlifdfJhXwF+qa5m+1OJ/satoLSHNT574o1oJsNmMxo2C5FeiHj3yN67eLj/Z03Xedo9ckLYITi76t+tR18c6jFVYbiu1iaW79oF1RqUQsdRlF8x+t1I3u8z7/2aH7jymold9QODbtZnJEWlxOrh4cDJDLMFzLSdwfgalbYC927SBTZH95rcd93ZNXdxG67lubUusqFBTlnBvT2vsyu5y+g3YQ7Xb2p/ltTIHGHUkbNrvryM2qAmdwf1A7M7qugnwK0cQv5FEbsEQTiJcQeKZNZ0xrb+rePPAHDRsqsatrt81XUAbBx5dN79OV6Vn279Btee+VrO7r2oYd2iN5yNLnsU7hukPd3JX7zin7nlNd/jtRe8h//5sr/n9ev+24LKvLhtBQYTJ4UWBEE4ETDG8Pjkhvhzvth8pLt3ejvPjTzOxFyOKwADm73n2Fh4YsZi3TCYmy1CRHEkduPnZnjNhZP5qNUM+XzCKRQdTtWdXcWSwfE0oElZ6ViI8ZqkYjkQDeF5kdAXHbNi4Xkeg8OKUikRakY6GNgc5IA1Slw+X2hUNmu47wGYnGxqrwrKGYph5S1Zhu/Yi1/z2D7xLDXv4EO34gGjDsehvt+0RXWTmQpHiwNUawbXbTJz5UGSHCCpqT2o4vzu6s5aP0tKzzQdYLq+M4eYEN4vTXL2xDnDiERlE36cKWqFuwjbsD4YTTi7Fih27dxlGBs3aA2tT/ST3jV+UGbBKDTRjPlESbqWFtezLPcQk+4u+qoPxOWO8ns1NUzVirPq2z+MUNmZ4+CFiEPNwhiTDsZoMB/Ve9Urx/ffM7mtPDP0UHDsRDL7ZuFi2Wka7uUDEQkjKjEcv/f+eQTpEMevBXmLmvBg351sGHqYWu3Q6zhmHmHBGt2Mve+xxmWJBOaHnvsv0WcURgLBK/w8qwwzb4XwvjLzhEVHzq5U+DK71l+Ir4Voy+AWbX5hxde8b+JbfmAgXKbNLLHLUjZ4BqVc0LqhSpuJXcFvUf1zHHI433WerOs56r1cru/AUuG9rQlyavn1PivjTpL2ZjhwZ7wIyU4bJiaaH8fe+SuswWfqC5qEMc7EKo1gj2/DGt8evASYKU6Hfd6+Ppfntxxob032fxDbCoIgnFA4A0XSiXxduyY3YSmb8xY35uK6YMmldGS62TD88Lz7e2Lg10xXJ3nLC39/1rr2Fy0jtbyN3J39QPDAes2aV/Gxa/+GN1z0vqZ5NJqxuH05AJMyI6MgCCcQVa9Mya8n9q1Um/dp0Vv0KNFvMxQGD49Z4VoznF165hN8PB2WhZuYrnzWwEN7KHfm1HIHHvU+/Cg88WTiUOF+FQpjfDzfMJWFsYlgecpKx+6LBc0yN8uhM2PQlVif2tFK/vESE1OKPYmIGd+kMAaU9jDaUN4wjl85sBM4Cp+x1dwzZk2HE/lFs3AZY6j15cPw0vrbe13x2PnwNFt25RidGmMw18f2iWfnOOe5hx2+NqEAEm1TD2Ot7cnHIU4zZ8KM2LkLNm/zo2/OeZwDU/+uPbYVe+DJebYNSOnmUxfe9fydfP3nj+LMnFU0upb8OVTRcL0VpukPzG7Nz8k3PhueNWzcOLP0zXN21VfW/9m/F0ZHg7AoAHuiiDdVxRlqlitvNloDFY0Z8EjvDpxNre44aX+6vo0PA7k9eNpFqebOrlTfA7Pq+1BnbNyzx7C3SeSzMWZegWhWGCM0iAKRGKZDNdBJ5OxL1qlx57HNENT5ozMm+TMz2stx6mX145yB9oxt5jqThTE0OcGDD8NYk5DrphiNNbETylPB3/qKOb9iZftR5akGUd5qT/Q9SWfQwbhx4hx9IaFjNyme1bwKY87mWeXTBGKSoS4qzZWzy1Y23nSNysYJys9OJA8d7mt2mbXn4+50MY4JnLf1bi1Yr2eHMRoMxtekJjZhZfc0FHkhzq6u2m66K9vw8g7ljRPN6zL5AqnJes8zPP5EIu9fwtmV2vkrUjvrk2IsLz7OisLDTcUuZ18Rd7zCsz+cYtOPmoeFK9/BKiSc33EY4zzXQFQR2gtE9uqMPtSyMNowFqZFSL6oWggidgmCcNLi7C+RWdsVf947vZ3V3eeQSbU2bGdbKS5beS0bh+d3dt2358csaVvB5StfNmudsi16futM8r8eRJcP4RV/yJK2YM7fKZmRURCEE4jIGRQxWJwdjrVg5njANUY3GpZmrM9WJthc2M2miQ3sKO7FKk+B7zQMWKyhDaR2/BI1MwfOQnLdFGpYE0UwelYOJ1+78S5cL3Di2MqOxRe/nGdN9mekvenZ+505QIuWJ5Zo44NuHPi64y4zH8V9Y6FVGnwXd6SMs69I8dkD/17EjoKDSJzk7MlTeW4Sp69QH7DlHfJ372docjeT3g5KTrnhHGeRHNT6GmdvIf6sTZizKw6N1PhGo42m8nyQ48Vo0zRnV0Sp7MXfXSiednly4L5EuQ5TPUgwOQUlf4x8eJrl8vxCS70QUe43i4mJSPSb29k1MVnXY5KimFUtBM6JhpCqRudFkqQTqvr4MOUnF/bsYWKhEpRTH8AnJ3KYro2zY2IjA6XN2PYBU3yFypQ+qNxhSXb3wfiMMbYxQWjxfQ/Mc9iZLixjYgcj1MUuPw4rNThxWHT9rCIH0GCuj6cHGw94wNlYwzZ84CF4JHwUjRKrz3TrNbuFy89OMP2TPnR1fqXQcQzZaZhyd5MrVsnXsgcoF6jsXqyJHaT2PYY1saM+0cJCRKrKdH0/ydn6GixMkfKkG1xEzdBll9qwHws4KurXEvvbV97AhLuDabdY3y/1PiIpds3slaN+Mum2bAhJJXJ2JdygJYPd5+ONVNBTGoZ8tOvNut+MqYtp8TIvDIlPRcnsE2WZoymT90dPZRvd1d1Unp3A6S+gc7P7ssb8ceDsK1DeWL9RPI+G+mtwds1B8sySL0NKj46QGs6RGivM/pJXD9GuPD+JrvqJGYbnEaMbhFGwyk36awNRqPjBRumK2CUIwkmJn3PQeadhJsa90zs4a9GFTbe/YtV17M/tmjP0puwWeWL/PbzinDdhW3bTbXpefzam4pG/+9ATNy9uD8Suw0kkLAiCcKQpObmGB+LdxS1Nt1vI+EdpFzR0PdlGdSD5RG8aHRWJVTWvwoaxxxmpTjCWmMRDlSYb3rJb+TDhfLVRnFvIE3DLpiEyu8bJ799PYfvGBsFL+1494XYYb2gnwhijt9Wt3uy362ZO0UJDrRjZT2Y97/ueYaZEoLWFTwZ8BxUmTZorV0qSaOC8fvdPydeyGGPwcw7lZyfi3FsNg+jyFGrP48EMYa6Oj+EXg/24uhLWixt+dw4pI1G02q4c5WcncAaDc9Y6zNkVV4zhoexTPDT1DEqFy/zGZNslJ0/ZLcZ1qgmdXQt9k1/NUy5PNOQuM81m7dT+rDxvjceYX7qJivzIY/DkUwcooDFoP6iPkeEgxDDp3pn51VJ5Rr68aORvfFLDG4NrUXtY4zuw9z4at0Gz0LFkVFWlAoW5Zk2b+T2t4+tPJa6/SOxKpYKQTgBPO8GsmwcYVdq778Xe9ev6ID9yFC0gh+lcYXla1/N3zclsFRo8Ta1Yxq/VILsPdzCP+3g2boyG2WJ9J8jbFYpd09UJqjOcpVqDObDcB4Dj+FArUp3uB8CicWbAZmJgJCLnf7lv3hlTt+8MnJsjznNsmLiLpwbun9dZZY1swpre17iwEglkM75XngrEMMCkgxllGxy2DW6g8K/nYMIwWHvf4w0uomYUHx6htK2e26rtnCg8OHEN6hl9QjSJSCikJnMhGmPI3zsQ118kiGmjY6delGss+ROSFN0zT3ik9mi0F4poJnB21bs1ExejUMvR2dIDwNi4YXxM42uPiqrOOkYzsSv6qYiIu910MC5pKnYmrwdtKG8IhLEkScF8Zs6uXM6wb9/c18jWLSYOH2w4rG/I5RLfC68Fb9pQ2zkVzBQZ5+yac/fJkuGVPVL7Zwi00W9nJHbNqKMDIWKXIAgnJU6Y+DK9NpiJ0fUdBnJ7OGvRuqbbX7nqegA2Dj/SdP1j++6m5ld45blvmfOY7dcsJ3N2F1P/vmvObQ7EotalWMpiSsIYBUE4gZiZA8bz6rM2wUFFDaC0g/IVaI/C7vqbfKM142NQqZj4c0TZLSYOojijdTlntAZh37GrJzFSUDMHyAfx9LvpeYuRUUNlcjLMF6XwjV8Xu8IE9bZlxwMr7QSDFV+1BoOUcJY1U83Hws2sRONOkVTfAzCwHd/zUTOcXb42cZhT5AzxjYWvMijPATsa6B1YyPO1h6rlsCd2MDz4BLWdOQr3D+LsLVDblZu1vT2xHeVWg9xnSfeVF4V2BkMEf56s5kP5fnKV+ixcUfPoUhDCqjGEUXvhoE2jjcHTPkR1NkPs2jD0MI/v+1WiLRJOjAVQff5BKlt/3Vg35dmJsu29j+Ju+lWDK6uh/ZRqmjg+Gnwm1xVLQQnnY88el4EhqIX6mtbMmaC+WJopdgUbWBM76kcxBmtyJ6pSHxi6rkPVKzeIG8nJNaemglC7hRA4uyJBMpl7znDRumCw7McZ6q0gQX0y5K9JdSivivKdeHcdzgDWxA6sqT0HLM+Dc2ShaC/tprf8XHjM5m3QTCweG9VMbN5GcddmrNFtFH/9HMWRSUJttZ6jzhjs0c1s2P1j3AaFMnkAs+DQzIw7yZrpX5Dqe4DayHMNO1tUfh6cAZ4eug/HrzE9bXCbhU7OcSzXa+wTozLNd+9Y0/tQTv3+8HKa3A+2Udk4wsxrOnB+7WzcQdJhONOiYzTW2GaswSAeN3mtJtmy1TA83Fx4sqxaw+dg12GfOeMFR31ih6QwptEFFz/nBBMJ9D+MqkwF5ZvRt0XfCrosM2uFKXvxZ+N7sy7ymutQ8ypxBMfoGAwPGvZmt7OlvLvxIMwtdiW7+yg0WLUGIaJ6Rki7MYbhfX7w+2GppjeeTrhIoe7sio6zb38gks4sR0RljnSN23fA+qfqn1V0z6hARDWl/IJydjUY0x65h3ZnEN2ViNAJLiUsgnPXInYJgnA6EIldmTWB2DWY34NvPM7ubS52nd17Ed0ti9kw1PyJ6b49P2JF5xpeMCO5fRJlKRa/+wJKj45Q65tj5pEDYFs2i1qXShijIAjHBWt4I9b4jvhz40AoHHAYg6+htZU5UQkXg6ddNo08Tm10M6ltP8PyHDBQc2BgSMdPpsEAASZCfcQkBhuBkyIsi7JotTLxMayR57D7H5nfAXIwsQ1KYTBMTsLAYJBUWmtvhqZkGhLU44SDLmUHCbnzQzja5f7t32G8tqtpEbRbDZwFd+/DbI3ylRncdcGgwLMhchD19QcDF60VvspgvBr+ZOgGmMPJobVhcNDg+wbt+VgDwcDCr+XxJhMJ/JuGWCVUm1DsKhQM5YJmZNTghmMU39e4nqFUgv0DpiG5/bbBZ9hxf30SAqslUEdNzQej8U3gdVGhw8ugG0eUwUk0Taof65vxyH5ho5u+ftjZ58XXSmGqzO6HNsYCa4RXzLFrdzCzW0QzXW/jc4bBIQPVHKYwHItdwYAr2GfKL5Hqe3DOMmk0fpQE2w9mPdU6yAvW6sx2mzvezATNwXGUW60P8JN1Fp7axoFHeHTvLxsG0b4/OyxuIcnLk0J0g6sGTTqlwkTakWgdiF0LnSjS98HSDovKmxf2hXnoKG6joxa47X3Xb94PzBQ1DFSLYXiyB7ps6C/to+pVYkeSb7xGMaVWYqow90vKoLoO4Owyhk6n7qKq+EH/YNCk/Tydtb24hV+Sr00zVhzkyafh/gdh796Zqmvz3ZfD2QlXnxG8qIjFruSssI5PbU++XhEzcLMaVZmELY/PryhEp9pohwKgUMtSrOVBa4pFgzM2jDW0IbFdY3sMDlF3DsVurfAwNkGoZFJUC//6cWKuel6/wARUd11F4ara8VFOMRDgy1MUS15jKKsx9f5f1SfNUKX6Re0XnPhwSnv1n6uwLqKQ0d62ZfWT04aKWwCbWfU5p9iV2CwWkNNBOWaKXfk87NzmMzYehErO3WQJscuaUxdrtjlqjg0LMyMZnTLaNejuNcHnWinOYajm+X1OrnKHK7S2TNC1eB/W8Ea0NhQKUZ93YJdYM0TsEgThpMTd3yh29We3A8zp7LKUxRWrXsazTZxdueokTw3ezyvPecsBE80vesd5kFJMfW/nvNvNx+L2FZKgXhCEY4Kfd5j+SR9+PnhQt3IDWJNB/1VxS02FhuAhXNGSmJ19chJ27U4OOOr/HisOMV4aZu9AkMdG+ZVofI4h8fDu+Q0PqsbXFGs5CrVp8rUpFPCKJVdz8fIX0ZZqjfvjrDPNVH7vLIeTaVuEXnxOXKK5uGvrD3niyaQ4oFmzVrOoJxizOY7C117DICOXM/TtTrGrz2XDRp/JsUg80kH9+C417YKVYiocwMY5lkqG1BY/SJgcPZ9PGCrlsOY6Nf4qD9/xAUOx5Swm3RV4Xpizy8pQ3V2kun06rKfm5/bcJtiyLWib1HZNaruFKlj4pu6mc7xa3ZmQ/KusID9KcTRwn5Wm6N8L27doxschGs1qX9PXB1u3wbbt8Myz9eOn9jS2R5SXRodilza6IUF9Mkm/Sji7muXjise80UB2Ia/yw21c4zI1HLjZqrkCBsPAAA2CV5Rzy3Vni0bBP4OB4+RUMKBM9T8MA+vrwlHCgdHmJhIyNyE5EcOEP0RVF+PvLi09PWvQ6bsundW+GQ0281wbrC64fg03zJnjuoaKHwy8XScIOUw+2RSLgfNCTe6eu8x+PShPeT7KuCxbChdeGIoACrxYRLKCMKmphc3YqR2HRZUtqFDRMFZ6/u3ny4cVrTKa1I5fYPc9NPc2EOQVcivoSOhQUNntUQ4n3sCAyu2H8W14M/pGz0mEvYZfLzkFKm5xTvNlbU8JZzS4H6yJnbQ7Q/G6qnYoFQ0GQ6sbvAD1rDRGBwJixI6ZwQRzHKvqBCsymWAWvzjvd+JaqW7JUtk0iTteaSoM6vIMdxZQHfRxh+ea2CBpywn+vW96F88MPojRHtks7N5l6uHnMHfeLqOxxp6P/gkErknjOo3nHBsOIxEkeknjz0pQH1kbjRO8dNFGMzkJG57zqCXChY2rZ9z/wQcrW0+6r4tesF/HYHx/VtjwRGk4eKHdtpQLll5Ku7UEfB1cv3YQXFl41sXLB2VxPVjUA+eekzxuY5VGzq74VKs+k+VR+qa2AsELJdzgd1XZVtMXG0kXabTPpLMrwkvk5ovqb8dO09gPRfncLDVrQgpVyzP9GBS2d4Y79Ov5Oxfq7KqCale0+mNgNGNjMDQExYJBJWap1dqQ3rawcZSIXYIgnJQ4AyWs7jR2TzB98N7p7VjKZk3PuXN+5/JV1zFS3MdwodHHf/+en+Bpl984/20HPG56WRvd/3Ut03fsjvOgHCyL25YzJTm7BEE4ymzf4eGGs6+5w405ZowxPLbvbgZzfbO+Fz18trnD2GHupqls4JqJSA7gdTwTYODsUb4DUaiJSiSl97xYBAOYKA6zfuBenhy4j6H8XtpT7aSsFCs6Vwf7CUrDeC3Ls7ltjW4WwGQ6MZ1ByMh8b47Hx2EqkdhXYUhnDN1dKh4UauOi/cCx5msYGYFqJUWpVuOJ4Z9gGSc8TugAiBLvWjY1v1ivN8eQedzDHtbovIuuGjztMljIUcjaONXASWXSNqbiQV8VRh0gcMtEYYzeZCJkqonlaHraxMm6tQ4ENlCgwPc9UEH+q02DG9mxfyTeLiqniWw4tXxw7GgwqqMwRhU2mRcMqEKsud4HhbNHQuTsqrsjwqkHAZ1owmjEauIZLxt2F+dPO4gwxnBg5eGRG82F5xGICdUa7EpoO1FoTntLfeA9c5yodXBt1B0y9TBGX88TXTpDnNLGj5cpDDucx2YlZU5iTe9hUWUL7W7QJiZx08T1kAzpHXmeHXt/HZ/A7snt9FXvx9VlXDcQPpKuq2o2y+N7/pPndv14jhMAo03DINEyHpYNyqqLXbEAaRRqKI+3YQQr23wWy6eeNvHsgKZapMWr58ubFZI8gyg6L91kotGoHm1dDUyKzuyQ1aRYbI1txt73eMMy16m3jzW2Has0QU07TDjTDfvxHR+Vbhw6P7H/Hh4buJdkBKEf7vvX9xoGHskztcFletpHl+v787xADO7bawDDSPVJnnKfIMoFlRS7ZqLmuO72hPm00umgzaMyjZeG4mgCE4s/Pmifoeo4Ba9MX79h+w6DX66L0NE9Wt7hUVqfiEaYIbTWFxtcouT2YMIbx/hg7ISg6bvomj9rlky3nLh2dCBoDAzB8IgmmnHzV7+u57urej6eZ+J8VKZWpLOwhVRiVmGdON8dO2FkVFOtBQWslevncf+On7A9G06lqU08WQGJWYljscuA71cYnN5D2a1P7pGrZelpXYKlLNb2nE+b3Qs6yM1oLBPXW3m7h/F9PBcWLYLeRfXTfmJ9FBYdEAlKkxNBSOt0eYKNw4/SNxGIXZ4LytfBdinVIMBFzmkzQ0EzJQeVq4TXWX25k2iPaOnefdRddoVhrLHNKOOCMbPFrmoek2oL7XgEF2Ccs+vAgrUxBu0aSAFWUDbtG0AH4d8JZ5cpONjTzfuamYjYJQjCSYk7UIxdXQD909tZ030uGbtlzu9cs/rVADy675cNy3+1+w4uWHIZ5/S+YEHHXvzbF+BNVCncM3AIJYcl7SuYqkgYoyAIR5ddu7x6DqbQdeO6Bs831PwmLgxT/6uMz6L8MywpPjV7u5qhdtcYlc1TQH2GKzscUSvtxgMyU0u8Zffrzq5aLcjTsqLtPC5b+dLgvyWXByvDh2Urng4uKteMUZ6y66JNuM7ufxgVT31eH7A1ouOk97YVil3aRxuwwkfj1ozh8ktTXHCe4vzzQIfnqND4mjg8o2G2NgOZZ5JuARftGAbLw7gGfOMHW1smSDjsezDloYYqmNBppbXCt1rRNR0nUG/m7PL3b6ItdIl4HigvLIoCz3gopai4JcYnYEtfUB86cnr4MDGpcB2DMYatxb541kU1Q1jb0+eRSogM3d1NqtMY7P3r43w+uhI4WUwUImkil5AGN2rTcICqG3N2JesyONEy1ugmnP4HyT1/O/aOX2Lv+CXW8HOzvqPd0NmER9orMJ3VKOOhlc1U+2WN28YCrDt7WbTKa1zuo+PWDq6ZuLQzCq+DpM9DJYb3PoXa+zAWYThPuG0yhCk1/EycGy5YF7owzWxXRPyvxPb924sUp4pxgXPVwNXlGYdyJRA+kiKlLkxT8atMObmG/TSegm4If1TGC0IXw7ZSiZxdWitU1cMCVK25cJXNGkbDx5705FZsHVzbnm/mD0+mXlctTR7vonW2rswtPjYRi00imVlNO9BSAuOjnCCHXX95iK2FZC4xg/Y8VCrqb8LFXhWlHSqeQ86Ebjo3ECDPmPpZfN47tnn4KhPvLV80aGNiMTbr9QOgTCUIkQ3z+7W446zM3Qe1RMxYky5tsjzKvulAzU2lAhGwWILstGHb+IY4qiEqv6lpMD5bC3tYn91EsQiOA7oa3ZfM3S5Gk+iU2TO1lacHH2Dn+HNsnwzzcxmFjupdQ82rnzvaJf+LfeR/0ZgY/+FHoFJO9gvBYTzXgK6HVkcX5ti4ZmgE0Bp/eITavz6GXXRp9eovk5Pi3vAoFMoapVQgrAztRGXDenchWxsOz8jEEzEoo+oR37EqA8XpIUae2clwvv7y3PN80lbiPFFgdCB+2nVzmlc0VH96J9oEwuTMgJKRRGSzZYGZ8Bne5zI8DKOFxlmSaw7BCwMdtm1Shwx/N4JuuH7RVB4cJL15BD1cIPuAgwnDmp1E8vsGbSoS6sNr0NJu8IzQYIUzgdCcbiOarcL4fv2l0DzEudaiWVNtFXdYqVRQJq9m4vrXOpikbKGI2CUIwkmJM0Ps2pvdwVm9zWdijDij+ywuWHIZD/b9Z7xsf24X28af4TfOf/uCj9358jNIr+lg6rs7DrxxExa3rWCqPNb0AV8QBOFIoSaK+E6U6Td4eNy3H0aGoVrLN4QYqPIEytQTwFrGRanA0ZFEa4OqGIbze8lvC57Ko7fgFlYwiMgnQudoFLsik0p2GrJTkKqex9KOVSxqWYmt28KCW8QzMMUPuAbfDcri5XXgQLBsGnJP+S6qmsMeejbYzVQf9tCzKAVpL0sqdGDlvGH2lgOhyIqcXTpwdtkEQttZZ0JrJnAjWJaitcMPB9thMuom4RnGgEqEAbmVCutHNlPwSkH+KlcFeass0JkU2vdIWYCv4sGt7yv8PRZe0cQJnY1vUOXJeLYrADu/jyWlDQBM7RsjtztyZgX5hibLI2QrgXtGO4GwaRf20+qO4vnQt1exb18Q8jft5tmR29NwOpGzq1J18Dyo6mkGquvjgUnD71fozFLTwUBMl1yM0eFgPtipUuFg0WsUu9BzhDGGqy1TQvkeOdvmGXcMs2gtpNtQpdlT8OlS4OYqphaj8BnZV8AyLr6VmZVQqn5NhiJUcQzcepiaMjoWu+oGNR1fbknHF8wItTM+65+Cnz51L794/G7Gxn0sXW3YWVKYsYvD6Fp9EoE4wi7ORVTfOKorpROhYCYQKtxysCxy8sX7n+Hs8qqJgWITJ1R0Pq528YwLmKA/gFgktqwZCerTKhhVzungMGgVqKaqMo1ntTPc/Uq2boX9e10efjTMjdaEqJ4zmSbrwvs1ZaqzDm3vX481vn32LJUGqFXif9e0C+kyqjQeiBtznILv+liZUIjPGkpPjZHfuYXx7X08MraZ3e6WOPeTmegPzjW6N9ANbb53v2ZqKhAMWp1RUqGDVmmH/bs8tn95jPbhfpbse5yULmMVEmGA4X6SaTnGS0OBi88CO6FsTk01PxddqJDafe/s5dFl5YNyy3iFJs+pDfe+Zriwl6pXwcKipSXow1vttrhfcKweirn69armCmMkcl0F56isQGhS4W/BTAFmwkR9gMEbnMZgSE+n8FXrrDBGXdOxsGhZ4T01NRT3sco1iWg9U3ctajDRdVdPJIg9GNivql6FcuhS9LSLbdXfDCgNXfvDcEfbNNzHXjE4oXSKWaneGtLlFTRmn0dqLB9cV4nf2mhb5WvKJTAq6HuDFR6UgnMLNMcZv1MKGA9+TywnWFdrNtNj9IUkoTpnJfOAhWK+scPKUlbg5NZevfOZo2+IHMDGCV44YRM4u4CUCq+hfFh/xrBrD2zb4M5jM25ExC5BEE46jDE4/QXSZyZmYszv4axF84tdADec/UY2jz3JYL4PgF/tugNL2bz63BsXfHxlKXrfdQHFh4Zx9jd/UJyPxe3L8Y1Hrjp54I0FQRAOEVVz8d16WMqzw4/wXGUzw9VJ3L0PY48E+VFq09M4o/sanF22qaIw+FYLuD6tj/dh5Srk8wYVjpWH8v0YX+NHIySjqe7zqW50sYst0a7qIVvGxAM11w0GA8YNprB/5DF46qkoXkIxMgqjo/XH1ImRNA8/HDz45jcYck84gQMsfJA2nqb4yCB+2WCsMIyiPM3wiGEqC0sLj9FdDZxH+6p1V5Blg/ZVMG29r2htUZy5RmHbVsPAZe1ZHmtWB4NT7YPxnGAQk9Q4kgMjoFjK4ZVCcaKoaB3LBOMlCxRpjO9iZwwmsGMFg5ycDzkfnxaccpig3nWw9z2OPfB0sGPfaTDkpIafRperwZt0rfAdj3392ymGsXomtES0Tz3H0uJTeJ7BEDrJ4hnMaCSeJTI4kGsq1OzBIIk3YWhepDPq+iBQTe9DZfdR2RjMdKkChSSqIVQodsWzGs4RxhiNKyNRx3StxHSvQS9/IaZzBcoPRsVqej/W0AasoQ2ogUD8K9mdgKKrthvbK+KTxswYUcYDSt8F38UeeHJGHSST9AeOyGqtPgptcHaZeg4wgGIth6PL+OEAcLpgEkmeZ4tdANqrC21x3uzEbI1TWUOxlAjmDLfX2sQ32Eh/MHiNyhIl9w/NdfWyJ9xXKnFcCHJQRYP9TdVN7NN7URgy5SmUBWOVLFRDAcHocDZLCyul4skImhPYdFw3uM5duxPf7sC1OxkZ9qhUYMcO8DxDuWzYvcewf6DRBRc5DJMtqUO3lK3LGBPkZZt66E68qSFUaRxrclf9+ovVDOgcfTwuVU07GDsQy+fKh2VNqCAsOZw8wB7SuIMlCvlghs18KCAa/Dj8FRJil/HR4QXl2D1oNNUqpIxPuxMk2HdSi8maLF35MSzjsGjLM9Dv4j+n67mSgooHaJjoyNcergvpsP85c20Q9lmrweBQICRu3Wbo3+UzOGQY2DjM9HSwHy9yACUcpEaDtW8Dw4/ng9lTE6xfr+NZPZXR+E6R5a3LOXfRC1m34kp625ainqlhPJ+qqbLX208pn7jOdHOxKxmKHidqV4AyGG0armGVaCg/zA2VeDdSb+rYXWYIJsOo37RuLXCQ+b6BpHasZ/RJ6dDdZeqJ76PwaoCxajDRgqf9ht+MzLhDqhyKnHbdGVYoGPYNgMIjlZ6t2SRFdOXX+w3XAx2Fhuog7LRcBrSmXIG9g4lk+mN95DY8BtoPupFE5anwdOIQ4CiiPZH83nguv/p1/X5pqNTw3yqwIgOB28t3NKTCcFXLJoo3jQWwOVRkFe4jdnalVF2hMj4Kg5sLai96IaccD9PSJK65CSJ2CYJw0uGNVtBlj5ZzewAYyO9GG3/O5PRJXnfhu2mx2/jOs18gWxnnp1u/wUvX/tfG2VMWwOJ3ngdKMXX7wSeqj6YllhkZBUE4qrgaHTm7PM1UeYyiX2RndRfVavTKXzOwp8BYwihjCGZLU5ZBqxascvDQmhrKsX2XQYdhD1WvjKn5uDpY7xuNXwxDHtx0uK8gZ5cyHpZXDgw9RtPhjHGpuxpnInhhkCrsp7u6k7ExQ3Za4bqJF+nKYLSFbUJ3TChmGcuKVRNvooI3WqSy24NwwFGqgBu7c4KdecZpeGhP2aArsLffx3UsMunorbWKc5AF5+YHhzKBs2vTRpfdfRqj3YTzB0wmMXIp+Nij6WDwO2vAYGMMpHsUKIXaVGBn3wBj4eQBg8OKgQFDrWYwhcmwHhR79xq8ajUYY1Q1qZqDH+VHc12KJUP2CYNyk6F6Xjzb5D6/n02T91I1VUb0cN2tZJq/JddhrrLODmhrAy8cbCXD7raMPcWz+W3BAKg8iapM4g5XAsdfclRldN3ZFQ0mtUEbH9uq1zWJr0SDm6hNfe1jUpnQyedgjW9DFcdR1en4fB1lcK3Q+e2Hzq45xC7ju6jqdMMyCAbTbsLZtW077Nxddyk2hjE2OmieHnyAXZVfhvsJwkotE4pz4XWY1A+MgalSPcl9FGGnEjNRZkeWkM+m0Bg87bE3u41aTVMoGKI0Sa3ueOBOApYthWXLvLDOGsepOunsSggPFbfEE/vvYffU5oaZUgHan9lNoVpk/KkCLY94gbPLN+QLoLBY1KtCYbMu5pXLQYiq63soNAqfbdshlwfP6gjPzEYZj472IJ91uQL79sGevqDOf/Vrw/oZkdTpRPqnKJQqFYYxDg3D6CgU9gZqjFfQeLlaIAr6icF8nIcNyn6FzpY25ox6BqwRG2M0qiscYHvVoBFnipZ4PPYE7AgfDS2i8GcfXIdqeimTHVcFgXIGeiu70IBWaYqZtaRGe2mfrJDxA6efk1qMm1oK2sNqt6ODxAyMTrNrdA/a+Hhe0J8BtKTgjDMCB16hAKVCivFxmB73yeeh0jdF/wMOpecy+PlQDogEOhu0bzH9kEN/eZC+cmPKjmJRx0KZ0QYz8hwtg0+BNihbYSk7CAv3fUb1CFm/QNmpX2cmIdxpX9P6RD+p/kkUfl34idKoRUqtNo3RqIkL2q0ZlHEwSoXXU31D7eqgjNqgXA8TOnMNGs8xTBUVW7YGYeDR1zb37+Pe7fuCFwE60TU2iD0+BkMuX3/h4foutqoLMJYOtsOvYay6WyyXI3Ss+aRTs8MYG/qhqC8wOvhNNBq0h+fpOHeZCjuMYsVnKN/HUKGf8aEyw/sMnuuHYYzBNq0t0NERiXdhOcPmHxutd0qpHXfTVd0dlsHgL++KxTplPBaXn6Wt1E/r+uA+s4wTRL1Gk00oGyrhrJ+R2DWPsyufNwz0e7Ew6KU641DaYXc7fbm9gInvJ4zBiLNLEIRTlVp/0IG2nNMF1GdiPLv3wGJXb9sy3vLC3+PuXd/no//5m9T8Kh+65uaDLkN6VQddr15N9vZdDbkfFsKS9pUAMiOjIAhHF9/HC6ejjybUiN6Y16JBrtakdJgRV4MqBI4fW9fA0miVwoSvfn23jNrWj70zFI58H10o4viBCBULQoAxkdgVvJHuqWxncf5J0n6elC7RYjTLdw+Sun89AIvLz2EZj9ExeCoc3KrwMdXoYD9Rnp+a9tlc2E1FO3Vnl6/Bd3EmNFE6smgwgNGx+8jVRezEiLatDRbvbsHfZKGMRTqtsEbSqDwNb+k9EwzwFT7a8whCbgzT/QPoXBg2ZoBkqNVEkKOsYtVzPUXP+7UzUvgroPXMeijmnq1lytUsuqMFLmxDG49qzY9D7YYf9um/c4RHH6qRnQazXdO6aRA/Oh/PJTcNlaxmaipwQlgqjdIwtnM7Ox/azbgzSsnJs9V5gik9yf7JaBY61TDjYRTGaKJcZYEmh+tHwmbS7WHwdX0igtbVNhDk7FLRSc8IY4wTz/vBIDA5SEzWUyz4hG2hjQ+p1rASiyjfwV9yPjvVK6maQODqXOSTWn5WsL2GSmpJnNstDsNMOrucUsMxI7SGjDvJktG7ASiaQrzNvv1QCm+bTMo0JCivVJPOFYOPFw8sVaio+EkXjYG+7DbS4aDQ8zWmoFGhG8aEo35FEN67qzzA7qmtrN8+zd5n6kqqMoBXDcRqBb6VC2Z1rJUaBtTeU9m6+zEhAEW596YqY2hfk55Mh3XuUvUmcXMeqmiBV8PCRxtNpQKplKG9NRy0JxSJxx6HZ/PbuX/iqeAkTeAUzdc6qKaXB/tWKSzjcsYZwXd27QpmwGyWjD4KY2xpCcT4zlo/UcWm/CIJo0k822L+KZe9e2HzFmhIcB3lY0NT0TV6WjsDQUDXK2qtfRaL7cUAWGXQPdD6wkXgVbEKI5Dd1zDhRlBXybxHBiu6f4yP9hy0yuDb7Uy0XwKAjQVoaqkloCzSU+20TmksXUOrNNNnX4Z/Zi8A6SVWvZ1DfvDEvfzsiWeZKI/gukEdqaKh5T6PRVXDResUF61TvPAFihterrj0IsMLLlKsXa0DB+m+NNZoOnDwRhHvGYWb7op/IxztNjqDTF099YyD4xj6dlrUqgZshWXZQT1oDzCk7BTl5Ex/tUSevGqgaqVG8rELDqinBVPhPWPqYlTciCGuqwO3q2VjtBX3Gb5veG6D5vkt8PwWQrFLh+GABuNB2YVh51m8io73OOqEM0IaAqXLAmOHQlMqCHPVBH17Ph8URZsgeX3KSl64ihZvilR1ElJ1V6Yd3nqWCZxdM8Uuv5nYFc4C7FWr2CObqE1O1ftr31DTBfa6j5KtTJAtjwfnuMOlvCWL7xvs/hymolm8JDy2qt8jaIOquVRK9fovFKHVDcYoll/FpG1SPcH1Z1lBW7ZV9seN1VnrRzsGookIko7eVAu1mmF8fI4wSa0Zn4DJUU2xCNgKNx2YGZTv4agKJbdEhXKQL4wgFLarNvdssklE7BIE4aTD2ROKXed2A8FMjLZKsab7vAV9/3ev+gRvfeEH6Wldwl+/6qvzzuA4H4vfcwHeaIXCvYMH3jj5vfbgIU9mZBQE4WiiXI0fhl75bv1BU2uYcLLhB49UmE/I2p0hvakdagqFH88CF4UZjFe30jKtMW7wRnti7wT59Q9RcYK4KU09tA0T5hTBMD0dvPnFQFttBLddoVCkM+C7ZnZOnRlMZoNBqR2WM+vlwRgmKhM0To8XPIQXng7DJ3QwaD5rVY3Vq2D5cnBNrcHkk0krurog7RqUytCSgtSeFjJPq1iAsYc24Ps1LCsQ7dr7f4kyweCoVIJ2d4RWZzRwdtnQkgpG/qYYzIroZUqkdSAqRQMUr60bfQakOlT9HKY1dk7jpB1Ma4oBbwfbJp6hHOZV8qZcrOkKtqkFSektC8sintVMew5KK7xWQ6mk0X4R3duJ8mHoofspeVWsqh18Nxx6lSpu6DJIvOoPWj2oVlMXuywLXD/K65bM22MaXTEWoA2eCZOcm+gUNcoLhx5ucE7lp8fRNa8uLPoO1tjWMH+SHySYTwwgtfEx4UQ0Vpi3q+R1snsPPDn5YrKtF7BoqaK9M02u7YVU0ivZo/cz4GyOiwrgqC4MCuW7qFDsSl6GUdjhosoW0C6ucRnQ+xN5fTSDYRqlltZE8mzAC10sZ62FtavBoe5kscLZDAfd+u//xuI2qm6eC5ZcGix4tIjZ6WJvz4XlMqhQfjQEubTQHhqNW3HIm9BBoRVuFCZqQdWfxPEGWGntDENwA3FX+z6oQNwoPjFJf/9mctUpvEINtI/WHmqoQGoyqPdpbx9jeoDyjjBkqTBCe2k3XugWtGwDXtDWKvHyTxvIOvlwMs4gZ1Kx5SxGul9JLb00qEeVImN7LA/N9ZNTgSNs1bLZCajPWjTChasnOecc6C0/x6Ly5lgAT/ulBkeMlxATC0XAaFRlOlxiAmeXrYLk9IbQ2WVYEg6ytTHkBnuojCwOBu1VhbcEfGcK8sNMZQ3lbBGMapjAQZtkPFwQqgaBQGRcNw671KH4aoXuUa3SgVvFz6B0EJapsaguAnXeMnpemiGzKhU6e2ZVDYODgRMxnQKVD87dmpzdr2rHR7XY2ErTsygUft0wHDQSTTOwLb+DncV68vXGmXB1KL4YPN+hVArOIz/toWxFKuw3Pd/FoLBbO3E8r94miTBaL5+YJdf4iZD3sDtRYXtpE7dvcC8k8l+5OsiJZ6UxWCiCsL7JyWDd4sWQSQdil8bEOayMB3aLwbM83C2a0p5EXDKR2BWUwbQFyeZN3NZ1cQwDvgkcvg05u4zCCkUwrHqfqezwr/FI2U3ErqTr04mE7PB3PMw15+RzcTm17zDhbiP5viBya3n5GrrqY48VMXu8+k+fIm5vv8+jZcMAqbFsQxmMStHqjLKosoWMnorPN0rBZfkOKT8fuxDdSR2/lMBz6tGPdpodO2Hjc81/540OQz1dP+hHU+BZ7eE6H2xQWtHv7YmdXR21QVr8qab7m4mIXYIgnHTU+vKothSpFUFn2J/dweqec+K3ogcibWf4yEs/zT+/6Ze89MzXHHI5ul61mtSKNqa+d3ChjIvbArFrUsQuQRCOJp4fv73V4QA8NFhQ8x2qNYNfLhKrFMVg4JXZ0UZHXytG+cFb8miA70yTcSfBQM0oUrrCcH+BanYP+wc0O/fUByBKh06aZGJcbVBGoZQOxK40uCaFrtZnhjRRZlogZavEcoNtahS9cmJOPIgeZY2vg2S4gPEUuubHiXh72mt0tJow/MnH1jNGF0DPIsOa1Sna0+GjsSIMrdPgwf49E/Eb+UgsqiccNrS7Qyi/hnIdTJeF6Qwe1E2rxlMGK5oRT0euA4tiZg2Z9qBRjNH4+BilqKZKweyM4dv8HVPbiRsPsMIBvrJTWMpgFcPcXFrT0d9Kx0SVDmc/lXP7ybTaKN+Qy5fxNSxOrSXD4tgt4ZJwuyTaKqqhaPBuWUH5ByvPka2MxyJYeOB4HypyY5Sn64JYYOxiwhvAeClMph3jTMdf1yUvCBl1Stijm1FelVRhgA5nPxYe2Vw9xFEbDenA2TU9/DQPTT5DRQfil2+1Md2yNiirBb7VwoS3CpQi6w0kqxCPDEYFedMisWumc0QZl4yfp+JVqZhArIwS0Wv8eFDa2kKYpylYZ0/uxNIOmQzYtqGjvV6rQfUYsm79/Mt+BaV9lneuoTXdHrvnlNHg+oFIoIIhviHIazU14WIwTLtZxtKj+K0aZcBxQ/ePAktpVqz06eiAri7FJZcoOjrA+D723lYyj3fijVfZ//gWntl0H6V79pJ5ZDt+cQzleAmXZnC9VRN5imy/hB8OUi07DBNToKKcVqN5rHwlrnMTCtWGxnDVVWsyXHCO05B8flFlC+fWfjVDYIG28ac5jycCQYfGdZZxZji7GhWhVm8cVRxNlMdAKpigAaAt08LFXedxXv5sMIErNEWKTr+Xc1rX0J3uQtsaz6vieYGQNzWlUboeOgjQW3yM3tLGsBDEbiXb1NCeg29lSKeIc+VlbCvoSVSGznQgMETnM2kmGcrcT82ycDMOhcpocP3OFLu0wd3s05E29PaSeOEwY7Oqj6n6tJzXjd1FbBHzHRWIbdF+0xD1q3pJZIEzlHd41Eb8UHgxOE5iJl5sLLcSOLtCAc/3XDSK1nSK6cwqKpGJ1Km3nTcV5vJSYIxDpaqZmjRUy4Hjc0APUPHz7J7YhBdantZdWBfrIQiFRLtoywp/P4LtRscB37B8GWRaQHkeqhbmXMSgfYPdqjE2TE/Dol0+XYUdsUAZCfhGGaqLA7FLpQNnlzFePJGrMuCZILS80dlFEFppoFjVDA4ZHM+j39vDdG6KnrHNtLU1CWNsInaBpqYLOKGFtFZRcX/d21PBThGU2vhQy2NF7raqj3bDH/2wb4bI2aXxXBP3e50TffXjmuBeTXuBkJX2i/Uat8PfId+hu7o7dFhCaXx54iTClyKuicMY5ww6NAZn0CfTP4FP4KTzrTYMNtorYSyTCOcMzsvSNXy7ba49NrCwzF6CIAgnELU9eVrO7gpmaiFwdp3Te9ExL4dKWfS+63zGv/Q87nCJ9KqOBX0vbWfoblnMlIQxCoJwNPF0kDS2FdzhMpbRCROOYWwcatNVWgDfaiWbN3Qbg6pZpEsWzw48z/LU2nhQ1F4eRqExupdaysZKGfoHB+jIFill2+htsFaEYXBh3iaDIm0HB1fGxbnQI+NZ6EoKJ193PZlxD0uFYk5sCwIfn7bh/TzWt5O014MNeFuq6EVhclvHD/LoADglit/+BZaVQnd7WIMupT2aVmWh8VnUZXH1ohfy9PSWeECoLEPreAZrTxqooVC4z+ZhsUeqr5XKuEd+eYlUqoNKdY7qrjpYk0NY6R6s7pWBCykdhHG22GC0Ch1U4HoK22qhpbUMa9J4+QqEPwk1XcBYFsqYOLlwMHiP3vKHD/xWmCsldO81JCFv8/GBTCqDYzTFskcbnSy3z2bM6sENR7YODq0Auj61O9SFLxWG0FlW5BYqsmHoYRa3J/NcBu3q66CtlQUqN4zfEjm7dLzPalZRKrfiu2VsPw92C8ZobCuFPVmf4TgIpa1isMkVbBYBvjb42oeWbmorL+H5wfup9axmqDJNTVu0WJ34pkaLxczJF+vhrlHkj7ZQVhrlO/Esl40GQ01HbT85Pc3uyvYGx0mwrUexFITstLYaHAIR1LYDwWVRZQstUylIWfFgVlsZUlbgJHK0R8kUaaMdT/uc07oSS1m0p9qZ8CdQqhWFITU4jV6hsVUqFLs0A4NQ0B5L8NCuh05plFakp1Okn7Lp0T7WCosgIXdsRQvchnYB7bv4+9PYysPowNlnjxsojmPlU0z176XXewlKQVcHUBqiQCB2RdVq6WD2Oa3ATmmcWpWh4m60G0wU1LarDyuVofry4NCOztK9qkC2mqPsT9Sv084aNWcSzxmj7Ad77/F3UkvVcJ0+3NSieNu8X8BGUXAnKOgirg4cpZ7VSs7fT7bgMFUNTJ7txTYm0lUotzDhttPijtNaDgbuFDymS4o2ayl+oFzTmm6hrHycCY3tZHB6a8HMrC4senoJRVXFVz6OcWIXnxUmdEqnDLqWwUOR8qboiBW3+tVkDZfwFit8q52XvBjuuzdYvnSJRWUKclYGyyUOtwVDyVShpRPHZHgs+xxefpS1hXXYZjN66bpYRFidMqyygtBYL23VL9IQe5/GSvl46UB8TC9rw16msMIoNIdOqmYxfrGfMb9Ep2qJFRjv7Bp2iwa/SnXMh0FQZ4d553Tg3kKFs9j6Psq24vx7Xhh+3d4e7C47DV1doKuh2y03gNm0HbgQY9vsqT1GrraXc73zsQoGy/YZM+M4nqFUcLDKZwKrUFa9LwwK4oPnBve4trAS7jrbGFKpoK9M7c7TWWnDW23jLdEYH4ytMeFLFVtX6B7bRvuifWELBG04Ng6DLRbXXL+I6Z8MQk5jVCJnVih2YYjdwFoblAn6T2OgUIVlGGqhWFUyRS6aGsXZOYZaszzofMIxjdaQ2TyEv7iDjIHOTjA9k+wdHWepD2ll4dTqfbQxZVpawHEKWLng3hrxqlR8l1RhNZ3uqkBoUgnBSYFTMQxH82RlNFapgqE1ds1plUq4dYOjuY6Jxa6Zk1HodEcsZZu2xajKFM6wJn1GS7S0IVQ+wnc9vGzodjs/hbIURtmYVCt+aZyMniSeYCSaUMX3F5yzS8QuQRBOOmq787S9IMhh4Pg1BvN9vPKcNx+Xsix+1wWM37qJqe/vZsUfX7bw77UvZ1IS1AuCcBTJlraxsvMMaA1mG7Sm63JGHBISJiCv2b145CkUoT18Ydq6sw3VUibNRJh/KRQttEE5Ocpnlunc04qatlm2JUVrzcY7NziG7UdhjMF3SlSptYzT1QFTFQ+rGzJtFgwppn4+gGk3kNWYQUMqfJs8M2m63+9Q1DY6pVjeDXrQpbxxEqvXYIZ2oCpJsa1GZjJDy0SaVItG2waVsTn7LE1lwqbn3DCBeRjSooHU3gyaUGgD9GANelxwg3JUXYdMpiOYAYtGJxRAKe/g1AxtdhXSFkb7VNG4GFrTaZaqNQzqfWhtKJWCcMe2NmBFBqcWJbK2qFgFjB2EU3k+YDTbnxwi5a8CwMpWMWlDSlewiiPodH3wGWHsIBGzbbcEecYc6GrPsKStxNJKluEodxWaXN7gjBgWt9Z3EJ9bJAaqemjM4JBhc3acy6NE4UFStcD1pOpf87QONKZwt36bxsrZjE6mcWsOTO/Csar4Z7ZihyqsKlmokoVpc7F1MDi3OhfjeoZt28HZ5/OSKwx7q/0UUl1kszBeeprhCrz/5TcymncY8hTWjGRKnR025GFPaYCOlhZqfi8ZlQ5ydoUiaeNsbwbLeNSoNYhg2hja2xSLFmlKYfRgS+hK8rwgn5TRgUhraRdMGgWkVIoyKaCGbVkUdYmt3mbOtM7GM348SGxTnTi6RjrdyqKlGl0uEszXGdgytDF4vkHh4SkX7XmYjCGtwFSAgqHV0VxevIYxcy9enIld42qfSed5PH8ax7HJtKggTEiBNWXQTgFcyGUdloazXba0qCCHvQM1fJJeiunaflpaFamsw1ihn6I7RdEZosWspbu6m3QahsOLob/6CJa9g9HpAmV3D4vPs2hvh51eDmu6D3+fT3812Hv3Gbt5erpAalmWTGEZm/PBKH1DYQsKRbkyzUS1nj7CUYto0Tk6TAeeCTw/vufiGx2KDRqMi2/ZmNYerP5JnHyNQmaUNm3TSYqaq4J+zVJYo2l0Tw2lFCYfJH63UhY1XWXD8BN195gOHCepFLRZvZQpxf0dwKJuyAKV9Eqq+7eyvzpG2yUvo6Wlnhqsxbbp6YGRcho7nCW1fttZpDIKz+hwyjyH0akd0A6qOo0fTqwU6WPKnSEkRA7ZnT6WMviLHbAUVk8GY/nxS4yyWoHamGW4sh9nsaJs0qCCnLikDabFQDgLa2ALdlAmnFUznJwkpVJBSHBK0ZoKXvyW/TJGKSwDdoePG74kiJxdVnUcL2fC7xfxvaH6feaBsTTasgIXlw6e93tLG6m5xfhmVRAkvPf9IGeXSXQ4QMbWWK0pLBXkWTQYMgUbOwPGBV/5GBNOeIIFRYPdHYTgBROrKEq1sP9XHp7xwxbycNxQRtHgmVogdkW5BTWoxCQIvg5tYmG72zakM4rKpiwta5fTur4fb2UX3lmLydTGaJ/cTbmwBr9L07MI+qq7wXQFZdJ2GPofhjZ61WBi4jBZpe7xKeVKeMDA4DgrzUasaLIQAEuhlAl/XMJFxoGqwUn10OJlg3IrG/zwS0oxPgHVUVBLo+eAGZebqb9hMIvOxHSdQXGqSI8XuRU9PC8MQZ8soXvbafNGUFN9UDPYSy1MR/3616k2fG8smCnVifYd3XwGbBG7BEE4BdFVD6e/wKK3nAPAQG5POBPjhcelPJm1nXTecAbZf9/J8o9egrJnvk5uzpK2FeLsEgThqFIt7MJZtIq+fli+UqNbgVBHikKwooSvTqqXQvsyFhf2UI6iChWoootq9fETc7Nrt0baH8PrCHbywvw6ijULHINfDQchYW4mP3w4HdGjtHs1LGws5aJUilQqyL3iOhosA7XGwZoVxLbUjxs93McP1YpKybB/FDKjsMwKCx0NFUtWkE8n7WFszWBljLzqprVmxwmq0aGwYxSqy8Karj9AKwUqMVtdNQxNi8Su5MAWAsea6yjoAGUbtPGomRSW5dHbnsGatnFdxegQ5HoUZyxZilnRw4r2tezpC8LsUt3dTK4o4tEZHMEHjGFiLEeGYKBg78phbBfHhYw/RVkbUHZDcmwUeJ6iPb2YotEoo0i3tGBh6PSDfC++agM8SmXIRPUQETuCwraoT3zJ9KjBdgz0JrY1Cq01yrKCCChj8D2NpUJ3gwKvQ9NSslBuioEhQ1sBJjsGqBTOpSM9hcGQ3tqGchTWWRq0T7tnkc2sJBumZ/F8n40bQZ01xeBgkI+psyMoW2+vomJXGRqvC3MRPT0W5GHT+CDd3bDHneKqzPVYYQihSbehw7xq9fM2dbEoQmtSukpLq08pp7GMQzplolXB3yjc1FKB0GLBZZkreaC2CyjQYmfi62/KTNAGpELFoo12tAnycXV2QMaphlJsIHd52uDiBgNH5WFcD91u6FqjyU/bGK1RpGgZNmRUOnCcAJhgwgDLLVFTVRy1Am+6Qq3bRbWAKmtoqWAKNriGlFuMB8eV86uwBVB2/Xowiow7jm5ZTnXPHiY6gvLlnf0UykXObwXXN1gDafQyB2V8zmpbxRmbLmC83Mslrz+DlhYFtSK234K/+BLyrcHkPVf1dkB5ArewnNrkcs46o4tsR4aLrECAcxzDbrvu5i+0nM9S9nNWWysj0YSFtTRrtItuqZGvXUS3tjiry4L2XixaMaVp9qgR0Dbt1U72DkBPKXAenZNeTTtRvQVOMUtZDUKuVi1EjsZlLb20bFnO6JJ+/LBPPPccMGVDFvCtDHmTQ5Usyq1ZLGtp3HdkUhZ4NkbZWI4muHkUxjJMXqmxLfC1QyqdCVyIqo3U7lZwDN6LoEV1oeyoU4+uUWahMfgVF6s1qCBl6Th/nOso/MTdn1MFLEKxywIsg3Kq2G0Kr2SwKg5kDK6vcUIRLHJ2YSnSdoaM3ULJDfI8KcLfgahLqfnge6QXW3jDQaF7nO1k3CzQiTYa7duYOAdkUM9OeZIOZwRvuh9luoPzUJAqjzCabydXttBhzq6ItKVRKSsQBE0gdqGhtc1gfINn6WDiB4LciaYaCMkGUHk7EN5DMfuZkccou4NcrC7GGB/HCWQUpQNnV8qAXSuhPI3v99IyWsUJc3tpE0pt2uD2eqQiR5XRGNcn7U2zeM8Gqj0G47i0uhnK6dUAeMolVZ2iw5miMN6OOtNKRiTimxpWCnxtYzCYVo0K+5PKdB7VYWh3XEgHLtNgxkwa8uulCByngZydDZeq+s+vMpRL4dUZOrv0jK7RJOy0E1NwxitW4jw/iTcddHYr8/fjea/HypbJ7BjFP6OLNnsfhZIPNUP3OVb0eAJYFMJcicY2oXAYBXIHYaT7dB8LQcQuQRBOKmo7cqANrRcFT9l7pxc+E+PRYvF7LmDfhx+g+NAwXa9cvbDvtC9n08jjR7lkgiCcziijGZvQOKt76dqznpIKBiYKFecXsowbuFGwZxqpUArSfoHu6g58b5JCuNx3PIwFVlvwpjqVz4Dt47cGM2sZQ5xjQ0cJzo3G2B2krCoKj66ODHarDRgqZR/aCIUnCxUl0zeh2KUiF1Kw3LW7MHYV09LFlq2KZR0+1TENK2FkBLq6DF2LEqKV9qj6DlknB+3d2KqeFwytwgFZ4OrQnSbI/2UgbbdwQdd5dHd3sjW3l6rvsCS0tphImEuIKpZxg1QlCrAJwsVaMviWwa5Z+Bqq5zph3iU48ywwS86nUxncZRZ63JA5/wywRpj0xsP6IyE8+YF4goGqobUd7BQ4KR1kpp4hzFygXkAtlQ7CHrXCTmUIJhRL1wcNpFC6hqUNOq2op6sJRcvoNBPOriVbfCzPMHiWxdJSivO6VzHsjgXuQSyUUsHgzpjgO1HoSTqoG+On4v3n8jCyx6U2XkR1t7CoEBwdR9E21kJnLkO2J4PbHpVL09kJpUS1VGuE+dig5geC1WUrric/OM6EGzwj2GHh8wXo6AgaTqs0ljseFCrdjvbLqOiSC5Nwu8qEQ6zgYJnKMK3eBG32K1hS2kGbO4qdCl6+aU04UA+NOIDSOnZ2aZVC/f/s/Xe8LMld342/q6rj5Dk53XNujhvu5qSwq4CEAJGN+In4I9jGxsY4EowMGLAfPw/JmMeAZTAGk2RMMAiDEEgggUBppc3h7u7N95574uTp7qrnj+rpmTnh7rmrXSGJ+bz27jlnpru6urq6uutTn+/na8AXHu30/qibOiGg0qmriIVVb0gDrubcysN0EodQOgghaXUiItOl8GwAOiaJE4xjMAtdmiuG4rUcxrE1doUgMZpO0kHqbjoZtTRLs+MSJpLNjQ5CdwCDmNbotQDZreNEtWwy3fUjXFybaS3tH1VVYbnTINKgVB0Tuyjp4BrF5OQm18wyUSxxzvqYiS6gmfLGULNFFoIS45XU38cfw/EeI1F53nD/JFJC8UqOzYdziEjjTJQotl1mO10qc1bt2BWG5YH7LnGm8bvLg4klESbh4kVsaHAIUscYkc9IgLIsc4QSyjGEUtJSgjiGq1cNnhcy2S6xmXZFrUGSkrjY0GItLLEgtKCyVqS7Isi3FIwlMG2zIvaiqrVw8fFoN2MaL3yczuYcwSfWEesF5CWfoFuilEuQFVIGQ7IpNkgIcIBmXMPLu4iok4W6yauavFthf/gq2vHvABCKHGOFKa4lL9i+qnzi7IY2fOy593PrzP184IV3M914joKo9irI5fhZXHt2GNdk7dTDxdoLzM/m0DWBaMVEbocPbnwEX1lSTGmF0QlCpVld4zz16CxglV2R7JvP606CuvZJ1KLK6qYkVBsuBriqLzMelckFAiPtWK0TwyN/9hg3113MIjg9Ylo6aGP44KUn4No+fO3gtVw2brVfO8JYtVkoaKUkmDLgNiOMhkTq1IPMPotIDCJ9ZnmPhmjHZM/FOEpD8oWDST37hAAZGSLdQhrwz38Y5YS0N+/qXUwrfE1Ju1hDY6mNF/UeJBraMZ7eBCV4LPo4IhLcxq0IErw5RSNqIzbsc1t262yuW9JZuQaiGLn+BBUV0wkCS7hntpMCE8eZEX3GkKk0nnFgIclJffliMajd1Blx2B8BScNIyRLLtE2bpmmgm5fwjE+7Yzh/Hp652uBAcx3xV5tcvnoBkzdM1DYRLUOh+wLeM2t0nSomjnFdyFUFG6mFW126nG2eJY6v2eeGsdX11uzYHOk2LQYWJ66DkUH9CCOM8FmF9pN2xSE4XgHg+TWbiXG+9NIyKr4cKL5hAWciYPWXn3rxjVOM52ZYaV3dMX59hBFGGOHlumnBWgABAABJREFUgSaOE5TogExonrfqoXm5kK3KShNRLtlV2UGya5D3cpIGMQlhkJYaJxhpkC50JiJkQRLPSGI/yd6fe2vGcUpQCTTaOLiOYHoqZmbKwfHsBKNe2+JxEyco2fPs6iMhIZIljHBIirNEWiEMmOdjRGwNh7uRVV7JnF2VtqbwcTrF74e+9LJFZlERGKgrRM6gc0mqo4F9/iSudPGkSztpU7bzbcyjXdQjtryGtwhAEF+zpsXShqmYpE3seqDsZFkpSEr9MT+XEjhCQBIaNk82yAXjOMJnTV/L/F56lWxMNrjY/QhNvc7EOFSrUC4LZuYUONKeb69wYzOkjbUfxtFNhAapfKS2KoyeF1fTm0MneUCjA0ViurSSdauqIGsyRGr6jjHINOlBqwlO40SmStI93xlpV/2NscoujA3ZVNJNvVecoWyOBzdjph7tsHalQq0BtQZ0mgLVknYyu+GwtgZBAOVKkprlyywLXhz3TcI7cQvfCRjPTzLhWsX3/kUQUlLTNu4wSXMuGGSfR3RzxGk2uwzGkJgEBzf7SHU3EAJ8VSNMMyo6abySMSnZZYY9w8IQmsH+Xs9DyGQoANYYcNIrJyLbYAaDkXbS7bRgovExjDGsdGp06eI3FFGji0FbsmvyCFNz+2kcvAYzZ5DC4CCITcLzzYss1y/b940B8higWdugdv6CvWDjCYn08dYddLSajQGxiYicaQadtD3h23s6MbjmInLzErnWCm+46Q7mKiEbyQbryZpVy3QEY8tNim4OISVKiX4GVqlASETSpVAQeN0u3UtNkoaB2IbI6XpEfK2BjgzxpiZZ05i1BHMxxlyMERdbmNiw3LcC6/sOZeHaEVq62TkYQGqJQtIKFugEE7Sa0Oq6bDQCdGxInHxGdrnKzRSLdn8HjcapK8TzCoHAa4A8B6Zhvep6hKfd1qANFK5qXvijh2k0LxOVYrx9kBQU/rpBRLZ6BkFbtogxCAndpAlOgDDtAflggsDF0Qle7SIYQ1FVODV9V6bwOpg/gVzWEDUgaiK6sGHW6MYtLsXXIF+h64wjY0hSotwIwDX4KsR46TVKBGudNR7uPsOTl2vIOGZNL5MYzYHcPMf8IyjhsHotph0JVlZg+UqOTteOMUJD2y3Scccx0sF0YqQrUOkYLdCUSgn+qr3PmjS50LlEYjQm9W28dt6QO6dZXb4GlxWOblh1nRS0Wlb5ZZN6SNx6v58qLAF38OuXCPz0fmpD+LGmDS8USZZ5VTsMqeLiBDqd7LYlidMMjMISe1rbcUcAUdxg+bzhyh8ZkpZBBNZjr5dFEWGfMddWNJ0Y2gc7uBVrpm+eegI33iTybWi90ILEJJRfs07plOJM+5xVsKXjkEyJOccxKN0h1jFhTlJIunbc9f10lE3DtHtrICnHJRyJFNDWG2yk/+LOo2zoDVbPXeBKfJmariGMRvQk4MJk7dTLJtkI7Pi6oq+xYdZZbS9z5dA11tpXqCVXOLv5LFfml1nmKs2rmzSe2+Di+z6K2mzhpaGSvVB11xcU5u0Nc6X4KmqmSS9ZjUmVZO6GS+mMonguQbe7I8+uEUYY4XMT7SfWEaGDt2hXk15Yf5KF8sE9Z2J8JSA9xdjbj3L1Jz9B+6l1gqOVF91nPJwmSjrUuuuU/OqLbj/CCCOMcKMQxpCQ4ChtPT1SoxhfBNyUv4MPrD8N+U0CX0BTYmSShs3057W9jIaJiXEcWJRLXEiWQdoX/Y35DvkTLskZg27Y8I8ho/TsTdtgEolB2WyMwkH5yk4GTEJBGGobdoVdxBqpLIlhjEbEguITORI0kWPDV5IE613UsmGGjtMLzTR0OiDDNAQjDaNLTJ/s0kajpcs91Zv56+Zj/fZC2PTtMcioaU9kc92WLxTdpIvnChb3GS4/BssNm8VPC2UVKQVIYoHGIJI2idEkjo+gjhCC6RlYnhI8v9WuUZCRPxKHnBynaZ6mkE6Qe5KI9el1avEctY0ajhNkSVq0ccERpPZrhAE0jLAqKwWOrtuyXZf4QoyzzwEMjhJEQiJiO8vTnmQteoaO2cTzIIgr1IzV8wkBp6cepPnu92TVlhEIpFUlGWwYoyBTR2mTenYlhpyTRyY9k3gHQwShgDoEbkTOExSezmE1W6A7wlqyCMFMchQZznEleg/Oc2vo2MGbAMfpBUnZ62+Mob1Sx088jNNG1hLclqGkoVMTPLP5JApJPQKVKNz2OtrVRNEEup0nahqUI+hF7AoSEjQKlwjbuHGU4DkQiFr6CTjC/laLm6y21qwSaGAe5k7u49iJO/nAb/8xCJgOZlhrrLNu1rJteoShjO15IAyaGKNh/LGY4JSgaTRtbwa4zKHqJBU/z+XL0HI7NJ0Q3y0Qh0/jxG1k3EB1WzR1TGISVltXaJmuvQfSm1sLl3Nd65Ok4y46NERzLt7HXaKgTc9ILCIiPuJxavEk/iOPEKqA51QdYWokic4Uf54BKSV3jb+Ki2GLegJL05Irjxla6wZ5hyDLntrViDSkzijPTsqB+vsuIi/24oQjUpmbzbbXMmx+JEJrg7loTwUB0mtgpkFM9NvcdA1cTYl2E1vySzjZ8VO2Mx2vJFopEg1a+dTCI+iJSYS4hDSb1jBcehjRAmHSqF0FxiBiiXIsMe9KqwqlacAIhBYYpVjTVxBEoCZJKoLllfN4uYjNsQjvoCByS8irBtmEqSmIrwnqBoSyer+uboObs2NZKhcTRkMXKlcfYd1sQlRA9Aj8dMjV17q4KwmynqpE3f1ozyDa63ieg3YDIlUk6AYY0th1CaaoCe4qUd9IE4f4GkhISpJYNllufYxO8QWqwIHcPJfkJqvaIEg4f0lAC1yRo9mxbnOe9KglK5wN7+eU9ySm00K4oPK2Hy5HTxCtn0FpSVSOcTccSKCm66ka0SDOCPKySJcVimd8nJkGsSris0YnchBaoIXMyDGRGIwSJEkHIxJU8xq5nIENq1KSwiZrSeQA2eVqdFdnGqZGz5sxfUWP41TlJVVGOkoFxBB3NsmtS2rLcPmxmODWPtudeSmavkpMOODPSqInLxE9XAdc2sEMPXJw1awg1x/nT0SCkj4KFxeHjrFklzAJvq9pNxI0McGCD2cMsXFwSmXi8lXcWKFrXehldOyNSanKbCV6hraxfUNp6IqQ6NJ51r3zmCLMrJVxnzHEboN6skxsajimTcxZYhGTrDXpBFN44lnyjTEOjN3B1NFFll+4iFqHmeAAd94JwckLPHHpr3mm+zTx5Rpys0UsQxzdQqVZWp19Ci9IiWihaCYr1qQeMNKqKZ0Njx4bGZnO8IrcdTAiu0YYYYTPKrQfXyM4Ws5e8p9fe5KDYyc/rXXodg2rq9Bq21WfbhfMvceRP/sYz/7Axwl/8LW4rg2r8FwrZ3fTnz2lwljOSvhXm1dGZNcII4zwisCqqRKUiNDCWBVCDEopSOzKsx+AkZJwWROs27AzVwd0afeCMMDaYSOlwBEui3KJurPONfU0kGahwxD5SepV3ichBskuEoGIHYyIUMpBSYkfrxKrPIX1mFpCP0wnfZFdCGaQMuZae9maCWMXNmo1q6CQJqYuNsjpkCTxaLkz5KJLdNJAvR5plxidhWJok4CWFPwcY6qSkRYIS3YJIyCJIW6ja+tAajCexkmVy4JzXo1awxJBxaJi2rHt0NGWhAuNoWkMGg+jrBJJSmF9ijKY7Fx1poCT5NQEm0iEiPGpUOmErCTPYlSb5qwkcmBzvcHl9mWOFJZIYgVOGjoIuNJBmC4Siev3ZziOcqEV4awoGDfki4L1NZCRRJiExBd009CQiXFYuyIyZZeUUCo6jCfzaKWpJ1dR3QTfk9lksZeNEWn5OaOt5xBxeiHSqmhcjOmgZU+lEiH3a8zjIVp4gKQdjVFtrCI8jdMyGBS5FY2z0sCIPE4XimsJafQNhQJsbD5PvLKOr0La+Uv4TxjGujFeXSBahuLFXNbyZemiqjnixgob/hRxa5nOCxH+bf2FM2E0MQnaqUJsSbjD6hDn9dP4so6U9rgyNen+SP2TFI2wZNdg7IxUlvSzsh0Knse4c5QPRx/KNlEDyi5jgKJBzhi6z9nv8w3FtVDTiRQ6CfEdh8BxcAQcnA6YOvxFfPzpP06TEoC3/izKaCIvh+zUMCah2TP2Tm+KjlMl1gFBvExkOiRGEeWKCCOI2l0o9EgxTVJxqExN47jPpddQYTOz6oxE92btOdT/7JI9VU+gY0t0AZi2yW5sEyWQkl0oF5Iuup2kRkAGnMDGAbZrEJaRjWskqUl1Ty0o9jtQlThPK1zlEq91wBGMLUpWH9eYlOySJoK85NKyw8LiwJ3XI4V65BWgcWz2yLYhV7TEiE4glB6oFmAYk+PUjbTebImD40vmblZcy7fQHywSnW3zyT/eYPbu+1jN16k1zyK8GQI5SbJ/E85oKlVJcRGUUCSVIpDgbhrcSYHIB5h2GzcMEBKipIVxbd8VcdrhjUY93EY92cU5ZPuV7Npsdz1j9P74naK1jpYJcu0FjPBACOZmYaG4n5XlApcvX862L81WudZKr9t4wqasUXPyuHKDKPIJL3h4NYE8qi2ZY0DoBMcTdBLwRN6OJEKQd/JoNnm28X6Qs5hGA+UJYj9mUzboiIbNXChsEgt3w3bTtXgDIxzQ4AgPmyc3ATTT1QhdCVm5mCCiFpGaxBFpnDQGp26oPJfQKl5j+coa1fMBImWdOjMJBceSXbHUhNFlVCehOzlF92qaoGQgJHZQ2WVtIVWmeHIcOw9IojaBCYA2K6swGyWAQKWkWu9K9IMBDSoU0G2wIWAtPIHx8hgsSRoT4fXGEGWouCVaxGAECkXunGTqOMROzMVNTTBRRJzZpNaQFBG0j7YILlZgM0Jfie3cIx2XEg1adICECTlFgQJO4HJVncJJNglEwOM8xsq5Z3BihziscS65Rj5K8LSmQRu3rHDOKWIDk9IQitBaEDgyI/eiGD7wF3D/SYU46BLHmtbUNcpXn0c904Zmyhsm9naXUjA7YwinBU8/uQE95a20zzXV7g+qbdNEj5RdI4wwwucajDY0P7FC5Yv3A9CN21ysPcfrDn0pRE3k8lOI5jVEcxXRqdlRVDr2AZKbwOQn0KV5CEp7Ol6nY3j6GXjiSXjyScP5C9YP5trKsHLBwuPNwSm++P0P8+++6gKP5eZ2LNN1Da4LYmISHoRv/seX0ZeP2lVwTfYzDGB6Bqan4OgRuO204LbT4Lp7XMoYYYQR/tZDSYMxCVLaMD7VlJQfKSDukERRqmQSQFtReiH1w8rNMVY3XLN51OhlgktMgqfshFDGGk95NtQQQRRZEUjLTzCtAYNuJJqEfBl0yyDXJeRsOnMpXYSSjI2B5zZwHEscREawTrpiHkFJ5tkgYYVr1E0NhM301WjYcAoprtFimUacY0LP0XEn4dY8te7zQJ/s0oMGyVl4IIiUnLNqGuvLY6SDFAK6dUizJLpSEaVklzEmU/oAVMcEbtNmuOoaiLRgXLg0sSFJRkncNCW959gX9pnp/sTHciDaerIISU5W2MRmFCyrwyStp6l3ZOrXBUYl1BJLtLWTDkmSA6dPTLnCQdBFIXF6Wd8RKGVJF6UtUSGFIJTjdOMVoMs5PkrZJDhJA9loAOMUrzkIE+EuKYp5l7nqLJsbhryaoi0aeG5PnZcqklKvZxvGaFJiwgYv9hapZiYFlZqms56qI9qbyLkA86SipeyzU20kKLmOJMHdTIi0wK0ZKEo6Rxfxy4/T/VCXOL2+ThrqkpgE5+4yxcoMXWFYaymmjwnadU097GVeAKecpzE1z2rzOJO3Nnj8r99P2JDMMU4WbkhCTEI9PAXNGl6ygS8CkkQg29c4cTyPlII4JbuykFgNTj/yEYQNG+pRwL7TC6e19GsUwZOPGm7aZ4ifbxI5GnlTwsP1R/D9w3Ta4McSg6G2FlF88hDuYhUhYd9CjcKUACHplE/RbFqjbyE00iuASKBTy15akoMRrIqsXom0scmxbnG2Vcf4s0iUJV8HUmlKXNR4gFMUxDVD3h1HmLPW28yAyWlyEz60AGMVfjiCeCBDqvW565FdA/Fiypqv63rXpuHDhpWKuI1cOwM1S3zFuRMgn+oTYr5ACIGXd5g+ssTaBx5DJzBzt09l09BIrEfb+oyP6EhqK4oolvRuCVGRsJpYr8B0bNNpNr1WEwpJF9kxtNpQwmZoyHsF2rQQ0sFoDbG90OMnHZxNWC/U2KitMxZ7dJ94AelO2/MRDq3Ts3i5WnbaxjUoIUkKw+91qjiOqUwwu+Swvg5d3QLlsZBf4Crrvb2Rl6/gJSEisddJaEGy2uFo5RaSRguBZNDITLbXEe7T0MF6CmqDUoIwkASem9YT7hu7hQtdl0bDEIYw4VX5eGONdsdlQkZ46+BHLvsbB7l8Js1WasDRDXzZZLNrQ6gVkghwhZepPREKuhqREzzXvshzJy7jfMyHlstEOE3Oc1hjma7uogVpqJpG4aBSG/0k6XCkPMvZYo5GMM6G0OQciYNVDAMUrsTIWBC4UE/WgZlMFRfNJ+gWmPMQm5jOZJPceR/hJ1yIz9tnXjTAVveeIYmxWV6lwqRhn2m3QcaG6TGf4mqb2qWE9vObFLrPcf62TepmCqFcG3LcGySEDbVvJx3OxTXWxHkqyRGrQNSKjmlR8jxKfg4TSubDKRrFS2gJsZG4TY2QhmIhgg2DW6ySD2s0kUSJVUS5gQeiQ5060khMAtcioL2CCDbwPQjiACkkyvGQKBwMoQm52bkVpxRSHAfR2iBwx3BVAyU7aEcifEMsjxIlkv2uYr3p2nBkV2ybHy0v2wZ08djYuMq4M4XxBKZpUJMKfdmSpAATEwJTkXR0LTO874UxqrYiTn/vmOZI2TXCCCN87qH7fA292SV32qZbfu7se9FGc+KTv0f+T3/BmvDuATo3gRk7gB47iB47gJ44RjJ5DMIq588b3v/n8OcfMDz6WD9j2cQELC3CPXfDzIxgahKC0KYc9327WhK3TpH8s+f5B2t/zvo3vY7m0iTxRhf15FXcC+s0C3muHNlHVyvqYpr3AHe+6ir7sfv3sl1JKWg0DFeuwqVL8Cu/Bv/9lw2VCrz58wxv+yrBxPiI9BphhBGuD6U0hgRJRDzwAiojq0Ky5AqIRDEzDZ0udLserpD4SwFJUocGNEyDGpvMOL6dtCQJSslMvXLtGrimS+wlWN9gO6l18dBOG3P3BrwXnJyk4Jao1xpI6YBS5PP9saxaBaMgLIA+C62Um1BCUaBIzVpGZzBoEtPBUdCOO6kXk6Q6Idh8OiUsehMVozGBlSBok9B8IqJ0szWY7pFIAkCLNNwJZGsDExmMUDhCYRJDpGNUkpoZp3DcVHEiAWNf9nPSS49rV7uD9G+BYHFBkExYI+cX1p7k+WsX0CaxJtiAL8sYIfu+Zanpebet7Zu7sGb6pIRSElkTZss5OSh/jG51HYHECwVxMUHVHJTqpYAXCGMY98dougdRkWaFh+kWY8LuFaRpI2Kb3fCm4s1c6HyYuOCgpEOp9RSy1WU9dxP5uIJSTVTcRRhhlV2kUWdpGKN00lmuIA2LEwQSfN/QEgPPbF9Tm3MpXLIkBh2Fro7TOORQeUTg/fUFOsLAMUW3C/XYo3ZYsZlyBxWlIbaqPWcqxKuGmAp0XYkZE8ROQlzsH09VBU85H6KriswG0yQTktV4Db+ZsKYFAkEnNnRNhJI+HWcKL9lAImmbIjpuI4NC2p5duqaT2f1sU3aJ3r2SKqBc6AInnZt4NP4kG5sw6yuunI1hQ9OaTKirTYzSlP065fw1vKRsPaLWQxDgjS+h4w6yW+8bjQUV2s4URq1ZIZ3jg+4RfD2vKYNMmbfWhCS8BiCJRRdPStquJo9DYhJL+Pb2EQ5CSXJHHDY/GjERzHKTE/OXNJBaYFxD3gvSEL4kPb6NQuyh/UQMU/0wxuw+Vh6iU7MEWGKJQ+PlEK1Vu0HcBi+HdiYQzhlMN2FyAsSC4NoauAWFlGUqR48hrz2FjC257nqC8XHI7e+w8pS9N3oDgphUMKHQK0ma6zIl6B17n8axgbM14lVbz5VVOD37Guaq8D7zuwhXoWMQ2kWm4c9SCFphC7/mYaY0ekohNgf6nF/ACz2UjEgSDY4lu4QvWD2qmD+XqjuFYGJGEnsChCEyLYwxHBk7yVU+mHYyg2zXgDD1eQMhFdGFBkVVJvJcoqRjQ0EB7RchWMckNmwt8RLce8okz64iOhKnkoOzkHNCcirk7HmHlTVw6+A6NkOfEQ4og0jD4qKO5MoTEcUDhu5qAdesUqw9zBVtE0YtqCW60kPhIESqfI0EJjEIJemOHYCNyxhhrA9edQmZbBKJNqtmJY3elsQJaG3JLHsyMT55jOOjjSKRNqwv8QT1eUX1CQibK+RLE/iewKTZK9zDCa2ggyQE355DO+7SnYjoTkRUdTVTJqu4fwMXOs8iWmBUmvZVCkgzGXoujI/D5ALMuD7+Oas6bq92cGjZxYbYt88fUm/I3rAgrKcejke7pBFdQ9ep4CRNtJMDWeem0mGaB/fTXj9PLifoALII8ZiB1jrJhvXh9KpT5MKzCBNbksiAXwhZv+UijcY6xadzJLFmrdGksBIAmpmJBPdsasTpKoggEQFE4GqPUPmUfUPc9chJFykCpDAI6YLQdEQeoRw8adt3ZQWe+oDgwKAqDjh3UTABeMJjrV0HFyjajJLOfkXsC9yJVBUaN3l09S/QxPhOyS4Yyd7YJdC+TsPuDUq67AUjsmuEEUb4rEHzY/YhXZi5SPCb/55zF98DOTjqjhHd8+UkM6cwxVlMOIbxS2CsJF5064jGNURjGblxDrn6HHL1DOqZ9+A2V7Lyr3VnuLJ6DLF5jNP54zz4VceZO7XI8eNyj+SSQ/fXX8+Zr/pDxn7oD5iaDomvtoZU5Mf2FTjwy2/ELMzwnl+E0/de4W237JQrpH+8dtvwsYfh3X9geNdvwv/6bcNXfaXh6792a0jMCCOMMEIfjjS0kzbPxp9gsrOOld1YUqBHdgkJdCWuKzg0eYTnV+u4i03y4SbNKzUMhivaqrxUGuoD4CiVRqYJ2h0IkgbGtdSaMQlrJxLGH/Fo0+aseZ7xXJ6J6iSONtTjS6mya5tEFiEgnxfU0797q8QlWcoMxntoJFdRIiAnbXjLU6uX8Cu3sbAgOPN0upFniTdtID7SRp2HgpMnqRna55MhQ2Ib6iHQVYVYAbo1ktgHz0cJB9FUbD7bpjSRGwhHITNKl8ImsU+0obDPQT+f0JoU5DoGPyW7pJDo3Fi273p7hWa0QSysx5YWCiEEh7y7mPeeYxkbTicQdDoGHDBKIweClJJE2EySaZnhQ5N4F84hE+sn1JnokqsHOMqmnieG293b2F+q8PALqYeW1HhilWhfnYmzY8AmE1XBeFly+YpAX30cp3wzrm4gU2M2P7lGITqPDCOMdNDapOF60IkjanqTgrSZwwRgjdEF4VMJcfEAdVcDZwFYKEzw3nmHaMZQeSFBdCCZmSeaqdOplfFMjoZQ6CUfNuHiRYdmesIltYAJzmFqGq01jnSQ0nqrdXoCvC2PWalsCoK2rnFuo47JQcd0ufL4MkEOogg2GpvkcVBhxBg3kW97CBLKIqC1blj3E4ROgC7R8y3CII+bg8IKlHIebpgarJcktUvPUX1EUiSPWG2TXDI4sshMNEnTNFFOh3OPn6M6JUhCjVICIRJEZwMhBHnh0cnPo9fKONLBkTaoC0CkHVA5VpUolJsmFHAh9cTpsU5CmKwx2lWBdiT+uYBEtslJh0hZVU5itM1aSoIRoIRVYmaZ3oQiqClcmdArMucGdFeehm7DKvqUIBm6v0g9lbYqu9Iwxs015Moz6X3bDznVc7elpyCQOYek26F8QOFMCM41F9i3WCBZ60Dq3Vp72Pp7ZcWLrlUbph5AtnyrCjOk5GSqzsuVPBoNe2MJIcjnrYp0YxPWny2x/56mrYeEc9RIkgJCCoQSVqXl2Zm+LkLtSJu11nlYhTHnCMbbRxBeoORHrLWuQqqMzeUEE0ckpUlD0hWoSzrzS7MhzjFKuOAVBnqwzrLsSS3BSVBFh85zg2Ok6BtG+SVM0KI9eYQknkUnHcTcFJx5DoFAFa1i1lMutZphc6M/1mMk1TI0Wi5G9o3K8QRiIyK5KGitjpO4EqPXSRLNxIQkPitxhA+J7kXw8vAjkqNpxsBE2eyLvT7lKJfICMqiStu0qYuNjJyUyH6aCKPJiwCE6PthpRkPhWv7QNCoo8IS4LOha/zptTMsTR6l04nIiTzRAYE4r1jL1XAcazVSkNpmHNYg4v67tSDC0V1kV9tQvTShgpPUkLKIVxToJuSq+Yx37rYThBS4DsjEwZj02dvzVUtVru5RQ4yCZyRoTd0/QOwneIHm6Nhs2jASXT0A9fOIRDBRESxLg1h/gVpix4Dy1DGuqA8jiEmkAAQlWeYmeROUY8SE9ZJbLCn0xhKuv4KpN7jUa98e2aVyrK6dpHD5OfIzOrvWinb/mddT7xoBQmSWAxubECPYHH5M4ziCXA5Eo8iKV+JDyTmCyjJUwIugW4ILdShgyb/W2G2U1ByNFbuwYQa6YuJrZqou6xdAib15NY/IrhFGGOGzBq2/egEVxFT+6lsxhUkemz9JsfEUpbe9i67YnfQxQQlTsqERg9qvCxcN//OXVjn/0Sc5mHuSe/c/wc2LT/Ka6AM29XAdzEdz6BcOpyqwVAk2dhBT3mdf0LbAWyhw5A+/iNX/8TSdp9bxlork75oivHmM5seucfYf/hnPvf2POPIHX0g5GONq/cKLnncQCO67B+67R3DxkuGd/9Xwi79k1Wff971w+NCI8BphhBG2Q2Bo6hVaapVID8z2k9T8XdrwEhHZt8mCV2KyMEHsP4FKs+oNwp2XyHWreHUcSxb0shYGvoQWyMM+TpIQRwJXubQBJMjAEj3ePSHRSgslFEJuJ7u2xiZk86os+GgYiWnj36NoPG5oX2uwONslnJkDx3oLqYUILvjEEwkIOFA5Tli7AkYTrxlE0R4vOt0ktyExM5okFIhKgngMjAkBjSsU6pJLcyPC3YyzjFT+FRf3vECYg6jnLiGNfc44pkF8qkXSVBCDkmkIKAJTmO6dHa2oDhjOS03RW6Ll2mdVXlXx1bk0BFD328UY1lkmNjZFvPYLdJ0iyl3LyK6p3ALVMKZetwqjqJQQB+BPByRrQGTwhMFxJPk81OIcKINjWpQmKrjn0pCmnlraGES7jXPtSdLTIIiv4iUb6HTCbpSfhTEKKTjfuWJ9eCTIdh1MjCcaNkGBtGqe6sEKogOH8/so+iX2L3XwPYFfP0lrc5N9S4dZKZ6nOzdHgsAvS0ROwGa/3+XlJIEq41fOkWykPjrpiv+xo4ILz/YUfsP9SjqW6DUaDo/fxFgl4Mz8hxgrQLkE7Y7h+atFhImo5Q8hknU8JyAmpjoFeBqnECKiBl0dczZ8gVwekjxELciVPbq+VVUdOngTqhQSFWx2UrcsMXXYCE8w02jQMA2b7a8F165KkoMarZL0/kvVYLFLecpj43mYcA/b80nDfESainKsGDLpHueA6yHoWPVklmmid+basmKxDRFrTgoc6eMWugRNRT5+CCl+nVi18NJ9i6qIlJN9chzoPtqAsx2qIsLxFVPjZQLXo9ttIB1wyhLaYFKVx8SUsKeSEuammxCvtumeq6PPNKmc6EJjA4DyvR7JoUXqWzJcm1iDdPAmJfkT9hrf/kWnaX50mW6zDq5L7rBDdzlBFQVJzRpbe42zoGwoYS8RxABvb8NOU1JQ+T4zRThrDBioVEjJL0O92b8eSgqabjVtf0s4SCHpjkWIBIoz6bVJhTOhqtAWilum74fi8+TcAk3/UtYvJyYEZsLBPJ9mRezVL718luzKo8dj5KqDMAY3nex7iSJydUZY9GBVob20uw56bJGul4e4jlE+sY4hsfdSWJ4kH97HbKHO8y9oyDlUylAqChZzkDQFuu2kIWWGGTmLKErMmiaJBQZJLPMIuYFIrBoyV9bUI2F93dJwZiMUOjGgFFoIHBzG1QTFXIlyeZqp8iq1dait5LlSXcZlHGGaIBy8NNGGwJAXOSDp5e7ICFepgoEGSH9KTWI0ndTgTyHQTpf6fJdu0iXvwfiYgM00vFhDwRQpEFCjBsJm1hRxCyNChLD9KohXSeaLiLVnUOenqJYr/aQOkX3eKAdE1wHhEJm4P4ynHVDNgA4c6s9cI45n0uMLfBVSzeWBNsKRdr6Rq0Ltml2gSZVi9aRJoHyCsIo5VmRt0+fowgGqZwN0C3J4GOHTCCbJ1c9TdDyi9Qt4U4rgNSGX/0+MWU0QngPNft0MEtXehF7ESZhg0hBjex8LG3i/dfohJbV6/0/PhfvvkJjnYfnJKUr+HEF0gVIaNhM40I5h0oeCb0f14uyrEJfGcOQTrHn7SHJP0B2PSIyDu6AptD02jLFk/h4wIrtGGGGEz3wYg/PYb9P8w+cozNbovuafEt32dTz17i/h8Pgt215iXwzLy4Zf+EXD//59cJwqb/n8e/m8L76PgwdsOY24i1x9Frn8BPLq48jVZ1HnPoT72G/1qyQUpryAri6hK0vo6n5M1f5UxVkmv2W7aX7hgVn2v/Mhnv2yP+Dyv/8YU3ctcKVx/obqPjcr+NffI/i8Nxp++N8b/t4/MLzje+HVrxoRXiOMMMIw/JkWbbmGI7okuoi1BvcI4pAkgUSE9mW1a19iReotBKCUwCgzoGBSxPfcTrB8heRSgqskeTckrxYACDxl/XrmHFgHVgXdiSnU6hpIiG5p4s4UkEUFNYNQLkOy1xSi50uYhR/an81gH83UcyX1ILafH1Pkg5BgcZyLlSe5Wnmcw7njLD54M+fLH8GMGbpLkVVFrEGgglSxoukGR9FFgOcwjkGdUlCz5Yuij3BBaweI0n0gcTXdDU3DWMPy8JKP4xtQOeSGQkqbQbHHE2ojuK14D7CctXGPMdBG04pT7xEhabjVjJwwKPzDDnotgMtks3OBBqWpCw25Kqa8QNxewXP9bJ4r/SALo1PpKvzGiS7lu8tsXr6EOaMxdQ1eG9cFLXIYAW68iZA5HOGihSBOVQMlv8xaR1rz6/S65NIsfkiruohMjqvLmlwZvKmjaP0s4aqH87yx3kSrZxDjtjf1DPlLVc36qQ6FcRd96HUUz/4BAOVTJ4nHE8pLDlU5yeNXYH4OXF9Sr1/j8cZjSFxcEXLzzL3MHnqBZ1YEsY5IZqQlBrb2qy1mxlLaZg9khcXKEbrrq7hzAcU568HWbhsu5gsIkxCXplCRQNQdq2o7BI4w5CYryHqXWnuDVtxG5sArQTOE8bl9nJdXuaV0lOqJo+DNUH8OPNPGO+IgXEVcmMDZDCgToIVDspxHrrfBgUR2Mcog0iyFrlMg93RE95qGoou7UCA6Y/sgru0b+/bBC2dPUIivImQXqTwyU7OMCjXgYckuBdoXdBaLyA1YW02YmS7QOiSouXVyl0tM+xNMe5NcVBMYxwcp0NX9UItACYLuMgRQ9os4JUnxFhenKpB1WP7rpBeViMwLdOUIPRO59tMbmE5KxPQiLdsdhAAVCkw+QJcXhlmpxGCM2ObVI/OpH95ikcBXBPsUujhD84Pn6F5LM4JKgUHRSRSmYWipdBgxlvDUaeiy8jyUCyLWxFoMHMpghMDo1PR+oFoy9eFzpFW+daYjjGOVUo7HgLuf9V1NXEVFTHBw8fVcUcDmMwPnmP4cUHbZny66vA91UhNdbjN/dp4wqrAKLOl5rsQdqncsIc/axBr+5kdZX14Ek1g1nRCYkr1HerBhjgYpbKi16y4gnWeALhIHb4A3MtKSWUZBSVbIaWXD0bQh1jZpR33DIRFQbj2O156hWjZUQsUyLsWPxZhVMKelTbzpWlVXToWUnDnKEzcjpaBQFnS81O/RMwSyQPPoVarPb+JrjYdPTio84XLb3H08+viHyV1s420aEh+k4+M61hxdZEqkAfkXluwSaJ5MHrdN7peBTQpejk02wQgmzSRaRtR0Lb3eIvXskkjdReuEYrVM+WSRk9eO0nzUgeWuDRHGoCObktWGojtIHOqsZs8tjaEeN22WYOlgFFxtPWJrp23/Eo6EuE9mj4fT1GrPMu6WuaYvWb8/HRMoe0+Z+ZOsXTlHzpsiEOs97gqEoJ47iHEMsJyZB0pXZP1MuAMduofYbqhmKwjR6j+t7SoXxvRVXT0U8lmEp62TIVN/eSJkxr+FUEwyHmnq/iL++AxXG2X2Tz7GhLpIvHQ/HVlJr5MHCIyE5r4OsXRYmla4z9t3B6VGyq4RRhjhcwFxF/8970B/6I9prfxDKt98P9Fd95DohOdWH+etJ75+z0UlieF//Tb8zM8Z4hi+9Evga/9/gvGtIYqOh546gZ46Aae+tP95t4Fcez4LgxTrLyDXnse98BFEt5FtZpSHrixiKkvoiSMks7eQzNwKuTFyt08y/g3HWfmFJzh46jBP6cdfUrPcc7fgF94J3/U9hu/+14bv+Efw5V86IrxGGOFzHb/8y7/MO9/5TpaXlzly5Ajf/d3fzZ133rnjtuqWLovdM7A8w7oRSDrMywVkmj2t404jxHOUtM9E5ajdJw01c5T1oemRXQaBEQYn9RtyXcE95Vt46qo9Vpimjoq1yQzqV4/fAskmE2ITPIMq+RBW0GEVpm+G5WcIlxStFwZ8bQqCBGvey4bJPH8S4WNSEkOpvu9yflEgC/cSrF6iOlekfRRiHZEvCfzxPpkWgyWxEGlIl8SIEBrlNJTGGvFnejXlIpSgc9EgtEalk2HmYy49vYwxhkoJkoImukXiPmq/FhLaqpr5bxkjcQYnxoiM0GrFDauGSqHpm50YIfBmFcb14LJVpaQFWiZNSvDLGAyxjvC8gN5TSAyEOwkJYQi1DjjKwZ2WcEbDUxHRvk2UA0iPRLkIGkgkDg5dIFl9BqGKLBSWOLTZJF632Tob/j4iWaDQPYsghvI4zeUnUM2YmgNl6YBWBOsOwq9Cadm2fWo2LSUYY82a9XhMMO4SDZhcHTgoyBccpqfh6jLZNQ/dPCvdq+l5RfhiDJ04NruoEHhvGCdeljhy+/Rm66KYo6wqYcq9yTZrz5S+Ry5IECYZSAqThvsICVKhtYZ0kqnTvY2BqHwILj5LzvF4/fg9tu9JB9e1c0zPBZWJrQbrpIkOTxLpEDYM9aSZMR0yN44zfTPehY/YTXMB+TsmWX8ubQvXsYbUruDB1xrUGcHGhTSMMcvyWLf2DlKjt3CBhYpPsgHdxGV60uHSmsNKswtCpqGHaV2lS3LizXD2HKJ+DXHKpbncobjuZxkZ3XGZXi8zFEWnfMDLZ8fMiC6AnkLj2rmeXR4oj8Ln3Uz9L65kjLeJNdqvIrZ49/lHKzjTOVTRhTR8Wc+eJpktw7VPWL5M2Yt79hyMNUCUB1rewEb+NLkjY0zct4D++GXoapKk12fT4wtBktjxUQ7M8nt1lqLfh3sdyQ/okw7Yfpx74wIY0L6CxkXYfIaCV6Le3cQEKdnd8zJMfwQqBDegevgtPPzCXzO+ERCkkZ6edFnKTVAYq8AYiLUXkBck8uy6HeukIj4oSfZJS3CliLRVoQkpkEV7DfwlH3G1y8KcYr1micW8E2Jy4xw46NI+Y3BwrJTKSeuagFQSrTxsDkKD176UkbAT4Sxe5wJuLEEL266OS4JBIjFDBndpNlFcEIaqmqUUQhB3EMJhQe1jX15gEqjmppn378Bc+hhgCMabTBz22PhEWk53E9odTEp2aZGG5AnSJAcQ5e4i8GvAJq5K7xUNKlH98dgAboiOmjb0VTdBgyc9TkcabyIkKUKUKp+UhG5slZmWVJcoMaxM7piYD619kv25OZAFxqcMnYsJwarAbRqUIxBKpWRX6uXo5DhZOGTHXKxfY3e+Ralky57ITwPn8KXt3EEA+RyUy5ornxA2yzA9AirtXirlvrzhMVNiffcKJx2Sk8dI/uwJRG85ZYDsgi1j5g7TEINAScHEhORKF1ruDBvhMer+EktTLuYFEAunicu32fZLDUaFEIRqHC0K1KkRhhKpDG46XsgdFjV2wojsGmGEET5z0Vwl/J1vR138KBfc7wSg8EZrfHl+4xk6SYvD4zfvqajnXzD88L8zPPY43H8v/JN/LJidvUFyyMujp0+hp08Nf26MzQK5Zskvuf4Ccu0FxNpzuM+9Dy99AOryAsm+e5h5y6tZ/SXBre85zp89+Ec3VocBVCuCn/hR+IEfMvzYTxiSBP7OV4wIrxFG+FzF7//+7/MjP/IjvOMd7+D222/nV3/1V/mWb/kWfu/3fo+5ue0ZYF8zdQe6nfDspuGj+lJK5cgsNX3sVCGsUCgdp9CxL8h2aieRUmCkIUmlBt1KjKP8jLhxvWETbj8luxJ0SpBJG6oSVvobSYHw8pjqfpQbgtGEBx1LdknHZmLTUHrTIvovH4H1KCPbpuckD7uKyrMJ+RxsblpDe3daMFG4laevHmL24CEu8ATNqM4nLr1/qC0S0VfzDOGqzfClpbZtI2wrCDdP0jZQcCHuZsquaLzL2jMbCASOI5ChoZsqICYnTrIeXaQbFTJyRRubVTGrh0no1aLZrfeaZQdIggCKudSwO3uxtzbHvTKuNC6gdYLr9gzwDU6uQCc9pJIwPgZVBxQKf0ZhZ1BgwjFU1+4VqxywjkRkZFekY0RzBZGfJFmH2kpE4IOvQzrdgM3gKI4Dc0WBc+U8G8lzdKNxPvIJQdyVCA/wi+hSCZqashOwyjpCCLQxaVuAEpJ44MpUK4Jq2m1MSnRIAdVwkrOp0sL3QXQFcWQnkwCRE4MUWRjjILYqu+gJhIyTXqd0ousGJDPHEOceBgxSKW4/DY1rksYnQaXZZJJEg5AkpXlWNp9GCDjg7ueCY7M99ybO6cEpFCT7FgT5tg2Bs+SxHKiOBjST9Q+xbtbTLmDP/dji6+yfPbWeHibghNP3ohJCIBzHGqZLp9/h3bY1ekcjFlzYgCRtpvFxeOrqSaTwKRQEsqemEin5m4ZMijR8CUCYGOEItJeGBXa3K+eQKaegdujgA+rMHtllYtNXdgLORIgzGRBfsdIvkxhMOI6RNawEs3/OTsVO+JPJY1YNJ5XNvC1dpNQsl+9DXuzvMzgr76oyWgbUF48SzkH0KIhE47oQ7ldwLsbVNcqNR9B63lZ5kMBOz69HcA9eG3dMMn5UEycVwBI50uvv3AurDNwc9e4mek7QzSlM1ZbV67Z51/r8HZk8zbNJC9O5TBrJjJAQLAwe2yasqL55HPPuCLGi0GVbUFf3dWZR0kWPScS6QJY82vceQE5cwlwBNyVYFIppf5y7J+6hERT4aO4Kk8vGEnwDbSCkAKHQg6RxmuFWaEHRHWPdrPJk56NUOpKKM4WWEa2mwOSWqPYuh7T+gznH5xbvNqbmyzQuNmmLLvkc+HkXm4nCtvHNh2a4/OET1JLL+F6Tiakgy1ep4gay1USo1JstfZYpNHEzVWw5kyDtOKzSxhaJQGqVhUpjFNKxhLJB0goqiBq40u1HSvrQM6gTEsLG80Tp9VFmUG06fO82khYgKU0lLAPlM7aOeqKvABauxCoyC9ZPLOk9WwztYwu4S5Ykmivv41huGs9RGGN9yObm0nNSIlXmmkzZBbYfayCu7IMLAxrEXpIJX+COTSGnupinHqPnE9jz7BICkrFD1Nc0pQlFeziSNlN2AUxNSR49b/+uBYcBm/hrZhqKxf79OPheMeYcwqhz1KmRZtNBKceq9gZVn9eBfPFNRhhhhBE+/RCNZXK//nXIq4/R+qKfYP25w7izOfwjdkL2zOojABweu+m65Rhj+L13G77pWw2XLsP3v0Pw73/kJRBd162swOQn0Qt3Et/8FXRf/U9pv/UnaX3979L4B39N86t+mc5r/yV66hTO039E6c+/g8njH2P6Twsk9S719tpLPrTvC77/+wQPPQg/+VOG3/rtnTxwRhhhhM8F/PzP/zxf/uVfzld+5Vdy6NAhvud7voeZmRl+5Vd+Zcftw9S/RIhsIdv6bxhjw9ikjxk7iAimsn10anrsSIEpJsS91e1DOQ6O3ZS9Xzq+y6CQIfAkkSwSJZqVaC01tfbB6as5EuFkEwgpZN+QC2y8TwoZKKQrEEdcZCmd9LmKTjoBvOfQ7UxMQCEvuG//m5ifc7jt3jLzS3Yi+vzaE0R68JUbjJCp8sKSCghJ8bVziJsDkoMda24snYw4Eb36CDDKRQiBFIIL8RWi2xscOj2T1huMnzZEOIbMl9FtSbOerqIjhub6dnJj0Yxq2TG2wgiFo+C2myMc1Vd2WacUk+0Tzdk2zLvpzApNWAizcqSEE8UD5Fx/y6q7xJQXcFLPra6bZhZE4qTHGvNShcDhHLnD6fGlYGohh0lVG8ZY1bSfjJGQ0IwSutpFo1ECysEEQitwc4w7E+xXhwickI32Cl3TseE0Qu5qR5B58kgo+2N9v23fklxRnBJQkKlW1A7KLoBm6ocm0v3tsa2xvs6MoxVIhesKZqcSDh6y6u9qZYCYEorNuMFqZ40zvsvVzipCQEmWswzOanDGltYnnxdpEoMeWTlQptHkuxfwkzqy52WnwEiFO59Py7QfO1smzJkrdlaYRAYiVXal/TDUluxKDP5Mnit3uhhXkAvBdQR+zkfIkEoZfK8n+xDZ/Zop39LOrH3bN7RnQ1tNJDADpvJWMZhmtJbDE9i0wD6US7SiSRoG4YLxi/1TGSANTaxBG0xpit1gxg9bVT62r+qp43T2vZrIKw1vmBa7fNeDtG9ezD72PFCu4PghzeyMQOV75J4m6J5Db16xSwaDl9cdGNOyiqdt6AkmXnOUeKEK9JNZ9NBNbGi274TZfj2iC2C2eBCBYiK04eLtlqDs2XGudy9IAeGhAYI3HVdl7QrGa9vGT8ftOImyey3WXZJFSenzl1B5u3+cDvdummFWCYnxi+THDjFVmGe6+hCZnWOvmsYqu1BOFoVpeSOTPnxs+J8hppXUudS5Co5LFGvO6Pup5w5tUwQpx3rv+TQzMtEPIJ9PFbPaln34AFTKNhxZNQ3ThUVah1okOd0fV6XVvCYkaOlj0OTFGlr6ODKfXbceQV18Koe6aknQ9KJYlRVW3ddxC2gRWLKrRxp52MUa7H2aaHstpSBd7Bg+wV73X+7Y938zLbl2s8O1U/Zf7eYwI7tweiy3QzJxFDN9KwAX2lcwboDn2ftFSoESHs+cUTztv5nV6Tf2jxfakMD6X0aYNDtmr53r/n5kOBCzCkiTJhU4ci8ojxMPHGD+ntuGL1L6GHKLZeYOVTj6BRPbruPgbS53IL2FGCa6eucxtEH/D5AmzRJrMHucxo2UXSOMMMJnHET9KuFvfAOifoXWl7+TqHgTtT/5dca//nj2kH5m5ZP4KmShfHjXctptw3/4UcP/+UO48w74vu8RjI19mpVPboCevx09fzvRHYCOkRc/xlj5/Vz9hMMtj9/E5i9+PtXbvpnolq+CgZe8vcJxBO/4Xogjw4/+hGFyEh64f6TwGmGEzyV0u10effRRvvVbv3Xo8wceeICPfexjO+9UmIRGA6XAceYh3gQBqmuIn4rIBXYlVzbIlBb9yTrEkxUSkxCVYya8m3EdD39JIergLJQQ2Ro6BK6i4S/yZP1J2t01XHGCnBxnU0p0fhqMRvsFei/9UqghsssIu0reC1sUAoQjEKHdXqUTcCktmXF46iDaJIRpaFS5LEi0/f1a4/J2/kioPgFhbGiWqviIeR/Tiq1CV6r+fl6R4m1tNs64UJ7C+AVk/XkSkZAPxxiPKzzLZWvY7PbqbLO3rQQuZ88aNvI2DFMOvLC3kk428+/E7XSi1Q/puvN2+PBHLbkgBKB7K+n2IKkmiGRRo1bAhALfCZgtLfLowodAGIJQsVa9n2XhMTO1ykL3k8wHR9FiYJLeC5FzLGmUOD4dd4YYSwqcKh7qq3ikRjigy4vgl5CJwDgK0U1IEnh0OY9pKlTLZqMzQmLQFJ0cBb/CWryeTdwkEKeKruXGJcT8MeLF+zKScSvyKX9SKNgwxmrVGoZPT8HFmsT3+squp67Z+KWdlF1SQNubRpqYozPLaEcQSJA1h+VrkEtDskRvgg7MTGnwpb06sneNbd9d6a6zvPJxTLxgeQQBAkWcksWeKzBeAdGtD0zYRFYXGJ4Ieh7k9CoSkZFar5q4HfVlJfRslehCw3oa7Yeb3jzVrwxsVzgIhQwFckBtpUsJarMDCRwrnuLJzWsIt8Ghg3abQwfBkw6lkuDuA/NsyGdQV+TAfdSbHac/nYDzlTejOx+yE95S73xt0F7PKy7RDCmAdoR0qH3CktPOva8mOTDZ/yo/MFWNDSYxQwTY9csVIB2kcraFyvVOR+d9jPKplGFjw16HjhQUfEOcZlnMdhEC6ld7fwCWZ+yRXWrrxByrRjxSupVrvSptURi6aZbWSjDOhY3ntp1C2R/nRP5WLl+Ahz9qmJgAnbrXR12r3kHYzJ19pGRX0kEvNNBjAabcP//QydGMGlxrXAbACVxEw+6TkV09HzKhSPa/Krt/8xMBEXYI0722Sex5SQEdWQAs+Vu+x6PVztNeMZmyUnUTBA4mKFBrbFo1WMGn9yjJOEIPIiEwkfVxq/tLFKoeeX0uPcWU9EoMc3NQLAZUKndT8CvE5YTW0SaVZyyBb1L1YUJMLHMYYgI3pjgW0kl8ezJmWI2pUEP3bS/jqZYOselghMAR7gChRsY+NgtHUKtPY1LSWuBgxJbFly1PKCEgGciuHhZldl8L1yF7Rrh5TFBEF9PFFungqR2StwhlZYQpdMFHGI2JjGUNhV10cV1bw0GlYufWBcRf2LFU5MO0fgLVu94pT68HOLypBybw5/LIK8ML7sZYothWdft9uxfL5b5XmEDELWR7w5Yn96bsGpFdI4wwwmcURP0q4a9/HaJ5jdaX/xf03G1s/sYzmK6m/Nb92XbPrDzCgbETWYarrVhbM/zL7zY88SR8yzcJvvbt218y/kYgHfTCXTj//zvh53+NOx++nUv3f5yjf/b/4H3oPxPd8ja6d30ThNUbKtZxBO/41/Dt/8Twjh8w/NRPwPFjnwHnO8III7wsWFtbI0kSxsfHhz6fmJhgeXl51/3y+Tzq0D3MfGKVIhrHN8jCAZpxnrJcJt8JCGOf8VNztJ/foKphw/eoVvMEruHag8fpbpzHKR9nbKzK+KECpYph6tAkZiPC9+3sqFyWeMsRuArP9TiWfwCVK6D9PLmZIwCMVccJnJB8M89YdZzctRzomKYPlCqw2SZ/sEShWiUKc/h+wtpVievGFEoFTpbfjFd4hHw+Tz5via1qdXisLK9UiJMII9rYgrOGIIgScp0QWj4oj2q1SqlVoRn4RDqmWCzT6eQIlaE0e4jq4X2oakznXA2KJZzLCt/3Obz/XgoXGziui5zUOMUijh+TC3O4D1UJnpKE6zHNhotX9inmQ6buKSF9wT3erVyeOc6Vms3EWwqrFJxxnnv+cUJVYW6uSv7JLr5TJJ/PEeRSkisoMjfrM64v0q5pvCM+IijhtA0Tq2NUqlOYGQBBtVolGCsQaE1pTJCv5SEsIPbfitzcwHXPolyXfD5PHGv8IMEJPFrF/SDmcN1zBEH/2Vos5ZEmT9yYRLghTscQlH1EYhUTupBnaqHA2YsO0giCfJGOkpSKPrlcjqDjE4Q5TKCJ3BjHjVGOjwkU7sQU1flDaKPJX91+TatVmJnRlEqSdhRSWS9QSQUXs6dL3H2oSi1uka/3FYQTY5MEqdKt10+i2OD7MZ5x2Vee5WqYIyzmkUmZbsdnuurh+z6FQo5yuYJZz1smMFdCVqs4MSz7Po4jCAIf7fsQhoh8HnPwbry1j5L38gSBpuu4TJSq+Ke/AKIWIrAEYpjLQcOnVCzgB4p8oYjftn30lpsdPvGMRuuQwHHxfUW1UEbkKjA5TpJfw/d9Jl8XsHhqFoBuPg++T7FcRQ60mXFvRzy7Ae0Z8skyIncTrVPnEPkaclIyNjbJQm2WFfNx8gNkUjEoUa1W0WNVDrQmqT3j40sfHQSEfoFqtUKhIEkqK4S3jJG/UsBdc9F3xkwcKlGYP4a5CqQepn7QRkmB6whyJReR61+jrTBrPuF+j+JDR4ZUfsGcpHbRjjHSU2iVkCsF5NKyZHX3dyWn3KWV17jFEvmSj+Pbtm65t1MNnkL4kly+iFYhp046zM/bPq+LKyjfJcrlyBdzuG6qdvUDul1wHYfQD1hasjxJrmT7WT7O4zp2Ih6mfaNarDJWrZLPd9P+PExMVKtVpidmGctNsZZcYf/YUR6+8JfZ92O5Kuv5PJs1CHPQaEJxYQzvckC8mpALIfAVhcBFFEKEG2C6OUzDto8f+jBd4M7jb+Yj5/4MgPnyfppRnbXmNSrhONVqlURr8vkIOXsn7sW/oDo1w+UVn4pfplIdy67J6fubXH7KZ2ND0835JC4IqcgXCjTahsbU68l5v0cQJpQmCzibZVQzRy4IcRyX/OoUMjSI8VOcf2KZQpgnnyuRj/KEoSBXyNH2BZOThsJ0wML9D/H4h55H5APyFUXQvAYRhEGOXLVK0opJSiuUSjbcrzI2juu6xDG4rotTnUIUXgDAxUX5IY6qAxFBsYrTKaCCHL72Kfr5TBU6NekjpUv9ioOjwMvliB0X7edJogahUyDwQwr50JJA+S6i7YDvI+MqruuiPEWQLyI4jNs+Q/PWLkiDq1zmcgt4/lkAO4ZgsmfpsaOK2WqRAjlLSo1XiPN1S95qg58v4+ftIOgUAqbGZyinc4Z8vu/JVi5L8sL2g/xYGX88wHd9vGKZMAf5vEc+38bzfYJcEd9PlbFjZfxxh8rxkOrULELZNglvV1z64w+jigFGJ3ieTxhI8nlFsVwmrFYolSLa9x4BY8jnA5SEcinG5PNQLGfjcb//ezjO9rlKPt/J7tdSOMNGso4MS9w9uwRPR1x0q+SKR3a79YcwIrtGGGGEzxx06gS/+a2W6Pqy/4KeO40xhpX/9iT+4TLhLXaSl+iYJ5Y/xucd+aodizl7zvDP/qVhZQV++AcFr3rgM4/0EUJQ/vIDHP6xNi9ML9J8/atxP/xfcT/y87if+DW6d30T0e1fB27uxQtLEQSC/+uH4Vu/zfAv/pXh534Gpqc+8859hBFGeOnYGu5lfTh2vs+FX6R+5XmMH9GNuiSxhiAhEF3aUY72agPvI3U6xS6dMUPj0Qa+J1na16JlOrRabda1x7p/jFJcZH19jbDdJJ+HWqOJbDbppOZQUbtLp5PQkh1Et0tiJG3ToGM6NBoNpgrzlMUU3W6bspqk04gw9RoYTTOcgVhQPJigJyLW1tZotZp0OmCiGBMltLpt6IQU3EkajX5CkLW14TDwZqNJlHQRrRay03/x116E6nZpNhrIVgvjGNbW1qjX6rTbbSITU2+1SdptWlLTrLdY911auk4nPV7TK9H1PJzicVonfZrL7wffkDSauB50qppGo0E38ok6CVEc0Wp36LSbxFVbl0ISsD9/ijOXnwLA0zmOTB1gv5pHoFhfX6PRwO7bapKsXcEAbe3hdTvkwy75MWi1Oxi/hYgMcSehvtkkiiNE2ia1mqHRgM16A7/RwCQOycYm9dwSUfQsWkCj0aDdNnQ70HUiOh1NWyZEUUS70zfLF40N3G6TZrMNjsaMhWyqAsGlVfu97lDIJRhi2o027U4X5XaRxqXZbNJpt2lpifQhiWLabTvxT1qSpOmxtraGMSa7rluvqf3M/hy89uVSk3Z7nVqznn0+lpuiVW/Toj20fZIYOh1Q3S65pECr1cLIJmHUYWW1SzFo0+l0aDUFYrOGajRsSJn2SNbWaNU26XQ6xFrSaDaQnQ7abWHS8hfFPtrtLo1Gl7ZWNFSR1qb1AqK1RrVapZX2yVazTqfjU2+0qKR9tN3uErdikm5EQkSnE9NsNjEExPUNGo0GSrtsdhpZ+8jbS4ggodZooofazEHf9zo67z3P/uIJmwxgbJpPrv42dCCOmzRaio1um0ajP3aoxF4LVasTdSK6UUwkImYn29Ro0e2us7YmcF47QatmaJxpEMURHSei022T1GrgjqPWrPop0QlRJyKREjEraFxssCsKC5i5aZL19aGPo6jVv+ZNrLF7U2PSbKjxDn2lh1a9RqfRQNU2aLSKBGlb75uHWgxOp0Oj2URLTacDa2u2LRrNBqIj0c0WXqdJFFlVTjtyef4Fm4kP2UEnAp1AM05oNFxarRZRbLdttduYRoOmbLEh7T0tBaytNbfVU+Cy1lnjePlOSIb7uPEbQ38DLC522ZzTmCsRIg/tTkxjcx0+/C7i41+AXFtBpvt0Oh20VrQbUVZOw2lyoHqKhTAi5xZYW1tjc8OOF89e9rgWvJaJBPY5U8y4k6wPXBOj20AXrQ3tWEIUQSuh3WnR7cJGLSE6vI8DS2doNBo0WnVaDUO32yWOI1gTbPhjvP8vGrTDJq7q0jANxO1lZF7QPNuk3bHjw9SkoIniqjpIp/Mc7TZ47Q7EHWRD0FlbI0nPKzhewVsqsr5Ro9uNiGLs8WJDnF73um7Q6RZoORFFoJUIdCsC2aZAh1i0SdJQRNftkg/A6BgdCaKOvbZJpKl31gh1jijq0mxqpJKYSY0qN2n8ZYdao0sxTujqLg2nQqeTYLqaWHSZUofIGUO18EZOlz7CHy9/iCS7VmkiBC1oNVvUDMhGg6RWIxqX6E5CfKVFZ1XSSc+p00roNCLWUjuURqOvrKrVoDY1A0LZ8WO/Qm9GNGMJ4TQ0zmOI6HS7NJqNrMx2q4k6epi58WdY3xzwuSvA4a+QNOpdzi0ntNsdPBcaDYHe3KS9Zmg0jE2UIoBGAylgY30N1Wigxfa+vL7e6CvGBtBoGIJux97vkeaIOEboTFNMrrLa7VDRYzS7ybb9dsLIs2uEEUb4zEDSJfjdb0euPkv7i34SPXcagOZfX6X1iRUmvvlENqF7euUTtOIGt87ev62YT3zS8Pf+gaHVgp/6ic9MoquHma88ZTPRvHsDPXmczuf/XzS//ndJFu/D/8BPkHvnm3A+8ev9uJ49oFoV/N//TtDpwvd+n8keniOMMMJnN6rVKkoprl27NvT5ysoKExMTO+80exPJvrvJjRXp3DQHSx7zixCm4RKFToRoGbwjZWTOrn8abQiDNDpBSBLTwUgXKdzhkIMtvkiOVCglSbQ1A5Zp9qnZ3EmKfoXjk7chhSRwctnv2dgWlACB9MRAaBZDPwsVxeI+WDpw/VfXSjA+vH8PQiJl75hm4DjCmuAb68Vk0u8dZRVV3sLASrSbxxRnyPtlcEPw++NrdLeDc9oqeDRqiIBMwn5Y1lb01MlKeEihsmppkXpsRQ0MEoSzJeTD/mEUuMpDOJK5OcHsovVe6UVtiS3qZyElYlplYUluGvHXs6wSA1nDZM+7SWqEI7JC83dMZpnWkvE8Yd6awksJa/EKF7qPEOQS296AiG3b2gMNPJOMsSbqbCdx9wLRM3Ae2PfWme3vBXaj9FSEQQ2E/fm+IIogyTKLDdfPpGXLXqY2ITG5MXRuDFOwoUR373s90+4kxtjuZQpvRC/slCE1DeUiDWMSw33ZSc2fhy6Z6PelpcoxXn3gC/vbj4U24cAOCnfpKwr3zWR1Hqv2LR/KFcnEuGFmenifatDvp0r0w7h8X3D06LCvWv9XG24rRO98+tvI1LNLSJC+JDg1NlzHvIu3mGYO9Us7Kr+cMR9V8XCmwn48006G9zvAP1rBXSjgLxaGskBINXgCgnzOvjv1NxCYyHo+CSUyn61IlUgSKOSHQ5ODIDXY36EOrvSye+z48T1VewhhoPBS+8BcCK9+AHKlwHoInvbIHboZk+9fN7FxPvOO6sF4xaE+Hzp5POWTcweytqanc20lrbcLS7k5/C0hwT0vQ8cBkyp+0CLLxGcAU/FwUq9FkYbfDQZWdCNj29joLARZVX1Uxd8+bg8eW0DxbrsA3O2Fy6Vhw7LoIQMHBvz/usdbRLf0vSATo0EIlGtVUJPFY5SdfZg0zrZnfef0PK2EoFyBiQmJFGmiKSltKL3wQICZPEp88EGEEnj5GghJLicAgRYa15Ps3ydZCk8yLxeYcQ7SHv8Guv6Wm28LysHYwPigyd02iTORhhRu4Xhc5bETtAY9eRw9YRVQ0kS2SKnQY/sBqJQFM9OCmZnhfTfDYzQPfsG2MuXx19v7YcvzuYfMCyztbuUymFRla3Lb31OuN+wbf7jvSfoG+gKzPTR5F4zIrhFGGOFvHsbg/+H34pz9Szqf90MkS/2X1WvvfBw15lP5soPZZw9f+iAAt0zfO1TMe//U8B3faaiU4Wd+WnDi+Gcu0QXgLRS4cOQKY+8Ls/TzZuwg7bf+JM2v/jX02EGC97yD8Fe+Gnn1sT2Xu7Qk+NffLXj8Cfjx/zgiu0YY4XMBnudx6tQpPvCBDwx9/sEPfpDbbrttx32EVJj8JLkcmJwHEy7KE7i+4OgRKJfT8JzJsO+DY4wlIdJJQ2xSX690AqAnjpLM37Ftgu0IReCFxPHwpK/glblr4aEdfZQGKgoInNI2hyASGTI2BsqVHDsq8HLX9+k4MXUHvhNs/0LIAdPwfg0FIuNfhFTWT8rozEtHFT3y980Q3jTGic9/gFPTd20nZraQFkEw/HdUPUZ86HXDu4iez88widWrYpKSi1K3MdIFIYjHj6F7Ie4pUTg2Pk319AL5OyYRC7ciZqw594njsLgPytUtJJOQiHkHeUca5uQLjhwGlV2ePompcikB1Nm0CfPSCbNKDfnb9x4gOjKFE0ikVDgOxMSs6csYN0SWl2xBun9iAk0+NQsXBrTZ2+p8D/ctfl7WdkPJDra0aw8Hx05QCSeGJtveAFHrOFaccu1aWoZf2HI9U1Ixc6K2XjqmsgTSIXTzFLySvTc03D75Vub9OwftcraVNZidcz08SS04mNY9PYQCk/ZhXdmXbSulHJ7YZhPOXaZzg23hF7m7ehOny8cwQvGq2xaYnEjJLCfkjvnXcGj8VLafsgxW9m6ydVba66fr4cnUwic18hnYLmtzBULJbRNb4Qi8pQEzencHTx8lKb52Hmeyn3RB7HWS6yvyd0wit5Qr5QBRA+S2COiFSM3wEQgF09MwNwvV2RLKgUFObr50gDANt+plVvTxmQqnuGP+NSxVj+E4gje+XjA/d+PvpFIqCunxpLIKfidn+8bEJAjHZdCzTV16GNHpq3GSiaO4TjB0j5T87aGfW6+Nv4MNVK8SxdMuxTtdTPrMMLHMyC6AerAfmRYgEqt0EwN+TcZYIlGjs8yfGTm/QxMJAZ1b5gleu4Aq2e3jdU1S62bZWrNsq1IhU+ZeVxL0rIdxQ4xyidMsLVNTX8WtJ7+SQxP3ZYQ5QMH3KYoyc3LBEp1C2jDcIiSFGbqqTOSU0ozEwrIo0ibfMNJB6BiEYmEBZmckR094SCk5slTlgXtCZtX8QBtAfOC15Bcf2Ha+c6X9HKie6N/XupflMT3HQS5zS3sN3hpmy+t/LThkk0j4xSEbwUq17xM41O473WZuOJSVdSuyjLES7r4TTt8K+EXiQw9hxg5Q3pIn4npkV+fkDN0jU9mYLrcmndmjd9+I7BphhBH+xuH+9c/hPv67dF71ncQn35p93n2hxub/OcfY1xy1KzYpHr78F+yvHKMS2lUCYwy/8muG7/s3hhMn4D//J8Hcy5lt8RXEyqubVC4VaT+xPvS5nr2F9lf+Au23/N+I2kXCX/5KvPf+EHTqeyr3VQ8IvuHr4Hf/N/zO/x4RXiOM8LmAb/zGb+Rd73oX73rXu3j22Wf54R/+YS5dusTb3va26+7XzwAmbMp4IfB9QY/0cYpeXy1hQPReKpVDQhdJmo1Qgp44ginObFN2CSE4Vr2XJBFDbNf1XmZ10XoPFV4zT/HVk1Y9tAUmzWiVzjAITm6fqA2dq3QZC6cxagvhJZUlCkyaer03SxACTTqZEG5Kdpkhcs6dCvEPlRnbN890YWHbMcs9NVmKuQXF/qWsytbk2g237QfW7HynubuRnm08CYkM6MwFCD8HQWpYZeyM5/TsA5SOzaDyHva1vq/GOXZUbFd2pec6SBoGgcjmy3LATdwdTwm51hWbEaxH0KV9xUubyPFTslIqNo81MUJgCtM44VhGznhujv1LMDsDS5UjLJQPgrHZ4W4EoZsncCw70ZuoyuukoN9fPW6vWXq5X7f/7iHVkutaH6Szl1yWC3cTz94+3Gm3qMd8f7iP9v6S6e0kUEghdlaq9T4amIXWgwN0lZ0B9vqBFGAcj/j4F2DSe2RHSAcjVUaM7Xq89DwKuUnGvQpIq7C8Z98bAHCks60PK2HTKWr6JOlO0NLDOJ4lU+wsOPvOGROIMQnzjrVk2NImQokhs/mdyK7su8GxYQej6+th6/0lJVzL385GcBQt/Ux51T/YIPsM5dMehaMOh04UueUmF2eraX0Kk7bVglrk5rGbKQfjO5uH30jdkRlx2muqUlEwf++dTN10zB53y7URqYE3wF373sg9C68fylBa3InsGiji5pusD6zxS9u2Qzq4VUllRlKopBkKddrGh8bS+rjoxbvt72GaKVALktTIX5NYZRcJIh1vtuRwGK6bsIs1MudmWRGRAt1KMmVX9vwSMhsXkryVK+nJ45iwgpEJAsnYeJG5ffdkZGwvE6sjYULN4AiHwk0uwWvyRKda6H0xru9TDw5ihLIJW0n7esbupJ1IKpQSuJ5gZqHKHTMPMFda2vnE/AJ3Lr1p28clvzqglAR6CwK9UxxYHxjPDyvEXvtqOJkqCJMt6wiRKqEXbrPPbmnJbNvu9vfu4Uk6t/YJud0e3YO3+VYr5GwMkzZpTObHlVqy3HoLmVLRlnWdFwTPQY/3mWVptpBd4XUW0AbrtKetRhhhhBFeIagz78P78x8nOvFWoru+eei7Sz/0EYSvGP/aY9lniY555MqHshDGJDH8+E8a/tP/a3j96+BH/4OgVLrO4PkZBvn6MrGMWf3Np7d/KQTx8S+g+Q2/T3T67bgP/w9y/+2LUM+9f09lf+PXC+69B37sJwxPPjUivEYY4bMdb3nLW/iu7/oufvqnf5ov/uIv5sMf/jA/+7M/y/z8/HX3y14ohUB4/TfV3qggENmqsX8gnXgjMNIjMZ104rtlYpdOnqanrIIIIPDd7AVb0MtEtTv03Gniw2/AGQtwKsOhGINhUlL2J7veXJ7KFx+47vkC4IYkUycwMmOcKLohlbfMkD/lULy/nB5HZuoVIRWJSQC9a2jITtg/ZsPsc66d2CklKBYFvWzuWycdg1ByOORxcGIulGOvy6v205kJLQmSTqqEvjFFVA+5Usj8POy7aWEo01xvsjhIEvoLitIdLm5VgjPcHm98vWB/KtxyUwLocGUfD506xMEj9u/AzVMIyxwYO85EYZZiUeC4AiEkvrJhaZHucqPohWRJ+qGFwM6KPtL+jeDmU4LZGZl9WvBKQ0RHx51Eut5wprSM5JLsX4LFRcG9i2/k5pl7hr4XaXYynewutOqVu/VrmRKX3XCWSBVtH9hLWKeQJAdeiylvJ2DTUxyCHjuE8UuYYJjs2N5uAlcoEH0CZ2thQ/2UVF3X+5fC9QRiv8tq9TaSfXdvHwyUhAESS7i7k5ZDGRj3qOzqb26P0T06le2eOHlqoQ3v8rbe6r0ZvLCiKXdcEi45mHRMGJ6gC1ShN54apIQwePneQaVUO6qeSgszmMmjw/XdAcXcOJ4T4EiXO+Zfw72Lb9wxsdNgCX7aHsnivcQHXrOlQk5aL8HBw2k2wiQdo+f75JjKlYkPvwHKc7Z8KdC9kGl0RqKKLZ3ChOVtdTt8CMIQisXBmgp0M8akZFc2lgmBMIJIFtGFYaJYugkHliQH08dHv6v25LRJVr5TFojQwZQTO14NcJYyI4joq+qy1QL7s3hPiPQE5XAsq5fdK43iuM4reda/MrmtGPpBYthXOcRtX/VmblkYDtt2HJHVVe/ggCIG+rYRql+oMeiJAibs3wy73WbazQ1ehhRpOGmfc9wRvi84dXLn714MvYWaDK6z84ZbMCK7RhhhhL8xiLXnCd79z9HTJ+m84fuHnuSbf3iWzT84y/R33oo73deYP73ySZpRnVtn7qfVMnzP9xn+5/+Ct381vON7xbZV1890zM4f4InDT7H2u2cyOfY2+EW6D303ra/+NUxQIvxffxf/D/4VtNavW7ZSNpxxbAy+9x2Gjc29e3+NMMIIn5l4+9vfznvf+14eeeQRfvM3f5O77rrrhvbvCZZEYP2p9ERfhVJ+6/4sEYgQ0iq7TIQUO6yg9rJWTQnKZbt/LnCJk96LfE8Rcp3KCAmO3/998KveO3iqKBNy74R99ihxAkyu5+Gl8KQNDfSmJE45nbgOVFAIaZVduh/GeD3cVTnF6fIxxnPTPHTwSzKfrx7KFZgYZ5s30uBxt6qShkRFqcIucioIkYaB9RQaybAvz65I21eX5tK/PUr3fD5q6iDlL9yP8FPiKK37iZsrjB09hAkqIMi8d0gVKjLs17fbEwCmyi5fuCyWxwnT8Mcgl0MEDjm3mGX0ctK+YlV29MPkUsjdZkkDUGkn7pFcJg0f87aq+TIMkLTlfQTSw/hFlqpHt82XtofoZHIsikWB8qzXUY/Y7NfJTi4TzY4hQYNlDf4f4MCCJfyMV8QgGfOKLOUXh/bM3ztN/p4dOpIb7j6z3JrMorJIcuDVEFZsmV6RIxM3c3Jyu7/YUm6O8XCSilvasawdmygNfeyhpyhtefNIb7vCSSgxFN4mvD0qu/bo2TWIe+4GPZbv132AIPK2Kbv6v/TIgfjAa/uh2z1FUEWQ/8I5RK4XxmhYmBMc3C+3tddLhRRySC2zM64T2j2g6CoH40M+XUObDZSdETvKBX+4nw+FqTop2WXsGN1rnp6iFcfP2lkgSbweKZig0/FcbAljTBbvRo/3/eXAKoRedX+qEhKSwilrGqVbsWWYGSBxgHrptA0NHrwEyic2CWHe74dBp9/PBDchhMCVTkZIK09kdRP01awm49RSsqt30mk7m16od2H4775K7sXJrt4YqMcPo8ePYMrz/WMC3bN1yoUJcuXSjsqonvhtJ7JrUME9JKczvWMPbLtLF+7sey2XKq/b8bveUHQd/vW6rwQ7Hs+doOnNURu/GyMd+w7jC/b6RjAiu0YYYYS/GXQbBL/zDzHSpf1FPwlu/yU1qUdc/Nd/RXCqysQ3nRja7eHL1q9rX3Av/+ifGD74F/DP/ong7/9dma3efTZhsXKEj978cczFLs2PLF93Wz1zE623/wad+/4hzhO/Z1VeT//RdfcplwU/+G8Ey8vwvd9X3zaxGGGEEf72wCDSd3KBKnskrwmIblI7Gn0LsGFSaOROybvl9s/KBRdjBM1W/7O9zvnMLq/ATW/ezqH93YiMndAvKxw/yvFbvhEjJGNemcxFfKdzVjaMUQwY1F8PJbdgw8Kyo/YmUT0iSzA7Iwiuo/TYqrIYmrwUJkEIOt4MAmmrnKpLTH7sRetnD+ARH30zZvxQ/7PBY6aTRZmyPvmcJKyWMdWlIcWQnjhK6U2LFB/qfxakl6RQHj6/2+ZfzeIXnab8pqU+SZE+n0u3e+TvnbZhVQaCgYzDp2cfyELrrntKshe+aMvMeyXGclOcmLx9x+0HzcRlWMU59WU8dOTvMF3Yh7PlMtuiBxnHdKqUKgWT1Hi+59V1bOJ0tpnWtnu9mPBIDDyH3/h6wezJ/ejSHJ3CfoyQ3Dl2ksPl4Qm/O53Dndl7dmYGT+M69+C+8mG8LcouPX0S5ZeYKR3ot90uZPQgjBgmeZyBIUL2I2z7cOQQwSWuF544GO54g8ousKF/WVEDghaASmV42x5xMkigDHq5ZQS5sIRrcvBBTG4sU8FtVSt9KpDIHZVdwxt96tP5wbK3Kd12gVX3CkgVbb32GhrSsqqlyi5hvc2MSEPHM3VmupXjbyfYBo8JeFMKmVN0X6jR+NCV9DgDJyBzIOTQVTD5CfTYIcSASXqQknoLY4u8buLuYe8/2SduxSBdIuw/mSm7euzOsLIrC7fsfR9WWC7czWru9K7nltW1Nz5IBz15tF/GIN9b3v0i9ezokuspu7QBIZEBIAQydLjtVrhvwAp5tymVVDIj8bZuklmnXadL7uWd4OjA8LdSvIfV/G1or0KycBfl+z3ECZe90lgjsmuEEUb49MNogj/4V8jV52l/4Y9heivOKa78h48RXWkx/+/u2/by85EL72Mud4x//h3jPPc8/Mi/FXzJF3/2kVw9LFaO8OTJZ0gCzfr/fPbFd1Ae0X3/gNbb34UpThP+7j/Cf/e/gPbmrrucPCH49m8TvPdPIn7l117Gyo8wwgifdVCufYn35vJMje8DJTIPpEEMEgTqOsquQUxPyQEhzHBYw4tiF+VI25lGz98G7vDLffH1CxQf3Dl8czDz2FRhgenyAV534K3kVABG2xDAnXyehLQmxls8u14JmCzsY4dMej2Vw8KdxEfeBMKG4cl0cpXM3YbJXz+b13CB11F+pGTXTQcWkRIKuZ70T4JnlTB6/AimOI0M1JCv0sI83HkHjE+poetXzU2xb/YoQg4od4Z8kOzvR8dv5c6512Yfj+WmCN3tGfm2nU5vopV5dklOzz5Awd8eAmW3G1bvDX4WbBEcWVJmcHI7oAobO5h5r0khuXvf66mmmTZlT9mVXKe5eyQoW8hdx0PP3YZwXAzSKmP2oHB7MfR5qht7RzK5cZKDD26pw+7Krn7xYmi79uKDXC49mG4//B1YtZYQIgudZgfPvsFtM+zRmHorxqowOUkWJuoo63FU3kLWZie0JXR3G+kgUmJLuRgn4HjhAPPBFBW3yNZzfakYDGPcKelB/v4ZSg9O7biv2cUncCcMXk/nOtdhuHIO+QKAsT5WaT2dwXoOqIe0A2EAjhuznrxgi9jq2fWiFbUHCZa8YcJnQO23GN7DmLNF2YX1IBsktMplwZ23w8GDA/0pP/hcG77ex4/ZkMrsw4HyB8Pl7a7b++jU/kmKk3aMqlZ3P0W9Wwb2gfs4d8fuGX577b9TMb1wT5MYEApvSpG/axzvQJGJCZFmkky33e02E/26ZNtssfb7VMmupSXBXBqFmikNBZAbA9dHSLHrAtlW7C3YcYQRRhjhZYT7oZ/BeeY9dB76HvS+u4e+az58jZVfeILxbzhO7tbhNLUb7VU+fvED6Me/nWICP/0fBUePfPYSXWDNlPdNH+a5O8/j/pbHzPfcgSpuX7FJNrpc/alP0vjLy/hHKkz/s9Por/5V3L/6Oby//GnU+Q/TedMPkyzeu8NR4Mu+FB5/wuNnfrbLqZNw6y2f3e02wggjvDR4E5LikSnUTJFFisyXDm5TFxmvAO1O9iq5k7LL7EB2OY6gMuYhlguY1Odr73O+XSbTiUbJ7aoPVdidjNpfPc65Dbt4YMyWt/DU7LeftnyQCFHcXDrC2WTzUya74kMPkajGrt/36tXzQxvEvXfDxUvgpB5GJ447PHNNXpezeqnohc8fXzrATfedQgpJp/edb0OeTBr2thVCCKoVSGpbL/JAm3rDE0ZdmsvIl7xb2KYq2gt8J0yL3PMMOftta5jkNlWPYEhpvtcO3OuvSXI9glcM/H87lIJYWpW63uH+umH0KvWSVe87KNy2FH0geC1HC63+NoOeXfk88fUi7HL2HMNbxgluHruuWfWQkf1LJLvuuF2wfs4eo1AUHDwEnnedtnF3JrsG9xisc6h8jhcPAKBftjBGlXF7OxXpToaImoL14c+T+duvn9xgC26kusbLI7oNkA7j41AsgHIGwi0HrnmmkkvJISFgZlrw0eQpUAJHDSu79lpRfzHAWZxh/befsx8PkF2zE3k6zVvRzpltu2+9/6vVHiMswBjMsRATSyDpK7vSfVxHIFw76kjTI7t695gzfPLZ86bPOB1PM8S3WiZTxe4E8yIBeu5sbihp17ZzTA+9UxijVIIE7GJUL0nHzPbkEbD7sDF42rspu3bORjtYwIuj2bQ/Dx+Cs+dgqRfZ7QRAZ88LAiNl1wgjjPBphTrzJ3gf/I9EJ7+E6PTbh74zsebCv/oL3Jkc0//8tuHvjOGnfuv/oEmYid7Cz/3nz36iq4cj47fwntN/jG7GrP3GdnVXvNLmzFf+Add+7jFk3mXj917gzJe9m+hql+jev0/rq38V3JDwXd+I96c/AlF7WxlCCH7g3xSYm4d3/IBhbW0UzjjCCH8bIYRAVfpylp3MipMDr0Ef+bzs7+t5dmX7zNkx+6Hbv4mJ0tvouBPp8fZcsZ0/18ZOmG5gwu4qjyMTN9vde6a2GRuRel2J7YoCIR3GvTKny8Ph83vF0CTFzfUN0nrfO9t9i3ZSduXzgiOH+xVbWFDcd3dqsy4Erzv0pRT8MofGXqLT73ClAUsgbJ0ImuIs8cEHMYWdlSM9bAs/G1RS+b12tglX9NxtfTP8l/gY6ikRu0nnRbZM6zBIvm2Z+iglsgQL9m9AeX0yd48dOKoeoenNEkXXUTW8SFGTkzA9vQNj8FKRsdUv8V1p6ObY+atQjbGQm0k/HI5V9LdyRVvKUGV/4LsXqeOgkb33qbfNffdJpqd2PqZ4EWVXP+HHdvKk12/MdULxbgRSyBdXy+yoBLqxa94jJxaun+sEAD12MN3JKmxc117bnZVd9kc+lBghmKwUbUKE9PNjJ1sc2M/ebUiy9t7SGAP7Hz4MD9xnyaltu+9CkJgBRdZy9X702AHEFm/Afhl98m57OXbbZOKYTSayQz8Iw12ytfbK2EXZZaI09NO/fv/vhQ+Xdkim2WunnrLretititt9Del7fvUyh16n6Bt9JRgbg7vvEoRhj0lLQ/n3WNKI7BphhBE+bTDLzxD8/r9AT5+i84Z/s22wvPbOx2k/usbcD949tGrfaBj+zQ8a3vPU7+FHS/zsD9/ExMTnBtEFcHj8Zp6ceBz37grL/+mTJI1+et34Woszb/tDOi/UOfA/3sjBX/08Dv3W55NsRpz7jj/HGIOePkXz7f+T7m1fi/fRXyT8H1+BvPLotuPk84J/+/2CWg2+/98akmREeI0wwt8WDGtKXmT8FNacNxOG7PS6qIbJrl44ei4M6fh9c+0bDU/ZpvDqGRDvNbQmK2XYvDx7sU9N33sVi5J+NkCREQyfWjKP3RRHyaHXER9989BnO5GNO5a5ZcJ198LrWKoe22Xrl4CByWLxdfPk7kzDZLwXDytEWcnGTuSVDFKyK+ifZzbRe4lsV4/sasfNPW0/lO1yh8nusaNi+7a9ELA9duB47Cir+duJ4uuF8IhdfreYGBfMz/eIlJdvivayFLWlkK3ZGO0vYqi9Clu80J2ZHDLnZEkOruc7tO3wg8qul4Hsui56aqStRvjZvTrYX3qyq3S8KUwRH3gNprQH1miv1XkRzy7jbA9XNEHlho7huoIH7rOhei8GU1m0JHhuLG0KA1JkBMcQ0ZG2pePA0SOQCySeCjhyFI4dgUPTixw+dOPv82Zr2LsavodzOcEDS2/eRirtpKRNd7LlIohVET11sm9kP6Rw7P10GPLs6qFHkufGSI583kBGxRdHxbHpbXdLtGE6VpUsgxcjuwT33s1w1sPesNJrJz1gLrjLOHx9smu43B5eLs8ugJtvgltu3q6+NDfQpjAiu0YYYYRPFzo14v/xjRjHp/3Wn+xn4ErRPVfnyo8+TOnNi5Q+rz9Reuppwzd9q+FPP1DDXXg/b73tC8jlPreGrl4K8/NfVyNebnPpBz6MMYbocpMzb/sjumfr7P9vr6Nwv109DU9Umf2eO2h88DKbv3/WFuIGNmPjl78T0WkQ/srbcP/qZ20e9AEcOij4p98h+PBH4Bd+cUR2jTDC3xbcdy/sWxwI2XgRCNH335I7TRB2mTS4W95DX2o0T7ZfRnbd2LjfIzXMFjMRoYeVXevtlf4xnQDj5tCTL4NiaicIuU2xo15kdb2HvWQp/FQwOFlURQ9vfuesbTvum16bYN/2c8mUXc4O7MhL5BQrgc2wOdFTFL1Y/QaNp/fYITO/oz22e4+Lqdc/Vb/wNFvbyxHG2Mvw/BIr5M7lcHrG7tvULTsRAHJY1SfEEOElfUXpjfsovGae/D3TyBdRqAwdb1DZ5b7C74DZ+QgQEtMLtc2UXf3t+gsBGZtwXYP1lwJ1fU7CEivzt9ttlEd8/Au2hOLuDbnc9RVHQ/DyGJkuiBhAbPe/g75KzoBdRJnzEAi8QHDbvruzkOQ9Ywu5mH28Q2ir74TbwtE9tUMlB8o7ckRy7Ej2Yfpje19vefPUwoOYfLooYD61ew2g6uxnKXg1U4WdiVKZigDU2Itf22JRZBkkB+GU0zLKPjuFWg4db5dTEQNxjFu7S6/I6yq79tjFfF/srL5MlV3SRNu/2wGfWzPGEUYY4TMTOiH4/X8Oa8/T/sIf3+YjYIzhwvd+CKEEcz9gPbzi2PBrv2H4e99m6Ebwjd/1R2i6vGb/F/5NnMErisXyEaby83yw8CdM/eNbWPuVp3n2rb/PU2/4HaILdQ784usp3Dv8Ul9922GC4xWu/PjDQxkWk6X7aX7dbxEfeRP+n/8YwW9+M6J+dWjft3y+4AveAr/wi/BXfz0ivEYY4W8D8nnBxNjeFTXDoV87TLzVzpPxrS/YnyrZJXrGIzccitULleuRXVvCGNM3+cNjNw0cU5EceghTvAHz9+vg6MSt7K8ev+42as+kxpbzeZmxTcVyI/tKQeUL9xEe2H4uPWJiqPysG760c/GdkIcOfgkzxcUX35jdlXbXRZYlco9k18DkbrdJ4qnJO5j0q/b3k1Z9sRVZpsY9kqDXgwgdvKUi+buuH4a6G/J3z1K6s6e+2r0N+/e42EZC3HMXPPTa4e1loG48s+Sgcsd9ZS2nszBGKYiPvonk0EO7bLhdcflyKvJ6Y8deijTh2Mt23D1DqL6ySwi8lEeK48Ft7A+DBgniphxjbz4IUpB3dycF3cpuTMuwzE2Vrq8O7Cl3eySeq3bZPi23WJQs9haFMj308NhllU0u9eDgQOe3923pNVMUHtgbCb+tCkjyamLX772FAsU3LOBO3iBBCFmotjsZUnz9At6+QjbGiF3Irus+u7fkaui9U/T+vq6t3qcamJMqu0Zk1wgjjPAZA++DP4nz3PtQX/DD6DRt9yDWfv0Z6n9ygZl/dRvuTI4nnjR867cZ/uN/MtxzD/z8zwkebf0qs8Uljk2e/vSfwCsMIQR3LbyOj1x8P2P/+CSz338XCEHxoTkOv/uLyN+zfeIllGTy226i8+Q6tfdeGP4yKNN5y3+g/aYfQV36BLn//iWoM+8b2uQ7/7Hg4EH4gX9ruHp1RHiNMMLfCtwA8zSoJNrJVwohs5C864UV7PmQW4iPpLhA16kMKLtu7A25l02yn8Z9axij/XssN4XTSxf/MplK97BQPsjBsev7f+1VsfVKK7v4FMguYNcL3SMOembkQ9t+Co+eG7lWL4XsypRdJr7+hikGCa4k2Xmb6eI+bikdBcAPBMXiDvXK+uunTugIIcidnrihcMEhDN7X1zOPH/jKbFHDSCn2nt3vOhi+3i/vfbr9YOkPmZJ3u5jzGzFQr60/PwVM5GcoBdVs7NiTH3+vjt4NkoifCqRKU4saEP2seYNkV9+gXmIE+F7I/PQh7l96067ZU8tvWaJw6279f1hpWHjV7K5ZeQfRCw3cXdm1JZsifVXw8Pgh0v/k8AJML+FI0cOZuHEyyhb94n1H5V9a4pQjhwVvfL1V7vVsYpKZm9GVRRuOegPVUWogjHTLNtna1Mvg2bUbevWN1N4UlCOya4QRRnhF4Tzxe3h/9bN0b/1q5F1fu+377rk6l77/wxReNYv3JUf5yZ/SfOvfN6yuwg/9gOCHf1BQ4zk+dunP+YJjX/PKv/D/DeGBpc+n0d3kz8/+PhPfeILDv/MWFv/ja/D37z6Yl79wP+5sjtX//uT2L4UgPvUlNN/+LnRxhvC3/h7Ju98BsV3l8n3r39WNrGF9HI8IrxFG+NuDPSi7BskudnlzlYr44IMkBx7cvZwbrFkP0fStXC0+QDJp46DkDb7k9yahJjOo76Woiof/ph+K9JIUQJ8iepOwI4fh9C3X2/KVrduNhonuFc5MjuBkleBEdduxZP7TkxR+L8TYNjVWL3Qt3psJ/qCyK96VH9vLNdzDbPEzDaVUrS/Vy0L2vChe6WP0zQp322Dgty11uUE/oZ1wy8x93Dn/YPb3niLjlEsyfzvJ/F2f8vH3jAFCVgiRJSSIhpRdtn2KfpkDYyc4lCppe757O0G4sq+u2/blFtWVK69L5t48cw/ThQV6z7xdlV3p/WaGUkn2QvW2q1IHw/yH6vUphDFuTZ7xisMN0TM37yod3O1UPI/sfL1eHo/URy9Jh6/rhTF6L5F778HkJ7lYfj1td28K7M/NWeMII4zwGQF55VH8P/xekoW76D74Xaw1r/HHz/4m73/ud2l0a+huwrnv+HMAPvbQfbzta+A3/id8yVvhl35B8NrX2FWI//XYf8GVHm868ra/4TN65XDn/IMsVo7w65/8aRK9y7LwFghHUv07h6n96UWiyzsb9ZqxA7Te9qt0b/ta9Ad/hvBXvxqxZlM171sQfPe/FHzyEfh/f3ZEdo0wwuc+bkwN09t6R8+uHrz89qxlA/B3WUjfhnSSaEJLimRRh5NFKl984Ib8faA/cRgKlROi79k1MKnJjIhvcDElmb+TZPrUDe2zFb1J3/4lweTk7tdHvtIT/JeasS/DLsouIQiOVIbINBko8vdOk7/jpYXX3XjNXvzcXnU/Q2GFPZ8mEbX2dIw9kV17USeZzz6yS0+eID78RnsPfzoWJD9d98Iuxxm8F+UWEt3sRqZ8Cthrk5ri7HXH4pcdPc8uhpVduUFh00DdZ8uLe07IsSsyRdHeGmUyP8ep6btwU0WXK69Pdg3ed/0Q+GG/wSxwcaAf6KlT6Op+TG58T/XaCadOCu684yXv/rJjd4P6fiO4HuTumMRdsIlMMmXXdS6P7wtOHof9e4tC3xFa7t2TbkR2jTDCCK8IxOYFgt/6e5jcOK0v/HE+dPF9fOl/PcWPvO/b+IE/+Ra+5tfu4q++/Vdo/vVVfmP+Xn70l3McOwb/9ecE3/kdkkLBjqQb7RX+z9O/whsOfyXVcPJv+KxeOUgh+Zpbv5OnVz7BL338R/e8X/XvHAZjWPuNZ3bfyPHoPvTdqK/5RWTtErlf+gqcx34LgAdfK/jKL4df+3V435+NCK8RRvhchh4/bH9xXzzURQjRn1fspuzaBa95Vf/3cK8RHW5IvP/V6OmTvYMCL52D6RkTD67kG6EgidLfh5VdOijfsLLLFKcx1f3Z34GyJ2uVBHvDXsPxXu4Qy23lv0JhjLvBnc698kbjKfbSdr6/JawwvUeycMYXwcum7DIvo0H9ywCzl2ycQgyQLJ8OdeQrfC9kHugvchwh+gR5Oq68HMquHQ4DvOTkpa8cep5dykuzMQpuO20z6GUYbMMbuGzJwl0ki/fucMydDepfDKFj+/GuY8EOYYyZsmunbIwD3wPgBujpU58S2TszJalWPv3q4t1w/WEzVXZ51kus1656D8ougPl5wZEjn55z/cwYSUcYYYTPLbQ3CH/z7yLiiOZX/AJPNs/y/e/9Zg5NnOTb7/kRYh3x/nf8Ovl3a959ywWen5/gx75FcNed2we+d374/2PvvuPbqO/Hj7/uNG1529l7h+wQIARSoBQos0AYpVBoaaHQQX8tpawC/RKgjA7aQmlT9h5lFsKehUAWSUjIXo7jON5Llq119/n9oWHJK7ZjW5L9fuaRh23pdProlu7e9/68P3cSMAJ8d+bPEvBB+tbxE85m7f5PeXLdnylr2Mv0IUcwLvcQxuZOId3W9shY9lEZZBw9jOrndjDo5zM7PDnTp5xE48Wv4njrWpxv30Bg7yp8x9/Ez650smmz4g93KSaOp3nocyFEv6Iyh4RG6uqiDjO72uBwaES6jXQ62AXgzIr+Gs0q6+a1Q176YKYOmhsfeNK05hFqYy5K1MgjUAH3QQeU7FYnx40/s1e62/d6F5eDDXYlsa5m7AFgcxIcvaBbI+sF20vObu9COVYPFqjvCcaYozvdlROI2a96fntyHTkkFCDt7eyxmAL1bYkdgTCyr2uRWoC9kNmVtDQNc/AkTLctuqgK8uOXWdw5aRfuXKiMtrM+VXj0Rs3v7lJTR+dMpMJTQpaj7fpUSreEttg2Ms9afS9oLX72kGQr06J3tL4iJeJaxHbHjoFAAIYPa/2SREmupSqESH1BP87//hKtroimM+/HlzOSuz75BXlpg7j/nDdI885i1RVOjl46g3VjfLx/1v3oJ53K4AnbWs1q2Z63eXPbU5w74wpGZk9IwIfpe7866h7OPORSPtvzJvcuu4ZfvnEa33lyIte+fT5bK9e1+ZrcCyYS2NuA54vSA85fZQzGe87D+Bb8AuvGV0h79gLsDYXc+nsNiwVu/j+Fz5dstw+FEAkRLV3T/Qtvu717VwQ9UAKF4Vlj40c71PTmDIyYYIJ2MG/SQmcvWDIc2QzLHNPp+bZba6aH9HbmWCJFMjOsXc26Sc/rdKZOerrGpHDiZPs1aTqT2ZVk3RgtNnC0fbOtTb24HdmGpGPNc/Z6N8ZolmN7u3LM+0eD0OFRXpXeC5ld4Z9Jl9kFkJ4PVnsHGVNaW792m8oZhUrLwczpWh+4bGc+x084mzRbO5mKkeN23H4XWeDxmV3RTK8e3g4TUTOyu46YrzF8WOvsR5tNY9ohPTMoRU+RYJcQoucYAZxLr8ZavBLfyXdhjjyM59bfx966HVw598/88+8Onjr9Kw79cg21c8Zy3ts/4o+n/YcGfx1XvnYiD6y4hVJ3ETVNFby08d/c/vEVTBt8GJfMvSbRn6zPWHQrVy24k9e+v52nz1/NHSc+xSVzr2FP7Tauev1UHlx1G4YZ30ci69ujsWTbqXmhg66MsXQLgQU/x3vOg+ieCtKfPo8Rde9w8+80tm2Hv92fjGdUQohE6U6w69A5MGVS99+zJ4JdrWdqac7AiJmxJRz46ss760eMPJ5DBh/a6ekd1m6O8NVXkjhYFrmIbHckth4ydozG7JlweHt1d2K3r0hQq5X2Hk8RfbIddP89NKflgC+P1JdrM4CTObh59LqYzK7oKK+90I0xqR0oy6kz2Yxdej8dY8zRqKwDj8DYtflGujHGZPyGo4uxmaGjcyYxMT/cT7Ong11JltnVkewsnfz85D3mx5JujEKInmEaON6+DuvOD/B+6/cEp5xKUe12nv3q70x1nstdv1rAWYUfcrp7N+lnT2DGnxegWXVmu45iyVkf8Niae3h108O8vPHf0VnOH3UC1x1zX/Kf5PcCTdMYkjGSIRkjmT/qBM6ZfgUPrr6N5zf8gxJ3ITcc+0D0xF13WMg+axw1z+1g+G1+LFmdywAwxhxN4/dfwbn0atLe+DXHzvk+P7zoNzz2tJ2ZMxSnfDs1vsiEEL3DolmBIJpmja/D0gn5+Rr53a/V29U6xF2bKcRndmltFLNPMr0dqDl4yft9EQwHIiIjX/amwYM7WA6x2194ZOSWzJyxWMo3gTXZ13d7+mA7OIggQ9aJow48UQcjk+rjjkbLXwfuqnBTwm2J3IQcSN0Y4cBdPrvZjTFRDlQrz26147DaiRTl70mpmF1ryU3+45QEu4QQB880cLx7E7atb+E77nqCsy9AKcXdH12L4Xex/ckb+E3NBwxzlzPkurkM+tmMuIN6btogfn30H/nuzJ+zpuR/BM0AUwcdypSCOSl58O8NLnsmvzrqHsblTOW+5TfiDfyQW7/1KPbwiFF5502k+vGt1L1eSN5Fk9uchy/opbhuF4Ncw6IBRJU5hKbzHsP+2b3Yv3yUXwzdQNFhf+OePw1i5AiYOUOWvxADlVW3AEF0LAzp6CK+F1l6NNgVO6pW7Ihqod8VyRvssvZC96ielbzfFZGuSyOzxie4JbHLqO1tTeWNI5g3rm+a0xuSPDvlgEXnAS3cBUuZba8jXbcwuWA21mHNA32ozGFodcW9ktkVqXuYldXxdIlwwOUZe8hN7k0jJC77snVmV+z3hurxboypsICaZR43Aj09+UNJyd9CIURyM/w43roO27a38R39KwKH/gClFIuff5ytjV8w5PM/8Zu6ldjdjUx94lSsxxW0O6vhWWMZnjW279qegs6c9iPSbC7++OmvuOWDS1kcDng5Z+bhnJpD9X92tAp2Fdft5PkN/+CjXa/iDTZi0+1cMOsqvj/n6tAw0BYb/mOvxRg+B+fbN/DHiedzrftv3HDTLP79Txg+LHkvYoQQvWdE1njWsrnLBep7ghGpI9+Th5/YrmMxd/AjFxlmu13LxAEl8Y0phzWN4yecnehmxC8js70q9qkuHChK4u3hQLRIhL2dYBeEaujZY0bqNIfOwhw8rVfak5mpcdSRivQDD6KbMO2u7rjRGFNgm4jtxhjuUhx/0733MtWSrUD9gViyUyOLUYJdQohuMcwgb25+jDVf3k+Op5oFc7/HvMMvo65a8bv7vmDb8N9zyO6zufJLKxZbgDEvnMSg4ydRU1OT6KanvJMmfRfQ+OOn/4/ff3BpNMMr9/yJ7F+8Gu+2WpyTcyis2cLT6/7KJ4X/xW5xcNr07zMxexar933Mk+v+TGOggZ/OvzU6X2PSSTTljsP52i/44yGXcJf6PdfecDb/uh8yMlLgJEUI0aPG5U1lavrEhAS7IkW+hwzpwZmGuxqZBZPiLmr0cJdGNQCDXZnfGgnBgfe5Ey6Ju8welD4KaBiDpqDS2795ejAimV3tBrva+oia1qv1ulyuJD0Hiw6b297IlbHBod5vzsGLaW/4+yA24youk026MSbM3NnQ5O3ctBLsEkJ0malM7vnwCj4oWsoYw8JXLiev736cnH3v4d43ieDwz5ldOp8fvfoNbPkOxj1zIvZRXRjNRxzQSZPOBxR//PRXXPP2ufxm4V8Ycc54Su9ey5YHPuK177zBp4VLSbdlcsHMqzhnxk8YO2wiNTU1nDjxPHKcBby0cQnzRhzLESOPj87XLJhE44XP43zzN9xo3sQzu7fw+//7LXffZUuq0VWEEL1P00hIoAvA4dD45rGqZ4874Wwa5cyJe3hSwUx0TSc3re3h7pPFgtEn9fg8LRk9dYEu3w9doal+mtkVvWDv3e1B5U/svZlbIjX8eu8t+o1I8KcTgZqUDebEjSjZTpaX6FMFBZ1f9ikRYxVCJJdXV9zOB0VL+Zkvk0ePe5wXLtrCSWmPUb1zJpqjhu/6fsqPnjkb+5B0xv/n2xLo6iUnTfouNx//IEW127ns5WP54XvHsGrmlwRfr2TLjtVcMvcanj5/FT867AaynfGVoi877HeMzp7EA8tvbjW6I2k5eM9egn/epVw47il+ZPsJS/5ek9TFm4UQ/U9PB9i18LFOtcjAcFrTmT7k8FC37iSWZnNF608lHbnu65TgmKNDv/TXLMJIxqQtifvcHUBnM7tSNnjTk6IjiXRiWktqLa9cRx66pjPKNbL5wZh1bk32MooCkGCXEKKL6jY8w2Mb/8nRKoNzzvkvDSO+xW13WPjvkpNYYDzKUzMe46j7xmIfnsG457+NbWjqnvCkgmPGns7j537Bjw+7kRlDjiB4QQb2oIM/VT/EJXOvIdOR0+brbBY7lx9+M8X1O/lf4RutJ9Ct+I+9Fu/Jd3PooHVc6LmANx7f3rsfRgiRVPrtxdxAGzFNJI8kD6geNE3HGHk4xqj5iW5Jt2nh0RjbK1DfPGEfNCbJRWKbnSn8nwrFzGPZHVl8s+BwslxDmx8MfydOnAALjuqZDWBkdqIHzohn6WeHqNTa6oQQiWP4sS/7G//5+n58Trji9JfY45vK765V7CmCn12pcfrQEoou/xjH+CzGPXMi1vzeH+ZbQLYzjwtmXRX9e++yT6l9bDuDLpmGfXj7WQDzR53AmJzJPL/hHxw37sw2L2yD075DMGcsWc9exRllF7LqmT9y+IXf7JXPIYQQfaIXa+sk0tzhCxNY5Fiu/DslxYpQd4fKSO7uwAcUyUBqJ/lO60o2U3/XhRsiWo8OrduzzPwJaL56VGZzkUiVno8x6ohQt3f3VqD546alaTidPfN5JhfMZnLB7B6ZV0/4xtFg9qPE0+Td6oQQSUOrKSTtuYvwfPkQr6QZnDTpu2zfOovLrlDU1sFf/6xxel4xRZd9hGNSNuOek0BXIg357VwASm5c3uGdSV3TOX/mz9hRtYE1JZ+2P8Phs+DHL1DJeI7d/3P2PPugFLMQQqSufprZlZs2qFWX9T7TX7MAe5ref/IMZs+EGb0zAGFCadFgVz+64u8tfVOirffZXRhjF7b6blCuQXEB6n6b7RzDZtNwOPrP55RglxCifUph3fgq6U+dg163l1fmnIJPBTE2/Yzf3awYPw4eXgKjv9zMnp98jHN6HuOfPQlrrgS6Esk+MoOhv5uH+8N9lN75ZYcBr+PHLyI/fSjPb7i/w3lacoaQ+YvHWdF4GtP3/wX3k9dCoJNDoQghRDIYAFk1idN/Lo56VT/aBgcP1hg2rP+td80WWke2oe1kxkcDPP3vs3dVNPjTQTdG28gMnNPz+qhFvSuSnWYdlJbglojO6j9HXCFEj9LcZTj/exXOd27AGDqLhu+/wqtln5PuXsh/n53IOWfDvbf48N6+jP2LV5N9ymjGP3cSluz+ecc81eR9fzL5P5xC5ZJN7L7gXeo/LG4z6GWz2Fk0/XLWlPyPXdWbOpynw5XGhKvv5pmKqxlSsRTj0UvQGsp76yMIIUSPCo4/luDoBYluRv8kF/6d099rdvUDmkUn69ujcc48QIBGNvlOBf5c8wbhnJjdN+3pBbHZXJpFI+Mbw3DNT/GuugOIBLuEEPGUiXX986Q/fjqWPcvwHXsd3nMe4unVmyhvLKLp60u49TqDiywb2XX8q9Qv3cOQa+cy6h/HoKf1n/T8VKdpGsNuPYIRdx2Jr9DNnh9+yIppj1Byy0qaNlTFTXva5O+TZnXx4tf/OuB80106x914GX/afz+Wml1YHj0PvXRDb30MIYToObZ0SO8fGQYiRfWjzK7+THda2u+yJpldzcIZXVoPj5ybvDSsec6krj8m4smaEkJEaTW7SXvhBzjf/z+MYbNovOS/eOf8gAcf1Xj408ex+AZz/2FTGH7Lq5Tds5aMo4cx8Z0zGPyLmZ0aiUX0LU3TyLtwMlOXLWL0P48la94Qqp/bzo7TlrLznLdpWF4KQIYjm1MmX8iHu16hqrHsgPPNytT4wa3f5I6yZ6isdeB49vtYN7cxoqMQQogBQs4BxAARDnJJrIvobq87+m/GohZzbNPkOJdyJNglhAAjgG3lv0l/4iz0yu14v30n3kUPsad+JL/4f4rHXy6mIGM1v3/5l/jvWIFthIvxL5/MmH8fl9KpyQOFZtPJPm0M054+nUO+PI9htx5OoLSR3ee/y97/9ymB8ibOnn4ZpjJ4ZdNDnZqny6Vx9R2T+FP9c3xZPgfnW7/F9umfQUlBVyGEGHDkyl8MNLLJgxEqjxGpc9bfDYQC9f3NwNgyhRDt0vetIe3p83B8di/Bid+i8Ydv4J1yJs88Dz/8saKoCL57zItc/eAvyNqfzsi/HM2El0/BdZj0V09Flkw7BZcewuQPvsPgX8+m7s09bPvmqzjfM/jm+LN5ddPD1DRVdGpeaWka/3dnLm/k/pvndn8Px6qHsL/yC/B7evlTCCGEEEKIRFLB0A3O/hzskgBXauu/W6YQokNaYxWOd24k/fmL0Hz1NJ35AL7T/sKKTfn88DLFA/9SHHWk4v6TNnLMP/MwcmDSG6eTe+4E6bLYD+hOK0N+PZtJ751J2vQ8in/1GYuePwPlNXnkyzs7PR+rVeO3v7VTc+TN3L7uZgJffYXx18ugYk8vtl4IIYRIXcqenugmiIMm58Iq0P+DXbGkG2PqkWrSQgw0poF1/fM4lv0Ngk3451+B/4gr2LTDySPXmSxfASNHwl23mox9YyU19+zg66mbmP7A6TjGZiW69aKHOcZmMu7ZE6l44GvK/rSOm7bfxF3fuZMFo7/NUaO/3bmZGIqTPZsp2WBn/cdXAGB9+C0GXzaKvN+cKsFRIYQQIiw46SQpVN8fyKkNKhge5ds6QLZnyfJKORLsEmIA0UvW4fjwNizlmwiOORrvcb9j1e6xPHujYuUqRXYWXPVzjdNm1FF6zTJqNlTxxbe/ZN2pm7lwwt2Jbr7oJZpFZ/BVs3BOyaHoF59yzWO/5gHzelznZzJ72FHtvs5UJrtWrMVzy3b0LX6yjh2OedQsPny3lm+UfUzJfVU0fvlvRj7xAzS7sw8/kRBCCJGkLLZEt0D0BIl7DIhujLEksyv1SLBLiIGgsRrHZ/di+epl3O6ZlOXezadfzuP9x11srwuSnW/hFz/TOGV6HU3/3UnhjVvQM2w03ZXDC97nuWn2EumzPgBknTSa8c+dxK4fvM9PH76ce/w/Zdb8Yzhvxs8YlzsVTdNw+2r5umwlK3d9gO2xeo76+Ag86R5e/O4ruBcEuezwm/jRT07gpRdmMOzxB+HzXLjoDobe/yNsQ8Yl+iMKIYQQQhw8OS/Gkm3HqPGhO/vvaIyxJNiVeiTYJUR/FvRhW/ME9pX/pvLrsez89BqU2wLUMo0PmAYoDawNaXCHSVGND6wa2aeOYejvD+PXXyxiqHU03xh7WqI/iegj6YcOYuJ/TmbXhe/ym8d/xb+Mh/jJjm9iszhwWJx4/PWMKRrN914/n8EV02k61ULWb6ZyopnLyxv/zU3vXcyFs/4fP7zgOooX/pKdP34ZVkwi77arKVxwNuPOuxCrXb56hBBCCJGClEp0C5JG2sx8HGOz0J1yXieSk2yZQvRHSmHd+ib2T/+C7i7hy+Xfx798FDsdBayfNZ2ZJ2Ux/xAfaQ2N+IsaCJY2otk0HBOzyT51DNaCND7Y+RJbK9dxw7H/wKLLoWIgcU7NZcLLp7D7wve46qGfUn++Ys/c/VBnMOrjQeR8asc2wsXIpxeQ8Y3hAMxkASdOPI9/rLiJZ9b/jT2127jxuAcY+fpZbPzWa+z64FQOH/EXdtz1OlsmLmbBmVNxueQOmRDFxcU88MADLF++nMrKSgYPHsx3vvMdrrzySux2e3S6kpISFi9ezPLly3E4HJxxxhlce+21cdMIIYToG5LYBZquYckeON9B0ssl9cgVrBD9iVJY9iyDj/6Os2YD2xqm89IH13Hc7lJKpo9n6l8WcOZU/YAH65qmCh5YcTNTBx3KN8ef3UeNF8nEMTaLiW+cRsnvV8JThcx8aigAeoaN/F9MZfDPZ6Cnx9cdsVns/OqoexiXewj/WP47rn37u9x24uNMfOBYdpz5JmvX38jkQ//OxJLzePb6S9g5/EpOPC2TWTPlBEIMXLt27UIpxeLFixkzZgzbtm3j5ptvpqmpieuuuw4AwzC44ooryM3N5ZlnnqG2tpbrrrsOpRQ333xzgj+BEEIMIJHELjlvESLpSbBLiP5AKYyt/6P+w7+zzVjHRsPF6oqTyPN9j/NLdpJ+9FBOevIotE6MluILNnHrh5fRFGjk2m/8Db2DEYOCwSCvvfYaAGeeeSZWq7XDxw8k9nWnnXYaS5cuxTAMACwWS3RewWCQl19+mdWrV3PYYYexaNGi6Hu8/fbbXHjhhQCcdNJJLFq0iO985zu89NJL/POf/2TQoEE8+eSTOJ1OXn75ZVasWMHu3bsZNGgQS5cuxTRN5syZQ1paGkuWLOHWW2/l1VdfJSsri2uuuQZd1/n3v/+Npmm88cYb5OTktNlugGOPPZazzjoLgMcee4yzzw4FDt977z2WLVsWXT5A3PJq+XfkszU0NHDxxRdTUVHBZZddxpo1a/joo4+YOHEiTz75JBkZGa2Wfey8fvjDH3ZqPURY85yMvu8Y/L+dS9OGavRMG67DB6Ondbw+zzzkUvLThnDHJz/lV0u/w+0nPMmgn86g4v4NNP74Mfy1T/B9/TFq/a/xz7/8jHsC53LMcTa+eZzGpIkS+BIDyzHHHMMxxxwT/XvUqFHs3r2bZ599Nhrs+uyzz9ixYwcff/wxQ4YMAeD666/n+uuv59e//jUZGRltzvvZZ5/F4/F06Tg8kASDQf7zn//w4osvMn78eG677TaczoMbTKO733899freEPudO3fuXCwWS9x3cm+8X8vvsdjv/O985zssXboUv9/P6tWr2bVrF+PHj2f+/PksWrQICH3vRZ4vLCzk3HPP5YwzzmDx4sWYpsm8efOw2+1x5xWx3+OvvPIKL7zwAkopxo0L1Zvcs2cPEyZM4LbbbgPglltuiZ4zrFmzhsLCQiZMmMDvf/973nzzTVauXImmaRx++OH84Ac/4KWXXop+pmRYr72hre23o2069rlvf/vb3HrrrezcuZNx48ZF12fLZeX1ernllluA0Dp45513Wr1fy+0VQjcN1q5d2+q8sauUGYp2aZbW5yqRz9PeuevB7ttdmUdH54P9dRvs7eNnRzW7urNu/H4/q1atYs+ePZx77rmcd955bV5LnXbaafz3v/9t87qno/d4+eWX445DB3pdd5Zf7P64ePHi6Hdoy30BQvvgl19+2en29IT+t5ULMZAEfVR88iaOdU/xpWMtf3P6aNANwAOZz/Pzx3LxWoaz6fIKsnwzGGQd3uHsSt1F3PnJz9lUvppbjn+I0TmT+uZziKRmH52JfXRml16zcOyp3ON8nlve/yE/efV4zlr4I+YvnUjR777E9sIitk+bStPaJ5nruplpxj/4cNmv+fHTZzN8uM5xxyoWHq0x7RCwtHEyKUR/53a7yc7Ojv69bt06Jk2aFA10ASxcuBC/38/XX3/NkUcemYhmCiHEwGOEU7vk/ESIpCfBLiFSkLe0iNK3XmR0xYuMtFZzq9XO++mNTM46mp8uvJZDBh9K6bNfU7NnA8suWcdLhc/yUOFdjM6ZxOT82eSmFZDpyMVuCUXfa72V7KnZyqp9H2HVbfz++IdZOPbUBH9KkepmDj2SB8/+iCWrFvPC1gdYdsJwfvXQL/jsV//mP2e8HJrIBbAL7RtXcdjRNzCq6hJefPFann7WTmYmHH6Y4sgjNOYfAfn5cmIp+r+ioiKeeuoprr/++uhjlZWVFBQUxE2XnZ2NzWajsrKyw/m5XC5yc3O7fffU5fIBkJvr6Nbrk1kwGMTlcmGz2XA4HOTm5vZIZpfL5QLo1nJv7/W5ubkH1a6DEWmT0+nE5XJFM7tafj4z3G79INvachkAce+fm5sbt97sdjsOhyP6XGT6yPM2my36nNPpxDRNXC4Xdrs9+hlavmfk9UopHI7Qth+7nQBx83I6nXHPRx6LtCX258Hsj8mure23o32i5XOR9RW7PtvK7Ios28iybuv9YrdXCGWVxG5D3T4mprkwtSDZeTk4cuNvBEbeOzazqzPLobO6Mo/29qODef9k15Xl05VjamSeOTm55Ka3/brurBubzRY9drTcLts6JnVl+43dDyKfoavbTGczu2L3x9jMrth9AZr3wc62pyf0v61ciH5Kq9tHzfIPYONSRrKeHNPCR96jeGj0DvaYm7hg1lVceuj1WHQLwRov9fdswzV/CFfc9mfO9vyaj3f/l69LV7ChbDluXy2NgYbovG0WB0MzRnH2tMs4d8aV5KcP6aAlQnRegWsYvzvun/ziyNvZUrGWJnc5Rz13JEdeugjX0UNJt2Xi9lbx5fqHeKfoTTYMfoB5332MczOuZPney/hoRQ5r32vgvaZypmS5GTs4wLAZLiacOQTXjDw0XQJgIjndd9993H///R1O8+KLLzJz5szo32VlZVx22WWcfPLJnHfeeXHTtte990Ddfj0eDzU1Nd0+ofR4QlkMNTWN3Xp9MgsGg3g8HgKBAD6fj5qamh4Jdnk8HoBuLfe2Xp+bm0tNTc1BtetgRNrk9XrxeDzRYFfLz2cNtzt4kG1tuQyAuPevqanB4/Hg9/vx+XzRn5HnItNHHg8EAtHnvF4vpmlG13vkM7R8z8jzSil8vlDAN3Y7AeLm5fV6456PPKZpWnS+B7NdpIq2tt+O9omWz0XWV+z6bCvY5fV6o69p7/1it1cIXWjHbkPdWQe5ubk0uN0ovwkNbmw1wTY/f2ywqzPLobO6Mo/29qODef9k19nl09VjamSedXV14LMc1HvHTuv3+6PHjpbbZVvHpK5sv7H7QeQ41NVtprPBrtj9MTbYFbsvQPM+2Nn2HEhnApb9bysXor/we7DsX4ex7VOCWz4lJ7ALF7C5bjpvZvyWpgUjeaz4etz+Gm46ZgnHjT8z+tL9i1djuP0Mv2M+mqYxJGMU3535c7478+fRaQKGn4DpRylFms3VYW0uIQ5WtjOf+aNOwFxssGP1G5h/qGXMKwuw5bsgawxTTniA8w0/b32xmEe2PcYfau7jl9u+5LTy42ksSgNAVYB/lxX9iyC7HwR3ZiYNJ09n3OXjmTDZgi6BL5FELrroIk49teMM2ZEjR0Z/Lysr45JLLmHOnDnRmkARBQUFfPXVV3GP1dXVEQgEyM/P77lGCyGE6JjRfs0u0b9JTdnUI8EuIRJNKbSGcvSa3ejVO9HLNqGVrMdSsxOUwmc6WFd5BFuMC8g7/BiO+PFwyvc+zEOrf8xg1wj+dtobTMifHp1d/YfF1L60i8G/no1zck67b2uz2LFZBs5wwSI56E4Lo+7/BrvOf4dd57/L6CXHkjYtDwCrxc5Jw69lxrsnUf3CNuyNdvYM3cfoYzYxcbaJ5ahj8Uw4ldUrc9n9+l4KVnzNyP8sp/DNz/nrTBfOGXNZMGM8h87VGDsGCX6JhMrLyyMvL69T00YCXdOnT+fOO+9E1+NvPsyZM4d//etflJeXM3jwYACWLVuG3W5nxowZPd72WGlO6OTHEALkxpno5yIF6qVm18DTUYF6kZwk2CVEXwn60Wv3oFXvRq/ZhV69C716N3r1LrRAc/eQfbtnUbjqBMzyc9ENjaaCbLLPGMeZ/28cq+o+4ZoPLqGwdisLx5zGNd+4lwx7VvS1Rr2fkhuW45yaw6Cf9+4FkBDdlTYtj3FPncieyz9ixylv4FowFGu+k6b1Vfj3uNFsOoNOm8Lekyp5oO4pan1VTNEzGbPhUwIbbmO3w87eBU0YR5pM3TGZRW+exZXLdVZ4/8bjtq+576MzsO+9mEPG5XPIVJh2iMbECTB0qBS8F8mnrKyMiy++mGHDhnHddddRXV0dfW7QoEFAqBj9xIkTufbaa7n22mupq6vj7rvv5vzzz293JMaesvBo2WdE5wTHHQNyE030d+FYl2R2CZH8JNglRE9SCppqQoGsmt3RYJZevRutvhhNmaHJ0PA5h1PGODY3fIePq+0UNph846s8Zhe78GQFqTyqAXuBwrWtieAjdax/9nM+OeYDjG8Z3HbCExw56sS4dFoVNNn7y08JVDQx+t/Hodvb7lMuRDJIn1PApHfPoOqRLbg/3oe3rBHn1BwKLj+ErFPGYBuUxmhgtv9bvLP9OZYXvcdGdyG2gI/R/gDf8hqMNyyMm6SRdWctez7M44iXD2P29tm8fMKrrD39Afbvv5INr/6EwLOhYIDdBqNGK8aOhrFjNUaNhEGDoKAACvLB4ZATV9H3li1bxp49e9izZw/HHHNM3HNbt24FQnVflixZwq233sr3vvc9nE4np59+Otddd10imixE2xxdG7VXiFQmwa6BRzK7Uo8Eu4ToDiOAVlccDmiFM7TCv2veuuhkpiUNT9pYqphBsfUMtlSOY2XRCNZ7qwkOXoU+9HP0greZWz6NH3x0Jmk+B2+d+B6fLfwCQzcwlYHlUAuz6w/jW+8cy1nvnIF9exbD8qbCSIgcc41aH8W//Rz3h/sYcdeRpM8uaLvdQiQRa66TIb+Zw5DfzGl3mgx7FudM/wnnTP9J3ONa7V4sez7DWrgMy86lFAxvpPHCfHZ9fCYXvnY+J23YzxPfeg7zooc4cfAVDA/+hH170yjcA1u3wUefKEwz/r2yshRZmeByQUZG6KcrHdLTwWoFiyX0P/S7Fve3zRb6bw//tNlDv7tckJMN2dmQk6N6YSmKVLdo0SIWLVp0wOmGDx/OkiVL+qBFQgghDkiXLrsDjtTsSjkS7BKiPdFaWoVotYXoNXvQawqpq97OlsY97NUCVGomPg1smgsnQ7AFp+H1zGTvvkP5au8ESjxDUZqBlvc1tpGfkTbmMQILV4HmxWFaWVh9At964RYytzqwzspizJ+PYe6UH3FtW+35Bbg/2cf+W1ez57KPsQ1LJ31+aNTEho/2YTQEGH7HfPIunNyni0mIRFA5owjmfI/g7O+B4Ucv34Jl/zomH7mO6vf2YHtnOlc/+Ev2TvmaV456no9H/pELbUO5dPZkso8egWHPoT6QTX0gixpvNpWebMrcWVQ2ZFPZmEWVJ5OSEgcej0ZTEwQNMAwwguGfZtcDV1ZrNTk5MHRIqEvlsKEwbJjGsKEwfBgMGQJWq5xICSGEEMlOMrsGHrvFkegmiC6SYJcYeIJ+NL8bfPVovga0pho0dym6ez+au7T594YytKAXD4q11gArbbDSqii0NEJ6aFaaYUcF01GWBjRLNdg3Q8abaMPtuI4YziBLEOrqGb13FIOqCxi7ehLDA98ktzEXW7GGWePHOjiNIX+aS+4549EsHd8lyjx2BBnvDqP+7SJqXy+kaU0FSkHm8SMpuHI6aYcceAhWIfodix1z2CzMYbPg0EvIOB0m1TdSdf8KLE9r/PLRGdQNqmHNxPXcOGY3eUN2Mj3Nx+FBP5ODoMempaeF/+eDsthQjixwZKIcWfjt6Wy1BNmvGTRZLNhtWeQ4BzMobRQ5aWMwLDn4tEwayaDEV8su90521n7F3oav8QTcmKaGxTuc+rrJlO+bx8fL5hH0NtdbsugwZKhi5AgYPhxGDNcYOQJGDA/97XR27cQ6YPjZWb0Rb9BDQfpwRmSNk5GEhBBCiJ4gN6cGDJc9k3RbhgS7UtCACHZtq/yKlcUfkJs2mDE5k5mcPwu71ZnoZvVrRq0P9ycluD8pwV9YT7DCi55pwzY0HdcRg8n4xnCc03LbvfBSSlHnraKysZSg6cdUCqtuxYqGJejHGvRj+OoIeGsJ+OrC/+vx+91oQS/2gA9H0I8t6MXu94K/kepAPRWmhyr81GiKWs1EBzKVRobSyDQt2MxsvIFcSoJpFKohFDmrqcsoBt1EBZ3olfPJblrIMOsRjM6ZyMjBeQwfBsOHKTILqqn2F1K6dQd16/dj/SpAwaYsMovTo5/LkufANjgN66A0bDMyyDh6KFknjUJ3dn5X1Kw62aePJfv0sQe5loTovyxZ6Qy+8ZsUXB2k9rXdZCzdQ/bnefDFsdFpyqx+itJ8qAywZdlxZdvJyLORnquwZHhpSKukRCtmt7uUvQ31lDdWYW9y4mpy4fTaQHNjavWY+jYMawAt3UNjuoeKjAb251TjcTViUzCBNIboTrBaKU7bQonjdczBCvtcjan2YYy3jGe4ORV7wxiaqkdQUV7AvtXprK1No8lIpymYRpORRkaWjby80Mh4+XmQmwt5eVq0y2WGCzRnDbubPmRDzbusr/iIxoA7+nnz04dyxMjj+daERcwaehS6jJomhBBCdIvcPBo45o86IdFNEN2UtMEuo97P/ttWA2AdkkbazHzSZxdgG5p+gFe2tnb/Zzy+5o+o8PAZVt3GxPyZzBwyn9lDFzBj6JFxI9qJrlNK4dtai/vjfbg/3IdnVTkYCmuBE8eUHNIPHYTh9uHfU4f7/WJgDbYhNrKOdJA5E+zDGtmrdrLOvZ3NtfsoctejvFacPidpXidp3jQ0pVGf4aY+s57arDrqM+tReie7EmmgWTVsARuOgJ30xmzSGnOxBuxoysBqGtgNDXvAjm7qBKxBrBY7s52HM9R3EVOypzHRMQJbpkHA7cG/p4Fg2eeYTUGUzyDoNahqMlBNQQYDg8lHT7eSfsRgMi4eimvBUBwTs7Fk2HpzNQghWtCdVvK+O4m8707CbAri3VaLd2st7v2V7C/ZTW1ZHZ6qWjS3In1vOlmbssj0ZKArGzAMF8OYAbQa2zR8R1cZCq29w1BGkPRhQTJGeHCNqCVreBX2jGK8Ri2bApWsVx6+CpTzmbWERu0zsAFDIH0wDDJ1hiiNbKWTZWpkKY1MZSHNdGI1HRiGA8PtxFdjZ78GJVYfhWnVlKRXojRFmj+DYdWHkF8zHauRhzu9jqqcLbxX/ypfrFrKhKIZTNl7FFn+wRQeOwwzM4By1NBkLcGjldBACfNzz+HQgpOw2zV0i4bFoqFbLVisOpol9FO3aFgtoTIWXT3v13XIyZGLBSGEEEII0fOSNtilgib+Ijf+PW6CFV5UIFRJ2DokjbTpeaTNzMc+NhProDSsBU70NCuaVUezahD5CaDgnOE/5qwzfkBNUyWFNZvZVrGeLZVreX/lC7xtPImuWRibM5WJeTMYljGaIZmjyLLnkWZLx2lJw6JFFpOGjo6laCWWvavYXajjDVgAC0rTUZoFND30t66DZgXdAroVdB1Nt4LFit3hIGgosFrRDR29SUNv0tAaNfQGhVYPWr0JdQa4DTQLoQsru4aWpoEz/FMBXhPlU9BkopoU+BXYQUsL/8/R0HMUeg5o2SZkKSzZBthMlG6AZqJ0E00PFaRRhokyTDANMEwwVehvwwQVfiwAZr2OUa+jquyo/WmoPRngDqV2akNqscwvxjq+ENvgYux4sagmnJqBaxb4PenUFI3Hs3MS3rcmUvVaJCV0HFMYx5RObiOmpmhIVzSk6wRsVkzdhtW0YjdNbKaBNWhgDZpYgiZWv8ISPJgtcj/V7AfAku/EPsKFfVQGeroVzWFBd1pC22CaFdvQdNKm5eE8JBfNJpkTQiQLPc1K+uwC0mcXkMdExnBk9LkKTwnbqzawr24Xu3yVONw2BnuHMsw3giEZI7FnpaFn2LDkOLDmOtDTmr8+lVKopiDBKh/BKi/BSi/+Ije+HXX4ttdRubqa8o/zgQlozgU4p+WSMyufEydkc3KGBs4g5Q2FVFeV4q6toanag1EVQK+1YKu34XA7sXltBHUDn91HfWY9deH/7gw3nnQPvvQgEwMZnFabzbyAlQlGEM1w4/dtINDowrsnF0/FDBrKv0WwMdR1MmgJUjqomGXWW3FbG8AADMg2NYabOjOqVnHU5ls6XKaG0jGVjqEsbf40lSU8jQWD+L9NpZMx1EJmtjX03alboNVIR82RRKVb8X/jN5hDZx7kliCEEEIIIfq7pA12WfOcjH/+2wCYPgPv5hoa11bQtKEa78Zq3J+UgNH1AsEuYC7DmMsw4NR2pqrEpBIP4Gl3TtOxhOfXdQFC4Z3W0Re/NYA7w407w019hpvGnEY0pWE1rNgCVhxeB/Z6Ow5/aA5+ux+/zY/P7sOX4SNgDWILWrH7HaR50sgsyyCzIROXN5IRp9GTq93EpDKvir1jNrF14ja2TthGfaa74xdl1cOwUpj/OdaAncnlhzO+cjYFgUnkWyeQlpmDNduOPceGLdeOM8+OM89GWrqG1d2EpaYRVdGIf5+HvH0eAiWeUIaVodAdoaCTnm5FSwv/HvmfbsWVl4lX+dHTmx/T06xoTkvz71Yd5TMwfQbKb2B6DfR0K9Y8J5YcB7rT0mPLTwiRHAa5hjPINbxbr9U0DS3dhj3dhn1URqvnlanwF7rRd3mpXF5E04Yqal7ahekOxE3nwoaLwQChY06BM3RDZ0IaliwbZtDAV9tIsKwJo9iHqvSD2ertaAQ2tGyjVcMx3knWcTbSp1hwTTBhuJu8oJPf+S/EqdvJ1NLJ03KwGQ4CfkUgoNgRUJjBcEDPNKP/MY3oT1ToZoimTDTa/6krAw0DKyaaMtA1E2eegdLN0DxCb9Q63hV5QA8HxYQQQgghhDiApA12xdIdFtLnFJA+pyD6mOk1CJY1EqhoIljlRXkNVCCUlaQCKpSBFDlhju1bEf49+lCLaXxGE7XeSpqCjfhNHwHDh4r9Fz4RV5ih33Utfj40zzfUtcUMTWuGLhRMw8RiteL3+VBKYegGwfQg/vQgwbQAQVsQpUKZVKapo5lZmErDr8CvaTSggQoAQRSRk34LkIEiE4VOAAsedKrRUJoFpbzoQR+WBgtOjxW724IlqIGhoysN3dAAHTQNpYf7ouhaKDtNDy+byA13HZRFw8zWMPN0VIGOnp5DmnUCh1mP5UirhtUKVguhri967CIJLSRds5CTVkBe2hAKXEO7WOwvDcjrwvTxcnNzqamp6fbrhRCiqzRdwzE+i9x5Y7CdEApmKVNhVHsx3AGMej9ANABvzXOgpx+4y7MKmhi1PoLVPoLVXoxaP8SMEqnZ9FDALN+JdUg6uqN1oH5M+H9LfVXVMkhbt32EEEKI5JR10iiUz0h0M4QQnZASwa626E4L9jGZ2Mdk9vi8h/X4HONJwEUIIQY2TdewFqRhLUjr/jys+kHPQwghhBCdp6dZIS1lL6GFGFCkP4AQQgghhBBCCCGE6Dck2CWEEEIIIYQQQggh+g0JdgkhhBBCCCGEEEKIfkOCXUIIIYQQQgghhBCi35BglxBCCCGEEEIIIYToNyTYJYQQQgghhBBCCCH6DQl2CSGEEEIIIYQQQoh+Q4JdQgghhBBCCCGEEKLfkGCXEEIIIYQQQgghhOg3JNglhBBCCCGEEEIIIfoNa3dfmJub25PtGHBk+SWOLPueEwwGcblcQGi5Wq3WDh+P/N3Z+blcLgzDAMBisUTnFZnO6XTicrni3iMzMzM6P5vNFn3e5XJhsViw2Wzk5uZGX+twOLDZbDgcDjRNQ9M0bDZb3HQAmqbhcrnQdR2LxYKmaeTm5pKTk9NmuyO/WyyW6O+6rrc5DdDh35HPFmmXxWKJfn5d17FareTm5pKRkdGqLbHzOtDyF71Pln/iDNRl3/IYKZpFjpeR74DYY/7BzhNaf/8dzOsTuf22/M61WCxx38m99X4Q/z0W+50fu97sdjsOhyP6XGT6yPOx5wJOpxPTNHG5XNjt9lbnFZH3jLxeKYXD4QCI206AuHk5nc645yOPRdoS+7M/749tbb8d7RMtn4s9J2vv2OX1eqPLtuX5VFvniJHzMMMw2jxv7KrOnMd2dO7a1nLorK7Mo6Pzwf66DXZl+fT0MbU768Zms0WPHS23y7aOSV3ZfmP3A+jcuUB3ttGW+2Pk95b7AjTvg51tT0/QlFKqV99BCCGEEEIIIYQQQog+It0YhRBCCCGEEEIIIUS/IcEuIYQQQgghhBBCCNFvSLBLCCGEEEIIIYQQQvQbEuwSQgghhBBCCCGEEP2GBLuEEEIIIYQQQgghRL8hwa4+UFxczI033sjxxx/PrFmzOOGEE/j73/+O3++Pm66kpIQrr7ySOXPmMH/+fG6//fZW04ju+ec//8kFF1zA7NmzOeyww9qcRpZ/73r66ac5/vjjmTlzJosWLWL16tWJblK/tGrVKq688koWLlzIlClTeP/99+OeV0px3333sXDhQmbNmsXFF1/M9u3bE9Ta/mXJkiWcc845zJ07lwULFvCzn/2MXbt2xU0jy7/3PPPMM5xxxhkceuihHHrooXz3u9/lk08+iT4/kJa9HG+TV08dJ/x+P7fddhvz589nzpw5XHnllZSWlvblRxExlixZwpQpU7jjjjuij8l6TA1lZWVcc801zJ8/n9mzZ3PmmWfy9ddfR5+X9Zj8gsEg9957b/Ra+1vf+hb3338/pmlGp5H1ODBJsKsP7Nq1C6UUixcvZunSpdxwww0899xz3HvvvdFpDMPgiiuuoLGxkWeeeYZ7772Xd955h7vvvjuBLe8/AoEAJ598Mt/73vfafF6Wf+968803ufPOO/npT3/Kq6++yrx587j88sspKSlJdNP6ncbGRqZMmcItt9zS5vMPPvggjz76KLfccgsvvvgiBQUFXHrppTQ0NPRxS/uflStXctFFF/HCCy/w6KOPYhgGP/7xj2lsbIxOI8u/9wwdOpRrrrmGl156iZdeeokjjzySn//859GT2YGy7OV4m9x66jhxxx138N5773HvvffyzDPP0NjYyBVXXIFhGIn4WAPa+vXref7555kyZUrc47Iek19dXR3f+973sNlsPPjggyxdupTrr7+erKys6DSyHpPfgw8+yHPPPcctt9zCm2++yW9/+1sefvhhnnzyybhpZD0OQEokxIMPPqiOP/746N8ff/yxmjp1qiotLY0+9sYbb6gZM2Yot9udiCb2Sy+99JKaN29eq8dl+feuc889V91yyy1xj5188snqT3/6U4JaNDBMnjxZvffee9G/TdNURx99tFqyZEn0MZ/Pp+bNm6eeffbZRDSxX6uqqlKTJ09WK1euVErJ8k+Eww8/XL3wwgsDatnL8Ta1dOc4UV9fr6ZPn66WLl0anaa0tFRNnTpV/e9//+vbDzDANTQ0qJNOOkktW7ZMff/731e33367UkrWY6r44x//qL73ve+1+7ysx9Twk5/8RN1www1xj/3iF79Q11xzjVJK1uNAJpldCeJ2u8nOzo7+vW7dOiZNmsSQIUOijy1cuBC/3x+XSit6hyz/3uP3+9m4cSMLFy6Me/zoo49m7dq1CWrVwFRcXExFRUXcurDb7Rx++OGyLnqB2+0GiB7rZfn3HcMwWLp0KY2NjcydO3fALHs53qae7hwnvv76awKBAEcffXR0miFDhjBp0iRZz31s8eLFHHvssRx11FFxj8t6TA0ffvghM2bM4Je//CULFizgrLPO4oUXXog+L+sxNcybN4/ly5eze/duALZs2cKXX37JscceC8h6HMisiW7AQFRUVMRTTz3F9ddfH32ssrKSgoKCuOmys7Ox2WxUVlb2dRMHHFn+vaempgbDMMjPz497vKCggIqKigS1amCKLO+21oV0cepZSinuvPNO5s2bx+TJkwFZ/n1h69atXHDBBfh8PtLT0/nHP/7BxIkTWbNmDdD/l70cb1NLd48TlZWV2Gy2uJumkWnknKXvLF26lE2bNvHiiy+2ek7WY2rYu3cvzz77LJdeeilXXnkl69ev5/bbb8dut3PWWWfJekwRl19+OW63m1NOOQWLxYJhGPz617/m9NNPB2R/HMgk2HUQ7rvvPu6///4Op3nxxReZOXNm9O+ysjIuu+wyTj75ZM4777y4aTVNa3Me7T0+0HVn+XdEln/varkclVKybBOkrXUhetbixYvZtm0bzzzzTKvnZPn3nnHjxvHqq69SX1/Pu+++y3XXXcdTTz0VfX6gLHs53qaGnj5O9NftORnt37+fO+64g0ceeQSHw9HudLIek5tSihkzZnD11VcDMG3aNHbs2MGzzz7LWWedFZ1O1mNye/PNN/nvf//Ln//8ZyZOnMjmzZu58847GTx4MGeffXZ0OlmPA48Euw7CRRddxKmnntrhNCNHjoz+XlZWxiWXXMKcOXO47bbb4qYrKCjgq6++inusrq6OQCDQKgotQrq6/Dsiy7/35ObmYrFYWt0VqaqqapVNJ3rXoEGDgNDdq8GDB0cfl3XRs2677TY+/PBDnnrqKYYOHRp9XJZ/77Pb7YwZMwaAmTNnsmHDBp544gkuv/xyoP8veznepo6DOU4UFBQQCASoq6uLy0Koqqpi7ty5ffQJBraNGzdSVVXFokWLoo8ZhsGqVat4+umnefvttwFZj8lu0KBBTJgwIe6x8ePH884770SfB1mPye6ee+7hJz/5CaeddhoAU6ZMoaSkhCVLlnD22WfLehzApGbXQcjLy2PChAkd/o/c7YkEuqZPn86dd96Jrscv+jlz5rB9+3bKy8ujjy1btgy73c6MGTP69HOliq4s/wOR5d977HY706dPZ9myZXGPf/755/Ll0cdGjhzJoEGD4taF3+9n1apVsi56gAqPuvvuu+/y+OOPM2rUqLjnZfn3PaUUfr9/wCx7Od4mv544TsyYMQObzRY3TXl5Odu3b5f13EeOPPJIXn/9dV599dXo/xkzZnDGGWfw6quvMmrUKFmPKeDQQw+N1nmKKCwsZMSIEYDsj6nC6/W2ytqyWCzRrCxZjwOXZHb1gbKyMi6++GKGDRvGddddR3V1dfS5SKR54cKFTJw4kWuvvZZrr72Wuro67r77bs4//3wyMjIS1fR+o6SkhLq6OkpKSjAMg82bNwMwevRoXC6XLP9edumll3LttdcyY8YM5s6dy/PPP8/+/fu54IILEt20fsfj8VBUVBT9u7i4mM2bN5Odnc3w4cO55JJLWLJkCWPHjmXMmDEsWbIEp9MZrWsguu/WW2/ljTfe4IEHHsDlckVrRGRmZuJ0OtE0TZZ/L/rLX/7CMcccw9ChQ/F4PLz55pusXLmShx56aEAtezneJreeOE5kZmZyzjnncPfdd5Obm0t2djZ33303kydPblUoXfSOjIyMaJ21iPT0dHJycqKPy3pMfj/4wQ/43ve+x7/+9S9OOeUU1q9fzwsvvMDixYsBZH9MEd/85jf517/+xfDhw6PdGB999FHOOeccQNbjQKYp6Yja615++WVuuOGGNp/bunVr9PeSkhJuvfVWli9fHt35rrvuOux2e181td+6/vrreeWVV1o9/sQTTzB//nxAln9ve/rpp3n44YcpLy9n8uTJ3HDDDRx++OGJbla/s2LFCi655JJWj5999tncddddKKW4//77ef7556mrq2P27NnccsstrU7aRddNmTKlzcfvvPPOaFcXWf6958Ybb2T58uWUl5eTmZnJlClTuPzyy6MjKw2kZS/H2+TVU8cJn8/HPffcwxtvvIHX62XBggX8/ve/Z9iwYX3yOURrF198MVOnTuV3v/sdIOsxVXz00Uf85S9/obCwkJEjR3LppZdy/vnnR5+X9Zj8Ghoa+Nvf/sb7779PVVUVgwcP5rTTTuPnP/959DpO1uPAJMEuIYQQQgghhBBCCNFvSM0uIYQQQgghhBBCCNFvSLBLCCGEEEIIIYQQQvQbEuwSQgghhBBCCCGEEP2GBLuEEEIIIYQQQgghRL8hwS4hhBBCCCGEEEII0W9IsEsIIYQQQgghhBBC9BsS7BJCCCGEEEIIIYQQ/YYEu4QQQgghhBBCCCFEvyHBLiGEEEIIIYQQQgjRb0iwSwghhBBCCCGEEEL0GxLsEkL0uffff5/HHnss0c0QQgghhBDtkPM1IUQqk2CXEKLPvf/++zzxxBOJboYQQgghhGiHnK8JIVKZBLuEEEIIIYQQQgghRL8hwS4hRJ+6/vrreeWVV9i3bx9TpkxhypQpXHzxxVRXV3PzzTdz4oknMmvWLI4//nhuuukmamtr415/8cUXc/HFF7ea75QpU7jvvvv66FMIIYQQQvRfcr4mhEh11kQ3QAgxsPzsZz+jurqaTZs2cf/99wOQkZFBbW0tDoeDa665hry8PMrLy3nkkUe47LLLePHFFxPcaiGEEEKIgUPO14QQqU6CXUKIPjV69Gjy8vKw2+3MmTMn7rmbbrop+rthGBx66KEcd9xxbNy4kenTp/dxS4UQQgghBiY5XxNCpDoJdgkhkoJSiqeffprnn3+e4uJiGhsbo88VFhbKyZMQQgghRILJ+ZoQIlVIsEsIkRSeeOIJ/vCHP3DppZeycOFCsrKyUEpx/vnn4/P5Et08IYQQQogBT87XhBCpQoJdQoik8NZbb7FgwQKuv/766GN79+5tNZ3dbsfj8cQ91rIoqhBCCCGE6HlyviaESBUyGqMQos/Z7fZWd/+8Xi9Wa3z8/bXXXmv12uHDh1NYWEggEIg+9r///a93GiqEEEIIMUDJ+ZoQIpVJsEsI0efGjx9PZWUl//nPf1i/fj27du3iG9/4Bp999hn/+te/+Pzzz7n33nt5/fXXW7321FNPpaamhptvvpnPP/+cJ554goceeigBn0IIIYQQov+S8zUhRCqTboxCiD537rnnsmbNGu666y4aGho44ogjePDBB6mvr+exxx7D5/NxxBFH8NBDD3HCCSfEvXbBggXcfPPNPPbYY7z11lvMmTOHv//973z7299O0KcRQgghhOh/5HxNCJHKNKWUSnQjhBBCCCGEEEIIIYToCdKNUQghhBBCCCGEEEL0GxLsEkIIIYQQQgghhBD9hgS7hBBCCCGEEEIIIUS/IcEuIYQQQgghhBBCCNFvSLBLCCGEEEIIIYQQQvQbEuwSQgghhBBCCCGEEP2GBLuEEEIIIYQQQgghRL9h7e4La2pqeqwR2dnZ1NXV9dj8RMdkefctWd59S5Z335Ll3bdkefeN3NzcRDehR5imKdtLPyD7feqTddg/yHrsH2Q99g+dOVdLiswuXU+KZgwYsrz7lizvviXLu2/J8u5bsrxFV8j20j/Iekx9sg77B1mP/YOsx4FD1rQQQgghhBBCCCGE6Dck2CWEEEIIIYQQQggh+g0JdgkhhBBCCCGEEEKIfiOpg13r9n/Ga5seSXQzhBBCCCGEEEIIEcNX6CZQ0ZToZgjRpqQOdn1dtop/rLiJCk9JopsihBBCCCGEEEKIsKavKvF8XproZgjRpqQOdp048VxMZfLhrlcS3RQhhBBCCCGEEEIIkQKSOtg1JGMUg1wjKKrdluimCCGEEEIIIYQQQogUkNTBLoCRWeMortud6GYIIYQQQgghhBBCiBSQ9MGu4VnjYFMTNa/sSnRThBBCCCGEEEIIIUSSS4Fg11hGbRhK8TXLMGp9iW6OEEIIIYQQQgghhEhiSR/sGpoxig1TN0JAUfd2UaKbI4QQQgghhBBCCCGSWNIHu/LShrBvaAlqjI3aV6V2lxBCCCGEEEIIIYRoX9IHu/LTh4AGjfNMmtZXoZRKdJOEEEIIIYQQQgghRJJK+mBXbtogAOoHezAbAhjVUrdLCCGEEEIIIYQQQrQt6YNdaTYX6bYMqvNqAPAVuhPcIiGEEEIIIYQQQgiRrJI+2AWQmzaY0uxSAPx7JNglhBBCCCGEEEIIIdqWEsGu/PTB7HMVga5JsEsIIYQQQgghhBBCtCslgl05zgJqgpXYRrgk2CWEEEIIIYQQQggh2pUSwa4sZx51vmrsozMk2CWEEEIIIYQQQggh2pUSwa4cZz513mrsYzLxF0mwSwghhBBCCCGEEEK0LSWCXdmOfExloIZYCFZ4MX1GopskhBBCCCGEEEIIIZJQSgS7spx5APgHmQAE9jcmsjlCCCGEEEIIIYQQIkmlRLArOxzs8ub7AAiUeBLZHCGEEEIIIYQQQgiRpFIk2JUPgDs3FOQK7JNglxBCCCGEEEIIIYRoLTWCXY5QZldNZg1o4JfMLiGEEEIIIYQQQgjRhpQIdmU5cwGoN2qwDk6TboxCCCGEEEIIIYQQok0pEexyWtNxWtOo81VjG+GSYJcQQgghhBBCCCGEaFNKBLsAshx51HmrsQ93Sc0uIYQQQgghhBBCCNGmlAl25Tjzqfc2Z3YppRLdJCGEEEIIIYQQQgiRZFIm2JXlzKfWW4VtuAuzMYhR5090k4QQQgghhBBCCCFEkkmZYFe2M496XzX2ES4A6coohBBCCCGEEEIkgPS0EskudYJd4ZpdtuES7BJCCCGEEEIIIYQQbUuZYFeWM48Gfx3aUAcAfhmRUQghhBBCCCGE6HuS2CWSXMoEu3Kc+QA0pjeiOS2S2SWEEEIIIYQQQgghWkmZYFeWMw+AOl8V9vCIjEIIIYQQQgghhOhjUrNLJLmUCXZlO8LBrnDdLgl2CSGEEEIIIYQQiSXF6kUySp1gVzizq95bjW2EC//ehgS3SAghhBBCCCGEGIBUO78LkSRSKNgVqtlV56vGMT6LYHkThtuf4FYJIYQQQvSsJUuWMGXKFO64447oY0op7rvvPhYuXMisWbO4+OKL2b59ewJbKYQQYkCLC3ZJtEskn5QJdmU5cgGo81bhmJgDgG9nfQJbJIQQQgjRs9avX8/zzz/PlClT4h5/8MEHefTRR7nlllt48cUXKSgo4NJLL6WhQTLdhRBCJJjEukQSSplgl0W3kmnPoc5bjWNiFgC+7XUJbpUQQgghRM/weDz89re/5fbbbyc7Ozv6uFKKJ554giuvvJKTTjqJyZMnc/fdd+P1ennjjTcS2GIhhBADVVydLgl2iSSUMsEuCI3IWOetxj4qA82h49tRm+gmCSGEEEL0iMWLF3Psscdy1FFHxT1eXFxMRUUFCxcujD5mt9s5/PDDWbt2bV83UwghhIgjBepFMrImugFdkePMp9ZbiWbRcYzLwrtDMruEEEIIkfqWLl3Kpk2bePHFF1s9V1FRAUB+fn7c4wUFBZSUlHQ439zc3J5rpEgYWY+pT9Zh/yDrsZnpC2K4qgDIzclBd6ROaEHW48CQOlskkJs2mOL6nQA4JmbTtLE6wS0SQgghhDg4+/fv54477uCRRx7B4XC0O52maXF/d+ZOek1NzUG3TyRWbm6urMcUJ+uwf5D1GM/0GXg8HgAs1bXoTkuCW9Q5sh77h84ELFOqG2N++mBqmsoBcE7Lw1/oxqiXERmFEEIIkbo2btxIVVUVixYtYtq0aUybNo2VK1fy5JNPMm3aNAoKCgCorKyMe11VVVX0OSGEEKJPqXb/ECIppFxmV523moDhJ312Piho2lBFxtHDEt00IYQQQohuOfLII3n99dfjHrvhhhsYP348l19+OaNGjWLQoEEsW7aMadOmAeD3+1m1ahXXXHNNIposhBBCNJNYl0hCKRXsyk8fAkCtt5K8WaE7mY3rKiXYJYQQQoiUlZGRweTJk+MeS09PJycnJ/r4JZdcwpIlSxg7dixjxoxhyZIlOJ1OTj/99EQ0WQghxIAXOxqjRLtE8kmpYFdu2iAAqhrLGDRoOI4JWTR9VZXgVgkhhBBC9K7LL78cn8/HrbfeSl1dHbNnz+aRRx4hIyMj0U0TQggxwEmsSySjlAp25aeFMruqw3W70mYX0PDZfpRSrYq2CiGEEEKkqieffDLub03TuOqqq7jqqqsS1KKQxkADVs2K3epMaDuEEEIkmGrndyGSREoVqC9whborVnr2A+CaP4RgeRP+3e5ENksIIYQQYkBYXvQeX+x9N9HNEEIIkUwktUskoZQKdmU787FZHJQ1FAOQcdRQABqW7U9ks4QQQgghBgzDNBLdBCGEEIkWG+CSWJdIQikV7NI1ncGuEZR79gFgH5OJbVQGns9LE9wyIYQQQgghhBBiAJJgV5z97j3R3mgicVIq2AWEgl3hzC4IZXc1fF6KMmUPE0IIIYToaW5fLfW+mkQ3QwghRBKJ67ko3RjjbC5fw/rS5YluxoCXcsGuIRkjo90YAVxHDcWo8eHdIidhQgghhBA9bVXxR6wu/jjRzRBCCJGklCHBLpF8Ui7YNTRzFFWNpfiDXqC5bpd0ZRRCCCGEEEIIIfpATHzLqPUlrh1CtCPlgl0jsyagUOxz7wbANiQdx8RsGpZJsEsIIYQQQgghhOhLwSpvopsgRCspF+wanTMJgL21O6KPZSwcimd5KaZPRgcSQgghhBBCCCF6VUydLtMr1+Ei+aRcsGtE1jg0NPbW7Yw+lnnCKExPkIZlMuKBEEIIIYQQQgjRZ2SwOJGEUi7Y5bCmMSRjFIW1W6OPuY4cgp5lo/6dvQlsmRBCCCGEEEIIMQCodn4XIkmkXLALYFLBTLZVfhX9W7dbyDx+JPXvFElXRiGEEEKIXqBkaHkhhBAtaaAks0skoZQMdk0pmMO++l00+Oqij+V9dyJGtY+6/xYmrmFCCCGEEP2Uklv3QgghIsJfCZquSWaXSEopGuyaC8DmijXRx1xHDcV5SC6VD25EGWaimiaEEEII0S9JZpcQQohWdE1qdomklJLBrmmD52G3OFm97+PoY5qmMfiXs/BuqaXqia3tv1gIIYQQQnSDXMwIIYQIi9wA0TW5GSKSUkoGuxzWNOYMO5qVxR/EPZ516mgyjx9B6R/WUP9BcYJaJ4QQQgjR/0g3RiGEEC1pOnIvRCSllAx2ARwx8nj21u2gpH5P9DFN0xj514U4p+aw59IPKb72c4LV3gS2UgghhBCifzCVlIkQQggREo1v6bp0YxRJKYWDXScAsLL4/bjHrTkOxj9/EoN+NoPal3ax/cT/4t1Sk4gmCiGEEEL0H9JNRQghREuS2SWSVMoGu4ZnjWFc7iG8v/OlVs/p6TaGXn8oE988Dc2qs/vC9whWSYaXEEIIIUR3STdG0RGtbh80yQ1mIQaM8A0QTddQktklklDKBrsATp9yMVsq1rC9akObzzun5DL2iRMw6v3sX7y6j1snhBBCCNF/SLBLdMSyfx3WPZ8nuhlCiL4S+UrQNcn8FUkppYNdJ0w8D6c1nTe2PNHuNM4pOQz66QxqX9lF06bqPmydEEIIIUT/IaNtCSGEaEnTkG6MIilZE92Ag+GyZ3L8+EV8sPMlTh1+M1+vzaS8QuHzQX6+xoSp9axsupOdw9fxo/QLKL9vPWP+eVyimy2EEEIIkXIks6tv6BVbMDOHgzMr0U0RQoj2xWZ2Ebohomla4tqThGSZJFZKB7sMQzHUcwne4FP85J5nMTZfjt0OQzMqsagSShZci5a3iTzm8uG8jzjxzePx723APioj0U0XQgghhEgpktnVB0wDvWonWs0ejMnfTnRrhBDiwMLBLkwFFgnsxDKViUWzJLoZA1ZKdmMMBhVvv6u45FLFA3fOwF59DJlH/I0nHt7Dsqt+zQsLj2HEyadjzV/Pd+p+Svlzr/G/3PGYmsmm+z5NdPOFEEIIIVKOZHYJIYSIiilQH/o7gW1JWrJQEillgl2NjYrt2xVPPaP47kWK2/+gcDjgjsUa91+6GENz8/D/TmFX4btcMzyXNTaDmx3TuVF7ivdufpRjpvyar6ZsxfdKCW+9FpC7k0IIIYQQXSHnTr1PmYlugRBCdE0k1iUjMrZiyjE9oVKiG+PXGxU//6XCMEJ/z50D11ytceQRhPrAqilckzabuxtW8wMXWJrc/PaYv7Nw/NkE3rqe3C//xLWnj+Ihx2DSb7fx8h/W8r+Vh3Hj9ZCZKamWQgghhBAHIpldfSFFl7Fc0Akx4KgWNbtS9fDVm5QcGxMqJYJd48bCb36t4XLBrBkwaFB8gMq65Q3O3LeTqUf+hk2DxzJ98BEMzxoDgO/bf0B3l+B8+3pO+c6fWfPwF5yugty8fB6X/xTuugPGjpGAlxBCCCFERyTY1QdSdRFL1p8QA5YWqdMlxwEgvr5lsnxvGoairByGDxtYcY+U6Mbocml853SNb31TaxXoIujD/ulfMIbNYcSC33DixPOigS4ArHa8Z/wd5chk/Md/Ys9JZeSUWrnvsnKamuAnP1V8tiw5NkIhhBBCiGQld6j7Qoqek8q2IcTAI5ldbYoNcCVLN8btO2DjJqiu7ngleTyK2tr+syJTItjVEdv659AbSvF942rQ2v44ylWA97Q/odXsYdr0/TSkN8A7y3l4ica4sXDjzYo3lvaflSqEEEII0dOk3mkfSNVlnKrtFkIcNE0LBbukZldIMmZ2BYOhn35/x9N9vhxWfdn77ekrqR3sCjRhW/FvgmOOxhx5eIeTmiMPx3/0/+ObJetZedhq1Ef1ZFTX8fd7Q7W/7vqj4rkXkmNjFEIIIYRINsly0t6/pegyTpLsBSFEH4oEdSIRhQQHu2qaKthetSGhbQiJCXYlyY0APbyOjAF2qE7pYJd183/Rm6rxL/h5p6YPHP5j7OOOxTjsC7wOL/vv+hKHQ+MPt2uc8C24/wHFk08nxwYphBBCCJFocXeok+SkvV9L2aBRqrZbCHHQkqQb49qSz9hbuyOxjSD+xlCydP+PBLvM5GhOn0ndYJdS2NY+iTFkJuawOZ17jabjPfkuvuly8d43PqTh/X24P9qH1apx840aJ38bljyoeOU1OZkTQgghhIirPSIBDdEeCYQKMfCEd3stEuySboxAcnZjtESCXUZi29HXUjbYZSn6AkvVTgJzvw9aF0YVSMtl1ikPsO6IZbgHV7Hvd8sxm4JYLBrX/1bjGwvhL39VvPt+cmyYQgghhBCJEp/ZJcGuXpeqQSPZNoQYuCI1uxLcjGSULAXqdUvoZyAIweDAWVMpG+yyrX0K01VAcMrJXX6tZeQRLBw+n2dOe55AsYfyv4SqsFmtGv93s8ahc+GOPyiWfT5wNgQhhBBCJMaSJUs455xzmDt3LgsWLOBnP/sZu3btiptGKcV9993HwoULmTVrFhdffDHbt2/vg9Yl36hS/VuKnnvKtiHEwJUkNbsiEv1dlczdGHcXwkefJLQpfSolg11abRGWXR8TmHUBWOzdmsfpRy1my5hC6md9SeWDm/EtWwWAw6Fx5+0ak6fA7xcrtm9Pjp1WCCGEEP3TypUrueiii3jhhRd49NFHMQyDH//4xzQ2NkanefDBB3n00Ue55ZZbePHFFykoKODSSy+loaGhV9uWjCft/VrKZnalaLtTgKlM9rv3JLoZQrRLS5KaXREJ/65Kwm6MXekI15+kZLDLtu5psFgJzvput+cxIX8684Yfy0NnLcPi9LH/6vewv34NWvUu0tM17vmDRlYWXH+ToqYmOTZSIYQQQvQ/Dz/8MIsWLWLSpElMnTqVO++8k5KSEjZu3AiEsrqeeOIJrrzySk466SQmT57M3Xffjdfr5Y033ujVtsV2YzSTKaChFHr5Jgh4E92SHpZEy7grEn1x2Y/tqd3K5vI1VHhKEt0UIeJFR2MMd2OUzC4gPsBlmMEEtqRZMn1996XUC3b5Pdi+fpnglFNRroKDmtV5M3/KXr2YvT8xce8fSd2bJbgeO4205y5iUNFz/OWmUurq4He3KAKBAbqFCCGEEKJPud1uALKzswEoLi6moqKChQsXRqex2+0cfvjhrF27tlfbEpfZlUwF6ptq0Kt3Yyldn+iW9KwUvSLRUjVIlwIChh8AX7C/BXYPjt8v21zS0CSzK+79YxZEg78ugS1plqJfLQfNmugGdJVt4yto/gYCcy8+6HnNG34sC0Z/m/v0P3LP9D+z58uzyLj4JOzbX8L5wWJmAO+cOYXn1x3H8389jot+MxMtUt1NCCGEEKKHKaW48847mTdvHpMnTwagoqICgPz8/LhpCwoKKCnpONsjNzf3oNrjC3pxuVwAZGZm4Gp09ch8D5ayG6gqF2S40BPclp6k7CbK5QLdGve5Er28D0RZA6F2Q79aHz0pJ1CBqtqFfkjX6g3nBvKoCZaRkenqle3g011vMiZ3EqNzJ/X4vHvLzl1Btmw1+NY37Tidfds/K9n3xb7k9VjB5SErLwfd1UR2VhaO3IyEtSfyXZWdk4PTltbhtL25HpsCDlyVobZgN5Nim8msDOJyNQ/FmJvraHM6l8sHQE6OHa0f9H1MrWCXaWBb8wTGiHmYQ6Yf9Ow0TeP/LbiLH718DK+c/Bpn/Pl49q8+lMG//CFazW6suz7BtfMjLpvyEDpLaLxvMJajLiEw+wKwu3rgAwkhhBBCNFu8eDHbtm3jmWeeafVcyxNP1YlbtTU1NQfVHl+wCY/HA0BtXW3094Od70Frqsfq8aCUAyPRbelJjbWhz6Vbo58rNzc38cv7ALSGGizhbSNYXd0/CsQEmsDqAO3gO8Lk5ubSsHMlAMEursuGhgY8Hg+lVSXk6EMOui0tlVeXUl5dSuaEg+sx05d27lR4PLB/v4ecnL7b1lJhX+xL/joPjR4Pyl1Ho8cDNbXYnIGEtSfy/VRdU0Warf1r9TbXY6AJUGBLP+h2NAU80baUBUqocSV+m6mrC+0zETU1jW1O5/GEziuqqjxYLMl9HO9MEDGlujFadryHXrcX/2E/6rF5FriG8ZuFf+bDzLepPMpDxQNf49/bgModR2DeD/Ge/zgNV37GgzX38FXZJByf/on0J85EL17dY20QQgghhLjtttv48MMPefzxxxk6dGj08UGDBgFQWVkZN31VVRUFBb17gdrlAvV93Vein9WKStnugLHroR+sE81TiXXnh2g1iS8Mr4cv10rqCyl1F/XovBNd26i7IiPLDdSuWUkjvAIiBeqTZX10pyi8deeHWHd+1KPt0DStze9NU5nUNlW28Yre05l1Y5qx3/e92Jg+lDrBLqWwr3oEM3ccxvjjenTWx477Dmcecin/mP9XDAz2XfdFXIE9PT2Hk68+nZu2/pvrtj+FqVlJ+88PsG58pUfbIYQQQoiBRynF4sWLeffdd3n88ccZNWpU3PMjR45k0KBBLFu2LPqY3+9n1apVzJ07t9fbFmHG1OxqK6tMr9iCdeubfRvs6Ccn5FGpeoXR34JddXtDPxuren7mXVw+Rsz0jQFPB1N2XbIUz+6qSLDLTP1NrX+IZHIOoAL1hqHigkOxIsE2DQ2zjS+pbZXrWFPyKU09vD93pHPBrq5NnwpSJthl2f0xlrIN+A+7tEfSiVu64oj/I2fsMN46/X0aPtvP/tvjM7dyczV+f5PGe1vncnvlfzDGHI3znRuxrn+hx9sihBBCiIHj1ltv5b///S9//vOfcblcVFRUUFFRgdcbKkitaRqXXHIJS5Ys4b333mPbtm3ccMMNOJ1OTj/99F5tW3xmV8fDqUezYEyj1XM9TYtezPSTM/KoFP08cVdGKfoZYoWDQJq/oRfm3bX9w1DNASmn9eC7WLU371Qiwa4kEdnV9eQKdvVFgfoPP4ZPP2vv/UPLQdctbbal3lcLgKF6/7uyuU0HniZ2f+ov+1Zq1Owy/Ng//TNm7jiC08/ulbewWxz8csGdXF1zNvPOWgAPbcY+3EXBZdOi0xw6V+OHl8Ajj7mYOfs+zhz/axwfLEZlj8AYc3SvtEsIIYQQ/duzzz4LwMUXxw++c+edd7Jo0SIALr/8cnw+H7feeit1dXXMnj2bRx55hIyM3i0GHJfZpVpkdsWU89i6TaFvURwymb65JRxpS3+5/RyRoM+jTIXpDmDJtnd3BjG/p/460cIBKc3vCX2enqxB1sULcTPugvjglq1pKnS9+bOkajfGyOow+i5WIDqghWs7Jcvm1J1ujN3hb6c8WeT9dfQ2s6AjATCNvquJ1bIZLY8Focfanz5VpUSwy77s71iqdtJ01r9A770mzxq6gOPHL+Kv2j3c6/0n+29bjSXPSe6i8dFpfnAxrPsK/vRXG9P+cQ+T3RfjfONqGr//Mip7RK+1TQghhBD909atWw84jaZpXHXVVVx11VV90KJm7dfsij8TLtoLwwPhx/ribnX0TLxnz8iVUmyu+JKC9GEMzkjAeV2CrjCaNlThL3STddIo9LRunGv3s26MxHbvUwZoPXj90cX9w4y5Aj2Y4FQgoPj4fzB5omLMmNBFbqQbY3dGXdu4SREMwuxZfV/EOpLZJcGuBIscriKFzA/i+KUMk7o39pA2uwDH2MyDa1aij0GRWmaa3mb2ZCLa13LVtLWq+mNmV3J3Y2yqwb7sr9hXP4x/9vcwxh/b62952WG/Q+mKV85/g4yFwyj+zTI8K8uiz1ssGrfcpOFMg5vuSKf+5PsAhfOta+O/GIUQQgghUl3MGXHAbL6N3eGd8744kY9mdvXse+13F1Hq3sveuh09Ot/OS0ywK1gV6jKrgt1dnjHtTvSFZk+I2dZ7/Kov6EMvWQdG50atM1QQhzUNAEX32+L3h36WlMbMOxLs6kaGScl+KK/odnMOSiTYFZRLr6QQLVBvHESwyx/atn3baw+6PYnOWIx8P1o0vc2oUqSOV18GvToT7OqPweOkDnalP3MB9hVLCExfhP+4G/rkPQdnjOD8mT/l3aL/0HR7DvYxmRT9/H/RkwCAgnyNW36nsWs3/PWJ4fhO+D8sJWuwrVjSJ20UQgghhOgLtXUmPn/orNhvNJ8LtdU1A8KjOfVlsKsHgkOVjaV8uPMVvMFGvMFQweA0W+92D21fgvqORN62u931YreH/tD/xQg21wju4UxFS+V29Pp90SL4B2IqA2u4Z0t3L+L9/nb210h3qh7oprljp2Lb9r5Z99KNMbG0xiq0hvLmfb0nanb14HGjve+nel8NxXW7Wj/RSwkrumZpc5+NBLn6qrtl6D3j/24rhi+ZXX3Me8a9NH7/JXwn3Q4WW5+97/kzf05++lD+9tX1DLt/AUatn72/+ixuhMbDD9P4/kXw2uvw5t5TCEw7C/vyf6LvX99n7RRCCCGE6E1r1im2bQ/97jd80cfbLFCPCl189kGB+uiZ+8FcKxgBCHjZXx8qrO/21UYzXcw+LBwcJ1GBosg5bjffX4vr9pf6V0maGURZHaE/euDzBM0gK2o2UBdo6HLwzDANrHqollp3MkH2Fis++RQaG9uYt4p0Yzz4S8LdhbCn6KBn0ymRzTQowa6EsBQtx1K8Knr81XqkG2PPHfvMdjIgVxd/zLbKr1o/YfRssCs6GqOmt/ldGQmA9WUGWmcyu5qamn9PkrEGDlpSB7vMwdMwB0/r2aKQnZBmc3H10X9iZ/VG/lJ2IwW/n03DJyVUPrw5brof/1Bj7hy450+KreNvRGUMxvnOjRD0tT1jIYQQQogkUOouImh2phtVTDdGwx/zcHuZXfGv6TU9kNll2bca684PovPQ0KIX/70e7DINtLp9bSzHBGd2dffaK3ZbSlSgsKeocHaiJVysvweCt00BDw3BRhqMxm4VqLfoFjRNw+xGMKG6OvSzwdP6uYPpxhjrs8/7druNZJ10NbNLq9vXPGqs6FnawcWFezLY1eWgcA9ndkVHY9QsHRao78vjfWeCXXX1sRO0PR+jIYBR52/7ySSU1MGuRJo/6gR+ceQdfF70Ftc7rkB900XZ3Wto2lQdncZq1fi/mzVcLrjxNhd1x92BXr0T+7K/J7DlQgghhBDt8/jdbCr/ks3law44bexd6bhujG2dCavQRajWJ5ldXa/ZpdUUohevbv67MXxOFwinvGgaRrjtvR3sshSvxLJ/HXjr4p9IVKwrfOWjDJOGL0ox6rt4MRNTf6pP1n9vCgfumjO7Dv7zBMJZkUqpLvcPMswgFs2ChtatzC6LJfQz2EZsO9qNsY1gV2Ojwu3u3AYZmxHSF7ob7LLsX4el7Oueb1AP2VW9mbKG4kQ3o9OiARMNNIsOxkFEu2KCXcFqL0Zt95NHNpd/2W5Xxjb1eDfGSLArFGpp2ZZEZHa1POy0dRiqrun4eQD3B8W4P97Xcw3rZRLs6sBZ037Mn095GYMgNx3+W7wuP3t/+SlmY/O3RX6+xuLfa+zbB7c9Mx//7Auxffko+r4Dn0AKIYQQQiSKJ1B/4IniRmM88MVDMEgfdWOLBLs6f0GjNdWgNVZG/1bO7NAvkYCTUtHMLqOXC5ZEAm1ay0BKoroAhvusBKu8BMubaPq6+gAvaCG22HoKd2PUaovQPOFtxBIKdvVE8M7rb6K8QtHkNbs+GqMysOhWdE1vt3tWR6zhYFegjev5jroxLvsClq+Eor3J158pstv3t5pdhTVb2Fi2KtHN6IKY7VGn+5mhxGd2NXy6H/cnJd2eV9AM4vG3//3W8rtMO0CwyzANSuoLO/3+kflHauG1DGr1Za2u9rT86qyqUjQ0wIjhbT/fUwxDseFrhc/XN8tAgl0HMHPokTx89iecevjFPHzGI3i311L0y09RMZHr2bM0rviJxocfwTMVV6OyR+F854bmO4VCCCGEEEkicqJtdOJudsvTUVu4a1d7J+umSR8VqI+8f1fu3huhwEUkeKGH68GGAzWGMqKZXW5fDR/ufIXapsq25tRzWi2rRHdj7G7NrgAq2u2v7e0qYPgTPkpahFazp83uiZbSDVhK1ob+sIY/Tw+0ubreh9cHpeVmTL25Dpa13wM+NwCGMtE1PVT/5yAyuwJtZHZFuzF2UDJm6zYIBBJ/cR6ru5ldycxIxYzIaLqgBrrWtWyqlg4mK6wtHWzTrb6/DrDsKxtL2FKxloYOAmhtzd+iWcJ/twh2hZdTMtXs8oYTt/Pz236+p1RUQmkZbO+jAY8l2NUJdquTK+ffyrcu+D4vnfIq7neLKb0zPnPre9+F478Jf/93GqtH3o5Wuxf7p/cmqMVCCCGEEG2LXDB3tWYXxAS72jwTVqHrlb6o2RTtxtj5l0QzdKJZSCZBZVDvD2V2GaYRXSaRi5AKT/ezCzolNoPM8GMp3dC779duO8LdGLt77WUEwJZGRzP5tHApG0qXd/MNepC3DkvZ12ie8jafjmQcKEvrboyBgGLPnq5fBRoq1CUraKpO7R/WXR9j3f2/0NsrE03TQ5ld3VhBkaQtfxs9UyMBlgMFKWpiujdVVim83q5lfPa0/pjZ5TP6uC9oT4gJbGu6dlBVzaOZXW0EqYLV3i7XiTI6+H5rFVg8wD7pC4YiQaYyCJQ1EvR2rtuj1k43RgCfX+H19c0GvGuXoqLFfZuWqyrydyQ43msF6vv4cCHBri44Z/oVpF84kk+O/JTKf2+i4p/Nfb41TePG6zQmT4L/99d5VE64BPu6p7AUrUhgi4UQQggh4jVndnV8oq2UanUH3KJZ25w2OvK8QY+MWW4YihUrFXV1bZ8Za+Gsly6dOYe7bBEptG8abHLvxB++kHH7a3D7auPfpwdGqWtLlb+OhmAjYKJXbse69c3m7nMJEB1xPJJd0dV65UagucZVG5ldkSBiVWNZl2a7pWItu2s2H3jCLtAiA0m12E69TSblFaHRR5uaVHNmV8x0W7fBth2hLj8ffqwoK+/c9ucPv2fQMLtVoF4nktnV9SvFyNv52ogVBMxILbGO2+RuCM9LKdaugxUxPe0SEXCKrJKuHGpMr4FnUwAVTK4stQhfMAWDXZEbBxqhzK6DKDIfWS+mp3WQquHT/QesE9Vy32j5/Vbp2R8zbcsCVh1vxJGRiA1fAM/yMjyrKjqcPkKPZHa1sX/t3g3LVyr8/oPbHv17G+J6nLVl1+42HmwnuS0S7OqtpDPVfkyzV0iwqws0TeOXC+5i9bkb2ThvK6V3rqHy0eYvYKdT464/aGRmwI+f/SWBrHE43r0Rmmo6mKsQQgghRN8xY07sI4GetqejVV8Gix4KdrXqBhIJdinaPEsOlDZ26ULd0wj17lBwoSWtvgS9PpRxdaBaK3HCn1uLCXbVBRqIFJrZV9f6iqAro9T5DR8f7nyF8oYDFO81DdbVbWFFzQZQJnrlttByNgLU1SlK9ifuYrzbF6tmEKytM7s8HoXHo2gMhKIleheDhyX1heyu3tK9NrUnEuyKyeYIBhWfLTMoC8fi/H6iNbtip4tkR3l9oSDPtja2z7b4/OGLZVN1uQuuqUx0zYKO1qo7VEtGrY+mzfHXHZHrYF8b9b4jF/GxAzKYXqM5+BmZR/jpSNNjs8R6ubxdmyLt6Mx7Byqa8G6txbe7Hl+Zia/k4KJzSima1ld1fRCHA4gEu7q6jySSZvRcZler1zZWoVds7cLL4zeGSD26iD2129qdtqPMLo/fHV03kaBSsKbj4vnNozGG1qVWuh7NHR/oD9XQM3G7aZ/RcfZ1oKyRxjUVeLfUdtyeNh5rVbC+i5ld3c3o7Otvt9TZm5JEms3Fb4/9G4+d9jhl8+vY//tV1L1RGH2+ID8U8CqvdfJ/X9+F5qnE+cbVB9xYhRBCCCH6QmygalfNpnanKy1rXWskWoOkVQEQ0FChi9AWFxL+Eg+eFWXUbaxjb3HnTnU102Bw/adY/LWtn/O1qJvS2ZNus0VmlzKxapYOr5g7qmXUUoMv1B2yxF3Y4XT+za80/9GisHvRXqiq6vRb9pxIGanuZL0oFQo6hjOhYgOQny8P/W8KB7sckYBYIhnxwS6fT/HRJ6C1LCLdxmiMLbvP+Tt5eu8NXyybkWUFaJ2MEikUWrhmV7vdGJVCq9mD+3/78G2rjQtWtZcFtaF0OZWe0nC7Qk8qU1H/ThFN6+KzDDvKpEpEsCuy/DtT5snzeSneLTVottBlr3mQMSqzIYBvdz2NazqX3dNZgXDDIjcUUoEKH780XQtFFRQYngDuT/ZhdrGLXsvsJL22CL2q84WdWn5Xteym3xTwtDtt3HdWzPeJYQZZsff96AiZwfAoD4EDFFePfMdGA5d1xVj2rW5zumA7i0lrKMe6/V2IjBrc1vuEl7EKdH0nbPUVHp5FZECLA36tdjNDUjK7UsD0IYdzzuwr+eOJd2HMtrP36mX4djYPHT15ksb/3aLx9voZPFR5G9a9y3F8/Ifeq/QmhBBCCNFJsRfMwXYyo5RSbNoMJuGT+2Dk5D1ScDe+Zk/k2lq1UbPLbAhddKz70mTL1s7dEdb89diNejIb2grGtThL7mx2V6tglxEO3rXfnq4EuyIXqzbd3u40/kAjX1Svb55/TLBL61QNtV4W7EbkIry+lW4NFYhqIyATudC0R7Kl+lhJfWFzMfZoZldovUfqUWkxmSBFe8EbbN2NMbLp+truCdkuvz+UQRmfEXngFyulUEqha1qHBeq1htJQHTJ3aXjWofcJVDRhbG07eloR260r0rW5LtzdsjK+S11Hwa5EdGOMZnZ14b01PdxNzqe69sKW7x0OLGiWnr1a71wNxSQTGeLToqGFuzH6ttdh1PoJlnZxoLbwNquXbkCrb66VaPrj15XXq9i6TbX6HmmV2RWzjg3TiGYxtjVt/IirzfP1GfGZz2b4827f1vo7w7+vgUD4M0f2p1A3eBUXXNu5S/H1xnBdQKVCIxi3QWuqBUBvbP/uRySoreld3xZbfg0bZnM5gtC8D/D67mYBS7ArNfzg0N8ydvBU7j7lDygHFF/7RdxdlIVHaVx/ncY/Pj2d97yXY/vqOez/u0cCXkIIIYRIqMgFs67pNPrdbV5kRS5gI92btoR7k0SzDmLOZ2K7O5oqPkNGr9yGWVtJ8T6FVtMIAaNzF8fhiw8TSyc+UOciDlqrbowmVt0Seq+gNzQKn9/TwRw6FunqYrXY2p2muK45U8GmW5sDb9Aiy6tnzheNho4voFXQpP7D4ua/IxkCXbkQiURANB0VWZ5hQ+o/oaBhBYZpoPndBAMNfTrqnC/YRFlDMVsq1rKrOhw4DS/z6PYQvhrSiG/X7qLQetTayOxqq0vggdoBYMYGuDqxHCL7n65ZOi5QH+lOFh4dTxmhYIDn81LU3nos5R31lWoOqgWrQh/Mkh0flIys4rY2y7ZGeext3anZpZpC6135QavbC4H2u3B3OB9/+E1t8ZfRW7cpKqu6v99G9guzhwb42Fm1EY+/4/XeLTHboDIM0MLBFk0jWNGEvyj8nuFjiAqYeLfWHvAmRzSr1AyiNTR3+VMtMsS+3hgKRte3HBixVc2u5g2zKRh/XG/djbHtzK6WddTc9UFqaxSaCpLduAmUwrezjtrXdtO4ugLPijJqmirYXP4lEMqE1uoNvHvig13NHZlNjHaCXcoS/q7tKAgaCTh1I6LTspuiUqDrof9tPd+qfZ0cPVOr24dWWxT9u7tlIbtLgl3dZLc4uPOkZ0kbks2zJzxH46py9jy8Mm6aU76t8f+u0vjtu/+Pz4xLsH/5GI63rj2oEykhhBBCiO7w+RS7disC4WiT05ZOg7+e1fs+bjVtNNhF/Jm4tY2aXaELztAZbF0dLP8iQGDPegj60Su3493xNfWVjTjqK7BvL2/7AtUIYN2yFK0uVO/KDJ8Rq04Fu8KNDXjbr5OqFKbXIFhvRoNKkUweDYXWGOq2pfnccTVzulKXpMMC00EfevFqPE1VuKxpjE4fhqHM+ABXTOCrJ7qGBcoacX9QjL+4oe0mBRVbvvQRqG1+X9WtzK7mYBe6tTmIo0xsRgOOxgoC9V78JbuoL9/BsqK32pyNYQbjlndPjPK3bM/bbCwLVVMPtsrsCmfohKfVWgYZNK3dTLW2RjZsj8+naGoK1/xRimDkor6DLokRZkxgWkNvv2ZXNE0iMm/V3MVJgW1XJVp9x8EdU5moptAyWtdiUNBot8E24jAtu2EppUCZvTrgQndGYzS9oX0t6DbRS77GUrzyAK9obz7hIKmt+dhkmoqivbB2XbdmGWpXOKjRE8FgX7CJPbXb2FD6xUHPq5XY9vmDaJbwiIPR9N7wc+FtsunrKrxbaghWHCC42E6mkOfz+BFxI5lQLUub+Vv0TzVi9mdvIP7au2WG5JflK9jp2Rt+svm5ysbSuOm+/sqHZ9tachs3kOnbjTNYQdPG+G6G69d+SsAbOsZomobtSwPvLiOaCaXiguqKXTUb+HDnK7TU2KSx4WtFUwc3LKLZVZauh3TaqtGv682HkmAAytsagCPQCD53u+urJcv+dXGjDPd1JqgEuw5CXvpg/nLqKww6ZyqbJm2h4u71/P2V30RrNgCcd47GZT/S+cXSa1nq/zXWrW+R/tQiLNvfkywvIYQQQvSZsjLYuQt27Qqd5Vr1UOZKo791MMQ0ody/iSYjvguFzRLq2hU0/FiKVqC596OVb8GiQhcawSCkB0oIlhehl4a662lKke3dSqZ3F1pjoO2T3UC4OHP1rtDf4dvdqq1T1ZZ1UMORIb16J5biVa2nBzCD1K/yU/9lAAx/qJuMUgRNI3RhHs7yOGrEcUzMn9H8VrFdHw9QfzVSl8loo1ulXr0TvaEMb81u0nQHNs2KqUwMwxsN6mjBng12RQpot1VI2+9XLPscSgqD0W58ENNFqyt9TCIXlZolHBwK/W001Iae3hSg8JFCyssN9hYZBNtZjp/sfp2vy5oDELHZFz0R+IoEamML1O/Zo/gqfB3WKtgFKC0+Uy2SxRTJ7Brk/hxqCtt9T59P8fGnBsX7It0YzeZMqHaDXbGZZJH1oYczu9pbDvHrSxkqmoEUiUFoLTIx0mwuAEZkjwtP11yYvmVNIqXif8Zq2Q3LNEEvWYtl7wr8rdJvekbko4Tq/Xdu21DeSNYbBKrM0IiubawDpVSrAv1xz/vCQVNr8zL3di9JLE5sRtfBBrwi+47RG0PqxS5vIwjh7pzRjLcW0xl14UzKA0QdlKlarQ+lFGZDaGdralJUVanmY2OLVeTxh66/B2eMAOK7hR4os6vWV0NhY0lcu0vdReytja8ZZvW3rBdptupCaN+osG1qzsjUDDP0Obzh7OeYYJfCZE/tDhoaFL4W+1xdTaiN5aXtd9PvTK2u9vYPUxGqBxbuLmmGM7us4cPkth3w1QZobIy5AWGY6BVb0at2dLiPdCSy/or3xc+7t0iw6yDlpBXw64V/5Jh//hibZiN/iYWr3jiNkvrC6DQ/vETjZ1fq/O7ty/hr7aOYuoO0139J+mOnYlv57+hdTCGEEEKI3hLJwGhsUuG/209PCQRNKgNbqTfi76rbLU4AjIYytMZKLPvWoFXuJN1f3GoeWjiIFtsLQzNMgo3B9osXh4MsKnwF3WY3xpbdOiIXiWYwVAMr5kKmtqmSDaUrUEaw+WU+X6jLIqG7/0Mc+dF6XulNtVgqtkdfH8kOsG5/D8vOD9tuc3Ta0Dw8fjd7atoeRawp2ECaxRnqwgh8VbmGDyvDAZ7YmjKd7CLSoRYZFrE2bwkVV9d98RdS3cvsiinColujy9JoDF8YGgq7P1QfSrUTxYtcfFZ4mre32NHUWo6sFssXbOLDna9Q1rA37vGWAQNLOLjbXKBesS3mWlZv0Y1RA7BY44KckUBVQ/ja2RGsgZKv221bRQVUBbZHA2lB3dacFdZeICK21lC0G6OOpmnt1uxqdeVvquiFcPRat8XFqakMhmeNIcOeHZ7OjHYlc/rKcQQqsBqeUFZkG90Ghw8Lt7HFqlEKdHcpHo/iiy8ClLWVHXKQYhdDR5kiscEAs8kfDbgY4Ytsy94VrV7TuLKcutcL251nJLMrdpGHE/foRumkqNggeUfbe9fm1QvBhNhgbNCIBntUwGgxWTi40xi+cXGAguYqYLYOdgWa32/HLo0165q3wZbr3e2rQ9M0pg8+HIc1LW7/bzpAZlf83YVQO/e7i2jJGmgOdilThbo+BxrQagpD2U6Ei9KH93GLZkFFasV5Q9m0se+tUNTWwe498PGnwWjA670PFOVlofZ3mNkV2b47OG63l4WqFFiLvsC6Z1mofWYoIGmxaPG1u2JWW1ydru58VxAzsiuw7IueuZHREQl29ZBhkycw4urDmLVpBmNWDOXqN89mX/3u6PMXXqBxza81nvx0Hhd99hKlR92Dcg3C8dm9uB4+gbTnLsS27mnwtZ1qLoQQQghxMCIXpUb4xH5o5mig7aLh++p3tTmPSPF109M8EplSoMcEoHQVwDBBC5dtiOtdohQN7+2l/u0WFxItL3LCjTXbSgdomTkVeW3k4iYmQ2pNyadUeEoIBJuLJZue5qBSUBmhcFr4gkprrMbWUBadZ2wGgNZBIfxgULFnj4FhKty+WnZWb8IbjC/QvN9biWEGSbc4sWmhYFetNyatKib42DLWVVJfSJ23i8M0dlAIONoVyBsIxagyQoGgaICkK3ftW2R2aaaB5i5FiwkCmSgwQG+nHlHAaF0EK/biv6Pi3ZG6RC0vTgNmy3kqUCo6KIBqsT61tgIMui0uuBqXxRTZRjoItgQNqAhsjtaxayBIoz/IvhLFjh3tvDCmXSom2KWbJmZ7pVCiXUcj/fsUKlzYO9rLNLJRRSYxg1g0a3SEVUMZ0Q0v07ODQQ0rGVr/MRm+3c3dmmO2S6czPN+6fYyoeSv6RlXVUFgYuojXlIGnFy5tIlkoLdvUarrY4ubeIJpVQ3domOGAv9ZYjWkq3vtAsbswnNUWLjKulW1qLvgfO0+fwc7dPuprY4Ip4WCX9SAGUowdLKSt7NCuzasXC6nFHqsDMZldgZbHcBX3+IFqPKmgiSXDiiWt+YBl+kLZXnpa84KNzKbl7JoCDbhsmWiahlW3xmd2BQ5Qsys2KBjeh4JmoFX9RT12PiZY6j1oVTvRmmpCI0cGGkPBrvA9Gk3TQVMoFEaTIhAgrity7O8mQYr2hrJuAbzhLsWG3xdeFkarmxHN+3j7x+v26gu2Fe+LBLmstvjHm/+IyfJqK9jVWI3a8EY08NeWlsfLTnUJD2efdYcEu3pQweXTcB05hHNePYtxm0bymzcXxQW8zjpT4493aezbb+GCu07j3eGP47nsfXzf+A0EvDg+vB3XQydgW/lgt4smCiGEEEK0JTJwViB8kjo8axxDM0eh6/HZU3UNJeysWE26b18osyNGJDBmBJoDOUrFF/fWlBl3190MX/Tk5RGXJRJ93mugfPEXZyoakGgdqdFadIOLjqQVCaS0ylhTGHV7Q1kdupVgdfM5lqFMbMEAs9LHMCtrMgA5tsyYYFfnuhMV74PSyiBVMWWKWo50Wdi4DysaBfYc0iIBxnDbg2awuZYUoRpAsbZUrOXLff/rVFuaRaJdoR+BQGgUsEBAYYms8oCJmeEg8/gRcS/pbD0WAGUa1K304y/zRWt26eWbMU0wt4XXIyb2Wit6uKtTwO2lfmdzEerC2taZcJ0ZNTQ0XWgZRoI2Ef4WATTDDMYHFFt0u4orRB+5RLLYo9ubaYZGHXXYI9OHL0Yjsb6aPXGB1tjnIIgNOwG7xuqK7VRXQ23NgTO7zEidMXSs+78KdQ1urMa3ux5fYUyXqhbbqW+PG8/K8tA8IquyxcWpoQwsui06wqqhQnWFlKnwGV68KrSfOII1zZld4Xnl50F6Wuh3W81WNEwsKrS8KyvB3RAqIK6r4AELXXeVUgrDAEdkF+oohhKTTWQ2+dEsYEnTMJqaH48EMIuKQGusil6oa6W7sOz7MvaNAWhsCLC9+itWbGneZqvCZZsOZoS52GyulsGYykrFjp0dL8gtFWup99WE5xUOgnhNmjZWxwVDAob/oIJhesxIiSpoNI9K2aJ5laUGm7fEZgK13/5AeRPB8iY0OziGx9RC8ykwg+hpltDyVyq6ewT8oWNadPZmMJq9adXtcZ/RG2yM65odu3wN02gRgWk+9uelDY67GRTZ53UdcJtYN1VhBmPCKUYgFOyKDnoRGY0RTG9ou42r2RWT1WSoIIV7YE9R5L3ib97Uv12E+5PQsvduq8Wo90ezDDvKmmvZxdYZ2W/aKVAPYI05lMbuX7GZXcrvRymFf19DNDurrmgfW7aCu7T9en0ta/wdaIALzVOBdc8ytJrdHU/YDgl29SDNqjPqH8fgnJjDhY+dx1W3X8a2419l0ykvs//21fiL3Mw/QuPf/9QYPBhuuEmx+L5hlE34MU0Xv0zj91/CGDkPx2d/If3pc9HLNib6IwkhhBCiH/D5VPSkMhLs0jUdW4uLAgBt18dYKzbhDJaT5d0e95zdGu7GGDMku2mCJZxFY+jO6GMRkWs4XQ8FxaIXzo0B/PsaqH+niPoP9sc3OJKGpsxQjaW4Qu4+lCMTY+is8IzCWVktRluMZEdoDWUYlVtCNXasTho3NkYv/gxlYNEsDHbkMciRC4DT4mBc9sTQrLswMprCjLuAiM3O8Bt+Gg0v49NH4LQ4SLekhdscmsbfIquovWHdu5LdVVOj8HgUkWjX3r2wvxT2Fjdf1GiGSUm5TlGrRLsuRCj8BoZH0bShHmUJZ0LZ0kPbW4NJg2rAi5f0vU5yv3ZgXxFg52sr2fDBJzQ1NeALNrGvrvWFjNvXnPVmdHBxHtl+tRZZgJGupC57ZmgeKthcrwsItgj+RAK2pVnHUZL9Lbw+MGNGzIzsP2NCCZHo4XVmmoCvAUvZ1+j718UvmnAkZaRtEnbNTna+k0pfKEilKQOMAFpDeXT6Ss9+/LFZiEbk4tqCbhqYysRa9AVNK3bQ9FXzthDNOgxfdPoLmzMror1MY64yTRWqI2TRLdHafYYZAENhmoriYBFfB78CwFlbgq0hlOEUiRGMH9dcg8mIBKTDT3qbzPDyDQUHujpy5YFE1kPkor3Dboyx67ixHnTQ08BsVBQXK8ormo+LmgaWouWhDB3TiNZYAtArtmDd+iZKKXy+8PZQvh/vlhoCAUV5ONHV54fSsq5F98oaiimpLwxl2umRwGP88WDtV7C7sP15BAw/JfWFrC7+GJTCqN4JykAvNPDtqCNQ0nzT4uuylWyr/OqA7Woru0zzVKBXbouZKCbY1cL2baFlHNHeMQ3A80Vo+9Ks4BxtIW2sBeXMwfQRqouV1jwKcNAIHZc/Wr+B1Wvju35G6vLZLDYCMSnFAcMfF7SK7UpoqCBaG5ldoe8Ga/S4UlWtIBgkJwdsNiAAOsFQXb/ozAKEHg3RNQ3NH8rsUv5wsKtFN8YIU4U2RHdkMMtIwDIYiA5qYTYEMH0G3s01NCzbH62H1irNLUZ1fP18jjg89HNvfK9vNG8d+XWfgxmMy1Bsih13JXYd+nwEK7w0rq7AqPLi8ynqwmXL62ojWaSxJwLhIGKLph4o2NVYXo7Xq7CUbQJv12sASrCrh9kGpTHh5ZMZ/ocjyTtuHPuHlbHdv5HKxzaz9dhX2Xv1MoYE3fz7AY3LfqTxwUfw3YsUjz2haMg4BO+Z/6Dp3Ech0EjasxdgW/N4fGdZIYQQQoguaGpS/O8zohdkkWCXho5Ft7W6qFE01yRS6GjhPhmmqbDqNjRNw4itL2WCxWzCaoGgHgrixGd2hX6Ggl1m9LTGs7KcxtWhRqlAMHQX39+Avm8Nyh/uSqQCWHe8j2Xf6tCLvPVo/kbMnNGotFBwyvT4Q92VIm8UblukYDwBL0EVRLOBsqUBChUIdWGE1hlBAOOyJ5LtzA8FuzpR5LnKW4TPrI87Zdtc8SUbSkM1gRrCXe2ydEf4PXWc9ozotMEW71G4p7m+VWwWQmR+LRluf3yXLWDLZsWu3aHAlWEaVIVHFrPo4PSGs/aCJsqis20HbPhaYUQuZjoZ7Gr8qpKGz0LBGmWNjMYYBBRV1aDbbASzS5tHPCSAXlJCIBwsLSnbRSAmkBXJvqj07GdTeXNWTUcFuyMXtbGjaJrKpMITCqDOGrqAdHsGhmlgKWseFSzYotuVHq2r5UTpdioqYc8+ezTQGtmmIxlFkeCYaRLteqgFQ5/L43dT563CFwhtizZ0TGXgzHRF388ZrMK6/V0sxavQavZgKpP1pctZV/p5dJpIV0udcM2u8MWx5m0eDCs8YfzPNp7Sgs0Xm5EAoUWzNgdYTANlmJiGiRbQsFfZUKVB1NYAjjWbCQRUNHCl681dnrxeLdzG0PLwNTUfT3SMXgl25TauJ5OycLvDT7QMitMc7NIrt6I1VqI5nOhODTMA1dWK0lLF58ubPxOEQ8NNNaHjUeRzVIeDsUYAf1PzezRuro5mzowJfEamdzsbNij0ii2hUetaiGzHlY2lbKkI1Q3cWLaKLRVr8fjdOCI1EWOOyfXuA++LsUF5s7aI4PatWLZVoKzhwI07ps0BN01ttI1AU3OwNOjlk92vU1y3M36a2O5pmhaKaIZHAkw/bBC2kc3HNAwTwsXZoZ1uby1o4fZaXKH6f8qnQpmikWBX+LhUE9xFhW8He6p3YpqKercKBwtD07XM7AqYfmwxwa7IMTVQ0UQwGGgRlAkvs3DwcfbQBYzJmUzZfhvKCLDTW4hP90BAhQLeenNkSDP8VFQqCveGAlkO0wHKRKEw/aFjSGyB+vT0mGBXOEQWjqVGjy8aJr66mJtL4f1L+c3o8mh5c6KsXLHs89By2duiNHgkwc3tNuIyiF3uzTiMGjRvXVywa8NGqKho/T5mXX20Tpu7IsD/PoOKstAyb4qM/Bpb0M8IQNDXKjjtO0A3xq1f1bJtR2gb0zyh75qvNypWrOzcd9RB9CzuPQ0NDVx88cXs2bOHwsLC6ONpaWnMmTOHmvCwMYcffjjBYJC33noLr9dLdnY2OTk5HHXUUdx888384Q9/wDAMTNOkqKiIc889l/POOw+r1UowGOTll19m9erVHHbYYSxatCj6+GuvvQbAmWeeibUbna91p5X8708m//uTSasZzzVvnUOWJ5vfFt5C3Yt7qH15F1knj+aUSdkcfYTB+rUGX/wtl/88O5ZTv2Ph3HPmM+TiV3G+dzOOj+9Cr9iK71v/B1Z7TyxeIYQQQgwAzz77LB6Ph2/MPIxMrx+3M5StZJgKpRS6pmPVraFuQaYRuug1g7g9wZi7uRq6ZsFQoYwsi2bBolsJBn3Rs0jDBLtRj26HIC4c1DSXENJtqHA2jUUHVHP2kxkuvNvUpLB4TYwGhc1hoLn3YwkXkHZWlVBXYZA1L5TFEjnZVVnDwQwSrDOpW1uGbZIDW35zN0ZTmTQGwhdmyiRgGtgMUDmD0TwVqKCiaegkNn3xH75Y9xHjskdx3vnnYbeFz7XMIBZdp7qxAr+/4YAnzPs8W4BQttDKlaEgzbzD5kVHuozUpLKZCnQITjwB1/oivOHn/Q5XOKvGZPfu0IX1mKYg6S47gZiudxa97Za4P9yHlm7BM18xNHMURlCDoInVqEerqKJsaA5bazaQY56Irrtw1axjsNtKlTEtFKQK8/shLQ2MYJDqxnLy0gfHnTPPnTsXi8WCxWLhzDPPDGUQNQXQAS0cAdGMACropdocjivPQPduIbaPUyjzLnTF1VhXR3BI8+fTNZ1gMMhzbzxJg1nDvMPmAfDqf19hy5qdHHbYYXznO99h6dKl+P1+Vq9ezfaKDRRMyGTu5COYfN4clFI8+PLfqfaXsmvXLu5f/yQLzp7BMUcfx5OPPIpOkBETpuENZmGOnotusWAaBps2rCFfK8M2y2TV6hUsX76cCRnFLJiwn9VNb7J7dxW5eSdx223nEvDrvP7K88xM30Xpfjv/e/5Ncuu3Yh86iPN/M51/vXEPO3fuQC+fj3MczJ46nOLS/XhVHSO0+DpAECro7s0cDIaPpurdkBPKWoxke2iahh4KFYdeoBQEA+CtA2d2c3ajMlv2JmuOW4YvTBuCVTQF9PD2ZMGiWakpN6mwBxhmKMxwBlh+hQOPNox0ivHXw8cxvWg1LeaiuQFyMqBgapA1WwyWfbqcQ/SdjBs3Dk0FaWw0eOmltq+tYq+7vv3tb3Prrbeyc2fotfPnz49en0HoOPHlGhg53Mu6D5/DYa8n55g/8d/X3ictzc+5U23o9jR8474Z3V6PGDeHzD0mg/z7UEqxqa6KyTNzmWydjFofAKeG01WIb9JwtLTQvq9ZQa/bi1kQel9lBHFvDJA2QgPDHxfsMpURqjmkDJyqjkxPDZ+tLyG3+AO8ejplrsnRz1xYvY3n3nmUcbbZFMy2o2kmvob4jNZId93YfX5F8wCleH1Blr7RelnGdvOtaywnsE+h18NX/q/wVHg5csgx2IJBXn31VXYEvuS4o46P30iCPqw7P8TMn4A5aGp09MLSul2MMjRU3vjQsokJJirNggo0Z3bZR2RgH5FBbXHomKaZCs0IdWm3WtvO7DIMhSUmM0zXY7peW2yYPoVmBlsFu0xl4ghUkmbuYsPXUyivAMuoAC4rvPTSS1QYRUyZFxpl1DCDKKXISxtMgy8UJFaYBGt9eD4vxRxcD+HRhL1FBt7ycjKOyYjWtMtwZGMxs9C0PRhBH6YWpNFRQb4/A78KRrs8m6bJ5g3r2F5rIX/qKI4c8S2MRk+4+yWYkcwuTJRpsmv3boycItbtWkVZRRpzRxscM+80vE3h/sGGn507d2IvtZBev52tW9exvbAQ/14/iwoWkR2+2aOnW1t1O9+0Cfx+g6effp8VKyqxWn2MGTuNY489HGu4j6KuggQNsOtAeARSpUzeeON1tpWMZ9q0o9DD/d1ra2HQIOLfp3ATqmAY1dUBHvi//1KVofGLk0eCBsGmRoLBIEtfeZERns0ANKzaTHqghkeWD8cXzGb8hAkccfjhBAKW6DaoNVWjMofFfRZdBWiyD0HpVWhBH6ZSNO7a9P/b++9wy676vgP+rF1PL/ee28v0qpmRRgUhJLoxGINNcQFj44ckEBLnzeMWJ8EJeY3Ng3Ec+4kLNm7EDQy2X+PYYAdsTJGEUNeMprc7t7fT265rvX/sc8+dqxmBuoTYn0fnGd1d11lln7W++1cwZRt4/VV96rHEll3PMtuL+/lfb/wbnJzLz+37Cbp/NczQvz9E575VVn/zOO5fn2H/zHl+fO3r/Pz85/jynzf4wXco/usvZvjK8K/j3Pb/wTzxNyT/8t1bzJxjYmJiYmJiYp4I2tIj5LtXxkSSSCkQQvTFk1D6aOvnMM7+P/wrrGiUEOj0gperKJW6Lgw8z+lnjtp4Ka4JCHuWXUFH4ddCQk1Dhqq/OFaaT7UbWWRsLH7OLlnMXA63xB3ZsJCS8yFhR+E3dLz5FrTqKDMFugVCx6/KntuOu7ngD3zum/9S3wpKKEmgAmbbS5zqnADdQvrgGyYNo4hQ0WIrLO5E5qLYVUKGtHruZhfKm9ZAG19WNBb68zKlFF0vUgdL9nYmjD1XtYFX72LdnUHvbJjZaKTNnhWEADc3dtU5VCM/k67r0e4obCNxzUDuG5YTa2sLnF57kPn6BZrNyEUx6a8hqov41UsIr4EnW4hmB/esj2h6keWFvrkc2HjrvlC9yMNLd/UDO4trWQz1m2ujA/Qsu5REuR18ZbNVm+vV85CPr0WLZqfZuWb8IEdtjWou+SZWIb0FclNWaLpVKt1VGjKKGZPqDiIQiJ5lFUpwqbmDUOmocKv71eWoKFIAAGYtSURBVOjgOgFbXywHEjSh0JBsRJ3eiGeji6hMzXaSlukgUSzYDR5YuGuz3EIiNImFTogAoWEY4PXiW/UtK7wWXuii1WYjC5uN8+VGgHodXegEMoyywCmJqM9hzNwJ3eqmVaPXgscEsd9oU60dLegvtL7KAwtf6X0Hg/BCB/8fAh592INAEfZcL0OrgGMOEejpq4L36/qmJZRCkMmAvpHk4Qq5Le0voZUvsrqW5+myuARdB+bnenWibVjcbfZf8dh4fSFoV/TdDgbzuV146YlIOOlIBtYeJH3pHJroPc82nj29QOH6iX/EXwloPOhHYpfTEyAB2VxCu+/zFBYfZWw0esYl9aj9HpuMYbmXQCFQHpqmIWpz1Obv2RJHbuN5fKV45ck2J9t/Q8NfIHSubSa3YdklulUazcsEToiQAm0jscBGsPOe9VAgA+brF/GCDQuc6Dtp5Qtoiw/h9axiRfUS+uqpzQDhMkBpJuH4URw5RGPZj6yY+pUWknt1IUp4ISWEatOSx5c4Z2uc/r2LrCxLwlDxpS/DhYtXCCgbgd01gdJ0Qk8HFSCsnmXPlZZI3hwZd5bV1Q1rtM2YXRoGQU/k2rD8TJlZXrbt9fiBYma+i3Iiq105cxIRBkihsfCAh7vYQSmFVBKt18nrdRBSMJAJsJKAECSFisaFDKPeoFuItA9CYqkUhHbPEkqBdW03RiEk9Ppd07rA+e4X+zGthArR9RCjoeEszFLyl0DzsNo61e6mJqBlzKuExI2x2W4nEFpAsXCaHduWuOUm0DTB0et7iWQCCEJFp+UhVc+VVwVXCFBRYWYfalNZDfuWXJotCDsK5QeUK2D2yrwRtw83shy8cuxlwkhoNNn63PU3MlfO3Yu+8OBV1pmaClDoYNjgd2gurZJ1L5H0n5guEotdzwHbCnv5zTd/nsn8Ln7+/h/nwe8/yYEHfpDDMz/GoTM/wsGT72THn38XRcvj/+v8E//qe9ucOAk/85/hbb/5fv6x+JuItXMk/+ztaHP3fusbxsTExMTExMT0uNKNyDQ23ixHU0BDM5DdBl3PR1QilxVvi5+BRiETudasl6P4HYYwmJ33OHExEmukAk95tGSLsOem1z0WcPYRn7+rLOI5m5YgjUyVlfZs38UwecMg3oFRhJJbkixurJ1Ub6a6cneH2X9cwb9cQSVyKKVwzjYIWyoSV3y5mcUudPsZ+gBwAjw/oOo0CawQmRpEBYpQ6PjCpu5FCySZHEAORNYAyLAf4FxcGWSkdw998WHKD93LiZMKL3T6GS6RFrZIRf8feND7nv5iz73t/jBaRGsGGSNDECocT8M3E1e1m6hH/icPH+ty8RJYWppABn1XqA2XEuVKlJJUeiJiubMSWeZJhSAkCAKqqw+TaF5A+Uv4MxWCNUmiK6MMfbqIApwrReCDMZLE9RwIFDN3P4x27stMOuewXR3tCteuL/2z4vKMj9YrpzD0KGYXkYVbiI0kWsk0x5IEpRJFUURaku7LBSopcC83qHejoDJj2W2EMsQLHQL1GHc0tgoHW/bpm+3z8PJdW1zAhOpZMaATyoDAz9FxSrSd5FUefxmrjae2il0bno6GkCh0NCSJ7jwAeq9MZZq09IBGysFzi8ycvERCRmJdN7OAoYfYfSc/cDI7eYBIyNxwgROBg++3+2LqmltFKbXpxig0jA0LS1+BCum2fFZXFeVLK7QqHnNf9ZBSRUHWgeShARKvmUIpsCwQXoB0uvjBZmBvebqDf7o3VhwvEtLWIksMPdmzMNFNNBVgnlnBPBP1sSstuyAKVi/ERjB9yYYlX0ZvkHdOU6lmCXqBvMNQ0ek8+RAt/SHWj2MW9ZEg1LckF7gyLpuQPffKRHSyi44yYbU7hGMO94+z1qt0V9vMzqp+rP8NN8bQvSK+U6MFtcWeaxqo9iqdYw6p0xewbRgqgS07vaQd1/6OIQGBDMht7L7i5cLh0ZdGX+GKeFOOjDyaZOduOP+PrAYX+zHXNoTisFMGFFp1hlrtEtKJhKZ+RsReMoaQnjVt0Obs+iOc2xDyr8ym21jE7axvqXQROJGoGriRxVVmjNN/3aG8FrJWVn1LYFGbxVq8G82vRG6zwWayEhVKnNNV1tbg2MOynxhg/ko3u42NGoBA+gaacUVcMKkwHyOgJ/2lXp2FfZdcvaea+dIj6Iknpm6ioTE/D18+dj/Nro8Kw/7ztBumqDckyytXJr2IbladcRh52Mdsh6hM9IJFGYlIiJGS0MqizASUXLqZEE3qeB6IgF7MMeg6HnfN/Q2L3gMkUxvFV73x0vt6XJnVOERqBpYZ4HW6KEATklTV6gtmQhdR3TzWaq5XXV3HxuiJr5YVoDk+YcvHsqDUvp/5BTh1Cu75ekCnsyl2hVJDX26QuH8WrdrBOrfKyb8vo3o+h/pgCq8hmTvvEIaR2FVoK0TXQ9NA+Q6ivUYq3IyxpRAoBbbY/L6GAd5GzLzey6WN8dvpKE6cVAgVooRBgEVQX8C/cG1X/scjFrueIwZTI/zaG/+GWyZfza/d9TP80r/8W9Z7sROEEGRePs6OT343OD53/MtX+cs/lfzkBy6T3vUl/uv/bxs/9KVPs9bKkfyrf4Vx3x/GcbxiYmJiYmJinhBXBoBNJEApD33tDKJ6CfviVyifPs83/mW2H+DdvTJdkoDJ4QK6q1hfV5w4CYYQOE5ARx/F9xVVr8mx4CFO+CeoyyYJG2rNNgtewOz5JC0n6C+OW+kGqM2A455mRP6NSqGuyKzVFyI8he8pKhVYWfJQ7Q5YWcKKi3O2ibcm0Zw6KpBItxdDKdiMb6IByXssOveHiFCgEgKVGSYYu5W1IFrkrzk9yzUrvRl/RQXsG7oBAF1dLXZBlG1udRUanQ5rG2vDUEfbsABaPYm+Grk3qp54o0mNzjkZWQboGRaXYHlVx+m5lgI8Il9KVZVQnoOozVGpRosVQ0Xiohc6BOtd6p+7jHQClBfS8ur4oYdtJOn4TTxHolc7CBVyrjZLo95BSVDhKrLloBQYG2KZJpis/SMpfx4/BD1toikN45KkcvISK5fmMZXH2HKa7Kkr3BH9ELU+s7lQ1jTYCHYeQKgsPBXV8a7SIGP6NAmRROoKz2sS7NQQrYDZy1FCpqQZrQA3XD+H9W3sHjgc1es3s+y6QuySUm4RCjas9gQaa+s+UpqESkdKDSvskJarpN0ZbH+dhOaiWVutDjxpoJTAEhKUwc7kEsnKcayg2rfsUkJiaSGhHllq6E6ZRBhZNiggTQlbKYLeClRaOVw9B8CFi5v3Cjpl6k2D2XnFw/UznG1f7o8TTeiYvb5ZudMBKVldDVlZhZnzHU7f41KZkVFw642FsKHh+tE9+xnYWtGCcuZyr35mA/zeMSrwwPNQpeh8Qxccug7SGQOhAvRqB73a4UL3n1lpX+bKfADJJNjVswgVMCrmMXumHqamUL1A70EYjYuz5+Cur9MPuv1E2RC7VO+BJjQfoUJarQSNyhUZ87xNq0AhI9FAmQpfmLQM8HVBtcJmxk0iKxfr3DK16maZ/LKkXZOcOgFOL3vjwj8cw6msYIYNVChpNR3WvbHo+SZBN8DCQSoB1xC7FOCpaDyP2AO9MkbPlEKyRMbKIYTYYtm1EV8q5ZeZ6SzRCldoynUuVE7w1Ut/j2wsIebuQbTXGbQK1LwmnhdCKNECQVvWqTUiKxhfPSZbaM9ib6MMYVfheC7nlr8BXpuwF5htff0kK2c+i9ZcisSupk8rzCCQZFsXCJYvglKIXiww1V6CQPbdGAFUoPoC/dbtV1g6VSPrN6EDmoZ0NTQtpJ+UVyoMg74VHsBg+yGUDAkCyfqqwYWLo2hsWMj5fcuuKN6k1r/v7NIKdx47x6PlWbR7BqncN4xE4rqKwOuJOj1htbVQJ69WCUIXleqNezOHIKS8Lpm5rIEw6NBE6h6a1Fgvw113gu8qVluS5VYD1fXIte5h21CDYqGJbgQIcXU/GRmG8bGQZD7AMEKQoKSgaLQRoUJNGKRuHiJzxxjo2lWWXVc+xSyrF99PKVr/skDz86exO5cxwnZfbE/5CwgkArDDLreV7mJURsK2Vo/6a9gO8Fo+vqdwU2mqFVg518bpClKuYqSh0M46WLaGkD5i9j6K/nK/HAqBlIKE8K8oW+Q6D1ESnfV1xfJCl+VlxexsZM0pVIgUBvcft/ibk8c5V13A0BXZ7FXVdk1ises5JGmm+dBr/w//6qYPcNflf+Ddf/lSPnbPf++btSb2FZj63y+ne6zMP77vV/jduduY2/Gj2G99Of4bf5Efuf9X+H/z303ia79K+3fejTN//nn+RjExMTExMTEvaEKF11uoadJhzJrBCsqszndxL53AEAaeD5pf7mXue4xll5Jk3RRDxyG5rlAKTCzUbAAPNalmb+JcN4ox5VjjVEXI2BgsaQusroPVEkhCFJITrfNUiqt0p/W+RYIj9SjOky4JXUV1NuTeM9O0Urtwgzyyq1iO5twYsh3FPdFslh85v2la4rUQtVlUJ7qm27PqEkJwy8D1GEKj1owWwMqOgkTX3C6X61FWsbJj84AzDmZqU+ySIRO5HSTNNMEVmSfbDT+yuFEKx4GO3+Grj2wGcZaBQUI6FL3NWDxSBoQyiAKoC9FfZGthlj36PoSwmVt0+5P+TLqLQxLlO+jLx0iUH0UEPrpM9drHxVvrglQ0/t8cQcXB7Ql8Q4zidRy8R6LA/0KFtNc8zIdTyI5ABqtUL67j+AHSDTCDGungAq5ysYMKQQAtadAsa1CXIENCsREkPaJWT3N5dgj8EP3KxbOm9+svCCCUFh3Nx9JMbnzZy5nevxtdmEgUKvQwC0m02hz6/BmQAYneQngj1polEkzldyEQKCUJQpvwMa6HAErf7K9CaH2LPIhErqgb68zPBwRBghANCxcpNUa693OoeIIb8vdiKI+hsa3uk52gSNcZIaV5hPkGutGOND1CdCGxzCZKKHKaQyA0DCExlOxbayglSDNA2uoS9hIhaNpWoSUMFUrT8RrzVHpiiwxhvrvCmbUHMc6GhAtdDGFAACESCNGUj+pKkpdnMZ0GgZ4mNNL9uEqhJmh2ovoyLUAQicVEQaETZorVcppz53plcQOk4+AJhTvkg65HwouKxC6UQqoAVzZY68z1A9RH30mgBy0m6l8kL6qkki4jI6Cd8eGUj0D2LbtqteicxuMkVkvr4TWtojbErsANEUKhCUVKa9BqJ+mWtc0sb1e6MsrIGgahqBjDdJMhgeyJ20JjvOc9rMsumufSN78zksjEEJV7fVyZYHktJJCw+HCNIOxZrsmAck3RlUnCZImwozB0SOBEbuJXfIdGQ7G4BEvLRZxe/0yTwHHVFgEdQCCYqZ6m7TVpOe1+4HKBAqkhVEhAwHIrWj/KzhqhkhA4lKwCuIJO0AUl0X2NpiwzX7kQJap4jNi13l6m2l3ru47V7/E48/VFtPoC+vpZ3J6VzfHluzndvBQJb5qB9EJCLY1CkGitoJZPIdvVvlXOymoXo9XEvFQmDEGYGiqQ9MJuQyj71mBy4yWH7yB67m96OnJjVJqJ0P2eeAhIFb2wwek174YZXh0ZwuyMjgoknhtZJfqh2/+tMTULTWj0wlBxYv4Y3bCM04W2LGBVNQIiMScIAvA7WHP34izPYi6fJKW1kUoiBwaQAztRholQEk+GKKEh0ZlT8wxr86SdOVZPtDGqDp2OooVkpeqiVaPvZzqrJBN+1I/EViE/VB7JJIwMuGhJHaFtxCkTyGQbU/l4IwprIoNesBGGQLZ9nDO1zT50xdi0jTrbL+cpPOShVWfQyudIrB3fcs9B7yya9PADyIRVcmaD6dFogBrLjf5FL5/3WV6Fc+vpKIZnazPRgYaCQGGZvey3vW7taKnefkkoNRLaZh80TTBrF9AWHqRcjrIFXzjjcvxRxfJX1hDNbhSpUOg0hUMn8HC+EWKc9dm+7dpZQB/LCzJA/YsZXTP4kev/I6/Z+RY++chv8LenPsHfnPwDDgzfxFR+F2caj7DvVZO84cvfzX+Z/BBjP3Mjx5a/zl88/Bvs2v3TtI+/my//w1Fy9hqlr36Ec4fGqV73bvbdvpvhoc0sNjExMTExMTExqXMhfgvEAcGQ/g0Gcy0mCw1m2xqNJuQHNyxxFlhuwq50Dm/DlQSBUCH586fZG9qsiMMkqVO7O4NeE3QSbVZm05Sz+0CN4OsNRCgRpR1YpUWogtFRBISEgxrhuIcgxCkJ5s9dJGVmqLvRVFRoCt+FlYd9AsNlZeIQevnOaIE+YCLKPobs4qxIFv7+HEtuhcn8TvKDu7D8iwSNMrJrQA66bh0suHH85WQa6yR1Gyc1iGXYeJYgUAG+E9ALRcZgJ0uhmowsq3pijb58HOk7GJpJ0FoErUAQKO6/P2BoKmSfD/WwzuXmX2K32qBHq2bHEQyGLdJlHVHQYUARdCqEQQdt4327FbksOr5FXitgqDozcx7Keg0yF2Cs3Y+jksjQx10ISdx3kfH0ZfQjt4KAsxe7tL9qkGwqLAt2UMaTLrpmYHzDxez6uK0GYKClJApBECi0ZQs1MUeynWFGzlJuK4YcWPPKnA0W2K32k/HhxJxNvarolhUTgyFePyOgxPcNVlYH0DQNzfG3ehooFZm2AM0mdGQXIRwG3/BSjJ3TGLMrdJOHyZrLtEKPfLFEyi6x0FlAay2TcUL02kka9kjUBj0XIl2L3Peqtf0sLpW29G+F7MfsAtCEwA03Y16Jnh9sGFgYPZfKUGlY0iWQCXQDBoqib+3hC5tXvkLywIM9tzdpEIYJJlILDAy08HIX0fWj3HjE5f6vzmAnyui2geoIAh0sIis0U3mkhE8bgYnJwd0u+ZUOHSILJF/P4pLFlk18H6ziKOHSPRhBAxB4mUkMZx65chZz/jBhvouR1hEdLRIcwiBy3VsHtRaiJboEohC5pIZRzK6Hjmu0bUECSFggExb6/BL5WohzUKeYGGK1raOJAIGO51Q421zG1zp0EyV0U++5yhkIIpfYkAARKGruGnfO/w2XOhb14BI3srO3wO7FuTJChkqw2BMwdeUTBJHKkExG2V5rdcjlrmjM0OOwvcJUsQXm1myTnY6i09lo0wCtZx2SJRJ1p7SLeN4OTBNE4LKte5LLepuwoRCui9zp4XR02rMmzVYkhAwOauSPmDgFQeWUQ6q7SOD0kif0Ys95LcUFawZbVsn6e9FUSIhEUxIlfSQaoUig8jm8kQGMla9hiw4y1LCNADvs4LrwjftgtQ0g6AY9YWnFYnUdhgcCzCRRpsNecg2Ab8z9E56n8OTO3vhTLC/paDLsC2B0FV5Qp/NQiNglyRRSCFcjUCFCSXR/c12ofEmAj5AB+sppRCKNKm7nQuUEA+mdSF/RbitagQcBWJaJ69QIkgEiiMbO5e4SsjYIRhAFqBcGwgxZXIRKw+PIWJfygsBDkAjWqTs+QZhCzyZwFzssLEbxvZz2Giurk1HBepZdTmcdOd0gxQCuDw+t+Iw2dXYUAlQvCFXGlhw+BOe+GHWGpjkCEvSwQRDCWHiKjHaZmc5OYMOdPaoDQ48suzbwumApCb4AUwOhcJRL11/l6/d8Ectx0aemaJ4+huUEqLygtc0hnMjCCj3zM9Cv05AXLOpeAzSF0hSis446voAJNLuSWvIyWVLY6w06KSIhGUFb1eEKq9V0GnzZIWnp4PoEwgItEvaU1KiMrRJMZJjIbgr6WiJ6Tjqnq7gX6ti78qggg9gQ+Xp9JR3WEU4yspjrhfXzyklM0WLkkM78otN/4QJXx5wbGY2yLQ7ogiCdwfVAEx5gsSOx3LN03YlmpYB6X3zu6Fno+vieASLA0gIK+TPs3W1hWbdgr59Fa6p+rDJdOgg3QF9vkVyeRY2GqCmDioravBM4BIGLJ5+YzVZs2fU8MZqd5qfv+FX+/Ifu48dv/DkMzeDBxTsZTI6w/7+8jvw7djDyFwns961yxx/fyEd+58O850/eRfZsjfJ167jju1m4+5Uk/ybHdx3/EeTHvp+/+6n/xZ/993/kk787xz/9k8+Fi6rvkx8TExMTExPznYXtNRguz0XLz7bCDFuYBpSSZWxL0G7DxbOR4rPqn+Sh5sloIeKFtDOvYHr41WQkpBcdLOViGzkyj3yZ4sMXEErgKI+le1ZYrUDGnkBXNqFyYOwg6XyUKcpwFJ5yaWcdwilBUlZwlIJuhfr8N6gtlTENELrCbfRi5IRmtLDtSJrJFu1EL76Y7OB3FG5vMRjKEPQERiGSkcLORqDiaFJs6QlUq0UpOUoyNUja2kmtqZB6iPI2J/JjrSL5ho2sev0FDIBWPoehFLKyRHc1hecpNOmxtOAzOwtnw9Nk3QvYznLfdcxy18i3VkjPFzBPRW+0VfUS0g3QjGiRrXSbsOnhBJHopetJQnxCLUmopdD1kCYF/KqkfcYnUCFWw8BuRyuU45cvslK7H9dVtHqGSEHo06oJqqfOo9bWmG+eph0sE3oegVGkqsbpGjtoK52aX8G0JEkLAhHQ0qOLrO3ucmr7Mo4FYc9iolYLmWuucD5cQEOyXt4MNK41HIQRkr3BRElFtxLSOdXGcyQX12tUj38DoUnSqegcy4RQJEiINPgdNFMjkTARroYKPZJ6VB+NnsfDRmIEXRj4QS97Y3drbDPZi8Bt+ZGLpy99mu6mUCKUQMoswZpN2j2NRCKVYNbfjutafSuPDdpGHl2HHdsWEQSEPbGs4+UZtkOUCEBAyvZImS2k0ljShvGkyYoz0b9Oo5mipAVoKIZFA4FkVN+OKWySRhqEYDUTZVz0fVD5Cdq+g4GBp+eoOym6jmLpnM7cnKJ+pgHLOqIdCX9GL+BTEPQSQnhJOtY4gbIIgi6z1bM81PgHnLCGpoGZM5D5BEoEFJ3IilJ3J/vZ7RQhLJdpO20WwyXS4wNoIrKoS+WiOGaa9JDKR/Mj6zRBZIXiigZSyS3WJJpQuM1M3/rLDIO+2NVfBG8ahUTt3FzC7lkRZnvWH0E1WtTf9XWo9KyCNOWjaQG+0shSRu8t5jv6UNTm7TV0FTJl1rGrDbodk2qmRLukU23so1zJo3SNYj5Ez2lMfM8+rFuK+MEKFxbP0Am6aKUcCA0/FHR6YowwexY2hJgiRJMu0gqRwsSyBWFDYicNdCR+EAkQ2aBCa2u+AJwgEolDp1cfXgBKRm7P8/dtOTaU0JU1xkZBqqCfFTdyR1TYd/s0/3mZoK4wT3VI6Qn0XnBxQWTZpTYC77shQeAzcTyN9ajAPt2FrqLhVFmonGKpWuX+Vp5qQzLpjbE3sw0hA1zp940pLrbnuefyGWa/EImM7LQROzesYT0uX+hSdjNc1C7gag9T797JbP08qZuH++JESy7TOnU3l++8iFZuYz40z+Ki4tTwgxx3z0X3KVe5bJxgpdskdH2kGX2nyUEf2xbIDbUmHamlZfdhlpZB79R6lZwgeVyj9qVZvLALXpvkuX9G89p9USVwozrSXI1QaZi6TbLQpeVfIjjpYJ4wCFehXIaMpUikdeRg0EsrDELftBlSus5sey4y+BOKohjAkF10DPIKQlNDWgpndUMO1npZjoMtFoDbt8Ge/W0mxKNRGTWzf79WJ0HbdpAJHy/waDSDSBhNbco5ypc4j66Smj3GROOLZFNt9F6927JL8tZJZGEa2VaYpqKxvoNBfZh8PqqLlhgGCeJCkvDyImYv5tbUJGSsEM0PwIDQTGMkBQkzJBGUsbQAoQfk8iBmoG7txpe9uGnC4lj5CM12kkYjgyUCdN1BCLAMRdjTKjbCLRhhC+EFGLqiKC+jLkfP4TLR71RIyFwwy9fKD/JEiC27nmdK6TF+9Iaf4kdv+Kkt29VHFZkbRyl/4jTtu5ZIHx4i99+28/nRv+WPj/8qObvA91ffweHfmObuv/03XHzHZzFKv0NWSA66OgcfsajfW2LJGaalDSETg+j5EumhQcR10+iZZBSgNTMSxaggtgqLiYmJiYl5MdGa88lsBDvpBVvWNMVKt4xtGr3FpoaOji8MXGHR6Lo0ugETu2xetWc73j0NmuVoMZnszNNtKoQQ2IZGYmSdRhWEB6lCBj2TYHnNRRcSEcLkmM1606OiyjQDl7HUMHtzLo9U1gnsMroaJVE+Rv7Ia2lpEmddRosFzcave+gdyXxqjsbgOgfcEjlPMNNexk9LwikNLZvAzpewlUZ9JsTqRkGD/U4XSGIFHlQWSdg5dg7s4sHjbRZ16BY9FucvwqBABKIfWke2/K3+H4DVXME9p9HODGH4TTLuDLrj0rgiCLYCdOURYpN3HoVHsyT03tv4ikbXnomy+vVSxfvLHt6XFmgPWpgmjA2Y1Oqb19N1iS8cHuiaDOg+a0EZdLDbHRbqOk13lVyzgjTzCGOEueVLtLoz1O0RChVJqLVwRRcvuISul6lXMwS5IpP5JCdJsGC0GRkAPBiaCjifa0EI1ew6NbmMlHOMb5NUZvLoTgWvK2iHLkr3EEBxxaE5lEDUWpCSmEWTlq1z8QwcwMfLKOphjbQ1hCYEqWTk1mIYUd9RagDhXyAIPXK5FEOtAUby01hGDl3T6XajhfRG7DNdM3ADDS8zh69HwllXtmjIMrIXpNz2Mkwa+wBoejU8T6frRO53frCDabWOLz0c3SNQOme7g4wbBkc0idIT+NtezuKJGmrDWkOXUcdWkZjkhmmUnkCIXta70COruTSlRUe3udQdJQzWo0olyhBot8a5UbhMMg+MkjZG2a5nGc0eYqV6H24vjtviIlhjJc40smSFYpe2m9n6OJX2GYy2hh42aZ49jd0Aw7GRBYk9rqOfktT9CWTKQgkDmU3QclYInDLLjo+ctqkEl9AOFdl90yjBCYMQSaKuMcXLuHx2CDucJRjO0vY00iuSwA0I0waJpAZIAhkwMGRTq0Ar8JBIdF8h0FC9gWPbIZ70EcKintgLXELTFJ3mdoLiAKyfJ1N3qco83YqPP9OGZBrX3VQahZJoV4iUViuBuSZpf20JdfMo0FurABoBmhZQCZMU7Co7RQ2AujZBSW+itTf6j8KSPutiEscxWV3LAlE09HBbAS1cQLNA6iZeegDXvgjtPLO5NDd+1w24j6zTmjXIVFygzspABltMsppaZfiCT9NrsGyHjGhRYPqg7LBcWKVhriH8EoamkQ3KeOUVYJwDB+DkBXDDAOWGeK7GUeMmUokcl70o6YHo1voWpwBuW+HIGpYREhIiEKTWDUj60FXguwRSRGJjSydYgkSQwENi92xa/F5w/suff4jMcQ9dKkTNIN/JkjqbonpglbPrD1K9qONKi5SeRq+amCUTPEH1ggShQTEkCBX+2hUqZdHsWxDpykOTLhVDo5askRJpkppgdqzFsdo3GDxyhKDl4V++gK6DvlTFWo2et+WypJFrkCsVaRk2y7UOImfjLwZUaz6VxnGUUURzegk6wsjV2chahG1BIziH0CYRG9azfki6lqAaLLNcXiXhtdHtIrK5zIR5Gwveg2hBgB2skTGzWNZBmuEy3uAlrKqDU9XI2opgXUPqioSu8OyNVJE6wctt1F2bMoocTSOXBaEVkhMFBjI2TXeZlmWyIz1EazKFWJhHdzaySvr95xsCUBrZTCeyZDbm0OvrKCAUJrKnyPt4+MAgacoV+OpsG3v8PgY1g23hNI4DaauLWDlNShkMTwu2hQusX4hqRVMSYyyPmA0IO4rtoxrBCthJA01AoQBZd5BHFw4w2FlGhZBLXGJp9+sp2mt4jTVMNwRdMLUjyXBdZ2G2CTQJlEaQ9MikFVLX6dZGqNuzSCkIhYmrNl9S2Ea3/zNr613CUAECzwN7ZIKit0DnzCCT+Tm6sgN+iuXwEg6SdG8+owlgq/fv4xKLXS9QhBAMvGMPA+/Ymr76XfwkN217BX/16Mf5y/CP+Zd3FXn/n/4bSn/2Oj7+Y5cpD0QZbYb1Am+Ww7yyOcCos0pKniLfLmN2A4hemHG34fMnCYcTWshUZZyXFW/lDW/+SUZH9z7XXzcmJiYmJibmGUadnafbTZPMguoqarLCYuBEcUcMd2NdTj4bsmIcJrgccv+Zi3RHO1w3nkXZOcKGwi5ojNmKHBdo9oShdFJH5g1Ep4oWGBgqy9SOBIVSk+6pNZRSjBVGMM1lGtY0Yp+OZjrcNDzJ2cYs7e0Osugj5kNSKfADl3rvZb1MmdjHFggJSY+HVHUDecssnUcmWZ3vkNmlCPcahHmbejBA5dIoteY8KIl9Qx5fBWhuA2v5UWpVxeU5DbF7GH9vF886wzn/ItmqghB0X/QXSLLVC5rfMNFNDz0psNrrtH0LZabwApNEUGZ8HB6ZrxFoGUYNg3WvhhG2GBpRqBlFGGoMG4MMTr2S43d/hfOlAH08jWalwQtQiUj8aVU0SjaMDBpoUx7dXugvwwhpG3N0KzYrTXCtEXS3QVhv0A5T6E4NO6ighY9ST3jMpu8knREk6oPoysBxINB9MuEKK7vrtI0sN3ET44UKZ1eqSM1GDZVgocxM7jJefgyv0SJjRQKdQMPMS7blyrjrSfSwyHyo4ZkdTAJSTY2EK1kpfYP25DLXq310e2HNuo6gru2nptpMlwwydp6BUuSWmDo8SPCQoKyX6NYfJTvaxR40GKuPkHc1VCpF0i7ieV10YdDuJFhaAl3odP0AP7FG1TzBiVOvZdY7jZQBrtUEJdBDm4TIsHvgEOudZdYrecIwxG8NI5VPxuhQE1BJlmmqS4R2FU/3ON8tsDPcBWYKV08/ZgBFC9OHmzvZPXAs2tazJHO7ZTSjS9MpIrVIqAzlpnhjK5tEN4MmBFovgL6nRcJZLhlZoSXzDjShok1TPu0x2y1wx1AC58I+rJSgZSbJdHRSykKgsbqsQQFkNuwbIOqaBZpBGII/YHH5wiUGzTr1bhq5W1ALZmgbM7jLD2Hz3XTH2kyFWUjkKe2ATEVysq3TGUmQXukQ+CFuHnK9FeLZznFuuf211M+ewg7WSagmmdoS+4e+j9NrDzI1oSh2JF3fZfnSGufFKK6aYkSrIrsKcygF56BY6TBSL1O7exh5VsMYDXF71p8AY8559GZU33pgklkrYKcVjEFntg1+gv3FNqdbuZ5ll0clTDE4AOPFZZrNBDPzFsMTFmmjJzSHAuEYNKwc68tppJSYRmQdYm3PkX/JTbD4MCo7waphsZTvoFWGmKlAai4gY5RpaSMkErO4YprFgSOUtk9TW/wq199usX68AQJy+SqGPUzY8LiYqLKeWKRTOwzJNVLJLsbCA5jmOKYZoglFp5Pg3NkuU2TQhYHeWEKIc7S9HF5Kcd3ETXztzP1kl0PCBySJmyGTcGn1xK7cuQRNLYmuOxB0mXXrpLQkQgqcs5KEncDVfSwiwbK6o4XUFSvtKAOo6Fmh6mioVgNt/TyOC16rZ8WYG0TJKbyTPppxnmZdw2wk8Q928e0AxNWZY6FnkYOiqxkoU+Hd1iLwNVquw1JthbZcYLkUYs04GCZU3EuMajl0YWDIDl0nIJMosFDzafsuQ9M26pzgBBfoLPsIfYpiRef0mYtYmSqer2NkWizioUsHK6hydF+Re8tL0JDowgaa6PPHMK0E2JP4nQ623M3hbfu5cOpfyJChIEvUunlSVoPQDEmlBc2uRphO027tpLqcpzjlYWZ7poVCI7QkQt/M3OoWTRo7QtQ5iVPwsacNpoZdOqfOAoLvv/VNuJ2v8w93n8MzLJBdtJ4YmXDzpCtrbMseBGC5eoru+hqH8rsQZR0pDEpDin+xLUJlMECOSht8/yL+cgN9AroXikgvy+F9DlIT4ChMS2ApDz0U6Ejkji7CTqDlFd6lEMoaoyMCuiZhRzE+IFi/pNA6GvpGhlBDIHMJ7Okc6ov3k24IRFKgp9IwMIR5cQktB/emSuwrnkcIMG0d83yV8mmH1niSwDZw2ewz+eQi+FEmVFt0aCtYLyuCABLDU4xU5kkUV5lbfJSm1SafHcJfgsREkYHsvyHkU2ga4D4xB8VY7Po2ZP/Qjfy3V38cpRRtv4nzxgor/+oePvh//jvF/3yQC7fO8Vdnf48/XL2TL0zs5t/c/PO8bPoN1B3F5bN1Tp25wP+79D/JztY5cvYwP3RxJ8lO9AM/9ytf5sTtf8Arfva1mAdeDVcM5JiYmJiYmJhvH0I3xNcFIq+htUMuhOfwnEjamcqUUFUFJkxfGkF2otTjrpToBUUxmUWZGfyaxBrREUJSuxTi+VlMmuzIjHEuq5GqhZi+iQxsbD1BQJe1U1GqN7OQwugapFI6Rs4gkRjABo6KnTxaXaNi+KRVQMLwCHdJyieLkBtlcDLJak2nk9NITYXcPjLN2spD+Nvnaa3soOsJsoHi4owLdYCbGZ2oEKy1ac4M4+Zt7NoCQhuiWla0xBDtugEDBegIGkmN7HqIvqAYvJxmnijGU+dsk8Az0Bd3gKaRyNfQ3FmkY3DuPNj5BEahSSGv0XykTk6NcWA6z9e8+ymJOQbtApYsEIYelgqYnbMAhapI2qZOSAJ/YhvCMGnWFR0pyewGDw0vdKj6M+S1KVRqHqdaRzSnWEvppI4USH7Dx/16jdTeImIxKm/TaNHNrIOuaKOY0Dro2PgBWOEaphlgZxWJ/BTXjVh4M2l2qJdSFnfBUAIWQA4GCMMkow2TsC4hvJBk+18Iu5KkUFiiRNiZgu4FAs3DNS6RC/aRxCFTkFRzNaSEuluhGszgXCwghIGlaWSn8hz4/legWz03uJRJuHcYo71Cox4wErqYAxrdiyFhxccYSpBOlqjXLhI4GeaXhjh5CpLbDRruhuWb4N7Ld9F1bWwzwDe6CNdEJCPBciq/m5HkbiByB3O9AoJVcomQMAOe7uIbHaCAS4KWthEx+1r03IfCJGthOvqz52b34MKXmEuWaXcHQIusTAKlYdvR9YaCEWZ9nUGrid4LoC97Mcj2bs9wrqkxOD7Lsr6XoL4bV5YxujCy6zYu3QM55bMytZOJZkg6kyRsK/Q9NuvpDKtignHhICVkCxqB6VNppmgPSFZ9E+00DCUmGNqT5JFzSwgBtgWr3qMMDLQoVoeZmuiiGyFtQ6EMjX2pNzJ7+LPIWgdpmqRG88hKjeCgjrWjCELDDJuQLJNbbzOaHOd0eA+FrELrKppraVZXwZw/T6M1xtzYIXYGCmsyWl9kRB1bOsjzZwnD/YiOj+uCdAIGKsvY6xIORbWeauXRjE3XLneujbUSQMllep9Fq90Gw6HtFsFOk0q66HUNb9ZhviXYd12U+c1op2j5eRojm5kas5k5Usm9XHcdYKYIt70sGkvFB6kNjTNQDfFMj7su/z9GQ4nmD8IOjZrQ0cJF0mGW8W0JRi2LhbrGamWJTljFnNiBc6mOvjqOH6ywrCaxKw5S9zFtyej0DGteA1t3SAc19mu3QdikmdhJY/kh5kLIIbjcUIzLM1irt1B9+H4IPSYLoLsuw/mQy+uTwBpa6IMMEX4Xpdu0/A71qkZbU9iujdIUJiZW6DCgZgnH02jn9vRGUCR2Zc00tfYadARuQ5Cat6EIS0MahmGjJ4cRjSnaqSSqbWOeACwf5SepBZcpGNvw9QyG7AVdDxuMDMOj9Z7buQEjmSJr88vMz+8EdZzQqVKQGbKmTltTtLwlQuWSFT6hrmg5Ccq+oqWaFAcHqN4scBod2ssBmdQSzcsL6AM1hnJpyCYIki4z7hy26nB9epRUdpBk7TwZp0O9mkVLS4Sr0RU+MzMKV6uBDq86chPG52dJ6xWEgGCiQNFoUin56PMGQkJgFFDaIFIzMDIJjG0GtEDpFqEMsG4cR7hRtt1H3XsY7onZoOHuvxlzoAqnIrdMzCTsv53cxRbG4AoirGMriw4hmtQRUqOgjUDgIZpruPcIjltriNUMbT1P4mXXsXDn10m7NmlMFh1w5DwyiEKOnRs6RyG5j4N7E/iroBoSUxeYykWTIoqnlZAow0IvmviXc5AfI3VDic69bWRbIV3FRopVX+ut/wsadrCOkdXRNAGegiEdO2kiRqZIJpZo5DXWrQT7BKT26/gkmJyAlYsj+O4ZOkESH4uqKpFMdNC6PnbPl9TsxeF6uP1yVE7j5iGdcE4h3Cad0MG/tU3lfoWqGKSmdmGpEUi8BJWbI/mYZAuPRyx2fRsjhCBj5cjcnCP3uRJzP3Un5Z9/hIGsyc+8+mdZ3V3hb8t/zm9d+E980f4TDqkbMR+VpB4R/PuV740ukrep7h3jAWsYt3OSKf8ce790iK89+jCZN3+Y0dFX4E/cgTF1kNzUGPmCQSdostS8jBt00YRGysxQSJTI2kV0Tf/mhY6JiYmJiYl5TgjMDis5iT12B7vWTzKxlkSUCgznPfZnD3PyH6tQTDO54zVkOzYPXjiD60F+Jol+r0twg4cSCayhkPa6RDegpe3gQPYYpmUxMj7F+vIlsrrFyDBkGxnMewOWmpcJhwXZA8O01ss4QTdyw0sU0Lw6BgEmFsvdLLtRpIJlSkckdiuBVUqQf9MIC1/R0GpfB2B4+HrWVh5C5H2yrzrM+cZ51BnIGx4TvXXs2ujLSezskCw38dQEze4yF6qKhr6HQE+gTB1N6NgWdGyBHBCY50OM3tvhVsbjwgVQF5ocui7KQufUi4jKdkK7xsz6fUylR5gNH6RR0yk0LIZGplBakSltjbFUhiHLp6rnmAscAi9D4OmAoiFrVMp5MnaeRWsQfbFBuQJyKEUma7CenwRnlfTkQ2wr6Dz0tUskwjGMznbC3BoDOw28CwmCOZi42KbTXiRJiqUDqwTGRYxeDKRCKkMgJAKBgcf0QInSzlEGj7wa90ITFSqSyx6lwvVsv0VxWTsLCYWmciQ0m3QyJC/aJDOXkRmdlDXNmthDQx8i7H4JQ/nUsj6Fdp1C0YGhLmhwpjnLQlilE6ZZGy2TXpbkcmBbSbTM1qXGbS+F8qVB5KPTTM510Ut2FPuprdANm3RyEFE+hdsa6ntzzV7W8X0zyqoHdGWVMND67l6au+n35Xlw+TIMujfjhk1a1EnpPpYZkrCitpZKkEy4ZNIhAzt2E07d+jgjaNNyYJUQgpCmzDCbyuH3XHuX02tMaoMY3SGkmEdHMjKi8JcsulLnRHsbB/RVVHYMiEQxXRdMjmSpdtdQxVW81KN05w4xcDrAMqOAz7ow2b9wCwntYTJ7DJqaxkr2dpa8hzGDNVwvgUSn5s0SptdZutUgnxME2OinDZLFLMPFAY7JWQw9xY7hcSzrMrqA9HwO99hFQKEVRtixL8nAhMbCA1H2OimybB/dzaXUw9FzRPoYuRxCa8BQh4E5m+an70OfOAE6GOcSzGsVEClAkq67ZM6GcBORZdeARmItcl1yOx5hwcZutAnuusTFB9coLLQBk0ZZEXhpEl2BnpNRNsGiTTDvo7VdjFEYq6yQ2rbEPff03EsnXwpfuJfEik4x69I6JQj3Q9kc5f6l3exKlvASOrblMz62xLFjHlOTa2Qzm618oXyCTC5g9KDGshAoHUIXZtDQD1SY3KPTXQI/mIFwmYlsinRgsC2Rwy06NHe4JA8P0Liwir6qKC3kWds2y1KrSi24ADVJqXSKwO8woq8hQ8moXycwMtSTBxDmGC13gNHVSTrHz1K+4DHsreOL61mz7iXdXGfgLLjrNma+gJBp0oS43gzmqMSojhH6DhLFaiPPwFQLmfaxVkxs2aENGEaAelUa8iMMr+ocNPego6j4NYyHUug1kMkicrdG19TQdpQQF0MS4QDNQofl1ABDx4dpNRYo5nVqxhrJG24nNTyBkGt4i+fZNtxkcAC8XuxF0xAYA1N8VxseqLvU/XMkDJ+iPYoySvgW0JojfeMSciABK9DsWjiaoJaeYiRVwEk3oQnZtM/QyCrivMK6J4P3khZmcRwpYPtcFjPRYVvOptscQ68m2N1aQkOibB/R1dBFhsp8EqNdZdcdDfSayQ69iZwsMNOeIpgqMnE0x/DyAvOPXKBOh+6ISdgwEALyLx/FWek9Z6w0Sik6Qw7HMsdIBAmCxDSe0unYR6j5r0BmRlETE0BP7DISaBmFbQvsUzn2HSjQ9r6BHlZYZLMzHlAWfpCnXRyiqnyqcoEBfRw/l8XXWwg/Q9BOILMGg0M+3eog1dkizsB5ljnPuQtZsmXAVcglSS4ok1QhRc0BwwQjgTmVwpvbhTGUxJzKwAMGYUcRdDS0pI47qrGSGeam77sD+ZXjZNzLGMmp3rMLZEkjkwH70AQla5psOkXywX9CIbDGNKztI4RfKlMtDnJ57iim6ACCYKSAaTi4VdhtRZ5og5k2bUvD13NofoBV6+CvS2R7DfdAG3QQgyHaDAgtyU37Q+rlaVQiTcEdfJzn9lZisetFgjWVYedfvp72nUvUP3eZxj/NY//fLj/Em4E394/zLI/OdSGZd+1m9LX7SFw3gNAErwA8bx+nTr+Vr3/6cxz56w782bv4xBs/S6D+D6l5QfkbihlN0pSCg2cPcOOjNzC+MoYealTzNRZHllge9CgXhwkL1zOVfCXj+QkGilAcgMHeZ2AAbDuODxYTExMTE/NsUt6/xgVvgmR+jqHVBknPQF4osO1d30Xrn+YoFKqYhSTCTJLPKYas/bS0kEJBI+kkcS81UBMH0Pb4yEceJZ8Hq2Sgh9NIYGTPDtYfvMSNuwfYd1jQ+ppJ0S9Sp4Kc0MmOl5CZKdzAwU04GLqFTA8xPbXKwwvbqYgxtumQqh5H1wUDLyvh1lNohs7BA4qFtZ20xQrZzBj64B78oM2tOyfZs76bfz5xF77sorQ2QqYJtSSLyuRC7UHcszXUUJp2OaBqZMjmJaXtGkLAwpKG60oOXX874bE2a6cVZWGwTImXhBqGDo4TZYwTlk6usIvZ+sOAYm5xGSlHcbZnmSgVGCpswy8VEPoUerHOkHmSVj3FYjjN/HCBI9tLtLQ27oqPa00yMHmYZdtCH7DRgyaiGWAceT2l4wuEHZ2V5jyrrQcwOxqTy7tIF0rcemgfrcx9VF42jH+nYHShiq9tI3M4QXJHiOeX2WkdYWV5EjPbpG1aTKuQke/2GBsooNmCQNMxBiM3EiFA2zbN9oEUl4ZHQejcVvw+LjpfQ9MV29MXAUEunWL79BCVRg46koS3h0RnhnO7JcltF8l2A5KlyA3v3rpJd1sJ6eo4BQ12akyOZ9m++2aEvtXVJJUSpHam0BbGqK7BYA7E4CCdhQ5BPUM2NYKUCtEe6cewCdwkpukzoHXJpF0sSyHru1FuFynnwDHpOiWUE3Luyw9TV6PstJbJOUss5dY56Z3ANA8xkS2gaT4ppTFcCJG6j8oMotIlCK4OAKNpZcJwmERyAZVcZbneYkIOcsZfR+QmWAsiq6VRe5gVa5amu0zNG+FV1/0YJxY/xT+fm6ARZphND8L0S4Cz/WsXU8O0vAZCCJIJ0KYfJXlBYOoWgwNRcWqeReXwS9k//RDNc+AbBfJqmrVwiRVHo21N4so59G06I8PRvHr7Do2JziGU1mWHt8bN9gr7th1lYOAwy73A/5mEhVg/j9Ag/aoR8iMa+sI3GHIfoaoUUlhMD2wjlTQ5sXIfTtBh4ideS2ptnfXjjzBSH0c0l9lb2Y0oSRoNj7WEDjoU8lAtb0Zkt4bTsN0m0DoYNR1vr0AGNrmGQ3dlmaC1hONY2LbPgws38tBSleFmjeTgEikVYu7KE641AA9DD1FrywTCZzVM9RrJgJoJGqSzOmvVSdYSO1hqheSbLYK8Rj7foTRYQ4jNrHdXstyK3PvSachMCfbk7uDe2TvRMQkTPoYBt0y+nLtnvsbePTrdromx8w1sTxyjvF6kWVrhX2b+Fm1Yoq/3AvcnjtGp5EjO7SKRGiWc7cKOi0zbAsfZwc6X7GK2s5NkDbrdAruNN1BkEYBWE0yzw9SExpQqkTgX0go7NK0048rhfMImR8BKukMyGGbEzmKpDpnRJOWx20jl72e1M8/2tc0kHC8fOIqXHKamN0iaCmGkQDronSStVgepwDxYRB3U4Rw8vDzAbYcEyTMFGoVVypUxRq2dDNglzPx5VgOBtHVGxjRKpQkWWrMUCi2EELhKYW7EzsqNM7Y/x/5HH+FY2+e6sSEaF9s4fhdtwqOZ9sgOS6CJmbJwOqPUxAp6Ksv1oy+j2/1nVAC7JzJ0kw1a56Nxqs9b7L3hdXgduFS4H8JpZGs/nUsmodDRjQDDd1itNBkKUmRv+gGqiw0mjBrFe/+Z+iM2thUgCwX2/cgeLs9CvqAjCt+DfuoCZy8cZ/i67ZTXJjGPmlgjAnJvIL0ywGB+G5cqpzm1dn8UR0t5KM3Eq0zTnhtDQyeVYmv8RyEwBhPsHjyMVCHm6TOMVsYgpbHs+yz3DpvUc4S5KcqDearrDxPgsRrO8EhNoCeaqKVRZE0ysbtAYbRCIjtJZ20nA4bNSutzXAwbjEzcQeGyjfR8DBWw16hhGSkQJkq3MIeSJA8NYI6nEZpAy6UIujl8Yxi9lMDraoAG+RItaxul8CzG8irZoybTr9uDliuQSApAIA7cgnAcMuk/xjMjq1YtlSD7mgmsP53FqCpU1aJY6JKd7uDoaWpOhum1FIXRS1jdAjt3JfFnq5QSHt3jXbSqxCl2UYWQo3t+ANE4A02D4R0jaGfWKBQ1YOiaY/laxGLXiwghBJmXj5N5+TjjShGsOTinq4QVBz1vY4ymsHflGBwpUa1ebbZtWYLrj8D1R95E5/1Vzvzbf+Btf/NOlh54DWf2nyFrKF65MMHkmWkMx0JmOqiJeZTRIVMbYMfDN6N7vXTaVpvLk7/LzPQcD00vUym2MJSBIU3M0CQhDJKaQUIYZIVNgSRFLUFOt0hpGrZIYJppjEQOO5FD2Cm0RAo9mcRIpjDSKfRECj2Zwkgm8U0JhkQXOpoWpXPVRJTpQhMaQmho4on59sbExMTExLxQ+fM//3P+8A//kLW1Nfbs2cMHPvABbr755mseqyVfi94JEKUulRNR8Of9I7fg3+2gtAGye3SUSKIAayzNUDGJW/0u9m/voi7XCVa7WNuyqIyBWdTwOhnGfngH7fvSyI5PNl/i4OQtsAKtu5YIWz4JI0VluIo5kMCwTPIvn2Q7ear1uyilRpGlMTKFdXJyidW1Gc7kBrE7UbCqxMQgakyyVF4jNAPc/CLIESw9Qaa4g7bXZCA9wGBGUBc5VtuLuOEX0JvXMZhP8vDsIwSGR7ENJW8PbX0Hohtg7U1zYH80B6j7N/PwzGn00hBlAy6rES5lZpi0XNJvmKR9os1itY1dCXB3DcKohT+UQ6zcT0K2yYRFbli+BVGKFjJJx+HgWAazlaZ6fgfpEYtE9gSBabDnjiHCzgEuLJ5kYsckR6eSfP0ekHaG0VFFYWWdzr2riGWfUUZwqjWabo2Ri3lSoc/AjgR7j+Zw7Bshl2S50Sbn3oWVzJD/4e9hZ/0SwdLDJIYOYm07QDYLl2WNkerXGB9NgW4hs6MAGAMJCt+/g5tfL1EqKvsNe38AQzPJ2hbjiQnW5rcx312hK10KZg5NaEztSZA63+ZyfYC8XGK70mgXy6wVVtibHSdgioY7CEmY2gPpFJimxu7h/Zgpm2tippkYh3Pn4dKCTaDvodN06P5Fh7Exk7HaEW6tzOF124w2zqHsQziZGS4GdUq5kNERRfWCQVjbQ7Dk4nW7tDvDdOYkRmKRQRZJFM6wtJzD1Fz2ZNpINLYf+nHk5y9QUDoT2l4a2ipO2GWhcQkdg7UwEj0uVB6lIhfRE6cQ5in8URNQaJ0Emc4IO4vXoQ3ouN59oDdJUyAhLiAEdNxBwvw0l5IH8YxlCDPo+tWZ0XcPHCJnF0hbeR5dvgdok0gYCCEYHweRtSBdIj1kYWYa7JnK0TgPiXCCZS/JF2SS3EFBcqcgV9DYN3QDZ9YeJpdK0UgfZlI+iNERvGLHLqCJmv06LyscImgtk5s+j5dNYE1YaM0T0Igyvw1lU9RqHa7PvQFd10hbUaa7h5buZCQzSSB9wvG9jNsG8rhOejULq4pUyqRhKroZF+NlOouPPownJjnyXTYNUSVz20FC9zT6iIHQBIX0WfaMrVH3AhY6eb6WHmbqcoPSiqC0nsAJDWqJJM1umnuPdUmaLaygiVVZQUgPL1fAqmVJ+xrOw2Wq/iSMeux7+RCznyxz6u8V4akMmmhQ2ruOHIm8TsJN7YdQhtQ66zh+Gzfosn/oRk6vPcjRvWPszA1w7yzsHT7I6GCaiXFBITFIxSZy5QIwk+i7buXQdIv75r+EUgo5Dll7CC5eYuRUBk1Z+NkBjG0aYi3DDWu3M3PuGOvpCTCn2LPDZLcKqFRAT2hoQciZxR1kU7MU9lYYGyjjr0lcL0O44wjewFlY8XBdG79r0sxnaXRHsOshO295F66rERopZtqvwOws023cT9dMoYkkmtBINFcYcy6gJwOUlqUSbKPuTqFGl2kfWGD7njy7S0fQ6y1EO82pFRi+fhers+uUzL0UX5JnpA7d5D5OqQuc736RAX+YoJti9+EB9EoVpRTb9nZIm68kYensKh3GMDPsH9vL1PoZ0sUdkL2XRrtMc2YGZ7BLzszQ8NuMjY2wUh2n6qwwMThAPjHAaGGC2vpxdpOEwd184+XH8RaGsasFtHsEKU9g6BbJAyMUDxyge7lOs5IgX19FFYepqXXSF4bwv+yQGsgw9YYDhOfPISoCs5TFvG0fRkFQKGz2jcLrtzH0jZ2Ea4IOsH882m4lB7h5+xvwAodLldMovYufvoPrR3dwoWKQ16ZZCB8AIuMOgMw7XglO5IItDI3SG3YTVlw6x0Bemme4YnNodYnFbga9EyAbXQI5imXY7D1wE494p+nkPUZKgnzWo9wZI5Fwee2eAl2pyGgzPNJxKISXGdL2cFJb4Uza5bBxFF9bxT07BI0u0uq5/PXCE9m7NrPq6lkbt709+g2ezsLcZl3c/F3TWBcjkd4saIjd+6/9TAdm/QL+tpdj6xZaWpHpGaxJpVHqtsiclzS3w3pjkGnfJ3kRGl9poMwBxgbq4IExkiSzb4TVxhKap5EZPkRw3TYO+RXEhToqkNg7cgQVh7AeuzF+RyOEwBxOYg4nv/XB1yA1VeSGv3sH1U+fx/6j04x9IQouqhcssm+aovi2naRvG4ne2HlttPJ5xOpZgjOX6DxSY+msQswU2fvlVyPQaGfrzO46y/zkZVYG16kmO9SQaJ5NtpWlXRlEro5irhbJr41i+tFgbFkus4OztAfWCQsVRKFGK1+llm5StjzWTZ+KEeApgaY0hBIIqaEpge3Z5JpZivUiI2vDDK8Pk2tlEQhCQ9LMd+nmHfy8D3mJlVGkMchIsFxF6PlINyRwFX4Q0rG6dMwuvungWx6uFeBYAa4V4to+nhEihSBJgoLIkdNy5EWRojWICHUCDTo4NGSLOi3qskFDNRDolMwhBo1RStY4JWuUhJnH0jKYWgI0PfroBkIYIDSEUEgZEMgAL3TwQw9XdukGLZywTddv0fG6aCqFEabR/BSanwE/hfIS+L4gDAVhANKHkRHB6167kZFqY1ImN/9WoJQCFEL1UhgrtXkoCoUEGUazCRVGATClH+VNJkTJALGxXwaoMEAQRH+HHirwQfkEO14FufEoy88V80PVuy9q845bif5uZLI0W02UVI/Zu/V6/euoqyeh0c7HnC+vPk75EtUKUc0A1QpQzRDlSoStgSUgoSEsDRIaaOC1OniNLl7TIXAcXMPDNV0cs0vXcAhtiZmxMNNJ7GyaVCZLJpEnaaTxQpemW2W5Oc9y6zJLzVm2F/fxr2/+wJbv84R4MscCXLOOIrzAJqh1n8ihT68cT+rCT/LaT+q6T/zCT7bIT+QEp6Xj1VvPXns/i/X85PqGIqy6+CsdvEtNnPN1/IUWI//pKOmbh59cGV9EfP7zn+cjH/kI/+N//A9uvPFG/uIv/oL3vve9fO5zn2N8fPyq4ydG8ui47NsxiZtMk0xIUrKEe66GOZoifesOpBsiWz7GYIJ9SjHdhVQqiT9iEzY8rB05MDWSrzmMnR4DIH3LZhukjwwRrDv4yx2QUHrZTta0FiOZyO3BKCUpkuRlhdeTMCJrDJUd41U3DDA4r5BWhWqwndBr4nYXobvUv7YQgp0DBxFCsGfw+ihNe+9t+f6xgwQrTTpeC1InaQMHD1hY3euxD3QZWTWpuiXaiyHJPbn+NQ9um6C1OsE3HgSZHMYZO4t1+SS57ChHjkBteoAz96XonlzBziYQtk6REYZe+3pGR5fRfYU+o6E8iTGYwF9sY1ZdEKCPlNBSGlozYF92kW3ToIsfxBg5QSk3TcIWvOqVG6kfM3Tu7+KvdNCLNumXjrA/GGHhi4/SvOzj3BKw/4dHMQyd4eIeqlTJ3J7CHXw5WlpH03Uo7sRGQ+anGNOjehnclkEPNOTIdaiBHVf1iURi88VfMbn5dtwc3M1EapBq+TitzjLZ1DTB0HUURlNojkv6EUnb7nDLaBsRCi4KybbRmzh49Fa8wKMVlMnaec6tH+e6kZv77X9NDAux46UMjOQ5ddYgdEJEKkC2fOYXawxWNPLtgESwwPSizbCYJTu3QmFJY1DLYFdWGVgNkWqdoXqL822TYiVg4pKLLwbZvrPLxSWLdFdHth30+iDu0gDBKYfM6ghpKUnPCzL6COmMwYUzDyBViJqpo6GxlriEulxnzDJQQGllCNNLk1qsUhIphi5HK9np+WmkCkhbGsPlLP7SbiYmRnAfrWLPC6bdCkJUScwdpPXwGvZspLZ0j5XRdZ0sScDjkLqehluFvIcxnCR5eBA9Y3KkV12SAxjArS8BSJC5tI+vHj+Na7fYYWgcGX0ppfQYo5mpSJAaSpDoXId0q8jhgwi/jTb/AJm1aNGqCkXMnTuRVhbt0lcACCdu5tbRI9w4uQYjE9HYNrPsGNhPx2ux2JhBKcX00BEyO3cQHNQI6z5yZQWtNEBCnKPhLpKwbULls6wucnzlG+iaTihDHtbXGdJGSKeruE6F9ZJAvnkCTZui/pcnsJMB2c48RuYynXCRVqnG0FBIlgoegkxe0JwG2RQ46zkyTYVveKwtzVE1O1SLHUr6CZrbfJoLAW7hIsv5h7BGp9DCSOySYUgrucpyeJEHl75CN4gs0NJWlpHMJCOZyd4LcsEP3fLWqzxRXrP7TTh6g05rc86TMjO8csf3IZXECx20YY0zyeO0Ei1KpUlUTmffq28hONtC1NNUxRrKMel8YxW3l2XviuSLlF69C91vMZRuYOU1rJKOfdM0qcn9nLl0nnBIJ+kXWV6d4C2vPMyxi3M4AwXmwwFSWXjJUfADqKwMMfvwATqeolGzOVWexPLWKZezhBjImw5Rq9moMQ2VmKY4/lWmC3sppUb57pfCqdOK+QWYOT3OgPp+ioMah24WQJ5QBtw6r7HeXsXVVrhYcVgyEhjBOr4AYeUYyI2xt3T95hfTTdIjvaBsB26nsHqCV2sGYTqkYKRpWwnE5M00KmkePmuyb3skxuwpHaHu+2hGDqwMt+77XtB03EsNwqqL8iUHx24j+5JI1E8WTOozgzQKBd56dJT05S75UommVmT4ugzaziTatu0UBkeo1RtXZd8F0DMmQ8OKZhO2TcNjf1otIzLssC3Bj77xlVhGgh0VxfxCSCHfopBvRwHUAa241dVOT5voaRNtbD/L86dIltcxay63kmD7iWUaThIGJSKrMfiyHYysXkdDrvOyqTdQewQamQewBwZJt2bJ6TrKNjk6eQnDgMrwa6Gxzlz3fjAeYrWZ4dJZuJgMqBU7HDRHyFRPoRtRv7P1BPnEIHI7hEkNPWfRzTq4vThaLa+OoRt4uSGE1yEcOwxeYzPjZY+228LrJX5Y8evYrShBhP4age8soPsetMEtC4yTklzD4LwuEKZFKQHYOph1tIIJ05IlK8uSOcFg8mhkuJKJXpooX5I8NIC9Kx+tv66xLrsWsdgV87gIXWPgR/Yy8CN7Cese0gsxBhMI7TEPBiuNHLsexq5HXA/pH4LdvV3BWofml+dp/ssCua8NceDhWx73fqqkE+wQeLdDOxfgeQGyLNHnsowsFcicSqHJp2adFVoO3uA6Xr5GiEL6NvmlAunTBXT5QhsGEqj2Po+P3vtc/d5UA3K9zwZO7/P4LPz6kyrk08Tg8R8/l3qfp0blKZ/53KBBPydJBtH769qZbaAFtJBEtVUEiuQ4wGHgMACn+MyzWt6YmBcC5ngKe3cePWt+64NfxHziE5/g7W9/Oz/4gz8IwM///M9z55138qlPfYqf+Zmfuep4XVcU8j57Bo9gjNzY357YX+iLRpqto/VSqgshIvcLwBxLY45dkZ1ueBfX+gW2JjNYk5kt225j9KrjNoSuDdJJm5fuuWnLtvXOMrrQyVh5QhWlmE9bUebCjX83yFg5Xjr1un6yHoCkkd4SO3Q4VIzWIXfFz2E2K3jJLYpGMxJg19eWuV/r9PePjgpG3pSAN23r11GvRoCegLMz+kcphXIj4UPoGsLUCIKA8OzmeUI32Dm0uejrX1MXpG8d2VpHVorp7znKI91ZlHn1IkxogsTB0pUbUAM7txxz5HoTjnwv6hqLuG+KZqDSQxxIvYpdodNvLz0J2ddOoOrf4Nj9LnbqOvI0GdEm2Db9WozkRuVuB6CUGiNppq99jytQ6RKDabhjCDbnBAkcJ0PYSnLv5z6FpwlSe0ZI1NsYjRZmo0k+m2f/+G7O1s8gu12mM/Nk/Sb5QoHi0Di5yWlURqfitXGEYLZRpV6WlDpDBGsOya6BUgqjAYZhsI8jeKrLXO08E+1tWCLJIXEjjXaH+fUllFKU/BFAw/NcUl2TYCUSO9JdE6kMzAakuhZ5LxXtX+5g1hTpjglCYNYU3kILsxYtzvylDlLXt7wBSJOEbIrEgSJ65ps/547uOMhYai/1us7OHWD2+oquGeiaEVVlageSSOxUZoJw++1olUvIwV1RsOwewa5XI5wGKhv1RbOwKVIKIdhRPBC1rrcfL3T6AqlhgVFMwvao/fcGaYyqzY78QZYebBLgc/P4q9B0DT/wOS5OsCZXaOyZRNM1TiOAJmFwnPTEA1SGTLYfDZntnqK+XCNI+mh5m9LkdkSmhLurQlm3UGYOdXGNufF5avk2B6+b43wwg0DQDpoU7tBJ+yGXv7FI+1wDDwetl1VTIgk1D09F7bd/6Cg5u0jSzFwVc3hs7OrxY+pWX3h+LJrQSBgpgmSAdWiSfBgSCIGuC4YzExgvMQiCgGojDVKRumUYvWdhiQLphmhJg8JUBlGZRKvPIxN55MBOsLMYwE0Tr6Djtmno95EcqzA2UGBquESrpZASbDsS55JANqPTfc0w99+9iAoFFXMfmPs40VtHFY0UE3ui/mPbJTr+d5Myr4gdtV+wc4ei3QbQyF7x+NU1g+unj/T/nq9foNpdB3uApBDsSI18S7Fbjt1ALj2ESg+DppMWGghBagTeMDLZPzRlZkhNv5LHOqDaO3JwtZbfRwkdObSfw5lBlJVGFa9wezNTCN24ptC1wZHD3/z5+ZKp12LpNpYercIGBgS5HJw7W/um512JX9LoZHPUmi2aQYXO9t0kX7oNMbQDLRetCzShUdCHMXW7/9tRsUZR6SGkaSHHrseqzYJhM5hLURiaIFkuU+musmQtM799iVl/AYRgXbRYas2gazoKRSivMHVMAyGEiyGz/kkA7ltIbh0Xi1+79vfwPVrJNQBOrd2PaUYGK6EMKWfPIKWEAuhDgkTZYMGfp5PyaB+QmC+dQtcq9FdvXdAcDdOwmR6MslPqAzapG0qR+382urYwNZ7oL9wLbZUf8wJFz1s8ldDzxlCK4g/upfiDe1FS4S+18S41CZsehAqRMiILtMkMRuFxzN17qEDiL3fw5lqEFRfphig3RAUSoQvQRPSvEKALtGTv2uNpjEEdzWtGlkcqhF7aXYWJV1F05to01+s40sETHmESrGweO5cnUyih20nCdkjYDgg6AbLto7oBqhsgOwF0A6S7+dCIDKIUrt8k1Lt0ui00JbCwSIgkGgJkGFkVyRAlJUr6tMIa7bBOoFz80CUkiLKmKHpl3nzcC6J03Low0IWGgYGlWVjCxNYsLN1A6gGh8PA1j4AAT3gEKuxbRUX2S5v/v+XibE7KFYLef5s7owO2NpLQQBPAFf/2t4nIQg2tFzSkt2/jo129jBKbN91yO3Xl/i1lFti2jdvL2nS15+rW8vYXHeLa+686+7FCrw4yI1Dp6ENGQ1kgPMBTaB4IF3AVSLCySZL5DIl8lmQqQzJMYnk2hmtEfakbvdn22l2cZptuq4UbdvFDD0MzsY0EOXsAU3+cifCTXdw8GR7n2ql0ik6780QOfZzrPv0yPDPXfnbK8eSb5JufsKW+XwDf78le+8lcWi/aGMMprOkMevo7W+QC8DyPEydO8L73vW/L9ttvv52HHnroSV1LPJvPiqdBKbUpkpk8sWzQG8l6roWuCwYGrt6ezQqy2SgukqZd/Xb4idaPEAKReGan0kIT1xS6ntxFnvr5G4v2xyPQLGr6MLqug3n1cU9E6PpmJBKCwChQyUTmFPrNt6EDl+sN7q9VCPfs4sgb38BlFeJ5HqfdBcqdMlOlFOLICEd/cC8AdfdRPM9jttNkxl9lzyGd9GvHmb+riZSS4es0LMsg//ppDMNgKNjH3/7t3+IDmddN0G5ZnDm5jlIKbyJa6V8OlvAnU2ReF1k+zX0tutbgQcGsU2fGj/anv2uCpvcIs90oJtfoYZ2B791Bq3oXALnXT2EYT6/fjI4YjI586+P6WGnk6KGrt5sp1DXa8bGkrSxpso+73zIS7B86ShAE6MJExyRrFzCMSOSZNq5DEnLHtjdu+e5BEFB9JAQbXrHjdXw1vI9Gs81AdQ/F3S/hZQfeclVdORMO//Cl+yk0B7lt4vW0TmpoaLxk8rX9+y09UGe13WVKPxj1VSAMQ9bbLtPGddwy8Zqn3QaPhxDimz9DNIE5lnrc+6uBnYSPEbEB8olB0kb+qu2ZzDWEcSHYc2uKk8uRCPGSnq3B0uJq/2/D2DzvSqFrA9sW2N98aQbAZH4Xk/ld3/rArQVE5Se/9XFPA5UaQOaeHUvwx/vNebIooXHWHeRyu8Xk0CT69Uf7/SK4RhxBAE9LEkze0j/uyhceuqazf+ho//z1Bz3Wm35kJW0c5eXb3tQ/r+U1cIIr5u9KEYQBi0YdUBwafsnmPa4Q5h/rWeN7Pl9oR8lkbpl4DbadQAgR3f8Rl3DDf9iA0A6ZWW1ihYJp6zpunfwuDMPYsp6zjeSW0ENCCKxtj//s+VbEYlfMc4bQBNZEBmvi6gfqEzrf0K75BvmJosxrP7GtPFg7oPAtr/DUFlvFYvGaMdJinh3i+n5uiev7uSWu7+9cqtUqYRgyOLjVLaJUKrG2tva456XTaYrF4rO2sPt2JggC0uk0pmli2zbFYpFE4vEsbZ/cNYGnVO+Pd36xWHxa5Xo6bJQpkUiQTqfRdR1d15+1fvXYOgC23L9YLPbbzTdzrMkMOWMAPTe85fiNdjVNs39eIpFASkk6ncayrP53eOw9N85XSmH3VvxX9hNgy7USicSW/RvbNspy5b8v5vF4rf4bBAGZXgCf4dLIVWJXLpPv70vaKSzDJmmlyGXyVx0P4DgOqUS6f87G+Y9tyyv7K0Ri15V96Km2wTcbixv33ljgXzlOnu6z4crrP5FrPN44ejr3f6HzZOrnmX6mPpW2MU2z/+x4bL+81jPpyfTfK8cBXD0XKHL19w+CgIezJwDYO3ndE+ojjuP04/tNDE/37xcEAcXM4KbYRTQGN6y0i5lBJoafvvD/rXjx9fKYmJiYmJiYmBcpj7UYUEp9UyuCdrtNtVp9US5sni5BENBut/F9H9d1qVarz4jY1Y58f55SvV/r/Odb5N4ok+M4tNvtvtj1bPWrx9YBsOX+1WqVdruN53m4rtv/d2PfxvEb233f7+9zHAcpZb/dN77DY++5sV8p1bcWv7KfAFuu5TjOlv0b24QQ/es+nX7x7cK1+u83GxOP3bfRXle257XELsdx+uc83v2u7K8QLbSv7ENPpQ2+1VjcuPeVYtcTqYcnypO5xuONo6dz/xc6T7R+no1n6lNpG8/z+s+Ox/bLaz2Tnkz/vXIcbDyHnmyfeaJi15Xj8Uqx68qxAJtj8ImW51vxRATLOD1dTExMTExMTMwLnGKxiK7rrK+vb9leLpcplUqPc1ZMTExMTExMzHcmsdgVExMTExMTE/MCx7IsrrvuOu66664t2++++26OHj36PJUqJiYmJiYmJuaFyYvPfjEmJiYmJiYm5kXIe97zHn7u536OQ4cOcfToUT796U+ztLTEO97xjue7aDExMTExMTExLyhisSsmJiYmJiYm5tuAN77xjVSrVT72sY+xurrK3r17+b3f+z0mJiae76LFxMTExMTExLygiMWumJiYmJiYmJhvE971rnfxrne96/kuRkxMTExMTEzMC5o4ZldMTExMTExMTExMTExMTExMzIuGWOyKiYmJiYmJiYmJiYmJiYmJiXnREItdMTExMTExMTExMTExMTExMTEvGmKxKyYmJiYmJiYmJiYmJiYmJibmRcNTDlBfLBafyXJsuZ5pmpimiRBiyzGapmGaJpqmIYTAtm10XUcIgaZpaJqGruvYtk2xWCSRSBAEAVJKTNMknU5TLBYxDIMgCEin0yQSiWtu3yiTYbw4Y/g/0+0X882J6/u5Ja7v55a4vp9b4vqOeTJcOceJ2crGnM80zS1zx2fimvDU5pGPd/7zOe4fO2fWdR1d15+1fvXYOgCumrNf2W6WZWHbdn/fxvEb+69cAyQSCaSUpNNpLMt63Pn/xvlKKWzbBtjST4At10okElv2b2zbKMuV/76Yx+O1+u83GxOP3bfRXle252PrynGcft1u1PW17ndlfwUIw/Cqdd9T4ZuNxY17h2EIsGWcPBNrzCdzjccbR0/n/i90nkz9PNPP1KfSNqZp9p8dj+2X13omPZn+e+U4gCc2F3gqffSx43Hj/x87FmBzDD7R8jwTCKWUelbvEBMTExMTExMTExMTExMTExMT8xwRuzHGxMTExMTExMTExMTExMTExLxoiMWumJiYmJiYmJiYmJiYmJiYmJgXDbHYFRMTExMTExMTExMTExMTExPzoiEWu2JiYmJiYmJiYmJiYmJiYmJiXjTEYldMTExMTExMTExMTExMTExMzIuG513s+vM//3Ne85rXcPjwYd72trdx//33P99FelFw33338f73v5877riDffv28U//9E9b9iul+M3f/E3uuOMOjhw5wo/92I9x7ty556m03958/OMf5+1vfztHjx7ltttu49//+3/PxYsXtxwT1/czxyc/+Une/OY3c+ONN3LjjTfywz/8w3zlK1/p74/r+tnl4x//OPv27ePDH/5wf1tc588cv/mbv8m+ffu2fG6//fb+/riuY54I8dzqhcszNWfwPI9f/MVf5NZbb+WGG27g/e9/P8vLy8/lV4m5gqf62xi34/PPysoKP/uzP8utt97K9ddfz/d///fz6KOP9vfH7fjCJwgCfv3Xf53XvOY1HDlyhNe+9rX81m/9FlLK/jFxO35n8ryKXZ///Of5yEc+wr/7d/+Oz372s9x00028973vZXFx8fks1ouCTqfDvn37+OAHP3jN/b//+7/PJz7xCT74wQ/yV3/1V5RKJd7znvfQarWe45J++3Pvvffyrne9i8985jN84hOfIAxD/vW//td0Op3+MXF9P3OMjo7ysz/7s/z1X/81f/3Xf81LX/pSfuInfqL/gxXX9bPHsWPH+PSnP82+ffu2bI/r/Jllz5493Hnnnf3P3/3d3/X3xXUd862I51YvbJ6pOcOHP/xhvvjFL/Lrv/7rfPKTn6TT6fBv/+2/JQzD5+NrfUfzdH4b43Z8fqnX67zzne/ENE1+//d/n8997nP8l//yX8jlcv1j4nZ84fP7v//7/MVf/AUf/OAH+fznP89/+k//iT/8wz/kT//0T7ccE7fjdyDqeeQHfuAH1Ac/+MEt297whjeoX/3VX32eSvTiZO/eveqLX/xi/28ppbr99tvVxz/+8f4213XVTTfdpD71qU89H0V8UVEul9XevXvVvffeq5SK6/u54JZbblGf+cxn4rp+Fmm1Wuq7v/u71V133aV+9Ed/VP3SL/2SUiru3880v/Ebv6G+7/u+75r74rqOeSLEc6tvL57KnKHRaKjrrrtOfe5zn+sfs7y8rPbv36+++tWvPrdf4Ducp/PbGLfj88///J//U73zne983P1xO3578L73vU/91//6X7ds+w//4T+on/3Zn1VKxe34nczzZtnleR4nTpzgjjvu2LL99ttv56GHHnqeSvWdwfz8PGtra1vq3rIsbrnllrjunwGazSYA+XweiOv72SQMQz73uc/R6XQ4evRoXNfPIh/60Id45Stfycte9rIt2+M6f+a5fPkyd9xxB695zWv4qZ/6Kebm5oC4rmO+NfHc6tuPpzJnePTRR/F9f4uL88jICHv27Inb+Tnm6fw2xu34/POlL32JQ4cO8R//43/ktttu4y1veQuf+cxn+vvjdvz24KabbuKee+7h0qVLAJw+fZoHHniAV77ylUDcjt/JGM/XjavVKmEYMjg4uGV7qVRibW3teSrVdwYb9Xutuo/dHJ4eSik+8pGPcNNNN7F3714gru9ngzNnzvCOd7wD13VJpVL89m//Nrt37+bBBx8E4rp+pvnc5z7HyZMn+au/+qur9sX9+5nlyJEjfPSjH2X79u2Uy2V+53d+h3e84x38/d//fVzXMd+SeG717cVTnTOsr69jmmZfILvymPX19eeg5DHw9H8b43Z8/pmbm+NTn/oU73nPe3j/+9/PsWPH+KVf+iUsy+Itb3lL3I7fJrz3ve+l2WzyPd/zPei6ThiG/NRP/RRvetObgHg8fifzvIldGwghtvytlLpqW8yzw7XqPubp8aEPfYizZ8/yyU9+8qp9cX0/c+zYsYPPfvazNBoNvvCFL/Cf//N/5s/+7M/6++O6fuZYWlriwx/+MH/0R3+EbduPe1xc588MG28hN7jhhht43etex2c/+1muv/56IK7rmG9NPLf69uCZnjPEz4LnjmfztzFux+cOpRSHDh3ip3/6pwE4ePAg58+f51Of+hRvectb+sfF7fjC5vOf/zz/9//+X/7X//pf7N69m1OnTvGRj3yE4eFh3vrWt/aPi9vxO4/nzY2xWCyi6/pVSmm5XKZUKj1PpfrOYGhoCCCu+2eYX/zFX+RLX/oSf/zHf8zo6Gh/e1zfzzyWZbFt2zYOHz7Mz/zMz7B//37+5E/+JK7rZ4ETJ05QLpd529vexsGDBzl48CD33nsvf/qnf8rBgwf79RrX+bNDKpVi7969zMzMxP075lsSz62+fXg6c4ZSqYTv+9Tr9cc9JubZ5Zn4bYzb8flnaGiIXbt2bdm2c+fOvrVPPB6/PfiVX/kV3ve+9/G93/u97Nu3j7e85S38+I//OB//+MeBuB2/k3nexC7Lsrjuuuu4666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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "az.plot_trace(idata, compact=False);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## CmdStanPy integration\n", - "ArviZ also has first class support for [CmdStanPy](https://mc-stan.org/cmdstanpy/). After creating and sampling a CmdStanPy model:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/oriol/bin/miniforge3/envs/general/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "from cmdstanpy import CmdStanModel\n", - "model = CmdStanModel(stan_file=\"schools.stan\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "19:42:30 - cmdstanpy - INFO - CmdStan start processing\n", - "chain 1 |\u001b[33m \u001b[0m| 00:00 Status\u001b[0m\n", - "chain 2 |\u001b[33m \u001b[0m| 00:00 Status\u001b[0m\u001b[A\n", - "\n", - "chain 3 |\u001b[33m \u001b[0m| 00:00 Status\u001b[0m\u001b[A\u001b[A\n", - "\n", - "\n", - "chain 4 |\u001b[33m \u001b[0m| 00:00 Status\u001b[0m\u001b[A\u001b[A\u001b[A\n", - "\n", - "chain 3 |\u001b[34m███████████████████████████████ \u001b[0m| 00:00 Iteration: 1600 / 2000 [ 80%] (Sampling)\u001b[0m\u001b[A\u001b[A\n", - "\n", - "\n", - "chain 1 |\u001b[34m███████████████████████████████████████████████████████████\u001b[0m| 00:00 Sampling completed\u001b[0m\u001b[A\u001b[A\u001b[A\n", - "\n", - "chain 2 |\u001b[34m███████████████████████████████████████████████████████████\u001b[0m| 00:00 Sampling completed\u001b[0m\u001b[A\n", - "chain 3 |\u001b[34m███████████████████████████████████████████████████████████\u001b[0m| 00:00 Sampling completed\u001b[0m\n", - "chain 4 |\u001b[34m███████████████████████████████████████████████████████████\u001b[0m| 00:00 Sampling completed\u001b[0m" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "19:42:30 - cmdstanpy - INFO - CmdStan done processing.\n", - "19:42:30 - cmdstanpy - WARNING - Some chains may have failed to converge.\n", - "\tChain 1 had 27 divergent transitions (2.7%)\n", - "\tChain 2 had 9 divergent transitions (0.9%)\n", - "\tChain 3 had 4 divergent transitions (0.4%)\n", - "\tChain 4 had 20 divergent transitions (2.0%)\n", - "\tUse the \"diagnose()\" method on the CmdStanMCMC object to see further information.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "fit = model.sample(data=\"schools.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The result can be used for plotting with ArviZ directly:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "idata = az.from_cmdstanpy(\n", - " fit,\n", - " posterior_predictive=\"y_hat\",\n", - " dims={\"y_hat\": [\"school\"], \"theta\": [\"school\"]},\n", - " coords={\"school\": schools}\n", - ")\n", - "az.plot_posterior(idata, var_names=[\"tau\", \"theta\"]);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{seealso}\n", - "- {ref}`creating_InferenceData`\n", - "- {ref}`working_with_InferenceData`\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:base] *", - "language": "python", - "name": "conda-base-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/doc/source/getting_started/WorkingWithInferenceData.ipynb b/doc/source/getting_started/WorkingWithInferenceData.ipynb deleted file mode 100644 index d9ad2c39f8..0000000000 --- a/doc/source/getting_started/WorkingWithInferenceData.ipynb +++ /dev/null @@ -1,36177 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(working_with_InferenceData)=\n", - "\n", - "# Working with InferenceData\n", - "\n", - "Here we present a collection of common manipulations you can use while working with `InferenceData`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import arviz as az\n", - "import numpy as np\n", - "import xarray as xr\n", - "\n", - "xr.set_options(display_expand_data=False, display_expand_attrs=False);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`display_expand_data=False` makes the default view for {class}`xarray.DataArray` fold the data values to a single line. To explore the values, click on the {fas}`database` icon on the left of the view, right under the `xarray.DataArray` text. It has no effect on `Dataset` objects that already default to folded views.\n", - "\n", - "`display_expand_attrs=False` folds the attributes in both `DataArray` and `Dataset` objects to keep the views shorter. In this page we print DataArrays and Datasets several times and they always have the same attributes." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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Rather than force multiple different sized arrays into one array, or have users manage multiple objects corresponding to different datasets, it is easier to hold references to each `xarray.Dataset` in an `InferenceData` object." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(netcdf)=\n", - "\n", - "## NetCDF\n", - "[NetCDF](https://www.unidata.ucar.edu/software/netcdf/) is a standard for referencing array oriented files. In other words, while `xarray.Dataset`s, and by extension `InferenceData`, are convenient for accessing arrays in Python memory, *netCDF* provides a convenient mechanism for persistence of model data on disk. In fact, the netCDF dataset was the inspiration for `InferenceData` as netCDF4 supports the concept of groups. `InferenceData` merely wraps `xarray.Dataset` with the same functionality.\n", - "\n", - "Most users will not have to concern themselves with the *netCDF* standard but for completeness it is good to make its usage transparent. It is also worth noting that the netCDF4 file standard is interoperable with HDF5 which may be familiar from other contexts.\n", - "\n", - "Earlier in this tutorial `InferenceData` was loaded from a *netCDF* file" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "data = az.load_arviz_data(\"centered_eight\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, the `InferenceData` objects can be persisted to disk in the netCDF format" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'eight_schools_model.nc'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.to_netcdf(\"eight_schools_model.nc\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Additional Reading\n", - "Additional documentation and tutorials exist for xarray and netCDF4. Check the following links:\n", - "\n", - "## `InferenceData`\n", - "* {ref}`working_with_InferenceData`: Tutorial covering the most common operations with `InferenceData` objects\n", - "* {ref}`creating_InferenceData`: Cookbook with examples of generating InferenceData objects from multiple sources, both external inference libraries like \n", - "* {ref}`data module API reference `\n", - "* {ref}`InferenceData API reference `: description of all available `InferenceData` methods, grouped by topic\n", - "\n", - "## xarray\n", - "* For getting to know xarray, check [xarray documentation](http://xarray.pydata.org/en/stable/why-xarray.html)\n", - "* Feel free to watch the Q/A session about xarray at [xarray lightning talk at SciPy 2015](https://www.youtube.com/watch?v=X0pAhJgySxk&t=949s)\n", - "\n", - "## NetCDF\n", - "* Get to know the introduction of netCDF at the official website of [NetCDF documentation](https://www.unidata.ucar.edu/software/netcdf/docs/.)\n", - "* Netcdf4-python library is a used to read/write netCDF files in both netCDF4 and netCDF3 format. Learn more about it by visitng its API documentation at [NetCDF4 API documentation](http://unidata.github.io/netcdf4-python/)\n", - "* xarray provides direct serialization and IO to netCDF format. Learn how to read/write netCDF files directly as xarray objects at {ref}`NetCDF usage in xarray `\n", - "* Check how to read/write netCDF4 files with HDF5 and vice versa at [NetCDF interoperability with HDF5](https://www.unidata.ucar.edu/software/netcdf/docs/interoperability_hdf5.html)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/doc/source/getting_started/index.md b/doc/source/getting_started/index.md deleted file mode 100644 index e3678b6f73..0000000000 --- a/doc/source/getting_started/index.md +++ /dev/null @@ -1,13 +0,0 @@ -(getting_started)= - -# Getting Started - -```{toctree} -:maxdepth: 2 - -Installation -Introduction -XarrayforArviZ -CreatingInferenceData -WorkingWithInferenceData -``` diff --git a/doc/source/getting_started/schools.json b/doc/source/getting_started/schools.json deleted file mode 100644 index ac1e998980..0000000000 --- a/doc/source/getting_started/schools.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "J": 8, - "y": [28, 8, -3, 7, -1, 1, 18, 12], - "sigma": [15, 10, 16, 11, 9, 11, 10, 18] -} diff --git a/doc/source/getting_started/schools.stan b/doc/source/getting_started/schools.stan deleted file mode 100644 index 87a91a92cf..0000000000 --- a/doc/source/getting_started/schools.stan +++ /dev/null @@ -1,26 +0,0 @@ -data { - int J; - array[J] real y; - array[J] real sigma; -} - -parameters { - real mu; - real tau; - array[J] real theta; -} - -model { - mu ~ normal(0, 5); - tau ~ cauchy(0, 5); - theta ~ normal(mu, tau); - y ~ normal(theta, sigma); -} -generated quantities { - array[J] real log_lik; - array[J] real y_hat; - for (j in 1:J) { - log_lik[j] = normal_lpdf(y[j] | theta[j], sigma[j]); - y_hat[j] = normal_rng(theta[j], sigma[j]); - } -} \ No newline at end of file diff --git a/doc/source/user_guide/Dask.ipynb b/doc/source/user_guide/Dask.ipynb deleted file mode 100644 index 26944638b9..0000000000 --- a/doc/source/user_guide/Dask.ipynb +++ /dev/null @@ -1,4476 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(dask_for_arviz)=\n", - "# Dask for ArviZ\n", - "\n", - "## Dask overview" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dask is a _big data processing_ library used for:\n", - "1. _Parallelizing_ the computation of the workflow consisting of NumPy, pandas, xarray and scikit-learn frameworks.\n", - "2. _Scaling_ the workflows up or down depending upon the hardware that is being used.\n", - "\n", - "Most notably, it provides the support for working with _larger-than-memory_ datasets. In this case, dask partitions the dataset into smaller chunks, then loads only a few chunks from the disk, and once the necessary processing is completed, it throws away the intermediate values. This way, the computations are performed without exceeding the memory limit.\n", - "\n", - "---\n", - "\n", - "Check out these links if you're unsure whether your workflow can benefit from using Dask or not:\n", - "- {doc}`dask:why`\n", - "- {doc}`dask:array-best-practices`\n", - "\n", - "Excerpt from \"Dask Array Best Practices\" doc.\n", - ">If your data fits comfortably in RAM and you are not performance bound, then using `NumPy` might be the right choice. Dask adds another layer of complexity which may get in the way.\n", - ">\n", - ">If you are just looking for speedups rather than scalability then you may want to consider a project like `Numba`.\n", - "\n", - "---\n", - "\n", - ":::{caution}\n", - "Dask is an optional dependency inside ArviZ, which is still being **actively developed**. Currently, few functions belonging to **diagnostics** and **stats** module can utilize Dask's capabilities. \n", - ":::" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import arviz as az\n", - "import numpy as np\n", - "import timeit\n", - "import dask\n", - "\n", - "from arviz.utils import conditional_jit, Dask" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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Error(`Multiple models are named '${e}'`)}}on_message(e,t){const s=this._message_callbacks.get(e);null==s?this._message_callbacks.set(e,new Set([t])):s.add(t)}remove_on_message(e,t){var s;null===(s=this._message_callbacks.get(e))||void 0===s||s.delete(t)}_trigger_on_message(e,t){const s=this._message_callbacks.get(e);if(null!=s)for(const e of s)e(t)}on_change(e,t=!1){this._callbacks.has(e)||this._callbacks.set(e,t)}remove_on_change(e){this._callbacks.delete(e)}_trigger_on_change(e){for(const[t,s]of this._callbacks)if(!s&&e instanceof S.DocumentEventBatch)for(const s of e.events)t(s);else t(e)}_notify_change(e,t,s,o,n){this._trigger_on_change(new S.ModelChangedEvent(this,e,t,s,o,null==n?void 0:n.setter_id,null==n?void 0:n.hint))}static _instantiate_object(e,t,s,o){const n=Object.assign(Object.assign({},s),{id:e,__deferred__:!0});return new(o.get(t))(n)}static _instantiate_references_json(e,t,s){var o;const n=new Map;for(const i of e){const e=i.id,r=i.type,l=null!==(o=i.attributes)&&void 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c.HasProps){if(n.has(e.id)&&!r.has(e.id)){r.add(e.id);const{instance:t,is_new:s}=n.get(e.id),{attributes:o}=t;for(const e of(0,v.values)(o))l(e);s&&(t.finalize(),i.push(t))}}else if((0,b.isArray)(e))for(const t of e)l(t);else if((0,b.isPlainObject)(e))for(const t of(0,v.values)(e))l(t)}for(const e of n.values())l(e.instance);for(const e of i)e.connect_signals()}static _event_for_attribute_change(e,t,s,o,n){if(o.get_model_by_id(e.id).property(t).syncable){const i={kind:\"ModelChanged\",model:{id:e.id},attr:t,new:s};return c.HasProps._json_record_references(o,s,n,{recursive:!0}),i}return null}static _events_to_sync_objects(e,t,s,o){const n=Object.keys(e.attributes),i=Object.keys(t.attributes),r=(0,g.difference)(n,i),l=(0,g.difference)(i,n),_=(0,g.intersection)(n,i),c=[];for(const e of r)a.logger.warn(`Server sent key ${e} but we don't seem to have it in our JSON`);for(const n of l){const i=t.attributes[n];c.push(E._event_for_attribute_change(e,n,i,s,o))}for(const n of _){const i=e.attributes[n],r=t.attributes[n];null==i&&null==r||(null==i||null==r?c.push(E._event_for_attribute_change(e,n,r,s,o)):(0,w.is_equal)(i,r)||c.push(E._event_for_attribute_change(e,n,r,s,o)))}return c.filter((e=>null!=e))}static _compute_patch_since_json(e,t){const s=t.to_json(!1);function o(e){const t=new Map;for(const s of e.roots.references)t.set(s.id,s);return t}const n=o(e),i=new Map,r=[];for(const t of e.roots.root_ids)i.set(t,n.get(t)),r.push(t);const l=o(s),a=new Map,_=[];for(const e of s.roots.root_ids)a.set(e,l.get(e)),_.push(e);if(r.sort(),_.sort(),(0,g.difference)(r,_).length>0||(0,g.difference)(_,r).length>0)throw new Error(\"Not implemented: computing add/remove of document roots\");const c=new Set;let h=[];for(const e of t._all_models.keys())if(n.has(e)){const s=E._events_to_sync_objects(n.get(e),l.get(e),t,c);h=h.concat(s)}const f=new d.Serializer({include_defaults:!1});return f.to_serializable([...c]),{references:[...f.definitions],events:h}}to_json_string(e=!0){return JSON.stringify(this.to_json(e))}to_json(e=!0){const t=new d.Serializer({include_defaults:e}),s=t.to_serializable(this._roots);return{version:l.version,title:this._title,roots:{root_ids:s.map((e=>e.id)),references:[...t.definitions]}}}static from_json_string(e){const t=JSON.parse(e);return E.from_json(t)}static from_json(e){a.logger.debug(\"Creating Document from JSON\");const t=e.version,s=-1!==t.indexOf(\"+\")||-1!==t.indexOf(\"-\"),o=`Library versions: JS (${l.version}) / Python (${t})`;s||l.version.replace(/-(dev|rc)\\./,\"$1\")==t?a.logger.debug(o):(a.logger.warn(\"JS/Python version mismatch\"),a.logger.warn(o));const n=new r.ModelResolver;null!=e.defs&&(0,j.resolve_defs)(e.defs,n);const i=e.roots,_=i.root_ids,c=i.references,d=E._instantiate_references_json(c,new Map,n);E._initialize_references_json(c,new Map,d,new Map);const h=new E({resolver:n});for(const e of _){const t=d.get(e);null!=t&&h.add_root(t)}return h.set_title(e.title),h}replace_with_json(e){E.from_json(e).destructively_move(this)}create_json_patch_string(e){return JSON.stringify(this.create_json_patch(e))}create_json_patch(e){for(const t of e)if(t.document!=this)throw new Error(\"Cannot create a patch using events from a different document\");const t=new d.Serializer,s=t.to_serializable(e);for(const e of this._all_models.values())t.remove_def(e);return{events:s,references:[...t.definitions]}}apply_json_patch(e,t=new Map,s){const o=e.references,n=e.events,i=E._instantiate_references_json(o,this._all_models,this._resolver);t instanceof Map||(t=new Map(t));for(const e of n)switch(e.kind){case\"RootAdded\":case\"RootRemoved\":case\"ModelChanged\":{const t=e.model.id,s=this._all_models.get(t);if(null!=s)i.set(t,s);else if(!i.has(t))throw a.logger.warn(`Got an event for unknown model ${e.model}\"`),new Error(\"event model wasn't known\");break}}const r=new Map(this._all_models),l=new Map;for(const[e,t]of i)r.has(e)||l.set(e,t);E._initialize_references_json(o,r,l,t);for(const e of n)switch(e.kind){case\"MessageSent\":{const{msg_type:s,msg_data:o}=e;let n;if(void 0===o){if(1!=t.size)throw new Error(\"expected exactly one buffer\");{const[[,e]]=t;n=e}}else n=E._resolve_refs(o,r,l,t);this._trigger_on_message(s,n);break}case\"ModelChanged\":{const o=e.model.id,n=this._all_models.get(o);if(null==n)throw new Error(`Cannot apply patch to ${o} which is not in the document`);const i=e.attr,a=E._resolve_refs(e.new,r,l,t);n.setv({[i]:a},{setter_id:s});break}case\"ColumnDataChanged\":{const o=e.column_source.id,n=this._all_models.get(o);if(null==n)throw new Error(`Cannot stream to ${o} which is not in the document`);const i=E._resolve_refs(e.new,new Map,new Map,t);if(null!=e.cols)for(const e in n.data)e in i||(i[e]=n.data[e]);n.setv({data:i},{setter_id:s,check_eq:!1});break}case\"ColumnsStreamed\":{const t=e.column_source.id,o=this._all_models.get(t);if(null==o)throw new Error(`Cannot stream to 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t={};for(const[s,n]of(0,l.entries)((0,f.isFunction)(e)?e(b):e)){const[e,r,i={}]=n;t[s]=[e,r,Object.assign(Object.assign({},i),{internal:!0})]}this.define(t)}static mixins(e){function t(e,t){const s={};for(const[n,r]of(0,l.entries)(t))s[e+n]=r;return s}const s={},n=[];for(const r of(0,f.isArray)(e)?e:[e])if((0,f.isArray)(r)){const[e,i]=r;(0,l.extend)(s,t(e,i)),n.push([e,i])}else{const e=r;(0,l.extend)(s,e),n.push([\"\",e])}this.define(s),this.prototype._mixins=[...this.prototype._mixins,...n]}static override(e){for(const[t,s]of(0,l.entries)(e)){const e=this._fix_default(s,t),n=this.prototype._props[t];if(null==n)throw new Error(`attempted to override nonexistent '${this.prototype.type}.${t}'`);const r=Object.assign({},this.prototype._props);r[t]=Object.assign(Object.assign({},n),{default_value:e}),this.prototype._props=r}}toString(){return`${this.type}(${this.id})`}property(e){const t=this.properties[e];if(null!=t)return t;throw new Error(`unknown property ${this.type}.${e}`)}get attributes(){const e={};for(const t of this)e[t.attr]=t.get_value();return e}[m.clone](e){const t=new Map;for(const s of this)s.dirty&&t.set(s.attr,e.clone(s.get_value()));return new this.constructor(t)}[y.equals](e,t){for(const s of this){const n=e.property(s.attr);if(!t.eq(s.get_value(),n.get_value()))return!1}return!0}[v.pretty](e){const t=e.token,s=[];for(const n of this)if(n.dirty){const r=n.get_value();s.push(`${n.attr}${t(\":\")} ${e.to_string(r)}`)}return`${this.constructor.__qualified__}${t(\"(\")}${t(\"{\")}${s.join(`${t(\",\")} `)}${t(\"}\")}${t(\")\")}`}[d.serialize](e){const t=this.ref();e.add_ref(this,t);const s=this.struct();for(const t of this)t.syncable&&(e.include_defaults||t.dirty)&&(s.attributes[t.attr]=e.to_serializable(t.get_value()));return e.add_def(this,s),t}finalize(){for(const e of this){if(!(e instanceof _.VectorSpec||e instanceof _.ScalarSpec))continue;const 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if((0,f.isArray)(t))for(const n of t)w._json_record_references(e,n,s,{recursive:r});else if((0,f.isPlainObject)(t))for(const n of(0,l.values)(t))w._json_record_references(e,n,s,{recursive:r})}static _value_record_references(e,t,s){const{recursive:n}=s;if(e instanceof w){if(!t.has(e)&&(t.add(e),n))for(const s of e.syncable_properties()){const e=s.get_value();w._value_record_references(e,t,{recursive:n})}}else if((0,f.isArray)(e))for(const s of e)w._value_record_references(s,t,{recursive:n});else if((0,f.isPlainObject)(e))for(const s of(0,l.values)(e))w._value_record_references(s,t,{recursive:n})}references(){const e=new Set;return w._value_record_references(this,e,{recursive:!0}),e}_doc_attached(){}_doc_detached(){}attach_document(e){if(null!=this.document&&this.document!=e)throw new Error(\"models must be owned by only a single document\");this.document=e,this._doc_attached()}detach_document(){this._doc_detached(),this.document=null}_needs_invalidate(e,t){const s=new 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e in l.named_colors}},\n", - " function _(r,t,n,a,o){a(),n.GeneratorFunction=Object.getPrototypeOf((function*(){})).constructor,n.ColorArray=Uint32Array,n.RGBAArray=Uint8ClampedArray,n.infer_type=function(r,t){return r instanceof Float64Array||r instanceof Array||t instanceof Float64Array||t instanceof Array?Float64Array:Float32Array},n.ScreenArray=Float32Array,n.to_screen=function(r){return r instanceof Float32Array?r:Float32Array.from(r)},o(\"Indices\",r(25).BitSet)},\n", - " function _(t,s,r,e,i){var n;e();const o=t(26),a=t(11);class _{constructor(t,s=0){this.size=t,this[n]=\"BitSet\",this._count=null,this._nwords=Math.ceil(t/32),0==s||1==s?(this._array=new Uint32Array(this._nwords),1==s&&this._array.fill(4294967295)):((0,a.assert)(s.length==this._nwords,\"Initializer size mismatch\"),this._array=s)}clone(){return new _(this.size,new 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e.get_transform(i);{const{x_range_name:e,y_range_name:t}=this.model,n=i.x_scales.get(e),s=i.y_scales.get(t);return new u.CoordinateTransform(n,s)}}get plot_view(){return this.parent}get plot_model(){return this.parent.model}get layer(){const{overlays:e,primary:i}=this.canvas;return\"overlay\"==this.model.level?e:i}get canvas(){return this.plot_view.canvas_view}request_render(){this.request_paint()}request_paint(){this.plot_view.request_paint(this)}request_layout(){this.plot_view.request_layout()}notify_finished(){this.plot_view.notify_finished()}notify_finished_after_paint(){this.plot_view.notify_finished_after_paint()}get needs_clip(){return!1}get has_webgl(){return!1}render(){this.model.visible&&this._render(),this._has_finished=!0}renderer_view(e){}}t.RendererView=p,p.__name__=\"RendererView\";class g extends h.Model{constructor(e){super(e)}}t.Renderer=g,a=g,g.__name__=\"Renderer\",a.define((({Boolean:e,String:i,Ref:t,Nullable:n})=>({group:[n(t(c)),null],level:[d.RenderLevel,\"image\"],visible:[e,!0],x_range_name:[i,\"default\"],y_range_name:[i,\"default\"],coordinates:[n(t(u.CoordinateMapping)),null]})))},\n", - " function _(t,e,s,i,n){i();const o=t(1),h=t(15),r=t(43),l=t(8),_=(0,o.__importDefault)(t(44));class d{constructor(t){this.removed=new h.Signal0(this,\"removed\"),this._ready=Promise.resolve(void 0),this._slots=new WeakMap,this._idle_notified=!1;const{model:e,parent:s}=t;this.model=e,this.parent=s,this.root=null==s?this:s.root,this.removed.emit()}get ready(){return this._ready}connect(t,e){let s=this._slots.get(e);return null==s&&(s=(t,s)=>{const i=Promise.resolve(e.call(this,t,s));this._ready=this._ready.then((()=>i))},this._slots.set(e,s)),t.connect(s,this)}disconnect(t,e){return t.disconnect(e,this)}initialize(){this._has_finished=!1,this.is_root&&(this._stylesheet=r.stylesheet);for(const t of this.styles())this.stylesheet.append(t)}async lazy_initialize(){}remove(){this.disconnect_signals(),this.removed.emit()}toString(){return`${this.model.type}View(${this.model.id})`}serializable_state(){return{type:this.model.type}}get is_root(){return null==this.parent}has_finished(){return this._has_finished}get is_idle(){return this.has_finished()}connect_signals(){}disconnect_signals(){h.Signal.disconnect_receiver(this)}on_change(t,e){for(const s of(0,l.isArray)(t)?t:[t])this.connect(s.change,e)}cursor(t,e){return null}get stylesheet(){return this.is_root?this._stylesheet:this.root.stylesheet}styles(){return[_.default]}notify_finished(){this.is_root?!this._idle_notified&&this.has_finished()&&null!=this.model.document&&(this._idle_notified=!0,this.model.document.notify_idle(this.model)):this.root.notify_finished()}}s.View=d,d.__name__=\"View\"},\n", - " function 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r(t)(e,...n)},n.div=r(\"div\"),n.span=r(\"span\"),n.canvas=r(\"canvas\"),n.link=r(\"link\"),n.style=r(\"style\"),n.a=r(\"a\"),n.p=r(\"p\"),n.i=r(\"i\"),n.pre=r(\"pre\"),n.button=r(\"button\"),n.label=r(\"label\"),n.input=r(\"input\"),n.select=r(\"select\"),n.option=r(\"option\"),n.optgroup=r(\"optgroup\"),n.textarea=r(\"textarea\"),n.createSVGElement=function(t,e,...n){const i=document.createElementNS(\"http://www.w3.org/2000/svg\",t);for(const[t,n]of(0,l.entries)(null!=e?e:{}))null==n||(0,s.isBoolean)(n)&&!n||i.setAttribute(t,n);function o(t){if((0,s.isString)(t))i.appendChild(document.createTextNode(t));else if(t instanceof Node)i.appendChild(t);else if(t instanceof NodeList||t instanceof HTMLCollection)for(const e of t)i.appendChild(e);else if(null!=t&&!1!==t)throw new Error(`expected a DOM element, string, false or null, got ${JSON.stringify(t)}`)}for(const t of n)if((0,s.isArray)(t))for(const e of t)o(e);else o(t);return i},n.nbsp=function(){return document.createTextNode(\"\\xa0\")},n.append=function(t,...e){for(const n of e)t.appendChild(n)},n.remove=a,n.removeElement=a,n.replaceWith=function(t,e){const n=t.parentNode;null!=n&&n.replaceChild(e,t)},n.prepend=c,n.empty=function(t,e=!1){let n;for(;n=t.firstChild;)t.removeChild(n);if(e&&t instanceof Element)for(const e of t.attributes)t.removeAttributeNode(e)},n.display=function(t){t.style.display=\"\"},n.undisplay=function(t){t.style.display=\"none\"},n.show=function(t){t.style.visibility=\"\"},n.hide=function(t){t.style.visibility=\"hidden\"},n.offset=function(t){const e=t.getBoundingClientRect();return{top:e.top+window.pageYOffset-document.documentElement.clientTop,left:e.left+window.pageXOffset-document.documentElement.clientLeft}},n.matches=d,n.parent=function(t,e){let n=t;for(;n=n.parentElement;)if(d(n,e))return n;return null},n.extents=f,n.size=u,n.scroll_size=function(t){return{width:Math.ceil(t.scrollWidth),height:Math.ceil(t.scrollHeight)}},n.outer_size=function(t){const{margin:{left:e,right:n,top:i,bottom:o}}=f(t),{width:s,height:l}=u(t);return{width:Math.ceil(s+e+n),height:Math.ceil(l+i+o)}},n.content_size=function(t){const{left:e,top:n}=t.getBoundingClientRect(),{padding:i}=f(t);let o=0,s=0;for(const l of t.children){const t=l.getBoundingClientRect();o=Math.max(o,Math.ceil(t.left-e-i.left+t.width)),s=Math.max(s,Math.ceil(t.top-n-i.top+t.height))}return{width:o,height:s}},n.position=function(t,e,n){const{style:i}=t;if(i.left=`${e.x}px`,i.top=`${e.y}px`,i.width=`${e.width}px`,i.height=`${e.height}px`,null==n)i.margin=\"\";else{const{top:t,right:e,bottom:o,left:s}=n;i.margin=`${t}px ${e}px ${o}px ${s}px`}},n.children=function(t){return Array.from(t.children)};class p{constructor(t){this.el=t,this.classList=t.classList}get values(){const t=[];for(let e=0;e{document.addEventListener(\"DOMContentLoaded\",(()=>t()),{once:!0})}))}},\n", - " function _(o,i,t,e,r){e(),t.root=\"bk-root\",t.default=\".bk-root{position:relative;width:auto;height:auto;box-sizing:border-box;font-family:Helvetica, Arial, sans-serif;font-size:13px;}.bk-root .bk,.bk-root .bk:before,.bk-root .bk:after{box-sizing:inherit;margin:0;border:0;padding:0;background-image:none;font-family:inherit;font-size:100%;line-height:1.42857143;}.bk-root pre.bk{font-family:Courier, monospace;}\"},\n", - " function _(e,t,r,a,c){a();const n=e(1),l=e(46);c(\"Line\",l.Line),c(\"LineScalar\",l.LineScalar),c(\"LineVector\",l.LineVector);const i=e(49);c(\"Fill\",i.Fill),c(\"FillScalar\",i.FillScalar),c(\"FillVector\",i.FillVector);const s=e(50);c(\"Text\",s.Text),c(\"TextScalar\",s.TextScalar),c(\"TextVector\",s.TextVector);const o=e(51);c(\"Hatch\",o.Hatch),c(\"HatchScalar\",o.HatchScalar),c(\"HatchVector\",o.HatchVector);const u=(0,n.__importStar)(e(48)),V=e(47);c(\"VisualProperties\",V.VisualProperties),c(\"VisualUniforms\",V.VisualUniforms);class h{constructor(e){this._visuals=[];for(const[t,r]of e.model._mixins){const a=(()=>{switch(r){case u.Line:return new l.Line(e,t);case u.LineScalar:return new l.LineScalar(e,t);case u.LineVector:return new l.LineVector(e,t);case u.Fill:return new i.Fill(e,t);case u.FillScalar:return new i.FillScalar(e,t);case u.FillVector:return new i.FillVector(e,t);case u.Text:return new s.Text(e,t);case u.TextScalar:return new s.TextScalar(e,t);case u.TextVector:return new s.TextVector(e,t);case u.Hatch:return new o.Hatch(e,t);case u.HatchScalar:return new o.HatchScalar(e,t);case u.HatchVector:return new o.HatchVector(e,t);default:throw new Error(\"unknown visual\")}})();a instanceof V.VisualProperties&&a.update(),this._visuals.push(a),Object.defineProperty(this,t+a.type,{get:()=>a,configurable:!1,enumerable:!0})}}*[Symbol.iterator](){yield*this._visuals}}r.Visuals=h,h.__name__=\"Visuals\"},\n", - " function _(e,t,i,l,s){l();const a=e(1),n=e(47),h=(0,a.__importStar)(e(48)),o=e(22),_=e(8);function r(e){if((0,_.isArray)(e))return e;switch(e){case\"solid\":return[];case\"dashed\":return[6];case\"dotted\":return[2,4];case\"dotdash\":return[2,4,6,4];case\"dashdot\":return[6,4,2,4];default:return e.split(\" \").map(Number).filter(_.isInteger)}}i.resolve_line_dash=r;class u extends n.VisualProperties{get doit(){const e=this.line_color.get_value(),t=this.line_alpha.get_value(),i=this.line_width.get_value();return!(null==e||0==t||0==i)}apply(e){const{doit:t}=this;return t&&(this.set_value(e),e.stroke()),t}values(){return{color:this.line_color.get_value(),alpha:this.line_alpha.get_value(),width:this.line_width.get_value(),join:this.line_join.get_value(),cap:this.line_cap.get_value(),dash:this.line_dash.get_value(),offset:this.line_dash_offset.get_value()}}set_value(e){const t=this.line_color.get_value(),i=this.line_alpha.get_value();e.strokeStyle=(0,o.color2css)(t,i),e.lineWidth=this.line_width.get_value(),e.lineJoin=this.line_join.get_value(),e.lineCap=this.line_cap.get_value(),e.lineDash=r(this.line_dash.get_value()),e.lineDashOffset=this.line_dash_offset.get_value()}}i.Line=u,u.__name__=\"Line\";class c extends n.VisualUniforms{get doit(){const e=this.line_color.value,t=this.line_alpha.value,i=this.line_width.value;return!(0==e||0==t||0==i)}apply(e){const{doit:t}=this;return t&&(this.set_value(e),e.stroke()),t}values(){return{color:this.line_color.value,alpha:this.line_alpha.value,width:this.line_width.value,join:this.line_join.value,cap:this.line_cap.value,dash:this.line_dash.value,offset:this.line_dash_offset.value}}set_value(e){const t=this.line_color.value,i=this.line_alpha.value;e.strokeStyle=(0,o.color2css)(t,i),e.lineWidth=this.line_width.value,e.lineJoin=this.line_join.value,e.lineCap=this.line_cap.value,e.lineDash=r(this.line_dash.value),e.lineDashOffset=this.line_dash_offset.value}}i.LineScalar=c,c.__name__=\"LineScalar\";class d extends n.VisualUniforms{get doit(){const{line_color:e}=this;if(e.is_Scalar()&&0==e.value)return!1;const{line_alpha:t}=this;if(t.is_Scalar()&&0==t.value)return!1;const{line_width:i}=this;return!i.is_Scalar()||0!=i.value}apply(e,t){const{doit:i}=this;return i&&(this.set_vectorize(e,t),e.stroke()),i}values(e){return{color:this.line_color.get(e),alpha:this.line_alpha.get(e),width:this.line_width.get(e),join:this.line_join.get(e),cap:this.line_cap.get(e),dash:this.line_dash.get(e),offset:this.line_dash_offset.get(e)}}set_vectorize(e,t){const i=this.line_color.get(t),l=this.line_alpha.get(t),s=this.line_width.get(t),a=this.line_join.get(t),n=this.line_cap.get(t),h=this.line_dash.get(t),_=this.line_dash_offset.get(t);e.strokeStyle=(0,o.color2css)(i,l),e.lineWidth=s,e.lineJoin=a,e.lineCap=n,e.lineDash=r(h),e.lineDashOffset=_}}i.LineVector=d,d.__name__=\"LineVector\",u.prototype.type=\"line\",u.prototype.attrs=Object.keys(h.Line),c.prototype.type=\"line\",c.prototype.attrs=Object.keys(h.LineScalar),d.prototype.type=\"line\",d.prototype.attrs=Object.keys(h.LineVector)},\n", - " function _(t,s,o,i,r){i();class e{constructor(t,s=\"\"){this.obj=t,this.prefix=s;const o=this;this._props=[];for(const i of this.attrs){const r=t.model.properties[s+i];r.change.connect((()=>this.update())),o[i]=r,this._props.push(r)}}*[Symbol.iterator](){yield*this._props}update(){}}o.VisualProperties=e,e.__name__=\"VisualProperties\";class p{constructor(t,s=\"\"){this.obj=t,this.prefix=s;for(const o of this.attrs)Object.defineProperty(this,o,{get:()=>t[s+o]})}*[Symbol.iterator](){for(const t of this.attrs)yield this.obj.model.properties[this.prefix+t]}update(){}}o.VisualUniforms=p,p.__name__=\"VisualUniforms\"},\n", - " function _(e,l,t,a,c){a();const r=e(1),o=(0,r.__importStar)(e(18)),n=e(20),i=(0,r.__importStar)(e(21)),_=e(13);t.Line={line_color:[i.Nullable(i.Color),\"black\"],line_alpha:[i.Alpha,1],line_width:[i.Number,1],line_join:[n.LineJoin,\"bevel\"],line_cap:[n.LineCap,\"butt\"],line_dash:[i.Or(n.LineDash,i.Array(i.Number)),[]],line_dash_offset:[i.Number,0]},t.Fill={fill_color:[i.Nullable(i.Color),\"gray\"],fill_alpha:[i.Alpha,1]},t.Hatch={hatch_color:[i.Nullable(i.Color),\"black\"],hatch_alpha:[i.Alpha,1],hatch_scale:[i.Number,12],hatch_pattern:[i.Nullable(i.Or(n.HatchPatternType,i.String)),null],hatch_weight:[i.Number,1],hatch_extra:[i.Dict(i.AnyRef()),{}]},t.Text={text_color:[i.Nullable(i.Color),\"#444444\"],text_alpha:[i.Alpha,1],text_font:[o.Font,\"helvetica\"],text_font_size:[i.FontSize,\"16px\"],text_font_style:[n.FontStyle,\"normal\"],text_align:[n.TextAlign,\"left\"],text_baseline:[n.TextBaseline,\"bottom\"],text_line_height:[i.Number,1.2]},t.LineScalar={line_color:[o.ColorScalar,\"black\"],line_alpha:[o.NumberScalar,1],line_width:[o.NumberScalar,1],line_join:[o.LineJoinScalar,\"bevel\"],line_cap:[o.LineCapScalar,\"butt\"],line_dash:[o.LineDashScalar,[]],line_dash_offset:[o.NumberScalar,0]},t.FillScalar={fill_color:[o.ColorScalar,\"gray\"],fill_alpha:[o.NumberScalar,1]},t.HatchScalar={hatch_color:[o.ColorScalar,\"black\"],hatch_alpha:[o.NumberScalar,1],hatch_scale:[o.NumberScalar,12],hatch_pattern:[o.NullStringScalar,null],hatch_weight:[o.NumberScalar,1],hatch_extra:[o.AnyScalar,{}]},t.TextScalar={text_color:[o.ColorScalar,\"#444444\"],text_alpha:[o.NumberScalar,1],text_font:[o.FontScalar,\"helvetica\"],text_font_size:[o.FontSizeScalar,\"16px\"],text_font_style:[o.FontStyleScalar,\"normal\"],text_align:[o.TextAlignScalar,\"left\"],text_baseline:[o.TextBaselineScalar,\"bottom\"],text_line_height:[o.NumberScalar,1.2]},t.LineVector={line_color:[o.ColorSpec,\"black\"],line_alpha:[o.NumberSpec,1],line_width:[o.NumberSpec,1],line_join:[o.LineJoinSpec,\"bevel\"],line_cap:[o.LineCapSpec,\"butt\"],line_dash:[o.LineDashSpec,[]],line_dash_offset:[o.NumberSpec,0]},t.FillVector={fill_color:[o.ColorSpec,\"gray\"],fill_alpha:[o.NumberSpec,1]},t.HatchVector={hatch_color:[o.ColorSpec,\"black\"],hatch_alpha:[o.NumberSpec,1],hatch_scale:[o.NumberSpec,12],hatch_pattern:[o.NullStringSpec,null],hatch_weight:[o.NumberSpec,1],hatch_extra:[o.AnyScalar,{}]},t.TextVector={text_color:[o.ColorSpec,\"#444444\"],text_alpha:[o.NumberSpec,1],text_font:[o.FontSpec,\"helvetica\"],text_font_size:[o.FontSizeSpec,\"16px\"],text_font_style:[o.FontStyleSpec,\"normal\"],text_align:[o.TextAlignSpec,\"left\"],text_baseline:[o.TextBaselineSpec,\"bottom\"],text_line_height:[o.NumberSpec,1.2]},t.attrs_of=function(e,l,t,a=!1){const c={};for(const r of(0,_.keys)(t)){const t=`${l}${r}`,o=e[t];c[a?t:r]=o}return c}},\n", - " function _(l,t,e,i,s){i();const a=l(1),o=l(47),r=(0,a.__importStar)(l(48)),_=l(22);class c extends o.VisualProperties{get doit(){const l=this.fill_color.get_value(),t=this.fill_alpha.get_value();return!(null==l||0==t)}apply(l,t){const{doit:e}=this;return e&&(this.set_value(l),l.fill(t)),e}values(){return{color:this.fill_color.get_value(),alpha:this.fill_alpha.get_value()}}set_value(l){const t=this.fill_color.get_value(),e=this.fill_alpha.get_value();l.fillStyle=(0,_.color2css)(t,e)}}e.Fill=c,c.__name__=\"Fill\";class h extends o.VisualUniforms{get doit(){const l=this.fill_color.value,t=this.fill_alpha.value;return!(0==l||0==t)}apply(l,t){const{doit:e}=this;return e&&(this.set_value(l),l.fill(t)),e}values(){return{color:this.fill_color.value,alpha:this.fill_alpha.value}}set_value(l){const t=this.fill_color.value,e=this.fill_alpha.value;l.fillStyle=(0,_.color2css)(t,e)}}e.FillScalar=h,h.__name__=\"FillScalar\";class u extends o.VisualUniforms{get doit(){const{fill_color:l}=this;if(l.is_Scalar()&&0==l.value)return!1;const{fill_alpha:t}=this;return!t.is_Scalar()||0!=t.value}apply(l,t,e){const{doit:i}=this;return i&&(this.set_vectorize(l,t),l.fill(e)),i}values(l){return{color:this.fill_color.get(l),alpha:this.fill_alpha.get(l)}}set_vectorize(l,t){const e=this.fill_color.get(t),i=this.fill_alpha.get(t);l.fillStyle=(0,_.color2css)(e,i)}}e.FillVector=u,u.__name__=\"FillVector\",c.prototype.type=\"fill\",c.prototype.attrs=Object.keys(r.Fill),h.prototype.type=\"fill\",h.prototype.attrs=Object.keys(r.FillScalar),u.prototype.type=\"fill\",u.prototype.attrs=Object.keys(r.FillVector)},\n", - " function _(t,e,l,s,_){s();const i=t(1),a=t(47),o=(0,i.__importStar)(t(48)),n=t(22);class h extends a.VisualProperties{get doit(){const t=this.text_color.get_value(),e=this.text_alpha.get_value();return!(null==t||0==e)}values(){return{color:this.text_color.get_value(),alpha:this.text_alpha.get_value(),font:this.text_font.get_value(),font_size:this.text_font_size.get_value(),font_style:this.text_font_style.get_value(),align:this.text_align.get_value(),baseline:this.text_baseline.get_value(),line_height:this.text_line_height.get_value()}}set_value(t){const e=this.text_color.get_value(),l=this.text_alpha.get_value();t.fillStyle=(0,n.color2css)(e,l),t.font=this.font_value(),t.textAlign=this.text_align.get_value(),t.textBaseline=this.text_baseline.get_value()}font_value(){return`${this.text_font_style.get_value()} ${this.text_font_size.get_value()} ${this.text_font.get_value()}`}}l.Text=h,h.__name__=\"Text\";class x extends a.VisualUniforms{get doit(){const t=this.text_color.value,e=this.text_alpha.value;return!(0==t||0==e)}values(){return{color:this.text_color.value,alpha:this.text_alpha.value,font:this.text_font.value,font_size:this.text_font_size.value,font_style:this.text_font_style.value,align:this.text_align.value,baseline:this.text_baseline.value,line_height:this.text_line_height.value}}set_value(t){const e=this.text_color.value,l=this.text_alpha.value,s=this.font_value(),_=this.text_align.value,i=this.text_baseline.value;t.fillStyle=(0,n.color2css)(e,l),t.font=s,t.textAlign=_,t.textBaseline=i}font_value(){return`${this.text_font_style.value} ${this.text_font_size.value} ${this.text_font.value}`}}l.TextScalar=x,x.__name__=\"TextScalar\";class u extends a.VisualUniforms{values(t){return{color:this.text_color.get(t),alpha:this.text_alpha.get(t),font:this.text_font.get(t),font_size:this.text_font_size.get(t),font_style:this.text_font_style.get(t),align:this.text_align.get(t),baseline:this.text_baseline.get(t),line_height:this.text_line_height.get(t)}}get doit(){const{text_color:t}=this;if(t.is_Scalar()&&0==t.value)return!1;const{text_alpha:e}=this;return!e.is_Scalar()||0!=e.value}set_vectorize(t,e){const l=this.text_color.get(e),s=this.text_alpha.get(e),_=this.font_value(e),i=this.text_align.get(e),a=this.text_baseline.get(e);t.fillStyle=(0,n.color2css)(l,s),t.font=_,t.textAlign=i,t.textBaseline=a}font_value(t){return`${this.text_font_style.get(t)} ${this.text_font_size.get(t)} ${this.text_font.get(t)}`}}l.TextVector=u,u.__name__=\"TextVector\",h.prototype.type=\"text\",h.prototype.attrs=Object.keys(o.Text),x.prototype.type=\"text\",x.prototype.attrs=Object.keys(o.TextScalar),u.prototype.type=\"text\",u.prototype.attrs=Object.keys(o.TextVector)},\n", - " function _(t,e,a,r,i){r();const h=t(1),s=t(47),n=t(52),c=(0,h.__importStar)(t(18)),_=(0,h.__importStar)(t(48));class l extends s.VisualProperties{constructor(){super(...arguments),this._update_iteration=0}update(){if(this._update_iteration++,this._hatch_image=null,!this.doit)return;const t=this.hatch_color.get_value(),e=this.hatch_alpha.get_value(),a=this.hatch_scale.get_value(),r=this.hatch_pattern.get_value(),i=this.hatch_weight.get_value(),h=t=>{this._hatch_image=t},s=this.hatch_extra.get_value()[r];if(null!=s){const r=s.get_pattern(t,e,a,i);if(r instanceof Promise){const{_update_iteration:t}=this;r.then((e=>{this._update_iteration==t&&(h(e),this.obj.request_render())}))}else h(r)}else{const s=this.obj.canvas.create_layer(),c=(0,n.get_pattern)(s,r,t,e,a,i);h(c)}}get doit(){const t=this.hatch_color.get_value(),e=this.hatch_alpha.get_value(),a=this.hatch_pattern.get_value();return!(null==t||0==e||\" \"==a||\"blank\"==a||null==a)}apply(t,e){const{doit:a}=this;return a&&(this.set_value(t),t.layer.undo_transform((()=>t.fill(e)))),a}set_value(t){const e=this.pattern(t);t.fillStyle=null!=e?e:\"transparent\"}pattern(t){const e=this._hatch_image;return null==e?null:t.createPattern(e,this.repetition())}repetition(){const t=this.hatch_pattern.get_value(),e=this.hatch_extra.get_value()[t];if(null==e)return\"repeat\";switch(e.repetition){case\"repeat\":return\"repeat\";case\"repeat_x\":return\"repeat-x\";case\"repeat_y\":return\"repeat-y\";case\"no_repeat\":return\"no-repeat\"}}}a.Hatch=l,l.__name__=\"Hatch\";class o extends s.VisualUniforms{constructor(){super(...arguments),this._static_doit=!1,this._update_iteration=0}_compute_static_doit(){const t=this.hatch_color.value,e=this.hatch_alpha.value,a=this.hatch_pattern.value;return!(null==t||0==e||\" \"==a||\"blank\"==a||null==a)}update(){this._update_iteration++;const t=this.hatch_color.length;if(this._hatch_image=new c.UniformScalar(null,t),this._static_doit=this._compute_static_doit(),!this._static_doit)return;const e=this.hatch_color.value,a=this.hatch_alpha.value,r=this.hatch_scale.value,i=this.hatch_pattern.value,h=this.hatch_weight.value,s=e=>{this._hatch_image=new c.UniformScalar(e,t)},_=this.hatch_extra.value[i];if(null!=_){const t=_.get_pattern(e,a,r,h);if(t instanceof Promise){const{_update_iteration:e}=this;t.then((t=>{this._update_iteration==e&&(s(t),this.obj.request_render())}))}else s(t)}else{const t=this.obj.canvas.create_layer(),c=(0,n.get_pattern)(t,i,e,a,r,h);s(c)}}get doit(){return this._static_doit}apply(t,e){const{doit:a}=this;return a&&(this.set_value(t),t.layer.undo_transform((()=>t.fill(e)))),a}set_value(t){var e;t.fillStyle=null!==(e=this.pattern(t))&&void 0!==e?e:\"transparent\"}pattern(t){const e=this._hatch_image.value;return null==e?null:t.createPattern(e,this.repetition())}repetition(){const t=this.hatch_pattern.value,e=this.hatch_extra.value[t];if(null==e)return\"repeat\";switch(e.repetition){case\"repeat\":return\"repeat\";case\"repeat_x\":return\"repeat-x\";case\"repeat_y\":return\"repeat-y\";case\"no_repeat\":return\"no-repeat\"}}}a.HatchScalar=o,o.__name__=\"HatchScalar\";class u extends s.VisualUniforms{constructor(){super(...arguments),this._static_doit=!1,this._update_iteration=0}_compute_static_doit(){const{hatch_color:t}=this;if(t.is_Scalar()&&0==t.value)return!1;const{hatch_alpha:e}=this;if(e.is_Scalar()&&0==e.value)return!1;const{hatch_pattern:a}=this;if(a.is_Scalar()){const t=a.value;if(\" \"==t||\"blank\"==t||null==t)return!1}return!0}update(){this._update_iteration++;const t=this.hatch_color.length;if(this._hatch_image=new c.UniformScalar(null,t),this._static_doit=this._compute_static_doit(),!this._static_doit)return;const e=(t,e,a,r,i,h)=>{const s=this.hatch_extra.value[t];if(null!=s){const t=s.get_pattern(e,a,r,i);if(t instanceof Promise){const{_update_iteration:e}=this;t.then((t=>{this._update_iteration==e&&(h(t),this.obj.request_render())}))}else h(t)}else{const s=this.obj.canvas.create_layer(),c=(0,n.get_pattern)(s,t,e,a,r,i);h(c)}};if(this.hatch_color.is_Scalar()&&this.hatch_alpha.is_Scalar()&&this.hatch_scale.is_Scalar()&&this.hatch_pattern.is_Scalar()&&this.hatch_weight.is_Scalar()){const a=this.hatch_color.value,r=this.hatch_alpha.value,i=this.hatch_scale.value;e(this.hatch_pattern.value,a,r,i,this.hatch_weight.value,(e=>{this._hatch_image=new c.UniformScalar(e,t)}))}else{const a=new Array(t);a.fill(null),this._hatch_image=new c.UniformVector(a);for(let r=0;r{a[r]=t}))}}}get doit(){return this._static_doit}apply(t,e,a){const{doit:r}=this;return r&&(this.set_vectorize(t,e),t.layer.undo_transform((()=>t.fill(a)))),r}set_vectorize(t,e){var a;t.fillStyle=null!==(a=this.pattern(t,e))&&void 0!==a?a:\"transparent\"}pattern(t,e){const a=this._hatch_image.get(e);return null==a?null:t.createPattern(a,this.repetition(e))}repetition(t){const e=this.hatch_pattern.get(t),a=this.hatch_extra.value[e];if(null==a)return\"repeat\";switch(a.repetition){case\"repeat\":return\"repeat\";case\"repeat_x\":return\"repeat-x\";case\"repeat_y\":return\"repeat-y\";case\"no_repeat\":return\"no-repeat\"}}}a.HatchVector=u,u.__name__=\"HatchVector\",l.prototype.type=\"hatch\",l.prototype.attrs=Object.keys(_.Hatch),o.prototype.type=\"hatch\",o.prototype.attrs=Object.keys(_.HatchScalar),u.prototype.type=\"hatch\",u.prototype.attrs=Object.keys(_.HatchVector)},\n", - " function _(e,o,a,s,r){s();const i=e(22);function l(e,o,a){e.moveTo(0,a+.5),e.lineTo(o,a+.5),e.stroke()}function n(e,o,a){e.moveTo(a+.5,0),e.lineTo(a+.5,o),e.stroke()}function t(e,o){e.moveTo(0,o),e.lineTo(o,0),e.stroke(),e.moveTo(0,0),e.lineTo(o,o),e.stroke()}a.hatch_aliases={\" \":\"blank\",\".\":\"dot\",o:\"ring\",\"-\":\"horizontal_line\",\"|\":\"vertical_line\",\"+\":\"cross\",'\"':\"horizontal_dash\",\":\":\"vertical_dash\",\"@\":\"spiral\",\"/\":\"right_diagonal_line\",\"\\\\\":\"left_diagonal_line\",x:\"diagonal_cross\",\",\":\"right_diagonal_dash\",\"`\":\"left_diagonal_dash\",v:\"horizontal_wave\",\">\":\"vertical_wave\",\"*\":\"criss_cross\"},a.get_pattern=function(e,o,s,r,c,k){return e.resize(c,c),e.prepare(),function(e,o,s,r,c,k){var _;const T=c,v=T/2,h=v/2,d=(0,i.color2css)(s,r);switch(e.strokeStyle=d,e.fillStyle=d,e.lineCap=\"square\",e.lineWidth=k,null!==(_=a.hatch_aliases[o])&&void 0!==_?_:o){case\"blank\":break;case\"dot\":e.arc(v,v,v/2,0,2*Math.PI,!0),e.fill();break;case\"ring\":e.arc(v,v,v/2,0,2*Math.PI,!0),e.stroke();break;case\"horizontal_line\":l(e,T,v);break;case\"vertical_line\":n(e,T,v);break;case\"cross\":l(e,T,v),n(e,T,v);break;case\"horizontal_dash\":l(e,v,v);break;case\"vertical_dash\":n(e,v,v);break;case\"spiral\":{const o=T/30;e.moveTo(v,v);for(let a=0;a<360;a++){const s=.1*a,r=v+o*s*Math.cos(s),i=v+o*s*Math.sin(s);e.lineTo(r,i)}e.stroke();break}case\"right_diagonal_line\":e.moveTo(.5-h,T),e.lineTo(h+.5,0),e.stroke(),e.moveTo(h+.5,T),e.lineTo(3*h+.5,0),e.stroke(),e.moveTo(3*h+.5,T),e.lineTo(5*h+.5,0),e.stroke(),e.stroke();break;case\"left_diagonal_line\":e.moveTo(h+.5,T),e.lineTo(.5-h,0),e.stroke(),e.moveTo(3*h+.5,T),e.lineTo(h+.5,0),e.stroke(),e.moveTo(5*h+.5,T),e.lineTo(3*h+.5,0),e.stroke(),e.stroke();break;case\"diagonal_cross\":t(e,T);break;case\"right_diagonal_dash\":e.moveTo(h+.5,3*h+.5),e.lineTo(3*h+.5,h+.5),e.stroke();break;case\"left_diagonal_dash\":e.moveTo(h+.5,h+.5),e.lineTo(3*h+.5,3*h+.5),e.stroke();break;case\"horizontal_wave\":e.moveTo(0,h),e.lineTo(v,3*h),e.lineTo(T,h),e.stroke();break;case\"vertical_wave\":e.moveTo(h,0),e.lineTo(3*h,v),e.lineTo(h,T),e.stroke();break;case\"criss_cross\":t(e,T),l(e,T,v),n(e,T,v)}}(e.ctx,o,s,r,c,k),e.canvas}},\n", - " function _(e,t,s,n,c){var a;n();const i=e(14),r=e(8),l=e(13),o=e(26),_=e(19);class h extends i.HasProps{constructor(e){super(e)}get is_syncable(){return this.syncable}[o.equals](e,t){return t.eq(this.id,e.id)&&super[o.equals](e,t)}initialize(){super.initialize(),this._js_callbacks=new Map}connect_signals(){super.connect_signals(),this._update_property_callbacks(),this.connect(this.properties.js_property_callbacks.change,(()=>this._update_property_callbacks())),this.connect(this.properties.js_event_callbacks.change,(()=>this._update_event_callbacks())),this.connect(this.properties.subscribed_events.change,(()=>this._update_event_callbacks()))}_process_event(e){var t;for(const s of null!==(t=this.js_event_callbacks[e.event_name])&&void 0!==t?t:[])s.execute(e);null!=this.document&&this.subscribed_events.some((t=>t==e.event_name))&&this.document.event_manager.send_event(e)}trigger_event(e){null!=this.document&&(e.origin=this,this.document.event_manager.trigger(e))}_update_event_callbacks(){null!=this.document?this.document.event_manager.subscribed_models.add(this):_.logger.warn(\"WARNING: Document not defined for updating event callbacks\")}_update_property_callbacks(){const e=e=>{const[t,s=null]=e.split(\":\");return null!=s?this.properties[s][t]:this[t]};for(const[t,s]of this._js_callbacks){const n=e(t);for(const e of s)this.disconnect(n,e)}this._js_callbacks.clear();for(const[t,s]of(0,l.entries)(this.js_property_callbacks)){const n=s.map((e=>()=>e.execute(this)));this._js_callbacks.set(t,n);const c=e(t);for(const e of n)this.connect(c,e)}}_doc_attached(){(0,l.isEmpty)(this.js_event_callbacks)&&0==this.subscribed_events.length||this._update_event_callbacks()}_doc_detached(){this.document.event_manager.subscribed_models.delete(this)}select(e){if((0,r.isString)(e))return[...this.references()].filter((t=>t instanceof h&&t.name===e));if(e.prototype instanceof i.HasProps)return[...this.references()].filter((t=>t instanceof e));throw new Error(\"invalid selector\")}select_one(e){const t=this.select(e);switch(t.length){case 0:return null;case 1:return t[0];default:throw new Error(\"found more than one object matching given selector\")}}}s.Model=h,a=h,h.__name__=\"Model\",a.define((({Any:e,Unknown:t,Boolean:s,String:n,Array:c,Dict:a,Nullable:i})=>({tags:[c(t),[]],name:[i(n),null],js_property_callbacks:[a(c(e)),{}],js_event_callbacks:[a(c(e)),{}],subscribed_events:[c(n),[]],syncable:[s,!0]})))},\n", - " function _(e,t,s,a,r){var c,n;a();const _=e(12),o=e(53),i=e(55),l=e(59),u=e(61),g=e(62),h=e(57),p=e(63),m=e(67);class x{constructor(e,t){this.x_scale=e,this.y_scale=t,this.x_source=this.x_scale.source_range,this.y_source=this.y_scale.source_range,this.ranges=[this.x_source,this.y_source],this.scales=[this.x_scale,this.y_scale]}map_to_screen(e,t){return[this.x_scale.v_compute(e),this.y_scale.v_compute(t)]}map_from_screen(e,t){return[this.x_scale.v_invert(e),this.y_scale.v_invert(t)]}}s.CoordinateTransform=x,x.__name__=\"CoordinateTransform\";class y extends o.Model{constructor(e){super(e)}get x_ranges(){return new Map([[\"default\",this.x_source]])}get y_ranges(){return new Map([[\"default\",this.y_source]])}_get_scale(e,t,s){if(e instanceof m.FactorRange!=t instanceof g.CategoricalScale)throw new Error(`Range ${e.type} is incompatible is Scale ${t.type}`);t instanceof u.LogScale&&e instanceof p.DataRange1d&&(e.scale_hint=\"log\");const a=t.clone();return a.setv({source_range:e,target_range:s}),a}get_transform(e){const{x_source:t,x_scale:s,x_target:a}=this,r=this._get_scale(t,s,a),{y_source:c,y_scale:n,y_target:_}=this,o=this._get_scale(c,n,_),i=new v({source_scale:r,source_range:r.source_range,target_scale:e.x_scale,target_range:e.x_target}),l=new v({source_scale:o,source_range:o.source_range,target_scale:e.y_scale,target_range:e.y_target});return new x(i,l)}}s.CoordinateMapping=y,c=y,y.__name__=\"CoordinateMapping\",c.define((({Ref:e})=>({x_source:[e(h.Range),()=>new p.DataRange1d],y_source:[e(h.Range),()=>new p.DataRange1d],x_scale:[e(i.Scale),()=>new l.LinearScale],y_scale:[e(i.Scale),()=>new l.LinearScale],x_target:[e(h.Range)],y_target:[e(h.Range)]})));class v extends i.Scale{constructor(e){super(e)}get s_compute(){const e=this.source_scale.s_compute,t=this.target_scale.s_compute;return s=>t(e(s))}get s_invert(){const e=this.source_scale.s_invert,t=this.target_scale.s_invert;return s=>e(t(s))}compute(e){return this.s_compute(e)}v_compute(e){const{s_compute:t}=this;return(0,_.map)(e,t)}invert(e){return this.s_invert(e)}v_invert(e){const{s_invert:t}=this;return(0,_.map)(e,t)}}s.CompositeScale=v,n=v,v.__name__=\"CompositeScale\",n.internal((({Ref:e})=>({source_scale:[e(i.Scale)],target_scale:[e(i.Scale)]})))},\n", - " function _(e,t,r,n,s){var _;n();const a=e(56),c=e(57),o=e(58),i=e(24);class u extends a.Transform{constructor(e){super(e)}compute(e){return this.s_compute(e)}v_compute(e){const t=new i.ScreenArray(e.length),{s_compute:r}=this;for(let n=0;n({source_range:[e(c.Range)],target_range:[e(o.Range1d)]})))},\n", - " function _(n,s,o,r,c){r();const e=n(53);class t extends e.Model{constructor(n){super(n)}}o.Transform=t,t.__name__=\"Transform\"},\n", - " function _(e,t,n,i,s){var r;i();const a=e(53);class l extends a.Model{constructor(e){super(e),this.have_updated_interactively=!1}get is_reversed(){return this.start>this.end}get is_valid(){return isFinite(this.min)&&isFinite(this.max)}get span(){return Math.abs(this.end-this.start)}}n.Range=l,r=l,l.__name__=\"Range\",r.define((({Number:e,Tuple:t,Or:n,Auto:i,Nullable:s})=>({bounds:[s(n(t(s(e),s(e)),i)),null],min_interval:[s(e),null],max_interval:[s(e),null]}))),r.internal((({Array:e,AnyRef:t})=>({plots:[e(t()),[]]})))},\n", - " function _(t,e,s,n,r){var a;n();const i=t(57);class _ extends i.Range{constructor(t){super(t)}_set_auto_bounds(){if(\"auto\"==this.bounds){const t=Math.min(this._reset_start,this._reset_end),e=Math.max(this._reset_start,this._reset_end);this.setv({bounds:[t,e]},{silent:!0})}}initialize(){super.initialize(),this._set_auto_bounds()}get min(){return Math.min(this.start,this.end)}get max(){return Math.max(this.start,this.end)}reset(){this._set_auto_bounds();const{_reset_start:t,_reset_end:e}=this;this.start!=t||this.end!=e?this.setv({start:t,end:e}):this.change.emit()}map(t){return new _({start:t(this.start),end:t(this.end)})}widen(t){let{start:e,end:s}=this;return this.is_reversed?(e+=t,s-=t):(e-=t,s+=t),new _({start:e,end:s})}}s.Range1d=_,a=_,_.__name__=\"Range1d\",a.define((({Number:t,Nullable:e})=>({start:[t,0],end:[t,1],reset_start:[e(t),null,{on_update(t,e){e._reset_start=null!=t?t:e.start}}],reset_end:[e(t),null,{on_update(t,e){e._reset_end=null!=t?t:e.end}}]})))},\n", - " function _(t,e,n,r,s){r();const a=t(60);class _ extends a.ContinuousScale{constructor(t){super(t)}get s_compute(){const[t,e]=this._linear_compute_state();return n=>t*n+e}get s_invert(){const[t,e]=this._linear_compute_state();return n=>(n-e)/t}_linear_compute_state(){const t=this.source_range.start,e=this.source_range.end,n=this.target_range.start,r=(this.target_range.end-n)/(e-t);return[r,-r*t+n]}}n.LinearScale=_,_.__name__=\"LinearScale\"},\n", - " function _(n,c,o,s,e){s();const t=n(55);class u extends t.Scale{constructor(n){super(n)}}o.ContinuousScale=u,u.__name__=\"ContinuousScale\"},\n", - " function _(t,e,a,o,s){o();const r=t(60);class n extends r.ContinuousScale{constructor(t){super(t)}get s_compute(){const[t,e,a,o]=this._compute_state();return s=>{if(0==a)return 0;{const r=(Math.log(s)-o)/a;return isFinite(r)?r*t+e:NaN}}}get s_invert(){const[t,e,a,o]=this._compute_state();return s=>{const r=(s-e)/t;return Math.exp(a*r+o)}}_get_safe_factor(t,e){let a=t<0?0:t,o=e<0?0:e;if(a==o)if(0==a)[a,o]=[1,10];else{const t=Math.log(a)/Math.log(10);a=10**Math.floor(t),o=Math.ceil(t)!=Math.floor(t)?10**Math.ceil(t):10**(Math.ceil(t)+1)}return[a,o]}_compute_state(){const t=this.source_range.start,e=this.source_range.end,a=this.target_range.start,o=this.target_range.end-a,[s,r]=this._get_safe_factor(t,e);let n,c;0==s?(n=Math.log(r),c=0):(n=Math.log(r)-Math.log(s),c=Math.log(s));return[o,a,n,c]}}a.LogScale=n,n.__name__=\"LogScale\"},\n", - " function _(t,e,c,a,s){a();const n=t(55),r=t(59),{_linear_compute_state:o}=r.LinearScale.prototype;class l extends n.Scale{constructor(t){super(t)}get s_compute(){const[t,e]=o.call(this),c=this.source_range;return a=>t*c.synthetic(a)+e}get s_invert(){const[t,e]=o.call(this);return c=>(c-e)/t}}c.CategoricalScale=l,l.__name__=\"CategoricalScale\"},\n", - " function _(t,i,n,a,e){a();const s=t(1);var l;const _=t(64),o=t(20),r=t(9),h=t(19),d=(0,s.__importStar)(t(65)),u=t(66);class g extends _.DataRange{constructor(t){super(t),this.have_updated_interactively=!1}initialize(){super.initialize(),this._initial_start=this.start,this._initial_end=this.end,this._initial_range_padding=this.range_padding,this._initial_range_padding_units=this.range_padding_units,this._initial_follow=this.follow,this._initial_follow_interval=this.follow_interval,this._initial_default_span=this.default_span,this._plot_bounds=new Map}get min(){return Math.min(this.start,this.end)}get max(){return Math.max(this.start,this.end)}computed_renderers(){const{renderers:t,names:i}=this,n=(0,r.concat)(this.plots.map((t=>t.data_renderers)));return(0,u.compute_renderers)(0==t.length?\"auto\":t,n,i)}_compute_plot_bounds(t,i){let n=d.empty();for(const a of t){const t=i.get(a);null==t||!a.visible&&this.only_visible||(n=d.union(n,t))}return n}adjust_bounds_for_aspect(t,i){const n=d.empty();let a=t.x1-t.x0;a<=0&&(a=1);let e=t.y1-t.y0;e<=0&&(e=1);const s=.5*(t.x1+t.x0),l=.5*(t.y1+t.y0);return al&&(\"start\"==this.follow?e=a+s*l:\"end\"==this.follow&&(a=e-s*l)),[a,e]}update(t,i,n,a){if(this.have_updated_interactively)return;const e=this.computed_renderers();let s=this._compute_plot_bounds(e,t);null!=a&&(s=this.adjust_bounds_for_aspect(s,a)),this._plot_bounds.set(n,s);const[l,_]=this._compute_min_max(this._plot_bounds.values(),i);let[o,r]=this._compute_range(l,_);null!=this._initial_start&&(\"log\"==this.scale_hint?this._initial_start>0&&(o=this._initial_start):o=this._initial_start),null!=this._initial_end&&(\"log\"==this.scale_hint?this._initial_end>0&&(r=this._initial_end):r=this._initial_end);let h=!1;\"auto\"==this.bounds&&(this.setv({bounds:[o,r]},{silent:!0}),h=!0);const[d,u]=[this.start,this.end];if(o!=d||r!=u){const t={};o!=d&&(t.start=o),r!=u&&(t.end=r),this.setv(t),h=!1}h&&this.change.emit()}reset(){this.have_updated_interactively=!1,this.setv({range_padding:this._initial_range_padding,range_padding_units:this._initial_range_padding_units,follow:this._initial_follow,follow_interval:this._initial_follow_interval,default_span:this._initial_default_span},{silent:!0}),this.change.emit()}}n.DataRange1d=g,l=g,g.__name__=\"DataRange1d\",l.define((({Boolean:t,Number:i,Nullable:n})=>({start:[i],end:[i],range_padding:[i,.1],range_padding_units:[o.PaddingUnits,\"percent\"],flipped:[t,!1],follow:[n(o.StartEnd),null],follow_interval:[n(i),null],default_span:[i,2],only_visible:[t,!1]}))),l.internal((({Enum:t})=>({scale_hint:[t(\"log\",\"auto\"),\"auto\"]})))},\n", - " function _(e,n,a,r,s){var t;r();const c=e(57);class _ extends c.Range{constructor(e){super(e)}}a.DataRange=_,t=_,_.__name__=\"DataRange\",t.define((({String:e,Array:n,AnyRef:a})=>({names:[n(e),[]],renderers:[n(a()),[]]})))},\n", - " function _(t,i,e,h,r){h();const s=t(24),n=t(26),{min:x,max:y}=Math;e.empty=function(){return{x0:1/0,y0:1/0,x1:-1/0,y1:-1/0}},e.positive_x=function(){return{x0:Number.MIN_VALUE,y0:-1/0,x1:1/0,y1:1/0}},e.positive_y=function(){return{x0:-1/0,y0:Number.MIN_VALUE,x1:1/0,y1:1/0}},e.union=function(t,i){return{x0:x(t.x0,i.x0),x1:y(t.x1,i.x1),y0:x(t.y0,i.y0),y1:y(t.y1,i.y1)}};class o{constructor(t){if(null==t)this.x0=0,this.y0=0,this.x1=0,this.y1=0;else if(\"x0\"in t){const{x0:i,y0:e,x1:h,y1:r}=t;if(!(i<=h&&e<=r))throw new Error(`invalid bbox {x0: ${i}, y0: ${e}, x1: ${h}, y1: ${r}}`);this.x0=i,this.y0=e,this.x1=h,this.y1=r}else if(\"x\"in t){const{x:i,y:e,width:h,height:r}=t;if(!(h>=0&&r>=0))throw new Error(`invalid bbox {x: ${i}, y: ${e}, width: ${h}, height: ${r}}`);this.x0=i,this.y0=e,this.x1=i+h,this.y1=e+r}else{let i,e,h,r;if(\"width\"in t)if(\"left\"in t)i=t.left,e=i+t.width;else if(\"right\"in t)e=t.right,i=e-t.width;else{const h=t.width/2;i=t.hcenter-h,e=t.hcenter+h}else i=t.left,e=t.right;if(\"height\"in t)if(\"top\"in t)h=t.top,r=h+t.height;else if(\"bottom\"in t)r=t.bottom,h=r-t.height;else{const i=t.height/2;h=t.vcenter-i,r=t.vcenter+i}else h=t.top,r=t.bottom;if(!(i<=e&&h<=r))throw new Error(`invalid bbox {left: ${i}, top: ${h}, right: ${e}, bottom: ${r}}`);this.x0=i,this.y0=h,this.x1=e,this.y1=r}}static from_rect({left:t,right:i,top:e,bottom:h}){return new o({x0:Math.min(t,i),y0:Math.min(e,h),x1:Math.max(t,i),y1:Math.max(e,h)})}equals(t){return this.x0==t.x0&&this.y0==t.y0&&this.x1==t.x1&&this.y1==t.y1}[n.equals](t,i){return i.eq(this.x0,t.x0)&&i.eq(this.y0,t.y0)&&i.eq(this.x1,t.x1)&&i.eq(this.y1,t.y1)}toString(){return`BBox({left: ${this.left}, top: ${this.top}, width: ${this.width}, height: ${this.height}})`}get left(){return this.x0}get top(){return this.y0}get right(){return this.x1}get bottom(){return this.y1}get p0(){return[this.x0,this.y0]}get p1(){return[this.x1,this.y1]}get x(){return this.x0}get y(){return this.y0}get width(){return this.x1-this.x0}get height(){return this.y1-this.y0}get size(){return{width:this.width,height:this.height}}get rect(){const{x0:t,y0:i,x1:e,y1:h}=this;return{p0:{x:t,y:i},p1:{x:e,y:i},p2:{x:e,y:h},p3:{x:t,y:h}}}get box(){const{x:t,y:i,width:e,height:h}=this;return{x:t,y:i,width:e,height:h}}get h_range(){return{start:this.x0,end:this.x1}}get v_range(){return{start:this.y0,end:this.y1}}get ranges(){return[this.h_range,this.v_range]}get aspect(){return this.width/this.height}get hcenter(){return(this.left+this.right)/2}get vcenter(){return(this.top+this.bottom)/2}get area(){return this.width*this.height}relative(){const{width:t,height:i}=this;return new o({x:0,y:0,width:t,height:i})}translate(t,i){const{x:e,y:h,width:r,height:s}=this;return new o({x:t+e,y:i+h,width:r,height:s})}relativize(t,i){return[t-this.x,i-this.y]}contains(t,i){return this.x0<=t&&t<=this.x1&&this.y0<=i&&i<=this.y1}clip(t,i){return tthis.x1&&(t=this.x1),ithis.y1&&(i=this.y1),[t,i]}grow_by(t){return new o({left:this.left-t,right:this.right+t,top:this.top-t,bottom:this.bottom+t})}shrink_by(t){return new o({left:this.left+t,right:this.right-t,top:this.top+t,bottom:this.bottom-t})}union(t){return new o({x0:x(this.x0,t.x0),y0:x(this.y0,t.y0),x1:y(this.x1,t.x1),y1:y(this.y1,t.y1)})}intersection(t){return this.intersects(t)?new o({x0:y(this.x0,t.x0),y0:y(this.y0,t.y0),x1:x(this.x1,t.x1),y1:x(this.y1,t.y1)}):null}intersects(t){return!(t.x1this.x1||t.y1this.y1)}get xview(){return{compute:t=>this.left+t,v_compute:t=>{const i=new s.ScreenArray(t.length),e=this.left;for(let h=0;hthis.bottom-t,v_compute:t=>{const i=new s.ScreenArray(t.length),e=this.bottom;for(let h=0;h0&&(r=r.filter((n=>(0,l.includes)(t,n.name)))),r}},\n", - " function _(t,n,e,i,s){var r;i();const a=t(57),o=t(20),g=t(21),p=t(24),c=t(9),l=t(8),u=t(11);function h(t,n,e=0){const i=new Map;for(let s=0;sa.get(t).value)));r.set(t,{value:l/s,mapping:a}),o+=s+n+p}return[r,(a.size-1)*n+g]}function _(t,n,e,i,s=0){var r;const a=new Map,o=new Map;for(const[n,e,i]of t){const t=null!==(r=o.get(n))&&void 0!==r?r:[];o.set(n,[...t,[e,i]])}let g=s,p=0;for(const[t,s]of o){const r=s.length,[o,l]=d(s,e,i,g);p+=l;const u=(0,c.sum)(s.map((([t])=>o.get(t).value)));a.set(t,{value:u/r,mapping:o}),g+=r+n+l}return[a,(o.size-1)*n+p]}e.Factor=(0,g.Or)(g.String,(0,g.Tuple)(g.String,g.String),(0,g.Tuple)(g.String,g.String,g.String)),e.FactorSeq=(0,g.Or)((0,g.Array)(g.String),(0,g.Array)((0,g.Tuple)(g.String,g.String)),(0,g.Array)((0,g.Tuple)(g.String,g.String,g.String))),e.map_one_level=h,e.map_two_levels=d,e.map_three_levels=_;class f extends a.Range{constructor(t){super(t)}get min(){return this.start}get max(){return this.end}initialize(){super.initialize(),this._init(!0)}connect_signals(){super.connect_signals(),this.connect(this.properties.factors.change,(()=>this.reset())),this.connect(this.properties.factor_padding.change,(()=>this.reset())),this.connect(this.properties.group_padding.change,(()=>this.reset())),this.connect(this.properties.subgroup_padding.change,(()=>this.reset())),this.connect(this.properties.range_padding.change,(()=>this.reset())),this.connect(this.properties.range_padding_units.change,(()=>this.reset()))}reset(){this._init(!1),this.change.emit()}_lookup(t){switch(t.length){case 1:{const[n]=t,e=this._mapping.get(n);return null!=e?e.value:NaN}case 2:{const[n,e]=t,i=this._mapping.get(n);if(null!=i){const t=i.mapping.get(e);if(null!=t)return t.value}return NaN}case 3:{const[n,e,i]=t,s=this._mapping.get(n);if(null!=s){const t=s.mapping.get(e);if(null!=t){const n=t.mapping.get(i);if(null!=n)return n.value}}return NaN}default:(0,u.unreachable)()}}synthetic(t){if((0,l.isNumber)(t))return t;if((0,l.isString)(t))return this._lookup([t]);let n=0;const e=t[t.length-1];return(0,l.isNumber)(e)&&(n=e,t=t.slice(0,-1)),this._lookup(t)+n}v_synthetic(t){const n=t.length,e=new p.ScreenArray(n);for(let i=0;i{if((0,c.every)(this.factors,l.isString)){const t=this.factors,[n,e]=h(t,this.factor_padding);return{levels:1,mapping:n,tops:null,mids:null,inside_padding:e}}if((0,c.every)(this.factors,(t=>(0,l.isArray)(t)&&2==t.length&&(0,l.isString)(t[0])&&(0,l.isString)(t[1])))){const t=this.factors,[n,e]=d(t,this.group_padding,this.factor_padding),i=[...n.keys()];return{levels:2,mapping:n,tops:i,mids:null,inside_padding:e}}if((0,c.every)(this.factors,(t=>(0,l.isArray)(t)&&3==t.length&&(0,l.isString)(t[0])&&(0,l.isString)(t[1])&&(0,l.isString)(t[2])))){const t=this.factors,[n,e]=_(t,this.group_padding,this.subgroup_padding,this.factor_padding),i=[...n.keys()],s=[];for(const[t,e]of n)for(const n of e.mapping.keys())s.push([t,n]);return{levels:3,mapping:n,tops:i,mids:s,inside_padding:e}}(0,u.unreachable)()})();this._mapping=e,this.tops=i,this.mids=s;let a=0,o=this.factors.length+r;if(\"percent\"==this.range_padding_units){const t=(o-a)*this.range_padding/2;a-=t,o+=t}else a-=this.range_padding,o+=this.range_padding;this.setv({start:a,end:o,levels:n},{silent:t}),\"auto\"==this.bounds&&this.setv({bounds:[a,o]},{silent:!0})}}e.FactorRange=f,r=f,f.__name__=\"FactorRange\",r.define((({Number:t})=>({factors:[e.FactorSeq,[]],factor_padding:[t,0],subgroup_padding:[t,.8],group_padding:[t,1.4],range_padding:[t,0],range_padding_units:[o.PaddingUnits,\"percent\"],start:[t],end:[t]}))),r.internal((({Number:t,String:n,Array:e,Tuple:i,Nullable:s})=>({levels:[t],mids:[s(e(i(n,n))),null],tops:[s(e(n)),null]})))},\n", - " function _(t,e,s,a,i){a();const n=t(1);var _;const r=t(69),o=t(112),l=t(48),d=t(20),h=t(24),c=t(113),u=(0,n.__importStar)(t(18)),v=t(10);class p extends r.DataAnnotationView{async lazy_initialize(){await super.lazy_initialize();const{start:t,end:e}=this.model;null!=t&&(this.start=await(0,c.build_view)(t,{parent:this})),null!=e&&(this.end=await(0,c.build_view)(e,{parent:this}))}set_data(t){var e,s;super.set_data(t),null===(e=this.start)||void 0===e||e.set_data(t),null===(s=this.end)||void 0===s||s.set_data(t)}remove(){var t,e;null===(t=this.start)||void 0===t||t.remove(),null===(e=this.end)||void 0===e||e.remove(),super.remove()}map_data(){const{frame:t}=this.plot_view;\"data\"==this.model.start_units?(this._sx_start=this.coordinates.x_scale.v_compute(this._x_start),this._sy_start=this.coordinates.y_scale.v_compute(this._y_start)):(this._sx_start=t.bbox.xview.v_compute(this._x_start),this._sy_start=t.bbox.yview.v_compute(this._y_start)),\"data\"==this.model.end_units?(this._sx_end=this.coordinates.x_scale.v_compute(this._x_end),this._sy_end=this.coordinates.y_scale.v_compute(this._y_end)):(this._sx_end=t.bbox.xview.v_compute(this._x_end),this._sy_end=t.bbox.yview.v_compute(this._y_end));const{_sx_start:e,_sy_start:s,_sx_end:a,_sy_end:i}=this,n=e.length,_=this._angles=new h.ScreenArray(n);for(let t=0;t({x_start:[u.XCoordinateSpec,{field:\"x_start\"}],y_start:[u.YCoordinateSpec,{field:\"y_start\"}],start_units:[d.SpatialUnits,\"data\"],start:[e(t(o.ArrowHead)),null],x_end:[u.XCoordinateSpec,{field:\"x_end\"}],y_end:[u.YCoordinateSpec,{field:\"y_end\"}],end_units:[d.SpatialUnits,\"data\"],end:[e(t(o.ArrowHead)),()=>new o.OpenHead]})))},\n", - " function _(t,e,n,s,a){s();const o=t(1);var i;const c=t(40),r=t(70),_=t(75),l=t(78),h=(0,o.__importStar)(t(18));class d extends c.AnnotationView{constructor(){super(...arguments),this._initial_set_data=!1}connect_signals(){super.connect_signals();const t=()=>{this.set_data(this.model.source),this._rerender()};this.connect(this.model.change,t),this.connect(this.model.source.streaming,t),this.connect(this.model.source.patching,t),this.connect(this.model.source.change,t)}_rerender(){this.request_render()}set_data(t){const e=this;for(const n of this.model)if(n instanceof h.VectorSpec||n instanceof h.ScalarSpec)if(n instanceof h.BaseCoordinateSpec){const s=n.array(t);e[`_${n.attr}`]=s}else{const s=n.uniform(t);e[`${n.attr}`]=s}this.plot_model.use_map&&(null!=e._x&&l.inplace.project_xy(e._x,e._y),null!=e._xs&&l.inplace.project_xsys(e._xs,e._ys));for(const t of this.visuals)t.update()}_render(){this._initial_set_data||(this.set_data(this.model.source),this._initial_set_data=!0),this.map_data(),this.paint(this.layer.ctx)}}n.DataAnnotationView=d,d.__name__=\"DataAnnotationView\";class u extends c.Annotation{constructor(t){super(t)}}n.DataAnnotation=u,i=u,u.__name__=\"DataAnnotation\",i.define((({Ref:t})=>({source:[t(r.ColumnarDataSource),()=>new _.ColumnDataSource]})))},\n", - " function _(t,e,n,s,a){var i;s();const r=t(71),l=t(15),c=t(19),o=t(73),h=t(8),u=t(9),g=t(13),d=t(72),_=t(74),m=t(29);class w extends r.DataSource{constructor(t){super(t),this.selection_manager=new o.SelectionManager(this)}get_array(t){let e=this.data[t];return null==e?this.data[t]=e=[]:(0,h.isArray)(e)||(this.data[t]=e=Array.from(e)),e}initialize(){super.initialize(),this._select=new l.Signal0(this,\"select\"),this.inspect=new l.Signal(this,\"inspect\"),this.streaming=new l.Signal0(this,\"streaming\"),this.patching=new l.Signal(this,\"patching\")}get_column(t){const e=this.data[t];return null!=e?e:null}columns(){return(0,g.keys)(this.data)}get_length(t=!0){const e=(0,u.uniq)((0,g.values)(this.data).map((t=>(0,m.is_NDArray)(t)?t.shape[0]:t.length)));switch(e.length){case 0:return null;case 1:return e[0];default:{const n=\"data source has columns of inconsistent lengths\";if(t)return c.logger.warn(n),e.sort()[0];throw new Error(n)}}}get length(){var t;return null!==(t=this.get_length())&&void 0!==t?t:0}clear(){const t={};for(const e of this.columns())t[e]=new this.data[e].constructor(0);this.data=t}}n.ColumnarDataSource=w,i=w,w.__name__=\"ColumnarDataSource\",i.define((({Ref:t})=>({selection_policy:[t(_.SelectionPolicy),()=>new _.UnionRenderers]}))),i.internal((({AnyRef:t})=>({inspected:[t(),()=>new d.Selection]})))},\n", - " function _(e,c,n,t,o){var a;t();const s=e(53),r=e(72);class l extends s.Model{constructor(e){super(e)}}n.DataSource=l,a=l,l.__name__=\"DataSource\",a.define((({Ref:e})=>({selected:[e(r.Selection),()=>new r.Selection]})))},\n", - " function _(i,e,s,t,n){var l;t();const c=i(53),d=i(9),h=i(13);class _ extends c.Model{constructor(i){super(i)}get_view(){return this.view}get selected_glyph(){return this.selected_glyphs.length>0?this.selected_glyphs[0]:null}add_to_selected_glyphs(i){this.selected_glyphs.push(i)}update(i,e=!0,s=\"replace\"){switch(s){case\"replace\":this.indices=i.indices,this.line_indices=i.line_indices,this.multiline_indices=i.multiline_indices,this.image_indices=i.image_indices,this.view=i.view,this.selected_glyphs=i.selected_glyphs;break;case\"append\":this.update_through_union(i);break;case\"intersect\":this.update_through_intersection(i);break;case\"subtract\":this.update_through_subtraction(i)}}clear(){this.indices=[],this.line_indices=[],this.multiline_indices={},this.image_indices=[],this.view=null,this.selected_glyphs=[]}map(i){return new _(Object.assign(Object.assign({},this.attributes),{indices:this.indices.map(i),multiline_indices:(0,h.to_object)((0,h.entries)(this.multiline_indices).map((([e,s])=>[i(Number(e)),s]))),image_indices:this.image_indices.map((e=>Object.assign(Object.assign({},e),{index:i(e.index)})))}))}is_empty(){return 0==this.indices.length&&0==this.line_indices.length&&0==this.image_indices.length}update_through_union(i){this.indices=(0,d.union)(this.indices,i.indices),this.selected_glyphs=(0,d.union)(i.selected_glyphs,this.selected_glyphs),this.line_indices=(0,d.union)(i.line_indices,this.line_indices),this.view=i.view,this.multiline_indices=(0,h.merge)(i.multiline_indices,this.multiline_indices)}update_through_intersection(i){this.indices=(0,d.intersection)(this.indices,i.indices),this.selected_glyphs=(0,d.union)(i.selected_glyphs,this.selected_glyphs),this.line_indices=(0,d.union)(i.line_indices,this.line_indices),this.view=i.view,this.multiline_indices=(0,h.merge)(i.multiline_indices,this.multiline_indices)}update_through_subtraction(i){this.indices=(0,d.difference)(this.indices,i.indices),this.selected_glyphs=(0,d.union)(i.selected_glyphs,this.selected_glyphs),this.line_indices=(0,d.union)(i.line_indices,this.line_indices),this.view=i.view,this.multiline_indices=(0,h.merge)(i.multiline_indices,this.multiline_indices)}}s.Selection=_,l=_,_.__name__=\"Selection\",l.define((({Int:i,Array:e,Dict:s})=>({indices:[e(i),[]],line_indices:[e(i),[]],multiline_indices:[s(e(i)),{}]}))),l.internal((({Int:i,Array:e,AnyRef:s,Struct:t,Nullable:n})=>({selected_glyphs:[e(s()),[]],view:[n(s()),null],image_indices:[e(t({index:i,dim1:i,dim2:i,flat_index:i})),[]]})))},\n", - " function _(e,t,o,s,c){s();const n=e(72);function i(e){return\"GlyphRenderer\"==e.model.type}function l(e){return\"GraphRenderer\"==e.model.type}class r{constructor(e){this.source=e,this.inspectors=new Map}select(e,t,o,s=\"replace\"){const c=[],n=[];for(const t of e)i(t)?c.push(t):l(t)&&n.push(t);let r=!1;for(const e of n){const c=e.model.selection_policy.hit_test(t,e);r=r||e.model.selection_policy.do_selection(c,e.model,o,s)}if(c.length>0){const e=this.source.selection_policy.hit_test(t,c);r=r||this.source.selection_policy.do_selection(e,this.source,o,s)}return r}inspect(e,t){let o=!1;if(i(e)){const s=e.hit_test(t);if(null!=s){o=!s.is_empty();const c=this.get_or_create_inspector(e.model);c.update(s,!0,\"replace\"),this.source.setv({inspected:c},{silent:!0}),this.source.inspect.emit([e.model,{geometry:t}])}}else if(l(e)){const s=e.model.inspection_policy.hit_test(t,e);o=o||e.model.inspection_policy.do_inspection(s,t,e,!1,\"replace\")}return o}clear(e){this.source.selected.clear(),null!=e&&this.get_or_create_inspector(e.model).clear()}get_or_create_inspector(e){let t=this.inspectors.get(e);return null==t&&(t=new n.Selection,this.inspectors.set(e,t)),t}}o.SelectionManager=r,r.__name__=\"SelectionManager\"},\n", - " function _(e,t,n,s,o){s();const r=e(53);class c extends r.Model{do_selection(e,t,n,s){return null!=e&&(t.selected.update(e,n,s),t._select.emit(),!t.selected.is_empty())}}n.SelectionPolicy=c,c.__name__=\"SelectionPolicy\";class l extends c{hit_test(e,t){const n=[];for(const s of t){const t=s.hit_test(e);null!=t&&n.push(t)}if(n.length>0){const e=n[0];for(const t of n)e.update_through_intersection(t);return e}return null}}n.IntersectRenderers=l,l.__name__=\"IntersectRenderers\";class _ extends c{hit_test(e,t){const n=[];for(const s of t){const t=s.hit_test(e);null!=t&&n.push(t)}if(n.length>0){const e=n[0];for(const t of n)e.update_through_union(t);return e}return null}}n.UnionRenderers=_,_.__name__=\"UnionRenderers\"},\n", - " function _(t,n,e,s,o){s();const r=t(1);var l;const c=t(70),i=t(8),a=t(13),u=(0,r.__importStar)(t(76)),h=t(77),d=t(35);function f(t,n,e){if((0,i.isArray)(t)){const s=t.concat(n);return null!=e&&s.length>e?s.slice(-e):s}if((0,i.isTypedArray)(t)){const s=t.length+n.length;if(null!=e&&s>e){const o=s-e,r=t.length;let l;t.length({data:[t(n),{}]})))},\n", - " function _(t,n,o,e,c){e(),o.concat=function(t,...n){let o=t.length;for(const t of n)o+=t.length;const e=new t.constructor(o);e.set(t,0);let c=t.length;for(const t of n)e.set(t,c),c+=t.length;return e}},\n", - " function _(n,o,t,e,f){function c(...n){const o=new Set;for(const t of n)for(const n of t)o.add(n);return o}e(),t.union=c,t.intersection=function(n,...o){const t=new Set;n:for(const e of n){for(const n of o)if(!n.has(e))continue n;t.add(e)}return t},t.difference=function(n,...o){const t=new Set(n);for(const n of c(...o))t.delete(n);return t}},\n", - " function _(n,t,e,o,r){o();const c=n(1),l=(0,c.__importDefault)(n(79)),i=(0,c.__importDefault)(n(80)),u=n(24),a=new i.default(\"GOOGLE\"),s=new i.default(\"WGS84\"),f=(0,l.default)(s,a);e.wgs84_mercator={compute:(n,t)=>isFinite(n)&&isFinite(t)?f.forward([n,t]):[NaN,NaN],invert:(n,t)=>isFinite(n)&&isFinite(t)?f.inverse([n,t]):[NaN,NaN]};const _={lon:[-20026376.39,20026376.39],lat:[-20048966.1,20048966.1]},p={lon:[-180,180],lat:[-85.06,85.06]},{min:g,max:h}=Math;function m(n,t){const o=g(n.length,t.length),r=(0,u.infer_type)(n,t),c=new r(o),l=new r(o);return e.inplace.project_xy(n,t,c,l),[c,l]}e.clip_mercator=function(n,t,e){const[o,r]=_[e];return[h(n,o),g(t,r)]},e.in_bounds=function(n,t){const[e,o]=p[t];return e2?void 0!==e.name&&\"geocent\"===e.name||void 0!==n.name&&\"geocent\"===n.name?\"number\"==typeof o.z?[o.x,o.y,o.z].concat(t.splice(3)):[o.x,o.y,t[2]].concat(t.splice(3)):[o.x,o.y].concat(t.splice(2)):[o.x,o.y]):(a=(0,c.default)(e,n,t,r),2===(i=Object.keys(t)).length||i.forEach((function(r){if(void 0!==e.name&&\"geocent\"===e.name||void 0!==n.name&&\"geocent\"===n.name){if(\"x\"===r||\"y\"===r||\"z\"===r)return}else if(\"x\"===r||\"y\"===r)return;a[r]=t[r]})),a)}function l(e){return e instanceof i.default?e:e.oProj?e.oProj:(0,i.default)(e)}t.default=function(e,n,t){e=l(e);var r,o=!1;return void 0===n?(n=e,e=u,o=!0):(void 0!==n.x||Array.isArray(n))&&(t=n,n=e,e=u,o=!0),n=l(n),t?f(e,n,t):(r={forward:function(t,r){return f(e,n,t,r)},inverse:function(t,r){return f(n,e,t,r)}},o&&(r.oProj=n),r)}},\n", - " function _(t,e,a,s,i){s();const l=t(1),u=(0,l.__importDefault)(t(81)),r=(0,l.__importDefault)(t(92)),d=(0,l.__importDefault)(t(93)),o=t(101),f=(0,l.__importDefault)(t(103)),p=(0,l.__importDefault)(t(104)),m=(0,l.__importDefault)(t(88)),n=t(105);function h(t,e){if(!(this instanceof h))return new h(t);e=e||function(t){if(t)throw t};var a=(0,u.default)(t);if(\"object\"==typeof a){var s=h.projections.get(a.projName);if(s){if(a.datumCode&&\"none\"!==a.datumCode){var i=(0,m.default)(f.default,a.datumCode);i&&(a.datum_params=a.datum_params||(i.towgs84?i.towgs84.split(\",\"):null),a.ellps=i.ellipse,a.datumName=i.datumName?i.datumName:a.datumCode)}a.k0=a.k0||1,a.axis=a.axis||\"enu\",a.ellps=a.ellps||\"wgs84\",a.lat1=a.lat1||a.lat0;var l=(0,o.sphere)(a.a,a.b,a.rf,a.ellps,a.sphere),d=(0,o.eccentricity)(l.a,l.b,l.rf,a.R_A),_=(0,n.getNadgrids)(a.nadgrids),c=a.datum||(0,p.default)(a.datumCode,a.datum_params,l.a,l.b,d.es,d.ep2,_);(0,r.default)(this,a),(0,r.default)(this,s),this.a=l.a,this.b=l.b,this.rf=l.rf,this.sphere=l.sphere,this.es=d.es,this.e=d.e,this.ep2=d.ep2,this.datum=c,this.init(),e(null,this)}else e(t)}else e(t)}h.projections=d.default,h.projections.start(),a.default=h},\n", - " function _(t,r,n,u,e){u();const f=t(1),i=(0,f.__importDefault)(t(82)),a=(0,f.__importDefault)(t(89)),o=(0,f.__importDefault)(t(84)),l=(0,f.__importDefault)(t(88));var C=[\"PROJECTEDCRS\",\"PROJCRS\",\"GEOGCS\",\"GEOCCS\",\"PROJCS\",\"LOCAL_CS\",\"GEODCRS\",\"GEODETICCRS\",\"GEODETICDATUM\",\"ENGCRS\",\"ENGINEERINGCRS\"];var d=[\"3857\",\"900913\",\"3785\",\"102113\"];n.default=function(t){if(!function(t){return\"string\"==typeof t}(t))return t;if(function(t){return t in i.default}(t))return i.default[t];if(function(t){return C.some((function(r){return t.indexOf(r)>-1}))}(t)){var r=(0,a.default)(t);if(function(t){var r=(0,l.default)(t,\"authority\");if(r){var n=(0,l.default)(r,\"epsg\");return n&&d.indexOf(n)>-1}}(r))return i.default[\"EPSG:3857\"];var n=function(t){var r=(0,l.default)(t,\"extension\");if(r)return(0,l.default)(r,\"proj4\")}(r);return n?(0,o.default)(n):r}return function(t){return\"+\"===t[0]}(t)?(0,o.default)(t):void 0}},\n", - " function _(t,r,i,e,n){e();const f=t(1),a=(0,f.__importDefault)(t(83)),l=(0,f.__importDefault)(t(84)),u=(0,f.__importDefault)(t(89));function o(t){var r=this;if(2===arguments.length){var i=arguments[1];\"string\"==typeof i?\"+\"===i.charAt(0)?o[t]=(0,l.default)(arguments[1]):o[t]=(0,u.default)(arguments[1]):o[t]=i}else if(1===arguments.length){if(Array.isArray(t))return t.map((function(t){Array.isArray(t)?o.apply(r,t):o(t)}));if(\"string\"==typeof t){if(t in o)return o[t]}else\"EPSG\"in t?o[\"EPSG:\"+t.EPSG]=t:\"ESRI\"in t?o[\"ESRI:\"+t.ESRI]=t:\"IAU2000\"in t?o[\"IAU2000:\"+t.IAU2000]=t:console.log(t);return}}(0,a.default)(o),i.default=o},\n", - " function _(t,l,G,S,e){S(),G.default=function(t){t(\"EPSG:4326\",\"+title=WGS 84 (long/lat) +proj=longlat +ellps=WGS84 +datum=WGS84 +units=degrees\"),t(\"EPSG:4269\",\"+title=NAD83 (long/lat) +proj=longlat +a=6378137.0 +b=6356752.31414036 +ellps=GRS80 +datum=NAD83 +units=degrees\"),t(\"EPSG:3857\",\"+title=WGS 84 / Pseudo-Mercator +proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 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o.push(!0),t[o[0].toLowerCase()]=o[1],t}),{}),c={proj:\"projName\",datum:\"datumCode\",rf:function(t){u.rf=parseFloat(t)},lat_0:function(t){u.lat0=t*r.D2R},lat_1:function(t){u.lat1=t*r.D2R},lat_2:function(t){u.lat2=t*r.D2R},lat_ts:function(t){u.lat_ts=t*r.D2R},lon_0:function(t){u.long0=t*r.D2R},lon_1:function(t){u.long1=t*r.D2R},lon_2:function(t){u.long2=t*r.D2R},alpha:function(t){u.alpha=parseFloat(t)*r.D2R},gamma:function(t){u.rectified_grid_angle=parseFloat(t)},lonc:function(t){u.longc=t*r.D2R},x_0:function(t){u.x0=parseFloat(t)},y_0:function(t){u.y0=parseFloat(t)},k_0:function(t){u.k0=parseFloat(t)},k:function(t){u.k0=parseFloat(t)},a:function(t){u.a=parseFloat(t)},b:function(t){u.b=parseFloat(t)},r_a:function(){u.R_A=!0},zone:function(t){u.zone=parseInt(t,10)},south:function(){u.utmSouth=!0},towgs84:function(t){u.datum_params=t.split(\",\").map((function(t){return parseFloat(t)}))},to_meter:function(t){u.to_meter=parseFloat(t)},units:function(t){u.units=t;var n=(0,l.default)(f.default,t);n&&(u.to_meter=n.to_meter)},from_greenwich:function(t){u.from_greenwich=t*r.D2R},pm:function(t){var n=(0,l.default)(i.default,t);u.from_greenwich=(n||parseFloat(t))*r.D2R},nadgrids:function(t){\"@null\"===t?u.datumCode=\"none\":u.nadgrids=t},axis:function(t){var n=\"ewnsud\";3===t.length&&-1!==n.indexOf(t.substr(0,1))&&-1!==n.indexOf(t.substr(1,1))&&-1!==n.indexOf(t.substr(2,1))&&(u.axis=t)},approx:function(){u.approx=!0}};for(n in e)o=e[n],n in c?\"function\"==typeof(a=c[n])?a(o):u[a]=o:u[n]=o;return\"string\"==typeof u.datumCode&&\"WGS84\"!==u.datumCode&&(u.datumCode=u.datumCode.toLowerCase()),u}},\n", - " function _(S,_,P,R,I){R(),P.PJD_3PARAM=1,P.PJD_7PARAM=2,P.PJD_GRIDSHIFT=3,P.PJD_WGS84=4,P.PJD_NODATUM=5,P.SRS_WGS84_SEMIMAJOR=6378137,P.SRS_WGS84_SEMIMINOR=6356752.314,P.SRS_WGS84_ESQUARED=.0066943799901413165,P.SEC_TO_RAD=484813681109536e-20,P.HALF_PI=Math.PI/2,P.SIXTH=.16666666666666666,P.RA4=.04722222222222222,P.RA6=.022156084656084655,P.EPSLN=1e-10,P.D2R=.017453292519943295,P.R2D=57.29577951308232,P.FORTPI=Math.PI/4,P.TWO_PI=2*Math.PI,P.SPI=3.14159265359},\n", - " function _(o,r,a,e,s){e();var n={};a.default=n,n.greenwich=0,n.lisbon=-9.131906111111,n.paris=2.337229166667,n.bogota=-74.080916666667,n.madrid=-3.687938888889,n.rome=12.452333333333,n.bern=7.439583333333,n.jakarta=106.807719444444,n.ferro=-17.666666666667,n.brussels=4.367975,n.stockholm=18.058277777778,n.athens=23.7163375,n.oslo=10.722916666667},\n", - " function _(t,e,f,o,u){o(),f.default={ft:{to_meter:.3048},\"us-ft\":{to_meter:1200/3937}}},\n", - " function _(e,r,t,a,n){a();var o=/[\\s_\\-\\/\\(\\)]/g;t.default=function(e,r){if(e[r])return e[r];for(var t,a=Object.keys(e),n=r.toLowerCase().replace(o,\"\"),f=-1;++f0?90:-90),e.lat_ts=e.lat1)}(n),n}},\n", - " function _(t,e,r,i,s){i(),r.default=function(t){return new d(t).output()};var h=/\\s/,o=/[A-Za-z]/,n=/[A-Za-z84]/,a=/[,\\]]/,u=/[\\d\\.E\\-\\+]/;function d(t){if(\"string\"!=typeof t)throw new Error(\"not a string\");this.text=t.trim(),this.level=0,this.place=0,this.root=null,this.stack=[],this.currentObject=null,this.state=1}d.prototype.readCharicter=function(){var t=this.text[this.place++];if(4!==this.state)for(;h.test(t);){if(this.place>=this.text.length)return;t=this.text[this.place++]}switch(this.state){case 1:return this.neutral(t);case 2:return this.keyword(t);case 4:return this.quoted(t);case 5:return this.afterquote(t);case 3:return this.number(t);case-1:return}},d.prototype.afterquote=function(t){if('\"'===t)return this.word+='\"',void(this.state=4);if(a.test(t))return this.word=this.word.trim(),void this.afterItem(t);throw new Error(\"havn't handled \\\"\"+t+'\" in afterquote yet, index '+this.place)},d.prototype.afterItem=function(t){return\",\"===t?(null!==this.word&&this.currentObject.push(this.word),this.word=null,void(this.state=1)):\"]\"===t?(this.level--,null!==this.word&&(this.currentObject.push(this.word),this.word=null),this.state=1,this.currentObject=this.stack.pop(),void(this.currentObject||(this.state=-1))):void 0},d.prototype.number=function(t){if(!u.test(t)){if(a.test(t))return this.word=parseFloat(this.word),void this.afterItem(t);throw new Error(\"havn't handled \\\"\"+t+'\" in number yet, index '+this.place)}this.word+=t},d.prototype.quoted=function(t){'\"'!==t?this.word+=t:this.state=5},d.prototype.keyword=function(t){if(n.test(t))this.word+=t;else{if(\"[\"===t){var e=[];return e.push(this.word),this.level++,null===this.root?this.root=e:this.currentObject.push(e),this.stack.push(this.currentObject),this.currentObject=e,void(this.state=1)}if(!a.test(t))throw new Error(\"havn't handled \\\"\"+t+'\" in keyword yet, index '+this.place);this.afterItem(t)}},d.prototype.neutral=function(t){if(o.test(t))return this.word=t,void(this.state=2);if('\"'===t)return this.word=\"\",void(this.state=4);if(u.test(t))return this.word=t,void(this.state=3);if(!a.test(t))throw new Error(\"havn't handled \\\"\"+t+'\" in neutral yet, index '+this.place);this.afterItem(t)},d.prototype.output=function(){for(;this.place90&&a*o.R2D<-90&&h*o.R2D>180&&h*o.R2D<-180)return null;if(Math.abs(Math.abs(a)-o.HALF_PI)<=o.EPSLN)return null;if(this.sphere)i=this.x0+this.a*this.k0*(0,n.default)(h-this.long0),s=this.y0+this.a*this.k0*Math.log(Math.tan(o.FORTPI+.5*a));else{var e=Math.sin(a),r=(0,l.default)(this.e,a,e);i=this.x0+this.a*this.k0*(0,n.default)(h-this.long0),s=this.y0-this.a*this.k0*Math.log(r)}return t.x=i,t.y=s,t}function M(t){var i,s,h=t.x-this.x0,a=t.y-this.y0;if(this.sphere)s=o.HALF_PI-2*Math.atan(Math.exp(-a/(this.a*this.k0)));else{var e=Math.exp(-a/(this.a*this.k0));if(-9999===(s=(0,u.default)(this.e,e)))return null}return i=(0,n.default)(this.long0+h/(this.a*this.k0)),t.x=i,t.y=s,t}s.init=f,s.forward=_,s.inverse=M,s.names=[\"Mercator\",\"Popular Visualisation Pseudo Mercator\",\"Mercator_1SP\",\"Mercator_Auxiliary_Sphere\",\"merc\"],s.default={init:f,forward:_,inverse:M,names:s.names}},\n", - " function _(t,n,r,u,a){u(),r.default=function(t,n,r){var u=t*n;return r/Math.sqrt(1-u*u)}},\n", - " function _(t,n,u,a,f){a();const e=t(1),o=t(85),_=(0,e.__importDefault)(t(97));u.default=function(t){return Math.abs(t)<=o.SPI?t:t-(0,_.default)(t)*o.TWO_PI}},\n", - " function _(n,t,u,f,c){f(),u.default=function(n){return n<0?-1:1}},\n", - " function _(t,n,a,o,u){o();const c=t(85);a.default=function(t,n,a){var o=t*a,u=.5*t;return o=Math.pow((1-o)/(1+o),u),Math.tan(.5*(c.HALF_PI-n))/o}},\n", - " function _(t,a,n,r,f){r();const h=t(85);n.default=function(t,a){for(var n,r,f=.5*t,o=h.HALF_PI-2*Math.atan(a),u=0;u<=15;u++)if(n=t*Math.sin(o),o+=r=h.HALF_PI-2*Math.atan(a*Math.pow((1-n)/(1+n),f))-o,Math.abs(r)<=1e-10)return o;return-9999}},\n", - " function _(n,i,e,t,r){function a(){}function f(n){return n}t(),e.init=a,e.forward=f,e.inverse=f,e.names=[\"longlat\",\"identity\"],e.default={init:a,forward:f,inverse:f,names:e.names}},\n", - " function _(t,r,e,a,n){a();const f=t(1),i=t(85),u=(0,f.__importStar)(t(102)),c=(0,f.__importDefault)(t(88));e.eccentricity=function(t,r,e,a){var n=t*t,f=r*r,u=(n-f)/n,c=0;return a?(n=(t*=1-u*(i.SIXTH+u*(i.RA4+u*i.RA6)))*t,u=0):c=Math.sqrt(u),{es:u,e:c,ep2:(n-f)/f}},e.sphere=function(t,r,e,a,n){if(!t){var f=(0,c.default)(u.default,a);f||(f=u.WGS84),t=f.a,r=f.b,e=f.rf}return e&&!r&&(r=(1-1/e)*t),(0===e||Math.abs(t-r)3&&(0===s.datum_params[3]&&0===s.datum_params[4]&&0===s.datum_params[5]&&0===s.datum_params[6]||(s.datum_type=d.PJD_7PARAM,s.datum_params[3]*=d.SEC_TO_RAD,s.datum_params[4]*=d.SEC_TO_RAD,s.datum_params[5]*=d.SEC_TO_RAD,s.datum_params[6]=s.datum_params[6]/1e6+1))),r&&(s.datum_type=d.PJD_GRIDSHIFT,s.grids=r),s.a=_,s.b=t,s.es=u,s.ep2=p,s}},\n", - " function _(t,e,n,r,i){r();var u={};function l(t){if(0===t.length)return null;var e=\"@\"===t[0];return e&&(t=t.slice(1)),\"null\"===t?{name:\"null\",mandatory:!e,grid:null,isNull:!0}:{name:t,mandatory:!e,grid:u[t]||null,isNull:!1}}function o(t){return t/3600*Math.PI/180}function a(t,e,n){return String.fromCharCode.apply(null,new Uint8Array(t.buffer.slice(e,n)))}function d(t){return t.map((function(t){return[o(t.longitudeShift),o(t.latitudeShift)]}))}function g(t,e,n){return{name:a(t,e+8,e+16).trim(),parent:a(t,e+24,e+24+8).trim(),lowerLatitude:t.getFloat64(e+72,n),upperLatitude:t.getFloat64(e+88,n),lowerLongitude:t.getFloat64(e+104,n),upperLongitude:t.getFloat64(e+120,n),latitudeInterval:t.getFloat64(e+136,n),longitudeInterval:t.getFloat64(e+152,n),gridNodeCount:t.getInt32(e+168,n)}}function s(t,e,n,r){for(var i=e+176,u=[],l=0;l1&&console.log(\"Only single NTv2 subgrids are currently supported, subsequent sub grids are ignored\");var l=function(t,e,n){for(var r=176,i=[],u=0;ua.y||f>a.x||N1e-12&&Math.abs(n.y)>1e-12);if(d<0)return console.log(\"Inverse grid shift iterator failed to converge.\"),a;a.x=(0,u.default)(l.x+t.ll[0]),a.y=l.y+t.ll[1]}else isNaN(l.x)||(a.x=r.x+l.x,a.y=r.y+l.y);return a}function f(r,e){var t,a={x:r.x/e.del[0],y:r.y/e.del[1]},i=Math.floor(a.x),l=Math.floor(a.y),n=a.x-1*i,o=a.y-1*l,u={x:Number.NaN,y:Number.NaN};if(i<0||i>=e.lim[0])return u;if(l<0||l>=e.lim[1])return u;t=l*e.lim[0]+i;var d=e.cvs[t][0],s=e.cvs[t][1];t++;var y=e.cvs[t][0],f=e.cvs[t][1];t+=e.lim[0];var x=e.cvs[t][0],m=e.cvs[t][1];t--;var N=e.cvs[t][0],c=e.cvs[t][1],_=n*o,g=n*(1-o),v=(1-n)*(1-o),S=(1-n)*o;return u.x=v*d+g*y+S*N+_*x,u.y=v*s+g*f+S*c+_*m,u}t.default=function(r,e,t){if((0,o.compareDatums)(r,e))return t;if(r.datum_type===n.PJD_NODATUM||e.datum_type===n.PJD_NODATUM)return t;var a=r.a,i=r.es;if(r.datum_type===n.PJD_GRIDSHIFT){if(0!==s(r,!1,t))return;a=n.SRS_WGS84_SEMIMAJOR,i=n.SRS_WGS84_ESQUARED}var l=e.a,u=e.b,y=e.es;if(e.datum_type===n.PJD_GRIDSHIFT&&(l=n.SRS_WGS84_SEMIMAJOR,u=n.SRS_WGS84_SEMIMINOR,y=n.SRS_WGS84_ESQUARED),i===y&&a===l&&!d(r.datum_type)&&!d(e.datum_type))return t;if(t=(0,o.geodeticToGeocentric)(t,i,a),d(r.datum_type)&&(t=(0,o.geocentricToWgs84)(t,r.datum_type,r.datum_params)),d(e.datum_type)&&(t=(0,o.geocentricFromWgs84)(t,e.datum_type,e.datum_params)),t=(0,o.geocentricToGeodetic)(t,y,l,u),e.datum_type===n.PJD_GRIDSHIFT&&0!==s(e,!0,t))return;return t},t.applyGridShift=s},\n", - " function _(a,t,r,m,s){m();const u=a(85);r.compareDatums=function(a,t){return a.datum_type===t.datum_type&&(!(a.a!==t.a||Math.abs(a.es-t.es)>5e-11)&&(a.datum_type===u.PJD_3PARAM?a.datum_params[0]===t.datum_params[0]&&a.datum_params[1]===t.datum_params[1]&&a.datum_params[2]===t.datum_params[2]:a.datum_type!==u.PJD_7PARAM||a.datum_params[0]===t.datum_params[0]&&a.datum_params[1]===t.datum_params[1]&&a.datum_params[2]===t.datum_params[2]&&a.datum_params[3]===t.datum_params[3]&&a.datum_params[4]===t.datum_params[4]&&a.datum_params[5]===t.datum_params[5]&&a.datum_params[6]===t.datum_params[6]))},r.geodeticToGeocentric=function(a,t,r){var m,s,_,e,n=a.x,d=a.y,i=a.z?a.z:0;if(d<-u.HALF_PI&&d>-1.001*u.HALF_PI)d=-u.HALF_PI;else if(d>u.HALF_PI&&d<1.001*u.HALF_PI)d=u.HALF_PI;else{if(d<-u.HALF_PI)return{x:-1/0,y:-1/0,z:a.z};if(d>u.HALF_PI)return{x:1/0,y:1/0,z:a.z}}return n>Math.PI&&(n-=2*Math.PI),s=Math.sin(d),e=Math.cos(d),_=s*s,{x:((m=r/Math.sqrt(1-t*_))+i)*e*Math.cos(n),y:(m+i)*e*Math.sin(n),z:(m*(1-t)+i)*s}},r.geocentricToGeodetic=function(a,t,r,m){var s,_,e,n,d,i,p,P,y,z,M,o,A,c,x,h=1e-12,f=a.x,I=a.y,F=a.z?a.z:0;if(s=Math.sqrt(f*f+I*I),_=Math.sqrt(f*f+I*I+F*F),s/r1e-24&&A<30);return{x:c,y:Math.atan(M/Math.abs(z)),z:x}},r.geocentricToWgs84=function(a,t,r){if(t===u.PJD_3PARAM)return{x:a.x+r[0],y:a.y+r[1],z:a.z+r[2]};if(t===u.PJD_7PARAM){var m=r[0],s=r[1],_=r[2],e=r[3],n=r[4],d=r[5],i=r[6];return{x:i*(a.x-d*a.y+n*a.z)+m,y:i*(d*a.x+a.y-e*a.z)+s,z:i*(-n*a.x+e*a.y+a.z)+_}}},r.geocentricFromWgs84=function(a,t,r){if(t===u.PJD_3PARAM)return{x:a.x-r[0],y:a.y-r[1],z:a.z-r[2]};if(t===u.PJD_7PARAM){var m=r[0],s=r[1],_=r[2],e=r[3],n=r[4],d=r[5],i=r[6],p=(a.x-m)/i,P=(a.y-s)/i,y=(a.z-_)/i;return{x:p+d*P-n*y,y:-d*p+P+e*y,z:n*p-e*P+y}}}},\n", - " function _(e,a,i,r,s){r(),i.default=function(e,a,i){var r,s,n,c=i.x,d=i.y,f=i.z||0,u={};for(n=0;n<3;n++)if(!a||2!==n||void 0!==i.z)switch(0===n?(r=c,s=-1!==\"ew\".indexOf(e.axis[n])?\"x\":\"y\"):1===n?(r=d,s=-1!==\"ns\".indexOf(e.axis[n])?\"y\":\"x\"):(r=f,s=\"z\"),e.axis[n]){case\"e\":u[s]=r;break;case\"w\":u[s]=-r;break;case\"n\":u[s]=r;break;case\"s\":u[s]=-r;break;case\"u\":void 0!==i[s]&&(u.z=r);break;case\"d\":void 0!==i[s]&&(u.z=-r);break;default:return null}return u}},\n", - " function _(n,t,e,u,f){u(),e.default=function(n){var t={x:n[0],y:n[1]};return n.length>2&&(t.z=n[2]),n.length>3&&(t.m=n[3]),t}},\n", - " function _(e,i,n,t,r){function o(e){if(\"function\"==typeof Number.isFinite){if(Number.isFinite(e))return;throw new TypeError(\"coordinates must be finite numbers\")}if(\"number\"!=typeof e||e!=e||!isFinite(e))throw new TypeError(\"coordinates must be finite numbers\")}t(),n.default=function(e){o(e.x),o(e.y)}},\n", - " function _(e,i,s,t,o){t();const n=e(1);var l,a,r,_,c;const d=e(53),v=e(42),u=(0,n.__importStar)(e(45)),h=e(48),m=(0,n.__importStar)(e(18));class T extends v.View{initialize(){super.initialize(),this.visuals=new u.Visuals(this)}request_render(){this.parent.request_render()}get canvas(){return this.parent.canvas}set_data(e){const i=this;for(const s of this.model){if(!(s instanceof m.VectorSpec||s instanceof m.ScalarSpec))continue;const t=s.uniform(e);i[`${s.attr}`]=t}}}s.ArrowHeadView=T,T.__name__=\"ArrowHeadView\";class p extends d.Model{constructor(e){super(e)}}s.ArrowHead=p,l=p,p.__name__=\"ArrowHead\",l.define((()=>({size:[m.NumberSpec,25]})));class V extends T{clip(e,i){this.visuals.line.set_vectorize(e,i);const s=this.size.get(i);e.moveTo(.5*s,s),e.lineTo(.5*s,-2),e.lineTo(-.5*s,-2),e.lineTo(-.5*s,s),e.lineTo(0,0),e.lineTo(.5*s,s)}render(e,i){if(this.visuals.line.doit){this.visuals.line.set_vectorize(e,i);const s=this.size.get(i);e.beginPath(),e.moveTo(.5*s,s),e.lineTo(0,0),e.lineTo(-.5*s,s),e.stroke()}}}s.OpenHeadView=V,V.__name__=\"OpenHeadView\";class f extends p{constructor(e){super(e)}}s.OpenHead=f,a=f,f.__name__=\"OpenHead\",a.prototype.default_view=V,a.mixins(h.LineVector);class w extends T{clip(e,i){this.visuals.line.set_vectorize(e,i);const s=this.size.get(i);e.moveTo(.5*s,s),e.lineTo(.5*s,-2),e.lineTo(-.5*s,-2),e.lineTo(-.5*s,s),e.lineTo(.5*s,s)}render(e,i){this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(e,i),this._normal(e,i),e.fill()),this.visuals.line.doit&&(this.visuals.line.set_vectorize(e,i),this._normal(e,i),e.stroke())}_normal(e,i){const s=this.size.get(i);e.beginPath(),e.moveTo(.5*s,s),e.lineTo(0,0),e.lineTo(-.5*s,s),e.closePath()}}s.NormalHeadView=w,w.__name__=\"NormalHeadView\";class H extends p{constructor(e){super(e)}}s.NormalHead=H,r=H,H.__name__=\"NormalHead\",r.prototype.default_view=w,r.mixins([h.LineVector,h.FillVector]),r.override({fill_color:\"black\"});class z extends T{clip(e,i){this.visuals.line.set_vectorize(e,i);const s=this.size.get(i);e.moveTo(.5*s,s),e.lineTo(.5*s,-2),e.lineTo(-.5*s,-2),e.lineTo(-.5*s,s),e.lineTo(0,.5*s),e.lineTo(.5*s,s)}render(e,i){this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(e,i),this._vee(e,i),e.fill()),this.visuals.line.doit&&(this.visuals.line.set_vectorize(e,i),this._vee(e,i),e.stroke())}_vee(e,i){const s=this.size.get(i);e.beginPath(),e.moveTo(.5*s,s),e.lineTo(0,0),e.lineTo(-.5*s,s),e.lineTo(0,.5*s),e.closePath()}}s.VeeHeadView=z,z.__name__=\"VeeHeadView\";class x extends p{constructor(e){super(e)}}s.VeeHead=x,_=x,x.__name__=\"VeeHead\",_.prototype.default_view=z,_.mixins([h.LineVector,h.FillVector]),_.override({fill_color:\"black\"});class g extends T{render(e,i){if(this.visuals.line.doit){this.visuals.line.set_vectorize(e,i);const s=this.size.get(i);e.beginPath(),e.moveTo(.5*s,0),e.lineTo(-.5*s,0),e.stroke()}}clip(e,i){}}s.TeeHeadView=g,g.__name__=\"TeeHeadView\";class b extends p{constructor(e){super(e)}}s.TeeHead=b,c=b,b.__name__=\"TeeHead\",c.prototype.default_view=g,c.mixins(h.LineVector)},\n", - " function _(n,e,t,i,o){i();const s=n(9);async function c(n,e,t){const i=new n(Object.assign(Object.assign({},t),{model:e}));return i.initialize(),await i.lazy_initialize(),i}t.build_view=async function(n,e={parent:null},t=(n=>n.default_view)){const i=await c(t(n),n,e);return i.connect_signals(),i},t.build_views=async function(n,e,t={parent:null},i=(n=>n.default_view)){const o=(0,s.difference)([...n.keys()],e);for(const e of o)n.get(e).remove(),n.delete(e);const a=[],f=e.filter((e=>!n.has(e)));for(const e of f){const o=await c(i(e),e,t);n.set(e,o),a.push(o)}for(const n of a)n.connect_signals();return a},t.remove_views=function(n){for(const[e,t]of n)t.remove(),n.delete(e)}},\n", - " function _(e,s,_,i,l){i();const t=e(1);var o;const r=e(115),p=(0,t.__importStar)(e(48));class h extends r.UpperLowerView{paint(e){e.beginPath(),e.moveTo(this._lower_sx[0],this._lower_sy[0]);for(let s=0,_=this._lower_sx.length;s<_;s++)e.lineTo(this._lower_sx[s],this._lower_sy[s]);for(let s=this._upper_sx.length-1;s>=0;s--)e.lineTo(this._upper_sx[s],this._upper_sy[s]);e.closePath(),this.visuals.fill.apply(e),e.beginPath(),e.moveTo(this._lower_sx[0],this._lower_sy[0]);for(let s=0,_=this._lower_sx.length;s<_;s++)e.lineTo(this._lower_sx[s],this._lower_sy[s]);this.visuals.line.apply(e),e.beginPath(),e.moveTo(this._upper_sx[0],this._upper_sy[0]);for(let s=0,_=this._upper_sx.length;s<_;s++)e.lineTo(this._upper_sx[s],this._upper_sy[s]);this.visuals.line.apply(e)}}_.BandView=h,h.__name__=\"BandView\";class n extends r.UpperLower{constructor(e){super(e)}}_.Band=n,o=n,n.__name__=\"Band\",o.prototype.default_view=h,o.mixins([p.Line,p.Fill]),o.override({fill_color:\"#fff9ba\",fill_alpha:.4,line_color:\"#cccccc\",line_alpha:.3})},\n", - " function _(e,t,i,s,o){s();const r=e(1);var n;const p=e(69),a=e(20),_=(0,r.__importStar)(e(18));class h extends p.DataAnnotationView{map_data(){const{frame:e}=this.plot_view,t=this.model.dimension,i=this.coordinates.x_scale,s=this.coordinates.y_scale,o=\"height\"==t?s:i,r=\"height\"==t?i:s,n=\"height\"==t?e.bbox.yview:e.bbox.xview,p=\"height\"==t?e.bbox.xview:e.bbox.yview;let a,_,h;a=\"data\"==this.model.properties.lower.units?o.v_compute(this._lower):n.v_compute(this._lower),_=\"data\"==this.model.properties.upper.units?o.v_compute(this._upper):n.v_compute(this._upper),h=\"data\"==this.model.properties.base.units?r.v_compute(this._base):p.v_compute(this._base);const[d,c]=\"height\"==t?[1,0]:[0,1],u=[a,h],l=[_,h];this._lower_sx=u[d],this._lower_sy=u[c],this._upper_sx=l[d],this._upper_sy=l[c]}}i.UpperLowerView=h,h.__name__=\"UpperLowerView\";class d extends _.CoordinateSpec{get dimension(){return\"width\"==this.obj.dimension?\"x\":\"y\"}get units(){var e;return null!==(e=this.spec.units)&&void 0!==e?e:\"data\"}}i.XOrYCoordinateSpec=d,d.__name__=\"XOrYCoordinateSpec\";class c extends p.DataAnnotation{constructor(e){super(e)}}i.UpperLower=c,n=c,c.__name__=\"UpperLower\",n.define((()=>({dimension:[a.Dimension,\"height\"],lower:[d,{field:\"lower\"}],upper:[d,{field:\"upper\"}],base:[d,{field:\"base\"}]})))},\n", - " function _(t,o,i,n,e){n();const s=t(1);var l;const r=t(40),a=(0,s.__importStar)(t(48)),c=t(20),h=t(65);i.EDGE_TOLERANCE=2.5;class b extends r.AnnotationView{constructor(){super(...arguments),this.bbox=new h.BBox}connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.request_render()))}_render(){const{left:t,right:o,top:i,bottom:n}=this.model;if(null==t&&null==o&&null==i&&null==n)return;const{frame:e}=this.plot_view,s=this.coordinates.x_scale,l=this.coordinates.y_scale,r=(t,o,i,n,e)=>{let s;return s=null!=t?this.model.screen?t:\"data\"==o?i.compute(t):n.compute(t):e,s};this.bbox=h.BBox.from_rect({left:r(t,this.model.left_units,s,e.bbox.xview,e.bbox.left),right:r(o,this.model.right_units,s,e.bbox.xview,e.bbox.right),top:r(i,this.model.top_units,l,e.bbox.yview,e.bbox.top),bottom:r(n,this.model.bottom_units,l,e.bbox.yview,e.bbox.bottom)}),this._paint_box()}_paint_box(){const{ctx:t}=this.layer;t.save();const{left:o,top:i,width:n,height:e}=this.bbox;t.beginPath(),t.rect(o,i,n,e),this.visuals.fill.apply(t),this.visuals.hatch.apply(t),this.visuals.line.apply(t),t.restore()}interactive_bbox(){const t=this.model.line_width+i.EDGE_TOLERANCE;return this.bbox.grow_by(t)}interactive_hit(t,o){if(null==this.model.in_cursor)return!1;return this.interactive_bbox().contains(t,o)}cursor(t,o){const{left:i,right:n,bottom:e,top:s}=this.bbox;return Math.abs(t-i)<3||Math.abs(t-n)<3?this.model.ew_cursor:Math.abs(o-e)<3||Math.abs(o-s)<3?this.model.ns_cursor:this.bbox.contains(t,o)?this.model.in_cursor:null}}i.BoxAnnotationView=b,b.__name__=\"BoxAnnotationView\";class u extends r.Annotation{constructor(t){super(t)}update({left:t,right:o,top:i,bottom:n}){this.setv({left:t,right:o,top:i,bottom:n,screen:!0})}}i.BoxAnnotation=u,l=u,u.__name__=\"BoxAnnotation\",l.prototype.default_view=b,l.mixins([a.Line,a.Fill,a.Hatch]),l.define((({Number:t,Nullable:o})=>({top:[o(t),null],top_units:[c.SpatialUnits,\"data\"],bottom:[o(t),null],bottom_units:[c.SpatialUnits,\"data\"],left:[o(t),null],left_units:[c.SpatialUnits,\"data\"],right:[o(t),null],right_units:[c.SpatialUnits,\"data\"],render_mode:[c.RenderMode,\"canvas\"]}))),l.internal((({Boolean:t,String:o,Nullable:i})=>({screen:[t,!1],ew_cursor:[i(o),null],ns_cursor:[i(o),null],in_cursor:[i(o),null]}))),l.override({fill_color:\"#fff9ba\",fill_alpha:.4,line_color:\"#cccccc\",line_alpha:.3})},\n", - " function _(t,e,i,o,n){o();const a=t(1);var r;const s=t(40),l=t(118),_=t(126),c=t(127),h=t(130),u=t(168),p=t(131),m=t(192),g=t(132),d=t(173),f=t(172),w=t(196),b=t(204),v=t(206),x=t(133),y=t(20),k=(0,a.__importStar)(t(48)),z=t(9),j=t(207),C=t(208),L=t(211),B=t(123),S=t(11),M=t(113),T=t(65),A=t(8);class O extends s.AnnotationView{get orientation(){return this._orientation}initialize(){super.initialize();const{ticker:t,formatter:e,color_mapper:i}=this.model;this._ticker=\"auto\"!=t?t:(()=>{switch(!0){case i instanceof w.LogColorMapper:return new u.LogTicker;case i instanceof w.ScanningColorMapper:return new u.BinnedTicker({mapper:i});case i instanceof w.CategoricalColorMapper:return new u.CategoricalTicker;default:return new u.BasicTicker}})(),this._formatter=\"auto\"!=e?e:(()=>{switch(!0){case this._ticker instanceof u.LogTicker:return new m.LogTickFormatter;case i instanceof w.CategoricalColorMapper:return new m.CategoricalTickFormatter;default:return new m.BasicTickFormatter}})(),this._major_range=(()=>{if(i instanceof w.CategoricalColorMapper){const{factors:t}=i;return new v.FactorRange({factors:t})}if(i instanceof f.ContinuousColorMapper){const{min:t,max:e}=i.metrics;return new v.Range1d({start:t,end:e})}(0,S.unreachable)()})(),this._major_scale=(()=>{if(i instanceof w.LinearColorMapper)return new b.LinearScale;if(i instanceof w.LogColorMapper)return new b.LogScale;if(i instanceof w.ScanningColorMapper){const{binning:t}=i.metrics;return new b.LinearInterpolationScale({binning:t})}if(i instanceof w.CategoricalColorMapper)return new b.CategoricalScale;(0,S.unreachable)()})(),this._minor_range=new v.Range1d({start:0,end:1}),this._minor_scale=new b.LinearScale;const o=k.attrs_of(this.model,\"major_label_\",k.Text,!0),n=k.attrs_of(this.model,\"major_tick_\",k.Line,!0),a=k.attrs_of(this.model,\"minor_tick_\",k.Line,!0),r=k.attrs_of(this.model,\"title_\",k.Text),s=i instanceof w.CategoricalColorMapper?c.CategoricalAxis:i instanceof w.LogColorMapper?c.LogAxis:c.LinearAxis;this._axis=new s(Object.assign(Object.assign(Object.assign({ticker:this._ticker,formatter:this._formatter,major_tick_in:this.model.major_tick_in,major_tick_out:this.model.major_tick_out,minor_tick_in:this.model.minor_tick_in,minor_tick_out:this.model.minor_tick_out,major_label_standoff:this.model.label_standoff,major_label_overrides:this.model.major_label_overrides,major_label_policy:this.model.major_label_policy,axis_line_color:null},o),n),a));const{title:_}=this.model;_&&(this._title=new l.Title(Object.assign({text:_,standoff:this.model.title_standoff},r)))}async lazy_initialize(){await super.lazy_initialize();const t=this,e={get parent(){return t.parent},get root(){return t.root},get frame(){return t._frame},get canvas_view(){return t.parent.canvas_view},request_layout(){t.parent.request_layout()}};this._axis_view=await(0,M.build_view)(this._axis,{parent:e}),null!=this._title&&(this._title_view=await(0,M.build_view)(this._title,{parent:e}))}remove(){var t;null===(t=this._title_view)||void 0===t||t.remove(),this._axis_view.remove(),super.remove()}connect_signals(){super.connect_signals(),this.connect(this._ticker.change,(()=>this.request_render())),this.connect(this._formatter.change,(()=>this.request_render())),this.connect(this.model.color_mapper.metrics_change,(()=>{const t=this._major_range,e=this._major_scale,{color_mapper:i}=this.model;if(i instanceof f.ContinuousColorMapper&&t instanceof v.Range1d){const{min:e,max:o}=i.metrics;t.setv({start:e,end:o})}if(i instanceof w.ScanningColorMapper&&e instanceof b.LinearInterpolationScale){const{binning:t}=i.metrics;e.binning=t}this._set_canvas_image(),this.plot_view.request_layout()}))}_set_canvas_image(){const{orientation:t}=this,e=(()=>{const{palette:e}=this.model.color_mapper;return\"vertical\"==t?(0,z.reversed)(e):e})(),[i,o]=\"vertical\"==t?[1,e.length]:[e.length,1],n=this._image=document.createElement(\"canvas\");n.width=i,n.height=o;const a=n.getContext(\"2d\"),r=a.getImageData(0,0,i,o),s=new w.LinearColorMapper({palette:e}).rgba_mapper.v_compute((0,z.range)(0,e.length));r.data.set(s),a.putImageData(r,0,0)}update_layout(){const{location:t,width:e,height:i,padding:o,margin:n}=this.model,[a,r]=(()=>{if(!(0,A.isString)(t))return[\"end\",\"start\"];switch(t){case\"top_left\":return[\"start\",\"start\"];case\"top\":case\"top_center\":return[\"start\",\"center\"];case\"top_right\":return[\"start\",\"end\"];case\"bottom_left\":return[\"end\",\"start\"];case\"bottom\":case\"bottom_center\":return[\"end\",\"center\"];case\"bottom_right\":return[\"end\",\"end\"];case\"left\":case\"center_left\":return[\"center\",\"start\"];case\"center\":case\"center_center\":return[\"center\",\"center\"];case\"right\":case\"center_right\":return[\"center\",\"end\"]}})(),s=this._orientation=(()=>{const{orientation:t}=this.model;return\"auto\"==t?null!=this.panel?this.panel.is_horizontal?\"horizontal\":\"vertical\":\"start\"==r||\"end\"==r||\"center\"==r&&\"center\"==a?\"vertical\":\"horizontal\":t})(),l=new C.NodeLayout,c=new C.VStack,h=new C.VStack,u=new C.HStack,p=new C.HStack;l.absolute=!0,c.absolute=!0,h.absolute=!0,u.absolute=!0,p.absolute=!0;const[m,g,d,f]=(()=>\"horizontal\"==s?[this._major_scale,this._minor_scale,this._major_range,this._minor_range]:[this._minor_scale,this._major_scale,this._minor_range,this._major_range])();this._frame=new _.CartesianFrame(m,g,d,f),l.on_resize((t=>this._frame.set_geometry(t)));const w=new L.BorderLayout;this._inner_layout=w,w.absolute=!0,w.center_panel=l,w.top_panel=c,w.bottom_panel=h,w.left_panel=u,w.right_panel=p;const b={left:o,right:o,top:o,bottom:o},v=(()=>{if(null==this.panel){if((0,A.isString)(t))return{left:n,right:n,top:n,bottom:n};{const[e,i]=t;return{left:e,right:n,top:n,bottom:i}}}if(!(0,A.isString)(t)){const[e,i]=t;return w.fixup_geometry=(t,o)=>{const n=t,a=this.layout.bbox,{width:r,height:s}=t;if(t=new T.BBox({left:a.left+e,bottom:a.bottom-i,width:r,height:s}),null!=o){const e=t.left-n.left,i=t.top-n.top,{left:a,top:r,width:s,height:l}=o;o=new T.BBox({left:a+e,top:r+i,width:s,height:l})}return[t,o]},{left:e,right:0,top:0,bottom:i}}w.fixup_geometry=(t,e)=>{const i=t;if(\"horizontal\"==s){const{top:e,width:i,height:o}=t;if(\"end\"==r){const{right:n}=this.layout.bbox;t=new T.BBox({right:n,top:e,width:i,height:o})}else if(\"center\"==r){const{hcenter:n}=this.layout.bbox;t=new T.BBox({hcenter:Math.round(n),top:e,width:i,height:o})}}else{const{left:e,width:i,height:o}=t;if(\"end\"==a){const{bottom:n}=this.layout.bbox;t=new T.BBox({left:e,bottom:n,width:i,height:o})}else if(\"center\"==a){const{vcenter:n}=this.layout.bbox;t=new T.BBox({left:e,vcenter:Math.round(n),width:i,height:o})}}if(null!=e){const o=t.left-i.left,n=t.top-i.top,{left:a,top:r,width:s,height:l}=e;e=new T.BBox({left:a+o,top:r+n,width:s,height:l})}return[t,e]}})();let x,y,k,z;if(w.padding=b,null!=this.panel?(x=\"max\",y=void 0,k=void 0,z=void 0):\"auto\"==(\"horizontal\"==s?e:i)?(x=\"fixed\",y=25*this.model.color_mapper.palette.length,k={percent:.3},z={percent:.8}):(x=\"fit\",y=void 0),\"horizontal\"==s){const t=\"auto\"==e?void 0:e,o=\"auto\"==i?25:i;w.set_sizing({width_policy:x,height_policy:\"min\",width:y,min_width:k,max_width:z,halign:r,valign:a,margin:v}),w.center_panel.set_sizing({width_policy:\"auto\"==e?\"fit\":\"fixed\",height_policy:\"fixed\",width:t,height:o})}else{const t=\"auto\"==e?25:e,o=\"auto\"==i?void 0:i;w.set_sizing({width_policy:\"min\",height_policy:x,height:y,min_height:k,max_height:z,halign:r,valign:a,margin:v}),w.center_panel.set_sizing({width_policy:\"fixed\",height_policy:\"auto\"==i?\"fit\":\"fixed\",width:t,height:o})}c.set_sizing({width_policy:\"fit\",height_policy:\"min\"}),h.set_sizing({width_policy:\"fit\",height_policy:\"min\"}),u.set_sizing({width_policy:\"min\",height_policy:\"fit\"}),p.set_sizing({width_policy:\"min\",height_policy:\"fit\"});const{_title_view:S}=this;null!=S&&(\"horizontal\"==s?(S.panel=new B.Panel(\"above\"),S.update_layout(),c.children.push(S.layout)):(S.panel=new B.Panel(\"left\"),S.update_layout(),u.children.push(S.layout)));const{panel:M}=this,O=null!=M&&s==M.orientation?M.side:\"horizontal\"==s?\"below\":\"right\",R=(()=>{switch(O){case\"above\":return c;case\"below\":return h;case\"left\":return u;case\"right\":return p}})(),{_axis_view:F}=this;if(F.panel=new B.Panel(O),F.update_layout(),R.children.push(F.layout),null!=this.panel){const t=new j.Grid([{layout:w,row:0,col:0}]);t.absolute=!0,\"horizontal\"==s?t.set_sizing({width_policy:\"max\",height_policy:\"min\"}):t.set_sizing({width_policy:\"min\",height_policy:\"max\"}),this.layout=t}else this.layout=this._inner_layout;const{visible:I}=this.model;this.layout.sizing.visible=I,this._set_canvas_image()}_render(){var t;const{ctx:e}=this.layer;e.save(),this._paint_bbox(e,this._inner_layout.bbox),this._paint_image(e,this._inner_layout.center_panel.bbox),null===(t=this._title_view)||void 0===t||t.render(),this._axis_view.render(),e.restore()}_paint_bbox(t,e){const{x:i,y:o}=e;let{width:n,height:a}=e;i+n>=this.parent.canvas_view.bbox.width&&(n-=1),o+a>=this.parent.canvas_view.bbox.height&&(a-=1),t.save(),this.visuals.background_fill.doit&&(this.visuals.background_fill.set_value(t),t.fillRect(i,o,n,a)),this.visuals.border_line.doit&&(this.visuals.border_line.set_value(t),t.strokeRect(i,o,n,a)),t.restore()}_paint_image(t,e){const{x:i,y:o,width:n,height:a}=e;t.save(),t.setImageSmoothingEnabled(!1),t.globalAlpha=this.model.scale_alpha,t.drawImage(this._image,i,o,n,a),this.visuals.bar_line.doit&&(this.visuals.bar_line.set_value(t),t.strokeRect(i,o,n,a)),t.restore()}serializable_state(){const t=super.serializable_state(),{children:e=[]}=t,i=(0,a.__rest)(t,[\"children\"]);return null!=this._title_view&&e.push(this._title_view.serializable_state()),e.push(this._axis_view.serializable_state()),Object.assign(Object.assign({},i),{children:e})}}i.ColorBarView=O,O.__name__=\"ColorBarView\";class R extends s.Annotation{constructor(t){super(t)}}i.ColorBar=R,r=R,R.__name__=\"ColorBar\",r.prototype.default_view=O,r.mixins([[\"major_label_\",k.Text],[\"title_\",k.Text],[\"major_tick_\",k.Line],[\"minor_tick_\",k.Line],[\"border_\",k.Line],[\"bar_\",k.Line],[\"background_\",k.Fill]]),r.define((({Alpha:t,Number:e,String:i,Tuple:o,Dict:n,Or:a,Ref:r,Auto:s,Nullable:l})=>({location:[a(y.Anchor,o(e,e)),\"top_right\"],orientation:[a(y.Orientation,s),\"auto\"],title:[l(i),null],title_standoff:[e,2],width:[a(e,s),\"auto\"],height:[a(e,s),\"auto\"],scale_alpha:[t,1],ticker:[a(r(h.Ticker),s),\"auto\"],formatter:[a(r(p.TickFormatter),s),\"auto\"],major_label_overrides:[n(a(i,r(x.BaseText))),{}],major_label_policy:[r(g.LabelingPolicy),()=>new g.NoOverlap],color_mapper:[r(d.ColorMapper)],label_standoff:[e,5],margin:[e,30],padding:[e,10],major_tick_in:[e,5],major_tick_out:[e,0],minor_tick_in:[e,0],minor_tick_out:[e,0]}))),r.override({background_fill_color:\"#ffffff\",background_fill_alpha:.95,bar_line_color:null,border_line_color:null,major_label_text_font_size:\"11px\",major_tick_line_color:\"#ffffff\",minor_tick_line_color:null,title_text_font_size:\"13px\",title_text_font_style:\"italic\"})},\n", - " function _(t,e,i,s,l){s();const o=t(1);var a;const n=t(119),r=t(20),c=t(120),h=(0,o.__importStar)(t(48));class _ extends n.TextAnnotationView{_get_location(){const t=this.model.offset,e=this.model.standoff/2;let i,s;const{bbox:l}=this.layout;switch(this.panel.side){case\"above\":case\"below\":switch(this.model.vertical_align){case\"top\":s=l.top+e;break;case\"middle\":s=l.vcenter;break;case\"bottom\":s=l.bottom-e}switch(this.model.align){case\"left\":i=l.left+t;break;case\"center\":i=l.hcenter;break;case\"right\":i=l.right-t}break;case\"left\":switch(this.model.vertical_align){case\"top\":i=l.left+e;break;case\"middle\":i=l.hcenter;break;case\"bottom\":i=l.right-e}switch(this.model.align){case\"left\":s=l.bottom-t;break;case\"center\":s=l.vcenter;break;case\"right\":s=l.top+t}break;case\"right\":switch(this.model.vertical_align){case\"top\":i=l.right-e;break;case\"middle\":i=l.hcenter;break;case\"bottom\":i=l.left+e}switch(this.model.align){case\"left\":s=l.top+t;break;case\"center\":s=l.vcenter;break;case\"right\":s=l.bottom-t}}return[i,s]}_render(){const{text:t}=this.model;if(null==t||0==t.length)return;this.model.text_baseline=this.model.vertical_align,this.model.text_align=this.model.align;const[e,i]=this._get_location(),s=this.panel.get_label_angle_heuristic(\"parallel\");(\"canvas\"==this.model.render_mode?this._canvas_text.bind(this):this._css_text.bind(this))(this.layer.ctx,t,e,i,s)}_get_size(){const{text:t}=this.model,e=new c.TextBox({text:t});e.visuals=this.visuals.text.values();const{width:i,height:s}=e.size();return{width:i,height:0==s?0:2+s+this.model.standoff}}}i.TitleView=_,_.__name__=\"TitleView\";class d extends n.TextAnnotation{constructor(t){super(t)}}i.Title=d,a=d,d.__name__=\"Title\",a.prototype.default_view=_,a.mixins([h.Text,[\"border_\",h.Line],[\"background_\",h.Fill]]),a.define((({Number:t,String:e})=>({text:[e,\"\"],vertical_align:[r.VerticalAlign,\"bottom\"],align:[r.TextAlign,\"left\"],offset:[t,0],standoff:[t,10]}))),a.prototype._props.text_align.options.internal=!0,a.prototype._props.text_baseline.options.internal=!0,a.override({text_font_size:\"13px\",text_font_style:\"bold\",text_line_height:1,background_fill_color:null,border_line_color:null})},\n", - " function _(e,t,s,i,l){var n;i();const o=e(40),a=e(43),r=e(20),d=e(120),u=e(123),c=e(11);class h extends o.AnnotationView{update_layout(){const{panel:e}=this;this.layout=null!=e?new u.SideLayout(e,(()=>this.get_size()),!0):void 0}initialize(){super.initialize(),\"css\"==this.model.render_mode&&(this.el=(0,a.div)(),this.plot_view.canvas_view.add_overlay(this.el))}remove(){null!=this.el&&(0,a.remove)(this.el),super.remove()}connect_signals(){super.connect_signals(),\"css\"==this.model.render_mode?this.connect(this.model.change,(()=>this.render())):this.connect(this.model.change,(()=>this.request_render()))}render(){this.model.visible||\"css\"!=this.model.render_mode||(0,a.undisplay)(this.el),super.render()}_canvas_text(e,t,s,i,l){const n=new d.TextBox({text:t});n.angle=l,n.position={sx:s,sy:i},n.visuals=this.visuals.text.values();const{background_fill:o,border_line:a}=this.visuals;if(o.doit||a.doit){const{p0:t,p1:s,p2:i,p3:l}=n.rect();e.beginPath(),e.moveTo(t.x,t.y),e.lineTo(s.x,s.y),e.lineTo(i.x,i.y),e.lineTo(l.x,l.y),e.closePath(),this.visuals.background_fill.apply(e),this.visuals.border_line.apply(e)}this.visuals.text.doit&&n.paint(e)}_css_text(e,t,s,i,l){const{el:n}=this;(0,c.assert)(null!=n),(0,a.undisplay)(n),n.textContent=t,this.visuals.text.set_value(e),n.style.position=\"absolute\",n.style.left=`${s}px`,n.style.top=`${i}px`,n.style.color=e.fillStyle,n.style.font=e.font,n.style.lineHeight=\"normal\",n.style.whiteSpace=\"pre\";const[o,r]=(()=>{switch(this.visuals.text.text_align.get_value()){case\"left\":return[\"left\",\"0%\"];case\"center\":return[\"center\",\"-50%\"];case\"right\":return[\"right\",\"-100%\"]}})(),[d,u]=(()=>{switch(this.visuals.text.text_baseline.get_value()){case\"top\":return[\"top\",\"0%\"];case\"middle\":return[\"center\",\"-50%\"];case\"bottom\":return[\"bottom\",\"-100%\"];default:return[\"center\",\"-50%\"]}})();let h=`translate(${r}, ${u})`;l&&(h+=`rotate(${l}rad)`),n.style.transformOrigin=`${o} ${d}`,n.style.transform=h,this.layout,this.visuals.background_fill.doit&&(this.visuals.background_fill.set_value(e),n.style.backgroundColor=e.fillStyle),this.visuals.border_line.doit&&(this.visuals.border_line.set_value(e),n.style.borderStyle=e.lineDash.length<2?\"solid\":\"dashed\",n.style.borderWidth=`${e.lineWidth}px`,n.style.borderColor=e.strokeStyle),(0,a.display)(n)}}s.TextAnnotationView=h,h.__name__=\"TextAnnotationView\";class _ extends o.Annotation{constructor(e){super(e)}}s.TextAnnotation=_,n=_,_.__name__=\"TextAnnotation\",n.define((()=>({render_mode:[r.RenderMode,\"canvas\"]})))},\n", - " function _(t,e,s,i,n){i();const h=t(65),o=t(121),r=t(9),a=t(8),c=t(122),_=t(22);s.text_width=(()=>{const t=document.createElement(\"canvas\").getContext(\"2d\");let e=\"\";return(s,i)=>(i!=e&&(e=i,t.font=i),t.measureText(s).width)})();class l{constructor(){this._position={sx:0,sy:0},this.font_size_scale=1,this.align=\"left\",this._base_font_size=13,this._x_anchor=\"left\",this._y_anchor=\"center\"}set base_font_size(t){null!=t&&(this._base_font_size=t)}get base_font_size(){return this._base_font_size}set position(t){this._position=t}get position(){return this._position}infer_text_height(){return\"ascent_descent\"}bbox(){const{p0:t,p1:e,p2:s,p3:i}=this.rect(),n=Math.min(t.x,e.x,s.x,i.x),o=Math.min(t.y,e.y,s.y,i.y),r=Math.max(t.x,e.x,s.x,i.x),a=Math.max(t.y,e.y,s.y,i.y);return new h.BBox({left:n,right:r,top:o,bottom:a})}size(){const{width:t,height:e}=this._size(),{angle:s}=this;if(s){const i=Math.cos(Math.abs(s)),n=Math.sin(Math.abs(s));return{width:Math.abs(t*i+e*n),height:Math.abs(t*n+e*i)}}return{width:t,height:e}}rect(){const t=this._rect(),{angle:e}=this;if(e){const{sx:s,sy:i}=this.position,n=new c.AffineTransform;return n.translate(s,i),n.rotate(e),n.translate(-s,-i),n.apply_rect(t)}return t}paint_rect(t){const{p0:e,p1:s,p2:i,p3:n}=this.rect();t.save(),t.strokeStyle=\"red\",t.lineWidth=1,t.beginPath();const{round:h}=Math;t.moveTo(h(e.x),h(e.y)),t.lineTo(h(s.x),h(s.y)),t.lineTo(h(i.x),h(i.y)),t.lineTo(h(n.x),h(n.y)),t.closePath(),t.stroke(),t.restore()}paint_bbox(t){const{x:e,y:s,width:i,height:n}=this.bbox();t.save(),t.strokeStyle=\"blue\",t.lineWidth=1,t.beginPath();const{round:h}=Math;t.moveTo(h(e),h(s)),t.lineTo(h(e),h(s+n)),t.lineTo(h(e+i),h(s+n)),t.lineTo(h(e+i),h(s)),t.closePath(),t.stroke(),t.restore()}}s.GraphicsBox=l,l.__name__=\"GraphicsBox\";class x extends l{constructor({text:t}){super(),this.text=t}set visuals(t){const e=t.color,s=t.alpha,i=t.font_style;let n=t.font_size;const h=t.font,{font_size_scale:r,base_font_size:a}=this,c=(0,o.parse_css_font_size)(n);if(null!=c){let{value:t,unit:e}=c;t*=r,\"em\"==e&&a&&(t*=a,e=\"px\"),n=`${t}${e}`}const l=`${i} ${n} ${h}`;this.font=l,this.color=(0,_.color2css)(e,s),this.line_height=t.line_height;const x=t.align;this._x_anchor=x;const u=t.baseline;this._y_anchor=(()=>{switch(u){case\"top\":return\"top\";case\"middle\":return\"center\";case\"bottom\":return\"bottom\";default:return\"baseline\"}})()}infer_text_height(){if(this.text.includes(\"\\n\"))return\"ascent_descent\";{function t(t){for(const e of new Set(t))if(!(\"0\"<=e&&e<=\"9\"))switch(e){case\",\":case\".\":case\"+\":case\"-\":case\"\\u2212\":case\"e\":continue;default:return!1}return!0}return t(this.text)?\"cap\":\"ascent_descent\"}}_text_line(t){var e;const s=null!==(e=this.text_height_metric)&&void 0!==e?e:this.infer_text_height(),i=(()=>{switch(s){case\"x\":case\"x_descent\":return t.x_height;case\"cap\":case\"cap_descent\":return t.cap_height;case\"ascent\":case\"ascent_descent\":return t.ascent}})(),n=(()=>{switch(s){case\"x\":case\"cap\":case\"ascent\":return 0;case\"x_descent\":case\"cap_descent\":case\"ascent_descent\":return t.descent}})();return{height:i+n,ascent:i,descent:n}}get nlines(){return this.text.split(\"\\n\").length}_size(){var t,e;const{font:i}=this,n=(0,o.font_metrics)(i),h=(this.line_height-1)*n.height,a=\"\"==this.text,c=this.text.split(\"\\n\"),_=c.length,l=c.map((t=>(0,s.text_width)(t,i))),x=this._text_line(n).height*_,u=\"%\"==(null===(t=this.width)||void 0===t?void 0:t.unit)?this.width.value:1,p=\"%\"==(null===(e=this.height)||void 0===e?void 0:e.unit)?this.height.value:1;return{width:(0,r.max)(l)*u,height:a?0:(x+h*(_-1))*p,metrics:n}}_computed_position(t,e,s){const{width:i,height:n}=t,{sx:h,sy:o,x_anchor:r=this._x_anchor,y_anchor:c=this._y_anchor}=this.position;return{x:h-(()=>{if((0,a.isNumber)(r))return r*i;switch(r){case\"left\":return 0;case\"center\":return.5*i;case\"right\":return i}})(),y:o-(()=>{var t;if((0,a.isNumber)(c))return c*n;switch(c){case\"top\":return 0;case\"center\":return.5*n;case\"bottom\":return n;case\"baseline\":if(1!=s)return.5*n;switch(null!==(t=this.text_height_metric)&&void 0!==t?t:this.infer_text_height()){case\"x\":case\"x_descent\":return e.x_height;case\"cap\":case\"cap_descent\":return e.cap_height;case\"ascent\":case\"ascent_descent\":return e.ascent}}})()}}_rect(){const{width:t,height:e,metrics:s}=this._size(),i=this.text.split(\"\\n\").length,{x:n,y:o}=this._computed_position({width:t,height:e},s,i);return new h.BBox({x:n,y:o,width:t,height:e}).rect}paint(t){var e,i;const{font:n}=this,h=(0,o.font_metrics)(n),a=(this.line_height-1)*h.height,c=this.text.split(\"\\n\"),_=c.length,l=c.map((t=>(0,s.text_width)(t,n))),x=this._text_line(h),u=x.height*_,p=\"%\"==(null===(e=this.width)||void 0===e?void 0:e.unit)?this.width.value:1,f=\"%\"==(null===(i=this.height)||void 0===i?void 0:i.unit)?this.height.value:1,g=(0,r.max)(l)*p,d=(u+a*(_-1))*f;t.save(),t.fillStyle=this.color,t.font=this.font,t.textAlign=\"left\",t.textBaseline=\"alphabetic\";const{sx:b,sy:m}=this.position,{align:y}=this,{angle:w}=this;w&&(t.translate(b,m),t.rotate(w),t.translate(-b,-m));let{x:v,y:z}=this._computed_position({width:g,height:d},h,_);if(\"justify\"==y)for(let e=0;e<_;e++){let i=v;const h=c[e].split(\" \"),o=h.length,_=h.map((t=>(0,s.text_width)(t,n))),l=(g-(0,r.sum)(_))/(o-1);for(let e=0;e{switch(y){case\"left\":return 0;case\"center\":return.5*(g-l[e]);case\"right\":return g-l[e]}})();t.fillStyle=this.color,t.fillText(c[e],s,z+x.ascent),z+=x.height+a}t.restore()}}s.TextBox=x,x.__name__=\"TextBox\";class u extends l{constructor(t,e){super(),this.base=t,this.expo=e}get children(){return[this.base,this.expo]}set base_font_size(t){super.base_font_size=t,this.base.base_font_size=t,this.expo.base_font_size=t}set position(t){this._position=t;const e=this.base.size(),s=this.expo.size(),i=this._shift_scale()*e.height,n=Math.max(e.height,i+s.height);this.base.position={sx:0,x_anchor:\"left\",sy:n,y_anchor:\"bottom\"},this.expo.position={sx:e.width,x_anchor:\"left\",sy:i,y_anchor:\"bottom\"}}get position(){return this._position}set visuals(t){this.expo.font_size_scale=.7,this.base.visuals=t,this.expo.visuals=t}_shift_scale(){if(this.base instanceof x&&1==this.base.nlines){const{x_height:t,cap_height:e}=(0,o.font_metrics)(this.base.font);return t/e}return 2/3}infer_text_height(){return this.base.infer_text_height()}_rect(){const t=this.base.bbox(),e=this.expo.bbox(),s=t.union(e),{x:i,y:n}=this._computed_position();return s.translate(i,n).rect}_size(){const t=this.base.size(),e=this.expo.size();return{width:t.width+e.width,height:Math.max(t.height,this._shift_scale()*t.height+e.height)}}paint(t){t.save();const{angle:e}=this;if(e){const{sx:s,sy:i}=this.position;t.translate(s,i),t.rotate(e),t.translate(-s,-i)}const{x:s,y:i}=this._computed_position();t.translate(s,i),this.base.paint(t),this.expo.paint(t),t.restore()}paint_bbox(t){super.paint_bbox(t);const{x:e,y:s}=this._computed_position();t.save(),t.translate(e,s);for(const e of this.children)e.paint_bbox(t);t.restore()}_computed_position(){const{width:t,height:e}=this._size(),{sx:s,sy:i,x_anchor:n=this._x_anchor,y_anchor:h=this._y_anchor}=this.position;return{x:s-(()=>{if((0,a.isNumber)(n))return n*t;switch(n){case\"left\":return 0;case\"center\":return.5*t;case\"right\":return t}})(),y:i-(()=>{if((0,a.isNumber)(h))return h*e;switch(h){case\"top\":return 0;case\"center\":return.5*e;case\"bottom\":return e;case\"baseline\":return.5*e}})()}}}s.BaseExpo=u,u.__name__=\"BaseExpo\";class p{constructor(t){this.items=t}set base_font_size(t){for(const e of this.items)e.base_font_size=t}get length(){return this.items.length}set visuals(t){for(const e of this.items)e.visuals=t;const e={x:0,cap:1,ascent:2,x_descent:3,cap_descent:4,ascent_descent:5},s=(0,r.max_by)(this.items.map((t=>t.infer_text_height())),(t=>e[t]));for(const t of this.items)t.text_height_metric=s}set angle(t){for(const e of this.items)e.angle=t}max_size(){let t=0,e=0;for(const s of this.items){const i=s.size();t=Math.max(t,i.width),e=Math.max(e,i.height)}return{width:t,height:e}}}s.GraphicsBoxes=p,p.__name__=\"GraphicsBoxes\"},\n", - " function _(t,e,n,r,l){r();const a=t(11),c=(()=>{try{return\"undefined\"!=typeof OffscreenCanvas&&null!=new OffscreenCanvas(0,0).getContext(\"2d\")}catch(t){return!1}})()?(t,e)=>new OffscreenCanvas(t,e):(t,e)=>{const n=document.createElement(\"canvas\");return n.width=t,n.height=e,n},o=(()=>{const t=c(0,0).getContext(\"2d\");return e=>{t.font=e;const n=t.measureText(\"M\"),r=t.measureText(\"x\"),l=t.measureText(\"\\xc5\\u015ag|\"),c=l.fontBoundingBoxAscent,o=l.fontBoundingBoxDescent;if(null!=c&&null!=o)return{height:c+o,ascent:c,descent:o,cap_height:n.actualBoundingBoxAscent,x_height:r.actualBoundingBoxAscent};const s=l.actualBoundingBoxAscent,u=l.actualBoundingBoxDescent;if(null!=s&&null!=u)return{height:s+u,ascent:s,descent:u,cap_height:n.actualBoundingBoxAscent,x_height:r.actualBoundingBoxAscent};(0,a.unreachable)()}})(),s=(()=>{const t=c(0,0).getContext(\"2d\");return(e,n)=>{t.font=n;const r=t.measureText(e),l=r.actualBoundingBoxAscent,c=r.actualBoundingBoxDescent;if(null!=l&&null!=c)return{width:r.width,height:l+c,ascent:l,descent:c};(0,a.unreachable)()}})(),u=(()=>{const t=document.createElement(\"canvas\"),e=t.getContext(\"2d\");let n=-1,r=-1;return(l,a=1)=>{e.font=l;const{width:c}=e.measureText(\"M\"),o=c*a,s=Math.ceil(o),u=Math.ceil(2*o),i=Math.ceil(1.5*o);n{let e=0;for(let n=0;n<=i;n++)for(let r=0;r{let e=t.length-4;for(let n=u;n>=i;n--)for(let r=0;r{const t=document.createElement(\"canvas\"),e=t.getContext(\"2d\");let n=-1,r=-1;return(l,a,c=1)=>{e.font=a;const{width:o}=e.measureText(\"M\"),s=o*c,u=Math.ceil(s),i=Math.ceil(2*s),f=Math.ceil(1.5*s);(n{let e=0;for(let n=0;n<=f;n++)for(let r=0;r{let e=t.length-4;for(let n=i;n>=f;n--)for(let r=0;r{try{return o(\"normal 10px sans-serif\"),o}catch(t){return u}})(),h=(()=>{try{return s(\"A\",\"normal 10px sans-serif\"),s}catch(t){return i}})(),g=new Map;function d(t){let e=g.get(t);return null==e&&(e={font:f(t),glyphs:new Map},g.set(t,e)),e.font}n.font_metrics=d,n.glyph_metrics=function(t,e){let n=g.get(e);null==n&&(d(e),n=g.get(e));let r=n.glyphs.get(t);return null==r&&(r=h(t,e),n.glyphs.set(t,r)),r},n.parse_css_font_size=function(t){const e=t.match(/^\\s*(\\d+(\\.\\d+)?)(\\w+)\\s*$/);if(null!=e){const[,t,,n]=e,r=Number(t);if(isFinite(r))return{value:r,unit:n}}return null}},\n", - " function _(t,s,r,n,i){n();const{sin:e,cos:a}=Math;class h{constructor(t=1,s=0,r=0,n=1,i=0,e=0){this.a=t,this.b=s,this.c=r,this.d=n,this.e=i,this.f=e}toString(){const{a:t,b:s,c:r,d:n,e:i,f:e}=this;return`matrix(${t}, ${s}, ${r}, ${n}, ${i}, ${e})`}static from_DOMMatrix(t){const{a:s,b:r,c:n,d:i,e,f:a}=t;return new h(s,r,n,i,e,a)}to_DOMMatrix(){const{a:t,b:s,c:r,d:n,e:i,f:e}=this;return new DOMMatrix([t,s,r,n,i,e])}clone(){const{a:t,b:s,c:r,d:n,e:i,f:e}=this;return new h(t,s,r,n,i,e)}get is_identity(){const{a:t,b:s,c:r,d:n,e:i,f:e}=this;return 1==t&&0==s&&0==r&&1==n&&0==i&&0==e}apply_point(t){const[s,r]=this.apply(t.x,t.y);return{x:s,y:r}}apply_rect(t){return{p0:this.apply_point(t.p0),p1:this.apply_point(t.p1),p2:this.apply_point(t.p2),p3:this.apply_point(t.p3)}}apply(t,s){const{a:r,b:n,c:i,d:e,e:a,f:h}=this;return[r*t+i*s+a,n*t+e*s+h]}iv_apply(t,s){const{a:r,b:n,c:i,d:e,e:a,f:h}=this,c=t.length;for(let o=0;o{const h={max:4,fit:3,min:2,fixed:1};return h[i]>h[t]};if(\"fixed\"!=n&&\"fixed\"!=s)if(n==s){const n=t,s=_(t/e),r=_(h*e),g=h;Math.abs(i.width-n)+Math.abs(i.height-s)<=Math.abs(i.width-r)+Math.abs(i.height-g)?(t=n,h=s):(t=r,h=g)}else r(n,s)?h=_(t/e):t=_(h*e);else\"fixed\"==n?h=_(t/e):\"fixed\"==s&&(t=_(h*e))}return{width:t,height:h}}measure(i){if(!this.sizing.visible)return{width:0,height:0};const t=i=>\"fixed\"==this.sizing.width_policy&&null!=this.sizing.width?this.sizing.width:i,h=i=>\"fixed\"==this.sizing.height_policy&&null!=this.sizing.height?this.sizing.height:i,e=new s.Sizeable(i).shrink_by(this.sizing.margin).map(t,h),n=this._measure(e),r=this.clip_size(n,e),g=t(r.width),l=h(r.height),a=this.apply_aspect(e,{width:g,height:l});return Object.assign(Object.assign({},n),a)}compute(i={}){const t=this.measure({width:null!=i.width&&this.is_width_expanding()?i.width:1/0,height:null!=i.height&&this.is_height_expanding()?i.height:1/0}),{width:h,height:e}=t,n=new r.BBox({left:0,top:0,width:h,height:e});let s;if(null!=t.inner){const{left:i,top:n,right:g,bottom:l}=t.inner;s=new r.BBox({left:i,top:n,right:h-g,bottom:e-l})}this.set_geometry(n,s)}get xview(){return this.bbox.xview}get yview(){return this.bbox.yview}clip_size(i,t){function h(i,t,h,e){return null==h?h=0:(0,g.isNumber)(h)||(h=Math.round(h.percent*t)),null==e?e=1/0:(0,g.isNumber)(e)||(e=Math.round(e.percent*t)),a(h,l(i,e))}return{width:h(i.width,t.width,this.sizing.min_width,this.sizing.max_width),height:h(i.height,t.height,this.sizing.min_height,this.sizing.max_height)}}has_size_changed(){const{_dirty:i}=this;return this._dirty=!1,i}}h.Layoutable=o,o.__name__=\"Layoutable\";class d extends o{_measure(i){const{width_policy:t,height_policy:h}=this.sizing;return{width:(()=>{const{width:h}=this.sizing;if(i.width==1/0)return null!=h?h:0;switch(t){case\"fixed\":return null!=h?h:0;case\"min\":return null!=h?l(i.width,h):0;case\"fit\":return null!=h?l(i.width,h):i.width;case\"max\":return null!=h?a(i.width,h):i.width}})(),height:(()=>{const{height:t}=this.sizing;if(i.height==1/0)return null!=t?t:0;switch(h){case\"fixed\":return null!=t?t:0;case\"min\":return null!=t?l(i.height,t):0;case\"fit\":return null!=t?l(i.height,t):i.height;case\"max\":return null!=t?a(i.height,t):i.height}})()}}}h.LayoutItem=d,d.__name__=\"LayoutItem\";class u extends o{_measure(i){const t=this._content_size(),h=i.bounded_to(this.sizing.size).bounded_to(t);return{width:(()=>{switch(this.sizing.width_policy){case\"fixed\":return null!=this.sizing.width?this.sizing.width:t.width;case\"min\":return t.width;case\"fit\":return h.width;case\"max\":return Math.max(t.width,h.width)}})(),height:(()=>{switch(this.sizing.height_policy){case\"fixed\":return null!=this.sizing.height?this.sizing.height:t.height;case\"min\":return t.height;case\"fit\":return h.height;case\"max\":return Math.max(t.height,h.height)}})()}}}h.ContentLayoutable=u,u.__name__=\"ContentLayoutable\"},\n", - " function _(e,t,s,a,_){a();const r=e(62),n=e(61),g=e(58),i=e(63),c=e(67),h=e(65),l=e(13),o=e(11);class x{constructor(e,t,s,a,_={},r={},n={},g={}){this.in_x_scale=e,this.in_y_scale=t,this.x_range=s,this.y_range=a,this.extra_x_ranges=_,this.extra_y_ranges=r,this.extra_x_scales=n,this.extra_y_scales=g,this._bbox=new h.BBox,(0,o.assert)(null==e.source_range&&null==e.target_range),(0,o.assert)(null==t.source_range&&null==t.target_range),this._configure_scales()}get bbox(){return this._bbox}_get_ranges(e,t){return new Map((0,l.entries)(Object.assign(Object.assign({},t),{default:e})))}_get_scales(e,t,s,a){var _;const g=new Map((0,l.entries)(Object.assign(Object.assign({},t),{default:e}))),h=new Map;for(const[t,l]of s){if(l instanceof c.FactorRange!=e instanceof r.CategoricalScale)throw new Error(`Range ${l.type} is incompatible is Scale ${e.type}`);e instanceof n.LogScale&&l instanceof i.DataRange1d&&(l.scale_hint=\"log\");const s=(null!==(_=g.get(t))&&void 0!==_?_:e).clone();s.setv({source_range:l,target_range:a}),h.set(t,s)}return h}_configure_frame_ranges(){const{bbox:e}=this;this._x_target=new g.Range1d({start:e.left,end:e.right}),this._y_target=new g.Range1d({start:e.bottom,end:e.top})}_configure_scales(){this._configure_frame_ranges(),this._x_ranges=this._get_ranges(this.x_range,this.extra_x_ranges),this._y_ranges=this._get_ranges(this.y_range,this.extra_y_ranges),this._x_scales=this._get_scales(this.in_x_scale,this.extra_x_scales,this._x_ranges,this._x_target),this._y_scales=this._get_scales(this.in_y_scale,this.extra_y_scales,this._y_ranges,this._y_target)}_update_scales(){this._configure_frame_ranges();for(const[,e]of this._x_scales)e.target_range=this._x_target;for(const[,e]of this._y_scales)e.target_range=this._y_target}set_geometry(e){this._bbox=e,this._update_scales()}get x_target(){return this._x_target}get y_target(){return this._y_target}get x_ranges(){return this._x_ranges}get y_ranges(){return this._y_ranges}get x_scales(){return this._x_scales}get y_scales(){return this._y_scales}get x_scale(){return this._x_scales.get(\"default\")}get y_scale(){return this._y_scales.get(\"default\")}get xscales(){return(0,l.to_object)(this.x_scales)}get yscales(){return(0,l.to_object)(this.y_scales)}}s.CartesianFrame=x,x.__name__=\"CartesianFrame\"},\n", - " function _(i,s,x,A,o){A(),o(\"Axis\",i(128).Axis),o(\"CategoricalAxis\",i(140).CategoricalAxis),o(\"ContinuousAxis\",i(143).ContinuousAxis),o(\"DatetimeAxis\",i(144).DatetimeAxis),o(\"LinearAxis\",i(145).LinearAxis),o(\"LogAxis\",i(162).LogAxis),o(\"MercatorAxis\",i(165).MercatorAxis)},\n", - " function _(t,e,i,s,a){s();const o=t(1);var l;const n=t(129),_=t(130),r=t(131),h=t(132),c=(0,o.__importStar)(t(48)),b=t(20),u=t(24),m=t(123),d=t(9),x=t(13),f=t(8),g=t(120),p=t(67),v=t(133),w=t(113),j=t(11),k=t(8),y=t(134),{abs:z}=Math;class M extends n.GuideRendererView{constructor(){super(...arguments),this._axis_label_view=null,this._major_label_views=new Map}async lazy_initialize(){await super.lazy_initialize(),await this._init_axis_label(),await this._init_major_labels()}async _init_axis_label(){const{axis_label:t}=this.model;if(null!=t){const e=(0,k.isString)(t)?(0,y.parse_delimited_string)(t):t;this._axis_label_view=await(0,w.build_view)(e,{parent:this})}else this._axis_label_view=null}async _init_major_labels(){const{major_label_overrides:t}=this.model;for(const[e,i]of(0,x.entries)(t)){const t=(0,k.isString)(i)?(0,y.parse_delimited_string)(i):i;this._major_label_views.set(e,await(0,w.build_view)(t,{parent:this}))}}update_layout(){this.layout=new m.SideLayout(this.panel,(()=>this.get_size()),!0),this.layout.on_resize((()=>this._coordinates=void 0))}get_size(){const{visible:t,fixed_location:e}=this.model;if(t&&null==e&&this.is_renderable){const{extents:t}=this;return{width:0,height:Math.round(t.tick+t.tick_label+t.axis_label)}}return{width:0,height:0}}get is_renderable(){const[t,e]=this.ranges;return t.is_valid&&e.is_valid}_render(){var t;if(!this.is_renderable)return;const{tick_coords:e,extents:i}=this,s=this.layer.ctx;s.save(),this._draw_rule(s,i),this._draw_major_ticks(s,i,e),this._draw_minor_ticks(s,i,e),this._draw_major_labels(s,i,e),this._draw_axis_label(s,i,e),null===(t=this._paint)||void 0===t||t.call(this,s,i,e),s.restore()}connect_signals(){super.connect_signals();const{axis_label:t,major_label_overrides:e}=this.model.properties;this.on_change(t,(async()=>{var t;null===(t=this._axis_label_view)||void 0===t||t.remove(),await this._init_axis_label()})),this.on_change(e,(async()=>{for(const t of this._major_label_views.values())t.remove();await this._init_major_labels()})),this.connect(this.model.change,(()=>this.plot_view.request_layout()))}get needs_clip(){return null!=this.model.fixed_location}_draw_rule(t,e){if(!this.visuals.axis_line.doit)return;const[i,s]=this.rule_coords,[a,o]=this.coordinates.map_to_screen(i,s),[l,n]=this.normals,[_,r]=this.offsets;this.visuals.axis_line.set_value(t),t.beginPath();for(let e=0;e0?s+i+3:0}_draw_axis_label(t,e,i){if(null==this._axis_label_view||null!=this.model.fixed_location)return;const[s,a]=(()=>{const{bbox:t}=this.layout;switch(this.panel.side){case\"above\":return[t.hcenter,t.bottom];case\"below\":return[t.hcenter,t.top];case\"left\":return[t.right,t.vcenter];case\"right\":return[t.left,t.vcenter]}})(),[o,l]=this.normals,n=e.tick+e.tick_label+this.model.axis_label_standoff,{vertical_align:_,align:r}=this.panel.get_label_text_heuristics(\"parallel\"),h={sx:s+o*n,sy:a+l*n,x_anchor:r,y_anchor:_},c=this._axis_label_view.graphics();c.visuals=this.visuals.axis_label_text.values(),c.angle=this.panel.get_label_angle_heuristic(\"parallel\"),this.plot_view.base_font_size&&(c.base_font_size=this.plot_view.base_font_size),c.position=h,c.align=r,c.paint(t)}_draw_ticks(t,e,i,s,a){if(!a.doit)return;const[o,l]=e,[n,_]=this.coordinates.map_to_screen(o,l),[r,h]=this.normals,[c,b]=this.offsets,[u,m]=[r*(c-i),h*(b-i)],[d,x]=[r*(c+s),h*(b+s)];a.set_value(t),t.beginPath();for(let e=0;et.bbox())),M=(()=>{const[t]=this.ranges;return t.is_reversed?0==this.dimension?(t,e)=>z[t].left-z[e].right:(t,e)=>z[e].top-z[t].bottom:0==this.dimension?(t,e)=>z[e].left-z[t].right:(t,e)=>z[t].top-z[e].bottom})(),{major_label_policy:O}=this.model,T=O.filter(k,z,M),A=[...T.ones()];if(0!=A.length){const t=this.parent.canvas_view.bbox,e=e=>{const i=z[e];if(i.left<0){const t=-i.left,{position:s}=y[e];y[e].position=Object.assign(Object.assign({},s),{sx:s.sx+t})}else if(i.right>t.width){const s=i.right-t.width,{position:a}=y[e];y[e].position=Object.assign(Object.assign({},a),{sx:a.sx-s})}},i=e=>{const i=z[e];if(i.top<0){const t=-i.top,{position:s}=y[e];y[e].position=Object.assign(Object.assign({},s),{sy:s.sy+t})}else if(i.bottom>t.height){const s=i.bottom-t.height,{position:a}=y[e];y[e].position=Object.assign(Object.assign({},a),{sy:a.sy-s})}},s=A[0],a=A[A.length-1];0==this.dimension?(e(s),e(a)):(i(s),i(a))}for(const e of T){y[e].paint(t)}}_tick_extent(){return this.model.major_tick_out}_tick_label_extents(){const t=this.tick_coords.major,e=this.compute_labels(t[this.dimension]),i=this.model.major_label_orientation,s=this.model.major_label_standoff,a=this.visuals.major_label_text;return[this._oriented_labels_extent(e,i,s,a)]}get extents(){const t=this._tick_label_extents();return{tick:this._tick_extent(),tick_labels:t,tick_label:(0,d.sum)(t),axis_label:this._axis_label_extent()}}_oriented_labels_extent(t,e,i,s){if(0==t.length||!s.doit)return 0;const a=this.panel.get_label_angle_heuristic(e);t.visuals=s.values(),t.angle=a,t.base_font_size=this.plot_view.base_font_size;const o=t.max_size(),l=0==this.dimension?o.height:o.width;return l>0?i+l+3:0}get normals(){return this.panel.normals}get dimension(){return this.panel.dimension}compute_labels(t){const e=this.model.formatter.format_graphics(t,this),{_major_label_views:i}=this,s=new Set;for(let a=0;az(l-n)?(t=r(_(a,o),l),s=_(r(a,o),n)):(t=_(a,o),s=r(a,o)),[t,s]}}get rule_coords(){const t=this.dimension,e=(t+1)%2,[i]=this.ranges,[s,a]=this.computed_bounds,o=[new Array(2),new Array(2)];return o[t][0]=Math.max(s,i.min),o[t][1]=Math.min(a,i.max),o[t][0]>o[t][1]&&(o[t][0]=o[t][1]=NaN),o[e][0]=this.loc,o[e][1]=this.loc,o}get tick_coords(){const t=this.dimension,e=(t+1)%2,[i]=this.ranges,[s,a]=this.computed_bounds,o=this.model.ticker.get_ticks(s,a,i,this.loc),l=o.major,n=o.minor,_=[[],[]],r=[[],[]],[h,c]=[i.min,i.max];for(let i=0;ic||(_[t].push(l[i]),_[e].push(this.loc));for(let i=0;ic||(r[t].push(n[i]),r[e].push(this.loc));return{major:_,minor:r}}get loc(){const{fixed_location:t}=this.model;if(null!=t){if((0,f.isNumber)(t))return t;const[,e]=this.ranges;if(e instanceof p.FactorRange)return e.synthetic(t);(0,j.unreachable)()}const[,e]=this.ranges;switch(this.panel.side){case\"left\":case\"below\":return e.start;case\"right\":case\"above\":return e.end}}serializable_state(){return Object.assign(Object.assign({},super.serializable_state()),{bbox:this.layout.bbox.box})}remove(){var t;null===(t=this._axis_label_view)||void 0===t||t.remove();for(const t of this._major_label_views.values())t.remove();super.remove()}has_finished(){if(!super.has_finished())return!1;if(null!=this._axis_label_view&&!this._axis_label_view.has_finished())return!1;for(const t of this._major_label_views.values())if(!t.has_finished())return!1;return!0}}i.AxisView=M,M.__name__=\"AxisView\";class O extends n.GuideRenderer{constructor(t){super(t)}}i.Axis=O,l=O,O.__name__=\"Axis\",l.prototype.default_view=M,l.mixins([[\"axis_\",c.Line],[\"major_tick_\",c.Line],[\"minor_tick_\",c.Line],[\"major_label_\",c.Text],[\"axis_label_\",c.Text]]),l.define((({Any:t,Int:e,Number:i,String:s,Ref:a,Dict:o,Tuple:l,Or:n,Nullable:c,Auto:u})=>({bounds:[n(l(i,i),u),\"auto\"],ticker:[a(_.Ticker)],formatter:[a(r.TickFormatter)],axis_label:[c(n(s,a(v.BaseText))),null],axis_label_standoff:[e,5],major_label_standoff:[e,5],major_label_orientation:[n(b.TickLabelOrientation,i),\"horizontal\"],major_label_overrides:[o(n(s,a(v.BaseText))),{}],major_label_policy:[a(h.LabelingPolicy),()=>new h.AllLabels],major_tick_in:[i,2],major_tick_out:[i,6],minor_tick_in:[i,0],minor_tick_out:[i,4],fixed_location:[c(n(i,t)),null]}))),l.override({axis_line_color:\"black\",major_tick_line_color:\"black\",minor_tick_line_color:\"black\",major_label_text_font_size:\"11px\",major_label_text_align:\"center\",major_label_text_baseline:\"alphabetic\",axis_label_text_font_size:\"13px\",axis_label_text_font_style:\"italic\"})},\n", - " function _(e,r,d,n,i){var s;n();const _=e(41);class u extends _.RendererView{}d.GuideRendererView=u,u.__name__=\"GuideRendererView\";class c extends _.Renderer{constructor(e){super(e)}}d.GuideRenderer=c,s=c,c.__name__=\"GuideRenderer\",s.override({level:\"guide\"})},\n", - " function _(c,e,n,s,o){s();const r=c(53);class t extends r.Model{constructor(c){super(c)}}n.Ticker=t,t.__name__=\"Ticker\"},\n", - " function _(t,o,r,e,c){e();const n=t(53),a=t(120);class m extends n.Model{constructor(t){super(t)}format_graphics(t,o){return this.doFormat(t,o).map((t=>new a.TextBox({text:t})))}compute(t,o){return this.doFormat([t],null!=o?o:{loc:0})[0]}v_compute(t,o){return this.doFormat(t,null!=o?o:{loc:0})}}r.TickFormatter=m,m.__name__=\"TickFormatter\"},\n", - " function _(e,n,s,t,i){var c,r;t();const l=e(53),o=e(13),a=e(34),u=e(8),d=e(24);class _ extends l.Model{constructor(e){super(e)}}s.LabelingPolicy=_,_.__name__=\"LabelingPolicy\";class f extends _{constructor(e){super(e)}filter(e,n,s){return e}}s.AllLabels=f,f.__name__=\"AllLabels\";class m extends _{constructor(e){super(e)}filter(e,n,s){const{min_distance:t}=this;let i=null;for(const n of e)null!=i&&s(i,n)({min_distance:[e,5]})));class b extends _{constructor(e){super(e)}get names(){return(0,o.keys)(this.args)}get values(){return(0,o.values)(this.args)}get func(){const e=(0,a.use_strict)(this.code);return new d.GeneratorFunction(\"indices\",\"bboxes\",\"distance\",...this.names,e)}filter(e,n,s){const t=Object.create(null),i=this.func.call(t,e,n,s,...this.values);let c=i.next();if(c.done&&void 0!==c.value){const{value:n}=c;return n instanceof d.Indices?n:void 0===n?e:(0,u.isIterable)(n)?d.Indices.from_indices(e.size,n):d.Indices.all_unset(e.size)}{const n=[];do{n.push(c.value),c=i.next()}while(!c.done);return d.Indices.from_indices(e.size,n)}}}s.CustomLabelingPolicy=b,r=b,b.__name__=\"CustomLabelingPolicy\",r.define((({Unknown:e,String:n,Dict:s})=>({args:[s(e),{}],code:[n,\"\"]})))},\n", - " function _(e,s,t,n,a){var _;n();const x=e(53),c=e(42);class i extends c.View{}t.BaseTextView=i,i.__name__=\"BaseTextView\";class o extends x.Model{constructor(e){super(e)}}t.BaseText=o,_=o,o.__name__=\"BaseText\",_.define((({String:e})=>({text:[e]})))},\n", - " function _(n,e,t,i,r){i();const s=n(135),l=n(139),d=[{start:\"$$\",end:\"$$\",inline:!1},{start:\"\\\\[\",end:\"\\\\]\",inline:!1},{start:\"\\\\(\",end:\"\\\\)\",inline:!0}];t.parse_delimited_string=function(n){for(const e of d){const t=n.indexOf(e.start),i=t+e.start.length;if(0==t){const t=n.indexOf(e.end,i),r=t;if(t==n.length-e.end.length)return new s.TeX({text:n.slice(i,r),inline:e.inline});break}}return new l.PlainText({text:n})}},\n", - " function _(t,e,s,i,n){var o,r,a;i();const h=t(8),_=t(136),l=t(22),c=t(120),d=t(121),u=t(122),g=t(65),p=t(133),x=t(137);class m extends p.BaseTextView{constructor(){super(...arguments),this._position={sx:0,sy:0},this.align=\"left\",this._x_anchor=\"left\",this._y_anchor=\"center\",this._base_font_size=13,this.font_size_scale=1,this.svg_image=null}graphics(){return this}infer_text_height(){return\"ascent_descent\"}set base_font_size(t){null!=t&&(this._base_font_size=t)}get base_font_size(){return this._base_font_size}get has_image_loaded(){return null!=this.svg_image}_rect(){const{width:t,height:e}=this._size(),{x:s,y:i}=this._computed_position();return new g.BBox({x:s,y:i,width:t,height:e}).rect}set position(t){this._position=t}get position(){return this._position}get text(){return this.model.text}get provider(){return x.default_provider}async lazy_initialize(){await super.lazy_initialize(),\"not_started\"==this.provider.status&&await this.provider.fetch(),\"not_started\"!=this.provider.status&&\"loading\"!=this.provider.status||this.provider.ready.connect((()=>this.load_image())),\"loaded\"==this.provider.status&&await this.load_image()}connect_signals(){super.connect_signals(),this.on_change(this.model.properties.text,(()=>this.load_image()))}set visuals(t){const e=t.color,s=t.alpha,i=t.font_style;let n=t.font_size;const o=t.font,{font_size_scale:r,_base_font_size:a}=this,h=(0,d.parse_css_font_size)(n);if(null!=h){let{value:t,unit:e}=h;t*=r,\"em\"==e&&a&&(t*=a,e=\"px\"),n=`${t}${e}`}const _=`${i} ${n} ${o}`;this.font=_,this.color=(0,l.color2css)(e,s)}_computed_position(){const{width:t,height:e}=this._size(),{sx:s,sy:i,x_anchor:n=this._x_anchor,y_anchor:o=this._y_anchor}=this.position;return{x:s-(()=>{if((0,h.isNumber)(n))return n*t;switch(n){case\"left\":return 0;case\"center\":return.5*t;case\"right\":return t}})(),y:i-(()=>{if((0,h.isNumber)(o))return o*e;switch(o){case\"top\":return 0;case\"center\":return.5*e;case\"bottom\":return e;case\"baseline\":return.5*e}})()}}size(){const{width:t,height:e}=this._size(),{angle:s}=this;if(s){const i=Math.cos(Math.abs(s)),n=Math.sin(Math.abs(s));return{width:Math.abs(t*i+e*n),height:Math.abs(t*n+e*i)}}return{width:t,height:e}}get_text_dimensions(){return{width:(0,c.text_width)(this.model.text,this.font),height:(0,d.font_metrics)(this.font).height}}get_image_dimensions(){var t,e,s,i;const n=parseFloat(null!==(e=null===(t=this.svg_element.getAttribute(\"height\"))||void 0===t?void 0:t.replace(/([A-z])/g,\"\"))&&void 0!==e?e:\"0\"),o=parseFloat(null!==(i=null===(s=this.svg_element.getAttribute(\"width\"))||void 0===s?void 0:s.replace(/([A-z])/g,\"\"))&&void 0!==i?i:\"0\");return{width:(0,d.font_metrics)(this.font).x_height*o,height:(0,d.font_metrics)(this.font).x_height*n}}_size(){return this.has_image_loaded?this.get_image_dimensions():this.get_text_dimensions()}bbox(){const{p0:t,p1:e,p2:s,p3:i}=this.rect(),n=Math.min(t.x,e.x,s.x,i.x),o=Math.min(t.y,e.y,s.y,i.y),r=Math.max(t.x,e.x,s.x,i.x),a=Math.max(t.y,e.y,s.y,i.y);return new g.BBox({left:n,right:r,top:o,bottom:a})}rect(){const t=this._rect(),{angle:e}=this;if(e){const{sx:s,sy:i}=this.position,n=new u.AffineTransform;return n.translate(s,i),n.rotate(e),n.translate(-s,-i),n.apply_rect(t)}return t}paint_rect(t){const{p0:e,p1:s,p2:i,p3:n}=this.rect();t.save(),t.strokeStyle=\"red\",t.lineWidth=1,t.beginPath();const{round:o}=Math;t.moveTo(o(e.x),o(e.y)),t.lineTo(o(s.x),o(s.y)),t.lineTo(o(i.x),o(i.y)),t.lineTo(o(n.x),o(n.y)),t.closePath(),t.stroke(),t.restore()}paint_bbox(t){const{x:e,y:s,width:i,height:n}=this.bbox();t.save(),t.strokeStyle=\"blue\",t.lineWidth=1,t.beginPath();const{round:o}=Math;t.moveTo(o(e),o(s)),t.lineTo(o(e),o(s+n)),t.lineTo(o(e+i),o(s+n)),t.lineTo(o(e+i),o(s)),t.closePath(),t.stroke(),t.restore()}async load_image(){if(null==this.provider.MathJax)return null;const t=this._process_text(this.model.text);if(null==t)return this._has_finished=!0,null;const e=t.children[0];this.svg_element=e,e.setAttribute(\"font\",this.font),e.setAttribute(\"stroke\",this.color);const s=e.outerHTML,i=new Blob([s],{type:\"image/svg+xml\"}),n=URL.createObjectURL(i);try{this.svg_image=await(0,_.load_image)(n)}finally{URL.revokeObjectURL(n)}return this.parent.request_layout(),this.svg_image}paint(t){t.save();const{sx:e,sy:s}=this.position;this.angle&&(t.translate(e,s),t.rotate(this.angle),t.translate(-e,-s));const{x:i,y:n}=this._computed_position();if(null!=this.svg_image){const{width:e,height:s}=this.get_image_dimensions();t.drawImage(this.svg_image,i,n,e,s)}else t.fillStyle=this.color,t.font=this.font,t.textAlign=\"left\",t.textBaseline=\"alphabetic\",t.fillText(this.model.text,i,n+(0,d.font_metrics)(this.font).ascent);t.restore(),this._has_finished||\"failed\"!=this.provider.status&&!this.has_image_loaded||(this._has_finished=!0,this.parent.notify_finished_after_paint())}}s.MathTextView=m,m.__name__=\"MathTextView\";class f extends p.BaseText{constructor(t){super(t)}}s.MathText=f,f.__name__=\"MathText\";class v extends m{_process_text(t){}}s.AsciiView=v,v.__name__=\"AsciiView\";class y extends f{constructor(t){super(t)}}s.Ascii=y,o=y,y.__name__=\"Ascii\",o.prototype.default_view=v;class w extends m{_process_text(t){var e;return null===(e=this.provider.MathJax)||void 0===e?void 0:e.mathml2svg(t.trim())}}s.MathMLView=w,w.__name__=\"MathMLView\";class b extends f{constructor(t){super(t)}}s.MathML=b,r=b,b.__name__=\"MathML\",r.prototype.default_view=w;class M extends m{_process_text(t){var e;return null===(e=this.provider.MathJax)||void 0===e?void 0:e.tex2svg(t,void 0,this.model.macros)}}s.TeXView=M,M.__name__=\"TeXView\";class T extends f{constructor(t){super(t)}}s.TeX=T,a=T,T.__name__=\"TeX\",a.prototype.default_view=M,a.define((({Boolean:t,Number:e,String:s,Dict:i,Tuple:n,Or:o})=>({macros:[i(o(s,n(s,e))),{}],inline:[t,!1]})))},\n", - " function _(i,e,t,s,o){s();const a=i(19);t.load_image=async function(i,e){return new n(i,e).promise};class n{constructor(i,e={}){this._image=new Image,this._finished=!1;const{attempts:t=1,timeout:s=1}=e;this.promise=new Promise(((o,n)=>{this._image.crossOrigin=\"anonymous\";let r=0;this._image.onerror=()=>{if(++r==t){const s=`unable to load ${i} image after ${t} attempts`;if(a.logger.warn(s),null==this._image.crossOrigin)return void(null!=e.failed&&e.failed());a.logger.warn(`attempting to load ${i} without a cross origin policy`),this._image.crossOrigin=null,r=0}setTimeout((()=>this._image.src=i),s)},this._image.onload=()=>{this._finished=!0,null!=e.loaded&&e.loaded(this._image),o(this._image)},this._image.src=i}))}get finished(){return this._finished}get image(){if(this._finished)return this._image;throw new Error(\"not loaded yet\")}}t.ImageLoader=n,n.__name__=\"ImageLoader\"},\n", - " function _(t,e,a,s,n){var r=this&&this.__createBinding||(Object.create?function(t,e,a,s){void 0===s&&(s=a),Object.defineProperty(t,s,{enumerable:!0,get:function(){return e[a]}})}:function(t,e,a,s){void 0===s&&(s=a),t[s]=e[a]}),i=this&&this.__setModuleDefault||(Object.create?function(t,e){Object.defineProperty(t,\"default\",{enumerable:!0,value:e})}:function(t,e){t.default=e}),d=this&&this.__importStar||function(t){if(t&&t.__esModule)return t;var e={};if(null!=t)for(var a in t)\"default\"!==a&&Object.prototype.hasOwnProperty.call(t,a)&&r(e,t,a);return i(e,t),e};s();const o=t(15),u=t(138);class c{constructor(){this.ready=new o.Signal0(this,\"ready\"),this.status=\"not_started\"}}a.MathJaxProvider=c,c.__name__=\"MathJaxProvider\";class h extends c{get MathJax(){return null}async fetch(){this.status=\"failed\"}}a.NoProvider=h,h.__name__=\"NoProvider\";class l extends c{get MathJax(){return\"undefined\"!=typeof MathJax?MathJax:null}async fetch(){const t=document.createElement(\"script\");t.src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js\",t.onload=()=>{this.status=\"loaded\",this.ready.emit()},t.onerror=()=>{this.status=\"failed\"},this.status=\"loading\",document.head.appendChild(t)}}a.CDNProvider=l,l.__name__=\"CDNProvider\";class _ extends c{get MathJax(){return this._mathjax}async fetch(){this.status=\"loading\";try{const e=await(0,u.load_module)(Promise.resolve().then((()=>d(t(515)))));this._mathjax=e,this.status=\"loaded\",this.ready.emit()}catch(t){this.status=\"failed\"}}}a.BundleProvider=_,_.__name__=\"BundleProvider\",a.default_provider=new _},\n", - " function _(n,r,o,t,c){t(),o.load_module=async function(n){try{return await n}catch(n){if((r=n)instanceof Error&&\"code\"in r&&\"MODULE_NOT_FOUND\"===n.code)return null;throw n}var r}},\n", - " function _(e,t,i,n,s){var a;n();const x=e(133),_=e(120);class l extends x.BaseTextView{initialize(){super.initialize(),this._has_finished=!0}graphics(){return new _.TextBox({text:this.model.text})}}i.PlainTextView=l,l.__name__=\"PlainTextView\";class r extends x.BaseText{constructor(e){super(e)}}i.PlainText=r,a=r,r.__name__=\"PlainText\",a.prototype.default_view=l},\n", - " function _(t,s,o,e,i){e();const r=t(1);var a;const l=t(128),_=t(141),n=t(142),p=(0,r.__importStar)(t(48)),c=t(20),h=t(120),m=t(8);class u extends l.AxisView{_paint(t,s,o){this._draw_group_separators(t,s,o)}_draw_group_separators(t,s,o){const[e]=this.ranges,[i,r]=this.computed_bounds;if(!e.tops||e.tops.length<2||!this.visuals.separator_line.doit)return;const a=this.dimension,l=(a+1)%2,_=[[],[]];let n=0;for(let t=0;ti&&pnew h.GraphicsBoxes(t.map((t=>(0,m.isString)(t)?new h.TextBox({text:t}):t))),_=t=>l(this.model.formatter.doFormat(t,this));if(1==t.levels){const t=_(i.major);a.push([t,r.major,this.model.major_label_orientation,this.visuals.major_label_text])}else if(2==t.levels){const t=_(i.major.map((t=>t[1])));a.push([t,r.major,this.model.major_label_orientation,this.visuals.major_label_text]),a.push([l(i.tops),r.tops,this.model.group_label_orientation,this.visuals.group_text])}else if(3==t.levels){const t=_(i.major.map((t=>t[2]))),s=i.mids.map((t=>t[1]));a.push([t,r.major,this.model.major_label_orientation,this.visuals.major_label_text]),a.push([l(s),r.mids,this.model.subgroup_label_orientation,this.visuals.subgroup_text]),a.push([l(i.tops),r.tops,this.model.group_label_orientation,this.visuals.group_text])}return a}get tick_coords(){const t=this.dimension,s=(t+1)%2,[o]=this.ranges,[e,i]=this.computed_bounds,r=this.model.ticker.get_ticks(e,i,o,this.loc),a={major:[[],[]],mids:[[],[]],tops:[[],[]],minor:[[],[]]};return a.major[t]=r.major,a.major[s]=r.major.map((()=>this.loc)),3==o.levels&&(a.mids[t]=r.mids,a.mids[s]=r.mids.map((()=>this.loc))),o.levels>1&&(a.tops[t]=r.tops,a.tops[s]=r.tops.map((()=>this.loc))),a}}o.CategoricalAxisView=u,u.__name__=\"CategoricalAxisView\";class d extends l.Axis{constructor(t){super(t)}}o.CategoricalAxis=d,a=d,d.__name__=\"CategoricalAxis\",a.prototype.default_view=u,a.mixins([[\"separator_\",p.Line],[\"group_\",p.Text],[\"subgroup_\",p.Text]]),a.define((({Number:t,Or:s})=>({group_label_orientation:[s(c.TickLabelOrientation,t),\"parallel\"],subgroup_label_orientation:[s(c.TickLabelOrientation,t),\"parallel\"]}))),a.override({ticker:()=>new _.CategoricalTicker,formatter:()=>new n.CategoricalTickFormatter,separator_line_color:\"lightgrey\",separator_line_width:2,group_text_font_style:\"bold\",group_text_font_size:\"11px\",group_text_color:\"grey\",subgroup_text_font_style:\"bold\",subgroup_text_font_size:\"11px\"})},\n", - " function _(t,c,o,s,e){s();const r=t(130);class i extends r.Ticker{constructor(t){super(t)}get_ticks(t,c,o,s){var e,r;return{major:this._collect(o.factors,o,t,c),minor:[],tops:this._collect(null!==(e=o.tops)&&void 0!==e?e:[],o,t,c),mids:this._collect(null!==(r=o.mids)&&void 0!==r?r:[],o,t,c)}}_collect(t,c,o,s){const e=[];for(const r of t){const t=c.synthetic(r);t>o&&tnew _.DatetimeTicker,formatter:()=>new m.DatetimeTickFormatter})},\n", - " function _(e,i,s,n,r){var t;n();const a=e(143),o=e(146),c=e(147);class _ extends a.ContinuousAxisView{}s.LinearAxisView=_,_.__name__=\"LinearAxisView\";class u extends a.ContinuousAxis{constructor(e){super(e)}}s.LinearAxis=u,t=u,u.__name__=\"LinearAxis\",t.prototype.default_view=_,t.override({ticker:()=>new c.BasicTicker,formatter:()=>new o.BasicTickFormatter})},\n", - " function _(i,t,e,n,o){var r;n();const s=i(131),c=i(34);function _(i){let t=\"\";for(const e of i)t+=\"-\"==e?\"\\u2212\":e;return t}e.unicode_replace=_;class a extends s.TickFormatter{constructor(i){super(i),this.last_precision=3}get scientific_limit_low(){return 10**this.power_limit_low}get scientific_limit_high(){return 10**this.power_limit_high}_need_sci(i){if(!this.use_scientific)return!1;const{scientific_limit_high:t}=this,{scientific_limit_low:e}=this,n=i.length<2?0:Math.abs(i[1]-i[0])/1e4;for(const o of i){const i=Math.abs(o);if(!(i<=n)&&(i>=t||i<=e))return!0}return!1}_format_with_precision(i,t,e){return t?i.map((i=>_(i.toExponential(e)))):i.map((i=>_((0,c.to_fixed)(i,e))))}_auto_precision(i,t){const e=new Array(i.length),n=this.last_precision<=15;i:for(let o=this.last_precision;n?o<=15:o>=1;n?o++:o--){if(t){e[0]=i[0].toExponential(o);for(let t=1;t({precision:[n(t,e),\"auto\"],use_scientific:[i,!0],power_limit_high:[t,5],power_limit_low:[t,-3]})))},\n", - " function _(c,e,s,i,n){i();const r=c(148);class t extends r.AdaptiveTicker{constructor(c){super(c)}}s.BasicTicker=t,t.__name__=\"BasicTicker\"},\n", - " function _(t,i,a,s,e){var n;s();const r=t(149),_=t(9),l=t(10);class h extends r.ContinuousTicker{constructor(t){super(t)}get_min_interval(){return this.min_interval}get_max_interval(){var t;return null!==(t=this.max_interval)&&void 0!==t?t:1/0}initialize(){super.initialize();const t=(0,_.nth)(this.mantissas,-1)/this.base,i=(0,_.nth)(this.mantissas,0)*this.base;this.extended_mantissas=[t,...this.mantissas,i],this.base_factor=0===this.get_min_interval()?1:this.get_min_interval()}get_interval(t,i,a){const s=i-t,e=this.get_ideal_interval(t,i,a),n=Math.floor((0,l.log)(e/this.base_factor,this.base)),r=this.base**n*this.base_factor,h=this.extended_mantissas,m=h.map((t=>Math.abs(a-s/(t*r)))),v=h[(0,_.argmin)(m)]*r;return(0,l.clamp)(v,this.get_min_interval(),this.get_max_interval())}}a.AdaptiveTicker=h,n=h,h.__name__=\"AdaptiveTicker\",n.define((({Number:t,Array:i,Nullable:a})=>({base:[t,10],mantissas:[i(t),[1,2,5]],min_interval:[t,0],max_interval:[a(t),null]})))},\n", - " function _(t,n,i,s,e){var o;s();const r=t(130),c=t(9);class _ extends r.Ticker{constructor(t){super(t)}get_ticks(t,n,i,s){return this.get_ticks_no_defaults(t,n,s,this.desired_num_ticks)}get_ticks_no_defaults(t,n,i,s){const e=this.get_interval(t,n,s),o=Math.floor(t/e),r=Math.ceil(n/e);let _;_=isFinite(o)&&isFinite(r)?(0,c.range)(o,r+1):[];const u=_.map((t=>t*e)).filter((i=>t<=i&&i<=n)),a=this.num_minor_ticks,f=[];if(a>0&&u.length>0){const i=e/a,s=(0,c.range)(0,a).map((t=>t*i));for(const i of s.slice(1)){const s=u[0]-i;t<=s&&s<=n&&f.push(s)}for(const i of u)for(const e of s){const s=i+e;t<=s&&s<=n&&f.push(s)}}return{major:u,minor:f}}get_ideal_interval(t,n,i){return(n-t)/i}}i.ContinuousTicker=_,o=_,_.__name__=\"ContinuousTicker\",o.define((({Int:t})=>({num_minor_ticks:[t,5],desired_num_ticks:[t,6]})))},\n", - " function _(s,t,e,n,i){n();var r;const o=(0,s(1).__importDefault)(s(151)),a=s(131),c=s(19),u=s(152),m=s(9),h=s(8);function d(s){return(0,o.default)(s,\"%Y %m %d %H %M %S\").split(/\\s+/).map((s=>parseInt(s,10)))}function l(s,t){if((0,h.isFunction)(t))return t(s);{const e=(0,u.sprintf)(\"$1%06d\",function(s){return Math.round(s/1e3%1*1e6)}(s));return-1==(t=t.replace(/((^|[^%])(%%)*)%f/,e)).indexOf(\"%\")?t:(0,o.default)(s,t)}}const f=[\"microseconds\",\"milliseconds\",\"seconds\",\"minsec\",\"minutes\",\"hourmin\",\"hours\",\"days\",\"months\",\"years\"];class _ extends a.TickFormatter{constructor(s){super(s),this.strip_leading_zeros=!0}initialize(){super.initialize(),this._update_width_formats()}_update_width_formats(){const s=+(0,o.default)(new Date),t=function(t){const e=t.map((t=>l(s,t).length)),n=(0,m.sort_by)((0,m.zip)(e,t),(([s])=>s));return(0,m.unzip)(n)};this._width_formats={microseconds:t(this.microseconds),milliseconds:t(this.milliseconds),seconds:t(this.seconds),minsec:t(this.minsec),minutes:t(this.minutes),hourmin:t(this.hourmin),hours:t(this.hours),days:t(this.days),months:t(this.months),years:t(this.years)}}_get_resolution_str(s,t){const e=1.1*s;switch(!1){case!(e<.001):return\"microseconds\";case!(e<1):return\"milliseconds\";case!(e<60):return t>=60?\"minsec\":\"seconds\";case!(e<3600):return t>=3600?\"hourmin\":\"minutes\";case!(e<86400):return\"hours\";case!(e<2678400):return\"days\";case!(e<31536e3):return\"months\";default:return\"years\"}}doFormat(s,t){if(0==s.length)return[];const e=Math.abs(s[s.length-1]-s[0])/1e3,n=e/(s.length-1),i=this._get_resolution_str(n,e),[,[r]]=this._width_formats[i],o=[],a=f.indexOf(i),u={};for(const s of f)u[s]=0;u.seconds=5,u.minsec=4,u.minutes=4,u.hourmin=3,u.hours=3;for(const t of s){let s,e;try{e=d(t),s=l(t,r)}catch(s){c.logger.warn(`unable to format tick for timestamp value ${t}`),c.logger.warn(` - ${s}`),o.push(\"ERR\");continue}let n=!1,m=a;for(;0==e[u[f[m]]];){let r;if(m+=1,m==f.length)break;if((\"minsec\"==i||\"hourmin\"==i)&&!n){if(\"minsec\"==i&&0==e[4]&&0!=e[5]||\"hourmin\"==i&&0==e[3]&&0!=e[4]){r=this._width_formats[f[a-1]][1][0],s=l(t,r);break}n=!0}r=this._width_formats[f[m]][1][0],s=l(t,r)}if(this.strip_leading_zeros){let t=s.replace(/^0+/g,\"\");t!=s&&isNaN(parseInt(t))&&(t=`0${t}`),o.push(t)}else o.push(s)}return o}}e.DatetimeTickFormatter=_,r=_,_.__name__=\"DatetimeTickFormatter\",r.define((({String:s,Array:t})=>({microseconds:[t(s),[\"%fus\"]],milliseconds:[t(s),[\"%3Nms\",\"%S.%3Ns\"]],seconds:[t(s),[\"%Ss\"]],minsec:[t(s),[\":%M:%S\"]],minutes:[t(s),[\":%M\",\"%Mm\"]],hourmin:[t(s),[\"%H:%M\"]],hours:[t(s),[\"%Hh\",\"%H:%M\"]],days:[t(s),[\"%m/%d\",\"%a%d\"]],months:[t(s),[\"%m/%Y\",\"%b %Y\"]],years:[t(s),[\"%Y\"]]})))},\n", - " function _(e,t,n,r,o){!function(e){\"object\"==typeof t&&t.exports?t.exports=e():\"function\"==typeof define?define(e):this.tz=e()}((function(){function e(e,t,n){var r,o=t.day[1];do{r=new Date(Date.UTC(n,t.month,Math.abs(o++)))}while(t.day[0]<7&&r.getUTCDay()!=t.day[0]);return(r={clock:t.clock,sort:r.getTime(),rule:t,save:6e4*t.save,offset:e.offset})[r.clock]=r.sort+6e4*t.time,r.posix?r.wallclock=r[r.clock]+(e.offset+t.saved):r.posix=r[r.clock]-(e.offset+t.saved),r}function t(t,n,r){var o,a,u,i,l,s,c,f=t[t.zone],h=[],T=new Date(r).getUTCFullYear(),g=1;for(o=1,a=f.length;o=T-g;--c)for(o=0,a=s.length;o=h[o][n]&&h[o][h[o].clock]>u[h[o].clock]&&(i=h[o])}return i&&((l=/^(.*)\\/(.*)$/.exec(u.format))?i.abbrev=l[i.save?2:1]:i.abbrev=u.format.replace(/%s/,i.rule.letter)),i||u}function n(e,n){return\"UTC\"==e.zone?n:(e.entry=t(e,\"posix\",n),n+e.entry.offset+e.entry.save)}function r(e,n){return\"UTC\"==e.zone?n:(e.entry=r=t(e,\"wallclock\",n),0<(o=n-r.wallclock)&&o9)t+=s*l[c-10];else{if(a=new Date(n(e,t)),c<7)for(;s;)a.setUTCDate(a.getUTCDate()+i),a.getUTCDay()==c&&(s-=i);else 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c.AdaptiveTicker({mantissas:[1,2,4,6,8,12],base:24,min_interval:T.ONE_HOUR,max_interval:12*T.ONE_HOUR,num_minor_ticks:0}),new _.DaysTicker({days:(0,t.range)(1,32)}),new _.DaysTicker({days:(0,t.range)(1,31,3)}),new _.DaysTicker({days:[1,8,15,22]}),new _.DaysTicker({days:[1,15]}),new k.MonthsTicker({months:(0,t.range)(0,12,1)}),new k.MonthsTicker({months:(0,t.range)(0,12,2)}),new k.MonthsTicker({months:(0,t.range)(0,12,4)}),new k.MonthsTicker({months:(0,t.range)(0,12,6)}),new o.YearsTicker({})]})},\n", - " function _(t,e,i,r,s){var n;r();const _=t(149),a=t(9);class l extends _.ContinuousTicker{constructor(t){super(t)}get min_intervals(){return this.tickers.map((t=>t.get_min_interval()))}get max_intervals(){return this.tickers.map((t=>t.get_max_interval()))}get_min_interval(){return this.min_intervals[0]}get_max_interval(){return this.max_intervals[0]}get_best_ticker(t,e,i){const r=e-t,s=this.get_ideal_interval(t,e,i),n=[(0,a.sorted_index)(this.min_intervals,s)-1,(0,a.sorted_index)(this.max_intervals,s)],_=[this.min_intervals[n[0]],this.max_intervals[n[1]]].map((t=>Math.abs(i-r/t)));let l;if((0,a.is_empty)(_.filter((t=>!isNaN(t)))))l=this.tickers[0];else{const t=n[(0,a.argmin)(_)];l=this.tickers[t]}return l}get_interval(t,e,i){return this.get_best_ticker(t,e,i).get_interval(t,e,i)}get_ticks_no_defaults(t,e,i,r){return this.get_best_ticker(t,e,r).get_ticks_no_defaults(t,e,i,r)}}i.CompositeTicker=l,n=l,l.__name__=\"CompositeTicker\",n.define((({Array:t,Ref:e})=>({tickers:[t(e(_.ContinuousTicker)),[]]})))},\n", - " function _(t,e,n,s,o){var a;s();const i=t(158),r=t(159),c=t(9);class _ extends i.SingleIntervalTicker{constructor(t){super(t)}initialize(){super.initialize();const t=this.days;t.length>1?this.interval=(t[1]-t[0])*r.ONE_DAY:this.interval=31*r.ONE_DAY}get_ticks_no_defaults(t,e,n,s){const o=function(t,e){const n=(0,r.last_month_no_later_than)(new Date(t)),s=(0,r.last_month_no_later_than)(new Date(e));s.setUTCMonth(s.getUTCMonth()+1);const o=[],a=n;for(;o.push((0,r.copy_date)(a)),a.setUTCMonth(a.getUTCMonth()+1),!(a>s););return o}(t,e),a=this.days,i=this.interval,_=(0,c.concat)(o.map((t=>((t,e)=>{const n=t.getUTCMonth(),s=[];for(const o of a){const a=(0,r.copy_date)(t);a.setUTCDate(o),new Date(a.getTime()+e/2).getUTCMonth()==n&&s.push(a)}return s})(t,i))));return{major:_.map((t=>t.getTime())).filter((n=>t<=n&&n<=e)),minor:[]}}}n.DaysTicker=_,a=_,_.__name__=\"DaysTicker\",a.define((({Int:t,Array:e})=>({days:[e(t),[]]}))),a.override({num_minor_ticks:0})},\n", - " function _(e,n,t,r,i){var a;r();const l=e(149);class s extends l.ContinuousTicker{constructor(e){super(e)}get_interval(e,n,t){return this.interval}get_min_interval(){return this.interval}get_max_interval(){return this.interval}}t.SingleIntervalTicker=s,a=s,s.__name__=\"SingleIntervalTicker\",a.define((({Number:e})=>({interval:[e]})))},\n", - " function _(t,n,e,_,E){function N(t){return new Date(t.getTime())}function O(t){const n=N(t);return n.setUTCDate(1),n.setUTCHours(0),n.setUTCMinutes(0),n.setUTCSeconds(0),n.setUTCMilliseconds(0),n}_(),e.ONE_MILLI=1,e.ONE_SECOND=1e3,e.ONE_MINUTE=60*e.ONE_SECOND,e.ONE_HOUR=60*e.ONE_MINUTE,e.ONE_DAY=24*e.ONE_HOUR,e.ONE_MONTH=30*e.ONE_DAY,e.ONE_YEAR=365*e.ONE_DAY,e.copy_date=N,e.last_month_no_later_than=O,e.last_year_no_later_than=function(t){const n=O(t);return n.setUTCMonth(0),n}},\n", - " function _(t,e,n,a,r){var s;a();const i=t(158),o=t(159),l=t(9);class _ extends i.SingleIntervalTicker{constructor(t){super(t)}initialize(){super.initialize();const t=this.months;t.length>1?this.interval=(t[1]-t[0])*o.ONE_MONTH:this.interval=12*o.ONE_MONTH}get_ticks_no_defaults(t,e,n,a){const r=function(t,e){const n=(0,o.last_year_no_later_than)(new Date(t)),a=(0,o.last_year_no_later_than)(new Date(e));a.setUTCFullYear(a.getUTCFullYear()+1);const r=[],s=n;for(;r.push((0,o.copy_date)(s)),s.setUTCFullYear(s.getUTCFullYear()+1),!(s>a););return r}(t,e),s=this.months;return{major:(0,l.concat)(r.map((t=>s.map((e=>{const n=(0,o.copy_date)(t);return n.setUTCMonth(e),n}))))).map((t=>t.getTime())).filter((n=>t<=n&&n<=e)),minor:[]}}}n.MonthsTicker=_,s=_,_.__name__=\"MonthsTicker\",s.define((({Int:t,Array:e})=>({months:[e(t),[]]})))},\n", - " function _(e,t,a,i,r){i();const n=e(147),_=e(158),s=e(159);class c extends _.SingleIntervalTicker{constructor(e){super(e)}initialize(){super.initialize(),this.interval=s.ONE_YEAR,this.basic_ticker=new n.BasicTicker({num_minor_ticks:0})}get_ticks_no_defaults(e,t,a,i){const r=(0,s.last_year_no_later_than)(new Date(e)).getUTCFullYear(),n=(0,s.last_year_no_later_than)(new Date(t)).getUTCFullYear();return{major:this.basic_ticker.get_ticks_no_defaults(r,n,a,i).major.map((e=>Date.UTC(e,0,1))).filter((a=>e<=a&&a<=t)),minor:[]}}}a.YearsTicker=c,c.__name__=\"YearsTicker\"},\n", - " function _(e,o,i,s,t){var n;s();const r=e(143),_=e(163),c=e(164);class a extends r.ContinuousAxisView{}i.LogAxisView=a,a.__name__=\"LogAxisView\";class u extends r.ContinuousAxis{constructor(e){super(e)}}i.LogAxis=u,n=u,u.__name__=\"LogAxis\",n.prototype.default_view=a,n.override({ticker:()=>new c.LogTicker,formatter:()=>new _.LogTickFormatter})},\n", - " function _(e,t,n,o,r){var i;o();const a=e(131),s=e(146),c=e(164),l=e(120),{abs:u,log:x,round:_}=Math;class p extends a.TickFormatter{constructor(e){super(e)}initialize(){super.initialize(),this.basic_formatter=new s.BasicTickFormatter}format_graphics(e,t){var n,o;if(0==e.length)return[];const r=null!==(o=null===(n=this.ticker)||void 0===n?void 0:n.base)&&void 0!==o?o:10,i=this._exponents(e,r);return null==i?this.basic_formatter.format_graphics(e,t):i.map((e=>{if(u(e)u(e)({ticker:[n(t(c.LogTicker)),null],min_exponent:[e,0]})))},\n", - " function _(t,o,e,s,n){var r;s();const i=t(148),a=t(9);class c extends i.AdaptiveTicker{constructor(t){super(t)}get_ticks_no_defaults(t,o,e,s){const n=this.num_minor_ticks,r=[],i=this.base,c=Math.log(t)/Math.log(i),f=Math.log(o)/Math.log(i),l=f-c;let h;if(isFinite(l))if(l<2){const e=this.get_interval(t,o,s),i=Math.floor(t/e),c=Math.ceil(o/e);if(h=(0,a.range)(i,c+1).filter((t=>0!=t)).map((t=>t*e)).filter((e=>t<=e&&e<=o)),n>0&&h.length>0){const t=e/n,o=(0,a.range)(0,n).map((o=>o*t));for(const t of o.slice(1))r.push(h[0]-t);for(const t of h)for(const e of o)r.push(t+e)}}else{const t=Math.ceil(.999999*c),o=Math.floor(1.000001*f),e=Math.ceil((o-t)/9);if(h=(0,a.range)(t-1,o+1,e).map((t=>i**t)),n>0&&h.length>0){const t=i**e/n,o=(0,a.range)(1,n+1).map((o=>o*t));for(const t of o)r.push(h[0]/t);r.push(h[0]);for(const t of h)for(const e of o)r.push(t*e)}}else h=[];return{major:h.filter((e=>t<=e&&e<=o)),minor:r.filter((e=>t<=e&&e<=o))}}}e.LogTicker=c,r=c,c.__name__=\"LogTicker\",r.override({mantissas:[1,5]})},\n", - " function _(e,r,t,i,a){var o;i();const s=e(128),c=e(145),n=e(166),_=e(167);class x extends s.AxisView{}t.MercatorAxisView=x,x.__name__=\"MercatorAxisView\";class d extends c.LinearAxis{constructor(e){super(e)}}t.MercatorAxis=d,o=d,d.__name__=\"MercatorAxis\",o.prototype.default_view=x,o.override({ticker:()=>new _.MercatorTicker({dimension:\"lat\"}),formatter:()=>new n.MercatorTickFormatter({dimension:\"lat\"})})},\n", - " function _(r,t,e,o,n){var i;o();const c=r(146),s=r(20),a=r(78);class l extends c.BasicTickFormatter{constructor(r){super(r)}doFormat(r,t){if(null==this.dimension)throw new Error(\"MercatorTickFormatter.dimension not configured\");if(0==r.length)return[];const e=r.length,o=new Array(e);if(\"lon\"==this.dimension)for(let n=0;n({dimension:[r(s.LatLon),null]})))},\n", - " function _(t,o,n,s,r){var e;s();const i=t(147),c=t(20),_=t(78);class a extends i.BasicTicker{constructor(t){super(t)}get_ticks_no_defaults(t,o,n,s){if(null==this.dimension)throw new Error(`${this}.dimension wasn't configured`);return[t,o]=(0,_.clip_mercator)(t,o,this.dimension),\"lon\"==this.dimension?this._get_ticks_lon(t,o,n,s):this._get_ticks_lat(t,o,n,s)}_get_ticks_lon(t,o,n,s){const[r]=_.wgs84_mercator.invert(t,n),[e,i]=_.wgs84_mercator.invert(o,n),c=super.get_ticks_no_defaults(r,e,n,s),a=[];for(const t of c.major)if((0,_.in_bounds)(t,\"lon\")){const[o]=_.wgs84_mercator.compute(t,i);a.push(o)}const m=[];for(const t of c.minor)if((0,_.in_bounds)(t,\"lon\")){const[o]=_.wgs84_mercator.compute(t,i);m.push(o)}return{major:a,minor:m}}_get_ticks_lat(t,o,n,s){const[,r]=_.wgs84_mercator.invert(n,t),[e,i]=_.wgs84_mercator.invert(n,o),c=super.get_ticks_no_defaults(r,i,n,s),a=[];for(const t of c.major)if((0,_.in_bounds)(t,\"lat\")){const[,o]=_.wgs84_mercator.compute(e,t);a.push(o)}const m=[];for(const t of c.minor)if((0,_.in_bounds)(t,\"lat\")){const[,o]=_.wgs84_mercator.compute(e,t);m.push(o)}return{major:a,minor:m}}}n.MercatorTicker=a,e=a,a.__name__=\"MercatorTicker\",e.define((({Nullable:t})=>({dimension:[t(c.LatLon),null]})))},\n", - " function _(e,i,r,c,k){c(),k(\"AdaptiveTicker\",e(148).AdaptiveTicker),k(\"BasicTicker\",e(147).BasicTicker),k(\"CategoricalTicker\",e(141).CategoricalTicker),k(\"CompositeTicker\",e(156).CompositeTicker),k(\"ContinuousTicker\",e(149).ContinuousTicker),k(\"DatetimeTicker\",e(155).DatetimeTicker),k(\"DaysTicker\",e(157).DaysTicker),k(\"FixedTicker\",e(169).FixedTicker),k(\"LogTicker\",e(164).LogTicker),k(\"MercatorTicker\",e(167).MercatorTicker),k(\"MonthsTicker\",e(160).MonthsTicker),k(\"SingleIntervalTicker\",e(158).SingleIntervalTicker),k(\"Ticker\",e(130).Ticker),k(\"YearsTicker\",e(161).YearsTicker),k(\"BinnedTicker\",e(170).BinnedTicker)},\n", - " function _(r,t,e,i,n){var s;i();const _=r(149);class c extends _.ContinuousTicker{constructor(r){super(r)}get_ticks_no_defaults(r,t,e,i){return{major:this.ticks,minor:this.minor_ticks}}get_interval(r,t,e){return 0}get_min_interval(){return 0}get_max_interval(){return 0}}e.FixedTicker=c,s=c,c.__name__=\"FixedTicker\",s.define((({Number:r,Array:t})=>({ticks:[t(r),[]],minor_ticks:[t(r),[]]})))},\n", - " function _(e,n,t,r,i){var o;r();const a=e(130),s=e(171),c=e(12);class m extends a.Ticker{constructor(e){super(e)}get_ticks(e,n,t,r){const{binning:i}=this.mapper.metrics,o=Math.max(0,(0,c.left_edge_index)(e,i)),a=Math.min((0,c.left_edge_index)(n,i)+1,i.length-1),s=[];for(let e=o;e<=a;e++)s.push(i[e]);const{num_major_ticks:m}=this,_=[],h=\"auto\"==m?s.length:m,l=Math.max(1,Math.floor(s.length/h));for(let e=0;e({mapper:[n(s.ScanningColorMapper)],num_major_ticks:[t(e,r),8]})))},\n", - " function _(n,e,i,r,o){r();const t=n(172),a=n(12);class c extends t.ContinuousColorMapper{constructor(n){super(n)}cmap(n,e,i,r,o){if(no.binning[o.binning.length-1])return r;return e[(0,a.left_edge_index)(n,o.binning)]}}i.ScanningColorMapper=c,c.__name__=\"ScanningColorMapper\"},\n", - " function _(t,e,o,n,s){var l;n();const c=t(173),i=t(175),a=t(9),h=t(8);class r extends c.ColorMapper{constructor(t){super(t),this._scan_data=null}connect_signals(){super.connect_signals();const t=()=>{for(const[t]of this.domain)this.connect(t.view.change,(()=>this.update_data())),this.connect(t.data_source.selected.change,(()=>this.update_data()))};this.connect(this.properties.domain.change,(()=>t())),t()}update_data(){const{domain:t,palette:e}=this,o=[...this._collect(t)];this._scan_data=this.scan(o,e.length),this.metrics_change.emit(),this.change.emit()}get metrics(){return null==this._scan_data&&this.update_data(),this._scan_data}*_collect(t){for(const[e,o]of t)for(const t of(0,h.isArray)(o)?o:[o]){let o=e.data_source.get_column(t);o=e.view.indices.select(o);const n=e.view.masked,s=e.data_source.selected.indices;let l;if(null!=n&&s.length>0?l=(0,a.intersection)([...n],s):null!=n?l=[...n]:s.length>0&&(l=s),null!=l&&(o=(0,a.map)(l,(t=>o[t]))),o.length>0&&!(0,h.isNumber)(o[0]))for(const t of o)yield*t;else yield*o}}_v_compute(t,e,o,n){const{nan_color:s}=n;let{low_color:l,high_color:c}=n;null==l&&(l=o[0]),null==c&&(c=o[o.length-1]);const{domain:i}=this,h=(0,a.is_empty)(i)?t:[...this._collect(i)];this._scan_data=this.scan(h,o.length),this.metrics_change.emit();for(let n=0,i=t.length;n({high:[a(t),null],low:[a(t),null],high_color:[a(n),null],low_color:[a(n),null],domain:[c(l(o(i.GlyphRenderer),s(e,c(e)))),[]]})))},\n", - " function _(e,r,t,n,o){var a;n();const c=e(174),i=e(15),_=e(24),l=e(22),s=e(27);function p(e){return(0,l.encode_rgba)((0,l.color2rgba)(e))}function u(e){const r=new Uint32Array(e.length);for(let t=0,n=e.length;te))),r}get rgba_mapper(){const e=this,r=u(this.palette),t=this._colors(p);return{v_compute(n){const o=new _.ColorArray(n.length);return e._v_compute(n,o,r,t),new Uint8ClampedArray((0,s.to_big_endian)(o).buffer)}}}_colors(e){return{nan_color:e(this.nan_color)}}}t.ColorMapper=h,a=h,h.__name__=\"ColorMapper\",a.define((({Color:e,Array:r})=>({palette:[r(e)],nan_color:[e,\"gray\"]})))},\n", - " function _(r,e,n,s,o){s();const p=r(56);class t extends p.Transform{constructor(r){super(r)}compute(r){throw new Error(\"mapping single values is not supported\")}}n.Mapper=t,t.__name__=\"Mapper\"},\n", - " function _(e,t,i,s,l){var h;s();const n=e(176),o=e(177),a=e(186),c=e(187),_=e(189),r=e(179),d=e(70),p=e(190),g=e(24),u=e(12),y=e(13),m=e(113),v=e(67),f={fill:{},line:{}},w={fill:{fill_alpha:.3,fill_color:\"grey\"},line:{line_alpha:.3,line_color:\"grey\"}},b={fill:{fill_alpha:.2},line:{}},V={fill:{},line:{}},x={fill:{fill_alpha:.2},line:{}};class G extends n.DataRendererView{get glyph_view(){return this.glyph}async lazy_initialize(){var e;await super.lazy_initialize();const t=this.model.glyph;this.glyph=await this.build_glyph_view(t);const i=\"fill\"in this.glyph.visuals,s=\"line\"in this.glyph.visuals,l=Object.assign({},t.attributes);function h(e){const h=(0,y.clone)(l);return i&&(0,y.extend)(h,e.fill),s&&(0,y.extend)(h,e.line),new t.constructor(h)}function n(e,t){return t instanceof r.Glyph?t:h(\"auto\"==t?e:{fill:{},line:{}})}delete l.id;let{selection_glyph:o,nonselection_glyph:a,hover_glyph:c,muted_glyph:_}=this.model;o=n(f,o),this.selection_glyph=await this.build_glyph_view(o),a=n(b,a),this.nonselection_glyph=await this.build_glyph_view(a),c=n(V,c),this.hover_glyph=await this.build_glyph_view(c),_=n(x,_),this.muted_glyph=await this.build_glyph_view(_);const d=n(w,\"auto\");this.decimated_glyph=await this.build_glyph_view(d),this.selection_glyph.set_base(this.glyph),this.nonselection_glyph.set_base(this.glyph),null===(e=this.hover_glyph)||void 0===e||e.set_base(this.glyph),this.muted_glyph.set_base(this.glyph),this.decimated_glyph.set_base(this.glyph),this.set_data()}async build_glyph_view(e){return(0,m.build_view)(e,{parent:this})}remove(){var e;this.glyph.remove(),this.selection_glyph.remove(),this.nonselection_glyph.remove(),null===(e=this.hover_glyph)||void 0===e||e.remove(),this.muted_glyph.remove(),this.decimated_glyph.remove(),super.remove()}connect_signals(){super.connect_signals();const e=()=>this.request_render(),t=()=>this.update_data();this.connect(this.model.change,e),this.connect(this.glyph.model.change,t),this.connect(this.selection_glyph.model.change,t),this.connect(this.nonselection_glyph.model.change,t),null!=this.hover_glyph&&this.connect(this.hover_glyph.model.change,t),this.connect(this.muted_glyph.model.change,t),this.connect(this.decimated_glyph.model.change,t),this.connect(this.model.data_source.change,t),this.connect(this.model.data_source.streaming,t),this.connect(this.model.data_source.patching,(e=>this.update_data(e))),this.connect(this.model.data_source.selected.change,e),this.connect(this.model.data_source._select,e),null!=this.hover_glyph&&this.connect(this.model.data_source.inspect,e),this.connect(this.model.properties.view.change,t),this.connect(this.model.view.properties.indices.change,t),this.connect(this.model.view.properties.masked.change,(()=>this.set_visuals())),this.connect(this.model.properties.visible.change,(()=>this.plot_view.invalidate_dataranges=!0));const{x_ranges:i,y_ranges:s}=this.plot_view.frame;for(const[,e]of i)e instanceof v.FactorRange&&this.connect(e.change,t);for(const[,e]of s)e instanceof v.FactorRange&&this.connect(e.change,t);const{transformchange:l,exprchange:h}=this.model.glyph;this.connect(l,t),this.connect(h,t)}_update_masked_indices(){const e=this.glyph.mask_data();return this.model.view.masked=e,e}update_data(e){this.set_data(e),this.request_render()}set_data(e){const t=this.model.data_source;this.all_indices=this.model.view.indices;const{all_indices:i}=this;this.glyph.set_data(t,i,e),this.set_visuals(),this._update_masked_indices();const{lod_factor:s}=this.plot_model,l=this.all_indices.count;this.decimated=new g.Indices(l);for(let e=0;e!n||n.is_empty()?[]:n.selected_glyph?this.model.view.convert_indices_from_subset(i):n.indices.length>0?n.indices:Object.keys(n.multiline_indices).map((e=>parseInt(e))))()),d=(0,u.filter)(i,(e=>r.has(t[e]))),{lod_threshold:p}=this.plot_model;let g,y,m;if(null!=this.model.document&&this.model.document.interactive_duration()>0&&!e&&null!=p&&t.length>p?(i=[...this.decimated],g=this.decimated_glyph,y=this.decimated_glyph,m=this.selection_glyph):(g=this.model.muted&&null!=this.muted_glyph?this.muted_glyph:this.glyph,y=this.nonselection_glyph,m=this.selection_glyph),null!=this.hover_glyph&&d.length){const e=new Set(i);for(const t of d)e.delete(t);i=[...e]}if(h.length){const e={};for(const t of h)e[t]=!0;const l=new Array,n=new Array;if(this.glyph instanceof o.LineView)for(const i of t)null!=e[i]?l.push(i):n.push(i);else for(const s of i)null!=e[t[s]]?l.push(s):n.push(s);y.render(s,n),m.render(s,l),null!=this.hover_glyph&&(this.glyph instanceof o.LineView?this.hover_glyph.render(s,this.model.view.convert_indices_from_subset(d)):this.hover_glyph.render(s,d))}else if(this.glyph instanceof o.LineView)this.hover_glyph&&d.length?this.hover_glyph.render(s,this.model.view.convert_indices_from_subset(d)):g.render(s,t);else if(this.glyph instanceof a.PatchView||this.glyph instanceof c.HAreaView||this.glyph instanceof _.VAreaView)if(0==n.selected_glyphs.length||null==this.hover_glyph)g.render(s,t);else for(const e of n.selected_glyphs)e==this.glyph.model&&this.hover_glyph.render(s,t);else g.render(s,i),this.hover_glyph&&d.length&&this.hover_glyph.render(s,d);s.restore()}draw_legend(e,t,i,s,l,h,n,o){0!=this.glyph.data_size&&(null==o&&(o=this.model.get_reference_point(h,n)),this.glyph.draw_legend_for_index(e,{x0:t,x1:i,y0:s,y1:l},o))}hit_test(e){if(!this.model.visible)return null;const t=this.glyph.hit_test(e);return null==t?null:this.model.view.convert_selection_from_subset(t)}}i.GlyphRendererView=G,G.__name__=\"GlyphRendererView\";class R extends n.DataRenderer{constructor(e){super(e)}initialize(){super.initialize(),this.view.source!=this.data_source&&(this.view.source=this.data_source,this.view.compute_indices())}get_reference_point(e,t){if(null!=e){const i=this.data_source.get_column(e);if(null!=i)for(const[e,s]of Object.entries(this.view.indices_map))if(i[parseInt(e)]==t)return s}return 0}get_selection_manager(){return this.data_source.selection_manager}}i.GlyphRenderer=R,h=R,R.__name__=\"GlyphRenderer\",h.prototype.default_view=G,h.define((({Boolean:e,Auto:t,Or:i,Ref:s,Null:l,Nullable:h})=>({data_source:[s(d.ColumnarDataSource)],view:[s(p.CDSView),e=>new p.CDSView({source:e.data_source})],glyph:[s(r.Glyph)],hover_glyph:[h(s(r.Glyph)),null],nonselection_glyph:[i(s(r.Glyph),t,l),\"auto\"],selection_glyph:[i(s(r.Glyph),t,l),\"auto\"],muted_glyph:[i(s(r.Glyph),t,l),\"auto\"],muted:[e,!1]})))},\n", - " function _(e,r,t,a,n){var s;a();const c=e(41);class _ extends c.RendererView{get xscale(){return this.coordinates.x_scale}get yscale(){return this.coordinates.y_scale}}t.DataRendererView=_,_.__name__=\"DataRendererView\";class i extends c.Renderer{constructor(e){super(e)}get selection_manager(){return this.get_selection_manager()}}t.DataRenderer=i,s=i,i.__name__=\"DataRenderer\",s.override({level:\"glyph\"})},\n", - " function _(e,t,i,s,n){s();const l=e(1);var _;const r=e(178),o=e(184),a=(0,l.__importStar)(e(48)),h=(0,l.__importStar)(e(185)),c=e(72);class d extends r.XYGlyphView{async lazy_initialize(){await super.lazy_initialize();const{webgl:t}=this.renderer.plot_view.canvas_view;if(null==t?void 0:t.regl_wrapper.has_webgl){const{LineGL:i}=await Promise.resolve().then((()=>(0,l.__importStar)(e(421))));this.glglyph=new i(t.regl_wrapper,this)}}_render(e,t,i){const{sx:s,sy:n}=null!=i?i:this;let l=null;const _=e=>null!=l&&e-l!=1;let r=!0;e.beginPath();for(const i of t){const t=s[i],o=n[i];isFinite(t+o)?r||_(i)?(e.moveTo(t,o),r=!1):e.lineTo(t,o):r=!0,l=i}this.visuals.line.set_value(e),e.stroke()}_hit_point(e){const t=new c.Selection,i={x:e.sx,y:e.sy};let s=9999;const n=Math.max(2,this.line_width.value/2);for(let e=0,l=this.sx.length-1;e({x:[c.XCoordinateSpec,{field:\"x\"}],y:[c.YCoordinateSpec,{field:\"y\"}]})))},\n", - " function _(e,t,s,i,n){i();const r=e(1),a=(0,r.__importStar)(e(18)),o=(0,r.__importStar)(e(65)),_=(0,r.__importStar)(e(45)),l=e(42),c=e(53),h=e(19),d=e(24),u=e(8),f=e(180),p=e(12),g=e(26),y=e(181),x=e(67),v=e(72),{abs:b,ceil:m}=Math;class w extends l.View{constructor(){super(...arguments),this._index=null,this._data_size=null,this._nohit_warned=new Set}get renderer(){return this.parent}get has_webgl(){return null!=this.glglyph}get index(){const{_index:e}=this;if(null!=e)return e;throw new Error(`${this}.index_data() wasn't called`)}get data_size(){const{_data_size:e}=this;if(null!=e)return e;throw new Error(`${this}.set_data() wasn't called`)}initialize(){super.initialize(),this.visuals=new _.Visuals(this)}request_render(){this.parent.request_render()}get canvas(){return this.renderer.parent.canvas_view}render(e,t,s){var i;null!=this.glglyph&&(this.renderer.needs_webgl_blit=this.glglyph.render(e,t,null!==(i=this.base)&&void 0!==i?i:this),this.renderer.needs_webgl_blit)||this._render(e,t,null!=s?s:this.base)}has_finished(){return!0}notify_finished(){this.renderer.notify_finished()}_bounds(e){return e}bounds(){return this._bounds(this.index.bbox)}log_bounds(){const{x0:e,x1:t}=this.index.bounds(o.positive_x()),{y0:s,y1:i}=this.index.bounds(o.positive_y());return this._bounds({x0:e,y0:s,x1:t,y1:i})}get_anchor_point(e,t,[s,i]){switch(e){case\"center\":case\"center_center\":{const[e,n]=this.scenterxy(t,s,i);return{x:e,y:n}}default:return null}}scenterx(e,t,s){return this.scenterxy(e,t,s)[0]}scentery(e,t,s){return this.scenterxy(e,t,s)[1]}sdist(e,t,s,i=\"edge\",n=!1){const r=t.length,a=new d.ScreenArray(r),o=e.s_compute;if(\"center\"==i)for(let e=0;em(e))),a}draw_legend_for_index(e,t,s){}hit_test(e){switch(e.type){case\"point\":if(null!=this._hit_point)return this._hit_point(e);break;case\"span\":if(null!=this._hit_span)return this._hit_span(e);break;case\"rect\":if(null!=this._hit_rect)return this._hit_rect(e);break;case\"poly\":if(null!=this._hit_poly)return this._hit_poly(e)}return this._nohit_warned.has(e.type)||(h.logger.debug(`'${e.type}' selection not available for ${this.model.type}`),this._nohit_warned.add(e.type)),null}_hit_rect_against_index(e){const{sx0:t,sx1:s,sy0:i,sy1:n}=e,[r,a]=this.renderer.coordinates.x_scale.r_invert(t,s),[o,_]=this.renderer.coordinates.y_scale.r_invert(i,n),l=[...this.index.indices({x0:r,x1:a,y0:o,y1:_})];return new v.Selection({indices:l})}_project_data(){}*_iter_visuals(){for(const e of this.visuals)for(const t of e)(t instanceof a.VectorSpec||t instanceof a.ScalarSpec)&&(yield t)}set_base(e){e!=this&&e instanceof this.constructor&&(this.base=e)}_configure(e,t){Object.defineProperty(this,(0,u.isString)(e)?e:e.attr,Object.assign({configurable:!0,enumerable:!0},t))}set_visuals(e,t){var s;for(const s of this._iter_visuals()){const{base:i}=this;if(null!=i){const e=i.model.properties[s.attr];if(null!=e&&(0,g.is_equal)(s.get_value(),e.get_value())){this._configure(s,{get:()=>i[`${s.attr}`]});continue}}const n=s.uniform(e).select(t);this._configure(s,{value:n})}for(const e of this.visuals)e.update();this._set_visuals(),null===(s=this.glglyph)||void 0===s||s.set_visuals_changed()}_set_visuals(){}set_data(e,t,s){var i;const{x_source:n,y_source:r}=this.renderer.coordinates,o=new Set(this._iter_visuals());this._data_size=t.count;for(const s of this.model)if((s instanceof a.VectorSpec||s instanceof a.ScalarSpec)&&!o.has(s))if(s instanceof a.BaseCoordinateSpec){const i=s.array(e);let o=t.select(i);const _=\"x\"==s.dimension?n:r;if(_ instanceof x.FactorRange)if(s instanceof a.CoordinateSpec)o=_.v_synthetic(o);else if(s instanceof a.CoordinateSeqSpec)for(let e=0;e{const s=new Uint32Array(r);for(let a=0;a>1;t[s]>i?e=s:n=s+1}return t[n]}class r extends d.default{get boxes(){return this._boxes}search_indices(i,t,n,e){if(this._pos!==this._boxes.length)throw new Error(\"Data not yet indexed - call index.finish().\");let s=this._boxes.length-4;const d=[],x=new o.Indices(this.numItems);for(;void 0!==s;){const o=Math.min(s+4*this.nodeSize,h(s,this._levelBounds));for(let h=s;h>2],r=this._boxes[h+0],l=this._boxes[h+1],a=this._boxes[h+2],_=this._boxes[h+3];na||t>_||(s<4*this.numItems?x.set(o):d.push(o)))}s=d.pop()}return x}}r.__name__=\"_FlatBush\";class l{constructor(i){this.index=null,i>0&&(this.index=new r(i))}add_rect(i,t,n,e){var s;isFinite(i+t+n+e)?null===(s=this.index)||void 0===s||s.add(i,t,n,e):this.add_empty()}add_point(i,t){var n;isFinite(i+t)?null===(n=this.index)||void 0===n||n.add(i,t,i,t):this.add_empty()}add_empty(){var i;null===(i=this.index)||void 0===i||i.add(1/0,1/0,-1/0,-1/0)}finish(){var i;null===(i=this.index)||void 0===i||i.finish()}_normalize(i){let{x0:t,y0:n,x1:e,y1:s}=i;return t>e&&([t,e]=[e,t]),n>s&&([n,s]=[s,n]),{x0:t,y0:n,x1:e,y1:s}}get bbox(){if(null==this.index)return(0,x.empty)();{const{minX:i,minY:t,maxX:n,maxY:e}=this.index;return{x0:i,y0:t,x1:n,y1:e}}}indices(i){if(null==this.index)return new o.Indices(0);{const{x0:t,y0:n,x1:e,y1:s}=this._normalize(i);return this.index.search_indices(t,n,e,s)}}bounds(i){const t=(0,x.empty)();if(null==this.index)return t;const{boxes:n}=this.index;for(const e of this.indices(i)){const s=n[4*e+0],d=n[4*e+1],o=n[4*e+2],x=n[4*e+3];s>=i.x0&&st.x1&&(t.x1=o),d>=i.y0&&dt.y1&&(t.y1=x)}return t}}n.SpatialIndex=l,l.__name__=\"SpatialIndex\"},\n", - " function _(t,s,i,e,h){e();const n=(0,t(1).__importDefault)(t(183)),o=[Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array];class r{static from(t){if(!(t instanceof ArrayBuffer))throw new Error(\"Data must be an instance of ArrayBuffer.\");const[s,i]=new Uint8Array(t,0,2);if(251!==s)throw new Error(\"Data does not appear to be in a Flatbush format.\");if(i>>4!=3)throw new Error(`Got v${i>>4} data when expected v3.`);const[e]=new Uint16Array(t,2,1),[h]=new Uint32Array(t,4,1);return new r(h,e,o[15&i],t)}constructor(t,s=16,i=Float64Array,e){if(void 0===t)throw new Error(\"Missing required argument: numItems.\");if(isNaN(t)||t<=0)throw new Error(`Unpexpected numItems value: ${t}.`);this.numItems=+t,this.nodeSize=Math.min(Math.max(+s,2),65535);let h=t,r=h;this._levelBounds=[4*h];do{h=Math.ceil(h/this.nodeSize),r+=h,this._levelBounds.push(4*r)}while(1!==h);this.ArrayType=i||Float64Array,this.IndexArrayType=r<16384?Uint16Array:Uint32Array;const a=o.indexOf(this.ArrayType),_=4*r*this.ArrayType.BYTES_PER_ELEMENT;if(a<0)throw new Error(`Unexpected typed array class: ${i}.`);e&&e instanceof ArrayBuffer?(this.data=e,this._boxes=new this.ArrayType(this.data,8,4*r),this._indices=new this.IndexArrayType(this.data,8+_,r),this._pos=4*r,this.minX=this._boxes[this._pos-4],this.minY=this._boxes[this._pos-3],this.maxX=this._boxes[this._pos-2],this.maxY=this._boxes[this._pos-1]):(this.data=new ArrayBuffer(8+_+r*this.IndexArrayType.BYTES_PER_ELEMENT),this._boxes=new this.ArrayType(this.data,8,4*r),this._indices=new this.IndexArrayType(this.data,8+_,r),this._pos=0,this.minX=1/0,this.minY=1/0,this.maxX=-1/0,this.maxY=-1/0,new Uint8Array(this.data,0,2).set([251,48+a]),new Uint16Array(this.data,2,1)[0]=s,new Uint32Array(this.data,4,1)[0]=t),this._queue=new n.default}add(t,s,i,e){const h=this._pos>>2;return this._indices[h]=h,this._boxes[this._pos++]=t,this._boxes[this._pos++]=s,this._boxes[this._pos++]=i,this._boxes[this._pos++]=e,tthis.maxX&&(this.maxX=i),e>this.maxY&&(this.maxY=e),h}finish(){if(this._pos>>2!==this.numItems)throw new Error(`Added ${this._pos>>2} items when expected ${this.numItems}.`);if(this.numItems<=this.nodeSize)return this._boxes[this._pos++]=this.minX,this._boxes[this._pos++]=this.minY,this._boxes[this._pos++]=this.maxX,void(this._boxes[this._pos++]=this.maxY);const t=this.maxX-this.minX,s=this.maxY-this.minY,i=new Uint32Array(this.numItems);for(let e=0;e>2]=t,this._boxes[this._pos++]=e,this._boxes[this._pos++]=h,this._boxes[this._pos++]=n,this._boxes[this._pos++]=o}}}search(t,s,i,e,h){if(this._pos!==this._boxes.length)throw new Error(\"Data not yet indexed - call index.finish().\");let n=this._boxes.length-4;const o=[],r=[];for(;void 0!==n;){const a=Math.min(n+4*this.nodeSize,_(n,this._levelBounds));for(let _=n;_>2];ithis._boxes[_+2]||s>this._boxes[_+3]||(n<4*this.numItems?(void 0===h||h(a))&&r.push(a):o.push(a)))}n=o.pop()}return r}neighbors(t,s,i=1/0,e=1/0,h){if(this._pos!==this._boxes.length)throw new Error(\"Data not yet indexed - call index.finish().\");let n=this._boxes.length-4;const o=this._queue,r=[],x=e*e;for(;void 0!==n;){const 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i=t^s,e=65535^i,h=65535^(t|s),n=t&(65535^s),o=i|e>>1,r=i>>1^i,a=h>>1^e&n>>1^h,_=i&h>>1^n>>1^n;i=o,e=r,h=a,n=_,o=i&i>>2^e&e>>2,r=i&e>>2^e&(i^e)>>2,a^=i&h>>2^e&n>>2,_^=e&h>>2^(i^e)&n>>2,i=o,e=r,h=a,n=_,o=i&i>>4^e&e>>4,r=i&e>>4^e&(i^e)>>4,a^=i&h>>4^e&n>>4,_^=e&h>>4^(i^e)&n>>4,i=o,e=r,h=a,n=_,a^=i&h>>8^e&n>>8,_^=e&h>>8^(i^e)&n>>8,i=a^a>>1,e=_^_>>1;let x=t^s,d=e|65535^(x|i);return x=16711935&(x|x<<8),x=252645135&(x|x<<4),x=858993459&(x|x<<2),x=1431655765&(x|x<<1),d=16711935&(d|d<<8),d=252645135&(d|d<<4),d=858993459&(d|d<<2),d=1431655765&(d|d<<1),(d<<1|x)>>>0}i.default=r},\n", - " function _(s,t,i,h,e){h();i.default=class{constructor(){this.ids=[],this.values=[],this.length=0}clear(){this.length=0}push(s,t){let i=this.length++;for(this.ids[i]=s,this.values[i]=t;i>0;){const s=i-1>>1,h=this.values[s];if(t>=h)break;this.ids[i]=this.ids[s],this.values[i]=h,i=s}this.ids[i]=s,this.values[i]=t}pop(){if(0===this.length)return;const s=this.ids[0];if(this.length--,this.length>0){const s=this.ids[0]=this.ids[this.length],t=this.values[0]=this.values[this.length],i=this.length>>1;let h=0;for(;h=t)break;this.ids[h]=e,this.values[h]=l,h=s}this.ids[h]=s,this.values[h]=t}return s}peek(){if(0!==this.length)return this.ids[0]}peekValue(){if(0!==this.length)return this.values[0]}}},\n", - " function _(e,n,a,t,i){t();const l=(0,e(1).__importStar)(e(185));function r(e,n,{x0:a,x1:t,y0:i,y1:l},r){n.save(),n.beginPath(),n.moveTo(a,(i+l)/2),n.lineTo(t,(i+l)/2),e.line.apply(n,r),n.restore()}function c(e,n,{x0:a,x1:t,y0:i,y1:l},r){var c,o;const _=.1*Math.abs(t-a),s=.1*Math.abs(l-i),y=a+_,p=t-_,g=i+s,h=l-s;n.beginPath(),n.rect(y,g,p-y,h-g),e.fill.apply(n,r),null===(c=e.hatch)||void 0===c||c.apply(n,r),null===(o=e.line)||void 0===o||o.apply(n,r)}a.generic_line_scalar_legend=function(e,n,{x0:a,x1:t,y0:i,y1:l}){n.save(),n.beginPath(),n.moveTo(a,(i+l)/2),n.lineTo(t,(i+l)/2),e.line.apply(n),n.restore()},a.generic_line_vector_legend=r,a.generic_line_legend=r,a.generic_area_scalar_legend=function(e,n,{x0:a,x1:t,y0:i,y1:l}){var r,c;const o=.1*Math.abs(t-a),_=.1*Math.abs(l-i),s=a+o,y=t-o,p=i+_,g=l-_;n.beginPath(),n.rect(s,p,y-s,g-p),e.fill.apply(n),null===(r=e.hatch)||void 0===r||r.apply(n),null===(c=e.line)||void 0===c||c.apply(n)},a.generic_area_vector_legend=c,a.generic_area_legend=c,a.line_interpolation=function(e,n,a,t,i,r){const{sx:c,sy:o}=n;let _,s,y,p;\"point\"==n.type?([y,p]=e.yscale.r_invert(o-1,o+1),[_,s]=e.xscale.r_invert(c-1,c+1)):\"v\"==n.direction?([y,p]=e.yscale.r_invert(o,o),[_,s]=[Math.min(a-1,i-1),Math.max(a+1,i+1)]):([_,s]=e.xscale.r_invert(c,c),[y,p]=[Math.min(t-1,r-1),Math.max(t+1,r+1)]);const{x:g,y:h}=l.check_2_segments_intersect(_,y,s,p,a,t,i,r);return[g,h]}},\n", - " function _(t,n,e,i,r){function s(t,n){return(t.x-n.x)**2+(t.y-n.y)**2}function o(t,n,e){const i=s(n,e);if(0==i)return s(t,n);const r=((t.x-n.x)*(e.x-n.x)+(t.y-n.y)*(e.y-n.y))/i;if(r<0)return s(t,n);if(r>1)return s(t,e);return s(t,{x:n.x+r*(e.x-n.x),y:n.y+r*(e.y-n.y)})}i(),e.point_in_poly=function(t,n,e,i){let r=!1,s=e[e.length-1],o=i[i.length-1];for(let u=0;u0&&_<1&&h>0&&h<1,x:t+_*(e-t),y:n+_*(i-n)}}}},\n", - " function _(t,s,e,i,a){i();const l=t(1);var n;const _=t(178),o=t(184),c=(0,l.__importStar)(t(185)),h=(0,l.__importStar)(t(48)),r=t(72);class p extends _.XYGlyphView{_render(t,s,e){const{sx:i,sy:a}=null!=e?e:this;let l=!0;t.beginPath();for(const e of s){const s=i[e],n=a[e];isFinite(s+n)?l?(t.moveTo(s,n),l=!1):t.lineTo(s,n):(t.closePath(),l=!0)}t.closePath(),this.visuals.fill.apply(t),this.visuals.hatch.apply(t),this.visuals.line.apply(t)}draw_legend_for_index(t,s,e){(0,o.generic_area_scalar_legend)(this.visuals,t,s)}_hit_point(t){const s=new r.Selection;return c.point_in_poly(t.sx,t.sy,this.sx,this.sy)&&(s.add_to_selected_glyphs(this.model),s.view=this),s}}e.PatchView=p,p.__name__=\"PatchView\";class d extends _.XYGlyph{constructor(t){super(t)}}e.Patch=d,n=d,d.__name__=\"Patch\",n.prototype.default_view=p,n.mixins([h.LineScalar,h.FillScalar,h.HatchScalar])},\n", - " function _(e,t,s,i,r){i();const n=e(1);var a;const _=e(24),h=e(188),o=(0,n.__importStar)(e(185)),l=(0,n.__importStar)(e(18)),c=e(72);class d extends h.AreaView{_index_data(e){const{min:t,max:s}=Math,{data_size:i}=this;for(let r=0;r=0;t--)e.lineTo(r[t],n[t]);e.closePath(),this.visuals.fill.apply(e),this.visuals.hatch.apply(e)}_hit_point(e){const t=this.sy.length,s=new _.ScreenArray(2*t),i=new _.ScreenArray(2*t);for(let e=0,r=t;e({x1:[l.XCoordinateSpec,{field:\"x1\"}],x2:[l.XCoordinateSpec,{field:\"x2\"}],y:[l.YCoordinateSpec,{field:\"y\"}]})))},\n", - " function _(e,a,r,_,s){_();const n=e(1);var c;const i=e(179),l=e(184),t=(0,n.__importStar)(e(48));class o extends i.GlyphView{draw_legend_for_index(e,a,r){(0,l.generic_area_scalar_legend)(this.visuals,e,a)}}r.AreaView=o,o.__name__=\"AreaView\";class d extends i.Glyph{constructor(e){super(e)}}r.Area=d,c=d,d.__name__=\"Area\",c.mixins([t.FillScalar,t.HatchScalar])},\n", - " function _(e,t,s,i,r){i();const n=e(1);var a;const _=e(24),h=e(188),o=(0,n.__importStar)(e(185)),l=(0,n.__importStar)(e(18)),c=e(72);class y extends h.AreaView{_index_data(e){const{min:t,max:s}=Math,{data_size:i}=this;for(let r=0;r=0;t--)e.lineTo(i[t],n[t]);e.closePath(),this.visuals.fill.apply(e),this.visuals.hatch.apply(e)}scenterxy(e){return[this.sx[e],(this.sy1[e]+this.sy2[e])/2]}_hit_point(e){const t=this.sx.length,s=new _.ScreenArray(2*t),i=new _.ScreenArray(2*t);for(let e=0,r=t;e({x:[l.XCoordinateSpec,{field:\"x\"}],y1:[l.YCoordinateSpec,{field:\"y1\"}],y2:[l.YCoordinateSpec,{field:\"y2\"}]})))},\n", - " function _(e,i,s,t,n){var c;t();const o=e(53),r=e(24),u=e(191),_=e(70);class a extends o.Model{constructor(e){super(e)}initialize(){super.initialize(),this.compute_indices()}connect_signals(){super.connect_signals(),this.connect(this.properties.filters.change,(()=>this.compute_indices()));const e=()=>{const e=()=>this.compute_indices();null!=this.source&&(this.connect(this.source.change,e),this.source instanceof _.ColumnarDataSource&&(this.connect(this.source.streaming,e),this.connect(this.source.patching,e)))};let i=null!=this.source;i?e():this.connect(this.properties.source.change,(()=>{i||(e(),i=!0)}))}compute_indices(){var e;const{source:i}=this;if(null==i)return;const s=null!==(e=i.get_length())&&void 0!==e?e:1,t=r.Indices.all_set(s);for(const e of this.filters)t.intersect(e.compute_indices(i));this.indices=t,this._indices=[...t],this.indices_map_to_subset()}indices_map_to_subset(){this.indices_map={};for(let e=0;ethis._indices[e]))}convert_selection_to_subset(e){return e.map((e=>this.indices_map[e]))}convert_indices_from_subset(e){return e.map((e=>this._indices[e]))}}s.CDSView=a,c=a,a.__name__=\"CDSView\",c.define((({Array:e,Ref:i})=>({filters:[e(i(u.Filter)),[]],source:[i(_.ColumnarDataSource)]}))),c.internal((({Int:e,Dict:i,Ref:s,Nullable:t})=>({indices:[s(r.Indices)],indices_map:[i(e),{}],masked:[t(s(r.Indices)),null]})))},\n", - " function _(e,t,n,s,c){s();const o=e(53);class r extends o.Model{constructor(e){super(e)}}n.Filter=r,r.__name__=\"Filter\"},\n", - " function _(t,r,a,e,c){e(),c(\"BasicTickFormatter\",t(146).BasicTickFormatter),c(\"CategoricalTickFormatter\",t(142).CategoricalTickFormatter),c(\"DatetimeTickFormatter\",t(150).DatetimeTickFormatter),c(\"FuncTickFormatter\",t(193).FuncTickFormatter),c(\"LogTickFormatter\",t(163).LogTickFormatter),c(\"MercatorTickFormatter\",t(166).MercatorTickFormatter),c(\"NumeralTickFormatter\",t(194).NumeralTickFormatter),c(\"PrintfTickFormatter\",t(195).PrintfTickFormatter),c(\"TickFormatter\",t(131).TickFormatter)},\n", - " function _(t,e,n,s,r){var c;s();const i=t(131),a=t(13),u=t(34);class o extends i.TickFormatter{constructor(t){super(t)}get names(){return(0,a.keys)(this.args)}get values(){return(0,a.values)(this.args)}_make_func(){const t=(0,u.use_strict)(this.code);return new Function(\"tick\",\"index\",\"ticks\",...this.names,t)}doFormat(t,e){const n=this._make_func().bind({});return t.map(((t,e,s)=>`${n(t,e,s,...this.values)}`))}}n.FuncTickFormatter=o,c=o,o.__name__=\"FuncTickFormatter\",c.define((({Unknown:t,String:e,Dict:n})=>({args:[n(t),{}],code:[e,\"\"]})))},\n", - " function _(r,n,t,o,e){o();var a;const u=(0,r(1).__importStar)(r(153)),c=r(131),i=r(20);class s extends c.TickFormatter{constructor(r){super(r)}get _rounding_fn(){switch(this.rounding){case\"round\":case\"nearest\":return Math.round;case\"floor\":case\"rounddown\":return Math.floor;case\"ceil\":case\"roundup\":return Math.ceil}}doFormat(r,n){const{format:t,language:o,_rounding_fn:e}=this;return r.map((r=>u.format(r,t,o,e)))}}t.NumeralTickFormatter=s,a=s,s.__name__=\"NumeralTickFormatter\",a.define((({String:r})=>({format:[r,\"0,0\"],language:[r,\"en\"],rounding:[i.RoundingFunction,\"round\"]})))},\n", - " function _(t,r,n,o,a){var e;o();const i=t(131),s=t(152);class c extends i.TickFormatter{constructor(t){super(t)}doFormat(t,r){return t.map((t=>(0,s.sprintf)(this.format,t)))}}n.PrintfTickFormatter=c,e=c,c.__name__=\"PrintfTickFormatter\",e.define((({String:t})=>({format:[t,\"%s\"]})))},\n", - " function _(r,o,a,p,e){p(),e(\"CategoricalColorMapper\",r(197).CategoricalColorMapper),e(\"CategoricalMarkerMapper\",r(199).CategoricalMarkerMapper),e(\"CategoricalPatternMapper\",r(200).CategoricalPatternMapper),e(\"ContinuousColorMapper\",r(172).ContinuousColorMapper),e(\"ColorMapper\",r(173).ColorMapper),e(\"LinearColorMapper\",r(201).LinearColorMapper),e(\"LogColorMapper\",r(202).LogColorMapper),e(\"ScanningColorMapper\",r(171).ScanningColorMapper),e(\"EqHistColorMapper\",r(203).EqHistColorMapper)},\n", - " function _(t,o,r,a,e){var c;a();const s=t(198),l=t(173),n=t(67);class _ extends l.ColorMapper{constructor(t){super(t)}_v_compute(t,o,r,{nan_color:a}){(0,s.cat_v_compute)(t,this.factors,r,o,this.start,this.end,a)}}r.CategoricalColorMapper=_,c=_,_.__name__=\"CategoricalColorMapper\",c.define((({Number:t,Nullable:o})=>({factors:[n.FactorSeq],start:[t,0],end:[o(t),null]})))},\n", - " function _(n,t,e,l,i){l();const c=n(12),u=n(8);function f(n,t){if(n.length!=t.length)return!1;for(let e=0,l=n.length;ef(n,h)))),s=_<0||_>=e.length?r:e[_],l[g]=s}}},\n", - " function _(e,r,a,t,s){var c;t();const l=e(198),n=e(67),u=e(174),o=e(20);class p extends u.Mapper{constructor(e){super(e)}v_compute(e){const r=new Array(e.length);return(0,l.cat_v_compute)(e,this.factors,this.markers,r,this.start,this.end,this.default_value),r}}a.CategoricalMarkerMapper=p,c=p,p.__name__=\"CategoricalMarkerMapper\",c.define((({Number:e,Array:r,Nullable:a})=>({factors:[n.FactorSeq],markers:[r(o.MarkerType)],start:[e,0],end:[a(e),null],default_value:[o.MarkerType,\"circle\"]})))},\n", - " function _(t,e,a,r,n){var s;r();const c=t(198),l=t(67),p=t(174),u=t(20);class o extends p.Mapper{constructor(t){super(t)}v_compute(t){const e=new Array(t.length);return(0,c.cat_v_compute)(t,this.factors,this.patterns,e,this.start,this.end,this.default_value),e}}a.CategoricalPatternMapper=o,s=o,o.__name__=\"CategoricalPatternMapper\",s.define((({Number:t,Array:e,Nullable:a})=>({factors:[l.FactorSeq],patterns:[e(u.HatchPatternType)],start:[t,0],end:[a(t),null],default_value:[u.HatchPatternType,\" \"]})))},\n", - " function _(n,r,o,t,a){t();const e=n(172),i=n(12);class s extends e.ContinuousColorMapper{constructor(n){super(n)}scan(n,r){const o=null!=this.low?this.low:(0,i.min)(n),t=null!=this.high?this.high:(0,i.max)(n);return{max:t,min:o,norm_factor:1/(t-o),normed_interval:1/r}}cmap(n,r,o,t,a){const e=r.length-1;if(n==a.max)return r[e];const i=(n-a.min)*a.norm_factor,s=Math.floor(i/a.normed_interval);return s<0?o:s>e?t:r[s]}}o.LinearColorMapper=s,s.__name__=\"LinearColorMapper\"},\n", - " function _(o,t,n,r,l){r();const a=o(172),s=o(12);class e extends a.ContinuousColorMapper{constructor(o){super(o)}scan(o,t){const n=null!=this.low?this.low:(0,s.min)(o),r=null!=this.high?this.high:(0,s.max)(o);return{max:r,min:n,scale:t/(Math.log(r)-Math.log(n))}}cmap(o,t,n,r,l){const a=t.length-1;if(o>l.max)return r;if(o==l.max)return t[a];if(oa&&(e=a),t[e]}}n.LogColorMapper=e,e.__name__=\"LogColorMapper\"},\n", - " function _(n,t,e,i,o){var s;i();const r=n(171),a=n(12),l=n(9),c=n(19);class h extends r.ScanningColorMapper{constructor(n){super(n)}scan(n,t){const e=null!=this.low?this.low:(0,a.min)(n),i=null!=this.high?this.high:(0,a.max)(n),o=this.bins,s=(0,l.linspace)(e,i,o+1),r=(0,a.bin_counts)(n,s),h=new Array(o);for(let n=0,t=s.length;nn/p));let m=t-1,f=[],M=0,_=2*t;for(;m!=t&&M<4&&0!=m;){const n=_/m;if(n>1e3)break;_=Math.round(Math.max(t*n,t));const e=(0,l.range)(0,_),i=(0,a.map)(u,(n=>n*(_-1)));f=(0,a.interpolate)(e,i,h);m=(0,l.uniq)(f).length-1,M++}if(0==m){f=[e,i];for(let n=0;n({bins:[n,65536]})))},\n", - " function _(a,e,l,c,n){c(),n(\"CategoricalScale\",a(62).CategoricalScale),n(\"ContinuousScale\",a(60).ContinuousScale),n(\"LinearScale\",a(59).LinearScale),n(\"LinearInterpolationScale\",a(205).LinearInterpolationScale),n(\"LogScale\",a(61).LogScale),n(\"Scale\",a(55).Scale)},\n", - " function _(e,r,n,t,a){var i;t();const s=e(55),o=e(59),c=e(12);class _ extends s.Scale{constructor(e){super(e)}connect_signals(){super.connect_signals();const{source_range:e,target_range:r}=this.properties;this.on_change([e,r],(()=>{this.linear_scale=new o.LinearScale({source_range:this.source_range,target_range:this.target_range})}))}get s_compute(){throw new Error(\"not implemented\")}get s_invert(){throw new Error(\"not implemented\")}compute(e){return e}v_compute(e){const{binning:r}=this,{start:n,end:t}=this.source_range,a=n,i=t,s=r.length,o=(t-n)/(s-1),_=new Float64Array(s);for(let e=0;e{if(ei)return i;const n=(0,c.left_edge_index)(e,r);if(-1==n)return a;if(n>=s-1)return i;const t=r[n],o=(e-t)/(r[n+1]-t),l=_[n];return l+o*(_[n+1]-l)}));return this.linear_scale.v_compute(l)}invert(e){return e}v_invert(e){return new Float64Array(e)}}n.LinearInterpolationScale=_,i=_,_.__name__=\"LinearInterpolationScale\",i.internal((({Arrayable:e,Ref:r})=>({binning:[e],linear_scale:[r(o.LinearScale),e=>new o.LinearScale({source_range:e.source_range,target_range:e.target_range})]})))},\n", - " function _(a,n,e,g,R){g(),R(\"DataRange\",a(64).DataRange),R(\"DataRange1d\",a(63).DataRange1d),R(\"FactorRange\",a(67).FactorRange),R(\"Range\",a(57).Range),R(\"Range1d\",a(58).Range1d)},\n", - " function _(a,o,i,t,e){t();var n=a(124);e(\"Sizeable\",n.Sizeable),e(\"SizingPolicy\",n.SizingPolicy);var c=a(125);e(\"Layoutable\",c.Layoutable),e(\"LayoutItem\",c.LayoutItem);var r=a(208);e(\"HStack\",r.HStack),e(\"VStack\",r.VStack);var l=a(209);e(\"Grid\",l.Grid),e(\"Row\",l.Row),e(\"Column\",l.Column);var S=a(210);e(\"ContentBox\",S.ContentBox),e(\"VariadicBox\",S.VariadicBox)},\n", - " function _(t,e,h,i,r){i();const n=t(125),o=t(65);class s extends n.Layoutable{constructor(){super(...arguments),this.children=[]}*[Symbol.iterator](){yield*this.children}}h.Stack=s,s.__name__=\"Stack\";class c extends s{_measure(t){let e=0,h=0;for(const t of this.children){const i=t.measure({width:0,height:0});e+=i.width,h=Math.max(h,i.height)}return{width:e,height:h}}_set_geometry(t,e){super._set_geometry(t,e);const h=this.absolute?t.top:0;let i=this.absolute?t.left:0;const{height:r}=t;for(const t of this.children){const{width:e}=t.measure({width:0,height:0});t.set_geometry(new o.BBox({left:i,width:e,top:h,height:r})),i+=e}}}h.HStack=c,c.__name__=\"HStack\";class a extends s{_measure(t){let e=0,h=0;for(const t of this.children){const i=t.measure({width:0,height:0});e=Math.max(e,i.width),h+=i.height}return{width:e,height:h}}_set_geometry(t,e){super._set_geometry(t,e);const h=this.absolute?t.left:0;let i=this.absolute?t.top:0;const{width:r}=t;for(const t of this.children){const{height:e}=t.measure({width:0,height:0});t.set_geometry(new o.BBox({top:i,height:e,left:h,width:r})),i+=e}}}h.VStack=a,a.__name__=\"VStack\";class l extends n.Layoutable{constructor(){super(...arguments),this.children=[]}*[Symbol.iterator](){yield*this.children}_measure(t){const{width_policy:e,height_policy:h}=this.sizing,{min:i,max:r}=Math;let n=0,o=0;for(const e of this.children){const{width:h,height:i}=e.measure(t);n=r(n,h),o=r(o,i)}return{width:(()=>{const{width:h}=this.sizing;if(t.width==1/0)return\"fixed\"==e&&null!=h?h:n;switch(e){case\"fixed\":return null!=h?h:n;case\"min\":return n;case\"fit\":return null!=h?i(t.width,h):t.width;case\"max\":return null!=h?r(t.width,h):t.width}})(),height:(()=>{const{height:e}=this.sizing;if(t.height==1/0)return\"fixed\"==h&&null!=e?e:o;switch(h){case\"fixed\":return null!=e?e:o;case\"min\":return o;case\"fit\":return null!=e?i(t.height,e):t.height;case\"max\":return null!=e?r(t.height,e):t.height}})()}}_set_geometry(t,e){super._set_geometry(t,e);const h=this.absolute?t:t.relative(),{left:i,right:r,top:n,bottom:s}=h,c=Math.round(h.vcenter),a=Math.round(h.hcenter);for(const e of this.children){const{margin:h,halign:l,valign:d}=e.sizing,{width:u,height:g,inner:_}=e.measure(t),w=(()=>{switch(`${d}_${l}`){case\"start_start\":return new o.BBox({left:i+h.left,top:n+h.top,width:u,height:g});case\"start_center\":return new o.BBox({hcenter:a,top:n+h.top,width:u,height:g});case\"start_end\":return new o.BBox({right:r-h.right,top:n+h.top,width:u,height:g});case\"center_start\":return new o.BBox({left:i+h.left,vcenter:c,width:u,height:g});case\"center_center\":return new o.BBox({hcenter:a,vcenter:c,width:u,height:g});case\"center_end\":return new o.BBox({right:r-h.right,vcenter:c,width:u,height:g});case\"end_start\":return new o.BBox({left:i+h.left,bottom:s-h.bottom,width:u,height:g});case\"end_center\":return new o.BBox({hcenter:a,bottom:s-h.bottom,width:u,height:g});case\"end_end\":return new o.BBox({right:r-h.right,bottom:s-h.bottom,width:u,height:g})}})(),m=null==_?w:new o.BBox({left:w.left+_.left,top:w.top+_.top,right:w.right-_.right,bottom:w.bottom-_.bottom});e.set_geometry(w,m)}}}h.NodeLayout=l,l.__name__=\"NodeLayout\"},\n", - " function _(t,i,s,e,o){e();const n=t(124),l=t(125),r=t(8),h=t(65),c=t(9),{max:a,round:g}=Math;class p{constructor(t){this.def=t,this._map=new Map}get(t){let i=this._map.get(t);return void 0===i&&(i=this.def(),this._map.set(t,i)),i}apply(t,i){const s=this.get(t);this._map.set(t,i(s))}}p.__name__=\"DefaultMap\";class f{constructor(){this._items=[],this._nrows=0,this._ncols=0}get nrows(){return this._nrows}get ncols(){return this._ncols}add(t,i){const{r1:s,c1:e}=t;this._nrows=a(this._nrows,s+1),this._ncols=a(this._ncols,e+1),this._items.push({span:t,data:i})}at(t,i){return this._items.filter((({span:s})=>s.r0<=t&&t<=s.r1&&s.c0<=i&&i<=s.c1)).map((({data:t})=>t))}row(t){return this._items.filter((({span:i})=>i.r0<=t&&t<=i.r1)).map((({data:t})=>t))}col(t){return this._items.filter((({span:i})=>i.c0<=t&&t<=i.c1)).map((({data:t})=>t))}foreach(t){for(const{span:i,data:s}of this._items)t(i,s)}map(t){const i=new f;for(const{span:s,data:e}of this._items)i.add(s,t(s,e));return i}}f.__name__=\"Container\";class _ extends l.Layoutable{constructor(t=[]){super(),this.items=t,this.rows=\"auto\",this.cols=\"auto\",this.spacing=0}*[Symbol.iterator](){for(const{layout:t}of this.items)yield t}is_width_expanding(){if(super.is_width_expanding())return!0;if(\"fixed\"==this.sizing.width_policy)return!1;const{cols:t}=this._state;return(0,c.some)(t,(t=>\"max\"==t.policy))}is_height_expanding(){if(super.is_height_expanding())return!0;if(\"fixed\"==this.sizing.height_policy)return!1;const{rows:t}=this._state;return(0,c.some)(t,(t=>\"max\"==t.policy))}_init(){var t,i,s,e;super._init();const o=new f;for(const{layout:t,row:i,col:s,row_span:e,col_span:n}of this.items)if(t.sizing.visible){const l=i,r=s,h=i+(null!=e?e:1)-1,c=s+(null!=n?n:1)-1;o.add({r0:l,c0:r,r1:h,c1:c},t)}const{nrows:n,ncols:l}=o,h=new Array(n);for(let s=0;s{var t;const i=(0,r.isPlainObject)(this.rows)?null!==(t=this.rows[s])&&void 0!==t?t:this.rows[\"*\"]:this.rows;return null==i?{policy:\"auto\"}:(0,r.isNumber)(i)?{policy:\"fixed\",height:i}:(0,r.isString)(i)?{policy:i}:i})(),n=null!==(t=e.align)&&void 0!==t?t:\"auto\";if(\"fixed\"==e.policy)h[s]={policy:\"fixed\",height:e.height,align:n};else if(\"min\"==e.policy)h[s]={policy:\"min\",align:n};else if(\"fit\"==e.policy||\"max\"==e.policy)h[s]={policy:e.policy,flex:null!==(i=e.flex)&&void 0!==i?i:1,align:n};else{if(\"auto\"!=e.policy)throw new Error(\"unrechable\");(0,c.some)(o.row(s),(t=>t.is_height_expanding()))?h[s]={policy:\"max\",flex:1,align:n}:h[s]={policy:\"min\",align:n}}}const a=new Array(l);for(let t=0;t{var i;const s=(0,r.isPlainObject)(this.cols)?null!==(i=this.cols[t])&&void 0!==i?i:this.cols[\"*\"]:this.cols;return null==s?{policy:\"auto\"}:(0,r.isNumber)(s)?{policy:\"fixed\",width:s}:(0,r.isString)(s)?{policy:s}:s})(),n=null!==(s=i.align)&&void 0!==s?s:\"auto\";if(\"fixed\"==i.policy)a[t]={policy:\"fixed\",width:i.width,align:n};else if(\"min\"==i.policy)a[t]={policy:\"min\",align:n};else if(\"fit\"==i.policy||\"max\"==i.policy)a[t]={policy:i.policy,flex:null!==(e=i.flex)&&void 0!==e?e:1,align:n};else{if(\"auto\"!=i.policy)throw new Error(\"unrechable\");(0,c.some)(o.col(t),(t=>t.is_width_expanding()))?a[t]={policy:\"max\",flex:1,align:n}:a[t]={policy:\"min\",align:n}}}const[g,p]=(0,r.isNumber)(this.spacing)?[this.spacing,this.spacing]:this.spacing;this._state={items:o,nrows:n,ncols:l,rows:h,cols:a,rspacing:g,cspacing:p}}_measure_totals(t,i){const{nrows:s,ncols:e,rspacing:o,cspacing:n}=this._state;return{height:(0,c.sum)(t)+(s-1)*o,width:(0,c.sum)(i)+(e-1)*n}}_measure_cells(t){const{items:i,nrows:s,ncols:e,rows:o,cols:l,rspacing:r,cspacing:h}=this._state,c=new Array(s);for(let t=0;t{const{r0:e,c0:f,r1:d,c1:u}=i,w=(d-e)*r,m=(u-f)*h;let y=0;for(let i=e;i<=d;i++)y+=t(i,f).height;y+=w;let x=0;for(let i=f;i<=u;i++)x+=t(e,i).width;x+=m;const b=s.measure({width:x,height:y});_.add(i,{layout:s,size_hint:b});const z=new n.Sizeable(b).grow_by(s.sizing.margin);z.height-=w,z.width-=m;const v=[];for(let t=e;t<=d;t++){const i=o[t];\"fixed\"==i.policy?z.height-=i.height:v.push(t)}if(z.height>0){const t=g(z.height/v.length);for(const i of v)c[i]=a(c[i],t)}const j=[];for(let t=f;t<=u;t++){const i=l[t];\"fixed\"==i.policy?z.width-=i.width:j.push(t)}if(z.width>0){const t=g(z.width/j.length);for(const i of j)p[i]=a(p[i],t)}}));return{size:this._measure_totals(c,p),row_heights:c,col_widths:p,size_hints:_}}_measure_grid(t){const{nrows:i,ncols:s,rows:e,cols:o,rspacing:n,cspacing:l}=this._state,r=this._measure_cells(((t,i)=>{const s=e[t],n=o[i];return{width:\"fixed\"==n.policy?n.width:1/0,height:\"fixed\"==s.policy?s.height:1/0}}));let h;h=\"fixed\"==this.sizing.height_policy&&null!=this.sizing.height?this.sizing.height:t.height!=1/0&&this.is_height_expanding()?t.height:r.size.height;let c,p=0;for(let t=0;t0)for(let t=0;ti?i:e,t--}}}c=\"fixed\"==this.sizing.width_policy&&null!=this.sizing.width?this.sizing.width:t.width!=1/0&&this.is_width_expanding()?t.width:r.size.width;let f=0;for(let t=0;t0)for(let t=0;ts?s:o,t--}}}const{row_heights:_,col_widths:d,size_hints:u}=this._measure_cells(((t,i)=>({width:r.col_widths[i],height:r.row_heights[t]})));return{size:this._measure_totals(_,d),row_heights:_,col_widths:d,size_hints:u}}_measure(t){const{size:i}=this._measure_grid(t);return i}_set_geometry(t,i){super._set_geometry(t,i);const{nrows:s,ncols:e,rspacing:o,cspacing:n}=this._state,{row_heights:l,col_widths:r,size_hints:c}=this._measure_grid(t),f=this._state.rows.map(((t,i)=>Object.assign(Object.assign({},t),{top:0,height:l[i],get bottom(){return this.top+this.height}}))),_=this._state.cols.map(((t,i)=>Object.assign(Object.assign({},t),{left:0,width:r[i],get right(){return this.left+this.width}}))),d=c.map(((t,i)=>Object.assign(Object.assign({},i),{outer:new h.BBox,inner:new h.BBox})));for(let i=0,e=this.absolute?t.top:0;i{const{layout:r,size_hint:c}=l,{sizing:a}=r,{width:p,height:d}=c,u=function(t,i){let s=(i-t)*n;for(let e=t;e<=i;e++)s+=_[e].width;return s}(i,e),w=function(t,i){let s=(i-t)*o;for(let e=t;e<=i;e++)s+=f[e].height;return s}(t,s),m=i==e&&\"auto\"!=_[i].align?_[i].align:a.halign,y=t==s&&\"auto\"!=f[t].align?f[t].align:a.valign;let x=_[i].left;\"start\"==m?x+=a.margin.left:\"center\"==m?x+=g((u-p)/2):\"end\"==m&&(x+=u-a.margin.right-p);let b=f[t].top;\"start\"==y?b+=a.margin.top:\"center\"==y?b+=g((w-d)/2):\"end\"==y&&(b+=w-a.margin.bottom-d),l.outer=new h.BBox({left:x,top:b,width:p,height:d})}));const u=f.map((()=>({start:new p((()=>0)),end:new p((()=>0))}))),w=_.map((()=>({start:new p((()=>0)),end:new p((()=>0))})));d.foreach((({r0:t,c0:i,r1:s,c1:e},{size_hint:o,outer:n})=>{const{inner:l}=o;null!=l&&(u[t].start.apply(n.top,(t=>a(t,l.top))),u[s].end.apply(f[s].bottom-n.bottom,(t=>a(t,l.bottom))),w[i].start.apply(n.left,(t=>a(t,l.left))),w[e].end.apply(_[e].right-n.right,(t=>a(t,l.right))))})),d.foreach((({r0:t,c0:i,r1:s,c1:e},o)=>{const{size_hint:n,outer:l}=o,r=t=>{const i=this.absolute?l:l.relative(),s=i.left+t.left,e=i.top+t.top,o=i.right-t.right,n=i.bottom-t.bottom;return new h.BBox({left:s,top:e,right:o,bottom:n})};if(null!=n.inner){let h=r(n.inner);if(!1!==n.align){const o=u[t].start.get(l.top),n=u[s].end.get(f[s].bottom-l.bottom),c=w[i].start.get(l.left),a=w[e].end.get(_[e].right-l.right);try{h=r({top:o,bottom:n,left:c,right:a})}catch(t){}}o.inner=h}else o.inner=l})),d.foreach(((t,{layout:i,outer:s,inner:e})=>{i.set_geometry(s,e)}))}}s.Grid=_,_.__name__=\"Grid\";class d extends _{constructor(t){super(),this.items=t.map(((t,i)=>({layout:t,row:0,col:i}))),this.rows=\"fit\"}}s.Row=d,d.__name__=\"Row\";class u extends _{constructor(t){super(),this.items=t.map(((t,i)=>({layout:t,row:i,col:0}))),this.cols=\"fit\"}}s.Column=u,u.__name__=\"Column\"},\n", - " function _(e,t,s,n,i){n();const a=e(125),c=e(124),o=e(43);class r extends a.ContentLayoutable{constructor(e){super(),this.content_size=(0,o.unsized)(e,(()=>new c.Sizeable((0,o.size)(e))))}_content_size(){return this.content_size}}s.ContentBox=r,r.__name__=\"ContentBox\";class _ extends a.Layoutable{constructor(e){super(),this.el=e}_measure(e){const t=new c.Sizeable(e).bounded_to(this.sizing.size);return(0,o.sized)(this.el,t,(()=>{const e=new c.Sizeable((0,o.content_size)(this.el)),{border:t,padding:s}=(0,o.extents)(this.el);return e.grow_by(t).grow_by(s).map(Math.ceil)}))}}s.VariadicBox=_,_.__name__=\"VariadicBox\";class h extends _{constructor(e){super(e),this._cache=new Map}_measure(e){const{width:t,height:s}=e,n=`${t},${s}`;let i=this._cache.get(n);return null==i&&(i=super._measure(e),this._cache.set(n,i)),i}invalidate_cache(){this._cache.clear()}}s.CachedVariadicBox=h,h.__name__=\"CachedVariadicBox\"},\n", - " function _(t,e,i,h,o){h();const s=t(124),r=t(125),n=t(65);class g extends r.Layoutable{constructor(){super(...arguments),this.min_border={left:0,top:0,right:0,bottom:0},this.padding={left:0,top:0,right:0,bottom:0}}*[Symbol.iterator](){yield this.top_panel,yield this.bottom_panel,yield this.left_panel,yield this.right_panel,yield this.center_panel}_measure(t){t=new s.Sizeable({width:\"fixed\"==this.sizing.width_policy||t.width==1/0?this.sizing.width:t.width,height:\"fixed\"==this.sizing.height_policy||t.height==1/0?this.sizing.height:t.height});const e=this.left_panel.measure({width:0,height:t.height}),i=Math.max(e.width,this.min_border.left)+this.padding.left,h=this.right_panel.measure({width:0,height:t.height}),o=Math.max(h.width,this.min_border.right)+this.padding.right,r=this.top_panel.measure({width:t.width,height:0}),n=Math.max(r.height,this.min_border.top)+this.padding.top,g=this.bottom_panel.measure({width:t.width,height:0}),a=Math.max(g.height,this.min_border.bottom)+this.padding.bottom,d=new s.Sizeable(t).shrink_by({left:i,right:o,top:n,bottom:a}),l=this.center_panel.measure(d);return{width:i+l.width+o,height:n+l.height+a,inner:{left:i,right:o,top:n,bottom:a},align:(()=>{const{width_policy:t,height_policy:e}=this.center_panel.sizing;return\"fixed\"!=t&&\"fixed\"!=e})()}}_set_geometry(t,e){super._set_geometry(t,e),this.center_panel.set_geometry(e);const i=this.left_panel.measure({width:0,height:t.height}),h=this.right_panel.measure({width:0,height:t.height}),o=this.top_panel.measure({width:t.width,height:0}),s=this.bottom_panel.measure({width:t.width,height:0}),{left:r,top:g,right:a,bottom:d}=e;this.top_panel.set_geometry(new n.BBox({left:r,right:a,bottom:g,height:o.height})),this.bottom_panel.set_geometry(new n.BBox({left:r,right:a,top:d,height:s.height})),this.left_panel.set_geometry(new n.BBox({top:g,bottom:d,right:r,width:i.width})),this.right_panel.set_geometry(new n.BBox({top:g,bottom:d,left:a,width:h.width}))}}i.BorderLayout=g,g.__name__=\"BorderLayout\"},\n", - " function _(t,e,i,s,l){s();const n=t(1);var o;const a=t(119),_=t(10),d=t(20),h=t(120),r=t(123),u=(0,n.__importStar)(t(48));class c extends a.TextAnnotationView{update_layout(){const{panel:t}=this;this.layout=null!=t?new r.SideLayout(t,(()=>this.get_size()),!1):void 0}_get_size(){const{text:t}=this.model,e=new h.TextBox({text:t}),{angle:i,angle_units:s}=this.model;e.angle=(0,_.resolve_angle)(i,s),e.visuals=this.visuals.text.values();const{width:l,height:n}=e.size();return{width:l,height:n}}_render(){const{angle:t,angle_units:e}=this.model,i=(0,_.resolve_angle)(t,e),s=null!=this.layout?this.layout:this.plot_view.frame,l=this.coordinates.x_scale,n=this.coordinates.y_scale;let o=\"data\"==this.model.x_units?l.compute(this.model.x):s.bbox.xview.compute(this.model.x),a=\"data\"==this.model.y_units?n.compute(this.model.y):s.bbox.yview.compute(this.model.y);o+=this.model.x_offset,a-=this.model.y_offset;(\"canvas\"==this.model.render_mode?this._canvas_text.bind(this):this._css_text.bind(this))(this.layer.ctx,this.model.text,o,a,i)}}i.LabelView=c,c.__name__=\"LabelView\";class x extends a.TextAnnotation{constructor(t){super(t)}}i.Label=x,o=x,x.__name__=\"Label\",o.prototype.default_view=c,o.mixins([u.Text,[\"border_\",u.Line],[\"background_\",u.Fill]]),o.define((({Number:t,String:e,Angle:i})=>({x:[t],x_units:[d.SpatialUnits,\"data\"],y:[t],y_units:[d.SpatialUnits,\"data\"],text:[e,\"\"],angle:[i,0],angle_units:[d.AngleUnits,\"rad\"],x_offset:[t,0],y_offset:[t,0]}))),o.override({background_fill_color:null,border_line_color:null})},\n", - " function _(t,e,s,i,l){i();const o=t(1);var a;const r=t(69),n=(0,o.__importStar)(t(48)),d=t(20),_=t(43),c=t(120),h=(0,o.__importStar)(t(18)),u=t(11);class v extends r.DataAnnotationView{set_data(t){var e;if(super.set_data(t),null===(e=this.els)||void 0===e||e.forEach((t=>(0,_.remove)(t))),\"css\"==this.model.render_mode){const t=this.els=[...this.text].map((()=>(0,_.div)({style:{display:\"none\"}})));for(const e of t)this.plot_view.canvas_view.add_overlay(e)}else delete this.els}remove(){var t;null===(t=this.els)||void 0===t||t.forEach((t=>(0,_.remove)(t))),super.remove()}_rerender(){\"css\"==this.model.render_mode?this.render():this.request_render()}map_data(){const{x_scale:t,y_scale:e}=this.coordinates,s=null!=this.layout?this.layout:this.plot_view.frame;this.sx=\"data\"==this.model.x_units?t.v_compute(this._x):s.bbox.xview.v_compute(this._x),this.sy=\"data\"==this.model.y_units?e.v_compute(this._y):s.bbox.yview.v_compute(this._y)}paint(){const t=\"canvas\"==this.model.render_mode?this._v_canvas_text.bind(this):this._v_css_text.bind(this),{ctx:e}=this.layer;for(let s=0,i=this.text.length;s{switch(this.visuals.text.text_align.get(e)){case\"left\":return[\"left\",\"0%\"];case\"center\":return[\"center\",\"-50%\"];case\"right\":return[\"right\",\"-100%\"]}})(),[d,c]=(()=>{switch(this.visuals.text.text_baseline.get(e)){case\"top\":return[\"top\",\"0%\"];case\"middle\":return[\"center\",\"-50%\"];case\"bottom\":return[\"bottom\",\"-100%\"];default:return[\"center\",\"-50%\"]}})();let h=`translate(${n}, ${c})`;o&&(h+=`rotate(${o}rad)`),a.style.transformOrigin=`${r} ${d}`,a.style.transform=h,this.layout,this.visuals.background_fill.doit&&(this.visuals.background_fill.set_vectorize(t,e),a.style.backgroundColor=t.fillStyle),this.visuals.border_line.doit&&(this.visuals.border_line.set_vectorize(t,e),a.style.borderStyle=t.lineDash.length<2?\"solid\":\"dashed\",a.style.borderWidth=`${t.lineWidth}px`,a.style.borderColor=t.strokeStyle),(0,_.display)(a)}}s.LabelSetView=v,v.__name__=\"LabelSetView\";class x extends r.DataAnnotation{constructor(t){super(t)}}s.LabelSet=x,a=x,x.__name__=\"LabelSet\",a.prototype.default_view=v,a.mixins([n.TextVector,[\"border_\",n.LineVector],[\"background_\",n.FillVector]]),a.define((()=>({x:[h.XCoordinateSpec,{field:\"x\"}],y:[h.YCoordinateSpec,{field:\"y\"}],x_units:[d.SpatialUnits,\"data\"],y_units:[d.SpatialUnits,\"data\"],text:[h.StringSpec,{field:\"text\"}],angle:[h.AngleSpec,0],x_offset:[h.NumberSpec,{value:0}],y_offset:[h.NumberSpec,{value:0}],render_mode:[d.RenderMode,\"canvas\"]}))),a.override({background_fill_color:null,border_line_color:null})},\n", - " function _(t,e,i,l,s){l();const n=t(1);var o;const h=t(40),a=t(215),_=t(20),r=(0,n.__importStar)(t(48)),d=t(15),c=t(123),g=t(121),m=t(65),b=t(9),f=t(8),u=t(11);class x extends h.AnnotationView{update_layout(){const{panel:t}=this;this.layout=null!=t?new c.SideLayout(t,(()=>this.get_size())):void 0}cursor(t,e){return\"none\"==this.model.click_policy?null:\"pointer\"}get legend_padding(){return null!=this.model.border_line_color?this.model.padding:0}connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.request_render())),this.connect(this.model.item_change,(()=>this.request_render()))}compute_legend_bbox(){const t=this.model.get_legend_names(),{glyph_height:e,glyph_width:i}=this.model,{label_height:l,label_width:s}=this.model;this.max_label_height=(0,b.max)([(0,g.font_metrics)(this.visuals.label_text.font_value()).height,l,e]);const{ctx:n}=this.layer;n.save(),this.visuals.label_text.set_value(n),this.text_widths=new Map;for(const e of t)this.text_widths.set(e,(0,b.max)([n.measureText(e).width,s]));this.visuals.title_text.set_value(n),this.title_height=this.model.title?(0,g.font_metrics)(this.visuals.title_text.font_value()).height+this.model.title_standoff:0,this.title_width=this.model.title?n.measureText(this.model.title).width:0,n.restore();const o=Math.max((0,b.max)([...this.text_widths.values()]),0),h=this.model.margin,{legend_padding:a}=this,_=this.model.spacing,{label_standoff:r}=this.model;let d,c;if(\"vertical\"==this.model.orientation)d=t.length*this.max_label_height+Math.max(t.length-1,0)*_+2*a+this.title_height,c=(0,b.max)([o+i+r+2*a,this.title_width+2*a]);else{let e=2*a+Math.max(t.length-1,0)*_;for(const[,t]of this.text_widths)e+=(0,b.max)([t,s])+i+r;c=(0,b.max)([this.title_width+2*a,e]),d=this.max_label_height+this.title_height+2*a}const x=null!=this.layout?this.layout:this.plot_view.frame,[p,w]=x.bbox.ranges,{location:v}=this.model;let y,k;if((0,f.isString)(v))switch(v){case\"top_left\":y=p.start+h,k=w.start+h;break;case\"top\":case\"top_center\":y=(p.end+p.start)/2-c/2,k=w.start+h;break;case\"top_right\":y=p.end-h-c,k=w.start+h;break;case\"bottom_right\":y=p.end-h-c,k=w.end-h-d;break;case\"bottom\":case\"bottom_center\":y=(p.end+p.start)/2-c/2,k=w.end-h-d;break;case\"bottom_left\":y=p.start+h,k=w.end-h-d;break;case\"left\":case\"center_left\":y=p.start+h,k=(w.end+w.start)/2-d/2;break;case\"center\":case\"center_center\":y=(p.end+p.start)/2-c/2,k=(w.end+w.start)/2-d/2;break;case\"right\":case\"center_right\":y=p.end-h-c,k=(w.end+w.start)/2-d/2}else if((0,f.isArray)(v)&&2==v.length){const[t,e]=v;y=x.bbox.xview.compute(t),k=x.bbox.yview.compute(e)-d}else(0,u.unreachable)();return new m.BBox({left:y,top:k,width:c,height:d})}interactive_bbox(){return this.compute_legend_bbox()}interactive_hit(t,e){return this.interactive_bbox().contains(t,e)}on_hit(t,e){let i;const{glyph_width:l}=this.model,{legend_padding:s}=this,n=this.model.spacing,{label_standoff:o}=this.model;let h=i=s;const a=this.compute_legend_bbox(),_=\"vertical\"==this.model.orientation;for(const r of this.model.items){const d=r.get_labels_list_from_label_prop();for(const c of d){const d=a.x+h,g=a.y+i+this.title_height;let b,f;[b,f]=_?[a.width-2*s,this.max_label_height]:[this.text_widths.get(c)+l+o,this.max_label_height];if(new m.BBox({left:d,top:g,width:b,height:f}).contains(t,e)){switch(this.model.click_policy){case\"hide\":for(const t of r.renderers)t.visible=!t.visible;break;case\"mute\":for(const t of r.renderers)t.muted=!t.muted}return!0}_?i+=this.max_label_height+n:h+=this.text_widths.get(c)+l+o+n}}return!1}_render(){if(0==this.model.items.length)return;if(!(0,b.some)(this.model.items,(t=>t.visible)))return;for(const t of this.model.items)t.legend=this.model;const{ctx:t}=this.layer,e=this.compute_legend_bbox();t.save(),this._draw_legend_box(t,e),this._draw_legend_items(t,e),this._draw_title(t,e),t.restore()}_draw_legend_box(t,e){t.beginPath(),t.rect(e.x,e.y,e.width,e.height),this.visuals.background_fill.apply(t),this.visuals.border_line.apply(t)}_draw_legend_items(t,e){const{glyph_width:i,glyph_height:l}=this.model,{legend_padding:s}=this,n=this.model.spacing,{label_standoff:o}=this.model;let h=s,a=s;const _=\"vertical\"==this.model.orientation;for(const r of this.model.items){if(!r.visible)continue;const d=r.get_labels_list_from_label_prop(),c=r.get_field_from_label_prop();if(0==d.length)continue;const g=(()=>{switch(this.model.click_policy){case\"none\":return!0;case\"hide\":return(0,b.every)(r.renderers,(t=>t.visible));case\"mute\":return(0,b.every)(r.renderers,(t=>!t.muted))}})();for(const m of d){const d=e.x+h,b=e.y+a+this.title_height,f=d+i,u=b+l;_?a+=this.max_label_height+n:h+=this.text_widths.get(m)+i+o+n,this.visuals.label_text.set_value(t),t.fillText(m,f+o,b+this.max_label_height/2);for(const e of r.renderers){const i=this.plot_view.renderer_view(e);null==i||i.draw_legend(t,d,f,b,u,c,m,r.index)}if(!g){let l,n;[l,n]=_?[e.width-2*s,this.max_label_height]:[this.text_widths.get(m)+i+o,this.max_label_height],t.beginPath(),t.rect(d,b,l,n),this.visuals.inactive_fill.set_value(t),t.fill()}}}}_draw_title(t,e){const{title:i}=this.model;i&&this.visuals.title_text.doit&&(t.save(),t.translate(e.x0,e.y0+this.title_height),this.visuals.title_text.set_value(t),t.fillText(i,this.legend_padding,this.legend_padding-this.model.title_standoff),t.restore())}_get_size(){const{width:t,height:e}=this.compute_legend_bbox();return{width:t+2*this.model.margin,height:e+2*this.model.margin}}}i.LegendView=x,x.__name__=\"LegendView\";class p extends h.Annotation{constructor(t){super(t)}initialize(){super.initialize(),this.item_change=new d.Signal0(this,\"item_change\")}get_legend_names(){const t=[];for(const e of this.items){const i=e.get_labels_list_from_label_prop();t.push(...i)}return t}}i.Legend=p,o=p,p.__name__=\"Legend\",o.prototype.default_view=x,o.mixins([[\"label_\",r.Text],[\"title_\",r.Text],[\"inactive_\",r.Fill],[\"border_\",r.Line],[\"background_\",r.Fill]]),o.define((({Number:t,String:e,Array:i,Tuple:l,Or:s,Ref:n,Nullable:o})=>({orientation:[_.Orientation,\"vertical\"],location:[s(_.LegendLocation,l(t,t)),\"top_right\"],title:[o(e),null],title_standoff:[t,5],label_standoff:[t,5],glyph_height:[t,20],glyph_width:[t,20],label_height:[t,20],label_width:[t,20],margin:[t,10],padding:[t,10],spacing:[t,3],items:[i(n(a.LegendItem)),[]],click_policy:[_.LegendClickPolicy,\"none\"]}))),o.override({border_line_color:\"#e5e5e5\",border_line_alpha:.5,border_line_width:1,background_fill_color:\"#ffffff\",background_fill_alpha:.95,inactive_fill_color:\"white\",inactive_fill_alpha:.7,label_text_font_size:\"13px\",label_text_baseline:\"middle\",title_text_font_size:\"13px\",title_text_font_style:\"italic\"})},\n", - " function _(e,r,l,n,t){n();const i=e(1);var s;const o=e(53),a=e(175),_=e(70),u=e(216),d=(0,i.__importStar)(e(18)),c=e(19),f=e(9);class h extends o.Model{constructor(e){super(e)}_check_data_sources_on_renderers(){if(null!=this.get_field_from_label_prop()){if(this.renderers.length<1)return!1;const e=this.renderers[0].data_source;if(null!=e)for(const r of this.renderers)if(r.data_source!=e)return!1}return!0}_check_field_label_on_data_source(){const e=this.get_field_from_label_prop();if(null!=e){if(this.renderers.length<1)return!1;const r=this.renderers[0].data_source;if(null!=r&&!(0,f.includes)(r.columns(),e))return!1}return!0}initialize(){super.initialize(),this.legend=null,this.connect(this.change,(()=>{var e;return null===(e=this.legend)||void 0===e?void 0:e.item_change.emit()}));this._check_data_sources_on_renderers()||c.logger.error(\"Non matching data sources on legend item renderers\");this._check_field_label_on_data_source()||c.logger.error(`Bad column name on label: ${this.label}`)}get_field_from_label_prop(){const{label:e}=this;return(0,u.isField)(e)?e.field:null}get_labels_list_from_label_prop(){if(!this.visible)return[];if((0,u.isValue)(this.label)){const{value:e}=this.label;return null!=e?[e]:[]}const e=this.get_field_from_label_prop();if(null!=e){let r;if(!this.renderers[0]||null==this.renderers[0].data_source)return[\"No source found\"];if(r=this.renderers[0].data_source,r instanceof _.ColumnarDataSource){const l=r.get_column(e);return null!=l?(0,f.uniq)(Array.from(l)):[\"Invalid field\"]}}return[]}}l.LegendItem=h,s=h,h.__name__=\"LegendItem\",s.define((({Boolean:e,Int:r,Array:l,Ref:n,Nullable:t})=>({label:[d.NullStringSpec,null],renderers:[l(n(a.GlyphRenderer)),[]],index:[t(r),null],visible:[e,!0]})))},\n", - " function _(i,n,e,t,u){t();const c=i(8);e.isValue=function(i){return(0,c.isPlainObject)(i)&&\"value\"in i},e.isField=function(i){return(0,c.isPlainObject)(i)&&\"field\"in i},e.isExpr=function(i){return(0,c.isPlainObject)(i)&&\"expr\"in i}},\n", - " function _(t,n,e,s,i){s();const o=t(1);var a;const l=t(40),c=(0,o.__importStar)(t(48)),r=t(20);class _ extends l.AnnotationView{connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.request_render()))}_render(){const{xs:t,ys:n}=this.model;if(t.length!=n.length)return;const e=t.length;if(e<3)return;const{frame:s}=this.plot_view,{ctx:i}=this.layer,o=this.coordinates.x_scale,a=this.coordinates.y_scale,{screen:l}=this.model;function c(t,n,e,s){return l?t:\"data\"==n?e.v_compute(t):s.v_compute(t)}const r=c(t,this.model.xs_units,o,s.bbox.xview),_=c(n,this.model.ys_units,a,s.bbox.yview);i.beginPath();for(let t=0;t({xs:[n(t),[]],xs_units:[r.SpatialUnits,\"data\"],ys:[n(t),[]],ys_units:[r.SpatialUnits,\"data\"]}))),a.internal((({Boolean:t})=>({screen:[t,!1]}))),a.override({fill_color:\"#fff9ba\",fill_alpha:.4,line_color:\"#cccccc\",line_alpha:.3})},\n", - " function _(e,t,n,o,i){o();const s=e(1);var l;const r=e(40),c=(0,s.__importStar)(e(48));class a extends r.AnnotationView{connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.request_render()))}_render(){const{gradient:e,y_intercept:t}=this.model;if(null==e||null==t)return;const{frame:n}=this.plot_view,o=this.coordinates.x_scale,i=this.coordinates.y_scale;let s,l,r,c;if(0==e)s=i.compute(t),l=s,r=n.bbox.left,c=r+n.bbox.width;else{s=n.bbox.top,l=s+n.bbox.height;const a=(i.invert(s)-t)/e,_=(i.invert(l)-t)/e;r=o.compute(a),c=o.compute(_)}const{ctx:a}=this.layer;a.save(),a.beginPath(),this.visuals.line.set_value(a),a.moveTo(r,s),a.lineTo(c,l),a.stroke(),a.restore()}}n.SlopeView=a,a.__name__=\"SlopeView\";class _ extends r.Annotation{constructor(e){super(e)}}n.Slope=_,l=_,_.__name__=\"Slope\",l.prototype.default_view=a,l.mixins(c.Line),l.define((({Number:e,Nullable:t})=>({gradient:[t(e),null],y_intercept:[t(e),null]}))),l.override({line_color:\"black\"})},\n", - " function _(e,t,i,o,n){o();const s=e(1);var l;const a=e(40),r=(0,s.__importStar)(e(48)),c=e(20);class d extends a.AnnotationView{connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.plot_view.request_paint(this)))}_render(){const{location:e}=this.model;if(null==e)return;const{frame:t}=this.plot_view,i=this.coordinates.x_scale,o=this.coordinates.y_scale,n=(t,i)=>\"data\"==this.model.location_units?t.compute(e):this.model.for_hover?e:i.compute(e);let s,l,a,r;\"width\"==this.model.dimension?(a=n(o,t.bbox.yview),l=t.bbox.left,r=t.bbox.width,s=this.model.line_width):(a=t.bbox.top,l=n(i,t.bbox.xview),r=this.model.line_width,s=t.bbox.height);const{ctx:c}=this.layer;c.save(),c.beginPath(),this.visuals.line.set_value(c),c.moveTo(l,a),\"width\"==this.model.dimension?c.lineTo(l+r,a):c.lineTo(l,a+s),c.stroke(),c.restore()}}i.SpanView=d,d.__name__=\"SpanView\";class _ extends a.Annotation{constructor(e){super(e)}}i.Span=_,l=_,_.__name__=\"Span\",l.prototype.default_view=d,l.mixins(r.Line),l.define((({Number:e,Nullable:t})=>({render_mode:[c.RenderMode,\"canvas\"],location:[t(e),null],location_units:[c.SpatialUnits,\"data\"],dimension:[c.Dimension,\"width\"]}))),l.internal((({Boolean:e})=>({for_hover:[e,!1]}))),l.override({line_color:\"black\"})},\n", - " function _(i,e,t,o,l){var s;o();const a=i(40),_=i(221),n=i(113),r=i(43),h=i(123),b=i(65);class v extends a.AnnotationView{constructor(){super(...arguments),this._invalidate_toolbar=!0,this._previous_bbox=new b.BBox}update_layout(){this.layout=new h.SideLayout(this.panel,(()=>this.get_size()),!0)}initialize(){super.initialize(),this.el=(0,r.div)(),this.plot_view.canvas_view.add_event(this.el)}async lazy_initialize(){await super.lazy_initialize(),this._toolbar_view=await(0,n.build_view)(this.model.toolbar,{parent:this}),this.plot_view.visibility_callbacks.push((i=>this._toolbar_view.set_visibility(i)))}remove(){this._toolbar_view.remove(),(0,r.remove)(this.el),super.remove()}render(){this.model.visible||(0,r.undisplay)(this.el),super.render()}_render(){const{bbox:i}=this.layout;this._previous_bbox.equals(i)||((0,r.position)(this.el,i),this._previous_bbox=i,this._invalidate_toolbar=!0),this._invalidate_toolbar&&(this.el.style.position=\"absolute\",this.el.style.overflow=\"hidden\",(0,r.empty)(this.el),this.el.appendChild(this._toolbar_view.el),this._toolbar_view.layout.bbox=i,this._toolbar_view.render(),this._invalidate_toolbar=!1),(0,r.display)(this.el)}_get_size(){const{tools:i,logo:e}=this.model.toolbar;return{width:30*i.length+(null!=e?25:0)+15,height:30}}}t.ToolbarPanelView=v,v.__name__=\"ToolbarPanelView\";class d extends a.Annotation{constructor(i){super(i)}}t.ToolbarPanel=d,s=d,d.__name__=\"ToolbarPanel\",s.prototype.default_view=v,s.define((({Ref:i})=>({toolbar:[i(_.Toolbar)]})))},\n", - " function _(t,e,s,i,o){var c;i();const n=t(8),a=t(9),l=t(13),r=t(222),_=t(223),u=t(232),p=t(233);function v(t){switch(t){case\"tap\":return\"active_tap\";case\"pan\":return\"active_drag\";case\"pinch\":case\"scroll\":return\"active_scroll\";case\"multi\":return\"active_multi\"}return null}function h(t){return\"tap\"==t||\"pan\"==t}s.Drag=r.Tool,s.Inspection=r.Tool,s.Scroll=r.Tool,s.Tap=r.Tool;class f extends p.ToolbarBase{constructor(t){super(t)}connect_signals(){super.connect_signals();const{tools:t,active_drag:e,active_inspect:s,active_scroll:i,active_tap:o,active_multi:c}=this.properties;this.on_change([t,e,s,i,o,c],(()=>this._init_tools()))}_init_tools(){if(super._init_tools(),\"auto\"==this.active_inspect);else if(this.active_inspect instanceof u.InspectTool){let t=!1;for(const e of this.inspectors)e!=this.active_inspect?e.active=!1:t=!0;t||(this.active_inspect=null)}else if((0,n.isArray)(this.active_inspect)){const t=(0,a.intersection)(this.active_inspect,this.inspectors);t.length!=this.active_inspect.length&&(this.active_inspect=t);for(const t of this.inspectors)(0,a.includes)(this.active_inspect,t)||(t.active=!1)}else if(null==this.active_inspect)for(const t of this.inspectors)t.active=!1;const t=t=>{t.active?this._active_change(t):t.active=!0};for(const t of(0,l.values)(this.gestures)){t.tools=(0,a.sort_by)(t.tools,(t=>t.default_order));for(const e of t.tools)this.connect(e.properties.active.change,(()=>this._active_change(e)))}for(const[e,s]of(0,l.entries)(this.gestures)){const i=v(e);if(i){const o=this[i];\"auto\"==o?0!=s.tools.length&&h(e)&&t(s.tools[0]):null!=o&&((0,a.includes)(this.tools,o)?t(o):this[i]=null)}}}}s.Toolbar=f,c=f,f.__name__=\"Toolbar\",c.prototype.default_view=p.ToolbarBaseView,c.define((({Or:t,Ref:e,Auto:i,Null:o})=>({active_drag:[t(e(s.Drag),i,o),\"auto\"],active_inspect:[t(e(s.Inspection),i,o),\"auto\"],active_scroll:[t(e(s.Scroll),i,o),\"auto\"],active_tap:[t(e(s.Tap),i,o),\"auto\"],active_multi:[t(e(_.GestureTool),i,o),\"auto\"]})))},\n", - " function _(t,e,n,o,s){var i;o();const a=t(42),r=t(9),l=t(53);class c extends a.View{get plot_view(){return this.parent}get plot_model(){return this.parent.model}connect_signals(){super.connect_signals(),this.connect(this.model.properties.active.change,(()=>{this.model.active?this.activate():this.deactivate()}))}activate(){}deactivate(){}}n.ToolView=c,c.__name__=\"ToolView\";class _ extends l.Model{constructor(t){super(t)}get synthetic_renderers(){return[]}_get_dim_limits([t,e],[n,o],s,i){const a=s.bbox.h_range;let l;\"width\"==i||\"both\"==i?(l=[(0,r.min)([t,n]),(0,r.max)([t,n])],l=[(0,r.max)([l[0],a.start]),(0,r.min)([l[1],a.end])]):l=[a.start,a.end];const c=s.bbox.v_range;let _;return\"height\"==i||\"both\"==i?(_=[(0,r.min)([e,o]),(0,r.max)([e,o])],_=[(0,r.max)([_[0],c.start]),(0,r.min)([_[1],c.end])]):_=[c.start,c.end],[l,_]}static register_alias(t,e){this.prototype._known_aliases.set(t,e)}static from_string(t){const e=this.prototype._known_aliases.get(t);if(null!=e)return e();{const e=[...this.prototype._known_aliases.keys()];throw new Error(`unexpected tool name '${t}', possible tools are ${e.join(\", \")}`)}}}n.Tool=_,i=_,_.__name__=\"Tool\",i.prototype._known_aliases=new Map,i.define((({String:t,Nullable:e})=>({description:[e(t),null]}))),i.internal((({Boolean:t})=>({active:[t,!1]})))},\n", - " function _(e,o,t,s,n){s();const u=e(224),_=e(231);class l extends u.ButtonToolView{}t.GestureToolView=l,l.__name__=\"GestureToolView\";class i extends u.ButtonTool{constructor(e){super(e),this.button_view=_.OnOffButtonView}}t.GestureTool=i,i.__name__=\"GestureTool\"},\n", - " function _(t,e,o,s,i){s();const n=t(1);var l;const r=(0,n.__importDefault)(t(225)),a=t(226),u=t(222),h=t(43),_=t(34),d=t(8),c=t(9),m=(0,n.__importStar)(t(227)),p=m,v=(0,n.__importDefault)(t(228)),f=(0,n.__importDefault)(t(229)),g=t(230);class b extends a.DOMView{initialize(){super.initialize();const t=this.model.menu;if(null!=t){const e=this.parent.model.toolbar_location,o=\"left\"==e||\"above\"==e,s=this.parent.model.horizontal?\"vertical\":\"horizontal\";this._menu=new g.ContextMenu(o?(0,c.reversed)(t):t,{orientation:s,prevent_hide:t=>t.target==this.el})}this._hammer=new r.default(this.el,{touchAction:\"auto\",inputClass:r.default.TouchMouseInput}),this.connect(this.model.change,(()=>this.render())),this._hammer.on(\"tap\",(t=>{var e;(null===(e=this._menu)||void 0===e?void 0:e.is_open)?this._menu.hide():t.target==this.el&&this._clicked()})),this._hammer.on(\"press\",(()=>this._pressed())),this.el.addEventListener(\"keydown\",(t=>{t.keyCode==h.Keys.Enter&&this._clicked()}))}remove(){var t;this._hammer.destroy(),null===(t=this._menu)||void 0===t||t.remove(),super.remove()}styles(){return[...super.styles(),m.default,v.default,f.default]}css_classes(){return super.css_classes().concat(p.toolbar_button)}render(){(0,h.empty)(this.el);const t=this.model.computed_icon;(0,d.isString)(t)&&((0,_.startsWith)(t,\"data:image\")?this.el.style.backgroundImage=`url(\"${t}\")`:this.el.classList.add(t)),this.el.title=this.model.tooltip,this.el.tabIndex=0,null!=this._menu&&this.root.el.appendChild(this._menu.el)}_pressed(){var t;const e=(()=>{switch(this.parent.model.toolbar_location){case\"right\":return{left_of:this.el};case\"left\":return{right_of:this.el};case\"above\":return{below:this.el};case\"below\":return{above:this.el}}})();null===(t=this._menu)||void 0===t||t.toggle(e)}}o.ButtonToolButtonView=b,b.__name__=\"ButtonToolButtonView\";class w extends u.ToolView{}o.ButtonToolView=w,w.__name__=\"ButtonToolView\";class y extends u.Tool{constructor(t){super(t)}_get_dim_tooltip(t){const{description:e,tool_name:o}=this;return null!=e?e:\"both\"==t?o:`${o} (${\"width\"==t?\"x\":\"y\"}-axis)`}get tooltip(){var t;return null!==(t=this.description)&&void 0!==t?t:this.tool_name}get computed_icon(){return this.icon}get menu(){return null}}o.ButtonTool=y,l=y,y.__name__=\"ButtonTool\",l.internal((({Boolean:t})=>({disabled:[t,!1]})))},\n", - " function _(t,e,i,n,r){\n", - " /*! 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e=t.scale<1?\"in\":\"out\";t.additionalEvent=this.options.event+e}this._super.emit.call(this,t)}}),g(zt,Pt,{defaults:{event:\"press\",pointers:1,time:251,threshold:9},getTouchAction:function(){return[yt]},process:function(t){var e=this.options,i=t.pointers.length===e.pointers,n=t.distancee.time;if(this._input=t,!n||!i||12&t.eventType&&!r)this.reset();else if(1&t.eventType)this.reset(),this._timer=l((function(){this.state=8,this.tryEmit()}),e.time,this);else if(4&t.eventType)return 8;return bt},reset:function(){clearTimeout(this._timer)},emit:function(t){8===this.state&&(t&&4&t.eventType?this.manager.emit(this.options.event+\"up\",t):(this._input.timeStamp=c(),this.manager.emit(this.options.event,this._input)))}}),g(Nt,Ot,{defaults:{event:\"rotate\",threshold:0,pointers:2},getTouchAction:function(){return[It]},attrTest:function(t){return this._super.attrTest.call(this,t)&&(Math.abs(t.rotation)>this.options.threshold||2&this.state)}}),g(Xt,Ot,{defaults:{event:\"swipe\",threshold:10,velocity:.3,direction:30,pointers:1},getTouchAction:function(){return Rt.prototype.getTouchAction.call(this)},attrTest:function(t){var e,i=this.options.direction;return 30&i?e=t.overallVelocity:6&i?e=t.overallVelocityX:i&Y&&(e=t.overallVelocityY),this._super.attrTest.call(this,t)&&i&t.offsetDirection&&t.distance>this.options.threshold&&t.maxPointers==this.options.pointers&&u(e)>this.options.velocity&&4&t.eventType},emit:function(t){var e=xt(t.offsetDirection);e&&this.manager.emit(this.options.event+e,t),this.manager.emit(this.options.event,t)}}),g(Yt,Pt,{defaults:{event:\"tap\",pointers:1,taps:1,interval:300,time:250,threshold:9,posThreshold:10},getTouchAction:function(){return[Et]},process:function(t){var e=this.options,i=t.pointers.length===e.pointers,n=t.distance .bk-divider{cursor:default;overflow:hidden;background-color:#e5e5e5;}.bk-root .bk-context-menu.bk-horizontal > .bk-divider{width:1px;margin:5px 0;}.bk-root .bk-context-menu.bk-vertical > .bk-divider{height:1px;margin:0 5px;}.bk-root .bk-context-menu > :not(.bk-divider){border:1px solid transparent;}.bk-root .bk-context-menu > :not(.bk-divider).bk-active{border-color:#26aae1;}.bk-root .bk-context-menu > :not(.bk-divider):hover{background-color:#f9f9f9;}.bk-root .bk-context-menu > :not(.bk-divider):focus,.bk-root .bk-context-menu > :not(.bk-divider):focus-visible{outline:1px dotted #26aae1;outline-offset:-1px;}.bk-root .bk-context-menu > :not(.bk-divider)::-moz-focus-inner{border:0;}.bk-root .bk-context-menu.bk-horizontal > :not(.bk-divider):first-child{border-top-left-radius:4px;border-bottom-left-radius:4px;}.bk-root .bk-context-menu.bk-horizontal > :not(.bk-divider):last-child{border-top-right-radius:4px;border-bottom-right-radius:4px;}.bk-root .bk-context-menu.bk-vertical > :not(.bk-divider):first-child{border-top-left-radius:4px;border-top-right-radius:4px;}.bk-root .bk-context-menu.bk-vertical > :not(.bk-divider):last-child{border-bottom-left-radius:4px;border-bottom-right-radius:4px;}.bk-root .bk-menu{position:absolute;left:0;width:100%;z-index:100;cursor:pointer;font-size:12px;background-color:#fff;border:1px solid #ccc;border-radius:4px;box-shadow:0 6px 12px rgba(0, 0, 0, 0.175);}.bk-root .bk-menu.bk-above{bottom:100%;}.bk-root .bk-menu.bk-below{top:100%;}.bk-root .bk-menu > .bk-divider{height:1px;margin:7.5px 0;overflow:hidden;background-color:#e5e5e5;}.bk-root .bk-menu > :not(.bk-divider){padding:6px 12px;}.bk-root .bk-menu > :not(.bk-divider):hover,.bk-root .bk-menu > :not(.bk-divider).bk-active{background-color:#e6e6e6;}.bk-root .bk-caret{display:inline-block;vertical-align:middle;width:0;height:0;margin:0 5px;}.bk-root .bk-caret.bk-down{border-top:4px solid;}.bk-root .bk-caret.bk-up{border-bottom:4px solid;}.bk-root .bk-caret.bk-down,.bk-root .bk-caret.bk-up{border-right:4px solid transparent;border-left:4px solid transparent;}.bk-root .bk-caret.bk-left{border-right:4px solid;}.bk-root .bk-caret.bk-right{border-left:4px solid;}.bk-root .bk-caret.bk-left,.bk-root .bk-caret.bk-right{border-top:4px solid transparent;border-bottom:4px solid transparent;}\"},\n", - " function _(t,e,i,n,o){n();const s=t(1),l=t(43),h=t(9),r=(0,s.__importStar)(t(229));class d{constructor(t,e={}){var i,n;this.items=t,this.el=(0,l.div)(),this._open=!1,this._item_click=t=>{var e;null===(e=t.handler)||void 0===e||e.call(t),this.hide()},this._on_mousedown=t=>{var e;const{target:i}=t;i instanceof Node&&this.el.contains(i)||(null===(e=this.prevent_hide)||void 0===e?void 0:e.call(this,t))||this.hide()},this._on_keydown=t=>{t.keyCode==l.Keys.Esc&&this.hide()},this._on_blur=()=>{this.hide()},this.orientation=null!==(i=e.orientation)&&void 0!==i?i:\"vertical\",this.reversed=null!==(n=e.reversed)&&void 0!==n&&n,this.prevent_hide=e.prevent_hide,(0,l.undisplay)(this.el)}get is_open(){return this._open}get can_open(){return 0!=this.items.length}remove(){(0,l.remove)(this.el),this._unlisten()}_listen(){document.addEventListener(\"mousedown\",this._on_mousedown),document.addEventListener(\"keydown\",this._on_keydown),window.addEventListener(\"blur\",this._on_blur)}_unlisten(){document.removeEventListener(\"mousedown\",this._on_mousedown),document.removeEventListener(\"keydown\",this._on_keydown),window.removeEventListener(\"blur\",this._on_blur)}_position(t){const e=this.el.parentElement;if(null!=e){const i=(()=>{if(\"left_of\"in t){const{left:e,top:i}=t.left_of.getBoundingClientRect();return{right:e,top:i}}if(\"right_of\"in t){const{top:e,right:i}=t.right_of.getBoundingClientRect();return{left:i,top:e}}if(\"below\"in t){const{left:e,bottom:i}=t.below.getBoundingClientRect();return{left:e,top:i}}if(\"above\"in t){const{left:e,top:i}=t.above.getBoundingClientRect();return{left:e,bottom:i}}return t})(),n=e.getBoundingClientRect();this.el.style.left=null!=i.left?i.left-n.left+\"px\":\"\",this.el.style.top=null!=i.top?i.top-n.top+\"px\":\"\",this.el.style.right=null!=i.right?n.right-i.right+\"px\":\"\",this.el.style.bottom=null!=i.bottom?n.bottom-i.bottom+\"px\":\"\"}}render(){var t;(0,l.empty)(this.el,!0),(0,l.classes)(this.el).add(\"bk-context-menu\",`bk-${this.orientation}`);const e=this.reversed?(0,h.reversed)(this.items):this.items;for(const i of e){let e;if(null==i)e=(0,l.div)({class:r.divider});else{if(null!=i.if&&!i.if())continue;if(null!=i.content)e=i.content;else{const n=null!=i.icon?(0,l.div)({class:[\"bk-menu-icon\",i.icon]}):null,o=[(null===(t=i.active)||void 0===t?void 0:t.call(i))?\"bk-active\":null,i.class];e=(0,l.div)({class:o,title:i.tooltip,tabIndex:0},n,i.label,i.content),e.addEventListener(\"click\",(()=>{this._item_click(i)})),e.addEventListener(\"keydown\",(t=>{t.keyCode==l.Keys.Enter&&this._item_click(i)}))}}this.el.appendChild(e)}}show(t){if(0!=this.items.length&&!this._open){if(this.render(),0==this.el.children.length)return;this._position(null!=t?t:{left:0,top:0}),(0,l.display)(this.el),this._listen(),this._open=!0}}hide(){this._open&&(this._open=!1,this._unlisten(),(0,l.undisplay)(this.el))}toggle(t){this._open?this.hide():this.show(t)}}i.ContextMenu=d,d.__name__=\"ContextMenu\"},\n", - " function _(t,e,i,n,o){n();const s=t(1),c=t(224),l=(0,s.__importStar)(t(227)),a=t(43);class _ extends c.ButtonToolButtonView{render(){super.render(),(0,a.classes)(this.el).toggle(l.active,this.model.active)}_clicked(){const{active:t}=this.model;this.model.active=!t}}i.OnOffButtonView=_,_.__name__=\"OnOffButtonView\"},\n", - " function _(e,o,t,n,s){var c;n();const l=e(224),_=e(231);class i extends l.ButtonToolView{}t.InspectToolView=i,i.__name__=\"InspectToolView\";class a extends l.ButtonTool{constructor(e){super(e),this.event_type=\"move\"}}t.InspectTool=a,c=a,a.__name__=\"InspectTool\",c.prototype.button_view=_.OnOffButtonView,c.define((({Boolean:e})=>({toggleable:[e,!0]}))),c.override({active:!0})},\n", - " function _(t,o,e,l,i){l();const s=t(1);var n,a;const r=t(19),c=t(43),h=t(113),_=t(226),u=t(20),v=t(9),d=t(234),p=t(13),b=t(8),g=t(235),f=t(65),m=t(53),w=t(222),y=t(223),T=t(238),z=t(239),x=t(232),B=t(230),C=(0,s.__importStar)(t(227)),k=C,L=(0,s.__importStar)(t(240)),M=L;class S extends m.Model{constructor(t){super(t)}get visible(){var t;return!this.autohide||null!==(t=this._visible)&&void 0!==t&&t}}e.ToolbarViewModel=S,n=S,S.__name__=\"ToolbarViewModel\",n.define((({Boolean:t})=>({autohide:[t,!1]}))),n.internal((({Boolean:t,Nullable:o})=>({_visible:[o(t),null]})));class $ extends _.DOMView{constructor(){super(...arguments),this.layout={bbox:new f.BBox}}initialize(){super.initialize(),this._tool_button_views=new Map,this._toolbar_view_model=new S({autohide:this.model.autohide});const{toolbar_location:t}=this.model,o=\"left\"==t||\"above\"==t,e=this.model.horizontal?\"vertical\":\"horizontal\";this._overflow_menu=new B.ContextMenu([],{orientation:e,reversed:o})}async lazy_initialize(){await super.lazy_initialize(),await this._build_tool_button_views()}connect_signals(){super.connect_signals(),this.connect(this.model.properties.tools.change,(async()=>{await this._build_tool_button_views(),this.render()})),this.connect(this.model.properties.autohide.change,(()=>{this._toolbar_view_model.autohide=this.model.autohide,this._on_visible_change()})),this.connect(this._toolbar_view_model.properties._visible.change,(()=>this._on_visible_change()))}styles(){return[...super.styles(),C.default,L.default]}remove(){(0,h.remove_views)(this._tool_button_views),super.remove()}async _build_tool_button_views(){const t=null!=this.model._proxied_tools?this.model._proxied_tools:this.model.tools;await(0,h.build_views)(this._tool_button_views,t,{parent:this},(t=>t.button_view))}set_visibility(t){t!=this._toolbar_view_model._visible&&(this._toolbar_view_model._visible=t)}_on_visible_change(){const{visible:t}=this._toolbar_view_model;(0,c.classes)(this.el).toggle(k.toolbar_hidden,!t)}render(){(0,c.empty)(this.el),this.el.classList.add(k.toolbar),this.el.classList.add(k[this.model.toolbar_location]),this._toolbar_view_model.autohide=this.model.autohide,this._on_visible_change();const{horizontal:t}=this.model;let o=0;if(null!=this.model.logo){const e=\"grey\"===this.model.logo?M.grey:null,l=(0,c.a)({href:\"https://bokeh.org/\",target:\"_blank\",class:[M.logo,M.logo_small,e]});this.el.appendChild(l);const{width:i,height:s}=l.getBoundingClientRect();o+=t?i:s}for(const[,t]of this._tool_button_views)t.render();const e=[],l=t=>this._tool_button_views.get(t).el,{gestures:i}=this.model;for(const t of(0,p.values)(i))e.push(t.tools.map(l));e.push(this.model.actions.map(l)),e.push(this.model.inspectors.filter((t=>t.toggleable)).map(l));const s=e.filter((t=>0!=t.length)),n=()=>(0,c.div)({class:k.divider}),{bbox:a}=this.layout;let r=!1;this.root.el.appendChild(this._overflow_menu.el);const h=(0,c.div)({class:k.tool_overflow,tabIndex:0},t?\"\\u22ee\":\"\\u22ef\"),_=()=>{const t=(()=>{switch(this.model.toolbar_location){case\"right\":return{left_of:h};case\"left\":return{right_of:h};case\"above\":return{below:h};case\"below\":return{above:h}}})();this._overflow_menu.toggle(t)};h.addEventListener(\"click\",(()=>{_()})),h.addEventListener(\"keydown\",(t=>{t.keyCode==c.Keys.Enter&&_()}));for(const e of(0,d.join)(s,n))if(r)this._overflow_menu.items.push({content:e,class:t?k.right:k.above});else{this.el.appendChild(e);const{width:l,height:i}=e.getBoundingClientRect();if(o+=t?l:i,r=t?o>a.width-15:o>a.height-15,r){this.el.removeChild(e),this.el.appendChild(h);const{items:t}=this._overflow_menu;t.splice(0,t.length),t.push({content:e})}}}update_layout(){}update_position(){}after_layout(){this._has_finished=!0}export(t,o=!0){const e=\"png\"==t?\"canvas\":\"svg\",l=new g.CanvasLayer(e,o);return l.resize(0,0),l}}function V(){return{pan:{tools:[],active:null},scroll:{tools:[],active:null},pinch:{tools:[],active:null},tap:{tools:[],active:null},doubletap:{tools:[],active:null},press:{tools:[],active:null},pressup:{tools:[],active:null},rotate:{tools:[],active:null},move:{tools:[],active:null},multi:{tools:[],active:null}}}e.ToolbarBaseView=$,$.__name__=\"ToolbarBaseView\";class A extends m.Model{constructor(t){super(t)}initialize(){super.initialize(),this._init_tools()}_init_tools(){const t=function(t,o){if(t.length!=o.length)return!0;const e=new Set(o.map((t=>t.id)));return(0,v.some)(t,(t=>!e.has(t.id)))},o=this.tools.filter((t=>t instanceof x.InspectTool));t(this.inspectors,o)&&(this.inspectors=o);const e=this.tools.filter((t=>t instanceof z.HelpTool));t(this.help,e)&&(this.help=e);const l=this.tools.filter((t=>t instanceof T.ActionTool));t(this.actions,l)&&(this.actions=l);const i=(t,o)=>{t in this.gestures||r.logger.warn(`Toolbar: unknown event type '${t}' for tool: ${o}`)},s={pan:{tools:[],active:null},scroll:{tools:[],active:null},pinch:{tools:[],active:null},tap:{tools:[],active:null},doubletap:{tools:[],active:null},press:{tools:[],active:null},pressup:{tools:[],active:null},rotate:{tools:[],active:null},move:{tools:[],active:null},multi:{tools:[],active:null}};for(const t of this.tools)if(t instanceof y.GestureTool&&t.event_type)if((0,b.isString)(t.event_type))s[t.event_type].tools.push(t),i(t.event_type,t);else{s.multi.tools.push(t);for(const o of t.event_type)i(o,t)}for(const o of Object.keys(s)){const e=this.gestures[o];t(e.tools,s[o].tools)&&(e.tools=s[o].tools),e.active&&(0,v.every)(e.tools,(t=>t.id!=e.active.id))&&(e.active=null)}}get horizontal(){return\"above\"===this.toolbar_location||\"below\"===this.toolbar_location}get vertical(){return\"left\"===this.toolbar_location||\"right\"===this.toolbar_location}_active_change(t){const{event_type:o}=t;if(null==o)return;const e=(0,b.isString)(o)?[o]:o;for(const o of e)if(t.active){const e=this.gestures[o].active;null!=e&&t!=e&&(r.logger.debug(`Toolbar: deactivating tool: ${e} for event type '${o}'`),e.active=!1),this.gestures[o].active=t,r.logger.debug(`Toolbar: activating tool: ${t} for event type '${o}'`)}else this.gestures[o].active=null}}e.ToolbarBase=A,a=A,A.__name__=\"ToolbarBase\",a.prototype.default_view=$,a.define((({Boolean:t,Array:o,Ref:e,Nullable:l})=>({tools:[o(e(w.Tool)),[]],logo:[l(u.Logo),\"normal\"],autohide:[t,!1]}))),a.internal((({Array:t,Struct:o,Ref:e,Nullable:l})=>{const i=o({tools:t(e(y.GestureTool)),active:l(e(w.Tool))});return{gestures:[o({pan:i,scroll:i,pinch:i,tap:i,doubletap:i,press:i,pressup:i,rotate:i,move:i,multi:i}),V],actions:[t(e(T.ActionTool)),[]],inspectors:[t(e(x.InspectTool)),[]],help:[t(e(z.HelpTool)),[]],toolbar_location:[u.Location,\"right\"]}}))},\n", - " function _(n,o,e,t,f){t();const r=n(9);function*i(n,o){const e=n.length;if(o>e)return;const t=(0,r.range)(o);for(yield t.map((o=>n[o]));;){let f;for(const n of(0,r.reversed)((0,r.range)(o)))if(t[n]!=n+e-o){f=n;break}if(null==f)return;t[f]+=1;for(const n of(0,r.range)(f+1,o))t[n]=t[n-1]+1;yield t.map((o=>n[o]))}}e.enumerate=function*(n){let o=0;for(const e of n)yield[e,o++]},e.join=function*(n,o){let e=!0;for(const t of n)e?e=!1:null!=o&&(yield o()),yield*t},e.combinations=i,e.subsets=function*(n){for(const o of(0,r.range)(n.length+1))yield*i(n,o)}},\n", - " function _(t,e,s,i,n){i();const o=t(236),a=t(65),r=t(43);function h(t){!function(t){void 0===t.lineDash&&Object.defineProperty(t,\"lineDash\",{get:()=>t.getLineDash(),set:e=>t.setLineDash(e)})}(t),function(t){t.setImageSmoothingEnabled=e=>{t.imageSmoothingEnabled=e,t.mozImageSmoothingEnabled=e,t.oImageSmoothingEnabled=e,t.webkitImageSmoothingEnabled=e,t.msImageSmoothingEnabled=e},t.getImageSmoothingEnabled=()=>{const e=t.imageSmoothingEnabled;return null==e||e}}(t),function(t){t.ellipse||(t.ellipse=function(e,s,i,n,o,a,r,h=!1){const l=.551784;t.translate(e,s),t.rotate(o);let c=i,g=n;h&&(c=-i,g=-n),t.moveTo(-c,0),t.bezierCurveTo(-c,g*l,-c*l,g,0,g),t.bezierCurveTo(c*l,g,c,g*l,c,0),t.bezierCurveTo(c,-g*l,c*l,-g,0,-g),t.bezierCurveTo(-c*l,-g,-c,-g*l,-c,0),t.rotate(-o),t.translate(-e,-s)})}(t)}const l={position:\"absolute\",top:\"0\",left:\"0\",width:\"100%\",height:\"100%\"};class c{constructor(t,e){switch(this.backend=t,this.hidpi=e,this.pixel_ratio=1,this.bbox=new a.BBox,t){case\"webgl\":case\"canvas\":{this._el=this._canvas=(0,r.canvas)({style:l});const t=this.canvas.getContext(\"2d\");if(null==t)throw new Error(\"unable to obtain 2D rendering context\");this._ctx=t,e&&(this.pixel_ratio=devicePixelRatio);break}case\"svg\":{const t=new o.SVGRenderingContext2D;this._ctx=t,this._canvas=t.get_svg(),this._el=(0,r.div)({style:l},this._canvas);break}}this._ctx.layer=this,h(this._ctx)}get canvas(){return this._canvas}get ctx(){return this._ctx}get el(){return this._el}resize(t,e){this.bbox=new a.BBox({left:0,top:0,width:t,height:e});const s=this._ctx instanceof o.SVGRenderingContext2D?this._ctx:this.canvas;s.width=t*this.pixel_ratio,s.height=e*this.pixel_ratio}undo_transform(t){const{ctx:e}=this;if(void 0===e.getTransform)t(e);else{const s=e.getTransform();e.setTransform(this._base_transform);try{t(e)}finally{e.setTransform(s)}}}prepare(){const{ctx:t,hidpi:e,pixel_ratio:s}=this;t.save(),e&&(t.scale(s,s),t.translate(.5,.5)),void 0!==t.getTransform&&(this._base_transform=t.getTransform()),this.clear()}clear(){const{x:t,y:e,width:s,height:i}=this.bbox;this.ctx.clearRect(t,e,s,i)}finish(){this.ctx.restore()}to_blob(){const{_canvas:t}=this;if(t instanceof HTMLCanvasElement)return null!=t.msToBlob?Promise.resolve(t.msToBlob()):new Promise(((e,s)=>{t.toBlob((t=>null!=t?e(t):s()),\"image/png\")}));{const t=this._ctx.get_serialized_svg(!0),e=new Blob([t],{type:\"image/svg+xml\"});return Promise.resolve(e)}}}s.CanvasLayer=c,c.__name__=\"CanvasLayer\"},\n", - " function _(t,e,i,s,r){s();const n=t(122),a=t(8),o=t(237),l=t(43);function h(t){var e;const i={left:\"start\",right:\"end\",center:\"middle\",start:\"start\",end:\"end\"};return null!==(e=i[t])&&void 0!==e?e:i.start}function _(t){var e;const i={alphabetic:\"alphabetic\",hanging:\"hanging\",top:\"text-before-edge\",bottom:\"text-after-edge\",middle:\"central\"};return null!==(e=i[t])&&void 0!==e?e:i.alphabetic}const c=function(t,e){const i=new Map,s=t.split(\",\");e=null!=e?e:10;for(let t=0;t=0?Math.acos(e):-Math.acos(e)}const v=b(f),A=b(g);this.lineTo(d+f[0]*r,m+f[1]*r),this.arc(d,m,r,v,A)}stroke(){\"path\"===this.__currentElement.nodeName&&this.__currentElement.setAttribute(\"paint-order\",\"fill\"),this.__applyCurrentDefaultPath(),this.__applyStyleToCurrentElement(\"stroke\"),null!=this._clip_path&&this.__currentElement.setAttribute(\"clip-path\",this._clip_path)}fill(t,e){let i=null;if(t instanceof Path2D)i=t;else{if(\"evenodd\"!=t&&\"nonzero\"!=t&&null!=t||null!=e)throw new Error(\"invalid arguments\");e=t}if(null!=i)throw new Error(\"not implemented\");\"none\"!=this.__currentElement.getAttribute(\"fill\")&&this.__init_element(),\"path\"===this.__currentElement.nodeName&&this.__currentElement.setAttribute(\"paint-order\",\"stroke\"),this.__applyCurrentDefaultPath(),this.__applyStyleToCurrentElement(\"fill\"),null!=e&&this.__currentElement.setAttribute(\"fill-rule\",e),null!=this._clip_path&&this.__currentElement.setAttribute(\"clip-path\",this._clip_path)}rect(t,e,i,s){isFinite(t+e+i+s)&&(this.moveTo(t,e),this.lineTo(t+i,e),this.lineTo(t+i,e+s),this.lineTo(t,e+s),this.lineTo(t,e))}fillRect(t,e,i,s){isFinite(t+e+i+s)&&(this.beginPath(),this.rect(t,e,i,s),this.fill())}strokeRect(t,e,i,s){isFinite(t+e+i+s)&&(this.beginPath(),this.rect(t,e,i,s),this.stroke())}__clearCanvas(){(0,l.empty)(this.__defs),(0,l.empty)(this.__root),this.__root.appendChild(this.__defs),this.__currentElement=this.__root}clearRect(t,e,i,s){if(!isFinite(t+e+i+s))return;if(0===t&&0===e&&i===this.width&&s===this.height)return void this.__clearCanvas();const r=this.__createElement(\"rect\",{x:t,y:e,width:i,height:s,fill:\"#FFFFFF\"},!0);this._apply_transform(r),this.__root.appendChild(r)}createLinearGradient(t,e,i,s){if(!isFinite(t+e+i+s))throw new Error(\"The provided double value is non-finite\");const[r,n]=this._transform.apply(t,e),[a,o]=this._transform.apply(i,s),l=this.__createElement(\"linearGradient\",{id:this._random_string(),x1:`${r}px`,x2:`${a}px`,y1:`${n}px`,y2:`${o}px`,gradientUnits:\"userSpaceOnUse\"},!1);return this.__defs.appendChild(l),new u(l,this)}createRadialGradient(t,e,i,s,r,n){if(!isFinite(t+e+i+s+r+n))throw new Error(\"The provided double value is non-finite\");const[a,o]=this._transform.apply(t,e),[l,h]=this._transform.apply(s,r),_=this.__createElement(\"radialGradient\",{id:this._random_string(),cx:`${l}px`,cy:`${h}px`,r:`${n}px`,r0:`${i}px`,fx:`${a}px`,fy:`${o}px`,gradientUnits:\"userSpaceOnUse\"},!1);return this.__defs.appendChild(_),new u(_,this)}__parseFont(){var t,e,i,s,r;const 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r=this.__parseFont(),n=this.__createElement(\"text\",{\"font-family\":r.family,\"font-size\":r.size,\"font-style\":r.style,\"font-weight\":r.weight,\"text-decoration\":r.decoration,x:e,y:i,\"text-anchor\":h(this.textAlign),\"dominant-baseline\":_(this.textBaseline)},!0);n.appendChild(this.__document.createTextNode(t)),this._apply_transform(n),this.__currentElement=n,this.__applyStyleToCurrentElement(s);const a=(()=>{if(null!=this._clip_path){const t=this.__createElement(\"g\");return t.setAttribute(\"clip-path\",this._clip_path),t.appendChild(n),t}return n})();this.__root.appendChild(a)}fillText(t,e,i){null!=t&&isFinite(e+i)&&this.__applyText(t,e,i,\"fill\")}strokeText(t,e,i){null!=t&&isFinite(e+i)&&this.__applyText(t,e,i,\"stroke\")}measureText(t){return this.__ctx.font=this.font,this.__ctx.measureText(t)}arc(t,e,i,s,r,n=!1){this.ellipse(t,e,i,i,0,s,r,n)}ellipse(t,e,i,s,r,n,a,o=!1){if(!isFinite(t+e+i+s+r+n+a))return;if(i<0||s<0)throw new DOMException(\"IndexSizeError, radius can't be negative\");(n%=2*Math.PI)===(a%=2*Math.PI)&&(a=(a+2*Math.PI-.001*(o?-1:1))%(2*Math.PI));const l=t+i*Math.cos(a),h=e+s*Math.sin(a),_=t+i*Math.cos(n),c=e+s*Math.sin(n),p=o?0:1;let u=0,d=a-n;d<0&&(d+=2*Math.PI),u=o?d>Math.PI?0:1:d>Math.PI?1:0,this.lineTo(_,c);const[m,f]=this._transform.apply(l,h),g=180*r/Math.PI;this.__addPathCommand(m,f,`A ${i} ${s} ${g} ${u} ${p} ${m} ${f}`)}clip(){const t=this.__createElement(\"clipPath\"),e=this._random_string();this.__applyCurrentDefaultPath(),t.setAttribute(\"id\",e),t.appendChild(this.__currentElement),this.__defs.appendChild(t),this._clip_path=`url(#${e})`}drawImage(t,...e){let i,s,r,n,a,o,l,h;if(2==e.length){if([i,s]=e,!isFinite(i+s))return;a=0,o=0,l=t.width,h=t.height,r=l,n=h}else if(4==e.length){if([i,s,r,n]=e,!isFinite(i+s+r+n))return;a=0,o=0,l=t.width,h=t.height}else{if(8!==e.length)throw new Error(`Inavlid number of arguments passed to drawImage: ${arguments.length}`);if([a,o,l,h,i,s,r,n]=e,!isFinite(a+o+l+h+i+s+r+n))return}const 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s.ButtonTool{constructor(o){super(o),this.button_view=l,this.do=new c.Signal(this,\"do\")}}n.ActionTool=d,d.__name__=\"ActionTool\"},\n", - " function _(o,e,t,l,i){var s;l();const n=o(238),r=o(228);class c extends n.ActionToolView{doit(){window.open(this.model.redirect)}}t.HelpToolView=c,c.__name__=\"HelpToolView\";class _ extends n.ActionTool{constructor(o){super(o),this.tool_name=\"Help\",this.icon=r.tool_icon_help}}t.HelpTool=_,s=_,_.__name__=\"HelpTool\",s.prototype.default_view=c,s.define((({String:o})=>({redirect:[o,\"https://docs.bokeh.org/en/latest/docs/user_guide/tools.html\"]}))),s.override({description:\"Click the question mark to learn more about Bokeh plot tools.\"}),s.register_alias(\"help\",(()=>new _))},\n", - " function _(o,l,g,A,r){A(),g.root=\"bk-root\",g.logo=\"bk-logo\",g.grey=\"bk-grey\",g.logo_small=\"bk-logo-small\",g.logo_notebook=\"bk-logo-notebook\",g.default=\".bk-root .bk-logo{margin:5px;position:relative;display:block;background-repeat:no-repeat;}.bk-root .bk-logo.bk-grey{filter:url(\\\"data:image/svg+xml;utf8,#grayscale\\\");filter:gray;-webkit-filter:grayscale(100%);}.bk-root .bk-logo-small{width:20px;height:20px;background-image:url(data:image/png;base64,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);}.bk-root .bk-logo-notebook{display:inline-block;vertical-align:middle;margin-right:5px;}\"},\n", - " function _(e,t,s,i,l){i();const o=e(1);var n;const a=e(40),h=e(20),r=e(43),c=(0,o.__importStar)(e(242)),d=c;class p extends a.AnnotationView{initialize(){super.initialize(),this.el=(0,r.div)({class:d.tooltip}),(0,r.undisplay)(this.el),this.plot_view.canvas_view.add_overlay(this.el)}remove(){(0,r.remove)(this.el),super.remove()}connect_signals(){super.connect_signals(),this.connect(this.model.properties.content.change,(()=>this.render())),this.connect(this.model.properties.position.change,(()=>this._reposition()))}styles(){return[...super.styles(),c.default]}render(){this.model.visible||(0,r.undisplay)(this.el),super.render()}_render(){const{content:e}=this.model;null!=e?((0,r.empty)(this.el),(0,r.classes)(this.el).toggle(\"bk-tooltip-custom\",this.model.custom),this.el.appendChild(e),this.model.show_arrow&&this.el.classList.add(d.tooltip_arrow)):(0,r.undisplay)(this.el)}_reposition(){const{position:e}=this.model;if(null==e)return void(0,r.undisplay)(this.el);const[t,s]=e,i=(()=>{const e=this.parent.layout.bbox.relative(),{attachment:i}=this.model;switch(i){case\"horizontal\":return t({attachment:[h.TooltipAttachment,\"horizontal\"],inner_only:[e,!0],show_arrow:[e,!0]}))),n.internal((({Boolean:e,Number:t,Tuple:s,Ref:i,Nullable:l})=>({position:[l(s(t,t)),null],content:[i(HTMLElement),()=>(0,r.div)()],custom:[e]}))),n.override({level:\"overlay\"})},\n", - " function _(o,t,r,e,l){e(),r.root=\"bk-root\",r.tooltip=\"bk-tooltip\",r.left=\"bk-left\",r.tooltip_arrow=\"bk-tooltip-arrow\",r.right=\"bk-right\",r.above=\"bk-above\",r.below=\"bk-below\",r.tooltip_row_label=\"bk-tooltip-row-label\",r.tooltip_row_value=\"bk-tooltip-row-value\",r.tooltip_color_block=\"bk-tooltip-color-block\",r.default='.bk-root{}.bk-root .bk-tooltip{font-weight:300;font-size:12px;position:absolute;padding:5px;border:1px solid #e5e5e5;color:#2f2f2f;background-color:white;pointer-events:none;opacity:0.95;z-index:100;}.bk-root .bk-tooltip > div:not(:first-child){margin-top:5px;border-top:#e5e5e5 1px dashed;}.bk-root .bk-tooltip.bk-left.bk-tooltip-arrow::before{position:absolute;margin:-7px 0 0 0;top:50%;width:0;height:0;border-style:solid;border-width:7px 0 7px 0;border-color:transparent;content:\" \";display:block;left:-10px;border-right-width:10px;border-right-color:#909599;}.bk-root .bk-tooltip.bk-left::before{left:-10px;border-right-width:10px;border-right-color:#909599;}.bk-root .bk-tooltip.bk-right.bk-tooltip-arrow::after{position:absolute;margin:-7px 0 0 0;top:50%;width:0;height:0;border-style:solid;border-width:7px 0 7px 0;border-color:transparent;content:\" \";display:block;right:-10px;border-left-width:10px;border-left-color:#909599;}.bk-root .bk-tooltip.bk-right::after{right:-10px;border-left-width:10px;border-left-color:#909599;}.bk-root .bk-tooltip.bk-above::before{position:absolute;margin:0 0 0 -7px;left:50%;width:0;height:0;border-style:solid;border-width:0 7px 0 7px;border-color:transparent;content:\" \";display:block;top:-10px;border-bottom-width:10px;border-bottom-color:#909599;}.bk-root .bk-tooltip.bk-below::after{position:absolute;margin:0 0 0 -7px;left:50%;width:0;height:0;border-style:solid;border-width:0 7px 0 7px;border-color:transparent;content:\" \";display:block;bottom:-10px;border-top-width:10px;border-top-color:#909599;}.bk-root .bk-tooltip-row-label{text-align:right;color:#26aae1;}.bk-root .bk-tooltip-row-value{color:default;}.bk-root .bk-tooltip-color-block{width:12px;height:12px;margin-left:5px;margin-right:5px;outline:#dddddd solid 1px;display:inline-block;}'},\n", - " function _(e,t,s,i,r){var a;i();const l=e(115),_=e(112),h=e(113),o=e(48);class n extends l.UpperLowerView{async lazy_initialize(){await super.lazy_initialize();const{lower_head:e,upper_head:t}=this.model;null!=e&&(this.lower_head=await(0,h.build_view)(e,{parent:this})),null!=t&&(this.upper_head=await(0,h.build_view)(t,{parent:this}))}set_data(e){var t,s;super.set_data(e),null===(t=this.lower_head)||void 0===t||t.set_data(e),null===(s=this.upper_head)||void 0===s||s.set_data(e)}paint(e){if(this.visuals.line.doit)for(let t=0,s=this._lower_sx.length;t({lower_head:[t(e(_.ArrowHead)),()=>new _.TeeHead({size:10})],upper_head:[t(e(_.ArrowHead)),()=>new _.TeeHead({size:10})]}))),a.override({level:\"underlay\"})},\n", - " function _(n,o,t,u,e){u(),e(\"CustomJS\",n(245).CustomJS),e(\"OpenURL\",n(247).OpenURL)},\n", - " function _(t,e,s,n,c){var a;n();const r=t(246),u=t(13),o=t(34);class i extends r.Callback{constructor(t){super(t)}get names(){return(0,u.keys)(this.args)}get values(){return(0,u.values)(this.args)}get func(){const t=(0,o.use_strict)(this.code);return new Function(...this.names,\"cb_obj\",\"cb_data\",t)}execute(t,e={}){return this.func.apply(t,this.values.concat(t,e))}}s.CustomJS=i,a=i,i.__name__=\"CustomJS\",a.define((({Unknown:t,String:e,Dict:s})=>({args:[s(t),{}],code:[e,\"\"]})))},\n", - " function _(c,a,l,n,s){n();const e=c(53);class o extends e.Model{constructor(c){super(c)}}l.Callback=o,o.__name__=\"Callback\"},\n", - " function _(e,t,n,o,i){var s;o();const c=e(246),r=e(152),a=e(8);class d extends c.Callback{constructor(e){super(e)}navigate(e){this.same_tab?window.location.href=e:window.open(e)}execute(e,{source:t}){const n=e=>{const n=(0,r.replace_placeholders)(this.url,t,e,void 0,void 0,encodeURI);if(!(0,a.isString)(n))throw new Error(\"HTML output is not supported in this context\");this.navigate(n)},{selected:o}=t;for(const e of o.indices)n(e);for(const e of o.line_indices)n(e)}}n.OpenURL=d,s=d,d.__name__=\"OpenURL\",s.define((({Boolean:e,String:t})=>({url:[t,\"http://\"],same_tab:[e,!1]})))},\n", - " function _(a,n,i,e,r){e(),r(\"Canvas\",a(249).Canvas),r(\"CartesianFrame\",a(126).CartesianFrame),r(\"CoordinateMapping\",a(54).CoordinateMapping)},\n", - " function _(e,t,i,s,a){var r,l=this&&this.__createBinding||(Object.create?function(e,t,i,s){void 0===s&&(s=i),Object.defineProperty(e,s,{enumerable:!0,get:function(){return t[i]}})}:function(e,t,i,s){void 0===s&&(s=i),e[s]=t[i]}),n=this&&this.__setModuleDefault||(Object.create?function(e,t){Object.defineProperty(e,\"default\",{enumerable:!0,value:t})}:function(e,t){e.default=t}),o=this&&this.__importStar||function(e){if(e&&e.__esModule)return e;var t={};if(null!=e)for(var i in e)\"default\"!==i&&Object.prototype.hasOwnProperty.call(e,i)&&l(t,e,i);return n(t,e),t};s();const h=e(14),c=e(226),u=e(19),_=e(43),d=e(20),p=e(13),b=e(250),v=e(65),g=e(138),w=e(235);const y=(()=>{let t;return async()=>void 0!==t?t:t=await async function(){const t=document.createElement(\"canvas\"),i=t.getContext(\"webgl\",{premultipliedAlpha:!0});if(null!=i){const s=await(0,g.load_module)(Promise.resolve().then((()=>o(e(410)))));if(null!=s){const e=s.get_regl(i);if(e.has_webgl)return{canvas:t,regl_wrapper:e};u.logger.trace(\"WebGL is supported, but not the required extensions\")}else u.logger.trace(\"WebGL is supported, but bokehjs(.min).js bundle is not available\")}else u.logger.trace(\"WebGL is not supported\");return null}()})(),m={position:\"absolute\",top:\"0\",left:\"0\",width:\"100%\",height:\"100%\"};class f extends c.DOMView{constructor(){super(...arguments),this.bbox=new v.BBox,this.webgl=null}initialize(){super.initialize(),this.underlays_el=(0,_.div)({style:m}),this.primary=this.create_layer(),this.overlays=this.create_layer(),this.overlays_el=(0,_.div)({style:m}),this.events_el=(0,_.div)({class:\"bk-canvas-events\",style:m});const e=[this.underlays_el,this.primary.el,this.overlays.el,this.overlays_el,this.events_el];(0,p.extend)(this.el.style,m),(0,_.append)(this.el,...e),this.ui_event_bus=new b.UIEventBus(this)}async lazy_initialize(){await super.lazy_initialize(),\"webgl\"==this.model.output_backend&&(this.webgl=await y())}remove(){this.ui_event_bus.destroy(),super.remove()}add_underlay(e){this.underlays_el.appendChild(e)}add_overlay(e){this.overlays_el.appendChild(e)}add_event(e){this.events_el.appendChild(e)}get pixel_ratio(){return this.primary.pixel_ratio}resize(e,t){this.bbox=new v.BBox({left:0,top:0,width:e,height:t}),this.primary.resize(e,t),this.overlays.resize(e,t)}prepare_webgl(e){const{webgl:t}=this;if(null!=t){const{width:i,height:s}=this.bbox;t.canvas.width=this.pixel_ratio*i,t.canvas.height=this.pixel_ratio*s;const[a,r,l,n]=e,{xview:o,yview:h}=this.bbox,c=o.compute(a),u=h.compute(r+n),_=this.pixel_ratio;t.regl_wrapper.set_scissor(_*c,_*u,_*l,_*n),this._clear_webgl()}}blit_webgl(e){const{webgl:t}=this;if(null!=t){if(u.logger.debug(\"Blitting WebGL canvas\"),e.restore(),e.drawImage(t.canvas,0,0),e.save(),this.model.hidpi){const t=this.pixel_ratio;e.scale(t,t),e.translate(.5,.5)}this._clear_webgl()}}_clear_webgl(){const{webgl:e}=this;if(null!=e){const{regl_wrapper:t,canvas:i}=e;t.clear(i.width,i.height)}}compose(){const e=this.create_layer(),{width:t,height:i}=this.bbox;return e.resize(t,i),e.ctx.drawImage(this.primary.canvas,0,0),e.ctx.drawImage(this.overlays.canvas,0,0),e}create_layer(){const{output_backend:e,hidpi:t}=this.model;return new w.CanvasLayer(e,t)}to_blob(){return this.compose().to_blob()}}i.CanvasView=f,f.__name__=\"CanvasView\";class x extends h.HasProps{constructor(e){super(e)}}i.Canvas=x,r=x,x.__name__=\"Canvas\",r.prototype.default_view=f,r.internal((({Boolean:e})=>({hidpi:[e,!0],output_backend:[d.OutputBackend,\"canvas\"]})))},\n", - " function _(t,e,s,n,i){n();const r=t(1),a=(0,r.__importDefault)(t(225)),_=t(15),h=t(19),o=t(43),l=(0,r.__importStar)(t(251)),c=t(252),p=t(9),u=t(8),v=t(27),d=t(230);class g{constructor(t){this.canvas_view=t,this.pan_start=new _.Signal(this,\"pan:start\"),this.pan=new _.Signal(this,\"pan\"),this.pan_end=new _.Signal(this,\"pan:end\"),this.pinch_start=new _.Signal(this,\"pinch:start\"),this.pinch=new _.Signal(this,\"pinch\"),this.pinch_end=new _.Signal(this,\"pinch:end\"),this.rotate_start=new _.Signal(this,\"rotate:start\"),this.rotate=new _.Signal(this,\"rotate\"),this.rotate_end=new _.Signal(this,\"rotate:end\"),this.tap=new _.Signal(this,\"tap\"),this.doubletap=new _.Signal(this,\"doubletap\"),this.press=new _.Signal(this,\"press\"),this.pressup=new _.Signal(this,\"pressup\"),this.move_enter=new _.Signal(this,\"move:enter\"),this.move=new _.Signal(this,\"move\"),this.move_exit=new _.Signal(this,\"move:exit\"),this.scroll=new _.Signal(this,\"scroll\"),this.keydown=new _.Signal(this,\"keydown\"),this.keyup=new _.Signal(this,\"keyup\"),this.hammer=new a.default(this.hit_area,{touchAction:\"auto\",inputClass:a.default.TouchMouseInput}),this._prev_move=null,this._curr_pan=null,this._curr_pinch=null,this._curr_rotate=null,this._configure_hammerjs(),this.hit_area.addEventListener(\"mousemove\",(t=>this._mouse_move(t))),this.hit_area.addEventListener(\"mouseenter\",(t=>this._mouse_enter(t))),this.hit_area.addEventListener(\"mouseleave\",(t=>this._mouse_exit(t))),this.hit_area.addEventListener(\"contextmenu\",(t=>this._context_menu(t))),this.hit_area.addEventListener(\"wheel\",(t=>this._mouse_wheel(t))),document.addEventListener(\"keydown\",this),document.addEventListener(\"keyup\",this),this.menu=new d.ContextMenu([],{prevent_hide:t=>2==t.button&&t.target==this.hit_area}),this.hit_area.appendChild(this.menu.el)}get hit_area(){return this.canvas_view.events_el}destroy(){this.menu.remove(),this.hammer.destroy(),document.removeEventListener(\"keydown\",this),document.removeEventListener(\"keyup\",this)}handleEvent(t){\"keydown\"==t.type?this._key_down(t):\"keyup\"==t.type&&this._key_up(t)}_configure_hammerjs(){this.hammer.get(\"doubletap\").recognizeWith(\"tap\"),this.hammer.get(\"tap\").requireFailure(\"doubletap\"),this.hammer.get(\"doubletap\").dropRequireFailure(\"tap\"),this.hammer.on(\"doubletap\",(t=>this._doubletap(t))),this.hammer.on(\"tap\",(t=>this._tap(t))),this.hammer.on(\"press\",(t=>this._press(t))),this.hammer.on(\"pressup\",(t=>this._pressup(t))),this.hammer.get(\"pan\").set({direction:a.default.DIRECTION_ALL}),this.hammer.on(\"panstart\",(t=>this._pan_start(t))),this.hammer.on(\"pan\",(t=>this._pan(t))),this.hammer.on(\"panend\",(t=>this._pan_end(t))),this.hammer.get(\"pinch\").set({enable:!0}),this.hammer.on(\"pinchstart\",(t=>this._pinch_start(t))),this.hammer.on(\"pinch\",(t=>this._pinch(t))),this.hammer.on(\"pinchend\",(t=>this._pinch_end(t))),this.hammer.get(\"rotate\").set({enable:!0}),this.hammer.on(\"rotatestart\",(t=>this._rotate_start(t))),this.hammer.on(\"rotate\",(t=>this._rotate(t))),this.hammer.on(\"rotateend\",(t=>this._rotate_end(t)))}register_tool(t){const e=t.model.event_type;null!=e&&((0,u.isString)(e)?this._register_tool(t,e):e.forEach(((e,s)=>this._register_tool(t,e,s<1))))}_register_tool(t,e,s=!0){const n=t,{id:i}=n.model,r=t=>e=>{e.id==i&&t(e.e)},a=t=>e=>{t(e.e)};switch(e){case\"pan\":null!=n._pan_start&&n.connect(this.pan_start,r(n._pan_start.bind(n))),null!=n._pan&&n.connect(this.pan,r(n._pan.bind(n))),null!=n._pan_end&&n.connect(this.pan_end,r(n._pan_end.bind(n)));break;case\"pinch\":null!=n._pinch_start&&n.connect(this.pinch_start,r(n._pinch_start.bind(n))),null!=n._pinch&&n.connect(this.pinch,r(n._pinch.bind(n))),null!=n._pinch_end&&n.connect(this.pinch_end,r(n._pinch_end.bind(n)));break;case\"rotate\":null!=n._rotate_start&&n.connect(this.rotate_start,r(n._rotate_start.bind(n))),null!=n._rotate&&n.connect(this.rotate,r(n._rotate.bind(n))),null!=n._rotate_end&&n.connect(this.rotate_end,r(n._rotate_end.bind(n)));break;case\"move\":null!=n._move_enter&&n.connect(this.move_enter,r(n._move_enter.bind(n))),null!=n._move&&n.connect(this.move,r(n._move.bind(n))),null!=n._move_exit&&n.connect(this.move_exit,r(n._move_exit.bind(n)));break;case\"tap\":null!=n._tap&&n.connect(this.tap,r(n._tap.bind(n))),null!=n._doubletap&&n.connect(this.doubletap,r(n._doubletap.bind(n)));break;case\"press\":null!=n._press&&n.connect(this.press,r(n._press.bind(n))),null!=n._pressup&&n.connect(this.pressup,r(n._pressup.bind(n)));break;case\"scroll\":null!=n._scroll&&n.connect(this.scroll,r(n._scroll.bind(n)));break;default:throw new Error(`unsupported event_type: ${e}`)}s&&(null!=n._keydown&&n.connect(this.keydown,a(n._keydown.bind(n))),null!=n._keyup&&n.connect(this.keyup,a(n._keyup.bind(n))),v.is_mobile&&null!=n._scroll&&\"pinch\"==e&&(h.logger.debug(\"Registering scroll on touch screen\"),n.connect(this.scroll,r(n._scroll.bind(n)))))}_hit_test_renderers(t,e,s){var n;const i=t.get_renderer_views();for(const t of(0,p.reversed)(i))if(null===(n=t.interactive_hit)||void 0===n?void 0:n.call(t,e,s))return t;return null}set_cursor(t=\"default\"){this.hit_area.style.cursor=t}_hit_test_frame(t,e,s){return t.frame.bbox.contains(e,s)}_hit_test_canvas(t,e,s){return t.layout.bbox.contains(e,s)}_hit_test_plot(t,e){for(const s of this.canvas_view.plot_views)if(s.layout.bbox.relative().contains(t,e))return s;return null}_trigger(t,e,s){var n;const{sx:i,sy:r}=e,a=this._hit_test_plot(i,r),_=t=>{const[s,n]=[i,r];return Object.assign(Object.assign({},e),{sx:s,sy:n})};if(\"panstart\"==e.type||\"pan\"==e.type||\"panend\"==e.type){let n;if(\"panstart\"==e.type&&null!=a?(this._curr_pan={plot_view:a},n=a):\"pan\"==e.type&&null!=this._curr_pan?n=this._curr_pan.plot_view:\"panend\"==e.type&&null!=this._curr_pan?(n=this._curr_pan.plot_view,this._curr_pan=null):n=null,null!=n){const e=_();this.__trigger(n,t,e,s)}}else if(\"pinchstart\"==e.type||\"pinch\"==e.type||\"pinchend\"==e.type){let n;if(\"pinchstart\"==e.type&&null!=a?(this._curr_pinch={plot_view:a},n=a):\"pinch\"==e.type&&null!=this._curr_pinch?n=this._curr_pinch.plot_view:\"pinchend\"==e.type&&null!=this._curr_pinch?(n=this._curr_pinch.plot_view,this._curr_pinch=null):n=null,null!=n){const e=_();this.__trigger(n,t,e,s)}}else if(\"rotatestart\"==e.type||\"rotate\"==e.type||\"rotateend\"==e.type){let 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t=this.properties.radius.uniform(r),n=this.properties.angle.uniform(r),e=\"anticlock\"==this.direction?-1:1,o=Math.min(t.length,n.length),i=new Float64Array(o),a=new Float64Array(o);for(let r=0;r({radius:[l.DistanceSpec,{field:\"radius\"}],angle:[l.AngleSpec,{field:\"angle\"}],direction:[c.Direction,\"anticlock\"]})))},\n", - " function _(e,t,l,r,i){r(),i(\"BooleanFilter\",e(263).BooleanFilter),i(\"CustomJSFilter\",e(264).CustomJSFilter),i(\"Filter\",e(191).Filter),i(\"GroupFilter\",e(265).GroupFilter),i(\"IndexFilter\",e(266).IndexFilter)},\n", - " function _(e,n,l,o,s){var t;o();const a=e(191),r=e(24);class c extends a.Filter{constructor(e){super(e)}compute_indices(e){const n=e.length,{booleans:l}=this;return null==l?r.Indices.all_set(n):r.Indices.from_booleans(n,l)}}l.BooleanFilter=c,t=c,c.__name__=\"BooleanFilter\",t.define((({Boolean:e,Array:n,Nullable:l})=>({booleans:[l(n(e)),null]})))},\n", - " function _(e,n,r,s,t){var i;s();const o=e(191),c=e(24),u=e(13),a=e(8),l=e(34);class f extends o.Filter{constructor(e){super(e)}get names(){return(0,u.keys)(this.args)}get values(){return(0,u.values)(this.args)}get func(){const e=(0,l.use_strict)(this.code);return new Function(...this.names,\"source\",e)}compute_indices(e){const n=e.length,r=this.func(...this.values,e);if(null==r)return c.Indices.all_set(n);if((0,a.isArrayOf)(r,a.isInteger))return c.Indices.from_indices(n,r);if((0,a.isArrayOf)(r,a.isBoolean))return c.Indices.from_booleans(n,r);throw new Error(`expect an array of integers or booleans, or null, got ${r}`)}}r.CustomJSFilter=f,i=f,f.__name__=\"CustomJSFilter\",i.define((({Unknown:e,String:n,Dict:r})=>({args:[r(e),{}],code:[n,\"\"]})))},\n", - " function _(n,e,t,o,r){var u;o();const s=n(191),c=n(24),i=n(19);class l extends s.Filter{constructor(n){super(n)}compute_indices(n){const e=n.get_column(this.column_name);if(null==e)return i.logger.warn(`${this}: groupby column '${this.column_name}' not found in the data source`),new c.Indices(n.length,1);{const t=new c.Indices(n.length);for(let n=0;n({column_name:[n],group:[n]})))},\n", - " function _(e,n,i,s,t){var l;s();const c=e(191),r=e(24);class d extends c.Filter{constructor(e){super(e)}compute_indices(e){const n=e.length,{indices:i}=this;return null==i?r.Indices.all_set(n):r.Indices.from_indices(n,i)}}i.IndexFilter=d,l=d,d.__name__=\"IndexFilter\",l.define((({Int:e,Array:n,Nullable:i})=>({indices:[i(n(e)),null]})))},\n", - " function _(e,a,l,i,t){i(),t(\"AnnularWedge\",e(268).AnnularWedge),t(\"Annulus\",e(269).Annulus),t(\"Arc\",e(270).Arc),t(\"Bezier\",e(271).Bezier),t(\"Circle\",e(272).Circle),t(\"Ellipse\",e(273).Ellipse),t(\"EllipseOval\",e(274).EllipseOval),t(\"Glyph\",e(179).Glyph),t(\"HArea\",e(187).HArea),t(\"HBar\",e(276).HBar),t(\"HexTile\",e(278).HexTile),t(\"Image\",e(279).Image),t(\"ImageRGBA\",e(281).ImageRGBA),t(\"ImageURL\",e(282).ImageURL),t(\"Line\",e(177).Line),t(\"MultiLine\",e(283).MultiLine),t(\"MultiPolygons\",e(284).MultiPolygons),t(\"Oval\",e(285).Oval),t(\"Patch\",e(186).Patch),t(\"Patches\",e(286).Patches),t(\"Quad\",e(287).Quad),t(\"Quadratic\",e(288).Quadratic),t(\"Ray\",e(289).Ray),t(\"Rect\",e(290).Rect),t(\"Scatter\",e(291).Scatter),t(\"Segment\",e(294).Segment),t(\"Spline\",e(295).Spline),t(\"Step\",e(297).Step),t(\"Text\",e(298).Text),t(\"VArea\",e(189).VArea),t(\"VBar\",e(299).VBar),t(\"Wedge\",e(300).Wedge)},\n", - " function _(e,t,s,i,r){i();const n=e(1);var a;const o=e(178),_=e(184),d=e(48),u=e(24),h=e(20),c=(0,n.__importStar)(e(18)),l=e(10),p=e(72);class x extends o.XYGlyphView{_map_data(){\"data\"==this.model.properties.inner_radius.units?this.sinner_radius=this.sdist(this.renderer.xscale,this._x,this.inner_radius):this.sinner_radius=(0,u.to_screen)(this.inner_radius),\"data\"==this.model.properties.outer_radius.units?this.souter_radius=this.sdist(this.renderer.xscale,this._x,this.outer_radius):this.souter_radius=(0,u.to_screen)(this.outer_radius)}_render(e,t,s){const{sx:i,sy:r,start_angle:n,end_angle:a,sinner_radius:o,souter_radius:_}=null!=s?s:this,d=\"anticlock\"==this.model.direction;for(const s of t){const t=i[s],u=r[s],h=o[s],c=_[s],l=n.get(s),p=a.get(s);if(!isFinite(t+u+h+c+l+p))continue;const x=p-l;e.translate(t,u),e.rotate(l),e.beginPath(),e.moveTo(c,0),e.arc(0,0,c,0,x,d),e.rotate(x),e.lineTo(h,0),e.arc(0,0,h,0,-x,!d),e.closePath(),e.rotate(-x-l),e.translate(-t,-u),this.visuals.fill.apply(e,s),this.visuals.hatch.apply(e,s),this.visuals.line.apply(e,s)}}_hit_point(e){const{sx:t,sy:s}=e,i=this.renderer.xscale.invert(t),r=this.renderer.yscale.invert(s);let n,a,o,_;if(\"data\"==this.model.properties.outer_radius.units)n=i-this.max_outer_radius,o=i+this.max_outer_radius,a=r-this.max_outer_radius,_=r+this.max_outer_radius;else{const e=t-this.max_outer_radius,i=t+this.max_outer_radius;[n,o]=this.renderer.xscale.r_invert(e,i);const r=s-this.max_outer_radius,d=s+this.max_outer_radius;[a,_]=this.renderer.yscale.r_invert(r,d)}const d=[];for(const e of this.index.indices({x0:n,x1:o,y0:a,y1:_})){const t=this.souter_radius[e]**2,s=this.sinner_radius[e]**2,[n,a]=this.renderer.xscale.r_compute(i,this._x[e]),[o,_]=this.renderer.yscale.r_compute(r,this._y[e]),u=(n-a)**2+(o-_)**2;u<=t&&u>=s&&d.push(e)}const u=\"anticlock\"==this.model.direction,h=[];for(const e of d){const i=Math.atan2(s-this.sy[e],t-this.sx[e]);(0,l.angle_between)(-i,-this.start_angle.get(e),-this.end_angle.get(e),u)&&h.push(e)}return new p.Selection({indices:h})}draw_legend_for_index(e,t,s){(0,_.generic_area_vector_legend)(this.visuals,e,t,s)}scenterxy(e){const t=(this.sinner_radius[e]+this.souter_radius[e])/2,s=(this.start_angle.get(e)+this.end_angle.get(e))/2;return[this.sx[e]+t*Math.cos(s),this.sy[e]+t*Math.sin(s)]}}s.AnnularWedgeView=x,x.__name__=\"AnnularWedgeView\";class g extends o.XYGlyph{constructor(e){super(e)}}s.AnnularWedge=g,a=g,g.__name__=\"AnnularWedge\",a.prototype.default_view=x,a.mixins([d.LineVector,d.FillVector,d.HatchVector]),a.define((({})=>({direction:[h.Direction,\"anticlock\"],inner_radius:[c.DistanceSpec,{field:\"inner_radius\"}],outer_radius:[c.DistanceSpec,{field:\"outer_radius\"}],start_angle:[c.AngleSpec,{field:\"start_angle\"}],end_angle:[c.AngleSpec,{field:\"end_angle\"}]})))},\n", - " function _(s,e,i,r,t){r();const n=s(1);var a;const u=s(178),o=s(24),_=s(48),d=(0,n.__importStar)(s(18)),h=s(27),c=s(72);class l extends u.XYGlyphView{_map_data(){\"data\"==this.model.properties.inner_radius.units?this.sinner_radius=this.sdist(this.renderer.xscale,this._x,this.inner_radius):this.sinner_radius=(0,o.to_screen)(this.inner_radius),\"data\"==this.model.properties.outer_radius.units?this.souter_radius=this.sdist(this.renderer.xscale,this._x,this.outer_radius):this.souter_radius=(0,o.to_screen)(this.outer_radius)}_render(s,e,i){const{sx:r,sy:t,sinner_radius:n,souter_radius:a}=null!=i?i:this;for(const i of e){const e=r[i],u=t[i],o=n[i],_=a[i];if(isFinite(e+u+o+_)){if(s.beginPath(),h.is_ie)for(const i of[!1,!0])s.moveTo(e,u),s.arc(e,u,o,0,Math.PI,i),s.moveTo(e+_,u),s.arc(e,u,_,Math.PI,0,!i);else s.arc(e,u,o,0,2*Math.PI,!0),s.moveTo(e+_,u),s.arc(e,u,_,2*Math.PI,0,!1);this.visuals.fill.apply(s,i),this.visuals.hatch.apply(s,i),this.visuals.line.apply(s,i)}}}_hit_point(s){const{sx:e,sy:i}=s,r=this.renderer.xscale.invert(e),t=this.renderer.yscale.invert(i);let n,a,u,o;if(\"data\"==this.model.properties.outer_radius.units)n=r-this.max_outer_radius,u=r+this.max_outer_radius,a=t-this.max_outer_radius,o=t+this.max_outer_radius;else{const s=e-this.max_outer_radius,r=e+this.max_outer_radius;[n,u]=this.renderer.xscale.r_invert(s,r);const t=i-this.max_outer_radius,_=i+this.max_outer_radius;[a,o]=this.renderer.yscale.r_invert(t,_)}const _=[];for(const s of this.index.indices({x0:n,x1:u,y0:a,y1:o})){const e=this.souter_radius[s]**2,i=this.sinner_radius[s]**2,[n,a]=this.renderer.xscale.r_compute(r,this._x[s]),[u,o]=this.renderer.yscale.r_compute(t,this._y[s]),d=(n-a)**2+(u-o)**2;d<=e&&d>=i&&_.push(s)}return new c.Selection({indices:_})}draw_legend_for_index(s,{x0:e,y0:i,x1:r,y1:t},n){const a=n+1,u=new Array(a);u[n]=(e+r)/2;const o=new Array(a);o[n]=(i+t)/2;const _=.5*Math.min(Math.abs(r-e),Math.abs(t-i)),d=new Array(a);d[n]=.4*_;const h=new Array(a);h[n]=.8*_,this._render(s,[n],{sx:u,sy:o,sinner_radius:d,souter_radius:h})}}i.AnnulusView=l,l.__name__=\"AnnulusView\";class x extends u.XYGlyph{constructor(s){super(s)}}i.Annulus=x,a=x,x.__name__=\"Annulus\",a.prototype.default_view=l,a.mixins([_.LineVector,_.FillVector,_.HatchVector]),a.define((({})=>({inner_radius:[d.DistanceSpec,{field:\"inner_radius\"}],outer_radius:[d.DistanceSpec,{field:\"outer_radius\"}]})))},\n", - " function _(e,i,s,t,n){t();const r=e(1);var a;const c=e(178),d=e(184),l=e(48),_=e(24),o=e(20),u=(0,r.__importStar)(e(18));class h extends c.XYGlyphView{_map_data(){\"data\"==this.model.properties.radius.units?this.sradius=this.sdist(this.renderer.xscale,this._x,this.radius):this.sradius=(0,_.to_screen)(this.radius)}_render(e,i,s){if(this.visuals.line.doit){const{sx:t,sy:n,sradius:r,start_angle:a,end_angle:c}=null!=s?s:this,d=\"anticlock\"==this.model.direction;for(const s of i){const i=t[s],l=n[s],_=r[s],o=a.get(s),u=c.get(s);isFinite(i+l+_+o+u)&&(e.beginPath(),e.arc(i,l,_,o,u,d),this.visuals.line.set_vectorize(e,s),e.stroke())}}}draw_legend_for_index(e,i,s){(0,d.generic_line_vector_legend)(this.visuals,e,i,s)}}s.ArcView=h,h.__name__=\"ArcView\";class g extends c.XYGlyph{constructor(e){super(e)}}s.Arc=g,a=g,g.__name__=\"Arc\",a.prototype.default_view=h,a.mixins(l.LineVector),a.define((({})=>({direction:[o.Direction,\"anticlock\"],radius:[u.DistanceSpec,{field:\"radius\"}],start_angle:[u.AngleSpec,{field:\"start_angle\"}],end_angle:[u.AngleSpec,{field:\"end_angle\"}]})))},\n", - " function _(e,t,i,n,s){n();const o=e(1);var c;const r=e(48),a=e(179),_=e(184),d=e(78),l=(0,o.__importStar)(e(18));function x(e,t,i,n,s,o,c,r){const a=[],_=[[],[]];for(let _=0;_<=2;_++){let d,l,x;if(0===_?(l=6*e-12*i+6*s,d=-3*e+9*i-9*s+3*c,x=3*i-3*e):(l=6*t-12*n+6*o,d=-3*t+9*n-9*o+3*r,x=3*n-3*t),Math.abs(d)<1e-12){if(Math.abs(l)<1e-12)continue;const e=-x/l;0({x0:[l.XCoordinateSpec,{field:\"x0\"}],y0:[l.YCoordinateSpec,{field:\"y0\"}],x1:[l.XCoordinateSpec,{field:\"x1\"}],y1:[l.YCoordinateSpec,{field:\"y1\"}],cx0:[l.XCoordinateSpec,{field:\"cx0\"}],cy0:[l.YCoordinateSpec,{field:\"cy0\"}],cx1:[l.XCoordinateSpec,{field:\"cx1\"}],cy1:[l.YCoordinateSpec,{field:\"cy1\"}]}))),c.mixins(r.LineVector)},\n", - " function _(s,i,e,t,r){t();const a=s(1);var n;const h=s(178),d=s(48),l=s(24),c=s(20),_=(0,a.__importStar)(s(185)),u=(0,a.__importStar)(s(18)),o=s(9),x=s(12),m=s(72);class p extends h.XYGlyphView{async lazy_initialize(){await super.lazy_initialize();const{webgl:i}=this.renderer.plot_view.canvas_view;if(null==i?void 0:i.regl_wrapper.has_webgl){const{MarkerGL:e}=await Promise.resolve().then((()=>(0,a.__importStar)(s(426))));this.glglyph=new e(i.regl_wrapper,this,\"circle\")}}get use_radius(){return!(this.radius.is_Scalar()&&isNaN(this.radius.value))}_set_data(s){super._set_data(s);const i=(()=>{if(this.use_radius)return 2*this.max_radius;{const{size:s}=this;return s.is_Scalar()?s.value:(0,x.max)(s.array)}})();this._configure(\"max_size\",{value:i})}_map_data(){if(this.use_radius)if(\"data\"==this.model.properties.radius.units)switch(this.model.radius_dimension){case\"x\":this.sradius=this.sdist(this.renderer.xscale,this._x,this.radius);break;case\"y\":this.sradius=this.sdist(this.renderer.yscale,this._y,this.radius);break;case\"max\":{const s=this.sdist(this.renderer.xscale,this._x,this.radius),i=this.sdist(this.renderer.yscale,this._y,this.radius);this.sradius=(0,x.map)(s,((s,e)=>Math.max(s,i[e])));break}case\"min\":{const s=this.sdist(this.renderer.xscale,this._x,this.radius),i=this.sdist(this.renderer.yscale,this._y,this.radius);this.sradius=(0,x.map)(s,((s,e)=>Math.min(s,i[e])));break}}else this.sradius=(0,l.to_screen)(this.radius);else{const s=l.ScreenArray.from(this.size);this.sradius=(0,x.map)(s,(s=>s/2))}}_mask_data(){const{frame:s}=this.renderer.plot_view,i=s.x_target,e=s.y_target;let t,r;return this.use_radius&&\"data\"==this.model.properties.radius.units?(t=i.map((s=>this.renderer.xscale.invert(s))).widen(this.max_radius),r=e.map((s=>this.renderer.yscale.invert(s))).widen(this.max_radius)):(t=i.widen(this.max_size).map((s=>this.renderer.xscale.invert(s))),r=e.widen(this.max_size).map((s=>this.renderer.yscale.invert(s)))),this.index.indices({x0:t.start,x1:t.end,y0:r.start,y1:r.end})}_render(s,i,e){const{sx:t,sy:r,sradius:a}=null!=e?e:this;for(const e of i){const i=t[e],n=r[e],h=a[e];isFinite(i+n+h)&&(s.beginPath(),s.arc(i,n,h,0,2*Math.PI,!1),this.visuals.fill.apply(s,e),this.visuals.hatch.apply(s,e),this.visuals.line.apply(s,e))}}_hit_point(s){const{sx:i,sy:e}=s,t=this.renderer.xscale.invert(i),r=this.renderer.yscale.invert(e),{hit_dilation:a}=this.model;let n,h,d,l;if(this.use_radius&&\"data\"==this.model.properties.radius.units)n=t-this.max_radius*a,h=t+this.max_radius*a,d=r-this.max_radius*a,l=r+this.max_radius*a;else{const s=i-this.max_size*a,t=i+this.max_size*a;[n,h]=this.renderer.xscale.r_invert(s,t);const r=e-this.max_size*a,c=e+this.max_size*a;[d,l]=this.renderer.yscale.r_invert(r,c)}const c=this.index.indices({x0:n,x1:h,y0:d,y1:l}),_=[];if(this.use_radius&&\"data\"==this.model.properties.radius.units)for(const s of c){const i=(this.sradius[s]*a)**2,[e,n]=this.renderer.xscale.r_compute(t,this._x[s]),[h,d]=this.renderer.yscale.r_compute(r,this._y[s]);(e-n)**2+(h-d)**2<=i&&_.push(s)}else for(const s of c){const t=(this.sradius[s]*a)**2;(this.sx[s]-i)**2+(this.sy[s]-e)**2<=t&&_.push(s)}return new m.Selection({indices:_})}_hit_span(s){const{sx:i,sy:e}=s,t=this.bounds();let r,a,n,h;if(\"h\"==s.direction){let s,e;if(n=t.y0,h=t.y1,this.use_radius&&\"data\"==this.model.properties.radius.units)s=i-this.max_radius,e=i+this.max_radius,[r,a]=this.renderer.xscale.r_invert(s,e);else{const t=this.max_size/2;s=i-t,e=i+t,[r,a]=this.renderer.xscale.r_invert(s,e)}}else{let s,i;if(r=t.x0,a=t.x1,this.use_radius&&\"data\"==this.model.properties.radius.units)s=e-this.max_radius,i=e+this.max_radius,[n,h]=this.renderer.yscale.r_invert(s,i);else{const t=this.max_size/2;s=e-t,i=e+t,[n,h]=this.renderer.yscale.r_invert(s,i)}}const d=[...this.index.indices({x0:r,x1:a,y0:n,y1:h})];return new m.Selection({indices:d})}_hit_rect(s){const{sx0:i,sx1:e,sy0:t,sy1:r}=s,[a,n]=this.renderer.xscale.r_invert(i,e),[h,d]=this.renderer.yscale.r_invert(t,r),l=[...this.index.indices({x0:a,x1:n,y0:h,y1:d})];return new m.Selection({indices:l})}_hit_poly(s){const{sx:i,sy:e}=s,t=(0,o.range)(0,this.sx.length),r=[];for(let s=0,a=t.length;s({angle:[u.AngleSpec,0],size:[u.ScreenSizeSpec,{value:4}],radius:[u.NullDistanceSpec,null],radius_dimension:[c.RadiusDimension,\"x\"],hit_dilation:[s,1]})))},\n", - " function _(e,l,s,i,_){var p;i();const t=e(274);class a extends t.EllipseOvalView{}s.EllipseView=a,a.__name__=\"EllipseView\";class n extends t.EllipseOval{constructor(e){super(e)}}s.Ellipse=n,p=n,n.__name__=\"Ellipse\",p.prototype.default_view=a},\n", - " function _(t,s,e,i,h){i();const n=t(1),r=t(275),a=(0,n.__importStar)(t(185)),l=t(24),_=t(72),o=(0,n.__importStar)(t(18));class d extends r.CenterRotatableView{_map_data(){\"data\"==this.model.properties.width.units?this.sw=this.sdist(this.renderer.xscale,this._x,this.width,\"center\"):this.sw=(0,l.to_screen)(this.width),\"data\"==this.model.properties.height.units?this.sh=this.sdist(this.renderer.yscale,this._y,this.height,\"center\"):this.sh=(0,l.to_screen)(this.height)}_render(t,s,e){const{sx:i,sy:h,sw:n,sh:r,angle:a}=null!=e?e:this;for(const e of s){const s=i[e],l=h[e],_=n[e],o=r[e],d=a.get(e);isFinite(s+l+_+o+d)&&(t.beginPath(),t.ellipse(s,l,_/2,o/2,d,0,2*Math.PI),this.visuals.fill.apply(t,e),this.visuals.hatch.apply(t,e),this.visuals.line.apply(t,e))}}_hit_point(t){let s,e,i,h,n,r,l,o,d;const{sx:c,sy:p}=t,w=this.renderer.xscale.invert(c),x=this.renderer.yscale.invert(p);\"data\"==this.model.properties.width.units?(s=w-this.max_width,e=w+this.max_width):(r=c-this.max_width,l=c+this.max_width,[s,e]=this.renderer.xscale.r_invert(r,l)),\"data\"==this.model.properties.height.units?(i=x-this.max_height,h=x+this.max_height):(o=p-this.max_height,d=p+this.max_height,[i,h]=this.renderer.yscale.r_invert(o,d));const m=this.index.indices({x0:s,x1:e,y0:i,y1:h}),y=[];for(const t of m)n=a.point_in_ellipse(c,p,this.angle.get(t),this.sh[t]/2,this.sw[t]/2,this.sx[t],this.sy[t]),n&&y.push(t);return new _.Selection({indices:y})}draw_legend_for_index(t,{x0:s,y0:e,x1:i,y1:h},n){const r=n+1,a=new Array(r);a[n]=(s+i)/2;const l=new Array(r);l[n]=(e+h)/2;const _=this.sw[n]/this.sh[n],d=.8*Math.min(Math.abs(i-s),Math.abs(h-e)),c=new Array(r),p=new Array(r);_>1?(c[n]=d,p[n]=d/_):(c[n]=d*_,p[n]=d);const w=new o.UniformScalar(0,r);this._render(t,[n],{sx:a,sy:l,sw:c,sh:p,angle:w})}}e.EllipseOvalView=d,d.__name__=\"EllipseOvalView\";class c extends r.CenterRotatable{constructor(t){super(t)}}e.EllipseOval=c,c.__name__=\"EllipseOval\"},\n", - " function _(e,t,i,a,n){a();const s=e(1);var r;const h=e(178),o=e(48),_=(0,s.__importStar)(e(18));class c extends h.XYGlyphView{get max_w2(){return\"data\"==this.model.properties.width.units?this.max_width/2:0}get max_h2(){return\"data\"==this.model.properties.height.units?this.max_height/2:0}_bounds({x0:e,x1:t,y0:i,y1:a}){const{max_w2:n,max_h2:s}=this;return{x0:e-n,x1:t+n,y0:i-s,y1:a+s}}}i.CenterRotatableView=c,c.__name__=\"CenterRotatableView\";class l extends h.XYGlyph{constructor(e){super(e)}}i.CenterRotatable=l,r=l,l.__name__=\"CenterRotatable\",r.mixins([o.LineVector,o.FillVector,o.HatchVector]),r.define((({})=>({angle:[_.AngleSpec,0],width:[_.DistanceSpec,{field:\"width\"}],height:[_.DistanceSpec,{field:\"height\"}]})))},\n", - " function _(t,e,s,i,h){i();const r=t(1);var a;const n=t(277),o=t(24),_=(0,r.__importStar)(t(18));class c extends n.BoxView{scenterxy(t){return[(this.sleft[t]+this.sright[t])/2,this.sy[t]]}_lrtb(t){const e=this._left[t],s=this._right[t],i=this._y[t],h=this.height.get(t)/2;return[Math.min(e,s),Math.max(e,s),i+h,i-h]}_map_data(){this.sy=this.renderer.yscale.v_compute(this._y),this.sh=this.sdist(this.renderer.yscale,this._y,this.height,\"center\"),this.sleft=this.renderer.xscale.v_compute(this._left),this.sright=this.renderer.xscale.v_compute(this._right);const t=this.sy.length;this.stop=new o.ScreenArray(t),this.sbottom=new o.ScreenArray(t);for(let e=0;e({left:[_.XCoordinateSpec,{value:0}],y:[_.YCoordinateSpec,{field:\"y\"}],height:[_.NumberSpec,{value:1}],right:[_.XCoordinateSpec,{field:\"right\"}]})))},\n", - " function _(t,e,s,r,i){var n;r();const a=t(48),h=t(179),o=t(184),c=t(72);class _ extends h.GlyphView{get_anchor_point(t,e,s){const r=Math.min(this.sleft[e],this.sright[e]),i=Math.max(this.sright[e],this.sleft[e]),n=Math.min(this.stop[e],this.sbottom[e]),a=Math.max(this.sbottom[e],this.stop[e]);switch(t){case\"top_left\":return{x:r,y:n};case\"top\":case\"top_center\":return{x:(r+i)/2,y:n};case\"top_right\":return{x:i,y:n};case\"bottom_left\":return{x:r,y:a};case\"bottom\":case\"bottom_center\":return{x:(r+i)/2,y:a};case\"bottom_right\":return{x:i,y:a};case\"left\":case\"center_left\":return{x:r,y:(n+a)/2};case\"center\":case\"center_center\":return{x:(r+i)/2,y:(n+a)/2};case\"right\":case\"center_right\":return{x:i,y:(n+a)/2}}}_index_data(t){const{min:e,max:s}=Math,{data_size:r}=this;for(let i=0;i({r:[l.NumberSpec,{field:\"r\"}],q:[l.NumberSpec,{field:\"q\"}],scale:[l.NumberSpec,1],size:[e,1],aspect_scale:[e,1],orientation:[_.HexTileOrientation,\"pointytop\"]}))),a.override({line_color:null})},\n", - " function _(e,a,t,_,r){var n;_();const s=e(280),o=e(173),i=e(201);class p extends s.ImageBaseView{connect_signals(){super.connect_signals(),this.connect(this.model.color_mapper.change,(()=>this._update_image()))}_update_image(){null!=this.image_data&&(this._set_data(null),this.renderer.request_render())}_flat_img_to_buf8(e){return this.model.color_mapper.rgba_mapper.v_compute(e)}}t.ImageView=p,p.__name__=\"ImageView\";class m extends s.ImageBase{constructor(e){super(e)}}t.Image=m,n=m,m.__name__=\"Image\",n.prototype.default_view=p,n.define((({Ref:e})=>({color_mapper:[e(o.ColorMapper),()=>new i.LinearColorMapper({palette:[\"#000000\",\"#252525\",\"#525252\",\"#737373\",\"#969696\",\"#bdbdbd\",\"#d9d9d9\",\"#f0f0f0\",\"#ffffff\"]})]})))},\n", - " function _(e,t,i,s,a){s();const h=e(1);var n;const r=e(178),_=e(24),d=(0,h.__importStar)(e(18)),l=e(72),g=e(9),o=e(29),c=e(11);class m extends r.XYGlyphView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.global_alpha.change,(()=>this.renderer.request_render()))}_render(e,t,i){const{image_data:s,sx:a,sy:h,sw:n,sh:r,global_alpha:_}=null!=i?i:this,d=e.getImageSmoothingEnabled();e.setImageSmoothingEnabled(!1);const l=_.is_Scalar();l&&(e.globalAlpha=_.value);for(const i of t){const t=s[i],_=a[i],d=h[i],g=n[i],o=r[i],c=this.global_alpha.get(i);if(null==t||!isFinite(_+d+g+o+c))continue;l||(e.globalAlpha=c);const m=d;e.translate(0,m),e.scale(1,-1),e.translate(0,-m),e.drawImage(t,0|_,0|d,g,o),e.translate(0,m),e.scale(1,-1),e.translate(0,-m)}e.setImageSmoothingEnabled(d)}_set_data(e){this._set_width_heigh_data();for(let t=0,i=this.image.length;t({image:[d.NDArraySpec,{field:\"image\"}],dw:[d.DistanceSpec,{field:\"dw\"}],dh:[d.DistanceSpec,{field:\"dh\"}],global_alpha:[d.NumberSpec,{value:1}],dilate:[e,!1]})))},\n", - " function _(e,a,t,r,_){var n;r();const s=e(280),m=e(8);class i extends s.ImageBaseView{_flat_img_to_buf8(e){let a;return a=(0,m.isArray)(e)?new Uint32Array(e):e,new Uint8ClampedArray(a.buffer)}}t.ImageRGBAView=i,i.__name__=\"ImageRGBAView\";class g extends s.ImageBase{constructor(e){super(e)}}t.ImageRGBA=g,n=g,g.__name__=\"ImageRGBA\",n.prototype.default_view=i},\n", - " function _(e,t,s,r,a){r();const i=e(1);var n;const o=e(178),c=e(24),_=e(20),h=(0,i.__importStar)(e(18)),l=e(12),d=e(136);class m extends o.XYGlyphView{constructor(){super(...arguments),this._images_rendered=!1,this._set_data_iteration=0}connect_signals(){super.connect_signals(),this.connect(this.model.properties.global_alpha.change,(()=>this.renderer.request_render()))}_index_data(e){const{data_size:t}=this;for(let s=0;s{this._set_data_iteration==r&&(this.image[a]=e,this.renderer.request_render())},attempts:t+1,timeout:s})}const a=\"data\"==this.model.properties.w.units,i=\"data\"==this.model.properties.h.units,n=this._x.length,o=new c.ScreenArray(a?2*n:n),_=new c.ScreenArray(i?2*n:n),{anchor:h}=this.model;function m(e,t){switch(h){case\"top_left\":case\"bottom_left\":case\"left\":case\"center_left\":return[e,e+t];case\"top\":case\"top_center\":case\"bottom\":case\"bottom_center\":case\"center\":case\"center_center\":return[e-t/2,e+t/2];case\"top_right\":case\"bottom_right\":case\"right\":case\"center_right\":return[e-t,e]}}function g(e,t){switch(h){case\"top_left\":case\"top\":case\"top_center\":case\"top_right\":return[e,e-t];case\"bottom_left\":case\"bottom\":case\"bottom_center\":case\"bottom_right\":return[e+t,e];case\"left\":case\"center_left\":case\"center\":case\"center_center\":case\"right\":case\"center_right\":return[e+t/2,e-t/2]}}if(a)for(let e=0;e({url:[h.StringSpec,{field:\"url\"}],anchor:[_.Anchor,\"top_left\"],global_alpha:[h.NumberSpec,{value:1}],angle:[h.AngleSpec,0],w:[h.NullDistanceSpec,null],h:[h.NullDistanceSpec,null],dilate:[e,!1],retry_attempts:[t,0],retry_timeout:[t,0]})))},\n", - " function _(e,t,s,i,n){i();const o=e(1);var r;const l=e(78),_=e(48),c=(0,o.__importStar)(e(185)),h=(0,o.__importStar)(e(18)),a=e(12),d=e(13),x=e(179),y=e(184),g=e(72);class p extends x.GlyphView{_project_data(){l.inplace.project_xy(this._xs.array,this._ys.array)}_index_data(e){const{data_size:t}=this;for(let s=0;s0&&o.set(e,s)}return new g.Selection({indices:[...o.keys()],multiline_indices:(0,d.to_object)(o)})}get_interpolation_hit(e,t,s){const i=this._xs.get(e),n=this._ys.get(e),o=i[t],r=n[t],l=i[t+1],_=n[t+1];return(0,y.line_interpolation)(this.renderer,s,o,r,l,_)}draw_legend_for_index(e,t,s){(0,y.generic_line_vector_legend)(this.visuals,e,t,s)}scenterxy(){throw new Error(`${this}.scenterxy() is not implemented`)}}s.MultiLineView=p,p.__name__=\"MultiLineView\";class u extends x.Glyph{constructor(e){super(e)}}s.MultiLine=u,r=u,u.__name__=\"MultiLine\",r.prototype.default_view=p,r.define((({})=>({xs:[h.XCoordinateSeqSpec,{field:\"xs\"}],ys:[h.YCoordinateSeqSpec,{field:\"ys\"}]}))),r.mixins(_.LineVector)},\n", - " function _(t,e,s,n,i){n();const o=t(1);var r;const l=t(181),h=t(179),a=t(184),_=t(12),c=t(12),d=t(48),x=(0,o.__importStar)(t(185)),y=(0,o.__importStar)(t(18)),f=t(72),g=t(11);class p extends h.GlyphView{_project_data(){}_index_data(t){const{min:e,max:s}=Math,{data_size:n}=this;for(let i=0;i1&&c.length>1)for(let s=1,n=i.length;s1){let r=!1;for(let t=1;t({xs:[y.XCoordinateSeqSeqSeqSpec,{field:\"xs\"}],ys:[y.YCoordinateSeqSeqSeqSpec,{field:\"ys\"}]}))),r.mixins([d.LineVector,d.FillVector,d.HatchVector])},\n", - " function _(a,e,l,s,_){var t;s();const i=a(274),n=a(12);class p extends i.EllipseOvalView{_map_data(){super._map_data(),(0,n.mul)(this.sw,.75)}}l.OvalView=p,p.__name__=\"OvalView\";class v extends i.EllipseOval{constructor(a){super(a)}}l.Oval=v,t=v,v.__name__=\"Oval\",t.prototype.default_view=p},\n", - " function _(e,t,s,i,n){i();const r=e(1);var a;const o=e(179),c=e(184),_=e(12),h=e(48),l=(0,r.__importStar)(e(185)),d=(0,r.__importStar)(e(18)),y=e(72),p=e(11),x=e(78);class f extends o.GlyphView{_project_data(){x.inplace.project_xy(this._xs.array,this._ys.array)}_index_data(e){const{data_size:t}=this;for(let s=0;s({xs:[d.XCoordinateSeqSpec,{field:\"xs\"}],ys:[d.YCoordinateSeqSpec,{field:\"ys\"}]}))),a.mixins([h.LineVector,h.FillVector,h.HatchVector])},\n", - " function _(t,e,o,i,s){i();const r=t(1);var n;const _=t(277),d=(0,r.__importStar)(t(18));class a extends _.BoxView{scenterxy(t){return[this.sleft[t]/2+this.sright[t]/2,this.stop[t]/2+this.sbottom[t]/2]}_lrtb(t){return[this._left[t],this._right[t],this._top[t],this._bottom[t]]}}o.QuadView=a,a.__name__=\"QuadView\";class c extends _.Box{constructor(t){super(t)}}o.Quad=c,n=c,c.__name__=\"Quad\",n.prototype.default_view=a,n.define((({})=>({right:[d.XCoordinateSpec,{field:\"right\"}],bottom:[d.YCoordinateSpec,{field:\"bottom\"}],left:[d.XCoordinateSpec,{field:\"left\"}],top:[d.YCoordinateSpec,{field:\"top\"}]})))},\n", - " function _(e,t,i,n,s){n();const c=e(1);var o;const r=e(48),a=e(78),_=e(179),d=e(184),l=(0,c.__importStar)(e(18));function x(e,t,i){if(t==(e+i)/2)return[e,i];{const n=(e-t)/(e-2*t+i),s=e*(1-n)**2+2*t*(1-n)*n+i*n**2;return[Math.min(e,i,s),Math.max(e,i,s)]}}class y extends _.GlyphView{_project_data(){a.inplace.project_xy(this._x0,this._y0),a.inplace.project_xy(this._x1,this._y1)}_index_data(e){const{_x0:t,_x1:i,_y0:n,_y1:s,_cx:c,_cy:o,data_size:r}=this;for(let a=0;a({x0:[l.XCoordinateSpec,{field:\"x0\"}],y0:[l.YCoordinateSpec,{field:\"y0\"}],x1:[l.XCoordinateSpec,{field:\"x1\"}],y1:[l.YCoordinateSpec,{field:\"y1\"}],cx:[l.XCoordinateSpec,{field:\"cx\"}],cy:[l.YCoordinateSpec,{field:\"cy\"}]}))),o.mixins(r.LineVector)},\n", - " function _(e,t,s,i,n){i();const l=e(1);var a;const r=e(178),o=e(184),h=e(48),_=e(24),c=(0,l.__importStar)(e(18));class g extends r.XYGlyphView{_map_data(){\"data\"==this.model.properties.length.units?this.slength=this.sdist(this.renderer.xscale,this._x,this.length):this.slength=(0,_.to_screen)(this.length);const{width:e,height:t}=this.renderer.plot_view.frame.bbox,s=2*(e+t),{slength:i}=this;for(let e=0,t=i.length;e({length:[c.DistanceSpec,0],angle:[c.AngleSpec,0]})))},\n", - " function _(t,e,s,i,r){var n,h=this&&this.__createBinding||(Object.create?function(t,e,s,i){void 0===i&&(i=s),Object.defineProperty(t,i,{enumerable:!0,get:function(){return e[s]}})}:function(t,e,s,i){void 0===i&&(i=s),t[i]=e[s]}),a=this&&this.__setModuleDefault||(Object.create?function(t,e){Object.defineProperty(t,\"default\",{enumerable:!0,value:e})}:function(t,e){t.default=e}),l=this&&this.__importStar||function(t){if(t&&t.__esModule)return t;var e={};if(null!=t)for(var s in t)\"default\"!==s&&Object.prototype.hasOwnProperty.call(t,s)&&h(e,t,s);return a(e,t),e};i();const o=t(275),c=t(184),_=t(24),d=t(12),f=t(72);class y extends o.CenterRotatableView{async lazy_initialize(){await super.lazy_initialize();const{webgl:e}=this.renderer.plot_view.canvas_view;if(null==e?void 0:e.regl_wrapper.has_webgl){const{RectGL:s}=await Promise.resolve().then((()=>l(t(425))));this.glglyph=new s(e.regl_wrapper,this)}}_map_data(){if(\"data\"==this.model.properties.width.units)[this.sw,this.sx0]=this._map_dist_corner_for_data_side_length(this._x,this.width,this.renderer.xscale);else{this.sw=(0,_.to_screen)(this.width);const t=this.sx.length;this.sx0=new _.ScreenArray(t);for(let e=0;e({dilate:[t,!1]})))},\n", - " function _(e,t,s,r,i){r();const a=e(1);var n;const l=e(292),_=e(293),c=(0,a.__importStar)(e(18));class o extends l.MarkerView{async lazy_initialize(){await super.lazy_initialize();const{webgl:t}=this.renderer.plot_view.canvas_view;if(null==t?void 0:t.regl_wrapper.has_webgl){const{MarkerGL:t}=await Promise.resolve().then((()=>(0,a.__importStar)(e(426))));this.glcls=t}}_init_webgl(){const{webgl:e}=this.renderer.plot_view.canvas_view;if(null!=e){const{regl_wrapper:t}=e;if(t.has_webgl){const e=new Set(null!=this.base?this.base.marker:this.marker);if(1==e.size){const[s]=[...e],r=this.glcls;if(null==r?void 0:r.is_supported(s)){const{glglyph:e}=this;if(null==e||e.marker_type!=s)return void(this.glglyph=new r(t,this,s))}}}}delete this.glglyph}_set_visuals(){this._init_webgl()}_render(e,t,s){const{sx:r,sy:i,size:a,angle:n,marker:l}=null!=s?s:this;for(const s of t){const t=r[s],c=i[s],o=a.get(s),g=n.get(s),h=l.get(s);if(!isFinite(t+c+o+g)||null==h)continue;const w=o/2;e.beginPath(),e.translate(t,c),g&&e.rotate(g),_.marker_funcs[h](e,s,w,this.visuals),g&&e.rotate(-g),e.translate(-t,-c)}}draw_legend_for_index(e,{x0:t,x1:s,y0:r,y1:i},a){const n=a+1,l=this.marker.get(a),_=Object.assign(Object.assign({},this._get_legend_args({x0:t,x1:s,y0:r,y1:i},a)),{marker:new c.UniformScalar(l,n)});this._render(e,[a],_)}}s.ScatterView=o,o.__name__=\"ScatterView\";class g extends l.Marker{constructor(e){super(e)}}s.Scatter=g,n=g,g.__name__=\"Scatter\",n.prototype.default_view=o,n.define((()=>({marker:[c.MarkerSpec,{value:\"circle\"}]})))},\n", - " function _(e,t,s,n,i){n();const r=e(1);var a;const c=e(178),o=e(48),_=(0,r.__importStar)(e(185)),h=(0,r.__importStar)(e(18)),l=e(9),x=e(72);class d extends c.XYGlyphView{_render(e,t,s){const{sx:n,sy:i,size:r,angle:a}=null!=s?s:this;for(const s of t){const t=n[s],c=i[s],o=r.get(s),_=a.get(s);if(!isFinite(t+c+o+_))continue;const h=o/2;e.beginPath(),e.translate(t,c),_&&e.rotate(_),this._render_one(e,s,h,this.visuals),_&&e.rotate(-_),e.translate(-t,-c)}}_mask_data(){const{x_target:e,y_target:t}=this.renderer.plot_view.frame,s=e.widen(this.max_size).map((e=>this.renderer.xscale.invert(e))),n=t.widen(this.max_size).map((e=>this.renderer.yscale.invert(e)));return this.index.indices({x0:s.start,x1:s.end,y0:n.start,y1:n.end})}_hit_point(e){const{sx:t,sy:s}=e,{max_size:n}=this,{hit_dilation:i}=this.model,r=t-n*i,a=t+n*i,[c,o]=this.renderer.xscale.r_invert(r,a),_=s-n*i,h=s+n*i,[l,d]=this.renderer.yscale.r_invert(_,h),y=this.index.indices({x0:c,x1:o,y0:l,y1:d}),g=[];for(const e of y){const n=this.size.get(e)/2*i;Math.abs(this.sx[e]-t)<=n&&Math.abs(this.sy[e]-s)<=n&&g.push(e)}return new x.Selection({indices:g})}_hit_span(e){const{sx:t,sy:s}=e,n=this.bounds(),i=this.max_size/2;let r,a,c,o;if(\"h\"==e.direction){c=n.y0,o=n.y1;const e=t-i,s=t+i;[r,a]=this.renderer.xscale.r_invert(e,s)}else{r=n.x0,a=n.x1;const e=s-i,t=s+i;[c,o]=this.renderer.yscale.r_invert(e,t)}const _=[...this.index.indices({x0:r,x1:a,y0:c,y1:o})];return new x.Selection({indices:_})}_hit_rect(e){const{sx0:t,sx1:s,sy0:n,sy1:i}=e,[r,a]=this.renderer.xscale.r_invert(t,s),[c,o]=this.renderer.yscale.r_invert(n,i),_=[...this.index.indices({x0:r,x1:a,y0:c,y1:o})];return new x.Selection({indices:_})}_hit_poly(e){const{sx:t,sy:s}=e,n=(0,l.range)(0,this.sx.length),i=[];for(let e=0,r=n.length;e({size:[h.ScreenSizeSpec,{value:4}],angle:[h.AngleSpec,0],hit_dilation:[e,1]})))},\n", - " function _(l,o,n,t,i){t();const e=Math.sqrt(3),a=Math.sqrt(5),c=(a+1)/4,p=Math.sqrt((5-a)/8),r=(a-1)/4,h=Math.sqrt((5+a)/8);function u(l,o){l.rotate(Math.PI/4),s(l,o),l.rotate(-Math.PI/4)}function f(l,o){const n=o*e,t=n/3;l.moveTo(-n/2,-t),l.lineTo(0,0),l.lineTo(n/2,-t),l.lineTo(0,0),l.lineTo(0,o)}function s(l,o){l.moveTo(0,o),l.lineTo(0,-o),l.moveTo(-o,0),l.lineTo(o,0)}function T(l,o){l.moveTo(0,o),l.lineTo(o/1.5,0),l.lineTo(0,-o),l.lineTo(-o/1.5,0),l.closePath()}function y(l,o){const n=o*e,t=n/3;l.moveTo(-o,t),l.lineTo(o,t),l.lineTo(0,t-n),l.closePath()}function v(l,o,n,t){l.arc(0,0,n,0,2*Math.PI,!1),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)}function d(l,o,n,t){T(l,n),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)}function _(l,o,n,t){!function(l,o){l.beginPath(),l.arc(0,0,o/4,0,2*Math.PI,!1),l.closePath()}(l,n),t.line.set_vectorize(l,o),l.fillStyle=l.strokeStyle,l.fill()}function P(l,o,n,t){!function(l,o){const n=o/2,t=e*n;l.moveTo(o,0),l.lineTo(n,-t),l.lineTo(-n,-t),l.lineTo(-o,0),l.lineTo(-n,t),l.lineTo(n,t),l.closePath()}(l,n),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)}function m(l,o,n,t){const i=2*n;l.rect(-n,-n,i,i),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)}function q(l,o,n,t){!function(l,o){const n=Math.sqrt(5-2*a)*o;l.moveTo(0,-o),l.lineTo(n*r,n*h-o),l.lineTo(n*(1+r),n*h-o),l.lineTo(n*(1+r-c),n*(h+p)-o),l.lineTo(n*(1+2*r-c),n*(2*h+p)-o),l.lineTo(0,2*n*h-o),l.lineTo(-n*(1+2*r-c),n*(2*h+p)-o),l.lineTo(-n*(1+r-c),n*(h+p)-o),l.lineTo(-n*(1+r),n*h-o),l.lineTo(-n*r,n*h-o),l.closePath()}(l,n),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)}function M(l,o,n,t){y(l,n),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)}n.marker_funcs={asterisk:function(l,o,n,t){s(l,n),u(l,n),t.line.apply(l,o)},circle:v,circle_cross:function(l,o,n,t){l.arc(0,0,n,0,2*Math.PI,!1),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.doit&&(t.line.set_vectorize(l,o),s(l,n),l.stroke())},circle_dot:function(l,o,n,t){v(l,o,n,t),_(l,o,n,t)},circle_y:function(l,o,n,t){l.arc(0,0,n,0,2*Math.PI,!1),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.doit&&(t.line.set_vectorize(l,o),f(l,n),l.stroke())},circle_x:function(l,o,n,t){l.arc(0,0,n,0,2*Math.PI,!1),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.doit&&(t.line.set_vectorize(l,o),u(l,n),l.stroke())},cross:function(l,o,n,t){s(l,n),t.line.apply(l,o)},diamond:d,diamond_dot:function(l,o,n,t){d(l,o,n,t),_(l,o,n,t)},diamond_cross:function(l,o,n,t){T(l,n),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.doit&&(t.line.set_vectorize(l,o),l.moveTo(0,n),l.lineTo(0,-n),l.moveTo(-n/1.5,0),l.lineTo(n/1.5,0),l.stroke())},dot:_,hex:P,hex_dot:function(l,o,n,t){P(l,o,n,t),_(l,o,n,t)},inverted_triangle:function(l,o,n,t){l.rotate(Math.PI),y(l,n),l.rotate(-Math.PI),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)},plus:function(l,o,n,t){const i=3*n/8,e=[i,i,n,n,i,i,-i,-i,-n,-n,-i,-i],a=[n,i,i,-i,-i,-n,-n,-i,-i,i,i,n];l.beginPath();for(let o=0;o<12;o++)l.lineTo(e[o],a[o]);l.closePath(),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)},square:m,square_cross:function(l,o,n,t){const i=2*n;l.rect(-n,-n,i,i),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.doit&&(t.line.set_vectorize(l,o),s(l,n),l.stroke())},square_dot:function(l,o,n,t){m(l,o,n,t),_(l,o,n,t)},square_pin:function(l,o,n,t){const i=3*n/8;l.moveTo(-n,-n),l.quadraticCurveTo(0,-i,n,-n),l.quadraticCurveTo(i,0,n,n),l.quadraticCurveTo(0,i,-n,n),l.quadraticCurveTo(-i,0,-n,-n),l.closePath(),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)},square_x:function(l,o,n,t){const i=2*n;l.rect(-n,-n,i,i),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.doit&&(t.line.set_vectorize(l,o),l.moveTo(-n,n),l.lineTo(n,-n),l.moveTo(-n,-n),l.lineTo(n,n),l.stroke())},star:q,star_dot:function(l,o,n,t){q(l,o,n,t),_(l,o,n,t)},triangle:M,triangle_dot:function(l,o,n,t){M(l,o,n,t),_(l,o,n,t)},triangle_pin:function(l,o,n,t){const i=n*e,a=i/3,c=3*a/8;l.moveTo(-n,a),l.quadraticCurveTo(0,c,n,a),l.quadraticCurveTo(e*c/2,c/2,0,a-i),l.quadraticCurveTo(-e*c/2,c/2,-n,a),l.closePath(),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)},dash:function(l,o,n,t){!function(l,o){l.moveTo(-o,0),l.lineTo(o,0)}(l,n),t.line.apply(l,o)},x:function(l,o,n,t){u(l,n),t.line.apply(l,o)},y:function(l,o,n,t){f(l,n),t.line.apply(l,o)}}},\n", - " function _(e,t,s,i,n){i();const r=e(1);var o;const _=(0,r.__importStar)(e(185)),h=(0,r.__importStar)(e(18)),c=e(48),a=e(78),d=e(179),x=e(184),l=e(72);class y extends d.GlyphView{_project_data(){a.inplace.project_xy(this._x0,this._y0),a.inplace.project_xy(this._x1,this._y1)}_index_data(e){const{min:t,max:s}=Math,{_x0:i,_x1:n,_y0:r,_y1:o,data_size:_}=this;for(let h=0;h<_;h++){const _=i[h],c=n[h],a=r[h],d=o[h];e.add_rect(t(_,c),t(a,d),s(_,c),s(a,d))}}_render(e,t,s){if(this.visuals.line.doit){const{sx0:i,sy0:n,sx1:r,sy1:o}=null!=s?s:this;for(const s of t){const t=i[s],_=n[s],h=r[s],c=o[s];isFinite(t+_+h+c)&&(e.beginPath(),e.moveTo(t,_),e.lineTo(h,c),this.visuals.line.set_vectorize(e,s),e.stroke())}}}_hit_point(e){const{sx:t,sy:s}=e,i={x:t,y:s},[n,r]=this.renderer.xscale.r_invert(t-2,t+2),[o,h]=this.renderer.yscale.r_invert(s-2,s+2),c=this.index.indices({x0:n,y0:o,x1:r,y1:h}),a=[];for(const e of c){const t=Math.max(2,this.line_width.get(e)/2)**2,s={x:this.sx0[e],y:this.sy0[e]},n={x:this.sx1[e],y:this.sy1[e]};_.dist_to_segment_squared(i,s,n)({x0:[h.XCoordinateSpec,{field:\"x0\"}],y0:[h.YCoordinateSpec,{field:\"y0\"}],x1:[h.XCoordinateSpec,{field:\"x1\"}],y1:[h.YCoordinateSpec,{field:\"y1\"}]}))),o.mixins(c.LineVector)},\n", - " function _(t,e,s,i,n){i();const o=t(1);var _;const l=t(178),a=(0,o.__importStar)(t(48)),c=t(296);class r extends l.XYGlyphView{_set_data(){const{tension:t,closed:e}=this.model;[this._xt,this._yt]=(0,c.catmullrom_spline)(this._x,this._y,20,t,e)}_map_data(){const{x_scale:t,y_scale:e}=this.renderer.coordinates;this.sxt=t.v_compute(this._xt),this.syt=e.v_compute(this._yt)}_render(t,e,s){const{sxt:i,syt:n}=null!=s?s:this;let o=!0;t.beginPath();const _=i.length;for(let e=0;e<_;e++){const s=i[e],_=n[e];isFinite(s+_)?o?(t.moveTo(s,_),o=!1):t.lineTo(s,_):o=!0}this.visuals.line.set_value(t),t.stroke()}}s.SplineView=r,r.__name__=\"SplineView\";class h extends l.XYGlyph{constructor(t){super(t)}}s.Spline=h,_=h,h.__name__=\"Spline\",_.prototype.default_view=r,_.mixins(a.LineScalar),_.define((({Boolean:t,Number:e})=>({tension:[e,.5],closed:[t,!1]})))},\n", - " function _(n,t,e,o,s){o();const c=n(24),l=n(11);e.catmullrom_spline=function(n,t,e=10,o=.5,s=!1){(0,l.assert)(n.length==t.length);const r=n.length,f=s?r+1:r,w=(0,c.infer_type)(n,t),i=new w(f+2),u=new w(f+2);i.set(n,1),u.set(t,1),s?(i[0]=n[r-1],u[0]=t[r-1],i[f]=n[0],u[f]=t[0],i[f+1]=n[1],u[f+1]=t[1]):(i[0]=n[0],u[0]=t[0],i[f+1]=n[r-1],u[f+1]=t[r-1]);const g=new w(4*(e+1));for(let n=0,t=0;n<=e;n++){const o=n/e,s=o**2,c=o*s;g[t++]=2*c-3*s+1,g[t++]=-2*c+3*s,g[t++]=c-2*s+o,g[t++]=c-s}const h=new w((f-1)*(e+1)),_=new w((f-1)*(e+1));for(let n=1,t=0;n1&&(e.stroke(),o=!1)}o?(e.lineTo(t,r),e.lineTo(a,c)):(e.beginPath(),e.moveTo(s[n],i[n]),o=!0),l=n}e.lineTo(s[a-1],i[a-1]),e.stroke()}}draw_legend_for_index(e,t,n){(0,r.generic_line_scalar_legend)(this.visuals,e,t)}}n.StepView=f,f.__name__=\"StepView\";class u extends a.XYGlyph{constructor(e){super(e)}}n.Step=u,l=u,u.__name__=\"Step\",l.prototype.default_view=f,l.mixins(c.LineScalar),l.define((()=>({mode:[_.StepMode,\"before\"]})))},\n", - " function _(t,e,s,i,n){i();const o=t(1);var _;const h=t(178),l=t(48),r=(0,o.__importStar)(t(185)),a=(0,o.__importStar)(t(18)),c=t(121),x=t(11),u=t(72);class f extends h.XYGlyphView{_rotate_point(t,e,s,i,n){return[(t-s)*Math.cos(n)-(e-i)*Math.sin(n)+s,(t-s)*Math.sin(n)+(e-i)*Math.cos(n)+i]}_text_bounds(t,e,s,i){return[[t,t+s,t+s,t,t],[e,e,e-i,e-i,e]]}_render(t,e,s){const{sx:i,sy:n,x_offset:o,y_offset:_,angle:h,text:l}=null!=s?s:this;this._sys=[],this._sxs=[];for(const s of e){const e=this._sxs[s]=[],r=this._sys[s]=[],a=i[s],x=n[s],u=o.get(s),f=_.get(s),p=h.get(s),g=l.get(s);if(isFinite(a+x+u+f+p)&&null!=g&&this.visuals.text.doit){const i=`${g}`;t.save(),t.translate(a+u,x+f),t.rotate(p),this.visuals.text.set_vectorize(t,s);const n=this.visuals.text.font_value(s),{height:o}=(0,c.font_metrics)(n),_=this.text_line_height.get(s)*o;if(-1==i.indexOf(\"\\n\")){t.fillText(i,0,0);const s=a+u,n=x+f,o=t.measureText(i).width,[h,l]=this._text_bounds(s,n,o,_);e.push(h),r.push(l)}else{const n=i.split(\"\\n\"),o=_*n.length,h=this.text_baseline.get(s);let l;switch(h){case\"top\":l=0;break;case\"middle\":l=-o/2+_/2;break;case\"bottom\":l=-o+_;break;default:l=0,console.warn(`'${h}' baseline not supported with multi line text`)}for(const s of n){t.fillText(s,0,l);const i=a+u,n=l+x+f,o=t.measureText(s).width,[h,c]=this._text_bounds(i,n,o,_);e.push(h),r.push(c),l+=_}}t.restore()}}}_hit_point(t){const{sx:e,sy:s}=t,i=[];for(let t=0;t({text:[a.NullStringSpec,{field:\"text\"}],angle:[a.AngleSpec,0],x_offset:[a.NumberSpec,0],y_offset:[a.NumberSpec,0]})))},\n", - " function _(t,s,e,i,r){i();const h=t(1);var o;const a=t(277),n=t(24),_=(0,h.__importStar)(t(18));class c extends a.BoxView{scenterxy(t){return[this.sx[t],(this.stop[t]+this.sbottom[t])/2]}_lrtb(t){const s=this.width.get(t)/2,e=this._x[t],i=this._top[t],r=this._bottom[t];return[e-s,e+s,Math.max(i,r),Math.min(i,r)]}_map_data(){this.sx=this.renderer.xscale.v_compute(this._x),this.sw=this.sdist(this.renderer.xscale,this._x,this.width,\"center\"),this.stop=this.renderer.yscale.v_compute(this._top),this.sbottom=this.renderer.yscale.v_compute(this._bottom);const t=this.sx.length;this.sleft=new n.ScreenArray(t),this.sright=new n.ScreenArray(t);for(let s=0;s({x:[_.XCoordinateSpec,{field:\"x\"}],bottom:[_.YCoordinateSpec,{value:0}],width:[_.NumberSpec,{value:1}],top:[_.YCoordinateSpec,{field:\"top\"}]})))},\n", - " function _(e,s,t,i,n){i();const r=e(1);var a;const c=e(178),l=e(184),d=e(48),o=e(24),h=e(20),_=(0,r.__importStar)(e(18)),u=e(10),g=e(72);class p extends c.XYGlyphView{_map_data(){\"data\"==this.model.properties.radius.units?this.sradius=this.sdist(this.renderer.xscale,this._x,this.radius):this.sradius=(0,o.to_screen)(this.radius)}_render(e,s,t){const{sx:i,sy:n,sradius:r,start_angle:a,end_angle:c}=null!=t?t:this,l=\"anticlock\"==this.model.direction;for(const t of s){const s=i[t],d=n[t],o=r[t],h=a.get(t),_=c.get(t);isFinite(s+d+o+h+_)&&(e.beginPath(),e.arc(s,d,o,h,_,l),e.lineTo(s,d),e.closePath(),this.visuals.fill.apply(e,t),this.visuals.hatch.apply(e,t),this.visuals.line.apply(e,t))}}_hit_point(e){let s,t,i,n,r,a,c,l,d;const{sx:o,sy:h}=e,_=this.renderer.xscale.invert(o),p=this.renderer.yscale.invert(h),x=2*this.max_radius;\"data\"===this.model.properties.radius.units?(a=_-x,c=_+x,l=p-x,d=p+x):(t=o-x,i=o+x,[a,c]=this.renderer.xscale.r_invert(t,i),n=h-x,r=h+x,[l,d]=this.renderer.yscale.r_invert(n,r));const y=[];for(const e of this.index.indices({x0:a,x1:c,y0:l,y1:d})){const a=this.sradius[e]**2;[t,i]=this.renderer.xscale.r_compute(_,this._x[e]),[n,r]=this.renderer.yscale.r_compute(p,this._y[e]),s=(t-i)**2+(n-r)**2,s<=a&&y.push(e)}const f=\"anticlock\"==this.model.direction,m=[];for(const e of y){const s=Math.atan2(h-this.sy[e],o-this.sx[e]);(0,u.angle_between)(-s,-this.start_angle.get(e),-this.end_angle.get(e),f)&&m.push(e)}return new g.Selection({indices:m})}draw_legend_for_index(e,s,t){(0,l.generic_area_vector_legend)(this.visuals,e,s,t)}scenterxy(e){const s=this.sradius[e]/2,t=(this.start_angle.get(e)+this.end_angle.get(e))/2;return[this.sx[e]+s*Math.cos(t),this.sy[e]+s*Math.sin(t)]}}t.WedgeView=p,p.__name__=\"WedgeView\";class x extends c.XYGlyph{constructor(e){super(e)}}t.Wedge=x,a=x,x.__name__=\"Wedge\",a.prototype.default_view=p,a.mixins([d.LineVector,d.FillVector,d.HatchVector]),a.define((({})=>({direction:[h.Direction,\"anticlock\"],radius:[_.DistanceSpec,{field:\"radius\"}],start_angle:[_.AngleSpec,{field:\"start_angle\"}],end_angle:[_.AngleSpec,{field:\"end_angle\"}]})))},\n", - " function _(t,_,r,o,a){o();const e=t(1);(0,e.__exportStar)(t(302),r),(0,e.__exportStar)(t(303),r),(0,e.__exportStar)(t(304),r)},\n", - " function _(e,t,d,n,s){n();const o=e(53),r=e(12),_=e(9),i=e(72);class c extends o.Model{constructor(e){super(e)}_hit_test(e,t,d){if(!t.model.visible)return null;const n=d.glyph.hit_test(e);return null==n?null:d.model.view.convert_selection_from_subset(n)}}d.GraphHitTestPolicy=c,c.__name__=\"GraphHitTestPolicy\";class a extends c{constructor(e){super(e)}hit_test(e,t){return this._hit_test(e,t,t.edge_view)}do_selection(e,t,d,n){if(null==e)return!1;const s=t.edge_renderer.data_source.selected;return s.update(e,d,n),t.edge_renderer.data_source._select.emit(),!s.is_empty()}do_inspection(e,t,d,n,s){if(null==e)return!1;const{edge_renderer:o}=d.model,r=o.get_selection_manager().get_or_create_inspector(d.edge_view.model);return r.update(e,n,s),d.edge_view.model.data_source.setv({inspected:r},{silent:!0}),d.edge_view.model.data_source.inspect.emit([d.edge_view.model,{geometry:t}]),!r.is_empty()}}d.EdgesOnly=a,a.__name__=\"EdgesOnly\";class l extends c{constructor(e){super(e)}hit_test(e,t){return this._hit_test(e,t,t.node_view)}do_selection(e,t,d,n){if(null==e)return!1;const s=t.node_renderer.data_source.selected;return s.update(e,d,n),t.node_renderer.data_source._select.emit(),!s.is_empty()}do_inspection(e,t,d,n,s){if(null==e)return!1;const{node_renderer:o}=d.model,r=o.get_selection_manager().get_or_create_inspector(d.node_view.model);return r.update(e,n,s),d.node_view.model.data_source.setv({inspected:r},{silent:!0}),d.node_view.model.data_source.inspect.emit([d.node_view.model,{geometry:t}]),!r.is_empty()}}d.NodesOnly=l,l.__name__=\"NodesOnly\";class u extends c{constructor(e){super(e)}hit_test(e,t){return this._hit_test(e,t,t.node_view)}get_linked_edges(e,t,d){let n=[];\"selection\"==d?n=e.selected.indices.map((t=>e.data.index[t])):\"inspection\"==d&&(n=e.inspected.indices.map((t=>e.data.index[t])));const s=[];for(let e=0;e(0,r.indexOf)(e.data.index,t)));return new i.Selection({indices:o})}do_selection(e,t,d,n){if(null==e)return!1;const s=t.edge_renderer.data_source.selected;s.update(e,d,n);const o=t.node_renderer.data_source.selected,r=this.get_linked_nodes(t.node_renderer.data_source,t.edge_renderer.data_source,\"selection\");return o.update(r,d,n),t.edge_renderer.data_source._select.emit(),!s.is_empty()}do_inspection(e,t,d,n,s){if(null==e)return!1;const o=d.edge_view.model.data_source.selection_manager.get_or_create_inspector(d.edge_view.model);o.update(e,n,s),d.edge_view.model.data_source.setv({inspected:o},{silent:!0});const r=d.node_view.model.data_source.selection_manager.get_or_create_inspector(d.node_view.model),_=this.get_linked_nodes(d.node_view.model.data_source,d.edge_view.model.data_source,\"inspection\");return r.update(_,n,s),d.node_view.model.data_source.setv({inspected:r},{silent:!0}),d.edge_view.model.data_source.inspect.emit([d.edge_view.model,{geometry:t}]),!o.is_empty()}}d.EdgesAndLinkedNodes=m,m.__name__=\"EdgesAndLinkedNodes\"},\n", - " function _(e,o,t,r,n){var s;r();const a=e(53),d=e(260);class _ extends a.Model{constructor(e){super(e)}get node_coordinates(){return new u({layout:this})}get edge_coordinates(){return new i({layout:this})}}t.LayoutProvider=_,_.__name__=\"LayoutProvider\";class c extends d.CoordinateTransform{constructor(e){super(e)}}t.GraphCoordinates=c,s=c,c.__name__=\"GraphCoordinates\",s.define((({Ref:e})=>({layout:[e(_)]})));class u extends c{constructor(e){super(e)}_v_compute(e){const[o,t]=this.layout.get_node_coordinates(e);return{x:o,y:t}}}t.NodeCoordinates=u,u.__name__=\"NodeCoordinates\";class i extends c{constructor(e){super(e)}_v_compute(e){const[o,t]=this.layout.get_edge_coordinates(e);return{x:o,y:t}}}t.EdgeCoordinates=i,i.__name__=\"EdgeCoordinates\"},\n", - " function _(t,a,l,e,n){var o;e();const r=t(303);class u extends r.LayoutProvider{constructor(t){super(t)}get_node_coordinates(t){var a;const l=null!==(a=t.data.index)&&void 0!==a?a:[],e=l.length,n=new Float64Array(e),o=new Float64Array(e);for(let t=0;t({graph_layout:[l(a(t,t)),{}]})))},\n", - " function _(i,d,n,r,G){r(),G(\"Grid\",i(306).Grid)},\n", - " function _(i,e,n,s,t){s();const r=i(1);var o;const d=i(127),_=i(129),a=i(130),l=(0,r.__importStar)(i(48)),h=i(8);class c extends _.GuideRendererView{_render(){const i=this.layer.ctx;i.save(),this._draw_regions(i),this._draw_minor_grids(i),this._draw_grids(i),i.restore()}connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.request_render()))}_draw_regions(i){if(!this.visuals.band_fill.doit&&!this.visuals.band_hatch.doit)return;const[e,n]=this.grid_coords(\"major\",!1);for(let s=0;sn[1]&&(t=n[1]);else{[s,t]=n;for(const i of this.plot_view.axis_views)i.dimension==this.model.dimension&&i.model.x_range_name==this.model.x_range_name&&i.model.y_range_name==this.model.y_range_name&&([s,t]=i.computed_bounds)}return[s,t]}grid_coords(i,e=!0){const n=this.model.dimension,s=(n+1)%2,[t,r]=this.ranges();let[o,d]=this.computed_bounds();[o,d]=[Math.min(o,d),Math.max(o,d)];const _=[[],[]],a=this.model.get_ticker();if(null==a)return _;const l=a.get_ticks(o,d,t,r.min)[i],h=t.min,c=t.max,u=r.min,m=r.max;e||(l[0]!=h&&l.splice(0,0,h),l[l.length-1]!=c&&l.push(c));for(let i=0;i({bounds:[r(t(i,i),e),\"auto\"],dimension:[n(0,1),0],axis:[o(s(d.Axis)),null],ticker:[o(s(a.Ticker)),null]}))),o.override({level:\"underlay\",band_fill_color:null,band_fill_alpha:0,grid_line_color:\"#e5e5e5\",minor_grid_line_color:null})},\n", - " function _(o,a,x,B,e){B(),e(\"Box\",o(308).Box),e(\"Column\",o(310).Column),e(\"GridBox\",o(311).GridBox),e(\"HTMLBox\",o(312).HTMLBox),e(\"LayoutDOM\",o(309).LayoutDOM),e(\"Panel\",o(313).Panel),e(\"Row\",o(314).Row),e(\"Spacer\",o(315).Spacer),e(\"Tabs\",o(316).Tabs),e(\"WidgetBox\",o(319).WidgetBox)},\n", - " function _(e,n,s,t,c){var i;t();const o=e(309);class r extends o.LayoutDOMView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.children.change,(()=>this.rebuild()))}get child_models(){return this.model.children}}s.BoxView=r,r.__name__=\"BoxView\";class a extends o.LayoutDOM{constructor(e){super(e)}}s.Box=a,i=a,a.__name__=\"Box\",i.define((({Number:e,Array:n,Ref:s})=>({children:[n(s(o.LayoutDOM)),[]],spacing:[e,0]})))},\n", - " function _(t,i,e,s,o){var l;s();const n=t(53),h=t(20),a=t(43),r=t(19),_=t(8),c=t(22),u=t(121),d=t(113),p=t(226),m=t(207),g=t(44),w=t(235);class f extends p.DOMView{constructor(){super(...arguments),this._offset_parent=null,this._viewport={}}get is_layout_root(){return this.is_root||!(this.parent instanceof f)}get base_font_size(){const t=getComputedStyle(this.el).fontSize,i=(0,u.parse_css_font_size)(t);if(null!=i){const{value:t,unit:e}=i;if(\"px\"==e)return t}return null}initialize(){super.initialize(),this.el.style.position=this.is_layout_root?\"relative\":\"absolute\",this._child_views=new Map}async lazy_initialize(){await super.lazy_initialize(),await this.build_child_views()}remove(){for(const t of this.child_views)t.remove();this._child_views.clear(),super.remove()}connect_signals(){super.connect_signals(),this.is_layout_root&&(this._on_resize=()=>this.resize_layout(),window.addEventListener(\"resize\",this._on_resize),this._parent_observer=setInterval((()=>{const t=this.el.offsetParent;this._offset_parent!=t&&(this._offset_parent=t,null!=t&&(this.compute_viewport(),this.invalidate_layout()))}),250));const t=this.model.properties;this.on_change([t.width,t.height,t.min_width,t.min_height,t.max_width,t.max_height,t.margin,t.width_policy,t.height_policy,t.sizing_mode,t.aspect_ratio,t.visible],(()=>this.invalidate_layout())),this.on_change([t.background,t.css_classes],(()=>this.invalidate_render()))}disconnect_signals(){null!=this._parent_observer&&clearTimeout(this._parent_observer),null!=this._on_resize&&window.removeEventListener(\"resize\",this._on_resize),super.disconnect_signals()}css_classes(){return super.css_classes().concat(this.model.css_classes)}get child_views(){return this.child_models.map((t=>this._child_views.get(t)))}async build_child_views(){await(0,d.build_views)(this._child_views,this.child_models,{parent:this})}render(){super.render(),(0,a.empty)(this.el);const{background:t}=this.model;this.el.style.backgroundColor=null!=t?(0,c.color2css)(t):\"\",(0,a.classes)(this.el).clear().add(...this.css_classes());for(const t of this.child_views)this.el.appendChild(t.el),t.render()}update_layout(){for(const t of this.child_views)t.update_layout();this._update_layout()}update_position(){this.el.style.display=this.model.visible?\"block\":\"none\";const t=this.is_layout_root?this.layout.sizing.margin:void 0;(0,a.position)(this.el,this.layout.bbox,t);for(const t of this.child_views)t.update_position()}after_layout(){for(const t of this.child_views)t.after_layout();this._has_finished=!0}compute_viewport(){this._viewport=this._viewport_size()}renderTo(t){t.appendChild(this.el),this._offset_parent=this.el.offsetParent,this.compute_viewport(),this.build(),this.notify_finished()}build(){if(!this.is_layout_root)throw new Error(`${this.toString()} is not a root layout`);return this.render(),this.update_layout(),this.compute_layout(),this}async rebuild(){await this.build_child_views(),this.invalidate_render()}compute_layout(){const t=Date.now();this.layout.compute(this._viewport),this.update_position(),this.after_layout(),r.logger.debug(`layout computed in ${Date.now()-t} ms`)}resize_layout(){this.root.compute_viewport(),this.root.compute_layout()}invalidate_layout(){this.root.update_layout(),this.root.compute_layout()}invalidate_render(){this.render(),this.invalidate_layout()}has_finished(){if(!super.has_finished())return!1;for(const t of this.child_views)if(!t.has_finished())return!1;return!0}_width_policy(){return null!=this.model.width?\"fixed\":\"fit\"}_height_policy(){return null!=this.model.height?\"fixed\":\"fit\"}box_sizing(){let{width_policy:t,height_policy:i,aspect_ratio:e}=this.model;\"auto\"==t&&(t=this._width_policy()),\"auto\"==i&&(i=this._height_policy());const{sizing_mode:s}=this.model;if(null!=s)if(\"fixed\"==s)t=i=\"fixed\";else if(\"stretch_both\"==s)t=i=\"max\";else if(\"stretch_width\"==s)t=\"max\";else if(\"stretch_height\"==s)i=\"max\";else switch(null==e&&(e=\"auto\"),s){case\"scale_width\":t=\"max\",i=\"min\";break;case\"scale_height\":t=\"min\",i=\"max\";break;case\"scale_both\":t=\"max\",i=\"max\"}const o={width_policy:t,height_policy:i},{min_width:l,min_height:n}=this.model;null!=l&&(o.min_width=l),null!=n&&(o.min_height=n);const{width:h,height:a}=this.model;null!=h&&(o.width=h),null!=a&&(o.height=a);const{max_width:r,max_height:c}=this.model;null!=r&&(o.max_width=r),null!=c&&(o.max_height=c),\"auto\"==e&&null!=h&&null!=a?o.aspect=h/a:(0,_.isNumber)(e)&&(o.aspect=e);const{margin:u}=this.model;if(null!=u)if((0,_.isNumber)(u))o.margin={top:u,right:u,bottom:u,left:u};else if(2==u.length){const[t,i]=u;o.margin={top:t,right:i,bottom:t,left:i}}else{const[t,i,e,s]=u;o.margin={top:t,right:i,bottom:e,left:s}}o.visible=this.model.visible;const{align:d}=this.model;return(0,_.isArray)(d)?[o.halign,o.valign]=d:o.halign=o.valign=d,o}_viewport_size(){return(0,a.undisplayed)(this.el,(()=>{let t=this.el;for(;t=t.parentElement;){if(t.classList.contains(g.root))continue;if(t==document.body){const{margin:{left:t,right:i,top:e,bottom:s}}=(0,a.extents)(document.body);return{width:Math.ceil(document.documentElement.clientWidth-t-i),height:Math.ceil(document.documentElement.clientHeight-e-s)}}const{padding:{left:i,right:e,top:s,bottom:o}}=(0,a.extents)(t),{width:l,height:n}=t.getBoundingClientRect(),h=Math.ceil(l-i-e),r=Math.ceil(n-s-o);if(h>0||r>0)return{width:h>0?h:void 0,height:r>0?r:void 0}}return{}}))}export(t,i=!0){const e=\"png\"==t?\"canvas\":\"svg\",s=new w.CanvasLayer(e,i),{width:o,height:l}=this.layout.bbox;s.resize(o,l);for(const e of this.child_views){const o=e.export(t,i),{x:l,y:n}=e.layout.bbox;s.ctx.drawImage(o.canvas,l,n)}return s}serializable_state(){return Object.assign(Object.assign({},super.serializable_state()),{bbox:this.layout.bbox.box,children:this.child_views.map((t=>t.serializable_state()))})}}e.LayoutDOMView=f,f.__name__=\"LayoutDOMView\";class y extends n.Model{constructor(t){super(t)}}e.LayoutDOM=y,l=y,y.__name__=\"LayoutDOM\",l.define((t=>{const{Boolean:i,Number:e,String:s,Auto:o,Color:l,Array:n,Tuple:a,Or:r,Null:_,Nullable:c}=t,u=a(e,e),d=a(e,e,e,e);return{width:[c(e),null],height:[c(e),null],min_width:[c(e),null],min_height:[c(e),null],max_width:[c(e),null],max_height:[c(e),null],margin:[c(r(e,u,d)),[0,0,0,0]],width_policy:[r(m.SizingPolicy,o),\"auto\"],height_policy:[r(m.SizingPolicy,o),\"auto\"],aspect_ratio:[r(e,o,_),null],sizing_mode:[c(h.SizingMode),null],visible:[i,!0],disabled:[i,!1],align:[r(h.Align,a(h.Align,h.Align)),\"start\"],background:[c(l),null],css_classes:[n(s),[]]}}))},\n", - " function _(o,s,t,i,e){var n;i();const a=o(308),l=o(209);class u extends a.BoxView{_update_layout(){const o=this.child_views.map((o=>o.layout));this.layout=new l.Column(o),this.layout.rows=this.model.rows,this.layout.spacing=[this.model.spacing,0],this.layout.set_sizing(this.box_sizing())}}t.ColumnView=u,u.__name__=\"ColumnView\";class _ extends a.Box{constructor(o){super(o)}}t.Column=_,n=_,_.__name__=\"Column\",n.prototype.default_view=u,n.define((({Any:o})=>({rows:[o,\"auto\"]})))},\n", - " function _(s,o,t,i,e){var n;i();const l=s(309),a=s(209);class r extends l.LayoutDOMView{connect_signals(){super.connect_signals();const{children:s,rows:o,cols:t,spacing:i}=this.model.properties;this.on_change([s,o,t,i],(()=>this.rebuild()))}get child_models(){return this.model.children.map((([s])=>s))}_update_layout(){this.layout=new a.Grid,this.layout.rows=this.model.rows,this.layout.cols=this.model.cols,this.layout.spacing=this.model.spacing;for(const[s,o,t,i,e]of this.model.children){const n=this._child_views.get(s);this.layout.items.push({layout:n.layout,row:o,col:t,row_span:i,col_span:e})}this.layout.set_sizing(this.box_sizing())}}t.GridBoxView=r,r.__name__=\"GridBoxView\";class c extends l.LayoutDOM{constructor(s){super(s)}}t.GridBox=c,n=c,c.__name__=\"GridBox\",n.prototype.default_view=r,n.define((({Any:s,Int:o,Number:t,Tuple:i,Array:e,Ref:n,Or:a,Opt:r})=>({children:[e(i(n(l.LayoutDOM),o,o,r(o),r(o))),[]],rows:[s,\"auto\"],cols:[s,\"auto\"],spacing:[a(t,i(t,t)),0]})))},\n", - " function _(t,e,o,s,n){s();const _=t(309),i=t(207);class a extends _.LayoutDOMView{get child_models(){return[]}_update_layout(){this.layout=new i.ContentBox(this.el),this.layout.set_sizing(this.box_sizing())}}o.HTMLBoxView=a,a.__name__=\"HTMLBoxView\";class u extends _.LayoutDOM{constructor(t){super(t)}}o.HTMLBox=u,u.__name__=\"HTMLBox\"},\n", - " function _(e,n,l,a,o){var t;a();const s=e(53),c=e(309);class d extends s.Model{constructor(e){super(e)}}l.Panel=d,t=d,d.__name__=\"Panel\",t.define((({Boolean:e,String:n,Ref:l})=>({title:[n,\"\"],child:[l(c.LayoutDOM)],closable:[e,!1],disabled:[e,!1]})))},\n", - " function _(o,s,t,i,e){var a;i();const n=o(308),l=o(209);class _ extends n.BoxView{_update_layout(){const o=this.child_views.map((o=>o.layout));this.layout=new l.Row(o),this.layout.cols=this.model.cols,this.layout.spacing=[0,this.model.spacing],this.layout.set_sizing(this.box_sizing())}}t.RowView=_,_.__name__=\"RowView\";class c extends n.Box{constructor(o){super(o)}}t.Row=c,a=c,c.__name__=\"Row\",a.prototype.default_view=_,a.define((({Any:o})=>({cols:[o,\"auto\"]})))},\n", - " function _(e,t,a,s,_){var o;s();const i=e(309),n=e(207);class u extends i.LayoutDOMView{get child_models(){return[]}_update_layout(){this.layout=new n.LayoutItem,this.layout.set_sizing(this.box_sizing())}}a.SpacerView=u,u.__name__=\"SpacerView\";class c extends i.LayoutDOM{constructor(e){super(e)}}a.Spacer=c,o=c,c.__name__=\"Spacer\",o.prototype.default_view=u},\n", - " function _(e,t,s,i,l){i();const h=e(1);var a;const o=e(207),d=e(43),r=e(9),c=e(10),n=e(20),_=e(309),p=e(313),b=(0,h.__importStar)(e(317)),m=b,u=(0,h.__importStar)(e(318)),g=u,v=(0,h.__importStar)(e(229)),w=v;class f extends _.LayoutDOMView{constructor(){super(...arguments),this._scroll_index=0}connect_signals(){super.connect_signals(),this.connect(this.model.properties.tabs.change,(()=>this.rebuild())),this.connect(this.model.properties.active.change,(()=>this.on_active_change()))}styles(){return[...super.styles(),u.default,v.default,b.default]}get child_models(){return this.model.tabs.map((e=>e.child))}_update_layout(){const e=this.model.tabs_location,t=\"above\"==e||\"below\"==e,{scroll_el:s,headers_el:i}=this;this.header=new class extends o.ContentBox{_measure(e){const l=(0,d.size)(s),h=(0,d.children)(i).slice(0,3).map((e=>(0,d.size)(e))),{width:a,height:o}=super._measure(e);if(t){const t=l.width+(0,r.sum)(h.map((e=>e.width)));return{width:e.width!=1/0?e.width:t,height:o}}{const t=l.height+(0,r.sum)(h.map((e=>e.height)));return{width:a,height:e.height!=1/0?e.height:t}}}}(this.header_el),t?this.header.set_sizing({width_policy:\"fit\",height_policy:\"fixed\"}):this.header.set_sizing({width_policy:\"fixed\",height_policy:\"fit\"});let l=1,h=1;switch(e){case\"above\":l-=1;break;case\"below\":l+=1;break;case\"left\":h-=1;break;case\"right\":h+=1}const a={layout:this.header,row:l,col:h},c=this.child_views.map((e=>({layout:e.layout,row:1,col:1})));this.layout=new o.Grid([a,...c]),this.layout.set_sizing(this.box_sizing())}update_position(){super.update_position(),this.header_el.style.position=\"absolute\",(0,d.position)(this.header_el,this.header.bbox);const e=this.model.tabs_location,t=\"above\"==e||\"below\"==e,s=(0,d.size)(this.scroll_el),i=(0,d.scroll_size)(this.headers_el);if(t){const{width:e}=this.header.bbox;i.width>e?(this.wrapper_el.style.maxWidth=e-s.width+\"px\",(0,d.display)(this.scroll_el),this.do_scroll(this.model.active)):(this.wrapper_el.style.maxWidth=\"\",(0,d.undisplay)(this.scroll_el))}else{const{height:e}=this.header.bbox;i.height>e?(this.wrapper_el.style.maxHeight=e-s.height+\"px\",(0,d.display)(this.scroll_el),this.do_scroll(this.model.active)):(this.wrapper_el.style.maxHeight=\"\",(0,d.undisplay)(this.scroll_el))}const{child_views:l}=this;for(const e of l)(0,d.hide)(e.el);const h=l[this.model.active];null!=h&&(0,d.show)(h.el)}render(){super.render();const{active:e}=this.model,t=this.model.tabs.map(((t,s)=>{const i=(0,d.div)({class:[m.tab,s==e?m.active:null]},t.title);if(i.addEventListener(\"click\",(e=>{this.model.disabled||e.target==e.currentTarget&&this.change_active(s)})),t.closable){const e=(0,d.div)({class:m.close});e.addEventListener(\"click\",(e=>{if(e.target==e.currentTarget){this.model.tabs=(0,r.remove_at)(this.model.tabs,s);const e=this.model.tabs.length;this.model.active>e-1&&(this.model.active=e-1)}})),i.appendChild(e)}return(this.model.disabled||t.disabled)&&i.classList.add(m.disabled),i}));this.headers_el=(0,d.div)({class:[m.headers]},t),this.wrapper_el=(0,d.div)({class:m.headers_wrapper},this.headers_el),this.left_el=(0,d.div)({class:[g.btn,g.btn_default],disabled:\"\"},(0,d.div)({class:[w.caret,m.left]})),this.right_el=(0,d.div)({class:[g.btn,g.btn_default]},(0,d.div)({class:[w.caret,m.right]})),this.left_el.addEventListener(\"click\",(()=>this.do_scroll(\"left\"))),this.right_el.addEventListener(\"click\",(()=>this.do_scroll(\"right\"))),this.scroll_el=(0,d.div)({class:g.btn_group},this.left_el,this.right_el);const s=this.model.tabs_location;this.header_el=(0,d.div)({class:[m.tabs_header,m[s]]},this.scroll_el,this.wrapper_el),this.el.appendChild(this.header_el)}do_scroll(e){const t=this.model.tabs.length;\"left\"==e?this._scroll_index-=1:\"right\"==e?this._scroll_index+=1:this._scroll_index=e,this._scroll_index=(0,c.clamp)(this._scroll_index,0,t-1),0==this._scroll_index?this.left_el.setAttribute(\"disabled\",\"\"):this.left_el.removeAttribute(\"disabled\"),this._scroll_index==t-1?this.right_el.setAttribute(\"disabled\",\"\"):this.right_el.removeAttribute(\"disabled\");const s=(0,d.children)(this.headers_el).slice(0,this._scroll_index).map((e=>e.getBoundingClientRect())),i=this.model.tabs_location;if(\"above\"==i||\"below\"==i){const e=-(0,r.sum)(s.map((e=>e.width)));this.headers_el.style.left=`${e}px`}else{const e=-(0,r.sum)(s.map((e=>e.height)));this.headers_el.style.top=`${e}px`}}change_active(e){e!=this.model.active&&(this.model.active=e)}on_active_change(){const e=this.model.active,t=(0,d.children)(this.headers_el);for(const e of t)e.classList.remove(m.active);t[e].classList.add(m.active);const{child_views:s}=this;for(const e of s)(0,d.hide)(e.el);(0,d.show)(s[e].el)}}s.TabsView=f,f.__name__=\"TabsView\";class x extends _.LayoutDOM{constructor(e){super(e)}}s.Tabs=x,a=x,x.__name__=\"Tabs\",a.prototype.default_view=f,a.define((({Int:e,Array:t,Ref:s})=>({tabs:[t(s(p.Panel)),[]],tabs_location:[n.Location,\"above\"],active:[e,0]})))},\n", - " function _(e,r,b,o,t){o(),b.root=\"bk-root\",b.tabs_header=\"bk-tabs-header\",b.btn_group=\"bk-btn-group\",b.btn=\"bk-btn\",b.headers_wrapper=\"bk-headers-wrapper\",b.above=\"bk-above\",b.right=\"bk-right\",b.below=\"bk-below\",b.left=\"bk-left\",b.headers=\"bk-headers\",b.tab=\"bk-tab\",b.active=\"bk-active\",b.close=\"bk-close\",b.disabled=\"bk-disabled\",b.default='.bk-root .bk-tabs-header{display:flex;flex-wrap:nowrap;align-items:center;overflow:hidden;user-select:none;-ms-user-select:none;-moz-user-select:none;-webkit-user-select:none;}.bk-root .bk-tabs-header .bk-btn-group{height:auto;margin-right:5px;}.bk-root .bk-tabs-header .bk-btn-group > .bk-btn{flex-grow:0;height:auto;padding:4px 4px;}.bk-root .bk-tabs-header .bk-headers-wrapper{flex-grow:1;overflow:hidden;color:#666666;}.bk-root .bk-tabs-header.bk-above .bk-headers-wrapper{border-bottom:1px solid #e6e6e6;}.bk-root .bk-tabs-header.bk-right .bk-headers-wrapper{border-left:1px solid #e6e6e6;}.bk-root .bk-tabs-header.bk-below .bk-headers-wrapper{border-top:1px solid #e6e6e6;}.bk-root .bk-tabs-header.bk-left .bk-headers-wrapper{border-right:1px solid #e6e6e6;}.bk-root .bk-tabs-header.bk-above,.bk-root .bk-tabs-header.bk-below{flex-direction:row;}.bk-root .bk-tabs-header.bk-above .bk-headers,.bk-root .bk-tabs-header.bk-below .bk-headers{flex-direction:row;}.bk-root .bk-tabs-header.bk-left,.bk-root .bk-tabs-header.bk-right{flex-direction:column;}.bk-root .bk-tabs-header.bk-left .bk-headers,.bk-root .bk-tabs-header.bk-right .bk-headers{flex-direction:column;}.bk-root .bk-tabs-header .bk-headers{position:relative;display:flex;flex-wrap:nowrap;align-items:center;}.bk-root .bk-tabs-header .bk-tab{padding:4px 8px;border:solid transparent;white-space:nowrap;cursor:pointer;}.bk-root .bk-tabs-header .bk-tab:hover{background-color:#f2f2f2;}.bk-root .bk-tabs-header .bk-tab.bk-active{color:#4d4d4d;background-color:white;border-color:#e6e6e6;}.bk-root .bk-tabs-header .bk-tab .bk-close{margin-left:10px;}.bk-root .bk-tabs-header .bk-tab.bk-disabled{cursor:not-allowed;pointer-events:none;opacity:0.65;}.bk-root .bk-tabs-header.bk-above .bk-tab{border-width:3px 1px 0px 1px;border-radius:4px 4px 0 0;}.bk-root .bk-tabs-header.bk-right .bk-tab{border-width:1px 3px 1px 0px;border-radius:0 4px 4px 0;}.bk-root .bk-tabs-header.bk-below .bk-tab{border-width:0px 1px 3px 1px;border-radius:0 0 4px 4px;}.bk-root .bk-tabs-header.bk-left .bk-tab{border-width:1px 0px 1px 3px;border-radius:4px 0 0 4px;}.bk-root .bk-close{display:inline-block;width:10px;height:10px;vertical-align:middle;background-image:url(\\'data:image/svg+xml;utf8, \\');}.bk-root .bk-close:hover{background-image:url(\\'data:image/svg+xml;utf8, \\');}'},\n", - " function _(o,b,r,t,e){t(),r.root=\"bk-root\",r.btn=\"bk-btn\",r.active=\"bk-active\",r.btn_default=\"bk-btn-default\",r.btn_primary=\"bk-btn-primary\",r.btn_success=\"bk-btn-success\",r.btn_warning=\"bk-btn-warning\",r.btn_danger=\"bk-btn-danger\",r.btn_light=\"bk-btn-light\",r.btn_group=\"bk-btn-group\",r.vertical=\"bk-vertical\",r.horizontal=\"bk-horizontal\",r.dropdown_toggle=\"bk-dropdown-toggle\",r.default=\".bk-root .bk-btn{height:100%;display:inline-block;text-align:center;vertical-align:middle;white-space:nowrap;cursor:pointer;padding:6px 12px;font-size:12px;border:1px solid transparent;border-radius:4px;outline:0;user-select:none;-ms-user-select:none;-moz-user-select:none;-webkit-user-select:none;}.bk-root .bk-btn:hover,.bk-root .bk-btn:focus{text-decoration:none;}.bk-root .bk-btn:active,.bk-root .bk-btn.bk-active{background-image:none;box-shadow:inset 0 3px 5px rgba(0, 0, 0, 0.125);}.bk-root .bk-btn[disabled]{cursor:not-allowed;pointer-events:none;opacity:0.65;box-shadow:none;}.bk-root .bk-btn-default{color:#333;background-color:#fff;border-color:#ccc;}.bk-root .bk-btn-default:hover{background-color:#f5f5f5;border-color:#b8b8b8;}.bk-root .bk-btn-default.bk-active{background-color:#ebebeb;border-color:#adadad;}.bk-root .bk-btn-default[disabled],.bk-root .bk-btn-default[disabled]:hover,.bk-root .bk-btn-default[disabled]:focus,.bk-root .bk-btn-default[disabled]:active,.bk-root .bk-btn-default[disabled].bk-active{background-color:#e6e6e6;border-color:#ccc;}.bk-root .bk-btn-primary{color:#fff;background-color:#428bca;border-color:#357ebd;}.bk-root .bk-btn-primary:hover{background-color:#3681c1;border-color:#2c699e;}.bk-root .bk-btn-primary.bk-active{background-color:#3276b1;border-color:#285e8e;}.bk-root .bk-btn-primary[disabled],.bk-root .bk-btn-primary[disabled]:hover,.bk-root .bk-btn-primary[disabled]:focus,.bk-root .bk-btn-primary[disabled]:active,.bk-root .bk-btn-primary[disabled].bk-active{background-color:#506f89;border-color:#357ebd;}.bk-root .bk-btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c;}.bk-root .bk-btn-success:hover{background-color:#4eb24e;border-color:#409240;}.bk-root .bk-btn-success.bk-active{background-color:#47a447;border-color:#398439;}.bk-root .bk-btn-success[disabled],.bk-root .bk-btn-success[disabled]:hover,.bk-root .bk-btn-success[disabled]:focus,.bk-root .bk-btn-success[disabled]:active,.bk-root .bk-btn-success[disabled].bk-active{background-color:#667b66;border-color:#4cae4c;}.bk-root .bk-btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236;}.bk-root .bk-btn-warning:hover{background-color:#eea43b;border-color:#e89014;}.bk-root .bk-btn-warning.bk-active{background-color:#ed9c28;border-color:#d58512;}.bk-root .bk-btn-warning[disabled],.bk-root .bk-btn-warning[disabled]:hover,.bk-root .bk-btn-warning[disabled]:focus,.bk-root .bk-btn-warning[disabled]:active,.bk-root .bk-btn-warning[disabled].bk-active{background-color:#c89143;border-color:#eea236;}.bk-root .bk-btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a;}.bk-root .bk-btn-danger:hover{background-color:#d5433e;border-color:#bd2d29;}.bk-root .bk-btn-danger.bk-active{background-color:#d2322d;border-color:#ac2925;}.bk-root .bk-btn-danger[disabled],.bk-root .bk-btn-danger[disabled]:hover,.bk-root .bk-btn-danger[disabled]:focus,.bk-root .bk-btn-danger[disabled]:active,.bk-root .bk-btn-danger[disabled].bk-active{background-color:#a55350;border-color:#d43f3a;}.bk-root .bk-btn-light{color:#333;background-color:#fff;border-color:#ccc;border-color:transparent;}.bk-root .bk-btn-light:hover{background-color:#f5f5f5;border-color:#b8b8b8;}.bk-root .bk-btn-light.bk-active{background-color:#ebebeb;border-color:#adadad;}.bk-root .bk-btn-light[disabled],.bk-root .bk-btn-light[disabled]:hover,.bk-root .bk-btn-light[disabled]:focus,.bk-root .bk-btn-light[disabled]:active,.bk-root .bk-btn-light[disabled].bk-active{background-color:#e6e6e6;border-color:#ccc;}.bk-root .bk-btn-group{height:100%;display:flex;flex-wrap:nowrap;align-items:center;}.bk-root .bk-btn-group:not(.bk-vertical),.bk-root .bk-btn-group.bk-horizontal{flex-direction:row;}.bk-root .bk-btn-group.bk-vertical{flex-direction:column;}.bk-root .bk-btn-group > .bk-btn{flex-grow:1;}.bk-root .bk-btn-group:not(.bk-vertical) > .bk-btn + .bk-btn{margin-left:-1px;}.bk-root .bk-btn-group.bk-vertical > .bk-btn + .bk-btn{margin-top:-1px;}.bk-root .bk-btn-group:not(.bk-vertical) > .bk-btn:first-child:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;}.bk-root .bk-btn-group.bk-vertical > .bk-btn:first-child:not(:last-child){border-bottom-left-radius:0;border-bottom-right-radius:0;}.bk-root .bk-btn-group:not(.bk-vertical) > .bk-btn:not(:first-child):last-child{border-bottom-left-radius:0;border-top-left-radius:0;}.bk-root .bk-btn-group.bk-vertical > .bk-btn:not(:first-child):last-child{border-top-left-radius:0;border-top-right-radius:0;}.bk-root .bk-btn-group > .bk-btn:not(:first-child):not(:last-child){border-radius:0;}.bk-root .bk-btn-group.bk-vertical > .bk-btn{width:100%;}.bk-root .bk-btn-group .bk-dropdown-toggle{flex:0 0 0;padding:6px 6px;}\"},\n", - " function _(e,t,o,n,_){var i;n();const s=e(310);class d extends s.ColumnView{}o.WidgetBoxView=d,d.__name__=\"WidgetBoxView\";class a extends s.Column{constructor(e){super(e)}}o.WidgetBox=a,i=a,a.__name__=\"WidgetBox\",i.prototype.default_view=d},\n", - " function _(t,a,i,e,M){e();var T=t(135);M(\"MathText\",T.MathText),M(\"Ascii\",T.Ascii),M(\"MathML\",T.MathML),M(\"TeX\",T.TeX),M(\"PlainText\",t(139).PlainText)},\n", - " function _(r,o,t,e,n){e(),n(\"CustomJSTransform\",r(322).CustomJSTransform),n(\"Dodge\",r(323).Dodge),n(\"Interpolator\",r(325).Interpolator),n(\"Jitter\",r(326).Jitter),n(\"LinearInterpolator\",r(327).LinearInterpolator),n(\"StepInterpolator\",r(328).StepInterpolator),n(\"Transform\",r(56).Transform)},\n", - " function _(r,t,s,n,e){var a;n();const u=r(56),o=r(13),m=r(34);class _ extends u.Transform{constructor(r){super(r)}get names(){return(0,o.keys)(this.args)}get values(){return(0,o.values)(this.args)}_make_transform(r,t){return new Function(...this.names,r,(0,m.use_strict)(t))}get scalar_transform(){return this._make_transform(\"x\",this.func)}get vector_transform(){return this._make_transform(\"xs\",this.v_func)}compute(r){return this.scalar_transform(...this.values,r)}v_compute(r){return this.vector_transform(...this.values,r)}}s.CustomJSTransform=_,a=_,_.__name__=\"CustomJSTransform\",a.define((({Unknown:r,String:t,Dict:s})=>({args:[s(r),{}],func:[t,\"\"],v_func:[t,\"\"]})))},\n", - " function _(e,n,r,o,s){var t;o();const u=e(324);class a extends u.RangeTransform{constructor(e){super(e)}_compute(e){return e+this.value}}r.Dodge=a,t=a,a.__name__=\"Dodge\",t.define((({Number:e})=>({value:[e,0]})))},\n", - " function _(e,n,t,r,a){var s;r();const c=e(56),o=e(57),i=e(67),u=e(24),h=e(8),l=e(11);class g extends c.Transform{constructor(e){super(e)}v_compute(e){let n;this.range instanceof i.FactorRange?n=this.range.v_synthetic(e):(0,h.isArrayableOf)(e,h.isNumber)?n=e:(0,l.unreachable)();const t=new((0,u.infer_type)(n))(n.length);for(let e=0;e({range:[n(e(o.Range)),null]})))},\n", - " function _(t,e,r,n,s){var o;n();const i=t(56),a=t(70),h=t(24),l=t(9),d=t(8);class c extends i.Transform{constructor(t){super(t),this._sorted_dirty=!0}connect_signals(){super.connect_signals(),this.connect(this.change,(()=>this._sorted_dirty=!0))}v_compute(t){const e=new((0,h.infer_type)(t))(t.length);for(let r=0;ro*(e[t]-e[r]))),this._x_sorted=new((0,h.infer_type)(e))(n),this._y_sorted=new((0,h.infer_type)(r))(n);for(let t=0;t({x:[o(r,s(e))],y:[o(r,s(e))],data:[i(n(a.ColumnarDataSource)),null],clip:[t,!0]})))},\n", - " function _(t,s,e,i,r){i();const n=t(1);var o;const a=t(324),u=t(67),h=t(20),c=t(8),m=t(12),f=(0,n.__importStar)(t(10)),_=t(11);class p extends a.RangeTransform{constructor(t){super(t)}v_compute(t){var s;let e;this.range instanceof u.FactorRange?e=this.range.v_synthetic(t):(0,c.isArrayableOf)(t,c.isNumber)?e=t:(0,_.unreachable)();const i=e.length;(null===(s=this.previous_offsets)||void 0===s?void 0:s.length)!=i&&(this.previous_offsets=new Array(i),this.previous_offsets=(0,m.map)(this.previous_offsets,(()=>this._compute())));const 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w.Title({text:e}):e}],title_location:[_(c.Location),\"above\"],above:[n(o(a(b.Annotation),a(p.Axis))),[]],below:[n(o(a(b.Annotation),a(p.Axis))),[]],left:[n(o(a(b.Annotation),a(p.Axis))),[]],right:[n(o(a(b.Annotation),a(p.Axis))),[]],center:[n(o(a(b.Annotation),a(f.Grid))),[]],renderers:[n(a(S.Renderer)),[]],x_range:[a(x.Range),()=>new L.DataRange1d],y_range:[a(x.Range),()=>new L.DataRange1d],x_scale:[a(v.Scale),()=>new y.LinearScale],y_scale:[a(v.Scale),()=>new y.LinearScale],extra_x_ranges:[i(a(x.Range)),{}],extra_y_ranges:[i(a(x.Range)),{}],extra_x_scales:[i(a(v.Scale)),{}],extra_y_scales:[i(a(v.Scale)),{}],lod_factor:[t,10],lod_interval:[t,300],lod_threshold:[_(t),2e3],lod_timeout:[t,500],hidpi:[e,!0],output_backend:[c.OutputBackend,\"canvas\"],min_border:[_(t),5],min_border_top:[_(t),null],min_border_left:[_(t),null],min_border_bottom:[_(t),null],min_border_right:[_(t),null],inner_width:[t,0],inner_height:[t,0],outer_width:[t,0],outer_height:[t,0],match_aspect:[e,!1],aspect_scale:[t,1],reset_policy:[c.ResetPolicy,\"standard\"]}))),a.override({width:600,height:600,outline_line_color:\"#e5e5e5\",border_fill_color:\"#ffffff\",background_fill_color:\"#ffffff\"})},\n", - " function _(e,t,i,s,a){s();const n=e(1),o=e(126),l=e(249),r=e(309),_=e(40),h=e(118),d=e(128),u=e(220),c=e(251),p=e(113),v=e(45),g=e(19),b=e(251),m=e(333),y=e(8),w=e(9),f=e(235),x=e(208),z=e(211),k=e(209),q=e(123),M=e(65),R=e(334),V=e(335),S=e(28);class O extends r.LayoutDOMView{constructor(){super(...arguments),this._outer_bbox=new M.BBox,this._inner_bbox=new M.BBox,this._needs_paint=!0,this._needs_layout=!1,this._invalidated_painters=new Set,this._invalidate_all=!0,this._needs_notify=!1}get canvas(){return this.canvas_view}get state(){return this._state_manager}set invalidate_dataranges(e){this._range_manager.invalidate_dataranges=e}renderer_view(e){const t=this.renderer_views.get(e);if(null==t)for(const[,t]of this.renderer_views){const i=t.renderer_view(e);if(null!=i)return i}return t}get is_paused(){return null!=this._is_paused&&0!==this._is_paused}get child_models(){return[]}pause(){null==this._is_paused?this._is_paused=1:this._is_paused+=1}unpause(e=!1){if(null==this._is_paused)throw new Error(\"wasn't paused\");this._is_paused-=1,0!=this._is_paused||e||this.request_paint(\"everything\")}notify_finished_after_paint(){this._needs_notify=!0}request_render(){this.request_paint(\"everything\")}request_paint(e){this.invalidate_painters(e),this.schedule_paint()}invalidate_painters(e){if(\"everything\"==e)this._invalidate_all=!0;else if((0,y.isArray)(e))for(const t of e)this._invalidated_painters.add(t);else this._invalidated_painters.add(e)}schedule_paint(){if(!this.is_paused){const e=this.throttled_paint();this._ready=this._ready.then((()=>e))}}request_layout(){this._needs_layout=!0,this.request_paint(\"everything\")}reset(){\"standard\"==this.model.reset_policy&&(this.state.clear(),this.reset_range(),this.reset_selection()),this.model.trigger_event(new c.Reset)}remove(){(0,p.remove_views)(this.renderer_views),(0,p.remove_views)(this.tool_views),this.canvas_view.remove(),super.remove()}render(){super.render(),this.el.appendChild(this.canvas_view.el),this.canvas_view.render()}initialize(){this.pause(),super.initialize(),this.lod_started=!1,this.visuals=new v.Visuals(this),this._initial_state={selection:new Map,dimensions:{width:0,height:0}},this.visibility_callbacks=[],this.renderer_views=new Map,this.tool_views=new Map,this.frame=new o.CartesianFrame(this.model.x_scale,this.model.y_scale,this.model.x_range,this.model.y_range,this.model.extra_x_ranges,this.model.extra_y_ranges,this.model.extra_x_scales,this.model.extra_y_scales),this._range_manager=new R.RangeManager(this),this._state_manager=new V.StateManager(this,this._initial_state),this.throttled_paint=(0,m.throttle)((()=>this.repaint()),1e3/60);const{title_location:e,title:t}=this.model;null!=e&&null!=t&&(this._title=t instanceof h.Title?t:new h.Title({text:t}));const{toolbar_location:i,toolbar:s}=this.model;null!=i&&null!=s&&(this._toolbar=new u.ToolbarPanel({toolbar:s}),s.toolbar_location=i)}async lazy_initialize(){await super.lazy_initialize();const{hidpi:e,output_backend:t}=this.model,i=new l.Canvas({hidpi:e,output_backend:t});this.canvas_view=await(0,p.build_view)(i,{parent:this}),this.canvas_view.plot_views=[this],await this.build_renderer_views(),await this.build_tool_views(),this._range_manager.update_dataranges(),this.unpause(!0),g.logger.debug(\"PlotView initialized\")}_width_policy(){return null==this.model.frame_width?super._width_policy():\"min\"}_height_policy(){return null==this.model.frame_height?super._height_policy():\"min\"}_update_layout(){var e,t,i,s,a;this.layout=new z.BorderLayout,this.layout.set_sizing(this.box_sizing());const n=(0,w.copy)(this.model.above),o=(0,w.copy)(this.model.below),l=(0,w.copy)(this.model.left),r=(0,w.copy)(this.model.right),d=e=>{switch(e){case\"above\":return n;case\"below\":return o;case\"left\":return l;case\"right\":return r}},{title_location:c,title:p}=this.model;null!=c&&null!=p&&d(c).push(this._title);const{toolbar_location:v,toolbar:g}=this.model;if(null!=v&&null!=g){const e=d(v);let t=!0;if(this.model.toolbar_sticky)for(let i=0;i{var i;const s=this.renderer_view(t);return s.panel=new q.Panel(e),null===(i=s.update_layout)||void 0===i||i.call(s),s.layout},m=(e,t)=>{const i=\"above\"==e||\"below\"==e,s=[];for(const a of t)if((0,y.isArray)(a)){const t=a.map((t=>{const s=b(e,t);if(t instanceof u.ToolbarPanel){const e=i?\"width_policy\":\"height_policy\";s.set_sizing(Object.assign(Object.assign({},s.sizing),{[e]:\"min\"}))}return s}));let n;i?(n=new k.Row(t),n.set_sizing({width_policy:\"max\",height_policy:\"min\"})):(n=new k.Column(t),n.set_sizing({width_policy:\"min\",height_policy:\"max\"})),n.absolute=!0,s.push(n)}else s.push(b(e,a));return s},f=null!==(e=this.model.min_border)&&void 0!==e?e:0;this.layout.min_border={left:null!==(t=this.model.min_border_left)&&void 0!==t?t:f,top:null!==(i=this.model.min_border_top)&&void 0!==i?i:f,right:null!==(s=this.model.min_border_right)&&void 0!==s?s:f,bottom:null!==(a=this.model.min_border_bottom)&&void 0!==a?a:f};const M=new x.NodeLayout,R=new x.VStack,V=new x.VStack,S=new x.HStack,O=new x.HStack;M.absolute=!0,R.absolute=!0,V.absolute=!0,S.absolute=!0,O.absolute=!0,M.children=this.model.center.filter((e=>e instanceof _.Annotation)).map((e=>{var t;const i=this.renderer_view(e);return null===(t=i.update_layout)||void 0===t||t.call(i),i.layout})).filter((e=>null!=e));const{frame_width:P,frame_height:j}=this.model;M.set_sizing(Object.assign(Object.assign({},null!=P?{width_policy:\"fixed\",width:P}:{width_policy:\"fit\"}),null!=j?{height_policy:\"fixed\",height:j}:{height_policy:\"fit\"})),M.on_resize((e=>this.frame.set_geometry(e))),R.children=(0,w.reversed)(m(\"above\",n)),V.children=m(\"below\",o),S.children=(0,w.reversed)(m(\"left\",l)),O.children=m(\"right\",r),R.set_sizing({width_policy:\"fit\",height_policy:\"min\"}),V.set_sizing({width_policy:\"fit\",height_policy:\"min\"}),S.set_sizing({width_policy:\"min\",height_policy:\"fit\"}),O.set_sizing({width_policy:\"min\",height_policy:\"fit\"}),this.layout.center_panel=M,this.layout.top_panel=R,this.layout.bottom_panel=V,this.layout.left_panel=S,this.layout.right_panel=O}get axis_views(){const e=[];for(const[,t]of this.renderer_views)t instanceof d.AxisView&&e.push(t);return e}set_toolbar_visibility(e){for(const t of this.visibility_callbacks)t(e)}update_range(e,t){this.pause(),this._range_manager.update(e,t),this.unpause()}reset_range(){this.update_range(null),this.trigger_ranges_update_event()}trigger_ranges_update_event(){const{x_range:e,y_range:t}=this.model;this.model.trigger_event(new b.RangesUpdate(e.start,e.end,t.start,t.end))}get_selection(){const e=new Map;for(const t of this.model.data_renderers){const{selected:i}=t.selection_manager.source;e.set(t,i)}return e}update_selection(e){for(const t of this.model.data_renderers){const i=t.selection_manager.source;if(null!=e){const s=e.get(t);null!=s&&i.selected.update(s,!0)}else i.selection_manager.clear()}}reset_selection(){this.update_selection(null)}_invalidate_layout(){(()=>{var e;for(const t of this.model.side_panels){const i=this.renderer_views.get(t);if(null===(e=i.layout)||void 0===e?void 0:e.has_size_changed())return this.invalidate_painters(i),!0}return!1})()&&this.root.compute_layout()}get_renderer_views(){return this.computed_renderers.map((e=>this.renderer_views.get(e)))}*_compute_renderers(){const{above:e,below:t,left:i,right:s,center:a,renderers:n}=this.model;yield*n,yield*e,yield*t,yield*i,yield*s,yield*a,null!=this._title&&(yield this._title),null!=this._toolbar&&(yield this._toolbar);for(const e of this.model.toolbar.tools)null!=e.overlay&&(yield e.overlay),yield*e.synthetic_renderers}async build_renderer_views(){this.computed_renderers=[...this._compute_renderers()],await(0,p.build_views)(this.renderer_views,this.computed_renderers,{parent:this})}async build_tool_views(){const e=this.model.toolbar.tools;(await(0,p.build_views)(this.tool_views,e,{parent:this})).map((e=>this.canvas_view.ui_event_bus.register_tool(e)))}connect_signals(){super.connect_signals();const{x_ranges:e,y_ranges:t}=this.frame;for(const[,t]of e)this.connect(t.change,(()=>{this._needs_layout=!0,this.request_paint(\"everything\")}));for(const[,e]of t)this.connect(e.change,(()=>{this._needs_layout=!0,this.request_paint(\"everything\")}));const{above:i,below:s,left:a,right:n,center:o,renderers:l}=this.model.properties;this.on_change([i,s,a,n,o,l],(async()=>await this.build_renderer_views())),this.connect(this.model.toolbar.properties.tools.change,(async()=>{await this.build_renderer_views(),await this.build_tool_views()})),this.connect(this.model.change,(()=>this.request_paint(\"everything\"))),this.connect(this.model.reset,(()=>this.reset()))}has_finished(){if(!super.has_finished())return!1;if(this.model.visible)for(const[,e]of this.renderer_views)if(!e.has_finished())return!1;return!0}after_layout(){var e;super.after_layout();for(const[,t]of this.renderer_views)t instanceof _.AnnotationView&&(null===(e=t.after_layout)||void 0===e||e.call(t));if(this._needs_layout=!1,this.model.setv({inner_width:Math.round(this.frame.bbox.width),inner_height:Math.round(this.frame.bbox.height),outer_width:Math.round(this.layout.bbox.width),outer_height:Math.round(this.layout.bbox.height)},{no_change:!0}),!1!==this.model.match_aspect&&(this.pause(),this._range_manager.update_dataranges(),this.unpause(!0)),!this._outer_bbox.equals(this.layout.bbox)){const{width:e,height:t}=this.layout.bbox;this.canvas_view.resize(e,t),this._outer_bbox=this.layout.bbox,this._invalidate_all=!0,this._needs_paint=!0}const{inner_bbox:t}=this.layout;this._inner_bbox.equals(t)||(this._inner_bbox=t,this._needs_paint=!0),this._needs_paint&&this.paint()}repaint(){this._needs_layout&&this._invalidate_layout(),this.paint()}paint(){this.is_paused||(this.model.visible&&(g.logger.trace(`${this.toString()}.paint()`),this._actual_paint()),this._needs_notify&&(this._needs_notify=!1,this.notify_finished()))}_actual_paint(){var e;const{document:t}=this.model;if(null!=t){const e=t.interactive_duration();e>=0&&e{t.interactive_duration()>this.model.lod_timeout&&t.interactive_stop(),this.request_paint(\"everything\")}),this.model.lod_timeout):t.interactive_stop()}this._range_manager.invalidate_dataranges&&(this._range_manager.update_dataranges(),this._invalidate_layout());let i=!1,s=!1;if(this._invalidate_all)i=!0,s=!0;else for(const e of this._invalidated_painters){const{level:t}=e.model;if(\"overlay\"!=t?i=!0:s=!0,i&&s)break}this._invalidated_painters.clear(),this._invalidate_all=!1;const a=[this.frame.bbox.left,this.frame.bbox.top,this.frame.bbox.width,this.frame.bbox.height],{primary:n,overlays:o}=this.canvas_view;i&&(n.prepare(),this.canvas_view.prepare_webgl(a),this._map_hook(n.ctx,a),this._paint_empty(n.ctx,a),this._paint_outline(n.ctx,a),this._paint_levels(n.ctx,\"image\",a,!0),this._paint_levels(n.ctx,\"underlay\",a,!0),this._paint_levels(n.ctx,\"glyph\",a,!0),this._paint_levels(n.ctx,\"guide\",a,!1),this._paint_levels(n.ctx,\"annotation\",a,!1),n.finish()),(s||S.settings.wireframe)&&(o.prepare(),this._paint_levels(o.ctx,\"overlay\",a,!1),S.settings.wireframe&&this._paint_layout(o.ctx,this.layout),o.finish()),null==this._initial_state.range&&(this._initial_state.range=null!==(e=this._range_manager.compute_initial())&&void 0!==e?e:void 0),this._needs_paint=!1}_paint_levels(e,t,i,s){for(const a of this.computed_renderers){if(a.level!=t)continue;const n=this.renderer_views.get(a);e.save(),(s||n.needs_clip)&&(e.beginPath(),e.rect(...i),e.clip()),n.render(),e.restore(),n.has_webgl&&n.needs_webgl_blit&&this.canvas_view.blit_webgl(e)}}_paint_layout(e,t){const{x:i,y:s,width:a,height:n}=t.bbox;e.strokeStyle=\"blue\",e.strokeRect(i,s,a,n);for(const a of t)e.save(),t.absolute||e.translate(i,s),this._paint_layout(e,a),e.restore()}_map_hook(e,t){}_paint_empty(e,t){const[i,s,a,n]=[0,0,this.layout.bbox.width,this.layout.bbox.height],[o,l,r,_]=t;this.visuals.border_fill.doit&&(this.visuals.border_fill.set_value(e),e.fillRect(i,s,a,n),e.clearRect(o,l,r,_)),this.visuals.background_fill.doit&&(this.visuals.background_fill.set_value(e),e.fillRect(o,l,r,_))}_paint_outline(e,t){if(this.visuals.outline_line.doit){e.save(),this.visuals.outline_line.set_value(e);let[i,s,a,n]=t;i+a==this.layout.bbox.width&&(a-=1),s+n==this.layout.bbox.height&&(n-=1),e.strokeRect(i,s,a,n),e.restore()}}to_blob(){return this.canvas_view.to_blob()}export(e,t=!0){const i=\"png\"==e?\"canvas\":\"svg\",s=new f.CanvasLayer(i,t),{width:a,height:n}=this.layout.bbox;s.resize(a,n);const{canvas:o}=this.canvas_view.compose();return s.ctx.drawImage(o,0,0),s}serializable_state(){const e=super.serializable_state(),{children:t}=e,i=(0,n.__rest)(e,[\"children\"]),s=this.get_renderer_views().map((e=>e.serializable_state())).filter((e=>null!=e.bbox));return Object.assign(Object.assign({},i),{children:[...null!=t?t:[],...s]})}}i.PlotView=O,O.__name__=\"PlotView\"},\n", - " function _(t,n,e,o,u){o(),e.throttle=function(t,n){let e=null,o=0,u=!1;return function(){return new Promise(((r,i)=>{const l=function(){o=Date.now(),e=null,u=!1;try{t(),r()}catch(t){i(t)}},a=Date.now(),c=n-(a-o);c<=0&&!u?(null!=e&&clearTimeout(e),u=!0,requestAnimationFrame(l)):e||u?r():e=setTimeout((()=>requestAnimationFrame(l)),c)}))}}},\n", - " function _(t,n,e,a,s){a();const o=t(63),r=t(19);class l{constructor(t){this.parent=t,this.invalidate_dataranges=!0}get frame(){return this.parent.frame}update(t,n){const{x_ranges:e,y_ranges:a}=this.frame;if(null==t){for(const[,t]of e)t.reset();for(const[,t]of a)t.reset();this.update_dataranges()}else{const s=[];for(const[n,a]of e)s.push([a,t.xrs.get(n)]);for(const[n,e]of a)s.push([e,t.yrs.get(n)]);(null==n?void 0:n.scrolling)&&this._update_ranges_together(s),this._update_ranges_individually(s,n)}}reset(){this.update(null)}_update_dataranges(t){const n=new Map,e=new Map;let a=!1;for(const[,n]of t.x_ranges)n instanceof o.DataRange1d&&\"log\"==n.scale_hint&&(a=!0);for(const[,n]of t.y_ranges)n instanceof o.DataRange1d&&\"log\"==n.scale_hint&&(a=!0);for(const t of this.parent.model.data_renderers){const s=this.parent.renderer_view(t);if(null==s)continue;const o=s.glyph_view.bounds();if(null!=o&&n.set(t,o),a){const n=s.glyph_view.log_bounds();null!=n&&e.set(t,n)}}let s=!1,l=!1;const i=t.x_target.span,d=t.y_target.span;let u;!1!==this.parent.model.match_aspect&&0!=i&&0!=d&&(u=1/this.parent.model.aspect_scale*(i/d));for(const[,a]of t.x_ranges){if(a instanceof o.DataRange1d){const t=\"log\"==a.scale_hint?e:n;a.update(t,0,this.parent.model,u),a.follow&&(s=!0)}null!=a.bounds&&(l=!0)}for(const[,a]of t.y_ranges){if(a instanceof o.DataRange1d){const t=\"log\"==a.scale_hint?e:n;a.update(t,1,this.parent.model,u),a.follow&&(s=!0)}null!=a.bounds&&(l=!0)}if(s&&l){r.logger.warn(\"Follow enabled so bounds are unset.\");for(const[,n]of t.x_ranges)n.bounds=null;for(const[,n]of t.y_ranges)n.bounds=null}}update_dataranges(){this._update_dataranges(this.frame);for(const t of this.parent.model.renderers){const{coordinates:n}=t;null!=n&&this._update_dataranges(n)}this.invalidate_dataranges=!1}compute_initial(){let t=!0;const{x_ranges:n,y_ranges:e}=this.frame,a=new Map,s=new Map;for(const[e,s]of n){const{start:n,end:o}=s;if(null==n||null==o||isNaN(n+o)){t=!1;break}a.set(e,{start:n,end:o})}if(t)for(const[n,a]of e){const{start:e,end:o}=a;if(null==e||null==o||isNaN(e+o)){t=!1;break}s.set(n,{start:e,end:o})}return t?{xrs:a,yrs:s}:(r.logger.warn(\"could not set initial ranges\"),null)}_update_ranges_together(t){let n=1;for(const[e,a]of t)n=Math.min(n,this._get_weight_to_constrain_interval(e,a));if(n<1)for(const[e,a]of t)a.start=n*a.start+(1-n)*e.start,a.end=n*a.end+(1-n)*e.end}_update_ranges_individually(t,n){const e=!!(null==n?void 0:n.panning),a=!!(null==n?void 0:n.scrolling);let s=!1;for(const[n,o]of t){if(!a){const t=this._get_weight_to_constrain_interval(n,o);t<1&&(o.start=t*o.start+(1-t)*n.start,o.end=t*o.end+(1-t)*n.end)}if(null!=n.bounds&&\"auto\"!=n.bounds){const[t,r]=n.bounds,l=Math.abs(o.end-o.start);n.is_reversed?(null!=t&&t>=o.end&&(s=!0,o.end=t,(e||a)&&(o.start=t+l)),null!=r&&r<=o.start&&(s=!0,o.start=r,(e||a)&&(o.end=r-l))):(null!=t&&t>=o.start&&(s=!0,o.start=t,(e||a)&&(o.end=t+l)),null!=r&&r<=o.end&&(s=!0,o.end=r,(e||a)&&(o.start=r-l)))}}if(!(a&&s&&(null==n?void 0:n.maintain_focus)))for(const[n,e]of t)n.have_updated_interactively=!0,n.start==e.start&&n.end==e.end||n.setv(e)}_get_weight_to_constrain_interval(t,n){const{min_interval:e}=t;let{max_interval:a}=t;if(null!=t.bounds&&\"auto\"!=t.bounds){const[n,e]=t.bounds;if(null!=n&&null!=e){const t=Math.abs(e-n);a=null!=a?Math.min(a,t):t}}let s=1;if(null!=e||null!=a){const o=Math.abs(t.end-t.start),r=Math.abs(n.end-n.start);null!=e&&e>0&&r0&&r>a&&(s=(a-o)/(r-o)),s=Math.max(0,Math.min(1,s))}return s}}e.RangeManager=l,l.__name__=\"RangeManager\"},\n", - " function _(t,i,s,e,n){e();const h=t(15);class a{constructor(t,i){this.parent=t,this.initial_state=i,this.changed=new h.Signal0(this.parent,\"state_changed\"),this.history=[],this.index=-1}_do_state_change(t){const i=null!=this.history[t]?this.history[t].state:this.initial_state;return null!=i.range&&this.parent.update_range(i.range),null!=i.selection&&this.parent.update_selection(i.selection),i}push(t,i){const{history:s,index:e}=this,n=null!=s[e]?s[e].state:{},h=Object.assign(Object.assign(Object.assign({},this.initial_state),n),i);this.history=this.history.slice(0,this.index+1),this.history.push({type:t,state:h}),this.index=this.history.length-1,this.changed.emit()}clear(){this.history=[],this.index=-1,this.changed.emit()}undo(){if(this.can_undo){this.index-=1;const t=this._do_state_change(this.index);return this.changed.emit(),t}return null}redo(){if(this.can_redo){this.index+=1;const t=this._do_state_change(this.index);return this.changed.emit(),t}return null}get can_undo(){return this.index>=0}get can_redo(){return this.indexm.emit();const s=encodeURIComponent,o=document.createElement(\"script\");o.type=\"text/javascript\",o.src=`https://maps.googleapis.com/maps/api/js?v=${s(e)}&key=${s(t)}&callback=_bokeh_gmaps_callback`,document.body.appendChild(o)}(t,e)}m.connect((()=>this.request_paint(\"everything\")))}this.unpause()}remove(){(0,p.remove)(this.map_el),super.remove()}update_range(t,e){var s,o;if(null==t)this.map.setCenter({lat:this.initial_lat,lng:this.initial_lng}),this.map.setOptions({zoom:this.initial_zoom}),super.update_range(null,e);else if(null!=t.sdx||null!=t.sdy)this.map.panBy(null!==(s=t.sdx)&&void 0!==s?s:0,null!==(o=t.sdy)&&void 0!==o?o:0),super.update_range(t,e);else if(null!=t.factor){if(10!==this.zoom_count)return void(this.zoom_count+=1);this.zoom_count=0,this.pause(),super.update_range(t,e);const s=t.factor<0?-1:1,o=this.map.getZoom();if(null!=o){const t=o+s;if(t>=2){this.map.setZoom(t);const[e,s]=this._get_projected_bounds();s-e<0&&this.map.setZoom(o)}}this.unpause()}this._set_bokeh_ranges()}_build_map(){const{maps:t}=google;this.map_types={satellite:t.MapTypeId.SATELLITE,terrain:t.MapTypeId.TERRAIN,roadmap:t.MapTypeId.ROADMAP,hybrid:t.MapTypeId.HYBRID};const e=this.model.map_options,s={center:new t.LatLng(e.lat,e.lng),zoom:e.zoom,disableDefaultUI:!0,mapTypeId:this.map_types[e.map_type],scaleControl:e.scale_control,tilt:e.tilt};null!=e.styles&&(s.styles=JSON.parse(e.styles)),this.map_el=(0,p.div)({style:{position:\"absolute\"}}),this.canvas_view.add_underlay(this.map_el),this.map=new t.Map(this.map_el,s),t.event.addListener(this.map,\"idle\",(()=>this._set_bokeh_ranges())),t.event.addListener(this.map,\"bounds_changed\",(()=>this._set_bokeh_ranges())),t.event.addListenerOnce(this.map,\"tilesloaded\",(()=>this._render_finished())),this.connect(this.model.properties.map_options.change,(()=>this._update_options())),this.connect(this.model.map_options.properties.styles.change,(()=>this._update_styles())),this.connect(this.model.map_options.properties.lat.change,(()=>this._update_center(\"lat\"))),this.connect(this.model.map_options.properties.lng.change,(()=>this._update_center(\"lng\"))),this.connect(this.model.map_options.properties.zoom.change,(()=>this._update_zoom())),this.connect(this.model.map_options.properties.map_type.change,(()=>this._update_map_type())),this.connect(this.model.map_options.properties.scale_control.change,(()=>this._update_scale_control())),this.connect(this.model.map_options.properties.tilt.change,(()=>this._update_tilt()))}_render_finished(){this._tiles_loaded=!0,this.notify_finished()}has_finished(){return super.has_finished()&&!0===this._tiles_loaded}_get_latlon_bounds(){const t=this.map.getBounds(),e=t.getNorthEast(),s=t.getSouthWest();return[s.lng(),e.lng(),s.lat(),e.lat()]}_get_projected_bounds(){const[t,e,s,o]=this._get_latlon_bounds(),[i,a]=l.wgs84_mercator.compute(t,s),[n,p]=l.wgs84_mercator.compute(e,o);return[i,n,a,p]}_set_bokeh_ranges(){const[t,e,s,o]=this._get_projected_bounds();this.frame.x_range.setv({start:t,end:e}),this.frame.y_range.setv({start:s,end:o})}_update_center(t){var e;const s=null===(e=this.map.getCenter())||void 0===e?void 0:e.toJSON();null!=s&&(s[t]=this.model.map_options[t],this.map.setCenter(s),this._set_bokeh_ranges())}_update_map_type(){this.map.setOptions({mapTypeId:this.map_types[this.model.map_options.map_type]})}_update_scale_control(){this.map.setOptions({scaleControl:this.model.map_options.scale_control})}_update_tilt(){this.map.setOptions({tilt:this.model.map_options.tilt})}_update_options(){this._update_styles(),this._update_center(\"lat\"),this._update_center(\"lng\"),this._update_zoom(),this._update_map_type()}_update_styles(){this.map.setOptions({styles:JSON.parse(this.model.map_options.styles)})}_update_zoom(){this.map.setOptions({zoom:this.model.map_options.zoom}),this._set_bokeh_ranges()}_map_hook(t,e){if(null==this.map&&\"undefined\"!=typeof google&&null!=google.maps&&this._build_map(),null!=this.map_el){const[t,s,o,i]=e;this.map_el.style.top=`${s}px`,this.map_el.style.left=`${t}px`,this.map_el.style.width=`${o}px`,this.map_el.style.height=`${i}px`}}_paint_empty(t,e){const s=this.layout.bbox.width,o=this.layout.bbox.height,[i,a,n,p]=e;t.clearRect(0,0,s,o),t.beginPath(),t.moveTo(0,0),t.lineTo(0,o),t.lineTo(s,o),t.lineTo(s,0),t.lineTo(0,0),t.moveTo(i,a),t.lineTo(i+n,a),t.lineTo(i+n,a+p),t.lineTo(i,a+p),t.lineTo(i,a),t.closePath(),null!=this.model.border_fill_color&&(t.fillStyle=(0,_.color2css)(this.model.border_fill_color),t.fill())}}s.GMapPlotView=d,d.__name__=\"GMapPlotView\"},\n", - " function _(t,_,n,o,r){o();(0,t(1).__exportStar)(t(132),n)},\n", - " function _(e,r,d,n,R){n(),R(\"GlyphRenderer\",e(175).GlyphRenderer),R(\"GraphRenderer\",e(339).GraphRenderer),R(\"GuideRenderer\",e(129).GuideRenderer);var G=e(41);R(\"Renderer\",G.Renderer),R(\"RendererGroup\",G.RendererGroup)},\n", - " function _(e,r,i,n,t){var d;n();const o=e(176),s=e(175),a=e(303),l=e(302),p=e(113),_=e(178),h=e(283),y=e(286);class c extends o.DataRendererView{get glyph_view(){return this.node_view.glyph}async lazy_initialize(){await super.lazy_initialize(),this.apply_coordinates();const{parent:e}=this,{edge_renderer:r,node_renderer:i}=this.model;this.edge_view=await(0,p.build_view)(r,{parent:e}),this.node_view=await(0,p.build_view)(i,{parent:e})}connect_signals(){super.connect_signals(),this.connect(this.model.layout_provider.change,(()=>{this.apply_coordinates(),this.edge_view.set_data(),this.node_view.set_data(),this.request_render()}))}apply_coordinates(){const{edge_renderer:e,node_renderer:r}=this.model;if(!(e.glyph instanceof h.MultiLine||e.glyph instanceof y.Patches))throw new Error(`${this}.edge_renderer.glyph must be a MultiLine glyph`);if(!(r.glyph instanceof _.XYGlyph))throw new Error(`${this}.node_renderer.glyph must be a XYGlyph glyph`);const i=this.model.layout_provider.edge_coordinates,n=this.model.layout_provider.node_coordinates;e.glyph.properties.xs.internal=!0,e.glyph.properties.ys.internal=!0,r.glyph.properties.x.internal=!0,r.glyph.properties.y.internal=!0,e.glyph.xs={expr:i.x},e.glyph.ys={expr:i.y},r.glyph.x={expr:n.x},r.glyph.y={expr:n.y},this.model.edge_renderer=e,this.model.node_renderer=r}remove(){this.edge_view.remove(),this.node_view.remove(),super.remove()}_render(){this.edge_view.render(),this.node_view.render()}renderer_view(e){if(e instanceof s.GlyphRenderer){if(e==this.edge_view.model)return this.edge_view;if(e==this.node_view.model)return this.node_view}return super.renderer_view(e)}}i.GraphRendererView=c,c.__name__=\"GraphRendererView\";class g extends o.DataRenderer{constructor(e){super(e)}get_selection_manager(){return this.node_renderer.data_source.selection_manager}}i.GraphRenderer=g,d=g,g.__name__=\"GraphRenderer\",d.prototype.default_view=c,d.define((({Ref:e})=>({layout_provider:[e(a.LayoutProvider)],node_renderer:[e(s.GlyphRenderer)],edge_renderer:[e(s.GlyphRenderer)],selection_policy:[e(l.GraphHitTestPolicy),()=>new l.NodesOnly],inspection_policy:[e(l.GraphHitTestPolicy),()=>new l.NodesOnly]})))},\n", - " function _(e,t,n,o,c){o();(0,e(1).__exportStar)(e(74),n),c(\"Selection\",e(72).Selection)},\n", - " function _(a,e,S,o,r){o(),r(\"ServerSentDataSource\",a(342).ServerSentDataSource),r(\"AjaxDataSource\",a(344).AjaxDataSource),r(\"ColumnDataSource\",a(75).ColumnDataSource),r(\"ColumnarDataSource\",a(70).ColumnarDataSource),r(\"CDSView\",a(190).CDSView),r(\"DataSource\",a(71).DataSource),r(\"GeoJSONDataSource\",a(345).GeoJSONDataSource),r(\"WebDataSource\",a(343).WebDataSource)},\n", - " function _(e,t,i,a,s){a();const n=e(343);class r extends n.WebDataSource{constructor(e){super(e),this.initialized=!1}setup(){if(!this.initialized){this.initialized=!0;new EventSource(this.data_url).onmessage=e=>{var t;this.load_data(JSON.parse(e.data),this.mode,null!==(t=this.max_size)&&void 0!==t?t:void 0)}}}}i.ServerSentDataSource=r,r.__name__=\"ServerSentDataSource\"},\n", - " function _(e,t,a,n,r){var s;n();const l=e(75),o=e(20);class c extends l.ColumnDataSource{constructor(e){super(e)}get_column(e){const t=this.data[e];return null!=t?t:[]}get_length(){var e;return null!==(e=super.get_length())&&void 0!==e?e:0}initialize(){super.initialize(),this.setup()}load_data(e,t,a){const{adapter:n}=this;let r;switch(r=null!=n?n.execute(this,{response:e}):e,t){case\"replace\":this.data=r;break;case\"append\":{const e=this.data;for(const t of this.columns()){const n=Array.from(e[t]),s=Array.from(r[t]),l=n.concat(s);r[t]=null!=a?l.slice(-a):l}this.data=r;break}}}}a.WebDataSource=c,s=c,c.__name__=\"WebDataSource\",s.define((({Any:e,Int:t,String:a,Nullable:n})=>({max_size:[n(t),null],mode:[o.UpdateMode,\"replace\"],adapter:[n(e),null],data_url:[a]})))},\n", - " function _(t,e,i,s,a){var n;s();const r=t(343),o=t(20),l=t(19),d=t(13);class h extends r.WebDataSource{constructor(t){super(t),this.interval=null,this.initialized=!1}destroy(){null!=this.interval&&clearInterval(this.interval),super.destroy()}setup(){if(!this.initialized&&(this.initialized=!0,this.get_data(this.mode),null!=this.polling_interval)){const t=()=>this.get_data(this.mode,this.max_size,this.if_modified);this.interval=setInterval(t,this.polling_interval)}}get_data(t,e=null,i=!1){const s=this.prepare_request();s.addEventListener(\"load\",(()=>this.do_load(s,t,null!=e?e:void 0))),s.addEventListener(\"error\",(()=>this.do_error(s))),s.send()}prepare_request(){const t=new XMLHttpRequest;t.open(this.method,this.data_url,!0),t.withCredentials=!1,t.setRequestHeader(\"Content-Type\",this.content_type);const e=this.http_headers;for(const[i,s]of(0,d.entries)(e))t.setRequestHeader(i,s);return t}do_load(t,e,i){if(200===t.status){const s=JSON.parse(t.responseText);this.load_data(s,e,i)}}do_error(t){l.logger.error(`Failed to fetch JSON from ${this.data_url} with code ${t.status}`)}}i.AjaxDataSource=h,n=h,h.__name__=\"AjaxDataSource\",n.define((({Boolean:t,Int:e,String:i,Dict:s,Nullable:a})=>({polling_interval:[a(e),null],content_type:[i,\"application/json\"],http_headers:[s(i),{}],method:[o.HTTPMethod,\"POST\"],if_modified:[t,!1]})))},\n", - " function _(e,t,o,r,n){var s;r();const a=e(70),i=e(19),l=e(9),c=e(13);function _(e){return null!=e?e:NaN}const{hasOwnProperty:g}=Object.prototype;class u extends a.ColumnarDataSource{constructor(e){super(e)}initialize(){super.initialize(),this._update_data()}connect_signals(){super.connect_signals(),this.connect(this.properties.geojson.change,(()=>this._update_data()))}_update_data(){this.data=this.geojson_to_column_data()}_get_new_list_array(e){return(0,l.range)(0,e).map((e=>[]))}_get_new_nan_array(e){return(0,l.range)(0,e).map((e=>NaN))}_add_properties(e,t,o,r){var n;const s=null!==(n=e.properties)&&void 0!==n?n:{};for(const[e,n]of(0,c.entries)(s))g.call(t,e)||(t[e]=this._get_new_nan_array(r)),t[e][o]=_(n)}_add_geometry(e,t,o){function r(e,t){return e.concat([[NaN,NaN,NaN]]).concat(t)}switch(e.type){case\"Point\":{const[r,n,s]=e.coordinates;t.x[o]=r,t.y[o]=n,t.z[o]=_(s);break}case\"LineString\":{const{coordinates:r}=e;for(let e=0;e1&&i.logger.warn(\"Bokeh does not support Polygons with holes in, only exterior ring used.\");const r=e.coordinates[0];for(let e=0;e1&&i.logger.warn(\"Bokeh does not support Polygons with holes in, only exterior ring used.\"),n.push(t[0]);const s=n.reduce(r);for(let e=0;e({geojson:[e]}))),s.internal((({Dict:e,Arrayable:t})=>({data:[e(t),{}]})))},\n", - " function _(e,r,T,o,S){o(),S(\"BBoxTileSource\",e(347).BBoxTileSource),S(\"MercatorTileSource\",e(348).MercatorTileSource),S(\"QUADKEYTileSource\",e(351).QUADKEYTileSource),S(\"TileRenderer\",e(352).TileRenderer),S(\"TileSource\",e(349).TileSource),S(\"TMSTileSource\",e(355).TMSTileSource),S(\"WMTSTileSource\",e(353).WMTSTileSource)},\n", - " function _(e,t,r,o,l){var i;o();const n=e(348);class s extends n.MercatorTileSource{constructor(e){super(e)}get_image_url(e,t,r){const o=this.string_lookup_replace(this.url,this.extra_url_vars);let l,i,n,s;return this.use_latlon?[i,s,l,n]=this.get_tile_geographic_bounds(e,t,r):[i,s,l,n]=this.get_tile_meter_bounds(e,t,r),o.replace(\"{XMIN}\",i.toString()).replace(\"{YMIN}\",s.toString()).replace(\"{XMAX}\",l.toString()).replace(\"{YMAX}\",n.toString())}}r.BBoxTileSource=s,i=s,s.__name__=\"BBoxTileSource\",i.define((({Boolean:e})=>({use_latlon:[e,!1]})))},\n", - " function _(t,e,i,_,s){var r;_();const o=t(349),n=t(9),l=t(350);class u extends o.TileSource{constructor(t){super(t)}initialize(){super.initialize(),this._resolutions=(0,n.range)(this.min_zoom,this.max_zoom+1).map((t=>this.get_resolution(t)))}_computed_initial_resolution(){return null!=this.initial_resolution?this.initial_resolution:2*Math.PI*6378137/this.tile_size}is_valid_tile(t,e,i){return!(!this.wrap_around&&(t<0||t>=2**i))&&!(e<0||e>=2**i)}parent_by_tile_xyz(t,e,i){const _=this.tile_xyz_to_quadkey(t,e,i),s=_.substring(0,_.length-1);return this.quadkey_to_tile_xyz(s)}get_resolution(t){return this._computed_initial_resolution()/2**t}get_resolution_by_extent(t,e,i){return[(t[2]-t[0])/i,(t[3]-t[1])/e]}get_level_by_extent(t,e,i){const _=(t[2]-t[0])/i,s=(t[3]-t[1])/e,r=Math.max(_,s);let o=0;for(const t of this._resolutions){if(r>t){if(0==o)return 0;if(o>0)return o-1}o+=1}return o-1}get_closest_level_by_extent(t,e,i){const _=(t[2]-t[0])/i,s=(t[3]-t[1])/e,r=Math.max(_,s),o=this._resolutions.reduce((function(t,e){return Math.abs(e-r)e?(u=o-s,a*=t):(u*=e,a=n-r)}const h=(u-(o-s))/2,c=(a-(n-r))/2;return[s-h,r-c,o+h,n+c]}tms_to_wmts(t,e,i){return[t,2**i-1-e,i]}wmts_to_tms(t,e,i){return[t,2**i-1-e,i]}pixels_to_meters(t,e,i){const _=this.get_resolution(i);return[t*_-this.x_origin_offset,e*_-this.y_origin_offset]}meters_to_pixels(t,e,i){const _=this.get_resolution(i);return[(t+this.x_origin_offset)/_,(e+this.y_origin_offset)/_]}pixels_to_tile(t,e){let i=Math.ceil(t/this.tile_size);i=0===i?i:i-1;return[i,Math.max(Math.ceil(e/this.tile_size)-1,0)]}pixels_to_raster(t,e,i){return[t,(this.tile_size<=l;t--)for(let i=n;i<=u;i++)this.is_valid_tile(i,t,e)&&h.push([i,t,e,this.get_tile_meter_bounds(i,t,e)]);return this.sort_tiles_from_center(h,[n,l,u,a]),h}quadkey_to_tile_xyz(t){let e=0,i=0;const _=t.length;for(let s=_;s>0;s--){const r=1<0;s--){const i=1<0;)if(s=s.substring(0,s.length-1),[t,e,i]=this.quadkey_to_tile_xyz(s),[t,e,i]=this.denormalize_xyz(t,e,i,_),this.tiles.has(this.tile_xyz_to_key(t,e,i)))return[t,e,i];return[0,0,0]}normalize_xyz(t,e,i){if(this.wrap_around){const _=2**i;return[(t%_+_)%_,e,i]}return[t,e,i]}denormalize_xyz(t,e,i,_){return[t+_*2**i,e,i]}denormalize_meters(t,e,i,_){return[t+2*_*Math.PI*6378137,e]}calculate_world_x_by_tile_xyz(t,e,i){return Math.floor(t/2**i)}}i.MercatorTileSource=u,r=u,u.__name__=\"MercatorTileSource\",r.define((({Boolean:t})=>({snap_to_zoom:[t,!1],wrap_around:[t,!0]}))),r.override({x_origin_offset:20037508.34,y_origin_offset:20037508.34,initial_resolution:156543.03392804097})},\n", - " function _(e,t,r,i,n){var l;i();const a=e(53),s=e(13);class c extends a.Model{constructor(e){super(e)}initialize(){super.initialize(),this.tiles=new Map,this._normalize_case()}connect_signals(){super.connect_signals(),this.connect(this.change,(()=>this._clear_cache()))}string_lookup_replace(e,t){let r=e;for(const[e,i]of(0,s.entries)(t))r=r.replace(`{${e}}`,i);return r}_normalize_case(){const e=this.url.replace(\"{x}\",\"{X}\").replace(\"{y}\",\"{Y}\").replace(\"{z}\",\"{Z}\").replace(\"{q}\",\"{Q}\").replace(\"{xmin}\",\"{XMIN}\").replace(\"{ymin}\",\"{YMIN}\").replace(\"{xmax}\",\"{XMAX}\").replace(\"{ymax}\",\"{YMAX}\");this.url=e}_clear_cache(){this.tiles=new Map}tile_xyz_to_key(e,t,r){return`${e}:${t}:${r}`}key_to_tile_xyz(e){const[t,r,i]=e.split(\":\").map((e=>parseInt(e)));return[t,r,i]}sort_tiles_from_center(e,t){const[r,i,n,l]=t,a=(n-r)/2+r,s=(l-i)/2+i;e.sort((function(e,t){return Math.sqrt((a-e[0])**2+(s-e[1])**2)-Math.sqrt((a-t[0])**2+(s-t[1])**2)}))}get_image_url(e,t,r){return this.string_lookup_replace(this.url,this.extra_url_vars).replace(\"{X}\",e.toString()).replace(\"{Y}\",t.toString()).replace(\"{Z}\",r.toString())}}r.TileSource=c,l=c,c.__name__=\"TileSource\",l.define((({Number:e,String:t,Dict:r,Nullable:i})=>({url:[t,\"\"],tile_size:[e,256],max_zoom:[e,30],min_zoom:[e,0],extra_url_vars:[r(t),{}],attribution:[t,\"\"],x_origin_offset:[e],y_origin_offset:[e],initial_resolution:[i(e),null]})))},\n", - " function _(t,e,r,n,o){n();const c=t(78);function _(t,e){return c.wgs84_mercator.compute(t,e)}function g(t,e){return c.wgs84_mercator.invert(t,e)}r.geographic_to_meters=_,r.meters_to_geographic=g,r.geographic_extent_to_meters=function(t){const[e,r,n,o]=t,[c,g]=_(e,r),[i,u]=_(n,o);return[c,g,i,u]},r.meters_extent_to_geographic=function(t){const[e,r,n,o]=t,[c,_]=g(e,r),[i,u]=g(n,o);return[c,_,i,u]}},\n", - " function _(e,t,r,s,_){s();const o=e(348);class c extends o.MercatorTileSource{constructor(e){super(e)}get_image_url(e,t,r){const s=this.string_lookup_replace(this.url,this.extra_url_vars),[_,o,c]=this.tms_to_wmts(e,t,r),i=this.tile_xyz_to_quadkey(_,o,c);return s.replace(\"{Q}\",i)}}r.QUADKEYTileSource=c,c.__name__=\"QUADKEYTileSource\"},\n", - " function _(t,e,i,s,_){s();const n=t(1);var a;const o=t(349),r=t(353),h=t(41),l=t(58),d=t(43),m=t(136),c=t(9),u=t(8),p=(0,n.__importStar)(t(354));class g extends h.RendererView{initialize(){this._tiles=[],super.initialize()}connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.request_render())),this.connect(this.model.tile_source.change,(()=>this.request_render()))}remove(){null!=this.attribution_el&&(0,d.removeElement)(this.attribution_el),super.remove()}styles(){return[...super.styles(),p.default]}get_extent(){return[this.x_range.start,this.y_range.start,this.x_range.end,this.y_range.end]}get map_plot(){return this.plot_model}get map_canvas(){return this.layer.ctx}get map_frame(){return this.plot_view.frame}get x_range(){return this.map_plot.x_range}get y_range(){return this.map_plot.y_range}_set_data(){this.extent=this.get_extent(),this._last_height=void 0,this._last_width=void 0}_update_attribution(){null!=this.attribution_el&&(0,d.removeElement)(this.attribution_el);const{attribution:t}=this.model.tile_source;if((0,u.isString)(t)&&t.length>0){const{layout:e,frame:i}=this.plot_view,s=e.bbox.width-i.bbox.right,_=e.bbox.height-i.bbox.bottom,n=i.bbox.width;this.attribution_el=(0,d.div)({class:p.tile_attribution,style:{position:\"absolute\",right:`${s}px`,bottom:`${_}px`,\"max-width\":n-4+\"px\",padding:\"2px\",\"background-color\":\"rgba(255,255,255,0.5)\",\"font-size\":\"9px\",\"line-height\":\"1.05\",\"white-space\":\"nowrap\",overflow:\"hidden\",\"text-overflow\":\"ellipsis\"}}),this.plot_view.canvas_view.add_event(this.attribution_el),this.attribution_el.innerHTML=t,this.attribution_el.title=this.attribution_el.textContent.replace(/\\s*\\n\\s*/g,\" \")}}_map_data(){this.initial_extent=this.get_extent();const t=this.model.tile_source.get_level_by_extent(this.initial_extent,this.map_frame.bbox.height,this.map_frame.bbox.width),e=this.model.tile_source.snap_to_zoom_level(this.initial_extent,this.map_frame.bbox.height,this.map_frame.bbox.width,t);this.x_range.start=e[0],this.y_range.start=e[1],this.x_range.end=e[2],this.y_range.end=e[3],this.x_range instanceof l.Range1d&&(this.x_range.reset_start=e[0],this.x_range.reset_end=e[2]),this.y_range instanceof l.Range1d&&(this.y_range.reset_start=e[1],this.y_range.reset_end=e[3]),this._update_attribution()}_create_tile(t,e,i,s,_=!1){const n=this.model.tile_source.tile_xyz_to_quadkey(t,e,i),a=this.model.tile_source.tile_xyz_to_key(t,e,i);if(this.model.tile_source.tiles.has(a))return;const[o,r,h]=this.model.tile_source.normalize_xyz(t,e,i),l=this.model.tile_source.get_image_url(o,r,h),d={img:void 0,tile_coords:[t,e,i],normalized_coords:[o,r,h],quadkey:n,cache_key:a,bounds:s,loaded:!1,finished:!1,x_coord:s[0],y_coord:s[3]};this.model.tile_source.tiles.set(a,d),this._tiles.push(d),new m.ImageLoader(l,{loaded:t=>{Object.assign(d,{img:t,loaded:!0}),_?(d.finished=!0,this.notify_finished()):this.request_render()},failed(){d.finished=!0}})}_enforce_aspect_ratio(){if(this._last_height!==this.map_frame.bbox.height||this._last_width!==this.map_frame.bbox.width){const t=this.get_extent(),e=this.model.tile_source.get_level_by_extent(t,this.map_frame.bbox.height,this.map_frame.bbox.width),i=this.model.tile_source.snap_to_zoom_level(t,this.map_frame.bbox.height,this.map_frame.bbox.width,e);this.x_range.setv({start:i[0],end:i[2]}),this.y_range.setv({start:i[1],end:i[3]}),this.extent=i,this._last_height=this.map_frame.bbox.height,this._last_width=this.map_frame.bbox.width}}has_finished(){if(!super.has_finished())return!1;if(0==this._tiles.length)return!1;for(const t of this._tiles)if(!t.finished)return!1;return!0}_render(){null==this.map_initialized&&(this._set_data(),this._map_data(),this.map_initialized=!0),this._enforce_aspect_ratio(),this._update(),null!=this.prefetch_timer&&clearTimeout(this.prefetch_timer),this.prefetch_timer=setTimeout(this._prefetch_tiles.bind(this),500),this.has_finished()&&this.notify_finished()}_draw_tile(t){const e=this.model.tile_source.tiles.get(t);if(null!=e&&e.loaded){const[[t],[i]]=this.coordinates.map_to_screen([e.bounds[0]],[e.bounds[3]]),[[s],[_]]=this.coordinates.map_to_screen([e.bounds[2]],[e.bounds[1]]),n=s-t,a=_-i,o=t,r=i,h=this.map_canvas.getImageSmoothingEnabled();this.map_canvas.setImageSmoothingEnabled(this.model.smoothing),this.map_canvas.drawImage(e.img,o,r,n,a),this.map_canvas.setImageSmoothingEnabled(h),e.finished=!0}}_set_rect(){const t=this.plot_model.outline_line_width,e=this.map_frame.bbox.left+t/2,i=this.map_frame.bbox.top+t/2,s=this.map_frame.bbox.width-t,_=this.map_frame.bbox.height-t;this.map_canvas.rect(e,i,s,_),this.map_canvas.clip()}_render_tiles(t){this.map_canvas.save(),this._set_rect(),this.map_canvas.globalAlpha=this.model.alpha;for(const e of t)this._draw_tile(e);this.map_canvas.restore()}_prefetch_tiles(){const{tile_source:t}=this.model,e=this.get_extent(),i=this.map_frame.bbox.height,s=this.map_frame.bbox.width,_=this.model.tile_source.get_level_by_extent(e,i,s),n=this.model.tile_source.get_tiles_by_extent(e,_);for(let e=0,i=Math.min(10,n.length);ei&&(s=this.extent,o=i,r=!0),r&&(this.x_range.setv({start:s[0],end:s[2]}),this.y_range.setv({start:s[1],end:s[3]})),this.extent=s;const h=t.get_tiles_by_extent(s,o),l=[],d=[],m=[],u=[];for(const e of h){const[i,s,n]=e,a=t.tile_xyz_to_key(i,s,n),o=t.tiles.get(a);if(null!=o&&o.loaded)d.push(a);else if(this.model.render_parents){const[e,a,o]=t.get_closest_parent_by_tile_xyz(i,s,n),r=t.tile_xyz_to_key(e,a,o),h=t.tiles.get(r);if(null!=h&&h.loaded&&!(0,c.includes)(m,r)&&m.push(r),_){const e=t.children_by_tile_xyz(i,s,n);for(const[i,s,_]of e){const e=t.tile_xyz_to_key(i,s,_);t.tiles.has(e)&&u.push(e)}}}null==o&&l.push(e)}this._render_tiles(m),this._render_tiles(u),this._render_tiles(d),null!=this.render_timer&&clearTimeout(this.render_timer),this.render_timer=setTimeout((()=>this._fetch_tiles(l)),65)}}i.TileRendererView=g,g.__name__=\"TileRendererView\";class b extends h.Renderer{constructor(t){super(t)}}i.TileRenderer=b,a=b,b.__name__=\"TileRenderer\",a.prototype.default_view=g,a.define((({Boolean:t,Number:e,Ref:i})=>({alpha:[e,1],smoothing:[t,!0],tile_source:[i(o.TileSource),()=>new r.WMTSTileSource],render_parents:[t,!0]}))),a.override({level:\"image\"})},\n", - " function _(t,e,r,o,s){o();const c=t(348);class i extends c.MercatorTileSource{constructor(t){super(t)}get_image_url(t,e,r){const o=this.string_lookup_replace(this.url,this.extra_url_vars),[s,c,i]=this.tms_to_wmts(t,e,r);return o.replace(\"{X}\",s.toString()).replace(\"{Y}\",c.toString()).replace(\"{Z}\",i.toString())}}r.WMTSTileSource=i,i.__name__=\"WMTSTileSource\"},\n", - " function _(t,o,i,b,r){b(),i.root=\"bk-root\",i.tile_attribution=\"bk-tile-attribution\",i.default=\".bk-root .bk-tile-attribution a{color:black;}\"},\n", - " function _(e,r,t,c,o){c();const i=e(348);class l extends i.MercatorTileSource{constructor(e){super(e)}get_image_url(e,r,t){return this.string_lookup_replace(this.url,this.extra_url_vars).replace(\"{X}\",e.toString()).replace(\"{Y}\",r.toString()).replace(\"{Z}\",t.toString())}}t.TMSTileSource=l,l.__name__=\"TMSTileSource\"},\n", - " function _(e,t,u,a,r){a(),r(\"CanvasTexture\",e(357).CanvasTexture),r(\"ImageURLTexture\",e(359).ImageURLTexture),r(\"Texture\",e(358).Texture)},\n", - " function _(t,e,n,c,s){var r;c();const o=t(358),a=t(34);class u extends o.Texture{constructor(t){super(t)}get func(){const t=(0,a.use_strict)(this.code);return new Function(\"ctx\",\"color\",\"scale\",\"weight\",t)}get_pattern(t,e,n){const c=document.createElement(\"canvas\");c.width=e,c.height=e;const s=c.getContext(\"2d\");return this.func.call(this,s,t,e,n),c}}n.CanvasTexture=u,r=u,u.__name__=\"CanvasTexture\",r.define((({String:t})=>({code:[t]})))},\n", - " function _(e,t,n,r,o){var i;r();const s=e(53),u=e(20);class c extends s.Model{constructor(e){super(e)}}n.Texture=c,i=c,c.__name__=\"Texture\",i.define((()=>({repetition:[u.TextureRepetition,\"repeat\"]})))},\n", - " function _(e,t,i,r,n){var a;r();const s=e(358),o=e(136);class u extends s.Texture{constructor(e){super(e)}initialize(){super.initialize(),this._loader=new o.ImageLoader(this.url)}get_pattern(e,t,i){const{_loader:r}=this;return this._loader.finished?r.image:r.promise}}i.ImageURLTexture=u,a=u,u.__name__=\"ImageURLTexture\",a.define((({String:e})=>({url:[e]})))},\n", - " function _(o,l,T,e,t){e(),t(\"ActionTool\",o(238).ActionTool),t(\"CustomAction\",o(361).CustomAction),t(\"HelpTool\",o(239).HelpTool),t(\"RedoTool\",o(362).RedoTool),t(\"ResetTool\",o(363).ResetTool),t(\"SaveTool\",o(364).SaveTool),t(\"UndoTool\",o(365).UndoTool),t(\"ZoomInTool\",o(366).ZoomInTool),t(\"ZoomOutTool\",o(369).ZoomOutTool),t(\"ButtonTool\",o(224).ButtonTool),t(\"EditTool\",o(370).EditTool),t(\"BoxEditTool\",o(371).BoxEditTool),t(\"FreehandDrawTool\",o(372).FreehandDrawTool),t(\"PointDrawTool\",o(373).PointDrawTool),t(\"PolyDrawTool\",o(374).PolyDrawTool),t(\"PolyTool\",o(375).PolyTool),t(\"PolyEditTool\",o(376).PolyEditTool),t(\"BoxSelectTool\",o(377).BoxSelectTool),t(\"BoxZoomTool\",o(379).BoxZoomTool),t(\"GestureTool\",o(223).GestureTool),t(\"LassoSelectTool\",o(380).LassoSelectTool),t(\"LineEditTool\",o(382).LineEditTool),t(\"PanTool\",o(384).PanTool),t(\"PolySelectTool\",o(381).PolySelectTool),t(\"RangeTool\",o(385).RangeTool),t(\"SelectTool\",o(378).SelectTool),t(\"TapTool\",o(386).TapTool),t(\"WheelPanTool\",o(387).WheelPanTool),t(\"WheelZoomTool\",o(388).WheelZoomTool),t(\"CrosshairTool\",o(389).CrosshairTool),t(\"CustomJSHover\",o(390).CustomJSHover),t(\"HoverTool\",o(391).HoverTool),t(\"InspectTool\",o(232).InspectTool),t(\"Tool\",o(222).Tool),t(\"ToolProxy\",o(394).ToolProxy),t(\"Toolbar\",o(221).Toolbar),t(\"ToolbarBase\",o(233).ToolbarBase),t(\"ProxyToolbar\",o(395).ProxyToolbar),t(\"ToolbarBox\",o(395).ToolbarBox)},\n", - " function _(t,o,e,s,n){var c;s();const i=t(238);class u extends i.ActionToolButtonView{css_classes(){return super.css_classes().concat(\"bk-toolbar-button-custom-action\")}}e.CustomActionButtonView=u,u.__name__=\"CustomActionButtonView\";class l extends i.ActionToolView{doit(){var t;null===(t=this.model.callback)||void 0===t||t.execute(this.model)}}e.CustomActionView=l,l.__name__=\"CustomActionView\";class a extends i.ActionTool{constructor(t){super(t),this.tool_name=\"Custom Action\",this.button_view=u}}e.CustomAction=a,c=a,a.__name__=\"CustomAction\",c.prototype.default_view=l,c.define((({Any:t,String:o,Nullable:e})=>({callback:[e(t)],icon:[o]}))),c.override({description:\"Perform a Custom Action\"})},\n", - " function _(e,o,t,i,s){var n;i();const l=e(238),_=e(228);class d extends l.ActionToolView{connect_signals(){super.connect_signals(),this.connect(this.plot_view.state.changed,(()=>this.model.disabled=!this.plot_view.state.can_redo))}doit(){const e=this.plot_view.state.redo();null!=(null==e?void 0:e.range)&&this.plot_view.trigger_ranges_update_event()}}t.RedoToolView=d,d.__name__=\"RedoToolView\";class a extends l.ActionTool{constructor(e){super(e),this.tool_name=\"Redo\",this.icon=_.tool_icon_redo}}t.RedoTool=a,n=a,a.__name__=\"RedoTool\",n.prototype.default_view=d,n.override({disabled:!0}),n.register_alias(\"redo\",(()=>new a))},\n", - " function _(e,o,t,s,i){var _;s();const n=e(238),l=e(228);class c extends n.ActionToolView{doit(){this.plot_view.reset()}}t.ResetToolView=c,c.__name__=\"ResetToolView\";class r extends n.ActionTool{constructor(e){super(e),this.tool_name=\"Reset\",this.icon=l.tool_icon_reset}}t.ResetTool=r,_=r,r.__name__=\"ResetTool\",_.prototype.default_view=c,_.register_alias(\"reset\",(()=>new r))},\n", - " function _(e,o,t,a,i){var s;a();const c=e(238),n=e(228);class l extends c.ActionToolView{async copy(){const e=await this.plot_view.to_blob(),o=new ClipboardItem({[e.type]:Promise.resolve(e)});await navigator.clipboard.write([o])}async save(e){const o=await this.plot_view.to_blob(),t=document.createElement(\"a\");t.href=URL.createObjectURL(o),t.download=e,t.target=\"_blank\",t.dispatchEvent(new MouseEvent(\"click\"))}doit(e=\"save\"){switch(e){case\"save\":this.save(\"bokeh_plot\");break;case\"copy\":this.copy()}}}t.SaveToolView=l,l.__name__=\"SaveToolView\";class r extends c.ActionTool{constructor(e){super(e),this.tool_name=\"Save\",this.icon=n.tool_icon_save}get menu(){return[{icon:\"bk-tool-icon-copy-to-clipboard\",tooltip:\"Copy image to clipboard\",if:()=>\"undefined\"!=typeof ClipboardItem,handler:()=>{this.do.emit(\"copy\")}}]}}t.SaveTool=r,s=r,r.__name__=\"SaveTool\",s.prototype.default_view=l,s.register_alias(\"save\",(()=>new r))},\n", - " function _(o,e,t,n,i){var s;n();const l=o(238),_=o(228);class d extends l.ActionToolView{connect_signals(){super.connect_signals(),this.connect(this.plot_view.state.changed,(()=>this.model.disabled=!this.plot_view.state.can_undo))}doit(){const o=this.plot_view.state.undo();null!=(null==o?void 0:o.range)&&this.plot_view.trigger_ranges_update_event()}}t.UndoToolView=d,d.__name__=\"UndoToolView\";class a extends l.ActionTool{constructor(o){super(o),this.tool_name=\"Undo\",this.icon=_.tool_icon_undo}}t.UndoTool=a,s=a,a.__name__=\"UndoTool\",s.prototype.default_view=d,s.override({disabled:!0}),s.register_alias(\"undo\",(()=>new a))},\n", - " function _(o,n,e,i,s){var t;i();const _=o(367),m=o(228);class a extends _.ZoomBaseToolView{}e.ZoomInToolView=a,a.__name__=\"ZoomInToolView\";class l extends _.ZoomBaseTool{constructor(o){super(o),this.sign=1,this.tool_name=\"Zoom In\",this.icon=m.tool_icon_zoom_in}}e.ZoomInTool=l,t=l,l.__name__=\"ZoomInTool\",t.prototype.default_view=a,t.register_alias(\"zoom_in\",(()=>new l({dimensions:\"both\"}))),t.register_alias(\"xzoom_in\",(()=>new l({dimensions:\"width\"}))),t.register_alias(\"yzoom_in\",(()=>new l({dimensions:\"height\"})))},\n", - " function _(o,t,e,i,s){var n;i();const a=o(238),_=o(20),l=o(368);class m extends a.ActionToolView{doit(){var o;const t=this.plot_view.frame,e=this.model.dimensions,i=\"width\"==e||\"both\"==e,s=\"height\"==e||\"both\"==e,n=(0,l.scale_range)(t,this.model.sign*this.model.factor,i,s);this.plot_view.state.push(\"zoom_out\",{range:n}),this.plot_view.update_range(n,{scrolling:!0,maintain_focus:this.model.maintain_focus}),null===(o=this.model.document)||void 0===o||o.interactive_start(this.plot_model),this.plot_view.trigger_ranges_update_event()}}e.ZoomBaseToolView=m,m.__name__=\"ZoomBaseToolView\";class h extends a.ActionTool{constructor(o){super(o),this.maintain_focus=!0}get tooltip(){return this._get_dim_tooltip(this.dimensions)}}e.ZoomBaseTool=h,n=h,h.__name__=\"ZoomBaseTool\",n.define((({Percent:o})=>({factor:[o,.1],dimensions:[_.Dimensions,\"both\"]})))},\n", - " function _(n,t,o,r,s){r();const c=n(10);function e(n,t,o){const[r,s]=[n.start,n.end],c=null!=o?o:(s+r)/2;return[r-(r-c)*t,s-(s-c)*t]}function a(n,[t,o]){const r=new Map;for(const[s,c]of n){const[n,e]=c.r_invert(t,o);r.set(s,{start:n,end:e})}return r}o.scale_highlow=e,o.get_info=a,o.scale_range=function(n,t,o=!0,r=!0,s){t=(0,c.clamp)(t,-.9,.9);const l=o?t:0,[u,i]=e(n.bbox.h_range,l,null!=s?s.x:void 0),_=a(n.x_scales,[u,i]),f=r?t:0,[g,x]=e(n.bbox.v_range,f,null!=s?s.y:void 0);return{xrs:_,yrs:a(n.y_scales,[g,x]),factor:t}}},\n", - " function _(o,e,t,i,s){var n;i();const _=o(367),a=o(228);class m extends _.ZoomBaseToolView{}t.ZoomOutToolView=m,m.__name__=\"ZoomOutToolView\";class l extends _.ZoomBaseTool{constructor(o){super(o),this.sign=-1,this.tool_name=\"Zoom Out\",this.icon=a.tool_icon_zoom_out}}t.ZoomOutTool=l,n=l,l.__name__=\"ZoomOutTool\",n.prototype.default_view=m,n.define((({Boolean:o})=>({maintain_focus:[o,!0]}))),n.register_alias(\"zoom_out\",(()=>new l({dimensions:\"both\"}))),n.register_alias(\"xzoom_out\",(()=>new l({dimensions:\"width\"}))),n.register_alias(\"yzoom_out\",(()=>new l({dimensions:\"height\"})))},\n", - " function _(e,t,s,o,n){var r;o();const i=e(9),c=e(8),a=e(11),_=e(175),l=e(223);class d extends l.GestureToolView{constructor(){super(...arguments),this._mouse_in_frame=!0}_select_mode(e){const{shiftKey:t,ctrlKey:s}=e;return t||s?t&&!s?\"append\":!t&&s?\"intersect\":t&&s?\"subtract\":void(0,a.unreachable)():\"replace\"}_move_enter(e){this._mouse_in_frame=!0}_move_exit(e){this._mouse_in_frame=!1}_map_drag(e,t,s){if(!this.plot_view.frame.bbox.contains(e,t))return null;const o=this.plot_view.renderer_view(s);if(null==o)return null;return[o.coordinates.x_scale.invert(e),o.coordinates.y_scale.invert(t)]}_delete_selected(e){const t=e.data_source,s=t.selected.indices;s.sort();for(const e of t.columns()){const o=t.get_array(e);for(let e=0;e({custom_icon:[n(t),null],empty_value:[e],renderers:[s(o(_.GlyphRenderer)),[]]})))},\n", - " function _(e,t,s,i,_){var o;i();const n=e(43),a=e(20),d=e(370),l=e(228);class r extends d.EditToolView{_tap(e){null==this._draw_basepoint&&null==this._basepoint&&this._select_event(e,this._select_mode(e),this.model.renderers)}_keyup(e){if(this.model.active&&this._mouse_in_frame)for(const t of this.model.renderers)if(e.keyCode===n.Keys.Backspace)this._delete_selected(t);else if(e.keyCode==n.Keys.Esc){t.data_source.selection_manager.clear()}}_set_extent([e,t],[s,i],_,o=!1){const n=this.model.renderers[0],a=this.plot_view.renderer_view(n);if(null==a)return;const d=n.glyph,l=n.data_source,[r,h]=a.coordinates.x_scale.r_invert(e,t),[p,u]=a.coordinates.y_scale.r_invert(s,i),[c,m]=[(r+h)/2,(p+u)/2],[f,b]=[h-r,u-p],[y,x]=[d.x.field,d.y.field],[w,v]=[d.width.field,d.height.field];if(_)this._pop_glyphs(l,this.model.num_objects),y&&l.get_array(y).push(c),x&&l.get_array(x).push(m),w&&l.get_array(w).push(f),v&&l.get_array(v).push(b),this._pad_empty_columns(l,[y,x,w,v]);else{const e=l.data[y].length-1;y&&(l.data[y][e]=c),x&&(l.data[x][e]=m),w&&(l.data[w][e]=f),v&&(l.data[v][e]=b)}this._emit_cds_changes(l,!0,!1,o)}_update_box(e,t=!1,s=!1){if(null==this._draw_basepoint)return;const i=[e.sx,e.sy],_=this.plot_view.frame,o=this.model.dimensions,n=this.model._get_dim_limits(this._draw_basepoint,i,_,o);if(null!=n){const[e,i]=n;this._set_extent(e,i,t,s)}}_doubletap(e){this.model.active&&(null!=this._draw_basepoint?(this._update_box(e,!1,!0),this._draw_basepoint=null):(this._draw_basepoint=[e.sx,e.sy],this._select_event(e,\"append\",this.model.renderers),this._update_box(e,!0,!1)))}_move(e){this._update_box(e,!1,!1)}_pan_start(e){if(e.shiftKey){if(null!=this._draw_basepoint)return;this._draw_basepoint=[e.sx,e.sy],this._update_box(e,!0,!1)}else{if(null!=this._basepoint)return;this._select_event(e,\"append\",this.model.renderers),this._basepoint=[e.sx,e.sy]}}_pan(e,t=!1,s=!1){if(e.shiftKey){if(null==this._draw_basepoint)return;this._update_box(e,t,s)}else{if(null==this._basepoint)return;this._drag_points(e,this.model.renderers)}}_pan_end(e){if(this._pan(e,!1,!0),e.shiftKey)this._draw_basepoint=null;else{this._basepoint=null;for(const e of this.model.renderers)this._emit_cds_changes(e.data_source,!1,!0,!0)}}}s.BoxEditToolView=r,r.__name__=\"BoxEditToolView\";class h extends d.EditTool{constructor(e){super(e),this.tool_name=\"Box Edit Tool\",this.icon=l.tool_icon_box_edit,this.event_type=[\"tap\",\"pan\",\"move\"],this.default_order=1}}s.BoxEditTool=h,o=h,h.__name__=\"BoxEditTool\",o.prototype.default_view=r,o.define((({Int:e})=>({dimensions:[a.Dimensions,\"both\"],num_objects:[e,0]})))},\n", - " function _(e,t,a,s,r){var _;s();const d=e(43),o=e(8),n=e(370),i=e(228);class l extends n.EditToolView{_draw(e,t,a=!1){if(!this.model.active)return;const s=this.model.renderers[0],r=this._map_drag(e.sx,e.sy,s);if(null==r)return;const[_,d]=r,n=s.data_source,i=s.glyph,[l,h]=[i.xs.field,i.ys.field];if(\"new\"==t)this._pop_glyphs(n,this.model.num_objects),l&&n.get_array(l).push([_]),h&&n.get_array(h).push([d]),this._pad_empty_columns(n,[l,h]);else if(\"add\"==t){if(l){const e=n.data[l].length-1;let t=n.get_array(l)[e];(0,o.isArray)(t)||(t=Array.from(t),n.data[l][e]=t),t.push(_)}if(h){const e=n.data[h].length-1;let t=n.get_array(h)[e];(0,o.isArray)(t)||(t=Array.from(t),n.data[h][e]=t),t.push(d)}}this._emit_cds_changes(n,!0,!0,a)}_pan_start(e){this._draw(e,\"new\")}_pan(e){this._draw(e,\"add\")}_pan_end(e){this._draw(e,\"add\",!0)}_tap(e){this._select_event(e,this._select_mode(e),this.model.renderers)}_keyup(e){if(this.model.active&&this._mouse_in_frame)for(const t of this.model.renderers)e.keyCode===d.Keys.Esc?t.data_source.selection_manager.clear():e.keyCode===d.Keys.Backspace&&this._delete_selected(t)}}a.FreehandDrawToolView=l,l.__name__=\"FreehandDrawToolView\";class h extends n.EditTool{constructor(e){super(e),this.tool_name=\"Freehand Draw Tool\",this.icon=i.tool_icon_freehand_draw,this.event_type=[\"pan\",\"tap\"],this.default_order=3}}a.FreehandDrawTool=h,_=h,h.__name__=\"FreehandDrawTool\",_.prototype.default_view=l,_.define((({Int:e})=>({num_objects:[e,0]}))),_.register_alias(\"freehand_draw\",(()=>new h))},\n", - " function _(e,t,s,o,a){var i;o();const n=e(43),_=e(370),r=e(228);class d extends _.EditToolView{_tap(e){if(this._select_event(e,this._select_mode(e),this.model.renderers).length||!this.model.add)return;const t=this.model.renderers[0],s=this._map_drag(e.sx,e.sy,t);if(null==s)return;const o=t.glyph,a=t.data_source,[i,n]=[o.x.field,o.y.field],[_,r]=s;this._pop_glyphs(a,this.model.num_objects),i&&a.get_array(i).push(_),n&&a.get_array(n).push(r),this._pad_empty_columns(a,[i,n]),a.change.emit(),a.data=a.data,a.properties.data.change.emit()}_keyup(e){if(this.model.active&&this._mouse_in_frame)for(const t of this.model.renderers)e.keyCode===n.Keys.Backspace?this._delete_selected(t):e.keyCode==n.Keys.Esc&&t.data_source.selection_manager.clear()}_pan_start(e){this.model.drag&&(this._select_event(e,\"append\",this.model.renderers),this._basepoint=[e.sx,e.sy])}_pan(e){this.model.drag&&null!=this._basepoint&&this._drag_points(e,this.model.renderers)}_pan_end(e){if(this.model.drag){this._pan(e);for(const e of this.model.renderers)this._emit_cds_changes(e.data_source,!1,!0,!0);this._basepoint=null}}}s.PointDrawToolView=d,d.__name__=\"PointDrawToolView\";class l extends _.EditTool{constructor(e){super(e),this.tool_name=\"Point Draw Tool\",this.icon=r.tool_icon_point_draw,this.event_type=[\"tap\",\"pan\",\"move\"],this.default_order=2}}s.PointDrawTool=l,i=l,l.__name__=\"PointDrawTool\",i.prototype.default_view=d,i.define((({Boolean:e,Int:t})=>({add:[e,!0],drag:[e,!0],num_objects:[t,0]})))},\n", - " function _(e,t,s,i,a){var r;i();const o=e(43),n=e(8),d=e(375),_=e(228);class h extends d.PolyToolView{constructor(){super(...arguments),this._drawing=!1,this._initialized=!1}_tap(e){this._drawing?this._draw(e,\"add\",!0):this._select_event(e,this._select_mode(e),this.model.renderers)}_draw(e,t,s=!1){const i=this.model.renderers[0],a=this._map_drag(e.sx,e.sy,i);if(this._initialized||this.activate(),null==a)return;const[r,o]=this._snap_to_vertex(e,...a),d=i.data_source,_=i.glyph,[h,l]=[_.xs.field,_.ys.field];if(\"new\"==t)this._pop_glyphs(d,this.model.num_objects),h&&d.get_array(h).push([r,r]),l&&d.get_array(l).push([o,o]),this._pad_empty_columns(d,[h,l]);else if(\"edit\"==t){if(h){const e=d.data[h][d.data[h].length-1];e[e.length-1]=r}if(l){const e=d.data[l][d.data[l].length-1];e[e.length-1]=o}}else if(\"add\"==t){if(h){const e=d.data[h].length-1;let t=d.get_array(h)[e];const s=t[t.length-1];t[t.length-1]=r,(0,n.isArray)(t)||(t=Array.from(t),d.data[h][e]=t),t.push(s)}if(l){const e=d.data[l].length-1;let t=d.get_array(l)[e];const s=t[t.length-1];t[t.length-1]=o,(0,n.isArray)(t)||(t=Array.from(t),d.data[l][e]=t),t.push(s)}}this._emit_cds_changes(d,!0,!1,s)}_show_vertices(){if(!this.model.active)return;const e=[],t=[];for(let s=0;sthis._show_vertices()))}this._initialized=!0}}deactivate(){this._drawing&&(this._remove(),this._drawing=!1),this.model.vertex_renderer&&this._hide_vertices()}}s.PolyDrawToolView=h,h.__name__=\"PolyDrawToolView\";class l extends d.PolyTool{constructor(e){super(e),this.tool_name=\"Polygon Draw Tool\",this.icon=_.tool_icon_poly_draw,this.event_type=[\"pan\",\"tap\",\"move\"],this.default_order=3}}s.PolyDrawTool=l,r=l,l.__name__=\"PolyDrawTool\",r.prototype.default_view=h,r.define((({Boolean:e,Int:t})=>({drag:[e,!0],num_objects:[t,0]})))},\n", - " function _(e,r,t,s,o){var _;s();const d=e(8),i=e(370);class l extends i.EditToolView{_set_vertices(e,r){const t=this.model.vertex_renderer.glyph,s=this.model.vertex_renderer.data_source,[o,_]=[t.x.field,t.y.field];o&&((0,d.isArray)(e)?s.data[o]=e:t.x={value:e}),_&&((0,d.isArray)(r)?s.data[_]=r:t.y={value:r}),this._emit_cds_changes(s,!0,!0,!1)}_hide_vertices(){this._set_vertices([],[])}_snap_to_vertex(e,r,t){if(this.model.vertex_renderer){const s=this._select_event(e,\"replace\",[this.model.vertex_renderer]),o=this.model.vertex_renderer.data_source,_=this.model.vertex_renderer.glyph,[d,i]=[_.x.field,_.y.field];if(s.length){const e=o.selected.indices[0];d&&(r=o.data[d][e]),i&&(t=o.data[i][e]),o.selection_manager.clear()}}return[r,t]}}t.PolyToolView=l,l.__name__=\"PolyToolView\";class n extends i.EditTool{constructor(e){super(e)}}t.PolyTool=n,_=n,n.__name__=\"PolyTool\",_.define((({AnyRef:e})=>({vertex_renderer:[e()]})))},\n", - " function _(e,t,s,r,i){var _;r();const d=e(43),n=e(8),l=e(375),a=e(228);class c extends l.PolyToolView{constructor(){super(...arguments),this._drawing=!1,this._cur_index=null}_doubletap(e){if(!this.model.active)return;const t=this._map_drag(e.sx,e.sy,this.model.vertex_renderer);if(null==t)return;const[s,r]=t,i=this._select_event(e,\"replace\",[this.model.vertex_renderer]),_=this.model.vertex_renderer.data_source,d=this.model.vertex_renderer.glyph,[n,l]=[d.x.field,d.y.field];if(i.length&&null!=this._selected_renderer){const e=_.selected.indices[0];this._drawing?(this._drawing=!1,_.selection_manager.clear()):(_.selected.indices=[e+1],n&&_.get_array(n).splice(e+1,0,s),l&&_.get_array(l).splice(e+1,0,r),this._drawing=!0),_.change.emit(),this._emit_cds_changes(this._selected_renderer.data_source)}else this._show_vertices(e)}_show_vertices(e){if(!this.model.active)return;const t=this.model.renderers[0],s=()=>this._update_vertices(t),r=null==t?void 0:t.data_source,i=this._select_event(e,\"replace\",this.model.renderers);if(!i.length)return this._set_vertices([],[]),this._selected_renderer=null,this._drawing=!1,this._cur_index=null,void(null!=r&&r.disconnect(r.properties.data.change,s));null!=r&&r.connect(r.properties.data.change,s),this._cur_index=i[0].data_source.selected.indices[0],this._update_vertices(i[0])}_update_vertices(e){const t=e.glyph,s=e.data_source,r=this._cur_index,[i,_]=[t.xs.field,t.ys.field];if(this._drawing)return;if(null==r&&(i||_))return;let d,l;i&&null!=r?(d=s.data[i][r],(0,n.isArray)(d)||(s.data[i][r]=d=Array.from(d))):d=t.xs.value,_&&null!=r?(l=s.data[_][r],(0,n.isArray)(l)||(s.data[_][r]=l=Array.from(l))):l=t.ys.value,this._selected_renderer=e,this._set_vertices(d,l)}_move(e){if(this._drawing&&null!=this._selected_renderer){const t=this.model.vertex_renderer,s=t.data_source,r=t.glyph,i=this._map_drag(e.sx,e.sy,t);if(null==i)return;let[_,d]=i;const n=s.selected.indices;[_,d]=this._snap_to_vertex(e,_,d),s.selected.indices=n;const[l,a]=[r.x.field,r.y.field],c=n[0];l&&(s.data[l][c]=_),a&&(s.data[a][c]=d),s.change.emit(),this._selected_renderer.data_source.change.emit()}}_tap(e){const t=this.model.vertex_renderer,s=this._map_drag(e.sx,e.sy,t);if(null==s)return;if(this._drawing&&this._selected_renderer){let[r,i]=s;const _=t.data_source,d=t.glyph,[n,l]=[d.x.field,d.y.field],a=_.selected.indices;[r,i]=this._snap_to_vertex(e,r,i);const c=a[0];if(_.selected.indices=[c+1],n){const e=_.get_array(n),t=e[c];e[c]=r,e.splice(c+1,0,t)}if(l){const e=_.get_array(l),t=e[c];e[c]=i,e.splice(c+1,0,t)}return _.change.emit(),void this._emit_cds_changes(this._selected_renderer.data_source,!0,!1,!0)}const r=this._select_mode(e);this._select_event(e,r,[t]),this._select_event(e,r,this.model.renderers)}_remove_vertex(){if(!this._drawing||!this._selected_renderer)return;const e=this.model.vertex_renderer,t=e.data_source,s=e.glyph,r=t.selected.indices[0],[i,_]=[s.x.field,s.y.field];i&&t.get_array(i).splice(r,1),_&&t.get_array(_).splice(r,1),t.change.emit(),this._emit_cds_changes(this._selected_renderer.data_source)}_pan_start(e){this._select_event(e,\"append\",[this.model.vertex_renderer]),this._basepoint=[e.sx,e.sy]}_pan(e){null!=this._basepoint&&(this._drag_points(e,[this.model.vertex_renderer]),this._selected_renderer&&this._selected_renderer.data_source.change.emit())}_pan_end(e){null!=this._basepoint&&(this._drag_points(e,[this.model.vertex_renderer]),this._emit_cds_changes(this.model.vertex_renderer.data_source,!1,!0,!0),this._selected_renderer&&this._emit_cds_changes(this._selected_renderer.data_source),this._basepoint=null)}_keyup(e){if(!this.model.active||!this._mouse_in_frame)return;let t;t=this._selected_renderer?[this.model.vertex_renderer]:this.model.renderers;for(const s of t)e.keyCode===d.Keys.Backspace?(this._delete_selected(s),this._selected_renderer&&this._emit_cds_changes(this._selected_renderer.data_source)):e.keyCode==d.Keys.Esc&&(this._drawing?(this._remove_vertex(),this._drawing=!1):this._selected_renderer&&this._hide_vertices(),s.data_source.selection_manager.clear())}deactivate(){this._selected_renderer&&(this._drawing&&(this._remove_vertex(),this._drawing=!1),this._hide_vertices())}}s.PolyEditToolView=c,c.__name__=\"PolyEditToolView\";class o extends l.PolyTool{constructor(e){super(e),this.tool_name=\"Poly Edit Tool\",this.icon=a.tool_icon_poly_edit,this.event_type=[\"tap\",\"pan\",\"move\"],this.default_order=4}}s.PolyEditTool=o,_=o,o.__name__=\"PolyEditTool\",_.prototype.default_view=c},\n", - " function _(e,t,o,s,i){var l;s();const n=e(378),_=e(116),c=e(20),r=e(228);class a extends n.SelectToolView{_compute_limits(e){const t=this.plot_view.frame,o=this.model.dimensions;let s=this._base_point;if(\"center\"==this.model.origin){const[t,o]=s,[i,l]=e;s=[t-(i-t),o-(l-o)]}return this.model._get_dim_limits(s,e,t,o)}_pan_start(e){const{sx:t,sy:o}=e;this._base_point=[t,o]}_pan(e){const{sx:t,sy:o}=e,s=[t,o],[i,l]=this._compute_limits(s);this.model.overlay.update({left:i[0],right:i[1],top:l[0],bottom:l[1]}),this.model.select_every_mousemove&&this._do_select(i,l,!1,this._select_mode(e))}_pan_end(e){const{sx:t,sy:o}=e,s=[t,o],[i,l]=this._compute_limits(s);this._do_select(i,l,!0,this._select_mode(e)),this.model.overlay.update({left:null,right:null,top:null,bottom:null}),this._base_point=null,this.plot_view.state.push(\"box_select\",{selection:this.plot_view.get_selection()})}_do_select([e,t],[o,s],i,l=\"replace\"){const n={type:\"rect\",sx0:e,sx1:t,sy0:o,sy1:s};this._select(n,i,l)}}o.BoxSelectToolView=a,a.__name__=\"BoxSelectToolView\";const h=()=>new _.BoxAnnotation({level:\"overlay\",top_units:\"screen\",left_units:\"screen\",bottom_units:\"screen\",right_units:\"screen\",fill_color:\"lightgrey\",fill_alpha:.5,line_color:\"black\",line_alpha:1,line_width:2,line_dash:[4,4]});class m extends n.SelectTool{constructor(e){super(e),this.tool_name=\"Box Select\",this.icon=r.tool_icon_box_select,this.event_type=\"pan\",this.default_order=30}get tooltip(){return this._get_dim_tooltip(this.dimensions)}}o.BoxSelectTool=m,l=m,m.__name__=\"BoxSelectTool\",l.prototype.default_view=a,l.define((({Boolean:e,Ref:t})=>({dimensions:[c.Dimensions,\"both\"],select_every_mousemove:[e,!1],overlay:[t(_.BoxAnnotation),h],origin:[c.BoxOrigin,\"corner\"]}))),l.register_alias(\"box_select\",(()=>new m)),l.register_alias(\"xbox_select\",(()=>new m({dimensions:\"width\"}))),l.register_alias(\"ybox_select\",(()=>new m({dimensions:\"height\"})))},\n", - " function _(e,t,s,n,r){var o;n();const c=e(223),i=e(175),a=e(339),l=e(176),d=e(66),_=e(20),h=e(43),p=e(251),u=e(15),m=e(11);class v extends c.GestureToolView{connect_signals(){super.connect_signals(),this.model.clear.connect((()=>this._clear()))}get computed_renderers(){const{renderers:e,names:t}=this.model,s=this.plot_model.data_renderers;return(0,d.compute_renderers)(e,s,t)}_computed_renderers_by_data_source(){var e;const t=new Map;for(const s of this.computed_renderers){let n;if(s instanceof i.GlyphRenderer)n=s.data_source;else{if(!(s instanceof a.GraphRenderer))continue;n=s.node_renderer.data_source}const r=null!==(e=t.get(n))&&void 0!==e?e:[];t.set(n,[...r,s])}return t}_select_mode(e){const{shiftKey:t,ctrlKey:s}=e;return t||s?t&&!s?\"append\":!t&&s?\"intersect\":t&&s?\"subtract\":void(0,m.unreachable)():this.model.mode}_keyup(e){e.keyCode==h.Keys.Esc&&this._clear()}_clear(){for(const e of this.computed_renderers)e.get_selection_manager().clear();const e=this.computed_renderers.map((e=>this.plot_view.renderer_view(e)));this.plot_view.request_paint(e)}_select(e,t,s){const n=this._computed_renderers_by_data_source();for(const[,r]of n){const n=r[0].get_selection_manager(),o=[];for(const e of r){const t=this.plot_view.renderer_view(e);null!=t&&o.push(t)}n.select(o,e,t,s)}null!=this.model.callback&&this._emit_callback(e),this._emit_selection_event(e,t)}_emit_selection_event(e,t=!0){const{x_scale:s,y_scale:n}=this.plot_view.frame;let r;switch(e.type){case\"point\":{const{sx:t,sy:o}=e,c=s.invert(t),i=n.invert(o);r=Object.assign(Object.assign({},e),{x:c,y:i});break}case\"span\":{const{sx:t,sy:o}=e,c=s.invert(t),i=n.invert(o);r=Object.assign(Object.assign({},e),{x:c,y:i});break}case\"rect\":{const{sx0:t,sx1:o,sy0:c,sy1:i}=e,[a,l]=s.r_invert(t,o),[d,_]=n.r_invert(c,i);r=Object.assign(Object.assign({},e),{x0:a,y0:d,x1:l,y1:_});break}case\"poly\":{const{sx:t,sy:o}=e,c=s.v_invert(t),i=n.v_invert(o);r=Object.assign(Object.assign({},e),{x:c,y:i});break}}this.plot_model.trigger_event(new p.SelectionGeometry(r,t))}}s.SelectToolView=v,v.__name__=\"SelectToolView\";class b extends c.GestureTool{constructor(e){super(e)}initialize(){super.initialize(),this.clear=new u.Signal0(this,\"clear\")}get menu(){return[{icon:\"bk-tool-icon-replace-mode\",tooltip:\"Replace the current selection\",active:()=>\"replace\"==this.mode,handler:()=>{this.mode=\"replace\",this.active=!0}},{icon:\"bk-tool-icon-append-mode\",tooltip:\"Append to the current selection (Shift)\",active:()=>\"append\"==this.mode,handler:()=>{this.mode=\"append\",this.active=!0}},{icon:\"bk-tool-icon-intersect-mode\",tooltip:\"Intersect with the current selection (Ctrl)\",active:()=>\"intersect\"==this.mode,handler:()=>{this.mode=\"intersect\",this.active=!0}},{icon:\"bk-tool-icon-subtract-mode\",tooltip:\"Subtract from the current selection (Shift+Ctrl)\",active:()=>\"subtract\"==this.mode,handler:()=>{this.mode=\"subtract\",this.active=!0}},null,{icon:\"bk-tool-icon-clear-selection\",tooltip:\"Clear the current selection (Esc)\",handler:()=>{this.clear.emit()}}]}}s.SelectTool=b,o=b,b.__name__=\"SelectTool\",o.define((({String:e,Array:t,Ref:s,Or:n,Auto:r})=>({renderers:[n(t(s(l.DataRenderer)),r),\"auto\"],names:[t(e),[]],mode:[_.SelectionMode,\"replace\"]})))},\n", - " function _(t,o,e,s,i){var n;s();const _=t(223),a=t(116),l=t(20),r=t(228);class h extends _.GestureToolView{_match_aspect(t,o,e){const s=e.bbox.aspect,i=e.bbox.h_range.end,n=e.bbox.h_range.start,_=e.bbox.v_range.end,a=e.bbox.v_range.start;let l=Math.abs(t[0]-o[0]),r=Math.abs(t[1]-o[1]);const h=0==r?0:l/r,[c]=h>=s?[1,h/s]:[s/h,1];let m,p,d,b;return t[0]<=o[0]?(m=t[0],p=t[0]+l*c,p>i&&(p=i)):(p=t[0],m=t[0]-l*c,m_&&(d=_)):(d=t[1],b=t[1]-l/s,bnew a.BoxAnnotation({level:\"overlay\",top_units:\"screen\",left_units:\"screen\",bottom_units:\"screen\",right_units:\"screen\",fill_color:\"lightgrey\",fill_alpha:.5,line_color:\"black\",line_alpha:1,line_width:2,line_dash:[4,4]});class m extends _.GestureTool{constructor(t){super(t),this.tool_name=\"Box Zoom\",this.icon=r.tool_icon_box_zoom,this.event_type=\"pan\",this.default_order=20}get tooltip(){return this._get_dim_tooltip(this.dimensions)}}e.BoxZoomTool=m,n=m,m.__name__=\"BoxZoomTool\",n.prototype.default_view=h,n.define((({Boolean:t,Ref:o})=>({dimensions:[l.Dimensions,\"both\"],overlay:[o(a.BoxAnnotation),c],match_aspect:[t,!1],origin:[l.BoxOrigin,\"corner\"]}))),n.register_alias(\"box_zoom\",(()=>new m({dimensions:\"both\"}))),n.register_alias(\"xbox_zoom\",(()=>new m({dimensions:\"width\"}))),n.register_alias(\"ybox_zoom\",(()=>new m({dimensions:\"height\"})))},\n", - " function _(s,e,t,o,_){var l;o();const i=s(378),a=s(217),c=s(381),n=s(43),h=s(228);class r extends i.SelectToolView{constructor(){super(...arguments),this.sxs=[],this.sys=[]}connect_signals(){super.connect_signals(),this.connect(this.model.properties.active.change,(()=>this._active_change()))}_active_change(){this.model.active||this._clear_overlay()}_keyup(s){s.keyCode==n.Keys.Enter&&this._clear_overlay()}_pan_start(s){this.sxs=[],this.sys=[];const{sx:e,sy:t}=s;this._append_overlay(e,t)}_pan(s){const[e,t]=this.plot_view.frame.bbox.clip(s.sx,s.sy);this._append_overlay(e,t),this.model.select_every_mousemove&&this._do_select(this.sxs,this.sys,!1,this._select_mode(s))}_pan_end(s){const{sxs:e,sys:t}=this;this._clear_overlay(),this._do_select(e,t,!0,this._select_mode(s)),this.plot_view.state.push(\"lasso_select\",{selection:this.plot_view.get_selection()})}_append_overlay(s,e){const{sxs:t,sys:o}=this;t.push(s),o.push(e),this.model.overlay.update({xs:t,ys:o})}_clear_overlay(){this.sxs=[],this.sys=[],this.model.overlay.update({xs:this.sxs,ys:this.sys})}_do_select(s,e,t,o){const _={type:\"poly\",sx:s,sy:e};this._select(_,t,o)}}t.LassoSelectToolView=r,r.__name__=\"LassoSelectToolView\";class y extends i.SelectTool{constructor(s){super(s),this.tool_name=\"Lasso Select\",this.icon=h.tool_icon_lasso_select,this.event_type=\"pan\",this.default_order=12}}t.LassoSelectTool=y,l=y,y.__name__=\"LassoSelectTool\",l.prototype.default_view=r,l.define((({Boolean:s,Ref:e})=>({select_every_mousemove:[s,!0],overlay:[e(a.PolyAnnotation),c.DEFAULT_POLY_OVERLAY]}))),l.register_alias(\"lasso_select\",(()=>new y))},\n", - " function _(e,t,s,l,o){var i;l();const a=e(378),_=e(217),c=e(43),n=e(9),h=e(228);class y extends a.SelectToolView{initialize(){super.initialize(),this.data={sx:[],sy:[]}}connect_signals(){super.connect_signals(),this.connect(this.model.properties.active.change,(()=>this._active_change()))}_active_change(){this.model.active||this._clear_data()}_keyup(e){e.keyCode==c.Keys.Enter&&this._clear_data()}_doubletap(e){this._do_select(this.data.sx,this.data.sy,!0,this._select_mode(e)),this.plot_view.state.push(\"poly_select\",{selection:this.plot_view.get_selection()}),this._clear_data()}_clear_data(){this.data={sx:[],sy:[]},this.model.overlay.update({xs:[],ys:[]})}_tap(e){const{sx:t,sy:s}=e;this.plot_view.frame.bbox.contains(t,s)&&(this.data.sx.push(t),this.data.sy.push(s),this.model.overlay.update({xs:(0,n.copy)(this.data.sx),ys:(0,n.copy)(this.data.sy)}))}_do_select(e,t,s,l){const o={type:\"poly\",sx:e,sy:t};this._select(o,s,l)}}s.PolySelectToolView=y,y.__name__=\"PolySelectToolView\";s.DEFAULT_POLY_OVERLAY=()=>new _.PolyAnnotation({level:\"overlay\",xs_units:\"screen\",ys_units:\"screen\",fill_color:\"lightgrey\",fill_alpha:.5,line_color:\"black\",line_alpha:1,line_width:2,line_dash:[4,4]});class d extends a.SelectTool{constructor(e){super(e),this.tool_name=\"Poly Select\",this.icon=h.tool_icon_polygon_select,this.event_type=\"tap\",this.default_order=11}}s.PolySelectTool=d,i=d,d.__name__=\"PolySelectTool\",i.prototype.default_view=y,i.define((({Ref:e})=>({overlay:[e(_.PolyAnnotation),s.DEFAULT_POLY_OVERLAY]}))),i.register_alias(\"poly_select\",(()=>new d))},\n", - " function _(e,t,s,i,r){var n;i();const _=e(20),d=e(383),o=e(228);class l extends d.LineToolView{constructor(){super(...arguments),this._drawing=!1}_doubletap(e){if(!this.model.active)return;const t=this.model.renderers;for(const s of t){1==this._select_event(e,\"replace\",[s]).length&&(this._selected_renderer=s)}this._show_intersections(),this._update_line_cds()}_show_intersections(){if(!this.model.active)return;if(null==this._selected_renderer)return;if(!this.model.renderers.length)return this._set_intersection([],[]),this._selected_renderer=null,void(this._drawing=!1);const e=this._selected_renderer.data_source,t=this._selected_renderer.glyph,[s,i]=[t.x.field,t.y.field],r=e.get_array(s),n=e.get_array(i);this._set_intersection(r,n)}_tap(e){const t=this.model.intersection_renderer;if(null==this._map_drag(e.sx,e.sy,t))return;if(this._drawing&&this._selected_renderer){const s=this._select_mode(e);if(0==this._select_event(e,s,[t]).length)return}const s=this._select_mode(e);this._select_event(e,s,[t]),this._select_event(e,s,this.model.renderers)}_update_line_cds(){if(null==this._selected_renderer)return;const e=this.model.intersection_renderer.glyph,t=this.model.intersection_renderer.data_source,[s,i]=[e.x.field,e.y.field];if(s&&i){const e=t.data[s],r=t.data[i];this._selected_renderer.data_source.data[s]=e,this._selected_renderer.data_source.data[i]=r}this._emit_cds_changes(this._selected_renderer.data_source,!0,!0,!1)}_pan_start(e){this._select_event(e,\"append\",[this.model.intersection_renderer]),this._basepoint=[e.sx,e.sy]}_pan(e){null!=this._basepoint&&(this._drag_points(e,[this.model.intersection_renderer],this.model.dimensions),this._selected_renderer&&this._selected_renderer.data_source.change.emit())}_pan_end(e){null!=this._basepoint&&(this._drag_points(e,[this.model.intersection_renderer]),this._emit_cds_changes(this.model.intersection_renderer.data_source,!1,!0,!0),this._selected_renderer&&this._emit_cds_changes(this._selected_renderer.data_source),this._basepoint=null)}activate(){this._drawing=!0}deactivate(){this._selected_renderer&&(this._drawing&&(this._drawing=!1),this._hide_intersections())}}s.LineEditToolView=l,l.__name__=\"LineEditToolView\";class h extends d.LineTool{constructor(e){super(e),this.tool_name=\"Line Edit Tool\",this.icon=o.tool_icon_line_edit,this.event_type=[\"tap\",\"pan\",\"move\"],this.default_order=4}get tooltip(){return this._get_dim_tooltip(this.dimensions)}}s.LineEditTool=h,n=h,h.__name__=\"LineEditTool\",n.prototype.default_view=l,n.define((()=>({dimensions:[_.Dimensions,\"both\"]})))},\n", - " function _(e,i,n,t,s){var o;t();const r=e(8),_=e(370);class d extends _.EditToolView{_set_intersection(e,i){const n=this.model.intersection_renderer.glyph,t=this.model.intersection_renderer.data_source,[s,o]=[n.x.field,n.y.field];s&&((0,r.isArray)(e)?t.data[s]=e:n.x={value:e}),o&&((0,r.isArray)(i)?t.data[o]=i:n.y={value:i}),this._emit_cds_changes(t,!0,!0,!1)}_hide_intersections(){this._set_intersection([],[])}}n.LineToolView=d,d.__name__=\"LineToolView\";class a extends _.EditTool{constructor(e){super(e)}}n.LineTool=a,o=a,a.__name__=\"LineTool\",o.define((({AnyRef:e})=>({intersection_renderer:[e()]})))},\n", - " function _(t,s,n,e,i){e();const o=t(1);var a;const _=t(223),l=t(20),r=(0,o.__importStar)(t(228));function h(t,s,n){const e=new Map;for(const[i,o]of t){const[t,a]=o.r_invert(s,n);e.set(i,{start:t,end:a})}return e}n.update_ranges=h;class d extends _.GestureToolView{_pan_start(t){var s;this.last_dx=0,this.last_dy=0;const{sx:n,sy:e}=t,i=this.plot_view.frame.bbox;if(!i.contains(n,e)){const t=i.h_range,s=i.v_range;(nt.end)&&(this.v_axis_only=!0),(es.end)&&(this.h_axis_only=!0)}null===(s=this.model.document)||void 0===s||s.interactive_start(this.plot_model)}_pan(t){var s;this._update(t.deltaX,t.deltaY),null===(s=this.model.document)||void 0===s||s.interactive_start(this.plot_model)}_pan_end(t){this.h_axis_only=!1,this.v_axis_only=!1,null!=this.pan_info&&this.plot_view.state.push(\"pan\",{range:this.pan_info}),this.plot_view.trigger_ranges_update_event()}_update(t,s){const n=this.plot_view.frame,e=t-this.last_dx,i=s-this.last_dy,o=n.bbox.h_range,a=o.start-e,_=o.end-e,l=n.bbox.v_range,r=l.start-i,d=l.end-i,p=this.model.dimensions;let c,u,m,v,x,g;\"width\"!=p&&\"both\"!=p||this.v_axis_only?(c=o.start,u=o.end,m=0):(c=a,u=_,m=-e),\"height\"!=p&&\"both\"!=p||this.h_axis_only?(v=l.start,x=l.end,g=0):(v=r,x=d,g=-i),this.last_dx=t,this.last_dy=s;const{x_scales:w,y_scales:y}=n,f=h(w,c,u),b=h(y,v,x);this.pan_info={xrs:f,yrs:b,sdx:m,sdy:g},this.plot_view.update_range(this.pan_info,{panning:!0})}}n.PanToolView=d,d.__name__=\"PanToolView\";class p extends _.GestureTool{constructor(t){super(t),this.tool_name=\"Pan\",this.event_type=\"pan\",this.default_order=10}get tooltip(){return this._get_dim_tooltip(this.dimensions)}}n.PanTool=p,a=p,p.__name__=\"PanTool\",a.prototype.default_view=d,a.define((()=>({dimensions:[l.Dimensions,\"both\",{on_update(t,s){switch(t){case\"both\":s.icon=r.tool_icon_pan;break;case\"width\":s.icon=r.tool_icon_xpan;break;case\"height\":s.icon=r.tool_icon_ypan}}}]}))),a.register_alias(\"pan\",(()=>new p({dimensions:\"both\"}))),a.register_alias(\"xpan\",(()=>new p({dimensions:\"width\"}))),a.register_alias(\"ypan\",(()=>new p({dimensions:\"height\"})))},\n", - " function _(e,t,i,s,n){var l;s();const a=e(116),r=e(58),o=e(19),_=e(223),h=e(228);function d(e){switch(e){case 1:return 2;case 2:return 1;case 4:return 5;case 5:return 4;default:return e}}function u(e,t,i,s){if(null==t)return!1;const n=i.compute(t);return Math.abs(e-n)n.right)&&(l=!1)}if(null!=n.bottom&&null!=n.top){const e=s.invert(t);(en.top)&&(l=!1)}return l}function g(e,t,i){let s=0;return e>=i.start&&e<=i.end&&(s+=1),t>=i.start&&t<=i.end&&(s+=1),s}function y(e,t,i,s){const n=t.compute(e),l=t.invert(n+i);return l>=s.start&&l<=s.end?l:e}function f(e,t,i){return e>t.start?(t.end=e,i):(t.end=t.start,t.start=e,d(i))}function v(e,t,i){return e=o&&(e.start=a,e.end=r)}i.flip_side=d,i.is_near=u,i.is_inside=c,i.sides_inside=g,i.compute_value=y,i.update_range_end_side=f,i.update_range_start_side=v,i.update_range=m;class p extends _.GestureToolView{initialize(){super.initialize(),this.side=0,this.model.update_overlay_from_ranges()}connect_signals(){super.connect_signals(),null!=this.model.x_range&&this.connect(this.model.x_range.change,(()=>this.model.update_overlay_from_ranges())),null!=this.model.y_range&&this.connect(this.model.y_range.change,(()=>this.model.update_overlay_from_ranges()))}_pan_start(e){this.last_dx=0,this.last_dy=0;const t=this.model.x_range,i=this.model.y_range,{frame:s}=this.plot_view,n=s.x_scale,l=s.y_scale,r=this.model.overlay,{left:o,right:_,top:h,bottom:d}=r,g=this.model.overlay.line_width+a.EDGE_TOLERANCE;null!=t&&this.model.x_interaction&&(u(e.sx,o,n,g)?this.side=1:u(e.sx,_,n,g)?this.side=2:c(e.sx,e.sy,n,l,r)&&(this.side=3)),null!=i&&this.model.y_interaction&&(0==this.side&&u(e.sy,d,l,g)&&(this.side=4),0==this.side&&u(e.sy,h,l,g)?this.side=5:c(e.sx,e.sy,n,l,this.model.overlay)&&(3==this.side?this.side=7:this.side=6))}_pan(e){const t=this.plot_view.frame,i=e.deltaX-this.last_dx,s=e.deltaY-this.last_dy,n=this.model.x_range,l=this.model.y_range,a=t.x_scale,r=t.y_scale;if(null!=n)if(3==this.side||7==this.side)m(n,a,i,t.x_range);else if(1==this.side){const e=y(n.start,a,i,t.x_range);this.side=v(e,n,this.side)}else if(2==this.side){const e=y(n.end,a,i,t.x_range);this.side=f(e,n,this.side)}if(null!=l)if(6==this.side||7==this.side)m(l,r,s,t.y_range);else if(4==this.side){const e=y(l.start,r,s,t.y_range);this.side=v(e,l,this.side)}else if(5==this.side){const e=y(l.end,r,s,t.y_range);this.side=f(e,l,this.side)}this.last_dx=e.deltaX,this.last_dy=e.deltaY}_pan_end(e){this.side=0,this.plot_view.trigger_ranges_update_event()}}i.RangeToolView=p,p.__name__=\"RangeToolView\";const x=()=>new a.BoxAnnotation({level:\"overlay\",fill_color:\"lightgrey\",fill_alpha:.5,line_color:\"black\",line_alpha:1,line_width:.5,line_dash:[2,2]});class w extends _.GestureTool{constructor(e){super(e),this.tool_name=\"Range Tool\",this.icon=h.tool_icon_range,this.event_type=\"pan\",this.default_order=1}initialize(){super.initialize(),this.overlay.in_cursor=\"grab\",this.overlay.ew_cursor=null!=this.x_range&&this.x_interaction?\"ew-resize\":null,this.overlay.ns_cursor=null!=this.y_range&&this.y_interaction?\"ns-resize\":null}update_overlay_from_ranges(){null==this.x_range&&null==this.y_range&&(this.overlay.left=null,this.overlay.right=null,this.overlay.bottom=null,this.overlay.top=null,o.logger.warn(\"RangeTool not configured with any Ranges.\")),null==this.x_range?(this.overlay.left=null,this.overlay.right=null):(this.overlay.left=this.x_range.start,this.overlay.right=this.x_range.end),null==this.y_range?(this.overlay.bottom=null,this.overlay.top=null):(this.overlay.bottom=this.y_range.start,this.overlay.top=this.y_range.end)}}i.RangeTool=w,l=w,w.__name__=\"RangeTool\",l.prototype.default_view=p,l.define((({Boolean:e,Ref:t,Nullable:i})=>({x_range:[i(t(r.Range1d)),null],x_interaction:[e,!0],y_range:[i(t(r.Range1d)),null],y_interaction:[e,!0],overlay:[t(a.BoxAnnotation),x]})))},\n", - " function _(e,t,s,o,i){var l;o();const a=e(378),n=e(20),c=e(228);class _ extends a.SelectToolView{_tap(e){\"tap\"==this.model.gesture&&this._handle_tap(e)}_doubletap(e){\"doubletap\"==this.model.gesture&&this._handle_tap(e)}_handle_tap(e){const{sx:t,sy:s}=e,o={type:\"point\",sx:t,sy:s};this._select(o,!0,this._select_mode(e))}_select(e,t,s){const{callback:o}=this.model;if(\"select\"==this.model.behavior){const i=this._computed_renderers_by_data_source();for(const[,l]of i){const i=l[0].get_selection_manager(),a=l.map((e=>this.plot_view.renderer_view(e))).filter((e=>null!=e));if(i.select(a,e,t,s)&&null!=o){const t=a[0].coordinates.x_scale.invert(e.sx),s=a[0].coordinates.y_scale.invert(e.sy),l={geometries:Object.assign(Object.assign({},e),{x:t,y:s}),source:i.source};o.execute(this.model,l)}}this._emit_selection_event(e),this.plot_view.state.push(\"tap\",{selection:this.plot_view.get_selection()})}else for(const t of this.computed_renderers){const s=this.plot_view.renderer_view(t);if(null==s)continue;const i=t.get_selection_manager();if(i.inspect(s,e)&&null!=o){const t=s.coordinates.x_scale.invert(e.sx),l=s.coordinates.y_scale.invert(e.sy),a={geometries:Object.assign(Object.assign({},e),{x:t,y:l}),source:i.source};o.execute(this.model,a)}}}}s.TapToolView=_,_.__name__=\"TapToolView\";class r extends a.SelectTool{constructor(e){super(e),this.tool_name=\"Tap\",this.icon=c.tool_icon_tap_select,this.event_type=\"tap\",this.default_order=10}}s.TapTool=r,l=r,r.__name__=\"TapTool\",l.prototype.default_view=_,l.define((({Any:e,Enum:t,Nullable:s})=>({behavior:[n.TapBehavior,\"select\"],gesture:[t(\"tap\",\"doubletap\"),\"tap\"],callback:[s(e)]}))),l.register_alias(\"click\",(()=>new r({behavior:\"inspect\"}))),l.register_alias(\"tap\",(()=>new r)),l.register_alias(\"doubletap\",(()=>new r({gesture:\"doubletap\"})))},\n", - " function _(e,t,s,n,i){var a;n();const o=e(223),l=e(20),_=e(228),r=e(384);class h extends o.GestureToolView{_scroll(e){let t=this.model.speed*e.delta;t>.9?t=.9:t<-.9&&(t=-.9),this._update_ranges(t)}_update_ranges(e){var t;const{frame:s}=this.plot_view,n=s.bbox.h_range,i=s.bbox.v_range,[a,o]=[n.start,n.end],[l,_]=[i.start,i.end];let h,d,p,c;switch(this.model.dimension){case\"height\":{const t=Math.abs(_-l);h=a,d=o,p=l-t*e,c=_-t*e;break}case\"width\":{const t=Math.abs(o-a);h=a-t*e,d=o-t*e,p=l,c=_;break}}const{x_scales:g,y_scales:u}=s,w={xrs:(0,r.update_ranges)(g,h,d),yrs:(0,r.update_ranges)(u,p,c),factor:e};this.plot_view.state.push(\"wheel_pan\",{range:w}),this.plot_view.update_range(w,{scrolling:!0}),null===(t=this.model.document)||void 0===t||t.interactive_start(this.plot_model,(()=>this.plot_view.trigger_ranges_update_event()))}}s.WheelPanToolView=h,h.__name__=\"WheelPanToolView\";class d extends o.GestureTool{constructor(e){super(e),this.tool_name=\"Wheel Pan\",this.icon=_.tool_icon_wheel_pan,this.event_type=\"scroll\",this.default_order=12}get tooltip(){return this._get_dim_tooltip(this.dimension)}}s.WheelPanTool=d,a=d,d.__name__=\"WheelPanTool\",a.prototype.default_view=h,a.define((()=>({dimension:[l.Dimension,\"width\"]}))),a.internal((({Number:e})=>({speed:[e,.001]}))),a.register_alias(\"xwheel_pan\",(()=>new d({dimension:\"width\"}))),a.register_alias(\"ywheel_pan\",(()=>new d({dimension:\"height\"})))},\n", - " function _(e,o,t,s,i){var n;s();const l=e(223),_=e(368),h=e(20),a=e(27),r=e(228);class m extends l.GestureToolView{_pinch(e){const{sx:o,sy:t,scale:s,ctrlKey:i,shiftKey:n}=e;let l;l=s>=1?20*(s-1):-20/s,this._scroll({type:\"wheel\",sx:o,sy:t,delta:l,ctrlKey:i,shiftKey:n})}_scroll(e){var o;const{frame:t}=this.plot_view,s=t.bbox.h_range,i=t.bbox.v_range,{sx:n,sy:l}=e,h=this.model.dimensions,a=(\"width\"==h||\"both\"==h)&&s.startthis.plot_view.trigger_ranges_update_event()))}}t.WheelZoomToolView=m,m.__name__=\"WheelZoomToolView\";class d extends l.GestureTool{constructor(e){super(e),this.tool_name=\"Wheel Zoom\",this.icon=r.tool_icon_wheel_zoom,this.event_type=a.is_mobile?\"pinch\":\"scroll\",this.default_order=10}get tooltip(){return this._get_dim_tooltip(this.dimensions)}}t.WheelZoomTool=d,n=d,d.__name__=\"WheelZoomTool\",n.prototype.default_view=m,n.define((({Boolean:e,Number:o})=>({dimensions:[h.Dimensions,\"both\"],maintain_focus:[e,!0],zoom_on_axis:[e,!0],speed:[o,1/600]}))),n.register_alias(\"wheel_zoom\",(()=>new d({dimensions:\"both\"}))),n.register_alias(\"xwheel_zoom\",(()=>new d({dimensions:\"width\"}))),n.register_alias(\"ywheel_zoom\",(()=>new d({dimensions:\"height\"})))},\n", - " function _(i,e,s,t,o){var n;t();const l=i(232),a=i(219),h=i(20),r=i(13),_=i(228);class c extends l.InspectToolView{_move(i){if(!this.model.active)return;const{sx:e,sy:s}=i;this.plot_view.frame.bbox.contains(e,s)?this._update_spans(e,s):this._update_spans(null,null)}_move_exit(i){this._update_spans(null,null)}_update_spans(i,e){const s=this.model.dimensions;\"width\"!=s&&\"both\"!=s||(this.model.spans.width.location=e),\"height\"!=s&&\"both\"!=s||(this.model.spans.height.location=i)}}s.CrosshairToolView=c,c.__name__=\"CrosshairToolView\";class p extends l.InspectTool{constructor(i){super(i),this.tool_name=\"Crosshair\",this.icon=_.tool_icon_crosshair}get tooltip(){return this._get_dim_tooltip(this.dimensions)}get synthetic_renderers(){return(0,r.values)(this.spans)}}s.CrosshairTool=p,n=p,p.__name__=\"CrosshairTool\",(()=>{function i(i,e){return new a.Span({for_hover:!0,dimension:e,location_units:\"screen\",level:\"overlay\",line_color:i.line_color,line_width:i.line_width,line_alpha:i.line_alpha})}n.prototype.default_view=c,n.define((({Alpha:i,Number:e,Color:s})=>({dimensions:[h.Dimensions,\"both\"],line_color:[s,\"black\"],line_width:[e,1],line_alpha:[i,1]}))),n.internal((({Struct:e,Ref:s})=>({spans:[e({width:s(a.Span),height:s(a.Span)}),e=>({width:i(e,\"width\"),height:i(e,\"height\")})]}))),n.register_alias(\"crosshair\",(()=>new p))})()},\n", - " function _(e,s,t,r,n){var o;r();const a=e(53),u=e(13),c=e(34);class i extends a.Model{constructor(e){super(e)}get values(){return(0,u.values)(this.args)}_make_code(e,s,t,r){return new Function(...(0,u.keys)(this.args),e,s,t,(0,c.use_strict)(r))}format(e,s,t){return this._make_code(\"value\",\"format\",\"special_vars\",this.code)(...this.values,e,s,t)}}t.CustomJSHover=i,o=i,i.__name__=\"CustomJSHover\",o.define((({Unknown:e,String:s,Dict:t})=>({args:[t(e),{}],code:[s,\"\"]})))},\n", - " function _(e,t,n,s,o){s();const i=e(1);var r;const l=e(232),a=e(390),c=e(241),_=e(175),d=e(339),p=e(176),h=e(177),u=e(283),m=(0,i.__importStar)(e(185)),y=e(152),f=e(43),v=e(22),x=e(13),w=e(234),g=e(8),b=e(113),k=e(20),C=e(228),S=e(15),T=e(66),$=(0,i.__importStar)(e(242)),R=e(392);function M(e,t,n,s,o,i){const r={x:o[e],y:i[e]},l={x:o[e+1],y:i[e+1]};let a,c;if(\"span\"==t.type)\"h\"==t.direction?(a=Math.abs(r.x-n),c=Math.abs(l.x-n)):(a=Math.abs(r.y-s),c=Math.abs(l.y-s));else{const e={x:n,y:s};a=m.dist_2_pts(r,e),c=m.dist_2_pts(l,e)}return adelete this._template_el)),this.on_change([e,t,n],(async()=>await this._update_ttmodels()))}async _update_ttmodels(){const{_ttmodels:e,computed_renderers:t}=this;e.clear();const{tooltips:n}=this.model;if(null!=n)for(const t of this.computed_renderers){const s=new c.Tooltip({custom:(0,g.isString)(n)||(0,g.isFunction)(n),attachment:this.model.attachment,show_arrow:this.model.show_arrow});t instanceof _.GlyphRenderer?e.set(t,s):t instanceof d.GraphRenderer&&(e.set(t.node_renderer,s),e.set(t.edge_renderer,s))}const s=await(0,b.build_views)(this._ttviews,[...e.values()],{parent:this.plot_view});for(const e of s)e.render();const o=[...function*(){for(const e of t)e instanceof _.GlyphRenderer?yield e:e instanceof d.GraphRenderer&&(yield e.node_renderer,yield e.edge_renderer)}()],i=this._slots.get(this._update);if(null!=i){const e=new Set(o.map((e=>e.data_source)));S.Signal.disconnect_receiver(this,i,e)}for(const e of o)this.connect(e.data_source.inspect,this._update)}get computed_renderers(){const{renderers:e,names:t}=this.model,n=this.plot_model.data_renderers;return(0,T.compute_renderers)(e,n,t)}get ttmodels(){return this._ttmodels}_clear(){this._inspect(1/0,1/0);for(const[,e]of this.ttmodels)e.clear()}_move(e){if(!this.model.active)return;const{sx:t,sy:n}=e;this.plot_view.frame.bbox.contains(t,n)?this._inspect(t,n):this._clear()}_move_exit(){this._clear()}_inspect(e,t){let n;if(\"mouse\"==this.model.mode)n={type:\"point\",sx:e,sy:t};else{n={type:\"span\",direction:\"vline\"==this.model.mode?\"h\":\"v\",sx:e,sy:t}}for(const e of this.computed_renderers){const t=e.get_selection_manager(),s=this.plot_view.renderer_view(e);null!=s&&t.inspect(s,n)}this._emit_callback(n)}_update([e,{geometry:t}]){var n,s;if(!this.model.active)return;if(\"point\"!=t.type&&\"span\"!=t.type)return;if(!(e instanceof _.GlyphRenderer))return;if(\"ignore\"==this.model.muted_policy&&e.muted)return;const o=this.ttmodels.get(e);if(null==o)return;const i=e.get_selection_manager(),r=i.inspectors.get(e),l=e.view.convert_selection_to_subset(r);if(r.is_empty())return void o.clear();const a=i.source,c=this.plot_view.renderer_view(e);if(null==c)return;const{sx:d,sy:p}=t,m=c.coordinates.x_scale,y=c.coordinates.y_scale,v=m.invert(d),w=y.invert(p),{glyph:g}=c,b=[];if(g instanceof h.LineView)for(const n of l.line_indices){let s,o,i=g._x[n+1],r=g._y[n+1],c=n;switch(this.model.line_policy){case\"interp\":[i,r]=g.get_interpolation_hit(n,t),s=m.compute(i),o=y.compute(r);break;case\"prev\":[[s,o],c]=G(g.sx,g.sy,n);break;case\"next\":[[s,o],c]=G(g.sx,g.sy,n+1);break;case\"nearest\":[[s,o],c]=M(n,t,d,p,g.sx,g.sy),i=g._x[c],r=g._y[c];break;default:[s,o]=[d,p]}const _={index:c,x:v,y:w,sx:d,sy:p,data_x:i,data_y:r,rx:s,ry:o,indices:l.line_indices,name:e.name};b.push([s,o,this._render_tooltips(a,c,_)])}for(const t of r.image_indices){const n={index:t.index,x:v,y:w,sx:d,sy:p,name:e.name},s=this._render_tooltips(a,t,n);b.push([d,p,s])}for(const o of l.indices)if(g instanceof u.MultiLineView&&!(0,x.isEmpty)(l.multiline_indices))for(const n of l.multiline_indices[o.toString()]){let s,i,r,c=g._xs.get(o)[n],h=g._ys.get(o)[n],u=n;switch(this.model.line_policy){case\"interp\":[c,h]=g.get_interpolation_hit(o,n,t),s=m.compute(c),i=y.compute(h);break;case\"prev\":[[s,i],u]=G(g.sxs.get(o),g.sys.get(o),n);break;case\"next\":[[s,i],u]=G(g.sxs.get(o),g.sys.get(o),n+1);break;case\"nearest\":[[s,i],u]=M(n,t,d,p,g.sxs.get(o),g.sys.get(o)),c=g._xs.get(o)[u],h=g._ys.get(o)[u];break;default:throw new Error(\"shouldn't have happened\")}r=e instanceof _.GlyphRenderer?e.view.convert_indices_from_subset([o])[0]:o;const f={index:r,x:v,y:w,sx:d,sy:p,data_x:c,data_y:h,segment_index:u,indices:l.multiline_indices,name:e.name};b.push([s,i,this._render_tooltips(a,r,f)])}else{const t=null===(n=g._x)||void 0===n?void 0:n[o],i=null===(s=g._y)||void 0===s?void 0:s[o];let r,c,h;if(\"snap_to_data\"==this.model.point_policy){let e=g.get_anchor_point(this.model.anchor,o,[d,p]);if(null==e&&(e=g.get_anchor_point(\"center\",o,[d,p]),null==e))continue;r=e.x,c=e.y}else[r,c]=[d,p];h=e instanceof _.GlyphRenderer?e.view.convert_indices_from_subset([o])[0]:o;const u={index:h,x:v,y:w,sx:d,sy:p,data_x:t,data_y:i,indices:l.indices,name:e.name};b.push([r,c,this._render_tooltips(a,h,u)])}if(0==b.length)o.clear();else{const{content:e}=o;(0,f.empty)(o.content);for(const[,,t]of b)null!=t&&e.appendChild(t);const[t,n]=b[b.length-1];o.setv({position:[t,n]},{check_eq:!1})}}_emit_callback(e){const{callback:t}=this.model;if(null!=t)for(const n of this.computed_renderers){if(!(n instanceof _.GlyphRenderer))continue;const s=this.plot_view.renderer_view(n);if(null==s)continue;const{x_scale:o,y_scale:i}=s.coordinates,r=o.invert(e.sx),l=i.invert(e.sy),a=n.data_source.inspected;t.execute(this.model,{geometry:Object.assign({x:r,y:l},e),renderer:n,index:a})}}_create_template(e){const t=(0,f.div)({style:{display:\"table\",borderSpacing:\"2px\"}});for(const[n]of e){const e=(0,f.div)({style:{display:\"table-row\"}});t.appendChild(e);const s=(0,f.div)({style:{display:\"table-cell\"},class:$.tooltip_row_label},0!=n.length?`${n}: `:\"\");e.appendChild(s);const o=(0,f.span)();o.dataset.value=\"\";const i=(0,f.span)({class:$.tooltip_color_block},\" \");i.dataset.swatch=\"\",(0,f.undisplay)(i);const r=(0,f.div)({style:{display:\"table-cell\"},class:$.tooltip_row_value},o,i);e.appendChild(r)}return t}_render_template(e,t,n,s,o){const i=e.cloneNode(!0),r=i.querySelectorAll(\"[data-value]\"),l=i.querySelectorAll(\"[data-swatch]\"),a=/\\$color(\\[.*\\])?:(\\w*)/,c=/\\$swatch:(\\w*)/;for(const[[,e],i]of(0,w.enumerate)(t)){const t=e.match(c),_=e.match(a);if(null!=t||null!=_){if(null!=t){const[,e]=t,o=n.get_column(e);if(null==o)r[i].textContent=`${e} unknown`;else{const e=(0,g.isNumber)(s)?o[s]:null;null!=e&&(l[i].style.backgroundColor=(0,v.color2css)(e),(0,f.display)(l[i]))}}if(null!=_){const[,e=\"\",t]=_,o=n.get_column(t);if(null==o){r[i].textContent=`${t} unknown`;continue}const a=e.indexOf(\"hex\")>=0,c=e.indexOf(\"swatch\")>=0,d=(0,g.isNumber)(s)?o[s]:null;if(null==d){r[i].textContent=\"(null)\";continue}r[i].textContent=a?(0,v.color2hex)(d):(0,v.color2css)(d),c&&(l[i].style.backgroundColor=(0,v.color2css)(d),(0,f.display)(l[i]))}}else{const t=(0,y.replace_placeholders)(e.replace(\"$~\",\"$data_\"),n,s,this.model.formatters,o);if((0,g.isString)(t))r[i].textContent=t;else for(const e of t)r[i].appendChild(e)}}return i}_render_tooltips(e,t,n){var s;const{tooltips:o}=this.model;if((0,g.isString)(o)){const s=(0,y.replace_placeholders)({html:o},e,t,this.model.formatters,n);return(0,f.div)(s)}if((0,g.isFunction)(o))return o(e,n);if(o instanceof R.Template)return this._template_view.update(e,t,n),this._template_view.el;if(null!=o){const i=null!==(s=this._template_el)&&void 0!==s?s:this._template_el=this._create_template(o);return this._render_template(i,o,e,t,n)}return null}}n.HoverToolView=z,z.__name__=\"HoverToolView\";class A extends l.InspectTool{constructor(e){super(e),this.tool_name=\"Hover\",this.icon=C.tool_icon_hover}}n.HoverTool=A,r=A,A.__name__=\"HoverTool\",r.prototype.default_view=z,r.define((({Any:e,Boolean:t,String:n,Array:s,Tuple:o,Dict:i,Or:r,Ref:l,Function:c,Auto:_,Nullable:d})=>({tooltips:[d(r(l(R.Template),n,s(o(n,n)),c())),[[\"index\",\"$index\"],[\"data (x, y)\",\"($x, $y)\"],[\"screen (x, y)\",\"($sx, $sy)\"]]],formatters:[i(r(l(a.CustomJSHover),y.FormatterType)),{}],renderers:[r(s(l(p.DataRenderer)),_),\"auto\"],names:[s(n),[]],mode:[k.HoverMode,\"mouse\"],muted_policy:[k.MutedPolicy,\"show\"],point_policy:[k.PointPolicy,\"snap_to_data\"],line_policy:[k.LinePolicy,\"nearest\"],show_arrow:[t,!0],anchor:[k.Anchor,\"center\"],attachment:[k.TooltipAttachment,\"horizontal\"],callback:[d(e)]}))),r.register_alias(\"hover\",(()=>new A))},\n", - " function _(e,t,s,n,a){n();const l=e(1);var i,_,o,r,c,d,p,u,m,w,f,h,x;const v=e(53),y=e(309),V=e(393);a(\"Styles\",V.Styles);const g=e(43),T=e(42),b=e(226),R=e(113),D=e(8),M=e(13),S=(0,l.__importStar)(e(242)),O=e(152);class C extends b.DOMView{}s.DOMNodeView=C,C.__name__=\"DOMNodeView\";class z extends v.Model{constructor(e){super(e)}}s.DOMNode=z,z.__name__=\"DOMNode\",z.__module__=\"bokeh.models.dom\";class P extends C{render(){super.render(),this.el.textContent=this.model.content}_createElement(){return document.createTextNode(\"\")}}s.TextView=P,P.__name__=\"TextView\";class A extends z{constructor(e){super(e)}}s.Text=A,i=A,A.__name__=\"Text\",i.prototype.default_view=P,i.define((({String:e})=>({content:[e,\"\"]})));class N extends C{}s.PlaceholderView=N,N.__name__=\"PlaceholderView\",N.tag_name=\"span\";class E extends z{constructor(e){super(e)}}s.Placeholder=E,_=E,E.__name__=\"Placeholder\",_.define((({})=>({})));class G extends N{update(e,t,s){this.el.textContent=t.toString()}}s.IndexView=G,G.__name__=\"IndexView\";class I extends E{constructor(e){super(e)}}s.Index=I,o=I,I.__name__=\"Index\",o.prototype.default_view=G,o.define((({})=>({})));class k extends N{update(e,t,s){const n=(0,O._get_column_value)(this.model.field,e,t),a=null==n?\"???\":`${n}`;this.el.textContent=a}}s.ValueRefView=k,k.__name__=\"ValueRefView\";class $ extends E{constructor(e){super(e)}}s.ValueRef=$,r=$,$.__name__=\"ValueRef\",r.prototype.default_view=k,r.define((({String:e})=>({field:[e]})));class B extends k{render(){super.render(),this.value_el=(0,g.span)(),this.swatch_el=(0,g.span)({class:S.tooltip_color_block},\" \"),this.el.appendChild(this.value_el),this.el.appendChild(this.swatch_el)}update(e,t,s){const n=(0,O._get_column_value)(this.model.field,e,t),a=null==n?\"???\":`${n}`;this.el.textContent=a}}s.ColorRefView=B,B.__name__=\"ColorRefView\";class L extends ${constructor(e){super(e)}}s.ColorRef=L,c=L,L.__name__=\"ColorRef\",c.prototype.default_view=B,c.define((({Boolean:e})=>({hex:[e,!0],swatch:[e,!0]})));class j extends C{constructor(){super(...arguments),this.child_views=new Map}async lazy_initialize(){await super.lazy_initialize();const e=this.model.children.filter((e=>e instanceof v.Model));await(0,R.build_views)(this.child_views,e,{parent:this})}render(){super.render();const{style:e}=this.model;if(null!=e)if(e instanceof V.Styles)for(const t of e){const e=t.get_value();if((0,D.isString)(e)){const s=t.attr.replace(/_/g,\"-\");this.el.style.hasOwnProperty(s)&&this.el.style.setProperty(s,e)}}else for(const[t,s]of(0,M.entries)(e)){const e=t.replace(/_/g,\"-\");this.el.style.hasOwnProperty(e)&&this.el.style.setProperty(e,s)}for(const e of this.model.children)if((0,D.isString)(e)){const t=document.createTextNode(e);this.el.appendChild(t)}else{this.child_views.get(e).renderTo(this.el)}}}s.DOMElementView=j,j.__name__=\"DOMElementView\";class q extends z{constructor(e){super(e)}}s.DOMElement=q,d=q,q.__name__=\"DOMElement\",d.define((({String:e,Array:t,Dict:s,Or:n,Nullable:a,Ref:l})=>({style:[a(n(l(V.Styles),s(e))),null],children:[t(n(e,l(z),l(y.LayoutDOM))),[]]})));class F extends T.View{}s.ActionView=F,F.__name__=\"ActionView\";class H extends v.Model{constructor(e){super(e)}}s.Action=H,p=H,H.__name__=\"Action\",H.__module__=\"bokeh.models.dom\",p.define((({})=>({})));class J extends j{constructor(){super(...arguments),this.action_views=new Map}async lazy_initialize(){await super.lazy_initialize(),await(0,R.build_views)(this.action_views,this.model.actions,{parent:this})}remove(){(0,R.remove_views)(this.action_views),super.remove()}update(e,t,s={}){!function n(a){for(const l of a.child_views.values())l instanceof N?l.update(e,t,s):l instanceof j&&n(l)}(this);for(const n of this.action_views.values())n.update(e,t,s)}}s.TemplateView=J,J.__name__=\"TemplateView\",J.tag_name=\"div\";class K extends q{}s.Template=K,u=K,K.__name__=\"Template\",u.prototype.default_view=J,u.define((({Array:e,Ref:t})=>({actions:[e(t(H)),[]]})));class Q extends j{}s.SpanView=Q,Q.__name__=\"SpanView\",Q.tag_name=\"span\";class U extends q{}s.Span=U,m=U,U.__name__=\"Span\",m.prototype.default_view=Q;class W extends j{}s.DivView=W,W.__name__=\"DivView\",W.tag_name=\"div\";class X extends q{}s.Div=X,w=X,X.__name__=\"Div\",w.prototype.default_view=W;class Y extends j{}s.TableView=Y,Y.__name__=\"TableView\",Y.tag_name=\"table\";class Z extends q{}s.Table=Z,f=Z,Z.__name__=\"Table\",f.prototype.default_view=Y;class ee extends j{}s.TableRowView=ee,ee.__name__=\"TableRowView\",ee.tag_name=\"tr\";class te extends q{}s.TableRow=te,h=te,te.__name__=\"TableRow\",h.prototype.default_view=ee;const se=e(41),ne=e(234);class ae extends F{update(e,t,s){for(const[e,s]of(0,ne.enumerate)(this.model.groups))e.visible=t==s}}s.ToggleGroupView=ae,ae.__name__=\"ToggleGroupView\";class le extends H{constructor(e){super(e)}}s.ToggleGroup=le,x=le,le.__name__=\"ToggleGroup\",x.prototype.default_view=ae,x.define((({Array:e,Ref:t})=>({groups:[e(t(se.RendererGroup)),[]]})))},\n", - " function _(l,n,u,_,e){var t;_();const o=l(53);class r extends 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- " function _(t,o,e,n,s){var i;n();const l=t(15),c=t(53),r=t(224),a=t(232),u=t(234);class h extends c.Model{constructor(t){super(t)}get button_view(){return this.tools[0].button_view}get event_type(){return this.tools[0].event_type}get tooltip(){return this.tools[0].tooltip}get tool_name(){return this.tools[0].tool_name}get icon(){return this.tools[0].computed_icon}get computed_icon(){return this.icon}get toggleable(){const t=this.tools[0];return t instanceof a.InspectTool&&t.toggleable}initialize(){super.initialize(),this.do=new l.Signal0(this,\"do\")}connect_signals(){super.connect_signals(),this.connect(this.do,(()=>this.doit())),this.connect(this.properties.active.change,(()=>this.set_active()));for(const t of this.tools)this.connect(t.properties.active.change,(()=>{this.active=t.active}))}doit(){for(const t of this.tools)t.do.emit()}set_active(){for(const t of this.tools)t.active=this.active}get menu(){const{menu:t}=this.tools[0];if(null==t)return null;const o=[];for(const[e,n]of(0,u.enumerate)(t))if(null==e)o.push(null);else{const t=()=>{var t,o,e;for(const s of this.tools)null===(e=null===(o=null===(t=s.menu)||void 0===t?void 0:t[n])||void 0===o?void 0:o.handler)||void 0===e||e.call(o)};o.push(Object.assign(Object.assign({},e),{handler:t}))}return o}}e.ToolProxy=h,i=h,h.__name__=\"ToolProxy\",i.define((({Boolean:t,Array:o,Ref:e})=>({tools:[o(e(r.ButtonTool)),[]],active:[t,!1],disabled:[t,!1]})))},\n", - " function _(o,t,s,e,i){var n,r;e();const l=o(20),c=o(9),h=o(13),a=o(233),_=o(221),p=o(394),u=o(309),f=o(207);class y extends a.ToolbarBase{constructor(o){super(o)}initialize(){super.initialize(),this._merge_tools()}_merge_tools(){this._proxied_tools=[];const o={},t={},s={},e=[],i=[];for(const o of this.help)(0,c.includes)(i,o.redirect)||(e.push(o),i.push(o.redirect));this._proxied_tools.push(...e),this.help=e;for(const[o,t]of(0,h.entries)(this.gestures)){o in s||(s[o]={});for(const e of t.tools)e.type in s[o]||(s[o][e.type]=[]),s[o][e.type].push(e)}for(const t of this.inspectors)t.type in o||(o[t.type]=[]),o[t.type].push(t);for(const o of this.actions)o.type in t||(t[o.type]=[]),t[o.type].push(o);const n=(o,t=!1)=>{const s=new p.ToolProxy({tools:o,active:t});return this._proxied_tools.push(s),s};for(const o of(0,h.keys)(s)){const t=this.gestures[o];t.tools=[];for(const e of(0,h.keys)(s[o])){const i=s[o][e];if(i.length>0)if(\"multi\"==o)for(const o of i){const s=n([o]);t.tools.push(s),this.connect(s.properties.active.change,(()=>this._active_change(s)))}else{const o=n(i);t.tools.push(o),this.connect(o.properties.active.change,(()=>this._active_change(o)))}}}this.actions=[];for(const[o,s]of(0,h.entries)(t))if(\"CustomAction\"==o)for(const o of s)this.actions.push(n([o]));else s.length>0&&this.actions.push(n(s));this.inspectors=[];for(const t of(0,h.values)(o))t.length>0&&this.inspectors.push(n(t,!0));for(const[o,t]of(0,h.entries)(this.gestures))0!=t.tools.length&&(t.tools=(0,c.sort_by)(t.tools,(o=>o.default_order)),\"pinch\"!=o&&\"scroll\"!=o&&\"multi\"!=o&&(t.tools[0].active=!0))}}s.ProxyToolbar=y,n=y,y.__name__=\"ProxyToolbar\",n.define((({Array:o,Ref:t})=>({toolbars:[o(t(_.Toolbar)),[]]})));class d extends u.LayoutDOMView{initialize(){this.model.toolbar.toolbar_location=this.model.toolbar_location,super.initialize()}get child_models(){return[this.model.toolbar]}_update_layout(){this.layout=new f.ContentBox(this.child_views[0].el);const{toolbar:o}=this.model;o.horizontal?this.layout.set_sizing({width_policy:\"fit\",min_width:100,height_policy:\"fixed\"}):this.layout.set_sizing({width_policy:\"fixed\",height_policy:\"fit\",min_height:100})}after_layout(){super.after_layout();const o=this.child_views[0];o.layout.bbox=this.layout.bbox,o.render()}}s.ToolbarBoxView=d,d.__name__=\"ToolbarBoxView\";class b extends u.LayoutDOM{constructor(o){super(o)}}s.ToolbarBox=b,r=b,b.__name__=\"ToolbarBox\",r.prototype.default_view=d,r.define((({Ref:o})=>({toolbar:[o(a.ToolbarBase)],toolbar_location:[l.Location,\"right\"]})))},\n", - " function _(e,n,r,t,o){t();const s=e(1),u=e(53),c=(0,s.__importStar)(e(21)),a=e(8),l=e(13);r.resolve_defs=function(e,n){var r,t,o,s;function i(e){return null!=e.module?`${e.module}.${e.name}`:e.name}function f(e){if((0,a.isString)(e))switch(e){case\"Any\":return c.Any;case\"Unknown\":return c.Unknown;case\"Boolean\":return c.Boolean;case\"Number\":return c.Number;case\"Int\":return c.Int;case\"String\":return c.String;case\"Null\":return c.Null}else switch(e[0]){case\"Nullable\":{const[,n]=e;return c.Nullable(f(n))}case\"Or\":{const[,...n]=e;return c.Or(...n.map(f))}case\"Tuple\":{const[,n,...r]=e;return c.Tuple(f(n),...r.map(f))}case\"Array\":{const[,n]=e;return c.Array(f(n))}case\"Struct\":{const[,...n]=e,r=n.map((([e,n])=>[e,f(n)]));return c.Struct((0,l.to_object)(r))}case\"Dict\":{const[,n]=e;return c.Dict(f(n))}case\"Map\":{const[,n,r]=e;return c.Map(f(n),f(r))}case\"Enum\":{const[,...n]=e;return c.Enum(...n)}case\"Ref\":{const[,r]=e,t=n.get(i(r));if(null!=t)return c.Ref(t);throw new Error(`${i(r)} wasn't defined before referencing it`)}case\"AnyRef\":return c.AnyRef()}}for(const c of e){const e=(()=>{if(null==c.extends)return u.Model;{const e=n.get(i(c.extends));if(null!=e)return e;throw new Error(`base model ${i(c.extends)} of ${i(c)} is not defined`)}})(),a=((s=class extends e{}).__name__=c.name,s.__module__=c.module,s);for(const e of null!==(r=c.properties)&&void 0!==r?r:[]){const n=f(null!==(t=e.kind)&&void 0!==t?t:\"Unknown\");a.define({[e.name]:[n,e.default]})}for(const e of null!==(o=c.overrides)&&void 0!==o?o:[])a.override({[e.name]:e.default});n.register(a)}}},\n", - " function _(n,e,t,o,i){o();const d=n(5),c=n(226),s=n(113),a=n(43),l=n(398);t.index={},t.add_document_standalone=async function(n,e,o=[],i=!1){const u=new Map;async function f(i){let d;const f=n.roots().indexOf(i),r=o[f];null!=r?d=r:e.classList.contains(l.BOKEH_ROOT)?d=e:(d=(0,a.div)({class:l.BOKEH_ROOT}),e.appendChild(d));const w=await(0,s.build_view)(i,{parent:null});return w instanceof c.DOMView&&w.renderTo(d),u.set(i,w),t.index[i.id]=w,w}for(const e of n.roots())await f(e);return i&&(window.document.title=n.title()),n.on_change((n=>{n instanceof d.RootAddedEvent?f(n.model):n instanceof d.RootRemovedEvent?function(n){const e=u.get(n);null!=e&&(e.remove(),u.delete(n),delete t.index[n.id])}(n.model):i&&n instanceof d.TitleChangedEvent&&(window.document.title=n.title)})),[...u.values()]}},\n", - " function _(o,e,n,t,r){t();const l=o(43),d=o(44);function u(o){let e=document.getElementById(o);if(null==e)throw new Error(`Error rendering Bokeh model: could not find #${o} HTML tag`);if(!document.body.contains(e))throw new Error(`Error rendering Bokeh model: element #${o} must be under `);if(\"SCRIPT\"==e.tagName){const o=(0,l.div)({class:n.BOKEH_ROOT});(0,l.replaceWith)(e,o),e=o}return e}n.BOKEH_ROOT=d.root,n._resolve_element=function(o){const{elementid:e}=o;return null!=e?u(e):document.body},n._resolve_root_elements=function(o){const e=[];if(null!=o.root_ids&&null!=o.roots)for(const n of o.root_ids)e.push(u(o.roots[n]));return e}},\n", - " function _(n,o,t,s,e){s();const c=n(400),r=n(19),a=n(397);t._get_ws_url=function(n,o){let t,s=\"ws:\";return\"https:\"==window.location.protocol&&(s=\"wss:\"),null!=o?(t=document.createElement(\"a\"),t.href=o):t=window.location,null!=n?\"/\"==n&&(n=\"\"):n=t.pathname.replace(/\\/+$/,\"\"),`${s}//${t.host}${n}/ws`};const i={};t.add_document_from_session=async function(n,o,t,s=[],e=!1){const l=window.location.search.substr(1);let d;try{d=await function(n,o,t){const s=(0,c.parse_token)(o).session_id;n in i||(i[n]={});const e=i[n];return s in e||(e[s]=(0,c.pull_session)(n,o,t)),e[s]}(n,o,l)}catch(n){const t=(0,c.parse_token)(o).session_id;throw r.logger.error(`Failed to load Bokeh session ${t}: ${n}`),n}return(0,a.add_document_standalone)(d.document,t,s,e)}},\n", - " function _(e,s,n,t,o){t();const r=e(19),i=e(5),c=e(401),l=e(402),_=e(403);n.DEFAULT_SERVER_WEBSOCKET_URL=\"ws://localhost:5006/ws\",n.DEFAULT_TOKEN=\"eyJzZXNzaW9uX2lkIjogImRlZmF1bHQifQ\";let h=0;function a(e){let s=e.split(\".\")[0];const n=s.length%4;return 0!=n&&(s+=\"=\".repeat(4-n)),JSON.parse(atob(s.replace(/_/g,\"/\").replace(/-/g,\"+\")))}n.parse_token=a;class d{constructor(e=n.DEFAULT_SERVER_WEBSOCKET_URL,s=n.DEFAULT_TOKEN,t=null){this.url=e,this.token=s,this.args_string=t,this._number=h++,this.socket=null,this.session=null,this.closed_permanently=!1,this._current_handler=null,this._pending_replies=new Map,this._pending_messages=[],this._receiver=new l.Receiver,this.id=a(s).session_id.split(\".\")[0],r.logger.debug(`Creating websocket ${this._number} to '${this.url}' session '${this.id}'`)}async connect(){if(this.closed_permanently)throw new Error(\"Cannot connect() a closed ClientConnection\");if(null!=this.socket)throw new Error(\"Already connected\");this._current_handler=null,this._pending_replies.clear(),this._pending_messages=[];try{let e=`${this.url}`;return null!=this.args_string&&this.args_string.length>0&&(e+=`?${this.args_string}`),this.socket=new WebSocket(e,[\"bokeh\",this.token]),new Promise(((e,s)=>{this.socket.binaryType=\"arraybuffer\",this.socket.onopen=()=>this._on_open(e,s),this.socket.onmessage=e=>this._on_message(e),this.socket.onclose=e=>this._on_close(e,s),this.socket.onerror=()=>this._on_error(s)}))}catch(e){throw r.logger.error(`websocket creation failed to url: ${this.url}`),r.logger.error(` - ${e}`),e}}close(){this.closed_permanently||(r.logger.debug(`Permanently closing websocket connection ${this._number}`),this.closed_permanently=!0,null!=this.socket&&this.socket.close(1e3,`close method called on ClientConnection 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connection was already closed\"),s(new Error(\"The connection has been closed\"));else{const s=i.Document.from_json(n),t=i.Document._compute_patch_since_json(n,s);if(t.events.length>0){r.logger.debug(`Sending ${t.events.length} changes from model construction back to server`);const e=c.Message.create(\"PATCH-DOC\",{},t);this.send(e)}this.session=new _.ClientSession(this,s,this.id);for(const e of this._pending_messages)this.session.handle(e);this._pending_messages=[],r.logger.debug(\"Created a new session from new pulled doc\"),e(this.session)}else this.session.document.replace_with_json(n),r.logger.debug(\"Updated existing session with new pulled doc\")}catch(e){null===(n=console.trace)||void 0===n||n.call(console,e),r.logger.error(`Failed to repull session ${e}`),s(e instanceof Error?e:`${e}`)}}_on_open(e,s){r.logger.info(`Websocket connection ${this._number} is now 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this.request_server_info()}_document_changed(e){if(e.setter_id===this.id)return;const t=e instanceof c.DocumentEventBatch?e.events:[e],n=this.document.create_json_patch(t),s=i.Message.create(\"PATCH-DOC\",{},n);this._connection.send(s)}_handle_patch(e){this.document.apply_json_patch(e.content,e.buffers,this.id)}_handle_ok(e){_.logger.trace(`Unhandled OK reply to ${e.reqid()}`)}_handle_error(e){_.logger.error(`Unhandled ERROR reply to ${e.reqid()}: ${e.content.text}`)}}n.ClientSession=r,r.__name__=\"ClientSession\"},\n", - " function _(e,o,t,n,r){n();const s=e(1),l=e(5),i=e(402),a=e(19),c=e(43),g=e(13),f=e(397),u=e(398),m=(0,s.__importDefault)(e(44)),p=(0,s.__importDefault)(e(240)),d=(0,s.__importDefault)(e(405));function _(e,o){o.buffers.length>0?e.consume(o.buffers[0].buffer):e.consume(o.content.data);const t=e.message;null!=t&&this.apply_json_patch(t.content,t.buffers)}function b(e,o){if(\"undefined\"!=typeof Jupyter&&null!=Jupyter.notebook.kernel){a.logger.info(`Registering Jupyter 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t.def(e.shared.vao,\".currentVAO?\",e.shared.vao,\".currentVAO.count:-1\")}));return m}return null}(),h=l(br,!1);return{elements:s,primitive:m,count:p,instances:h,offset:d,vao:f,vaoActive:i,elementsActive:u,static:a}}(e,d),A=function(e,t){var r=e.static,n=e.dynamic,i={};return k.forEach((function(e){var o=E(e);function f(t,a){if(e in r){var f=t(r[e]);i[o]=an((function(){return f}))}else if(e in n){var u=n[e];i[o]=on(u,(function(e,t){return a(e,t,e.invoke(t,u))}))}}switch(e){case Nt:case It:case Vt:case er:case Mt:case ir:case Xt:case Kt:case Jt:case Gt:return f((function(r){return _.commandType(r,\"boolean\",e,t.commandStr),r}),(function(t,r,n){return _.optional((function(){t.assert(r,\"typeof \"+n+'===\"boolean\"',\"invalid flag \"+e,t.commandStr)})),n}));case Wt:return f((function(r){return _.commandParameter(r,$r,\"invalid \"+e,t.commandStr),$r[r]}),(function(t,r,n){var a=t.constants.compareFuncs;return _.optional((function(){t.assert(r,n+\" in \"+a,\"invalid \"+e+\", must be one of 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_.optional((function(){e.assert(t,e.shared.isArrayLike+\"(\"+r+\")&&\"+r+\".length===4\",\"blend.color must be a 4d array\")})),V(4,(function(e){return t.def(\"+\",r,\"[\",e,\"]\")}))}));case tr:return f((function(e){return _.commandType(e,\"number\",o,t.commandStr),0|e}),(function(e,t,r){return _.optional((function(){e.assert(t,\"typeof \"+r+'===\"number\"',\"invalid stencil.mask\")})),t.def(r,\"|0\")}));case rr:return f((function(r){_.commandType(r,\"object\",o,t.commandStr);var n=r.cmp||\"keep\",a=r.ref||0,i=\"mask\"in r?r.mask:-1;return _.commandParameter(n,$r,e+\".cmp\",t.commandStr),_.commandType(a,\"number\",e+\".ref\",t.commandStr),_.commandType(i,\"number\",e+\".mask\",t.commandStr),[$r[n],a,i]}),(function(e,t,r){var n=e.constants.compareFuncs;return _.optional((function(){function a(){e.assert(t,Array.prototype.join.call(arguments,\"\"),\"invalid stencil.func\")}a(r+\"&&typeof \",r,'===\"object\"'),a('!(\"cmp\" in ',r,\")||(\",r,\".cmp in \",n,\")\")})),[t.def('\"cmp\" in 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_.commandType(r,\"number\",o+\".factor\",t.commandStr),_.commandType(n,\"number\",o+\".units\",t.commandStr),[r,n]}),(function(t,r,n){return _.optional((function(){t.assert(r,n+\"&&typeof \"+n+'===\"object\"',\"invalid \"+e)})),[r.def(n,\".factor|0\"),r.def(n,\".units|0\")]}));case qt:return f((function(e){var r=0;return\"front\"===e?r=Hr:\"back\"===e&&(r=Nr),_.command(!!r,o,t.commandStr),r}),(function(e,t,r){return _.optional((function(){e.assert(t,r+'===\"front\"||'+r+'===\"back\"',\"invalid cull.face\")})),t.def(r,'===\"front\"?',Hr,\":\",Nr)}));case Yt:return f((function(e){return _.command(\"number\"==typeof e&&e>=a.lineWidthDims[0]&&e<=a.lineWidthDims[1],\"invalid line width, must be a positive number between \"+a.lineWidthDims[0]+\" and \"+a.lineWidthDims[1],t.commandStr),e}),(function(e,t,r){return _.optional((function(){e.assert(t,\"typeof \"+r+'===\"number\"&&'+r+\">=\"+a.lineWidthDims[0]+\"&&\"+r+\"<=\"+a.lineWidthDims[1],\"invalid line width\")})),r}));case Qt:return f((function(e){return _.commandParameter(e,Zr,o,t.commandStr),Zr[e]}),(function(e,t,r){return _.optional((function(){e.assert(t,r+'===\"cw\"||'+r+'===\"ccw\"',\"invalid frontFace, must be one of cw,ccw\")})),t.def(r+'===\"cw\"?2304:'+qr)}));case Ht:return f((function(e){return _.command(me(e)&&4===e.length,\"color.mask must be length 4 array\",t.commandStr),e.map((function(e){return!!e}))}),(function(e,t,r){return _.optional((function(){e.assert(t,e.shared.isArrayLike+\"(\"+r+\")&&\"+r+\".length===4\",\"invalid color.mask\")})),V(4,(function(e){return\"!!\"+r+\"[\"+e+\"]\"}))}));case Zt:return f((function(e){_.command(\"object\"==typeof e&&e,o,t.commandStr);var r=\"value\"in e?e.value:1,n=!!e.invert;return _.command(\"number\"==typeof r&&r>=0&&r<=1,\"sample.coverage.value must be a number between 0 and 1\",t.commandStr),[r,n]}),(function(e,t,r){return _.optional((function(){e.assert(t,r+\"&&typeof \"+r+'===\"object\"',\"invalid sample.coverage\")})),[t.def('\"value\" in 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t=x[e];t&&(A[e]=t)}O(fr),O(E(or));var T=Object.keys(A).length>0,D={framebuffer:y,draw:w,shader:S,state:A,dirty:T,scopeVAO:null,drawVAO:null,useVAO:!1,attributes:{}};if(D.profile=function(e){var t,r=e.static,n=e.dynamic;if(ur in r){var a=!!r[ur];(t=an((function(e,t){return a}))).enable=a}else if(ur in n){var i=n[ur];t=on(i,(function(e,t){return e.invoke(t,i)}))}return t}(e),D.uniforms=function(e,t){var r=e.static,n=e.dynamic,a={};return Object.keys(r).forEach((function(e){var n,i=r[e];if(\"number\"==typeof i||\"boolean\"==typeof i)n=an((function(){return i}));else if(\"function\"==typeof i){var o=i._reglType;\"texture2d\"===o||\"textureCube\"===o?n=an((function(e){return e.link(i)})):\"framebuffer\"===o||\"framebufferCube\"===o?(_.command(i.color.length>0,'missing color attachment for framebuffer sent to uniform \"'+e+'\"',t.commandStr),n=an((function(e){return e.link(i.color[0])}))):_.commandRaise('invalid data for uniform \"'+e+'\"',t.commandStr)}else me(i)?n=an((function(t){return 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e=0;e=0},read:z,destroy:function(){I.length=0,le(),V&&(V.removeEventListener(cn,de),V.removeEventListener(ln,me)),k.clear(),E.clear(),O.clear(),A.clear(),S.clear(),w.clear(),x.clear(),m&&m.clear(),q.forEach((function(e){e()}))},_gl:a,_refresh:xe,poll:function(){ye(),m&&m.update()},now:we,stats:l});return n.onDone(null,Ae),Ae}},\"object\"==typeof r&&void 0!==t?t.exports=o():\"function\"==typeof define&&define.amd?define(o):i.createREGL=o()},\n", - " 413: function _(t,e,s,a,r){a();const n=t(414),_=t(12);class o{constructor(t){this._regl=t,this._map=new Map}_create_texture(t){const e=t.length;let s=0;const a=[];let r=0,_=0;for(let n=0;nc[f+1]&&f++;const a=t[f],n=c[f]+.5*a;let o=.5*a-Math.abs(s-n);f%2==1&&(o=-o),m[e]=Math.round(255*(o-r)/(_-r))}return[[s,u,r,_],this._regl.texture({shape:[l,1,1],data:m,wrapS:\"repeat\",format:\"alpha\",type:\"uint8\",mag:\"linear\",min:\"linear\"})]}_get_key(t){return t.join(\",\")}_get_or_create(t){const e=this._get_key(t);let s=this._map.get(e);if(null==s){const a=(0,n.gcd)(t);if(a>1){t=(0,_.map)(t,(t=>t/a)),s=this._get_or_create(t);const[r,n,o]=s;s=[r,n,a],this._map.set(e,s)}else{const[r,n]=this._create_texture(t);s=[r,n,a],this._map.set(e,s)}}return s}get(t){return t.length%2==1&&(t=t.slice(0,-1)),this._get_or_create(t)}}s.DashCache=o,o.__name__=\"DashCache\"},\n", - " 414: function _(n,t,e,r,o){function u(n,t){let e,r;n>t?(e=n,r=t):(e=t,r=n);let o=e%r;for(;0!=o;)e=r,r=o,o=e%r;return r}r(),e.gcd=function(n){let t=n[0];for(let e=1;e u_miter_limit);\\n miter_too_large_end = (miter_factor_end > u_miter_limit);\\n }\\n\\n float sign_at_start = -sign(a_position.x); // +ve at segment start, -ve end.\\n vec2 point = sign_at_start > 0.0 ? a_point_start : a_point_end;\\n vec2 adjacent_point =\\n sign_at_start > 0.0 ? (has_start_cap ? a_point_start : a_point_prev)\\n : (has_end_cap ? a_point_end : a_point_next);\\n\\n if ( (has_start_cap && sign_at_start > 0.0) ||\\n (has_end_cap && sign_at_start < 0.0) ) {\\n // Cap.\\n xy = point - segment_right*(halfwidth*a_position.y);\\n if (cap_type == butt_cap)\\n xy -= sign_at_start*0.5*u_antialias*segment_along;\\n else\\n xy -= sign_at_start*halfwidth*segment_along;\\n }\\n else { // Join.\\n // +ve if turning to right, -ve if to left.\\n float turn_sign = sign_at_start > 0.0 ? turn_right_start : turn_right_end;\\n\\n vec2 adjacent_right = sign_at_start*normalize(right_vector(point - adjacent_point));\\n vec2 point_right = normalize(segment_right + adjacent_right);\\n float miter_factor = sign_at_start > 0.0 ? miter_factor_start : miter_factor_end;\\n bool miter_too_large = sign_at_start > 0.0 ? miter_too_large_start : miter_too_large_end;\\n\\n if (abs(a_position.x) > 1.5) {\\n // Outer point, meets prev/next segment.\\n float factor; // multiplied by halfwidth...\\n\\n if (join_type == bevel_join || (join_type == miter_join && miter_too_large))\\n factor = 1.0 / miter_factor; // cos_theta.\\n else if (join_type == round_join &&\\n miter_factor > min_miter_factor_round_join_mesh)\\n factor = 1.0;\\n else // miter, or round (small angle only).\\n factor = miter_factor;\\n\\n xy = point - point_right*(halfwidth*turn_sign*factor);\\n v_coords.y = turn_sign*halfwidth*factor / miter_factor;\\n }\\n else if (turn_sign*a_position.y < 0.0) {\\n // Inner point, meets prev/next segment.\\n float len = halfwidth*miter_factor;\\n float segment_len = v_segment_length;\\n float adjacent_len = distance(point, adjacent_point);\\n\\n if (len <= min(segment_len, adjacent_len))\\n // Normal behaviour.\\n xy = point - point_right*(len*a_position.y);\\n else\\n // For short wide line segments the inner point using the above\\n // calculation can be outside of the line. Here clipping it.\\n xy = point + segment_right*(halfwidth*turn_sign);\\n }\\n else {\\n // Point along outside edge.\\n xy = point - segment_right*(halfwidth*a_position.y);\\n if (join_type == round_join &&\\n miter_factor > min_miter_factor_round_join_mesh) {\\n xy = line_intersection(xy, segment_along,\\n point - turn_sign*point_right*halfwidth,\\n right_vector(point_right));\\n }\\n }\\n }\\n\\n vec2 pos = xy + 0.5; // Bokeh's offset.\\n pos /= u_canvas_size / u_pixel_ratio; // in 0..1\\n gl_Position = vec4(2.0*pos.x - 1.0, 1.0 - 2.0*pos.y, 0.0, 1.0);\\n\\n v_coords.x = dot(xy - a_point_start, segment_along);\\n v_flags = float(int(has_start_cap) +\\n 2*int(has_end_cap) +\\n 4*int(miter_too_large_start) +\\n 8*int(miter_too_large_end));\\n v_cos_theta_turn_right_start = cos_theta_start*turn_right_start;\\n v_cos_theta_turn_right_end = cos_theta_end*turn_right_end;\\n\\n#ifdef DASHED\\n v_length_so_far = a_length_so_far;\\n#endif\\n}\\n\"},\n", - " 416: function _(n,t,a,i,e){i();a.default=\"\\nprecision mediump float;\\n\\nconst int butt_cap = 0;\\nconst int round_cap = 1;\\nconst int square_cap = 2;\\n\\nconst int miter_join = 0;\\nconst int round_join = 1;\\nconst int bevel_join = 2;\\n\\nuniform float u_linewidth;\\nuniform float u_antialias;\\nuniform float u_line_join;\\nuniform float u_line_cap;\\nuniform vec4 u_line_color;\\n#ifdef DASHED\\nuniform sampler2D u_dash_tex;\\nuniform vec4 u_dash_tex_info;\\nuniform float u_dash_scale;\\nuniform float u_dash_offset;\\n#endif\\n\\nvarying float v_segment_length;\\nvarying vec2 v_coords;\\nvarying float v_flags;\\nvarying float v_cos_theta_turn_right_start;\\nvarying float v_cos_theta_turn_right_end;\\n#ifdef DASHED\\nvarying float v_length_so_far;\\n#endif\\n\\nfloat cross_z(in vec2 v0, in vec2 v1)\\n{\\n return v0.x*v1.y - v0.y*v1.x;\\n}\\n\\nfloat point_line_side(in vec2 point, in vec2 start, in vec2 end)\\n{\\n // +ve if point to right of line.\\n // Alternatively could do dot product with right_vector.\\n return cross_z(point - start, end - start);\\n}\\n\\nfloat point_line_distance(in vec2 point, in vec2 start, in vec2 end)\\n{\\n return point_line_side(point, start, end) / distance(start, end);\\n}\\n\\nvec2 right_vector(in vec2 v)\\n{\\n return vec2(v.y, -v.x);\\n}\\n\\nfloat bevel_join_distance(in float sign_start, in float halfwidth)\\n{\\n float cos_theta_turn_right = sign_start > 0.0 ? v_cos_theta_turn_right_start\\n : v_cos_theta_turn_right_end;\\n float cos_theta = abs(cos_theta_turn_right);\\n float turn_right = sign(cos_theta_turn_right);\\n float distance_along = sign_start > 0.0 ? 0.0 : v_segment_length;\\n\\n // In v_coords reference frame (x is along segment, y across).\\n vec2 line_start = vec2(distance_along, halfwidth*turn_right);\\n float sin_alpha = cos_theta;\\n float cos_alpha = sqrt(1.0 - sin_alpha*sin_alpha);\\n vec2 line_along = vec2(-sign_start*turn_right*sin_alpha, -cos_alpha);\\n\\n return halfwidth + sign_start*point_line_distance(\\n v_coords, line_start, line_start+line_along);\\n}\\n\\nfloat cap(in int cap_type, in float x, in float y)\\n{\\n // x is distance along segment in direction away from end of segment,\\n // y is distance across segment.\\n if (cap_type == butt_cap)\\n return max(0.5*u_linewidth - x, abs(y));\\n else if (cap_type == square_cap)\\n return max(-x, abs(y));\\n else // cap_type == round_cap\\n return distance(vec2(min(x, 0.0), y), vec2(0.0, 0.0));\\n}\\n\\nfloat distance_to_alpha(in float dist)\\n{\\n return 1.0 - smoothstep(0.5*(u_linewidth - u_antialias),\\n 0.5*(u_linewidth + u_antialias), dist);\\n}\\n\\n#ifdef DASHED\\nfloat dash_distance(in float x)\\n{\\n // x is in direction of v_coords.x, i.e. along segment.\\n float tex_length = u_dash_tex_info.x;\\n float tex_offset = u_dash_tex_info.y;\\n float tex_dist_min = u_dash_tex_info.z;\\n float tex_dist_max = u_dash_tex_info.w;\\n\\n // Apply offset.\\n x += v_length_so_far - u_dash_scale*tex_offset + u_dash_offset;\\n\\n // Interpolate within texture to obtain distance to dash.\\n float dist = texture2D(u_dash_tex,\\n vec2(x / (tex_length*u_dash_scale), 0.0)).a;\\n\\n // Scale distance within min and max limits.\\n dist = tex_dist_min + dist*(tex_dist_max - tex_dist_min);\\n\\n return u_dash_scale*dist;\\n}\\n\\nmat2 rotation_matrix(in float sign_start)\\n{\\n // Rotation matrix for v_coords from this segment to prev or next segment.\\n float cos_theta_turn_right = sign_start > 0.0 ? v_cos_theta_turn_right_start\\n : v_cos_theta_turn_right_end;\\n float cos_theta = abs(cos_theta_turn_right);\\n float turn_right = sign(cos_theta_turn_right);\\n\\n float sin_theta = sqrt(1.0 - cos_theta*cos_theta)*sign_start*turn_right;\\n float cos_2theta = 2.0*cos_theta*cos_theta - 1.0;\\n float sin_2theta = 2.0*sin_theta*cos_theta;\\n return mat2(cos_2theta, -sin_2theta, sin_2theta, cos_2theta);\\n}\\n#endif\\n\\nvoid main()\\n{\\n int join_type = int(u_line_join + 0.5);\\n int cap_type = int(u_line_cap + 0.5);\\n float halfwidth = 0.5*(u_linewidth + u_antialias);\\n\\n // Extract flags.\\n int flags = int(v_flags + 0.5);\\n bool miter_too_large_end = (flags / 8 > 0);\\n flags -= 8*int(miter_too_large_end);\\n bool miter_too_large_start = (flags / 4 > 0);\\n flags -= 4*int(miter_too_large_start);\\n bool has_end_cap = (flags / 2 > 0);\\n flags -= 2*int(has_end_cap);\\n bool has_start_cap = flags > 0;\\n\\n float dist = v_coords.y; // For straight segment, and miter join.\\n\\n if (v_coords.x <= 0.5*u_antialias) {\\n // At start of segment, either cap or join.\\n if (has_start_cap)\\n dist = cap(cap_type, v_coords.x, v_coords.y);\\n else if (join_type == round_join)\\n dist = distance(v_coords, vec2(0.0, 0.0));\\n else if (join_type == bevel_join ||\\n (join_type == miter_join && miter_too_large_start))\\n dist = max(abs(dist), bevel_join_distance(1.0, halfwidth));\\n // else a miter join which uses the default dist calculation.\\n }\\n else if (v_coords.x >= v_segment_length - 0.5*u_antialias) {\\n // At end of segment, either cap or join.\\n if (has_end_cap)\\n dist = cap(cap_type, v_segment_length - v_coords.x, v_coords.y);\\n else if (join_type == round_join)\\n dist = distance(v_coords, vec2(v_segment_length, 0));\\n else if ((join_type == bevel_join ||\\n (join_type == miter_join && miter_too_large_end)))\\n dist = max(abs(dist), bevel_join_distance(-1.0, halfwidth));\\n // else a miter join which uses the default dist calculation.\\n }\\n\\n float alpha = distance_to_alpha(abs(dist));\\n\\n#ifdef DASHED\\n if (u_dash_tex_info.x >= 0.0) {\\n // Dashes in straight segments (outside of joins) are easily calculated.\\n dist = dash_distance(v_coords.x);\\n\\n if (!has_start_cap && cap_type == butt_cap) {\\n if (v_coords.x < 0.5*u_antialias) {\\n // Outer of start join rendered solid color or not at all\\n // depending on whether corner point is in dash or gap, with\\n // antialiased ends.\\n dist = (dash_distance(0.0) > 0.0 ? 0.5*u_antialias - v_coords.x\\n : -0.5*u_linewidth);\\n\\n // Also consider antialiased end of dash just outside corner.\\n float end_dist = dash_distance(0.5*u_antialias) + v_coords.x -\\n 0.5*u_antialias;\\n dist = max(dist, end_dist);\\n }\\n\\n vec2 prev_coords = rotation_matrix(1.0)*v_coords;\\n\\n if (abs(prev_coords.y) < halfwidth &&\\n prev_coords.x < 0.5*u_antialias) {\\n // Extend dashes across from previous segment, with antialiased\\n // end.\\n float new_dist = dash_distance(min(prev_coords.x,\\n -0.5*u_antialias));\\n\\n if (prev_coords.x > -0.5*u_antialias)\\n new_dist -= prev_coords.x + 0.5*u_antialias;\\n\\n new_dist = min(new_dist, 0.5*u_linewidth - abs(prev_coords.y));\\n dist = max(dist, new_dist);\\n }\\n }\\n\\n if (!has_end_cap && cap_type == butt_cap) {\\n if (v_coords.x > v_segment_length - 0.5*u_antialias) {\\n // Similar for end join.\\n dist = (dash_distance(v_segment_length) > 0.0\\n ? v_coords.x - v_segment_length + 0.5*u_antialias\\n : -0.5*u_linewidth);\\n\\n float end_dist =\\n dash_distance(v_segment_length - 0.5*u_antialias) -\\n v_coords.x + v_segment_length - 0.5*u_antialias;\\n dist = max(dist, end_dist);\\n }\\n\\n vec2 next_coords =\\n rotation_matrix(-1.0)*(v_coords - vec2(v_segment_length, 0.0));\\n\\n if (abs(next_coords.y) < halfwidth &&\\n next_coords.x > -0.5*u_antialias) {\\n // Extend dashes across from next segment, with antialiased end.\\n float new_dist = dash_distance(v_segment_length +\\n max(next_coords.x, 0.5*u_antialias));\\n\\n if (next_coords.x < 0.5*u_antialias)\\n new_dist += next_coords.x - 0.5*u_antialias;\\n\\n new_dist = min(new_dist, 0.5*u_linewidth - abs(next_coords.y));\\n dist = max(dist, new_dist);\\n }\\n }\\n\\n dist = cap(cap_type, dist, v_coords.y);\\n\\n float dash_alpha = distance_to_alpha(dist);\\n alpha = min(alpha, dash_alpha);\\n }\\n#endif\\n\\n alpha = u_line_color.a*alpha;\\n gl_FragColor = vec4(u_line_color.rgb*alpha, alpha); // Premultiplied alpha.\\n}\\n\"},\n", - " 417: function _(n,i,o,a,t){a();o.default=\"\\nprecision mediump float;\\n\\nattribute vec2 a_position;\\nattribute vec2 a_center;\\nattribute float a_size;\\nattribute float a_angle; // in radians\\nattribute float a_linewidth;\\nattribute vec4 a_line_color;\\nattribute vec4 a_fill_color;\\nattribute float a_show;\\n\\nuniform float u_pixel_ratio;\\nuniform vec2 u_canvas_size;\\nuniform float u_antialias;\\n\\nvarying float v_linewidth;\\nvarying float v_size;\\nvarying vec4 v_line_color;\\nvarying vec4 v_fill_color;\\nvarying vec2 v_coords;\\n\\nvoid main()\\n{\\n v_size = a_size;\\n v_linewidth = a_linewidth;\\n v_line_color = a_line_color;\\n v_fill_color = a_fill_color;\\n\\n if (a_show < 0.5) {\\n // Do not show this marker.\\n gl_Position = vec4(-2.0, -2.0, 0.0, 1.0);\\n return;\\n }\\n\\n float enclosing_size = v_size + 2.0*v_linewidth + 3.0*u_antialias;\\n\\n // Coordinates in rotated frame with respect to center of marker, used in\\n // distance functions in fragment shader.\\n v_coords = a_position*enclosing_size;\\n\\n float c = cos(-a_angle);\\n float s = sin(-a_angle);\\n mat2 rotation = mat2(c, -s, s, c);\\n\\n vec2 pos = a_center + rotation*v_coords;\\n pos += 0.5; // make up for Bokeh's offset\\n pos /= u_canvas_size / u_pixel_ratio; // in 0..1\\n gl_Position = vec4(2.0*pos.x - 1.0, 1.0 - 2.0*pos.y, 0.0, 1.0);\\n}\\n\"},\n", - " 418: function _(n,a,e,t,s){t();e.default=\"\\nprecision mediump float;\\n\\nconst float SQRT_2 = 1.4142135623730951;\\nconst float SQRT_3 = sqrt(3.0);\\nconst float PI = 3.14159265358979323846264;\\n\\nconst float IN_ANGLE = 0.6283185307179586; // PI/5. = 36 degrees (star of 5 pikes)\\n//const float OUT_ANGLE = PI/2. - IN_ANGLE; // External angle for regular stars\\nconst float COS_A = 0.8090169943749475; // cos(IN_ANGLE)\\nconst float SIN_A = 0.5877852522924731; // sin(IN_ANGLE)\\nconst float COS_B = 0.5877852522924731; // cos(OUT_ANGLE)\\nconst float SIN_B = 0.8090169943749475; // sin(OUT_ANGLE)\\n\\nuniform float u_antialias;\\n\\nvarying vec4 v_line_color;\\nvarying vec4 v_fill_color;\\nvarying float v_linewidth;\\nvarying float v_size;\\nvarying vec2 v_coords;\\n\\n#ifdef USE_ASTERISK\\n// asterisk\\nfloat marker(vec2 P, float size)\\n{\\n // Masks\\n float diamond = max(abs(SQRT_2 / 2.0 * (P.x - P.y)), abs(SQRT_2 / 2.0 * (P.x + P.y))) - size / 2.0;\\n float square = max(abs(P.x), abs(P.y)) - size / 2.0;\\n // Shapes\\n float X = min(abs(P.x - P.y), abs(P.x + P.y));\\n float cross = min(abs(P.x), abs(P.y));\\n // Result is union of masked shapes\\n float result = min(max(X, diamond), max(cross, square));\\n return max(result - v_linewidth/2.0 + u_antialias/2.0, 0.0);\\n}\\n#endif\\n\\n#ifdef USE_CIRCLE\\n// circle\\nfloat marker(vec2 P, float size)\\n{\\n return length(P) - size/2.0;\\n}\\n#endif\\n\\n#ifdef USE_DOT\\nfloat marker(vec2 P, float size)\\n{\\n return max(length(P) - size/8.0 - v_linewidth/15.0 + u_antialias, 0.0);\\n}\\n#endif\\n\\n#ifdef USE_CIRCLE_DOT\\nfloat marker(vec2 P, float size)\\n{\\n float circle = length(P) - size/2.0;\\n float dot_ = min(size/8.0 + v_linewidth/15.0 - u_antialias - length(P), 0.0);\\n return max(circle, dot_);\\n}\\n#endif\\n\\n#ifdef USE_SQUARE\\n// square\\nfloat marker(vec2 P, float size)\\n{\\n return max(abs(P.x), abs(P.y)) - size/2.0;\\n}\\n#endif\\n\\n#ifdef USE_SQUARE_DOT\\nfloat marker(vec2 P, float size)\\n{\\n float square = max(abs(P.x), abs(P.y)) - size/2.0;\\n float dot_ = min(size/8.0 + v_linewidth/15.0 - u_antialias - length(P), 0.0);\\n return max(square, dot_);\\n}\\n#endif\\n\\n#ifdef USE_DIAMOND\\n// diamond\\nfloat marker(vec2 P, float size)\\n{\\n float x = SQRT_2 / 2.0 * (P.x * 1.5 - P.y);\\n float y = SQRT_2 / 2.0 * (P.x * 1.5 + P.y);\\n float r1 = max(abs(x), abs(y)) - size / (2.0 * SQRT_2);\\n return r1 / SQRT_2;\\n}\\n#endif\\n\\n#ifdef USE_DIAMOND_DOT\\nfloat marker(vec2 P, float size)\\n{\\n float x = SQRT_2 / 2.0 * (P.x * 1.5 - P.y);\\n float y = SQRT_2 / 2.0 * (P.x * 1.5 + P.y);\\n float r1 = max(abs(x), abs(y)) - size / (2.0 * SQRT_2);\\n float diamond = r1 / SQRT_2;\\n float dot_ = min(size/8.0 + v_linewidth/15.0 - u_antialias - length(P), 0.0);\\n return max(diamond, dot_);\\n}\\n#endif\\n\\n#ifdef USE_HEX\\n// hex\\nfloat marker(vec2 P, float size)\\n{\\n vec2 q = abs(P);\\n return max(q.y * 0.57735 + q.x - 1.0 * size/2.0, q.y - 0.866 * size/2.0);\\n}\\n#endif\\n\\n#ifdef USE_HEX_DOT\\nfloat marker(vec2 P, float size)\\n{\\n vec2 q = abs(P);\\n float hex = max(q.y * 0.57735 + q.x - 1.0 * size/2.0, q.y - 0.866 * size/2.0);\\n float dot_ = min(size/8.0 + v_linewidth/15.0 - u_antialias - length(P), 0.0);\\n return max(hex, dot_);\\n}\\n#endif\\n\\n#ifdef USE_STAR\\n// star\\n// https://iquilezles.org/www/articles/distfunctions2d/distfunctions2d.htm\\nfloat marker(vec2 P, float size)\\n{\\n float bn = mod(atan(P.x, -P.y), 2.0*IN_ANGLE) - IN_ANGLE;\\n P = length(P)*vec2(cos(bn), abs(sin(bn)));\\n P -= size*vec2(COS_A, SIN_A)/2.;\\n P += vec2(COS_B, SIN_B)*clamp(-(P.x*COS_B + P.y*SIN_B), 0.0, size*SIN_A/SIN_B/2.);\\n return length(P)*sign(P.x);\\n}\\n#endif\\n\\n#ifdef USE_STAR_DOT\\nfloat marker(vec2 P, float size)\\n{\\n float dot_ = min(size/8.0 + v_linewidth/15.0 - u_antialias - length(P), 0.0);\\n float bn = mod(atan(P.x, -P.y), 2.0*IN_ANGLE) - IN_ANGLE;\\n P = length(P)*vec2(cos(bn), abs(sin(bn)));\\n P -= size*vec2(COS_A, SIN_A)/2.;\\n P += vec2(COS_B, SIN_B)*clamp(-(P.x*COS_B + P.y*SIN_B), 0.0, size*SIN_A/SIN_B/2.);\\n float star = length(P)*sign(P.x);\\n return max(star, dot_);\\n}\\n#endif\\n\\n#ifdef USE_TRIANGLE\\n// triangle\\nfloat marker(vec2 P, float size)\\n{\\n P.y -= size * 0.3;\\n float x = SQRT_2 / 2.0 * (P.x * 1.7 - P.y);\\n float y = SQRT_2 / 2.0 * (P.x * 1.7 + P.y);\\n float r1 = max(abs(x), abs(y)) - size / 1.6;\\n float r2 = P.y;\\n return max(r1 / SQRT_2, r2); // Intersect diamond with rectangle\\n}\\n#endif\\n\\n#ifdef USE_TRIANGLE_DOT\\nfloat marker(vec2 P, float size)\\n{\\n float dot_ = min(size/8.0 + v_linewidth/15.0 - u_antialias - length(P), 0.0);\\n P.y -= size * 0.3;\\n float x = SQRT_2 / 2.0 * (P.x * 1.7 - P.y);\\n float y = SQRT_2 / 2.0 * (P.x * 1.7 + P.y);\\n float r1 = max(abs(x), abs(y)) - size / 1.6;\\n float r2 = P.y;\\n float triangle = max(r1 / SQRT_2, r2); // Intersect diamond with rectangle\\n return max(triangle, dot_);\\n}\\n#endif\\n\\n#ifdef USE_INVERTED_TRIANGLE\\n// inverted_triangle\\nfloat marker(vec2 P, float size)\\n{\\n P.y += size * 0.3;\\n float x = SQRT_2 / 2.0 * (P.x * 1.7 - P.y);\\n float y = SQRT_2 / 2.0 * (P.x * 1.7 + P.y);\\n float r1 = max(abs(x), abs(y)) - size / 1.6;\\n float r2 = - P.y;\\n return max(r1 / SQRT_2, r2); // Intersect diamond with rectangle\\n}\\n#endif\\n\\n#ifdef USE_CROSS\\n// cross\\nfloat marker(vec2 P, float size)\\n{\\n float square = max(abs(P.x), abs(P.y)) - size / 2.0;\\n float cross = min(abs(P.x), abs(P.y));\\n cross = max(cross - v_linewidth/2.0 + u_antialias/2.0, 0.0);\\n return max(square, cross);\\n}\\n#endif\\n\\n#ifdef USE_DASH\\nfloat marker(vec2 P, float size)\\n{\\n float square = max(abs(P.x), abs(P.y)) - size / 2.0;\\n float cross = abs(P.y);\\n cross = max(cross - v_linewidth/2.0 + u_antialias/2.0, 0.0);\\n return max(square, cross);\\n}\\n#endif\\n\\n#ifdef USE_CIRCLE_CROSS\\n// circle_cross\\nfloat marker(vec2 P, float size)\\n{\\n // Define quadrants\\n float qs = size / 2.0; // quadrant size\\n float s1 = max(abs(P.x - qs), abs(P.y - qs)) - qs;\\n float s2 = max(abs(P.x + qs), abs(P.y - qs)) - qs;\\n float s3 = max(abs(P.x - qs), abs(P.y + qs)) - qs;\\n float s4 = max(abs(P.x + qs), abs(P.y + qs)) - qs;\\n // Intersect main shape with quadrants (to form cross)\\n float circle = length(P) - size/2.0;\\n float c1 = max(circle, s1);\\n float c2 = max(circle, s2);\\n float c3 = max(circle, s3);\\n float c4 = max(circle, s4);\\n // Union\\n return min(min(min(c1, c2), c3), c4);\\n}\\n#endif\\n\\n#ifdef USE_SQUARE_CROSS\\n// square_cross\\nfloat marker(vec2 P, float size)\\n{\\n // Define quadrants\\n float qs = size / 2.0; // quadrant size\\n float s1 = max(abs(P.x - qs), abs(P.y - qs)) - qs;\\n float s2 = max(abs(P.x + qs), abs(P.y - qs)) - qs;\\n float s3 = max(abs(P.x - qs), abs(P.y + qs)) - qs;\\n float s4 = max(abs(P.x + qs), abs(P.y + qs)) - qs;\\n // Intersect main shape with quadrants (to form cross)\\n float square = max(abs(P.x), abs(P.y)) - size/2.0;\\n float c1 = max(square, s1);\\n float c2 = max(square, s2);\\n float c3 = max(square, s3);\\n float c4 = max(square, s4);\\n // Union\\n return min(min(min(c1, c2), c3), c4);\\n}\\n#endif\\n\\n#ifdef USE_DIAMOND_CROSS\\n// diamond_cross\\nfloat marker(vec2 P, float size)\\n{\\n // Define quadrants\\n float qs = size / 2.0; // quadrant size\\n float s1 = max(abs(P.x - qs), abs(P.y - qs)) - qs;\\n float s2 = max(abs(P.x + qs), abs(P.y - qs)) - qs;\\n float s3 = max(abs(P.x - qs), abs(P.y + qs)) - qs;\\n float s4 = max(abs(P.x + qs), abs(P.y + qs)) - qs;\\n // Intersect main shape with quadrants (to form cross)\\n float x = SQRT_2 / 2.0 * (P.x * 1.5 - P.y);\\n float y = SQRT_2 / 2.0 * (P.x * 1.5 + P.y);\\n float diamond = max(abs(x), abs(y)) - size / (2.0 * SQRT_2);\\n diamond /= SQRT_2;\\n float c1 = max(diamond, s1);\\n float c2 = max(diamond, s2);\\n float c3 = max(diamond, s3);\\n float c4 = max(diamond, s4);\\n // Union\\n return min(min(min(c1, c2), c3), c4);\\n}\\n#endif\\n\\n#ifdef USE_X\\n// x\\nfloat marker(vec2 P, float size)\\n{\\n float circle = length(P) - size / 2.0;\\n float X = min(abs(P.x - P.y), abs(P.x + P.y));\\n X = max(X - v_linewidth/2.0, 0.0);\\n return max(circle, X);\\n}\\n#endif\\n\\n#ifdef USE_Y\\nfloat marker(vec2 P, float size)\\n{\\n float circle = length(P) - size / 2.0;\\n\\n float dx = 1.0 / SQRT_3;\\n float dy = SQRT_2 / SQRT_3;\\n\\n // Sideways distance from the three spokes.\\n float d0 = abs(P.x);\\n float d1 = abs(dot(P, vec2(dx, dy)));\\n float d2 = abs(dot(P, vec2(dx, -dy)));\\n\\n // Clip each spoke to semicircle.\\n d0 = max(d0, -P.y);\\n d1 = max(d1, dot(P, vec2(-dy, dx)));\\n d2 = max(d2, dot(P, vec2(dy, dx)));\\n\\n float Y = min(min(d0, d1), d2);\\n Y = max(Y - v_linewidth/2.0 + u_antialias/2.0, 0.0);\\n return max(circle, Y);\\n}\\n#endif\\n\\n#ifdef USE_CIRCLE_X\\n// circle_x\\nfloat marker(vec2 P, float size)\\n{\\n float x = P.x - P.y;\\n float y = P.x + P.y;\\n // Define quadrants\\n float qs = size / 2.0; // quadrant size\\n float s1 = max(abs(x - qs), abs(y - qs)) - qs;\\n float s2 = max(abs(x + qs), abs(y - qs)) - qs;\\n float s3 = max(abs(x - qs), abs(y + qs)) - qs;\\n float s4 = max(abs(x + qs), abs(y + qs)) - qs;\\n // Intersect main shape with quadrants (to form cross)\\n float circle = length(P) - size/2.0;\\n float c1 = max(circle, s1);\\n float c2 = max(circle, s2);\\n float c3 = max(circle, s3);\\n float c4 = max(circle, s4);\\n // Union\\n return min(min(min(c1, c2), c3), c4);\\n}\\n#endif\\n\\n#ifdef USE_CIRCLE_Y\\nfloat marker(vec2 P, float size)\\n{\\n float circle = length(P) - size/2.0;\\n\\n float dx = 1.0 / SQRT_3;\\n float dy = SQRT_2 / SQRT_3;\\n\\n // Sideways distance from the three spokes.\\n float d0 = abs(P.x);\\n float d1 = abs(dot(P, vec2(dx, dy)));\\n float d2 = abs(dot(P, vec2(dx, -dy)));\\n\\n // Clip each spoke to semicircle.\\n d0 = max(d0, -P.y);\\n d1 = max(d1, dot(P, vec2(-dy, dx)));\\n d2 = max(d2, dot(P, vec2(dy, dx)));\\n\\n float Y = min(min(d0, d1), d2);\\n Y = min(v_linewidth/2.0 - u_antialias/2.0 - Y, 0.0);\\n\\n return max(circle, Y);\\n}\\n#endif\\n\\n#ifdef USE_SQUARE_X\\n// square_x\\nfloat marker(vec2 P, float size)\\n{\\n float x = P.x - P.y;\\n float y = P.x + P.y;\\n // Define quadrants\\n float qs = size / 2.0; // quadrant size\\n float s1 = max(abs(x - qs), abs(y - qs)) - qs;\\n float s2 = max(abs(x + qs), abs(y - qs)) - qs;\\n float s3 = max(abs(x - qs), abs(y + qs)) - qs;\\n float s4 = max(abs(x + qs), abs(y + qs)) - qs;\\n // Intersect main shape with quadrants (to form cross)\\n float square = max(abs(P.x), abs(P.y)) - size/2.0;\\n float c1 = max(square, s1);\\n float c2 = max(square, s2);\\n float c3 = max(square, s3);\\n float c4 = max(square, s4);\\n // Union\\n return min(min(min(c1, c2), c3), c4);\\n}\\n#endif\\n\\n#ifdef USE_PLUS\\nfloat marker(vec2 P, float size)\\n{\\n vec2 size2 = vec2(size*0.5, size*0.2);\\n P = abs(P);\\n P = (P.y > P.x) ? P.yx : P.xy;\\n vec2 q = P - size2;\\n float k = max(q.y, q.x);\\n vec2 w = (k > 0.0) ? q : vec2(size2.y - P.x, -k);\\n return sign(k)*length(max(w, 0.0));\\n}\\n#endif\\n\\n#ifdef USE_SQUARE_PIN\\nfloat marker(vec2 P, float size)\\n{\\n float actual_size = size*1.2;\\n float radius = 0.75*actual_size; // Radius of curvature of edges.\\n float offset = actual_size/2.0 + sqrt(radius*radius - actual_size*actual_size/4.0);\\n vec2 centerx = vec2(offset, 0.0);\\n vec2 centery = vec2(0.0, offset);\\n\\n float right = length(P - centerx);\\n float left = length(P + centerx);\\n float up = length(P - centery);\\n float down = length(P + centery);\\n float pin = radius - min(min(right, left), min(up, down));\\n\\n float circle = length(P) - actual_size*0.6;\\n return max(circle, pin);\\n}\\n#endif\\n\\n#ifdef USE_TRIANGLE_PIN\\nfloat marker(vec2 P, float size)\\n{\\n float actual_size = size*1.2;\\n float radius = 1.2*actual_size; // Radius of curvature of edges.\\n\\n float angle = 2.0*PI / 3.0;\\n float c = cos(angle);\\n float s = sin(angle);\\n mat2 rotation = mat2(c, -s, s, c);\\n\\n // Half the length of straight triangle edge.\\n float half_edge = actual_size*SQRT_3/4.0;\\n // Distance from center of triangle to middle of straight edge.\\n float centre_middle_edge = 0.25*actual_size;\\n float offset = centre_middle_edge + sqrt(radius*radius - half_edge*half_edge);\\n // Centre of curvature.\\n vec2 center = vec2(0.0, offset);\\n\\n float dist0 = length(P - center);\\n P = rotation*P;\\n float dist1 = length(P - center);\\n P = rotation*P;\\n float dist2 = length(P - center);\\n float pin = radius - min(min(dist0, dist1), dist2);\\n\\n float circle = length(P) - actual_size / 2.0;\\n return max(circle, pin);\\n}\\n#endif\\n\\nvec4 outline(float distance, float linewidth, float antialias, vec4 line_color,\\n vec4 fill_color)\\n{\\n vec4 frag_color;\\n float t = min(linewidth/2.0 - antialias, 0.0); // Line half-width.\\n float signed_distance = distance;\\n float border_distance = abs(signed_distance) - t;\\n float alpha = border_distance/antialias;\\n alpha = exp(-alpha*alpha);\\n\\n // If line alpha is zero, it probably means no outline. To avoid a dark\\n // outline shining through due to antialiasing, we set the line color to the\\n // fill color.\\n float select = float(bool(line_color.a));\\n line_color.rgb = select*line_color.rgb + (1.0 - select)*fill_color.rgb;\\n // Similarly, if we want a transparent fill.\\n select = float(bool(fill_color.a));\\n fill_color.rgb = select*fill_color.rgb + (1.0 - select)*line_color.rgb;\\n\\n if (border_distance < 0.0)\\n frag_color = line_color;\\n else if (signed_distance < 0.0)\\n frag_color = mix(fill_color, line_color, sqrt(alpha));\\n else {\\n if (abs(signed_distance) < linewidth/2.0 + antialias)\\n frag_color = vec4(line_color.rgb, line_color.a*alpha);\\n else\\n discard;\\n }\\n return frag_color;\\n}\\n\\nvoid main()\\n{\\n float distance = marker(v_coords, v_size);\\n gl_FragColor = outline(\\n distance, v_linewidth, u_antialias, v_line_color, v_fill_color);\\n gl_FragColor.rgb *= gl_FragColor.a; // Premultiplied alpha.\\n}\\n\"},\n", - " 419: function _(n,t,a,i,e){i();a.default=\"\\nprecision mediump float;\\n\\nattribute vec2 a_position;\\nattribute vec2 a_center;\\nattribute float a_width;\\nattribute float a_height;\\nattribute float a_angle; // In radians\\nattribute float a_linewidth;\\nattribute vec4 a_line_color;\\nattribute vec4 a_fill_color;\\nattribute float a_line_join;\\nattribute float a_show;\\n#ifdef HATCH\\nattribute float a_hatch_pattern;\\nattribute float a_hatch_scale;\\nattribute float a_hatch_weight;\\nattribute vec4 a_hatch_color;\\n#endif\\n\\nuniform float u_pixel_ratio;\\nuniform vec2 u_canvas_size;\\nuniform float u_antialias;\\n\\nvarying float v_linewidth;\\nvarying vec2 v_size; // 2D size for rects compared to 1D for markers.\\nvarying vec4 v_line_color;\\nvarying vec4 v_fill_color;\\nvarying float v_line_join;\\nvarying vec2 v_coords;\\n#ifdef HATCH\\nvarying float v_hatch_pattern;\\nvarying float v_hatch_scale;\\nvarying float v_hatch_weight;\\nvarying vec4 v_hatch_color;\\nvarying vec2 v_hatch_coords;\\n#endif\\n\\nvoid main()\\n{\\n if (a_show < 0.5) {\\n // Do not show this rect.\\n gl_Position = vec4(-2.0, -2.0, 0.0, 1.0);\\n return;\\n }\\n\\n v_size = vec2(a_width, a_height);\\n v_linewidth = a_linewidth;\\n v_line_color = a_line_color;\\n v_fill_color = a_fill_color;\\n v_line_join = a_line_join;\\n\\n if (v_linewidth < 1.0) {\\n // Linewidth less than 1 is implemented as 1 but with reduced alpha.\\n v_line_color.a *= v_linewidth;\\n v_linewidth = 1.0;\\n }\\n\\n#ifdef HATCH\\n v_hatch_pattern = a_hatch_pattern;\\n v_hatch_scale = a_hatch_scale;\\n v_hatch_weight = a_hatch_weight;\\n v_hatch_color = a_hatch_color;\\n#endif\\n\\n vec2 enclosing_size = v_size + v_linewidth + u_antialias;\\n\\n // Coordinates in rotated frame with respect to center of marker, used for\\n // distance functions in fragment shader.\\n v_coords = a_position*enclosing_size;\\n\\n float c = cos(-a_angle);\\n float s = sin(-a_angle);\\n mat2 rotation = mat2(c, -s, s, c);\\n\\n vec2 pos = a_center + rotation*v_coords;\\n#ifdef HATCH\\n // Coordinates for hatching in unrotated frame of reference.\\n v_hatch_coords = pos - 0.5;\\n#endif\\n pos += 0.5; // Make up for Bokeh's offset.\\n pos /= u_canvas_size / u_pixel_ratio; // 0 to 1.\\n gl_Position = vec4(2.0*pos.x - 1.0, 1.0 - 2.0*pos.y, 0.0, 1.0);\\n}\\n\"},\n", - " 420: function _(n,a,t,o,r){o();t.default=\"\\nprecision mediump float;\\n\\nconst float SQRT2 = sqrt(2.0);\\nconst float INVSQRT2 = 1.0/SQRT2;\\nconst float PI = 3.14159265358979323846;\\n\\nconst int miter_join = 0;\\nconst int round_join = 1;\\nconst int bevel_join = 2;\\n#ifdef HATCH\\nconst int hatch_dot = 1;\\nconst int hatch_ring = 2;\\nconst int hatch_horizontal_line = 3;\\nconst int hatch_vertical_line = 4;\\nconst int hatch_cross = 5;\\nconst int hatch_horizontal_dash = 6;\\nconst int hatch_vertical_dash = 7;\\nconst int hatch_spiral = 8;\\nconst int hatch_right_diagonal_line = 9;\\nconst int hatch_left_diagonal_line = 10;\\nconst int hatch_diagonal_cross = 11;\\nconst int hatch_right_diagonal_dash = 12;\\nconst int hatch_left_diagonal_dash = 13;\\nconst int hatch_horizontal_wave = 14;\\nconst int hatch_vertical_wave = 15;\\nconst int hatch_criss_cross = 16;\\n#endif\\n\\nuniform float u_antialias;\\n\\nvarying float v_linewidth;\\nvarying vec2 v_size;\\nvarying vec4 v_line_color;\\nvarying vec4 v_fill_color;\\nvarying float v_line_join;\\nvarying vec2 v_coords;\\n#ifdef HATCH\\nvarying float v_hatch_pattern;\\nvarying float v_hatch_scale;\\nvarying float v_hatch_weight;\\nvarying vec4 v_hatch_color;\\nvarying vec2 v_hatch_coords;\\n#endif\\n\\n// Distance is zero on edge of marker, +ve outside and -ve inside.\\nfloat marker_distance(vec2 p, int line_join)\\n{\\n vec2 dist2 = abs(p) - v_size/2.0;\\n float dist = max(dist2.x, dist2.y);\\n\\n if (dist2.x > 0.0 && dist2.y > 0.0) {\\n // Outside of corner needs correct join, default is miter.\\n if (line_join == round_join)\\n dist = length(dist2);\\n else if (line_join == bevel_join) {\\n vec2 normal = vec2(INVSQRT2, INVSQRT2);\\n dist = dot(dist2, normal) + 0.5*v_linewidth*(1.0 - INVSQRT2);\\n }\\n }\\n\\n return dist;\\n}\\n\\n// Convert distance from edge of marker to fraction in range 0 to 1, depending\\n// on antialiasing width.\\nfloat distance_to_fraction(float dist)\\n{\\n return 1.0 - smoothstep(-0.5*u_antialias, 0.5*u_antialias, dist);\\n}\\n\\n// Return fraction from 0 (no fill color) to 1 (full fill color).\\nfloat fill_fraction(float dist)\\n{\\n return distance_to_fraction(dist);\\n}\\n\\n// Return fraction in range 0 (no line color) to 1 (full line color).\\nfloat line_fraction(float dist)\\n{\\n return distance_to_fraction(abs(dist) - 0.5*v_linewidth);\\n}\\n\\n// Return fraction (in range 0 to 1) of a color, with premultiplied alpha.\\nvec4 fractional_color(vec4 color, float fraction)\\n{\\n color.a *= fraction;\\n color.rgb *= color.a;\\n return color;\\n}\\n\\n// Blend colors that have premultiplied alpha.\\nvec4 blend_colors(vec4 src, vec4 dest)\\n{\\n return (1.0 - src.a)*dest + src;\\n}\\n\\n#ifdef HATCH\\n// Wrap coordinate(s) by removing integer part to give distance from center of\\n// repeat, in the range -0.5 to +0.5.\\nfloat wrap(float x)\\n{\\n return fract(x) - 0.5;\\n}\\n\\nvec2 wrap(vec2 xy)\\n{\\n return fract(xy) - 0.5;\\n}\\n\\n// Return fraction from 0 (no hatch color) to 1 (full hatch color).\\nfloat hatch_fraction(vec2 coords, int hatch_pattern)\\n{\\n float scale = v_hatch_scale; // Hatch repeat distance.\\n\\n // Coordinates and linewidth/halfwidth are scaled to hatch repeat distance.\\n coords = coords / scale;\\n float halfwidth = 0.5*v_hatch_weight / scale; // Half the hatch linewidth.\\n\\n // Default is to return fraction of zero, i.e. no pattern.\\n float dist = u_antialias;\\n\\n if (hatch_pattern == hatch_dot) {\\n const float dot_radius = 0.25;\\n dist = length(wrap(coords)) - dot_radius;\\n }\\n else if (hatch_pattern == hatch_ring) {\\n const float ring_radius = 0.25;\\n dist = abs(length(wrap(coords)) - ring_radius) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_horizontal_line) {\\n dist = abs(wrap(coords.y)) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_vertical_line) {\\n dist = abs(wrap(coords.x)) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_cross) {\\n dist = min(abs(wrap(coords.x)), abs(wrap(coords.y))) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_horizontal_dash) {\\n // Dashes have square caps.\\n const float halflength = 0.25;\\n dist = max(abs(wrap(coords.y)),\\n abs(wrap(coords.x) + 0.25) - halflength) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_vertical_dash) {\\n const float halflength = 0.25;\\n dist = max(abs(wrap(coords.x)),\\n abs(wrap(coords.y) + 0.25) - halflength) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_spiral) {\\n vec2 wrap2 = wrap(coords);\\n float angle = wrap(atan(wrap2.y, wrap2.x) / (2.0*PI));\\n // Canvas spiral radius increases by scale*pi/15 each rotation.\\n const float dr = PI/15.0;\\n float radius = length(wrap2);\\n // At any angle, spiral lines are equally spaced dr apart.\\n // Find distance to nearest of these lines.\\n float frac = fract((radius - dr*angle) / dr); // 0 to 1.\\n dist = dr*(abs(frac - 0.5));\\n dist = min(dist, radius) - halfwidth; // Consider center point also.\\n }\\n else if (hatch_pattern == hatch_right_diagonal_line) {\\n dist = abs(wrap(2.0*coords.x + coords.y))/sqrt(5.0) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_left_diagonal_line) {\\n dist = abs(wrap(2.0*coords.x - coords.y))/sqrt(5.0) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_diagonal_cross) {\\n coords = vec2(coords.x + coords.y + 0.5, coords.x - coords.y + 0.5);\\n dist = min(abs(wrap(coords.x)), abs(wrap(coords.y))) / SQRT2 - halfwidth;\\n }\\n else if (hatch_pattern == hatch_right_diagonal_dash) {\\n float across = coords.x + coords.y + 0.5;\\n dist = abs(wrap(across)) / SQRT2; // Distance to nearest solid line.\\n\\n across = floor(across); // Offset for dash.\\n float along = wrap(0.5*(coords.x - coords.y + across));\\n const float halflength = 0.25;\\n along = abs(along) - halflength; // Distance along line.\\n\\n dist = max(dist, along) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_left_diagonal_dash) {\\n float across = coords.x - coords.y + 0.5;\\n dist = abs(wrap(across)) / SQRT2; // Distance to nearest solid line.\\n\\n across = floor(across); // Offset for dash.\\n float along = wrap(0.5*(coords.x + coords.y + across));\\n const float halflength = 0.25;\\n along = abs(along) - halflength; // Distance along line.\\n\\n dist = max(dist, along) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_horizontal_wave) {\\n float wrapx = wrap(coords.x);\\n float wrapy = wrap(coords.y - 0.25 + abs(wrapx));\\n dist = abs(wrapy) / SQRT2 - halfwidth;\\n }\\n else if (hatch_pattern == hatch_vertical_wave) {\\n float wrapy = wrap(coords.y);\\n float wrapx = wrap(coords.x - 0.25 + abs(wrapy));\\n dist = abs(wrapx) / SQRT2 - halfwidth;\\n }\\n else if (hatch_pattern == hatch_criss_cross) {\\n float plus = min(abs(wrap(coords.x)), abs(wrap(coords.y)));\\n\\n coords = vec2(coords.x + coords.y + 0.5, coords.x - coords.y + 0.5);\\n float X = min(abs(wrap(coords.x)), abs(wrap(coords.y))) / SQRT2;\\n\\n dist = min(plus, X) - halfwidth;\\n }\\n\\n return distance_to_fraction(dist*scale);\\n}\\n#endif\\n\\nvoid main()\\n{\\n int line_join = int(v_line_join + 0.5);\\n#ifdef HATCH\\n int hatch_pattern = int(v_hatch_pattern + 0.5);\\n#endif\\n\\n float dist = marker_distance(v_coords, line_join);\\n\\n float fill_frac = fill_fraction(dist);\\n vec4 color = fractional_color(v_fill_color, fill_frac);\\n\\n#ifdef HATCH\\n if (hatch_pattern > 0 && fill_frac > 0.0) {\\n float hatch_frac = hatch_fraction(v_hatch_coords, hatch_pattern);\\n vec4 hatch_color = fractional_color(v_hatch_color, hatch_frac*fill_frac);\\n color = blend_colors(hatch_color, color);\\n }\\n#endif\\n\\n float line_frac = line_fraction(dist);\\n if (line_frac > 0.0) {\\n vec4 line_color = fractional_color(v_line_color, line_frac);\\n color = blend_colors(line_color, color);\\n }\\n\\n gl_FragColor = color;\\n}\\n\"},\n", - " 421: function _(s,i,t,e,_){e();const h=s(422),l=s(22),a=s(46),n=s(423),o=s(424),r=-1e4;class p extends h.BaseGLGlyph{constructor(s,i){super(s,i),this.glyph=i,this._antialias=1.5,this._miter_limit=5}draw(s,i,t){const e=i.glglyph;this.visuals_changed&&(this._set_visuals(),this.visuals_changed=!1),e.data_changed&&(e._set_data(),e.data_changed=!1);const _=this.glyph.visuals.line,h=n.cap_lookup[_.line_cap.value],l=n.join_lookup[_.line_join.value];if(this._is_dashed()){const s={scissor:this.regl_wrapper.scissor,viewport:this.regl_wrapper.viewport,canvas_size:[t.width,t.height],pixel_ratio:t.pixel_ratio,line_color:this._color,linewidth:this._linewidth,antialias:this._antialias,miter_limit:this._miter_limit,points:e._points,nsegments:e._nsegments,line_join:l,line_cap:h,length_so_far:e._length_so_far,dash_tex:this._dash_tex,dash_tex_info:this._dash_tex_info,dash_scale:this._dash_scale,dash_offset:this._dash_offset};this.regl_wrapper.dashed_line()(s)}else{const s={scissor:this.regl_wrapper.scissor,viewport:this.regl_wrapper.viewport,canvas_size:[t.width,t.height],pixel_ratio:t.pixel_ratio,line_color:this._color,linewidth:this._linewidth,antialias:this._antialias,miter_limit:this._miter_limit,points:e._points,nsegments:e._nsegments,line_join:l,line_cap:h};this.regl_wrapper.solid_line()(s)}}_is_dashed(){return this._line_dash.length>0}_set_data(){const s=this.glyph.sx.length;this._nsegments=s-1,null==this._is_closed&&(this._is_closed=this.glyph.sx[0]==this.glyph.sx[s-1]&&this.glyph.sy[0]==this.glyph.sy[s-1]&&isFinite(this.glyph.sx[0])&&isFinite(this.glyph.sy[0])),null==this._points&&(this._points=new o.Float32Buffer(this.regl_wrapper));const i=this._points.get_sized_array(2*(s+2));for(let t=1;t-9e3&&i[2*e+4]>-9e3&&(t+=Math.sqrt((i[2*e+4]-i[2*e+2])**2+(i[2*e+5]-i[2*e+3])**2));this._length_so_far.update()}}_set_visuals(){const s=this.glyph.visuals.line,i=(0,l.color2rgba)(s.line_color.value,s.line_alpha.value);this._color=i.map((s=>s/255)),this._linewidth=s.line_width.value,this._linewidth<1&&(this._color[3]*=this._linewidth,this._linewidth=1),this._line_dash=(0,a.resolve_line_dash)(s.line_dash.value),1==this._line_dash.length&&this._line_dash.push(this._line_dash[0]),this._is_dashed()&&([this._dash_tex_info,this._dash_tex,this._dash_scale]=this.regl_wrapper.get_dash(this._line_dash),this._dash_offset=s.line_dash_offset.value)}}t.LineGL=p,p.__name__=\"LineGL\"},\n", - " 422: function _(e,t,s,i,h){i();class a{constructor(e,t){this.glyph=t,this.nvertices=0,this.size_changed=!1,this.data_changed=!1,this.visuals_changed=!1,this.regl_wrapper=e}set_data_changed(){const{data_size:e}=this.glyph;e!=this.nvertices&&(this.nvertices=e,this.size_changed=!0),this.data_changed=!0}set_visuals_changed(){this.visuals_changed=!0}render(e,t,s){if(0==t.length)return!0;const{width:i,height:h}=this.glyph.renderer.plot_view.canvas_view.webgl.canvas,a={pixel_ratio:this.glyph.renderer.plot_view.canvas_view.pixel_ratio,width:i,height:h};return this.draw(t,s,a),!0}}s.BaseGLGlyph=a,a.__name__=\"BaseGLGlyph\"},\n", - " 423: function _(a,o,i,n,l){n();const t=a(52);i.cap_lookup={butt:0,round:1,square:2},i.join_lookup={miter:0,round:1,bevel:2};const _={blank:0,dot:1,ring:2,horizontal_line:3,vertical_line:4,cross:5,horizontal_dash:6,vertical_dash:7,spiral:8,right_diagonal_line:9,left_diagonal_line:10,diagonal_cross:11,right_diagonal_dash:12,left_diagonal_dash:13,horizontal_wave:14,vertical_wave:15,criss_cross:16};i.hatch_pattern_to_index=function(a){var o,i;return null!==(i=_[null!==(o=t.hatch_aliases[a])&&void 0!==o?o:a])&&void 0!==i?i:0}},\n", - " 424: function _(r,t,a,e,s){e();const i=r(423),_=r(22);class n{constructor(r){this.regl_wrapper=r,this.is_scalar=!0}get_sized_array(r){return null!=this.array&&this.array.length==r||(this.array=this.new_array(r)),this.array}is_normalized(){return!1}get length(){return null!=this.array?this.array.length:0}set_from_array(r){const t=r.length,a=this.get_sized_array(t);for(let e=0;ethis.render()))}remove(){null!=this.icon_view&&this.icon_view.remove(),super.remove()}styles(){return[...super.styles(),d.default]}_render_button(...t){return(0,c.button)({type:\"button\",disabled:this.model.disabled,class:[h.btn,h[`btn_${this.model.button_type}`]]},...t)}render(){super.render(),this.button_el=this._render_button(this.model.label),this.button_el.addEventListener(\"click\",(()=>this.click())),null!=this.icon_view&&(\"\"!=this.model.label?(0,c.prepend)(this.button_el,this.icon_view.el,(0,c.nbsp)()):(0,c.prepend)(this.button_el,this.icon_view.el),this.icon_view.render()),this.group_el=(0,c.div)({class:h.btn_group},this.button_el),this.el.appendChild(this.group_el)}click(){}}n.AbstractButtonView=b,b.__name__=\"AbstractButtonView\";class p extends _.Control{constructor(t){super(t)}}n.AbstractButton=p,o=p,p.__name__=\"AbstractButton\",o.define((({String:t,Ref:e,Nullable:n})=>({label:[t,\"Button\"],icon:[n(e(a.AbstractIcon)),null],button_type:[r.ButtonType,\"default\"]})))},\n", - " 439: function _(t,e,o,s,n){s();const i=t(508),l=t(43);class c extends i.WidgetView{connect_signals(){super.connect_signals();const t=this.model.properties;this.on_change(t.disabled,(()=>{for(const t of this.controls())(0,l.toggle_attribute)(t,\"disabled\",this.model.disabled)}))}}o.ControlView=c,c.__name__=\"ControlView\";class r extends i.Widget{constructor(t){super(t)}}o.Control=r,r.__name__=\"Control\"},\n", - " 508: function _(i,e,t,n,o){var r;n();const s=i(312);class _ extends s.HTMLBoxView{get orientation(){return\"horizontal\"}get default_size(){return this.model.default_size}_width_policy(){return\"horizontal\"==this.orientation?super._width_policy():\"fixed\"}_height_policy(){return\"horizontal\"==this.orientation?\"fixed\":super._height_policy()}box_sizing(){const i=super.box_sizing();return\"horizontal\"==this.orientation?null==i.width&&(i.width=this.default_size):null==i.height&&(i.height=this.default_size),i}}t.WidgetView=_,_.__name__=\"WidgetView\";class h extends s.HTMLBox{constructor(i){super(i)}}t.Widget=h,r=h,h.__name__=\"Widget\",r.define((({Number:i})=>({default_size:[i,300]}))),r.override({margin:[5,5,5,5]})},\n", - " 441: function _(c,t,s,n,e){n();const o=c(53),_=c(226);class a extends _.DOMView{}s.AbstractIconView=a,a.__name__=\"AbstractIconView\";class r extends o.Model{constructor(c){super(c)}}s.AbstractIcon=r,r.__name__=\"AbstractIcon\"},\n", - " 442: function _(e,t,n,s,i){s();const h=e(1);var o;const _=e(443),u=e(43),r=e(10),c=(0,h.__importStar)(e(229)),a=c;class l extends _.TextInputView{constructor(){super(...arguments),this._open=!1,this._last_value=\"\",this._hover_index=0}styles(){return[...super.styles(),c.default]}render(){super.render(),this.input_el.addEventListener(\"keydown\",(e=>this._keydown(e))),this.input_el.addEventListener(\"keyup\",(e=>this._keyup(e))),this.menu=(0,u.div)({class:[a.menu,a.below]}),this.menu.addEventListener(\"click\",(e=>this._menu_click(e))),this.menu.addEventListener(\"mouseover\",(e=>this._menu_hover(e))),this.el.appendChild(this.menu),(0,u.undisplay)(this.menu)}change_input(){this._open&&this.menu.children.length>0?(this.model.value=this.menu.children[this._hover_index].textContent,this.input_el.focus(),this._hide_menu()):this.model.restrict||super.change_input()}_update_completions(e){(0,u.empty)(this.menu);for(const t of e){const e=(0,u.div)(t);this.menu.appendChild(e)}e.length>0&&this.menu.children[0].classList.add(a.active)}_show_menu(){if(!this._open){this._open=!0,this._hover_index=0,this._last_value=this.model.value,(0,u.display)(this.menu);const e=t=>{const{target:n}=t;n instanceof HTMLElement&&!this.el.contains(n)&&(document.removeEventListener(\"click\",e),this._hide_menu())};document.addEventListener(\"click\",e)}}_hide_menu(){this._open&&(this._open=!1,(0,u.undisplay)(this.menu))}_menu_click(e){e.target!=e.currentTarget&&e.target instanceof Element&&(this.model.value=e.target.textContent,this.input_el.focus(),this._hide_menu())}_menu_hover(e){if(e.target!=e.currentTarget&&e.target instanceof Element){let t=0;for(t=0;t0&&(this.menu.children[this._hover_index].classList.remove(a.active),this._hover_index=(0,r.clamp)(e,0,t-1),this.menu.children[this._hover_index].classList.add(a.active))}_keydown(e){}_keyup(e){switch(e.keyCode){case u.Keys.Enter:this.change_input();break;case u.Keys.Esc:this._hide_menu();break;case u.Keys.Up:this._bump_hover(this._hover_index-1);break;case u.Keys.Down:this._bump_hover(this._hover_index+1);break;default:{const e=this.input_el.value;if(e.lengthe:e=>e.toLowerCase();for(const n of this.model.completions)s(n).startsWith(s(e))&&t.push(n);this._update_completions(t),0==t.length?this._hide_menu():this._show_menu()}}}}n.AutocompleteInputView=l,l.__name__=\"AutocompleteInputView\";class d extends _.TextInput{constructor(e){super(e)}}n.AutocompleteInput=d,o=d,d.__name__=\"AutocompleteInput\",o.prototype.default_view=l,o.define((({Boolean:e,Int:t,String:n,Array:s})=>({completions:[s(n),[]],min_characters:[t,2],case_sensitive:[e,!0],restrict:[e,!0]})))},\n", - " 443: function _(t,e,n,p,_){p();const u=t(1);var i;const s=t(444),r=t(43),x=(0,u.__importStar)(t(446));class a extends s.TextLikeInputView{_render_input(){this.input_el=(0,r.input)({type:\"text\",class:x.input})}}n.TextInputView=a,a.__name__=\"TextInputView\";class c extends s.TextLikeInput{constructor(t){super(t)}}n.TextInput=c,i=c,c.__name__=\"TextInput\",i.prototype.default_view=a},\n", - " 444: function _(e,t,n,i,l){var s;i();const h=e(445);class a extends h.InputWidgetView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.name.change,(()=>{var e;return this.input_el.name=null!==(e=this.model.name)&&void 0!==e?e:\"\"})),this.connect(this.model.properties.value.change,(()=>this.input_el.value=this.model.value)),this.connect(this.model.properties.value_input.change,(()=>this.input_el.value=this.model.value_input)),this.connect(this.model.properties.disabled.change,(()=>this.input_el.disabled=this.model.disabled)),this.connect(this.model.properties.placeholder.change,(()=>this.input_el.placeholder=this.model.placeholder)),this.connect(this.model.properties.max_length.change,(()=>{const{max_length:e}=this.model;null!=e?this.input_el.maxLength=e:this.input_el.removeAttribute(\"maxLength\")}))}render(){var e;super.render(),this._render_input();const{input_el:t}=this;t.name=null!==(e=this.model.name)&&void 0!==e?e:\"\",t.value=this.model.value,t.disabled=this.model.disabled,t.placeholder=this.model.placeholder,null!=this.model.max_length&&(t.maxLength=this.model.max_length),t.addEventListener(\"change\",(()=>this.change_input())),t.addEventListener(\"input\",(()=>this.change_input_value())),this.group_el.appendChild(t)}change_input(){this.model.value=this.input_el.value,super.change_input()}change_input_value(){this.model.value_input=this.input_el.value,super.change_input()}}n.TextLikeInputView=a,a.__name__=\"TextLikeInputView\";class u extends h.InputWidget{constructor(e){super(e)}}n.TextLikeInput=u,s=u,u.__name__=\"TextLikeInput\",s.define((({Int:e,String:t,Nullable:n})=>({value:[t,\"\"],value_input:[t,\"\"],placeholder:[t,\"\"],max_length:[n(e),null]})))},\n", - " 445: function _(e,t,n,s,l){s();const i=e(1);var o;const r=e(439),_=e(43),p=(0,i.__importStar)(e(446)),a=p;class c extends r.ControlView{*controls(){yield this.input_el}connect_signals(){super.connect_signals(),this.connect(this.model.properties.title.change,(()=>{this.label_el.textContent=this.model.title}))}styles(){return[...super.styles(),p.default]}render(){super.render();const{title:e}=this.model;this.label_el=(0,_.label)({style:{display:0==e.length?\"none\":\"\"}},e),this.group_el=(0,_.div)({class:a.input_group},this.label_el),this.el.appendChild(this.group_el)}change_input(){}}n.InputWidgetView=c,c.__name__=\"InputWidgetView\";class d extends r.Control{constructor(e){super(e)}}n.InputWidget=d,o=d,d.__name__=\"InputWidget\",o.define((({String:e})=>({title:[e,\"\"]})))},\n", - " 446: function _(o,p,t,n,i){n(),t.root=\"bk-root\",t.input=\"bk-input\",t.input_group=\"bk-input-group\",t.inline=\"bk-inline\",t.spin_wrapper=\"bk-spin-wrapper\",t.spin_btn=\"bk-spin-btn\",t.spin_btn_up=\"bk-spin-btn-up\",t.spin_btn_down=\"bk-spin-btn-down\",t.default='.bk-root .bk-input{display:inline-block;width:100%;flex-grow:1;min-height:31px;padding:0 12px;background-color:#fff;border:1px solid #ccc;border-radius:4px;}.bk-root .bk-input:focus{border-color:#66afe9;outline:0;box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(102, 175, 233, 0.6);}.bk-root .bk-input::placeholder,.bk-root .bk-input:-ms-input-placeholder,.bk-root .bk-input::-moz-placeholder,.bk-root .bk-input::-webkit-input-placeholder{color:#999;opacity:1;}.bk-root .bk-input[disabled]{cursor:not-allowed;background-color:#eee;opacity:1;}.bk-root select:not([multiple]).bk-input,.bk-root select:not([size]).bk-input{height:auto;appearance:none;-webkit-appearance:none;background-image:url(\\'data:image/svg+xml;utf8,\\');background-position:right 0.5em center;background-size:8px 6px;background-repeat:no-repeat;}.bk-root select[multiple].bk-input,.bk-root select[size].bk-input,.bk-root textarea.bk-input{height:auto;}.bk-root .bk-input-group{width:100%;height:100%;display:inline-flex;flex-wrap:nowrap;align-items:start;flex-direction:column;white-space:nowrap;}.bk-root .bk-input-group.bk-inline{flex-direction:row;}.bk-root .bk-input-group.bk-inline > *:not(:first-child){margin-left:5px;}.bk-root .bk-input-group input[type=\"checkbox\"] + span,.bk-root .bk-input-group input[type=\"radio\"] + span{position:relative;top:-2px;margin-left:3px;}.bk-root .bk-input-group > .bk-spin-wrapper{display:inherit;width:inherit;height:inherit;position:relative;overflow:hidden;padding:0;vertical-align:middle;}.bk-root .bk-input-group > .bk-spin-wrapper input{padding-right:20px;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn{position:absolute;display:block;height:50%;min-height:0;min-width:0;width:30px;padding:0;margin:0;right:0;border:none;background:none;cursor:pointer;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn:before{content:\"\";display:inline-block;transform:translateY(-50%);border-left:5px solid transparent;border-right:5px solid transparent;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn.bk-spin-btn-up{top:0;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn.bk-spin-btn-up:before{border-bottom:5px solid black;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn.bk-spin-btn-up:disabled:before{border-bottom-color:grey;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn.bk-spin-btn-down{bottom:0;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn.bk-spin-btn-down:before{border-top:5px solid black;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn.bk-spin-btn-down:disabled:before{border-top-color:grey;}'},\n", - " 447: function _(t,e,n,o,c){var s;o();const u=t(438),r=t(251);class i extends u.AbstractButtonView{click(){this.model.trigger_event(new r.ButtonClick),super.click()}}n.ButtonView=i,i.__name__=\"ButtonView\";class _ extends u.AbstractButton{constructor(t){super(t)}}n.Button=_,s=_,_.__name__=\"Button\",s.prototype.default_view=i,s.override({label:\"Button\"})},\n", - " 448: function _(t,e,o,c,a){c();const s=t(1);var n;const i=t(449),r=t(43),u=(0,s.__importStar)(t(318));class _ extends i.ButtonGroupView{get active(){return new Set(this.model.active)}change_active(t){const{active:e}=this;e.has(t)?e.delete(t):e.add(t),this.model.active=[...e].sort()}_update_active(){const{active:t}=this;this._buttons.forEach(((e,o)=>{(0,r.classes)(e).toggle(u.active,t.has(o))}))}}o.CheckboxButtonGroupView=_,_.__name__=\"CheckboxButtonGroupView\";class h extends i.ButtonGroup{constructor(t){super(t)}}o.CheckboxButtonGroup=h,n=h,h.__name__=\"CheckboxButtonGroup\",n.prototype.default_view=_,n.define((({Int:t,Array:e})=>({active:[e(t),[]]})))},\n", - " 449: function _(t,e,n,s,i){s();const o=t(1);var r;const a=t(450),l=t(20),d=t(43),u=(0,o.__importStar)(t(318)),_=u;class c extends a.OrientedControlView{get default_size(){return\"horizontal\"==this.orientation?this.model.default_size:void 0}*controls(){yield*this._buttons}connect_signals(){super.connect_signals();const t=this.model.properties;this.on_change(t.button_type,(()=>this.render())),this.on_change(t.labels,(()=>this.render())),this.on_change(t.active,(()=>this._update_active()))}styles(){return[...super.styles(),u.default]}render(){super.render(),this._buttons=this.model.labels.map(((t,e)=>{const n=(0,d.div)({class:[_.btn,_[`btn_${this.model.button_type}`]],disabled:this.model.disabled},t);return n.addEventListener(\"click\",(()=>this.change_active(e))),n})),this._update_active();const t=\"horizontal\"==this.model.orientation?_.horizontal:_.vertical,e=(0,d.div)({class:[_.btn_group,t]},this._buttons);this.el.appendChild(e)}}n.ButtonGroupView=c,c.__name__=\"ButtonGroupView\";class h extends a.OrientedControl{constructor(t){super(t)}}n.ButtonGroup=h,r=h,h.__name__=\"ButtonGroup\",r.define((({String:t,Array:e})=>({labels:[e(t),[]],button_type:[l.ButtonType,\"default\"]})))},\n", - " 450: function _(n,t,e,o,r){var i;o();const a=n(439),l=n(20);class s extends a.ControlView{get orientation(){return this.model.orientation}}e.OrientedControlView=s,s.__name__=\"OrientedControlView\";class _ extends a.Control{constructor(n){super(n)}}e.OrientedControl=_,i=_,_.__name__=\"OrientedControl\",i.define((()=>({orientation:[l.Orientation,\"horizontal\"]})))},\n", - " 451: function _(e,t,n,i,s){i();const o=e(1);var a;const c=e(452),l=e(43),d=e(9),p=(0,o.__importStar)(e(446));class r extends c.InputGroupView{render(){super.render();const e=(0,l.div)({class:[p.input_group,this.model.inline?p.inline:null]});this.el.appendChild(e);const{active:t,labels:n}=this.model;this._inputs=[];for(let i=0;ithis.change_active(i))),this._inputs.push(s),this.model.disabled&&(s.disabled=!0),(0,d.includes)(t,i)&&(s.checked=!0);const o=(0,l.label)(s,(0,l.span)(n[i]));e.appendChild(o)}}change_active(e){const t=new Set(this.model.active);t.has(e)?t.delete(e):t.add(e),this.model.active=[...t].sort()}}n.CheckboxGroupView=r,r.__name__=\"CheckboxGroupView\";class h extends c.InputGroup{constructor(e){super(e)}}n.CheckboxGroup=h,a=h,h.__name__=\"CheckboxGroup\",a.prototype.default_view=r,a.define((({Boolean:e,Int:t,String:n,Array:i})=>({active:[i(t),[]],labels:[i(n),[]],inline:[e,!1]})))},\n", - " 452: function _(n,t,e,s,o){s();const r=n(1),u=n(439),c=(0,r.__importDefault)(n(446));class _ extends u.ControlView{*controls(){yield*this._inputs}connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.render()))}styles(){return[...super.styles(),c.default]}}e.InputGroupView=_,_.__name__=\"InputGroupView\";class i extends u.Control{constructor(n){super(n)}}e.InputGroup=i,i.__name__=\"InputGroup\"},\n", - " 453: function _(e,t,i,n,o){n();const s=e(1);var l;const r=e(445),c=e(43),a=e(22),d=(0,s.__importStar)(e(446));class h extends r.InputWidgetView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.name.change,(()=>{var e;return this.input_el.name=null!==(e=this.model.name)&&void 0!==e?e:\"\"})),this.connect(this.model.properties.color.change,(()=>this.input_el.value=(0,a.color2hexrgb)(this.model.color))),this.connect(this.model.properties.disabled.change,(()=>this.input_el.disabled=this.model.disabled))}render(){super.render(),this.input_el=(0,c.input)({type:\"color\",class:d.input,name:this.model.name,value:this.model.color,disabled:this.model.disabled}),this.input_el.addEventListener(\"change\",(()=>this.change_input())),this.group_el.appendChild(this.input_el)}change_input(){this.model.color=this.input_el.value,super.change_input()}}i.ColorPickerView=h,h.__name__=\"ColorPickerView\";class p extends r.InputWidget{constructor(e){super(e)}}i.ColorPicker=p,l=p,p.__name__=\"ColorPicker\",l.prototype.default_view=h,l.define((({Color:e})=>({color:[e,\"#000000\"]})))},\n", - " 454: function _(e,t,i,n,s){n();const a=e(1);var l;const o=(0,a.__importDefault)(e(455)),d=e(445),r=e(43),c=e(20),u=e(8),h=(0,a.__importStar)(e(446)),_=(0,a.__importDefault)(e(456));function p(e){const t=[];for(const i of e)if((0,u.isString)(i))t.push(i);else{const[e,n]=i;t.push({from:e,to:n})}return t}class m extends d.InputWidgetView{connect_signals(){super.connect_signals();const{value:e,min_date:t,max_date:i,disabled_dates:n,enabled_dates:s,position:a,inline:l}=this.model.properties;this.connect(e.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.setDate(this.model.value)})),this.connect(t.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.set(\"minDate\",this.model.min_date)})),this.connect(i.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.set(\"maxDate\",this.model.max_date)})),this.connect(n.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.set(\"disable\",this.model.disabled_dates)})),this.connect(s.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.set(\"enable\",this.model.enabled_dates)})),this.connect(a.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.set(\"position\",this.model.position)})),this.connect(l.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.set(\"inline\",this.model.inline)}))}remove(){var e;null===(e=this._picker)||void 0===e||e.destroy(),super.remove()}styles(){return[...super.styles(),_.default]}render(){var e,t;null==this._picker&&(super.render(),this.input_el=(0,r.input)({type:\"text\",class:h.input,disabled:this.model.disabled}),this.group_el.appendChild(this.input_el),this._picker=(0,o.default)(this.input_el,{defaultDate:this.model.value,minDate:null!==(e=this.model.min_date)&&void 0!==e?e:void 0,maxDate:null!==(t=this.model.max_date)&&void 0!==t?t:void 0,inline:this.model.inline,position:this.model.position,disable:p(this.model.disabled_dates),enable:p(this.model.enabled_dates),onChange:(e,t,i)=>this._on_change(e,t,i)}))}_on_change(e,t,i){this.model.value=t,this.change_input()}}i.DatePickerView=m,m.__name__=\"DatePickerView\";class v extends d.InputWidget{constructor(e){super(e)}}i.DatePicker=v,l=v,v.__name__=\"DatePicker\",l.prototype.default_view=m,l.define((({Boolean:e,String:t,Array:i,Tuple:n,Or:s,Nullable:a})=>{const l=i(s(t,n(t,t)));return{value:[t],min_date:[a(t),null],max_date:[a(t),null],disabled_dates:[l,[]],enabled_dates:[l,[]],position:[c.CalendarPosition,\"auto\"],inline:[e,!1]}}))},\n", - " 455: function _(e,n,t,a,i){\n", - " /* flatpickr v4.6.6, @license MIT */var o,r;o=this,r=function(){\"use strict\";\n", - " /*! *****************************************************************************\n", - " Copyright (c) Microsoft Corporation.\n", - " \n", - " Permission to use, copy, modify, and/or distribute this software for any\n", - " purpose with or without fee is hereby granted.\n", - " \n", - " THE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\n", - " REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\n", - " AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\n", - " INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\n", - " LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\n", - " OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\n", - " PERFORMANCE OF THIS SOFTWARE.\n", - " ***************************************************************************** */var e=function(){return e=Object.assign||function(e){for(var n,t=1,a=arguments.length;t\",noCalendar:!1,now:new Date,onChange:[],onClose:[],onDayCreate:[],onDestroy:[],onKeyDown:[],onMonthChange:[],onOpen:[],onParseConfig:[],onReady:[],onValueUpdate:[],onYearChange:[],onPreCalendarPosition:[],plugins:[],position:\"auto\",positionElement:void 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e.slice().map((function(e){return\"string\"==typeof e||\"number\"==typeof e||e instanceof Date?w.parseDate(e,void 0,!0):e&&\"object\"==typeof e&&e.from&&e.to?{from:w.parseDate(e.from,void 0),to:w.parseDate(e.to,void 0)}:e})).filter((function(e){return e}))}function pe(e,n){if(void 0!==w.config){var t=w.config[e];if(void 0!==t&&t.length>0)for(var a=0;t[a]&&a1||\"static\"===w.config.monthSelectorType?w.monthElements[n].textContent=h(t.getMonth(),w.config.shorthandCurrentMonth,w.l10n)+\" \":w.monthsDropdownContainer.value=t.getMonth().toString(),e.value=t.getFullYear().toString()})),w._hidePrevMonthArrow=void 0!==w.config.minDate&&(w.currentYear===w.config.minDate.getFullYear()?w.currentMonth<=w.config.minDate.getMonth():w.currentYearw.config.maxDate.getMonth():w.currentYear>w.config.maxDate.getFullYear()))}function we(e){return w.selectedDates.map((function(n){return w.formatDate(n,e)})).filter((function(e,n,t){return\"range\"!==w.config.mode||w.config.enableTime||t.indexOf(e)===n})).join(\"range\"!==w.config.mode?w.config.conjunction:w.l10n.rangeSeparator)}function be(e){void 0===e&&(e=!0),void 0!==w.mobileInput&&w.mobileFormatStr&&(w.mobileInput.value=void 0!==w.latestSelectedDateObj?w.formatDate(w.latestSelectedDateObj,w.mobileFormatStr):\"\"),w.input.value=we(w.config.dateFormat),void 0!==w.altInput&&(w.altInput.value=we(w.config.altFormat)),!1!==e&&pe(\"onValueUpdate\")}function Ce(e){var n=g(e),t=w.prevMonthNav.contains(n),a=w.nextMonthNav.contains(n);t||a?G(t?-1:1):w.yearElements.indexOf(n)>=0?n.select():n.classList.contains(\"arrowUp\")?w.changeYear(w.currentYear+1):n.classList.contains(\"arrowDown\")&&w.changeYear(w.currentYear-1)}return function(){w.element=w.input=p,w.isOpen=!1,function(){var n=[\"wrap\",\"weekNumbers\",\"allowInput\",\"allowInvalidPreload\",\"clickOpens\",\"time_24hr\",\"enableTime\",\"noCalendar\",\"altInput\",\"shorthandCurrentMonth\",\"inline\",\"static\",\"enableSeconds\",\"disableMobile\"],i=e(e({},JSON.parse(JSON.stringify(p.dataset||{}))),v),o={};w.config.parseDate=i.parseDate,w.config.formatDate=i.formatDate,Object.defineProperty(w.config,\"enable\",{get:function(){return w.config._enable},set:function(e){w.config._enable=ge(e)}}),Object.defineProperty(w.config,\"disable\",{get:function(){return w.config._disable},set:function(e){w.config._disable=ge(e)}});var r=\"time\"===i.mode;if(!i.dateFormat&&(i.enableTime||r)){var l=k.defaultConfig.dateFormat||a.dateFormat;o.dateFormat=i.noCalendar||r?\"H:i\"+(i.enableSeconds?\":S\":\"\"):l+\" H:i\"+(i.enableSeconds?\":S\":\"\")}if(i.altInput&&(i.enableTime||r)&&!i.altFormat){var d=k.defaultConfig.altFormat||a.altFormat;o.altFormat=i.noCalendar||r?\"h:i\"+(i.enableSeconds?\":S K\":\" K\"):d+\" h:i\"+(i.enableSeconds?\":S\":\"\")+\" K\"}Object.defineProperty(w.config,\"minDate\",{get:function(){return w.config._minDate},set:oe(\"min\")}),Object.defineProperty(w.config,\"maxDate\",{get:function(){return w.config._maxDate},set:oe(\"max\")});var s=function(e){return function(n){w.config[\"min\"===e?\"_minTime\":\"_maxTime\"]=w.parseDate(n,\"H:i:S\")}};Object.defineProperty(w.config,\"minTime\",{get:function(){return w.config._minTime},set:s(\"min\")}),Object.defineProperty(w.config,\"maxTime\",{get:function(){return w.config._maxTime},set:s(\"max\")}),\"time\"===i.mode&&(w.config.noCalendar=!0,w.config.enableTime=!0),Object.assign(w.config,o,i);for(var u=0;u-1?w.config[m]=c(f[m]).map(x).concat(w.config[m]):void 0===i[m]&&(w.config[m]=f[m])}i.altInputClass||(w.config.altInputClass=re().className+\" \"+w.config.altInputClass),pe(\"onParseConfig\")}(),le(),w.input=re(),w.input?(w.input._type=w.input.type,w.input.type=\"text\",w.input.classList.add(\"flatpickr-input\"),w._input=w.input,w.config.altInput&&(w.altInput=s(w.input.nodeName,w.config.altInputClass),w._input=w.altInput,w.altInput.placeholder=w.input.placeholder,w.altInput.disabled=w.input.disabled,w.altInput.required=w.input.required,w.altInput.tabIndex=w.input.tabIndex,w.altInput.type=\"text\",w.input.setAttribute(\"type\",\"hidden\"),!w.config.static&&w.input.parentNode&&w.input.parentNode.insertBefore(w.altInput,w.input.nextSibling)),w.config.allowInput||w._input.setAttribute(\"readonly\",\"readonly\"),w._positionElement=w.config.positionElement||w._input):w.config.errorHandler(new Error(\"Invalid input element specified\")),function(){w.selectedDates=[],w.now=w.parseDate(w.config.now)||new Date;var e=w.config.defaultDate||(\"INPUT\"!==w.input.nodeName&&\"TEXTAREA\"!==w.input.nodeName||!w.input.placeholder||w.input.value!==w.input.placeholder?w.input.value:null);e&&me(e,w.config.dateFormat),w._initialDate=w.selectedDates.length>0?w.selectedDates[0]:w.config.minDate&&w.config.minDate.getTime()>w.now.getTime()?w.config.minDate:w.config.maxDate&&w.config.maxDate.getTime()0&&(w.latestSelectedDateObj=w.selectedDates[0]),void 0!==w.config.minTime&&(w.config.minTime=w.parseDate(w.config.minTime,\"H:i\")),void 0!==w.config.maxTime&&(w.config.maxTime=w.parseDate(w.config.maxTime,\"H:i\")),w.minDateHasTime=!!w.config.minDate&&(w.config.minDate.getHours()>0||w.config.minDate.getMinutes()>0||w.config.minDate.getSeconds()>0),w.maxDateHasTime=!!w.config.maxDate&&(w.config.maxDate.getHours()>0||w.config.maxDate.getMinutes()>0||w.config.maxDate.getSeconds()>0)}(),w.utils={getDaysInMonth:function(e,n){return void 0===e&&(e=w.currentMonth),void 0===n&&(n=w.currentYear),1===e&&(n%4==0&&n%100!=0||n%400==0)?29:w.l10n.daysInMonth[e]}},w.isMobile||function(){var e=window.document.createDocumentFragment();if(w.calendarContainer=s(\"div\",\"flatpickr-calendar\"),w.calendarContainer.tabIndex=-1,!w.config.noCalendar){if(e.appendChild((w.monthNav=s(\"div\",\"flatpickr-months\"),w.yearElements=[],w.monthElements=[],w.prevMonthNav=s(\"span\",\"flatpickr-prev-month\"),w.prevMonthNav.innerHTML=w.config.prevArrow,w.nextMonthNav=s(\"span\",\"flatpickr-next-month\"),w.nextMonthNav.innerHTML=w.config.nextArrow,q(),Object.defineProperty(w,\"_hidePrevMonthArrow\",{get:function(){return w.__hidePrevMonthArrow},set:function(e){w.__hidePrevMonthArrow!==e&&(d(w.prevMonthNav,\"flatpickr-disabled\",e),w.__hidePrevMonthArrow=e)}}),Object.defineProperty(w,\"_hideNextMonthArrow\",{get:function(){return w.__hideNextMonthArrow},set:function(e){w.__hideNextMonthArrow!==e&&(d(w.nextMonthNav,\"flatpickr-disabled\",e),w.__hideNextMonthArrow=e)}}),w.currentYearElement=w.yearElements[0],De(),w.monthNav)),w.innerContainer=s(\"div\",\"flatpickr-innerContainer\"),w.config.weekNumbers){var n=function(){w.calendarContainer.classList.add(\"hasWeeks\");var e=s(\"div\",\"flatpickr-weekwrapper\");e.appendChild(s(\"span\",\"flatpickr-weekday\",w.l10n.weekAbbreviation));var n=s(\"div\",\"flatpickr-weeks\");return e.appendChild(n),{weekWrapper:e,weekNumbers:n}}(),t=n.weekWrapper,a=n.weekNumbers;w.innerContainer.appendChild(t),w.weekNumbers=a,w.weekWrapper=t}w.rContainer=s(\"div\",\"flatpickr-rContainer\"),w.rContainer.appendChild($()),w.daysContainer||(w.daysContainer=s(\"div\",\"flatpickr-days\"),w.daysContainer.tabIndex=-1),J(),w.rContainer.appendChild(w.daysContainer),w.innerContainer.appendChild(w.rContainer),e.appendChild(w.innerContainer)}w.config.enableTime&&e.appendChild(function(){w.calendarContainer.classList.add(\"hasTime\"),w.config.noCalendar&&w.calendarContainer.classList.add(\"noCalendar\"),w.timeContainer=s(\"div\",\"flatpickr-time\"),w.timeContainer.tabIndex=-1;var e=s(\"span\",\"flatpickr-time-separator\",\":\"),n=m(\"flatpickr-hour\",{\"aria-label\":w.l10n.hourAriaLabel});w.hourElement=n.getElementsByTagName(\"input\")[0];var t=m(\"flatpickr-minute\",{\"aria-label\":w.l10n.minuteAriaLabel});if(w.minuteElement=t.getElementsByTagName(\"input\")[0],w.hourElement.tabIndex=w.minuteElement.tabIndex=-1,w.hourElement.value=o(w.latestSelectedDateObj?w.latestSelectedDateObj.getHours():w.config.time_24hr?w.config.defaultHour:function(e){switch(e%24){case 0:case 12:return 12;default:return 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a=m(\"flatpickr-second\");w.secondElement=a.getElementsByTagName(\"input\")[0],w.secondElement.value=o(w.latestSelectedDateObj?w.latestSelectedDateObj.getSeconds():w.config.defaultSeconds),w.secondElement.setAttribute(\"step\",w.minuteElement.getAttribute(\"step\")),w.secondElement.setAttribute(\"min\",\"0\"),w.secondElement.setAttribute(\"max\",\"59\"),w.timeContainer.appendChild(s(\"span\",\"flatpickr-time-separator\",\":\")),w.timeContainer.appendChild(a)}return w.config.time_24hr||(w.amPM=s(\"span\",\"flatpickr-am-pm\",w.l10n.amPM[r((w.latestSelectedDateObj?w.hourElement.value:w.config.defaultHour)>11)]),w.amPM.title=w.l10n.toggleTitle,w.amPM.tabIndex=-1,w.timeContainer.appendChild(w.amPM)),w.timeContainer}()),d(w.calendarContainer,\"rangeMode\",\"range\"===w.config.mode),d(w.calendarContainer,\"animate\",!0===w.config.animate),d(w.calendarContainer,\"multiMonth\",w.config.showMonths>1),w.calendarContainer.appendChild(e);var i=void 0!==w.config.appendTo&&void 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null!==o&&!1!==o&&(o=Number(o.toFixed(a))),null!==s&&!1!==s&&(s=Number(s.toFixed(a))),[s,o]}f(b=w,r.cssClasses.target),0===r.dir?f(b,r.cssClasses.ltr):f(b,r.cssClasses.rtl),0===r.ort?f(b,r.cssClasses.horizontal):f(b,r.cssClasses.vertical),f(b,\"rtl\"===getComputedStyle(b).direction?r.cssClasses.textDirectionRtl:r.cssClasses.textDirectionLtr),a=T(b,r.cssClasses.base),function(t,e){var n=T(e,r.cssClasses.connects);p=[],(m=[]).push(z(n,t[0]));for(var i=0;i=0&&t .noUi-tooltip{-webkit-transform:translate(50%, 0);transform:translate(50%, 0);left:auto;bottom:10px;}.bk-root .noUi-vertical .noUi-origin > .noUi-tooltip{-webkit-transform:translate(0, -18px);transform:translate(0, -18px);top:auto;right:28px;}.bk-root .noUi-handle{cursor:grab;cursor:-webkit-grab;}.bk-root .noUi-handle.noUi-active{cursor:grabbing;cursor:-webkit-grabbing;}.bk-root .noUi-handle:after,.bk-root .noUi-handle:before{display:none;}.bk-root .noUi-tooltip{display:none;white-space:nowrap;}.bk-root .noUi-handle:hover 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i=e.split(this.options.tokenSeparator),n=0,s=i.length;n0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1?arguments[1]:void 0,i=this.list,n={},s=[];if(\"string\"==typeof i[0]){for(var r=0,o=i.length;r1)throw new Error(\"Key weight has to be > 0 and <= 1\");p=p.name}else a[p]={weight:1};this._analyze({key:p,value:this.options.getFn(h,p),record:h,index:c},{resultMap:n,results:s,tokenSearchers:e,fullSearcher:t})}return{weights:a,results:s}}},{key:\"_analyze\",value:function(e,t){var i=e.key,n=e.arrayIndex,s=void 0===n?-1:n,r=e.value,o=e.record,c=e.index,l=t.tokenSearchers,h=void 0===l?[]:l,u=t.fullSearcher,d=void 0===u?[]:u,p=t.resultMap,m=void 0===p?{}:p,f=t.results,v=void 0===f?[]:f;if(null!=r){var g=!1,_=-1,b=0;if(\"string\"==typeof r){this._log(\"\\nKey: \".concat(\"\"===i?\"-\":i));var y=d.search(r);if(this._log('Full text: \"'.concat(r,'\", score: ').concat(y.score)),this.options.tokenize){for(var E=r.split(this.options.tokenSeparator),I=[],S=0;S-1&&(P=(P+_)/2),this._log(\"Score average:\",P);var D=!this.options.tokenize||!this.options.matchAllTokens||b>=h.length;if(this._log(\"\\nCheck Matches: \".concat(D)),(g||y.isMatch)&&D){var M=m[c];M?M.output.push({key:i,arrayIndex:s,value:r,score:P,matchedIndices:y.matchedIndices}):(m[c]={item:o,output:[{key:i,arrayIndex:s,value:r,score:P,matchedIndices:y.matchedIndices}]},v.push(m[c]))}}else if(a(r))for(var N=0,F=r.length;N-1&&(o.arrayIndex=r.arrayIndex),t.matches.push(o)}}})),this.options.includeScore&&s.push((function(e,t){t.score=e.score}));for(var r=0,o=e.length;ri)return s(e,this.pattern,n);var o=this.options,a=o.location,c=o.distance,l=o.threshold,h=o.findAllMatches,u=o.minMatchCharLength;return r(e,this.pattern,this.patternAlphabet,{location:a,distance:c,threshold:l,findAllMatches:h,minMatchCharLength:u})}}])&&n(t.prototype,i),a&&n(t,a),e}();e.exports=a},function(e,t){var i=/[\\-\\[\\]\\/\\{\\}\\(\\)\\*\\+\\?\\.\\\\\\^\\$\\|]/g;e.exports=function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:/ +/g,s=new RegExp(t.replace(i,\"\\\\$&\").replace(n,\"|\")),r=e.match(s),o=!!r,a=[];if(o)for(var c=0,l=r.length;c=P;N-=1){var F=N-1,j=i[e.charAt(F)];if(j&&(E[F]=1),M[N]=(M[N+1]<<1|1)&j,0!==T&&(M[N]|=(O[N+1]|O[N])<<1|1|O[N+1]),M[N]&L&&(C=n(t,{errors:T,currentLocation:F,expectedLocation:v,distance:l}))<=_){if(_=C,(b=F)<=v)break;P=Math.max(1,2*v-b)}}if(n(t,{errors:T+1,currentLocation:v,expectedLocation:v,distance:l})>_)break;O=M}return{isMatch:b>=0,score:0===C?.001:C,matchedIndices:s(E,f)}}},function(e,t){e.exports=function(e,t){var i=t.errors,n=void 0===i?0:i,s=t.currentLocation,r=void 0===s?0:s,o=t.expectedLocation,a=void 0===o?0:o,c=t.distance,l=void 0===c?100:c,h=n/e.length,u=Math.abs(a-r);return l?h+u/l:u?1:h}},function(e,t){e.exports=function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 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e(e){var t=e.element,i=e.type,n=e.classNames,s=e.position;this.element=t,this.classNames=n,this.type=i,this.position=s,this.isOpen=!1,this.isFlipped=!1,this.isFocussed=!1,this.isDisabled=!1,this.isLoading=!1,this._onFocus=this._onFocus.bind(this),this._onBlur=this._onBlur.bind(this)}var t=e.prototype;return t.addEventListeners=function(){this.element.addEventListener(\"focus\",this._onFocus),this.element.addEventListener(\"blur\",this._onBlur)},t.removeEventListeners=function(){this.element.removeEventListener(\"focus\",this._onFocus),this.element.removeEventListener(\"blur\",this._onBlur)},t.shouldFlip=function(e){if(\"number\"!=typeof e)return!1;var t=!1;return\"auto\"===this.position?t=!window.matchMedia(\"(min-height: 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0===t&&(t=document.createElement(\"div\")),e.nextSibling?e.parentNode.insertBefore(t,e.nextSibling):e.parentNode.appendChild(t),t.appendChild(e)}(e,this.element)},t.unwrap=function(e){this.element.parentNode.insertBefore(e,this.element),this.element.parentNode.removeChild(this.element)},t.addLoadingState=function(){this.element.classList.add(this.classNames.loadingState),this.element.setAttribute(\"aria-busy\",\"true\"),this.isLoading=!0},t.removeLoadingState=function(){this.element.classList.remove(this.classNames.loadingState),this.element.removeAttribute(\"aria-busy\"),this.isLoading=!1},t._onFocus=function(){this.isFocussed=!0},t._onBlur=function(){this.isFocussed=!1},e}();function le(e,t){for(var i=0;i0?this.element.scrollTop+o-s:e.offsetTop;requestAnimationFrame((function(){i._animateScroll(a,t)}))}},t._scrollDown=function(e,t,i){var n=(i-e)/t,s=n>1?n:1;this.element.scrollTop=e+s},t._scrollUp=function(e,t,i){var n=(e-i)/t,s=n>1?n:1;this.element.scrollTop=e-s},t._animateScroll=function(e,t){var i=this,n=this.element.scrollTop,s=!1;t>0?(this._scrollDown(n,4,e),ne&&(s=!0)),s&&requestAnimationFrame((function(){i._animateScroll(e,t)}))},e}();function de(e,t){for(var i=0;i0?\"treeitem\":\"option\"),Object.assign(g.dataset,{choice:\"\",id:l,value:h,selectText:i}),m?(g.classList.add(a),g.dataset.choiceDisabled=\"\",g.setAttribute(\"aria-disabled\",\"true\")):(g.classList.add(r),g.dataset.choiceSelectable=\"\"),g},input:function(e,t){var i=e.input,n=e.inputCloned,s=Object.assign(document.createElement(\"input\"),{type:\"text\",className:i+\" \"+n,autocomplete:\"off\",autocapitalize:\"off\",spellcheck:!1});return s.setAttribute(\"role\",\"textbox\"),s.setAttribute(\"aria-autocomplete\",\"list\"),s.setAttribute(\"aria-label\",t),s},dropdown:function(e){var t=e.list,i=e.listDropdown,n=document.createElement(\"div\");return n.classList.add(t,i),n.setAttribute(\"aria-expanded\",\"false\"),n},notice:function(e,t,i){var n=e.item,s=e.itemChoice,r=e.noResults,o=e.noChoices;void 0===i&&(i=\"\");var a=[n,s];return\"no-choices\"===i?a.push(o):\"no-results\"===i&&a.push(r),Object.assign(document.createElement(\"div\"),{innerHTML:t,className:a.join(\" \")})},option:function(e){var t=e.label,i=e.value,n=e.customProperties,s=e.active,r=e.disabled,o=new Option(t,i,!1,s);return n&&(o.dataset.customProperties=n),o.disabled=r,o}},be=function(e){return void 0===e&&(e=!0),{type:q,active:e}},ye=function(e,t){return{type:$,id:e,highlighted:t}},Ee=function(e){var t=e.value,i=e.id,n=e.active,s=e.disabled;return{type:z,value:t,id:i,active:n,disabled:s}},Ie=function(e){return{type:\"SET_IS_LOADING\",isLoading:e}};function Se(e,t){for(var i=0;i=0?this._store.getGroupById(s):null;return this._store.dispatch(ye(i,!0)),t&&this.passedElement.triggerEvent(H,{id:i,value:o,label:c,groupValue:l&&l.value?l.value:null}),this},r.unhighlightItem=function(e){if(!e)return this;var t=e.id,i=e.groupId,n=void 0===i?-1:i,s=e.value,r=void 0===s?\"\":s,o=e.label,a=void 0===o?\"\":o,c=n>=0?this._store.getGroupById(n):null;return this._store.dispatch(ye(t,!1)),this.passedElement.triggerEvent(H,{id:t,value:r,label:a,groupValue:c&&c.value?c.value:null}),this},r.highlightAll=function(){var e=this;return this._store.items.forEach((function(t){return e.highlightItem(t)})),this},r.unhighlightAll=function(){var e=this;return this._store.items.forEach((function(t){return e.unhighlightItem(t)})),this},r.removeActiveItemsByValue=function(e){var t=this;return this._store.activeItems.filter((function(t){return t.value===e})).forEach((function(e){return t._removeItem(e)})),this},r.removeActiveItems=function(e){var t=this;return this._store.activeItems.filter((function(t){return t.id!==e})).forEach((function(e){return t._removeItem(e)})),this},r.removeHighlightedItems=function(e){var t=this;return void 0===e&&(e=!1),this._store.highlightedActiveItems.forEach((function(i){t._removeItem(i),e&&t._triggerChange(i.value)})),this},r.showDropdown=function(e){var t=this;return this.dropdown.isActive||requestAnimationFrame((function(){t.dropdown.show(),t.containerOuter.open(t.dropdown.distanceFromTopWindow),!e&&t._canSearch&&t.input.focus(),t.passedElement.triggerEvent(D,{})})),this},r.hideDropdown=function(e){var t=this;return this.dropdown.isActive?(requestAnimationFrame((function(){t.dropdown.hide(),t.containerOuter.close(),!e&&t._canSearch&&(t.input.removeActiveDescendant(),t.input.blur()),t.passedElement.triggerEvent(M,{})})),this):this},r.getValue=function(e){void 0===e&&(e=!1);var t=this._store.activeItems.reduce((function(t,i){var n=e?i.value:i;return t.push(n),t}),[]);return this._isSelectOneElement?t[0]:t},r.setValue=function(e){var t=this;return this.initialised?(e.forEach((function(e){return t._setChoiceOrItem(e)})),this):this},r.setChoiceByValue=function(e){var t=this;return!this.initialised||this._isTextElement||(Array.isArray(e)?e:[e]).forEach((function(e){return t._findAndSelectChoiceByValue(e)})),this},r.setChoices=function(e,t,i,n){var s=this;if(void 0===e&&(e=[]),void 0===t&&(t=\"value\"),void 0===i&&(i=\"label\"),void 0===n&&(n=!1),!this.initialised)throw new ReferenceError(\"setChoices was called on a non-initialized instance of Choices\");if(!this._isSelectElement)throw new TypeError(\"setChoices can't be used with INPUT based Choices\");if(\"string\"!=typeof t||!t)throw new TypeError(\"value parameter must be a name of 'value' field in passed objects\");if(n&&this.clearChoices(),\"function\"==typeof e){var r=e(this);if(\"function\"==typeof Promise&&r instanceof Promise)return new Promise((function(e){return requestAnimationFrame(e)})).then((function(){return s._handleLoadingState(!0)})).then((function(){return r})).then((function(e){return s.setChoices(e,t,i,n)})).catch((function(e){s.config.silent||console.error(e)})).then((function(){return s._handleLoadingState(!1)})).then((function(){return s}));if(!Array.isArray(r))throw new TypeError(\".setChoices first argument function must return either array of choices or Promise, got: \"+typeof r);return this.setChoices(r,t,i,!1)}if(!Array.isArray(e))throw new TypeError(\".setChoices must be called either with array of choices with a function resulting into Promise of array of choices\");return this.containerOuter.removeLoadingState(),this._startLoading(),e.forEach((function(e){e.choices?s._addGroup({id:parseInt(e.id,10)||null,group:e,valueKey:t,labelKey:i}):s._addChoice({value:e[t],label:e[i],isSelected:e.selected,isDisabled:e.disabled,customProperties:e.customProperties,placeholder:e.placeholder})})),this._stopLoading(),this},r.clearChoices=function(){return this._store.dispatch({type:U}),this},r.clearStore=function(){return this._store.dispatch({type:\"CLEAR_ALL\"}),this},r.clearInput=function(){var e=!this._isSelectOneElement;return this.input.clear(e),!this._isTextElement&&this._canSearch&&(this._isSearching=!1,this._store.dispatch(be(!0))),this},r._render=function(){if(!this._store.isLoading()){this._currentState=this._store.state;var e=this._currentState.choices!==this._prevState.choices||this._currentState.groups!==this._prevState.groups||this._currentState.items!==this._prevState.items,t=this._isSelectElement,i=this._currentState.items!==this._prevState.items;e&&(t&&this._renderChoices(),i&&this._renderItems(),this._prevState=this._currentState)}},r._renderChoices=function(){var e=this,t=this._store,i=t.activeGroups,n=t.activeChoices,s=document.createDocumentFragment();if(this.choiceList.clear(),this.config.resetScrollPosition&&requestAnimationFrame((function(){return e.choiceList.scrollToTop()})),i.length>=1&&!this._isSearching){var r=n.filter((function(e){return!0===e.placeholder&&-1===e.groupId}));r.length>=1&&(s=this._createChoicesFragment(r,s)),s=this._createGroupsFragment(i,n,s)}else n.length>=1&&(s=this._createChoicesFragment(n,s));if(s.childNodes&&s.childNodes.length>0){var o=this._store.activeItems,a=this._canAddItem(o,this.input.value);a.response?(this.choiceList.append(s),this._highlightChoice()):this.choiceList.append(this._getTemplate(\"notice\",a.notice))}else{var c,l;this._isSearching?(l=\"function\"==typeof this.config.noResultsText?this.config.noResultsText():this.config.noResultsText,c=this._getTemplate(\"notice\",l,\"no-results\")):(l=\"function\"==typeof this.config.noChoicesText?this.config.noChoicesText():this.config.noChoicesText,c=this._getTemplate(\"notice\",l,\"no-choices\")),this.choiceList.append(c)}},r._renderItems=function(){var e=this._store.activeItems||[];this.itemList.clear();var t=this._createItemsFragment(e);t.childNodes&&this.itemList.append(t)},r._createGroupsFragment=function(e,t,i){var n=this;return void 0===i&&(i=document.createDocumentFragment()),this.config.shouldSort&&e.sort(this.config.sorter),e.forEach((function(e){var s=function(e){return t.filter((function(t){return n._isSelectOneElement?t.groupId===e.id:t.groupId===e.id&&(\"always\"===n.config.renderSelectedChoices||!t.selected)}))}(e);if(s.length>=1){var r=n._getTemplate(\"choiceGroup\",e);i.appendChild(r),n._createChoicesFragment(s,i,!0)}})),i},r._createChoicesFragment=function(e,t,i){var n=this;void 0===t&&(t=document.createDocumentFragment()),void 0===i&&(i=!1);var s=this.config,r=s.renderSelectedChoices,o=s.searchResultLimit,a=s.renderChoiceLimit,c=this._isSearching?w:this.config.sorter,l=function(e){if(\"auto\"!==r||n._isSelectOneElement||!e.selected){var i=n._getTemplate(\"choice\",e,n.config.itemSelectText);t.appendChild(i)}},h=e;\"auto\"!==r||this._isSelectOneElement||(h=e.filter((function(e){return!e.selected})));var u=h.reduce((function(e,t){return t.placeholder?e.placeholderChoices.push(t):e.normalChoices.push(t),e}),{placeholderChoices:[],normalChoices:[]}),d=u.placeholderChoices,p=u.normalChoices;(this.config.shouldSort||this._isSearching)&&p.sort(c);var m=h.length,f=this._isSelectOneElement?[].concat(d,p):p;this._isSearching?m=o:a&&a>0&&!i&&(m=a);for(var v=0;v=n){var o=s?this._searchChoices(e):0;this.passedElement.triggerEvent(j,{value:e,resultCount:o})}else r&&(this._isSearching=!1,this._store.dispatch(be(!0)))}},r._canAddItem=function(e,t){var i=!0,n=\"function\"==typeof this.config.addItemText?this.config.addItemText(t):this.config.addItemText;if(!this._isSelectOneElement){var s=function(e,t,i){return void 0===i&&(i=\"value\"),e.some((function(e){return\"string\"==typeof t?e[i]===t.trim():e[i]===t}))}(e,t);this.config.maxItemCount>0&&this.config.maxItemCount<=e.length&&(i=!1,n=\"function\"==typeof this.config.maxItemText?this.config.maxItemText(this.config.maxItemCount):this.config.maxItemText),!this.config.duplicateItemsAllowed&&s&&i&&(i=!1,n=\"function\"==typeof this.config.uniqueItemText?this.config.uniqueItemText(t):this.config.uniqueItemText),this._isTextElement&&this.config.addItems&&i&&\"function\"==typeof this.config.addItemFilter&&!this.config.addItemFilter(t)&&(i=!1,n=\"function\"==typeof this.config.customAddItemText?this.config.customAddItemText(t):this.config.customAddItemText)}return{response:i,notice:n}},r._searchChoices=function(e){var t=\"string\"==typeof e?e.trim():e,i=\"string\"==typeof this._currentValue?this._currentValue.trim():this._currentValue;if(t.length<1&&t===i+\" \")return 0;var n=this._store.searchableChoices,r=t,o=[].concat(this.config.searchFields),a=Object.assign(this.config.fuseOptions,{keys:o}),c=new s.a(n,a).search(r);return this._currentValue=t,this._highlightPosition=0,this._isSearching=!0,this._store.dispatch(function(e){return{type:G,results:e}}(c)),c.length},r._addEventListeners=function(){var e=document.documentElement;e.addEventListener(\"touchend\",this._onTouchEnd,!0),this.containerOuter.element.addEventListener(\"keydown\",this._onKeyDown,!0),this.containerOuter.element.addEventListener(\"mousedown\",this._onMouseDown,!0),e.addEventListener(\"click\",this._onClick,{passive:!0}),e.addEventListener(\"touchmove\",this._onTouchMove,{passive:!0}),this.dropdown.element.addEventListener(\"mouseover\",this._onMouseOver,{passive:!0}),this._isSelectOneElement&&(this.containerOuter.element.addEventListener(\"focus\",this._onFocus,{passive:!0}),this.containerOuter.element.addEventListener(\"blur\",this._onBlur,{passive:!0})),this.input.element.addEventListener(\"keyup\",this._onKeyUp,{passive:!0}),this.input.element.addEventListener(\"focus\",this._onFocus,{passive:!0}),this.input.element.addEventListener(\"blur\",this._onBlur,{passive:!0}),this.input.element.form&&this.input.element.form.addEventListener(\"reset\",this._onFormReset,{passive:!0}),this.input.addEventListeners()},r._removeEventListeners=function(){var e=document.documentElement;e.removeEventListener(\"touchend\",this._onTouchEnd,!0),this.containerOuter.element.removeEventListener(\"keydown\",this._onKeyDown,!0),this.containerOuter.element.removeEventListener(\"mousedown\",this._onMouseDown,!0),e.removeEventListener(\"click\",this._onClick),e.removeEventListener(\"touchmove\",this._onTouchMove),this.dropdown.element.removeEventListener(\"mouseover\",this._onMouseOver),this._isSelectOneElement&&(this.containerOuter.element.removeEventListener(\"focus\",this._onFocus),this.containerOuter.element.removeEventListener(\"blur\",this._onBlur)),this.input.element.removeEventListener(\"keyup\",this._onKeyUp),this.input.element.removeEventListener(\"focus\",this._onFocus),this.input.element.removeEventListener(\"blur\",this._onBlur),this.input.element.form&&this.input.element.form.removeEventListener(\"reset\",this._onFormReset),this.input.removeEventListeners()},r._onKeyDown=function(e){var t,i=e.target,n=e.keyCode,s=e.ctrlKey,r=e.metaKey,o=this._store.activeItems,a=this.input.isFocussed,c=this.dropdown.isActive,l=this.itemList.hasChildren(),h=String.fromCharCode(n),u=J,d=Y,p=Z,m=Q,f=ee,v=te,g=ie,_=ne,b=se,y=s||r;!this._isTextElement&&/[a-zA-Z0-9-_ ]/.test(h)&&this.showDropdown();var E=((t={})[m]=this._onAKey,t[p]=this._onEnterKey,t[f]=this._onEscapeKey,t[v]=this._onDirectionKey,t[_]=this._onDirectionKey,t[g]=this._onDirectionKey,t[b]=this._onDirectionKey,t[d]=this._onDeleteKey,t[u]=this._onDeleteKey,t);E[n]&&E[n]({event:e,target:i,keyCode:n,metaKey:r,activeItems:o,hasFocusedInput:a,hasActiveDropdown:c,hasItems:l,hasCtrlDownKeyPressed:y})},r._onKeyUp=function(e){var t=e.target,i=e.keyCode,n=this.input.value,s=this._store.activeItems,r=this._canAddItem(s,n),o=J,a=Y;if(this._isTextElement)if(r.notice&&n){var c=this._getTemplate(\"notice\",r.notice);this.dropdown.element.innerHTML=c.outerHTML,this.showDropdown(!0)}else this.hideDropdown(!0);else{var l=(i===o||i===a)&&!t.value,h=!this._isTextElement&&this._isSearching,u=this._canSearch&&r.response;l&&h?(this._isSearching=!1,this._store.dispatch(be(!0))):u&&this._handleSearch(this.input.value)}this._canSearch=this.config.searchEnabled},r._onAKey=function(e){var t=e.hasItems;e.hasCtrlDownKeyPressed&&t&&(this._canSearch=!1,this.config.removeItems&&!this.input.value&&this.input.element===document.activeElement&&this.highlightAll())},r._onEnterKey=function(e){var t=e.event,i=e.target,n=e.activeItems,s=e.hasActiveDropdown,r=Z,o=i.hasAttribute(\"data-button\");if(this._isTextElement&&i.value){var a=this.input.value;this._canAddItem(n,a).response&&(this.hideDropdown(!0),this._addItem({value:a}),this._triggerChange(a),this.clearInput())}if(o&&(this._handleButtonAction(n,i),t.preventDefault()),s){var c=this.dropdown.getChild(\".\"+this.config.classNames.highlightedState);c&&(n[0]&&(n[0].keyCode=r),this._handleChoiceAction(n,c)),t.preventDefault()}else this._isSelectOneElement&&(this.showDropdown(),t.preventDefault())},r._onEscapeKey=function(e){e.hasActiveDropdown&&(this.hideDropdown(!0),this.containerOuter.focus())},r._onDirectionKey=function(e){var t,i,n,s=e.event,r=e.hasActiveDropdown,o=e.keyCode,a=e.metaKey,c=ie,l=ne,h=se;if(r||this._isSelectOneElement){this.showDropdown(),this._canSearch=!1;var u,d=o===c||o===h?1:-1,p=\"[data-choice-selectable]\";if(a||o===h||o===l)u=d>0?this.dropdown.element.querySelector(\"[data-choice-selectable]:last-of-type\"):this.dropdown.element.querySelector(p);else{var m=this.dropdown.element.querySelector(\".\"+this.config.classNames.highlightedState);u=m?function(e,t,i){if(void 0===i&&(i=1),e instanceof Element&&\"string\"==typeof t){for(var n=(i>0?\"next\":\"previous\")+\"ElementSibling\",s=e[n];s;){if(s.matches(t))return s;s=s[n]}return s}}(m,p,d):this.dropdown.element.querySelector(p)}u&&(t=u,i=this.choiceList.element,void 0===(n=d)&&(n=1),t&&(n>0?i.scrollTop+i.offsetHeight>=t.offsetTop+t.offsetHeight:t.offsetTop>=i.scrollTop)||this.choiceList.scrollToChildElement(u,d),this._highlightChoice(u)),s.preventDefault()}},r._onDeleteKey=function(e){var t=e.event,i=e.target,n=e.hasFocusedInput,s=e.activeItems;!n||i.value||this._isSelectOneElement||(this._handleBackspace(s),t.preventDefault())},r._onTouchMove=function(){this._wasTap&&(this._wasTap=!1)},r._onTouchEnd=function(e){var t=(e||e.touches[0]).target;this._wasTap&&this.containerOuter.element.contains(t)&&((t===this.containerOuter.element||t===this.containerInner.element)&&(this._isTextElement?this.input.focus():this._isSelectMultipleElement&&this.showDropdown()),e.stopPropagation()),this._wasTap=!0},r._onMouseDown=function(e){var t=e.target;if(t instanceof HTMLElement){if(we&&this.choiceList.element.contains(t)){var i=this.choiceList.element.firstElementChild,n=\"ltr\"===this._direction?e.offsetX>=i.offsetWidth:e.offsetX0&&this.unhighlightAll(),this.containerOuter.removeFocusState(),this.hideDropdown(!0))},r._onFocus=function(e){var t,i=this,n=e.target;this.containerOuter.element.contains(n)&&((t={}).text=function(){n===i.input.element&&i.containerOuter.addFocusState()},t[\"select-one\"]=function(){i.containerOuter.addFocusState(),n===i.input.element&&i.showDropdown(!0)},t[\"select-multiple\"]=function(){n===i.input.element&&(i.showDropdown(!0),i.containerOuter.addFocusState())},t)[this.passedElement.element.type]()},r._onBlur=function(e){var t=this,i=e.target;if(this.containerOuter.element.contains(i)&&!this._isScrollingOnIe){var n,s=this._store.activeItems.some((function(e){return e.highlighted}));((n={}).text=function(){i===t.input.element&&(t.containerOuter.removeFocusState(),s&&t.unhighlightAll(),t.hideDropdown(!0))},n[\"select-one\"]=function(){t.containerOuter.removeFocusState(),(i===t.input.element||i===t.containerOuter.element&&!t._canSearch)&&t.hideDropdown(!0)},n[\"select-multiple\"]=function(){i===t.input.element&&(t.containerOuter.removeFocusState(),t.hideDropdown(!0),s&&t.unhighlightAll())},n)[this.passedElement.element.type]()}else this._isScrollingOnIe=!1,this.input.element.focus()},r._onFormReset=function(){this._store.dispatch({type:\"RESET_TO\",state:this._initialState})},r._highlightChoice=function(e){var t=this;void 0===e&&(e=null);var i=Array.from(this.dropdown.element.querySelectorAll(\"[data-choice-selectable]\"));if(i.length){var n=e;Array.from(this.dropdown.element.querySelectorAll(\".\"+this.config.classNames.highlightedState)).forEach((function(e){e.classList.remove(t.config.classNames.highlightedState),e.setAttribute(\"aria-selected\",\"false\")})),n?this._highlightPosition=i.indexOf(n):(n=i.length>this._highlightPosition?i[this._highlightPosition]:i[i.length-1])||(n=i[0]),n.classList.add(this.config.classNames.highlightedState),n.setAttribute(\"aria-selected\",\"true\"),this.passedElement.triggerEvent(B,{el:n}),this.dropdown.isActive&&(this.input.setActiveDescendant(n.id),this.containerOuter.setActiveDescendant(n.id))}},r._addItem=function(e){var t=e.value,i=e.label,n=void 0===i?null:i,s=e.choiceId,r=void 0===s?-1:s,o=e.groupId,a=void 0===o?-1:o,c=e.customProperties,l=void 0===c?null:c,h=e.placeholder,u=void 0!==h&&h,d=e.keyCode,p=void 0===d?null:d,m=\"string\"==typeof t?t.trim():t,f=p,v=l,g=this._store.items,_=n||m,b=r||-1,y=a>=0?this._store.getGroupById(a):null,E=g?g.length+1:1;return this.config.prependValue&&(m=this.config.prependValue+m.toString()),this.config.appendValue&&(m+=this.config.appendValue.toString()),this._store.dispatch(function(e){var t=e.value,i=e.label,n=e.id,s=e.choiceId,r=e.groupId,o=e.customProperties,a=e.placeholder,c=e.keyCode;return{type:W,value:t,label:i,id:n,choiceId:s,groupId:r,customProperties:o,placeholder:a,keyCode:c}}({value:m,label:_,id:E,choiceId:b,groupId:a,customProperties:l,placeholder:u,keyCode:f})),this._isSelectOneElement&&this.removeActiveItems(E),this.passedElement.triggerEvent(K,{id:E,value:m,label:_,customProperties:v,groupValue:y&&y.value?y.value:void 0,keyCode:f}),this},r._removeItem=function(e){if(!e||!E(\"Object\",e))return this;var t=e.id,i=e.value,n=e.label,s=e.choiceId,r=e.groupId,o=r>=0?this._store.getGroupById(r):null;return this._store.dispatch(function(e,t){return{type:X,id:e,choiceId:t}}(t,s)),o&&o.value?this.passedElement.triggerEvent(R,{id:t,value:i,label:n,groupValue:o.value}):this.passedElement.triggerEvent(R,{id:t,value:i,label:n}),this},r._addChoice=function(e){var t=e.value,i=e.label,n=void 0===i?null:i,s=e.isSelected,r=void 0!==s&&s,o=e.isDisabled,a=void 0!==o&&o,c=e.groupId,l=void 0===c?-1:c,h=e.customProperties,u=void 0===h?null:h,d=e.placeholder,p=void 0!==d&&d,m=e.keyCode,f=void 0===m?null:m;if(null!=t){var v=this._store.choices,g=n||t,_=v?v.length+1:1,b=this._baseId+\"-\"+this._idNames.itemChoice+\"-\"+_;this._store.dispatch(function(e){var t=e.value,i=e.label,n=e.id,s=e.groupId,r=e.disabled,o=e.elementId,a=e.customProperties,c=e.placeholder,l=e.keyCode;return{type:V,value:t,label:i,id:n,groupId:s,disabled:r,elementId:o,customProperties:a,placeholder:c,keyCode:l}}({id:_,groupId:l,elementId:b,value:t,label:g,disabled:a,customProperties:u,placeholder:p,keyCode:f})),r&&this._addItem({value:t,label:g,choiceId:_,customProperties:u,placeholder:p,keyCode:f})}},r._addGroup=function(e){var t=this,i=e.group,n=e.id,s=e.valueKey,r=void 0===s?\"value\":s,o=e.labelKey,a=void 0===o?\"label\":o,c=E(\"Object\",i)?i.choices:Array.from(i.getElementsByTagName(\"OPTION\")),l=n||Math.floor((new Date).valueOf()*Math.random()),h=!!i.disabled&&i.disabled;c?(this._store.dispatch(Ee({value:i.label,id:l,active:!0,disabled:h})),c.forEach((function(e){var i=e.disabled||e.parentNode&&e.parentNode.disabled;t._addChoice({value:e[r],label:E(\"Object\",e)?e[a]:e.innerHTML,isSelected:e.selected,isDisabled:i,groupId:l,customProperties:e.customProperties,placeholder:e.placeholder})}))):this._store.dispatch(Ee({value:i.label,id:i.id,active:!1,disabled:i.disabled}))},r._getTemplate=function(e){var t;if(!e)return null;for(var i=this.config.classNames,n=arguments.length,s=new Array(n>1?n-1:0),r=1;r{var e;return this.input_el.name=null!==(e=this.model.name)&&void 0!==e?e:\"\"})),this.connect(this.model.properties.value.change,(()=>{this.input_el.value=this.format_value,this.old_value=this.input_el.value})),this.connect(this.model.properties.low.change,(()=>{const{value:e,low:t,high:l}=this.model;null!=t&&null!=l&&(0,p.assert)(t<=l,\"Invalid bounds, low must be inferior to high\"),null!=e&&null!=t&&e{const{value:e,low:t,high:l}=this.model;null!=t&&null!=l&&(0,p.assert)(l>=t,\"Invalid bounds, high must be superior to low\"),null!=e&&null!=l&&e>l&&(this.model.value=l)})),this.connect(this.model.properties.high.change,(()=>this.input_el.placeholder=this.model.placeholder)),this.connect(this.model.properties.disabled.change,(()=>this.input_el.disabled=this.model.disabled)),this.connect(this.model.properties.placeholder.change,(()=>this.input_el.placeholder=this.model.placeholder))}get format_value(){return null!=this.model.value?this.model.pretty(this.model.value):\"\"}_set_input_filter(e){this.input_el.addEventListener(\"input\",(()=>{const{selectionStart:t,selectionEnd:l}=this.input_el;if(e(this.input_el.value))this.old_value=this.input_el.value;else{const e=this.old_value.length-this.input_el.value.length;this.input_el.value=this.old_value,t&&l&&this.input_el.setSelectionRange(t-1,l+e)}}))}render(){super.render(),this.input_el=(0,r.input)({type:\"text\",class:_.input,name:this.model.name,value:this.format_value,disabled:this.model.disabled,placeholder:this.model.placeholder}),this.old_value=this.format_value,this.set_input_filter(),this.input_el.addEventListener(\"change\",(()=>this.change_input())),this.input_el.addEventListener(\"focusout\",(()=>this.input_el.value=this.format_value)),this.group_el.appendChild(this.input_el)}set_input_filter(){\"int\"==this.model.mode?this._set_input_filter((e=>m.test(e))):\"float\"==this.model.mode&&this._set_input_filter((e=>c.test(e)))}bound_value(e){let t=e;const{low:l,high:i}=this.model;return t=null!=l?Math.max(l,t):t,t=null!=i?Math.min(i,t):t,t}get value(){let e=\"\"!=this.input_el.value?Number(this.input_el.value):null;return null!=e&&(e=this.bound_value(e)),e}change_input(){null==this.value?this.model.value=null:Number.isNaN(this.value)||(this.model.value=this.value)}}l.NumericInputView=v,v.__name__=\"NumericInputView\";class g extends o.InputWidget{constructor(e){super(e)}_formatter(e,t){return(0,d.isString)(t)?h.format(e,t):t.doFormat([e],{loc:0})[0]}pretty(e){return null!=this.format?this._formatter(e,this.format):`${e}`}}l.NumericInput=g,u=g,g.__name__=\"NumericInput\",u.prototype.default_view=v,u.define((({Number:e,String:t,Enum:l,Ref:i,Or:n,Nullable:s})=>({value:[s(e),null],placeholder:[t,\"\"],mode:[l(\"int\",\"float\"),\"int\"],format:[s(n(t,i(a.TickFormatter))),null],low:[s(e),null],high:[s(e),null]})))},\n", - " 475: function _(e,t,r,s,n){var a;s();const o=e(464),_=e(43);class p extends o.MarkupView{render(){super.render();const e=(0,_.pre)({style:{overflow:\"auto\"}},this.model.text);this.markup_el.appendChild(e)}}r.PreTextView=p,p.__name__=\"PreTextView\";class u extends o.Markup{constructor(e){super(e)}}r.PreText=u,a=u,u.__name__=\"PreText\",a.prototype.default_view=p},\n", - " 476: function _(t,o,e,a,i){a();const n=t(1);var u;const s=t(449),c=t(43),_=(0,n.__importStar)(t(318));class r extends s.ButtonGroupView{change_active(t){this.model.active!==t&&(this.model.active=t)}_update_active(){const{active:t}=this.model;this._buttons.forEach(((o,e)=>{(0,c.classes)(o).toggle(_.active,t===e)}))}}e.RadioButtonGroupView=r,r.__name__=\"RadioButtonGroupView\";class l extends s.ButtonGroup{constructor(t){super(t)}}e.RadioButtonGroup=l,u=l,l.__name__=\"RadioButtonGroup\",u.prototype.default_view=r,u.define((({Int:t,Nullable:o})=>({active:[o(t),null]})))},\n", - " 477: function _(e,n,i,t,a){t();const s=e(1);var l;const o=e(43),d=e(34),p=e(452),r=(0,s.__importStar)(e(446));class u extends p.InputGroupView{render(){super.render();const e=(0,o.div)({class:[r.input_group,this.model.inline?r.inline:null]});this.el.appendChild(e);const n=(0,d.uniqueId)(),{active:i,labels:t}=this.model;this._inputs=[];for(let a=0;athis.change_active(a))),this._inputs.push(s),this.model.disabled&&(s.disabled=!0),a==i&&(s.checked=!0);const l=(0,o.label)(s,(0,o.span)(t[a]));e.appendChild(l)}}change_active(e){this.model.active=e}}i.RadioGroupView=u,u.__name__=\"RadioGroupView\";class c extends p.InputGroup{constructor(e){super(e)}}i.RadioGroup=c,l=c,c.__name__=\"RadioGroup\",l.prototype.default_view=u,l.define((({Boolean:e,Int:n,String:i,Array:t,Nullable:a})=>({active:[a(n),null],labels:[t(i),[]],inline:[e,!1]})))},\n", - " 478: function _(e,r,t,a,i){a();var n;const o=(0,e(1).__importStar)(e(153)),s=e(458),_=e(8);class d extends s.AbstractRangeSliderView{}t.RangeSliderView=d,d.__name__=\"RangeSliderView\";class c extends s.AbstractSlider{constructor(e){super(e),this.behaviour=\"drag\",this.connected=[!1,!0,!1]}_formatter(e,r){return(0,_.isString)(r)?o.format(e,r):r.compute(e)}}t.RangeSlider=c,n=c,c.__name__=\"RangeSlider\",n.prototype.default_view=d,n.override({format:\"0[.]00\"})},\n", - " 479: function _(e,t,n,s,i){s();const l=e(1);var u;const a=e(43),o=e(8),p=e(13),_=e(445),r=(0,l.__importStar)(e(446));class c extends _.InputWidgetView{constructor(){super(...arguments),this._known_values=new Set}connect_signals(){super.connect_signals();const{value:e,options:t}=this.model.properties;this.on_change(e,(()=>{this._update_value()})),this.on_change(t,(()=>{(0,a.empty)(this.input_el),(0,a.append)(this.input_el,...this.options_el()),this._update_value()}))}options_el(){const{_known_values:e}=this;function t(t){return t.map((t=>{let n,s;return(0,o.isString)(t)?n=s=t:[n,s]=t,e.add(n),(0,a.option)({value:n},s)}))}e.clear();const{options:n}=this.model;return(0,o.isArray)(n)?t(n):(0,p.entries)(n).map((([e,n])=>(0,a.optgroup)({label:e},t(n))))}render(){super.render(),this.input_el=(0,a.select)({class:r.input,name:this.model.name,disabled:this.model.disabled},this.options_el()),this._update_value(),this.input_el.addEventListener(\"change\",(()=>this.change_input())),this.group_el.appendChild(this.input_el)}change_input(){const e=this.input_el.value;this.model.value=e,super.change_input()}_update_value(){const{value:e}=this.model;this._known_values.has(e)?this.input_el.value=e:this.input_el.removeAttribute(\"value\")}}n.SelectView=c,c.__name__=\"SelectView\";class h extends _.InputWidget{constructor(e){super(e)}}n.Select=h,u=h,h.__name__=\"Select\",u.prototype.default_view=c,u.define((({String:e,Array:t,Tuple:n,Dict:s,Or:i})=>{const l=t(i(e,n(e,e)));return{value:[e,\"\"],options:[i(l,s(l)),[]]}}))},\n", - " 480: function _(e,t,r,i,a){i();var o;const s=(0,e(1).__importStar)(e(153)),_=e(458),n=e(8);class c extends _.AbstractSliderView{}r.SliderView=c,c.__name__=\"SliderView\";class d extends _.AbstractSlider{constructor(e){super(e),this.behaviour=\"tap\",this.connected=[!0,!1]}_formatter(e,t){return(0,n.isString)(t)?s.format(e,t):t.compute(e)}}r.Slider=d,o=d,d.__name__=\"Slider\",o.prototype.default_view=c,o.override({format:\"0[.]00\"})},\n", - " 481: function _(e,t,i,n,s){var l;n();const o=e(474),r=e(43),{min:a,max:h,floor:_,abs:u}=Math;function d(e){return _(e)!==e?e.toFixed(16).replace(/0+$/,\"\").split(\".\")[1].length:0}class p extends o.NumericInputView{*buttons(){yield this.btn_up_el,yield this.btn_down_el}initialize(){super.initialize(),this._handles={interval:void 0,timeout:void 0},this._interval=200}connect_signals(){super.connect_signals();const e=this.model.properties;this.on_change(e.disabled,(()=>{for(const e of this.buttons())(0,r.toggle_attribute)(e,\"disabled\",this.model.disabled)}))}render(){super.render(),this.wrapper_el=(0,r.div)({class:\"bk-spin-wrapper\"}),this.group_el.replaceChild(this.wrapper_el,this.input_el),this.btn_up_el=(0,r.button)({class:\"bk-spin-btn bk-spin-btn-up\"}),this.btn_down_el=(0,r.button)({class:\"bk-spin-btn bk-spin-btn-down\"}),this.wrapper_el.appendChild(this.input_el),this.wrapper_el.appendChild(this.btn_up_el),this.wrapper_el.appendChild(this.btn_down_el);for(const e of this.buttons())(0,r.toggle_attribute)(e,\"disabled\",this.model.disabled),e.addEventListener(\"mousedown\",(e=>this._btn_mouse_down(e))),e.addEventListener(\"mouseup\",(()=>this._btn_mouse_up())),e.addEventListener(\"mouseleave\",(()=>this._btn_mouse_leave()));this.input_el.addEventListener(\"keydown\",(e=>this._input_key_down(e))),this.input_el.addEventListener(\"keyup\",(()=>this.model.value_throttled=this.model.value)),this.input_el.addEventListener(\"wheel\",(e=>this._input_mouse_wheel(e))),this.input_el.addEventListener(\"wheel\",function(e,t,i=!1){let n;return function(...s){const l=this,o=i&&void 0===n;void 0!==n&&clearTimeout(n),n=setTimeout((function(){n=void 0,i||e.apply(l,s)}),t),o&&e.apply(l,s)}}((()=>{this.model.value_throttled=this.model.value}),this.model.wheel_wait,!1))}get precision(){const{low:e,high:t,step:i}=this.model,n=d;return h(n(u(null!=e?e:0)),n(u(null!=t?t:0)),n(u(i)))}remove(){this._stop_incrementation(),super.remove()}_start_incrementation(e){clearInterval(this._handles.interval),this._counter=0;const{step:t}=this.model,i=e=>{if(this._counter+=1,this._counter%5==0){const t=Math.floor(this._counter/5);t<10?(clearInterval(this._handles.interval),this._handles.interval=setInterval((()=>i(e)),this._interval/(t+1))):t>=10&&t<=13&&(clearInterval(this._handles.interval),this._handles.interval=setInterval((()=>i(2*e)),this._interval/10))}this.increment(e)};this._handles.interval=setInterval((()=>i(e*t)),this._interval)}_stop_incrementation(){clearTimeout(this._handles.timeout),this._handles.timeout=void 0,clearInterval(this._handles.interval),this._handles.interval=void 0,this.model.value_throttled=this.model.value}_btn_mouse_down(e){e.preventDefault();const t=e.currentTarget===this.btn_up_el?1:-1;this.increment(t*this.model.step),this.input_el.focus(),this._handles.timeout=setTimeout((()=>this._start_incrementation(t)),this._interval)}_btn_mouse_up(){this._stop_incrementation()}_btn_mouse_leave(){this._stop_incrementation()}_input_mouse_wheel(e){if(document.activeElement===this.input_el){e.preventDefault();const t=e.deltaY>0?-1:1;this.increment(t*this.model.step)}}_input_key_down(e){switch(e.keyCode){case r.Keys.Up:return e.preventDefault(),this.increment(this.model.step);case r.Keys.Down:return e.preventDefault(),this.increment(-this.model.step);case r.Keys.PageUp:return e.preventDefault(),this.increment(this.model.page_step_multiplier*this.model.step);case r.Keys.PageDown:return e.preventDefault(),this.increment(-this.model.page_step_multiplier*this.model.step)}}adjust_to_precision(e){return this.bound_value(Number(e.toFixed(this.precision)))}increment(e){const{low:t,high:i}=this.model;null==this.model.value?e>0?this.model.value=null!=t?t:null!=i?a(0,i):0:e<0&&(this.model.value=null!=i?i:null!=t?h(t,0):0):this.model.value=this.adjust_to_precision(this.model.value+e)}change_input(){super.change_input(),this.model.value_throttled=this.model.value}}i.SpinnerView=p,p.__name__=\"SpinnerView\";class m extends o.NumericInput{constructor(e){super(e)}}i.Spinner=m,l=m,m.__name__=\"Spinner\",l.prototype.default_view=p,l.define((({Number:e,Nullable:t})=>({value_throttled:[t(e),null],step:[e,1],page_step_multiplier:[e,10],wheel_wait:[e,100]}))),l.override({mode:\"float\"})},\n", - " 482: function _(e,t,s,n,i){n();const o=e(1);var r;const c=e(444),l=e(43),p=(0,o.__importStar)(e(446));class _ extends c.TextLikeInputView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.rows.change,(()=>this.input_el.rows=this.model.rows)),this.connect(this.model.properties.cols.change,(()=>this.input_el.cols=this.model.cols))}_render_input(){this.input_el=(0,l.textarea)({class:p.input})}render(){super.render(),this.input_el.cols=this.model.cols,this.input_el.rows=this.model.rows}}s.TextAreaInputView=_,_.__name__=\"TextAreaInputView\";class a extends c.TextLikeInput{constructor(e){super(e)}}s.TextAreaInput=a,r=a,a.__name__=\"TextAreaInput\",r.prototype.default_view=_,r.define((({Int:e})=>({cols:[e,20],rows:[e,2]}))),r.override({max_length:500})},\n", - " 483: function _(e,t,s,c,i){c();const o=e(1);var a;const n=e(438),l=e(43),_=(0,o.__importStar)(e(318));class r extends n.AbstractButtonView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.active.change,(()=>this._update_active()))}render(){super.render(),this._update_active()}click(){this.model.active=!this.model.active,super.click()}_update_active(){(0,l.classes)(this.button_el).toggle(_.active,this.model.active)}}s.ToggleView=r,r.__name__=\"ToggleView\";class g extends n.AbstractButton{constructor(e){super(e)}}s.Toggle=g,a=g,g.__name__=\"Toggle\",a.prototype.default_view=r,a.define((({Boolean:e})=>({active:[e,!1]}))),a.override({label:\"Toggle\"})},\n", - " }, 436, {\"models/widgets/main\":436,\"models/widgets/index\":437,\"models/widgets/abstract_button\":438,\"models/widgets/control\":439,\"models/widgets/widget\":508,\"models/widgets/abstract_icon\":441,\"models/widgets/autocomplete_input\":442,\"models/widgets/text_input\":443,\"models/widgets/text_like_input\":444,\"models/widgets/input_widget\":445,\"styles/widgets/inputs.css\":446,\"models/widgets/button\":447,\"models/widgets/checkbox_button_group\":448,\"models/widgets/button_group\":449,\"models/widgets/oriented_control\":450,\"models/widgets/checkbox_group\":451,\"models/widgets/input_group\":452,\"models/widgets/color_picker\":453,\"models/widgets/date_picker\":454,\"styles/widgets/flatpickr.css\":456,\"models/widgets/date_range_slider\":457,\"models/widgets/abstract_slider\":458,\"styles/widgets/sliders.css\":460,\"styles/widgets/nouislider.css\":461,\"models/widgets/date_slider\":462,\"models/widgets/div\":463,\"models/widgets/markup\":464,\"styles/clearfix.css\":465,\"models/widgets/dropdown\":466,\"models/widgets/file_input\":467,\"models/widgets/multiselect\":468,\"models/widgets/paragraph\":469,\"models/widgets/password_input\":470,\"models/widgets/multichoice\":471,\"styles/widgets/choices.css\":473,\"models/widgets/numeric_input\":474,\"models/widgets/pretext\":475,\"models/widgets/radio_button_group\":476,\"models/widgets/radio_group\":477,\"models/widgets/range_slider\":478,\"models/widgets/selectbox\":479,\"models/widgets/slider\":480,\"models/widgets/spinner\":481,\"models/widgets/textarea_input\":482,\"models/widgets/toggle\":483}, {});});\n", - "\n", - " /* END bokeh-widgets.min.js */\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " /* BEGIN bokeh-tables.min.js */\n", - " /*!\n", - " * Copyright (c) 2012 - 2021, Anaconda, Inc., and Bokeh Contributors\n", - " * All rights reserved.\n", - " * \n", - " * Redistribution and use in source and binary forms, with or without modification,\n", - " * are permitted provided that the following conditions are met:\n", - " * \n", - " * Redistributions of source code must retain the above copyright notice,\n", - " * this list of conditions and the following disclaimer.\n", - " * \n", - " * Redistributions in binary form must reproduce the above copyright notice,\n", - " * this list of conditions and the following disclaimer in the documentation\n", - " * and/or other materials provided with the distribution.\n", - " * \n", - " * Neither the name of Anaconda nor the names of any contributors\n", - " * may be used to endorse or promote products derived from this software\n", - " * without specific prior written permission.\n", - " * \n", - " * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n", - " * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n", - " * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n", - " * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n", - " * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n", - " * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n", - " * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n", - " * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n", - " * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n", - " * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF\n", - " * THE POSSIBILITY OF SUCH DAMAGE.\n", - " */\n", - " (function(root, factory) {\n", - " factory(root[\"Bokeh\"], \"2.4.1\");\n", - " })(this, function(Bokeh, version) {\n", - " let define;\n", - " return (function(modules, entry, aliases, externals) {\n", - " const bokeh = typeof Bokeh !== \"undefined\" && (version != null ? Bokeh[version] : Bokeh);\n", - " if (bokeh != null) {\n", - " return bokeh.register_plugin(modules, entry, aliases);\n", - " } else {\n", - " throw new Error(\"Cannot find Bokeh \" + version + \". You have to load it prior to loading plugins.\");\n", - " }\n", - " })\n", - " ({\n", - " 484: function _(t,e,o,r,s){r();const _=(0,t(1).__importStar)(t(485));o.Tables=_;(0,t(7).register_models)(_)},\n", - " 485: function _(g,a,r,e,t){e();const o=g(1);(0,o.__exportStar)(g(486),r),(0,o.__exportStar)(g(489),r),t(\"DataTable\",g(492).DataTable),t(\"TableColumn\",g(510).TableColumn),t(\"TableWidget\",g(509).TableWidget);var n=g(512);t(\"AvgAggregator\",n.AvgAggregator),t(\"MinAggregator\",n.MinAggregator),t(\"MaxAggregator\",n.MaxAggregator),t(\"SumAggregator\",n.SumAggregator);var A=g(513);t(\"GroupingInfo\",A.GroupingInfo),t(\"DataCube\",A.DataCube)},\n", - " 486: function _(e,t,i,s,a){s();const r=e(1);var l,n,u,d,o,p,_,c,h;const E=e(43),V=e(226),m=e(53),f=e(487),v=(0,r.__importStar)(e(488));class w extends V.DOMView{constructor(e){const{model:t,parent:i}=e.column;super(Object.assign({model:t,parent:i},e)),this.args=e,this.initialize(),this.render()}get emptyValue(){return null}initialize(){super.initialize(),this.inputEl=this._createInput(),this.defaultValue=null}async lazy_initialize(){throw new Error(\"unsupported\")}css_classes(){return super.css_classes().concat(v.cell_editor)}render(){super.render(),this.args.container.append(this.el),this.el.appendChild(this.inputEl),this.renderEditor(),this.disableNavigation()}renderEditor(){}disableNavigation(){this.inputEl.addEventListener(\"keydown\",(e=>{switch(e.keyCode){case E.Keys.Left:case E.Keys.Right:case E.Keys.Up:case E.Keys.Down:case E.Keys.PageUp:case E.Keys.PageDown:e.stopImmediatePropagation()}}))}destroy(){this.remove()}focus(){this.inputEl.focus()}show(){}hide(){}position(){}getValue(){return this.inputEl.value}setValue(e){this.inputEl.value=e}serializeValue(){return this.getValue()}isValueChanged(){return!(\"\"==this.getValue()&&null==this.defaultValue)&&this.getValue()!==this.defaultValue}applyValue(e,t){const i=this.args.grid.getData(),s=i.index.indexOf(e[f.DTINDEX_NAME]);i.setField(s,this.args.column.field,t)}loadValue(e){const t=e[this.args.column.field];this.defaultValue=null!=t?t:this.emptyValue,this.setValue(this.defaultValue)}validateValue(e){if(this.args.column.validator){const t=this.args.column.validator(e);if(!t.valid)return t}return{valid:!0,msg:null}}validate(){return this.validateValue(this.getValue())}}i.CellEditorView=w,w.__name__=\"CellEditorView\";class g extends m.Model{}i.CellEditor=g,g.__name__=\"CellEditor\";class x extends w{get emptyValue(){return\"\"}_createInput(){return(0,E.input)({type:\"text\"})}renderEditor(){this.inputEl.focus(),this.inputEl.select()}loadValue(e){super.loadValue(e),this.inputEl.defaultValue=this.defaultValue,this.inputEl.select()}}i.StringEditorView=x,x.__name__=\"StringEditorView\";class y extends g{}i.StringEditor=y,l=y,y.__name__=\"StringEditor\",l.prototype.default_view=x,l.define((({String:e,Array:t})=>({completions:[t(e),[]]})));class I extends w{_createInput(){return(0,E.textarea)()}renderEditor(){this.inputEl.focus(),this.inputEl.select()}}i.TextEditorView=I,I.__name__=\"TextEditorView\";class b extends g{}i.TextEditor=b,n=b,b.__name__=\"TextEditor\",n.prototype.default_view=I;class N extends w{_createInput(){return(0,E.select)()}renderEditor(){for(const e of this.model.options)this.inputEl.appendChild((0,E.option)({value:e},e));this.focus()}}i.SelectEditorView=N,N.__name__=\"SelectEditorView\";class C extends g{}i.SelectEditor=C,u=C,C.__name__=\"SelectEditor\",u.prototype.default_view=N,u.define((({String:e,Array:t})=>({options:[t(e),[]]})));class D extends w{_createInput(){return(0,E.input)({type:\"text\"})}}i.PercentEditorView=D,D.__name__=\"PercentEditorView\";class S extends g{}i.PercentEditor=S,d=S,S.__name__=\"PercentEditor\",d.prototype.default_view=D;class k extends w{_createInput(){return(0,E.input)({type:\"checkbox\"})}renderEditor(){this.focus()}loadValue(e){this.defaultValue=!!e[this.args.column.field],this.inputEl.checked=this.defaultValue}serializeValue(){return this.inputEl.checked}}i.CheckboxEditorView=k,k.__name__=\"CheckboxEditorView\";class z extends g{}i.CheckboxEditor=z,o=z,z.__name__=\"CheckboxEditor\",o.prototype.default_view=k;class P extends w{_createInput(){return(0,E.input)({type:\"text\"})}renderEditor(){this.inputEl.focus(),this.inputEl.select()}remove(){super.remove()}serializeValue(){var e;return null!==(e=parseInt(this.getValue(),10))&&void 0!==e?e:0}loadValue(e){super.loadValue(e),this.inputEl.defaultValue=this.defaultValue,this.inputEl.select()}validateValue(e){return isNaN(e)?{valid:!1,msg:\"Please enter a valid integer\"}:super.validateValue(e)}}i.IntEditorView=P,P.__name__=\"IntEditorView\";class T extends g{}i.IntEditor=T,p=T,T.__name__=\"IntEditor\",p.prototype.default_view=P,p.define((({Int:e})=>({step:[e,1]})));class K extends w{_createInput(){return(0,E.input)({type:\"text\"})}renderEditor(){this.inputEl.focus(),this.inputEl.select()}remove(){super.remove()}serializeValue(){var e;return null!==(e=parseFloat(this.getValue()))&&void 0!==e?e:0}loadValue(e){super.loadValue(e),this.inputEl.defaultValue=this.defaultValue,this.inputEl.select()}validateValue(e){return isNaN(e)?{valid:!1,msg:\"Please enter a valid number\"}:super.validateValue(e)}}i.NumberEditorView=K,K.__name__=\"NumberEditorView\";class A extends g{}i.NumberEditor=A,_=A,A.__name__=\"NumberEditor\",_.prototype.default_view=K,_.define((({Number:e})=>({step:[e,.01]})));class M extends w{_createInput(){return(0,E.input)({type:\"text\"})}}i.TimeEditorView=M,M.__name__=\"TimeEditorView\";class O extends g{}i.TimeEditor=O,c=O,O.__name__=\"TimeEditor\",c.prototype.default_view=M;class F extends w{_createInput(){return(0,E.input)({type:\"text\"})}get emptyValue(){return new Date}renderEditor(){this.inputEl.focus(),this.inputEl.select()}destroy(){super.destroy()}show(){super.show()}hide(){super.hide()}position(){return super.position()}getValue(){}setValue(e){}}i.DateEditorView=F,F.__name__=\"DateEditorView\";class L extends g{}i.DateEditor=L,h=L,L.__name__=\"DateEditor\",h.prototype.default_view=F},\n", - " 487: function _(_,n,i,t,d){t(),i.DTINDEX_NAME=\"__bkdt_internal_index__\"},\n", - " 488: function _(e,l,o,t,r){t(),o.root=\"bk-root\",o.data_table=\"bk-data-table\",o.cell_special_defaults=\"bk-cell-special-defaults\",o.cell_select=\"bk-cell-select\",o.cell_index=\"bk-cell-index\",o.header_index=\"bk-header-index\",o.cell_editor=\"bk-cell-editor\",o.cell_editor_completion=\"bk-cell-editor-completion\",o.default='.bk-root .bk-data-table{box-sizing:content-box;font-size:11px;}.bk-root .bk-data-table input[type=\"checkbox\"]{margin-left:4px;margin-right:4px;}.bk-root .bk-cell-special-defaults{border-right-color:silver;border-right-style:solid;background:#f5f5f5;}.bk-root .bk-cell-select{border-right-color:silver;border-right-style:solid;background:#f5f5f5;}.bk-root .slick-cell.bk-cell-index{border-right-color:silver;border-right-style:solid;background:#f5f5f5;text-align:right;background:#f0f0f0;color:#909090;}.bk-root .bk-header-index .slick-column-name{float:right;}.bk-root .slick-row.selected .bk-cell-index{background-color:transparent;}.bk-root .slick-row.odd{background:#f0f0f0;}.bk-root .slick-cell{padding-left:4px;padding-right:4px;border-right-color:transparent;border:0.25px solid transparent;}.bk-root .slick-cell .bk{line-height:inherit;}.bk-root .slick-cell.active{border-style:dashed;}.bk-root .slick-cell.selected{background-color:#F0F8FF;}.bk-root .slick-cell.editable{padding-left:0;padding-right:0;}.bk-root .bk-cell-editor{display:contents;}.bk-root .bk-cell-editor input,.bk-root .bk-cell-editor select{width:100%;height:100%;border:0;margin:0;padding:0;outline:0;background:transparent;vertical-align:baseline;}.bk-root .bk-cell-editor input{padding-left:4px;padding-right:4px;}.bk-root .bk-cell-editor-completion{font-size:11px;}'},\n", - " 489: function _(t,e,r,n,a){n();const o=t(1);var l,s,i,c,u,m;const _=(0,o.__importDefault)(t(151)),d=(0,o.__importStar)(t(153)),f=t(490),g=t(43),h=t(20),F=t(8),p=t(34),b=t(22),S=t(53);class x extends S.Model{constructor(t){super(t)}doFormat(t,e,r,n,a){return null==r?\"\":`${r}`.replace(/&/g,\"&\").replace(//g,\">\")}}r.CellFormatter=x,x.__name__=\"CellFormatter\";class M extends x{constructor(t){super(t)}doFormat(t,e,r,n,a){const{font_style:o,text_align:l,text_color:s}=this,i=(0,g.div)(null==r?\"\":`${r}`);switch(o){case\"bold\":i.style.fontWeight=\"bold\";break;case\"italic\":i.style.fontStyle=\"italic\"}return 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