Update to v0.14.0

Author: dprohe

docs/source/example_problems/airplane_modal_test.rst

@@ -849,7 +849,7 @@ function wants as its arguments.  The first argument is ``sensors_to_keep``,
 which is the number we just defined.  The second is the ``shape_matrix``.
 Reading the documentation, this ``shape_matrix`` should have its first dimension
 corresponding to each sensor (here a sensor could be a channel or group of
-channels for a triaxial accelerometer) and its last dimension be correspond to
+channels for a triaxial accelerometer) and its last dimension correspond to
 each target mode.  We will therefore want to set up a matrix with shape 
 (``candidate_node_ids.size`` x 3 x ``target_shapes.size``).  This way, the
 target shape dimension is last, and the group of channels corresponding to each
@@ -1726,29 +1726,29 @@ look through all the functions to ensure they look right.
   :width: 600
   :alt: Drive points used for a MIMO modal test.
 
-Fitting Modes using PolyMax
----------------------------
+Fitting Modes using PolyPy
+--------------------------
 
 Now that we have frequency response functions created, we can fit modes to them.
 SDynPy has two mode fitters implemented,
-:py:class:`PolyMax<sdynpy.modal.sdynpy_polymax.PolyMax_GUI>` and 
+:py:class:`PolyPy<sdynpy.modal.sdynpy_polypy.PolyPy_GUI>` and 
 :py:class:`SMAC<sdynpy.modal.sdynpy_smac.SMAC_GUI>`.  Both curve fitters can
 be used via graphical user interface or via Python commands if it is desirable
 to automate the curve fitting.  This example will use the
-:py:class:`sdpy.PolyMax_GUI<sdynpy.modal.sdynpy_polymax.PolyMax_GUI>` approach.
+:py:class:`sdpy.PolyPy_GUI<sdynpy.modal.sdynpy_polypy.PolyPy_GUI>` approach.
 
-Running PolyMax
+Running PolyPy
 ~~~~~~~~~~~~~~~
 
-We open the PolyMax GUI by initializing the
-:py:class:`sdpy.PolyMax_GUI<sdynpy.modal.sdynpy_polymax.PolyMax_GUI>` class
+We open the PolyPy GUI by initializing the
+:py:class:`sdpy.PolyPy_GUI<sdynpy.modal.sdynpy_polypy.PolyPy_GUI>` class
 with our frequency response function dataset ``frf_sampled``
 
 .. code-block:: python
 
     # Now that we have FRFs we can go fit modes.  We will first look at using
-    # PolyMax
-    pm = sdpy.PolyMax_GUI(frf_sampled)
+    # PolyPy
+    pm = sdpy.PolyPy_GUI(frf_sampled)
     
 The initial screen shows mode indicator functions, as well as options for
 computing the initial stabilization diagram.  We can see from the shown
@@ -1757,25 +1757,25 @@ modes.  We can drag the frequency region on the figure to select the frequency
 range of interest, set the polynomial orders, and press the button to compute
 the stabilization curve.
 
-.. image:: figures/airplane_polymax_stabilization.png
+.. image:: figures/airplane_polypy_stabilization.png
   :width: 600
-  :alt: Setting up parameters to compute the stabilization plot in PolyMax
+  :alt: Setting up parameters to compute the stabilization plot in PolyPy
 
 Once the stabilization plot is computed, stable poles can be selected by
 clicking on them in the stabilization plot.  Once all poles are selected,
 shapes can be computed.
 
-.. image:: figures/airplane_polymax_stabilization_selection.png
+.. image:: figures/airplane_polypy_stabilization_selection.png
   :width: 600
-  :alt: Selecting poles on the stabilization plot in PolyMax
+  :alt: Selecting poles on the stabilization plot in PolyPy
 
-The final tab of the PolyMax implementation allows you to see how well the
+The final tab of the PolyPy implementation allows you to see how well the
 modes fit to the measured frequency response data.  On this page, modes can be
 saved to a file.
 
-.. image:: figures/airplane_polymax_stabilization_resynthesis.png
+.. image:: figures/airplane_polypy_stabilization_resynthesis.png
   :width: 600
-  :alt: Visualizing the modal fits in PolyMax
+  :alt: Visualizing the modal fits in PolyPy
 
 Comparing Test and Finite Element Modes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1789,15 +1789,15 @@ function.
 
 .. code-block:: python
 
-    # In the PolyMax GUI we saved the shapes to disk, so we will now load them.
-    test_shapes_polymax = sdpy.shape.load('shapes_polymax.npy')
+    # In the PolyPy GUI we saved the shapes to disk, so we will now load them.
+    test_shapes_polypy = sdpy.shape.load('shapes_polypy.npy')
     
     # Let's compare the shapes to the finite element model shapes
-    mac = sdpy.shape.mac(test_shapes,test_shapes_polymax)
+    mac = sdpy.shape.mac(test_shapes,test_shapes_polypy)
     sdpy.correlation.matrix_plot(
         mac,text_size=6)
     shape_correspondences = np.where(mac > 0.9)
-    shape_1 = test_shapes_polymax[shape_correspondences[1]]
+    shape_1 = test_shapes_polypy[shape_correspondences[1]]
     shape_2 = test_shapes[shape_correspondences[0]]
     print(sdpy.shape.shape_comparison_table(shape_1, shape_2,
                                             percent_error_format='{:0.4f}%'))
@@ -1901,14 +1901,14 @@ and global finite element geometry are handled automatically by SDynPy.
 
     # Perform the expansion using the finite element shapes in the bandwidth
     expansion_basis = shapes_global[shapes_global.frequency < shape_bandwidth]
-    expanded_shapes = test_shapes_polymax.expand(test_geometry,geometry_global,
+    expanded_shapes = test_shapes_polypy.expand(test_geometry,geometry_global,
                                                  expansion_basis)
     # We can then plot the expanded shapes on the original finite element geometry
     geometry_global.plot_shape(expanded_shapes,plot_options)
     
     # Or overlay the geometries and shapes
     expansion_comparison_geometry,expansion_comparison_shapes = sdpy.shape.overlay_shapes(
-        (test_geometry,geometry_global),(test_shapes_polymax,expanded_shapes),[1,7])
+        (test_geometry,geometry_global),(test_shapes_polypy,expanded_shapes),[1,7])
     expansion_comparison_geometry.plot_shape(expansion_comparison_shapes,plot_options,
                                              deformed_opacity=0.5,undeformed_opacity=0)
                                              

…gures/airplane_polymax_stabilization.png …igures/airplane_polypy_stabilization.pngdocs/source/example_problems/figures/airplane_polymax_stabilization.png renamed to docs/source/example_problems/figures/airplane_polypy_stabilization.png docs/source/example_problems/figures/airplane_polymax_stabilization.png renamed to docs/source/example_problems/figures/airplane_polypy_stabilization.png

@@ -445,7 +445,7 @@ Fitting Modes to the FRFs
 -------------------------
 
 Now that we have data and geometry, we can perform experimental modal analysis.
-We will fit modes both with SMAC and PolyMax.  First we will do SMAC, so we can
+We will fit modes both with SMAC and PolyPy.  First we will do SMAC, so we can
 call :py:class:`sdpy.SMAC_GUI<sdynpy.modal.sdynpy_smac.SMAC_GUI>` in the
 IPython console to load the GUI.
 
@@ -508,39 +508,39 @@ The MAC can also be plotted to visualize the independence of the shapes.
 Shapes are saved to the file :code:`'rattlesnake_test_shapes_smac.npy'` so they
 can be loaded into the script without re-running SMAC.
 
-Similarly, PolyMax can be run by calling the :py:class:`sdpy.PolyMax_GUI<sdynpy.modal.sdynpy_polymax.PolyMax_GUI>`
+Similarly, PolyPy can be run by calling the :py:class:`sdpy.PolyPy_GUI<sdynpy.modal.sdynpy_polypy.PolyPy_GUI>`
 class from the IPython console.
 
 .. code-block:: console
 
-    In [13]: sdynpy.PolyMax_GUI(frfs)
-    Out[13]: <sdynpy.modal.sdynpy_polymax.PolyMax_GUI at 0x14fff4c5d30>
+    In [13]: sdynpy.PolyPy_GUI(frfs)
+    Out[13]: <sdynpy.modal.sdynpy_polypy.PolyPy_GUI at 0x14fff4c5d30>
     
-The first page of the SDynPy PolyMax implementation allows users to select the
+The first page of the SDynPy PolyPy implementation allows users to select the
 frequency range of the analysis.  Also selected is the number of poles to use
 in the polynomial fitter.
 
-.. image:: figures/sdynpy_demo_polymax_setup.png
+.. image:: figures/sdynpy_demo_polypy_setup.png
   :width: 600
-  :alt: Initial setup for PolyMax frequency range
+  :alt: Initial setup for PolyPy frequency range
 
 Once the poles are solved, a stability plot is presented.  The user can click
 on individual icons on the stability plot to select given poles to use in the
 resynthesis.
 
-.. image:: figures/sdynpy_demo_polymax_stability_plot.png
+.. image:: figures/sdynpy_demo_polypy_stability_plot.png
   :width: 600
-  :alt: PolyMax stability plot
+  :alt: PolyPy stability plot
 
 With the poles selected, shapes can be computed.  Resynthesized FRFs are plotted
 for the user to investigate.
 
-.. image:: figures/sdynpy_demo_polymax_resynthesize.png
+.. image:: figures/sdynpy_demo_polypy_resynthesize.png
   :width: 600
-  :alt: PolyMax resynthesis
+  :alt: PolyPy resynthesis
 
-Shapes are saved to the file :code:`'rattlesnake_test_shapes_polymax.npy'` so they
-can be loaded into the script without re-running PolyMax.
+Shapes are saved to the file :code:`'rattlesnake_test_shapes_polypy.npy'` so they
+can be loaded into the script without re-running PolyPy.
 
 Analyzing Modal Parameters
 --------------------------
@@ -553,7 +553,7 @@ shape files into our script for analysis using the
 
     # Load the shape files from our curve fitting into the script
     test_shapes_smac = sdpy.shape.load('rattlesnake_test_shapes_smac.npy')
-    test_shapes_polymax = sdpy.shape.load('rattlesnake_test_shapes_smac.npy')
+    test_shapes_polypy = sdpy.shape.load('rattlesnake_test_shapes_smac.npy')
     
 We also want to extract finite element shapes from the Exodus file using the
 :py:func:`sdpy.shape.from_exodus<sdynpy.core.sdynpy_shape.ShapeArray.from_exodus>`
@@ -584,7 +584,7 @@ finite element shapes reduced to the test degrees of freedom.
     response_dofs = test_shapes_smac[0].coordinate
     # Can extract a shape matrix by indexing the shape arrays with a CoordinateArray
     test_shape_matrix_smac = test_shapes_smac[response_dofs].T
-    test_shape_matrix_polymax = test_shapes_smac[response_dofs].T
+    test_shape_matrix_polypy = test_shapes_smac[response_dofs].T
     test_shape_matrix_fem = fem_shapes[response_dofs].T
     # Create an actual ShapeArray object
     fem_shapes_reduced = sdpy.shape_array(response_dofs,
@@ -601,11 +601,11 @@ function that provides a nice visualization of the Modal Assurance Criterion mat
 .. code-block:: python
 
     # Look at correlation between shapes from different tests/analyses
-    mac_smac_polymax = sdpy.correlation.mac(test_shape_matrix_smac,test_shape_matrix_polymax)
+    mac_smac_polypy = sdpy.correlation.mac(test_shape_matrix_smac,test_shape_matrix_polypy)
     mac_smac_fem = sdpy.correlation.mac(test_shape_matrix_smac,test_shape_matrix_fem)
-    mac_polymax_fem = sdpy.correlation.mac(test_shape_matrix_polymax,test_shape_matrix_fem)
+    mac_polypy_fem = sdpy.correlation.mac(test_shape_matrix_polypy,test_shape_matrix_fem)
     # Plot the mac matrix between the 
-    sdpy.correlation.matrix_plot(mac_polymax_fem)
+    sdpy.correlation.matrix_plot(mac_polypy_fem)
 
 .. image:: figures/sdynpy_demo_mac_test_fem.png
   :width: 600
@@ -625,11 +625,11 @@ of the first mode to a :code:`.gif` file.
 
     # Compare shapes by overlaying them
     # Find the best match in the FEM to each of the test shapes
-    fem_matches = np.argmax(mac_polymax_fem,axis=1)
+    fem_matches = np.argmax(mac_polypy_fem,axis=1)
     # Overlay the shapes and geometry
     combined_geometry,combined_shapes = sdpy.shape.overlay_shapes(
         (test_geometry,test_geometry),
-        (test_shapes_polymax,fem_shapes_reduced[fem_matches]),
+        (test_shapes_polypy,fem_shapes_reduced[fem_matches]),
         color_override=[1,11])
     # Plot the shapes to set up the animation
     geometry_kwargs = {'view_up':[0,1,0],'line_width':5,'node_size':8}
@@ -679,7 +679,7 @@ slide that does not have a subtitle placeholder on it.
                                   title='BARC Modal with Rattlesnake',
                                   subtitle='A SDynPy Demonstration',
                                   geometry = test_geometry,
-                                  shapes = test_shapes_polymax,
+                                  shapes = test_shapes_polypy,
                                   frfs=frfs,
                                   geometry_plot_kwargs = geometry_kwargs,
                                   content_slide_layout_index=2,
@@ -724,7 +724,7 @@ to allow animation of the shapes inside the exported PDF.
     sdpy.doc.create_latex_summary(
         figure_basename='figures/rattlesnake_test.png', 
         geometry = test_geometry, 
-        shapes = test_shapes_polymax, 
+        shapes = test_shapes_polypy, 
         frfs = frfs,
         output_file = 'rattlesnake_test_memo_content.tex',
         geometry_plot_kwargs = geometry_kwargs)
@@ -798,7 +798,7 @@ shapes should be included in the expansion.
 
 .. code-block:: python
 
-    q = np.linalg.lstsq(test_shape_matrix_fem[:,:14],test_shape_matrix_polymax)[0]
+    q = np.linalg.lstsq(test_shape_matrix_fem[:,:14],test_shape_matrix_polypy)[0]
 
 We then want to multiply this coefficient by the full finite element shapes to
 estimate what the test shapes would look like in the full finite element space.
@@ -824,7 +824,7 @@ us to fill that information in with the test frequencies.
     # Linearly combine the existing shapes into new shapes
     fexo_repack = fexo.repack(q)
     # Fill in the abscissa data (frequencies in this case)
-    fexo_repack.time = test_shapes_polymax.frequency
+    fexo_repack.time = test_shapes_polypy.frequency
     
 If we want to plot the new shapes in SDynPy, we could load the shapes back into
 a :py:class:`sdpy.ShapeArray<sdynpy.core.sdynpy_shape.ShapeArray>` object, and
@@ -834,11 +834,11 @@ even combine with the finite element shapes for comparison.
 
     # Plot expanded shapes against finite element shapes
     # Load shapes into sdynpy ShapeArray object
-    expanded_shapes_polymax = sdpy.shape.from_exodus(fexo_repack)
+    expanded_shapes_polypy = sdpy.shape.from_exodus(fexo_repack)
     # combine the shapes
     combined_expanded_geometry,combined_expanded_shapes = sdpy.shape.overlay_shapes(
         (fem_geometry,fem_geometry),
-        (expanded_shapes_polymax,fem_shapes[fem_matches]),color_override=[1,11])
+        (expanded_shapes_polypy,fem_shapes[fem_matches]),color_override=[1,11])
     # Plot the shapes overlaid
     plotter = combined_expanded_geometry.plot_shape(
         combined_expanded_shapes,{'view_up':[0,1,0],'node_size':0},
@@ -855,11 +855,11 @@ to visualize in external software.
 .. code-block:: python
 
     # Create an ExodusInMemory object from sdynpy objects
-    fexo_out = sdpy.ExodusInMemory.from_sdynpy(test_geometry,test_shapes_polymax)
+    fexo_out = sdpy.ExodusInMemory.from_sdynpy(test_geometry,test_shapes_polypy)
     # Write it to a file
     fexo_out.write_to_file('rattlesnake_test_output.exo',clobber=True)
     # Repeat with the expanded data
-    fexo_out = sdpy.ExodusInMemory.from_sdynpy(fem_geometry,expanded_shapes_polymax)
+    fexo_out = sdpy.ExodusInMemory.from_sdynpy(fem_geometry,expanded_shapes_polypy)
     fexo_out.write_to_file('rattlesnake_test_output_expanded.exo',clobber=True)
 
 The Exodus files can then be loaded into an external software such as Paraview

…ne_polymax_stabilization_resynthesis.png …ane_polypy_stabilization_resynthesis.pngdocs/source/example_problems/figures/airplane_polymax_stabilization_resynthesis.png renamed to docs/source/example_problems/figures/airplane_polypy_stabilization_resynthesis.png docs/source/example_problems/figures/airplane_polymax_stabilization_resynthesis.png renamed to docs/source/example_problems/figures/airplane_polypy_stabilization_resynthesis.png

@@ -13,7 +13,7 @@
 Welcome to SDynPy's documentation!
 ==================================
 
-|documentation| |build| |codecov| |coveralls| |codefactor| |pylint| |docker|
+|docs| |tests| |coverage| |lint| |version|
 
 **SDynPy** is a package for performing structural dynamic analyses using Python.
 It contains several objects that represent various structural dynamics data
@@ -30,10 +30,11 @@ an overview of recommended workflows and a summary of current functionality.
 Information
 ***********
 
-- `Documentation <https://sandialabs.github.io/sdynpy/>`_
-- `Project <https://github.com/sandialabs/sdynpy>`_
-- `Releases <https://github.com/sandialabs/sdynpy/releases>`_
-- `Tutorial <https://sandialabs.github.io/sdynpy/sdynpy_showcase.html>`_
+- `Contributing <https://cee-gitlab.sandia.gov/structMechTools/structural-dynamics-python-libraries/-/blob/main/CONTRIBUTING.rst>`_
+- `Documentation <http://structmechtools.cee-gitlab.lan/structural-dynamics-python-libraries/>`_
+- `Project <https://cee-gitlab.sandia.gov/structMechTools/structural-dynamics-python-libraries/>`_
+- `Releases <https://cee-gitlab.sandia.gov/structMechTools/structural-dynamics-python-libraries/-/releases>`_
+- `Tutorial <http://structmechtools.cee-gitlab.lan/structural-dynamics-python-libraries/sdynpy_showcase.html>`_
 
 .. toctree::
    :maxdepth: 2
@@ -56,23 +57,21 @@ Indices and tables
 ..
     Badges ========================================================================
 
-.. |documentation| image:: https://img.shields.io/github/actions/workflow/status/sandialabs/sdynpy/pages.yml?branch=main&label=Documentation
-    :target: https://sandialabs.github.io/sdynpy/
+.. |docs| image:: https://cee-gitlab.sandia.gov/structMechTools/structural-dynamics-python-libraries/-/jobs/artifacts/main/raw/badges/docs.svg?job=pages
+    :target: http://structmechtools.cee-gitlab.lan/structural-dynamics-python-libraries/
+    :alt: docs
 
-.. |build| image:: https://img.shields.io/github/actions/workflow/status/sandialabs/sdynpy/main.yml?branch=main&label=GitHub&logo=github
-    :target: https://github.com/sandialabs/sdynpy
+.. |tests| image:: https://cee-gitlab.sandia.gov/structMechTools/structural-dynamics-python-libraries/-/jobs/artifacts/main/raw/badges/tests.svg?job=basic-tests
+    :target: https://cee-gitlab.sandia.gov/structMechTools/structural-dynamics-python-libraries/-/jobs/artifacts/main/raw/logs/report.xml?job=basic-tests
+    :alt: tests
 
-.. |pylint| image:: https://raw.githubusercontent.com/sandialabs/sdynpy/gh-pages/pylint.svg
-    :target: https://github.com/sandialabs/sdynpy
+.. |coverage| image:: https://cee-gitlab.sandia.gov/structMechTools/structural-dynamics-python-libraries/badges/main/coverage.svg
+    :target: https://cee-gitlab.sandia.gov/structMechTools/structural-dynamics-python-libraries/-/pipelines/latest
+    :alt: coverage
 
-.. |coveralls| image:: https://img.shields.io/coveralls/github/sandialabs/sdynpy?logo=coveralls&label=Coveralls
-    :target: https://coveralls.io/github/sandialabs/sdynpy?branch=main
+.. |lint| image:: https://cee-gitlab.sandia.gov/structMechTools/structural-dynamics-python-libraries/-/jobs/artifacts/main/raw/badges/lint.svg?job=static-code-checks
+    :target: https://cee-gitlab.sandia.gov/structMechTools/structural-dynamics-python-libraries/-/jobs/artifacts/main/raw/logs/lint.log?job=static-code-checks
+    :alt: lint
 
-.. |codecov| image:: https://img.shields.io/codecov/c/github/sandialabs/sdynpy?label=Codecov&logo=codecov
-    :target: https://codecov.io/gh/sandialabs/sdynpy
-
-.. |codefactor| image:: https://img.shields.io/codefactor/grade/github/sandialabs/sdynpy?label=Codefactor&logo=codefactor
-   :target: https://www.codefactor.io/repository/github/sandialabs/sdynpy
-
-.. |docker| image:: https://img.shields.io/docker/v/dprohe/sdynpy?color=0db7ed&label=Docker%20Hub&logo=docker&logoColor=0db7ed
-    :target: https://hub.docker.com/r/dprohe/sdynpy
+.. |version| image:: https://cee-gitlab.sandia.gov/structMechTools/structural-dynamics-python-libraries/-/jobs/artifacts/main/raw/badges/version.svg?job=badges
+    :target: https://cee-gitlab.sandia.gov/structMechTools/structural-dynamics-python-libraries/-/releases