diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml new file mode 100644 index 0000000..830855e --- /dev/null +++ b/.github/workflows/lint.yaml @@ -0,0 +1,34 @@ +# This file will lint the specified files/directories. + +name: lint + +on: + push: + branches: [main] + pull_request: + workflow_dispatch: + +jobs: + lint: + runs-on: ubuntu-latest + steps: + + - name: Check out repository + uses: actions/checkout@v4 + + - name: Install python and dependencies + uses: actions/setup-python@v4 + with: + python-version: '3.13' + cache: 'pip' + - run: pip install -r requirements.txt + + - name: Lint model code + run: | + flake8 ./simulation + pylint ./simulation + + - name: Lint tests + run: | + flake8 ./tests + pylint ./tests diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml index 7530402..40c777d 100644 --- a/.github/workflows/tests.yaml +++ b/.github/workflows/tests.yaml @@ -1,4 +1,4 @@ -# This file runs the tests in `tests/` with every push to GitHub +# This file runs the tests in `tests/` # They will run in parallel, and run on three operating systems: # Ubuntu, Windows and Mac. @@ -6,6 +6,8 @@ name: tests on: push: + branches: [main] + pull_request: workflow_dispatch: jobs: diff --git a/.pylintrc b/.pylintrc new file mode 100644 index 0000000..c1b664a --- /dev/null +++ b/.pylintrc @@ -0,0 +1,4 @@ +[MESSAGES CONTROL] +disable= + too-few-public-methods, + too-many-instance-attributes \ No newline at end of file diff --git a/CITATION.cff b/CITATION.cff index 6698525..1200eff 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -3,8 +3,7 @@ cff-version: 1.2.0 title: >- - Simple Reproducible Python Discrete-Event Simulation (DES) - Template + Python DES RAP Template message: >- If you use this software, please cite it using the metadata from this file. @@ -18,8 +17,8 @@ authors: repository-code: >- https://github.com/pythonhealthdatascience/rap_template_python_des abstract: >- - Template reproducible analytical pipeline (RAP) for simple - python DES model. + A template for creating discrete-event simulation (DES) models in Python + within a reproducible analytical pipeline (RAP). license: MIT # TODO: Manually update with each GitHub release version: '0.1.0' diff --git a/README.md b/README.md index 58523a3..6aa5a16 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,14 @@
-# Simple Reproducible Python
Discrete-Event Simulation (DES) Template +# Python DES RAP Template [![python](https://img.shields.io/badge/-Python_3.13-306998?logo=python&logoColor=white)](https://www.python.org/) [![licence](https://img.shields.io/badge/Licence-MIT-green.svg?labelColor=gray)](https://github.com/pythonhealthdatascience/rap_template_python_des/blob/main/LICENSE) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14622466.svg)](https://doi.org/10.5281/zenodo.14622466) [![Tests](https://github.com/pythonhealthdatascience/rap_template_python_des/actions/workflows/tests.yaml/badge.svg)](https://github.com/pythonhealthdatascience/rap_template_python_des/actions/workflows/tests.yaml) -A simple template for creating DES models in Python, within a **reproducible analytical pipeline (RAP)**
+
A template for creating **discrete-event simulation (DES)** models in Python
+within a **reproducible analytical pipeline (RAP)**.

Click on Use this template to initialise new repository.
A `README` template is provided at the **end of this file**. @@ -41,9 +42,9 @@ This repository provides a template for building discrete-event simulation (DES) ♻️ **Reproducible:** This template is designed to function as a RAP. It adheres to reproducibility recommendations from: * ["Levels of RAP" framework](https://nhsdigital.github.io/rap-community-of-practice/introduction_to_RAP/levels_of_RAP/) from the NHS RAP Community of Practice (`docs/nhs_rap.md`). -* Recommendations from [Heather et al. 2025](TODO:ADDLINK) "*On the reproducibility of discrete-event simulation studies in health research: an empirical study using open models*" (`docs/heather_2025.md`). +* Recommendations from [Heather et al. 2025](https://doi.org/10.48550/arXiv.2501.13137) "*On the reproducibility of discrete-event simulation studies in health research: an empirical study using open models*" (`docs/heather_2025.md`). -🚀 **Extendable:** This template adapts from and complements material from Sammi Rosser and Dan Chalk (2024) ["HSMA - the little book of DES"](https://github.com/hsma-programme/hsma6_des_book). The book includes additional advanced features that can be used to extend the model in this template, including: +🚀 **Extendable:** This template adapts from Sammi Rosser and Dan Chalk (2024) ["HSMA - the little book of DES"](https://github.com/hsma-programme/hsma6_des_book). The book includes additional advanced features that can be used to extend the model in this template, including: * Multiple activities * Branching paths @@ -149,6 +150,12 @@ To run tests, ensure environment is active and located in main directory (i.e. p pytest ``` +To run tests in parallel - + +``` +pytest -n auto +``` + The repository contains a GitHub action `tests.yaml` which will automatically run tests with new commits to GitHub. This is continuous integration, helping to catch bugs early and keep the code stable. It will run the tests on three operating systems: Ubuntu, Windows and Mac. If you have changed the model behaviour, you may wish to amend, remove or write new tests. @@ -184,10 +191,12 @@ This section describes the purposes of each class in the simulation. * `generate_patient_arrivals()` to handle patient creation, then sending them on to `attend_clinic()`. * `interval_audit()` to record utilisation and wait times at specified intervals during the simulation. -**Trial Class Usage:** +**Runner Class Usage:** + +Having set up `experiment = Runner()`... -* **Single Run:** Use `trial.run_single()` to execute a single model run. -* **Multiple Runs:** Use `trial.run_trial()` to perform multiple replications of the model. +* **Single Run:** Use `experiment.run_single()` to execute a single model run. +* **Multiple Runs:** Use `experiment.run_reps()` to perform multiple replications of the model.
@@ -210,6 +219,7 @@ repo/ ├── simulation/ # Local package containing code for the DES model ├── tests/ # Unit and back testing of the DES model ├── .gitignore # Untracked files +├── .pylintrc # Pylint settings ├── CHANGELOG.md # Describes changes between releases ├── CITATION.cff # How to cite the repository ├── CONTRIBUTING.md # Contribution instructions @@ -230,6 +240,8 @@ The overall run time will vary depending on how the template model is used. A fe * `choosing_parameters.ipynb` - 22s * `generate_exp_results.ipynb` - 0s + + These times were obtained on an Intel Core i7-12700H with 32GB RAM running Ubuntu 24.04.1 Linux.
diff --git a/docs/heather_2025.md b/docs/heather_2025.md index 4bf6ffc..cd74a26 100644 --- a/docs/heather_2025.md +++ b/docs/heather_2025.md @@ -1,6 +1,6 @@ # Reproducibility recommendations from Heather et al. 2025 -As part of the project STARS (Sharing Tools and Artefacts for Reproducible Simulations), a series of computational reproducibility assessments were conducted by Heather et al. 2025 (**TODO: add DOI of pre-print**). From these, several recommendations were shared to support reproducibility of healthcare discrete-event simulation (DES) models. These are copied below. Those marked with a star (⭐) were identified as having the greatest impact in Heather et al. 2025. +As part of the project STARS (Sharing Tools and Artefacts for Reproducible Simulations), a series of computational reproducibility assessments were conducted by [Heather et al. 2025](https://doi.org/10.48550/arXiv.2501.13137). From these, several recommendations were shared to support reproducibility of healthcare discrete-event simulation (DES) models. These are copied below. Those marked with a star (⭐) were identified as having the greatest impact in Heather et al. 2025. ## Recommendations to support reproduction diff --git a/docs/hsma_changes.md b/docs/hsma_changes.md index 26eb513..7508b8b 100644 --- a/docs/hsma_changes.md +++ b/docs/hsma_changes.md @@ -132,9 +132,9 @@ param.patient_inter = 10 model = Model(param) ``` -## Saving trial results +## Saving replication results -To save trial-level results in the HSMA models, an empty dataframe is initialised during the __init__ method of the `Trial` class. +To save results from each run in the HSMA models, an empty dataframe is initialised during the __init__ method of the `Trial` class (equivalent to the `Runner` class in this template). ``` self.df_trial_results = pd.DataFrame() @@ -164,7 +164,7 @@ In the template, results are instead saved as a dictionary into a list as the ru Also, some of the calculations have been performed directly during the `run_single()` method, instead of from a seperate method `calculate_run_results()`. This is to help simplify the code, as it makes clear how each metric was calculated in one place, rather than needing to refer elsewhere. ``` -trial_results = { +run_results = { 'run_number': run, 'scenario': self.param.scenario_name, 'arrivals': len(patient_results), @@ -176,7 +176,7 @@ trial_results = { ... -self.trial_results_df = pd.DataFrame(trial_results_list) +self.run_results_df = pd.DataFrame(run_results_list) ``` ## Extra features diff --git a/docs/nhs_rap.md b/docs/nhs_rap.md index 2250234..0b05fa1 100644 --- a/docs/nhs_rap.md +++ b/docs/nhs_rap.md @@ -15,7 +15,7 @@ RAP fundamentals offering resilience against future change. | Data produced by code in an open-source language (e.g., Python, R, SQL). | ✅ | Python | | Code is version controlled (see [Git basics](https://nhsdigital.github.io/rap-community-of-practice/training_resources/git/introduction-to-git/) and [using Git collaboratively](https://nhsdigital.github.io/rap-community-of-practice/training_resources/git/using-git-collaboratively/) guides). | ✅ | [GitHub](https://github.com/pythonhealthdatascience/rap_template_python_des/) | | Repository includes a README.md file (or equivalent) that clearly details steps a user must follow to reproduce the code (use [NHS Open Source Policy section on Readmes](https://github.com/nhsx/open-source-policy/blob/main/open-source-policy.md#b-readmes) as a guide). | ✅ | - | -| Code has been [peer reviewed](https://nhsdigital.github.io/rap-community-of-practice/implementing_RAP/workflow/code-review/). | ❌ | **TODO: Have code peer reviewed, record on GitHub - this would typically be through working on branches and then reviewing code in a pull request before it is merged into the already approved code. Could create empty branch.** | +| Code has been [peer reviewed](https://nhsdigital.github.io/rap-community-of-practice/implementing_RAP/workflow/code-review/). | ✅ | Peer reviewed by Tom Monks | | Code is [published in the open](https://nhsdigital.github.io/rap-community-of-practice/implementing_RAP/publishing_code/how-to-publish-your-code-in-the-open/) and linked to & from accompanying publication (if relevant). | ✅ & N/A | Shared openly. No publication. | ## 🥈 Silver diff --git a/docs/time_weighted_averages.ipynb b/docs/time_weighted_averages.ipynb new file mode 100644 index 0000000..9894ae1 --- /dev/null +++ b/docs/time_weighted_averages.ipynb @@ -0,0 +1,373 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Time-weighted averages\n", + "\n", + "This notebook provides some simple examples to explain the time-weighted average calculation used in the model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext pycodestyle_magic\n", + "%pycodestyle_on\n", + "\n", + "import plotly.express as px\n", + "import plotly.io as pio\n", + "\n", + "pio.renderers.default = 'svg'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 1: Queue size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we have a given queue size over time:\n", + "\n", + "| Interval | Queue Size | Duration |\n", + "|-------------|------------|----------|\n", + "| 0 - 14.4 | 0 | 14.4 |\n", + "| 14.4 - 15.2 | 1 | 0.8 |\n", + "| 15.2 - 16.1 | 2 | 0.9 |\n", + "| 16.1 - 17.0 | 3 | 0.9 |\n", + "\n", + "You can see that, for most of the time, there is no-one in the queue, but then a few people join at the end.\n", + "\n", + "Hence, we would logically expect to see an average queue size fairly close to 0." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "02468101214160123TimeQueue size" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Given data\n", + "time = [0.0, 14.4, 15.2, 16.1, 17.0]\n", + "queue = [0, 1, 2, 3, 3]\n", + "\n", + "fig = px.line(x=time, y=queue)\n", + "fig.update_traces(mode='lines', line_shape='hv')\n", + "fig.update_xaxes(dtick=2)\n", + "fig.update_yaxes(dtick=1)\n", + "fig.update_layout(\n", + " xaxis_title='Time',\n", + " yaxis_title='Queue size',\n", + " template='plotly_white')\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simple average\n", + "\n", + "#### Formula for simple average queue size:\n", + "\n", + "$$\n", + "\\text{Simple Average} = \\frac{\\sum (\\text{Queue Size})}{\\text{Number of Intervals}}\n", + "$$\n", + "\n", + "#### Applying values:\n", + "\n", + "$$\n", + "= \\frac{0 + 1 + 2 + 3}{4}\n", + "$$\n", + "\n", + "$$\n", + "= \\frac{6}{4} = 1.5\n", + "$$\n", + "\n", + "With a simple average, we find quite a high average: 1.5." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simple Average Queue Size: 1.5\n" + ] + } + ], + "source": [ + "simple_avg = sum(queue[:-1]) / len(queue[:-1])\n", + "print(f'Simple Average Queue Size: {simple_avg}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time-weighted average\n", + "\n", + "#### Formula for time-weighted average queue size:\n", + "\n", + "$$\n", + "\\text{Time-Weighted Average} = \\frac{\\sum (\\text{Queue Size} \\times \\text{Time Duration})}{\\text{Total Time}}\n", + "$$\n", + "\n", + "#### Applying values:\n", + "\n", + "$$\n", + "= \\frac{(0 \\times 14.4) + (1 \\times 0.8) + (2 \\times 0.9) + (3 \\times 0.9)}{17.0}\n", + "$$\n", + "\n", + "$$\n", + "= \\frac{0 + 0.8 + 1.8 + 2.7}{17.0}\n", + "$$\n", + "\n", + "$$\n", + "= \\frac{5.3}{17.0} \\approx 0.312\n", + "$$\n", + "\n", + "The time-weighted average better meets our expectations, being fairly close to 0." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time-Weighted Average Queue Size: 0.3117647058823529\n" + ] + } + ], + "source": [ + "# Compute time-weighted sum\n", + "weighted_sum = sum(\n", + " queue[i] * (time[i+1] - time[i]) for i in range(len(time)-1))\n", + "\n", + "# Total time duration\n", + "total_time = time[-1] - time[0]\n", + "\n", + "# Compute time-weighted average\n", + "time_weighted_avg = weighted_sum / total_time\n", + "\n", + "print(f'Time-Weighted Average Queue Size: {time_weighted_avg}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 2: Utilisation\n", + "\n", + "Building on our queue example:\n", + "\n", + "* There are **2 nurses** in this system.\n", + "* Five patients arrive during the observation period - 2 at the start, and 3 near the end.\n", + "* **Patients A and B** are seen immediately, and each has a long consultation.\n", + "* **Patients C, D, and E** arrive and wait in the queue since no nurses are available, and are still waiting at the end of the observation period." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Time | Event | Patients in system | Nurses busy | Queue size |\n", + "| - | - | - | - | - |\n", + "| 0.0 | Clinic opens |0 | 0 | 0 |\n", + "| 1.2 | Patient A arrives, sees nurse for 19 | 1 | 1 | 0 |\n", + "| 2.3 | Patient B arrives, sees nurse for 20 | 2 | 2 | 0 |\n", + "| 14.4 | Patient C arrives, waits for nurse | 3 | 2 | 1 |\n", + "| 15.2 | Patient D arrives, waits for nurse | 4 | 2 | 2 |\n", + "| 16.1 | Patient E arrives, waits for nurse | 5 | 2 | 3 |\n", + "| 17.0 | Simulation ends, three patients waiting still | 5 | 2 | 3 |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The utilisation of the nurses will depend on how many are busy at each point in time.\n", + "\n", + "* When **no nurses** are busy, utilisation is **0.0** (i.e. 0%).\n", + "* When **one nurse** is busy, utilisation is **0.5** (i.e. 50%).\n", + "* When **two nurses** are busy, utilisation is **1.0** (i.e. 100%)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "024681012141600.51TimeNurse utilisation" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time = [0.0, 1.2, 2.3, 14.4, 15.2, 16.1, 17.0]\n", + "utilisation = [0, 0.5, 1.0, 1.0, 1.0, 1.0, 1.0]\n", + "\n", + "fig = px.line(x=time, y=utilisation)\n", + "fig.update_traces(mode='lines', line_shape='hv')\n", + "fig.update_xaxes(dtick=2)\n", + "fig.update_yaxes(dtick=0.5)\n", + "fig.update_layout(\n", + " xaxis_title='Time',\n", + " yaxis_title='Nurse utilisation',\n", + " template='plotly_white')\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this scenario, we'd expect to see an average utilisation close to 1, as there was full utilisation for most of the observation period." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simple average\n", + "\n", + "#### Formula for simple average utilisation\n", + "\n", + "$$\n", + "\\text{Simple Average} = \\frac{\\sum (\\text{Utilisation})}{\\text{Number of Intervals}}\n", + "$$\n", + "\n", + "#### Applying values:\n", + "\n", + "$$\n", + "= \\frac{0 + 0.5 + 1 + 1 + 1 + 1 + 1}{7} = 0.7857\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simple Average Utilisation: 0.7857142857142857\n" + ] + } + ], + "source": [ + "simple_avg = sum(utilisation) / len(utilisation)\n", + "print(f'Simple Average Utilisation: {simple_avg}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time-weighted average\n", + "\n", + "#### Formula for time-weighted utilisation\n", + "\n", + "$$\n", + "\\text{Time-Weighted Average} = \\frac{\\sum (\\text{Utilisation} \\times \\text{Time Duration})}{\\text{Total Time}}\n", + "$$\n", + "\n", + "#### Applying values:\n", + "\n", + "$$\n", + "= \\frac{(0 \\times 1.2) + (0.5 \\times 1.1) + (1 \\times 14.7)}{17.0}\n", + "$$\n", + "\n", + "$$\n", + "= \\frac{0 + 0.55 + 14.7}{17.0} \\approx 0.897\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time-Weighted Average Utilisation: 0.8970588235294119\n" + ] + } + ], + "source": [ + "weighted_sum = sum(\n", + " utilisation[i] * (time[i+1] - time[i]) for i in range(len(time)-1)\n", + ")\n", + "\n", + "# Total time duration\n", + "total_time = time[-1] - time[0]\n", + "\n", + "time_weighted_avg = weighted_sum / total_time\n", + "print(f'Time-Weighted Average Utilisation: {time_weighted_avg}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When calculating the time-weighted average, we indeed observe a higher average utilisation, better reflecting the reality of utilisation during the observation period." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "template-des", + "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": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/environment.yaml b/environment.yaml index f05fd0c..f34ad17 100644 --- a/environment.yaml +++ b/environment.yaml @@ -14,7 +14,9 @@ dependencies: - pip=24.3.1 - plotly_express=0.4.1 - pycodestyle=2.12.1 + - pylint=3.3.3 - pytest=8.3.4 + - pytest-xdist=3.6.1 - python=3.13.0 - simpy=4.1.1 - pip: diff --git a/images/model_structure.drawio b/images/model_structure.drawio index 204927a..fffe8e4 100644 --- a/images/model_structure.drawio +++ b/images/model_structure.drawio @@ -1,58 +1,58 @@ - + - + - + - + - + - + - + - + - + - + - - + + - + - + - + @@ -60,7 +60,7 @@ - + @@ -77,19 +77,19 @@ - + - + - + - + @@ -99,35 +99,35 @@ - + - + - + - + - + - + - + - + - - + + @@ -139,13 +139,13 @@ - + - - + + - - + + @@ -153,34 +153,34 @@ - - + + - + - - + + - + - - + + - + - + - + - + - + @@ -190,10 +190,10 @@ - + - + @@ -203,7 +203,7 @@ - + @@ -213,43 +213,58 @@ - + - - + + - - + + - - - - + + - - - - - - - + + - + - + - - + + - + - + + + + + + + + + + + + + + + + + + + + + + + diff --git a/images/model_structure.png b/images/model_structure.png index b8b399d..10326e7 100644 Binary files a/images/model_structure.png and b/images/model_structure.png differ diff --git a/notebooks/analysis.ipynb b/notebooks/analysis.ipynb index 1d9eb88..d7a2e7f 100644 --- a/notebooks/analysis.ipynb +++ b/notebooks/analysis.ipynb @@ -16,7 +16,7 @@ "\n", "Credit:\n", "\n", - "* Analysis of the spread of replication results was adapted from Tom Monks (2024) HPDM097 - Making a difference with health data (https://github.com/health-data-science-OR/stochastic_systems) (MIT License)." + "* Analysis of the spread of replication results was adapted from Tom Monks (2024) HPDM097 - Making a difference with health data (https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence)." ] }, { @@ -98,17 +98,17 @@ " # set up ONE trace\n", " first_col = included_columns[0]\n", " first_friendly_name = (\n", - " name_mappings[first_col][\"friendly_name\"]\n", + " name_mappings[first_col]['friendly_name']\n", " if name_mappings and first_col in name_mappings\n", " else first_col\n", " )\n", " first_units = (\n", - " name_mappings[first_col][\"units\"]\n", + " name_mappings[first_col]['units']\n", " if name_mappings and first_col in name_mappings\n", - " else \"\"\n", + " else ''\n", " )\n", " first_x_title = (\n", - " f\"{first_friendly_name} ({first_units})\"\n", + " f'{first_friendly_name} ({first_units})'\n", " if first_units\n", " else first_friendly_name\n", " )\n", @@ -121,18 +121,18 @@ " # create list of drop down items - KPIs\n", " for col in included_columns:\n", " if name_mappings and col in name_mappings:\n", - " friendly_name = name_mappings[col][\"friendly_name\"]\n", - " units = name_mappings[col][\"units\"]\n", - " x_title = f\"{friendly_name} ({units})\" if units else friendly_name\n", + " friendly_name = name_mappings[col]['friendly_name']\n", + " units = name_mappings[col]['units']\n", + " x_title = f'{friendly_name} ({units})' if units else friendly_name\n", " else:\n", " friendly_name = col\n", " x_title = col\n", "\n", " buttons.append(\n", " dict(\n", - " method=\"update\",\n", + " method='update',\n", " label=friendly_name,\n", - " args=[{\"x\": [results[col]]}, {\"xaxis\": {\"title\": x_title}}],\n", + " args=[{'x': [results[col]]}, {'xaxis': {'title': x_title}}],\n", " )\n", " )\n", "\n", @@ -141,9 +141,9 @@ " your_menu = dict()\n", " updatemenu.append(your_menu)\n", "\n", - " updatemenu[0][\"buttons\"] = buttons\n", - " updatemenu[0][\"direction\"] = \"down\"\n", - " updatemenu[0][\"showactive\"] = True\n", + " updatemenu[0]['buttons'] = buttons\n", + " updatemenu[0]['direction'] = 'down'\n", + " updatemenu[0]['showactive'] = True\n", "\n", " # add dropdown menus to the figure\n", " fig.update_layout(showlegend=False, updatemenus=updatemenu)\n", @@ -151,9 +151,9 @@ " # Add annotation as instruction\n", " if include_instruct:\n", " fig.add_annotation(\n", - " text=\"Select a KPI from the drop down list\",\n", - " xref=\"paper\",\n", - " yref=\"paper\",\n", + " text='Select a KPI from the drop down list',\n", + " xref='paper',\n", + " yref='paper',\n", " x=0.0,\n", " y=1.1, # Positions the text above the plot\n", " showarrow=False,\n", @@ -216,21 +216,22 @@ "metadata": {}, "outputs": [], "source": [ - "import itertools\n", "import os\n", "import pandas as pd\n", "import plotly.express as px\n", + "import plotly.io as pio\n", "import time\n", "\n", "from simulation.logging import SimLogger\n", - "from simulation.model import Defaults, Model, Trial, summary_stats" + "from simulation.model import (\n", + " Defaults, Model, Runner, summary_stats, run_scenarios)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Start timer." + "Display plotly express figures as non-interactive figures. This means they will be visible when browsing the notebooks on GitHub. To switch these back to interactive figures, simply remove this line." ] }, { @@ -238,6 +239,22 @@ "execution_count": 7, "metadata": {}, "outputs": [], + "source": [ + "pio.renderers.default = 'svg'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Start timer." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], "source": [ "start_time = time.time()" ] @@ -251,11 +268,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "output_dir = \"../outputs/\"" + "output_dir = '../outputs/'" ] }, { @@ -274,13 +291,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "param = Defaults()\n", - "trial = Trial(param)\n", - "trial.run_trial()" + "experiment = Runner(param)\n", + "experiment.run_reps()" ] }, { @@ -293,7 +310,7 @@ "\n", "```python\n", "# Save file\n", - "trial.patient_results_df.to_csv(\n", + "experiment.patient_results_df.to_csv(\n", " os.path.join(output_dir, 'example_patient.csv.gz'),\n", " index=False, compression='gzip')\n", "\n", @@ -306,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -396,12 +413,12 @@ } ], "source": [ - "display(trial.patient_results_df.head())" + "display(experiment.patient_results_df.head())" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -431,6 +448,8 @@ " mean_q_time_nurse\n", " mean_time_with_nurse\n", " mean_nurse_utilisation\n", + " mean_nurse_utilisation_tw\n", + " mean_nurse_q_length\n", " \n", " \n", " \n", @@ -442,6 +461,8 @@ " 0.504541\n", " 9.842268\n", " 0.499639\n", + " 0.499740\n", + " 0.128144\n", " \n", " \n", " 1\n", @@ -451,6 +472,8 @@ " 0.514151\n", " 10.060481\n", " 0.501991\n", + " 0.502069\n", + " 0.128347\n", " \n", " \n", " 2\n", @@ -460,6 +483,8 @@ " 0.523235\n", " 9.925025\n", " 0.498130\n", + " 0.498260\n", + " 0.132669\n", " \n", " \n", " 3\n", @@ -469,6 +494,8 @@ " 0.479149\n", " 9.937057\n", " 0.498220\n", + " 0.498269\n", + " 0.120131\n", " \n", " \n", " 4\n", @@ -478,6 +505,8 @@ " 0.461457\n", " 10.015904\n", " 0.496870\n", + " 0.497106\n", + " 0.114510\n", " \n", " \n", "\n", @@ -491,12 +520,12 @@ "3 3 0 10831 0.479149 9.937057 \n", "4 4 0 10720 0.461457 10.015904 \n", "\n", - " mean_nurse_utilisation \n", - "0 0.499639 \n", - "1 0.501991 \n", - "2 0.498130 \n", - "3 0.498220 \n", - "4 0.496870 " + " mean_nurse_utilisation mean_nurse_utilisation_tw mean_nurse_q_length \n", + "0 0.499639 0.499740 0.128144 \n", + "1 0.501991 0.502069 0.128347 \n", + "2 0.498130 0.498260 0.132669 \n", + "3 0.498220 0.498269 0.120131 \n", + "4 0.496870 0.497106 0.114510 " ] }, "metadata": {}, @@ -504,15 +533,14 @@ } ], "source": [ - "display(trial.trial_results_df.head())\n", - "trial.trial_results_df.to_csv(\n", - " os.path.join(output_dir, \"example_trial.csv\"), index=False\n", - ")" + "display(experiment.run_results_df.head())\n", + "experiment.run_results_df.to_csv(\n", + " os.path.join(output_dir, 'example_run.csv'), index=False)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -615,15 +643,14 @@ } ], "source": [ - "display(trial.interval_audit_df.head())\n", - "trial.interval_audit_df.to_csv(\n", - " os.path.join(output_dir, \"example_interval_audit.csv\"), index=False\n", - ")" + "display(experiment.interval_audit_df.head())\n", + "experiment.interval_audit_df.to_csv(\n", + " os.path.join(output_dir, 'example_interval_audit.csv'), index=False)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -651,6 +678,8 @@ " mean_q_time_nurse\n", " mean_time_with_nurse\n", " mean_nurse_utilisation\n", + " mean_nurse_utilisation_tw\n", + " mean_nurse_q_length\n", " \n", " \n", " \n", @@ -660,6 +689,8 @@ " 0.499037\n", " 9.978457\n", " 0.497670\n", + " 0.497804\n", + " 0.124599\n", " \n", " \n", " std_dev\n", @@ -667,6 +698,8 @@ " 0.067393\n", " 0.115138\n", " 0.007524\n", + " 0.007541\n", + " 0.017213\n", " \n", " \n", " lower_95_ci\n", @@ -674,6 +707,8 @@ " 0.474317\n", " 9.936224\n", " 0.494910\n", + " 0.495038\n", + " 0.118285\n", " \n", " \n", " upper_95_ci\n", @@ -681,6 +716,8 @@ " 0.523757\n", " 10.020690\n", " 0.500430\n", + " 0.500570\n", + " 0.130912\n", " \n", " \n", "\n", @@ -693,11 +730,17 @@ "lower_95_ci 10734.264952 0.474317 9.936224 \n", "upper_95_ci 10819.218919 0.523757 10.020690 \n", "\n", - " mean_nurse_utilisation \n", - "mean 0.497670 \n", - "std_dev 0.007524 \n", - "lower_95_ci 0.494910 \n", - "upper_95_ci 0.500430 " + " mean_nurse_utilisation mean_nurse_utilisation_tw \\\n", + "mean 0.497670 0.497804 \n", + "std_dev 0.007524 0.007541 \n", + "lower_95_ci 0.494910 0.495038 \n", + "upper_95_ci 0.500430 0.500570 \n", + "\n", + " mean_nurse_q_length \n", + "mean 0.124599 \n", + "std_dev 0.017213 \n", + "lower_95_ci 0.118285 \n", + "upper_95_ci 0.130912 " ] }, "metadata": {}, @@ -705,10 +748,9 @@ } ], "source": [ - "display(trial.overall_results_df.head())\n", - "trial.overall_results_df.to_csv(\n", - " os.path.join(output_dir, \"example_overall.csv\"), index=False\n", - ")" + "display(experiment.overall_results_df.head())\n", + "experiment.overall_results_df.to_csv(\n", + " os.path.join(output_dir, 'example_overall.csv'), index=False)" ] }, { @@ -722,1146 +764,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - 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" + "image/svg+xml": [ + "10.6k10.8k11k024681012Patient Arrivals (n)Select a KPI from the drop down listPatient Arrivals▼" ] }, "metadata": {}, @@ -1871,23 +780,23 @@ "source": [ "# optional name mappings to help user. if excluded then column names used.\n", "name_mappings = {\n", - " \"arrivals\": {\"friendly_name\": \"Patient Arrivals\", \"units\": \"n\"},\n", - " \"mean_q_time_nurse\": {\n", - " \"friendly_name\": \"Nurse waiting time\",\n", - " \"units\": \"minutes\",\n", + " 'arrivals': {'friendly_name': 'Patient Arrivals', 'units': 'n'},\n", + " 'mean_q_time_nurse': {\n", + " 'friendly_name': 'Nurse waiting time',\n", + " 'units': 'minutes',\n", " },\n", - " \"mean_time_with_nurse\": {\n", - " \"friendly_name\": \"Patient contact time\",\n", - " \"units\": \"minutes\",\n", + " 'mean_time_with_nurse': {\n", + " 'friendly_name': 'Patient contact time',\n", + " 'units': 'minutes',\n", " },\n", - " \"mean_nurse_utilisation\": {\n", - " \"friendly_name\": \"Nurse Utilization\",\n", - " \"units\": \"%\",\n", + " 'mean_nurse_utilisation': {\n", + " 'friendly_name': 'Nurse Utilization',\n", + " 'units': '%',\n", " },\n", "}\n", "\n", "create_user_controlled_hist(\n", - " trial.trial_results_df,\n", + " experiment.run_results_df,\n", " exclude_columns=[0, 1],\n", " name_mappings=name_mappings,\n", " include_instruct=True,\n", @@ -1896,7 +805,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1905,9 +814,6 @@ " Plot spread of results from across replications, for chosen column.\n", " Show figure and save under specified file name.\n", "\n", - " TM patch so that `trial.trial_results_df` is a parameter rather \n", - " than using notebook scope variable.\n", - "\n", " Arguments:\n", " rep_results (pandas.DataFrame)\n", " The dataframe of replication results. \n", @@ -1919,7 +825,11 @@ " Filename to save figure to.\n", " \"\"\"\n", " fig = px.histogram(rep_results[column])\n", - " fig.update_layout(xaxis_title=x_label, yaxis_title=\"Frequency\")\n", + " fig.update_layout(\n", + " xaxis_title=x_label,\n", + " yaxis_title='Frequency',\n", + " template='plotly_white',\n", + " showlegend=False)\n", "\n", " # Show figure\n", " fig.show()\n", @@ -1930,944 +840,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "alignmentgroup": "True", - "bingroup": "x", - "hovertemplate": "variable=arrivals
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" + "image/svg+xml": [ + "0.480.490.50.51012345678Mean nurse utilisationFrequency" ] }, "metadata": {}, @@ -5696,31 +882,31 @@ ], "source": [ "plot_results_spread(\n", - " rep_results=trial.trial_results_df,\n", - " column=\"arrivals\", \n", - " x_label=\"Arrivals\", \n", - " file=\"spread_arrivals.png\"\n", + " rep_results=experiment.run_results_df,\n", + " column='arrivals', \n", + " x_label='Arrivals', \n", + " file='spread_arrivals.png'\n", ")\n", "\n", "plot_results_spread(\n", - " rep_results=trial.trial_results_df,\n", - " column=\"mean_q_time_nurse\",\n", - " x_label=\"Mean wait time for nurse\",\n", - " file=\"spread_nurse_wait.png\",\n", + " rep_results=experiment.run_results_df,\n", + " column='mean_q_time_nurse',\n", + " x_label='Mean wait time for nurse',\n", + " file='spread_nurse_wait.png',\n", ")\n", "\n", "plot_results_spread(\n", - " rep_results=trial.trial_results_df,\n", - " column=\"mean_time_with_nurse\",\n", - " x_label=\"Mean length of nurse consultation\",\n", - " file=\"spread_nurse_time.png\",\n", + " rep_results=experiment.run_results_df,\n", + " column='mean_time_with_nurse',\n", + " x_label='Mean length of nurse consultation',\n", + " file='spread_nurse_time.png',\n", ")\n", "\n", "plot_results_spread(\n", - " rep_results=trial.trial_results_df,\n", - " column=\"mean_nurse_utilisation\",\n", - " x_label=\"Mean nurse utilisation\",\n", - " file=\"spread_nurse_util.png\",\n", + " rep_results=experiment.run_results_df,\n", + " column='mean_nurse_utilisation',\n", + " x_label='Mean nurse utilisation',\n", + " file='spread_nurse_util.png',\n", ")" ] }, @@ -5728,52 +914,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Scenario analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "def run_scenarios(scenarios):\n", - " \"\"\"\n", - " Run a set of scenarios and return the scenario-level results.\n", + "## Scenario analysis\n", "\n", - " Arguments:\n", - " scenarios (dict):\n", - " Dictionary where key is name of parameter and value is a list\n", - " with different values to run in scenarios.\n", - " \"\"\"\n", - " # Find every possible permutation of the scenarios\n", - " all_scenarios_tuples = list(itertools.product(*scenarios.values()))\n", - " # Convert back into dictionaries\n", - " all_scenarios_dicts = [\n", - " dict(zip(scenarios.keys(), p)) for p in all_scenarios_tuples\n", - " ]\n", - " # Preview some of the scenarios\n", - " print(f\"There are {len(all_scenarios_dicts)} scenarios. Running:\")\n", - "\n", - " # Run the scenarios...\n", - " results = []\n", - " for index, scenario_to_run in enumerate(all_scenarios_dicts):\n", - " print(scenario_to_run)\n", - "\n", - " # Overwrite defaults from the passed dictionary\n", - " param = Defaults()\n", - " param.scenario_name = index\n", - " for key in scenario_to_run:\n", - " setattr(param, key, scenario_to_run[key])\n", - "\n", - " # Run trial and keep trial-level results, adding the scenario values to\n", - " # the results dataframe\n", - " scenario_trial = Trial(param)\n", - " scenario_trial.run_trial()\n", - " for key in scenario_to_run:\n", - " scenario_trial.trial_results_df[key] = scenario_to_run[key]\n", - " results.append(scenario_trial.trial_results_df)\n", - " return pd.concat(results)" + "We can use the `run_scenarios()` function to run scenarios on the default model, executing it with every possible combination of the specified parameters." ] }, { @@ -5812,7 +955,7 @@ "source": [ "# Run scenarios\n", "scenario_results = run_scenarios(\n", - " {\"patient_inter\": [3, 4, 5, 6, 7], \"number_of_nurses\": [5, 6, 7, 8]}\n", + " {'patient_inter': [3, 4, 5, 6, 7], 'number_of_nurses': [5, 6, 7, 8]}\n", ")" ] }, @@ -5848,6 +991,8 @@ " mean_q_time_nurse\n", " mean_time_with_nurse\n", " mean_nurse_utilisation\n", + " mean_nurse_utilisation_tw\n", + " mean_nurse_q_length\n", " patient_inter\n", " number_of_nurses\n", " \n", @@ -5861,6 +1006,8 @@ " 1.906132\n", " 9.949058\n", " 0.667461\n", + " 0.667636\n", + " 0.639392\n", " 3\n", " 5\n", " \n", @@ -5872,6 +1019,8 @@ " 1.918952\n", " 10.148115\n", " 0.676693\n", + " 0.677008\n", + " 0.640044\n", " 3\n", " 5\n", " \n", @@ -5883,6 +1032,8 @@ " 1.976377\n", " 9.931685\n", " 0.665045\n", + " 0.665109\n", + " 0.661766\n", " 3\n", " 5\n", " \n", @@ -5894,6 +1045,8 @@ " 1.959961\n", " 10.003235\n", " 0.667725\n", + " 0.667792\n", + " 0.655062\n", " 3\n", " 5\n", " \n", @@ -5905,6 +1058,8 @@ " 1.780232\n", " 9.980463\n", " 0.664761\n", + " 0.664891\n", + " 0.592875\n", " 3\n", " 5\n", " \n", @@ -5920,12 +1075,19 @@ "3 3 0 14424 1.959961 10.003235 \n", "4 4 0 14387 1.780232 9.980463 \n", "\n", - " mean_nurse_utilisation patient_inter number_of_nurses \n", - "0 0.667461 3 5 \n", - "1 0.676693 3 5 \n", - "2 0.665045 3 5 \n", - "3 0.667725 3 5 \n", - "4 0.664761 3 5 " + " mean_nurse_utilisation mean_nurse_utilisation_tw mean_nurse_q_length \\\n", + "0 0.667461 0.667636 0.639392 \n", + "1 0.676693 0.677008 0.640044 \n", + "2 0.665045 0.665109 0.661766 \n", + "3 0.667725 0.667792 0.655062 \n", + "4 0.664761 0.664891 0.592875 \n", + "\n", + " patient_inter number_of_nurses \n", + "0 3 5 \n", + "1 3 5 \n", + "2 3 5 \n", + "3 3 5 \n", + "4 3 5 " ] }, "execution_count": 19, @@ -5950,15 +1112,8 @@ "metadata": {}, "outputs": [], "source": [ - "def plot_scenario(\n", - " results,\n", - " x_var,\n", - " result_var,\n", - " colour_var,\n", - " xaxis_title,\n", - " yaxis_title,\n", - " legend_title,\n", - "):\n", + "def plot_scenario(results, x_var, result_var, colour_var, xaxis_title,\n", + " yaxis_title, legend_title):\n", " \"\"\"\n", " Plot results from different model scenarios.\n", "\n", @@ -5992,28 +1147,23 @@ " .apply(pd.Series)\n", " .reset_index()\n", " )\n", - " df.columns = list(df.columns[:-4]) + [\n", - " \"mean\",\n", - " \"std_dev\",\n", - " \"ci_lower\",\n", - " \"ci_upper\",\n", - " ]\n", + " df.columns = (list(df.columns[:-4]) +\n", + " ['mean', 'std_dev', 'ci_lower', 'ci_upper'])\n", "\n", " # Plot mean line\n", - " fig = px.line(df, x=x_var, y=\"mean\", color=colour_var)\n", + " fig = px.line(df, x=x_var, y='mean', color=colour_var)\n", " fig.update_layout(\n", " xaxis_title=xaxis_title,\n", " yaxis_title=yaxis_title,\n", " legend_title_text=legend_title,\n", + " template='plotly_white'\n", " )\n", "\n", " # Plot confidence interval lines\n", - " for ci in [\"ci_upper\", \"ci_lower\"]:\n", - " trace = (\n", - " px.line(df, x=x_var, y=ci, color=colour_var)\n", - " .update_traces(opacity=0.5, showlegend=False)\n", - " .select_traces()\n", - " )\n", + " for ci in ['ci_upper', 'ci_lower']:\n", + " trace = (px.line(df, x=x_var, y=ci, color=colour_var)\n", + " .update_traces(opacity=0.5, showlegend=False)\n", + " .select_traces())\n", " # Add to figure\n", " fig.add_traces(list(trace))\n", "\n", @@ -6034,1278 +1184,8 @@ "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "number_of_nurses=5
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" + "image/svg+xml": [ + "345670.20.30.40.50.6Nurses5678Patient inter-arrival timeMean nurse utilisation" ] }, "metadata": {}, @@ -8623,17 +1232,16 @@ "source": [ "result, fig = plot_scenario(\n", " results=scenario_results,\n", - " x_var=\"patient_inter\",\n", - " result_var=\"mean_nurse_utilisation\",\n", - " colour_var=\"number_of_nurses\",\n", - " xaxis_title=\"Patient inter-arrival time\",\n", - " yaxis_title=\"Mean nurse utilisation\",\n", - " legend_title=\"Nurses\",\n", - ")\n", + " x_var='patient_inter',\n", + " result_var='mean_nurse_utilisation',\n", + " colour_var='number_of_nurses',\n", + " xaxis_title='Patient inter-arrival time',\n", + " yaxis_title='Mean nurse utilisation',\n", + " legend_title='Nurses')\n", "\n", "fig.show()\n", "\n", - "fig.write_image(os.path.join(output_dir, \"scenario_nurse_util.png\"))" + "fig.write_image(os.path.join(output_dir, 'scenario_nurse_util.png'))" ] }, { @@ -8672,24 +1280,23 @@ "table = result.copy()\n", "\n", "# Combine mean and CI into single column, and round\n", - "table[\"mean_ci\"] = table.apply(\n", - " lambda row: f\"{row['mean']:.2f} ({row['ci_lower']:.2f}, {row['ci_upper']:.2f})\",\n", - " axis=1,\n", + "table['mean_ci'] = table.apply(\n", + " lambda row:\n", + " f'{row['mean']:.2f} ({row['ci_lower']:.2f}, {row['ci_upper']:.2f})', axis=1\n", ")\n", "\n", "# Convert from long to wide format\n", "table = (\n", - " table.pivot(\n", - " index=\"patient_inter\", columns=\"number_of_nurses\", values=\"mean_ci\"\n", - " )\n", - " .rename_axis(\"Patient inter-arrival time\", axis=\"index\")\n", - " .rename_axis(\"Number of nurses\", axis=\"columns\")\n", + " table\n", + " .pivot(index='patient_inter', columns='number_of_nurses', values='mean_ci')\n", + " .rename_axis('Patient inter-arrival time', axis='index')\n", + " .rename_axis('Number of nurses', axis='columns')\n", ")\n", "\n", "# Convert to latex, display and save\n", "table_latex = table.to_latex()\n", "print(table_latex)\n", - "with open(os.path.join(output_dir, \"scenario_nurse_util.tex\"), \"w\") as tf:\n", + "with open(os.path.join(output_dir, 'scenario_nurse_util.tex'), 'w') as tf:\n", " tf.write(table_latex)" ] }, @@ -8731,7 +1338,7 @@ "source": [ "# Run scenarios\n", "sensitivity_consult = run_scenarios(\n", - " {\"mean_n_consult_time\": [8, 9, 10, 11, 12, 13, 14, 15]}\n", + " {'mean_n_consult_time': [8, 9, 10, 11, 12, 13, 14, 15]}\n", ")" ] }, @@ -8742,1003 +1349,8 @@ "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "mean_n_consult_time=%{x}
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" + "image/svg+xml": [ + "89101112131415012345Mean nurse consultation time (minutes)Mean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -9748,17 +1360,17 @@ "source": [ "result, fig = plot_scenario(\n", " results=sensitivity_consult,\n", - " x_var=\"mean_n_consult_time\",\n", - " result_var=\"mean_q_time_nurse\",\n", + " x_var='mean_n_consult_time',\n", + " result_var='mean_q_time_nurse',\n", " colour_var=None,\n", - " xaxis_title=\"Mean nurse consultation time (minutes)\",\n", - " yaxis_title=\"Mean wait time for nurse (minutes)\",\n", - " legend_title=\"Nurses\",\n", + " xaxis_title='Mean nurse consultation time (minutes)',\n", + " yaxis_title='Mean wait time for nurse (minutes)',\n", + " legend_title='Nurses',\n", ")\n", "\n", "fig.show()\n", "\n", - "fig.write_image(os.path.join(output_dir, \"sensitivity_consult_time.png\"))" + "fig.write_image(os.path.join(output_dir, 'sensitivity_consult_time.png'))" ] }, { @@ -9792,22 +1404,22 @@ "table = result.copy()\n", "\n", "# Combine mean and CI into single column, and round\n", - "table[\"mean_ci\"] = table.apply(\n", - " lambda row: f\"{row['mean']:.2f} ({row['ci_lower']:.2f}, {row['ci_upper']:.2f})\",\n", - " axis=1,\n", + "table['mean_ci'] = table.apply(\n", + " lambda row:\n", + " f'{row['mean']:.2f} ({row['ci_lower']:.2f}, {row['ci_upper']:.2f})', axis=1\n", ")\n", "\n", "# Select relevant columns and rename\n", "cols = {\n", - " \"mean_n_consult_time\": \"Mean nurse consultation time\",\n", - " \"mean_ci\": \"Mean wait time for nurse (95 percent confidence interval)\",\n", + " 'mean_n_consult_time': 'Mean nurse consultation time',\n", + " 'mean_ci': 'Mean wait time for nurse (95 percent confidence interval)'\n", "}\n", "table = table[cols.keys()].rename(columns=cols)\n", "\n", "# Convert to latex, display and save\n", "table_latex = table.to_latex()\n", "print(table_latex)\n", - "with open(os.path.join(output_dir, \"sensitivity_consult_time.tex\"), \"w\") as tf:\n", + "with open(os.path.join(output_dir, 'sensitivity_consult_time.tex'), 'w') as tf:\n", " tf.write(table_latex)" ] }, @@ -9915,9 +1527,9 @@ "source": [ "param = Defaults()\n", "param.patient_inter = 2\n", - "trial = Trial(param)\n", - "trial.run_trial()\n", - "trial.patient_results_df.tail()" + "nan_experiment = Runner(param)\n", + "nan_experiment.run_reps()\n", + "nan_experiment.patient_results_df.tail()" ] }, { @@ -9942,70 +1554,73 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-01-24 17:33:23,382 - INFO - logging.py:log():128 - Initialised model: {'param': , 'run_number': 0, 'env': , 'nurse': , 'patients': [], 'nurse_time_used': 0, 'nurse_consult_count': 0, 'running_mean_nurse_wait': 0, 'audit_list': [], 'results_list': [], 'patient_inter_arrival_dist': , 'nurse_consult_time_dist': }\n", - "2025-01-24 17:33:23,382 - INFO - logging.py:log():128 - Parameters: {'_initialising': False, 'patient_inter': 4, 'mean_n_consult_time': 10, 'number_of_nurses': 5, 'warm_up_period': 50, 'data_collection_period': 100, 'number_of_runs': 1, 'audit_interval': 120, 'scenario_name': 0, 'cores': 0, 'logger': }\n", - "2025-01-24 17:33:23,383 - INFO - logging.py:log():128 - Patient X (warm-up) arrives at: 0.000\n", - "2025-01-24 17:33:23,383 - INFO - logging.py:log():128 - Patient X (warm-up) is seen by nurse after 0.000 minutes. Consultation length: 8.031 minutes.\n", - "2025-01-24 17:33:23,383 - INFO - logging.py:log():128 - Patient X (warm-up) arrives at: 13.174\n", - "2025-01-24 17:33:23,384 - INFO - logging.py:log():128 - Patient X (warm-up) is seen by nurse after 13.174 minutes. Consultation length: 3.820 minutes.\n", - "2025-01-24 17:33:23,384 - INFO - logging.py:log():128 - Patient X (warm-up) arrives at: 16.227\n", - "2025-01-24 17:33:23,385 - INFO - logging.py:log():128 - Patient X (warm-up) is seen by nurse after 16.227 minutes. Consultation length: 3.642 minutes.\n", - "2025-01-24 17:33:23,385 - INFO - logging.py:log():128 - Patient X (warm-up) arrives at: 21.236\n", - "2025-01-24 17:33:23,386 - INFO - logging.py:log():128 - Patient X (warm-up) is seen by nurse after 21.236 minutes. Consultation length: 5.295 minutes.\n", - "2025-01-24 17:33:23,386 - INFO - logging.py:log():128 - Patient X (warm-up) arrives at: 22.140\n", - "2025-01-24 17:33:23,386 - INFO - logging.py:log():128 - Patient X (warm-up) is seen by nurse after 22.140 minutes. Consultation length: 27.884 minutes.\n", - "2025-01-24 17:33:23,386 - INFO - logging.py:log():128 - Patient X (warm-up) arrives at: 23.023\n", - "2025-01-24 17:33:23,387 - INFO - logging.py:log():128 - Patient X (warm-up) is seen by nurse after 23.023 minutes. Consultation length: 19.610 minutes.\n", - "2025-01-24 17:33:23,387 - INFO - logging.py:log():128 - Patient X (warm-up) arrives at: 30.223\n", - "2025-01-24 17:33:23,387 - INFO - logging.py:log():128 - Patient X (warm-up) is seen by nurse after 30.223 minutes. Consultation length: 9.490 minutes.\n", - "2025-01-24 17:33:23,387 - INFO - logging.py:log():128 - Patient X (warm-up) arrives at: 30.487\n", - "2025-01-24 17:33:23,388 - INFO - logging.py:log():128 - Patient X (warm-up) is seen by nurse after 30.487 minutes. Consultation length: 41.665 minutes.\n", - "2025-01-24 17:33:23,388 - INFO - logging.py:log():128 - Patient X (warm-up) arrives at: 34.089\n", - "2025-01-24 17:33:23,389 - INFO - logging.py:log():128 - Patient X (warm-up) is seen by nurse after 34.089 minutes. Consultation length: 5.874 minutes.\n", - "2025-01-24 17:33:23,389 - INFO - logging.py:log():128 - Patient X (warm-up) arrives at: 35.270\n", - "2025-01-24 17:33:23,389 - INFO - logging.py:log():128 - Patient X (warm-up) is seen by nurse after 35.270 minutes. Consultation length: 27.882 minutes.\n", - "2025-01-24 17:33:23,389 - INFO - logging.py:log():128 - Patient X (warm-up) arrives at: 44.470\n", - "2025-01-24 17:33:23,390 - INFO - logging.py:log():128 - Patient X (warm-up) is seen by nurse after 44.470 minutes. Consultation length: 24.915 minutes.\n", - "2025-01-24 17:33:23,390 - INFO - logging.py:log():128 - Patient 1 arrives at: 51.904\n", - "2025-01-24 17:33:23,390 - INFO - logging.py:log():128 - Patient 1 is seen by nurse after 51.904 minutes. Consultation length: 18.079 minutes.\n", - "2025-01-24 17:33:23,390 - INFO - logging.py:log():128 - Patient 2 arrives at: 51.963\n", - "2025-01-24 17:33:23,391 - INFO - logging.py:log():128 - Patient 2 is seen by nurse after 51.963 minutes. Consultation length: 3.102 minutes.\n", - "2025-01-24 17:33:23,391 - INFO - logging.py:log():128 - Patient 3 arrives at: 74.349\n", - "2025-01-24 17:33:23,391 - INFO - logging.py:log():128 - Patient 3 is seen by nurse after 74.349 minutes. Consultation length: 26.745 minutes.\n", - "2025-01-24 17:33:23,392 - INFO - logging.py:log():128 - Patient 4 arrives at: 77.534\n", - "2025-01-24 17:33:23,392 - INFO - logging.py:log():128 - Patient 4 is seen by nurse after 77.534 minutes. Consultation length: 0.748 minutes.\n", - "2025-01-24 17:33:23,393 - INFO - logging.py:log():128 - Patient 5 arrives at: 78.932\n", - "2025-01-24 17:33:23,393 - INFO - logging.py:log():128 - Patient 5 is seen by nurse after 78.932 minutes. Consultation length: 0.528 minutes.\n", - "2025-01-24 17:33:23,393 - INFO - logging.py:log():128 - Patient 6 arrives at: 86.815\n", - "2025-01-24 17:33:23,393 - INFO - logging.py:log():128 - Patient 6 is seen by nurse after 86.815 minutes. Consultation length: 2.435 minutes.\n", - "2025-01-24 17:33:23,394 - INFO - logging.py:log():128 - Patient 7 arrives at: 89.783\n", - "2025-01-24 17:33:23,394 - INFO - logging.py:log():128 - Patient 7 is seen by nurse after 89.783 minutes. Consultation length: 9.666 minutes.\n", - "2025-01-24 17:33:23,394 - INFO - logging.py:log():128 - Patient 8 arrives at: 89.807\n", - "2025-01-24 17:33:23,394 - INFO - logging.py:log():128 - Patient 8 is seen by nurse after 89.807 minutes. Consultation length: 7.005 minutes.\n", - "2025-01-24 17:33:23,395 - INFO - logging.py:log():128 - Patient 9 arrives at: 93.118\n", - "2025-01-24 17:33:23,395 - INFO - logging.py:log():128 - Patient 9 is seen by nurse after 93.118 minutes. Consultation length: 20.185 minutes.\n", - "2025-01-24 17:33:23,395 - INFO - logging.py:log():128 - Patient 10 arrives at: 95.598\n", - "2025-01-24 17:33:23,395 - INFO - logging.py:log():128 - Patient 10 is seen by nurse after 95.598 minutes. Consultation length: 7.651 minutes.\n", - "2025-01-24 17:33:23,395 - INFO - logging.py:log():128 - Patient 11 arrives at: 98.019\n", - "2025-01-24 17:33:23,396 - INFO - logging.py:log():128 - Patient 11 is seen by nurse after 98.019 minutes. Consultation length: 27.908 minutes.\n", - "2025-01-24 17:33:23,396 - INFO - logging.py:log():128 - Patient 12 arrives at: 109.379\n", - "2025-01-24 17:33:23,396 - INFO - logging.py:log():128 - Patient 12 is seen by nurse after 109.379 minutes. Consultation length: 16.811 minutes.\n", - "2025-01-24 17:33:23,396 - INFO - logging.py:log():128 - Patient 13 arrives at: 110.322\n", - "2025-01-24 17:33:23,397 - INFO - logging.py:log():128 - Patient 13 is seen by nurse after 110.322 minutes. Consultation length: 24.157 minutes.\n", - "2025-01-24 17:33:23,397 - INFO - logging.py:log():128 - Patient 14 arrives at: 120.342\n", - "2025-01-24 17:33:23,397 - INFO - logging.py:log():128 - Patient 14 is seen by nurse after 120.342 minutes. Consultation length: 1.451 minutes.\n", - "2025-01-24 17:33:23,397 - INFO - logging.py:log():128 - Patient 15 arrives at: 121.643\n", - "2025-01-24 17:33:23,397 - INFO - logging.py:log():128 - Patient 15 is seen by nurse after 121.643 minutes. Consultation length: 1.343 minutes.\n", - "2025-01-24 17:33:23,398 - INFO - logging.py:log():128 - Patient 16 arrives at: 127.827\n", - "2025-01-24 17:33:23,398 - INFO - logging.py:log():128 - Patient 16 is seen by nurse after 127.827 minutes. Consultation length: 1.554 minutes.\n", - "2025-01-24 17:33:23,398 - INFO - logging.py:log():128 - Patient 17 arrives at: 132.055\n", - "2025-01-24 17:33:23,398 - INFO - logging.py:log():128 - Patient 17 is seen by nurse after 132.055 minutes. Consultation length: 15.623 minutes.\n", - "2025-01-24 17:33:23,399 - INFO - logging.py:log():128 - Patient 18 arrives at: 133.617\n", - "2025-01-24 17:33:23,399 - INFO - logging.py:log():128 - Patient 18 is seen by nurse after 133.617 minutes. Consultation length: 4.688 minutes.\n", - "2025-01-24 17:33:23,399 - INFO - logging.py:log():128 - Patient 19 arrives at: 136.346\n", - "2025-01-24 17:33:23,400 - INFO - logging.py:log():128 - Patient 19 is seen by nurse after 136.346 minutes. Consultation length: 2.612 minutes.\n", - "2025-01-24 17:33:23,400 - INFO - logging.py:log():128 - Patient 20 arrives at: 144.142\n", - "2025-01-24 17:33:23,400 - INFO - logging.py:log():128 - Patient 20 is seen by nurse after 144.142 minutes. Consultation length: 6.348 minutes.\n" + "2025-01-29 13:39:29,576 - INFO - logging.py:log():128 - Initialised model: {'param': , 'run_number': 0, 'env': , 'nurse': , 'patients': [], 'nurse_time_used': 0, 'nurse_consult_count': 0, 'running_mean_nurse_wait': 0, 'audit_list': [], 'results_list': [], 'patient_inter_arrival_dist': , 'nurse_consult_time_dist': }\n", + "2025-01-29 13:39:29,577 - INFO - logging.py:log():128 - Parameters: {'_initialising': False, 'patient_inter': 4, 'mean_n_consult_time': 10, 'number_of_nurses': 5, 'warm_up_period': 50, 'data_collection_period': 100, 'number_of_runs': 1, 'audit_interval': 120, 'scenario_name': 0, 'cores': 0, 'logger': }\n", + "2025-01-29 13:39:29,577 - INFO - logging.py:log():128 - Patient 1 arrives at: 0.000\n", + "2025-01-29 13:39:29,577 - INFO - logging.py:log():128 - Patient 1 is seen by nurse after 0.000 minutes. Consultation length: 8.031 minutes.\n", + "2025-01-29 13:39:29,578 - INFO - logging.py:log():128 - Patient 2 arrives at: 13.174\n", + "2025-01-29 13:39:29,578 - INFO - logging.py:log():128 - Patient 2 is seen by nurse after 13.174 minutes. Consultation length: 3.820 minutes.\n", + "2025-01-29 13:39:29,578 - INFO - logging.py:log():128 - Patient 3 arrives at: 16.227\n", + "2025-01-29 13:39:29,578 - INFO - logging.py:log():128 - Patient 3 is seen by nurse after 16.227 minutes. Consultation length: 3.642 minutes.\n", + "2025-01-29 13:39:29,578 - INFO - logging.py:log():128 - Patient 4 arrives at: 21.236\n", + "2025-01-29 13:39:29,579 - INFO - logging.py:log():128 - Patient 4 is seen by nurse after 21.236 minutes. Consultation length: 5.295 minutes.\n", + "2025-01-29 13:39:29,579 - INFO - logging.py:log():128 - Patient 5 arrives at: 22.140\n", + "2025-01-29 13:39:29,579 - INFO - logging.py:log():128 - Patient 5 is seen by nurse after 22.140 minutes. Consultation length: 27.884 minutes.\n", + "2025-01-29 13:39:29,579 - INFO - logging.py:log():128 - Patient 6 arrives at: 23.023\n", + "2025-01-29 13:39:29,579 - INFO - logging.py:log():128 - Patient 6 is seen by nurse after 23.023 minutes. Consultation length: 19.610 minutes.\n", + "2025-01-29 13:39:29,579 - INFO - logging.py:log():128 - Patient 7 arrives at: 30.223\n", + "2025-01-29 13:39:29,580 - INFO - logging.py:log():128 - Patient 7 is seen by nurse after 30.223 minutes. Consultation length: 9.490 minutes.\n", + "2025-01-29 13:39:29,580 - INFO - logging.py:log():128 - Patient 8 arrives at: 30.487\n", + "2025-01-29 13:39:29,580 - INFO - logging.py:log():128 - Patient 8 is seen by nurse after 30.487 minutes. Consultation length: 41.665 minutes.\n", + "2025-01-29 13:39:29,580 - INFO - logging.py:log():128 - Patient 9 arrives at: 34.089\n", + "2025-01-29 13:39:29,580 - INFO - logging.py:log():128 - Patient 9 is seen by nurse after 34.089 minutes. Consultation length: 5.874 minutes.\n", + "2025-01-29 13:39:29,581 - INFO - logging.py:log():128 - Patient 10 arrives at: 35.270\n", + "2025-01-29 13:39:29,581 - INFO - logging.py:log():128 - Patient 10 is seen by nurse after 35.270 minutes. Consultation length: 27.882 minutes.\n", + "2025-01-29 13:39:29,581 - INFO - logging.py:log():128 - Patient 11 arrives at: 44.470\n", + "2025-01-29 13:39:29,581 - INFO - logging.py:log():128 - Patient 11 is seen by nurse after 44.470 minutes. Consultation length: 24.915 minutes.\n", + "2025-01-29 13:39:29,582 - INFO - logging.py:log():128 - ──────────\n", + "2025-01-29 13:39:29,582 - INFO - logging.py:log():128 - 50.00: Warm up complete.\n", + "2025-01-29 13:39:29,582 - INFO - logging.py:log():128 - ──────────\n", + "2025-01-29 13:39:29,582 - INFO - logging.py:log():128 - Patient 1 arrives at: 51.904\n", + "2025-01-29 13:39:29,582 - INFO - logging.py:log():128 - Patient 1 is seen by nurse after 51.904 minutes. Consultation length: 18.079 minutes.\n", + "2025-01-29 13:39:29,583 - INFO - logging.py:log():128 - Patient 2 arrives at: 51.963\n", + "2025-01-29 13:39:29,583 - INFO - logging.py:log():128 - Patient 2 is seen by nurse after 51.963 minutes. Consultation length: 3.102 minutes.\n", + "2025-01-29 13:39:29,583 - INFO - logging.py:log():128 - Patient 3 arrives at: 74.349\n", + "2025-01-29 13:39:29,583 - INFO - logging.py:log():128 - Patient 3 is seen by nurse after 74.349 minutes. Consultation length: 26.745 minutes.\n", + "2025-01-29 13:39:29,583 - INFO - logging.py:log():128 - Patient 4 arrives at: 77.534\n", + "2025-01-29 13:39:29,584 - INFO - logging.py:log():128 - Patient 4 is seen by nurse after 77.534 minutes. Consultation length: 0.748 minutes.\n", + "2025-01-29 13:39:29,584 - INFO - logging.py:log():128 - Patient 5 arrives at: 78.932\n", + "2025-01-29 13:39:29,584 - INFO - logging.py:log():128 - Patient 5 is seen by nurse after 78.932 minutes. Consultation length: 0.528 minutes.\n", + "2025-01-29 13:39:29,584 - INFO - logging.py:log():128 - Patient 6 arrives at: 86.815\n", + "2025-01-29 13:39:29,584 - INFO - logging.py:log():128 - Patient 6 is seen by nurse after 86.815 minutes. Consultation length: 2.435 minutes.\n", + "2025-01-29 13:39:29,585 - INFO - logging.py:log():128 - Patient 7 arrives at: 89.783\n", + "2025-01-29 13:39:29,585 - INFO - logging.py:log():128 - Patient 7 is seen by nurse after 89.783 minutes. Consultation length: 9.666 minutes.\n", + "2025-01-29 13:39:29,585 - INFO - logging.py:log():128 - Patient 8 arrives at: 89.807\n", + "2025-01-29 13:39:29,585 - INFO - logging.py:log():128 - Patient 8 is seen by nurse after 89.807 minutes. Consultation length: 7.005 minutes.\n", + "2025-01-29 13:39:29,585 - INFO - logging.py:log():128 - Patient 9 arrives at: 93.118\n", + "2025-01-29 13:39:29,586 - INFO - logging.py:log():128 - Patient 9 is seen by nurse after 93.118 minutes. Consultation length: 20.185 minutes.\n", + "2025-01-29 13:39:29,586 - INFO - logging.py:log():128 - Patient 10 arrives at: 95.598\n", + "2025-01-29 13:39:29,586 - INFO - logging.py:log():128 - Patient 10 is seen by nurse after 95.598 minutes. Consultation length: 7.651 minutes.\n", + "2025-01-29 13:39:29,587 - INFO - logging.py:log():128 - Patient 11 arrives at: 98.019\n", + "2025-01-29 13:39:29,587 - INFO - logging.py:log():128 - Patient 11 is seen by nurse after 98.019 minutes. Consultation length: 27.908 minutes.\n", + "2025-01-29 13:39:29,587 - INFO - logging.py:log():128 - Patient 12 arrives at: 109.379\n", + "2025-01-29 13:39:29,587 - INFO - logging.py:log():128 - Patient 12 is seen by nurse after 109.379 minutes. Consultation length: 16.811 minutes.\n", + "2025-01-29 13:39:29,587 - INFO - logging.py:log():128 - Patient 13 arrives at: 110.322\n", + "2025-01-29 13:39:29,588 - INFO - logging.py:log():128 - Patient 13 is seen by nurse after 110.322 minutes. Consultation length: 24.157 minutes.\n", + "2025-01-29 13:39:29,588 - INFO - logging.py:log():128 - Patient 14 arrives at: 120.342\n", + "2025-01-29 13:39:29,588 - INFO - logging.py:log():128 - Patient 14 is seen by nurse after 120.342 minutes. Consultation length: 1.451 minutes.\n", + "2025-01-29 13:39:29,588 - INFO - logging.py:log():128 - Patient 15 arrives at: 121.643\n", + "2025-01-29 13:39:29,588 - INFO - logging.py:log():128 - Patient 15 is seen by nurse after 121.643 minutes. Consultation length: 1.343 minutes.\n", + "2025-01-29 13:39:29,589 - INFO - logging.py:log():128 - Patient 16 arrives at: 127.827\n", + "2025-01-29 13:39:29,589 - INFO - logging.py:log():128 - Patient 16 is seen by nurse after 127.827 minutes. Consultation length: 1.554 minutes.\n", + "2025-01-29 13:39:29,589 - INFO - logging.py:log():128 - Patient 17 arrives at: 132.055\n", + "2025-01-29 13:39:29,589 - INFO - logging.py:log():128 - Patient 17 is seen by nurse after 132.055 minutes. Consultation length: 15.623 minutes.\n", + "2025-01-29 13:39:29,589 - INFO - logging.py:log():128 - Patient 18 arrives at: 133.617\n", + "2025-01-29 13:39:29,590 - INFO - logging.py:log():128 - Patient 18 is seen by nurse after 133.617 minutes. Consultation length: 4.688 minutes.\n", + "2025-01-29 13:39:29,590 - INFO - logging.py:log():128 - Patient 19 arrives at: 136.346\n", + "2025-01-29 13:39:29,590 - INFO - logging.py:log():128 - Patient 19 is seen by nurse after 136.346 minutes. Consultation length: 2.612 minutes.\n", + "2025-01-29 13:39:29,590 - INFO - logging.py:log():128 - Patient 20 arrives at: 144.142\n", + "2025-01-29 13:39:29,590 - INFO - logging.py:log():128 - Patient 20 is seen by nurse after 144.142 minutes. Consultation length: 6.348 minutes.\n" ] } ], @@ -10155,13 +1770,13 @@ "runtime = round(end_time - start_time)\n", "\n", "# Display converted to minutes and seconds\n", - "print(f\"Notebook run time: {runtime // 60}m {runtime % 60}s\")" + "print(f'Notebook run time: {runtime // 60}m {runtime % 60}s')" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "template-des", "language": "python", "name": "python3" }, diff --git a/notebooks/changes.md b/notebooks/changes.md deleted file mode 100644 index 5b65c4c..0000000 --- a/notebooks/changes.md +++ /dev/null @@ -1,10 +0,0 @@ -## Added - -* `analysis.ipynb`: Added `create_user_controlled_hist` to allow users to decide the KPI histogram to view. - -## Changed - -* `analysis.ipynb`: modified `plot_results_spread` to accept `DataFrame` parameter containing results rather than using a variable with notebook scope. -* `environment.yaml`: patched to include channel. It now installs correctly -* `environment.yaml`: added `nbqa` and `black` to auto-format notebooks. -* `environment.yaml`: added `jinja2` upgraded from 3.1.4 to 3.1.5 due to dependabot security flag. \ No newline at end of file diff --git a/notebooks/choosing_parameters.ipynb b/notebooks/choosing_parameters.ipynb index 44d707e..23e89c6 100644 --- a/notebooks/choosing_parameters.ipynb +++ b/notebooks/choosing_parameters.ipynb @@ -11,19 +11,20 @@ "* Length of warm-up period\n", "* Number of replications\n", "* Number of CPU cores\n", - "* Sampling distributions\n", "\n", "The run time is provided at the end of the notebook.\n", "\n", + "\n", + "\n", "Credit:\n", "\n", - "* Code for choice of warm-up period, replication number and distributions was adapted from Tom Monks (2024) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT License).\n", + "* Code for choice of warm-up period and replication number was adapted from Tom Monks (2024) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence).\n", "* Code for running the model with a varying number of CPU cores was adapted from Sammi Rosser and Dan Chalk (2024) [HSMA - the\n", - " little book of DES](https://github.com/hsma-programme/hsma6_des_book) (MIT License).\n", + " little book of DES](https://github.com/hsma-programme/hsma6_des_book) (MIT Licence).\n", "\n", - "License:\n", + "Licence:\n", "\n", - "* This project is licensed under the MIT License. See the `LICENSE` file for more details." + "* This project is licensed under the MIT Licence. See the `LICENSE` file for more details." ] }, { @@ -79,11 +80,16 @@ "metadata": {}, "outputs": [], "source": [ - "from simulation.model import Defaults, Trial, summary_stats\n", - "import time\n", + "from simulation.model import Defaults, Runner, summary_stats\n", + "\n", "from IPython.display import display\n", + "import os\n", "import pandas as pd\n", "import plotly.express as px\n", + "import plotly.io as pio\n", + "import plotly.graph_objects as go\n", + "import plotly.subplots as sp\n", + "import time\n", "import warnings" ] }, @@ -91,7 +97,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Start timer." + "Display plotly express figures as non-interactive figures. This means they will be visible when browsing the notebooks on GitHub. To switch these back to interactive figures, simply remove this line." ] }, { @@ -99,10 +105,42 @@ "execution_count": 5, "metadata": {}, "outputs": [], + "source": [ + "pio.renderers.default = 'svg'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Start timer." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], "source": [ "notebook_start_time = time.time()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define path to outputs folder" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "output_dir = '../outputs/'" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -118,15 +156,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "def time_series_inspection(data_collection_period, warm_up=None):\n", + "def time_series_inspection(file, data_collection_period, warm_up=None):\n", " \"\"\"\n", " Time series inspection method for determining length of warm-up.\n", "\n", " Arguments:\n", + " file (str):\n", + " Filename to save figure to.\n", " data_collection_period (float):\n", " Length of time to run the simulation for.\n", " warm_up (float, optional):\n", @@ -142,8 +182,8 @@ " # display(param.__dict__)\n", "\n", " # Run model\n", - " choose_warmup = Trial(param)\n", - " choose_warmup.run_trial()\n", + " choose_warmup = Runner(param)\n", + " choose_warmup.run_reps()\n", "\n", " # Filter to nurse results\n", " nurse = choose_warmup.interval_audit_df[\n", @@ -154,6 +194,11 @@ " 'utilisation': 'Cumulative mean nurse utilisation',\n", " 'running_mean_wait_time': 'Running mean nurse wait time'\n", " }\n", + "\n", + " # Create 1x2 subplot\n", + " full_figure = sp.make_subplots(rows=2, cols=1, shared_xaxes=True)\n", + "\n", + " i = 1\n", " for var, label in plot.items():\n", " # Reformat so index is simulation time and columns are each run\n", " reformat = (\n", @@ -169,17 +214,45 @@ " elif var == 'running_mean_wait_time':\n", " cumulative = reformat.copy()\n", "\n", - " # Create plot. If specified, add vertical line to indicate suggested\n", - " # warm-up length\n", - " fig = px.line(cumulative)\n", - " fig.update_layout(\n", - " xaxis_title='Run time (minutes)',\n", - " yaxis_title=label,\n", - " showlegend=False\n", + " # Create plot (using go.Scatter instead of px.express, for sub-plots)\n", + " full_figure.add_trace(\n", + " go.Scatter(\n", + " x=cumulative.index,\n", + " y=cumulative[0],\n", + " mode='lines',\n", + " line=dict(color='royalblue')\n", + " ),\n", + " row=i, col=1\n", " )\n", + "\n", + " # Add y axis label\n", + " full_figure.update_yaxes(title_text=label, row=i, col=1)\n", + "\n", + " # Add vertical line for warm-up period specified\n", " if warm_up is not None:\n", - " fig.add_vline(x=warm_up, line_color='red', line_dash='dash')\n", - " fig.show()" + " full_figure.add_vline(\n", + " x=warm_up, line_color='red', line_dash='dash',\n", + " annotation_text='Suggested warm-up length',\n", + " annotation_position='top left',\n", + " annotation_font_color='red')\n", + " i += 1\n", + "\n", + " # Add x axis title\n", + " full_figure.update_xaxes(title_text='Run time (minutes)')\n", + "\n", + " # Set figure dimensions and hide legend\n", + " full_figure.update_layout(\n", + " width=1400,\n", + " height=800,\n", + " showlegend=False,\n", + " template='plotly_white'\n", + " )\n", + "\n", + " # Show figure\n", + " full_figure.show()\n", + "\n", + " # Save figure\n", + " full_figure.write_image(os.path.join(output_dir, file))" ] }, { @@ -191,1952 +264,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "run=0
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"anchor": "y", - "domain": [ - 0, - 1 - ], - "title": { - "text": "Run time (minutes)" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "Running mean nurse wait time" - } - } - } - } + "image/svg+xml": [ + "0.30.350.40.450.50.550.610k20k30k40k50k0.10.20.30.40.50.60.70.8Run time (minutes)Run time (minutes)Cumulative mean nurse utilisationRunning mean nurse wait timeSuggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" + ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "time_series_inspection(data_collection_period=1440*40, warm_up=1440*13)" + "time_series_inspection(file='choose_param_time_series_2.png',\n", + " data_collection_period=1440*40, warm_up=1440*13)" ] }, { @@ -5889,34 +326,38 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "def confidence_interval_method(replications, metric, desired_precision,\n", - " min_rep=None):\n", + "def confidence_interval_method(file, replications, metric, desired_precision,\n", + " yaxis_title, min_rep=None):\n", " \"\"\"\n", " Use the confidence interval method to select the number of replications.\n", "\n", " Arguments:\n", + " file (str):\n", + " Filename to save figure to.\n", " replications (int):\n", " Number of times to run the model.\n", " metric (string):\n", " Name of performance metric to assess.\n", " desired_precision (float):\n", " Desired mean deviation from confidence interval.\n", + " yaxis_title (str):\n", + " Label for y axis.\n", " min_rep (int):\n", " A suggested minimum number of replications.\n", " \"\"\"\n", " param = Defaults()\n", " param.number_of_runs = replications\n", - " choose_rep = Trial(param)\n", - " choose_rep.run_trial()\n", + " choose_rep = Runner(param)\n", + " choose_rep.run_reps()\n", "\n", " # If mean of metric is less than 1, multiply by 100\n", - " if choose_rep.trial_results_df[metric].mean() < 1:\n", - " choose_rep.trial_results_df[f'adj_{metric}'] = (\n", - " choose_rep.trial_results_df[metric]*100)\n", + " if choose_rep.run_results_df[metric].mean() < 1:\n", + " choose_rep.run_results_df[f'adj_{metric}'] = (\n", + " choose_rep.run_results_df[metric]*100)\n", " metric = f'adj_{metric}'\n", "\n", " # Initialise list to store the results\n", @@ -5926,7 +367,7 @@ " # then perform calculations\n", " for i in range(1, replications+1):\n", " mean, std_dev, ci_lower, ci_upper = summary_stats(\n", - " choose_rep.trial_results_df[metric].iloc[:i])\n", + " choose_rep.run_results_df[metric].iloc[:i])\n", " deviation = ((ci_upper-mean)/mean)*100\n", " cumulative_list.append({\n", " 'replications': i,\n", @@ -5955,16 +396,22 @@ " y=['cumulative_mean', 'lower_ci', 'upper_ci'])\n", " fig.update_layout(\n", " xaxis_title='Number of replications',\n", - 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"## Run time with varying number of CPU cores" + "## Run time with varying number of CPU cores\n", + "\n", + "These are rounded to the nearest .5 seconds, as we will see minor differences in run time between re-runs of this notebook on the same machine, so rounding prevents the figure from changing with every run!" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Running with cores: 1.\n", + "Running with cores: 2.\n", + "Running with cores: 3.\n", + "Running with cores: 4.\n", + "Running with cores: 5.\n", + "Running with cores: 6.\n", + "Running with cores: 7.\n", + "Running with cores: 8.\n", " cores run_time\n", - "0 1 4.431\n", - "1 2 3.058\n", - "2 3 2.194\n", - "3 4 1.973\n", - "4 5 1.768\n", - "5 6 1.680\n", - "6 7 1.559\n", - "7 8 0.955\n" + "0 1 5.0\n", + "1 2 3.5\n", + "2 3 2.5\n", + "3 4 2.0\n", + "4 5 2.0\n", + "5 6 2.0\n", + "6 7 2.0\n", + "7 8 1.0\n" ] }, { "data": { - 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"linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "title": { - "text": "Number of cores" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "Run time (seconds)" - } - } - } - } + "image/svg+xml": [ + "1234567811.522.533.544.55Number of coresRun time (rounded to nearest .5 seconds)" + ] }, "metadata": {}, "output_type": "display_data" @@ -11444,25 +1685,32 @@ "# Run with 1 to 8 cores\n", "speed = []\n", "for i in range(1, 9):\n", + " print(f'Running with cores: {i}.')\n", " start_time = time.time()\n", "\n", " param = Defaults()\n", " param.cores = i\n", - " my_trial = Trial(param)\n", - " my_trial.run_trial()\n", + " experiment = Runner(param)\n", + " experiment.run_reps()\n", "\n", - " run_time = round((time.time() - start_time), 3)\n", + " # Round time to nearest .5 seconds\n", + " run_time = round((time.time() - start_time)*2)/2\n", " speed.append({'cores': i, 'run_time': run_time})\n", "\n", - "# Display and plot time by number of cores\n", + "# Plot time by number of cores\n", "timing_results = pd.DataFrame(speed)\n", "print(timing_results)\n", "fig = px.line(timing_results, x='cores', y='run_time')\n", "fig.update_layout(\n", " xaxis_title='Number of cores',\n", - " yaxis_title='Run time (seconds)'\n", + " yaxis_title='Run time (rounded to nearest .5 seconds)',\n", + " template='plotly_white'\n", ")\n", - "fig.show()" + "\n", + "# Display and save figure\n", + "fig.show()\n", + "fig.write_image(os.path.join(output_dir, 'choose_param_cores.png'),\n", + " width=800, height=600)" ] }, { @@ -11474,14 +1722,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 22s\n" + "Notebook run time: 0m 25s\n" ] } ], diff --git a/notebooks/generate_exp_results.ipynb b/notebooks/generate_exp_results.ipynb index 1cf2832..1ae6846 100644 --- a/notebooks/generate_exp_results.ipynb +++ b/notebooks/generate_exp_results.ipynb @@ -67,7 +67,7 @@ "outputs": [], "source": [ "# Import required packages\n", - "from simulation.model import Defaults, Trial\n", + "from simulation.model import Defaults, Runner\n", "import os\n", "import time" ] @@ -131,9 +131,9 @@ "param.scenario_name = 0\n", "param.cores = 1\n", "\n", - "# Run the trial\n", - "trial = Trial(param)\n", - "trial.run_trial()" + "# Run the replications\n", + "experiment = Runner(param)\n", + "experiment.run_reps()" ] }, { @@ -286,8 +286,8 @@ ], "source": [ "# Patient-level results\n", - "display(trial.patient_results_df)\n", - "trial.patient_results_df.to_csv(\n", + "display(experiment.patient_results_df)\n", + "experiment.patient_results_df.to_csv(\n", " os.path.join(tests, 'patient.csv'), index=False)" ] }, @@ -323,6 +323,8 @@ " mean_q_time_nurse\n", " mean_time_with_nurse\n", " mean_nurse_utilisation\n", + " mean_nurse_utilisation_tw\n", + " mean_nurse_q_length\n", " \n", " \n", " \n", @@ -334,6 +336,8 @@ " 2.065396\n", " 9.622971\n", " 0.643006\n", + " 0.643006\n", + " 0.554903\n", " \n", " \n", " 1\n", @@ -343,6 +347,8 @@ " 1.396755\n", " 10.186235\n", " 0.645640\n", + " 0.647690\n", + " 0.355707\n", " \n", " \n", " 2\n", @@ -352,6 +358,8 @@ " 3.484284\n", " 10.733822\n", " 0.660790\n", + " 0.668319\n", + " 0.858219\n", " \n", " \n", " 3\n", @@ -361,6 +369,8 @@ " 2.050522\n", " 10.511447\n", " 0.634541\n", + " 0.638407\n", + " 0.502188\n", " \n", " \n", " 4\n", @@ -370,6 +380,8 @@ " 1.836706\n", " 9.742127\n", " 0.597097\n", + " 0.601461\n", + " 0.451830\n", " \n", " \n", "\n", @@ -383,12 +395,12 @@ "3 3 0 367 2.050522 10.511447 \n", "4 4 0 369 1.836706 9.742127 \n", "\n", - " mean_nurse_utilisation \n", - "0 0.643006 \n", - "1 0.645640 \n", - "2 0.660790 \n", - "3 0.634541 \n", - "4 0.597097 " + " mean_nurse_utilisation mean_nurse_utilisation_tw mean_nurse_q_length \n", + "0 0.643006 0.643006 0.554903 \n", + "1 0.645640 0.647690 0.355707 \n", + "2 0.660790 0.668319 0.858219 \n", + "3 0.634541 0.638407 0.502188 \n", + "4 0.597097 0.601461 0.451830 " ] }, "metadata": {}, @@ -396,10 +408,10 @@ } ], "source": [ - "# Trial-level results\n", - "display(trial.trial_results_df)\n", - "trial.trial_results_df.to_csv(\n", - " os.path.join(tests, 'trial.csv'), index=False)" + "# Run results\n", + "display(experiment.run_results_df)\n", + "experiment.run_results_df.to_csv(\n", + " os.path.join(tests, 'run.csv'), index=False)" ] }, { @@ -577,8 +589,8 @@ ], "source": [ "# Interval audit results\n", - "display(trial.interval_audit_df)\n", - "trial.interval_audit_df.to_csv(\n", + "display(experiment.interval_audit_df)\n", + "experiment.interval_audit_df.to_csv(\n", " os.path.join(tests, 'interval.csv'), index=False)" ] }, @@ -612,6 +624,8 @@ " mean_q_time_nurse\n", " mean_time_with_nurse\n", " mean_nurse_utilisation\n", + " mean_nurse_utilisation_tw\n", + " mean_nurse_q_length\n", " \n", " \n", " \n", @@ -621,6 +635,8 @@ " 2.166733\n", " 10.159320\n", " 0.636215\n", + " 0.639776\n", + " 0.544569\n", " \n", " \n", " std_dev\n", @@ -628,6 +644,8 @@ " 0.784458\n", " 0.478668\n", " 0.023832\n", + " 0.024278\n", + " 0.190098\n", " \n", " \n", " lower_95_ci\n", @@ -635,6 +653,8 @@ " 1.192699\n", " 9.564976\n", " 0.606623\n", + " 0.609631\n", + " 0.308532\n", " \n", " \n", " upper_95_ci\n", @@ -642,6 +662,8 @@ " 3.140766\n", " 10.753665\n", " 0.665806\n", + " 0.669921\n", + " 0.780607\n", " \n", " \n", "\n", @@ -654,11 +676,17 @@ "lower_95_ci 359.128341 1.192699 9.564976 \n", "upper_95_ci 396.871659 3.140766 10.753665 \n", "\n", - " mean_nurse_utilisation \n", - "mean 0.636215 \n", - "std_dev 0.023832 \n", - "lower_95_ci 0.606623 \n", - "upper_95_ci 0.665806 " + " mean_nurse_utilisation mean_nurse_utilisation_tw \\\n", + "mean 0.636215 0.639776 \n", + "std_dev 0.023832 0.024278 \n", + "lower_95_ci 0.606623 0.609631 \n", + "upper_95_ci 0.665806 0.669921 \n", + "\n", + " mean_nurse_q_length \n", + "mean 0.544569 \n", + "std_dev 0.190098 \n", + "lower_95_ci 0.308532 \n", + "upper_95_ci 0.780607 " ] }, "metadata": {}, @@ -667,8 +695,8 @@ ], "source": [ "# Overall results\n", - "display(trial.overall_results_df)\n", - "trial.overall_results_df.to_csv(\n", + "display(experiment.overall_results_df)\n", + "experiment.overall_results_df.to_csv(\n", " os.path.join(tests, 'overall.csv'), index=True)" ] }, diff --git a/outputs/choose_param_conf_int_1.png b/outputs/choose_param_conf_int_1.png new file mode 100644 index 0000000..1df7b26 Binary files /dev/null and b/outputs/choose_param_conf_int_1.png differ diff --git a/outputs/choose_param_conf_int_2.png b/outputs/choose_param_conf_int_2.png new file mode 100644 index 0000000..1aae52e Binary files /dev/null and b/outputs/choose_param_conf_int_2.png differ diff --git a/outputs/choose_param_conf_int_3.png b/outputs/choose_param_conf_int_3.png new file mode 100644 index 0000000..26c64e3 Binary files /dev/null and b/outputs/choose_param_conf_int_3.png differ diff --git a/outputs/choose_param_cores.png b/outputs/choose_param_cores.png new file mode 100644 index 0000000..5693345 Binary files /dev/null and b/outputs/choose_param_cores.png differ diff --git a/outputs/choose_param_time_series_1.png b/outputs/choose_param_time_series_1.png new file mode 100644 index 0000000..cae4d17 Binary files /dev/null and b/outputs/choose_param_time_series_1.png differ diff --git a/outputs/choose_param_time_series_2.png b/outputs/choose_param_time_series_2.png new file mode 100644 index 0000000..8eba128 Binary files /dev/null and b/outputs/choose_param_time_series_2.png differ diff --git a/outputs/example_overall.csv b/outputs/example_overall.csv index 3a0741e..8deedfd 100644 --- a/outputs/example_overall.csv +++ b/outputs/example_overall.csv @@ -1,5 +1,5 @@ -arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation -10776.741935483871,0.499036905566402,9.978456743390534,0.49767035119563796 -115.80327218806197,0.06739346957542013,0.11513823236530764,0.007524465631261531 -10734.264952313013,0.47431678135150124,9.936223698796068,0.4949103549196282 -10819.21891865473,0.5237570297813028,10.020689787985,0.5004303474716477 +arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length +10776.741935483871,0.499036905566402,9.978456743390534,0.49767035119563796,0.497804034286856,0.12459850290556283 +115.80327218806197,0.06739346957542013,0.11513823236530764,0.007524465631261531,0.0075405891452702024,0.017212719807337778 +10734.264952313013,0.47431678135150124,9.936223698796068,0.4949103549196282,0.4950381238581607,0.11828482630760263 +10819.21891865473,0.5237570297813028,10.020689787985,0.5004303474716477,0.5005699447155513,0.13091217950352302 diff --git a/outputs/example_trial.csv b/outputs/example_run.csv similarity index 58% rename from outputs/example_trial.csv rename to outputs/example_run.csv index f5dd427..cdec45f 100644 --- a/outputs/example_trial.csv +++ b/outputs/example_run.csv @@ -1,32 +1,32 @@ -run_number,scenario,arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation -0,0,10972,0.504541081338615,9.84226781662332,0.4996386466177992 -1,0,10784,0.514150649003393,10.060480983450425,0.5019905433327594 -2,0,10854,0.5232349226016817,9.925024519746302,0.4981297032213408 -3,0,10831,0.4791488631810612,9.9370571543943,0.49822049332297624 -4,0,10720,0.46145745726579823,10.015904147971671,0.4968703372055205 -5,0,10772,0.3882646868128185,9.884995942861282,0.492904233058074 -6,0,10831,0.4669381081669874,10.041799654744745,0.503356251584998 -7,0,10781,0.625888360901447,10.086979128063648,0.5034489381047406 -8,0,10772,0.4684971326393017,10.202270228377186,0.508790994907774 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+30,0,10796,0.5102078888697092,9.922063713004379,0.49578999982774674,0.49597325153194355,0.12767905794112022 diff --git a/outputs/scenario_nurse_util.png b/outputs/scenario_nurse_util.png index 5f4ec00..cc3822e 100644 Binary files a/outputs/scenario_nurse_util.png and b/outputs/scenario_nurse_util.png differ diff --git a/outputs/scenario_nurse_wait.png b/outputs/scenario_nurse_wait.png index 7bea20b..f768431 100644 Binary files a/outputs/scenario_nurse_wait.png and b/outputs/scenario_nurse_wait.png differ diff --git a/outputs/sensitivity_consult_time.png b/outputs/sensitivity_consult_time.png index cc102e2..241803b 100644 Binary files a/outputs/sensitivity_consult_time.png and b/outputs/sensitivity_consult_time.png differ diff --git a/outputs/spread_arrivals.png b/outputs/spread_arrivals.png index c310678..4e08329 100644 Binary files a/outputs/spread_arrivals.png and b/outputs/spread_arrivals.png differ diff --git a/outputs/spread_nurse_time.png b/outputs/spread_nurse_time.png index 88b8860..bc6a988 100644 Binary files a/outputs/spread_nurse_time.png and b/outputs/spread_nurse_time.png differ diff --git a/outputs/spread_nurse_util.png b/outputs/spread_nurse_util.png index a0b7f03..2b9cd72 100644 Binary files a/outputs/spread_nurse_util.png and b/outputs/spread_nurse_util.png differ diff --git a/outputs/spread_nurse_wait.png b/outputs/spread_nurse_wait.png index 5080c0a..d864b5f 100644 Binary files a/outputs/spread_nurse_wait.png and b/outputs/spread_nurse_wait.png differ diff --git a/requirements.txt b/requirements.txt index 5a312ed..2a6123d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,6 +10,8 @@ pip==24.3.1 plotly_express==0.4.1 pycodestyle==2.12.1 pycodestyle_magic==0.5 +pylint==3.3.3 pytest==8.3.4 +pytest-xdist==3.6.1 simpy==4.1.1 -e .[dev] \ No newline at end of file diff --git a/simulation/logging.py b/simulation/logging.py index e7744aa..1549a91 100644 --- a/simulation/logging.py +++ b/simulation/logging.py @@ -7,7 +7,7 @@ > This code is adapted from NHS Digital (2024) RAP repository template (https://github.com/NHSDigital/rap-package-template) (MIT Licence). -License: +Licence: This project is licensed under the MIT Licence. See the LICENSE file for more details. diff --git a/simulation/model.py b/simulation/model.py index 49bfbd0..2a37de8 100644 --- a/simulation/model.py +++ b/simulation/model.py @@ -9,33 +9,40 @@ > This code is adapted from Sammi Rosser and Dan Chalk (2024) HSMA - the little book of DES (https://github.com/hsma-programme/hsma6_des_book) (MIT Licence). - > The distribution class is copied from Tom Monks (2021) sim-tools: + > The Exponential class is based on Tom Monks (2021) sim-tools: fundamental tools to support the simulation process in python (https://github.com/TomMonks/sim-tools) (MIT Licence). For other distributions (bernoulli, lognormal, normal, uniform, triangular, fixed, combination, continuous empirical, erlang, weibull, gamma, beta, discrete, truncated, raw empirical, pearsonV, pearsonVI, erlangK, poisson), check out the sim-tools package. + > The MonitoredResource class is based on Tom Monks, Alison Harper and Amy + Heather (2025) An introduction to Discrete-Event Simulation (DES) using + Free and Open Source Software + (https://github.com/pythonhealthdatascience/intro-open-sim/tree/main) + (MIT Licence). They based it on the method described in Law. Simulation + Modeling and Analysis 4th Ed. Pages 14 - 17. Licence: This project is licensed under the MIT Licence. See the LICENSE file for more details. Typical usage example: - trial = Trial(param=Defaults()) - trial.run_trial() - print(trial.trial_results_df) + experiment = Runner(param=Defaults()) + experiment.run_reps() + print(experiment.run_results_df) """ +import itertools from joblib import Parallel, delayed -from simulation.logging import SimLogger import numpy as np import pandas as pd import scipy.stats as st import simpy +from simulation.logging import SimLogger -class Defaults(): +class Defaults: """ Default parameters for simulation. @@ -91,12 +98,25 @@ def __setattr__(self, name, value): """ Prevent addition of new attributes. - Only allow modification of existing attributes, and not the addition - of new attributes. This helps avoid an error where a parameter appears - to have been changed, but remains the same as the attribute name - used was incorrect. + This method overrides the default `__setattr__` behavior to restrict + the addition of new attributes to the instance. It allows modification + of existing attributes but raises an `AttributeError` if an attempt is + made to create a new attribute. This ensures that accidental typos in + attribute names do not silently create new attributes. + + Arguments: + name (str): + The name of the attribute to set. + value (Any): + The value to assign to the attribute. + + Raises: + AttributeError: + If `name` is not an existing attribute and an attempt is made + to add it to the instance. """ # Skip the check if the object is still initialising + # pylint: disable=maybe-no-member if hasattr(self, '_initialising') and self._initialising: super().__setattr__(name, value) else: @@ -116,6 +136,7 @@ def summary_stats(data): Arguments: data (pd.Series): Data to use in calculation. + Returns: tuple: (mean, standard deviation, CI lower, CI upper). """ @@ -168,6 +189,118 @@ def __init__(self, patient_id): self.time_with_nurse = np.nan +class MonitoredResource(simpy.Resource): + """ + Subclass of simpy.Resource used to monitor resource usage during the run. + + Calculates resource utilisation and the queue length during the model run. + As it is a subclass, it inherits the attributes and methods from + simpy.Resource, which is referred to as the superclass or parent class. + + Attributes: + time_last_event (float): + Time of last resource request or release. + area_n_in_queue (float): + Total time that patients have spent queueing for the resource + (i.e. sum of the times each patient spent waiting). Used to + calculate the average queue length. + area_resource_busy (float): + Total time that resources have been in use during the simulation + (i.e. sum of the times each individual resource was busy). Used + to calculated utilisation. + """ + def __init__(self, *args, **kwargs): + """ + Initialises a MonitoredResource - which involves initialising a SimPy + resource and resetting monitoring attributes. + + Arguments: + *args: + Positional arguments to be passed to the parent class. + **kwargs: + Keyword arguments to be passed to the parent class. + """ + # Initialise a SimPy Resource + super().__init__(*args, **kwargs) + # Run the init_results() method + self.init_results() + + def init_results(self): + """ + Resets monitoring attributes to initial values. + """ + self.time_last_event = self._env.now + self.area_n_in_queue = 0.0 + self.area_resource_busy = 0.0 + + def request(self, *args, **kwargs): + """ + Requests a resource, but updates time-weighted statistics BEFORE + making the request. + + Arguments: + *args: + Positional arguments to be passed to the parent class. + **kwargs: + Keyword arguments to be passed to the parent class. + + Returns: + simpy.events.Event: Event representing the request. + """ + # Update time-weighted statistics + self.update_time_weighted_stats() + # Request a resource + return super().request(*args, **kwargs) + + def release(self, *args, **kwargs): + """ + Releases a resource, but updates time-weighted statistics BEFORE + releasing it. + + Arguments: + *args: + Positional arguments to be passed to the parent class. + **kwargs: + Keyword arguments to be passed to the parent class.# + + Returns: + simpy.events.Event: Event representing the request. + """ + # Update time-weighted statistics + self.update_time_weighted_stats() + # Release a resource + return super().release(*args, **kwargs) + + def update_time_weighted_stats(self): + """ + Update the time-weighted statistics for resource usage. + + Between every request or release of the resource, it calculates the + relevant statistics - e.g.: + - Total queue time (number of requests in queue * time) + - Total resource use (number of resources in use * time) + These are summed to return the totals from across the whole simulation. + In Runner.run_single(), these are then used to calculate utilisation. + + Further details: + - These sums can be referred to as "the area under the curve". + - They are called "time-weighted" statistics as they account for how + long certain events or states (such as resource use or queue length) + persist over time. + """ + # Calculate time since last event + time_since_last_event = self._env.now - self.time_last_event + + # Update self.time_last_event to current time + self.time_last_event = self._env.now + + # Update the statistics + # len(self.queue) is the number of requests queued + self.area_n_in_queue += len(self.queue) * time_since_last_event + # self.count is the number of resources in use + self.area_resource_busy += self.count * time_since_last_event + + class Exponential: """ Generate samples from an exponential distribution. @@ -175,11 +308,8 @@ class Exponential: Attributes: mean (float): Mean of the exponential distribution. - random_seed (int|None): - Random seed to reproduce samples. - size (int|None): - Number of samples to return. If set to none, then returns a single - sample. + rand (numpy.random.Generator): + Random number generator for producing samples. """ def __init__(self, mean, random_seed): """ @@ -202,6 +332,11 @@ def sample(self, size=None): size (int|None): Number of samples to return. If set to none, then returns a single sample. + + Returns: + float or numpy.ndarray: + A single sample if size is None, or an array of samples if + size is specified. """ return self.rand.exponential(self.mean, size=size) @@ -220,8 +355,9 @@ class Model: Run number for random seed generation. env (simpy.Environment): The SimPy environment for the simulation. - nurse (simpy.Resource): - SimPy resource representing nurses. + nurse (MonitoredResource): + Subclass of SimPy resource representing nurses (whilst monitoring + the resource during the simulation run). patients (list): List containing the generated patient objects. nurse_time_used (float): @@ -259,8 +395,8 @@ def __init__(self, param, run_number): # Create simpy environment and resource self.env = simpy.Environment() - self.nurse = simpy.Resource(self.env, - capacity=self.param.number_of_nurses) + self.nurse = MonitoredResource(self.env, + capacity=self.param.number_of_nurses) # Initialise attributes to store results self.patients = [] @@ -317,14 +453,10 @@ def generate_patient_arrivals(self): p = Patient(len(self.patients) + 1) p.arrival_time = self.env.now - # If the warm-up period has passed, add the patient to the list. + # Add the patient to the list. # The list stores a reference to the patient object, so any updates # to the patient attributes will be reflected in the list as well - if self.env.now >= self.param.warm_up_period: - self.patients.append(p) - # If still during warm-up, amend ID (so not set to 1) - else: - p.patient_id = 'X (warm-up)' + self.patients.append(p) # Log arrival time self.param.logger.log( @@ -371,16 +503,15 @@ def attend_clinic(self, patient): f'{patient.time_with_nurse:.3f} minutes.' ) - # If warm-up period has passed, update the total nurse time used. + # Update the total nurse time used. # This is used to calculate utilisation. To avoid overestimation, # if the consultation would overrun the simulation, just record # time to end of the simulation. - if self.env.now >= self.param.warm_up_period: - remaining_time = ( - self.param.warm_up_period + - self.param.data_collection_period) - self.env.now - self.nurse_time_used += min( - patient.time_with_nurse, remaining_time) + remaining_time = ( + self.param.warm_up_period + + self.param.data_collection_period) - self.env.now + self.nurse_time_used += min( + patient.time_with_nurse, remaining_time) # Pass time spent with nurse yield self.env.timeout(patient.time_with_nurse) @@ -399,59 +530,103 @@ def interval_audit(self, interval): Time between audits in minutes. """ while True: - # Only save results if the warm-up period has passed - if self.env.now >= self.param.warm_up_period: - self.audit_list.append({ - 'resource_name': 'nurse', - 'simulation_time': self.env.now, - 'utilisation': self.nurse.count / self.nurse.capacity, - 'queue_length': len(self.nurse.queue), - 'running_mean_wait_time': self.running_mean_nurse_wait - }) + self.audit_list.append({ + 'resource_name': 'nurse', + 'simulation_time': self.env.now, + 'utilisation': self.nurse.count / self.nurse.capacity, + 'queue_length': len(self.nurse.queue), + 'running_mean_wait_time': self.running_mean_nurse_wait + }) # Trigger next audit after desired interval has passed yield self.env.timeout(interval) + def init_results_variables(self): + """ + Resets all results collection variables to their initial values. + """ + self.patients = [] + self.nurse_time_used = 0 + self.audit_list = [] + self.nurse.init_results() + + def warm_up_complete(self): + """ + Resets all results collection variables once warm-up period has passed. + """ + # Delay process until warm-up period has completed + yield self.env.timeout(self.param.warm_up_period) + + # Reset results collection variables + self.init_results_variables() + + # If there was a warm-up period, log that this time has passed so we + # can distinguish between patients before and after warm-up in logs + if self.param.warm_up_period > 0: + self.param.logger.log('─' * 10) + self.param.logger.log(f'{self.env.now:.2f}: Warm up complete.') + self.param.logger.log('─' * 10) + def run(self): """ Runs the simulation for the specified duration. """ - # Start patient generator + # Calculate the total run length + run_length = (self.param.warm_up_period + + self.param.data_collection_period) + + # Schedule process which will reset results when warm-up period ends + self.env.process(self.warm_up_complete()) + + # Schedule patient generator to run during simulation self.env.process(self.generate_patient_arrivals()) - # Start interval auditor + # Schedule interval auditor to run during simulation self.env.process( self.interval_audit(interval=self.param.audit_interval)) - # Run for specified duration (which is the warm-up period + the - # data collection period) - self.env.run(until=self.param.data_collection_period + - self.param.warm_up_period) + # Run the simulation + self.env.run(until=run_length) + + # If the simulation ends while resources are still in use or requests + # are still in the queue, the time between the last recorded event and + # the simulation end will not have been accounted for. Hence, we call + # update_time_weighted_stats() to run for last event --> end. + self.nurse.update_time_weighted_stats() + + # Error handling - if there was no data collection period, the + # simulation ends before it has a chance to reset the results, + # so we do so manually + if self.param.data_collection_period == 0: + self.init_results_variables() # Convert list of patient objects into a list that just contains the # attributes of each of those patients as dictionaries self.results_list = [x.__dict__ for x in self.patients] -class Trial: +class Runner: """ - Manages multiple simulation runs. + Run the simulation. + + Manages simulation runs, either running the model once or multiple times + (replications). Attributes: param (Defaults): Simulation parameters. patient_results_df (pandas.DataFrame): Dataframe to store patient-level results. - trial_results_df (pandas.DataFrame): - Dataframe to store trial-level results. + run_results_df (pandas.DataFrame): + Dataframe to store results from each run. interval_audit_df (pandas.DataFrame): Dataframe to store interval audit results. overall_results_df (pandas.DataFrame): - Dataframe to store average results from runs of the trial. + Dataframe to store average results from across the runs. """ def __init__(self, param): ''' - Initialise a new instance of the trial class. + Initialise a new instance of the Runner class. Arguments: param (Defaults): @@ -461,7 +636,7 @@ def __init__(self, param): self.param = param # Initialise empty dataframes to store results self.patient_results_df = pd.DataFrame() - self.trial_results_df = pd.DataFrame() + self.run_results_df = pd.DataFrame() self.interval_audit_df = pd.DataFrame() self.overall_results_df = pd.DataFrame() @@ -475,8 +650,8 @@ def run_single(self, run): Returns: dict: - A dictionary containing the patient-level results, trial-level - results, and interval audit results. + A dictionary containing the patient-level results, results + from each run, and interval audit results. """ # Run model model = Model(param=self.param, run_number=run) @@ -486,8 +661,9 @@ def run_single(self, run): patient_results = pd.DataFrame(model.results_list) patient_results['run'] = run - # Create dictionary recording the trial-level results - trial_results = { + # Create dictionary recording the run results + # Currently has two alternative methods of measuring utilisation + run_results = { 'run_number': run, 'scenario': self.param.scenario_name, 'arrivals': len(patient_results), @@ -495,7 +671,12 @@ def run_single(self, run): 'mean_time_with_nurse': patient_results['time_with_nurse'].mean(), 'mean_nurse_utilisation': (model.nurse_time_used / (self.param.number_of_nurses * - self.param.data_collection_period)) + self.param.data_collection_period)), + 'mean_nurse_utilisation_tw': (model.nurse.area_resource_busy / + (self.param.number_of_nurses * + self.param.data_collection_period)), + 'mean_nurse_q_length': (model.nurse.area_n_in_queue / + self.param.data_collection_period) } # Convert interval audit results to a dataframe and add run column @@ -504,16 +685,15 @@ def run_single(self, run): return { 'patient': patient_results, - 'trial': trial_results, + 'run': run_results, 'interval_audit': interval_audit_df } - def run_trial(self): + def run_reps(self): """ Execute a single model configuration for multiple runs/replications. - This is known as a trial, experiment, batch or scenario. These can be - run sequentially or in parallel. + These can be run sequentially or in parallel. """ # Sequential execution if self.param.cores == 1: @@ -528,28 +708,70 @@ def run_trial(self): # Seperate results from each run into appropriate lists patient_results_list = [ result['patient'] for result in all_results] - trial_results_list = [ - result['trial'] for result in all_results] + run_results_list = [ + result['run'] for result in all_results] interval_audit_list = [ result['interval_audit'] for result in all_results] # Convert lists into dataframes self.patient_results_df = pd.concat(patient_results_list, ignore_index=True) - self.trial_results_df = pd.DataFrame(trial_results_list) + self.run_results_df = pd.DataFrame(run_results_list) self.interval_audit_df = pd.concat(interval_audit_list, ignore_index=True) - # Calculate average results and uncertainty from across all trials + # Calculate average results and uncertainty from across all runs uncertainty_metrics = {} - trial_col = self.trial_results_df.columns + run_col = self.run_results_df.columns - # Loop through the trial-level performance measure columns + # Loop through the run performance measure columns # Calculate mean, standard deviation and 95% confidence interval - for col in trial_col[~trial_col.isin(['run_number', 'scenario'])]: + for col in run_col[~run_col.isin(['run_number', 'scenario'])]: uncertainty_metrics[col] = dict(zip( ['mean', 'std_dev', 'lower_95_ci', 'upper_95_ci'], - summary_stats(self.trial_results_df[col]) + summary_stats(self.run_results_df[col]) )) # Convert to dataframe self.overall_results_df = pd.DataFrame(uncertainty_metrics) + + +def run_scenarios(scenarios): + """ + Execute a set of scenarios and return the results from each run. + + Arguments: + scenarios (dict): + Dictionary where key is name of parameter and value is a list + with different values to run in scenarios. + + Returns: + pandas.dataframe: + Dataframe with results from each run of each scenario. + """ + # Find every possible permutation of the scenarios + all_scenarios_tuples = list(itertools.product(*scenarios.values())) + # Convert back into dictionaries + all_scenarios_dicts = [ + dict(zip(scenarios.keys(), p)) for p in all_scenarios_tuples] + # Preview some of the scenarios + print(f'There are {len(all_scenarios_dicts)} scenarios. Running:') + + # Run the scenarios... + results = [] + for index, scenario_to_run in enumerate(all_scenarios_dicts): + print(scenario_to_run) + + # Overwrite defaults from the passed dictionary + param = Defaults() + param.scenario_name = index + for key in scenario_to_run: + setattr(param, key, scenario_to_run[key]) + + # Perform replications and keep results from each run, adding the + # scenario values to the results dataframe + scenario_exp = Runner(param) + scenario_exp.run_reps() + for key in scenario_to_run: + scenario_exp.run_results_df[key] = scenario_to_run[key] + results.append(scenario_exp.run_results_df) + return pd.concat(results) diff --git a/tests/exp_results/overall.csv b/tests/exp_results/overall.csv index db7bde0..dbf57c7 100644 --- a/tests/exp_results/overall.csv +++ b/tests/exp_results/overall.csv @@ -1,5 +1,5 @@ -,arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation -mean,378.0,2.1667325673589564,10.159320371752361,0.6362147508884318 -std_dev,15.198684153570664,0.7844583147114635,0.4786676748781607,0.02383218108751965 -lower_95_ci,359.12834106644124,1.192698919890132,9.564975952752244,0.606623189633386 -upper_95_ci,396.87165893355876,3.1407662148277806,10.75366479075248,0.6658063121434776 +,arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length +mean,378.0,2.1667325673589564,10.159320371752361,0.6362147508884318,0.6397764054245421,0.5445692746656905 +std_dev,15.198684153570664,0.7844583147114635,0.4786676748781607,0.02383218108751965,0.024277980144559095,0.19009766156834382 +lower_95_ci,359.12834106644124,1.192698919890132,9.564975952752244,0.606623189633386,0.6096313115299372,0.3085318521535542 +upper_95_ci,396.87165893355876,3.1407662148277806,10.75366479075248,0.6658063121434776,0.6699214993191469,0.7806066971778267 diff --git a/tests/exp_results/run.csv b/tests/exp_results/run.csv new file mode 100644 index 0000000..b7c840d --- /dev/null +++ b/tests/exp_results/run.csv @@ -0,0 +1,6 @@ +run_number,scenario,arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length +0,0,403,2.065395937787463,9.622970542788952,0.6430060048077357,0.6430060048077361,0.5549030419522317 +1,0,382,1.3967545762785822,10.18623477209793,0.6456396558371599,0.6476900423541169,0.35570683209227894 +2,0,369,3.4842840540548505,10.733822459914006,0.6607899582685668,0.6683185188812463,0.8582185938875108 +3,0,367,2.0505219928782124,10.511446608041043,0.6345411517895263,0.6384068134475295,0.5021881615506957 +4,0,369,1.8367062757956718,9.742127475919874,0.5970969837391709,0.6014606476320816,0.45182974384573527 diff --git a/tests/exp_results/trial.csv b/tests/exp_results/trial.csv deleted file mode 100644 index b443878..0000000 --- a/tests/exp_results/trial.csv +++ /dev/null @@ -1,6 +0,0 @@ -run_number,scenario,arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation -0,0,403,2.065395937787463,9.622970542788952,0.6430060048077357 -1,0,382,1.3967545762785822,10.18623477209793,0.6456396558371599 -2,0,369,3.4842840540548505,10.733822459914006,0.6607899582685668 -3,0,367,2.0505219928782124,10.511446608041043,0.6345411517895263 -4,0,369,1.8367062757956718,9.742127475919874,0.5970969837391709 diff --git a/tests/test_backtest.py b/tests/test_backtest.py index 3bcdf4b..61d97f9 100644 --- a/tests/test_backtest.py +++ b/tests/test_backtest.py @@ -2,7 +2,7 @@ These check that the model code produces results consistent with prior code. -License: +Licence: This project is licensed under the MIT Licence. See the LICENSE file for more details. @@ -12,9 +12,9 @@ """ -from simulation.model import Defaults, Trial -import pandas as pd from pathlib import Path +import pandas as pd +from simulation.model import Defaults, Runner def test_reproduction(): @@ -34,26 +34,26 @@ def test_reproduction(): param.scenario_name = 0 param.cores = 1 - # Run the trial - trial = Trial(param) - trial.run_trial() + # Run the replications + experiment = Runner(param) + experiment.run_reps() # Compare patient-level results exp_patient = pd.read_csv( Path(__file__).parent.joinpath('exp_results/patient.csv')) - pd.testing.assert_frame_equal(trial.patient_results_df, exp_patient) + pd.testing.assert_frame_equal(experiment.patient_results_df, exp_patient) - # Compare trial-level results - exp_trial = pd.read_csv( - Path(__file__).parent.joinpath('exp_results/trial.csv')) - pd.testing.assert_frame_equal(trial.trial_results_df, exp_trial) + # Compare run results + exp_run = pd.read_csv( + Path(__file__).parent.joinpath('exp_results/run.csv')) + pd.testing.assert_frame_equal(experiment.run_results_df, exp_run) # Compare interval audit results exp_interval = pd.read_csv( Path(__file__).parent.joinpath('exp_results/interval.csv')) - pd.testing.assert_frame_equal(trial.interval_audit_df, exp_interval) + pd.testing.assert_frame_equal(experiment.interval_audit_df, exp_interval) # Compare overall results exp_overall = pd.read_csv( Path(__file__).parent.joinpath('exp_results/overall.csv'), index_col=0) - pd.testing.assert_frame_equal(trial.overall_results_df, exp_overall) + pd.testing.assert_frame_equal(experiment.overall_results_df, exp_overall) diff --git a/tests/test_unittest_logger.py b/tests/test_unittest_logger.py index 98c70cf..ef447c5 100644 --- a/tests/test_unittest_logger.py +++ b/tests/test_unittest_logger.py @@ -3,7 +3,7 @@ These check specific parts of the simulation and code, ensuring they work correctly and as expected. -License: +Licence: This project is licensed under the MIT Licence. See the LICENSE file for more details. @@ -15,9 +15,9 @@ from io import StringIO import logging import os +from unittest.mock import patch, MagicMock import pytest from simulation.logging import SimLogger -from unittest.mock import patch, MagicMock def test_log_to_console(): diff --git a/tests/test_unittest_model.py b/tests/test_unittest_model.py index 733c1ee..3e64e51 100644 --- a/tests/test_unittest_model.py +++ b/tests/test_unittest_model.py @@ -3,7 +3,7 @@ These check specific parts of the simulation and code, ensuring they work correctly and as expected. -License: +Licence: This project is licensed under the MIT Licence. See the LICENSE file for more details. @@ -12,10 +12,12 @@ pytest """ -from simulation.model import Defaults, Exponential, Model, Trial import numpy as np import pandas as pd import pytest +import simpy +from simulation.model import ( + Defaults, Exponential, Model, Runner, MonitoredResource) def test_new_attribute(): @@ -57,7 +59,7 @@ def test_negative_inputs(param_name, value, rule): # Construct the expected error message if rule == 'positive': expected_message = f'Parameter "{param_name}" must be greater than 0.' - elif rule == 'non_negative': + else: expected_message = (f'Parameter "{param_name}" must be greater than ' + 'or equal to 0.') @@ -101,18 +103,18 @@ def test_high_demand(): param = Defaults() param.number_of_nurses = 1 param.patient_inter = 0.1 - trial = Trial(param) - results = trial.run_single(run=0) + experiment = Runner(param) + results = experiment.run_single(run=0) # Check that the utilisation as calculated from total_nurse_time_used # does not exceed 1 or drop below 0 - util = results['trial']['mean_nurse_utilisation'] + util = results['run']['mean_nurse_utilisation'] assert util <= 1, ( - 'The trial `mean_nurse_utilisation` should not exceed 1, but ' + + 'The run `mean_nurse_utilisation` should not exceed 1, but ' + f'found utilisation of {util}.' ) assert util >= 0, ( - 'The trial `mean_nurse_utilisation` should not drop below 0, but ' + + 'The run `mean_nurse_utilisation` should not drop below 0, but ' + f'found utilisation of {util}.' ) @@ -186,8 +188,8 @@ def helper_warmup(warm_up_period): param.patient_inter = 1 param.warm_up_period = warm_up_period param.data_collection_period = 1500 - trial = Trial(param) - return trial.run_single(run=0) + experiment = Runner(param) + return experiment.run_single(run=0) # Run model with and without warm-up period results_warmup = helper_warmup(warm_up_period=500) @@ -201,18 +203,14 @@ def helper_warmup(warm_up_period): # and queue time greater than 0 assert first_warmup['arrival_time'] > 500, ( 'Expect first patient to arrive after time 500 when model is run ' + - f'with warm-up, but got {first_warmup["arrival_time"]}.' + f'with warm-up (length 500), but got {first_warmup["arrival_time"]}.' ) assert first_warmup['q_time_nurse'] > 0, ( 'Expect first patient to need to queue in model with warm-up and ' + f'high arrival rate, but got {first_warmup["q_time_nurse"]}.' ) - # Check that model without warm-up has arrival and queue time of 0 - assert first_none['arrival_time'] == 0, ( - 'Expect first patient to arrive at time 0 when model is run ' + - f'without warm-up, but got {first_none["arrival_time"]}.' - ) + # Check that, in model without warm-up, first patient has 0 queue time assert first_none['q_time_nurse'] == 0, ( 'Expect first patient to have no wait time in model without warm-up ' + f'but got {first_none["q_time_nurse"]}.' @@ -221,19 +219,22 @@ def helper_warmup(warm_up_period): def test_arrivals(): """ - Check that trial-level count of arrivals is consistent with the number of + Check that count of arrivals in each run is consistent with the number of patients recorded in the patient-level results. """ - trial = Trial(Defaults()) - trial.run_trial() + experiment = Runner(Defaults()) + experiment.run_reps() - # Get count of patients from patient-level and trial-level results - patient_n = trial.patient_results_df.groupby('run')['patient_id'].count() - trial_n = trial.trial_results_df['arrivals'] + # Get count of patients from patient-level and run results + patient_n = (experiment + .patient_results_df + .groupby('run')['patient_id'] + .count()) + run_n = experiment.run_results_df['arrivals'] # Compare the counts from each run - assert all(patient_n == trial_n), ( - 'The number of arrivals in the trial-level results should be ' + + assert all(patient_n == run_n), ( + 'The number of arrivals in the results from each run should be ' + 'consistent with the number of patients in the patient-level results.' ) @@ -283,9 +284,9 @@ def helper_param(param_name, value): # Modify chosen parameter for the test setattr(param, param_name, value) - # Run the trial and return the mean queue time for nurses - trial = Trial(param) - return trial.run_single(run=0)['trial'] + # Run replications and return the mean queue time for nurses + experiment = Runner(param) + return experiment.run_single(run=0)['run'] # Run model with initial and adjusted values initial_results = helper_param(param_name, initial_value) @@ -321,14 +322,14 @@ def test_arrivals_decrease(param_name, initial_value, adjusted_value): # Run model with initial value param = Defaults() setattr(param, param_name, initial_value) - trial = Trial(param) - initial_arrivals = trial.run_single(run=0)['trial']['arrivals'] + experiment = Runner(param) + initial_arrivals = experiment.run_single(run=0)['run']['arrivals'] # Run model with adjusted value param = Defaults() setattr(param, param_name, adjusted_value) - trial = Trial(param) - adjusted_arrivals = trial.run_single(run=0)['trial']['arrivals'] + experiment = Runner(param) + adjusted_arrivals = experiment.run_single(run=0)['run']['arrivals'] # Check that arrivals from adjusted model are less assert initial_arrivals > adjusted_arrivals, ( @@ -342,11 +343,11 @@ def test_seed_stability(): """ Check that two runs using the same random seed return the same results. """ - # Run trial twice, with same run number (and therefore same seed) each time - trial1 = Trial(param=Defaults()) - result1 = trial1.run_single(run=33) - trial2 = Trial(param=Defaults()) - result2 = trial2.run_single(run=33) + # Run model twice, with same run number (and therefore same seed) each time + experiment1 = Runner(param=Defaults()) + result1 = experiment1.run_single(run=33) + experiment2 = Runner(param=Defaults()) + result2 = experiment2.run_single(run=33) # Check that dataframes with patient-level results are equal pd.testing.assert_frame_equal(result1['patient'], result2['patient']) @@ -356,10 +357,10 @@ def test_interval_audit_time(): """ Check that length of interval audit is less than the length of simulation. """ - # Run single trial with default parameters and get max time from audit + # Run model once with default parameters and get max time from audit param = Defaults() - trial = Trial(param) - results = trial.run_single(run=0) + experiment = Runner(param) + results = experiment.run_single(run=0) max_time = max(results['interval_audit']['simulation_time']) # Check that max time in audit is less than simulation length @@ -378,7 +379,7 @@ def test_exponentional(): d = Exponential(mean=10, random_seed=42) # Check that sample is a float - assert type(d.sample()) == float, ( + assert isinstance(d.sample(), float), ( f'Expected sample() to return a float - instead: {type(d.sample())}' ) @@ -417,12 +418,90 @@ def test_parallel(): for mode, cores in [('seq', 1), ('par', -1)]: param = Defaults() param.cores = cores - trial = Trial(param) - results[mode] = trial.run_single(run=0) + experiment = Runner(param) + results[mode] = experiment.run_single(run=0) # Verify results are identical pd.testing.assert_frame_equal( results['seq']['patient'], results['par']['patient']) pd.testing.assert_frame_equal( results['seq']['interval_audit'], results['par']['interval_audit']) - assert results['seq']['trial'] == results['par']['trial'] + assert results['seq']['run'] == results['par']['run'] + + +def test_consistent_metrics(): + """ + Presently, the simulation code includes a few different methods to + calculate the same metrics. We would expect each to return similar results + (with a little tolerance, as expect some deviation due to methodological + differences). + """ + # Run default model + experiment = Runner(Defaults()) + experiment.run_reps() + + # Absolute tolerance (atol) = +- 0.001 + + # Check nurse utilisation + pd.testing.assert_series_equal( + experiment.run_results_df['mean_nurse_utilisation'], + experiment.run_results_df['mean_nurse_utilisation_tw'], + atol=0.001, + check_names=False) + + +def test_monitoredresource_cleanup(): + """ + Run simple example and check that the monitored resource calculations + are as expected (e.g. clean-up was performed appropriately at end of + simulation). + """ + # Simulation setup + env = simpy.Environment() + resource = MonitoredResource(env, capacity=1) + + def process_task(env, resource, duration): + """Simulate a task that requests the resource.""" + with resource.request() as req: + yield req + yield env.timeout(duration) + + # Set run length + run_length = 12 + + # Schedule tasks to occur during the simulation + env.process(process_task(env, resource, duration=5)) # Task A + env.process(process_task(env, resource, duration=10)) # Task B + env.process(process_task(env, resource, duration=15)) # Task C + + # Run the simulation + env.run(until=run_length) + + # If the simulation ends while resources are still in use or requests are + # still in the queue, the time between the last recorded event and the + # simulation end will not have been accounted for. Hence, we call + # update_time_weighted_stats() to run for time between last event and end. + resource.update_time_weighted_stats() + + # Assertions + # At time=12: + # - Task A is done (0-5) + # - Task B is still using the resource (5-15) + # - Task C is still waiting in the queue (15-30) + # Hence... + + # Expected queue time: 17, as task B (5) + task C (12) + expected_queue_time = 17.0 + + # Expected busy time: 12, as one resource busy for the whole simulation + expected_busy_time = 12.0 + + # Run assertions + assert resource.area_n_in_queue == expected_queue_time, ( + f'Expected queue time {expected_queue_time} but ' + + f'observed {resource.area_n_in_queue}.' + ) + assert resource.area_resource_busy == expected_busy_time, ( + f'Expected queue time {expected_busy_time} but ' + + f'observed {resource.area_resource_busy}.' + )