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Merge pull request #50 from pythonhealthdatascience/dev
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‎README.md

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@@ -55,8 +55,7 @@ The simulation code is in the `simulation/` folder as a local package. Example a
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%autoreload 1
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%aimport simulation
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from simulation.model import Param, Runner, run_scenarios
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from simulation.helper import summary_stats
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from simulation import Param, Runner, run_scenarios, summary_stats
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# etc.
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```
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@@ -247,9 +246,9 @@ This repository was developed with thanks to several others sources. These are a
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| - | - |
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| Amy Heather, Thomas Monks, Alison Harper, Navonil Mustafee, Andrew Mayne (2025) On the reproducibility of discrete-event simulation studies in health research: an empirical study using open models (https://doi.org/10.48550/arXiv.2501.13137). | `docs/heather_2025.md` |
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| NHS Digital (2024) RAP repository template (https://github.com/NHSDigital/rap-package-template) (MIT Licence) | `simulation/logging.py`<br>`docs/nhs_rap.md` |
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| Sammi Rosser and Dan Chalk (2024) HSMA - the little book of DES (https://github.com/hsma-programme/hsma6_des_book) (MIT Licence) | `simulation/model.py`<br>`notebooks/choosing_cores.ipynb` |
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| Tom Monks (2025) sim-tools: tools to support the Discrete-Event Simulation process in python (https://github.com/TomMonks/sim-tools) (MIT Licence)<br>Who themselves cite Hoad, Robinson, & Davies (2010). Automated selection of the number of replications for a discrete-event simulation (https://www.jstor.org/stable/40926090), and Knuth. D "The Art of Computer Programming" Vol 2. 2nd ed. Page 216. | `simulation/model.py`<br>`simulation/replications.py`<br>`notebooks/choosing_replications.ipynb` |
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| 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) - who themselves also cite Law. Simulation Modeling and Analysis 4th Ed. Pages 14 - 17. | `simulation/model.py` |
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| Sammi Rosser and Dan Chalk (2024) HSMA - the little book of DES (https://github.com/hsma-programme/hsma6_des_book) (MIT Licence) | `simulation/model.py`<br>`simulation/patient.py`<br>`simulation/runner.py`<br>`notebooks/choosing_cores.ipynb` |
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| Tom Monks (2025) sim-tools: tools to support the Discrete-Event Simulation process in python (https://github.com/TomMonks/sim-tools) (MIT Licence)<br>Who themselves cite Hoad, Robinson, & Davies (2010). Automated selection of the number of replications for a discrete-event simulation (https://www.jstor.org/stable/40926090), and Knuth. D "The Art of Computer Programming" Vol 2. 2nd ed. Page 216. | `simulation/confidence_interval_method.py`<br>`simulation/onlinestatistics.py`<br>`simulation/plotly_confidence_interval_method.py`<br>`simulation/replicationsalgorithm.py`<br>`simulation/replicationtabulizer.py`<br>`notebooks/choosing_replications.ipynb` |
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| 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) - who themselves also cite Law. Simulation Modeling and Analysis 4th Ed. Pages 14 - 17. | `simulation/monitoredresource.py` |
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| Tom Monks (2024) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence). | `notebooks/analysis.ipynb`<br>`notebooks/choosing_replications.ipynb`<br>`notebooks/choosing_warmup.ipynb` |
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| Monks T and Harper A. Improving the usability of open health service delivery simulation models using Python and web apps (https://doi.org/10.3310/nihropenres.13467.2) [version 2; peer review: 3 approved]. NIHR Open Res 2023, 3:48.<br>Who themselves cite a [Stack Overflow](https://stackoverflow.com/questions/59406167/plotly-how-to-filter-a-pandas-dataframe-using-a-dropdown-menu) post. | `notebooks/analysis.ipynb` |
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‎notebooks/analysis.ipynb

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‎notebooks/choosing_cores.ipynb

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‎notebooks/choosing_replications.ipynb

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‎notebooks/choosing_warmup.ipynb

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‎notebooks/generate_exp_results.ipynb

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@@ -56,8 +56,7 @@
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"import time\n",
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"from IPython.display import display\n",
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"\n",
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"from simulation.model import Param, Runner, run_scenarios\n",
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"from simulation.replications import confidence_interval_method"
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"from simulation import confidence_interval_method, Param, Runner, run_scenarios"
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]
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},
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{
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"outputs": [],
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"source": [
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"# Define path to folder for expected results for tests\n",
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"TESTS = '../tests/exp_results/'"
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"TESTS = \"../tests/exp_results/\""
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]
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},
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{
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"# Patient-level results\n",
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"display(experiment.patient_results_df)\n",
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"experiment.patient_results_df.to_csv(\n",
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" os.path.join(TESTS, 'patient.csv'), index=False)"
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" os.path.join(TESTS, \"patient.csv\"), index=False)"
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]
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},
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{
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"# Run results\n",
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"display(experiment.run_results_df)\n",
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"experiment.run_results_df.to_csv(\n",
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" os.path.join(TESTS, 'run.csv'), index=False)"
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" os.path.join(TESTS, \"run.csv\"), index=False)"
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]
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},
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{
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"# Interval audit results\n",
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"display(experiment.interval_audit_df)\n",
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"experiment.interval_audit_df.to_csv(\n",
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" os.path.join(TESTS, 'interval.csv'), index=False)"
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" os.path.join(TESTS, \"interval.csv\"), index=False)"
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]
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},
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{
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"# Overall results\n",
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"display(experiment.overall_results_df)\n",
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"experiment.overall_results_df.to_csv(\n",
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" os.path.join(TESTS, 'overall.csv'), index=True)"
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" os.path.join(TESTS, \"overall.csv\"), index=True)"
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]
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},
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{
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" cores=1\n",
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")\n",
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"scenario_results = run_scenarios(\n",
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" scenarios={'patient_inter': [3, 4],\n",
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" 'number_of_nurses': [6, 7]},\n",
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" scenarios={\"patient_inter\": [3, 4],\n",
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" \"number_of_nurses\": [6, 7]},\n",
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" param=param\n",
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")\n",
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"\n",
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"# Preview\n",
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"display(scenario_results.head())\n",
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"\n",
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"# Save to csv\n",
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"scenario_results.to_csv(os.path.join(TESTS, 'scenario.csv'), index=True)"
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"scenario_results.to_csv(os.path.join(TESTS, \"scenario.csv\"), index=True)"
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]
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},
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{
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"\n",
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"_, man_df = confidence_interval_method(\n",
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" replications=40,\n",
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" metrics=['mean_time_with_nurse',\n",
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" 'mean_q_time_nurse',\n",
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" 'mean_nurse_utilisation'],\n",
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" metrics=[\"mean_time_with_nurse\",\n",
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" \"mean_q_time_nurse\",\n",
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" \"mean_nurse_utilisation\"],\n",
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" param=param)\n",
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"\n",
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"display(man_df)\n",
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"\n",
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"man_df.to_csv(\n",
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" os.path.join(TESTS, 'replications.csv'), index=False)"
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" os.path.join(TESTS, \"replications.csv\"), index=False)"
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]
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},
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{
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"runtime = round(end_time - start_time)\n",
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"\n",
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"# Display converted to minutes and seconds\n",
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"print(f'Notebook run time: {runtime // 60}m {runtime % 60}s')"
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"print(f\"Notebook run time: {runtime // 60}m {runtime % 60}s\")"
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]
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}
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],

‎notebooks/input_modelling.ipynb

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@@ -7563,49 +7563,49 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[16-06-2025 14:16:54] [distfit.distfit] fit\n"
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"[20-06-2025 09:52:08] [distfit.distfit] fit\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[16-06-2025 14:16:54] [distfit.distfit] transform\n"
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"[20-06-2025 09:52:08] [distfit.distfit] transform\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[16-06-2025 14:16:54] [distfit.distfit] [expon] [0.00 sec] [RSS: 2.2484] [loc=0.000 scale=3.984]\n"
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"[20-06-2025 09:52:08] [distfit.distfit] [expon] [0.00 sec] [RSS: 2.2484] [loc=0.000 scale=3.984]\n"
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},
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[16-06-2025 14:16:54] [distfit.distfit] [expon] [0.00 sec] [RSS: 2.2484] [loc=0.000 scale=3.984]\n"
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"[20-06-2025 09:52:08] [distfit.distfit] [expon] [0.00 sec] [RSS: 2.2484] [loc=0.000 scale=3.984]\n"
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[16-06-2025 14:16:54] [distfit.distfit] Compute confidence intervals [parametric]\n"
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"[20-06-2025 09:52:08] [distfit.distfit] Compute confidence intervals [parametric]\n"
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]
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},
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[16-06-2025 14:16:54] [distfit.distfit] Create pdf plot for the parametric method.\n"
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"[20-06-2025 09:52:08] [distfit.distfit] Create pdf plot for the parametric method.\n"
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]
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},
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[16-06-2025 14:16:54] [distfit.distfit] Estimated distribution: Expon(loc:0.000000, scale:3.984361)\n"
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"[20-06-2025 09:52:08] [distfit.distfit] Estimated distribution: Expon(loc:0.000000, scale:3.984361)\n"
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]
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{

‎notebooks/logs.ipynb

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@@ -34,8 +34,7 @@
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"import time\n",
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"from IPython.display import display\n",
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"\n",
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"from simulation.logging import SimLogger\n",
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"from simulation.model import Param, Model"
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"from simulation import Model, Param, SimLogger"
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]
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},
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{
@@ -80,12 +79,12 @@
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"text": [
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"\u001b[1m{\u001b[0m \u001b[32m'audit_list'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m, \n",
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" \u001b[32m'env'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m<\u001b[0m\u001b[32msimpy.core.Environment\u001b[0m\u001b[32m>'\u001b[0m\u001b[39m,\u001b[0m \n",
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"\u001b[39m \u001b[0m\u001b[32m'nurse'\u001b[0m\u001b[39m: \u001b[0m\u001b[32m'<simulation.model.MonitoredResource>'\u001b[0m\u001b[39m,\u001b[0m \n",
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"\u001b[39m \u001b[0m\u001b[32m'nurse'\u001b[0m\u001b[39m: \u001b[0m\u001b[32m'<simulation.monitoredresource.MonitoredResource>'\u001b[0m\u001b[39m,\u001b[0m \n",
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"\u001b[39m \u001b[0m\u001b[32m'nurse_consult_count'\u001b[0m\u001b[39m: \u001b[0m\u001b[1;36m0\u001b[0m\u001b[39m,\u001b[0m \n",
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"\u001b[39m \u001b[0m\u001b[32m'nurse_consult_time_dist'\u001b[0m\u001b[39m: \u001b[0m\u001b[32m'<sim_tools.distributions.Exponential>'\u001b[0m\u001b[39m,\u001b[0m \n",
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"\u001b[39m \u001b[0m\u001b[32m'nurse_time_used'\u001b[0m\u001b[39m: \u001b[0m\u001b[1;36m0\u001b[0m\u001b[39m,\u001b[0m \n",
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"\u001b[39m \u001b[0m\u001b[32m'nurse_time_used_correction'\u001b[0m\u001b[39m: \u001b[0m\u001b[1;36m0\u001b[0m\u001b[39m,\u001b[0m \n",
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"\u001b[39m \u001b[0m\u001b[32m'param'\u001b[0m\u001b[39m: \u001b[0m\u001b[32m'<simulation.model.Param>'\u001b[0m\u001b[39m,\u001b[0m \n",
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"\u001b[39m \u001b[0m\u001b[32m'param'\u001b[0m\u001b[39m: \u001b[0m\u001b[32m'<simulation.param.Param>'\u001b[0m\u001b[39m,\u001b[0m \n",
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"\u001b[39m \u001b[0m\u001b[32m'patient_inter_arrival_dist'\u001b[0m\u001b[39m: \u001b[0m\u001b[32m'<sim_tools.distributions.Exponential\u001b[0m\u001b[32m>\u001b[0m\u001b[32m'\u001b[0m, \n",
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" \u001b[32m'patients'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m, \n",
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" \u001b[32m'results_list'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m, \n",
@@ -109,7 +108,7 @@
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" \u001b[32m'audit_interval'\u001b[0m: \u001b[1;36m120\u001b[0m, \n",
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" \u001b[32m'cores'\u001b[0m: \u001b[1;36m1\u001b[0m, \n",
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" \u001b[32m'data_collection_period'\u001b[0m: \u001b[1;36m50\u001b[0m, \n",
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" \u001b[32m'logger'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m<\u001b[0m\u001b[32msimulation.logging.SimLogger\u001b[0m\u001b[32m>\u001b[0m\u001b[32m'\u001b[0m, \n",
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" \u001b[32m'logger'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m<\u001b[0m\u001b[32msimulation.simlogger.SimLogger\u001b[0m\u001b[32m>\u001b[0m\u001b[32m'\u001b[0m, \n",
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" \u001b[32m'mean_n_consult_time'\u001b[0m: \u001b[1;36m10\u001b[0m, \n",
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" \u001b[32m'number_of_nurses'\u001b[0m: \u001b[1;36m1\u001b[0m, \n",
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" \u001b[32m'number_of_runs'\u001b[0m: \u001b[1;36m1\u001b[0m, \n",
@@ -186,7 +185,7 @@
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"output_type": "stream",
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"text": [
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"\u001b[1;36m28.667\u001b[0m: 🛠 Patient \u001b[1;36m4\u001b[0m starts consultation with \u001b[1;36m1.333\u001b[0m left of warm-up \u001b[1m(\u001b[0mwhich is \u001b[1;36m30.000\u001b[0m\u001b[1m)\u001b[0m. As their consultation is for \n",
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"\u001b[1;36m5.295\u001b[0m, they will exceed warmup by \u001b[1;36m3.962\u001b[0m, so we correct for this. \n"
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"\u001b[1;36m5.295\u001b[0m, they will exceed warmup by \u001b[1;36m3.962\u001b[0m,so we correct for this. \n"
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]
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},
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{
@@ -304,7 +303,7 @@
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" number_of_runs=1,\n",
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" cores=1,\n",
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" logger=SimLogger(log_to_console=True, log_to_file=True,\n",
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" file_path='../outputs/logs/log_example.log',\n",
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" file_path=\"../outputs/logs/log_example.log\",\n",
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" sanitise=True)\n",
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")\n",
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"\n",
@@ -404,7 +403,7 @@
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"runtime = round(end_time - start_time)\n",
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"\n",
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"# Display converted to minutes and seconds\n",
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"print(f'Notebook run time: {runtime // 60}m {runtime % 60}s')"
406+
"print(f\"Notebook run time: {runtime // 60}m {runtime % 60}s\")"
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]
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}
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],

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