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Merge pull request #23 from pythonhealthdatascience/dev
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‎.gitignore

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.pytest_cache
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.pytype
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# Log messages, except the example one generated by logs.ipynb
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*.log
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!log_example.log
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# vscode
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.vscode/*

‎CITATION.cff

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email: [email protected]
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affiliation: University of Exeter
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orcid: 'https://orcid.org/0000-0002-6596-3479'
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- given-names: Thomas
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family-names: Monks
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email: [email protected]
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affiliation: University of Exeter
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orcid: 'https://orcid.org/0000-0003-2631-4481'
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repository-code: >-
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https://github.com/pythonhealthdatascience/rap_template_python_des
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abstract: >-

‎README.md

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**Model Run Process:**
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1. **Set Parameters:** Create a `Defaults` instance and modify it with desired model parameters.
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1. **Set Parameters:** Create a `Param` instance with desired model parameters.
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2. **Initialise Model:** Instantiate `Model` using the parameters. During setup, `Model` creates `Exponential` instances for each distribution.
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3. **Run Simulation:** Call `model.run()` to execute the simulation within the SimPy environment, running two processes:
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If you use this template, please cite the archived repository:
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> Heather, A. (2025). Simple Reproducible Python Discrete-Event Simulation (DES) Template. Zenodo. https://doi.org/10.5281/zenodo.14622466
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> Heather, A. Monks, T. (2025). Python DES RAP Template. Zenodo. https://doi.org/10.5281/zenodo.14622466
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You can also cite the GitHub repository:
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> Heather, A. (2025). Simple Reproducible Python Discrete-Event Simulation (DES) Template. GitHub. https://github.com/pythonhealthdatascience/rap_template_python_des.
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> Heather, A. Monks, T. (2025). Python DES RAP Template. GitHub. https://github.com/pythonhealthdatascience/rap_template_python_des.
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Researcher details:
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| Contributor | ORCID | GitHub |
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| --- | --- | --- |
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| Amy Heather | [![ORCID: Heather](https://img.shields.io/badge/ORCID-0000--0002--6596--3479-brightgreen)](https://orcid.org/0000-0002-6596-3479) | https://github.com/amyheather |
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| Contributor | Role | ORCID | GitHub |
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| --- | --- | --- | --- |
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| Amy Heather | Author | [![ORCID: Heather](https://img.shields.io/badge/ORCID-0000--0002--6596--3479-brightgreen)](https://orcid.org/0000-0002-6596-3479) | https://github.com/amyheather |
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| Tom Monks | Code review | [![ORCID: Monks](https://img.shields.io/badge/ORCID-0000--0003--2631--4481-brightgreen)](https://orcid.org/0000-0003-2631-4481) | https://github.com/TomMonks |
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<br>
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‎docs/time_weighted_averages.ipynb

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‎environment.yaml

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- pytest=8.3.4
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- pytest-xdist=3.6.1
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- python=3.13.1
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- rich=13.9.4
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- simpy=4.1.1
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- pip:
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- kaleido==0.2.1

‎images/model_structure.drawio

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‎images/model_structure.png

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‎lint.sh

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nbqa pylint ./notebooks

‎notebooks/analysis.ipynb

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‎notebooks/choosing_parameters.ipynb

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‎notebooks/generate_exp_results.ipynb

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"# Define model parameters\n",
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"param.patient_inter = 4\n",
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"param.mean_n_consult_time = 10\n",
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" mean_n_consult_time = 10,\n",
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" data_collection_period = 1500,\n",
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")\n",
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