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Merge pull request #11 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/*
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‎CHANGELOG.md‎

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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). Dates formatted as YYYY-MM-DD as per [ISO standard](https://www.iso.org/iso-8601-date-and-time-format.html).
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## v0.1.0 - 2025-06-02
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🌱 First release of the repository. Contains implementation of the DES model from Monks et al. 2016, with reproduction of all tables and figures with results from the article and supplementary material. Also includes some tests, and a demonstration of logging, and of how parameters could be stored in a `.csv`.

‎CITATION.cff‎

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from Monks et al. 2016.
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license: MIT
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version: '0.1.0'
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date-released: '2025-03-26'
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date-released: '2025-06-02'

‎README.md‎

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From this repository:
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![](outputs/occupancy_freq_asu.png)
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![](outputs/figure1_asu.png)
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**Figure 3**
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From this repository:
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![](outputs/delay_prob_asu.png)
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![](outputs/figure3_asu.png)
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<br>
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## Run time and machine specification
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TBC
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The run time for this analysis (`notebooks/analysis.ipynb`) is 10 seconds. This was on an Intel Core i7-12700H, 32GB RAM, Ubuntu 24.04.1.
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<!-- State the run time, and give the specification of the machine used (which achieved that run time).
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**Example:** Intel Core i7-12700H with 32GB RAM running Ubuntu 24.04.1 Linux.
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To find this information:
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* **Linux:** Run `neofetch` on the terminal and record your CPU, memory and operating system.
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* **Windows:** Open "Task Manager" (Ctrl + Shift + Esc), go to the "Performance" tab, then select "CPU" and "Memory" for relevant information.
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* **Mac:** Click the "Apple Menu", select "About This Mac", then window will display the details.-->
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The other notebooks generate results for tests and illustrate other functionality (e.g. importing parameters from csv, running with logs), and these just take a second or two.
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<br>
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‎notebooks/analysis.ipynb‎

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"\n",
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"# pylint: disable=wrong-import-position\n",
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"import os\n",
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"import time\n",
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"\n",
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"from IPython.display import display\n",
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"import numpy as np\n",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Start timer\n",
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"start_time = time.time()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"OUTPUT_DIR = \"../outputs/\""
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},
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"## Run time"
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"source": [
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"# Get run time in seconds\n",
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"end_time = time.time()\n",
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‎notebooks/generate_exp_results.ipynb‎

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"source": [
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"# Get run time in seconds\n",
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