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Merge pull request #37 from pythonhealthdatascience/dev
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2 parents 08d20d3 + d4c0717 commit 73ccf92

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‎.github/workflows/lint.yaml

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on:
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branches: [main]
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‎docs/hsma_changes.md

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self.time_with_nurse = np.nan
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```
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This enables metrics like `count_unseen` and `mean_q_time_unseen`, which can show backlog in the system at the end of the simulation.
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This enables metrics like `count_nurse_unseen` and `mean_q_time_nurse_unseen`, which can show backlog in the system at the end of the simulation.
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## Seeds
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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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"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\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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]
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},
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" <th>q_time_nurse</th>\n",
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" <th>time_with_nurse</th>\n",
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" <th>run</th>\n",
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" <th>q_time_unseen</th>\n",
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" <th>q_time_unseen_nurse</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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"1888 368 1988.648731 0.0 24.898160 4 \n",
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"1889 369 1993.658168 0.0 3.353385 4 \n",
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"\n",
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" q_time_unseen \n",
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"0 NaN \n",
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"1 NaN \n",
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"2 NaN \n",
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"3 NaN \n",
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"4 NaN \n",
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"... ... \n",
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"1885 NaN \n",
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"1886 NaN \n",
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"1887 NaN \n",
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"1888 NaN \n",
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"1889 NaN \n",
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" q_time_unseen_nurse \n",
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"0 NaN \n",
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"1 NaN \n",
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"2 NaN \n",
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"3 NaN \n",
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"4 NaN \n",
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"... ... \n",
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"1885 NaN \n",
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"1886 NaN \n",
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"1887 NaN \n",
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"1888 NaN \n",
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"1889 NaN \n",
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"\n",
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"[1890 rows x 6 columns]"
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]
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" <th>mean_nurse_utilisation</th>\n",
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" <th>mean_nurse_utilisation_tw</th>\n",
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" <th>mean_nurse_q_length</th>\n",
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" <th>count_unseen</th>\n",
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" <th>mean_q_time_unseen</th>\n",
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" <th>count_nurse_unseen</th>\n",
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" <th>mean_q_time_nurse_unseen</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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"3 0.639573 0.639573 0.544915 \n",
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"4 0.601042 0.601042 0.406760 \n",
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"\n",
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" count_unseen mean_q_time_unseen \n",
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"0 0 NaN \n",
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"1 0 NaN \n",
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"2 0 NaN \n",
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"3 0 NaN \n",
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"4 0 NaN "
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" count_nurse_unseen mean_q_time_nurse_unseen \n",
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"0 0 NaN \n",
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"1 0 NaN \n",
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"2 0 NaN \n",
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"3 0 NaN \n",
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"4 0 NaN "
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]
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},
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"metadata": {},
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" <th>mean_nurse_utilisation</th>\n",
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" <th>mean_nurse_utilisation_tw</th>\n",
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" <th>mean_nurse_q_length</th>\n",
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" <th>count_unseen</th>\n",
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" <th>mean_q_time_unseen</th>\n",
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" <th>count_nurse_unseen</th>\n",
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" <th>mean_q_time_nurse_unseen</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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"lower_95_ci 0.609435 0.609435 \n",
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"upper_95_ci 0.669667 0.669667 \n",
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"\n",
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" mean_nurse_q_length count_unseen mean_q_time_unseen \n",
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"mean 0.531240 0.0 NaN \n",
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"std_dev 0.195914 0.0 NaN \n",
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"lower_95_ci 0.287981 0.0 NaN \n",
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"upper_95_ci 0.774500 0.0 NaN "
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" mean_nurse_q_length count_nurse_unseen mean_q_time_nurse_unseen \n",
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"mean 0.531240 0.0 NaN \n",
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"std_dev 0.195914 0.0 NaN \n",
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"lower_95_ci 0.287981 0.0 NaN \n",
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"upper_95_ci 0.774500 0.0 NaN "
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]
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},
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"metadata": {},
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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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Scenario analysis"
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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": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"There are 4 scenarios. Running:\n",
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"{'patient_inter': 3, 'number_of_nurses': 6}\n",
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"{'patient_inter': 3, 'number_of_nurses': 7}\n",
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"{'patient_inter': 4, 'number_of_nurses': 6}\n",
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"{'patient_inter': 4, 'number_of_nurses': 7}\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" <th></th>\n",
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" <th>run_number</th>\n",
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" <th>scenario</th>\n",
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" <th>arrivals</th>\n",
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" <th>mean_q_time_nurse</th>\n",
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" <th>mean_time_with_nurse</th>\n",
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" <th>mean_nurse_utilisation</th>\n",
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" <th>mean_nurse_utilisation_tw</th>\n",
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" <th>mean_nurse_q_length</th>\n",
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" <th>count_nurse_unseen</th>\n",
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" <th>mean_q_time_nurse_unseen</th>\n",
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" <th>patient_inter</th>\n",
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" <th>number_of_nurses</th>\n",
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" <th>1</th>\n",
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"text/plain": [
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" run_number scenario arrivals mean_q_time_nurse mean_time_with_nurse \\\n",
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"0 0 0 2004 0.486680 9.925934 \n",
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"1 1 0 1993 0.746175 10.377197 \n",
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"2 2 0 2017 0.386324 9.856724 \n",
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"0 0 1 2004 0.121476 9.925934 \n",
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"1 1 1 1993 0.226682 10.377197 \n",
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"\n",
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" mean_nurse_utilisation mean_nurse_utilisation_tw mean_nurse_q_length \\\n",
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"0 0.553955 0.553955 0.162551 \n",
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"1 0.573874 0.573874 0.247854 \n",
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"0 0.474819 0.474819 0.040573 \n",
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"1 0.491892 0.491892 0.075296 \n",
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"\n",
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" count_nurse_unseen mean_q_time_nurse_unseen patient_inter \\\n",
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"0 0 NaN 3 \n",
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"1 0 NaN 3 \n",
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"2 0 NaN 3 \n",
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"0 0 NaN 3 \n",
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"\n",
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" number_of_nurses \n",
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"0 6 \n",
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"1 6 \n",
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"2 6 \n",
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"0 7 \n",
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"1 7 "
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Run scenarios\n",
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"param = Param(\n",
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" patient_inter=4,\n",
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" mean_n_consult_time=10,\n",
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" number_of_nurses=5,\n",
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" warm_up_period=2000,\n",
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" data_collection_period=6000,\n",
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" number_of_runs=3,\n",
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" audit_interval=120,\n",
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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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" 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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]
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},
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"execution_count": 12,
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}
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"metadata": {
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"kernelspec": {
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"display_name": "template-des",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",

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