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fix(warmup): typo
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‎content/13_warm_up.ipynb

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@@ -9,7 +9,7 @@
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"\n",
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"## Why do you need a warm-up period?\n",
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"\n",
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"Typically when you are modelling a non-terminating system, you will need to deal with **initialisation bias**. That is the real system always has work-in-progress (e.g. patients in queues and in service), but the model starts from empty. One way to do this is to split the model's run length into warm-up and data collection periods. We discard all results in the warm-up period.\n",
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"Typically when you are modelling a non-terminating system, you will need to deal with **initialisation bias**. That is the real system always has work-in-progress (e.g. patients in queues and in service), but the model starts from empty. One way to deal with this is to split the model's run length into warm-up and data collection periods. We discard all results in the warm-up period.\n",
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"\n",
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"> In this tutorial we will focus on coding a warm-up period in Python and SimPy rather than analysis to determine its length\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"id": "c1cee9f9-8696-4b13-94ff-bee2a2a2e5f8",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"id": "ea3d507f-9e6d-4ff0-8b90-f9c63c8a8bdf",
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"metadata": {},
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"outputs": [],
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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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"execution_count": null,
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"id": "1ecf0429-f03f-4ad2-abb4-46692a74e559",
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"metadata": {},
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"outputs": [],
@@ -96,7 +96,7 @@
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"\n",
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"# run variables (units = days)\n",
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"WU_PERIOD = 0.0\n",
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"RC_PERIOD = 100"
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"RC_PERIOD = 100.0"
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]
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},
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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": 4,
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"execution_count": null,
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"id": "52c9271f-1d05-454d-a199-8768bdf5b6e8",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"id": "576ae9b4-b21b-4ed0-9b13-e5898d423173",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"id": "dff74a08-37fd-4b18-8bcd-97994f38369a",
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"metadata": {},
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"outputs": [],
@@ -248,7 +248,7 @@
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" Parameters:\n",
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" ----------\n",
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" warm_up_period: float\n",
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" Duration of warm-up period in simultion time units\n",
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" Duration of warm-up period in simulation time units\n",
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"\n",
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" env: simpy.Environment\n",
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" The simpy environment\n",
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"id": "911528e1-e4eb-4307-bb26-632faf7769d1",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"id": "b3e686ce-5371-4471-a052-b9d43309bc85",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": null,
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"id": "0d0ea6cf-7d95-4d2c-9690-fcdbdae35d84",
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"metadata": {},
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"outputs": [],
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"id": "c13f5e57-723c-409b-a1ce-cdb831b4e166",
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"metadata": {},
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"source": [
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"## Quick check 1: No warm-up"
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"## No warm-up"
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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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"execution_count": null,
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"id": "caf52390-5455-4fa1-bb22-60b5b91ad8d0",
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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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"0.00: 🥵 Warm up complete.\n",
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"3.29: Stroke arrival.\n",
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"3.29: Patient 1 admitted to acute ward.(waited 0.00 days)\n",
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"4.06: Stroke arrival.\n",
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"4.06: Patient 2 admitted to acute ward.(waited 0.00 days)\n",
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"5.31: Stroke arrival.\n",
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"5.31: Patient 3 admitted to acute ward.(waited 0.00 days)\n",
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"5.53: Stroke arrival.\n",
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"5.53: Patient 4 admitted to acute ward.(waited 0.00 days)\n",
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"5.76: Stroke arrival.\n",
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"5.76: Patient 5 admitted to acute ward.(waited 0.00 days)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'mean_acute_wait': 0.0}"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"TRACE = True\n",
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"experiment = Experiment()\n",
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"results = single_run(experiment, rep=0, wu_period=0.0, rc_period=6.0)\n",
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"results = single_run(experiment, rep=2, wu_period=10.0, rc_period=6.0)\n",
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"results"
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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": 11,
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"execution_count": null,
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"id": "ddedb4f1-207d-4295-9ae4-c49b2c7cdcaf",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'n_arrivals': 5, 'waiting_acute': [0.0, 0.0, 0.0, 0.0, 0.0]}"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"# check how many patient waiting times recorded.\n",
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"experiment.results"
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"id": "660ea2e1-d9c2-4355-876c-43dfd9dab0fe",
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"metadata": {},
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"source": [
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"## Quick check 2: Include a warm-up"
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"## Include a warm-up"
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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": 12,
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"execution_count": null,
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"id": "72b5284a-1fcb-4126-b663-c0ef0002e4bf",
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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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"3.29: Stroke arrival.\n",
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"3.29: Patient 1 admitted to acute ward.(waited 0.00 days)\n",
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"4.06: Stroke arrival.\n",
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"4.06: Patient 2 admitted to acute ward.(waited 0.00 days)\n",
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"5.00: 🥵 Warm up complete.\n",
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"5.31: Stroke arrival.\n",
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"5.31: Patient 3 admitted to acute ward.(waited 0.00 days)\n",
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"5.53: Stroke arrival.\n",
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"5.53: Patient 4 admitted to acute ward.(waited 0.00 days)\n",
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"5.76: Stroke arrival.\n",
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"5.76: Patient 5 admitted to acute ward.(waited 0.00 days)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'mean_acute_wait': 0.0}"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"TRACE = True\n",
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"experiment = Experiment()\n",
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": null,
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"id": "7f5e282b-0f41-41df-bdca-f128e7d418c1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'n_arrivals': 3, 'waiting_acute': [0.0, 0.0, 0.0]}"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"# check how many patient waiting times recorded.\n",
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"experiment.results"

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