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Merged
TomMonks merged 3 commits into from
Jan 2, 2026
Merged

Init #14

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9dfcd16
feat(init_cond): fixed typos
TomMonks Dec 30, 2025
c6fa0c3
feat(init_cond): +image
TomMonks Jan 1, 2026
5f4e1bf
feat(init_cond): extra trace info for exp
TomMonks Jan 1, 2026
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206 changes: 79 additions & 127 deletions content/14_initial_conditions.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -5,20 +5,39 @@
"id": "0be7dabf-cb34-4faf-abb1-e2c8e735beda",
"metadata": {},
"source": [
"# Setting initial conditions\n",
"# Setting Initial Conditions\n",
"\n",
"An alternative to including a warm-up period in a model is to set initial conditions. In our stroke example this would mean adding patients to beds and if required the admission queue. \n",
"\n",
"**IMPORTANT: There is still an initialisation bias problem:**\n",
"🎓 **Why use Initial-Conditions over a Warm-Up?**\n",
"\n",
"* While warm-up periods are simpler to code, they can also increase how long it takes to run your model. Setting initial conditions is in theory computationally more efficient (no wasted run-time).\n",
"\n",
"* Setting initial-conditions also allows you to test specific starting scenarios, such as \"What happens if the day begins with a full ward?\"\n",
"\n",
"**In general, adding initial conditions is more complex than including a warm-up period.**\n",
"\n",
"Initial conditions could be \n",
"\n",
"1. Fixed i.e. the same number of patients for each replication.\n",
"2. Random i.e. sampled from a discrete distribution specified by a user.\n",
"\n",
"In this notebook we will learn how to add initial conditions to a SimPy model. We will again use the acute stroke pathway model from the warm-up example notebook. Our initial conditions approach will load stroke patients to the queue before the simulation begins.\n",
"\n",
"<img src=\"img/acute_stroke_pathway.png\" alt=\"stroke pathway\" width=\"400\"/>\n",
"\n",
"\n",
"## IMPORTANT: There is still an initialisation bias problem\n",
"\n",
"* We load patients into the model before we begin the run. \n",
"* If there are $n$ acute stroke beds then the first $n$ patients loaded into the queue will begin service immediately and have a zero queuing time.\n",
"* This applies if we have one queue in the model or if we have multiple queues and activities: the time in system metrics will be biased for initial condition patients.\n",
"* This is the same problem faced when starting the model from time zero with no patients.\n",
"\n",
"**Some Options:**\n",
"* Include a setting in a process to switch results collection on and off.\n",
"* Code a seperate process for initial conditions that does not include results collection.\n",
"* Mixed initial conditions and (a shorter) warm-up period.\n",
"1. Include a setting in a process to switch results collection on and off.\n",
"2. Code a seperate process for initial conditions that does not include results collection.\n",
"3. Mixed initial conditions and (a shorter) warm-up period.\n",
" * You will need to do some analysis to ensure this is working acceptably.\n",
" * The warm-up pushes patients into service and also resets results collection. (deletes an initial transient)."
]
Expand Down Expand Up @@ -83,7 +102,7 @@
"\n",
"# Acute LoS (Lognormal)\n",
"ACUTE_LOS_MEAN = 7.0\n",
"ACUTE_LOC_STD = 1.0\n",
"ACUTE_LOS_STD = 1.0\n",
"\n",
"# initial conditions for acute queue + service \n",
"# if there are 9 beds then 10 = 1 queuing\n",
Expand All @@ -93,11 +112,12 @@
"\n",
"INIT_COND_PARAMS = {\n",
" \"mode\": \"fixed\",\n",
" \"fixed\": 8,\n",
" \"fixed\": 10,\n",
" \"rnd\": {\n",
" \"values\":[8, 9, 10, 11, 12, 13],\n",
" \"freq\":[25, 25, 2, 1, 1, 1]\n",
" }\n",
" \"freq\":[15, 15, 25, 20, 5, 5]\n",
" },\n",
" \"collect_results\": False\n",
"}\n",
" \n",
"# sampling settings\n",
Expand Down Expand Up @@ -129,7 +149,7 @@
"source": [
"def trace(msg):\n",
" \"\"\"\n",
" Turing printing of events on and off.\n",
" Turning printing of events on and off.\n",
"\n",
" Params:\n",
" -------\n",
Expand Down Expand Up @@ -159,14 +179,17 @@
" \"\"\"\n",
" Encapsulates the concept of an experiment 🧪 for the stroke pathway\n",
" bed blocking simulator. Manages parameters, PRNG streams and results.\n",
"\n",
" There is a new parameter `init_cond_params` that stores the initial\n",
" conditions to use in an experiment.\n",
" \"\"\"\n",
" def __init__(\n",
" self,\n",
" random_number_set=DEFAULT_RND_SET,\n",
" n_streams=N_STREAMS,\n",
" iat_strokes=IAT_STROKES,\n",
" acute_los_mean=ACUTE_LOS_MEAN,\n",
" acute_los_std=ACUTE_LOC_STD,\n",
" acute_los_std=ACUTE_LOS_STD,\n",
" n_acute_beds=N_ACUTE_BEDS,\n",
" init_cond_params=INIT_COND_PARAMS, \n",
" ):\n",
Expand All @@ -182,7 +205,7 @@
" self.acute_los_mean = acute_los_mean\n",
" self.acute_los_std = acute_los_std\n",
"\n",
" # stored initial conditions\n",
" # ---- stored initial conditions -------\n",
" self.init_cond_params = init_cond_params\n",
"\n",
" # place holder for resources\n",
Expand All @@ -198,8 +221,9 @@
" def set_random_no_set(self, random_number_set):\n",
" \"\"\"\n",
" Controls the random sampling\n",
" \n",
" Parameters:\n",
" ----------\n",
" -----------\n",
" random_number_set: int\n",
" Used to control the set of pseudo random numbers used by\n",
" the distributions in the simulation.\n",
Expand Down Expand Up @@ -231,12 +255,14 @@
" self.init_conds = FixedDistribution(\n",
" self.init_cond_params[\"fixed\"]\n",
" )\n",
" else:\n",
" elif self.init_cond_params[\"mode\"] == \"rnd\":\n",
" self.init_conds = DiscreteEmpirical(\n",
" values = self.init_cond_params[\"rnd\"][\"values\"],\n",
" freq = self.init_cond_params[\"rnd\"][\"freq\"],\n",
" random_seed=self.seeds[2]\n",
" )\n",
" else:\n",
" raise ValueError(\"Initial conditions mode must be 'fixed' or 'rnd'\")\n",
" \n",
"\n",
" def init_results_variables(self):\n",
Expand Down Expand Up @@ -273,7 +299,7 @@
" Parameters:\n",
" ----------\n",
" warm_up_period: float\n",
" Duration of warm-up period in simultion time units\n",
" Duration of warm-up period in simulation time units\n",
"\n",
" env: simpy.Environment\n",
" The simpy environment\n",
Expand All @@ -296,7 +322,7 @@
"\n",
"The key things to recognise are \n",
"\n",
"* We include a optional parameter called `collection_results` that defaults to `True`. We may set this `False` in our functions that setup initial conditions"
"* We include a optional parameter called `collect_results` that defaults to `True`. We may set this `False` in our functions that setup initial conditions"
]
},
{
Expand All @@ -307,8 +333,7 @@
"outputs": [],
"source": [
"def acute_stroke_pathway(patient_id, env, args, collect_results=True):\n",
" \"\"\"Process a patient through the acute ward\n",
" Simpy generator function.\n",
" \"\"\"Process a patient through the acute ward. Simpy generator function.\n",
" \n",
" Parameters:\n",
" -----------\n",
Expand All @@ -329,6 +354,7 @@
" acute_admit_time = env.now\n",
" wait_for_acute = acute_admit_time - arrival_time\n",
"\n",
" # used to avoid collecting stats from initial conditions...\n",
" if collect_results:\n",
" args.results['waiting_acute'].append(wait_for_acute)\n",
" \n",
Expand Down Expand Up @@ -396,27 +422,25 @@
"source": [
"def setup_initial_conditions(\n",
" env: simpy.Environment, \n",
" args: Experiment,\n",
" collect_results: bool = False,\n",
" args: Experiment\n",
"):\n",
" \"\"\"Set up initial conditions with patients already in the acute\n",
" stroke queue\n",
" \"\"\"Set up initial conditions with patients already in the acute stroke queue\n",
"\n",
" Parmaters:\n",
" ---------\n",
" Parameters:\n",
" -----------\n",
" env: simpy.Environment\n",
" The simpy environment for the simulation\n",
"\n",
" args: Experiment\n",
" The settings and input parameters for the simulation.\n",
"\n",
" collect_results: bool, optional (default = False)#\n",
" Should results be collected for initial conditions patients?\n",
" \n",
" The settings and input parameters for the simulation. \n",
" \"\"\"\n",
" # sample the no. patients to load into queue\n",
" patients_to_load = args.init_conds.sample()\n",
" trace(f\"Patient to load: {patients_to_load}\")\n",
"\n",
" # collect results or not?\n",
" collect_results = args.init_cond_params[\"collect_results\"]\n",
" \n",
" for initial_id in range(1, patients_to_load+1):\n",
" # Create patients with negative IDs to distinguish them as init cond.\n",
" # we may or may not want collect results for initial conditions\n",
Expand Down Expand Up @@ -477,15 +501,19 @@
" # simpy resources\n",
" experiment.acute_ward = simpy.Resource(env, experiment.n_acute_beds)\n",
"\n",
" # load the acute stroke queue\n",
" setup_initial_conditions(env, experiment)\n",
" trace(\"--- Pre-Simulation Actions ---\")\n",
"\n",
" # schedule a warm_up period\n",
" env.process(warmup_complete(wu_period, env, experiment))\n",
" \n",
" # load the acute stroke queue\n",
" setup_initial_conditions(env, experiment)\n",
" \n",
" # we pass all arrival generators to simpy \n",
" env.process(stroke_arrivals_generator(env, experiment))\n",
"\n",
" trace(\"--- Start Simulation ---\")\n",
" \n",
" # run model\n",
" env.run(until=wu_period+rc_period)\n",
"\n",
Expand All @@ -501,123 +529,47 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"id": "caf52390-5455-4fa1-bb22-60b5b91ad8d0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.00: Patient -1 loaded into queue\n",
"0.00: Patient -2 loaded into queue\n",
"0.00: Patient -3 loaded into queue\n",
"0.00: Patient -4 loaded into queue\n",
"0.00: Patient -5 loaded into queue\n",
"0.00: Patient -6 loaded into queue\n",
"0.00: Patient -7 loaded into queue\n",
"0.00: Patient -8 loaded into queue\n",
"0.00: Patient -9 loaded into queue\n",
"0.00: Patient -10 loaded into queue\n",
"0.00: Patient -1 admitted to acute ward.(waited 0.00 days)\n",
"0.00: Patient -2 admitted to acute ward.(waited 0.00 days)\n",
"0.00: Patient -3 admitted to acute ward.(waited 0.00 days)\n",
"0.00: Patient -4 admitted to acute ward.(waited 0.00 days)\n",
"0.00: Patient -5 admitted to acute ward.(waited 0.00 days)\n",
"0.00: Patient -6 admitted to acute ward.(waited 0.00 days)\n",
"0.00: Patient -7 admitted to acute ward.(waited 0.00 days)\n",
"0.00: Patient -8 admitted to acute ward.(waited 0.00 days)\n",
"0.00: Patient -9 admitted to acute ward.(waited 0.00 days)\n",
"0.00: 🥵 Warm up complete.\n",
"2.74: Patient 1. Stroke arrival.\n",
"2.78: Patient 2. Stroke arrival.\n",
"3.36: Patient 3. Stroke arrival.\n",
"4.42: Patient 4. Stroke arrival.\n",
"4.68: Patient 5. Stroke arrival.\n",
"5.80: Patient -3 discharged.\n",
"5.80: Patient -10 admitted to acute ward.(waited 5.80 days)\n",
"5.80: Patient -8 discharged.\n",
"5.80: Patient 1 admitted to acute ward.(waited 3.06 days)\n",
"5.97: Patient 6. Stroke arrival.\n",
"6.08: Patient -6 discharged.\n",
"6.08: Patient 2 admitted to acute ward.(waited 3.30 days)\n",
"6.22: Patient -7 discharged.\n",
"6.22: Patient 3 admitted to acute ward.(waited 2.86 days)\n",
"6.33: Patient 7. Stroke arrival.\n",
"7.28: Patient 8. Stroke arrival.\n",
"7.41: Patient -4 discharged.\n",
"7.41: Patient 4 admitted to acute ward.(waited 2.99 days)\n",
"7.43: Patient 9. Stroke arrival.\n",
"7.55: Patient 10. Stroke arrival.\n",
"7.94: Patient -5 discharged.\n",
"7.94: Patient 5 admitted to acute ward.(waited 3.26 days)\n",
"8.11: Patient -2 discharged.\n",
"8.11: Patient 6 admitted to acute ward.(waited 2.14 days)\n",
"8.26: Patient 11. Stroke arrival.\n",
"8.42: Patient 12. Stroke arrival.\n",
"8.68: Patient 13. Stroke arrival.\n",
"8.72: Patient 14. Stroke arrival.\n",
"8.82: Patient -9 discharged.\n",
"8.82: Patient 7 admitted to acute ward.(waited 2.50 days)\n",
"8.95: Patient 15. Stroke arrival.\n",
"9.18: Patient 16. Stroke arrival.\n",
"9.87: Patient -1 discharged.\n",
"9.87: Patient 8 admitted to acute ward.(waited 2.58 days)\n",
"9.92: Patient 17. Stroke arrival.\n"
]
},
{
"data": {
"text/plain": [
"{'mean_acute_wait': 2.8362718457918676}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"TRACE = True\n",
"\n",
"# settings dictionary\n",
"init_cond_params = INIT_COND_PARAMS.copy()\n",
"init_cond_params[\"mode\"] = \"fixed\"\n",
"\n",
"# uncomment to vary the fixed amount 10 = 1 in queue.\n",
"# vary the fixed amount. Interpretation 10 = 1 in queue.\n",
"init_cond_params[\"fixed\"] = 10\n",
"\n",
"# vary if we collect results from initial condition stroke patients\n",
"init_cond_params[\"collect_results\"] = False\n",
"\n",
"# create experiment and pass in initial conditions\n",
"experiment = Experiment(init_cond_params=init_cond_params)\n",
"\n",
"results = single_run(experiment, rep=1, wu_period=0.0, rc_period=10.0)\n",
"results"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"id": "0aaef408-09ca-49e0-8d39-f4a088a4ef1b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'n_arrivals': 17,\n",
" 'waiting_acute': [3.0586175577655674,\n",
" 3.303449502025928,\n",
" 2.857579305401165,\n",
" 2.9908615153438225,\n",
" 3.2622390398417513,\n",
" 2.136685286636955,\n",
" 2.496306611009193,\n",
" 2.58443594831056]}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"experiment.results"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d5266e1-d8f3-4755-838b-89b9b416faef",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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