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feat(init): updates arrival msg
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lines changed

2 files changed

+89
-60
lines changed

‎content/13_warm_up.ipynb‎

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@@ -11,7 +11,7 @@
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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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"\n",
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"> In this tutorial we will focus on coding a warm-up period rather than analysis to determine its length\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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"## But how do you code it?\n",
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"\n",
@@ -443,7 +443,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 10,
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"id": "caf52390-5455-4fa1-bb22-60b5b91ad8d0",
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"metadata": {},
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"outputs": [
@@ -470,7 +470,7 @@
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"{'mean_acute_wait': 0.0}"
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]
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},
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"execution_count": 22,
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -484,7 +484,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 11,
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"id": "ddedb4f1-207d-4295-9ae4-c49b2c7cdcaf",
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"metadata": {},
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"outputs": [
@@ -494,7 +494,7 @@
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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": 23,
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -514,7 +514,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 12,
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"id": "72b5284a-1fcb-4126-b663-c0ef0002e4bf",
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"metadata": {},
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"outputs": [
@@ -541,7 +541,7 @@
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"{'mean_acute_wait': 0.0}"
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]
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},
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"execution_count": 24,
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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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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 13,
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"id": "7f5e282b-0f41-41df-bdca-f128e7d418c1",
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"metadata": {},
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"outputs": [
@@ -565,7 +565,7 @@
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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": 25,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}

‎content/14_initial_conditions.ipynb‎

Lines changed: 80 additions & 51 deletions
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@@ -69,7 +69,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 3,
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"id": "1ecf0429-f03f-4ad2-abb4-46692a74e559",
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"metadata": {},
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"outputs": [],
@@ -95,8 +95,8 @@
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" \"mode\": \"fixed\",\n",
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" \"fixed\": 8,\n",
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" \"rnd\": {\n",
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" \"values\":[6, 7, 8, 9, 10, 11],\n",
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" \"freq\":[0.25, 0.25, 0.2, 0.1, 0.1, 0.1]\n",
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" \"values\":[8, 9, 10, 11, 12, 13],\n",
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" \"freq\":[25, 25, 2, 1, 1, 1]\n",
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" }\n",
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"}\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": 35,
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"execution_count": 8,
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"id": "b3e686ce-5371-4471-a052-b9d43309bc85",
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"metadata": {},
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"outputs": [],
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"\n",
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" args.results[\"n_arrivals\"] += 1\n",
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" \n",
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" trace(f\"{env.now:.2f}: Stroke arrival.\")\n",
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" trace(f\"{env.now:.2f}: Patient {patient_id}. Stroke arrival.\")\n",
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"\n",
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" # patient enters pathway\n",
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" env.process(acute_stroke_pathway(patient_id, env, args))"
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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": 9,
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"id": "ca37c9cd-158c-416b-8490-b4c5c2e63412",
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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": 13,
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"execution_count": 10,
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"id": "0d0ea6cf-7d95-4d2c-9690-fcdbdae35d84",
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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": 34,
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"execution_count": 11,
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"id": "caf52390-5455-4fa1-bb22-60b5b91ad8d0",
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"metadata": {},
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"outputs": [
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"0.00: Patient -6 loaded into queue\n",
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"0.00: Patient -7 loaded into queue\n",
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"0.00: Patient -8 loaded into queue\n",
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"0.00: Patient -9 loaded into queue\n",
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"0.00: Patient -10 loaded into queue\n",
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"0.00: Patient -1 admitted to acute ward.(waited 0.00 days)\n",
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"0.00: Patient -2 admitted to acute ward.(waited 0.00 days)\n",
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"0.00: Patient -3 admitted to acute ward.(waited 0.00 days)\n",
@@ -525,54 +527,52 @@
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"0.00: Patient -6 admitted to acute ward.(waited 0.00 days)\n",
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"0.00: Patient -7 admitted to acute ward.(waited 0.00 days)\n",
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"0.00: Patient -8 admitted to acute ward.(waited 0.00 days)\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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"4.22: Patient -3 discharged.\n",
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"4.22: Patient 2 admitted to acute ward.(waited 0.16 days)\n",
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"5.00: 🥵 Warm up complete.\n",
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"5.28: Patient -2 discharged.\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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"5.76: Stroke arrival.\n",
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"5.98: Patient -8 discharged.\n",
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"5.98: Patient 4 admitted to acute ward.(waited 0.44 days)\n",
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"6.60: Patient -6 discharged.\n",
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"6.60: Patient 5 admitted to acute ward.(waited 0.84 days)\n",
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"7.25: Patient -7 discharged.\n",
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"7.56: Stroke arrival.\n",
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"7.56: Patient 6 admitted to acute ward.(waited 0.00 days)\n",
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"7.62: Stroke arrival.\n",
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"7.77: Patient -1 discharged.\n",
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"7.77: Patient 7 admitted to acute ward.(waited 0.15 days)\n",
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"7.91: Patient -4 discharged.\n",
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"8.33: Patient -5 discharged.\n",
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"8.52: Stroke arrival.\n",
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"8.52: Patient 8 admitted to acute ward.(waited 0.00 days)\n",
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"8.82: Stroke arrival.\n",
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"8.82: Patient 9 admitted to acute ward.(waited 0.00 days)\n",
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"9.80: Patient 1 discharged.\n",
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"11.12: Stroke arrival.\n",
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"11.12: Patient 10 admitted to acute ward.(waited 0.00 days)\n",
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"11.93: Patient 2 discharged.\n",
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"12.98: Stroke arrival.\n",
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"12.98: Patient 11 admitted to acute ward.(waited 0.00 days)\n",
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"12.99: Stroke arrival.\n",
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"13.26: Patient 3 discharged.\n",
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"13.26: Patient 12 admitted to acute ward.(waited 0.26 days)\n",
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"13.80: Patient 5 discharged.\n",
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"14.19: Patient 7 discharged.\n",
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"14.20: Patient 4 discharged.\n"
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"0.00: Patient -9 admitted to acute ward.(waited 0.00 days)\n",
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"0.00: 🥵 Warm up complete.\n",
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"2.74: Patient 1. Stroke arrival.\n",
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"2.78: Patient 2. Stroke arrival.\n",
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"3.36: Patient 3. Stroke arrival.\n",
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"4.42: Patient 4. Stroke arrival.\n",
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"4.68: Patient 5. Stroke arrival.\n",
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"5.80: Patient -3 discharged.\n",
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"5.80: Patient -10 admitted to acute ward.(waited 5.80 days)\n",
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"5.80: Patient -8 discharged.\n",
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"5.80: Patient 1 admitted to acute ward.(waited 3.06 days)\n",
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"5.97: Patient 6. Stroke arrival.\n",
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"6.08: Patient -6 discharged.\n",
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"6.08: Patient 2 admitted to acute ward.(waited 3.30 days)\n",
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"6.22: Patient -7 discharged.\n",
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"6.22: Patient 3 admitted to acute ward.(waited 2.86 days)\n",
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"6.33: Patient 7. Stroke arrival.\n",
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"7.28: Patient 8. Stroke arrival.\n",
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"7.41: Patient -4 discharged.\n",
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"7.41: Patient 4 admitted to acute ward.(waited 2.99 days)\n",
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"7.43: Patient 9. Stroke arrival.\n",
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"7.55: Patient 10. Stroke arrival.\n",
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"7.94: Patient -5 discharged.\n",
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"7.94: Patient 5 admitted to acute ward.(waited 3.26 days)\n",
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"8.11: Patient -2 discharged.\n",
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"8.11: Patient 6 admitted to acute ward.(waited 2.14 days)\n",
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"8.26: Patient 11. Stroke arrival.\n",
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"8.42: Patient 12. Stroke arrival.\n",
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"8.68: Patient 13. Stroke arrival.\n",
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"8.72: Patient 14. Stroke arrival.\n",
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"8.82: Patient -9 discharged.\n",
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"8.82: Patient 7 admitted to acute ward.(waited 2.50 days)\n",
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"8.95: Patient 15. Stroke arrival.\n",
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"9.18: Patient 16. Stroke arrival.\n",
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"9.87: Patient -1 discharged.\n",
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"9.87: Patient 8 admitted to acute ward.(waited 2.58 days)\n",
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"9.92: Patient 17. Stroke arrival.\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': 0.16965284328644098}"
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"{'mean_acute_wait': 2.8362718457918676}"
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]
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},
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"execution_count": 34,
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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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"init_cond_params[\"mode\"] = \"fixed\"\n",
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"\n",
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"# uncomment to vary the fixed amount 10 = 1 in queue.\n",
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"# init_cond_params[\"fixed\"] = 10\n",
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"init_cond_params[\"fixed\"] = 10\n",
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"\n",
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"experiment = Experiment(init_cond_params=init_cond_params)\n",
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"results = single_run(experiment, rep=0, wu_period=5.0, rc_period=10.0)\n",
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"results = single_run(experiment, rep=1, wu_period=0.0, rc_period=10.0)\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": 12,
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"id": "0aaef408-09ca-49e0-8d39-f4a088a4ef1b",
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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': 17,\n",
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" 'waiting_acute': [3.0586175577655674,\n",
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" 3.303449502025928,\n",
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" 2.857579305401165,\n",
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" 2.9908615153438225,\n",
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" 3.2622390398417513,\n",
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" 2.136685286636955,\n",
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" 2.496306611009193,\n",
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" 2.58443594831056]}"
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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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"source": [
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"experiment.results"
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]
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}
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],
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"metadata": {

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