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Update documentation
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‎.buildinfo‎

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
3-
config: 3c4fc6418eec8a888d9bfced2b71f28c
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config: 862fd0596be33d5281a36c240023a5e9
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎_downloads/0d89a4d8d5ce98cb84c41e2a3c1ea369/29_stroke_rehab_interface.py‎

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import streamlit as st
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import numpy as np
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from stroke_rehab_model import (
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Experiment, multiple_replications, combine_pdelay_results,
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combine_occup_results, mean_results, prob_delay_plot,
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occupancy_plot, summary_table
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)
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def main():
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st.title("Stroke Rehabilitation Model Simulation")
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# Sidebar for Experiment parameters
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st.sidebar.header("Simulation Parameters")
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# Inter-arrival rates
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st.sidebar.subheader("Inter-arrival Rates")
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stroke_mean = st.sidebar.number_input("Stroke patients", value=1.2, step=0.1)
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tia_mean = st.sidebar.number_input("TIA patients", value=9.3, step=0.1)
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neuro_mean = st.sidebar.number_input("Complex Neurological patients", value=3.6, step=0.1)
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other_mean = st.sidebar.number_input("Other patients", value=3.2, step=0.1)
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# Model control
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st.sidebar.subheader("Model Control")
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trace = st.sidebar.checkbox("Trace patients in simulation", value=False)
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warm_up = st.sidebar.number_input("Warm-up period", value=1095, step=1)
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# Number of replications
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num_replications = st.number_input("Number of replications", value=100, min_value=1, step=1)
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if st.button("Simulate"):
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with st.spinner("Please wait for results..."):
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# Create an instance of Experiment with user-defined parameters
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experiment = Experiment(
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stroke_mean=stroke_mean,
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tia_mean=tia_mean,
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neuro_mean=neuro_mean,
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other_mean=other_mean,
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trace=trace,
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warm_up=warm_up
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)
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# Run multiple replications
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rep_results = multiple_replications(experiment, num_replications)
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# Combine results and take the mean
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pd_asu, pd_rehab = combine_pdelay_results(rep_results)
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rel_asu, rel_rehab = combine_occup_results(rep_results)
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mean_pd_asu, mean_pd_rehab = mean_results(pd_asu), mean_results(pd_rehab)
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mean_rel_asu, mean_rel_rehab = mean_results(rel_asu), mean_results(rel_rehab)
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# Display tables
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st.subheader("Acute Care Results")
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df_acute = summary_table(mean_pd_asu, 9, 14, "acute")
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st.table(df_acute)
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st.subheader("Rehabilitation Results")
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df_rehab = summary_table(mean_pd_rehab, 10, 16, "rehab")
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st.table(df_rehab)
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# Display plots
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st.subheader("Probability Delay Plots")
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fig_pd_asu, ax_pd_asu = prob_delay_plot(mean_pd_asu, np.arange(0, 30))
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st.pyplot(fig_pd_asu)
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fig_pd_rehab, ax_pd_rehab = prob_delay_plot(mean_pd_rehab, np.arange(0, 30), "No. rehab beds available")
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st.pyplot(fig_pd_rehab)
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st.subheader("Occupancy Plots")
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fig_occ_asu, ax_occ_asu = occupancy_plot(mean_rel_asu, np.arange(0, 30))
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st.pyplot(fig_occ_asu)
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fig_occ_rehab, ax_occ_rehab = occupancy_plot(mean_rel_rehab, np.arange(0, 30), "No. people in rehab")
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st.pyplot(fig_occ_rehab)
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if __name__ == "__main__":
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main()

‎_downloads/25c06ed77ce5dea48f78b951a2b8298c/iteration_21.py‎

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import streamlit as st
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import simpy
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import numpy as np
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import pandas as pd
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class Experiment:
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def __init__(self,
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interarrival_means=[22.72, 26.0, 37.0, 47.2, 575.0, 17.91],
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stay_distributions=[(128.79, 267.51), (177.89, 276.54), (140.15, 218.02), (212.86, 457.67), (87.53, 108.67), 57.34],
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elective_treatment_mean=57.34,
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num_critical_care_beds=24,
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intensive_cleaning_duration=5,
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warm_up_period=30*24,
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results_collection_period=12*30*24,
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trace=False,
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random_number_set=0):
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self.interarrival_means = interarrival_means
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self.stay_distributions = stay_distributions
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self.elective_treatment_mean = elective_treatment_mean
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self.num_critical_care_beds = num_critical_care_beds
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self.intensive_cleaning_duration = intensive_cleaning_duration
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self.warm_up_period = warm_up_period
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self.results_collection_period = results_collection_period
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self.total_treatment_time = 0
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self.cancelled_elective_count = 0
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self.mean_waiting_time_unplanned = 0
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self.total_unplanned_admissions = 0
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self.bed_occupancy = 0
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self.trace = trace
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self.patient_count = 0
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self.random_number_set = random_number_set
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self.streams = []
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self.setup_streams(random_number_set)
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def reset_kpi(self):
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self.total_treatment_time = 0
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self.cancelled_elective_count = 0
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self.mean_waiting_time_unplanned = 0
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self.total_unplanned_admissions = 0
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self.bed_occupancy = 0
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self.patient_count = 0
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def setup_streams(self, random_number_set):
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self.streams = []
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rng = np.random.default_rng(random_number_set)
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seeds = rng.integers(0, np.iinfo(np.int64).max, size=12)
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for seed in seeds:
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self.streams.append(np.random.default_rng(seed))
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class CCUModel:
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def __init__(self, env, experiment):
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self.env = env
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self.experiment = experiment
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self.patient_count = 0
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self.critical_care_beds = simpy.Resource(env, capacity=experiment.num_critical_care_beds)
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def patient_arrival_AE(self):
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while True:
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yield self.env.timeout(self.experiment.streams[0].exponential(self.experiment.interarrival_means[0]))
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self.patient_count += 1
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if self.experiment.trace:
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print(f"Patient {self.patient_count} arrived from Accident and Emergency at time {self.env.now}")
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self.env.process(self.unplanned_admission(self.experiment.stay_distributions[0]))
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def patient_arrival_wards(self):
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while True:
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yield self.env.timeout(self.experiment.streams[1].exponential(self.experiment.interarrival_means[1]))
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self.patient_count += 1
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if self.experiment.trace:
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print(f"Patient {self.patient_count} arrived from the Wards at time {self.env.now}")
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self.env.process(self.unplanned_admission(self.experiment.stay_distributions[1]))
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def patient_arrival_surgery(self):
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while True:
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yield self.env.timeout(self.experiment.streams[2].exponential(self.experiment.interarrival_means[2]))
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self.patient_count += 1
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if self.experiment.trace:
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print(f"Patient {self.patient_count} arrived from Emergency surgery at time {self.env.now}")
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self.env.process(self.unplanned_admission(self.experiment.stay_distributions[2]))
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def patient_arrival_other_hospitals(self):
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while True:
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yield self.env.timeout(self.experiment.streams[3].exponential(self.experiment.interarrival_means[3]))
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self.patient_count += 1
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if self.experiment.trace:
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print(f"Patient {self.patient_count} arrived from other hospitals at time {self.env.now}")
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self.env.process(self.unplanned_admission(self.experiment.stay_distributions[3]))
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def patient_arrival_X_ray(self):
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while True:
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yield self.env.timeout(self.experiment.streams[4].exponential(self.experiment.interarrival_means[4]))
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self.patient_count += 1
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if self.experiment.trace:
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print(f"Patient {self.patient_count} arrived from the X-Ray department at time {self.env.now}")
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self.env.process(self.unplanned_admission(self.experiment.stay_distributions[4]))
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def patient_arrival_elective_surgery(self):
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while True:
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yield self.env.timeout(self.experiment.streams[5].normal(self.experiment.interarrival_means[5], 3.16))
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self.patient_count += 1
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if self.experiment.trace:
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print(f"Elective surgery patient {self.patient_count} arrived at time {self.env.now}")
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if len(self.critical_care_beds.users) == self.critical_care_beds.capacity:
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if self.experiment.trace:
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print(f"Elective surgery for patient {self.patient_count} cancelled due to no available critical care beds at time {self.env.now}")
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if self.env.now > self.experiment.warm_up_period:
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self.experiment.cancelled_elective_count += 1
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else:
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self.env.process(self.elective_surgery_process(self.experiment.elective_treatment_mean))
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def unplanned_admission(self, stay_distribution):
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arrival_time = self.env.now
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with self.critical_care_beds.request() as req:
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yield req
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wait_time = self.env.now - arrival_time
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if self.experiment.trace:
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print(f"Patient {self.patient_count} admitted to critical care bed at time {self.env.now}")
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treatment_time = self.experiment.streams[6].lognormal(np.log(stay_distribution[0]) - 0.5 * np.log(1 + (stay_distribution[1] / stay_distribution[0])**2),
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np.sqrt(np.log(1 + (stay_distribution[1] / stay_distribution[0])**2)))
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yield self.env.timeout(treatment_time)
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if self.experiment.trace:
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print(f"Patient {self.patient_count} discharged from critical care bed at time {self.env.now}")
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if self.env.now > self.experiment.warm_up_period:
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self.experiment.total_treatment_time += treatment_time
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self.experiment.mean_waiting_time_unplanned += wait_time
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self.experiment.total_unplanned_admissions += 1
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yield self.env.timeout(self.experiment.intensive_cleaning_duration)
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if self.experiment.trace:
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print(f"Critical care bed is available for next patient at time {self.env.now}")
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def elective_surgery_process(self, treatment_mean):
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with self.critical_care_beds.request() as req:
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yield req
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if self.experiment.trace:
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print(f"Elective surgery patient {self.patient_count} admitted to critical care bed at time {self.env.now}")
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treatment_time = self.experiment.streams[7].exponential(treatment_mean)
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yield self.env.timeout(treatment_time)
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if self.experiment.trace:
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print(f"Elective surgery patient {self.patient_count} discharged from critical care bed at time {self.env.now}")
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if self.env.now > self.experiment.warm_up_period:
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self.experiment.total_treatment_time += treatment_time
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yield self.env.timeout(self.experiment.intensive_cleaning_duration)
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if self.experiment.trace:
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print(f"Critical care bed is available for next patient at time {self.env.now}")
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def warmup_complete(self):
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yield self.env.timeout(self.experiment.warm_up_period)
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self.patient_count = 0
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if self.experiment.trace:
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print("Warm-up complete")
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def run(self):
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self.env.process(self.patient_arrival_AE())
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self.env.process(self.patient_arrival_wards())
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self.env.process(self.patient_arrival_surgery())
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self.env.process(self.patient_arrival_other_hospitals())
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self.env.process(self.patient_arrival_X_ray())
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self.env.process(self.patient_arrival_elective_surgery())
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self.env.process(self.warmup_complete())
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self.env.run(until=self.experiment.results_collection_period + self.experiment.warm_up_period)
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if self.env.now > self.experiment.warm_up_period:
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mean_waiting_time_unplanned = self.experiment.mean_waiting_time_unplanned / self.experiment.total_unplanned_admissions
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bed_utilization = self.experiment.total_treatment_time / (self.experiment.num_critical_care_beds * self.experiment.results_collection_period)
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bed_occupancy = bed_utilization * self.experiment.num_critical_care_beds
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performance_measures = pd.DataFrame({'Cancelled Elective Operations': [self.experiment.cancelled_elective_count], 'Bed Utilization': [bed_utilization], 'Mean Waiting Time Unplanned': [mean_waiting_time_unplanned], 'Bed Occupancy': [bed_occupancy], 'Patient Count': [self.patient_count]})
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return performance_measures
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import pandas as pd
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def multiple_replications(experiment, num_replications=5):
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all_results = []
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for i in range(num_replications):
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experiment.setup_streams(i) # Call the setup_streams method and pass in the current replication number
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model = CCUModel(simpy.Environment(), experiment)
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experiment.reset_kpi()
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results = model.run()
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results.insert(0, 'Replication', i+1)
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all_results.append(results)
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return pd.concat(all_results, ignore_index=True)
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def results_summary(results):
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# Drop the 'Replication' column
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results = results.drop(columns='Replication')
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# Calculate the mean and standard deviation of each column
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mean = results.mean()
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std = results.std()
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# Create a summary dataframe
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summary = pd.DataFrame({'Mean': mean, 'Standard Deviation': std})
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return summary
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def get_experiments():
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experiments = {}
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for i in range(23, 29):
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exp = Experiment(num_critical_care_beds=i)
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experiments[f'Experiment_{i}'] = exp
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return experiments
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def run_all_experiments(experiments, num_replications):
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summaries = {}
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for name, exp in experiments.items():
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print(f"Running experiment: {name}")
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model = CCUModel(simpy.Environment(), exp)
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results = multiple_replications(exp, num_replications)
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summary = results_summary(results)
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summaries[name] = summary
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return summaries
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def summary_of_experiments(experiment_summaries):
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return pd.concat(experiment_summaries, axis=1)
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import streamlit as st
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import pandas as pd
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def main():
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st.title('A simulation model of bed-occupancy in a critical care unit')
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st.write('This model is a recreation of the model reported in a published academic study:')
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st.write('J D Griffiths, M Jones, M S Read & J E Williams (2010) A simulation model of bed-occupancy in a critical care unit, Journal of Simulation, 4:1, 52-59, DOI: 10.1057/jos.2009.22')
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st.write('Original Study: [Journal of Simulation](https://www.tandfonline.com/doi/full/10.1057/jos.2009.22)')
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with st.sidebar:
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st.subheader('Experiment Parameters')
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num_beds = st.slider('Number of Critical Care Beds', 23, 28, 23)
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cleaning_duration = st.slider('Intensive Cleaning Duration', 1, 10, 5)
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trace = st.checkbox('Enable Trace', False)
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num_replications = st.number_input('Number of Replications', 1, 10, 5)
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experiment = Experiment(num_critical_care_beds=num_beds, intensive_cleaning_duration=cleaning_duration, trace=trace)
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simulate_button = st.button('Simulate')
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if simulate_button:
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with st.spinner('Please wait for results...'):
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results = multiple_replications(experiment, num_replications)
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summary = results_summary(results)
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st.write(summary)
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if __name__ == '__main__':
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main()
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‎_downloads/86cbae788477e7196251bb08da1276df/28_stroke_rehab_interface.py‎

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import streamlit as st
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import numpy as np
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from stroke_rehab_model import (
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Experiment, multiple_replications, combine_pdelay_results,
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combine_occup_results, mean_results, prob_delay_plot,
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occupancy_plot, summary_table
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)
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def main():
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st.title("Stroke Rehabilitation Model Simulation")
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if st.button("Simulate"):
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with st.spinner("Please wait for results..."):
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# Create an instance of Experiment (default params, default warm-up and rcp)
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experiment = Experiment()
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# Run multiple replications
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rep_results = multiple_replications(experiment, 100)
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# Combine results and take the mean
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pd_asu, pd_rehab = combine_pdelay_results(rep_results)
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rel_asu, rel_rehab = combine_occup_results(rep_results)
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mean_pd_asu, mean_pd_rehab = mean_results(pd_asu), mean_results(pd_rehab)
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mean_rel_asu, mean_rel_rehab = mean_results(rel_asu), mean_results(rel_rehab)
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# Display tables
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st.subheader("Acute Care Results")
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df_acute = summary_table(mean_pd_asu, 9, 14, "acute")
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st.table(df_acute)
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st.subheader("Rehabilitation Results")
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df_rehab = summary_table(mean_pd_rehab, 10, 16, "rehab")
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st.table(df_rehab)
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# Display plots
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st.subheader("Probability Delay Plots")
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fig_pd_asu, ax_pd_asu = prob_delay_plot(mean_pd_asu, np.arange(0, 30))
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st.pyplot(fig_pd_asu)
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fig_pd_rehab, ax_pd_rehab = prob_delay_plot(mean_pd_rehab, np.arange(0, 30), "No. rehab beds available")
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st.pyplot(fig_pd_rehab)
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st.subheader("Occupancy Plots")
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fig_occ_asu, ax_occ_asu = occupancy_plot(mean_rel_asu, np.arange(0, 30))
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st.pyplot(fig_occ_asu)
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fig_occ_rehab, ax_occ_rehab = occupancy_plot(mean_rel_rehab, np.arange(0, 30), "No. people in rehab")
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st.pyplot(fig_occ_rehab)
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if __name__ == "__main__":
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main()

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