Initial commit with right files

.gitignore

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LICENSE

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OOP_ED_v2.0_gr.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Apr 22 10:55:25 2024
+
+@author: varad
+"""
+#OOP model for a real ED with 3 processes and 3 resources
+#import libraries
+import simpy
+import matplotlib
+import matplotlib.pyplot as plt
+import math
+import numpy as np
+import pandas as pd
+import random
+import csv
+import gradio as gr
+import plotly.graph_objects as go
+
+class g (object):
+    '''
+    A class that holds all the global variables that will be passed into the different processes.
+    It's easier to change the global variables here rather than change them in the process code as it could lead to errors
+    '''
+    #service times (these are more or less constant and hence won't be changed through user input, although it is possible to change them too,
+    #but for the sake of this program, they won't be changed)
+    ed_inter_arrival = 6 #mins
+    mean_registeration = 2 #mins
+    mean_triage = 6 #mins
+    mean_acu_ass = 60 #mins
+    mean_ed_ass = 30 #mins
+    
+    #resources (this will need to me moved from a static variable to a variable within the ED class as it will then be fed into the gradio app
+    #as variables to be modified through user input)
+    receptionist = 1
+    nurse = 2
+    ed_doc = 2
+    acu_doc = 1
+    
+    #simulation variables
+    number_of_runs = 100
+    warmup_time = 1440 #24 hours and 60 minutes per hour
+    run_time = 100 # mins
+
+class ed_patient (object):
+    '''
+    Defines the patient characteristics. Could be interesting if we have different types of patients
+    But for this exercise we have only one type of patient that is a regular patient that has a 20% chance 
+    of going to the ACU. Also important to store patient level variables to then summarize and plot
+    '''
+    def __init__(self, uhid):
+        self.id = uhid
+        
+        #declaring these variables as they will be recorded and manipulated with later
+        self.time_entered_in_system = 0
+        self.q_reception = 0 
+        self.service_reception = 0
+        self.q_nurse = 0
+        self.service_nurse = 0
+        self.q_edd_ass = 0
+        self.ed_ass_time = 0
+        self.acu_ass_time = 0
+        self.q_acu_ass = 0
+        self.time_exit_system = 0
+        self.tot_system_time = 0
+
+class ED_sim (object):
+    '''
+    This is the actual clinic where everything is simulated.
+    '''
+    def __init__(self, run_number,receptionist = 3, nurse = 2, ed_doc = 2, acu_doc = 1  ):
+        #declaring the environment
+        self.env = simpy.Environment()
+        
+        
+        
+        #declaring resource capacity as variables to later be changed through gradio inputs
+        self.rec_no = receptionist
+        self.nurse_no = nurse
+        self.ed_doc_no = ed_doc
+        self.acu_doc_no = acu_doc
+        
+
+        self.patient_counter = 0
+        #declaring resources
+        self.receptionist = simpy.Resource(self.env, capacity = self.rec_no)
+        self.nurse = simpy.Resource(self.env, capacity = self.nurse_no)
+        self.ed_doc = simpy.Resource(self.env, capacity = self.ed_doc_no)
+        self.acu_doc = simpy.Resource(self.env, capacity = self.acu_doc_no)
+        self.run_number = run_number + 1
+        
+        #initiating a dataframe with required columns
+        self.individual_level_results = pd.DataFrame({
+            "UHID" :[],
+            "Time_entered_in_system" : [],
+             "Q_time_receptionist":[], 
+             "Q_time_nurse":[],
+             "Q_time_acu_doc":[],
+             "Q_time_ed_doc":[],
+             "Service_time_receptionist":[],
+             "Service_time_nurse":[],
+             "Service_time_acu_doc":[],
+             "Service_time_ed_doc":[],
+             "Time_exit_system":[],
+             "Total time in System":[]
+             })
+        self.individual_level_results.set_index('UHID', inplace= True) #sets the index to UHID which is just 1,2,3,4 etc
+        
+        #declaring mean variables for or KPIs to be calculated at the end of one run
+        self.Mean_Q_Rec_time = 0
+        self.Mean_Q_Nurse_time = 0
+        self.Mean_Q_ED_time = 0
+        self.Mean_Q_ACU_time = 0
+        self.Rec_utilize = 0
+        self.Nurse_utilize = 0
+        self.ED_doc_utilize = 0
+        self.ACU_doc_utilize = 0
+        
+        
+        
+    def generate_ed_arrivals(self):
+        while True:
+            self.patient_counter += 1 #this is also the UHID of the patient
+            
+            ed_pt = ed_patient(self.patient_counter)
+            
+            ed_pt.time_entered_in_system = self.env.now #Used to calculate total time spent in the system
+            
+            #Patient goes to registeration
+            self.env.process(self.registration(ed_pt))
+            
+            #print(self.individual_level_results)
+            #draws a random value from an exponential distribution with lambda = interarrival time
+            ed_arrival_time = random.expovariate(1/g.ed_inter_arrival)
+            
+            yield self.env.timeout(ed_arrival_time)
+        
+    
+    def registration(self, patient):
+        
+        start_q_rec = self.env.now
+        
+        with self.receptionist.request() as req:
+            yield req
+        
+            end_q_rec = self.env.now
+            
+        #storing patient level values in patient level variables
+            patient.q_reception = end_q_rec - start_q_rec
+        
+        #patient goes to triage
+            self.env.process(self.triage(patient))
+        
+        #register_time = random.triangular(0,g.mean_registeration, 2*g.mean_registeration)
+            register_time = random.expovariate(1/g.mean_registeration)
+        
+            patient.service_reception = register_time
+       
+     
+        
+            yield self.env.timeout(register_time)
+    
+    def triage (self, patient):
+        start_q_nurse = self.env.now
+        with self.nurse.request() as req:
+            yield req
+        
+            end_q_nurse = self.env.now
+        
+            patient.q_nurse = end_q_nurse - start_q_nurse
+        
+        #patient goes either to ACU or to ED based on probability
+            if random.random() > 0.2: #80% chance that the patient goes to ED
+                self.env.process(self.ed_ass(patient))
+            else:
+                self.env.process(self.acu_ass(patient))
+
+        
+            triage_time = random.triangular(g.mean_triage/2, g.mean_triage, g.mean_triage*2 )
+        
+            patient.service_nurse = triage_time
+        
+        
+        
+            yield self.env.timeout(triage_time)       
+    
+    def ed_ass (self, patient):
+        start_ed_q = self.env.now
+        with self.ed_doc.request() as req:
+            yield req
+            end_ed_q = self.env.now
+            patient.q_edd_ass = end_ed_q - start_ed_q
+        
+        
+            ed_ass_time = random.triangular(g.mean_ed_ass/2, g.mean_ed_ass, g.mean_ed_ass*2)
+            patient.ed_ass_time = ed_ass_time
+        
+        
+        
+            yield self.env.timeout(ed_ass_time)
+            
+            patient.time_exit_system = self.env.now    
+        
+            patient.tot_system_time = patient.time_exit_system - patient.time_entered_in_system
+    
+            ED_sim.add_to_df(self, patient)
+        
+        
+    def acu_ass (self, patient):
+        start_acu_q = self.env.now
+        
+        with self.acu_doc.request() as req:
+            yield req
+            
+            end_acu_q = self.env.now
+            patient.q_acu_ass = end_acu_q - start_acu_q
+        
+            acu_ass_time = random.triangular(g.mean_acu_ass/2, g.mean_acu_ass, g.mean_acu_ass*2)
+        
+            patient.acu_ass_time = acu_ass_time
+            yield self.env.timeout(acu_ass_time)
+        
+            patient.time_exit_system = self.env.now
+            
+            patient.tot_system_time = patient.time_exit_system - patient.time_entered_in_system
+            ED_sim.add_to_df(self, patient)
+        
+    
+    def add_to_df(self, patient):
+        '''
+        Basically takes all the variables and adds them to the dataframe without having to enter them manually with 
+        12 line codes in every function
+        '''
+        df_to_add = pd.DataFrame({
+            "UHID" :[patient.id],
+            "Time_entered_in_system" : [patient.time_entered_in_system],
+            "Q_time_receptionist":[patient.q_reception],
+            
+            #using zeros as placeholders
+            "Q_time_nurse":[patient.q_nurse],
+            "Q_time_acu_doc":[patient.q_acu_ass],
+            "Q_time_ed_doc":[patient.q_edd_ass],
+            "Service_time_receptionist":[patient.service_reception],
+            "Service_time_nurse":[patient.service_nurse],
+            "Service_time_acu_doc":[patient.acu_ass_time],
+            "Service_time_ed_doc":[patient.ed_ass_time],
+            "Time_exit_system" :[patient.time_exit_system],
+            "Total time in System":[patient.tot_system_time]        
+              })
+        
+        df_to_add.set_index('UHID', inplace=True)
+        self.individual_level_results = self.individual_level_results._append(df_to_add)
+        #print(self.individual_level_results)
+        
+        
+    def mean_calculator (self):
+        '''
+        calculates the average statistic for each individual run for all the different KPIs and maybe stores it in a global database
+        '''
+        #mean queuing times
+        self.Mean_Q_Rec_time = self.individual_level_results['Q_time_receptionist'].mean()
+        self.Mean_Q_Nurse_time = self.individual_level_results['Q_time_nurse'].mean()
+        self.Mean_Q_ED_time = self.individual_level_results['Q_time_ed_doc'].mean()
+        self.Mean_Q_ACU_time = self.individual_level_results['Q_time_acu_doc'].mean()
+        
+        #%resource utilisation
+        self.Rec_utilize = self.individual_level_results[
+            'Service_time_receptionist'].sum()/(g.run_time*self.rec_no)
+        self.Nurse_utilize = self.individual_level_results['Service_time_nurse'].sum()/(g.run_time*self.nurse_no)
+        self.ED_doc_utilize = self.individual_level_results['Service_time_ed_doc'].sum()/(g.run_time*self.ed_doc_no)
+        self.ACU_doc_utilize = self.individual_level_results['Service_time_acu_doc'].sum()/(g.run_time*self.acu_doc_no)
+    
+        
+    
+    def export_row_to_csv(self):
+        '''
+        Writes the results of an individual run as a row in a csv file in the desired folder
+        '''
+    
+        with open (r"C:\Users\varad\Desktop\Education Material\Mathematical Modelling\HSMA\HSMA_modelling_ED\trial_results.csv",'a') as f:
+            writer = csv.writer(f, delimiter = ',')
+            row_to_add = [self.run_number,
+                self.Mean_Q_Rec_time,
+                               self.Mean_Q_Nurse_time,
+                               self.Mean_Q_ED_time,
+                               self.Mean_Q_ACU_time,
+                               self.Rec_utilize,
+                               self.Nurse_utilize,
+                               self.ED_doc_utilize,
+                               self.ACU_doc_utilize]
+            writer.writerow(row_to_add)
+            
+            
+    def run (self):
+        '''
+        suns the simulation program
+        '''
+        self.env.process(self.generate_ed_arrivals())
+        self.env.run(until = g.run_time)
+        #print(self.individual_level_results)
+        self.individual_level_results.to_csv(r"C:\Users\varad\Desktop\Education Material\Mathematical Modelling\HSMA\HSMA_modelling_ED\individual_results.csv")
+        self.mean_calculator()
+        self.export_row_to_csv()
+
+class summary_statistics(object):
+    '''
+    This object calculates the mean, median or other summary statistics from a dataframe that has means of individual runs
+    So this object calculates the means of means
+    '''
+    def __init__(self):
+        self.total_mean_df = pd.DataFrame({
+            "Median_q_rec_time":[],
+            "25_q_rec_time":[],
+            "75_q_rec_time":[],
+            
+            "Median_q_nurse_time":[],
+            "25_q_nurse_time":[],
+            "75_q_nurse_time":[],
+            
+            "Median_q_ed_doc_time":[],
+            "25_q_ed_doc_time":[],
+            "75_q_ed_doc_time":[],
+            
+            "Median_q_acu_doc_time":[],
+            "25_q_acu_doc_time":[],
+            "75_q_acu_doc_time":[],
+            
+            "Median_%_utilize_rec":[],
+           
+            "Median_%_utilize_nurse":[],
+            
+            
+            "Median_%_utilize_ed_doc":[],
+           
+            
+            "Median_%_utilize_acu_doc":[],
+            
+            })
+        filepath = r"C:\Users\varad\Desktop\Education Material\Mathematical Modelling\HSMA\HSMA_modelling_ED\trial_results.csv"
+        self.dataframe = pd.read_csv(filepath)
+    
+    def mean_of_means(self):
+        
+        median_q_rec = self.dataframe["Mean_Q_Rec_time"].median()
+        twofive_q_rec = self.dataframe["Mean_Q_Rec_time"].quantile(0.25)
+        sevfive_q_rec = self.dataframe["Mean_Q_Rec_time"].quantile(0.75)
+        
+        median_q_nurse = self.dataframe["Mean_Q_Nurse_time"].median()
+        twofive_q_nurse = self.dataframe["Mean_Q_Nurse_time"].quantile(0.25)
+        sevfive_q_nurse = self.dataframe["Mean_Q_Nurse_time"].quantile(0.75)
+        
+        median_q_ed_doc = self.dataframe["Mean_Q_ED_time"].median()
+        twofive_q_ed_doc = self.dataframe["Mean_Q_ED_time"].quantile(0.25)
+        sevfive_q_ed_doc = self.dataframe["Mean_Q_ED_time"].quantile(0.75)
+        
+        median_q_acu_doc = self.dataframe["Mean_Q_ACU_time"].median()
+        twofive_q_acu_doc = self.dataframe["Mean_Q_ACU_time"].quantile(0.25)
+        sevfive_q_acu_doc = self.dataframe["Mean_Q_ACU_time"].quantile(0.75)
+        
+        median_rec_uti = self.dataframe["Rec_%_utilize"].median()
+        median_nurse_uti = self.dataframe["Nurse_%_utilize"].median()
+        median_ed_doc_uti = self.dataframe["ED_doc_%_utilize"].median()
+        median_acu_doc_uti = self.dataframe["ACU_doc_%_utilize"].median()
+        
+        
+        print("Results of " +  str(g.number_of_runs) + " runs")
+        print("-------------")
+        
+        self.total_mean_df = pd.DataFrame({
+            "Median_q_rec_time":[median_q_rec],
+            "25_q_rec_time":[twofive_q_rec],
+            "75_q_rec_time":[sevfive_q_rec],
+            
+            "Median_q_nurse_time":[median_q_nurse],
+            "25_q_nurse_time":[twofive_q_nurse],
+            "75_q_nurse_time":[sevfive_q_nurse],
+            
+            "Median_q_ed_doc_time":[median_q_ed_doc],
+            "25_q_ed_doc_time":[twofive_q_ed_doc],
+            "75_q_ed_doc_time":[sevfive_q_ed_doc],
+            
+            "Median_q_acu_doc_time":[median_q_acu_doc],
+            "25_q_acu_doc_time":[twofive_q_acu_doc],
+            "75_q_acu_doc_time":[sevfive_q_acu_doc],
+            
+            "Median_%_utilize_rec":[median_rec_uti],
+           
+            
+            "Median_%_utilize_nurse":[median_nurse_uti],
+           
+            
+            "Median_%_utilize_ed_doc":[median_ed_doc_uti],
+          
+            
+            "Median_%_utilize_acu_doc":[median_acu_doc_uti],
+           
+            })
+    
+        print(self.total_mean_df)
+        
+        with open (r"C:\Users\varad\Desktop\Education Material\Mathematical Modelling\HSMA\HSMA_modelling_ED\mean_per_lambda.csv",'a') as f:
+            writer = csv.writer(f, delimiter = ',')
+            row_to_add = [g.ed_inter_arrival,
+                          median_q_rec,
+                          twofive_q_rec,
+                          sevfive_q_rec,
+                          
+                          median_q_nurse,
+                          twofive_q_nurse,
+                          sevfive_q_nurse,
+                          
+                          median_q_ed_doc,
+                          twofive_q_ed_doc,
+                          sevfive_q_ed_doc,
+                          
+                          median_q_acu_doc,
+                          twofive_q_acu_doc,
+                          sevfive_q_acu_doc,
+                          
+                          median_rec_uti,
+                          median_nurse_uti,
+                          median_ed_doc_uti,
+                          median_acu_doc_uti
+                          ]
+            writer.writerow(row_to_add)
+        
+def file_opener():
+    '''
+    Adds one row to the filename that is passed to it
+    '''
+    #This is not the most compulsory function here as the above function will also create a file if it does not exist already
+    
+    with open (r"C:\Users\varad\Desktop\Education Material\Mathematical Modelling\HSMA\HSMA_modelling_ED\trial_results.csv",'w') as f:
+        writer = csv.writer(f, delimiter = ',')
+        columns_headers = ["Run_Number",
+                          "Mean_Q_Rec_time",
+                          "Mean_Q_Nurse_time",
+                          "Mean_Q_ED_time",
+                          "Mean_Q_ACU_time",
+                          "Rec_%_utilize",
+                          "Nurse_%_utilize",
+                          "ED_doc_%_utilize",
+                          "ACU_doc_%_utilize"]
+        writer.writerow(columns_headers)
+        
+        with open (r"C:\Users\varad\Desktop\Education Material\Mathematical Modelling\HSMA\HSMA_modelling_ED\mean_per_lambda.csv",'w') as f:
+            writer = csv.writer(f, delimiter = ',')
+            columns_headers = ["Pt Interarrival Time (lambda)",
+                              "Median_Q_Rec_time",
+                              "25_Q_rec_time",
+                              "75_Q_rec_time",
+                              
+                              "Median_Q_Nurse_time",
+                              "25_Q_Nurse_time",
+                              "75_Q_Nurse_time",
+                              
+                              "Median_Q_ED_time",
+                              "25_Q_ED_time",
+                              "75_Q_ED_time",
+                              
+                              "Median_Q_ACU_time",
+                              "25_Q_ACU_time",
+                              "75_Q_ACU_time",
+                              
+                              "Median_Rec_%_utilize",
+                              "Median_Nurse_%_utilize",
+                              "Median_ED_doc_%_utilize",
+                              "Median_ACU_doc_%_utilize"]
+            
+            writer.writerow(columns_headers)
+
+def Plotter():
+    filepath = r"C:\Users\varad\Desktop\Education Material\Mathematical Modelling\HSMA\HSMA_modelling_ED\mean_per_lambda.csv"
+    df_to_plot = pd.read_csv(filepath)
+
+    figure = plt.subplots()
+    plt.plot(df_to_plot["Pt Interarrival Time (lambda)"], df_to_plot['Median_Q_Rec_time'], color = 'green', linestyle = '-', label = 'Queue for reception')
+    plt.plot(df_to_plot["Pt Interarrival Time (lambda)"], df_to_plot['Median_Q_Nurse_time'], color = 'blue', linestyle = ':', label = 'Queue for nurses')
+    plt.plot(df_to_plot["Pt Interarrival Time (lambda)"], df_to_plot['Median_Q_ED_time'], color = 'red', linestyle = '--', label = 'Queue for ED_doc')
+    plt.plot(df_to_plot["Pt Interarrival Time (lambda)"], df_to_plot['Median_Q_ACU_time'], color = 'black', linestyle = '-.', label = 'Queue for ACU_doc')
+
+    plt.xlabel("Pt interarrival time (min)")
+    plt.ylabel("Time in minutes")
+    plt.title("Queuing times for the Emergency room")
+
+    plt.text(3,10,"Rec = 1, Nur = 2, ED_doc = 2, ACU_doc = 1")
+    plt.legend()
+
+    
+
+    figure = plt.subplots()
+    plt.plot(df_to_plot["Pt Interarrival Time (lambda)"], df_to_plot['Median_Rec_%_utilize'], color = 'green', linestyle = '-', label = '% utilise of reception')
+    plt.plot(df_to_plot["Pt Interarrival Time (lambda)"], df_to_plot['Median_Nurse_%_utilize'], color = 'blue', linestyle = ':', label = '% utilise of nurses')
+    plt.plot(df_to_plot["Pt Interarrival Time (lambda)"], df_to_plot['Median_ED_doc_%_utilize'], color = 'red', linestyle = '--', label = '%utilise of ED_doc')
+    plt.plot(df_to_plot["Pt Interarrival Time (lambda)"], df_to_plot['Median_ACU_doc_%_utilize'], color = 'black', linestyle = '-.', label = '%utilise of ACU_doc')
+
+    plt.xlabel("Pt interarrival time (min)")
+    plt.ylabel("Time in minutes")
+    plt.title("Percentage utlisation for the Emergency room different HR")
+
+    plt.text(3,10,"Rec = 1, Nur = 2, ED_doc = 2, ACU_doc = 1")
+    plt.legend()
+
+    
+    return figure
+
+def plotly_plotter():
+    '''
+    Uses the Plotly library to plot the graphs as the plotly library is web application friendly
+    '''
+    filepath = r"C:\Users\varad\Desktop\Education Material\Mathematical Modelling\HSMA\HSMA_modelling_ED\mean_per_lambda.csv"
+    df_to_plot = pd.read_csv(filepath)
+
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x = df_to_plot["Pt Interarrival Time (lambda)"], y = df_to_plot['Median_Rec_%_utilize'], name='Receptionist utilization%'))
+    fig.add_trace(go.Scatter(x = df_to_plot["Pt Interarrival Time (lambda)"], y = df_to_plot['Median_Nurse_%_utilize'], name='Nurse utilization%'))
+    fig.add_trace(go.Scatter(x = df_to_plot["Pt Interarrival Time (lambda)"], y = df_to_plot['Median_ED_doc_%_utilize'], name='ED Doc utilization%'))
+    fig.add_trace(go.Scatter(x = df_to_plot["Pt Interarrival Time (lambda)"], y = df_to_plot['Median_ACU_doc_%_utilize'], name='ACU Doc utilization%'))
+    fig.update_layout(title = "% utilization of different HR")
+    #fig.show()
+
+    return fig
+
+
+
+def main(receptionist, nurse, ed_doc, acu_doc):
+    '''
+    this is going to to be the main function that gradio will operate on
+    It will take input parameters as model parameters which will be modified by the users
+    It will output a plot or a set of plots which will then be plotted in a given block in gradio
+    '''
+    file_opener()
+    for l in range(1,11):
+        print("Pt interarrival time = ", l)
+        for run in range (g.number_of_runs):
+            print(f"Run {run + 1} of {g.number_of_runs}")
+            my_ED_model = ED_sim(run, receptionist, nurse, ed_doc, acu_doc)
+            my_ED_model.run()
+        g.ed_inter_arrival = l 
+        my_sum_stats = summary_statistics()
+        my_sum_stats.mean_of_means()
+    return plotly_plotter()
+
+def get_data_gradio():
+    filepath = r"C:\Users\varad\Desktop\Education Material\Mathematical Modelling\HSMA\HSMA_modelling_ED\mean_per_lambda.csv"
+    return pd.read_csv(filepath)
+
+
+
+#main()
+
+
+    
+with gr.Blocks() as demo:
+    gr.Markdown(r"A Discrete Event Simulation run of an imaginary Emergency Room")
+    gr.Image(r"C:\Users\varad\Desktop\Education Material\Mathematical Modelling\HSMA\HSMA_modelling_ED\Ed_process_map.png")
+    with gr.Row():
+        gr.HighlightedText(label = "Modify these parameters (number of different human resources) using the sliders below")
+    with gr.Row():
+        receptionist = gr.Slider(minimum=1, maximum=10,label = "No of Receptionists")
+        nurse = gr.Slider(minimum=1, maximum=10, label = "No of Nurses")
+    with gr.Row():    
+        ed_doc = gr.Slider(minimum=1, maximum=10, label = "No of ED doctors")
+        acu_doc = gr.Slider(minimum=1, maximum=10,label = "No of ACU Doctors")
+    
+    
+    with gr.Row():
+        btn = gr.Button(value = "Run the Simulation")
+
+        
+    with gr.Row():
+        output = gr.Plot(label = "Simulation Run")
+        btn.click(main,[receptionist,nurse,ed_doc,acu_doc], output)
+demo.launch(share = True)   
+
+
+
+
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