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app.py
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app.py
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from shiny import (App, ui, render, reactive, Inputs, Outputs, Session)
from shinywidgets import output_widget, render_widget
import shinyswatch
import plotly.graph_objects as go
import pandas as pd
# import the wrapper objects for model interaction.
from ciw_model import Experiment, multiple_replications
ABOUT = """## About
This work is produced using entirely free and open software in python.
> This model is independent research supported by the National Institute for Health Research
Applied Research Collaboration South West Peninsula.
The views expressed in this publication are those of the author(s) and not necessarily
those of the National Institute for Health Research or the Department of Health and Social Care."""
SIMSOFTWARE = """## Modelling and Simulation Software
The model is written in python3 and `ciw`. The simulation libary `ciw` is a network based DES package.
> Detailed documentation for `ciw` and additional models can be found here: https://ciw.readthedocs.io
"""
DOCS_LINK = """### Model Documentation
Live documentation including STRESS-DES reporting for the model and
is available https://pythonhealthdatascience.github.io/stars-ciw-examplar"""
app_ui = ui.page_fluid(
shinyswatch.theme.journal(),
ui.h1("Ciw Urgent Care Call Centre Model"),
ui.markdown("""
This app is based on a
[ciw example](https://health-data-science-or.github.io/simpy-streamlit-tutorial/content/03_streamlit/13_ciw_backend.html)
that simulates a simple call centre model.
"""),
ui.navset_tab(
ui.nav("Interactive simulation",
ui.layout_sidebar(
ui.panel_sidebar(
# number of call operators
ui.input_slider("n_operators", "Call operators", 1, 40, 13, sep=5),
# nurses on duty
ui.input_slider("n_nurses", "Nurse practitioners", 1, 20, 9),
# chance of nurse call back
ui.input_slider("chance_callback", "Probability of nurse callback", 0.0, 1.0, 0.4),
# Number of replications
ui.input_numeric("n_reps", "Replications", value=5),
# run simulation model button
ui.input_action_button("run_sim", "Run Simulation", class_="btn-primary"),
width=2
),
ui.panel_main(
ui.row(
ui.column(5, ui.output_data_frame("result_table"),),
ui.column(7, output_widget("histogram"),),
),
)
)
),
ui.nav("About",
ui.markdown(ABOUT),
ui.markdown(SIMSOFTWARE),
ui.markdown(DOCS_LINK)),
)
)
def server(input: Inputs, output: Outputs, session: Session):
# reactive value for replication results.
replication_results = reactive.Value()
def run_simulation():
'''
Run the simulation model
Returns:
--------
pd.DataFrame
Pandas Dataframe containing replications by performance
measures
'''
# create the experiment
user_experiment = Experiment(n_operators=input.n_operators(),
n_nurses=input.n_nurses(),
chance_callback=input.chance_callback())
# run multiple replications
results = multiple_replications(user_experiment, n_reps=input.n_reps())
return results
def summary_results(replications):
'''
Convert the replication results into a summary table
Returns:
-------
pd.DataFrame
'''
summary = replications.describe().round(2).T
# resetting index because cannot figure out how to show index
summary = summary.reset_index()
summary = summary.rename(columns={'index': 'metric'})
return summary
def create_user_filtered_hist(results):
'''
Create a plotly histogram that includes a drop down list that allows a user
to select which KPI is displayed as a histogram
Params:
-------
results: pd.Dataframe
rows = replications, cols = KPIs
Returns:
-------
plotly.figure
Sources:
------
The code in this function was partly adapted from two sources:
1. https://stackoverflow.com/questions/59406167/plotly-how-to-filter-a-pandas-dataframe-using-a-dropdown-menu
Thanks and credit to `vestland` the author of the reponse.
2. https://plotly.com/python/dropdowns/
'''
# create a figure
fig = go.Figure()
# set up a trace
fig.add_trace(go.Histogram(x=results[results.columns[0]]))
buttons = []
# create list of drop down items - KPIs
# the params in the code would need to vary depending on the type of chart.
# The histogram will show the first KPI by default
for col in results.columns:
buttons.append(dict(method='restyle',
label=col,
visible=True,
args=[{'x':[results[col]],
'type':'histogram'}, [0]],
)
)
# create update menu and parameters
updatemenu = []
your_menu = dict()
updatemenu.append(your_menu)
updatemenu[0]['buttons'] = buttons
updatemenu[0]['direction'] = 'down'
updatemenu[0]['showactive'] = True
updatemenu[0]['x'] = 0.25
updatemenu[0]['y'] = 1.1
updatemenu[0]['xanchor'] = 'right'
updatemenu[0]['yanchor'] = 'bottom'
# add dropdown menus to the figure
fig.update_layout(showlegend=False,
updatemenus=updatemenu)
# add label for selecting performance measure
fig.update_layout(
annotations=[
dict(text="Performance measure", x=0, xref="paper", y=1.25,
yref="paper", align="left", showarrow=False)
])
return fig
@output
@render.data_frame
def result_table():
'''
Reactive event to when the run simulation button
is clicked.
'''
return summary_results(replication_results())
@output
@render_widget
def histogram():
'''
Updates the interactive histogram
Returns:
-------
plotly.figure
'''
return create_user_filtered_hist(replication_results())
@reactive.Effect
@reactive.event(input.run_sim)
async def _():
'''
Runs simulation model when button is clicked.
This is a reactive effect. Once replication_results
is set it invalidates results_table and histogram.
These are rerun by Shiny
'''
# set to empty - forces shiny to dim output widgets
# helps with the feeling of waiting for simulation to complete
replication_results.set([])
ui.notification_show("Simulation running. Please wait", type='warning')
replication_results.set(run_simulation())
ui.notification_show("Simulation complete.", type='message')
app = App(app_ui, server)