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Adding a plot

Adding a plot#

In this exercise we will again return to the treatment centre model. We will enhance the app by adding in a simple plot.

Imports#

import plotly.express as px

from treat_sim.model import Scenario, multiple_replications

Exercise 1#

Create a new python file called app_with_plot.py and copy as paste in the code from app_with_sidebar.py shown below

'''
Script to complete a basic run of the model and display a table of
results in a streamlit app.

The model is imported from a pypi package 'treat_sim'

Full documentation and source code for `treat_sim` is available as 
* Jupyter Book: https://tommonks.github.io/treatment-centre-sim/
* github: https://github.com/TomMonks/treatment-centre-sim

A conda environment has been provided locally,but the model can be pip installed
`pip install treat_sim==1.0.0`
'''
from treat_sim.model import Scenario, multiple_replications
import streamlit as st

INTRO_FILE = 'resources/overview.md'

def read_file_contents(file_name):
    ''''
    Read the contents of a file.

    Params:
    ------
    file_name: str
        Path to file.

    Returns:
    -------
    str
    '''
    with open(file_name) as f:
        return f.read()

# give the page a title
st.title('Treatment Centre Simulation Model')

# show the introductory markdown
st.markdown(read_file_contents(INTRO_FILE))

################################################################################
# MODIFICATION: create a sidebar for sliders
with st.sidebar:
    n_triage = st.slider('Triage bays', 1, 5, 1)
    n_exam = st.slider('Exam rooms', 1, 5, 3)
    n_cubicles_1 = st.slider('Non-Trauma Treatment cubicles', 1, 5, 1, 
                             help='Set the number of non trauma pathway '
                             + 'treatment cubicles')

    # examination mean
    exam_mean = st.slider('Mean examination time', 10.0, 45.0, 
                           16.0, 1.0)

    # runs
    replications = st.slider('No. replications', 1, 50, 10)
################################################################################

# Setup scenario using supplied variables
args = Scenario(n_triage=n_triage, n_exam=n_exam, n_cubicles_1=n_cubicles_1,
                exam_mean=exam_mean)

# Only execute model if a streamlit button is pressed.
if st.button('Simulate treatment centre'):

    # in this example run a single replication of the model.
    with st.spinner('Simulating the treatment centre...'):
        results = multiple_replications(args, n_reps=replications)

    st.success('Done!')

    # display results using st.table (or st.dataframe)
    st.table(results.mean().round(1))

Exercise 2#

The code below creates apandas.DataFrame called results in the same format as code in the app_with_plot.py.

Each row in the DataFrame is a replication and each column is a performance measure. You can quickly run the code and view the format.

Task:

  • Create a function called create_hist(). The function should accept results and create a histogram of the results of a performance measure of your choice. For example, 03a_examination_wait.

  • The function should return a plotly figure object to the calling code. It is recommended you use the fig naming convention.

  • Include a violin plot above the histogram using plotly express.

Hints:

  • It is recommended that you complete this exercise in the Jupyter notebook before modifying app_with_plot.py

  • At the top of this notebook we have imported plotly.express

  • To have a useful histogram you may want to run more than 10 replications. e.g. 50 to 100.

  • Jupyter notebooks will render the histogram with calling fig.show(), just leave the last line of code in a cell as fig. In a python module you will need to call fig.show()

default_scenario = Scenario()
results = multiple_replications(default_scenario, n_reps=50)
results.info()

<class 'pandas.core.frame.DataFrame'>
Index: 50 entries, 1 to 50
Data columns (total 16 columns):
 #   Column                          Non-Null Count  Dtype  
---  ------                          --------------  -----  
 0   00_arrivals                     50 non-null     float64
 1   01a_triage_wait                 50 non-null     float64
 2   01b_triage_util                 50 non-null     float64
 3   02a_registration_wait           50 non-null     float64
 4   02b_registration_util           50 non-null     float64
 5   03a_examination_wait            50 non-null     float64
 6   03b_examination_util            50 non-null     float64
 7   04a_treatment_wait(non_trauma)  50 non-null     float64
 8   04b_treatment_util(non_trauma)  50 non-null     float64
 9   05_total_time(non-trauma)       50 non-null     float64
 10  06a_trauma_wait                 50 non-null     float64
 11  06b_trauma_util                 50 non-null     float64
 12  07a_treatment_wait(trauma)      50 non-null     float64
 13  07b_treatment_util(trauma)      50 non-null     float64
 14  08_total_time(trauma)           50 non-null     float64
 15  09_throughput                   50 non-null     float64
dtypes: float64(16)
memory usage: 6.6 KB

# your code here ...

Exercise 3:#

Modify app_with_plot.py to use create_hist() and display histogram of your chosen KPI to a user.

Hints:

  • Don’t forget to import plotly.express

# your code here ...
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