Introduction.py

import gc
import streamlit as st
from helper_functions import add_logo, mermaid

st.set_page_config(
    page_title="Introduction",
    page_icon="👋",
    layout="wide",
    initial_sidebar_state="expanded",
)

add_logo()

with open("style.css") as css:
    st.markdown( f'<style>{css.read()}</style>' , unsafe_allow_html= True)

st.title("Welcome to the Discrete Event Simulation Playground! 👋")

gc.collect()

st.markdown(
"""
This is a discrete event simulation playground based on the Monks et al (2022), which is itself an implementation of the Treatment Centre Model from Nelson (2013).

By working through the pages on the left in order, you will
- see how a discrete event simulation builds from simple beginnings up to the point of being able to model a complex system
- understand the impact of variability and randomness on systems
- have a go at changing parameters to find the best configuration that balances the average use of resources (like treatment bays or nurses) and the average time a patient will wait at each step of the process.

The treatment centre we want to build a model of looks like this:
"""
)


mermaid(height=450, code=
"""
    %%{ init: { 'flowchart': { 'curve': 'step'} } }%%
    %%{ init: {  'theme': 'base', 'themeVariables': {'lineColor': '#b4b4b4'} } }%%
    flowchart LR
        A[Arrival] --> B{Trauma or non-trauma}
        B --> B1{Trauma Pathway}
        B --> B2{Non-Trauma Pathway}

        B1 --> C[Stabilisation]
        C --> E[Treatment]

        B2 --> D[Registration]
        D --> G[Examination]

        G --> H[Treat?]
        H ----> F

        H --> I[Non-Trauma Treatment]
        I --> F

        C -.-> Z([Trauma Room\n<b>RESOURCE</b>])
        Z -.-> C

        E -.-> Y([Cubicle - 1\n<b>RESOURCE</b>])
        Y -.-> E

        D -.-> X([Clerks\n<b>RESOURCE</b>])
        X -.-> D

        G -.-> W([Exam Room\n<b>RESOURCE</b>])
        W -.-> G

        I -.-> V([Cubicle - 2\n<b>RESOURCE</b>])
        V -.-> I

        E ----> F[Discharge]

        classDef ZZ1 fill:#47D7FF,font-family:lexend
        classDef ZZ2 fill:#5DFDA0,font-family:lexend
        classDef ZZ2a fill:#02CD55,font-family:lexend, color:#FFF
        classDef ZZ3 fill: #D45E5E,font-family:lexend
        classDef ZZ3a fill: #932727,font-family:lexend, color:#FFF
        classDef ZZ4 fill: #611D67,font-family:lexend, color:#FFF

        class A,B ZZ1
        class C,E ZZ2
        class D,G ZZ3
        class X,W ZZ3a
        class Z,Y ZZ2a
        class I,V ZZ4;
    """
)

st.markdown(
"""
## References

1. *Monks.T, Harper.A, Anagnoustou. A, Allen.M, Taylor.S. (2022) Open Science for Computer Simulation*; [Repository Link](https://github.com/TomMonks/treatment-centre-sim/tree/main)
2. *Nelson. B.L. (2013). [Foundations and methods of stochastic simulation](https://www.amazon.co.uk/Foundations-Methods-Stochastic-Simulation-International/dp/1461461596/ref=sr_1_1?dchild=1&keywords=foundations+and+methods+of+stochastic+simulation&qid=1617050801&sr=8-1). Springer.*
3. https://health-data-science-or.github.io/simpy-streamlit-tutorial/
"""
)