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Merged
merged 2 commits into from
Mar 21, 2020
Merged

Extract sidebar #163

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6a01b46
Update type annotation
jlubken Mar 21, 2020
551250e
Merge branch 'develop' into extract_sidebar
quinn-dougherty Mar 21, 2020
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154 changes: 5 additions & 149 deletions app.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,12 +1,12 @@
import altair as alt
import streamlit as st

from penn_chime.defaults import RateLos
from penn_chime.models import Parameters
from penn_chime.presentation import (
additional_projections_chart,
admitted_patients_chart,
display_header,
display_sidebar,
display_n_days_slider,
draw_census_table,
draw_projected_admissions_table,
draw_raw_sir_simulation_table,
Expand All @@ -27,143 +27,8 @@
# In dev, this should be shown
st.markdown(hide_menu_style, unsafe_allow_html=True)


def display_sidebar(st, d) -> Parameters:
# Initialize variables
# these functions create input elements and bind the values they are set to
# to the variables they are set equal to
# it's kindof like ember or angular if you are familiar with those

if d.known_infected < 1:
raise ValueError("Known cases must be larger than one to enable predictions.")

current_hospitalized = st.sidebar.number_input(
"Currently Hospitalized COVID-19 Patients",
min_value=0,
value=d.current_hospitalized,
step=1,
format="%i"
)

doubling_time = st.sidebar.number_input(
"Doubling time before social distancing (days)",
min_value=0,
value=d.doubling_time,
step=1,
format="%i"
)

relative_contact_rate = (
st.sidebar.number_input(
"Social distancing (% reduction in social contact)",
min_value=0,
max_value=100,
value=d.relative_contact_rate * 100,
step=5,
format="%i",
)
/ 100.0
)

hospitalized_rate = (
st.sidebar.number_input(
"Hospitalization %(total infections)",
min_value=0.001,
max_value=100.0,
value=d.hospitalized.rate * 100,
step=1.0, format="%f",
)
/ 100.0
)
icu_rate = (
st.sidebar.number_input(
"ICU %(total infections)",
min_value=0.0,
max_value=100.0,
value=d.icu.rate * 100,
step=1.0,
format="%f"
)
/ 100.0
)
ventilated_rate = (
st.sidebar.number_input(
"Ventilated %(total infections)",
min_value=0.0,
max_value=100.0,
value=d.ventilated.rate * 100,
step=1.0,
format="%f"
)
/ 100.0
)

hospitalized_los = st.sidebar.number_input(
"Hospital Length of Stay",
min_value=0,
value=d.hospitalized.length_of_stay,
step=1,
format="%i",
)
icu_los = st.sidebar.number_input(
"ICU Length of Stay",
min_value=0,
value=d.icu.length_of_stay,
step=1,
format="%i",
)
ventilated_los = st.sidebar.number_input(
"Vent Length of Stay",
min_value=0,
value=d.ventilated.length_of_stay,
step=1,
format="%i",
)

market_share = (
st.sidebar.number_input(
"Hospital Market Share (%)",
min_value=0.001,
max_value=100.0,
value=d.market_share * 100,
step=1.0,
format="%f"
)
/ 100.0
)
susceptible = st.sidebar.number_input(
"Regional Population",
min_value=1,
value=d.region.susceptible,
step=100000,
format="%i"
)

known_infected = st.sidebar.number_input(
"Currently Known Regional Infections (only used to compute detection rate - does not change projections)",
min_value=0,
value=d.known_infected,
step=10,
format="%i",
)

return Parameters(
current_hospitalized=current_hospitalized,
doubling_time=doubling_time,
known_infected=known_infected,
market_share=market_share,
relative_contact_rate=relative_contact_rate,
susceptible=susceptible,

hospitalized=RateLos(hospitalized_rate, hospitalized_los),
icu=RateLos(icu_rate, icu_los),
ventilated=RateLos(ventilated_rate, ventilated_los)
)


p = display_sidebar(st, DEFAULTS)

# PRESENTATION
display_header(
st,
total_infections=p.infected,
Expand Down Expand Up @@ -195,22 +60,13 @@ def display_sidebar(st, d) -> Parameters:

)

# PRESENTATION
# One more combination variable initialization / input element creation
p.n_days = st.slider(
"Number of days to project",
min_value=30,
max_value=200,
value=DEFAULTS.n_days,
step=1,
format="%i"
)
display_n_days_slider(st, p, DEFAULTS)

# format data
# begin format data
projection_admits = build_admissions_df(p.n_days, *p.dispositions)
census_df = build_census_df(projection_admits, *p.lengths_of_stay)
# end format data

# PRESENTATION
st.subheader("New Admissions")
st.markdown("Projected number of **daily** COVID-19 admissions at Penn hospitals")
st.altair_chart(
Expand Down
148 changes: 147 additions & 1 deletion penn_chime/presentation.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,8 @@
import numpy as np
import pandas as pd

from .defaults import Constants
from .defaults import Constants, RateLos
from .models import Parameters

hide_menu_style = """
<style>
Expand Down Expand Up @@ -88,6 +89,151 @@ def display_header(
return None


def display_sidebar(st, d: Constants) -> Parameters:
# Initialize variables
# these functions create input elements and bind the values they are set to
# to the variables they are set equal to
# it's kindof like ember or angular if you are familiar with those

if d.known_infected < 1:
raise ValueError("Known cases must be larger than one to enable predictions.")

current_hospitalized = st.sidebar.number_input(
"Currently Hospitalized COVID-19 Patients",
min_value=0,
value=d.current_hospitalized,
step=1,
format="%i"
)

doubling_time = st.sidebar.number_input(
"Doubling time before social distancing (days)",
min_value=0,
value=d.doubling_time,
step=1,
format="%i"
)

relative_contact_rate = (
st.sidebar.number_input(
"Social distancing (% reduction in social contact)",
min_value=0,
max_value=100,
value=d.relative_contact_rate * 100,
step=5,
format="%i",
)
/ 100.0
)

hospitalized_rate = (
st.sidebar.number_input(
"Hospitalization %(total infections)",
min_value=0.001,
max_value=100.0,
value=d.hospitalized.rate * 100,
step=1.0, format="%f",
)
/ 100.0
)
icu_rate = (
st.sidebar.number_input(
"ICU %(total infections)",
min_value=0.0,
max_value=100.0,
value=d.icu.rate * 100,
step=1.0,
format="%f"
)
/ 100.0
)
ventilated_rate = (
st.sidebar.number_input(
"Ventilated %(total infections)",
min_value=0.0,
max_value=100.0,
value=d.ventilated.rate * 100,
step=1.0,
format="%f"
)
/ 100.0
)

hospitalized_los = st.sidebar.number_input(
"Hospital Length of Stay",
min_value=0,
value=d.hospitalized.length_of_stay,
step=1,
format="%i",
)
icu_los = st.sidebar.number_input(
"ICU Length of Stay",
min_value=0,
value=d.icu.length_of_stay,
step=1,
format="%i",
)
ventilated_los = st.sidebar.number_input(
"Vent Length of Stay",
min_value=0,
value=d.ventilated.length_of_stay,
step=1,
format="%i",
)

market_share = (
st.sidebar.number_input(
"Hospital Market Share (%)",
min_value=0.001,
max_value=100.0,
value=d.market_share * 100,
step=1.0,
format="%f"
)
/ 100.0
)
susceptible = st.sidebar.number_input(
"Regional Population",
min_value=1,
value=d.region.susceptible,
step=100000,
format="%i"
)

known_infected = st.sidebar.number_input(
"Currently Known Regional Infections (only used to compute detection rate - does not change projections)",
min_value=0,
value=d.known_infected,
step=10,
format="%i",
)

return Parameters(
current_hospitalized=current_hospitalized,
doubling_time=doubling_time,
known_infected=known_infected,
market_share=market_share,
relative_contact_rate=relative_contact_rate,
susceptible=susceptible,

hospitalized=RateLos(hospitalized_rate, hospitalized_los),
icu=RateLos(icu_rate, icu_los),
ventilated=RateLos(ventilated_rate, ventilated_los)
)


def display_n_days_slider(st, p: Parameters, d: Constants):
"""Display n_days_slider."""
p.n_days = st.slider(
"Number of days to project",
min_value=30,
max_value=200,
value=d.n_days,
step=1,
format="%i"
)


def show_more_info_about_this_tool(
st,
recovery_days,
Expand Down