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graphing.py
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graphing.py
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import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from utils import COLOR_MAP
from data import constants
from data.constants import SymptomState
TEMPLATE = "plotly_white"
def _set_title(fig):
fig.layout.update(
title=dict(y=0.95, x=0, xanchor="left", yanchor="top"), titlefont=dict(size=14)
)
def _set_plot_font(fig):
fig.layout.update(font=dict(family="Arial"))
def _set_legends(fig):
fig.layout.update(legend=dict(x=-0.1, y=1.2))
fig.layout.update(legend_orientation="h")
def plot_historical_data(df):
# Convert wide to long
df = pd.melt(
df,
id_vars="Date",
value_vars=["Confirmed", "Deaths", "Recovered"],
var_name="Status",
value_name="Number",
)
fig = px.scatter(
df, x="Date", y="Number", color="Status", template=TEMPLATE, opacity=0.8
)
_set_legends(fig)
return fig
def plot_true_versus_confirmed(confirmed, predicted):
df = pd.DataFrame(
{
"Status": ["Confirmed", "Predicted"],
"Cases": [confirmed, predicted],
"Color": ["b", "r"],
}
)
fig = px.bar(df, x="Status", y="Cases", color="Color", template=TEMPLATE)
fig.layout.update(showlegend=False)
return fig
def infection_graph(df, y_max, contact_rate):
asymptomatic_contact_rate = contact_rate[SymptomState.ASYMPTOMATIC]
symptomatic_contact_rate = contact_rate[SymptomState.SYMPTOMATIC]
# We cannot explicitly set graph width here, have to do it as injected css: see interface.css
fig = go.Figure(layout=dict(template=TEMPLATE))
susceptible, infected, recovered = (
df.loc[df.Status == "Susceptible"],
df.loc[df.Status == "Infected"],
df.loc[df.Status == "Recovered"],
)
fig.add_scatter(
x=susceptible.Days,
y=susceptible.Forecast,
fillcolor=COLOR_MAP["susceptible"],
fill="tozeroy",
mode="lines",
line=dict(width=0),
name="Uninfected",
opacity=0.5,
)
fig.add_scatter(
x=recovered.Days,
y=recovered.Forecast,
fillcolor=COLOR_MAP["recovered"],
fill="tozeroy",
mode="lines",
line=dict(width=0),
name="Recovered",
opacity=0.5,
)
fig.add_scatter(
x=infected.Days,
y=infected.Forecast,
fillcolor="#FFA000",
fill="tozeroy",
mode="lines",
line=dict(width=0),
name="Infected",
opacity=0.5,
)
fig.update_yaxes(range=[0, y_max])
fig.layout.update(xaxis_title="Number of days from today")
fig.layout.update(
title=dict(
text=f"Disease propagation with symptomatic people meeting <b>{int(symptomatic_contact_rate)} "
f"</b> and asymptomatic meeting <b>{int(asymptomatic_contact_rate)}</b> people a day"
)
)
_set_legends(fig)
_set_title(fig)
_set_plot_font(fig)
return fig
def age_segregated_mortality(df, contact_rate):
asymptomatic_contact_rate = contact_rate[SymptomState.ASYMPTOMATIC]
symptomatic_contact_rate = contact_rate[SymptomState.SYMPTOMATIC]
df = df.rename(index={ag: "0-30" for ag in ["0-9", "10-19", "20-29"]}).reset_index()
df = pd.melt(df, id_vars="Age Group", var_name="Status", value_name="Forecast")
# Add up values for < 30
df = (
df.groupby(["Age Group", "Status"])
.sum()
.reset_index(1)
.sort_values(by="Status", ascending=False)
)
df["Status"] = df["Status"].apply(
lambda x: {"Need Hospitalization": "Hospitalized"}.get(x, x)
)
fig = px.bar(
df,
x=df.index,
y="Forecast",
color="Status",
template=TEMPLATE,
opacity=0.7,
color_discrete_sequence=["pink", "red"],
barmode="group",
)
fig.layout.update(
xaxis_title="",
yaxis_title="",
font=dict(family="Arial", size=15, color=COLOR_MAP["default"]),
title=dict(
text=f"Casualties and hospitalizations with symptomatic people meeting <b>{int(symptomatic_contact_rate)}</b> "
f"and asymptomatic meeting <b>{int(asymptomatic_contact_rate)}</b> people a day"
),
)
_set_legends(fig)
_set_title(fig)
_set_plot_font(fig)
fig.layout.update(legend=dict(y=1.05))
return fig
def num_beds_occupancy_comparison_chart(
num_beds_available, max_num_beds_needed, contact_rate
):
"""
A horizontal bar chart comparing # of beds available compared to
max number number of beds needed
"""
asymptomatic_contact_rate = contact_rate[SymptomState.ASYMPTOMATIC]
symptomatic_contact_rate = contact_rate[SymptomState.SYMPTOMATIC]
num_beds_available, max_num_beds_needed = (
int(num_beds_available),
int(max_num_beds_needed),
)
df = pd.DataFrame(
{
"Label": ["Total Beds ", "Peak Occupancy "],
"Value": [num_beds_available, max_num_beds_needed],
"Text": [f"{num_beds_available:,} ", f"{max_num_beds_needed:,} "],
"Color": ["b", "r"],
}
)
fig = px.bar(
df,
x="Value",
y="Label",
color="Color",
text="Text",
orientation="h",
opacity=0.7,
template=TEMPLATE,
height=300,
)
fig.layout.update(
showlegend=False,
xaxis_title="",
xaxis_showticklabels=False,
yaxis_title="",
yaxis_showticklabels=True,
font=dict(family="Arial", size=15, color=COLOR_MAP["default"]),
title=dict(
text=f"Peak occupancy with symptomatic people meeting <b>{int(symptomatic_contact_rate)} "
f"</b> and asymptomatic meeting <b>{int(asymptomatic_contact_rate)}</b> people a day"
),
)
fig.update_traces(textposition="outside", cliponaxis=False)
_set_title(fig)
_set_plot_font(fig)
return fig