def prepare_visualization_group(df: DataFrame = None, **kwargs) -> List[Any]:
"""Creates plot, table and download link for data frame.
"""
result = [{}, None, None]
if df is not None and isinstance(df, DataFrame):
plot_data = plot_dataframe(
df.dropna().set_index("date").drop(columns=["day"]),
max_y_axis=kwargs.get("max_y_axis", None),
)
table = (
df_to_html_table(
build_table(
df=df,
labels=kwargs.get("labels", df.columns),
modulo=kwargs.get("table_mod", 7),
),
format={
float: int,
(date, datetime): lambda d: d.strftime(DATE_FORMAT),
},
)
# if kwargs.get("show_tables", None)
# else None
)
csv = build_csv_download(df)
result = [plot_data, table, csv]
return result
def build_tables(model, labels):
'''
builds CHIME tables
'''
tables = list()
for title, attr in (
('New Admissions', 'admits_floor_df'),
('Admitted Patients (Census)', 'census_floor_df'),
('Susceptible, Infected, and Recovered', 'sim_sir_w_date_floor_df'),
):
df = getattr(model, attr)
columns = [{'field': c, 'title': c} for c in df.columns]
data = build_table(df=df, labels=labels)
tables.append({
'title': title,
'id': attr,
'columns': columns,
'data': data.to_json(orient='records'),
})
return tables
admits_df=m.admits_df,
max_y_axis=p.max_y_axis)
st.altair_chart(admits_chart, use_container_width=True)
st.markdown(build_descriptions(chart=admits_chart, labels=p.labels))
display_download_link(
st,
filename=f"{p.current_date}_projected_admits.csv",
df=m.admits_df,
)
if st.checkbox("Show Projected Admissions in tabular form"):
admits_modulo = 1
if not st.checkbox("Show Daily Counts"):
admits_modulo = 7
table_df = build_table(df=m.admits_df,
labels=p.labels,
modulo=admits_modulo)
st.table(table_df)
st.subheader("Admitted Patients (Census)")
st.markdown(
"Projected **census** of COVID-19 patients, accounting for arrivals and discharges at Penn hospitals"
)
census_chart = build_census_chart(alt=alt,
census_df=m.census_df,
max_y_axis=p.max_y_axis)
st.altair_chart(census_chart, use_container_width=True)
st.markdown(
build_descriptions(chart=census_chart, labels=p.labels, suffix=" Census"))
display_download_link(
st,
def display_body_charts(m, p: "Parameters", d: "Parameters",
actuals: "pd.DataFrame", mode: "Mode"):
st.subheader("New Hospital Admissions")
admits_chart = build_admits_chart(alt=alt,
admits_floor_df=m.admits_floor_df,
p=p,
actuals=actuals)
st.altair_chart(admits_chart, use_container_width=True)
zero_admits_warning(p)
st.markdown(
build_descriptions(
chart=admits_chart,
labels=p.admits_patient_chart_desc,
))
display_download_link(
filename=f"{p.current_date}_projected_admits.csv",
df=m.admits_df,
)
if st.checkbox("Show Projected Admissions in tabular form"):
admits_modulo = 1
if not st.checkbox("Show Daily Counts"):
admits_modulo = 7
table_df = build_table(df=m.admits_floor_df,
labels=p.labels,
modulo=admits_modulo)
st.table(table_df)
st.subheader("Hospital Census")
census_chart = build_census_chart(alt=alt,
census_floor_df=m.census_floor_df,
p=p,
actuals=actuals)
st.altair_chart(census_chart, use_container_width=True)
# Display census mismatch message if appropriate
census_mismatch_message(parameters=p, actuals=actuals, st=st)
st.markdown(
build_descriptions(
chart=census_chart,
labels=p.census_patient_chart_desc,
))
display_download_link(
filename=f"{p.current_date}_projected_census.csv",
df=m.census_df,
)
if st.checkbox("Show Projected Census in tabular form"):
census_modulo = 1
if not st.checkbox("Show Daily Census Counts"):
census_modulo = 7
table_df = build_table(df=m.census_floor_df,
labels=p.labels,
modulo=census_modulo)
st.table(table_df)
st.subheader("COVID-19 Capacity")
beds_chart = build_beds_chart(alt=alt,
beds_floor_df=m.beds_df,
parameters=p)
st.altair_chart(beds_chart, use_container_width=True)
st.markdown(
build_bed_descriptions(chart=beds_chart, labels=p.beds_chart_desc))
display_download_link(
filename=f"{p.current_date}_projected_capacity.csv",
df=m.beds_df,
)
if st.checkbox("Show Projected Capacity in tabular form"):
beds_modulo = 1
if not st.checkbox("Show Daily Capacity Counts"):
beds_modulo = 7
table_df = build_table(df=m.beds_floor_df,
labels=p.labels,
modulo=beds_modulo)
st.table(table_df)
st.subheader("Susceptible, Infected, and Recovered")
sim_sir_w_date_chart = build_sim_sir_w_date_chart(
alt=alt,
sim_sir_w_date_floor_df=m.sim_sir_w_date_floor_df,
actuals=actuals,
p=p)
st.altair_chart(sim_sir_w_date_chart, use_container_width=True)
display_download_link(
filename=f"{p.current_date}_sim_sir_w_date.csv",
df=m.sim_sir_w_date_df,
)
if st.checkbox("Show SIR Simulation in tabular form"):
table_df = build_table(df=m.sim_sir_w_date_floor_df, labels=p.labels)
st.table(table_df)
### PPE Section
st.subheader("Personal Protection Equipment")
show_ppe_section = st.checkbox("Show PPE Charts", value=p.show_ppe_section)
p.show_ppe_section = show_ppe_section
if show_ppe_section:
st.markdown("Quantity of PPE required per day")
for pc in list(p.ppe_labels.keys())[3:]:
ppe_chart = build_ppe_chart(alt=alt,
ppe_floor_df=m.ppe_floor_df,
p=p,
plot_columns=pc)
st.altair_chart(ppe_chart, use_container_width=True)
st.markdown(
build_ppe_descriptions(chart=ppe_chart,
label=p.ppe_labels[pc]["label"]))
st.markdown(" \n \n")
display_download_link(
filename=f"{p.current_date}_projected_ppe_required.csv",
df=m.ppe_df,
)
if st.checkbox("Show Projected PPE Required in tabular form"):
ppe_modulo = 1
if not st.checkbox("Show Daily PPE Required"):
ppe_modulo = 7
table_df = build_table(df=m.ppe_floor_df,
labels=p.labels,
modulo=ppe_modulo)
st.dataframe(table_df)
### Staffing Section
st.subheader("Staffing")
show_staffing_section = st.checkbox("Show Staffing Charts",
value=p.show_staffing_section)
p.show_staffing_section = show_staffing_section
if show_staffing_section:
st.markdown("Number of staff required per day")
for pc in list(p.staffing_labels.keys())[3:]:
staffing_chart = build_staffing_chart(
alt=alt,
staffing_floor_df=m.staffing_floor_df,
p=p,
plot_columns=pc)
st.altair_chart(staffing_chart, use_container_width=True)
st.markdown(
build_staffing_descriptions(
chart=staffing_chart,
label=p.staffing_labels[pc]["label"],
shift_duration=p.shift_duration))
st.markdown(" \n \n")
display_download_link(
filename=f"{p.current_date}_projected_staffing_required.csv",
df=m.staffing_df,
)
if st.checkbox("Show Projected Staffing Required in tabular form"):
staffing_modulo = 1
if not st.checkbox("Show Daily Staffing Required"):
staffing_modulo = 7
table_df = build_table(df=m.staffing_floor_df,
labels=p.labels,
modulo=staffing_modulo)
st.dataframe(table_df)
### Export Full Data and Parameters
st.header("Export Full Data and Parameters")
df = build_data_and_params(projection_admits=m.admits_df,
census_df=m.census_df,
beds_df=m.beds_df,
ppe_df=m.ppe_df,
staffing_df=m.staffing_df,
model=m,
parameters=p)
if st.checkbox("Show full data and parameters to be exported"):
st.dataframe(df)
filename = "Data" + "_" + p.author + "_" + p.scenario + "_" + (
datetime.datetime.utcnow() -
datetime.timedelta(hours=6)).isoformat() + ".csv"
csv = dataframe_to_base64(df)
st.markdown("""
<a download="{filename}" href="data:text/plain;base64,{csv}">Download full table as CSV</a>
""".format(csv=csv, filename=filename),
unsafe_allow_html=True)
if actuals is not None:
if st.checkbox("Display Uploaded Actuals"):
st.dataframe(actuals)
def prepare_visualization_group(df: DataFrame = None, **kwargs) -> List[Any]:
"""Creates plot, table and download link for data frame.
Arguments:
df: The Dataframe to plot
content: Dict[str, str]
Mapping for translating columns and index.
max_y_axis: int
Maximal value on y-axis
labels: List[str]
Columns to display
table_mod: int
Displays only each `table_mod` row in table
"""
result = [{}, None, None]
if df is not None and isinstance(df, DataFrame):
date_column = "date"
day_column = "day"
# Translate column and index if specified
content = kwargs.get("content", None)
if content:
columns = {
col: content[col]
for col in df.columns if col in content
}
index = ({
df.index.name: content[df.index.name]
} if df.index.name and df.index.name in content else None)
df = df.rename(columns=columns, index=index)
date_column = content.get(date_column, date_column)
day_column = content.get(day_column, day_column)
plot_data = plot_dataframe(
df.dropna().set_index(date_column).drop(columns=[day_column]),
max_y_axis=kwargs.get("max_y_axis", None),
)
# translate back for backwards compability of build_table
column_map = {day_column: "day", date_column: "date"}
table = (
df_to_html_table(
build_table(
df=df.rename(columns=column_map),
labels=kwargs.get("labels", df.columns),
modulo=kwargs.get("table_mod", 7),
),
formats={
float: int,
(date, datetime): lambda d: d.strftime(DATE_FORMAT),
},
)
# if kwargs.get("show_tables", None)
# else None
)
# Convert columnnames to lowercase
column_map = {col: col.lower() for col in df.columns}
csv = build_csv_download(df.rename(columns=column_map))
result = [plot_data, table, csv]
return result
