def collect_inputs(parent_region="BR", where=st.sidebar):
"""
Collect input parameters for the app to run.
Returns:
Dictionary with the following keys:
parent_region, regions, columns, targets, days
"""
st = where
kind = st.selectbox(_("Select scenario"), list(REGIONS_TYPES[parent_region]))
query = REGIONS_TYPES[parent_region][kind]
regions = get_regions(**query)
msg = _("Columns")
columns = st.multiselect(msg, COLUMNS, default=COLUMNS_DEFAULT)
msg = _("Isolation scores")
kwargs = {"default": TARGETS_DEFAULT, "format_func": lambda x: f"{x}%"}
targets = st.multiselect(msg, TARGETS, **kwargs)
msg = _("Show values for the given days")
days = st.multiselect(msg, DAYS, default=DAYS_DEFAULT)
return {
"parent_region": parent_region,
"regions": regions,
"columns": columns,
"targets": targets,
"days": days,
}
def ask(self, parent_region="BR", where=st.sidebar):
st = where
scenario_kind = st.selectbox(_("Select scenario"), list(REGIONS_TYPES[parent_region]))
query = REGIONS_TYPES[parent_region][scenario_kind]
regions = self.__datahandler.get_regions(**query)
message = _("Columns")
columns = st.multiselect(message, COLUMNS, default=COLUMNS_DEFAULT)
message = _("Isolation scores")
kwargs = {"default": TARGETS_DEFAULT, "format_func": lambda x: f"{x}%"}
targets = st.multiselect(message, TARGETS, **kwargs)
message = _("Show values for the given days")
days = st.multiselect(message, DAYS, default=DAYS_DEFAULT)
self.__datahandler.user_inputs = {
"parent_region": parent_region,
"regions": regions,
"columns": columns,
"targets": targets,
"days": days,
"disease": "covid-19"
}
def ask(self, parent_region="BR", where=st.sidebar):
st = where
scenario_kind = st.selectbox(_("Select scenario"), list(REGIONS_TYPES[parent_region]))
scenario = REGIONS_TYPES[parent_region][scenario_kind]
regions = self.__datahandler.get_regions(**scenario["query"])
if "filter" in scenario:
filtering = scenario["filter"]
format_func = filtering.pop("format_func", None)
if format_func is not None:
function = format_func
def format_func(x):
if x == "all":
return _("All")
return function(x)
field, message = filtering.popitem()
groups = sk.group_by(lambda x: getattr(x, field), regions)
key = st.selectbox(message, ["all", *groups], format_func=format_func)
if key != "all":
regions = groups[key]
message = _("Columns")
columns = st.multiselect(message, COLUMNS, default=COLUMNS_DEFAULT)
message = _("Isolation scores")
kwargs = {"default": TARGETS_DEFAULT, "format_func": lambda x: f"{x}%"}
targets = st.multiselect(message, TARGETS, **kwargs)
message = _("Show values for the given days")
days = st.multiselect(message, DAYS, default=DAYS_DEFAULT)
if any(not isinstance(d, int) for d in days):
day_max = sk.pipe(
days,
sk.remove(lambda d: isinstance(d, int)),
sk.map(lambda d: int(NUMBER.search(d).groups()[0])),
max,
)
days = list(range(1, day_max + 1))
message = _("Transpose data")
transpose = st.checkbox(message, value=False)
self.__datahandler.user_inputs = {
"parent_region": parent_region,
"regions": regions,
"columns": columns,
"targets": targets,
"days": days,
"scenario": scenario,
"transpose": transpose,
"disease": "covid-19"
}
def options(where=st):
st = where
regs = regions("BR", type="state")
# Regions to highlight
st.subheader(_("Highlight"))
opts = {
"BR-1": _("North"),
"BR-2": _("Northeast"),
"BR-3": _("Southeast"),
"BR-4": _("South"),
"BR-5": _("Midwest"),
"select": _("Select states"),
# 'top5': _('Top 5'), NotImplemented
}
msg = _("Which states do you want to highlight?")
opt = st.radio(msg, [*opts], format_func=opts.get)
if opt == "select":
fmt = {r.id: r.name for r in regs}.get
ids = [r.id for r in regs]
msg = _("Select states to highlight")
select = set(
st.multiselect(msg, ids, format_func=fmt, default=[regs[0].id]))
highlight = [r for r in regs if r.id in select]
elif opt == "top5":
highlight = opt
else:
highlight = [r for r in regs if r.parent_id == opt]
# Options
st.subheader(_("Options"))
logy = not st.checkbox(_("Linear scale"))
column = "deaths" if st.checkbox(_("Plot deaths")) else "cases"
diff = st.checkbox(_("Plot new daily cases"))
population_adj = st.checkbox(_("Adjust for population"))
default = (20 if diff else 100) // (1 if column == "cases" else 10)
thresh = st.number_input(_("Minimum number of cases"),
min_value=0,
value=default)
default = 7 if diff else 0
smooth = st.number_input(_("Smooth over n days"),
min_value=0,
value=default)
return {
"regions": regs,
"highlight": highlight,
"diff": diff,
"thresh": thresh,
"smoothing": smooth,
"column": column,
"logy": logy,
"population_adj": population_adj,
}
def sidebar(parent_region="BR", where=st.sidebar):
"""
Collect input parameters for the app to run.
Returns:
Dictionary with the following keys:
parent_region, regions, columns, targets, days
"""
st = where
kind = st.selectbox(_("Select scenario"), list(REGIONS_TYPES[parent_region]))
scenario = REGIONS_TYPES[parent_region][kind]
regions = get_regions(**scenario["query"])
if "filter" in scenario:
filtering = scenario["filter"]
format_func = filtering.pop("format_func", None)
if format_func is not None:
fn = format_func
def format_func(x):
if x == "all":
return _("All")
return fn(x)
field, msg = filtering.popitem()
groups = sk.group_by(lambda x: getattr(x, field), regions)
key = st.selectbox(msg, ["all", *groups], format_func=format_func)
if key != "all":
regions = groups[key]
msg = _("Columns")
columns = st.multiselect(msg, COLUMNS, default=COLUMNS_DEFAULT)
msg = _("Isolation scores")
kwargs = {"default": TARGETS_DEFAULT, "format_func": lambda x: f"{x}%"}
targets = st.multiselect(msg, TARGETS, **kwargs)
msg = _("Show values for the given days")
days = st.multiselect(msg, DAYS, default=DAYS_DEFAULT)
if any(not isinstance(d, int) for d in days):
day_max = sk.pipe(
days,
sk.remove(lambda d: isinstance(d, int)),
sk.map(lambda d: int(NUMBER.search(d).groups()[0])),
max,
)
days = list(range(1, day_max + 1))
msg = _("Transpose data")
transpose = st.checkbox(msg, value=False)
return {
"parent_region": parent_region,
"regions": regions,
"columns": columns,
"targets": targets,
"days": days,
"scenario": scenario,
"transpose": transpose,
}
def show_outputs(base, group, region: RegionT, plot_opts, clinical_opts,
**kwargs):
"""
Show results from user input.
"""
cmodels = group.clinical.overflow_model(**clinical_opts)
cforecast = cmodels[0]
start = base.info["event.simulation_start"]
#
# Introduction
#
st.header(_("Introduction"))
st.markdown(report_intro(region))
st.cards(
{
_("Basic reproduction number"): fmt(base.R0),
_("Ascertainment rate"): pc(
base.info["observed.notification_rate"]),
},
color="st-gray-900",
)
#
# Forecast
#
st.header(_("Forecasts"))
st.markdown(forecast_intro(region))
# Infectious curve
group["infectious:dates"].plot(**plot_opts)
mark_x(start.date, "k--")
plt.legend()
plt.title(_("Active cases"))
plt.tight_layout()
st.pyplot()
st.markdown("#### " + _("Download data"))
opts = ["critical", "severe", "infectious", "cases", "deaths"]
default_columns = ["critical", "severe", "cases", "deaths"]
columns = st.multiselect(_("Select columns"),
opts,
default=default_columns)
rename = dict(zip(range(len(columns)), columns))
columns = [c + ":dates" for c in columns]
data = pd.concat([cm[columns].rename(rename, axis=1) for cm in cmodels],
axis=1,
keys=cmodels.names)
st.data_anchor(data.astype(int), f"data-{region.id}.csv")
#
# Reopening
#
st.header(_("When can we reopen?"))
st.markdown(reopening_intro(region))
st.subheader(_("Step 1: Controlling the curve"))
st.markdown(rt_intro(region))
st.subheader(_("Step 2: Testing"))
st.markdown(rt_intro(region))
if kwargs.get("show_weekday_rate"):
region.ui.weekday_rate()
st.subheader(_("Step 3: Hospital capacity"))
st.markdown(rt_intro(region))
# Hospitalization
cmodels["critical:dates"].plot(**plot_opts)
mark_x(start.date, "k--")
mark_y(cforecast.icu_surge_capacity, "k:")
plt.legend()
plt.title(_("Critical cases"))
plt.tight_layout()
st.pyplot()
mul = notification if is_reported else 1.0
progression_cards(cm["cases"] * mul, color="st-gray-900")
st.subheader(_("Hospitalizations"))
hospitalization_chart(cm, shift=len(cases))
st.subheader(_("Projections"))
m = cm.infection_model
date = m.dates[data_size]
base = cm.infection_model.trim_dates(0, data_size)
assert len(base.data) == data_size, (data_size, len(base.data), len(m.times))
valid_rates = [0.1 * i for i in range(1, 10)]
rates = [valid_rates[i] for i in [6, 4, 2]]
msg = _("Isolation score")
rates = st.multiselect(msg, valid_rates, format_func=pc, default=rates)
columns = ["severe", "critical", "cases", "deaths"]
valid_columns = [*columns, "infectious", "recovered", "asymptomatic"]
msg = _("Columns")
columns = st.multiselect(msg,
valid_columns,
format_func=str.title,
default=columns)
ms = [
base.copy(R0=base.R0 * (1 - rate),
name=f"Isolation {pc(rate)}").clinical.overflow_model()
for rate in rates
]
