def sari_br_dataframe():
"""
Aggregate SARI vigilance for all states.
"""
states = mundi.regions_dataframe("BR", type="state").index
data = [sari_br_state_dataframe(state) for state in states]
return pd.concat(data)
def get_regions(**query):
"""
Get all children in region that have the same values of the parameters passed
as keyword arguments.
"""
return [
mundi.region(id_) for id_ in mundi.regions_dataframe(**query).index
]
def _brazil_map():
num_codes = (mundi.regions_dataframe(
"BR", type="state").mundi["numeric_code"].astype(object)
["numeric_code"].to_dict())
translate = {v: k for k, v in num_codes.items()}
geo = geopandas.read_file(
PATH / "databases/maps/br/estados.dbf")[["CD_GEOCUF", "geometry"]]
geo.index = geo.pop("CD_GEOCUF").apply(translate.__getitem__)
geo["geometry"] = geo.simplify(0.1)
return geo
def select_regions(where=st.sidebar):
"""
Select regions for app.
"""
st = where
opts = {
"BR": _("Brazil (everything)"),
"BR-1": _("North Region"),
"BR-2": _("Northeast Region"),
"BR-3": _("Southeast Region"),
"BR-4": _("South Region"),
"BR-5": _("Center-West Region"),
}
opt = st.radio(_("What do you want to show?"), list(opts), format_func=opts.get)
if opt == "BR":
df = mundi.regions_dataframe("BR", type="state")
else:
df = mundi.regions_dataframe(type="state", parent_id=opt)
return df.index
def sub_regions(code, **kwargs):
"""
Return a list of mundi codes starting with the given code, followed by all
sub-regions queried from the given arguments.
"""
if len(code) == 2:
kwargs["country_code"] = code
else:
kwargs["parent_id"] = code
sub_df = mundi.regions_dataframe(**kwargs)
return tuple(sub_df.index)
def collect_inputs(region="BR", where=st.sidebar):
states = mundi.regions_dataframe(region, type="state")
highlight = where.selectbox(_("Select a state to highlight"), states.index)
msg = _("Which kind of curve to you want to analyze?")
which = where.selectbox(msg, ["cases", "deaths"])
return {
"loc": highlight,
"idx": int(which == "deaths"),
"states": states,
"which": which,
}
def abstract_str(top=10, kind=_("Federal Units"), date=None):
"""
Create a markdown string with an abstract to the dashboard.
"""
children_refs = mundi.regions_dataframe(country_id="BR",
type="state").index
children = [mundi.region(ref) for ref in children_refs]
n_children = len(children)
curves = [child.pydemic.epidemic_curve().diff() for child in children]
date = date or max(curve.index.max() for curve in curves)
cases = pd.Series([c.loc[date, "cases"] for c in curves],
index=children_refs)
deaths = pd.Series([c.loc[date, "deaths"] for c in curves],
index=children_refs)
def list_top(data: pd.Series):
*head, last = sk.pipe(
data.sort_values(ascending=False).index[:top],
sk.map(mundi.region),
sk.map("{0.name}".format),
)
head = ", ".join(head)
return _(" and ").join([head, last])
return _(ABSTRACT).format(
cases="{:n}".format(cases.sum()),
deaths="{:n}".format(deaths.sum()),
date=date,
n_children=n_children,
n_top_cases=min(top, n_children),
n_top_deaths=min(top, n_children),
top_cases=list_top(cases),
top_deaths=list_top(deaths),
unit_kind=_(kind),
)
def regions(*args, **kwargs):
refs = mundi.regions_dataframe(*args, **kwargs).index
return [mundi.region(ref) for ref in refs]
def fetch_states():
return tuple(map(mundi.region, mundi.regions_dataframe("BR", type="state").index))