def explore_object(name, obj, where=st):
"""
Explore methods of object.
"""
fn = select_method(name, obj, where=where)
args, kwargs = select_arguments(fn, where=where)
t0 = time.time()
msg = st.empty()
result = fn(*args, **kwargs)
msg.info(_("Method executed in {} seconds.").format(fmt(time.time() - t0)))
st.line()
st.subheader(_("Method help and signature"))
st.help(fn)
if result is not None:
st.line()
st.subheader(_("Function output"))
if isinstance(result, plt.Axes):
st.markdown(_("Function returned a matplotlib **ax** object. Showing it..."))
st.pyplot(result.get_figure())
else:
st.write(result)
def backcasting(
predictor,
window,
curves,
distance="RMS",
columns=("cases", "deaths"),
min_series=14,
step=1,
):
"""
Perform a backcasting performance analysis of the given model. For the sake
of this method, the model is just a function that receives an epidemic curve
dataframe and a list of time windows and return the forecasts for cases and
deaths for the specified times.
"""
windows = np.array(as_seq(windows))
min_window = windows.min(initial=len(curves))
def distance(x, y):
return (x - y).dropna().abs() / x
results = []
for k in range(min_window, len(curves) - min_series, step):
data = curves.iloc[:-k]
prediction = fn(data, windows)
results.append(distance(curves, prediction))
st.write(results[-1])
return pd.concat(results, axis=0)
def main(**kwargs):
df = sari_br_dataframe()
st.write(
df.astype({
k: str
for k in df.dtypes[(df.dtypes == "string")
| (df.dtypes == "category")].index
}))
x = df.reset_index()[["age", "id"]].groupby("age").count()
st.bar_chart(x)
def main(region="BR", disease=covid19):
st.css()
params = sidebar(
region=region,
disease=disease,
secret_date=datetime.date(1904, 11, 10),
secret_function=lambda: easter_egg(disease),
)
if params is None: # Secret function was activated
return
debug = False
if DEBUG and st.checkbox(_("Enable debug")):
st.info(_("Running in debug mode!"))
st.html(
"""
<ul>
<li><a href="#debug">{}</a></li>
</ul>""".format(
_("Debug section!")
)
)
debug = True
epidemiology = extract_keys(PARAMS, params)
clinical = extract_keys(CLINICAL, params)
# Run infections model
m = cm = results = None
try:
m = model(disease=disease, **epidemiology)
cm = m.clinical.overflow_model(icu_occupancy=0, hospital_occupancy=0, **clinical)
cm.extra_info = params
output(cm)
finally:
if debug:
if results:
st.html('<div style="height: 15rem"></div>')
st.html('<h2 id="debug">{}</h2>'.format(_("Debug information")))
st.subheader(_("Generic parameters"))
st.write(params)
st.subheader(_("Epidemiological parameters"))
st.write(epidemiology)
st.subheader(_("Clinical parameters"))
st.write(clinical)
st.subheader(_("Output"))
st.write(results)
if m:
st.line_chart(m.data)
if cm:
st.line_chart(cm[["infectious", "severe", "critical"]])
st.subheader(_("Distribution of deaths"))
df = cm[DEATH_DISTRIBUTION_COLUMNS]
df.columns = [DEATH_DISTRIBUTION_COL_NAMES[k] for k in df.columns]
st.area_chart(df)
right: 0;
bottom: 0;
padding: 20px;
border: 1px solid #ccc;
}
[type=radio]:checked ~ label {
background: white;
border-bottom: 1px solid white;
z-index: 2;
}
[type=radio]:checked ~ label ~ .content {
z-index: 1;
}
</style>
""")
st.write("dfsd")
st.html("""
<div class="tabs">
<div class="tab">
<input type="radio" id="tab-1" name="tab-group-1" checked/>
<label for="tab-1">Tab One</label>
<div class="content">stuff one</div>
</div>
<div class="tab">
<input type="radio" id="tab-2" name="tab-group-1">
<label for="tab-2">Tab Two</label>
<div class="content">stuff two</div>
</div>
</div>
""")
"xlsx": _("Excel"),
}
mime = {"csv": "text/csv", "xls": "application/vnd.ms-excel"}
opt = st.radio(_("How do you want your data?"),
list(opts),
format_func=opts.get)
df = pd.concat(data, axis=1)
df.index = base.to_dates(df.index)
df = df.astype(int)
if not st.checkbox(_("Keep observed period")):
df = df.iloc[data_size:]
if opt == "show":
st.write(df)
else:
df.index = [d.strftime("%Y-%m-%d") for d in df.index]
if opt == "csv":
fd = io.StringIO()
df.to_csv(fd)
data = fd.getvalue().encode("utf8")
else:
fd = io.BytesIO()
df.to_excel(fd)
data = fd.getvalue()
data = base64.b64encode(data).decode("utf8")
name = f"curves-%s.{opt}" % region.id
msg = _("Right click link to download")
st.html(f'<a href="data:{opt};base64,{data}" download="{name}">{msg}</a>')