def select_arguments(fn, where=st):
"""
Select arguments from a callable object.
This method only works if we are able to introspect the object signature.
"""
st = where
sig = inspect.Signature.from_callable(fn)
args = []
kwargs = {}
show_title = True
for k, v in sig.parameters.items():
if k == "where":
continue
if show_title:
st.subheader(_("Arguments"))
show_title = False
if v.annotation == str:
kwargs[k] = st.text_input(k)
elif v.annotation == bool:
kwargs[k] = st.checkbox(k)
elif v.annotation == float:
kwargs[k] = st.number_input(k)
elif v.annotation == int:
kwargs[k] = st.number_input(k)
else:
st.text(k)
return args, kwargs
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(title, where=st.sidebar, embed=False, disease="covid-19"):
"""
Collect inputs in the sidebar or other location.
"""
st = where
region = st.region_input("BR", text=True)
title(_("Covid risk factors ({name})").format(name=_(region.name)))
# Scenarios
model = start_model(region, disease)
R0 = model.R0
st.header(_("Forecast scenarios"))
subs = {"R0": fmt(R0), "place": _(region.name)}
st.markdown(
_("""The computed value of R0 for {place} is **{R0}**. Let us
consider 3 different scenarios: the first progress with this value of R0, the
second increases social isolation to obtain a lower R0 and the third loosen
social isolation and correspond to a higher R0.""").format(**subs))
st.subheader("Scenario 1: more isolation")
msg = _("What is the new R0?")
R0_tight = st.slider(msg, 0.1, R0, R0 * 0.66)
st.subheader("Scenario 2: less isolation")
R0_loose = st.slider(msg, R0, max(2 * R0, 3.0), 1.33 * R0)
R0_list = (R0, R0_tight, R0_loose)
# Parameters
st.header(_("Parameters"))
st.subheader(_("Healthcare system"))
if np.isnan(region.icu_capacity):
population = region.population
msg = _("Total ICU beds")
icu = int(population / 10_000)
icu = st.number_input(msg, min_value=0, value=icu)
msg = _("Total hospital beds")
hospital = int(population / 1_000)
hospital = st.number_input(msg, min_value=0, value=hospital)
else:
icu = region.icu_capacity
hospital = region.hospital_capacity
msg = _("Fraction of ICU beds that is occupied?")
rate = 1 - st.slider(msg, 0, 100, value=75) / 100
# Options
def ask(title, **kwargs):
if embed:
return True
return st.checkbox(title, **kwargs)
st.header(_("Options"))
st.subheader(_("Plotting options"))
options = {
"logy": not ask(_("Linear scale")),
"grid": not ask(_("Hide grid"))
}
st.subheader(_("Advanced information"))
options = {
"plot_opts": options,
"show_weekday_rate": ask(_("Notification per weekday")),
}
return {
"region": region,
"icu_capacity": icu,
"hospital_capacity": hospital,
"icu_surge_capacity": rate * icu,
"hospital_surge_capacity": rate * hospital,
"R0_list": R0_list,
**options,
}
from collections import namedtuple
import numpy as np
import pandas as pd
from scipy import special as sfn, optimize
from pydemic_ui import st
MeanStd = namedtuple("MeanStd", ("mean", "std"))
r = st.number_input("Growth factor", value=1.2)
N = st.number_input("N", value=50)
alpha = st.number_input("alpha", value=2.0, format="%0.4g")
rng = np.random.default_rng()
# @st.cache
def test_data(r, N, alpha):
X = np.linspace(0, 10, N)
R = rng.gamma(alpha, r / alpha, size=N)
Y = np.multiply.accumulate(R)
return pd.Series(Y, index=X)
def log_Zk(alpha, Y, approx=False):
alpha = np.asarray(alpha)
if not alpha.shape:
return np.sum(log_Zk(np.array([alpha]), Y, approx))
eta = (Y[1:] / Y[:-1])[None, :]
delta = np.log(eta.mean()) - np.log(eta).mean()