def plot(data):
agg = {col: "sum" for col in BEDCOUNT_COLUMNS}
data = data.groupby(["date", "department"]).agg(agg)
data = data.reset_index()
data["pct_occ"] = (data["n_covid_occ"] /
(data["n_covid_occ"] + data["n_covid_free"])).fillna(0)
fig, ax = plt.subplots(1, figsize=(7, 4))
date_idx_range = np.arange(len(data.date.unique()))
for department, d in data.groupby("department"):
d = d.sort_values(by="date")
plot_int(
date_idx_range,
d["pct_occ"],
ax=ax,
color=DEPARTMENT_COLOR[department],
label=department,
lw=2,
)
ge_pct_occ = data.groupby("date").pct_occ.mean().sort_index().values
plot_int(
date_idx_range,
ge_pct_occ,
ax=ax,
color="k",
marker=False,
label="Grand Est",
lw=4,
)
dates = np.array(sorted(data.date.unique().flatten()))
xticks = np.arange(0, len(dates), 3)
ax.set_xticks(xticks)
ax.set_xticklabels(
[date.strftime("%d-%m") for date in dates[xticks]],
rotation=45,
)
ax.legend(
ncol=2,
handles=[
matplotlib.patches.Patch(
facecolor=DEPARTMENT_COLOR[department],
label=department,
linewidth=3,
) for department in sorted(data.department.unique())
] + [
matplotlib.patches.Patch(
facecolor="k",
label="Grand Est",
linewidth=3,
)
],
loc="lower right",
)
ax.set_ylabel("Covid+ beds occupancy percentage")
tikzplotlib_kwargs = dict(
axis_width="14cm",
axis_height="8cm",
)
return fig, tikzplotlib_kwargs
def plot(data):
agg = {col: "sum" for col in BEDCOUNT_COLUMNS}
data = data.groupby(["date"]).agg(agg)
data = data.sort_index().reset_index()
data["pct_deaths"] = data["n_covid_deaths"] / (
data["n_covid_deaths"] + data["n_covid_healed"]
)
data["pct_healed"] = data["n_covid_healed"] / (
data["n_covid_healed"] + data["n_covid_deaths"]
)
fig, ax = plt.subplots(1, figsize=(7, 4))
for col in ["pct_deaths", "pct_healed"]:
plot_int(
np.arange(len(data)),
data[col],
ax=ax,
color=COL_COLOR[col],
label=COLUMN_TO_HUMAN_READABLE[col],
marker=next(RANDOM_MARKERS),
lw=2,
)
dates = np.array(sorted(data.date.unique().flatten()))
xticks = np.arange(0, len(dates), 3)
ax.set_xticks(xticks)
ax.set_xticklabels(
[date.strftime("%d-%m") for date in dates[xticks]], rotation=45,
)
ax.set_ylabel("Percentage")
ax.legend()
tikzplotlib_kwargs = dict(axis_width="14cm", axis_height="6cm",)
return fig, tikzplotlib_kwargs
def plot(data):
fig, (ax1, ax2) = plt.subplots(2, figsize=(10, 10), sharex=True)
date_range_idx = np.arange(len(data.date.unique()))
for dept, dg in data.groupby("department"):
dg = dg.sort_values(by="date")
plot_int(
x=date_range_idx,
y=dg.n_hospitalised_patients / dg.department_pop * 100e3,
ax=ax1,
marker=None,
color=DEPARTMENT_COLOR[dept],
label=dept,
lw=2,
)
y1 = dg.n_icu_patients_icubam / dg.department_pop * 100e3
plot_int(
x=date_range_idx,
y=y1,
ax=ax2,
marker=None,
color=DEPARTMENT_COLOR[dept],
ls="dashed",
lw=2,
)
y2 = dg.n_icu_patients_public / dg.department_pop * 100e3
plot_int(
x=date_range_idx,
y=y2,
ax=ax2,
marker=None,
color=DEPARTMENT_COLOR[dept],
ls="solid",
label=dept,
lw=2,
)
ax2.fill_between(
x=date_range_idx,
y1=y1,
y2=y2,
facecolor=DEPARTMENT_COLOR[dept],
alpha=0.3,
)
ax1.set_ylabel("Population-normalised nb of hospitalised patients")
ax1.set_title(
"Evolution of nb of hospitalised patients / 100,000 inhabitants")
ax1.legend(ncol=2)
ax2.set_ylabel("Population-normalised nb of ICU patients")
ax2.set_title("Evolution of nb of ICU patients / 100,000 inhabitants")
dates = np.array(sorted(data.date.unique().flatten()))
xticks = np.arange(0, len(dates), 3)
for ax in [ax1, ax2]:
ax.set_xticks(xticks)
ax.set_xticklabels(
[date.strftime("%d-%m") for date in dates[xticks]],
rotation=45,
)
ax2.legend(
[
matplotlib.lines.Line2D([0], [0], color="k", lw=2, ls="solid"),
matplotlib.lines.Line2D([0], [0], color="k", lw=2, ls="dashed"),
],
["Public data", "ICUBAM data"],
ncol=1,
)
ax2.set_xlim(0, len(date_range_idx) - 1)
tikzplotlib_kwargs = dict(
axis_width="12cm",
axis_height="8cm",
)
return fig, tikzplotlib_kwargs
def plot(data):
agg = {col: "sum" for col in BEDCOUNT_COLUMNS}
data = data.groupby(["date", "department"]).agg(agg)
data = data.reset_index()
data = compute_flow_per_dpt(data)
fig, ax = plt.subplots(1, figsize=(7, 4))
date_idx_range = np.arange(len(data.date.unique()))
sorted_depts = list(
data.groupby("department").cum_flow.max().sort_values(
ascending=False).reset_index().department)
for i, department in enumerate(sorted_depts):
y = (data.loc[data.department.isin(
sorted_depts[i:])].groupby("date").flow.sum().sort_index().values)
plot_int(
date_idx_range,
y,
ax=ax,
color=DEPARTMENT_COLOR[department],
label=department,
lw=1,
fill_below=True,
)
if i == 0:
plot_int(
date_idx_range,
y,
ax=ax,
color="black",
label="Grand Est",
lw=3,
)
dates = np.array(sorted(data.date.unique().flatten()))
xticks = np.arange(0, len(dates), 3)
ax.set_xticks(xticks)
ax.set_xticklabels(
[date.strftime("%d-%m") for date in dates[xticks]],
rotation=45,
)
ax.legend(
ncol=2,
handles=[
matplotlib.patches.Patch(
facecolor=DEPARTMENT_COLOR[dpt],
label=dpt,
linewidth=3,
) for dpt in sorted_depts
] + [
matplotlib.patches.Patch(
facecolor="black",
label="Grand Est",
linewidth=1,
)
],
loc="upper left",
)
tikzplotlib_kwargs = dict(
axis_width="14cm",
axis_height="8cm",
)
return fig, tikzplotlib_kwargs
def plot(data):
n_occ = data.groupby("date").sum()["n_covid_occ"]
n_free = data.groupby("date").sum()["n_covid_free"]
n_transfered = (
data.groupby("date").sum()["n_covid_transfered"].diff(1).fillna(0))
n_tot = n_occ + n_free
n_req = n_occ + n_transfered
fig, ax = plt.subplots(1, figsize=(18, 12))
x_icubam_beg = np.argwhere(
np.sort(data.date.unique()) == datetime.date(2020, 3, 25)).flatten()[0]
ax.axvline(x=x_icubam_beg, c="red", lw=7, ls="dashed")
text = ax.text(
x=x_icubam_beg - 0.5,
y=n_tot.max() * 0.95,
s=r"Start \texttt{ICUBAM}",
fontsize="xx-large",
fontweight="bold",
color="red",
)
text.set_horizontalalignment("right")
date_range_idx = np.arange(len(n_req))
ax = plot_int(
date_range_idx,
n_tot.values,
ax=ax,
color=next(RANDOM_COLORS),
marker=next(RANDOM_MARKERS),
label="Total (beds)",
lw=2,
)
ax = plot_int(
date_range_idx,
n_req.values,
ax=ax,
color=next(RANDOM_COLORS),
marker=next(RANDOM_MARKERS),
label="Nb of occupied beds + transfers",
lw=2,
)
ax = plot_int(
date_range_idx,
n_occ.values,
ax=ax,
color=next(RANDOM_COLORS),
marker=next(RANDOM_MARKERS),
label="Nb of occupied beds",
lw=2,
)
ax.set_ylabel("Number of patients / beds")
ax.legend(loc="lower right")
dates = np.array(sorted(data.date.unique().flatten()))
xticks = np.arange(0, len(dates), 3)
ax.set_xticks(xticks)
ax.set_xticklabels(
[date.strftime("%d-%m") for date in dates[xticks]],
rotation=45,
)
tikzplotlib_kwargs = dict(
axis_width="13.5cm",
axis_height="7.2cm",
)
return fig, tikzplotlib_kwargs