def plot_regional_outputs(
plotter,
calib_dir_path,
mcmc_tables,
targets,
regions,
indicator,
file_name,
max_y_values=(),
):
chosen_outputs = [
indicator + "_for_cluster_" + i_region.replace("-", "_")
for i_region in regions
]
uncertainty_df = get_uncertainty_df(calib_dir_path, mcmc_tables, targets)
x_low, x_up = STANDARD_X_LIMITS
plots.uncertainty.plots.plot_multi_output_timeseries_with_uncertainty(
plotter,
uncertainty_df,
chosen_outputs,
[0],
targets,
False,
x_low,
x_up,
STANDARD_N_TICKS,
title_font_size=STANDARD_TITLE_FONTSIZE,
label_font_size=STANDARD_LABEL_FONTSIZE,
file_name=file_name,
max_y_values=max_y_values,
custom_titles=[i_region.replace("-", " ") for i_region in regions],
custom_sup_title=indicator.replace("_", " "),
)
def plot_seroprevalence_by_age(
plotter: StreamlitPlotter,
calib_dir_path: str,
mcmc_tables: List[pd.DataFrame],
mcmc_params: List[pd.DataFrame],
targets: dict,
app_name: str,
region: str,
):
n_columns = 2
n_rows = 2
fig = pyplot.figure(constrained_layout=True,
figsize=(n_columns * 7, n_rows * 5)) # (w, h)
spec = fig.add_gridspec(ncols=n_columns, nrows=n_rows)
i_row = 0
i_col = 0
for region in Region.PHILIPPINES_REGIONS:
calib_dir_path = calib_dir_path.replace("philippines", region)
uncertainty_df = get_uncertainty_df(calib_dir_path, mcmc_tables,
targets)
# available_scenarios = uncertainty_df["scenario"].unique()
# selected_scenario = st.sidebar.selectbox("Select scenario", available_scenarios, key=str())
selected_scenario = 0
# min_time = int(min(uncertainty_df["time"]))
# max_time = int(max(uncertainty_df["time"]))
# time = st.sidebar.slider("time", min_time, max_time, max_time)
time = 397
with pyplot.style.context("ggplot"):
ax = fig.add_subplot(spec[i_row, i_col])
_, _, _ = plots.uncertainty.plots.plot_seroprevalence_by_age(
plotter,
uncertainty_df,
selected_scenario,
time,
axis=ax,
name=region.title())
i_col += 1
if i_col >= n_columns:
i_col = 0
i_row += 1
plotter.save_figure(fig,
filename="sero_by_age",
subdir="outputs",
title_text="")
def plot_scenarios_multioutput(
plotter: StreamlitPlotter,
calib_dir_path: str,
mcmc_tables: List[pd.DataFrame],
mcmc_params: List[pd.DataFrame],
targets: dict,
app_name: str,
region: str,
):
uncertainty_df = get_uncertainty_df(calib_dir_path, mcmc_tables, targets)
(
dpi_request,
capitalise_first_letter,
is_logscale,
is_targets,
is_overlay_uncertainty,
is_legend,
) = (300, False, False, True, True, True)
scenario_outputs = [
"notifications", "infection_deaths", "icu_occupancy",
"hospital_occupancy"
]
# From Litton et al.
icu_capacities = [{}] * len(scenario_outputs)
icu_capacities[2] = \
{
"base ICU beds": 499,
"max physical ICU beds": 1092,
"max surge ICU capacity": 1665,
}
plots.uncertainty.plots.plot_multi_output_timeseries_with_uncertainty(
plotter,
uncertainty_df,
scenario_outputs,
uncertainty_df["scenario"].unique(),
targets,
is_logscale,
STANDARD_X_LIMITS[0],
426,
title_font_size=STANDARD_TITLE_FONTSIZE,
label_font_size=STANDARD_LABEL_FONTSIZE,
file_name="multi_scenario",
multi_panel_hlines=icu_capacities,
max_y_values=(24e3, 750, 3e3, 3e4),
)
def multi_country_uncertainty(plotter, calib_dir_path, mcmc_tables,
mcmc_params, targets, app_name, region_names):
"""
Code taken directly from the fit calibration file at this stage.
"""
is_logscale = st.sidebar.checkbox("Log scale")
n_xticks = st.sidebar.slider("Number of x ticks", 1, 10, 6)
title_font_size = st.sidebar.slider("Title font size", 1, 30, 12)
label_font_size = st.sidebar.slider("Label font size", 1, 30, 10)
uncertainty_df = []
for i_region in range(len(mcmc_tables)):
uncertainty_df.append(
get_uncertainty_df(calib_dir_path[i_region], mcmc_tables[i_region],
targets))
if i_region == 0:
available_outputs = [
o["output_key"] for o in targets[i_region].values()
]
chosen_output = st.sidebar.selectbox("Select calibration target",
available_outputs)
x_min = round(min(uncertainty_df[0]["time"]))
x_max = round(max(uncertainty_df[0]["time"]))
x_low, x_up = selectors.create_xrange_selector(x_min, x_max)
available_scenarios = uncertainty_df[0]["scenario"].unique()
selected_scenarios = st.multiselect("Select scenarios",
available_scenarios)
plots.uncertainty.plots.plot_multicountry_timeseries_with_uncertainty(
plotter,
uncertainty_df,
chosen_output,
selected_scenarios,
targets,
region_names,
is_logscale,
x_low,
x_up,
n_xticks,
title_font_size=title_font_size,
label_font_size=label_font_size,
)
def plot_scenarios(
plotter: StreamlitPlotter,
calib_dir_path: str,
mcmc_tables: List[pd.DataFrame],
mcmc_params: List[pd.DataFrame],
targets: dict,
app_name: str,
region: str,
):
chosen_output = "notifications"
targets = {
k: v
for k, v in targets.items() if v["output_key"] == chosen_output
}
uncertainty_df = get_uncertainty_df(calib_dir_path, mcmc_tables, targets)
selected_scenarios = uncertainty_df["scenario"].unique()
(
title_font_size,
label_font_size,
dpi_request,
capitalise_first_letter,
is_logscale,
is_targets,
is_overlay_uncertainty,
is_legend,
) = (8, 8, 300, False, False, True, True, True)
plots.uncertainty.plots.plot_timeseries_with_uncertainty(
plotter,
uncertainty_df,
chosen_output,
selected_scenarios,
targets,
is_logscale,
STANDARD_X_LIMITS[0],
426,
add_targets=is_targets,
overlay_uncertainty=is_overlay_uncertainty,
title_font_size=title_font_size,
label_font_size=label_font_size,
dpi_request=dpi_request,
capitalise_first_letter=capitalise_first_letter,
legend=is_legend,
)
def plot_multiple_timeseries_with_uncertainty(
plotter: StreamlitPlotter,
calib_dir_path: str,
mcmc_tables: List[pd.DataFrame],
mcmc_params: List[pd.DataFrame],
targets: dict,
app_name: str,
region: str,
):
plt.style.use("ggplot")
uncertainty_df = get_uncertainty_df(calib_dir_path, mcmc_tables, targets)
chosen_outputs = STATEWIDE_OUTPUTS
# Add vertical lines for the dates of specific policy interventions to the first panel
multi_panel_vlines = \
[{}] * len(chosen_outputs)
multi_panel_vlines[0] = \
{
"postcodes": 184, # 2nd July (11:59pm 1st)
"stage 3": 191, # 9th July (11:59pm 8th)
"face coverings": 205, # 23rd July (11:59pm 22nd)
"stage 4": 215.75 # 6pm 2nd August
}
x_low, x_up = STANDARD_X_LIMITS
plots.uncertainty.plots.plot_multi_output_timeseries_with_uncertainty(
plotter,
uncertainty_df,
chosen_outputs,
[0],
targets,
False,
x_low,
x_up,
STANDARD_N_TICKS,
title_font_size=STANDARD_TITLE_FONTSIZE,
label_font_size=STANDARD_LABEL_FONTSIZE,
file_name="multi_output",
multi_panel_vlines=multi_panel_vlines,
)