def plot_param_matrix(
plotter: StreamlitPlotter,
mcmc_params: List[pd.DataFrame],
parameters: List,
label_param_string=False,
show_ticks=False,
file_name="",
):
burn_in, label_font_size, label_chars, bins, style, dpi_request = BURN_INS, 8, 2, 20, "Shade", 300
plots.calibration.plots.plot_param_vs_param(
plotter,
mcmc_params,
parameters,
burn_in,
style,
bins,
label_font_size,
label_chars,
dpi_request,
label_param_string=label_param_string,
show_ticks=show_ticks,
file_name=file_name,
)
param_names = [get_plot_text_dict(param) for param in parameters]
params_df = pd.DataFrame({"names": param_names})
params_df["numbers"] = range(1, len(params_df) + 1)
create_downloadable_csv(params_df, "parameter_indices")
key_string = ""
for i_param, param_name in enumerate(param_names):
key_string += str(i_param + 1) + ", " + param_name + "; "
st.write(key_string)
st.dataframe(params_df)
def write_mcmc_centiles(
mcmc_params,
mcmc_tables,
burn_in,
sig_figs,
centiles,
):
"""
Write a table of parameter centiles from the MCMC chain outputs.
"""
# Get parameter names
parameters = get_epi_params(mcmc_params)
# Create empty dataframe
params_df = pd.DataFrame(index=parameters, columns=centiles)
# Populate with data
for param_name in parameters:
param_values = get_posterior(mcmc_params, mcmc_tables, param_name,
burn_in)
centile_values = np.percentile(param_values, centiles)
rounded_centile_values = [
round_sig_fig(i_value, sig_figs) for i_value in centile_values
]
params_df.loc[param_name] = rounded_centile_values
# Display
create_downloadable_csv(params_df, "posterior_centiles")
st.write(params_df)
def print_mle_parameters(
plotter: StreamlitPlotter,
calib_dir_path: str,
mcmc_tables: List[pd.DataFrame],
mcmc_params: List[pd.DataFrame],
targets: dict,
app_name: str,
region: str,
):
df = db.load.append_tables(mcmc_tables)
param_df = db.load.append_tables(mcmc_params)
params = db.process.find_mle_params(df, param_df)
create_downloadable_csv(pd.Series(params), "mle_parameters")
st.write(params)
def create_seroprev_csv(seroprev_by_age):
"""
Create downloadable CSV from seroprevalence by age data as it is created in the seroprevalence plotting function.
"""
# Hack to convert single-element lists from seroprevalence data to just single values
for age_group in seroprev_by_age.keys():
for centile in seroprev_by_age[age_group]:
if len(seroprev_by_age[age_group][centile]) == 1:
seroprev_by_age[age_group][centile] = seroprev_by_age[
age_group][centile][0]
elif len(seroprev_by_age[age_group][centile]) == 0:
seroprev_by_age[age_group][centile] = "no estimate"
# Create the CSV
create_downloadable_csv(
pd.DataFrame.from_dict(seroprev_by_age),
"seroprev_by_age",
text="click to download age-specific seroprevalence values",
)
def plot_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,
):
available_outputs = [o["output_key"] for o in targets.values()]
chosen_output = st.sidebar.selectbox("Select calibration target",
available_outputs)
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)
x_min = round(min(uncertainty_df["time"]))
x_max = round(max(uncertainty_df["time"]))
x_low, x_up = selectors.create_xrange_selector(x_min, x_max)
available_scenarios = uncertainty_df["scenario"].unique()
selected_scenarios = st.multiselect("Select scenarios",
available_scenarios)
(
title_font_size,
label_font_size,
dpi_request,
capitalise_first_letter,
) = selectors.create_standard_plotting_sidebar()
is_logscale = st.sidebar.checkbox("Log scale")
is_targets = st.sidebar.checkbox("Show targets")
is_overlay_unceratinty = st.sidebar.checkbox("Overlay uncertainty")
is_legend = st.sidebar.checkbox("Add legend")
quantiles = plots.uncertainty.plots.plot_timeseries_with_uncertainty(
plotter,
uncertainty_df,
chosen_output,
selected_scenarios,
targets,
is_logscale,
x_low,
x_up,
add_targets=is_targets,
overlay_uncertainty=is_overlay_unceratinty,
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,
)
# Provide outputs as CSV
if selected_scenarios:
scenario_for_csv = selected_scenarios[0]
st.write(
"The following downloadable CSV is for the first scenario selected in the select box above:"
)
create_downloadable_csv(
quantiles[scenario_for_csv],
f"output_quantiles_for_scenario_{scenario_for_csv}_for_indicator_{chosen_output}",
include_row=False,
)