dynode.utils.vis_utils.plot_model_overview_subplot_matplotlib

dynode.utils.vis_utils.plot_model_overview_subplot_matplotlib(timeseries_df: DataFrame, pop_sizes: dict[str, int], plot_types: ndarray = array(['seasonality_coef', 'vaccination_', '_external_introductions', '_strain_proportion', '_average_immunity', 'total_infection_incidence', 'pred_hosp_'], dtype='<U25'), plot_titles: ndarray = array(['Seasonality Coefficient', 'Vaccination Rate By Age', 'External Introductions by Strain (per 100k)', 'Strain Proportion of New Infections', 'Average Population Immunity Against Strains', 'Total Infection Incidence (per 100k)', 'Predicted Hospitalizations (per 100k)'], dtype='<U43'), plot_normalizations: ndarray = array([1, 1, 100000, 1, 1, 100000, 100000]), matplotlib_style: list[str] | str = ['seaborn-v0_8-colorblind']) → Figure

Generate an overview figure containing subplots for various model metrics.

Parameters

timeseries_dfpd.DataFrame

DataFrame containing at least [“date”, “chain_particle”, “region”] followed by columns for different time series to be plotted.

pop_sizesdict[str, int]

Population sizes for each region as a dictionary. Keys must match the values in the “region” column of timeseries_df.

plot_typesnp.ndarray[str], optional

Types of plots to be generated. Elements not found in timeseries_df are skipped.

plot_titlesnp.ndarray[str], optional

Titles for each subplot corresponding to plot_types.

plot_normalizationsnp.ndarray[int]

Normalization factors for each plot type.

matplotlib_style: list[str] | str

Matplotlib style to use for plotting.

Returns

plt.Figure

Matplotlib Figure containing subplots with one column per region and one row per plot type.