evofr.plotting package

Submodules

evofr.plotting.plot_functions module

add_dates(ax, dates, sep=1)
add_dates_sep(ax, dates, sep=7)
plot_R(ax, samples, ps, alphas, colors, forecast=False, plot_neutral_line=True)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • colors (List[str])

  • forecast (bool | None)

  • plot_neutral_line (bool | None)

plot_R_censored(ax, samples, ps, alphas, colors, forecast=False, thres=0.001, plot_neutral_line=True)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • colors (List[str])

  • forecast (bool | None)

  • thres (float | None)

  • plot_neutral_line (bool | None)

plot_cases(ax, LD)
plot_ga_time_censored(ax, samples, ps, alphas, colors, forecast=False, thres=0.001, plot_pivot_line=True, dates=None)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • colors (List[str])

  • forecast (bool | None)

  • thres (float | None)

  • plot_pivot_line (bool | None)

  • dates (List | None)

plot_growth_advantage(ax, samples, LD, ps, alphas, colors, plot_pivot_line=True)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • colors (List[str])

  • plot_pivot_line (bool | None)

plot_little_r_censored(ax, samples, ps, alphas, colors, forecast=False, thres=0.001, plot_neutral_line=False)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • colors (List[str])

  • forecast (bool | None)

  • thres (float | None)

  • plot_neutral_line (bool | None)

plot_observed_frequency(ax, LD, colors)
Parameters:

colors (List[str])

plot_observed_frequency_size(ax, LD, colors, size)
Parameters:

  • colors (List[str])

  • size (Callable)

plot_posterior_I(ax, samples, ps, alphas, colors, forecast=False)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • colors (List[str])

  • forecast (bool | None)

plot_posterior_average_R(ax, samples, ps, alphas, color, plot_neutral_line=True)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • color (str)

  • plot_neutral_line (bool | None)

plot_posterior_frequency(ax, samples, ps, alphas, colors, forecast=False)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • colors (List[str])

  • forecast (bool | None)

plot_posterior_smooth_EC(ax, samples, ps, alphas, color)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • color (str)

plot_posterior_time(ax, t, med, quants, alphas, colors, included=None)

Loop over variants to plot medians and quantiles at specifed points. Plots all time points unless time points to be included are specified in ‘included’.

Parameters:

  • ax – Matplotlib axis to plot on.

  • t – Time points to plot over.

  • med – Median values.

  • quants – Quantiles to be plotted. Organized as a list of CIs as Arrays.

  • alphas (List[float]) – Transparency for each quantile.

  • colors (List[str]) – List of colors to use for each variant.

  • included (List[bool] | None) – optional list of bools which determine which time points and variants to include observations from.

plot_ppc_cases(ax, samples, ps, alphas, color)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • color (str)

plot_ppc_frequency(ax, samples, LD, ps, alphas, colors, forecast=False)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • colors (List[str])

  • forecast (bool | None)

plot_ppc_seq_counts(ax, samples, ps, alphas, colors, forecast=False)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • colors (List[str])

  • forecast (bool | None)

plot_site(ax, site, samples, ps, alphas, colors, forecast, times)
plot_time_varying_single(ax, site, samples, ps, alphas, color)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • color (str)

plot_time_varying_variant(ax, site, samples, ps, alphas, colors, forecast=False)
Parameters:

  • ps (List[float])

  • alphas (List[float])

  • colors (List[str])

  • forecast (bool | None)

plot_total_by_median_frequency(ax, samples, LD, total, colors)
Parameters:

colors (List[str])

plot_total_by_obs_frequency(ax, LD, total, colors)
Parameters:

colors (List[str])

prep_posterior_for_plot(site, samples, ps, forecast=False)

Prep posteriors for plotting by finding time span, medians, and quantiles.

Parameters:

  • site – Name of the site to access from samples.

  • samples – Dictionary with keys being site or variable names. Values are Arrays containing posterior samples with shape (sample_number, site_shape).

  • ps (List[float]) – Levels of confidence to generate quantiles for.

  • forecast (bool | None) – Prep posterior for forecasts? Defaults to False.

Module contents