Source code for validphys.closuretest.closure_plots

"""
closuretest/plots.py

Plots of statistical estimators for closure tests
"""
from reportengine.figure import figure
from validphys import plotutils


@figure
def plot_biases(biases_table):
    """
    Plot the bias of each experiment for all fits with bars. For information on
    how biases is calculated see `bias_experiment`
    """
    fig, ax = plotutils.barplot(
        biases_table.values.T,
        collabels=biases_table.index.values,
        datalabels=biases_table.columns.droplevel(1).values,
    )
    ax.set_title("Biases per experiment for each fit")
    ax.legend()
    return fig


@figure
def plot_delta_chi2(delta_chi2_bootstrap, fits):
    """Plots distributions of delta chi2 for each fit in `fits`.
    Distribution is generated by bootstrapping. For more information
    on delta chi2 see `delta_chi2_bootstrap`
    """
    delta_chi2 = delta_chi2_bootstrap.T
    labels = [fit.label for fit in fits]
    fig, ax = plotutils.subplots()
    for i, label in enumerate(labels):
        ax.hist(delta_chi2[:, i], alpha=0.3, label=label, zorder=100)
    ax.set_xlabel(r"$\Delta_{\chi^{2}}$")
    l = ax.legend()
    l.set_zorder(1000)
    ax.set_title(r"Total $\Delta_{\chi^{2}}$ for each fit")
    return fig


def errorbar_figure_from_table(df):
    """Given a table with even columns as central values as odd columns as errors
    plot an errorbar plot"""
    fig, ax = plotutils.plot_horizontal_errorbars(
        df.values[:, ::2].T,
        df.values[:, 1::2].T,
        df.index.values,
        df.columns.unique(0),
        xlim=0,
    )
    return fig, ax


@figure
def plot_fits_bootstrap_variance(fits_bootstrap_variance_table):
    """Plot variance as error bars, with mean and central value calculated
    from bootstrap sample
    """
    fig, ax = errorbar_figure_from_table(fits_bootstrap_variance_table)
    ax.set_title("Variance by experiment for closure fits")
    return fig


@figure
def plot_fits_bootstrap_bias(fits_bootstrap_bias_table):
    """Plot the bias for each experiment for all `fits` as a point with an error bar,
    where the error bar is given by bootstrapping the bias across replicas

    The number of bootstrap samples can be controlled by the parameter `bootstrap_samples`
    """
    fig, ax = errorbar_figure_from_table(fits_bootstrap_bias_table)
    ax.set_title("Bias by experiment for closure fits")
    return fig