m-thesis-introduction/simulations/airshower_beacon_simulation/lib/figlib.py

import matplotlib.pyplot as plt
import numpy as np

from scipy.stats import norm

def phase_comparison_figure(
        measured_phases,
        true_phases,
        plot_residuals=True,
        f_beacon=None,
        hist_kwargs={},
        sc_kwargs={},
        colors=['blue', 'orange'],
        legend_on_scatter=True,
        secondary_axis='time',
        fit_gaussian=False,
        **fig_kwargs
        ):
    """
    Create a figure comparing measured_phase against true_phase
    by both plotting the values, and the residuals.
    """
    default_fig_kwargs = dict(sharex=True)
    fig_kwargs = {**default_fig_kwargs, **fig_kwargs}

    do_hist_plot = hist_kwargs is not False
    do_scatter_plot = sc_kwargs is not False

    fig, axs = plt.subplots(0+do_hist_plot+do_scatter_plot, 1, **fig_kwargs)

    if not hasattr(axs, '__len__'):
        axs = [axs]

    if f_beacon and secondary_axis in ['phase', 'time']:
        phase2time = lambda x: x/(2*np.pi*f_beacon)
        time2phase = lambda x: 2*np.pi*x*f_beacon

        if secondary_axis == 'time':
            functions = (phase2time, time2phase)
            label = 'Time $\\varphi/(2\\pi f_{beac})$ [ns]'
        else:
            functions = (time2phase, phase2time)
            label = 'Phase $2\\pi t f_{beac}$ [rad]'

        secax = axs[0].secondary_xaxis('top', functions=functions)

    # Histogram
    if do_hist_plot:
        i=0
        default_hist_kwargs = dict(bins='sqrt', density=False, alpha=0.8, histtype='step')
        hist_kwargs = {**default_hist_kwargs, **hist_kwargs}

        axs[i].set_ylabel("#")

        _counts, _bins, _patches = axs[i].hist(measured_phases, color=colors[0], label='Measured', ls='solid', **hist_kwargs)
        if not plot_residuals: # also plot the true clock phases
            axs[i].hist(true_phases, color=colors[1], label='Actual', ls='dashed', **{**hist_kwargs, **dict(bins=_bins)})

        if fit_gaussian:# Fit a gaussian to the histogram
            gauss_kwargs = dict(color=colors[0], ls='dotted')
            xs = np.linspace(min(_bins), max(_bins))

            mean, std = norm.fit(measured_phases)
            dx = _bins[1] - _bins[0]
            scale = len(measured_phases)*dx
            fit_ys = norm.pdf(xs, mean, std) * scale

            axs[i].axvline(mean, ls='dotted', color='k', lw=2)

            axs[i].plot( xs, fit_ys, label='fit', **gauss_kwargs)

            # put mean, sigma and chisq on plot
            text_str = f"$\\mu$ = {mean: .2e}\n$\\sigma$ = {std: .2e}"
            text_kwargs = dict( transform=axs[i].transAxes, fontsize=14, verticalalignment='top')
            axs[i].text(0.02, 0.95, text_str, **text_kwargs)

    # Scatter plot
    if do_scatter_plot:
        i=1
        default_sc_kwargs = dict(alpha=0.6, ls='none')
        sc_kwargs = {**default_sc_kwargs, **sc_kwargs}

        axs[i].set_ylabel("Antenna no.")
        axs[i].plot(measured_phases, np.arange(len(measured_phases)), marker='x' if plot_residuals else '3', color=colors[0], label='Measured', **sc_kwargs)

        if not plot_residuals: # also plot the true clock phases
            axs[i].plot(true_phases, np.arange(len(true_phases)), marker='4', color=colors[1], label='Actual', **sc_kwargs)

        if not plot_residuals and legend_on_scatter:
            axs[i].legend()

    fig.tight_layout()

    return fig