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

73 lines
2.5 KiB
Python

import matplotlib.pyplot as plt
import numpy as np
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',
**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)})
# 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