ZH: save phase_time_residuals for SNR plotting required for (a585677)

This commit is contained in:
Eric Teunis de Boone 2023-03-27 16:56:19 +02:00
parent 97f7b0ba8c
commit 878c476a4a

View file

@ -7,6 +7,7 @@ import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
import matplotlib as mpl
import numpy as np
import json
import aa_generate_beacon as beacon
import lib
@ -119,6 +120,11 @@ if __name__ == "__main__":
mean_clock_phase = np.nanmean(clock_phase_matrix[my_mask], axis=0)
std_clock_phase = np.nanstd( clock_phase_matrix[my_mask], axis=0)
# Remove the mean from the matrix
if False:
clock_phase_matrix = clock_phase_matrix - np.mean(mean_clock_phase)
mean_clock_phase = np.nanmean(clock_phase_matrix[my_mask], axis=0)
std_clock_phase = np.nanstd( clock_phase_matrix[my_mask], axis=0)
# Show resulting matrix as figure
if True:
@ -175,6 +181,7 @@ if __name__ == "__main__":
global_phase_shift = actual_antenna_phase_shifts[0] - mean_clock_phase[0]
actual_antenna_phase_shifts = lib.phase_mod(actual_antenna_phase_shifts - global_phase_shift )
fit_info = {}
for i in range(2):
plot_residuals = i == 1
true_phases = actual_antenna_phase_shifts
@ -184,7 +191,7 @@ if __name__ == "__main__":
if plot_residuals:
measured_phases = lib.phase_mod(measured_phases - actual_antenna_phase_shifts)
fig = figlib.phase_comparison_figure(
fig, _fit_info = figlib.phase_comparison_figure(
measured_phases,
true_phases,
plot_residuals=plot_residuals,
@ -192,6 +199,7 @@ if __name__ == "__main__":
figsize=figsize,
hist_kwargs=hist_kwargs,
fit_gaussian=plot_residuals,
return_fit_info = True,
)
axs = fig.get_axes()
@ -215,6 +223,19 @@ if __name__ == "__main__":
extra_name = "residuals"
fig.savefig(path.join(fig_dir, path.basename(__file__) + f".phase.{extra_name}.pdf"))
# Save fit_info to data file
if fname_dir and plot_residuals:
with open(path.join(fname_dir, 'phase_time_residuals.json'), 'w') as fp:
json.dump(
{
'mean': np.mean(measured_phases),
'std': np.std(measured_phases),
'values': measured_phases.tolist(),
},
fp)
##########################
##########################
##########################