mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2025-10-31 03:46:44 +01:00
293 lines
11 KiB
Python
Executable file
293 lines
11 KiB
Python
Executable file
#!/usr/bin/env python3
|
|
# vim: fdm=indent ts=4
|
|
|
|
import h5py
|
|
from itertools import combinations, zip_longest
|
|
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
|
|
from lib import figlib
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from os import path
|
|
import sys
|
|
|
|
import os
|
|
import matplotlib
|
|
if os.name == 'posix' and "DISPLAY" not in os.environ:
|
|
matplotlib.use('Agg')
|
|
|
|
from scriptlib import MyArgumentParser
|
|
parser = MyArgumentParser()
|
|
args = parser.parse_args()
|
|
|
|
figsize = (12,8)
|
|
|
|
show_plots = args.show_plots
|
|
ref_ant_id = None # leave None for all baselines
|
|
|
|
####
|
|
fname_dir = args.data_dir
|
|
antennas_fname = path.join(fname_dir, beacon.antennas_fname)
|
|
time_diffs_fname = 'time_diffs.hdf5' if False else antennas_fname
|
|
fig_dir = args.fig_dir # set None to disable saving
|
|
|
|
basenames, time_diffs, f_beacons, clock_phase_diffs, k_periods = beacon.read_baseline_time_diffs_hdf5(time_diffs_fname)
|
|
|
|
f_beacon, tx, antennas = beacon.read_beacon_hdf5(antennas_fname)
|
|
|
|
# TODO: allow multiple frequencies
|
|
if (f_beacon != f_beacons).any():
|
|
raise NotImplementedError
|
|
|
|
N_base = len(basenames)
|
|
N_ant = len(antennas)
|
|
|
|
# reshape time_diffs into N_ant x N_ant matrix
|
|
clock_phase_matrix = np.full( (N_ant, N_ant), np.nan, dtype=float)
|
|
|
|
## set i=i terms to 0
|
|
for i in range(N_ant):
|
|
clock_phase_matrix[i,i] = 0
|
|
|
|
## fill matrix
|
|
name2idx = lambda name: int(name)-1
|
|
for i, b in enumerate(basenames):
|
|
idx = (name2idx(b[0]), name2idx(b[1]))
|
|
|
|
clock_phase_matrix[(idx[0], idx[1])] = lib.phase_mod(clock_phase_diffs[i])
|
|
clock_phase_matrix[(idx[1], idx[0])] = lib.phase_mod(-1*clock_phase_diffs[i])
|
|
|
|
mat_kwargs = dict(
|
|
norm = Normalize(vmin=-np.pi, vmax=+np.pi),
|
|
cmap = mpl.cm.get_cmap('Spectral_r')
|
|
)
|
|
|
|
# Show Matrix as figure
|
|
if True:
|
|
fig, ax = plt.subplots(figsize=figsize)
|
|
ax.set_title("Measured clock phase differences Baseline i,j")
|
|
ax.set_ylabel("Antenna no. i")
|
|
ax.set_xlabel("Antenna no. j")
|
|
|
|
im = ax.imshow(clock_phase_matrix, interpolation='none', **mat_kwargs)
|
|
fig.colorbar(im, ax=ax)
|
|
|
|
if fig_dir:
|
|
fig.savefig(path.join(fig_dir, path.basename(__file__) + f".matrix.original.pdf"))
|
|
|
|
plt.close(fig)
|
|
|
|
|
|
# Modify the matrix to let each column represent multiple
|
|
# measurements of the same baseline (j,0) phase difference
|
|
if True:
|
|
# for each row j subtract the 0,j element from the whole row
|
|
# and apply phase_mod
|
|
first_row = -1*(clock_phase_matrix[0,:] * np.ones_like(clock_phase_matrix)).T
|
|
|
|
# Show subtraction Matrix as figure
|
|
if True:
|
|
fig, ax = plt.subplots(figsize=figsize)
|
|
ax.set_title("Clock Phase Subtraction matrix i,j")
|
|
ax.set_ylabel("Antenna no. i")
|
|
ax.set_xlabel("Antenna no. j")
|
|
|
|
im = ax.imshow(first_row, interpolation='none', **mat_kwargs)
|
|
fig.colorbar(im, ax=ax)
|
|
|
|
if fig_dir:
|
|
fig.savefig(path.join(fig_dir, path.basename(__file__) + f".matrix.first_row.pdf"))
|
|
|
|
plt.close(fig)
|
|
|
|
clock_phase_matrix = clock_phase_matrix - first_row
|
|
clock_phase_matrix = lib.phase_mod(clock_phase_matrix)
|
|
|
|
# Except for the first row, these are all separate measurements
|
|
# Condense into phase offset per antenna
|
|
if True: # do not use the first row
|
|
my_mask = np.arange(1, len(clock_phase_matrix), dtype=int)
|
|
else:
|
|
my_mask = np.arange(0, len(clock_phase_matrix), dtype=int)
|
|
|
|
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:
|
|
fig, axs = plt.subplots(2,1, sharex=True, figsize=figsize)
|
|
axs[0].set_title("Modified clock phase differences Baseline 0,j")
|
|
axs[0].set_ylabel("Antenna no. 0")
|
|
axs[-1].set_xlabel("Antenna no. j")
|
|
|
|
im = axs[0].imshow(clock_phase_matrix, interpolation='none', **mat_kwargs)
|
|
fig.colorbar(im, ax=axs)
|
|
axs[0].set_aspect('auto')
|
|
|
|
colours = [mat_kwargs['cmap'](mat_kwargs['norm'](x)) for x in mean_clock_phase]
|
|
axs[1].set_ylabel("Mean Baseline Phase (0, j)[rad]")
|
|
axs[1].errorbar(np.arange(N_ant), mean_clock_phase, yerr=std_clock_phase, ls='none')
|
|
axs[1].scatter(np.arange(N_ant), mean_clock_phase, c=colours,s=4)
|
|
|
|
if fig_dir:
|
|
fig.savefig(path.join(fig_dir, path.basename(__file__) + f".matrix.modified.pdf"))
|
|
|
|
plt.close(fig)
|
|
|
|
# write into antenna hdf5
|
|
with h5py.File(antennas_fname, 'a') as fp:
|
|
group = fp['antennas']
|
|
|
|
freq_name = None
|
|
for i, ant in enumerate(antennas):
|
|
h5ant = group[ant.name]
|
|
|
|
h5beacon_info = h5ant['beacon_info']
|
|
|
|
# find out freq_name
|
|
if freq_name is None:
|
|
freq_name = [ k for k in h5beacon_info.keys() if np.isclose(h5beacon_info[k].attrs['freq'], f_beacon)][0]
|
|
|
|
h5attrs = h5beacon_info[freq_name].attrs
|
|
|
|
idx = name2idx(ant.name)
|
|
h5attrs['clock_phase_mean'] = mean_clock_phase[idx]
|
|
h5attrs['clock_phase_std'] = std_clock_phase[idx]
|
|
|
|
|
|
##############################
|
|
# Compare actual time shifts #
|
|
##############################
|
|
actual_antenna_time_shifts = { a.name: a.attrs['clock_offset'] for a in sorted(antennas, key=lambda a: int(a.name)) }
|
|
|
|
if True:
|
|
actual_antenna_phase_shifts = [ -1*lib.phase_mod(2*np.pi*f_beacon*v) for k,v in actual_antenna_time_shifts.items() ]
|
|
antenna_names = [int(k)-1 for k,v in actual_antenna_time_shifts.items() ]
|
|
|
|
# Make sure to shift all antennas by a global phase
|
|
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
|
|
measured_phases = mean_clock_phase
|
|
|
|
hist_kwargs = {}
|
|
if plot_residuals:
|
|
measured_phases = lib.phase_mod(measured_phases - actual_antenna_phase_shifts)
|
|
|
|
fig, _fit_info = figlib.phase_comparison_figure(
|
|
measured_phases,
|
|
true_phases,
|
|
plot_residuals=plot_residuals,
|
|
f_beacon=f_beacon,
|
|
figsize=figsize,
|
|
hist_kwargs=hist_kwargs,
|
|
fit_gaussian=plot_residuals,
|
|
return_fit_info = True,
|
|
)
|
|
|
|
axs = fig.get_axes()
|
|
|
|
if plot_residuals:
|
|
fig.suptitle("Difference between Measured and Actual phases (minus global phase)\n for Antenna $i$")
|
|
axs[-1].set_xlabel("Antenna Mean Phase Residual $\\Delta_\\varphi$")
|
|
else:
|
|
fig.suptitle("Comparison Measured and Actual phases (minus global phase)\n for Antenna $i$")
|
|
axs[-1].set_xlabel("Antenna Mean Phase $\\varphi$")
|
|
|
|
i=1
|
|
axs[i].set_ylabel("Antenna no.")
|
|
#axs[i].errorbar(mean_clock_phase, np.arange(N_ant), yerr=std_clock_phase, marker='4', alpha=0.7, ls='none', color=colors[0], label='Measured')
|
|
|
|
fig.tight_layout()
|
|
|
|
if fig_dir:
|
|
extra_name = "measured"
|
|
if plot_residuals:
|
|
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)
|
|
|
|
|
|
|
|
##########################
|
|
##########################
|
|
##########################
|
|
|
|
actual_baseline_time_shifts = []
|
|
for i,b in enumerate(basenames):
|
|
actual_baseline_time_shift = actual_antenna_time_shifts[b[0]] - actual_antenna_time_shifts[b[1]]
|
|
|
|
actual_baseline_time_shifts.append(actual_baseline_time_shift)
|
|
|
|
# unpack mean_clock_phase back into a list of time diffs
|
|
measured_baseline_time_diffs = []
|
|
for i,b in enumerate(basenames):
|
|
phase0, phase1 = mean_clock_phase[name2idx(b[0])], mean_clock_phase[name2idx(b[1])]
|
|
measured_baseline_time_diffs.append(lib.phase_mod(phase1 - phase0)/(2*np.pi*f_beacon))
|
|
|
|
# Make a plot
|
|
if True:
|
|
for i in range(2):
|
|
fig, ax = plt.subplots(figsize=figsize)
|
|
ax.set_title("Baseline Time difference reconstruction" + ( '' if i == 0 else ' (wrapped time)'))
|
|
ax.set_xlabel("Baseline no.")
|
|
ax.set_ylabel("Time $\\Delta t$ [ns]")
|
|
if True:
|
|
forward = lambda x: x/(2*np.pi*f_beacon)
|
|
inverse = lambda x: 2*np.pi*x*f_beacon
|
|
secax = ax.secondary_yaxis('right', functions=(inverse, forward))
|
|
secax.set_ylabel('Phase $\\Delta \\varphi$ [rad]')
|
|
|
|
if True: # indicate single beacon period span
|
|
ax.plot((-1, -1), (-1/(2*f_beacon), 1/(2*f_beacon)), marker='3', ms=10, label='1/f_beacon')
|
|
if i == 0:
|
|
ax.plot(np.arange(N_base), actual_baseline_time_shifts, ls='none', marker='h', alpha=0.8, label='actual time shifts')
|
|
else:
|
|
ax.plot(np.arange(N_base), (actual_baseline_time_shifts+1/(2*f_beacon))%(1/f_beacon) - 1/(2*f_beacon), ls='none', marker='h', label='actual time shifts', alpha=0.8)
|
|
ax.plot(np.arange(N_base), measured_baseline_time_diffs, ls='none', alpha=0.8, marker='x', label='calculated')
|
|
|
|
ax.legend()
|
|
if fig_dir:
|
|
extra_name = ''
|
|
if i == 1:
|
|
extra_name = '.wrapped'
|
|
|
|
old_lims = (ax.get_xlim(), ax.get_ylim())
|
|
for j in range(2):
|
|
if j == 1:
|
|
ax.set_xlim(-5, 50)
|
|
extra_name += '.n50'
|
|
fig.savefig(path.join(fig_dir, path.basename(__file__) + f".time_comparison{extra_name}.pdf"))
|
|
|
|
ax.set_xlim(*old_lims[0])
|
|
ax.set_ylim(*old_lims[1])
|
|
|
|
if show_plots:
|
|
plt.show()
|