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