In [13]:
for ant_to_plot in ants_to_plot:
amplitude_plot(ant_to_plot)
Antenna 3 Amplitude Waterfalls
Antenna 239 Amplitude Waterfalls
by Josh Dillon, last updated March 29, 2023
This notebook runs calibration smoothing to the gains coming out of file_calibration notebook. It removes any flags founds on by that notebook and replaces them with flags generated from full_day_rfi and full_day_antenna_flagging. It also plots the results for a couple of antennas.
Here's a set of links to skip to particular figures and tables:
smooth_cal¶smooth_cal¶import time
tstart = time.time()
import os
os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'
import h5py
import hdf5plugin # REQUIRED to have the compression plugins available
import numpy as np
import glob
import copy
import warnings
import matplotlib
import matplotlib.pyplot as plt
from hera_cal import io, utils, smooth_cal
from hera_qm.time_series_metrics import true_stretches
%matplotlib inline
from IPython.display import display, HTML
# get files
SUM_FILE = os.environ.get("SUM_FILE", None)
# SUM_FILE = '/users/jsdillon/lustre/H6C/abscal/2459853/zen.2459853.25518.sum.uvh5'
SUM_SUFFIX = os.environ.get("SUM_SUFFIX", 'sum.uvh5')
CAL_SUFFIX = os.environ.get("CAL_SUFFIX", 'sum.omni.calfits')
SMOOTH_CAL_SUFFIX = os.environ.get("SMOOTH_CAL_SUFFIX", 'sum.smooth.calfits')
ANT_FLAG_SUFFIX = os.environ.get("ANT_FLAG_SUFFIX", 'sum.antenna_flags.h5')
RFI_FLAG_SUFFIX = os.environ.get("RFI_FLAG_SUFFIX", 'sum.flag_waterfall.h5')
FREQ_SMOOTHING_SCALE = float(os.environ.get("FREQ_SMOOTHING_SCALE", 10.0)) # MHz
TIME_SMOOTHING_SCALE = float(os.environ.get("TIME_SMOOTHING_SCALE", 6e5)) # seconds
EIGENVAL_CUTOFF = float(os.environ.get("EIGENVAL_CUTOFF", 1e-12))
OUT_YAML_SUFFIX = os.environ.get("OUT_YAML_SUFFIX", '_aposteriori_flags.yaml')
OUT_YAML_DIR = os.environ.get("OUT_YAML_DIR", None)
if OUT_YAML_DIR is None:
OUT_YAML_DIR = os.path.dirname(SUM_FILE)
out_yaml_file = os.path.join(OUT_YAML_DIR, SUM_FILE.split('.')[-4] + OUT_YAML_SUFFIX)
for setting in ['SUM_FILE', 'SUM_SUFFIX', 'CAL_SUFFIX', 'SMOOTH_CAL_SUFFIX', 'ANT_FLAG_SUFFIX', 'RFI_FLAG_SUFFIX',
'FREQ_SMOOTHING_SCALE', 'TIME_SMOOTHING_SCALE', 'EIGENVAL_CUTOFF', 'out_yaml_file']:
print(f'{setting} = {eval(setting)}')
SUM_FILE = /lustre/aoc/projects/hera/h6c-analysis/IDR2/2459866/zen.2459866.25292.sum.uvh5 SUM_SUFFIX = sum.uvh5 CAL_SUFFIX = sum.omni.calfits SMOOTH_CAL_SUFFIX = sum.smooth.calfits ANT_FLAG_SUFFIX = sum.antenna_flags.h5 RFI_FLAG_SUFFIX = sum.flag_waterfall.h5 FREQ_SMOOTHING_SCALE = 10.0 TIME_SMOOTHING_SCALE = 600000.0 EIGENVAL_CUTOFF = 1e-12 out_yaml_file = /lustre/aoc/projects/hera/h6c-analysis/IDR2/2459866/2459866_aposteriori_flags.yaml
sum_glob = '.'.join(SUM_FILE.split('.')[:-3]) + '.*.' + SUM_SUFFIX
cal_files_glob = sum_glob.replace(SUM_SUFFIX, CAL_SUFFIX)
cal_files = sorted(glob.glob(cal_files_glob))
print(f'Found {len(cal_files)} *.{CAL_SUFFIX} files starting with {cal_files[0]}.')
Found 1862 *.sum.omni.calfits files starting with /lustre/aoc/projects/hera/h6c-analysis/IDR2/2459866/zen.2459866.25292.sum.omni.calfits.
rfi_flag_files_glob = sum_glob.replace(SUM_SUFFIX, RFI_FLAG_SUFFIX)
rfi_flag_files = sorted(glob.glob(rfi_flag_files_glob))
print(f'Found {len(rfi_flag_files)} *.{RFI_FLAG_SUFFIX} files starting with {rfi_flag_files[0]}.')
Found 1862 *.sum.flag_waterfall.h5 files starting with /lustre/aoc/projects/hera/h6c-analysis/IDR2/2459866/zen.2459866.25292.sum.flag_waterfall.h5.
ant_flag_files_glob = sum_glob.replace(SUM_SUFFIX, ANT_FLAG_SUFFIX)
ant_flag_files = sorted(glob.glob(ant_flag_files_glob))
print(f'Found {len(ant_flag_files)} *.{ANT_FLAG_SUFFIX} files starting with {ant_flag_files[0]}.')
Found 1862 *.sum.antenna_flags.h5 files starting with /lustre/aoc/projects/hera/h6c-analysis/IDR2/2459866/zen.2459866.25292.sum.antenna_flags.h5.
cs = smooth_cal.CalibrationSmoother(cal_files, flag_file_list=(ant_flag_files + rfi_flag_files), ignore_calflags=True,
pick_refant=True, propagate_refant_flags=True, load_chisq=True, load_cspa=True)
for pol in cs.refant:
print(f'Reference antenna {cs.refant[pol][0]} selected for {pol}.')
Mean of empty slice
Reference antenna 101 selected for Jnn. Reference antenna 101 selected for Jee.
# duplicate a small number of abscal gains for plotting
antnums = set([ant[0] for ant in cs.ants])
flags_per_antnum = [np.sum(cs.flag_grids[ant, 'Jnn']) + np.sum(cs.flag_grids[ant, 'Jee']) for ant in antnums]
refant_nums = [ant[0] for ant in cs.refant.values()]
candidate_ants = [ant for ant, nflags in zip(antnums, flags_per_antnum) if (ant not in refant_nums) and (nflags <= np.percentile(flags_per_antnum, 25))
and not np.all(cs.flag_grids[ant, 'Jee']) and not np.all(cs.flag_grids[ant, 'Jnn'])]
ants_to_plot = [func(candidate_ants) for func in (np.min, np.max)]
abscal_gains = {(ant, pol): np.array(cs.gain_grids[(ant, pol)]) for ant in ants_to_plot for pol in ['Jee', 'Jnn']}
cs.time_freq_2D_filter(freq_scale=FREQ_SMOOTHING_SCALE, time_scale=TIME_SMOOTHING_SCALE, eigenval_cutoff=EIGENVAL_CUTOFF,
method='DPSS', fit_method='lu_solve', fix_phase_flips=True, flag_phase_flip_ints=True)
No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
23 phase-flipped integrations detected on antenna (121, 'Jee') between 2459866.3770726025 and 2459866.379533261. 1879 phase-flipped integrations detected on antenna (144, 'Jnn') between 2459866.4592809677 and 2459866.6693317294.
lst_grid = utils.JD2LST(cs.time_grid) * 12 / np.pi
lst_grid[lst_grid > lst_grid[-1]] -= 24
def amplitude_plot(ant_to_plot):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# Pick vmax to not saturate 90% of the abscal gains
vmax = np.max([np.percentile(np.abs(cs.gain_grids[ant_to_plot, pol][~cs.flag_grids[ant_to_plot, pol]]), 99) for pol in ['Jee', 'Jnn']])
display(HTML(f'<h2>Antenna {ant_to_plot} Amplitude Waterfalls</h2>'))
# Plot abscal gain amplitude waterfalls for a single antenna
fig, axes = plt.subplots(4, 2, figsize=(14,14), gridspec_kw={'height_ratios': [1, 1, .4, .4]})
for ax, pol in zip(axes[0], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
extent=[cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
im = ax.imshow(np.where(cs.flag_grids[ant], np.nan, np.abs(cs.gain_grids[ant])), aspect='auto', cmap='inferno',
interpolation='nearest', vmin=0, vmax=vmax, extent=extent)
ax.set_title(f'Smoothcal Gain Amplitude of Antenna {ant[0]}: {pol[-1]}-polarized' )
ax.set_xlabel('Frequency (MHz)')
ax.set_ylabel('LST (Hours)')
ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
ax.set_yticklabels(ax.get_yticks() % 24)
plt.colorbar(im, ax=ax, orientation='horizontal', pad=.15)
# Now flagged plot abscal waterfall
for ax, pol in zip(axes[1], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
extent=[cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
im = ax.imshow(np.where(cs.flag_grids[ant], np.nan, np.abs(abscal_gains[ant])), aspect='auto', cmap='inferno',
interpolation='nearest', vmin=0, vmax=vmax, extent=extent)
ax.set_title(f'Abscal Gain Amplitude of Antenna {ant[0]}: {pol[-1]}-polarized' )
ax.set_xlabel('Frequency (MHz)')
ax.set_ylabel('LST (Hours)')
ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
ax.set_yticklabels(ax.get_yticks() % 24)
plt.colorbar(im, ax=ax, orientation='horizontal', pad=.15)
# Now plot mean gain spectra
for ax, pol in zip(axes[2], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
nflags_spectrum = np.sum(cs.flag_grids[ant], axis=0)
to_plot = nflags_spectrum <= np.percentile(nflags_spectrum, 75)
ax.plot(cs.freqs[to_plot] / 1e6, np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.abs(abscal_gains[ant])), axis=0)[to_plot], 'r.', label='Abscal')
ax.plot(cs.freqs[to_plot] / 1e6, np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.abs(cs.gain_grids[ant])), axis=0)[to_plot], 'k.', ms=2, label='Smoothed')
ax.set_ylim([0, vmax])
ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
ax.set_xlabel('Frequency (MHz)')
ax.set_ylabel('|g| (unitless)')
ax.set_title(f'Mean Infrequently-Flagged Gain Amplitude of Antenna {ant[0]}: {pol[-1]}-polarized')
ax.legend(loc='upper left')
# Now plot mean gain time series
for ax, pol in zip(axes[3], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
nflags_series = np.sum(cs.flag_grids[ant], axis=1)
to_plot = nflags_series <= np.percentile(nflags_series, 75)
ax.plot(lst_grid[to_plot], np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.abs(abscal_gains[ant])), axis=1)[to_plot], 'r.', label='Abscal')
ax.plot(lst_grid[to_plot], np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.abs(cs.gain_grids[ant])), axis=1)[to_plot], 'k.', ms=2, label='Smoothed')
ax.set_ylim([0, vmax])
ax.set_xlabel('LST (hours)')
ax.set_ylabel('|g| (unitless)')
ax.set_title(f'Mean Infrequently-Flagged Gain Amplitude of Antenna {ant[0]}: {pol[-1]}-polarized')
ax.set_xticklabels(ax.get_xticks() % 24)
ax.legend(loc='upper left')
plt.tight_layout()
plt.show()
def phase_plot(ant_to_plot):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
display(HTML(f'<h2>Antenna {ant_to_plot} Phase Waterfalls</h2>'))
fig, axes = plt.subplots(4, 2, figsize=(14,14), gridspec_kw={'height_ratios': [1, 1, .4, .4]})
# Plot phase waterfalls for a single antenna
for ax, pol in zip(axes[0], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
extent=[cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
im = ax.imshow(np.where(cs.flag_grids[ant], np.nan, np.angle(cs.gain_grids[ant])), aspect='auto', cmap='inferno',
interpolation='nearest', vmin=-np.pi, vmax=np.pi, extent=extent)
ax.set_title(f'Smoothcal Gain Phase of Ant {ant[0]} / Ant {cs.refant[pol][0]}: {pol[-1]}-polarized')
ax.set_xlabel('Frequency (MHz)')
ax.set_ylabel('LST (Hours)')
ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
ax.set_yticklabels(ax.get_yticks() % 24)
plt.colorbar(im, ax=ax, orientation='horizontal', pad=.15)
# Now plot abscal phase waterfall
for ax, pol in zip(axes[1], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
extent=[cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
im = ax.imshow(np.where(cs.flag_grids[ant], np.nan, np.angle(abscal_gains[ant])), aspect='auto', cmap='inferno',
interpolation='nearest', vmin=-np.pi, vmax=np.pi, extent=extent)
ax.set_title(f'Abscal Gain Phase of Ant {ant[0]} / Ant {cs.refant[pol][0]}: {pol[-1]}-polarized')
ax.set_xlabel('Frequency (MHz)')
ax.set_ylabel('LST (Hours)')
ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
ax.set_yticklabels(ax.get_yticks() % 24)
plt.colorbar(im, ax=ax, orientation='horizontal', pad=.15)
# Now plot median gain spectra
for ax, pol in zip(axes[2], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
nflags_spectrum = np.sum(cs.flag_grids[ant], axis=0)
to_plot = nflags_spectrum <= np.percentile(nflags_spectrum, 75)
ax.plot(cs.freqs[to_plot] / 1e6, np.nanmedian(np.where(cs.flag_grids[ant], np.nan, np.angle(abscal_gains[ant])), axis=0)[to_plot], 'r.', label='Abscal')
ax.plot(cs.freqs[to_plot] / 1e6, np.nanmedian(np.where(cs.flag_grids[ant], np.nan, np.angle(cs.gain_grids[ant])), axis=0)[to_plot], 'k.', ms=2, label='Smoothed')
ax.set_ylim([-np.pi, np.pi])
ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
ax.set_xlabel('Frequency (MHz)')
ax.set_ylabel(f'Phase of g$_{{{ant[0]}}}$ / g$_{{{cs.refant[pol][0]}}}$')
ax.set_title(f'Median Infrequently-Flagged Gain Phase of Ant {ant[0]} / Ant {cs.refant[pol][0]}: {pol[-1]}-polarized')
ax.legend(loc='upper left')
# # Now plot median gain time series
for ax, pol in zip(axes[3], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
nflags_series = np.sum(cs.flag_grids[ant], axis=1)
to_plot = nflags_series <= np.percentile(nflags_series, 75)
ax.plot(lst_grid[to_plot], np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.angle(abscal_gains[ant])), axis=1)[to_plot], 'r.', label='Abscal')
ax.plot(lst_grid[to_plot], np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.angle(cs.gain_grids[ant])), axis=1)[to_plot], 'k.', ms=2, label='Smoothed')
ax.set_ylim([-np.pi, np.pi])
ax.set_xlabel('LST (hours)')
ax.set_ylabel(f'Phase of g$_{{{ant[0]}}}$ / g$_{{{cs.refant[pol][0]}}}$')
ax.set_title(f'Mean Infrequently-Flagged Gain Phase of Ant {ant[0]} / Ant {cs.refant[pol][0]}: {pol[-1]}-polarized')
ax.set_xticklabels(ax.get_xticks() % 24)
ax.legend(loc='upper left')
plt.tight_layout()
plt.show()
smooth_cal¶Here we plot abscal and smooth_cal gain amplitudes for both of the sample antennas. We also show means across time/frequency, excluding frequencies/times that are frequently flagged.
for ant_to_plot in ants_to_plot:
amplitude_plot(ant_to_plot)
smooth_cal¶Here we plot abscal and smooth_cal phases relative to each polarization's reference antenna for both of the sample antennas. We also show medians across time/frequency, excluding frequencies/times that are frequently flagged.
for ant_to_plot in ants_to_plot:
phase_plot(ant_to_plot)
def chisq_plot():
fig, axes = plt.subplots(1, 2, figsize=(14, 10), sharex=True, sharey=True)
extent = [cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
for ax, pol in zip(axes, ['Jee', 'Jnn']):
im = ax.imshow(np.where(cs.flag_grids[cs.refant[pol]], np.nan, cs.chisq_grids[pol]), vmin=1, vmax=5,
aspect='auto', cmap='turbo', interpolation='none', extent=extent)
ax.set_yticklabels(ax.get_yticks() % 24)
ax.set_title(f'{pol[1:]}-Polarized $\\chi^2$ / DoF')
ax.set_xlabel('Frequency (MHz)')
axes[0].set_ylabel('LST (hours)')
plt.tight_layout()
fig.colorbar(im, ax=axes, pad=.07, label='$\\chi^2$ / DoF', orientation='horizontal', extend='both', aspect=50)
Here we plot $\chi^2$ per degree of freedom from redundant-baseline calibration for both polarizations separately. While this plot is a little out of place, as it was not produced by this notebook, it is a convenient place where all the necessary components are readily available. If the array were perfectly redundant and any non-redundancies in the calibrated visibilities were explicable by thermal noise alone, this waterfall should be all 1.
chisq_plot()
FixedFormatter should only be used together with FixedLocator
avg_cspa_vs_time = {ant: np.nanmean(np.where(cs.flag_grids[ant], np.nan, cs.cspa_grids[ant]), axis=1) for ant in cs.ants}
avg_cspa_vs_freq = {ant: np.nanmean(np.where(cs.flag_grids[ant], np.nan, cs.cspa_grids[ant]), axis=0) for ant in cs.ants}
Mean of empty slice Mean of empty slice
def cspa_vs_time_plot():
fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True, sharey=True, gridspec_kw={'hspace': 0})
for ax, pol in zip(axes, ['Jee', 'Jnn']):
detail_cutoff = np.percentile([np.nanmean(m) for ant, m in avg_cspa_vs_time.items()
if ant[1] == pol and np.isfinite(np.nanmean(m))], 95)
for ant in avg_cspa_vs_time:
if ant[1] == pol and not np.all(cs.flag_grids[ant]):
if np.nanmean(avg_cspa_vs_time[ant]) > detail_cutoff:
ax.plot(lst_grid, avg_cspa_vs_time[ant], label=ant, zorder=100)
else:
ax.plot(lst_grid, avg_cspa_vs_time[ant], c='grey', alpha=.2, lw=.5)
ax.legend(title=f'{pol[1:]}-Polarized', ncol=2)
ax.set_ylabel('Mean Unflagged $\\chi^2$ per Antenna')
ax.set_xlabel('LST (hours)')
ax.set_xticklabels(ax.get_xticks() % 24)
plt.ylim([1, 5.4])
plt.tight_layout()
def cspa_vs_freq_plot():
fig, axes = plt.subplots(2, 1, figsize=(14, 6), sharex=True, sharey=True, gridspec_kw={'hspace': 0})
for ax, pol in zip(axes, ['Jee', 'Jnn']):
detail_cutoff = np.percentile([np.nanmean(m) for ant, m in avg_cspa_vs_freq.items()
if ant[1] == pol and np.isfinite(np.nanmean(m))], 95)
for ant in avg_cspa_vs_freq:
if ant[1] == pol and not np.all(cs.flag_grids[ant]):
if np.nanmean(avg_cspa_vs_freq[ant]) > detail_cutoff:
ax.plot(cs.freqs / 1e6, avg_cspa_vs_freq[ant], label=ant, zorder=100)
else:
ax.plot(cs.freqs / 1e6, avg_cspa_vs_freq[ant], c='grey', alpha=.2, lw=.5)
ax.legend(title=f'{pol[1:]}-Polarized', ncol=2)
ax.set_ylabel('Mean Unflagged $\\chi^2$ per Antenna')
ax.set_xlabel('Frequency (MHz)')
plt.ylim([1, 5.4])
plt.tight_layout()
Here we plot $\chi^2$ per antenna from redundant-baseline calibration, separating polarizations and averaging the unflagged pixels in the waterfalls over frequency or time. The worst 5% of antennas are shown in color and highlighted in the legends, the rest are shown in grey.
cspa_vs_time_plot()
cspa_vs_freq_plot()
Mean of empty slice FixedFormatter should only be used together with FixedLocator
add_to_history = 'Produced by calibration_smoothing notebook with the following environment:\n' + '=' * 65 + '\n' + os.popen('conda env export').read() + '=' * 65
cs.write_smoothed_cal(output_replace=(CAL_SUFFIX, SMOOTH_CAL_SUFFIX), add_to_history=add_to_history, clobber=True)
Mean of empty slice
# write summary of entirely flagged times/freqs/ants to yaml
all_flagged_times = np.all([np.all(cs.flag_grids[ant], axis=1) for ant in cs.flag_grids], axis=0)
all_flagged_freqs = np.all([np.all(cs.flag_grids[ant], axis=0) for ant in cs.flag_grids], axis=0)
all_flagged_ants = sorted([ant for ant in cs.flag_grids if np.all(cs.flag_grids[ant])])
out_yml_str = 'JD_flags: ' + str([[cs.time_grid[flag_stretch][0] - cs.dt, cs.time_grid[flag_stretch][-1] + cs.dt]
for flag_stretch in true_stretches(all_flagged_times)])
chan_res = np.median(np.diff(cs.freqs))
out_yml_str += '\n\nfreq_flags: ' + str([[cs.freqs[flag_stretch][0] - chan_res / 2, cs.freqs[flag_stretch][-1] + chan_res / 2]
for flag_stretch in true_stretches(all_flagged_freqs)])
out_yml_str += '\n\nex_ants: ' + str(all_flagged_ants).replace("'", "").replace('(', '[').replace(')', ']')
print(f'Writing the following to {out_yaml_file}\n' + '-' * (25 + len(out_yaml_file)))
print(out_yml_str)
with open(out_yaml_file, 'w') as outfile:
outfile.writelines(out_yml_str)
Writing the following to /lustre/aoc/projects/hera/h6c-analysis/IDR2/2459866/2459866_aposteriori_flags.yaml ----------------------------------------------------------------------------------------------------------- JD_flags: [[2459866.257842511, 2459866.258066207], [2459866.261645347, 2459866.261869043], [2459866.266566664, 2459866.26679036], [2459866.2669022083, 2459866.2671259046], [2459866.268468082, 2459866.268691778], [2459866.270593196, 2459866.2708168924], [2459866.2738367915, 2459866.274396032], [2459866.274396032, 2459866.2747315764], [2459866.2785344124, 2459866.2787581086], [2459866.2883770466, 2459866.288600743], [2459866.295535326, 2459866.2957590222], [2459866.2968775034, 2459866.2977722883], [2459866.30123958, 2459866.3015751243], [2459866.3015751243, 2459866.3019106686], [2459866.304147631, 2459866.304371327], [2459866.3132073283, 2459866.313878417], [2459866.3222670257, 2459866.32260257], [2459866.336024344, 2459866.33624804], [2459866.340050876, 2459866.3402745724], [2459866.350564599, 2459866.3509001434], [2459866.362867892, 2459866.363203436], [2459866.368683994, 2459866.36890769], [2459866.3749474883, 2459866.3751711845], [2459866.3799806535, 2459866.3802043498], [2459866.3809872866, 2459866.381210983], [2459866.381210983, 2459866.381434679], [2459866.383447945, 2459866.385461211], [2459866.3876981735, 2459866.3887048066], [2459866.393066883, 2459866.3932905793], [2459866.397876352, 2459866.3982118964], [2459866.415324658, 2459866.4155483544], [2459866.434450686, 2459866.4347862303], [2459866.4484317005, 2459866.4486553967], [2459866.4515634477, 2459866.45201084], [2459866.4524582326, 2459866.452681929], [2459866.452793777, 2459866.453017473], [2459866.4551425874, 2459866.455478132], [2459866.455478132, 2459866.4563729167], [2459866.456484765, 2459866.456708461], [2459866.5003292263, 2459866.5005529225], [2459866.5005529225, 2459866.5007766187], [2459866.506816417, 2459866.507040113], [2459866.5080467463, 2459866.5082704425], [2459866.5199026465, 2459866.520126343], [2459866.5371272564, 2459866.5374628007], [2459866.5374628007, 2459866.5381338894], [2459866.553345233, 2459866.5537926257], [2459866.5691158175, 2459866.569339514], [2459866.5711290836, 2459866.57135278], [2459866.573589742, 2459866.5738134384], [2459866.584215313, 2459866.5845508575], [2459866.6011043787, 2459866.601328075], [2459866.667206615, 2459866.6694435775]] freq_flags: [[49911499.0234375, 50033569.3359375], [62362670.8984375, 62606811.5234375], [69931030.2734375, 70053100.5859375], [87509155.2734375, 108016967.7734375], [113632202.1484375, 113754272.4609375], [116439819.3359375, 116561889.6484375], [116683959.9609375, 116806030.2734375], [124374389.6484375, 125228881.8359375], [136215209.9609375, 136459350.5859375], [136947631.8359375, 137313842.7734375], [137435913.0859375, 137924194.3359375], [141464233.3984375, 141830444.3359375], [142074584.9609375, 142318725.5859375], [143539428.7109375, 143661499.0234375], [143783569.3359375, 144027709.9609375], [144638061.5234375, 144760131.8359375], [145492553.7109375, 145614624.0234375], [145858764.6484375, 145980834.9609375], [147079467.7734375, 147323608.3984375], [147445678.7109375, 147567749.0234375], [148178100.5859375, 148422241.2109375], [149154663.0859375, 149276733.3984375], [154281616.2109375, 154403686.5234375], [155014038.0859375, 155258178.7109375], [158187866.2109375, 158309936.5234375], [161361694.3359375, 161483764.6484375], [175155639.6484375, 175277709.9609375], [183212280.2734375, 183334350.5859375], [187362670.8984375, 187606811.5234375], [189193725.5859375, 189315795.8984375], [190902709.9609375, 191513061.5234375], [195663452.1484375, 195785522.4609375], [197006225.5859375, 197494506.8359375], [199203491.2109375, 199325561.5234375], [201766967.7734375, 201889038.0859375], [215194702.1484375, 215316772.4609375], [223129272.4609375, 223373413.0859375], [227401733.3984375, 227523803.7109375], [229110717.7734375, 229354858.3984375], [231063842.7734375, 231185913.0859375]] ex_ants: [[4, Jee], [18, Jee], [18, Jnn], [22, Jee], [22, Jnn], [27, Jee], [27, Jnn], [28, Jee], [28, Jnn], [32, Jee], [32, Jnn], [33, Jnn], [34, Jee], [43, Jee], [44, Jee], [46, Jnn], [47, Jee], [50, Jnn], [51, Jee], [51, Jnn], [54, Jee], [54, Jnn], [55, Jnn], [57, Jee], [58, Jee], [58, Jnn], [59, Jee], [59, Jnn], [60, Jee], [60, Jnn], [63, Jnn], [68, Jnn], [73, Jee], [73, Jnn], [74, Jee], [74, Jnn], [75, Jee], [75, Jnn], [77, Jee], [77, Jnn], [78, Jee], [82, Jnn], [84, Jnn], [86, Jnn], [87, Jee], [92, Jee], [92, Jnn], [99, Jee], [99, Jnn], [102, Jee], [102, Jnn], [103, Jee], [103, Jnn], [104, Jnn], [108, Jee], [109, Jnn], [110, Jnn], [111, Jnn], [116, Jnn], [117, Jee], [117, Jnn], [119, Jnn], [120, Jnn], [126, Jee], [135, Jnn], [140, Jnn], [141, Jnn], [142, Jnn], [143, Jee], [145, Jnn], [147, Jee], [147, Jnn], [148, Jee], [148, Jnn], [149, Jee], [149, Jnn], [150, Jee], [150, Jnn], [151, Jee], [153, Jee], [155, Jee], [156, Jee], [158, Jnn], [161, Jnn], [162, Jee], [165, Jee], [166, Jee], [166, Jnn], [167, Jee], [167, Jnn], [169, Jee], [169, Jnn], [170, Jee], [170, Jnn], [173, Jee], [173, Jnn], [176, Jnn], [179, Jee], [179, Jnn], [180, Jnn], [182, Jee], [183, Jee], [184, Jee], [184, Jnn], [185, Jee], [185, Jnn], [186, Jee], [186, Jnn], [187, Jee], [187, Jnn], [190, Jee], [190, Jnn], [192, Jnn], [193, Jee], [200, Jee], [200, Jnn], [201, Jee], [201, Jnn], [203, Jee], [203, Jnn], [219, Jee], [222, Jee], [320, Jee], [320, Jnn], [321, Jee], [321, Jnn], [322, Jee], [322, Jnn], [323, Jee], [323, Jnn], [324, Jee], [324, Jnn], [325, Jee], [325, Jnn], [329, Jee], [329, Jnn], [333, Jee], [333, Jnn]]
for repo in ['hera_cal', 'hera_qm', 'hera_filters', 'hera_notebook_templates', 'pyuvdata']:
exec(f'from {repo} import __version__')
print(f'{repo}: {__version__}')
hera_cal: 3.2.3 hera_qm: 2.1.1 hera_filters: 0.1.4.dev2+ga4ff591 hera_notebook_templates: 0.1.dev531+gfe314a8 pyuvdata: 2.3.3.dev39+g16031096
print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')
Finished execution in 34.18 minutes.