Second Round of Full Day RFI Flagging¶
by Josh Dillon, last updated July 31, 2023
This notebook is synthesizes information from individual delay_filtered_average_zscore notebooks to find low-level RFI and flag it. That notebook takes smooth_cal
ibrated data, redundantly averages it, performs a high-pass delay filter, and then incoherently averages across baselines, creating a per-polarization z-score. This notebook then takes that whole night of z-scores and finds a new set of flags to both add to the smooth_cal
files, which are updated in place, and to write down as new UVFlag
waterfall-type .h5
files.
Here's a set of links to skip to particular figures and tables:
• Figure 1: Waterfall of Maximum z-Score of Either Polarization Before Round 2 Flagging¶
• Figure 2: Histogram of z-scores¶
• Figure 3: Waterfall of Maximum z-Score of Either Polarization After Round 2 Flagging¶
• Figure 4: Spectra of Time-Averaged z-Scores¶
• Figure 5: Summary of Flags Before and After Round 2 Flagging¶
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 matplotlib.pyplot as plt
import matplotlib
import copy
import warnings
from pyuvdata import UVFlag, UVCal
from hera_cal import utils
from hera_qm import xrfi
from hera_qm.time_series_metrics import true_stretches
from IPython.display import display, HTML
%matplotlib inline
display(HTML("<style>.container { width:100% !important; }</style>"))
_ = np.seterr(all='ignore') # get rid of red warnings
%config InlineBackend.figure_format = 'retina'
# get input data file names
SUM_FILE = os.environ.get("SUM_FILE", None)
# SUM_FILE = '/lustre/aoc/projects/hera/h6c-analysis/IDR2/2459861/zen.2459861.25297.sum.uvh5'
SUM_SUFFIX = os.environ.get("SUM_SUFFIX", 'sum.uvh5')
# get input and output suffixes
SMOOTH_CAL_SUFFIX = os.environ.get("SMOOTH_CAL_SUFFIX", 'sum.smooth.calfits')
ZSCORE_SUFFIX = os.environ.get("ZSCORE_SUFFIX", 'sum.red_avg_zscore.h5')
FLAG_WATERFALL2_SUFFIX = os.environ.get("FLAG_WATERFALL2_SUFFIX", 'sum.flag_waterfall_round_2.h5')
OUT_YAML_SUFFIX = os.environ.get("OUT_YAML_SUFFIX", '_aposteriori_flags.yaml')
OUT_YAML_DIR = os.environ.get("OUT_YAML_DIR", None)
# build globs
sum_glob = '.'.join(SUM_FILE.split('.')[:-3]) + '.*.' + SUM_SUFFIX
cal_files_glob = sum_glob.replace(SUM_SUFFIX, SMOOTH_CAL_SUFFIX)
zscore_glob = sum_glob.replace(SUM_SUFFIX, ZSCORE_SUFFIX)
# build out yaml file
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)
# get flagging parameters
Z_THRESH = float(os.environ.get("Z_THRESH", 5))
WS_Z_THRESH = float(os.environ.get("WS_Z_THRESH", 4))
AVG_Z_THRESH = float(os.environ.get("AVG_Z_THRESH", 1))
MAX_FREQ_FLAG_FRAC = float(os.environ.get("MAX_FREQ_FLAG_FRAC", .25))
MAX_TIME_FLAG_FRAC = float(os.environ.get("MAX_TIME_FLAG_FRAC", .1))
for setting in ['Z_THRESH', 'WS_Z_THRESH', 'AVG_Z_THRESH', 'MAX_FREQ_FLAG_FRAC', 'MAX_TIME_FLAG_FRAC']:
print(f'{setting} = {eval(setting)}')
Z_THRESH = 5.0 WS_Z_THRESH = 4.0 AVG_Z_THRESH = 1.0 MAX_FREQ_FLAG_FRAC = 0.25 MAX_TIME_FLAG_FRAC = 0.1
Load z-scores¶
# load z-scores
zscore_files = sorted(glob.glob(zscore_glob))
print(f'Found {len(zscore_files)} *.{ZSCORE_SUFFIX} files starting with {zscore_files[0]}.')
uvf = UVFlag(zscore_files, use_future_array_shapes=True)
Found 1571 *.sum.red_avg_zscore.h5 files starting with /mnt/sn1/data2/2460552/zen.2460552.16914.sum.red_avg_zscore.h5.
# get calibration solution files
cal_files = sorted(glob.glob(cal_files_glob))
print(f'Found {len(cal_files)} *.{SMOOTH_CAL_SUFFIX} files starting with {cal_files[0]}.')
Found 1571 *.sum.smooth.calfits files starting with /mnt/sn1/data2/2460552/zen.2460552.16914.sum.smooth.calfits.
assert len(zscore_files) == len(cal_files)
# extract z-scores and correct by a single number per polarization to account for biases created by filtering
zscore = {pol: uvf.metric_array[:, :, np.argwhere(uvf.polarization_array == utils.polstr2num(pol, x_orientation=uvf.x_orientation))[0][0]] for pol in ['ee', 'nn']}
zscore = {pol: zscore[pol] - np.nanmedian(zscore[pol]) for pol in zscore}
freqs = uvf.freq_array
times = uvf.time_array
extent = [freqs[0] / 1e6, freqs[-1] / 1e6, times[-1] - int(times[0]), times[0] - int(times[0])]
def plot_max_z_score(zscore, flags=None):
if flags is None:
flags = np.any(~np.isfinite(list(zscore.values())), axis=0)
plt.figure(figsize=(14,10), dpi=100)
plt.imshow(np.where(flags, np.nan, np.nanmax([zscore['ee'], zscore['nn']], axis=0)), aspect='auto',
cmap='coolwarm', interpolation='none', vmin=-10, vmax=10, extent=extent)
plt.colorbar(location='top', label='Max z-score of either polarization', extend='both', aspect=40, pad=.02)
plt.xlabel('Frequency (MHz)')
plt.ylabel(f'JD - {int(times[0])}')
plt.tight_layout()
Figure 1: Waterfall of Maximum z-Score of Either Polarization Before Round 2 Flagging¶
Shows the worse of the two results from delay_filtered_average_zscore from either polarization. Dips near flagged channels are expected, due to overfitting of noise. Positive-going excursions are problematic and likely evidence of RFI.
plot_max_z_score(zscore)
All-NaN axis encountered
def plot_histogram():
plt.figure(figsize=(14,4), dpi=100)
bins = np.arange(-50, 100, .1)
hist_ee = plt.hist(np.ravel(zscore['ee']), bins=bins, density=True, label='ee-polarized z-scores', alpha=.5)
hist_nn = plt.hist(np.ravel(zscore['nn']), bins=bins, density=True, label='nn-polarized z-scores', alpha=.5)
plt.plot(bins, (2*np.pi)**-.5 * np.exp(-bins**2 / 2), 'k:', label='Gaussian approximate\nnoise-only distribution')
plt.axvline(WS_Z_THRESH, c='r', ls='--', label='Watershed z-score')
plt.axvline(Z_THRESH, c='r', ls='-', label='Threshold z-score')
plt.yscale('log')
all_densities = np.concatenate([hist_ee[0][hist_ee[0] > 0], hist_nn[0][hist_nn[0] > 0]])
plt.ylim(np.min(all_densities) / 2, np.max(all_densities) * 2)
plt.xlim([-50, 100])
plt.legend()
plt.xlabel('z-score')
plt.ylabel('Density')
plt.tight_layout()
Figure 2: Histogram of z-scores¶
Shows a comparison of the histogram of z-scores in this file (one per polarization) to a Gaussian approximation of what one might expect from thermal noise. Without filtering, the actual distribution is a weighted sum of Rayleigh distributions. Filtering further complicates this. To make the z-scores more reliable, a single per-polarization median is subtracted from each waterfall, which allows us to flag low-level outliers with more confidence. Any points beyond the solid red line are flagged. Any points neighboring a flag beyond the dashed red line are also flagged. Finally, flagging is performed for low-level outliers in whole times or channels.
plot_histogram()
Perform flagging¶
def iteratively_flag_on_averaged_zscore(flags, zscore, avg_z_thresh=1.5, verbose=True):
'''Flag whole integrations or channels based on average z-score. This is done
iteratively to prevent bad times affecting channel averages or vice versa.'''
flagged_chan_count = 0
flagged_int_count = 0
while True:
zspec = np.nanmean(np.where(flags, np.nan, zscore), axis=0)
ztseries = np.nanmean(np.where(flags, np.nan, zscore), axis=1)
if (np.nanmax(zspec) < avg_z_thresh) and (np.nanmax(ztseries) < avg_z_thresh):
break
if np.nanmax(zspec) >= np.nanmax(ztseries):
flagged_chan_count += np.sum((zspec >= np.nanmax(ztseries)) & (zspec >= avg_z_thresh))
flags[:, (zspec >= np.nanmax(ztseries)) & (zspec >= avg_z_thresh)] = True
else:
flagged_int_count += np.sum((ztseries >= np.nanmax(zspec)) & (ztseries >= avg_z_thresh))
flags[(ztseries >= np.nanmax(zspec)) & (ztseries >= avg_z_thresh), :] = True
if verbose:
print(f'\tFlagging an additional {flagged_int_count} integrations and {flagged_chan_count} channels.')
def impose_max_chan_flag_frac(flags, max_flag_frac=.25, verbose=True):
'''Flag channels already flagged more than max_flag_frac (excluding completely flagged times).'''
unflagged_times = ~np.all(flags, axis=1)
frequently_flagged_chans = np.mean(flags[unflagged_times, :], axis=0) >= max_flag_frac
if verbose:
print(f'\tFlagging {np.sum(frequently_flagged_chans) - np.sum(np.all(flags, axis=0))} channels previously flagged {max_flag_frac:.2%} or more.')
flags[:, frequently_flagged_chans] = True
def impose_max_time_flag_frac(flags, max_flag_frac=.25, verbose=True):
'''Flag times already flagged more than max_flag_frac (excluding completely flagged channels).'''
unflagged_chans = ~np.all(flags, axis=0)
frequently_flagged_times = np.mean(flags[:, unflagged_chans], axis=1) >= max_flag_frac
if verbose:
print(f'\tFlagging {np.sum(frequently_flagged_times) - np.sum(np.all(flags, axis=1))} times previously flagged {max_flag_frac:.2%} or more.')
flags[frequently_flagged_times, :] = True
flags = np.any(~np.isfinite(list(zscore.values())), axis=0)
print(f'{np.mean(flags):.3%} of waterfall flagged to start.')
# flag largest outliers
for pol in ['ee', 'nn']:
flags |= (zscore[pol] > Z_THRESH)
print(f'{np.mean(flags):.3%} of waterfall flagged after flagging z > {Z_THRESH} outliers.')
# watershed flagging
while True:
nflags = np.sum(flags)
for pol in ['ee', 'nn']:
flags |= xrfi._ws_flag_waterfall(zscore[pol], flags, WS_Z_THRESH)
if np.sum(flags) == nflags:
break
print(f'{np.mean(flags):.3%} of waterfall flagged after watershed flagging on z > {WS_Z_THRESH} neightbors of prior flags.')
# flag whole integrations or channels
while True:
nflags = np.sum(flags)
for pol in ['ee', 'nn']:
iteratively_flag_on_averaged_zscore(flags, zscore[pol], avg_z_thresh=AVG_Z_THRESH, verbose=True)
impose_max_chan_flag_frac(flags, max_flag_frac=MAX_FREQ_FLAG_FRAC, verbose=True)
impose_max_time_flag_frac(flags, max_flag_frac=MAX_TIME_FLAG_FRAC, verbose=True)
if np.sum(flags) == nflags:
break
print(f'{np.mean(flags):.3%} of waterfall flagged after flagging whole times and channels with average z > {AVG_Z_THRESH}.')
39.959% of waterfall flagged to start. 42.548% of waterfall flagged after flagging z > 5.0 outliers.
42.992% of waterfall flagged after watershed flagging on z > 4.0 neightbors of prior flags.
Mean of empty slice Mean of empty slice
Flagging an additional 0 integrations and 81 channels. Flagging 16 channels previously flagged 25.00% or more. Flagging 33 times previously flagged 10.00% or more. Flagging an additional 0 integrations and 0 channels.
Flagging 0 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more. Flagging an additional 0 integrations and 0 channels.
Flagging 0 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more. Flagging an additional 0 integrations and 0 channels. Flagging 0 channels previously flagged 25.00% or more.
Flagging 0 times previously flagged 10.00% or more. 47.173% of waterfall flagged after flagging whole times and channels with average z > 1.0.
Show results of flagging¶
Figure 3: Waterfall of Maximum z-Score of Either Polarization After Round 2 Flagging¶
The same as Figure 1, but after the flagging performed in this notebook.
plot_max_z_score(zscore, flags=flags)
All-NaN axis encountered
def zscore_spectra():
fig, axes = plt.subplots(2, 1, figsize=(14,6), dpi=100, sharex=True, sharey=True, gridspec_kw={'hspace': 0})
for ax, pol in zip(axes, ['ee', 'nn']):
ax.plot(freqs / 1e6, np.nanmean(zscore[pol], axis=0),'r', label=f'{pol}-Polarization Before Round 2 Flagging', lw=.5)
ax.plot(freqs / 1e6, np.nanmean(np.where(flags, np.nan, zscore[pol]), axis=0), label=f'{pol}-Polarization After Round 2 Flagging')
ax.legend(loc='lower right')
ax.set_ylabel('Time-Averged Z-Score\n(Excluding Flags)')
ax.set_ylim(-11, 11)
axes[1].set_xlabel('Frequency (MHz)')
plt.tight_layout()
Figure 4: Spectra of Time-Averaged z-Scores¶
The average along the time axis of Figures 1 and 3 (though now separated per-polarization). This plot is useful for showing channels with repeated low-level RFI.
zscore_spectra()
Mean of empty slice Mean of empty slice
def summarize_flagging():
plt.figure(figsize=(14,10), dpi=100)
cmap = matplotlib.colors.ListedColormap(((0, 0, 0),) + matplotlib.cm.get_cmap("Set2").colors[0:2])
plt.imshow(np.where(np.any(~np.isfinite(list(zscore.values())), axis=0), 1, np.where(flags, 2, 0)),
aspect='auto', cmap=cmap, interpolation='none', extent=extent)
plt.clim([-.5, 2.5])
cbar = plt.colorbar(location='top', aspect=40, pad=.02)
cbar.set_ticks([0, 1, 2])
cbar.set_ticklabels(['Unflagged', 'Previously Flagged', 'Flagged Here Using Delayed Filtered z-Scores'])
plt.xlabel('Frequency (MHz)')
plt.ylabel(f'JD - {int(times[0])}')
plt.tight_layout()
Figure 5: Summary of Flags Before and After Round 2 Flagging¶
This plot shows which times and frequencies were flagged before and after this notebook. It is directly comparable to Figure 5 of the first round full_day_rfi notebook.
summarize_flagging()
The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
Save results¶
add_to_history = 'by full_day_rfi_round_2 notebook with the following environment:\n' + '=' * 65 + '\n' + os.popen('conda env export').read() + '=' * 65
tind = 0
always_flagged_ants = set()
ever_unflagged_ants = set()
for cal_file in cal_files:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# update cal_file
uvc = UVCal()
uvc.read(cal_file, use_future_array_shapes=True)
uvc.flag_array |= (flags[tind:tind + len(uvc.time_array), :].T)[None, :, :, None]
uvc.history += 'Modified ' + add_to_history
uvc.write_calfits(cal_file, clobber=True)
# keep track of flagged antennas
for antnum in uvc.ant_array:
for antpol in ['Jee', 'Jnn']:
if np.all(uvc.get_flags(antnum, antpol)):
if (antnum, antpol) not in ever_unflagged_ants:
always_flagged_ants.add((antnum, antpol))
else:
ever_unflagged_ants.add((antnum, antpol))
always_flagged_ants.discard((antnum, antpol))
# Create new flag object
uvf_out = UVFlag(uvc, waterfall=True, mode='flag')
uvf_out.flag_array |= flags[tind:tind + len(uvc.time_array), :, None]
uvf_out.history += 'Produced ' + add_to_history
uvf_out.write(cal_file.replace(SMOOTH_CAL_SUFFIX, FLAG_WATERFALL2_SUFFIX), clobber=True)
# increment time index
tind += len(uvc.time_array)
print(f'Saved {len(cal_files)} *.{FLAG_WATERFALL2_SUFFIX} files starting with {cal_files[0].replace(SMOOTH_CAL_SUFFIX, FLAG_WATERFALL2_SUFFIX)}.')
Saved 1571 *.sum.flag_waterfall_round_2.h5 files starting with /mnt/sn1/data2/2460552/zen.2460552.16914.sum.flag_waterfall_round_2.h5.
# write summary of entirely flagged times/freqs/ants to yaml
all_flagged_times = np.all(flags, axis=1)
all_flagged_freqs = np.all(flags, axis=0)
all_flagged_ants = sorted(always_flagged_ants)
dt = np.median(np.diff(times))
out_yml_str = 'JD_flags: ' + str([[times[flag_stretch][0] - dt / 2, times[flag_stretch][-1] + dt / 2]
for flag_stretch in true_stretches(all_flagged_times)])
df = np.median(np.diff(freqs))
out_yml_str += '\n\nfreq_flags: ' + str([[freqs[flag_stretch][0] - df / 2, freqs[flag_stretch][-1] + df / 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 /mnt/sn1/data2/2460552/2460552_aposteriori_flags.yaml ------------------------------------------------------------------------------ JD_flags: [[2460552.169024528, 2460552.256489747], [2460552.2566015953, 2460552.2568252916], [2460552.257160836, 2460552.2574963802], [2460552.258391165, 2460552.2597333426], [2460552.2598451907, 2460552.260068887], [2460552.261411064, 2460552.261522912], [2460552.2628650893, 2460552.2630887856], [2460552.2632006337, 2460552.263648026], [2460552.265437596, 2460552.2656612922], [2460552.2724840264, 2460552.2725958745], [2460552.2752802293, 2460552.2755039255], [2460552.2757276217, 2460552.275951318], [2460552.2870242805, 2460552.2871361286], [2460552.289820483, 2460552.289932331], [2460552.292616686, 2460552.292840382], [2460552.2965313694, 2460552.2967550657], [2460552.296866914, 2460552.296978762], [2460552.2983209393, 2460552.2984327874], [2460552.29888018, 2460552.298992028], [2460552.3011171417, 2460552.30122899], [2460552.301452686, 2460552.301564534], [2460552.30771618, 2460552.307828028], [2460552.310400535, 2460552.310512383], [2460552.3134204336, 2460552.31364413], [2460552.3184535983, 2460552.3186772945], [2460552.319907624, 2460552.320019472], [2460552.3215853455, 2460552.3216971937], [2460552.321809042, 2460552.32192089], [2460552.325276333, 2460552.3256118773], [2460552.3303094977, 2460552.330421346], [2460552.3310924345, 2460552.3313161307], [2460552.3319872194, 2460552.3320990675], [2460552.338027017, 2460552.3381388653], [2460552.3426127895, 2460552.3427246376], [2460552.3428364857, 2460552.342948334], [2460552.344178663, 2460552.344290511], [2460552.344737903, 2460552.3448497513], [2460552.3468630174, 2460552.3469748655], [2460552.34731041, 2460552.3477578023], [2460552.3486525873, 2460552.3487644354], [2460552.3489881316, 2460552.349211828], [2460552.3496592203, 2460552.3497710684], [2460552.3510013972, 2460552.3513369416], [2460552.351560638, 2460552.351784334], [2460552.3530146633, 2460552.3531265114], [2460552.353462056, 2460552.353685752], [2460552.356034562, 2460552.35614641], [2460552.3576004356, 2460552.3577122837], [2460552.3582715243, 2460552.3586070687], [2460552.358718917, 2460552.358942613], [2460552.3595018536, 2460552.35972555], [2460552.360955879, 2460552.3615151197], [2460552.3618506636, 2460552.36207436], [2460552.363304689, 2460552.363416537], [2460552.364311322, 2460552.36442317], [2460552.369456335, 2460552.369568183], [2460552.380193753, 2460552.3804174494], [2460552.3822070193, 2460552.3823188674], [2460552.3826544117, 2460552.38276626], [2460552.3875757284, 2460552.3877994246], [2460552.3886942095, 2460552.3888060576], [2460552.3894771463, 2460552.3895889944], [2460552.3902600827, 2460552.390483779], [2460552.3911548676, 2460552.391378564], [2460552.3930562856, 2460552.393279982], [2460552.3947340073, 2460552.3950695517], [2460552.3951813993, 2460552.3952932474], [2460552.3980894503, 2460552.3982012984], [2460552.398648691, 2460552.398760539], [2460552.399767172, 2460552.39987902], [2460552.401109349, 2460552.401221197], [2460552.402004134, 2460552.4025633745], [2460552.405024033, 2460552.405135881], [2460552.40580697, 2460552.405918818], [2460552.406142514, 2460552.4062543623], [2460552.408491324, 2460552.4089387166], [2460552.409274261, 2460552.409497957], [2460552.4105045903, 2460552.4106164384], [2460552.4115112233, 2460552.4116230714], [2460552.416656236, 2460552.4168799324], [2460552.4172154767, 2460552.417327325], [2460552.4230315783, 2460552.4231434264], [2460552.424261907, 2460552.4244856033], [2460552.4284002874, 2460552.4285121355], [2460552.432426819, 2460552.432538667], [2460552.434328237, 2460552.434440085], [2460552.436677047, 2460552.436788895], [2460552.439585098, 2460552.439696946], [2460552.4402561863, 2460552.4403680344], [2460552.4412628193, 2460552.4413746675], [2460552.4415983637, 2460552.441710212], [2460552.44182206, 2460552.441933908], [2460552.442045756, 2460552.4421576043], [2460552.4422694524, 2460552.4423813005], [2460552.44361163, 2460552.443723478], [2460552.4455130477, 2460552.445624896], [2460552.445736744, 2460552.445848592], [2460552.446184136, 2460552.446295984], [2460552.4466315284, 2460552.4468552247], [2460552.447302617, 2460552.4474144652], [2460552.448197402, 2460552.4484210983], [2460552.4496514276, 2460552.449875124], [2460552.450993605, 2460552.4513291493], [2460552.452335782, 2460552.45244763], [2460552.4546845923, 2460552.4547964404], [2460552.4714618083, 2460552.4715736564], [2460552.472021049, 2460552.472132897], [2460552.482534771, 2460552.482646619], [2460552.484548037, 2460552.484771733], [2460552.4866731507, 2460552.486896847], [2460552.487791632, 2460552.488015328], [2460552.4882390243, 2460552.4883508724], [2460552.491258923, 2460552.491370771], [2460552.4936077334, 2460552.4938314296], [2460552.494949911, 2460552.495173607], [2460552.4953973033, 2460552.4956209995], [2460552.497410569, 2460552.497522417], [2460552.5006541642, 2460552.5007660124], [2460552.501213405, 2460552.501437101], [2460552.5053517846, 2460552.5054636328], [2460552.5070295064, 2460552.5072532026], [2460552.5073650507, 2460552.507476899], [2460552.507924291, 2460552.508147987], [2460552.513069304, 2460552.5131811523], [2460552.5168721396, 2460552.517095836], [2460552.517990621, 2460552.518102469], [2460552.518214317, 2460552.5184380133], [2460552.5188854057, 2460552.518997254]] freq_flags: [[46981811.5234375, 48202514.6484375], [49911499.0234375, 50033569.3359375], [62362670.8984375, 62606811.5234375], [69931030.2734375, 70053100.5859375], [87387084.9609375, 108261108.3984375], [109970092.7734375, 110092163.0859375], [121444702.1484375, 121566772.4609375], [122909545.8984375, 123275756.8359375], [123519897.4609375, 124130249.0234375], [124618530.2734375, 125350952.1484375], [125839233.3984375, 126449584.9609375], [126571655.2734375, 132431030.2734375], [132553100.5859375, 134262084.9609375], [134750366.2109375, 134872436.5234375], [136215209.9609375, 136337280.2734375], [136947631.8359375, 138046264.6484375], [141464233.3984375, 141586303.7109375], [141708374.0234375, 141830444.3359375], [142074584.9609375, 142318725.5859375], [143783569.3359375, 144027709.9609375], [147445678.7109375, 147567749.0234375], [149154663.0859375, 149276733.3984375], [149887084.9609375, 150009155.2734375], [154159545.8984375, 154403686.5234375], [169906616.2109375, 170150756.8359375], [175155639.6484375, 175277709.9609375], [187362670.8984375, 187606811.5234375], [189926147.4609375, 190048217.7734375], [191146850.5859375, 191513061.5234375], [198104858.3984375, 198348999.0234375], [199203491.2109375, 199325561.5234375], [201644897.4609375, 201889038.0859375], [204940795.8984375, 205062866.2109375], [208480834.9609375, 208724975.5859375], [209945678.7109375, 210067749.0234375], [212142944.3359375, 212265014.6484375], [220565795.8984375, 220809936.5234375], [223129272.4609375, 223373413.0859375], [227401733.3984375, 227523803.7109375], [229110717.7734375, 229354858.3984375], [229965209.9609375, 230087280.2734375], [231063842.7734375, 231185913.0859375]] ex_ants: [[8, Jee], [8, Jnn], [15, Jnn], [18, Jee], [18, Jnn], [21, Jee], [22, Jnn], [27, Jee], [27, Jnn], [28, Jee], [28, Jnn], [31, Jnn], [32, Jnn], [33, Jnn], [34, Jee], [34, Jnn], [36, Jee], [37, Jee], [37, Jnn], [40, Jnn], [42, Jee], [42, Jnn], [45, Jee], [46, Jee], [46, Jnn], [47, Jee], [47, Jnn], [51, Jee], [54, Jee], [54, Jnn], [61, Jee], [61, Jnn], [62, Jee], [63, Jee], [63, Jnn], [64, Jee], [64, Jnn], [69, Jee], [72, Jee], [72, Jnn], [73, Jee], [73, Jnn], [77, Jee], [77, Jnn], [78, Jee], [78, Jnn], [81, Jnn], [83, Jee], [83, Jnn], [84, Jnn], [85, Jnn], [86, Jee], [86, Jnn], [87, Jee], [88, Jee], [88, Jnn], [89, Jee], [89, Jnn], [90, Jee], [90, Jnn], [91, Jee], [91, Jnn], [92, Jee], [97, Jnn], [98, Jee], [98, Jnn], [99, Jee], [99, Jnn], [100, Jee], [100, Jnn], [104, Jnn], [105, Jee], [105, Jnn], [106, Jee], [106, Jnn], [107, Jee], [107, Jnn], [108, Jee], [108, Jnn], [109, Jnn], [111, Jee], [116, Jnn], [117, Jee], [117, Jnn], [120, Jee], [121, Jee], [124, Jee], [124, Jnn], [125, Jee], [125, Jnn], [126, Jee], [126, Jnn], [130, Jee], [131, Jee], [131, Jnn], [135, Jee], [136, Jee], [136, Jnn], [144, Jee], [144, Jnn], [145, Jee], [145, Jnn], [155, Jee], [155, Jnn], [159, Jnn], [161, Jnn], [165, Jnn], [166, Jnn], [170, Jee], [171, Jnn], [172, Jee], [172, Jnn], [173, Jnn], [175, Jee], [175, Jnn], [176, Jee], [176, Jnn], [177, Jee], [177, Jnn], [178, Jee], [178, Jnn], [179, Jee], [179, Jnn], [180, Jee], [180, Jnn], [182, Jee], [183, Jnn], [184, Jee], [188, Jee], [188, Jnn], [193, Jee], [196, Jee], [199, Jnn], [200, Jee], [200, Jnn], [201, Jnn], [202, Jnn], [204, Jee], [208, Jnn], [209, Jnn], [212, Jee], [212, Jnn], [215, Jee], [215, Jnn], [216, Jee], [218, Jnn], [231, Jee], [231, Jnn], [232, Jee], [233, Jee], [241, Jee], [241, Jnn], [242, Jee], [242, Jnn], [243, Jee], [243, Jnn], [245, Jnn], [246, Jee], [250, Jee], [250, Jnn], [251, Jee], [251, Jnn], [252, Jee], [252, Jnn], [255, Jnn], [256, Jee], [256, Jnn], [262, Jee], [262, Jnn], [266, Jee], [268, Jnn], [270, Jee], [272, Jee], [272, Jnn], [281, Jee], [285, Jee], [320, Jee], [320, Jnn], [321, Jee], [321, Jnn], [323, Jee], [323, Jnn], [324, Jee], [324, Jnn], [325, Jee], [325, Jnn], [326, Jee], [326, Jnn], [327, Jee], [327, Jnn], [328, Jee], [328, Jnn], [329, Jee], [329, Jnn], [331, Jee], [331, Jnn], [332, Jee], [332, Jnn], [333, Jee], [333, Jnn], [336, Jee], [336, Jnn], [340, Jee], [340, Jnn]]
Metadata¶
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: 3.6.2.dev110+g0529798 hera_qm: 2.2.0 hera_filters: 0.1.6.dev1+g297dcce
hera_notebook_templates: 0.1.dev931+g5bbc1c0 pyuvdata: 3.0.1.dev46+g690c7ce
print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')
Finished execution in 31.19 minutes.