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_calibrated 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¶
In [1]:
import time
tstart = time.time()
In [2]:
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'
In [3]:
# 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¶
In [4]:
# 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 1851 *.sum.red_avg_zscore.h5 files starting with /mnt/sn1/data2/2460602/zen.2460602.25235.sum.red_avg_zscore.h5.
In [5]:
# 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 1851 *.sum.smooth.calfits files starting with /mnt/sn1/data2/2460602/zen.2460602.25235.sum.smooth.calfits.
In [6]:
assert len(zscore_files) == len(cal_files)
In [7]:
# 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}
In [8]:
freqs = uvf.freq_array
times = uvf.time_array
In [9]:
extent = [freqs[0] / 1e6, freqs[-1] / 1e6, times[-1] - int(times[0]), times[0] - int(times[0])]
In [10]:
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.
In [11]:
plot_max_z_score(zscore)
All-NaN axis encountered
In [12]:
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.
In [13]:
plot_histogram()
Perform flagging¶
In [14]:
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
In [15]:
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}.')
21.632% of waterfall flagged to start. 25.455% of waterfall flagged after flagging z > 5.0 outliers.
26.152% 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 60 channels. Flagging 30 channels previously flagged 25.00% or more. Flagging 141 times previously flagged 10.00% or more. Flagging an additional 0 integrations and 0 channels. Flagging 1 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. 31.839% 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.
In [16]:
plot_max_z_score(zscore, flags=flags)
All-NaN axis encountered
In [17]:
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.
In [18]:
zscore_spectra()
Mean of empty slice Mean of empty slice
In [19]:
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.
In [20]:
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¶
In [21]:
add_to_history = 'by full_day_rfi_round_2 notebook with the following environment:\n' + '=' * 65 + '\n' + os.popen('conda env export').read() + '=' * 65
In [22]:
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 1851 *.sum.flag_waterfall_round_2.h5 files starting with /mnt/sn1/data2/2460602/zen.2460602.25235.sum.flag_waterfall_round_2.h5.
In [23]:
# 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/data2/2460602/2460602_aposteriori_flags.yaml ------------------------------------------------------------------------------ JD_flags: [[2460602.25503891, 2460602.255150758], [2460602.2553744544, 2460602.2554863025], [2460602.2555981507, 2460602.255709999], [2460602.256045543, 2460602.2566047837], [2460602.2571640243, 2460602.2574995686], [2460602.258058809, 2460602.2581706573], [2460602.260183923, 2460602.260407619], [2460602.261973493, 2460602.262085341], [2460602.262309037, 2460602.2625327334], [2460602.26331567, 2460602.263986759], [2460602.264545999, 2460602.2647696952], [2460602.2668948094, 2460602.267230354], [2460602.2676777462, 2460602.2677895944], [2460602.269579164, 2460602.2696910123], [2460602.2714805817, 2460602.271816126], [2460602.2740530884, 2460602.2741649365], [2460602.275954506, 2460602.276178202], [2460602.2793099494, 2460602.2794217975], [2460602.280987671, 2460602.2812113673], [2460602.2910540006, 2460602.2911658487], [2460602.2919487855, 2460602.2920606337], [2460602.292619874, 2460602.2930672662], [2460602.2950805323, 2460602.2951923804], [2460602.295639773, 2460602.2961990135], [2460602.296646406, 2460602.296758254], [2460602.2970937984, 2460602.2972056465], [2460602.297764887, 2460602.2982122796], [2460602.2984359753, 2460602.2993307603], [2460602.2994426084, 2460602.2995544565], [2460602.299890001, 2460602.300001849], [2460602.300113697, 2460602.302015115], [2460602.3022388113, 2460602.3023506594], [2460602.3024625075, 2460602.302798052], [2460602.3029099, 2460602.303021748], [2460602.303133596, 2460602.304475773], [2460602.3049231656, 2460602.305146862], [2460602.3067127354, 2460602.30704828], [2460602.3079430647, 2460602.308390457], [2460602.3101800266, 2460602.310403723], [2460602.3108511153, 2460602.3109629634], [2460602.311522204, 2460602.3117459], [2460602.3119695964, 2460602.312528837], [2460602.3127525332, 2460602.31353547], [2460602.3154368876, 2460602.3155487357], [2460602.31588428, 2460602.315996128], [2460602.316667217, 2460602.317002761], [2460602.317785698, 2460602.3181212423], [2460602.3182330905, 2460602.318568635], [2460602.318680483, 2460602.318904179], [2460602.3190160273, 2460602.3191278754], [2460602.3193515716, 2460602.319575268], [2460602.3213648372, 2460602.3214766853], [2460602.321924078, 2460602.322035926], [2460602.3223714703, 2460602.3224833184], [2460602.3234899514, 2460602.3236017996], [2460602.3255032175, 2460602.3256150656], [2460602.325838762, 2460602.32595061], [2460602.3318785597, 2460602.331990408], [2460602.3321022554, 2460602.3323259517], [2460602.342839674, 2460602.342951522], [2460602.3430633703, 2460602.3432870666], [2460602.3477609907, 2460602.348208383], [2460602.3483202313, 2460602.3485439275], [2460602.348767624, 2460602.348879472], [2460602.3501098007, 2460602.350221649], [2460602.350333497, 2460602.350557193], [2460602.351563826, 2460602.3516756743], [2460602.3517875224, 2460602.352234915], [2460602.356820687, 2460602.3569325353], [2460602.3625249406, 2460602.362748637], [2460602.3659922318, 2460602.36610408], [2460602.368229194, 2460602.368341042], [2460602.371696485, 2460602.3719201814], [2460602.3738215994, 2460602.3739334475], [2460602.37617041, 2460602.376282258], [2460602.377736283, 2460602.377959979], [2460602.3780718273, 2460602.3781836755], [2460602.3791903085, 2460602.3794140047], [2460602.387690765, 2460602.387802613], [2460602.395520132, 2460602.3957438283], [2460602.405139069, 2460602.405250917], [2460602.41632388, 2460602.416547576], [2460602.4295219565, 2460602.4297456527], [2460602.437127628, 2460602.437239476], [2460602.440483071, 2460602.440594919], [2460602.4470821093, 2460602.4471939574], [2460602.451891578, 2460602.452003426], [2460602.4579313756, 2460602.4580432237], [2460602.464530414, 2460602.464642262], [2460602.4649778064, 2460602.4650896545], [2460602.475155984, 2460602.475267832], [2460602.4819787187, 2460602.482090567], [2460602.484663073, 2460602.484886769], [2460602.4861170985, 2460602.4862289466], [2460602.4871237315, 2460602.487459276], [2460602.4891369976, 2460602.4892488457], [2460602.4903673264, 2460602.4904791745], [2460602.493834618, 2460602.493946466], [2460602.494841251, 2460602.494953099], [2460602.495400491, 2460602.4956241874], [2460602.4967426686, 2460602.497190061], [2460602.4976374535, 2460602.4977493016], [2460602.502223226, 2460602.50255877], [2460602.515085758, 2460602.5164279356], [2460602.5186648974, 2460602.5188885937], [2460602.5197833786, 2460602.5198952267], [2460602.521013708, 2460602.521237404], [2460602.523698062, 2460602.5238099103], [2460602.5319748223, 2460602.5320866704], [2460602.5405871263, 2460602.5408108225], [2460602.541481911, 2460602.5419293037], [2460602.543159633, 2460602.543271481], [2460602.5445018103, 2460602.5446136585], [2460602.5515482407, 2460602.551660089], [2460602.5550155323, 2460602.5553510766], [2460602.5573643423, 2460602.5575880385], [2460602.5585946715, 2460602.558818368], [2460602.559153912, 2460602.559825001], [2460602.559936849, 2460602.5607197857], [2460602.5625093556, 2460602.562733052], [2460602.5659766467, 2460602.566088495], [2460602.56698328, 2460602.567095128], [2460602.567206976, 2460602.567318824], [2460602.5783917867, 2460602.578615483], [2460602.5797339636, 2460602.580181356], [2460602.581970926, 2460602.582082774], [2460602.582194622, 2460602.5823064703], [2460602.5867803944, 2460602.5868922425], [2460602.590247686, 2460602.590359534], [2460602.59472161, 2460602.5949453064], [2460602.5958400914, 2460602.5960637876], [2460602.5961756357, 2460602.5966230277], [2460602.597965205, 2460602.5983007494], [2460602.6000903193, 2460602.6003140155], [2460602.6005377118, 2460602.60064956], [2460602.602327281, 2460602.6026628255], [2460602.605011636, 2460602.605123484], [2460602.6072485982, 2460602.6073604464], [2460602.6120580668, 2460602.612169915], [2460602.6129528517, 2460602.613176548], [2460602.615749054, 2460602.616531991], [2460602.6169793834, 2460602.6173149277], [2460602.617426776, 2460602.617538624], [2460602.6180978646, 2460602.618321561], [2460602.619104497, 2460602.6192163453], [2460602.6265983204, 2460602.6267101686], [2460602.6276049535, 2460602.6278286497], [2460602.6297300677, 2460602.629953764], [2460602.6308485484, 2460602.631407789], [2460602.636888346, 2460602.6370001943], [2460602.6415859666, 2460602.6416978147], [2460602.644717714, 2460602.645053258], [2460602.6463954356, 2460602.6465072837], [2460602.6475139163, 2460602.6476257644], [2460602.6551195877, 2460602.655231436], [2460602.657468398, 2460602.657580246], [2460602.6594816637, 2460602.659593512], [2460602.6629489553, 2460602.6663043983]] freq_flags: [[49911499.0234375, 50033569.3359375], [62240600.5859375, 62850952.1484375], [69931030.2734375, 70053100.5859375], [87387084.9609375, 108016967.7734375], [109970092.7734375, 110092163.0859375], [112655639.6484375, 112777709.9609375], [112899780.2734375, 113021850.5859375], [113143920.8984375, 113754272.4609375], [114974975.5859375, 115463256.8359375], [115707397.4609375, 117294311.5234375], [117538452.1484375, 118026733.3984375], [118637084.9609375, 118759155.2734375], [124618530.2734375, 125473022.4609375], [127548217.7734375, 127670288.0859375], [129989624.0234375, 130111694.3359375], [136215209.9609375, 136459350.5859375], [136947631.8359375, 138046264.6484375], [138656616.2109375, 138778686.5234375], [141464233.3984375, 141586303.7109375], [141708374.0234375, 141830444.3359375], [142074584.9609375, 142318725.5859375], [142684936.5234375, 142807006.8359375], [142929077.1484375, 143295288.0859375], [143783569.3359375, 144027709.9609375], [144638061.5234375, 144760131.8359375], [145858764.6484375, 145980834.9609375], [147445678.7109375, 147567749.0234375], [148178100.5859375, 148422241.2109375], [149154663.0859375, 149276733.3984375], [149887084.9609375, 150009155.2734375], [153671264.6484375, 153793334.9609375], [154159545.8984375, 154403686.5234375], [155014038.0859375, 155258178.7109375], [157577514.6484375, 157699584.9609375], [158187866.2109375, 158309936.5234375], [160263061.5234375, 160385131.8359375], [161361694.3359375, 161483764.6484375], [169906616.2109375, 170150756.8359375], [170883178.7109375, 171005249.0234375], [171249389.6484375, 171371459.9609375], [171737670.8984375, 171859741.2109375], [175155639.6484375, 175399780.2734375], [181137084.9609375, 181381225.5859375], [183212280.2734375, 183334350.5859375], [187362670.8984375, 187728881.8359375], [189926147.4609375, 190048217.7734375], [191024780.2734375, 191513061.5234375], [195663452.1484375, 195785522.4609375], [197128295.8984375, 197372436.5234375], [197494506.8359375, 198837280.2734375], [199203491.2109375, 199325561.5234375], [201156616.2109375, 202499389.6484375], [204940795.8984375, 205062866.2109375], [205184936.5234375, 205307006.8359375], [207138061.5234375, 207260131.8359375], [207870483.3984375, 209335327.1484375], [209945678.7109375, 210067749.0234375], [212142944.3359375, 212265014.6484375], [215194702.1484375, 215316772.4609375], [219833374.0234375, 219955444.3359375], [220687866.2109375, 220809936.5234375], [221176147.4609375, 221298217.7734375], [222763061.5234375, 223739624.0234375], [227157592.7734375, 227523803.7109375], [227645874.0234375, 227767944.3359375], [229110717.7734375, 229354858.3984375], [229965209.9609375, 230087280.2734375], [230819702.1484375, 231307983.3984375]] ex_ants: [[7, Jee], [8, Jee], [9, Jee], [15, Jnn], [18, Jee], [18, Jnn], [21, Jee], [22, Jee], [22, Jnn], [27, Jee], [27, Jnn], [28, Jee], [28, Jnn], [31, Jnn], [32, Jnn], [33, Jnn], [34, Jee], [34, Jnn], [35, Jee], [35, Jnn], [36, Jee], [36, Jnn], [37, Jnn], [40, Jnn], [42, Jnn], [45, Jee], [46, Jee], [47, Jee], [47, Jnn], [48, Jee], [48, Jnn], [49, Jee], [49, Jnn], [51, Jee], [54, Jnn], [57, Jee], [61, Jee], [61, Jnn], [62, Jee], [62, Jnn], [63, Jee], [63, Jnn], [64, Jee], [64, Jnn], [69, Jee], [72, Jnn], [73, Jee], [77, Jee], [77, Jnn], [78, Jee], [78, Jnn], [83, Jee], [83, Jnn], [84, Jnn], [85, Jnn], [86, Jee], [86, Jnn], [87, Jee], [87, Jnn], [88, Jee], [88, Jnn], [89, Jee], [90, Jee], [90, Jnn], [92, Jee], [93, Jee], [96, Jee], [97, Jnn], [98, Jee], [98, Jnn], [100, Jnn], [101, Jnn], [102, Jnn], [103, Jee], [104, Jnn], [107, Jee], [107, Jnn], [108, Jnn], [109, Jnn], [120, Jee], [120, Jnn], [121, Jee], [121, Jnn], [122, Jnn], [125, Jee], [125, Jnn], [127, Jee], [127, Jnn], [130, Jee], [130, Jnn], [131, Jee], [134, Jee], [136, Jee], [136, Jnn], [137, Jee], [142, Jnn], [147, Jee], [147, Jnn], [148, Jee], [161, Jnn], [170, Jee], [171, Jnn], [175, Jnn], [176, Jee], [176, Jnn], [177, Jee], [177, Jnn], [178, Jee], [178, Jnn], [179, Jee], [179, Jnn], [180, Jee], [180, Jnn], [182, Jee], [184, Jee], [188, Jnn], [189, Jee], [189, Jnn], [193, Jee], [194, Jee], [199, Jnn], [200, Jee], [200, Jnn], [201, Jnn], [202, Jnn], [204, Jee], [206, Jee], [208, Jee], [209, Jnn], [212, Jnn], [213, Jee], [213, Jnn], [215, Jee], [215, Jnn], [216, Jee], [218, Jnn], [221, Jee], [227, Jee], [231, Jee], [232, Jee], [234, Jnn], [235, Jee], [239, Jee], [241, Jee], [241, Jnn], [242, Jee], [242, Jnn], [243, Jee], [243, Jnn], [245, Jnn], [246, Jee], [250, Jee], [251, Jee], [253, Jnn], [255, Jnn], [256, Jee], [256, Jnn], [262, Jee], [262, Jnn], [266, Jee], [268, Jnn], [270, Jee], [270, Jnn], [272, Jee], [281, Jee], [281, Jnn], [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¶
In [24]:
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.dev936+gdc93cad pyuvdata: 3.0.1.dev70+g283dda3
In [25]:
print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')
Finished execution in 38.96 minutes.