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 1925 *.sum.red_avg_zscore.h5 files starting with /mnt/sn1/data1/2460599/zen.2460599.25252.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 1925 *.sum.smooth.calfits files starting with /mnt/sn1/data1/2460599/zen.2460599.25252.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}.')
28.478% of waterfall flagged to start. 32.740% of waterfall flagged after flagging z > 5.0 outliers.
33.405% 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 4 channels. Flagging 156 channels previously flagged 25.00% or more. Flagging 185 times previously flagged 10.00% or more.
Flagging an additional 0 integrations and 18 channels. Flagging 0 channels previously flagged 25.00% or more. Flagging 2 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. 42.108% 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 1925 *.sum.flag_waterfall_round_2.h5 files starting with /mnt/sn1/data1/2460599/zen.2460599.25252.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/data1/2460599/2460599_aposteriori_flags.yaml ------------------------------------------------------------------------------ JD_flags: [[2460599.253634952, 2460599.2537468], [2460599.2582207243, 2460599.2585562686], [2460599.2616880154, 2460599.2619117117], [2460599.2628064966, 2460599.263030193], [2460599.2639249777, 2460599.264036826], [2460599.2644842183, 2460599.2645960664], [2460599.266497484, 2460599.266609332], [2460599.2687344463, 2460599.2688462944], [2460599.2710832567, 2460599.271195105], [2460599.2763401177, 2460599.276451966], [2460599.277123054, 2460599.2773467503], [2460599.278241535, 2460599.2784652314], [2460599.2788007758, 2460599.278912624], [2460599.280142953, 2460599.2804784975], [2460599.2812614343, 2460599.2813732824], [2460599.2814851305, 2460599.2815969787], [2460599.2912159157, 2460599.291327764], [2460599.29334103, 2460599.293452878], [2460599.2970320173, 2460599.2971438654], [2460599.2974794097, 2460599.297814954], [2460599.300051916, 2460599.300163764], [2460599.3024007264, 2460599.3025125745], [2460599.304078448, 2460599.304190296], [2460599.3066509543, 2460599.3077694355], [2460599.3079931317, 2460599.30810498], [2460599.308328676, 2460599.308440524], [2460599.3088879166, 2460599.309223461], [2460599.310006398, 2460599.310118246], [2460599.3105656384, 2460599.310789334], [2460599.3111248785, 2460599.3113485747], [2460599.311460423, 2460599.311572271], [2460599.3123552077, 2460599.3128026], [2460599.3132499927, 2460599.313473689], [2460599.313697385, 2460599.3138092333], [2460599.3142566257, 2460599.314368474], [2460599.3149277144, 2460599.3150395625], [2460599.315375107, 2460599.315710651], [2460599.3158224993, 2460599.3161580437], [2460599.3162698913, 2460599.3163817395], [2460599.3164935876, 2460599.3166054357], [2460599.316717284, 2460599.31694098], [2460599.3173883725, 2460599.3175002206], [2460599.3181713093, 2460599.3182831574], [2460599.3185068537, 2460599.31873055], [2460599.3192897905, 2460599.3244348033], [2460599.3245466514, 2460599.3247703477], [2460599.324882196, 2460599.325105892], [2460599.3253295883, 2460599.3254414364], [2460599.3255532845, 2460599.3256651326], [2460599.3257769807, 2460599.325888829], [2460599.3265599175, 2460599.3266717657], [2460599.326783614, 2460599.32700731], [2460599.3273428543, 2460599.3277902463], [2460599.3279020945, 2460599.329244272], [2460599.3296916643, 2460599.3299153605], [2460599.330474601, 2460599.3305864492], [2460599.3308101455, 2460599.33114569], [2460599.331257538, 2460599.331369386], [2460599.331481234, 2460599.3315930823], [2460599.3318167785, 2460599.3319286266], [2460599.332264171, 2460599.332376019], [2460599.3332708036, 2460599.3334945], [2460599.333830044, 2460599.3339418923], [2460599.3351722215, 2460599.3352840696], [2460599.3361788546, 2460599.336402551], [2460599.3372973357, 2460599.337521032], [2460599.33763288, 2460599.337744728], [2460599.3378565763, 2460599.3379684244], [2460599.338527665, 2460599.338639513], [2460599.3394224495, 2460599.3395342976], [2460599.342666045, 2460599.343001589], [2460599.3441200703, 2460599.3442319185], [2460599.345126703, 2460599.345238551], [2460599.3465807284, 2460599.3466925765], [2460599.361792071, 2460599.361903919], [2460599.3625750076, 2460599.362798704], [2460599.363917185, 2460599.364029033], [2460599.3651475143, 2460599.3652593624], [2460599.366042299, 2460599.3661541473], [2460599.36716078, 2460599.3678318686], [2460599.368391109, 2460599.3685029573], [2460599.3688385016, 2460599.3689503497], [2460599.370963616, 2460599.371075464], [2460599.371187312, 2460599.37129916], [2460599.3715228564, 2460599.3718584008], [2460599.372082097, 2460599.3756612362], [2460599.3757730843, 2460599.3759967806], [2460599.378904831, 2460599.37957592], [2460599.379687768, 2460599.379799616], [2460599.3800233123, 2460599.3801351604], [2460599.3823721227, 2460599.382483971], [2460599.383937996, 2460599.384049844], [2460599.3856157176, 2460599.3857275657], [2460599.388859313, 2460599.388971161], [2460599.3898659456, 2460599.3899777937], [2460599.3915436673, 2460599.3916555154], [2460599.392214756, 2460599.392326604], [2460599.392997693, 2460599.393333237], [2460599.394228022, 2460599.39433987], [2460599.396353136, 2460599.396464984], [2460599.400715212, 2460599.40082706], [2460599.409774909, 2460599.409886757], [2460599.411005238, 2460599.411117086], [2460599.4126829593, 2460599.4129066556], [2460599.4165976434, 2460599.4167094915], [2460599.4174924283, 2460599.4176042764], [2460599.4280061503, 2460599.4281179984], [2460599.4294601753, 2460599.429907568], [2460599.435164429, 2460599.435276277], [2460599.4368421505, 2460599.4369539986], [2460599.438184328, 2460599.438296176], [2460599.4398620497, 2460599.4399738978], [2460599.440533138, 2460599.440644986], [2460599.4420990115, 2460599.4422108596], [2460599.443329341, 2460599.443441189], [2460599.443553037, 2460599.443664885], [2460599.4437767332, 2460599.4438885814], [2460599.4442241257, 2460599.444335974], [2460599.4463492394, 2460599.4464610876], [2460599.4472440244, 2460599.4473558725], [2460599.447803265, 2460599.447915113], [2460599.4500402273, 2460599.4501520754], [2460599.4514942523, 2460599.4516061004], [2460599.4570866576, 2460599.4571985058], [2460599.4577577463, 2460599.4578695945], [2460599.459547316, 2460599.4596591643], [2460599.460442101, 2460599.4605539492], [2460599.464692329, 2460599.464804177], [2460599.4660345064, 2460599.4661463546], [2460599.4671529876, 2460599.467376684], [2460599.4695017976, 2460599.4696136457], [2460599.470732127, 2460599.470843975], [2460599.475988988, 2460599.476100836], [2460599.4783377983, 2460599.4784496464], [2460599.4785614945, 2460599.4787851907], [2460599.479008887, 2460599.479120735], [2460599.4794562794, 2460599.4795681275], [2460599.482588026, 2460599.4826998743], [2460599.4830354187, 2460599.483147267], [2460599.483482811, 2460599.4838183555], [2460599.485943469, 2460599.4860553173], [2460599.4881804315, 2460599.4888515202], [2460599.490864786, 2460599.490976634], [2460599.492095115, 2460599.4922069632], [2460599.4933254444, 2460599.4934372925], [2460599.495003166, 2460599.4951150143], [2460599.4953387105, 2460599.4954505586], [2460599.497351976, 2460599.502608837], [2460599.503503622, 2460599.50361547], [2460599.5057405843, 2460599.5059642806], [2460599.509878964, 2460599.5101026604], [2460599.5132344076, 2460599.5133462558], [2460599.5136817996, 2460599.5137936478], [2460599.515023977, 2460599.515135825], [2460599.5183794205, 2460599.5184912686], [2460599.5189386606, 2460599.519162357], [2460599.5196097493, 2460599.5197215974], [2460599.5227414966, 2460599.5228533447], [2460599.525425851, 2460599.525649547], [2460599.5300116236, 2460599.5301234717], [2460599.532919674, 2460599.5330315223], [2460599.533926307, 2460599.5340381553], [2460599.5341500035, 2460599.5343736997], [2460599.534597396, 2460599.534709244], [2460599.5363869653, 2460599.5364988134], [2460599.536946206, 2460599.537058054], [2460599.5376172946, 2460599.537840991], [2460599.537952839, 2460599.538176535], [2460599.5384002314, 2460599.5387357757], [2460599.538959472, 2460599.5394068644], [2460599.5395187126, 2460599.540077953], [2460599.5416438263, 2460599.5417556744], [2460599.543433396, 2460599.5435452443], [2460599.5436570924, 2460599.544440029], [2460599.5466769915, 2460599.5467888396], [2460599.5479073203, 2460599.5527167893], [2460599.552828637, 2460599.552940485], [2460599.5532760294, 2460599.5533878775], [2460599.5534997256, 2460599.5536115738], [2460599.553723422, 2460599.55383527], [2460599.5542826625, 2460599.5543945106], [2460599.5562959285, 2460599.556631473], [2460599.556743321, 2460599.5570788654], [2460599.5573025616, 2460599.5574144097], [2460599.557526258, 2460599.557638106], [2460599.565467473, 2460599.5656911694], [2460599.5660267137, 2460599.566362258], [2460599.567145195, 2460599.567257043], [2460599.571395423, 2460599.571619119], [2460599.57452717, 2460599.574639018], [2460599.5748627144, 2460599.5750864106], [2460599.5770996762, 2460599.5772115244], [2460599.579784031, 2460599.579895879], [2460599.580790664, 2460599.580902512], [2460599.5817972966, 2460599.5819091448], [2460599.5879489426, 2460599.588172639], [2460599.5938768922, 2460599.5941005885], [2460599.5962257027, 2460599.596449399], [2460599.5985745126, 2460599.5986863608], [2460599.6055090954, 2460599.6057327916], [2460599.6077460577, 2460599.607857906], [2460599.6089763865, 2460599.609311931], [2460599.6099830195, 2460599.610430412], [2460599.6111015007, 2460599.611213349], [2460599.612555526, 2460599.6126673743], [2460599.6151280324, 2460599.6152398805], [2460599.6153517286, 2460599.6154635767], [2460599.619601957, 2460599.6198256533], [2460599.620944134, 2460599.621055982], [2460599.622845552, 2460599.623069248], [2460599.6254180586, 2460599.625641755], [2460599.638616135, 2460599.638727983], [2460599.6418597302, 2460599.6420834265], [2460599.6436492996, 2460599.643872996], [2460599.6454388695, 2460599.6455507176], [2460599.6462218063, 2460599.6466691988], [2460599.6474521356, 2460599.64778768], [2460599.651366819, 2460599.6515905154], [2460599.6525971484, 2460599.6527089966], [2460599.652932693, 2460599.653156389], [2460599.6536037815, 2460599.6538274777], [2460599.6541630216, 2460599.6542748697], [2460599.6549459584, 2460599.6551696546], [2460599.65841325, 2460599.658525098], [2460599.660985756, 2460599.6612094524], [2460599.662887174, 2460599.6631108704], [2460599.6640056553, 2460599.6642293516], [2460599.6643411997, 2460599.6854804917]] freq_flags: [[47714233.3984375, 47836303.7109375], [49911499.0234375, 50033569.3359375], [62362670.8984375, 62850952.1484375], [66513061.5234375, 66635131.8359375], [69931030.2734375, 70053100.5859375], [87387084.9609375, 108139038.0859375], [109970092.7734375, 110092163.0859375], [112655639.6484375, 112777709.9609375], [113632202.1484375, 113754272.4609375], [116439819.3359375, 116561889.6484375], [118881225.5859375, 119003295.8984375], [119247436.5234375, 119613647.4609375], [119979858.3984375, 125595092.7734375], [125717163.0859375, 140975952.1484375], [141342163.0859375, 141952514.6484375], [142074584.9609375, 142318725.5859375], [142807006.8359375, 143417358.3984375], [143661499.0234375, 144149780.2734375], [144638061.5234375, 144882202.1484375], [145004272.4609375, 145248413.0859375], [145736694.3359375, 145980834.9609375], [147445678.7109375, 147567749.0234375], [149154663.0859375, 149276733.3984375], [149887084.9609375, 150009155.2734375], [154159545.8984375, 154403686.5234375], [159164428.7109375, 159286499.0234375], [169906616.2109375, 170150756.8359375], [170883178.7109375, 171005249.0234375], [175155639.6484375, 175277709.9609375], [181137084.9609375, 181259155.2734375], [183212280.2734375, 183334350.5859375], [187362670.8984375, 187606811.5234375], [189926147.4609375, 190048217.7734375], [191146850.5859375, 191513061.5234375], [197128295.8984375, 197372436.5234375], [198104858.3984375, 198348999.0234375], [199203491.2109375, 199325561.5234375], [201766967.7734375, 201889038.0859375], [204940795.8984375, 205062866.2109375], [208480834.9609375, 208724975.5859375], [209945678.7109375, 210067749.0234375], [212142944.3359375, 212265014.6484375], [220687866.2109375, 220809936.5234375], [223129272.4609375, 223373413.0859375], [227401733.3984375, 227523803.7109375], [229110717.7734375, 229354858.3984375], [229965209.9609375, 230087280.2734375], [231063842.7734375, 231185913.0859375]] ex_ants: [[3, Jee], [3, Jnn], [4, Jee], [4, Jnn], [5, Jee], [5, Jnn], [7, Jee], [8, Jee], [9, Jee], [15, Jee], [15, Jnn], [17, Jnn], [18, Jee], [18, Jnn], [21, Jee], [22, Jee], [22, Jnn], [27, Jee], [27, Jnn], [28, Jee], [28, Jnn], [29, Jnn], [31, Jnn], [33, Jnn], [34, Jee], [34, Jnn], [35, Jee], [35, Jnn], [36, Jee], [36, Jnn], [37, Jee], [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], [55, 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], [81, Jnn], [82, Jnn], [84, Jnn], [85, Jnn], [86, Jee], [86, Jnn], [87, Jee], [87, Jnn], [88, Jee], [88, Jnn], [90, Jee], [90, Jnn], [92, Jee], [92, Jnn], [93, Jee], [96, Jee], [97, Jee], [97, Jnn], [98, Jnn], [100, Jnn], [101, Jnn], [102, Jnn], [103, Jnn], [104, Jnn], [107, Jee], [107, Jnn], [108, Jnn], [109, Jnn], [111, Jee], [116, Jee], [116, Jnn], [119, Jnn], [120, Jee], [120, Jnn], [121, Jee], [121, Jnn], [122, Jnn], [123, Jnn], [127, Jnn], [130, Jee], [130, Jnn], [131, Jnn], [134, Jee], [136, Jee], [136, Jnn], [137, Jee], [142, Jnn], [145, Jnn], [148, Jee], [155, Jee], [155, Jnn], [159, Jnn], [160, Jnn], [161, Jnn], [166, Jnn], [170, Jee], [171, Jnn], [176, Jee], [176, Jnn], [177, Jee], [177, Jnn], [178, Jee], [178, Jnn], [179, Jee], [179, Jnn], [180, Jee], [180, Jnn], [181, Jnn], [182, Jee], [183, Jnn], [184, Jee], [188, Jnn], [189, Jee], [189, Jnn], [193, Jee], [198, Jnn], [199, Jnn], [200, Jee], [200, Jnn], [201, Jnn], [202, Jnn], [204, Jnn], [205, Jnn], [206, Jee], [207, Jee], [207, Jnn], [208, Jee], [209, Jnn], [212, Jnn], [213, Jee], [213, Jnn], [215, Jee], [215, Jnn], [216, Jee], [218, Jnn], [221, Jee], [232, Jee], [234, Jnn], [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], [268, Jnn], [270, Jee], [270, Jnn], [272, Jee], [281, Jee], [281, Jnn], [295, 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 276.82 minutes.