Second Round of Full Day RFI Flagging¶
by Josh Dillon, last updated October 13, 2024
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 hera_filters import dspec
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", 4))
WS_Z_THRESH = float(os.environ.get("WS_Z_THRESH", 2))
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))
AVG_SPECTRUM_FILTER_DELAY = float(os.environ.get("AVG_SPECTRUM_FILTER_DELAY", 250)) # in ns
EIGENVAL_CUTOFF = float(os.environ.get("EIGENVAL_CUTOFF", 1e-12))
TIME_AVG_DELAY_FILT_SNR_THRESH = float(os.environ.get("TIME_AVG_DELAY_FILT_SNR_THRESH", 4.0))
TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE = float(os.environ.get("TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE", 1.5))
for setting in ['Z_THRESH', 'WS_Z_THRESH', 'AVG_Z_THRESH', 'MAX_FREQ_FLAG_FRAC', 'MAX_TIME_FLAG_FRAC', 'AVG_SPECTRUM_FILTER_DELAY',
'EIGENVAL_CUTOFF', 'TIME_AVG_DELAY_FILT_SNR_THRESH', 'TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE']:
print(f'{setting} = {eval(setting)}')
Z_THRESH = 4.0 WS_Z_THRESH = 2.0 AVG_Z_THRESH = 1.0 MAX_FREQ_FLAG_FRAC = 0.25 MAX_TIME_FLAG_FRAC = 0.1 AVG_SPECTRUM_FILTER_DELAY = 250.0 EIGENVAL_CUTOFF = 1e-12 TIME_AVG_DELAY_FILT_SNR_THRESH = 4.0 TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE = 1.5
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 1850 *.sum.red_avg_zscore.h5 files starting with /mnt/sn1/data2/2460746/zen.2460746.25256.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 1850 *.sum.smooth.calfits files starting with /mnt/sn1/data2/2460746/zen.2460746.25256.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, vmin=-5, vmax=5):
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=vmin, vmax=vmax, 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_func=np.nanmean, avg_z_thresh=AVG_Z_THRESH, 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 = avg_func(np.where(flags, np.nan, zscore), axis=0)
ztseries = avg_func(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=MAX_FREQ_FLAG_FRAC, 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=MAX_TIME_FLAG_FRAC, 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
def time_avg_zscore_dly_filt_SNRs(flags, filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF):
"""Produces SNRs after time-averaging z-scores and delay filtering, accounting for flagging's effect on the filter."""
# figure out high and low band based on FM gap at 100 MHz
flagged_stretches = true_stretches(np.all(flags, axis=0))
FM_gap = [fs for fs in flagged_stretches if fs.start <= np.argmin(np.abs(freqs - 100e6)) < fs.stop][0]
low_band = slice((0 if flagged_stretches[0].start != 0 else flagged_stretches[0].stop), FM_gap.start)
high_band = slice(FM_gap.stop, (len(freqs) if flagged_stretches[-1].stop != len(freqs) else flagged_stretches[-1].start))
filt_SNR = {}
for pol in zscore:
# calculate timeavg_SNR and filter
noise_prediction = 1.0 / np.sum(~flags, axis=0)**.5
timeavg_SNR = np.nanmean(np.where(flags, np.nan, zscore[pol] / noise_prediction), axis=0)
wgts = np.where(np.isfinite(timeavg_SNR), 1, 0)
model = np.zeros_like(timeavg_SNR)
for band in [low_band, high_band]:
model[band], _, _ = dspec.fourier_filter(freqs[band], np.where(np.isfinite(timeavg_SNR[band]), timeavg_SNR[band], 0),
wgts[band], [0], [AVG_SPECTRUM_FILTER_DELAY / 1e9], mode="dpss_solve",
eigenval_cutoff=[EIGENVAL_CUTOFF], suppression_factors=[EIGENVAL_CUTOFF])
filt_SNR[pol] = timeavg_SNR - model
# correct for impact of filter
correction_factors = np.ones_like(wgts) * np.nan
for band in [low_band, high_band]:
X = dspec.dpss_operator(freqs[band], [0], filter_half_widths=[AVG_SPECTRUM_FILTER_DELAY / 1e9], eigenval_cutoff=[EIGENVAL_CUTOFF])[0]
W = wgts[band]
leverage = np.diag(X @ np.linalg.pinv(np.dot(X.T * W, X)) @ (X.T * W))
correction_factors[band] = np.where(leverage > 0, (1 - leverage)**.5, np.nan) # because the underlying data should be gaussian
filt_SNR[pol] /= correction_factors
return filt_SNR
def iteratively_flag_on_delay_filtered_time_avg_zscore(flags, thresh=TIME_AVG_DELAY_FILT_SNR_THRESH, dynamic_range=TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE,
filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF):
"""Flag whole channels based on their outlierness after delay-filterd time-averaged zscores.
This is done iteratively since the delay filter can be unduly influenced by large outliers."""
filt_SNR = time_avg_zscore_dly_filt_SNRs(flags, filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF)
while True:
largest_SNR = np.nanmax(list(filt_SNR.values()))
if largest_SNR < thresh:
break
#
cut = np.max([thresh, largest_SNR / dynamic_range])
for pol in filt_SNR:
flags[:, filt_SNR[pol] > cut] = True
filt_SNR = time_avg_zscore_dly_filt_SNRs(flags, filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF)
In [15]:
flags = np.any(~np.isfinite(list(zscore.values())), axis=0)
print(f'{np.mean(flags):.3%} of waterfall flagged to start.')
# flag whole integrations or channels using outliers in median
while True:
nflags = np.sum(flags)
for pol in ['ee', 'nn']:
iteratively_flag_on_averaged_zscore(flags, zscore[pol], avg_func=np.nanmedian, 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 median z > {AVG_Z_THRESH}.')
# 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} neighbors of prior flags.')
# flag whole integrations or channels using outliers in mean
while True:
nflags = np.sum(flags)
for pol in ['ee', 'nn']:
iteratively_flag_on_averaged_zscore(flags, zscore[pol], avg_func=np.nanmean, 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}.')
# flag channels based on delay filter
iteratively_flag_on_delay_filtered_time_avg_zscore(flags, thresh=TIME_AVG_DELAY_FILT_SNR_THRESH, dynamic_range=TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE,
filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF)
print(f'{np.mean(flags):.3%} of flagging channels that are {TIME_AVG_DELAY_FILT_SNR_THRESH}σ outliers after delay filtering the time average.')
# watershed flagging again
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 another round of watershed flagging on z > {WS_Z_THRESH} neighbors of prior flags.')
23.926% of waterfall flagged to start.
All-NaN slice encountered
Flagging an additional 1 integrations and 14 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 5 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. 24.979% of waterfall flagged after flagging whole times and channels with median z > 1.0. 25.909% of waterfall flagged after flagging z > 4.0 outliers.
27.639% of waterfall flagged after watershed flagging on z > 2.0 neighbors of prior flags. Flagging an additional 0 integrations and 0 channels. Flagging 15 channels previously flagged 25.00% or more. Flagging 278 times previously flagged 10.00% or more.
Mean of empty slice Mean of empty slice
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. 33.579% of waterfall flagged after flagging whole times and channels with average z > 1.0.
Mean of empty slice Casting complex values to real discards the imaginary part Casting complex values to real discards the imaginary part
37.448% of flagging channels that are 4.0σ outliers after delay filtering the time average.
37.828% of waterfall flagged after another round of watershed flagging on z > 2.0 neighbors of prior flags.
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(ylim=[-3, 3], flags=flags):
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(ylim)
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(flags=flags):
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 in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` 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 1850 *.sum.flag_waterfall_round_2.h5 files starting with /mnt/sn1/data2/2460746/zen.2460746.25256.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/data2/2460746/2460746_aposteriori_flags.yaml ------------------------------------------------------------------------------ JD_flags: [[np.float64(2460746.2526735025), np.float64(2460746.253120895)], [np.float64(2460746.2543512243), np.float64(2460746.2547986167)], [np.float64(2460746.2587133003), np.float64(2460746.259160693)], [np.float64(2460746.26050287), np.float64(2460746.2609502627)], [np.float64(2460746.265647883), np.float64(2460746.2658715793)], [np.float64(2460746.2715758327), np.float64(2460746.271799529)], [np.float64(2460746.2748194276), np.float64(2460746.275154972)], [np.float64(2460746.2754905163), np.float64(2460746.2758260607)], [np.float64(2460746.2764971494), np.float64(2460746.2766089975)], [np.float64(2460746.276944542), np.float64(2460746.277168238)], [np.float64(2460746.2776156305), np.float64(2460746.278063023)], [np.float64(2460746.278174871), np.float64(2460746.278286719)], [np.float64(2460746.2802999853), np.float64(2460746.280523681)], [np.float64(2460746.282425099), np.float64(2460746.2826487953)], [np.float64(2460746.2860042383), np.float64(2460746.2861160864)], [np.float64(2460746.287793808), np.float64(2460746.2879056563)], [np.float64(2460746.288464897), np.float64(2460746.288688593)], [np.float64(2460746.290590011), np.float64(2460746.2909255554)], [np.float64(2460746.29182034), np.float64(2460746.292044036)], [np.float64(2460746.2931625172), np.float64(2460746.2932743654)], [np.float64(2460746.297971986), np.float64(2460746.29830753)], [np.float64(2460746.299202315), np.float64(2460746.299314163)], [np.float64(2460746.2994260113), np.float64(2460746.2995378594)], [np.float64(2460746.3000971), np.float64(2460746.3005444924)], [np.float64(2460746.3119529993), np.float64(2460746.3121766956)], [np.float64(2460746.315196594), np.float64(2460746.3155321386)], [np.float64(2460746.3165387716), np.float64(2460746.316874316)], [np.float64(2460746.318887582), np.float64(2460746.3192231264)], [np.float64(2460746.3225785694), np.float64(2460746.3226904175)], [np.float64(2460746.325151076), np.float64(2460746.32548662)], [np.float64(2460746.3271643417), np.float64(2460746.327388038)], [np.float64(2460746.327499886), np.float64(2460746.3277235823)], [np.float64(2460746.3320856583), np.float64(2460746.3324212027)], [np.float64(2460746.3373425193), np.float64(2460746.3374543674)], [np.float64(2460746.340138722), np.float64(2460746.3402505703)], [np.float64(2460746.340697963), np.float64(2460746.340809811)], [np.float64(2460746.340921659), np.float64(2460746.341033507)], [np.float64(2460746.3414808996), np.float64(2460746.3415927477)], [np.float64(2460746.341704596), np.float64(2460746.341928292)], [np.float64(2460746.343382317), np.float64(2460746.3439415577)], [np.float64(2460746.344053406), np.float64(2460746.344165254)], [np.float64(2460746.3457311275), np.float64(2460746.345954824)], [np.float64(2460746.34617852), np.float64(2460746.3464022162)], [np.float64(2460746.3465140644), np.float64(2460746.3466259125)], [np.float64(2460746.3468496087), np.float64(2460746.347073305)], [np.float64(2460746.349422115), np.float64(2460746.3497576592)], [np.float64(2460746.3503169), np.float64(2460746.350540596)], [np.float64(2460746.351435381), np.float64(2460746.351659077)], [np.float64(2460746.3531131027), np.float64(2460746.353224951)], [np.float64(2460746.353336799), np.float64(2460746.353560495)], [np.float64(2460746.3551263683), np.float64(2460746.3553500646)], [np.float64(2460746.3562448495), np.float64(2460746.35680409)], [np.float64(2460746.3583699637), np.float64(2460746.35859366)], [np.float64(2460746.3604950774), np.float64(2460746.3606069256)], [np.float64(2460746.3641860653), np.float64(2460746.3642979134)], [np.float64(2460746.368436293), np.float64(2460746.3685481413)], [np.float64(2460746.3713443438), np.float64(2460746.371456192)], [np.float64(2460746.371791736), np.float64(2460746.372350977)], [np.float64(2460746.372462825), np.float64(2460746.372686521)], [np.float64(2460746.374476091), np.float64(2460746.3746997872)], [np.float64(2460746.3748116354), np.float64(2460746.3751471797)], [np.float64(2460746.375259028), np.float64(2460746.375482724)], [np.float64(2460746.3758182684), np.float64(2460746.3759301165)], [np.float64(2460746.3761538123), np.float64(2460746.3762656604)], [np.float64(2460746.3804040407), np.float64(2460746.380515889)], [np.float64(2460746.381969914), np.float64(2460746.382081762)], [np.float64(2460746.3837594837), np.float64(2460746.383871332)], [np.float64(2460746.3843187243), np.float64(2460746.3844305724)], [np.float64(2460746.3846542686), np.float64(2460746.384877965)], [np.float64(2460746.38577275), np.float64(2460746.3862201422)], [np.float64(2460746.3863319904), np.float64(2460746.3864438385)], [np.float64(2460746.3880097116), np.float64(2460746.388233408)], [np.float64(2460746.3886808003), np.float64(2460746.3889044966)], [np.float64(2460746.389240041), np.float64(2460746.390358522)], [np.float64(2460746.393490269), np.float64(2460746.393825813)], [np.float64(2460746.398523434), np.float64(2460746.398635282)], [np.float64(2460746.398858978), np.float64(2460746.399082674)], [np.float64(2460746.3995300666), np.float64(2460746.3996419148)], [np.float64(2460746.400648548), np.float64(2460746.400760396)], [np.float64(2460746.401655181), np.float64(2460746.4021025733)], [np.float64(2460746.402549966), np.float64(2460746.402773662)], [np.float64(2460746.40288551), np.float64(2460746.403556599)], [np.float64(2460746.4046750795), np.float64(2460746.4047869276)], [np.float64(2460746.406129105), np.float64(2460746.406240953)], [np.float64(2460746.4092608523), np.float64(2460746.4093727004)], [np.float64(2460746.411609662), np.float64(2460746.4117215103)], [np.float64(2460746.418656093), np.float64(2460746.4188797893)], [np.float64(2460746.4223470804), np.float64(2460746.4224589285)], [np.float64(2460746.4232418654), np.float64(2460746.4233537135)], [np.float64(2460746.4243603465), np.float64(2460746.4245840427)], [np.float64(2460746.424807739), np.float64(2460746.425031435)], [np.float64(2460746.4279394858), np.float64(2460746.428163182)], [np.float64(2460746.4287224226), np.float64(2460746.429057967)], [np.float64(2460746.4295053594), np.float64(2460746.4296172075)], [np.float64(2460746.430176448), np.float64(2460746.430288296)], [np.float64(2460746.4331963467), np.float64(2460746.433420043)], [np.float64(2460746.4337555873), np.float64(2460746.4338674354)], [np.float64(2460746.4349859166), np.float64(2460746.435433309)], [np.float64(2460746.435545157), np.float64(2460746.4356570053)], [np.float64(2460746.4357688534), np.float64(2460746.4358807015)], [np.float64(2460746.4367754865), np.float64(2460746.4368873346)], [np.float64(2460746.4380058153), np.float64(2460746.4381176634)], [np.float64(2460746.4382295115), np.float64(2460746.4383413596)], [np.float64(2460746.4384532077), np.float64(2460746.438565056)], [np.float64(2460746.4402427776), np.float64(2460746.4403546257)], [np.float64(2460746.4412494106), np.float64(2460746.441473107)], [np.float64(2460746.4428152842), np.float64(2460746.4432626767)], [np.float64(2460746.4515394364), np.float64(2460746.4517631326)], [np.float64(2460746.4548948794), np.float64(2460746.4552304237)], [np.float64(2460746.4562370568), np.float64(2460746.456348905)], [np.float64(2460746.45724369), np.float64(2460746.457467386)], [np.float64(2460746.458250323), np.float64(2460746.458474019)], [np.float64(2460746.460710981), np.float64(2460746.460822829)], [np.float64(2460746.4631716395), np.float64(2460746.463507184)], [np.float64(2460746.464513817), np.float64(2460746.464625665)], [np.float64(2460746.4651849056), np.float64(2460746.465408602)], [np.float64(2460746.4665270825), np.float64(2460746.4667507787)], [np.float64(2460746.466974475), np.float64(2460746.467198171)], [np.float64(2460746.4673100193), np.float64(2460746.4674218674)], [np.float64(2460746.4675337155), np.float64(2460746.4676455637)], [np.float64(2460746.469882526), np.float64(2460746.470106222)], [np.float64(2460746.4702180703), np.float64(2460746.4703299184)], [np.float64(2460746.4712247034), np.float64(2460746.4714483996)], [np.float64(2460746.473126121), np.float64(2460746.473237969)], [np.float64(2460746.474020906), np.float64(2460746.4746919945)], [np.float64(2460746.4765934125), np.float64(2460746.476928957)], [np.float64(2460746.477040805), np.float64(2460746.477152653)], [np.float64(2460746.477264501), np.float64(2460746.4773763493)], [np.float64(2460746.4781592856), np.float64(2460746.478382982)], [np.float64(2460746.47849483), np.float64(2460746.478606678)], [np.float64(2460746.479277767), np.float64(2460746.4797251592)], [np.float64(2460746.4881137675), np.float64(2460746.48856116)], [np.float64(2460746.4913573624), np.float64(2460746.4915810586)], [np.float64(2460746.4968379196), np.float64(2460746.497285312)], [np.float64(2460746.4975090083), np.float64(2460746.4977327045)], [np.float64(2460746.4979564007), np.float64(2460746.498180097)], [np.float64(2460746.4985156413), np.float64(2460746.4986274894)], [np.float64(2460746.4988511857), np.float64(2460746.498963034)], [np.float64(2460746.4995222744), np.float64(2460746.4996341225)], [np.float64(2460746.4997459706), np.float64(2460746.499969667)], [np.float64(2460746.5000815145), np.float64(2460746.5001933626)], [np.float64(2460746.5003052107), np.float64(2460746.500417059)], [np.float64(2460746.5011999956), np.float64(2460746.501423692)], [np.float64(2460746.5028777174), np.float64(2460746.5029895655)], [np.float64(2460746.5031014136), np.float64(2460746.503436958)], [np.float64(2460746.503548806), np.float64(2460746.5037725023)], [np.float64(2460746.5039961985), np.float64(2460746.5041080466)], [np.float64(2460746.50567392), np.float64(2460746.5063450085)], [np.float64(2460746.5065687047), np.float64(2460746.507016097)], [np.float64(2460746.510595237), np.float64(2460746.510707085)], [np.float64(2460746.5133914393), np.float64(2460746.5135032875)], [np.float64(2460746.5137269837), np.float64(2460746.51395068)], [np.float64(2460746.514174376), np.float64(2460746.5143980724)], [np.float64(2460746.5152928573), np.float64(2460746.5154047054)], [np.float64(2460746.519319389), np.float64(2460746.5196549334)], [np.float64(2460746.520214174), np.float64(2460746.520326022)], [np.float64(2460746.5205497183), np.float64(2460746.5206615664)], [np.float64(2460746.5207734145), np.float64(2460746.5209971108)], [np.float64(2460746.521444503), np.float64(2460746.5217800476)], [np.float64(2460746.52222744), np.float64(2460746.5224511363)], [np.float64(2460746.522674832), np.float64(2460746.5228985283)], [np.float64(2460746.523681465), np.float64(2460746.5239051613)], [np.float64(2460746.5262539717), np.float64(2460746.526477668)], [np.float64(2460746.527596149), np.float64(2460746.5278198454)], [np.float64(2460746.532964858), np.float64(2460746.5333004026)], [np.float64(2460746.535984757), np.float64(2460746.536208453)], [np.float64(2460746.554663391), np.float64(2460746.554775239)], [np.float64(2460746.555781872), np.float64(2460746.55589372)], [np.float64(2460746.5605913405), np.float64(2460746.5607031886)], [np.float64(2460746.5619335175), np.float64(2460746.5621572137)], [np.float64(2460746.57065767), np.float64(2460746.570769518)], [np.float64(2460746.5772567084), np.float64(2460746.5775922528)], [np.float64(2460746.5933628357), np.float64(2460746.593586532)], [np.float64(2460746.596830127), np.float64(2460746.597053823)], [np.float64(2460746.598843393), np.float64(2460746.598955241)], [np.float64(2460746.6102519), np.float64(2460746.610475596)], [np.float64(2460746.6185286595), np.float64(2460746.618976052)], [np.float64(2460746.622890736), np.float64(2460746.6233381284)], [np.float64(2460746.6247921535), np.float64(2460746.625239546)], [np.float64(2460746.626358027), np.float64(2460746.626469875)], [np.float64(2460746.6266935715), np.float64(2460746.6268054196)], [np.float64(2460746.6269172677), np.float64(2460746.627029116)], [np.float64(2460746.627252812), np.float64(2460746.6274765083)], [np.float64(2460746.6275883564), np.float64(2460746.629377926)], [np.float64(2460746.629489774), np.float64(2460746.629825318)], [np.float64(2460746.6301608626), np.float64(2460746.6302727107)], [np.float64(2460746.630384559), np.float64(2460746.630496407)], [np.float64(2460746.630720103), np.float64(2460746.635976964)], [np.float64(2460746.6360888123), np.float64(2460746.6362006604)], [np.float64(2460746.6363125085), np.float64(2460746.636648053)], [np.float64(2460746.636759901), np.float64(2460746.636871749)], [np.float64(2460746.6370954453), np.float64(2460746.6372072934)], [np.float64(2460746.6375428378), np.float64(2460746.646378838)], [np.float64(2460746.646490686), np.float64(2460746.647497319)], [np.float64(2460746.6477210154), np.float64(2460746.6478328635)], [np.float64(2460746.6480565597), np.float64(2460746.648168408)], [np.float64(2460746.648392104), np.float64(2460746.648503952)], [np.float64(2460746.6488394965), np.float64(2460746.6489513447)], [np.float64(2460746.6498461296), np.float64(2460746.650293522)], [np.float64(2460746.6508527626), np.float64(2460746.6509646107)], [np.float64(2460746.651300155), np.float64(2460746.651412003)], [np.float64(2460746.6519712433), np.float64(2460746.6523067877)], [np.float64(2460746.652642332), np.float64(2460746.6530897245)], [np.float64(2460746.6532015726), np.float64(2460746.653425269)], [np.float64(2460746.653537117), np.float64(2460746.653648965)], [np.float64(2460746.6539845094), np.float64(2460746.6540963575)], [np.float64(2460746.6542082056), np.float64(2460746.654767446)], [np.float64(2460746.6584584336), np.float64(2460746.6585702817)], [np.float64(2460746.6610309402), np.float64(2460746.6613664846)], [np.float64(2460746.662708662), np.float64(2460746.66282051)], [np.float64(2460746.6646100795), np.float64(2460746.6648337757)]] freq_flags: [[np.float64(49911499.0234375), np.float64(50155639.6484375)], [np.float64(51742553.7109375), np.float64(51986694.3359375)], [np.float64(52230834.9609375), np.float64(52352905.2734375)], [np.float64(59188842.7734375), np.float64(59310913.0859375)], [np.float64(61996459.9609375), np.float64(63095092.7734375)], [np.float64(66024780.2734375), np.float64(66146850.5859375)], [np.float64(66268920.8984375), np.float64(66390991.2109375)], [np.float64(66513061.5234375), np.float64(66757202.1484375)], [np.float64(69931030.2734375), np.float64(70053100.5859375)], [np.float64(72006225.5859375), np.float64(72128295.8984375)], [np.float64(73959350.5859375), np.float64(74447631.8359375)], [np.float64(77499389.6484375), np.float64(77987670.8984375)], [np.float64(87509155.2734375), np.float64(108139038.0859375)], [np.float64(109970092.7734375), np.float64(110092163.0859375)], [np.float64(112167358.3984375), np.float64(113143920.8984375)], [np.float64(113265991.2109375), np.float64(113510131.8359375)], [np.float64(113632202.1484375), np.float64(113754272.4609375)], [np.float64(116073608.3984375), np.float64(116195678.7109375)], [np.float64(116439819.3359375), np.float64(116561889.6484375)], [np.float64(116683959.9609375), np.float64(116806030.2734375)], [np.float64(124740600.5859375), np.float64(125228881.8359375)], [np.float64(127548217.7734375), np.float64(127670288.0859375)], [np.float64(129989624.0234375), np.float64(130111694.3359375)], [np.float64(136337280.2734375), np.float64(136459350.5859375)], [np.float64(136947631.8359375), np.float64(138046264.6484375)], [np.float64(138290405.2734375), np.float64(138412475.5859375)], [np.float64(138656616.2109375), np.float64(138778686.5234375)], [np.float64(141464233.3984375), np.float64(141586303.7109375)], [np.float64(141708374.0234375), np.float64(141830444.3359375)], [np.float64(142074584.9609375), np.float64(142318725.5859375)], [np.float64(142562866.2109375), np.float64(143539428.7109375)], [np.float64(143783569.3359375), np.float64(144027709.9609375)], [np.float64(144882202.1484375), np.float64(145004272.4609375)], [np.float64(145492553.7109375), np.float64(145736694.3359375)], [np.float64(145858764.6484375), np.float64(145980834.9609375)], [np.float64(146224975.5859375), np.float64(146347045.8984375)], [np.float64(147445678.7109375), np.float64(147567749.0234375)], [np.float64(148422241.2109375), np.float64(148544311.5234375)], [np.float64(149154663.0859375), np.float64(149276733.3984375)], [np.float64(149642944.3359375), np.float64(149765014.6484375)], [np.float64(149887084.9609375), np.float64(150009155.2734375)], [np.float64(153060913.0859375), np.float64(153182983.3984375)], [np.float64(153427124.0234375), np.float64(153549194.3359375)], [np.float64(154159545.8984375), np.float64(154403686.5234375)], [np.float64(155014038.0859375), np.float64(155136108.3984375)], [np.float64(155258178.7109375), np.float64(155380249.0234375)], [np.float64(155868530.2734375), np.float64(156112670.8984375)], [np.float64(157577514.6484375), np.float64(157699584.9609375)], [np.float64(158187866.2109375), np.float64(158309936.5234375)], [np.float64(158432006.8359375), np.float64(158554077.1484375)], [np.float64(159164428.7109375), np.float64(159286499.0234375)], [np.float64(159896850.5859375), np.float64(160385131.8359375)], [np.float64(160507202.1484375), np.float64(160629272.4609375)], [np.float64(169174194.3359375), np.float64(169296264.6484375)], [np.float64(169906616.2109375), np.float64(170639038.0859375)], [np.float64(170883178.7109375), np.float64(171005249.0234375)], [np.float64(171249389.6484375), np.float64(171615600.5859375)], [np.float64(171737670.8984375), np.float64(171859741.2109375)], [np.float64(171981811.5234375), np.float64(172103881.8359375)], [np.float64(174789428.7109375), np.float64(175033569.3359375)], [np.float64(175155639.6484375), np.float64(175765991.2109375)], [np.float64(181137084.9609375), np.float64(181381225.5859375)], [np.float64(183212280.2734375), np.float64(183334350.5859375)], [np.float64(186386108.3984375), np.float64(186630249.0234375)], [np.float64(187362670.8984375), np.float64(187606811.5234375)], [np.float64(189926147.4609375), np.float64(190048217.7734375)], [np.float64(190902709.9609375), np.float64(191879272.4609375)], [np.float64(193222045.8984375), np.float64(193344116.2109375)], [np.float64(195175170.8984375), np.float64(195297241.2109375)], [np.float64(196395874.0234375), np.float64(196517944.3359375)], [np.float64(196884155.2734375), np.float64(197006225.5859375)], [np.float64(197128295.8984375), np.float64(197372436.5234375)], [np.float64(198104858.3984375), np.float64(198348999.0234375)], [np.float64(199203491.2109375), np.float64(199325561.5234375)], [np.float64(200057983.3984375), np.float64(200180053.7109375)], [np.float64(200790405.2734375), np.float64(200912475.5859375)], [np.float64(201278686.5234375), np.float64(202133178.7109375)], [np.float64(203231811.5234375), np.float64(203353881.8359375)], [np.float64(203964233.3984375), np.float64(204086303.7109375)], [np.float64(204574584.9609375), np.float64(204696655.2734375)], [np.float64(204940795.8984375), np.float64(205062866.2109375)], [np.float64(205184936.5234375), np.float64(205307006.8359375)], [np.float64(206893920.8984375), np.float64(207015991.2109375)], [np.float64(207138061.5234375), np.float64(207382202.1484375)], [np.float64(207504272.4609375), np.float64(207626342.7734375)], [np.float64(208480834.9609375), np.float64(208724975.5859375)], [np.float64(209945678.7109375), np.float64(210067749.0234375)], [np.float64(210433959.9609375), np.float64(210556030.2734375)], [np.float64(211776733.3984375), np.float64(212631225.5859375)], [np.float64(215194702.1484375), np.float64(215316772.4609375)], [np.float64(215682983.3984375), np.float64(215805053.7109375)], [np.float64(220565795.8984375), np.float64(220809936.5234375)], [np.float64(221176147.4609375), np.float64(221298217.7734375)], [np.float64(221420288.0859375), np.float64(221542358.3984375)], [np.float64(223129272.4609375), np.float64(223373413.0859375)], [np.float64(225692749.0234375), np.float64(225814819.3359375)], [np.float64(227401733.3984375), np.float64(227523803.7109375)], [np.float64(229110717.7734375), np.float64(229598999.0234375)], [np.float64(229965209.9609375), np.float64(230087280.2734375)], [np.float64(231063842.7734375), np.float64(231185913.0859375)], [np.float64(233383178.7109375), np.float64(233505249.0234375)], [np.float64(233627319.3359375), np.float64(233871459.9609375)], [np.float64(234237670.8984375), np.float64(234359741.2109375)]] ex_ants: [[np.int64[4], Jee], [np.int64[4], Jnn], [np.int64[7], Jee], [np.int64[8], Jee], [np.int64[8], Jnn], [np.int64[10], Jee], [np.int64[10], Jnn], [np.int64[16], Jee], [np.int64[17], Jnn], [np.int64[18], Jee], [np.int64[18], Jnn], [np.int64[20], Jee], [np.int64[20], Jnn], [np.int64[21], Jee], [np.int64[22], Jee], [np.int64[22], Jnn], [np.int64[27], Jee], [np.int64[27], Jnn], [np.int64[28], Jee], [np.int64[28], Jnn], [np.int64[29], Jee], [np.int64[29], Jnn], [np.int64[31], Jnn], [np.int64[32], Jnn], [np.int64[33], Jee], [np.int64[33], Jnn], [np.int64[34], Jee], [np.int64[34], Jnn], [np.int64[35], Jnn], [np.int64[37], Jnn], [np.int64[40], Jnn], [np.int64[42], Jnn], [np.int64[44], Jee], [np.int64[45], Jnn], [np.int64[46], Jee], [np.int64[47], Jee], [np.int64[49], Jnn], [np.int64[51], Jee], [np.int64[55], Jee], [np.int64[58], Jnn], [np.int64[59], Jnn], [np.int64[64], Jnn], [np.int64[65], Jee], [np.int64[67], Jnn], [np.int64[70], Jee], [np.int64[70], Jnn], [np.int64[71], Jnn], [np.int64[72], Jnn], [np.int64[73], Jnn], [np.int64[75], Jee], [np.int64[75], Jnn], [np.int64[78], Jee], [np.int64[80], Jnn], [np.int64[81], Jnn], [np.int64[82], Jnn], [np.int64[85], Jee], [np.int64[86], Jee], [np.int64[88], Jee], [np.int64[89], Jee], [np.int64[89], Jnn], [np.int64[90], Jnn], [np.int64[91], Jee], [np.int64[91], Jnn], [np.int64[92], Jee], [np.int64[92], Jnn], [np.int64[93], Jnn], [np.int64[95], Jee], [np.int64[97], Jnn], [np.int64[98], Jnn], [np.int64[99], Jee], [np.int64[99], Jnn], [np.int64[102], Jnn], [np.int64[104], Jnn], [np.int64[107], Jee], [np.int64[107], Jnn], [np.int64[108], Jnn], [np.int64[109], Jnn], [np.int64[112], Jee], [np.int64[115], Jnn], [np.int64[116], Jnn], [np.int64[117], Jee], [np.int64[120], Jee], [np.int64[120], Jnn], [np.int64[121], Jee], [np.int64[121], Jnn], [np.int64[124], Jnn], [np.int64[125], Jnn], [np.int64[128], Jnn], [np.int64[130], Jee], [np.int64[130], Jnn], [np.int64[133], Jee], [np.int64[134], Jnn], [np.int64[135], Jee], [np.int64[137], Jee], [np.int64[140], Jee], [np.int64[143], Jnn], [np.int64[147], Jnn], [np.int64[148], Jnn], [np.int64[150], Jnn], [np.int64[154], Jnn], [np.int64[155], Jee], [np.int64[155], Jnn], [np.int64[156], Jee], [np.int64[158], Jnn], [np.int64[161], Jnn], [np.int64[170], Jee], [np.int64[170], Jnn], [np.int64[171], Jnn], [np.int64[175], Jnn], [np.int64[176], Jnn], [np.int64[180], Jee], [np.int64[180], Jnn], [np.int64[183], Jee], [np.int64[187], Jee], [np.int64[188], Jnn], [np.int64[189], Jnn], [np.int64[190], Jnn], [np.int64[191], Jee], [np.int64[191], Jnn], [np.int64[198], Jnn], [np.int64[199], Jee], [np.int64[199], Jnn], [np.int64[200], Jee], [np.int64[200], Jnn], [np.int64[202], Jnn], [np.int64[204], Jee], [np.int64[208], Jee], [np.int64[209], Jnn], [np.int64[212], Jnn], [np.int64[213], Jee], [np.int64[215], Jnn], [np.int64[216], Jnn], [np.int64[218], Jee], [np.int64[218], Jnn], [np.int64[221], Jee], [np.int64[232], Jee], [np.int64[235], Jee], [np.int64[238], Jnn], [np.int64[239], Jee], [np.int64[246], Jee], [np.int64[246], Jnn], [np.int64[250], Jee], [np.int64[251], Jee], [np.int64[253], Jnn], [np.int64[255], Jnn], [np.int64[261], Jee], [np.int64[261], Jnn], [np.int64[262], Jee], [np.int64[262], Jnn], [np.int64[268], Jee], [np.int64[268], Jnn], [np.int64[320], Jee], [np.int64[320], Jnn], [np.int64[321], Jee], [np.int64[321], Jnn], [np.int64[322], Jee], [np.int64[322], Jnn], [np.int64[323], Jee], [np.int64[323], Jnn], [np.int64[324], Jee], [np.int64[324], Jnn], [np.int64[325], Jee], [np.int64[325], Jnn], [np.int64[326], Jee], [np.int64[326], Jnn], [np.int64[327], Jee], [np.int64[327], Jnn], [np.int64[328], Jee], [np.int64[328], Jnn], [np.int64[329], Jee], [np.int64[329], Jnn], [np.int64[331], Jee], [np.int64[331], Jnn], [np.int64[332], Jee], [np.int64[332], Jnn], [np.int64[333], Jee], [np.int64[333], Jnn], [np.int64[336], Jee], [np.int64[336], Jnn], [np.int64[340], Jee], [np.int64[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: 3.7.1.dev18+g10e9584 hera_qm: 2.2.1.dev2+ga535e9e hera_filters: 0.1.6.dev9+gf165ec1
hera_notebook_templates: 0.1.dev989+gee0995d pyuvdata: 3.1.3
In [25]:
print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')
Finished execution in 39.57 minutes.