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 1571 *.sum.red_avg_zscore.h5 files starting with /mnt/sn1/data2/2460564/zen.2460564.16917.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 1571 *.sum.smooth.calfits files starting with /mnt/sn1/data2/2460564/zen.2460564.16917.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}.')
25.263% of waterfall flagged to start. 29.918% of waterfall flagged after flagging z > 5.0 outliers.
30.830% 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 40 integrations and 38 channels. Flagging 57 channels previously flagged 25.00% or more. Flagging 675 times previously flagged 10.00% or more.
Flagging an additional 0 integrations and 2 channels. Flagging 4 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more.
Flagging an additional 0 integrations and 51 channels. Flagging 0 channels previously flagged 25.00% or more. Flagging 5 times previously flagged 10.00% or more. Flagging an additional 0 integrations and 0 channels. Flagging 0 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more.
Flagging an additional 0 integrations and 0 channels. Flagging 0 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more. Flagging an additional 0 integrations and 0 channels. Flagging 0 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more. 51.592% 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 1571 *.sum.flag_waterfall_round_2.h5 files starting with /mnt/sn1/data2/2460564/zen.2460564.16917.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/data2/2460564/2460564_aposteriori_flags.yaml ------------------------------------------------------------------------------ JD_flags: [[2460564.1690588077, 2460564.181250251], [2460564.181473947, 2460564.1830398208], [2460564.183487213, 2460564.186283416], [2460564.1867308086, 2460564.187625593], [2460564.1878492893, 2460564.1891914667], [2460564.189415163, 2460564.1898625554], [2460564.1900862516, 2460564.203060632], [2460564.2032843283, 2460564.2048502015], [2460564.2050738977, 2460564.205409442], [2460564.20552129, 2460564.2059686827], [2460564.206192379, 2460564.20731086], [2460564.2075345563, 2460564.2086530374], [2460564.2088767337, 2460564.209324126], [2460564.209547822, 2460564.209771518], [2460564.2103307587, 2460564.211561088], [2460564.211784784, 2460564.2120084804], [2460564.2122321767, 2460564.212344025], [2460564.212455873, 2460564.2129032654], [2460564.213350658, 2460564.2139098984], [2460564.2140217465, 2460564.2148046833], [2460564.2153639235, 2460564.216035012], [2460564.2163705565, 2460564.216929797], [2460564.2171534933, 2460564.218831215], [2460564.218943063, 2460564.2309108106], [2460564.2310226588, 2460564.232588532], [2460564.2329240763, 2460564.233818861], [2460564.2341544055, 2460564.2500368366], [2460564.2501486847, 2460564.2518264065], [2460564.2520501027, 2460564.252273799], [2460564.2530567357, 2460564.253168584], [2460564.25339228, 2460564.253504128], [2460564.2538396725, 2460564.2539515207], [2460564.260215014, 2460564.2603268623], [2460564.2612216473, 2460564.2613334954], [2460564.2614453435, 2460564.2615571916], [2460564.263794154, 2460564.263906002], [2460564.269162863, 2460564.2694984074], [2460564.2758737495, 2460564.2759855976], [2460564.2779988633, 2460564.2781107114], [2460564.2782225595, 2460564.2783344076], [2460564.285045294, 2460564.285157142], [2460564.2854926866, 2460564.2856045347], [2460564.2861637753, 2460564.2864993196], [2460564.2866111677, 2460564.286723016], [2460564.287729649, 2460564.287841497], [2460564.288400737, 2460564.288512585], [2460564.290302155, 2460564.290414003], [2460564.294328687, 2460564.294664231], [2460564.2948879274, 2460564.295447168], [2460564.306967523, 2460564.307079371], [2460564.3077504598, 2460564.307862308], [2460564.3086452447, 2460564.308980789], [2460564.311105903, 2460564.311217751], [2460564.3125599283, 2460564.3127836245], [2460564.314349498, 2460564.3145731944], [2460564.3179286374, 2460564.3180404855], [2460564.3182641817, 2460564.31837603], [2460564.322738106, 2460564.322849954], [2460564.3230736502, 2460564.3231854984], [2460564.324975068, 2460564.3250869163], [2460564.3251987644, 2460564.325646157], [2460564.3262053975, 2460564.3263172456], [2460564.3269883343, 2460564.3271001824], [2460564.3273238786, 2460564.3277712706], [2460564.327994967, 2460564.329896385], [2460564.330008233, 2460564.33079117], [2460564.331126714, 2460564.3312385622], [2460564.3313504104, 2460564.3340347647], [2460564.334146613, 2460564.3350413977], [2460564.3356006383, 2460564.3358243345], [2460564.33727836, 2460564.3378376006], [2460564.3379494487, 2460564.338061297], [2460564.341528588, 2460564.341640436], [2460564.3421996767, 2460564.342311525], [2460564.3444366385, 2460564.3446603348], [2460564.345107727, 2460564.3452195753], [2460564.3472328414, 2460564.3474565377], [2460564.3507001325, 2460564.351147525], [2460564.3537200317, 2460564.35383188], [2460564.3542792723, 2460564.3543911204], [2460564.3559569935, 2460564.3560688416], [2460564.3588650445, 2460564.359312437], [2460564.3595361332, 2460564.3596479814], [2460564.3597598295, 2460564.3598716776], [2460564.36031907, 2460564.360430918], [2460564.3608783106, 2460564.361102007], [2460564.361549399, 2460564.361773095], [2460564.361884943, 2460564.3619967913], [2460564.3630034244, 2460564.3633389687], [2460564.3643456018, 2460564.364569298], [2460564.365464083, 2460564.365687779], [2460564.3657996273, 2460564.366470716], [2460564.3674773485, 2460564.367701045], [2460564.369826159, 2460564.3713920326], [2460564.3737408426, 2460564.374971172], [2460564.3765370455, 2460564.3767607417], [2460564.381346514, 2460564.38157021], [2460564.3835834763, 2460564.383807172], [2460564.3881692486, 2460564.3882810967], [2460564.388504793, 2460564.388616641], [2460564.3924194765, 2460564.3925313246], [2460564.3926431728, 2460564.392866869], [2460564.396110464, 2460564.3964460082], [2460564.3967815526, 2460564.397005249], [2460564.3974526413, 2460564.3977881856], [2460564.3979000337, 2460564.398011882], [2460564.39812373, 2460564.398347426], [2460564.398906667, 2460564.399018515], [2460564.3995777555, 2460564.3996896036], [2460564.3998014517, 2460564.3999133], [2460564.400136996, 2460564.4004725404], [2460564.4009199324, 2460564.4010317805], [2460564.4011436286, 2460564.4012554768], [2460564.401479173, 2460564.4019265654], [2460564.4020384136, 2460564.402485806], [2460564.402597654, 2460564.403268743], [2460564.403380591, 2460564.405282009], [2460564.405393857, 2460564.405505705], [2460564.4056175533, 2460564.4059530976], [2460564.4060649457, 2460564.406176794], [2460564.4062886415, 2460564.406736034], [2460564.406847882, 2460564.40695973], [2460564.4071834264, 2460564.4072952745], [2460564.407518971, 2460564.4079663632], [2460564.4081900595, 2460564.4083019076], [2460564.4094203888, 2460564.4099796293], [2460564.412104743, 2460564.412216591], [2460564.4123284393, 2460564.4133350723], [2460564.413782465, 2460564.413894313], [2460564.4144535535, 2460564.4145654016], [2460564.415124642, 2460564.41702606], [2460564.4171379083, 2460564.4173616045], [2460564.417473452, 2460564.4175853003], [2460564.4179208446, 2460564.4180326927], [2460564.418480085, 2460564.4185919333], [2460564.4199341107, 2460564.420045959], [2460564.426644997, 2460564.4267568453], [2460564.428546415, 2460564.4286582633], [2460564.4333558837, 2460564.43357958], [2460564.433691428, 2460564.4339151243], [2460564.4364876305, 2460564.4365994786], [2460564.4392838334, 2460564.439731226], [2460564.439954922, 2460564.440178618], [2460564.4406260103, 2460564.4408497065], [2460564.442303732, 2460564.44241558], [2460564.450580492, 2460564.4508041884], [2460564.4614297585, 2460564.4615416066], [2460564.467134012, 2460564.46724586], [2460564.472502721, 2460564.4727264172], [2460564.474963379, 2460564.475075227], [2460564.4834638354, 2460564.4835756836], [2460564.4843586204, 2460564.4845823166], [2460564.4897273295, 2460564.4898391776], [2460564.502030621, 2460564.502254317], [2460564.504938672, 2460564.505274216], [2460564.507623026, 2460564.5077348743], [2460564.5099718366, 2460564.510195533], [2460564.5107547734, 2460564.5108666215], [2460564.5150050013, 2460564.5151168494], [2460564.5171301155, 2460564.51746566], [2460564.51981447, 2460564.519926318], [2460564.5203737104, 2460564.5204855585]] freq_flags: [[46859741.2109375, 46981811.5234375], [47103881.8359375, 49179077.1484375], [49789428.7109375, 50155639.6484375], [50399780.2734375, 50643920.8984375], [51132202.1484375, 51376342.7734375], [54061889.6484375, 54916381.8359375], [62240600.5859375, 63217163.0859375], [65780639.6484375, 66390991.2109375], [68344116.2109375, 68466186.5234375], [69931030.2734375, 70053100.5859375], [70175170.8984375, 70541381.8359375], [72616577.1484375, 72738647.4609375], [74325561.5234375, 74935913.0859375], [77377319.3359375, 78353881.8359375], [85433959.9609375, 85556030.2734375], [85800170.8984375, 86044311.5234375], [87265014.6484375, 108261108.3984375], [109970092.7734375, 110092163.0859375], [112167358.3984375, 112411499.0234375], [112655639.6484375, 112777709.9609375], [113632202.1484375, 113754272.4609375], [116439819.3359375, 116561889.6484375], [122787475.5859375, 123275756.8359375], [123519897.4609375, 124130249.0234375], [124496459.9609375, 125473022.4609375], [125839233.3984375, 135604858.3984375], [136215209.9609375, 136459350.5859375], [136947631.8359375, 138046264.6484375], [138656616.2109375, 138778686.5234375], [141464233.3984375, 141586303.7109375], [141708374.0234375, 141830444.3359375], [142074584.9609375, 142318725.5859375], [143783569.3359375, 144027709.9609375], [147445678.7109375, 147567749.0234375], [149154663.0859375, 149276733.3984375], [149887084.9609375, 150009155.2734375], [153793334.9609375, 153915405.2734375], [154159545.8984375, 154403686.5234375], [155258178.7109375, 155380249.0234375], [169906616.2109375, 170150756.8359375], [170883178.7109375, 171005249.0234375], [175155639.6484375, 175277709.9609375], [181137084.9609375, 181259155.2734375], [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], [231063842.7734375, 231185913.0859375]] ex_ants: [[8, Jee], [8, Jnn], [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], [37, Jee], [37, Jnn], [40, Jnn], [45, Jee], [46, Jee], [46, Jnn], [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], [73, Jnn], [77, Jee], [77, Jnn], [78, Jee], [78, Jnn], [82, Jnn], [83, Jnn], [85, Jnn], [86, Jee], [86, Jnn], [87, Jee], [88, Jee], [88, Jnn], [90, Jee], [90, Jnn], [91, Jee], [91, Jnn], [92, Jee], [96, Jee], [97, Jnn], [98, Jnn], [100, Jnn], [104, Jnn], [105, Jee], [105, Jnn], [107, Jee], [107, Jnn], [108, Jee], [108, Jnn], [109, Jnn], [117, Jee], [120, Jee], [121, Jee], [125, Jee], [125, Jnn], [130, Jee], [131, Jee], [131, Jnn], [135, Jee], [136, Jee], [136, Jnn], [152, Jee], [152, Jnn], [153, Jee], [153, Jnn], [154, Jee], [154, Jnn], [155, Jee], [161, Jnn], [170, Jee], [171, Jnn], [172, Jee], [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, Jee], [188, Jnn], [193, Jee], [199, Jnn], [200, Jee], [200, Jnn], [201, Jnn], [202, Jnn], [204, Jee], [209, Jnn], [212, Jee], [212, Jnn], [215, Jee], [215, Jnn], [218, Jee], [218, Jnn], [227, Jee], [231, Jee], [231, Jnn], [232, Jee], [233, Jee], [233, Jnn], [234, Jee], [234, Jnn], [241, Jee], [241, Jnn], [242, Jee], [242, Jnn], [243, Jee], [243, Jnn], [245, Jnn], [246, Jee], [250, Jee], [251, Jee], [252, Jee], [252, Jnn], [255, Jnn], [256, Jee], [256, Jnn], [262, Jee], [262, Jnn], [268, Jnn], [270, Jee], [270, Jnn], [272, Jee], [272, Jnn], [281, Jee], [281, Jnn], [285, Jee], [320, Jee], [320, Jnn], [321, Jee], [321, Jnn], [323, Jee], [323, Jnn], [324, Jee], [324, Jnn], [325, Jee], [325, Jnn], [326, Jee], [326, Jnn], [327, Jee], [327, Jnn], [328, Jee], [328, Jnn], [329, Jee], [329, Jnn], [331, Jee], [331, Jnn], [332, Jee], [332, Jnn], [333, Jee], [333, Jnn], [336, Jee], [336, Jnn], [340, Jee], [340, Jnn]]
Metadata¶
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 33.57 minutes.