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 1860 *.sum.red_avg_zscore.h5 files starting with /mnt/sn1/2460420/zen.2460420.16906.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 1860 *.sum.smooth.calfits files starting with /mnt/sn1/2460420/zen.2460420.16906.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
No description has been provided for this image
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()
No description has been provided for this image

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}.')

22.230% of waterfall flagged to start.
24.453% of waterfall flagged after flagging z > 5.0 outliers.

24.813% 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 2 channels.
	Flagging 18 channels previously flagged 25.00% or more.
	Flagging 432 times previously flagged 10.00% or more.

	Flagging an additional 0 integrations and 2 channels.
	Flagging 1 channels previously flagged 25.00% or more.
	Flagging 0 times previously flagged 10.00% or more.

	Flagging an additional 0 integrations and 1 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.
	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.
33.882% 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
No description has been provided for this image
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
No description has been provided for this image
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.
No description has been provided for this image

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 1860 *.sum.flag_waterfall_round_2.h5 files starting with /mnt/sn1/2460420/zen.2460420.16906.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/sn1/2460420/2460420_aposteriori_flags.yaml
------------------------------------------------------------------------
JD_flags: [[2460420.168943419, 2460420.1799045336], [2460420.1810230147, 2460420.181134863], [2460420.1825888883, 2460420.1827007364], [2460420.1988068637, 2460420.198918712], [2460420.2021623068, 2460420.202497851], [2460420.204399269, 2460420.2046229653], [2460420.20965613, 2460420.209767978], [2460420.2152485354, 2460420.2154722316], [2460420.216814409, 2460420.216926257], [2460420.2184921303, 2460420.2189395227], [2460420.2250911687, 2460420.225538561], [2460420.228782156, 2460420.2292295485], [2460420.2294532447, 2460420.229788789], [2460420.233703473, 2460420.2338153212], [2460420.23504565, 2460420.2352693463], [2460420.2381773973, 2460420.2382892454], [2460420.2384010935, 2460420.2385129416], [2460420.2386247898, 2460420.238736638], [2460420.2407499035, 2460420.2409735997], [2460420.241085448, 2460420.241197296], [2460420.241309144, 2460420.241420992], [2460420.2426513215, 2460420.242986866], [2460420.2489148155, 2460420.2490266636], [2460420.258198208, 2460420.2583100563], [2460420.2639024616, 2460420.2640143097], [2460420.264126158, 2460420.264238006], [2460420.2690474745, 2460420.2691593226], [2460420.269494867, 2460420.269606715], [2460420.271396285, 2460420.271619981], [2460420.272626614, 2460420.2730740067], [2460420.273185855, 2460420.273297703], [2460420.273633247, 2460420.273745095], [2460420.2771005384, 2460420.2773242346], [2460420.283811425, 2460420.284035121], [2460420.2850417537, 2460420.285377298], [2460420.296114716, 2460420.2962265643], [2460420.2963384124, 2460420.2964502606], [2460420.302042666, 2460420.302154514], [2460420.304279628, 2460420.3045033244], [2460420.3108786666, 2460420.311102363], [2460420.3141222615, 2460420.314569654], [2460420.3196028187, 2460420.319714667], [2460420.319826515, 2460420.320050211], [2460420.3266492495, 2460420.326984794], [2460420.327991427, 2460420.3285506675], [2460420.3286625156, 2460420.3287743637], [2460420.328886212, 2460420.329221756], [2460420.3326890473, 2460420.3329127436], [2460420.335597098, 2460420.335932642], [2460420.3370511234, 2460420.3371629715], [2460420.342755377, 2460420.342867225], [2460420.343985706, 2460420.3443212504], [2460420.347341149, 2460420.347452997], [2460420.350920289, 2460420.351032137], [2460420.370046315, 2460420.370270011], [2460420.3704937072, 2460420.3707174035], [2460420.371164796, 2460420.371276644], [2460420.372171429, 2460420.372395125], [2460420.3735136064, 2460420.3736254545], [2460420.3840273283, 2460420.3841391765], [2460420.3852576576, 2460420.3853695057], [2460420.3865998345, 2460420.3867116827], [2460420.387942012, 2460420.388165708], [2460420.3895078856, 2460420.3896197337], [2460420.394317354, 2460420.394429202], [2460420.405725861, 2460420.405837709], [2460420.4084102158, 2460420.408522064], [2460420.4104234814, 2460420.4105353295], [2460420.411877507, 2460420.412101203], [2460420.4132196843, 2460420.4134433805], [2460420.441405407, 2460420.4415172553], [2460420.44476085, 2460420.4450963945], [2460420.4462148757, 2460420.446326724], [2460420.4495703187, 2460420.449682167], [2460420.4639987247, 2460420.464110573], [2460420.4742887504, 2460420.4745124467], [2460420.481111485, 2460420.481223333], [2460420.48256551, 2460420.482677358], [2460420.4903948777, 2460420.490506726], [2460420.492855536, 2460420.492967384], [2460420.4936384726, 2460420.4937503207], [2460420.4952043463, 2460420.4953161944], [2460420.5019152327, 2460420.502027081], [2460420.5154488534, 2460420.5155607015], [2460420.5165673345, 2460420.5167910308], [2460420.518804297, 2460420.519139841], [2460420.522271588, 2460420.522383436], [2460420.5228308286, 2460420.528311386], [2460420.5290943226, 2460420.531554981], [2460420.5317786774, 2460420.5318905255], [2460420.5325616137, 2460420.532673462], [2460420.532897158, 2460420.5333445505], [2460420.533903791, 2460420.5351341204], [2460420.5352459685, 2460420.5353578166], [2460420.535693361, 2460420.535805209], [2460420.5360289053, 2460420.5361407534], [2460420.536699994, 2460420.536811842], [2460420.5370355383, 2460420.540055437], [2460420.540167285, 2460420.5402791332], [2460420.540838374, 2460420.54106207], [2460420.5415094625, 2460420.5416213106], [2460420.5488914377, 2460420.549003286], [2460420.55168764, 2460420.585018376]]

freq_flags: [[49911499.0234375, 50033569.3359375], [53695678.7109375, 53817749.0234375], [62240600.5859375, 62728881.8359375], [66268920.8984375, 66390991.2109375], [69931030.2734375, 70053100.5859375], [73959350.5859375, 75057983.3984375], [77621459.9609375, 78109741.2109375], [87265014.6484375, 108261108.3984375], [109970092.7734375, 110092163.0859375], [113265991.2109375, 113388061.5234375], [116073608.3984375, 116195678.7109375], [116439819.3359375, 116561889.6484375], [116683959.9609375, 116806030.2734375], [119857788.0859375, 119979858.3984375], [124618530.2734375, 125228881.8359375], [129989624.0234375, 130111694.3359375], [136337280.2734375, 136459350.5859375], [136947631.8359375, 137313842.7734375], [137435913.0859375, 138046264.6484375], [138656616.2109375, 138778686.5234375], [141464233.3984375, 141586303.7109375], [141708374.0234375, 141830444.3359375], [142074584.9609375, 142318725.5859375], [142929077.1484375, 143051147.4609375], [143783569.3359375, 144027709.9609375], [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], [171737670.8984375, 171859741.2109375], [175155639.6484375, 175399780.2734375], [181137084.9609375, 181381225.5859375], [187362670.8984375, 187606811.5234375], [189926147.4609375, 190048217.7734375], [191146850.5859375, 191513061.5234375], [197128295.8984375, 197372436.5234375], [198104858.3984375, 198226928.7109375], [199203491.2109375, 199325561.5234375], [201766967.7734375, 201889038.0859375], [204940795.8984375, 205062866.2109375], [208480834.9609375, 208724975.5859375], [209945678.7109375, 210067749.0234375], [212142944.3359375, 212265014.6484375], [215194702.1484375, 215316772.4609375], [220687866.2109375, 220809936.5234375], [222885131.8359375, 223617553.7109375], [227401733.3984375, 227523803.7109375], [227645874.0234375, 227767944.3359375], [229110717.7734375, 229354858.3984375], [229965209.9609375, 230087280.2734375], [231063842.7734375, 231185913.0859375], [232772827.1484375, 232894897.4609375], [233139038.0859375, 233383178.7109375], [233505249.0234375, 233749389.6484375], [233871459.9609375, 233993530.2734375]]

ex_ants: [[3, Jnn], [7, Jee], [18, Jee], [18, Jnn], [21, Jee], [21, Jnn], [27, Jee], [27, Jnn], [28, Jee], [28, Jnn], [31, Jnn], [32, Jnn], [34, Jee], [34, Jnn], [35, Jnn], [37, Jee], [40, Jnn], [46, Jee], [47, Jee], [47, Jnn], [51, Jee], [53, Jnn], [54, Jee], [54, Jnn], [55, Jee], [61, Jee], [61, Jnn], [63, Jee], [63, Jnn], [64, Jee], [64, Jnn], [66, Jnn], [68, Jnn], [77, Jee], [77, Jnn], [78, Jee], [78, Jnn], [81, Jee], [81, Jnn], [86, Jee], [86, Jnn], [87, Jee], [88, Jee], [88, Jnn], [90, Jee], [90, Jnn], [92, Jee], [93, Jee], [93, Jnn], [98, Jnn], [99, Jee], [100, Jnn], [104, Jnn], [107, Jee], [107, Jnn], [109, Jnn], [112, Jee], [115, Jee], [115, Jnn], [119, Jee], [119, Jnn], [130, Jee], [130, Jnn], [131, Jnn], [142, Jnn], [161, Jnn], [162, Jee], [163, Jee], [163, Jnn], [170, Jee], [171, Jnn], [176, Jee], [176, Jnn], [177, Jee], [177, Jnn], [178, Jee], [178, Jnn], [180, Jnn], [182, Jnn], [183, Jee], [183, Jnn], [194, Jee], [194, Jnn], [196, Jee], [196, Jnn], [199, Jnn], [200, Jee], [200, Jnn], [202, Jnn], [204, Jnn], [208, Jee], [212, Jnn], [213, Jee], [217, Jee], [217, Jnn], [218, Jnn], [229, Jee], [231, Jnn], [232, Jee], [241, Jee], [241, Jnn], [242, Jee], [242, Jnn], [243, Jee], [243, Jnn], [245, Jnn], [246, Jee], [246, Jnn], [250, Jee], [251, Jee], [255, Jee], [255, Jnn], [261, Jee], [261, Jnn], [262, Jee], [262, Jnn], [266, Jee], [269, Jnn], [270, Jee], [270, Jnn], [272, Jee], [272, Jnn], [281, 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.dev6+gf0cfd8d
hera_qm: 2.1.3.dev5+g3e71720
hera_filters: 0.1.5

hera_notebook_templates: 0.1.dev734+g90f16f4
pyuvdata: 2.4.2
In [25]:
print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')

Finished execution in 45.99 minutes.