import time
tstart = time.time()

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'

# 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
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 1532 *.sum.red_avg_zscore.h5 files starting with /mnt/sn1/data2/2460466/zen.2460466.16905.sum.red_avg_zscore.h5.

# 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 1532 *.sum.smooth.calfits files starting with /mnt/sn1/data2/2460466/zen.2460466.16905.sum.smooth.calfits.

assert len(zscore_files) == len(cal_files)

# 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}

freqs = uvf.freq_array
times = uvf.time_array

extent = [freqs[0] / 1e6, freqs[-1] / 1e6, times[-1] - int(times[0]), times[0] - int(times[0])]

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()

plot_max_z_score(zscore)

All-NaN axis encountered

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()

plot_histogram()

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

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}.')

30.423% of waterfall flagged to start.
36.582% of waterfall flagged after flagging z > 5.0 outliers.

37.119% 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 1 integrations and 5 channels.
	Flagging 160 channels previously flagged 25.00% or more.
	Flagging 508 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 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.
51.594% of waterfall flagged after flagging whole times and channels with average z > 1.0.

plot_max_z_score(zscore, flags=flags)

All-NaN axis encountered

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()

zscore_spectra()

Mean of empty slice
Mean of empty slice

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()

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.

add_to_history = 'by full_day_rfi_round_2 notebook with the following environment:\n' + '=' * 65 + '\n' + os.popen('conda env export').read() + '=' * 65

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)}.')

File /mnt/sn1/data2/2460466/zen.2460466.16905.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data2/2460466/zen.2460466.16927.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data2/2460466/zen.2460466.16950.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data2/2460466/zen.2460466.16972.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data2/2460466/zen.2460466.16994.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data2/2460466/zen.2460466.17017.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data2/2460466/zen.2460466.17039.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data2/2460466/zen.2460466.17061.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data2/2460466/zen.2460466.17084.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data2/2460466/zen.2460466.17106.sum.flag_waterfall_round_2.h5 exists; clobbering

# 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/data2/2460466/2460466_aposteriori_flags.yaml
------------------------------------------------------------------------------
JD_flags: [[2460466.168936099, 2460466.169159795], [2460466.1697190357, 2460466.169942732], [2460466.170166428, 2460466.1703901244], [2460466.1705019725, 2460466.1706138207], [2460466.170837517, 2460466.1713967575], [2460466.171508605, 2460466.1716204532], [2460466.1720678457, 2460466.172291542], [2460466.17240339, 2460466.1728507825], [2460466.173410023, 2460466.1736337193], [2460466.1739692637, 2460466.174081112], [2460466.17419296, 2460466.174416656], [2460466.1747522005, 2460466.1749758967], [2460466.175087745, 2460466.175311441], [2460466.1756469854, 2460466.1757588335], [2460466.176206226, 2460466.176318074], [2460466.176989163, 2460466.177101011], [2460466.177436555, 2460466.177548403], [2460466.1779957954, 2460466.1781076435], [2460466.178555036, 2460466.178666884], [2460466.1791142765, 2460466.1792261247], [2460466.179449821, 2460466.1797853652], [2460466.1798972134, 2460466.1802327577], [2460466.180344606, 2460466.1809038464], [2460466.1811275426, 2460466.181463087], [2460466.182246024, 2460466.1826934163], [2460466.1833645045, 2460466.185713315], [2460466.185825163, 2460466.1860488593], [2460466.1861607074, 2460466.1863844036], [2460466.1864962517, 2460466.186831796], [2460466.1870554923, 2460466.1871673404], [2460466.1873910367, 2460466.187502885], [2460466.1885095173, 2460466.1888450617], [2460466.18895691, 2460466.189068758], [2460466.1894043023, 2460466.1896279985], [2460466.1906346316, 2460466.190858328], [2460466.190970176, 2460466.1914175684], [2460466.1923123533, 2460466.1924242014], [2460466.192871594, 2460466.19309529], [2460466.193207138, 2460466.1933189863], [2460466.193766379, 2460466.193878227], [2460466.195220404, 2460466.195332252], [2460466.195555948, 2460466.1956677963], [2460466.1968981256, 2460466.197121822], [2460466.1979047586, 2460466.1980166067], [2460466.198463999, 2460466.1985758473], [2460466.1989113917, 2460466.1995824804], [2460466.199806176, 2460466.2000298724], [2460466.200700961, 2460466.200812809], [2460466.2011483535, 2460466.201483898], [2460466.201595746, 2460466.2019312903], [2460466.202714227, 2460466.2030497715], [2460466.2032734677, 2460466.203385316], [2460466.2040564045, 2460466.2041682526], [2460466.204503797, 2460466.204727493], [2460466.2048393413, 2460466.205286734], [2460466.205845974, 2460466.205957822], [2460466.20606967, 2460466.2061815183], [2460466.2084184806, 2460466.208642177], [2460466.208977721, 2460466.2090895693], [2460466.2095369617, 2460466.20964881], [2460466.209760658, 2460466.209984354], [2460466.210767291, 2460466.210879139], [2460466.211326531, 2460466.2114383793], [2460466.2115502274, 2460466.2117739236], [2460466.2136753416, 2460466.2137871897], [2460466.2143464303, 2460466.2144582784], [2460466.215017519, 2460466.215129367], [2460466.2155767595, 2460466.2156886077], [2460466.216024152, 2460466.216136], [2460466.2162478482, 2460466.2168070883], [2460466.2169189365, 2460466.2170307846], [2460466.217478177, 2460466.2177018733], [2460466.2181492657, 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[2460466.2374989884, 2460466.2376108365], [2460466.238058229, 2460466.238170077], [2460466.238281925, 2460466.2383937733], [2460466.2398477984, 2460466.2399596465], [2460466.2418610645, 2460466.2419729126], [2460466.242420305, 2460466.2428676975], [2460466.2453283556, 2460466.2454402037], [2460466.246894229, 2460466.2470060773], [2460466.247565318, 2460466.2479008622], [2460466.248571951, 2460466.248683799], [2460466.248795647, 2460466.2489074953], [2460466.2492430396, 2460466.2493548878], [2460466.249466736, 2460466.249690432], [2460466.2502496727, 2460466.2505852166], [2460466.250920761, 2460466.251144457], [2460466.2512563053, 2460466.2513681534], [2460466.25215109, 2460466.2522629383], [2460466.253045875, 2460466.253157723], [2460466.25394066, 2460466.254052508], [2460466.2546117487, 2460466.254723597], [2460466.25628947, 2460466.256401318], [2460466.258414584, 2460466.2585264323], [2460466.259085673, 2460466.259197521], [2460466.2627766603, 2460466.2630003565], [2460466.265461015, 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freq_flags: [[46981811.5234375, 56259155.2734375], [59921264.6484375, 60409545.8984375], [60653686.5234375, 61019897.4609375], [62118530.2734375, 63095092.7734375], [63461303.7109375, 63705444.3359375], [65170288.0859375, 70419311.5234375], [87387084.9609375, 108627319.3359375], [108993530.2734375, 117050170.8984375], [117172241.2109375, 117538452.1484375], [117782592.7734375, 118026733.3984375], [118515014.6484375, 118637084.9609375], [124862670.8984375, 125228881.8359375], [127548217.7734375, 127670288.0859375], [129989624.0234375, 130111694.3359375], [136337280.2734375, 136459350.5859375], [136825561.5234375, 138290405.2734375], [138656616.2109375, 138778686.5234375], [141464233.3984375, 141586303.7109375], [141708374.0234375, 141830444.3359375], [142074584.9609375, 142318725.5859375], [142929077.1484375, 143051147.4609375], [143783569.3359375, 144027709.9609375], [145736694.3359375, 145980834.9609375], [147445678.7109375, 147567749.0234375], [149887084.9609375, 150009155.2734375], [154159545.8984375, 154403686.5234375], [169906616.2109375, 170150756.8359375], [170883178.7109375, 171005249.0234375], [171737670.8984375, 171859741.2109375], [175155639.6484375, 175277709.9609375], [181137084.9609375, 181259155.2734375], [183212280.2734375, 183334350.5859375], [187362670.8984375, 187606811.5234375], [189926147.4609375, 190048217.7734375], [191146850.5859375, 191513061.5234375], [197128295.8984375, 197372436.5234375], [198104858.3984375, 198348999.0234375], [199203491.2109375, 199325561.5234375], [201644897.4609375, 201889038.0859375], [204940795.8984375, 205062866.2109375], [207138061.5234375, 207260131.8359375], [208480834.9609375, 208724975.5859375], [209945678.7109375, 210067749.0234375], [212142944.3359375, 212265014.6484375], [215194702.1484375, 215316772.4609375], [220565795.8984375, 220809936.5234375], [223007202.1484375, 223495483.3984375], [227401733.3984375, 227523803.7109375], [227645874.0234375, 227767944.3359375], [229110717.7734375, 229354858.3984375], [229965209.9609375, 230087280.2734375], [231063842.7734375, 231185913.0859375]]

ex_ants: [[3, Jnn], [8, Jee], [8, Jnn], [9, Jnn], [10, Jnn], [15, Jnn], [18, Jee], [18, Jnn], [22, Jnn], [27, Jee], [27, Jnn], [28, Jee], [28, Jnn], [29, Jnn], [31, Jnn], [33, Jee], [34, Jee], [35, Jnn], [37, Jee], [37, Jnn], [40, Jnn], [45, Jee], [46, Jee], [47, Jee], [47, Jnn], [51, Jee], [54, Jnn], [61, Jee], [61, Jnn], [63, Jee], [63, Jnn], [64, Jee], [64, Jnn], [69, Jee], [73, Jee], [73, Jnn], [77, Jee], [77, Jnn], [78, Jee], [78, Jnn], [81, Jnn], [82, Jnn], [86, Jee], [86, Jnn], [87, Jee], [88, Jee], [88, Jnn], [90, Jee], [90, Jnn], [92, Jee], [96, Jnn], [97, Jnn], [104, Jnn], [107, Jee], [107, Jnn], [109, Jnn], [111, Jee], [112, Jnn], [115, Jnn], [116, Jnn], [117, Jee], [117, Jnn], [119, Jnn], [131, Jnn], [134, Jnn], [136, Jnn], [154, Jnn], [160, Jnn], [161, Jnn], [166, Jnn], [168, Jee], [168, Jnn], [169, Jnn], [170, Jee], [171, Jnn], [172, Jnn], [173, Jnn], [174, Jnn], [175, Jnn], [176, Jee], [176, Jnn], [177, Jee], [177, Jnn], [178, Jee], [178, Jnn], [180, Jnn], [183, Jee], [183, Jnn], [188, Jnn], [195, Jnn], [196, Jee], [196, Jnn], [197, Jnn], [199, Jee], [199, Jnn], [200, Jee], [200, Jnn], [202, Jnn], [204, Jnn], [209, Jnn], [212, Jee], [212, Jnn], [215, Jnn], [217, Jee], [218, Jee], [218, Jnn], [229, Jee], [231, Jee], [231, Jnn], [232, Jee], [232, Jnn], [235, Jee], [235, Jnn], [241, Jee], [241, Jnn], [242, Jee], [242, Jnn], [243, Jee], [243, Jnn], [245, Jnn], [246, Jee], [251, Jee], [252, Jnn], [255, Jee], [255, Jnn], [262, Jnn], [266, Jee], [267, Jnn], [268, 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]]

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.dev110+gcc0a13d
hera_qm: 2.1.5.dev6+g23b7cf7
hera_filters: 0.1.5

hera_notebook_templates: 0.1.dev734+g90f16f4
pyuvdata: 2.4.2

print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')

Finished execution in 44.88 minutes.

Second Round of Full Day RFI Flagging¶

• 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 ¶

Load z-scores¶

Figure 1: Waterfall of Maximum z-Score of Either Polarization Before Round 2 Flagging¶

Figure 2: Histogram of z-scores¶

Perform flagging¶

Show results of flagging¶

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¶

Save results¶

Metadata¶

Second Round of Full Day RFI Flagging¶

• 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¶

Load z-scores¶

Figure 1: Waterfall of Maximum z-Score of Either Polarization Before Round 2 Flagging¶

Figure 2: Histogram of z-scores¶

Perform flagging¶

Show results of flagging¶

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¶

Save results¶

Metadata¶

• 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 ¶