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 1944 *.sum.red_avg_zscore.h5 files starting with /mnt/sn1/data1/2460593/zen.2460593.25239.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 1944 *.sum.smooth.calfits files starting with /mnt/sn1/data1/2460593/zen.2460593.25239.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}.')

28.966% of waterfall flagged to start.
30.265% of waterfall flagged after flagging z > 5.0 outliers.

30.713% 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 5 channels.
	Flagging 13 channels previously flagged 25.00% or more.
	Flagging 192 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 4 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.
35.217% 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/data1/2460593/zen.2460593.25239.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460593/zen.2460593.25261.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460593/zen.2460593.25284.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460593/zen.2460593.25306.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460593/zen.2460593.25328.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460593/zen.2460593.25351.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460593/zen.2460593.25373.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460593/zen.2460593.25396.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460593/zen.2460593.25418.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460593/zen.2460593.25440.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/data1/2460593/2460593_aposteriori_flags.yaml
------------------------------------------------------------------------------
JD_flags: [[2460593.2522773845, 2460593.252612929], [2460593.2530603213, 2460593.253507714], [2460593.2547380426, 2460593.2556328275], [2460593.25608022, 2460593.2564157643], [2460593.25786979, 2460593.257981638], [2460593.258093486, 2460593.258876423], [2460593.259547511, 2460593.2598830555], [2460593.2601067517, 2460593.260442296], [2460593.265811005, 2460593.2659228533], [2460593.268383512, 2460593.2688309043], [2460593.270732322, 2460593.27084417], [2460593.270956018, 2460593.2712915624], [2460593.2733048284, 2460593.2734166766], [2460593.2735285247, 2460593.273752221], [2460593.273864069, 2460593.273975917], [2460593.280239411, 2460593.281246044], [2460593.2814697404, 2460593.2816934367], [2460593.286279209, 2460593.286391057], [2460593.2866147533, 2460593.2867266014], [2460593.2890754114, 2460593.2897465], [2460593.2901938925, 2460593.2903057407], [2460593.290641285, 2460593.290753133], [2460593.292542703, 2460593.292654551], [2460593.2931019436, 2460593.2934374874], [2460593.2954507535, 2460593.295786298], [2460593.3080895897, 2460593.308201438], [2460593.3087606784, 2460593.3088725265], [2460593.3096554633, 2460593.3098791596], [2460593.3149123243, 2460593.3150241724], [2460593.315583413, 2460593.316030805], [2460593.317820375, 2460593.317932223], [2460593.318044071, 2460593.3183796154], [2460593.3196099447, 2460593.319833641], [2460593.3201691853, 2460593.3202810334], [2460593.3205047296, 2460593.320728426], [2460593.320840274, 2460593.320952122], [2460593.3212876664, 2460593.32934073], [2460593.330571059, 2460593.3306829073], [2460593.3310184516, 2460593.3311302997], [2460593.3321369328, 2460593.332472477], [2460593.3333672616, 2460593.333590958], [2460593.333814654, 2460593.3340383503], [2460593.3341501984, 2460593.3343738946], [2460593.3344857427, 2460593.3351568314], [2460593.3352686795, 2460593.3353805277], [2460593.335492376, 2460593.33582792], [2460593.3359397682, 2460593.337505642], [2460593.33761749, 2460593.337729338], [2460593.337841186, 2460593.3379530343], [2460593.3380648824, 2460593.338847819], [2460593.3392952112, 2460593.3395189075], [2460593.3397426037, 2460593.339854452], [2460593.3399663, 2460593.340078148], [2460593.340189996, 2460593.3403018443], [2460593.3404136924, 2460593.3405255405], [2460593.3406373886, 2460593.341867718], [2460593.341979566, 2460593.3422032623], [2460593.343769136, 2460593.3439928317], [2460593.344216528, 2460593.344328376], [2460593.344552072, 2460593.3446639203], [2460593.3456705534, 2460593.3457824015], [2460593.3460060977, 2460593.346117946], [2460593.346341642, 2460593.34645349], [2460593.3469008827, 2460593.347012731], [2460593.3540591616, 2460593.3541710097], [2460593.3547302503, 2460593.3549539465], [2460593.3617766807, 2460593.361888529], [2460593.3633425543, 2460593.3634544024], [2460593.363789947, 2460593.364125491], [2460593.3675927822, 2460593.3677046304], [2460593.372849643, 2460593.3729614913], [2460593.373744428, 2460593.3739681244], 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[2460593.444656128, 2460593.444767976], [2460593.4464456975, 2460593.4465575456], [2460593.447116786, 2460593.4472286343], [2460593.448011571, 2460593.4484589635], [2460593.4491300522, 2460593.4493537485], [2460593.460426711, 2460593.460538559], [2460593.4672494456, 2460593.467473142], [2460593.4707167367, 2460593.471052281], [2460593.4714996736, 2460593.47172337], [2460593.4726181547, 2460593.472730003], [2460593.47642099, 2460593.4766446864], [2460593.476980231, 2460593.477092079], [2460593.477203927, 2460593.4775394714], [2460593.4812304587, 2460593.481566003], [2460593.4817896993, 2460593.4820133955], [2460593.4822370918, 2460593.48234894], [2460593.4864873197, 2460593.486711016], [2460593.4889479782, 2460593.4890598264], [2460593.4907375476, 2460593.4908493957], [2460593.491296788, 2460593.4914086363], [2460593.4916323326, 2460593.4917441807], [2460593.491967877, 2460593.492079725], [2460593.4952114723, 2460593.4970010417], [2460593.4994617, 2460593.5006920295], [2460593.50181051, 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ex_ants: [[5, Jee], [7, Jee], [8, Jee], [8, Jnn], [9, Jee], [10, Jee], [15, Jnn], [18, Jee], [18, Jnn], [20, Jee], [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], [36, Jee], [36, Jnn], [37, Jnn], [40, Jnn], [42, Jnn], [45, Jee], [45, Jnn], [46, Jee], [46, Jnn], [47, Jee], [47, Jnn], [48, Jee], [48, Jnn], [49, Jee], [49, Jnn], [51, Jee], [54, Jnn], [55, Jee], [61, Jee], [61, Jnn], [62, Jee], [62, Jnn], [63, Jee], [63, Jnn], [64, Jee], [64, Jnn], [69, Jee], [73, Jee], [73, Jnn], [77, Jee], [77, Jnn], [78, Jee], [78, Jnn], [80, Jnn], [82, Jnn], [84, Jnn], [85, Jnn], [86, Jee], [86, Jnn], [87, Jee], [87, Jnn], [88, Jee], [88, Jnn], [90, Jee], [90, Jnn], [92, Jee], [92, Jnn], [93, Jee], [96, Jee], [97, Jnn], [98, Jnn], [100, Jee], [100, Jnn], [102, Jnn], [104, Jee], [104, Jnn], [107, Jee], [107, Jnn], [109, Jnn], [120, Jee], [120, Jnn], [121, Jee], [121, Jnn], [125, Jee], [125, Jnn], [127, Jee], [130, Jee], [130, Jnn], [131, Jee], [132, Jee], [132, Jnn], [134, Jee], [136, Jee], [136, Jnn], [137, Jee], [142, Jnn], [144, Jee], [144, Jnn], [145, Jnn], [155, Jee], [155, Jnn], [161, Jnn], [170, Jee], [171, Jnn], [175, Jnn], [176, Jee], [176, Jnn], [177, Jee], [177, Jnn], [178, Jee], [178, Jnn], [179, Jee], [179, Jnn], [180, Jee], [180, Jnn], [181, Jnn], [182, Jee], [184, Jee], [188, Jnn], [189, Jee], [189, Jnn], [193, Jee], [194, Jee], [199, Jnn], [200, Jee], [200, Jnn], [201, Jnn], [202, Jnn], [204, Jee], [204, Jnn], [206, Jee], [208, Jee], [212, Jnn], [213, Jee], [213, Jnn], [215, Jee], [215, Jnn], [216, Jee], [218, Jnn], [232, Jee], [235, Jee], [240, Jee], [240, Jnn], [241, Jee], [241, Jnn], [242, Jee], [242, Jnn], [243, Jee], [243, Jnn], [245, Jnn], [246, Jee], [250, Jee], [251, Jee], [253, Jnn], [255, Jnn], [256, Jee], [256, Jnn], [262, Jee], [262, Jnn], [268, Jnn], [270, Jee], [270, Jnn], [272, Jee], [281, Jee], [281, Jnn], [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: 3.6.2.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

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

Finished execution in 284.19 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 ¶