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 hera_filters import dspec

from IPython.display import display, HTML
%matplotlib inline
display(HTML("<style>.container { width:100% !important; }</style>"))
_ = np.seterr(all='ignore')  # get rid of red warnings
%config InlineBackend.figure_format = 'retina'

# get input data file names
SUM_FILE = os.environ.get("SUM_FILE", None)
# SUM_FILE = '/lustre/aoc/projects/hera/h6c-analysis/IDR2/2459861/zen.2459861.25297.sum.uvh5'
SUM_SUFFIX = os.environ.get("SUM_SUFFIX", 'sum.uvh5')

# get input and output suffixes
SMOOTH_CAL_SUFFIX = os.environ.get("SMOOTH_CAL_SUFFIX", 'sum.smooth.calfits')
ZSCORE_SUFFIX =  os.environ.get("ZSCORE_SUFFIX", 'sum.red_avg_zscore.h5')
FLAG_WATERFALL2_SUFFIX = os.environ.get("FLAG_WATERFALL2_SUFFIX", 'sum.flag_waterfall_round_2.h5')
OUT_YAML_SUFFIX = os.environ.get("OUT_YAML_SUFFIX", '_aposteriori_flags.yaml')
OUT_YAML_DIR = os.environ.get("OUT_YAML_DIR", None)

# build globs
sum_glob = '.'.join(SUM_FILE.split('.')[:-3]) + '.*.' + SUM_SUFFIX
cal_files_glob = sum_glob.replace(SUM_SUFFIX, SMOOTH_CAL_SUFFIX)
zscore_glob = sum_glob.replace(SUM_SUFFIX, ZSCORE_SUFFIX)

# build out yaml file
if OUT_YAML_DIR is None:
    OUT_YAML_DIR = os.path.dirname(SUM_FILE)
out_yaml_file = os.path.join(OUT_YAML_DIR, SUM_FILE.split('.')[-4] + OUT_YAML_SUFFIX)    

# get flagging parameters
Z_THRESH = float(os.environ.get("Z_THRESH", 4))
WS_Z_THRESH = float(os.environ.get("WS_Z_THRESH", 2))
AVG_Z_THRESH = float(os.environ.get("AVG_Z_THRESH", 1))
MAX_FREQ_FLAG_FRAC = float(os.environ.get("MAX_FREQ_FLAG_FRAC", .25))
MAX_TIME_FLAG_FRAC = float(os.environ.get("MAX_TIME_FLAG_FRAC", .1))
AVG_SPECTRUM_FILTER_DELAY = float(os.environ.get("AVG_SPECTRUM_FILTER_DELAY", 250)) # in ns
EIGENVAL_CUTOFF = float(os.environ.get("EIGENVAL_CUTOFF", 1e-12))
TIME_AVG_DELAY_FILT_SNR_THRESH = float(os.environ.get("TIME_AVG_DELAY_FILT_SNR_THRESH", 4.0))
TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE = float(os.environ.get("TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE", 1.5))

for setting in ['Z_THRESH', 'WS_Z_THRESH', 'AVG_Z_THRESH', 'MAX_FREQ_FLAG_FRAC', 'MAX_TIME_FLAG_FRAC', 'AVG_SPECTRUM_FILTER_DELAY',
               'EIGENVAL_CUTOFF', 'TIME_AVG_DELAY_FILT_SNR_THRESH', 'TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE']:
    print(f'{setting} = {eval(setting)}')

Z_THRESH = 4.0
WS_Z_THRESH = 2.0
AVG_Z_THRESH = 1.0
MAX_FREQ_FLAG_FRAC = 0.25
MAX_TIME_FLAG_FRAC = 0.1
AVG_SPECTRUM_FILTER_DELAY = 250.0
EIGENVAL_CUTOFF = 1e-12
TIME_AVG_DELAY_FILT_SNR_THRESH = 4.0
TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE = 1.5

# load z-scores
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 1851 *.sum.red_avg_zscore.h5 files starting with /mnt/sn1/data1/2460637/zen.2460637.25248.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 1851 *.sum.smooth.calfits files starting with /mnt/sn1/data1/2460637/zen.2460637.25248.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, vmin=-5, vmax=5):
    if flags is None:
        flags = np.any(~np.isfinite(list(zscore.values())), axis=0)
    plt.figure(figsize=(14,10), dpi=100)
    plt.imshow(np.where(flags, np.nan, np.nanmax([zscore['ee'], zscore['nn']], axis=0)), aspect='auto', 
               cmap='coolwarm', interpolation='none', vmin=vmin, vmax=vmax, extent=extent)
    plt.colorbar(location='top', label='Max z-score of either polarization', extend='both', aspect=40, pad=.02)
    plt.xlabel('Frequency (MHz)')
    plt.ylabel(f'JD - {int(times[0])}')
    plt.tight_layout()

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_func=np.nanmean, avg_z_thresh=AVG_Z_THRESH, verbose=True):
    '''Flag whole integrations or channels based on average z-score. This is done
    iteratively to prevent bad times affecting channel averages or vice versa.'''
    flagged_chan_count = 0
    flagged_int_count = 0
    while True:
        zspec = avg_func(np.where(flags, np.nan, zscore), axis=0)
        ztseries = avg_func(np.where(flags, np.nan, zscore), axis=1)

        if (np.nanmax(zspec) < avg_z_thresh) and (np.nanmax(ztseries) < avg_z_thresh):
            break

        if np.nanmax(zspec) >= np.nanmax(ztseries):
            flagged_chan_count += np.sum((zspec >= np.nanmax(ztseries)) & (zspec >= avg_z_thresh))
            flags[:, (zspec >= np.nanmax(ztseries)) & (zspec >= avg_z_thresh)] = True
        else:
            flagged_int_count += np.sum((ztseries >= np.nanmax(zspec)) & (ztseries >= avg_z_thresh))
            flags[(ztseries >= np.nanmax(zspec)) & (ztseries >= avg_z_thresh), :] = True

    if verbose:
        print(f'\tFlagging an additional {flagged_int_count} integrations and {flagged_chan_count} channels.')

def impose_max_chan_flag_frac(flags, max_flag_frac=MAX_FREQ_FLAG_FRAC, verbose=True):
    '''Flag channels already flagged more than max_flag_frac (excluding completely flagged times).'''
    unflagged_times = ~np.all(flags, axis=1)
    frequently_flagged_chans =  np.mean(flags[unflagged_times, :], axis=0) >= max_flag_frac
    if verbose:
        print(f'\tFlagging {np.sum(frequently_flagged_chans) - np.sum(np.all(flags, axis=0))} channels previously flagged {max_flag_frac:.2%} or more.')        
    flags[:, frequently_flagged_chans] = True 
        
def impose_max_time_flag_frac(flags, max_flag_frac=MAX_TIME_FLAG_FRAC, verbose=True):
    '''Flag times already flagged more than max_flag_frac (excluding completely flagged channels).'''
    unflagged_chans = ~np.all(flags, axis=0)
    frequently_flagged_times =  np.mean(flags[:, unflagged_chans], axis=1) >= max_flag_frac
    if verbose:
        print(f'\tFlagging {np.sum(frequently_flagged_times) - np.sum(np.all(flags, axis=1))} times previously flagged {max_flag_frac:.2%} or more.')
    flags[frequently_flagged_times, :] = True

def time_avg_zscore_dly_filt_SNRs(flags, filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF):
    """Produces SNRs after time-averaging z-scores and delay filtering, accounting for flagging's effect on the filter."""
    # figure out high and low band based on FM gap at 100 MHz
    flagged_stretches = true_stretches(np.all(flags, axis=0))
    FM_gap = [fs for fs in flagged_stretches if fs.start <= np.argmin(np.abs(freqs - 100e6)) < fs.stop][0]
    low_band = slice((0 if flagged_stretches[0].start != 0 else flagged_stretches[0].stop), FM_gap.start)
    high_band = slice(FM_gap.stop, (len(freqs) if flagged_stretches[-1].stop != len(freqs) else flagged_stretches[-1].start))
    
    filt_SNR = {}
    for pol in zscore:
        # calculate timeavg_SNR and filter
        noise_prediction = 1.0 / np.sum(~flags, axis=0)**.5
        timeavg_SNR = np.nanmean(np.where(flags, np.nan, zscore[pol] / noise_prediction), axis=0) 
        wgts = np.where(np.isfinite(timeavg_SNR), 1, 0)
        model = np.zeros_like(timeavg_SNR)
        for band in [low_band, high_band]:
            model[band], _, _ = dspec.fourier_filter(freqs[band], np.where(np.isfinite(timeavg_SNR[band]), timeavg_SNR[band], 0),
                                                     wgts[band], [0], [AVG_SPECTRUM_FILTER_DELAY / 1e9], mode="dpss_solve", 
                                                     eigenval_cutoff=[EIGENVAL_CUTOFF], suppression_factors=[EIGENVAL_CUTOFF])
        filt_SNR[pol] = timeavg_SNR - model

        # correct for impact of filter
        correction_factors = np.ones_like(wgts) * np.nan
        for band in [low_band, high_band]:
            X = dspec.dpss_operator(freqs[band], [0], filter_half_widths=[AVG_SPECTRUM_FILTER_DELAY / 1e9], eigenval_cutoff=[EIGENVAL_CUTOFF])[0]
            W = wgts[band]
            leverage = np.diag(X @ np.linalg.pinv(np.dot(X.T * W, X)) @ (X.T * W))
            correction_factors[band] = np.where(leverage > 0, (1 - leverage)**.5, np.nan) # because the underlying data should be gaussian
        filt_SNR[pol] /= correction_factors
    
    return filt_SNR

def iteratively_flag_on_delay_filtered_time_avg_zscore(flags, thresh=TIME_AVG_DELAY_FILT_SNR_THRESH, dynamic_range=TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE,
                                                       filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF):
    """Flag whole channels based on their outlierness after delay-filterd time-averaged zscores.
    This is done iteratively since the delay filter can be unduly influenced by large outliers."""
    filt_SNR = time_avg_zscore_dly_filt_SNRs(flags, filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF)
    while True:
        largest_SNR = np.nanmax(list(filt_SNR.values()))
        if largest_SNR < thresh:
            break
        # 
        cut = np.max([thresh, largest_SNR / dynamic_range])
        for pol in filt_SNR:
            flags[:, filt_SNR[pol] > cut] = True
        filt_SNR = time_avg_zscore_dly_filt_SNRs(flags, filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF)

flags = np.any(~np.isfinite(list(zscore.values())), axis=0)
print(f'{np.mean(flags):.3%} of waterfall flagged to start.')

# flag whole integrations or channels using outliers in median
while True:
    nflags = np.sum(flags)
    for pol in ['ee', 'nn']:    
        iteratively_flag_on_averaged_zscore(flags, zscore[pol], avg_func=np.nanmedian, avg_z_thresh=AVG_Z_THRESH, verbose=True)
        impose_max_chan_flag_frac(flags, max_flag_frac=MAX_FREQ_FLAG_FRAC, verbose=True)
        impose_max_time_flag_frac(flags, max_flag_frac=MAX_TIME_FLAG_FRAC, verbose=True)
    if np.sum(flags) == nflags:
        break  
print(f'{np.mean(flags):.3%} of waterfall flagged after flagging whole times and channels with median z > {AVG_Z_THRESH}.')

# flag largest outliers
for pol in ['ee', 'nn']:
    flags |= (zscore[pol] > Z_THRESH) 
print(f'{np.mean(flags):.3%} of waterfall flagged after flagging z > {Z_THRESH} outliers.')
    
# watershed flagging
while True:
    nflags = np.sum(flags)
    for pol in ['ee', 'nn']:
        flags |= xrfi._ws_flag_waterfall(zscore[pol], flags, WS_Z_THRESH)
    if np.sum(flags) == nflags:
        break
print(f'{np.mean(flags):.3%} of waterfall flagged after watershed flagging on z > {WS_Z_THRESH} neighbors of prior flags.')
        
# flag whole integrations or channels using outliers in mean
while True:
    nflags = np.sum(flags)
    for pol in ['ee', 'nn']:    
        iteratively_flag_on_averaged_zscore(flags, zscore[pol], avg_func=np.nanmean, avg_z_thresh=AVG_Z_THRESH, verbose=True)
        impose_max_chan_flag_frac(flags, max_flag_frac=MAX_FREQ_FLAG_FRAC, verbose=True)
        impose_max_time_flag_frac(flags, max_flag_frac=MAX_TIME_FLAG_FRAC, verbose=True)
    if np.sum(flags) == nflags:
        break  
print(f'{np.mean(flags):.3%} of waterfall flagged after flagging whole times and channels with average z > {AVG_Z_THRESH}.')

# flag channels based on delay filter
iteratively_flag_on_delay_filtered_time_avg_zscore(flags, thresh=TIME_AVG_DELAY_FILT_SNR_THRESH, dynamic_range=TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE,
                                                   filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF)
print(f'{np.mean(flags):.3%} of flagging channels that are {TIME_AVG_DELAY_FILT_SNR_THRESH}σ outliers after delay filtering the time average.')

# watershed flagging again
while True:
    nflags = np.sum(flags)
    for pol in ['ee', 'nn']:
        flags |= xrfi._ws_flag_waterfall(zscore[pol], flags, WS_Z_THRESH)
    if np.sum(flags) == nflags:
        break
print(f'{np.mean(flags):.3%} of waterfall flagged after another round of watershed flagging on z > {WS_Z_THRESH} neighbors of prior flags.')

32.973% of waterfall flagged to start.

All-NaN slice encountered

	Flagging an additional 0 integrations and 74 channels.
	Flagging 0 channels previously flagged 25.00% or more.
	Flagging 1 times previously flagged 10.00% or more.

	Flagging an additional 3 integrations and 19 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.
37.912% of waterfall flagged after flagging whole times and channels with median z > 1.0.
39.330% of waterfall flagged after flagging z > 4.0 outliers.

41.480% of waterfall flagged after watershed flagging on z > 2.0 neighbors of prior flags.
	Flagging an additional 0 integrations and 0 channels.
	Flagging 74 channels previously flagged 25.00% or more.
	Flagging 281 times previously flagged 10.00% or more.

Mean of empty slice
Mean of empty slice

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

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

plot_max_z_score(zscore, flags=flags)

All-NaN axis encountered

def zscore_spectra(ylim=[-3, 3], flags=flags):
    fig, axes = plt.subplots(2, 1, figsize=(14,6), dpi=100, sharex=True, sharey=True, gridspec_kw={'hspace': 0})
    for ax, pol in zip(axes, ['ee', 'nn']):

        ax.plot(freqs / 1e6, np.nanmean(zscore[pol], axis=0),'r', label=f'{pol}-Polarization Before Round 2 Flagging', lw=.5)
        ax.plot(freqs / 1e6, np.nanmean(np.where(flags, np.nan, zscore[pol]), axis=0), label=f'{pol}-Polarization After Round 2 Flagging')
        ax.legend(loc='lower right')
        ax.set_ylabel('Time-Averged Z-Score\n(Excluding Flags)')
        ax.set_ylim(ylim)
    axes[1].set_xlabel('Frequency (MHz)')
    plt.tight_layout()

zscore_spectra()

Mean of empty slice

Mean of empty slice

def summarize_flagging(flags=flags):
    plt.figure(figsize=(14,10), dpi=100)
    cmap = matplotlib.colors.ListedColormap(((0, 0, 0),) + matplotlib.cm.get_cmap("Set2").colors[0:2])
    plt.imshow(np.where(np.any(~np.isfinite(list(zscore.values())), axis=0), 1, np.where(flags, 2, 0)), 
               aspect='auto', cmap=cmap, interpolation='none', extent=extent)
    plt.clim([-.5, 2.5])
    cbar = plt.colorbar(location='top', aspect=40, pad=.02)
    cbar.set_ticks([0, 1, 2])
    cbar.set_ticklabels(['Unflagged', 'Previously Flagged', 'Flagged Here Using Delayed Filtered z-Scores'])
    plt.xlabel('Frequency (MHz)')
    plt.ylabel(f'JD - {int(times[0])}')
    plt.tight_layout()

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/data1/2460637/zen.2460637.25248.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460637/zen.2460637.25270.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460637/zen.2460637.25293.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460637/zen.2460637.25315.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460637/zen.2460637.25338.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460637/zen.2460637.25360.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460637/zen.2460637.25382.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460637/zen.2460637.25405.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460637/zen.2460637.25427.sum.flag_waterfall_round_2.h5 exists; clobbering

File /mnt/sn1/data1/2460637/zen.2460637.25449.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/data1/2460637/2460637_aposteriori_flags.yaml
------------------------------------------------------------------------------
JD_flags: [[2460637.2596390746, 2460637.259862771], [2460637.269705404, 2460637.2699291003], [2460637.270823885, 2460637.2709357333], [2460637.2764162906, 2460637.276863683], [2460637.2808902147, 2460637.281113911], [2460637.283686417, 2460637.2837982653], [2460637.286594468, 2460637.2868181644], [2460637.2870418606, 2460637.2871537087], [2460637.287601101, 2460637.2878247974], [2460637.291851329, 2460637.2920750254], [2460637.2921868735, 2460637.292746114], [2460637.2931935065, 2460637.293529051], [2460637.2938645952, 2460637.2939764434], [2460637.294535684, 2460637.2949830764], [2460637.295206772, 2460637.296213405], [2460637.2964371014, 2460637.2965489496], [2460637.2972200383, 2460637.2973318864], [2460637.297779279, 2460637.298002975], [2460637.298114823, 2460637.2984503675], [2460637.29889776, 2460637.299121456], [2460637.305161254, 2460637.3054967984], [2460637.3123195325, 2460637.3124313806], [2460637.31366171, 2460637.313773558], [2460637.3144446467, 2460637.314668343], [2460637.3153394316, 2460637.315563128], [2460637.3176882416, 2460637.3178000897], [2460637.3288730523, 2460637.3289849004], [2460637.331557407, 2460637.331669255], [2460637.339722319, 2460637.3399460153], [2460637.354374421, 2460637.354486269], [2460637.356275839, 2460637.356611383], [2460637.3567232313, 2460637.357058775], [2460637.3582891044, 2460637.3584009525], [2460637.3585128007, 2460637.359072041], [2460637.3592957375, 2460637.3595194337], [2460637.365223687, 2460637.3653355353], [2460637.36623032, 2460637.3665658645], [2460637.3673488013, 2460637.3674606495], [2460637.3736122954, 2460637.3741715355], [2460637.3773032827, 2460637.377750675], [2460637.380770574, 2460637.381106118], [2460637.3821127512, 2460637.3824482956], [2460637.3831193843, 2460637.383790473], [2460637.384797106, 2460637.384908954], [2460637.3930738657, 2460637.393185714], [2460637.3979951823, 2460637.398442575], [2460637.4012387777, 2460637.4019098664], [2460637.4087326005, 2460637.409068145], [2460637.4095155373, 2460637.410074778], [2460637.410186626, 2460637.410298474], [2460637.4104103222, 2460637.4106340185], [2460637.412199892, 2460637.4125354365], [2460637.4139894615, 2460637.414325006], [2460637.4147723983, 2460637.4170093606], [2460637.4171212087, 2460637.417233057], [2460637.417568601, 2460637.4176804493], [2460637.418351538, 2460637.424950576], [2460637.4252861203, 2460637.4253979684], [2460637.4257335127, 2460637.426180905], [2460637.426851994, 2460637.4272993864], [2460637.427746779, 2460637.427970475], [2460637.42886526, 2460637.4292008043], [2460637.4295363487, 2460637.429983741], [2460637.434233969, 2460637.4347932097], [2460637.435016906, 2460637.435128754], [2460637.4353524502, 2460637.4354642984], [2460637.4356879946, 2460637.4357998427], [2460637.436359083, 2460637.436470931], [2460637.43770126, 2460637.4378131083], [2460637.4381486527, 2460637.438260501], [2460637.43949083, 2460637.439602678], [2460637.4398263744, 2460637.4399382225], [2460637.4401619188, 2460637.4407211593], [2460637.4408330075, 2460637.442846273], [2460637.4432936655, 2460637.4434055137], [2460637.443517362, 2460637.44362921], [2460637.443852906, 2460637.444523995], [2460637.4449713873, 2460637.4453069316], [2460637.4454187797, 2460637.445530628], [2460637.445866172, 2460637.4460898684], [2460637.446537261, 2460637.446649109], [2460637.447208349, 2460637.4473201972], [2460637.4477675897, 2460637.448103134], [2460637.448214982, 2460637.4484386784], [2460637.448886071, 2460637.448997919], [2460637.4506756407, 2460637.450787489], [2460637.4513467294, 2460637.4514585775], [2460637.45190597, 2460637.4522415143], [2460637.452577058, 2460637.4529126026], [2460637.453136299, 2460637.453248147], [2460637.4538073875, 2460637.4539192356], [2460637.454142932, 2460637.45425478], [2460637.4548140205, 2460637.455261413], [2460637.4569391347, 2460637.457050983], [2460637.4577220716, 2460637.458057616], [2460637.4600708815, 2460637.460406426], [2460637.461413059, 2460637.461524907], [2460637.4621959957, 2460637.462643388], [2460637.463538173, 2460637.4637618694], [2460637.465886983, 2460637.4661106793], [2460637.4691305785, 2460637.4695779704], [2460637.478302123, 2460637.4786376674], [2460637.489598782, 2460637.4900461743], [2460637.4912765035, 2460637.4915002], [2460637.491723896, 2460637.4920594404], [2460637.4973163013, 2460637.4975399976], [2460637.497651845, 2460637.4978755414], [2460637.501342833, 2460637.501454681], [2460637.502237618, 2460637.502461314], [2460637.503020555, 2460637.503132403], [2460637.503467947, 2460637.503803491], [2460637.515323846, 2460637.5154356943], [2460637.5174489603, 2460637.5176726566], [2460637.5249427836, 2460637.52516648], [2460637.534897265, 2460637.5351209613], [2460637.5384764043, 2460637.5387001005], [2460637.539147493, 2460637.539371189], [2460637.5397067335, 2460637.5398185817], [2460637.543285873, 2460637.543397721], [2460637.5439569615, 2460637.544292506], [2460637.5468650125, 2460637.5470887087], [2460637.5477597974, 2460637.5478716455], [2460637.5553654684, 2460637.5554773165], [2460637.561293418, 2460637.5615171143], [2460637.5638659247, 2460637.564089621], [2460637.566326583, 2460637.566550279], [2460637.5677806083, 2460637.568228001], [2460637.569346482, 2460637.569570178], [2460637.5713597476, 2460637.571695292], [2460637.57180714, 2460637.5722545325], [2460637.572701925, 2460637.572925621], [2460637.5761692165, 2460637.576392913], [2460637.5796365077, 2460637.579860204], [2460637.5984269897, 2460637.598650686], [2460637.6075985343, 2460637.6078222306], [2460637.6089407117, 2460637.609276256], [2460637.6111776736, 2460637.61140137], [2460637.612408003, 2460637.612743547], [2460637.616993775, 2460637.6172174714], [2460637.6191188893, 2460637.619566282], [2460637.6216913955, 2460637.6218032436], [2460637.6219150918, 2460637.62202694], [2460637.6252705352, 2460637.6256060796], [2460637.627619345, 2460637.6278430414], [2460637.629297067, 2460637.629408915], [2460637.6303037, 2460637.630527396], [2460637.6307510925, 2460637.6319814217], [2460637.632316966, 2460637.632428814], [2460637.6325406623, 2460637.6326525104], [2460637.632876206, 2460637.6664306386]]

freq_flags: [[49911499.0234375, 50155639.6484375], [52719116.2109375, 52841186.5234375], [58700561.5234375, 58822631.8359375], [62118530.2734375, 62973022.4609375], [66146850.5859375, 66268920.8984375], [69564819.3359375, 69686889.6484375], [69931030.2734375, 70053100.5859375], [81771850.5859375, 81893920.8984375], [87387084.9609375, 108627319.3359375], [109970092.7734375, 110092163.0859375], [112167358.3984375, 112411499.0234375], [112533569.3359375, 113143920.8984375], [113265991.2109375, 113388061.5234375], [113632202.1484375, 113754272.4609375], [113998413.0859375, 114242553.7109375], [116073608.3984375, 116195678.7109375], [116439819.3359375, 116561889.6484375], [119979858.3984375, 120101928.7109375], [123153686.5234375, 123275756.8359375], [124618530.2734375, 125473022.4609375], [125717163.0859375, 125961303.7109375], [127548217.7734375, 127670288.0859375], [128280639.6484375, 128402709.9609375], [128524780.2734375, 128646850.5859375], [129989624.0234375, 130111694.3359375], [130966186.5234375, 131088256.8359375], [131454467.7734375, 131576538.0859375], [131698608.3984375, 131820678.7109375], [132308959.9609375, 132553100.5859375], [132675170.8984375, 132797241.2109375], [132919311.5234375, 133041381.8359375], [133407592.7734375, 133529663.0859375], [134140014.6484375, 134262084.9609375], [134506225.5859375, 134750366.2109375], [134872436.5234375, 135238647.4609375], [135482788.0859375, 136581420.8984375], [136703491.2109375, 138412475.5859375], [138656616.2109375, 138778686.5234375], [141464233.3984375, 141586303.7109375], [141708374.0234375, 141830444.3359375], [141952514.6484375, 142318725.5859375], [142807006.8359375, 143661499.0234375], [143783569.3359375, 144027709.9609375], [144638061.5234375, 145004272.4609375], [145492553.7109375, 145980834.9609375], [146102905.2734375, 146224975.5859375], [146347045.8984375, 146469116.2109375], [147079467.7734375, 147323608.3984375], [147445678.7109375, 147567749.0234375], [147933959.9609375, 148056030.2734375], [148178100.5859375, 148544311.5234375], [149154663.0859375, 149398803.7109375], [149887084.9609375, 150009155.2734375], [151351928.7109375, 151473999.0234375], [151840209.9609375, 151962280.2734375], [152938842.7734375, 153182983.3984375], [153793334.9609375, 154037475.5859375], [154159545.8984375, 154403686.5234375], [154525756.8359375, 154647827.1484375], [154769897.4609375, 154891967.7734375], [155014038.0859375, 155380249.0234375], [155868530.2734375, 156234741.2109375], [157089233.3984375, 157211303.7109375], [157577514.6484375, 157699584.9609375], [157943725.5859375, 158065795.8984375], [158187866.2109375, 158432006.8359375], [159164428.7109375, 159408569.3359375], [160263061.5234375, 160385131.8359375], [161239624.0234375, 161605834.9609375], [169906616.2109375, 170150756.8359375], [170272827.1484375, 170394897.4609375], [170883178.7109375, 171005249.0234375], [171127319.3359375, 171859741.2109375], [174911499.0234375, 175033569.3359375], [175155639.6484375, 175399780.2734375], [175521850.5859375, 175643920.8984375], [176864624.0234375, 176986694.3359375], [177108764.6484375, 177597045.8984375], [179183959.9609375, 179428100.5859375], [179916381.8359375, 180404663.0859375], [180526733.3984375, 180648803.7109375], [181137084.9609375, 181381225.5859375], [181503295.8984375, 181625366.2109375], [182113647.4609375, 182601928.7109375], [182723999.0234375, 182846069.3359375], [183212280.2734375, 183700561.5234375], [184188842.7734375, 184432983.3984375], [184555053.7109375, 185043334.9609375], [185287475.5859375, 185775756.8359375], [186141967.7734375, 186874389.6484375], [187118530.2734375, 187850952.1484375], [188583374.0234375, 188949584.9609375], [189193725.5859375, 202499389.6484375], [202987670.8984375, 205673217.7734375], [205795288.0859375, 206161499.0234375], [206527709.9609375, 208114624.0234375], [208236694.3359375, 209701538.0859375], [209945678.7109375, 210067749.0234375], [210189819.3359375, 210556030.2734375], [210678100.5859375, 211776733.3984375], [211898803.7109375, 212875366.2109375], [213241577.1484375, 213363647.4609375], [213485717.7734375, 213729858.3984375], [214828491.2109375, 214950561.5234375], [215072631.8359375, 215560913.0859375], [215682983.3984375, 215805053.7109375], [216659545.8984375, 216781616.2109375], [218856811.5234375, 219100952.1484375], [219589233.3984375, 219955444.3359375], [220321655.2734375, 221298217.7734375], [221786499.0234375, 222152709.9609375], [222274780.2734375, 222396850.5859375], [222518920.8984375, 224105834.9609375], [224227905.2734375, 224472045.8984375], [224594116.2109375, 224716186.5234375], [225692749.0234375, 225936889.6484375], [226181030.2734375, 226791381.8359375], [227279663.0859375, 227890014.6484375], [228988647.4609375, 229476928.7109375], [229965209.9609375, 230331420.8984375], [230941772.4609375, 231552124.0234375], [232894897.4609375, 233016967.7734375], [233383178.7109375, 233505249.0234375]]

ex_ants: [[4, Jee], [5, Jee], [5, Jnn], [7, Jee], [9, Jee], [15, Jee], [15, Jnn], [16, Jee], [18, Jee], [18, Jnn], [19, Jee], [20, Jee], [21, Jee], [21, Jnn], [22, Jee], [22, Jnn], [27, Jee], [27, Jnn], [28, Jee], [28, Jnn], [29, Jee], [31, Jnn], [32, Jnn], [33, Jnn], [34, Jee], [34, Jnn], [35, Jee], [35, Jnn], [36, Jee], [36, Jnn], [37, Jee], [37, Jnn], [40, Jnn], [41, Jee], [42, Jnn], [45, Jee], [45, Jnn], [46, Jee], [46, Jnn], [47, Jee], [47, Jnn], [48, Jee], [48, Jnn], [49, Jee], [49, Jnn], [50, Jnn], [51, Jee], [55, Jee], [57, Jee], [61, Jee], [61, Jnn], [62, Jee], [62, Jnn], [63, Jee], [63, Jnn], [64, Jee], [64, Jnn], [65, Jee], [65, Jnn], [66, Jnn], [67, Jee], [68, Jee], [68, Jnn], [72, Jee], [72, Jnn], [73, Jee], [73, Jnn], [77, Jee], [77, Jnn], [78, Jee], [78, Jnn], [81, Jee], [81, Jnn], [83, Jnn], [84, Jee], [84, Jnn], [85, Jnn], [86, Jee], [86, Jnn], [87, Jee], [88, Jee], [88, Jnn], [89, Jee], [89, Jnn], [90, Jee], [90, Jnn], [92, Jee], [94, Jee], [97, Jnn], [98, Jnn], [99, Jnn], [100, Jee], [100, Jnn], [104, Jnn], [107, Jee], [107, Jnn], [108, Jnn], [109, Jnn], [110, Jee], [111, Jee], [116, Jee], [116, Jnn], [117, Jnn], [118, Jee], [120, Jee], [121, Jee], [121, Jnn], [124, Jee], [125, Jee], [125, Jnn], [127, Jee], [130, Jee], [130, Jnn], [134, Jee], [135, Jnn], [136, Jnn], [137, Jee], [142, Jnn], [143, Jnn], [144, Jee], [144, Jnn], [148, Jee], [148, Jnn], [155, Jnn], [160, Jnn], [161, Jnn], [165, Jnn], [170, Jee], [171, Jnn], [176, Jee], [176, Jnn], [177, Jee], [177, Jnn], [178, Jee], [178, Jnn], [179, Jee], [179, Jnn], [180, Jnn], [182, Jee], [182, Jnn], [184, Jee], [188, Jnn], [189, Jee], [189, Jnn], [194, Jee], [199, Jnn], [200, Jee], [200, Jnn], [201, Jnn], [202, Jnn], [204, Jee], [209, Jnn], [212, Jnn], [213, Jee], [215, Jee], [215, Jnn], [216, Jee], [218, Jnn], [221, Jee], [226, Jnn], [232, Jee], [235, Jee], [235, Jnn], [239, 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], [262, Jee], [262, Jnn], [266, Jnn], [268, Jnn], [270, Jee], [270, Jnn], [295, 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 427.59 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 ¶