Calibration Smoothing¶

by Josh Dillon, last updated March 29, 2023

This notebook runs calibration smoothing to the gains coming out of file_calibration notebook. It removes any flags founds on by that notebook and replaces them with flags generated from full_day_rfi and full_day_antenna_flagging. It also plots the results for a couple of antennas.

Here's a set of links to skip to particular figures and tables:

• Figure 1: Full-Day Gain Amplitudes Before and After smooth_cal¶

• Figure 2: Full-Day Gain Phases Before and After smooth_cal¶

• Figure 3: Full-Day $\chi^2$ / DoF Waterfall from Redundant-Baseline Calibration¶

• Figure 4: Average $\chi^2$ per Antenna vs. Time and Frequency¶

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 copy
import warnings
import matplotlib
import matplotlib.pyplot as plt
from hera_cal import io, utils, smooth_cal
from hera_qm.time_series_metrics import true_stretches
%matplotlib inline
from IPython.display import display, HTML

Parse inputs¶

In [3]:
# get files
SUM_FILE = os.environ.get("SUM_FILE", None)
# SUM_FILE = '/users/jsdillon/lustre/H6C/abscal/2459853/zen.2459853.25518.sum.uvh5'
SUM_SUFFIX = os.environ.get("SUM_SUFFIX", 'sum.uvh5')
CAL_SUFFIX = os.environ.get("CAL_SUFFIX", 'sum.omni.calfits')
SMOOTH_CAL_SUFFIX = os.environ.get("SMOOTH_CAL_SUFFIX", 'sum.smooth.calfits')
ANT_FLAG_SUFFIX = os.environ.get("ANT_FLAG_SUFFIX", 'sum.antenna_flags.h5')
RFI_FLAG_SUFFIX = os.environ.get("RFI_FLAG_SUFFIX", 'sum.flag_waterfall.h5')
FREQ_SMOOTHING_SCALE = float(os.environ.get("FREQ_SMOOTHING_SCALE", 10.0)) # MHz
TIME_SMOOTHING_SCALE = float(os.environ.get("TIME_SMOOTHING_SCALE", 6e5)) # seconds
EIGENVAL_CUTOFF = float(os.environ.get("EIGENVAL_CUTOFF", 1e-12))
OUT_YAML_SUFFIX = os.environ.get("OUT_YAML_SUFFIX", '_aposteriori_flags.yaml')
OUT_YAML_DIR = os.environ.get("OUT_YAML_DIR", None)

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)    

for setting in ['SUM_FILE', 'SUM_SUFFIX', 'CAL_SUFFIX', 'SMOOTH_CAL_SUFFIX', 'ANT_FLAG_SUFFIX', 'RFI_FLAG_SUFFIX',
                'FREQ_SMOOTHING_SCALE', 'TIME_SMOOTHING_SCALE', 'EIGENVAL_CUTOFF', 'out_yaml_file']:
        print(f'{setting} = {eval(setting)}')

SUM_FILE = /lustre/aoc/projects/hera/h6c-analysis/IDR2/2459861/zen.2459861.25297.sum.uvh5
SUM_SUFFIX = sum.uvh5
CAL_SUFFIX = sum.omni.calfits
SMOOTH_CAL_SUFFIX = sum.smooth.calfits
ANT_FLAG_SUFFIX = sum.antenna_flags.h5
RFI_FLAG_SUFFIX = sum.flag_waterfall.h5
FREQ_SMOOTHING_SCALE = 10.0
TIME_SMOOTHING_SCALE = 600000.0
EIGENVAL_CUTOFF = 1e-12
out_yaml_file = /lustre/aoc/projects/hera/h6c-analysis/IDR2/2459861/2459861_aposteriori_flags.yaml

Load files¶

In [4]:
sum_glob = '.'.join(SUM_FILE.split('.')[:-3]) + '.*.' + SUM_SUFFIX
cal_files_glob = sum_glob.replace(SUM_SUFFIX, CAL_SUFFIX)
cal_files = sorted(glob.glob(cal_files_glob))
print(f'Found {len(cal_files)} *.{CAL_SUFFIX} files starting with {cal_files[0]}.')

Found 1862 *.sum.omni.calfits files starting with /lustre/aoc/projects/hera/h6c-analysis/IDR2/2459861/zen.2459861.25297.sum.omni.calfits.
In [5]:
rfi_flag_files_glob = sum_glob.replace(SUM_SUFFIX, RFI_FLAG_SUFFIX)
rfi_flag_files = sorted(glob.glob(rfi_flag_files_glob))
print(f'Found {len(rfi_flag_files)} *.{RFI_FLAG_SUFFIX} files starting with {rfi_flag_files[0]}.')

Found 1862 *.sum.flag_waterfall.h5 files starting with /lustre/aoc/projects/hera/h6c-analysis/IDR2/2459861/zen.2459861.25297.sum.flag_waterfall.h5.
In [6]:
ant_flag_files_glob = sum_glob.replace(SUM_SUFFIX, ANT_FLAG_SUFFIX)
ant_flag_files = sorted(glob.glob(ant_flag_files_glob))
print(f'Found {len(ant_flag_files)} *.{ANT_FLAG_SUFFIX} files starting with {ant_flag_files[0]}.')

Found 1862 *.sum.antenna_flags.h5 files starting with /lustre/aoc/projects/hera/h6c-analysis/IDR2/2459861/zen.2459861.25297.sum.antenna_flags.h5.
In [7]:
cs = smooth_cal.CalibrationSmoother(cal_files, flag_file_list=(ant_flag_files + rfi_flag_files), ignore_calflags=True,
                                    pick_refant=True, propagate_refant_flags=True, load_chisq=True, load_cspa=True)
for pol in cs.refant:
    print(f'Reference antenna {cs.refant[pol][0]} selected for {pol}.')

Mean of empty slice

Reference antenna 123 selected for Jnn.
Reference antenna 122 selected for Jee.
In [8]:
# duplicate a small number of abscal gains for plotting
antnums = set([ant[0] for ant in cs.ants])
flags_per_antnum = [np.sum(cs.flag_grids[ant, 'Jnn']) + np.sum(cs.flag_grids[ant, 'Jee']) for ant in antnums]
refant_nums = [ant[0] for ant in cs.refant.values()]
candidate_ants = [ant for ant, nflags in zip(antnums, flags_per_antnum) if (ant not in refant_nums) and (nflags <= np.percentile(flags_per_antnum, 25))
                  and not np.all(cs.flag_grids[ant, 'Jee']) and not np.all(cs.flag_grids[ant, 'Jnn'])]
ants_to_plot = [func(candidate_ants) for func in (np.min, np.max)]
abscal_gains = {(ant, pol): np.array(cs.gain_grids[(ant, pol)]) for ant in ants_to_plot for pol in ['Jee', 'Jnn']}

Perform smoothing¶

In [9]:
cs.time_freq_2D_filter(freq_scale=FREQ_SMOOTHING_SCALE, time_scale=TIME_SMOOTHING_SCALE, eigenval_cutoff=EIGENVAL_CUTOFF, 
                       method='DPSS', fit_method='lu_solve', fix_phase_flips=True, flag_phase_flip_ints=True)

No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)

Plot results¶

In [10]:
lst_grid = utils.JD2LST(cs.time_grid) * 12 / np.pi
lst_grid[lst_grid > lst_grid[-1]] -= 24
In [11]:
def amplitude_plot(ant_to_plot):
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        # Pick vmax to not saturate 90% of the abscal gains
        vmax = np.max([np.percentile(np.abs(cs.gain_grids[ant_to_plot, pol][~cs.flag_grids[ant_to_plot, pol]]), 99) for pol in ['Jee', 'Jnn']])

        display(HTML(f'<h2>Antenna {ant_to_plot} Amplitude Waterfalls</h2>'))    

        # Plot abscal gain amplitude waterfalls for a single antenna
        fig, axes = plt.subplots(4, 2, figsize=(14,14), gridspec_kw={'height_ratios': [1, 1, .4, .4]})
        for ax, pol in zip(axes[0], ['Jee', 'Jnn']):
            ant = (ant_to_plot, pol)
            extent=[cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
            im = ax.imshow(np.where(cs.flag_grids[ant], np.nan, np.abs(cs.gain_grids[ant])), aspect='auto', cmap='inferno', 
                           interpolation='nearest', vmin=0, vmax=vmax, extent=extent)
            ax.set_title(f'Smoothcal Gain Amplitude of Antenna {ant[0]}: {pol[-1]}-polarized' )
            ax.set_xlabel('Frequency (MHz)')
            ax.set_ylabel('LST (Hours)')
            ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
            ax.set_yticklabels(ax.get_yticks() % 24)
            plt.colorbar(im, ax=ax,  orientation='horizontal', pad=.15)

        # Now flagged plot abscal waterfall    
        for ax, pol in zip(axes[1], ['Jee', 'Jnn']):
            ant = (ant_to_plot, pol)
            extent=[cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
            im = ax.imshow(np.where(cs.flag_grids[ant], np.nan, np.abs(abscal_gains[ant])), aspect='auto', cmap='inferno', 
                           interpolation='nearest', vmin=0, vmax=vmax, extent=extent)
            ax.set_title(f'Abscal Gain Amplitude of Antenna {ant[0]}: {pol[-1]}-polarized' )
            ax.set_xlabel('Frequency (MHz)')
            ax.set_ylabel('LST (Hours)')
            ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
            ax.set_yticklabels(ax.get_yticks() % 24)
            plt.colorbar(im, ax=ax,  orientation='horizontal', pad=.15)
            
        # Now plot mean gain spectra 
        for ax, pol in zip(axes[2], ['Jee', 'Jnn']):
            ant = (ant_to_plot, pol)   
            nflags_spectrum = np.sum(cs.flag_grids[ant], axis=0)
            to_plot = nflags_spectrum <= np.percentile(nflags_spectrum, 75)
            ax.plot(cs.freqs[to_plot] / 1e6, np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.abs(abscal_gains[ant])), axis=0)[to_plot], 'r.', label='Abscal')        
            ax.plot(cs.freqs[to_plot] / 1e6, np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.abs(cs.gain_grids[ant])), axis=0)[to_plot], 'k.', ms=2, label='Smoothed')        
            ax.set_ylim([0, vmax])
            ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])    
            ax.set_xlabel('Frequency (MHz)')
            ax.set_ylabel('|g| (unitless)')
            ax.set_title(f'Mean Infrequently-Flagged Gain Amplitude of Antenna {ant[0]}: {pol[-1]}-polarized')
            ax.legend(loc='upper left')

        # Now plot mean gain time series
        for ax, pol in zip(axes[3], ['Jee', 'Jnn']):
            ant = (ant_to_plot, pol)
            nflags_series = np.sum(cs.flag_grids[ant], axis=1)
            to_plot = nflags_series <= np.percentile(nflags_series, 75)
            ax.plot(lst_grid[to_plot], np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.abs(abscal_gains[ant])), axis=1)[to_plot], 'r.', label='Abscal')        
            ax.plot(lst_grid[to_plot], np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.abs(cs.gain_grids[ant])), axis=1)[to_plot], 'k.', ms=2, label='Smoothed')        
            ax.set_ylim([0, vmax])
            ax.set_xlabel('LST (hours)')
            ax.set_ylabel('|g| (unitless)')
            ax.set_title(f'Mean Infrequently-Flagged Gain Amplitude of Antenna {ant[0]}: {pol[-1]}-polarized')
            ax.set_xticklabels(ax.get_xticks() % 24)
            ax.legend(loc='upper left')

        plt.tight_layout()
        plt.show()
In [12]:
def phase_plot(ant_to_plot):
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")    
        display(HTML(f'<h2>Antenna {ant_to_plot} Phase Waterfalls</h2>'))
        fig, axes = plt.subplots(4, 2, figsize=(14,14), gridspec_kw={'height_ratios': [1, 1, .4, .4]})

        # Plot phase waterfalls for a single antenna    
        for ax, pol in zip(axes[0], ['Jee', 'Jnn']):
            ant = (ant_to_plot, pol)
            extent=[cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
            im = ax.imshow(np.where(cs.flag_grids[ant], np.nan, np.angle(cs.gain_grids[ant])), aspect='auto', cmap='inferno', 
                           interpolation='nearest', vmin=-np.pi, vmax=np.pi, extent=extent)
            ax.set_title(f'Smoothcal Gain Phase of Ant {ant[0]} / Ant {cs.refant[pol][0]}: {pol[-1]}-polarized')
            ax.set_xlabel('Frequency (MHz)')
            ax.set_ylabel('LST (Hours)')
            ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
            ax.set_yticklabels(ax.get_yticks() % 24)
            plt.colorbar(im, ax=ax,  orientation='horizontal', pad=.15)

        # Now plot abscal phase waterfall    
        for ax, pol in zip(axes[1], ['Jee', 'Jnn']):
            ant = (ant_to_plot, pol)
            extent=[cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
            im = ax.imshow(np.where(cs.flag_grids[ant], np.nan, np.angle(abscal_gains[ant])), aspect='auto', cmap='inferno', 
                           interpolation='nearest', vmin=-np.pi, vmax=np.pi, extent=extent)
            ax.set_title(f'Abscal Gain Phase of Ant {ant[0]} / Ant {cs.refant[pol][0]}: {pol[-1]}-polarized')
            ax.set_xlabel('Frequency (MHz)')
            ax.set_ylabel('LST (Hours)')
            ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
            ax.set_yticklabels(ax.get_yticks() % 24)
            plt.colorbar(im, ax=ax,  orientation='horizontal', pad=.15)
            
        # Now plot median gain spectra 
        for ax, pol in zip(axes[2], ['Jee', 'Jnn']):
            ant = (ant_to_plot, pol)   
            nflags_spectrum = np.sum(cs.flag_grids[ant], axis=0)
            to_plot = nflags_spectrum <= np.percentile(nflags_spectrum, 75)
            ax.plot(cs.freqs[to_plot] / 1e6, np.nanmedian(np.where(cs.flag_grids[ant], np.nan, np.angle(abscal_gains[ant])), axis=0)[to_plot], 'r.', label='Abscal')        
            ax.plot(cs.freqs[to_plot] / 1e6, np.nanmedian(np.where(cs.flag_grids[ant], np.nan, np.angle(cs.gain_grids[ant])), axis=0)[to_plot], 'k.', ms=2, label='Smoothed')        
            ax.set_ylim([-np.pi, np.pi])
            ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])    
            ax.set_xlabel('Frequency (MHz)')
            ax.set_ylabel(f'Phase of g$_{{{ant[0]}}}$ / g$_{{{cs.refant[pol][0]}}}$')
            ax.set_title(f'Median Infrequently-Flagged Gain Phase of Ant {ant[0]} / Ant {cs.refant[pol][0]}: {pol[-1]}-polarized')
            ax.legend(loc='upper left')

        # # Now plot median gain time series
        for ax, pol in zip(axes[3], ['Jee', 'Jnn']):
            ant = (ant_to_plot, pol)
            nflags_series = np.sum(cs.flag_grids[ant], axis=1)
            to_plot = nflags_series <= np.percentile(nflags_series, 75)
            ax.plot(lst_grid[to_plot], np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.angle(abscal_gains[ant])), axis=1)[to_plot], 'r.', label='Abscal')        
            ax.plot(lst_grid[to_plot], np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.angle(cs.gain_grids[ant])), axis=1)[to_plot], 'k.', ms=2, label='Smoothed')        
            ax.set_ylim([-np.pi, np.pi])    
            ax.set_xlabel('LST (hours)')
            ax.set_ylabel(f'Phase of g$_{{{ant[0]}}}$ / g$_{{{cs.refant[pol][0]}}}$')
            ax.set_title(f'Mean Infrequently-Flagged Gain Phase of Ant {ant[0]} / Ant {cs.refant[pol][0]}: {pol[-1]}-polarized')
            ax.set_xticklabels(ax.get_xticks() % 24)    
            ax.legend(loc='upper left')

        plt.tight_layout()
        plt.show()

Figure 1: Full-Day Gain Amplitudes Before and After smooth_cal¶

Here we plot abscal and smooth_cal gain amplitudes for both of the sample antennas. We also show means across time/frequency, excluding frequencies/times that are frequently flagged.

In [13]:
for ant_to_plot in ants_to_plot:
    amplitude_plot(ant_to_plot)

Antenna 3 Amplitude Waterfalls

Antenna 239 Amplitude Waterfalls

Figure 2: Full-Day Gain Phases Before and After smooth_cal¶

Here we plot abscal and smooth_cal phases relative to each polarization's reference antenna for both of the sample antennas. We also show medians across time/frequency, excluding frequencies/times that are frequently flagged.

In [14]:
for ant_to_plot in ants_to_plot:
    phase_plot(ant_to_plot)

Antenna 3 Phase Waterfalls

Antenna 239 Phase Waterfalls

Examine $\chi^2$¶

In [15]:
def chisq_plot():
    fig, axes = plt.subplots(1, 2, figsize=(14, 10), sharex=True, sharey=True)
    extent = [cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
    for ax, pol in zip(axes, ['Jee', 'Jnn']):

        im = ax.imshow(np.where(cs.flag_grids[cs.refant[pol]], np.nan, cs.chisq_grids[pol]), vmin=1, vmax=5, 
                       aspect='auto', cmap='turbo', interpolation='none', extent=extent)
        ax.set_yticklabels(ax.get_yticks() % 24)
        ax.set_title(f'{pol[1:]}-Polarized $\\chi^2$ / DoF')
        ax.set_xlabel('Frequency (MHz)')

    axes[0].set_ylabel('LST (hours)')
    plt.tight_layout()
    fig.colorbar(im, ax=axes, pad=.07, label='$\\chi^2$ / DoF', orientation='horizontal', extend='both', aspect=50)

Figure 3: Full-Day $\chi^2$ / DoF Waterfall from Redundant-Baseline Calibration¶

Here we plot $\chi^2$ per degree of freedom from redundant-baseline calibration for both polarizations separately. While this plot is a little out of place, as it was not produced by this notebook, it is a convenient place where all the necessary components are readily available. If the array were perfectly redundant and any non-redundancies in the calibrated visibilities were explicable by thermal noise alone, this waterfall should be all 1.

In [16]:
chisq_plot()

FixedFormatter should only be used together with FixedLocator
In [17]:
avg_cspa_vs_time = {ant: np.nanmean(np.where(cs.flag_grids[ant], np.nan, cs.cspa_grids[ant]), axis=1) for ant in cs.ants}
avg_cspa_vs_freq = {ant: np.nanmean(np.where(cs.flag_grids[ant], np.nan, cs.cspa_grids[ant]), axis=0) for ant in cs.ants}

Mean of empty slice
Mean of empty slice
In [18]:
def cspa_vs_time_plot():
    fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True, sharey=True, gridspec_kw={'hspace': 0})
    for ax, pol in zip(axes, ['Jee', 'Jnn']):
        detail_cutoff = np.percentile([np.nanmean(m) for ant, m in avg_cspa_vs_time.items() 
                                       if ant[1] == pol and np.isfinite(np.nanmean(m))], 95)
        for ant in avg_cspa_vs_time:
            if ant[1] == pol and not np.all(cs.flag_grids[ant]):
                if np.nanmean(avg_cspa_vs_time[ant]) > detail_cutoff:
                    ax.plot(lst_grid, avg_cspa_vs_time[ant], label=ant, zorder=100)
                else:
                    ax.plot(lst_grid, avg_cspa_vs_time[ant], c='grey', alpha=.2, lw=.5)
        ax.legend(title=f'{pol[1:]}-Polarized', ncol=2)
        ax.set_ylabel('Mean Unflagged $\\chi^2$ per Antenna')
        ax.set_xlabel('LST (hours)')
        ax.set_xticklabels(ax.get_xticks() % 24)

    plt.ylim([1, 5.4])
    plt.tight_layout()
In [19]:
def cspa_vs_freq_plot():
    fig, axes = plt.subplots(2, 1, figsize=(14, 6), sharex=True, sharey=True, gridspec_kw={'hspace': 0})
    for ax, pol in zip(axes, ['Jee', 'Jnn']):
        detail_cutoff = np.percentile([np.nanmean(m) for ant, m in avg_cspa_vs_freq.items() 
                                       if ant[1] == pol and np.isfinite(np.nanmean(m))], 95)
        for ant in avg_cspa_vs_freq:
            if ant[1] == pol and not np.all(cs.flag_grids[ant]):
                if np.nanmean(avg_cspa_vs_freq[ant]) > detail_cutoff:
                    ax.plot(cs.freqs / 1e6, avg_cspa_vs_freq[ant], label=ant, zorder=100)
                else:
                    ax.plot(cs.freqs / 1e6, avg_cspa_vs_freq[ant], c='grey', alpha=.2, lw=.5)
        ax.legend(title=f'{pol[1:]}-Polarized', ncol=2)
        ax.set_ylabel('Mean Unflagged $\\chi^2$ per Antenna')
        ax.set_xlabel('Frequency (MHz)')

    plt.ylim([1, 5.4])
    plt.tight_layout()

Figure 4: Average $\chi^2$ per Antenna vs. Time and Frequency¶

Here we plot $\chi^2$ per antenna from redundant-baseline calibration, separating polarizations and averaging the unflagged pixels in the waterfalls over frequency or time. The worst 5% of antennas are shown in color and highlighted in the legends, the rest are shown in grey.

In [20]:
cspa_vs_time_plot()
cspa_vs_freq_plot()

Mean of empty slice
FixedFormatter should only be used together with FixedLocator

Save Results¶

In [21]:
add_to_history = 'Produced by calibration_smoothing notebook with the following environment:\n' + '=' * 65 + '\n' + os.popen('conda env export').read() + '=' * 65
In [22]:
cs.write_smoothed_cal(output_replace=(CAL_SUFFIX, SMOOTH_CAL_SUFFIX), add_to_history=add_to_history, clobber=True)

Mean of empty slice
In [23]:
# write summary of entirely flagged times/freqs/ants to yaml
all_flagged_times = np.all([np.all(cs.flag_grids[ant], axis=1) for ant in cs.flag_grids], axis=0)
all_flagged_freqs = np.all([np.all(cs.flag_grids[ant], axis=0) for ant in cs.flag_grids], axis=0)
all_flagged_ants = sorted([ant for ant in cs.flag_grids if np.all(cs.flag_grids[ant])])

out_yml_str = 'JD_flags: ' + str([[cs.time_grid[flag_stretch][0] - cs.dt, cs.time_grid[flag_stretch][-1] + cs.dt] 
                                  for flag_stretch in true_stretches(all_flagged_times)])
chan_res = np.median(np.diff(cs.freqs))
out_yml_str += '\n\nfreq_flags: ' + str([[cs.freqs[flag_stretch][0] - chan_res / 2, cs.freqs[flag_stretch][-1] + chan_res / 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 /lustre/aoc/projects/hera/h6c-analysis/IDR2/2459861/2459861_aposteriori_flags.yaml
-----------------------------------------------------------------------------------------------------------
JD_flags: [[2459861.256770222, 2459861.256993918], [2459861.2677313373, 2459861.26817873], [2459861.2707512365, 2459861.2709749327], [2459861.2709749327, 2459861.271310477], [2459861.271310477, 2459861.2716460214], [2459861.2792516933, 2459861.2794753895], [2459861.283837466, 2459861.2840611623], [2459861.3026279495, 2459861.302963494], [2459861.303075342, 2459861.303299038], [2459861.3094506846, 2459861.309674381], [2459861.3105691657, 2459861.310792862], [2459861.3111284063, 2459861.3113521026], [2459861.3113521026, 2459861.311575799], [2459861.3132535205, 2459861.3134772168], [2459861.3190696226, 2459861.319293319], [2459861.319405167, 2459861.319628863], [2459861.319628863, 2459861.3199644075], [2459861.3208591924, 2459861.3210828886], [2459861.325444965, 2459861.3257805095], [2459861.32633975, 2459861.3265634463], [2459861.332155852, 2459861.3323795483], [2459861.332938789, 2459861.333162485], [2459861.3336098776, 2459861.333833574], [2459861.3365179286, 2459861.3375245617], [2459861.33763641, 2459861.3381956504], [2459861.340544461, 2459861.342557727], [2459861.3433406637, 2459861.34356436], [2459861.3449065373, 2459861.3451302336], [2459861.345465778, 2459861.345689474], [2459861.345689474, 2459861.3469198034], [2459861.3472553478, 2459861.347479044], [2459861.3498278544, 2459861.3500515507], [2459861.3500515507, 2459861.3508344875], [2459861.351393728, 2459861.3516174243], [2459861.352512209, 2459861.3527359054], [2459861.3537425385, 2459861.3539662347], [2459861.354189931, 2459861.354413627], [2459861.3556439565, 2459861.3558676527], [2459861.358104615, 2459861.3583283112], [2459861.3593349443, 2459861.3595586405], [2459861.3786846683, 2459861.3789083646], [2459861.3805860863, 2459861.3808097825], [2459861.3950144933, 2459861.3952381895], [2459861.4088836596, 2459861.409107356], [2459861.412127255, 2459861.412350951], [2459861.415818243, 2459861.416041939], [2459861.420068471, 2459861.4202921675], [2459861.4223054335, 2459861.42252913], [2459861.424766092, 2459861.4249897883], [2459861.431588827, 2459861.4318125234], [2459861.4330428527, 2459861.433266549], [2459861.4336020933, 2459861.4338257895], [2459861.4347205744, 2459861.4349442706], [2459861.4368456886, 2459861.437069385], [2459861.4375167773, 2459861.4377404735], [2459861.4378523217, 2459861.438076018], [2459861.438187866, 2459861.4384115622], [2459861.4407603727, 2459861.440984069], [2459861.4424380944, 2459861.4426617906], [2459861.4427736388, 2459861.442997335], [2459861.4482541964, 2459861.4484778927], [2459861.448589741, 2459861.448813437], [2459861.4540702985, 2459861.4551887796], [2459861.4551887796, 2459861.455412476], [2459861.4629062996, 2459861.463241844], [2459861.467156528, 2459861.4673802243], [2459861.4714067564, 2459861.471742301], [2459861.471742301, 2459861.472077845], [2459861.4761043773, 2459861.4763280735], [2459861.4967962787, 2459861.497131823], [2459861.527778207, 2459861.5283374474], [2459861.5311336503, 2459861.5331469164], [2459861.534600942, 2459861.53650236], [2459861.5398578034, 2459861.5400814996], [2459861.54421988, 2459861.5447791205], [2459861.5450028167, 2459861.545226513], [2459861.5556283877, 2459861.5561876283], [2459861.5631222115, 2459861.563457756], [2459861.5639051483, 2459861.5642406926], [2459861.567260592, 2459861.567484288], [2459861.5709515796, 2459861.5718463645], [2459861.5747544155, 2459861.5756492005], [2459861.5880643413, 2459861.5882880376], [2459861.5890709744, 2459861.5892946706], [2459861.591531633, 2459861.5920908735], [2459861.5999202416, 2459861.600255786], [2459861.60596004, 2459861.606183736], [2459861.6328035877, 2459861.63325098], [2459861.645554273, 2459861.645777969]]

freq_flags: [[48202514.6484375, 48324584.9609375], [49911499.0234375, 50155639.6484375], [51620483.3984375, 51742553.7109375], [62240600.5859375, 62728881.8359375], [65902709.9609375, 66024780.2734375], [69931030.2734375, 70053100.5859375], [78109741.2109375, 78231811.5234375], [87509155.2734375, 108016967.7734375], [113632202.1484375, 113754272.4609375], [124618530.2734375, 125350952.1484375], [136215209.9609375, 136459350.5859375], [136947631.8359375, 137924194.3359375], [138168334.9609375, 138290405.2734375], [141464233.3984375, 141830444.3359375], [142074584.9609375, 142318725.5859375], [143783569.3359375, 144027709.9609375], [145736694.3359375, 145980834.9609375], [147445678.7109375, 147567749.0234375], [154159545.8984375, 154403686.5234375], [175155639.6484375, 175277709.9609375], [183212280.2734375, 183334350.5859375], [187362670.8984375, 187606811.5234375], [189193725.5859375, 189315795.8984375], [191146850.5859375, 191390991.2109375], [197128295.8984375, 197372436.5234375], [198104858.3984375, 198226928.7109375], [199203491.2109375, 199325561.5234375], [201766967.7734375, 201889038.0859375], [208480834.9609375, 208724975.5859375], [212142944.3359375, 212265014.6484375], [220687866.2109375, 220809936.5234375], [223129272.4609375, 223373413.0859375], [227401733.3984375, 227523803.7109375], [229110717.7734375, 229354858.3984375], [231063842.7734375, 231185913.0859375]]

ex_ants: [[10, Jee], [18, Jee], [18, Jnn], [22, Jee], [22, Jnn], [27, Jee], [27, Jnn], [28, Jee], [28, Jnn], [32, Jnn], [33, Jnn], [34, Jee], [43, Jee], [44, Jee], [46, Jnn], [47, Jee], [49, Jnn], [50, Jnn], [51, Jee], [51, Jnn], [54, Jee], [54, Jnn], [55, Jnn], [57, Jee], [58, Jee], [58, Jnn], [59, Jee], [60, Jee], [60, Jnn], [63, Jnn], [68, Jnn], [73, Jee], [73, Jnn], [74, Jee], [74, Jnn], [75, Jee], [75, Jnn], [77, Jee], [77, Jnn], [78, Jee], [81, Jee], [81, Jnn], [82, Jee], [82, Jnn], [83, Jee], [83, Jnn], [84, Jnn], [87, Jee], [92, Jee], [92, Jnn], [100, Jee], [100, Jnn], [102, Jee], [102, Jnn], [103, Jee], [103, Jnn], [104, Jnn], [109, Jnn], [110, Jee], [110, Jnn], [111, Jnn], [117, Jee], [117, Jnn], [119, Jee], [119, Jnn], [120, Jnn], [135, Jnn], [138, Jee], [138, Jnn], [140, Jnn], [141, Jnn], [142, Jnn], [143, Jee], [143, Jnn], [144, Jee], [144, Jnn], [145, Jee], [145, Jnn], [147, Jee], [147, Jnn], [148, Jee], [148, Jnn], [149, Jee], [149, Jnn], [150, Jee], [150, Jnn], [151, Jee], [153, Jee], [155, Jee], [156, Jee], [158, Jnn], [161, Jnn], [162, Jee], [163, Jee], [163, Jnn], [164, Jee], [164, Jnn], [165, Jee], [165, Jnn], [166, Jee], [166, Jnn], [167, Jee], [167, Jnn], [168, Jee], [168, Jnn], [169, Jee], [169, Jnn], [170, Jee], [170, Jnn], [173, Jee], [173, Jnn], [179, Jee], [179, Jnn], [180, Jnn], [183, Jee], [184, Jee], [184, Jnn], [185, Jee], [185, Jnn], [186, Jee], [186, Jnn], [187, Jee], [187, Jnn], [190, Jee], [190, Jnn], [192, Jnn], [193, Jee], [200, Jee], [200, Jnn], [201, Jee], [201, Jnn], [203, Jee], [203, Jnn], [219, Jee], [320, Jee], [320, Jnn], [321, Jee], [321, Jnn], [322, Jee], [322, Jnn], [323, Jee], [323, Jnn], [324, Jee], [324, Jnn], [325, Jee], [325, Jnn], [329, Jee], [329, Jnn], [333, Jee], [333, 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: 3.2.3
hera_qm: 2.1.1
hera_filters: 0.1.4.dev2+ga4ff591
hera_notebook_templates: 0.1.dev531+gfe314a8
pyuvdata: 2.3.3.dev39+g16031096
In [25]:
print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')

Finished execution in 40.49 minutes.