by Josh Dillon last updated October 17, 2022
This notebook parses and summarizes the output of the file_calibration
notebook to produce a report on per-antenna malfunctions on a daily basis.
Quick links:
import numpy as np
import pandas as pd
import glob
import os
import matplotlib.pyplot as plt
from hera_cal import io, utils
from hera_qm import ant_class
from uvtools.plot import plot_antpos, plot_antclass
%matplotlib inline
from IPython.display import display, HTML
# Parse settings from environment
ANT_CLASS_FOLDER = os.environ.get("ANT_CLASS_FOLDER", "./")
SUM_FILE = os.environ.get("SUM_FILE", None)
OC_SKIP_OUTRIGGERS = os.environ.get("OC_SKIP_OUTRIGGERS", "TRUE").upper() == "TRUE"
for param in ['ANT_CLASS_FOLDER', 'SUM_FILE', 'OC_SKIP_OUTRIGGERS']:
print(f"{param} = '{eval(param)}'")
ANT_CLASS_FOLDER = '/mnt/sn1/2460125' SUM_FILE = '/mnt/sn1/2460125/zen.2460125.46146.sum.uvh5' OC_SKIP_OUTRIGGERS = 'True'
if SUM_FILE is not None:
from astropy.time import Time, TimeDelta
utc = Time(float(SUM_FILE.split('zen.')[-1].split('.sum.uvh5')[0]), format='jd').datetime
print(f'Date: {utc.month}-{utc.day}-{utc.year}')
Date: 6-29-2023
# set thresholds for fraction of the day
overall_thresh = .1
all_zero_thresh = .1
eo_zeros_thresh = .1
cross_pol_thresh = .5
bad_fem_thresh = .1
high_power_thresh = .1
low_power_thresh = .1
low_corr_thresh = .1
bad_shape_thresh = .5
excess_rfi_thresh = .1
chisq_thresh = .25
# Load csvs
csv_files = sorted(glob.glob(os.path.join(ANT_CLASS_FOLDER, '*.ant_class.csv')))
jds = [float(f.split('/')[-1].split('zen.')[-1].split('.sum')[0]) for f in csv_files]
tables = [pd.read_csv(f).dropna(axis=0, how='all') for f in csv_files]
table_cols = tables[0].columns[1::2]
class_cols = tables[0].columns[2::2]
print(f'Found {len(csv_files)} csv files starting with {csv_files[0]}')
Found 360 csv files starting with /mnt/sn1/2460125/zen.2460125.42120.sum.ant_class.csv
# parse ant_strings
ap_strs = np.array(tables[0]['Antenna'])
ants = sorted(set(int(a[:-1]) for a in ap_strs))
translator = ''.maketrans('e', 'n') | ''.maketrans('n', 'e')
# get node numbers
node_dict = {ant: 'Unknown' for ant in ants}
try:
from hera_mc import cm_hookup
hookup = cm_hookup.get_hookup('default')
for ant_name in hookup:
ant = int("".join(filter(str.isdigit, ant_name)))
if ant in node_dict:
if hookup[ant_name].get_part_from_type('node')['E<ground'] is not None:
node_dict[ant] = int(hookup[ant_name].get_part_from_type('node')['E<ground'][1:])
except:
pass
nodes = sorted(set(node_dict.values()))
def classification_array(col):
class_array = np.vstack([t[col] for t in tables])
class_array[class_array == 'good'] = 1.7
class_array[class_array == 'suspect'] = 1
class_array[class_array == 'bad'] = 0
return class_array.astype(float)
if SUM_FILE is not None:
hd = io.HERADataFastReader(SUM_FILE)
ap_tuples = [(int(ap[:-1]), {'e': 'Jee', 'n': 'Jnn'}[ap[-1]]) for ap in ap_strs]
bad_bools = np.mean(classification_array('Antenna Class') == 0, axis=0) > overall_thresh
bad_aps = [ap_tuples[i] for i in np.arange(len(ap_tuples))[bad_bools]]
suspect_bools = np.mean(classification_array('Antenna Class') == 1, axis=0) > overall_thresh
suspect_aps = [ap_tuples[i] for i in np.arange(len(ap_tuples))[suspect_bools] if ap_tuples[i] not in bad_aps]
good_aps = [ap for ap in ap_tuples if ap not in bad_aps and ap not in suspect_aps]
overall_class = ant_class.AntennaClassification(bad=bad_aps, suspect=suspect_aps, good=good_aps)
autos, _, _ = hd.read(bls=[bl for bl in hd.bls if utils.split_bl(bl)[0] == utils.split_bl(bl)[1]], read_flags=False, read_nsamples=False)
avg_unflagged_auto = {pol: np.mean([autos[bl] for bl in autos if bl[2] == pol and overall_class[utils.split_bl(bl)[0]] != 'bad'], axis=(0, 1)) for pol in ['ee', 'nn']}
def print_issue_summary(bad_ant_strs, title, notes='', plot=False):
'''Print report for list of bad antenna polarizations strings'''
unique_bad_antnums = [int(ap[:-1]) for ap in bad_ant_strs]
display(HTML(f'<h2>{title}: ({len(bad_ant_strs)} antpols across {len(set([ba[:-1] for ba in bad_ant_strs]))} antennas)</h2>'))
if len(notes) > 0:
display(HTML(f'<h4>{notes}</h4>'))
if len(bad_ant_strs) > 0:
print(f'All Bad Antpols: {", ".join(bad_ant_strs)}\n')
for node in nodes:
if np.any([node == node_dict[a] for a in unique_bad_antnums]):
aps = [ap for ap in bad_ant_strs if node_dict[int(ap[:-1])] == node]
whole_ants = [str(wa) for wa in set([int(ap[:-1]) for ap in aps if ap.translate(translator) in bad_ant_strs])]
single_pols = [ap for ap in aps if ap.translate(translator) not in bad_ant_strs]
print(f'Node {node}:')
print(f'\tAntpols ({len(aps)} total): {", ".join(aps)}')
print(f'\tWhole Ants ({len(whole_ants)} total): {", ".join(whole_ants)}')
print(f'\tSingle Pols ({len(single_pols)} total): {", ".join(single_pols)}')
if plot and SUM_FILE is not None:
fig, axes = plt.subplots(1, 2, figsize=(12,4), dpi=70, sharey=True, gridspec_kw={'wspace': 0})
for ax, pol in zip(axes, ['ee', 'nn']):
ax.semilogy(autos.freqs / 1e6, avg_unflagged_auto[pol], 'k--', label='Average\nUnflagged\nAuto')
for ap in aps:
ant = int(ap[:-1]), utils.comply_pol(ap[-1])
auto_bl = utils.join_bl(ant, ant)
if auto_bl[2] == pol:
ax.semilogy(autos.freqs / 1e6, np.mean(autos[auto_bl], axis=0), label=ap)
ax.legend()
ax.set_xlim([40, 299])
ax.set_title(f'{title} on Node {node} ({pol}-antennas)')
ax.set_xlabel('Frequency (MHz)')
axes[0].set_ylabel('Single File Raw Autocorrelation')
plt.tight_layout()
plt.show()
# precompute various helpful quantities
all_slopes = np.vstack([t['Autocorr Slope'] for t in tables])
median_slope = np.median(all_slopes)
bad_slopes = np.vstack([t['Autocorr Slope Class'] for t in tables]) == 'bad'
suspect_slopes = np.vstack([t['Autocorr Slope Class'] for t in tables]) == 'suspect'
bad_shapes = np.vstack([t['Autocorr Shape Class'] for t in tables]) == 'bad'
suspect_shapes = np.vstack([t['Autocorr Shape Class'] for t in tables]) == 'suspect'
all_powers = np.vstack([t['Autocorr Power'] for t in tables])
median_power = np.median(all_powers)
bad_powers = np.vstack([t['Autocorr Power Class'] for t in tables]) == 'bad'
suspect_powers = np.vstack([t['Autocorr Power Class'] for t in tables]) == 'suspect'
bad_rfi = np.vstack([t['RFI in Autos Class'] for t in tables]) == 'bad'
suspect_rfi = np.vstack([t['RFI in Autos Class'] for t in tables]) == 'suspect'
# find all zeros
all_zeros_strs = ap_strs[np.mean(np.vstack([t['Dead? Class'] for t in tables]) == 'bad', axis=0) > all_zero_thresh]
# find even/odd zeros
eo_zeros_strs = ap_strs[np.mean(np.vstack([t['Even/Odd Zeros Class'] for t in tables]) == 'bad', axis=0) > eo_zeros_thresh]
eo_zeros_strs = [ap for ap in eo_zeros_strs if ap not in all_zeros_strs]
# find cross-polarized antennas
cross_pol_strs = ap_strs[np.mean(np.vstack([t['Cross-Polarized Class'] for t in tables]) == 'bad', axis=0) > cross_pol_thresh]
# find FEM power issues: must be low power, high slope, and bad or suspect in power, slope, rfi, and shape
fem_off_prod = (bad_powers + .5 * suspect_powers) * (bad_slopes + .5 * suspect_slopes)
fem_off_prod *= (bad_rfi + .5 * suspect_rfi) * (bad_shapes + .5 * suspect_shapes)
fem_off_strs = ap_strs[np.mean(fem_off_prod * (all_powers < median_power) * (all_slopes > median_slope), axis=0) > .1]
# find high power issues
high_power_strs = ap_strs[np.mean(bad_powers & (all_powers > median_power), axis=0) > high_power_thresh]
# find other low power issues
low_power_strs = ap_strs[np.mean(bad_powers & (all_powers < median_power), axis=0) > low_power_thresh]
low_power_strs = [ap for ap in low_power_strs if ap not in all_zeros_strs and ap not in fem_off_strs]
# find low correlation (but not low power)
low_corr_strs = ap_strs[np.mean(np.vstack([t['Low Correlation Class'] for t in tables]) == 'bad', axis=0) > low_corr_thresh]
low_corr_strs = [ap for ap in low_corr_strs if ap not in (set(low_power_strs) | set(all_zeros_strs) | set(fem_off_strs))]
# find bad bandpasses
bad_bandpass_strs = ap_strs[np.mean(bad_shapes, axis=0) > bad_shape_thresh]
bad_bandpass_strs = [ap for ap in bad_bandpass_strs if ap not in (set(low_power_strs) | set(all_zeros_strs) | set(high_power_strs) | set(fem_off_strs))]
# find antennas with excess RFI
excess_rfi_strs = ap_strs[np.mean(np.vstack([t['RFI in Autos Class'] for t in tables]) == 'bad', axis=0) > excess_rfi_thresh]
excess_rfi_strs = [ap for ap in excess_rfi_strs if ap not in (set(low_power_strs) | set(all_zeros_strs) | set(fem_off_strs) |
set(bad_bandpass_strs) | set(high_power_strs))]
# find antennas with high redcal chi^2
chisq_strs = ap_strs[np.mean(np.vstack([t['Redcal chi^2 Class'] for t in tables]) == 'bad', axis=0) > chisq_thresh]
chisq_strs = [ap for ap in chisq_strs if ap not in (set(bad_bandpass_strs) | set(low_power_strs) | set(excess_rfi_strs) | set(low_corr_strs) |
set(all_zeros_strs) | set(high_power_strs) | set(fem_off_strs) | set(eo_zeros_strs))]
if OC_SKIP_OUTRIGGERS:
chisq_strs = [ap for ap in chisq_strs if int(ap[:-1]) < 320]
# collect all results
to_print = [(all_zeros_strs, 'All-Zeros', 'These antennas have visibilities that are more than half zeros.'),
(eo_zeros_strs, 'Excess Zeros in Either Even or Odd Spectra',
'These antennas are showing evidence of packet loss or X-engine failure.', True),
(cross_pol_strs, 'Cross-Polarized', 'These antennas have their east and north cables swapped.'),
(fem_off_strs, 'Likely FEM Power Issue', 'These antennas have low power, anomolously high slopes, and extra channels identified as RFI.', True),
(high_power_strs, 'High Power', 'These antennas have high median power.', True),
(low_power_strs, 'Other Low Power Issues', 'These antennas have low power, but are not all-zeros and not FEM off.', True),
(low_corr_strs, 'Low Correlation, But Not Low Power', 'These antennas are low correlation, but their autocorrelation power levels look OK.'),
(bad_bandpass_strs, 'Bad Bandpass Shapes, But Not Bad Power',
'These antennas have unusual bandpass shapes, but are not all-zeros, high power, low power, or FEM off.', True),
(excess_rfi_strs, 'Excess RFI', 'These antennas have excess strucutre (identified as possible RFI) in their bandpassed relative to the ' + \
'median antenna, but not low or high power or a bad bandpass.', True),
(chisq_strs, 'Redcal chi^2', 'These antennas have been idenfied as not redundantly calibrating well, even after passing the above checks.')]
def print_high_level_summary():
for tp in sorted(to_print, key=lambda x: len(x[0]), reverse=True):
print(f'{len(tp[0])} antpols (on {len(set([ap[:-1] for ap in tp[0]]))} antennas) frequently flagged for {tp[1]}.')
def print_all_issue_summaries():
for tp in to_print:
print_issue_summary(*tp)
print_high_level_summary()
60 antpols (on 30 antennas) frequently flagged for All-Zeros. 47 antpols (on 34 antennas) frequently flagged for High Power. 24 antpols (on 19 antennas) frequently flagged for Likely FEM Power Issue. 12 antpols (on 12 antennas) frequently flagged for Bad Bandpass Shapes, But Not Bad Power. 8 antpols (on 8 antennas) frequently flagged for Low Correlation, But Not Low Power. 7 antpols (on 6 antennas) frequently flagged for Excess RFI. 4 antpols (on 3 antennas) frequently flagged for Redcal chi^2. 3 antpols (on 2 antennas) frequently flagged for Other Low Power Issues. 0 antpols (on 0 antennas) frequently flagged for Excess Zeros in Either Even or Odd Spectra. 0 antpols (on 0 antennas) frequently flagged for Cross-Polarized.
print_all_issue_summaries()
All Bad Antpols: 43e, 43n, 44e, 44n, 58e, 58n, 59e, 59n, 60e, 60n, 74e, 74n, 115e, 115n, 131e, 131n, 133e, 133n, 143e, 143n, 144e, 144n, 145e, 145n, 146e, 146n, 163e, 163n, 164e, 164n, 165e, 165n, 166e, 166n, 184e, 184n, 185e, 185n, 186e, 186n, 187e, 187n, 200e, 200n, 201e, 201n, 202e, 202n, 220e, 220n, 221e, 221n, 222e, 222n, 237e, 237n, 238e, 238n, 239e, 239n Node 5: Antpols (12 total): 43e, 43n, 44e, 44n, 58e, 58n, 59e, 59n, 60e, 60n, 74e, 74n Whole Ants (6 total): 74, 43, 44, 58, 59, 60 Single Pols (0 total): Node 11: Antpols (6 total): 115e, 115n, 131e, 131n, 133e, 133n Whole Ants (3 total): 115, 131, 133 Single Pols (0 total): Node 14: Antpols (24 total): 143e, 143n, 144e, 144n, 145e, 145n, 146e, 146n, 163e, 163n, 164e, 164n, 165e, 165n, 166e, 166n, 184e, 184n, 185e, 185n, 186e, 186n, 187e, 187n Whole Ants (12 total): 163, 164, 165, 166, 143, 144, 145, 146, 184, 185, 186, 187 Single Pols (0 total): Node 18: Antpols (18 total): 200e, 200n, 201e, 201n, 202e, 202n, 220e, 220n, 221e, 221n, 222e, 222n, 237e, 237n, 238e, 238n, 239e, 239n Whole Ants (9 total): 200, 201, 202, 237, 238, 239, 220, 221, 222 Single Pols (0 total):
All Bad Antpols: 27e, 29e, 29n, 34e, 47e, 47n, 55e, 92e, 92n, 93n, 109n, 124e, 124n, 141e, 153e, 174n, 182n, 183n, 211n, 213n, 225n, 246n, 329e, 329n Node 1: Antpols (3 total): 27e, 29e, 29n Whole Ants (1 total): 29 Single Pols (1 total): 27e
Node 4: Antpols (1 total): 55e Whole Ants (0 total): Single Pols (1 total): 55e
Node 6: Antpols (3 total): 34e, 47e, 47n Whole Ants (1 total): 47 Single Pols (1 total): 34e
Node 9: Antpols (2 total): 124e, 124n Whole Ants (1 total): 124 Single Pols (0 total):
Node 10: Antpols (4 total): 92e, 92n, 93n, 109n Whole Ants (1 total): 92 Single Pols (2 total): 93n, 109n
Node 12: Antpols (2 total): 329e, 329n Whole Ants (1 total): 329 Single Pols (0 total):
Node 13: Antpols (3 total): 141e, 182n, 183n Whole Ants (0 total): Single Pols (3 total): 141e, 182n, 183n
Node 16: Antpols (3 total): 153e, 174n, 213n Whole Ants (0 total): Single Pols (3 total): 153e, 174n, 213n
Node 19: Antpols (1 total): 225n Whole Ants (0 total): Single Pols (1 total): 225n
Node 20: Antpols (2 total): 211n, 246n Whole Ants (0 total): Single Pols (2 total): 211n, 246n
All Bad Antpols: 8e, 8n, 19n, 30e, 62n, 63n, 78n, 80n, 84e, 84n, 93e, 96e, 113e, 113n, 114e, 121e, 123e, 123n, 127e, 134n, 139e, 155e, 160e, 173e, 173n, 175e, 175n, 180n, 189e, 189n, 192e, 192n, 193e, 193n, 195e, 207e, 213e, 224e, 224n, 229n, 324e, 324n, 332e, 332n, 333n, 340e, 340n Node 1: Antpols (1 total): 30e Whole Ants (0 total): Single Pols (1 total): 30e
Node 2: Antpols (3 total): 8e, 8n, 19n Whole Ants (1 total): 8 Single Pols (1 total): 19n
Node 4: Antpols (2 total): 324e, 324n Whole Ants (1 total): 324 Single Pols (0 total):
Node 6: Antpols (3 total): 62n, 63n, 78n Whole Ants (0 total): Single Pols (3 total): 62n, 63n, 78n
Node 8: Antpols (5 total): 84e, 84n, 121e, 123e, 123n Whole Ants (2 total): 123, 84 Single Pols (1 total): 121e
Node 10: Antpols (2 total): 93e, 127e Whole Ants (0 total): Single Pols (2 total): 93e, 127e
Node 11: Antpols (6 total): 80n, 96e, 113e, 113n, 114e, 134n Whole Ants (1 total): 113 Single Pols (4 total): 80n, 96e, 114e, 134n
Node 12: Antpols (2 total): 155e, 333n Whole Ants (0 total): Single Pols (2 total): 155e, 333n
Node 13: Antpols (3 total): 139e, 160e, 180n Whole Ants (0 total): Single Pols (3 total): 139e, 160e, 180n
Node 15: Antpols (2 total): 189e, 189n Whole Ants (1 total): 189 Single Pols (0 total):
Node 16: Antpols (7 total): 173e, 173n, 192e, 192n, 193e, 193n, 213e Whole Ants (3 total): 192, 193, 173 Single Pols (1 total): 213e
Node 19: Antpols (3 total): 207e, 224e, 224n Whole Ants (1 total): 224 Single Pols (1 total): 207e
Node 20: Antpols (1 total): 229n Whole Ants (0 total): Single Pols (1 total): 229n
Node 21: Antpols (7 total): 175e, 175n, 195e, 332e, 332n, 340e, 340n Whole Ants (3 total): 332, 340, 175 Single Pols (1 total): 195e
All Bad Antpols: 208n, 209e, 209n Node 20: Antpols (3 total): 208n, 209e, 209n Whole Ants (1 total): 209 Single Pols (1 total): 208n
All Bad Antpols: 27n, 28n, 61e, 86e, 93e, 174e, 194e, 214e Node 1: Antpols (2 total): 27n, 28n Whole Ants (0 total): Single Pols (2 total): 27n, 28n Node 6: Antpols (1 total): 61e Whole Ants (0 total): Single Pols (1 total): 61e Node 8: Antpols (1 total): 86e Whole Ants (0 total): Single Pols (1 total): 86e Node 10: Antpols (1 total): 93e Whole Ants (0 total): Single Pols (1 total): 93e Node 16: Antpols (2 total): 174e, 194e Whole Ants (0 total): Single Pols (2 total): 174e, 194e Node 21: Antpols (1 total): 214e Whole Ants (0 total): Single Pols (1 total): 214e
All Bad Antpols: 15e, 27n, 28n, 32e, 78e, 87e, 96n, 104n, 110e, 111e, 142n, 161n Node 1: Antpols (3 total): 15e, 27n, 28n Whole Ants (0 total): Single Pols (3 total): 15e, 27n, 28n
Node 2: Antpols (1 total): 32e Whole Ants (0 total): Single Pols (1 total): 32e
Node 6: Antpols (1 total): 78e Whole Ants (0 total): Single Pols (1 total): 78e
Node 8: Antpols (2 total): 87e, 104n Whole Ants (0 total): Single Pols (2 total): 87e, 104n
Node 10: Antpols (2 total): 110e, 111e Whole Ants (0 total): Single Pols (2 total): 110e, 111e
Node 11: Antpols (1 total): 96n Whole Ants (0 total): Single Pols (1 total): 96n
Node 13: Antpols (2 total): 142n, 161n Whole Ants (0 total): Single Pols (2 total): 142n, 161n
All Bad Antpols: 10e, 18e, 18n, 40n, 51e, 174e, 214e Node 1: Antpols (2 total): 18e, 18n Whole Ants (1 total): 18 Single Pols (0 total):
Node 2: Antpols (1 total): 10e Whole Ants (0 total): Single Pols (1 total): 10e
Node 3: Antpols (1 total): 51e Whole Ants (0 total): Single Pols (1 total): 51e
Node 4: Antpols (1 total): 40n Whole Ants (0 total): Single Pols (1 total): 40n
Node 16: Antpols (1 total): 174e Whole Ants (0 total): Single Pols (1 total): 174e
Node 21: Antpols (1 total): 214e Whole Ants (0 total): Single Pols (1 total): 214e
All Bad Antpols: 54e, 54n, 87n, 151e Node 4: Antpols (2 total): 54e, 54n Whole Ants (1 total): 54 Single Pols (0 total): Node 8: Antpols (1 total): 87n Whole Ants (0 total): Single Pols (1 total): 87n Node 16: Antpols (1 total): 151e Whole Ants (0 total): Single Pols (1 total): 151e
def classification_plot(col):
class_array = classification_array(col)
plt.figure(figsize=(12, len(ants) / 10), dpi=100)
plt.imshow(class_array.T, aspect='auto', interpolation='none', cmap='RdYlGn', vmin=0, vmax=2,
extent=[jds[0] - np.floor(jds[0]), jds[-1] - np.floor(jds[0]), len(ants), 0])
plt.xlabel(f'JD - {int(jds[0])}')
plt.yticks(ticks=np.arange(.5, len(ants)+.5), labels=[ant for ant in ants], fontsize=6)
plt.ylabel('Antenna Number (East First, Then North)')
plt.gca().tick_params(right=True, top=True, labelright=True, labeltop=True)
plt.tight_layout()
plt.title(f'{col}: Green is "good", Yellow is "suspect", Red is "bad"')
This "big green board" shows the overall (i.e. after redundant calibration) classification of antennas on a per-file basis. This is useful for looking at time-dependent effects across the array. While only antenna numbers are labeled, both polarizations are shown, first East then North going down, above and below the antenna's tick mark.
classification_plot('Antenna Class')
# compute flag fractions for all classifiers and antennas
frac_flagged = []
for col in class_cols[1:]:
class_array = np.vstack([t[col] for t in tables])
class_array[class_array == 'good'] = False
class_array[class_array == 'suspect'] = False
class_array[class_array == 'bad'] = True
frac_flagged.append(np.sum(class_array, axis=0))
def plot_flag_frac_all_classifiers():
ticks = []
for i, col in enumerate(list(class_cols[1:])):
ticks.append(f'{col} ({np.nanmean(np.array(frac_flagged).astype(float)[i]) / len(csv_files):.2%})')
plt.figure(figsize=(8, len(ants) / 10), dpi=100)
plt.imshow(np.array(frac_flagged).astype(float).T, aspect='auto', interpolation='none', cmap='viridis')
plt.xticks(ticks=np.arange(len(list(class_cols[1:]))), labels=ticks, rotation=-45, ha='left')
plt.yticks(ticks=np.arange(.5, len(ap_strs)+.5, 2), labels=[ant for ant in ants], fontsize=6)
plt.ylabel('Antenna Number (East First, Then North)')
plt.gca().tick_params(right=True, labelright=True,)
ax2 = plt.gca().twiny()
ax2.set_xticks(ticks=np.arange(len(list(class_cols[1:]))), labels=ticks, rotation=45, ha='left')
plt.colorbar(ax=plt.gca(), label=f'Number of Files Flagged Out of {len(csv_files)}', aspect=50)
plt.tight_layout()
This plot shows the fraction of files flagged for each reason for each antenna. It's useful for seeing which problems are transitory and which ones are more common. Note that not all flags are independent and in particular redcal chi^2 takes an OR of other classifications as an input. Also note that only antenna numbers are labeled, both polarizations are shown, first East then North going down, above and below the antenna's tick mark.
plot_flag_frac_all_classifiers()
def array_class_plot():
fig, axes = plt.subplots(1, 2, figsize=(14, 6), dpi=100, gridspec_kw={'width_ratios': [2, 1]})
plot_antclass(hd.antpos, overall_class, ax=axes[0], ants=[ant for ant in hd.data_ants if ant < 320], legend=False,
title=f'HERA Core: Overall Flagging Based on {overall_thresh:.1%} Daily Threshold')
plot_antclass(hd.antpos, overall_class, ax=axes[1], ants=[ant for ant in hd.data_ants if ant >= 320], radius=50, title='Outriggers')
Overall classification of antenna-polarizations shown on the array layout. If any antenna is marked bad for any reason more than the threshold (default 10%), it is marked bad here. Likewise, if any antenna is marked suspect for more than 10% of the night (but not bad), it's suspect here.
if SUM_FILE is not None: array_class_plot()