Antenna Classification Daily Summary¶
by Josh Dillon last updated June 19, 2023
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:
• Summary of Per Antenna Issues¶
• Figure 1: Per File Overall Antenna Classification Summary¶
• Figure 2: Per Classifier Antenna Flagging Summary¶
• Figure 3: Array Visualization of Overall Daily Classification¶
In [1]:
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 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
display(HTML("<style>.container { width:100% !important; }</style>"))
_ = np.seterr(all='ignore') # get rid of red warnings
%config InlineBackend.figure_format = 'retina'
Settings¶
In [2]:
# Parse settings from environment
ANT_CLASS_FOLDER = os.environ.get("ANT_CLASS_FOLDER", "./")
SUM_FILE = os.environ.get("SUM_FILE", None)
# ANT_CLASS_FOLDER = "/mnt/sn1/2460330"
# SUM_FILE = "/mnt/sn1/2460330/zen.2460330.25463.sum.uvh5"
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/data1/2460941' SUM_FILE = '/mnt/sn1/data1/2460941/zen.2460941.53446.sum.uvh5' OC_SKIP_OUTRIGGERS = 'True'
In [3]:
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: 9-23-2025
In [4]:
# set thresholds for fraction of the day
overall_thresh = .1
all_zero_thresh = .1
eo_zeros_thresh = .1
xengine_diff_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 classifications and other metadata¶
In [5]:
# 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 1157 csv files starting with /mnt/sn1/data1/2460941/zen.2460941.33581.sum.ant_class.csv
In [6]:
# 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')
In [7]:
# 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()))
In [8]:
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)
In [9]:
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 = {}
for pol in ['ee', 'nn']:
unflagged_autos = [autos[bl] for bl in autos if bl[2] == pol and overall_class[utils.split_bl(bl)[0]] != 'bad']
if len(unflagged_autos) > 0:
avg_unflagged_auto[pol] = np.mean(unflagged_autos, axis=(0, 1))
else:
avg_unflagged_auto[pol] = np.zeros(len(hd.freqs), dtype=complex)
Figure out and summarize per-antenna issues¶
In [10]:
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()
In [11]:
# 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['Auto RFI RMS Class'] for t in tables]) == 'bad'
suspect_rfi = np.vstack([t['Auto RFI RMS Class'] for t in tables]) == 'suspect'
In [12]:
# 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]
In [13]:
# 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]
In [14]:
# 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]
cross_pol_strs = [ap for ap in cross_pol_strs if ap not in all_zeros_strs]
In [15]:
# 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]
In [16]:
# find high power issues
high_power_strs = ap_strs[np.mean(bad_powers & (all_powers > median_power), axis=0) > high_power_thresh]
In [17]:
# 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]
In [18]:
# 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))]
In [19]:
# 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))]
In [20]:
# find antennas with excess RFI
excess_rfi_strs = ap_strs[np.mean(np.vstack([t['Auto RFI RMS 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))]
In [21]:
# find bad x-engine diffs
xengine_diff_strs = ap_strs[np.mean(np.vstack([t['Bad Diff X-Engines Class'] for t in tables]) == 'bad', axis=0) > xengine_diff_thresh]
xengine_diff_strs = [ap for ap in xengine_diff_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))]
In [22]:
# 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) | set(xengine_diff_strs))]
if OC_SKIP_OUTRIGGERS:
chisq_strs = [ap for ap in chisq_strs if int(ap[:-1]) < 320]
In [23]:
# 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),
(xengine_diff_strs, 'Excess Power in X-Engine Diffs',
'These antennas are showing evidence of mis-written packets in either the evens or the odds.', 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 and anomolously high slopes.', 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 RMS after DPSS filtering (likely RFI), 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.')]
In [24]:
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)
Summary of Per-Antenna Issues¶
In [25]:
print_high_level_summary()
382 antpols (on 191 antennas) frequently flagged for All-Zeros. 41 antpols (on 22 antennas) frequently flagged for Low Correlation, But Not Low Power. 36 antpols (on 25 antennas) frequently flagged for Redcal chi^2. 28 antpols (on 17 antennas) frequently flagged for High Power. 22 antpols (on 19 antennas) frequently flagged for Excess RFI. 13 antpols (on 9 antennas) frequently flagged for Bad Bandpass Shapes, But Not Bad Power. 8 antpols (on 6 antennas) frequently flagged for Excess Zeros in Either Even or Odd Spectra. 2 antpols (on 1 antennas) frequently flagged for Cross-Polarized. 1 antpols (on 1 antennas) frequently flagged for Excess Power in X-Engine Diffs. 0 antpols (on 0 antennas) frequently flagged for Likely FEM Power Issue. 0 antpols (on 0 antennas) frequently flagged for Other Low Power Issues.
In [26]:
print_all_issue_summaries()
All-Zeros: (382 antpols across 191 antennas)
These antennas have visibilities that are more than half zeros.
All Bad Antpols: 3e, 3n, 4e, 4n, 5e, 5n, 7e, 7n, 8e, 8n, 9e, 9n, 10e, 10n, 15e, 15n, 16e, 16n, 17e, 17n, 19e, 19n, 20e, 20n, 21e, 21n, 22e, 22n, 31e, 31n, 32e, 32n, 34e, 34n, 35e, 35n, 37e, 37n, 38e, 38n, 40e, 40n, 41e, 41n, 42e, 42n, 43e, 43n, 44e, 44n, 47e, 47n, 48e, 48n, 49e, 49n, 51e, 51n, 53e, 53n, 54e, 54n, 56e, 56n, 57e, 57n, 58e, 58n, 59e, 59n, 60e, 60n, 61e, 61n, 62e, 62n, 63e, 63n, 64e, 64n, 68e, 68n, 69e, 69n, 72e, 72n, 74e, 74n, 77e, 77n, 78e, 78n, 79e, 79n, 80e, 80n, 85e, 85n, 86e, 86n, 87e, 87n, 88e, 88n, 90e, 90n, 91e, 91n, 95e, 95n, 96e, 96n, 101e, 101n, 102e, 102n, 105e, 105n, 106e, 106n, 107e, 107n, 108e, 108n, 109e, 109n, 110e, 110n, 111e, 111n, 113e, 113n, 114e, 114n, 115e, 115n, 122e, 122n, 125e, 125n, 126e, 126n, 129e, 129n, 130e, 130n, 131e, 131n, 133e, 133n, 139e, 139n, 140e, 140n, 141e, 141n, 142e, 142n, 143e, 143n, 144e, 144n, 145e, 145n, 146e, 146n, 147e, 147n, 148e, 148n, 149e, 149n, 150e, 150n, 156e, 156n, 157e, 157n, 158e, 158n, 159e, 159n, 160e, 160n, 163e, 163n, 164e, 164n, 166e, 166n, 167e, 167n, 168e, 168n, 169e, 169n, 170e, 170n, 173e, 173n, 179e, 179n, 180e, 180n, 181e, 181n, 183e, 183n, 186e, 186n, 187e, 187n, 188e, 188n, 189e, 189n, 190e, 190n, 191e, 191n, 192e, 192n, 193e, 193n, 196e, 196n, 197e, 197n, 198e, 198n, 211e, 211n, 218e, 218n, 225e, 225n, 226e, 226n, 227e, 227n, 228e, 228n, 229e, 229n, 233e, 233n, 234e, 234n, 237e, 237n, 238e, 238n, 239e, 239n, 240e, 240n, 241e, 241n, 242e, 242n, 243e, 243n, 244e, 244n, 245e, 245n, 246e, 246n, 250e, 250n, 251e, 251n, 252e, 252n, 254e, 254n, 255e, 255n, 256e, 256n, 257e, 257n, 261e, 261n, 262e, 262n, 266e, 266n, 267e, 267n, 268e, 268n, 269e, 269n, 270e, 270n, 271e, 271n, 272e, 272n, 273e, 273n, 277e, 277n, 278e, 278n, 281e, 281n, 284e, 284n, 285e, 285n, 286e, 286n, 287e, 287n, 290e, 290n, 291e, 291n, 292e, 292n, 295e, 295n, 299e, 299n, 300e, 300n, 301e, 301n, 302e, 302n, 303e, 303n, 304e, 304n, 305e, 305n, 306e, 306n, 307e, 307n, 311e, 311n, 312e, 312n, 313e, 313n, 314e, 314n, 318e, 318n, 319e, 319n, 320e, 320n, 324e, 324n, 326e, 326n, 327e, 327n, 328e, 328n, 329e, 329n, 331e, 331n, 332e, 332n, 333e, 333n, 336e, 336n, 339e, 339n, 340e, 340n, 345e, 345n, 347e, 347n Node 1: Antpols (12 total): 3e, 3n, 4e, 4n, 5e, 5n, 15e, 15n, 16e, 16n, 17e, 17n Whole Ants (6 total): 3, 4, 5, 15, 16, 17 Single Pols (0 total): Node 2: Antpols (18 total): 7e, 7n, 8e, 8n, 9e, 9n, 10e, 10n, 19e, 19n, 20e, 20n, 21e, 21n, 31e, 31n, 32e, 32n Whole Ants (9 total): 32, 7, 8, 9, 10, 19, 20, 21, 31 Single Pols (0 total): Node 3: Antpols (12 total): 37e, 37n, 38e, 38n, 51e, 51n, 53e, 53n, 68e, 68n, 320e, 320n Whole Ants (6 total): 320, 68, 37, 38, 51, 53 Single Pols (0 total): Node 4: Antpols (18 total): 40e, 40n, 41e, 41n, 42e, 42n, 54e, 54n, 56e, 56n, 57e, 57n, 69e, 69n, 72e, 72n, 324e, 324n Whole Ants (9 total): 324, 69, 40, 41, 42, 72, 54, 56, 57 Single Pols (0 total): 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 6: Antpols (24 total): 22e, 22n, 34e, 34n, 35e, 35n, 47e, 47n, 48e, 48n, 49e, 49n, 61e, 61n, 62e, 62n, 63e, 63n, 64e, 64n, 77e, 77n, 78e, 78n Whole Ants (12 total): 64, 34, 35, 77, 78, 47, 48, 49, 22, 61, 62, 63 Single Pols (0 total): Node 8: Antpols (12 total): 85e, 85n, 86e, 86n, 87e, 87n, 101e, 101n, 102e, 102n, 122e, 122n Whole Ants (6 total): 101, 102, 85, 86, 87, 122 Single Pols (0 total): Node 9: Antpols (18 total): 88e, 88n, 90e, 90n, 91e, 91n, 105e, 105n, 106e, 106n, 107e, 107n, 108e, 108n, 125e, 125n, 126e, 126n Whole Ants (9 total): 105, 106, 107, 108, 88, 90, 91, 125, 126 Single Pols (0 total): Node 10: Antpols (12 total): 109e, 109n, 110e, 110n, 111e, 111n, 129e, 129n, 130e, 130n, 328e, 328n Whole Ants (6 total): 129, 130, 328, 109, 110, 111 Single Pols (0 total): Node 11: Antpols (18 total): 79e, 79n, 80e, 80n, 95e, 95n, 96e, 96n, 113e, 113n, 114e, 114n, 115e, 115n, 131e, 131n, 133e, 133n Whole Ants (9 total): 96, 131, 133, 79, 80, 113, 114, 115, 95 Single Pols (0 total): Node 12: Antpols (12 total): 156e, 156n, 157e, 157n, 158e, 158n, 179e, 179n, 329e, 329n, 333e, 333n Whole Ants (6 total): 329, 333, 179, 156, 157, 158 Single Pols (0 total): Node 13: Antpols (18 total): 139e, 139n, 140e, 140n, 141e, 141n, 142e, 142n, 159e, 159n, 160e, 160n, 180e, 180n, 181e, 181n, 183e, 183n Whole Ants (9 total): 160, 139, 140, 141, 142, 180, 181, 183, 159 Single Pols (0 total): Node 14: Antpols (18 total): 143e, 143n, 144e, 144n, 145e, 145n, 146e, 146n, 163e, 163n, 164e, 164n, 166e, 166n, 186e, 186n, 187e, 187n Whole Ants (9 total): 163, 164, 166, 143, 144, 145, 146, 186, 187 Single Pols (0 total): Node 15: Antpols (24 total): 147e, 147n, 148e, 148n, 149e, 149n, 150e, 150n, 167e, 167n, 168e, 168n, 169e, 169n, 170e, 170n, 188e, 188n, 189e, 189n, 190e, 190n, 191e, 191n Whole Ants (12 total): 167, 168, 169, 170, 147, 148, 149, 150, 188, 189, 190, 191 Single Pols (0 total): Node 16: Antpols (6 total): 173e, 173n, 192e, 192n, 193e, 193n Whole Ants (3 total): 192, 193, 173 Single Pols (0 total): Node 17: Antpols (12 total): 196e, 196n, 197e, 197n, 198e, 198n, 218e, 218n, 233e, 233n, 234e, 234n Whole Ants (6 total): 196, 197, 198, 233, 234, 218 Single Pols (0 total): Node 18: Antpols (6 total): 237e, 237n, 238e, 238n, 239e, 239n Whole Ants (3 total): 237, 238, 239 Single Pols (0 total): Node 19: Antpols (12 total): 225e, 225n, 226e, 226n, 240e, 240n, 241e, 241n, 242e, 242n, 243e, 243n Whole Ants (6 total): 225, 226, 240, 241, 242, 243 Single Pols (0 total): Node 20: Antpols (18 total): 211e, 211n, 227e, 227n, 228e, 228n, 229e, 229n, 244e, 244n, 245e, 245n, 246e, 246n, 261e, 261n, 262e, 262n Whole Ants (9 total): 227, 228, 229, 261, 262, 211, 244, 245, 246 Single Pols (0 total): Node 21: Antpols (12 total): 326e, 326n, 327e, 327n, 331e, 331n, 332e, 332n, 336e, 336n, 340e, 340n Whole Ants (6 total): 326, 327, 331, 332, 336, 340 Single Pols (0 total): Node 22: Antpols (18 total): 250e, 250n, 251e, 251n, 252e, 252n, 266e, 266n, 267e, 267n, 268e, 268n, 269e, 269n, 281e, 281n, 295e, 295n Whole Ants (9 total): 295, 266, 267, 268, 269, 281, 250, 251, 252 Single Pols (0 total): Node 23: Antpols (24 total): 254e, 254n, 255e, 255n, 256e, 256n, 257e, 257n, 270e, 270n, 271e, 271n, 272e, 272n, 273e, 273n, 284e, 284n, 285e, 285n, 286e, 286n, 287e, 287n Whole Ants (12 total): 256, 257, 287, 286, 270, 271, 272, 273, 284, 285, 254, 255 Single Pols (0 total): Node 27: Antpols (18 total): 299e, 299n, 300e, 300n, 301e, 301n, 302e, 302n, 311e, 311n, 312e, 312n, 313e, 313n, 314e, 314n, 347e, 347n Whole Ants (9 total): 299, 300, 301, 302, 311, 312, 313, 314, 347 Single Pols (0 total): Node 28: Antpols (12 total): 290e, 290n, 291e, 291n, 303e, 303n, 304e, 304n, 305e, 305n, 318e, 318n Whole Ants (6 total): 290, 291, 303, 304, 305, 318 Single Pols (0 total): Node 29: Antpols (16 total): 277e, 277n, 278e, 278n, 292e, 292n, 306e, 306n, 307e, 307n, 319e, 319n, 339e, 339n, 345e, 345n Whole Ants (8 total): 292, 306, 307, 339, 277, 278, 345, 319 Single Pols (0 total):
Excess Zeros in Either Even or Odd Spectra: (8 antpols across 6 antennas)
These antennas are showing evidence of packet loss or X-engine failure.
All Bad Antpols: 84e, 84n, 103e, 103n, 104e, 120n, 121e, 123e Node 8: Antpols (8 total): 84e, 84n, 103e, 103n, 104e, 120n, 121e, 123e Whole Ants (2 total): 84, 103 Single Pols (4 total): 104e, 120n, 121e, 123e
Excess Power in X-Engine Diffs: (1 antpols across 1 antennas)
These antennas are showing evidence of mis-written packets in either the evens or the odds.
All Bad Antpols: 185e Node 14: Antpols (1 total): 185e Whole Ants (0 total): Single Pols (1 total): 185e
Cross-Polarized: (2 antpols across 1 antennas)
These antennas have their east and north cables swapped.
All Bad Antpols: 294e, 294n Node 29: Antpols (2 total): 294e, 294n Whole Ants (1 total): 294 Single Pols (0 total):
Likely FEM Power Issue: (0 antpols across 0 antennas)
These antennas have low power and anomolously high slopes.
High Power: (28 antpols across 17 antennas)
These antennas have high median power.
All Bad Antpols: 36e, 36n, 81e, 84e, 84n, 94e, 94n, 99e, 99n, 103e, 103n, 104e, 104n, 118n, 120e, 120n, 121e, 121n, 123e, 123n, 137e, 137n, 201n, 315e, 315n, 316n, 317e, 322e Node 3: Antpols (2 total): 36e, 36n Whole Ants (1 total): 36 Single Pols (0 total):
Node 5: Antpols (1 total): 322e Whole Ants (0 total): Single Pols (1 total): 322e
Node 7: Antpols (6 total): 81e, 99e, 99n, 118n, 137e, 137n Whole Ants (2 total): 137, 99 Single Pols (2 total): 81e, 118n
Node 8: Antpols (12 total): 84e, 84n, 103e, 103n, 104e, 104n, 120e, 120n, 121e, 121n, 123e, 123n Whole Ants (6 total): 103, 104, 84, 120, 121, 123 Single Pols (0 total):
Node 10: Antpols (2 total): 94e, 94n Whole Ants (1 total): 94 Single Pols (0 total):
Node 18: Antpols (1 total): 201n Whole Ants (0 total): Single Pols (1 total): 201n
Node 28: Antpols (4 total): 315e, 315n, 316n, 317e Whole Ants (1 total): 315 Single Pols (2 total): 316n, 317e
Other Low Power Issues: (0 antpols across 0 antennas)
These antennas have low power, but are not all-zeros and not FEM off.
Low Correlation, But Not Low Power: (41 antpols across 22 antennas)
These antennas are low correlation, but their autocorrelation power levels look OK.
All Bad Antpols: 18n, 27e, 27n, 28e, 28n, 30e, 30n, 36e, 36n, 84e, 84n, 94e, 94n, 99e, 99n, 103e, 103n, 104e, 104n, 120e, 120n, 121e, 121n, 123e, 123n, 135e, 137e, 137n, 200e, 253e, 253n, 282e, 282n, 283e, 283n, 294e, 294n, 321e, 321n, 346e, 346n Node 1: Antpols (7 total): 18n, 27e, 27n, 28e, 28n, 30e, 30n Whole Ants (3 total): 27, 28, 30 Single Pols (1 total): 18n Node 2: Antpols (2 total): 321e, 321n Whole Ants (1 total): 321 Single Pols (0 total): Node 3: Antpols (2 total): 36e, 36n Whole Ants (1 total): 36 Single Pols (0 total): Node 7: Antpols (4 total): 99e, 99n, 137e, 137n Whole Ants (2 total): 137, 99 Single Pols (0 total): Node 8: Antpols (12 total): 84e, 84n, 103e, 103n, 104e, 104n, 120e, 120n, 121e, 121n, 123e, 123n Whole Ants (6 total): 103, 104, 84, 120, 121, 123 Single Pols (0 total): Node 10: Antpols (2 total): 94e, 94n Whole Ants (1 total): 94 Single Pols (0 total): Node 12: Antpols (1 total): 135e Whole Ants (0 total): Single Pols (1 total): 135e Node 18: Antpols (1 total): 200e Whole Ants (0 total): Single Pols (1 total): 200e Node 22: Antpols (6 total): 253e, 253n, 282e, 282n, 283e, 283n Whole Ants (3 total): 282, 283, 253 Single Pols (0 total): Node 27: Antpols (2 total): 346e, 346n Whole Ants (1 total): 346 Single Pols (0 total): Node 29: Antpols (2 total): 294e, 294n Whole Ants (1 total): 294 Single Pols (0 total):
Bad Bandpass Shapes, But Not Bad Power: (13 antpols across 9 antennas)
These antennas have unusual bandpass shapes, but are not all-zeros, high power, low power, or FEM off.
All Bad Antpols: 18e, 27e, 27n, 28e, 29e, 29n, 30e, 30n, 46e, 76e, 76n, 135e, 161n Node 1: Antpols (8 total): 18e, 27e, 27n, 28e, 29e, 29n, 30e, 30n Whole Ants (3 total): 27, 29, 30 Single Pols (2 total): 18e, 28e
Node 5: Antpols (3 total): 46e, 76e, 76n Whole Ants (1 total): 76 Single Pols (1 total): 46e
Node 12: Antpols (1 total): 135e Whole Ants (0 total): Single Pols (1 total): 135e
Node 13: Antpols (1 total): 161n Whole Ants (0 total): Single Pols (1 total): 161n
Excess RFI: (22 antpols across 19 antennas)
These antennas have excess RMS after DPSS filtering (likely RFI), but not low or high power or a bad bandpass.
All Bad Antpols: 18n, 33n, 92e, 98n, 117e, 127e, 127n, 161e, 176e, 182e, 200e, 202n, 206n, 210n, 212n, 213e, 253n, 321e, 321n, 342n, 346e, 346n Node 1: Antpols (1 total): 18n Whole Ants (0 total): Single Pols (1 total): 18n
Node 2: Antpols (3 total): 33n, 321e, 321n Whole Ants (1 total): 321 Single Pols (1 total): 33n
Node 7: Antpols (2 total): 98n, 117e Whole Ants (0 total): Single Pols (2 total): 98n, 117e
Node 10: Antpols (3 total): 92e, 127e, 127n Whole Ants (1 total): 127 Single Pols (1 total): 92e
Node 12: Antpols (1 total): 176e Whole Ants (0 total): Single Pols (1 total): 176e
Node 13: Antpols (2 total): 161e, 182e Whole Ants (0 total): Single Pols (2 total): 161e, 182e
Node 16: Antpols (1 total): 213e Whole Ants (0 total): Single Pols (1 total): 213e
Node 18: Antpols (2 total): 200e, 202n Whole Ants (0 total): Single Pols (2 total): 200e, 202n
Node 19: Antpols (1 total): 206n Whole Ants (0 total): Single Pols (1 total): 206n
Node 20: Antpols (1 total): 210n Whole Ants (0 total): Single Pols (1 total): 210n
Node 21: Antpols (1 total): 212n Whole Ants (0 total): Single Pols (1 total): 212n
Node 22: Antpols (1 total): 253n Whole Ants (0 total): Single Pols (1 total): 253n
Node 27: Antpols (3 total): 342n, 346e, 346n Whole Ants (1 total): 346 Single Pols (1 total): 342n
Redcal chi^2: (36 antpols across 25 antennas)
These antennas have been idenfied as not redundantly calibrating well, even after passing the above checks.
All Bad Antpols: 55e, 65n, 66e, 66n, 70e, 70n, 71e, 71n, 75n, 116e, 116n, 135n, 136e, 136n, 154e, 155e, 165n, 174e, 174n, 175e, 175n, 184e, 184n, 185n, 194e, 194n, 195e, 195n, 204n, 209n, 213n, 214n, 215n, 232e, 293e, 293n Node 3: Antpols (3 total): 65n, 66e, 66n Whole Ants (1 total): 66 Single Pols (1 total): 65n Node 4: Antpols (5 total): 55e, 70e, 70n, 71e, 71n Whole Ants (2 total): 70, 71 Single Pols (1 total): 55e Node 5: Antpols (1 total): 75n Whole Ants (0 total): Single Pols (1 total): 75n Node 7: Antpols (2 total): 116e, 116n Whole Ants (1 total): 116 Single Pols (0 total): Node 12: Antpols (4 total): 135n, 136e, 136n, 155e Whole Ants (1 total): 136 Single Pols (2 total): 135n, 155e Node 14: Antpols (4 total): 165n, 184e, 184n, 185n Whole Ants (1 total): 184 Single Pols (2 total): 165n, 185n Node 16: Antpols (6 total): 154e, 174e, 174n, 194e, 194n, 213n Whole Ants (2 total): 194, 174 Single Pols (2 total): 154e, 213n Node 17: Antpols (1 total): 215n Whole Ants (0 total): Single Pols (1 total): 215n Node 19: Antpols (1 total): 204n Whole Ants (0 total): Single Pols (1 total): 204n Node 20: Antpols (1 total): 209n Whole Ants (0 total): Single Pols (1 total): 209n Node 21: Antpols (6 total): 175e, 175n, 195e, 195n, 214n, 232e Whole Ants (2 total): 195, 175 Single Pols (2 total): 214n, 232e Node 29: Antpols (2 total): 293e, 293n Whole Ants (1 total): 293 Single Pols (0 total):
Full-Day Visualizations¶
In [27]:
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"')
Figure 1: Per-File Overall Antenna Classification Summary¶
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.
In [28]:
classification_plot('Antenna Class')
In [29]:
# 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))
In [30]:
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()
Figure 2: Per-Classifier Antenna Flagging Summary¶
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.
In [31]:
plot_flag_frac_all_classifiers()
In [32]:
def array_class_plot():
fig, axes = plt.subplots(1, 2, figsize=(14, 6), dpi=100, gridspec_kw={'width_ratios': [2, 1]})
if len([ant for ant in hd.data_ants if ant < 320]) > 0:
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')
if len([ant for ant in hd.data_ants if ant >= 320]) > 0:
plot_antclass(hd.antpos, overall_class, ax=axes[1], ants=[ant for ant in hd.data_ants if ant >= 320], radius=50, title='Outriggers')
Figure 3: Array Visualization of Overall Daily Classification¶
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.
In [33]:
if SUM_FILE is not None: array_class_plot()
WARNING:matplotlib.axes._base:Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.
WARNING:matplotlib.axes._base:Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.
WARNING:matplotlib.axes._base:Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.
WARNING:matplotlib.axes._base:Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.
WARNING:matplotlib.axes._base:Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.
In [34]:
for repo in ['pyuvdata', 'hera_cal', 'hera_qm', 'hera_notebook_templates']:
exec(f'from {repo} import __version__')
print(f'{repo}: {__version__}')
pyuvdata: 3.2.5.dev1+g5a985ae31 hera_cal: 3.7.7.dev68+g3286222d3 hera_qm: 2.2.1.dev4+gf6d02113b
hera_notebook_templates: 0.1.dev989+gee0995d