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/data2/2460742'
SUM_FILE = '/mnt/sn1/data2/2460742/zen.2460742.50797.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: 3-8-2025
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 1411 csv files starting with /mnt/sn1/data2/2460742/zen.2460742.34937.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()

264 antpols (on 180 antennas) frequently flagged for Excess Power in X-Engine Diffs.
101 antpols (on 85 antennas) frequently flagged for Redcal chi^2.
56 antpols (on 28 antennas) frequently flagged for All-Zeros.
38 antpols (on 35 antennas) frequently flagged for Excess RFI.
21 antpols (on 19 antennas) frequently flagged for Likely FEM Power Issue.
16 antpols (on 13 antennas) frequently flagged for Low Correlation, But Not Low Power.
11 antpols (on 9 antennas) frequently flagged for Other Low Power Issues.
10 antpols (on 10 antennas) frequently flagged for Bad Bandpass Shapes, But Not Bad Power.
8 antpols (on 4 antennas) frequently flagged for Cross-Polarized.
5 antpols (on 4 antennas) frequently flagged for High Power.
0 antpols (on 0 antennas) frequently flagged for Excess Zeros in Either Even or Odd Spectra.
In [26]:
print_all_issue_summaries()

All-Zeros: (56 antpols across 28 antennas)

These antennas have visibilities that are more than half zeros.

All Bad Antpols: 35e, 35n, 45e, 45n, 46e, 46n, 47e, 47n, 48e, 48n, 49e, 49n, 61e, 61n, 62e, 62n, 63e, 63n, 64e, 64n, 73e, 73n, 77e, 77n, 78e, 78n, 143e, 143n, 144e, 144n, 145e, 145n, 146e, 146n, 147e, 147n, 148e, 148n, 149e, 149n, 163e, 163n, 164e, 164n, 165e, 165n, 166e, 166n, 184e, 184n, 185e, 185n, 186e, 186n, 187e, 187n

Node 5:
	Antpols (6 total): 45e, 45n, 46e, 46n, 73e, 73n
	Whole Ants (3 total): 73, 45, 46
	Single Pols (0 total): 
Node 6:
	Antpols (20 total): 35e, 35n, 47e, 47n, 48e, 48n, 49e, 49n, 61e, 61n, 62e, 62n, 63e, 63n, 64e, 64n, 77e, 77n, 78e, 78n
	Whole Ants (10 total): 64, 35, 77, 78, 47, 48, 49, 61, 62, 63
	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 15:
	Antpols (6 total): 147e, 147n, 148e, 148n, 149e, 149n
	Whole Ants (3 total): 147, 148, 149
	Single Pols (0 total):

Excess Zeros in Either Even or Odd Spectra: (0 antpols across 0 antennas)

These antennas are showing evidence of packet loss or X-engine failure.

Excess Power in X-Engine Diffs: (264 antpols across 180 antennas)

These antennas are showing evidence of mis-written packets in either the evens or the odds.

All Bad Antpols: 3e, 4n, 5e, 7e, 9e, 9n, 15e, 15n, 16e, 16n, 17e, 19e, 19n, 30e, 30n, 31e, 32e, 32n, 33e, 34n, 37e, 38e, 38n, 40e, 41e, 41n, 43e, 43n, 44n, 50e, 52e, 52n, 53e, 53n, 54e, 54n, 55e, 56e, 56n, 58e, 59e, 60e, 60n, 66e, 67e, 68e, 68n, 69e, 70e, 70n, 71e, 72e, 72n, 79e, 79n, 80e, 82e, 83e, 83n, 84e, 84n, 85n, 86e, 87n, 88e, 89e, 89n, 91n, 93e, 94e, 94n, 95e, 96e, 96n, 97e, 98e, 99e, 100e, 100n, 101e, 101n, 103n, 105n, 106n, 108e, 108n, 109e, 110e, 110n, 111e, 111n, 112n, 113e, 115e, 115n, 116e, 116n, 117n, 118n, 119e, 119n, 121n, 122e, 122n, 123e, 123n, 124e, 126e, 127e, 127n, 128e, 128n, 129e, 130e, 131e, 131n, 132e, 132n, 133e, 133n, 134e, 134n, 135n, 136e, 137n, 138e, 138n, 140e, 140n, 141e, 141n, 142e, 142n, 150e, 150n, 151n, 152e, 152n, 153e, 153n, 154e, 154n, 155e, 156n, 157e, 157n, 158e, 158n, 159e, 159n, 160e, 161e, 162e, 162n, 167e, 167n, 168e, 168n, 169e, 169n, 170n, 171e, 172n, 173e, 174e, 175e, 175n, 176n, 177e, 177n, 179e, 179n, 181e, 181n, 182e, 183e, 183n, 188e, 188n, 189e, 189n, 190e, 190n, 191e, 191n, 192e, 193e, 193n, 194e, 195e, 195n, 196e, 196n, 197e, 197n, 198n, 201e, 201n, 203n, 204e, 204n, 206e, 206n, 207n, 208n, 209e, 210e, 210n, 214e, 214n, 216e, 217n, 219n, 220e, 222e, 223e, 223n, 226e, 227e, 228n, 229e, 232n, 233e, 235e, 235n, 237e, 237n, 241e, 243n, 244e, 244n, 245e, 245n, 246e, 251n, 252e, 252n, 253e, 255e, 256e, 256n, 261n, 266e, 266n, 267e, 267n, 268e, 269e, 281e, 281n, 282e, 283e, 285n, 295e, 295n, 320n, 321e, 322n, 323e, 327e, 333n, 336e, 336n, 340e

Node 1:
	Antpols (10 total): 3e, 4n, 5e, 15e, 15n, 16e, 16n, 17e, 30e, 30n
	Whole Ants (3 total): 16, 30, 15
	Single Pols (4 total): 3e, 4n, 5e, 17e

Casting complex values to real discards the imaginary part
Casting complex values to real discards the imaginary part
No description has been provided for this image 
Node 2:
	Antpols (11 total): 7e, 9e, 9n, 19e, 19n, 31e, 32e, 32n, 33e, 321e, 323e
	Whole Ants (3 total): 32, 9, 19
	Single Pols (5 total): 7e, 31e, 33e, 321e, 323e
No description has been provided for this image 
Node 3:
	Antpols (13 total): 37e, 38e, 38n, 50e, 52e, 52n, 53e, 53n, 66e, 67e, 68e, 68n, 320n
	Whole Ants (4 total): 52, 53, 38, 68
	Single Pols (5 total): 37e, 50e, 66e, 67e, 320n
No description has been provided for this image 
Node 4:
	Antpols (14 total): 40e, 41e, 41n, 54e, 54n, 55e, 56e, 56n, 69e, 70e, 70n, 71e, 72e, 72n
	Whole Ants (5 total): 70, 72, 41, 54, 56
	Single Pols (4 total): 40e, 55e, 69e, 71e
No description has been provided for this image 
Node 5:
	Antpols (8 total): 43e, 43n, 44n, 58e, 59e, 60e, 60n, 322n
	Whole Ants (2 total): 43, 60
	Single Pols (4 total): 44n, 58e, 59e, 322n
No description has been provided for this image 
Node 6:
	Antpols (1 total): 34n
	Whole Ants (0 total): 
	Single Pols (1 total): 34n

Data has no positive values, and therefore cannot be log-scaled.
No description has been provided for this image 
Node 7:
	Antpols (16 total): 82e, 83e, 83n, 98e, 99e, 100e, 100n, 116e, 116n, 117n, 118n, 119e, 119n, 137n, 138e, 138n
	Whole Ants (5 total): 100, 138, 83, 116, 119
	Single Pols (6 total): 82e, 98e, 99e, 117n, 118n, 137n
No description has been provided for this image 
Node 8:
	Antpols (13 total): 84e, 84n, 85n, 86e, 87n, 101e, 101n, 103n, 121n, 122e, 122n, 123e, 123n
	Whole Ants (4 total): 122, 123, 84, 101
	Single Pols (5 total): 85n, 86e, 87n, 103n, 121n
No description has been provided for this image 
Node 9:
	Antpols (10 total): 88e, 89e, 89n, 91n, 105n, 106n, 108e, 108n, 124e, 126e
	Whole Ants (2 total): 89, 108
	Single Pols (6 total): 88e, 91n, 105n, 106n, 124e, 126e
No description has been provided for this image 
Node 10:
	Antpols (15 total): 93e, 94e, 94n, 109e, 110e, 110n, 111e, 111n, 112n, 127e, 127n, 128e, 128n, 129e, 130e
	Whole Ants (5 total): 128, 110, 111, 94, 127
	Single Pols (5 total): 93e, 109e, 112n, 129e, 130e
No description has been provided for this image 
Node 11:
	Antpols (18 total): 79e, 79n, 80e, 95e, 96e, 96n, 97e, 113e, 115e, 115n, 131e, 131n, 132e, 132n, 133e, 133n, 134e, 134n
	Whole Ants (7 total): 96, 131, 132, 133, 134, 79, 115
	Single Pols (4 total): 80e, 95e, 97e, 113e
No description has been provided for this image 
Node 12:
	Antpols (14 total): 135n, 136e, 155e, 156n, 157e, 157n, 158e, 158n, 176n, 177e, 177n, 179e, 179n, 333n
	Whole Ants (4 total): 177, 179, 157, 158
	Single Pols (6 total): 135n, 136e, 155e, 156n, 176n, 333n
No description has been provided for this image 
Node 13:
	Antpols (17 total): 140e, 140n, 141e, 141n, 142e, 142n, 159e, 159n, 160e, 161e, 162e, 162n, 181e, 181n, 182e, 183e, 183n
	Whole Ants (7 total): 162, 140, 141, 142, 181, 183, 159
	Single Pols (3 total): 160e, 161e, 182e
No description has been provided for this image 
Node 15:
	Antpols (17 total): 150e, 150n, 167e, 167n, 168e, 168n, 169e, 169n, 170n, 188e, 188n, 189e, 189n, 190e, 190n, 191e, 191n
	Whole Ants (8 total): 167, 168, 169, 150, 188, 189, 190, 191
	Single Pols (1 total): 170n
No description has been provided for this image 
Node 16:
	Antpols (15 total): 151n, 152e, 152n, 153e, 153n, 154e, 154n, 171e, 172n, 173e, 174e, 192e, 193e, 193n, 194e
	Whole Ants (4 total): 152, 153, 154, 193
	Single Pols (7 total): 151n, 171e, 172n, 173e, 174e, 192e, 194e
No description has been provided for this image 
Node 17:
	Antpols (10 total): 196e, 196n, 197e, 197n, 198n, 216e, 217n, 233e, 235e, 235n
	Whole Ants (3 total): 235, 196, 197
	Single Pols (4 total): 198n, 216e, 217n, 233e
No description has been provided for this image 
Node 18:
	Antpols (8 total): 201e, 201n, 203n, 219n, 220e, 222e, 237e, 237n
	Whole Ants (2 total): 201, 237
	Single Pols (4 total): 203n, 219n, 220e, 222e
No description has been provided for this image 
Node 19:
	Antpols (10 total): 204e, 204n, 206e, 206n, 207n, 223e, 223n, 226e, 241e, 243n
	Whole Ants (3 total): 204, 206, 223
	Single Pols (4 total): 207n, 226e, 241e, 243n
No description has been provided for this image 
Node 20:
	Antpols (13 total): 208n, 209e, 210e, 210n, 227e, 228n, 229e, 244e, 244n, 245e, 245n, 246e, 261n
	Whole Ants (3 total): 210, 244, 245
	Single Pols (7 total): 208n, 209e, 227e, 228n, 229e, 246e, 261n
No description has been provided for this image 
Node 21:
	Antpols (11 total): 175e, 175n, 195e, 195n, 214e, 214n, 232n, 327e, 336e, 336n, 340e
	Whole Ants (4 total): 336, 195, 214, 175
	Single Pols (3 total): 232n, 327e, 340e
No description has been provided for this image 
Node 22:
	Antpols (16 total): 251n, 252e, 252n, 253e, 266e, 266n, 267e, 267n, 268e, 269e, 281e, 281n, 282e, 283e, 295e, 295n
	Whole Ants (5 total): 295, 266, 267, 281, 252
	Single Pols (6 total): 251n, 253e, 268e, 269e, 282e, 283e
No description has been provided for this image 
Node 23:
	Antpols (4 total): 255e, 256e, 256n, 285n
	Whole Ants (1 total): 256
	Single Pols (2 total): 255e, 285n
No description has been provided for this image

Cross-Polarized: (8 antpols across 4 antennas)

These antennas have their east and north cables swapped.

All Bad Antpols: 20e, 20n, 70e, 70n, 89e, 89n, 92e, 92n

Node 2:
	Antpols (2 total): 20e, 20n
	Whole Ants (1 total): 20
	Single Pols (0 total): 
Node 4:
	Antpols (2 total): 70e, 70n
	Whole Ants (1 total): 70
	Single Pols (0 total): 
Node 9:
	Antpols (2 total): 89e, 89n
	Whole Ants (1 total): 89
	Single Pols (0 total): 
Node 10:
	Antpols (2 total): 92e, 92n
	Whole Ants (1 total): 92
	Single Pols (0 total):

Likely FEM Power Issue: (21 antpols across 19 antennas)

These antennas have low power and anomolously high slopes.

All Bad Antpols: 4e, 10n, 29e, 44e, 75e, 75n, 104n, 109n, 112e, 135e, 170e, 200e, 216n, 218e, 238n, 239e, 322e, 326n, 329n, 332e, 332n

Node 1:
	Antpols (2 total): 4e, 29e
	Whole Ants (0 total): 
	Single Pols (2 total): 4e, 29e
No description has been provided for this image 
Node 2:
	Antpols (1 total): 10n
	Whole Ants (0 total): 
	Single Pols (1 total): 10n
No description has been provided for this image 
Node 5:
	Antpols (4 total): 44e, 75e, 75n, 322e
	Whole Ants (1 total): 75
	Single Pols (2 total): 44e, 322e
No description has been provided for this image 
Node 8:
	Antpols (1 total): 104n
	Whole Ants (0 total): 
	Single Pols (1 total): 104n
No description has been provided for this image 
Node 10:
	Antpols (2 total): 109n, 112e
	Whole Ants (0 total): 
	Single Pols (2 total): 109n, 112e
No description has been provided for this image 
Node 12:
	Antpols (2 total): 135e, 329n
	Whole Ants (0 total): 
	Single Pols (2 total): 135e, 329n
No description has been provided for this image 
Node 15:
	Antpols (1 total): 170e
	Whole Ants (0 total): 
	Single Pols (1 total): 170e
No description has been provided for this image 
Node 17:
	Antpols (2 total): 216n, 218e
	Whole Ants (0 total): 
	Single Pols (2 total): 216n, 218e
No description has been provided for this image 
Node 18:
	Antpols (3 total): 200e, 238n, 239e
	Whole Ants (0 total): 
	Single Pols (3 total): 200e, 238n, 239e
No description has been provided for this image 
Node 21:
	Antpols (3 total): 326n, 332e, 332n
	Whole Ants (1 total): 332
	Single Pols (1 total): 326n
No description has been provided for this image

High Power: (5 antpols across 4 antennas)

These antennas have high median power.

All Bad Antpols: 8e, 8n, 31n, 125n, 232e

Node 2:
	Antpols (3 total): 8e, 8n, 31n
	Whole Ants (1 total): 8
	Single Pols (1 total): 31n
No description has been provided for this image 
Node 9:
	Antpols (1 total): 125n
	Whole Ants (0 total): 
	Single Pols (1 total): 125n
No description has been provided for this image 
Node 21:
	Antpols (1 total): 232e
	Whole Ants (0 total): 
	Single Pols (1 total): 232e
No description has been provided for this image

Other Low Power Issues: (11 antpols across 9 antennas)

These antennas have low power, but are not all-zeros and not FEM off.

All Bad Antpols: 20e, 71n, 81n, 82n, 99n, 114e, 114n, 218n, 251e, 262e, 262n

Node 2:
	Antpols (1 total): 20e
	Whole Ants (0 total): 
	Single Pols (1 total): 20e
No description has been provided for this image 
Node 4:
	Antpols (1 total): 71n
	Whole Ants (0 total): 
	Single Pols (1 total): 71n
No description has been provided for this image 
Node 7:
	Antpols (3 total): 81n, 82n, 99n
	Whole Ants (0 total): 
	Single Pols (3 total): 81n, 82n, 99n
No description has been provided for this image 
Node 11:
	Antpols (2 total): 114e, 114n
	Whole Ants (1 total): 114
	Single Pols (0 total):
No description has been provided for this image 
Node 17:
	Antpols (1 total): 218n
	Whole Ants (0 total): 
	Single Pols (1 total): 218n
No description has been provided for this image 
Node 20:
	Antpols (2 total): 262e, 262n
	Whole Ants (1 total): 262
	Single Pols (0 total):
No description has been provided for this image 
Node 22:
	Antpols (1 total): 251e
	Whole Ants (0 total): 
	Single Pols (1 total): 251e
No description has been provided for this image

Low Correlation, But Not Low Power: (16 antpols across 13 antennas)

These antennas are low correlation, but their autocorrelation power levels look OK.

All Bad Antpols: 10e, 20n, 22e, 22n, 27e, 28e, 28n, 92e, 92n, 171n, 200n, 255n, 326e, 328e, 329e, 331e

Node 1:
	Antpols (3 total): 27e, 28e, 28n
	Whole Ants (1 total): 28
	Single Pols (1 total): 27e
Node 2:
	Antpols (2 total): 10e, 20n
	Whole Ants (0 total): 
	Single Pols (2 total): 10e, 20n
Node 6:
	Antpols (2 total): 22e, 22n
	Whole Ants (1 total): 22
	Single Pols (0 total): 
Node 10:
	Antpols (3 total): 92e, 92n, 328e
	Whole Ants (1 total): 92
	Single Pols (1 total): 328e
Node 12:
	Antpols (1 total): 329e
	Whole Ants (0 total): 
	Single Pols (1 total): 329e
Node 16:
	Antpols (1 total): 171n
	Whole Ants (0 total): 
	Single Pols (1 total): 171n
Node 18:
	Antpols (1 total): 200n
	Whole Ants (0 total): 
	Single Pols (1 total): 200n
Node 21:
	Antpols (2 total): 326e, 331e
	Whole Ants (0 total): 
	Single Pols (2 total): 326e, 331e
Node 23:
	Antpols (1 total): 255n
	Whole Ants (0 total): 
	Single Pols (1 total): 255n

Bad Bandpass Shapes, But Not Bad Power: (10 antpols across 10 antennas)

These antennas have unusual bandpass shapes, but are not all-zeros, high power, low power, or FEM off.

All Bad Antpols: 27e, 28e, 33n, 98n, 130n, 161n, 180n, 199n, 209n, 340n

Node 1:
	Antpols (2 total): 27e, 28e
	Whole Ants (0 total): 
	Single Pols (2 total): 27e, 28e
No description has been provided for this image 
Node 2:
	Antpols (1 total): 33n
	Whole Ants (0 total): 
	Single Pols (1 total): 33n
No description has been provided for this image 
Node 7:
	Antpols (1 total): 98n
	Whole Ants (0 total): 
	Single Pols (1 total): 98n
No description has been provided for this image 
Node 10:
	Antpols (1 total): 130n
	Whole Ants (0 total): 
	Single Pols (1 total): 130n
No description has been provided for this image 
Node 13:
	Antpols (2 total): 161n, 180n
	Whole Ants (0 total): 
	Single Pols (2 total): 161n, 180n
No description has been provided for this image 
Node 17:
	Antpols (1 total): 199n
	Whole Ants (0 total): 
	Single Pols (1 total): 199n
No description has been provided for this image 
Node 20:
	Antpols (1 total): 209n
	Whole Ants (0 total): 
	Single Pols (1 total): 209n
No description has been provided for this image 
Node 21:
	Antpols (1 total): 340n
	Whole Ants (0 total): 
	Single Pols (1 total): 340n
No description has been provided for this image

Excess RFI: (38 antpols across 35 antennas)

These antennas have excess RMS after DPSS filtering (likely RFI), but not low or high power or a bad bandpass.

All Bad Antpols: 18e, 18n, 21e, 27n, 29n, 37n, 40n, 42e, 42n, 51e, 67n, 80n, 81e, 90e, 92n, 97n, 102n, 103e, 104e, 106e, 107e, 107n, 117e, 120e, 121e, 137e, 180e, 199e, 202n, 208e, 212n, 213e, 215n, 250e, 253n, 268n, 320e, 333e

Node 1:
	Antpols (4 total): 18e, 18n, 27n, 29n
	Whole Ants (1 total): 18
	Single Pols (2 total): 27n, 29n
No description has been provided for this image 
Node 2:
	Antpols (1 total): 21e
	Whole Ants (0 total): 
	Single Pols (1 total): 21e
No description has been provided for this image 
Node 3:
	Antpols (4 total): 37n, 51e, 67n, 320e
	Whole Ants (0 total): 
	Single Pols (4 total): 37n, 51e, 67n, 320e
No description has been provided for this image 
Node 4:
	Antpols (3 total): 40n, 42e, 42n
	Whole Ants (1 total): 42
	Single Pols (1 total): 40n
No description has been provided for this image 
Node 7:
	Antpols (3 total): 81e, 117e, 137e
	Whole Ants (0 total): 
	Single Pols (3 total): 81e, 117e, 137e
No description has been provided for this image 
Node 8:
	Antpols (5 total): 102n, 103e, 104e, 120e, 121e
	Whole Ants (0 total): 
	Single Pols (5 total): 102n, 103e, 104e, 120e, 121e
No description has been provided for this image 
Node 9:
	Antpols (4 total): 90e, 106e, 107e, 107n
	Whole Ants (1 total): 107
	Single Pols (2 total): 90e, 106e
No description has been provided for this image 
Node 10:
	Antpols (1 total): 92n
	Whole Ants (0 total): 
	Single Pols (1 total): 92n
No description has been provided for this image 
Node 11:
	Antpols (2 total): 80n, 97n
	Whole Ants (0 total): 
	Single Pols (2 total): 80n, 97n
No description has been provided for this image 
Node 12:
	Antpols (1 total): 333e
	Whole Ants (0 total): 
	Single Pols (1 total): 333e
No description has been provided for this image 
Node 13:
	Antpols (1 total): 180e
	Whole Ants (0 total): 
	Single Pols (1 total): 180e
No description has been provided for this image 
Node 16:
	Antpols (1 total): 213e
	Whole Ants (0 total): 
	Single Pols (1 total): 213e
No description has been provided for this image 
Node 17:
	Antpols (2 total): 199e, 215n
	Whole Ants (0 total): 
	Single Pols (2 total): 199e, 215n
No description has been provided for this image 
Node 18:
	Antpols (1 total): 202n
	Whole Ants (0 total): 
	Single Pols (1 total): 202n
No description has been provided for this image 
Node 20:
	Antpols (1 total): 208e
	Whole Ants (0 total): 
	Single Pols (1 total): 208e
No description has been provided for this image 
Node 21:
	Antpols (1 total): 212n
	Whole Ants (0 total): 
	Single Pols (1 total): 212n
No description has been provided for this image 
Node 22:
	Antpols (3 total): 250e, 253n, 268n
	Whole Ants (0 total): 
	Single Pols (3 total): 250e, 253n, 268n
No description has been provided for this image

Redcal chi^2: (101 antpols across 85 antennas)

These antennas have been idenfied as not redundantly calibrating well, even after passing the above checks.

All Bad Antpols: 3n, 5n, 7n, 17n, 21n, 34e, 36e, 36n, 50n, 51n, 55n, 57e, 57n, 58n, 59n, 65e, 65n, 66n, 69n, 85e, 86n, 87e, 88n, 90n, 91e, 93n, 95n, 102e, 105e, 113n, 118e, 120n, 124n, 125e, 126n, 129n, 136n, 139e, 139n, 151e, 155n, 156e, 160n, 172e, 173n, 174n, 176e, 178e, 178n, 182n, 192n, 194n, 198e, 202e, 203e, 205e, 205n, 207e, 211e, 211n, 212e, 213n, 215e, 217e, 219e, 220n, 221e, 221n, 222n, 224e, 224n, 225e, 225n, 226n, 227n, 228e, 229n, 231e, 231n, 233n, 234e, 234n, 238e, 239n, 240e, 240n, 241n, 242e, 242n, 243e, 246n, 250n, 261e, 269n, 270e, 270n, 272e, 272n, 282n, 283n, 285e

Node 1:
	Antpols (3 total): 3n, 5n, 17n
	Whole Ants (0 total): 
	Single Pols (3 total): 3n, 5n, 17n
Node 2:
	Antpols (2 total): 7n, 21n
	Whole Ants (0 total): 
	Single Pols (2 total): 7n, 21n
Node 3:
	Antpols (7 total): 36e, 36n, 50n, 51n, 65e, 65n, 66n
	Whole Ants (2 total): 65, 36
	Single Pols (3 total): 50n, 51n, 66n
Node 4:
	Antpols (4 total): 55n, 57e, 57n, 69n
	Whole Ants (1 total): 57
	Single Pols (2 total): 55n, 69n
Node 5:
	Antpols (2 total): 58n, 59n
	Whole Ants (0 total): 
	Single Pols (2 total): 58n, 59n
Node 6:
	Antpols (1 total): 34e
	Whole Ants (0 total): 
	Single Pols (1 total): 34e
Node 7:
	Antpols (1 total): 118e
	Whole Ants (0 total): 
	Single Pols (1 total): 118e
Node 8:
	Antpols (5 total): 85e, 86n, 87e, 102e, 120n
	Whole Ants (0 total): 
	Single Pols (5 total): 85e, 86n, 87e, 102e, 120n
Node 9:
	Antpols (7 total): 88n, 90n, 91e, 105e, 124n, 125e, 126n
	Whole Ants (0 total): 
	Single Pols (7 total): 88n, 90n, 91e, 105e, 124n, 125e, 126n
Node 10:
	Antpols (2 total): 93n, 129n
	Whole Ants (0 total): 
	Single Pols (2 total): 93n, 129n
Node 11:
	Antpols (2 total): 95n, 113n
	Whole Ants (0 total): 
	Single Pols (2 total): 95n, 113n
Node 12:
	Antpols (6 total): 136n, 155n, 156e, 176e, 178e, 178n
	Whole Ants (1 total): 178
	Single Pols (4 total): 136n, 155n, 156e, 176e
Node 13:
	Antpols (4 total): 139e, 139n, 160n, 182n
	Whole Ants (1 total): 139
	Single Pols (2 total): 160n, 182n
Node 16:
	Antpols (7 total): 151e, 172e, 173n, 174n, 192n, 194n, 213n
	Whole Ants (0 total): 
	Single Pols (7 total): 151e, 172e, 173n, 174n, 192n, 194n, 213n
Node 17:
	Antpols (6 total): 198e, 215e, 217e, 233n, 234e, 234n
	Whole Ants (1 total): 234
	Single Pols (4 total): 198e, 215e, 217e, 233n
Node 18:
	Antpols (9 total): 202e, 203e, 219e, 220n, 221e, 221n, 222n, 238e, 239n
	Whole Ants (1 total): 221
	Single Pols (7 total): 202e, 203e, 219e, 220n, 222n, 238e, 239n
Node 19:
	Antpols (14 total): 205e, 205n, 207e, 224e, 224n, 225e, 225n, 226n, 240e, 240n, 241n, 242e, 242n, 243e
	Whole Ants (5 total): 224, 225, 205, 240, 242
	Single Pols (4 total): 207e, 226n, 241n, 243e
Node 20:
	Antpols (7 total): 211e, 211n, 227n, 228e, 229n, 246n, 261e
	Whole Ants (1 total): 211
	Single Pols (5 total): 227n, 228e, 229n, 246n, 261e
Node 21:
	Antpols (3 total): 212e, 231e, 231n
	Whole Ants (1 total): 231
	Single Pols (1 total): 212e
Node 22:
	Antpols (4 total): 250n, 269n, 282n, 283n
	Whole Ants (0 total): 
	Single Pols (4 total): 250n, 269n, 282n, 283n
Node 23:
	Antpols (5 total): 270e, 270n, 272e, 272n, 285e
	Whole Ants (2 total): 272, 270
	Single Pols (1 total): 285e

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')
No description has been provided for this image
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()
No description has been provided for this image
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.
No description has been provided for this image
In [34]:
for repo in ['pyuvdata', 'hera_cal', 'hera_qm', 'hera_notebook_templates']:
    exec(f'from {repo} import __version__')
    print(f'{repo}: {__version__}')

pyuvdata: 3.1.3
hera_cal: 3.7.1.dev18+g10e9584
hera_qm: 2.2.1.dev2+ga535e9e

hera_notebook_templates: 0.1.dev989+gee0995d