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/data1/2460911'
SUM_FILE = '/mnt/sn1/data1/2460911/zen.2460911.43867.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: 8-23-2025
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 1756 csv files starting with /mnt/sn1/data1/2460911/zen.2460911.21094.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()

108 antpols (on 54 antennas) frequently flagged for All-Zeros.
34 antpols (on 27 antennas) frequently flagged for Likely FEM Power Issue.
31 antpols (on 26 antennas) frequently flagged for High Power.
30 antpols (on 28 antennas) frequently flagged for Excess RFI.
27 antpols (on 20 antennas) frequently flagged for Redcal chi^2.
16 antpols (on 15 antennas) frequently flagged for Bad Bandpass Shapes, But Not Bad Power.
11 antpols (on 8 antennas) frequently flagged for Low Correlation, But Not Low Power.
7 antpols (on 7 antennas) frequently flagged for Excess Power in X-Engine Diffs.
4 antpols (on 2 antennas) frequently flagged for Cross-Polarized.
3 antpols (on 3 antennas) frequently flagged for Other Low Power Issues.
0 antpols (on 0 antennas) frequently flagged for Excess Zeros in Either Even or Odd Spectra.
In [26]:
print_all_issue_summaries()

All-Zeros: (108 antpols across 54 antennas)

These antennas have visibilities that are more than half zeros.

All Bad Antpols: 22e, 22n, 33e, 33n, 34e, 34n, 35e, 35n, 41e, 41n, 57e, 57n, 88e, 88n, 90e, 90n, 91e, 91n, 105e, 105n, 107e, 107n, 108e, 108n, 115e, 115n, 131e, 131n, 133e, 133n, 152e, 152n, 153e, 153n, 154e, 154n, 166e, 166n, 186e, 186n, 187e, 187n, 220e, 220n, 221e, 221n, 222e, 222n, 225e, 225n, 226e, 226n, 227e, 227n, 228e, 228n, 229e, 229n, 240e, 240n, 251e, 251n, 253e, 253n, 257e, 257n, 270e, 270n, 271e, 271n, 272e, 272n, 273e, 273n, 281e, 281n, 282e, 282n, 283e, 283n, 284e, 284n, 285e, 285n, 286e, 286n, 287e, 287n, 295e, 295n, 303e, 303n, 304e, 304n, 305e, 305n, 321e, 321n, 323e, 323n, 324e, 324n, 344e, 344n, 348e, 348n, 349e, 349n

Node 2:
	Antpols (6 total): 33e, 33n, 321e, 321n, 323e, 323n
	Whole Ants (3 total): 321, 33, 323
	Single Pols (0 total): 
Node 4:
	Antpols (6 total): 41e, 41n, 57e, 57n, 324e, 324n
	Whole Ants (3 total): 41, 324, 57
	Single Pols (0 total): 
Node 6:
	Antpols (6 total): 22e, 22n, 34e, 34n, 35e, 35n
	Whole Ants (3 total): 34, 35, 22
	Single Pols (0 total): 
Node 9:
	Antpols (12 total): 88e, 88n, 90e, 90n, 91e, 91n, 105e, 105n, 107e, 107n, 108e, 108n
	Whole Ants (6 total): 105, 107, 108, 88, 90, 91
	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 (6 total): 166e, 166n, 186e, 186n, 187e, 187n
	Whole Ants (3 total): 186, 187, 166
	Single Pols (0 total): 
Node 16:
	Antpols (6 total): 152e, 152n, 153e, 153n, 154e, 154n
	Whole Ants (3 total): 152, 153, 154
	Single Pols (0 total): 
Node 18:
	Antpols (6 total): 220e, 220n, 221e, 221n, 222e, 222n
	Whole Ants (3 total): 220, 221, 222
	Single Pols (0 total): 
Node 19:
	Antpols (6 total): 225e, 225n, 226e, 226n, 240e, 240n
	Whole Ants (3 total): 240, 225, 226
	Single Pols (0 total): 
Node 20:
	Antpols (6 total): 227e, 227n, 228e, 228n, 229e, 229n
	Whole Ants (3 total): 227, 228, 229
	Single Pols (0 total): 
Node 22:
	Antpols (12 total): 251e, 251n, 253e, 253n, 281e, 281n, 282e, 282n, 283e, 283n, 295e, 295n
	Whole Ants (6 total): 295, 251, 281, 282, 283, 253
	Single Pols (0 total): 
Node 23:
	Antpols (18 total): 257e, 257n, 270e, 270n, 271e, 271n, 272e, 272n, 273e, 273n, 284e, 284n, 285e, 285n, 286e, 286n, 287e, 287n
	Whole Ants (9 total): 257, 270, 271, 272, 273, 284, 285, 286, 287
	Single Pols (0 total): 
Node 28:
	Antpols (12 total): 303e, 303n, 304e, 304n, 305e, 305n, 344e, 344n, 348e, 348n, 349e, 349n
	Whole Ants (6 total): 303, 304, 305, 344, 348, 349
	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: (7 antpols across 7 antennas)

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

All Bad Antpols: 28n, 29n, 30e, 113n, 159n, 202e, 245n

Node 1:
	Antpols (3 total): 28n, 29n, 30e
	Whole Ants (0 total): 
	Single Pols (3 total): 28n, 29n, 30e
No description has been provided for this image 
Node 11:
	Antpols (1 total): 113n
	Whole Ants (0 total): 
	Single Pols (1 total): 113n
No description has been provided for this image 
Node 13:
	Antpols (1 total): 159n
	Whole Ants (0 total): 
	Single Pols (1 total): 159n
No description has been provided for this image 
Node 18:
	Antpols (1 total): 202e
	Whole Ants (0 total): 
	Single Pols (1 total): 202e
No description has been provided for this image 
Node 20:
	Antpols (1 total): 245n
	Whole Ants (0 total): 
	Single Pols (1 total): 245n
No description has been provided for this image

Cross-Polarized: (4 antpols across 2 antennas)

These antennas have their east and north cables swapped.

All Bad Antpols: 316e, 316n, 318e, 318n

Node 28:
	Antpols (4 total): 316e, 316n, 318e, 318n
	Whole Ants (2 total): 316, 318
	Single Pols (0 total):

Likely FEM Power Issue: (34 antpols across 27 antennas)

These antennas have low power and anomolously high slopes.

All Bad Antpols: 19n, 44e, 44n, 53e, 62n, 74e, 99e, 109n, 118n, 120e, 124e, 128e, 135e, 164e, 164n, 167n, 170e, 200e, 255e, 290e, 291e, 312n, 320e, 320n, 322e, 326e, 326n, 332e, 332n, 336e, 346e, 346n, 347e, 347n

Node 2:
	Antpols (1 total): 19n
	Whole Ants (0 total): 
	Single Pols (1 total): 19n
No description has been provided for this image 
Node 3:
	Antpols (3 total): 53e, 320e, 320n
	Whole Ants (1 total): 320
	Single Pols (1 total): 53e
No description has been provided for this image 
Node 5:
	Antpols (4 total): 44e, 44n, 74e, 322e
	Whole Ants (1 total): 44
	Single Pols (2 total): 74e, 322e
No description has been provided for this image 
Node 6:
	Antpols (1 total): 62n
	Whole Ants (0 total): 
	Single Pols (1 total): 62n
No description has been provided for this image 
Node 7:
	Antpols (2 total): 99e, 118n
	Whole Ants (0 total): 
	Single Pols (2 total): 99e, 118n
No description has been provided for this image 
Node 8:
	Antpols (1 total): 120e
	Whole Ants (0 total): 
	Single Pols (1 total): 120e
No description has been provided for this image 
Node 9:
	Antpols (1 total): 124e
	Whole Ants (0 total): 
	Single Pols (1 total): 124e
No description has been provided for this image 
Node 10:
	Antpols (2 total): 109n, 128e
	Whole Ants (0 total): 
	Single Pols (2 total): 109n, 128e
No description has been provided for this image 
Node 12:
	Antpols (1 total): 135e
	Whole Ants (0 total): 
	Single Pols (1 total): 135e
No description has been provided for this image 
Node 14:
	Antpols (2 total): 164e, 164n
	Whole Ants (1 total): 164
	Single Pols (0 total):
No description has been provided for this image 
Node 15:
	Antpols (2 total): 167n, 170e
	Whole Ants (0 total): 
	Single Pols (2 total): 167n, 170e
No description has been provided for this image 
Node 18:
	Antpols (1 total): 200e
	Whole Ants (0 total): 
	Single Pols (1 total): 200e
No description has been provided for this image 
Node 21:
	Antpols (5 total): 326e, 326n, 332e, 332n, 336e
	Whole Ants (2 total): 332, 326
	Single Pols (1 total): 336e
No description has been provided for this image 
Node 23:
	Antpols (1 total): 255e
	Whole Ants (0 total): 
	Single Pols (1 total): 255e
No description has been provided for this image 
Node 27:
	Antpols (5 total): 312n, 346e, 346n, 347e, 347n
	Whole Ants (2 total): 346, 347
	Single Pols (1 total): 312n
No description has been provided for this image 
Node 28:
	Antpols (2 total): 290e, 291e
	Whole Ants (0 total): 
	Single Pols (2 total): 290e, 291e
No description has been provided for this image

High Power: (31 antpols across 26 antennas)

These antennas have high median power.

All Bad Antpols: 15n, 94e, 103e, 134e, 139e, 174n, 176e, 188n, 208n, 211e, 231e, 232e, 238e, 243e, 254e, 290n, 300e, 301e, 313e, 313n, 315e, 315n, 316e, 316n, 317e, 317n, 318e, 318n, 329n, 331e, 340e

Node 1:
	Antpols (1 total): 15n
	Whole Ants (0 total): 
	Single Pols (1 total): 15n
No description has been provided for this image 
Node 8:
	Antpols (1 total): 103e
	Whole Ants (0 total): 
	Single Pols (1 total): 103e
No description has been provided for this image 
Node 10:
	Antpols (1 total): 94e
	Whole Ants (0 total): 
	Single Pols (1 total): 94e
No description has been provided for this image 
Node 11:
	Antpols (1 total): 134e
	Whole Ants (0 total): 
	Single Pols (1 total): 134e
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Node 12:
	Antpols (2 total): 176e, 329n
	Whole Ants (0 total): 
	Single Pols (2 total): 176e, 329n
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Node 13:
	Antpols (1 total): 139e
	Whole Ants (0 total): 
	Single Pols (1 total): 139e
No description has been provided for this image 
Node 15:
	Antpols (1 total): 188n
	Whole Ants (0 total): 
	Single Pols (1 total): 188n
No description has been provided for this image 
Node 16:
	Antpols (1 total): 174n
	Whole Ants (0 total): 
	Single Pols (1 total): 174n
No description has been provided for this image 
Node 18:
	Antpols (1 total): 238e
	Whole Ants (0 total): 
	Single Pols (1 total): 238e
No description has been provided for this image 
Node 19:
	Antpols (1 total): 243e
	Whole Ants (0 total): 
	Single Pols (1 total): 243e
No description has been provided for this image 
Node 20:
	Antpols (2 total): 208n, 211e
	Whole Ants (0 total): 
	Single Pols (2 total): 208n, 211e
No description has been provided for this image 
Node 21:
	Antpols (4 total): 231e, 232e, 331e, 340e
	Whole Ants (0 total): 
	Single Pols (4 total): 231e, 232e, 331e, 340e
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Node 23:
	Antpols (1 total): 254e
	Whole Ants (0 total): 
	Single Pols (1 total): 254e
No description has been provided for this image 
Node 27:
	Antpols (4 total): 300e, 301e, 313e, 313n
	Whole Ants (1 total): 313
	Single Pols (2 total): 300e, 301e
No description has been provided for this image 
Node 28:
	Antpols (9 total): 290n, 315e, 315n, 316e, 316n, 317e, 317n, 318e, 318n
	Whole Ants (4 total): 315, 316, 317, 318
	Single Pols (1 total): 290n
No description has been provided for this image

Other Low Power Issues: (3 antpols across 3 antennas)

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

All Bad Antpols: 81e, 104n, 291n

Node 7:
	Antpols (1 total): 81e
	Whole Ants (0 total): 
	Single Pols (1 total): 81e
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 28:
	Antpols (1 total): 291n
	Whole Ants (0 total): 
	Single Pols (1 total): 291n
No description has been provided for this image

Low Correlation, But Not Low Power: (11 antpols across 8 antennas)

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

All Bad Antpols: 27e, 27n, 28e, 255n, 290n, 312e, 316e, 316n, 318e, 318n, 328e

Node 1:
	Antpols (3 total): 27e, 27n, 28e
	Whole Ants (1 total): 27
	Single Pols (1 total): 28e
Node 10:
	Antpols (1 total): 328e
	Whole Ants (0 total): 
	Single Pols (1 total): 328e
Node 23:
	Antpols (1 total): 255n
	Whole Ants (0 total): 
	Single Pols (1 total): 255n
Node 27:
	Antpols (1 total): 312e
	Whole Ants (0 total): 
	Single Pols (1 total): 312e
Node 28:
	Antpols (5 total): 290n, 316e, 316n, 318e, 318n
	Whole Ants (2 total): 316, 318
	Single Pols (1 total): 290n

Bad Bandpass Shapes, But Not Bad Power: (16 antpols across 15 antennas)

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

All Bad Antpols: 18e, 27n, 28e, 30n, 32n, 46e, 76e, 76n, 78e, 87e, 161n, 180n, 182e, 191n, 209n, 300n

Node 1:
	Antpols (4 total): 18e, 27n, 28e, 30n
	Whole Ants (0 total): 
	Single Pols (4 total): 18e, 27n, 28e, 30n
No description has been provided for this image 
Node 2:
	Antpols (1 total): 32n
	Whole Ants (0 total): 
	Single Pols (1 total): 32n
No description has been provided for this image 
Node 5:
	Antpols (3 total): 46e, 76e, 76n
	Whole Ants (1 total): 76
	Single Pols (1 total): 46e
No description has been provided for this image 
Node 6:
	Antpols (1 total): 78e
	Whole Ants (0 total): 
	Single Pols (1 total): 78e