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/data1/2461143'
SUM_FILE = '/mnt/sn1/data1/2461143/zen.2461143.42795.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: 4-12-2026
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 2129 csv files starting with /mnt/sn1/data1/2461143/zen.2461143.18993.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()

206 antpols (on 103 antennas) frequently flagged for All-Zeros.
35 antpols (on 24 antennas) frequently flagged for Likely FEM Power Issue.
30 antpols (on 24 antennas) frequently flagged for High Power.
30 antpols (on 24 antennas) frequently flagged for Low Correlation, But Not Low Power.
22 antpols (on 21 antennas) frequently flagged for Excess RFI.
12 antpols (on 11 antennas) frequently flagged for Bad Bandpass Shapes, But Not Bad Power.
5 antpols (on 4 antennas) frequently flagged for Other Low Power Issues.
5 antpols (on 4 antennas) frequently flagged for Redcal chi^2.
3 antpols (on 3 antennas) frequently flagged for Excess Power in X-Engine Diffs.
2 antpols (on 1 antennas) frequently flagged for Cross-Polarized.
0 antpols (on 0 antennas) frequently flagged for Excess Zeros in Either Even or Odd Spectra.
In [26]:
print_all_issue_summaries()

All-Zeros: (206 antpols across 103 antennas)

These antennas have visibilities that are more than half zeros.

All Bad Antpols: 18e, 18n, 27e, 27n, 28e, 28n, 33e, 33n, 36e, 36n, 44e, 44n, 50e, 50n, 52e, 52n, 58e, 58n, 59e, 59n, 81e, 81n, 82e, 82n, 83e, 83n, 85e, 85n, 86e, 86n, 88e, 88n, 90e, 90n, 97e, 97n, 102e, 102n, 107e, 107n, 112e, 112n, 127e, 127n, 128e, 128n, 132e, 132n, 134e, 134n, 140e, 140n, 141e, 141n, 142e, 142n, 147e, 147n, 148e, 148n, 149e, 149n, 151e, 151n, 156e, 156n, 157e, 157n, 158e, 158n, 160e, 160n, 171e, 171n, 172e, 172n, 173e, 173n, 174e, 174n, 175e, 175n, 180e, 180n, 181e, 181n, 189e, 189n, 190e, 190n, 191e, 191n, 192e, 192n, 193e, 193n, 194e, 194n, 195e, 195n, 196e, 196n, 197e, 197n, 198e, 198n, 200e, 200n, 201e, 201n, 202e, 202n, 213e, 213n, 214e, 214n, 215e, 215n, 216e, 216n, 217e, 217n, 225e, 225n, 226e, 226n, 227e, 227n, 228e, 228n, 229e, 229n, 237e, 237n, 238e, 238n, 239e, 239n, 240e, 240n, 241e, 241n, 242e, 242n, 243e, 243n, 246e, 246n, 250e, 250n, 254e, 254n, 255e, 255n, 256e, 256n, 261e, 261n, 262e, 262n, 266e, 266n, 269e, 269n, 284e, 284n, 285e, 285n, 286e, 286n, 290e, 290n, 291e, 291n, 293e, 293n, 294e, 294n, 299e, 299n, 300e, 300n, 301e, 301n, 302e, 302n, 311e, 311n, 312e, 312n, 318e, 318n, 321e, 321n, 323e, 323n, 326e, 326n, 327e, 327n, 331e, 331n, 339e, 339n, 345e, 345n

Node 1:
	Antpols (6 total): 18e, 18n, 27e, 27n, 28e, 28n
	Whole Ants (3 total): 18, 27, 28
	Single Pols (0 total): 
Node 2:
	Antpols (6 total): 33e, 33n, 321e, 321n, 323e, 323n
	Whole Ants (3 total): 321, 33, 323
	Single Pols (0 total): 
Node 3:
	Antpols (6 total): 36e, 36n, 50e, 50n, 52e, 52n
	Whole Ants (3 total): 50, 36, 52
	Single Pols (0 total): 
Node 5:
	Antpols (6 total): 44e, 44n, 58e, 58n, 59e, 59n
	Whole Ants (3 total): 58, 59, 44
	Single Pols (0 total): 
Node 7:
	Antpols (6 total): 81e, 81n, 82e, 82n, 83e, 83n
	Whole Ants (3 total): 81, 82, 83
	Single Pols (0 total): 
Node 8:
	Antpols (6 total): 85e, 85n, 86e, 86n, 102e, 102n
	Whole Ants (3 total): 102, 85, 86
	Single Pols (0 total): 
Node 9:
	Antpols (6 total): 88e, 88n, 90e, 90n, 107e, 107n
	Whole Ants (3 total): 88, 90, 107
	Single Pols (0 total): 
Node 10:
	Antpols (6 total): 112e, 112n, 127e, 127n, 128e, 128n
	Whole Ants (3 total): 112, 128, 127
	Single Pols (0 total): 
Node 11:
	Antpols (6 total): 97e, 97n, 132e, 132n, 134e, 134n
	Whole Ants (3 total): 97, 132, 134
	Single Pols (0 total): 
Node 12:
	Antpols (6 total): 156e, 156n, 157e, 157n, 158e, 158n
	Whole Ants (3 total): 156, 157, 158
	Single Pols (0 total): 
Node 13:
	Antpols (12 total): 140e, 140n, 141e, 141n, 142e, 142n, 160e, 160n, 180e, 180n, 181e, 181n
	Whole Ants (6 total): 160, 140, 141, 142, 180, 181
	Single Pols (0 total): 
Node 15:
	Antpols (12 total): 147e, 147n, 148e, 148n, 149e, 149n, 189e, 189n, 190e, 190n, 191e, 191n
	Whole Ants (6 total): 147, 148, 149, 189, 190, 191
	Single Pols (0 total): 
Node 16:
	Antpols (18 total): 151e, 151n, 171e, 171n, 172e, 172n, 173e, 173n, 174e, 174n, 192e, 192n, 193e, 193n, 194e, 194n, 213e, 213n
	Whole Ants (9 total): 192, 193, 194, 171, 172, 173, 174, 213, 151
	Single Pols (0 total): 
Node 17:
	Antpols (12 total): 196e, 196n, 197e, 197n, 198e, 198n, 215e, 215n, 216e, 216n, 217e, 217n
	Whole Ants (6 total): 196, 197, 198, 215, 216, 217
	Single Pols (0 total): 
Node 18:
	Antpols (12 total): 200e, 200n, 201e, 201n, 202e, 202n, 237e, 237n, 238e, 238n, 239e, 239n
	Whole Ants (6 total): 200, 201, 202, 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 (12 total): 227e, 227n, 228e, 228n, 229e, 229n, 246e, 246n, 261e, 261n, 262e, 262n
	Whole Ants (6 total): 227, 228, 261, 229, 262, 246
	Single Pols (0 total): 
Node 21:
	Antpols (12 total): 175e, 175n, 195e, 195n, 214e, 214n, 326e, 326n, 327e, 327n, 331e, 331n
	Whole Ants (6 total): 195, 326, 327, 331, 175, 214
	Single Pols (0 total): 
Node 22:
	Antpols (6 total): 250e, 250n, 266e, 266n, 269e, 269n
	Whole Ants (3 total): 250, 266, 269
	Single Pols (0 total): 
Node 23:
	Antpols (12 total): 254e, 254n, 255e, 255n, 256e, 256n, 284e, 284n, 285e, 285n, 286e, 286n
	Whole Ants (6 total): 256, 286, 284, 285, 254, 255
	Single Pols (0 total): 
Node 27:
	Antpols (12 total): 299e, 299n, 300e, 300n, 301e, 301n, 302e, 302n, 311e, 311n, 312e, 312n
	Whole Ants (6 total): 299, 300, 301, 302, 311, 312
	Single Pols (0 total): 
Node 28:
	Antpols (6 total): 290e, 290n, 291e, 291n, 318e, 318n
	Whole Ants (3 total): 290, 291, 318
	Single Pols (0 total): 
Node 29:
	Antpols (8 total): 293e, 293n, 294e, 294n, 339e, 339n, 345e, 345n
	Whole Ants (4 total): 345, 339, 293, 294
	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: (3 antpols across 3 antennas)

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

All Bad Antpols: 29n, 209n, 307n

Node 1:
	Antpols (1 total): 29n
	Whole Ants (0 total): 
	Single Pols (1 total): 29n
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 29:
	Antpols (1 total): 307n
	Whole Ants (0 total): 
	Single Pols (1 total): 307n
No description has been provided for this image

Cross-Polarized: (2 antpols across 1 antennas)

These antennas have their east and north cables swapped.

All Bad Antpols: 144e, 144n

Node 14:
	Antpols (2 total): 144e, 144n
	Whole Ants (1 total): 144
	Single Pols (0 total):

Likely FEM Power Issue: (35 antpols across 24 antennas)

These antennas have low power and anomolously high slopes.

All Bad Antpols: 4e, 5e, 41e, 75e, 75n, 76e, 101n, 159e, 170e, 207n, 268e, 268n, 271e, 273n, 314e, 314n, 320e, 320n, 322e, 329e, 329n, 332e, 332n, 333e, 333n, 340e, 340n, 343n, 344e, 344n, 346n, 347e, 347n, 348e, 348n

Node 1:
	Antpols (2 total): 4e, 5e
	Whole Ants (0 total): 
	Single Pols (2 total): 4e, 5e
No description has been provided for this image 
Node 3:
	Antpols (2 total): 320e, 320n
	Whole Ants (1 total): 320
	Single Pols (0 total):
No description has been provided for this image 
Node 4:
	Antpols (1 total): 41e
	Whole Ants (0 total): 
	Single Pols (1 total): 41e
No description has been provided for this image 
Node 5:
	Antpols (4 total): 75e, 75n, 76e, 322e
	Whole Ants (1 total): 75
	Single Pols (2 total): 76e, 322e
No description has been provided for this image 
Node 8:
	Antpols (1 total): 101n
	Whole Ants (0 total): 
	Single Pols (1 total): 101n
No description has been provided for this image 
Node 12:
	Antpols (4 total): 329e, 329n, 333e, 333n
	Whole Ants (2 total): 329, 333
	Single Pols (0 total):
No description has been provided for this image 
Node 13:
	Antpols (1 total): 159e
	Whole Ants (0 total): 
	Single Pols (1 total): 159e
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 19:
	Antpols (1 total): 207n
	Whole Ants (0 total): 
	Single Pols (1 total): 207n
No description has been provided for this image 
Node 21:
	Antpols (4 total): 332e, 332n, 340e, 340n
	Whole Ants (2 total): 332, 340
	Single Pols (0 total):
No description has been provided for this image 
Node 22:
	Antpols (2 total): 268e, 268n
	Whole Ants (1 total): 268
	Single Pols (0 total):
No description has been provided for this image 
Node 23:
	Antpols (2 total): 271e, 273n
	Whole Ants (0 total): 
	Single Pols (2 total): 271e, 273n
No description has been provided for this image 
Node 27:
	Antpols (6 total): 314e, 314n, 343n, 346n, 347e, 347n
	Whole Ants (2 total): 314, 347
	Single Pols (2 total): 343n, 346n
No description has been provided for this image 
Node 28:
	Antpols (4 total): 344e, 344n, 348e, 348n
	Whole Ants (2 total): 344, 348
	Single Pols (0 total):
No description has been provided for this image

High Power: (30 antpols across 24 antennas)

These antennas have high median power.

All Bad Antpols: 8e, 8n, 89e, 99n, 103e, 103n, 118n, 120n, 122e, 167n, 176e, 178e, 178n, 183n, 187n, 208n, 224e, 224n, 231e, 232e, 257e, 273e, 282n, 287e, 292n, 313e, 313n, 336n, 349e, 349n

Node 2:
	Antpols (2 total): 8e, 8n
	Whole Ants (1 total): 8
	Single Pols (0 total):
No description has been provided for this image 
Node 7:
	Antpols (2 total): 99n, 118n
	Whole Ants (0 total): 
	Single Pols (2 total): 99n, 118n
No description has been provided for this image 
Node 8:
	Antpols (4 total): 103e, 103n, 120n, 122e
	Whole Ants (1 total): 103
	Single Pols (2 total): 120n, 122e
No description has been provided for this image 
Node 9:
	Antpols (1 total): 89e
	Whole Ants (0 total): 
	Single Pols (1 total): 89e
No description has been provided for this image 
Node 12:
	Antpols (3 total): 176e, 178e, 178n
	Whole Ants (1 total): 178
	Single Pols (1 total): 176e
No description has been provided for this image 
Node 13:
	Antpols (1 total): 183n
	Whole Ants (0 total): 
	Single Pols (1 total): 183n
No description has been provided for this image 
Node 14:
	Antpols (1 total): 187n
	Whole Ants (0 total): 
	Single Pols (1 total): 187n
No description has been provided for this image 
Node 15:
	Antpols (1 total): 167n
	Whole Ants (0 total): 
	Single Pols (1 total): 167n
No description has been provided for this image 
Node 19:
	Antpols (2 total): 224e, 224n
	Whole Ants (1 total): 224
	Single Pols (0 total):
No description has been provided for this image 
Node 20:
	Antpols (1 total): 208n
	Whole Ants (0 total): 
	Single Pols (1 total): 208n
No description has been provided for this image 
Node 21:
	Antpols (3 total): 231e, 232e, 336n
	Whole Ants (0 total): 
	Single Pols (3 total): 231e, 232e, 336n
No description has been provided for this image 
Node 22:
	Antpols (1 total): 282n
	Whole Ants (0 total): 
	Single Pols (1 total): 282n
No description has been provided for this image 
Node 23:
	Antpols (3 total): 257e, 273e, 287e
	Whole Ants (0 total): 
	Single Pols (3 total): 257e, 273e, 287e
No description has been provided for this image 
Node 27:
	Antpols (2 total): 313e, 313n
	Whole Ants (1 total): 313
	Single Pols (0 total):
No description has been provided for this image 
Node 28:
	Antpols (2 total): 349e, 349n
	Whole Ants (1 total): 349
	Single Pols (0 total):
No description has been provided for this image 
Node 29:
	Antpols (1 total): 292n
	Whole Ants (0 total): 
	Single Pols (1 total): 292n
No description has been provided for this image

Other Low Power Issues: (5 antpols across 4 antennas)

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

All Bad Antpols: 57n, 104n, 218n, 295e, 295n

Node 4:
	Antpols (1 total): 57n
	Whole Ants (0 total): 
	Single Pols (1 total): 57n
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 17:
	Antpols (1 total): 218n
	Whole Ants (0 total): 
	Single Pols (1 total): 218n
No description has been provided for this image 
Node 22:
	Antpols (2 total): 295e, 295n
	Whole Ants (1 total): 295
	Single Pols (0 total):
No description has been provided for this image

Low Correlation, But Not Low Power: (30 antpols across 24 antennas)

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

All Bad Antpols: 4n, 21n, 76n, 135e, 144e, 144n, 234n, 251e, 251n, 273e, 277e, 278e, 281e, 287n, 307e, 313e, 315n, 322n, 324e, 324n, 325n, 328e, 328n, 336e, 336n, 342n, 343e, 346e, 349e, 349n

Node 1:
	Antpols (1 total): 4n
	Whole Ants (0 total): 
	Single Pols (1 total): 4n
Node 2:
	Antpols (1 total): 21n
	Whole Ants (0 total): 
	Single Pols (1 total): 21n
Node 4:
	Antpols (2 total): 324e, 324n
	Whole Ants (1 total): 324
	Single Pols (0 total): 
Node 5:
	Antpols (2 total): 76n, 322n
	Whole Ants (0 total): 
	Single Pols (2 total): 76n, 322n
Node 9:
	Antpols (1 total): 325n
	Whole Ants (0 total): 
	Single Pols (1 total): 325n
Node 10:
	Antpols (2 total): 328e, 328n
	Whole Ants (1 total): 328
	Single Pols (0 total): 
Node 12:
	Antpols (1 total): 135e
	Whole Ants (0 total): 
	Single Pols (1 total): 135e
Node 14:
	Antpols (2 total): 144e, 144n
	Whole Ants (1 total): 144
	Single Pols (0 total): 
Node 17:
	Antpols (1 total): 234n
	Whole Ants (0 total): 
	Single Pols (1 total): 234n
Node 21:
	Antpols (2 total): 336e, 336n
	Whole Ants (1 total): 336
	Single Pols (0 total): 
Node 22:
	Antpols (3 total): 251e, 251n, 281e
	Whole Ants (1 total): 251
	Single Pols (1 total): 281e
Node 23:
	Antpols (2 total): 273e, 287n
	Whole Ants (0 total): 
	Single Pols (2 total): 273e, 287n
Node 27:
	Antpols (4 total): 313e, 342n, 343e, 346e
	Whole Ants (0 total): 
	Single Pols (4 total): 313e, 342n, 343e, 346e
Node 28:
	Antpols (3 total): 315n, 349e, 349n
	Whole Ants (1 total): 349
	Single Pols (1 total): 315n
Node 29:
	Antpols (3 total): 277e, 278e, 307e
	Whole Ants (0 total): 
	Single Pols (3 total): 277e, 278e, 307e

Bad Bandpass Shapes, But Not Bad Power: (12 antpols across 11 antennas)

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

All Bad Antpols: 4n, 21n, 32n, 46e, 71e, 71n, 87e, 135e, 161n, 206n, 277e, 307e

Node 1:
	Antpols (1 total): 4n
	Whole Ants (0 total): 
	Single Pols (1 total): 4n
No description has been provided for this image 
Node 2:
	Antpols (2 total): 21n, 32n
	Whole Ants (0 total): 
	Single Pols (2 total): 21n, 32n
No description has been provided for this image 
Node 4:
	Antpols (2 total): 71e, 71n
	Whole Ants (1 total): 71
	Single Pols (0 total):
No description has been provided for this image 
Node 5:
	Antpols (1 total): 46e
	Whole Ants (0 total): 
	Single Pols (1 total): 46e
No description has been provided for this image 
Node 8:
	Antpols (1 total): 87e
	Whole Ants (0 total): 
	Single Pols (1 total): 87e
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 13:
	Antpols (1 total): 161n
	Whole Ants (0 total): 
	Single Pols (1 total): 161n
No description has been provided for this image 
Node 19:
	Antpols (1 total): 206n
	Whole Ants (0 total): 
	Single Pols (1 total): 206n
No description has been provided for this image 
Node 29:
	Antpols (2 total): 277e, 307e
	Whole Ants (0 total): 
	Single Pols (2 total): 277e, 307e
No description has been provided for this image

Excess RFI: (22 antpols across 21 antennas)

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

All Bad Antpols: 10e, 15e, 30n, 37n, 40n, 60n, 117e, 121e, 164e, 169e, 212n, 234n, 251e, 253n, 271n, 287n, 306n, 336e, 342e, 342n, 343e, 346e

Node 1:
	Antpols (2 total): 15e, 30n
	Whole Ants (0 total): 
	Single Pols (2 total): 15e, 30n
No description has been provided for this image 
Node 2:
	Antpols (1 total): 10e
	Whole Ants (0 total): 
	Single Pols (1 total): 10e
No description has been provided for this image 
Node 3:
	Antpols (1 total): 37n
	Whole Ants (0 total): 
	Single Pols (1 total): 37n
No description has been provided for this image 
Node 4:
	Antpols (1 total): 40n
	Whole Ants (0 total): 
	Single Pols (1 total): 40n
No description has been provided for this image 
Node 5:
	Antpols (1 total): 60n
	Whole Ants (0 total): 
	Single Pols (1 total): 60n
No description has been provided for this image 
Node 7:
	Antpols (1 total): 117e
	Whole Ants (0 total): 
	Single Pols (1 total): 117e
No description has been provided for this image 
Node 8:
	Antpols (1 total): 121e
	Whole Ants (0 total): 
	Single Pols (1 total): 121e
No description has been provided for this image 
Node 14:
	Antpols (1 total): 164e
	Whole Ants (0 total): 
	Single Pols (1 total): 164e
No description has been provided for this image 
Node 15:
	Antpols (1 total): 169e
	Whole Ants (0 total): 
	Single Pols (1 total): 169e
No description has been provided for this image 
Node 17:
	Antpols (1 total): 234n
	Whole Ants (0 total): 
	Single Pols (1 total): 234n
No description has been provided for this image 
Node 21:
	Antpols (2 total): 212n, 336e
	Whole Ants (0 total): 
	Single Pols (2 total): 212n, 336e
No description has been provided for this image 
Node 22:
	Antpols (2 total): 251e, 253n
	Whole Ants (0 total): 
	Single Pols (2 total): 251e, 253n
No description has been provided for this image 
Node 23:
	Antpols (2 total): 271n, 287n
	Whole Ants (0 total): 
	Single Pols (2 total): 271n, 287n
No description has been provided for this image 
Node 27:
	Antpols (4 total): 342e, 342n, 343e, 346e
	Whole Ants (1 total): 342
	Single Pols (2 total): 343e, 346e
No description has been provided for this image 
Node 29:
	Antpols (1 total): 306n
	Whole Ants (0 total): 
	Single Pols (1 total): 306n
No description has been provided for this image

Redcal chi^2: (5 antpols across 4 antennas)

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

All Bad Antpols: 94n, 136n, 170n, 204e, 204n

Node 10:
	Antpols (1 total): 94n
	Whole Ants (0 total): 
	Single Pols (1 total): 94n
Node 12:
	Antpols (1 total): 136n
	Whole Ants (0 total): 
	Single Pols (1 total): 136n
Node 15:
	Antpols (1 total): 170n
	Whole Ants (0 total): 
	Single Pols (1 total): 170n
Node 19:
	Antpols (2 total): 204e, 204n
	Whole Ants (1 total): 204
	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')
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: 3.2.5.dev1+g5a985ae31
hera_cal: 3.7.7.dev97+gc2668d3f7
hera_qm: 2.2.1.dev4+gf6d02113b

hera_notebook_templates: 0.0.1.dev1313+g92178a09c