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/data1/2460579' SUM_FILE = '/mnt/sn1/data1/2460579/zen.2460579.46990.sum.uvh5' OC_SKIP_OUTRIGGERS = 'True'
In [3]:
if SUM_FILE is not None:
from astropy.time import Time, TimeDelta
utc = Time(float(SUM_FILE.split('zen.')[-1].split('.sum.uvh5')[0]), format='jd').datetime
print(f'Date: {utc.month}-{utc.day}-{utc.year}')
Date: 9-25-2024
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 1942 csv files starting with /mnt/sn1/data1/2460579/zen.2460579.25247.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()
192 antpols (on 96 antennas) frequently flagged for All-Zeros. 20 antpols (on 19 antennas) frequently flagged for Excess RFI. 12 antpols (on 11 antennas) frequently flagged for Bad Bandpass Shapes, But Not Bad Power. 11 antpols (on 9 antennas) frequently flagged for Likely FEM Power Issue. 6 antpols (on 6 antennas) frequently flagged for Redcal chi^2. 5 antpols (on 4 antennas) frequently flagged for Excess Power in X-Engine Diffs. 4 antpols (on 2 antennas) frequently flagged for Cross-Polarized. 4 antpols (on 3 antennas) frequently flagged for Low Correlation, But Not Low Power. 3 antpols (on 2 antennas) frequently flagged for High Power. 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: (192 antpols across 96 antennas)
These antennas have visibilities that are more than half zeros.
All Bad Antpols: 22e, 22n, 34e, 34n, 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, 88e, 88n, 90e, 90n, 103e, 103n, 104e, 104n, 107e, 107n, 120e, 120n, 150e, 150n, 151e, 151n, 152e, 152n, 153e, 153n, 154e, 154n, 167e, 167n, 171e, 171n, 172e, 172n, 174e, 174n, 176e, 176n, 177e, 177n, 178e, 178n, 179e, 179n, 188e, 188n, 194e, 194n, 199e, 199n, 200e, 200n, 201e, 201n, 202e, 202n, 204e, 204n, 205e, 205n, 206e, 206n, 207e, 207n, 211e, 211n, 212e, 212n, 213e, 213n, 215e, 215n, 216e, 216n, 217e, 217n, 218e, 218n, 220e, 220n, 221e, 221n, 222e, 222n, 223e, 223n, 224e, 224n, 225e, 225n, 226e, 226n, 231e, 231n, 232e, 232n, 233e, 233n, 234e, 234n, 235e, 235n, 240e, 240n, 241e, 241n, 242e, 242n, 243e, 243n, 244e, 244n, 245e, 245n, 246e, 246n, 250e, 250n, 251e, 251n, 252e, 252n, 253e, 253n, 255e, 255n, 256e, 256n, 261e, 261n, 262e, 262n, 266e, 266n, 267e, 267n, 268e, 268n, 269e, 269n, 270e, 270n, 281e, 281n, 282e, 282n, 283e, 283n, 285e, 285n, 295e, 295n, 326e, 326n, 327e, 327n, 329e, 329n, 331e, 331n, 332e, 332n, 333e, 333n, 336e, 336n, 340e, 340n 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 (24 total): 22e, 22n, 34e, 34n, 35e, 35n, 47e, 47n, 48e, 48n, 49e, 49n, 61e, 61n, 62e, 62n, 63e, 63n, 64e, 64n, 77e, 77n, 78e, 78n Whole Ants (12 total): 64, 34, 35, 77, 78, 47, 48, 49, 22, 61, 62, 63 Single Pols (0 total): Node 8: Antpols (6 total): 103e, 103n, 104e, 104n, 120e, 120n Whole Ants (3 total): 104, 120, 103 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 12: Antpols (12 total): 176e, 176n, 177e, 177n, 178e, 178n, 179e, 179n, 329e, 329n, 333e, 333n Whole Ants (6 total): 329, 333, 176, 177, 178, 179 Single Pols (0 total): Node 15: Antpols (6 total): 150e, 150n, 167e, 167n, 188e, 188n Whole Ants (3 total): 188, 150, 167 Single Pols (0 total): Node 16: Antpols (18 total): 151e, 151n, 152e, 152n, 153e, 153n, 154e, 154n, 171e, 171n, 172e, 172n, 174e, 174n, 194e, 194n, 213e, 213n Whole Ants (9 total): 194, 171, 172, 174, 213, 151, 152, 153, 154 Single Pols (0 total): Node 17: Antpols (16 total): 199e, 199n, 215e, 215n, 216e, 216n, 217e, 217n, 218e, 218n, 233e, 233n, 234e, 234n, 235e, 235n Whole Ants (8 total): 199, 233, 234, 235, 215, 216, 217, 218 Single Pols (0 total): Node 18: Antpols (12 total): 200e, 200n, 201e, 201n, 202e, 202n, 220e, 220n, 221e, 221n, 222e, 222n Whole Ants (6 total): 200, 201, 202, 220, 221, 222 Single Pols (0 total): Node 19: Antpols (24 total): 204e, 204n, 205e, 205n, 206e, 206n, 207e, 207n, 223e, 223n, 224e, 224n, 225e, 225n, 226e, 226n, 240e, 240n, 241e, 241n, 242e, 242n, 243e, 243n Whole Ants (12 total): 224, 225, 226, 204, 205, 206, 207, 240, 241, 242, 243, 223 Single Pols (0 total): Node 20: Antpols (12 total): 211e, 211n, 244e, 244n, 245e, 245n, 246e, 246n, 261e, 261n, 262e, 262n Whole Ants (6 total): 261, 262, 211, 244, 245, 246 Single Pols (0 total): Node 21: Antpols (18 total): 212e, 212n, 231e, 231n, 232e, 232n, 326e, 326n, 327e, 327n, 331e, 331n, 332e, 332n, 336e, 336n, 340e, 340n Whole Ants (9 total): 326, 327, 232, 231, 331, 332, 336, 212, 340 Single Pols (0 total): Node 22: Antpols (24 total): 250e, 250n, 251e, 251n, 252e, 252n, 253e, 253n, 266e, 266n, 267e, 267n, 268e, 268n, 269e, 269n, 281e, 281n, 282e, 282n, 283e, 283n, 295e, 295n Whole Ants (12 total): 295, 266, 267, 268, 269, 282, 283, 281, 250, 251, 252, 253 Single Pols (0 total): Node 23: Antpols (8 total): 255e, 255n, 256e, 256n, 270e, 270n, 285e, 285n Whole Ants (4 total): 256, 285, 270, 255 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: (5 antpols across 4 antennas)
These antennas are showing evidence of mis-written packets in either the evens or the odds.
All Bad Antpols: 28n, 111e, 136e, 136n, 197n Node 1: Antpols (1 total): 28n Whole Ants (0 total): Single Pols (1 total): 28n
Node 10: Antpols (1 total): 111e Whole Ants (0 total): Single Pols (1 total): 111e
Node 12: Antpols (2 total): 136e, 136n Whole Ants (1 total): 136 Single Pols (0 total):
Node 17: Antpols (1 total): 197n Whole Ants (0 total): Single Pols (1 total): 197n
Cross-Polarized: (4 antpols across 2 antennas)
These antennas have their east and north cables swapped.
All Bad Antpols: 86e, 86n, 136e, 136n Node 8: Antpols (2 total): 86e, 86n Whole Ants (1 total): 86 Single Pols (0 total): Node 12: Antpols (2 total): 136e, 136n Whole Ants (1 total): 136 Single Pols (0 total):
Likely FEM Power Issue: (11 antpols across 9 antennas)
These antennas have low power and anomolously high slopes.
All Bad Antpols: 15n, 36e, 36n, 86n, 96e, 109n, 170e, 182e, 189e, 189n, 272e Node 1: Antpols (1 total): 15n Whole Ants (0 total): Single Pols (1 total): 15n
Node 3: Antpols (2 total): 36e, 36n Whole Ants (1 total): 36 Single Pols (0 total):
Node 8: Antpols (1 total): 86n Whole Ants (0 total): Single Pols (1 total): 86n
Node 10: Antpols (1 total): 109n Whole Ants (0 total): Single Pols (1 total): 109n
Node 11: Antpols (1 total): 96e Whole Ants (0 total): Single Pols (1 total): 96e
Node 13: Antpols (1 total): 182e Whole Ants (0 total): Single Pols (1 total): 182e
Node 15: Antpols (3 total): 170e, 189e, 189n Whole Ants (1 total): 189 Single Pols (1 total): 170e
Node 23: Antpols (1 total): 272e Whole Ants (0 total): Single Pols (1 total): 272e
High Power: (3 antpols across 2 antennas)
These antennas have high median power.
All Bad Antpols: 8e, 8n, 272n Node 2: Antpols (2 total): 8e, 8n Whole Ants (1 total): 8 Single Pols (0 total):
Node 23: Antpols (1 total): 272n Whole Ants (0 total): Single Pols (1 total): 272n
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: 57e, 100n, 137e Node 4: Antpols (1 total): 57e Whole Ants (0 total): Single Pols (1 total): 57e
Node 7: Antpols (2 total): 100n, 137e Whole Ants (0 total): Single Pols (2 total): 100n, 137e
Low Correlation, But Not Low Power: (4 antpols across 3 antennas)
These antennas are low correlation, but their autocorrelation power levels look OK.
All Bad Antpols: 27e, 28e, 328e, 328n Node 1: Antpols (2 total): 27e, 28e Whole Ants (0 total): Single Pols (2 total): 27e, 28e Node 10: Antpols (2 total): 328e, 328n Whole Ants (1 total): 328 Single Pols (0 total):
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: 18e, 27e, 28e, 32n, 33n, 87e, 130e, 130n, 142n, 161n, 180n, 184e Node 1: Antpols (3 total): 18e, 27e, 28e Whole Ants (0 total): Single Pols (3 total): 18e, 27e, 28e
Node 2: Antpols (2 total): 32n, 33n Whole Ants (0 total): Single Pols (2 total): 32n, 33n
Node 8: Antpols (1 total): 87e Whole Ants (0 total): Single Pols (1 total): 87e
Node 10: Antpols (2 total): 130e, 130n Whole Ants (1 total): 130 Single Pols (0 total):
Node 13: Antpols (3 total): 142n, 161n, 180n Whole Ants (0 total): Single Pols (3 total): 142n, 161n, 180n
Node 14: Antpols (1 total): 184e Whole Ants (0 total): Single Pols (1 total): 184e
Excess RFI: (20 antpols across 19 antennas)
These antennas have excess RMS after DPSS filtering (likely RFI), but not low or high power or a bad bandpass.
All Bad Antpols: 9e, 18n, 21e, 27n, 31n, 37n, 40n, 42n, 51e, 54n, 69e, 72n, 85n, 86e, 92e, 97n, 121e, 127e, 144e, 144n Node 1: Antpols (2 total): 18n, 27n Whole Ants (0 total): Single Pols (2 total): 18n, 27n
Node 2: Antpols (3 total): 9e, 21e, 31n Whole Ants (0 total): Single Pols (3 total): 9e, 21e, 31n
Node 3: Antpols (2 total): 37n, 51e Whole Ants (0 total): Single Pols (2 total): 37n, 51e
Node 4: Antpols (5 total): 40n, 42n, 54n, 69e, 72n Whole Ants (0 total): Single Pols (5 total): 40n, 42n, 54n, 69e, 72n
Node 8: Antpols (3 total): 85n, 86e, 121e Whole Ants (0 total): Single Pols (3 total): 85n, 86e, 121e
Node 10: Antpols (2 total): 92e, 127e Whole Ants (0 total): Single Pols (2 total): 92e, 127e
Node 11: Antpols (1 total): 97n Whole Ants (0 total): Single Pols (1 total): 97n
Node 14: Antpols (2 total): 144e, 144n Whole Ants (1 total): 144 Single Pols (0 total):
Redcal chi^2: (6 antpols across 6 antennas)
These antennas have been idenfied as not redundantly calibrating well, even after passing the above checks.
All Bad Antpols: 83n, 84n, 101n, 117e, 131e, 155n Node 7: Antpols (2 total): 83n, 117e Whole Ants (0 total): Single Pols (2 total): 83n, 117e Node 8: Antpols (2 total): 84n, 101n Whole Ants (0 total): Single Pols (2 total): 84n, 101n Node 11: Antpols (1 total): 131e Whole Ants (0 total): Single Pols (1 total): 131e Node 12: Antpols (1 total): 155n Whole Ants (0 total): Single Pols (1 total): 155n
Full-Day Visualizations¶
In [27]:
def classification_plot(col):
class_array = classification_array(col)
plt.figure(figsize=(12, len(ants) / 10), dpi=100)
plt.imshow(class_array.T, aspect='auto', interpolation='none', cmap='RdYlGn', vmin=0, vmax=2,
extent=[jds[0] - np.floor(jds[0]), jds[-1] - np.floor(jds[0]), len(ants), 0])
plt.xlabel(f'JD - {int(jds[0])}')
plt.yticks(ticks=np.arange(.5, len(ants)+.5), labels=[ant for ant in ants], fontsize=6)
plt.ylabel('Antenna Number (East First, Then North)')
plt.gca().tick_params(right=True, top=True, labelright=True, labeltop=True)
plt.tight_layout()
plt.title(f'{col}: Green is "good", Yellow is "suspect", Red is "bad"')
Figure 1: Per-File Overall Antenna Classification Summary¶
This "big green board" shows the overall (i.e. after redundant calibration) classification of antennas on a per-file basis. This is useful for looking at time-dependent effects across the array. While only antenna numbers are labeled, both polarizations are shown, first East then North going down, above and below the antenna's tick mark.
In [28]:
classification_plot('Antenna Class')
In [29]:
# compute flag fractions for all classifiers and antennas
frac_flagged = []
for col in class_cols[1:]:
class_array = np.vstack([t[col] for t in tables])
class_array[class_array == 'good'] = False
class_array[class_array == 'suspect'] = False
class_array[class_array == 'bad'] = True
frac_flagged.append(np.sum(class_array, axis=0))
In [30]:
def plot_flag_frac_all_classifiers():
ticks = []
for i, col in enumerate(list(class_cols[1:])):
ticks.append(f'{col} ({np.nanmean(np.array(frac_flagged).astype(float)[i]) / len(csv_files):.2%})')
plt.figure(figsize=(8, len(ants) / 10), dpi=100)
plt.imshow(np.array(frac_flagged).astype(float).T, aspect='auto', interpolation='none', cmap='viridis')
plt.xticks(ticks=np.arange(len(list(class_cols[1:]))), labels=ticks, rotation=-45, ha='left')
plt.yticks(ticks=np.arange(.5, len(ap_strs)+.5, 2), labels=[ant for ant in ants], fontsize=6)
plt.ylabel('Antenna Number (East First, Then North)')
plt.gca().tick_params(right=True, labelright=True,)
ax2 = plt.gca().twiny()
ax2.set_xticks(ticks=np.arange(len(list(class_cols[1:]))), labels=ticks, rotation=45, ha='left')
plt.colorbar(ax=plt.gca(), label=f'Number of Files Flagged Out of {len(csv_files)}', aspect=50)
plt.tight_layout()
Figure 2: Per-Classifier Antenna Flagging Summary¶
This plot shows the fraction of files flagged for each reason for each antenna. It's useful for seeing which problems are transitory and which ones are more common. Note that not all flags are independent and in particular redcal chi^2 takes an OR of other classifications as an input. Also note that only antenna numbers are labeled, both polarizations are shown, first East then North going down, above and below the antenna's tick mark.
In [31]:
plot_flag_frac_all_classifiers()
In [32]:
def array_class_plot():
fig, axes = plt.subplots(1, 2, figsize=(14, 6), dpi=100, gridspec_kw={'width_ratios': [2, 1]})
plot_antclass(hd.antpos, overall_class, ax=axes[0], ants=[ant for ant in hd.data_ants if ant < 320], legend=False,
title=f'HERA Core: Overall Flagging Based on {overall_thresh:.1%} Daily Threshold')
plot_antclass(hd.antpos, overall_class, ax=axes[1], ants=[ant for ant in hd.data_ants if ant >= 320], radius=50, title='Outriggers')
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()
In [34]:
for repo in ['pyuvdata', 'hera_cal', 'hera_qm', 'hera_notebook_templates']:
exec(f'from {repo} import __version__')
print(f'{repo}: {__version__}')
pyuvdata: 3.0.1.dev70+g283dda3 hera_cal: 3.6.2.dev110+g0529798 hera_qm: 2.2.0
hera_notebook_templates: 0.1.dev936+gdc93cad