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/sn1/2460341' SUM_FILE = '/mnt/sn1/2460341/zen.2460341.45068.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: 1-31-2024
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 1749 csv files starting with /mnt/sn1/2460341/zen.2460341.25450.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['RFI in Autos Class'] for t in tables]) == 'bad'
suspect_rfi = np.vstack([t['RFI in Autos 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['RFI in Autos 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, anomolously high slopes, and extra channels identified as RFI.', 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 strucutre (identified as possible RFI) in their bandpassed relative to the ' + \
'median antenna, 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()
34 antpols (on 27 antennas) frequently flagged for Likely FEM Power Issue. 30 antpols (on 15 antennas) frequently flagged for All-Zeros. 19 antpols (on 18 antennas) frequently flagged for Excess RFI. 12 antpols (on 10 antennas) frequently flagged for Other Low Power Issues. 12 antpols (on 10 antennas) frequently flagged for Low Correlation, But Not Low Power. 7 antpols (on 7 antennas) frequently flagged for Excess Power in X-Engine Diffs. 6 antpols (on 3 antennas) frequently flagged for Cross-Polarized. 5 antpols (on 5 antennas) frequently flagged for Bad Bandpass Shapes, But Not Bad Power. 3 antpols (on 3 antennas) frequently flagged for Redcal chi^2. 2 antpols (on 2 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: (30 antpols across 15 antennas)
These antennas have visibilities that are more than half zeros.
All Bad Antpols: 45e, 45n, 46e, 46n, 63e, 63n, 64e, 64n, 73e, 73n, 78e, 78n, 246e, 246n, 261e, 261n, 262e, 262n, 270e, 270n, 271e, 271n, 287e, 287n, 326e, 326n, 327e, 327n, 331e, 331n 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 (6 total): 63e, 63n, 64e, 64n, 78e, 78n Whole Ants (3 total): 64, 78, 63 Single Pols (0 total): Node 20: Antpols (6 total): 246e, 246n, 261e, 261n, 262e, 262n Whole Ants (3 total): 261, 246, 262 Single Pols (0 total): Node 21: Antpols (6 total): 326e, 326n, 327e, 327n, 331e, 331n Whole Ants (3 total): 331, 326, 327 Single Pols (0 total): Node 23: Antpols (6 total): 270e, 270n, 271e, 271n, 287e, 287n Whole Ants (3 total): 287, 270, 271 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: 20e, 28n, 32n, 93e, 95e, 111e, 183n Node 1: Antpols (1 total): 28n Whole Ants (0 total): Single Pols (1 total): 28n
Node 2: Antpols (2 total): 20e, 32n Whole Ants (0 total): Single Pols (2 total): 20e, 32n
Node 10: Antpols (2 total): 93e, 111e Whole Ants (0 total): Single Pols (2 total): 93e, 111e
Node 11: Antpols (1 total): 95e Whole Ants (0 total): Single Pols (1 total): 95e
Node 13: Antpols (1 total): 183n Whole Ants (0 total): Single Pols (1 total): 183n
Cross-Polarized: (6 antpols across 3 antennas)
These antennas have their east and north cables swapped.
All Bad Antpols: 93e, 93n, 232e, 232n, 255e, 255n Node 10: Antpols (2 total): 93e, 93n Whole Ants (1 total): 93 Single Pols (0 total): Node 21: Antpols (2 total): 232e, 232n Whole Ants (1 total): 232 Single Pols (0 total): Node 23: Antpols (2 total): 255e, 255n Whole Ants (1 total): 255 Single Pols (0 total):
Likely FEM Power Issue: (34 antpols across 27 antennas)
These antennas have low power, anomolously high slopes, and extra channels identified as RFI.
All Bad Antpols: 3n, 34e, 68n, 86e, 86n, 93n, 104n, 109n, 112e, 114n, 115e, 170e, 174e, 174n, 176e, 194e, 194n, 196n, 200e, 205e, 217e, 253e, 256n, 269n, 272n, 283e, 283n, 285n, 286e, 286n, 329e, 329n, 332e, 332n Node 1: Antpols (1 total): 3n Whole Ants (0 total): Single Pols (1 total): 3n
Node 3: Antpols (1 total): 68n Whole Ants (0 total): Single Pols (1 total): 68n
Node 6: Antpols (1 total): 34e Whole Ants (0 total): Single Pols (1 total): 34e
Node 8: Antpols (3 total): 86e, 86n, 104n Whole Ants (1 total): 86 Single Pols (1 total): 104n
Node 10: Antpols (3 total): 93n, 109n, 112e Whole Ants (0 total): Single Pols (3 total): 93n, 109n, 112e
Node 11: Antpols (2 total): 114n, 115e Whole Ants (0 total): Single Pols (2 total): 114n, 115e
Node 12: Antpols (3 total): 176e, 329e, 329n Whole Ants (1 total): 329 Single Pols (1 total): 176e
Node 15: Antpols (1 total): 170e Whole Ants (0 total): Single Pols (1 total): 170e
Node 16: Antpols (4 total): 174e, 174n, 194e, 194n Whole Ants (2 total): 194, 174 Single Pols (0 total):
Node 17: Antpols (2 total): 196n, 217e Whole Ants (0 total): Single Pols (2 total): 196n, 217e
Node 18: Antpols (1 total): 200e Whole Ants (0 total): Single Pols (1 total): 200e
Node 19: Antpols (1 total): 205e Whole Ants (0 total): Single Pols (1 total): 205e
Node 21: Antpols (2 total): 332e, 332n Whole Ants (1 total): 332 Single Pols (0 total):
Node 22: Antpols (4 total): 253e, 269n, 283e, 283n Whole Ants (1 total): 283 Single Pols (2 total): 253e, 269n
Node 23: Antpols (5 total): 256n, 272n, 285n, 286e, 286n Whole Ants (1 total): 286 Single Pols (3 total): 256n, 272n, 285n
High Power: (2 antpols across 2 antennas)
These antennas have high median power.
All Bad Antpols: 232e, 255e Node 21: Antpols (1 total): 232e Whole Ants (0 total): Single Pols (1 total): 232e
Node 23: Antpols (1 total): 255e Whole Ants (0 total): Single Pols (1 total): 255e
Other Low Power Issues: (12 antpols across 10 antennas)
These antennas have low power, but are not all-zeros and not FEM off.
All Bad Antpols: 98n, 99e, 99n, 100n, 119n, 199n, 208n, 209e, 209n, 218n, 251e, 295e Node 7: Antpols (5 total): 98n, 99e, 99n, 100n, 119n Whole Ants (1 total): 99 Single Pols (3 total): 98n, 100n, 119n
Node 17: Antpols (2 total): 199n, 218n Whole Ants (0 total): Single Pols (2 total): 199n, 218n
Node 20: Antpols (3 total): 208n, 209e, 209n Whole Ants (1 total): 209 Single Pols (1 total): 208n
Node 22: Antpols (2 total): 251e, 295e Whole Ants (0 total): Single Pols (2 total): 251e, 295e
Low Correlation, But Not Low Power: (12 antpols across 10 antennas)
These antennas are low correlation, but their autocorrelation power levels look OK.
All Bad Antpols: 27e, 28e, 61e, 114e, 183e, 196e, 232e, 232n, 255e, 255n, 272e, 328e Node 1: Antpols (2 total): 27e, 28e Whole Ants (0 total): Single Pols (2 total): 27e, 28e Node 6: Antpols (1 total): 61e Whole Ants (0 total): Single Pols (1 total): 61e Node 10: Antpols (1 total): 328e Whole Ants (0 total): Single Pols (1 total): 328e Node 11: Antpols (1 total): 114e Whole Ants (0 total): Single Pols (1 total): 114e Node 13: Antpols (1 total): 183e Whole Ants (0 total): Single Pols (1 total): 183e Node 17: Antpols (1 total): 196e Whole Ants (0 total): Single Pols (1 total): 196e Node 21: Antpols (2 total): 232e, 232n Whole Ants (1 total): 232 Single Pols (0 total): Node 23: Antpols (3 total): 255e, 255n, 272e Whole Ants (1 total): 255 Single Pols (1 total): 272e
Bad Bandpass Shapes, But Not Bad Power: (5 antpols across 5 antennas)
These antennas have unusual bandpass shapes, but are not all-zeros, high power, low power, or FEM off.
All Bad Antpols: 27e, 28e, 161n, 180n, 266e Node 1: Antpols (2 total): 27e, 28e Whole Ants (0 total): Single Pols (2 total): 27e, 28e
Node 13: Antpols (2 total): 161n, 180n Whole Ants (0 total): Single Pols (2 total): 161n, 180n
Node 22: Antpols (1 total): 266e Whole Ants (0 total): Single Pols (1 total): 266e
Excess RFI: (19 antpols across 18 antennas)
These antennas have excess strucutre (identified as possible RFI) in their bandpassed relative to the median antenna, but not low or high power or a bad bandpass.
All Bad Antpols: 7e, 18n, 27n, 31n, 34n, 40n, 47e, 51e, 55e, 79e, 92e, 97n, 120e, 120n, 130n, 202n, 212n, 213e, 216e Node 1: Antpols (2 total): 18n, 27n Whole Ants (0 total): Single Pols (2 total): 18n, 27n
Node 2: Antpols (2 total): 7e, 31n Whole Ants (0 total): Single Pols (2 total): 7e, 31n
Node 3: Antpols (1 total): 51e Whole Ants (0 total): Single Pols (1 total): 51e
Node 4: Antpols (2 total): 40n, 55e Whole Ants (0 total): Single Pols (2 total): 40n, 55e
Node 6: Antpols (2 total): 34n, 47e Whole Ants (0 total): Single Pols (2 total): 34n, 47e
Node 8: Antpols (2 total): 120e, 120n Whole Ants (1 total): 120 Single Pols (0 total):
Node 10: Antpols (2 total): 92e, 130n Whole Ants (0 total): Single Pols (2 total): 92e, 130n
Node 11: Antpols (2 total): 79e, 97n Whole Ants (0 total): Single Pols (2 total): 79e, 97n
Node 16: Antpols (1 total): 213e Whole Ants (0 total): Single Pols (1 total): 213e
Node 17: Antpols (1 total): 216e Whole Ants (0 total): Single Pols (1 total): 216e
Node 18: Antpols (1 total): 202n Whole Ants (0 total): Single Pols (1 total): 202n
Node 21: Antpols (1 total): 212n Whole Ants (0 total): Single Pols (1 total): 212n
Redcal chi^2: (3 antpols across 3 antennas)
These antennas have been idenfied as not redundantly calibrating well, even after passing the above checks.
All Bad Antpols: 239e, 256e, 285e Node 18: Antpols (1 total): 239e Whole Ants (0 total): Single Pols (1 total): 239e Node 23: Antpols (2 total): 256e, 285e Whole Ants (0 total): Single Pols (2 total): 256e, 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')