Antenna Classification Daily SummaryĀ¶
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:
ā¢ Summary of Per Antenna IssuesĀ¶
ā¢ Figure 1: Per File Overall Antenna Classification SummaryĀ¶
ā¢ Figure 2: Per Classifier Antenna Flagging SummaryĀ¶
ā¢ Figure 3: Array Visualization of Overall Daily ClassificationĀ¶
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'
A module that was compiled using NumPy 1.x cannot be run in NumPy 2.2.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel_launcher.py", line 18, in <module> app.launch_new_instance() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/traitlets/config/application.py", line 1075, in launch_instance app.start() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelapp.py", line 739, in start self.io_loop.start() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/tornado/platform/asyncio.py", line 205, in start self.asyncio_loop.run_forever() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/asyncio/base_events.py", line 640, in run_forever self._run_once() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/asyncio/base_events.py", line 1992, in _run_once handle._run() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/asyncio/events.py", line 88, in _run self._context.run(self._callback, *self._args) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelbase.py", line 545, in dispatch_queue await self.process_one() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelbase.py", line 534, in process_one await dispatch(*args) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelbase.py", line 437, in dispatch_shell await result File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/ipkernel.py", line 362, in execute_request await super().execute_request(stream, ident, parent) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelbase.py", line 778, in execute_request reply_content = await reply_content File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/ipkernel.py", line 449, in do_execute res = shell.run_cell( File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/zmqshell.py", line 549, in run_cell return super().run_cell(*args, **kwargs) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3077, in run_cell result = self._run_cell( File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3132, in _run_cell result = runner(coro) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/async_helpers.py", line 128, in _pseudo_sync_runner coro.send(None) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3336, in run_cell_async has_raised = await self.run_ast_nodes(code_ast.body, cell_name, File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3519, in run_ast_nodes if await self.run_code(code, result, async_=asy): File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3579, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "/tmp/ipykernel_14065/3852672648.py", line 10, in <module> from hera_cal import io, utils File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/hera_cal/__init__.py", line 22, in <module> from . import utils File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/hera_cal/utils.py", line 35, in <module> import aipy File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/aipy/__init__.py", line 15, in <module> from . import phs, const, coord, deconv File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/aipy/phs.py", line 10, in <module> from .miriad import ij2bl, bl2ij File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/aipy/miriad.py", line 12, in <module> from . import _miriad
--------------------------------------------------------------------------- ImportError Traceback (most recent call last) File ~/mambaforge/envs/RTP/lib/python3.12/site-packages/numpy/core/_multiarray_umath.py:44, in __getattr__(attr_name) 39 # Also print the message (with traceback). This is because old versions 40 # of NumPy unfortunately set up the import to replace (and hide) the 41 # error. The traceback shouldn't be needed, but e.g. pytest plugins 42 # seem to swallow it and we should be failing anyway... 43 sys.stderr.write(msg + tb_msg) ---> 44 raise ImportError(msg) 46 ret = getattr(_multiarray_umath, attr_name, None) 47 if ret is None: ImportError: A module that was compiled using NumPy 1.x cannot be run in NumPy 2.2.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2.
A module that was compiled using NumPy 1.x cannot be run in NumPy 2.2.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel_launcher.py", line 18, in <module> app.launch_new_instance() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/traitlets/config/application.py", line 1075, in launch_instance app.start() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelapp.py", line 739, in start self.io_loop.start() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/tornado/platform/asyncio.py", line 205, in start self.asyncio_loop.run_forever() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/asyncio/base_events.py", line 640, in run_forever self._run_once() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/asyncio/base_events.py", line 1992, in _run_once handle._run() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/asyncio/events.py", line 88, in _run self._context.run(self._callback, *self._args) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelbase.py", line 545, in dispatch_queue await self.process_one() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelbase.py", line 534, in process_one await dispatch(*args) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelbase.py", line 437, in dispatch_shell await result File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/ipkernel.py", line 362, in execute_request await super().execute_request(stream, ident, parent) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelbase.py", line 778, in execute_request reply_content = await reply_content File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/ipkernel.py", line 449, in do_execute res = shell.run_cell( File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/zmqshell.py", line 549, in run_cell return super().run_cell(*args, **kwargs) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3077, in run_cell result = self._run_cell( File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3132, in _run_cell result = runner(coro) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/async_helpers.py", line 128, in _pseudo_sync_runner coro.send(None) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3336, in run_cell_async has_raised = await self.run_ast_nodes(code_ast.body, cell_name, File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3519, in run_ast_nodes if await self.run_code(code, result, async_=asy): File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3579, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "/tmp/ipykernel_14065/3852672648.py", line 10, in <module> from hera_cal import io, utils File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/hera_cal/__init__.py", line 23, in <module> from . import redcal File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/hera_cal/redcal.py", line 13, in <module> from .noise import predict_noise_variance_from_autos, infer_dt File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/hera_cal/noise.py", line 12, in <module> from . import io File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/hera_cal/io.py", line 40, in <module> import aipy File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/aipy/__init__.py", line 15, in <module> from . import phs, const, coord, deconv File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/aipy/phs.py", line 10, in <module> from .miriad import ij2bl, bl2ij File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/aipy/miriad.py", line 12, in <module> from . import _miriad
--------------------------------------------------------------------------- ImportError Traceback (most recent call last) File ~/mambaforge/envs/RTP/lib/python3.12/site-packages/numpy/core/_multiarray_umath.py:44, in __getattr__(attr_name) 39 # Also print the message (with traceback). This is because old versions 40 # of NumPy unfortunately set up the import to replace (and hide) the 41 # error. The traceback shouldn't be needed, but e.g. pytest plugins 42 # seem to swallow it and we should be failing anyway... 43 sys.stderr.write(msg + tb_msg) ---> 44 raise ImportError(msg) 46 ret = getattr(_multiarray_umath, attr_name, None) 47 if ret is None: ImportError: A module that was compiled using NumPy 1.x cannot be run in NumPy 2.2.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2.
A module that was compiled using NumPy 1.x cannot be run in NumPy 2.2.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel_launcher.py", line 18, in <module> app.launch_new_instance() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/traitlets/config/application.py", line 1075, in launch_instance app.start() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelapp.py", line 739, in start self.io_loop.start() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/tornado/platform/asyncio.py", line 205, in start self.asyncio_loop.run_forever() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/asyncio/base_events.py", line 640, in run_forever self._run_once() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/asyncio/base_events.py", line 1992, in _run_once handle._run() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/asyncio/events.py", line 88, in _run self._context.run(self._callback, *self._args) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelbase.py", line 545, in dispatch_queue await self.process_one() File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelbase.py", line 534, in process_one await dispatch(*args) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelbase.py", line 437, in dispatch_shell await result File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/ipkernel.py", line 362, in execute_request await super().execute_request(stream, ident, parent) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/kernelbase.py", line 778, in execute_request reply_content = await reply_content File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/ipkernel.py", line 449, in do_execute res = shell.run_cell( File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/ipykernel/zmqshell.py", line 549, in run_cell return super().run_cell(*args, **kwargs) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3077, in run_cell result = self._run_cell( File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3132, in _run_cell result = runner(coro) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/async_helpers.py", line 128, in _pseudo_sync_runner coro.send(None) File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3336, in run_cell_async has_raised = await self.run_ast_nodes(code_ast.body, cell_name, File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3519, in run_ast_nodes if await self.run_code(code, result, async_=asy): File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/IPython/core/interactiveshell.py", line 3579, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "/tmp/ipykernel_14065/3852672648.py", line 10, in <module> from hera_cal import io, utils File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/hera_cal/__init__.py", line 26, in <module> from . import abscal File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/hera_cal/abscal.py", line 43, in <module> from .smooth_cal import pick_reference_antenna, rephase_to_refant File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/hera_cal/smooth_cal.py", line 15, in <module> import aipy File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/aipy/__init__.py", line 15, in <module> from . import phs, const, coord, deconv File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/aipy/phs.py", line 10, in <module> from .miriad import ij2bl, bl2ij File "/home/obs/mambaforge/envs/RTP/lib/python3.12/site-packages/aipy/miriad.py", line 12, in <module> from . import _miriad
--------------------------------------------------------------------------- ImportError Traceback (most recent call last) File ~/mambaforge/envs/RTP/lib/python3.12/site-packages/numpy/core/_multiarray_umath.py:44, in __getattr__(attr_name) 39 # Also print the message (with traceback). This is because old versions 40 # of NumPy unfortunately set up the import to replace (and hide) the 41 # error. The traceback shouldn't be needed, but e.g. pytest plugins 42 # seem to swallow it and we should be failing anyway... 43 sys.stderr.write(msg + tb_msg) ---> 44 raise ImportError(msg) 46 ret = getattr(_multiarray_umath, attr_name, None) 47 if ret is None: ImportError: A module that was compiled using NumPy 1.x cannot be run in NumPy 2.2.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2.
SettingsĀ¶
# Parse settings from environment
ANT_CLASS_FOLDER = os.environ.get("ANT_CLASS_FOLDER", "./")
SUM_FILE = os.environ.get("SUM_FILE", None)
# ANT_CLASS_FOLDER = "/mnt/sn1/2460330"
# SUM_FILE = "/mnt/sn1/2460330/zen.2460330.25463.sum.uvh5"
OC_SKIP_OUTRIGGERS = os.environ.get("OC_SKIP_OUTRIGGERS", "TRUE").upper() == "TRUE"
for param in ['ANT_CLASS_FOLDER', 'SUM_FILE', 'OC_SKIP_OUTRIGGERS']:
print(f"{param} = '{eval(param)}'")
ANT_CLASS_FOLDER = '/mnt/sn1/data2/2460718' SUM_FILE = '/mnt/sn1/data2/2460718/zen.2460718.45941.sum.uvh5' OC_SKIP_OUTRIGGERS = 'True'
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: 2-11-2025
# 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 metadataĀ¶
# 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 1851 csv files starting with /mnt/sn1/data2/2460718/zen.2460718.25249.sum.ant_class.csv
# 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')
# 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()))
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)
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 issuesĀ¶
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()
# 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'
# 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]
# 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]
# 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]
# 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]
# find high power issues
high_power_strs = ap_strs[np.mean(bad_powers & (all_powers > median_power), axis=0) > high_power_thresh]
# 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]
# 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))]
# 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))]
# 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))]
# 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))]
# 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]
# 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.')]
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 IssuesĀ¶
print_high_level_summary()
68 antpols (on 34 antennas) frequently flagged for All-Zeros. 42 antpols (on 40 antennas) frequently flagged for Excess RFI. 25 antpols (on 22 antennas) frequently flagged for Likely FEM Power Issue. 13 antpols (on 10 antennas) frequently flagged for Other Low Power Issues. 11 antpols (on 9 antennas) frequently flagged for Low Correlation, But Not Low Power. 10 antpols (on 10 antennas) frequently flagged for Bad Bandpass Shapes, But Not Bad Power. 4 antpols (on 4 antennas) frequently flagged for Excess Power in X-Engine Diffs. 3 antpols (on 3 antennas) frequently flagged for High Power. 3 antpols (on 3 antennas) frequently flagged for Redcal chi^2. 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.
print_all_issue_summaries()
All-Zeros: (68 antpols across 34 antennas)
These antennas have visibilities that are more than half zeros.
All Bad Antpols: 43e, 43n, 45e, 45n, 46e, 46n, 60e, 60n, 63e, 63n, 64e, 64n, 73e, 73n, 78e, 78n, 89e, 89n, 124e, 124n, 143e, 143n, 144e, 144n, 145e, 145n, 146e, 146n, 147e, 147n, 148e, 148n, 149e, 149n, 163e, 163n, 164e, 164n, 165e, 165n, 166e, 166n, 184e, 184n, 185e, 185n, 186e, 186n, 187e, 187n, 199e, 199n, 203e, 203n, 219e, 219n, 235e, 235n, 322e, 322n, 325e, 325n, 326e, 326n, 327e, 327n, 331e, 331n Node 5: Antpols (12 total): 43e, 43n, 45e, 45n, 46e, 46n, 60e, 60n, 73e, 73n, 322e, 322n Whole Ants (6 total): 322, 73, 43, 45, 46, 60 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 9: Antpols (6 total): 89e, 89n, 124e, 124n, 325e, 325n Whole Ants (3 total): 89, 124, 325 Single Pols (0 total): Node 14: Antpols (24 total): 143e, 143n, 144e, 144n, 145e, 145n, 146e, 146n, 163e, 163n, 164e, 164n, 165e, 165n, 166e, 166n, 184e, 184n, 185e, 185n, 186e, 186n, 187e, 187n Whole Ants (12 total): 163, 164, 165, 166, 143, 144, 145, 146, 184, 185, 186, 187 Single Pols (0 total): Node 15: Antpols (6 total): 147e, 147n, 148e, 148n, 149e, 149n Whole Ants (3 total): 147, 148, 149 Single Pols (0 total): Node 17: Antpols (4 total): 199e, 199n, 235e, 235n Whole Ants (2 total): 235, 199 Single Pols (0 total): Node 18: Antpols (4 total): 203e, 203n, 219e, 219n Whole Ants (2 total): 203, 219 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):
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: (4 antpols across 4 antennas)
These antennas are showing evidence of mis-written packets in either the evens or the odds.
All Bad Antpols: 15n, 209n, 226n, 340e Node 1: Antpols (1 total): 15n Whole Ants (0 total): Single Pols (1 total): 15n
Node 19: Antpols (1 total): 226n Whole Ants (0 total): Single Pols (1 total): 226n
Node 20: Antpols (1 total): 209n Whole Ants (0 total): Single Pols (1 total): 209n
Node 21: Antpols (1 total): 340e Whole Ants (0 total): Single Pols (1 total): 340e
Cross-Polarized: (2 antpols across 1 antennas)
These antennas have their east and north cables swapped.
All Bad Antpols: 70e, 70n Node 4: Antpols (2 total): 70e, 70n Whole Ants (1 total): 70 Single Pols (0 total):
Likely FEM Power Issue: (25 antpols across 22 antennas)
These antennas have low power and anomolously high slopes.
All Bad Antpols: 9e, 20e, 20n, 22n, 30e, 34e, 48e, 48n, 51n, 61e, 68e, 77n, 109n, 120e, 135e, 170e, 182e, 200e, 216n, 218e, 238n, 239e, 329n, 332e, 332n Node 1: Antpols (1 total): 30e Whole Ants (0 total): Single Pols (1 total): 30e
Node 2: Antpols (3 total): 9e, 20e, 20n Whole Ants (1 total): 20 Single Pols (1 total): 9e
Node 3: Antpols (2 total): 51n, 68e Whole Ants (0 total): Single Pols (2 total): 51n, 68e
Node 6: Antpols (6 total): 22n, 34e, 48e, 48n, 61e, 77n Whole Ants (1 total): 48 Single Pols (4 total): 22n, 34e, 61e, 77n
Node 8: Antpols (1 total): 120e Whole Ants (0 total): Single Pols (1 total): 120e
Node 10: Antpols (1 total): 109n Whole Ants (0 total): Single Pols (1 total): 109n
Node 12: Antpols (2 total): 135e, 329n Whole Ants (0 total): Single Pols (2 total): 135e, 329n
Node 13: Antpols (1 total): 182e Whole Ants (0 total): Single Pols (1 total): 182e
Node 15: Antpols (1 total): 170e Whole Ants (0 total): Single Pols (1 total): 170e
Node 17: Antpols (2 total): 216n, 218e Whole Ants (0 total): Single Pols (2 total): 216n, 218e
Node 18: Antpols (3 total): 200e, 238n, 239e Whole Ants (0 total): Single Pols (3 total): 200e, 238n, 239e
Node 21: Antpols (2 total): 332e, 332n Whole Ants (1 total): 332 Single Pols (0 total):
High Power: (3 antpols across 3 antennas)
These antennas have high median power.
All Bad Antpols: 201n, 232e, 320n Node 3: Antpols (1 total): 320n Whole Ants (0 total): Single Pols (1 total): 320n
Node 18: Antpols (1 total): 201n Whole Ants (0 total): Single Pols (1 total): 201n
Node 21: Antpols (1 total): 232e Whole Ants (0 total): Single Pols (1 total): 232e
Other Low Power Issues: (13 antpols across 10 antennas)
These antennas have low power, but are not all-zeros and not FEM off.
All Bad Antpols: 67n, 82e, 82n, 99e, 100n, 104n, 114e, 114n, 137e, 218n, 251e, 262e, 262n Node 3: Antpols (1 total): 67n Whole Ants (0 total): Single Pols (1 total): 67n
Node 7: Antpols (5 total): 82e, 82n, 99e, 100n, 137e Whole Ants (1 total): 82 Single Pols (3 total): 99e, 100n, 137e
Node 8: Antpols (1 total): 104n Whole Ants (0 total): Single Pols (1 total): 104n
Node 11: Antpols (2 total): 114e, 114n Whole Ants (1 total): 114 Single Pols (0 total):
Node 17: Antpols (1 total): 218n Whole Ants (0 total): Single Pols (1 total): 218n
Node 20: Antpols (2 total): 262e, 262n Whole Ants (1 total): 262 Single Pols (0 total):
Node 22: Antpols (1 total): 251e Whole Ants (0 total): Single Pols (1 total): 251e
Low Correlation, But Not Low Power: (11 antpols across 9 antennas)
These antennas are low correlation, but their autocorrelation power levels look OK.
All Bad Antpols: 27e, 28e, 28n, 70e, 70n, 104e, 171n, 200n, 255n, 328e, 329e Node 1: Antpols (3 total): 27e, 28e, 28n Whole Ants (1 total): 28 Single Pols (1 total): 27e Node 4: Antpols (2 total): 70e, 70n Whole Ants (1 total): 70 Single Pols (0 total): Node 8: Antpols (1 total): 104e Whole Ants (0 total): Single Pols (1 total): 104e Node 10: Antpols (1 total): 328e Whole Ants (0 total): Single Pols (1 total): 328e Node 12: Antpols (1 total): 329e Whole Ants (0 total): Single Pols (1 total): 329e Node 16: Antpols (1 total): 171n Whole Ants (0 total): Single Pols (1 total): 171n Node 18: Antpols (1 total): 200n Whole Ants (0 total): Single Pols (1 total): 200n Node 23: Antpols (1 total): 255n Whole Ants (0 total): Single Pols (1 total): 255n
Bad Bandpass Shapes, But Not Bad Power: (10 antpols across 10 antennas)
These antennas have unusual bandpass shapes, but are not all-zeros, high power, low power, or FEM off.
All Bad Antpols: 27e, 28e, 32n, 33n, 130n, 142n, 161n, 180n, 188n, 340n Node 1: Antpols (2 total): 27e, 28e Whole Ants (0 total): Single Pols (2 total): 27e, 28e
Node 2: Antpols (2 total): 32n, 33n Whole Ants (0 total): Single Pols (2 total): 32n, 33n
Node 10: Antpols (1 total): 130n Whole Ants (0 total): Single Pols (1 total): 130n
Node 13: Antpols (3 total): 142n, 161n, 180n Whole Ants (0 total): Single Pols (3 total): 142n, 161n, 180n
Node 15: Antpols (1 total): 188n Whole Ants (0 total): Single Pols (1 total): 188n
Node 21: Antpols (1 total): 340n Whole Ants (0 total): Single Pols (1 total): 340n
Excess RFI: (42 antpols across 40 antennas)
These antennas have excess RMS after DPSS filtering (likely RFI), but not low or high power or a bad bandpass.
All Bad Antpols: 16e, 18n, 21e, 27n, 29e, 29n, 37n, 40n, 42n, 47e, 51e, 55e, 58e, 72n, 77e, 86e, 92e, 93e, 95e, 97n, 98n, 103e, 104e, 107n, 120n, 121e, 121n, 134e, 158n, 198n, 200n, 202n, 208e, 212n, 213e, 215n, 246e, 250e, 253n, 268n, 320e, 333e Node 1: Antpols (5 total): 16e, 18n, 27n, 29e, 29n Whole Ants (1 total): 29 Single Pols (3 total): 16e, 18n, 27n
Node 2: Antpols (1 total): 21e Whole Ants (0 total): Single Pols (1 total): 21e
Node 3: Antpols (3 total): 37n, 51e, 320e Whole Ants (0 total): Single Pols (3 total): 37n, 51e, 320e
Node 4: Antpols (4 total): 40n, 42n, 55e, 72n Whole Ants (0 total): Single Pols (4 total): 40n, 42n, 55e, 72n
Node 5: Antpols (1 total): 58e Whole Ants (0 total): Single Pols (1 total): 58e
Node 6: Antpols (2 total): 47e, 77e Whole Ants (0 total): Single Pols (2 total): 47e, 77e
Node 7: Antpols (1 total): 98n Whole Ants (0 total): Single Pols (1 total): 98n
Node 8: Antpols (6 total): 86e, 103e, 104e, 120n, 121e, 121n Whole Ants (1 total): 121 Single Pols (4 total): 86e, 103e, 104e, 120n
Node 9: Antpols (1 total): 107n Whole Ants (0 total): Single Pols (1 total): 107n
Node 10: Antpols (2 total): 92e, 93e Whole Ants (0 total): Single Pols (2 total): 92e, 93e
Node 11: Antpols (3 total): 95e, 97n, 134e Whole Ants (0 total): Single Pols (3 total): 95e, 97n, 134e
Node 12: Antpols (2 total): 158n, 333e Whole Ants (0 total): Single Pols (2 total): 158n, 333e
Node 16: Antpols (1 total): 213e Whole Ants (0 total): Single Pols (1 total): 213e
Node 17: Antpols (2 total): 198n, 215n Whole Ants (0 total): Single Pols (2 total): 198n, 215n
Node 18: Antpols (2 total): 200n, 202n Whole Ants (0 total): Single Pols (2 total): 200n, 202n
Node 20: Antpols (2 total): 208e, 246e Whole Ants (0 total): Single Pols (2 total): 208e, 246e
Node 21: Antpols (1 total): 212n Whole Ants (0 total): Single Pols (1 total): 212n
Node 22: Antpols (3 total): 250e, 253n, 268n Whole Ants (0 total): Single Pols (3 total): 250e, 253n, 268n
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: 83n, 136n, 154n Node 7: Antpols (1 total): 83n Whole Ants (0 total): Single Pols (1 total): 83n Node 12: Antpols (1 total): 136n Whole Ants (0 total): Single Pols (1 total): 136n Node 16: Antpols (1 total): 154n Whole Ants (0 total): Single Pols (1 total): 154n
Full-Day VisualizationsĀ¶
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 SummaryĀ¶
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.
classification_plot('Antenna Class')
# 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))
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 SummaryĀ¶
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.
plot_flag_frac_all_classifiers()
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 ClassificationĀ¶
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.
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.
for repo in ['pyuvdata', 'hera_cal', 'hera_qm', 'hera_notebook_templates']:
exec(f'from {repo} import __version__')
print(f'{repo}: {__version__}')
pyuvdata: 3.1.3 hera_cal: 3.7.1.dev11+g34d99e4 hera_qm: 2.2.1.dev2+ga535e9e
hera_notebook_templates: 0.1.dev989+gee0995d