In [19]:
rfi_plot()
by Josh Dillon, Aaron Parsons, Tyler Cox, and Zachary Martinot, last updated March 14, 2023
This notebook is designed to infer as much information about the array from a single file, including pushing the calibration and RFI mitigation as far as possible. Calibration includes redundant-baseline calibration, RFI-based calibration of delay slopes, model-based calibration of overall amplitudes, and a full per-frequency phase slope absolute calibration if abscal model files are available.
Here's a set of links to skip to particular figures and tables:
import time
tstart = time.time()
import os
os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'
import h5py
import hdf5plugin # REQUIRED to have the compression plugins available
import numpy as np
from scipy import constants, interpolate
import copy
import glob
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.max_rows', 1000)
from uvtools.plot import plot_antpos, plot_antclass
from hera_qm import ant_metrics, ant_class, xrfi
from hera_cal import io, utils, redcal, apply_cal, datacontainer, abscal
from hera_notebook_templates.data import DATA_PATH as HNBT_DATA
from IPython.display import display, HTML
import linsolve
display(HTML("<style>.container { width:100% !important; }</style>"))
_ = np.seterr(all='ignore') # get rid of red warnings
%config InlineBackend.figure_format = 'retina'
# this enables better memory management on linux
import ctypes
def malloc_trim():
try:
ctypes.CDLL('libc.so.6').malloc_trim(0)
except OSError:
pass
To use this notebook interactively, you will have to provide a sum filename path if none exists as an environment variable. All other parameters have reasonable default values.
# figure out whether to save results
SAVE_RESULTS = os.environ.get("SAVE_RESULTS", "TRUE").upper() == "TRUE"
# get infile names
SUM_FILE = os.environ.get("SUM_FILE", None)
# SUM_FILE = '/mnt/sn1/2459797/zen.2459797.30001.sum.uvh5' # If sum_file is not defined in the environment variables, define it here.
DIFF_FILE = SUM_FILE.replace('sum', 'diff')
# get outfilenames
AM_FILE = (SUM_FILE.replace('.uvh5', '.ant_metrics.hdf5') if SAVE_RESULTS else None)
ANTCLASS_FILE = (SUM_FILE.replace('.uvh5', '.ant_class.csv') if SAVE_RESULTS else None)
OMNICAL_FILE = (SUM_FILE.replace('.uvh5', '.omni.calfits') if SAVE_RESULTS else None)
OMNIVIS_FILE = (SUM_FILE.replace('.uvh5', '.omni_vis.uvh5') if SAVE_RESULTS else None)
for fname in ['SUM_FILE', 'DIFF_FILE', 'AM_FILE', 'ANTCLASS_FILE', 'OMNICAL_FILE', 'OMNIVIS_FILE']:
print(f"{fname} = '{eval(fname)}'")
SUM_FILE = '/mnt/sn1/2460024/zen.2460024.42004.sum.uvh5' DIFF_FILE = '/mnt/sn1/2460024/zen.2460024.42004.diff.uvh5' AM_FILE = '/mnt/sn1/2460024/zen.2460024.42004.sum.ant_metrics.hdf5' ANTCLASS_FILE = '/mnt/sn1/2460024/zen.2460024.42004.sum.ant_class.csv' OMNICAL_FILE = '/mnt/sn1/2460024/zen.2460024.42004.sum.omni.calfits' OMNIVIS_FILE = '/mnt/sn1/2460024/zen.2460024.42004.sum.omni_vis.uvh5'
Load settings relating to the operation of the notebook, then print what was loaded (or default).
# parse plotting settings
PLOT = os.environ.get("PLOT", "TRUE").upper() == "TRUE"
if PLOT:
%matplotlib inline
# parse omnical settings
OC_MAX_DIMS = int(os.environ.get("OC_MAX_DIMS", 4))
OC_MIN_DIM_SIZE = int(os.environ.get("OC_MIN_DIM_SIZE", 8))
OC_SKIP_OUTRIGGERS = os.environ.get("OC_SKIP_OUTRIGGERS", "TRUE").upper() == "TRUE"
OC_MIN_BL_LEN = float(os.environ.get("OC_MIN_BL_LEN", 1))
OC_MAX_BL_LEN = float(os.environ.get("OC_MAX_BL_LEN", 1e100))
OC_MAXITER = int(os.environ.get("OC_MAXITER", 50))
OC_MAX_RERUN = int(os.environ.get("OC_MAX_RERUN", 4))
OC_USE_GPU = os.environ.get("SAVE_RESULTS", "FALSE").upper() == "TRUE"
# parse RFI settings
RFI_DPSS_HALFWIDTH = float(os.environ.get("RFI_DPSS_HALFWIDTH", 300e-9))
RFI_NSIG = float(os.environ.get("RFI_NSIG", 6))
# parse abscal settings
ABSCAL_MODEL_FILES_GLOB = os.environ.get("ABSCAL_MODEL_FILES_GLOB", None)
ABSCAL_MIN_BL_LEN = float(os.environ.get("ABSCAL_MIN_BL_LEN", 1.0))
ABSCAL_MAX_BL_LEN = float(os.environ.get("ABSCAL_MAX_BL_LEN", 140.0))
# print settings
for setting in ['PLOT', 'SAVE_RESULTS', 'OC_MAX_DIMS', 'OC_MIN_DIM_SIZE', 'OC_SKIP_OUTRIGGERS',
'OC_MIN_BL_LEN', 'OC_MAX_BL_LEN', 'OC_MAXITER', 'OC_MAX_RERUN',
'OC_USE_GPU', 'RFI_DPSS_HALFWIDTH', 'RFI_NSIG', 'ABSCAL_MODEL_FILES_GLOB',
'ABSCAL_MIN_BL_LEN', 'ABSCAL_MAX_BL_LEN']:
print(f'{setting} = {eval(setting)}')
PLOT = True SAVE_RESULTS = True OC_MAX_DIMS = 4 OC_MIN_DIM_SIZE = 8 OC_SKIP_OUTRIGGERS = True OC_MIN_BL_LEN = 1.0 OC_MAX_BL_LEN = 1e+100 OC_MAXITER = 50 OC_MAX_RERUN = 4 OC_USE_GPU = False RFI_DPSS_HALFWIDTH = 3e-07 RFI_NSIG = 6.0 ABSCAL_MODEL_FILES_GLOB = None ABSCAL_MIN_BL_LEN = 1.0 ABSCAL_MAX_BL_LEN = 140.0
Load settings related to classifying antennas as good, suspect, or bad, then print what was loaded (or default).
# ant_metrics bounds for low correlation / dead antennas
am_corr_bad = (0, float(os.environ.get("AM_CORR_BAD", 0.3)))
am_corr_suspect = (float(os.environ.get("AM_CORR_BAD", 0.3)), float(os.environ.get("AM_CORR_SUSPECT", 0.5)))
# ant_metrics bounds for cross-polarized antennas
am_xpol_bad = (-1, float(os.environ.get("AM_XPOL_BAD", -0.1)))
am_xpol_suspect = (float(os.environ.get("AM_XPOL_BAD", -0.1)), float(os.environ.get("AM_XPOL_SUSPECT", 0)))
# bounds on solar altitude (in degrees)
good_solar_altitude = (-90, float(os.environ.get("SUSPECT_SOLAR_ALTITUDE", 0)))
suspect_solar_altitude = (float(os.environ.get("SUSPECT_SOLAR_ALTITUDE", 0)), 90)
# bounds on zeros in spectra
good_zeros_per_eo_spectrum = (0, int(os.environ.get("MAX_ZEROS_PER_EO_SPEC_GOOD", 2)))
suspect_zeros_per_eo_spectrum = (0, int(os.environ.get("MAX_ZEROS_PER_EO_SPEC_SUSPECT", 8)))
# bounds on autocorrelation power
auto_power_good = (float(os.environ.get("AUTO_POWER_GOOD_LOW", 5)), float(os.environ.get("AUTO_POWER_GOOD_HIGH", 30)))
auto_power_suspect = (float(os.environ.get("AUTO_POWER_SUSPECT_LOW", 1)), float(os.environ.get("AUTO_POWER_SUSPECT_HIGH", 60)))
# bounds on autocorrelation slope
auto_slope_good = (float(os.environ.get("AUTO_SLOPE_GOOD_LOW", -0.4)), float(os.environ.get("AUTO_SLOPE_GOOD_HIGH", 0.4)))
auto_slope_suspect = (float(os.environ.get("AUTO_SLOPE_SUSPECT_LOW", -0.6)), float(os.environ.get("AUTO_SLOPE_SUSPECT_HIGH", 0.6)))
# bounds on autocorrelation RFI
auto_rfi_good = (0, float(os.environ.get("AUTO_RFI_GOOD", 0.015)))
auto_rfi_suspect = (0, float(os.environ.get("AUTO_RFI_SUSPECT", 0.03)))
# bounds on autocorrelation shape
auto_shape_good = (0, float(os.environ.get("AUTO_SHAPE_GOOD", 0.1)))
auto_shape_suspect = (0, float(os.environ.get("AUTO_SHAPE_SUSPECT", 0.2)))
# bounds on chi^2 per antenna in omnical
oc_cspa_good = (0, float(os.environ.get("OC_CSPA_GOOD", 2)))
oc_cspa_suspect = (0, float(os.environ.get("OC_CSPA_SUSPECT", 3)))
# print bounds
for bound in ['am_corr_bad', 'am_corr_suspect', 'am_xpol_bad', 'am_xpol_suspect',
'good_solar_altitude', 'suspect_solar_altitude',
'good_zeros_per_eo_spectrum', 'suspect_zeros_per_eo_spectrum',
'auto_power_good', 'auto_power_suspect', 'auto_slope_good', 'auto_slope_suspect',
'auto_rfi_good', 'auto_rfi_suspect', 'auto_shape_good', 'auto_shape_suspect',
'oc_cspa_good', 'oc_cspa_suspect']:
print(f'{bound} = {eval(bound)}')
am_corr_bad = (0, 0.2) am_corr_suspect = (0.2, 0.4) am_xpol_bad = (-1, -0.1) am_xpol_suspect = (-0.1, 0.0) good_solar_altitude = (-90, 0.0) suspect_solar_altitude = (0.0, 90) good_zeros_per_eo_spectrum = (0, 2) suspect_zeros_per_eo_spectrum = (0, 8) auto_power_good = (5.0, 30.0) auto_power_suspect = (1.0, 60.0) auto_slope_good = (-0.4, 0.4) auto_slope_suspect = (-0.6, 0.6) auto_rfi_good = (0, 0.015) auto_rfi_suspect = (0, 0.03) auto_shape_good = (0, 0.1) auto_shape_suspect = (0, 0.2) oc_cspa_good = (0, 2.0) oc_cspa_suspect = (0, 3.0)
hd = io.HERADataFastReader(SUM_FILE)
data, _, _ = hd.read(read_flags=False, read_nsamples=False)
hd_diff = io.HERADataFastReader(DIFF_FILE)
diff_data, _, _ = hd_diff.read(read_flags=False, read_nsamples=False)
ants = sorted(set([ant for bl in hd.bls for ant in utils.split_bl(bl)]))
auto_bls = [bl for bl in data if (bl[0] == bl[1]) and (utils.split_pol(bl[2])[0] == utils.split_pol(bl[2])[1])]
antpols = sorted(set([ant[1] for ant in ants]))
# print basic information about the file
print(f'File: {SUM_FILE}')
print(f'JDs: {hd.times} ({np.median(np.diff(hd.times)) * 24 * 3600:.5f} s integrations)')
print(f'LSTS: {hd.lsts * 12 / np.pi } hours')
print(f'Frequencies: {len(hd.freqs)} {np.median(np.diff(hd.freqs)) / 1e6:.5f} MHz channels from {hd.freqs[0] / 1e6:.5f} to {hd.freqs[-1] / 1e6:.5f} MHz')
print(f'Antennas: {len(hd.data_ants)}')
print(f'Polarizations: {hd.pols}')
File: /mnt/sn1/2460024/zen.2460024.42004.sum.uvh5 JDs: [2460024.41998074 2460024.42009258] (9.66368 s integrations) LSTS: [11.38649506 11.38918676] hours Frequencies: 1536 0.12207 MHz channels from 46.92078 to 234.29871 MHz Antennas: 198 Polarizations: ['nn', 'ee', 'ne', 'en']
ant_metrics¶This classifies antennas as cross-polarized, low-correlation, or dead. Such antennas are excluded from any calibration.
am = ant_metrics.AntennaMetrics(SUM_FILE, DIFF_FILE, sum_data=data, diff_data=diff_data)
am.iterative_antenna_metrics_and_flagging(crossCut=am_xpol_bad[1], deadCut=am_corr_bad[1])
am.all_metrics = {} # this saves time and disk by getting rid of per-iteration information we never use
if SAVE_RESULTS:
am.save_antenna_metrics(AM_FILE, overwrite=True)
# Turn ant metrics into classifications
totally_dead_ants = [ant for ant, i in am.xants.items() if i == -1]
am_totally_dead = ant_class.AntennaClassification(good=[ant for ant in ants if ant not in totally_dead_ants], bad=totally_dead_ants)
am_corr = ant_class.antenna_bounds_checker(am.final_metrics['corr'], bad=[am_corr_bad], suspect=[am_corr_suspect], good=[(0, 1)])
am_xpol = ant_class.antenna_bounds_checker(am.final_metrics['corrXPol'], bad=[am_xpol_bad], suspect=[am_xpol_suspect], good=[(-1, 1)])
ant_metrics_class = am_totally_dead + am_corr + am_xpol
min_sun_alt = np.min(utils.get_sun_alt(hd.times))
solar_class = ant_class.antenna_bounds_checker({ant: min_sun_alt for ant in ants}, good=[good_solar_altitude], suspect=[suspect_solar_altitude])
This classifier looks for X-engine failure or packet loss specific to an antenna which causes either the even visibilities (or the odd ones, or both) to be 0s.
zeros_class = ant_class.even_odd_zeros_checker(data, diff_data, good=good_zeros_per_eo_spectrum, suspect=suspect_zeros_per_eo_spectrum)
# delete diffs to save memory
del diff_data, hd_diff
malloc_trim()
These classifiers look for antennas with too high or low power, to steep a slope, or too much excess RFI.
auto_power_class = ant_class.auto_power_checker(data, good=auto_power_good, suspect=auto_power_suspect)
auto_slope_class = ant_class.auto_slope_checker(data, good=auto_slope_good, suspect=auto_slope_suspect, edge_cut=100, filt_size=17)
cache = {}
auto_rfi_class = ant_class.auto_rfi_checker(data, good=auto_rfi_good, suspect=auto_rfi_suspect,
filter_half_widths=[RFI_DPSS_HALFWIDTH], nsig=RFI_NSIG, cache=cache)
auto_class = auto_power_class + auto_slope_class + auto_rfi_class
del cache
malloc_trim()
# Compute int_count for all unflagged autocorrelations averaged together
int_time = 24 * 3600 * np.median(np.diff(data.times_by_bl[auto_bls[0][0:2]]))
chan_res = np.median(np.diff(data.freqs))
final_class = ant_metrics_class + zeros_class + auto_class
int_count = int(int_time * chan_res) * (len(final_class.good_ants) + len(final_class.suspect_ants))
avg_auto = {(-1, -1, 'ee'): np.mean([data[bl] for bl in auto_bls if final_class[utils.split_bl(bl)[0]] != 'bad'], axis=0)}
# Flag RFI first with channel differences and then with DPSS
antenna_flags, _ = xrfi.flag_autos(avg_auto, int_count=int_count, nsig=(RFI_NSIG * 5))
_, rfi_flags = xrfi.flag_autos(avg_auto, int_count=int_count, flag_method='dpss_flagger',
flags=antenna_flags, freqs=data.freqs, filter_centers=[0],
filter_half_widths=[RFI_DPSS_HALFWIDTH], eigenval_cutoff=[1e-9], nsig=RFI_NSIG)
malloc_trim()
def rfi_plot():
plt.figure(figsize=(12, 5), dpi=100)
plt.semilogy(hd.freqs / 1e6, np.where(rfi_flags, np.nan, avg_auto[(-1, -1, 'ee')])[1], label = 'Average Good or Suspect Autocorrelation', zorder=100)
plt.semilogy(hd.freqs / 1e6, np.where(False, np.nan, avg_auto[(-1, -1, 'ee')])[1], 'r', lw=.5, label=f'{np.sum(rfi_flags[0])} Channels Flagged for RFI')
plt.legend()
plt.xlabel('Frequency (MHz)')
plt.ylabel('Uncalibrated Autocorrelation')
plt.tight_layout()
This figure shows RFI identified using the average of all autocorrelations---excluding bad antennas---for the first integration in the file.
rfi_plot()
def autocorr_plot(cls):
fig, axes = plt.subplots(1, 2, figsize=(14, 5), dpi=100, sharey=True, gridspec_kw={'wspace': 0})
labels = []
colors = ['darkgreen', 'goldenrod', 'maroon']
for ax, pol in zip(axes, antpols):
for ant in cls.ants:
if ant[1] == pol:
color = colors[cls.quality_classes.index(cls[ant])]
ax.semilogy(np.mean(data[utils.join_bl(ant, ant)], axis=0), color=color, lw=.5)
ax.set_xlabel('Channel', fontsize=12)
ax.set_title(f'{utils.join_pol(pol, pol)}-Polarized Autos')
axes[0].set_ylabel('Raw Autocorrelation', fontsize=12)
axes[1].legend([matplotlib.lines.Line2D([0], [0], color=color) for color in colors],
[cls.capitalize() for cls in auto_class.quality_classes], ncol=1, fontsize=12, loc='upper right', framealpha=1)
plt.tight_layout()
auto_shape_class = ant_class.auto_shape_checker(data, good=auto_shape_good, suspect=auto_shape_suspect,
flag_spectrum=np.sum(rfi_flags, axis=0).astype(bool), antenna_class=final_class)
auto_class += auto_shape_class
This figure shows a plot of all autocorrelations in the array, split by polarization. Antennas are classified based on their autocorrelations into good, suspect, and bad, by examining power, slope, and RFI-occupancy.
if PLOT: autocorr_plot(auto_class)
final_class = ant_metrics_class + solar_class + zeros_class + auto_class
def array_class_plot(cls, extra_label=""):
outriggers = [ant for ant in hd.data_ants if ant >= 320]
if len(outriggers) > 0:
fig, axes = plt.subplots(1, 2, figsize=(14, 6), dpi=100, gridspec_kw={'width_ratios': [2, 1]})
plot_antclass(hd.antpos, cls, ax=axes[0], ants=[ant for ant in hd.data_ants if ant < 320], legend=False, title=f'HERA Core{extra_label}')
plot_antclass(hd.antpos, cls, ax=axes[1], ants=outriggers, radius=50, title='Outriggers')
else:
fig, axes = plt.subplots(1, 1, figsize=(9, 6), dpi=100)
plot_antclass(hd.antpos, cls, ax=axes, ants=[ant for ant in hd.data_ants if ant < 320], legend=False, title=f'HERA Core{extra_label}')
This figure shows the location and classification of all antennas prior to calibration.
Antennas are split along the diagonal, with ee-polarized antpols represented by the southeast half of each antenna and nn-polarized antpols represented by the northwest half.
Outriggers are split from the core and shown at exaggerated size in the right-hand panel. This classification includes ant_metrics, a count of the zeros in the even or odd visibilities, and autocorrelation power, slope, and RFI occupancy.
An antenna classified as bad in any classification will be considered bad.
An antenna marked as suspect any in any classification will be considered suspect unless it is also classified as bad elsewhere.
if PLOT: array_class_plot(final_class)
def classify_off_grid(reds, all_ants):
'''Returns AntennaClassification of all_ants where good ants are in reds while bad ants are not.'''
ants_in_reds = set([ant for red in reds for bl in red for ant in utils.split_bl(bl)])
on_grid = [ant for ant in all_ants if ant in ants_in_reds]
off_grid = [ant for ant in all_ants if ant not in ants_in_reds]
return ant_class.AntennaClassification(good=on_grid, bad=off_grid)
redcal¶redcal_start = time.time()
rc_settings = {'max_dims': OC_MAX_DIMS, 'oc_conv_crit': 1e-10, 'gain': 0.4, 'run_logcal': False,
'oc_maxiter': OC_MAXITER, 'check_after': OC_MAXITER, 'use_gpu': OC_USE_GPU}
fr_settings = {'max_dims': OC_MAX_DIMS, 'min_dim_size': OC_MIN_DIM_SIZE, 'min_bl_cut': OC_MIN_BL_LEN, 'max_bl_cut': OC_MAX_BL_LEN}
# figure out and filter reds and classify antennas based on whether or not they are on the main grid
reds = redcal.get_reds(hd.data_antpos, pols=['ee', 'nn'], pol_mode='2pol')
reds = redcal.filter_reds(reds, ex_ants=final_class.bad_ants, antpos=hd.data_antpos, **fr_settings)
if OC_SKIP_OUTRIGGERS:
reds = redcal.filter_reds(reds, ex_ants=[ant for ant in ants if ant[0] >= 320])
redcal_class = classify_off_grid(reds, ants)
# perform first stage of redundant calibration,
meta, sol = redcal.redundantly_calibrate(data, reds, **rc_settings)
malloc_trim()
max_dly = np.max(np.abs(list(meta['fc_meta']['dlys'].values())))
med_cspa = {ant: np.median(meta['chisq_per_ant'][ant]) for ant in meta['chisq_per_ant']}
cspa_class = ant_class.antenna_bounds_checker(med_cspa, good=np.array(oc_cspa_good)*5, suspect=np.array(oc_cspa_suspect)*5, bad=(0, np.inf))
redcal_class += cspa_class
print(f'Removing {cspa_class.bad_ants} for high chi^2.')
# iteratively rerun redundant calibration
for i in range(OC_MAX_RERUN):
# refilter reds and update classification to reflect new off-grid ants, if any
reds = redcal.filter_reds(reds, ex_ants=(final_class + redcal_class).bad_ants, antpos=hd.data_antpos, **fr_settings)
reds = sorted(reds, key=len, reverse=True)
redcal_class += classify_off_grid(reds, ants)
ants_in_reds = set([ant for red in reds for bl in red for ant in utils.split_bl(bl)])
# re-run redundant calibration using previous solution, updating bad and suspicious antennas
meta, sol = redcal.redundantly_calibrate(data, reds, sol0=sol, **rc_settings)
malloc_trim()
# recompute chi^2 for bad antennas without bad antennas to make sure they are actually bad
med_cspa = {ant: np.median(meta['chisq_per_ant'][ant]) for ant in meta['chisq_per_ant']}
sol2 = redcal.RedSol(sol.reds, gains={ant: sol[ant] for ant in med_cspa if med_cspa[ant] <= oc_cspa_suspect[1]}, vis=sol.vis)
new_chisq_per_ant = {ant: np.array(meta['chisq_per_ant'][ant]) for ant in sol2.gains}
redcal.expand_omni_gains(sol2, reds, data, chisq_per_ant=new_chisq_per_ant)
med_cspa = {ant: np.min([med_cspa[ant], np.median(new_chisq_per_ant[ant])]) for ant in med_cspa}
# remove bad antennas
cspa_class = ant_class.antenna_bounds_checker(med_cspa, good=oc_cspa_good, suspect=oc_cspa_suspect, bad=(0, np.inf))
redcal_class += cspa_class
print(f'Removing {cspa_class.bad_ants} for high chi^2.')
if len(cspa_class.bad_ants) == 0:
break # no new antennas to flag
print(f'Finished redcal in {(time.time() - redcal_start) / 60:.2f} minutes.')
Removing set() for high chi^2.
Removing {(95, 'Jee'), (95, 'Jnn'), (54, 'Jnn'), (55, 'Jee')} for high chi^2.
Removing set() for high chi^2.
Finished redcal in 10.24 minutes.
final_class += redcal_class
expanded_reds = redcal.get_reds(hd.data_antpos, pols=['ee', 'nn'], pol_mode='2pol')
expanded_reds = redcal.filter_reds(expanded_reds, ex_ants=(ant_metrics_class + solar_class + zeros_class + auto_class).bad_ants,
max_dims=OC_MAX_DIMS, min_dim_size=OC_MIN_DIM_SIZE)
if OC_SKIP_OUTRIGGERS:
expanded_reds = redcal.filter_reds(expanded_reds, ex_ants=[ant for ant in ants if ant[0] >= 320])
redcal.expand_omni_vis(sol, expanded_reds, data, chisq=meta['chisq'], chisq_per_ant=meta['chisq_per_ant'])
# now figure out flags, nsamples etc.
omni_flags = {ant: ~np.isfinite(g) for ant, g in sol.gains.items()}
vissol_flags = datacontainer.RedDataContainer({bl: ~np.isfinite(v) for bl, v in sol.vis.items()}, reds=sol.vis.reds)
single_nsamples_array = np.ones((len(hd.times), len(hd.freqs)), dtype=float)
nsamples = datacontainer.DataContainer({bl: single_nsamples_array for bl in data})
vissol_nsamples = redcal.count_redundant_nsamples(nsamples, expanded_reds, good_ants=sol.gains)
sol.make_sol_finite()
firstcal delay slope degeneracy using RFI transmitters¶# find channels clearly contaminated by RFI
not_bad_ants = [ant for ant in final_class.ants if final_class[ant] != 'bad']
chan_flags = np.mean([xrfi.detrend_medfilt(data[utils.join_bl(ant, ant)], Kf=8, Kt=2) for ant in not_bad_ants], axis=(0, 1)) > 5
# hardcoded RFI transmitters and their headings
# channel: frequency (Hz), heading (rad), chi^2
phs_sol = {359: ( 90744018.5546875, 0.7853981, 23.3),
360: ( 90866088.8671875, 0.7853981, 10.8),
385: ( 93917846.6796875, 0.7853981, 27.3),
386: ( 94039916.9921875, 0.7853981, 18.1),
400: ( 95748901.3671875, 6.0632738, 24.0),
441: (100753784.1796875, 0.7853981, 21.7),
442: (100875854.4921875, 0.7853981, 19.4),
455: (102462768.5546875, 6.0632738, 18.8),
456: (102584838.8671875, 6.0632738, 8.8),
471: (104415893.5546875, 0.7853981, 13.3),
484: (106002807.6171875, 6.0632738, 21.2),
485: (106124877.9296875, 6.0632738, 4.0),
1181: (191085815.4296875, 0.7853981, 26.3),
1182: (191207885.7421875, 0.7853981, 27.0),
1183: (191329956.0546875, 0.7853981, 25.6),
1448: (223678588.8671875, 2.6075219, 25.7),
1449: (223800659.1796875, 2.6075219, 22.6),
1450: (223922729.4921875, 2.6075219, 11.6),
1451: (224044799.8046875, 2.6075219, 5.9),
1452: (224166870.1171875, 2.6075219, 22.6),
1510: (231246948.2421875, 0.1068141, 23.9)}
if not np.isclose(hd.freqs[0], 46920776.3671875, atol=0.001) or len(hd.freqs) != 1536:
# We have less frequencies than usual (maybe testing)
phs_sol = {np.argmin(np.abs(hd.freqs - freq)): (freq, heading, chisq) for chan, (freq, heading, chisq) in phs_sol.items() if hd.freqs[0] <= freq <= hd.freqs[-1]}
rfi_chans = [chan for chan in phs_sol if chan_flags[chan]]
print('Channels used for delay-slope calibration with RFI:', rfi_chans)
rfi_angles = np.array([phs_sol[chan][1] for chan in rfi_chans])
rfi_headings = np.array([np.cos(rfi_angles), np.sin(rfi_angles), np.zeros_like(rfi_angles)])
rfi_chisqs = np.array([phs_sol[chan][2] for chan in rfi_chans])
Channels used for delay-slope calibration with RFI: [359, 360, 385, 386, 400, 441, 442, 455, 456, 471, 484, 485, 1182]
# resolve firstcal degeneracy with delay slopes set by RFI transmitters, update cal
RFI_dly_slope_gains = abscal.RFI_delay_slope_cal([red for red in expanded_reds if red[0] in sol.vis], hd.antpos, sol.vis, hd.freqs, rfi_chans, rfi_headings, rfi_wgts=rfi_chisqs**-1,
min_tau=-max_dly, max_tau=max_dly, delta_tau=0.1e-9, return_gains=True, gain_ants=sol.gains.keys())
sol.gains = {ant: g * RFI_dly_slope_gains[ant] for ant, g in sol.gains.items()}
apply_cal.calibrate_in_place(sol.vis, RFI_dly_slope_gains)
malloc_trim()
# Load simulated and then downsampled model of autocorrelations that includes receiver noise, then interpolate to upsample
hd_model = io.HERADataFastReader(f'{HNBT_DATA}/SSM_autocorrelations_downsampled.uvh5')
model, _, _ = hd_model.read(read_flags=False, read_nsamples=False)
per_pol_interpolated_model = {}
for bl in model:
sorted_lsts, lst_indices = np.unique(model.lsts, return_index=True)
periodic_model = np.vstack([model[bl][lst_indices, :], model[bl][lst_indices[0], :]])
periodic_lsts = np.append(sorted_lsts, sorted_lsts[0] + 2 * np.pi)
lst_interpolated = interpolate.CubicSpline(periodic_lsts, periodic_model, axis=0, bc_type='periodic')(data.lsts)
per_pol_interpolated_model[bl[2]] = interpolate.CubicSpline(model.freqs, lst_interpolated, axis=1)(data.freqs)
model = {bl: per_pol_interpolated_model[bl[2]] for bl in auto_bls if utils.split_bl(bl)[0] not in final_class.bad_ants}
# Run abscal and update omnical gains with abscal gains
redcaled_autos = {bl: sol.calibrate_bl(bl, data[bl]) for bl in auto_bls if utils.split_bl(bl)[0] not in final_class.bad_ants}
g_abscal = abscal.abs_amp_logcal(model, redcaled_autos, verbose=False, return_gains=True, gain_ants=sol.gains)
sol.gains = {ant: g * g_abscal[ant] for ant, g in sol.gains.items()}
apply_cal.calibrate_in_place(sol.vis, g_abscal)
del hd_model, model, redcaled_autos, g_abscal
malloc_trim()
If an ABSCAL_MODEL_FILES_GLOB is provided, try to perform a full absolute calibration of tip-tilt phase slopes using that those model files. Specifically, this step calibrates omnical visbility solutions using unique baselines simulated with a model of the sky and HERA's beam.
if ABSCAL_MODEL_FILES_GLOB is not None:
abscal_model_files = sorted(glob.glob(ABSCAL_MODEL_FILES_GLOB))
else:
# try to find files on site
abscal_model_files = sorted(glob.glob('/mnt/sn1/abscal_models/H4C_1/abscal_files_unique_baselines/zen.2458894.?????.uvh5'))
if len(abscal_model_files) == 0:
# try to find files at NRAO
abscal_model_files = sorted(glob.glob('/lustre/aoc/projects/hera/zmartino/hera_calib_model/H4C_1/abscal_files_unique_baselines/zen.2458894.?????.uvh5'))
print(f'Found {len(abscal_model_files)} abscal model files{" in " + os.path.dirname(abscal_model_files[0]) if len(abscal_model_files) > 0 else ""}.')
Found 0 abscal model files.
# Try to perform a full abscal of phase
if len(abscal_model_files) == 0:
DO_FULL_ABSCAL = False
print('No model files found... not performing full absolute calibration of phase slopes.')
else:
# figure out which model files match the LSTs of the data
matched_model_files = sorted(set(abscal.match_times(SUM_FILE, abscal_model_files, filetype='uvh5')))
if len(matched_model_files) == 0:
DO_FULL_ABSCAL = False
print('No model files found matching the LSTs of this file... not performing full absolute calibration of phase slopes.')
else:
DO_FULL_ABSCAL = True
# figure out appropriate model times to load
hdm = io.HERAData(matched_model_files)
all_model_times, all_model_lsts = abscal.get_all_times_and_lsts(hdm, unwrap=True)
d2m_time_map = abscal.get_d2m_time_map(data.times, np.unwrap(data.lsts), all_model_times, all_model_lsts, extrap_limit=.5)
No model files found... not performing full absolute calibration of phase slopes.
if DO_FULL_ABSCAL:
abscal_start = time.time()
for pol in ['ee', 'nn']:
print(f'Performing absolute phase slope calibration of {pol}-polarized visibility solutions...')
# load matching times and baselines
unflagged_data_bls = [bl for bl in vissol_flags if not np.all(vissol_flags[bl])]
model_bls = copy.deepcopy(hdm.bls)
model_antpos = hdm.data_antpos
if len(matched_model_files) > 1: # in this case, it's a dictionary
model_bls = list(set([bl for bls in list(hdm.bls.values()) for bl in bls]))
model_antpos = {ant: pos for antpos in hdm.data_antpos.values() for ant, pos in antpos.items()}
data_bls, model_bls, data_to_model_bl_map = abscal.match_baselines(unflagged_data_bls, model_bls, data.antpos, model_antpos=model_antpos,
pols=[pol], data_is_redsol=True, model_is_redundant=True, tol=1.0,
min_bl_cut=ABSCAL_MIN_BL_LEN, max_bl_cut=ABSCAL_MAX_BL_LEN, verbose=True)
model, model_flags, _ = io.partial_time_io(hdm, np.unique([d2m_time_map[time] for time in data.times]), bls=model_bls)
model_bls = [data_to_model_bl_map[bl] for bl in data_bls]
# rephase model to match in lsts
model_blvecs = {bl: model.antpos[bl[0]] - model.antpos[bl[1]] for bl in model.keys()}
utils.lst_rephase(model, model_blvecs, model.freqs, data.lsts - model.lsts,
lat=hdm.telescope_location_lat_lon_alt_degrees[0], inplace=True)
# run abscal and apply
abscal_meta, delta_gains = abscal.complex_phase_abscal(sol.vis, model, sol.reds, data_bls, model_bls)
# apply gains
sol.gains = {antpol : g * delta_gains.get(antpol, 1) for antpol, g in sol.gains.items()}
apply_cal.calibrate_in_place(sol.vis, delta_gains)
del hdm, model, model_flags, delta_gains
malloc_trim()
print(f'Finished absolute calibration of tip-tilt phase slopes in {(time.time() - abscal_start) / 60:.2f} minutes.')
def redundant_group_plot():
fig, axes = plt.subplots(2, 2, figsize=(14, 6), dpi=100, sharex='col', sharey='row', gridspec_kw={'hspace': 0, 'wspace': 0})
for i, pol in enumerate(['ee', 'nn']):
reds_here = redcal.get_reds(hd.data_antpos, pols=[pol], pol_mode='1pol')
red = sorted(redcal.filter_reds(reds_here, ex_ants=final_class.bad_ants), key=len, reverse=True)[0]
rc_data = {bl: sol.calibrate_bl(bl, data[bl]) for bl in red}
for bl in red:
axes[0, i].plot(hd.freqs/1e6, np.angle(rc_data[bl][0]), alpha=.5, lw=.5)
axes[1, i].semilogy(hd.freqs/1e6, np.abs(rc_data[bl][0]), alpha=.5, lw=.5)
axes[0, i].plot(hd.freqs / 1e6, np.angle(sol.vis[red[0]][0]), lw=1, c='k')
axes[1, i].semilogy(hd.freqs / 1e6, np.abs(sol.vis[red[0]][0]), lw=1, c='k', label=f'Baseline Group:\n{red[0]}')
axes[1, i].set_xlabel('Frequency (MHz)')
axes[1, i].legend(loc='upper right')
axes[0, 0].set_ylabel('Visibility Phase (radians)')
axes[1, 0].set_ylabel('Visibility Amplitude (Jy)')
plt.tight_layout()
The results of a redundant-baseline calibration of a single integration and a single group, the one with the highest redundancy in each polarization after antenna classification and excision based on the above, plus the removal of antennas with high chi^2 per antenna. The black line is the redundant visibility solution. Each thin colored line is a different baseline group. Phases are shown in the top row, amplitudes in the bottom, ee-polarized visibilities in the left column, and nn-polarized visibilities in the right.
if PLOT: redundant_group_plot()
This attempts to calibrate bad antennas using information from good or suspect antennas without allowing bad antennas to affect their calibration.
However antennas flagged for ant_metrics or lots of zeros in the even or odd visibilities are considered beyond saving.
Likewise, some antennas would add extra degeneracies (controlled by OC_MAX_DIMS, OC_MIN_DIM_SIZE, and OC_SKIP_OUTRIGGERS) are excluded.
expanded_reds = redcal.get_reds(hd.data_antpos, pols=['ee', 'nn'], pol_mode='2pol')
sol.vis.build_red_keys(expanded_reds)
redcal.expand_omni_gains(sol, expanded_reds, data, chisq_per_ant=meta['chisq_per_ant'])
redcal.expand_omni_vis(sol, expanded_reds, data)
sol.make_sol_finite()
malloc_trim()
def array_chisq_plot():
fig, axes = plt.subplots(1, 2, figsize=(14, 5), dpi=100)
for ax, pol in zip(axes, ['ee', 'nn']):
ants_to_plot = set([ant for ant in meta['chisq_per_ant'] if utils.join_pol(ant[1], ant[1]) == pol])
cspas = np.array([np.median(meta['chisq_per_ant'][ant]) for ant in ants_to_plot])
xpos = [hd.antpos[ant[0]][0] for ant in ants_to_plot]
ypos = [hd.antpos[ant[0]][1] for ant in ants_to_plot]
scatter = ax.scatter(xpos, ypos, s=300, c=cspas, lw=.25, edgecolors=np.where(np.isfinite(cspas) & (cspas > 0), 'none', 'k'),
norm=matplotlib.colors.LogNorm(vmin=1, vmax=oc_cspa_suspect[1]))
for ant in ants_to_plot:
ax.text(hd.antpos[ant[0]][0], hd.antpos[ant[0]][1], ant[0], va='center', ha='center', fontsize=8,
c=('r' if ant in final_class.bad_ants else 'w'))
plt.colorbar(scatter, ax=ax, extend='both')
ax.axis('equal')
ax.set_xlabel('East-West Position (meters)')
ax.set_ylabel('North-South Position (meters)')
ax.set_title(f'{pol}-pol $\\chi^2$ / Antenna (Red is Flagged)')
plt.tight_layout()
This plot shows median (taken over time and frequency) of the normalized chi^2 per antenna. The expectation value for this quantity when the array is perfectly redundant is 1.0. Antennas that are classified as bad for any reason have their numbers shown in red. Some of those antennas were classified as bad during redundant calibration for high chi^2. Some of those antennas were originally excluded from redundant calibration because they were classified as bad earlier for some reason. See here for more details. Note that the color scale saturates at below 1 and above 10.
if PLOT: array_chisq_plot()
This figure is the same as Figure 2, except that it now includes additional suspect or bad antennas based on redundant calibration. This can include antennas with high chi^2, but it can also include antennas classified as "bad" because they would add extra degeneracies to calibration.
if PLOT: array_class_plot(final_class, extra_label=", Post-Redcal")
to_show = {'Antenna': [f'{ant[0]}{ant[1][-1]}' for ant in ants]}
classes = {'Antenna': [final_class[ant] if ant in final_class else '-' for ant in ants]}
to_show['Dead?'] = [{'good': 'No', 'bad': 'Yes'}[am_totally_dead[ant]] if (ant in am_totally_dead) else '' for ant in ants]
classes['Dead?'] = [am_totally_dead[ant] if (ant in am_totally_dead) else '' for ant in ants]
for title, ac in [('Low Correlation', am_corr),
('Cross-Polarized', am_xpol),
('Solar Alt', solar_class),
('Even/Odd Zeros', zeros_class),
('Autocorr Power', auto_power_class),
('Autocorr Slope', auto_slope_class),
('RFI in Autos', auto_rfi_class),
('Autocorr Shape', auto_shape_class)]:
to_show[title] = [f'{ac._data[ant]:.2G}' if (ant in ac._data) else '' for ant in ants]
classes[title] = [ac[ant] if ant in ac else '' for ant in ants]
to_show['Redcal chi^2'] = [f'{np.median(meta["chisq_per_ant"][ant]):.3G}' if (ant in meta['chisq_per_ant']) else '-' for ant in ants]
classes['Redcal chi^2'] = [redcal_class[ant] if ant in redcal_class else '' for ant in ants]
df = pd.DataFrame(to_show)
df_classes = pd.DataFrame(classes)
colors = {'good': 'darkgreen', 'suspect': 'goldenrod', 'bad': 'maroon'}
df_colors = df_classes.applymap(lambda x: f'background-color: {colors.get(x, None)}')
table = df.style.hide_index() \
.apply(lambda x: pd.DataFrame(df_colors.values, columns=x.columns), axis=None) \
.set_properties(subset=['Antenna'], **{'font-weight': 'bold', 'border-right': "3pt solid black"}) \
.set_properties(subset=df.columns[1:], **{'border-left': "1pt solid black"}) \
.set_properties(**{'text-align': 'center', 'color': 'white'})
This table summarizes the results of the various classifications schemes detailed above.
As before, green is good, yellow is suspect, and red is bad. The color for each antenna (first column) is the final summary of all other classifications.
Antennas missing from redcal $\chi^2$ were excluded redundant-baseline calibration, either because they were flagged by ant_metrics or the even/odd zeros check, or because they would add unwanted extra degeneracies.
HTML(table.render())
| Antenna | Dead? | Low Correlation | Cross-Polarized | Solar Alt | Even/Odd Zeros | Autocorr Power | Autocorr Slope | RFI in Autos | Autocorr Shape | Redcal chi^2 |
|---|---|---|---|---|---|---|---|---|---|---|
| 3e | No | 0.55 | 0.47 | -58 | 0 | 12 | 0.22 | 0.0078 | 0.051 | 1.43 |
| 3n | No | 0.046 | 0.47 | -58 | 0 | 0.62 | 0.59 | 0.036 | 0.18 | 1.15 |
| 4e | No | 0.56 | 0.33 | -58 | 0 | 20 | 0.14 | 0.0016 | 0.022 | 1.48 |
| 4n | No | 0.42 | 0.33 | -58 | 0 | 22 | 0.87 | 0 | 0.28 | 1.99 |
| 5e | No | 0.041 | 0.0022 | -58 | 1 | 0.7 | 0.54 | 0.025 | 0.13 | 1.15 |
| 5n | No | 0.036 | 0.0022 | -58 | 0 | 0.67 | 0.58 | 0.013 | 0.18 | 1.13 |
| 7e | No | 0.57 | 0.31 | -58 | 0 | 15 | 0.093 | 0.002 | 0.024 | 1.62 |
| 7n | No | 0.58 | 0.31 | -58 | 0 | 13 | -0.072 | 0.00033 | 0.032 | 1.84 |
| 8e | No | 0.57 | 0.3 | -58 | 0 | 57 | 0.045 | 0.075 | 0.06 | 2.25 |
| 8n | No | 0.58 | 0.3 | -58 | 0 | 60 | -0.012 | 0 | 0.064 | 3.18 |
| 9e | No | 0.57 | 0.33 | -58 | 0 | 4.4 | 0.21 | 0.0036 | 0.04 | 1.52 |
| 9n | No | 0.61 | 0.33 | -58 | 0 | 15 | -0.033 | 0.00033 | 0.024 | 2.4 |
| 10e | No | 0.59 | 0.31 | -58 | 0 | 22 | 0.11 | 0.041 | 0.024 | 3.55 |
| 10n | No | 0.6 | 0.31 | -58 | 0 | 17 | -0.041 | 0.00033 | 0.022 | 2.73 |
| 15e | No | 0.41 | 0.32 | -58 | 0 | 10 | 1 | 0 | 0.29 | 2.65 |
| 15n | No | 0.56 | 0.32 | -58 | 0 | 12 | -0.072 | 0 | 0.027 | 1.33 |
| 16e | No | 0.58 | 0.49 | -58 | 0 | 14 | 0.11 | 0.0062 | 0.034 | 1.52 |
| 16n | No | 0.043 | 0.49 | -58 | 0 | 0.62 | 0.6 | 0.061 | 0.18 | 1.13 |
| 17e | No | 0.58 | 0.38 | -58 | 0 | 9.1 | 0.084 | 0.00065 | 0.021 | 1.47 |
| 17n | No | 0.41 | 0.38 | -58 | 0 | 0.94 | 0.29 | 0.013 | 0.097 | 1.46 |
| 18e | No | 0.039 | 0.3 | -58 | 0 | 0.69 | 0.54 | 0.03 | 0.13 | 1.13 |
| 18n | No | 0.38 | 0.3 | -58 | 0 | 10 | -0.12 | 0.21 | 0.24 | 1.44 |
| 19e | No | 0.59 | 0.3 | -58 | 0 | 14 | 0.081 | 0.002 | 0.028 | 1.43 |
| 19n | No | 0.6 | 0.3 | -58 | 0 | 33 | -0.05 | 0 | 0.022 | 1.75 |
| 20e | No | 0.59 | 0.32 | -58 | 0 | 6.5 | 0.13 | 0.0036 | 0.034 | 1.45 |
| 20n | No | 0.62 | 0.32 | -58 | 0 | 15 | -0.07 | 0 | 0.023 | 1.8 |
| 21e | No | 0.59 | 0.3 | -58 | 0 | 13 | 0.21 | 0.002 | 0.044 | 1.58 |
| 21n | No | 0.6 | 0.3 | -58 | 0 | 12 | -0.0082 | 0.00065 | 0.03 | 2.18 |
| 22e | No | 0.56 | 0.31 | -58 | 0 | 24 | 0.12 | 0.00098 | 0.012 | 1.61 |
| 22n | No | 0.58 | 0.31 | -58 | 0 | 23 | -0.042 | 0.00033 | 0.018 | 2.74 |
| 27e | No | 0.035 | 0.0012 | -58 | 0 | 0.68 | 0.49 | 0.083 | 0.12 | 1.12 |
| 27n | No | 0.031 | 0.0012 | -58 | 0 | 0.65 | 0.55 | 0.052 | 0.17 | 1.11 |
| 28e | No | 0.032 | 0.2 | -58 | 0 | 0.71 | 0.52 | 0.053 | 0.13 | 1.11 |
| 28n | No | 0.26 | 0.2 | -58 | 0 | 5 | 0.36 | 0.3 | 0.32 | 1.25 |
| 29e | No | 0.6 | 0.31 | -58 | 0 | 14 | 0.076 | 0.0023 | 0.019 | 1.5 |
| 29n | No | 0.61 | 0.31 | -58 | 0 | 15 | -0.049 | 0.0036 | 0.022 | 1.61 |
| 30e | No | 0.59 | 0.31 | -58 | 0 | 9.7 | 0.15 | 0.0013 | 0.038 | 1.42 |
| 30n | No | 0.61 | 0.31 | -58 | 0 | 17 | -0.07 | 0.00065 | 0.019 | 1.59 |
| 31e | No | 0.6 | 0.29 | -58 | 0 | 8.1 | 0.018 | 0.0016 | 0.035 | 1.48 |
| 31n | No | 0.61 | 0.29 | -58 | 0 | 7.3 | -0.0058 | 0.00065 | 0.021 | 1.63 |
| 32e | No | 0.48 | 0.19 | -58 | 0 | 16 | 0.8 | 0.0085 | 0.22 | 3.13 |
| 32n | No | 0.52 | 0.19 | -58 | 0 | 16 | 0.77 | 0.0081 | 0.25 | 2.99 |
| 34e | No | 0.038 | 0.011 | -58 | 0 | 2.9 | 0.57 | 0.03 | 0.14 | 1.2 |
| 34n | No | 0.055 | 0.011 | -58 | 0 | 2.9 | 0.61 | 0.064 | 0.18 | 1.21 |
| 35e | No | 0.56 | 0.3 | -58 | 0 | 34 | 0.067 | 0.00033 | 0.025 | 1.57 |
| 35n | No | 0.58 | 0.3 | -58 | 0 | 19 | 0.022 | 0 | 0.018 | 2.26 |
| 36e | No | 0.55 | 0.35 | -58 | 0 | 8.3 | -0.24 | 0.0026 | 0.11 | 1.4 |
| 36n | No | 0.52 | 0.35 | -58 | 0 | 9.6 | -0.38 | 0.00098 | 0.1 | 1.38 |
| 37e | No | 0.55 | 0.43 | -58 | 0 | 23 | 0.24 | 0 | 0.041 | 1.44 |
| 37n | No | 0.035 | 0.43 | -58 | 1 | 0.37 | 0.9 | 0.03 | 0.24 | 1.16 |
| 38e | No | 0.58 | 0.35 | -58 | 0 | 13 | 0.085 | 0.01 | 0.05 | 1.53 |
| 38n | No | 0.53 | 0.35 | -58 | 0 | 13 | 0.17 | 0 | 0.059 | 1.38 |
| 40e | No | 0.28 | -0.26 | -58 | 0 | 12 | 0.02 | 0 | 0.042 | 3 |
| 40n | No | 0.27 | -0.26 | -58 | 0 | 15 | 0.1 | 0.0026 | 0.053 | 2.43 |
| 41e | No | 0.59 | 0.3 | -58 | 0 | 8 | 0.04 | 0.0078 | 0.033 | 1.53 |
| 41n | No | 0.59 | 0.3 | -58 | 0 | 6.9 | -0.084 | 0.00098 | 0.03 | 1.48 |
| 42e | No | 0.32 | -0.25 | -58 | 0 | 14 | 0.071 | 0 | 0.032 | 3.15 |
| 42n | No | 0.31 | -0.25 | -58 | 0 | 17 | -0.017 | 0.0013 | 0.062 | 3.17 |
| 43e | No | 0.61 | 0.29 | -58 | 0 | 22 | 0.048 | 0.0013 | 0.032 | 1.43 |
| 43n | No | 0.62 | 0.29 | -58 | 0 | 10 | 0.056 | 0 | 0.037 | 1.47 |
| 44e | No | 0.61 | 0.29 | -58 | 0 | 19 | 0.079 | 0.00033 | 0.022 | 1.45 |
| 44n | No | 0.62 | 0.29 | -58 | 0 | 13 | -0.036 | 0.00033 | 0.026 | 1.57 |
| 45e | No | 0.6 | 0.29 | -58 | 0 | 9.2 | 0.087 | 0.00098 | 0.03 | 1.44 |
| 45n | No | 0.61 | 0.29 | -58 | 0 | 9.8 | -0.11 | 0.02 | 0.2 | 2 |
| 46e | No | 0.61 | 0.31 | -58 | 0 | 12 | 0.1 | 0.0016 | 0.022 | 1.47 |
| 46n | No | 0.63 | 0.31 | -58 | 0 | 20 | -0.11 | 0 | 0.03 | 1.82 |
| 47e | No | 0.033 | 0.017 | -58 | 0 | 3 | 0.54 | 0.091 | 0.13 | 1.17 |
| 47n | No | 0.057 | 0.017 | -58 | 0 | 3.2 | 0.59 | 0.024 | 0.18 | 1.19 |
| 48e | No | 0.57 | 0.32 | -58 | 0 | 23 | 0.2 | 0.0039 | 0.028 | 1.57 |
| 48n | No | 0.6 | 0.32 | -58 | 0 | 38 | 0.0027 | 0 | 0.028 | 2.16 |
| 49e | No | 0.53 | 0.32 | -58 | 0 | 11 | 0.2 | 0.0023 | 0.028 | 1.6 |
| 49n | No | 0.58 | 0.32 | -58 | 0 | 24 | 0.043 | 0.00033 | 0.025 | 2.96 |
| 50e | No | 0.55 | 0.35 | -58 | 0 | 10 | 0.11 | 0.0013 | 0.046 | 1.51 |
| 50n | No | 0.53 | 0.35 | -58 | 0 | 7.4 | -0.12 | 0.00098 | 0.052 | 1.38 |
| 51e | No | 0.57 | 0.34 | -58 | 0 | 12 | 0.13 | 0.0049 | 0.064 | 1.53 |
| 51n | No | 0.56 | 0.34 | -58 | 0 | 15 | -0.13 | 0.0078 | 0.057 | 1.38 |
| 52e | No | 0.58 | 0.33 | -58 | 0 | 9.9 | -0.23 | 0.0023 | 0.1 | 1.88 |
| 52n | No | 0.56 | 0.33 | -58 | 0 | 11 | -0.31 | 0.0016 | 0.088 | 1.5 |
| 53e | No | 0.59 | 0.34 | -58 | 0 | 13 | 0.11 | 0.0065 | 0.043 | 1.57 |
| 53n | No | 0.6 | 0.34 | -58 | 0 | 24 | -0.17 | 0 | 0.054 | 1.56 |
| 54e | No | 0.29 | 0.15 | -58 | 0 | 51 | 0.5 | 0 | 0.12 | 2.23 |
| 54n | No | 0.37 | 0.15 | -58 | 0 | 28 | -0.033 | 0 | 0.068 | 3.31 |
| 55e | No | 0.3 | 0.13 | -58 | 0 | 32 | 0.0031 | 0 | 0.035 | 3.03 |
| 55n | No | 0.047 | 0.13 | -58 | 0 | 1.9 | 1.9 | 0.022 | 0.49 | 1.11 |
| 56e | No | 0.6 | 0.28 | -58 | 0 | 20 | 0.059 | 0.00033 | 0.028 | 1.53 |
| 56n | No | 0.6 | 0.28 | -58 | 0 | 5.8 | -0.031 | 0.00033 | 0.032 | 1.56 |
| 57e | No | 0.62 | 0.29 | -58 | 0 | 22 | 0.092 | 0 | 0.023 | 1.58 |
| 57n | No | 0.62 | 0.29 | -58 | 0 | 15 | 0.0081 | 0.0029 | 0.046 | 1.73 |
| 58e | No | 0.042 | 0.0039 | -58 | 0 | 0.7 | 0.49 | 0.031 | 0.13 | 1.11 |
| 58n | No | 0.038 | 0.0039 | -58 | 0 | 0.63 | 0.56 | 0.021 | 0.17 | 1.11 |
| 59e | No | 0.05 | 0.44 | -58 | 0 | 0.8 | 0.53 | 0.016 | 0.13 | 1.12 |
| 59n | No | 0.62 | 0.44 | -58 | 0 | 11 | 0.1 | 0.00033 | 0.044 | 1.61 |
| 60e | No | 0.61 | 0.47 | -58 | 0 | 12 | 0.22 | 0.00033 | 0.038 | 1.47 |
| 60n | No | 0.085 | 0.47 | -58 | 0 | 0.63 | 0.56 | 0.086 | 0.17 | 1.13 |
| 61e | No | 0.54 | 0.32 | -58 | 0 | 10 | 0.29 | 0.00033 | 0.048 | 1.52 |
| 61n | No | 0.6 | 0.32 | -58 | 0 | 17 | 0.074 | 0.00033 | 0.028 | 1.86 |
| 62e | No | 0.54 | 0.33 | -58 | 0 | 13 | 0.26 | 0.0026 | 0.041 | 1.46 |
| 62n | No | 0.6 | 0.33 | -58 | 0 | 35 | -0.018 | 0.00033 | 0.033 | 1.78 |
| 63e | No | 0.57 | 0.42 | -58 | 0 | 21 | 0.13 | 0.0013 | 0.029 | 1.56 |
| 63n | No | 0.05 | 0.42 | -58 | 0 | 2.9 | 0.57 | 0.087 | 0.17 | 1.16 |
| 64e | No | 0.56 | 0.32 | -58 | 0 | 19 | 0.17 | 0.0016 | 0.026 | 1.65 |
| 64n | No | 0.57 | 0.32 | -58 | 0 | 14 | 0.039 | 0 | 0.025 | 2.08 |
| 65e | No | 0.024 | 0.012 | -58 | 1 | 0.31 | 1 | 0.07 | 0.23 | 1.15 |
| 65n | No | 0.037 | 0.012 | -58 | 1 | 0.33 | 0.89 | 0.15 | 0.24 | 1.15 |
| 66e | No | 0.23 | 0.11 | -58 | 0 | 50 | -0.025 | 0 | 0.059 | 2.76 |
| 66n | No | 0.055 | 0.11 | -58 | 1 | 0.32 | 0.93 | 0.13 | 0.26 | 1.13 |
| 67e | No | 0.57 | 0.32 | -58 | 0 | 16 | 0.059 | 0.0013 | 0.035 | 1.5 |
| 67n | No | 0.57 | 0.32 | -58 | 0 | 9.6 | -0.053 | 0 | 0.031 | 1.46 |
| 68e | No | 0.036 | 0.43 | -58 | 1 | 0.31 | 1.1 | 0.11 | 0.24 | 1.09 |
| 68n | No | 0.59 | 0.43 | -58 | 0 | 35 | -0.024 | 0 | 0.023 | 1.58 |
| 69e | No | 0.59 | 0.31 | -58 | 0 | 7.8 | 0.11 | 0.0068 | 0.026 | 1.66 |
| 69n | No | 0.6 | 0.31 | -58 | 0 | 16 | 0.026 | 0.002 | 0.033 | 1.62 |
| 70e | No | 0.32 | -0.25 | -58 | 0 | 8.3 | 0.092 | 0.00065 | 0.031 | 3.25 |
| 70n | No | 0.31 | -0.25 | -58 | 0 | 5.3 | 0.071 | 0.00033 | 0.049 | 3.03 |
| 71e | No | 0.61 | 0.29 | -58 | 0 | 14 | -0.15 | 0 | 0.096 | 1.44 |
| 71n | No | 0.63 | 0.29 | -58 | 0 | 12 | -0.027 | 0 | 0.024 | 1.5 |
| 72e | No | 0.35 | -0.25 | -58 | 0 | 5.6 | 0.023 | 0.0085 | 0.04 | 3.15 |
| 72n | No | 0.34 | -0.25 | -58 | 0 | 8.7 | 0.12 | 0.0081 | 0.053 | 3.28 |
| 73e | No | 0.62 | 0.28 | -58 | 0 | 15 | 0.1 | 0.00033 | 0.027 | 1.48 |
| 73n | No | 0.63 | 0.28 | -58 | 0 | 11 | -0.13 | 0 | 0.038 | 1.55 |
| 74e | No | 0.61 | 0.28 | -58 | 0 | 24 | 0.07 | 0 | 0.03 | 1.48 |
| 74n | No | 0.63 | 0.28 | -58 | 0 | 13 | -0.0096 | 0.00033 | 0.03 | 1.72 |
| 77e | No | 0.29 | 0.24 | -58 | 0 | 21 | 2.1 | 0.0049 | 0.48 | 1.51 |
| 77n | No | 0.48 | 0.24 | -58 | 0 | 24 | 0.76 | 0.00033 | 0.22 | 2.57 |
| 78e | No | 0.39 | 0.33 | -58 | 0 | 18 | 1.1 | 0.0042 | 0.31 | 2.46 |
| 78n | No | 0.6 | 0.33 | -58 | 0 | 37 | -0.033 | 0 | 0.03 | 1.61 |
| 79e | No | 0.55 | 0.41 | -58 | 0 | 16 | 0.26 | 0.00065 | 0.04 | 1.58 |
| 79n | No | 0.044 | 0.41 | -58 | 0 | 2.9 | 0.58 | 0.01 | 0.18 | 1.14 |
| 80e | No | 0.57 | 0.42 | -58 | 0 | 26 | 0.19 | 0.00065 | 0.027 | 1.55 |
| 80n | No | 0.084 | 0.42 | -58 | 0 | 2.9 | 0.61 | 0.041 | 0.18 | 1.16 |
| 81e | No | 0.018 | 0.00034 | -58 | 72 | 0.003 | -0.18 | 0.36 | 4.3 | 1.09 |
| 81n | No | 0.017 | 0.00034 | -58 | 1 | 0.035 | -0.067 | 0.43 | 0.49 | 1.1 |
| 82e | No | 0.015 | -5.2E-05 | -58 | 7 | 0.0092 | -0.15 | 0.45 | 0.67 | 1.07 |
| 82n | No | 0.016 | -5.2E-05 | -58 | 1.2E+02 | 0.0039 | -0.1 | 0.46 | 0.79 | 1.07 |
| 83e | No | 0.017 | 0.00044 | -58 | 2 | 0.032 | -0.13 | 0.44 | 0.47 | 1.06 |
| 83n | No | 0.016 | 0.00044 | -58 | 3 | 0.031 | -0.035 | 0.43 | 0.55 | 1.06 |
| 84e | No | 0.18 | 0.11 | -58 | 0 | 0.33 | 0.82 | 0.062 | 0.23 | 1.18 |
| 84n | No | 0.039 | 0.11 | -58 | 1 | 0.28 | 0.94 | 0.12 | 0.29 | 1.12 |
| 85e | No | 0.61 | 0.3 | -58 | 0 | 25 | 0.0003 | 0.00033 | 0.038 | 1.53 |
| 85n | No | 0.6 | 0.3 | -58 | 0 | 17 | 0.016 | 0.00033 | 0.025 | 1.5 |
| 86e | No | 0.6 | 0.3 | -58 | 0 | 11 | 0.033 | 0.0016 | 0.054 | 1.57 |
| 86n | No | 0.63 | 0.3 | -58 | 0 | 15 | -0.12 | 0.0016 | 0.043 | 1.54 |
| 87e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 87n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 88e | No | 0.61 | 0.28 | -58 | 0 | 9.5 | 0.037 | 0.002 | 0.041 | 1.52 |
| 88n | No | 0.62 | 0.28 | -58 | 0 | 8.2 | -0.08 | 0 | 0.033 | 1.53 |
| 89e | No | 0.61 | 0.29 | -58 | 0 | 9.5 | 0.091 | 0 | 0.036 | 1.53 |
| 89n | No | 0.64 | 0.29 | -58 | 0 | 8.8 | -0.089 | 0 | 0.033 | 1.65 |
| 90e | No | 0.6 | 0.3 | -58 | 0 | 13 | 0.21 | 0.002 | 0.036 | 1.52 |
| 90n | No | 0.64 | 0.3 | -58 | 0 | 23 | -0.058 | 0.0046 | 0.019 | 1.72 |
| 91e | No | 0.6 | 0.3 | -58 | 0 | 8.9 | 0.11 | 0 | 0.033 | 1.55 |
| 91n | No | 0.63 | 0.3 | -58 | 0 | 10 | -0.051 | 0.00065 | 0.035 | 1.97 |
| 92e | No | 0.041 | 0.38 | -58 | 0 | 0.7 | 0.51 | 0.019 | 0.13 | 1.14 |
| 92n | No | 0.62 | 0.38 | -58 | 0 | 11 | -0.086 | 0 | 0.027 | 1.82 |
| 93e | No | 0.035 | 0.0057 | -58 | 0 | 0.68 | 0.53 | 0.083 | 0.13 | 1.12 |
| 93n | No | 0.024 | 0.0057 | -58 | 0 | 0.62 | 0.57 | 0.04 | 0.17 | 1.12 |
| 94e | No | 0.025 | 0.0028 | -58 | 0 | 0.66 | 0.56 | 0.018 | 0.14 | 1.12 |
| 94n | No | 0.027 | 0.0028 | -58 | 1 | 0.65 | 0.58 | 0.019 | 0.18 | 1.11 |
| 95e | No | 0.34 | 0.15 | -58 | 0 | 22 | 0.22 | 0 | 0.061 | 3.25 |
| 95n | No | 0.36 | 0.15 | -58 | 0 | 33 | 0.036 | 0 | 0.054 | 3.47 |
| 96e | No | 0.58 | 0.32 | -58 | 0 | 35 | 0.13 | 0 | 0.02 | 1.53 |
| 96n | No | 0.48 | 0.32 | -58 | 0 | 25 | 0.81 | 0.00033 | 0.26 | 3.09 |
| 97e | No | 0.55 | 0.32 | -58 | 0 | 20 | 0.15 | 0.0013 | 0.016 | 1.57 |
| 97n | No | 0.55 | 0.32 | -58 | 0 | 13 | 0.15 | 0.016 | 0.057 | 1.81 |
| 101e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 101n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 102e | No | 0.61 | 0.32 | -58 | 0 | 21 | 0.07 | 0 | 0.027 | 1.48 |
| 102n | No | 0.62 | 0.32 | -58 | 0 | 18 | 0.021 | 0 | 0.04 | 1.48 |
| 103e | No | 0.61 | 0.3 | -58 | 0 | 43 | 0.06 | 0.023 | 0.043 | 1.53 |
| 103n | No | 0.62 | 0.3 | -58 | 0 | 19 | -0.29 | 0 | 0.094 | 1.56 |
| 104e | No | 0.61 | 0.28 | -58 | 0 | 8.8 | -0.2 | 0.0039 | 0.092 | 1.55 |
| 104n | No | 0.6 | 0.28 | -58 | 0 | 1.7 | 1.9 | 0.0026 | 0.53 | 1.76 |
| 105e | No | 0.6 | 0.27 | -58 | 0 | 10 | 0.071 | 0.00033 | 0.035 | 1.53 |
| 105n | No | 0.62 | 0.27 | -58 | 0 | 8.6 | -0.11 | 0.00033 | 0.037 | 1.67 |
| 106e | No | 0.61 | 0.28 | -58 | 0 | 12 | 0.023 | 0.0023 | 0.044 | 1.73 |
| 106n | No | 0.63 | 0.28 | -58 | 0 | 12 | -0.15 | 0.0023 | 0.045 | 1.95 |
| 107e | No | 0.61 | 0.29 | -58 | 0 | 13 | 0.18 | 0.0023 | 0.043 | 1.71 |
| 107n | No | 0.63 | 0.29 | -58 | 0 | 15 | -0.066 | 0.0013 | 0.032 | 2.18 |
| 108e | No | 0.038 | 0.17 | -58 | 0 | 0.7 | 0.52 | 0.049 | 0.13 | 1.12 |
| 108n | No | 0.35 | 0.17 | -58 | 0 | 12 | 1.4 | 0.00098 | 0.42 | 3.78 |
| 109e | No | 0.064 | 0.018 | -58 | 0 | 0.69 | 0.5 | 0.016 | 0.13 | 1.14 |
| 109n | No | 0.039 | 0.018 | -58 | 0 | 0.67 | 0.56 | 0.057 | 0.17 | 1.14 |
| 110e | No | 0.48 | 0.31 | -58 | 0 | 12 | 1.1 | 0 | 0.28 | 2.06 |
| 110n | No | 0.62 | 0.31 | -58 | 0 | 13 | -0.04 | 0 | 0.038 | 1.66 |
| 111e | No | 0.47 | 0.32 | -58 | 0 | 8.5 | 1.1 | 0 | 0.3 | 2 |
| 111n | No | 0.066 | 0.32 | -58 | 0 | 0.65 | 0.56 | 0.049 | 0.17 | 1.12 |
| 112e | No | 0.29 | -0.19 | -58 | 0 | 7.2 | 0.11 | 0.00033 | 0.026 | 2.4 |
| 112n | No | 0.18 | -0.19 | -58 | 0 | 0.87 | 0.37 | 0.023 | 0.12 | 1.39 |
| 113e | No | 0.037 | 0.0047 | -58 | 0 | 3.2 | 0.58 | 0.036 | 0.14 | 1.14 |
| 113n | No | 0.031 | 0.0047 | -58 | 0 | 2.9 | 0.6 | 0.028 | 0.18 | 1.13 |
| 114e | No | 0.053 | 0.4 | -58 | 0 | 3.1 | 0.61 | 0.035 | 0.14 | 1.15 |
| 114n | No | 0.57 | 0.4 | -58 | 0 | 26 | 0.00073 | 0.0094 | 0.035 | 1.67 |
| 115e | No | 0.54 | 0.34 | -58 | 0 | 18 | 0.19 | 0.0026 | 0.023 | 1.41 |
| 115n | No | 0.57 | 0.34 | -58 | 0 | 27 | 0.0039 | 0.00065 | 0.023 | 1.83 |
| 117e | No | 0.018 | -0.00022 | -58 | 3 | 0.031 | -0.1 | 0.42 | 0.42 | 1.08 |
| 117n | No | 0.017 | -0.00022 | -58 | 5.5E+02 | 0.00091 | -0.21 | 0.46 | 0.87 | 1.07 |
| 118e | No | 0.027 | 0.0065 | -58 | 3 | 0.031 | -0.092 | 0.44 | 0.42 | 1.08 |
| 118n | No | 0.032 | 0.0065 | -58 | 2 | 0.025 | -0.16 | 0.44 | 0.55 | 1.08 |
| 120e | No | 0.6 | 0.31 | -58 | 0 | 5.3 | 0.17 | 0.00098 | 0.093 | 1.63 |
| 120n | No | 0.62 | 0.31 | -58 | 0 | 16 | -0.061 | 0 | 0.05 | 1.54 |
| 121e | No | 0.61 | 0.29 | -58 | 0 | 18 | 0.12 | 0.00033 | 0.043 | 1.62 |
| 121n | No | 0.6 | 0.29 | -58 | 0 | 2.1 | -0.18 | 0.0062 | 0.055 | 1.52 |
| 122e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 122n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 123e | No | 0.62 | 0.29 | -58 | 0 | 8.5 | -0.21 | 0.00065 | 0.1 | 1.57 |
| 123n | No | 0.64 | 0.29 | -58 | 0 | 8.4 | -0.4 | 0.00065 | 0.11 | 1.6 |
| 124e | No | 0.045 | 0.38 | -58 | 0 | 0.65 | 0.52 | 0.021 | 0.13 | 1.13 |
| 124n | No | 0.63 | 0.38 | -58 | 0 | 9.8 | -0.054 | 0 | 0.035 | 1.82 |
| 125e | No | 0.61 | 0.29 | -58 | 0 | 7.9 | 0.071 | 0 | 0.028 | 1.77 |
| 125n | No | 0.62 | 0.29 | -58 | 0 | 8.6 | 0.03 | 0 | 0.063 | 2.08 |
| 126e | No | 0.5 | 0.31 | -58 | 0 | 9.6 | 0.93 | 0 | 0.25 | 2.31 |
| 126n | No | 0.63 | 0.31 | -58 | 0 | 8.8 | -0.11 | 0.00033 | 0.032 | 2.06 |
| 127e | No | 0.035 | 0.34 | -58 | 0 | 0.7 | 0.5 | 0.015 | 0.13 | 1.14 |
| 127n | No | 0.6 | 0.34 | -58 | 0 | 3.7 | 0.013 | 0.0023 | 0.026 | 1.76 |
| 128e | No | 0.61 | 0.32 | -58 | 0 | 15 | 0.083 | 0.0081 | 0.034 | 1.43 |
| 128n | No | 0.62 | 0.32 | -58 | 0 | 25 | -0.0098 | 0 | 0.021 | 1.69 |
| 131e | No | 0.55 | 0.38 | -58 | 0 | 24 | 0.15 | 0.00033 | 0.014 | 1.44 |
| 131n | No | 0.22 | 0.38 | -58 | 0 | 2.9 | 0.56 | 0.0046 | 0.17 | 1.31 |
| 132e | No | 0.56 | 0.33 | -58 | 0 | 22 | 0.13 | 0.00033 | 0.011 | 1.38 |
| 132n | No | 0.56 | 0.33 | -58 | 0 | 18 | 0.06 | 0 | 0.029 | 1.39 |
| 133e | No | 0.54 | 0.34 | -58 | 0 | 16 | 0.18 | 0.0055 | 0.028 | 1.35 |
| 133n | No | 0.56 | 0.34 | -58 | 0 | 23 | 0.018 | 0.0013 | 0.013 | 1.49 |
| 134e | No | 0.044 | 0.0043 | -58 | 0 | 3.7 | 0.57 | 0.022 | 0.14 | 1.17 |
| 134n | No | 0.04 | 0.0043 | -58 | 0 | 3.4 | 0.62 | 0.026 | 0.18 | 1.16 |
| 135e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 135n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 136e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 136n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 137e | No | 0.016 | 0.0032 | -58 | 20 | 0.0047 | -0.18 | 0.46 | 0.83 | 1.08 |
| 137n | No | 0.018 | 0.0032 | -58 | 14 | 0.0097 | -0.037 | 0.45 | 0.76 | 1.08 |
| 139e | No | 0.58 | 0.3 | -58 | 0 | 34 | 0.08 | 0 | 0.036 | 1.86 |
| 139n | No | 0.59 | 0.3 | -58 | 0 | 20 | 0.017 | 0 | 0.029 | 1.8 |
| 140e | No | 0.57 | 0.3 | -58 | 0 | 14 | 0.23 | 0.00033 | 0.086 | 1.78 |
| 140n | No | 0.62 | 0.3 | -58 | 0 | 23 | -0.061 | 0 | 0.016 | 1.69 |
| 141e | No | 0.6 | 0.29 | -58 | 0 | 12 | 0.12 | 0.00033 | 0.023 | 1.64 |
| 141n | No | 0.62 | 0.29 | -58 | 0 | 30 | -0.044 | 0 | 0.013 | 1.69 |
| 142e | No | 0.61 | 0.48 | -58 | 0 | 14 | 0.15 | 0.0013 | 0.034 | 1.58 |
| 142n | No | 0.052 | 0.48 | -58 | 1 | 0.63 | 0.56 | 0.035 | 0.17 | 1.17 |
| 143e | No | 0.1 | 0.055 | -58 | 0 | 0.74 | 0.56 | 0.016 | 0.14 | 1.28 |
| 143n | No | 0.033 | 0.055 | -58 | 1 | 0.63 | 0.56 | 0.03 | 0.17 | 1.13 |
| 144e | No | 0.62 | 0.3 | -58 | 0 | 15 | 0.077 | 0.00033 | 0.04 | 1.71 |
| 144n | No | 0.61 | 0.3 | -58 | 0 | 4.2 | 0.0028 | 0.0081 | 0.033 | 1.99 |
| 145e | No | 0.62 | 0.29 | -58 | 0 | 12 | 0.079 | 0 | 0.032 | 1.64 |
| 145n | No | 0.62 | 0.29 | -58 | 0 | 11 | 0.09 | 0.00065 | 0.04 | 2.05 |
| 146e | No | 0.58 | 0.29 | -58 | 0 | 18 | 0.21 | 0 | 0.027 | 1.74 |
| 146n | No | 0.6 | 0.29 | -58 | 0 | 21 | 0.058 | 0 | 0.026 | 2.03 |
| 147e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 147n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 148e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 148n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 149e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 149n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 150e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 150n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 151e | No | 0.42 | 0.29 | -58 | 0 | 21 | 0.83 | 0.0026 | 0.23 | 2.65 |
| 151n | No | 0.53 | 0.29 | -58 | 0 | 9.5 | 0.071 | 0 | 0.029 | 1.34 |
| 155e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 155n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 156e | No | 0.41 | 0.31 | -58 | 0 | 1.2 | 0.28 | 0.027 | 0.061 | 1.39 |
| 156n | No | 0.043 | 0.31 | -58 | 1 | 0.65 | 0.55 | 0.058 | 0.17 | 1.23 |
| 157e | No | 0.57 | 0.33 | -58 | 0 | 10 | 0.085 | 0.0068 | 0.031 | 1.41 |
| 157n | No | 0.59 | 0.33 | -58 | 0 | 9.3 | -0.023 | 0.0016 | 0.026 | 1.66 |
| 158e | No | 0.59 | 0.32 | -58 | 0 | 18 | 0.086 | 0.0068 | 0.032 | 1.49 |
| 158n | No | 0.6 | 0.32 | -58 | 0 | 19 | 0.064 | 0.00033 | 0.031 | 1.57 |
| 159e | No | 0.56 | 0.29 | -58 | 0 | 14 | 0.18 | 0 | 0.032 | 1.8 |
| 159n | No | 0.47 | 0.29 | -58 | 0 | 17 | 0.88 | 0.0013 | 0.26 | 3.23 |
| 160e | No | 0.6 | 0.31 | -58 | 0 | 12 | 0.037 | 0.0036 | 0.036 | 1.79 |
| 160n | No | 0.63 | 0.31 | -58 | 0 | 15 | 0.0039 | 0 | 0.025 | 1.73 |
| 161e | No | 0.6 | 0.28 | -58 | 0 | 10 | 0.11 | 0.00098 | 0.025 | 1.79 |
| 161n | No | 0.5 | 0.28 | -58 | 0 | 14 | 0.99 | 0 | 0.29 | 3.34 |
| 162e | No | 0.61 | 0.31 | -58 | 0 | 25 | 0.13 | 0.0039 | 0.02 | 1.87 |
| 162n | No | 0.64 | 0.31 | -58 | 0 | 20 | -0.027 | 0 | 0.02 | 1.95 |
| 163e | No | 0.61 | 0.3 | -58 | 0 | 11 | 0.014 | 0.00098 | 0.032 | 1.72 |
| 163n | No | 0.62 | 0.3 | -58 | 0 | 10 | -0.13 | 0.00098 | 0.04 | 1.85 |
| 164e | No | 0.6 | 0.27 | -58 | 0 | 8.2 | 0.16 | 0.00033 | 0.026 | 1.88 |
| 164n | No | 0.61 | 0.27 | -58 | 0 | 8.3 | -0.079 | 0 | 0.038 | 2.23 |
| 165e | No | 0.47 | 0.29 | -58 | 0 | 12 | 1 | 0 | 0.28 | 4.09 |
| 165n | No | 0.61 | 0.29 | -58 | 0 | 14 | -0.084 | 0 | 0.022 | 2.13 |
| 166e | No | 0.6 | 0.28 | -58 | 0 | 8.7 | 0.1 | 0.00065 | 0.022 | 1.72 |
| 166n | No | 0.61 | 0.28 | -58 | 0 | 31 | -0.0048 | 0 | 0.026 | 2.09 |
| 167e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 167n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 168e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 168n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 169e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 169n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 170e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 170n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 171e | No | 0.51 | 0.34 | -58 | 0 | 9.6 | 0.22 | 0 | 0.031 | 1.35 |
| 171n | No | 0.56 | 0.34 | -58 | 0 | 21 | -0.0064 | 0.00033 | 0.011 | 1.43 |
| 173e | No | 0.52 | 0.33 | -58 | 0 | 73 | 0.011 | 0 | 0.088 | 1.67 |
| 173n | No | 0.51 | 0.33 | -58 | 0 | 77 | -0.0011 | 0 | 0.098 | 1.67 |
| 179e | No | 0.58 | 0.32 | -58 | 0 | 12 | 0.17 | 0.00065 | 0.027 | 1.58 |
| 179n | No | 0.61 | 0.32 | -58 | 0 | 17 | -0.01 | 0.00033 | 0.021 | 1.56 |
| 180e | No | 0.6 | 0.49 | -58 | 0 | 16 | 0.13 | 0.01 | 0.031 | 1.69 |
| 180n | No | 0.059 | 0.49 | -58 | 0 | 0.61 | 0.59 | 0.05 | 0.17 | 1.37 |
| 181e | No | 0.59 | 0.31 | -58 | 0 | 7.9 | 0.11 | 0.00033 | 0.036 | 1.69 |
| 181n | No | 0.61 | 0.31 | -58 | 0 | 9.4 | -0.0026 | 0.00033 | 0.027 | 1.73 |
| 182e | No | 0.61 | 0.43 | -58 | 0 | 16 | 0.11 | 0.0091 | 0.038 | 1.77 |
| 182n | No | 0.054 | 0.43 | -58 | 0 | 0.67 | 0.55 | 0.026 | 0.17 | 1.23 |
| 183e | No | 0.59 | 0.29 | -58 | 0 | 10 | 0.045 | 0.00065 | 0.029 | 1.63 |
| 183n | No | 0.61 | 0.29 | -58 | 0 | 8.9 | -0.048 | 0.00065 | 0.032 | 1.77 |
| 184e | No | 0.4 | 0.33 | -58 | 0 | 1.7 | 0.88 | 0.014 | 0.21 | 2.16 |
| 184n | No | 0.62 | 0.33 | -58 | 0 | 16 | -0.069 | 0 | 0.025 | 1.84 |
| 185e | No | 0.61 | 0.29 | -58 | 0 | 18 | 0.084 | 0.002 | 0.03 | 1.62 |
| 185n | No | 0.61 | 0.29 | -58 | 0 | 13 | -0.031 | 0 | 0.026 | 1.82 |
| 186e | No | 0.6 | 0.29 | -58 | 0 | 27 | 0.083 | 0 | 0.013 | 1.77 |
| 186n | No | 0.61 | 0.29 | -58 | 0 | 23 | -0.029 | 0 | 0.019 | 1.9 |
| 187e | No | 0.59 | 0.3 | -58 | 0 | 15 | 0.16 | 0.00065 | 0.034 | 1.59 |
| 187n | No | 0.6 | 0.3 | -58 | 0 | 27 | -0.018 | 0 | 0.021 | 2 |
| 189e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 189n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 190e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 190n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 191e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 191n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 192e | No | 0.55 | 0.33 | -58 | 0 | 50 | 0.083 | 0 | 0.04 | 1.35 |
| 192n | No | 0.52 | 0.33 | -58 | 0 | 82 | -0.0021 | 0 | 0.11 | 1.64 |
| 193e | No | 0.52 | 0.32 | -58 | 0 | 80 | 0.018 | 0 | 0.1 | 1.66 |
| 193n | No | 0.51 | 0.32 | -58 | 0 | 75 | -9.9E-05 | 0 | 0.092 | 1.65 |
| 200e | No | 0.047 | 0.15 | -58 | 0 | 3.1 | 0.58 | 0.058 | 0.14 | 1.16 |
| 200n | No | 0.24 | 0.15 | -58 | 0 | 22 | 1.4 | 0 | 0.39 | 1.71 |
| 201e | No | 0.57 | 0.3 | -58 | 0 | 51 | 0.1 | 0 | 0.044 | 1.46 |
| 201n | No | 0.58 | 0.3 | -58 | 0 | 70 | -0.0068 | 0 | 0.081 | 1.76 |
| 202e | No | 0.6 | 0.31 | -58 | 0 | 35 | 0.12 | 0 | 0.02 | 1.46 |
| 202n | No | 0.59 | 0.31 | -58 | 0 | 15 | 0.15 | 0.3 | 0.05 | 1.81 |
| 204e | No | 0.59 | 0.31 | -58 | 0 | 7.1 | -0.25 | 0.0016 | 0.12 | 1.97 |
| 204n | No | 0.61 | 0.31 | -58 | 0 | 18 | -0.48 | 0 | 0.14 | 2.34 |
| 205e | No | 0.41 | 0.36 | -58 | 0 | 4.6 | 0.41 | 0.016 | 0.087 | 1.41 |
| 205n | No | 0.59 | 0.36 | -58 | 0 | 17 | 0.053 | 0.1 | 0.029 | 2.15 |
| 206e | No | 0.52 | 0.28 | -58 | 0 | 11 | 0.22 | 0.0033 | 0.033 | 1.7 |
| 206n | No | 0.46 | 0.28 | -58 | 0 | 5.7 | 0.25 | 0.0046 | 0.082 | 1.69 |
| 207e | No | 0.56 | 0.29 | -58 | 0 | 20 | 0.16 | 0.0036 | 0.013 | 1.41 |
| 207n | No | 0.56 | 0.29 | -58 | 0 | 14 | 0.12 | 0.0013 | 0.04 | 1.64 |
| 208e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 208n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 209e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 209n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 210e | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 210n | Yes | -58 | 1.5E+03 | 0 | 0 | 0 | INF | 0 | ||
| 211e | No | 0.53 | 0.43 | -58 | 0 | 16 | 0.23 | 0.00065 | 0.032 | 1.33 |
| 211n | No | 0.043 | 0.43 | -58 | 0 | 2.8 | 0.58 | 0.045 | 0.18 | 1.16 |
| 220e | No | 0.59 | 0.31 | -58 | 0 | 26 | 0.13 | 0.00065 | 0.013 | 1.41 |
| 220n | No | 0.6 | 0.31 | -58 | 0 | 22 | -0.017 | 0 | 0.014 | 1.57 |
| 221e | No | 0.57 | 0.31 | -58 | 0 | 20 | 0.16 | 0 | 0.02 | 1.44 |
| 221n | No | 0.59 | 0.31 | -58 | 0 | 21 | 0.055 | 0 | 0.022 | 1.53 |
| 222e | No | 0.57 | 0.3 | -58 | 0 | 24 | 0.2 | 0 | 0.027 | 1.46 |
| 222n | No | 0.59 | 0.3 | -58 | 0 | 26 | 0.015 | 0 | 0.025 | 1.54 |
| 223e | No | 0.56 | 0.31 | -58 | 0 | 15 | 0.13 | 0 | 0.014 | 1.47 |
| 223n | No | 0.58 | 0.31 | -58 | 0 | 22 | -0.054 | 0 | 0.015 | 1.57 |
| 224e | No | 0.51 | 0.3 | -58 | 0 | 84 | 0.029 | 0.0059 | 0.11 | 1.79 |
| 224n | No | 0.54 | 0.3 | -58 | 0 | 80 | 0.0004 | 0 | 0.098 | 1.96 |
| 225e | No | 0.57 | 0.44 | -58 | 0 | 30 | 0.11 | 0 | 0.012 | 1.61 |
| 225n | No | 0.14 | 0.44 | -58 | 0 | 3.1 | 0.56 | 0.058 | 0.17 | 1.22 |
| 226e | No | 0.56 | 0.29 | -58 | 0 | 20 | 0.1 | 0 | 0.021 | 1.43 |
| 226n | No | 0.46 | 0.29 | -58 | 0 | 30 | 0.69 | 0 | 0.22 | 3.1 |
| 227e | No | 0.45 | 0.34 | -58 | 0 | 6.6 | 0.33 | 0.0033 | 0.066 | 1.35 |
| 227n | No | 0.54 | 0.34 | -58 | 0 | 15 | 0.082 | 0 | 0.031 | 1.59 |
| 228e | No | 0.56 | 0.32 | -58 | 0 | 30 | 0.12 | 0 | 0.023 | 1.42 |
| 228n | No | 0.54 | 0.32 | -58 | 0 | 18 | 0.048 | 0 | 0.028 | 1.5 |
| 229e | No | 0.54 | 0.33 | -58 | 0 | 30 | 0.14 | 0 | 0.019 | 1.36 |
| 229n | No | 0.54 | 0.33 | -58 | 0 | 36 | -0.024 | 0 | 0.025 | 1.44 |
| 237e | No | 0.52 | 0.32 | -58 | 0 | 11 | 0.27 | 0.00033 | 0.042 | 1.32 |
| 237n | No | 0.57 | 0.32 | -58 | 0 | 18 | 0.042 | 0 | 0.024 | 1.39 |
| 238e | No | 0.58 | 0.32 | -58 | 0 | 30 | 0.089 | 0.00033 | 0.031 | 1.35 |
| 238n | No | 0.58 | 0.32 | -58 | 0 | 30 | 0.044 | 0 | 0.023 | 1.4 |
| 239e | No | 0.57 | 0.31 | -58 | 0 | 26 | 0.12 | 0 | 0.017 | 1.36 |
| 239n | No | 0.58 | 0.31 | -58 | 0 | 30 | -0.0049 | 0 | 0.015 | 1.41 |
| 240e | No | 0.53 | 0.33 | -58 | 0 | 11 | 0.22 | 0.0029 | 0.036 | 1.35 |
| 240n | No | 0.57 | 0.33 | -58 | 0 | 20 | 0.055 | 0 | 0.025 | 1.42 |
| 241e | No | 0.56 | 0.32 | -58 | 0 | 22 | 0.09 | 0.00033 | 0.011 | 1.37 |
| 241n | No | 0.57 | 0.32 | -58 | 0 | 27 | -0.021 | 0 | 0.013 | 1.48 |
| 242e | No | 0.42 | 0.32 | -58 | 0 | 26 | 0.78 | 0 | 0.21 | 2.68 |
| 242n | No | 0.56 | 0.32 | -58 | 0 | 38 | -0.012 | 0 | 0.032 | 1.58 |
| 243e | No | 0.43 | 0.32 | -58 | 0 | 33 | 0.75 | 0 | 0.2 | 2.94 |
| 243n | No | 0.54 | 0.32 | -58 | 0 | 18 | 0.032 | 0 | 0.018 | 1.76 |
| 244e | No | 0.52 | 0.31 | -58 | 0 | 13 | 0.2 | 0.0055 | 0.039 | 1.44 |
| 244n | No | 0.54 | 0.31 | -58 | 0 | 17 | 0.059 | 0 | 0.022 | 1.56 |
| 245e | No | 0.55 | 0.32 | -58 | 0 | 32 | 0.12 | 0 | 0.021 | 1.4 |
| 245n | No | 0.53 | 0.32 | -58 | 0 | 22 | 0.072 | 0 | 0.028 | 1.6 |
| 246e | No | 0.52 | 0.42 | -58 | 0 | 20 | 0.17 | 0.00065 | 0.023 | 1.45 |
| 246n | No | 0.043 | 0.42 | -58 | 0 | 3.2 | 0.59 | 0.023 | 0.18 | 1.18 |
| 261e | No | 0.52 | 0.31 | -58 | 0 | 26 | 0.09 | 0.00033 | 0.016 | 1.56 |
| 261n | No | 0.52 | 0.31 | -58 | 0 | 25 | 0.0038 | 0 | 0.019 | 1.63 |
| 262e | No | 0.54 | 0.35 | -58 | 0 | 11 | -0.29 | 0.00065 | 0.13 | 1.79 |
| 262n | No | 0.53 | 0.35 | -58 | 0 | 11 | -0.54 | 0.00065 | 0.16 | 1.78 |
| 320e | No | 0.47 | 0.34 | -58 | 0 | 46 | 0.024 | 0.0016 | 0.051 | - |
| 320n | No | 0.45 | 0.34 | -58 | 0 | 36 | -0.11 | 0.00065 | 0.041 | - |
| 324e | No | 0.47 | 0.34 | -58 | 0 | 33 | 0.12 | 0.0013 | 0.036 | - |
| 324n | No | 0.44 | 0.34 | -58 | 0 | 39 | -0.035 | 0 | 0.049 | - |
| 325e | No | 0.49 | 0.36 | -58 | 0 | 31 | 0.09 | 0.0026 | 0.027 | - |
| 325n | No | 0.46 | 0.36 | -58 | 0 | 18 | -0.016 | 0.00065 | 0.018 | - |
| 329e | No | 0.48 | 0.35 | -58 | 0 | 32 | 0.17 | 0.055 | 0.037 | - |
| 329n | No | 0.46 | 0.35 | -58 | 0 | 23 | -0.0086 | 0.011 | 0.025 | - |
| 333e | No | 0.44 | 0.31 | -58 | 0 | 14 | 0.26 | 0.011 | 0.054 | - |
| 333n | No | 0.41 | 0.31 | -58 | 0 | 17 | 0.18 | 0.0029 | 0.062 | - |
# Save antenna classification table as a csv
if SAVE_RESULTS:
for ind, col in zip(np.arange(len(df.columns), 0, -1), df_classes.columns[::-1]):
df.insert(int(ind), col + ' Class', df_classes[col])
df.to_csv(ANTCLASS_FILE)
print('Final Ant-Pol Classification:\n\n', final_class)
Final Ant-Pol Classification: Jee: ---------- good (100 antpols): 3, 4, 7, 16, 17, 19, 20, 21, 22, 29, 30, 31, 37, 38, 41, 43, 44, 45, 46, 48, 49, 50, 51, 53, 56, 57, 60, 61, 62, 63, 64, 67, 69, 71, 73, 74, 79, 80, 85, 86, 88, 89, 90, 91, 97, 102, 104, 105, 106, 107, 115, 120, 121, 125, 128, 131, 132, 133, 140, 141, 142, 144, 145, 146, 157, 158, 159, 160, 161, 162, 163, 164, 166, 171, 179, 180, 181, 182, 183, 185, 186, 187, 206, 207, 211, 220, 221, 222, 223, 225, 226, 227, 229, 237, 239, 240, 241, 244, 246, 261 suspect (20 antpols): 9, 35, 36, 52, 54, 66, 96, 103, 123, 139, 156, 192, 201, 202, 204, 205, 228, 238, 245, 262 bad (78 antpols): 5, 8, 10, 15, 18, 27, 28, 32, 34, 40, 42, 47, 55, 58, 59, 65, 68, 70, 72, 77, 78, 81, 82, 83, 84, 87, 92, 93, 94, 95, 101, 108, 109, 110, 111, 112, 113, 114, 117, 118, 122, 124, 126, 127, 134, 135, 136, 137, 143, 147, 148, 149, 150, 151, 155, 165, 167, 168, 169, 170, 173, 184, 189, 190, 191, 193, 200, 208, 209, 210, 224, 242, 243, 320, 324, 325, 329, 333 Jnn: ---------- good (74 antpols): 7, 15, 20, 29, 30, 31, 38, 41, 43, 44, 46, 50, 51, 52, 53, 56, 57, 59, 61, 67, 69, 71, 73, 74, 85, 86, 88, 89, 90, 92, 102, 103, 105, 106, 110, 114, 115, 120, 124, 128, 132, 133, 139, 140, 141, 151, 157, 158, 160, 162, 163, 171, 179, 181, 183, 184, 185, 186, 206, 207, 220, 221, 222, 223, 227, 228, 237, 239, 240, 241, 243, 244, 245, 261 suspect (34 antpols): 9, 10, 19, 21, 22, 35, 36, 48, 49, 62, 64, 68, 78, 91, 97, 107, 121, 123, 125, 126, 127, 131, 144, 145, 146, 164, 165, 166, 187, 204, 229, 238, 242, 262 bad (90 antpols): 3, 4, 5, 8, 16, 17, 18, 27, 28, 32, 34, 37, 40, 42, 45, 47, 54, 55, 58, 60, 63, 65, 66, 70, 72, 77, 79, 80, 81, 82, 83, 84, 87, 93, 94, 95, 96, 101, 104, 108, 109, 111, 112, 113, 117, 118, 122, 134, 135, 136, 137, 142, 143, 147, 148, 149, 150, 155, 156, 159, 161, 167, 168, 169, 170, 173, 180, 182, 189, 190, 191, 192, 193, 200, 201, 202, 205, 208, 209, 210, 211, 224, 225, 226, 246, 320, 324, 325, 329, 333
# update flags in omnical gains and visibility solutions
for ant in omni_flags:
omni_flags[ant] |= rfi_flags
for bl in vissol_flags:
vissol_flags[bl] |= rfi_flags
if SAVE_RESULTS:
add_to_history = 'Produced by file_calibration notebook with the following environment:\n' + '=' * 65 + '\n' + os.popen('conda env export').read() + '=' * 65
hd_vissol = io.HERAData(SUM_FILE)
hc_omni = hd_vissol.init_HERACal(gain_convention='divide', cal_style='redundant')
hc_omni.update(gains=sol.gains, flags=omni_flags, quals=meta['chisq_per_ant'], total_qual=meta['chisq'])
hc_omni.history += add_to_history
hc_omni.write_calfits(OMNICAL_FILE, clobber=True)
del hc_omni
malloc_trim()
# output results, harmonizing keys over polarizations
all_reds = redcal.get_reds(hd.data_antpos, pols=['ee', 'nn'], pol_mode='2pol')
bl_to_red_map = {bl: red[0] for red in all_reds for bl in red}
hd_vissol.read(bls=[bl_to_red_map[bl] for bl in sol.vis], return_data=False)
hd_vissol.empty_arrays()
hd_vissol.history += add_to_history
hd_vissol.update(data={bl_to_red_map[bl]: sol.vis[bl] for bl in sol.vis},
flags={bl_to_red_map[bl]: vissol_flags[bl] for bl in vissol_flags},
nsamples={bl_to_red_map[bl]: vissol_nsamples[bl] for bl in vissol_nsamples})
hd_vissol.write_uvh5(OMNIVIS_FILE, clobber=True)
del hd_vissol
malloc_trim()
OMNICAL_FILE is not written¶if SAVE_RESULTS and not os.path.exists(OMNICAL_FILE):
print(f'WARNING: No calibration file produced at {OMNICAL_FILE}. Creating a fully-flagged placeholder calibration file.')
hd_writer = io.HERAData(SUM_FILE)
io.write_cal(OMNICAL_FILE, freqs=hd_writer.freqs, times=hd_writer.times,
gains={ant: np.ones((hd_writer.Ntimes, hd_writer.Nfreqs), dtype=np.complex64) for ant in ants},
flags={ant: np.ones((len(data.times), len(data.freqs)), dtype=bool) for ant in ants},
quality=None, total_qual=None, outdir='', overwrite=True, history=utils.history_string(add_to_history),
x_orientation=hd_writer.x_orientation, telescope_location=hd_writer.telescope_location, lst_array=np.unique(hd_writer.lsts),
antenna_positions=np.array([hd_writer.antenna_positions[hd_writer.antenna_numbers == antnum].flatten() for antnum in set(ant[0] for ant in ants)]),
antnums2antnames=dict(zip(hd_writer.antenna_numbers, hd_writer.antenna_names)))
OMNIVIS_FILE is not written¶if SAVE_RESULTS and not os.path.exists(OMNIVIS_FILE):
print(f'WARNING: No omnivis file produced at {OMNIVIS_FILE}. Creating an empty visibility solution file.')
hd_writer = io.HERAData(SUM_FILE)
hd_writer.initialize_uvh5_file(OMNIVIS_FILE, clobber=True)
for repo in ['hera_cal', 'hera_qm', 'hera_notebook_templates']:
exec(f'from {repo} import __version__')
print(f'{repo}: {__version__}')
hera_cal: 3.2.3.dev133+g7c00d5f hera_qm: 2.0.5.dev13+gd6c757c hera_notebook_templates: 0.1.dev486+gfb8560a
print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')
Finished execution in 15.17 minutes.