Calibration Smoothing¶
by Josh Dillon, last updated October 2, 2025
This notebook runs calibration smoothing to the gains coming out of file_calibration notebook. It removes any flags founds on by that notebook and replaces them with flags generated from full_day_rfi and full_day_antenna_flagging. It flags antennas with high relative difference between the original gains and smoothed gains. It also plots the results for a couple of antennas.
Here's a set of links to skip to particular figures and tables:
• Figure 1: Identifying and Blacklisting abscal Failures¶
• Figure 2: Antenna Phases with Identified Phase Flips¶
• Figure 3: Full-Day Gain Amplitudes Before and After smooth_cal¶
• Figure 4: Full-Day Gain Phases Before and After smooth_cal¶
• Figure 5: Full-Day $\chi^2$ / DoF Waterfall from Redundant-Baseline Calibration¶
• Figure 6: Average $\chi^2$ per Antenna¶
• Figure 7: Relative Difference Before and After Smoothing¶
In [1]:
import time
tstart = time.time()
In [2]:
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 glob
import copy
import warnings
import matplotlib
import matplotlib.pyplot as plt
from hera_cal import io, utils, smooth_cal
from hera_qm.time_series_metrics import true_stretches
%matplotlib inline
from IPython.display import display, HTML
Parse inputs¶
In [3]:
# get files
SUM_FILE = os.environ.get("SUM_FILE", None)
# SUM_FILE = "/lustre/aoc/projects/hera/h6c-analysis/IDR3/2459893/zen.2459893.25258.sum.uvh5"
SUM_SUFFIX = os.environ.get("SUM_SUFFIX", 'sum.uvh5')
CAL_SUFFIX = os.environ.get("CAL_SUFFIX", 'sum.omni.calfits')
SMOOTH_CAL_SUFFIX = os.environ.get("SMOOTH_CAL_SUFFIX", 'sum.smooth.calfits')
ANT_FLAG_SUFFIX = os.environ.get("ANT_FLAG_SUFFIX", 'sum.antenna_flags.h5')
RFI_FLAG_SUFFIX = os.environ.get("RFI_FLAG_SUFFIX", 'sum.flag_waterfall.h5')
FREQ_SMOOTHING_SCALE = float(os.environ.get("FREQ_SMOOTHING_SCALE", 30.0)) # MHz
TIME_SMOOTHING_SCALE = float(os.environ.get("TIME_SMOOTHING_SCALE", 1e4)) # seconds
EIGENVAL_CUTOFF = float(os.environ.get("EIGENVAL_CUTOFF", 1e-12))
PER_POL_REFANT = os.environ.get("PER_POL_REFANT", "False").upper() == "TRUE"
BLACKLIST_TIMESCALE_FACTOR = float(os.environ.get("BLACKLIST_TIMESCALE_FACTOR", 4.0))
BLACKLIST_RELATIVE_ERROR_THRESH = float(os.environ.get("BLACKLIST_RELATIVE_ERROR_THRESH", 1))
BLACKLIST_RELATIVE_WEIGHT = float(os.environ.get("BLACKLIST_RELATIVE_WEIGHT", 0.1))
FM_LOW_FREQ = float(os.environ.get("FM_LOW_FREQ", 87.5)) # in MHz
FM_HIGH_FREQ = float(os.environ.get("FM_HIGH_FREQ", 108.0)) # in MHz
SC_RELATIVE_DIFF_CUTOFF = float(os.environ.get("SC_RELATIVE_DIFF_CUTOFF", 0.2))
for setting in ['SUM_FILE', 'SUM_SUFFIX', 'CAL_SUFFIX', 'SMOOTH_CAL_SUFFIX', 'ANT_FLAG_SUFFIX',
'RFI_FLAG_SUFFIX', 'FREQ_SMOOTHING_SCALE', 'TIME_SMOOTHING_SCALE', 'EIGENVAL_CUTOFF',
'PER_POL_REFANT', 'BLACKLIST_TIMESCALE_FACTOR', 'BLACKLIST_RELATIVE_ERROR_THRESH',
'BLACKLIST_RELATIVE_WEIGHT', 'FM_LOW_FREQ', 'FM_HIGH_FREQ', 'SC_RELATIVE_DIFF_CUTOFF']:
if issubclass(type(eval(setting)), str):
print(f'{setting} = "{eval(setting)}"')
else:
print(f'{setting} = {eval(setting)}')
SUM_FILE = "/lustre/aoc/projects/hera/h6c-analysis/IDR3/2459896/zen.2459896.25289.sum.uvh5" SUM_SUFFIX = "sum.uvh5" CAL_SUFFIX = "sum.omni.calfits" SMOOTH_CAL_SUFFIX = "sum.smooth.calfits" ANT_FLAG_SUFFIX = "sum.antenna_flags.h5" RFI_FLAG_SUFFIX = "sum.flag_waterfall.h5" FREQ_SMOOTHING_SCALE = 30.0 TIME_SMOOTHING_SCALE = 10000.0 EIGENVAL_CUTOFF = 1e-12 PER_POL_REFANT = False BLACKLIST_TIMESCALE_FACTOR = 4.0 BLACKLIST_RELATIVE_ERROR_THRESH = 1.0 BLACKLIST_RELATIVE_WEIGHT = 0.1 FM_LOW_FREQ = 87.5 FM_HIGH_FREQ = 108.0 SC_RELATIVE_DIFF_CUTOFF = 0.2
Load files and select reference antenna(s)¶
In [4]:
sum_glob = '.'.join(SUM_FILE.split('.')[:-3]) + '.*.' + SUM_SUFFIX
cal_files_glob = sum_glob.replace(SUM_SUFFIX, CAL_SUFFIX)
cal_files = sorted(glob.glob(cal_files_glob))
print(f'Found {len(cal_files)} *.{CAL_SUFFIX} files starting with {cal_files[0]}.')
Found 1849 *.sum.omni.calfits files starting with /lustre/aoc/projects/hera/h6c-analysis/IDR3/2459896/zen.2459896.25289.sum.omni.calfits.
In [5]:
rfi_flag_files_glob = sum_glob.replace(SUM_SUFFIX, RFI_FLAG_SUFFIX)
rfi_flag_files = sorted(glob.glob(rfi_flag_files_glob))
print(f'Found {len(rfi_flag_files)} *.{RFI_FLAG_SUFFIX} files starting with {rfi_flag_files[0]}.')
Found 1849 *.sum.flag_waterfall.h5 files starting with /lustre/aoc/projects/hera/h6c-analysis/IDR3/2459896/zen.2459896.25289.sum.flag_waterfall.h5.
In [6]:
ant_flag_files_glob = sum_glob.replace(SUM_SUFFIX, ANT_FLAG_SUFFIX)
ant_flag_files = sorted(glob.glob(ant_flag_files_glob))
print(f'Found {len(ant_flag_files)} *.{ANT_FLAG_SUFFIX} files starting with {ant_flag_files[0]}.')
Found 1849 *.sum.antenna_flags.h5 files starting with /lustre/aoc/projects/hera/h6c-analysis/IDR3/2459896/zen.2459896.25289.sum.antenna_flags.h5.
In [7]:
cs = smooth_cal.CalibrationSmoother(cal_files, flag_file_list=(ant_flag_files + rfi_flag_files),
ignore_calflags=True, pick_refant=False, load_chisq=True, load_cspa=True)
In [8]:
cs.refant = smooth_cal.pick_reference_antenna(cs.gain_grids, cs.flag_grids, cs.freqs, per_pol=True)
for pol in cs.refant:
print(f'Reference antenna {cs.refant[pol][0]} selected for smoothing {pol} gains.')
if not PER_POL_REFANT:
# in this case, rephase both pols separately before smoothing, but also smooth the relative polarization calibration phasor
overall_refant = smooth_cal.pick_reference_antenna({ant: cs.gain_grids[ant] for ant in cs.refant.values()},
{ant: cs.flag_grids[ant] for ant in cs.refant.values()},
cs.freqs, per_pol=False)
print(f'Overall reference antenna {overall_refant} selected.')
other_refant = [ant for ant in cs.refant.values() if ant != overall_refant][0]
relative_pol_phasor = cs.gain_grids[overall_refant] * cs.gain_grids[other_refant].conj() # TODO: is this conjugation right?
relative_pol_phasor /= np.abs(relative_pol_phasor)
abscal_refants = {cs.refant[pol]: cs.gain_grids[cs.refant[pol]] for pol in ['Jee', 'Jnn']}
Reference antenna 109 selected for smoothing Jee gains. Reference antenna 167 selected for smoothing Jnn gains.
Overall reference antenna (np.int64(167), 'Jnn') selected.
In [9]:
cs.rephase_to_refant(propagate_refant_flags=True)
In [10]:
lst_grid = utils.JD2LST(cs.time_grid) * 12 / np.pi
lst_grid[lst_grid > lst_grid[-1]] -= 24
Find consistent outliers in relative error after a coarse smoothing¶
These are typically a sign of failures of abscal.
In [11]:
relative_error_samples = {pol: np.zeros_like(cs.gain_grids[cs.refant[pol]], dtype=float) for pol in ['Jee', 'Jnn']}
sum_relative_error = {pol: np.zeros_like(cs.gain_grids[cs.refant[pol]], dtype=float) for pol in ['Jee', 'Jnn']}
per_ant_avg_relative_error = {}
# perform a 2D DPSS filter with a BLACKLIST_TIMESCALE_FACTOR longer timescale, averaging the results per-pol
for ant in cs.gain_grids:
if np.all(cs.flag_grids[ant]):
continue
filtered, _ = smooth_cal.time_freq_2D_filter(gains=cs.gain_grids[ant],
wgts=(~cs.flag_grids[ant]).astype(float),
freqs=cs.freqs,
times=cs.time_grid,
freq_scale=FREQ_SMOOTHING_SCALE,
time_scale=TIME_SMOOTHING_SCALE * BLACKLIST_TIMESCALE_FACTOR,
eigenval_cutoff=EIGENVAL_CUTOFF,
method='DPSS',
fit_method='lu_solve',
fix_phase_flips=True,
flag_phase_flip_ints=True,
skip_flagged_edges=True,
freq_cuts=[(FM_LOW_FREQ + FM_HIGH_FREQ) * .5e6],
)
relative_error = np.where(cs.flag_grids[ant], 0, np.abs(cs.gain_grids[ant] - filtered) / np.abs(filtered))
per_ant_avg_relative_error[ant] = np.nanmean(np.where(cs.flag_grids[ant], np.nan, relative_error))
relative_error_samples[ant[1]] += (~cs.flag_grids[ant]).astype(float)
sum_relative_error[ant[1]] += relative_error
# figure out per-antpol cuts for where to set weights to 0 for the main smooth_cal (but not necessarily flags)
cs.blacklist_wgt = BLACKLIST_RELATIVE_WEIGHT
for pol in ['Jee', 'Jnn']:
avg_rel_error = sum_relative_error[pol] / relative_error_samples[pol]
to_blacklist = np.where(relative_error_samples[pol] > 0, avg_rel_error > BLACKLIST_RELATIVE_ERROR_THRESH, False)
for ant in cs.ants:
if ant[1] == pol:
cs.waterfall_blacklist[ant] = to_blacklist
invalid value encountered in divide
In [12]:
def plot_relative_error():
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fig, axes = plt.subplots(1, 3, figsize=(14, 7))
extent = [cs.freqs[0] / 1e6, cs.freqs[-1] / 1e6, lst_grid[-1], lst_grid[0]]
cmap = plt.get_cmap('Greys', 256)
cmap.set_over('red')
for ax, pol in zip(axes[0:2], ['Jee', 'Jnn']):
to_plot = sum_relative_error[pol] / relative_error_samples[pol]
im = ax.imshow(np.where(np.isfinite(to_plot), to_plot, np.nan), aspect='auto', interpolation='none',
vmin=0, vmax=BLACKLIST_RELATIVE_ERROR_THRESH, extent=extent, cmap=cmap)
ax.set_title(pol)
ax.set_yticklabels(ax.get_yticks() % 24)
ax.set_ylabel('LST (hours)')
ax.set_xlabel('Frequency (MHz)')
plt.colorbar(im, ax=axes[0:2], location='top', extend='max', label='Average Relative Error on Initial Smoothing')
for pol in ['Jee', 'Jnn']:
axes[2].hist((sum_relative_error[pol] / relative_error_samples[pol]).ravel(), bins=np.arange(0,2,.01), alpha=.5, label=pol)
axes[2].set_yscale('log')
axes[2].set_ylabel('Number of Waterfall Pixels')
axes[2].set_xlabel('Relative Error')
axes[2].axvline(BLACKLIST_RELATIVE_ERROR_THRESH, ls='--', c='r', label='Blacklist Threshold')
axes[2].legend()
Figure 1: Identifying and Blacklisting abscal Failures¶
This plot highlights regions of the waterfall that are per-polarization blacklisted (i.e. given 0 weight in the main smooth_cal fit, but not necessarily flagged). This is usually a sign of problems with abscal and often occurs because
In [13]:
plot_relative_error()
In [14]:
# duplicate a small number of abscal gains for plotting
antnums = set([ant[0] for ant in cs.ants])
flags_per_antnum = [np.sum(cs.flag_grids[ant, 'Jnn']) + np.sum(cs.flag_grids[ant, 'Jee']) for ant in antnums]
larger_relative_error = np.array([np.max([per_ant_avg_relative_error.get((ant, pol), np.inf) for pol in ['Jee', 'Jnn']]) for ant in antnums])
refant_nums = [ant[0] for ant in cs.refant.values()]
candidate_ants = [ant for ant, nflags, rel_err in zip(antnums, flags_per_antnum, larger_relative_error)
if (ant not in refant_nums) and (nflags <= np.percentile(flags_per_antnum, 25))
and (rel_err <= SC_RELATIVE_DIFF_CUTOFF)
and not np.all(cs.flag_grids[ant, 'Jee']) and not np.all(cs.flag_grids[ant, 'Jnn'])]
ants_to_plot = [func(candidate_ants) for func in (np.min, np.max)]
abscal_gains = {}
for pol in ['Jee', 'Jnn']:
for antnum in ants_to_plot:
if PER_POL_REFANT:
abscal_gains[antnum, pol] = cs.gain_grids[(antnum, pol)] * np.abs(abscal_refants[cs.refant[pol]]) / abscal_refants[cs.refant[pol]]
else:
abscal_gains[antnum, pol] = cs.gain_grids[(antnum, pol)] / np.abs(abscal_refants[cs.refant[pol]]) * abscal_refants[cs.refant[pol]]
abscal_gains[antnum, pol] *= np.abs(abscal_refants[overall_refant]) / abscal_refants[overall_refant]
Perform smoothing¶
In [15]:
if not PER_POL_REFANT:
# treat the relative_pol_phasor as if it were antenna -1
cs.gain_grids[(-1, other_refant[1])] = relative_pol_phasor
cs.flag_grids[(-1, other_refant[1])] = cs.flag_grids[overall_refant] | cs.flag_grids[other_refant]
cs.waterfall_blacklist[(-1, other_refant[1])] = cs.waterfall_blacklist[cs.ants[0][0], 'Jee'] | cs.waterfall_blacklist[cs.ants[0][0], 'Jnn']
In [16]:
meta = cs.time_freq_2D_filter(freq_scale=FREQ_SMOOTHING_SCALE,
time_scale=TIME_SMOOTHING_SCALE,
eigenval_cutoff=EIGENVAL_CUTOFF,
method='DPSS',
fit_method='lu_solve',
fix_phase_flips=True,
flag_phase_flip_ints=True,
skip_flagged_edges=True,
freq_cuts=[(FM_LOW_FREQ + FM_HIGH_FREQ) * .5e6],)
36 phase flips detected on antenna (np.int64(206), 'Jee'). A total of 24 integrations were phase-flipped relative to the 0th integration between 2459896.4027626463 and 2459896.4117104956.
2 phase flips detected on antenna (np.int64(9), 'Jee'). A total of 1 integrations were phase-flipped relative to the 0th integration between 2459896.643459792 and 2459896.643459792.
4 phase flips detected on antenna (np.int64(21), 'Jee'). A total of 2 integrations were phase-flipped relative to the 0th integration between 2459896.643459792 and 2459896.644690121.
In [17]:
# calculate average chi^2 per antenna before additional flagging
avg_cspa_vs_time = {ant: np.nanmean(np.where(cs.flag_grids[ant], np.nan, cs.cspa_grids[ant]), axis=1) for ant in cs.ants}
avg_cspa_vs_freq = {ant: np.nanmean(np.where(cs.flag_grids[ant], np.nan, cs.cspa_grids[ant]), axis=0) for ant in cs.ants}
avg_cspa = {ant: np.nanmean(np.where(cs.flag_grids[ant], np.nan, cs.cspa_grids[ant])) for ant in cs.ants}
Mean of empty slice
Mean of empty slice
Mean of empty slice
In [18]:
# Pick out antennas with too high relative differences before and after smoothing and flag them.
avg_relative_diffs = {ant: np.nanmean(rel_diff) for ant, rel_diff in meta['freq_avg_rel_diff'].items()}
to_cut = sorted([ant for ant, diff in avg_relative_diffs.items() if ant[0] >= 0 and diff > SC_RELATIVE_DIFF_CUTOFF])
if len(to_cut) > 0:
for ant in to_cut:
print(f'Flagging antenna {ant[0]}{ant[1][-1]} with a relative difference before and after smoothing of {avg_relative_diffs[ant]:.2%} '
f'(compared to the {SC_RELATIVE_DIFF_CUTOFF:.2%} cutoff).')
cs.flag_grids[ant] |= True
else:
print(f'No antennas have a relative difference above the {SC_RELATIVE_DIFF_CUTOFF:.2%} cutoff.')
Flagging antenna 3n with a relative difference before and after smoothing of 51.65% (compared to the 20.00% cutoff). Flagging antenna 4n with a relative difference before and after smoothing of 57.80% (compared to the 20.00% cutoff). Flagging antenna 5n with a relative difference before and after smoothing of 63.72% (compared to the 20.00% cutoff). Flagging antenna 7n with a relative difference before and after smoothing of 78.41% (compared to the 20.00% cutoff). Flagging antenna 8n with a relative difference before and after smoothing of 87.07% (compared to the 20.00% cutoff). Flagging antenna 9n with a relative difference before and after smoothing of 96.60% (compared to the 20.00% cutoff). Flagging antenna 10n with a relative difference before and after smoothing of 106.71% (compared to the 20.00% cutoff). Flagging antenna 15n with a relative difference before and after smoothing of 50.66% (compared to the 20.00% cutoff). Flagging antenna 16n with a relative difference before and after smoothing of 56.51% (compared to the 20.00% cutoff). Flagging antenna 17n with a relative difference before and after smoothing of 63.07% (compared to the 20.00% cutoff). Flagging antenna 19n with a relative difference before and after smoothing of 77.85% (compared to the 20.00% cutoff). Flagging antenna 20n with a relative difference before and after smoothing of 86.31% (compared to the 20.00% cutoff). Flagging antenna 21n with a relative difference before and after smoothing of 96.30% (compared to the 20.00% cutoff). Flagging antenna 30n with a relative difference before and after smoothing of 62.01% (compared to the 20.00% cutoff). Flagging antenna 31n with a relative difference before and after smoothing of 69.38% (compared to the 20.00% cutoff). Flagging antenna 34n with a relative difference before and after smoothing of 34.52% (compared to the 20.00% cutoff). Flagging antenna 35n with a relative difference before and after smoothing of 42.93% (compared to the 20.00% cutoff). Flagging antenna 36n with a relative difference before and after smoothing of 24.50% (compared to the 20.00% cutoff). Flagging antenna 37n with a relative difference before and after smoothing of 27.70% (compared to the 20.00% cutoff). Flagging antenna 38n with a relative difference before and after smoothing of 30.79% (compared to the 20.00% cutoff). Flagging antenna 40n with a relative difference before and after smoothing of 38.84% (compared to the 20.00% cutoff). Flagging antenna 41n with a relative difference before and after smoothing of 43.46% (compared to the 20.00% cutoff). Flagging antenna 42n with a relative difference before and after smoothing of 48.65% (compared to the 20.00% cutoff). Flagging antenna 47n with a relative difference before and after smoothing of 29.50% (compared to the 20.00% cutoff). Flagging antenna 48n with a relative difference before and after smoothing of 36.67% (compared to the 20.00% cutoff). Flagging antenna 49n with a relative difference before and after smoothing of 42.48% (compared to the 20.00% cutoff). Flagging antenna 50n with a relative difference before and after smoothing of 21.41% (compared to the 20.00% cutoff). Flagging antenna 52n with a relative difference before and after smoothing of 26.42% (compared to the 20.00% cutoff). Flagging antenna 53n with a relative difference before and after smoothing of 29.71% (compared to the 20.00% cutoff). Flagging antenna 57n with a relative difference before and after smoothing of 47.85% (compared to the 20.00% cutoff). Flagging antenna 61n with a relative difference before and after smoothing of 26.05% (compared to the 20.00% cutoff). Flagging antenna 62n with a relative difference before and after smoothing of 30.79% (compared to the 20.00% cutoff). Flagging antenna 64n with a relative difference before and after smoothing of 42.05% (compared to the 20.00% cutoff). Flagging antenna 66n with a relative difference before and after smoothing of 20.80% (compared to the 20.00% cutoff).
Flagging antenna 67n with a relative difference before and after smoothing of 22.83% (compared to the 20.00% cutoff). Flagging antenna 69n with a relative difference before and after smoothing of 28.79% (compared to the 20.00% cutoff). Flagging antenna 70n with a relative difference before and after smoothing of 32.62% (compared to the 20.00% cutoff). Flagging antenna 71n with a relative difference before and after smoothing of 37.07% (compared to the 20.00% cutoff). Flagging antenna 72n with a relative difference before and after smoothing of 41.82% (compared to the 20.00% cutoff). Flagging antenna 78n with a relative difference before and after smoothing of 30.46% (compared to the 20.00% cutoff). Flagging antenna 79n with a relative difference before and after smoothing of 35.98% (compared to the 20.00% cutoff). Flagging antenna 85n with a relative difference before and after smoothing of 24.57% (compared to the 20.00% cutoff). Flagging antenna 86n with a relative difference before and after smoothing of 28.12% (compared to the 20.00% cutoff). Flagging antenna 87n with a relative difference before and after smoothing of 31.90% (compared to the 20.00% cutoff). Flagging antenna 88n with a relative difference before and after smoothing of 36.33% (compared to the 20.00% cutoff). Flagging antenna 89n with a relative difference before and after smoothing of 41.27% (compared to the 20.00% cutoff). Flagging antenna 90n with a relative difference before and after smoothing of 46.59% (compared to the 20.00% cutoff). Flagging antenna 91n with a relative difference before and after smoothing of 52.58% (compared to the 20.00% cutoff). Flagging antenna 93n with a relative difference before and after smoothing of 20.42% (compared to the 20.00% cutoff). Flagging antenna 94n with a relative difference before and after smoothing of 25.02% (compared to the 20.00% cutoff). Flagging antenna 95n with a relative difference before and after smoothing of 30.14% (compared to the 20.00% cutoff). Flagging antenna 102n with a relative difference before and after smoothing of 20.83% (compared to the 20.00% cutoff). Flagging antenna 105n with a relative difference before and after smoothing of 31.23% (compared to the 20.00% cutoff). Flagging antenna 106n with a relative difference before and after smoothing of 35.63% (compared to the 20.00% cutoff). Flagging antenna 107n with a relative difference before and after smoothing of 40.60% (compared to the 20.00% cutoff). Flagging antenna 108n with a relative difference before and after smoothing of 44.07% (compared to the 20.00% cutoff). Flagging antenna 112n with a relative difference before and after smoothing of 24.76% (compared to the 20.00% cutoff). Flagging antenna 114n with a relative difference before and after smoothing of 35.46% (compared to the 20.00% cutoff). Flagging antenna 115n with a relative difference before and after smoothing of 41.79% (compared to the 20.00% cutoff). Flagging antenna 121n with a relative difference before and after smoothing of 20.01% (compared to the 20.00% cutoff). Flagging antenna 122n with a relative difference before and after smoothing of 22.96% (compared to the 20.00% cutoff). Flagging antenna 123n with a relative difference before and after smoothing of 26.57% (compared to the 20.00% cutoff). Flagging antenna 124n with a relative difference before and after smoothing of 30.62% (compared to the 20.00% cutoff). Flagging antenna 125n with a relative difference before and after smoothing of 35.15% (compared to the 20.00% cutoff). Flagging antenna 126n with a relative difference before and after smoothing of 38.99% (compared to the 20.00% cutoff). Flagging antenna 132n with a relative difference before and after smoothing of 30.20% (compared to the 20.00% cutoff). Flagging antenna 136n with a relative difference before and after smoothing of 20.45% (compared to the 20.00% cutoff). Flagging antenna 157n with a relative difference before and after smoothing of 42.38% (compared to the 20.00% cutoff). Flagging antenna 160n with a relative difference before and after smoothing of 56.48% (compared to the 20.00% cutoff). Flagging antenna 181n with a relative difference before and after smoothing of 55.69% (compared to the 20.00% cutoff).
Flagging antenna 183n with a relative difference before and after smoothing of 68.18% (compared to the 20.00% cutoff). Flagging antenna 206n with a relative difference before and after smoothing of 94.99% (compared to the 20.00% cutoff). Flagging antenna 220n with a relative difference before and after smoothing of 54.57% (compared to the 20.00% cutoff). Flagging antenna 221n with a relative difference before and after smoothing of 60.67% (compared to the 20.00% cutoff). Flagging antenna 222n with a relative difference before and after smoothing of 67.82% (compared to the 20.00% cutoff). Flagging antenna 223n with a relative difference before and after smoothing of 75.01% (compared to the 20.00% cutoff). Flagging antenna 237n with a relative difference before and after smoothing of 49.17% (compared to the 20.00% cutoff). Flagging antenna 238n with a relative difference before and after smoothing of 54.21% (compared to the 20.00% cutoff). Flagging antenna 239n with a relative difference before and after smoothing of 60.27% (compared to the 20.00% cutoff).
In [19]:
if not PER_POL_REFANT:
# put back in the smoothed phasor, ensuring the amplitude is 1 and that data are flagged anywhere either polarization's refant is flagged
smoothed_relative_pol_phasor = cs.gain_grids[(-1, other_refant[-1])] / np.abs(cs.gain_grids[(-1, other_refant[-1])])
for ant in cs.gain_grids:
if ant[0] >= 0 and ant[1] == other_refant[1]:
cs.gain_grids[ant] /= smoothed_relative_pol_phasor
cs.flag_grids[ant] |= (cs.flag_grids[(-1, other_refant[1])])
cs.refant = overall_refant
In [20]:
def phase_flip_diagnostic_plot():
'''Shows time-smoothed antenna avg phases after taking out a delay and filtering in time.'''
if not np.any([np.any(meta['phase_flipped'][ant]) for ant in meta['phase_flipped']]):
print("No antennas have phase flips identified. Nothing to plot.")
return
plt.figure(figsize=(14,4))
for ant in meta['phase_flipped']:
if np.any(meta['phase_flipped'][ant]):
plt.plot(cs.time_grid - int(cs.time_grid[0]),
np.angle(np.exp(1.0j * (meta['phases'][ant] - meta['time_smoothed_phases'][ant]))), label=f'{ant[0]}{ant[1][-1]}')
plt.legend(title='Antennas with Identified Phase Flips', ncol=4)
plt.xlabel(f'JD - {int(cs.time_grid[0])}')
plt.ylabel('Average Phase After Filtering (radians)')
plt.tight_layout()
Figure 2: Antenna Phases with Identified Phase Flips¶
In [21]:
phase_flip_diagnostic_plot()
Plot results¶
In [22]:
def amplitude_plot(ant_to_plot):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# Pick vmax to not saturate 90% of the abscal gains
vmax = np.max([np.percentile(np.abs(cs.gain_grids[ant_to_plot, pol][~cs.flag_grids[ant_to_plot, pol]]), 99) for pol in ['Jee', 'Jnn']])
display(HTML(f'<h2>Antenna {ant_to_plot} Amplitude Waterfalls</h2>'))
# Plot abscal gain amplitude waterfalls for a single antenna
fig, axes = plt.subplots(4, 2, figsize=(14,14), gridspec_kw={'height_ratios': [1, 1, .4, .4]})
for ax, pol in zip(axes[0], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
extent=[cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
im = ax.imshow(np.where(cs.flag_grids[ant], np.nan, np.abs(cs.gain_grids[ant])), aspect='auto', cmap='inferno',
interpolation='nearest', vmin=0, vmax=vmax, extent=extent)
ax.set_title(f'Smoothcal Gain Amplitude of Antenna {ant[0]}: {pol[-1]}-polarized' )
ax.set_xlabel('Frequency (MHz)')
ax.set_ylabel('LST (Hours)')
ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
ax.set_yticklabels(ax.get_yticks() % 24)
plt.colorbar(im, ax=ax, orientation='horizontal', pad=.15)
# Now flagged plot abscal waterfall
for ax, pol in zip(axes[1], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
extent=[cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
im = ax.imshow(np.where(cs.flag_grids[ant], np.nan, np.abs(abscal_gains[ant])), aspect='auto', cmap='inferno',
interpolation='nearest', vmin=0, vmax=vmax, extent=extent)
ax.set_title(f'Abscal Gain Amplitude of Antenna {ant[0]}: {pol[-1]}-polarized' )
ax.set_xlabel('Frequency (MHz)')
ax.set_ylabel('LST (Hours)')
ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
ax.set_yticklabels(ax.get_yticks() % 24)
plt.colorbar(im, ax=ax, orientation='horizontal', pad=.15)
# Now plot mean gain spectra
for ax, pol in zip(axes[2], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
nflags_spectrum = np.sum(cs.flag_grids[ant], axis=0)
to_plot = nflags_spectrum <= np.percentile(nflags_spectrum, 75)
ax.plot(cs.freqs[to_plot] / 1e6, np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.abs(abscal_gains[ant])), axis=0)[to_plot], 'r.', label='Abscal')
ax.plot(cs.freqs[to_plot] / 1e6, np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.abs(cs.gain_grids[ant])), axis=0)[to_plot], 'k.', ms=2, label='Smoothed')
ax.set_ylim([0, vmax])
ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
ax.set_xlabel('Frequency (MHz)')
ax.set_ylabel('|g| (unitless)')
ax.set_title(f'Mean Infrequently-Flagged Gain Amplitude of Antenna {ant[0]}: {pol[-1]}-polarized')
ax.legend(loc='upper left')
# Now plot mean gain time series
for ax, pol in zip(axes[3], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
nflags_series = np.sum(cs.flag_grids[ant], axis=1)
to_plot = nflags_series <= np.percentile(nflags_series, 75)
ax.plot(lst_grid[to_plot], np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.abs(abscal_gains[ant])), axis=1)[to_plot], 'r.', label='Abscal')
ax.plot(lst_grid[to_plot], np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.abs(cs.gain_grids[ant])), axis=1)[to_plot], 'k.', ms=2, label='Smoothed')
ax.set_ylim([0, vmax])
ax.set_xlabel('LST (hours)')
ax.set_ylabel('|g| (unitless)')
ax.set_title(f'Mean Infrequently-Flagged Gain Amplitude of Antenna {ant[0]}: {pol[-1]}-polarized')
ax.set_xticklabels(ax.get_xticks() % 24)
ax.legend(loc='upper left')
plt.tight_layout()
plt.show()
In [23]:
def phase_plot(ant_to_plot):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
display(HTML(f'<h2>Antenna {ant_to_plot} Phase Waterfalls</h2>'))
fig, axes = plt.subplots(4, 2, figsize=(14,14), gridspec_kw={'height_ratios': [1, 1, .4, .4]})
# Plot phase waterfalls for a single antenna
for ax, pol in zip(axes[0], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
extent=[cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
im = ax.imshow(np.where(cs.flag_grids[ant], np.nan, np.angle(cs.gain_grids[ant])), aspect='auto', cmap='inferno',
interpolation='nearest', vmin=-np.pi, vmax=np.pi, extent=extent)
refant = (cs.refant[pol] if isinstance(cs.refant, dict) else cs.refant)
ax.set_title(f'Smoothcal Gain Phase of Ant {ant[0]}{pol[-1]} / Ant {refant[0]}{refant[1][-1]}')
ax.set_xlabel('Frequency (MHz)')
ax.set_ylabel('LST (Hours)')
ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
ax.set_yticklabels(ax.get_yticks() % 24)
plt.colorbar(im, ax=ax, orientation='horizontal', pad=.15)
# Now plot abscal phase waterfall
for ax, pol in zip(axes[1], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
extent=[cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
im = ax.imshow(np.where(cs.flag_grids[ant], np.nan, np.angle(abscal_gains[ant])), aspect='auto', cmap='inferno',
interpolation='nearest', vmin=-np.pi, vmax=np.pi, extent=extent)
refant = (cs.refant[pol] if isinstance(cs.refant, dict) else cs.refant)
ax.set_title(f'Abscal Gain Phase of Ant {ant[0]}{pol[-1]} / Ant {refant[0]}{refant[1][-1]}')
ax.set_xlabel('Frequency (MHz)')
ax.set_ylabel('LST (Hours)')
ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
ax.set_yticklabels(ax.get_yticks() % 24)
plt.colorbar(im, ax=ax, orientation='horizontal', pad=.15)
# Now plot median gain spectra
for ax, pol in zip(axes[2], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
nflags_spectrum = np.sum(cs.flag_grids[ant], axis=0)
to_plot = nflags_spectrum <= np.percentile(nflags_spectrum, 75)
ax.plot(cs.freqs[to_plot] / 1e6, np.nanmedian(np.where(cs.flag_grids[ant], np.nan, np.angle(abscal_gains[ant])), axis=0)[to_plot], 'r.', label='Abscal')
ax.plot(cs.freqs[to_plot] / 1e6, np.nanmedian(np.where(cs.flag_grids[ant], np.nan, np.angle(cs.gain_grids[ant])), axis=0)[to_plot], 'k.', ms=2, label='Smoothed')
ax.set_ylim([-np.pi, np.pi])
ax.set_xlim([cs.freqs[0]/1e6, cs.freqs[-1]/1e6])
ax.set_xlabel('Frequency (MHz)')
refant = (cs.refant[pol] if isinstance(cs.refant, dict) else cs.refant)
ax.set_ylabel(f'Phase of g$_{{{ant[0]}{pol[-1]}}}$ / g$_{{{refant[0]}{refant[1][-1]}}}$')
ax.set_title(f'Median Infrequently-Flagged Gain Phase of Ant {ant[0]}{pol[-1]} / Ant {refant[0]}{refant[1][-1]}')
ax.legend(loc='upper left')
# # Now plot median gain time series
for ax, pol in zip(axes[3], ['Jee', 'Jnn']):
ant = (ant_to_plot, pol)
nflags_series = np.sum(cs.flag_grids[ant], axis=1)
to_plot = nflags_series <= np.percentile(nflags_series, 75)
ax.plot(lst_grid[to_plot], np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.angle(abscal_gains[ant])), axis=1)[to_plot], 'r.', label='Abscal')
ax.plot(lst_grid[to_plot], np.nanmean(np.where(cs.flag_grids[ant], np.nan, np.angle(cs.gain_grids[ant])), axis=1)[to_plot], 'k.', ms=2, label='Smoothed')
ax.set_ylim([-np.pi, np.pi])
ax.set_xlabel('LST (hours)')
refant = (cs.refant[pol] if isinstance(cs.refant, dict) else cs.refant)
ax.set_ylabel(f'Phase of g$_{{{ant[0]}{pol[-1]}}}$ / g$_{{{refant[0]}{refant[1][-1]}}}$')
ax.set_title(f'Mean Infrequently-Flagged Gain Phase of Ant {ant[0]}{pol[-1]} / Ant {refant[0]}{refant[1][-1]}')
ax.set_xticklabels(ax.get_xticks() % 24)
ax.legend(loc='upper left')
plt.tight_layout()
plt.show()
Figure 3: Full-Day Gain Amplitudes Before and After smooth_cal¶
Here we plot abscal and smooth_cal gain amplitudes for both of the sample antennas. We also show means across time/frequency, excluding frequencies/times that are frequently flagged.
In [24]:
for ant_to_plot in ants_to_plot:
amplitude_plot(ant_to_plot)
Antenna 128 Amplitude Waterfalls
Antenna 191 Amplitude Waterfalls
Figure 4: Full-Day Gain Phases Before and After smooth_cal¶
Here we plot abscal and smooth_cal phases relative to each polarization's reference antenna for both of the sample antennas. We also show medians across time/frequency, excluding frequencies/times that are frequently flagged.
In [25]:
for ant_to_plot in ants_to_plot:
phase_plot(ant_to_plot)
Antenna 128 Phase Waterfalls
Antenna 191 Phase Waterfalls
Examine $\chi^2$¶
In [26]:
def chisq_plot():
fig, axes = plt.subplots(1, 2, figsize=(14, 10), sharex=True, sharey=True)
extent = [cs.freqs[0]/1e6, cs.freqs[-1]/1e6, lst_grid[-1], lst_grid[0]]
for ax, pol in zip(axes, ['Jee', 'Jnn']):
refant = (cs.refant[pol] if isinstance(cs.refant, dict) else cs.refant)
im = ax.imshow(np.where(cs.flag_grids[refant], np.nan, cs.chisq_grids[pol]), vmin=1, vmax=5,
aspect='auto', cmap='turbo', interpolation='none', extent=extent)
ax.set_yticklabels(ax.get_yticks() % 24)
ax.set_title(f'{pol[1:]}-Polarized $\\chi^2$ / DoF')
ax.set_xlabel('Frequency (MHz)')
axes[0].set_ylabel('LST (hours)')
plt.tight_layout()
fig.colorbar(im, ax=axes, pad=.07, label='$\\chi^2$ / DoF', orientation='horizontal', extend='both', aspect=50)
Figure 5: Full-Day $\chi^2$ / DoF Waterfall from Redundant-Baseline Calibration¶
Here we plot $\chi^2$ per degree of freedom from redundant-baseline calibration for both polarizations separately. While this plot is a little out of place, as it was not produced by this notebook, it is a convenient place where all the necessary components are readily available. If the array were perfectly redundant and any non-redundancies in the calibrated visibilities were explicable by thermal noise alone, this waterfall should be all 1.
In [27]:
chisq_plot()
set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
In [28]:
def cspa_vs_time_plot():
fig, axes = plt.subplots(2, 1, figsize=(14, 6), sharex=True, sharey=True, gridspec_kw={'hspace': 0})
for ax, pol in zip(axes, ['Jee', 'Jnn']):
detail_cutoff = np.percentile([np.nanmean(m) for ant, m in avg_cspa_vs_time.items()
if ant[1] == pol and np.isfinite(np.nanmean(m))], 95)
for ant in avg_cspa_vs_time:
if ant[1] == pol and not np.all(cs.flag_grids[ant]):
if np.nanmean(avg_cspa_vs_time[ant]) > detail_cutoff:
ax.plot(lst_grid, avg_cspa_vs_time[ant], label=str((int(ant[0]), ant[1])), zorder=100)
else:
ax.plot(lst_grid, avg_cspa_vs_time[ant], c='grey', alpha=.2, lw=.5)
ax.legend(title=f'{pol[1:]}-Polarized', ncol=2)
ax.set_ylabel('Mean Unflagged $\\chi^2$ per Antenna')
ax.set_xlabel('LST (hours)')
ax.set_xticklabels(ax.get_xticks() % 24)
plt.ylim([1, 5.4])
plt.tight_layout()
In [29]:
def cspa_vs_freq_plot():
fig, axes = plt.subplots(2, 1, figsize=(14, 6), sharex=True, sharey=True, gridspec_kw={'hspace': 0})
for ax, pol in zip(axes, ['Jee', 'Jnn']):
detail_cutoff = np.percentile([np.nanmean(m) for ant, m in avg_cspa_vs_freq.items()
if ant[1] == pol and np.isfinite(np.nanmean(m))], 95)
for ant in avg_cspa_vs_freq:
if ant[1] == pol and not np.all(cs.flag_grids[ant]):
if np.nanmean(avg_cspa_vs_freq[ant]) > detail_cutoff:
ax.plot(cs.freqs / 1e6, avg_cspa_vs_freq[ant], label=str((int(ant[0]), ant[1])), zorder=100)
else:
ax.plot(cs.freqs / 1e6, avg_cspa_vs_freq[ant], c='grey', alpha=.2, lw=.5)
ax.legend(title=f'{pol[1:]}-Polarized', ncol=2)
ax.set_ylabel('Mean Unflagged $\\chi^2$ per Antenna')
ax.set_xlabel('Frequency (MHz)')
plt.ylim([1, 5.4])
plt.tight_layout()
In [30]:
def avg_cspa_array_plot():
hd = io.HERAData(SUM_FILE)
fig, axes = plt.subplots(1, 2, figsize=(14, 8), sharex=True, sharey=True, gridspec_kw={'wspace': 0})
for pol, ax in zip(['Jee', 'Jnn'], axes):
ants_here = [ant for ant in avg_cspa if np.isfinite(avg_cspa[ant]) and ant[1] == pol if ant[0] in hd.antpos]
avg_chisqs = [avg_cspa[ant] for ant in ants_here]
xs = [hd.antpos[ant[0]][0] for ant in ants_here]
ys = [hd.antpos[ant[0]][1] for ant in ants_here]
names = [ant[0] for ant in ants_here]
im = ax.scatter(x=xs, y=ys, c=avg_chisqs, s=200, vmin=1, vmax=3, cmap='turbo')
ax.set_aspect('equal')
for x,y,n in zip(xs, ys, names):
ax.text(x, y, str(n), va='center', ha='center', fontsize=8)
ax.set_title(pol)
ax.set_xlabel('East-West Antenna Position (m)')
axes[0].set_ylabel('North-South Antenna Position (m)')
plt.tight_layout()
plt.colorbar(im, ax=axes, location='top', aspect=60, pad=.04, label='Mean Unflagged $\\chi^2$ per Antenna', extend='both')
Figure 6: Average $\chi^2$ per Antenna¶
Here we plot $\chi^2$ per antenna from redundant-baseline calibration, separating polarizations and averaging the unflagged pixels in the waterfalls over frequency or time. The worst 5% of antennas are shown in color and highlighted in the legends, the rest are shown in grey. We also show time- and frequency-averaged $\chi^2$ for each antennas as a scatter plot with array position.
In [31]:
cspa_vs_freq_plot()
cspa_vs_time_plot()
avg_cspa_array_plot()
Mean of empty slice
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. Mean of empty slice No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
Examine relative differences before and after smoothing¶
In [32]:
def time_avg_diff_plot():
fig, axes = plt.subplots(2, 1, figsize=(14, 6), sharex=True, sharey=True, gridspec_kw={'hspace': 0})
for ax, pol in zip(axes, ['Jee', 'Jnn']):
detail_cutoff = np.percentile([np.nanmean(diff) for ant, diff in meta['time_avg_rel_diff'].items()
if ant[1] == pol and np.isfinite(np.nanmean(diff))], 95)
for ant, rel_diff in meta['time_avg_rel_diff'].items():
if ant[0] >= 0 and ant[1] == pol and np.any(np.isfinite(rel_diff)):
if np.nanmean(rel_diff) > detail_cutoff:
if np.all(cs.flag_grids[ant]):
ax.plot(cs.freqs / 1e6, rel_diff, label=str((int(ant[0]), ant[1])), zorder=99, ls='--', c='r', lw=.5)
else:
ax.plot(cs.freqs / 1e6, rel_diff, label=str((int(ant[0]), ant[1])), zorder=100)
else:
ax.plot(cs.freqs / 1e6, rel_diff, c='grey', alpha=.2, lw=.5)
med_rel_diff = np.nanmedian([diff for ant, diff in meta['time_avg_rel_diff'].items() if ant[1] == pol], axis=0)
ax.plot(cs.freqs / 1e6, med_rel_diff, 'k--', label='Median')
ax.set_ylim([0, 1.05])
ax.legend(title=f'{pol[1:]}-Polarized', ncol=2)
ax.set_ylabel('Time-Averaged Relative Difference\nBefore and After Smoothing')
ax.set_xlabel('Frequency (MHz)')
plt.tight_layout()
In [33]:
def freq_avg_diff_plot():
fig, axes = plt.subplots(2, 1, figsize=(14, 6), sharex=True, sharey=True, gridspec_kw={'hspace': 0})
for ax, pol in zip(axes, ['Jee', 'Jnn']):
detail_cutoff = np.percentile([np.nanmean(m) for ant, m in meta['freq_avg_rel_diff'].items()
if ant[1] == pol and np.isfinite(np.nanmean(m))], 95)
for ant, rel_diff in meta['freq_avg_rel_diff'].items():
if ant[0] >= 0 and ant[1] == pol and np.any(np.isfinite(rel_diff)):
if np.nanmean(rel_diff) > detail_cutoff:
if np.all(cs.flag_grids[ant]):
ax.plot(lst_grid, rel_diff, label=str((int(ant[0]), ant[1])), zorder=99, ls='--', c='r', lw=.5)
else:
ax.plot(lst_grid, rel_diff, label=str((int(ant[0]), ant[1])), zorder=100)
else:
ax.plot(lst_grid, rel_diff, c='grey', alpha=.2, lw=.5)
med_rel_diff = np.nanmedian([diff for ant, diff in meta['freq_avg_rel_diff'].items() if ant[1] == pol], axis=0)
ax.plot(lst_grid, med_rel_diff, 'k--', label='Median', zorder=101)
ax.set_ylim([0, 1.05])
ax.legend(title=f'{pol[1:]}-Polarized', ncol=2)
ax.set_ylabel('Frequency-Averaged Relative Difference\nBefore and After Smoothing')
ax.set_xlabel('LST (hours)')
ax.set_xticklabels(ax.get_xticks() % 24)
plt.tight_layout()
In [34]:
def avg_difference_array_plot():
hd = io.HERAData(SUM_FILE)
fig, axes = plt.subplots(1, 2, figsize=(14, 8), sharex=True, sharey=True, gridspec_kw={'wspace': 0})
for pol, ax in zip(['Jee', 'Jnn'], axes):
avg_diffs = [np.nanmean(meta['time_avg_rel_diff'][ant]) for ant in meta['time_avg_rel_diff'] if ant[1] == pol if ant[0] in hd.antpos]
xs = [hd.antpos[ant[0]][0] for ant in meta['time_avg_rel_diff'] if ant[1] == pol if ant[0] in hd.antpos]
ys = [hd.antpos[ant[0]][1] for ant in meta['time_avg_rel_diff'] if ant[1] == pol if ant[0] in hd.antpos]
names = [ant[0] for ant in meta['time_avg_rel_diff'] if ant[1] == pol if ant[0] in hd.antpos]
im = ax.scatter(x=xs, y=ys, c=avg_diffs, s=200, vmin=0, vmax=.25, cmap='turbo')
ax.set_aspect('equal')
for x,y,n in zip(xs, ys, names):
color = ('w' if np.all(cs.flag_grids[n, pol]) else 'k')
ax.text(x, y, str(n), va='center', ha='center', fontsize=8, c=color)
ax.set_title(pol)
ax.set_xlabel('East-West Antenna Position (m)')
axes[0].set_ylabel('North-South Antenna Position (m)')
plt.tight_layout()
plt.colorbar(im, ax=axes, location='top', aspect=60, pad=.04, label='Average Relative Difference Before and After Smoothing', extend='max')
Figure 7: Relative Difference Before and After Smoothing¶
Similar to the above plots, here we show the relative difference before and after smoothing, compared to the magnitude of the smoothed calibration solution. Totally flagged antennas (because they are above the SC_RELATIVE_DIFF_CUTOFF) are red in the first two plots, and their numbers are white in the last plot.
In [35]:
time_avg_diff_plot()
freq_avg_diff_plot()
avg_difference_array_plot()
All-NaN slice encountered
All-NaN slice encountered set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. All-NaN slice encountered set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
Save Results¶
In [36]:
add_to_history = 'Produced by calibration_smoothing notebook with the following environment:\n' + '=' * 65 + '\n' + os.popen('conda env export').read() + '=' * 65
In [37]:
cs.write_smoothed_cal(output_replace=(CAL_SUFFIX, SMOOTH_CAL_SUFFIX), add_to_history=add_to_history, clobber=True)
Mean of empty slice
Metadata¶
In [38]:
for repo in ['hera_cal', 'hera_qm', 'hera_filters', 'hera_notebook_templates', 'pyuvdata']:
exec(f'from {repo} import __version__')
print(f'{repo}: {__version__}')
hera_cal: 3.7.7.dev67+g3ed1b8028 hera_qm: 2.2.1.dev4+gf6d0211 hera_filters: 0.1.7
hera_notebook_templates: 0.0.1.dev1269+g4fd50fce5 pyuvdata: 3.2.3.dev10+g11d3f658
In [39]:
print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')
Finished execution in 181.27 minutes.