Calibration Smoothing¶
by Josh Dillon, last updated December 20, 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 = "/mnt/sn1/data1/2461149/zen.2461149.19005.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 = 10.0 TIME_SMOOTHING_SCALE = 600000.0 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]:
hd = io.HERAData(SUM_FILE)
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 2130 *.sum.omni.calfits files starting with /mnt/sn1/data1/2461149/zen.2461149.19005.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 2130 *.sum.flag_waterfall.h5 files starting with /mnt/sn1/data1/2461149/zen.2461149.19005.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 2130 *.sum.antenna_flags.h5 files starting with /mnt/sn1/data1/2461149/zen.2461149.19005.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)
invalid value encountered in multiply
In [8]:
# Pick reference antenna(s) but don't let ants known to flip phases get picked as reference antennas
banned_refants = [(144, 'Jnn'), (121, 'Jee'), (71, 'Jnn')]
cs.refant = smooth_cal.pick_reference_antenna({ant: cs.gain_grids[ant] for ant in cs.gain_grids if ant not in banned_refants},
{ant: cs.flag_grids[ant] for ant in cs.gain_grids if ant not in banned_refants},
cs.freqs, per_pol=True, acceptable_candidate_frac=0.25, antpos=hd.antpos)
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 123 selected for smoothing Jnn gains. Reference antenna 123 selected for smoothing Jee gains.
Overall reference antenna (np.int64(123), '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,
phase_flip_time_scale = TIME_SMOOTHING_SCALE / 2,
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()]
# pick candidates that don't exhibit too many flags or non-smooth structure on first pass
candidate_ants = []
rel_error_factor = 1
while len(candidate_ants) < 6: # Select more candidates to ensure we have enough after potential flagging
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 * rel_error_factor)
and not np.all(cs.flag_grids[ant, 'Jee']) and not np.all(cs.flag_grids[ant, 'Jnn'])]
rel_error_factor += .1
# choose antennas to plot: select up to 6 candidates, prioritizing diversity across antenna numbers
candidate_ants_sorted = sorted(candidate_ants)
step = max(1, len(candidate_ants_sorted) // 6) # spread them out
_candidates = sorted(candidate_ants_sorted[::step][:6])
ants_to_plot_candidates = [_candidates[i//2] if i % 2 == 0 else _candidates[-(i//2)-1] for i in range(len(_candidates))]
# Store abscal gains for all candidates
abscal_gains = {}
for pol in ['Jee', 'Jnn']:
for antnum in ants_to_plot_candidates:
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,
phase_flip_time_scale = TIME_SMOOTHING_SCALE / 2,
flag_phase_flip_ints=True,
skip_flagged_edges=True,
freq_cuts=[(FM_LOW_FREQ + FM_HIGH_FREQ) * .5e6],)
1 phase flips detected on antenna (np.int64(80), 'Jee'). A total of 3951 integrations were phase-flipped relative to the 0th integration between 2461149.22461231 and 2461149.666412368.
2 phase flips detected on antenna (np.int64(176), 'Jnn'). A total of 1 integrations were phase-flipped relative to the 0th integration between 2461149.58554618 and 2461149.58554618.
1 phase flips detected on antenna (np.int64(214), 'Jnn'). A total of 3909 integrations were phase-flipped relative to the 0th integration between 2461149.229309931 and 2461149.666412368.
1 phase flips detected on antenna (np.int64(214), 'Jee'). A total of 3925 integrations were phase-flipped relative to the 0th integration between 2461149.227520361 and 2461149.666412368.
1 phase flips detected on antenna (np.int64(115), 'Jee'). A total of 3940 integrations were phase-flipped relative to the 0th integration between 2461149.2258426393 and 2461149.666412368.
1 phase flips detected on antenna (np.int64(133), 'Jee'). A total of 3942 integrations were phase-flipped relative to the 0th integration between 2461149.225618943 and 2461149.666412368.
1 phase flips detected on antenna (np.int64(195), 'Jee'). A total of 3917 integrations were phase-flipped relative to the 0th integration between 2461149.228415146 and 2461149.666412368.
1 phase flips detected on antenna (np.int64(154), 'Jee'). A total of 3915 integrations were phase-flipped relative to the 0th integration between 2461149.2286388422 and 2461149.666412368.
1 phase flips detected on antenna (np.int64(97), 'Jee'). A total of 3932 integrations were phase-flipped relative to the 0th integration between 2461149.2267374243 and 2461149.666412368.
1 phase flips detected on antenna (np.int64(153), 'Jee'). A total of 3921 integrations were phase-flipped relative to the 0th integration between 2461149.2279677535 and 2461149.666412368.
1 phase flips detected on antenna (np.int64(174), 'Jee'). A total of 3920 integrations were phase-flipped relative to the 0th integration between 2461149.2280796017 and 2461149.666412368.
1 phase flips detected on antenna (np.int64(175), 'Jee'). A total of 3917 integrations were phase-flipped relative to the 0th integration between 2461149.228415146 and 2461149.666412368.
16 phase flips detected on antenna (np.int64(114), 'Jnn'). A total of 11 integrations were phase-flipped relative to the 0th integration between 2461149.5723481025 and 2461149.5827499772.
30 phase flips detected on antenna (np.int64(80), 'Jnn'). A total of 25 integrations were phase-flipped relative to the 0th integration between 2461149.5715651657 and 2461149.5830855216.
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 3e with a relative difference before and after smoothing of 33.44% (compared to the 20.00% cutoff). Flagging antenna 3n with a relative difference before and after smoothing of 35.63% (compared to the 20.00% cutoff). Flagging antenna 5n with a relative difference before and after smoothing of 29.87% (compared to the 20.00% cutoff). Flagging antenna 7e with a relative difference before and after smoothing of 35.27% (compared to the 20.00% cutoff). Flagging antenna 9e with a relative difference before and after smoothing of 42.72% (compared to the 20.00% cutoff). Flagging antenna 9n with a relative difference before and after smoothing of 40.94% (compared to the 20.00% cutoff). Flagging antenna 16n with a relative difference before and after smoothing of 26.28% (compared to the 20.00% cutoff). Flagging antenna 17e with a relative difference before and after smoothing of 27.82% (compared to the 20.00% cutoff). Flagging antenna 21e with a relative difference before and after smoothing of 38.28% (compared to the 20.00% cutoff). Flagging antenna 30e with a relative difference before and after smoothing of 26.23% (compared to the 20.00% cutoff). Flagging antenna 31n with a relative difference before and after smoothing of 27.12% (compared to the 20.00% cutoff). Flagging antenna 32e with a relative difference before and after smoothing of 26.69% (compared to the 20.00% cutoff). Flagging antenna 33e with a relative difference before and after smoothing of 37.74% (compared to the 20.00% cutoff). Flagging antenna 36e with a relative difference before and after smoothing of 27.22% (compared to the 20.00% cutoff). Flagging antenna 36n with a relative difference before and after smoothing of 28.96% (compared to the 20.00% cutoff). Flagging antenna 43e with a relative difference before and after smoothing of 21.97% (compared to the 20.00% cutoff). Flagging antenna 43n with a relative difference before and after smoothing of 20.75% (compared to the 20.00% cutoff). Flagging antenna 45e with a relative difference before and after smoothing of 23.46% (compared to the 20.00% cutoff). Flagging antenna 45n with a relative difference before and after smoothing of 27.00% (compared to the 20.00% cutoff). Flagging antenna 46e with a relative difference before and after smoothing of 30.14% (compared to the 20.00% cutoff). Flagging antenna 46n with a relative difference before and after smoothing of 29.52% (compared to the 20.00% cutoff). Flagging antenna 50e with a relative difference before and after smoothing of 29.12% (compared to the 20.00% cutoff). Flagging antenna 50n with a relative difference before and after smoothing of 35.69% (compared to the 20.00% cutoff). Flagging antenna 52n with a relative difference before and after smoothing of 24.11% (compared to the 20.00% cutoff). Flagging antenna 59n with a relative difference before and after smoothing of 21.58% (compared to the 20.00% cutoff). Flagging antenna 60e with a relative difference before and after smoothing of 27.39% (compared to the 20.00% cutoff). Flagging antenna 65e with a relative difference before and after smoothing of 27.82% (compared to the 20.00% cutoff). Flagging antenna 65n with a relative difference before and after smoothing of 33.42% (compared to the 20.00% cutoff). Flagging antenna 67e with a relative difference before and after smoothing of 21.16% (compared to the 20.00% cutoff). Flagging antenna 79e with a relative difference before and after smoothing of 44.26% (compared to the 20.00% cutoff). Flagging antenna 80e with a relative difference before and after smoothing of 66.91% (compared to the 20.00% cutoff). Flagging antenna 80n with a relative difference before and after smoothing of 40.10% (compared to the 20.00% cutoff). Flagging antenna 82n with a relative difference before and after smoothing of 28.18% (compared to the 20.00% cutoff). Flagging antenna 91e with a relative difference before and after smoothing of 23.28% (compared to the 20.00% cutoff). Flagging antenna 91n with a relative difference before and after smoothing of 22.79% (compared to the 20.00% cutoff). Flagging antenna 92e with a relative difference before and after smoothing of 26.68% (compared to the 20.00% cutoff). Flagging antenna 92n with a relative difference before and after smoothing of 29.25% (compared to the 20.00% cutoff). Flagging antenna 93e with a relative difference before and after smoothing of 30.02% (compared to the 20.00% cutoff). Flagging antenna 93n with a relative difference before and after smoothing of 35.33% (compared to the 20.00% cutoff). Flagging antenna 94e with a relative difference before and after smoothing of 30.66% (compared to the 20.00% cutoff). Flagging antenna 95e with a relative difference before and after smoothing of 35.24% (compared to the 20.00% cutoff). Flagging antenna 96e with a relative difference before and after smoothing of 43.83% (compared to the 20.00% cutoff). Flagging antenna 96n with a relative difference before and after smoothing of 51.03% (compared to the 20.00% cutoff). Flagging antenna 97e with a relative difference before and after smoothing of 70.41% (compared to the 20.00% cutoff). Flagging antenna 98e with a relative difference before and after smoothing of 23.71% (compared to the 20.00% cutoff). Flagging antenna 98n with a relative difference before and after smoothing of 32.54% (compared to the 20.00% cutoff). Flagging antenna 100e with a relative difference before and after smoothing of 22.39% (compared to the 20.00% cutoff).
Flagging antenna 100n with a relative difference before and after smoothing of 23.95% (compared to the 20.00% cutoff). Flagging antenna 113e with a relative difference before and after smoothing of 41.47% (compared to the 20.00% cutoff). Flagging antenna 113n with a relative difference before and after smoothing of 48.84% (compared to the 20.00% cutoff). Flagging antenna 114e with a relative difference before and after smoothing of 40.96% (compared to the 20.00% cutoff). Flagging antenna 114n with a relative difference before and after smoothing of 41.58% (compared to the 20.00% cutoff). Flagging antenna 115e with a relative difference before and after smoothing of 70.98% (compared to the 20.00% cutoff). Flagging antenna 116e with a relative difference before and after smoothing of 33.79% (compared to the 20.00% cutoff). Flagging antenna 116n with a relative difference before and after smoothing of 27.84% (compared to the 20.00% cutoff). Flagging antenna 117n with a relative difference before and after smoothing of 30.39% (compared to the 20.00% cutoff). Flagging antenna 118e with a relative difference before and after smoothing of 21.99% (compared to the 20.00% cutoff). Flagging antenna 119n with a relative difference before and after smoothing of 21.25% (compared to the 20.00% cutoff). Flagging antenna 129e with a relative difference before and after smoothing of 29.43% (compared to the 20.00% cutoff). Flagging antenna 129n with a relative difference before and after smoothing of 35.04% (compared to the 20.00% cutoff). Flagging antenna 130e with a relative difference before and after smoothing of 29.88% (compared to the 20.00% cutoff). Flagging antenna 130n with a relative difference before and after smoothing of 40.98% (compared to the 20.00% cutoff). Flagging antenna 131e with a relative difference before and after smoothing of 38.41% (compared to the 20.00% cutoff). Flagging antenna 131n with a relative difference before and after smoothing of 39.67% (compared to the 20.00% cutoff). Flagging antenna 132e with a relative difference before and after smoothing of 39.71% (compared to the 20.00% cutoff). Flagging antenna 132n with a relative difference before and after smoothing of 45.52% (compared to the 20.00% cutoff). Flagging antenna 133e with a relative difference before and after smoothing of 67.14% (compared to the 20.00% cutoff). Flagging antenna 133n with a relative difference before and after smoothing of 64.59% (compared to the 20.00% cutoff). Flagging antenna 135n with a relative difference before and after smoothing of 37.35% (compared to the 20.00% cutoff). Flagging antenna 136e with a relative difference before and after smoothing of 26.62% (compared to the 20.00% cutoff). Flagging antenna 138n with a relative difference before and after smoothing of 23.90% (compared to the 20.00% cutoff). Flagging antenna 146n with a relative difference before and after smoothing of 25.29% (compared to the 20.00% cutoff). Flagging antenna 147n with a relative difference before and after smoothing of 28.48% (compared to the 20.00% cutoff). Flagging antenna 148e with a relative difference before and after smoothing of 28.52% (compared to the 20.00% cutoff). Flagging antenna 148n with a relative difference before and after smoothing of 33.13% (compared to the 20.00% cutoff). Flagging antenna 149e with a relative difference before and after smoothing of 32.02% (compared to the 20.00% cutoff). Flagging antenna 149n with a relative difference before and after smoothing of 38.95% (compared to the 20.00% cutoff). Flagging antenna 150e with a relative difference before and after smoothing of 35.62% (compared to the 20.00% cutoff). Flagging antenna 150n with a relative difference before and after smoothing of 44.59% (compared to the 20.00% cutoff). Flagging antenna 152e with a relative difference before and after smoothing of 46.02% (compared to the 20.00% cutoff). Flagging antenna 152n with a relative difference before and after smoothing of 63.20% (compared to the 20.00% cutoff). Flagging antenna 153e with a relative difference before and after smoothing of 65.93% (compared to the 20.00% cutoff). Flagging antenna 154e with a relative difference before and after smoothing of 72.24% (compared to the 20.00% cutoff). Flagging antenna 155e with a relative difference before and after smoothing of 37.14% (compared to the 20.00% cutoff). Flagging antenna 155n with a relative difference before and after smoothing of 33.50% (compared to the 20.00% cutoff). Flagging antenna 156e with a relative difference before and after smoothing of 25.42% (compared to the 20.00% cutoff). Flagging antenna 157e with a relative difference before and after smoothing of 22.25% (compared to the 20.00% cutoff). Flagging antenna 157n with a relative difference before and after smoothing of 23.29% (compared to the 20.00% cutoff). Flagging antenna 158e with a relative difference before and after smoothing of 24.57% (compared to the 20.00% cutoff). Flagging antenna 167e with a relative difference before and after smoothing of 22.37% (compared to the 20.00% cutoff). Flagging antenna 173e with a relative difference before and after smoothing of 42.43% (compared to the 20.00% cutoff). Flagging antenna 174e with a relative difference before and after smoothing of 65.96% (compared to the 20.00% cutoff). Flagging antenna 175e with a relative difference before and after smoothing of 68.44% (compared to the 20.00% cutoff). Flagging antenna 176n with a relative difference before and after smoothing of 37.90% (compared to the 20.00% cutoff). Flagging antenna 177e with a relative difference before and after smoothing of 29.75% (compared to the 20.00% cutoff).
Flagging antenna 177n with a relative difference before and after smoothing of 32.29% (compared to the 20.00% cutoff). Flagging antenna 178e with a relative difference before and after smoothing of 22.24% (compared to the 20.00% cutoff). Flagging antenna 179e with a relative difference before and after smoothing of 21.86% (compared to the 20.00% cutoff). Flagging antenna 179n with a relative difference before and after smoothing of 23.27% (compared to the 20.00% cutoff). Flagging antenna 187e with a relative difference before and after smoothing of 21.22% (compared to the 20.00% cutoff). Flagging antenna 188e with a relative difference before and after smoothing of 21.01% (compared to the 20.00% cutoff). Flagging antenna 189e with a relative difference before and after smoothing of 27.12% (compared to the 20.00% cutoff). Flagging antenna 190e with a relative difference before and after smoothing of 26.85% (compared to the 20.00% cutoff). Flagging antenna 190n with a relative difference before and after smoothing of 42.13% (compared to the 20.00% cutoff). Flagging antenna 191e with a relative difference before and after smoothing of 37.59% (compared to the 20.00% cutoff). Flagging antenna 191n with a relative difference before and after smoothing of 55.46% (compared to the 20.00% cutoff). Flagging antenna 192e with a relative difference before and after smoothing of 43.41% (compared to the 20.00% cutoff). Flagging antenna 193e with a relative difference before and after smoothing of 48.68% (compared to the 20.00% cutoff). Flagging antenna 193n with a relative difference before and after smoothing of 54.51% (compared to the 20.00% cutoff). Flagging antenna 195e with a relative difference before and after smoothing of 71.43% (compared to the 20.00% cutoff). Flagging antenna 206e with a relative difference before and after smoothing of 23.82% (compared to the 20.00% cutoff). Flagging antenna 210e with a relative difference before and after smoothing of 36.68% (compared to the 20.00% cutoff). Flagging antenna 210n with a relative difference before and after smoothing of 39.67% (compared to the 20.00% cutoff). Flagging antenna 211n with a relative difference before and after smoothing of 47.16% (compared to the 20.00% cutoff). Flagging antenna 213e with a relative difference before and after smoothing of 43.34% (compared to the 20.00% cutoff). Flagging antenna 213n with a relative difference before and after smoothing of 64.42% (compared to the 20.00% cutoff). Flagging antenna 214e with a relative difference before and after smoothing of 76.72% (compared to the 20.00% cutoff). Flagging antenna 214n with a relative difference before and after smoothing of 77.69% (compared to the 20.00% cutoff). Flagging antenna 216e with a relative difference before and after smoothing of 29.63% (compared to the 20.00% cutoff). Flagging antenna 216n with a relative difference before and after smoothing of 24.95% (compared to the 20.00% cutoff). Flagging antenna 217n with a relative difference before and after smoothing of 26.10% (compared to the 20.00% cutoff). Flagging antenna 220n with a relative difference before and after smoothing of 20.12% (compared to the 20.00% cutoff). Flagging antenna 223n with a relative difference before and after smoothing of 24.17% (compared to the 20.00% cutoff). Flagging antenna 225n with a relative difference before and after smoothing of 36.17% (compared to the 20.00% cutoff). Flagging antenna 226e with a relative difference before and after smoothing of 27.80% (compared to the 20.00% cutoff). Flagging antenna 226n with a relative difference before and after smoothing of 29.92% (compared to the 20.00% cutoff). Flagging antenna 228e with a relative difference before and after smoothing of 29.20% (compared to the 20.00% cutoff). Flagging antenna 228n with a relative difference before and after smoothing of 56.17% (compared to the 20.00% cutoff). Flagging antenna 229e with a relative difference before and after smoothing of 41.21% (compared to the 20.00% cutoff). Flagging antenna 229n with a relative difference before and after smoothing of 55.70% (compared to the 20.00% cutoff). Flagging antenna 233e with a relative difference before and after smoothing of 23.45% (compared to the 20.00% cutoff). Flagging antenna 237e with a relative difference before and after smoothing of 22.26% (compared to the 20.00% cutoff). Flagging antenna 237n with a relative difference before and after smoothing of 23.20% (compared to the 20.00% cutoff). Flagging antenna 238n with a relative difference before and after smoothing of 22.66% (compared to the 20.00% cutoff). Flagging antenna 239e with a relative difference before and after smoothing of 20.89% (compared to the 20.00% cutoff). Flagging antenna 241e with a relative difference before and after smoothing of 24.66% (compared to the 20.00% cutoff). Flagging antenna 241n with a relative difference before and after smoothing of 33.67% (compared to the 20.00% cutoff). Flagging antenna 242e with a relative difference before and after smoothing of 26.91% (compared to the 20.00% cutoff). Flagging antenna 242n with a relative difference before and after smoothing of 24.71% (compared to the 20.00% cutoff). Flagging antenna 243n with a relative difference before and after smoothing of 45.18% (compared to the 20.00% cutoff). Flagging antenna 244e with a relative difference before and after smoothing of 32.33% (compared to the 20.00% cutoff). Flagging antenna 244n with a relative difference before and after smoothing of 48.32% (compared to the 20.00% cutoff).
Flagging antenna 245e with a relative difference before and after smoothing of 35.94% (compared to the 20.00% cutoff). Flagging antenna 245n with a relative difference before and after smoothing of 37.59% (compared to the 20.00% cutoff). Flagging antenna 246n with a relative difference before and after smoothing of 46.22% (compared to the 20.00% cutoff). Flagging antenna 252e with a relative difference before and after smoothing of 22.14% (compared to the 20.00% cutoff). Flagging antenna 261e with a relative difference before and after smoothing of 40.46% (compared to the 20.00% cutoff). Flagging antenna 261n with a relative difference before and after smoothing of 45.64% (compared to the 20.00% cutoff). Flagging antenna 262e with a relative difference before and after smoothing of 43.72% (compared to the 20.00% cutoff). Flagging antenna 262n with a relative difference before and after smoothing of 51.56% (compared to the 20.00% cutoff). Flagging antenna 267n with a relative difference before and after smoothing of 34.26% (compared to the 20.00% cutoff). Flagging antenna 270e with a relative difference before and after smoothing of 21.36% (compared to the 20.00% cutoff). Flagging antenna 272e with a relative difference before and after smoothing of 29.97% (compared to the 20.00% cutoff). Flagging antenna 282e with a relative difference before and after smoothing of 33.77% (compared to the 20.00% cutoff). Flagging antenna 283e with a relative difference before and after smoothing of 25.67% (compared to the 20.00% cutoff). Flagging antenna 283n with a relative difference before and after smoothing of 28.57% (compared to the 20.00% cutoff). Flagging antenna 286e with a relative difference before and after smoothing of 25.04% (compared to the 20.00% cutoff). Flagging antenna 315e with a relative difference before and after smoothing of 45.55% (compared to the 20.00% cutoff). Flagging antenna 315n with a relative difference before and after smoothing of 44.18% (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]):
to_plot = np.angle(np.exp(1.0j * (meta['phases'][ant] - meta['time_smoothed_phases'][ant])))
to_plot[to_plot < -np.pi / 2] += 2 * np.pi
plt.plot(cs.time_grid - int(cs.time_grid[0]), to_plot, 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()
In [24]:
# Select first 2 unflagged antennas from candidates for amplitude plotting
ants_to_plot = []
for ant_candidate in ants_to_plot_candidates:
if not (np.all(cs.flag_grids[ant_candidate, 'Jee']) and np.all(cs.flag_grids[ant_candidate, 'Jnn'])):
ants_to_plot.append(ant_candidate)
if len(ants_to_plot) >= 2:
break
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 [25]:
if len(ants_to_plot) == 0:
print("Warning: No unflagged antennas available for plotting.")
else:
for ant_to_plot in ants_to_plot:
amplitude_plot(ant_to_plot)
Antenna 55 Amplitude Waterfalls
Antenna 124 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 [26]:
# Use the same selected unflagged antennas for phase plotting
if len(ants_to_plot) == 0:
print("Warning: No unflagged antennas available for plotting.")
else:
for ant_to_plot in ants_to_plot:
phase_plot(ant_to_plot)
Antenna 55 Phase Waterfalls
Antenna 124 Phase Waterfalls
Examine $\chi^2$¶
In [27]:
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 [28]:
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 [29]:
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 [30]:
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 [31]:
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 [32]:
cspa_vs_freq_plot()
cspa_vs_time_plot()
avg_cspa_array_plot()
Mean of empty slice
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 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 [33]:
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 [34]:
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 [35]:
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 [36]:
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 [37]:
add_to_history = 'Produced by calibration_smoothing notebook with the following environment:\n' + '=' * 65 + '\n' + os.popen('conda env export').read() + '=' * 65
In [38]:
cs.write_smoothed_cal(output_replace=(CAL_SUFFIX, SMOOTH_CAL_SUFFIX), add_to_history=add_to_history, clobber=True)
Mean of empty slice
invalid value encountered in multiply invalid value encountered in divide
Metadata¶
In [39]:
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.dev97+gc2668d3f7 hera_qm: 2.2.1.dev4+gf6d02113b hera_filters: 0.1.7
hera_notebook_templates: 0.0.1.dev1313+g92178a09c pyuvdata: 3.2.5.dev1+g5a985ae31
In [40]:
print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')
Finished execution in 59.39 minutes.