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/data1/2461067/zen.2461067.25245.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 1758 *.sum.omni.calfits files starting with /mnt/sn1/data1/2461067/zen.2461067.25245.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 1758 *.sum.flag_waterfall.h5 files starting with /mnt/sn1/data1/2461067/zen.2461067.25245.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 1758 *.sum.antenna_flags.h5 files starting with /mnt/sn1/data1/2461067/zen.2461067.25245.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 56 selected for smoothing Jnn gains. Reference antenna 104 selected for smoothing Jee gains.
Overall reference antenna (np.int64(56), '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],)
14 phase flips detected on antenna (np.int64(113), 'Jee'). A total of 73 integrations were phase-flipped relative to the 0th integration between 2461067.4265939174 and 2461067.435653615.
8 phase flips detected on antenna (np.int64(173), 'Jee'). A total of 181 integrations were phase-flipped relative to the 0th integration between 2461067.422455537 and 2461067.4432592867.
8 phase flips detected on antenna (np.int64(79), 'Jee'). A total of 9 integrations were phase-flipped relative to the 0th integration between 2461067.4303967534 and 2461067.434870678.
18 phase flips detected on antenna (np.int64(214), 'Jee'). A total of 294 integrations were phase-flipped relative to the 0th integration between 2461067.4113825737 and 2461067.4457199452.
4 phase flips detected on antenna (np.int64(192), 'Jee'). A total of 58 integrations were phase-flipped relative to the 0th integration between 2461067.4283834873 and 2461067.4355417667.
12 phase flips detected on antenna (np.int64(231), 'Jnn'). A total of 84 integrations were phase-flipped relative to the 0th integration between 2461067.4264820693 and 2461067.436772096.
12 phase flips detected on antenna (np.int64(231), 'Jee'). A total of 186 integrations were phase-flipped relative to the 0th integration between 2461067.4217844484 and 2461067.4431474386.
2 phase flips detected on antenna (np.int64(214), 'Jnn'). A total of 191 integrations were phase-flipped relative to the 0th integration between 2461067.423238474 and 2461067.444489616.
10 phase flips detected on antenna (np.int64(152), 'Jee'). A total of 174 integrations were phase-flipped relative to the 0th integration between 2461067.4220081447 and 2461067.442588198.
10 phase flips detected on antenna (np.int64(193), 'Jnn'). A total of 49 integrations were phase-flipped relative to the 0th integration between 2461067.428271639 and 2461067.434646982.
10 phase flips detected on antenna (np.int64(152), 'Jnn'). A total of 21 integrations were phase-flipped relative to the 0th integration between 2461067.430061209 and 2461067.433864045.
16 phase flips detected on antenna (np.int64(134), 'Jee'). A total of 291 integrations were phase-flipped relative to the 0th integration between 2461067.4113825737 and 2461067.4448251603.
16 phase flips detected on antenna (np.int64(195), 'Jee'). A total of 307 integrations were phase-flipped relative to the 0th integration between 2461067.4102640925 and 2461067.4460554896.
10 phase flips detected on antenna (np.int64(294), 'Jnn'). A total of 37 integrations were phase-flipped relative to the 0th integration between 2461067.430061209 and 2461067.4345351337.
6 phase flips detected on antenna (np.int64(211), 'Jee'). A total of 46 integrations were phase-flipped relative to the 0th integration between 2461067.4297256647 and 2461067.434982526.
4 phase flips detected on antenna (np.int64(212), 'Jee'). A total of 86 integrations were phase-flipped relative to the 0th integration between 2461067.4264820693 and 2461067.4361010073.
2 phase flips detected on antenna (np.int64(133), 'Jnn'). A total of 77 integrations were phase-flipped relative to the 0th integration between 2461067.4264820693 and 2461067.434982526.
20 phase flips detected on antenna (np.int64(115), 'Jee'). A total of 268 integrations were phase-flipped relative to the 0th integration between 2461067.4119418142 and 2461067.4442659197.
12 phase flips detected on antenna (np.int64(232), 'Jee'). A total of 243 integrations were phase-flipped relative to the 0th integration between 2461067.415409106 and 2461067.4457199452.
8 phase flips detected on antenna (np.int64(97), 'Jee'). A total of 195 integrations were phase-flipped relative to the 0th integration between 2461067.4199948786 and 2461067.442588198.
2 phase flips detected on antenna (np.int64(232), 'Jnn'). A total of 184 integrations were phase-flipped relative to the 0th integration between 2461067.4240214108 and 2461067.444489616.
18 phase flips detected on antenna (np.int64(174), 'Jee'). A total of 296 integrations were phase-flipped relative to the 0th integration between 2461067.4113825737 and 2461067.4457199452.
2 phase flips detected on antenna (np.int64(306), 'Jee'). A total of 1 integrations were phase-flipped relative to the 0th integration between 2461067.431850779 and 2461067.431850779.
2 phase flips detected on antenna (np.int64(319), 'Jee'). A total of 65 integrations were phase-flipped relative to the 0th integration between 2461067.4287190316 and 2461067.435877311.
14 phase flips detected on antenna (np.int64(307), 'Jnn'). A total of 21 integrations were phase-flipped relative to the 0th integration between 2461067.430061209 and 2461067.4344232855.
2 phase flips detected on antenna (np.int64(195), 'Jnn'). A total of 195 integrations were phase-flipped relative to the 0th integration between 2461067.4227910815 and 2461067.444489616.
4 phase flips detected on antenna (np.int64(96), 'Jee'). A total of 116 integrations were phase-flipped relative to the 0th integration between 2461067.4239095626 and 2461067.436883944.
2 phase flips detected on antenna (np.int64(153), 'Jnn'). A total of 118 integrations were phase-flipped relative to the 0th integration between 2461067.4246924995 and 2461067.437778729.
8 phase flips detected on antenna (np.int64(80), 'Jee'). A total of 140 integrations were phase-flipped relative to the 0th integration between 2461067.4235740183 and 2461067.441469717.
6 phase flips detected on antenna (np.int64(173), 'Jnn'). A total of 87 integrations were phase-flipped relative to the 0th integration between 2461067.4264820693 and 2461067.436772096.
4 phase flips detected on antenna (np.int64(213), 'Jnn'). A total of 179 integrations were phase-flipped relative to the 0th integration between 2461067.4239095626 and 2461067.4439303754.
6 phase flips detected on antenna (np.int64(132), 'Jee'). A total of 98 integrations were phase-flipped relative to the 0th integration between 2461067.4246924995 and 2461067.4361010073.
16 phase flips detected on antenna (np.int64(175), 'Jee'). A total of 311 integrations were phase-flipped relative to the 0th integration between 2461067.4102640925 and 2461067.446502882.
2 phase flips detected on antenna (np.int64(115), 'Jnn'). A total of 97 integrations were phase-flipped relative to the 0th integration between 2461067.425139892 and 2461067.435877311.
4 phase flips detected on antenna (np.int64(175), 'Jnn'). A total of 216 integrations were phase-flipped relative to the 0th integration between 2461067.4210015116 and 2461067.445272553.
2 phase flips detected on antenna (np.int64(134), 'Jnn'). A total of 128 integrations were phase-flipped relative to the 0th integration between 2461067.4240214108 and 2461067.4382261215.
6 phase flips detected on antenna (np.int64(174), 'Jnn'). A total of 177 integrations were phase-flipped relative to the 0th integration between 2461067.4237977145 and 2461067.443706679.
4 phase flips detected on antenna (np.int64(154), 'Jnn'). A total of 195 integrations were phase-flipped relative to the 0th integration between 2461067.4227910815 and 2461067.444601464.
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 7e with a relative difference before and after smoothing of 24.03% (compared to the 20.00% cutoff). Flagging antenna 8e with a relative difference before and after smoothing of 26.35% (compared to the 20.00% cutoff). Flagging antenna 9e with a relative difference before and after smoothing of 29.21% (compared to the 20.00% cutoff). Flagging antenna 9n with a relative difference before and after smoothing of 20.06% (compared to the 20.00% cutoff). Flagging antenna 19e with a relative difference before and after smoothing of 23.40% (compared to the 20.00% cutoff). Flagging antenna 31e with a relative difference before and after smoothing of 20.59% (compared to the 20.00% cutoff). Flagging antenna 32e with a relative difference before and after smoothing of 24.73% (compared to the 20.00% cutoff). Flagging antenna 33e with a relative difference before and after smoothing of 28.20% (compared to the 20.00% cutoff). Flagging antenna 45e with a relative difference before and after smoothing of 21.82% (compared to the 20.00% cutoff). Flagging antenna 50n with a relative difference before and after smoothing of 25.62% (compared to the 20.00% cutoff). Flagging antenna 51n with a relative difference before and after smoothing of 20.69% (compared to the 20.00% cutoff). Flagging antenna 60e with a relative difference before and after smoothing of 22.57% (compared to the 20.00% cutoff). Flagging antenna 65n with a relative difference before and after smoothing of 26.15% (compared to the 20.00% cutoff). Flagging antenna 66n with a relative difference before and after smoothing of 23.02% (compared to the 20.00% cutoff). Flagging antenna 79e with a relative difference before and after smoothing of 38.22% (compared to the 20.00% cutoff). Flagging antenna 79n with a relative difference before and after smoothing of 31.54% (compared to the 20.00% cutoff). Flagging antenna 80e with a relative difference before and after smoothing of 43.65% (compared to the 20.00% cutoff). Flagging antenna 80n with a relative difference before and after smoothing of 35.29% (compared to the 20.00% cutoff). Flagging antenna 82n with a relative difference before and after smoothing of 23.89% (compared to the 20.00% cutoff). Flagging antenna 83n with a relative difference before and after smoothing of 20.29% (compared to the 20.00% cutoff). Flagging antenna 93n with a relative difference before and after smoothing of 22.72% (compared to the 20.00% cutoff). Flagging antenna 94e with a relative difference before and after smoothing of 32.80% (compared to the 20.00% cutoff). Flagging antenna 94n with a relative difference before and after smoothing of 25.59% (compared to the 20.00% cutoff). Flagging antenna 95n with a relative difference before and after smoothing of 30.48% (compared to the 20.00% cutoff). Flagging antenna 96e with a relative difference before and after smoothing of 40.13% (compared to the 20.00% cutoff). Flagging antenna 96n with a relative difference before and after smoothing of 34.29% (compared to the 20.00% cutoff). Flagging antenna 97e with a relative difference before and after smoothing of 42.95% (compared to the 20.00% cutoff). Flagging antenna 98e with a relative difference before and after smoothing of 22.20% (compared to the 20.00% cutoff). Flagging antenna 100n with a relative difference before and after smoothing of 22.24% (compared to the 20.00% cutoff). Flagging antenna 109e with a relative difference before and after smoothing of 21.52% (compared to the 20.00% cutoff). Flagging antenna 110e with a relative difference before and after smoothing of 28.03% (compared to the 20.00% cutoff). Flagging antenna 110n with a relative difference before and after smoothing of 20.95% (compared to the 20.00% cutoff). Flagging antenna 111e with a relative difference before and after smoothing of 29.59% (compared to the 20.00% cutoff). Flagging antenna 111n with a relative difference before and after smoothing of 24.32% (compared to the 20.00% cutoff). Flagging antenna 112e with a relative difference before and after smoothing of 33.34% (compared to the 20.00% cutoff). Flagging antenna 112n with a relative difference before and after smoothing of 27.96% (compared to the 20.00% cutoff). Flagging antenna 113e with a relative difference before and after smoothing of 37.19% (compared to the 20.00% cutoff). Flagging antenna 115e with a relative difference before and after smoothing of 45.13% (compared to the 20.00% cutoff). Flagging antenna 115n with a relative difference before and after smoothing of 40.45% (compared to the 20.00% cutoff). Flagging antenna 116e with a relative difference before and after smoothing of 24.22% (compared to the 20.00% cutoff). Flagging antenna 116n with a relative difference before and after smoothing of 31.34% (compared to the 20.00% cutoff). Flagging antenna 117n with a relative difference before and after smoothing of 27.90% (compared to the 20.00% cutoff). Flagging antenna 118n with a relative difference before and after smoothing of 24.13% (compared to the 20.00% cutoff). Flagging antenna 119n with a relative difference before and after smoothing of 20.83% (compared to the 20.00% cutoff). Flagging antenna 128e with a relative difference before and after smoothing of 23.09% (compared to the 20.00% cutoff). Flagging antenna 129e with a relative difference before and after smoothing of 27.21% (compared to the 20.00% cutoff). Flagging antenna 129n with a relative difference before and after smoothing of 22.76% (compared to the 20.00% cutoff). Flagging antenna 130e with a relative difference before and after smoothing of 31.36% (compared to the 20.00% cutoff). Flagging antenna 130n with a relative difference before and after smoothing of 26.42% (compared to the 20.00% cutoff). Flagging antenna 131e with a relative difference before and after smoothing of 36.14% (compared to the 20.00% cutoff). Flagging antenna 131n with a relative difference before and after smoothing of 31.25% (compared to the 20.00% cutoff). Flagging antenna 132e with a relative difference before and after smoothing of 42.86% (compared to the 20.00% cutoff). Flagging antenna 132n with a relative difference before and after smoothing of 35.56% (compared to the 20.00% cutoff). Flagging antenna 133n with a relative difference before and after smoothing of 39.80% (compared to the 20.00% cutoff). Flagging antenna 134e with a relative difference before and after smoothing of 46.75% (compared to the 20.00% cutoff). Flagging antenna 134n with a relative difference before and after smoothing of 42.68% (compared to the 20.00% cutoff). Flagging antenna 135n with a relative difference before and after smoothing of 34.08% (compared to the 20.00% cutoff).
Flagging antenna 136e with a relative difference before and after smoothing of 23.10% (compared to the 20.00% cutoff). Flagging antenna 137n with a relative difference before and after smoothing of 27.13% (compared to the 20.00% cutoff). Flagging antenna 138n with a relative difference before and after smoothing of 23.15% (compared to the 20.00% cutoff). Flagging antenna 147e with a relative difference before and after smoothing of 20.93% (compared to the 20.00% cutoff). Flagging antenna 148n with a relative difference before and after smoothing of 21.61% (compared to the 20.00% cutoff). Flagging antenna 149e with a relative difference before and after smoothing of 28.65% (compared to the 20.00% cutoff). Flagging antenna 149n with a relative difference before and after smoothing of 25.16% (compared to the 20.00% cutoff). Flagging antenna 150e with a relative difference before and after smoothing of 32.71% (compared to the 20.00% cutoff). Flagging antenna 150n with a relative difference before and after smoothing of 28.91% (compared to the 20.00% cutoff). Flagging antenna 152e with a relative difference before and after smoothing of 41.57% (compared to the 20.00% cutoff). Flagging antenna 152n with a relative difference before and after smoothing of 37.65% (compared to the 20.00% cutoff). Flagging antenna 153n with a relative difference before and after smoothing of 42.93% (compared to the 20.00% cutoff). Flagging antenna 154n with a relative difference before and after smoothing of 44.28% (compared to the 20.00% cutoff). Flagging antenna 155e with a relative difference before and after smoothing of 27.96% (compared to the 20.00% cutoff). Flagging antenna 155n with a relative difference before and after smoothing of 36.37% (compared to the 20.00% cutoff). Flagging antenna 156e with a relative difference before and after smoothing of 23.41% (compared to the 20.00% cutoff). Flagging antenna 156n with a relative difference before and after smoothing of 31.28% (compared to the 20.00% cutoff). Flagging antenna 157e with a relative difference before and after smoothing of 20.16% (compared to the 20.00% cutoff). Flagging antenna 157n with a relative difference before and after smoothing of 27.86% (compared to the 20.00% cutoff). Flagging antenna 159n with a relative difference before and after smoothing of 20.95% (compared to the 20.00% cutoff). Flagging antenna 168e with a relative difference before and after smoothing of 22.51% (compared to the 20.00% cutoff). Flagging antenna 168n with a relative difference before and after smoothing of 20.52% (compared to the 20.00% cutoff). Flagging antenna 169n with a relative difference before and after smoothing of 23.91% (compared to the 20.00% cutoff). Flagging antenna 173e with a relative difference before and after smoothing of 43.67% (compared to the 20.00% cutoff). Flagging antenna 173n with a relative difference before and after smoothing of 38.80% (compared to the 20.00% cutoff). Flagging antenna 174e with a relative difference before and after smoothing of 44.75% (compared to the 20.00% cutoff). Flagging antenna 174n with a relative difference before and after smoothing of 42.31% (compared to the 20.00% cutoff). Flagging antenna 175e with a relative difference before and after smoothing of 57.90% (compared to the 20.00% cutoff). Flagging antenna 175n with a relative difference before and after smoothing of 47.48% (compared to the 20.00% cutoff). Flagging antenna 176n with a relative difference before and after smoothing of 34.40% (compared to the 20.00% cutoff). Flagging antenna 177e with a relative difference before and after smoothing of 22.56% (compared to the 20.00% cutoff). Flagging antenna 177n with a relative difference before and after smoothing of 30.10% (compared to the 20.00% cutoff). Flagging antenna 178n with a relative difference before and after smoothing of 26.42% (compared to the 20.00% cutoff). Flagging antenna 179n with a relative difference before and after smoothing of 23.55% (compared to the 20.00% cutoff). Flagging antenna 188e with a relative difference before and after smoothing of 21.34% (compared to the 20.00% cutoff). Flagging antenna 188n with a relative difference before and after smoothing of 23.34% (compared to the 20.00% cutoff). Flagging antenna 189e with a relative difference before and after smoothing of 24.14% (compared to the 20.00% cutoff). Flagging antenna 189n with a relative difference before and after smoothing of 23.08% (compared to the 20.00% cutoff). Flagging antenna 190e with a relative difference before and after smoothing of 28.08% (compared to the 20.00% cutoff). Flagging antenna 190n with a relative difference before and after smoothing of 26.49% (compared to the 20.00% cutoff). Flagging antenna 191e with a relative difference before and after smoothing of 31.26% (compared to the 20.00% cutoff). Flagging antenna 192e with a relative difference before and after smoothing of 35.34% (compared to the 20.00% cutoff). Flagging antenna 192n with a relative difference before and after smoothing of 33.79% (compared to the 20.00% cutoff). Flagging antenna 193n with a relative difference before and after smoothing of 38.85% (compared to the 20.00% cutoff). Flagging antenna 195e with a relative difference before and after smoothing of 47.21% (compared to the 20.00% cutoff). Flagging antenna 195n with a relative difference before and after smoothing of 47.68% (compared to the 20.00% cutoff). Flagging antenna 196e with a relative difference before and after smoothing of 25.20% (compared to the 20.00% cutoff). Flagging antenna 196n with a relative difference before and after smoothing of 32.85% (compared to the 20.00% cutoff). Flagging antenna 197e with a relative difference before and after smoothing of 22.53% (compared to the 20.00% cutoff). Flagging antenna 198n with a relative difference before and after smoothing of 25.47% (compared to the 20.00% cutoff). Flagging antenna 207e with a relative difference before and after smoothing of 20.31% (compared to the 20.00% cutoff). Flagging antenna 208e with a relative difference before and after smoothing of 23.22% (compared to the 20.00% cutoff). Flagging antenna 208n with a relative difference before and after smoothing of 23.66% (compared to the 20.00% cutoff). Flagging antenna 209e with a relative difference before and after smoothing of 27.37% (compared to the 20.00% cutoff). Flagging antenna 210e with a relative difference before and after smoothing of 33.70% (compared to the 20.00% cutoff). Flagging antenna 211e with a relative difference before and after smoothing of 33.96% (compared to the 20.00% cutoff). Flagging antenna 212e with a relative difference before and after smoothing of 39.57% (compared to the 20.00% cutoff). Flagging antenna 213n with a relative difference before and after smoothing of 50.13% (compared to the 20.00% cutoff).
Flagging antenna 214e with a relative difference before and after smoothing of 45.60% (compared to the 20.00% cutoff). Flagging antenna 214n with a relative difference before and after smoothing of 46.40% (compared to the 20.00% cutoff). Flagging antenna 215e with a relative difference before and after smoothing of 24.89% (compared to the 20.00% cutoff). Flagging antenna 215n with a relative difference before and after smoothing of 32.03% (compared to the 20.00% cutoff). Flagging antenna 216e with a relative difference before and after smoothing of 20.73% (compared to the 20.00% cutoff). Flagging antenna 216n with a relative difference before and after smoothing of 28.57% (compared to the 20.00% cutoff). Flagging antenna 217n with a relative difference before and after smoothing of 24.79% (compared to the 20.00% cutoff). Flagging antenna 225n with a relative difference before and after smoothing of 20.84% (compared to the 20.00% cutoff). Flagging antenna 226e with a relative difference before and after smoothing of 22.12% (compared to the 20.00% cutoff). Flagging antenna 228e with a relative difference before and after smoothing of 32.24% (compared to the 20.00% cutoff). Flagging antenna 228n with a relative difference before and after smoothing of 29.22% (compared to the 20.00% cutoff). Flagging antenna 229e with a relative difference before and after smoothing of 36.61% (compared to the 20.00% cutoff). Flagging antenna 229n with a relative difference before and after smoothing of 32.28% (compared to the 20.00% cutoff). Flagging antenna 231e with a relative difference before and after smoothing of 41.10% (compared to the 20.00% cutoff). Flagging antenna 231n with a relative difference before and after smoothing of 41.31% (compared to the 20.00% cutoff). Flagging antenna 232e with a relative difference before and after smoothing of 45.33% (compared to the 20.00% cutoff). Flagging antenna 232n with a relative difference before and after smoothing of 47.40% (compared to the 20.00% cutoff). Flagging antenna 233e with a relative difference before and after smoothing of 24.83% (compared to the 20.00% cutoff). Flagging antenna 234e with a relative difference before and after smoothing of 20.60% (compared to the 20.00% cutoff). Flagging antenna 234n with a relative difference before and after smoothing of 28.06% (compared to the 20.00% cutoff). Flagging antenna 237n with a relative difference before and after smoothing of 20.49% (compared to the 20.00% cutoff). Flagging antenna 242n with a relative difference before and after smoothing of 21.03% (compared to the 20.00% cutoff). Flagging antenna 243e with a relative difference before and after smoothing of 21.54% (compared to the 20.00% cutoff). Flagging antenna 243n with a relative difference before and after smoothing of 23.18% (compared to the 20.00% cutoff). Flagging antenna 244e with a relative difference before and after smoothing of 24.00% (compared to the 20.00% cutoff). Flagging antenna 244n with a relative difference before and after smoothing of 24.70% (compared to the 20.00% cutoff). Flagging antenna 245e with a relative difference before and after smoothing of 27.42% (compared to the 20.00% cutoff). Flagging antenna 245n with a relative difference before and after smoothing of 27.84% (compared to the 20.00% cutoff). Flagging antenna 246e with a relative difference before and after smoothing of 31.47% (compared to the 20.00% cutoff). Flagging antenna 246n with a relative difference before and after smoothing of 31.58% (compared to the 20.00% cutoff). Flagging antenna 250n with a relative difference before and after smoothing of 30.81% (compared to the 20.00% cutoff). Flagging antenna 252n with a relative difference before and after smoothing of 25.31% (compared to the 20.00% cutoff). Flagging antenna 254n with a relative difference before and after smoothing of 20.92% (compared to the 20.00% cutoff). Flagging antenna 257n with a relative difference before and after smoothing of 20.43% (compared to the 20.00% cutoff). Flagging antenna 261e with a relative difference before and after smoothing of 28.38% (compared to the 20.00% cutoff). Flagging antenna 261n with a relative difference before and after smoothing of 31.03% (compared to the 20.00% cutoff). Flagging antenna 262n with a relative difference before and after smoothing of 31.91% (compared to the 20.00% cutoff). Flagging antenna 267e with a relative difference before and after smoothing of 21.42% (compared to the 20.00% cutoff). Flagging antenna 268n with a relative difference before and after smoothing of 25.65% (compared to the 20.00% cutoff). Flagging antenna 269n with a relative difference before and after smoothing of 23.58% (compared to the 20.00% cutoff). Flagging antenna 270n with a relative difference before and after smoothing of 22.71% (compared to the 20.00% cutoff). Flagging antenna 272n with a relative difference before and after smoothing of 22.02% (compared to the 20.00% cutoff). Flagging antenna 273n with a relative difference before and after smoothing of 23.38% (compared to the 20.00% cutoff). Flagging antenna 277n with a relative difference before and after smoothing of 31.55% (compared to the 20.00% cutoff). Flagging antenna 278n with a relative difference before and after smoothing of 36.53% (compared to the 20.00% cutoff). Flagging antenna 281e with a relative difference before and after smoothing of 23.04% (compared to the 20.00% cutoff). Flagging antenna 281n with a relative difference before and after smoothing of 30.76% (compared to the 20.00% cutoff). Flagging antenna 282e with a relative difference before and after smoothing of 20.79% (compared to the 20.00% cutoff). Flagging antenna 282n with a relative difference before and after smoothing of 29.00% (compared to the 20.00% cutoff). Flagging antenna 283n with a relative difference before and after smoothing of 26.39% (compared to the 20.00% cutoff). Flagging antenna 284n with a relative difference before and after smoothing of 24.91% (compared to the 20.00% cutoff). Flagging antenna 285n with a relative difference before and after smoothing of 23.77% (compared to the 20.00% cutoff). Flagging antenna 292e with a relative difference before and after smoothing of 31.42% (compared to the 20.00% cutoff). Flagging antenna 292n with a relative difference before and after smoothing of 35.48% (compared to the 20.00% cutoff). Flagging antenna 293n with a relative difference before and after smoothing of 39.75% (compared to the 20.00% cutoff). Flagging antenna 294n with a relative difference before and after smoothing of 43.42% (compared to the 20.00% cutoff). Flagging antenna 295e with a relative difference before and after smoothing of 24.11% (compared to the 20.00% cutoff).
Flagging antenna 306e with a relative difference before and after smoothing of 34.18% (compared to the 20.00% cutoff). Flagging antenna 307n with a relative difference before and after smoothing of 43.06% (compared to the 20.00% cutoff). Flagging antenna 315e with a relative difference before and after smoothing of 25.52% (compared to the 20.00% cutoff). Flagging antenna 315n with a relative difference before and after smoothing of 31.61% (compared to the 20.00% cutoff). Flagging antenna 316e with a relative difference before and after smoothing of 27.54% (compared to the 20.00% cutoff). Flagging antenna 316n with a relative difference before and after smoothing of 33.81% (compared to the 20.00% cutoff). Flagging antenna 317e with a relative difference before and after smoothing of 31.02% (compared to the 20.00% cutoff). Flagging antenna 317n with a relative difference before and after smoothing of 38.03% (compared to the 20.00% cutoff). Flagging antenna 319e with a relative difference before and after smoothing of 37.78% (compared to the 20.00% cutoff). Flagging antenna 319n with a relative difference before and after smoothing of 44.08% (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 38 Amplitude Waterfalls
Antenna 162 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 38 Phase Waterfalls
Antenna 162 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 51.25 minutes.