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/data1/2461105/zen.2461105.23167.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 1581 *.sum.omni.calfits files starting with /mnt/sn1/data1/2461105/zen.2461105.23167.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 1581 *.sum.flag_waterfall.h5 files starting with /mnt/sn1/data1/2461105/zen.2461105.23167.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 1581 *.sum.antenna_flags.h5 files starting with /mnt/sn1/data1/2461105/zen.2461105.23167.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 127 selected for smoothing Jee gains.
Reference antenna 238 selected for smoothing Jnn gains.

Overall reference antenna (np.int64(238), '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()
No description has been provided for this image
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],)

16 phase flips detected on antenna (np.int64(60), 'Jnn'). A total of 45 integrations were phase-flipped relative to the 0th integration between 2461105.310748379 and 2461105.3294270146.

50 phase flips detected on antenna (np.int64(132), 'Jnn'). A total of 1052 integrations were phase-flipped relative to the 0th integration between 2461105.3081758725 and 2461105.6437202203.

60 phase flips detected on antenna (np.int64(131), 'Jnn'). A total of 1002 integrations were phase-flipped relative to the 0th integration between 2461105.3081758725 and 2461105.6437202203.

14 phase flips detected on antenna (np.int64(79), 'Jnn'). A total of 426 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.4557035374.

16 phase flips detected on antenna (np.int64(95), 'Jnn'). A total of 406 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.4557035374.

10 phase flips detected on antenna (np.int64(228), 'Jnn'). A total of 356 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.5060351896.

39 phase flips detected on antenna (np.int64(229), 'Jnn'). A total of 1188 integrations were phase-flipped relative to the 0th integration between 2461105.307616632 and 2461105.645397942.

44 phase flips detected on antenna (np.int64(192), 'Jnn'). A total of 654 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.5107328105.

8 phase flips detected on antenna (np.int64(51), 'Jnn'). A total of 179 integrations were phase-flipped relative to the 0th integration between 2461105.3195843804 and 2461105.3399407375.

4 phase flips detected on antenna (np.int64(315), 'Jnn'). A total of 2 integrations were phase-flipped relative to the 0th integration between 2461105.3294270146 and 2461105.330657344.

26 phase flips detected on antenna (np.int64(96), 'Jnn'). A total of 407 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.4557035374.

33 phase flips detected on antenna (np.int64(232), 'Jnn'). A total of 1705 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.645397942.

32 phase flips detected on antenna (np.int64(128), 'Jnn'). A total of 271 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.4458609032.

94 phase flips detected on antenna (np.int64(246), 'Jnn'). A total of 878 integrations were phase-flipped relative to the 0th integration between 2461105.307616632 and 2461105.6437202203.

28 phase flips detected on antenna (np.int64(208), 'Jnn'). A total of 172 integrations were phase-flipped relative to the 0th integration between 2461105.315222304 and 2461105.3382630157.

33 phase flips detected on antenna (np.int64(231), 'Jnn'). A total of 1723 integrations were phase-flipped relative to the 0th integration between 2461105.308735113 and 2461105.645397942.

20 phase flips detected on antenna (np.int64(112), 'Jnn'). A total of 419 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.4527954864.

50 phase flips detected on antenna (np.int64(278), 'Jnn'). A total of 697 integrations were phase-flipped relative to the 0th integration between 2461105.3089588094 and 2461105.5156541276.

38 phase flips detected on antenna (np.int64(133), 'Jnn'). A total of 1012 integrations were phase-flipped relative to the 0th integration between 2461105.3081758725 and 2461105.64137141.

18 phase flips detected on antenna (np.int64(46), 'Jnn'). A total of 165 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.330657344.

27 phase flips detected on antenna (np.int64(294), 'Jnn'). A total of 1648 integrations were phase-flipped relative to the 0th integration between 2461105.307616632 and 2461105.645397942.

50 phase flips detected on antenna (np.int64(211), 'Jnn'). A total of 974 integrations were phase-flipped relative to the 0th integration between 2461105.3081758725 and 2461105.6437202203.

38 phase flips detected on antenna (np.int64(193), 'Jnn'). A total of 691 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.512969773.

16 phase flips detected on antenna (np.int64(50), 'Jnn'). A total of 247 integrations were phase-flipped relative to the 0th integration between 2461105.3157815444 and 2461105.5109565067.

25 phase flips detected on antenna (np.int64(152), 'Jnn'). A total of 1718 integrations were phase-flipped relative to the 0th integration between 2461105.307616632 and 2461105.645397942.

20 phase flips detected on antenna (np.int64(196), 'Jnn'). A total of 191 integrations were phase-flipped relative to the 0th integration between 2461105.3157815444 and 2461105.340611826.

16 phase flips detected on antenna (np.int64(316), 'Jnn'). A total of 172 integrations were phase-flipped relative to the 0th integration between 2461105.317459266 and 2461105.3398288894.

4 phase flips detected on antenna (np.int64(198), 'Jnn'). A total of 2 integrations were phase-flipped relative to the 0th integration between 2461105.3294270146 and 2461105.330657344.

6 phase flips detected on antenna (np.int64(245), 'Jnn'). A total of 291 integrations were phase-flipped relative to the 0th integration between 2461105.3118668604 and 2461105.3448620546.

32 phase flips detected on antenna (np.int64(93), 'Jnn'). A total of 233 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.4460845995.

6 phase flips detected on antenna (np.int64(169), 'Jnn'). A total of 326 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.445413511.

2 phase flips detected on antenna (np.int64(127), 'Jnn'). A total of 1 integrations were phase-flipped relative to the 0th integration between 2461105.3160052407 and 2461105.3160052407.

20 phase flips detected on antenna (np.int64(293), 'Jnn'). A total of 418 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.511180203.

12 phase flips detected on antenna (np.int64(65), 'Jnn'). A total of 247 integrations were phase-flipped relative to the 0th integration between 2461105.3157815444 and 2461105.5109565067.

2 phase flips detected on antenna (np.int64(19), 'Jnn'). A total of 1 integrations were phase-flipped relative to the 0th integration between 2461105.3160052407 and 2461105.3160052407.

10 phase flips detected on antenna (np.int64(116), 'Jnn'). A total of 278 integrations were phase-flipped relative to the 0th integration between 2461105.315222304 and 2461105.5109565067.

18 phase flips detected on antenna (np.int64(261), 'Jnn'). A total of 208 integrations were phase-flipped relative to the 0th integration between 2461105.3157815444 and 2461105.3410592186.

22 phase flips detected on antenna (np.int64(38), 'Jnn'). A total of 63 integrations were phase-flipped relative to the 0th integration between 2461105.322045039 and 2461105.3369208383.

22 phase flips detected on antenna (np.int64(307), 'Jnn'). A total of 683 integrations were phase-flipped relative to the 0th integration between 2461105.3089588094 and 2461105.5211346853.

10 phase flips detected on antenna (np.int64(190), 'Jnn'). A total of 306 integrations were phase-flipped relative to the 0th integration between 2461105.310412835 and 2461105.345309447.

18 phase flips detected on antenna (np.int64(244), 'Jnn'). A total of 88 integrations were phase-flipped relative to the 0th integration between 2461105.3160052407 and 2461105.3334535467.

4 phase flips detected on antenna (np.int64(109), 'Jnn'). A total of 4 integrations were phase-flipped relative to the 0th integration between 2461105.3160052407 and 2461105.316452633.

10 phase flips detected on antenna (np.int64(177), 'Jnn'). A total of 12 integrations were phase-flipped relative to the 0th integration between 2461105.326854508 and 2461105.330657344.

20 phase flips detected on antenna (np.int64(176), 'Jnn'). A total of 204 integrations were phase-flipped relative to the 0th integration between 2461105.3157815444 and 2461105.3410592186.

12 phase flips detected on antenna (np.int64(98), 'Jnn'). A total of 261 integrations were phase-flipped relative to the 0th integration between 2461105.315446 and 2461105.5109565067.

28 phase flips detected on antenna (np.int64(147), 'Jnn'). A total of 61 integrations were phase-flipped relative to the 0th integration between 2461105.3130971896 and 2461105.33088104.

4 phase flips detected on antenna (np.int64(59), 'Jnn'). A total of 4 integrations were phase-flipped relative to the 0th integration between 2461105.3160052407 and 2461105.316452633.

18 phase flips detected on antenna (np.int64(168), 'Jnn'). A total of 222 integrations were phase-flipped relative to the 0th integration between 2461105.3118668604 and 2461105.3401644337.

6 phase flips detected on antenna (np.int64(111), 'Jnn'). A total of 330 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.3465397763.

16 phase flips detected on antenna (np.int64(292), 'Jnn'). A total of 418 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.511403899.

2 phase flips detected on antenna (np.int64(226), 'Jnn'). A total of 3 integrations were phase-flipped relative to the 0th integration between 2461105.326854508 and 2461105.327078204.

4 phase flips detected on antenna (np.int64(149), 'Jnn'). A total of 340 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.448321562.

18 phase flips detected on antenna (np.int64(21), 'Jnn'). A total of 176 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.331887673.

12 phase flips detected on antenna (np.int64(277), 'Jnn'). A total of 331 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.5067062783.

12 phase flips detected on antenna (np.int64(150), 'Jnn'). A total of 19 integrations were phase-flipped relative to the 0th integration between 2461105.326854508 and 2461105.3378156233.

10 phase flips detected on antenna (np.int64(3), 'Jnn'). A total of 18 integrations were phase-flipped relative to the 0th integration between 2461105.4450779664 and 2461105.450334828.

8 phase flips detected on antenna (np.int64(66), 'Jnn'). A total of 179 integrations were phase-flipped relative to the 0th integration between 2461105.3195843804 and 2461105.3399407375.

14 phase flips detected on antenna (np.int64(319), 'Jnn'). A total of 556 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.515877824.

30 phase flips detected on antenna (np.int64(92), 'Jnn'). A total of 146 integrations were phase-flipped relative to the 0th integration between 2461105.310412835 and 2461105.331887673.

31 phase flips detected on antenna (np.int64(153), 'Jnn'). A total of 1688 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.645397942.

6 phase flips detected on antenna (np.int64(148), 'Jnn'). A total of 332 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.451565157.

18 phase flips detected on antenna (np.int64(45), 'Jnn'). A total of 21 integrations were phase-flipped relative to the 0th integration between 2461105.313432734 and 2461105.3294270146.

12 phase flips detected on antenna (np.int64(129), 'Jnn'). A total of 334 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.44843341.

60 phase flips detected on antenna (np.int64(130), 'Jnn'). A total of 724 integrations were phase-flipped relative to the 0th integration between 2461105.3081758725 and 2461105.6409240174.

6 phase flips detected on antenna (np.int64(157), 'Jnn'). A total of 7 integrations were phase-flipped relative to the 0th integration between 2461105.326854508 and 2461105.330657344.

47 phase flips detected on antenna (np.int64(213), 'Jnn'). A total of 1240 integrations were phase-flipped relative to the 0th integration between 2461105.3089588094 and 2461105.645397942.

10 phase flips detected on antenna (np.int64(189), 'Jnn'). A total of 272 integrations were phase-flipped relative to the 0th integration between 2461105.311419468 and 2461105.3427369404.

18 phase flips detected on antenna (np.int64(20), 'Jnn'). A total of 39 integrations were phase-flipped relative to the 0th integration between 2461105.311419468 and 2461105.3294270146.

10 phase flips detected on antenna (np.int64(155), 'Jnn'). A total of 257 integrations were phase-flipped relative to the 0th integration between 2461105.315446 and 2461105.3446383583.

20 phase flips detected on antenna (np.int64(317), 'Jnn'). A total of 172 integrations were phase-flipped relative to the 0th integration between 2461105.3160052407 and 2461105.340611826.

84 phase flips detected on antenna (np.int64(214), 'Jnn'). A total of 1322 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.6160937357.

56 phase flips detected on antenna (np.int64(114), 'Jnn'). A total of 1057 integrations were phase-flipped relative to the 0th integration between 2461105.307840328 and 2461105.6437202203.

8 phase flips detected on antenna (np.int64(135), 'Jnn'). A total of 251 integrations were phase-flipped relative to the 0th integration between 2461105.3157815444 and 2461105.344190966.

36 phase flips detected on antenna (np.int64(194), 'Jnn'). A total of 884 integrations were phase-flipped relative to the 0th integration between 2461105.309406202 and 2461105.5089432406.

8 phase flips detected on antenna (np.int64(36), 'Jnn'). A total of 214 integrations were phase-flipped relative to the 0th integration between 2461105.3176829624 and 2461105.343296181.

3 phase flips detected on antenna (np.int64(195), 'Jnn'). A total of 2621 integrations were phase-flipped relative to the 0th integration between 2461105.352020334 and 2461105.645397942.
In [17]:
# calculate average chi^2 per antenna before additional flagging
avg_cspa_vs_time = {ant: np.nanmean(np.where(cs.flag_grids[ant], np.nan, cs.cspa_grids[ant]), axis=1) for ant in cs.ants}
avg_cspa_vs_freq = {ant: np.nanmean(np.where(cs.flag_grids[ant], np.nan, cs.cspa_grids[ant]), axis=0) for ant in cs.ants}
avg_cspa = {ant: np.nanmean(np.where(cs.flag_grids[ant], np.nan, cs.cspa_grids[ant])) for ant in cs.ants}

Mean of empty slice

Mean of empty slice

Mean of empty slice
In [18]:
# Pick out antennas with too high relative differences before and after smoothing and flag them.
avg_relative_diffs = {ant: np.nanmean(rel_diff) for ant, rel_diff in meta['freq_avg_rel_diff'].items()}
to_cut = sorted([ant for ant, diff in avg_relative_diffs.items() if ant[0] >= 0 and diff > SC_RELATIVE_DIFF_CUTOFF])
if len(to_cut) > 0:
    for ant in to_cut:
        print(f'Flagging antenna {ant[0]}{ant[1][-1]} with a relative difference before and after smoothing of {avg_relative_diffs[ant]:.2%} '
              f'(compared to the {SC_RELATIVE_DIFF_CUTOFF:.2%} cutoff).')
        cs.flag_grids[ant] |= True
else:
    print(f'No antennas have a relative difference above the {SC_RELATIVE_DIFF_CUTOFF:.2%} cutoff.')

Flagging antenna 3n with a relative difference before and after smoothing of 35.69% (compared to the 20.00% cutoff).
Flagging antenna 5n with a relative difference before and after smoothing of 33.47% (compared to the 20.00% cutoff).
Flagging antenna 16n with a relative difference before and after smoothing of 30.07% (compared to the 20.00% cutoff).
Flagging antenna 17n with a relative difference before and after smoothing of 32.15% (compared to the 20.00% cutoff).
Flagging antenna 19n with a relative difference before and after smoothing of 34.51% (compared to the 20.00% cutoff).
Flagging antenna 20n with a relative difference before and after smoothing of 38.10% (compared to the 20.00% cutoff).
Flagging antenna 21n with a relative difference before and after smoothing of 46.98% (compared to the 20.00% cutoff).
Flagging antenna 30n with a relative difference before and after smoothing of 29.35% (compared to the 20.00% cutoff).
Flagging antenna 31n with a relative difference before and after smoothing of 30.49% (compared to the 20.00% cutoff).
Flagging antenna 36n with a relative difference before and after smoothing of 60.33% (compared to the 20.00% cutoff).
Flagging antenna 38n with a relative difference before and after smoothing of 28.93% (compared to the 20.00% cutoff).
Flagging antenna 41n with a relative difference before and after smoothing of 24.18% (compared to the 20.00% cutoff).
Flagging antenna 43n with a relative difference before and after smoothing of 25.87% (compared to the 20.00% cutoff).
Flagging antenna 45n with a relative difference before and after smoothing of 31.08% (compared to the 20.00% cutoff).
Flagging antenna 46n with a relative difference before and after smoothing of 39.00% (compared to the 20.00% cutoff).
Flagging antenna 50n with a relative difference before and after smoothing of 49.52% (compared to the 20.00% cutoff).
Flagging antenna 51n with a relative difference before and after smoothing of 36.55% (compared to the 20.00% cutoff).
Flagging antenna 52n with a relative difference before and after smoothing of 25.38% (compared to the 20.00% cutoff).
Flagging antenna 53n with a relative difference before and after smoothing of 25.24% (compared to the 20.00% cutoff).
Flagging antenna 54n with a relative difference before and after smoothing of 22.18% (compared to the 20.00% cutoff).
Flagging antenna 58n with a relative difference before and after smoothing of 24.60% (compared to the 20.00% cutoff).
Flagging antenna 59n with a relative difference before and after smoothing of 26.27% (compared to the 20.00% cutoff).
Flagging antenna 60n with a relative difference before and after smoothing of 30.20% (compared to the 20.00% cutoff).
Flagging antenna 65n with a relative difference before and after smoothing of 46.52% (compared to the 20.00% cutoff).
Flagging antenna 66n with a relative difference before and after smoothing of 37.73% (compared to the 20.00% cutoff).
Flagging antenna 68n with a relative difference before and after smoothing of 22.71% (compared to the 20.00% cutoff).
Flagging antenna 69n with a relative difference before and after smoothing of 22.31% (compared to the 20.00% cutoff).
Flagging antenna 71n with a relative difference before and after smoothing of 20.32% (compared to the 20.00% cutoff).
Flagging antenna 73n with a relative difference before and after smoothing of 22.46% (compared to the 20.00% cutoff).
Flagging antenna 74n with a relative difference before and after smoothing of 23.16% (compared to the 20.00% cutoff).
Flagging antenna 79n with a relative difference before and after smoothing of 61.58% (compared to the 20.00% cutoff).
Flagging antenna 84n with a relative difference before and after smoothing of 21.03% (compared to the 20.00% cutoff).
Flagging antenna 85n with a relative difference before and after smoothing of 20.17% (compared to the 20.00% cutoff).
Flagging antenna 91n with a relative difference before and after smoothing of 24.43% (compared to the 20.00% cutoff).
Flagging antenna 92n with a relative difference before and after smoothing of 34.89% (compared to the 20.00% cutoff).
Flagging antenna 93n with a relative difference before and after smoothing of 40.87% (compared to the 20.00% cutoff).
Flagging antenna 95n with a relative difference before and after smoothing of 57.95% (compared to the 20.00% cutoff).
Flagging antenna 96n with a relative difference before and after smoothing of 57.76% (compared to the 20.00% cutoff).
Flagging antenna 98n with a relative difference before and after smoothing of 44.72% (compared to the 20.00% cutoff).
Flagging antenna 101n with a relative difference before and after smoothing of 20.78% (compared to the 20.00% cutoff).
Flagging antenna 109n with a relative difference before and after smoothing of 25.88% (compared to the 20.00% cutoff).
Flagging antenna 111n with a relative difference before and after smoothing of 49.26% (compared to the 20.00% cutoff).
Flagging antenna 112n with a relative difference before and after smoothing of 56.33% (compared to the 20.00% cutoff).
Flagging antenna 114n with a relative difference before and after smoothing of 218.57% (compared to the 20.00% cutoff).
Flagging antenna 116n with a relative difference before and after smoothing of 46.68% (compared to the 20.00% cutoff).
Flagging antenna 127n with a relative difference before and after smoothing of 22.93% (compared to the 20.00% cutoff).
Flagging antenna 128n with a relative difference before and after smoothing of 38.68% (compared to the 20.00% cutoff).
Flagging antenna 129n with a relative difference before and after smoothing of 48.17% (compared to the 20.00% cutoff).
Flagging antenna 130n with a relative difference before and after smoothing of 113.97% (compared to the 20.00% cutoff).
Flagging antenna 131n with a relative difference before and after smoothing of 221.10% (compared to the 20.00% cutoff).
Flagging antenna 132n with a relative difference before and after smoothing of 237.30% (compared to the 20.00% cutoff).
Flagging antenna 133n with a relative difference before and after smoothing of 223.58% (compared to the 20.00% cutoff).
Flagging antenna 135n with a relative difference before and after smoothing of 41.75% (compared to the 20.00% cutoff).
Flagging antenna 146n with a relative difference before and after smoothing of 21.30% (compared to the 20.00% cutoff).
Flagging antenna 147n with a relative difference before and after smoothing of 24.25% (compared to the 20.00% cutoff).

Flagging antenna 148n with a relative difference before and after smoothing of 46.73% (compared to the 20.00% cutoff).
Flagging antenna 149n with a relative difference before and after smoothing of 47.45% (compared to the 20.00% cutoff).
Flagging antenna 150n with a relative difference before and after smoothing of 32.20% (compared to the 20.00% cutoff).
Flagging antenna 152n with a relative difference before and after smoothing of 316.57% (compared to the 20.00% cutoff).
Flagging antenna 153n with a relative difference before and after smoothing of 316.31% (compared to the 20.00% cutoff).
Flagging antenna 155n with a relative difference before and after smoothing of 41.32% (compared to the 20.00% cutoff).
Flagging antenna 157n with a relative difference before and after smoothing of 20.45% (compared to the 20.00% cutoff).
Flagging antenna 168n with a relative difference before and after smoothing of 34.58% (compared to the 20.00% cutoff).
Flagging antenna 169n with a relative difference before and after smoothing of 44.14% (compared to the 20.00% cutoff).
Flagging antenna 176n with a relative difference before and after smoothing of 35.39% (compared to the 20.00% cutoff).
Flagging antenna 177e with a relative difference before and after smoothing of 21.01% (compared to the 20.00% cutoff).
Flagging antenna 177n with a relative difference before and after smoothing of 22.00% (compared to the 20.00% cutoff).
Flagging antenna 188n with a relative difference before and after smoothing of 23.93% (compared to the 20.00% cutoff).
Flagging antenna 189n with a relative difference before and after smoothing of 36.85% (compared to the 20.00% cutoff).
Flagging antenna 190n with a relative difference before and after smoothing of 40.29% (compared to the 20.00% cutoff).
Flagging antenna 192n with a relative difference before and after smoothing of 107.94% (compared to the 20.00% cutoff).
Flagging antenna 193n with a relative difference before and after smoothing of 126.46% (compared to the 20.00% cutoff).
Flagging antenna 194n with a relative difference before and after smoothing of 370.53% (compared to the 20.00% cutoff).
Flagging antenna 195n with a relative difference before and after smoothing of 64.54% (compared to the 20.00% cutoff).
Flagging antenna 196n with a relative difference before and after smoothing of 30.43% (compared to the 20.00% cutoff).
Flagging antenna 208n with a relative difference before and after smoothing of 29.97% (compared to the 20.00% cutoff).
Flagging antenna 211n with a relative difference before and after smoothing of 229.65% (compared to the 20.00% cutoff).
Flagging antenna 213n with a relative difference before and after smoothing of 269.38% (compared to the 20.00% cutoff).
Flagging antenna 214n with a relative difference before and after smoothing of 392.29% (compared to the 20.00% cutoff).
Flagging antenna 215e with a relative difference before and after smoothing of 22.14% (compared to the 20.00% cutoff).
Flagging antenna 216e with a relative difference before and after smoothing of 21.39% (compared to the 20.00% cutoff).
Flagging antenna 226n with a relative difference before and after smoothing of 21.23% (compared to the 20.00% cutoff).
Flagging antenna 228n with a relative difference before and after smoothing of 47.23% (compared to the 20.00% cutoff).
Flagging antenna 229n with a relative difference before and after smoothing of 322.28% (compared to the 20.00% cutoff).
Flagging antenna 231n with a relative difference before and after smoothing of 326.53% (compared to the 20.00% cutoff).
Flagging antenna 232n with a relative difference before and after smoothing of 309.78% (compared to the 20.00% cutoff).
Flagging antenna 233e with a relative difference before and after smoothing of 21.57% (compared to the 20.00% cutoff).
Flagging antenna 234e with a relative difference before and after smoothing of 20.84% (compared to the 20.00% cutoff).
Flagging antenna 244n with a relative difference before and after smoothing of 23.99% (compared to the 20.00% cutoff).
Flagging antenna 245n with a relative difference before and after smoothing of 38.93% (compared to the 20.00% cutoff).
Flagging antenna 246n with a relative difference before and after smoothing of 224.03% (compared to the 20.00% cutoff).
Flagging antenna 250e with a relative difference before and after smoothing of 22.11% (compared to the 20.00% cutoff).
Flagging antenna 261n with a relative difference before and after smoothing of 32.00% (compared to the 20.00% cutoff).
Flagging antenna 267e with a relative difference before and after smoothing of 23.18% (compared to the 20.00% cutoff).
Flagging antenna 277n with a relative difference before and after smoothing of 46.89% (compared to the 20.00% cutoff).
Flagging antenna 278n with a relative difference before and after smoothing of 137.90% (compared to the 20.00% cutoff).
Flagging antenna 281e with a relative difference before and after smoothing of 21.40% (compared to the 20.00% cutoff).
Flagging antenna 282e with a relative difference before and after smoothing of 20.86% (compared to the 20.00% cutoff).
Flagging antenna 292n with a relative difference before and after smoothing of 58.53% (compared to the 20.00% cutoff).
Flagging antenna 293n with a relative difference before and after smoothing of 62.51% (compared to the 20.00% cutoff).
Flagging antenna 294n with a relative difference before and after smoothing of 388.42% (compared to the 20.00% cutoff).
Flagging antenna 301n with a relative difference before and after smoothing of 20.54% (compared to the 20.00% cutoff).
Flagging antenna 302n with a relative difference before and after smoothing of 21.10% (compared to the 20.00% cutoff).
Flagging antenna 307n with a relative difference before and after smoothing of 154.28% (compared to the 20.00% cutoff).
Flagging antenna 311e with a relative difference before and after smoothing of 21.52% (compared to the 20.00% cutoff).
Flagging antenna 315n with a relative difference before and after smoothing of 26.19% (compared to the 20.00% cutoff).
Flagging antenna 316n with a relative difference before and after smoothing of 32.78% (compared to the 20.00% cutoff).
Flagging antenna 317n with a relative difference before and after smoothing of 38.22% (compared to the 20.00% cutoff).
Flagging antenna 319n with a relative difference before and after smoothing of 104.28% (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()
No description has been provided for this image

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 140 Amplitude Waterfalls

No description has been provided for this image

Antenna 221 Amplitude Waterfalls

No description has been provided for this image

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 140 Phase Waterfalls

No description has been provided for this image

Antenna 221 Phase Waterfalls

No description has been provided for this image

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.
No description has been provided for this image
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

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
Mean of empty slice
set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image

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.
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image

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 45.78 minutes.