Second Round of Full Day RFI Flagging¶
by Josh Dillon, last updated October 13, 2024
This notebook is synthesizes information from individual delay_filtered_average_zscore notebooks to find low-level RFI and flag it. That notebook takes smooth_calibrated data, redundantly averages it, performs a high-pass delay filter, and then incoherently averages across baselines, creating a per-polarization z-score. This notebook then takes that whole night of z-scores and finds a new set of flags to both add to the smooth_cal files, which are updated in place, and to write down as new UVFlag waterfall-type .h5 files.
Here's a set of links to skip to particular figures and tables:
• Figure 1: Waterfall of Maximum z-Score of Either Polarization Before Round 2 Flagging¶
• Figure 2: Histogram of z-scores¶
• Figure 3: Waterfall of Maximum z-Score of Either Polarization After Round 2 Flagging¶
• Figure 4: Spectra of Time-Averaged z-Scores¶
• Figure 5: Summary of Flags Before and After Round 2 Flagging¶
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 matplotlib.pyplot as plt
import matplotlib
import copy
import warnings
from pyuvdata import UVFlag, UVCal
from hera_cal import utils
from hera_qm import xrfi
from hera_qm.time_series_metrics import true_stretches
from hera_filters import dspec
from IPython.display import display, HTML
%matplotlib inline
display(HTML("<style>.container { width:100% !important; }</style>"))
_ = np.seterr(all='ignore') # get rid of red warnings
%config InlineBackend.figure_format = 'retina'
In [3]:
# get input data file names
SUM_FILE = os.environ.get("SUM_FILE", None)
# SUM_FILE = '/lustre/aoc/projects/hera/h6c-analysis/IDR2/2459861/zen.2459861.25297.sum.uvh5'
SUM_SUFFIX = os.environ.get("SUM_SUFFIX", 'sum.uvh5')
# get input and output suffixes
SMOOTH_CAL_SUFFIX = os.environ.get("SMOOTH_CAL_SUFFIX", 'sum.smooth.calfits')
ZSCORE_SUFFIX = os.environ.get("ZSCORE_SUFFIX", 'sum.red_avg_zscore.h5')
FLAG_WATERFALL2_SUFFIX = os.environ.get("FLAG_WATERFALL2_SUFFIX", 'sum.flag_waterfall_round_2.h5')
OUT_YAML_SUFFIX = os.environ.get("OUT_YAML_SUFFIX", '_aposteriori_flags.yaml')
OUT_YAML_DIR = os.environ.get("OUT_YAML_DIR", None)
# build globs
sum_glob = '.'.join(SUM_FILE.split('.')[:-3]) + '.*.' + SUM_SUFFIX
cal_files_glob = sum_glob.replace(SUM_SUFFIX, SMOOTH_CAL_SUFFIX)
zscore_glob = sum_glob.replace(SUM_SUFFIX, ZSCORE_SUFFIX)
# build out yaml file
if OUT_YAML_DIR is None:
OUT_YAML_DIR = os.path.dirname(SUM_FILE)
out_yaml_file = os.path.join(OUT_YAML_DIR, SUM_FILE.split('.')[-4] + OUT_YAML_SUFFIX)
# get flagging parameters
Z_THRESH = float(os.environ.get("Z_THRESH", 4))
WS_Z_THRESH = float(os.environ.get("WS_Z_THRESH", 2))
AVG_Z_THRESH = float(os.environ.get("AVG_Z_THRESH", 1))
MAX_FREQ_FLAG_FRAC = float(os.environ.get("MAX_FREQ_FLAG_FRAC", .25))
MAX_TIME_FLAG_FRAC = float(os.environ.get("MAX_TIME_FLAG_FRAC", .1))
AVG_SPECTRUM_FILTER_DELAY = float(os.environ.get("AVG_SPECTRUM_FILTER_DELAY", 250)) # in ns
EIGENVAL_CUTOFF = float(os.environ.get("EIGENVAL_CUTOFF", 1e-12))
TIME_AVG_DELAY_FILT_SNR_THRESH = float(os.environ.get("TIME_AVG_DELAY_FILT_SNR_THRESH", 4.0))
TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE = float(os.environ.get("TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE", 1.5))
for setting in ['Z_THRESH', 'WS_Z_THRESH', 'AVG_Z_THRESH', 'MAX_FREQ_FLAG_FRAC', 'MAX_TIME_FLAG_FRAC', 'AVG_SPECTRUM_FILTER_DELAY',
'EIGENVAL_CUTOFF', 'TIME_AVG_DELAY_FILT_SNR_THRESH', 'TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE']:
print(f'{setting} = {eval(setting)}')
Z_THRESH = 4.0 WS_Z_THRESH = 2.0 AVG_Z_THRESH = 1.0 MAX_FREQ_FLAG_FRAC = 0.25 MAX_TIME_FLAG_FRAC = 0.1 AVG_SPECTRUM_FILTER_DELAY = 250.0 EIGENVAL_CUTOFF = 1e-12 TIME_AVG_DELAY_FILT_SNR_THRESH = 4.0 TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE = 1.5
Load z-scores¶
In [4]:
# load z-scores
zscore_files = sorted(glob.glob(zscore_glob))
print(f'Found {len(zscore_files)} *.{ZSCORE_SUFFIX} files starting with {zscore_files[0]}.')
uvf = UVFlag(zscore_files, use_future_array_shapes=True)
Found 1851 *.sum.red_avg_zscore.h5 files starting with /mnt/sn1/data2/2460692/zen.2460692.25232.sum.red_avg_zscore.h5.
In [5]:
# get calibration solution files
cal_files = sorted(glob.glob(cal_files_glob))
print(f'Found {len(cal_files)} *.{SMOOTH_CAL_SUFFIX} files starting with {cal_files[0]}.')
Found 1851 *.sum.smooth.calfits files starting with /mnt/sn1/data2/2460692/zen.2460692.25232.sum.smooth.calfits.
In [6]:
assert len(zscore_files) == len(cal_files)
In [7]:
# extract z-scores and correct by a single number per polarization to account for biases created by filtering
zscore = {pol: uvf.metric_array[:, :, np.argwhere(uvf.polarization_array == utils.polstr2num(pol, x_orientation=uvf.x_orientation))[0][0]] for pol in ['ee', 'nn']}
zscore = {pol: zscore[pol] - np.nanmedian(zscore[pol]) for pol in zscore}
In [8]:
freqs = uvf.freq_array
times = uvf.time_array
In [9]:
extent = [freqs[0] / 1e6, freqs[-1] / 1e6, times[-1] - int(times[0]), times[0] - int(times[0])]
In [10]:
def plot_max_z_score(zscore, flags=None, vmin=-5, vmax=5):
if flags is None:
flags = np.any(~np.isfinite(list(zscore.values())), axis=0)
plt.figure(figsize=(14,10), dpi=100)
plt.imshow(np.where(flags, np.nan, np.nanmax([zscore['ee'], zscore['nn']], axis=0)), aspect='auto',
cmap='coolwarm', interpolation='none', vmin=vmin, vmax=vmax, extent=extent)
plt.colorbar(location='top', label='Max z-score of either polarization', extend='both', aspect=40, pad=.02)
plt.xlabel('Frequency (MHz)')
plt.ylabel(f'JD - {int(times[0])}')
plt.tight_layout()
Figure 1: Waterfall of Maximum z-Score of Either Polarization Before Round 2 Flagging¶
Shows the worse of the two results from delay_filtered_average_zscore from either polarization. Dips near flagged channels are expected, due to overfitting of noise. Positive-going excursions are problematic and likely evidence of RFI.
In [11]:
plot_max_z_score(zscore)
All-NaN axis encountered
In [12]:
def plot_histogram():
plt.figure(figsize=(14,4), dpi=100)
bins = np.arange(-50, 100, .1)
hist_ee = plt.hist(np.ravel(zscore['ee']), bins=bins, density=True, label='ee-polarized z-scores', alpha=.5)
hist_nn = plt.hist(np.ravel(zscore['nn']), bins=bins, density=True, label='nn-polarized z-scores', alpha=.5)
plt.plot(bins, (2*np.pi)**-.5 * np.exp(-bins**2 / 2), 'k:', label='Gaussian approximate\nnoise-only distribution')
plt.axvline(WS_Z_THRESH, c='r', ls='--', label='Watershed z-score')
plt.axvline(Z_THRESH, c='r', ls='-', label='Threshold z-score')
plt.yscale('log')
all_densities = np.concatenate([hist_ee[0][hist_ee[0] > 0], hist_nn[0][hist_nn[0] > 0]])
plt.ylim(np.min(all_densities) / 2, np.max(all_densities) * 2)
plt.xlim([-50, 100])
plt.legend()
plt.xlabel('z-score')
plt.ylabel('Density')
plt.tight_layout()
Figure 2: Histogram of z-scores¶
Shows a comparison of the histogram of z-scores in this file (one per polarization) to a Gaussian approximation of what one might expect from thermal noise. Without filtering, the actual distribution is a weighted sum of Rayleigh distributions. Filtering further complicates this. To make the z-scores more reliable, a single per-polarization median is subtracted from each waterfall, which allows us to flag low-level outliers with more confidence. Any points beyond the solid red line are flagged. Any points neighboring a flag beyond the dashed red line are also flagged. Finally, flagging is performed for low-level outliers in whole times or channels.
In [13]:
plot_histogram()
Perform flagging¶
In [14]:
def iteratively_flag_on_averaged_zscore(flags, zscore, avg_func=np.nanmean, avg_z_thresh=AVG_Z_THRESH, verbose=True):
'''Flag whole integrations or channels based on average z-score. This is done
iteratively to prevent bad times affecting channel averages or vice versa.'''
flagged_chan_count = 0
flagged_int_count = 0
while True:
zspec = avg_func(np.where(flags, np.nan, zscore), axis=0)
ztseries = avg_func(np.where(flags, np.nan, zscore), axis=1)
if (np.nanmax(zspec) < avg_z_thresh) and (np.nanmax(ztseries) < avg_z_thresh):
break
if np.nanmax(zspec) >= np.nanmax(ztseries):
flagged_chan_count += np.sum((zspec >= np.nanmax(ztseries)) & (zspec >= avg_z_thresh))
flags[:, (zspec >= np.nanmax(ztseries)) & (zspec >= avg_z_thresh)] = True
else:
flagged_int_count += np.sum((ztseries >= np.nanmax(zspec)) & (ztseries >= avg_z_thresh))
flags[(ztseries >= np.nanmax(zspec)) & (ztseries >= avg_z_thresh), :] = True
if verbose:
print(f'\tFlagging an additional {flagged_int_count} integrations and {flagged_chan_count} channels.')
def impose_max_chan_flag_frac(flags, max_flag_frac=MAX_FREQ_FLAG_FRAC, verbose=True):
'''Flag channels already flagged more than max_flag_frac (excluding completely flagged times).'''
unflagged_times = ~np.all(flags, axis=1)
frequently_flagged_chans = np.mean(flags[unflagged_times, :], axis=0) >= max_flag_frac
if verbose:
print(f'\tFlagging {np.sum(frequently_flagged_chans) - np.sum(np.all(flags, axis=0))} channels previously flagged {max_flag_frac:.2%} or more.')
flags[:, frequently_flagged_chans] = True
def impose_max_time_flag_frac(flags, max_flag_frac=MAX_TIME_FLAG_FRAC, verbose=True):
'''Flag times already flagged more than max_flag_frac (excluding completely flagged channels).'''
unflagged_chans = ~np.all(flags, axis=0)
frequently_flagged_times = np.mean(flags[:, unflagged_chans], axis=1) >= max_flag_frac
if verbose:
print(f'\tFlagging {np.sum(frequently_flagged_times) - np.sum(np.all(flags, axis=1))} times previously flagged {max_flag_frac:.2%} or more.')
flags[frequently_flagged_times, :] = True
def time_avg_zscore_dly_filt_SNRs(flags, filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF):
"""Produces SNRs after time-averaging z-scores and delay filtering, accounting for flagging's effect on the filter."""
# figure out high and low band based on FM gap at 100 MHz
flagged_stretches = true_stretches(np.all(flags, axis=0))
FM_gap = [fs for fs in flagged_stretches if fs.start <= np.argmin(np.abs(freqs - 100e6)) < fs.stop][0]
low_band = slice((0 if flagged_stretches[0].start != 0 else flagged_stretches[0].stop), FM_gap.start)
high_band = slice(FM_gap.stop, (len(freqs) if flagged_stretches[-1].stop != len(freqs) else flagged_stretches[-1].start))
filt_SNR = {}
for pol in zscore:
# calculate timeavg_SNR and filter
noise_prediction = 1.0 / np.sum(~flags, axis=0)**.5
timeavg_SNR = np.nanmean(np.where(flags, np.nan, zscore[pol] / noise_prediction), axis=0)
wgts = np.where(np.isfinite(timeavg_SNR), 1, 0)
model = np.zeros_like(timeavg_SNR)
for band in [low_band, high_band]:
model[band], _, _ = dspec.fourier_filter(freqs[band], np.where(np.isfinite(timeavg_SNR[band]), timeavg_SNR[band], 0),
wgts[band], [0], [AVG_SPECTRUM_FILTER_DELAY / 1e9], mode="dpss_solve",
eigenval_cutoff=[EIGENVAL_CUTOFF], suppression_factors=[EIGENVAL_CUTOFF])
filt_SNR[pol] = timeavg_SNR - model
# correct for impact of filter
correction_factors = np.ones_like(wgts) * np.nan
for band in [low_band, high_band]:
X = dspec.dpss_operator(freqs[band], [0], filter_half_widths=[AVG_SPECTRUM_FILTER_DELAY / 1e9], eigenval_cutoff=[EIGENVAL_CUTOFF])[0]
W = wgts[band]
leverage = np.diag(X @ np.linalg.pinv(np.dot(X.T * W, X)) @ (X.T * W))
correction_factors[band] = np.where(leverage > 0, (1 - leverage)**.5, np.nan) # because the underlying data should be gaussian
filt_SNR[pol] /= correction_factors
return filt_SNR
def iteratively_flag_on_delay_filtered_time_avg_zscore(flags, thresh=TIME_AVG_DELAY_FILT_SNR_THRESH, dynamic_range=TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE,
filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF):
"""Flag whole channels based on their outlierness after delay-filterd time-averaged zscores.
This is done iteratively since the delay filter can be unduly influenced by large outliers."""
filt_SNR = time_avg_zscore_dly_filt_SNRs(flags, filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF)
while True:
largest_SNR = np.nanmax(list(filt_SNR.values()))
if largest_SNR < thresh:
break
#
cut = np.max([thresh, largest_SNR / dynamic_range])
for pol in filt_SNR:
flags[:, filt_SNR[pol] > cut] = True
filt_SNR = time_avg_zscore_dly_filt_SNRs(flags, filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF)
In [15]:
flags = np.any(~np.isfinite(list(zscore.values())), axis=0)
print(f'{np.mean(flags):.3%} of waterfall flagged to start.')
# flag whole integrations or channels using outliers in median
while True:
nflags = np.sum(flags)
for pol in ['ee', 'nn']:
iteratively_flag_on_averaged_zscore(flags, zscore[pol], avg_func=np.nanmedian, avg_z_thresh=AVG_Z_THRESH, verbose=True)
impose_max_chan_flag_frac(flags, max_flag_frac=MAX_FREQ_FLAG_FRAC, verbose=True)
impose_max_time_flag_frac(flags, max_flag_frac=MAX_TIME_FLAG_FRAC, verbose=True)
if np.sum(flags) == nflags:
break
print(f'{np.mean(flags):.3%} of waterfall flagged after flagging whole times and channels with median z > {AVG_Z_THRESH}.')
# flag largest outliers
for pol in ['ee', 'nn']:
flags |= (zscore[pol] > Z_THRESH)
print(f'{np.mean(flags):.3%} of waterfall flagged after flagging z > {Z_THRESH} outliers.')
# watershed flagging
while True:
nflags = np.sum(flags)
for pol in ['ee', 'nn']:
flags |= xrfi._ws_flag_waterfall(zscore[pol], flags, WS_Z_THRESH)
if np.sum(flags) == nflags:
break
print(f'{np.mean(flags):.3%} of waterfall flagged after watershed flagging on z > {WS_Z_THRESH} neighbors of prior flags.')
# flag whole integrations or channels using outliers in mean
while True:
nflags = np.sum(flags)
for pol in ['ee', 'nn']:
iteratively_flag_on_averaged_zscore(flags, zscore[pol], avg_func=np.nanmean, avg_z_thresh=AVG_Z_THRESH, verbose=True)
impose_max_chan_flag_frac(flags, max_flag_frac=MAX_FREQ_FLAG_FRAC, verbose=True)
impose_max_time_flag_frac(flags, max_flag_frac=MAX_TIME_FLAG_FRAC, verbose=True)
if np.sum(flags) == nflags:
break
print(f'{np.mean(flags):.3%} of waterfall flagged after flagging whole times and channels with average z > {AVG_Z_THRESH}.')
# flag channels based on delay filter
iteratively_flag_on_delay_filtered_time_avg_zscore(flags, thresh=TIME_AVG_DELAY_FILT_SNR_THRESH, dynamic_range=TIME_AVG_DELAY_FILT_SNR_DYNAMIC_RANGE,
filter_delay=AVG_SPECTRUM_FILTER_DELAY, eigenval_cutoff=EIGENVAL_CUTOFF)
print(f'{np.mean(flags):.3%} of flagging channels that are {TIME_AVG_DELAY_FILT_SNR_THRESH}σ outliers after delay filtering the time average.')
# watershed flagging again
while True:
nflags = np.sum(flags)
for pol in ['ee', 'nn']:
flags |= xrfi._ws_flag_waterfall(zscore[pol], flags, WS_Z_THRESH)
if np.sum(flags) == nflags:
break
print(f'{np.mean(flags):.3%} of waterfall flagged after another round of watershed flagging on z > {WS_Z_THRESH} neighbors of prior flags.')
28.130% of waterfall flagged to start.
All-NaN slice encountered
Flagging an additional 1 integrations and 8 channels. Flagging 1 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more.
Flagging an additional 1 integrations and 10 channels. Flagging 0 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more.
Flagging an additional 0 integrations and 0 channels. Flagging 0 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more.
Flagging an additional 0 integrations and 0 channels. Flagging 0 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more. 29.122% of waterfall flagged after flagging whole times and channels with median z > 1.0. 30.780% of waterfall flagged after flagging z > 4.0 outliers.
35.197% of waterfall flagged after watershed flagging on z > 2.0 neighbors of prior flags. Flagging an additional 0 integrations and 0 channels.
Mean of empty slice Mean of empty slice
Flagging 93 channels previously flagged 25.00% or more. Flagging 1116 times previously flagged 10.00% or more.
Flagging an additional 0 integrations and 0 channels. Flagging 1 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more.
Flagging an additional 0 integrations and 0 channels. Flagging 0 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more.
Flagging an additional 0 integrations and 0 channels. Flagging 0 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more. 58.599% of waterfall flagged after flagging whole times and channels with average z > 1.0.
Mean of empty slice Casting complex values to real discards the imaginary part Casting complex values to real discards the imaginary part
59.915% of flagging channels that are 4.0σ outliers after delay filtering the time average.
60.668% of waterfall flagged after another round of watershed flagging on z > 2.0 neighbors of prior flags.
Show results of flagging¶
Figure 3: Waterfall of Maximum z-Score of Either Polarization After Round 2 Flagging¶
The same as Figure 1, but after the flagging performed in this notebook.
In [16]:
plot_max_z_score(zscore, flags=flags)
All-NaN axis encountered
In [17]:
def zscore_spectra(ylim=[-3, 3], flags=flags):
fig, axes = plt.subplots(2, 1, figsize=(14,6), dpi=100, sharex=True, sharey=True, gridspec_kw={'hspace': 0})
for ax, pol in zip(axes, ['ee', 'nn']):
ax.plot(freqs / 1e6, np.nanmean(zscore[pol], axis=0),'r', label=f'{pol}-Polarization Before Round 2 Flagging', lw=.5)
ax.plot(freqs / 1e6, np.nanmean(np.where(flags, np.nan, zscore[pol]), axis=0), label=f'{pol}-Polarization After Round 2 Flagging')
ax.legend(loc='lower right')
ax.set_ylabel('Time-Averged Z-Score\n(Excluding Flags)')
ax.set_ylim(ylim)
axes[1].set_xlabel('Frequency (MHz)')
plt.tight_layout()
Figure 4: Spectra of Time-Averaged z-Scores¶
The average along the time axis of Figures 1 and 3 (though now separated per-polarization). This plot is useful for showing channels with repeated low-level RFI.
In [18]:
zscore_spectra()
Mean of empty slice Mean of empty slice
In [19]:
def summarize_flagging(flags=flags):
plt.figure(figsize=(14,10), dpi=100)
cmap = matplotlib.colors.ListedColormap(((0, 0, 0),) + matplotlib.cm.get_cmap("Set2").colors[0:2])
plt.imshow(np.where(np.any(~np.isfinite(list(zscore.values())), axis=0), 1, np.where(flags, 2, 0)),
aspect='auto', cmap=cmap, interpolation='none', extent=extent)
plt.clim([-.5, 2.5])
cbar = plt.colorbar(location='top', aspect=40, pad=.02)
cbar.set_ticks([0, 1, 2])
cbar.set_ticklabels(['Unflagged', 'Previously Flagged', 'Flagged Here Using Delayed Filtered z-Scores'])
plt.xlabel('Frequency (MHz)')
plt.ylabel(f'JD - {int(times[0])}')
plt.tight_layout()
Figure 5: Summary of Flags Before and After Round 2 Flagging¶
This plot shows which times and frequencies were flagged before and after this notebook. It is directly comparable to Figure 5 of the first round full_day_rfi notebook.
In [20]:
summarize_flagging()
The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
Save results¶
In [21]:
add_to_history = 'by full_day_rfi_round_2 notebook with the following environment:\n' + '=' * 65 + '\n' + os.popen('conda env export').read() + '=' * 65
In [22]:
tind = 0
always_flagged_ants = set()
ever_unflagged_ants = set()
for cal_file in cal_files:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# update cal_file
uvc = UVCal()
uvc.read(cal_file, use_future_array_shapes=True)
uvc.flag_array |= (flags[tind:tind + len(uvc.time_array), :].T)[None, :, :, None]
uvc.history += 'Modified ' + add_to_history
uvc.write_calfits(cal_file, clobber=True)
# keep track of flagged antennas
for antnum in uvc.ant_array:
for antpol in ['Jee', 'Jnn']:
if np.all(uvc.get_flags(antnum, antpol)):
if (antnum, antpol) not in ever_unflagged_ants:
always_flagged_ants.add((antnum, antpol))
else:
ever_unflagged_ants.add((antnum, antpol))
always_flagged_ants.discard((antnum, antpol))
# Create new flag object
uvf_out = UVFlag(uvc, waterfall=True, mode='flag')
uvf_out.flag_array |= flags[tind:tind + len(uvc.time_array), :, None]
uvf_out.history += 'Produced ' + add_to_history
uvf_out.write(cal_file.replace(SMOOTH_CAL_SUFFIX, FLAG_WATERFALL2_SUFFIX), clobber=True)
# increment time index
tind += len(uvc.time_array)
print(f'Saved {len(cal_files)} *.{FLAG_WATERFALL2_SUFFIX} files starting with {cal_files[0].replace(SMOOTH_CAL_SUFFIX, FLAG_WATERFALL2_SUFFIX)}.')
Saved 1851 *.sum.flag_waterfall_round_2.h5 files starting with /mnt/sn1/data2/2460692/zen.2460692.25232.sum.flag_waterfall_round_2.h5.
In [23]:
# write summary of entirely flagged times/freqs/ants to yaml
all_flagged_times = np.all(flags, axis=1)
all_flagged_freqs = np.all(flags, axis=0)
all_flagged_ants = sorted(always_flagged_ants)
dt = np.median(np.diff(times))
out_yml_str = 'JD_flags: ' + str([[times[flag_stretch][0] - dt / 2, times[flag_stretch][-1] + dt / 2]
for flag_stretch in true_stretches(all_flagged_times)])
df = np.median(np.diff(freqs))
out_yml_str += '\n\nfreq_flags: ' + str([[freqs[flag_stretch][0] - df / 2, freqs[flag_stretch][-1] + df / 2]
for flag_stretch in true_stretches(all_flagged_freqs)])
out_yml_str += '\n\nex_ants: ' + str(all_flagged_ants).replace("'", "").replace('(', '[').replace(')', ']')
print(f'Writing the following to {out_yaml_file}\n' + '-' * (25 + len(out_yaml_file)))
print(out_yml_str)
with open(out_yaml_file, 'w') as outfile:
outfile.writelines(out_yml_str)
Writing the following to /mnt/sn1/data2/2460692/2460692_aposteriori_flags.yaml ------------------------------------------------------------------------------ JD_flags: [[2460692.252207371, 2460692.2526547634], [2460692.2534377, 2460692.255339118], [2460692.256345751, 2460692.2565694475], [2460692.2572405357, 2460692.257352384], [2460692.258359017, 2460692.2588064093], [2460692.2597011942, 2460692.2611552197], [2460692.261267068, 2460692.261378916], [2460692.261490764, 2460692.2618263084], [2460692.2620500047, 2460692.262161853], [2460692.262385549, 2460692.262497397], [2460692.262721093, 2460692.263727726], [2460692.263839574, 2460692.263951422], [2460692.2641751184, 2460692.2643988146], [2460692.2645106628, 2460692.264622511], [2460692.264958055, 2460692.26585284], [2460692.2659646883, 2460692.267530562], [2460692.26764241, 2460692.267754258], [2460692.2682016506, 2460692.2683134987], [2460692.2684253464, 2460692.268872739], [2460692.268984587, 2460692.270214916], [2460692.2704386124, 2460692.2706623087], [2460692.270774157, 2460692.2722281823], [2460692.2723400304, 2460692.2733466635], [2460692.2734585116, 2460692.275359929], [2460692.2755836253, 2460692.276366562], [2460692.27647841, 2460692.278379828], [2460692.2784916763, 2460692.2793864612], [2460692.279610157, 2460692.2808404863], [2460692.2809523344, 2460692.2811760306], [2460692.281399727, 2460692.2840840816], [2460692.284307778, 2460692.285202563], [2460692.2854262586, 2460692.2860973473], [2460692.2865447397, 2460692.286656588], [2460692.2872158284, 2460692.29124236], [2460692.2913542083, 2460692.301420538], [2460692.3015323863, 2460692.3016442345], [2460692.3017560826, 2460692.3038811963], [2460692.3039930444, 2460692.3041048925], [2460692.3042167407, 2460692.308802513], [2460692.3093617535, 2460692.3111513234], [2460692.3112631715, 2460692.317974058], [2460692.318085906, 2460692.320546564], [2460692.3206584123, 2460692.3207702604], [2460692.3208821085, 2460692.323342767], [2460692.323454615, 2460692.327592995], [2460692.327704843, 2460692.335981603], [2460692.3378830208, 2460692.3384422613], [2460692.34325173, 2460692.3439228185], [2460692.3457123884, 2460692.3460479327], [2460692.346271629, 2460692.3468308696], [2460692.348732287, 2460692.3490678314], [2460692.3491796795, 2460692.349627072], [2460692.3510810975, 2460692.351752186], [2460692.3534299075, 2460692.353765452], [2460692.3598052496, 2460692.36036449], [2460692.3604763383, 2460692.360923731], [2460692.361147427, 2460692.3617066676], [2460692.363160693, 2460692.3636080856], [2460692.363831782, 2460692.364391022], [2460692.36450287, 2460692.3657331993], [2460692.366627984, 2460692.367299073], [2460692.369536035, 2460692.370654516], [2460692.3709900603, 2460692.3713256046], [2460692.371772997, 2460692.3721085414], [2460692.372444086, 2460692.3728914782], [2460692.3732270226, 2460692.375352137], [2460692.3755758326, 2460692.3762469213], [2460692.3763587694, 2460692.3764706175], [2460692.3765824656, 2460692.37691801], [2460692.377029858, 2460692.378148339], [2460692.3802734534, 2460692.380832694], [2460692.3809445417, 2460692.38105639], [2460692.381168238, 2460692.381280086], [2460692.3815037822, 2460692.3825104153], [2460692.3829578077, 2460692.383181504], [2460692.3836288964, 2460692.3837407446], [2460692.383964441, 2460692.384076289], [2460692.384188137, 2460692.3847473776], [2460692.3856421625, 2460692.385977707], [2460692.386089555, 2460692.386201403], [2460692.386313251, 2460692.388326517], [2460692.388550213, 2460692.389668694], [2460692.3897805423, 2460692.3898923905], [2460692.3901160867, 2460692.390339783], [2460692.390451631, 2460692.3906753273], [2460692.3911227197, 2460692.3934715297], [2460692.393583378, 2460692.393695226], [2460692.3941426184, 2460692.3942544665], [2460692.3943663146, 2460692.394925555], [2460692.3952610996, 2460692.3960440364], [2460692.3961558845, 2460692.3962677326], [2460692.396491429, 2460692.396603277], [2460692.396715125, 2460692.3972743656], [2460692.397498062, 2460692.397721758], [2460692.397945454, 2460692.398057302], [2460692.39816915, 2460692.398280998], [2460692.3983928463, 2460692.3993994794], [2460692.3995113275, 2460692.3997350237], [2460692.39995872, 2460692.400070568], [2460692.4005179605, 2460692.400853505], [2460692.4013008974, 2460692.4024193785], [2460692.4025312266, 2460692.4026430747], [2460692.403090467, 2460692.4032023153], [2460692.403426011, 2460692.4036497073], [2460692.4037615554, 2460692.4111435306], [2460692.4112553787, 2460692.411590923], [2460692.4120383156, 2460692.4138278854], [2460692.4146108218, 2460692.4152819104], [2460692.4153937586, 2460692.416735936], [2460692.4170714803, 2460692.4171833284], [2460692.4174070247, 2460692.417518873], [2460692.417742569, 2460692.4190847464], [2460692.4193084426, 2460692.4194202907], [2460692.419532139, 2460692.419755835], [2460692.419867683, 2460692.4200913794], [2460692.4205387714, 2460692.4214335564], [2460692.4215454045, 2460692.422216493], [2460692.4225520375, 2460692.4233349743], [2460692.4234468224, 2460692.4235586706], [2460692.4236705187, 2460692.423894215], [2460692.424006063, 2460692.424117911], [2460692.4244534555, 2460692.4245653036], [2460692.4246771517, 2460692.4254600885], [2460692.4255719366, 2460692.4266904173], [2460692.4268022655, 2460692.4269141136], [2460692.42713781, 2460692.4278088985], [2460692.428144443, 2460692.428256291], [2460692.4287036834, 2460692.429039228], [2460692.429262924, 2460692.4299340127], [2460692.430381405, 2460692.4304932533], [2460692.4306051014, 2460692.431052494], [2460692.431723582, 2460692.43183543], [2460692.4319472783, 2460692.432394671], [2460692.432506519, 2460692.432618367], [2460692.4328420633, 2460692.4330657595], [2460692.433401304, 2460692.433513152], [2460692.433736848, 2460692.4348553293], [2460692.4349671775, 2460692.4350790256], [2460692.4351908737, 2460692.435302722], [2460692.43541457, 2460692.436421203], [2460692.436644899, 2460692.4367567473], [2460692.4370922917, 2460692.4373159874], [2460692.4374278355, 2460692.437651532], [2460692.43776338, 2460692.4380989242], [2460692.4382107724, 2460692.438881861], [2460692.438993709, 2460692.4394411016], [2460692.439776646, 2460692.439888494], [2460692.4402240384, 2460692.4403358866], [2460692.4404477347, 2460692.440671431], [2460692.440895127, 2460692.4410069752], [2460692.4411188234, 2460692.4413425196], [2460692.4414543677, 2460692.441789912], [2460692.4420136083, 2460692.4421254564], [2460692.4423491526, 2460692.442572849], [2460692.442684697, 2460692.442796545], [2460692.4429083928, 2460692.443020241], [2460692.443132089, 2460692.443243937], [2460692.4435794814, 2460692.4436913296], [2460692.443915026, 2460692.44425057], [2460692.4444742664, 2460692.4445861145], [2460692.4446979626, 2460692.4448098107], [2460692.444921659, 2460692.445033507], [2460692.4457045957, 2460692.446151988], [2460692.446263836, 2460692.447941558], [2460692.449283735, 2460692.4500666717], [2460692.45017852, 2460692.450514064], [2460692.4507377604, 2460692.4518562416], [2460692.4519680897, 2460692.452191786], [2460692.4527510265, 2460692.457448647], [2460692.457560495, 2460692.4577841912], [2460692.4580078875, 2460692.4581197356], [2460692.4587908243, 2460692.4604685456], [2460692.4605803937, 2460692.460692242], [2460692.461698875, 2460692.4622581154], [2460692.462817356, 2460692.463041052], [2460692.4633765966, 2460692.463600293], [2460692.4657254065, 2460692.4658372547], [2460692.466060951, 2460692.466172799], [2460692.4663964952, 2460692.4667320396], [2460692.467179432, 2460692.4677386726], [2460692.4684097613, 2460692.4694163944], [2460692.4703111793, 2460692.470982268], [2460692.4710941156, 2460692.4712059638], [2460692.4738903185, 2460692.474449559], [2460692.4748969516, 2460692.475232496], [2460692.4757917365, 2460692.476462825], [2460692.476686521, 2460692.4771339134], [2460692.4783642427, 2460692.478476091], [2460692.478587939, 2460692.478699787], [2460692.482838167, 2460692.483173711], [2460692.4863054585, 2460692.4865291547], [2460692.4875357877, 2460692.488095028], [2460692.488318724, 2460692.489101661], [2460692.4907793826, 2460692.491338623], [2460692.4914504713, 2460692.491897864], [2460692.493687433, 2460692.4940229775], [2460692.5072210543, 2460692.5075565986], [2460692.51930065, 2460692.519412498], [2460692.5231034853, 2460692.5233271816], [2460692.523886422, 2460692.5242219665], [2460692.526794473, 2460692.5269063213], [2460692.527241865, 2460692.5275774095], [2460692.5339527517, 2460692.534176448], [2460692.5345119922, 2460692.5349593847], [2460692.538538524, 2460692.538650372], [2460692.5393214608, 2460692.539433309], [2460692.5411110306, 2460692.541334727], [2460692.541670271, 2460692.5417821193], [2460692.5430124486, 2460692.543571689], [2460692.550170727, 2460692.5506181195], [2460692.5507299677, 2460692.550841816], [2460692.555427588, 2460692.5557631324], [2460692.5568816136, 2460692.55710531], [2460692.560237057, 2460692.5605726014], [2460692.5631451076, 2460692.5632569557], [2460692.563704348, 2460692.5639280444], [2460692.565270222, 2460692.565493918], [2460692.5745536145, 2460692.5747773107], [2460692.58484364, 2460692.5852910327], [2460692.5873042988, 2460692.587639843], [2460692.58842278, 2460692.5893175644], [2460692.589764957, 2460692.590659742], [2460692.590883438, 2460692.5924493116], [2460692.592673008, 2460692.592784856], [2460692.593008552, 2460692.5940151853], [2460692.5941270334, 2460692.594574426], [2460692.595133666, 2460692.595357362], [2460692.596140299, 2460692.596252147], [2460692.5964758433, 2460692.596923236], [2460692.598265413, 2460692.5983772613], [2460692.600054983, 2460692.6002786793], [2460692.6083317427, 2460692.608667287], [2460692.6112397937, 2460692.611351642], [2460692.611575338, 2460692.611799034], [2460692.6133649074, 2460692.6134767556], [2460692.615042629, 2460692.6151544773], [2460692.616720351, 2460692.616944047], [2460692.6170558953, 2460692.617391439], [2460692.624661566, 2460692.6248852625], [2460692.6261155917, 2460692.62622744], [2460692.6268985285, 2460692.6270103767], [2460692.6279051616, 2460692.628352554], [2460692.6290236423, 2460692.6292473385], [2460692.629582883, 2460692.6300302753], [2460692.631596149, 2460692.631819845], [2460692.6320435414, 2460692.6332738707], [2460692.633721263, 2460692.6339449594], [2460692.6345041995, 2460692.6346160476], [2460692.6355108325, 2460692.6356226807], [2460692.63685301, 2460692.6374122505], [2460692.638978124, 2460692.6393136685], [2460692.64568901, 2460692.6460245545], [2460692.6461364026, 2460692.646471947], [2460692.6481496687, 2460692.648261517], [2460692.6490444536, 2460692.6500510867], [2460692.6540776184, 2460692.6543013146], [2460692.656538277, 2460692.656985669], [2460692.657880454, 2460692.65810415], [2460692.6613477455, 2460692.666269062]] freq_flags: [[49911499.0234375, 50155639.6484375], [62240600.5859375, 62973022.4609375], [69931030.2734375, 70053100.5859375], [78842163.0859375, 78964233.3984375], [87509155.2734375, 108016967.7734375], [109970092.7734375, 110092163.0859375], [112167358.3984375, 112411499.0234375], [112655639.6484375, 113143920.8984375], [113265991.2109375, 113388061.5234375], [113632202.1484375, 113754272.4609375], [114486694.3359375, 114608764.6484375], [116073608.3984375, 116195678.7109375], [116439819.3359375, 117050170.8984375], [120468139.6484375, 120590209.9609375], [121444702.1484375, 122177124.0234375], [124496459.9609375, 125350952.1484375], [127548217.7734375, 127670288.0859375], [129867553.7109375, 130233764.6484375], [135848999.0234375, 135971069.3359375], [136093139.6484375, 138290405.2734375], [138656616.2109375, 138778686.5234375], [141464233.3984375, 141586303.7109375], [141708374.0234375, 141952514.6484375], [142074584.9609375, 142318725.5859375], [142684936.5234375, 142929077.1484375], [143051147.4609375, 143539428.7109375], [143783569.3359375, 144027709.9609375], [145492553.7109375, 146102905.2734375], [147445678.7109375, 147567749.0234375], [148422241.2109375, 148544311.5234375], [149154663.0859375, 149276733.3984375], [149642944.3359375, 149765014.6484375], [149887084.9609375, 150009155.2734375], [150131225.5859375, 150253295.8984375], [154159545.8984375, 154403686.5234375], [155014038.0859375, 155136108.3984375], [155258178.7109375, 155380249.0234375], [157577514.6484375, 157699584.9609375], [158187866.2109375, 158432006.8359375], [158676147.4609375, 158798217.7734375], [159164428.7109375, 159286499.0234375], [169906616.2109375, 170150756.8359375], [170883178.7109375, 171005249.0234375], [171249389.6484375, 171371459.9609375], [171737670.8984375, 171859741.2109375], [175155639.6484375, 175277709.9609375], [181137084.9609375, 181259155.2734375], [183212280.2734375, 183334350.5859375], [187362670.8984375, 187606811.5234375], [189926147.4609375, 190048217.7734375], [191024780.2734375, 191879272.4609375], [193222045.8984375, 193344116.2109375], [195663452.1484375, 195785522.4609375], [197128295.8984375, 197372436.5234375], [197738647.4609375, 197860717.7734375], [198104858.3984375, 198348999.0234375], [198471069.3359375, 198959350.5859375], [199203491.2109375, 199325561.5234375], [201644897.4609375, 201889038.0859375], [202865600.5859375, 202987670.8984375], [203231811.5234375, 203353881.8359375], [204940795.8984375, 205062866.2109375], [205184936.5234375, 205307006.8359375], [207138061.5234375, 207382202.1484375], [208480834.9609375, 208724975.5859375], [209823608.3984375, 210189819.3359375], [211776733.3984375, 212631225.5859375], [212997436.5234375, 213485717.7734375], [214950561.5234375, 215438842.7734375], [219833374.0234375, 219955444.3359375], [220565795.8984375, 220809936.5234375], [221176147.4609375, 221298217.7734375], [222885131.8359375, 223495483.3984375], [225204467.7734375, 234359741.2109375]] ex_ants: [[8, Jee], [8, Jnn], [9, Jee], [15, Jnn], [16, Jee], [17, Jnn], [18, Jee], [18, Jnn], [21, Jee], [22, Jee], [22, Jnn], [27, Jee], [27, Jnn], [28, Jee], [28, Jnn], [29, Jee], [29, Jnn], [30, Jee], [30, Jnn], [31, Jnn], [32, Jnn], [33, Jnn], [34, Jee], [34, Jnn], [35, Jee], [35, Jnn], [37, Jnn], [40, Jee], [40, Jnn], [41, Jee], [41, Jnn], [42, Jee], [42, Jnn], [45, Jee], [46, Jee], [47, Jee], [47, Jnn], [48, Jee], [48, Jnn], [49, Jee], [49, Jnn], [51, Jee], [54, Jee], [54, Jnn], [55, Jee], [55, Jnn], [56, Jee], [56, Jnn], [57, Jee], [57, Jnn], [61, Jee], [61, Jnn], [62, Jee], [62, Jnn], [63, Jee], [63, Jnn], [64, Jee], [64, Jnn], [68, Jee], [68, Jnn], [69, Jee], [69, Jnn], [70, Jee], [70, Jnn], [71, Jee], [71, Jnn], [72, Jee], [72, Jnn], [73, Jee], [77, Jee], [77, Jnn], [78, Jee], [78, Jnn], [83, Jee], [83, Jnn], [86, Jee], [87, Jee], [88, Jee], [88, Jnn], [89, Jee], [90, Jee], [90, Jnn], [92, Jee], [93, Jee], [95, Jee], [97, Jnn], [100, Jnn], [103, Jnn], [104, Jee], [104, Jnn], [107, Jee], [107, Jnn], [108, Jnn], [109, Jnn], [117, Jee], [119, Jee], [119, Jnn], [120, Jee], [120, Jnn], [121, Jee], [121, Jnn], [125, Jee], [125, Jnn], [127, Jee], [127, Jnn], [130, Jnn], [134, Jee], [135, Jee], [140, Jee], [142, Jnn], [155, Jee], [161, Jee], [161, Jnn], [170, Jee], [171, Jnn], [176, Jnn], [179, Jee], [179, Jnn], [180, Jee], [180, Jnn], [182, Jee], [182, Jnn], [184, Jee], [187, Jee], [188, Jnn], [198, Jnn], [199, Jee], [199, Jnn], [200, Jee], [200, Jnn], [201, Jnn], [202, Jnn], [204, Jee], [208, Jee], [209, Jnn], [212, Jnn], [213, Jee], [215, Jnn], [216, Jee], [218, Jee], [218, Jnn], [221, Jee], [226, Jnn], [232, Jee], [235, Jnn], [240, Jee], [240, Jnn], [241, Jee], [241, Jnn], [242, Jee], [242, Jnn], [243, Jee], [243, Jnn], [245, Jnn], [246, Jee], [250, Jee], [251, Jee], [253, Jnn], [255, Jnn], [262, Jee], [262, Jnn], [266, Jee], [268, Jnn], [320, Jee], [320, Jnn], [321, Jee], [321, Jnn], [323, Jee], [323, Jnn], [324, Jee], [324, Jnn], [325, Jee], [325, Jnn], [326, Jee], [326, Jnn], [327, Jee], [327, Jnn], [328, Jee], [328, Jnn], [329, Jee], [329, Jnn], [331, Jee], [331, Jnn], [332, Jee], [332, Jnn], [333, Jee], [333, Jnn], [336, Jee], [336, Jnn], [340, Jee], [340, Jnn]]
Metadata¶
In [24]:
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: 3.6.2.dev110+g0529798 hera_qm: 2.2.0 hera_filters: 0.1.6.dev1+g297dcce
hera_notebook_templates: 0.1.dev936+gdc93cad pyuvdata: 3.0.1.dev70+g283dda3
In [25]:
print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')
Finished execution in 40.58 minutes.