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/data1/2460747/zen.2460747.25250.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/data1/2460747/zen.2460747.25250.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.')
28.382% of waterfall flagged to start.
All-NaN slice encountered
Flagging an additional 318 integrations and 12 channels. Flagging 0 channels previously flagged 25.00% or more. Flagging 1 times previously flagged 10.00% or more.
Flagging an additional 0 integrations and 11 channels. Flagging 0 channels previously flagged 25.00% or more. Flagging 0 times previously flagged 10.00% or more.
Flagging an additional 1 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.
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. 36.637% of waterfall flagged after flagging whole times and channels with median z > 1.0. 37.985% of waterfall flagged after flagging z > 4.0 outliers.
41.178% of waterfall flagged after watershed flagging on z > 2.0 neighbors of prior flags. Flagging an additional 0 integrations and 0 channels. Flagging 32 channels previously flagged 25.00% or more. Flagging 809 times previously flagged 10.00% or more.
Mean of empty slice Mean of empty slice
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. 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. 56.821% 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
59.294% of flagging channels that are 4.0σ outliers after delay filtering the time average.
59.575% 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 in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` 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/data1/2460747/zen.2460747.25250.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/data1/2460747/2460747_aposteriori_flags.yaml ------------------------------------------------------------------------------ JD_flags: [[np.float64(2460747.252392636), np.float64(2460747.2604457)], [np.float64(2460747.2605575477), np.float64(2460747.260893092)], [np.float64(2460747.2611167883), np.float64(2460747.2612286364)], [np.float64(2460747.2614523326), np.float64(2460747.261787877)], [np.float64(2460747.2621234213), np.float64(2460747.2623471175)], [np.float64(2460747.2625708138), np.float64(2460747.26279451)], [np.float64(2460747.262906358), np.float64(2460747.2632419025)], [np.float64(2460747.2634655987), np.float64(2460747.263689295)], [np.float64(2460747.263912991), np.float64(2460747.2640248393)], [np.float64(2460747.2641366874), np.float64(2460747.2644722317)], [np.float64(2460747.264695928), np.float64(2460747.264919624)], [np.float64(2460747.2650314723), np.float64(2460747.2651433204)], [np.float64(2460747.2652551685), np.float64(2460747.265478865)], [np.float64(2460747.265590713), np.float64(2460747.265814409)], [np.float64(2460747.266038105), np.float64(2460747.266149953)], [np.float64(2460747.266261801), np.float64(2460747.2665973455)], [np.float64(2460747.2667091936), np.float64(2460747.267156586)], [np.float64(2460747.2673802823), np.float64(2460747.2678276747)], [np.float64(2460747.267939523), np.float64(2460747.268051371)], [np.float64(2460747.268163219), np.float64(2460747.268275067)], [np.float64(2460747.2684987634), np.float64(2460747.2687224597)], [np.float64(2460747.268834308), np.float64(2460747.268946156)], [np.float64(2460747.2692817003), np.float64(2460747.2693935484)], [np.float64(2460747.2695053965), np.float64(2460747.2696172446)], [np.float64(2460747.2697290927), np.float64(2460747.269952789)], [np.float64(2460747.270064637), np.float64(2460747.270176485)], [np.float64(2460747.2702883333), np.float64(2460747.2705120295)], [np.float64(2460747.2706238776), np.float64(2460747.2707357258)], [np.float64(2460747.270847574), np.float64(2460747.27107127)], [np.float64(2460747.271183118), np.float64(2460747.2712949663)], [np.float64(2460747.2714068145), np.float64(2460747.2716305107)], [np.float64(2460747.2719660546), np.float64(2460747.272189751)], [np.float64(2460747.272301599), np.float64(2460747.272525295)], [np.float64(2460747.2728608395), np.float64(2460747.2729726876)], [np.float64(2460747.27342008), np.float64(2460747.273531928)], [np.float64(2460747.274091169), np.float64(2460747.274203017)], [np.float64(2460747.274426713), np.float64(2460747.2745385612)], [np.float64(2460747.2746504094), np.float64(2460747.2749859537)], [np.float64(2460747.275321498), np.float64(2460747.275433346)], [np.float64(2460747.2756570424), np.float64(2460747.2757688905)], [np.float64(2460747.276328131), np.float64(2460747.2765518273)], [np.float64(2460747.2768873717), np.float64(2460747.277222916)], [np.float64(2460747.277446612), np.float64(2460747.27755846)], [np.float64(2460747.2780058524), np.float64(2460747.2781177005)], [np.float64(2460747.2783413967), np.float64(2460747.278453245)], [np.float64(2460747.278565093), np.float64(2460747.278676941)], [np.float64(2460747.2789006373), np.float64(2460747.2791243335)], [np.float64(2460747.2793480298), np.float64(2460747.279459878)], [np.float64(2460747.279795422), np.float64(2460747.2799072703)], [np.float64(2460747.280690207), np.float64(2460747.2809139034)], [np.float64(2460747.281473144), np.float64(2460747.281584992)], [np.float64(2460747.2818086883), np.float64(2460747.2819205364)], [np.float64(2460747.2820323845), np.float64(2460747.2821442327)], [np.float64(2460747.2832627133), np.float64(2460747.2833745615)], [np.float64(2460747.2841574983), np.float64(2460747.2842693464)], [np.float64(2460747.284716739), np.float64(2460747.284940435)], [np.float64(2460747.2853878275), np.float64(2460747.2854996757)], [np.float64(2460747.2862826125), np.float64(2460747.2863944606)], [np.float64(2460747.286730005), np.float64(2460747.2870655493)], [np.float64(2460747.2871773974), np.float64(2460747.2872892455)], [np.float64(2460747.28762479), np.float64(2460747.2880721823)], [np.float64(2460747.2882958786), np.float64(2460747.288519575)], [np.float64(2460747.2896380555), np.float64(2460747.2899736)], [np.float64(2460747.2917631697), np.float64(2460747.292098714)], [np.float64(2460747.2924342584), np.float64(2460747.293217195)], [np.float64(2460747.293776436), np.float64(2460747.29411198)], [np.float64(2460747.294559372), np.float64(2460747.2947830684)], [np.float64(2460747.2948949165), np.float64(2460747.2951186127)], [np.float64(2460747.295342309), np.float64(2460747.296348942)], [np.float64(2460747.297131879), np.float64(2460747.2976911194)], [np.float64(2460747.29825036), np.float64(2460747.2997043855)], [np.float64(2460747.3001517775), np.float64(2460747.3021650435)], [np.float64(2460747.302500588), np.float64(2460747.303507221)], [np.float64(2460747.303619069), np.float64(2460747.3923146175)], [np.float64(2460747.3924264656), np.float64(2460747.392650162)], [np.float64(2460747.392873858), np.float64(2460747.398801808)], [np.float64(2460747.398913656), np.float64(2460747.4002558333)], [np.float64(2460747.4004795295), np.float64(2460747.4017098583)], [np.float64(2460747.4018217064), np.float64(2460747.403499428)], [np.float64(2460747.4040586688), np.float64(2460747.404170517)], [np.float64(2460747.4047297575), np.float64(2460747.4059600867)], [np.float64(2460747.406407479), np.float64(2460747.408644441)], [np.float64(2460747.4088681373), np.float64(2460747.409651074)], [np.float64(2460747.4098747703), np.float64(2460747.4102103147)], [np.float64(2460747.410657707), np.float64(2460747.4109932515)], [np.float64(2460747.4112169477), np.float64(2460747.411328796)], [np.float64(2460747.4118880364), np.float64(2460747.4121117326)], [np.float64(2460747.4122235808), np.float64(2460747.412335429)], [np.float64(2460747.4125591246), np.float64(2460747.412894669)], [np.float64(2460747.41401315), np.float64(2460747.414684239)], [np.float64(2460747.414796087), np.float64(2460747.4152434794)], [np.float64(2460747.4154671757), np.float64(2460747.416921201)], [np.float64(2460747.4171448974), np.float64(2460747.41815153)], [np.float64(2460747.418375226), np.float64(2460747.4187107706)], [np.float64(2460747.4188226187), np.float64(2460747.418934467)], [np.float64(2460747.419270011), np.float64(2460747.4193818592)], [np.float64(2460747.4208358848), np.float64(2460747.420947733)], [np.float64(2460747.4215069734), np.float64(2460747.4216188216)], [np.float64(2460747.421954366), np.float64(2460747.4222899103)], [np.float64(2460747.422849151), np.float64(2460747.4232965433)], [np.float64(2460747.4238557834), np.float64(2460747.424415024)], [np.float64(2460747.4267638344), np.float64(2460747.427099379)], [np.float64(2460747.429895581), np.float64(2460747.4300074293)], [np.float64(2460747.43056667), np.float64(2460747.4309022143)], [np.float64(2460747.4353761384), np.float64(2460747.435823531)], [np.float64(2460747.437724949), np.float64(2460747.438060493)], [np.float64(2460747.4391789744), np.float64(2460747.439850063)], [np.float64(2460747.441080392), np.float64(2460747.441415936)], [np.float64(2460747.4453306203), np.float64(2460747.446113557)], [np.float64(2460747.448574215), np.float64(2460747.4491334558)], [np.float64(2460747.449245304), np.float64(2460747.4496926963)], [np.float64(2460747.4574102154), np.float64(2460747.4575220635)], [np.float64(2460747.458081304), np.float64(2460747.458193152)], [np.float64(2460747.4718386214), np.float64(2460747.4720623177)], [np.float64(2460747.483023432), np.float64(2460747.4832471283)], [np.float64(2460747.4841419132), np.float64(2460747.4842537614)], [np.float64(2460747.4845893057), np.float64(2460747.484701154)], [np.float64(2460747.486378875), np.float64(2460747.486490723)], [np.float64(2460747.48727366), np.float64(2460747.4874973563)], [np.float64(2460747.4913001917), np.float64(2460747.491523888)], [np.float64(2460747.499576952), np.float64(2460747.4996888)], [np.float64(2460747.503267939), np.float64(2460747.5036034836)], [np.float64(2460747.506735231), np.float64(2460747.506958927)], [np.float64(2460747.507853712), np.float64(2460747.50796556)], [np.float64(2460747.539730422), np.float64(2460747.5399541184)], [np.float64(2460747.5409607515), np.float64(2460747.5411844477)], [np.float64(2460747.5499086), np.float64(2460747.550020448)], [np.float64(2460747.5576261193), np.float64(2460747.5577379675)], [np.float64(2460747.5599749293), np.float64(2460747.5601986255)], [np.float64(2460747.563442221), np.float64(2460747.563554069)], [np.float64(2460747.5643370054), np.float64(2460747.5644488535)], [np.float64(2460747.571271588), np.float64(2460747.5714952843)], [np.float64(2460747.5759692085), np.float64(2460747.5760810566)], [np.float64(2460747.5794365), np.float64(2460747.579548348)], [np.float64(2460747.5807786775), np.float64(2460747.5808905256)], [np.float64(2460747.584469665), np.float64(2460747.584581513)], [np.float64(2460747.6089644004), np.float64(2460747.6090762485)], [np.float64(2460747.6104184254), np.float64(2460747.6105302735)], [np.float64(2460747.613662021), np.float64(2460747.613885717)], [np.float64(2460747.6158989826), np.float64(2460747.616346375)], [np.float64(2460747.6169056157), np.float64(2460747.6175767044)], [np.float64(2460747.6250705277), np.float64(2460747.625406072)], [np.float64(2460747.6259653126), np.float64(2460747.6264127046)], [np.float64(2460747.6282022744), np.float64(2460747.6283141226)], [np.float64(2460747.6293207556), np.float64(2460747.629544452)], [np.float64(2460747.6299918443), np.float64(2460747.6301036924)], [np.float64(2460747.6303273886), np.float64(2460747.661197466)], [np.float64(2460747.661309314), np.float64(2460747.6619804027)], [np.float64(2460747.662092251), np.float64(2460747.662204099)], [np.float64(2460747.6625396432), np.float64(2460747.6626514914)], [np.float64(2460747.6628751876), np.float64(2460747.6654476942)], [np.float64(2460747.6655595424), np.float64(2460747.666454327)]] freq_flags: [[np.float64(49911499.0234375), np.float64(50155639.6484375)], [np.float64(61874389.6484375), np.float64(63461303.7109375)], [np.float64(69931030.2734375), np.float64(70053100.5859375)], [np.float64(77621459.9609375), np.float64(77743530.2734375)], [np.float64(87387084.9609375), np.float64(108139038.0859375)], [np.float64(109970092.7734375), np.float64(110092163.0859375)], [np.float64(112167358.3984375), np.float64(112411499.0234375)], [np.float64(112655639.6484375), np.float64(113143920.8984375)], [np.float64(113265991.2109375), np.float64(113388061.5234375)], [np.float64(113632202.1484375), np.float64(113754272.4609375)], [np.float64(115219116.2109375), np.float64(115341186.5234375)], [np.float64(115707397.4609375), 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np.float64(212631225.5859375)], [np.float64(215194702.1484375), np.float64(215316772.4609375)], [np.float64(215682983.3984375), np.float64(215805053.7109375)], [np.float64(220321655.2734375), np.float64(220932006.8359375)], [np.float64(221176147.4609375), np.float64(221298217.7734375)], [np.float64(222885131.8359375), np.float64(223007202.1484375)], [np.float64(223129272.4609375), np.float64(223617553.7109375)], [np.float64(225692749.0234375), np.float64(225814819.3359375)], [np.float64(227401733.3984375), np.float64(227523803.7109375)], [np.float64(229110717.7734375), np.float64(229476928.7109375)], [np.float64(229965209.9609375), np.float64(230087280.2734375)], [np.float64(231063842.7734375), np.float64(231185913.0859375)], [np.float64(232406616.2109375), np.float64(232528686.5234375)], [np.float64(232772827.1484375), np.float64(234359741.2109375)]] ex_ants: [[np.int64[4], Jee], [np.int64[7], Jee], [np.int64[8], Jee], [np.int64[8], Jnn], [np.int64[10], Jee], [np.int64[10], Jnn], 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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: 3.7.1.dev18+g10e9584 hera_qm: 2.2.1.dev2+ga535e9e hera_filters: 0.1.6.dev9+gf165ec1
hera_notebook_templates: 0.1.dev989+gee0995d pyuvdata: 3.1.3
In [25]:
print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')
Finished execution in 54.44 minutes.