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 1567 *.sum.red_avg_zscore.h5 files starting with /mnt/sn1/data2/2460772/zen.2460772.21079.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 1567 *.sum.smooth.calfits files starting with /mnt/sn1/data2/2460772/zen.2460772.21079.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.')
38.599% of waterfall flagged to start.
All-NaN slice encountered
Flagging an additional 3 integrations and 11 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 6 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. 39.471% of waterfall flagged after flagging whole times and channels with median z > 1.0. 40.321% of waterfall flagged after flagging z > 4.0 outliers.
42.519% of waterfall flagged after watershed flagging on z > 2.0 neighbors of prior flags. Flagging an additional 0 integrations and 0 channels. Flagging 35 channels previously flagged 25.00% or more. Flagging 380 times previously flagged 10.00% or more.
Mean of empty slice Mean of empty slice
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. 52.248% 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
55.112% of flagging channels that are 4.0σ outliers after delay filtering the time average.
55.464% 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 1567 *.sum.flag_waterfall_round_2.h5 files starting with /mnt/sn1/data2/2460772/zen.2460772.21079.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 = [(int(ant[0]), ant[1]) for ant in sorted(always_flagged_ants)]
dt = np.median(np.diff(times))
out_yml_str = 'JD_flags: ' + str([[float(times[flag_stretch][0] - dt / 2), float(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([[float(freqs[flag_stretch][0] - df / 2), float(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/data2/2460772/2460772_aposteriori_flags.yaml ------------------------------------------------------------------------------ JD_flags: [[2460772.210679005, 2460772.2351737404], [2460772.2352855885, 2460772.235956677], [2460772.2365159173, 2460772.23696331], [2460772.237187006, 2460772.2376343985], [2460772.239200272, 2460772.2395358165], [2460772.2397595127, 2460772.2407661458], [2460772.240877994, 2460772.241549082], [2460772.2418846264, 2460772.2419964746], [2460772.2421083227, 2460772.242220171], [2460772.242443867, 2460772.244345285], [2460772.244457133, 2460772.2446808293], [2460772.2447926775, 2460772.2449045256], [2460772.2455756143, 2460772.24758888], [2460772.2489310573, 2460772.2502732347], [2460772.2527338928, 2460772.253740526], [2460772.255753792, 2460772.25586564], [2460772.257878906, 2460772.2582144504], [2460772.2597803236, 2460772.260115868], [2460772.2624646784, 2460772.2628002227], [2460772.264589792, 2460772.2649253365], [2460772.2650371846, 2460772.2651490327], [2460772.2660438176, 2460772.266379362], [2460772.26649121, 2460772.2667149063], [2460772.269734805, 2460772.271636223], [2460772.2721954635, 2460772.272866552], [2460772.27476797, 2460772.2763338434], [2460772.2774523245, 2460772.277787869], [2460772.277899717, 2460772.278011565], [2460772.2792418944, 2460772.280024831], [2460772.2801366793, 2460772.2802485274], [2460772.2804722236, 2460772.2844987554], [2460772.286176477, 2460772.286623869], [2460772.2868475653, 2460772.287518654], [2460772.287630502, 2460772.2880778946], [2460772.2888608314, 2460772.2890845276], [2460772.2906504013, 2460772.29132149], [2460772.292663667, 2460772.292887363], [2460772.293893996, 2460772.2940058443], [2460772.294900629, 2460772.2950124773], [2460772.2952361736, 2460772.2953480217], [2460772.295795414, 2460772.2959072622], [2460772.2962428066, 2460772.2963546547], [2460772.296578351, 2460772.296690199], [2460772.296802047, 2460772.2970257434], [2460772.297920528, 2460772.298144224], [2460772.298256072, 2460772.2983679203], [2460772.2984797684, 2460772.298927161], [2460772.299039009, 2460772.299150857], [2460772.2995982496, 2460772.2997100977], [2460772.300045642, 2460772.3002693383], [2460772.3007167308, 2460772.300828579], [2460772.301052275, 2460772.3012759713], [2460772.301723364, 2460772.3021707563], [2460772.302729997, 2460772.302841845], [2460772.303065541, 2460772.3031773893], [2460772.3038484775, 2460772.3039603257], [2460772.304072174, 2460772.304184022], [2460772.30429587, 2460772.304407718], [2460772.3046314144, 2460772.3047432625], [2460772.3048551106, 2460772.305078807], [2460772.305414351, 2460772.3055261993], [2460772.3056380474, 2460772.3058617436], [2460772.306197288, 2460772.3066446804], [2460772.3067565286, 2460772.307092073], [2460772.3075394654, 2460772.3077631616], [2460772.308098706, 2460772.3101119716], [2460772.310335668, 2460772.3108949084], [2460772.3111186046, 2460772.311342301], [2460772.311677845, 2460772.3121252377], [2460772.312348934, 2460772.3129081745], [2460772.3130200226, 2460772.3136911113], [2460772.3139148075, 2460772.314474048], [2460772.3145858957, 2460772.314809592], [2460772.31492144, 2460772.3153688326], [2460772.3154806807, 2460772.3161517694], [2460772.3162636175, 2460772.3173820986], [2460772.3174939468, 2460772.3182768836], [2460772.318612428, 2460772.3193953647], [2460772.319507213, 2460772.3199546053], [2460772.320066453, 2460772.3204019973], [2460772.3206256935, 2460772.321184934], [2460772.3214086303, 2460772.3225271115], [2460772.3226389596, 2460772.322862656], [2460772.322974504, 2460772.324875922], [2460772.32498777, 2460772.325099618], [2460772.3252114663, 2460772.3254351625], [2460772.3255470106, 2460772.3259944026], [2460772.3261062508, 2460772.326329947], [2460772.326441795, 2460772.326553643], [2460772.3266654913, 2460772.3275602763], [2460772.3276721244, 2460772.3278958206], [2460772.3280076687, 2460772.3285669093], [2460772.3289024536, 2460772.330020935], [2460772.330132783, 2460772.3330408335], [2460772.333376378, 2460772.333711922], [2460772.3338237703, 2460772.3351659477], [2460772.3357251883, 2460772.3381858463], [2460772.3382976945, 2460772.338633239], [2460772.338745087, 2460772.338968783], [2460772.3391924794, 2460772.339639872], [2460772.33975172, 2460772.3403109605], [2460772.340534657, 2460772.340646505], [2460772.340758353, 2460772.3413175936], [2460772.34154129, 2460772.343219011], [2460772.343330859, 2460772.344001948], [2460772.344225644, 2460772.3444493404], [2460772.344896733, 2460772.3454559734], [2460772.346015214, 2460772.34623891], [2460772.3464626065, 2460772.3466863027], [2460772.346798151, 2460772.347021847], [2460772.3472455433, 2460772.347804784], [2460772.348140328, 2460772.348364024], [2460772.348475872, 2460772.3486995683], [2460772.3489232645, 2460772.3500417457], [2460772.350265442, 2460772.3507128344], [2460772.3508246825, 2460772.351160227], [2460772.351272075, 2460772.351383923], [2460772.3516076193, 2460772.352278708], [2460772.352390556, 2460772.353061645], [2460772.353173493, 2460772.354627518], [2460772.354739366, 2460772.355522303], [2460772.356417088, 2460772.356752632], [2460772.3569763284, 2460772.357759265], [2460772.3578711133, 2460772.3591014426], [2460772.3592132907, 2460772.359996227], [2460772.360108075, 2460772.3603317714], [2460772.3605554677, 2460772.360667316], [2460772.360779164, 2460772.3612265564], [2460772.3613384045, 2460772.3615621007], [2460772.361673949, 2460772.361897645], [2460772.362009493, 2460772.362680582], [2460772.36279243, 2460772.3632398224], [2460772.3633516706, 2460772.3634635187], [2460772.363575367, 2460772.363910911], [2460772.3640227593, 2460772.3642464555], [2460772.3643583036, 2460772.364582], [2460772.364693848, 2460772.364917544], [2460772.36514124, 2460772.365253088], [2460772.365364936, 2460772.3655886324], [2460772.3657004805, 2460772.3658123286], [2460772.3659241768, 2460772.366036025], [2460772.366147873, 2460772.366259721], [2460772.366371569, 2460772.3667071136], [2460772.3668189617, 2460772.367042658], [2460772.367154506, 2460772.3674900504], [2460772.3677137466, 2460772.368049291], [2460772.368161139, 2460772.368272987], [2460772.3683848353, 2460772.3684966834], [2460772.3687203797, 2460772.369055924], [2460772.369167772, 2460772.3692796202], [2460772.3693914684, 2460772.3695033165], [2460772.3696151646, 2460772.3697270127], [2460772.369838861, 2460772.369950709], [2460772.3702862533, 2460772.3708454934], [2460772.3709573415, 2460772.3711810377], [2460772.371404734, 2460772.37162843], [2460772.3717402783, 2460772.3719639746], [2460772.372187671, 2460772.372411367], [2460772.372523215, 2460772.3726350632], [2460772.3728587595, 2460772.3729706076], [2460772.373306152, 2460772.3738653925], [2460772.3739772406, 2460772.374312785], [2460772.374536481, 2460772.3748720256], [2460772.3749838737, 2460772.375095722], [2460772.37520757, 2460772.375431266], [2460772.3755431143, 2460772.3756549624], [2460772.3758786586, 2460772.3759905067], [2460772.376214203, 2460772.376326051], [2460772.376549747, 2460772.376885291], [2460772.3769971393, 2460772.3771089874], [2460772.3773326837, 2460772.377444532], [2460772.37755638, 2460772.377780076], [2460772.3780037723, 2460772.378563013], [2460772.378674861, 2460772.378786709], [2460772.3788985573, 2460772.3790104054], [2460772.3791222535, 2460772.3792341016], [2460772.3793459497, 2460772.379681494], [2460772.3799051903, 2460772.3801288866], [2460772.380464431, 2460772.380576279], [2460772.3809118234, 2460772.381359216], [2460772.381471064, 2460772.38169476], [2460772.382030304, 2460772.382142152], [2460772.3822540003, 2460772.3825895446], [2460772.382813241, 2460772.383036937], [2460772.383148785, 2460772.383819874], [2460772.38404357, 2460772.3841554183], [2460772.3842672664, 2460772.3844909626], [2460772.384714659, 2460772.384826507], [2460772.384938355, 2460772.3856094438], [2460772.38583314, 2460772.385944988], [2460772.386056836, 2460772.3865042287], [2460772.386839773, 2460772.386951621], [2460772.3870634693, 2460772.3873990136], [2460772.3875108617, 2460772.38762271], [2460772.3878464056, 2460772.388070102], [2460772.388293798, 2460772.3885174943], [2460772.3886293424, 2460772.3887411905], [2460772.388964887, 2460772.389076735], [2460772.389188583, 2460772.3894122792], [2460772.3895241274, 2460772.3898596717], [2460772.38997152, 2460772.3904189123], [2460772.3905307604, 2460772.3906426085], [2460772.3907544566, 2460772.391090001], [2460772.391201849, 2460772.391313697], [2460772.3914255453, 2460772.392208482], [2460772.3927677227, 2460772.392879571], [2460772.392991419, 2460772.393215115], [2460772.393550659, 2460772.3941098996], [2460772.394445444, 2460772.395340229], [2460772.395452077, 2460772.395563925], [2460772.3957876214, 2460772.3960113176], [2460772.39645871, 2460772.3966824063], [2460772.397129799, 2460772.397241647], [2460772.397465343, 2460772.3975771912], [2460772.3978008875, 2460772.3980245837], [2460772.39824828, 2460772.398360128], [2460772.3986956724, 2460772.398919368], [2460772.399702305, 2460772.399926001], [2460772.4002615456, 2460772.4003733937], [2460772.40059709, 2460772.4009326342], [2460772.4010444824, 2460772.4012681786], [2460772.401603723, 2460772.401715571], [2460772.4021629635, 2460772.4022748116], [2460772.402610356, 2460772.402722204], [2460772.402834052, 2460772.4029459003], [2460772.4032814447, 2460772.403393293], [2460772.403616989, 2460772.4038406853], [2460772.4040643815, 2460772.4041762296], [2460772.404959166, 2460772.405071014], [2460772.4056302547, 2460772.405742103], [2460772.406189495, 2460772.4063013433], [2460772.4066368877, 2460772.406860584], [2460772.4076435207, 2460772.407755369], [2460772.4085383057, 2460772.408762002], [2460772.4093212425, 2460772.4094330906], [2460772.410216027, 2460772.410327875], [2460772.41122266, 2460772.411446356], [2460772.412229293, 2460772.412341141], [2460772.4127885336, 2460772.4129003817], [2460772.41301223, 2460772.413124078], [2460772.413347774, 2460772.4135714704], [2460772.414130711, 2460772.414354407], [2460772.415137344, 2460772.4153610403], [2460772.4159202804, 2460772.4160321285], [2460772.416703217, 2460772.4168150653], [2460772.4177098502, 2460772.4179335465], [2460772.418380939, 2460772.418492787], [2460772.4189401795, 2460772.4191638757], [2460772.4197231163, 2460772.4198349644], [2460772.4199468126, 2460772.4200586607], [2460772.420506053, 2460772.4207297494], [2460772.42128899, 2460772.4215126857], [2460772.4221837744, 2460772.4222956225], [2460772.4225193188, 2460772.422631167], [2460772.422743015, 2460772.422854863], [2460772.4233022556, 2460772.423525952], [2460772.4239733443, 2460772.4240851924], [2460772.424644433, 2460772.424756281], [2460772.4252036735, 2460772.4253155217], [2460772.425762914, 2460772.4259866104], [2460772.4263221547, 2460772.426434003], [2460772.427105091, 2460772.427216939], [2460772.4275524835, 2460772.4276643316], [2460772.428894661, 2460772.429006509], [2460772.430907927, 2460772.431019775], [2460772.43191456, 2460772.432026408], [2460772.433144889, 2460772.433256737], [2460772.433368585, 2460772.433480433], [2460772.4335922813, 2460772.4338159775], [2460772.435046307, 2460772.435158155], [2460772.436164788, 2460772.436276636], [2460772.437171421, 2460772.4373951172], [2460772.437954358, 2460772.438066206], [2460772.4392965348, 2460772.439408383], [2460772.439743927, 2460772.4398557753], [2460772.4417571933, 2460772.4418690414], [2460772.4419808895, 2460772.4420927376], [2460772.4429875226, 2460772.443434915], [2460772.448579928, 2460772.448691776], [2460772.4493628647, 2460772.4496984086], [2460772.4504813454, 2460772.4505931935], [2460772.451152434, 2460772.451264282], [2460772.4515998266, 2460772.4517116747], [2460772.452942004, 2460772.453053852], [2460772.453724941, 2460772.453836789], [2460772.455290814, 2460772.4556263583], [2460772.4560737507, 2460772.456185599], [2460772.457080384, 2460772.457192232], [2460772.4598765867, 2460772.459988435], [2460772.461666156, 2460772.461778004], [2460772.462337245, 2460772.462560941], [2460772.464574207, 2460772.464686055], [2460772.4649097514, 2460772.4651334477], [2460772.468824435, 2460772.4690481313], [2460772.469942916, 2460772.4700547643], [2460772.4754234734, 2460772.4755353215], [2460772.4777722834, 2460772.4778841315], [2460772.4781078277, 2460772.478219676], [2460772.4810158787, 2460772.481127727], [2460772.4869438284, 2460772.4870556765], [2460772.4902992714, 2460772.4904111195], [2460772.490634816, 2460772.490746664], [2460772.493878411, 2460772.4939902592], [2460772.5039447406, 2460772.5040565887], [2460772.508418665, 2460772.508642361], [2460772.510543779, 2460772.510655627], [2460772.5216167415, 2460772.5217285897], [2460772.521952286, 2460772.524301096], [2460772.524412944, 2460772.5256432733], [2460772.5257551214, 2460772.5259788176], [2460772.529781653, 2460772.529893501], [2460772.5301171974, 2460772.530452742], [2460772.5343674254, 2460772.5345911216], [2460772.53705178, 2460772.5372754764]] freq_flags: [[46859741.2109375, 46981811.5234375], [49911499.0234375, 50033569.3359375], [62240600.5859375, 62973022.4609375], [64682006.8359375, 65536499.0234375], [65780639.6484375, 65902709.9609375], [66268920.8984375, 66513061.5234375], [66879272.4609375, 67123413.0859375], [69931030.2734375, 70053100.5859375], [78964233.3984375, 79086303.7109375], [80551147.4609375, 80673217.7734375], [87387084.9609375, 108139038.0859375], [109970092.7734375, 110092163.0859375], [112167358.3984375, 112289428.7109375], [112655639.6484375, 113143920.8984375], [113265991.2109375, 113510131.8359375], [113632202.1484375, 113754272.4609375], [116073608.3984375, 116195678.7109375], [116439819.3359375, 116561889.6484375], [116683959.9609375, 116806030.2734375], [124862670.8984375, 125228881.8359375], [127548217.7734375, 127670288.0859375], [129989624.0234375, 130111694.3359375], [131698608.3984375, 131820678.7109375], [135848999.0234375, 135971069.3359375], [136337280.2734375, 136459350.5859375], [136703491.2109375, 138290405.2734375], [138412475.5859375, 138534545.8984375], [138656616.2109375, 138778686.5234375], [141464233.3984375, 141586303.7109375], [141708374.0234375, 141830444.3359375], [142074584.9609375, 142318725.5859375], [142684936.5234375, 142807006.8359375], [142929077.1484375, 143295288.0859375], [143783569.3359375, 144027709.9609375], [144638061.5234375, 144760131.8359375], [144882202.1484375, 145004272.4609375], [145492553.7109375, 145614624.0234375], [146224975.5859375, 146347045.8984375], [147445678.7109375, 147567749.0234375], [148056030.2734375, 148544311.5234375], [149154663.0859375, 149276733.3984375], [149887084.9609375, 150009155.2734375], [153427124.0234375, 153549194.3359375], [154159545.8984375, 154403686.5234375], [155014038.0859375, 155136108.3984375], [155258178.7109375, 155380249.0234375], [155990600.5859375, 156112670.8984375], [157577514.6484375, 157699584.9609375], [158187866.2109375, 158309936.5234375], [158432006.8359375, 158676147.4609375], [159164428.7109375, 159286499.0234375], [160018920.8984375, 160385131.8359375], [161361694.3359375, 161483764.6484375], [169906616.2109375, 170639038.0859375], [170883178.7109375, 171005249.0234375], [171249389.6484375, 171859741.2109375], [174911499.0234375, 175033569.3359375], [175155639.6484375, 175399780.2734375], [175521850.5859375, 175765991.2109375], [179672241.2109375, 179794311.5234375], [180892944.3359375, 181747436.5234375], [183212280.2734375, 183334350.5859375], [186386108.3984375, 186508178.7109375], [187362670.8984375, 187606811.5234375], [189926147.4609375, 190048217.7734375], [190292358.3984375, 190414428.7109375], [191024780.2734375, 191879272.4609375], [193222045.8984375, 193344116.2109375], [194564819.3359375, 194686889.6484375], [195663452.1484375, 195785522.4609375], [196395874.0234375, 196517944.3359375], [196884155.2734375, 197006225.5859375], [197128295.8984375, 197372436.5234375], [197860717.7734375, 198593139.6484375], [199203491.2109375, 199325561.5234375], [199935913.0859375, 200180053.7109375], [200912475.5859375, 201034545.8984375], [201644897.4609375, 201889038.0859375], [203231811.5234375, 203353881.8359375], [203964233.3984375, 204086303.7109375], [204940795.8984375, 205062866.2109375], [205184936.5234375, 205307006.8359375], [206893920.8984375, 207015991.2109375], [207138061.5234375, 207382202.1484375], [207504272.4609375, 207626342.7734375], [208480834.9609375, 208724975.5859375], [209945678.7109375, 210067749.0234375], [210433959.9609375, 210556030.2734375], [211166381.8359375, 211288452.1484375], [212142944.3359375, 212265014.6484375], [215194702.1484375, 215438842.7734375], [215682983.3984375, 215805053.7109375], [216659545.8984375, 216781616.2109375], [219833374.0234375, 219955444.3359375], [220565795.8984375, 220809936.5234375], [221176147.4609375, 221298217.7734375], [222763061.5234375, 223495483.3984375], [223617553.7109375, 223739624.0234375], [225692749.0234375, 225814819.3359375], [227401733.3984375, 227523803.7109375], [227645874.0234375, 227767944.3359375], [229110717.7734375, 229354858.3984375], [229965209.9609375, 230087280.2734375], [231063842.7734375, 231307983.3984375], [232650756.8359375, 232894897.4609375], [233016967.7734375, 234359741.2109375]] ex_ants: [[4, Jee], [7, Jee], [8, Jee], [8, Jnn], [10, Jee], [10, Jnn], [16, Jee], [18, Jee], [18, Jnn], [20, Jnn], [21, Jee], [22, Jee], [22, Jnn], [27, Jee], [27, Jnn], [28, Jee], [28, Jnn], [29, Jnn], [30, Jee], [32, Jnn], [33, Jnn], [34, Jee], [34, Jnn], [37, Jnn], [40, Jnn], [42, Jee], [42, Jnn], [44, Jee], [46, Jee], [49, Jnn], [51, Jee], [55, Jee], [64, Jnn], [67, Jnn], [71, Jnn], [75, Jnn], [78, Jee], [80, Jnn], [81, Jee], [81, Jnn], [82, Jnn], [83, Jee], [83, Jnn], [86, Jee], [86, Jnn], [87, Jee], [90, Jee], [92, Jee], [97, Jnn], [98, Jee], [98, Jnn], [99, Jnn], [102, Jnn], [104, Jnn], [105, Jee], [107, Jee], [107, Jnn], [109, Jnn], [112, Jee], [113, Jnn], [115, Jee], [115, Jnn], [116, Jee], [117, Jee], [120, Jee], [120, Jnn], [121, Jee], [130, Jee], [130, Jnn], [134, Jnn], [135, Jee], [137, Jee], [137, Jnn], [143, Jnn], [149, Jee], [154, Jnn], [155, Jnn], [161, Jnn], [164, Jee], [167, Jnn], [169, Jee], [170, Jee], [171, Jnn], [174, Jnn], [175, Jnn], [176, Jnn], [180, Jnn], [187, Jnn], [189, Jee], [189, Jnn], [195, Jnn], [197, Jnn], [199, Jnn], [200, Jee], [200, Jnn], [202, Jnn], [206, Jnn], [209, Jnn], [212, Jnn], [213, Jee], [213, Jnn], [214, Jee], [214, Jnn], [215, Jnn], [216, Jee], [216, Jnn], [218, Jnn], [227, Jee], [227, Jnn], [232, Jee], [233, Jnn], [238, Jnn], [239, Jee], [244, Jee], [246, Jee], [250, Jee], [251, Jee], [253, Jnn], [255, Jee], [255, Jnn], [261, Jnn], [262, Jee], [262, Jnn], [266, Jee], [266, Jnn], [268, Jee], [268, Jnn], [320, Jee], [320, Jnn], [321, Jee], [321, Jnn], [322, Jee], [322, 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: 3.7.1.dev45+g4a0c6f1 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 202.39 minutes.