Collection of scripts and small programs used by the EVN Support Scientists at JIVE during the regular data processing of EVN observations.
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#!/usr/bin/env python3
"""
Flag visibilities with weights below the provided threshold.
Usage: flag_weights.py msdata threshold
Options:
msdata : str MS data set containing the data to be flagged.
threshold : float Visibilities with a weight below the specified
value will be flagged. Must be positive.
Version: 3.1
Date: Mar 2020
Written by Benito Marcote (marcote@jive.eu)
version 3.1 changes (Mar 2020)
- Progress bar added.
version 3.0 changes (Apr 2019)
- Refactoring code (thanks to Harro).
version 2.0 changes
- Major revision. Now it does not modify the weights anymore. Instead, it
flags those data with weights below the given threshold by modifying the
FLAG table.
- Small change in print messages to show '100%' instead of '1e+02%' in certain
cases.
version 1.4 changes
- Now it also reports the percentage or data that were different from
zero and will be flagged (not only the total data as before).
version 1.3 changes
- Minor fixes (prog name in optparse info).
version 1.2 changes
- Minor fixes.
version 1.1 changes
- Added option -v that allows you to just get how many visibilities will
be flagged (but without actually flagging the data).
"""
from pyrap import tables as pt
import numpy as np
import sys
__version__ = 3.0
help_msdata = 'Measurement set containing the data to be corrected.'
help_threshold = 'Visibilities with a weight below this value will be flagged. Must be positive.'
help_v = 'Only checks the visibilities to flag (do not flag the data).'
try:
usage = "%(prog)s [-h] <measurement set> <weight threshold>"
description="""Flag visibilities with weights below the provided threshold.
"""
import argparse
parser = argparse.ArgumentParser(description=description, prog='flag_weights.py', usage=usage)
parser.add_argument('msdata', type=str, help=help_msdata)
parser.add_argument('threshold', type=float, help=help_threshold)
parser.add_argument('--version', action='version', version='%(prog)s {}'.format(__version__))
parser.add_argument("-v", "--verbose", default=True, action="store_false" , help=help_v)
arguments = parser.parse_args()
#print('The arguments ', arguments)
verbose = arguments.verbose
msdata = arguments.msdata[:-1] if arguments.msdata[-1]=='/' else arguments.msdata
threshold = arguments.threshold
except ImportError:
usage = "%prog [-h] [-v] <measurement set> <weight threshold>"
description="""Flag visibilities with weights below the provided threshold.
"""
# Compatibility with Python 2.7 in eee
import optparse
parser = optparse.OptionParser(usage=usage, description=description, prog='flag_weights.py', version='%prog 1.3')
parser.add_option("-v", action="store_false", dest="verbose", default=True, help=help_v)
theparser = parser.parse_args()
verbose = theparser[0].verbose
arguments = theparser[1]
#arguments = parser.parse_args()[1]
if len(arguments) != 2:
print('Two arguments must be provided: flag_weights.py [-h] [-v] <measurement set> <weight threshold>')
print('Use -h to get help.')
sys.exit(1)
msdata = arguments[0][:-1] if arguments[0][-1]=='/' else arguments[0]
threshold = float(arguments[1])
assert threshold > 0.0
def chunkert(f, l, cs, verbose=True):
while f<l:
n = min(cs, l-f)
yield (f, n)
f = f + n
def cli_progress_bar(current_val, end_val, bar_length=40):
percent = current_val/end_val
hashes = '#'*int(round(percent*bar_length))
spaces = ' '*(bar_length-len(hashes))
sys.stdout.write("\rProgress: [{0}] {1}%".format(hashes+spaces, int(round(percent*100))))
sys.stdout.flush()
percent = lambda x, y: (float(x)/float(y))*100.0
with pt.table(msdata, readonly=False, ack=False) as ms:
total_number = 0
flagged_before, flagged_after = (0, 0)
flagged_nonzero, flagged_nonzero_before, flagged_nonzero_after = (0, 0, 0)
# WEIGHT: (nrow, npol)
# WEIGHT_SPECTRUM: (nrow, npol, nfreq)
# flags[weight < threshold] = True
weightcol = 'WEIGHT_SPECTRUM' if 'WEIGHT_SPECTRUM' in ms.colnames() else 'WEIGHT'
transpose = (lambda x:x) if weightcol == 'WEIGHT_SPECTRUM' else (lambda x: x.transpose((1, 0, 2)))
for (start, nrow) in chunkert(0, len(ms), 5000):
cli_progress_bar(start, len(ms), bar_length=40)
# shape: (nrow, npol, nfreq)
flags = transpose(ms.getcol("FLAG", startrow=start, nrow=nrow))
total_number += np.product(flags.shape)
# count how much data is already flagged
flagged_before += np.sum(flags)
# extract weights and compute new flags based on threshold
weights = ms.getcol(weightcol, startrow=start, nrow=nrow)
# how many non-zero did we flag
flagged_nonzero_before = np.logical_and(flags, weights > 0)
# join with existing flags and count again
flags = np.logical_or(flags, weights < threshold)
flagged_after += np.sum(flags)
flagged_nonzero_after = np.logical_and(flags, weights > 0)
# Saving the total of nonzero flags (in this and previous runs)
# flagged_nonzero += np.sum(np.logical_xor(flagged_nonzero_before, flagged_nonzero_after))
flagged_nonzero += np.sum(flagged_nonzero_after)
# one thing left to do: write the updated flags to disk
#flags = ms.putcol("FLAG", flags.transpose((1, 0 , 2)), startrow=start, nrow=nrow)
if verbose:
flags = ms.putcol("FLAG", transpose(flags), startrow=start, nrow=nrow)
print("\nGot {0:11} visibilities".format(total_number))
print("Got {0:11} visibilities to flag using threshold {1}\n".format(flagged_after-flagged_before,
threshold))
print("{0:.2f}% total vis. flagged ({2:.2f}% to flag in this execution).\n{1:.2f}% data with non-zero weights flagged.\n".format(percent(flagged_after, total_number), percent(flagged_nonzero, total_number), percent(flagged_after-flagged_before, total_number)))
ms.close()
if verbose:
print('Done.')
else:
print('Flags have not been applied.')