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: msdata threshold
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: 3.0
Date: Apr 2019
Written by Benito Marcote (
version 3.0 changes
- 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
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).'
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='', 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='', 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: [-h] [-v] <measurement set> <weight threshold>')
print('Use -h to get help.')
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
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):
# 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("Got {0:11} visibilities".format(total_number))
print("Got {0:11} visibilities to flag using threshold {1}\n".format(flagged_after-flagged_before,
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)))
if verbose:
print('Flags have not been applied.')