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#!/usr/bin/env python3
#
# Copyright (c) 2017 Aaron LI
# MIT license
#
# Create image from OSKAR simulated visibility data using `WSClean`.
# WSClean: https://sourceforge.net/p/wsclean/
#
# 2017-09-01
#
import os
import argparse
import subprocess
import time
def wsclean(args):
t1 = time.perf_counter()
cmd = ["wsclean"] + args
print("CMD: %s" % " ".join(cmd))
subprocess.check_call(cmd)
t2 = time.perf_counter()
print("-----------------------------------------------------------")
print("WSClean Elapsed time: %.1f [min]" % ((t2-t1)/60))
print("-----------------------------------------------------------")
def main():
parser = argparse.ArgumentParser(description="Run WSClean")
parser.add_argument("--args", dest="args", nargs="*",
help="additional arguments for WSClean")
parser.add_argument("--dirty", dest="dirty", action="store_true",
help="only create dirty images (by setting niter=0)")
parser.add_argument("--update-model", dest="update_model",
action="store_true",
help="update the MODEL_DATA column in MS")
parser.add_argument("--weight-briggs", dest="briggs",
type=float, default=0.0,
help="Briggs weight parameter")
parser.add_argument("--niter", dest="niter", type=int, default=100000,
help="maximum number of CLEAN iterations")
parser.add_argument("--gain", dest="gain", type=float, default=0.1,
help="CLEAN gain for each minor iteration")
parser.add_argument("--mgain", dest="mgain", type=float, default=0.85,
help="CLEAN gain for major iterations")
parser.add_argument("--size", dest="size", type=int,
required=True,
help="output image size (pixel number on a side)")
parser.add_argument("--pixelsize", dest="pixelsize", type=float,
required=True,
help="output image pixel size [arcsec]")
parser.add_argument("--taper-gaus", dest="taper_gaus", type=float,
help="taper the weights with a Gaussian function " +
"to reduce the contribution of long baselines. " +
"Gaussian beam size in [arcsec].")
parser.add_argument("--fit-spec-order", dest="fit_spec_order", type=int,
help="do joined-channel CLEAN by fitting the " +
"spectra with [order] polynomial in normal-space")
#
exgrp = parser.add_mutually_exclusive_group()
exgrp.add_argument("--threshold-auto", dest="threshold_auto",
type=float, default=1.5,
help="estimate noise level and stop at <sigma>*<std>")
exgrp.add_argument("--threshold", dest="threshold", type=float,
help="stopping CLEAN threshold [Jy]")
#
parser.add_argument("--name", dest="name", required=True,
help="filename prefix for the output files")
parser.add_argument("--ms", nargs="+", help="input visibility MSs")
args = parser.parse_args()
nms = len(args.ms) # i.e., number of MS == number of channels
cmdargs = [
"-verbose",
"-log-time",
"-pol", "XX", # OSKAR "Scalar" simulation only give "XX" component
"-make-psf", # always make the PSF, even no cleaning performed
]
if args.dirty:
cmdargs += ["-niter", str(0)] # make dirty image only
else:
cmdargs += ["-niter", str(args.niter)]
cmdargs += ["-weight", "briggs", str(args.briggs)]
cmdargs += ["-gain", str(args.gain)]
cmdargs += ["-mgain", str(args.mgain)]
cmdargs += ["-size", str(args.size), str(args.size)]
cmdargs += ["-scale", "{0}asec".format(args.pixelsize)]
if args.fit_spec_order:
cmdargs += ["-joinchannels", "-channelsout", str(nms),
"-fit-spectral-pol", str(args.fit_spec_order+1)]
if args.update_model:
cmdargs += ["-update-model-required"]
else:
cmdargs += ["-no-update-model-required"]
if args.threshold:
cmdargs += ["-threshold", str(args.threshold)]
else:
cmdargs += ["-auto-threshold", str(args.threshold_auto)]
if args.taper_gaus:
cmdargs += ["-taper-gaussian", str(args.taper_gaus)]
# additional WSClean arguments
if args.args:
cmdargs += args.args
nameprefix = args.name.rstrip("-_")
cmdargs += ["-name", nameprefix]
cmdargs += args.ms
wsclean(cmdargs)
if args.dirty:
# Remove the output "-image" since it is identical to "-dirty"
os.remove(nameprefix+"-image.fits")
if __name__ == "__main__":
main()
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