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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 re
import argparse
import subprocess
import time
def wsclean(args, dryrun=False):
# NOTE: Convert all arguments to strings
cmd = ["wsclean"] + [str(arg) for arg in args]
print("CMD: %s" % " ".join(cmd))
if dryrun:
print(">>> DRY RUN MODE <<<")
return
t1 = time.perf_counter()
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 with more handy arguments")
parser.add_argument("-a", "--args", dest="args",
help="additional arguments for WSClean, " +
"in a quoted string separated by space, e.g.," +
"' -simulate-noise 0.001' (NOTE the beginning space!)")
parser.add_argument("-d", "--dirty", dest="dirty", action="store_true",
help="only create dirty images (by setting niter=0)")
parser.add_argument("-n", "--dry-run", dest="dryrun", action="store_true",
help="do not actually run WSClean")
parser.add_argument("--update-model", dest="update_model",
action="store_true",
help="write/update the MODEL_DATA column in MS")
parser.add_argument("--save-weights", dest="save_weights",
action="store_true",
help="save gridded weights in <name>-weights.fits")
parser.add_argument("--save-uv", dest="save_uv",
action="store_true",
help="save gridded uv plane (i.e., FFT of the " +
"residual image) in <name>-uv-{real,imag}.fits")
parser.add_argument("--uv-range", dest="uv_range", default=":",
help="uv range [lambda] (i.e., baseline lengths) " +
"used for imaging; syntax: '<min>:<max>' " +
"(default: ':', i.e., all uv/baselines)")
parser.add_argument("-w", "--weight", dest="weight", default="uniform",
choices=["uniform", "natural", "briggs"],
help="weighting method (default: 'briggs')")
parser.add_argument("-B", "--briggs", dest="briggs",
type=float, default=0.0,
help="Briggs robustness parameter (default: 0); " +
"-1 (uniform) -> 1 (natural)")
parser.add_argument("-#", "--niter", dest="niter",
type=int, default=200000,
help="maximum number of CLEAN iterations " +
"(default: 200,000)")
parser.add_argument("--gain", dest="gain", type=float, default=0.1,
help="CLEAN gain for each minor iteration " +
"(default: 0.1)")
parser.add_argument("--mgain", dest="mgain", type=float, default=0.85,
help="CLEAN gain for major iterations " +
"(default: 0.85)")
parser.add_argument("-s", "--size", dest="size", type=int,
required=True,
help="output image size (pixel number on a side)")
parser.add_argument("-p", "--pixelsize", dest="pixelsize", type=float,
required=True,
help="output image pixel size [arcsec]")
parser.add_argument("-G", "--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("-S", "--threshold-nsigma", dest="threshold_nsigma",
type=float, default=2.0,
help="estimate the noise level <sigma> and stop at " +
"nsigma*<sigma> (default: 2.0 <sigma>)")
exgrp.add_argument("-t", "--threshold", dest="threshold", type=float,
help="stopping CLEAN threshold [mJy]")
#
parser.add_argument("-N", "--name", dest="name", required=True,
help="filename prefix for the output files")
parser.add_argument("-m", "--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
"-tempdir", "/tmp",
]
if args.dirty:
cmdargs += ["-niter", 0] # make dirty image only
else:
cmdargs += ["-niter", args.niter]
if args.weight == "uniform":
cmdargs += ["-weight", "uniform",
"-weighting-rank-filter", 3]
elif args.weight == "briggs":
cmdargs += ["-weight", "briggs", args.briggs]
else:
cmdargs += ["-weight", args.weight] # natural
cmdargs += ["-gain", args.gain]
cmdargs += ["-mgain", args.mgain]
cmdargs += ["-size", args.size, args.size]
cmdargs += ["-scale", "{0}asec".format(args.pixelsize)]
if args.fit_spec_order:
cmdargs += ["-joinchannels", "-channelsout", nms,
"-fit-spectral-pol", args.fit_spec_order+1]
if args.update_model:
cmdargs += ["-update-model-required"]
else:
cmdargs += ["-no-update-model-required"]
if args.save_weights:
cmdargs += ["-saveweights"]
if args.save_uv:
cmdargs += ["-saveuv"]
# uv/baseline range
uvmin, uvmax = args.uv_range.strip().split(":")
if uvmin:
cmdargs += ["-minuv-l", float(uvmin)]
if uvmax:
cmdargs += ["-maxuv-l", float(uvmax)]
if args.threshold:
cmdargs += ["-threshold", args.threshold*1e-3] # [mJy] -> [Jy]
else:
cmdargs += ["-auto-threshold", args.threshold_nsigma]
if args.taper_gaus:
cmdargs += ["-taper-gaussian", args.taper_gaus]
# additional WSClean arguments
if args.args:
extra_args = re.split(r"\s+", args.args.strip())
print("Additional WSClean arguments:", extra_args)
cmdargs += extra_args
nameprefix = args.name.rstrip("-_")
cmdargs += ["-name", nameprefix]
cmdargs += args.ms
wsclean(cmdargs, dryrun=args.dryrun)
if args.dirty and not args.dryrun:
# Remove the output "-image" since it is identical to "-dirty"
os.remove(nameprefix+"-image.fits")
if __name__ == "__main__":
main()
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