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-rwxr-xr-xastro/oskar/wsclean.py120
1 files changed, 120 insertions, 0 deletions
diff --git a/astro/oskar/wsclean.py b/astro/oskar/wsclean.py
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+++ b/astro/oskar/wsclean.py
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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 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("Elapsed time: %.1f [min]" % ((t2-t1)/60))
+
+
+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-gaussian", dest="taper_gaussian",
+ 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_gaussian:
+ cmdargs += ["-taper-gaussian", str(args.taper_gaussian)]
+
+ # additional WSClean arguments
+ if args.args:
+ cmdargs += args.args
+
+ cmdargs += ["-name", args.name.rstrip("-_")]
+ cmdargs += args.ms
+
+ wsclean(cmdargs)
+
+
+if __name__ == "__main__":
+ main()