makecleanmask_10sb_wsclean.py

#!/usr/bin/python

if __name__ == '__main__':

    import os
    import numpy
    import pyrap.images
    import pyfits
    import sys
    import lofar.bdsm as bdsm

    argc=len(sys.argv)
    from optparse import OptionParser
    parser = OptionParser(usage='%prog [options] <Input image>')
    #parser.add_option('--outfile', dest='file_clusters', help='Name of the output skymodel file [default catalogue.clusters.skymodel]', \
    #                      metavar='VAL', default='catalog.clusters.skymodel')
    parser.add_option('--threshpix', dest='threshpix', help='Pixel threshold [default: 5]', metavar='VAL', default=5)
    parser.add_option('--threshisl', dest='threshisl', help='Island threshold [default: 3]', metavar='VAL', default=3)
    parser.add_option('--atrous_do', dest='atrous_do', help='Decompose in wavelet scales threshold [default: False]', metavar='VAL', default='False')
    parser.add_option('--casaregion',dest='casaregion',help='Casa region file [default: ""]', metavar='VAL', default='')
    (o, args) = parser.parse_args()


    # convert to boolean
    if o.atrous_do == 'False':
        o.atrous_do = False
    else:
        o.atrous_do = True
        o.threshisl = 3.0
        print 'Changing island threshold to 3 because atrous_do=True'


    image_name = args[0]

    mask_name  = image_name.split('-image.fits')[0] + '.fitsmask'


    print '\n\n\n'
    print 'Making mask:', mask_name
    print '\n\n\n'

    os.system('rm -rf ' + mask_name)


    # DO THE SOURCE DETECTION
    img = bdsm.process_image(image_name, mean_map='zero', rms_box=(60,10), thresh_pix=numpy.float(o.threshpix), \
                             thresh_isl=numpy.float(o.threshisl), atrous_do=o.atrous_do,  \
                             adaptive_rms_box=False, adaptive_thresh=150, rms_box_bright=(60,10), atrous_jmax=3)

    #img.show_fit()

    # WRITE THE MASK FITS
    img.export_image(img_type='island_mask', img_format='fits', outfile=mask_name)


    # convert mask to casapy format

    #sys.exit()

    #img         = pyrap.images.image(mask_name)
    #pixels      = numpy.copy(img.getdata())
    #pixels_mask = 0.*numpy.copy(pixels)

    #hdulist   = pyfits.open(maskmodel + '.fits')
    #pixels_gs = hdulist[0].data

    #idx = numpy.where(pixels_gs > 0.0)
    #pixels_mask[idx] = 1.0

    #img.putdata(pixels_mask)