Predict visibilities from a FITS image

predict_skylens.py

import stimela

INPUT = 'input'
OUTPUT = 'output'
MSDIR = 'msdir'

MODEL = 'skylens.fits'
MS = 'test_predict_with_custom_fits.ms'
PREFIX = MS[:-3]

recipe = stimela.Recipe('Test lwimger predict', ms_dir=MSDIR)



recipe.add('cab/simms', 'create_empty_ms', {
        "msname"    :   MS, 
        "synthesis" :   6, # length of observation in hours
        "dtime"     :   2, # integration time in seconds
        "telescope" :   'ska1mid197',
        "direction" :   'J2000,0deg,-30deg',
        # This frequency information will be
        # imposed when FITS image is simulated into
        # MS
        "freq0"     :   '1.400GHz',
        "nchan"     :   1,  # If your FITS image has multiple frequency planes, then make this value match that
        "dfreq"     :   '1MHz',
},
    input=INPUT,
    output=OUTPUT,
    label='make_ms:: Make empty MS')


recipe.add('cab/lwimager', 'predict', {
        "msname"    :   MS, 
        "simulate_fits" :   MODEL,
        "column"    :   'DATA', # data will simulated into this column
        "cellsize"  :   "0.2arcsec", 
        # If not specified will use appropriate
        # CDELT value in FITS header if its there,
        # else assumes 1arcsec
},
    input=INPUT,
    output=OUTPUT,
    label='predict:: Predict from FITS')


recipe.add('cab/simulator', 'addnoise', {
        "msname"    :    MS,
        "addnoise"  :    True,
        "sefd"      :    386,
        "mode"      :    'add', # add simulated to noise to the column specified bellow
        "input-column"  :  'DATA'
        "column"    :    'DATA',
},
    input=INPUT,
    output=OUTPUT,
    label='addnoise:: Add noise')


recipe.add('cab/wsclean', 'make_image', {
        "msname"    :   MS, 
        "column"    :   'DATA',
        "prefix"    :   PREFIX,
        "npix"      :   1500,
        "weight"    :   "briggs -2",
        "niter"     :   100000,
        "auto-threshold":   5, # stop at 5sigma
        "mgain"     :   0.9,
        "cellsize"  :   "0.2arcsec",
},
    input=INPUT,
    output=OUTPUT,
    label="make_image:: Make image")

recipe.run()
#recipe.run(['make_ms','predict', 'addnoise','make_image'])