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import sys
import numpy
from pywsclean import *
if len(sys.argv) < 2:
print("Syntax: example.py <ms>\n")
else:
parameters = ImagingParameters()
parameters.msPath = sys.argv[1]
parameters.imageWidth = 1024
parameters.imageHeight = 1024
parameters.pixelScaleX = "1amin"
parameters.pixelScaleY = "1amin"
parameters.extraParameters = "-weight natural"
# Test the operator
with Operator(parameters) as o:
data, weights = o.read()
image = numpy.zeros(parameters.imageWidth * parameters.imageHeight)
o.backward(image, data)
o.forward(data, image)
data = numpy.ones(o.data_size(), dtype=numpy.complex128)
o.backward(image, data)
o.write("name.fits", image)
try:
o.backward(data, image) # data and image are swapped
except Exception as e:
print("- Ok, specifying wrong input raised:", e)
try:
o.backward(image, data) # Outside with block, should raise
except Exception as e:
print("- Ok, using operator outside with block raised:", e)
# Test if a second use of operator works fine
with Operator(parameters) as o:
data, weights = o.read() # Reread data
try:
o.backward(
image, numpy.ones(o.data_size(), dtype=numpy.complex64)
) # Use wrong type
except Exception as e:
print("- Ok, wrong type in backward raised:", e)
try:
o.forward(
numpy.ones(o.data_size(), dtype=numpy.complex64), image
) # Use wrong type
except Exception as e:
print("- Ok, wrong type in forward raised:", e)
# Test the full cleaning command
wsc = WSClean()
wsc.width = 1536
wsc.height = 1536
wsc.scale = "5asec"
wsc.datacolumn = "DATA"
wsc.niter = 250
wsc.set_uniform_weighting()
# Alternatively: wsc.set_natural_weighting()
# This makes wsclean-dirty.fits (et al) from the DATA column
wsc.image([sys.argv[1]], "wsclean")
# This predicts from wsclean-model.fits into the MODEL_DATA column
# (note that the column is always called MODEL_DATA, the datacolumn parameter
# only sets the column used in the 'image' command)
wsc.predict([sys.argv[1]], "wsclean")
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