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"""
A Python interface to WSClean
This wrapper can be used to call the (C++) WSClean imager.
"""
import _wsclean
import numpy
class ImagingParameters(object):
"""Parameters for imaging"""
msPath = ""
imageWidth = 512
imageHeight = 512
pixelScaleX = "1asec"
pixelScaleY = "1asec"
doNormalize = 1
extraParameters = ""
class ImagingData(object):
"""Information about the imaging operation"""
dataSize = 0
class Operator(object):
"""Class that wraps WSClean as an operator, so that it is easy
to get an image from data 'in memory' (and the inverse). Currently, the
operator will write that data to the MODEL_DATA Measurement Set before imaging.
The read/write/backward/forward methods can only be used within a "with" context.
"""
_userdata = None
_parameters = None
_imagingdata = None
def __init__(self, parameters):
"""Constructor: only sets parameters"""
self._parameters = parameters
def __enter__(self):
"""Context manager entrance: initialize WSClean"""
self._userdata, self._imagingdata = _wsclean.initialize(
self._parameters
)
return self
def __exit__(self, type, value, traceback):
"""Destructor: release WSClean resources"""
if self._userdata != None:
print("Releasing resources for WSClean...")
_wsclean.deinitialize(self._userdata)
self._userdata = None
self._imagingdata = None
def data_size(self):
"""Get the number of visibilities"""
if self._userdata == None:
raise RuntimeError(
'Operator.data_size() was called outside "with" block'
)
return self._imagingdata.dataSize
def image_size(self):
"""Get the number of pixels in the image"""
if self._userdata == None:
raise RuntimeError(
'Operator.image_size() was called outside "with" block'
)
return self._parameters.imageWidth * self._parameters.imageHeight
def read(self):
"""Read the visibilities and return as a (data,weight) tuple."""
if self._userdata == None:
raise RuntimeError(
'Operator.read() was called outside "with" block'
)
data = numpy.ascontiguousarray(
numpy.zeros(self._imagingdata.dataSize, dtype=numpy.complex128)
)
weights = numpy.ascontiguousarray(
numpy.zeros(self._imagingdata.dataSize, dtype=numpy.float64)
)
_wsclean.read(self._userdata, data, weights)
return data, weights
def write(self, filename, data):
"""Write a FITS image with the correct keywords etc."""
if self._userdata == None:
raise RuntimeError(
'Operator.write() was called outside "with" block'
)
dataCont = numpy.ascontiguousarray(data)
_wsclean.write(self._userdata, filename, data)
def forward(self, dataOut, dataIn):
"""Perform the forward operation. This is 'prediction': convert
an image into visibilities. dataOut should be an complex double array
that will be filled with visibilities, dataIn should be an array
of doubles, representing the image for the operator input."""
if self._userdata == None:
raise RuntimeError(
'Operator.forward() was called outside "with" block'
)
if numpy.shape(dataOut)[0] != self.data_size():
raise RuntimeError(
"Size of output argument ("
+ str(numpy.shape(dataOut)[0])
+ ") does not match the image size ("
+ str(self.data_size())
+ ")"
)
if numpy.shape(dataIn)[0] != self.image_size():
raise RuntimeError(
"Shape of input argument ("
+ str(numpy.shape(dataIn)[0])
+ ") does not match the number of visibilities ("
+ str(self.image_size())
+ ")"
)
if dataOut.dtype.name != "complex128":
raise RuntimeError(
"The dataOut parameter of forward() should be of type complex128, but was "
+ dataOut.dtype.name
)
if dataIn.dtype.name != "float64":
raise RuntimeError(
"The dataIn parameter of forward() should be of type float64, but was "
+ dataIn.dtype.name
)
dataOutCont = numpy.ascontiguousarray(dataOut)
dataInCont = numpy.ascontiguousarray(dataIn)
_wsclean.operator_A(self._userdata, dataOut, dataIn)
def backward(self, dataOut, dataIn):
"""Perform the backward operation. This is the 'imaging' step:
convert visibilities into an image. dataOut should be an array
of doubles, which will be filled with the image, dataOut should be an array
of complex doubles, representing the visibilities for the operator input.
"""
if self._userdata == None:
raise RuntimeError(
'Operator.backward() was called outside "with" block'
)
if numpy.shape(dataOut)[0] != self.image_size():
raise RuntimeError(
"Size of output argument ("
+ str(numpy.shape(dataOut)[0])
+ ") does not match the image size ("
+ str(self.image_size())
+ ")"
)
if numpy.shape(dataIn)[0] != self.data_size():
raise RuntimeError(
"Shape of input argument ("
+ str(numpy.shape(dataIn)[0])
+ ") does not match the number of visibilities ("
+ str(self.data_size())
+ ")"
)
if dataOut.dtype.name != "float64":
raise RuntimeError(
"The dataOut parameter of forward() should be of type float64, but was "
+ dataOut.dtype.name
)
if dataIn.dtype.name != "complex128":
raise RuntimeError(
"The dataIn parameter of forward() should be of type complex128, but was "
+ dataIn.dtype.name
)
dataOutCont = numpy.ascontiguousarray(dataOut)
dataInCont = numpy.ascontiguousarray(dataIn)
_wsclean.operator_At(self._userdata, dataOut, dataIn)
class WSClean(object):
"""The Python interface to WSClean"""
datacolumn = ""
"""The column used for imaging; empty means CORRECTED_DATA if it exists, otherwise
use DATA."""
width = 1024
"""Image width"""
height = 1024
"""Image height"""
scale = "1asec"
"""Pixel scale of image. Units can e.g. be deg, amin, asec, masec. There should not
be a space between the number and its unit."""
niter = 0
"""Number of clean or moresane iterations"""
gain = -1
"""Gain per minor iteration. -1 means use WSClean's default."""
mgain = -1
"""Gain per major iteration. -1 means use WSClean's default."""
__weightpar = ""
def __init__(self):
return
def image(self, msnames, nameprefix):
"""Run WSClean to make an image on the specified list of measurement sets"""
plist = self.__get_parameterlist(nameprefix)
import os
msnamelist = " ".join(msnames)
cmd = "wsclean " + str(plist) + " " + msnamelist
print(cmd)
os.system(cmd)
return
def predict(self, msnames, nameprefix):
"""Run WSClean to predict"""
plist = self.__get_parameterlist(nameprefix)
import os
msnamelist = " ".join(msnames)
cmd = "wsclean -predict " + str(plist) + " " + msnamelist
print(cmd)
os.system(cmd)
return
def set_uniform_weighting(self):
"""Enable uniform weighting"""
self.__weightpar = "-weight uniform"
def set_natural_weighting(self):
"""Enable natural weighting"""
self.__weightpar = "-weight natural"
def set_briggs_weighting(self, robustness):
"""Enable Briggs' weighting with a given robustness"""
self.__weightpar = "-weight briggs " + str(robustness)
def __get_parameterlist(self, prefixname):
plist = (
"-size "
+ str(self.width)
+ " "
+ str(self.height)
+ " -scale "
+ str(self.scale)
)
if self.datacolumn != "":
plist += " -data-column " + self.datacolumn
if self.__weightpar != "":
plist += " " + self.__weightpar
if self.niter != 0:
plist += " -niter " + str(self.niter)
if self.gain != -1:
plist += " -gain " + str(self.gain)
if self.mgain != -1:
plist += " -mgain " + str(self.mgain)
if prefixname != "":
plist += " -name " + prefixname
return plist
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