def invert_with_raster_iterator(vis, im, dopsf=False,
normalize=True, invert_function=invert_2d_base, facets=1,
**kwargs) -> (Image, numpy.ndarray):
""" Predict using image partitions, calling specified predict function
:param vis: Visibility to be inverted
:param im: image template (not changed)
:param image_iterator: Iterator to use for partitioning
:param dopsf: Make the psf instead of the dirty image
:param normalize: Normalize by the sum of weights (True)
:return: resulting image[nchan, npol, ny, nx], sum of weights[nchan, npol]
"""
log.info("invert_with_image_iterator: Inverting by image partitions")
i = 0
nchan, npol, _, _ = im.shape
totalwt = numpy.zeros([nchan, npol])
for dpatch in image_raster_iter(im, facets=facets):
result, sumwt = invert_function(vis, dpatch, dopsf, normalize=False, **kwargs)
totalwt = sumwt
# Ensure that we fill in the elements of dpatch instead of creating a new numpy arrray
dpatch.data[...] = result.data[...]
assert numpy.max(numpy.abs(dpatch.data)), "Partition image %d appears to be empty" % i
i += 1
assert numpy.max(numpy.abs(im.data)), "Output image appears to be empty"
if normalize:
im = normalize_sumwt(im, totalwt)
return im, totalwt
def image_gather_facets(image_list: List[Image], im: Image, facets=1) -> Image:
"""Gather a list of subimages back into an image using the image_raster_iterator
:param image_list: List of subimages
:param im: Output image
:param facets: Number of image partitions on each axis (2)
:return: list of subimages
"""
for i, facet in enumerate(image_raster_iter(im, facets=facets)):
facet.data[...] = image_list[i].data[...]
return im
def image_scatter_facets(im: Image, facets=1) -> List[Image]:
"""Scatter an image into a list of subimages using the image_raster_iterator
:param im: Image
:param facets: Number of image partitions on each axis (2)
:return: list of subimages
"""
image_list = list()
for facet in image_raster_iter(im, facets=facets):
image_list.append(facet)
return image_list
def predict_with_raster_iterator(vis: Visibility, model: Image,
predict_function=predict_2d_base, facets=1,
**kwargs) -> Visibility:
""" Predict using image partitions, calling specified predict function
:param vis: Visibility to be predicted
:param model: model image
:param image_iterator: Image iterator used to access the image
:param predict_function: Function to be used for prediction (allows nesting)
:return: resulting visibility (in place works)
"""
log.info("predict_with_image_iterator: Predicting by image partitions")
result = copy_visibility(vis)
for dpatch in image_raster_iter(model, facets=facets):
result.data['vis'][...] = 0.0
result = predict_function(result, dpatch, **kwargs)
vis.data['vis'] += result.data['vis']
return vis
def test_rasterise(self):
m31original = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
assert numpy.max(numpy.abs(m31original.data)), "Original is empty"
for nraster in [2, 4, 8]:
m31model = create_test_image(polarisation_frame=PolarisationFrame('stokesI'))
for patch in image_raster_iter(m31model, facets=nraster):
assert patch.data.shape[3] == (m31model.data.shape[3] // nraster), \
"Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[3],
(m31model.data.shape[3] // nraster))
assert patch.data.shape[2] == (m31model.data.shape[2] // nraster), \
"Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[2],
(m31model.data.shape[2] // nraster))
patch.data *= 2.0
diff = m31model.data - 2.0 * m31original.data
assert numpy.max(numpy.abs(m31model.data)), "Raster is empty for %d" % nraster
assert numpy.max(numpy.abs(diff)) == 0.0, "Raster set failed for %d" % nraster