def test_griddata_invert_wterm(self):
self.actualSetUp(zerow=False)
gcf, cf = create_awterm_convolutionfunction(self.model,
nw=100,
wstep=8.0,
oversampling=8,
support=32,
use_aaf=True)
cf_image = convert_convolutionfunction_to_image(cf)
cf_image.data = numpy.real(cf_image.data)
if self.persist:
export_image_to_fits(cf_image,
"%s/test_gridding_wterm_cf.fits" % self.dir)
griddata = create_griddata_from_image(self.model, nw=1)
griddata, sumwt = grid_visibility_to_griddata(self.vis,
griddata=griddata,
cf=cf)
im = fft_griddata_to_image(griddata, gcf)
im = normalize_sumwt(im, sumwt)
if self.persist:
export_image_to_fits(
im, '%s/test_gridding_dirty_wterm.fits' % self.dir)
self.check_peaks(im, 97.13215242859648)
def test_griddata_invert_aterm_noover(self):
self.actualSetUp(zerow=True)
make_pb = functools.partial(create_pb_generic,
diameter=35.0,
blockage=0.0,
use_local=False)
pb = make_pb(self.model)
if self.persist:
export_image_to_fits(pb,
"%s/test_gridding_aterm_pb.fits" % self.dir)
gcf, cf = create_awterm_convolutionfunction(self.model,
make_pb=make_pb,
nw=1,
oversampling=1,
support=16,
use_aaf=True)
griddata = create_griddata_from_image(self.model)
griddata, sumwt = grid_visibility_to_griddata(self.vis,
griddata=griddata,
cf=cf)
im = fft_griddata_to_image(griddata, gcf)
im = normalize_sumwt(im, sumwt)
if self.persist:
export_image_to_fits(
im, '%s/test_gridding_dirty_aterm_noover.fits' % self.dir)
self.check_peaks(im, 97.10594988491549)
def test_griddata_invert_awterm(self):
self.actualSetUp(zerow=False)
make_pb = functools.partial(create_pb_generic,
diameter=35.0,
blockage=0.0,
use_local=False)
pb = make_pb(self.model)
if self.persist:
export_image_to_fits(pb,
"%s/test_gridding_awterm_pb.fits" % self.dir)
gcf, cf = create_awterm_convolutionfunction(self.model,
make_pb=make_pb,
nw=100,
wstep=8.0,
oversampling=16,
support=32,
use_aaf=True)
cf_image = convert_convolutionfunction_to_image(cf)
cf_image.data = numpy.real(cf_image.data)
if self.persist:
export_image_to_fits(cf_image,
"%s/test_gridding_awterm_cf.fits" % self.dir)
griddata = create_griddata_from_image(self.model, nw=100, wstep=8.0)
griddata, sumwt = grid_visibility_to_griddata(self.vis,
griddata=griddata,
cf=cf)
im = fft_griddata_to_image(griddata, gcf)
im = normalize_sumwt(im, sumwt)
if self.persist:
export_image_to_fits(
im, '%s/test_gridding_dirty_awterm.fits' % self.dir)
self.check_peaks(im, 97.13240677427714)
def test_griddata_predict_awterm(self):
self.actualSetUp(zerow=False)
make_pb = functools.partial(create_pb_generic,
diameter=35.0,
blockage=0.0,
use_local=False)
pb = make_pb(self.model)
if self.persist:
export_image_to_fits(pb,
"%s/test_gridding_awterm_pb.fits" % self.dir)
gcf, cf = create_awterm_convolutionfunction(self.model,
make_pb=make_pb,
nw=100,
wstep=8.0,
oversampling=16,
support=32,
use_aaf=True)
griddata = create_griddata_from_image(self.model, nw=100, wstep=8.0)
griddata = fft_image_to_griddata(self.model, griddata, gcf)
newvis = degrid_visibility_from_griddata(self.vis,
griddata=griddata,
cf=cf)
qa = qa_visibility(newvis)
assert qa.data['rms'] < 120.0, str(qa)
self.plot_vis(newvis, 'awterm')
def test_readwritegriddata(self):
im = create_test_image()
gd = create_griddata_from_image(im)
export_griddata_to_hdf5(gd, '%s/test_data_model_helpers_griddata.hdf' % self.dir)
newgd = import_griddata_from_hdf5('%s/test_data_model_helpers_griddata.hdf' % self.dir)
assert newgd.data.shape == gd.data.shape
assert numpy.max(numpy.abs(gd.data - newgd.data)) < 1e-15
def test_griddata_predict_pswf(self):
self.actualSetUp(zerow=True)
gcf, cf = create_pswf_convolutionfunction(self.model, support=6, oversampling=256)
griddata = create_griddata_from_image(self.model)
griddata = fft_image_to_griddata(self.model, griddata, gcf)
newvis = degrid_visibility_from_griddata(self.vis, griddata=griddata, cf=cf)
newvis.data['vis'][...] -= self.vis.data['vis'][...]
qa = qa_visibility(newvis)
assert qa.data['rms'] < 0.7, str(qa)
def test_griddata_invert_fast(self):
self.actualSetUp(zerow=True)
gcf, cf = create_box_convolutionfunction(self.model)
griddata = create_griddata_from_image(self.model)
griddata, sumwt = grid_visibility_to_griddata(self.vis, griddata=griddata, cf=cf)
im = fft_griddata_to_image(griddata, gcf)
im = normalize_sumwt(im, sumwt)
export_image_to_fits(im, '%s/test_gridding_dirty_fast.fits' % self.dir)
self.check_peaks(im, 96.74492791536595, tol=1e-7)
def test_griddata_predict_box(self):
self.actualSetUp(zerow=True)
gcf, cf = create_box_convolutionfunction(self.model)
griddata = create_griddata_from_image(self.model)
griddata = fft_image_to_griddata(self.model, griddata, gcf)
newvis = degrid_visibility_from_griddata(self.vis, griddata=griddata, cf=cf)
newvis.data['vis'][...] -= self.vis.data['vis'][...]
qa = qa_visibility(newvis)
assert qa.data['rms'] < 46.0, str(qa)
def test_griddata_invert_pswf_w(self):
self.actualSetUp(zerow=False)
gcf, cf = create_pswf_convolutionfunction(self.model, support=6, oversampling=32)
griddata = create_griddata_from_image(self.model)
griddata, sumwt = grid_visibility_to_griddata(self.vis, griddata=griddata, cf=cf)
im = fft_griddata_to_image(griddata, gcf)
im = normalize_sumwt(im, sumwt)
export_image_to_fits(im, '%s/test_gridding_dirty_pswf_w.fits' % self.dir)
self.check_peaks(im, 96.62754566597258, tol=1e-7)
def test_griddata_invert_pswf(self):
self.actualSetUp(zerow=True)
gcf, cf = create_pswf_convolutionfunction(self.model, support=6, oversampling=32)
griddata = create_griddata_from_image(self.model)
griddata, sumwt = grid_visibility_to_griddata(self.vis, griddata=griddata, cf=cf)
im = fft_griddata_to_image(griddata, gcf)
im = normalize_sumwt(im, sumwt)
if self.persist:
export_image_to_fits(im, '%s/test_gridding_dirty_pswf.fits' % self.dir)
self.check_peaks(im, 97.00435128311616, tol=1e-7)
def test_griddata_invert_box(self):
self.actualSetUp(zerow=True)
gcf, cf = create_box_convolutionfunction(self.model)
griddata = create_griddata_from_image(self.model)
griddata, sumwt = grid_visibility_to_griddata(self.vis, griddata=griddata, cf=cf)
im = fft_griddata_to_image(griddata, gcf)
im = normalize_sumwt(im, sumwt)
if self.persist:
export_image_to_fits(im, '%s/test_gridding_dirty_box.fits' % self.dir)
self.check_peaks(im, 97.11833094588997, tol=1e-7)
def test_griddata_predict_wterm(self):
self.actualSetUp(zerow=False)
gcf, cf = create_awterm_convolutionfunction(self.model, nw=100, wstep=10.0, oversampling=8, support=30,
use_aaf=True)
griddata = create_griddata_from_image(self.model, nw=100, wstep=10.0)
griddata = fft_image_to_griddata(self.model, griddata, gcf)
newvis = degrid_visibility_from_griddata(self.vis, griddata=griddata, cf=cf)
newvis.data['vis'][...] -= self.vis.data['vis'][...]
qa = qa_visibility(newvis)
self.plot_vis(newvis, 'wterm')
assert qa.data['rms'] < 11.0, str(qa)
def test_griddata_predict_aterm(self):
self.actualSetUp(zerow=True)
make_pb = functools.partial(create_pb_generic, diameter=35.0, blockage=0.0)
griddata = create_griddata_from_image(self.model)
gcf, cf = create_awterm_convolutionfunction(self.model, make_pb=make_pb, nw=1,
oversampling=16, support=16,
use_aaf=True)
griddata = fft_image_to_griddata(self.model, griddata, gcf)
newvis = degrid_visibility_from_griddata(self.vis, griddata=griddata, cf=cf)
qa = qa_visibility(newvis)
assert qa.data['rms'] < 120.0, str(qa)
def test_griddata_weight(self):
self.actualSetUp(zerow=True)
gcf, cf = create_box_convolutionfunction(self.model)
gd = create_griddata_from_image(self.model)
gd_list = [grid_weight_to_griddata(self.vis, gd, cf) for i in range(10)]
gd, sumwt = griddata_merge_weights(gd_list, algorithm='uniform')
self.vis = griddata_reweight(self.vis, gd, cf)
gd, sumwt = grid_visibility_to_griddata(self.vis, griddata=gd, cf=cf)
im = fft_griddata_to_image(gd, gcf)
im = normalize_sumwt(im, sumwt)
if self.persist:
export_image_to_fits(im, '%s/test_gridding_dirty_2d_uniform.fits' % self.dir)
self.check_peaks(im, 99.42031190701735)
def test_readwritegriddata(self):
im = create_test_image()
gd = create_griddata_from_image(im)
config = {
"buffer": {
"directory": self.dir
},
"griddata": {
"name": "test_buffergriddata.hdf",
"data_model": "GridData"
}
}
bdm = BufferGridData(config["buffer"], config["griddata"], gd)
bdm.sync()
new_bdm = BufferGridData(config["buffer"], config["griddata"])
new_bdm.sync()
newgd = bdm.memory_data_model
assert newgd.data.shape == gd.data.shape
assert numpy.max(numpy.abs(gd.data - newgd.data)) < 1e-15
def weight_visibility(vis, model, gcfcf=None, weighting='uniform', **kwargs):
""" Weight the visibility data
This is done collectively so the weights are summed over all vis_lists and then
corrected
:param vis_list:
:param model_imagelist: Model required to determine weighting parameters
:param weighting: Type of weighting
:param kwargs: Parameters for functions in graphs
:return: List of vis_graphs
"""
assert isinstance(vis, Visibility), vis
if gcfcf is None:
gcfcf = create_pswf_convolutionfunction(model)
griddata = create_griddata_from_image(model)
griddata, sumwt = grid_weight_to_griddata(vis, griddata, gcfcf[1])
vis = griddata_reweight(vis, griddata, gcfcf[1])
return vis
def test_convert_griddata_to_image(self):
m31model_by_image = create_griddata_from_image(self.m31image)
m31_converted = convert_griddata_to_image(m31model_by_image)
def test_create_griddata_from_image(self):
m31model_by_image = create_griddata_from_image(self.m31image)
assert m31model_by_image.shape[0] == self.m31image.shape[0]
assert m31model_by_image.shape[1] == self.m31image.shape[1]
assert m31model_by_image.shape[3] == self.m31image.shape[2]
assert m31model_by_image.shape[4] == self.m31image.shape[3]
