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_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_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_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_fill_box_to_convolutionfunction(self):
gcf, cf = create_box_convolutionfunction(self.image)
assert numpy.max(numpy.abs(cf.data)) > 0.0
export_image_to_fits(
gcf, "%s/test_convolutionfunction_box_gcf.fits" % self.dir)
cf_image = convert_convolutionfunction_to_image(cf)
cf_image.data = numpy.real(cf_image.data)
export_image_to_fits(
cf_image, "%s/test_convolutionfunction_box_cf.fits" % self.dir)
peak_location = numpy.unravel_index(numpy.argmax(numpy.abs(cf.data)),
cf.shape)
assert numpy.abs(cf.data[peak_location] -
1.0) < 1e-15, "Peak is incorrect %s" % str(
cf.data[peak_location] - 1.0)
assert peak_location == (0, 0, 0, 0, 0, 2, 2), peak_location