def predict_wstack_single(vis, model, remove=True, facets=1, vis_slices=1, **kwargs) -> Visibility: """ Predict using a single w slices. This processes a single w plane, rotating out the w beam for the average w :param vis: Visibility to be predicted :param model: model image :return: resulting visibility (in place works) """ if not isinstance(vis, Visibility): log.debug("predict_wstack_single: Coalescing") avis = coalesce_visibility(vis, **kwargs) else: avis = vis log.debug("predict_wstack_single: predicting using single w slice") avis.data['vis'] *= 0.0 # We might want to do wprojection so we remove the average w w_average = numpy.average(avis.w) avis.data['uvw'][..., 2] -= w_average tempvis = copy_visibility(avis) # Calculate w beam and apply to the model. The imaginary part is not needed workimage = copy_image(model) w_beam = create_w_term_like(model, w_average, vis.phasecentre) # Do the real part workimage.data = w_beam.data.real * model.data avis = predict_2d(avis, workimage, facets=1, vis_slices=1, **kwargs) # and now the imaginary part workimage.data = w_beam.data.imag * model.data tempvis = predict_2d(tempvis, workimage, facets=facets, vis_slices=vis_slices, **kwargs) avis.data['vis'] -= 1j * tempvis.data['vis'] if not remove: avis.data['uvw'][..., 2] += w_average if isinstance(vis, BlockVisibility) and isinstance(avis, Visibility): log.debug("imaging.predict decoalescing post prediction") return decoalesce_visibility(avis) else: return avis
def test_create_w_term_image(self): m31image = create_test_image(cellsize=0.001) im = create_w_term_like(m31image, w=20000.0, remove_shift=True) im.data = im.data.real for x in [64, 64 + 128]: for y in [64, 64 + 128]: self.assertAlmostEqual(im.data[0, 0, y, x], 0.84946344276442431, 7) export_image_to_fits(im, '%s/test_wterm.fits' % self.dir) assert im.data.shape == (1, 1, 256, 256) self.assertAlmostEqual(numpy.max(im.data.real), 1.0, 7)
def test_convert_image_to_kernel(self): m31image = create_test_image(cellsize=0.001, frequency=[1e8], canonical=True) screen = create_w_term_like(m31image, w=20000.0, remove_shift=True) screen_fft = fft_image(screen) converted = convert_image_to_kernel(screen_fft, 8, 8) assert converted.shape == (1, 1, 8, 8, 8, 8) with self.assertRaises(AssertionError): converted = convert_image_to_kernel(m31image, 15, 1) with self.assertRaises(AssertionError): converted = convert_image_to_kernel(m31image, 15, 1000)
def invert_wstack_single(vis: Visibility, im: Image, dopsf, normalize=True, remove=True, facets=1, vis_slices=1, **kwargs) -> (Image, numpy.ndarray): """Process single w slice :param vis: Visibility to be inverted :param im: image template (not changed) :param dopsf: Make the psf instead of the dirty image :param normalize: Normalize by the sum of weights (True) """ log.debug("invert_wstack_single: predicting using single w slice") kwargs['imaginary'] = True assert isinstance(vis, Visibility), vis # We might want to do wprojection so we remove the average w w_average = numpy.average(vis.w) vis.data['uvw'][..., 2] -= w_average reWorkimage, sumwt, imWorkimage = invert_2d(vis, im, dopsf, normalize=normalize, facets=facets, vis_slices=vis_slices, **kwargs) if not remove: vis.data['uvw'][..., 2] += w_average # Calculate w beam and apply to the model. The imaginary part is not needed w_beam = create_w_term_like(im, w_average, vis.phasecentre) reWorkimage.data = w_beam.data.real * reWorkimage.data - w_beam.data.imag * imWorkimage.data return reWorkimage, sumwt