def test_pad_image(self): m31image = create_test_image(cellsize=0.001, frequency=[1e8], canonical=True) padded = pad_image(m31image, [1, 1, 1024, 1024]) assert padded.shape == (1, 1, 1024, 1024) padded = pad_image(m31image, [3, 4, 2048, 2048]) assert padded.shape == (3, 4, 2048, 2048) with self.assertRaises(ValueError): padded = pad_image(m31image, [1, 1, 100, 100]) with self.assertRaises(IndexError): padded = pad_image(m31image, [1, 1])
def test_fftim_factors(self): for i in [3, 5, 7]: npixel = 256 * i m31image = create_test_image(cellsize=0.001, frequency=[1e8], canonical=True) padded = pad_image(m31image, [1, 1, npixel, npixel]) assert padded.shape == (1, 1, npixel, npixel) padded_fft = fft_image(padded) padded_fft_ifft = fft_image(padded_fft, m31image) numpy.testing.assert_array_almost_equal(padded.data, padded_fft_ifft.data.real, 12) padded_fft.data = numpy.abs(padded_fft.data) export_image_to_fits(padded_fft, fitsfile='%s/test_m31_fft_%d.fits' % (self.dir, npixel))
def get_kernel_list(vis: Visibility, im: Image, **kwargs): """Get the list of kernels, one per visibility """ shape = im.data.shape npixel = shape[3] cellsize = numpy.pi * im.wcs.wcs.cdelt[1] / 180.0 kernelname = get_parameter(kwargs, "kernel", "2d") oversampling = get_parameter(kwargs, "oversampling", 8) padding = get_parameter(kwargs, "padding", 2) gcf, _ = anti_aliasing_calculate((padding * npixel, padding * npixel), oversampling) wabsmax = numpy.max(numpy.abs(vis.w)) if kernelname == 'wprojection' and wabsmax > 0.0: # wprojection needs a lot of commentary! log.debug("get_kernel_list: Using wprojection kernel") # The field of view must be as padded! R_F is for reporting only so that # need not be padded. fov = cellsize * npixel * padding r_f = (cellsize * npixel / 2) ** 2 / abs(cellsize) log.debug("get_kernel_list: Fresnel number = %f" % (r_f)) delA = get_parameter(kwargs, 'wloss', 0.02) advice = advise_wide_field(vis, delA) wstep = get_parameter(kwargs, "wstep", advice['w_sampling_primary_beam']) log.debug("get_kernel_list: Using w projection with wstep = %f" % (wstep)) # Now calculate the maximum support for the w kernel kernelwidth = get_parameter(kwargs, "kernelwidth", (2 * int(round(numpy.sin(0.5 * fov) * npixel * wabsmax * cellsize)))) kernelwidth = max(kernelwidth, 8) assert kernelwidth % 2 == 0 log.debug("get_kernel_list: Maximum w kernel full width = %d pixels" % (kernelwidth)) padded_shape=[im.shape[0], im.shape[1], im.shape[2] * padding, im.shape[3] * padding] remove_shift = get_parameter(kwargs, "remove_shift", True) padded_image = pad_image(im, padded_shape) kernel_list = w_kernel_list(vis, padded_image, oversampling=oversampling, wstep=wstep, kernelwidth=kernelwidth, remove_shift=remove_shift) else: kernelname = '2d' kernel_list = standard_kernel_list(vis, (padding * npixel, padding * npixel), oversampling=oversampling) return kernelname, gcf, kernel_list
def w_kernel_list(vis: Visibility, im: Image, oversampling=1, wstep=50.0, kernelwidth=16, **kwargs): """ Calculate w convolution kernels Uses create_w_term_like to calculate the w screen. This is exactly as wstacking does. Returns (indices to the w kernel for each row, kernels) Each kernel has axes [centre_v, centre_u, offset_v, offset_u]. We currently use the same convolution function for all channels and polarisations. Changing that behaviour would require modest changes here and to the gridding/degridding routines. :param vis: visibility :param image: Template image (padding, if any, occurs before this) :param oversampling: Oversampling factor :param wstep: Step in w between cached functions :return: (indices to the w kernel for each row, kernels) """ nchan, npol, ny, nx = im.shape gcf, _ = anti_aliasing_calculate((ny, nx)) assert oversampling % 2 == 0 or oversampling == 1, "oversampling must be unity or even" assert kernelwidth % 2 == 0, "kernelwidth must be even" wmaxabs = numpy.max(numpy.abs(vis.w)) log.debug( "w_kernel_list: Maximum absolute w = %.1f, step is %.1f wavelengths" % (wmaxabs, wstep)) def digitise(w, wstep): return numpy.ceil((w + wmaxabs) / wstep).astype('int') # Find all the unique indices for which we need a kernel nwsteps = digitise(wmaxabs, wstep) + 1 w_list = numpy.linspace(-wmaxabs, +wmaxabs, nwsteps) print('====', nwsteps, wstep, len(w_list)) wtemplate = copy_image(im) wtemplate.data = numpy.zeros(wtemplate.shape, dtype=im.data.dtype) padded_shape = list(wtemplate.shape) padded_shape[3] *= oversampling padded_shape[2] *= oversampling # For all the unique indices, calculate the corresponding w kernel kernels = list() for w in w_list: # Make a w screen wscreen = create_w_term_like(wtemplate, w, vis.phasecentre, **kwargs) wscreen.data /= gcf assert numpy.max(numpy.abs(wscreen.data)) > 0.0, 'w screen is empty' wscreen_padded = pad_image(wscreen, padded_shape) wconv = fft_image(wscreen_padded) wconv.data *= float(oversampling)**2 # For the moment, ignore the polarisation and channel axes kernels.append( convert_image_to_kernel(wconv, oversampling, kernelwidth).data[0, 0, ...]) # Now make a lookup table from row number of vis to the kernel kernel_indices = digitise(vis.w, wstep) assert numpy.max(kernel_indices) < len(kernels), "wabsmax %f wstep %f" % ( wmaxabs, wstep) assert numpy.min(kernel_indices) >= 0, "wabsmax %f wstep %f" % (wmaxabs, wstep) return kernel_indices, kernels