def test_deconvolve_and_restore_cube_mmclean(self): self.actualSetUp(add_errors=True) dirty_imagelist = invert_list_serial_workflow(self.vis_list, self.model_imagelist, context='2d', dopsf=False, normalize=True) psf_imagelist = invert_list_serial_workflow(self.vis_list, self.model_imagelist, context='2d', dopsf=True, normalize=True) dec_imagelist = deconvolve_list_serial_workflow( dirty_imagelist, psf_imagelist, self.model_imagelist, niter=1000, fractional_threshold=0.01, scales=[0, 3, 10], algorithm='mmclean', nmoment=3, nchan=self.freqwin, threshold=0.1, gain=0.7) residual_imagelist = residual_list_serial_workflow( self.vis_list, model_imagelist=dec_imagelist, context='2d') restored = restore_list_serial_workflow( model_imagelist=dec_imagelist, psf_imagelist=psf_imagelist, residual_imagelist=residual_imagelist, empty=self.model_imagelist)[0] export_image_to_fits( restored, '%s/test_imaging_serial_mmclean_restored.fits' % (self.dir))
def test_residual_list(self): self.actualSetUp(zerow=True) centre = self.freqwin // 2 residual_image_list = residual_list_serial_workflow(self.vis_list, self.model_list, context='2d') qa = qa_image(residual_image_list[centre][0]) assert numpy.abs(qa.data['max'] - 0.35139716991480785) < 1.0, str(qa) assert numpy.abs(qa.data['min'] + 0.7681701460717593) < 1.0, str(qa)