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_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_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_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 weight_blockvisibility(vis, model, gcfcf=None, weighting="uniform", robustness=0.0, **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, BlockVisibility), vis assert image_is_canonical(model) if gcfcf is None: gcfcf = create_pswf_convolutionfunction(model) griddata = create_griddata_from_image(model, vis) griddata, sumwt = grid_blockvisibility_weight_to_griddata( vis, griddata, gcfcf[1]) vis = griddata_blockvisibility_reweight(vis, griddata, gcfcf[1], weighting=weighting, robustness=robustness) return vis
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 invert_2d(vis: Visibility, im: Image, dopsf: bool = False, normalize: bool = True, gcfcf=None, **kwargs) -> (Image, numpy.ndarray): """ Invert using 2D convolution function, using the specified convolution function Use the image im as a template. Do PSF in a separate call. This is at the bottom of the layering i.e. all transforms are eventually expressed in terms of this function. Any shifting needed is performed here. :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) :param gcfcf: (Grid correction function i.e. in image space, Convolution function i.e. in uv space) :return: resulting image """ assert isinstance(vis, Visibility), vis svis = copy_visibility(vis) if dopsf: svis.data['vis'][...] = 1.0 + 0.0j svis = shift_vis_to_image(svis, im, tangent=True, inverse=False) if gcfcf is None: gcf, cf = create_pswf_convolutionfunction( im, support=get_parameter(kwargs, "support", 6), oversampling=get_parameter(kwargs, "oversampling", 128)) else: gcf, cf = gcfcf griddata = create_griddata_from_image(im) griddata, sumwt = grid_visibility_to_griddata(svis, griddata=griddata, cf=cf) imaginary = get_parameter(kwargs, "imaginary", False) if imaginary: result0, result1 = fft_griddata_to_image(griddata, gcf, imaginary=imaginary) log.debug("invert_2d: retaining imaginary part of dirty image") if normalize: result0 = normalize_sumwt(result0, sumwt) result1 = normalize_sumwt(result1, sumwt) return result0, sumwt, result1 else: result = fft_griddata_to_image(griddata, gcf) if normalize: result = normalize_sumwt(result, sumwt) return result, sumwt
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_predict_box(self): self.actualSetUp(zerow=True, image_pol=PolarisationFrame("stokesIQUV")) gcf, cf = create_box_convolutionfunction(self.model) modelIQUV = convert_stokes_to_polimage(self.model, self.vis.polarisation_frame) griddata = create_griddata_from_image(modelIQUV, self.vis) griddata = fft_image_to_griddata(modelIQUV, 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'] < 58.0, str(qa)
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_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, 97.10594988491546, tol=1e-7)
def test_griddata_invert_pswf_w(self): self.actualSetUp(zerow=False) gcf, cf = create_pswf_convolutionfunction(self.model) griddata = create_griddata_from_image(self.model, self.vis) griddata, sumwt = grid_visibility_to_griddata(self.vis, griddata=griddata, cf=cf) cim = fft_griddata_to_image(griddata, gcf) cim = normalize_sumwt(cim, sumwt) im = convert_polimage_to_stokes(cim) if self.persist: export_image_to_fits( im, '%s/test_gridding_dirty_pswf_w.fits' % self.dir) self.check_peaks(im, 97.13240718331633, tol=1e-7)
def test_griddata_invert_pswf_stokesIQ(self): self.actualSetUp(zerow=True, image_pol=PolarisationFrame("stokesIQ")) gcf, cf = create_pswf_convolutionfunction(self.model) griddata = create_griddata_from_image(self.model, self.vis) griddata, sumwt = grid_visibility_to_griddata(self.vis, griddata=griddata, cf=cf) cim = fft_griddata_to_image(griddata, gcf) cim = normalize_sumwt(cim, sumwt) im = convert_polimage_to_stokes(cim) if self.persist: export_image_to_fits(im, '%s/test_gridding_dirty_pswf.fits' % self.dir) self.check_peaks(im, 97.10594988491545, tol=1e-7)
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) if self.persist: export_image_to_fits( im, '%s/test_gridding_dirty_pswf_w.fits' % self.dir) self.check_peaks(im, 97.01838776845877, 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, 99.40822097133994)
def test_griddata_predict_wterm(self): self.actualSetUp(zerow=False) gcf, cf = create_awterm_convolutionfunction(self.model, nw=100, wstep=10.0, oversampling=16, support=32, 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_visibility_weight_IQ(self): self.actualSetUp(zerow=True, image_pol=PolarisationFrame("stokesIQUV")) gcf, cf = create_pswf_convolutionfunction(self.model) gd = create_griddata_from_image(self.model, self.vis) gd_list = [ grid_visibility_weight_to_griddata(self.vis, gd, cf) for i in range(10) ] gd, sumwt = griddata_merge_weights(gd_list, algorithm='uniform') self.vis = griddata_visibility_reweight(self.vis, gd, cf) gd, sumwt = grid_visibility_to_griddata(self.vis, griddata=gd, cf=cf) cim = fft_griddata_to_image(gd, gcf) cim = normalize_sumwt(cim, sumwt) im = convert_polimage_to_stokes(cim) if self.persist: export_image_to_fits( im, '%s/test_gridding_dirty_2d_IQ_uniform.fits' % self.dir) self.check_peaks(im, 99.40822097133994)
def predict_2d(vis: Union[BlockVisibility, Visibility], model: Image, gcfcf=None, **kwargs) -> Union[BlockVisibility, Visibility]: """ Predict using convolutional degridding. This is at the bottom of the layering i.e. all transforms are eventually expressed in terms of this function. Any shifting needed is performed here. :param vis: Visibility to be predicted :param model: model image :param gcfcf: (Grid correction function i.e. in image space, Convolution function i.e. in uv space) :return: resulting visibility (in place works) """ if model is None: return vis assert isinstance(vis, Visibility) or isinstance(vis, BlockVisibility), vis _, _, ny, nx = model.data.shape if gcfcf is None: gcf, cf = create_pswf_convolutionfunction( model, support=get_parameter(kwargs, "support", 8), oversampling=get_parameter(kwargs, "oversampling", 127)) else: gcf, cf = gcfcf griddata = create_griddata_from_image(model, vis) polmodel = convert_stokes_to_polimage(model, vis.polarisation_frame) griddata = fft_image_to_griddata(polmodel, griddata, gcf) if isinstance(vis, Visibility): vis = degrid_visibility_from_griddata(vis, griddata=griddata, cf=cf) else: vis = degrid_blockvisibility_from_griddata(vis, griddata=griddata, cf=cf) # Now we can shift the visibility from the image frame to the original visibility frame svis = shift_vis_to_image(vis, model, tangent=True, inverse=True) return svis
def test_griddata_predict_aterm(self): self.actualSetUp(zerow=True) make_pb = functools.partial(create_pb_generic, diameter=35.0, blockage=0.0, use_local=False) griddata = create_griddata_from_image(self.model) gcf, cf = create_awterm_convolutionfunction(self.model, make_pb=make_pb, nw=1, oversampling=16, support=32, 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_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 test_griddata_blockvisibility_weight(self): self.actualSetUp(zerow=True, block=True, image_pol=PolarisationFrame("stokesIQUV")) gcf, cf = create_pswf_convolutionfunction(self.model) gd = create_griddata_from_image(self.model, self.vis) gd_list = [ grid_blockvisibility_weight_to_griddata(self.vis, gd, cf) for i in range(10) ] assert numpy.max(numpy.abs(gd_list[0][0].data)) > 10.0 gd, sumwt = griddata_merge_weights(gd_list, algorithm='uniform') self.vis = griddata_blockvisibility_reweight(self.vis, gd, cf) gd, sumwt = grid_blockvisibility_to_griddata(self.vis, griddata=gd, cf=cf) cim = fft_griddata_to_image(gd, gcf) cim = normalize_sumwt(cim, sumwt) im = convert_polimage_to_stokes(cim) if self.persist: export_image_to_fits( im, '%s/test_gridding_dirty_2d_uniform_block.fits' % self.dir) self.check_peaks(im, 100.13540418821904)
def test_create_griddata_from_image(self): m31model_by_image = create_griddata_from_image(self.m31image, None) 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]
def test_convert_griddata_to_image(self): m31model_by_image = create_griddata_from_image(self.m31image, None) m31_converted = convert_griddata_to_image(m31model_by_image)