def test_deconvolve_mmclean_quadratic_psf(self): self.comp, self.residual = deconvolve_cube(self.dirty, self.psf, niter=self.niter, gain=0.1, algorithm='mmclean', scales=[0, 3, 10], threshold=0.01, nmoments=2, findpeak='ARL', fractional_threshold=0.01, window=self.innerquarter, psf_support=32) export_image_to_fits( self.comp, "%s/test_deconvolve_mmclean_quadratic_psf-comp.fits" % self.dir) export_image_to_fits( self.residual, "%s/test_deconvolve_mmclean_quadratic_psf-residual.fits" % self.dir) self.cmodel = restore_cube(self.comp, self.psf, self.residual) export_image_to_fits( self.cmodel, "%s/test_deconvolve_mmclean_quadratic_psf-clean.fits" % self.dir) assert numpy.max(self.residual.data) < 3.0
def test_deconvolve_hogbom(self): self.comp, self.residual = deconvolve_cube(self.dirty, self.psf, niter=10000, gain=0.1, algorithm='hogbom', threshold=0.01) export_image_to_fits(self.residual, "%s/test_deconvolve_hogbom-residual.fits" % (self.dir)) self.cmodel = restore_cube(self.comp, self.psf, self.residual) export_image_to_fits(self.cmodel, "%s/test_deconvolve_hogbom-clean.fits" % (self.dir)) assert numpy.max(self.residual.data) < 1.2
def test_deconvolve_and_restore_cube_mmclean(self): self.bigmodel.data *= 0.0 visres, model, residual = solve_image(self.vis, self.bigmodel, nmajor=3, niter=1000, threshold=0.01, gain=0.7, window='quarter', scales=[0, 3, 10], fractional_threshold=0.1, algorithm='mmclean', nmoments=3) export_image_to_fits( model, '%s/test_solve_image_mmclean_solution.fits' % (self.dir)) residual, sumwt = invert_2d(visres, model) export_image_to_fits( residual, '%s/test_solve_image_mmclean_residual.fits' % (self.dir)) psf, sumwt = invert_2d(self.vis, model, dopsf=True) export_image_to_fits( psf, '%s/test_solve_image_mmclean_psf.fits' % (self.dir)) restored = restore_cube(model=model, psf=psf, residual=residual) export_image_to_fits( restored, '%s/test_solve_image_mmclean_restored.fits' % (self.dir)) assert numpy.max(numpy.abs(residual.data)) < 1.2
def test_deconvolve_and_restore_cube_hogbom(self): self.bigmodel.data *= 0.0 visres, model, _ = solve_image(self.vis, self.bigmodel, nmajor=5, niter=1000, threshold=0.01, gain=0.1, psf_support=200, window='quarter', fractional_threshold=0.1, algorithm='hogbom') assert numpy.max(numpy.abs(model.data)) > 0.0, "Model image is empty" export_image_to_fits( model, '%s/test_solve_skycomponent_hogbom_solution.fits' % (self.dir)) residual, sumwt = invert_2d(visres, model) export_image_to_fits( residual, '%s/test_solve_skycomponent_msclean_residual.fits' % (self.dir)) psf, sumwt = invert_2d(self.vis, model, dopsf=True) export_image_to_fits( psf, '%s/test_solve_skycomponent_hogbom_psf.fits' % (self.dir)) restored = restore_cube(model=model, psf=psf, residual=residual) export_image_to_fits( restored, '%s/test_solve_skycomponent_hogbom_restored.fits' % (self.dir)) assert numpy.max(numpy.abs(residual.data)) < 0.5
def test_deconvolve_msclean(self): self.comp, self.residual = deconvolve_cube(self.dirty, self.psf, niter=1000, gain=0.7, algorithm='msclean', scales=[0, 3, 10, 30], threshold=0.01) export_image_to_fits(self.comp, "%s/test_deconvolve_msclean-comp.fits" % (self.dir)) export_image_to_fits(self.residual, "%s/test_deconvolve_msclean-residual.fits" % (self.dir)) self.cmodel = restore_cube(self.comp, self.psf, self.residual) export_image_to_fits(self.cmodel, "%s/test_deconvolve_msclean-clean.fits" % (self.dir)) assert numpy.max(self.residual.data) < 1.2
def test_deconvolve_hogbom_subpsf(self): self.comp, self.residual = deconvolve_cube(self.dirty, psf=self.psf, psf_support=200, window='quarter', niter=10000, gain=0.1, algorithm='hogbom', threshold=0.01) export_image_to_fits(self.residual, "%s/test_deconvolve_hogbom_subpsf-residual.fits" % (self.dir)) self.cmodel = restore_cube(self.comp, self.psf, self.residual) export_image_to_fits(self.cmodel, "%s/test_deconvolve_hogbom_subpsf-clean.fits" % (self.dir)) assert numpy.max(self.residual.data[..., 56:456, 56:456]) < 1.2
def test_deconvolve_mmclean_no_taylor_noscales(self): self.comp, self.residual = deconvolve_cube(self.dirty, self.psf, niter=self.niter, gain=0.1, algorithm='mmclean', scales=[0], threshold=0.01, nmoment=1, findpeak='ARL', fractional_threshold=0.01, window=self.innerquarter) if self.persist: export_image_to_fits(self.comp, "%s/test_deconvolve_mmclean_notaylor_noscales-comp.fits" % self.dir) if self.persist: export_image_to_fits(self.residual, "%s/test_deconvolve_mmclean_notaylor_noscales-residual.fits" % self.dir) self.cmodel = restore_cube(self.comp, self.psf, self.residual) if self.persist: export_image_to_fits(self.cmodel, "%s/test_deconvolve_mmclean_notaylor_noscales-clean.fits" % self.dir) assert numpy.max(self.residual.data) < 3.0
def test_deconvolve_mmclean_no_taylor_edge(self): self.comp, self.residual = deconvolve_cube(self.dirty, self.psf, niter=self.niter, gain=0.1, algorithm='mmclean', scales=[0, 3, 10], threshold=0.01, nmoment=1, findpeak='ARL', fractional_threshold=0.01, window_shape='no_edge', window_edge=32) export_image_to_fits(self.comp, "%s/test_deconvolve_mmclean_notaylor-comp.fits" % self.dir) export_image_to_fits(self.residual, "%s/test_deconvolve_mmclean_notaylor-residual.fits" % self.dir) self.cmodel = restore_cube(self.comp, self.psf, self.residual) export_image_to_fits(self.cmodel, "%s/test_deconvolve_mmclean_notaylor-clean.fits" % self.dir) assert numpy.max(self.residual.data) < 3.0
def test_deconvolve_msclean_subpsf(self): self.comp, self.residual = deconvolve_cube(self.dirty, psf=self.psf, psf_support=200, window=self.innerquarter, niter=1000, gain=0.7, algorithm='msclean', scales=[0, 3, 10, 30], threshold=0.01) export_image_to_fits(self.comp, "%s/test_deconvolve_msclean_subpsf-comp.fits" % (self.dir)) export_image_to_fits(self.residual, "%s/test_deconvolve_msclean_subpsf-residual.fits" % (self.dir)) self.cmodel = restore_cube(self.comp, self.psf, self.residual) export_image_to_fits(self.cmodel, "%s/test_deconvolve_msclean_subpsf-clean.fits" % (self.dir)) assert numpy.max(self.residual.data[..., 56:456, 56:456]) < 1.0
def main(): dlg_string = os.environ['DLG_UID'] dlg_string = dlg_string[(dlg_string.rindex('_') + 1):len(dlg_string)] dlg_uid = dlg_string.split('/') Freq_Iteration = int(dlg_uid[1]) # derived from ID Facet_Iteration = int(dlg_uid[2]) # derived from ID vt = load(1) phasecentre_array = [[+15, -45], [+15.2, -45], [+15, -44], [+14.8, -45], [+15, -46]] phasecentre = SkyCoord(ra=phasecentre_array[Facet_Iteration][0] * u.deg, dec=phasecentre_array[Facet_Iteration][1] * u.deg, frame='icrs', equinox='J2000') model = create_image_from_visibility(vt, phasecentre=phasecentre, cellsize=0.001, npixel=256) dirty, sumwt = invert_function(vt, model) psf, sumwt = invert_function(vt, model, dopsf=True) #show_image(dirty) print("Max, min in dirty image = %.6f, %.6f, sumwt = %f" % (dirty.data.max(), dirty.data.min(), sumwt)) print("Max, min in PSF = %.6f, %.6f, sumwt = %f" % (psf.data.max(), psf.data.min(), sumwt)) export_image_to_fits( dirty, '%s/imaging_dirty_%02d_%02d.fits' % (results_dir, Freq_Iteration, Facet_Iteration)) export_image_to_fits( psf, '%s/imaging_psf_%02d_%02d.fits' % (results_dir, Freq_Iteration, Facet_Iteration)) # Deconvolve using clean comp, residual = deconvolve_cube(dirty, psf, niter=1000, threshold=0.001, fracthresh=0.01, window_shape='quarter', gain=0.7, scales=[0, 3, 10, 30]) restored = restore_cube(comp, psf, residual) export_image_to_fits( restored, '%s/imaging_clean%02d_%02d.fits' % (results_dir, Freq_Iteration, Facet_Iteration)) dump(2, restored)
def restore_list_serial_workflow(model_imagelist, psf_imagelist, residual_imagelist, **kwargs): """ Create a graph to calculate the restored image :param model_imagelist: Model list :param psf_imagelist: PSF list :param residual_imagelist: Residual list :param kwargs: Parameters for functions in components :return: """ return [ restore_cube(model_imagelist[i], psf_imagelist[i][0], residual_imagelist[i][0], **kwargs) for i, _ in enumerate(model_imagelist) ]
def test_restore(self): self.cmodel = restore_cube(self.model, self.psf)
def dprepb_imaging(vis_input): """The DPrepB/C imaging pipeline for visibility data. Args: vis_input (array): array of ARL visibility data and parameters. Returns: restored: clean image. """ # Load the Input Data # ------------------------------------------------------ vis1 = vis_input[0] vis2 = vis_input[1] channel = vis_input[2] stations = vis_input[3] lofar_stat_pos = vis_input[4] APPLY_IONO = vis_input[5] APPLY_BEAM = vis_input[6] MAKE_PLOTS = vis_input[7] UV_CUTOFF = vis_input[8] PIXELS_PER_BEAM = vis_input[9] POLDEF = vis_input[10] RESULTS_DIR = vis_input[11] FORCE_RESOLUTION = vis_input[12] ionRM1 = vis_input[13] times1 = vis_input[14] time_indices1 = vis_input[15] ionRM2 = vis_input[16] times2 = vis_input[17] time_indices2 = vis_input[18] twod_imaging = vis_input[19] npixel_advice = vis_input[20] cell_advice = vis_input[21] # Make a results directory on the worker: os.makedirs(RESULTS_DIR, exist_ok=True) # Redirect stdout, as Dask cannot print on workers # ------------------------------------------------------ sys.stdout = open('%s/dask-log.txt' % (RESULTS_DIR), 'w') # Prepare Measurement Set # ------------------------------------------------------ # Combine MSSS snapshots: vis = append_visibility(vis1, vis2) # Apply a uv-distance cut to the data: vis = uv_cut(vis, UV_CUTOFF) # Make some basic plots: if MAKE_PLOTS: uv_cov(vis) uv_dist(vis) # Imaging and Deconvolution # ------------------------------------------------------ # Convert from XX/XY/YX/YY to I/Q/U/V: vis = convert_to_stokes(vis, POLDEF) # Image I, Q, U, V, per channel: if twod_imaging: dirty, psf = image_2d(vis, npixel_advice, cell_advice, channel, RESULTS_DIR) else: dirty, psf = wstack(vis, npixel_advice, cell_advice, channel, RESULTS_DIR) # Deconvolve (using complex Hogbom clean): comp, residual = deconvolve_cube_complex(dirty, psf, niter=100, threshold=0.001, fracthresh=0.001, window_shape='', gain=0.1, algorithm='hogbom-complex') # Convert resolution (FWHM in arcmin) to a psfwidth (standard deviation in pixels): clean_res = (((FORCE_RESOLUTION / 2.35482004503) / 60.0) * np.pi / 180.0) / cell_advice # Create the restored image: restored = restore_cube(comp, psf, residual, psfwidth=clean_res) # Save to disk: export_image_to_fits( restored, '%s/imaging_clean_WStack-%s.fits' % (RESULTS_DIR, channel)) return restored
def ical_serial(block_vis: BlockVisibility, model: Image, components=None, context='2d', controls=None, **kwargs): """ Post observation image, deconvolve, and self-calibrate :param vis: :param model: Model image :param components: Initial components :param context: Imaging context :param controls: calibration controls dictionary :return: model, residual, restored """ nmajor = get_parameter(kwargs, 'nmajor', 5) log.info("ical_serial: Performing %d major cycles" % nmajor) do_selfcal = get_parameter(kwargs, "do_selfcal", False) if controls is None: controls = create_calibration_controls(**kwargs) # The model is added to each major cycle and then the visibilities are # calculated from the full model vis = convert_blockvisibility_to_visibility(block_vis) block_vispred = copy_visibility(block_vis, zero=True) vispred = convert_blockvisibility_to_visibility(block_vispred) vispred.data['vis'][...] = 0.0 visres = copy_visibility(vispred) vispred = predict_serial(vispred, model, context=context, **kwargs) if components is not None: vispred = predict_skycomponent_visibility(vispred, components) if do_selfcal: vis, gaintables = calibrate_function(vis, vispred, 'TGB', controls, iteration=-1) visres.data['vis'] = vis.data['vis'] - vispred.data['vis'] dirty, sumwt = invert_serial(visres, model, context=context, **kwargs) log.info("Maximum in residual image is %.6f" % (numpy.max(numpy.abs(dirty.data)))) psf, sumwt = invert_serial(visres, model, dopsf=True, context=context, **kwargs) thresh = get_parameter(kwargs, "threshold", 0.0) for i in range(nmajor): log.info("ical_serial: Start of major cycle %d of %d" % (i, nmajor)) cc, res = deconvolve_cube(dirty, psf, **kwargs) model.data += cc.data vispred.data['vis'][...] = 0.0 vispred = predict_serial(vispred, model, context=context, **kwargs) if do_selfcal: vis, gaintables = calibrate_function(vis, vispred, 'TGB', controls, iteration=i) visres.data['vis'] = vis.data['vis'] - vispred.data['vis'] dirty, sumwt = invert_serial(visres, model, context=context, **kwargs) log.info("Maximum in residual image is %s" % (numpy.max(numpy.abs(dirty.data)))) if numpy.abs(dirty.data).max() < 1.1 * thresh: log.info("ical_serial: Reached stopping threshold %.6f Jy" % thresh) break log.info("ical_serial: End of major cycle") log.info("ical_serial: End of major cycles") restored = restore_cube(model, psf, dirty, **kwargs) return model, dirty, restored