def test_predict(self): self.actualSetUp(zerow=True) self.skymodel_list = [ arlexecute.execute(create_low_test_skymodel_from_gleam)( npixel=self.npixel, cellsize=self.cellsize, frequency=[self.frequency[f]], phasecentre=self.phasecentre, polarisation_frame=PolarisationFrame("stokesI"), flux_limit=0.3, flux_threshold=0.3, flux_max=5.0) for f, freq in enumerate(self.frequency) ] self.skymodel_list = arlexecute.compute(self.skymodel_list, sync=True) assert isinstance(self.skymodel_list[0].images[0], Image), self.skymodel_list[0].images[0] assert isinstance(self.skymodel_list[0].components[0], Skycomponent), self.skymodel_list[0].components[0] assert len(self.skymodel_list[0].components) == 119, len( self.skymodel_list[0].components) assert len(self.skymodel_list[0].images) == 1, len( self.skymodel_list[0].images) assert numpy.max(numpy.abs( self.skymodel_list[0].images[0].data)) > 0.0, "Image is empty" self.skymodel_list = arlexecute.scatter(self.skymodel_list) skymodel_vislist = predict_skymodel_list_arlexecute_workflow( self.vis_list, self.skymodel_list, context='2d') skymodel_vislist = arlexecute.compute(skymodel_vislist, sync=True) assert numpy.max(numpy.abs(skymodel_vislist[0].vis)) > 0.0
def test_zero_list(self): self.actualSetUp() centre = self.freqwin // 2 vis_list = zero_list_arlexecute_workflow(self.vis_list) vis_list = arlexecute.compute(vis_list, sync=True) assert numpy.max(numpy.abs(vis_list[centre].vis)) < 1e-15, numpy.max( numpy.abs(vis_list[centre].vis)) predicted_vis_list = [ arlexecute.execute(predict_skycomponent_visibility)( vis_list[freqwin], self.components_list[freqwin]) for freqwin, _ in enumerate(self.frequency) ] predicted_vis_list = arlexecute.compute(predicted_vis_list, sync=True) assert numpy.max(numpy.abs(predicted_vis_list[centre].vis)) > 0.0, \ numpy.max(numpy.abs(predicted_vis_list[centre].vis)) diff_vis_list = subtract_list_arlexecute_workflow( self.vis_list, predicted_vis_list) diff_vis_list = arlexecute.compute(diff_vis_list, sync=True) assert numpy.max(numpy.abs( diff_vis_list[centre].vis)) < 1e-15, numpy.max( numpy.abs(diff_vis_list[centre].vis))
def test_crosssubtract_datamodel(self): self.actualSetUp(zerow=True) future_vis = arlexecute.scatter(self.vis_list[0]) future_skymodel_list = arlexecute.scatter(self.skymodel_list) skymodel_vislist = predict_skymodel_list_arlexecute_workflow( future_vis, future_skymodel_list, context='2d', docal=True) skymodel_vislist = arlexecute.compute(skymodel_vislist, sync=True) vobs = sum_predict_results(skymodel_vislist) future_vobs = arlexecute.scatter(vobs) skymodel_vislist = crosssubtract_datamodels_skymodel_list_arlexecute_workflow( future_vobs, skymodel_vislist) skymodel_vislist = arlexecute.compute(skymodel_vislist, sync=True) result_skymodel = [ SkyModel(components=None, image=self.skymodel_list[-1].image) for v in skymodel_vislist ] self.vis_list = arlexecute.scatter(self.vis_list) result_skymodel = invert_skymodel_list_arlexecute_workflow( skymodel_vislist, result_skymodel, context='2d', docal=True) results = arlexecute.compute(result_skymodel, sync=True) assert numpy.max(numpy.abs(results[0][0].data)) > 0.0 assert numpy.max(numpy.abs(results[0][1])) > 0.0 if self.plot: import matplotlib.pyplot as plt from wrappers.arlexecute.image.operations import show_image show_image(results[0][0], title='Dirty image after cross-subtraction', vmax=0.1, vmin=-0.01) plt.show()
def actualSetUp(self, freqwin=1, block=True, dopol=False, zerow=False): self.npixel = 1024 self.low = create_named_configuration('LOWBD2', rmax=550.0) self.freqwin = freqwin self.blockvis_list = list() self.ntimes = 5 self.cellsize = 0.0005 # Choose the interval so that the maximum change in w is smallish integration_time = numpy.pi * (24 / (12 * 60)) self.times = numpy.linspace(-integration_time * (self.ntimes // 2), integration_time * (self.ntimes // 2), self.ntimes) if freqwin > 1: self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin) self.channelwidth = numpy.array(freqwin * [self.frequency[1] - self.frequency[0]]) else: self.frequency = numpy.array([1.0e8]) self.channelwidth = numpy.array([4e7]) if dopol: self.vis_pol = PolarisationFrame('linear') self.image_pol = PolarisationFrame('stokesIQUV') f = numpy.array([100.0, 20.0, -10.0, 1.0]) else: self.vis_pol = PolarisationFrame('stokesI') self.image_pol = PolarisationFrame('stokesI') f = numpy.array([100.0]) self.phasecentre = SkyCoord(ra=+0.0 * u.deg, dec=-40.0 * u.deg, frame='icrs', equinox='J2000') self.blockvis_list = [arlexecute.execute(ingest_unittest_visibility)(self.low, [self.frequency[freqwin]], [self.channelwidth[freqwin]], self.times, self.vis_pol, self.phasecentre, block=block, zerow=zerow) for freqwin, _ in enumerate(self.frequency)] self.blockvis_list = arlexecute.compute(self.blockvis_list, sync=True) self.vis_list = [arlexecute.execute(convert_blockvisibility_to_visibility)(bv) for bv in self.blockvis_list] self.vis_list = arlexecute.compute(self.vis_list, sync=True) self.skymodel_list = [arlexecute.execute(create_low_test_skymodel_from_gleam) (npixel=self.npixel, cellsize=self.cellsize, frequency=[self.frequency[f]], phasecentre=self.phasecentre, polarisation_frame=PolarisationFrame("stokesI"), flux_limit=0.6, flux_threshold=1.0, flux_max=5.0) for f, freq in enumerate(self.frequency)] self.skymodel_list = arlexecute.compute(self.skymodel_list, sync=True) assert isinstance(self.skymodel_list[0].image, Image), self.skymodel_list[0].image assert isinstance(self.skymodel_list[0].components[0], Skycomponent), self.skymodel_list[0].components[0] assert len(self.skymodel_list[0].components) == 19, len(self.skymodel_list[0].components) self.skymodel_list = expand_skymodel_by_skycomponents(self.skymodel_list[0]) assert len(self.skymodel_list) == 20, len(self.skymodel_list) assert numpy.max(numpy.abs(self.skymodel_list[-1].image.data)) > 0.0, "Image is empty" self.vis_list = [copy_visibility(self.vis_list[0], zero=True) for i, _ in enumerate(self.skymodel_list)]
def test_create_simulate_vis_list(self): vis_list = simulate_list_arlexecute_workflow( frequency=self.frequency, channel_bandwidth=self.channel_bandwidth) assert len(vis_list) == len(self.frequency) vt = arlexecute.compute(vis_list[0], sync=True) assert isinstance(vt, BlockVisibility) assert vt.nvis > 0
def test_deconvolve_spectral(self): self.actualSetUp(add_errors=True) dirty_imagelist = invert_list_arlexecute_workflow(self.vis_list, self.model_imagelist, context='2d', dopsf=False, normalize=True) psf_imagelist = invert_list_arlexecute_workflow(self.vis_list, self.model_imagelist, context='2d', dopsf=True, normalize=True) dirty_imagelist = arlexecute.persist(dirty_imagelist) psf_imagelist = arlexecute.persist(psf_imagelist) deconvolved = deconvolve_list_arlexecute_workflow( dirty_imagelist, psf_imagelist, self.model_imagelist, niter=1000, fractional_threshold=0.1, scales=[0, 3, 10], threshold=0.1, gain=0.7) deconvolved = arlexecute.persist(deconvolved) deconvolved = arlexecute.compute(deconvolved, sync=True) export_image_to_fits( deconvolved[0], '%s/test_imaging_%s_deconvolve_spectral.fits' % (self.dir, arlexecute.type()))
def test_continuum_imaging_pipeline(self): self.actualSetUp(add_errors=False, block=True) continuum_imaging_list = \ continuum_imaging_list_arlexecute_workflow(self.vis_list, model_imagelist=self.model_imagelist, context='2d', algorithm='mmclean', facets=1, scales=[0, 3, 10], niter=1000, fractional_threshold=0.1, nmoments=2, nchan=self.freqwin, threshold=2.0, nmajor=5, gain=0.1, deconvolve_facets=8, deconvolve_overlap=16, deconvolve_taper='tukey') clean, residual, restored = arlexecute.compute(continuum_imaging_list, sync=True) export_image_to_fits( clean[0], '%s/test_pipelines_continuum_imaging_pipeline_clean.fits' % self.dir) export_image_to_fits( residual[0][0], '%s/test_pipelines_continuum_imaging_pipeline_residual.fits' % self.dir) export_image_to_fits( restored[0], '%s/test_pipelines_continuum_imaging_pipeline_restored.fits' % self.dir) qa = qa_image(restored[0]) assert numpy.abs(qa.data['max'] - 116.9) < 1.0, str(qa) assert numpy.abs(qa.data['min'] + 0.118) < 1.0, str(qa)
def test_continuum_imaging_pipeline_serialclean(self): self.actualSetUp(add_errors=False, zerow=True) continuum_imaging_list = \ continuum_imaging_list_arlexecute_workflow(self.vis_list, model_imagelist=self.model_imagelist, context='2d', algorithm='mmclean', facets=1, scales=[0, 3, 10], niter=1000, fractional_threshold=0.1, threshold=0.1, nmoment=3, nmajor=5, gain=0.1, deconvolve_facets=4, deconvolve_overlap=32, deconvolve_taper='tukey', psf_support=64, use_serial_clean=True) clean, residual, restored = arlexecute.compute(continuum_imaging_list, sync=True) centre = len(clean) // 2 if self.persist: export_image_to_fits( clean[centre], '%s/test_pipelines_continuum_imaging_pipeline_arlexecute_clean.fits' % self.dir) export_image_to_fits( residual[centre][0], '%s/test_pipelines_continuum_imaging_pipeline_arlexecute_residual.fits' % self.dir) export_image_to_fits( restored[centre], '%s/test_pipelines_continuum_imaging_pipeline_arlexecute_restored.fits' % self.dir) qa = qa_image(restored[centre]) assert numpy.abs(qa.data['max'] - 100.13762476849081) < 1.0, str(qa) assert numpy.abs(qa.data['min'] + 0.03627273884170454) < 1.0, str(qa)
def _invert_base(self, context, extra='', fluxthreshold=1.0, positionthreshold=1.0, check_components=True, facets=1, vis_slices=1, gcfcf=None, **kwargs): centre = self.freqwin // 2 dirty = invert_list_arlexecute_workflow(self.vis_list, self.model_list, context=context, dopsf=False, normalize=True, facets=facets, vis_slices=vis_slices, gcfcf=gcfcf, **kwargs) dirty = arlexecute.compute(dirty, sync=True)[centre] export_image_to_fits( dirty[0], '%s/test_imaging_invert_%s%s_%s_dirty.fits' % (self.dir, context, extra, arlexecute.type())) assert numpy.max(numpy.abs(dirty[0].data)), "Image is empty" if check_components: self._checkcomponents(dirty[0], fluxthreshold, positionthreshold)
def _predict_base(self, context='2d', extra='', fluxthreshold=1.0, facets=1, vis_slices=1, **kwargs): vis_list = zero_list_arlexecute_workflow(self.vis_list) vis_list = predict_list_arlexecute_workflow(vis_list, self.model_list, context=context, vis_slices=vis_slices, facets=facets, **kwargs) vis_list = subtract_list_arlexecute_workflow(self.vis_list, vis_list)[0] vis_list = arlexecute.compute(vis_list, sync=True) dirty = invert_list_arlexecute_workflow([vis_list], [self.model_list[0]], context='2d', dopsf=False, normalize=True)[0] dirty = arlexecute.compute(dirty, sync=True) assert numpy.max(numpy.abs(dirty[0].data)), "Residual image is empty" export_image_to_fits(dirty[0], '%s/test_imaging_predict_%s%s_%s_dirty.fits' % (self.dir, context, extra, arlexecute.type())) maxabs = numpy.max(numpy.abs(dirty[0].data)) assert maxabs < fluxthreshold, "Error %.3f greater than fluxthreshold %.3f " % (maxabs, fluxthreshold)
def test_useFunction(self): def square(x): return x**2 arlexecute.set_client(use_dask=False) graph = arlexecute.execute(square)(numpy.arange(10)) assert (arlexecute.compute(graph) == numpy.array( [0, 1, 4, 9, 16, 25, 36, 49, 64, 81])).all() arlexecute.close()
def test_residual_list(self): self.actualSetUp(zerow=True) centre = self.freqwin // 2 residual_image_list = residual_list_arlexecute_workflow(self.vis_list, self.model_list, context='2d') residual_image_list = arlexecute.compute(residual_image_list, sync=True) 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)
def test_useDaskSync(self): def square(x): return x**2 arlexecute.set_client(use_dask=True) graph = arlexecute.execute(square)(numpy.arange(10)) result = arlexecute.compute(graph, sync=True) assert (result == numpy.array([0, 1, 4, 9, 16, 25, 36, 49, 64, 81])).all() arlexecute.close()
def test_calibrate_arlexecute_empty(self): amp_errors = {'T': 0.0, 'G': 0.0} phase_errors = {'T': 1.0, 'G': 0.0} self.actualSetUp(amp_errors=amp_errors, phase_errors=phase_errors) for v in self.blockvis_list: v.data['vis'][...] = 0.0 + 0.0j controls = create_calibration_controls() controls['T']['first_selfcal'] = 0 controls['T']['timescale'] = 'auto' calibrate_list = \ calibrate_list_arlexecute_workflow(self.error_blockvis_list, self.blockvis_list, calibration_context='T', controls=controls, do_selfcal=True, global_solution=False) calibrate_list = arlexecute.compute(calibrate_list, sync=True) assert len(calibrate_list[1][0]) > 0
def test_deconvolve_and_restore_cube_mmclean_facets(self): self.actualSetUp(add_errors=True) dirty_imagelist = invert_list_arlexecute_workflow(self.vis_list, self.model_imagelist, context='2d', dopsf=False, normalize=True) psf_imagelist = invert_list_arlexecute_workflow(self.vis_list, self.model_imagelist, context='2d', dopsf=True, normalize=True) dirty_imagelist = arlexecute.persist(dirty_imagelist) psf_imagelist = arlexecute.persist(psf_imagelist) dec_imagelist = deconvolve_list_arlexecute_workflow( dirty_imagelist, psf_imagelist, self.model_imagelist, niter=1000, fractional_threshold=0.1, scales=[0, 3, 10], algorithm='mmclean', nmoment=3, nchan=self.freqwin, threshold=0.01, gain=0.7, deconvolve_facets=8, deconvolve_overlap=8, deconvolve_taper='tukey') dec_imagelist = arlexecute.persist(dec_imagelist) residual_imagelist = residual_list_arlexecute_workflow( self.vis_list, model_imagelist=dec_imagelist, context='2d') residual_imagelist = arlexecute.persist(residual_imagelist) restored_list = restore_list_arlexecute_workflow( model_imagelist=dec_imagelist, psf_imagelist=psf_imagelist, residual_imagelist=residual_imagelist, empty=self.model_imagelist) restored = arlexecute.compute(restored_list, sync=True)[0] export_image_to_fits( restored, '%s/test_imaging_%s_overlap_mmclean_restored.fits' % (self.dir, arlexecute.type()))
def test_ical_pipeline(self): amp_errors = {'T': 0.0, 'G': 0.00, 'B': 0.0} phase_errors = {'T': 0.1, 'G': 0.0, 'B': 0.0} self.actualSetUp(add_errors=True, block=True, amp_errors=amp_errors, phase_errors=phase_errors) controls = create_calibration_controls() controls['T']['first_selfcal'] = 1 controls['G']['first_selfcal'] = 3 controls['B']['first_selfcal'] = 4 controls['T']['timescale'] = 'auto' controls['G']['timescale'] = 'auto' controls['B']['timescale'] = 1e5 ical_list = \ ical_list_arlexecute_workflow(self.vis_list, model_imagelist=self.model_imagelist, context='2d', calibration_context='T', controls=controls, do_selfcal=True, global_solution=False, algorithm='mmclean', facets=1, scales=[0, 3, 10], niter=1000, fractional_threshold=0.1, nmoments=2, nchan=self.freqwin, threshold=2.0, nmajor=5, gain=0.1, deconvolve_facets=8, deconvolve_overlap=16, deconvolve_taper='tukey') clean, residual, restored = arlexecute.compute(ical_list, sync=True) centre = len(clean) // 2 export_image_to_fits( clean[centre], '%s/test_pipelines_ical_pipeline_clean.fits' % self.dir) export_image_to_fits( residual[centre][0], '%s/test_pipelines_ical_pipeline_residual.fits' % self.dir) export_image_to_fits( restored[centre], '%s/test_pipelines_ical_pipeline_restored.fits' % self.dir) qa = qa_image(restored[centre]) assert numpy.abs(qa.data['max'] - 100.13739440876233) < 1.0, str(qa) assert numpy.abs(qa.data['min'] + 0.03644435471804354) < 1.0, str(qa)
def test_deconvolve_and_restore_cube_mmclean(self): self.actualSetUp(add_errors=True) dirty_imagelist = invert_list_arlexecute_workflow(self.vis_list, self.model_imagelist, context='2d', dopsf=False, normalize=True) psf_imagelist = invert_list_arlexecute_workflow(self.vis_list, self.model_imagelist, context='2d', dopsf=True, normalize=True) dec_imagelist, _ = deconvolve_list_arlexecute_workflow(dirty_imagelist, psf_imagelist, self.model_imagelist, niter=1000, fractional_threshold=0.01, scales=[0, 3, 10], algorithm='mmclean', nmoments=3, nchan=self.freqwin, threshold=0.1, gain=0.7) residual_imagelist = residual_list_arlexecute_workflow(self.vis_list, model_imagelist=dec_imagelist, context='wstack', vis_slices=51) restored = restore_list_arlexecute_workflow(model_imagelist=dec_imagelist, psf_imagelist=psf_imagelist, residual_imagelist=residual_imagelist, empty=self.model_imagelist)[0] restored = arlexecute.compute(restored, sync=True) export_image_to_fits(restored, '%s/test_imaging_%s_mmclean_restored.fits' % (self.dir, arlexecute.type()))
def test_ical_pipeline_global(self): self.actualSetUp(add_errors=True) controls = create_calibration_controls() controls['T']['first_selfcal'] = 1 controls['T']['timescale'] = 'auto' ical_list = \ ical_list_arlexecute_workflow(self.vis_list, model_imagelist=self.model_imagelist, context='2d', algorithm='mmclean', facets=1, scales=[0, 3, 10], niter=1000, fractional_threshold=0.1, threshold=0.1, nmoment=3, nmajor=1, gain=0.1, deconvolve_facets=4, deconvolve_overlap=32, deconvolve_taper='tukey', psf_support=64, calibration_context='T', controls=controls, do_selfcal=True, global_solution=True) clean, residual, restored, gt_list = arlexecute.compute(ical_list, sync=True) centre = len(clean) // 2 if self.persist: export_image_to_fits( clean[centre], '%s/test_pipelines_ical_global_pipeline_arlexecute_clean.fits' % self.dir) export_image_to_fits( residual[centre][0], '%s/test_pipelines_ical_global_pipeline_arlexecute_residual.fits' % self.dir) export_image_to_fits( restored[centre], '%s/test_pipelines_ical_global_pipeline_arlexecute_restored.fits' % self.dir) export_gaintable_to_hdf5( gt_list[0]['T'], '%s/test_pipelines_ical_global_pipeline_arlexecute_gaintable.hdf5' % self.dir) qa = qa_image(restored[centre]) assert numpy.abs(qa.data['max'] - 98.92656340122159) < 1.0, str(qa) assert numpy.abs(qa.data['min'] + 0.7024492707920869) < 1.0, str(qa)
def test_calibrate_arlexecute_global(self): amp_errors = {'T': 0.0, 'G': 0.0} phase_errors = {'T': 1.0, 'G': 0.0} self.actualSetUp(amp_errors=amp_errors, phase_errors=phase_errors) controls = create_calibration_controls() controls['T']['first_selfcal'] = 0 controls['T']['timescale'] = 'auto' calibrate_list = \ calibrate_list_arlexecute_workflow(self.error_blockvis_list, self.blockvis_list, calibration_context='T', controls=controls, do_selfcal=True, global_solution=True) calibrate_list = arlexecute.compute(calibrate_list, sync=True) assert numpy.max(calibrate_list[1]['T'].residual) < 7e-6, numpy.max( calibrate_list[1]['T'].residual) assert numpy.max( numpy.abs(calibrate_list[0][0].vis - self.blockvis_list[0].vis)) < 2e-6
def test_predictcal(self): self.actualSetUp(zerow=True) future_vis = arlexecute.scatter(self.vis_list[0]) future_skymodel = arlexecute.scatter(self.skymodel_list) skymodel_vislist = predict_skymodel_list_arlexecute_workflow(future_vis, future_skymodel, context='2d', docal=True) skymodel_vislist = arlexecute.compute(skymodel_vislist, sync=True) vobs = sum_predict_results(skymodel_vislist) if self.plot: def plotvis(i, v): import matplotlib.pyplot as plt uvr = numpy.hypot(v.u, v.v) amp = numpy.abs(v.vis[:, 0]) plt.plot(uvr, amp, '.') plt.title(str(i)) plt.show() plotvis(0, vobs)
def actualSetUp(self, nfreqwin=3, dospectral=True, dopol=False, amp_errors=None, phase_errors=None, zerow=True): if amp_errors is None: amp_errors = {'T': 0.0, 'G': 0.1} if phase_errors is None: phase_errors = {'T': 1.0, 'G': 0.0} self.npixel = 512 self.low = create_named_configuration('LOWBD2', rmax=750.0) self.freqwin = nfreqwin self.vis_list = list() self.ntimes = 1 self.times = numpy.linspace(-3.0, +3.0, self.ntimes) * numpy.pi / 12.0 self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin) if self.freqwin > 1: self.channelwidth = numpy.array( self.freqwin * [self.frequency[1] - self.frequency[0]]) else: self.channelwidth = numpy.array([1e6]) if dopol: self.vis_pol = PolarisationFrame('linear') self.image_pol = PolarisationFrame('stokesIQUV') f = numpy.array([100.0, 20.0, -10.0, 1.0]) else: self.vis_pol = PolarisationFrame('stokesI') self.image_pol = PolarisationFrame('stokesI') f = numpy.array([100.0]) if dospectral: flux = numpy.array( [f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency]) else: flux = numpy.array([f]) self.phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000') self.blockvis_list = [ arlexecute.execute(ingest_unittest_visibility, nout=1)(self.low, [self.frequency[i]], [self.channelwidth[i]], self.times, self.vis_pol, self.phasecentre, block=True, zerow=zerow) for i in range(nfreqwin) ] self.blockvis_list = arlexecute.compute(self.blockvis_list, sync=True) for v in self.blockvis_list: v.data['vis'][...] = 1.0 + 0.0j self.error_blockvis_list = [ arlexecute.execute(copy_visibility(v)) for v in self.blockvis_list ] gt = arlexecute.execute(create_gaintable_from_blockvisibility)( self.blockvis_list[0]) gt = arlexecute.execute(simulate_gaintable)(gt, phase_error=0.1, amplitude_error=0.0, smooth_channels=1, leakage=0.0, seed=180555) self.error_blockvis_list = [ arlexecute.execute(apply_gaintable)(self.error_blockvis_list[i], gt) for i in range(self.freqwin) ] self.error_blockvis_list = arlexecute.compute(self.error_blockvis_list, sync=True) assert numpy.max( numpy.abs(self.error_blockvis_list[0].vis - self.blockvis_list[0].vis)) > 0.0
def create_surface_errors_gaintable_arlexecute_workflow( band, sub_bvis_list, sub_components, vp_directory, use_radec=False, elevation_sampling=5.0, show=False, basename=''): def get_band_vp(band, el): if band == 'B1': vpa = import_image_from_fits( '%s/B1_%d_0565_real_interpolated.fits' % (vp_directory, int(el))) vpa_imag = import_image_from_fits( '%s/B1_%d_0565_imag_interpolated.fits' % (vp_directory, int(el))) elif band == 'B2': vpa = import_image_from_fits( '%s/B2_%d_1360_real_interpolated.fits' % (vp_directory, int(el))) vpa_imag = import_image_from_fits( '%s/B2_%d_1360_imag_interpolated.fits' % (vp_directory, int(el))) elif band == 'Ku': vpa = import_image_from_fits( '%s/Ku_%d_11700_real_interpolated.fits' % (vp_directory, int(el))) vpa_imag = import_image_from_fits( '%s/Ku_%d_11700_imag_interpolated.fits' % (vp_directory, int(el))) else: raise ValueError("Unknown band %s" % band) vpa.data = vpa.data + 1j * vpa_imag.data return vpa def find_vp(band, vis): ha = numpy.pi * numpy.average(vis.time) / 43200.0 dec = vis.phasecentre.dec.rad latitude = vis.configuration.location.lat.rad az, el = hadec_to_azel(ha, dec, latitude) el_deg = el * 180.0 / numpy.pi el_table = max( 0.0, min( 90.1, elevation_sampling * ((el_deg + elevation_sampling / 2.0) // elevation_sampling))) return get_band_vp(band, el_table) def find_vp_nominal(band): el_nominal_deg = 45.0 return get_band_vp(band, el_nominal_deg) error_pt_list = [ arlexecute.execute(create_pointingtable_from_blockvisibility)(bvis) for bvis in sub_bvis_list ] no_error_pt_list = [ arlexecute.execute(create_pointingtable_from_blockvisibility)(bvis) for bvis in sub_bvis_list ] vp_nominal_list = [ arlexecute.execute(find_vp_nominal)(band) for bv in sub_bvis_list ] vp_actual_list = [ arlexecute.execute(find_vp)(band, bv) for bv in sub_bvis_list ] # Create the gain tables, one per Visibility and per component no_error_gt_list = [ arlexecute.execute(simulate_gaintable_from_pointingtable)( bvis, sub_components, no_error_pt_list[ibv], vp_nominal_list[ibv], use_radec=use_radec) for ibv, bvis in enumerate(sub_bvis_list) ] error_gt_list = [ arlexecute.execute(simulate_gaintable_from_pointingtable)( bvis, sub_components, error_pt_list[ibv], vp_actual_list[ibv], use_radec=use_radec) for ibv, bvis in enumerate(sub_bvis_list) ] if show: plot_file = 'gaintable.png' tmp_gt_list = arlexecute.compute(error_gt_list, sync=True) plot_gaintable(tmp_gt_list, plot_file=plot_file, title=basename) return no_error_gt_list, error_gt_list
frequency = numpy.linspace(0.9e8, 1.1e8, nfreqwin) channel_bandwidth = numpy.array(nfreqwin * [frequency[1] - frequency[0]]) times = numpy.linspace(-numpy.pi / 3.0, numpy.pi / 3.0, ntimes) phasecentre = SkyCoord(ra=+30.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000') vis_list = simulate_list_arlexecute_workflow('LOWBD2', rmax=rmax, frequency=frequency, channel_bandwidth=channel_bandwidth, times=times, phasecentre=phasecentre, order='frequency') print('%d elements in vis_list' % len(vis_list)) print('About to make visibility') vis_list = arlexecute.persist(vis_list) vis_list = arlexecute.compute(vis_list, sync=True) print(vis_list[0]) advice_low = advise_wide_field(vis_list[0], guard_band_image=8.0, delA=0.02, wprojection_planes=1) advice_high = advise_wide_field(vis_list[-1], guard_band_image=8.0, delA=0.02, wprojection_planes=1) vis_slices = advice_low['vis_slices'] npixel = advice_high['npixels2'] cellsize = min(advice_low['cellsize'], advice_high['cellsize'])
def create_pointing_errors_gaintable_arlexecute_workflow( sub_bvis_list, sub_components, sub_vp_list, use_radec=False, pointing_error=0.0, static_pointing_error=None, global_pointing_error=None, time_series='', time_series_type='', seed=None, pointing_directory=None, show=False, basename=''): if global_pointing_error is None: global_pointing_error = [0.0, 0.0] # One pointing table per visibility error_pt_list = [ arlexecute.execute(create_pointingtable_from_blockvisibility)(bvis) for bvis in sub_bvis_list ] no_error_pt_list = [ arlexecute.execute(create_pointingtable_from_blockvisibility)(bvis) for bvis in sub_bvis_list ] if time_series is '': error_pt_list = [ arlexecute.execute(simulate_pointingtable)( pt, pointing_error=pointing_error, static_pointing_error=static_pointing_error, global_pointing_error=global_pointing_error, seed=seed) for ipt, pt in enumerate(error_pt_list) ] else: error_pt_list = [ arlexecute.execute(simulate_pointingtable_from_timeseries)( pt, type=time_series, time_series_type=time_series_type, pointing_directory=pointing_directory, seed=seed) for ipt, pt in enumerate(error_pt_list) ] if show: tmp_error_pt_list = arlexecute.compute(error_pt_list, sync=True) if time_series != "": plot_file = 'pointing_error_%s.png' % (time_series_type) else: r2s = 180 * 3600.0 / numpy.pi plot_file = 'pointing_error_dynamic_%.2f_static_(%.2f,%.2f)_global_(%.2f,%.2f).png' % \ (r2s * pointing_error, r2s * static_pointing_error[0], r2s * static_pointing_error[1], r2s * global_pointing_error[0], r2s * global_pointing_error[1]) plot_pointingtable(tmp_error_pt_list, plot_file=plot_file, title=basename) # Create the gain tables, one per Visibility and per component no_error_gt_list = [ arlexecute.execute(simulate_gaintable_from_pointingtable)( bvis, sub_components, no_error_pt_list[ibv], sub_vp_list[ibv], use_radec=use_radec) for ibv, bvis in enumerate(sub_bvis_list) ] error_gt_list = [ arlexecute.execute(simulate_gaintable_from_pointingtable)( bvis, sub_components, error_pt_list[ibv], sub_vp_list[ibv], use_radec=use_radec) for ibv, bvis in enumerate(sub_bvis_list) ] if show: tmp_gt_list = arlexecute.compute(error_gt_list, sync=True) if time_series_type != "": plot_file = 'gaintable_%s.png' % time_series_type else: r2s = 180 * 3600.0 / numpy.pi plot_file = 'gaintable_dynamic_%.2f_static_(%.2f,%.2f)_global_(%.2f,%.2f).png' % \ (r2s * pointing_error, r2s * static_pointing_error[0], r2s * static_pointing_error[1], r2s * global_pointing_error[0], r2s * global_pointing_error[1]) plot_gaintable(tmp_gt_list, title="%s: dish 0 amplitude gain, %s" % (basename, time_series_type), plot_file=plot_file) return no_error_gt_list, error_gt_list
def actualSetUp(self, add_errors=False, freqwin=3, block=False, dospectral=True, dopol=False, zerow=False, makegcfcf=False): self.npixel = 256 self.low = create_named_configuration('LOWBD2', rmax=750.0) self.freqwin = freqwin self.vis_list = list() self.ntimes = 5 self.cellsize = 0.0005 # Choose the interval so that the maximum change in w is smallish integration_time = numpy.pi * (24 / (12 * 60)) self.times = numpy.linspace(-integration_time * (self.ntimes // 2), integration_time * (self.ntimes // 2), self.ntimes) if freqwin > 1: self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin) self.channelwidth = numpy.array(freqwin * [self.frequency[1] - self.frequency[0]]) else: self.frequency = numpy.array([1.0e8]) self.channelwidth = numpy.array([4e7]) if dopol: self.vis_pol = PolarisationFrame('linear') self.image_pol = PolarisationFrame('stokesIQUV') f = numpy.array([100.0, 20.0, -10.0, 1.0]) else: self.vis_pol = PolarisationFrame('stokesI') self.image_pol = PolarisationFrame('stokesI') f = numpy.array([100.0]) if dospectral: flux = numpy.array([f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency]) else: flux = numpy.array([f]) self.phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000') self.vis_list = [arlexecute.execute(ingest_unittest_visibility)(self.low, [self.frequency[freqwin]], [self.channelwidth[freqwin]], self.times, self.vis_pol, self.phasecentre, block=block, zerow=zerow) for freqwin, _ in enumerate(self.frequency)] self.model_list = [arlexecute.execute(create_unittest_model, nout=freqwin)(self.vis_list[freqwin], self.image_pol, cellsize=self.cellsize, npixel=self.npixel) for freqwin, _ in enumerate(self.frequency)] self.components_list = [arlexecute.execute(create_unittest_components)(self.model_list[freqwin], flux[freqwin, :][numpy.newaxis, :], single=True) for freqwin, _ in enumerate(self.frequency)] self.components_list = arlexecute.compute(self.components_list, sync=True) self.model_list = [arlexecute.execute(insert_skycomponent, nout=1)(self.model_list[freqwin], self.components_list[freqwin]) for freqwin, _ in enumerate(self.frequency)] self.model_list = arlexecute.compute(self.model_list, sync=True) self.vis_list = [arlexecute.execute(predict_skycomponent_visibility)(self.vis_list[freqwin], self.components_list[freqwin]) for freqwin, _ in enumerate(self.frequency)] centre = self.freqwin // 2 # Calculate the model convolved with a Gaussian. self.model = self.model_list[centre] self.cmodel = smooth_image(self.model) export_image_to_fits(self.model, '%s/test_imaging_model.fits' % self.dir) export_image_to_fits(self.cmodel, '%s/test_imaging_cmodel.fits' % self.dir) if add_errors and block: self.vis_list = [arlexecute.execute(insert_unittest_errors)(self.vis_list[i]) for i, _ in enumerate(self.frequency)] self.components = self.components_list[centre] if makegcfcf: self.gcfcf = [create_awterm_convolutionfunction(self.model, nw=61, wstep=16.0, oversampling=8, support=64, use_aaf=True)] self.gcfcf_clipped = [(self.gcfcf[0][0], apply_bounding_box_convolutionfunction(self.gcfcf[0][1], fractional_level=1e-3))] self.gcfcf_joint = [create_awterm_convolutionfunction(self.model, nw=11, wstep=16.0, oversampling=8, support=64, use_aaf=True)] else: self.gcfcf = None self.gcfcf_clipped = None self.gcfcf_joint = None
def trial_case(results, seed=180555, context='wstack', nworkers=8, threads_per_worker=1, memory=8, processes=True, order='frequency', nfreqwin=7, ntimes=3, rmax=750.0, facets=1, wprojection_planes=1, use_dask=True, use_serial_imaging=True, flux_limit=0.3, nmajor=5, dft_threshold=1.0, use_serial_clean=True, write_fits=False): """ Single trial for performance-timings Simulates visibilities from GLEAM including phase errors Makes dirty image and PSF Runs ICAL pipeline The results are in a dictionary: 'context': input - a string describing concisely the purpose of the test 'time overall', overall execution time (s) 'time predict', time to execute GLEAM prediction graph 'time invert', time to make dirty image 'time invert graph', time to make dirty image graph 'time ICAL graph', time to create ICAL graph 'time ICAL', time to execute ICAL graph 'context', type of imaging e.g. 'wstack' 'nworkers', number of workers to create 'threads_per_worker', 'nnodes', Number of nodes, 'processes', 'order', Ordering of data_models 'nfreqwin', Number of frequency windows in simulation 'ntimes', Number of hour angles in simulation 'rmax', Maximum radius of stations used in simulation (m) 'facets', Number of facets in deconvolution and imaging 'wprojection_planes', Number of wprojection planes 'vis_slices', Number of visibility slices (per Visibbility) 'npixel', Number of pixels in image 'cellsize', Cellsize in radians 'seed', Random number seed 'dirty_max', Maximum in dirty image 'dirty_min', Minimum in dirty image 'restored_max', 'restored_min', 'deconvolved_max', 'deconvolved_min', 'residual_max', 'residual_min', 'git_info', GIT hash (not definitive since local mods are possible) :param results: Initial state :param seed: Random number seed (used in gain simulations) :param context: imaging context :param context: Type of context: '2d'|'timeslice'|'wstack' :param nworkers: Number of dask workers to use :param threads_per_worker: Number of threads per worker :param processes: Use processes instead of threads 'processes'|'threads' :param order: See simulate_list_list_arlexecute_workflow_workflowkflow :param nfreqwin: See simulate_list_list_arlexecute_workflow_workflowkflow :param ntimes: See simulate_list_list_arlexecute_workflow_workflowkflow :param rmax: See simulate_list_list_arlexecute_workflow_workflowkflow :param facets: Number of facets to use :param wprojection_planes: Number of wprojection planes to use :param use_dask: Use dask or immediate evaluation :return: results dictionary """ if use_dask: client = get_dask_Client(threads_per_worker=threads_per_worker, processes=threads_per_worker == 1, memory_limit=memory * 1024 * 1024 * 1024, n_workers=nworkers) arlexecute.set_client(client) nodes = findNodes(arlexecute.client) print("Defined %d workers on %d nodes" % (nworkers, len(nodes))) print("Workers are: %s" % str(nodes)) else: arlexecute.set_client(use_dask=use_dask) results['nnodes'] = 1 def init_logging(): logging.basicConfig( filename='pipelines_arlexecute_timings.log', filemode='w', format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s', datefmt='%H:%M:%S', level=logging.INFO) init_logging() log = logging.getLogger() # Initialise logging on the workers. This appears to only work using the process scheduler. arlexecute.run(init_logging) def lprint(*args): log.info(*args) print(*args) lprint("Starting pipelines_arlexecute_timings") numpy.random.seed(seed) results['seed'] = seed start_all = time.time() results['context'] = context results['hostname'] = socket.gethostname() results['git_hash'] = git_hash() results['epoch'] = time.strftime("%Y-%m-%d %H:%M:%S") lprint("Context is %s" % context) results['nworkers'] = nworkers results['threads_per_worker'] = threads_per_worker results['processes'] = processes results['memory'] = memory results['order'] = order results['nfreqwin'] = nfreqwin results['ntimes'] = ntimes results['rmax'] = rmax results['facets'] = facets results['wprojection_planes'] = wprojection_planes results['dft threshold'] = dft_threshold results['use_dask'] = use_dask lprint("At start, configuration is:") lprint(sort_dict(results)) # Parameters determining scale of simulation. frequency = numpy.linspace(1.0e8, 1.2e8, nfreqwin) centre = nfreqwin // 2 if nfreqwin > 1: channel_bandwidth = numpy.array(nfreqwin * [frequency[1] - frequency[0]]) else: channel_bandwidth = numpy.array([1e6]) times = numpy.linspace(-numpy.pi / 4.0, numpy.pi / 4.0, ntimes) phasecentre = SkyCoord(ra=+0.0 * u.deg, dec=-40.0 * u.deg, frame='icrs', equinox='J2000') lprint("****** Visibility creation ******") # Create the empty BlockVisibility's and persist these on the cluster tmp_bvis_list = simulate_list_arlexecute_workflow( 'LOWBD2', frequency=frequency, channel_bandwidth=channel_bandwidth, times=times, phasecentre=phasecentre, order=order, format='blockvis', rmax=rmax) tmp_vis_list = [ arlexecute.execute(convert_blockvisibility_to_visibility)(bv) for bv in tmp_bvis_list ] tmp_vis_list = arlexecute.client.compute(tmp_vis_list, sync=True) vis_list = arlexecute.gather(tmp_vis_list) import matplotlib.pyplot as plt plt.clf() plt.hist(vis_list[0].w, bins=100) plt.title('Histogram of w samples: rms=%.1f (wavelengths)' % numpy.std(vis_list[0].w)) plt.xlabel('W (wavelengths)') plt.show() plt.clf() plt.hist(vis_list[0].uvdist, bins=100) plt.title('Histogram of uvdistance samples') plt.xlabel('UV Distance (wavelengths)') plt.show() arlexecute.client.cancel(tmp_vis_list) future_vis_list = arlexecute.scatter(vis_list) # Find the best imaging parameters but don't bring the vis_list back here print("****** Finding wide field parameters ******") future_advice = [ arlexecute.execute(advise_wide_field)( v, guard_band_image=6.0, delA=0.1, facets=facets, wprojection_planes=wprojection_planes, oversampling_synthesised_beam=4.0) for v in future_vis_list ] future_advice = arlexecute.compute(future_advice) advice = arlexecute.client.gather(future_advice)[-1] arlexecute.client.cancel(future_advice) # Deconvolution via sub-images requires 2^n npixel = advice['npixels2'] results['npixel'] = npixel cellsize = advice['cellsize'] results['cellsize'] = cellsize lprint("Image will have %d by %d pixels, cellsize = %.6f rad" % (npixel, npixel, cellsize)) # Create an empty model image tmp_model_list = [ arlexecute.execute(create_image)( npixel=npixel, cellsize=cellsize, frequency=[frequency[f]], channel_bandwidth=[channel_bandwidth[f]], phasecentre=phasecentre, polarisation_frame=PolarisationFrame("stokesI")) for f, freq in enumerate(frequency) ] model_list = arlexecute.compute(tmp_model_list, sync=True) future_model_list = arlexecute.scatter(model_list) lprint("****** Setting up imaging parameters ******") # Now set up the imaging parameters template_model = create_image( npixel=npixel, cellsize=cellsize, frequency=[frequency[centre]], phasecentre=phasecentre, channel_bandwidth=[channel_bandwidth[centre]], polarisation_frame=PolarisationFrame("stokesI")) gcfcf = [create_pswf_convolutionfunction(template_model)] if context == 'timeslice': vis_slices = ntimes lprint("Using timeslice with %d slices" % vis_slices) elif context == '2d': vis_slices = 1 elif context == "wprojection": wstep = advice['wstep'] nw = advice['wprojection_planes'] vis_slices = 1 support = advice['nwpixels'] results['wprojection_planes'] = nw lprint("****** Starting W projection kernel creation ******") lprint("Using wprojection with %d planes with wstep %.1f wavelengths" % (nw, wstep)) lprint("Support of wprojection = %d pixels" % support) gcfcf = [ create_awterm_convolutionfunction(template_model, nw=nw, wstep=wstep, oversampling=4, support=support, use_aaf=True) ] lprint("Size of W projection gcf, cf = %.2E bytes" % get_size(gcfcf)) else: context = 'wstack' vis_slices = advice['vis_slices'] lprint("Using wstack with %d slices" % vis_slices) gcfcf = arlexecute.scatter(gcfcf, broadcast=True) results['vis_slices'] = vis_slices # Make a skymodel from gleam, with bright sources as components and weak sources in an image lprint("****** Starting GLEAM skymodel creation ******") future_skymodel_list = [ arlexecute.execute(create_low_test_skymodel_from_gleam)( npixel=npixel, cellsize=cellsize, frequency=[frequency[f]], phasecentre=phasecentre, polarisation_frame=PolarisationFrame("stokesI"), flux_limit=flux_limit, flux_threshold=dft_threshold, flux_max=5.0) for f, freq in enumerate(frequency) ] # We use predict_skymodel so that we can use skycomponents as well as images lprint("****** Starting GLEAM skymodel prediction ******") predicted_vis_list = [ predict_skymodel_list_arlexecute_workflow(future_vis_list[f], [future_skymodel_list[f]], context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf)[0] for f, freq in enumerate(frequency) ] # Corrupt the visibility for the GLEAM model lprint("****** Visibility corruption ******") tmp_corrupted_vis_list = corrupt_list_arlexecute_workflow( predicted_vis_list, phase_error=1.0, seed=seed) lprint("****** Weighting and tapering ******") tmp_corrupted_vis_list = weight_list_arlexecute_workflow( tmp_corrupted_vis_list, future_model_list) tmp_corrupted_vis_list = taper_list_arlexecute_workflow( tmp_corrupted_vis_list, 0.003 * 750.0 / rmax) tmp_corrupted_vis_list = arlexecute.compute(tmp_corrupted_vis_list, sync=True) corrupted_vis_list = arlexecute.gather(tmp_corrupted_vis_list) # arlexecute.client.cancel(predicted_vis_list) arlexecute.client.cancel(tmp_corrupted_vis_list) future_corrupted_vis_list = arlexecute.scatter(corrupted_vis_list) # At this point the only futures are of scatter'ed data so no repeated calculations should be # incurred. lprint("****** Starting dirty image calculation ******") start = time.time() dirty_list = invert_list_arlexecute_workflow( future_corrupted_vis_list, future_model_list, vis_slices=vis_slices, context=context, facets=facets, use_serial_invert=use_serial_imaging, gcfcf=gcfcf) results['size invert graph'] = get_size(dirty_list) lprint('Size of dirty graph is %.3E bytes' % (results['size invert graph'])) end = time.time() results['time invert graph'] = end - start lprint("Construction of invert graph took %.3f seconds" % (end - start)) start = time.time() dirty, sumwt = arlexecute.compute(dirty_list, sync=True)[centre] end = time.time() results['time invert'] = end - start lprint("Dirty image invert took %.3f seconds" % (end - start)) lprint("Maximum in dirty image is %f, sumwt is %s" % (numpy.max(numpy.abs(dirty.data)), str(sumwt))) qa = qa_image(dirty) results['dirty_max'] = qa.data['max'] results['dirty_min'] = qa.data['min'] if write_fits: export_image_to_fits( dirty, "pipelines_arlexecute_timings-%s-dirty.fits" % context) lprint("****** Starting prediction ******") start = time.time() tmp_vis_list = predict_list_arlexecute_workflow( future_corrupted_vis_list, future_model_list, vis_slices=vis_slices, context=context, facets=facets, use_serial_predict=use_serial_imaging, gcfcf=gcfcf) result = arlexecute.compute(tmp_vis_list, sync=True) # arlexecute.client.cancel(tmp_vis_list) end = time.time() results['time predict'] = end - start lprint("Predict took %.3f seconds" % (end - start)) # Create the ICAL pipeline to run major cycles, starting selfcal at cycle 1. A global solution across all # frequencies (i.e. Visibilities) is performed. print("Using subimage clean") deconvolve_facets = 8 deconvolve_overlap = 16 deconvolve_taper = 'tukey' lprint("****** Starting ICAL graph creation ******") controls = create_calibration_controls() controls['T']['first_selfcal'] = 1 controls['T']['timescale'] = 'auto' start = time.time() ical_list = ical_list_arlexecute_workflow( future_corrupted_vis_list, model_imagelist=future_model_list, context=context, vis_slices=vis_slices, scales=[0, 3, 10], algorithm='mmclean', nmoment=3, niter=1000, fractional_threshold=0.1, threshold=0.01, nmajor=nmajor, gain=0.25, psf_support=64, deconvolve_facets=deconvolve_facets, deconvolve_overlap=deconvolve_overlap, deconvolve_taper=deconvolve_taper, timeslice='auto', global_solution=True, do_selfcal=True, calibration_context='T', controls=controls, use_serial_predict=use_serial_imaging, use_serial_invert=use_serial_imaging, use_serial_clean=use_serial_clean, gcfcf=gcfcf) results['size ICAL graph'] = get_size(ical_list) lprint('Size of ICAL graph is %.3E bytes' % results['size ICAL graph']) end = time.time() results['time ICAL graph'] = end - start lprint("Construction of ICAL graph took %.3f seconds" % (end - start)) print("Current objects on cluster: ") pp.pprint(arlexecute.client.who_has()) # # Execute the graph lprint("****** Executing ICAL graph ******") start = time.time() deconvolved, residual, restored, gaintables = arlexecute.compute(ical_list, sync=True) end = time.time() results['time ICAL'] = end - start lprint("ICAL graph execution took %.3f seconds" % (end - start)) qa = qa_image(deconvolved[centre]) results['deconvolved_max'] = qa.data['max'] results['deconvolved_min'] = qa.data['min'] deconvolved_cube = image_gather_channels(deconvolved) if write_fits: export_image_to_fits( deconvolved_cube, "pipelines_arlexecute_timings-%s-ical_deconvolved.fits" % context) qa = qa_image(residual[centre][0]) results['residual_max'] = qa.data['max'] results['residual_min'] = qa.data['min'] residual_cube = remove_sumwt(residual) residual_cube = image_gather_channels(residual_cube) if write_fits: export_image_to_fits( residual_cube, "pipelines_arlexecute_timings-%s-ical_residual.fits" % context) qa = qa_image(restored[centre]) results['restored_max'] = qa.data['max'] results['restored_min'] = qa.data['min'] restored_cube = image_gather_channels(restored) if write_fits: export_image_to_fits( restored_cube, "pipelines_arlexecute_timings-%s-ical_restored.fits" % context) # arlexecute.close() end_all = time.time() results['time overall'] = end_all - start_all lprint("At end, results are:") results = sort_dict(results) lprint(results) return results
def actualSetUp(self, add_errors=False, freqwin=7, block=False, dospectral=True, dopol=False, zerow=True): self.npixel = 256 self.low = create_named_configuration('LOWBD2', rmax=750.0) self.freqwin = freqwin self.vis_list = list() self.ntimes = 5 cellsize = 0.001 self.times = numpy.linspace(-3.0, +3.0, self.ntimes) * numpy.pi / 12.0 self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin) if freqwin > 1: self.channelwidth = numpy.array( freqwin * [self.frequency[1] - self.frequency[0]]) else: self.channelwidth = numpy.array([1e6]) if dopol: self.vis_pol = PolarisationFrame('linear') self.image_pol = PolarisationFrame('stokesIQUV') f = numpy.array([100.0, 20.0, -10.0, 1.0]) else: self.vis_pol = PolarisationFrame('stokesI') self.image_pol = PolarisationFrame('stokesI') f = numpy.array([100.0]) if dospectral: flux = numpy.array( [f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency]) else: flux = numpy.array([f]) self.phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000') self.vis_list = [ arlexecute.execute(ingest_unittest_visibility)( self.low, [self.frequency[freqwin]], [self.channelwidth[freqwin]], self.times, self.vis_pol, self.phasecentre, block=block, zerow=zerow) for freqwin, _ in enumerate(self.frequency) ] self.model_imagelist = [ arlexecute.execute(create_unittest_model, nout=freqwin)(self.vis_list[freqwin], self.image_pol, cellsize=cellsize, npixel=self.npixel) for freqwin, _ in enumerate(self.frequency) ] self.componentlist = [ arlexecute.execute(create_unittest_components)( self.model_imagelist[freqwin], flux[freqwin, :][numpy.newaxis, :]) for freqwin, _ in enumerate(self.frequency) ] self.model_imagelist = [ arlexecute.execute(insert_skycomponent, nout=1)(self.model_imagelist[freqwin], self.componentlist[freqwin]) for freqwin, _ in enumerate(self.frequency) ] self.vis_list = [ arlexecute.execute(predict_skycomponent_visibility)( self.vis_list[freqwin], self.componentlist[freqwin]) for freqwin, _ in enumerate(self.frequency) ] # Calculate the model convolved with a Gaussian. self.model_imagelist = arlexecute.compute(self.model_imagelist, sync=True) model = self.model_imagelist[0] self.cmodel = smooth_image(model) export_image_to_fits( model, '%s/test_imaging_arlexecute_deconvolved_model.fits' % self.dir) export_image_to_fits( self.cmodel, '%s/test_imaging_arlexecute_deconvolved_cmodel.fits' % self.dir) if add_errors and block: self.vis_list = [ arlexecute.execute(insert_unittest_errors)(self.vis_list[i]) for i, _ in enumerate(self.frequency) ] # self.vis_list = arlexecute.compute(self.vis_list, sync=True) self.vis_list = arlexecute.persist(self.vis_list) self.model_imagelist = arlexecute.scatter(self.model_imagelist)
threshold=0.1, nmajor=5, gain=0.25, deconvolve_facets=8, deconvolve_overlap=32, deconvolve_taper='tukey', vis_slices=ntimes, timeslice='auto', global_solution=False, psf_support=64, do_selfcal=True) # In[ ]: log.info('About to run ical_list_serial_workflow') result = arlexecute.compute(ical_list, sync=True) deconvolved = result[0][0] residual = result[1][0] restored = result[2][0] arlexecute.close() show_image(deconvolved, title='Clean image', cm='Greys', vmax=0.1, vmin=-0.01) print(qa_image(deconvolved, context='Clean image')) plt.show() export_image_to_fits( deconvolved, '%s/gleam_ical_arlexecute_deconvolved.fits' % (results_dir))
print(contexts) #dict_keys(['facets_wstack', 'facets_timeslice', '2d', 'wstack', 'wprojection', 'timeslice', 'facets', 'wsnapshots']) print(gcfcf_clipped[1]) contexts = ['2d', 'facets', 'timeslice', 'wstack', 'wprojection'] for context in contexts: print('Processing context %s' % context) vtpredict_list =[create_visibility(lowcore, times, frequency, channel_bandwidth=channel_bandwidth, weight=1.0, phasecentre=phasecentre, polarisation_frame=PolarisationFrame('stokesI'))] model_list = [model] vtpredict_list = arlexecute.compute(vtpredict_list, sync=True) vtpredict_list = arlexecute.scatter(vtpredict_list) if context == 'wprojection': future = predict_list_arlexecute_workflow(vtpredict_list, model_list, context='2d', gcfcf=[gcfcf_clipped]) elif context == 'facets': future = predict_list_arlexecute_workflow(vtpredict_list, model_list, context=context, facets=8) elif context == 'timeslice': future = predict_list_arlexecute_workflow(vtpredict_list, model_list, context=context, vis_slices=vis_timeslices( vtpredict, 'auto')) elif context == 'wstack': future = predict_list_arlexecute_workflow(vtpredict_list, model_list, context=context, vis_slices=31)
def actualSetUp(self, add_errors=False, nfreqwin=7, dospectral=True, dopol=False, zerow=True): self.npixel = 512 self.low = create_named_configuration('LOWBD2', rmax=750.0) self.freqwin = nfreqwin self.vis_list = list() self.ntimes = 5 self.times = numpy.linspace(-3.0, +3.0, self.ntimes) * numpy.pi / 12.0 self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin) if self.freqwin > 1: self.channelwidth = numpy.array( self.freqwin * [self.frequency[1] - self.frequency[0]]) else: self.channelwidth = numpy.array([1e6]) if dopol: self.vis_pol = PolarisationFrame('linear') self.image_pol = PolarisationFrame('stokesIQUV') f = numpy.array([100.0, 20.0, -10.0, 1.0]) else: self.vis_pol = PolarisationFrame('stokesI') self.image_pol = PolarisationFrame('stokesI') f = numpy.array([100.0]) if dospectral: flux = numpy.array( [f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency]) else: flux = numpy.array([f]) self.phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000') self.blockvis_list = [ arlexecute.execute(ingest_unittest_visibility, nout=1)(self.low, [self.frequency[i]], [self.channelwidth[i]], self.times, self.vis_pol, self.phasecentre, block=True, zerow=zerow) for i in range(nfreqwin) ] self.blockvis_list = arlexecute.compute(self.blockvis_list, sync=True) self.blockvis_list = arlexecute.scatter(self.blockvis_list) self.vis_list = [ arlexecute.execute(convert_blockvisibility_to_visibility, nout=1)(bv) for bv in self.blockvis_list ] self.vis_list = arlexecute.compute(self.vis_list, sync=True) self.vis_list = arlexecute.scatter(self.vis_list) self.model_imagelist = [ arlexecute.execute(create_unittest_model, nout=1)(self.vis_list[i], self.image_pol, npixel=self.npixel, cellsize=0.0005) for i in range(nfreqwin) ] self.model_imagelist = arlexecute.compute(self.model_imagelist, sync=True) self.model_imagelist = arlexecute.scatter(self.model_imagelist) self.components_list = [ arlexecute.execute(create_unittest_components)( self.model_imagelist[freqwin], flux[freqwin, :][numpy.newaxis, :]) for freqwin, m in enumerate(self.model_imagelist) ] self.components_list = arlexecute.compute(self.components_list, sync=True) self.components_list = arlexecute.scatter(self.components_list) self.blockvis_list = [ arlexecute.execute(predict_skycomponent_visibility)( self.blockvis_list[freqwin], self.components_list[freqwin]) for freqwin, _ in enumerate(self.blockvis_list) ] self.blockvis_list = arlexecute.compute(self.blockvis_list, sync=True) self.vis = self.blockvis_list[0] self.blockvis_list = arlexecute.scatter(self.blockvis_list) self.model_imagelist = [ arlexecute.execute(insert_skycomponent, nout=1)(self.model_imagelist[freqwin], self.components_list[freqwin]) for freqwin in range(nfreqwin) ] self.model_imagelist = arlexecute.compute(self.model_imagelist, sync=True) model = self.model_imagelist[0] self.cmodel = smooth_image(model) if self.persist: export_image_to_fits( model, '%s/test_pipelines_arlexecute_model.fits' % self.dir) export_image_to_fits( self.cmodel, '%s/test_pipelines_arlexecute_cmodel.fits' % self.dir) if add_errors: gt = create_gaintable_from_blockvisibility(self.vis) gt = simulate_gaintable(gt, phase_error=0.1, amplitude_error=0.0, smooth_channels=1, leakage=0.0, seed=180555) self.blockvis_list = [ arlexecute.execute(apply_gaintable, nout=1)(self.blockvis_list[i], gt) for i in range(self.freqwin) ] self.blockvis_list = arlexecute.compute(self.blockvis_list, sync=True) self.blockvis_list = arlexecute.scatter(self.blockvis_list) self.vis_list = [ arlexecute.execute(convert_blockvisibility_to_visibility)(bv) for bv in self.blockvis_list ] self.vis_list = arlexecute.compute(self.vis_list, sync=True) self.vis_list = arlexecute.scatter(self.vis_list) self.model_imagelist = [ arlexecute.execute(create_unittest_model, nout=1)(self.vis_list[i], self.image_pol, npixel=self.npixel, cellsize=0.0005) for i in range(nfreqwin) ] self.model_imagelist = arlexecute.compute(self.model_imagelist, sync=True) self.model_imagelist = arlexecute.scatter(self.model_imagelist)