def test_im_to_vis_phase_centre(): """ The simplest test here is to see if a single source at the phase centre returns simply the flux everywhere with zero imaginary part """ from africanus.dft.kernels import im_to_vis nrow = 100 uvw = np.random.random(size=(nrow, 3)) npix = 35 # must be odd for this test to work x = np.linspace(-0.1, 0.1, npix) ll, mm = np.meshgrid(x, x) lm = np.vstack((ll.flatten(), mm.flatten())).T nchan = 11 frequency = np.linspace(1.0, 2.0, nchan, endpoint=True) image = np.zeros([npix, npix, nchan], dtype=np.float64) I0 = 1.0 ref_freq = frequency[nchan//2] Inu = I0*(frequency/ref_freq)**(-0.7) image[npix//2, npix//2, :] = Inu image = image.reshape(npix**2, nchan) vis = im_to_vis(image, uvw, lm, frequency) for i in range(nchan): tmp = vis[:, i] - Inu[i] assert np.all(tmp.real < 1e-13) assert np.all(tmp.imag < 1e-13)
def test_im_to_vis_zero_w(): """ This test checks that the result matches the analytic result in the case when w = 0 for multiple channels and sources. """ from africanus.dft.kernels import im_to_vis from africanus.constants import minus_two_pi_over_c np.random.seed(123) nrow = 100 uvw = np.random.random(size=(nrow, 3)) uvw[:, 2] = 0.0 nchan = 3 frequency = np.linspace(1.e9, 2.e9, nchan, endpoint=True) nsource = 5 I0 = np.random.randn(nsource) ref_freq = frequency[nchan//2] image = I0[:, None] * (frequency/ref_freq)**(-0.7) l = 0.001 + 0.1*np.random.random(nsource) m = 0.001 + 0.1*np.random.random(nsource) lm = np.vstack((l,m)).T vis = im_to_vis(image, uvw, lm, frequency) vis_true = np.zeros([nrow, nchan], dtype=np.complex128) for ch in range(nchan): for source in range(nsource): vis_true[:, ch] += image[source, ch]*np.exp( minus_two_pi_over_c*frequency[ch] * 1.0j * (uvw[:,0]*lm[source,0]+uvw[:,1]*lm[source,1])) assert np.allclose(vis, vis_true)
def test_im_to_vis_simple(): """ This test checks that the result matches the analytic result w = 0 for multiple channels and sources but a single correlation """ from africanus.dft.kernels import im_to_vis from africanus.constants import minus_two_pi_over_c np.random.seed(123) nrow = 100 uvw = np.random.random(size=(nrow, 3)) nchan = 3 frequency = np.linspace(1.e9, 2.e9, nchan, endpoint=True) nsource = 5 I0 = np.random.randn(nsource) ref_freq = frequency[nchan // 2] image = I0[:, None] * (frequency / ref_freq)**(-0.7) # add correlation axis image = image[:, :, None] l = 0.001 + 0.1 * np.random.random(nsource) # noqa m = 0.001 + 0.1 * np.random.random(nsource) lm = np.vstack((l, m)).T vis = im_to_vis(image, uvw, lm, frequency).squeeze() vis_true = np.zeros([nrow, nchan], dtype=np.complex128) for ch in range(nchan): for source in range(nsource): l, m = lm[source] n = np.sqrt(1.0 - l**2 - m**2) phase = minus_two_pi_over_c*frequency[ch] * 1.0j * \ (uvw[:, 0]*l + uvw[:, 1]*m + uvw[:, 2]*(n-1)) vis_true[:, ch] += image[source, ch, 0] * np.exp(phase) assert_array_almost_equal(vis, vis_true, decimal=14)
def test_symmetric_covariance(): """ Test that the image plane precision matrix R^H Sigma^-1R is Hermitian (symmetric since its real). """ from africanus.dft.kernels import vis_to_im, im_to_vis np.random.seed(123) nsource = 25 ncorr = 1 nchan = 1 lmmax = 0.05 ll = -lmmax + 2 * lmmax * np.random.random(nsource) # noqa mm = -lmmax + 2 * lmmax * np.random.random(nsource) lm = np.vstack((ll, mm)).T nrows = 1000 uvw = np.random.randn(nrows, 3) * 1000 uvw[:, 2] = 0.0 freq = np.array([1.0e9]) flags = np.zeros((nrows, nchan, ncorr), dtype=np.bool) # get the "psf" matrix at source locations psf_source = np.zeros((nsource, nsource), dtype=np.float64) point_source = np.ones((1, nchan, ncorr), dtype=np.float64) for source in range(nsource): lm_i = lm[source].reshape(1, 2) Ki = im_to_vis(point_source, uvw, lm_i, freq) psf_source[:, source] = vis_to_im(Ki, uvw, lm, freq, flags).squeeze() assert_array_almost_equal(psf_source, psf_source.T, decimal=14)
def test_degrid_dft_packed_dask_dft_check(): da = pytest.importorskip("dask.array") # construct kernel W = 5 OS = 3 kern = kernels.pack_kernel(kernels.kbsinc(W, oversample=OS), W, oversample=OS) nrow = 100 nrow_chunk = nrow // 8 uvw = np.column_stack( (5000.0 * np.cos(np.linspace(0, 2 * np.pi, nrow)), 5000.0 * np.sin(np.linspace(0, 2 * np.pi, nrow)), np.zeros(nrow))) pxacrossbeam = 10 nchan = 16 frequency = np.linspace(1.0e9, 1.4e9, nchan) wavelength = lightspeed / frequency cell = np.rad2deg( wavelength[0] / (2 * max(np.max(np.abs(uvw[:, 0])), np.max(np.abs(uvw[:, 1]))) * pxacrossbeam)) npix = 512 mod = np.zeros((1, npix, npix), dtype=np.complex64) mod[0, npix // 2 - 5, npix // 2 - 5] = 1.0 ftmod = np.fft.ifftshift(np.fft.fft2(np.fft.fftshift( mod[0, :, :]))).reshape((1, 1, npix, npix)) chanmap = np.zeros(nchan, dtype=np.int64) dec, ra = np.meshgrid( np.arange(-npix // 2, npix // 2) * np.deg2rad(cell), np.arange(-npix // 2, npix // 2) * np.deg2rad(cell)) radec = np.column_stack((ra.flatten(), dec.flatten())) vis_dft = im_to_vis(mod[0, :, :].reshape(1, 1, npix * npix).T.copy(), uvw, radec, frequency) vis_degrid = dwrap.degridder( da.from_array(uvw, chunks=(nrow_chunk, 3)), da.from_array(ftmod, chunks=(1, 1, npix, npix)), da.from_array(wavelength, chunks=(nchan, )), da.from_array(chanmap, chunks=(nchan, )), cell * 3600.0, da.from_array(np.array([[0, np.pi / 4.0]]), chunks=(1, 2)), (0, np.pi / 4.0), kern, W, OS, "None", # no faceting "None", # no faceting "XXYY_FROM_I", "conv_1d_axisymmetric_packed_gather") vis_degrid = vis_degrid.compute() assert np.percentile( np.abs(vis_dft[:, 0, 0].real - vis_degrid[:, 0, 0].real), 99.0) < 0.05 assert np.percentile( np.abs(vis_dft[:, 0, 0].imag - vis_degrid[:, 0, 0].imag), 99.0) < 0.05
def test_im_to_vis_fft(convention): """ Test against the fft when uv on regular and w is zero. """ from africanus.dft.kernels import im_to_vis np.random.seed(123) Fs = np.fft.fftshift iFs = np.fft.ifftshift # set image and take fft npix = 29 ncorr = 1 image = np.zeros((npix, npix, ncorr), dtype=np.float64) fft_image = np.zeros((npix, npix, ncorr), dtype=np.complex128) nsource = 25 for corr in range(ncorr): Ix = np.random.randint(5, npix - 5, nsource) Iy = np.random.randint(5, npix - 5, nsource) image[Ix, Iy, corr] = np.random.randn(nsource) fft_image[:, :, corr] = Fs(np.fft.fft2(iFs(image[:, :, corr]))) # image space coords deltal = 0.001 # this assumes npix is odd l_coord = np.arange(-(npix // 2), npix // 2 + 1) * deltal ll, mm = np.meshgrid(l_coord, l_coord) lm = np.vstack((ll.flatten(), mm.flatten())).T # uv-space coords u = Fs(np.fft.fftfreq(npix, d=deltal)) uu, vv = np.meshgrid(u, u) uvw = np.zeros((npix**2, 3), dtype=np.float64) uvw[:, 0] = uu.flatten() uvw[:, 1] = vv.flatten() nchan = 1 image = image.reshape(npix**2, nchan, ncorr) frequency = np.ones(nchan, dtype=np.float64) from africanus.constants import c as lightspeed frequency *= lightspeed # makes result independent of frequency # take DFT and compare vis = im_to_vis(image, uvw, lm, frequency, convention=convention) fft_image = fft_image.reshape(npix**2, nchan, ncorr) fft_image = np.conj(fft_image) if convention == 'casa' else fft_image assert_array_almost_equal(vis, fft_image, decimal=13)
def test_im_to_vis_single_baseline_and_chan(): """ Here we check that the result is consistent for a single baseline, source and channel. """ from africanus.dft.kernels import im_to_vis from africanus.constants import minus_two_pi_over_c nrow = 1 uvw = np.random.random(size=(nrow, 3)) frequency = np.array([1.5e9]) l = 0.015 m = -0.0123 lm = np.array([[l, m]]) n = np.sqrt(1 - l**2 - m**2) image = np.array([[1.0]]) vis = im_to_vis(image, uvw, lm, frequency) vis_true = image*np.exp(minus_two_pi_over_c * frequency * 1.0j * (uvw[:,0]*l + uvw[:,1]*m + uvw[:,2]*(n - 1.0))) assert np.allclose(vis, vis_true)
def gen_MODEL_DATA(sel_FREQ, sel_UVW, sel_LSM, gen_lm, n_chan, n_row, n_dir): # Create initial model array model = np.zeros((n_dir, n_chan, 4), dtype=np.float64) # Cycle coordinates creating a source with flux for d, source in enumerate(sel_LSM.sources): flux = getattr(source, 'flux') for i, stokes in enumerate(['I', 'Q', 'U', 'V']): stokes_val = getattr(flux, stokes) if stokes_val: # Get spectrum (only spi currently supported) tmp_spec = source.spectrum spi = [tmp_spec.spi if tmp_spec is not None else 0.0] ref_freq = [tmp_spec.freq0 if tmp_spec is not None else 1.0] # Generate model flux model[d, :, i] = stokes_val\ * (sel_FREQ/ref_freq)**spi # Get model visibilities model_vis = np.zeros((n_row, n_chan, n_dir, 4), dtype=np.complex128) for s in range(n_dir): model_vis[:, :, s] = im_to_vis( model[s].reshape((1, n_chan, 4)), sel_UVW, gen_lm[s].reshape((1, 2)), sel_FREQ, dtype=np.complex64) # Convert Stokes to corr in_schema = ['I', 'Q', 'U', 'V'] out_schema = [['RR', 'RL'], ['LR', 'LL']] model_vis = convert(model_vis, in_schema, out_schema) return model_vis
def test_wcorrection_faceting_forward(tmp_path_factory): # construct kernel W = 5 OS = 9 kern = kernels.pack_kernel(kernels.kbsinc(W, oversample=OS), W, OS) nrow = 5000 np.random.seed(0) # simulate some ficticious baselines rotated by an hour angle uvw = np.zeros((nrow, 3), dtype=np.float64) blpos = np.random.uniform(26, 10000, size=(25, 3)) ntime = int(nrow / 25.0) d0 = np.pi / 4.0 for n in range(25): for ih0, h0 in enumerate( np.linspace(np.deg2rad(-20), np.deg2rad(20), ntime)): s = np.sin c = np.cos R = np.array([[s(h0), c(h0), 0], [-s(d0) * c(h0), s(d0) * s(h0), c(d0)], [c(d0) * c(h0), -c(d0) * s(h0), s(d0)]]) uvw[n * ntime + ih0, :] = np.dot(R, blpos[n, :].T) pxacrossbeam = 5 frequency = np.array([1.4e9]) wavelength = lightspeed / frequency cell = np.rad2deg( wavelength[0] / (max(np.max(np.abs(uvw[:, 0])), np.max(np.abs(uvw[:, 1]))) * pxacrossbeam)) npixfacet = 100 mod = np.ones((1, 1, 1), dtype=np.complex64) deltaradec = np.array([[20 * np.deg2rad(cell), 20 * np.deg2rad(cell)]]) lm = radec_to_lmn(deltaradec + np.array([[0, d0]]), phase_centre=np.array([0, d0])) vis_dft = im_to_vis(mod, uvw, lm[:, 0:2], frequency).repeat(2).reshape(nrow, 1, 2) chanmap = np.array([0]) ftmod = np.ones((1, npixfacet, npixfacet), dtype=np.complex64) # point source at centre of facet vis_degrid = degridder.degridder( uvw, ftmod, wavelength, chanmap, cell * 3600.0, (deltaradec + np.array([[0, d0]]))[0, :], (0, d0), kern, W, OS, "rotate", # no faceting "phase_rotate", # no faceting "XXYY_FROM_I", "conv_1d_axisymmetric_packed_gather") try: import matplotlib except ImportError: pass else: matplotlib.use("agg") from matplotlib import pyplot as plt plot_dir = tmp_path_factory.mktemp("wcorrection_forward") plt.figure() plt.plot(vis_degrid[:, 0, 0].real, label=r"$\Re(\mathtt{degrid facet})$") plt.plot(vis_dft[:, 0, 0].real, label=r"$\Re(\mathtt{dft})$") plt.plot(np.abs(vis_dft[:, 0, 0].real - vis_degrid[:, 0, 0].real), label="Error") plt.legend() plt.xlabel("sample") plt.ylabel("Real of predicted") plt.savefig(plot_dir / "facet_degrid_vs_dft_re_packed.png") plt.figure() plt.plot(vis_degrid[:, 0, 0].imag, label=r"$\Im(\mathtt{degrid facet})$") plt.plot(vis_dft[:, 0, 0].imag, label=r"$\Im(\mathtt{dft})$") plt.plot(np.abs(vis_dft[:, 0, 0].imag - vis_degrid[:, 0, 0].imag), label="Error") plt.legend() plt.xlabel("sample") plt.ylabel("Imag of predicted") plt.savefig(plot_dir / "facet_degrid_vs_dft_im_packed.png") assert np.percentile( np.abs(vis_dft[:, 0, 0].real - vis_degrid[:, 0, 0].real), 99.0) < 0.05 assert np.percentile( np.abs(vis_dft[:, 0, 0].imag - vis_degrid[:, 0, 0].imag), 99.0) < 0.05
def test_wcorrection_faceting_backward(tmp_path_factory): # construct kernel W = 5 OS = 9 kern = kernels.pack_kernel(kernels.kbsinc(W, oversample=OS), W, OS) nrow = 5000 np.random.seed(0) # simulate some ficticious baselines rotated by an hour angle uvw = np.zeros((nrow, 3), dtype=np.float64) blpos = np.random.uniform(26, 10000, size=(25, 3)) ntime = int(nrow / 25.0) d0 = np.pi / 4.0 for n in range(25): for ih0, h0 in enumerate( np.linspace(np.deg2rad(-20), np.deg2rad(20), ntime)): s = np.sin c = np.cos R = np.array([[s(h0), c(h0), 0], [-s(d0) * c(h0), s(d0) * s(h0), c(d0)], [c(d0) * c(h0), -c(d0) * s(h0), s(d0)]]) uvw[n * ntime + ih0, :] = np.dot(R, blpos[n, :].T) pxacrossbeam = 5 frequency = np.array([1.4e9]) wavelength = lightspeed / frequency cell = np.rad2deg( wavelength[0] / (max(np.max(np.abs(uvw[:, 0])), np.max(np.abs(uvw[:, 1]))) * pxacrossbeam)) npix = 2048 npixfacet = 100 fftpad = 1.1 mod = np.ones((1, 1, 1), dtype=np.complex64) deltaradec = np.array([[600 * np.deg2rad(cell), 600 * np.deg2rad(cell)]]) lm = radec_to_lmn(deltaradec + np.array([[0, d0]]), phase_centre=np.array([0, d0])) vis_dft = im_to_vis(mod, uvw, lm[:, 0:2], frequency).repeat(2).reshape(nrow, 1, 2) chanmap = np.array([0]) detaper = kernels.compute_detaper_dft_seperable( int(npix * fftpad), kernels.unpack_kernel(kern, W, OS), W, OS) vis_grid_nofacet = gridder.gridder( uvw, vis_dft, wavelength, chanmap, int(npix * fftpad), cell * 3600.0, (0, d0), (0, d0), kern, W, OS, "None", # no faceting "None", # no faceting "I_FROM_XXYY", "conv_1d_axisymmetric_packed_scatter", do_normalize=True) ftvis = (np.fft.fftshift( np.fft.ifft2(np.fft.ifftshift(vis_grid_nofacet[0, :, :]))).reshape( (1, int(npix * fftpad), int(npix * fftpad)))).real / detaper * int( npix * fftpad)**2 ftvis = ftvis[:, int(npix * fftpad) // 2 - npix // 2:int(npix * fftpad) // 2 - npix // 2 + npix, int(npix * fftpad) // 2 - npix // 2:int(npix * fftpad) // 2 - npix // 2 + npix] detaper_facet = kernels.compute_detaper_dft_seperable( int(npixfacet * fftpad), kernels.unpack_kernel(kern, W, OS), W, OS) vis_grid_facet = gridder.gridder(uvw, vis_dft, wavelength, chanmap, int(npixfacet * fftpad), cell * 3600.0, (deltaradec + np.array([[0, d0]]))[0, :], (0, d0), kern, W, OS, "rotate", "phase_rotate", "I_FROM_XXYY", "conv_1d_axisymmetric_packed_scatter", do_normalize=True) ftvisfacet = (np.fft.fftshift( np.fft.ifft2(np.fft.ifftshift(vis_grid_facet[0, :, :]))).reshape( (1, int(npixfacet * fftpad), int( npixfacet * fftpad)))).real / detaper_facet * int( npixfacet * fftpad)**2 ftvisfacet = ftvisfacet[:, int(npixfacet * fftpad) // 2 - npixfacet // 2:int(npixfacet * fftpad) // 2 - npixfacet // 2 + npixfacet, int(npixfacet * fftpad) // 2 - npixfacet // 2:int(npixfacet * fftpad) // 2 - npixfacet // 2 + npixfacet] try: import matplotlib except ImportError: pass else: matplotlib.use("agg") from matplotlib import pyplot as plt plot_dir = tmp_path_factory.mktemp("wcorrection_backward") plt.figure() plt.subplot(121) plt.imshow(ftvis[0, 1624 - 50:1624 + 50, 1447 - 50:1447 + 50]) plt.colorbar() plt.title("Offset FFT (peak={0:.1f})".format(np.max(ftvis))) plt.subplot(122) plt.imshow(ftvisfacet[0, :, :]) plt.colorbar() plt.title("Faceted FFT (peak={0:.1f})".format(np.max(ftvisfacet))) plt.savefig(plot_dir / "facet_imaging.png") assert (np.abs(np.max(ftvisfacet[0, :, :]) - 1.0) < 1.0e-6)
def test_grid_dft_packed(tmp_path_factory): # construct kernel W = 7 OS = 1009 kern = kernels.pack_kernel(kernels.kbsinc(W, oversample=OS), W, OS) nrow = 5000 np.random.seed(0) uvw = np.random.normal(scale=6000, size=(nrow, 3)) uvw[:, 2] = 0.0 # ignore widefield effects for now pxacrossbeam = 10 frequency = np.array([30.0e9]) wavelength = lightspeed / frequency cell = np.rad2deg( wavelength[0] / (2 * max(np.max(np.abs(uvw[:, 0])), np.max(np.abs(uvw[:, 1]))) * pxacrossbeam)) npix = 256 fftpad = 1.25 mod = np.zeros((1, npix, npix), dtype=np.complex64) for n in [int(n) for n in np.linspace(npix // 8, 2 * npix // 5, 5)]: mod[0, npix // 2 + n, npix // 2 + n] = 1.0 mod[0, npix // 2 + n, npix // 2 - n] = 1.0 mod[0, npix // 2 - n, npix // 2 - n] = 1.0 mod[0, npix // 2 - n, npix // 2 + n] = 1.0 mod[0, npix // 2, npix // 2 + n] = 1.0 mod[0, npix // 2, npix // 2 - n] = 1.0 mod[0, npix // 2 - n, npix // 2] = 1.0 mod[0, npix // 2 + n, npix // 2] = 1.0 dec, ra = np.meshgrid( np.arange(-npix // 2, npix // 2) * np.deg2rad(cell), np.arange(-npix // 2, npix // 2) * np.deg2rad(cell)) radec = np.column_stack((ra.flatten(), dec.flatten())) vis_dft = im_to_vis(mod[0, :, :].reshape(1, 1, npix * npix).T.copy(), uvw, radec, frequency).repeat(2).reshape(nrow, 1, 2) chanmap = np.array([0]) detaper = kernels.compute_detaper_dft_seperable( int(npix * fftpad), kernels.unpack_kernel(kern, W, OS), W, OS) vis_grid = gridder.gridder( uvw, vis_dft, wavelength, chanmap, int(npix * fftpad), cell * 3600.0, (0, np.pi / 4.0), (0, np.pi / 4.0), kern, W, OS, "None", # no faceting "None", # no faceting "I_FROM_XXYY", "conv_1d_axisymmetric_packed_scatter", do_normalize=True) ftvis = (np.fft.fftshift(np.fft.ifft2(np.fft.ifftshift( vis_grid[0, :, :]))).reshape((1, int(npix * fftpad), int( npix * fftpad)))).real / detaper * int(npix * fftpad)**2 ftvis = ftvis[:, int(npix * fftpad) // 2 - npix // 2:int(npix * fftpad) // 2 - npix // 2 + npix, int(npix * fftpad) // 2 - npix // 2:int(npix * fftpad) // 2 - npix // 2 + npix] dftvis = vis_to_im(vis_dft, uvw, radec, frequency, np.zeros(vis_dft.shape, dtype=np.bool)).T.copy().reshape( 2, 1, npix, npix) / nrow try: import matplotlib except ImportError: pass else: matplotlib.use("agg") from matplotlib import pyplot as plt plt.figure() plt.subplot(131) plt.title("FFT") plt.imshow(ftvis[0, :, :]) plt.colorbar() plt.subplot(132) plt.title("DFT") plt.imshow(dftvis[0, 0, :, :]) plt.colorbar() plt.subplot(133) plt.title("ABS diff") plt.imshow(np.abs(ftvis[0, :, :] - dftvis[0, 0, :, :])) plt.colorbar() plt.savefig( tmp_path_factory.mktemp("grid_dft_packed") / "grid_diff_dft_packed.png") assert (np.percentile(np.abs(ftvis[0, :, :] - dftvis[0, 0, :, :]), 95.0) < 0.15)
def test_degrid_dft_packed(tmp_path_factory): # construct kernel W = 5 OS = 3 kern = kernels.pack_kernel(kernels.kbsinc(W, oversample=OS), W, oversample=OS) uvw = np.column_stack( (5000.0 * np.cos(np.linspace(0, 2 * np.pi, 1000)), 5000.0 * np.sin(np.linspace(0, 2 * np.pi, 1000)), np.zeros(1000))) pxacrossbeam = 10 frequency = np.array([1.4e9]) wavelength = lightspeed / frequency cell = np.rad2deg( wavelength[0] / (2 * max(np.max(np.abs(uvw[:, 0])), np.max(np.abs(uvw[:, 1]))) * pxacrossbeam)) npix = 512 mod = np.zeros((1, npix, npix), dtype=np.complex64) mod[0, npix // 2 - 5, npix // 2 - 5] = 1.0 ftmod = np.fft.ifftshift(np.fft.fft2(np.fft.fftshift( mod[0, :, :]))).reshape((1, npix, npix)) chanmap = np.array([0]) vis_degrid = degridder.degridder( uvw, ftmod, wavelength, chanmap, cell * 3600.0, (0, np.pi / 4.0), (0, np.pi / 4.0), kern, W, OS, "None", # no faceting "None", # no faceting "XXYY_FROM_I", "conv_1d_axisymmetric_packed_gather") dec, ra = np.meshgrid( np.arange(-npix // 2, npix // 2) * np.deg2rad(cell), np.arange(-npix // 2, npix // 2) * np.deg2rad(cell)) radec = np.column_stack((ra.flatten(), dec.flatten())) vis_dft = im_to_vis(mod[0, :, :].reshape(1, 1, npix * npix).T.copy(), uvw, radec, frequency) try: import matplotlib except ImportError: pass else: matplotlib.use("agg") from matplotlib import pyplot as plt plt.figure() plt.plot(vis_degrid[:, 0, 0].real, label=r"$\Re(\mathtt{degrid})$") plt.plot(vis_dft[:, 0, 0].real, label=r"$\Re(\mathtt{dft})$") plt.plot(np.abs(vis_dft[:, 0, 0].real - vis_degrid[:, 0, 0].real), label="Error") plt.legend() plt.xlabel("sample") plt.ylabel("Real of predicted") plt.savefig( os.path.join(os.environ.get("TMPDIR", "/tmp"), "degrid_vs_dft_re_packed.png")) plt.figure() plt.plot(vis_degrid[:, 0, 0].imag, label=r"$\Im(\mathtt{degrid})$") plt.plot(vis_dft[:, 0, 0].imag, label=r"$\Im(\mathtt{dft})$") plt.plot(np.abs(vis_dft[:, 0, 0].imag - vis_degrid[:, 0, 0].imag), label="Error") plt.legend() plt.xlabel("sample") plt.ylabel("Imag of predicted") plt.savefig( tmp_path_factory.mktemp("degrid_dft_packed") / "degrid_vs_dft_im_packed.png") assert np.percentile( np.abs(vis_dft[:, 0, 0].real - vis_degrid[:, 0, 0].real), 99.0) < 0.05 assert np.percentile( np.abs(vis_dft[:, 0, 0].imag - vis_degrid[:, 0, 0].imag), 99.0) < 0.05