import numpy as np import torch as t from cdtools.tools import initializers from cdtools.datasets import Ptycho2DDataset def test_exit_wave_geometry(): # First test a simple case where nothing need change basis = t.Tensor([[0, -30e-6, 0], [-20e-6, 0, 0]]).transpose(0, 1) shape = t.Size([73, 56]) wavelength = 1e-9 distance = 1. rs_basis = initializers.exit_wave_geometry(basis, shape, wavelength, distance) assert t.allclose(rs_basis[0, 1], t.Tensor([-8.928571428571428e-07])) assert t.allclose(rs_basis[1, 0], t.Tensor([-4.5662100456621004e-07])) def test_calc_object_setup(): # First just try a simple case probe_shape = t.Size([120, 57]) translations = t.rand((30, 2)) * 300 t_max = t.max(translations, dim=0)[0] t_min = t.min(translations, dim=0)[0] obj_shape, min_translation = initializers.calc_object_setup(probe_shape, translations) exp_shape = t.ceil(t_max - t_min).to(t.int32) + t.Tensor(list(probe_shape)).to(t.int32) assert t.allclose(min_translation, t_min) assert obj_shape == t.Size(exp_shape) # Then add some padding padding = 5 obj_shape, min_translation = initializers.calc_object_setup(probe_shape, translations, padding=padding) assert t.allclose(min_translation, t_min - padding) assert obj_shape == t.Size(exp_shape + 2 * padding) def test_gaussian(): # Generate gaussian as a numpy array (square array) shape = [10, 10] sigma = [2.5, 2.5] center = ((shape[0] - 1) / 2, (shape[1] - 1) / 2) y, x = np.mgrid[:shape[0], :shape[1]] np_result = 10 * np.exp(-0.5 * ((x - center[1]) / sigma[1])**2 - 0.5 * ((y - center[0]) / sigma[0])**2) init_result = initializers.gaussian(shape, sigma, amplitude=10).numpy() assert np.allclose(init_result, np_result) # Generate gaussian as a numpy array (rectangular array) shape = [10, 5] sigma = [2.5, 3] center = ((shape[0] - 1) / 2, (shape[1] - 1) / 2) y, x = np.mgrid[:shape[0], :shape[1]] np_result = np.exp(-0.5 * ((x - center[1]) / sigma[1])**2 - 0.5 * ((y - center[0]) / sigma[0])**2) init_result = initializers.gaussian(shape, sigma).numpy() assert np.allclose(init_result, np_result) # Generate gaussian with curvature shape = [20, 30] sigma = [2.5, 5] curvature = [1, 0.6] center = ((shape[0] - 1) / 2 + 3, (shape[1] - 1) / 2 - 1.4) y, x = np.mgrid[:shape[0], :shape[1]] np_result = (10 + 0j) * np.exp(-0.5 * ((x - center[1]) / sigma[1])**2 - 0.5 * ((y - center[0]) / sigma[0])**2) np_result *= np.exp(0.5j * curvature[1] * (x - center[1])**2 + 0.5j * curvature[0] * (y - center[0])**2) init_result = initializers.gaussian(shape, sigma, center=center, curvature=curvature, amplitude=10).numpy() assert np.allclose(init_result, np_result) def test_gaussian_probe(ptycho_cxi_1): dataset = Ptycho2DDataset.from_cxi(ptycho_cxi_1[0]) det_basis = t.Tensor(dataset.detector_geometry['basis']) det_shape = t.Size(dataset.patterns.shape[-2:]) wavelength = dataset.wavelength distance = dataset.detector_geometry['distance'] basis = initializers.exit_wave_geometry(det_basis, det_shape, wavelength, distance) # Basis is around 60nm in the i(y) direction, 85nm in the j(x) direction # Full window is therefore about 15 um in i(y) and 20 um in the j(x) dir # Come up with a roughly matching set of probe parameters sigma = 5e-7 # Build a stage explicitly with numpy to compare against x = (np.arange(256) - 127.5) * (-basis[0, 1]).numpy() y = (np.arange(256) - 127.5) * (-basis[1, 0]).numpy() Xs, Ys = np.meshgrid(x, y) Rs = np.sqrt(Xs**2 + Ys**2) # Now we first test the non-propagated probe np_probe = np.exp(- 1 / (2 * sigma**2) * Rs**2) normalization = 0 for params, im in dataset: normalization += np.sum(im.cpu().numpy()) normalization /= len(dataset) normalization_1 = np.sqrt(normalization / np.sum(np.abs(np_probe)**2)) probe = initializers.gaussian_probe( dataset, basis, det_shape, sigma).numpy() assert np.allclose(probe, normalization_1 * np_probe) # And then a propagated probe z = 1e-4 # nm k = 2 * np.pi / wavelength w0 = np.sqrt(2) * sigma zr = np.pi * w0**2 / wavelength wz = w0 * np.sqrt(1 + (z / zr)**2) Rz = z * (1 + (zr / z)**2) np_probe = np.exp(-Rs**2 / wz**2) * np.exp(-1j * k * Rs**2 / (2 * Rz)) normalization_2 = np.sqrt(normalization / np.sum(np.abs(np_probe)**2)) probe = initializers.gaussian_probe(dataset, basis, det_shape, sigma, propagation_distance=z).numpy() assert np.allclose(probe, normalization_2 * np_probe) def test_SHARP_style_probe(ptycho_cxi_1): # This code will probably change and honestly it doesn't need to # be exactly the final thing. So just test that the function doesn't # throw an error. dataset = Ptycho2DDataset.from_cxi(ptycho_cxi_1[0]) det_basis = t.Tensor(dataset.detector_geometry['basis']) det_shape = t.Size(dataset.patterns.shape[-2:]) wavelength = dataset.wavelength distance = dataset.detector_geometry['distance'] basis = initializers.exit_wave_geometry(det_basis, det_shape, wavelength, distance) assert basis.shape == t.Size([3, 2]) probe = initializers.SHARP_style_probe(dataset) assert probe.shape == t.Size([256, 256]) probe = initializers.SHARP_style_probe(dataset, propagation_distance=20e-6) assert probe.shape == t.Size([256, 256]) def test_RPI_spectral_init(): # I think we can only really meaningfully test that it doesn't throw errors, # since the original implementation is in numpy and there aren't any clear # cases that can be calculated analytically. pattern = np.random.rand(230, 253).astype(np.float32) probe = np.random.rand(230, 253).astype(np.complex64) obj_shape = [37, 53] mask = t.Tensor(np.random.rand(*pattern.shape) > 0.04) background = t.as_tensor(np.random.rand(*pattern.shape), dtype=t.float32) * 0.05 probe = t.as_tensor(probe) pattern = t.as_tensor(pattern) obj = initializers.RPI_spectral_init(pattern, probe, obj_shape) assert list(obj.shape) == [1] + obj_shape obj = initializers.RPI_spectral_init(pattern, probe, obj_shape, n_modes=2, mask=mask) assert list(obj.shape) == [2] + obj_shape obj = initializers.RPI_spectral_init(pattern, probe, obj_shape, n_modes=2, background=background) assert list(obj.shape) == [2] + obj_shape obj = initializers.RPI_spectral_init(pattern, probe, obj_shape, n_modes=2, mask=mask, background=background) assert list(obj.shape) == [2] + obj_shape