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