def test_correlation_methods(lt_ctx, cls, dtype, kwargs): shape = np.array([128, 128]) zero = shape / 2 + np.random.uniform(-1, 1, size=2) a = np.array([27.17, 0.]) + np.random.uniform(-1, 1, size=2) b = np.array([0., 29.19]) + np.random.uniform(-1, 1, size=2) indices = np.mgrid[-2:3, -2:3] indices = np.concatenate(indices.T) radius = 8 data, indices, peaks = cbed_frame(*shape, zero, a, b, indices, radius) dataset = MemoryDataSet(data=data, tileshape=(1, *shape), num_partitions=1, sig_dims=2) template = m.radial_gradient(centerX=radius + 1, centerY=radius + 1, imageSizeX=2 * radius + 2, imageSizeY=2 * radius + 2, radius=radius) match_patterns = [ blobfinder.RadialGradient(radius=radius), blobfinder.Circular(radius=radius), blobfinder.BackgroundSubtraction(radius=radius), blobfinder.RadialGradientBackgroundSubtraction(radius=radius), blobfinder.UserTemplate(template=template) ] print("zero: ", zero) print("a: ", a) print("b: ", b) for match_pattern in match_patterns: print("refining using template %s" % type(match_pattern)) udf = cls(match_pattern=match_pattern, peaks=peaks.astype(dtype), **kwargs) res = lt_ctx.run_udf(dataset=dataset, udf=udf) print(peaks) print(res['refineds'].data[0]) print(peaks - res['refineds'].data[0]) print(res['peak_values'].data[0]) print(res['peak_elevations'].data[0]) # import matplotlib.pyplot as plt # fig, ax = plt.subplots() # plt.imshow(data[0]) # for p in np.flip(res['refineds'].data[0], axis=-1): # ax.add_artist(plt.Circle(p, radius, fill=False, color='y')) # plt.show() assert np.allclose(res['refineds'].data[0], peaks, atol=0.5)
def test_run_refine_fastmatch(lt_ctx): shape = np.array([128, 128]) zero = shape / 2 + np.random.uniform(-1, 1, size=2) a = np.array([27.17, 0.]) + np.random.uniform(-1, 1, size=2) b = np.array([0., 29.19]) + np.random.uniform(-1, 1, size=2) indices = np.mgrid[-2:3, -2:3] indices = np.concatenate(indices.T) drop = np.random.choice([True, False], size=len(indices), p=[0.9, 0.1]) indices = indices[drop] radius = 10 data, indices, peaks = cbed_frame(*shape, zero, a, b, indices, radius) dataset = MemoryDataSet(data=data, tileshape=(1, *shape), num_partitions=1, sig_dims=2) matcher = grm.Matcher() template = m.radial_gradient(centerX=radius + 1, centerY=radius + 1, imageSizeX=2 * radius + 2, imageSizeY=2 * radius + 2, radius=radius) match_patterns = [ blobfinder.RadialGradient(radius=radius), blobfinder.Circular(radius=radius), blobfinder.BackgroundSubtraction(radius=radius), blobfinder.RadialGradientBackgroundSubtraction(radius=radius), blobfinder.UserTemplate(template=template) ] print("zero: ", zero) print("a: ", a) print("b: ", b) for match_pattern in match_patterns: print("refining using template %s" % type(match_pattern)) (res, real_indices) = blobfinder.run_refine( ctx=lt_ctx, dataset=dataset, zero=zero + np.random.uniform(-1, 1, size=2), a=a + np.random.uniform(-1, 1, size=2), b=b + np.random.uniform(-1, 1, size=2), matcher=matcher, match_pattern=match_pattern) print(peaks - grm.calc_coords(res['zero'].data[0], res['a'].data[0], res['b'].data[0], indices)) assert np.allclose(res['zero'].data[0], zero, atol=0.5) assert np.allclose(res['a'].data[0], a, atol=0.2) assert np.allclose(res['b'].data[0], b, atol=0.2)