def setup(dials_data): from dials.algorithms.profile_model.gaussian_rs import BBoxCalculator3D from dials.model.serialize import load sequence = load.sequence( dials_data("centroid_test_data").join("sweep.json").strpath) fixture = {} # Get the models fixture["beam"] = sequence.get_beam() fixture["detector"] = sequence.get_detector() fixture["gonio"] = sequence.get_goniometer() fixture["scan"] = sequence.get_scan() # Set the delta_divergence/mosaicity n_sigma = 5 sigma_divergence = 0.060 * math.pi / 180 mosaicity = 0.154 * math.pi / 180 fixture["delta_divergence"] = n_sigma * sigma_divergence fixture["delta_mosaicity"] = n_sigma * mosaicity # Create the bounding box calculator fixture["calculate_bbox"] = BBoxCalculator3D( fixture["beam"], fixture["detector"], fixture["gonio"], fixture["scan"], fixture["delta_divergence"], fixture["delta_mosaicity"], ) return fixture
def test_run(dials_data): from dials.algorithms.profile_model.gaussian_rs import transform from dials.algorithms.profile_model.gaussian_rs import CoordinateSystem from scitbx import matrix from dials.model.serialize import load sequence = load.sequence( dials_data("centroid_test_data").join("sweep.json").strpath) # Get the models beam = sequence.get_beam() detector = sequence.get_detector() gonio = sequence.get_goniometer() assert len(detector) == 1 # Get some stuff s0 = beam.get_s0() m2 = gonio.get_rotation_axis() image_size = detector[0].get_image_size() # Get a random s1/phi i = random.uniform(0, image_size[0]) j = random.uniform(1, image_size[1]) s1 = matrix.col(detector[0].get_pixel_lab_coord((i, j))) s1 = s1.normalize() * matrix.col(s0).length() phi = random.uniform(0, 5) x0 = int(math.floor(i - 10)) y0 = int(math.floor(j - 10)) # Set some parameters sigma_divergence = beam.get_sigma_divergence(deg=False) delta_divergence = 3 * sigma_divergence grid_half_size = 4 step_size = (delta_divergence / grid_half_size, delta_divergence / grid_half_size) # Create the coordinate system cs = CoordinateSystem(m2, s0, s1, phi) # Create the map of s1 coordinates s1_map = transform.beam_vector_map(detector[0], beam, True) # Create the grid index generator generate_indices = transform.GridIndexGenerator(cs, x0, y0, step_size, grid_half_size, s1_map) for j in range(0, 20): for i in range(0, 20): xx = x0 + i yy = y0 + j if xx < 0 or yy < 0 or xx >= image_size[0] or yy >= image_size[0]: continue # Get the grid indices gi_1, gj_1 = generate_indices(j, i) # Get the grid indices xyz = matrix.col(detector[0].get_pixel_lab_coord((x0 + i, y0 + j))) xyz = xyz.normalize() * matrix.col(s0).length() c1, c2 = matrix.col(cs.from_beam_vector(xyz)) gi_2 = grid_half_size + c1 / step_size[0] + 0.5 gj_2 = grid_half_size + c2 / step_size[1] + 0.5 # Check both are the same eps = 1e-7 assert abs(gj_1 - gj_2) <= eps, (gi_1, gi_2, gj_1, gj_2) assert abs(gi_1 - gi_2) <= eps, (gi_1, gi_2, gj_1, gj_2)
def test_forward(dials_data): sequence = load.sequence( dials_data("centroid_test_data").join("sweep.json").strpath) # Get the models beam = sequence.get_beam() detector = sequence.get_detector() gonio = sequence.get_goniometer() scan = sequence.get_scan() # Set some parameters sigma_divergence = beam.get_sigma_divergence(deg=False) mosaicity = 0.157 * math.pi / 180 n_sigma = 3 grid_size = 7 delta_divergence = n_sigma * sigma_divergence step_size = delta_divergence / grid_size delta_divergence2 = delta_divergence + step_size * 0.5 delta_mosaicity = n_sigma * mosaicity # Create the bounding box calculator calculate_bbox = BBoxCalculator3D(beam, detector, gonio, scan, delta_divergence2, delta_mosaicity) # Initialise the transform spec = transform.TransformSpec(beam, detector, gonio, scan, sigma_divergence, mosaicity, n_sigma + 1, grid_size) # tst_conservation_of_counts(self): assert len(detector) == 1 s0 = beam.get_s0() m2 = gonio.get_rotation_axis() s0_length = matrix.col(beam.get_s0()).length() # Create an s1 map s1_map = transform.beam_vector_map(detector[0], beam, True) for i in range(100): # Get random x, y, z x = random.uniform(300, 1800) y = random.uniform(300, 1800) z = random.uniform(0, 9) # Get random s1, phi, panel s1 = matrix.col(detector[0].get_pixel_lab_coord( (x, y))).normalize() * s0_length phi = scan.get_angle_from_array_index(z, deg=False) panel = 0 # Calculate the bounding box bbox = calculate_bbox(s1, z, panel) x0, x1, y0, y1, z0, z1 = bbox # Create the coordinate system cs = CoordinateSystem(m2, s0, s1, phi) # The grid index generator step_size = delta_divergence / grid_size grid_index = transform.GridIndexGenerator(cs, x0, y0, (step_size, step_size), grid_size, s1_map) # Create the image # image = flex.double(flex.grid(z1 - z0, y1 - y0, x1 - x0), 1) image = gaussian((z1 - z0, y1 - y0, x1 - x0), 10.0, (z - z0, y - y0, x - x0), (2.0, 2.0, 2.0)) mask = flex.bool(flex.grid(image.all()), False) for j in range(y1 - y0): for i in range(x1 - x0): inside = False gx00, gy00 = grid_index(j, i) gx01, gy01 = grid_index(j, i + 1) gx10, gy10 = grid_index(j + 1, i) gx11, gy11 = grid_index(j + 1, i + 1) mingx = min([gx00, gx01, gx10, gx11]) maxgx = max([gx00, gx01, gx10, gx11]) mingy = min([gy00, gy01, gy10, gy11]) maxgy = max([gy00, gy01, gy10, gy11]) if (mingx >= 0 and maxgx < 2 * grid_size + 1 and mingy >= 0 and maxgy < 2 * grid_size + 1): inside = True for k in range(1, z1 - z0 - 1): mask[k, j, i] = inside # Transform the image to the grid transformed = transform.TransformForward(spec, cs, bbox, 0, image.as_double(), mask) grid = transformed.profile() # Get the sums and ensure they're the same eps = 1e-7 sum_grid = flex.sum(grid) sum_image = flex.sum(flex.double(flex.select(image, flags=mask))) assert abs(sum_grid - sum_image) <= eps # Test passed # tst_transform_with_background(self): assert len(detector) == 1 s0 = beam.get_s0() m2 = gonio.get_rotation_axis() s0_length = matrix.col(beam.get_s0()).length() # Create an s1 map s1_map = transform.beam_vector_map(detector[0], beam, True) for i in range(100): # Get random x, y, z x = random.uniform(300, 1800) y = random.uniform(300, 1800) z = random.uniform(0, 9) # Get random s1, phi, panel s1 = matrix.col(detector[0].get_pixel_lab_coord( (x, y))).normalize() * s0_length phi = scan.get_angle_from_array_index(z, deg=False) panel = 0 # Calculate the bounding box bbox = calculate_bbox(s1, z, panel) x0, x1, y0, y1, z0, z1 = bbox # Create the coordinate system cs = CoordinateSystem(m2, s0, s1, phi) # The grid index generator step_size = delta_divergence / grid_size grid_index = transform.GridIndexGenerator(cs, x0, y0, (step_size, step_size), grid_size, s1_map) # Create the image # image = flex.double(flex.grid(z1 - z0, y1 - y0, x1 - x0), 1) image = gaussian((z1 - z0, y1 - y0, x1 - x0), 10.0, (z - z0, y - y0, x - x0), (2.0, 2.0, 2.0)) background = flex.random_double(len(image)) background.resize(image.accessor()) mask = flex.bool(flex.grid(image.all()), False) for j in range(y1 - y0): for i in range(x1 - x0): inside = False gx00, gy00 = grid_index(j, i) gx01, gy01 = grid_index(j, i + 1) gx10, gy10 = grid_index(j + 1, i) gx11, gy11 = grid_index(j + 1, i + 1) mingx = min([gx00, gx01, gx10, gx11]) maxgx = max([gx00, gx01, gx10, gx11]) mingy = min([gy00, gy01, gy10, gy11]) maxgy = max([gy00, gy01, gy10, gy11]) if (mingx >= 0 and maxgx <= 2 * grid_size + 1 and mingy >= 0 and maxgy <= 2 * grid_size + 1): inside = True for k in range(1, z1 - z0 - 1): mask[k, j, i] = inside # Transform the image to the grid transformed = transform.TransformForward(spec, cs, bbox, 0, image.as_double(), background.as_double(), mask) igrid = transformed.profile() bgrid = transformed.background() # Get the sums and ensure they're the same eps = 1e-7 sum_igrid = flex.sum(igrid) sum_bgrid = flex.sum(bgrid) sum_image = flex.sum(flex.double(flex.select(image, flags=mask))) sum_bkgrd = flex.sum(flex.double(flex.select(background, flags=mask))) try: assert abs(sum_igrid - sum_image) <= eps assert abs(sum_bgrid - sum_bkgrd) <= eps except Exception: print("Failed for: ", (x, y, z)) raise
def test_forward_no_model(dials_data): sequence = load.sequence( dials_data("centroid_test_data").join("sweep.json").strpath) # Get the models beam = sequence.get_beam() detector = sequence.get_detector() gonio = sequence.get_goniometer() scan = sequence.get_scan() scan.set_image_range((0, 1000)) # Set some parameters sigma_divergence = beam.get_sigma_divergence(deg=False) mosaicity = 0.157 * math.pi / 180 n_sigma = 3 grid_size = 20 delta_divergence = n_sigma * sigma_divergence step_size = delta_divergence / grid_size delta_divergence2 = delta_divergence + step_size * 0.5 delta_mosaicity = n_sigma * mosaicity # Create the bounding box calculator calculate_bbox = BBoxCalculator3D(beam, detector, gonio, scan, delta_divergence2, delta_mosaicity) # Initialise the transform spec = transform.TransformSpec(beam, detector, gonio, scan, sigma_divergence, mosaicity, n_sigma + 1, grid_size) # tst_conservation_of_counts(self): random.seed(0) assert len(detector) == 1 s0 = beam.get_s0() m2 = gonio.get_rotation_axis() s0_length = matrix.col(beam.get_s0()).length() # Create an s1 map s1_map = transform.beam_vector_map(detector[0], beam, True) for i in range(100): # Get random x, y, z x = random.uniform(300, 1800) y = random.uniform(300, 1800) z = random.uniform(500, 600) # Get random s1, phi, panel s1 = matrix.col(detector[0].get_pixel_lab_coord( (x, y))).normalize() * s0_length phi = scan.get_angle_from_array_index(z, deg=False) panel = 0 # Calculate the bounding box bbox = calculate_bbox(s1, z, panel) x0, x1, y0, y1, z0, z1 = bbox # Create the coordinate system cs = CoordinateSystem(m2, s0, s1, phi) if abs(cs.zeta()) < 0.1: continue # The grid index generator step_size = delta_divergence / grid_size grid_index = transform.GridIndexGenerator(cs, x0, y0, (step_size, step_size), grid_size, s1_map) # Create the image # image = flex.double(flex.grid(z1 - z0, y1 - y0, x1 - x0), 1) image = gaussian((z1 - z0, y1 - y0, x1 - x0), 10.0, (z - z0, y - y0, x - x0), (2.0, 2.0, 2.0)) mask = flex.bool(flex.grid(image.all()), False) for j in range(y1 - y0): for i in range(x1 - x0): inside = False gx00, gy00 = grid_index(j, i) gx01, gy01 = grid_index(j, i + 1) gx10, gy10 = grid_index(j + 1, i) gx11, gy11 = grid_index(j + 1, i + 1) mingx = min([gx00, gx01, gx10, gx11]) maxgx = max([gx00, gx01, gx10, gx11]) mingy = min([gy00, gy01, gy10, gy11]) maxgy = max([gy00, gy01, gy10, gy11]) if (mingx >= 0 and maxgx < 2 * grid_size + 1 and mingy >= 0 and maxgy < 2 * grid_size + 1): inside = True for k in range(1, z1 - z0 - 1): mask[k, j, i] = inside # Transform the image to the grid transformed = transform.TransformForwardNoModel( spec, cs, bbox, 0, image.as_double(), mask) grid = transformed.profile() # Get the sums and ensure they're the same eps = 1e-7 sum_grid = flex.sum(grid) sum_image = flex.sum(flex.double(flex.select(image, flags=mask))) assert abs(sum_grid - sum_image) <= eps mask = flex.bool(flex.grid(image.all()), True) transformed = transform.TransformForwardNoModel( spec, cs, bbox, 0, image.as_double(), mask) grid = transformed.profile() # Boost the bbox to make sure all intensity is included x0, x1, y0, y1, z0, z1 = bbox bbox2 = (x0 - 10, x1 + 10, y0 - 10, y1 + 10, z0 - 10, z1 + 10) # Do the reverse transform transformed = transform.TransformReverseNoModel( spec, cs, bbox2, 0, grid) image2 = transformed.profile() # Check the sum of pixels are the same sum_grid = flex.sum(grid) sum_image = flex.sum(image2) assert abs(sum_grid - sum_image) <= eps # Do the reverse transform transformed = transform.TransformReverseNoModel( spec, cs, bbox, 0, grid) image2 = transformed.profile() from dials.algorithms.statistics import pearson_correlation_coefficient cc = pearson_correlation_coefficient(image.as_1d().as_double(), image2.as_1d()) assert cc >= 0.99
def test_map_frames_forward(dials_data): from dials.model.serialize import load from dials.algorithms.profile_model.gaussian_rs.transform import MapFramesForward from dials.algorithms.profile_model.gaussian_rs import BBoxCalculator3D sequence = load.sequence( dials_data("centroid_test_data").join("sweep.json").strpath) # Get the models beam = sequence.get_beam() detector = sequence.get_detector() gonio = sequence.get_goniometer() scan = sequence.get_scan() # Set the delta_divergence/mosaicity n_sigma = 3 sigma_divergence = 0.060 * math.pi / 180 mosaicity = 0.154 * math.pi / 180 delta_divergence = n_sigma * sigma_divergence delta_mosaicity = n_sigma * mosaicity # Set the grid size grid_size = (4, 4, 4) # Create the E3 fraction object transform = MapFramesForward( scan.get_array_range()[0], scan.get_oscillation(deg=False)[0], scan.get_oscillation(deg=False)[1], mosaicity, n_sigma, grid_size[2], ) # Create the bounding box calculator calculate_bbox = BBoxCalculator3D(beam, detector, gonio, scan, delta_divergence, delta_mosaicity) from dials.algorithms.profile_model.gaussian_rs import CoordinateSystem from scitbx.array_family import flex assert len(detector) == 1 s0 = beam.get_s0() m2 = gonio.get_rotation_axis() s0_length = matrix.col(beam.get_s0()).length() for i in range(100): # Get random x, y, z x = random.uniform(0, 2000) y = random.uniform(0, 2000) z = random.uniform(0, 9) # Get random s1, phi, panel s1 = matrix.col(detector[0].get_pixel_lab_coord( (x, y))).normalize() * s0_length phi = scan.get_angle_from_array_index(z, deg=False) panel = 0 # Calculate the bounding box bbox = calculate_bbox(s1, z, panel) # Create the XDS coordinate system xcs = CoordinateSystem(m2, s0, s1, phi) # Calculate the transform fraction fraction = transform(bbox[4:], phi, xcs.zeta()) # Ensure the minimum and maximum are 0 < 1 fmax = flex.max(fraction) fmin = flex.min(fraction) assert fmax <= ( 1.0 + 5e-15) and fmax > 0.0, "%.16f not between 0 and 1" % fmax assert fmin >= 0.0 and fmin <= 1.0 # Ensure the fraction for each image frame adds up to 1.0 for # all those frames completely within the grid for j in range(1, fraction.all()[0] - 1): tot = flex.sum(fraction[j:j + 1, :]) assert abs(tot - 1.0) < 1e-7 # Ensure the frames follow a progression through the grid. I.e, # check that values increase then decrease and don't jump around for j in range(fraction.all()[0]): f = fraction[j:j + 1, :] last = f[0] rev = False for i in range(1, len(f)): curr = f[1] if rev is False: if curr < last: rev = True else: assert curr <= last last = curr
def test_map_forward_reverse(dials_data): from dials.model.serialize import load from dials.algorithms.profile_model.gaussian_rs.transform import MapFramesReverse from dials.algorithms.profile_model.gaussian_rs.transform import MapFramesForward from dials.algorithms.profile_model.gaussian_rs import BBoxCalculator3D sequence = load.sequence( dials_data("centroid_test_data").join("sweep.json").strpath) # Get the models beam = sequence.get_beam() detector = sequence.get_detector() gonio = sequence.get_goniometer() scan = sequence.get_scan() # Set the delta_divergence/mosaicity n_sigma = 3 sigma_divergence = 0.060 * math.pi / 180 mosaicity = 0.154 * math.pi / 180 delta_divergence = n_sigma * sigma_divergence delta_mosaicity = n_sigma * mosaicity # Set the grid size grid_size = (4, 4, 4) # Create the E3 fraction object transform_forward = MapFramesForward( scan.get_array_range()[0], scan.get_oscillation(deg=False)[0], scan.get_oscillation(deg=False)[1], mosaicity, n_sigma, grid_size[2], ) # Create the E3 fraction object transform_reverse = MapFramesReverse( scan.get_array_range()[0], scan.get_oscillation(deg=False)[0], scan.get_oscillation(deg=False)[1], mosaicity, n_sigma, grid_size[2], ) # Create the bounding box calculator calculate_bbox = BBoxCalculator3D(beam, detector, gonio, scan, delta_divergence, delta_mosaicity) from dials.algorithms.profile_model.gaussian_rs import CoordinateSystem s0 = beam.get_s0() m2 = gonio.get_rotation_axis() s0_length = matrix.col(beam.get_s0()).length() for i in range(100): # Get random x, y, z x = random.uniform(0, 2000) y = random.uniform(0, 2000) z = random.uniform(0, 9) # Get random s1, phi, panel s1 = matrix.col(detector[0].get_pixel_lab_coord( (x, y))).normalize() * s0_length phi = scan.get_angle_from_array_index(z, deg=False) panel = 0 # Calculate the bounding box bbox = calculate_bbox(s1, phi, panel) # Create the XDS coordinate system xcs = CoordinateSystem(m2, s0, s1, phi) # Calculate the transform fraction forward_fraction = transform_forward(bbox[4:], phi, xcs.zeta()) # Calculate the transform fraction reverse_fraction = transform_reverse(bbox[4:], phi, xcs.zeta()) # Check the same points are non-zero eps = 1e-7 for j in range(forward_fraction.all()[0]): for i in range(forward_fraction.all()[1]): if forward_fraction[j, i] > 0.0: assert reverse_fraction[i, j] > 0.0 else: assert reverse_fraction[i, j] < eps