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Dev/rosenblatttransform (#241)

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- added rosenblatttransform 
- added 3x3 eta methods 
- interpolation can be used with various eta functions
- added documentation for interpolation, eta calculation 
- exposed full eta struct in python 
- disable ClusterFinder for 2x2 clusters 
- factory function for ClusterVector

---------

Co-authored-by: Dhanya Thattil <dhanya.thattil@psi.ch>
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
This commit is contained in:
mazzol_a
2025-11-21 14:48:46 +01:00
committed by GitHub
parent 7fb500c44c
commit 267ca87ab0
49 changed files with 3253 additions and 1172 deletions
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29
python/tests/test_Interpolation.py
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@@ -24,10 +24,6 @@ def create_photon_hit_with_gaussian_distribution(mean, covariance_matrix, data_p
probability_values = gaussian.pdf(data_points)
return (probability_values.reshape(X.shape)).round() #python bindings only support frame types of uint16_t
def photon_hit_in_euclidean_space(cluster_center, pixels_per_superpixel, photon_hit):
scaled_photon_hit_x = cluster_center - (1 - photon_hit[0][0])*pixels_per_superpixel*pixel_width
scaled_photon_hit_y = cluster_center - (1 - photon_hit[0][1])*pixels_per_superpixel*pixel_width
return (scaled_photon_hit_x, scaled_photon_hit_y)
def create_2x2cluster_from_frame(frame, pixels_per_superpixel):
return Cluster2x2d(1, 1, np.array([frame[0:pixels_per_superpixel, 0:pixels_per_superpixel].sum(),
@@ -48,10 +44,10 @@ def create_3x3cluster_from_frame(frame, pixels_per_superpixel):
frame[2*pixels_per_superpixel:3*pixels_per_superpixel, 2*pixels_per_superpixel:3*pixels_per_superpixel].sum()], dtype=np.float64))
def calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, cluster_2x2 = True):
def calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, bin_edges_x = bh.axis.Regular(100, -0.2, 1.2), bin_edges_y = bh.axis.Regular(100, -0.2, 1.2), cluster_2x2 = True):
hist = bh.Histogram(
bh.axis.Regular(100, -0.2, 1.2),
bh.axis.Regular(100, -0.2, 1.2), bh.axis.Regular(1, 0, num_pixels*num_pixels*1/(variance*2*np.pi)))
bin_edges_x,
bin_edges_y, bh.axis.Regular(1, 0, num_pixels*num_pixels*1/(variance*2*np.pi)))
for _ in range(0, num_frames):
mean_x = random_number_generator.uniform(pixels_per_superpixel*pixel_width, 2*pixels_per_superpixel*pixel_width)
@@ -66,7 +62,7 @@ def calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_
cluster = create_3x3cluster_from_frame(frame, pixels_per_superpixel)
eta2 = calculate_eta2(cluster)
hist.fill(eta2[0], eta2[1], eta2[2])
hist.fill(eta2.x, eta2.y, eta2.sum)
return hist
@@ -85,9 +81,9 @@ def test_interpolation_of_2x2_cluster(test_data_path):
pixels_per_superpixel = int(num_pixels*0.5)
random_number_generator = np.random.default_rng(42)
eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator)
eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, bin_edges_x = bh.axis.Regular(100, -0.1, 0.6), bin_edges_y = bh.axis.Regular(100, -0.1, 0.6))
interpolation = Interpolator(eta_distribution, eta_distribution.axes[0].edges[:-1], eta_distribution.axes[1].edges[:-1], eta_distribution.axes[2].edges[:-1])
interpolation = Interpolator(eta_distribution, eta_distribution.axes[0].edges, eta_distribution.axes[1].edges, eta_distribution.axes[2].edges)
#actual photon hit
mean = 1.2*pixels_per_superpixel*pixel_width
@@ -104,7 +100,7 @@ def test_interpolation_of_2x2_cluster(test_data_path):
cluster_center = 1.5*pixels_per_superpixel*pixel_width
scaled_photon_hit = photon_hit_in_euclidean_space(cluster_center, pixels_per_superpixel, interpolated_photon)
scaled_photon_hit = (interpolated_photon[0][0]*pixels_per_superpixel*pixel_width, interpolated_photon[0][1]*pixels_per_superpixel*pixel_width)
assert (np.linalg.norm(scaled_photon_hit - mean) < np.linalg.norm(np.array([cluster_center, cluster_center] - mean)))
@@ -123,13 +119,14 @@ def test_interpolation_of_3x3_cluster(test_data_path):
num_frames = 1000
pixels_per_superpixel = int(num_pixels/3)
random_number_generator = np.random.default_rng(42)
eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, False)
eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, bin_edges_x = bh.axis.Regular(100, -0.1, 1.1), bin_edges_y = bh.axis.Regular(100, -0.1, 1.1), cluster_2x2 = False)
interpolation = Interpolator(eta_distribution, eta_distribution.axes[0].edges[:-1], eta_distribution.axes[1].edges[:-1], eta_distribution.axes[2].edges[:-1])
interpolation = Interpolator(eta_distribution, eta_distribution.axes[0].edges, eta_distribution.axes[1].edges, eta_distribution.axes[2].edges)
#actual photon hit
mean = 1.2*pixels_per_superpixel*pixel_width
mean = np.array([mean, mean])
mean_x = (1 + 0.8)*pixels_per_superpixel*pixel_width
mean_y = (1 + 0.2)*pixels_per_superpixel*pixel_width
mean = np.array([mean_x, mean_y])
frame = create_photon_hit_with_gaussian_distribution(mean, covariance_matrix, data_points)
cluster = create_3x3cluster_from_frame(frame, pixels_per_superpixel)
@@ -142,7 +139,7 @@ def test_interpolation_of_3x3_cluster(test_data_path):
cluster_center = 1.5*pixels_per_superpixel*pixel_width
scaled_photon_hit = photon_hit_in_euclidean_space(cluster_center, pixels_per_superpixel, interpolated_photon)
scaled_photon_hit = (interpolated_photon[0][0]*pixels_per_superpixel*pixel_width, interpolated_photon[0][1]*pixels_per_superpixel*pixel_width)
assert (np.linalg.norm(scaled_photon_hit - mean) < np.linalg.norm(np.array([cluster_center, cluster_center] - mean)))