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adresses SonarQube comments

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mazzol_a
2025-10-08 18:19:17 +02:00
parent 5329be816e
commit 516ef88d10
3 changed files with 15 additions and 9 deletions
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2
include/aare/CalculateEta.hpp
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@@ -59,7 +59,7 @@ template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
Eta2<T> Eta2<T>
calculate_eta2(const Cluster<T, ClusterSizeX, ClusterSizeY, CoordType> &cl) { calculate_eta2(const Cluster<T, ClusterSizeX, ClusterSizeY, CoordType> &cl) {
assert(ClusterSizeX > 1 && ClusterSizeY > 1); static_assert(ClusterSizeX > 1 && ClusterSizeY > 1);
Eta2<T> eta{}; Eta2<T> eta{};
size_t cluster_center_index = size_t cluster_center_index =

17
python/tests/test_Interpolation.py
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@@ -17,6 +17,8 @@ data_points = np.stack([X.ravel(), Y.ravel()], axis=1)
variance = 10*pixel_width variance = 10*pixel_width
covariance_matrix = np.array([[variance, 0],[0, variance]]) covariance_matrix = np.array([[variance, 0],[0, variance]])
def create_photon_hit_with_gaussian_distribution(mean, covariance_matrix, data_points): def create_photon_hit_with_gaussian_distribution(mean, covariance_matrix, data_points):
gaussian = multivariate_normal(mean=mean, cov=covariance_matrix) gaussian = multivariate_normal(mean=mean, cov=covariance_matrix)
probability_values = gaussian.pdf(data_points) probability_values = gaussian.pdf(data_points)
@@ -46,14 +48,14 @@ 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)) 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, cluster_2x2 = True): def calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, cluster_2x2 = True):
hist = bh.Histogram( hist = bh.Histogram(
bh.axis.Regular(100, -0.2, 1.2), 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))) bh.axis.Regular(100, -0.2, 1.2), bh.axis.Regular(1, 0, num_pixels*num_pixels*1/(variance*2*np.pi)))
for frame_index in range(0, num_frames): for _ in range(0, num_frames):
mean_x = np.random.uniform(pixels_per_superpixel*pixel_width, 2*pixels_per_superpixel*pixel_width) mean_x = random_number_generator.uniform(pixels_per_superpixel*pixel_width, 2*pixels_per_superpixel*pixel_width)
mean_y = np.random.uniform(pixels_per_superpixel*pixel_width, 2*pixels_per_superpixel*pixel_width) mean_y = random_number_generator.uniform(pixels_per_superpixel*pixel_width, 2*pixels_per_superpixel*pixel_width)
frame = create_photon_hit_with_gaussian_distribution(np.array([mean_x, mean_y]), variance, data_points) frame = create_photon_hit_with_gaussian_distribution(np.array([mean_x, mean_y]), variance, data_points)
cluster = None cluster = None
@@ -81,7 +83,9 @@ def test_interpolation_of_2x2_cluster(test_data_path):
num_frames = 1000 num_frames = 1000
pixels_per_superpixel = int(num_pixels*0.5) pixels_per_superpixel = int(num_pixels*0.5)
eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel) random_number_generator = np.random.default_rng(42)
eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator)
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[:-1], eta_distribution.axes[1].edges[:-1], eta_distribution.axes[2].edges[:-1])
@@ -118,7 +122,8 @@ def test_interpolation_of_3x3_cluster(test_data_path):
num_frames = 1000 num_frames = 1000
pixels_per_superpixel = int(num_pixels/3) pixels_per_superpixel = int(num_pixels/3)
eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, False) random_number_generator = np.random.default_rng(42)
eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, 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[:-1], eta_distribution.axes[1].edges[:-1], eta_distribution.axes[2].edges[:-1])

5
src/ClusterFile.test.cpp
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@@ -279,7 +279,8 @@ TEST_CASE("Read cluster from multiple frame file", "[.with-data]") {
} }
} }
TEST_CASE("Write cluster with potential padding", "[.with-data][.ClusterFile]") { TEST_CASE("Write cluster with potential padding",
"[.with-data][.ClusterFile]") {
using ClusterType = Cluster<double, 3, 3>; using ClusterType = Cluster<double, 3, 3>;
@@ -290,7 +291,7 @@ TEST_CASE("Write cluster with potential padding", "[.with-data][.ClusterFile]")
ClusterFile<ClusterType> file(fpath, 1000, "w"); ClusterFile<ClusterType> file(fpath, 1000, "w");
ClusterVector<ClusterType> clustervec(2); ClusterVector<ClusterType> clustervec(2);
int16_t coordinate = 5; uint16_t coordinate = 5;
clustervec.push_back(ClusterType{ clustervec.push_back(ClusterType{
coordinate, coordinate, {0., 0., 0., 0., 0., 0., 0., 0., 0.}}); coordinate, coordinate, {0., 0., 0., 0., 0., 0., 0., 0., 0.}});
clustervec.push_back(ClusterType{ clustervec.push_back(ClusterType{