aare/python/tests/test_interpolation.ipynb at c20a94015cf4ade6d295e4eda4041ddb14c97d4a

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CI/CD: Integrate pre-commit hooks and GitHub Actions workflow (#303 )

To improve codebase quality and reduce human error, this PR introduces
the pre-commit framework. This ensures that all code adheres to project
standards before it is even committed, maintaining a consistent style
and catching common mistakes early.

Key Changes:

- Code Formatting: Automated C++ formatting using clang-format (based on
the project's .clang-format file).
- Syntax Validation: Basic checks for file integrity and syntax.
- Spell Check: Automated scanning for typos in source code and comments.
- CMake Formatting: Standardization of CMakeLists.txt and .cmake
configuration files.
- GitHub Workflow: Added a CI action that validates every Pull Request
against the pre-commit configuration to ensure compliance.

The configuration includes a [ci] block to handle automated fixes within
the PR. Currently, this is disabled. If we want the CI to automatically
commit formatting fixes back to the PR branch, this can be toggled to
true in .pre-commit-config.yaml.

```yaml
ci:
  autofix_commit_msg: [pre-commit] auto fixes from pre-commit hooks
  autofix_prs: false
  autoupdate_schedule: monthly
```

The last large commit with the fit functions, for example, was not
formatted according to the clang-format rules. This PR would allow to
avoid similar mistakes in the future.

Python fomat with `ruff` for tests and sanitiser for `.ipynb` notebooks
can be added as well.

2026-04-14 11:52:23 +02:00

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In [1]:

import numpy as np 
from scipy.stats import multivariate_normal
import matplotlib.pyplot as plt
from aare import ClusterFinder, calculate_eta2, Interpolator
import boost_histogram as bh
from aare._aare import Cluster2x2d, ClusterVector_Cluster2x2d
import pickle
import os

from test_Interpolation import create_photon_hit_with_gaussian_distribution, create_2x2cluster_from_frame, create_3x3cluster_from_frame, calculate_eta_distribution, photon_hit_in_euclidean_space

In [2]:

### plotting functions

def plot_eta_distribution(eta_distribution):
    plt.imshow(
    eta_distribution[:, :, 0].view().transpose(),   
    origin='lower',                  
    extent=[eta_distribution.axes[0].edges[0], eta_distribution.axes[0].edges[-1], eta_distribution.axes[1].edges[0], eta_distribution.axes[1].edges[-1]],
    aspect='auto')
    plt.colorbar()
    plt.xlabel('eta_x')
    plt.ylabel('eta_y')

def plot_uniform_eta_distribution(uniform_etax, uniform_etay, eta_extent): 
    fig, axes = plt.subplots(1, 2, figsize=(10, 5))  # (rows, cols)

    im1 = axes[0].imshow(
    uniform_etax[:, :, 0].view().transpose(),   
    origin='lower',                  
    extent=[eta_extent[0], eta_extent[1], eta_extent[0], eta_extent[1]],
    aspect='auto'
    )
    axes[0].set_title("uniform distribution of etax")
    fig.colorbar(im1, ax=axes[0], fraction=0.046, pad=0.04)
    axes[0].set_xlabel("eta_x")
    axes[0].set_ylabel("eta_y")

    im2 = axes[1].imshow(
    uniform_etay[:, :, 0].view().transpose(),   
    origin='lower',                  
    extent=[eta_extent[0], eta_extent[1], eta_extent[0], eta_extent[1]],
    aspect='auto'
    )
    axes[1].set_title("uniform distribution of etay")
    fig.colorbar(im2, ax=axes[1], fraction=0.046, pad=0.04)
    axes[1].set_xlabel("eta_x")
    axes[1].set_ylabel("eta_y")

    plt.tight_layout()
    plt.show()

def plot_photon_hit_distribution(eta_distribution, marg_CDF_etax, cond_CDF_etay): 
    normalized_eta_distribution = eta_distribution.values()[:,:, 0] / eta_distribution.values()[: , :, 0].sum() #normnalize 

    n_samples = 1000
    rng = np.random.default_rng(42)

    flat_indices = rng.choice(
        normalized_eta_distribution.size,
        size=n_samples,
        p=normalized_eta_distribution.ravel())

    x_indices, y_indices = np.unravel_index(flat_indices, normalized_eta_distribution.shape)

    photon_position_x = marg_CDF_etax[x_indices, y_indices]
    photon_position_y = cond_CDF_etay[x_indices, y_indices]

    fig, axes = plt.subplots(1, 2, figsize=(10, 5))  # (rows, cols)

    axes[0].scatter(eta_distribution.axes[0].edges[:-1][x_indices], eta_distribution.axes[1].edges[:-1][y_indices], s=1, alpha=0.5)
    axes[0].set_title("Eta distribution (eta_x, eta_y)")
    #plt.gca().set_aspect('equal')
    axes[0].set_xlabel("eta_x")
    axes[0].set_ylabel("eta_y")
    #axes[0].set_xlim(0,1)
    #axes[0].set_ylim(0,1)

    axes[1].scatter(photon_position_x, photon_position_y, s=1, alpha=0.5)
    axes[1].set_title("uniform Photon positions")
    #plt.gca().set_aspect('equal')
    axes[1].set_xlabel("x")
    axes[1].set_ylabel("y")
    #axes[1].set_xlim(0,1)
    #axes[1].set_ylim(0,1)

In [3]:

pixel_width = 1e-4

values = np.arange(0.5*pixel_width, 0.1, pixel_width)

num_pixels = values.size

print(f"num pixels: ({values.size} x {values.size})")

X, Y = np.meshgrid(values, values)

data_points = np.stack([X.ravel(), Y.ravel()], axis=1)

num pixels: (1000 x 1000)

In [4]:

# mean is in bottom right quadrant 
variance = 10*pixel_width

covariance_matrix = np.array([[variance, 0], [0, variance]])

mean = 650*pixel_width

mean = np.array([mean, mean])

base_frame = create_photon_hit_with_gaussian_distribution(mean, variance, data_points)

pixels_per_superpixel = int(num_pixels*0.5)

plt.imshow(base_frame) 

plt.colorbar()

plt.axvline(x=pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)
plt.axhline(y=pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)

plt.xlabel('x_0')
plt.ylabel('y_0')

Out [4]:

Text(0, 0.5, 'y_0')

In [5]:

### create eta distribution
num_frames = 5000
random_number_generator = np.random.default_rng(42)
eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, bh.axis.Regular(100, -0.1, 0.6), bh.axis.Regular(100, -0.1, 0.6))

test_data_path = os.getenv("AARE_TEST_DATA") + "/eta_distributions"
filename = test_data_path + "/eta_distribution_2x2cluster_gaussian.pkl"
with open(filename, "wb") as f:
    pickle.dump(eta_distribution, f)

In [7]:

filename = os.getenv("AARE_TEST_DATA") + "/eta_distributions/eta_distribution_2x2cluster_gaussian.pkl"
with open(filename, "rb") as f:
        eta_distribution = pickle.load(f)

In [8]:

plot_eta_distribution(eta_distribution)

In [9]:

interpolator = Interpolator(eta_distribution, eta_distribution.axes[0].edges, eta_distribution.axes[1].edges, eta_distribution.axes[2].edges[:-1])
#interpolator = Interpolator(eta_distribution.axes[0].edges, eta_distribution.axes[1].edges, eta_distribution.axes[2].edges[:-1])

#interpolator.rosenblatttransform(eta_distribution.values())

In [18]:

marg_CDF_etax = interpolator.get_ietax()
cond_CDF_etay = interpolator.get_ietay()

plot_photon_hit_distribution(eta_distribution, marg_CDF_etax, cond_CDF_etay)

In [10]:

marg_CDF_etax = interpolator.get_ietax()
cond_CDF_etay = interpolator.get_ietay()

#plot_uniform_eta_distribution(uniform_etax, uniform_etay, [eta_distribution.axes[0].edges[0], eta_distribution.axes[0].edges[-1]])

In [11]:


cluster = create_2x2cluster_from_frame(base_frame, pixels_per_superpixel)

print("cluster.x:", cluster.x)
print("cluster.y:", cluster.y)
clustervec = ClusterVector_Cluster2x2d()
clustervec.push_back(cluster)

eta = calculate_eta2(cluster)
print("eta: ", eta)
bin_size = (eta_distribution.axes[0].edges[-1] - eta_distribution.axes[0].edges[0])/eta_distribution.axes[0].edges.shape[0]

bin_index_x = int((eta[0] - eta_distribution.axes[0].edges[0])/bin_size)
bin_index_y = int((eta[1] - eta_distribution.axes[1].edges[0])/bin_size)

print("distance x:", marg_CDF_etax[bin_index_x, bin_index_y, 0])
print("distance y:", cond_CDF_etay[bin_index_x, bin_index_y, 0])

photon_hit = interpolator.interpolate(clustervec)

print(photon_hit)

cluster.x: 1
cluster.y: 1
eta:  (0.3519491547025806, 0.3519491547025806, 71553511.0)
distance x: 0.7083333333333334
distance y: 0.7564102564102564
[(0.78166653, 0.81284991, 71553511.)]

In [12]:

# scale to cluster pixel width 
#cluster_center = num_pixels*0.75*pixel_width
cluster_center = (pixels_per_superpixel*pixel_width*(cluster.x + 0.5), pixels_per_superpixel*pixel_width*(cluster.y + 0.5))
#scaled_photon_hit = photon_hit_in_euclidean_space(cluster_center, pixels_per_superpixel, photon_hit)

print(photon_hit[0])

scaled_photon_hit = ((photon_hit[0][0])*pixels_per_superpixel*pixel_width, (photon_hit[0][1])*pixels_per_superpixel*pixel_width)

print(f"previous center: ({cluster_center[0]}, {cluster_center[1]})")
print(f"interpolated center: ({photon_hit[0][0]},{photon_hit[0][1]})")
print(f"scaled interpolated center: ({scaled_photon_hit[0]},{scaled_photon_hit[1]})")
print(f"actual center: ({mean},{mean})")

(0.7816665310999981, 0.8128499138725078, 71553511.0)
previous center: (0.07500000000000001, 0.07500000000000001)
interpolated center: (0.7816665310999981,0.8128499138725078)
scaled interpolated center: (0.03908332655499991,0.040642495693625394)
actual center: ([0.065 0.065],[0.065 0.065])

In [13]:

plt.imshow(base_frame) 

plt.colorbar()

plt.axvline(x=pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)
plt.axhline(y=pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)

plt.scatter(mean/pixel_width, mean/pixel_width, color='red', s=40, marker='x', label='Actual photon hit')
plt.scatter(scaled_photon_hit[0]/pixel_width, scaled_photon_hit[1]/pixel_width, color = 'blue', s=40, marker='x', label='interpolated photon hit')

plt.xlabel('x_0')
plt.ylabel('y_0')

Out [13]:

Text(0, 0.5, 'y_0')

3x3 Cluster

In [14]:

from aare._aare import Cluster3x3d, ClusterVector_Cluster3x3d

In [15]:

# mean is in center quadrant 
variance = 10*pixel_width

covariance_matrix = np.array([[variance, 0], [0, variance]])

mean_x = (1 + 0.8)*(num_pixels/3)*pixel_width
mean_y = (1 + 0.2)*(num_pixels/3)*pixel_width

mean = np.array([mean_x, mean_y])

base_frame = create_photon_hit_with_gaussian_distribution(mean, variance, data_points)

pixels_per_superpixel = int(num_pixels/3)

plt.imshow(base_frame) 

plt.colorbar()

plt.axvline(x=pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)
plt.axhline(y=pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)
plt.axvline(x=2*pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)
plt.axhline(y=2*pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)

plt.xlabel('x_0')
plt.ylabel('y_0')

Out [15]:

Text(0, 0.5, 'y_0')

In [ ]:

### calculate eta distribution

num_frames = 5000

random_number_generator = np.random.default_rng(42)
eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, bh.axis.Regular(100, -0.1, 1.0), bh.axis.Regular(100, -0.1, 1.0), False)

filename = os.getenv("AARE_TEST_DATA") + "/eta_distributions/eta_distribution_3x3cluster_gaussian.pkl"
with open(filename, "wb") as f:
    pickle.dump(eta_distribution, f)

In [ ]:

filename = os.getenv("AARE_TEST_DATA") + "/eta_distributions/eta_distribution_3x3cluster_gaussian.pkl"
with open(filename, "rb") as f:
        eta_distribution = pickle.load(f)

In [7]:

plot_eta_distribution(eta_distribution)

In [ ]:

In [14]:

#interpolator = Interpolator(eta_distribution, eta_distribution.axes[0].edges, eta_distribution.axes[1].edges, eta_distribution.axes[2].edges[:-1])

interpolator = Interpolator(eta_distribution.axes[0].edges, eta_distribution.axes[1].edges, eta_distribution.axes[2].edges[:-1])
interpolator.rosenblatttransform(eta_distribution.values())
marg_CDF_etax = interpolator.get_ietax()
cond_CDF_etay = interpolator.get_ietay()

In [15]:


plot_photon_hit_distribution(eta_distribution, marg_CDF_etax, cond_CDF_etay)

In [ ]:



plot_uniform_eta_distribution(uniform_etax, uniform_etay, [eta_distribution.axes[0].edges[0], eta_distribution.axes[0].edges[-1]])

In [16]:


cluster = create_3x3cluster_from_frame(base_frame, pixels_per_superpixel)

eta = calculate_eta2(cluster)
print(eta)
print(eta_distribution.axes[0].edges)
clustervec = ClusterVector_Cluster3x3d()
#print(marg_CDF_etax)
print(marg_CDF_etax[93, 0,0])
clustervec.push_back(cluster)



photon_hit = interpolator.interpolate(clustervec)

(0.5499154381017892, 0.44933336606613455, 48591988.0)
[-0.1   -0.093 -0.086 -0.079 -0.072 -0.065 -0.058 -0.051 -0.044 -0.037
 -0.03  -0.023 -0.016 -0.009 -0.002  0.005  0.012  0.019  0.026  0.033
  0.04   0.047  0.054  0.061  0.068  0.075  0.082  0.089  0.096  0.103
  0.11   0.117  0.124  0.131  0.138  0.145  0.152  0.159  0.166  0.173
  0.18   0.187  0.194  0.201  0.208  0.215  0.222  0.229  0.236  0.243
  0.25   0.257  0.264  0.271  0.278  0.285  0.292  0.299  0.306  0.313
  0.32   0.327  0.334  0.341  0.348  0.355  0.362  0.369  0.376  0.383
  0.39   0.397  0.404  0.411  0.418  0.425  0.432  0.439  0.446  0.453
  0.46   0.467  0.474  0.481  0.488  0.495  0.502  0.509  0.516  0.523
  0.53   0.537  0.544  0.551  0.558  0.565  0.572  0.579  0.586  0.593
  0.6  ]
0.9999999999999998
dX: 1
dY: 0
u: 1
v: 0

In [17]:

## scale to cluster pixel width 
#cluster_center = (1+0.5)*pixels_per_superpixel*pixel_width

cluster_center = (pixels_per_superpixel*pixel_width*(cluster.x + 0.5), pixels_per_superpixel*pixel_width*(cluster.y + 0.5))

print(photon_hit[0])

scaled_photon_hit = (photon_hit[0][0]*pixels_per_superpixel*pixel_width, photon_hit[0][1]*pixels_per_superpixel*pixel_width)

print(f"previous center: ({cluster_center[0]}, {cluster_center[1]})")
print(f"interpolated center: ({photon_hit[0][0]},{photon_hit[0][1]})")
print(f"scaled interpolated center: ({scaled_photon_hit[0]},{scaled_photon_hit[1]})")
print(f"actual center: ({mean},{mean})")

(1.5000000000000002, 1.5, 48591988.0)
previous center: (0.04995000000000001, 0.04995000000000001)
interpolated center: (1.5000000000000002,1.5)
scaled interpolated center: (0.04995000000000001,0.04995)
actual center: ([0.06 0.04],[0.06 0.04])

In [18]:

plt.imshow(base_frame) 

plt.colorbar()

plt.axvline(x=pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)
plt.axhline(y=pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)
plt.axvline(x=2*pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)
plt.axhline(y=2*pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)

plt.scatter(mean_x/pixel_width, mean_y/pixel_width, color='red', s=40, marker='x', label='Actual photon hit')
plt.scatter(scaled_photon_hit[0]/pixel_width, scaled_photon_hit[1]/pixel_width, color = 'blue', s=40, marker='x', label='interpolated photon hit')

plt.xlabel('x_0')
plt.ylabel('y_0')

Out [18]:

Text(0, 0.5, 'y_0')

Interpolation with Eta3

In [6]:

from aare._aare import Cluster3x3d, ClusterVector_Cluster3x3d, calculate_eta3, calculate_cross_eta3

In [4]:

# mean is in center quadrant 
variance = 10*pixel_width

covariance_matrix = np.array([[variance, 0], [0, variance]])

mean = (1 + 0.3)*(num_pixels/3)*pixel_width

mean = np.array([mean, mean])

base_frame = create_photon_hit_with_gaussian_distribution(mean, variance, data_points)

pixels_per_superpixel = int(num_pixels/3)

plt.imshow(base_frame) 

plt.colorbar()

plt.axvline(x=pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)
plt.axhline(y=pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)
plt.axvline(x=2*pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)
plt.axhline(y=2*pixels_per_superpixel, color='black', linestyle='--', linewidth=0.5)

plt.xlabel('x_0')
plt.ylabel('y_0')

Out [4]:

Text(0, 0.5, 'y_0')

In [7]:

num_frames = 2000

random_number_generator = np.random.default_rng(42)
eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, calculate_eta3, False)

In [8]:

plot_eta_distribution(eta_distribution)

In [9]:

num_frames = 2000

random_number_generator = np.random.default_rng(42)
eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, calculate_cross_eta3, False)

In [10]:

plot_eta_distribution(eta_distribution)

In [ ]:

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3x3 Cluster

Interpolation with Eta3

923 KiB

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