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https://github.com/slsdetectorgroup/aare.git
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AliceMazzoleni99
e795310b16
- fixed failed tests - removed import of pickle, scipy - still requires boost_histogram, pytest_check Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
290 lines
11 KiB
Python
290 lines
11 KiB
Python
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#test script to test interpolation on simulated data
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import pytest
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import pytest_check as check
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import numpy as np
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import boost_histogram as bh
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from aare import Interpolator, calculate_eta2, calculate_cross_eta3, calculate_full_eta2, calculate_eta3
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from aare import ClusterFile
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from conftest import test_data_path
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## TODO: is there something like a test fixture setup/teardown in pytest?
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def calculate_eta_distribution(cv, calculate_eta, edges_x=[-0.5,0.5], edges_y=[-0.5,0.5], nbins = 101):
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energy_bins = bh.axis.Regular(1, 0, 16) # max and min energy of simulated photons
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eta_distribution = bh.Histogram(
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bh.axis.Regular(nbins, edges_x[0], edges_x[1]),
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bh.axis.Regular(nbins, edges_y[0], edges_y[1]), energy_bins)
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eta = calculate_eta(cv)
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eta_distribution.fill(eta['x'], eta['y'], eta['sum'])
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return eta_distribution
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@pytest.fixture
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def load_data(test_data_path):
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"""Load simulated cluster data and ground truth positions"""
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f = ClusterFile(test_data_path / "clust" / "simulated_clusters.clust", dtype=np.float64, mode="r")
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cv = f.read_frame()
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ground_truths = np.load(test_data_path / "interpolation/ground_truth_simulated.npy")
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return cv, ground_truths
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@pytest.mark.withdata
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def test_eta2_interpolation(load_data, check):
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"""Test eta2 interpolation on simulated data"""
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cv, ground_truths = load_data
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num_bins = 201
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eta_distribution = calculate_eta_distribution(cv, calculate_eta2, edges_x=[-0.1,1.1], edges_y=[-0.1,1.1], nbins=num_bins)
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interpolator = Interpolator(eta_distribution, eta_distribution.axes[0].edges, eta_distribution.axes[1].edges, eta_distribution.axes[2].edges)
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assert interpolator.get_ietax().shape == (num_bins,num_bins,1)
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assert interpolator.get_ietay().shape == (num_bins,num_bins,1)
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interpolated_photons = interpolator.interpolate(cv)
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assert interpolated_photons.size == cv.size
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interpolated_photons["x"] += 1.0 #groud truth label uses 5x5 clusters
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interpolated_photons["y"] += 1.0
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residuals_interpolated_x = abs(ground_truths[:, 0] - interpolated_photons["x"])
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residuals_interpolated_y = abs(ground_truths[:, 1] - interpolated_photons["y"])
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"""
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residuals_center_pixel_x = abs(ground_truths[:, 0] - 2.5)
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residuals_center_pixel_y = abs(ground_truths[:, 1] - 2.5)
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# interpolation needs to perform better than center pixel assignment - not true for photon close to the center
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assert (residuals_interpolated_x < residuals_center_pixel_x).all()
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assert (residuals_interpolated_y < residuals_center_pixel_y).all()
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"""
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# check within photon hit pixel for all
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with check:
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assert np.allclose(interpolated_photons["x"], ground_truths[:, 0], atol=5e-1)
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with check:
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assert np.allclose(interpolated_photons["y"], ground_truths[:, 1], atol=5e-1)
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# check mean and std of residuals
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with check:
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assert residuals_interpolated_y.mean() <= 0.1
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with check:
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assert residuals_interpolated_x.mean() <= 0.1
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with check:
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assert residuals_interpolated_x.std() <= 0.05
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with check:
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assert residuals_interpolated_y.std() <= 0.05
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@pytest.mark.withdata
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def test_eta2_interpolation_rosenblatt(load_data, check):
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"""Test eta2 interpolation on simulated data using Rosenblatt transform"""
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cv, ground_truths = load_data
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num_bins = 201
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eta_distribution = calculate_eta_distribution(cv, calculate_eta2, edges_x=[-0.1,1.1], edges_y=[-0.1,1.1], nbins=num_bins)
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interpolator = Interpolator(eta_distribution.axes[0].edges, eta_distribution.axes[1].edges, eta_distribution.axes[2].edges)
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interpolator.rosenblatttransform(eta_distribution)
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assert interpolator.get_ietax().shape == (num_bins,num_bins,1)
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assert interpolator.get_ietay().shape == (num_bins,num_bins,1)
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interpolated_photons = interpolator.interpolate(cv)
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assert interpolated_photons.size == cv.size
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interpolated_photons["x"] += 1.0 #groud truth label uses 5x5 clusters
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interpolated_photons["y"] += 1.0
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residuals_interpolated_x = abs(ground_truths[:, 0] - interpolated_photons["x"])
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residuals_interpolated_y = abs(ground_truths[:, 1] - interpolated_photons["y"])
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"""
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residuals_center_pixel_x = abs(ground_truths[:, 0] - 2.5)
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residuals_center_pixel_y = abs(ground_truths[:, 1] - 2.5)
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# interpolation needs to perform better than center pixel assignment - not true for photon close to the center
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assert (residuals_interpolated_x < residuals_center_pixel_x).all()
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assert (residuals_interpolated_y < residuals_center_pixel_y).all()
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"""
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# check within photon hit pixel for all
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with check:
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assert np.allclose(interpolated_photons["x"], ground_truths[:, 0], atol=5e-1)
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with check:
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assert np.allclose(interpolated_photons["y"], ground_truths[:, 1], atol=5e-1)
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# check mean and std of residuals
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with check:
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assert residuals_interpolated_y.mean() <= 0.1
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with check:
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assert residuals_interpolated_x.mean() <= 0.1
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with check:
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assert residuals_interpolated_x.std() <= 0.055 #performs slightly worse
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with check:
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assert residuals_interpolated_y.std() <= 0.055 #performs slightly worse
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@pytest.mark.withdata
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def test_cross_eta_interpolation(load_data, check):
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"""Test cross eta interpolation on simulated data"""
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cv, ground_truths = load_data
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num_bins = 201
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eta_distribution = calculate_eta_distribution(cv, calculate_cross_eta3, edges_x=[-0.5,0.5], edges_y=[-0.5,0.5], nbins=num_bins)
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interpolator = Interpolator(eta_distribution, eta_distribution.axes[0].edges, eta_distribution.axes[1].edges, eta_distribution.axes[2].edges)
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assert interpolator.get_ietax().shape == (num_bins,num_bins,1)
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assert interpolator.get_ietay().shape == (num_bins,num_bins,1)
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interpolated_photons = interpolator.interpolate_cross_eta3(cv)
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assert interpolated_photons.size == cv.size
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interpolated_photons["x"] += 1.0 #groud truth label uses 5x5 clusters
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interpolated_photons["y"] += 1.0
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residuals_interpolated_x = abs(ground_truths[:, 0] - interpolated_photons["x"])
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residuals_interpolated_y = abs(ground_truths[:, 1] - interpolated_photons["y"])
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"""
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residuals_center_pixel_x = abs(ground_truths[:, 0] - 2.5)
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residuals_center_pixel_y = abs(ground_truths[:, 1] - 2.5)
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# interpolation needs to perform better than center pixel assignment - not true for photon close to the center
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assert (residuals_interpolated_x < residuals_center_pixel_x).all()
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assert (residuals_interpolated_y < residuals_center_pixel_y).all()
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"""
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# check within photon hit pixel for all
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# TODO: fails as eta_x = 0, eta_y = 0 is not leading to offset (0.5,0.5)
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with check:
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assert np.allclose(interpolated_photons["x"], ground_truths[:, 0], atol=5e-1)
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with check:
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assert np.allclose(interpolated_photons["y"], ground_truths[:, 1], atol=5e-1)
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# check mean and std of residuals
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with check:
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assert residuals_interpolated_y.mean() <= 0.1
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with check:
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assert residuals_interpolated_x.mean() <= 0.1
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with check:
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assert residuals_interpolated_x.std() <= 0.05
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with check:
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assert residuals_interpolated_y.std() <= 0.05
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@pytest.mark.withdata
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def test_eta3_interpolation(load_data, check):
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"""Test eta3 interpolation on simulated data"""
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cv, ground_truths = load_data
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num_bins = 201
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eta_distribution = calculate_eta_distribution(cv, calculate_eta3, edges_x=[-0.5,0.5], edges_y=[-0.5,0.5], nbins=num_bins)
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interpolator = Interpolator(eta_distribution, eta_distribution.axes[0].edges, eta_distribution.axes[1].edges, eta_distribution.axes[2].edges)
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assert interpolator.get_ietax().shape == (num_bins,num_bins,1)
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assert interpolator.get_ietay().shape == (num_bins,num_bins,1)
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interpolated_photons = interpolator.interpolate_eta3(cv)
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assert interpolated_photons.size == cv.size
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interpolated_photons["x"] += 1.0 #groud truth label uses 5x5 clusters
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interpolated_photons["y"] += 1.0
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residuals_interpolated_x = abs(ground_truths[:, 0] - interpolated_photons["x"])
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residuals_interpolated_y = abs(ground_truths[:, 1] - interpolated_photons["y"])
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"""
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residuals_center_pixel_x = abs(ground_truths[:, 0] - 2.5)
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residuals_center_pixel_y = abs(ground_truths[:, 1] - 2.5)
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# interpolation needs to perform better than center pixel assignment - not true for photon close to the center
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assert (residuals_interpolated_x < residuals_center_pixel_x).all()
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assert (residuals_interpolated_y < residuals_center_pixel_y).all()
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"""
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# check within photon hit pixel for all
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# TODO: fails as eta_x = 0, eta_y = 0 is not leading to offset (0.5,0.5)
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with check:
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assert np.allclose(interpolated_photons["x"], ground_truths[:, 0], atol=5e-1)
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with check:
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assert np.allclose(interpolated_photons["y"], ground_truths[:, 1], atol=5e-1)
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# check mean and std of residuals
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with check:
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assert residuals_interpolated_y.mean() <= 0.1
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with check:
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assert residuals_interpolated_x.mean() <= 0.1
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with check:
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assert residuals_interpolated_x.std() <= 0.05
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with check:
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assert residuals_interpolated_y.std() <= 0.05
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@pytest.mark.withdata
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def test_full_eta2_interpolation(load_data, check):
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"""Test full eta2 interpolation on simulated data"""
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cv, ground_truths = load_data
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num_bins = 201
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eta_distribution = calculate_eta_distribution(cv, calculate_full_eta2, edges_x=[-0.1,1.1], edges_y=[-0.1,1.1], nbins=num_bins)
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interpolator = Interpolator(eta_distribution, eta_distribution.axes[0].edges, eta_distribution.axes[1].edges, eta_distribution.axes[2].edges)
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assert interpolator.get_ietax().shape == (num_bins,num_bins,1)
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assert interpolator.get_ietay().shape == (num_bins,num_bins,1)
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interpolated_photons = interpolator.interpolate_full_eta2(cv)
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assert interpolated_photons.size == cv.size
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interpolated_photons["x"] += 1.0 #groud truth label uses 5x5 clusters
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interpolated_photons["y"] += 1.0
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residuals_interpolated_x = abs(ground_truths[:, 0] - interpolated_photons["x"])
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residuals_interpolated_y = abs(ground_truths[:, 1] - interpolated_photons["y"])
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"""
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residuals_center_pixel_x = abs(ground_truths[:, 0] - 2.5)
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residuals_center_pixel_y = abs(ground_truths[:, 1] - 2.5)
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# interpolation needs to perform better than center pixel assignment - not true for photon close to the center
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assert (residuals_interpolated_x < residuals_center_pixel_x).all()
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assert (residuals_interpolated_y < residuals_center_pixel_y).all()
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"""
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# check within photon hit pixel for all
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with check:
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assert np.allclose(interpolated_photons["x"], ground_truths[:, 0], atol=5e-1)
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with check:
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assert np.allclose(interpolated_photons["y"], ground_truths[:, 1], atol=5e-1)
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# check mean and std of residuals
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with check:
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assert residuals_interpolated_y.mean() <= 0.1
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with check:
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assert residuals_interpolated_x.mean() <= 0.1
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with check:
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assert residuals_interpolated_x.std() <= 0.05
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with check:
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assert residuals_interpolated_y.std() <= 0.05
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