Martino Ferrari
03b63ba03a
Full implementation of Laguerre-Gauss modal reconstruction for gyrotron beam diagnostics, per the approved design spec, plus tests, docs, and a runnable end-to-end example. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
import numpy as np
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import pytest
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from he11lib.noise import NoiseEstimator
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def test_estimate_std_recovers_known_noise_on_flat_image():
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rng = np.random.default_rng(0)
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true_std = 0.05
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image = np.ones((200, 200)) * 10.0 + rng.normal(0, true_std, size=(200, 200))
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estimated = NoiseEstimator().estimate_std(image)
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assert estimated == pytest.approx(true_std, rel=0.15)
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def test_estimate_std_recovers_known_noise_on_smooth_bump():
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rng = np.random.default_rng(1)
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x = np.linspace(-3, 3, 200)
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xx, yy = np.meshgrid(x, x)
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smooth = np.exp(-(xx**2 + yy**2))
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true_std = 0.01
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image = smooth + rng.normal(0, true_std, size=smooth.shape)
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estimated = NoiseEstimator().estimate_std(image)
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assert estimated == pytest.approx(true_std, rel=0.35)
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def test_estimate_std_near_zero_for_noise_free_image():
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x = np.linspace(-3, 3, 100)
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xx, yy = np.meshgrid(x, x)
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smooth = np.exp(-(xx**2 + yy**2))
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estimated = NoiseEstimator().estimate_std(smooth)
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assert estimated < 1e-6
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def test_weights_are_uniform_and_match_shape():
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rng = np.random.default_rng(2)
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image = np.ones((50, 50)) * 5.0 + rng.normal(0, 0.1, size=(50, 50))
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weights = NoiseEstimator().weights(image)
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assert weights.shape == image.shape
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assert np.allclose(weights, weights.flat[0])
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expected_std = NoiseEstimator().estimate_std(image)
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assert weights.flat[0] == pytest.approx(1.0 / expected_std**2, rel=1e-6)
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