43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
import numpy as np
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import pytest
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from tests.node_tests._shared import make_field
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def test_wiener_preserves_shape():
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from backend.nodes.deconvolution import Deconvolution
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node = Deconvolution()
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field = make_field(shape=(32, 32))
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result, = node.process(field, "wiener", 2.0, 0.01, 10)
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assert result.data.shape == (32, 32)
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assert np.isfinite(result.data).all()
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def test_richardson_lucy_preserves_shape():
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from backend.nodes.deconvolution import Deconvolution
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node = Deconvolution()
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field = make_field(data=np.abs(np.random.default_rng(42).standard_normal((32, 32))) + 0.1)
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result, = node.process(field, "richardson_lucy", 2.0, 0.01, 5)
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assert result.data.shape == (32, 32)
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assert np.isfinite(result.data).all()
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def test_wiener_flat_field():
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from backend.nodes.deconvolution import Deconvolution
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node = Deconvolution()
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field = make_field(data=np.ones((32, 32)))
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result, = node.process(field, "wiener", 2.0, 0.01, 10)
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# A flat field convolved with anything is still flat; Wiener should preserve it
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assert result.data.shape == (32, 32)
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def test_unknown_method():
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from backend.nodes.deconvolution import Deconvolution
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node = Deconvolution()
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field = make_field(shape=(32, 32))
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with pytest.raises(ValueError):
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node.process(field, "unknown", 2.0, 0.01, 10)
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