38 lines
1.2 KiB
Python
38 lines
1.2 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_uniform_field():
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"""Uniform field should remain approximately the same after filtering."""
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from backend.nodes.trimmed_mean import TrimmedMean
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node = TrimmedMean()
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data = np.full((16, 16), 3.0, dtype=np.float64)
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field = make_field(data=data)
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result, = node.process(field, radius=2, trim_fraction=0.1)
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assert np.allclose(result.data, 3.0, atol=1e-10)
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def test_shape_preserved():
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"""Output shape should match input shape."""
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from backend.nodes.trimmed_mean import TrimmedMean
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node = TrimmedMean()
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field = make_field(shape=(16, 16))
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result, = node.process(field, radius=2, trim_fraction=0.1)
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assert result.data.shape == (16, 16)
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def test_reduces_outliers():
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"""A spike in the field should be reduced by the trimmed mean filter."""
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from backend.nodes.trimmed_mean import TrimmedMean
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node = TrimmedMean()
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data = np.zeros((16, 16), dtype=np.float64)
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data[8, 8] = 100.0 # large spike
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field = make_field(data=data)
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result, = node.process(field, radius=2, trim_fraction=0.1)
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# The spike should be significantly reduced
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assert result.data[8, 8] < 50.0
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