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import pytest | ||
import numpy as np | ||
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from eo_tides.utils import idw | ||
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# Test Inverse Distance Weighted function | ||
def test_idw(): | ||
# Basic psuedo-1D example | ||
input_z = [1, 2, 3, 4, 5] | ||
input_x = [0, 1, 2, 3, 4] | ||
input_y = [0, 0, 0, 0, 0] | ||
output_x = [0.5, 1.5, 2.5, 3.5] | ||
output_y = [0.0, 0.0, 0.0, 0.0] | ||
out = idw(input_z, input_x, input_y, output_x, output_y, k=2) | ||
assert np.allclose(out, [1.5, 2.5, 3.5, 4.5]) | ||
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# Verify that k > input points gives error | ||
with pytest.raises(ValueError): | ||
idw(input_z, input_x, input_y, output_x, output_y, k=6) | ||
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# 2D nearest neighbour case | ||
input_z = [1, 2, 3, 4] | ||
input_x = [0, 4, 0, 4] | ||
input_y = [0, 0, 4, 4] | ||
output_x = [1, 4, 0, 3] | ||
output_y = [0, 1, 3, 4] | ||
out = idw(input_z, input_x, input_y, output_x, output_y, k=1) | ||
assert np.allclose(out, [1, 2, 3, 4]) | ||
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# Two neighbours | ||
input_z = [1, 2, 3, 4] | ||
input_x = [0, 4, 0, 4] | ||
input_y = [0, 0, 4, 4] | ||
output_x = [2, 0, 4, 2] | ||
output_y = [0, 2, 2, 4] | ||
out = idw(input_z, input_x, input_y, output_x, output_y, k=2) | ||
assert np.allclose(out, [1.5, 2, 3, 3.5]) | ||
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# Four neighbours | ||
out = idw(input_z, input_x, input_y, output_x, output_y, k=4) | ||
assert np.allclose(out, [2.11, 2.30, 2.69, 2.88], rtol=0.01) | ||
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# Four neighbours; max distance of 2 | ||
out = idw(input_z, input_x, input_y, output_x, output_y, k=4, max_dist=2) | ||
assert np.allclose(out, [1.5, 2, 3, 3.5]) | ||
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# Four neighbours; max distance of 2, k_min of 4 (should return NaN) | ||
out = idw(input_z, input_x, input_y, output_x, output_y, k=4, max_dist=2, k_min=4) | ||
assert np.isnan(out).all() | ||
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# Four neighbours; power function p=0 | ||
out = idw(input_z, input_x, input_y, output_x, output_y, k=4, p=0) | ||
assert np.allclose(out, [2.5, 2.5, 2.5, 2.5]) | ||
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# Four neighbours; power function p=2 | ||
out = idw(input_z, input_x, input_y, output_x, output_y, k=4, p=2) | ||
assert np.allclose(out, [1.83, 2.17, 2.83, 3.17], rtol=0.01) | ||
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# Different units, nearest neighbour case | ||
input_z = [10, 20, 30, 40] | ||
input_x = [1125296, 1155530, 1125296, 1155530] | ||
input_y = [-4169722, -4169722, -4214782, -4214782] | ||
output_x = [1124952, 1159593, 1120439, 1155284] | ||
output_y = [-4169749, -4172892, -4211108, -4214332] | ||
out = idw(input_z, input_x, input_y, output_x, output_y, k=1) | ||
assert np.allclose(out, [10, 20, 30, 40]) | ||
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# Verify distance works on different units | ||
output_x = [1142134, 1138930] | ||
output_y = [-4171232, -4213451] | ||
out = idw(input_z, input_x, input_y, output_x, output_y, k=4, max_dist=20000) | ||
assert np.allclose(out, [15, 35], rtol=0.1) | ||
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# Test multidimensional input | ||
input_z = np.column_stack(([1, 2, 3, 4], [10, 20, 30, 40])) | ||
input_x = [0, 4, 0, 4] | ||
input_y = [0, 0, 4, 4] | ||
output_x = [1, 4, 0, 3] | ||
output_y = [0, 1, 3, 4] | ||
out = idw(input_z, input_x, input_y, output_x, output_y, k=1) | ||
assert input_z.shape == out.shape | ||
assert np.allclose(out[:, 0], [1, 2, 3, 4]) | ||
assert np.allclose(out[:, 1], [10, 20, 30, 40]) | ||
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