Differentiation of Multidimensional Arrays in SINDyDerivative #476
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## master #476 +/- ##
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+ Coverage 94.40% 94.42% +0.01%
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Files 38 38
Lines 4060 4069 +9
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+ Hits 3833 3842 +9
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Jacob-Stevens-Haas
requested changes
Feb 23, 2024
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| def test_multidimensional_differentiation(): | ||
| X = np.random.random(size=(10, 100, 2)) | ||
| t = np.arange(0, 10, 0.1) | ||
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| X_dot = SINDyDerivative(kind="kalman", axis=-2)._differentiate(X, t) | ||
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| assert X_dot.shape == X.shape |
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Let's make sure we differentiate the correct axis, rather than just verifying that it runs without error. E.g.:
- create a short arange of ints
- tile it /repeat it to make it 3D, so that x[i,i,:] is the original arange
- differentiate it (use something like finite difference, so that we know the expected result)
- test that every slice is what we expect (ones)
Change the name to test_nd_diff_sindy_derivative() to reflect that we're only testing one class.
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It's also worth checking whether this preserves AxesArray types. If not, we can perhaps add an override for np.moveaxis
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This PR addresses the issue and lets SINDyDerivative handle arrays with more dimensions than 2.