ENH: Validate data objects' attributes at instantiation #305
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So a refactoring will be required if we go for this. Even if this requires more code than the checks removed in the PRs referenced in issue #302, these are no longer spread across the code base, and we do not run the risk of forgetting checks, or producing e.g. numpy error messages downstream that are harder to debug. |
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jhlegarreta
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Nov 18, 2025
src/nifreeze/data/dmri.py
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| except (TypeError, ValueError): | ||
| data_vols = None | ||
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| if data_vols is not None: |
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This is now duplicating par of what _normalize_gradients does: leaving here until PR #327 gets merged.
jhlegarreta
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Nov 18, 2025
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| @pytest.mark.random_uniform_spatial_data((2, 2, 2, 4), 0.0, 1.0) | ||
| def test_brainmask_volume_mismatch_error(request, setup_random_uniform_spatial_data): |
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src/nifreeze/data/dmri.py
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| if value is None: | ||
| raise ValueError(GRADIENT_ABSENCE_ERROR_MSG) | ||
| if not isinstance(value, np.ndarray) or value.shape[1] != 4: | ||
| raise ValueError(GRADIENT_SHAPE_ERROR_MSG) |
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This is failing for the HDF5 file because it is a 4x102 matrix, and the functions runs before __attrs_post_init__ which would transpose it.
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Validate DWI data objects' attributes at instantiation: ensures that the attributes are present and match the expected dimensionalities.
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Validate data objects' attributes at instantiation: ensures that the attributes are present and match the expected dimensionalities.
Fixes #302.