Add features for conversion QC and scalar export to original formats #40
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| # file generated by setuptools-scm | ||
| # don't change, don't track in version control | ||
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| __all__ = [ | ||
| "__version__", | ||
| "__version_tuple__", | ||
| "version", | ||
| "version_tuple", | ||
| "__commit_id__", | ||
| "commit_id", | ||
| ] | ||
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| TYPE_CHECKING = False | ||
| if TYPE_CHECKING: | ||
| from typing import Tuple | ||
| from typing import Union | ||
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| VERSION_TUPLE = Tuple[Union[int, str], ...] | ||
| COMMIT_ID = Union[str, None] | ||
| else: | ||
| VERSION_TUPLE = object | ||
| COMMIT_ID = object | ||
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| version: str | ||
| __version__: str | ||
| __version_tuple__: VERSION_TUPLE | ||
| version_tuple: VERSION_TUPLE | ||
| commit_id: COMMIT_ID | ||
| __commit_id__: COMMIT_ID | ||
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| __version__ = version = '0.1.dev115+g7f4c6030e' | ||
| __version_tuple__ = version_tuple = (0, 1, 'dev115', 'g7f4c6030e') | ||
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| __commit_id__ = commit_id = None |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,116 @@ | ||
| """Diagnostic image helpers for conversion commands.""" | ||
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| from __future__ import annotations | ||
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| from pathlib import Path | ||
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| import nibabel as nb | ||
| import numpy as np | ||
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| from modelarrayio.utils.cifti import extract_cifti_scalar_data | ||
| from modelarrayio.utils.fixels import nifti2_to_mif | ||
| from modelarrayio.utils.voxels import flattened_image | ||
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| def summarize_rows(rows) -> dict[str, np.ndarray]: | ||
| """Compute common diagnostics from a sequence of 1-D subject arrays.""" | ||
| stacked = np.vstack(rows) | ||
| return { | ||
| 'mean': np.nanmean(stacked, axis=0).astype(np.float32), | ||
| 'n_non_nan': np.sum(~np.isnan(stacked), axis=0).astype(np.float32), | ||
| 'element_id': np.arange(stacked.shape[1], dtype=np.float32), | ||
| } | ||
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| def verify_nifti_element_mapping(group_mask_img, group_mask_matrix): | ||
| """Verify NIfTI group-mask flattening order matches element indices.""" | ||
| expected = np.arange(int(group_mask_matrix.sum()), dtype=np.float32) | ||
| element_volume = np.zeros(group_mask_matrix.shape, dtype=np.float32) | ||
| element_volume[group_mask_matrix] = expected | ||
| element_img = nb.Nifti1Image( | ||
| element_volume, | ||
| affine=group_mask_img.affine, | ||
| header=group_mask_img.header, | ||
| ) | ||
| extracted = flattened_image(element_img, group_mask_img, group_mask_matrix) | ||
| if not np.array_equal(extracted.astype(np.int64), expected.astype(np.int64)): | ||
| raise ValueError('Element ID mapping check failed for NIfTI group-mask flattening.') | ||
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| def write_nifti_diagnostics( | ||
| *, | ||
| maps: list[str], | ||
| scalar_name: str, | ||
| diagnostics: dict[str, np.ndarray], | ||
| group_mask_img, | ||
| group_mask_matrix, | ||
| output_dir: Path, | ||
| ): | ||
| header = group_mask_img.header.copy() | ||
| header.set_data_dtype(np.float32) | ||
| for name in maps: | ||
| out_file = output_dir / f'{scalar_name}_{name}.nii.gz' | ||
| data = np.zeros(group_mask_matrix.shape, dtype=np.float32) | ||
| data[group_mask_matrix] = diagnostics[name] | ||
| nb.Nifti1Image(data, affine=group_mask_img.affine, header=header).to_filename(out_file) | ||
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| def verify_cifti_element_mapping(template_cifti, reference_brain_names): | ||
| """Verify CIFTI extraction order matches element indices.""" | ||
| expected = np.arange(reference_brain_names.shape[0], dtype=np.float32) | ||
| test_img = nb.Cifti2Image( | ||
| expected.reshape(1, -1), | ||
| header=template_cifti.header, | ||
| nifti_header=template_cifti.nifti_header, | ||
| ) | ||
| recovered, _ = extract_cifti_scalar_data(test_img, reference_brain_names=reference_brain_names) | ||
| if not np.array_equal(recovered.astype(np.int64), expected.astype(np.int64)): | ||
| raise ValueError('Element ID mapping check failed for CIFTI greyordinate ordering.') | ||
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| def write_cifti_diagnostics( | ||
| *, | ||
| maps: list[str], | ||
| scalar_name: str, | ||
| diagnostics: dict[str, np.ndarray], | ||
| template_cifti, | ||
| output_dir: Path, | ||
| ): | ||
| for name in maps: | ||
| out_file = output_dir / f'{scalar_name}_{name}.dscalar.nii' | ||
| nb.Cifti2Image( | ||
| diagnostics[name].reshape(1, -1), | ||
| header=template_cifti.header, | ||
| nifti_header=template_cifti.nifti_header, | ||
| ).to_filename(out_file) | ||
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| def verify_mif_element_mapping(template_nifti2, num_elements: int): | ||
| """Verify fixel vector reshape/squeeze mapping remains identity.""" | ||
| expected = np.arange(num_elements, dtype=np.float32) | ||
| test_img = nb.Nifti2Image( | ||
| expected.reshape(-1, 1, 1), | ||
| affine=template_nifti2.affine, | ||
| header=template_nifti2.header, | ||
| ) | ||
| recovered = test_img.get_fdata(dtype=np.float32).squeeze() | ||
| if not np.array_equal(recovered.astype(np.int64), expected.astype(np.int64)): | ||
| raise ValueError('Element ID mapping check failed for MIF fixel vector ordering.') | ||
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| def write_mif_diagnostics( | ||
| *, | ||
| maps: list[str], | ||
| scalar_name: str, | ||
| diagnostics: dict[str, np.ndarray], | ||
| template_nifti2, | ||
| output_dir: Path, | ||
| ): | ||
| for name in maps: | ||
| out_file = output_dir / f'{scalar_name}_{name}.mif' | ||
| temp_nifti2 = nb.Nifti2Image( | ||
| diagnostics[name].reshape(-1, 1, 1), | ||
| affine=template_nifti2.affine, | ||
| header=template_nifti2.header, | ||
| ) | ||
| nifti2_to_mif(temp_nifti2, out_file) |
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