chore: remove unused private functions

bdpy/dataform/features.py

@@ -466,9 +466,6 @@ def __parse_dir(self, path: str, keys: Optional[List[str]] = None) -> 'FileDatab
 
         return db
 
-    def __init_db(self, keys):
-        raise NotImplementedError
-
 
 class FileDatabase(object):
     def __init__(self, keys: List[str]):

bdpy/dl/torch/dataset.py

@@ -12,31 +12,6 @@
 _FeatureTypeNP = Dict[str, np.ndarray]
 
 
-def _removesuffix(s: str, suffix: str) -> str:
-    """Remove suffix from string.
-
-    Note
-    ----
-    This function is available from Python 3.9 as `str.removesuffix`. We can
-    remove this function when we drop support for Python 3.8.
-
-    Parameters
-    ----------
-    s : str
-        String.
-    suffix : str
-        Suffix to remove.
-
-    Returns
-    -------
-    str
-        String without suffix.
-    """
-    if suffix and s.endswith(suffix):
-        return s[: -len(suffix)]
-    return s[:]
-
-
 class FeaturesDataset(Dataset):
     """Dataset of features.
 

bdpy/mri/fmriprep.py

@@ -94,13 +94,13 @@
         # File name patterns
         # FIXME
         if self.__fmriprep_version == '1.2':
             file_pattern = {'volume_native'   : '.*_space-T1w_desc-preproc_bold\.nii\.gz$',
                             'volume_standard' : '.*_space-MNI152NLin2009cAsym_desc-preproc_bold\.nii\.gz$',
                             'surf_native_left'  : '.*_space-fsnative_hemi-L\.func\.gii$',
                             'surf_native_right' : '.*_space-fsnative_hemi-R\.func\.gii$',
                             'surf_standard_left'  : '.*_space-fsaverage_hemi-L\.func\.gii$',
                             'surf_standard_right' : '.*_space-fsaverage_hemi-R\.func\.gii$',
                             'surf_standard_41k_left'  : '.*_space-fsaverage6_hemi-L\.func\.gii$',
                             'surf_standard_41k_right' : '.*_space-fsaverage6_hemi-R\.func\.gii$',
                             'surf_standard_10k_left'  : '.*_space-fsaverage5_hemi-L\.func\.gii$',
                             'surf_standard_10k_right' : '.*_space-fsaverage5_hemi-R\.func\.gii$',
@@ -813,52 +813,6 @@
     return bdata
 
 
-def __get_xyz(img):
-    if len(img.shape) == 4:
-        # 4D-image
-        i_len, j_len, k_len, t = img.shape
-        affine = np.delete(np.delete(img.coordmap.affine, 3, axis=0), 3, axis=1)
-    else:
-        # 3D-image
-        i_len, j_len, k_len = img.shape
-        affine = img.coordmap.affine
-    ijk = np.array(list(itertools.product(range(i_len),
-                                          range(j_len),
-                                          range(k_len),
-                                          [1]))).T
-    return np.dot(affine, ijk)[:-1]
-
-
-def __load_mri(fpath):
-    """Load a MRI image.
-
-    - Returns data as 2D array (sample x voxel)
-    - Returns voxle xyz coordinates (3 x voxel)
-    - Returns voxel ijk indexes (3 x voxel)
-    - Data, xyz, and ijk are flattened by Fortran-like index order
-    """
-    img = nibabel.load(fpath)
-
-    data = img.get_fdata()
-    if data.ndim == 4:
-        data = data.reshape(-1, data.shape[-1], order='F').T
-        i_len, j_len, k_len, t = img.shape
-        affine = np.delete(np.delete(img.affine, 3, axis=0), 3, axis=1)
-    elif data.ndim == 3:
-        data = data.flatten(order='F')
-        i_len, j_len, k_len = img.shape
-        affine = img.affine
-    else:
-        raise ValueError('Invalid shape.')
-
-    ijk = np.array(np.unravel_index(np.arange(i_len * j_len * k_len),
-                                    (i_len, j_len, k_len), order='F'))
-    ijk_b = np.vstack([ijk, np.ones((1, i_len * j_len * k_len))])
-    xyz_b = np.dot(affine, ijk_b)
-    xyz = xyz_b[:-1]
-
-    return data, xyz, ijk
-
 
 if __name__ == '__main__':
     pass

bdpy/recon/torch/task/inversion.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Callable, Dict, Iterable
+from typing import TYPE_CHECKING, Dict, Iterable
 
 from bdpy.task import BaseTask
 from bdpy.task.callback import BaseCallback, unused
@@ -14,12 +14,6 @@
     _FeatureType = Dict[str, torch.Tensor]
 
 
-def _apply_to_features(
-    fn: Callable[[torch.Tensor], torch.Tensor], features: _FeatureType
-) -> _FeatureType:
-    return {k: fn(v) for k, v in features.items()}
-
-
 class FeatureInversionCallback(BaseCallback):
     """Callback for feature inversion task.