Drop gifti support

tedana/model/fit.py

@@ -197,9 +197,6 @@ def fitmodels_direct(catd, mmix, mask, t2s, t2sG, tes, combmode, ref_img,
             out[:, 2] = np.squeeze(utils.unmask(F_S0_maps[:, i], t2s != 0))
             out[:, 3] = np.squeeze(utils.unmask(Z_maps[:, i], mask))
 
-            if utils.get_dtype(ref_img) == 'GIFTI':
-                continue  # TODO: pass through GIFTI file data as below
-
             ccimg = utils.new_nii_like(ref_img, out)
 
             # Do simple clustering on F

tedana/tests/test_utils.py

@@ -17,8 +17,6 @@
 def test_get_dtype():
     # various combinations of input types
     good_inputs = [
-        (['echo1.func.gii', 'echo2.func.gii', 'echo3.func.gii'], 'GIFTI'),
-        ('echo1.func.gii', 'GIFTI'),
         (['echo1.nii.gz', 'echo2.nii.gz', 'echo3.nii.gz'], 'NIFTI'),
         ('echo1.nii.gz', 'NIFTI'),
         (['echo1.unknown', 'echo2.unknown', 'echo3.unknown'], 'OTHER'),
@@ -31,7 +29,7 @@ def test_get_dtype():
         assert utils.get_dtype(input) == expected
 
     with pytest.raises(ValueError):  # mixed arrays don't work
-        utils.get_dtype(['echo1.func.gii', 'echo1.nii.gz'])
+        utils.get_dtype(['echo1.unknown', 'echo1.nii.gz'])
 
     with pytest.raises(TypeError):  # non-img_like inputs don't work
         utils.get_dtype(rs.rand(100, 100))
@@ -184,11 +182,3 @@ def test_new_nii_like():
 
 def test_filewrite():
     pass
-
-
-def test_new_gii_like():
-    pass
-
-
-def test_new_gii_darray():
-    pass

tedana/utils/utils.py

@@ -1,8 +1,6 @@
 """
 Utilities for tedana package
 """
-import os.path as op
-
 import nibabel as nib
 from nibabel.filename_parser import splitext_addext
 from nilearn.image import new_img_like
@@ -13,8 +11,7 @@
 
 from ..due import due, BibTeX
 
-FORMATS = {'.nii': 'NIFTI',
-           '.gii': 'GIFTI'}
+FORMATS = {'.nii': 'NIFTI'}
 
 
 def get_dtype(data):
@@ -28,7 +25,7 @@ def get_dtype(data):
 
     Returns
     -------
-    dtype : {'NIFTI', 'GIFTI', 'OTHER'} str
+    dtype : {'NIFTI', 'OTHER'} str
         Format of input data
     """
 
@@ -93,11 +90,7 @@ def load_image(data):
     """
 
     if isinstance(data, str):
-        if get_dtype(data) == 'GIFTI':
-            fdata = np.column_stack([f.data for f in nib.load(data).darrays])
-            return fdata
-        elif get_dtype(data) == 'NIFTI':
-            data = check_niimg(data).get_data()
+        data = check_niimg(data).get_data()
     elif isinstance(data, nib.spatialimages.SpatialImage):
         data = check_niimg(data).get_data()
 
@@ -115,7 +108,7 @@ def load_data(data, n_echos=None):
     data : (X x Y x M x T) array_like or :obj:`list` of img_like
         Input multi-echo data array, where `X` and `Y` are spatial dimensions,
         `M` is the Z-spatial dimensions with all the input echos concatenated,
-        and `T` is time. A list of image-like objects (e.g., .nii or .gii) are
+        and `T` is time. A list of image-like objects (e.g., .nii) are
         accepted, as well
     n_echos : :obj:`int`, optional
         Number of echos in provided data array. Only necessary if `data` is
@@ -139,13 +132,8 @@ def load_data(data, n_echos=None):
             raise ValueError('Cannot run `tedana` with only two echos: '
                              '{}'.format(data))
         else:  # individual echo files were provided (surface or volumetric)
-            if get_dtype(data) == 'GIFTI':  # NOTE: only handles .[L/R].func.gii files
-                echos = np.array_split(data, n_echos)
-                fdata = np.stack([np.vstack([load_image(f) for f in e]) for e in echos], axis=1)
-                return np.atleast_3d(fdata), data[0]
-            else:
-                fdata = np.stack([load_image(f) for f in data], axis=1)
-                return np.atleast_3d(fdata), data[0]
+            fdata = np.stack([load_image(f) for f in data], axis=1)
+            return np.atleast_3d(fdata), data[0]
 
     img = check_niimg(data)
     (nx, ny), nz = img.shape[:2], img.shape[2] // n_echos
@@ -240,14 +228,10 @@ def make_min_mask(data):
     return data.prod(axis=-1).prod(axis=-1).astype(bool)
 
 
-def filewrite(data, filename, ref_img, gzip=False, copy_header=True,
-              copy_meta=False):
+def filewrite(data, filename, ref_img, gzip=False, copy_header=True):
     """
     Writes `data` to `filename` in format of `ref_img`
 
-    If `ref_img` dtype is GIFTI, then `data` is assumed to be stacked L/R
-    hemispheric and will be split and saved as two files
-
     Parameters
     ----------
     data : (S [x T]) array_like
@@ -261,9 +245,6 @@ def filewrite(data, filename, ref_img, gzip=False, copy_header=True,
         if output dtype is NIFTI. Default: False
     copy_header : :obj:`bool`, optional
         Whether to copy header from `ref_img` to new image. Default: True
-    copy_meta : :obj:`bool`, optional
-        Whether to copy meta from `ref_img` to new image. Only applies if
-        output dtype is GIFTI. Default: False
 
     Returns
     -------
@@ -286,17 +267,6 @@ def filewrite(data, filename, ref_img, gzip=False, copy_header=True,
         out = new_nii_like(ref_img, data, copy_header=copy_header)
         name = '{}.{}'.format(root, 'nii.gz' if gzip else 'nii')
         out.to_filename(name)
-    elif dtype == 'GIFTI':
-        # remove possible hemispheric denotations from root
-        root = op.join(op.dirname(root), op.basename(root).split('.')[0])
-        # save hemispheres separately
-        for n, (hdata, hemi) in enumerate(zip(np.split(data, 2, axis=0),
-                                              ['L', 'R'])):
-            out = new_gii_like(ref_img, hdata,
-                               copy_header=copy_header,
-                               copy_meta=copy_meta)
-            name = '{}.{}.func.gii'.format(root, hemi)
-            out.to_filename(name)
 
     return name
 
@@ -332,77 +302,6 @@ def new_nii_like(ref_img, data, affine=None, copy_header=True):
     return nii
 
 
-def new_gii_like(ref_img, data, copy_header=True, copy_meta=False):
-    """
-    Coerces `data` into GiftiImage format like `ref_img`
-
-    Parameters
-    ----------
-    ref_img : :obj:`str` or img_like
-        Reference image
-    data : (S [x T]) array_like
-        Data to be saved
-    copy_header : :obj:`bool`, optional
-        Whether to copy header from `ref_img` to new image. Default: True
-    copy_meta : :obj:`bool`, optional
-        Whether to copy meta from `ref_img` to new image. Default: False
-
-    Returns
-    -------
-    gii : :obj:`nibabel.gifti.gifti.GiftiImage`
-        GiftiImage
-    """
-
-    if isinstance(ref_img, str):
-        ref_img = nib.load(ref_img)
-
-    if data.ndim == 1:
-        data = np.atleast_2d(data).T
-
-    darrays = [make_gii_darray(ref_img.darrays[n], d, copy_meta=copy_meta)
-               for n, d in enumerate(data.T)]
-    gii = nib.gifti.GiftiImage(header=ref_img.header if copy_header else None,
-                               extra=ref_img.extra,
-                               meta=ref_img.meta if copy_meta else None,
-                               labeltable=ref_img.labeltable,
-                               darrays=darrays)
-
-    return gii
-
-
-def make_gii_darray(ref_array, data, copy_meta=False):
-    """
-    Converts `data` into GiftiDataArray format like `ref_array`
-
-    Parameters
-    ----------
-    ref_array : :obj:`str` or img_like
-        Reference array
-    data : (S,) array_like
-        Data to be saved
-    copy_meta : :obj:`bool`, optional
-        Whether to copy meta from `ref_img` to new image. Default: False
-
-    Returns
-    -------
-    gii : :obj:`nibabel.gifti.gifti.GiftiDataArray`
-        Output data array instance
-    """
-
-    if not isinstance(ref_array, nib.gifti.GiftiDataArray):
-        raise TypeError('Provided reference is not a GiftiDataArray.')
-    darray = nib.gifti.GiftiDataArray(data,
-                                      intent=ref_array.intent,
-                                      datatype=data.dtype,
-                                      encoding=ref_array.encoding,
-                                      endian=ref_array.endian,
-                                      coordsys=ref_array.coordsys,
-                                      ordering=ref_array.ind_ord,
-                                      meta=ref_array.meta if copy_meta else None)
-
-    return darray
-
-
 def unmask(data, mask):
     """
     Unmasks `data` using non-zero entries of `mask`