RF: Remove deprecated nibabel get_* methods

Nibabel deprecated the use of img.get_header() and img.get_affine()
functions in favor of img.header and img.affine in 2.0.0, and
img.get_shape() was deprecated in nibabel 1.2.0.

Nipype has a nibabel >= 2.0.1 requirement, so these changes are safe.

Author: effigies

doc/devel/python_interface_devel.rst

@@ -41,7 +41,7 @@ do is to define inputs, outputs, _run_interface() (not run()), and _list_outputs
             thresholded_map = np.zeros(data.shape)
             thresholded_map[active_map] = data[active_map]
 
-            new_img = nb.Nifti1Image(thresholded_map, img.get_affine(), img.get_header())
+            new_img = nb.Nifti1Image(thresholded_map, img.affine, img.header)
             _, base, _ = split_filename(fname)
             nb.save(new_img, base + '_thresholded.nii')
 

examples/dmri_camino_dti.py

@@ -39,7 +39,7 @@ def get_vox_dims(volume):
     if isinstance(volume, list):
         volume = volume[0]
     nii = nb.load(volume)
-    hdr = nii.get_header()
+    hdr = nii.header
     voxdims = hdr.get_zooms()
     return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
 
@@ -49,15 +49,15 @@ def get_data_dims(volume):
     if isinstance(volume, list):
         volume = volume[0]
     nii = nb.load(volume)
-    hdr = nii.get_header()
+    hdr = nii.header
     datadims = hdr.get_data_shape()
     return [int(datadims[0]), int(datadims[1]), int(datadims[2])]
 
 
 def get_affine(volume):
     import nibabel as nb
     nii = nb.load(volume)
-    return nii.get_affine()
+    return nii.affine
 
 subject_list = ['subj1']
 fsl.FSLCommand.set_default_output_type('NIFTI')

examples/dmri_connectivity.py

@@ -77,7 +77,7 @@ def get_vox_dims(volume):
     if isinstance(volume, list):
         volume = volume[0]
     nii = nb.load(volume)
-    hdr = nii.get_header()
+    hdr = nii.header
     voxdims = hdr.get_zooms()
     return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
 
@@ -87,15 +87,15 @@ def get_data_dims(volume):
     if isinstance(volume, list):
         volume = volume[0]
     nii = nb.load(volume)
-    hdr = nii.get_header()
+    hdr = nii.header
     datadims = hdr.get_data_shape()
     return [int(datadims[0]), int(datadims[1]), int(datadims[2])]
 
 
 def get_affine(volume):
     import nibabel as nb
     nii = nb.load(volume)
-    return nii.get_affine()
+    return nii.affine
 
 
 def select_aparc(list_of_files):

examples/fmri_ants_openfmri.py

@@ -80,8 +80,8 @@ def median(in_files):
             average = data
         else:
             average = average + data
-    median_img = nb.Nifti1Image(average / float(idx + 1),
-                                img.get_affine(), img.get_header())
+    median_img = nb.Nifti1Image(average / float(idx + 1), img.affine,
+                                img.header)
     filename = os.path.join(os.getcwd(), 'median.nii.gz')
     median_img.to_filename(filename)
     return filename

examples/fmri_fsl.py

@@ -108,7 +108,7 @@ def getmiddlevolume(func):
     funcfile = func
     if isinstance(func, list):
         funcfile = func[0]
-    _, _, _, timepoints = load(funcfile).get_shape()
+    _, _, _, timepoints = load(funcfile).shape
     return int(timepoints / 2) - 1
 
 preproc.connect(inputnode, ('func', getmiddlevolume), extract_ref, 't_min')

examples/fmri_spm_auditory.py

@@ -123,7 +123,7 @@ def get_vox_dims(volume):
     if isinstance(volume, list):
         volume = volume[0]
     nii = nb.load(volume)
-    hdr = nii.get_header()
+    hdr = nii.header
     voxdims = hdr.get_zooms()
     return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
 

examples/fmri_spm_face.py

@@ -117,7 +117,7 @@ def get_vox_dims(volume):
     if isinstance(volume, list):
         volume = volume[0]
     nii = nb.load(volume)
-    hdr = nii.get_header()
+    hdr = nii.header
     voxdims = hdr.get_zooms()
     return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
 

examples/rsfmri_vol_surface_preprocessing.py

@@ -125,8 +125,8 @@ def median(in_files):
             average = data
         else:
             average = average + data
-    median_img = nb.Nifti1Image(average / float(idx + 1),
-                                img.get_affine(), img.get_header())
+    median_img = nb.Nifti1Image(average / float(idx + 1), img.affine,
+                                img.header)
     filename = os.path.join(os.getcwd(), 'median.nii.gz')
     median_img.to_filename(filename)
     return filename
@@ -165,8 +165,7 @@ def bandpass_filter(files, lowpass_freq, highpass_freq, fs):
             filtered_data = data
         else:
             filtered_data = np.real(np.fft.ifftn(np.fft.fftn(data) * F))
-        img_out = nb.Nifti1Image(filtered_data, img.get_affine(),
-                                 img.get_header())
+        img_out = nb.Nifti1Image(filtered_data, img.affine, img.header)
         img_out.to_filename(out_file)
         out_files.append(out_file)
     return list_to_filename(out_files)

examples/rsfmri_vol_surface_preprocessing_nipy.py

@@ -119,8 +119,8 @@ def median(in_files):
             average = data
         else:
             average = average + data
-    median_img = nb.Nifti1Image(average / float(idx + 1),
-                                img.get_affine(), img.get_header())
+    median_img = nb.Nifti1Image(average / float(idx + 1), img.affine,
+                                img.header)
     filename = os.path.join(os.getcwd(), 'median.nii.gz')
     median_img.to_filename(filename)
     return filename
@@ -156,8 +156,7 @@ def bandpass_filter(files, lowpass_freq, highpass_freq, fs):
             filtered_data = data
         else:
             filtered_data = np.real(np.fft.ifftn(np.fft.fftn(data) * F))
-        img_out = nb.Nifti1Image(filtered_data, img.get_affine(),
-                                 img.get_header())
+        img_out = nb.Nifti1Image(filtered_data, img.affine, img.header)
         img_out.to_filename(out_file)
         out_files.append(out_file)
     return list_to_filename(out_files)

nipype/algorithms/icc.py

@@ -49,19 +49,19 @@ def _run_interface(self, runtime):
             icc[x], subject_var[x], session_var[x], session_F[x], _, _ = ICC_rep_anova(Y)
 
         nim = nb.load(self.inputs.subjects_sessions[0][0])
-        new_data = np.zeros(nim.get_shape())
+        new_data = np.zeros(nim.shape)
         new_data[maskdata] = icc.reshape(-1,)
-        new_img = nb.Nifti1Image(new_data, nim.get_affine(), nim.get_header())
+        new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)
         nb.save(new_img, 'icc_map.nii')
 
-        new_data = np.zeros(nim.get_shape())
+        new_data = np.zeros(nim.shape)
         new_data[maskdata] = session_var.reshape(-1,)
-        new_img = nb.Nifti1Image(new_data, nim.get_affine(), nim.get_header())
+        new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)
         nb.save(new_img, 'session_var_map.nii')
 
-        new_data = np.zeros(nim.get_shape())
+        new_data = np.zeros(nim.shape)
         new_data[maskdata] = subject_var.reshape(-1,)
-        new_img = nb.Nifti1Image(new_data, nim.get_affine(), nim.get_header())
+        new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)
         nb.save(new_img, 'subject_var_map.nii')
 
         return runtime

nipype/algorithms/mesh.py

@@ -111,13 +111,12 @@ def _run_interface(self, runtime):
         points = np.array(mesh.points)
         warp_dims = nb.funcs.four_to_three(nb.load(self.inputs.warp))
 
-        affine = warp_dims[0].get_affine()
-        voxsize = warp_dims[0].get_header().get_zooms()
+        affine = warp_dims[0].affine
+        voxsize = warp_dims[0].header.get_zooms()
         vox2ras = affine[0:3, 0:3]
         ras2vox = np.linalg.inv(vox2ras)
         origin = affine[0:3, 3]
-        voxpoints = np.array([np.dot(ras2vox,
-                                     (p - origin)) for p in points])
+        voxpoints = np.array([np.dot(ras2vox, (p - origin)) for p in points])
 
         warps = []
         for axis in warp_dims:

nipype/algorithms/metrics.py

@@ -92,9 +92,9 @@ def _eucl_min(self, nii1, nii2):
         origdata2 = nii2.get_data().astype(np.bool)
         border2 = self._find_border(origdata2)
 
-        set1_coordinates = self._get_coordinates(border1, nii1.get_affine())
+        set1_coordinates = self._get_coordinates(border1, nii1.affine)
 
-        set2_coordinates = self._get_coordinates(border2, nii2.get_affine())
+        set2_coordinates = self._get_coordinates(border2, nii2.affine)
 
         dist_matrix = cdist(set1_coordinates.T, set2_coordinates.T)
         (point1, point2) = np.unravel_index(
@@ -108,7 +108,7 @@ def _eucl_cog(self, nii1, nii2):
         origdata1 = nii1.get_data().astype(np.bool)
         cog_t = np.array(center_of_mass(origdata1)).reshape(-1, 1)
         cog_t = np.vstack((cog_t, np.array([1])))
-        cog_t_coor = np.dot(nii1.get_affine(), cog_t)[:3, :]
+        cog_t_coor = np.dot(nii1.affine, cog_t)[:3, :]
 
         origdata2 = nii2.get_data().astype(np.bool)
         (labeled_data, n_labels) = label(origdata2)
@@ -119,7 +119,7 @@ def _eucl_cog(self, nii1, nii2):
             cogs[:3, i] = np.array(center_of_mass(origdata2,
                                                   labeled_data, i + 1))
 
-        cogs_coor = np.dot(nii2.get_affine(), cogs)[:3, :]
+        cogs_coor = np.dot(nii2.affine, cogs)[:3, :]
 
         dist_matrix = cdist(cog_t_coor.T, cogs_coor.T)
 
@@ -131,8 +131,8 @@ def _eucl_mean(self, nii1, nii2, weighted=False):
 
         origdata2 = nii2.get_data().astype(np.bool)
 
-        set1_coordinates = self._get_coordinates(border1, nii1.get_affine())
-        set2_coordinates = self._get_coordinates(origdata2, nii2.get_affine())
+        set1_coordinates = self._get_coordinates(border1, nii1.affine)
+        set2_coordinates = self._get_coordinates(origdata2, nii2.affine)
 
         dist_matrix = cdist(set1_coordinates.T, set2_coordinates.T)
         min_dist_matrix = np.amin(dist_matrix, axis=0)
@@ -172,8 +172,8 @@ def _eucl_max(self, nii1, nii2):
         border1 = self._find_border(origdata1)
         border2 = self._find_border(origdata2)
 
-        set1_coordinates = self._get_coordinates(border1, nii1.get_affine())
-        set2_coordinates = self._get_coordinates(border2, nii2.get_affine())
+        set1_coordinates = self._get_coordinates(border1, nii1.affine)
+        set2_coordinates = self._get_coordinates(border2, nii2.affine)
         distances = cdist(set1_coordinates.T, set2_coordinates.T)
         mins = np.concatenate(
             (np.amin(distances, axis=0), np.amin(distances, axis=1)))
@@ -283,7 +283,7 @@ def _run_interface(self, runtime):
         scale = 1.0
 
         if self.inputs.vol_units == 'mm':
-            voxvol = nii1.get_header().get_zooms()
+            voxvol = nii1.header.get_zooms()
             for i in range(nii1.get_data().ndim - 1):
                 scale = scale * voxvol[i]
 
@@ -330,8 +330,8 @@ def _run_interface(self, runtime):
         both_data = np.zeros(data1.shape)
         both_data[(data1 - data2) != 0] = 1
 
-        nb.save(nb.Nifti1Image(both_data, nii1.get_affine(),
-                               nii1.get_header()), self.inputs.out_file)
+        nb.save(nb.Nifti1Image(both_data, nii1.affine, nii1.header),
+                self.inputs.out_file)
 
         self._labels = labels
         self._ove_rois = results
@@ -452,8 +452,9 @@ def _run_interface(self, runtime):
             ch[msk == 0] = 0
             diff += w * ch
 
-        nb.save(nb.Nifti1Image(diff, nb.load(self.inputs.in_ref[0]).get_affine(),
-                               nb.load(self.inputs.in_ref[0]).get_header()), self.inputs.out_file)
+        nb.save(nb.Nifti1Image(diff, nb.load(self.inputs.in_ref[0]).affine,
+                               nb.load(self.inputs.in_ref[0]).header),
+                self.inputs.out_file)
 
         return runtime
 
@@ -549,7 +550,7 @@ def _run_interface(self, runtime):
 
         errmap = errvectorexp.reshape(mapshape)
 
-        hdr = nii_ref.get_header().copy()
+        hdr = nii_ref.header.copy()
         hdr.set_data_dtype(np.float32)
         hdr['data_type'] = 16
         hdr.set_data_shape(mapshape)
@@ -563,7 +564,7 @@ def _run_interface(self, runtime):
         else:
             self._out_file = self.inputs.out_map
 
-        nb.Nifti1Image(errmap.astype(np.float32), nii_ref.get_affine(),
+        nb.Nifti1Image(errmap.astype(np.float32), nii_ref.affine,
                        hdr).to_filename(self._out_file)
 
         return runtime