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