RF: Finish purging get_data() from algorithms

Author: effigies

nipype/algorithms/icc.py

@@ -41,12 +41,12 @@ class ICC(BaseInterface):
     output_spec = ICCOutputSpec
 
     def _run_interface(self, runtime):
-        maskdata = nb.load(self.inputs.mask).get_data()
+        maskdata = nb.load(self.inputs.mask).get_fdata()
         maskdata = np.logical_not(np.logical_or(maskdata == 0, np.isnan(maskdata)))
 
         session_datas = [
             [
-                nb.load(fname, mmap=NUMPY_MMAP).get_data()[maskdata].reshape(-1, 1)
+                nb.load(fname, mmap=NUMPY_MMAP).get_fdata()[maskdata].reshape(-1, 1)
                 for fname in sessions
             ]
             for sessions in self.inputs.subjects_sessions
@@ -66,17 +66,17 @@ def _run_interface(self, runtime):
 
         nim = nb.load(self.inputs.subjects_sessions[0][0])
         new_data = np.zeros(nim.shape)
-        new_data[maskdata] = icc.reshape(-1,)
+        new_data[maskdata] = icc.reshape(-1)
         new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)
         nb.save(new_img, "icc_map.nii")
 
         new_data = np.zeros(nim.shape)
-        new_data[maskdata] = session_var.reshape(-1,)
+        new_data[maskdata] = session_var.reshape(-1)
         new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)
         nb.save(new_img, "session_var_map.nii")
 
         new_data = np.zeros(nim.shape)
-        new_data[maskdata] = subject_var.reshape(-1,)
+        new_data[maskdata] = subject_var.reshape(-1)
         new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)
         nb.save(new_img, "subject_var_map.nii")
 

nipype/algorithms/mesh.py

@@ -113,9 +113,8 @@ def _run_interface(self, runtime):
 
         warps = []
         for axis in warp_dims:
-            wdata = axis.get_data()
+            wdata = axis.dataobj  # four_to_three ensures this is an array
             if np.any(wdata != 0):
-
                 warp = ndimage.map_coordinates(wdata, voxpoints.transpose())
             else:
                 warp = np.zeros((points.shape[0],))

nipype/algorithms/rapidart.py

@@ -496,7 +496,7 @@ def _detect_outliers_core(self, imgfile, motionfile, runidx, cwd=None):
         # compute global intensity signal
         (x, y, z, timepoints) = nim.shape
 
-        data = nim.get_data()
+        data = nim.get_fdata(dtype=np.float32)
         affine = nim.affine
         g = np.zeros((timepoints, 1))
         masktype = self.inputs.mask_type
@@ -526,7 +526,7 @@ def _detect_outliers_core(self, imgfile, motionfile, runidx, cwd=None):
                     g[t0] = np.nansum(vol * mask_tmp) / np.nansum(mask_tmp)
         elif masktype == "file":  # uses a mask image to determine intensity
             maskimg = load(self.inputs.mask_file, mmap=NUMPY_MMAP)
-            mask = maskimg.get_data()
+            mask = maskimg.get_fdata()
             affine = maskimg.affine
             mask = mask > 0.5
             for t0 in range(timepoints):

nipype/algorithms/stats.py

@@ -50,7 +50,7 @@ class ActivationCount(SimpleInterface):
     output_spec = ActivationCountOutputSpec
 
     def _run_interface(self, runtime):
-        allmaps = nb.concat_images(self.inputs.in_files).get_data()
+        allmaps = nb.concat_images(self.inputs.in_files).dataobj
         acm_pos = np.mean(allmaps > self.inputs.threshold, axis=3, dtype=np.float32)
         acm_neg = np.mean(
             allmaps < -1.0 * self.inputs.threshold, axis=3, dtype=np.float32