RF: Purge get_data from nipy, dipy interfaces

Author: effigies

nipype/interfaces/dipy/anisotropic_power.py

@@ -43,11 +43,11 @@ def _run_interface(self, runtime):
         gtab = self._get_gradient_table()
 
         img = nb.load(self.inputs.in_file)
-        data = img.get_data()
+        data = np.asanyarray(img.dataobj)
         affine = img.affine
         mask = None
         if isdefined(self.inputs.mask_file):
-            mask = nb.load(self.inputs.mask_file).get_data()
+            mask = np.asanyarray(nb.load(self.inputs.mask_file).dataobj)
 
         # Fit it
         model = shm.QballModel(gtab, 8)

nipype/interfaces/dipy/preprocess.py

@@ -166,7 +166,7 @@ def _run_interface(self, runtime):
         settings = dict(mask=None, rician=(self.inputs.noise_model == "rician"))
 
         if isdefined(self.inputs.in_mask):
-            settings["mask"] = nb.load(self.inputs.in_mask).get_data()
+            settings["mask"] = np.asanyarray(nb.load(self.inputs.in_mask).dataobj)
 
         if isdefined(self.inputs.patch_radius):
             settings["patch_radius"] = self.inputs.patch_radius
@@ -180,10 +180,10 @@ def _run_interface(self, runtime):
 
         signal_mask = None
         if isdefined(self.inputs.signal_mask):
-            signal_mask = nb.load(self.inputs.signal_mask).get_data()
+            signal_mask = np.asanyarray(nb.load(self.inputs.signal_mask).dataobj)
         noise_mask = None
         if isdefined(self.inputs.noise_mask):
-            noise_mask = nb.load(self.inputs.noise_mask).get_data()
+            noise_mask = np.asanyarray(nb.load(self.inputs.noise_mask).dataobj)
 
         _, s = nlmeans_proxy(
             self.inputs.in_file,
@@ -224,7 +224,7 @@ def resample_proxy(in_file, order=3, new_zooms=None, out_file=None):
 
     img = nb.load(in_file, mmap=NUMPY_MMAP)
     hdr = img.header.copy()
-    data = img.get_data().astype(np.float32)
+    data = img.get_fdata(dtype=np.float32)
     affine = img.affine
     im_zooms = hdr.get_zooms()[:3]
 
@@ -264,7 +264,7 @@ def nlmeans_proxy(in_file, settings, snr=None, smask=None, nmask=None, out_file=
 
     img = nb.load(in_file, mmap=NUMPY_MMAP)
     hdr = img.header
-    data = img.get_data()
+    data = img.get_fdata()
     aff = img.affine
 
     if data.ndim < 4:

nipype/interfaces/dipy/reconstruction.py

@@ -92,17 +92,19 @@ def _run_interface(self, runtime):
         img = nb.load(self.inputs.in_file)
         hdr = img.header.copy()
         affine = img.affine
-        data = img.get_data()
+        data = img.get_fdata()
         gtab = self._get_gradient_table()
 
         if isdefined(self.inputs.in_mask):
-            msk = nb.load(self.inputs.in_mask).get_data().astype(np.uint8)
+            msk = np.asanyarray(nb.load(self.inputs.in_mask).dataobj).astype(np.uint8)
         else:
             msk = np.ones(data.shape[:3], dtype=np.uint8)
 
         try_b0 = True
         if isdefined(self.inputs.noise_mask):
-            noise_msk = nb.load(self.inputs.noise_mask).get_data().reshape(-1)
+            noise_msk = (
+                nb.load(self.inputs.noise_mask).get_fdata(dtype=np.float32).reshape(-1)
+            )
             noise_msk[noise_msk > 0.5] = 1
             noise_msk[noise_msk < 1.0] = 0
             noise_msk = noise_msk.astype(np.uint8)
@@ -232,16 +234,16 @@ def _run_interface(self, runtime):
         affine = img.affine
 
         if isdefined(self.inputs.in_mask):
-            msk = nb.load(self.inputs.in_mask).get_data()
+            msk = np.asanyarray(nb.load(self.inputs.in_mask).dataobj)
             msk[msk > 0] = 1
             msk[msk < 0] = 0
         else:
             msk = np.ones(imref.shape)
 
-        data = img.get_data().astype(np.float32)
+        data = img.get_fdata(dtype=np.float32)
         gtab = self._get_gradient_table()
 
-        evals = np.nan_to_num(nb.load(self.inputs.in_evals).get_data())
+        evals = np.nan_to_num(nb.load(self.inputs.in_evals).dataobj)
         FA = np.nan_to_num(fractional_anisotropy(evals)) * msk
         indices = np.where(FA > self.inputs.fa_thresh)
         S0s = data[indices][:, np.nonzero(gtab.b0s_mask)[0]]
@@ -260,7 +262,7 @@ def _run_interface(self, runtime):
             indices = np.logical_or(
                 FA >= 0.4, (np.logical_and(FA >= 0.15, MD >= 0.0011))
             )
-            data = nb.load(self.inputs.in_file).get_data()
+            data = np.asanyarray(nb.load(self.inputs.in_file).dataobj)
             response = recursive_response(
                 gtab,
                 data,
@@ -359,11 +361,11 @@ def _run_interface(self, runtime):
         imref = nb.four_to_three(img)[0]
 
         if isdefined(self.inputs.in_mask):
-            msk = nb.load(self.inputs.in_mask).get_data()
+            msk = np.asanyarray(nb.load(self.inputs.in_mask).dataobj)
         else:
             msk = np.ones(imref.shape)
 
-        data = img.get_data().astype(np.float32)
+        data = img.get_fdata(dtype=np.float32)
 
         gtab = self._get_gradient_table()
         resp_file = np.loadtxt(self.inputs.response)

nipype/interfaces/dipy/simulate.py

@@ -143,15 +143,15 @@ def _run_interface(self, runtime):
         vfs = np.squeeze(
             nb.concat_images(
                 [nb.load(f, mmap=NUMPY_MMAP) for f in self.inputs.in_vfms]
-            ).get_data()
+            ).dataobj
         )
         if nballs == 1:
             vfs = vfs[..., np.newaxis]
         total_vf = np.sum(vfs, axis=3)
 
         # Generate a mask
         if isdefined(self.inputs.in_mask):
-            msk = nb.load(self.inputs.in_mask).get_data()
+            msk = np.asanyarray(nb.load(self.inputs.in_mask).dataobj)
             msk[msk > 0.0] = 1.0
             msk[msk < 1.0] = 0.0
         else:
@@ -165,7 +165,7 @@ def _run_interface(self, runtime):
         ffsim = nb.concat_images(
             [nb.load(f, mmap=NUMPY_MMAP) for f in self.inputs.in_frac]
         )
-        ffs = np.nan_to_num(np.squeeze(ffsim.get_data()))  # fiber fractions
+        ffs = np.nan_to_num(np.squeeze(ffsim.dataobj))  # fiber fractions
         ffs = np.clip(ffs, 0.0, 1.0)
         if nsticks == 1:
             ffs = ffs[..., np.newaxis]
@@ -207,7 +207,7 @@ def _run_interface(self, runtime):
         dirs = None
         for i in range(nsticks):
             f = self.inputs.in_dirs[i]
-            fd = np.nan_to_num(nb.load(f, mmap=NUMPY_MMAP).get_data())
+            fd = np.nan_to_num(nb.load(f, mmap=NUMPY_MMAP).dataobj)
             w = np.linalg.norm(fd, axis=3)[..., np.newaxis]
             w[w < np.finfo(float).eps] = 1.0
             fd /= w
@@ -230,7 +230,7 @@ def _run_interface(self, runtime):
 
         mevals = [sf_evals] * nsticks + [[ba_evals[d]] * 3 for d in range(nballs)]
 
-        b0 = b0_im.get_data()[msk > 0]
+        b0 = b0_im.get_fdata()[msk > 0]
         args = []
         for i in range(nvox):
             args.append(

nipype/interfaces/dipy/tensors.py

@@ -47,11 +47,11 @@ def _run_interface(self, runtime):
         gtab = self._get_gradient_table()
 
         img = nb.load(self.inputs.in_file)
-        data = img.get_data()
+        data = img.get_fdata()
         affine = img.affine
         mask = None
         if isdefined(self.inputs.mask_file):
-            mask = nb.load(self.inputs.mask_file).get_data()
+            mask = np.asanyarray(nb.load(self.inputs.mask_file).dataobj)
 
         # Fit it
         tenmodel = dti.TensorModel(gtab)
@@ -120,7 +120,7 @@ def _run_interface(self, runtime):
 
         # Load the 4D image files
         img = nb.load(self.inputs.in_file)
-        data = img.get_data()
+        data = img.get_fdata()
         affine = img.affine
 
         # Load the gradient strengths and directions

nipype/interfaces/dipy/tracks.py

@@ -233,7 +233,7 @@ def _run_interface(self, runtime):
         imref = nb.four_to_three(img)[0]
         affine = img.affine
 
-        data = img.get_data().astype(np.float32)
+        data = img.get_fdata(dtype=np.float32)
         hdr = imref.header.copy()
         hdr.set_data_dtype(np.float32)
         hdr["data_type"] = 16
@@ -274,7 +274,7 @@ def _run_interface(self, runtime):
         IFLOGGER.info("Performing tractography")
 
         if isdefined(self.inputs.tracking_mask):
-            msk = nb.load(self.inputs.tracking_mask).get_data()
+            msk = np.asanyarray(nb.load(self.inputs.tracking_mask).dataobj)
             msk[msk > 0] = 1
             msk[msk < 0] = 0
         else:
@@ -287,7 +287,7 @@ def _run_interface(self, runtime):
             seeds = np.loadtxt(self.inputs.seed_coord)
 
         elif isdefined(self.inputs.seed_mask):
-            seedmsk = nb.load(self.inputs.seed_mask).get_data()
+            seedmsk = np.asanyarray(nb.load(self.inputs.seed_mask).dataobj)
             assert seedmsk.shape == data.shape[:3]
             seedmsk[seedmsk > 0] = 1
             seedmsk[seedmsk < 1] = 0

nipype/interfaces/nipy/preprocess.py

@@ -57,8 +57,7 @@ def _run_interface(self, runtime):
             value = getattr(self.inputs, key)
             if isdefined(value):
                 if key in ["mean_volume", "reference_volume"]:
-                    nii = nb.load(value, mmap=NUMPY_MMAP)
-                    value = nii.get_data()
+                    value = np.asanyarray(nb.load(value, mmap=NUMPY_MMAP).dataobj)
                 args[key] = value
 
         brain_mask = compute_mask(**args)
@@ -264,7 +263,7 @@ def _run_interface(self, runtime):
             s = slice(self.inputs.begin_index, nii.shape[3])
         else:
             s = slice(self.inputs.begin_index, self.inputs.end_index)
-        nii2 = nb.Nifti1Image(nii.get_data()[..., s], nii.affine, nii.header)
+        nii2 = nb.Nifti1Image(nii.dataobj[..., s], nii.affine, nii.header)
         nb.save(nii2, out_file)
         return runtime
 

nipype/interfaces/nipy/utils.py

@@ -74,12 +74,12 @@ def _run_interface(self, runtime):
         vol2_nii = nb.load(self.inputs.volume2)
 
         if isdefined(self.inputs.mask1):
-            mask1 = nb.load(self.inputs.mask1).get_data() == 1
+            mask1 = np.asanyarray(nb.load(self.inputs.mask1).dataobj) == 1
         else:
             mask1 = None
 
         if isdefined(self.inputs.mask2):
-            mask2 = nb.load(self.inputs.mask2).get_data() == 1
+            mask2 = np.asanyarray(nb.load(self.inputs.mask2).dataobj) == 2
         else:
             mask2 = None