Merge pull request #3099 from effigies/mnt/nibabel3

ENH: Pacify DeprecationWarnings caused by nibabel 3 pre-release

Author: effigies

nipype/algorithms/confounds.py

@@ -623,12 +623,10 @@ def _run_interface(self, runtime):
         skip_vols = self.inputs.ignore_initial_volumes
         if skip_vols:
             imgseries = imgseries.__class__(
-                imgseries.get_data()[..., skip_vols:],
-                imgseries.affine,
-                imgseries.header,
+                imgseries.dataobj[..., skip_vols:], imgseries.affine, imgseries.header
             )
 
-        mask_images = self._process_masks(mask_images, imgseries.get_data())
+        mask_images = self._process_masks(mask_images, imgseries.dataobj)
 
         TR = 0
         if self.inputs.pre_filter == "cosine":
@@ -664,7 +662,7 @@ def _run_interface(self, runtime):
             )
 
         components, filter_basis, metadata = compute_noise_components(
-            imgseries.get_data(),
+            imgseries.get_fdata(dtype=np.float32),
             mask_images,
             components_criterion,
             self.inputs.pre_filter,
@@ -837,8 +835,9 @@ def __init__(self, *args, **kwargs):
     def _process_masks(self, mask_images, timeseries=None):
         out_images = []
         self._mask_files = []
+        timeseries = np.asanyarray(timeseries)
         for i, img in enumerate(mask_images):
-            mask = img.get_data().astype(np.bool)
+            mask = np.asanyarray(img.dataobj).astype(np.bool)
             imgseries = timeseries[mask, :]
             imgseries = regress_poly(2, imgseries)[0]
             tSTD = _compute_tSTD(imgseries, 0, axis=-1)
@@ -920,11 +919,13 @@ class TSNR(BaseInterface):
     def _run_interface(self, runtime):
         img = nb.load(self.inputs.in_file[0], mmap=NUMPY_MMAP)
         header = img.header.copy()
-        vollist = [
-            nb.load(filename, mmap=NUMPY_MMAP) for filename in self.inputs.in_file
-        ]
+        vollist = [nb.load(filename) for filename in self.inputs.in_file]
         data = np.concatenate(
-            [vol.get_data().reshape(vol.shape[:3] + (-1,)) for vol in vollist], axis=3
+            [
+                vol.get_fdata(dtype=np.float32).reshape(vol.shape[:3] + (-1,))
+                for vol in vollist
+            ],
+            axis=3,
         )
         data = np.nan_to_num(data)
 
@@ -985,7 +986,7 @@ class NonSteadyStateDetector(BaseInterface):
     def _run_interface(self, runtime):
         in_nii = nb.load(self.inputs.in_file)
         global_signal = (
-            in_nii.get_data()[:, :, :, :50].mean(axis=0).mean(axis=0).mean(axis=0)
+            in_nii.dataobj[:, :, :, :50].mean(axis=0).mean(axis=0).mean(axis=0)
         )
 
         self._results = {"n_volumes_to_discard": is_outlier(global_signal)}
@@ -1032,8 +1033,8 @@ def compute_dvars(
     from nitime.algorithms import AR_est_YW
     import warnings
 
-    func = nb.load(in_file, mmap=NUMPY_MMAP).get_data().astype(np.float32)
-    mask = nb.load(in_mask, mmap=NUMPY_MMAP).get_data().astype(np.uint8)
+    func = nb.load(in_file).get_fdata(dtype=np.float32)
+    mask = np.asanyarray(nb.load(in_mask).dataobj).astype(np.uint8)
 
     if len(func.shape) != 4:
         raise RuntimeError("Input fMRI dataset should be 4-dimensional")
@@ -1278,20 +1279,22 @@ def combine_mask_files(mask_files, mask_method=None, mask_index=None):
     if mask_method == "union":
         mask = None
         for filename in mask_files:
-            img = nb.load(filename, mmap=NUMPY_MMAP)
+            img = nb.load(filename)
+            img_as_mask = np.asanyarray(img.dataobj).astype("int32") > 0
             if mask is None:
-                mask = img.get_data() > 0
-            np.logical_or(mask, img.get_data() > 0, mask)
+                mask = img_as_mask
+            np.logical_or(mask, img_as_mask, mask)
         img = nb.Nifti1Image(mask, img.affine, header=img.header)
         return [img]
 
     if mask_method == "intersect":
         mask = None
         for filename in mask_files:
-            img = nb.load(filename, mmap=NUMPY_MMAP)
+            img = nb.load(filename)
+            img_as_mask = np.asanyarray(img.dataobj).astype("int32") > 0
             if mask is None:
-                mask = img.get_data() > 0
-            np.logical_and(mask, img.get_data() > 0, mask)
+                mask = img_as_mask
+            np.logical_and(mask, img_as_mask, mask)
         img = nb.Nifti1Image(mask, img.affine, header=img.header)
         return [img]
 
@@ -1374,7 +1377,7 @@ def compute_noise_components(
     md_retained = []
 
     for name, img in zip(mask_names, mask_images):
-        mask = nb.squeeze_image(img).get_data().astype(np.bool)
+        mask = np.asanyarray(nb.squeeze_image(img).dataobj).astype(np.bool)
         if imgseries.shape[:3] != mask.shape:
             raise ValueError(
                 "Inputs for CompCor, timeseries and mask, do not have "

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/metrics.py

@@ -89,10 +89,10 @@ def _get_coordinates(self, data, affine):
     def _eucl_min(self, nii1, nii2):
         from scipy.spatial.distance import cdist, euclidean
 
-        origdata1 = nii1.get_data().astype(np.bool)
+        origdata1 = np.asanyarray(nii1.dataobj).astype(np.bool)
         border1 = self._find_border(origdata1)
 
-        origdata2 = nii2.get_data().astype(np.bool)
+        origdata2 = np.asanyarray(nii2.dataobj).astype(np.bool)
         border2 = self._find_border(origdata2)
 
         set1_coordinates = self._get_coordinates(border1, nii1.affine)
@@ -111,16 +111,14 @@ def _eucl_cog(self, nii1, nii2):
         from scipy.spatial.distance import cdist
         from scipy.ndimage.measurements import center_of_mass, label
 
-        origdata1 = np.logical_and(
-            nii1.get_data() != 0, np.logical_not(np.isnan(nii1.get_data()))
-        )
-        cog_t = np.array(center_of_mass(origdata1.copy())).reshape(-1, 1)
+        origdata1 = np.asanyarray(nii1.dataobj)
+        origdata1 = (np.rint(origdata1) != 0) & ~np.isnan(origdata1)
+        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.affine, cog_t)[:3, :]
 
-        origdata2 = np.logical_and(
-            nii2.get_data() != 0, np.logical_not(np.isnan(nii2.get_data()))
-        )
+        origdata2 = np.asanyarray(nii2.dataobj)
+        origdata2 = (np.rint(origdata2) != 0) & ~np.isnan(origdata2)
         (labeled_data, n_labels) = label(origdata2)
 
         cogs = np.ones((4, n_labels))
@@ -137,10 +135,10 @@ def _eucl_cog(self, nii1, nii2):
     def _eucl_mean(self, nii1, nii2, weighted=False):
         from scipy.spatial.distance import cdist
 
-        origdata1 = nii1.get_data().astype(np.bool)
+        origdata1 = np.asanyarray(nii1.dataobj).astype(np.bool)
         border1 = self._find_border(origdata1)
 
-        origdata2 = nii2.get_data().astype(np.bool)
+        origdata2 = np.asanyarray(nii2.dataobj).astype(np.bool)
 
         set1_coordinates = self._get_coordinates(border1, nii1.affine)
         set2_coordinates = self._get_coordinates(origdata2, nii2.affine)
@@ -159,26 +157,26 @@ def _eucl_mean(self, nii1, nii2, weighted=False):
         plt.close()
 
         if weighted:
-            return np.average(min_dist_matrix, weights=nii2.get_data()[origdata2].flat)
+            return np.average(min_dist_matrix, weights=nii2.dataobj[origdata2].flat)
         else:
             return np.mean(min_dist_matrix)
 
     def _eucl_max(self, nii1, nii2):
         from scipy.spatial.distance import cdist
 
-        origdata1 = nii1.get_data()
-        origdata1 = np.logical_not(np.logical_or(origdata1 == 0, np.isnan(origdata1)))
-        origdata2 = nii2.get_data()
-        origdata2 = np.logical_not(np.logical_or(origdata2 == 0, np.isnan(origdata2)))
+        origdata1 = np.asanyarray(nii1.dataobj)
+        origdata1 = (np.rint(origdata1) != 0) & ~np.isnan(origdata1)
+        origdata2 = np.asanyarray(nii2.dataobj)
+        origdata2 = (np.rint(origdata2) != 0) & ~np.isnan(origdata2)
 
         if isdefined(self.inputs.mask_volume):
-            maskdata = nb.load(self.inputs.mask_volume).get_data()
-            maskdata = np.logical_not(np.logical_or(maskdata == 0, np.isnan(maskdata)))
+            maskdata = np.asanyarray(nb.load(self.inputs.mask_volume).dataobj)
+            maskdata = (np.rint(maskdata) != 0) & ~np.isnan(maskdata)
             origdata1 = np.logical_and(maskdata, origdata1)
             origdata2 = np.logical_and(maskdata, origdata2)
 
         if origdata1.max() == 0 or origdata2.max() == 0:
-            return np.NaN
+            return np.nan
 
         border1 = self._find_border(origdata1)
         border2 = self._find_border(origdata2)
@@ -302,19 +300,17 @@ def _run_interface(self, runtime):
         scale = 1.0
 
         if self.inputs.vol_units == "mm":
-            voxvol = nii1.header.get_zooms()
-            for i in range(nii1.get_data().ndim - 1):
-                scale = scale * voxvol[i]
+            scale = np.prod(nii1.header.get_zooms()[:3])
 
-        data1 = nii1.get_data()
+        data1 = np.asanyarray(nii1.dataobj)
         data1[np.logical_or(data1 < 0, np.isnan(data1))] = 0
         max1 = int(data1.max())
         data1 = data1.astype(np.min_scalar_type(max1))
-        data2 = nii2.get_data().astype(np.min_scalar_type(max1))
+        data2 = np.asanyarray(nii2.dataobj).astype(np.min_scalar_type(max1))
         data2[np.logical_or(data1 < 0, np.isnan(data1))] = 0
 
         if isdefined(self.inputs.mask_volume):
-            maskdata = nb.load(self.inputs.mask_volume).get_data()
+            maskdata = np.asanyarray(nb.load(self.inputs.mask_volume).dataobj)
             maskdata = ~np.logical_or(maskdata == 0, np.isnan(maskdata))
             data1[~maskdata] = 0
             data2[~maskdata] = 0
@@ -445,8 +441,8 @@ class FuzzyOverlap(SimpleInterface):
 
     def _run_interface(self, runtime):
         # Load data
-        refdata = nb.concat_images(self.inputs.in_ref).get_data()
-        tstdata = nb.concat_images(self.inputs.in_tst).get_data()
+        refdata = nb.concat_images(self.inputs.in_ref).dataobj
+        tstdata = nb.concat_images(self.inputs.in_tst).dataobj
 
         # Data must have same shape
         if not refdata.shape == tstdata.shape:
@@ -460,8 +456,7 @@ def _run_interface(self, runtime):
         # Load mask
         mask = np.ones_like(refdata, dtype=bool)
         if isdefined(self.inputs.in_mask):
-            mask = nb.load(self.inputs.in_mask).get_data()
-            mask = mask > 0
+            mask = np.asanyarray(nb.load(self.inputs.in_mask).dataobj) > 0
             mask = np.repeat(mask[..., np.newaxis], ncomp, -1)
             assert mask.shape == refdata.shape
 
@@ -565,8 +560,8 @@ class ErrorMap(BaseInterface):
     def _run_interface(self, runtime):
         # Get two numpy data matrices
         nii_ref = nb.load(self.inputs.in_ref)
-        ref_data = np.squeeze(nii_ref.get_data())
-        tst_data = np.squeeze(nb.load(self.inputs.in_tst).get_data())
+        ref_data = np.squeeze(nii_ref.dataobj)
+        tst_data = np.squeeze(nb.load(self.inputs.in_tst).dataobj)
         assert ref_data.ndim == tst_data.ndim
 
         # Load mask
@@ -578,7 +573,7 @@ def _run_interface(self, runtime):
             mapshape = ref_data.shape[:-1]
 
         if isdefined(self.inputs.mask):
-            msk = nb.load(self.inputs.mask).get_data()
+            msk = np.asanyarray(nb.load(self.inputs.mask).dataobj)
             if mapshape != msk.shape:
                 raise RuntimeError(
                     "Mask should match volume shape, \
@@ -701,7 +696,7 @@ def _run_interface(self, runtime):
         vol1_nii = nb.load(self.inputs.volume1)
         vol2_nii = nb.load(self.inputs.volume2)
 
-        dims = vol1_nii.get_data().ndim
+        dims = len(vol1_nii.shape)
 
         if dims == 3 or dims == 2:
             vols1 = [vol1_nii]
@@ -716,12 +711,12 @@ def _run_interface(self, runtime):
             )
 
         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) == 1
         else:
             mask2 = None