Replace deprecated HasTraits.set with trait_set

circle.yml

@@ -1,4 +1,6 @@
 machine:
+  pre:
+    - curl -sSL https://s3.amazonaws.com/circle-downloads/install-circleci-docker.sh | bash -s -- 1.10.0
   environment:
     OSF_NIPYPE_URL: "https://files.osf.io/v1/resources/nefdp/providers/osfstorage"
     DATA_NIPYPE_TUTORIAL_URL: "${OSF_NIPYPE_URL}/57f4739cb83f6901ed94bf21"

nipype/algorithms/confounds.py

@@ -398,7 +398,7 @@ def _run_interface(self, runtime):
         if len(mask_images) == 0:
             img = nb.Nifti1Image(np.ones(imgseries.shape[:3], dtype=np.bool),
                                  affine=imgseries.affine,
-                                 header=imgseries.get_header())
+                                 header=imgseries.header)
             mask_images = [img]
 
         mask_images = self._process_masks(mask_images, imgseries.get_data())
@@ -501,7 +501,7 @@ def _process_masks(self, mask_images, timeseries=None):
             mask_data = np.zeros_like(mask)
             mask_data[mask != 0] = tSTD >= threshold_std
             out_image = nb.Nifti1Image(mask_data, affine=img.affine,
-                                       header=img.get_header())
+                                       header=img.header)
 
             # save mask
             mask_file = os.path.abspath('mask_{:03d}.nii.gz'.format(i))
@@ -561,7 +561,7 @@ def _run_interface(self, runtime):
         header = img.header.copy()
         vollist = [nb.load(filename, mmap=NUMPY_MMAP) for filename in self.inputs.in_file]
         data = np.concatenate([vol.get_data().reshape(
-            vol.get_shape()[:3] + (-1,)) for vol in vollist], axis=3)
+            vol.shape[:3] + (-1,)) for vol in vollist], axis=3)
         data = np.nan_to_num(data)
 
         if data.dtype.kind == 'i':
@@ -570,18 +570,18 @@ def _run_interface(self, runtime):
 
         if isdefined(self.inputs.regress_poly):
             data = regress_poly(self.inputs.regress_poly, data, remove_mean=False)
-            img = nb.Nifti1Image(data, img.get_affine(), header)
+            img = nb.Nifti1Image(data, img.affine, header)
             nb.save(img, op.abspath(self.inputs.detrended_file))
 
         meanimg = np.mean(data, axis=3)
         stddevimg = np.std(data, axis=3)
         tsnr = np.zeros_like(meanimg)
         tsnr[stddevimg > 1.e-3] = meanimg[stddevimg > 1.e-3] / stddevimg[stddevimg > 1.e-3]
-        img = nb.Nifti1Image(tsnr, img.get_affine(), header)
+        img = nb.Nifti1Image(tsnr, img.affine, header)
         nb.save(img, op.abspath(self.inputs.tsnr_file))
-        img = nb.Nifti1Image(meanimg, img.get_affine(), header)
+        img = nb.Nifti1Image(meanimg, img.affine, header)
         nb.save(img, op.abspath(self.inputs.mean_file))
-        img = nb.Nifti1Image(stddevimg, img.get_affine(), header)
+        img = nb.Nifti1Image(stddevimg, img.affine, header)
         nb.save(img, op.abspath(self.inputs.stddev_file))
         return runtime
 
@@ -872,7 +872,7 @@ def combine_mask_files(mask_files, mask_method=None, mask_index=None):
             if mask is None:
                 mask = img.get_data() > 0
             np.logical_or(mask, img.get_data() > 0, mask)
-        img = nb.Nifti1Image(mask, img.affine, header=img.get_header())
+        img = nb.Nifti1Image(mask, img.affine, header=img.header)
         return [img]
 
     if mask_method == 'intersect':
@@ -882,7 +882,7 @@ def combine_mask_files(mask_files, mask_method=None, mask_index=None):
             if mask is None:
                 mask = img.get_data() > 0
             np.logical_and(mask, img.get_data() > 0, mask)
-        img = nb.Nifti1Image(mask, img.affine, header=img.get_header())
+        img = nb.Nifti1Image(mask, img.affine, header=img.header)
         return [img]
 
 

nipype/algorithms/tests/test_tsnr.py

@@ -113,7 +113,7 @@ def assert_default_outputs(self, outputs):
 
     def assert_unchanged(self, expected_ranges):
         for key, (min_, max_) in expected_ranges.items():
-            data = np.asarray(nb.load(self.out_filenames[key])._data)
+            data = np.asarray(nb.load(self.out_filenames[key]).dataobj)
             npt.assert_almost_equal(np.amin(data), min_, decimal=1)
             npt.assert_almost_equal(np.amax(data), max_, decimal=1)
 

nipype/caching/memory.py

@@ -70,7 +70,7 @@ def __call__(self, **kwargs):
         kwargs = modify_paths(kwargs, relative=False)
         interface = self.interface()
         # Set the inputs early to get some argument checking
-        interface.inputs.set(**kwargs)
+        interface.inputs.trait_set(**kwargs)
         # Make a name for our node
         inputs = interface.inputs.get_hashval()
         hasher = hashlib.new('md5')

nipype/interfaces/afni/utils.py

@@ -171,13 +171,10 @@ def aggregate_outputs(self, runtime=None, needed_outputs=None):
             m = re.search(pattern, line)
             if m:
                 d = m.groupdict()
-                for k in list(d.keys()):
-                    d[k] = int(d[k])
-                outputs.set(**d)
+                outputs.trait_set(**{k: int(d[k]) for k in d.keys()})
         return outputs
 
 
-
 class BrickStatInputSpec(CommandLineInputSpec):
     in_file = File(
         desc='input file to 3dmaskave',

nipype/interfaces/base.py

@@ -367,7 +367,7 @@ def __init__(self, **kwargs):
                 undefined_traits[trait] = Undefined
         self.trait_set(trait_change_notify=False, **undefined_traits)
         self._generate_handlers()
-        self.set(**kwargs)
+        self.trait_set(**kwargs)
 
     def items(self):
         """ Name, trait generator for user modifiable traits
@@ -650,7 +650,7 @@ def __deepcopy__(self, memo):
                 pass
         # clone twice
         dup = self.clone_traits(memo=memo)
-        dup.set(**dup_dict)
+        dup.trait_set(**dup_dict)
         return dup
 
 
@@ -1060,7 +1060,7 @@ def run(self, **inputs):
         results :  an InterfaceResult object containing a copy of the instance
         that was executed, provenance information and, if successful, results
         """
-        self.inputs.set(**inputs)
+        self.inputs.trait_set(**inputs)
         self._check_mandatory_inputs()
         self._check_version_requirements(self.inputs)
         interface = self.__class__

nipype/interfaces/dipy/reconstruction.py

@@ -74,8 +74,8 @@ def _run_interface(self, runtime):
         import gc
 
         img = nb.load(self.inputs.in_file)
-        hdr = img.get_header().copy()
-        affine = img.get_affine()
+        hdr = img.header.copy()
+        affine = img.affine
         data = img.get_data()
         gtab = self._get_gradient_table()
 
@@ -210,14 +210,14 @@ def _run_interface(self, runtime):
 
         img = nb.load(self.inputs.in_file)
         imref = nb.four_to_three(img)[0]
-        affine = img.get_affine()
+        affine = img.affine
 
         if isdefined(self.inputs.in_mask):
             msk = nb.load(self.inputs.in_mask).get_data()
             msk[msk > 0] = 1
             msk[msk < 0] = 0
         else:
-            msk = np.ones(imref.get_shape())
+            msk = np.ones(imref.shape)
 
         data = img.get_data().astype(np.float32)
         gtab = self._get_gradient_table()
@@ -326,15 +326,15 @@ def _run_interface(self, runtime):
 
         img = nb.load(self.inputs.in_file)
         imref = nb.four_to_three(img)[0]
-        affine = img.get_affine()
+        affine = img.affine
 
         if isdefined(self.inputs.in_mask):
             msk = nb.load(self.inputs.in_mask).get_data()
         else:
-            msk = np.ones(imref.get_shape())
+            msk = np.ones(imref.shape)
 
         data = img.get_data().astype(np.float32)
-        hdr = imref.get_header().copy()
+        hdr = imref.header.copy()
 
         gtab = self._get_gradient_table()
         resp_file = np.loadtxt(self.inputs.response)
@@ -359,7 +359,7 @@ def _run_interface(self, runtime):
         if self.inputs.save_fods:
             sphere = get_sphere('symmetric724')
             fods = csd_fit.odf(sphere)
-            nb.Nifti1Image(fods.astype(np.float32), img.get_affine(),
+            nb.Nifti1Image(fods.astype(np.float32), img.affine,
                            None).to_filename(self._gen_filename('fods'))
 
         return runtime

nipype/interfaces/dipy/tensors.py

@@ -127,7 +127,7 @@ def _run_interface(self, runtime):
         # Load the 4D image files
         img = nb.load(self.inputs.in_file)
         data = img.get_data()
-        affine = img.get_affine()
+        affine = img.affine
 
         # Load the gradient strengths and directions
         gtab = self._get_gradient_table()

nipype/interfaces/dipy/tracks.py

@@ -177,10 +177,10 @@ def _run_interface(self, runtime):
 
         img = nb.load(self.inputs.in_file)
         imref = nb.four_to_three(img)[0]
-        affine = img.get_affine()
+        affine = img.affine
 
         data = img.get_data().astype(np.float32)
-        hdr = imref.get_header().copy()
+        hdr = imref.header.copy()
         hdr.set_data_dtype(np.float32)
         hdr['data_type'] = 16
 
@@ -222,7 +222,7 @@ def _run_interface(self, runtime):
             msk[msk > 0] = 1
             msk[msk < 0] = 0
         else:
-            msk = np.ones(imref.get_shape())
+            msk = np.ones(imref.shape)
 
         gfa = peaks.gfa * msk
         seeds = self.inputs.num_seeds

nipype/interfaces/utility/base.py

@@ -72,7 +72,7 @@ def __init__(self, fields=None, mandatory_inputs=True, **inputs):
         # Adding any traits wipes out all input values set in superclass initialization,
         # even it the trait is not in the add_traits argument. The work-around is to reset
         # the values after adding the traits.
-        self.inputs.set(**inputs)
+        self.inputs.trait_set(**inputs)
 
     def _add_output_traits(self, base):
         return add_traits(base, self._fields)
@@ -319,7 +319,7 @@ class Split(IOBase):
 
     >>> from nipype.interfaces.utility import Split
     >>> sp = Split()
-    >>> _ = sp.inputs.set(inlist=[1, 2, 3], splits=[2, 1])
+    >>> _ = sp.inputs.trait_set(inlist=[1, 2, 3], splits=[2, 1])
     >>> out = sp.run()
     >>> out.outputs.out1
     [1, 2]
@@ -373,12 +373,12 @@ class Select(IOBase):
 
     >>> from nipype.interfaces.utility import Select
     >>> sl = Select()
-    >>> _ = sl.inputs.set(inlist=[1, 2, 3, 4, 5], index=[3])
+    >>> _ = sl.inputs.trait_set(inlist=[1, 2, 3, 4, 5], index=[3])
     >>> out = sl.run()
     >>> out.outputs.out
     4
 
-    >>> _ = sl.inputs.set(inlist=[1, 2, 3, 4, 5], index=[3, 4])
+    >>> _ = sl.inputs.trait_set(inlist=[1, 2, 3, 4, 5], index=[3, 4])
     >>> out = sl.run()
     >>> out.outputs.out
     [4, 5]

nipype/pipeline/engine/nodes.py

@@ -561,7 +561,7 @@ def _load_results(self, cwd):
             logger.debug('aggregating results')
             if attribute_error:
                 old_inputs = loadpkl(op.join(cwd, '_inputs.pklz'))
-                self.inputs.set(**old_inputs)
+                self.inputs.trait_set(**old_inputs)
             if not isinstance(self, MapNode):
                 self._copyfiles_to_wd(cwd, True, linksonly=True)
                 aggouts = self._interface.aggregate_outputs(
@@ -1121,7 +1121,7 @@ def _make_nodes(self, cwd=None):
                         base_dir=op.join(cwd, 'mapflow'),
                         name=nodename)
             node.plugin_args = self.plugin_args
-            node._interface.inputs.set(
+            node._interface.inputs.trait_set(
                 **deepcopy(self._interface.inputs.get()))
             for field in self.iterfield:
                 if self.nested:
@@ -1246,7 +1246,7 @@ def _get_inputs(self):
         old_inputs = self._inputs.get()
         self._inputs = self._create_dynamic_traits(self._interface.inputs,
                                                    fields=self.iterfield)
-        self._inputs.set(**old_inputs)
+        self._inputs.trait_set(**old_inputs)
         super(MapNode, self)._get_inputs()
 
     def _check_iterfield(self):

nipype/workflows/dmri/fsl/epi.py

@@ -743,13 +743,15 @@ def _dilate_mask(in_file, iterations=4):
     import os
     from nipype.utils import NUMPY_MMAP
     img = nb.load(in_file, mmap=NUMPY_MMAP)
-    img._data = ndimage.binary_dilation(img.get_data(), iterations=iterations)
+    dilated_img = img.__class__(ndimage.binary_dilation(img.get_data(),
+                                                        iterations=iterations),
+                                img.affine, img.header)
 
     name, fext = os.path.splitext(os.path.basename(in_file))
     if fext == '.gz':
         name, _ = os.path.splitext(name)
     out_file = os.path.abspath('./%s_dil.nii.gz' % name)
-    nb.save(img, out_file)
+    nb.save(dilated_img, out_file)
     return out_file
 
 
@@ -781,12 +783,13 @@ def _vsm_remove_mean(in_file, mask_file, in_unwarped):
     img_data[msk == 0] = 0
     vsmmag_masked = ma.masked_values(img_data.reshape(-1), 0.0)
     vsmmag_masked = vsmmag_masked - vsmmag_masked.mean()
-    img._data = vsmmag_masked.reshape(img.shape)
+    masked_img = img.__class__(vsmmag_masked.reshape(img.shape),
+                               img.affine, img.header)
     name, fext = os.path.splitext(os.path.basename(in_file))
     if fext == '.gz':
         name, _ = os.path.splitext(name)
     out_file = os.path.abspath('./%s_demeaned.nii.gz' % name)
-    nb.save(img, out_file)
+    nb.save(masked_img, out_file)
     return out_file