[DATALAD RUNCMD] RF: .get_affine() -> .affine to address nibabel deprecation

=== Do not change lines below ===
{
 "chain": [],
 "cmd": "git-sedi '\\.get_affine()' .affine",
 "exit": 0,
 "extra_inputs": [],
 "inputs": [],
 "outputs": [
  "doc",
  "src",
  "test"
 ],
 "pwd": "."
}
^^^ Do not change lines above ^^^

Author: yarikoptic

doc/Python_Tutorial.rst

@@ -62,7 +62,7 @@ the array of voxel data, get the affine transform, or create a Nifti1Image.
 .. code-block:: python
     
     >>> stack_data = my_stack.get_data()
-    >>> stack_affine = my_stack.get_affine()
+    >>> stack_affine = my_stack.affine
     >>> nii = my_stack.to_nifti()
     
 Embedding Meta Data

src/dcmstack/dcmmeta.py

@@ -1304,7 +1304,7 @@ def meta_valid(self, classification):
             return False
 
         slice_dim = hdr.get_dim_info()[2]
-        slice_dir = self.nii_img.get_affine()[slice_dim, :3]
+        slice_dir = self.nii_img.affine[slice_dim, :3]
         slices_aligned = np.allclose(slice_dir,
                                      self.meta_ext.slice_normal,
                                      atol=1e-6)
@@ -1533,7 +1533,7 @@ def from_dicom_wrapper(klass, dcm_wrp, meta_dict=None):
         data = dcm_wrp.get_data()
 
         #The Nifti patient space flips the x and y directions
-        affine = np.dot(np.diag([-1., -1., 1., 1.]), dcm_wrp.get_affine())
+        affine = np.dot(np.diag([-1., -1., 1., 1.]), dcm_wrp.affine)
 
         #Make 2D data 3D
         if len(data.shape) == 2:
@@ -1608,7 +1608,7 @@ def from_sequence(klass, seq, dim=None):
         first_nii = first_input.nii_img
         first_hdr = first_nii.header
         shape = first_nii.shape
-        affine = first_nii.get_affine().copy()
+        affine = first_nii.affine.copy()
 
         #If dim is None, choose a sane default
         if dim is None:
@@ -1683,7 +1683,7 @@ def from_sequence(klass, seq, dim=None):
 
             input_wrp = seq[input_idx]
             input_nii = input_wrp.nii_img
-            input_aff = input_nii.get_affine()
+            input_aff = input_nii.affine
             input_hdr = input_nii.header
 
             #Check that the affines match appropriately
@@ -1768,7 +1768,7 @@ def from_sequence(klass, seq, dim=None):
         #If we joined along a spatial dim, rescale the appropriate axis
         scaled_dim_dir = None
         if dim < 3:
-            scaled_dim_dir = seq[1].nii_img.get_affine()[:3, 3] - trans
+            scaled_dim_dir = seq[1].nii_img.affine[:3, 3] - trans
             affine[:3, dim] = scaled_dim_dir
 
         #Create the resulting Nifti and wrapper

src/dcmstack/dcmstack.py

@@ -356,7 +356,7 @@ def _make_dummy(reference, meta, iop):
     data[...] = np.iinfo(np.int16).max
 
     #Create the nifti image and set header data
-    aff = reference.nii_img.get_affine().copy()
+    aff = reference.nii_img.affine.copy()
     aff[:3, 3] = [iop[1], iop[0], iop[2]]
     nii_img = nb.nifti1.Nifti1Image(data, aff)
     hdr = nii_img.header
@@ -842,12 +842,12 @@ def get_affine(self):
         files_per_vol = len(self._files_info) // n_vols
 
         #Pull the DICOM Patient Space affine from the first input
-        aff = self._files_info[0][0].nii_img.get_affine()
+        aff = self._files_info[0][0].nii_img.affine
 
         #If there is more than one file per volume, we need to fix slice scaling
         if files_per_vol > 1:
             first_offset = aff[:3, 3]
-            second_offset = self._files_info[1][0].nii_img.get_affine()[:3, 3]
+            second_offset = self._files_info[1][0].nii_img.affine[:3, 3]
             scaled_slc_dir = second_offset - first_offset
             aff[:3, 2] = scaled_slc_dir
 
@@ -873,7 +873,7 @@ def to_nifti(self, voxel_order='LAS', embed_meta=False):
         '''
         #Get the voxel data and affine
         data = self.get_data()
-        affine = self.get_affine()
+        affine = self.affine
 
         #Figure out the number of three (or two) dimensional volumes
         n_vols = 1

test/test_dcmmeta.py

@@ -957,7 +957,7 @@ def test_slice_dim_changed(self):
                 ok_(self.nw.meta_valid(classes))
 
     def test_slice_dir_changed(self):
-        aff = self.nw.nii_img.get_affine()
+        aff = self.nw.nii_img.affine
         aff[:] = np.c_[aff[2, :],
                        aff[1, :],
                        aff[0, :],
@@ -1092,7 +1092,7 @@ def setUp(self):
     def test_split_slice(self):
         for split_idx, nw_split in enumerate(self.nw.split(2)):
             eq_(nw_split.nii_img.shape, (3, 3, 1, 5, 7))
-            ok_(np.allclose(nw_split.nii_img.get_affine(),
+            ok_(np.allclose(nw_split.nii_img.affine,
                             np.c_[[1.1, 0.0, 0.0, 0.0],
                                   [0.0, 1.1, 0.0, 0.0],
                                   [0.0, 0.0, 1.1, 0.0],
@@ -1107,7 +1107,7 @@ def test_split_slice(self):
     def test_split_time(self):
         for split_idx, nw_split in enumerate(self.nw.split(3)):
             eq_(nw_split.nii_img.shape, (3, 3, 3, 1, 7))
-            ok_(np.allclose(nw_split.nii_img.get_affine(),
+            ok_(np.allclose(nw_split.nii_img.affine,
                             np.diag([1.1, 1.1, 1.1, 1.0])))
             ok_(np.all(nw_split.nii_img.get_data() ==
                        self.arr[:, :, :, split_idx:split_idx+1, :])
@@ -1116,7 +1116,7 @@ def test_split_time(self):
     def test_split_vector(self):
         for split_idx, nw_split in enumerate(self.nw.split(4)):
             eq_(nw_split.nii_img.shape, (3, 3, 3, 5))
-            ok_(np.allclose(nw_split.nii_img.get_affine(),
+            ok_(np.allclose(nw_split.nii_img.affine,
                             np.diag([1.1, 1.1, 1.1, 1.0])))
             ok_(np.all(nw_split.nii_img.get_data() ==
                        self.arr[:, :, :, :, split_idx])
@@ -1143,8 +1143,8 @@ def test_from_dicom():
     nw = dcmmeta.NiftiWrapper.from_dicom(src_dcm, meta)
     hdr = nw.nii_img.header
     eq_(nw.nii_img.shape, (192, 192, 1))
-    ok_(np.allclose(np.dot(np.diag([-1., -1., 1., 1.]), src_dw.get_affine()),
-                    nw.nii_img.get_affine())
+    ok_(np.allclose(np.dot(np.diag([-1., -1., 1., 1.]), src_dw.affine),
+                    nw.nii_img.affine)
        )
     eq_(hdr.get_xyzt_units(), ('mm', 'sec'))
     eq_(hdr.get_dim_info(), (0, 1, 2))
@@ -1169,7 +1169,7 @@ def test_from_2d_slice_to_3d():
 
     merged = dcmmeta.NiftiWrapper.from_sequence(slice_nws, 2)
     eq_(merged.nii_img.shape, (4, 4, 3))
-    ok_(np.allclose(merged.nii_img.get_affine(),
+    ok_(np.allclose(merged.nii_img.affine,
                     np.diag((1.1, 1.1, 0.5, 1.0)))
        )
     eq_(merged.meta_ext.get_values_and_class('EchoTime'),
@@ -1208,7 +1208,7 @@ def test_from_3d_time_to_4d():
 
     merged = dcmmeta.NiftiWrapper.from_sequence(time_nws, 3)
     eq_(merged.nii_img.shape, (4, 4, 4, 3))
-    ok_(np.allclose(merged.nii_img.get_affine(),
+    ok_(np.allclose(merged.nii_img.affine,
                     np.diag((1.1, 1.1, 1.1, 1.0)))
        )
     eq_(merged.meta_ext.get_values_and_class('PatientID'),
@@ -1254,7 +1254,7 @@ def test_from_3d_vector_to_4d():
 
     merged = dcmmeta.NiftiWrapper.from_sequence(vector_nws, 4)
     eq_(merged.nii_img.shape, (4, 4, 4, 1, 3))
-    ok_(np.allclose(merged.nii_img.get_affine(),
+    ok_(np.allclose(merged.nii_img.affine,
                     np.diag((1.1, 1.1, 1.1, 1.0)))
        )
     eq_(merged.meta_ext.get_values_and_class('PatientID'),
@@ -1326,7 +1326,7 @@ def test_merge_with_slc_and_without():
 
     merged = dcmmeta.NiftiWrapper.from_sequence(input_nws)
     eq_(merged.nii_img.shape, (4, 4, 4, 3))
-    ok_(np.allclose(merged.nii_img.get_affine(),
+    ok_(np.allclose(merged.nii_img.affine,
                     np.diag((1.1, 1.1, 1.1, 1.0)))
        )
     eq_(merged.meta_ext.get_values_and_class('PatientID'),

test/test_dcmstack.py

@@ -532,15 +532,15 @@ def setUp(self):
     def test_single_slice(self):
         stack = dcmstack.DicomStack()
         stack.add_dcm(self.inputs[0])
-        affine = stack.get_affine()
+        affine = stack.affine
         ref = np.load(path.join(self.data_dir, 'single_slice_aff.npy'))
         ok_(np.allclose(affine, ref))
 
     def test_three_dim(self):
         stack = dcmstack.DicomStack()
         stack.add_dcm(self.inputs[0])
         stack.add_dcm(self.inputs[1])
-        affine = stack.get_affine()
+        affine = stack.affine
         ref = np.load(path.join(self.data_dir, 'single_vol_aff.npy'))
         ok_(np.allclose(affine, ref))