sty: format changed files

Author: oesteban

nitransforms/base.py

@@ -7,6 +7,7 @@
 #
 ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
 """Common interface for transforms."""
+
 from pathlib import Path
 import numpy as np
 import h5py
@@ -146,13 +147,13 @@ def from_arrays(cls, coordinates, triangles):
         darrays = [
             nb.gifti.GiftiDataArray(
                 coordinates.astype(np.float32),
-                intent=nb.nifti1.intent_codes['NIFTI_INTENT_POINTSET'],
-                datatype=nb.nifti1.data_type_codes['NIFTI_TYPE_FLOAT32'],
+                intent=nb.nifti1.intent_codes["NIFTI_INTENT_POINTSET"],
+                datatype=nb.nifti1.data_type_codes["NIFTI_TYPE_FLOAT32"],
             ),
             nb.gifti.GiftiDataArray(
                 triangles.astype(np.int32),
-                intent=nb.nifti1.intent_codes['NIFTI_INTENT_TRIANGLE'],
-                datatype=nb.nifti1.data_type_codes['NIFTI_TYPE_INT32'],
+                intent=nb.nifti1.intent_codes["NIFTI_INTENT_TRIANGLE"],
+                datatype=nb.nifti1.data_type_codes["NIFTI_TYPE_INT32"],
             ),
         ]
         gii = nb.gifti.GiftiImage(darrays=darrays)
@@ -282,7 +283,7 @@ def __add__(self, b):
     def __len__(self):
         """
         Enable ``len()``.
-        
+
         By default, all transforms are of length one.
         This must be overriden by transforms arrays and chains.
 
@@ -345,10 +346,8 @@ def apply(self, *args, **kwargs):
 
         Deprecated. Please use ``nitransforms.resampling.apply`` instead.
         """
-        message = (
-            "The `apply` method is deprecated. Please use `nitransforms.resampling.apply` instead."
-        )
-        warnings.warn(message, DeprecationWarning, stacklevel=2)
+        _msg = "This method is deprecated. Please use `nitransforms.resampling.apply` instead."
+        warnings.warn(_msg, DeprecationWarning, stacklevel=2)
         from .resampling import apply
 
         return apply(self, *args, **kwargs)

nitransforms/resampling.py

@@ -188,7 +188,11 @@ def apply(
         )
 
     if isinstance(_ref, ImageGrid):  # If reference is grid, reshape
-        hdr = _ref.header.copy() if _ref.header is not None else spatialimage.header.__class__()
+        hdr = (
+            _ref.header.copy()
+            if _ref.header is not None
+            else spatialimage.header.__class__()
+        )
         hdr.set_data_dtype(output_dtype or spatialimage.header.get_data_dtype())
 
         moved = spatialimage.__class__(

nitransforms/tests/test_base.py

@@ -1,4 +1,5 @@
 """Tests of the base module."""
+
 import numpy as np
 import nibabel as nb
 from nibabel.arrayproxy import get_obj_dtype
@@ -114,7 +115,9 @@ def _to_hdf5(klass, x5_root):
     xfm.reference = fname
     moved = apply(xfm, fname, order=0)
 
-    assert np.all(imgdata == np.asanyarray(moved.dataobj, dtype=get_obj_dtype(moved.dataobj)))
+    assert np.all(
+        imgdata == np.asanyarray(moved.dataobj, dtype=get_obj_dtype(moved.dataobj))
+    )
 
     # Test ndim returned by affine
     assert nitl.Affine().ndim == 3
@@ -168,7 +171,10 @@ def test_concatenation(testdata_path):
 
 def test_SurfaceMesh(testdata_path):
     surf_path = testdata_path / "sub-200148_hemi-R_pial.surf.gii"
-    shape_path = testdata_path / "sub-sid000005_ses-budapest_acq-MPRAGE_hemi-R_thickness.shape.gii"
+    shape_path = (
+        testdata_path
+        / "sub-sid000005_ses-budapest_acq-MPRAGE_hemi-R_thickness.shape.gii"
+    )
     img_path = testdata_path / "bold.nii.gz"
 
     mesh = SurfaceMesh(nb.load(surf_path))

nitransforms/tests/test_linear.py

@@ -1,20 +1,26 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 """Tests of linear transforms."""
-import os
+
 import pytest
 import numpy as np
 import h5py
 
-import nibabel as nb
 from nibabel.eulerangles import euler2mat
 from nibabel.affines import from_matvec
 from nitransforms import linear as nitl
 from nitransforms import io
 from .utils import assert_affines_by_filename
 
 
-@pytest.mark.parametrize("matrix", [[0.0], np.ones((3, 3, 3)), np.ones((3, 4)), ])
+@pytest.mark.parametrize(
+    "matrix",
+    [
+        [0.0],
+        np.ones((3, 3, 3)),
+        np.ones((3, 4)),
+    ],
+)
 def test_linear_typeerrors1(matrix):
     """Exercise errors in Affine creation."""
     with pytest.raises(TypeError):
@@ -136,7 +142,9 @@ def test_loadsave(tmp_path, data_path, testdata_path, autofmt, fmt):
 
         assert np.allclose(
             xfm.matrix,
-            nitl.load(fname, fmt=supplied_fmt, reference=ref_file, moving=ref_file).matrix,
+            nitl.load(
+                fname, fmt=supplied_fmt, reference=ref_file, moving=ref_file
+            ).matrix,
         )
     else:
         assert xfm == nitl.load(fname, fmt=supplied_fmt, reference=ref_file)
@@ -146,7 +154,9 @@ def test_loadsave(tmp_path, data_path, testdata_path, autofmt, fmt):
     if fmt == "fsl":
         assert np.allclose(
             xfm.matrix,
-            nitl.load(fname, fmt=supplied_fmt, reference=ref_file, moving=ref_file).matrix,
+            nitl.load(
+                fname, fmt=supplied_fmt, reference=ref_file, moving=ref_file
+            ).matrix,
             rtol=1e-2,  # FSL incurs into large errors due to rounding
         )
     else:
@@ -160,7 +170,9 @@ def test_loadsave(tmp_path, data_path, testdata_path, autofmt, fmt):
     if fmt == "fsl":
         assert np.allclose(
             xfm.matrix,
-            nitl.load(fname, fmt=supplied_fmt, reference=ref_file, moving=ref_file).matrix,
+            nitl.load(
+                fname, fmt=supplied_fmt, reference=ref_file, moving=ref_file
+            ).matrix,
             rtol=1e-2,  # FSL incurs into large errors due to rounding
         )
     else:
@@ -170,7 +182,9 @@ def test_loadsave(tmp_path, data_path, testdata_path, autofmt, fmt):
     if fmt == "fsl":
         assert np.allclose(
             xfm.matrix,
-            nitl.load(fname, fmt=supplied_fmt, reference=ref_file, moving=ref_file).matrix,
+            nitl.load(
+                fname, fmt=supplied_fmt, reference=ref_file, moving=ref_file
+            ).matrix,
             rtol=1e-2,  # FSL incurs into large errors due to rounding
         )
     else:
@@ -190,12 +204,15 @@ def test_linear_save(tmpdir, data_path, get_testdata, image_orientation, sw_tool
         T = np.linalg.inv(T)
 
     xfm = (
-        nitl.Affine(T) if (sw_tool, image_orientation) != ("afni", "oblique") else
+        nitl.Affine(T)
+        if (sw_tool, image_orientation) != ("afni", "oblique")
         # AFNI is special when moving or reference are oblique - let io do the magic
-        nitl.Affine(io.afni.AFNILinearTransform.from_ras(T).to_ras(
-            reference=img,
-            moving=img,
-        ))
+        else nitl.Affine(
+            io.afni.AFNILinearTransform.from_ras(T).to_ras(
+                reference=img,
+                moving=img,
+            )
+        )
     )
     xfm.reference = img
 

nitransforms/tests/test_manip.py

@@ -1,6 +1,7 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 """Tests of nonlinear transforms."""
+
 import pytest
 
 import numpy as np

nitransforms/tests/test_nonlinear.py

@@ -1,9 +1,8 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 """Tests of nonlinear transforms."""
+
 import os
-import shutil
-from subprocess import check_call
 import pytest
 
 import numpy as np
@@ -14,7 +13,6 @@
 from nitransforms.nonlinear import (
     BSplineFieldTransform,
     DenseFieldTransform,
-    load as nlload,
 )
 from ..io.itk import ITKDisplacementsField
 

nitransforms/tests/test_resampling.py

@@ -1,6 +1,7 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 """Exercise the standalone ``apply()`` implementation."""
+
 import os
 import pytest
 import numpy as np
@@ -50,9 +51,19 @@
 }
 
 
-@pytest.mark.parametrize("image_orientation", ["RAS", "LAS", "LPS", 'oblique', ])
+@pytest.mark.parametrize(
+    "image_orientation",
+    [
+        "RAS",
+        "LAS",
+        "LPS",
+        "oblique",
+    ],
+)
 @pytest.mark.parametrize("sw_tool", ["itk", "fsl", "afni", "fs"])
-def test_apply_linear_transform(tmpdir, get_testdata, get_testmask, image_orientation, sw_tool):
+def test_apply_linear_transform(
+    tmpdir, get_testdata, get_testmask, image_orientation, sw_tool
+):
     """Check implementation of exporting affines to formats."""
     tmpdir.chdir()
 
@@ -107,7 +118,7 @@ def test_apply_linear_transform(tmpdir, get_testdata, get_testmask, image_orient
     nt_moved_mask.to_filename("ntmask.nii.gz")
     diff = np.asanyarray(sw_moved_mask.dataobj) - np.asanyarray(nt_moved_mask.dataobj)
 
-    assert np.sqrt((diff ** 2).mean()) < RMSE_TOL_LINEAR
+    assert np.sqrt((diff**2).mean()) < RMSE_TOL_LINEAR
     brainmask = np.asanyarray(nt_moved_mask.dataobj, dtype=bool)
 
     cmd = APPLY_LINEAR_CMD[sw_tool](
@@ -123,19 +134,17 @@ def test_apply_linear_transform(tmpdir, get_testdata, get_testmask, image_orient
     sw_moved.set_data_dtype(img.get_data_dtype())
 
     nt_moved = apply(xfm, img, order=0)
-    diff = (
-        np.asanyarray(sw_moved.dataobj, dtype=sw_moved.get_data_dtype())
-        - np.asanyarray(nt_moved.dataobj, dtype=nt_moved.get_data_dtype())
-    )
+    diff = np.asanyarray(
+        sw_moved.dataobj, dtype=sw_moved.get_data_dtype()
+    ) - np.asanyarray(nt_moved.dataobj, dtype=nt_moved.get_data_dtype())
 
     # A certain tolerance is necessary because of resampling at borders
     assert np.sqrt((diff[brainmask] ** 2).mean()) < RMSE_TOL_LINEAR
 
     nt_moved = apply(xfm, "img.nii.gz", order=0)
-    diff = (
-        np.asanyarray(sw_moved.dataobj, dtype=sw_moved.get_data_dtype())
-        - np.asanyarray(nt_moved.dataobj, dtype=nt_moved.get_data_dtype())
-    )
+    diff = np.asanyarray(
+        sw_moved.dataobj, dtype=sw_moved.get_data_dtype()
+    ) - np.asanyarray(nt_moved.dataobj, dtype=nt_moved.get_data_dtype())
     # A certain tolerance is necessary because of resampling at borders
     assert np.sqrt((diff[brainmask] ** 2).mean()) < RMSE_TOL_LINEAR
 
@@ -281,7 +290,8 @@ def test_apply_transformchain(tmp_path, testdata_path):
 
     ref_fname = tmp_path / "reference.nii.gz"
     nb.Nifti1Image(
-        np.zeros(xfm.reference.shape, dtype="uint16"), xfm.reference.affine,
+        np.zeros(xfm.reference.shape, dtype="uint16"),
+        xfm.reference.affine,
     ).to_filename(str(ref_fname))
 
     # Then apply the transform and cross-check with software
@@ -310,7 +320,9 @@ def test_apply_transformchain(tmp_path, testdata_path):
 
 
 @pytest.mark.parametrize("serialize_4d", [True, False])
-def test_LinearTransformsMapping_apply(tmp_path, data_path, testdata_path, serialize_4d):
+def test_LinearTransformsMapping_apply(
+    tmp_path, data_path, testdata_path, serialize_4d
+):
     """Apply transform mappings."""
     hmc = nitl.load(
         data_path / "hmc-itk.tfm", fmt="itk", reference=testdata_path / "sbref.nii.gz"
@@ -333,7 +345,8 @@ def test_LinearTransformsMapping_apply(tmp_path, data_path, testdata_path, seria
     )
 
     nii = apply(
-        hmcinv, testdata_path / "fmap.nii.gz",
+        hmcinv,
+        testdata_path / "fmap.nii.gz",
         order=1,
         serialize_nvols=2 if serialize_4d else np.inf,
     )