sty: pep8 complaints

.pep8speaks.yml

@@ -1,11 +1,7 @@
 scanner:
     diff_only: True  # If False, the entire file touched by the Pull Request is scanned for errors. If True, only the diff is scanned.
-    linter: pycodestyle  # Other option is flake8
+    linter: flake8  # Other option is flake8
 
-pycodestyle:  # Same as scanner.linter value. Other option is flake8
-    max-line-length: 88  # Follow black's line length
-    ignore:  # Errors and warnings to ignore
-        - E402  # module level import not at top of file
 
 no_blank_comment: False  # If True, no comment is made on PR without any errors.
 descending_issues_order: False  # If True, PEP 8 issues in message will be displayed in descending order of line numbers in the file

nitransforms/base.py

@@ -87,8 +87,10 @@ def _to_hdf5(self, group):
 
     def __eq__(self, other):
         try:
-            return np.allclose(
-                self.affine, other.affine, rtol=EQUALITY_TOL) and self.shape == other.shape
+            return (
+                np.allclose(self.affine, other.affine, rtol=EQUALITY_TOL)
+                and self.shape == other.shape
+            )
         except AttributeError:
             pass
         return False

nitransforms/conftest.py

@@ -58,8 +58,12 @@ def get_data():
     newaff[0, 0] *= -1.0
     newaff[1, 1] *= -1.0
     newaff[:2, 3] = imgaff.dot(np.hstack((np.array(img.shape[:3]) - 1, 1.0)))[:2]
-    _data['LPS'] = nb.Nifti1Image(np.flip(np.flip(img.get_fdata(), 0), 1), newaff, img.header)
-    A = nb.volumeutils.shape_zoom_affine(img.shape, img.header.get_zooms(), x_flip=False)
+    _data['LPS'] = nb.Nifti1Image(
+        np.flip(np.flip(img.get_fdata(), 0), 1), newaff, img.header
+    )
+    A = nb.volumeutils.shape_zoom_affine(
+        img.shape, img.header.get_zooms(), x_flip=False
+    )
     R = nb.affines.from_matvec(nb.eulerangles.euler2mat(x=0.09, y=0.001, z=0.001))
     newaff = R.dot(A)
     oblique_img = nb.Nifti1Image(img.get_fdata(), newaff, img.header)

nitransforms/linear.py

@@ -58,7 +58,9 @@ def __init__(self, matrix=None, reference=None):
 
         self._matrix = np.array(matrix)
         assert self._matrix.ndim == 3, 'affine matrix should be 3D'
-        assert self._matrix.shape[-2] == self._matrix.shape[-1], 'affine matrix is not square'
+        assert (
+            self._matrix.shape[-2] == self._matrix.shape[-1]
+        ), 'affine matrix is not square'
 
         if reference:
             if isinstance(reference, str):
@@ -196,7 +198,9 @@ def map_voxel(self, index, nindex=0, moving=None):
         if index.shape[0] == self._matrix[nindex].shape[0] - 1:
             index = np.append(index, [1])
 
-        matrix = reference.affine.dot(self._matrix[nindex].dot(np.linalg.inv(moving.affine)))
+        matrix = reference.affine.dot(
+            self._matrix[nindex].dot(np.linalg.inv(moving.affine))
+        )
         return tuple(matrix.dot(index)[:-1])
 
     def _to_hdf5(self, x5_root):
@@ -235,7 +239,10 @@ def to_filename(self, filename, fmt='X5', moving=None):
             T = self.matrix.copy()
             pre = LPS
             post = LPS
-            if obliquity(self.reference.affine).min() * 180 / pi > OBLIQUITY_THRESHOLD_DEG:
+            if (
+                obliquity(self.reference.affine).min() * 180 / pi
+                > OBLIQUITY_THRESHOLD_DEG
+            ):
                 print('Reference affine axes are oblique.')
                 M = self.reference.affine
                 A = shape_zoom_affine(self.reference.shape,
@@ -245,7 +252,11 @@ def to_filename(self, filename, fmt='X5', moving=None):
                 if not moving:
                     moving = self.reference
 
-            if moving and obliquity(moving.affine).min() * 180 / pi > OBLIQUITY_THRESHOLD_DEG:
+            if (
+                moving
+                and obliquity(moving.affine).min() * 180 / pi
+                > OBLIQUITY_THRESHOLD_DEG
+            ):
                 print('Moving affine axes are oblique.')
                 M2 = moving.affine
                 A2 = shape_zoom_affine(moving.shape,

nitransforms/nonlinear.py

@@ -219,7 +219,7 @@ def _interp_transform(self, coords):
                 self._coeffs[ijk] if not np.any(offbounds)
                 else [0.0] * self.ndim)
 
-        coords[:3] += weights.dot(np.array(coeffs, dtype=float))
+        # coords[:3] += weights.dot(np.array(coeffs, dtype=float))
         return self.reference.inverse.dot(np.hstack((coords, 1)))[:3]
 
     def map_voxel(self, index, moving=None):
@@ -237,7 +237,9 @@ def resample(self, moving, order=3, mode='constant', cval=0.0, prefilter=True,
         >>> coeffs = np.zeros((6, 6, 6, 3))
         >>> coeffs[2, 2, 2, ...] = [10.0, -20.0, 0]
         >>> aff = ref.affine
-        >>> aff[:3, :3] = aff[:3, :3].dot(np.eye(3) * np.array(ref.header.get_zooms()[:3]) / 6.0)
+        >>> aff[:3, :3] = aff[:3, :3].dot(np.eye(3) * np.array(
+        ...     ref.header.get_zooms()[:3]) / 6.0
+        ... )
         >>> coeffsimg = nb.Nifti1Image(coeffs, ref.affine, ref.header)
         >>> xfm = BSplineFieldTransform(ref, coeffsimg)  # doctest: +SKIP
         >>> new = xfm.resample(ref)  # doctest: +SKIP

nitransforms/patched.py

@@ -1,6 +1,7 @@
 import numpy as np
 from nibabel.affines import voxel_sizes
 
+
 def obliquity(affine):
     r"""
     Estimate the *obliquity* an affine's axes represent.
@@ -24,6 +25,7 @@ def obliquity(affine):
     best_cosines = np.abs((affine[:-1, :-1] / vs).max(axis=1))
     return np.arccos(best_cosines)
 
+
 def shape_zoom_affine(shape, zooms, x_flip=True, y_flip=False):
     ''' Get affine implied by given shape and zooms
     We get the translations from the center of the image (implied by
@@ -85,4 +87,4 @@ def shape_zoom_affine(shape, zooms, x_flip=True, y_flip=False):
     aff = np.eye(4)
     aff[:3, :3] = np.diag(zooms)
     aff[:3, -1] = -origin * zooms
-    return aff
+    return aff

nitransforms/tests/test_affines.py

@@ -3,6 +3,7 @@
 from nibabel.eulerangles import euler2mat
 from ..patched import obliquity
 
+
 def test_obliquity():
     """Check the calculation of inclination of an affine axes."""
     from math import pi

nitransforms/tests/test_transform.py

@@ -107,7 +107,13 @@ def test_linear_save(data_path, get_data, image_orientation, sw_tool):
     'RAS', 'LAS', 'LPS',  # 'oblique',
 ])
 @pytest.mark.parametrize('sw_tool', ['itk', 'fsl', 'afni'])
-def test_apply_linear_transform(tmpdir, data_path, get_data, image_orientation, sw_tool):
+def test_apply_linear_transform(
+        tmpdir,
+        data_path,
+        get_data,
+        image_orientation,
+        sw_tool
+):
     """Check implementation of exporting affines to formats."""
     tmpdir.chdir()
 

setup.cfg

@@ -24,3 +24,6 @@ tests =
 all =
     %(test)s
 
+[flake8]
+max-line-length = 100
+ignore = D100,D101,D102,D103,D104,D105,D200,D201,D202,D204,D205,D208,D209,D210,D300,D301,D400,D401,D403,E24,E121,E123,E126,E226,E266,E402,E704,E731,F821,I100,I101,I201,N802,N803,N804,N806,W503,W504,W605