STY: autopep8 on test code.

Author: Ben Cipollini

nibabel/freesurfer/tests/test_mghformat.py

@@ -37,7 +37,7 @@
 v2r = np.array([[1, 2, 3, -13], [2, 3, 1, -11.5],
                 [3, 1, 2, -11.5], [0, 0, 0, 1]], dtype=np.float32)
 # sample voxel to ras - tkr matrix (mri_info --vox2ras-tkr)
-v2rtkr = np.array([[-1.0, 0.0, 0.0,  1.5],
+v2rtkr = np.array([[-1.0, 0.0, 0.0, 1.5],
                    [0.0, 0.0, 1.0, -2.5],
                    [0.0, -1.0, 0.0, 2.0],
                    [0.0, 0.0, 0.0, 1.0]], dtype=np.float32)

nibabel/gifti/tests/test_gifti.py

@@ -24,7 +24,7 @@ def test_gifti_image():
     # arguments.
     gi = GiftiImage()
     assert_equal(gi.darrays, [])
-    arr = np.zeros((2,3))
+    arr = np.zeros((2, 3))
     gi.darrays.append(arr)
     # Now check we didn't overwrite the default arg
     gi = GiftiImage()
@@ -63,9 +63,9 @@ def test_gifti_image():
 def test_dataarray():
     for dt_code in data_type_codes.value_set():
         data_type = data_type_codes.type[dt_code]
-        if data_type is np.void: # not supported
+        if data_type is np.void:  # not supported
             continue
-        arr = np.zeros((10,3), dtype=data_type)
+        arr = np.zeros((10, 3), dtype=data_type)
         da = GiftiDataArray.from_array(arr, 'triangle')
         assert_equal(da.datatype, data_type_codes[arr.dtype])
         bs_arr = arr.byteswap().newbyteorder()
@@ -142,7 +142,7 @@ def assign_rgba(gl, val):
 
 def test_print_summary():
     for fil in [DATA_FILE1, DATA_FILE2, DATA_FILE3, DATA_FILE4,
-                            DATA_FILE5, DATA_FILE6]:
+                DATA_FILE5, DATA_FILE6]:
         gimg = nib.load(fil)
         gimg.print_summary()
 

nibabel/gifti/tests/test_giftiio.py

@@ -19,6 +19,7 @@
 
 
 class TestGiftiIO(object):
+
     def setUp(self):
         with clear_and_catch_warnings() as w:
             warnings.simplefilter('always', DeprecationWarning)

nibabel/gifti/tests/test_parse_gifti_fast.py

@@ -45,56 +45,56 @@
 numDA = [2, 1, 1, 1, 2, 1]
 
 DATA_FILE1_darr1 = np.array(
-       [[-16.07201 , -66.187515,  21.266994],
-       [-16.705893, -66.054337,  21.232786],
-       [-17.614349, -65.401642,  21.071466]])
-DATA_FILE1_darr2 = np.array( [0,1,2] )
-
-DATA_FILE2_darr1 = np.array([[ 0.43635699],
-       [ 0.270017  ],
-       [ 0.133239  ],
-       [ 0.35054299],
-       [ 0.26538199],
-       [ 0.32122701],
-       [ 0.23495001],
-       [ 0.26671499],
-       [ 0.306851  ],
-       [ 0.36302799]], dtype=np.float32)
+    [[-16.07201, -66.187515, 21.266994],
+        [-16.705893, -66.054337, 21.232786],
+        [-17.614349, -65.401642, 21.071466]])
+DATA_FILE1_darr2 = np.array([0, 1, 2])
+
+DATA_FILE2_darr1 = np.array([[0.43635699],
+                             [0.270017],
+                             [0.133239],
+                             [0.35054299],
+                             [0.26538199],
+                             [0.32122701],
+                             [0.23495001],
+                             [0.26671499],
+                             [0.306851],
+                             [0.36302799]], dtype=np.float32)
 
 DATA_FILE3_darr1 = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0])
 
 DATA_FILE4_darr1 = np.array([[-0.57811606],
-       [-0.53871965],
-       [-0.44602534],
-       [-0.56532663],
-       [-0.51392376],
-       [-0.43225467],
-       [-0.54646534],
-       [-0.48011276],
-       [-0.45624232],
-       [-0.31101292]], dtype=np.float32)
-
-DATA_FILE5_darr1 = np.array([[ 155.17539978,  135.58103943,   98.30715179],
-       [ 140.33973694,  190.0491333 ,   73.24776459],
-       [ 157.3598938 ,  196.97969055,   83.65809631],
-       [ 171.46174622,  137.43661499,   78.4709549 ],
-       [ 148.54592896,   97.06752777,   65.96373749],
-       [ 123.45701599,  111.46841431,   66.3571167 ],
-       [ 135.30892944,  202.28720093,   36.38148499],
-       [ 178.28155518,  162.59469604,   37.75128937],
-       [ 178.11087036,  115.28820038,   57.17986679],
-       [ 142.81582642,   82.82115173,   31.02205276]], dtype=np.float32)
-
-DATA_FILE5_darr2 = np.array([[ 6402, 17923, 25602],
-       [14085, 25602, 17923],
-       [25602, 14085,  4483],
-       [17923,  1602, 14085],
-       [ 4483, 25603, 25602],
-       [25604, 25602, 25603],
-       [25602, 25604,  6402],
-       [25603,  3525, 25604],
-       [ 1123, 17922, 12168],
-       [25604, 12168, 17922]], dtype=np.int32)
+                             [-0.53871965],
+                             [-0.44602534],
+                             [-0.56532663],
+                             [-0.51392376],
+                             [-0.43225467],
+                             [-0.54646534],
+                             [-0.48011276],
+                             [-0.45624232],
+                             [-0.31101292]], dtype=np.float32)
+
+DATA_FILE5_darr1 = np.array([[155.17539978, 135.58103943, 98.30715179],
+                             [140.33973694, 190.0491333, 73.24776459],
+                             [157.3598938, 196.97969055, 83.65809631],
+                             [171.46174622, 137.43661499, 78.4709549],
+                             [148.54592896, 97.06752777, 65.96373749],
+                             [123.45701599, 111.46841431, 66.3571167],
+                             [135.30892944, 202.28720093, 36.38148499],
+                             [178.28155518, 162.59469604, 37.75128937],
+                             [178.11087036, 115.28820038, 57.17986679],
+                             [142.81582642, 82.82115173, 31.02205276]], dtype=np.float32)
+
+DATA_FILE5_darr2 = np.array([[6402, 17923, 25602],
+                             [14085, 25602, 17923],
+                             [25602, 14085, 4483],
+                             [17923, 1602, 14085],
+                             [4483, 25603, 25602],
+                             [25604, 25602, 25603],
+                             [25602, 25604, 6402],
+                             [25603, 3525, 25604],
+                             [1123, 17922, 12168],
+                             [25604, 12168, 17922]], dtype=np.int32)
 
 DATA_FILE6_darr1 = np.array([9182740, 9182740, 9182740], dtype=np.float32)
 
@@ -107,15 +107,15 @@ def test_read_ordering():
     assert_equal(img2.darrays[0].data.shape, (143479, 1))
     # Read image for which we know output shape
     img = load(DATA_FILE1)
-    assert_equal(img.darrays[0].data.shape, (3,3))
+    assert_equal(img.darrays[0].data.shape, (3, 3))
 
 
 def test_load_metadata():
     for i, dat in enumerate(datafiles):
         img = load(dat)
         me = img.meta
         assert_equal(numDA[i], img.numDA)
-        assert_equal(img.version,'1.0')
+        assert_equal(img.version, '1.0')
 
 
 def test_metadata_deprecations():
@@ -142,13 +142,15 @@ def test_load_dataarray1():
     for img in (img1, bimg):
         assert_array_almost_equal(img.darrays[0].data, DATA_FILE1_darr1)
         assert_array_almost_equal(img.darrays[1].data, DATA_FILE1_darr2)
-        me=img.darrays[0].meta.metadata
+        me = img.darrays[0].meta.metadata
         assert_true('AnatomicalStructurePrimary' in me)
         assert_true('AnatomicalStructureSecondary' in me)
         assert_equal(me['AnatomicalStructurePrimary'], 'CortexLeft')
-        assert_array_almost_equal(img.darrays[0].coordsys.xform, np.eye(4,4))
-        assert_equal(xform_codes.niistring[img.darrays[0].coordsys.dataspace],'NIFTI_XFORM_TALAIRACH')
-        assert_equal(xform_codes.niistring[img.darrays[0].coordsys.xformspace],'NIFTI_XFORM_TALAIRACH')
+        assert_array_almost_equal(img.darrays[0].coordsys.xform, np.eye(4, 4))
+        assert_equal(xform_codes.niistring[img.darrays[
+                     0].coordsys.dataspace], 'NIFTI_XFORM_TALAIRACH')
+        assert_equal(xform_codes.niistring[img.darrays[
+                     0].coordsys.xformspace], 'NIFTI_XFORM_TALAIRACH')
 
 
 def test_load_dataarray2():
@@ -223,19 +225,19 @@ def test_readwritedata():
     with InTemporaryDirectory():
         save(img, 'test.gii')
         img2 = load('test.gii')
-        assert_equal(img.numDA,img2.numDA)
+        assert_equal(img.numDA, img2.numDA)
         assert_array_almost_equal(img.darrays[0].data,
                                   img2.darrays[0].data)
 
 
 def test_write_newmetadata():
     img = gi.GiftiImage()
-    attr = gi.GiftiNVPairs(name = 'mykey', value = 'val1')
+    attr = gi.GiftiNVPairs(name='mykey', value='val1')
     newmeta = gi.GiftiMetaData(attr)
     img.meta = newmeta
     myme = img.meta.metadata
     assert_true('mykey' in myme)
-    newmeta = gi.GiftiMetaData.from_dict( {'mykey1' : 'val2'} )
+    newmeta = gi.GiftiMetaData.from_dict({'mykey1': 'val2'})
     img.meta = newmeta
     myme = img.meta.metadata
     assert_true('mykey1' in myme)

nibabel/nicom/tests/data_pkgs.py

@@ -3,14 +3,14 @@
 from ... import data as nibd
 
 PUBLIC_PKG_DEF = dict(
-    relpath = 'nipy/dicom/public',
-    name = 'nipy-dicom-public',
-    version = '0.1')
+    relpath='nipy/dicom/public',
+    name='nipy-dicom-public',
+    version='0.1')
 
 PRIVATE_PKG_DEF = dict(
-    relpath = 'nipy/dicom/private',
-    name = 'nipy-dicom-private',
-    version = '0.1')
+    relpath='nipy/dicom/private',
+    name='nipy-dicom-private',
+    version='0.1')
 
 
 PUBLIC_DS = nibd.datasource_or_bomber(PUBLIC_PKG_DEF)

nibabel/nicom/tests/test_csareader.py

@@ -25,9 +25,9 @@
 @dicom_test
 def test_csa_header_read():
     hdr = csa.get_csa_header(DATA, 'image')
-    assert_equal(hdr['n_tags'],83)
-    assert_equal(csa.get_csa_header(DATA,'series')['n_tags'],65)
-    assert_raises(ValueError, csa.get_csa_header, DATA,'xxxx')
+    assert_equal(hdr['n_tags'], 83)
+    assert_equal(csa.get_csa_header(DATA, 'series')['n_tags'], 65)
+    assert_raises(ValueError, csa.get_csa_header, DATA, 'xxxx')
     assert_true(csa.is_mosaic(hdr))
     # Get a shallow copy of the data, lacking the CSA marker
     # Need to do it this way because del appears broken in pydicom 0.9.7
@@ -94,7 +94,7 @@ def test_csa_params():
         snv = csa.get_slice_normal(csa_info)
         assert_equal(snv.shape, (3,))
         assert_true(np.allclose(1,
-                np.sqrt((snv * snv).sum())))
+                                np.sqrt((snv * snv).sum())))
         amt = csa.get_acq_mat_txt(csa_info)
         assert_equal(amt, '128p*128')
     csa_info = csa.read(CSA2_B0)
@@ -106,7 +106,7 @@ def test_csa_params():
     assert_equal(g_vector, None)
     csa_info = csa.read(CSA2_B1000)
     b_matrix = csa.get_b_matrix(csa_info)
-    assert_equal(b_matrix.shape, (3,3))
+    assert_equal(b_matrix.shape, (3, 3))
     # check (by absence of error) that the B matrix is positive
     # semi-definite.
     q = dwp.B2q(b_matrix)
@@ -122,10 +122,10 @@ def test_ice_dims():
     ex_dims0 = ['X', '1', '1', '1', '1', '1', '1',
                 '48', '1', '1', '1', '1', '201']
     ex_dims1 = ['X', '1', '1', '1', '2', '1', '1',
-               '48', '1', '1', '1', '1', '201']
+                '48', '1', '1', '1', '1', '201']
     for csa_str, ex_dims in ((CSA2_B0, ex_dims0),
                              (CSA2_B1000, ex_dims1)):
         csa_info = csa.read(csa_str)
         assert_equal(csa.get_ice_dims(csa_info),
-                           ex_dims)
+                     ex_dims)
     assert_equal(csa.get_ice_dims({}), None)

nibabel/nicom/tests/test_dicomreaders.py

@@ -16,11 +16,12 @@
 
 from numpy.testing import assert_array_equal, assert_array_almost_equal
 
+
 @dicom_test
 def test_read_dwi():
     img = didr.mosaic_to_nii(DATA)
     arr = img.get_data()
-    assert_equal(arr.shape, (128,128,48))
+    assert_equal(arr.shape, (128, 128, 48))
     assert_array_almost_equal(img.affine, EXPECTED_AFFINE)