FIX: PEP8 - E502 the backslash is redundant between brackets

Author: miykael

nipype/interfaces/camino/dti.py

@@ -262,29 +262,29 @@ def _gen_outfilename(self):
 
 
 class DTLUTGenInputSpec(StdOutCommandLineInputSpec):
-    lrange = traits.List(traits.Float, desc='Index to one-tensor LUTs. This is the ratio L1/L3 and L2 / L3.' \
-                         'The LUT is square, with half the values calculated (because L2 / L3 cannot be less than L1 / L3 by definition).' \
-                         'The minimum must be >= 1. For comparison, a ratio L1 / L3 = 10 with L2 / L3 = 1 corresponds to an FA of 0.891, '\
-                         'and L1 / L3 = 15 with L2 / L3 = 1 corresponds to an FA of 0.929. The default range is 1 to 10.', \
+    lrange = traits.List(traits.Float, desc='Index to one-tensor LUTs. This is the ratio L1/L3 and L2 / L3.'
+                         'The LUT is square, with half the values calculated (because L2 / L3 cannot be less than L1 / L3 by definition).'
+                         'The minimum must be >= 1. For comparison, a ratio L1 / L3 = 10 with L2 / L3 = 1 corresponds to an FA of 0.891, '
+                         'and L1 / L3 = 15 with L2 / L3 = 1 corresponds to an FA of 0.929. The default range is 1 to 10.',
                          argstr='-lrange %s', minlen=2, maxlen=2, position=1,
                          units='NA')
 
-    frange = traits.List(traits.Float, desc='Index to two-tensor LUTs. This is the fractional anisotropy \
-        of the two tensors. The default is 0.3 to 0.94', \
+    frange = traits.List(traits.Float, desc='Index to two-tensor LUTs. This is the fractional anisotropy'
+                         ' of the two tensors. The default is 0.3 to 0.94',
                          argstr='-frange %s', minlen=2, maxlen=2, position=1,
                          units='NA')
 
     step = traits.Float(argstr='-step %f', units='NA',
-                        desc='Distance between points in the LUT.' \
-                        'For example, if lrange is 1 to 10 and the step is 0.1, LUT entries will be computed ' \
-                        'at L1 / L3 = 1, 1.1, 1.2 ... 10.0 and at L2 / L3 = 1.0, 1.1 ... L1 / L3.' \
+                        desc='Distance between points in the LUT.'
+                        'For example, if lrange is 1 to 10 and the step is 0.1, LUT entries will be computed '
+                        'at L1 / L3 = 1, 1.1, 1.2 ... 10.0 and at L2 / L3 = 1.0, 1.1 ... L1 / L3.'
                         'For single tensor LUTs, the default step is 0.2, for two-tensor LUTs it is 0.02.')
 
     samples = traits.Int(argstr='-samples %d', units='NA',
                          desc='The number of synthetic measurements to generate at each point in the LUT. The default is 2000.')
 
     snr = traits.Float(argstr='-snr %f', units='NA',
-                       desc='The signal to noise ratio of the unweighted (q = 0) measurements.'\
+                       desc='The signal to noise ratio of the unweighted (q = 0) measurements.'
                        'This should match the SNR (in white matter) of the images that the LUTs are used with.')
 
     bingham = traits.Bool(argstr='-bingham', desc="Compute a LUT for the Bingham PDF. This is the default.")
@@ -351,27 +351,27 @@ class PicoPDFsInputSpec(StdOutCommandLineInputSpec):
                              argstr='-inputmodel %s', position=2, desc='input model type', usedefault=True)
 
     luts = InputMultiPath(File(exists=True), argstr='-luts %s', mandatory=True,
-                          desc='Files containing the lookup tables.'\
-                          'For tensor data, one lut must be specified for each type of inversion used in the image (one-tensor, two-tensor, three-tensor).'\
-                          'For pds, the number of LUTs must match -numpds (it is acceptable to use the same LUT several times - see example, above).'\
+                          desc='Files containing the lookup tables.'
+                          'For tensor data, one lut must be specified for each type of inversion used in the image (one-tensor, two-tensor, three-tensor).'
+                          'For pds, the number of LUTs must match -numpds (it is acceptable to use the same LUT several times - see example, above).'
                           'These LUTs may be generated with dtlutgen.')
 
     pdf = traits.Enum('bingham', 'watson', 'acg',
-                      argstr='-pdf %s', position=4, desc=' Specifies the PDF to use. There are three choices:'\
-                      'watson - The Watson distribution. This distribution is rotationally symmetric.'\
-                      'bingham - The Bingham distributionn, which allows elliptical probability density contours.'\
+                      argstr='-pdf %s', position=4, desc=' Specifies the PDF to use. There are three choices:'
+                      'watson - The Watson distribution. This distribution is rotationally symmetric.'
+                      'bingham - The Bingham distributionn, which allows elliptical probability density contours.'
                       'acg - The Angular Central Gaussian distribution, which also allows elliptical probability density contours', usedefault=True)
 
     directmap = traits.Bool(argstr='-directmap', desc="Only applicable when using pds as the inputmodel. Use direct mapping between the eigenvalues and the distribution parameters instead of the log of the eigenvalues.")
 
     maxcomponents = traits.Int(argstr='-maxcomponents %d', units='NA',
-                               desc='The maximum number of tensor components in a voxel (default 2) for multitensor data.'\
+                               desc='The maximum number of tensor components in a voxel (default 2) for multitensor data.'
                                'Currently, only the default is supported, but future releases may allow the input of three-tensor data using this option.')
 
     numpds = traits.Int(argstr='-numpds %d', units='NA',
-                        desc='The maximum number of PDs in a voxel (default 3) for PD data.' \
-                        'This option determines the size of the input and output voxels.' \
-                        'This means that the data file may be large enough to accomodate three or more PDs,'\
+                        desc='The maximum number of PDs in a voxel (default 3) for PD data.'
+                        'This option determines the size of the input and output voxels.'
+                        'This means that the data file may be large enough to accomodate three or more PDs,'
                         'but does not mean that any of the voxels are classified as containing three or more PDs.')
 
 
@@ -835,19 +835,19 @@ class ComputeMeanDiffusivityInputSpec(CommandLineInputSpec):
 
     inputmodel = traits.Enum('dt', 'twotensor', 'threetensor',
                              argstr='-inputmodel %s',
-                             desc='Specifies the model that the input tensor data contains parameters for.' \
-                             'Possible model types are: "dt" (diffusion-tensor data), "twotensor" (two-tensor data), '\
-                             '"threetensor" (three-tensor data). By default, the program assumes that the input data '\
+                             desc='Specifies the model that the input tensor data contains parameters for.'
+                             'Possible model types are: "dt" (diffusion-tensor data), "twotensor" (two-tensor data), '
+                             '"threetensor" (three-tensor data). By default, the program assumes that the input data '
                              'contains a single diffusion tensor in each voxel.')
 
     inputdatatype = traits.Enum('char', 'short', 'int', 'long', 'float', 'double',
                                 argstr='-inputdatatype %s',
-                                desc='Specifies the data type of the input file. The data type can be any of the' \
+                                desc='Specifies the data type of the input file. The data type can be any of the'
                                 'following strings: "char", "short", "int", "long", "float" or "double".')
 
     outputdatatype = traits.Enum('char', 'short', 'int', 'long', 'float', 'double',
                                  argstr='-outputdatatype %s',
-                                 desc='Specifies the data type of the output data. The data type can be any of the' \
+                                 desc='Specifies the data type of the output data. The data type can be any of the'
                                  'following strings: "char", "short", "int", "long", "float" or "double".')
 
 
@@ -891,19 +891,19 @@ class ComputeFractionalAnisotropyInputSpec(StdOutCommandLineInputSpec):
 
     inputmodel = traits.Enum('dt', 'twotensor', 'threetensor', 'multitensor',
                              argstr='-inputmodel %s',
-                             desc='Specifies the model that the input tensor data contains parameters for.' \
-                             'Possible model types are: "dt" (diffusion-tensor data), "twotensor" (two-tensor data), '\
-                             '"threetensor" (three-tensor data). By default, the program assumes that the input data '\
+                             desc='Specifies the model that the input tensor data contains parameters for.'
+                             'Possible model types are: "dt" (diffusion-tensor data), "twotensor" (two-tensor data), '
+                             '"threetensor" (three-tensor data). By default, the program assumes that the input data '
                              'contains a single diffusion tensor in each voxel.')
 
     inputdatatype = traits.Enum('char', 'short', 'int', 'long', 'float', 'double',
                                 argstr='-inputdatatype %s',
-                                desc='Specifies the data type of the input file. The data type can be any of the' \
+                                desc='Specifies the data type of the input file. The data type can be any of the'
                                 'following strings: "char", "short", "int", "long", "float" or "double".')
 
     outputdatatype = traits.Enum('char', 'short', 'int', 'long', 'float', 'double',
                                  argstr='-outputdatatype %s',
-                                 desc='Specifies the data type of the output data. The data type can be any of the' \
+                                 desc='Specifies the data type of the output data. The data type can be any of the'
                                  'following strings: "char", "short", "int", "long", "float" or "double".')
 
 
@@ -953,19 +953,19 @@ class ComputeTensorTraceInputSpec(StdOutCommandLineInputSpec):
 
     inputmodel = traits.Enum('dt', 'twotensor', 'threetensor', 'multitensor',
                              argstr='-inputmodel %s',
-                             desc='Specifies the model that the input tensor data contains parameters for.' \
-                             'Possible model types are: "dt" (diffusion-tensor data), "twotensor" (two-tensor data), '\
-                             '"threetensor" (three-tensor data). By default, the program assumes that the input data '\
+                             desc='Specifies the model that the input tensor data contains parameters for.'
+                             'Possible model types are: "dt" (diffusion-tensor data), "twotensor" (two-tensor data), '
+                             '"threetensor" (three-tensor data). By default, the program assumes that the input data '
                              'contains a single diffusion tensor in each voxel.')
 
     inputdatatype = traits.Enum('char', 'short', 'int', 'long', 'float', 'double',
                                 argstr='-inputdatatype %s',
-                                desc='Specifies the data type of the input file. The data type can be any of the' \
+                                desc='Specifies the data type of the input file. The data type can be any of the'
                                 'following strings: "char", "short", "int", "long", "float" or "double".')
 
     outputdatatype = traits.Enum('char', 'short', 'int', 'long', 'float', 'double',
                                  argstr='-outputdatatype %s',
-                                 desc='Specifies the data type of the output data. The data type can be any of the' \
+                                 desc='Specifies the data type of the output data. The data type can be any of the'
                                  'following strings: "char", "short", "int", "long", "float" or "double".')
 
 

nipype/interfaces/cmtk/nbs.py

@@ -43,7 +43,7 @@ class NetworkBasedStatisticInputSpec(BaseInterfaceInputSpec):
     number_of_permutations = traits.Int(1000, usedefault=True, desc='Number of permutations to perform')
     threshold = traits.Float(3, usedefault=True, desc='T-statistic threshold')
     t_tail = traits.Enum('left', 'right', 'both', usedefault=True, desc='Can be one of "left", "right", or "both"')
-    edge_key = traits.Str('number_of_fibers', usedefault=True, desc='Usually "number_of_fibers, "fiber_length_mean", "fiber_length_std" for matrices made with CMTK' \
+    edge_key = traits.Str('number_of_fibers', usedefault=True, desc='Usually "number_of_fibers, "fiber_length_mean", "fiber_length_std" for matrices made with CMTK'
                           'Sometimes "weight" or "value" for functional networks.')
     out_nbs_network = File(desc='Output network with edges identified by the NBS')
     out_nbs_pval_network = File(desc='Output network with p-values to weight the edges identified by the NBS')

nipype/interfaces/cmtk/nx.py

@@ -505,7 +505,7 @@ def _gen_outfilename(self, name, ext):
 
 class AverageNetworksInputSpec(BaseInterfaceInputSpec):
     in_files = InputMultiPath(File(exists=True), mandatory=True, desc='Networks for a group of subjects')
-    resolution_network_file = File(exists=True, desc='Parcellation files from Connectome Mapping Toolkit. This is not necessary' \
+    resolution_network_file = File(exists=True, desc='Parcellation files from Connectome Mapping Toolkit. This is not necessary'
                                    ', but if included, the interface will output the statistical maps as networkx graphs.')
     group_id = traits.Str('group1', usedefault=True, desc='ID for group')
     out_gpickled_groupavg = File(desc='Average network saved as a NetworkX .pck')

nipype/interfaces/freesurfer/utils.py

@@ -814,7 +814,7 @@ class MRIsConvertInputSpec(FSTraitedSpec):
     # Not really sure why the ./ is necessary but the module fails without it
 
     out_datatype = traits.Enum("ico", "tri", "stl", "vtk", "gii", "mgh", "mgz", mandatory=True,
-                               desc="These file formats are supported:  ASCII:       .asc" \
+                               desc="These file formats are supported:  ASCII:       .asc"
                                "ICO: .ico, .tri GEO: .geo STL: .stl VTK: .vtk GIFTI: .gii MGH surface-encoded 'volume': .mgh, .mgz")
 
 

nipype/interfaces/fsl/tests/test_dti.py

@@ -285,11 +285,11 @@ def test_Randomise_parallel():
                'TFCE_height_param':                  ('--tfce_H=0.11', 0.11),
                'TFCE_extent_param':                  ('--tfce_E=0.50', 0.50),
                'TFCE_connectivity':                  ('--tfce_C=0.30', 0.30),
-               'list_num_voxel_EVs_pos':             ('--vxl=' \
-                                                      + repr([1, 2, 3, 4]),
+               'list_num_voxel_EVs_pos':             ('--vxl=' +
+                                                      repr([1, 2, 3, 4]),
                                                       repr([1, 2, 3, 4])),
-               'list_img_voxel_EVs':               ('--vxf=' \
-                                                    + repr([6, 7, 8, 9, 3]),
+               'list_img_voxel_EVs':               ('--vxf=' +
+                                                    repr([6, 7, 8, 9, 3]),
                                                     repr([6, 7, 8, 9, 3]))}
 
     for name, settings in list(opt_map.items()):

nipype/interfaces/meshfix.py

@@ -38,7 +38,7 @@ class MeshFixInputSpec(CommandLineInputSpec):
 
     uniform_remeshing_steps = traits.Int(argstr='-u %d', requires=['uniform_remeshing_vertices'], desc="Number of steps for uniform remeshing of the whole mesh")
 
-    uniform_remeshing_vertices = traits.Int(argstr='--vertices %d', requires=['uniform_remeshing_steps'], desc="Constrains the number of vertices." \
+    uniform_remeshing_vertices = traits.Int(argstr='--vertices %d', requires=['uniform_remeshing_steps'], desc="Constrains the number of vertices."
                                             "Must be used with uniform_remeshing_steps")
 
     laplacian_smoothing_steps = traits.Int(argstr='--smooth %d', desc="The number of laplacian smoothing steps to apply")
@@ -48,23 +48,23 @@ class MeshFixInputSpec(CommandLineInputSpec):
     # Cutting, decoupling, dilation
     cut_outer = traits.Int(argstr='--cut-outer %d', desc="Remove triangles of 1st that are outside of the 2nd shell.")
     cut_inner = traits.Int(argstr='--cut-inner %d', desc="Remove triangles of 1st that are inside of the 2nd shell. Dilate 2nd by N; Fill holes and keep only 1st afterwards.")
-    decouple_inin = traits.Int(argstr='--decouple-inin %d', desc="Treat 1st file as inner, 2nd file as outer component." \
+    decouple_inin = traits.Int(argstr='--decouple-inin %d', desc="Treat 1st file as inner, 2nd file as outer component."
                                "Resolve overlaps by moving inners triangles inwards. Constrain the min distance between the components > d.")
-    decouple_outin = traits.Int(argstr='--decouple-outin %d', desc="Treat 1st file as outer, 2nd file as inner component." \
+    decouple_outin = traits.Int(argstr='--decouple-outin %d', desc="Treat 1st file as outer, 2nd file as inner component."
                                 "Resolve overlaps by moving outers triangles inwards. Constrain the min distance between the components > d.")
-    decouple_outout = traits.Int(argstr='--decouple-outout %d', desc="Treat 1st file as outer, 2nd file as inner component." \
+    decouple_outout = traits.Int(argstr='--decouple-outout %d', desc="Treat 1st file as outer, 2nd file as inner component."
                                  "Resolve overlaps by moving outers triangles outwards. Constrain the min distance between the components > d.")
 
     finetuning_inwards = traits.Bool(argstr='--fineTuneIn ', requires=['finetuning_distance', 'finetuning_substeps'])
     finetuning_outwards = traits.Bool(argstr='--fineTuneIn ', requires=['finetuning_distance', 'finetuning_substeps'], xor=['finetuning_inwards'],
                                       desc='Similar to finetuning_inwards, but ensures minimal distance in the other direction')
-    finetuning_distance = traits.Float(argstr='%f', requires=['finetuning_substeps'], desc="Used to fine-tune the minimal distance between surfaces." \
+    finetuning_distance = traits.Float(argstr='%f', requires=['finetuning_substeps'], desc="Used to fine-tune the minimal distance between surfaces."
                                        "A minimal distance d is ensured, and reached in n substeps. When using the surfaces for subsequent volume meshing by gmsh, this step prevent too flat tetrahedra2)")
-    finetuning_substeps = traits.Int(argstr='%d', requires=['finetuning_distance'], desc="Used to fine-tune the minimal distance between surfaces." \
+    finetuning_substeps = traits.Int(argstr='%d', requires=['finetuning_distance'], desc="Used to fine-tune the minimal distance between surfaces."
                                      "A minimal distance d is ensured, and reached in n substeps. When using the surfaces for subsequent volume meshing by gmsh, this step prevent too flat tetrahedra2)")
 
     dilation = traits.Int(argstr='--dilate %d', desc="Dilate the surface by d. d < 0 means shrinking.")
-    set_intersections_to_one = traits.Bool(argstr='--intersect', desc="If the mesh contains intersections, return value = 1." \
+    set_intersections_to_one = traits.Bool(argstr='--intersect', desc="If the mesh contains intersections, return value = 1."
                                            "If saved in gmsh format, intersections will be highlighted.")
 
     in_file1 = File(exists=True, argstr="%s", position=1, mandatory=True)