Skip to content

Commit 400b30a

Browse files
satrasatra
committed
Merge pull request nipy#439 from satra/fix/makecheck
sty: white space
2 parents a77edf9 + f0ecde3 commit 400b30a

27 files changed

+141
-141
lines changed

‎nipype/interfaces/afni/preprocess.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1331,7 +1331,7 @@ class TCorrelate(AFNICommand):
13311331

13321332
def _list_outputs(self):
13331333
outputs = self.output_spec().get()
1334-
1334+
13351335
if not isdefined(self.inputs.out_file):
13361336
outputs['out_file'] = self._gen_fname(self.inputs.xset,
13371337
suffix=self.inputs.suffix)

‎nipype/interfaces/freesurfer/utils.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -308,7 +308,7 @@ class SurfaceTransformInputSpec(FSTraitedSpec):
308308
help="subject id of target surface")
309309
target_ico_order = traits.Enum(1, 2, 3, 4, 5, 6, 7, argstr="--trgicoorder %d",
310310
help="order of the icosahedron if target_subject is 'ico'")
311-
source_type = traits.Enum(filetypes, argstr='--sfmt %s', requires=['source_file'],
311+
source_type = traits.Enum(filetypes, argstr='--sfmt %s', requires=['source_file'],
312312
help="source file format")
313313
target_type = traits.Enum(filetypes, argstr='--tfmt %s', help="output format")
314314
reshape = traits.Bool(argstr="--reshape", help="reshape output surface to conform with Nifti")
@@ -988,7 +988,7 @@ class MakeAverageSubjectOutputSpec(TraitedSpec):
988988

989989
class MakeAverageSubject(FSCommand):
990990
"""Make an average freesurfer subject
991-
991+
992992
Examples
993993
--------
994994

‎nipype/interfaces/fsl/maths.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -23,7 +23,7 @@ class MathsInput(FSLCommandInputSpec):
2323
output_datatype = traits.Enum(*_dtypes,
2424
position=-1, argstr="-odt %s",
2525
desc="datatype to use for output (default uses input type)")
26-
26+
2727
nan2zeros = traits.Bool(position=3, argstr='-nan',
2828
desc='change NaNs to zeros before doing anything')
2929

‎nipype/interfaces/fsl/utils.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -464,7 +464,7 @@ def aggregate_outputs(self, runtime=None, needed_outputs=None):
464464
return outputs
465465

466466
class AvScaleInputSpec(FSLCommandInputSpec):
467-
mat_file = File(exists=True, argstr="%s",
467+
mat_file = File(exists=True, argstr="%s",
468468
desc='mat file to read', position=0)
469469

470470

@@ -477,7 +477,7 @@ class AvScaleOutputSpec(TraitedSpec):
477477
forward_half_transform = traits.Any(desc='Forward Half Transform')
478478
backward_half_transform = traits.Any(desc='Backwards Half Transform')
479479
left_right_orientation_preserved = traits.Bool(desc='True if LR orientation preserved')
480-
480+
481481
class AvScale(FSLCommand):
482482
"""Use FSL avscale command to extract info from mat file output of FLIRT
483483
@@ -505,7 +505,7 @@ def lines_to_float(lines):
505505
values = line.split()
506506
out.append([float(val) for val in values])
507507
return out
508-
508+
509509
out = runtime.stdout.split('\n')
510510

511511
outputs.rotation_translation_matrix = lines_to_float(out[1:5])

‎nipype/interfaces/io.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -1117,7 +1117,7 @@ def capture_provenance():
11171117
def push_provenance():
11181118
pass
11191119

1120-
1120+
11211121
class SQLiteSinkInputSpec(DynamicTraitedSpec, BaseInterfaceInputSpec):
11221122
database_file = File(exists=True, mandatory = True)
11231123
table_name = traits.Str(mandatory=True)
@@ -1206,7 +1206,7 @@ def _list_outputs(self):
12061206
"""
12071207
import MySQLdb
12081208
if isdefined(self.inputs.config):
1209-
conn = MySQLdb.connect(db=self.inputs.database_name,
1209+
conn = MySQLdb.connect(db=self.inputs.database_name,
12101210
read_default_file=self.inputs.config)
12111211
else:
12121212
conn = MySQLdb.connect(host=self.inputs.host,

‎nipype/interfaces/nipy/preprocess.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -100,7 +100,7 @@ class FmriRealign4dInputSpec(BaseInterfaceInputSpec):
100100

101101
class FmriRealign4dOutputSpec(TraitedSpec):
102102

103-
out_file = OutputMultiPath(File(exists=True),
103+
out_file = OutputMultiPath(File(exists=True),
104104
desc="Realigned files")
105105
par_file = OutputMultiPath(File(exists=True),
106106
desc="Motion parameter files")

‎nipype/interfaces/nipy/utils.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -61,14 +61,14 @@ def _run_interface(self, runtime):
6161

6262
vol1_nii = nb.load(self.inputs.volume1)
6363
vol2_nii = nb.load(self.inputs.volume2)
64-
64+
6565
if isdefined(self.inputs.mask1):
6666
mask1_nii = nb.load(self.inputs.mask1)
6767
mask1_nii = nb.Nifti1Image(nb.load(self.inputs.mask1).get_data() == 1, mask1_nii.get_affine(),
6868
mask1_nii.get_header())
6969
else:
7070
mask1_nii = None
71-
71+
7272
if isdefined(self.inputs.mask2):
7373
mask2_nii = nb.load(self.inputs.mask2)
7474
mask2_nii = nb.Nifti1Image(nb.load(self.inputs.mask2).get_data() == 1, mask2_nii.get_affine(),

‎nipype/interfaces/slicer/converters.py

Lines changed: 10 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
1-
"""Autogenerated file - DO NOT EDIT
1+
"""Autogenerated file - DO NOT EDIT
22
If you spot a bug, please report it on the mailing list and/or change the generator."""
33

44
from nipype.interfaces.base import CommandLine, CommandLineInputSpec, TraitedSpec, File, Directory, traits, isdefined, InputMultiPath, OutputMultiPath
@@ -21,27 +21,27 @@ class DicomToNrrdConverterOutputSpec(TraitedSpec):
2121

2222

2323
class DicomToNrrdConverter(SlicerCommandLine):
24-
"""title:
25-
Dicom to Nrrd Converter
26-
24+
"""title:
25+
Dicom to Nrrd Converter
2726
28-
category:
27+
28+
category:
2929
Converters
30-
3130
32-
description:
31+
32+
description:
3333
Converts diffusion weighted MR images in dicom series into Nrrd format for analysis in Slicer. This program has been tested on only a limited subset of DTI dicom formats available from Siemens, GE, and Phillips scanners. Work in progress to support dicom multi-frame data. The program parses dicom header to extract necessary information about measurement frame, diffusion weighting directions, b-values, etc, and write out a nrrd image. For non-diffusion weighted dicom images, it loads in an entire dicom series and writes out a single dicom volume in a .nhdr/.raw pair.
34-
34+
3535
3636
version: 0.2.0.$Revision: 916 $(alpha)
3737
3838
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/DicomToNrrdConverter
3939
40-
license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt
40+
license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt
4141
4242
contributor: Xiaodong Tao
4343
44-
acknowledgements:
44+
acknowledgements:
4545
This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Additional support for DTI data produced on Philips scanners was contributed by Vincent Magnotta and Hans Johnson at the University of Iowa.
4646
4747

‎nipype/interfaces/slicer/diffusion/denoising.py

Lines changed: 7 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
1-
"""Autogenerated file - DO NOT EDIT
1+
"""Autogenerated file - DO NOT EDIT
22
If you spot a bug, please report it on the mailing list and/or change the generator."""
33

44
from nipype.interfaces.base import CommandLine, CommandLineInputSpec, TraitedSpec, File, Directory, traits, isdefined, InputMultiPath, OutputMultiPath
@@ -23,19 +23,19 @@ class jointLMMSE(SlicerCommandLine):
2323
2424
category: Diffusion.Denoising
2525
26-
description:
26+
description:
2727
This module reduces Rician noise (or unwanted detail) on a set of diffusion weighted images. For this, it filters the image in the mean squared error sense using a Rician noise model. The N closest gradient directions to the direction being processed are filtered together to improve the results: the noise-free signal is seen as an n-diemensional vector which has to be estimated with the LMMSE method from a set of corrupted measurements. To that end, the covariance matrix of the noise-free vector and the cross covariance between this signal and the noise have to be estimated, which is done taking into account the image formation process.
2828
The noise parameter is automatically estimated from a rough segmentation of the background of the image. In this area the signal is simply 0, so that Rician statistics reduce to Rayleigh and the noise power can be easily estimated from the mode of the histogram.
2929
A complete description of the algorithm may be found in:
30-
Antonio Tristan-Vega and Santiago Aja-Fernandez, DWI filtering using joint information for DTI and HARDI, Medical Image Analysis, Volume 14, Issue 2, Pages 205-218. 2010.
31-
30+
Antonio Tristan-Vega and Santiago Aja-Fernandez, DWI filtering using joint information for DTI and HARDI, Medical Image Analysis, Volume 14, Issue 2, Pages 205-218. 2010.
31+
3232
3333
version: 0.1.1.$Revision: 1 $(alpha)
3434
3535
documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.0/Modules/JointRicianLMMSEImageFilter
3636
3737
contributor: Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).
38-
38+
3939
4040
"""
4141

@@ -68,11 +68,11 @@ class dwiNoiseFilter(SlicerCommandLine):
6868
6969
category: Diffusion.Denoising
7070
71-
description:
71+
description:
7272
This module reduces noise (or unwanted detail) on a set of diffusion weighted images. For this, it filters the image in the mean squared error sense using a Rician noise model. Images corresponding to each gradient direction, including baseline, are processed individually. The noise parameter is automatically estimated (noise estimation improved but slower).
7373
Note that this is a general purpose filter for MRi images. The module jointLMMSE has been specifically designed for DWI volumes and shows a better performance, so its use is recommended instead.
7474
A complete description of the algorithm in this module can be found in:
75-
S. Aja-Fernandez, M. Niethammer, M. Kubicki, M. Shenton, and C.-F. Westin. Restoration of DWI data using a Rician LMMSE estimator. IEEE Transactions on Medical Imaging, 27(10): pp. 1389-1403, Oct. 2008.
75+
S. Aja-Fernandez, M. Niethammer, M. Kubicki, M. Shenton, and C.-F. Westin. Restoration of DWI data using a Rician LMMSE estimator. IEEE Transactions on Medical Imaging, 27(10): pp. 1389-1403, Oct. 2008.
7676
7777
7878
version: 0.1.1.$Revision: 1 $(alpha)

‎nipype/interfaces/slicer/diffusion/gtract.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
1-
"""Autogenerated file - DO NOT EDIT
1+
"""Autogenerated file - DO NOT EDIT
22
If you spot a bug, please report it on the mailing list and/or change the generator."""
33

44
from nipype.interfaces.base import CommandLine, CommandLineInputSpec, TraitedSpec, File, Directory, traits, isdefined, InputMultiPath, OutputMultiPath
@@ -102,7 +102,7 @@ class gtractCostFastMarching(SlicerCommandLine):
102102
103103
category: Diffusion.GTRACT
104104
105-
description: This program will use a fast marching fiber tracking algorithm to identify fiber tracts from a tensor image. This program is the first portion of the algorithm. The user must first run gtractFastMarchingTracking to generate the actual fiber tracts. This algorithm is roughly based on the work by G. Parker et al. from IEEE Transactions On Medical Imaging, 21(5): 505-512, 2002. An additional feature of including anisotropy into the vcl_cost function calculation is included.
105+
description: This program will use a fast marching fiber tracking algorithm to identify fiber tracts from a tensor image. This program is the first portion of the algorithm. The user must first run gtractFastMarchingTracking to generate the actual fiber tracts. This algorithm is roughly based on the work by G. Parker et al. from IEEE Transactions On Medical Imaging, 21(5): 505-512, 2002. An additional feature of including anisotropy into the vcl_cost function calculation is included.
106106
107107
version: 4.0.0
108108
@@ -201,7 +201,7 @@ class gtractTransformToDeformationField(SlicerCommandLine):
201201
202202
license: http://mri.radiology.uiowa.edu/copyright/GTRACT-Copyright.txt
203203
204-
contributor: This tool was developed by Vincent Magnotta, Madhura Ingalhalikar, and Greg Harris
204+
contributor: This tool was developed by Vincent Magnotta, Madhura Ingalhalikar, and Greg Harris
205205
206206
acknowledgements: Funding for this version of the GTRACT program was provided by NIH/NINDS R01NS050568-01A2S1
207207

0 commit comments

Comments
 (0)