sty: black

Author: oesteban

nipype/info.py

@@ -160,14 +160,14 @@ def get_nipype_gitversion():
 EXTRA_REQUIRES = {
     "data": ["datalad"],
     "doc": [
-        'dipy',
-        'ipython',
-        'matplotlib',
-        'nbsphinx',
-        'sphinx-argparse',
-        'sphinx>=2.1.2',
-        'sphinxcontrib-apidoc',
-        'sphinxcontrib-napoleon',
+        "dipy",
+        "ipython",
+        "matplotlib",
+        "nbsphinx",
+        "sphinx-argparse",
+        "sphinx>=2.1.2",
+        "sphinxcontrib-apidoc",
+        "sphinxcontrib-napoleon",
     ],
     "duecredit": ["duecredit"],
     "nipy": ["nitime", "nilearn<0.5.0", "dipy", "nipy", "matplotlib"],

nipype/interfaces/afni/preprocess.py

@@ -3483,7 +3483,8 @@ class QwarpInputSpec(AFNICommandInputSpec):
   or the inverse can be computed as needed in 3dNwarpApply, like
   3dNwarpApply -nwarp 'INV(Z_WARP+tlrc)' -source Dataset.nii ...
 
-""")
+""",
+    )
     resample = traits.Bool(
         desc="""\
 This option simply resamples the source dataset to match the
@@ -3579,7 +3580,8 @@ class QwarpInputSpec(AFNICommandInputSpec):
   use the option '-penold'.To be completely compatible with
   the older 3dQwarp, you'll also have to use '-penfac 0.2'.
 
-""")
+""",
+    )
     noweight = traits.Bool(
         desc="If you want a binary weight (the old default), use this option."
         "That is, each voxel in the base volume automask will be"
@@ -3624,7 +3626,7 @@ class QwarpInputSpec(AFNICommandInputSpec):
         argstr="-wball %s",
         minlen=5,
         maxlen=5,
-        xor=['wmask'],
+        xor=["wmask"],
     )
     traits.Tuple((traits.Float(), traits.Float()), argstr="-bpass %f %f")
     wmask = traits.Tuple(
@@ -3643,7 +3645,7 @@ class QwarpInputSpec(AFNICommandInputSpec):
 
 """,
         argstr="-wpass %s %f",
-        xor=['wball'],
+        xor=["wball"],
     )
     out_weight_file = File(
         argstr="-wtprefix %s", desc="Write the weight volume to disk as a dataset"

nipype/interfaces/afni/utils.py

@@ -1034,24 +1034,35 @@ class Edge3(AFNICommand):
     input_spec = Edge3InputSpec
     output_spec = AFNICommandOutputSpec
     references_ = [
-        {'entry': BibTeX("""\
+        {
+            "entry": BibTeX(
+                """\
 @article{Deriche1987,
 author={R. Deriche},
 title={Optimal edge detection using recursive filtering},
 journal={International Journal of Computer Vision},
 volume={2},'
 pages={167-187},
 year={1987},
-}"""), 'tags': ['method']},
-        {'entry': BibTeX("""\
+}"""
+            ),
+            "tags": ["method"],
+        },
+        {
+            "entry": BibTeX(
+                """\
 @article{MongaDericheMalandainCocquerez1991,
     author={O. Monga, R. Deriche, G. Malandain, J.P. Cocquerez},
     title={Recursive filtering and edge tracking: two primary tools for 3D edge detection},
     journal={Image and vision computing},
     volume={9},'
     pages={203-214},
     year={1991},
-}"""), 'tags': ['method']}]
+}"""
+            ),
+            "tags": ["method"],
+        },
+    ]
 
 
 class EvalInputSpec(AFNICommandInputSpec):

nipype/interfaces/ants/segmentation.py

@@ -610,9 +610,7 @@ class CorticalThicknessInputSpec(ANTSCommandInputSpec):
         0,
         1,
         argstr="-u %d",
-        desc=(
-            "Use random number generated from system clock in Atropos (default = 1)"
-        ),
+        desc=("Use random number generated from system clock in Atropos (default = 1)"),
     )
     b_spline_smoothing = traits.Bool(
         argstr="-v",
@@ -855,9 +853,7 @@ class BrainExtractionInputSpec(ANTSCommandInputSpec):
         "highres001_",
         argstr="-o %s",
         usedefault=True,
-        desc=(
-            "Prefix that is prepended to all output files"
-        ),
+        desc=("Prefix that is prepended to all output files"),
     )
 
     extraction_registration_mask = File(
@@ -879,9 +875,7 @@ class BrainExtractionInputSpec(ANTSCommandInputSpec):
         0,
         1,
         argstr="-u %d",
-        desc=(
-            "Use random number generated from system clock in Atropos (default = 1)"
-        ),
+        desc=("Use random number generated from system clock in Atropos (default = 1)"),
     )
     keep_temporary_files = traits.Int(
         argstr="-k %d",
@@ -1156,9 +1150,7 @@ class JointFusionInputSpec(ANTSCommandInputSpec):
         minlen=3,
         maxlen=3,
         argstr="-rp %s",
-        desc=(
-            "Patch radius for similarity measures, scalar or vector. Default: 2x2x2"
-        ),
+        desc=("Patch radius for similarity measures, scalar or vector. Default: 2x2x2"),
     )
     search_radius = traits.ListInt(
         minlen=3, maxlen=3, argstr="-rs %s", desc="Local search radius. Default: 3x3x3"
@@ -1816,7 +1808,8 @@ class KellyKapowski(ANTSCommand):
 
     references_ = [
         {
-            "entry": BibTeX("""\
+            "entry": BibTeX(
+                """\
 @book{Das2009867,
   author={Sandhitsu R. Das and Brian B. Avants and Murray Grossman and James C. Gee},
   title={Registration based cortical thickness measurement.},
@@ -1828,7 +1821,8 @@ class KellyKapowski(ANTSCommand):
   issn={1053-8119},
   url={http://www.sciencedirect.com/science/article/pii/S1053811908012780},
   doi={https://doi.org/10.1016/j.neuroimage.2008.12.016}
-}"""),
+}"""
+            ),
             "description": "The details on the implementation of DiReCT.",
             "tags": ["implementation"],
         }

nipype/interfaces/ants/visualization.py

@@ -32,8 +32,21 @@ class ConvertScalarImageToRGBInputSpec(ANTSCommandInputSpec):
         "none", argstr="%s", exists=True, desc="mask image", position=3, usedefault=True
     )
     colormap = traits.Enum(
-        "grey", "red", "green", "blue", "copper", "jet", "hsv", "spring", "summer",
-        "autumn", "winter", "hot", "cool", "overunder", "custom",
+        "grey",
+        "red",
+        "green",
+        "blue",
+        "copper",
+        "jet",
+        "hsv",
+        "spring",
+        "summer",
+        "autumn",
+        "winter",
+        "hot",
+        "cool",
+        "overunder",
+        "custom",
         argstr="%s",
         desc="Select a colormap",
         mandatory=True,
@@ -48,12 +61,8 @@ class ConvertScalarImageToRGBInputSpec(ANTSCommandInputSpec):
     maximum_input = traits.Int(
         argstr="%d", desc="maximum input", mandatory=True, position=7
     )
-    minimum_RGB_output = traits.Int(
-        0, usedefault=True, argstr="%d", position=8
-    )
-    maximum_RGB_output = traits.Int(
-        255, usedefault=True, argstr="%d", position=9
-    )
+    minimum_RGB_output = traits.Int(0, usedefault=True, argstr="%d", position=8)
+    maximum_RGB_output = traits.Int(255, usedefault=True, argstr="%d", position=9)
 
 
 class ConvertScalarImageToRGBOutputSpec(TraitedSpec):

nipype/interfaces/diffusion_toolkit/dti.py

@@ -210,12 +210,12 @@ class DTITrackerInputSpec(CommandLineInputSpec):
     )
     angle_threshold_weight = traits.Float(
         desc="set angle threshold weighting factor. weighting will be be applied "
-             "on top of the angle_threshold",
+        "on top of the angle_threshold",
         argstr="-atw %f",
     )
     random_seed = traits.Int(
         desc="use random location in a voxel instead of the center of the voxel "
-             "to seed. can also define number of seed per voxel. default is 1",
+        "to seed. can also define number of seed per voxel. default is 1",
         argstr="-rseed %d",
     )
     invert_x = traits.Bool(desc="invert x component of the vector", argstr="-ix")
@@ -229,13 +229,13 @@ class DTITrackerInputSpec(CommandLineInputSpec):
     )
     mask1_threshold = traits.Float(
         desc="threshold value for the first mask image, if not given, the program will "
-             "try automatically find the threshold",
+        "try automatically find the threshold",
         position=3,
     )
     mask2_file = File(desc="second mask image", argstr="-m2 %s", position=4)
     mask2_threshold = traits.Float(
         desc="threshold value for the second mask image, if not given, the program will "
-             "try automatically find the threshold",
+        "try automatically find the threshold",
         position=5,
     )
     input_data_prefix = traits.Str(

nipype/interfaces/diffusion_toolkit/odf.py

@@ -297,13 +297,13 @@ class ODFTrackerInputSpec(CommandLineInputSpec):
     )
     mask1_threshold = traits.Float(
         desc="threshold value for the first mask image, if not given, the program will "
-             "try automatically find the threshold",
+        "try automatically find the threshold",
         position=3,
     )
     mask2_file = File(desc="second mask image", argstr="-m2 %s", position=4)
     mask2_threshold = traits.Float(
         desc="threshold value for the second mask image, if not given, the program will "
-             "try automatically find the threshold",
+        "try automatically find the threshold",
         position=5,
     )
     limit = traits.Int(

nipype/interfaces/freesurfer/model.py

@@ -175,8 +175,8 @@ class MRISPreprocReconAllInputSpec(MRISPreprocInputSpec):
     )
     copy_inputs = traits.Bool(
         desc="If running as a node, set this to True "
-             "this will copy some implicit inputs to the "
-             "node directory."
+        "this will copy some implicit inputs to the "
+        "node directory."
     )
 
 
@@ -1073,8 +1073,8 @@ class SegStatsReconAllInputSpec(SegStatsInputSpec):
     aseg = File(exists=True, desc="Mandatory implicit input in 5.3")
     copy_inputs = traits.Bool(
         desc="If running as a node, set this to True "
-             "otherwise, this will copy the implicit inputs "
-             "to the node directory."
+        "otherwise, this will copy the implicit inputs "
+        "to the node directory."
     )
 
 

nipype/interfaces/freesurfer/utils.py

@@ -1685,7 +1685,7 @@ class SmoothTessellationInputSpec(FSTraitedSpec):
         argstr="-n %d", desc="Number of smoothing iterations (default=10)"
     )
     snapshot_writing_iterations = traits.Int(
-        argstr="-w %d", desc='Write snapshot every *n* iterations'
+        argstr="-w %d", desc="Write snapshot every *n* iterations"
     )
 
     use_gaussian_curvature_smoothing = traits.Bool(
@@ -3835,8 +3835,8 @@ class Aparc2AsegInputSpec(FSTraitedSpec):
     a2009s = traits.Bool(argstr="--a2009s", desc="Using the a2009s atlas")
     copy_inputs = traits.Bool(
         desc="If running as a node, set this to True."
-             "This will copy the input files to the node "
-             "directory."
+        "This will copy the input files to the node "
+        "directory."
     )
 
 

nipype/interfaces/niftyseg/maths.py

@@ -149,7 +149,8 @@ class UnaryMathsInput(MathsInput):
     * 4to5 - Flip the 4th and 5th dimension.
     * range - Reset the image range to the min max.
 
-""")
+""",
+    )
 
 
 class UnaryMaths(MathsCommand):
@@ -406,7 +407,8 @@ class BinaryMathsInputInteger(MathsInput):
     * crop - <int> - Crop <int> voxels around each 3D volume.
     * pad - <int> -  Pad <int> voxels with NaN value around each 3D volume.
 
-""")
+""",
+    )
 
     operand_value = traits.Int(
         argstr="%d",
@@ -475,7 +477,8 @@ class TupleMathsInput(MathsInput):
     * lssd <file> <std> Local SSD between current img and <file> on a kernel with <std>
     * lltsnorm <file_norm> <float>  Linear LTS normalisation assuming <float> percent outliers
 
-""")
+""",
+    )
 
     operand_file1 = File(
         exists=True,
@@ -559,8 +562,11 @@ class MergeInput(MathsInput):
 
     dimension = traits.Int(mandatory=True, desc="Dimension to merge the images.")
     merge_files = traits.List(
-        File(exists=True), argstr="%s", mandatory=True, position=4,
-        desc="List of images to merge to the working image <input>."
+        File(exists=True),
+        argstr="%s",
+        mandatory=True,
+        position=4,
+        desc="List of images to merge to the working image <input>.",
     )