STY: Manual tweaks to pyupgrade+black

Author: effigies

nipype/algorithms/confounds.py

@@ -465,12 +465,12 @@ class CompCorInputSpec(BaseInterfaceInputSpec):
         "cosine",
         False,
         usedefault=True,
-        desc="Detrend time series prior to component " "extraction",
+        desc="Detrend time series prior to component extraction",
     )
     use_regress_poly = traits.Bool(
         deprecated="0.15.0",
         new_name="pre_filter",
-        desc=("use polynomial regression " "pre-component extraction"),
+        desc=("use polynomial regression pre-component extraction"),
     )
     regress_poly_degree = traits.Range(
         low=1, value=1, usedefault=True, desc="the degree polynomial to use"
@@ -806,7 +806,7 @@ class TCompCorInputSpec(CompCorInputSpec):
 class TCompCorOutputSpec(CompCorOutputSpec):
     # and all the fields in CompCorOutputSpec
     high_variance_masks = OutputMultiPath(
-        File(exists=True), desc=("voxels exceeding the variance" " threshold")
+        File(exists=True), desc=("voxels exceeding the variance threshold")
     )
 
 
@@ -856,7 +856,7 @@ def _process_masks(self, mask_images, timeseries=None):
             mask_file = os.path.abspath(f"mask_{i:03d}.nii.gz")
             out_image.to_filename(mask_file)
             IFLOGGER.debug(
-                "tCompcor computed and saved mask of shape %s to " "mask_file %s",
+                "tCompcor computed and saved mask of shape %s to mask_file %s",
                 str(mask.shape),
                 mask_file,
             )
@@ -1287,9 +1287,7 @@ def combine_mask_files(mask_files, mask_method=None, mask_index=None):
             mask = nb.load(mask_files[mask_index])
             return [mask]
         raise ValueError(
-            ("mask_index {} must be less than number of mask " "files {}").format(
-                mask_index, len(mask_files)
-            )
+            f"mask_index {mask_index} must be less than number of mask files {len(mask_files)}"
         )
     masks = []
     if mask_method == "none":

nipype/algorithms/mesh.py

@@ -141,12 +141,12 @@ class ComputeMeshWarpInputSpec(BaseInterfaceInputSpec):
     surface1 = File(
         exists=True,
         mandatory=True,
-        desc=("Reference surface (vtk format) to which compute " "distance."),
+        desc=("Reference surface (vtk format) to which compute distance."),
     )
     surface2 = File(
         exists=True,
         mandatory=True,
-        desc=("Test surface (vtk format) from which compute " "distance."),
+        desc=("Test surface (vtk format) from which compute distance."),
     )
     metric = traits.Enum(
         "euclidean", "sqeuclidean", usedefault=True, desc="norm used to report distance"
@@ -163,7 +163,7 @@ class ComputeMeshWarpInputSpec(BaseInterfaceInputSpec):
     out_warp = File(
         "surfwarp.vtk",
         usedefault=True,
-        desc="vtk file based on surface1 and warpings mapping it " "to surface2",
+        desc="vtk file based on surface1 and warpings mapping it to surface2",
     )
     out_file = File(
         "distance.npy",
@@ -176,7 +176,7 @@ class ComputeMeshWarpOutputSpec(TraitedSpec):
     distance = traits.Float(desc="computed distance")
     out_warp = File(
         exists=True,
-        desc=("vtk file with the vertex-wise " "mapping of surface1 to surface2"),
+        desc=("vtk file with the vertex-wise mapping of surface1 to surface2"),
     )
     out_file = File(
         exists=True, desc="numpy file keeping computed distances and weights"
@@ -308,15 +308,15 @@ class MeshWarpMathsInputSpec(BaseInterfaceInputSpec):
     out_warp = File(
         "warp_maths.vtk",
         usedefault=True,
-        desc="vtk file based on in_surf and warpings mapping it " "to out_file",
+        desc="vtk file based on in_surf and warpings mapping it to out_file",
     )
     out_file = File("warped_surf.vtk", usedefault=True, desc="vtk with surface warped")
 
 
 class MeshWarpMathsOutputSpec(TraitedSpec):
     out_warp = File(
         exists=True,
-        desc=("vtk file with the vertex-wise " "mapping of surface1 to surface2"),
+        desc=("vtk file with the vertex-wise mapping of surface1 to surface2"),
     )
     out_file = File(exists=True, desc="vtk with surface warped")
 

nipype/algorithms/misc.py

@@ -870,7 +870,7 @@ def _run_interface(self, runtime):
             import pandas as pd
         except ImportError as e:
             raise ImportError(
-                "This interface requires pandas " "(http://pandas.pydata.org/) to run."
+                "This interface requires pandas (http://pandas.pydata.org/) to run."
             ) from e
 
         try:
@@ -1000,7 +1000,7 @@ class AddNoiseInputSpec(TraitedSpec):
     )
     in_mask = File(
         exists=True,
-        desc=("input mask, voxels outside this mask " "will be considered background"),
+        desc=("input mask, voxels outside this mask will be considered background"),
     )
     snr = traits.Float(10.0, desc="desired output SNR in dB", usedefault=True)
     dist = traits.Enum(
@@ -1015,7 +1015,7 @@ class AddNoiseInputSpec(TraitedSpec):
         "rayleigh",
         usedefault=True,
         mandatory=True,
-        desc=("desired noise distribution, currently " "only normal is implemented"),
+        desc=("desired noise distribution, currently only normal is implemented"),
     )
     out_file = File(desc="desired output filename")
 
@@ -1118,7 +1118,7 @@ def gen_noise(self, image, mask=None, snr_db=10.0, dist="normal", bg_dist="norma
             im_noise = np.sqrt((image + stde_1) ** 2 + (stde_2) ** 2)
         else:
             raise NotImplementedError(
-                "Only normal and rician distributions " "are supported"
+                "Only normal and rician distributions are supported"
             )
 
         return im_noise

nipype/algorithms/modelgen.py

@@ -614,11 +614,8 @@ def _concatenate_info(self, infolist):
                         infoout.durations[j].extend(info.durations[j])
                     else:
                         raise ValueError(
-                            "Mismatch in number of onsets and \
-                                          durations for run {}, condition \
-                                          {}".format(
-                                i + 2, j + 1
-                            )
+                            f"Mismatch in number of onsets and durations for run {i + 2}, "
+                            f"condition {j + 1}"
                         )
 
                 if hasattr(info, "amplitudes") and info.amplitudes:

nipype/algorithms/rapidart.py

@@ -168,7 +168,7 @@ def _calc_norm_affine(affines, use_differences, brain_pts=None):
 class ArtifactDetectInputSpec(BaseInterfaceInputSpec):
     realigned_files = InputMultiPath(
         File(exists=True),
-        desc=("Names of realigned functional data " "files"),
+        desc=("Names of realigned functional data files"),
         mandatory=True,
     )
     realignment_parameters = InputMultiPath(
@@ -224,12 +224,12 @@ class ArtifactDetectInputSpec(BaseInterfaceInputSpec):
     rotation_threshold = traits.Float(
         mandatory=True,
         xor=["norm_threshold"],
-        desc=("Threshold (in radians) to use to " "detect rotation-related outliers"),
+        desc=("Threshold (in radians) to use to detect rotation-related outliers"),
     )
     translation_threshold = traits.Float(
         mandatory=True,
         xor=["norm_threshold"],
-        desc=("Threshold (in mm) to use to " "detect translation-related " "outliers"),
+        desc=("Threshold (in mm) to use to detect translation-related outliers"),
     )
     zintensity_threshold = traits.Float(
         mandatory=True,
@@ -257,12 +257,12 @@ class ArtifactDetectInputSpec(BaseInterfaceInputSpec):
     )
     mask_file = File(exists=True, desc="Mask file to be used if mask_type is 'file'.")
     mask_threshold = traits.Float(
-        desc=("Mask threshold to be used if mask_type" " is 'thresh'.")
+        desc=("Mask threshold to be used if mask_type is 'thresh'.")
     )
     intersect_mask = traits.Bool(
         True,
         usedefault=True,
-        desc=("Intersect the masks when computed from " "spm_global."),
+        desc=("Intersect the masks when computed from spm_global."),
     )
     save_plot = traits.Bool(
         True, desc="save plots containing outliers", usedefault=True
@@ -288,7 +288,7 @@ class ArtifactDetectInputSpec(BaseInterfaceInputSpec):
     )
     global_threshold = traits.Float(
         8.0,
-        desc=("use this threshold when mask " "type equal's spm_global"),
+        desc=("use this threshold when mask type equal's spm_global"),
         usedefault=True,
     )
 
@@ -312,7 +312,7 @@ class ArtifactDetectOutputSpec(TraitedSpec):
         ),
     )
     norm_files = OutputMultiPath(
-        File, desc=("One file for each functional run " "containing the composite norm")
+        File, desc=("One file for each functional run containing the composite norm")
     )
     statistic_files = OutputMultiPath(
         File(exists=True),
@@ -329,7 +329,7 @@ class ArtifactDetectOutputSpec(TraitedSpec):
     plot_files = OutputMultiPath(
         File,
         desc=(
-            "One image file for each functional run " "containing the detected outliers"
+            "One image file for each functional run containing the detected outliers"
         ),
     )
     mask_files = OutputMultiPath(
@@ -698,28 +698,27 @@ class StimCorrInputSpec(BaseInterfaceInputSpec):
         File(exists=True),
         mandatory=True,
         desc=(
-            "Names of realignment "
-            "parameters corresponding to "
+            "Names of realignment parameters corresponding to "
             "the functional data files"
         ),
     )
     intensity_values = InputMultiPath(
         File(exists=True),
         mandatory=True,
-        desc=("Name of file containing intensity " "values"),
+        desc=("Name of file containing intensity values"),
     )
     spm_mat_file = File(
         exists=True, mandatory=True, desc="SPM mat file (use pre-estimate SPM.mat file)"
     )
     concatenated_design = traits.Bool(
         mandatory=True,
-        desc=("state if the design matrix " "contains concatenated sessions"),
+        desc=("state if the design matrix contains concatenated sessions"),
     )
 
 
 class StimCorrOutputSpec(TraitedSpec):
     stimcorr_files = OutputMultiPath(
-        File(exists=True), desc=("List of files containing " "correlation values")
+        File(exists=True), desc=("List of files containing correlation values")
     )
 
 

nipype/external/cloghandler.py

@@ -267,7 +267,7 @@ def doRollover(self):
             except OSError:
                 exc_value = sys.exc_info()[1]
                 self._degrade(
-                    True, "rename failed.  File in use?  " "exception=%s", exc_value
+                    True, "rename failed.  File in use?  exception=%s", exc_value
                 )
                 return
 

nipype/interfaces/afni/model.py

@@ -196,7 +196,7 @@ class DeconvolveInputSpec(AFNICommandInputSpec):
         desc="output the sample variance (MSE) for each stimulus", argstr="-vout"
     )
     nofdr = traits.Bool(
-        desc="Don't compute the statistic-vs-FDR curves for the bucket " "dataset.",
+        desc="Don't compute the statistic-vs-FDR curves for the bucket dataset.",
         argstr="-noFDR",
     )
     global_times = traits.Bool(
@@ -218,7 +218,7 @@ class DeconvolveInputSpec(AFNICommandInputSpec):
             File(desc="stimulus timing file", exists=True),
             Str(desc="model"),
         ),
-        desc="generate a response model from a set of stimulus times" " given in file.",
+        desc="generate a response model from a set of stimulus times given in file.",
         argstr="-stim_times %d %s '%s'...",
         position=-5,
     )
@@ -569,14 +569,14 @@ class RemlfitInputSpec(AFNICommandInputSpec):
 
 class RemlfitOutputSpec(AFNICommandOutputSpec):
     out_file = File(
-        desc="dataset for beta + statistics from the REML estimation (if " "generated"
+        desc="dataset for beta + statistics from the REML estimation (if generated)"
     )
     var_file = File(desc="dataset for REML variance parameters (if generated)")
     rbeta_file = File(
-        desc="dataset for beta weights from the REML estimation (if " "generated)"
+        desc="dataset for beta weights from the REML estimation (if generated)"
     )
     rbeta_file = File(
-        desc="output dataset for beta weights from the REML estimation (if " "generated"
+        desc="output dataset for beta weights from the REML estimation (if generated)"
     )
     glt_file = File(
         desc="output dataset for beta + statistics from the REML estimation, "
@@ -585,25 +585,25 @@ class RemlfitOutputSpec(AFNICommandOutputSpec):
     )
     fitts_file = File(desc="output dataset for REML fitted model (if generated)")
     errts_file = File(
-        desc="output dataset for REML residuals = data - fitted model (if " "generated"
+        desc="output dataset for REML residuals = data - fitted model (if generated)"
     )
     wherr_file = File(
         desc="dataset for REML residual, whitened using the estimated "
         "ARMA(1,1) correlation matrix of the noise (if generated)"
     )
     ovar = File(desc="dataset for OLSQ st.dev. parameter (if generated)")
     obeta = File(
-        desc="dataset for beta weights from the OLSQ estimation (if " "generated)"
+        desc="dataset for beta weights from the OLSQ estimation (if generated)"
     )
     obuck = File(
-        desc="dataset for beta + statistics from the OLSQ estimation (if " "generated)"
+        desc="dataset for beta + statistics from the OLSQ estimation (if generated)"
     )
     oglt = File(
-        desc="dataset for beta + statistics from 'gltsym' options (if " "generated"
+        desc="dataset for beta + statistics from 'gltsym' options (if generated)"
     )
     ofitts = File(desc="dataset for OLSQ fitted model (if generated)")
     oerrts = File(
-        desc="dataset for OLSQ residuals = data - fitted model (if " "generated"
+        desc="dataset for OLSQ residuals = data - fitted model (if generated)"
     )
 
 
@@ -650,13 +650,13 @@ def _list_outputs(self):
 
 class SynthesizeInputSpec(AFNICommandInputSpec):
     cbucket = File(
-        desc="Read the dataset output from " "3dDeconvolve via the '-cbucket' option.",
+        desc="Read the dataset output from 3dDeconvolve via the '-cbucket' option.",
         argstr="-cbucket %s",
         copyfile=False,
         mandatory=True,
     )
     matrix = File(
-        desc="Read the matrix output from " "3dDeconvolve via the '-x1D' option.",
+        desc="Read the matrix output from 3dDeconvolve via the '-x1D' option.",
         argstr="-matrix %s",
         copyfile=False,
         mandatory=True,
@@ -681,7 +681,7 @@ class SynthesizeInputSpec(AFNICommandInputSpec):
         argstr="-prefix %s",
     )
     dry_run = traits.Bool(
-        desc="Don't compute the output, just " "check the inputs.", argstr="-dry"
+        desc="Don't compute the output, just check the inputs.", argstr="-dry"
     )
     TR = traits.Float(
         desc="TR to set in the output.  The default value of "

nipype/interfaces/afni/utils.py

@@ -120,7 +120,7 @@ class AFNItoNIFTIInputSpec(AFNICommandInputSpec):
         argstr="-denote",
     )
     oldid = traits.Bool(
-        desc="Give the new dataset the input dataset" "s AFNI ID code.",
+        desc="Give the new dataset the input datasets AFNI ID code.",
         argstr="-oldid",
         xor=["newid"],
     )
@@ -1686,7 +1686,7 @@ class LocalstatInputSpec(AFNICommandInputSpec):
         traits.Tuple(traits.Float, traits.Float, traits.Float),
         argstr="-reduce_restore_grid %s",
         xor=["reduce_max_vox", "reduce_grid"],
-        desc="Like reduce_grid, but also resample output back to input" "grid.",
+        desc="Like reduce_grid, but also resample output back to input grid.",
     )
     reduce_max_vox = traits.Float(
         argstr="-reduce_max_vox %s",
@@ -2036,7 +2036,7 @@ class NwarpApplyInputSpec(CommandLineInputSpec):
         traits.List(File(exists=True)),
         mandatory=True,
         argstr="-source %s",
-        desc="the name of the dataset to be warped " "can be multiple datasets",
+        desc="the name of the dataset to be warped can be multiple datasets",
     )
     warp = traits.String(
         desc="the name of the warp dataset. "
@@ -2135,7 +2135,7 @@ class NwarpCatInputSpec(AFNICommandInputSpec):
         position=-1,
     )
     space = traits.String(
-        desc="string to attach to the output dataset as its atlas space " "marker.",
+        desc="string to attach to the output dataset as its atlas space marker.",
         argstr="-space %s",
     )
     inv_warp = traits.Bool(desc="invert the final warp before output", argstr="-iwarp")