MAINT: fix additional code style issues reported by codacy

docker/bidsapp/run.py

@@ -97,7 +97,7 @@ def main(bids_dir, output_dir, subject, p_stacksOrder, session, paramTV=None, nu
                 sr_list = participants_params[sub]
                 print(sr_list)
 
-                for iSr, sr_params in enumerate(sr_list):
+                for sr_params in sr_list:
 
                     ses = sr_params["session"] if "session" in sr_params.keys() else None
 

pymialsrtk/interfaces/preprocess.py

@@ -521,7 +521,7 @@ class MialsrtkSliceBySliceN4BiasFieldCorrectionOutputSpec(TraitedSpec):
     Attributes
     -----------
     out_im_file <string>
-        Output N4 bias field corrected image file 
+        Output N4 bias field corrected image file
     out_fld_file <string>
         Output bias field
 
@@ -753,9 +753,9 @@ class MialsrtkSliceBySliceCorrectBiasFieldInputSpec(BaseInterfaceInputSpec):
 
 class MialsrtkSliceBySliceCorrectBiasFieldOutputSpec(TraitedSpec):
     """Class used to represent outputs of the MialsrtkSliceBySliceCorrectBiasField interface.
-    
+
     Attributes
-    -----------   
+    -----------
     out_im_file <string>
         Output bias field corrected image file
 
@@ -1960,8 +1960,8 @@ class MultipleBrainExtractionOutputSpec(TraitedSpec):
 class MultipleBrainExtraction(BaseInterface):
     """Runs on multiple images the automatic brain extraction module.
 
-    It calls on a list of images the :class:`pymialsrtk.interfaces.preprocess.BrainExtraction.BrainExtraction` module 
-    that implements a brain extraction algorithm based on a 2D U-Net (Ronneberger et al. [1]_) using 
+    It calls on a list of images the :class:`pymialsrtk.interfaces.preprocess.BrainExtraction.BrainExtraction` module
+    that implements a brain extraction algorithm based on a 2D U-Net (Ronneberger et al. [1]_) using
     the pre-trained weights from Salehi et al. [2]_.
 
     References

pymialsrtk/pipelines/anatomical/srr.py

@@ -500,9 +500,9 @@ def create_workflow(self):
     def run(self, number_of_cores=1):
         """Execute the workflow of the super-resolution reconstruction pipeline.
 
-        Nipype execution engine will take care of the management and execution of 
+        Nipype execution engine will take care of the management and execution of
         all processing steps involved in the super-resolution reconstruction pipeline.
-        Note that the complete execution graph is saved as a PNG image to support 
+        Note that the complete execution graph is saved as a PNG image to support
         transparency on the whole processing.
 
         Parameters