Update preprocess.py

Code changes to satisfy codacy (white spaces, variable name, overindentation, etc.)

pymialsrtk/interfaces/preprocess.py

@@ -2,9 +2,11 @@
 #
 #  This software is distributed under the open-source license Modified BSD.
 
-""" PyMIALSRTK preprocessing functions including BTK Non-local-mean denoising, slice intensity correction
+"""
+PyMIALSRTK preprocessing functions including BTK Non-local-mean denoising, slice intensity correction
 slice N4 bias field correction, slice-by-slice correct bias field, intensity standardization,
 histogram normalization and both manual or deep learning based automatic brain extraction.
+
 """
 
 import os
@@ -58,6 +60,7 @@ class BtkNLMDenoisingOutputSpec(TraitedSpec):
     out_file = File(desc='Output denoised image')
 
 class BtkNLMDenoising(BaseInterface):
+
     """
     Runs the non-local mean denoising module: implementation by Rousseau et al. [1]_ of the method proposed by Coupé et al. [2]_.
 
@@ -93,7 +96,6 @@ class BtkNLMDenoising(BaseInterface):
     >>> nlmDenoise.inputs.weight = 0.2
     >>> nlmDenoise.run() # doctest: +SKIP
     """
-
     input_spec = BtkNLMDenoisingInputSpec
     output_spec = BtkNLMDenoisingOutputSpec
 
@@ -128,12 +130,13 @@ class MultipleBtkNLMDenoisingInputSpec(BaseInterfaceInputSpec):
     input_masks = InputMultiPath(File(desc='Input mask filenames', mandatory=False))
     weight = traits.Float(0.1, desc='NLM smoothing parameter (0.1 by default)', usedefault=True)
     out_postfix = traits.Str("_nlm", desc='Suffix to be added to input image filenames to construst denoised output filenames',usedefault=True)
-    stacksOrder = traits.List(desc='Order of images index. To ensure images are processed with their correct corresponding mask', mandatory=False)
+    stacks_order = traits.List(desc='Order of images index. To ensure images are processed with their correct corresponding mask', mandatory=False)
 
 class MultipleBtkNLMDenoisingOutputSpec(TraitedSpec):
     output_images = OutputMultiPath(File(desc='Output denoised images'))
 
 class MultipleBtkNLMDenoising(BaseInterface):
+
     """
     Apply the non-local mean (NLM) denoising module on multiple inputs.
     NLM denoising implementation by Rousseau et al. [1]_ of the method proposed by Coupé et al. [2]_.
@@ -155,7 +158,7 @@ class MultipleBtkNLMDenoising(BaseInterface):
     weight <float>
         smoothing parameter (high beta produces smoother result, default is 0.1)
 
-    stacksOrder <list<int>>
+    stacks_order <list<int>>
         order of images index. To ensure images are processed with their correct corresponding mask.
 
     References
@@ -177,7 +180,6 @@ class MultipleBtkNLMDenoising(BaseInterface):
     --------
     pymialsrtk.interfaces.preprocess.BtkNLMDenoising
     """
-
     input_spec = MultipleBtkNLMDenoisingInputSpec
     output_spec = MultipleBtkNLMDenoisingOutputSpec
 
@@ -239,6 +241,7 @@ class MialsrtkCorrectSliceIntensityOutputSpec(TraitedSpec):
     out_file = File(desc='Output image with corrected slice intensities')
 
 class MialsrtkCorrectSliceIntensity(BaseInterface):
+
     """
     Runs the MIAL SRTK mean slice intensity correction module [to_be_cited].
 
@@ -264,9 +267,7 @@ class MialsrtkCorrectSliceIntensity(BaseInterface):
     >>> sliceIntensityCorr.inputs.in_file = 'my_image.nii.gz'
     >>> sliceIntensityCorr.inputs.in_mask = 'my_mask.nii.gz'
     >>> sliceIntensityCorr.run() # doctest: +SKIP
-
-    """
-
+    """       
     input_spec = MialsrtkCorrectSliceIntensityInputSpec
     output_spec = MialsrtkCorrectSliceIntensityOutputSpec
 
@@ -301,6 +302,7 @@ class MultipleMialsrtkCorrectSliceIntensityOutputSpec(TraitedSpec):
     output_images = OutputMultiPath(File())
 
 class MultipleMialsrtkCorrectSliceIntensity(BaseInterface):
+
     """
     Apply the MIAL SRTK slice intensity correction module [to_be_cited] on multiple images.
     Calls MialsrtkCorrectSliceIntensity interface with a list of images/masks.
@@ -335,7 +337,6 @@ class MultipleMialsrtkCorrectSliceIntensity(BaseInterface):
     ------------
     pymialsrtk.interfaces.preprocess.MialsrtkCorrectSliceIntensity
     """
-
     input_spec = MultipleMialsrtkCorrectSliceIntensityInputSpec
     output_spec = MultipleMialsrtkCorrectSliceIntensityOutputSpec
 
@@ -395,6 +396,7 @@ class MialsrtkSliceBySliceN4BiasFieldCorrectionOutputSpec(TraitedSpec):
     out_fld_file = File(desc='Filename bias field extracted slice by slice from input image.')
 
 class MialsrtkSliceBySliceN4BiasFieldCorrection(BaseInterface):
+
     """
     Runs the MIAL SRTK slice by slice N4 bias field correction module that implements the method proposed by Tustison et al. [1]_.
 
@@ -428,7 +430,6 @@ class MialsrtkSliceBySliceN4BiasFieldCorrection(BaseInterface):
     >>> N4biasFieldCorr.inputs.in_mask = 'my_mask.nii.gz'
     >>> N4biasFieldCorr.run() # doctest: +SKIP
     """
-
     input_spec = MialsrtkSliceBySliceN4BiasFieldCorrectionInputSpec
     output_spec = MialsrtkSliceBySliceN4BiasFieldCorrectionOutputSpec
 
@@ -475,6 +476,7 @@ class MultipleMialsrtkSliceBySliceN4BiasFieldCorrectionOutputSpec(TraitedSpec):
     output_fields = OutputMultiPath(File())
 
 class MultipleMialsrtkSliceBySliceN4BiasFieldCorrection(BaseInterface):
+
     """
     Runs on multiple images the MIAL SRTK slice by slice N4 bias field correction module that implements the method proposed by Tustison et al. [1]_.
     Calls MialsrtkSliceBySliceN4BiasFieldCorrection interface with a list of images/masks.
@@ -518,7 +520,6 @@ class MultipleMialsrtkSliceBySliceN4BiasFieldCorrection(BaseInterface):
     .. [1] Tustison et al.; Medical Imaging, IEEE Transactions, 2010. `(link to paper) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3071855>`_
 
     """
-
     input_spec = MultipleMialsrtkSliceBySliceN4BiasFieldCorrectionInputSpec
     output_spec = MultipleMialsrtkSliceBySliceN4BiasFieldCorrectionOutputSpec
 
@@ -574,6 +575,7 @@ class MialsrtkSliceBySliceCorrectBiasFieldOutputSpec(TraitedSpec):
     out_im_file = File(desc='Bias field corrected image')
 
 class MialsrtkSliceBySliceCorrectBiasField(BaseInterface):
+
     """
     Runs the MIAL SRTK independant slice by slice bias field correction module [To_be_cited].
 
@@ -607,7 +609,6 @@ class MialsrtkSliceBySliceCorrectBiasField(BaseInterface):
     >>> biasFieldCorr.inputs.in_field = 'my_field.nii.gz'
     >>> biasFieldCorr.run() # doctest: +SKIP
     """
-
     input_spec = MialsrtkSliceBySliceCorrectBiasFieldInputSpec
     output_spec = MialsrtkSliceBySliceCorrectBiasFieldOutputSpec
 
@@ -643,6 +644,7 @@ class MultipleMialsrtkSliceBySliceCorrectBiasFieldOutputSpec(TraitedSpec):
 
 
 class MultipleMialsrtkSliceBySliceCorrectBiasField(BaseInterface):
+
     """
     Runs the MIAL SRTK slice by slice bias field correction module [to_be_cited] on multiple images.
     Calls MialsrtkSliceBySliceCorrectBiasField interface with a list of images/masks/fields.
@@ -680,9 +682,7 @@ class MultipleMialsrtkSliceBySliceCorrectBiasField(BaseInterface):
     See also
     ------------
     pymialsrtk.interfaces.preprocess.MialsrtkCorrectSliceIntensity
-    
     """
-
     input_spec = MultipleMialsrtkSliceBySliceCorrectBiasFieldInputSpec
     output_spec = MultipleMialsrtkSliceBySliceCorrectBiasFieldOutputSpec
 
@@ -737,12 +737,13 @@ class MialsrtkIntensityStandardizationInputSpec(BaseInterfaceInputSpec):
     input_images = InputMultiPath(File(desc='files to be corrected for intensity', mandatory=True))
     out_postfix = traits.Str("", desc='Suffix to be added to intensity corrected input_images', usedefault=True)
     in_max = traits.Float(desc='Maximal intensity',usedefault=False)
-    stacksOrder = traits.List(desc='Order of images index. To ensure images are processed with their correct corresponding mask', mandatory=False) # ToDo: Can be removed -> Also in pymialsrtk.pipelines.anatomical.srr.AnatomicalPipeline !!!
+    stacks_order = traits.List(desc='Order of images index. To ensure images are processed with their correct corresponding mask', mandatory=False) # ToDo: Can be removed -> Also in pymialsrtk.pipelines.anatomical.srr.AnatomicalPipeline !!!
 
 class MialsrtkIntensityStandardizationOutputSpec(TraitedSpec):
     output_images = OutputMultiPath(File())
 
 class MialsrtkIntensityStandardization(BaseInterface):
+
     """
     Runs the MIAL SRTK intensity standardization module [To_be_cited].
     Rescale image intensity by linear transformation
@@ -769,7 +770,6 @@ class MialsrtkIntensityStandardization(BaseInterface):
     >>> intensityStandardization.inputs.input_images = ['image1.nii.gz','image2.nii.gz']
     >>> intensityStandardization.run() # doctest: +SKIP
     """
-
     input_spec = MialsrtkIntensityStandardizationInputSpec
     output_spec = MialsrtkIntensityStandardizationOutputSpec
 
@@ -806,7 +806,8 @@ class MialsrtkHistogramNormalizationInputSpec(BaseInterfaceInputSpec):
     bids_dir = Directory(desc='BIDS root directory', mandatory=True, exists=True)
     input_images = InputMultiPath(File(desc='Input image filenames to be normalized', mandatory=True))
     input_masks = InputMultiPath(File(desc='Input mask filenames', mandatory=False))
-    out_postfix = traits.Str("_histnorm", desc='Suffix to be added to normalized input image filenames to construct ouptut normalized image filenames', usedefault=True)
+    out_postfix = traits.Str("_histnorm", desc='Suffix to be added to normalized input image filenames to construct ouptut normalized image filenames',
+                             usedefault=True)
     stacksOrder = traits.List(desc='Order of images index. To ensure images are processed with their correct corresponding mask', mandatory=False)
 
 
@@ -815,6 +816,7 @@ class MialsrtkHistogramNormalizationOutputSpec(TraitedSpec):
 
 
 class MialsrtkHistogramNormalization(BaseInterface):
+
     """
     Runs the MIAL SRTK histogram normalizaton module that implements the method proposed by Nyúl et al. [1]_.
 
@@ -849,8 +851,7 @@ class MialsrtkHistogramNormalization(BaseInterface):
     >>> histNorm.inputs.out_postfix = '_histnorm'
     >>> histNorm.inputs.stacksOrder = [0,1]
     >>> histNorm.run()  # doctest: +SKIP
-    """
-
+    """       
     input_spec = MialsrtkHistogramNormalizationInputSpec
     output_spec = MialsrtkHistogramNormalizationOutputSpec
 
@@ -913,6 +914,7 @@ class MialsrtkMaskImageOutputSpec(TraitedSpec):
     out_im_file = File(desc='Masked image')
 
 class MialsrtkMaskImage(BaseInterface):
+
     """
     Runs the MIAL SRTK mask image module.
 
@@ -940,7 +942,6 @@ class MialsrtkMaskImage(BaseInterface):
     >>> maskImg.inputs.out_im_postfix = '_masked'
     >>> maskImg.run() # doctest: +SKIP
     """
-
     input_spec = MialsrtkMaskImageInputSpec
     output_spec = MialsrtkMaskImageOutputSpec
 
@@ -975,6 +976,7 @@ class MultipleMialsrtkMaskImageOutputSpec(TraitedSpec):
     output_images = OutputMultiPath(File(desc='Output masked image filenames'))
 
 class MultipleMialsrtkMaskImage(BaseInterface):
+
     """
     Runs the MIAL SRTK mask image module on multiple images.
     Calls MialsrtkMaskImage interface with a list of images/masks.
@@ -1013,8 +1015,7 @@ class MultipleMialsrtkMaskImage(BaseInterface):
     See also
     ------------
     pymialsrtk.interfaces.preprocess.MialsrtkMaskImage
-    """
-
+    """       
     input_spec = MultipleMialsrtkMaskImageInputSpec
     output_spec = MultipleMialsrtkMaskImageOutputSpec
 
@@ -1069,6 +1070,7 @@ class BrainExtractionOutputSpec(TraitedSpec):
 
 
 class BrainExtraction(BaseInterface):
+
     """
     Runs the automatic brain extraction module based on a 2D U-Net (Ronneberger et al. [1]_)
     using the pre-trained weights from Salehi et al. [2]_.
@@ -1114,7 +1116,6 @@ class BrainExtraction(BaseInterface):
     >>> brainmask.inputs.out_postfix = '_brainMask.nii.gz'
     >>> brainmask.run() # doctest: +SKIP
     """
-
     input_spec = BrainExtractionInputSpec
     output_spec = BrainExtractionOutputSpec
 
@@ -1141,7 +1142,7 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
           
         """
 
-        # Step1: Main part brain localization
+        ##### Step2: Brain localization ##### 
         normalize = "local_max"
         width = 128
         height = 128
@@ -1160,16 +1161,16 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
                                    fy=height)
 
             if normalize:
-                if normalize == "local_max":
-                    images[slice_counter, :, :, 0] = img_patch / np.max(img_patch)
-                elif normalize == "global_max":
-                    images[slice_counter, :, :, 0] = img_patch / max_val
-                elif normalize == "mean_std":
-                    images[slice_counter, :, :, 0] = (img_patch-np.mean(img_patch))/np.std(img_patch)
-                else:
-                    raise ValueError('Please select a valid normalization')
+               if normalize == "local_max":
+                  images[slice_counter, :, :, 0] = img_patch / np.max(img_patch)
+               elif normalize == "global_max":
+                  images[slice_counter, :, :, 0] = img_patch / max_val
+               elif normalize == "mean_std":
+                  images[slice_counter, :, :, 0] = (img_patch-np.mean(img_patch))/np.std(img_patch)
+               else:
+                  raise ValueError('Please select a valid normalization')
             else:
-                images[slice_counter, :, :, 0] = img_patch
+               images[slice_counter, :, :, 0] = img_patch
 
             slice_counter += 1
 
@@ -1276,7 +1277,7 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
             y_beg = med_y-half_max_y-border_y
             y_end = med_y+half_max_y+border_y
 
-        # Step2: Brain segmentation
+        ##### Step2: Brain segmentation ##### 
         width = 96
         height = 96
 
@@ -1287,12 +1288,12 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
             img_patch = cv2.resize(image_data[x_beg:x_end, y_beg:y_end, ii], dsize=(width, height))
 
             if normalize:
-                if normalize == "local_max":
-                     images[slice_counter, :, :, 0] = img_patch / np.max(img_patch)
-                elif normalize ==  "mean_std":
-                     images[slice_counter, :, :, 0] = (img_patch-np.mean(img_patch))/np.std(img_patch)
-                else:
-                     raise ValueError('Please select a valid normalization')
+               if normalize == "local_max":
+                  images[slice_counter, :, :, 0] = img_patch / np.max(img_patch)
+               elif normalize ==  "mean_std":
+                  images[slice_counter, :, :, 0] = (img_patch-np.mean(img_patch))/np.std(img_patch)
+               else:
+                  raise ValueError('Please select a valid normalization')
             else:
                 images[slice_counter, :, :, 0] = img_patch
 
@@ -1346,7 +1347,7 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
             pred = conv_2d(conv9, 2, 1,  activation='linear', padding='valid')
 
         with tf.Session(graph=g) as sess_test_seg:
-        # Restore the model
+            # Restore the model
             tf_saver = tf.train.Saver()
             tf_saver.restore(sess_test_seg, modelCkptSeg)