removed var not used

pyalfe/tasks/dicom_processing.py

@@ -9,20 +9,16 @@
     PipelineDataDir,
     PatientDicomDataDir,
     Modality,
-    Orientation
+    Orientation,
 )
 from pyalfe.tasks import Task
-from pyalfe.utils.dicom import (
-    extract_dicom_meta,
-    get_max_echo_series_crc,
-    ImageMeta
-)
+from pyalfe.utils.dicom import extract_dicom_meta, get_max_echo_series_crc, ImageMeta
 from pyalfe.utils.technique import detect_modality, detect_orientation
 
 
 class DicomProcessing(Task):
     """This task classifies raw dicom images into their modality and converts
-    them to NIfTI.  
+    them to NIfTI.
     """
 
     logger = logging.getLogger('DicomProcessing')
@@ -32,77 +28,82 @@ def __init__(
         pipeline_dir: PipelineDataDir,
         dicom_dir: PatientDicomDataDir,
         overwrite: bool = True,
-        ) -> None:
+    ) -> None:
         super().__init__()
         self.pipeline_dir = pipeline_dir
         self.dicom_dir = dicom_dir
         self.overwrite = overwrite
-    
+
     @staticmethod
     def dicom2nifti(
         dcm_series_dir: str,
         nifti_path: str,
         series_crc: str = None,
-        ):
-        """This function is a wrapper around dcm2niix that converts a 
+    ):
+        """This function is a wrapper around dcm2niix that converts a
         dicom series to a NIfTI file.
 
         Parameters
         ----------
         dcm_series_dir: str
             The path to a dicom series directory
-        nifti_path: str 
+        nifti_path: str
             The path to the output NIfTI file
         series_crc: str, default None
             The Series CRC number that should be converted. This option should
-            be set when there are that one echo numbers in one series. 
+            be set when there are that one echo numbers in one series.
 
         Returns
         -------
         """
         nifti_dir = os.path.dirname(nifti_path)
         Path(nifti_dir).mkdir(parents=True, exist_ok=True)
         nifti_name = os.path.basename(nifti_path).split('.')[0]
-        cmd_parts = [ 
+        cmd_parts = [
             'dcm2niix',
-            '-z', 'y',
-            '-b', 'y',
-            '-f', nifti_name,
-            '-o', nifti_dir]
+            '-z',
+            'y',
+            '-b',
+            'y',
+            '-f',
+            nifti_name,
+            '-o',
+            nifti_dir,
+        ]
 
         if series_crc is not None:
             cmd_parts += ['-n', series_crc]
-            
+
         return subprocess.run(cmd_parts + [dcm_series_dir])
 
     @staticmethod
     def get_best(image_list: list[ImageMeta]) -> ImageMeta:
         """This function returns the image with the smallest slice thickness.
-        
+
         Paramters
         ---------
         image_list: list[ImageMeta]
             List of image meta data
-        
+
         Returns
         -------
         ImageMeta
             The image meta for the image with the smallest slice thickness.
         """
+
         def none_to_inf(val):
             if val is not None:
                 return val
             else:
                 return math.inf
+
         return min(
-            image_list,
-            key=lambda image_meta: none_to_inf(image_meta.slice_thickness)
-            )
+            image_list, key=lambda image_meta: none_to_inf(image_meta.slice_thickness)
+        )
 
-
     @staticmethod
     def select_orientation(
-            orientation_dict: dict[Orientation, ImageMeta]
+        orientation_dict: dict[Orientation, ImageMeta]
     ) -> tuple[Orientation, ImageMeta]:
         selected_orientation = None
         selected_image = None
@@ -120,28 +121,25 @@ def select_orientation(
                         selected_orientation = orientation
                         selected_image = image_meta
         return selected_orientation, selected_image
-    
+
     def run(self, accession: str) -> None:
         classified = defaultdict(defaultdict(list).copy)
         converted, conversion_failed = defaultdict(str), defaultdict(str)
         skipped = []
-        meta_series_map = {}
-        all_series_instances = self.dicom_dir.get_all_dicom_series_instances(
-            accession)
+        all_series_instances = self.dicom_dir.get_all_dicom_series_instances(accession)
 
         for series_uid, instances in all_series_instances.items():
             if len(instances) == 0:
                 continue
 
-            dicom_meta = extract_dicom_meta(
-                instances[0], series_uid=series_uid)
+            dicom_meta = extract_dicom_meta(instances[0], series_uid=series_uid)
             modality = detect_modality(
                 dicom_meta.series_desc,
                 dicom_meta.seq,
                 dicom_meta.contrast_agent,
-                dicom_meta.te)
-            orientation = detect_orientation(
-                dicom_meta.patient_orientation_vector)
+                dicom_meta.te,
+            )
+            orientation = detect_orientation(dicom_meta.patient_orientation_vector)
 
             if modality is None or orientation is None:
                 skipped.append(dicom_meta)
@@ -151,36 +149,30 @@ def run(self, accession: str) -> None:
 
         for modality, orientations in classified.items():
             orientation_best = defaultdict(Modality)
-            
+
             for orientation, image_list in orientations.items():
                 orientation_best[orientation] = self.get_best(image_list)
 
-            selected_orientation, selected_image_meta = self.select_orientation(orientation_best)
+            selected_orientation, selected_image_meta = self.select_orientation(
+                orientation_best
+            )
             nifti_path = self.pipeline_dir.get_input_image(
-                accession=accession, modality=modality)
+                accession=accession, modality=modality
+            )
             selected_series_uid = selected_image_meta.series_uid
-            dicom_dir = self.dicom_dir.get_series(
-                accession, selected_series_uid)
+            dicom_dir = self.dicom_dir.get_series(accession, selected_series_uid)
             max_echo_series_crc = get_max_echo_series_crc(
-                all_series_instances[selected_series_uid])
+                all_series_instances[selected_series_uid]
+            )
 
             conversion_status = self.dicom2nifti(
                 dcm_series_dir=dicom_dir,
                 nifti_path=nifti_path,
-                series_crc=max_echo_series_crc)
-
+                series_crc=max_echo_series_crc,
+            )
+
             if conversion_status == 0:
                 self.logger.warning(f'failed to convert {dicom_dir} to nifti.')
                 conversion_failed[modality] = selected_image_meta
                 continue
             converted[modality] = selected_image_meta
-
-
-
-
-
-
-
-
-
-