fix ruff issues + linting

src/scportrait/pipeline/_base.py

@@ -11,6 +11,7 @@
 
 from scportrait.pipeline._utils.helper import read_config
 
+
 class Logable:
     """Create log entries.
 
@@ -179,13 +180,13 @@ class ProcessingStep(Logable):
     def __init__(
         self,
         config,
-        directory = None,
-        project_location = None,
+        directory=None,
+        project_location=None,
         debug=False,
         overwrite=False,
         project=None,
         filehandler=None,
-        from_project:bool = False,
+        from_project: bool = False,
     ):
         super().__init__(directory=directory)
 
@@ -205,7 +206,7 @@ def __init__(
         if isinstance(config, str):
             config = read_config(config)
             if self.__class__.__name__ in config.keys():
-                self.config = config[self.__class__.__name__ ]
+                self.config = config[self.__class__.__name__]
             else:
                 self.config = config
         else:

src/scportrait/pipeline/_utils/helper.py

@@ -1,8 +1,10 @@
 from typing import TypeVar
+
 import yaml
 
 T = TypeVar("T")
 
+
 def read_config(config_path: str) -> dict:
     with open(config_path) as stream:
         try:
@@ -11,6 +13,7 @@ def read_config(config_path: str) -> dict:
             print(exc)
     return config
 
+
 def flatten(nested_list: list[list[T]]) -> list[T | tuple[T]]:
     """Flatten a list of lists into a single list.
 

src/scportrait/pipeline/featurization.py

@@ -5,8 +5,8 @@
 from contextlib import redirect_stdout
 from functools import partial as func_partial
 
-import numpy as np
 import h5py
+import numpy as np
 import pandas as pd
 import pytorch_lightning as pl
 import torch
@@ -18,6 +18,7 @@
 from scportrait.tools.ml.datasets import HDF5SingleCellDataset
 from scportrait.tools.ml.plmodels import MultilabelSupervisedModel
 
+
 class _FeaturizationBase(ProcessingStep):
     PRETRAINED_MODEL_NAMES = [
         "autophagy_classifier",
@@ -175,9 +176,7 @@ def _get_nmasks(self):
             try:
                 self.n_masks = h5py.File(self.extraction_file, "r")["n_masks"][()].item()
             except Exception as e:
-                raise ValueError(
-                    f"Could not extract number of masks from HDF5 file. Error: {e}"
-                ) from e
+                raise ValueError(f"Could not extract number of masks from HDF5 file. Error: {e}") from e
 
     def _general_setup(self, extraction_dir: str, return_results: bool = False):
         """Helper function to execute all setup functions that are common to all featurization steps."""
@@ -892,7 +891,7 @@ class based on the previous single-cell extraction. Therefore, only the second a
         self.log("Started MLClusterClassifier classification.")
 
         # perform setup
-        self._setup(extraction_dir = extraction_dir, return_results=return_results)
+        self._setup(extraction_dir=extraction_dir, return_results=return_results)
 
         self.dataloader = self.generate_dataloader(
             extraction_dir,
@@ -975,8 +974,8 @@ def _load_models(self):
         memory_usage = self._get_gpu_memory_usage()
         self.log(f"GPU memory usage after loading models: {memory_usage}")
 
-    def _setup(self, extraction_dir: str):
-        self._general_set(extraction_dir=extraction_dir)
+    def _setup(self, extraction_dir: str, return_results: bool):
+        self._general_setup(extraction_dir=extraction_dir, return_results=return_results)
         self._get_model_specs()
         self._setup_transforms()
 
@@ -988,7 +987,7 @@ def _setup(self, extraction_dir: str):
 
         self._load_models()
 
-    def process(self, extraction_dir:str, size:int = 0, return_results:bool = False):
+    def process(self, extraction_dir: str, size: int = 0, return_results: bool = False):
         """
         Function called to perform classification on the provided HDF5 dataset.
 
@@ -1335,12 +1334,12 @@ def __init__(self, *args, **kwargs):
 
         self.channel_selection = None  # ensure that all images are passed to the function
 
-    def _setup(self, extraction_dir:str, return_results:bool):
+    def _setup(self, extraction_dir: str, return_results: bool):
         self._general_setup(extraction_dir=extraction_dir, return_results=return_results)
         self._setup_transforms()
         self._get_channel_specs()
 
-    def process(self, extraction_dir: str, size: int =0, return_results: bool = False):
+    def process(self, extraction_dir: str, size: int = 0, return_results: bool = False):
         """
         Perform featurization on the provided HDF5 dataset.
 
@@ -1453,7 +1452,7 @@ def _setup_channel_selection(self):
             self.channel_selection = [0, self.channel_selection]
         return
 
-    def _setup(self, extraction_dir:str, return_results:bool):
+    def _setup(self, extraction_dir: str, return_results: bool):
         self._general_setup(extraction_dir=extraction_dir, return_results=return_results)
         self._setup_channel_selection()
         self._setup_transforms()

src/scportrait/pipeline/project.py

@@ -307,7 +307,7 @@ def _setup_featurization_f(self, featurization_f):
                 self.featurization_directory,
                 project_location=self.project_location,
                 debug=self.debug,
-                overwrite=False,  #this needs to be set to false as the featurization step should not remove previously created features
+                overwrite=False,  # this needs to be set to false as the featurization step should not remove previously created features
                 project=self,
                 filehandler=self.filehandler,
                 from_project=True,