More docstrings

Signed-off-by: zethson <lukas.heumos@posteo.net>

src/scportrait/io/convert.py

@@ -54,9 +54,9 @@ def __init__(
         self.debug = debug
         self.overwrite = overwrite
 
-        self.input_image_status = None
-        self.segmentation_status = None
-        self.centers_status = None
+        self.input_image_status: bool | None = None
+        self.segmentation_status: bool | None = None
+        self.centers_status: bool | None = None
 
     def _check_memory(self, item) -> bool:
         """Check if item can fit in available memory.
@@ -77,7 +77,7 @@ def _create_temp_dir(self) -> None:
         Creates directory in cache location specified in config or current working directory.
         """
         if self.cache is not None:
-            path = os.path.join(self.config["cache"], f"{self.__class__.__name__}_")
+            path = os.path.join(self.directory["cache"], f"{self.__class__.__name__}_")  # type: ignore
         else:
             path = os.path.join(os.getcwd(), f"{self.__class__.__name__}_")
 
@@ -338,10 +338,10 @@ def write_centers_to_spatialdata(self, coordinate_system: str = "global") -> Non
                 _ids = cPickle.load(input_file)
             centroids = self._make_centers_object(centers, _ids, transform, coordinate_system=coordinate_system)
         else:
-            if self.segementation_status:
+            if self.segmentation_status:
                 self.log("No centers found in project. Recalculating based on the provided segmentation mask.")
 
-                if self.check_memory(mask):
+                if self._check_memory(mask):
                     self.log("Calculating centers using numba This should be quick.")
                     centers, _, _ids = numba_mask_centroid(mask.values)
                     centroids = self._make_centers_object(centers, _ids, transform, coordinate_system=coordinate_system)

src/scportrait/io/daskmmap.py

@@ -77,8 +77,8 @@ def calculate_chunk_sizes_chunks(
     target_size_bytes = target_size_gb * 1024**3
     total_elements_per_chunk = target_size_bytes // element_size
 
-    HDF5_chunk_size = list(HDF5_chunk_size)
-    chunk_sizes = HDF5_chunk_size.copy()
+    HDF5_chunk_size = list(HDF5_chunk_size)  # type: ignore
+    chunk_sizes = HDF5_chunk_size.copy()  # type: ignore
 
     while np.prod(chunk_sizes) < total_elements_per_chunk:
         for i in range(len(chunk_sizes)):
@@ -209,4 +209,4 @@ def load_hdf5_chunk(file_path: str, container_name: str, slices: tuple[slice, ..
         The sliced chunk from the HDF5 dataset
     """
     with h5py.File(file_path, "r") as f:
-        data = f[container_name][slices]
+        f[container_name][slices]

src/scportrait/pipeline/_utils/helper.py

@@ -1,6 +1,7 @@
 from typing import TypeVar
 
-T = TypeVar('T')
+T = TypeVar("T")
+
 
 def flatten(nested_list: list[list[T]]) -> list[T]:
     """Flatten a list of lists into a single list.
@@ -16,4 +17,4 @@ def flatten(nested_list: list[list[T]]) -> list[T]:
         >>> flatten(nested_list)
         [1, 2, 3, 4, 5, 6, 7, 8, 9]
     """
-    return [item for sublist in nested_list for item in sublist]
+    return [item for sublist in nested_list for item in sublist]