Pre commits

Notion task: https://www.notion.so/qim-dtu/Pre-commit-pipeline-1728f41887814960b6f5870460995cf4

All developers need to hook the pre-commit into the git commit command. The required packages are pre-commit and ruff. After they have been installed, the pre-commits are hooked onto git by running pre-commit install.

The details on how it works and how to use it are all in described in Notion.

stuff.py

 """Provides filter functions and classes for image processing."""
 
 from typing import Type
+
 import dask.array as da
+import dask_image.ndfilters as dask_ndfilters
+import numpy as np
 from scipy import ndimage
 from skimage import morphology
-import dask_image.ndfilters as dask_ndfilters
+
 from qim3d.utils import log
-import os
-import numpy as np
 
 __all__ = [
-    "FilterBase",
-    "Gaussian",
-    "Median",
-    "Pipeline",
-    "gaussian",
-    "median",
-    "tophat",
+    'FilterBase',
+    'Gaussian',
+    'Median',
+    'Pipeline',
+    'gaussian',
+    'median',
+    'tophat',
 ]
 
+
 class FilterBase:
-    def __init__(self, *args, dask: bool = False, chunks: str = "auto", **kwargs):
+    def __init__(self, *args, dask: bool = False, chunks: str = 'auto', **kwargs):
         """
         Base class for image filters.
 
         Args:
+            chunks: ??
+            dask: ??
             *args: Additional positional arguments for filter initialization.
             **kwargs: Additional keyword arguments for filter initialization.
 
@@ -90,6 +94,7 @@ class Median(FilterBase):
         self.size = size
         self.footprint = footprint
 
+
 class Pipeline:
 
     """
@@ -184,6 +189,7 @@ class Pipeline:
         """
         self._add_filter(str(len(self.filters)), fn)
 
+
 def gaussian(
     vol: np.ndarray, sigma: float, dask: bool = False, chunks: str = 'auto', **kwargs
 ) -> np.ndarray:
@@ -214,7 +220,13 @@ def gaussian(
         return res
 
 
-def median(vol: np.ndarray, size: float = None, footprint: np.ndarray = None, dask: bool = False, chunks: str = 'auto', **kwargs,
+def median(
+    vol: np.ndarray,
+    size: float = None,
+    footprint: np.ndarray = None,
+    dask: bool = False,
+    chunks: str = 'auto',
+    **kwargs,
 ) -> np.ndarray:
     """
     Applies a median filter to the input volume using scipy.ndimage.median_filter or dask_image.ndfilters.median_filter.
@@ -248,6 +260,7 @@ def median(vol: np.ndarray, size: float = None, footprint: np.ndarray = None, da
         res = ndimage.median_filter(vol, size, footprint, **kwargs)
         return res
 
+
 def tophat(vol: np.ndarray, dask: bool = False, **kwargs):
     """
     Remove background from the volume.