fix

qim3d/generate/_aggregators.py

@@ -147,12 +147,12 @@ def noise_object_collection(
     Generate a 3D volume of multiple synthetic objects using Perlin noise.
 
     Args:
-        collection_shape (tuple, optional): Shape of the final collection volume to generate. Defaults to (200, 200, 200).
+        collection_shape (tuple of ints, optional): Shape of the final collection volume to generate. Defaults to (200, 200, 200).
         num_objects (int, optional): Number of synthetic objects to include in the collection. Defaults to 15.
         positions (list[tuple], optional): List of specific positions as (z, y, x) coordinates for the objects. If not provided, they are placed randomly into the collection. Defaults to None.
-        min_shape (tuple, optional): Minimum shape of the objects. Defaults to (40, 40, 40).
-        max_shape (tuple, optional): Maximum shape of the objects. Defaults to (60, 60, 60).
-        object_shape_zoom (tuple, optional): Scaling factors for each dimension of each object. Defaults to (1.0, 1.0, 1.0).
+        min_shape (tuple of ints, optional): Minimum shape of the objects. Defaults to (40, 40, 40).
+        max_shape (tuple of ints, optional): Maximum shape of the objects. Defaults to (60, 60, 60).
+        object_shape_zoom (tuple of ints, optional): Scaling factors for each dimension of each object. Defaults to (1.0, 1.0, 1.0).
         min_object_noise (float, optional): Minimum scale factor for Perlin noise. Defaults to 0.02.
         max_object_noise (float, optional): Maximum scale factor for Perlin noise. Defaults to 0.05.
         min_rotation_degrees (int, optional): Minimum rotation angle in degrees. Defaults to 0.

qim3d/generate/_generators.py

@@ -20,8 +20,8 @@ def noise_object(
     Generate a 3D volume with Perlin noise, spherical gradient, and optional scaling and gamma correction.
 
     Args:
-        base_shape (tuple, optional): Shape of the initial volume to generate. Defaults to (128, 128, 128).
-        final_shape (tuple, optional): Desired shape of the final volume. Defaults to (128, 128, 128).
+        base_shape (tuple of ints, optional): Shape of the initial volume to generate. Defaults to (128, 128, 128).
+        final_shape (tuple of ints, optional): Desired shape of the final volume. Defaults to (128, 128, 128).
         noise_scale (float, optional): Scale factor for Perlin noise. Defaults to 0.05.
         order (int, optional): Order of the spline interpolation used in resizing. Defaults to 1.
         gamma (float, optional): Gamma correction factor. Defaults to 1.0.
@@ -29,7 +29,7 @@ def noise_object(
         threshold (float, optional): Threshold value for clipping low intensity values. Defaults to 0.5.
         smooth_borders (bool, optional): Flag for automatic computation of the threshold value to ensure a blob with no straight edges. If True, the `threshold` parameter is ignored. Defaults to False.
         object_shape (str, optional): Shape of the object to generate, either "cylinder", or "tube". Defaults to None.
-        dtype (str, optional): Desired data type of the output volume. Defaults to "uint8".
+        dtype (data-type, optional): Desired data type of the output volume. Defaults to "uint8".
 
     Returns:
         noise_object (numpy.ndarray): Generated 3D volume with specified parameters.

qim3d/io/_downloader.py

@@ -16,7 +16,7 @@ class Downloader:
     """Class for downloading large data files available on the [QIM data repository](https://data.qim.dk/).
 
     Attributes:
-        folder_name (str): Folder class with the name of the folder in <https://data.qim.dk/>
+        folder_name (str or os.PathLike): Folder class with the name of the folder in <https://data.qim.dk/>
 
     Syntax for downloading and loading a file is `qim3d.io.Downloader().{folder_name}.{file_name}(load_file=True)`
 

qim3d/io/_ome_zarr.py

@@ -197,7 +197,7 @@ def export_ome_zarr(
     This function generates a multi-scale OME-Zarr representation of the input data, which is commonly used for large imaging datasets. The downsampled scales are calculated such that the smallest scale fits within the specified `chunk_size`.
 
     Args:
-        path (str): The directory where the OME-Zarr data will be stored.
+        path (str or os.PathLike): The directory where the OME-Zarr data will be stored.
         data (np.ndarray or dask.array): The 3D image data to be exported. Supports both NumPy and Dask arrays.
         chunk_size (int, optional): The size of the chunks for storing data. This affects both the original data and the downsampled scales. Defaults to 256.
         downsample_rate (int, optional): The factor by which to downsample the data for each scale. Must be greater than 1. Defaults to 2.
@@ -220,9 +220,6 @@ def export_ome_zarr(
 
         qim3d.io.export_ome_zarr("Escargot.zarr", data, chunk_size=100, downsample_rate=2)
         ```
-
-    Returns:
-        None (None): This function writes the OME-Zarr data to the specified directory and does not return any value.
     """
 
     # Check if directory exists
@@ -307,7 +304,7 @@ def import_ome_zarr(path, scale=0, load=True):
     The image data can be lazily loaded (as Dask arrays) or fully computed into memory.
 
     Args:
-        path (str): The file path to the OME-Zarr data.
+        path (str or os.PathLike): The file path to the OME-Zarr data.
         scale (int or str, optional): The scale level to load.
             If 'highest', loads the finest scale (scale 0).
             If 'lowest', loads the coarsest scale (last available scale). Defaults to 0.

qim3d/io/_saving.py

@@ -413,7 +413,7 @@ def save(
     """Save data to a specified file path.
 
     Args:
-        path (str): The path to save file to. File format is chosen based on the extension. 
+        path (str or os.PathLike): The path to save file to. File format is chosen based on the extension. 
             Supported extensions are: <em>'.tif', '.tiff', '.nii', '.nii.gz', '.h5', '.vol', '.vgi', '.dcm', '.DCM', '.zarr', '.jpeg', '.jpg', '.png'</em>
         data (numpy.ndarray): The data to be saved
         replace (bool, optional): Specifies if an existing file with identical path should be replaced.
@@ -469,7 +469,7 @@ def save_mesh(filename, mesh):
     Save a trimesh object to an .obj file.
 
     Args:
-        filename (str): The name of the file to save the mesh.
+        filename (str or os.PathLike): The name of the file to save the mesh.
         mesh (trimesh.Trimesh): A trimesh.Trimesh object representing the mesh.
 
     Example:

qim3d/processing/_layers.py

@@ -9,13 +9,13 @@ def segment_layers(data:np.ndarray, inverted:bool = False, n_layers:int = 1, del
     Now uses only MaxflowBuilder for solving.
 
     Args:
-        data: 2D or 3D array on which it will be computed
-        inverted: if True, it will invert the brightness of the image
-        n_layers: How many layers are we looking for (result in a layer and background)
-        delta: Smoothness parameter
-        min_margin: If we want more layers, we have to have a margin otherwise they are all going to be exactly the same
-        max_margin: Maximum margin between layers
-        wrap: If True, starting and ending point of the border between layers are at the same level
+        data (np.ndarray): 2D or 3D array on which it will be computed
+        inverted (bool): if True, it will invert the brightness of the image
+        n_layers (int): How many layers are we looking for (result in a layer and background)
+        delta (float): Smoothness parameter
+        min_margin (int): If we want more layers, we have to have a margin otherwise they are all going to be exactly the same
+        max_margin (int): Maximum margin between layers
+        wrap (bool): If True, starting and ending point of the border between layers are at the same level
 
     Returns:
         segmentations (list[np.ndarray]): list of numpy arrays, even if n_layers == 1, each array is only 0s and 1s, 1s segmenting this specific layer
@@ -88,7 +88,7 @@ def get_lines(segmentations:list|np.ndarray) -> list:
     so it could be plotted. Used with qim3d.processing.segment_layers
 
     Args:
-        segmentations: list of arrays where each array is 2D segmentation with only 2 classes
+        segmentations (list of arrays): list of arrays where each array is 2D segmentation with only 2 classes
 
     Returns:
         segmentation_lines: list of 1D numpy arrays