trying to update google format

cellpose/dynamics.py

@@ -110,8 +110,9 @@ def get_centers(masks, slices):
         slices (ndarray): The slices of the masks.
 
     Returns:
-        ndarray: The centers of the masks.
-        ndarray: The extents of the masks.
+        tuple containing
+            - centers (ndarray): The centers of the masks.
+            - ext (ndarray): The extents of the masks.
     """
     centers = np.zeros((len(slices), 2), "int32")
     ext = np.zeros((len(slices),), "int32")

cellpose/models.py

@@ -140,7 +140,7 @@ def eval(self, x, batch_size=8, channels=[0,0], channel_axis=None, invert=False,
             do_3D (bool, optional): Set to True to run 3D segmentation on 4D image input. Defaults to False.
 
         Returns:
-            tuple: Tuple containing masks, flows, styles, and diams.
+            tuple containing
                 - masks (list of 2D arrays or single 3D array): Labelled image, where 0=no masks; 1,2,...=mask labels.
                 - flows (list of lists 2D arrays or list of 3D arrays): 
                     - flows[k][0] = XY flow in HSV 0-255

cellpose/transforms.py

@@ -99,11 +99,12 @@ def make_tiles(imgi, bsize=224, augment=False, tile_overlap=0.1):
         tile_overlap (float, optional): Fraction of overlap of tiles. Defaults to 0.1.
 
     Returns:
-        np.ndarray: Array of shape (ntiles, nchan, bsize, bsize) representing the tiles.
-        list: List of arrays with start and end of tiles in Y of length ntiles.
-        list: List of arrays with start and end of tiles in X of length ntiles.
-        int: Height of the input image.
-        int: Width of the input image.
+        tuple containing
+            - IMG (np.ndarray): Array of shape (ntiles, nchan, bsize, bsize) representing the tiles.
+            - ysub (list): List of arrays with start and end of tiles in Y of length ntiles.
+            - xsub (list): List of arrays with start and end of tiles in X of length ntiles.
+            - Ly (int): Height of the input image.
+            - Lx (int): Width of the input image.
     """
     nchan, Ly, Lx = imgi.shape
     if augment:
@@ -736,10 +737,11 @@ def pad_image_ND(img0, div=16, extra=1, min_size=None):
         extra (int, optional): Extra padding. Defaults to 1.
         min_size (tuple, optional): Minimum size of the image. Defaults to None.
 
-    Returns:
-        ndarray: Padded image.
-        ndarray: Y range of pixels in the padded image corresponding to img0.
-        ndarray: X range of pixels in the padded image corresponding to img0.
+    Returns:   
+        tuple containing 
+            - I (ndarray): Padded image.
+            - ysub (ndarray): Y range of pixels in the padded image corresponding to img0.
+            - xsub (ndarray): X range of pixels in the padded image corresponding to img0.
     """
     if min_size is None or img0.shape[-2] >= min_size[-2]:
         Lpad = int(div * np.ceil(img0.shape[-2] / div) - img0.shape[-2])
@@ -789,9 +791,10 @@ def random_rotate_and_resize(X, Y=None, scale_range=1., xy=(224, 224), do_3D=Fal
         random_per_image (bool, optional): Different random rotate and resize per image. Defaults to True.
 
     Returns:
-        imgi (ND-array, float): Transformed images in array [nimg x nchan x xy[0] x xy[1]].
-        lbl (ND-array, float): Transformed labels in array [nimg x nchan x xy[0] x xy[1]].
-        scale (array, float): Amount each image was resized by.
+        tuple containing
+            - imgi (ND-array, float): Transformed images in array [nimg x nchan x xy[0] x xy[1]].
+            - lbl (ND-array, float): Transformed labels in array [nimg x nchan x xy[0] x xy[1]].
+            - scale (array, float): Amount each image was resized by.
     """
     scale_range = max(0, min(2, float(scale_range)))
     nimg = len(X)