updating docstrings for google format

cellpose/models.py

@@ -432,6 +432,7 @@ def eval(self, x, batch_size=8, resample=True, channels=None, channel_axis=None,
             progress (QProgressBar, optional): pyqt progress bar. Defaults to None.
 
         Returns:
+            A tuple containing (masks, flows, styles, diams): 
             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; flows[k][1] = XY flows at each pixel; 
             flows[k][2] = cell probability (if > cellprob_threshold, pixel used for dynamics); 

cellpose/transforms.py

@@ -97,12 +97,12 @@ 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:
-        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.
+        A tuple containing (IMG, ysub, xsub, Ly, Lx):
+        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:
@@ -859,10 +859,8 @@ def pad_image_ND(img0, div=16, extra=1, min_size=None, zpad=False):
         min_size (tuple, optional): Minimum size of the image. Defaults to None.
 
     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.
+        A tuple containing (I, ysub, xsub) or (I, ysub, xsub, zsub), I is padded image, -sub are ranges of pixels in the padded image corresponding to img0.
+            
     """
     Ly, Lx = img0.shape[-2:]
     ypad1, ypad2, xpad1, xpad2 = get_pad_yx(Ly, Lx, div=div, extra=extra, min_size=min_size)
@@ -910,10 +908,9 @@ 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:
-        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.
+        A tuple containing (imgi, lbl, scale): 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)

cellpose/utils.py

@@ -561,7 +561,7 @@ def diameters(masks):
     masks (ndarray): masks (0=no cells, 1=first cell, 2=second cell,...)
 
     Returns:
-    tuple: A tuple containing the median diameter and an array of diameters for each object.
+        tuple: A tuple containing the median diameter and an array of diameters for each object.
 
     Examples:
     >>> masks = np.array([[0, 1, 1], [1, 0, 0], [1, 1, 0]])
@@ -586,10 +586,7 @@ def radius_distribution(masks, bins):
         bins (int): Number of bins for the histogram.
 
     Returns:
-        tuple: A tuple containing:
-            - nb (ndarray): Normalized histogram of radii.
-            - md (float): Median radius.
-            - radii (ndarray): Array of radii.
+        A tuple containing a normalized histogram of radii, median radius, array of radii.
 
     """
     unique, counts = np.unique(masks, return_counts=True)
@@ -634,9 +631,9 @@ def fill_holes_and_remove_small_masks(masks, min_size=15):
         Set to -1 to turn off this functionality. Default is 15.
 
     Returns:
-    ndarray: Int, 2D or 3D array of masks with holes filled and small masks removed.
-        0 represents no mask, while positive integers represent mask labels.
-        The size is [Ly x Lx] or [Lz x Ly x Lx].
+        ndarray: Int, 2D or 3D array of masks with holes filled and small masks removed.
+            0 represents no mask, while positive integers represent mask labels.
+            The size is [Ly x Lx] or [Lz x Ly x Lx].
     """
 
     if masks.ndim > 3 or masks.ndim < 2: