docs pattern of markdown, link to related functions

docs/napari_points_mask.md

@@ -1,22 +1,22 @@
 ## Make Mask of Napari Points
 
-This function is to generate a mask from napari point information.
+This function is to generate a mask from Napari point information.
 This application of this function could be to get information about image
 at particular points (e.g. color or intensity information)
 
-**plantcv.annotate.napari_points_mask*(*img, viewer)
+**plantcv.annotate.napari_points_mask**(*img, viewer*)
 
-**returns** dictionary of masks for each class
+**returns** dictionary of masks, one for each class where the class label is the key to access
 
 - **Parameters:**
     - img - image data (compatible with gray, RGB, and hyperspectral data. If data is hyperspecral it should be the array e.g. hyperspectral.array_data)
-    - viewer = Napari Viewer with point classes labeled. The size of the points in the mask will be from the viewer parameters.
+    - viewer = Napari Viewer with point classes labeled (likely created with [`napari_label_classes`](napari_label_classes.md)). The size of the points in the mask will be determined from the viewer parameters.
 
 - **Context:**
     - This function can be used to generate a mask from Napari points in order to get information about point data. 
 
 - **Example use:**
-    - An application of this function might be collection of color data for the Naive Bayes module.
+    - An application of this function might be collection of color data for the [Naive Bayes module](https://plantcv.readthedocs.io/en/latest/tutorials/machine_learning_tutorial/).
 
 
 ```python
@@ -27,11 +27,15 @@ import napari
 # Create an instance of the Points class
 img, path, name = pcv.readimage("./wheat.png")
 
+# Should open interactive napari viewer
 viewer = pcvan.napari_label_classes(img,['background','healthy', 'rust', 'chlorosis'], size = 4)
 
 maskdict = pcvan.napari_points_mask(img,viewer)
 
-# Should open interactive napari viewer
+pcv.plot_image(maskdict['background'])
+pcv.plot_image(maskdict['healthy'])
+pcv.plot_image(maskdict['rust'])
+pcv.plot_image(maskdict['chlorosis'])
 
 ```