Small doc fixes

Author: dnerini

examples/LK_buffer_mask.py

@@ -97,7 +97,7 @@
 # Sparse Lucas-Kanade
 # -------------------
 #
-# By setting the optional argument 'dense=False' in 'x,y,u,v = LK_optflow(.....)',
+# By setting the optional argument 'dense=False' in 'xy, uv = LK_optflow(.....)',
 # the LK algorithm returns the motion vectors detected by the Lucas-Kanade scheme
 # without interpolating them on the grid.
 # This allows us to better identify the presence of wrongly detected
@@ -138,8 +138,8 @@
 # One way to mitigate this effect of the boundaries is to introduce a slight buffer
 # of the no-data mask so that the algorithm will ignore all the portions of the
 # radar domain that are nearby no-data areas.
-# This is achieved by setting the keyword argument 'buffer_mask = 20' in
-# 'x,y,u,v = LK_optflow(.....)'.
+# This is achieved by passing the keyword argument 'buffer_mask = 10' within the
+# feature detection optional arguments 'fd_kwargs'.
 
 # with buffer
 buffer = 10

pysteps/motion/lucaskanade.py

@@ -148,7 +148,7 @@ def dense_lucaskanade(input_images,
         Sparse vectors within this declustering scale are averaged together.
         If set to less than 2 pixels, the declustering is not perfomed.
         See the documentation of
-        :py:func:`pysteps.cleansing.cleansing.decluster`.
+        :py:func:`pysteps.utils.cleansing.decluster`.
 
     verbose : bool, optional
         If set to True, print some information about the program.
@@ -157,9 +157,11 @@ def dense_lucaskanade(input_images,
     -------
 
     out : array_like or tuple
-        If **dense=True** (the default), it returns the three-dimensional array
-        (2, m, n) containing the dense x- and y-components of the motion field
-        in units of [pixels/timestep] as given by the input array input_images.
+        If **dense=True** (the default), return the advection field having shape
+        (2, m, n), where out[0, :, :] contains the x-components of the motion
+        vectors and out[1, :, :] contains the y-components.
+        The velocities are in units of pixels / timestep, where timestep is the
+        time difference between the two input images.
         Return a zero motion field of shape (2, m, n) when no motion is
         detected.
 

pysteps/utils/interpolate.py

@@ -76,7 +76,7 @@ def rbfinterp2d(
 
     The coordinates are normalized before computing the Euclidean norms:
 
-        x = (x - min(x)) / max[max(x) - min(x), max(y) - min(y)],
+        x = (x - min(x)) / max[max(x) - min(x), max(y) - min(y)],\n
         y = (y - min(y)) / max[max(x) - min(x), max(y) - min(y)],
 
     where the min and max values are taken as the 2nd and 98th percentiles.