Added doc on mgrid and ogrid in doc/library/tensor/basic.txt

doc/library/tensor/basic.txt

@@ -1643,28 +1643,49 @@ Linear Algebra
     :note: See :func:`tensordot` and :func:`batched_dot` for
         supplementary documentation.
 
-.. function:: mgrid()
-
-    :param
-    
-    :returns: a dense (or fleshed out) mesh-grid when indexed, so that 
-	      each returned argument has the same shape. The dimensions 
-	      and number of the output arrays are equal to the number of 
-	      indexing dimensions. If the step length is not a complex 
-	      number, then the stop is not inclusive.
+.. function:: mgrid
 
-.. function:: ogrid()
+    :returns: an instance which returns a dense (or fleshed out) mesh-grid 
+              when indexed, so that each returned argument has the same shape. 
+              The dimensions and number of the output arrays are equal to the 
+              number of indexing dimensions. If the step length is not a complex 
+	      number, then the stop is not inclusive.
 
-    :param
-    
-    :returns: an open (i.e. not fleshed out) mesh-grid when indexed, 
-              so that only one dimension of each returned array is 
-              greater than 1. The dimension and number of the output 
-              arrays are equal to the number of indexing dimensions. 
-              If the step length is not a complex number, then the 
-              stop is not inclusive.
+    Example:
+    >>> a = T.mgrid[0:5, 0:3]
+    >>> a[0].eval()
+    array([[0, 0, 0],
+           [1, 1, 1],
+           [2, 2, 2],
+           [3, 3, 3],
+           [4, 4, 4]], dtype=int8)
+    >>> a[1].eval()
+    array([[0, 1, 2],
+           [0, 1, 2],
+           [0, 1, 2],
+           [0, 1, 2],
+           [0, 1, 2]], dtype=int8)	    
+	    
+.. function:: ogrid
 
+    :returns: an instance which returns an open (i.e. not fleshed out) mesh-grid 
+              when indexed, so that only one dimension of each returned array is 
+              greater than 1. The dimension and number of the output arrays are 
+              equal to the number of indexing dimensions. If the step length is 
+              not a complex number, then the stop is not inclusive.
 
+    Example:
+    >>> b = T.ogrid[0:5, 0:3]
+    >>> b[0].eval()
+    array([[0],
+           [1],
+           [2],
+           [3],
+           [4]], dtype=int8)
+    >>> b[1].eval()
+    array([[0, 1, 2, 3]], dtype=int8)
+      
+      
 Gradient / Differentiation
 ==========================