Document new heapify() function.
diff --git a/Doc/lib/libheapq.tex b/Doc/lib/libheapq.tex
index d196fda..d1aaaae 100644
--- a/Doc/lib/libheapq.tex
+++ b/Doc/lib/libheapq.tex
@@ -25,13 +25,16 @@
(a) We use zero-based indexing. This makes the relationship between the
index for a node and the indexes for its children slightly less
obvious, but is more suitable since Python uses zero-based indexing.
-(b) Our pop method returns the smallest item, not the largest.
+(b) Our pop method returns the smallest item, not the largest (called a
+"min heap" in textbooks; a "max heap" is more common in texts because
+of its suitability for in-place sorting).
These two make it possible to view the heap as a regular Python list
without surprises: \code{\var{heap}[0]} is the smallest item, and
\code{\var{heap}.sort()} maintains the heap invariant!
-To create a heap, use a list initialized to \code{[]}.
+To create a heap, use a list initialized to \code{[]}, or you can
+transform a populated list into a heap via function \function{heapify()}.
The following functions are provided:
@@ -45,6 +48,10 @@
heap invariant.
\end{funcdesc}
+\begin{funcdesc}{heapify}{x}
+Transform list \var{x} into a heap, in-place, in linear time.
+\end{funcdesc}
+
Example of use:
\begin{verbatim}
@@ -53,17 +60,17 @@
>>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
>>> for item in data:
... heappush(heap, item)
-...
+...
>>> sorted = []
>>> while heap:
... sorted.append(heappop(heap))
-...
+...
>>> print sorted
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> data.sort()
>>> print data == sorted
True
->>>
+>>>
\end{verbatim}