From: Georg Brandl <georg@python.org>
Date: Sat, 22 Nov 2008 08:54:21 +0000 (+0000)
Subject: Fix two mp doc issues from #4012.
X-Git-Tag: v3.0~77
X-Git-Url: https://granicus.if.org/sourcecode?a=commitdiff_plain;h=e3d70aed8ef41d321f528016ec58c9b92438a0ac;p=python

Fix two mp doc issues from #4012.
---

diff --git a/Doc/library/multiprocessing.rst b/Doc/library/multiprocessing.rst
index e15ff4d4d6..8ea561f217 100644
--- a/Doc/library/multiprocessing.rst
+++ b/Doc/library/multiprocessing.rst
@@ -232,7 +232,7 @@ For example::
 
    if __name__ == '__main__':
        pool = Pool(processes=4)           # start 4 worker processes
-       result = pool.applyAsync(f, [10])  # evaluate "f(10)" asynchronously
+       result = pool.apply_async(f, [10]) # evaluate "f(10)" asynchronously
        print(result.get(timeout=1))       # prints "100" unless your computer is *very* slow
        print(pool.map(f, range(10)))      # prints "[0, 1, 4,..., 81]"
 
@@ -1505,7 +1505,7 @@ with the :class:`Pool` class.
    The class of the result returned by :meth:`Pool.apply_async` and
    :meth:`Pool.map_async`.
 
-   .. method:: get([timeout)
+   .. method:: get([timeout])
 
       Return the result when it arrives.  If *timeout* is not ``None`` and the
       result does not arrive within *timeout* seconds then
@@ -1535,7 +1535,7 @@ The following example demonstrates the use of a pool::
    if __name__ == '__main__':
        pool = Pool(processes=4)              # start 4 worker processes
 
-       result = pool.applyAsync(f, (10,))    # evaluate "f(10)" asynchronously
+       result = pool.apply_async(f, (10,))   # evaluate "f(10)" asynchronously
        print(result.get(timeout=1))          # prints "100" unless your computer is *very* slow
 
        print(pool.map(f, range(10)))         # prints "[0, 1, 4,..., 81]"
@@ -1546,7 +1546,7 @@ The following example demonstrates the use of a pool::
        print(it.next(timeout=1))             # prints "4" unless your computer is *very* slow
 
        import time
-       result = pool.applyAsync(time.sleep, (10,))
+       result = pool.apply_async(time.sleep, (10,))
        print(result.get(timeout=1))          # raises TimeoutError