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Little grammar fix, trailing whitespace deleted
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@@ -12,7 +12,7 @@ The `Python Imaging Library <http://www.pythonware.com/products/pil/>`_, or PIL
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for short, is *the* library for image manipulation in Python.
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It works with Python 1.5.2 and above, including 2.5, 2.6 and 2.7. Unfortunately,
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it doesn't work with 3.0+ yet.
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it doesn't work with 3.0+ yet.
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Installation
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~~~~~~~~~~~~
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@@ -20,7 +20,7 @@ Installation
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PIL has a reputation of not being very straightforward to install. Listed below
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are installation notes on various systems.
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Also, there's a fork named `Pillow <http://pypi.python.org/pypi/Pillow>`_ which is easier
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Also, there's a fork named `Pillow <http://pypi.python.org/pypi/Pillow>`_ which is easier
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to install. It has good setup instructions for all platforms.
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Installing on Linux
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@@ -6,19 +6,19 @@ Twisted
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`Twisted <http://twistedmatrix.com/trac/>`_ is an event-driven networking engine. It can be
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used to build applications around many different networking protocols, including http servers
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and clients, applications using SMTP, POP3, IMAP or SSH protocols, instant messaging and
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`many more <http://twistedmatrix.com/trac/wiki/Documentation>`_.
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and clients, applications using SMTP, POP3, IMAP or SSH protocols, instant messaging and
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many more <http://twistedmatrix.com/trac/wiki/Documentation>`_.
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PyZMQ
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-----
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`PyZMQ <http://zeromq.github.com/pyzmq/>`_ is the Python binding for `ZeroMQ <http://www.zeromq.org/>`_,
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which is a high-performance asynchronous messaging library. One great advantage is that ZeroMQ
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can be used for message queuing without message broker. The basic patterns for this are:
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can be used for message queuing without a message broker. The basic patterns for this are:
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- request-reply: connects a set of clients to a set of services. This is a remote procedure call
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and task distribution pattern.
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- publish-subscribe: connects a set of publishers to a set of subscribers. This is a data
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- publish-subscribe: connects a set of publishers to a set of subscribers. This is a data
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distribution pattern.
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- push-pull (or pipeline): connects nodes in a fan-out / fan-in pattern that can have multiple
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steps, and loops. This is a parallel task distribution and collection pattern.
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