Merge with Fernando's trunk-dev.

docs/source/attic/parallel_task_old.txt

@@ -0,0 +1,240 b''
+.. _paralleltask:
+
+==========================
+The IPython task interface
+==========================
+
+.. contents::
+
+The ``Task`` interface to the controller presents the engines as a fault tolerant, dynamic load-balanced system or workers. Unlike the ``MultiEngine`` interface, in the ``Task`` interface, the user have no direct access to individual engines. In some ways, this interface is simpler, but in other ways it is more powerful. Best of all the user can use both of these interfaces at the same time to take advantage or both of their strengths.  When the user can break up the user's work into segments that do not depend on previous execution, the ``Task`` interface is ideal.  But it also has more power and flexibility, allowing the user to guide the distribution of jobs, without having to assign Tasks to engines explicitly.
+
+Starting the IPython controller and engines
+===========================================
+
+To follow along with this tutorial, the user will need to start the IPython
+controller and four IPython engines. The simplest way of doing this is to
+use the ``ipcluster`` command::
+
+	$ ipcluster -n 4
+	
+For more detailed information about starting the controller and engines, see our :ref:`introduction <ip1par>` to using IPython for parallel computing.
+
+The magic here is that this single controller and set of engines is running both the MultiEngine and ``Task`` interfaces simultaneously.
+
+QuickStart Task Farming
+=======================
+
+First, a quick example of how to start running the most basic Tasks.
+The first step is to import the IPython ``client`` module and then create a ``TaskClient`` instance::
+    
+    In [1]: from IPython.kernel import client
+    
+    In [2]: tc = client.TaskClient()
+
+Then the user wrap the commands the user want to run in Tasks::
+
+    In [3]: tasklist = []
+    In [4]: for n in range(1000):
+        ...     tasklist.append(client.Task("a = %i"%n, pull="a"))
+
+The first argument of the ``Task`` constructor is a string, the command to be executed.  The most important optional keyword argument is ``pull``, which can be a string or list of strings, and it specifies the variable names to be saved as results of the ``Task``.
+
+Next, the user need to submit the Tasks to the ``TaskController`` with the ``TaskClient``::
+
+    In [5]: taskids = [ tc.run(t) for t in tasklist ]
+
+This will give the user a list of the TaskIDs used by the controller to keep track of the Tasks and their results.  Now at some point the user are going to want to get those results back.  The ``barrier`` method allows the user to wait for the Tasks to finish running::
+
+    In [6]: tc.barrier(taskids)
+
+This command will block until all the Tasks in ``taskids`` have finished.  Now, the user probably want to look at the user's results::
+
+    In [7]: task_results = [ tc.get_task_result(taskid) for taskid in taskids ]
+
+Now the user have a list of ``TaskResult`` objects, which have the actual result as a dictionary, but also keep track of some useful metadata about the ``Task``::
+
+    In [8]: tr = ``Task``_results[73]
+    
+    In [9]: tr
+    Out[9]: ``TaskResult``[ID:73]:{'a':73}
+    
+    In [10]: tr.engineid
+    Out[10]: 1
+    
+    In [11]: tr.submitted, tr.completed, tr.duration
+    Out[11]: ("2008/03/08 03:41:42", "2008/03/08 03:41:44", 2.12345)
+
+The actual results are stored in a dictionary, ``tr.results``, and a namespace object ``tr.ns`` which accesses the result keys by attribute::
+
+    In [12]: tr.results['a']
+    Out[12]: 73
+    
+    In [13]: tr.ns.a
+    Out[13]: 73
+
+That should cover the basics of running simple Tasks.  There are several more powerful things the user can do with Tasks covered later.  The most useful probably being using a ``MutiEngineClient`` interface to initialize all the engines with the import dependencies necessary to run the user's Tasks.
+
+There are many options for running and managing Tasks. The best way to learn further about the ``Task`` interface is to study the examples in ``docs/examples``. If the user do so and learn a lots about this interface, we encourage the user to expand this documentation about the ``Task`` system.
+
+Overview of the Task System
+===========================
+
+The user's view of the ``Task`` system has three basic objects:  The ``TaskClient``, the ``Task``, and the ``TaskResult``.  The names of these three objects well indicate their role.
+
+The ``TaskClient`` is the user's ``Task`` farming connection to the IPython cluster.  Unlike the ``MultiEngineClient``, the ``TaskControler`` handles all the scheduling and distribution of work, so the ``TaskClient`` has no notion of engines, it just submits Tasks and requests their results.  The Tasks are described as ``Task`` objects, and their results are wrapped in ``TaskResult`` objects.  Thus, there are very few necessary methods for the user to manage.
+
+Inside the task system is a Scheduler object, which assigns tasks to workers.  The default scheduler is a simple FIFO queue.  Subclassing the Scheduler should be easy, just implementing your own priority system.
+
+The TaskClient
+==============
+
+The ``TaskClient`` is the object the user use to connect to the ``Controller`` that is managing the user's Tasks.  It is the analog of the ``MultiEngineClient`` for the standard IPython multiplexing interface.  As with all client interfaces, the first step is to import the IPython Client Module::
+
+    In [1]: from IPython.kernel import client
+
+Just as with the ``MultiEngineClient``, the user create the ``TaskClient`` with a tuple, containing the ip-address and port of the ``Controller``.  the ``client`` module conveniently has the default address of the ``Task`` interface of the controller.  Creating a default ``TaskClient`` object would be done with this::
+
+    In [2]: tc = client.TaskClient(client.default_task_address)
+
+or, if the user want to specify a non default location of the ``Controller``, the user can specify explicitly::
+
+    In [3]: tc = client.TaskClient(("192.168.1.1", 10113))
+
+As discussed earlier, the ``TaskClient`` only has a few basic methods.
+
+ * ``tc.run(task)``
+    ``run`` is the method by which the user submits Tasks.  It takes exactly one argument, a ``Task`` object.  All the advanced control of ``Task`` behavior is handled by properties of the ``Task`` object, rather than the submission command, so they will be discussed later in the `Task`_ section.  ``run`` returns an integer, the ``Task``ID by which the ``Task`` and its results can be tracked and retrieved::
+    
+        In [4]: ``Task``ID = tc.run(``Task``)
+    
+ * ``tc.get_task_result(taskid, block=``False``)``
+    ``get_task_result`` is the method by which results are retrieved.  It takes a single integer argument, the ``Task``ID`` of the result the user wish to retrieve.  ``get_task_result`` also takes a keyword argument ``block``.  ``block`` specifies whether the user actually want to wait for the result.  If ``block`` is false, as it is by default, ``get_task_result`` will return immediately.  If the ``Task`` has completed, it will return the ``TaskResult`` object for that ``Task``.  But if the ``Task`` has not completed, it will return ``None``.  If the user specify ``block=``True``, then ``get_task_result`` will wait for the ``Task`` to complete, and always return the ``TaskResult`` for the requested ``Task``.
+ * ``tc.barrier(taskid(s))``
+    ``barrier`` is a synchronization method.  It takes exactly one argument, a ``Task``ID or list of taskIDs.  ``barrier`` will block until all the specified Tasks have completed.  In practice, a barrier is often called between the ``Task`` submission section of the code and the result gathering section::
+        
+        In [5]: taskIDs = [ tc.run(``Task``) for ``Task`` in myTasks ]
+        
+        In [6]: tc.get_task_result(taskIDs[-1]) is None
+        Out[6]: ``True``
+        
+        In [7]: tc.barrier(``Task``ID)
+        
+        In [8]: results = [ tc.get_task_result(tid) for tid in taskIDs ]
+        
+ * ``tc.queue_status(verbose=``False``)``
+    ``queue_status`` is a method for querying the state of the ``TaskControler``.  ``queue_status`` returns a dict of the form::
+        
+        {'scheduled': Tasks that have been submitted but yet run
+         'pending'  : Tasks that are currently running
+         'succeeded': Tasks that have completed successfully
+         'failed'   : Tasks that have finished with a failure
+        }
+    
+    if @verbose is not specified (or is ``False``), then the values of the dict are integers - the number of Tasks in each state.  if @verbose is ``True``, then each element in the dict is a list of the taskIDs in that state::
+    
+        In [8]: tc.queue_status()
+        Out[8]: {'scheduled': 4,
+                'pending'  : 2,
+                'succeeded': 5,
+                'failed'   : 1
+                }
+        
+        In [9]: tc.queue_status(verbose=True)
+        Out[9]: {'scheduled': [8,9,10,11],
+                'pending'  : [6,7],
+                'succeeded': [0,1,2,4,5],
+                'failed'   : [3]
+                }
+        
+ * ``tc.abort(taskid)``
+    ``abort`` allows the user to abort Tasks that have already been submitted.  ``abort`` will always return immediately.  If the ``Task`` has completed, ``abort`` will raise an ``IndexError ``Task`` Already Completed``.  An obvious case for ``abort`` would be where the user submits a long-running ``Task`` with a number of retries (see ``Task``_ section for how to specify retries) in an interactive session, but realizes there has been a typo.  The user can then abort the ``Task``, preventing certain failures from cluttering up the queue.  It can also be used for parallel search-type problems, where only one ``Task`` will give the solution, so once the user find the solution, the user would want to abort all remaining Tasks to prevent wasted work.
+ * ``tc.spin()``
+    ``spin`` simply triggers the scheduler in the ``TaskControler``.  Under most normal circumstances, this will do nothing.  The primary known usage case involves the ``Task`` dependency (see `Dependencies`_).  The dependency is a function of an Engine's ``properties``, but changing the ``properties`` via the ``MutliEngineClient`` does not trigger a reschedule event.  The main example case for this requires the following event sequence:
+     * ``engine`` is available, ``Task`` is submitted, but ``engine`` does not have ``Task``'s dependencies.
+     * ``engine`` gets necessary dependencies while no new Tasks are submitted or completed.
+     * now ``engine`` can run ``Task``, but a ``Task`` event is required for the ``TaskControler`` to try scheduling ``Task`` again.
+     
+    ``spin`` is just an empty ping method to ensure that the Controller has scheduled all available Tasks, and should not be needed under most normal circumstances.
+
+That covers the ``TaskClient``, a simple interface to the cluster.  With this, the user can submit jobs (and abort if necessary), request their results, synchronize on arbitrary subsets of jobs.
+
+.. _task: The Task Object
+
+The Task Object
+===============
+
+The ``Task`` is the basic object for describing a job.  It can be used in a very simple manner, where the user just specifies a command string to be executed as the ``Task``.  The usage of this first argument is exactly the same as the ``execute`` method of the ``MultiEngine`` (in fact, ``execute`` is called to run the code)::
+
+    In [1]: t = client.Task("a = str(id)")
+    
+This ``Task`` would run, and store the string representation of the ``id`` element in ``a`` in each worker's namespace, but it is fairly useless because the user does not know anything about the state of the ``worker`` on which it ran at the time of retrieving results.  It is important that each ``Task`` not expect the state of the ``worker`` to persist after the ``Task`` is completed.  
+There are many different situations for using ``Task`` Farming, and the ``Task`` object has many attributes for use in customizing the ``Task`` behavior.  All of a ``Task``'s attributes may be specified in the constructor, through keyword arguments, or after ``Task`` construction through attribute assignment.
+
+Data Attributes
+***************
+It is likely that the user may want to move data around before or after executing the ``Task``.  We provide methods of sending data to initialize the worker's namespace, and specifying what data to bring back as the ``Task``'s results.
+
+ * pull = []
+    The obvious case is as above, where ``t`` would execute and store the result of ``myfunc`` in ``a``, it is likely that the user would want to bring ``a`` back to their namespace.  This is done through the ``pull`` attribute.  ``pull`` can be a string or list of strings, and it specifies the names of variables to be retrieved.  The ``TaskResult`` object retrieved by ``get_task_result`` will have a dictionary of keys and values, and the ``Task``'s ``pull`` attribute determines what goes into it::
+        
+        In [2]: t = client.Task("a = str(id)", pull = "a")
+        
+        In [3]: t = client.Task("a = str(id)", pull = ["a", "id"])
+        
+ * push = {}
+    A user might also want to initialize some data into the namespace before the code part of the ``Task`` is run.  Enter ``push``.  ``push`` is a dictionary of key/value pairs to be loaded from the user's namespace into the worker's immediately before execution::
+
+        In [4]: t = client.Task("a = f(submitted)", push=dict(submitted=time.time()), pull="a")
+
+push and pull result directly in calling an ``engine``'s ``push`` and ``pull`` methods before and after ``Task`` execution respectively, and thus their api is the same.
+
+Namespace Cleaning
+******************
+When a user is running a large number of Tasks, it is likely that the namespace of the worker's could become cluttered.  Some Tasks might be sensitive to clutter, while others might be known to cause namespace pollution.  For these reasons, Tasks have two boolean attributes for cleaning up the namespace.
+
+ * ``clear_after``
+    if clear_after is specified ``True``, the worker on which the ``Task`` was run will be reset (via ``engine.reset``) upon completion of the ``Task``.  This can be useful for both Tasks that produce clutter or Tasks whose intermediate data one might wish to be kept private::
+    
+        In [5]: t = client.Task("a = range(1e10)", pull = "a",clear_after=True)
+        
+    
+ * ``clear_before``
+    as one might guess, clear_before is identical to ``clear_after``, but it takes place before the ``Task`` is run.  This ensures that the ``Task`` runs on a fresh worker::
+
+        In [6]: t = client.Task("a = globals()", pull = "a",clear_before=True)
+
+Of course, a user can both at the same time, ensuring that all workers are clear except when they are currently running a job.  Both of these default to ``False``.
+
+Fault Tolerance
+***************
+It is possible that Tasks might fail, and there are a variety of reasons this could happen.  One might be that the worker it was running on disconnected, and there was nothing wrong with the ``Task`` itself.  With the fault tolerance attributes of the ``Task``, the user can specify how many times to resubmit the ``Task``, and what to do if it never succeeds.
+
+ * ``retries``
+    ``retries`` is an integer, specifying the number of times a ``Task`` is to be retried.  It defaults to zero.  It is often a good idea for this number to be 1 or 2, to protect the ``Task`` from disconnecting engines, but not a large number.  If a ``Task`` is failing 100 times, there is probably something wrong with the ``Task``.  The canonical bad example:
+    
+        In [7]: t = client.Task("os.kill(os.getpid(), 9)", retries=99)
+        
+    This would actually take down 100 workers.
+    
+ * ``recovery_task``
+    ``recovery_task`` is another ``Task`` object, to be run in the event of the original ``Task`` still failing after running out of retries.  Since ``recovery_task`` is another ``Task`` object, it can have its own ``recovery_task``.  The chain of Tasks is limitless, except loops are not allowed (that would be bad!).
+
+Dependencies
+************
+Dependencies are the most powerful part of the ``Task`` farming system, because it allows the user to do some classification of the workers, and guide the ``Task`` distribution without meddling with the controller directly.  It makes use of two objects - the ``Task``'s ``depend`` attribute, and the engine's ``properties``.  See the `MultiEngine`_ reference for how to use engine properties.  The engine properties api exists for extending IPython, allowing conditional execution and new controllers that make decisions based on properties of its engines.  Currently the ``Task`` dependency is the only internal use of the properties api.
+
+.. _MultiEngine: ./parallel_multiengine
+
+The ``depend`` attribute of a ``Task`` must be a function of exactly one argument, the worker's properties dictionary, and it should return ``True`` if the ``Task`` should be allowed to run on the worker and ``False`` if not.  The usage in the controller is fault tolerant, so exceptions raised by ``Task.depend`` will be ignored and functionally equivalent to always returning ``False``.  Tasks`` with invalid ``depend`` functions will never be assigned to a worker::
+
+    In [8]: def dep(properties):
+        ...     return properties["RAM"] > 2**32 # have at least 4GB
+    In [9]: t = client.Task("a = bigfunc()", depend=dep)
+    
+It is important to note that assignment of values to the properties dict is done entirely by the user, either locally (in the engine) using the EngineAPI, or remotely, through the ``MultiEngineClient``'s get/set_properties methods.
+
+
+
+
+
+    

docs/source/install/install.txt

@@ -0,0 +1,191 b''
+Overview
+========
+
+This document describes the steps required to install IPython.  IPython is organized into a number of subpackages, each of which has its own dependencies.  All of the subpackages come with IPython, so you don't need to download and install them separately.  However, to use a given subpackage, you will need to install all of its dependencies.  
+
+
+Please let us know if you have problems installing IPython or any of its
+dependencies. IPython requires Python version 2.4 or greater.  We have not tested
+IPython with the upcoming 2.6 or 3.0 versions.
+
+.. warning::
+
+	IPython will not work with Python 2.3 or below.
+
+Some of the installation approaches use the :mod:`setuptools` package and its :command:`easy_install` command line program.  In many scenarios, this provides the most simple method of installing IPython and its dependencies.  It is not required though.  More information about :mod:`setuptools` can be found on its website.
+
+More general information about installing Python packages can be found in Python's documentation at http://www.python.org/doc/.
+
+Installing IPython itself
+=========================
+
+Given a properly built Python, the basic interactive IPython shell will work with no external dependencies.  However, some Python distributions (particularly on Windows and OS X), don't come with a working :mod:`readline` module.  The IPython shell will work without :mod:`readline`, but will lack many features that users depend on, such as tab completion and command line editing.  See below for details of how to make sure you have a working :mod:`readline`.
+
+Installation using easy_install
+-------------------------------
+
+If you have :mod:`setuptools` installed, the easiest way of getting IPython is to simple use :command:`easy_install`::
+
+	$ easy_install IPython
+
+That's it.
+
+Installation from source
+------------------------
+
+If you don't want to use :command:`easy_install`, or don't have it installed, just grab the latest stable build of IPython from `here <http://ipython.scipy.org/dist/>`_.  Then do the following::
+
+	$ tar -xzf ipython.tar.gz
+	$ cd ipython
+	$ python setup.py install
+
+If you are installing to a location (like ``/usr/local``) that requires higher permissions, you may need to run the last command with :command:`sudo`.
+
+Windows
+-------
+
+There are a few caveats for Windows users.  The main issue is that a basic ``python setup.py install`` approach won't create ``.bat`` file or Start Menu shortcuts, which most users want.  To get an installation with these, there are two choices:
+
+1.  Install using :command:`easy_install`.
+
+2.  Install using our binary ``.exe`` Windows installer, which can be found at `here <http://ipython.scipy.org/dist/>`_
+
+3.  Install from source, but using :mod:`setuptools` (``python setupegg.py install``).
+
+Installing the development version
+----------------------------------
+
+It is also possible to install the development version of IPython from our `Bazaar <http://bazaar-vcs.org/>`_ source code
+repository.  To do this you will need to have Bazaar installed on your system.  Then just do::
+
+	$ bzr branch lp:ipython
+	$ cd ipython
+	$ python setup.py install
+
+Again, this last step on Windows won't create ``.bat`` files or Start Menu shortcuts, so you will have to use one of the other approaches listed above.
+
+Some users want to be able to follow the development branch as it changes.  If you have :mod:`setuptools` installed, this is easy. Simply replace the last step by::
+
+	$ python setupegg.py develop
+
+This creates links in the right places and installs the command line script to the appropriate places.  Then, if you want to update your IPython at any time, just do::
+
+	$ bzr pull
+
+Basic optional dependencies
+===========================
+
+There are a number of basic optional dependencies that most users will want to get.  These are:
+
+* readline (for command line editing, tab completion, etc.)
+* nose (to run the IPython test suite)
+* pexpect (to use things like irunner)
+
+If you are comfortable installing these things yourself, have at it, otherwise read on for more details.
+
+readline
+--------
+
+In principle, all Python distributions should come with a working :mod:`readline` module.  But, reality is not quite that simple.  There are two common situations where you won't have a working :mod:`readline` module:
+
+* If you are using the built-in Python on Mac OS X.
+
+* If you are running Windows, which doesn't have a :mod:`readline` module.
+
+On OS X, the built-in Python doesn't not have :mod:`readline` because of license issues.  Starting with OS X 10.5 (Leopard), Apple's built-in Python has a BSD-licensed not-quite-compatible readline replacement. As of IPython 0.9, many of the issues related to the differences between readline and libedit have been resolved.  For many users, libedit may be sufficient.
+
+Most users on OS X will want to get the full :mod:`readline` module.  To get a working :mod:`readline` module, just do (with :mod:`setuptools` installed)::
+
+	$ easy_install readline
+
+.. note:
+
+    Other Python distributions on OS X (such as fink, MacPorts and the 
+    official python.org binaries) already have readline installed so 
+    you don't have to do this step.
+
+If needed, the readline egg can be build and installed from source (see the wiki page at http://ipython.scipy.org/moin/InstallationOSXLeopard).
+
+On Windows, you will need the PyReadline module.  PyReadline is a separate, Windows only implementation of readline that uses native Windows calls through :mod:`ctypes`.  The easiest way of installing PyReadline is you use the binary installer available `here <http://ipython.scipy.org/dist/>`_.  The :mod:`ctypes` module, which comes with Python 2.5 and greater, is required by PyReadline.  It is available for Python 2.4 at http://python.net/crew/theller/ctypes.
+
+nose
+----
+
+To run the IPython test suite you will need the :mod:`nose` package.  Nose provides a great way of sniffing out and running all of the IPython tests.  The simplest way of getting nose, is to use :command:`easy_install`::
+
+	$ easy_install nose
+
+Another way of getting this is to do::
+
+    $ easy_install IPython[test]
+
+For more installation options, see the `nose website <http://somethingaboutorange.com/mrl/projects/nose/>`_.  Once you have nose installed, you can run IPython's test suite using the iptest command::
+
+	$ iptest
+
+
+pexpect
+-------
+
+The `pexpect <http://www.noah.org/wiki/Pexpect>`_ package is used in IPython's :command:`irunner` script.  On Unix platforms (including OS X), just do::
+
+	$ easy_install pexpect
+
+Windows users are out of luck as pexpect does not run there.
+
+Dependencies for IPython.kernel (parallel computing)
+====================================================
+
+The IPython kernel provides a nice architecture for parallel computing.  The main focus of this architecture is on interactive parallel computing.  These features require a number of additional packages:
+
+* zope.interface (yep, we use interfaces)
+* Twisted (asynchronous networking framework)
+* Foolscap (a nice, secure network protocol)
+* pyOpenSSL (security for network connections)
+
+On a Unix style platform (including OS X), if you want to use :mod:`setuptools`, you can just do::
+
+    $ easy_install IPython[kernel]    # the first three
+    $ easy_install IPython[security]  # pyOpenSSL
+
+zope.interface and Twisted
+--------------------------
+
+On Unix style platforms (including OS X), the simplest way of getting the these is to use :command:`easy_install`::
+
+	$ easy_install zope.interface
+	$ easy_install Twisted
+
+Of course, you can also download the source tarballs from the `Twisted website <twistedmatrix.org>`_ and the `zope.interface page at PyPI <http://pypi.python.org/pypi/zope.interface>`_ and do the usual ``python setup.py install`` if you prefer.
+
+Windows is a bit different.  For zope.interface and Twisted, simply get the latest binary ``.exe`` installer from the Twisted website.  This installer includes both zope.interface and Twisted and should just work.
+
+Foolscap
+--------
+
+Foolscap uses Twisted to provide a very nice secure RPC protocol that we use to implement our parallel computing features.
+
+On all platforms a simple::
+
+    $ easy_install foolscap
+
+should work.  You can also download the source tarballs from the `Foolscap website <http://foolscap.lothar.com/trac>`_ and do ``python setup.py install`` if you prefer.
+
+pyOpenSSL
+---------
+
+IPython requires an older version of pyOpenSSL (0.6 rather than the current 0.7).  There are a couple of options for getting this:
+
+1.  Most Linux distributions have packages for pyOpenSSL.
+2.  The built-in Python 2.5 on OS X 10.5 already has it installed.
+3.  There are source tarballs on the pyOpenSSL website.  On Unix-like
+    platforms, these can be built using ``python seutp.py install``.
+4.  There is also a binary ``.exe`` Windows installer on the `pyOpenSSL website <http://pyopenssl.sourceforge.net/>`_.
+
+Dependencies for IPython.frontend (the IPython GUI)
+===================================================
+
+wxPython
+--------
+
+Starting with IPython 0.9, IPython has a new IPython.frontend package that has a nice wxPython based IPython GUI.  As you would expect, this GUI requires wxPython.  Most Linux distributions have wxPython packages available and the built-in Python on OS X comes with wxPython preinstalled.  For Windows, a binary installer is available on the `wxPython website <http://www.wxpython.org/>`_. 
  No newline at end of file

docs/sphinxext/inheritance_diagram.py

@@ -0,0 +1,423 b''
+"""
+Defines a docutils directive for inserting inheritance diagrams.
+
+Provide the directive with one or more classes or modules (separated
+by whitespace).  For modules, all of the classes in that module will
+be used.
+
+Example::
+
+   Given the following classes:
+
+   class A: pass
+   class B(A): pass
+   class C(A): pass
+   class D(B, C): pass
+   class E(B): pass
+
+   .. inheritance-diagram: D E
+
+   Produces a graph like the following:
+
+               A
+              / \
+             B   C
+            / \ /
+           E   D
+
+The graph is inserted as a PNG+image map into HTML and a PDF in
+LaTeX.
+"""
+
+import inspect
+import os
+import re
+import subprocess
+try:
+    from hashlib import md5
+except ImportError:
+    from md5 import md5
+
+from docutils.nodes import Body, Element
+from docutils.writers.html4css1 import HTMLTranslator
+from sphinx.latexwriter import LaTeXTranslator
+from docutils.parsers.rst import directives
+from sphinx.roles import xfileref_role
+
+class DotException(Exception):
+    pass
+
+class InheritanceGraph(object):
+    """
+    Given a list of classes, determines the set of classes that
+    they inherit from all the way to the root "object", and then
+    is able to generate a graphviz dot graph from them.
+    """
+    def __init__(self, class_names, show_builtins=False):
+        """
+        *class_names* is a list of child classes to show bases from.
+
+        If *show_builtins* is True, then Python builtins will be shown
+        in the graph.
+        """
+        self.class_names = class_names
+        self.classes = self._import_classes(class_names)
+        self.all_classes = self._all_classes(self.classes)
+        if len(self.all_classes) == 0:
+            raise ValueError("No classes found for inheritance diagram")
+        self.show_builtins = show_builtins
+
+    py_sig_re = re.compile(r'''^([\w.]*\.)?    # class names
+                           (\w+)  \s* $        # optionally arguments
+                           ''', re.VERBOSE)
+
+    def _import_class_or_module(self, name):
+        """
+        Import a class using its fully-qualified *name*.
+        """
+        try:
+            path, base = self.py_sig_re.match(name).groups()
+        except:
+            raise ValueError(
+                "Invalid class or module '%s' specified for inheritance diagram" % name)
+        fullname = (path or '') + base
+        path = (path and path.rstrip('.'))
+        if not path:
+            path = base
+        if not path:
+            raise ValueError(
+                "Invalid class or module '%s' specified for inheritance diagram" % name)
+        try:
+            module = __import__(path, None, None, [])
+        except ImportError:
+            raise ValueError(
+                "Could not import class or module '%s' specified for inheritance diagram" % name)
+
+        try:
+            todoc = module
+            for comp in fullname.split('.')[1:]:
+                todoc = getattr(todoc, comp)
+        except AttributeError:
+            raise ValueError(
+                "Could not find class or module '%s' specified for inheritance diagram" % name)
+
+        # If a class, just return it
+        if inspect.isclass(todoc):
+            return [todoc]
+        elif inspect.ismodule(todoc):
+            classes = []
+            for cls in todoc.__dict__.values():
+                if inspect.isclass(cls) and cls.__module__ == todoc.__name__:
+                    classes.append(cls)
+            return classes
+        raise ValueError(
+            "'%s' does not resolve to a class or module" % name)
+
+    def _import_classes(self, class_names):
+        """
+        Import a list of classes.
+        """
+        classes = []
+        for name in class_names:
+            classes.extend(self._import_class_or_module(name))
+        return classes
+
+    def _all_classes(self, classes):
+        """
+        Return a list of all classes that are ancestors of *classes*.
+        """
+        all_classes = {}
+
+        def recurse(cls):
+            all_classes[cls] = None
+            for c in cls.__bases__:
+                if c not in all_classes:
+                    recurse(c)
+
+        for cls in classes:
+            recurse(cls)
+
+        return all_classes.keys()
+
+    def class_name(self, cls, parts=0):
+        """
+        Given a class object, return a fully-qualified name.  This
+        works for things I've tested in matplotlib so far, but may not
+        be completely general.
+        """
+        module = cls.__module__
+        if module == '__builtin__':
+            fullname = cls.__name__
+        else:
+            fullname = "%s.%s" % (module, cls.__name__)
+        if parts == 0:
+            return fullname
+        name_parts = fullname.split('.')
+        return '.'.join(name_parts[-parts:])
+
+    def get_all_class_names(self):
+        """
+        Get all of the class names involved in the graph.
+        """
+        return [self.class_name(x) for x in self.all_classes]
+
+    # These are the default options for graphviz
+    default_graph_options = {
+        "rankdir": "LR",
+        "size": '"8.0, 12.0"'
+        }
+    default_node_options = {
+        "shape": "box",
+        "fontsize": 10,
+        "height": 0.25,
+        "fontname": "Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",
+        "style": '"setlinewidth(0.5)"'
+        }
+    default_edge_options = {
+        "arrowsize": 0.5,
+        "style": '"setlinewidth(0.5)"'
+        }
+
+    def _format_node_options(self, options):
+        return ','.join(["%s=%s" % x for x in options.items()])
+    def _format_graph_options(self, options):
+        return ''.join(["%s=%s;\n" % x for x in options.items()])
+
+    def generate_dot(self, fd, name, parts=0, urls={},
+                     graph_options={}, node_options={},
+                     edge_options={}):
+        """
+        Generate a graphviz dot graph from the classes that
+        were passed in to __init__.
+
+        *fd* is a Python file-like object to write to.
+
+        *name* is the name of the graph
+
+        *urls* is a dictionary mapping class names to http urls
+
+        *graph_options*, *node_options*, *edge_options* are
+        dictionaries containing key/value pairs to pass on as graphviz
+        properties.
+        """
+        g_options = self.default_graph_options.copy()
+        g_options.update(graph_options)
+        n_options = self.default_node_options.copy()
+        n_options.update(node_options)
+        e_options = self.default_edge_options.copy()
+        e_options.update(edge_options)
+
+        fd.write('digraph %s {\n' % name)
+        fd.write(self._format_graph_options(g_options))
+
+        for cls in self.all_classes:
+            if not self.show_builtins and cls in __builtins__.values():
+                continue
+
+            name = self.class_name(cls, parts)
+
+            # Write the node
+            this_node_options = n_options.copy()
+            url = urls.get(self.class_name(cls))
+            if url is not None:
+                this_node_options['URL'] = '"%s"' % url
+            fd.write('  "%s" [%s];\n' %
+                     (name, self._format_node_options(this_node_options)))
+
+            # Write the edges
+            for base in cls.__bases__:
+                if not self.show_builtins and base in __builtins__.values():
+                    continue
+
+                base_name = self.class_name(base, parts)
+                fd.write('  "%s" -> "%s" [%s];\n' %
+                         (base_name, name,
+                          self._format_node_options(e_options)))
+        fd.write('}\n')
+
+    def run_dot(self, args, name, parts=0, urls={},
+                graph_options={}, node_options={}, edge_options={}):
+        """
+        Run graphviz 'dot' over this graph, returning whatever 'dot'
+        writes to stdout.
+
+        *args* will be passed along as commandline arguments.
+
+        *name* is the name of the graph
+
+        *urls* is a dictionary mapping class names to http urls
+
+        Raises DotException for any of the many os and
+        installation-related errors that may occur.
+        """
+        try:
+            dot = subprocess.Popen(['dot'] + list(args),
+                                   stdin=subprocess.PIPE, stdout=subprocess.PIPE,
+                                   close_fds=True)
+        except OSError:
+            raise DotException("Could not execute 'dot'.  Are you sure you have 'graphviz' installed?")
+        except ValueError:
+            raise DotException("'dot' called with invalid arguments")
+        except:
+            raise DotException("Unexpected error calling 'dot'")
+
+        self.generate_dot(dot.stdin, name, parts, urls, graph_options,
+                          node_options, edge_options)
+        dot.stdin.close()
+        result = dot.stdout.read()
+        returncode = dot.wait()
+        if returncode != 0:
+            raise DotException("'dot' returned the errorcode %d" % returncode)
+        return result
+
+class inheritance_diagram(Body, Element):
+    """
+    A docutils node to use as a placeholder for the inheritance
+    diagram.
+    """
+    pass
+
+def inheritance_diagram_directive_run(class_names, options, state):
+    """
+    Run when the inheritance_diagram directive is first encountered.
+    """
+    node = inheritance_diagram()
+
+    # Create a graph starting with the list of classes
+    graph = InheritanceGraph(class_names)
+
+    # Create xref nodes for each target of the graph's image map and
+    # add them to the doc tree so that Sphinx can resolve the
+    # references to real URLs later.  These nodes will eventually be
+    # removed from the doctree after we're done with them.
+    for name in graph.get_all_class_names():
+        refnodes, x = xfileref_role(
+            'class', ':class:`%s`' % name, name, 0, state)
+        node.extend(refnodes)
+    # Store the graph object so we can use it to generate the
+    # dot file later
+    node['graph'] = graph
+    # Store the original content for use as a hash
+    node['parts'] = options.get('parts', 0)
+    node['content'] = " ".join(class_names)
+    return [node]
+
+def get_graph_hash(node):
+    return md5(node['content'] + str(node['parts'])).hexdigest()[-10:]
+
+def html_output_graph(self, node):
+    """
+    Output the graph for HTML.  This will insert a PNG with clickable
+    image map.
+    """
+    graph = node['graph']
+    parts = node['parts']
+
+    graph_hash = get_graph_hash(node)
+    name = "inheritance%s" % graph_hash
+    png_path = os.path.join('_static', name + ".png")
+
+    path = '_static'
+    source = self.document.attributes['source']
+    count = source.split('/doc/')[-1].count('/')
+    for i in range(count):
+        if os.path.exists(path): break
+        path = '../'+path
+    path = '../'+path #specifically added for matplotlib
+
+    # Create a mapping from fully-qualified class names to URLs.
+    urls = {}
+    for child in node:
+        if child.get('refuri') is not None:
+            urls[child['reftitle']] = child.get('refuri')
+        elif child.get('refid') is not None:
+            urls[child['reftitle']] = '#' + child.get('refid')
+
+    # These arguments to dot will save a PNG file to disk and write
+    # an HTML image map to stdout.
+    image_map = graph.run_dot(['-Tpng', '-o%s' % png_path, '-Tcmapx'],
+                              name, parts, urls)
+    return ('<img src="%s/%s.png" usemap="#%s" class="inheritance"/>%s' %
+            (path, name, name, image_map))
+
+def latex_output_graph(self, node):
+    """
+    Output the graph for LaTeX.  This will insert a PDF.
+    """
+    graph = node['graph']
+    parts = node['parts']
+
+    graph_hash = get_graph_hash(node)
+    name = "inheritance%s" % graph_hash
+    pdf_path = os.path.join('_static', name + ".pdf")
+
+    graph.run_dot(['-Tpdf', '-o%s' % pdf_path],
+                  name, parts, graph_options={'size': '"6.0,6.0"'})
+    return '\\includegraphics{../../%s}' % pdf_path
+
+def visit_inheritance_diagram(inner_func):
+    """
+    This is just a wrapper around html/latex_output_graph to make it
+    easier to handle errors and insert warnings.
+    """
+    def visitor(self, node):
+        try:
+            content = inner_func(self, node)
+        except DotException, e:
+            # Insert the exception as a warning in the document
+            warning = self.document.reporter.warning(str(e), line=node.line)
+            warning.parent = node
+            node.children = [warning]
+        else:
+            source = self.document.attributes['source']
+            self.body.append(content)
+            node.children = []
+    return visitor
+
+def do_nothing(self, node):
+    pass
+
+options_spec = {
+    'parts': directives.nonnegative_int
+    }
+
+# Deal with the old and new way of registering directives
+try:
+    from docutils.parsers.rst import Directive
+except ImportError:
+    from docutils.parsers.rst.directives import _directives
+    def inheritance_diagram_directive(name, arguments, options, content, lineno,
+                                      content_offset, block_text, state,
+                                      state_machine):
+        return inheritance_diagram_directive_run(arguments, options, state)
+    inheritance_diagram_directive.__doc__ = __doc__
+    inheritance_diagram_directive.arguments = (1, 100, 0)
+    inheritance_diagram_directive.options = options_spec
+    inheritance_diagram_directive.content = 0
+    _directives['inheritance-diagram'] = inheritance_diagram_directive
+else:
+    class inheritance_diagram_directive(Directive):
+        has_content = False
+        required_arguments = 1
+        optional_arguments = 100
+        final_argument_whitespace = False
+        option_spec = options_spec
+
+        def run(self):
+            return inheritance_diagram_directive_run(
+                self.arguments, self.options, self.state)
+    inheritance_diagram_directive.__doc__ = __doc__
+
+    directives.register_directive('inheritance-diagram',
+                                  inheritance_diagram_directive)
+
+def setup(app):
+    app.add_node(inheritance_diagram)
+
+    HTMLTranslator.visit_inheritance_diagram = \
+        visit_inheritance_diagram(html_output_graph)
+    HTMLTranslator.depart_inheritance_diagram = do_nothing
+
+    LaTeXTranslator.visit_inheritance_diagram = \
+        visit_inheritance_diagram(latex_output_graph)
+    LaTeXTranslator.depart_inheritance_diagram = do_nothing

docs/sphinxext/ipython_console_highlighting.py

@@ -0,0 +1,75 b''
+from pygments.lexer import Lexer, do_insertions
+from pygments.lexers.agile import PythonConsoleLexer, PythonLexer, \
+    PythonTracebackLexer
+from pygments.token import Comment, Generic
+from sphinx import highlighting
+import re
+
+line_re = re.compile('.*?\n')
+
+class IPythonConsoleLexer(Lexer):
+    """
+    For IPython console output or doctests, such as:
+
+    Tracebacks are not currently supported.
+
+    .. sourcecode:: ipython
+
+      In [1]: a = 'foo'
+
+      In [2]: a
+      Out[2]: 'foo'
+
+      In [3]: print a
+      foo
+
+      In [4]: 1 / 0
+    """
+    name = 'IPython console session'
+    aliases = ['ipython']
+    mimetypes = ['text/x-ipython-console']
+    input_prompt = re.compile("(In \[[0-9]+\]: )|(   \.\.\.+:)")
+    output_prompt = re.compile("(Out\[[0-9]+\]: )|(   \.\.\.+:)")
+    continue_prompt = re.compile("   \.\.\.+:")
+    tb_start = re.compile("\-+")
+
+    def get_tokens_unprocessed(self, text):
+        pylexer = PythonLexer(**self.options)
+        tblexer = PythonTracebackLexer(**self.options)
+
+        curcode = ''
+        insertions = []
+        for match in line_re.finditer(text):
+            line = match.group()
+            input_prompt = self.input_prompt.match(line)
+            continue_prompt = self.continue_prompt.match(line.rstrip())
+            output_prompt = self.output_prompt.match(line)
+            if line.startswith("#"):
+                insertions.append((len(curcode),
+                                   [(0, Comment, line)]))
+            elif input_prompt is not None:
+                insertions.append((len(curcode),
+                                   [(0, Generic.Prompt, input_prompt.group())]))
+                curcode += line[input_prompt.end():]
+            elif continue_prompt is not None:
+                insertions.append((len(curcode),
+                                   [(0, Generic.Prompt, continue_prompt.group())]))
+                curcode += line[continue_prompt.end():]
+            elif output_prompt is not None:
+                insertions.append((len(curcode),
+                                   [(0, Generic.Output, output_prompt.group())]))
+                curcode += line[output_prompt.end():]
+            else:
+                if curcode:
+                    for item in do_insertions(insertions,
+                                              pylexer.get_tokens_unprocessed(curcode)):
+                        yield item
+                        curcode = ''
+                        insertions = []
+                yield match.start(), Generic.Output, line
+        if curcode:
+            for item in do_insertions(insertions,
+                                      pylexer.get_tokens_unprocessed(curcode)):
+                yield item
+
+highlighting.lexers['ipython'] = IPythonConsoleLexer()

docs/sphinxext/only_directives.py

@@ -0,0 +1,87 b''
+#
+# A pair of directives for inserting content that will only appear in
+# either html or latex.
+#
+
+from docutils.nodes import Body, Element
+from docutils.writers.html4css1 import HTMLTranslator
+from sphinx.latexwriter import LaTeXTranslator
+from docutils.parsers.rst import directives
+
+class html_only(Body, Element):
+    pass
+
+class latex_only(Body, Element):
+    pass
+
+def run(content, node_class, state, content_offset):
+    text = '\n'.join(content)
+    node = node_class(text)
+    state.nested_parse(content, content_offset, node)
+    return [node]
+
+try:
+    from docutils.parsers.rst import Directive
+except ImportError:
+    from docutils.parsers.rst.directives import _directives
+
+    def html_only_directive(name, arguments, options, content, lineno,
+                            content_offset, block_text, state, state_machine):
+        return run(content, html_only, state, content_offset)
+
+    def latex_only_directive(name, arguments, options, content, lineno,
+                             content_offset, block_text, state, state_machine):
+        return run(content, latex_only, state, content_offset)
+
+    for func in (html_only_directive, latex_only_directive):
+        func.content = 1
+        func.options = {}
+        func.arguments = None
+
+    _directives['htmlonly'] = html_only_directive
+    _directives['latexonly'] = latex_only_directive
+else:
+    class OnlyDirective(Directive):
+        has_content = True
+        required_arguments = 0
+        optional_arguments = 0
+        final_argument_whitespace = True
+        option_spec = {}
+
+        def run(self):
+            self.assert_has_content()
+            return run(self.content, self.node_class,
+                       self.state, self.content_offset)
+
+    class HtmlOnlyDirective(OnlyDirective):
+        node_class = html_only
+
+    class LatexOnlyDirective(OnlyDirective):
+        node_class = latex_only
+
+    directives.register_directive('htmlonly', HtmlOnlyDirective)
+    directives.register_directive('latexonly', LatexOnlyDirective)
+
+def setup(app):
+    app.add_node(html_only)
+    app.add_node(latex_only)
+
+    # Add visit/depart methods to HTML-Translator:
+    def visit_perform(self, node):
+        pass
+    def depart_perform(self, node):
+        pass
+    def visit_ignore(self, node):
+        node.children = []
+    def depart_ignore(self, node):
+        node.children = []
+
+    HTMLTranslator.visit_html_only = visit_perform
+    HTMLTranslator.depart_html_only = depart_perform
+    HTMLTranslator.visit_latex_only = visit_ignore
+    HTMLTranslator.depart_latex_only = depart_ignore
+
+    LaTeXTranslator.visit_html_only = visit_ignore
+    LaTeXTranslator.depart_html_only = depart_ignore
+    LaTeXTranslator.visit_latex_only = visit_perform
+    LaTeXTranslator.depart_latex_only = depart_perform

docs/sphinxext/plot_directive.py

@@ -0,0 +1,155 b''
+"""A special directive for including a matplotlib plot.
+
+Given a path to a .py file, it includes the source code inline, then:
+
+- On HTML, will include a .png with a link to a high-res .png.
+
+- On LaTeX, will include a .pdf
+
+This directive supports all of the options of the `image` directive,
+except for `target` (since plot will add its own target).
+
+Additionally, if the :include-source: option is provided, the literal
+source will be included inline, as well as a link to the source.
+"""
+
+import sys, os, glob, shutil
+from docutils.parsers.rst import directives
+
+try:
+    # docutils 0.4
+    from docutils.parsers.rst.directives.images import align
+except ImportError:
+    # docutils 0.5
+    from docutils.parsers.rst.directives.images import Image
+    align = Image.align
+
+
+import matplotlib
+import IPython.Shell
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+mplshell = IPython.Shell.MatplotlibShell('mpl')
+
+options = {'alt': directives.unchanged,
+           'height': directives.length_or_unitless,
+           'width': directives.length_or_percentage_or_unitless,
+           'scale': directives.nonnegative_int,
+           'align': align,
+           'class': directives.class_option,
+           'include-source': directives.flag }
+
+template = """
+.. htmlonly::
+
+   [`source code <../%(srcdir)s/%(basename)s.py>`__,
+   `png <../%(srcdir)s/%(basename)s.hires.png>`__,
+   `pdf <../%(srcdir)s/%(basename)s.pdf>`__]
+
+   .. image:: ../%(srcdir)s/%(basename)s.png
+%(options)s
+
+.. latexonly::
+   .. image:: ../%(srcdir)s/%(basename)s.pdf
+%(options)s
+
+"""
+
+def makefig(fullpath, outdir):
+    """
+    run a pyplot script and save the low and high res PNGs and a PDF in _static
+    """
+
+    fullpath = str(fullpath)  # todo, why is unicode breaking this
+    formats = [('png', 100),
+               ('hires.png', 200),
+               ('pdf', 72),
+               ]
+
+    basedir, fname = os.path.split(fullpath)
+    basename, ext = os.path.splitext(fname)
+    all_exists = True
+
+    if basedir != outdir:
+        shutil.copyfile(fullpath, os.path.join(outdir, fname))
+
+    for format, dpi in formats:
+        outname = os.path.join(outdir, '%s.%s' % (basename, format))
+        if not os.path.exists(outname):
+            all_exists = False
+            break
+
+    if all_exists:
+        print '    already have %s'%fullpath
+        return
+
+    print '    building %s'%fullpath
+    plt.close('all')    # we need to clear between runs
+    matplotlib.rcdefaults()
+
+    mplshell.magic_run(fullpath)
+    for format, dpi in formats:
+        outname = os.path.join(outdir, '%s.%s' % (basename, format))
+        if os.path.exists(outname): continue
+        plt.savefig(outname, dpi=dpi)
+
+def run(arguments, options, state_machine, lineno):
+    reference = directives.uri(arguments[0])
+    basedir, fname = os.path.split(reference)
+    basename, ext = os.path.splitext(fname)
+
+    # todo - should we be using the _static dir for the outdir, I am
+    # not sure we want to corrupt that dir with autogenerated files
+    # since it also has permanent files in it which makes it difficult
+    # to clean (save an rm -rf followed by and svn up)
+    srcdir = 'pyplots'
+
+    makefig(os.path.join(srcdir, reference), srcdir)
+
+    # todo: it is not great design to assume the makefile is putting
+    # the figs into the right place, so we may want to do that here instead.
+
+    if options.has_key('include-source'):
+        lines = ['.. literalinclude:: ../pyplots/%(reference)s' % locals()]
+        del options['include-source']
+    else:
+        lines = []
+
+    options = ['      :%s: %s' % (key, val) for key, val in
+               options.items()]
+    options = "\n".join(options)
+
+    lines.extend((template % locals()).split('\n'))
+
+    state_machine.insert_input(
+        lines, state_machine.input_lines.source(0))
+    return []
+
+
+try:
+    from docutils.parsers.rst import Directive
+except ImportError:
+    from docutils.parsers.rst.directives import _directives
+
+    def plot_directive(name, arguments, options, content, lineno,
+                       content_offset, block_text, state, state_machine):
+        return run(arguments, options, state_machine, lineno)
+    plot_directive.__doc__ = __doc__
+    plot_directive.arguments = (1, 0, 1)
+    plot_directive.options = options
+
+    _directives['plot'] = plot_directive
+else:
+    class plot_directive(Directive):
+        required_arguments = 1
+        optional_arguments = 0
+        final_argument_whitespace = True
+        option_spec = options
+        def run(self):
+            return run(self.arguments, self.options,
+                       self.state_machine, self.lineno)
+    plot_directive.__doc__ = __doc__
+
+    directives.register_directive('plot', plot_directive)
+

IPython/Release.py

@@ -1,99 +1,97 b''
 # -*- coding: utf-8 -*-
-"""Release data for the IPython project.
-
-$Id: Release.py 3002 2008-02-01 07:17:00Z fperez $"""
+"""Release data for the IPython project."""
 
 #*****************************************************************************
 #       Copyright (C) 2001-2006 Fernando Perez <[e-mail]>
 #
 #       Copyright (c) 2001 Janko Hauser <[e-mail]> and Nathaniel Gray
 #       <[e-mail]>
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #*****************************************************************************
 
 # Name of the package for release purposes.  This is the name which labels
 # the tarballs and RPMs made by distutils, so it's best to lowercase it.
 name = 'ipython'
 
 # For versions with substrings (like 0.6.16.svn), use an extra . to separate
 # the new substring.  We have to avoid using either dashes or underscores,
 # because bdist_rpm does not accept dashes (an RPM) convention, and
 # bdist_deb does not accept underscores (a Debian convention).
 
 development = False    # change this to False to do a release
-version_base = '0.9.rc1'
+version_base = '0.9.1'
 branch = 'ipython'
-revision = '1124'
+revision = '1143'
 
 if development:
     if branch == 'ipython':
         version = '%s.bzr.r%s' % (version_base, revision)
     else:
         version = '%s.bzr.r%s.%s' % (version_base, revision, branch)
 else:
     version = version_base
 
 
 description = "Tools for interactive development in Python."
 
 long_description = \
 """
 IPython provides a replacement for the interactive Python interpreter with
 extra functionality.
 
 Main features:
 
  * Comprehensive object introspection.
 
  * Input history, persistent across sessions.
 
  * Caching of output results during a session with automatically generated
    references.
 
  * Readline based name completion.
 
  * Extensible system of 'magic' commands for controlling the environment and
    performing many tasks related either to IPython or the operating system.
 
  * Configuration system with easy switching between different setups (simpler
    than changing $PYTHONSTARTUP environment variables every time).
 
  * Session logging and reloading.
 
  * Extensible syntax processing for special purpose situations.
 
  * Access to the system shell with user-extensible alias system.
 
  * Easily embeddable in other Python programs.
 
  * Integrated access to the pdb debugger and the Python profiler. 
 
  The latest development version is always available at the IPython subversion
  repository_.
 
 .. _repository: http://ipython.scipy.org/svn/ipython/ipython/trunk#egg=ipython-dev
  """
 
 license = 'BSD'
 
 authors = {'Fernando' : ('Fernando Perez','[e-mail]'),
            'Janko'    : ('Janko Hauser','[e-mail]'),
            'Nathan'   : ('Nathaniel Gray','[e-mail]'),
            'Ville'    : ('Ville Vainio','[e-mail]'),
            'Brian'    : ('Brian E Granger', '[e-mail]'),
            'Min'      : ('Min Ragan-Kelley', '[e-mail]')
            }
 
 author = 'The IPython Development Team'
 
 author_email = '[e-mail]'
 
 url = 'http://ipython.scipy.org'
 
 download_url = 'http://ipython.scipy.org/dist'
 
 platforms = ['Linux','Mac OSX','Windows XP/2000/NT','Windows 95/98/ME']
 
 keywords = ['Interactive','Interpreter','Shell','Parallel','Distributed']

IPython/frontend/frontendbase.py

@@ -1,360 +1,429 b''
 # encoding: utf-8
 # -*- test-case-name: IPython.frontend.tests.test_frontendbase -*-
 """
 frontendbase provides an interface and base class for GUI frontends for 
 IPython.kernel/IPython.kernel.core.
 
 Frontend implementations will likely want to subclass FrontEndBase. 
 
 Author: Barry Wark
 """
 __docformat__ = "restructuredtext en"
 
 #-------------------------------------------------------------------------------
 #  Copyright (C) 2008  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-------------------------------------------------------------------------------
 
 #-------------------------------------------------------------------------------
 # Imports
 #-------------------------------------------------------------------------------
 import string
-import uuid
-import _ast
+
+try:
+    import _ast
+except ImportError:
+    # Python 2.4 hackish workaround.
+    class bunch: pass
+    _ast = bunch()
+    _ast.PyCF_ONLY_AST = 1024
+    
+
+
+try:
+    import uuid
+except ImportError:
+    # Python 2.4 hackish workaround.
+    class UUID:
+        def __init__(self,bytes):
+            version = 4
+            int = long(('%02x'*16) % tuple(map(ord, bytes)), 16)
+            # Set the variant to RFC 4122.
+            int &= ~(0xc000 << 48L)
+            int |= 0x8000 << 48L
+            # Set the version number.
+            int &= ~(0xf000 << 64L)
+            int |= version << 76L
+            self.__dict__['int'] = int
+
+        def __cmp__(self, other):
+            if isinstance(other, UUID):
+                return cmp(self.int, other.int)
+            return NotImplemented
+
+        def __hash__(self):
+            return hash(self.int)
+
+        def __int__(self):
+            return self.int
+
+        def __repr__(self):
+            return 'UUID(%r)' % str(self)
+
+        def __setattr__(self, name, value):
+            raise TypeError('UUID objects are immutable')
+
+        def __str__(self):
+            hex = '%032x' % self.int
+            return '%s-%s-%s-%s-%s' % (
+                hex[:8], hex[8:12], hex[12:16], hex[16:20], hex[20:])
+
+        def get_bytes(self):
+            bytes = ''
+            for shift in range(0, 128, 8):
+                bytes = chr((self.int >> shift) & 0xff) + bytes
+            return bytes
+
+        bytes = property(get_bytes)
+ 
+    
+    def _u4():
+        "Fake random uuid"
+        
+        import random
+        bytes = [chr(random.randrange(256)) for i in range(16)]
+        return UUID(bytes)
+
+    class bunch: pass
+    uuid = bunch()
+    uuid.uuid4 = _u4
+    del _u4
+
+
 
 from IPython.frontend.zopeinterface import (
     Interface, 
     Attribute, 
 )
 from IPython.kernel.core.history import FrontEndHistory
 from IPython.kernel.core.util import Bunch
 
 ##############################################################################
 # TEMPORARY!!! fake configuration, while we decide whether to use tconfig or
 # not
 
 rc = Bunch()
 rc.prompt_in1 = r'In [$number]:  '
 rc.prompt_in2 = r'...'
 rc.prompt_out = r'Out [$number]:  '
 
 ##############################################################################
 # Interface definitions
 ##############################################################################
 
 class IFrontEndFactory(Interface):
     """Factory interface for frontends."""
     
     def __call__(engine=None, history=None):
         """
         Parameters:
         interpreter : IPython.kernel.engineservice.IEngineCore
         """
         
         pass
 
 
 class IFrontEnd(Interface):
     """Interface for frontends. All methods return t.i.d.Deferred"""
     
     Attribute("input_prompt_template", "string.Template instance\
                 substituteable with execute result.")
     Attribute("output_prompt_template", "string.Template instance\
                 substituteable with execute result.")
     Attribute("continuation_prompt_template", "string.Template instance\
                 substituteable with execute result.")
     
     def update_cell_prompt(result, blockID=None):
         """Subclass may override to update the input prompt for a block. 
 
         In asynchronous frontends, this method will be called as a
         twisted.internet.defer.Deferred's callback/errback.
         Implementations should thus return result when finished.
         
         Result is a result dict in case of success, and a
         twisted.python.util.failure.Failure in case of an error
         """
         
         pass
     
     def render_result(result):
         """Render the result of an execute call. Implementors may choose the
          method of rendering.
         For example, a notebook-style frontend might render a Chaco plot 
         inline.
         
         Parameters:
             result : dict (result of IEngineBase.execute )
                 blockID = result['blockID']
         
         Result:
             Output of frontend rendering
         """
         
         pass
     
     def render_error(failure):
         """Subclasses must override to render the failure. 
         
         In asynchronous frontend, since this method will be called as a
         twisted.internet.defer.Deferred's callback. Implementations
         should thus return result when finished.
         
         blockID = failure.blockID
         """
         
         pass
     
     def input_prompt(number=''):
         """Returns the input prompt by subsituting into 
         self.input_prompt_template
         """
         pass
     
     def output_prompt(number=''):
         """Returns the output prompt by subsituting into 
         self.output_prompt_template
         """
         
         pass
     
     def continuation_prompt():
         """Returns the continuation prompt by subsituting into 
         self.continuation_prompt_template
         """
         
         pass
     
     def is_complete(block):
         """Returns True if block is complete, False otherwise."""
         
         pass
     
     def compile_ast(block):
         """Compiles block to an _ast.AST"""
         
         pass
     
     def get_history_previous(current_block):
         """Returns the block previous in  the history. Saves currentBlock if
         the history_cursor is currently at the end of the input history"""
         pass
     
     def get_history_next():
         """Returns the next block in the history."""
         
         pass
     
     def complete(self, line):
         """Returns the list of possible completions, and the completed
             line.
 
         The input argument is the full line to be completed. This method
         returns both the line completed as much as possible, and the list
         of further possible completions (full words).
         """
         pass
 
 
 ##############################################################################
 # Base class for all the frontends. 
 ##############################################################################
 
 class FrontEndBase(object):
     """
     FrontEndBase manages the state tasks for a CLI frontend:
         - Input and output history management
         - Input/continuation and output prompt generation
         
     Some issues (due to possibly unavailable engine):
         - How do we get the current cell number for the engine?
         - How do we handle completions?
     """
     
     history_cursor = 0
     
     input_prompt_template = string.Template(rc.prompt_in1)
     output_prompt_template = string.Template(rc.prompt_out)
     continuation_prompt_template = string.Template(rc.prompt_in2)
     
     def __init__(self, shell=None, history=None):
         self.shell = shell
         if history is None:
                 self.history = FrontEndHistory(input_cache=[''])
         else:
             self.history = history
         
     
     def input_prompt(self, number=''):
         """Returns the current input prompt
         
         It would be great to use ipython1.core.prompts.Prompt1 here
         """
         return self.input_prompt_template.safe_substitute({'number':number})
     
     
     def continuation_prompt(self):
         """Returns the current continuation prompt"""
         
         return self.continuation_prompt_template.safe_substitute()
     
     def output_prompt(self, number=''):
         """Returns the output prompt for result"""
         
         return self.output_prompt_template.safe_substitute({'number':number})
     
     
     def is_complete(self, block):
         """Determine if block is complete.
         
         Parameters
         block : string
         
         Result 
         True if block can be sent to the engine without compile errors.
         False otherwise.
         """
         
         try:
             ast = self.compile_ast(block)
         except:
             return False
         
         lines = block.split('\n')
         return (len(lines)==1 or str(lines[-1])=='')
     
     
     def compile_ast(self, block):
         """Compile block to an AST
         
         Parameters:
             block : str
         
         Result:
             AST
         
         Throws:
             Exception if block cannot be compiled
         """
         
         return compile(block, "<string>", "exec", _ast.PyCF_ONLY_AST)
     
     
     def execute(self, block, blockID=None):
         """Execute the block and return the result.
         
         Parameters:
             block : {str, AST}
             blockID : any
                 Caller may provide an ID to identify this block. 
                 result['blockID'] := blockID
         
         Result:
             Deferred result of self.interpreter.execute
         """
         
         if(not self.is_complete(block)):
             raise Exception("Block is not compilable")
         
         if(blockID == None):
             blockID = uuid.uuid4() #random UUID
         
         try:
             result = self.shell.execute(block)
         except Exception,e:
             e = self._add_block_id_for_failure(e, blockID=blockID)
             e = self.update_cell_prompt(e, blockID=blockID)
             e = self.render_error(e)
         else:
             result = self._add_block_id_for_result(result, blockID=blockID)
             result = self.update_cell_prompt(result, blockID=blockID)
             result = self.render_result(result)
         
         return result
     
     
     def _add_block_id_for_result(self, result, blockID):
         """Add the blockID to result or failure. Unfortunatley, we have to 
         treat failures differently than result dicts.
         """
         
         result['blockID'] = blockID
         
         return result
     
     def _add_block_id_for_failure(self, failure, blockID):
         """_add_block_id_for_failure"""
         failure.blockID = blockID
         return failure
     
     
     def _add_history(self, result, block=None):
         """Add block to the history"""
         
         assert(block != None)
         self.history.add_items([block])
         self.history_cursor += 1
         
         return result
     
     
     def get_history_previous(self, current_block):
         """ Returns previous history string and decrement history cursor.
         """
         command = self.history.get_history_item(self.history_cursor - 1)
         
         if command is not None:
             if(self.history_cursor+1 == len(self.history.input_cache)):
                 self.history.input_cache[self.history_cursor] = current_block
             self.history_cursor -= 1
         return command
     
     
     def get_history_next(self):
         """ Returns next history string and increment history cursor.
         """
         command = self.history.get_history_item(self.history_cursor+1)
         
         if command is not None:
             self.history_cursor += 1
         return command
     
     ###
     # Subclasses probably want to override these methods...
     ###
     
     def update_cell_prompt(self, result, blockID=None):
         """Subclass may override to update the input prompt for a block. 
 
         This method only really makes sens in asyncrhonous frontend.
         Since this method will be called as a 
         twisted.internet.defer.Deferred's callback, implementations should 
         return result when finished.
         """
         
         raise NotImplementedError
     
     
     def render_result(self, result):
         """Subclasses must override to render result. 
         
         In asynchronous frontends, this method will be called as a
         twisted.internet.defer.Deferred's callback. Implementations
         should thus return result when finished.
         """
         
         raise NotImplementedError
     
     
     def render_error(self, failure):
         """Subclasses must override to render the failure. 
         
         In asynchronous frontends, this method will be called as a
         twisted.internet.defer.Deferred's callback. Implementations
         should thus return result when finished.
         """
         
         raise NotImplementedError
 

IPython/frontend/linefrontendbase.py

@@ -1,320 +1,333 b''
 """
 Base front end class for all line-oriented frontends, rather than
 block-oriented.
 
 Currently this focuses on synchronous frontends.
 """
 __docformat__ = "restructuredtext en"
 
 #-------------------------------------------------------------------------------
 #  Copyright (C) 2008  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-------------------------------------------------------------------------------
 
 #-------------------------------------------------------------------------------
 # Imports
 #-------------------------------------------------------------------------------
 import re
 
 import IPython
 import sys
 import codeop
 import traceback
 
 from frontendbase import FrontEndBase
 from IPython.kernel.core.interpreter import Interpreter
 
 def common_prefix(strings):
     """ Given a list of strings, return the common prefix between all
         these strings.
     """
     ref = strings[0]
     prefix = ''
     for size in range(len(ref)):
         test_prefix = ref[:size+1]
         for string in strings[1:]:
             if not string.startswith(test_prefix):
                 return prefix
         prefix = test_prefix
 
     return prefix
 
 #-------------------------------------------------------------------------------
 # Base class for the line-oriented front ends
 #-------------------------------------------------------------------------------
 class LineFrontEndBase(FrontEndBase):
     """ Concrete implementation of the FrontEndBase class. This is meant
     to be the base class behind all the frontend that are line-oriented,
     rather than block-oriented.
     """
     
     # We need to keep the prompt number, to be able to increment
     # it when there is an exception.
     prompt_number = 1
 
     # We keep a reference to the last result: it helps testing and
     # programatic control of the frontend. 
     last_result = dict(number=0)
 
     # The input buffer being edited
     input_buffer = ''
 
     # Set to true for debug output
     debug = False
 
     # A banner to print at startup
     banner = None
 
     #--------------------------------------------------------------------------
     # FrontEndBase interface
     #--------------------------------------------------------------------------
 
     def __init__(self, shell=None, history=None, banner=None, *args, **kwargs):
         if shell is None:
             shell = Interpreter()
         FrontEndBase.__init__(self, shell=shell, history=history)
 
         if banner is not None:
             self.banner = banner
 
     def start(self):
         """ Put the frontend in a state where it is ready for user
             interaction.
         """
         if self.banner is not None:
             self.write(self.banner, refresh=False)
 
         self.new_prompt(self.input_prompt_template.substitute(number=1))
 
 
     def complete(self, line):
         """Complete line in engine's user_ns
         
         Parameters
         ----------
         line : string
         
         Result
         ------
         The replacement for the line and the list of possible completions.
         """
         completions = self.shell.complete(line)
         complete_sep =  re.compile('[\s\{\}\[\]\(\)\=]')
         if completions:
             prefix = common_prefix(completions) 
             residual = complete_sep.split(line)[:-1]
             line = line[:-len(residual)] + prefix
         return line, completions 
  
 
     def render_result(self, result):
         """ Frontend-specific rendering of the result of a calculation
         that has been sent to an engine.
         """
         if 'stdout' in result and result['stdout']:
             self.write('\n' + result['stdout'])
         if 'display' in result and result['display']:
             self.write("%s%s\n" % ( 
                             self.output_prompt_template.substitute(
                                     number=result['number']),
                             result['display']['pprint']
                             ) )
        
 
     def render_error(self, failure):
         """ Frontend-specific rendering of error. 
         """
         self.write('\n\n'+str(failure)+'\n\n')
         return failure
 
 
     def is_complete(self, string):
         """ Check if a string forms a complete, executable set of
         commands.
 
         For the line-oriented frontend, multi-line code is not executed
         as soon as it is complete: the users has to enter two line
         returns.
         """
         if string in ('', '\n'):
             # Prefiltering, eg through ipython0, may return an empty
             # string although some operations have been accomplished. We
             # thus want to consider an empty string as a complete
             # statement.
             return True
         elif ( len(self.input_buffer.split('\n'))>2 
                         and not re.findall(r"\n[\t ]*\n[\t ]*$", string)):
             return False
         else:
             self.capture_output()
             try:
                 # Add line returns here, to make sure that the statement is
                 # complete.
                 is_complete = codeop.compile_command(string.rstrip() + '\n\n',
                             "<string>", "exec")
                 self.release_output()
             except Exception, e:
                 # XXX: Hack: return True so that the
                 # code gets executed and the error captured.
                 is_complete = True
             return is_complete
 
 
     def write(self, string, refresh=True):
         """ Write some characters to the display.
 
             Subclass should overide this method.
 
             The refresh keyword argument is used in frontends with an
             event loop, to choose whether the write should trigget an UI
             refresh, and thus be syncrhonous, or not.
         """
         print >>sys.__stderr__, string
 
 
     def execute(self, python_string, raw_string=None):
         """ Stores the raw_string in the history, and sends the
         python string to the interpreter.
         """
         if raw_string is None:
             raw_string = python_string
         # Create a false result, in case there is an exception
         self.last_result = dict(number=self.prompt_number)
+
+        ## try:
+        ##     self.history.input_cache[-1] = raw_string.rstrip()
+        ##     result = self.shell.execute(python_string)
+        ##     self.last_result = result
+        ##     self.render_result(result)
+        ## except:
+        ##     self.show_traceback()
+        ## finally:
+        ##     self.after_execute()
+
         try:
-            self.history.input_cache[-1] = raw_string.rstrip()
-            result = self.shell.execute(python_string)
-            self.last_result = result
-            self.render_result(result)
-        except:
-            self.show_traceback()
+            try:
+                self.history.input_cache[-1] = raw_string.rstrip()
+                result = self.shell.execute(python_string)
+                self.last_result = result
+                self.render_result(result)
+            except:
+                self.show_traceback()
         finally:
             self.after_execute()
 
+
     #--------------------------------------------------------------------------
     # LineFrontEndBase interface
     #--------------------------------------------------------------------------
 
     def prefilter_input(self, string):
         """ Prefilter the input to turn it in valid python.
         """
         string = string.replace('\r\n', '\n')
         string = string.replace('\t', 4*' ')
         # Clean the trailing whitespace
         string = '\n'.join(l.rstrip()  for l in string.split('\n'))
         return string
 
 
     def after_execute(self):
         """ All the operations required after an execution to put the
             terminal back in a shape where it is usable.
         """
         self.prompt_number += 1
         self.new_prompt(self.input_prompt_template.substitute(
                             number=(self.last_result['number'] + 1)))
         # Start a new empty history entry
         self._add_history(None, '')
         self.history_cursor = len(self.history.input_cache) - 1
 
 
     def complete_current_input(self):
         """ Do code completion on current line. 
         """
         if self.debug:
             print >>sys.__stdout__, "complete_current_input", 
         line = self.input_buffer
         new_line, completions = self.complete(line)
         if len(completions)>1:
             self.write_completion(completions, new_line=new_line)
         elif not line == new_line:
             self.input_buffer = new_line
         if self.debug:
             print >>sys.__stdout__, 'line', line
             print >>sys.__stdout__, 'new_line', new_line
             print >>sys.__stdout__, completions 
 
 
     def get_line_width(self):
         """ Return the width of the line in characters.
         """
         return 80
 
 
     def write_completion(self, possibilities, new_line=None):
         """ Write the list of possible completions.
 
             new_line is the completed input line that should be displayed
             after the completion are writen. If None, the input_buffer
             before the completion is used.
         """
         if new_line is None:
             new_line = self.input_buffer
         
         self.write('\n')
         max_len = len(max(possibilities, key=len)) + 1
         
         # Now we check how much symbol we can put on a line...
         chars_per_line = self.get_line_width()
         symbols_per_line = max(1, chars_per_line/max_len)
 
         pos = 1
         buf = []
         for symbol in possibilities:
             if pos < symbols_per_line:
                 buf.append(symbol.ljust(max_len))
                 pos += 1
             else:
                 buf.append(symbol.rstrip() + '\n')
                 pos = 1
         self.write(''.join(buf))
         self.new_prompt(self.input_prompt_template.substitute(
                             number=self.last_result['number'] + 1))
         self.input_buffer = new_line
 
 
     def new_prompt(self, prompt):
         """ Prints a prompt and starts a new editing buffer. 
 
             Subclasses should use this method to make sure that the
             terminal is put in a state favorable for a new line
             input.
         """
         self.input_buffer = ''
         self.write(prompt)
 
 
     #--------------------------------------------------------------------------
     # Private API
     #--------------------------------------------------------------------------
  
     def _on_enter(self):
         """ Called when the return key is pressed in a line editing
             buffer.
         """
         current_buffer = self.input_buffer
         cleaned_buffer = self.prefilter_input(current_buffer)
         if self.is_complete(cleaned_buffer):
             self.execute(cleaned_buffer, raw_string=current_buffer)
         else:
             self.input_buffer += self._get_indent_string(
                                                 current_buffer[:-1])
             if len(current_buffer.split('\n')) == 2:
                 self.input_buffer += '\t\t'
             if current_buffer[:-1].split('\n')[-1].rstrip().endswith(':'):
                 self.input_buffer += '\t'
 
 
     def _get_indent_string(self, string):
         """ Return the string of whitespace that prefixes a line. Used to
         add the right amount of indendation when creating a new line.
         """
         string = string.replace('\t', ' '*4)
         string = string.split('\n')[-1]
         indent_chars = len(string) - len(string.lstrip())
         indent_string = '\t'*(indent_chars // 4) + \
                             ' '*(indent_chars % 4)
 
         return indent_string
  
     

IPython/frontend/prefilterfrontend.py

@@ -1,230 +1,246 b''
 """
 Frontend class that uses IPython0 to prefilter the inputs.
 
 Using the IPython0 mechanism gives us access to the magics.
 
 This is a transitory class, used here to do the transition between
 ipython0 and ipython1. This class is meant to be short-lived as more
 functionnality is abstracted out of ipython0 in reusable functions and
 is added on the interpreter. This class can be a used to guide this
 refactoring.
 """
 __docformat__ = "restructuredtext en"
 
 #-------------------------------------------------------------------------------
 #  Copyright (C) 2008  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-------------------------------------------------------------------------------
 
 #-------------------------------------------------------------------------------
 # Imports
 #-------------------------------------------------------------------------------
 import sys
 
 from linefrontendbase import LineFrontEndBase, common_prefix
 from frontendbase import FrontEndBase
 
 from IPython.ipmaker import make_IPython
 from IPython.ipapi import IPApi
 from IPython.kernel.core.redirector_output_trap import RedirectorOutputTrap
 
 from IPython.kernel.core.sync_traceback_trap import SyncTracebackTrap
 
 from IPython.genutils import Term
 import pydoc
 import os
 import sys
 
 
 def mk_system_call(system_call_function, command):
     """ given a os.system replacement, and a leading string command,
         returns a function that will execute the command with the given
         argument string.
     """
     def my_system_call(args):
         system_call_function("%s %s" % (command, args))
     return my_system_call
 
 #-------------------------------------------------------------------------------
 # Frontend class using ipython0 to do the prefiltering. 
 #-------------------------------------------------------------------------------
 class PrefilterFrontEnd(LineFrontEndBase):
     """ Class that uses ipython0 to do prefilter the input, do the
     completion and the magics.
 
     The core trick is to use an ipython0 instance to prefilter the
     input, and share the namespace between the interpreter instance used
     to execute the statements and the ipython0 used for code
     completion...
     """
 
     debug = False
     
     def __init__(self, ipython0=None, *args, **kwargs):
         """ Parameters:
             -----------
 
             ipython0: an optional ipython0 instance to use for command
             prefiltering and completion.
         """
         LineFrontEndBase.__init__(self, *args, **kwargs)
         self.shell.output_trap = RedirectorOutputTrap(
                             out_callback=self.write,
                             err_callback=self.write,
                                             )
         self.shell.traceback_trap = SyncTracebackTrap(
                         formatters=self.shell.traceback_trap.formatters,
                             )
 
         # Start the ipython0 instance:
         self.save_output_hooks()
         if ipython0 is None:
             # Instanciate an IPython0 interpreter to be able to use the
             # prefiltering.
             # XXX: argv=[] is a bit bold.
             ipython0 = make_IPython(argv=[], 
                                     user_ns=self.shell.user_ns,
                                     user_global_ns=self.shell.user_global_ns)
         self.ipython0 = ipython0
         # Set the pager:
         self.ipython0.set_hook('show_in_pager', 
                     lambda s, string: self.write("\n" + string))
         self.ipython0.write = self.write
         self._ip = _ip = IPApi(self.ipython0)
         # Make sure the raw system call doesn't get called, as we don't
         # have a stdin accessible.
         self._ip.system = self.system_call
         # XXX: Muck around with magics so that they work better
         # in our environment
         self.ipython0.magic_ls = mk_system_call(self.system_call, 
                                                             'ls -CF')
         # And now clean up the mess created by ipython0
         self.release_output()
 
 
         if not 'banner' in kwargs and self.banner is None:
             self.banner = self.ipython0.BANNER + """
 This is the wx frontend, by Gael Varoquaux. This is EXPERIMENTAL code."""
 
         self.start()
 
     #--------------------------------------------------------------------------
     # FrontEndBase interface 
     #--------------------------------------------------------------------------
 
     def show_traceback(self):
         """ Use ipython0 to capture the last traceback and display it.
         """
         self.capture_output()
         self.ipython0.showtraceback(tb_offset=-1)
         self.release_output()
 
 
     def execute(self, python_string, raw_string=None):
         if self.debug:
             print 'Executing Python code:', repr(python_string)
         self.capture_output()
         LineFrontEndBase.execute(self, python_string,
                                     raw_string=raw_string)
         self.release_output()
 
 
     def save_output_hooks(self):
         """ Store all the output hooks we can think of, to be able to
         restore them. 
         
         We need to do this early, as starting the ipython0 instance will
         screw ouput hooks.
         """
         self.__old_cout_write = Term.cout.write
         self.__old_cerr_write = Term.cerr.write
         self.__old_stdout = sys.stdout
         self.__old_stderr= sys.stderr
         self.__old_help_output = pydoc.help.output
         self.__old_display_hook = sys.displayhook
 
 
     def capture_output(self):
         """ Capture all the output mechanisms we can think of.
         """
         self.save_output_hooks()
         Term.cout.write = self.write
         Term.cerr.write = self.write
         sys.stdout = Term.cout
         sys.stderr = Term.cerr
         pydoc.help.output = self.shell.output_trap.out
 
 
     def release_output(self):
         """ Release all the different captures we have made.
         """
         Term.cout.write = self.__old_cout_write
         Term.cerr.write = self.__old_cerr_write
         sys.stdout = self.__old_stdout
         sys.stderr = self.__old_stderr
         pydoc.help.output = self.__old_help_output 
         sys.displayhook = self.__old_display_hook 
 
 
     def complete(self, line):
         # FIXME: This should be factored out in the linefrontendbase
         # method.
         word = line.split('\n')[-1].split(' ')[-1]
         completions = self.ipython0.complete(word)
         # FIXME: The proper sort should be done in the complete method.
         key = lambda x: x.replace('_', '')
         completions.sort(key=key)
         if completions:
             prefix = common_prefix(completions) 
             line = line[:-len(word)] + prefix
         return line, completions 
  
     
     #--------------------------------------------------------------------------
     # LineFrontEndBase interface 
     #--------------------------------------------------------------------------
 
     def prefilter_input(self, input_string):
         """ Using IPython0 to prefilter the commands to turn them
         in executable statements that are valid Python strings.
         """
         input_string = LineFrontEndBase.prefilter_input(self, input_string)
         filtered_lines = []
         # The IPython0 prefilters sometime produce output. We need to
         # capture it.
         self.capture_output()
         self.last_result = dict(number=self.prompt_number)
+        
+        ## try:
+        ##     for line in input_string.split('\n'):
+        ##         filtered_lines.append(
+        ##                 self.ipython0.prefilter(line, False).rstrip())
+        ## except:
+        ##     # XXX: probably not the right thing to do.
+        ##     self.ipython0.showsyntaxerror()
+        ##     self.after_execute()
+        ## finally:
+        ##     self.release_output()
+
+
         try:
-            for line in input_string.split('\n'):
-                filtered_lines.append(
-                        self.ipython0.prefilter(line, False).rstrip())
-        except:
-            # XXX: probably not the right thing to do.
-            self.ipython0.showsyntaxerror()
-            self.after_execute()
+            try:
+                for line in input_string.split('\n'):
+                    filtered_lines.append(
+                            self.ipython0.prefilter(line, False).rstrip())
+            except:
+                # XXX: probably not the right thing to do.
+                self.ipython0.showsyntaxerror()
+                self.after_execute()
         finally:
             self.release_output()
 
+
+
         # Clean up the trailing whitespace, to avoid indentation errors
         filtered_string = '\n'.join(filtered_lines)
         return filtered_string
 
 
     #--------------------------------------------------------------------------
     # PrefilterFrontEnd interface 
     #--------------------------------------------------------------------------
  
     def system_call(self, command_string):
         """ Allows for frontend to define their own system call, to be
             able capture output and redirect input.
         """
         return os.system(command_string)
 
 
     def do_exit(self):
         """ Exit the shell, cleanup and save the history.
         """
         self.ipython0.atexit_operations()
 

IPython/kernel/contexts.py

@@ -1,143 +1,141 b''
 # encoding: utf-8
 # -*- test-case-name: IPython.kernel.test.test_contexts -*-
 """Context managers for IPython.
 
 Python 2.5 introduced the `with` statement, which is based on the context
 manager protocol.  This module offers a few context managers for common cases,
 which can also be useful as templates for writing new, application-specific
 managers.
 """
 
-from __future__ import with_statement
-
 __docformat__ = "restructuredtext en"
 
 #-------------------------------------------------------------------------------
 #  Copyright (C) 2008  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-------------------------------------------------------------------------------
 
 #-------------------------------------------------------------------------------
 # Imports
 #-------------------------------------------------------------------------------
 
 import linecache
 import sys
 
 from twisted.internet.error import ConnectionRefusedError
 
 from IPython.ultraTB import _fixed_getinnerframes, findsource
 from IPython import ipapi
 
 from IPython.kernel import error
 
 #---------------------------------------------------------------------------
 # Utility functions needed by all context managers.
 #---------------------------------------------------------------------------
 
 def remote():
     """Raises a special exception meant to be caught by context managers.
     """
     m = 'Special exception to stop local execution of parallel code.'
     raise error.StopLocalExecution(m)
 
 
 def strip_whitespace(source,require_remote=True):
     """strip leading whitespace from input source.
 
     :Parameters:
 
     """
     remote_mark = 'remote()'
     # Expand tabs to avoid any confusion.
     wsource = [l.expandtabs(4) for l in source]
     # Detect the indentation level
     done = False
     for line in wsource:
         if line.isspace():
             continue
         for col,char in enumerate(line):
             if char != ' ':
                 done = True
                 break
         if done:
             break
     # Now we know how much leading space there is in the code.  Next, we
     # extract up to the first line that has less indentation.
     # WARNINGS: we skip comments that may be misindented, but we do NOT yet
     # detect triple quoted strings that may have flush left text.
     for lno,line in enumerate(wsource):
         lead = line[:col]
         if lead.isspace():
             continue
         else:
             if not lead.lstrip().startswith('#'):
                 break
     # The real 'with' source is up to lno
     src_lines = [l[col:] for l in wsource[:lno+1]]
 
     # Finally, check that the source's first non-comment line begins with the
     # special call 'remote()'
     if require_remote:
         for nline,line in enumerate(src_lines):
             if line.isspace() or line.startswith('#'):
                 continue
             if line.startswith(remote_mark):
                 break
             else:
                 raise ValueError('%s call missing at the start of code' %
                                  remote_mark)
         out_lines = src_lines[nline+1:]
     else:
         # If the user specified that the remote() call wasn't mandatory
         out_lines = src_lines
 
     # src = ''.join(out_lines)  # dbg
     #print 'SRC:\n<<<<<<<>>>>>>>\n%s<<<<<>>>>>>' % src  # dbg
     return ''.join(out_lines)
 
 class RemoteContextBase(object):
     def __init__(self):
         self.ip = ipapi.get()
 
     def _findsource_file(self,f):
         linecache.checkcache()
         s = findsource(f.f_code)
         lnum = f.f_lineno
         wsource = s[0][f.f_lineno:]
         return strip_whitespace(wsource)
 
     def _findsource_ipython(self,f):
         from IPython import ipapi
         self.ip = ipapi.get()
         buf = self.ip.IP.input_hist_raw[-1].splitlines()[1:]
         wsource = [l+'\n' for l in buf ]
 
         return strip_whitespace(wsource)
 
     def findsource(self,frame):
         local_ns = frame.f_locals
         global_ns = frame.f_globals
         if frame.f_code.co_filename == '<ipython console>':
             src = self._findsource_ipython(frame)
         else:
             src = self._findsource_file(frame)
         return src
 
     def __enter__(self):
         raise NotImplementedError
 
     def __exit__ (self, etype, value, tb):
         if issubclass(etype,error.StopLocalExecution):
             return True
 
 class RemoteMultiEngine(RemoteContextBase):
     def __init__(self,mec):
         self.mec = mec
         RemoteContextBase.__init__(self)
 
     def __enter__(self):
         src = self.findsource(sys._getframe(1))
         return self.mec.execute(src)

IPython/kernel/tests/test_contexts.py

@@ -1,41 +1,43 b''
-from __future__ import with_statement
+#from __future__ import with_statement
+
+# XXX This file is currently disabled to preserve 2.4 compatibility.
 
 #def test_simple():
 if 0:
 
     # XXX - for now, we need a running cluster to be started separately.  The
     # daemon work is almost finished, and will make much of this unnecessary.
     from IPython.kernel import client
     mec = client.MultiEngineClient(('127.0.0.1',10105))
 
     try:
         mec.get_ids()
     except ConnectionRefusedError:
         import os, time
         os.system('ipcluster -n 2 &')
         time.sleep(2)
         mec = client.MultiEngineClient(('127.0.0.1',10105))
 
     mec.block = False
 
     import itertools
     c = itertools.count()
 
     parallel = RemoteMultiEngine(mec)
 
     mec.pushAll()
 
-    with parallel as pr:
-        # A comment
-        remote()  # this means the code below only runs remotely
-        print 'Hello remote world'
-        x = range(10)
-        # Comments are OK
-        # Even misindented.
-        y = x+1
+    ## with parallel as pr:
+    ##     # A comment
+    ##     remote()  # this means the code below only runs remotely
+    ##     print 'Hello remote world'
+    ##     x = range(10)
+    ##     # Comments are OK
+    ##     # Even misindented.
+    ##     y = x+1
 
 
-    with pfor('i',sequence) as pr:
-        print x[i]
+    ## with pfor('i',sequence) as pr:
+    ##     print x[i]
 
     print pr.x + pr.y

docs/examples/kernel/nwmerge.py

@@ -1,120 +1,123 b''
 """Example showing how to merge multiple remote data streams.
 """
 # Slightly modified version of:
 # http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/511509
 
 import heapq
 from IPython.kernel.error import CompositeError
 
 def mergesort(list_of_lists, key=None):
     """ Perform an N-way merge operation on sorted lists.
 
     @param list_of_lists: (really iterable of iterable) of sorted elements
     (either by naturally or by C{key})
     @param key: specify sort key function (like C{sort()}, C{sorted()})
 
     Yields tuples of the form C{(item, iterator)}, where the iterator is the
     built-in list iterator or something you pass in, if you pre-generate the
     iterators.
 
     This is a stable merge; complexity O(N lg N)
 
     Examples::
 
     >>> print list(mergesort([[1,2,3,4],
     ...                      [2,3.25,3.75,4.5,6,7],
     ...                      [2.625,3.625,6.625,9]]))
     [1, 2, 2, 2.625, 3, 3.25, 3.625, 3.75, 4, 4.5, 6, 6.625, 7, 9]
 
     # note stability
     >>> print list(mergesort([[1,2,3,4],
     ...                      [2,3.25,3.75,4.5,6,7],
     ...                      [2.625,3.625,6.625,9]],
     ...                      key=int))
     [1, 2, 2, 2.625, 3, 3.25, 3.75, 3.625, 4, 4.5, 6, 6.625, 7, 9]
 
 
     >>> print list(mergesort([[4, 3, 2, 1],
     ...                      [7, 6, 4.5, 3.75, 3.25, 2],
     ...                      [9, 6.625, 3.625, 2.625]],
     ...                      key=lambda x: -x))
     [9, 7, 6.625, 6, 4.5, 4, 3.75, 3.625, 3.25, 3, 2.625, 2, 2, 1]
     """
 
     heap = []
     for i, itr in enumerate(iter(pl) for pl in list_of_lists):
         try:
             item = itr.next()
-            toadd = (key(item), i, item, itr) if key else (item, i, itr)
+            if key:
+                toadd = (key(item), i, item, itr)
+            else:
+                toadd = (item, i, itr)
             heap.append(toadd)
         except StopIteration:
             pass
     heapq.heapify(heap)
 
     if key:
         while heap:
             _, idx, item, itr = heap[0]
             yield item
             try:
                 item = itr.next()
                 heapq.heapreplace(heap, (key(item), idx, item, itr) )
             except StopIteration:
                 heapq.heappop(heap)
 
     else:
         while heap:
             item, idx, itr = heap[0]
             yield item
             try:
                 heapq.heapreplace(heap, (itr.next(), idx, itr))
             except StopIteration:
                 heapq.heappop(heap)
 
 
 def remote_iterator(rc,engine,name):
     """Return an iterator on an object living on a remote engine.
     """
     # Check that the object exists on the engine and pin a reference to it
     iter_name = '_%s_rmt_iter_' % name
     rc.execute('%s = iter(%s)' % (iter_name,name), targets=engine)
     tpl = '_tmp = %s.next()' % iter_name
     while True:
         try:
             rc.execute(tpl, targets=engine)
             result = rc.pull('_tmp', targets=engine)[0]
         # This causes the StopIteration exception to be raised.
         except CompositeError, e:
             e.raise_exception()
         else:
             yield result
 
 # Main, interactive testing
 if __name__ == '__main__':
 
     from IPython.kernel import client
     ipc = client.MultiEngineClient()
     print 'Engine IDs:',ipc.get_ids()
 
     # Make a set of 'sorted datasets'
     a0 = range(5,20)
     a1 = range(10)
     a2 = range(15,25)
 
     # Now, imagine these had been created in the remote engines by some long
     # computation.  In this simple example, we just send them over into the
     # remote engines.  They will all be called 'a' in each engine.
     ipc.push(dict(a=a0), targets=0)
     ipc.push(dict(a=a1), targets=1)
     ipc.push(dict(a=a2), targets=2)
 
     # And we now make a local object which represents the remote iterator
     aa0 = remote_iterator(ipc,0,'a')
     aa1 = remote_iterator(ipc,1,'a')
     aa2 = remote_iterator(ipc,2,'a')
 
     # Let's merge them, both locally and remotely:
     print 'Merge the local datasets:'
     print list(mergesort([a0,a1,a2]))
     
     print 'Locally merge the remote sets:'
     print list(mergesort([aa0,aa1,aa2]))

docs/source/changes.txt

@@ -1,252 +1,325 b''
 .. _changes:
 
 ==========
 What's new
 ==========
 
 .. contents::
 ..
     1  Release 0.9
       1.1  New features
       1.2  Bug fixes
       1.3  Backwards incompatible changes
       1.4  Changes merged in from IPython1
         1.4.1  New features
         1.4.2  Bug fixes
         1.4.3  Backwards incompatible changes
     2  Release 0.8.4
-    3  Release 0.8.2
-    4  Release 0.8.3
+    3  Release 0.8.3
+    4  Release 0.8.2
     5  Older releases
 ..
 
 
 Release 0.9
 ===========
 
 New features
 ------------
 
+* All furl files and security certificates are now put in a read-only directory
+  named ~./ipython/security.
+
+* A single function :func:`get_ipython_dir`, in :mod:`IPython.genutils` that
+  determines the user's IPython directory in a robust manner.
+
 * Laurent's WX application has been given a top-level script called ipython-wx,
   and it has received numerous fixes.  We expect this code to be
   architecturally better integrated with Gael's WX 'ipython widget' over the
   next few releases.
 
 * The Editor synchronization work by Vivian De Smedt has been merged in.  This
   code adds a number of new editor hooks to synchronize with editors under
   Windows.
 
 * A new, still experimental but highly functional, WX shell by Gael Varoquaux.
   This work was sponsored by Enthought, and while it's still very new, it is
   based on a more cleanly organized arhictecture of the various IPython
   components.  We will continue to develop this over the next few releases as a
   model for GUI components that use IPython.
 
 * Another GUI frontend, Cocoa based (Cocoa is the OSX native GUI framework),
   authored by Barry Wark.  Currently the WX and the Cocoa ones have slightly
   different internal organizations, but the whole team is working on finding
   what the right abstraction points are for a unified codebase.
 
 * As part of the frontend work, Barry Wark also implemented an experimental
   event notification system that various ipython components can use.  In the
   next release the implications and use patterns of this system regarding the
   various GUI options will be worked out.
 
 * IPython finally has a full test system, that can test docstrings with
   IPython-specific functionality.  There are still a few pieces missing for it
   to be widely accessible to all users (so they can run the test suite at any
   time and report problems), but it now works for the developers.  We are
   working hard on continuing to improve it, as this was probably IPython's
   major Achilles heel (the lack of proper test coverage made it effectively
-  impossible to do large-scale refactoring).
-
-	* The notion of a task has been completely reworked.  An `ITask` interface has
-	  been created.  This interface defines the methods that tasks need to implement.
-	  These methods are now responsible for things like submitting tasks and processing
-	  results.  There are two basic task types: :class:`IPython.kernel.task.StringTask`
-	  (this is the old `Task` object, but renamed) and the new 
-	  :class:`IPython.kernel.task.MapTask`, which is based on a function.
-	* A new interface, :class:`IPython.kernel.mapper.IMapper` has been defined to
-	  standardize the idea of a `map` method.  This interface has a single
-	  `map` method that has the same syntax as the built-in `map`.  We have also defined
-	  a `mapper` factory interface that creates objects that implement
-	  :class:`IPython.kernel.mapper.IMapper` for different controllers.  Both
-	  the multiengine and task controller now have mapping capabilties.
-	* The parallel function capabilities have been reworks.  The major changes are that
-	  i) there is now an `@parallel` magic that creates parallel functions, ii)
-	  the syntax for mulitple variable follows that of `map`, iii) both the
-	  multiengine and task controller now have a parallel function implementation.
-	* All of the parallel computing capabilities from `ipython1-dev` have been merged into
-	  IPython proper.  This resulted in the following new subpackages:  
-	  :mod:`IPython.kernel`, :mod:`IPython.kernel.core`, :mod:`IPython.config`,
-	  :mod:`IPython.tools` and :mod:`IPython.testing`.
-	* As part of merging in the `ipython1-dev` stuff, the `setup.py` script and friends
-	  have been completely refactored.  Now we are checking for dependencies using
-	  the approach that matplotlib uses.
-	* The documentation has been completely reorganized to accept the documentation
-	  from `ipython1-dev`.
-	* We have switched to using Foolscap for all of our network protocols in
-	  :mod:`IPython.kernel`.  This gives us secure connections that are both encrypted
-	  and authenticated.
-	* We have a brand new `COPYING.txt` files that describes the IPython license
-	  and copyright.  The biggest change is that we are putting "The IPython
-	  Development Team" as the copyright holder.  We give more details about exactly
-	  what this means in this file.  All developer should read this and use the new
-	  banner in all IPython source code files.
-	* sh profile: ./foo runs foo as system command, no need to do !./foo anymore
-	* String lists now support 'sort(field, nums = True)' method (to easily
-	  sort system command output). Try it with 'a = !ls -l ; a.sort(1, nums=1)'
-	* '%cpaste foo' now assigns the pasted block as string list, instead of string
-	* The ipcluster script now run by default with no security.  This is done because
-	  the main usage of the script is for starting things on localhost.  Eventually
-	  when ipcluster is able to start things on other hosts, we will put security
-	  back.
-	* 'cd --foo' searches directory history for string foo, and jumps to that dir.
-	  Last part of dir name is checked first. If no matches for that are found,
-	  look at the whole path.
+  impossible to do large-scale refactoring).  The full test suite can now
+  be run using the :command:`iptest` command line program.
+
+* The notion of a task has been completely reworked.  An `ITask` interface has
+  been created.  This interface defines the methods that tasks need to
+  implement.  These methods are now responsible for things like submitting
+  tasks and processing results.  There are two basic task types:
+  :class:`IPython.kernel.task.StringTask` (this is the old `Task` object, but
+  renamed) and the new :class:`IPython.kernel.task.MapTask`, which is based on
+  a function.
+
+* A new interface, :class:`IPython.kernel.mapper.IMapper` has been defined to
+  standardize the idea of a `map` method.  This interface has a single `map`
+  method that has the same syntax as the built-in `map`.  We have also defined
+  a `mapper` factory interface that creates objects that implement
+  :class:`IPython.kernel.mapper.IMapper` for different controllers.  Both the
+  multiengine and task controller now have mapping capabilties.
+
+* The parallel function capabilities have been reworks.  The major changes are
+  that i) there is now an `@parallel` magic that creates parallel functions,
+  ii) the syntax for mulitple variable follows that of `map`, iii) both the
+  multiengine and task controller now have a parallel function implementation.
+
+* All of the parallel computing capabilities from `ipython1-dev` have been
+  merged into IPython proper.  This resulted in the following new subpackages:
+  :mod:`IPython.kernel`, :mod:`IPython.kernel.core`, :mod:`IPython.config`,
+  :mod:`IPython.tools` and :mod:`IPython.testing`.
+
+* As part of merging in the `ipython1-dev` stuff, the `setup.py` script and
+  friends have been completely refactored.  Now we are checking for
+  dependencies using the approach that matplotlib uses.
+
+* The documentation has been completely reorganized to accept the documentation
+  from `ipython1-dev`.
+
+* We have switched to using Foolscap for all of our network protocols in
+  :mod:`IPython.kernel`.  This gives us secure connections that are both
+  encrypted and authenticated.
 
+* We have a brand new `COPYING.txt` files that describes the IPython license
+  and copyright.  The biggest change is that we are putting "The IPython
+  Development Team" as the copyright holder.  We give more details about
+  exactly what this means in this file.  All developer should read this and use
+  the new banner in all IPython source code files.
+
+* sh profile: ./foo runs foo as system command, no need to do !./foo anymore
+
+* String lists now support ``sort(field, nums = True)`` method (to easily sort
+  system command output). Try it with ``a = !ls -l ; a.sort(1, nums=1)``.
+
+* '%cpaste foo' now assigns the pasted block as string list, instead of string
+
+* The ipcluster script now run by default with no security.  This is done
+  because the main usage of the script is for starting things on localhost.
+  Eventually when ipcluster is able to start things on other hosts, we will put
+  security back.
+
+* 'cd --foo' searches directory history for string foo, and jumps to that dir.
+  Last part of dir name is checked first. If no matches for that are found,
+  look at the whole path.
+
+  
 Bug fixes
 ---------
 
-	* The colors escapes in the multiengine client are now turned off on win32 as they
-	  don't print correctly.
-	* The :mod:`IPython.kernel.scripts.ipengine` script was exec'ing mpi_import_statement
-	  incorrectly, which was leading the engine to crash when mpi was enabled.
-	* A few subpackages has missing `__init__.py` files.
-	* The documentation is only created is Sphinx is found.  Previously, the `setup.py`
-	  script would fail if it was missing.
-	* Greedy 'cd' completion has been disabled again (it was enabled in 0.8.4)
+* The Windows installer has been fixed.  Now all IPython scripts have ``.bat``
+  versions created.  Also, the Start Menu shortcuts have been updated.
+
+* The colors escapes in the multiengine client are now turned off on win32 as
+  they don't print correctly.
+
+* The :mod:`IPython.kernel.scripts.ipengine` script was exec'ing
+  mpi_import_statement incorrectly, which was leading the engine to crash when
+  mpi was enabled.
+
+* A few subpackages had missing ``__init__.py`` files.
+
+* The documentation is only created if Sphinx is found.  Previously, the
+  ``setup.py`` script would fail if it was missing.
+
+* Greedy ``cd`` completion has been disabled again (it was enabled in 0.8.4) as
+  it caused problems on certain platforms.
           
 
 Backwards incompatible changes
 ------------------------------
 
+* The ``clusterfile`` options of the :command:`ipcluster` command has been
+  removed as it was not working and it will be replaced soon by something much
+  more robust.
+
+* The :mod:`IPython.kernel` configuration now properly find the user's
+  IPython directory.
+
 * In ipapi, the :func:`make_user_ns` function has been replaced with
   :func:`make_user_namespaces`, to support dict subclasses in namespace
   creation.
 
-	* :class:`IPython.kernel.client.Task` has been renamed
-	  :class:`IPython.kernel.client.StringTask` to make way for new task types.
-	* The keyword argument `style` has been renamed `dist` in `scatter`, `gather`
-	  and `map`.
-	* Renamed the values that the rename `dist` keyword argument can have from
-	  `'basic'` to `'b'`.
-	* IPython has a larger set of dependencies if you want all of its capabilities.
-	  See the `setup.py` script for details.
-	* The constructors for :class:`IPython.kernel.client.MultiEngineClient` and 
-	  :class:`IPython.kernel.client.TaskClient` no longer take the (ip,port) tuple.
-	  Instead they take the filename of a file that contains the FURL for that
-	  client.  If the FURL file is in your IPYTHONDIR, it will be found automatically
-	  and the constructor can be left empty.
-	* The asynchronous clients in :mod:`IPython.kernel.asyncclient` are now created 
-	  using the factory functions :func:`get_multiengine_client` and 
-	  :func:`get_task_client`.  These return a `Deferred` to the actual client.
-	* The command line options to `ipcontroller` and `ipengine` have changed to
-	  reflect the new Foolscap network protocol and the FURL files.  Please see the
-	  help for these scripts for details.
-	* The configuration files for the kernel have changed because of the Foolscap stuff.
-	  If you were using custom config files before, you should delete them and regenerate
-	  new ones.
+* :class:`IPython.kernel.client.Task` has been renamed
+  :class:`IPython.kernel.client.StringTask` to make way for new task types.
+
+* The keyword argument `style` has been renamed `dist` in `scatter`, `gather`
+  and `map`.
+
+* Renamed the values that the rename `dist` keyword argument can have from
+  `'basic'` to `'b'`.
+
+* IPython has a larger set of dependencies if you want all of its capabilities.
+  See the `setup.py` script for details.
+
+* The constructors for :class:`IPython.kernel.client.MultiEngineClient` and 
+  :class:`IPython.kernel.client.TaskClient` no longer take the (ip,port) tuple.
+  Instead they take the filename of a file that contains the FURL for that
+  client.  If the FURL file is in your IPYTHONDIR, it will be found automatically
+  and the constructor can be left empty.
+
+* The asynchronous clients in :mod:`IPython.kernel.asyncclient` are now created 
+  using the factory functions :func:`get_multiengine_client` and 
+  :func:`get_task_client`.  These return a `Deferred` to the actual client.
+
+* The command line options to `ipcontroller` and `ipengine` have changed to
+  reflect the new Foolscap network protocol and the FURL files.  Please see the
+  help for these scripts for details.
+
+* The configuration files for the kernel have changed because of the Foolscap
+  stuff.  If you were using custom config files before, you should delete them
+  and regenerate new ones.
 
 Changes merged in from IPython1
 -------------------------------
 
 New features
 ............
 
-	* Much improved ``setup.py`` and ``setupegg.py`` scripts.  Because Twisted
-	  and zope.interface are now easy installable, we can declare them as dependencies
-	  in our setupegg.py script.
-	* IPython is now compatible with Twisted 2.5.0 and 8.x.
-	* Added a new example of how to use :mod:`ipython1.kernel.asynclient`.
-	* Initial draft of a process daemon in :mod:`ipython1.daemon`.  This has not
-	  been merged into IPython and is still in `ipython1-dev`.
-	* The ``TaskController`` now has methods for getting the queue status.
-	* The ``TaskResult`` objects not have information about how long the task
-	  took to run.
-	* We are attaching additional attributes to exceptions ``(_ipython_*)`` that 
-	  we use to carry additional info around.
-	* New top-level module :mod:`asyncclient` that has asynchronous versions (that
-	  return deferreds) of the client classes.  This is designed to users who want
-	  to run their own Twisted reactor
-	* All the clients in :mod:`client` are now based on Twisted.  This is done by 
-	  running the Twisted reactor in a separate thread and using the
-	  :func:`blockingCallFromThread` function that is in recent versions of Twisted.
-	* Functions can now be pushed/pulled to/from engines using
-	  :meth:`MultiEngineClient.push_function` and :meth:`MultiEngineClient.pull_function`.
-	* Gather/scatter are now implemented in the client to reduce the work load
-	  of the controller and improve performance.
-	* Complete rewrite of the IPython docuementation.  All of the documentation
-	  from the IPython website has been moved into docs/source as restructured
-	  text documents.  PDF and HTML documentation are being generated using 
-	  Sphinx.
-	* New developer oriented documentation: development guidelines and roadmap. 
-	* Traditional ``ChangeLog`` has been changed to a more useful ``changes.txt`` file
-	  that is organized by release and is meant to provide something more relevant
-	  for users.
+* Much improved ``setup.py`` and ``setupegg.py`` scripts.  Because Twisted and
+  zope.interface are now easy installable, we can declare them as dependencies
+  in our setupegg.py script.
+
+* IPython is now compatible with Twisted 2.5.0 and 8.x.
+
+* Added a new example of how to use :mod:`ipython1.kernel.asynclient`.
+
+* Initial draft of a process daemon in :mod:`ipython1.daemon`.  This has not
+  been merged into IPython and is still in `ipython1-dev`.
+  
+* The ``TaskController`` now has methods for getting the queue status.
+
+* The ``TaskResult`` objects not have information about how long the task
+  took to run.
+  
+* We are attaching additional attributes to exceptions ``(_ipython_*)`` that 
+  we use to carry additional info around.
+  
+* New top-level module :mod:`asyncclient` that has asynchronous versions (that
+  return deferreds) of the client classes.  This is designed to users who want
+  to run their own Twisted reactor.
+  
+* All the clients in :mod:`client` are now based on Twisted.  This is done by 
+  running the Twisted reactor in a separate thread and using the
+  :func:`blockingCallFromThread` function that is in recent versions of Twisted.
+
+* Functions can now be pushed/pulled to/from engines using
+  :meth:`MultiEngineClient.push_function` and
+  :meth:`MultiEngineClient.pull_function`.
+
+* Gather/scatter are now implemented in the client to reduce the work load
+  of the controller and improve performance.
+
+* Complete rewrite of the IPython docuementation.  All of the documentation
+  from the IPython website has been moved into docs/source as restructured
+  text documents.  PDF and HTML documentation are being generated using 
+  Sphinx.
+
+* New developer oriented documentation: development guidelines and roadmap. 
+
+* Traditional ``ChangeLog`` has been changed to a more useful ``changes.txt``
+  file that is organized by release and is meant to provide something more
+  relevant for users.
 
 Bug fixes
 .........
 
-	* Created a proper ``MANIFEST.in`` file to create source distributions.
-	* Fixed a bug in the ``MultiEngine`` interface.  Previously, multi-engine 
-	  actions were being collected with a :class:`DeferredList` with 
-	  ``fireononeerrback=1``.  This meant that methods were returning 
-	  before all engines had given their results.  This was causing extremely odd 
-	  bugs in certain cases. To fix this problem, we have 1) set 
-	  ``fireononeerrback=0`` to make sure all results (or exceptions) are in 
-	  before returning and 2) introduced a :exc:`CompositeError` exception 
-	  that wraps all of the engine exceptions.  This is a huge change as it means 
-	  that users will have to catch :exc:`CompositeError` rather than the actual
-	  exception.
+* Created a proper ``MANIFEST.in`` file to create source distributions.
+
+* Fixed a bug in the ``MultiEngine`` interface.  Previously, multi-engine 
+  actions were being collected with a :class:`DeferredList` with 
+  ``fireononeerrback=1``.  This meant that methods were returning 
+  before all engines had given their results.  This was causing extremely odd 
+  bugs in certain cases. To fix this problem, we have 1) set 
+  ``fireononeerrback=0`` to make sure all results (or exceptions) are in 
+  before returning and 2) introduced a :exc:`CompositeError` exception 
+  that wraps all of the engine exceptions.  This is a huge change as it means 
+  that users will have to catch :exc:`CompositeError` rather than the actual
+  exception.
 
 Backwards incompatible changes
 ..............................
 
-	* All names have been renamed to conform to the lowercase_with_underscore
-	  convention.  This will require users to change references to all names like
-	  ``queueStatus`` to ``queue_status``.
-	* Previously, methods like :meth:`MultiEngineClient.push` and    	
-	  :meth:`MultiEngineClient.push` used ``*args`` and ``**kwargs``.  This was 
-	  becoming a problem as we weren't able to introduce new keyword arguments into
-	  the API.  Now these methods simple take a dict or sequence.  This has also allowed
-	  us to get rid of the ``*All`` methods like :meth:`pushAll` and :meth:`pullAll`.
-	  These things are now handled with the ``targets`` keyword argument that defaults
-	  to ``'all'``.
-	* The :attr:`MultiEngineClient.magicTargets` has been renamed to
-	  :attr:`MultiEngineClient.targets`. 
-	* All methods in the MultiEngine interface now accept the optional keyword argument
-	  ``block``.
-	* Renamed :class:`RemoteController` to :class:`MultiEngineClient` and 
-	  :class:`TaskController` to :class:`TaskClient`.
-	* Renamed the top-level module from :mod:`api` to :mod:`client`.
-	* Most methods in the multiengine interface now raise a :exc:`CompositeError` exception
-	  that wraps the user's exceptions, rather than just raising the raw user's exception.
-	* Changed the ``setupNS`` and ``resultNames`` in the ``Task`` class to ``push`` 
-	  and ``pull``.
+* All names have been renamed to conform to the lowercase_with_underscore
+  convention.  This will require users to change references to all names like
+  ``queueStatus`` to ``queue_status``.
+
+* Previously, methods like :meth:`MultiEngineClient.push` and
+  :meth:`MultiEngineClient.push` used ``*args`` and ``**kwargs``.  This was
+  becoming a problem as we weren't able to introduce new keyword arguments into
+  the API.  Now these methods simple take a dict or sequence.  This has also
+  allowed us to get rid of the ``*All`` methods like :meth:`pushAll` and
+  :meth:`pullAll`.  These things are now handled with the ``targets`` keyword
+  argument that defaults to ``'all'``.
+
+* The :attr:`MultiEngineClient.magicTargets` has been renamed to
+  :attr:`MultiEngineClient.targets`. 
+
+* All methods in the MultiEngine interface now accept the optional keyword
+  argument ``block``.
+
+* Renamed :class:`RemoteController` to :class:`MultiEngineClient` and 
+  :class:`TaskController` to :class:`TaskClient`.
+
+* Renamed the top-level module from :mod:`api` to :mod:`client`.
+
+* Most methods in the multiengine interface now raise a :exc:`CompositeError`
+  exception that wraps the user's exceptions, rather than just raising the raw
+  user's exception.
+
+* Changed the ``setupNS`` and ``resultNames`` in the ``Task`` class to ``push`` 
+  and ``pull``.
 
+  
 Release 0.8.4
 =============
 
-Someone needs to describe what went into 0.8.4.
+This was a quick release to fix an unfortunate bug that slipped into the 0.8.3
+release.  The ``--twisted`` option was disabled, as it turned out to be broken
+across several platforms.
 
-Release 0.8.2
-=============
 
-* %pushd/%popd behave differently; now "pushd /foo" pushes CURRENT directory 
-  and jumps to /foo. The current behaviour is closer to the documented 
-  behaviour, and should not trip anyone.
-  
 Release 0.8.3
 =============
   
 * pydb is now disabled by default (due to %run -d problems). You can enable
   it by passing -pydb command line argument to IPython. Note that setting
   it in config file won't work.
 
+  
+Release 0.8.2
+=============
+
+* %pushd/%popd behave differently; now "pushd /foo" pushes CURRENT directory 
+  and jumps to /foo. The current behaviour is closer to the documented 
+  behaviour, and should not trip anyone.
+
+  
 Older releases
 ==============
 
-Changes in earlier releases of IPython are described in the older file ``ChangeLog``.  
-Please refer to this document for details.
+Changes in earlier releases of IPython are described in the older file
+``ChangeLog``.  Please refer to this document for details.
 

docs/source/conf.py

@@ -1,180 +1,187 b''
 # -*- coding: utf-8 -*-
 #
-# IPython documentation build configuration file, created by
-# sphinx-quickstart on Thu May  8 16:45:02 2008.
+# IPython documentation build configuration file.
 
 # NOTE: This file has been edited manually from the auto-generated one from
 # sphinx.  Do NOT delete and re-generate.  If any changes from sphinx are
 # needed, generate a scratch one and merge by hand any new fields needed.
 
 #
 # This file is execfile()d with the current directory set to its containing dir.
 #
 # The contents of this file are pickled, so don't put values in the namespace
 # that aren't pickleable (module imports are okay, they're removed automatically).
 #
 # All configuration values have a default value; values that are commented out
 # serve to show the default value.
 
 import sys, os
 
 # If your extensions are in another directory, add it here. If the directory
 # is relative to the documentation root, use os.path.abspath to make it
 # absolute, like shown here.
-#sys.path.append(os.path.abspath('some/directory'))
+sys.path.append(os.path.abspath('../sphinxext'))
+
+# Import support for ipython console session syntax highlighting (lives
+# in the sphinxext directory defined above)
+import ipython_console_highlighting
 
 # We load the ipython release info into a dict by explicit execution
 iprelease = {}
 execfile('../../IPython/Release.py',iprelease)
 
 # General configuration
 # ---------------------
 
 # Add any Sphinx extension module names here, as strings. They can be extensions
 # coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
-#extensions = []
+extensions = ['sphinx.ext.autodoc',
+              'inheritance_diagram', 'only_directives',
+              'ipython_console_highlighting', 
+              # 'plot_directive', # disabled for now, needs matplotlib
+              ]
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
 
 # The suffix of source filenames.
 source_suffix = '.txt'
 
 # The master toctree document.
 master_doc = 'index'
 
 # General substitutions.
 project = 'IPython'
 copyright = '2008, The IPython Development Team'
 
 # The default replacements for |version| and |release|, also used in various
 # other places throughout the built documents.
 #
 # The full version, including alpha/beta/rc tags.
 release = iprelease['version']
 # The short X.Y version.
 version = '.'.join(release.split('.',2)[:2])
 
 
 # There are two options for replacing |today|: either, you set today to some
 # non-false value, then it is used:
 #today = ''
 # Else, today_fmt is used as the format for a strftime call.
 today_fmt = '%B %d, %Y'
 
 # List of documents that shouldn't be included in the build.
 #unused_docs = []
 
 # List of directories, relative to source directories, that shouldn't be searched
 # for source files.
-#exclude_dirs = []
+exclude_dirs = ['attic']
 
 # If true, '()' will be appended to :func: etc. cross-reference text.
 #add_function_parentheses = True
 
 # If true, the current module name will be prepended to all description
 # unit titles (such as .. function::).
 #add_module_names = True
 
 # If true, sectionauthor and moduleauthor directives will be shown in the
 # output. They are ignored by default.
 #show_authors = False
 
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = 'sphinx'
 
 
 # Options for HTML output
 # -----------------------
 
 # The style sheet to use for HTML and HTML Help pages. A file of that name
 # must exist either in Sphinx' static/ path, or in one of the custom paths
 # given in html_static_path.
 html_style = 'default.css'
 
 # The name for this set of Sphinx documents.  If None, it defaults to
 # "<project> v<release> documentation".
 #html_title = None
 
 # The name of an image file (within the static path) to place at the top of
 # the sidebar.
 #html_logo = None
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
 html_static_path = ['_static']
 
 # If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
 # using the given strftime format.
 html_last_updated_fmt = '%b %d, %Y'
 
 # If true, SmartyPants will be used to convert quotes and dashes to
 # typographically correct entities.
 #html_use_smartypants = True
 
 # Custom sidebar templates, maps document names to template names.
 #html_sidebars = {}
 
 # Additional templates that should be rendered to pages, maps page names to
 # template names.
 #html_additional_pages = {}
 
 # If false, no module index is generated.
 #html_use_modindex = True
 
 # If true, the reST sources are included in the HTML build as _sources/<name>.
 #html_copy_source = True
 
 # If true, an OpenSearch description file will be output, and all pages will
 # contain a <link> tag referring to it.  The value of this option must be the
 # base URL from which the finished HTML is served.
 #html_use_opensearch = ''
 
 # If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml").
 #html_file_suffix = ''
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'IPythondoc'
+htmlhelp_basename = 'ipythondoc'
 
 
 # Options for LaTeX output
 # ------------------------
 
 # The paper size ('letter' or 'a4').
 latex_paper_size = 'letter'
 
 # The font size ('10pt', '11pt' or '12pt').
 latex_font_size = '11pt'
 
 # Grouping the document tree into LaTeX files. List of tuples
 # (source start file, target name, title, author, document class [howto/manual]).
 
 latex_documents = [ ('index', 'ipython.tex', 'IPython Documentation',
-                     ur"""Brian Granger, Fernando Pérez and Ville Vainio\\
-                     \ \\
-                     With contributions from:\\
-                     Benjamin Ragan-Kelley and Barry Wark.""",
+                     ur"""The IPython Development Team""",
                      'manual'),
                     ]
 
 # The name of an image file (relative to this directory) to place at the top of
 # the title page.
 #latex_logo = None
 
 # For "manual" documents, if this is true, then toplevel headings are parts,
 # not chapters.
 #latex_use_parts = False
 
 # Additional stuff for the LaTeX preamble.
 #latex_preamble = ''
 
 # Documents to append as an appendix to all manuals.
 #latex_appendices = []
 
 # If false, no module index is generated.
 #latex_use_modindex = True
 
 
-# Cleanup: delete release info to avoid pickling errors from sphinx
+# Cleanup
+# -------
+# delete release info to avoid pickling errors from sphinx
+
 del iprelease

docs/source/config/customization.txt

@@ -1,284 +1,286 b''
 .. _customization:
 
 ========================
 Customization of IPython
 ========================
 
 There are 2 ways to configure IPython - the old way of using ipythonrc
 files (an INI-file like format), and the new way that involves editing
 your ipy_user_conf.py. Both configuration systems work at the same
 time, so you can set your options in both, but if you are hesitating
 about which alternative to choose, we recommend the ipy_user_conf.py
 approach, as it will give you more power and control in the long
 run. However, there are few options such as pylab_import_all that can
 only be specified in ipythonrc file or command line - the reason for
 this is that they are needed before IPython has been started up, and
 the IPApi object used in ipy_user_conf.py is not yet available at that
 time. A hybrid approach of specifying a few options in ipythonrc and
 doing the more advanced configuration in ipy_user_conf.py is also
 possible.
 
+.. _ipythonrc:
+
 The ipythonrc approach
 ======================
 
 As we've already mentioned, IPython reads a configuration file which can
 be specified at the command line (-rcfile) or which by default is
 assumed to be called ipythonrc. Such a file is looked for in the current
 directory where IPython is started and then in your IPYTHONDIR, which
 allows you to have local configuration files for specific projects. In
 this section we will call these types of configuration files simply
 rcfiles (short for resource configuration file).
 
 The syntax of an rcfile is one of key-value pairs separated by
 whitespace, one per line. Lines beginning with a # are ignored as
 comments, but comments can not be put on lines with data (the parser is
 fairly primitive). Note that these are not python files, and this is
 deliberate, because it allows us to do some things which would be quite
 tricky to implement if they were normal python files.
 
-First, an rcfile can contain permanent default values for almost all
-command line options (except things like -help or -Version). Sec
-`command line options`_ contains a description of all command-line
-options. However, values you explicitly specify at the command line
-override the values defined in the rcfile.
+First, an rcfile can contain permanent default values for almost all command
+line options (except things like -help or -Version). :ref:`This section
+<command_line_options>` contains a description of all command-line
+options. However, values you explicitly specify at the command line override
+the values defined in the rcfile.
 
 Besides command line option values, the rcfile can specify values for
 certain extra special options which are not available at the command
 line. These options are briefly described below.
 
 Each of these options may appear as many times as you need it in the file.
 
     * include <file1> <file2> ...: you can name other rcfiles you want
       to recursively load up to 15 levels (don't use the <> brackets in
       your names!). This feature allows you to define a 'base' rcfile
       with general options and special-purpose files which can be loaded
       only when needed with particular configuration options. To make
       this more convenient, IPython accepts the -profile <name> option
       (abbreviates to -p <name>) which tells it to look for an rcfile
       named ipythonrc-<name>.
     * import_mod <mod1> <mod2> ...: import modules with 'import
       <mod1>,<mod2>,...'
     * import_some <mod> <f1> <f2> ...: import functions with 'from
       <mod> import <f1>,<f2>,...'
     * import_all <mod1> <mod2> ...: for each module listed import
       functions with ``from <mod> import *``.
     * execute <python code>: give any single-line python code to be
       executed.
     * execfile <filename>: execute the python file given with an
       'execfile(filename)' command. Username expansion is performed on
       the given names. So if you need any amount of extra fancy
       customization that won't fit in any of the above 'canned' options,
       you can just put it in a separate python file and execute it.
     * alias <alias_def>: this is equivalent to calling
       '%alias <alias_def>' at the IPython command line. This way, from
       within IPython you can do common system tasks without having to
       exit it or use the ! escape. IPython isn't meant to be a shell
       replacement, but it is often very useful to be able to do things
       with files while testing code. This gives you the flexibility to
       have within IPython any aliases you may be used to under your
       normal system shell.
 
 ipy_user_conf.py
 ================
 
 There should be a simple template ipy_user_conf.py file in your
 ~/.ipython directory. It is a plain python module that is imported
 during IPython startup, so you can do pretty much what you want there
 - import modules, configure extensions, change options, define magic
 commands, put variables and functions in the IPython namespace,
 etc. You use the IPython extension api object, acquired by
 IPython.ipapi.get() and documented in the "IPython extension API"
 chapter, to interact with IPython. A sample ipy_user_conf.py is listed
 below for reference::
 
     # Most of your config files and extensions will probably start
     # with this import
 
     import IPython.ipapi
     ip = IPython.ipapi.get()
 
     # You probably want to uncomment this if you did %upgrade -nolegacy
     # import ipy_defaults    
 
     import os   
 
     def main():   
 
         #ip.dbg.debugmode = True
         ip.dbg.debug_stack()
 
         # uncomment if you want to get ipython -p sh behaviour
         # without having to use command line switches  
         import ipy_profile_sh
         import jobctrl
 
         # Configure your favourite editor?
         # Good idea e.g. for %edit os.path.isfile
 
         #import ipy_editors
 
         # Choose one of these:
 
         #ipy_editors.scite()
         #ipy_editors.scite('c:/opt/scite/scite.exe')
         #ipy_editors.komodo()
         #ipy_editors.idle()
         # ... or many others, try 'ipy_editors??' after import to see them
 
         # Or roll your own:
         #ipy_editors.install_editor("c:/opt/jed +$line $file")
 
 
         o = ip.options
         # An example on how to set options
         #o.autocall = 1
         o.system_verbose = 0
 
         #import_all("os sys")
         #execf('~/_ipython/ns.py')
 
 
         # -- prompt
         # A different, more compact set of prompts from the default ones, that
         # always show your current location in the filesystem:
 
         #o.prompt_in1 = r'\C_LightBlue[\C_LightCyan\Y2\C_LightBlue]\C_Normal\n\C_Green|\#>'
         #o.prompt_in2 = r'.\D: '
         #o.prompt_out = r'[\#] '
 
         # Try one of these color settings if you can't read the text easily
         # autoexec is a list of IPython commands to execute on startup
         #o.autoexec.append('%colors LightBG')
         #o.autoexec.append('%colors NoColor')
         o.autoexec.append('%colors Linux')
 
 
     # some config helper functions you can use 
     def import_all(modules):
         """ Usage: import_all("os sys") """ 
         for m in modules.split():
             ip.ex("from %s import *" % m)
 
     def execf(fname):
         """ Execute a file in user namespace """
         ip.ex('execfile("%s")' % os.path.expanduser(fname))
 
     main()
 
 .. _Prompts:
 
 Fine-tuning your prompt
 =======================
 
 IPython's prompts can be customized using a syntax similar to that of
 the bash shell. Many of bash's escapes are supported, as well as a few
 additional ones. We list them below::
 
     \#
         the prompt/history count number. This escape is automatically
         wrapped in the coloring codes for the currently active color scheme. 
     \N
         the 'naked' prompt/history count number: this is just the number
         itself, without any coloring applied to it. This lets you produce
         numbered prompts with your own colors. 
     \D
         the prompt/history count, with the actual digits replaced by dots.
         Used mainly in continuation prompts (prompt_in2) 
     \w
         the current working directory 
     \W
         the basename of current working directory 
     \Xn
         where $n=0\ldots5.$ The current working directory, with $HOME
         replaced by ~, and filtered out to contain only $n$ path elements 
     \Yn
         Similar to \Xn, but with the $n+1$ element included if it is ~ (this
         is similar to the behavior of the %cn escapes in tcsh) 
     \u
         the username of the current user 
     \$
         if the effective UID is 0, a #, otherwise a $ 
     \h
         the hostname up to the first '.' 
     \H
         the hostname 
     \n
         a newline 
     \r
         a carriage return 
     \v
         IPython version string 
 
 In addition to these, ANSI color escapes can be insterted into the
 prompts, as \C_ColorName. The list of valid color names is: Black, Blue,
 Brown, Cyan, DarkGray, Green, LightBlue, LightCyan, LightGray,
 LightGreen, LightPurple, LightRed, NoColor, Normal, Purple, Red, White,
 Yellow.
 
 Finally, IPython supports the evaluation of arbitrary expressions in
 your prompt string. The prompt strings are evaluated through the syntax
 of PEP 215, but basically you can use $x.y to expand the value of x.y,
 and for more complicated expressions you can use braces: ${foo()+x} will
 call function foo and add to it the value of x, before putting the
 result into your prompt. For example, using
 prompt_in1 '${commands.getoutput("uptime")}\nIn [\#]: ' 
 will print the result of the uptime command on each prompt (assuming the
 commands module has been imported in your ipythonrc file).
 
 
       Prompt examples
 
 The following options in an ipythonrc file will give you IPython's
 default prompts::
 
     prompt_in1 'In [\#]:' 
     prompt_in2 '   .\D.:' 
     prompt_out 'Out[\#]:'
 
 which look like this::
 
     In [1]: 1+2 
     Out[1]: 3
 
     In [2]: for i in (1,2,3): 
        ...:    print i, 
        ...: 
     1 2 3
 
 These will give you a very colorful prompt with path information::
 
     #prompt_in1 '\C_Red\u\C_Blue[\C_Cyan\Y1\C_Blue]\C_LightGreen\#>' 
     prompt_in2 ' ..\D>' 
     prompt_out '<\#>'
 
 which look like this::
 
     fperez[~/ipython]1> 1+2 
                     <1> 3 
     fperez[~/ipython]2> for i in (1,2,3): 
                    ...>     print i, 
                    ...> 
     1 2 3
 
 
 .. _Profiles:
 
 IPython profiles
 ================
 
-As we already mentioned, IPython supports the -profile command-line
-option (see sec. `command line options`_). A profile is nothing more
-than a particular configuration file like your basic ipythonrc one,
-but with particular customizations for a specific purpose. When you
-start IPython with 'ipython -profile <name>', it assumes that in your
-IPYTHONDIR there is a file called ipythonrc-<name> or
-ipy_profile_<name>.py, and loads it instead of the normal ipythonrc.
+As we already mentioned, IPython supports the -profile command-line option (see
+:ref:`here <command_line_options>`).  A profile is nothing more than a
+particular configuration file like your basic ipythonrc one, but with
+particular customizations for a specific purpose. When you start IPython with
+'ipython -profile <name>', it assumes that in your IPYTHONDIR there is a file
+called ipythonrc-<name> or ipy_profile_<name>.py, and loads it instead of the
+normal ipythonrc.
 
 This system allows you to maintain multiple configurations which load
 modules, set options, define functions, etc. suitable for different
 tasks and activate them in a very simple manner. In order to avoid
 having to repeat all of your basic options (common things that don't
 change such as your color preferences, for example), any profile can
 include another configuration file. The most common way to use profiles
 is then to have each one include your basic ipythonrc file as a starting
 point, and then add further customizations.
  No newline at end of file

docs/source/config/initial_config.txt

@@ -1,243 +1,244 b''
 .. _initial config:
 
 =========================================
 Initial configuration of your environment
 =========================================
 
 This section will help you set various things in your environment for
 your IPython sessions to be as efficient as possible. All of IPython's
 configuration information, along with several example files, is stored
 in a directory named by default $HOME/.ipython. You can change this by
 defining the environment variable IPYTHONDIR, or at runtime with the
 command line option -ipythondir.
 
-If all goes well, the first time you run IPython it should
-automatically create a user copy of the config directory for you,
-based on its builtin defaults. You can look at the files it creates to
-learn more about configuring the system. The main file you will modify
-to configure IPython's behavior is called ipythonrc (with a .ini
-extension under Windows), included for reference in `ipythonrc`_
-section. This file is very commented and has many variables you can
-change to suit your taste, you can find more details in
-Sec. customization_. Here we discuss the basic things you will want to
-make sure things are working properly from the beginning.
+If all goes well, the first time you run IPython it should automatically create
+a user copy of the config directory for you, based on its builtin defaults. You
+can look at the files it creates to learn more about configuring the
+system. The main file you will modify to configure IPython's behavior is called
+ipythonrc (with a .ini extension under Windows), included for reference
+:ref:`here <ipythonrc>`. This file is very commented and has many variables you
+can change to suit your taste, you can find more details :ref:`here
+<customization>`. Here we discuss the basic things you will want to make sure
+things are working properly from the beginning.
 
 
-.. _Accessing help:
+.. _accessing_help:
 
 Access to the Python help system
 ================================
 
-This is true for Python in general (not just for IPython): you should
-have an environment variable called PYTHONDOCS pointing to the directory
-where your HTML Python documentation lives. In my system it's
-/usr/share/doc/python-docs-2.3.4/html, check your local details or ask
-your systems administrator.
+This is true for Python in general (not just for IPython): you should have an
+environment variable called PYTHONDOCS pointing to the directory where your
+HTML Python documentation lives. In my system it's
+:file:`/usr/share/doc/python-doc/html`, check your local details or ask your
+systems administrator.
 
 This is the directory which holds the HTML version of the Python
 manuals. Unfortunately it seems that different Linux distributions
 package these files differently, so you may have to look around a bit.
 Below I show the contents of this directory on my system for reference::
 
     [html]> ls
-    about.dat acks.html dist/ ext/ index.html lib/ modindex.html
-    stdabout.dat tut/ about.html api/ doc/ icons/ inst/ mac/ ref/ style.css
+    about.html  dist/  icons/      lib/           python2.5.devhelp.gz  whatsnew/
+    acks.html   doc/   index.html  mac/           ref/
+    api/        ext/   inst/       modindex.html  tut/
 
 You should really make sure this variable is correctly set so that
 Python's pydoc-based help system works. It is a powerful and convenient
 system with full access to the Python manuals and all modules accessible
 to you.
 
 Under Windows it seems that pydoc finds the documentation automatically,
 so no extra setup appears necessary.
 
 
 Editor
 ======
 
 The %edit command (and its alias %ed) will invoke the editor set in your
 environment as EDITOR. If this variable is not set, it will default to
 vi under Linux/Unix and to notepad under Windows. You may want to set
 this variable properly and to a lightweight editor which doesn't take
 too long to start (that is, something other than a new instance of
 Emacs). This way you can edit multi-line code quickly and with the power
 of a real editor right inside IPython.
 
 If you are a dedicated Emacs user, you should set up the Emacs server so
 that new requests are handled by the original process. This means that
 almost no time is spent in handling the request (assuming an Emacs
 process is already running). For this to work, you need to set your
 EDITOR environment variable to 'emacsclient'. The code below, supplied
 by Francois Pinard, can then be used in your .emacs file to enable the
 server::
 
     (defvar server-buffer-clients)
     (when (and (fboundp 'server-start) (string-equal (getenv "TERM") 'xterm))
       (server-start)
       (defun fp-kill-server-with-buffer-routine ()
         (and server-buffer-clients (server-done)))
       (add-hook 'kill-buffer-hook 'fp-kill-server-with-buffer-routine))
 
 You can also set the value of this editor via the commmand-line option
 '-editor' or in your ipythonrc file. This is useful if you wish to use
 specifically for IPython an editor different from your typical default
 (and for Windows users who tend to use fewer environment variables).
 
 
 Color
 =====
 
 The default IPython configuration has most bells and whistles turned on
 (they're pretty safe). But there's one that may cause problems on some
 systems: the use of color on screen for displaying information. This is
 very useful, since IPython can show prompts and exception tracebacks
 with various colors, display syntax-highlighted source code, and in
 general make it easier to visually parse information.
 
 The following terminals seem to handle the color sequences fine:
 
     * Linux main text console, KDE Konsole, Gnome Terminal, E-term,
       rxvt, xterm.
     * CDE terminal (tested under Solaris). This one boldfaces light colors.
     * (X)Emacs buffers. See the emacs_ section for more details on
       using IPython with (X)Emacs.
     * A Windows (XP/2k) command prompt with pyreadline_.
     * A Windows (XP/2k) CygWin shell. Although some users have reported
       problems; it is not clear whether there is an issue for everyone
       or only under specific configurations. If you have full color
       support under cygwin, please post to the IPython mailing list so
       this issue can be resolved for all users.
 
+.. _pyreadline: https://code.launchpad.net/pyreadline
+      
 These have shown problems:
 
     * Windows command prompt in WinXP/2k logged into a Linux machine via
       telnet or ssh.
     * Windows native command prompt in WinXP/2k, without Gary Bishop's
       extensions. Once Gary's readline library is installed, the normal
       WinXP/2k command prompt works perfectly.
 
 Currently the following color schemes are available:
 
     * NoColor: uses no color escapes at all (all escapes are empty '' ''
       strings). This 'scheme' is thus fully safe to use in any terminal.
     * Linux: works well in Linux console type environments: dark
       background with light fonts. It uses bright colors for
       information, so it is difficult to read if you have a light
       colored background.
     * LightBG: the basic colors are similar to those in the Linux scheme
       but darker. It is easy to read in terminals with light backgrounds.
 
 IPython uses colors for two main groups of things: prompts and
 tracebacks which are directly printed to the terminal, and the object
 introspection system which passes large sets of data through a pager.
 
 
 Input/Output prompts and exception tracebacks
 =============================================
 
 You can test whether the colored prompts and tracebacks work on your
 system interactively by typing '%colors Linux' at the prompt (use
 '%colors LightBG' if your terminal has a light background). If the input
 prompt shows garbage like::
 
     [0;32mIn [[1;32m1[0;32m]: [0;00m
 
 instead of (in color) something like::
 
     In [1]:
 
 this means that your terminal doesn't properly handle color escape
 sequences. You can go to a 'no color' mode by typing '%colors NoColor'.
 
 You can try using a different terminal emulator program (Emacs users,
 see below). To permanently set your color preferences, edit the file
 $HOME/.ipython/ipythonrc and set the colors option to the desired value.
 
 
 Object details (types, docstrings, source code, etc.)
 =====================================================
 
-IPython has a set of special functions for studying the objects you
-are working with, discussed in detail in Sec. `dynamic object
-information`_. But this system relies on passing information which is
-longer than your screen through a data pager, such as the common Unix
-less and more programs. In order to be able to see this information in
-color, your pager needs to be properly configured. I strongly
-recommend using less instead of more, as it seems that more simply can
+IPython has a set of special functions for studying the objects you are working
+with, discussed in detail :ref:`here <dynamic_object_info>`. But this system
+relies on passing information which is longer than your screen through a data
+pager, such as the common Unix less and more programs. In order to be able to
+see this information in color, your pager needs to be properly configured. I
+strongly recommend using less instead of more, as it seems that more simply can
 not understand colored text correctly.
 
 In order to configure less as your default pager, do the following:
 
    1. Set the environment PAGER variable to less.
    2. Set the environment LESS variable to -r (plus any other options
       you always want to pass to less by default). This tells less to
       properly interpret control sequences, which is how color
       information is given to your terminal.
 
 For the csh or tcsh shells, add to your ~/.cshrc file the lines::
 
     setenv PAGER less
     setenv LESS -r
 
 There is similar syntax for other Unix shells, look at your system
 documentation for details.
 
 If you are on a system which lacks proper data pagers (such as Windows),
 IPython will use a very limited builtin pager.
 
 .. _emacs:
 
 (X)Emacs configuration
 ======================
 
 Thanks to the work of Alexander Schmolck and Prabhu Ramachandran,
 currently (X)Emacs and IPython get along very well.
 
 Important note: You will need to use a recent enough version of
 python-mode.el, along with the file ipython.el. You can check that the
 version you have of python-mode.el is new enough by either looking at
 the revision number in the file itself, or asking for it in (X)Emacs via
 M-x py-version. Versions 4.68 and newer contain the necessary fixes for
 proper IPython support.
 
 The file ipython.el is included with the IPython distribution, in the
 documentation directory (where this manual resides in PDF and HTML
 formats).
 
 Once you put these files in your Emacs path, all you need in your .emacs
 file is::
 
     (require 'ipython)
 
 This should give you full support for executing code snippets via
 IPython, opening IPython as your Python shell via ``C-c !``, etc.
 
 You can customize the arguments passed to the IPython instance at startup by
 setting the ``py-python-command-args`` variable.  For example, to start always
 in ``pylab`` mode with hardcoded light-background colors, you can use::
 
     (setq py-python-command-args '("-pylab" "-colors" "LightBG"))
 
 If you happen to get garbage instead of colored prompts as described in
 the previous section, you may need to set also in your .emacs file::
 
     (setq ansi-color-for-comint-mode t)
 
 Notes:
 
     * There is one caveat you should be aware of: you must start the
       IPython shell before attempting to execute any code regions via
       ``C-c |``. Simply type C-c ! to start IPython before passing any code
       regions to the interpreter, and you shouldn't experience any
       problems.
       This is due to a bug in Python itself, which has been fixed for
       Python 2.3, but exists as of Python 2.2.2 (reported as SF bug [
       737947 ]).
     * The (X)Emacs support is maintained by Alexander Schmolck, so all
       comments/requests should be directed to him through the IPython
       mailing lists.
     * This code is still somewhat experimental so it's a bit rough
       around the edges (although in practice, it works quite well).
     * Be aware that if you customize py-python-command previously, this
       value will override what ipython.el does (because loading the
       customization variables comes later).

docs/source/credits.txt

@@ -1,139 +1,207 b''
 .. _credits:
 
 =======
 Credits
 =======
 
-IPython is mainly developed by Fernando Pérez
-<[e-mail]>, but the project was born from mixing in
-Fernando's code with the IPP project by Janko Hauser
-<jhauser-AT-zscout.de> and LazyPython by Nathan Gray
-<n8gray-AT-caltech.edu>. For all IPython-related requests, please
-contact Fernando.
-
-As of early 2006, the following developers have joined the core team:
-
-    * [Robert Kern] <rkern-AT-enthought.com>: co-mentored the 2005
-      Google Summer of Code project to develop python interactive
-      notebooks (XML documents) and graphical interface. This project
-      was awarded to the students Tzanko Matev <tsanko-AT-gmail.com> and
-      Toni Alatalo <antont-AT-an.org>
-    * [Brian Granger] <bgranger-AT-scu.edu>: extending IPython to allow
-      support for interactive parallel computing.
-    * [Ville Vainio] <vivainio-AT-gmail.com>: Ville is the new
-      maintainer for the main trunk of IPython after version 0.7.1.
+IPython is led by Fernando Pérez.
+
+As of this writing, the following developers have joined the core team:
+
+* [Robert Kern] <rkern-AT-enthought.com>: co-mentored the 2005
+  Google Summer of Code project to develop python interactive
+  notebooks (XML documents) and graphical interface. This project
+  was awarded to the students Tzanko Matev <tsanko-AT-gmail.com> and
+  Toni Alatalo <antont-AT-an.org>.
+
+* [Brian Granger] <ellisonbg-AT-gmail.com>: extending IPython to allow
+  support for interactive parallel computing.
+
+* [Benjamin (Min) Ragan-Kelley]: key work on IPython's parallel
+  computing infrastructure.
+
+* [Ville Vainio] <vivainio-AT-gmail.com>: Ville has made many improvements
+  to the core of IPython and was the maintainer of the main IPython
+  trunk from version 0.7.1 to 0.8.4.
+
+* [Gael Varoquaux] <gael.varoquaux-AT-normalesup.org>: work on the merged
+  architecture for the interpreter as of version 0.9, implementing a new WX GUI
+  based on this system.
+
+* [Barry Wark] <barrywark-AT-gmail.com>: implementing a new Cocoa GUI, as well
+  as work on the new interpreter architecture and Twisted support.
+
+* [Laurent Dufrechou] <laurent.dufrechou-AT-gmail.com>: development of the WX
+  GUI support.
+
+* [Jörgen Stenarson] <jorgen.stenarson-AT-bostream.nu>: maintainer of the
+  PyReadline project, necessary for IPython under windows.
+
 
 The IPython project is also very grateful to:
 
 Bill Bumgarner <bbum-AT-friday.com>: for providing the DPyGetOpt module
 which gives very powerful and convenient handling of command-line
 options (light years ahead of what Python 2.1.1's getopt module does).
 
 Ka-Ping Yee <ping-AT-lfw.org>: for providing the Itpl module for
 convenient and powerful string interpolation with a much nicer syntax
 than formatting through the '%' operator.
 
 Arnd Baecker <baecker-AT-physik.tu-dresden.de>: for his many very useful
 suggestions and comments, and lots of help with testing and
 documentation checking. Many of IPython's newer features are a result of
 discussions with him (bugs are still my fault, not his).
 
 Obviously Guido van Rossum and the whole Python development team, that
 goes without saying.
 
 IPython's website is generously hosted at http://ipython.scipy.orgby
 Enthought (http://www.enthought.com). I am very grateful to them and all
 of the SciPy team for their contribution.
 
 Fernando would also like to thank Stephen Figgins <fig-AT-monitor.net>,
 an O'Reilly Python editor. His Oct/11/2001 article about IPP and
 LazyPython, was what got this project started. You can read it at:
 http://www.onlamp.com/pub/a/python/2001/10/11/pythonnews.html.
 
-And last but not least, all the kind IPython users who have emailed new
-code, bug reports, fixes, comments and ideas. A brief list follows,
-please let me know if I have ommitted your name by accident:
-
-    * [Jack Moffit] <jack-AT-xiph.org> Bug fixes, including the infamous
-      color problem. This bug alone caused many lost hours and
-      frustration, many thanks to him for the fix. I've always been a
-      fan of Ogg & friends, now I have one more reason to like these folks.
-      Jack is also contributing with Debian packaging and many other
-      things.
-    * [Alexander Schmolck] <a.schmolck-AT-gmx.net> Emacs work, bug
-      reports, bug fixes, ideas, lots more. The ipython.el mode for
-      (X)Emacs is Alex's code, providing full support for IPython under
-      (X)Emacs.
-    * [Andrea Riciputi] <andrea.riciputi-AT-libero.it> Mac OSX
-      information, Fink package management.
-    * [Gary Bishop] <gb-AT-cs.unc.edu> Bug reports, and patches to work
-      around the exception handling idiosyncracies of WxPython. Readline
-      and color support for Windows.
-    * [Jeffrey Collins] <Jeff.Collins-AT-vexcel.com> Bug reports. Much
-      improved readline support, including fixes for Python 2.3.
-    * [Dryice Liu] <dryice-AT-liu.com.cn> FreeBSD port.
-    * [Mike Heeter] <korora-AT-SDF.LONESTAR.ORG>
-    * [Christopher Hart] <hart-AT-caltech.edu> PDB integration.
-    * [Milan Zamazal] <pdm-AT-zamazal.org> Emacs info.
-    * [Philip Hisley] <compsys-AT-starpower.net>
-    * [Holger Krekel] <pyth-AT-devel.trillke.net> Tab completion, lots
-      more.
-    * [Robin Siebler] <robinsiebler-AT-starband.net>
-    * [Ralf Ahlbrink] <ralf_ahlbrink-AT-web.de>
-    * [Thorsten Kampe] <thorsten-AT-thorstenkampe.de>
-    * [Fredrik Kant] <fredrik.kant-AT-front.com> Windows setup.
-    * [Syver Enstad] <syver-en-AT-online.no> Windows setup.
-    * [Richard] <rxe-AT-renre-europe.com> Global embedding.
-    * [Hayden Callow] <h.callow-AT-elec.canterbury.ac.nz> Gnuplot.py 1.6
-      compatibility.
-    * [Leonardo Santagada] <retype-AT-terra.com.br> Fixes for Windows
-      installation.
-    * [Christopher Armstrong] <radix-AT-twistedmatrix.com> Bugfixes.
-    * [Francois Pinard] <pinard-AT-iro.umontreal.ca> Code and
-      documentation fixes.
-    * [Cory Dodt] <cdodt-AT-fcoe.k12.ca.us> Bug reports and Windows
-      ideas. Patches for Windows installer.
-    * [Olivier Aubert] <oaubert-AT-bat710.univ-lyon1.fr> New magics.
-    * [King C. Shu] <kingshu-AT-myrealbox.com> Autoindent patch.
-    * [Chris Drexler] <chris-AT-ac-drexler.de> Readline packages for
-      Win32/CygWin.
-    * [Gustavo Cordova Avila] <gcordova-AT-sismex.com> EvalDict code for
-      nice, lightweight string interpolation.
-    * [Kasper Souren] <Kasper.Souren-AT-ircam.fr> Bug reports, ideas.
-    * [Gever Tulley] <gever-AT-helium.com> Code contributions.
-    * [Ralf Schmitt] <ralf-AT-brainbot.com> Bug reports & fixes.
-    * [Oliver Sander] <osander-AT-gmx.de> Bug reports.
-    * [Rod Holland] <rhh-AT-structurelabs.com> Bug reports and fixes to
-      logging module.
-    * [Daniel 'Dang' Griffith] <pythondev-dang-AT-lazytwinacres.net>
-      Fixes, enhancement suggestions for system shell use.
-    * [Viktor Ransmayr] <viktor.ransmayr-AT-t-online.de> Tests and
-      reports on Windows installation issues. Contributed a true Windows
-      binary installer.
-    * [Mike Salib] <msalib-AT-mit.edu> Help fixing a subtle bug related
-      to traceback printing.
-    * [W.J. van der Laan] <gnufnork-AT-hetdigitalegat.nl> Bash-like
-      prompt specials.
-    * [Antoon Pardon] <Antoon.Pardon-AT-rece.vub.ac.be> Critical fix for
-      the multithreaded IPython.
-    * [John Hunter] <jdhunter-AT-nitace.bsd.uchicago.edu> Matplotlib
-      author, helped with all the development of support for matplotlib
-      in IPyhton, including making necessary changes to matplotlib itself.
-    * [Matthew Arnison] <maffew-AT-cat.org.au> Bug reports, '%run -d' idea.
-    * [Prabhu Ramachandran] <prabhu_r-AT-users.sourceforge.net> Help
-      with (X)Emacs support, threading patches, ideas...
-    * [Norbert Tretkowski] <tretkowski-AT-inittab.de> help with Debian
-      packaging and distribution.
-    * [George Sakkis] <gsakkis-AT-eden.rutgers.edu> New matcher for
-      tab-completing named arguments of user-defined functions.
-    * [Jörgen Stenarson] <jorgen.stenarson-AT-bostream.nu> Wildcard
-      support implementation for searching namespaces.
-    * [Vivian De Smedt] <vivian-AT-vdesmedt.com> Debugger enhancements,
-      so that when pdb is activated from within IPython, coloring, tab
-      completion and other features continue to work seamlessly.
-    * [Scott Tsai] <scottt958-AT-yahoo.com.tw> Support for automatic
-      editor invocation on syntax errors (see
-      http://www.scipy.net/roundup/ipython/issue36).
-    * [Alexander Belchenko] <bialix-AT-ukr.net> Improvements for win32
-      paging system.
-    * [Will Maier] <willmaier-AT-ml1.net> Official OpenBSD port.
  No newline at end of file
+And last but not least, all the kind IPython users who have emailed new code,
+bug reports, fixes, comments and ideas. A brief list follows, please let us
+know if we have ommitted your name by accident:
+
+* Dan Milstein <danmil-AT-comcast.net>.  A bold refactoring of the 
+  core prefilter stuff in the IPython interpreter.
+
+* [Jack Moffit] <jack-AT-xiph.org> Bug fixes, including the infamous
+  color problem. This bug alone caused many lost hours and
+  frustration, many thanks to him for the fix. I've always been a
+  fan of Ogg & friends, now I have one more reason to like these folks.
+  Jack is also contributing with Debian packaging and many other
+  things.
+ 
+* [Alexander Schmolck] <a.schmolck-AT-gmx.net> Emacs work, bug
+  reports, bug fixes, ideas, lots more. The ipython.el mode for
+  (X)Emacs is Alex's code, providing full support for IPython under
+  (X)Emacs.
+
+* [Andrea Riciputi] <andrea.riciputi-AT-libero.it> Mac OSX
+  information, Fink package management.
+
+* [Gary Bishop] <gb-AT-cs.unc.edu> Bug reports, and patches to work
+  around the exception handling idiosyncracies of WxPython. Readline
+  and color support for Windows.
+
+* [Jeffrey Collins] <Jeff.Collins-AT-vexcel.com> Bug reports. Much
+  improved readline support, including fixes for Python 2.3.
+
+* [Dryice Liu] <dryice-AT-liu.com.cn> FreeBSD port.
+
+* [Mike Heeter] <korora-AT-SDF.LONESTAR.ORG>
+
+* [Christopher Hart] <hart-AT-caltech.edu> PDB integration.
+
+* [Milan Zamazal] <pdm-AT-zamazal.org> Emacs info.
+
+* [Philip Hisley] <compsys-AT-starpower.net>
+
+* [Holger Krekel] <pyth-AT-devel.trillke.net> Tab completion, lots
+  more.
+
+* [Robin Siebler] <robinsiebler-AT-starband.net>
+
+* [Ralf Ahlbrink] <ralf_ahlbrink-AT-web.de>
+
+* [Thorsten Kampe] <thorsten-AT-thorstenkampe.de>
+
+* [Fredrik Kant] <fredrik.kant-AT-front.com> Windows setup.
+
+* [Syver Enstad] <syver-en-AT-online.no> Windows setup.
+
+* [Richard] <rxe-AT-renre-europe.com> Global embedding.
+
+* [Hayden Callow] <h.callow-AT-elec.canterbury.ac.nz> Gnuplot.py 1.6
+  compatibility.
+  
+* [Leonardo Santagada] <retype-AT-terra.com.br> Fixes for Windows
+  installation.
+  
+* [Christopher Armstrong] <radix-AT-twistedmatrix.com> Bugfixes.
+
+* [Francois Pinard] <pinard-AT-iro.umontreal.ca> Code and
+  documentation fixes.
+  
+* [Cory Dodt] <cdodt-AT-fcoe.k12.ca.us> Bug reports and Windows
+  ideas. Patches for Windows installer.
+  
+* [Olivier Aubert] <oaubert-AT-bat710.univ-lyon1.fr> New magics.
+
+* [King C. Shu] <kingshu-AT-myrealbox.com> Autoindent patch.
+
+* [Chris Drexler] <chris-AT-ac-drexler.de> Readline packages for
+  Win32/CygWin.
+  
+* [Gustavo Cordova Avila] <gcordova-AT-sismex.com> EvalDict code for
+  nice, lightweight string interpolation.
+  
+* [Kasper Souren] <Kasper.Souren-AT-ircam.fr> Bug reports, ideas.
+
+* [Gever Tulley] <gever-AT-helium.com> Code contributions.
+
+* [Ralf Schmitt] <ralf-AT-brainbot.com> Bug reports & fixes.
+
+* [Oliver Sander] <osander-AT-gmx.de> Bug reports.
+
+* [Rod Holland] <rhh-AT-structurelabs.com> Bug reports and fixes to
+  logging module.
+
+* [Daniel 'Dang' Griffith] <pythondev-dang-AT-lazytwinacres.net>
+  Fixes, enhancement suggestions for system shell use.
+
+* [Viktor Ransmayr] <viktor.ransmayr-AT-t-online.de> Tests and
+  reports on Windows installation issues. Contributed a true Windows
+  binary installer.
+
+* [Mike Salib] <msalib-AT-mit.edu> Help fixing a subtle bug related
+  to traceback printing.
+
+* [W.J. van der Laan] <gnufnork-AT-hetdigitalegat.nl> Bash-like
+  prompt specials.
+
+* [Antoon Pardon] <Antoon.Pardon-AT-rece.vub.ac.be> Critical fix for
+  the multithreaded IPython.
+
+* [John Hunter] <jdhunter-AT-nitace.bsd.uchicago.edu> Matplotlib
+  author, helped with all the development of support for matplotlib
+  in IPyhton, including making necessary changes to matplotlib itself.
+
+* [Matthew Arnison] <maffew-AT-cat.org.au> Bug reports, '%run -d' idea.
+
+* [Prabhu Ramachandran] <prabhu_r-AT-users.sourceforge.net> Help
+  with (X)Emacs support, threading patches, ideas...
+
+* [Norbert Tretkowski] <tretkowski-AT-inittab.de> help with Debian
+  packaging and distribution.
+
+* [George Sakkis] <gsakkis-AT-eden.rutgers.edu> New matcher for
+  tab-completing named arguments of user-defined functions.
+
+* [Jörgen Stenarson] <jorgen.stenarson-AT-bostream.nu> Wildcard
+  support implementation for searching namespaces.
+
+* [Vivian De Smedt] <vivian-AT-vdesmedt.com> Debugger enhancements,
+  so that when pdb is activated from within IPython, coloring, tab
+  completion and other features continue to work seamlessly.
+
+* [Scott Tsai] <scottt958-AT-yahoo.com.tw> Support for automatic
+  editor invocation on syntax errors (see
+  http://www.scipy.net/roundup/ipython/issue36).
+
+* [Alexander Belchenko] <bialix-AT-ukr.net> Improvements for win32
+  paging system.
+
+* [Will Maier] <willmaier-AT-ml1.net> Official OpenBSD port.
+
+* [Ondrej Certik] <ondrej-AT-certik.cz>: set up the IPython docs to use the new
+  Sphinx system used by Python, Matplotlib and many more projects.
+
+* [Stefan van der Walt] <stefan-AT-sun.ac.za>: support for the new config system.

docs/source/development/development.txt

@@ -1,397 +1,426 b''
 .. _development:
 
 ==================================
 IPython development guidelines
 ==================================
 
 .. contents::
 
 
 Overview
 ========
 
 IPython is the next generation of IPython.  It is named such for two reasons:
 
 - Eventually, IPython will become IPython version 1.0.
 - This new code base needs to be able to co-exist with the existing IPython until
   it is a full replacement for it.  Thus we needed a different name.  We couldn't
   use ``ipython`` (lowercase) as some files systems are case insensitive.
 
 There are two, no three, main goals of the IPython effort:
 
 1. Clean up the existing codebase and write lots of tests.
 2. Separate the core functionality of IPython from the terminal to enable IPython
    to be used from within a variety of GUI applications.
 3. Implement a system for interactive parallel computing.
 
-While the third goal may seem a bit unrelated to the main focus of IPython, it turns
-out that the technologies required for this goal are nearly identical with those
-required for goal two. This is the main reason the interactive parallel computing
-capabilities are being put into IPython proper. Currently the third of these goals is
-furthest along.
+While the third goal may seem a bit unrelated to the main focus of IPython, it
+turns out that the technologies required for this goal are nearly identical
+with those required for goal two. This is the main reason the interactive
+parallel computing capabilities are being put into IPython proper. Currently
+the third of these goals is furthest along.
 
 This document describes IPython from the perspective of developers.  
 
 
 Project organization
 ====================
 
 Subpackages
 -----------
 
-IPython is organized into semi self-contained subpackages.  Each of the subpackages will have its own:
+IPython is organized into semi self-contained subpackages.  Each of the
+subpackages will have its own:
 
 - **Dependencies**.  One of the most important things to keep in mind in
   partitioning code amongst subpackages, is that they should be used to cleanly
-  encapsulate dependencies.  
+  encapsulate dependencies.
+  
 - **Tests**.  Each subpackage shoud have its own ``tests`` subdirectory that
-  contains all of the tests for that package.  For information about writing tests
-  for IPython, see the `Testing System`_ section of this document.
-- **Configuration**.  Each subpackage should have its own ``config`` subdirectory
-  that contains the configuration information for the components of the 
-  subpackage.  For information about how the IPython configuration system
-  works, see the `Configuration System`_ section of this document.
-- **Scripts**.  Each subpackage should have its own ``scripts`` subdirectory that
-  contains all of the command line scripts associated with the subpackage.
+  contains all of the tests for that package.  For information about writing
+  tests for IPython, see the `Testing System`_ section of this document.
+  
+- **Configuration**.  Each subpackage should have its own ``config``
+  subdirectory that contains the configuration information for the components
+  of the subpackage.  For information about how the IPython configuration
+  system works, see the `Configuration System`_ section of this document.
+  
+- **Scripts**.  Each subpackage should have its own ``scripts`` subdirectory
+  that contains all of the command line scripts associated with the subpackage.
 
 Installation and dependencies
 -----------------------------
    
-IPython will not use `setuptools`_ for installation. Instead, we will use standard
-``setup.py`` scripts that use `distutils`_. While there are a number a extremely nice
-features that `setuptools`_ has (like namespace packages), the current implementation
-of `setuptools`_ has performance problems, particularly on shared file systems. In
-particular, when Python packages are installed on NSF file systems, import times
-become much too long (up towards 10 seconds). 
+IPython will not use `setuptools`_ for installation. Instead, we will use
+standard ``setup.py`` scripts that use `distutils`_. While there are a number a
+extremely nice features that `setuptools`_ has (like namespace packages), the
+current implementation of `setuptools`_ has performance problems, particularly
+on shared file systems. In particular, when Python packages are installed on
+NSF file systems, import times become much too long (up towards 10 seconds).
 
 Because IPython is being used extensively in the context of high performance
-computing, where performance is critical but shared file systems are common, we feel
-these performance hits are not acceptable. Thus, until the performance problems
-associated with `setuptools`_ are addressed, we will stick with plain `distutils`_. We
-are hopeful that these problems will be addressed and that we will eventually begin
-using `setuptools`_. Because of this, we are trying to organize IPython in a way that
-will make the eventual transition to `setuptools`_ as painless as possible.
-
-Because we will be using `distutils`_, there will be no method for automatically installing dependencies.  Instead, we are following the approach of `Matplotlib`_ which can be summarized as follows:
+computing, where performance is critical but shared file systems are common, we
+feel these performance hits are not acceptable. Thus, until the performance
+problems associated with `setuptools`_ are addressed, we will stick with plain
+`distutils`_. We are hopeful that these problems will be addressed and that we
+will eventually begin using `setuptools`_. Because of this, we are trying to
+organize IPython in a way that will make the eventual transition to
+`setuptools`_ as painless as possible.
+
+Because we will be using `distutils`_, there will be no method for
+automatically installing dependencies.  Instead, we are following the approach
+of `Matplotlib`_ which can be summarized as follows:
 
 - Distinguish between required and optional dependencies.  However, the required
   dependencies for IPython should be only the Python standard library.
-- Upon installation check to see which optional dependencies are present and tell
-  the user which parts of IPython need which optional dependencies.
+  
+- Upon installation check to see which optional dependencies are present and
+  tell the user which parts of IPython need which optional dependencies.
 
-It is absolutely critical that each subpackage of IPython has a clearly specified set
-of dependencies and that dependencies are not carelessly inherited from other IPython
-subpackages. Furthermore, tests that have certain dependencies should not fail if
-those dependencies are not present. Instead they should be skipped and print a
-message.
+It is absolutely critical that each subpackage of IPython has a clearly
+specified set of dependencies and that dependencies are not carelessly
+inherited from other IPython subpackages. Furthermore, tests that have certain
+dependencies should not fail if those dependencies are not present. Instead
+they should be skipped and print a message.
 
 .. _setuptools: http://peak.telecommunity.com/DevCenter/setuptools
 .. _distutils: http://docs.python.org/lib/module-distutils.html
 .. _Matplotlib: http://matplotlib.sourceforge.net/
 
 Specific subpackages
 --------------------
 
 ``core``
     This is the core functionality of IPython that is independent of the
     terminal, network and GUIs.  Most of the code that is in the current
     IPython trunk will be refactored, cleaned up and moved here.
 
 ``kernel``
     The enables the IPython core to be expose to a the network.  This is
     also where all of the parallel computing capabilities are to be found.
     
 ``config``
     The configuration package used by IPython.
 
 ``frontends``
     The various frontends for IPython.  A frontend is the end-user application
-    that exposes the capabilities of IPython to the user.  The most basic frontend
-    will simply be a terminal based application that looks just like today 's 
-    IPython.  Other frontends will likely be more powerful and based on GUI toolkits.
+    that exposes the capabilities of IPython to the user.  The most basic
+    frontend will simply be a terminal based application that looks just like
+    today 's IPython.  Other frontends will likely be more powerful and based
+    on GUI toolkits.
 
 ``notebook``
     An application that allows users to work with IPython notebooks.
 
 ``tools``
     This is where general utilities go.
 
 
 Version control
 ===============
 
-In the past, IPython development has been done using `Subversion`__.  Recently, we made the transition to using `Bazaar`__ and `Launchpad`__.  This makes it much easier for people
-to contribute code to IPython.  Here is a sketch of how to use Bazaar for IPython
-development.  First, you should install Bazaar.  After you have done that, make
-sure that it is working by getting the latest main branch of IPython::
+In the past, IPython development has been done using `Subversion`__.  Recently,
+we made the transition to using `Bazaar`__ and `Launchpad`__.  This makes it
+much easier for people to contribute code to IPython.  Here is a sketch of how
+to use Bazaar for IPython development.  First, you should install Bazaar.
+After you have done that, make sure that it is working by getting the latest
+main branch of IPython::
 
     $ bzr branch lp:ipython
 
 Now you can create a new branch for you to do your work in::
 
     $ bzr branch ipython ipython-mybranch
 
-The typical work cycle in this branch will be to make changes in `ipython-mybranch`
-and then commit those changes using the commit command::
+The typical work cycle in this branch will be to make changes in
+``ipython-mybranch`` and then commit those changes using the commit command::
 
     $ ...do work in ipython-mybranch...
     $ bzr ci -m "the commit message goes here"
 
-Please note that since we now don't use an old-style linear ChangeLog
-(that tends to cause problems with distributed version control
-systems), you should ensure that your log messages are reasonably
-detailed.  Use a docstring-like approach in the commit messages
-(including the second line being left *blank*)::
+Please note that since we now don't use an old-style linear ChangeLog (that
+tends to cause problems with distributed version control systems), you should
+ensure that your log messages are reasonably detailed.  Use a docstring-like
+approach in the commit messages (including the second line being left
+*blank*)::
 
   Single line summary of  changes being committed.
 
   - more details when warranted ...
   - including crediting outside contributors if they sent the
     code/bug/idea!
 
-If we couple this with a policy of making single commits for each
-reasonably atomic change, the bzr log should give an excellent view of
-the project, and the `--short` log option becomes a nice summary.
+If we couple this with a policy of making single commits for each reasonably
+atomic change, the bzr log should give an excellent view of the project, and
+the `--short` log option becomes a nice summary.
 
-While working with this branch, it is a good idea to merge in changes that have been
-made upstream in the parent branch.  This can be done by doing::
+While working with this branch, it is a good idea to merge in changes that have
+been made upstream in the parent branch.  This can be done by doing::
 
     $ bzr pull
     
-If this command shows that the branches have diverged, then you should do a merge
-instead::
+If this command shows that the branches have diverged, then you should do a
+merge instead::
 
     $ bzr merge lp:ipython
 
-If you want others to be able to see your branch, you can create an account with
-launchpad and push the branch to your own workspace::
+If you want others to be able to see your branch, you can create an account
+with launchpad and push the branch to your own workspace::
 
     $ bzr push bzr+ssh://<me>@bazaar.launchpad.net/~<me>/+junk/ipython-mybranch
 
-Finally, once the work in your branch is done, you can merge your changes back into
-the `ipython` branch by using merge::
+Finally, once the work in your branch is done, you can merge your changes back
+into the `ipython` branch by using merge::
 
     $ cd ipython
     $ merge ../ipython-mybranch
     [resolve any conflicts]
     $ bzr ci -m "Fixing that bug"
     $ bzr push
 
-But this will require you to have write permissions to the `ipython` branch.  It you don't
-you can tell one of the IPython devs about your branch and they can do the merge for you.
+But this will require you to have write permissions to the `ipython` branch.
+It you don't you can tell one of the IPython devs about your branch and they
+can do the merge for you.
 
 More information about Bazaar workflows can be found `here`__.
 
 .. __: http://subversion.tigris.org/
 .. __: http://bazaar-vcs.org/
 .. __: http://www.launchpad.net/ipython
 .. __: http://doc.bazaar-vcs.org/bzr.dev/en/user-guide/index.html
 
 Documentation
 =============
 
 Standalone documentation
 ------------------------
 
-All standalone documentation should be written in plain text (``.txt``) files using
-`reStructuredText`_ for markup and formatting. All such documentation should be placed
-in the top level directory ``docs`` of the IPython source tree. Or, when appropriate,
-a suitably named subdirectory should be used. The documentation in this location will
-serve as the main source for IPython documentation and all existing documentation
-should be converted to this format.
+All standalone documentation should be written in plain text (``.txt``) files
+using `reStructuredText`_ for markup and formatting. All such documentation
+should be placed in the top level directory ``docs`` of the IPython source
+tree. Or, when appropriate, a suitably named subdirectory should be used. The
+documentation in this location will serve as the main source for IPython
+documentation and all existing documentation should be converted to this
+format.
 
-In the future, the text files in the ``docs`` directory will be used to generate all
-forms of documentation for IPython. This include documentation on the IPython website
-as well as *pdf* documentation.
+In the future, the text files in the ``docs`` directory will be used to
+generate all forms of documentation for IPython. This include documentation on
+the IPython website as well as *pdf* documentation.
 
 .. _reStructuredText: http://docutils.sourceforge.net/rst.html
 
 Docstring format
 ----------------
 
-Good docstrings are very important. All new code will use `Epydoc`_ for generating API
-docs, so we will follow the `Epydoc`_ conventions. More specifically, we will use
-`reStructuredText`_ for markup and formatting, since it is understood by a wide
-variety of tools. This means that if in the future we have any reason to change from
-`Epydoc`_ to something else, we'll have fewer transition pains.
+Good docstrings are very important. All new code will use `Epydoc`_ for
+generating API docs, so we will follow the `Epydoc`_ conventions. More
+specifically, we will use `reStructuredText`_ for markup and formatting, since
+it is understood by a wide variety of tools. This means that if in the future
+we have any reason to change from `Epydoc`_ to something else, we'll have fewer
+transition pains.
 
 Details about using `reStructuredText`_ for docstrings can be found `here
 <http://epydoc.sourceforge.net/manual-othermarkup.html>`_.
 
 .. _Epydoc: http://epydoc.sourceforge.net/
 
 Additional PEPs of interest regarding documentation of code:
 
 - `Docstring Conventions <http://www.python.org/peps/pep-0257.html>`_
 - `Docstring Processing System Framework <http://www.python.org/peps/pep-0256.html>`_
 - `Docutils Design Specification <http://www.python.org/peps/pep-0258.html>`_
 
 
 Coding conventions
 ==================
 
 General
 -------
 
-In general, we'll try to follow the standard Python style conventions as described here:
+In general, we'll try to follow the standard Python style conventions as
+described here:
 
 - `Style Guide for Python Code  <http://www.python.org/peps/pep-0008.html>`_  
 
 
 Other comments:
 
 - In a large file, top level classes and functions should be 
   separated by 2-3 lines to make it easier to separate them visually.
 - Use 4 spaces for indentation.
 - Keep the ordering of methods the same in classes that have the same 
   methods.  This is particularly true for classes that implement 
   similar interfaces and for interfaces that are similar.
 
 Naming conventions
 ------------------
 
-In terms of naming conventions, we'll follow the guidelines from the `Style Guide for
-Python Code`_.
+In terms of naming conventions, we'll follow the guidelines from the `Style
+Guide for Python Code`_.
 
 For all new IPython code (and much existing code is being refactored), we'll use:
 
 - All ``lowercase`` module names.
 
 - ``CamelCase`` for class names.
 
-- ``lowercase_with_underscores`` for methods, functions, variables and attributes.
+- ``lowercase_with_underscores`` for methods, functions, variables and
+  attributes.
 
-This may be confusing as most of the existing IPython codebase uses a different convention (``lowerCamelCase`` for methods and attributes).  Slowly, we will move IPython over to the new
-convention, providing shadow names for backward compatibility in public interfaces. 
+This may be confusing as most of the existing IPython codebase uses a different
+convention (``lowerCamelCase`` for methods and attributes).  Slowly, we will
+move IPython over to the new convention, providing shadow names for backward
+compatibility in public interfaces.
 
-There are, however, some important exceptions to these rules.  In some cases, IPython
-code will interface with packages (Twisted, Wx, Qt) that use other conventions.  At some level this makes it impossible to adhere to our own standards at all times.  In particular, when subclassing classes that use other naming conventions, you must follow their naming conventions.  To deal with cases like this, we propose the following policy:
+There are, however, some important exceptions to these rules.  In some cases,
+IPython code will interface with packages (Twisted, Wx, Qt) that use other
+conventions.  At some level this makes it impossible to adhere to our own
+standards at all times.  In particular, when subclassing classes that use other
+naming conventions, you must follow their naming conventions.  To deal with
+cases like this, we propose the following policy:
 
 - If you are subclassing a class that uses different conventions, use its
-  naming conventions throughout your subclass.  Thus, if you are creating a 
-  Twisted Protocol class, used Twisted's ``namingSchemeForMethodsAndAttributes.``
-
-- All IPython's official interfaces should use our conventions.  In some cases 
-  this will mean that you need to provide shadow names (first implement ``fooBar``
-  and then ``foo_bar = fooBar``).  We want to avoid this at all costs, but it 
-  will probably be necessary at times.  But, please use this sparingly!
-
-Implementation-specific *private* methods will use ``_single_underscore_prefix``.
-Names with a leading double underscore will *only* be used in special cases, as they
-makes subclassing difficult (such names are not easily seen by child classes).
-
-Occasionally some run-in lowercase names are used, but mostly for very short names or
-where we are implementing methods very similar to existing ones in a base class (like
-``runlines()`` where ``runsource()`` and ``runcode()`` had established precedent).
+  naming conventions throughout your subclass.  Thus, if you are creating a
+  Twisted Protocol class, used Twisted's
+  ``namingSchemeForMethodsAndAttributes.``
+
+- All IPython's official interfaces should use our conventions.  In some cases
+  this will mean that you need to provide shadow names (first implement
+  ``fooBar`` and then ``foo_bar = fooBar``).  We want to avoid this at all
+  costs, but it will probably be necessary at times.  But, please use this
+  sparingly!
+
+Implementation-specific *private* methods will use
+``_single_underscore_prefix``.  Names with a leading double underscore will
+*only* be used in special cases, as they makes subclassing difficult (such
+names are not easily seen by child classes).
+
+Occasionally some run-in lowercase names are used, but mostly for very short
+names or where we are implementing methods very similar to existing ones in a
+base class (like ``runlines()`` where ``runsource()`` and ``runcode()`` had
+established precedent).
 
 The old IPython codebase has a big mix of classes and modules prefixed with an
-explicit ``IP``. In Python this is mostly unnecessary, redundant and frowned upon, as
-namespaces offer cleaner prefixing. The only case where this approach is justified is
-for classes which are expected to be imported into external namespaces and a very
-generic name (like Shell) is too likely to clash with something else. We'll need to
-revisit this issue as we clean up and refactor the code, but in general we should
-remove as many unnecessary ``IP``/``ip`` prefixes as possible. However, if a prefix
-seems absolutely necessary the more specific ``IPY`` or ``ipy`` are preferred.
+explicit ``IP``. In Python this is mostly unnecessary, redundant and frowned
+upon, as namespaces offer cleaner prefixing. The only case where this approach
+is justified is for classes which are expected to be imported into external
+namespaces and a very generic name (like Shell) is too likely to clash with
+something else. We'll need to revisit this issue as we clean up and refactor
+the code, but in general we should remove as many unnecessary ``IP``/``ip``
+prefixes as possible. However, if a prefix seems absolutely necessary the more
+specific ``IPY`` or ``ipy`` are preferred.
 
 .. _devel_testing:
 
 Testing system
 ==============
 
-It is extremely important that all code contributed to IPython has tests. Tests should
-be written as unittests, doctests or as entities that the `Nose`_ testing package will
-find. Regardless of how the tests are written, we will use `Nose`_ for discovering and
-running the tests. `Nose`_ will be required to run the IPython test suite, but will
-not be required to simply use IPython.
+It is extremely important that all code contributed to IPython has tests. Tests
+should be written as unittests, doctests or as entities that the `Nose`_
+testing package will find. Regardless of how the tests are written, we will use
+`Nose`_ for discovering and running the tests. `Nose`_ will be required to run
+the IPython test suite, but will not be required to simply use IPython.
 
 .. _Nose: http://code.google.com/p/python-nose/
 
-Tests of `Twisted`__ using code should be written by subclassing the ``TestCase`` class
-that comes with ``twisted.trial.unittest``. When this is done, `Nose`_ will be able to
-run the tests and the twisted reactor will be handled correctly.
+Tests of `Twisted`__ using code should be written by subclassing the
+``TestCase`` class that comes with ``twisted.trial.unittest``. When this is
+done, `Nose`_ will be able to run the tests and the twisted reactor will be
+handled correctly.
 
 .. __: http://www.twistedmatrix.com
 
-Each subpackage in IPython should have its own ``tests`` directory that contains all
-of the tests for that subpackage. This allows each subpackage to be self-contained. If
-a subpackage has any dependencies beyond the Python standard library, the tests for
-that subpackage should be skipped if the dependencies are not found. This is very
-important so users don't get tests failing simply because they don't have dependencies.
+Each subpackage in IPython should have its own ``tests`` directory that
+contains all of the tests for that subpackage. This allows each subpackage to
+be self-contained. If a subpackage has any dependencies beyond the Python
+standard library, the tests for that subpackage should be skipped if the
+dependencies are not found. This is very important so users don't get tests
+failing simply because they don't have dependencies.
 
-We also need to look into use Noses ability to tag tests to allow a more modular
-approach of running tests.
+We also need to look into use Noses ability to tag tests to allow a more
+modular approach of running tests.
 
 .. _devel_config:
 
 Configuration system
 ====================
 
 IPython uses `.ini`_ files for configuration purposes. This represents a huge
-improvement over the configuration system used in IPython. IPython works with these
-files using the `ConfigObj`_ package, which IPython includes as
+improvement over the configuration system used in IPython. IPython works with
+these files using the `ConfigObj`_ package, which IPython includes as
 ``ipython1/external/configobj.py``.
 
-Currently, we are using raw `ConfigObj`_ objects themselves. Each subpackage of IPython
-should contain a ``config`` subdirectory that contains all of the configuration
-information for the subpackage. To see how configuration information is defined (along
-with defaults) see at the examples in ``ipython1/kernel/config`` and
-``ipython1/core/config``. Likewise, to see how the configuration information is used,
-see examples in ``ipython1/kernel/scripts/ipengine.py``.
-
-Eventually, we will add a new layer on top of the raw `ConfigObj`_ objects. We are
-calling this new layer, ``tconfig``, as it will use a `Traits`_-like validation model.
-We won't actually use `Traits`_, but will implement something similar in pure Python.
-But, even in this new system, we will still use `ConfigObj`_ and `.ini`_ files
-underneath the hood. Talk to Fernando if you are interested in working on this part of
-IPython. The current prototype of ``tconfig`` is located in the IPython sandbox.
+Currently, we are using raw `ConfigObj`_ objects themselves. Each subpackage of
+IPython should contain a ``config`` subdirectory that contains all of the
+configuration information for the subpackage. To see how configuration
+information is defined (along with defaults) see at the examples in
+``ipython1/kernel/config`` and ``ipython1/core/config``. Likewise, to see how
+the configuration information is used, see examples in
+``ipython1/kernel/scripts/ipengine.py``.
+
+Eventually, we will add a new layer on top of the raw `ConfigObj`_ objects. We
+are calling this new layer, ``tconfig``, as it will use a `Traits`_-like
+validation model.  We won't actually use `Traits`_, but will implement
+something similar in pure Python.  But, even in this new system, we will still
+use `ConfigObj`_ and `.ini`_ files underneath the hood. Talk to Fernando if you
+are interested in working on this part of IPython. The current prototype of
+``tconfig`` is located in the IPython sandbox.
 
 .. _.ini: http://docs.python.org/lib/module-ConfigParser.html
 .. _ConfigObj: http://www.voidspace.org.uk/python/configobj.html
 .. _Traits: http://code.enthought.com/traits/
 
 Installation and testing scenarios
 ==================================
 
-This section outlines the various scenarios that we need to test before we release an IPython version.  These scenarios represent different ways of installing IPython and its dependencies.
+This section outlines the various scenarios that we need to test before we
+release an IPython version.  These scenarios represent different ways of
+installing IPython and its dependencies.
 
 Installation scenarios under Linux and OS X
 -------------------------------------------
 
-    1.  Install from tarball using `python setup.py install`.
+    1.  Install from tarball using ``python setup.py install``.
         a.  With only readline+nose dependencies installed.
-        b.  With all dependencies installed (readline, zope.interface,
-            Twisted, foolscap, Sphinx, nose, pyOpenSSL).
+        b.  With all dependencies installed (readline, zope.interface, Twisted,
+        foolscap, Sphinx, nose, pyOpenSSL).
+	    
     2.  Install using easy_install.
+
         a.  With only readline+nose dependencies installed.
-            i.  Default dependencies: `easy_install ipython-0.9.beta3-py2.5.egg`
-            ii. Optional dependency sets: `easy_install -f ipython-0.9.beta3-py2.5.egg IPython[kernel,doc,test,security]`
+            i.  Default dependencies: ``easy_install ipython-0.9.beta3-py2.5.egg``
+            ii. Optional dependency sets: ``easy_install -f ipython-0.9.beta3-py2.5.egg IPython[kernel,doc,test,security]``
+
         b.  With all dependencies already installed.
+	
 
 Installation scenarios under Win32
 ----------------------------------
 
 	1.  Install everything from .exe installers
 	2.  easy_install?
 
 
 Tests to run for these scenarios
 --------------------------------
 
     1.  Run the full test suite.
     2.  Start a controller and engines and try a few things by hand.
         a.  Using ipcluster.
         b.  Using ipcontroller/ipengine by hand.
+
     3.  Run a few of the parallel examples.
     4.  Try the kernel with and without security with and without PyOpenSSL
         installed.
     5.  Beat on the IPython terminal a bunch.
     6.  Make sure that furl files are being put in proper locations.
-
-
-
-
-
-
-
-
-

docs/source/development/index.txt

@@ -1,9 +1,10 b''
 ==================
 Development
 ==================
 
 .. toctree::
    :maxdepth: 2
 
    development.txt
    roadmap.txt
+   notification_blueprint.txt

docs/source/development/notification_blueprint.txt

@@ -1,47 +1,49 b''
-.. Notification:
+.. _notification:
 
 ==========================================
 IPython.kernel.core.notification blueprint
 ==========================================
 
 Overview
 ========
 The :mod:`IPython.kernel.core.notification` module will provide a simple implementation of a notification center and support for the observer pattern within the :mod:`IPython.kernel.core`. The main intended use case is to provide notification of Interpreter events to an observing frontend during the execution of a single block of code.
 
 Functional Requirements
 =======================
 The notification center must:
-	* Provide synchronous notification of events to all registered observers.
-	* Provide typed or labeled notification types
-	* Allow observers to register callbacks for individual or all notification types
-	* Allow observers to register callbacks for events from individual or all notifying objects
-	* Notification to the observer consists of the notification type, notifying object and user-supplied extra information [implementation: as keyword parameters to the registered callback]
-	* Perform as O(1) in the case of no registered observers.
-	* Permit out-of-process or cross-network extension.
-	
+    * Provide synchronous notification of events to all registered observers.
+    * Provide typed or labeled notification types
+    * Allow observers to register callbacks for individual or all notification types
+    * Allow observers to register callbacks for events from individual or all notifying objects
+    * Notification to the observer consists of the notification type, notifying object and user-supplied extra information [implementation: as keyword parameters to the registered callback]
+    * Perform as O(1) in the case of no registered observers.
+    * Permit out-of-process or cross-network extension.
+    
 What's not included
 ==============================================================
 As written, the :mod:`IPython.kernel.core.notificaiton` module does not:
- 	* Provide out-of-process or network notifications [these should be handled by a separate, Twisted aware module in :mod:`IPython.kernel`].
-	* Provide zope.interface-style interfaces for the notification system [these should also be provided by the :mod:`IPython.kernel` module]
-	
+    * Provide out-of-process or network notifications [these should be handled by a separate, Twisted aware module in :mod:`IPython.kernel`].
+    * Provide zope.interface-style interfaces for the notification system [these should also be provided by the :mod:`IPython.kernel` module]
+    
 Use Cases
 =========
 The following use cases describe the main intended uses of the notificaiton module and illustrate the main success scenario for each use case:
 
-	1. Dwight Schroot is writing a frontend for the IPython project. His frontend is stuck in the stone age and must communicate synchronously with an IPython.kernel.core.Interpreter instance. Because code is executed in blocks by the Interpreter, Dwight's UI freezes every time he executes a long block of code. To keep track of the progress of his long running block, Dwight adds the following code to his frontend's set-up code::
-		from IPython.kernel.core.notification import NotificationCenter
-		center = NotificationCenter.sharedNotificationCenter
-		center.registerObserver(self, type=IPython.kernel.core.Interpreter.STDOUT_NOTIFICATION_TYPE, notifying_object=self.interpreter, callback=self.stdout_notification)
-	
-	and elsewhere in his front end::
-		def stdout_notification(self, type, notifying_object, out_string=None):
-			self.writeStdOut(out_string)
-	
-	If everything works, the Interpreter will (according to its published API) fire a notification via the :data:`IPython.kernel.core.notification.sharedCenter` of type :const:`STD_OUT_NOTIFICATION_TYPE` before writing anything to stdout [it's up to the Intereter implementation to figure out when to do this]. The notificaiton center will then call the registered callbacks for that event type (in this case, Dwight's frontend's stdout_notification method). Again, according to its API, the Interpreter provides an additional keyword argument when firing the notificaiton of out_string, a copy of the string it will write to stdout.
-
-	Like magic, Dwight's frontend is able to provide output, even during long-running calculations. Now if Jim could just convince Dwight to use Twisted...
-
-	2. Boss Hog is writing a frontend for the IPython project. Because Boss Hog is stuck in the stone age, his frontend will be written in a new Fortran-like dialect of python and will run only from the command line. Because he doesn't need any fancy notification system and is used to worrying about every cycle on his rat-wheel powered mini, Boss Hog is adamant that the new notification system not produce any performance penalty. As they say in Hazard county, there's no such thing as a free lunch. If he wanted zero overhead, he should have kept using IPython 0.8. Instead, those tricky Duke boys slide in a suped-up bridge-out jumpin' awkwardly confederate-lovin' notification module that imparts only a constant (and small) performance penalty when the Interpreter (or any other object) fires an event for which there are no registered observers. Of course, the same notificaiton-enabled Interpreter can then be used in frontends that require notifications, thus saving the IPython project from a nasty civil war.
-	
-	3. Barry is wrting a frontend for the IPython project. Because Barry's front end is the *new hotness*, it uses an asynchronous event model to communicate with a Twisted :mod:`~IPython.kernel.engineservice` that communicates with the IPython :class:`~IPython.kernel.core.interpreter.Interpreter`. Using the :mod:`IPython.kernel.notification` module, an asynchronous wrapper on the :mod:`IPython.kernel.core.notification` module, Barry's frontend can register for notifications from the interpreter that are delivered asynchronously. Even if Barry's frontend is running on a separate process or even host from the Interpreter, the notifications are delivered, as if by dark and twisted magic. Just like Dwight's frontend, Barry's frontend can now recieve notifications of e.g. writing to stdout/stderr, opening/closing an external file, an exception in the executing code, etc.
  No newline at end of file
+    1. Dwight Schroot is writing a frontend for the IPython project. His frontend is stuck in the stone age and must communicate synchronously with an IPython.kernel.core.Interpreter instance. Because code is executed in blocks by the Interpreter, Dwight's UI freezes every time he executes a long block of code. To keep track of the progress of his long running block, Dwight adds the following code to his frontend's set-up code::
+
+        from IPython.kernel.core.notification import NotificationCenter
+        center = NotificationCenter.sharedNotificationCenter
+        center.registerObserver(self, type=IPython.kernel.core.Interpreter.STDOUT_NOTIFICATION_TYPE, notifying_object=self.interpreter, callback=self.stdout_notification)
+    
+    and elsewhere in his front end::
+
+        def stdout_notification(self, type, notifying_object, out_string=None):
+            self.writeStdOut(out_string)
+    
+    If everything works, the Interpreter will (according to its published API) fire a notification via the :data:`IPython.kernel.core.notification.sharedCenter` of type :const:`STD_OUT_NOTIFICATION_TYPE` before writing anything to stdout [it's up to the Intereter implementation to figure out when to do this]. The notificaiton center will then call the registered callbacks for that event type (in this case, Dwight's frontend's stdout_notification method). Again, according to its API, the Interpreter provides an additional keyword argument when firing the notificaiton of out_string, a copy of the string it will write to stdout.
+
+    Like magic, Dwight's frontend is able to provide output, even during long-running calculations. Now if Jim could just convince Dwight to use Twisted...
+
+    2. Boss Hog is writing a frontend for the IPython project. Because Boss Hog is stuck in the stone age, his frontend will be written in a new Fortran-like dialect of python and will run only from the command line. Because he doesn't need any fancy notification system and is used to worrying about every cycle on his rat-wheel powered mini, Boss Hog is adamant that the new notification system not produce any performance penalty. As they say in Hazard county, there's no such thing as a free lunch. If he wanted zero overhead, he should have kept using IPython 0.8. Instead, those tricky Duke boys slide in a suped-up bridge-out jumpin' awkwardly confederate-lovin' notification module that imparts only a constant (and small) performance penalty when the Interpreter (or any other object) fires an event for which there are no registered observers. Of course, the same notificaiton-enabled Interpreter can then be used in frontends that require notifications, thus saving the IPython project from a nasty civil war.
+    
+    3. Barry is wrting a frontend for the IPython project. Because Barry's front end is the *new hotness*, it uses an asynchronous event model to communicate with a Twisted :mod:`~IPython.kernel.engineservice` that communicates with the IPython :class:`~IPython.kernel.core.interpreter.Interpreter`. Using the :mod:`IPython.kernel.notification` module, an asynchronous wrapper on the :mod:`IPython.kernel.core.notification` module, Barry's frontend can register for notifications from the interpreter that are delivered asynchronously. Even if Barry's frontend is running on a separate process or even host from the Interpreter, the notifications are delivered, as if by dark and twisted magic. Just like Dwight's frontend, Barry's frontend can now recieve notifications of e.g. writing to stdout/stderr, opening/closing an external file, an exception in the executing code, etc.
  No newline at end of file

docs/source/development/roadmap.txt

@@ -1,96 +1,107 b''
 .. _roadmap:
 
 ===================
 Development roadmap
 ===================
 
 .. contents::
 
 IPython is an ambitious project that is still under heavy development.  However, we want IPython to become useful to as many people as possible, as quickly as possible.  To help us accomplish this, we are laying out a roadmap of where we are headed and what needs to happen to get there.  Hopefully, this will help the IPython developers figure out the best things to work on for each upcoming release.
 
 Speaking of releases, we are going to begin releasing a new version of IPython every four weeks.  We are hoping that a regular release schedule, along with a clear roadmap of where we are headed will propel the project forward.
 
 Where are we headed
 ===================
 
 Our goal with IPython is simple: to provide a *powerful*, *robust* and *easy to use* framework for parallel computing.  While there are other secondary goals you will hear us talking about at various times, this is the primary goal of IPython that frames the roadmap.
 
 Steps along the way
 ===================
 
 Here we describe the various things that we need to work on to accomplish this goal.
 
 Setting up for regular release schedule
 ---------------------------------------
 
 We would like to begin to release IPython regularly (probably a 4 week release cycle).  To get ready for this, we need to revisit the development guidelines and put in information about releasing IPython.  
 
 Process startup and management
 ------------------------------
 
 IPython is implemented using a distributed set of processes that communicate using TCP/IP network channels.  Currently, users have to start each of the various processes separately using command line scripts.  This is both difficult and error prone.  Furthermore, there are a number of things that often need to be managed once the processes have been started, such as the sending of signals and the shutting down and cleaning up of processes.
 
 We need to build a system that makes it trivial for users to start and manage IPython processes.  This system should have the following properties:
 
-	* It should possible to do everything through an extremely simple API that users
-	  can call from their own Python script.  No shell commands should be needed.
-	* This simple API should be configured using standard .ini files.
-	* The system should make it possible to start processes using a number of different
-	  approaches:  SSH, PBS/Torque, Xgrid, Windows Server, mpirun, etc.
-	* The controller and engine processes should each have a daemon for monitoring, 
-	  signaling and clean up.
-	* The system should be secure.
-	* The system should work under all the major operating systems, including
-	  Windows.
+* It should possible to do everything through an extremely simple API that users
+  can call from their own Python script.  No shell commands should be needed.
+  
+* This simple API should be configured using standard .ini files.
+
+* The system should make it possible to start processes using a number of different
+  approaches:  SSH, PBS/Torque, Xgrid, Windows Server, mpirun, etc.
+  
+* The controller and engine processes should each have a daemon for monitoring, 
+  signaling and clean up.
+  
+* The system should be secure.
+
+* The system should work under all the major operating systems, including
+  Windows.
 
 Initial work has begun on the daemon infrastructure, and some of the needed logic is contained in the ipcluster script.
 
 Ease of use/high-level approaches to parallelism
 ------------------------------------------------
 
 While our current API for clients is well designed, we can still do a lot better in designing a user-facing API that is super simple.  The main goal here is that it should take *almost no extra code* for users to get their code running in parallel.  For this to be possible, we need to tie into Python's standard idioms that enable efficient coding.  The biggest ones we are looking at are using context managers (i.e., Python 2.5's ``with`` statement) and decorators.  Initial work on this front has begun, but more work is needed.
 
 We also need to think about new models for expressing parallelism.  This is fun work as most of the foundation has already been established.
 
 Security
 --------
 
 Currently, IPython has no built in security or security model.  Because we would like IPython to be usable on public computer systems and over wide area networks, we need to come up with a robust solution for security.  Here are some of the specific things that need to be included:
 
-	* User authentication between all processes (engines, controller and clients).
-	* Optional TSL/SSL based encryption of all communication channels.
-	* A good way of picking network ports so multiple users on the same system can
-	  run their own controller and engines without interfering with those of others.
-	* A clear model for security that enables users to evaluate the security risks
-	  associated with using IPython in various manners.
+* User authentication between all processes (engines, controller and clients).
+
+* Optional TSL/SSL based encryption of all communication channels.
+
+* A good way of picking network ports so multiple users on the same system can
+  run their own controller and engines without interfering with those of others.
+  
+* A clear model for security that enables users to evaluate the security risks
+  associated with using IPython in various manners.
 
 For the implementation of this, we plan on using Twisted's support for SSL and authentication.  One things that we really should look at is the `Foolscap`_ network protocol, which provides many of these things out of the box.
 
 .. _Foolscap: http://foolscap.lothar.com/trac
 
 The security work needs to be done in conjunction with other network protocol stuff.
 
+As of the 0.9 release of IPython, we are using Foolscap and we have implemented
+a full security model.
+
 Latent performance issues
 -------------------------
 
 Currently, we have a number of performance issues that are waiting to bite users:
 
 	* The controller store a large amount of state in Python dictionaries.  Under heavy
 	  usage, these dicts with get very large, causing memory usage problems.  We need to 
 	  develop more scalable solutions to this problem, such as using a sqlite database
 	  to store this state.  This will also help the controller to be more fault tolerant.
 	* Currently, the client to controller connections are done through XML-RPC using 
 	  HTTP 1.0.  This is very inefficient as XML-RPC is a very verbose protocol and 
 	  each request must be handled with a new connection.  We need to move these network
-	  connections over to PB or Foolscap.
+	  connections over to PB or Foolscap.  Done!
 	* We currently don't have a good way of handling large objects in the controller.
 	  The biggest problem is that because we don't have any way of streaming objects,
 	  we get lots of temporary copies in the low-level buffers.  We need to implement
 	  a better serialization approach and true streaming support.
 	* The controller currently unpickles and repickles objects.  We need to use the 
 	  [push|pull]_serialized methods instead.
 	* Currently the controller is a bottleneck.  We need the ability to scale the 
 	  controller by aggregating multiple controllers into one effective controller.
 
 
 

docs/source/faq.txt

@@ -1,93 +1,105 b''
 .. _faq:
 
 ========================================
 Frequently asked questions
 ========================================
 
 General questions
 =================
 
 Questions about parallel computing with IPython
 ================================================
 
 Will IPython speed my Python code up?
 --------------------------------------
 
 Yes and no. When converting a serial code to run in parallel, there often many
 difficulty questions that need to be answered, such as:
 
-	* How should data be decomposed onto the set of processors?
-	* What are the data movement patterns?
-	* Can the algorithm be structured to minimize data movement?
-	* Is dynamic load balancing important?
+* How should data be decomposed onto the set of processors?
+
+* What are the data movement patterns?
+
+* Can the algorithm be structured to minimize data movement?
+
+* Is dynamic load balancing important?
 
 We can't answer such questions for you. This is the hard (but fun) work of parallel
 computing. But, once you understand these things IPython will make it easier for you to
 implement a good solution quickly. Most importantly, you will be able to use the
 resulting parallel code interactively.
 
 With that said, if your problem is trivial to parallelize, IPython has a number of
 different interfaces that will enable you to parallelize things is almost no time at
-all.  A good place to start is the ``map`` method of our `multiengine interface`_.
-
-.. _multiengine interface: ./parallel_multiengine
+all.  A good place to start is the ``map`` method of our :class:`MultiEngineClient`.
 
 What is the best way to use MPI from Python?
 --------------------------------------------
 
 What about all the other parallel computing packages in Python?
 ---------------------------------------------------------------
 
 Some of the unique characteristic of IPython are:
 
-	* IPython is the only architecture that abstracts out the notion of a 
-	  parallel computation in such a way that new models of parallel computing
-	  can be explored quickly and easily.  If you don't like the models we
-	  provide, you can simply create your own using the capabilities we provide.
-	* IPython is asynchronous from the ground up (we use `Twisted`_).
-	* IPython's architecture is designed to avoid subtle problems 
-	  that emerge because of Python's global interpreter lock (GIL).
-	* While IPython'1 architecture is designed to support a wide range
-	  of novel parallel computing models, it is fully interoperable with
-	  traditional MPI applications.
-	* IPython has been used and tested extensively on modern supercomputers.
-	* IPython's networking layers are completely modular.  Thus, is 
-	  straightforward to replace our existing network protocols with 
-	  high performance alternatives (ones based upon Myranet/Infiniband).
-	* IPython is designed from the ground up to support collaborative
-	  parallel computing.  This enables multiple users to actively develop
-	  and run the *same* parallel computation.
-	* Interactivity is a central goal for us.  While IPython does not have
-	  to be used interactivly, is can be.
-	  
+* IPython is the only architecture that abstracts out the notion of a 
+  parallel computation in such a way that new models of parallel computing
+  can be explored quickly and easily.  If you don't like the models we
+  provide, you can simply create your own using the capabilities we provide.
+  
+* IPython is asynchronous from the ground up (we use `Twisted`_).
+
+* IPython's architecture is designed to avoid subtle problems 
+  that emerge because of Python's global interpreter lock (GIL).
+  
+* While IPython's architecture is designed to support a wide range
+  of novel parallel computing models, it is fully interoperable with
+  traditional MPI applications.
+  
+* IPython has been used and tested extensively on modern supercomputers.
+
+* IPython's networking layers are completely modular.  Thus, is 
+  straightforward to replace our existing network protocols with 
+  high performance alternatives (ones based upon Myranet/Infiniband).
+  
+* IPython is designed from the ground up to support collaborative
+  parallel computing.  This enables multiple users to actively develop
+  and run the *same* parallel computation.
+  
+* Interactivity is a central goal for us.  While IPython does not have
+  to be used interactivly, it can be.
+      
 .. _Twisted: http://www.twistedmatrix.com
 
 Why The IPython controller a bottleneck in my parallel calculation?
 -------------------------------------------------------------------
 
 A golden rule in parallel computing is that you should only move data around if you
 absolutely need to. The main reason that the controller becomes a bottleneck is that
 too much data is being pushed and pulled to and from the engines. If your algorithm
 is structured in this way, you really should think about alternative ways of
 handling the data movement. Here are some ideas:
 
-	1. Have the engines write data to files on the locals disks of the engines.
-	2. Have the engines write data to files on a file system that is shared by
-	   the engines.
-	3. Have the engines write data to a database that is shared by the engines.
-	4. Simply keep data in the persistent memory of the engines and move the
-	   computation to the data (rather than the data to the computation).
-	5. See if you can pass data directly between engines using MPI.
+1. Have the engines write data to files on the locals disks of the engines.
+
+2. Have the engines write data to files on a file system that is shared by
+   the engines.
+
+3. Have the engines write data to a database that is shared by the engines.
+
+4. Simply keep data in the persistent memory of the engines and move the
+   computation to the data (rather than the data to the computation).
+
+5. See if you can pass data directly between engines using MPI.
 
 Isn't Python slow to be used for high-performance parallel computing?
 ---------------------------------------------------------------------
 
 
 
 
  
 
 
 
 
 

docs/source/history.txt

@@ -1,56 +1,38 b''
 .. _history:
 
 =======
 History
 =======
 
 Origins
 =======
 
-The current IPython system grew out of the following three projects:
-
-    * [ipython] by Fernando Pérez. I was working on adding
-      Mathematica-type prompts and a flexible configuration system
-      (something better than $PYTHONSTARTUP) to the standard Python
-      interactive interpreter.
-    * [IPP] by Janko Hauser. Very well organized, great usability. Had
-      an old help system. IPP was used as the 'container' code into
-      which I added the functionality from ipython and LazyPython.
-    * [LazyPython] by Nathan Gray. Simple but very powerful. The quick
-      syntax (auto parens, auto quotes) and verbose/colored tracebacks
-      were all taken from here.
-
-When I found out about IPP and LazyPython I tried to join all three
-into a unified system. I thought this could provide a very nice
-working environment, both for regular programming and scientific
-computing: shell-like features, IDL/Matlab numerics, Mathematica-type
-prompt history and great object introspection and help facilities. I
-think it worked reasonably well, though it was a lot more work than I
-had initially planned.
-
-
-Current status
-==============
-
-The above listed features work, and quite well for the most part. But
-until a major internal restructuring is done (see below), only bug
-fixing will be done, no other features will be added (unless very minor
-and well localized in the cleaner parts of the code).
-
-IPython consists of some 18000 lines of pure python code, of which
-roughly two thirds is reasonably clean. The rest is, messy code which
-needs a massive restructuring before any further major work is done.
-Even the messy code is fairly well documented though, and most of the
-problems in the (non-existent) class design are well pointed to by a
-PyChecker run. So the rewriting work isn't that bad, it will just be
-time-consuming.
-
-
-Future
-------
-
-See the separate new_design document for details. Ultimately, I would
-like to see IPython become part of the standard Python distribution as a
-'big brother with batteries' to the standard Python interactive
-interpreter. But that will never happen with the current state of the
-code, so all contributions are welcome.
  No newline at end of file
+IPython was starting in 2001 by Fernando Perez.  IPython as we know it
+today grew out of the following three projects:
+
+* ipython by Fernando Pérez. I was working on adding
+  Mathematica-type prompts and a flexible configuration system
+  (something better than $PYTHONSTARTUP) to the standard Python
+  interactive interpreter.
+* IPP by Janko Hauser. Very well organized, great usability. Had
+  an old help system. IPP was used as the 'container' code into
+  which I added the functionality from ipython and LazyPython.
+* LazyPython by Nathan Gray. Simple but very powerful. The quick
+  syntax (auto parens, auto quotes) and verbose/colored tracebacks
+  were all taken from here.
+
+Here is how Fernando describes it:
+
+    When I found out about IPP and LazyPython I tried to join all three
+    into a unified system. I thought this could provide a very nice
+    working environment, both for regular programming and scientific
+    computing: shell-like features, IDL/Matlab numerics, Mathematica-type
+    prompt history and great object introspection and help facilities. I
+    think it worked reasonably well, though it was a lot more work than I
+    had initially planned.
+
+Today and how we got here
+=========================
+
+This needs to be filled in.
+

docs/source/index.txt

@@ -1,28 +1,32 b''
 =====================
 IPython Documentation
 =====================
 
-Contents
-========
+.. htmlonly::
+
+   :Release: |version|
+   :Date: |today|
+
+   Contents:
 
 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 2
 
    overview.txt
    install/index.txt
    interactive/index.txt
    parallel/index.txt
    config/index.txt
    changes.txt
    development/index.txt
    faq.txt
    history.txt
    license_and_copyright.txt
    credits.txt
 
-Indices and tables
-==================
 
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
  No newline at end of file
+.. htmlonly::
+
+  * :ref:`genindex`
+  * :ref:`modindex`
+  * :ref:`search`

docs/source/install/index.txt

@@ -1,11 +1,10 b''
 .. _install_index:
 
 ==================
 Installation
 ==================
 
 .. toctree::
    :maxdepth: 2
 
-   basic.txt
-   advanced.txt
+   install.txt

docs/source/interactive/reference.txt

@@ -1,3163 +1,3162 b''
 .. IPython documentation master file, created by sphinx-quickstart.py on Mon Mar 24 17:01:34 2008.
    You can adapt this file completely to your liking, but it should at least
    contain the root 'toctree' directive.
 
 =================
 IPython reference
 =================
 
 .. contents::
 
-.. _Command line options:
+.. _command_line_options:
 
 Command-line usage
 ==================
 
 You start IPython with the command::
 
     $ ipython [options] files
 
 If invoked with no options, it executes all the files listed in sequence
 and drops you into the interpreter while still acknowledging any options
 you may have set in your ipythonrc file. This behavior is different from
 standard Python, which when called as python -i will only execute one
 file and ignore your configuration setup.
 
 Please note that some of the configuration options are not available at
 the command line, simply because they are not practical here. Look into
 your ipythonrc configuration file for details on those. This file
 typically installed in the $HOME/.ipython directory. For Windows users,
 $HOME resolves to C:\\Documents and Settings\\YourUserName in most
 instances. In the rest of this text, we will refer to this directory as
 IPYTHONDIR.
 
 .. _Threading options:
 
 
 Special Threading Options
 -------------------------
 
 The following special options are ONLY valid at the beginning of the
 command line, and not later. This is because they control the initial-
 ization of ipython itself, before the normal option-handling mechanism
 is active.
 
     -gthread, -qthread, -q4thread, -wthread, -pylab: 
 	Only one of these can be given, and it can only be given as
 	the first option passed to IPython (it will have no effect in
 	any other position).  They provide threading support for the
 	GTK, Qt (versions 3 and 4) and WXPython toolkits, and for the
 	matplotlib library.
 
 	With any of the first four options, IPython starts running a
 	separate thread for the graphical toolkit's operation, so that
 	you can open and control graphical elements from within an
 	IPython command line, without blocking. All four provide
 	essentially the same functionality, respectively for GTK, Qt3,
 	Qt4 and WXWidgets (via their Python interfaces).
 
 	Note that with -wthread, you can additionally use the
 	-wxversion option to request a specific version of wx to be
 	used.  This requires that you have the wxversion Python module
 	installed, which is part of recent wxPython distributions.
 
 	If -pylab is given, IPython loads special support for the mat
 	plotlib library (http://matplotlib.sourceforge.net), allowing
 	interactive usage of any of its backends as defined in the
 	user's ~/.matplotlib/matplotlibrc file. It automatically
 	activates GTK, Qt or WX threading for IPyhton if the choice of
 	matplotlib backend requires it. It also modifies the %run
 	command to correctly execute (without blocking) any
 	matplotlib-based script which calls show() at the end.
 
     -tk
 	The -g/q/q4/wthread options, and -pylab (if matplotlib is
 	configured to use GTK, Qt3, Qt4 or WX), will normally block Tk
 	graphical interfaces. This means that when either GTK, Qt or WX
 	threading is active, any attempt to open a Tk GUI will result in a
 	dead window, and possibly cause the Python interpreter to crash.
 	An extra option, -tk, is available to address this issue. It can
 	only be given as a second option after any of the above (-gthread,
 	-wthread or -pylab).
 
 	If -tk is given, IPython will try to coordinate Tk threading
 	with GTK, Qt or WX. This is however potentially unreliable, and
 	you will have to test on your platform and Python configuration to
 	determine whether it works for you. Debian users have reported
 	success, apparently due to the fact that Debian builds all of Tcl,
 	Tk, Tkinter and Python with pthreads support. Under other Linux
 	environments (such as Fedora Core 2/3), this option has caused
 	random crashes and lockups of the Python interpreter. Under other
 	operating systems (Mac OSX and Windows), you'll need to try it to
 	find out, since currently no user reports are available.
     
 	There is unfortunately no way for IPython to determine at run time
 	whether -tk will work reliably or not, so you will need to do some
 	experiments before relying on it for regular work.
 
 
 
 Regular Options
 ---------------
 
 After the above threading options have been given, regular options can
 follow in any order. All options can be abbreviated to their shortest
 non-ambiguous form and are case-sensitive. One or two dashes can be
 used. Some options have an alternate short form, indicated after a ``|``.
 
 Most options can also be set from your ipythonrc configuration file. See
 the provided example for more details on what the options do. Options
 given at the command line override the values set in the ipythonrc file.
 
 All options with a [no] prepended can be specified in negated form
 (-nooption instead of -option) to turn the feature off.
 
     -help  print a help message and exit.
 
     -pylab
 	this can only be given as the first option passed to IPython
 	(it will have no effect in any other position). It adds
 	special support for the matplotlib library
 	(http://matplotlib.sourceforge.ne), allowing interactive usage
 	of any of its backends as defined in the user's .matplotlibrc
 	file.  It automatically activates GTK or WX threading for
 	IPyhton if the choice of matplotlib backend requires it. It
 	also modifies the %run command to correctly execute (without
 	blocking) any matplotlib-based script which calls show() at
 	the end. See `Matplotlib support`_ for more details.
 
     -autocall <val>
 	Make IPython automatically call any callable object even if you
 	didn't type explicit parentheses. For example, 'str 43' becomes
 	'str(43)' automatically. The value can be '0' to disable the feature,
 	'1' for smart autocall, where it is not applied if there are no more
 	arguments on the line, and '2' for full autocall, where all callable
 	objects are automatically called (even if no arguments are
 	present). The default is '1'.
 
     -[no]autoindent    
 	Turn automatic indentation on/off.
 
     -[no]automagic 
 	make magic commands automatic (without needing their first character
 	to be %). Type %magic at the IPython prompt for more information.
 
     -[no]autoedit_syntax 
 	When a syntax error occurs after editing a file, automatically
 	open the file to the trouble causing line for convenient
 	fixing.
 
     -[no]banner		Print the initial information banner (default on).
 
     -c <command>
 	execute the given command string. This is similar to the -c
 	option in the normal Python interpreter.
 
     -cache_size, cs <n> 
 	size of the output cache (maximum number of entries to hold in
 	memory). The default is 1000, you can change it permanently in your
 	config file. Setting it to 0 completely disables the caching system,
 	and the minimum value accepted is 20 (if you provide a value less than
 	20, it is reset to 0 and a warning is issued) This limit is defined
 	because otherwise you'll spend more time re-flushing a too small cache
 	than working.
 
     -classic, cl 
 	Gives IPython a similar feel to the classic Python
 	prompt.
 
     -colors <scheme> 
 	Color scheme for prompts and exception reporting. Currently
 	implemented: NoColor, Linux and LightBG.
 
     -[no]color_info 
 	IPython can display information about objects via a set of functions,
 	and optionally can use colors for this, syntax highlighting source
 	code and various other elements.  However, because this information is
 	passed through a pager (like 'less') and many pagers get confused with
 	color codes, this option is off by default. You can test it and turn
 	it on permanently in your ipythonrc file if it works for you. As a
 	reference, the 'less' pager supplied with Mandrake 8.2 works ok, but
 	that in RedHat 7.2 doesn't.
 
 	Test it and turn it on permanently if it works with your
 	system. The magic function %color_info allows you to toggle this
 	interactively for testing.
 
     -[no]debug 
 	Show information about the loading process. Very useful to pin down
 	problems with your configuration files or to get details about
 	session restores.
 
     -[no]deep_reload: 
 	IPython can use the deep_reload module which reloads changes in
 	modules recursively (it replaces the reload() function, so you don't
 	need to change anything to use it).  deep_reload() forces a full
 	reload of modules whose code may have changed, which the default
 	reload() function does not.
 
 	When deep_reload is off, IPython will use the normal reload(),
 	but deep_reload will still be available as dreload(). This
 	feature is off by default [which means that you have both
 	normal reload() and dreload()].
 
     -editor <name>
 	Which editor to use with the %edit command. By default,
 	IPython will honor your EDITOR environment variable (if not
 	set, vi is the Unix default and notepad the Windows one).
 	Since this editor is invoked on the fly by IPython and is
 	meant for editing small code snippets, you may want to use a
 	small, lightweight editor here (in case your default EDITOR is
 	something like Emacs).
 
     -ipythondir <name> 
 	name of your IPython configuration directory IPYTHONDIR. This
 	can also be specified through the environment variable
 	IPYTHONDIR.
 
     -log, l 
 	generate a log file of all input. The file is named
 	ipython_log.py in your current directory (which prevents logs
 	from multiple IPython sessions from trampling each other). You
 	can use this to later restore a session by loading your
 	logfile as a file to be executed with option -logplay (see
 	below).
 
     -logfile, lf <name>   specify the name of your logfile.
 
     -logplay, lp <name> 
 
       you can replay a previous log. For restoring a session as close as
       possible to the state you left it in, use this option (don't just run
       the logfile). With -logplay, IPython will try to reconstruct the
       previous working environment in full, not just execute the commands in
       the logfile.
 
       When a session is restored, logging is automatically turned on
       again with the name of the logfile it was invoked with (it is
       read from the log header). So once you've turned logging on for
       a session, you can quit IPython and reload it as many times as
       you want and it will continue to log its history and restore
       from the beginning every time.
       
       Caveats: there are limitations in this option. The history
       variables _i*,_* and _dh don't get restored properly. In the
       future we will try to implement full session saving by writing
       and retrieving a 'snapshot' of the memory state of IPython. But
       our first attempts failed because of inherent limitations of
       Python's Pickle module, so this may have to wait.
 
     -[no]messages   
 	Print messages which IPython collects about its startup
 	process (default on).
 
     -[no]pdb
 	Automatically call the pdb debugger after every uncaught
 	exception. If you are used to debugging using pdb, this puts
 	you automatically inside of it after any call (either in
 	IPython or in code called by it) which triggers an exception
 	which goes uncaught.
 
     -pydb
 	Makes IPython use the third party "pydb" package as debugger,
 	instead of pdb. Requires that pydb is installed.
 	
     -[no]pprint
 	ipython can optionally use the pprint (pretty printer) module
 	for displaying results. pprint tends to give a nicer display
 	of nested data structures. If you like it, you can turn it on
 	permanently in your config file (default off).
 
     -profile, p <name>
 
 	assume that your config file is ipythonrc-<name> or
 	ipy_profile_<name>.py (looks in current dir first, then in
 	IPYTHONDIR).  This is a quick way to keep and load multiple
 	config files for different tasks, especially if you use the
 	include option of config files. You can keep a basic
 	IPYTHONDIR/ipythonrc file and then have other 'profiles' which
 	include this one and load extra things for particular
 	tasks. For example:
 
 	1. $HOME/.ipython/ipythonrc : load basic things you always want.
 	2. $HOME/.ipython/ipythonrc-math : load (1) and basic math-related modules.
 	3. $HOME/.ipython/ipythonrc-numeric : load (1) and Numeric and plotting modules.
 
 	Since it is possible to create an endless loop by having
 	circular file inclusions, IPython will stop if it reaches 15
 	recursive inclusions.
 
     -prompt_in1, pi1 <string>
-	Specify the string used for input prompts. Note that if you
-	are using numbered prompts, the number is represented with a
-	'\#' in the string. Don't forget to quote strings with spaces
-	embedded in them. Default: 'In [\#]:'.  Sec. Prompts_
-	discusses in detail all the available escapes to customize
-	your prompts.
+
+        Specify the string used for input prompts. Note that if you are using
+	numbered prompts, the number is represented with a '\#' in the
+	string. Don't forget to quote strings with spaces embedded in
+	them. Default: 'In [\#]:'.  The :ref:`prompts section <prompts>`
+	discusses in detail all the available escapes to customize your
+	prompts.
 
     -prompt_in2, pi2 <string>
 	Similar to the previous option, but used for the continuation
 	prompts. The special sequence '\D' is similar to '\#', but
 	with all digits replaced dots (so you can have your
 	continuation prompt aligned with your input prompt).  Default:
 	' .\D.:' (note three spaces at the start for alignment with
 	'In [\#]').
 
     -prompt_out,po <string> 
 	String used for output prompts, also uses numbers like
 	prompt_in1. Default: 'Out[\#]:'
 
     -quick   start in bare bones mode (no config file loaded).
 
     -rcfile <name>   
 	name of your IPython resource configuration file. Normally
 	IPython loads ipythonrc (from current directory) or
 	IPYTHONDIR/ipythonrc.
 
 	If the loading of your config file fails, IPython starts with
 	a bare bones configuration (no modules loaded at all).
 
     -[no]readline
 	use the readline library, which is needed to support name
 	completion and command history, among other things.  It is
 	enabled by default, but may cause problems for users of
 	X/Emacs in Python comint or shell buffers.
 
 	Note that X/Emacs 'eterm' buffers (opened with M-x term) support
 	IPython's readline and syntax coloring fine, only 'emacs' (M-x
 	shell and C-c !) buffers do not.
 
     -screen_length, sl <n>
 	number of lines of your screen. This is used to control
 	printing of very long strings. Strings longer than this number
 	of lines will be sent through a pager instead of directly
 	printed.
 
 	The default value for this is 0, which means IPython will
 	auto-detect your screen size every time it needs to print certain
 	potentially long strings (this doesn't change the behavior of the
 	'print' keyword, it's only triggered internally). If for some
 	reason this isn't working well (it needs curses support), specify
 	it yourself. Otherwise don't change the default.
 
     -separate_in, si <string>
 
 	separator before input prompts.
 	Default: '\n'
 
     -separate_out, so <string>  
         separator before output prompts.
         Default: nothing.
 
     -separate_out2, so2
 	separator after output prompts.
 	Default: nothing.
 	For these three options, use the value 0 to specify no separator.
 
     -nosep   
 	shorthand for '-SeparateIn 0 -SeparateOut 0 -SeparateOut2
 	0'. Simply removes all input/output separators.
 
     -upgrade 
 	allows you to upgrade your IPYTHONDIR configuration when you
 	install a new version of IPython. Since new versions may
 	include new command line options or example files, this copies
 	updated ipythonrc-type files. However, it backs up (with a
 	.old extension) all files which it overwrites so that you can
 	merge back any customizations you might have in your personal
 	files. Note that you should probably use %upgrade instead,
 	it's a safer alternative.
 
 
     -Version    print version information and exit.
 
     -wxversion <string> 
 	Select a specific version of wxPython (used in conjunction
 	with -wthread). Requires the wxversion module, part of recent
 	wxPython distributions
 
     -xmode <modename>
 
 	Mode for exception reporting.
 
 	Valid modes: Plain, Context and Verbose.
 
 	* Plain: similar to python's normal traceback printing.
 	* Context: prints 5 lines of context source code around each
 	  line in the traceback.
 	* Verbose: similar to Context, but additionally prints the
 	  variables currently visible where the exception happened
 	  (shortening their strings if too long). This can potentially be
 	  very slow, if you happen to have a huge data structure whose
 	  string representation is complex to compute. Your computer may
 	  appear to freeze for a while with cpu usage at 100%. If this
 	  occurs, you can cancel the traceback with Ctrl-C (maybe hitting it
 	  more than once).
 
 Interactive use
 ===============
 
 Warning: IPython relies on the existence of a global variable called
 _ip which controls the shell itself. If you redefine _ip to anything,
 bizarre behavior will quickly occur.
 
 Other than the above warning, IPython is meant to work as a drop-in
 replacement for the standard interactive interpreter. As such, any code
 which is valid python should execute normally under IPython (cases where
 this is not true should be reported as bugs). It does, however, offer
 many features which are not available at a standard python prompt. What
 follows is a list of these.
 
 
 Caution for Windows users
 -------------------------
 
 Windows, unfortunately, uses the '\' character as a path
 separator. This is a terrible choice, because '\' also represents the
 escape character in most modern programming languages, including
 Python. For this reason, using '/' character is recommended if you
 have problems with ``\``.  However, in Windows commands '/' flags
 options, so you can not use it for the root directory. This means that
 paths beginning at the root must be typed in a contrived manner like:
 ``%copy \opt/foo/bar.txt \tmp``
 
 .. _magic:
 
 Magic command system
 --------------------
 
 IPython will treat any line whose first character is a % as a special
 call to a 'magic' function. These allow you to control the behavior of
 IPython itself, plus a lot of system-type features. They are all
 prefixed with a % character, but parameters are given without
 parentheses or quotes.
 
 Example: typing '%cd mydir' (without the quotes) changes you working
 directory to 'mydir', if it exists.
 
 If you have 'automagic' enabled (in your ipythonrc file, via the command
 line option -automagic or with the %automagic function), you don't need
 to type in the % explicitly. IPython will scan its internal list of
 magic functions and call one if it exists. With automagic on you can
 then just type 'cd mydir' to go to directory 'mydir'. The automagic
 system has the lowest possible precedence in name searches, so defining
 an identifier with the same name as an existing magic function will
 shadow it for automagic use. You can still access the shadowed magic
 function by explicitly using the % character at the beginning of the line.
 
 An example (with automagic on) should clarify all this::
 
     In [1]: cd ipython # %cd is called by automagic
 
     /home/fperez/ipython
 
     In [2]: cd=1 # now cd is just a variable
 
     In [3]: cd .. # and doesn't work as a function anymore
 
     ------------------------------
 
         File "<console>", line 1
 
           cd ..
 
               ^
 
     SyntaxError: invalid syntax
 
     In [4]: %cd .. # but %cd always works
 
     /home/fperez
 
     In [5]: del cd # if you remove the cd variable
 
     In [6]: cd ipython # automagic can work again
 
     /home/fperez/ipython
 
 You can define your own magic functions to extend the system. The
 following example defines a new magic command, %impall::
 
     import IPython.ipapi 
 
     ip = IPython.ipapi.get()
 
     def doimp(self, arg):
 
         ip = self.api
 
         ip.ex("import %s; reload(%s); from %s import *" % (
 
         arg,arg,arg)
 
         )
 
     ip.expose_magic('impall', doimp)
 
 You can also define your own aliased names for magic functions. In your
 ipythonrc file, placing a line like:
 
 execute __IP.magic_cl = __IP.magic_clear
 
 will define %cl as a new name for %clear.
 
 Type %magic for more information, including a list of all available
 magic functions at any time and their docstrings. You can also type
 %magic_function_name? (see sec. 6.4 <#sec:dyn-object-info> for
 information on the '?' system) to get information about any particular
 magic function you are interested in.
 
 
 Magic commands
 --------------
 
 The rest of this section is automatically generated for each release
 from the docstrings in the IPython code. Therefore the formatting is
 somewhat minimal, but this method has the advantage of having
 information always in sync with the code.
 
 A list of all the magic commands available in IPython's default
 installation follows. This is similar to what you'll see by simply
 typing %magic at the prompt, but that will also give you information
 about magic commands you may have added as part of your personal
 customizations.
 
 .. magic_start
 
 **%Exit**::
 
 	Exit IPython without confirmation.
 
 **%Pprint**::
 
 	Toggle pretty printing on/off.
 
 **%alias**::
 
 	Define an alias for a system command.
 
         '%alias alias_name cmd' defines 'alias_name' as an alias for 'cmd'
 
         Then, typing 'alias_name params' will execute the system command 'cmd
         params' (from your underlying operating system).
 
         Aliases have lower precedence than magic functions and Python normal
         variables, so if 'foo' is both a Python variable and an alias, the
         alias can not be executed until 'del foo' removes the Python variable.
 
         You can use the %l specifier in an alias definition to represent the
         whole line when the alias is called.  For example:
 
           In [2]: alias all echo "Input in brackets: <%l>"\
           In [3]: all hello world\
           Input in brackets: <hello world>
 
         You can also define aliases with parameters using %s specifiers (one
         per parameter):
         
           In [1]: alias parts echo first %s second %s\
           In [2]: %parts A B\
           first A second B\
           In [3]: %parts A\
           Incorrect number of arguments: 2 expected.\
           parts is an alias to: 'echo first %s second %s'
 
         Note that %l and %s are mutually exclusive.  You can only use one or
         the other in your aliases.
 
         Aliases expand Python variables just like system calls using ! or !! 
         do: all expressions prefixed with '$' get expanded.  For details of
         the semantic rules, see PEP-215:
         http://www.python.org/peps/pep-0215.html.  This is the library used by
         IPython for variable expansion.  If you want to access a true shell
         variable, an extra $ is necessary to prevent its expansion by IPython:
 
         In [6]: alias show echo\
         In [7]: PATH='A Python string'\
         In [8]: show $PATH\
         A Python string\
         In [9]: show $$PATH\
         /usr/local/lf9560/bin:/usr/local/intel/compiler70/ia32/bin:...
 
         You can use the alias facility to acess all of $PATH.  See the %rehash
         and %rehashx functions, which automatically create aliases for the
         contents of your $PATH.
 
         If called with no parameters, %alias prints the current alias table.
 
 **%autocall**::
 
 	Make functions callable without having to type parentheses.
 
         Usage:
 
            %autocall [mode]
 
         The mode can be one of: 0->Off, 1->Smart, 2->Full.  If not given, the
         value is toggled on and off (remembering the previous state).
 
         In more detail, these values mean:
 
         0 -> fully disabled
 
         1 -> active, but do not apply if there are no arguments on the line.
 
         In this mode, you get:
 
         In [1]: callable
         Out[1]: <built-in function callable>
 
         In [2]: callable 'hello'
         ------> callable('hello')
         Out[2]: False
 
         2 -> Active always.  Even if no arguments are present, the callable
         object is called:
 
         In [4]: callable
         ------> callable()
 
         Note that even with autocall off, you can still use '/' at the start of
         a line to treat the first argument on the command line as a function
         and add parentheses to it:
 
         In [8]: /str 43
         ------> str(43)
         Out[8]: '43'
 
 **%autoindent**::
 
 	Toggle autoindent on/off (if available).
 
 **%automagic**::
 
 	Make magic functions callable without having to type the initial %.
 
         Without argumentsl toggles on/off (when off, you must call it as
         %automagic, of course).  With arguments it sets the value, and you can
         use any of (case insensitive):
 
          - on,1,True: to activate
 
          - off,0,False: to deactivate.
 
         Note that magic functions have lowest priority, so if there's a
         variable whose name collides with that of a magic fn, automagic won't
         work for that function (you get the variable instead). However, if you
         delete the variable (del var), the previously shadowed magic function
         becomes visible to automagic again.
 
 **%bg**::
 
 	Run a job in the background, in a separate thread.
 
         For example,
 
           %bg myfunc(x,y,z=1)
 
         will execute 'myfunc(x,y,z=1)' in a background thread.  As soon as the
         execution starts, a message will be printed indicating the job
         number.  If your job number is 5, you can use
 
           myvar = jobs.result(5)  or  myvar = jobs[5].result
 
         to assign this result to variable 'myvar'.
 
         IPython has a job manager, accessible via the 'jobs' object.  You can
         type jobs? to get more information about it, and use jobs.<TAB> to see
         its attributes.  All attributes not starting with an underscore are
         meant for public use.
 
         In particular, look at the jobs.new() method, which is used to create
         new jobs.  This magic %bg function is just a convenience wrapper
         around jobs.new(), for expression-based jobs.  If you want to create a
         new job with an explicit function object and arguments, you must call
         jobs.new() directly.
 
         The jobs.new docstring also describes in detail several important
         caveats associated with a thread-based model for background job
         execution.  Type jobs.new? for details.
 
         You can check the status of all jobs with jobs.status().
 
         The jobs variable is set by IPython into the Python builtin namespace.
         If you ever declare a variable named 'jobs', you will shadow this
         name.  You can either delete your global jobs variable to regain
         access to the job manager, or make a new name and assign it manually
         to the manager (stored in IPython's namespace).  For example, to
         assign the job manager to the Jobs name, use:
 
           Jobs = __builtins__.jobs
 
 **%bookmark**::
 
 	Manage IPython's bookmark system.
 
         %bookmark <name>       - set bookmark to current dir
         %bookmark <name> <dir> - set bookmark to <dir>
         %bookmark -l           - list all bookmarks
         %bookmark -d <name>    - remove bookmark
         %bookmark -r           - remove all bookmarks
 
         You can later on access a bookmarked folder with:
           %cd -b <name>
         or simply '%cd <name>' if there is no directory called <name> AND
         there is such a bookmark defined.
 
         Your bookmarks persist through IPython sessions, but they are
         associated with each profile.
 
 **%cd**::
 
 	Change the current working directory.
 
         This command automatically maintains an internal list of directories
         you visit during your IPython session, in the variable _dh. The
         command %dhist shows this history nicely formatted. You can also
         do 'cd -<tab>' to see directory history conveniently.
 
         Usage:
 
           cd 'dir': changes to directory 'dir'.
 
           cd -: changes to the last visited directory.
 
           cd -<n>: changes to the n-th directory in the directory history.
 
           cd -b <bookmark_name>: jump to a bookmark set by %bookmark
              (note: cd <bookmark_name> is enough if there is no
               directory <bookmark_name>, but a bookmark with the name exists.)
               'cd -b <tab>' allows you to tab-complete bookmark names. 
 
         Options:
 
         -q: quiet.  Do not print the working directory after the cd command is
         executed.  By default IPython's cd command does print this directory,
         since the default prompts do not display path information.
         
         Note that !cd doesn't work for this purpose because the shell where
         !command runs is immediately discarded after executing 'command'.
 
 **%clear**::
 
 	 Clear various data (e.g. stored history data)
     
     %clear out - clear output history
     %clear in  - clear input history
     %clear shadow_compress - Compresses shadow history (to speed up ipython)
     %clear shadow_nuke - permanently erase all entries in shadow history
     %clear dhist - clear dir history
 
 **%color_info**::
 
 	Toggle color_info.
 
         The color_info configuration parameter controls whether colors are
         used for displaying object details (by things like %psource, %pfile or
         the '?' system). This function toggles this value with each call.
 
         Note that unless you have a fairly recent pager (less works better
         than more) in your system, using colored object information displays
         will not work properly. Test it and see.
 
 **%colors**::
 
 	Switch color scheme for prompts, info system and exception handlers.
 
         Currently implemented schemes: NoColor, Linux, LightBG.
 
         Color scheme names are not case-sensitive.
 
 **%cpaste**::
 
 	Allows you to paste & execute a pre-formatted code block from clipboard
         
         You must terminate the block with '--' (two minus-signs) alone on the
         line. You can also provide your own sentinel with '%paste -s %%' ('%%' 
         is the new sentinel for this operation)
         
         The block is dedented prior to execution to enable execution of method
         definitions. '>' and '+' characters at the beginning of a line are
         ignored, to allow pasting directly from e-mails or diff files. The
         executed block is also assigned to variable named 'pasted_block' for
         later editing with '%edit pasted_block'.
         
         You can also pass a variable name as an argument, e.g. '%cpaste foo'.
         This assigns the pasted block to variable 'foo' as string, without 
         dedenting or executing it.
         
         Do not be alarmed by garbled output on Windows (it's a readline bug). 
         Just press enter and type -- (and press enter again) and the block 
         will be what was just pasted.
         
         IPython statements (magics, shell escapes) are not supported (yet).
 
 **%debug**::
 
 	Activate the interactive debugger in post-mortem mode.
 
         If an exception has just occurred, this lets you inspect its stack
         frames interactively.  Note that this will always work only on the last
         traceback that occurred, so you must call this quickly after an
         exception that you wish to inspect has fired, because if another one
         occurs, it clobbers the previous one.
 
         If you want IPython to automatically do this on every exception, see
         the %pdb magic for more details.
 
 **%dhist**::
 
 	Print your history of visited directories.
 
         %dhist       -> print full history\
         %dhist n     -> print last n entries only\
         %dhist n1 n2 -> print entries between n1 and n2 (n1 not included)\
 
         This history is automatically maintained by the %cd command, and
         always available as the global list variable _dh. You can use %cd -<n>
         to go to directory number <n>.
         
         Note that most of time, you should view directory history by entering
         cd -<TAB>.
 
 **%dirs**::
 
 	Return the current directory stack.
 
 **%doctest_mode**::
 
 	Toggle doctest mode on and off.
 
         This mode allows you to toggle the prompt behavior between normal
         IPython prompts and ones that are as similar to the default IPython
         interpreter as possible.
 
         It also supports the pasting of code snippets that have leading '>>>'
         and '...' prompts in them.  This means that you can paste doctests from
         files or docstrings (even if they have leading whitespace), and the
         code will execute correctly.  You can then use '%history -tn' to see
         the translated history without line numbers; this will give you the
         input after removal of all the leading prompts and whitespace, which
         can be pasted back into an editor.
 
         With these features, you can switch into this mode easily whenever you
         need to do testing and changes to doctests, without having to leave
         your existing IPython session.
 
 **%ed**::
 
 	Alias to %edit.
 
 **%edit**::
 
 	Bring up an editor and execute the resulting code.
 
         Usage:
           %edit [options] [args]
 
         %edit runs IPython's editor hook.  The default version of this hook is
         set to call the __IPYTHON__.rc.editor command.  This is read from your
         environment variable $EDITOR.  If this isn't found, it will default to
         vi under Linux/Unix and to notepad under Windows.  See the end of this
         docstring for how to change the editor hook.
 
         You can also set the value of this editor via the command line option
         '-editor' or in your ipythonrc file. This is useful if you wish to use
         specifically for IPython an editor different from your typical default
         (and for Windows users who typically don't set environment variables).
 
         This command allows you to conveniently edit multi-line code right in
         your IPython session.
         
         If called without arguments, %edit opens up an empty editor with a
         temporary file and will execute the contents of this file when you
         close it (don't forget to save it!).
 
 
         Options:
 
         -n <number>: open the editor at a specified line number.  By default,
         the IPython editor hook uses the unix syntax 'editor +N filename', but
         you can configure this by providing your own modified hook if your
         favorite editor supports line-number specifications with a different
         syntax.
         
         -p: this will call the editor with the same data as the previous time
         it was used, regardless of how long ago (in your current session) it
         was.
 
         -r: use 'raw' input.  This option only applies to input taken from the
         user's history.  By default, the 'processed' history is used, so that
         magics are loaded in their transformed version to valid Python.  If
         this option is given, the raw input as typed as the command line is
         used instead.  When you exit the editor, it will be executed by
         IPython's own processor.
         
         -x: do not execute the edited code immediately upon exit. This is
         mainly useful if you are editing programs which need to be called with
         command line arguments, which you can then do using %run.
 
 
         Arguments:
 
         If arguments are given, the following possibilites exist:
 
         - The arguments are numbers or pairs of colon-separated numbers (like
         1 4:8 9). These are interpreted as lines of previous input to be
         loaded into the editor. The syntax is the same of the %macro command.
 
         - If the argument doesn't start with a number, it is evaluated as a
         variable and its contents loaded into the editor. You can thus edit
         any string which contains python code (including the result of
         previous edits).
 
         - If the argument is the name of an object (other than a string),
         IPython will try to locate the file where it was defined and open the
         editor at the point where it is defined. You can use `%edit function`
         to load an editor exactly at the point where 'function' is defined,
         edit it and have the file be executed automatically.
 
         If the object is a macro (see %macro for details), this opens up your
         specified editor with a temporary file containing the macro's data.
         Upon exit, the macro is reloaded with the contents of the file.
 
         Note: opening at an exact line is only supported under Unix, and some
         editors (like kedit and gedit up to Gnome 2.8) do not understand the
         '+NUMBER' parameter necessary for this feature. Good editors like
         (X)Emacs, vi, jed, pico and joe all do.
 
         - If the argument is not found as a variable, IPython will look for a
         file with that name (adding .py if necessary) and load it into the
         editor. It will execute its contents with execfile() when you exit,
         loading any code in the file into your interactive namespace.
 
         After executing your code, %edit will return as output the code you
         typed in the editor (except when it was an existing file). This way
         you can reload the code in further invocations of %edit as a variable,
         via _<NUMBER> or Out[<NUMBER>], where <NUMBER> is the prompt number of
         the output.
 
         Note that %edit is also available through the alias %ed.
 
         This is an example of creating a simple function inside the editor and
         then modifying it. First, start up the editor:
 
         In [1]: ed\
         Editing... done. Executing edited code...\
         Out[1]: 'def foo():\n    print "foo() was defined in an editing session"\n'
 
         We can then call the function foo():
         
         In [2]: foo()\
         foo() was defined in an editing session
 
         Now we edit foo.  IPython automatically loads the editor with the
         (temporary) file where foo() was previously defined:
         
         In [3]: ed foo\
         Editing... done. Executing edited code...
 
         And if we call foo() again we get the modified version:
         
         In [4]: foo()\
         foo() has now been changed!
 
         Here is an example of how to edit a code snippet successive
         times. First we call the editor:
 
         In [8]: ed\
         Editing... done. Executing edited code...\
         hello\
         Out[8]: "print 'hello'\n"
 
         Now we call it again with the previous output (stored in _):
 
         In [9]: ed _\
         Editing... done. Executing edited code...\
         hello world\
         Out[9]: "print 'hello world'\n"
 
         Now we call it with the output #8 (stored in _8, also as Out[8]):
 
         In [10]: ed _8\
         Editing... done. Executing edited code...\
         hello again\
         Out[10]: "print 'hello again'\n"
 
 
         Changing the default editor hook:
 
         If you wish to write your own editor hook, you can put it in a
         configuration file which you load at startup time.  The default hook
         is defined in the IPython.hooks module, and you can use that as a
         starting example for further modifications.  That file also has
         general instructions on how to set a new hook for use once you've
         defined it.
 
 **%env**::
 
 	List environment variables.
 
 **%exit**::
 
 	Exit IPython, confirming if configured to do so.
 
         You can configure whether IPython asks for confirmation upon exit by
         setting the confirm_exit flag in the ipythonrc file.
 
 **%hist**::
 
 	Alternate name for %history.
 
 **%history**::
 
 	Print input history (_i<n> variables), with most recent last.
     
     %history       -> print at most 40 inputs (some may be multi-line)\
     %history n     -> print at most n inputs\
     %history n1 n2 -> print inputs between n1 and n2 (n2 not included)\
     
     Each input's number <n> is shown, and is accessible as the
     automatically generated variable _i<n>.  Multi-line statements are
     printed starting at a new line for easy copy/paste.
     
 
     Options:
 
       -n: do NOT print line numbers. This is useful if you want to get a
       printout of many lines which can be directly pasted into a text
       editor.
 
       This feature is only available if numbered prompts are in use.
 
       -t: (default) print the 'translated' history, as IPython understands it.
       IPython filters your input and converts it all into valid Python source
       before executing it (things like magics or aliases are turned into
       function calls, for example). With this option, you'll see the native
       history instead of the user-entered version: '%cd /' will be seen as
       '_ip.magic("%cd /")' instead of '%cd /'.
       
       -r: print the 'raw' history, i.e. the actual commands you typed.
       
       -g: treat the arg as a pattern to grep for in (full) history.
       This includes the "shadow history" (almost all commands ever written).
       Use '%hist -g' to show full shadow history (may be very long).
       In shadow history, every index nuwber starts with 0.
 
       -f FILENAME: instead of printing the output to the screen, redirect it to
        the given file.  The file is always overwritten, though IPython asks for
        confirmation first if it already exists.
 
 **%logoff**::
 
 	Temporarily stop logging.
 
         You must have previously started logging.
 
 **%logon**::
 
 	Restart logging.
 
         This function is for restarting logging which you've temporarily
         stopped with %logoff. For starting logging for the first time, you
         must use the %logstart function, which allows you to specify an
         optional log filename.
 
 **%logstart**::
 
 	Start logging anywhere in a session.
 
         %logstart [-o|-r|-t] [log_name [log_mode]]
 
         If no name is given, it defaults to a file named 'ipython_log.py' in your
         current directory, in 'rotate' mode (see below).
 
         '%logstart name' saves to file 'name' in 'backup' mode.  It saves your
         history up to that point and then continues logging.
 
         %logstart takes a second optional parameter: logging mode. This can be one
         of (note that the modes are given unquoted):\
           append: well, that says it.\
           backup: rename (if exists) to name~ and start name.\
           global: single logfile in your home dir, appended to.\
           over  : overwrite existing log.\
           rotate: create rotating logs name.1~, name.2~, etc.
 
         Options:
 
           -o: log also IPython's output.  In this mode, all commands which
           generate an Out[NN] prompt are recorded to the logfile, right after
           their corresponding input line.  The output lines are always
           prepended with a '#[Out]# ' marker, so that the log remains valid
           Python code.
 
           Since this marker is always the same, filtering only the output from
           a log is very easy, using for example a simple awk call:
 
             awk -F'#\[Out\]# ' '{if($2) {print $2}}' ipython_log.py
 
           -r: log 'raw' input.  Normally, IPython's logs contain the processed
           input, so that user lines are logged in their final form, converted
           into valid Python.  For example, %Exit is logged as
           '_ip.magic("Exit").  If the -r flag is given, all input is logged
           exactly as typed, with no transformations applied.
 
           -t: put timestamps before each input line logged (these are put in
           comments).
 
 **%logstate**::
 
 	Print the status of the logging system.
 
 **%logstop**::
 
 	Fully stop logging and close log file.
 
         In order to start logging again, a new %logstart call needs to be made,
         possibly (though not necessarily) with a new filename, mode and other
         options.
 
 **%lsmagic**::
 
 	List currently available magic functions.
 
 **%macro**::
 
 	Define a set of input lines as a macro for future re-execution.
 
         Usage:\
           %macro [options] name n1-n2 n3-n4 ... n5 .. n6 ...
 
         Options:
         
           -r: use 'raw' input.  By default, the 'processed' history is used,
           so that magics are loaded in their transformed version to valid
           Python.  If this option is given, the raw input as typed as the
           command line is used instead.
 
         This will define a global variable called `name` which is a string
         made of joining the slices and lines you specify (n1,n2,... numbers
         above) from your input history into a single string. This variable
         acts like an automatic function which re-executes those lines as if
         you had typed them. You just type 'name' at the prompt and the code
         executes.
 
         The notation for indicating number ranges is: n1-n2 means 'use line
         numbers n1,...n2' (the endpoint is included).  That is, '5-7' means
         using the lines numbered 5,6 and 7.
 
         Note: as a 'hidden' feature, you can also use traditional python slice
         notation, where N:M means numbers N through M-1.
 
         For example, if your history contains (%hist prints it):
         
           44: x=1\
           45: y=3\
           46: z=x+y\
           47: print x\
           48: a=5\
           49: print 'x',x,'y',y\
 
         you can create a macro with lines 44 through 47 (included) and line 49
         called my_macro with:
 
           In [51]: %macro my_macro 44-47 49
 
         Now, typing `my_macro` (without quotes) will re-execute all this code
         in one pass.
 
         You don't need to give the line-numbers in order, and any given line
         number can appear multiple times. You can assemble macros with any
         lines from your input history in any order.
 
         The macro is a simple object which holds its value in an attribute,
         but IPython's display system checks for macros and executes them as
         code instead of printing them when you type their name.
 
         You can view a macro's contents by explicitly printing it with:
         
           'print macro_name'.
 
         For one-off cases which DON'T contain magic function calls in them you
         can obtain similar results by explicitly executing slices from your
         input history with:
 
           In [60]: exec In[44:48]+In[49]
 
 **%magic**::
 
 	Print information about the magic function system.
 
 **%mglob**::
 
 	    This program allows specifying filenames with "mglob" mechanism.
     Supported syntax in globs (wilcard matching patterns)::
     
      *.cpp ?ellowo*                
          - obvious. Differs from normal glob in that dirs are not included.
            Unix users might want to write this as: "*.cpp" "?ellowo*"
      rec:/usr/share=*.txt,*.doc    
          - get all *.txt and *.doc under /usr/share, 
            recursively
      rec:/usr/share
          - All files under /usr/share, recursively
      rec:*.py
          - All .py files under current working dir, recursively
      foo                           
          - File or dir foo
      !*.bak readme*                   
          - readme*, exclude files ending with .bak
      !.svn/ !.hg/ !*_Data/ rec:.
          - Skip .svn, .hg, foo_Data dirs (and their subdirs) in recurse.
            Trailing / is the key, \ does not work!
      dir:foo                       
          - the directory foo if it exists (not files in foo)
      dir:*                         
          - all directories in current folder
      foo.py bar.* !h* rec:*.py
          - Obvious. !h* exclusion only applies for rec:*.py.
            foo.py is *not* included twice.
      @filelist.txt
          - All files listed in 'filelist.txt' file, on separate lines.
 
 **%page**::
 
 	Pretty print the object and display it through a pager.
         
         %page [options] OBJECT
 
         If no object is given, use _ (last output).
         
         Options:
 
           -r: page str(object), don't pretty-print it.
 
 **%pdb**::
 
 	Control the automatic calling of the pdb interactive debugger.
 
         Call as '%pdb on', '%pdb 1', '%pdb off' or '%pdb 0'. If called without
         argument it works as a toggle.
 
         When an exception is triggered, IPython can optionally call the
         interactive pdb debugger after the traceback printout. %pdb toggles
         this feature on and off.
 
         The initial state of this feature is set in your ipythonrc
         configuration file (the variable is called 'pdb').
 
         If you want to just activate the debugger AFTER an exception has fired,
         without having to type '%pdb on' and rerunning your code, you can use
         the %debug magic.
 
 **%pdef**::
 
 	Print the definition header for any callable object.
 
         If the object is a class, print the constructor information.
 
 **%pdoc**::
 
 	Print the docstring for an object.
 
         If the given object is a class, it will print both the class and the
         constructor docstrings.
 
 **%pfile**::
 
 	Print (or run through pager) the file where an object is defined.
 
         The file opens at the line where the object definition begins. IPython
         will honor the environment variable PAGER if set, and otherwise will
         do its best to print the file in a convenient form.
 
         If the given argument is not an object currently defined, IPython will
         try to interpret it as a filename (automatically adding a .py extension
         if needed). You can thus use %pfile as a syntax highlighting code
         viewer.
 
 **%pinfo**::
 
 	Provide detailed information about an object.
     
         '%pinfo object' is just a synonym for object? or ?object.
 
 **%popd**::
 
 	Change to directory popped off the top of the stack.
 
 **%profile**::
 
 	Print your currently active IPyhton profile.
 
 **%prun**::
 
 	Run a statement through the python code profiler.
 
         Usage:\
           %prun [options] statement
 
         The given statement (which doesn't require quote marks) is run via the
         python profiler in a manner similar to the profile.run() function.
         Namespaces are internally managed to work correctly; profile.run
         cannot be used in IPython because it makes certain assumptions about
         namespaces which do not hold under IPython.
 
         Options:
 
         -l <limit>: you can place restrictions on what or how much of the
         profile gets printed. The limit value can be:
 
           * A string: only information for function names containing this string
           is printed.
 
           * An integer: only these many lines are printed.
 
           * A float (between 0 and 1): this fraction of the report is printed
           (for example, use a limit of 0.4 to see the topmost 40% only).
 
         You can combine several limits with repeated use of the option. For
         example, '-l __init__ -l 5' will print only the topmost 5 lines of
         information about class constructors.
 
         -r: return the pstats.Stats object generated by the profiling. This
         object has all the information about the profile in it, and you can
         later use it for further analysis or in other functions.
 
        -s <key>: sort profile by given key. You can provide more than one key
         by using the option several times: '-s key1 -s key2 -s key3...'. The
         default sorting key is 'time'.
 
         The following is copied verbatim from the profile documentation
         referenced below:
 
         When more than one key is provided, additional keys are used as
         secondary criteria when the there is equality in all keys selected
         before them.
         
         Abbreviations can be used for any key names, as long as the
         abbreviation is unambiguous.  The following are the keys currently
         defined:
 
                 Valid Arg       Meaning\
                   "calls"      call count\
                   "cumulative" cumulative time\
                   "file"       file name\
                   "module"     file name\
                   "pcalls"     primitive call count\
                   "line"       line number\
                   "name"       function name\
                   "nfl"        name/file/line\
                   "stdname"    standard name\
                   "time"       internal time
 
         Note that all sorts on statistics are in descending order (placing
         most time consuming items first), where as name, file, and line number
         searches are in ascending order (i.e., alphabetical). The subtle
         distinction between "nfl" and "stdname" is that the standard name is a
         sort of the name as printed, which means that the embedded line
         numbers get compared in an odd way.  For example, lines 3, 20, and 40
         would (if the file names were the same) appear in the string order
         "20" "3" and "40".  In contrast, "nfl" does a numeric compare of the
         line numbers.  In fact, sort_stats("nfl") is the same as
         sort_stats("name", "file", "line").
 
         -T <filename>: save profile results as shown on screen to a text
         file. The profile is still shown on screen.
 
         -D <filename>: save (via dump_stats) profile statistics to given
         filename. This data is in a format understod by the pstats module, and
         is generated by a call to the dump_stats() method of profile
         objects. The profile is still shown on screen.
 
         If you want to run complete programs under the profiler's control, use
         '%run -p [prof_opts] filename.py [args to program]' where prof_opts
         contains profiler specific options as described here.
         
         You can read the complete documentation for the profile module with:\
           In [1]: import profile; profile.help()
 
 **%psearch**::
 
 	Search for object in namespaces by wildcard.
 
         %psearch [options] PATTERN [OBJECT TYPE]
 
         Note: ? can be used as a synonym for %psearch, at the beginning or at
         the end: both a*? and ?a* are equivalent to '%psearch a*'.  Still, the
         rest of the command line must be unchanged (options come first), so
         for example the following forms are equivalent
 
         %psearch -i a* function
         -i a* function?
         ?-i a* function
 
         Arguments:
         
           PATTERN
 
           where PATTERN is a string containing * as a wildcard similar to its
           use in a shell.  The pattern is matched in all namespaces on the
           search path. By default objects starting with a single _ are not
           matched, many IPython generated objects have a single
           underscore. The default is case insensitive matching. Matching is
           also done on the attributes of objects and not only on the objects
           in a module.
 
           [OBJECT TYPE]
 
           Is the name of a python type from the types module. The name is
           given in lowercase without the ending type, ex. StringType is
           written string. By adding a type here only objects matching the
           given type are matched. Using all here makes the pattern match all
           types (this is the default).
 
         Options:
 
           -a: makes the pattern match even objects whose names start with a
           single underscore.  These names are normally ommitted from the
           search.
 
           -i/-c: make the pattern case insensitive/sensitive.  If neither of
           these options is given, the default is read from your ipythonrc
           file.  The option name which sets this value is
           'wildcards_case_sensitive'.  If this option is not specified in your
           ipythonrc file, IPython's internal default is to do a case sensitive
           search.
 
           -e/-s NAMESPACE: exclude/search a given namespace.  The pattern you
           specifiy can be searched in any of the following namespaces:
           'builtin', 'user', 'user_global','internal', 'alias', where
           'builtin' and 'user' are the search defaults.  Note that you should
           not use quotes when specifying namespaces.
 
           'Builtin' contains the python module builtin, 'user' contains all
           user data, 'alias' only contain the shell aliases and no python
           objects, 'internal' contains objects used by IPython.  The
           'user_global' namespace is only used by embedded IPython instances,
           and it contains module-level globals.  You can add namespaces to the
           search with -s or exclude them with -e (these options can be given
           more than once).
     
         Examples:
        
         %psearch a*            -> objects beginning with an a
         %psearch -e builtin a* -> objects NOT in the builtin space starting in a
         %psearch a* function   -> all functions beginning with an a
         %psearch re.e*         -> objects beginning with an e in module re
         %psearch r*.e*         -> objects that start with e in modules starting in r
         %psearch r*.* string   -> all strings in modules beginning with r
 
         Case sensitve search:
        
         %psearch -c a*         list all object beginning with lower case a
 
         Show objects beginning with a single _:
        
         %psearch -a _*         list objects beginning with a single underscore
 
 **%psource**::
 
 	Print (or run through pager) the source code for an object.
 
 **%pushd**::
 
 	Place the current dir on stack and change directory.
         
         Usage:\
           %pushd ['dirname']
 
 **%pwd**::
 
 	Return the current working directory path.
 
 **%pycat**::
 
 	Show a syntax-highlighted file through a pager.
 
         This magic is similar to the cat utility, but it will assume the file
         to be Python source and will show it with syntax highlighting.
 
 **%quickref**::
 
 	 Show a quick reference sheet
 
 **%quit**::
 
 	Exit IPython, confirming if configured to do so (like %exit)
 
 **%r**::
 
 	Repeat previous input.
 
         Note: Consider using the more powerfull %rep instead!
         
         If given an argument, repeats the previous command which starts with
         the same string, otherwise it just repeats the previous input.
 
         Shell escaped commands (with ! as first character) are not recognized
         by this system, only pure python code and magic commands.
 
 **%rehashdir**::
 
 	 Add executables in all specified dirs to alias table
      
     Usage:
 
     %rehashdir c:/bin;c:/tools
       - Add all executables under c:/bin and c:/tools to alias table, in 
       order to make them directly executable from any directory.
         
       Without arguments, add all executables in current directory.
 
 **%rehashx**::
 
 	Update the alias table with all executable files in $PATH.
 
         This version explicitly checks that every entry in $PATH is a file
         with execute access (os.X_OK), so it is much slower than %rehash.
 
         Under Windows, it checks executability as a match agains a
         '|'-separated string of extensions, stored in the IPython config
         variable win_exec_ext.  This defaults to 'exe|com|bat'.
         
         This function also resets the root module cache of module completer,
         used on slow filesystems.
 
 **%rep**::
 
 	 Repeat a command, or get command to input line for editing
 
     - %rep (no arguments):
     
     Place a string version of last computation result (stored in the special '_'
     variable) to the next input prompt. Allows you to create elaborate command
     lines without using copy-paste::
     
         $ l = ["hei", "vaan"]       
         $ "".join(l)        
         ==> heivaan        
         $ %rep        
         $ heivaan_ <== cursor blinking    
     
     %rep 45
     
     Place history line 45 to next input prompt. Use %hist to find out the
     number.
     
     %rep 1-4 6-7 3
     
     Repeat the specified lines immediately. Input slice syntax is the same as
     in %macro and %save.
     
     %rep foo
     
     Place the most recent line that has the substring "foo" to next input.
     (e.g. 'svn ci -m foobar').
 
 **%reset**::
 
 	Resets the namespace by removing all names defined by the user.
 
         Input/Output history are left around in case you need them.
 
 **%run**::
 
 	Run the named file inside IPython as a program.
 
         Usage:\
           %run [-n -i -t [-N<N>] -d [-b<N>] -p [profile options]] file [args]
         
         Parameters after the filename are passed as command-line arguments to
         the program (put in sys.argv). Then, control returns to IPython's
         prompt.
 
         This is similar to running at a system prompt:\
           $ python file args\
         but with the advantage of giving you IPython's tracebacks, and of
         loading all variables into your interactive namespace for further use
         (unless -p is used, see below).
 
         The file is executed in a namespace initially consisting only of
         __name__=='__main__' and sys.argv constructed as indicated. It thus
         sees its environment as if it were being run as a stand-alone program
         (except for sharing global objects such as previously imported
         modules). But after execution, the IPython interactive namespace gets
         updated with all variables defined in the program (except for __name__
         and sys.argv). This allows for very convenient loading of code for
         interactive work, while giving each program a 'clean sheet' to run in.
 
         Options:
         
         -n: __name__ is NOT set to '__main__', but to the running file's name
         without extension (as python does under import).  This allows running
         scripts and reloading the definitions in them without calling code
         protected by an ' if __name__ == "__main__" ' clause.
 
         -i: run the file in IPython's namespace instead of an empty one. This
         is useful if you are experimenting with code written in a text editor
         which depends on variables defined interactively.
 
         -e: ignore sys.exit() calls or SystemExit exceptions in the script
         being run.  This is particularly useful if IPython is being used to
         run unittests, which always exit with a sys.exit() call.  In such
         cases you are interested in the output of the test results, not in
         seeing a traceback of the unittest module.
 
         -t: print timing information at the end of the run.  IPython will give
         you an estimated CPU time consumption for your script, which under
         Unix uses the resource module to avoid the wraparound problems of
         time.clock().  Under Unix, an estimate of time spent on system tasks
         is also given (for Windows platforms this is reported as 0.0).
 
         If -t is given, an additional -N<N> option can be given, where <N>
         must be an integer indicating how many times you want the script to
         run.  The final timing report will include total and per run results.
 
         For example (testing the script uniq_stable.py):
 
             In [1]: run -t uniq_stable
 
             IPython CPU timings (estimated):\
               User  :    0.19597 s.\
               System:        0.0 s.\
 
             In [2]: run -t -N5 uniq_stable
 
             IPython CPU timings (estimated):\
             Total runs performed: 5\
               Times :      Total       Per run\
               User  :   0.910862 s,  0.1821724 s.\
               System:        0.0 s,        0.0 s.
 
         -d: run your program under the control of pdb, the Python debugger.
         This allows you to execute your program step by step, watch variables,
         etc.  Internally, what IPython does is similar to calling:
         
           pdb.run('execfile("YOURFILENAME")')
 
         with a breakpoint set on line 1 of your file.  You can change the line
         number for this automatic breakpoint to be <N> by using the -bN option
         (where N must be an integer).  For example:
 
           %run -d -b40 myscript
 
         will set the first breakpoint at line 40 in myscript.py.  Note that
         the first breakpoint must be set on a line which actually does
         something (not a comment or docstring) for it to stop execution.
 
         When the pdb debugger starts, you will see a (Pdb) prompt.  You must
         first enter 'c' (without qoutes) to start execution up to the first
         breakpoint.
 
         Entering 'help' gives information about the use of the debugger.  You
         can easily see pdb's full documentation with "import pdb;pdb.help()"
         at a prompt.
 
         -p: run program under the control of the Python profiler module (which
         prints a detailed report of execution times, function calls, etc).
 
         You can pass other options after -p which affect the behavior of the
         profiler itself. See the docs for %prun for details.
 
         In this mode, the program's variables do NOT propagate back to the
         IPython interactive namespace (because they remain in the namespace
         where the profiler executes them).
 
         Internally this triggers a call to %prun, see its documentation for
         details on the options available specifically for profiling.
 
         There is one special usage for which the text above doesn't apply:
         if the filename ends with .ipy, the file is run as ipython script,
         just as if the commands were written on IPython prompt.
 
 **%runlog**::
 
 	Run files as logs.
 
         Usage:\
           %runlog file1 file2 ...
 
         Run the named files (treating them as log files) in sequence inside
         the interpreter, and return to the prompt.  This is much slower than
         %run because each line is executed in a try/except block, but it
         allows running files with syntax errors in them.
 
         Normally IPython will guess when a file is one of its own logfiles, so
         you can typically use %run even for logs. This shorthand allows you to
         force any file to be treated as a log file.
 
 **%save**::
 
 	Save a set of lines to a given filename.
 
         Usage:\
           %save [options] filename n1-n2 n3-n4 ... n5 .. n6 ...
 
         Options:
         
           -r: use 'raw' input.  By default, the 'processed' history is used,
           so that magics are loaded in their transformed version to valid
           Python.  If this option is given, the raw input as typed as the
           command line is used instead.
 
         This function uses the same syntax as %macro for line extraction, but
         instead of creating a macro it saves the resulting string to the
         filename you specify.
 
         It adds a '.py' extension to the file if you don't do so yourself, and
         it asks for confirmation before overwriting existing files.
 
 **%sc**::
 
 	Shell capture - execute a shell command and capture its output.
 
         DEPRECATED. Suboptimal, retained for backwards compatibility.
         
         You should use the form 'var = !command' instead. Example:
          
          "%sc -l myfiles = ls ~" should now be written as
             
          "myfiles = !ls ~"
          
         myfiles.s, myfiles.l and myfiles.n still apply as documented
         below.
 
         --
         %sc [options] varname=command
 
         IPython will run the given command using commands.getoutput(), and
         will then update the user's interactive namespace with a variable
         called varname, containing the value of the call.  Your command can
         contain shell wildcards, pipes, etc.
 
         The '=' sign in the syntax is mandatory, and the variable name you
         supply must follow Python's standard conventions for valid names.
         
         (A special format without variable name exists for internal use)
 
         Options:
 
           -l: list output.  Split the output on newlines into a list before
           assigning it to the given variable.  By default the output is stored
           as a single string.
 
           -v: verbose.  Print the contents of the variable.
 
         In most cases you should not need to split as a list, because the
         returned value is a special type of string which can automatically
         provide its contents either as a list (split on newlines) or as a
         space-separated string.  These are convenient, respectively, either
         for sequential processing or to be passed to a shell command.
 
         For example:
 
             # Capture into variable a
             In [9]: sc a=ls *py
 
             # a is a string with embedded newlines
             In [10]: a
             Out[10]: 'setup.py win32_manual_post_install.py'
 
             # which can be seen as a list:
             In [11]: a.l
             Out[11]: ['setup.py', 'win32_manual_post_install.py']
 
             # or as a whitespace-separated string:
             In [12]: a.s
             Out[12]: 'setup.py win32_manual_post_install.py'
 
             # a.s is useful to pass as a single command line:
             In [13]: !wc -l $a.s
               146 setup.py
               130 win32_manual_post_install.py
               276 total
 
             # while the list form is useful to loop over:
             In [14]: for f in a.l:
                ....:      !wc -l $f
                ....:
             146 setup.py
             130 win32_manual_post_install.py
 
         Similiarly, the lists returned by the -l option are also special, in
         the sense that you can equally invoke the .s attribute on them to
         automatically get a whitespace-separated string from their contents:
 
             In [1]: sc -l b=ls *py
 
             In [2]: b
             Out[2]: ['setup.py', 'win32_manual_post_install.py']
 
             In [3]: b.s
             Out[3]: 'setup.py win32_manual_post_install.py'
 
         In summary, both the lists and strings used for ouptut capture have
         the following special attributes:
 
             .l (or .list) : value as list.
             .n (or .nlstr): value as newline-separated string.
             .s (or .spstr): value as space-separated string.
 
 **%store**::
 
 	Lightweight persistence for python variables.
 
     Example:
     
     ville@badger[~]|1> A = ['hello',10,'world']\
     ville@badger[~]|2> %store A\
     ville@badger[~]|3> Exit
     
     (IPython session is closed and started again...)
     
     ville@badger:~$ ipython -p pysh\
     ville@badger[~]|1> print A
     
     ['hello', 10, 'world']
     
     Usage:
     
     %store          - Show list of all variables and their current values\
     %store <var>    - Store the *current* value of the variable to disk\
     %store -d <var> - Remove the variable and its value from storage\
     %store -z       - Remove all variables from storage\
     %store -r       - Refresh all variables from store (delete current vals)\
     %store foo >a.txt  - Store value of foo to new file a.txt\
     %store foo >>a.txt - Append value of foo to file a.txt\   
     
     It should be noted that if you change the value of a variable, you
     need to %store it again if you want to persist the new value.
     
     Note also that the variables will need to be pickleable; most basic
     python types can be safely %stored.
     
     Also aliases can be %store'd across sessions.
 
 **%sx**::
 
 	Shell execute - run a shell command and capture its output.
 
         %sx command
 
         IPython will run the given command using commands.getoutput(), and
         return the result formatted as a list (split on '\n').  Since the
         output is _returned_, it will be stored in ipython's regular output
         cache Out[N] and in the '_N' automatic variables.
 
         Notes:
 
         1) If an input line begins with '!!', then %sx is automatically
         invoked.  That is, while:
           !ls
         causes ipython to simply issue system('ls'), typing
           !!ls
         is a shorthand equivalent to:
           %sx ls
         
         2) %sx differs from %sc in that %sx automatically splits into a list,
         like '%sc -l'.  The reason for this is to make it as easy as possible
         to process line-oriented shell output via further python commands.
         %sc is meant to provide much finer control, but requires more
         typing.
 
         3) Just like %sc -l, this is a list with special attributes:
 
           .l (or .list) : value as list.
           .n (or .nlstr): value as newline-separated string.
           .s (or .spstr): value as whitespace-separated string.
 
         This is very useful when trying to use such lists as arguments to
         system commands.
 
 **%system_verbose**::
 
 	Set verbose printing of system calls.
 
         If called without an argument, act as a toggle
 
 **%time**::
 
 	Time execution of a Python statement or expression.
 
         The CPU and wall clock times are printed, and the value of the
         expression (if any) is returned.  Note that under Win32, system time
         is always reported as 0, since it can not be measured.
 
         This function provides very basic timing functionality.  In Python
         2.3, the timeit module offers more control and sophistication, so this
         could be rewritten to use it (patches welcome).
         
         Some examples:
 
           In [1]: time 2**128
           CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s
           Wall time: 0.00
           Out[1]: 340282366920938463463374607431768211456L
 
           In [2]: n = 1000000
 
           In [3]: time sum(range(n))
           CPU times: user 1.20 s, sys: 0.05 s, total: 1.25 s
           Wall time: 1.37
           Out[3]: 499999500000L
 
           In [4]: time print 'hello world'
           hello world
           CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s
           Wall time: 0.00
 
           Note that the time needed by Python to compile the given expression
           will be reported if it is more than 0.1s.  In this example, the
           actual exponentiation is done by Python at compilation time, so while
           the expression can take a noticeable amount of time to compute, that
           time is purely due to the compilation:
 
           In [5]: time 3**9999;
           CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s
           Wall time: 0.00 s
 
           In [6]: time 3**999999;
           CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s
           Wall time: 0.00 s
           Compiler : 0.78 s
 
 **%timeit**::
 
 	Time execution of a Python statement or expression
 
         Usage:\
           %timeit [-n<N> -r<R> [-t|-c]] statement
 
         Time execution of a Python statement or expression using the timeit
         module.
 
         Options:
         -n<N>: execute the given statement <N> times in a loop. If this value
         is not given, a fitting value is chosen. 
         
         -r<R>: repeat the loop iteration <R> times and take the best result.
         Default: 3
         
         -t: use time.time to measure the time, which is the default on Unix.
         This function measures wall time.
         
         -c: use time.clock to measure the time, which is the default on
         Windows and measures wall time. On Unix, resource.getrusage is used
         instead and returns the CPU user time.
 
         -p<P>: use a precision of <P> digits to display the timing result.
         Default: 3
 
         
         Examples:\
           In [1]: %timeit pass
           10000000 loops, best of 3: 53.3 ns per loop
 
           In [2]: u = None
 
           In [3]: %timeit u is None
           10000000 loops, best of 3: 184 ns per loop
 
           In [4]: %timeit -r 4 u == None
           1000000 loops, best of 4: 242 ns per loop
 
           In [5]: import time
 
           In [6]: %timeit -n1 time.sleep(2)
           1 loops, best of 3: 2 s per loop
           
 
         The times reported by %timeit will be slightly higher than those
         reported by the timeit.py script when variables are accessed. This is
         due to the fact that %timeit executes the statement in the namespace
         of the shell, compared with timeit.py, which uses a single setup
         statement to import function or create variables. Generally, the bias
         does not matter as long as results from timeit.py are not mixed with
         those from %timeit.
 
 **%unalias**::
 
 	Remove an alias
 
 **%upgrade**::
 
 	 Upgrade your IPython installation
         
         This will copy the config files that don't yet exist in your 
         ipython dir from the system config dir. Use this after upgrading 
         IPython if you don't wish to delete your .ipython dir.
 
         Call with -nolegacy to get rid of ipythonrc* files (recommended for
         new users)
 
 **%which**::
 
 	 %which <cmd> => search PATH for files matching cmd. Also scans aliases.
 
     Traverses PATH and prints all files (not just executables!) that match the
     pattern on command line. Probably more useful in finding stuff
     interactively than 'which', which only prints the first matching item.
     
     Also discovers and expands aliases, so you'll see what will be executed
     when you call an alias.
     
     Example:
     
     [~]|62> %which d
     d -> ls -F --color=auto
       == c:\cygwin\bin\ls.exe
     c:\cygwin\bin\d.exe
     
     [~]|64> %which diff*
     diff3 -> diff3
       == c:\cygwin\bin\diff3.exe
     diff -> diff
       == c:\cygwin\bin\diff.exe
     c:\cygwin\bin\diff.exe
     c:\cygwin\bin\diff3.exe
 
 **%who**::
 
 	Print all interactive variables, with some minimal formatting.
 
         If any arguments are given, only variables whose type matches one of
         these are printed.  For example:
 
           %who function str
 
         will only list functions and strings, excluding all other types of
         variables.  To find the proper type names, simply use type(var) at a
         command line to see how python prints type names.  For example:
 
           In [1]: type('hello')\
           Out[1]: <type 'str'>
 
         indicates that the type name for strings is 'str'.
 
         %who always excludes executed names loaded through your configuration
         file and things which are internal to IPython.
 
         This is deliberate, as typically you may load many modules and the
         purpose of %who is to show you only what you've manually defined.
 
 **%who_ls**::
 
 	Return a sorted list of all interactive variables.
 
         If arguments are given, only variables of types matching these
         arguments are returned.
 
 **%whos**::
 
 	Like %who, but gives some extra information about each variable.
 
         The same type filtering of %who can be applied here.
 
         For all variables, the type is printed. Additionally it prints:
         
           - For {},[],(): their length.
 
           - For numpy and Numeric arrays, a summary with shape, number of
           elements, typecode and size in memory.
 
           - Everything else: a string representation, snipping their middle if
           too long.
 
 **%xmode**::
 
 	Switch modes for the exception handlers.
 
         Valid modes: Plain, Context and Verbose.
 
         If called without arguments, acts as a toggle.
 
 .. magic_end
 
 Access to the standard Python help
 ----------------------------------
 
-As of Python 2.1, a help system is available with access to object
-docstrings and the Python manuals. Simply type 'help' (no quotes) to
-access it. You can also type help(object) to obtain information about a
-given object, and help('keyword') for information on a keyword. As noted
-in sec. `accessing help`_, you need to properly configure
-your environment variable PYTHONDOCS for this feature to work correctly.
+As of Python 2.1, a help system is available with access to object docstrings
+and the Python manuals. Simply type 'help' (no quotes) to access it. You can
+also type help(object) to obtain information about a given object, and
+help('keyword') for information on a keyword. As noted :ref:`here
+<accessing_help>`, you need to properly configure your environment variable
+PYTHONDOCS for this feature to work correctly.
 
+.. _dynamic_object_info:
 
 Dynamic object information
 --------------------------
 
 Typing ?word or word? prints detailed information about an object. If
 certain strings in the object are too long (docstrings, code, etc.) they
 get snipped in the center for brevity. This system gives access variable
 types and values, full source code for any object (if available),
 function prototypes and other useful information.
 
 Typing ??word or word?? gives access to the full information without
 snipping long strings. Long strings are sent to the screen through the
 less pager if longer than the screen and printed otherwise. On systems
 lacking the less command, IPython uses a very basic internal pager.
 
 The following magic functions are particularly useful for gathering
 information about your working environment. You can get more details by
 typing %magic or querying them individually (use %function_name? with or
 without the %), this is just a summary:
 
     * **%pdoc <object>**: Print (or run through a pager if too long) the
       docstring for an object. If the given object is a class, it will
       print both the class and the constructor docstrings.
     * **%pdef <object>**: Print the definition header for any callable
       object. If the object is a class, print the constructor information.
     * **%psource <object>**: Print (or run through a pager if too long)
       the source code for an object.
     * **%pfile <object>**: Show the entire source file where an object was
       defined via a pager, opening it at the line where the object
       definition begins.
     * **%who/%whos**: These functions give information about identifiers
       you have defined interactively (not things you loaded or defined
       in your configuration files). %who just prints a list of
       identifiers and %whos prints a table with some basic details about
       each identifier.
 
 Note that the dynamic object information functions (?/??, %pdoc, %pfile,
 %pdef, %psource) give you access to documentation even on things which
 are not really defined as separate identifiers. Try for example typing
 {}.get? or after doing import os, type os.path.abspath??.
 
 
-.. _Readline:
+.. _readline:
 
 Readline-based features
 -----------------------
 
 These features require the GNU readline library, so they won't work if
 your Python installation lacks readline support. We will first describe
 the default behavior IPython uses, and then how to change it to suit
 your preferences.
 
 
 Command line completion
 +++++++++++++++++++++++
 
 At any time, hitting TAB will complete any available python commands or
 variable names, and show you a list of the possible completions if
 there's no unambiguous one. It will also complete filenames in the
 current directory if no python names match what you've typed so far.
 
 
 Search command history
 ++++++++++++++++++++++
 
 IPython provides two ways for searching through previous input and thus
 reduce the need for repetitive typing:
 
    1. Start typing, and then use Ctrl-p (previous,up) and Ctrl-n
       (next,down) to search through only the history items that match
       what you've typed so far. If you use Ctrl-p/Ctrl-n at a blank
       prompt, they just behave like normal arrow keys.
    2. Hit Ctrl-r: opens a search prompt. Begin typing and the system
       searches your history for lines that contain what you've typed so
       far, completing as much as it can.
 
 
 Persistent command history across sessions
 ++++++++++++++++++++++++++++++++++++++++++
 
 IPython will save your input history when it leaves and reload it next
 time you restart it. By default, the history file is named
 $IPYTHONDIR/history, but if you've loaded a named profile,
 '-PROFILE_NAME' is appended to the name. This allows you to keep
 separate histories related to various tasks: commands related to
 numerical work will not be clobbered by a system shell history, for
 example.
 
 
 Autoindent
 ++++++++++
 
 IPython can recognize lines ending in ':' and indent the next line,
 while also un-indenting automatically after 'raise' or 'return'.
 
 This feature uses the readline library, so it will honor your ~/.inputrc
 configuration (or whatever file your INPUTRC variable points to). Adding
 the following lines to your .inputrc file can make indenting/unindenting
 more convenient (M-i indents, M-u unindents)::
 
     $if Python 
     "\M-i": "    "  
     "\M-u": "\d\d\d\d"  
     $endif
 
 Note that there are 4 spaces between the quote marks after "M-i" above.
 
 Warning: this feature is ON by default, but it can cause problems with
 the pasting of multi-line indented code (the pasted code gets
 re-indented on each line). A magic function %autoindent allows you to
 toggle it on/off at runtime. You can also disable it permanently on in
 your ipythonrc file (set autoindent 0).
 
 
 Customizing readline behavior
 +++++++++++++++++++++++++++++
 
 All these features are based on the GNU readline library, which has an
 extremely customizable interface. Normally, readline is configured via a
 file which defines the behavior of the library; the details of the
 syntax for this can be found in the readline documentation available
 with your system or on the Internet. IPython doesn't read this file (if
 it exists) directly, but it does support passing to readline valid
 options via a simple interface. In brief, you can customize readline by
 setting the following options in your ipythonrc configuration file (note
 that these options can not be specified at the command line):
 
     * **readline_parse_and_bind**: this option can appear as many times as
       you want, each time defining a string to be executed via a
       readline.parse_and_bind() command. The syntax for valid commands
       of this kind can be found by reading the documentation for the GNU
       readline library, as these commands are of the kind which readline
       accepts in its configuration file.
     * **readline_remove_delims**: a string of characters to be removed
       from the default word-delimiters list used by readline, so that
       completions may be performed on strings which contain them. Do not
       change the default value unless you know what you're doing.
     * **readline_omit__names**: when tab-completion is enabled, hitting
       <tab> after a '.' in a name will complete all attributes of an
       object, including all the special methods whose names include
       double underscores (like __getitem__ or __class__). If you'd
       rather not see these names by default, you can set this option to
       1. Note that even when this option is set, you can still see those
       names by explicitly typing a _ after the period and hitting <tab>:
       'name._<tab>' will always complete attribute names starting with '_'.
       
       This option is off by default so that new users see all
       attributes of any objects they are dealing with.
 
 You will find the default values along with a corresponding detailed
 explanation in your ipythonrc file.
 
 
 Session logging and restoring
 -----------------------------
 
-You can log all input from a session either by starting IPython with
-the command line switches -log or -logfile (see sec. `command line
-options`_) or by activating the logging at any moment with the magic
-function %logstart.
+You can log all input from a session either by starting IPython with the
+command line switches -log or -logfile (see :ref:`here <command_line_options>`)
+or by activating the logging at any moment with the magic function %logstart.
 
 Log files can later be reloaded with the -logplay option and IPython
 will attempt to 'replay' the log by executing all the lines in it, thus
 restoring the state of a previous session. This feature is not quite
 perfect, but can still be useful in many cases.
 
 The log files can also be used as a way to have a permanent record of
 any code you wrote while experimenting. Log files are regular text files
 which you can later open in your favorite text editor to extract code or
 to 'clean them up' before using them to replay a session.
 
 The %logstart function for activating logging in mid-session is used as
 follows:
 
 %logstart [log_name [log_mode]]
 
 If no name is given, it defaults to a file named 'log' in your
 IPYTHONDIR directory, in 'rotate' mode (see below).
 
 '%logstart name' saves to file 'name' in 'backup' mode. It saves your
 history up to that point and then continues logging.
 
 %logstart takes a second optional parameter: logging mode. This can be
 one of (note that the modes are given unquoted):
 
     * [over:] overwrite existing log_name.
     * [backup:] rename (if exists) to log_name~ and start log_name.
     * [append:] well, that says it.
     * [rotate:] create rotating logs log_name.1~, log_name.2~, etc.
 
 The %logoff and %logon functions allow you to temporarily stop and
 resume logging to a file which had previously been started with
 %logstart. They will fail (with an explanation) if you try to use them
 before logging has been started.
 
+.. _system_shell_access:
+
 System shell access
 -------------------
 
 Any input line beginning with a ! character is passed verbatim (minus
 the !, of course) to the underlying operating system. For example,
 typing !ls will run 'ls' in the current directory.
 
 Manual capture of command output
 --------------------------------
 
 If the input line begins with two exclamation marks, !!, the command is
 executed but its output is captured and returned as a python list, split
 on newlines. Any output sent by the subprocess to standard error is
 printed separately, so that the resulting list only captures standard
 output. The !! syntax is a shorthand for the %sx magic command.
 
 Finally, the %sc magic (short for 'shell capture') is similar to %sx,
 but allowing more fine-grained control of the capture details, and
 storing the result directly into a named variable. The direct use of 
 %sc is now deprecated, and you should ise the ``var = !cmd`` syntax
 instead.
 
 IPython also allows you to expand the value of python variables when
 making system calls. Any python variable or expression which you prepend
 with $ will get expanded before the system call is made::
 
     In [1]: pyvar='Hello world' 
     In [2]: !echo "A python variable: $pyvar"  
     A python variable: Hello world
 
 If you want the shell to actually see a literal $, you need to type it
 twice::
 
     In [3]: !echo "A system variable: $$HOME"  
     A system variable: /home/fperez
 
 You can pass arbitrary expressions, though you'll need to delimit them
 with {} if there is ambiguity as to the extent of the expression::
 
     In [5]: x=10 
     In [6]: y=20 
     In [13]: !echo $x+y  
     10+y  
     In [7]: !echo ${x+y}  
     30
 
 Even object attributes can be expanded::
 
     In [12]: !echo $sys.argv  
     [/home/fperez/usr/bin/ipython]
 
 
 System command aliases
 ----------------------
 
 The %alias magic function and the alias option in the ipythonrc
 configuration file allow you to define magic functions which are in fact
 system shell commands. These aliases can have parameters.
 
 '%alias alias_name cmd' defines 'alias_name' as an alias for 'cmd'
 
 Then, typing '%alias_name params' will execute the system command 'cmd
 params' (from your underlying operating system).
 
 You can also define aliases with parameters using %s specifiers (one per
 parameter). The following example defines the %parts function as an
 alias to the command 'echo first %s second %s' where each %s will be
 replaced by a positional parameter to the call to %parts::
 
     In [1]: alias parts echo first %s second %s 
     In [2]: %parts A B 
     first A second B  
     In [3]: %parts A  
     Incorrect number of arguments: 2 expected.  
     parts is an alias to: 'echo first %s second %s'
 
 If called with no parameters, %alias prints the table of currently
 defined aliases.
 
 The %rehash/rehashx magics allow you to load your entire $PATH as
 ipython aliases. See their respective docstrings (or sec. 6.2
 <#sec:magic> for further details).
 
 
 .. _dreload:
 
 Recursive reload
 ----------------
 
 The dreload function does a recursive reload of a module: changes made
 to the module since you imported will actually be available without
 having to exit.
 
 
 Verbose and colored exception traceback printouts
 -------------------------------------------------
 
 IPython provides the option to see very detailed exception tracebacks,
 which can be especially useful when debugging large programs. You can
 run any Python file with the %run function to benefit from these
 detailed tracebacks. Furthermore, both normal and verbose tracebacks can
 be colored (if your terminal supports it) which makes them much easier
 to parse visually.
 
 See the magic xmode and colors functions for details (just type %magic).
 
 These features are basically a terminal version of Ka-Ping Yee's cgitb
 module, now part of the standard Python library.
 
 
-.. _Input caching:
+.. _input_caching:
 
 Input caching system
 --------------------
 
 IPython offers numbered prompts (In/Out) with input and output caching.
 All input is saved and can be retrieved as variables (besides the usual
 arrow key recall).
 
 The following GLOBAL variables always exist (so don't overwrite them!):
 _i: stores previous input. _ii: next previous. _iii: next-next previous.
 _ih : a list of all input _ih[n] is the input from line n and this list
 is aliased to the global variable In. If you overwrite In with a
 variable of your own, you can remake the assignment to the internal list
 with a simple 'In=_ih'.
 
 Additionally, global variables named _i<n> are dynamically created (<n>
 being the prompt counter), such that
 _i<n> == _ih[<n>] == In[<n>].
 
 For example, what you typed at prompt 14 is available as _i14, _ih[14]
 and In[14].
 
 This allows you to easily cut and paste multi line interactive prompts
 by printing them out: they print like a clean string, without prompt
 characters. You can also manipulate them like regular variables (they
 are strings), modify or exec them (typing 'exec _i9' will re-execute the
 contents of input prompt 9, 'exec In[9:14]+In[18]' will re-execute lines
 9 through 13 and line 18).
 
 You can also re-execute multiple lines of input easily by using the
 magic %macro function (which automates the process and allows
 re-execution without having to type 'exec' every time). The macro system
 also allows you to re-execute previous lines which include magic
 function calls (which require special processing). Type %macro? or see
 sec. 6.2 <#sec:magic> for more details on the macro system.
 
 A history function %hist allows you to see any part of your input
 history by printing a range of the _i variables.
 
-.. _Output caching:
+.. _output_caching:
 
 Output caching system
 ---------------------
 
 For output that is returned from actions, a system similar to the input
 cache exists but using _ instead of _i. Only actions that produce a
 result (NOT assignments, for example) are cached. If you are familiar
 with Mathematica, IPython's _ variables behave exactly like
 Mathematica's % variables.
 
 The following GLOBAL variables always exist (so don't overwrite them!):
 
     * [_] (a single underscore) : stores previous output, like Python's
       default interpreter.
     * [__] (two underscores): next previous.
     * [___] (three underscores): next-next previous.
 
 Additionally, global variables named _<n> are dynamically created (<n>
 being the prompt counter), such that the result of output <n> is always
 available as _<n> (don't use the angle brackets, just the number, e.g.
 _21).
 
 These global variables are all stored in a global dictionary (not a
 list, since it only has entries for lines which returned a result)
 available under the names _oh and Out (similar to _ih and In). So the
 output from line 12 can be obtained as _12, Out[12] or _oh[12]. If you
 accidentally overwrite the Out variable you can recover it by typing
 'Out=_oh' at the prompt.
 
 This system obviously can potentially put heavy memory demands on your
 system, since it prevents Python's garbage collector from removing any
 previously computed results. You can control how many results are kept
 in memory with the option (at the command line or in your ipythonrc
 file) cache_size. If you set it to 0, the whole system is completely
 disabled and the prompts revert to the classic '>>>' of normal Python.
 
 
 Directory history
 -----------------
 
 Your history of visited directories is kept in the global list _dh, and
 the magic %cd command can be used to go to any entry in that list. The
 %dhist command allows you to view this history. do ``cd -<TAB`` to
 conventiently view the directory history.
 
 
 Automatic parentheses and quotes
 --------------------------------
 
 These features were adapted from Nathan Gray's LazyPython. They are
 meant to allow less typing for common situations.
 
 
 Automatic parentheses
 ---------------------
 
 Callable objects (i.e. functions, methods, etc) can be invoked like this
 (notice the commas between the arguments)::
 
     >>> callable_ob arg1, arg2, arg3
 
 and the input will be translated to this::
 
     -> callable_ob(arg1, arg2, arg3)
 
 You can force automatic parentheses by using '/' as the first character
 of a line. For example::
 
     >>> /globals # becomes 'globals()'
 
 Note that the '/' MUST be the first character on the line! This won't work::
 
     >>> print /globals # syntax error
 
 In most cases the automatic algorithm should work, so you should rarely
 need to explicitly invoke /. One notable exception is if you are trying
 to call a function with a list of tuples as arguments (the parenthesis
 will confuse IPython)::
 
     In [1]: zip (1,2,3),(4,5,6) # won't work
 
 but this will work::
 
     In [2]: /zip (1,2,3),(4,5,6) 
     ---> zip ((1,2,3),(4,5,6))  
     Out[2]= [(1, 4), (2, 5), (3, 6)]
 
 IPython tells you that it has altered your command line by displaying
 the new command line preceded by ->. e.g.::
 
     In [18]: callable list 
     ----> callable (list)
 
 
 Automatic quoting
 -----------------
 
 You can force automatic quoting of a function's arguments by using ','
 or ';' as the first character of a line. For example::
 
     >>> ,my_function /home/me # becomes my_function("/home/me")
 
 If you use ';' instead, the whole argument is quoted as a single string
 (while ',' splits on whitespace)::
 
     >>> ,my_function a b c # becomes my_function("a","b","c")
 
     >>> ;my_function a b c # becomes my_function("a b c")
 
 Note that the ',' or ';' MUST be the first character on the line! This
 won't work::
 
     >>> x = ,my_function /home/me # syntax error
 
 IPython as your default Python environment
 ==========================================
 
 Python honors the environment variable PYTHONSTARTUP and will execute at
 startup the file referenced by this variable. If you put at the end of
 this file the following two lines of code::
 
     import IPython 
     IPython.Shell.IPShell().mainloop(sys_exit=1)
 
 then IPython will be your working environment anytime you start Python.
 The sys_exit=1 is needed to have IPython issue a call to sys.exit() when
 it finishes, otherwise you'll be back at the normal Python '>>>'
 prompt.
 
 This is probably useful to developers who manage multiple Python
 versions and don't want to have correspondingly multiple IPython
 versions. Note that in this mode, there is no way to pass IPython any
 command-line options, as those are trapped first by Python itself.
 
 .. _Embedding:
 
 Embedding IPython
 =================
 
 It is possible to start an IPython instance inside your own Python
 programs. This allows you to evaluate dynamically the state of your
 code, operate with your variables, analyze them, etc. Note however that
 any changes you make to values while in the shell do not propagate back
 to the running code, so it is safe to modify your values because you
 won't break your code in bizarre ways by doing so.
 
 This feature allows you to easily have a fully functional python
 environment for doing object introspection anywhere in your code with a
 simple function call. In some cases a simple print statement is enough,
 but if you need to do more detailed analysis of a code fragment this
 feature can be very valuable.
 
 It can also be useful in scientific computing situations where it is
 common to need to do some automatic, computationally intensive part and
 then stop to look at data, plots, etc.
 Opening an IPython instance will give you full access to your data and
 functions, and you can resume program execution once you are done with
 the interactive part (perhaps to stop again later, as many times as
 needed).
 
 The following code snippet is the bare minimum you need to include in
 your Python programs for this to work (detailed examples follow later)::
 
     from IPython.Shell import IPShellEmbed
 
     ipshell = IPShellEmbed()
 
     ipshell() # this call anywhere in your program will start IPython
 
 You can run embedded instances even in code which is itself being run at
 the IPython interactive prompt with '%run <filename>'. Since it's easy
 to get lost as to where you are (in your top-level IPython or in your
 embedded one), it's a good idea in such cases to set the in/out prompts
 to something different for the embedded instances. The code examples
 below illustrate this.
 
 You can also have multiple IPython instances in your program and open
 them separately, for example with different options for data
 presentation. If you close and open the same instance multiple times,
 its prompt counters simply continue from each execution to the next.
 
 Please look at the docstrings in the Shell.py module for more details on
 the use of this system.
 
 The following sample file illustrating how to use the embedding
 functionality is provided in the examples directory as example-embed.py.
 It should be fairly self-explanatory::
 
 
     #!/usr/bin/env python
 
     """An example of how to embed an IPython shell into a running program.
 
     Please see the documentation in the IPython.Shell module for more details.
 
     The accompanying file example-embed-short.py has quick code fragments for
     embedding which you can cut and paste in your code once you understand how
     things work.
 
     The code in this file is deliberately extra-verbose, meant for learning."""
 
     # The basics to get you going:
 
     # IPython sets the __IPYTHON__ variable so you can know if you have nested
     # copies running.
 
     # Try running this code both at the command line and from inside IPython (with
     # %run example-embed.py)
     try:
         __IPYTHON__
     except NameError:
         nested = 0
         args = ['']
     else:
         print "Running nested copies of IPython."
         print "The prompts for the nested copy have been modified"
         nested = 1
         # what the embedded instance will see as sys.argv:
         args = ['-pi1','In <\\#>: ','-pi2','   .\\D.: ',
                 '-po','Out<\\#>: ','-nosep']
 
     # First import the embeddable shell class
     from IPython.Shell import IPShellEmbed
 
     # Now create an instance of the embeddable shell. The first argument is a
     # string with options exactly as you would type them if you were starting
     # IPython at the system command line. Any parameters you want to define for
     # configuration can thus be specified here.
     ipshell = IPShellEmbed(args,
                            banner = 'Dropping into IPython',
                            exit_msg = 'Leaving Interpreter, back to program.')
 
     # Make a second instance, you can have as many as you want.
     if nested:
         args[1] = 'In2<\\#>'
     else:
         args = ['-pi1','In2<\\#>: ','-pi2','   .\\D.: ',
                 '-po','Out<\\#>: ','-nosep']
     ipshell2 = IPShellEmbed(args,banner = 'Second IPython instance.')
 
     print '\nHello. This is printed from the main controller program.\n'
 
     # You can then call ipshell() anywhere you need it (with an optional
     # message):
     ipshell('***Called from top level. '
             'Hit Ctrl-D to exit interpreter and continue program.\n'
             'Note that if you use %kill_embedded, you can fully deactivate\n'
             'This embedded instance so it will never turn on again')
 
     print '\nBack in caller program, moving along...\n'
 
     #---------------------------------------------------------------------------
     # More details:
 
     # IPShellEmbed instances don't print the standard system banner and
     # messages. The IPython banner (which actually may contain initialization
     # messages) is available as <instance>.IP.BANNER in case you want it.
 
     # IPShellEmbed instances print the following information everytime they
     # start:
 
     # - A global startup banner.
 
     # - A call-specific header string, which you can use to indicate where in the
     # execution flow the shell is starting.
 
     # They also print an exit message every time they exit.
 
     # Both the startup banner and the exit message default to None, and can be set
     # either at the instance constructor or at any other time with the
     # set_banner() and set_exit_msg() methods.
 
     # The shell instance can be also put in 'dummy' mode globally or on a per-call
     # basis. This gives you fine control for debugging without having to change
     # code all over the place.
 
     # The code below illustrates all this.
 
 
     # This is how the global banner and exit_msg can be reset at any point
     ipshell.set_banner('Entering interpreter - New Banner')
     ipshell.set_exit_msg('Leaving interpreter - New exit_msg')
 
     def foo(m):
         s = 'spam'
         ipshell('***In foo(). Try @whos, or print s or m:')
         print 'foo says m = ',m
 
     def bar(n):
         s = 'eggs'
         ipshell('***In bar(). Try @whos, or print s or n:')
         print 'bar says n = ',n
 
     # Some calls to the above functions which will trigger IPython:
     print 'Main program calling foo("eggs")\n'
     foo('eggs')
 
     # The shell can be put in 'dummy' mode where calls to it silently return. This
     # allows you, for example, to globally turn off debugging for a program with a
     # single call.
     ipshell.set_dummy_mode(1)
     print '\nTrying to call IPython which is now "dummy":'
     ipshell()
     print 'Nothing happened...'
     # The global 'dummy' mode can still be overridden for a single call
     print '\nOverriding dummy mode manually:'
     ipshell(dummy=0)
 
     # Reactivate the IPython shell
     ipshell.set_dummy_mode(0)
 
     print 'You can even have multiple embedded instances:'
     ipshell2()
 
     print '\nMain program calling bar("spam")\n'
     bar('spam')
 
     print 'Main program finished. Bye!'
 
     #********************** End of file <example-embed.py> ***********************
 
 Once you understand how the system functions, you can use the following
 code fragments in your programs which are ready for cut and paste::
 
 
     """Quick code snippets for embedding IPython into other programs.
 
     See example-embed.py for full details, this file has the bare minimum code for
     cut and paste use once you understand how to use the system."""
 
     #---------------------------------------------------------------------------
     # This code loads IPython but modifies a few things if it detects it's running
     # embedded in another IPython session (helps avoid confusion)
 
     try:
         __IPYTHON__
     except NameError:
         argv = ['']
         banner = exit_msg = ''
     else:
         # Command-line options for IPython (a list like sys.argv)
         argv = ['-pi1','In <\\#>:','-pi2','   .\\D.:','-po','Out<\\#>:']
         banner = '*** Nested interpreter ***'
         exit_msg = '*** Back in main IPython ***'
 
     # First import the embeddable shell class
     from IPython.Shell import IPShellEmbed
     # Now create the IPython shell instance. Put ipshell() anywhere in your code
     # where you want it to open.
     ipshell = IPShellEmbed(argv,banner=banner,exit_msg=exit_msg)
 
     #---------------------------------------------------------------------------
     # This code will load an embeddable IPython shell always with no changes for
     # nested embededings.
 
     from IPython.Shell import IPShellEmbed
     ipshell = IPShellEmbed()
     # Now ipshell() will open IPython anywhere in the code.
 
     #---------------------------------------------------------------------------
     # This code loads an embeddable shell only if NOT running inside
     # IPython. Inside IPython, the embeddable shell variable ipshell is just a
     # dummy function.
 
     try:
         __IPYTHON__
     except NameError:
         from IPython.Shell import IPShellEmbed
         ipshell = IPShellEmbed()
         # Now ipshell() will open IPython anywhere in the code
     else:
         # Define a dummy ipshell() so the same code doesn't crash inside an
         # interactive IPython
         def ipshell(): pass
 
     #******************* End of file <example-embed-short.py> ********************
 
 Using the Python debugger (pdb)
 ===============================
 
 Running entire programs via pdb
 -------------------------------
 
 pdb, the Python debugger, is a powerful interactive debugger which
 allows you to step through code, set breakpoints, watch variables,
 etc.  IPython makes it very easy to start any script under the control
 of pdb, regardless of whether you have wrapped it into a 'main()'
 function or not. For this, simply type '%run -d myscript' at an
 IPython prompt. See the %run command's documentation (via '%run?' or
 in Sec. magic_ for more details, including how to control where pdb
 will stop execution first.
 
 For more information on the use of the pdb debugger, read the included
 pdb.doc file (part of the standard Python distribution). On a stock
 Linux system it is located at /usr/lib/python2.3/pdb.doc, but the
 easiest way to read it is by using the help() function of the pdb module
 as follows (in an IPython prompt):
 
 In [1]: import pdb 
 In [2]: pdb.help()
 
 This will load the pdb.doc document in a file viewer for you automatically.
 
 
 Automatic invocation of pdb on exceptions
 -----------------------------------------
 
 IPython, if started with the -pdb option (or if the option is set in
 your rc file) can call the Python pdb debugger every time your code
 triggers an uncaught exception. This feature
 can also be toggled at any time with the %pdb magic command. This can be
 extremely useful in order to find the origin of subtle bugs, because pdb
 opens up at the point in your code which triggered the exception, and
 while your program is at this point 'dead', all the data is still
 available and you can walk up and down the stack frame and understand
 the origin of the problem.
 
 Furthermore, you can use these debugging facilities both with the
 embedded IPython mode and without IPython at all. For an embedded shell
 (see sec. Embedding_), simply call the constructor with
 '-pdb' in the argument string and automatically pdb will be called if an
 uncaught exception is triggered by your code.
 
 For stand-alone use of the feature in your programs which do not use
 IPython at all, put the following lines toward the top of your 'main'
 routine::
 
     import sys,IPython.ultraTB  
     sys.excepthook = IPython.ultraTB.FormattedTB(mode='Verbose',
     color_scheme='Linux', call_pdb=1)
 
 The mode keyword can be either 'Verbose' or 'Plain', giving either very
 detailed or normal tracebacks respectively. The color_scheme keyword can
 be one of 'NoColor', 'Linux' (default) or 'LightBG'. These are the same
 options which can be set in IPython with -colors and -xmode.
 
 This will give any of your programs detailed, colored tracebacks with
 automatic invocation of pdb.
 
 
 Extensions for syntax processing
 ================================
 
 This isn't for the faint of heart, because the potential for breaking
 things is quite high. But it can be a very powerful and useful feature.
 In a nutshell, you can redefine the way IPython processes the user input
 line to accept new, special extensions to the syntax without needing to
 change any of IPython's own code.
 
 In the IPython/Extensions directory you will find some examples
 supplied, which we will briefly describe now. These can be used 'as is'
 (and both provide very useful functionality), or you can use them as a
 starting point for writing your own extensions.
 
 
 Pasting of code starting with '>>> ' or '... '
 ----------------------------------------------
 
 In the python tutorial it is common to find code examples which have
 been taken from real python sessions. The problem with those is that all
 the lines begin with either '>>> ' or '... ', which makes it impossible
 to paste them all at once. One must instead do a line by line manual
 copying, carefully removing the leading extraneous characters.
 
 This extension identifies those starting characters and removes them
 from the input automatically, so that one can paste multi-line examples
 directly into IPython, saving a lot of time. Please look at the file
 InterpreterPasteInput.py in the IPython/Extensions directory for details
 on how this is done.
 
 IPython comes with a special profile enabling this feature, called
 tutorial. Simply start IPython via 'ipython -p tutorial' and the feature
 will be available. In a normal IPython session you can activate the
 feature by importing the corresponding module with:
 In [1]: import IPython.Extensions.InterpreterPasteInput
 
 The following is a 'screenshot' of how things work when this extension
 is on, copying an example from the standard tutorial::
 
     IPython profile: tutorial 
 
     *** Pasting of code with ">>>" or "..." has been enabled. 
 
     In [1]: >>> def fib2(n): # return Fibonacci series up to n 
        ...: ...     """Return a list containing the Fibonacci series up to
     n.""" 
        ...: ...     result = [] 
        ...: ...     a, b = 0, 1 
        ...: ...     while b < n: 
        ...: ...         result.append(b)    # see below 
        ...: ...         a, b = b, a+b 
        ...: ...     return result 
        ...: 
 
     In [2]: fib2(10) 
     Out[2]: [1, 1, 2, 3, 5, 8]
 
 Note that as currently written, this extension does not recognize
 IPython's prompts for pasting. Those are more complicated, since the
 user can change them very easily, they involve numbers and can vary in
 length. One could however extract all the relevant information from the
 IPython instance and build an appropriate regular expression. This is
 left as an exercise for the reader.
 
 
 Input of physical quantities with units
 ---------------------------------------
 
 The module PhysicalQInput allows a simplified form of input for physical
 quantities with units. This file is meant to be used in conjunction with
 the PhysicalQInteractive module (in the same directory) and
 Physics.PhysicalQuantities from Konrad Hinsen's ScientificPython
 (http://dirac.cnrs-orleans.fr/ScientificPython/).
 
 The Physics.PhysicalQuantities module defines PhysicalQuantity objects,
 but these must be declared as instances of a class. For example, to
 define v as a velocity of 3 m/s, normally you would write::
 
     In [1]: v = PhysicalQuantity(3,'m/s')
 
 Using the PhysicalQ_Input extension this can be input instead as: 
 In [1]: v = 3 m/s
 which is much more convenient for interactive use (even though it is
 blatantly invalid Python syntax).
 
 The physics profile supplied with IPython (enabled via 'ipython -p
 physics') uses these extensions, which you can also activate with:
 
 from math import * # math MUST be imported BEFORE PhysicalQInteractive 
 from IPython.Extensions.PhysicalQInteractive import * 
 import IPython.Extensions.PhysicalQInput
 
 
 Threading support
 =================
 
 WARNING: The threading support is still somewhat experimental, and it
 has only seen reasonable testing under Linux. Threaded code is
 particularly tricky to debug, and it tends to show extremely
 platform-dependent behavior. Since I only have access to Linux machines,
 I will have to rely on user's experiences and assistance for this area
 of IPython to improve under other platforms.
 
 IPython, via the -gthread , -qthread, -q4thread and -wthread options
 (described in Sec. `Threading options`_), can run in
 multithreaded mode to support pyGTK, Qt3, Qt4 and WXPython applications
 respectively. These GUI toolkits need to control the python main loop of
 execution, so under a normal Python interpreter, starting a pyGTK, Qt3,
 Qt4 or WXPython application will immediately freeze the shell.
 
 IPython, with one of these options (you can only use one at a time),
 separates the graphical loop and IPython's code execution run into
 different threads. This allows you to test interactively (with %run, for
 example) your GUI code without blocking.
 
 A nice mini-tutorial on using IPython along with the Qt Designer
 application is available at the SciPy wiki:
 http://www.scipy.org/Cookbook/Matplotlib/Qt_with_IPython_and_Designer.
 
 
 Tk issues
 ---------
 
 As indicated in Sec. `Threading options`_, a special -tk option is
 provided to try and allow Tk graphical applications to coexist
 interactively with WX, Qt or GTK ones. Whether this works at all,
 however, is very platform and configuration dependent. Please
 experiment with simple test cases before committing to using this
 combination of Tk and GTK/Qt/WX threading in a production environment.
 
 
 I/O pitfalls
 ------------
 
 Be mindful that the Python interpreter switches between threads every
 $N$ bytecodes, where the default value as of Python 2.3 is $N=100.$ This
 value can be read by using the sys.getcheckinterval() function, and it
 can be reset via sys.setcheckinterval(N). This switching of threads can
 cause subtly confusing effects if one of your threads is doing file I/O.
 In text mode, most systems only flush file buffers when they encounter a
 '\n'. An instruction as simple as:: 
 
   print >> filehandle, ''hello world''
 
 actually consists of several bytecodes, so it is possible that the
 newline does not reach your file before the next thread switch.
 Similarly, if you are writing to a file in binary mode, the file won't
 be flushed until the buffer fills, and your other thread may see
 apparently truncated files.
 
 For this reason, if you are using IPython's thread support and have (for
 example) a GUI application which will read data generated by files
 written to from the IPython thread, the safest approach is to open all
 of your files in unbuffered mode (the third argument to the file/open
 function is the buffering value)::
 
   filehandle = open(filename,mode,0)
 
 This is obviously a brute force way of avoiding race conditions with the
 file buffering. If you want to do it cleanly, and you have a resource
 which is being shared by the interactive IPython loop and your GUI
 thread, you should really handle it with thread locking and
 syncrhonization properties. The Python documentation discusses these.
 
-.. _Interactive demos:
+.. _interactive_demos:
 
 Interactive demos with IPython
 ==============================
 
 IPython ships with a basic system for running scripts interactively in
 sections, useful when presenting code to audiences. A few tags embedded
 in comments (so that the script remains valid Python code) divide a file
 into separate blocks, and the demo can be run one block at a time, with
 IPython printing (with syntax highlighting) the block before executing
 it, and returning to the interactive prompt after each block. The
 interactive namespace is updated after each block is run with the
 contents of the demo's namespace.
 
 This allows you to show a piece of code, run it and then execute
 interactively commands based on the variables just created. Once you
 want to continue, you simply execute the next block of the demo. The
 following listing shows the markup necessary for dividing a script into
 sections for execution as a demo::
 
 
     """A simple interactive demo to illustrate the use of IPython's Demo class.
 
     Any python script can be run as a demo, but that does little more than showing
     it on-screen, syntax-highlighted in one shot.  If you add a little simple
     markup, you can stop at specified intervals and return to the ipython prompt,
     resuming execution later.
     """
 
     print 'Hello, welcome to an interactive IPython demo.'
     print 'Executing this block should require confirmation before proceeding,'
     print 'unless auto_all has been set to true in the demo object'
 
     # The mark below defines a block boundary, which is a point where IPython will
     # stop execution and return to the interactive prompt.
     # Note that in actual interactive execution, 
     # <demo> --- stop ---
 
     x = 1
     y = 2
 
     # <demo> --- stop ---
 
     # the mark below makes this block as silent
     # <demo> silent
 
     print 'This is a silent block, which gets executed but not printed.'
 
     # <demo> --- stop ---
     # <demo> auto
     print 'This is an automatic block.'
     print 'It is executed without asking for confirmation, but printed.'
     z = x+y
 
     print 'z=',x
 
     # <demo> --- stop ---
     # This is just another normal block.
     print 'z is now:', z
 
     print 'bye!'
 
 In order to run a file as a demo, you must first make a Demo object out
 of it. If the file is named myscript.py, the following code will make a
 demo::
 
     from IPython.demo import Demo
 
     mydemo = Demo('myscript.py')
 
 This creates the mydemo object, whose blocks you run one at a time by
 simply calling the object with no arguments. If you have autocall active
 in IPython (the default), all you need to do is type::
 
     mydemo 
 
 and IPython will call it, executing each block. Demo objects can be
 restarted, you can move forward or back skipping blocks, re-execute the
 last block, etc. Simply use the Tab key on a demo object to see its
 methods, and call '?' on them to see their docstrings for more usage
 details. In addition, the demo module itself contains a comprehensive
 docstring, which you can access via::
 
     from IPython import demo
 
     demo?
 
 Limitations: It is important to note that these demos are limited to
 fairly simple uses. In particular, you can not put division marks in
 indented code (loops, if statements, function definitions, etc.)
 Supporting something like this would basically require tracking the
 internal execution state of the Python interpreter, so only top-level
 divisions are allowed. If you want to be able to open an IPython
 instance at an arbitrary point in a program, you can use IPython's
 embedding facilities, described in detail in Sec. 9
 
 
 .. _Matplotlib support:
 
 Plotting with matplotlib
 ========================
 
 The matplotlib library (http://matplotlib.sourceforge.net
 http://matplotlib.sourceforge.net) provides high quality 2D plotting for
 Python. Matplotlib can produce plots on screen using a variety of GUI
 toolkits, including Tk, GTK and WXPython. It also provides a number of
 commands useful for scientific computing, all with a syntax compatible
 with that of the popular Matlab program.
 
-IPython accepts the special option -pylab (Sec. `Command line
-options`_). This configures it to support matplotlib, honoring the
-settings in the .matplotlibrc file. IPython will detect the user's
-choice of matplotlib GUI backend, and automatically select the proper
-threading model to prevent blocking. It also sets matplotlib in
-interactive mode and modifies %run slightly, so that any
-matplotlib-based script can be executed using %run and the final
-show() command does not block the interactive shell.
-
-The -pylab option must be given first in order for IPython to
-configure its threading mode. However, you can still issue other
-options afterwards. This allows you to have a matplotlib-based
-environment customized with additional modules using the standard
-IPython profile mechanism (Sec. Profiles_): ''ipython -pylab -p
-myprofile'' will load the profile defined in ipythonrc-myprofile after
-configuring matplotlib.
-
-
+IPython accepts the special option -pylab (see :ref:`here
+<command_line_options>`). This configures it to support matplotlib, honoring
+the settings in the .matplotlibrc file. IPython will detect the user's choice
+of matplotlib GUI backend, and automatically select the proper threading model
+to prevent blocking. It also sets matplotlib in interactive mode and modifies
+%run slightly, so that any matplotlib-based script can be executed using %run
+and the final show() command does not block the interactive shell.
+
+The -pylab option must be given first in order for IPython to configure its
+threading mode. However, you can still issue other options afterwards. This
+allows you to have a matplotlib-based environment customized with additional
+modules using the standard IPython profile mechanism (see :ref:`here
+<profiles>`): ``ipython -pylab -p myprofile`` will load the profile defined in
+ipythonrc-myprofile after configuring matplotlib.

docs/source/interactive/tutorial.txt

@@ -1,315 +1,317 b''
 .. _tutorial:
 
 ======================
 Quick IPython tutorial
 ======================
 
 .. contents::
 
 IPython can be used as an improved replacement for the Python prompt,
 and for that you don't really need to read any more of this manual. But
 in this section we'll try to summarize a few tips on how to make the
 most effective use of it for everyday Python development, highlighting
 things you might miss in the rest of the manual (which is getting long).
 We'll give references to parts in the manual which provide more detail
 when appropriate.
 
 The following article by Jeremy Jones provides an introductory tutorial
 about IPython: http://www.onlamp.com/pub/a/python/2005/01/27/ipython.html
 
 Highlights
 ==========
 
 Tab completion
 --------------
 
 TAB-completion, especially for attributes, is a convenient way to explore the
-structure of any object you're dealing with. Simply type object_name.<TAB>
-and a list of the object's attributes will be printed (see readline_ for
-more). Tab completion also works on file and directory names, which combined
-with IPython's alias system allows you to do from within IPython many of the
-things you normally would need the system shell for.
+structure of any object you're dealing with. Simply type object_name.<TAB> and
+a list of the object's attributes will be printed (see :ref:`the readline
+section <readline>` for more). Tab completion also works on file and directory
+names, which combined with IPython's alias system allows you to do from within
+IPython many of the things you normally would need the system shell for.
 
 Explore your objects
 --------------------
 
 Typing object_name? will print all sorts of details about any object,
 including docstrings, function definition lines (for call arguments) and
 constructor details for classes. The magic commands %pdoc, %pdef, %psource
 and %pfile will respectively print the docstring, function definition line,
 full source code and the complete file for any object (when they can be
 found). If automagic is on (it is by default), you don't need to type the '%'
-explicitly. See sec. `dynamic object information`_ for more.
+explicitly. See :ref:`this section <dynamic_object_info>` for more.
 
 The `%run` magic command
 ------------------------
 
-The %run magic command allows you to run any python script and load all of
-its data directly into the interactive namespace. Since the file is re-read
-from disk each time, changes you make to it are reflected immediately (in
-contrast to the behavior of import). I rarely use import for code I am
-testing, relying on %run instead. See magic_ section for more on this and
-other magic commands, or type the name of any magic command and ? to get
-details on it. See also sec. dreload_ for a recursive reload command. %run
+The %run magic command allows you to run any python script and load all of its
+data directly into the interactive namespace. Since the file is re-read from
+disk each time, changes you make to it are reflected immediately (in contrast
+to the behavior of import). I rarely use import for code I am testing, relying
+on %run instead. See :ref:`this section <magic>` for more on this and other
+magic commands, or type the name of any magic command and ? to get details on
+it. See also :ref:`this section <dreload>` for a recursive reload command. %run
 also has special flags for timing the execution of your scripts (-t) and for
 executing them under the control of either Python's pdb debugger (-d) or
 profiler (-p). With all of these, %run can be used as the main tool for
 efficient interactive development of code which you write in your editor of
 choice.
 
 Debug a Python script
 ---------------------
 
-Use the Python debugger, pdb. The %pdb command allows you to toggle on and
-off the automatic invocation of an IPython-enhanced pdb debugger (with
-coloring, tab completion and more) at any uncaught exception. The advantage
-of this is that pdb starts inside the function where the exception occurred,
-with all data still available. You can print variables, see code, execute
-statements and even walk up and down the call stack to track down the true
-source of the problem (which often is many layers in the stack above where
-the exception gets triggered). Running programs with %run and pdb active can
-be an efficient to develop and debug code, in many cases eliminating the need
-for print statements or external debugging tools. I often simply put a 1/0 in
-a place where I want to take a look so that pdb gets called, quickly view
-whatever variables I need to or test various pieces of code and then remove
-the 1/0. Note also that '%run -d' activates pdb and automatically sets
-initial breakpoints for you to step through your code, watch variables, etc.
-See Sec. `Output caching`_ for details.
+Use the Python debugger, pdb. The %pdb command allows you to toggle on and off
+the automatic invocation of an IPython-enhanced pdb debugger (with coloring,
+tab completion and more) at any uncaught exception. The advantage of this is
+that pdb starts inside the function where the exception occurred, with all data
+still available. You can print variables, see code, execute statements and even
+walk up and down the call stack to track down the true source of the problem
+(which often is many layers in the stack above where the exception gets
+triggered). Running programs with %run and pdb active can be an efficient to
+develop and debug code, in many cases eliminating the need for print statements
+or external debugging tools. I often simply put a 1/0 in a place where I want
+to take a look so that pdb gets called, quickly view whatever variables I need
+to or test various pieces of code and then remove the 1/0. Note also that '%run
+-d' activates pdb and automatically sets initial breakpoints for you to step
+through your code, watch variables, etc.  The :ref:`output caching section
+<output_caching>` has more details.
 
 Use the output cache
 --------------------
 
 All output results are automatically stored in a global dictionary named Out
 and variables named _1, _2, etc. alias them. For example, the result of input
 line 4 is available either as Out[4] or as _4. Additionally, three variables
 named _, __ and ___ are always kept updated with the for the last three
 results. This allows you to recall any previous result and further use it for
-new calculations. See Sec. `Output caching`_ for more.
+new calculations. See :ref:`the output caching section <output_caching>` for
+more.
 
 Suppress output
 ---------------
 
 Put a ';' at the end of a line to suppress the printing of output. This is
 useful when doing calculations which generate long output you are not
 interested in seeing. The _* variables and the Out[] list do get updated with
 the contents of the output, even if it is not printed. You can thus still
 access the generated results this way for further processing.
 
 Input cache
 -----------
 
 A similar system exists for caching input. All input is stored in a global
 list called In , so you can re-execute lines 22 through 28 plus line 34 by
 typing 'exec In[22:29]+In[34]' (using Python slicing notation). If you need
 to execute the same set of lines often, you can assign them to a macro with
-the %macro function. See sec. `Input caching`_ for more.
+the %macro function. See :ref:`here <input_caching>` for more.
 
 Use your input history
 ----------------------
 
 The %hist command can show you all previous input, without line numbers if
 desired (option -n) so you can directly copy and paste code either back in
 IPython or in a text editor. You can also save all your history by turning on
 logging via %logstart; these logs can later be either reloaded as IPython
 sessions or used as code for your programs.
 
 Define your own system aliases
 ------------------------------
 
 Even though IPython gives you access to your system shell via the ! prefix,
 it is convenient to have aliases to the system commands you use most often.
 This allows you to work seamlessly from inside IPython with the same commands
 you are used to in your system shell. IPython comes with some pre-defined
 aliases and a complete system for changing directories, both via a stack (see
 %pushd, %popd and %dhist) and via direct %cd. The latter keeps a history of
 visited directories and allows you to go to any previously visited one.
 
 Call system shell commands
 --------------------------
 
 Use Python to manipulate the results of system commands. The '!!' special
 syntax, and the %sc and %sx magic commands allow you to capture system output
 into Python variables.
 
 Use Python variables when calling the shell
 -------------------------------------------
 
-Expand python variables when calling the shell (either via '!' and '!!' or
-via aliases) by prepending a $ in front of them. You can also expand complete
-python expressions. See `System shell access`_ for more.
+Expand python variables when calling the shell (either via '!' and '!!' or via
+aliases) by prepending a $ in front of them. You can also expand complete
+python expressions. See :ref:`our shell section <system_shell_access>` for
+more details.
 
 Use profiles
 ------------
 
 Use profiles to maintain different configurations (modules to load, function
 definitions, option settings) for particular tasks. You can then have
-customized versions of IPython for specific purposes. See sec. profiles_ for
-more.
+customized versions of IPython for specific purposes. :ref:`This section
+<profiles>` has more details.
 
 
 Embed IPython in your programs
 ------------------------------
 
 A few lines of code are enough to load a complete IPython inside your own
 programs, giving you the ability to work with your data interactively after
-automatic processing has been completed. See sec. embedding_ for more.
+automatic processing has been completed. See :ref:`here <embedding>` for more.
 
 Use the Python profiler
 -----------------------
 
 When dealing with performance issues, the %run command with a -p option
 allows you to run complete programs under the control of the Python profiler.
 The %prun command does a similar job for single Python expressions (like
 function calls).
 
 Use IPython to present interactive demos
 ----------------------------------------
 
 Use the IPython.demo.Demo class to load any Python script as an interactive
-demo. With a minimal amount of simple markup, you can control the execution
-of the script, stopping as needed. See sec. `interactive demos`_ for more.
+demo. With a minimal amount of simple markup, you can control the execution of
+the script, stopping as needed. See :ref:`here <interactive_demos>` for more.
 
 Run doctests
 ------------
 
 Run your doctests from within IPython for development and debugging. The
 special %doctest_mode command toggles a mode where the prompt, output and
 exceptions display matches as closely as possible that of the default Python
 interpreter. In addition, this mode allows you to directly paste in code that
 contains leading '>>>' prompts, even if they have extra leading whitespace
 (as is common in doctest files). This combined with the '%history -tn' call
 to see your translated history (with these extra prompts removed and no line
 numbers) allows for an easy doctest workflow, where you can go from doctest
 to interactive execution to pasting into valid Python code as needed.
 
 Source code handling tips
 =========================
 
 IPython is a line-oriented program, without full control of the
 terminal. Therefore, it doesn't support true multiline editing. However,
 it has a number of useful tools to help you in dealing effectively with
 more complex editing.
 
 The %edit command gives a reasonable approximation of multiline editing,
 by invoking your favorite editor on the spot. IPython will execute the
 code you type in there as if it were typed interactively. Type %edit?
 for the full details on the edit command.
 
 If you have typed various commands during a session, which you'd like to
 reuse, IPython provides you with a number of tools. Start by using %hist
 to see your input history, so you can see the line numbers of all input.
 Let us say that you'd like to reuse lines 10 through 20, plus lines 24
 and 28. All the commands below can operate on these with the syntax::
 
     %command 10-20 24 28 
 
 where the command given can be:
 
     * %macro <macroname>: this stores the lines into a variable which,
       when called at the prompt, re-executes the input. Macros can be
       edited later using '%edit macroname', and they can be stored
       persistently across sessions with '%store macroname' (the storage
       system is per-profile). The combination of quick macros,
       persistent storage and editing, allows you to easily refine
       quick-and-dirty interactive input into permanent utilities, always
       available both in IPython and as files for general reuse.
     * %edit: this will open a text editor with those lines pre-loaded
       for further modification. It will then execute the resulting
       file's contents as if you had typed it at the prompt.
     * %save <filename>: this saves the lines directly to a named file on
       disk.
 
 While %macro saves input lines into memory for interactive re-execution,
 sometimes you'd like to save your input directly to a file. The %save
 magic does this: its input sytnax is the same as %macro, but it saves
 your input directly to a Python file. Note that the %logstart command
 also saves input, but it logs all input to disk (though you can
 temporarily suspend it and reactivate it with %logoff/%logon); %save
 allows you to select which lines of input you need to save.
 
 
 Lightweight 'version control'
 =============================
 
 When you call %edit with no arguments, IPython opens an empty editor
 with a temporary file, and it returns the contents of your editing
 session as a string variable. Thanks to IPython's output caching
 mechanism, this is automatically stored::
 
     In [1]: %edit
 
     IPython will make a temporary file named: /tmp/ipython_edit_yR-HCN.py
 
     Editing... done. Executing edited code...
 
     hello - this is a temporary file
 
     Out[1]: "print 'hello - this is a temporary file'\n"
 
 Now, if you call '%edit -p', IPython tries to open an editor with the
 same data as the last time you used %edit. So if you haven't used %edit
 in the meantime, this same contents will reopen; however, it will be
 done in a new file. This means that if you make changes and you later
 want to find an old version, you can always retrieve it by using its
 output number, via '%edit _NN', where NN is the number of the output
 prompt.
 
 Continuing with the example above, this should illustrate this idea::
 
     In [2]: edit -p
 
     IPython will make a temporary file named: /tmp/ipython_edit_nA09Qk.py
 
     Editing... done. Executing edited code...
 
     hello - now I made some changes
 
     Out[2]: "print 'hello - now I made some changes'\n"
 
     In [3]: edit _1
 
     IPython will make a temporary file named: /tmp/ipython_edit_gy6-zD.py
 
     Editing... done. Executing edited code...
 
     hello - this is a temporary file
 
     IPython version control at work :)
 
     Out[3]: "print 'hello - this is a temporary file'\nprint 'IPython version control at work :)'\n"
 
 
 This section was written after a contribution by Alexander Belchenko on
 the IPython user list.
 
 
 Effective logging
 =================
 
 A very useful suggestion sent in by Robert Kern follows:
 
 I recently happened on a nifty way to keep tidy per-project log files. I
 made a profile for my project (which is called "parkfield")::
 
     include ipythonrc
 
     # cancel earlier logfile invocation:
 
     logfile ''
 
     execute import time
 
     execute __cmd = '/Users/kern/research/logfiles/parkfield-%s.log rotate'
 
     execute __IP.magic_logstart(__cmd % time.strftime('%Y-%m-%d'))
 
 I also added a shell alias for convenience::
 
     alias parkfield="ipython -pylab -profile parkfield" 
 
 Now I have a nice little directory with everything I ever type in,
 organized by project and date.
 
 Contribute your own: If you have your own favorite tip on using IPython
 efficiently for a certain task (especially things which can't be done in
 the normal Python interpreter), don't hesitate to send it!

docs/source/license_and_copyright.txt

@@ -1,61 +1,87 b''
 .. _license:
 
-=============================
-License and Copyright 
-=============================
+=====================
+License and Copyright
+=====================
 
-This files needs to be updated to reflect what the new COPYING.txt files says about our license and copyright!
+License
+=======
 
-IPython is released under the terms of the BSD license, whose general
-form can be found at: http://www.opensource.org/licenses/bsd-license.php. The full text of the
-IPython license is reproduced below::
+IPython is licensed under the terms of the new or revised BSD license, as follows::
 
-    IPython is released under a BSD-type license.
+    Copyright (c) 2008, IPython Development Team
 
-    Copyright (c) 2001, 2002, 2003, 2004 Fernando Perez
-    <[e-mail]>.
+    All rights reserved.
 
-    Copyright (c) 2001 Janko Hauser <[e-mail]> and  
-    Nathaniel Gray <[e-mail]>.
+    Redistribution and use in source and binary forms, with or without modification,
+    are permitted provided that the following conditions are met:
 
-    All rights reserved.
+    Redistributions of source code must retain the above copyright notice, this list of
+    conditions and the following disclaimer.
+
+    Redistributions in binary form must reproduce the above copyright notice, this list
+    of conditions and the following disclaimer in the documentation and/or other
+    materials provided with the distribution.
+
+    Neither the name of the IPython Development Team nor the names of its contributors
+    may be used to endorse or promote products derived from this software without
+    specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
+    EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+    IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+    NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+    PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+About the IPython Development Team
+==================================
+
+Fernando Perez began IPython in 2001 based on code from Janko Hauser <[e-mail]> 
+and Nathaniel Gray <[e-mail]>.  Fernando is still the project lead.
+
+The IPython Development Team is the set of all contributors to the IPython project.
+This includes all of the IPython subprojects. Here is a list of the currently active contributors:
+
+	* Matthieu Brucher
+	* Ondrej Certik
+	* Laurent Dufrechou
+	* Robert Kern
+	* Brian E. Granger
+	* Fernando Perez (project leader)
+	* Benjamin Ragan-Kelley
+	* Ville M. Vainio
+	* Gael Varoququx
+	* Stefan van der Walt
+	* Tech-X Corporation
+	* Barry Wark
+
+If your name is missing, please add it.  
+
+Our Copyright Policy
+====================
+
+IPython uses a shared copyright model. Each contributor maintains copyright over
+their contributions to IPython. But, it is important to note that these
+contributions are typically only changes to the repositories. Thus, the IPython
+source code, in its entirety is not the copyright of any single person or
+institution. Instead, it is the collective copyright of the entire IPython
+Development Team. If individual contributors want to maintain a record of what
+changes/contributions they have specific copyright on, they should indicate their
+copyright in the commit message of the change, when they commit the change to
+one of the IPython repositories.
 
-    Redistribution and use in source and binary forms, with or without
-    modification, are permitted provided that the following conditions
-    are met:
-
-    a. Redistributions of source code must retain the above copyright
-    notice, this list of conditions and the following disclaimer.
-
-    b. Redistributions in binary form must reproduce the above copyright
-    notice, this list of conditions and the following disclaimer in the
-    documentation and/or other materials provided with the distribution.
-
-    c. Neither the name of the copyright holders nor the names of any
-    contributors to this software may be used to endorse or promote
-    products derived from this software without specific prior written
-    permission.
-
-    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-    FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-    REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-    INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-    BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-    LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-    CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-    LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-    ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-    POSSIBILITY OF SUCH DAMAGE. 
-
-Individual authors are the holders of the copyright for their code and
-are listed in each file.
+Miscellaneous
+=============
 
 Some files (DPyGetOpt.py, for example) may be licensed under different
 conditions. Ultimately each file indicates clearly the conditions under
 which its author/authors have decided to publish the code.
 
 Versions of IPython up to and including 0.6.3 were released under the
 GNU Lesser General Public License (LGPL), available at
 http://www.gnu.org/copyleft/lesser.html.
  No newline at end of file

docs/source/overview.txt

@@ -1,174 +1,233 b''
 .. _overview:
 
 ============
 Introduction
 ============
 
 Overview
 ========
 
 One of Python's most useful features is its interactive interpreter.
 This system allows very fast testing of ideas without the overhead of
 creating test files as is typical in most programming languages.
 However, the interpreter supplied with the standard Python distribution
 is somewhat limited for extended interactive use.
 
 The goal of IPython is to create a comprehensive environment for
 interactive and exploratory computing.  To support, this goal, IPython
 has two main components:
 
-	* An enhanced interactive Python shell.
-	* An architecture for interactive parallel computing.
+* An enhanced interactive Python shell.
+* An architecture for interactive parallel computing.
 
 All of IPython is open source (released under the revised BSD license).
 
 Enhanced interactive Python shell
 =================================
 
-IPython's interactive shell (`ipython`), has the following goals:
-
-   1. Provide an interactive shell superior to Python's default. IPython
-      has many features for object introspection, system shell access,
-      and its own special command system for adding functionality when
-      working interactively. It tries to be a very efficient environment
-      both for Python code development and for exploration of problems
-      using Python objects (in situations like data analysis).
-   2. Serve as an embeddable, ready to use interpreter for your own
-      programs. IPython can be started with a single call from inside
-      another program, providing access to the current namespace. This
-      can be very useful both for debugging purposes and for situations
-      where a blend of batch-processing and interactive exploration are
-      needed.
-   3. Offer a flexible framework which can be used as the base
-      environment for other systems with Python as the underlying
-      language. Specifically scientific environments like Mathematica,
-      IDL and Matlab inspired its design, but similar ideas can be
-      useful in many fields.
-   4. Allow interactive testing of threaded graphical toolkits. IPython
-      has support for interactive, non-blocking control of GTK, Qt and
-      WX applications via special threading flags. The normal Python
-      shell can only do this for Tkinter applications.
+IPython's interactive shell (:command:`ipython`), has the following goals,
+amongst others:
+
+1. Provide an interactive shell superior to Python's default. IPython
+   has many features for object introspection, system shell access,
+   and its own special command system for adding functionality when
+   working interactively. It tries to be a very efficient environment
+   both for Python code development and for exploration of problems
+   using Python objects (in situations like data analysis).
+  
+2. Serve as an embeddable, ready to use interpreter for your own
+   programs. IPython can be started with a single call from inside
+   another program, providing access to the current namespace. This
+   can be very useful both for debugging purposes and for situations
+   where a blend of batch-processing and interactive exploration are
+   needed.  New in the 0.9 version of IPython is a reusable wxPython
+   based IPython widget.
+  
+3. Offer a flexible framework which can be used as the base
+   environment for other systems with Python as the underlying
+   language. Specifically scientific environments like Mathematica,
+   IDL and Matlab inspired its design, but similar ideas can be
+   useful in many fields.
+  
+4. Allow interactive testing of threaded graphical toolkits. IPython
+   has support for interactive, non-blocking control of GTK, Qt and
+   WX applications via special threading flags. The normal Python
+   shell can only do this for Tkinter applications.
 
 Main features of the interactive shell
 --------------------------------------
 
-	* Dynamic object introspection. One can access docstrings, function
-	  definition prototypes, source code, source files and other details
-	  of any object accessible to the interpreter with a single
-	  keystroke (:samp:`?`, and using :samp:`??` provides additional detail).
-	* Searching through modules and namespaces with :samp:`*` wildcards, both
-	  when using the :samp:`?` system and via the :samp:`%psearch` command.
-	* Completion in the local namespace, by typing :kbd:`TAB` at the prompt.
-	  This works for keywords, modules, methods, variables and files in the
-	  current directory. This is supported via the readline library, and
-	  full access to configuring readline's behavior is provided. 
-	  Custom completers can be implemented easily for different purposes
-	  (system commands, magic arguments etc.)
-	* Numbered input/output prompts with command history (persistent
-	  across sessions and tied to each profile), full searching in this
-	  history and caching of all input and output.
-	* User-extensible 'magic' commands. A set of commands prefixed with
-	  :samp:`%` is available for controlling IPython itself and provides
-	  directory control, namespace information and many aliases to
-	  common system shell commands.
-	* Alias facility for defining your own system aliases.
-	* Complete system shell access. Lines starting with :samp:`!` are passed
-	  directly to the system shell, and using :samp:`!!` or :samp:`var = !cmd` 
-	  captures shell output into python variables for further use.
-	* Background execution of Python commands in a separate thread.
-	  IPython has an internal job manager called jobs, and a
-	  conveninence backgrounding magic function called :samp:`%bg`.
-	* The ability to expand python variables when calling the system
-	  shell. In a shell command, any python variable prefixed with :samp:`$` is
-	  expanded. A double :samp:`$$` allows passing a literal :samp:`$` to the shell (for
-	  access to shell and environment variables like :envvar:`PATH`).
-	* Filesystem navigation, via a magic :samp:`%cd` command, along with a
-	  persistent bookmark system (using :samp:`%bookmark`) for fast access to
-	  frequently visited directories.
-	* A lightweight persistence framework via the :samp:`%store` command, which
-	  allows you to save arbitrary Python variables. These get restored
-	  automatically when your session restarts.
-	* Automatic indentation (optional) of code as you type (through the
-	  readline library).
-	* Macro system for quickly re-executing multiple lines of previous
-	  input with a single name. Macros can be stored persistently via
-	  :samp:`%store` and edited via :samp:`%edit`.
-	* Session logging (you can then later use these logs as code in your
-	  programs). Logs can optionally timestamp all input, and also store
-	  session output (marked as comments, so the log remains valid
-	  Python source code).
-	* Session restoring: logs can be replayed to restore a previous
-	  session to the state where you left it.
-	* Verbose and colored exception traceback printouts. Easier to parse
-	  visually, and in verbose mode they produce a lot of useful
-	  debugging information (basically a terminal version of the cgitb
-	  module).
-	* Auto-parentheses: callable objects can be executed without
-	  parentheses: :samp:`sin 3` is automatically converted to :samp:`sin(3)`.
-	* Auto-quoting: using :samp:`,`, or :samp:`;` as the first character forces
-	  auto-quoting of the rest of the line: :samp:`,my_function a b` becomes
-	  automatically :samp:`my_function("a","b")`, while :samp:`;my_function a b`
-	  becomes :samp:`my_function("a b")`.
-	* Extensible input syntax. You can define filters that pre-process
-	  user input to simplify input in special situations. This allows
-	  for example pasting multi-line code fragments which start with
-	  :samp:`>>>` or :samp:`...` such as those from other python sessions or the
-	  standard Python documentation.
-	* Flexible configuration system. It uses a configuration file which
-	  allows permanent setting of all command-line options, module
-	  loading, code and file execution. The system allows recursive file
-	  inclusion, so you can have a base file with defaults and layers
-	  which load other customizations for particular projects.
-	* Embeddable. You can call IPython as a python shell inside your own
-	  python programs. This can be used both for debugging code or for
-	  providing interactive abilities to your programs with knowledge
-	  about the local namespaces (very useful in debugging and data
-	  analysis situations).
-	* Easy debugger access. You can set IPython to call up an enhanced
-	  version of the Python debugger (pdb) every time there is an
-	  uncaught exception. This drops you inside the code which triggered
-	  the exception with all the data live and it is possible to
-	  navigate the stack to rapidly isolate the source of a bug. The
-	  :samp:`%run` magic command (with the :samp:`-d` option) can run any script under
-	  pdb's control, automatically setting initial breakpoints for you.
-	  This version of pdb has IPython-specific improvements, including
-	  tab-completion and traceback coloring support. For even easier
-	  debugger access, try :samp:`%debug` after seeing an exception. winpdb is 
-	  also supported, see ipy_winpdb extension.
-	* Profiler support. You can run single statements (similar to
-	  :samp:`profile.run()`) or complete programs under the profiler's control.
-	  While this is possible with standard cProfile or profile modules,
-	  IPython wraps this functionality with magic commands (see :samp:`%prun`
-	  and :samp:`%run -p`) convenient for rapid interactive work.
-	* Doctest support. The special :samp:`%doctest_mode` command toggles a mode
-	  that allows you to paste existing doctests (with leading :samp:`>>>`
-	  prompts and whitespace) and uses doctest-compatible prompts and
-	  output, so you can use IPython sessions as doctest code.
+* Dynamic object introspection. One can access docstrings, function
+  definition prototypes, source code, source files and other details
+  of any object accessible to the interpreter with a single
+  keystroke (:samp:`?`, and using :samp:`??` provides additional detail).
+  
+* Searching through modules and namespaces with :samp:`*` wildcards, both
+  when using the :samp:`?` system and via the :samp:`%psearch` command.
+
+* Completion in the local namespace, by typing :kbd:`TAB` at the prompt.
+  This works for keywords, modules, methods, variables and files in the
+  current directory. This is supported via the readline library, and
+  full access to configuring readline's behavior is provided. 
+  Custom completers can be implemented easily for different purposes
+  (system commands, magic arguments etc.)
+
+* Numbered input/output prompts with command history (persistent
+  across sessions and tied to each profile), full searching in this
+  history and caching of all input and output.
+
+* User-extensible 'magic' commands. A set of commands prefixed with
+  :samp:`%` is available for controlling IPython itself and provides
+  directory control, namespace information and many aliases to
+  common system shell commands.
+
+* Alias facility for defining your own system aliases.
+
+* Complete system shell access. Lines starting with :samp:`!` are passed
+  directly to the system shell, and using :samp:`!!` or :samp:`var = !cmd` 
+  captures shell output into python variables for further use.
+
+* Background execution of Python commands in a separate thread.
+  IPython has an internal job manager called jobs, and a
+  convenience backgrounding magic function called :samp:`%bg`.
+
+* The ability to expand python variables when calling the system
+  shell. In a shell command, any python variable prefixed with :samp:`$` is
+  expanded. A double :samp:`$$` allows passing a literal :samp:`$` to the shell (for
+  access to shell and environment variables like :envvar:`PATH`).
+
+* Filesystem navigation, via a magic :samp:`%cd` command, along with a
+  persistent bookmark system (using :samp:`%bookmark`) for fast access to
+  frequently visited directories.
+
+* A lightweight persistence framework via the :samp:`%store` command, which
+  allows you to save arbitrary Python variables. These get restored
+  automatically when your session restarts.
+
+* Automatic indentation (optional) of code as you type (through the
+  readline library).
+
+* Macro system for quickly re-executing multiple lines of previous
+  input with a single name. Macros can be stored persistently via
+  :samp:`%store` and edited via :samp:`%edit`.
+
+* Session logging (you can then later use these logs as code in your
+  programs). Logs can optionally timestamp all input, and also store
+  session output (marked as comments, so the log remains valid
+  Python source code).
+
+* Session restoring: logs can be replayed to restore a previous
+  session to the state where you left it.
+
+* Verbose and colored exception traceback printouts. Easier to parse
+  visually, and in verbose mode they produce a lot of useful
+  debugging information (basically a terminal version of the cgitb
+  module).
+
+* Auto-parentheses: callable objects can be executed without
+  parentheses: :samp:`sin 3` is automatically converted to :samp:`sin(3)`.
+
+* Auto-quoting: using :samp:`,`, or :samp:`;` as the first character forces
+  auto-quoting of the rest of the line: :samp:`,my_function a b` becomes
+  automatically :samp:`my_function("a","b")`, while :samp:`;my_function a b`
+  becomes :samp:`my_function("a b")`.
+
+* Extensible input syntax. You can define filters that pre-process
+  user input to simplify input in special situations. This allows
+  for example pasting multi-line code fragments which start with
+  :samp:`>>>` or :samp:`...` such as those from other python sessions or the
+  standard Python documentation.
+
+* Flexible configuration system. It uses a configuration file which
+  allows permanent setting of all command-line options, module
+  loading, code and file execution. The system allows recursive file
+  inclusion, so you can have a base file with defaults and layers
+  which load other customizations for particular projects.
+
+* Embeddable. You can call IPython as a python shell inside your own
+  python programs. This can be used both for debugging code or for
+  providing interactive abilities to your programs with knowledge
+  about the local namespaces (very useful in debugging and data
+  analysis situations).
+
+* Easy debugger access. You can set IPython to call up an enhanced
+  version of the Python debugger (pdb) every time there is an
+  uncaught exception. This drops you inside the code which triggered
+  the exception with all the data live and it is possible to
+  navigate the stack to rapidly isolate the source of a bug. The
+  :samp:`%run` magic command (with the :samp:`-d` option) can run any script under
+  pdb's control, automatically setting initial breakpoints for you.
+  This version of pdb has IPython-specific improvements, including
+  tab-completion and traceback coloring support. For even easier
+  debugger access, try :samp:`%debug` after seeing an exception. winpdb is 
+  also supported, see ipy_winpdb extension.
+
+* Profiler support. You can run single statements (similar to
+  :samp:`profile.run()`) or complete programs under the profiler's control.
+  While this is possible with standard cProfile or profile modules,
+  IPython wraps this functionality with magic commands (see :samp:`%prun`
+  and :samp:`%run -p`) convenient for rapid interactive work.
+
+* Doctest support. The special :samp:`%doctest_mode` command toggles a mode
+  that allows you to paste existing doctests (with leading :samp:`>>>`
+  prompts and whitespace) and uses doctest-compatible prompts and
+  output, so you can use IPython sessions as doctest code.
 
 Interactive parallel computing
 ==============================
 
 Increasingly, parallel computer hardware, such as multicore CPUs, clusters and supercomputers, is becoming ubiquitous. Over the last 3 years, we have developed an
 architecture within IPython that allows such hardware to be used quickly and easily
 from Python. Moreover, this architecture is designed to support interactive and
 collaborative parallel computing.
 
+The main features of this system are:
+
+* Quickly parallelize Python code from an interactive Python/IPython session.
+
+* A flexible and dynamic process model that be deployed on anything from 
+  multicore workstations to supercomputers.
+
+* An architecture that supports many different styles of parallelism, from
+  message passing to task farming.  And all of these styles can be handled
+  interactively.
+
+* Both blocking and fully asynchronous interfaces.
+
+* High level APIs that enable many things to be parallelized in a few lines
+  of code.
+
+* Write parallel code that will run unchanged on everything from multicore
+  workstations to supercomputers.
+
+* Full integration with Message Passing libraries (MPI).
+
+* Capabilities based security model with full encryption of network connections.
+
+* Share live parallel jobs with other users securely.  We call this collaborative
+  parallel computing.
+
+* Dynamically load balanced task farming system.
+
+* Robust error handling.  Python exceptions raised in parallel execution are
+  gathered and presented to the top-level code.
+
 For more information, see our :ref:`overview <parallel_index>` of using IPython for
 parallel computing.
 
 Portability and Python requirements
 -----------------------------------
 
 As of the 0.9 release, IPython requires Python 2.4 or greater.  We have
 not begun to test IPython on Python 2.6 or 3.0, but we expect it will
 work with some minor changes.
 
 IPython is known to work on the following operating systems:
 
 	* Linux
 	* AIX
 	* Most other Unix-like OSs (Solaris, BSD, etc.)
 	* Mac OS X
 	* Windows (CygWin, XP, Vista, etc.)
 
 See :ref:`here <install_index>` for instructions on how to install IPython.
  No newline at end of file

docs/source/parallel/index.txt

@@ -1,17 +1,14 b''
 .. _parallel_index:
 
 ====================================
-Using IPython for Parallel computing
+Using IPython for parallel computing
 ====================================
 
-User Documentation
-==================
-
 .. toctree::
    :maxdepth: 2
 
    parallel_intro.txt
    parallel_multiengine.txt
    parallel_task.txt
    parallel_mpi.txt
 

docs/source/parallel/parallel_intro.txt

@@ -1,242 +1,327 b''
 .. _ip1par:
 
-======================================
-Using IPython for parallel computing
-======================================
+============================
+Overview and getting started
+============================
 
 .. contents::
 
 Introduction
 ============
 
-This file gives an overview of IPython. IPython has a sophisticated and
+This file gives an overview of IPython's sophisticated and
 powerful architecture for parallel and distributed computing. This
 architecture abstracts out parallelism in a very general way, which
 enables IPython to support many different styles of parallelism
 including:
 
-	* Single program, multiple data (SPMD) parallelism.
-	* Multiple program, multiple data (MPMD) parallelism.
-	* Message passing using ``MPI``.
-	* Task farming.
-	* Data parallel.
-	* Combinations of these approaches.
-	* Custom user defined approaches.
+* Single program, multiple data (SPMD) parallelism.
+* Multiple program, multiple data (MPMD) parallelism.
+* Message passing using ``MPI``.
+* Task farming.
+* Data parallel.
+* Combinations of these approaches.
+* Custom user defined approaches.
 
 Most importantly, IPython enables all types of parallel applications to
 be developed, executed, debugged and monitored *interactively*. Hence,
 the ``I`` in IPython.  The following are some example usage cases for IPython:
 
-	* Quickly parallelize algorithms that are embarrassingly parallel
-	  using a number of simple approaches.  Many simple things can be
-	  parallelized interactively in one or two lines of code.
-	* Steer traditional MPI applications on a supercomputer from an
-	  IPython session on your laptop.
-	* Analyze and visualize large datasets (that could be remote and/or
-	  distributed) interactively using IPython and tools like
-	  matplotlib/TVTK.
-	* Develop, test and debug new parallel algorithms 
-	  (that may use MPI) interactively.
-	* Tie together multiple MPI jobs running on different systems into
-	  one giant distributed and parallel system.
-	* Start a parallel job on your cluster and then have a remote 	
-	  collaborator connect to it and pull back data into their 
-	  local IPython session for plotting and analysis.
-	* Run a set of tasks on a set of CPUs using dynamic load balancing.
+* Quickly parallelize algorithms that are embarrassingly parallel
+  using a number of simple approaches.  Many simple things can be
+  parallelized interactively in one or two lines of code.
+
+* Steer traditional MPI applications on a supercomputer from an
+  IPython session on your laptop.
+
+* Analyze and visualize large datasets (that could be remote and/or
+  distributed) interactively using IPython and tools like
+  matplotlib/TVTK.
+
+* Develop, test and debug new parallel algorithms 
+  (that may use MPI) interactively.
+
+* Tie together multiple MPI jobs running on different systems into
+  one giant distributed and parallel system.
+
+* Start a parallel job on your cluster and then have a remote 	
+  collaborator connect to it and pull back data into their 
+  local IPython session for plotting and analysis.
+
+* Run a set of tasks on a set of CPUs using dynamic load balancing.
 
 Architecture overview
 =====================
 
 The IPython architecture consists of three components:
 
-	* The IPython engine.
-	* The IPython controller.
-	* Various controller Clients.
+* The IPython engine.
+* The IPython controller.
+* Various controller clients.
+
+These components live in the :mod:`IPython.kernel` package and are
+installed with IPython.  They do, however, have additional dependencies
+that must be installed.  For more information, see our
+:ref:`installation documentation <install_index>`.
 
 IPython engine
 ---------------
 
 The IPython engine is a Python instance that takes Python commands over a
 network connection. Eventually, the IPython engine will be a full IPython
 interpreter, but for now, it is a regular Python interpreter. The engine
 can also handle incoming and outgoing Python objects sent over a network
 connection.  When multiple engines are started, parallel and distributed
 computing becomes possible. An important feature of an IPython engine is
 that it blocks while user code is being executed. Read on for how the
 IPython controller solves this problem to expose a clean asynchronous API
 to the user.
 
 IPython controller
 ------------------
 
 The IPython controller provides an interface for working with a set of
 engines. At an general level, the controller is a process to which
 IPython engines can connect. For each connected engine, the controller
 manages a queue. All actions that can be performed on the engine go
 through this queue. While the engines themselves block when user code is
 run, the controller hides that from the user to provide a fully
-asynchronous interface to a set of engines. Because the controller
-listens on a network port for engines to connect to it, it must be
-started before any engines are started.  
+asynchronous interface to a set of engines. 
+
+.. note::
+
+    Because the controller listens on a network port for engines to 
+    connect to it, it must be started *before* any engines are started.  
 
 The controller also provides a single point of contact for users who wish
 to utilize the engines connected to the controller. There are different
 ways of working with a controller. In IPython these ways correspond to different interfaces that the controller is adapted to.  Currently we have two default interfaces to the controller: 
 
-	* The MultiEngine interface.
-	* The Task interface.
+* The MultiEngine interface, which provides the simplest possible way of working
+  with engines interactively.
+* The Task interface, which provides presents the engines as a load balanced
+  task farming system.
 
 Advanced users can easily add new custom interfaces to enable other
 styles of parallelism. 
 
 .. note:: 
 
 	A single controller and set of engines can be accessed 
 	through multiple interfaces simultaneously.  This opens the
 	door for lots of interesting things.  
 
 Controller clients
 ------------------
 
 For each controller interface, there is a corresponding client. These
 clients allow users to interact with a set of engines through the
-interface.
+interface.  Here are the two default clients:
+
+* The :class:`MultiEngineClient` class.
+* The :class:`TaskClient` class.
 
 Security
 --------
 
-By default (as long as `pyOpenSSL` is installed) all network connections between the controller and engines and the controller and clients are secure.  What does this mean?  First of all, all of the connections will be encrypted using SSL.  Second, the connections are authenticated.  We handle authentication in a `capabilities`__ based security model.  In this model,  a "capability (known in some systems as a key) is a communicable, unforgeable token of authority".  Put simply, a capability is like a key to your house.  If you have the key to your house, you can get in, if not you can't.  
+By default (as long as `pyOpenSSL` is installed) all network connections between the controller and engines and the controller and clients are secure.  What does this mean?  First of all, all of the connections will be encrypted using SSL.  Second, the connections are authenticated.  We handle authentication in a `capabilities`__ based security model.  In this model,  a "capability (known in some systems as a key) is a communicable, unforgeable token of authority".  Put simply, a capability is like a key to your house.  If you have the key to your house, you can get in.  If not, you can't.  
 
 .. __: http://en.wikipedia.org/wiki/Capability-based_security
 
-In our architecture, the controller is the only process that listens on network ports, and is thus responsible to creating these keys.  In IPython, these keys are known as Foolscap URLs, or FURLs, because of the underlying network protocol we are using.  As a user, you don't need to know anything about the details of these FURLs, other than that when the controller starts, it saves a set of FURLs to files named something.furl.  The default location of these files is your ~./ipython directory.
+In our architecture, the controller is the only process that listens on network ports, and is thus responsible to creating these keys.  In IPython, these keys are known as Foolscap URLs, or FURLs, because of the underlying network protocol we are using.  As a user, you don't need to know anything about the details of these FURLs, other than that when the controller starts, it saves a set of FURLs to files named :file:`something.furl`.  The default location of these files is the :file:`~./ipython/security` directory.
 
-To connect and authenticate to the controller an engine or client simply needs to present an appropriate furl (that was originally created by the controller) to the controller.  Thus, the .furl files need to be copied to a location where the clients and engines can find them.  Typically, this is the ~./ipython directory on the host where the client/engine is running (which could be a different host than the controller).  Once the .furl files are copied over, everything should work fine.
+To connect and authenticate to the controller an engine or client simply needs to present an appropriate furl (that was originally created by the controller) to the controller.  Thus, the .furl files need to be copied to a location where the clients and engines can find them.  Typically, this is the :file:`~./ipython/security` directory on the host where the client/engine is running (which could be a different host than the controller).  Once the .furl files are copied over, everything should work fine.
+
+Currently, there are three .furl files that the controller creates:
+
+ipcontroller-engine.furl
+    This ``.furl`` file is the key that gives an engine the ability to connect
+    to a controller.
+
+ipcontroller-tc.furl
+    This ``.furl`` file is the key that a :class:`TaskClient` must use to
+    connect to the task interface of a controller.
+    
+ipcontroller-mec.furl
+    This ``.furl`` file is the key that a :class:`MultiEngineClient` must use to
+    connect to the multiengine interface of a controller.
+
+More details of how these ``.furl`` files are used are given below.
 
 Getting Started
 ===============
 
 To use IPython for parallel computing, you need to start one instance of
 the controller and one or more instances of the engine. The controller
 and each engine can run on different machines or on the same machine.
 Because of this, there are many different possibilities for setting up
 the IP addresses and ports used by the various processes.
 
 Starting the controller and engine on your local machine
 --------------------------------------------------------
 
 This is the simplest configuration that can be used and is useful for
 testing the system and on machines that have multiple cores and/or
-multple CPUs. The easiest way of doing this is using the ``ipcluster``
+multple CPUs. The easiest way of getting started is to use the :command:`ipcluster`
 command::
 
 	$ ipcluster -n 4
-	
+
 This will start an IPython controller and then 4 engines that connect to
 the controller. Lastly, the script will print out the Python commands
 that you can use to connect to the controller. It is that easy.
 
-Underneath the hood, the ``ipcluster`` script uses two other top-level
+.. warning::
+
+    The :command:`ipcluster` does not currently work on Windows.  We are
+    working on it though.
+
+Underneath the hood, the controller creates ``.furl`` files in the
+:file:`~./ipython/security` directory.  Because the engines are on the
+same host, they automatically find the needed :file:`ipcontroller-engine.furl`
+there and use it to connect to the controller.
+
+The :command:`ipcluster` script uses two other top-level
 scripts that you can also use yourself. These scripts are
-``ipcontroller``, which starts the controller and ``ipengine`` which
+:command:`ipcontroller`, which starts the controller and :command:`ipengine` which
 starts one engine. To use these scripts to start things on your local
 machine, do the following.
 
 First start the controller::
 
-	$ ipcontroller &
+	$ ipcontroller
 	
 Next, start however many instances of the engine you want using (repeatedly) the command::
 
-	$ ipengine &
+	$ ipengine
+
+The engines should start and automatically connect to the controller using the ``.furl`` files in :file:`~./ipython/security`. You are now ready to use the controller and engines from IPython.
 
 .. warning:: 
 	
 	The order of the above operations is very important.  You *must*
  	start the controller before the engines, since the engines connect 
 	to the controller as they get started.
 
-On some platforms you may need to give these commands in the form
-``(ipcontroller &)`` and ``(ipengine &)`` for them to work properly. The
-engines should start and automatically connect to the controller on the
-default ports, which are chosen for this type of setup. You are now ready
-to use the controller and engines from IPython.
+.. note::
 
-Starting the controller and engines on different machines
----------------------------------------------------------
+    On some platforms (OS X), to put the controller and engine into the background
+    you may need to give these commands in the form ``(ipcontroller &)`` 
+    and ``(ipengine &)`` (with the parentheses) for them to work properly. 
 
-This section needs to be updated to reflect the new Foolscap capabilities based
-model.
 
-Using ``ipcluster`` with ``ssh``
---------------------------------
+Starting the controller and engines on different hosts
+------------------------------------------------------
 
-The ``ipcluster`` command can also start a controller and engines using
-``ssh``.  We need more documentation on this, but for now here is any
-example startup script::
+When the controller and engines are running on different hosts, things are
+slightly more complicated, but the underlying ideas are the same:
 
-	controller = dict(host='myhost',
-	                  engine_port=None, # default is 10105
-	                  control_port=None,
-	                  )
+1. Start the controller on a host using :command:`ipcontroler`.
+2. Copy :file:`ipcontroller-engine.furl` from :file:`~./ipython/security` on the controller's host to the host where the engines will run.
+3. Use :command:`ipengine` on the engine's hosts to start the engines.
 
-	# keys are hostnames, values are the number of engine on that host
-	engines = dict(node1=2,
-	               node2=2,
-	               node3=2,
-	               node3=2,
-	               )
+The only thing you have to be careful of is to tell :command:`ipengine` where the :file:`ipcontroller-engine.furl` file is located.  There are two ways you can do this:
+
+* Put :file:`ipcontroller-engine.furl` in the :file:`~./ipython/security` directory
+  on the engine's host, where it will be found automatically.
+* Call :command:`ipengine` with the ``--furl-file=full_path_to_the_file`` flag.
+
+The ``--furl-file`` flag works like this::
+
+    $ ipengine --furl-file=/path/to/my/ipcontroller-engine.furl
+
+.. note::
+    
+    If the controller's and engine's hosts all have a shared file system  
+    (:file:`~./ipython/security` is the same on all of them), then things
+    will just work!
+
+Make .furl files persistent
+---------------------------
+
+At fist glance it may seem that that managing the ``.furl`` files is a bit annoying.  Going back to the house and key analogy, copying the ``.furl`` around each time you start the controller is like having to make a new key everytime you want to unlock the door and enter your house.  As with your house, you want to be able to create the key (or ``.furl`` file) once, and then simply use it at any point in the future.
+
+This is possible.  The only thing you have to do is decide what ports the controller will listen on for the engines and clients.  This is done as follows::
+
+    $ ipcontroller --client-port=10101 --engine-port=10102
+
+Then, just copy the furl files over the first time and you are set.  You can start and stop the controller and engines any many times as you want in the future, just make sure to tell the controller to use the *same* ports.
+
+.. note::
+
+    You may ask the question: what ports does the controller listen on if you
+    don't tell is to use specific ones?  The default is to use high random port
+    numbers.  We do this for two reasons: i) to increase security through obcurity
+    and ii) to multiple controllers on a given host to start and automatically
+    use different ports.
 
 Starting engines using ``mpirun``
 ---------------------------------
 
 The IPython engines can be started using ``mpirun``/``mpiexec``, even if
-the engines don't call MPI_Init() or use the MPI API in any way. This is
+the engines don't call ``MPI_Init()`` or use the MPI API in any way. This is
 supported on modern MPI implementations like `Open MPI`_.. This provides
 an really nice way of starting a bunch of engine. On a system with MPI
 installed you can do::
 
-	mpirun -n 4 ipengine --controller-port=10000 --controller-ip=host0
+	mpirun -n 4 ipengine
+
+to start 4 engine on a cluster.  This works even if you don't have any
+Python-MPI bindings installed.
 
 .. _Open MPI: http://www.open-mpi.org/
 
 More details on using MPI with IPython can be found :ref:`here <parallelmpi>`.
 
 Log files
 ---------
 
 All of the components of IPython have log files associated with them.
 These log files can be extremely useful in debugging problems with
 IPython and can be found in the directory ``~/.ipython/log``.  Sending
 the log files to us will often help us to debug any problems.
 
 Next Steps
 ==========
 
 Once you have started the IPython controller and one or more engines, you
-are ready to use the engines to do somnething useful. To make sure
+are ready to use the engines to do something useful. To make sure
 everything is working correctly, try the following commands::
 
 	In [1]: from IPython.kernel import client
 	
-	In [2]: mec = client.MultiEngineClient()    # This looks for .furl files in ~./ipython
+	In [2]: mec = client.MultiEngineClient()
 	
 	In [4]: mec.get_ids()
 	Out[4]: [0, 1, 2, 3]
 	
 	In [5]: mec.execute('print "Hello World"')
 	Out[5]: 
 	<Results List>
 	[0] In [1]: print "Hello World"
 	[0] Out[1]: Hello World
 
 	[1] In [1]: print "Hello World"
 	[1] Out[1]: Hello World
 
 	[2] In [1]: print "Hello World"
 	[2] Out[1]: Hello World
 
 	[3] In [1]: print "Hello World"
 	[3] Out[1]: Hello World
 
-If this works, you are ready to learn more about the :ref:`MultiEngine <parallelmultiengine>` and :ref:`Task <paralleltask>` interfaces to the controller.
+Remember, a client also needs to present a ``.furl`` file to the controller.  How does this happen?  When a multiengine client is created with no arguments, the client tries to find the corresponding ``.furl`` file in the local :file:`~./ipython/security` directory.  If it finds it, you are set.  If you have put the ``.furl`` file in a different location or it has a different name, create the client like this::
+
+    mec = client.MultiEngineClient('/path/to/my/ipcontroller-mec.furl')
+
+Same thing hold true of creating a task client::
+
+    tc = client.TaskClient('/path/to/my/ipcontroller-tc.furl')
+
+You are now ready to learn more about the :ref:`MultiEngine <parallelmultiengine>` and :ref:`Task <paralleltask>` interfaces to the controller.
+
+.. note:: 
+
+    Don't forget that the engine, multiengine client and task client all have
+    *different* furl files.  You must move *each* of these around to an appropriate
+    location so that the engines and clients can use them to connect to the controller.

docs/source/parallel/parallel_multiengine.txt

@@ -1,728 +1,783 b''
 .. _parallelmultiengine:
 
-=================================
-IPython's MultiEngine interface
-=================================
+===============================
+IPython's multiengine interface
+===============================
 
 .. contents::
 
-The MultiEngine interface represents one possible way of working with a
-set of IPython engines. The basic idea behind the MultiEngine interface is
-that the capabilities of each engine are explicitly exposed to the user.
-Thus, in the MultiEngine interface, each engine is given an id that is
-used to identify the engine and give it work to do. This interface is very
-intuitive and is designed with interactive usage in mind, and is thus the
-best place for new users of IPython to begin.
+The multiengine interface represents one possible way of working with a set of
+IPython engines. The basic idea behind the multiengine interface is that the
+capabilities of each engine are directly and explicitly exposed to the user.
+Thus, in the multiengine interface, each engine is given an id that is used to
+identify the engine and give it work to do. This interface is very intuitive
+and is designed with interactive usage in mind, and is thus the best place for
+new users of IPython to begin.
 
 Starting the IPython controller and engines
 ===========================================
 
 To follow along with this tutorial, you will need to start the IPython
-controller and four IPython engines. The simplest way of doing this is to
-use the ``ipcluster`` command::
+controller and four IPython engines. The simplest way of doing this is to use
+the :command:`ipcluster` command::
 
 	$ ipcluster -n 4
 	
-For more detailed information about starting the controller and engines, see our :ref:`introduction <ip1par>` to using IPython for parallel computing.
+For more detailed information about starting the controller and engines, see
+our :ref:`introduction <ip1par>` to using IPython for parallel computing.
 
 Creating a ``MultiEngineClient`` instance
 =========================================
 
-The first step is to import the IPython ``client`` module and then create a ``MultiEngineClient`` instance::
+The first step is to import the IPython :mod:`IPython.kernel.client` module
+and then create a :class:`MultiEngineClient` instance::
 
 	In [1]: from IPython.kernel import client
 	
 	In [2]: mec = client.MultiEngineClient()
 
-To make sure there are engines connected to the controller, use can get a list of engine ids::
+This form assumes that the :file:`ipcontroller-mec.furl` is in the
+:file:`~./ipython/security` directory on the client's host. If not, the
+location of the ``.furl`` file must be given as an argument to the
+constructor::
+
+    In[2]: mec = client.MultiEngineClient('/path/to/my/ipcontroller-mec.furl')
+
+To make sure there are engines connected to the controller, use can get a list
+of engine ids::
 
 	In [3]: mec.get_ids()
 	Out[3]: [0, 1, 2, 3]
 
 Here we see that there are four engines ready to do work for us.
 
+Quick and easy parallelism
+==========================
+
+In many cases, you simply want to apply a Python function to a sequence of objects, but *in parallel*.  The multiengine interface provides two simple ways of accomplishing this:  a parallel version of :func:`map` and ``@parallel`` function decorator.
+
+Parallel map
+------------
+
+Python's builtin :func:`map` functions allows a function to be applied to a
+sequence element-by-element. This type of code is typically trivial to
+parallelize. In fact, the multiengine interface in IPython already has a
+parallel version of :meth:`map` that works just like its serial counterpart::
+
+	In [63]: serial_result = map(lambda x:x**10, range(32))
+
+	In [64]: parallel_result = mec.map(lambda x:x**10, range(32))
+
+	In [65]: serial_result==parallel_result
+	Out[65]: True
+
+.. note::
+
+    The multiengine interface version of :meth:`map` does not do any load
+    balancing.  For a load balanced version, see the task interface.
+
+.. seealso::
+
+    The :meth:`map` method has a number of options that can be controlled by
+    the :meth:`mapper` method.  See its docstring for more information.
+
+Parallel function decorator
+---------------------------
+
+Parallel functions are just like normal function, but they can be called on sequences and *in parallel*.  The multiengine interface provides a decorator that turns any Python function into a parallel function::
+
+    In [10]: @mec.parallel()
+       ....: def f(x):
+       ....:     return 10.0*x**4
+       ....: 
+
+    In [11]: f(range(32))    # this is done in parallel
+    Out[11]: 
+    [0.0,10.0,160.0,...]
+
+See the docstring for the :meth:`parallel` decorator for options.
+
 Running Python commands
 =======================
 
-The most basic type of operation that can be performed on the engines is to execute Python code. Executing Python code can be done in blocking or non-blocking mode (blocking is default) using the ``execute`` method.
+The most basic type of operation that can be performed on the engines is to
+execute Python code. Executing Python code can be done in blocking or
+non-blocking mode (blocking is default) using the :meth:`execute` method.
 
 Blocking execution
 ------------------
 
-In blocking mode, the ``MultiEngineClient`` object (called ``mec`` in
+In blocking mode, the :class:`MultiEngineClient` object (called ``mec`` in
 these examples) submits the command to the controller, which places the
-command in the engines' queues for execution. The ``execute`` call then
+command in the engines' queues for execution. The :meth:`execute` call then
 blocks until the engines are done executing the command::
 
 	# The default is to run on all engines
 	In [4]: mec.execute('a=5')
 	Out[4]: 
 	<Results List>
 	[0] In [1]: a=5
 	[1] In [1]: a=5
 	[2] In [1]: a=5
 	[3] In [1]: a=5
 
 	In [5]: mec.execute('b=10')
 	Out[5]: 
 	<Results List>
 	[0] In [2]: b=10
 	[1] In [2]: b=10
 	[2] In [2]: b=10
 	[3] In [2]: b=10
 
-Python commands can be executed on specific engines by calling execute using the ``targets`` keyword argument::
+Python commands can be executed on specific engines by calling execute using
+the ``targets`` keyword argument::
 
 	In [6]: mec.execute('c=a+b',targets=[0,2])
 	Out[6]: 
 	<Results List>
 	[0] In [3]: c=a+b
 	[2] In [3]: c=a+b
 
 
 	In [7]: mec.execute('c=a-b',targets=[1,3])
 	Out[7]: 
 	<Results List>
 	[1] In [3]: c=a-b
 	[3] In [3]: c=a-b
 
 
 	In [8]: mec.execute('print c')
 	Out[8]: 
 	<Results List>
 	[0] In [4]: print c
 	[0] Out[4]: 15
 
 	[1] In [4]: print c
 	[1] Out[4]: -5
 
 	[2] In [4]: print c
 	[2] Out[4]: 15
 
 	[3] In [4]: print c
 	[3] Out[4]: -5
 
-This example also shows one of the most important things about the IPython engines: they have a persistent user namespaces.  The ``execute`` method returns a Python ``dict`` that contains useful information::
+This example also shows one of the most important things about the IPython
+engines: they have a persistent user namespaces. The :meth:`execute` method
+returns a Python ``dict`` that contains useful information::
 
 	In [9]: result_dict = mec.execute('d=10; print d')
 
 	In [10]: for r in result_dict:
 	   ....:     print r
 	   ....:     
 	   ....:     
 	{'input': {'translated': 'd=10; print d', 'raw': 'd=10; print d'}, 'number': 5, 'id': 0, 'stdout': '10\n'}
 	{'input': {'translated': 'd=10; print d', 'raw': 'd=10; print d'}, 'number': 5, 'id': 1, 'stdout': '10\n'}
 	{'input': {'translated': 'd=10; print d', 'raw': 'd=10; print d'}, 'number': 5, 'id': 2, 'stdout': '10\n'}
 	{'input': {'translated': 'd=10; print d', 'raw': 'd=10; print d'}, 'number': 5, 'id': 3, 'stdout': '10\n'}
 
 Non-blocking execution
 ----------------------
 
-In non-blocking mode, ``execute`` submits the command to be executed and then returns a
-``PendingResult`` object immediately. The ``PendingResult`` object gives you a way of getting a
-result at a later time through its ``get_result`` method or ``r`` attribute. This allows you to
-quickly submit long running commands without blocking your local Python/IPython session::
+In non-blocking mode, :meth:`execute` submits the command to be executed and
+then returns a :class:`PendingResult` object immediately. The
+:class:`PendingResult` object gives you a way of getting a result at a later
+time through its :meth:`get_result` method or :attr:`r` attribute. This allows
+you to quickly submit long running commands without blocking your local
+Python/IPython session::
 
 	# In blocking mode
 	In [6]: mec.execute('import time')
 	Out[6]:
 	<Results List>
 	[0] In [1]: import time
 	[1] In [1]: import time
 	[2] In [1]: import time
 	[3] In [1]: import time
 
 	# In non-blocking mode
 	In [7]: pr = mec.execute('time.sleep(10)',block=False)
 
 	# Now block for the result
 	In [8]: pr.get_result()
 	Out[8]:
 	<Results List>
 	[0] In [2]: time.sleep(10)
 	[1] In [2]: time.sleep(10)
 	[2] In [2]: time.sleep(10)
 	[3] In [2]: time.sleep(10)
 
 	# Again in non-blocking mode
 	In [9]: pr = mec.execute('time.sleep(10)',block=False)
 
 	# Poll to see if the result is ready
 	In [10]: pr.get_result(block=False)
 
 	# A shorthand for get_result(block=True)
 	In [11]: pr.r
 	Out[11]:
 	<Results List>
 	[0] In [3]: time.sleep(10)
 	[1] In [3]: time.sleep(10)
 	[2] In [3]: time.sleep(10)
 	[3] In [3]: time.sleep(10)
 
-Often, it is desirable to wait until a set of ``PendingResult`` objects are done.  For this, there is a the method ``barrier``.  This method takes a tuple of ``PendingResult`` objects and blocks until all of the associated results are ready::
+Often, it is desirable to wait until a set of :class:`PendingResult` objects
+are done. For this, there is a the method :meth:`barrier`. This method takes a
+tuple of :class:`PendingResult` objects and blocks until all of the associated
+results are ready::
 
 	In [72]: mec.block=False
 
 	# A trivial list of PendingResults objects
 	In [73]: pr_list = [mec.execute('time.sleep(3)') for i in range(10)]
 
 	# Wait until all of them are done
 	In [74]: mec.barrier(pr_list)
 
 	# Then, their results are ready using get_result or the r attribute
 	In [75]: pr_list[0].r
 	Out[75]: 
 	<Results List>
 	[0] In [20]: time.sleep(3)
 	[1] In [19]: time.sleep(3)
 	[2] In [20]: time.sleep(3)
 	[3] In [19]: time.sleep(3)
 
 
 The ``block`` and ``targets`` keyword arguments and attributes
 --------------------------------------------------------------
 
-Most commands in the multiengine interface (like ``execute``) accept ``block`` and ``targets``
-as keyword arguments. As we have seen above, these keyword arguments control the blocking mode
-and which engines the command is applied to. The ``MultiEngineClient`` class also has ``block``
-and ``targets`` attributes that control the default behavior when the keyword arguments are not
-provided. Thus the following logic is used for ``block`` and ``targets``:
+Most methods in the multiengine interface (like :meth:`execute`) accept
+``block`` and ``targets`` as keyword arguments. As we have seen above, these
+keyword arguments control the blocking mode and which engines the command is
+applied to. The :class:`MultiEngineClient` class also has :attr:`block` and
+:attr:`targets` attributes that control the default behavior when the keyword
+arguments are not provided. Thus the following logic is used for :attr:`block`
+and :attr:`targets`:
 
-	* If no keyword argument is provided, the instance attributes are used.
-	* Keyword argument, if provided override the instance attributes.
+* If no keyword argument is provided, the instance attributes are used.
+* Keyword argument, if provided override the instance attributes.
 
 The following examples demonstrate how to use the instance attributes::
 
 	In [16]: mec.targets = [0,2]
 
 	In [17]: mec.block = False
 
 	In [18]: pr = mec.execute('a=5')
 
 	In [19]: pr.r
 	Out[19]: 
 	<Results List>
 	[0] In [6]: a=5
 	[2] In [6]: a=5
 
 	# Note targets='all' means all engines
 	In [20]: mec.targets = 'all'
 
 	In [21]: mec.block = True
 
 	In [22]: mec.execute('b=10; print b')
 	Out[22]: 
 	<Results List>
 	[0] In [7]: b=10; print b
 	[0] Out[7]: 10
 
 	[1] In [6]: b=10; print b
 	[1] Out[6]: 10
 
 	[2] In [7]: b=10; print b
 	[2] Out[7]: 10
 
 	[3] In [6]: b=10; print b
 	[3] Out[6]: 10
 
-The ``block`` and ``targets`` instance attributes also determine the behavior of the parallel
-magic commands...
+The :attr:`block` and :attr:`targets` instance attributes also determine the
+behavior of the parallel magic commands.
 
 
 Parallel magic commands
 -----------------------
 
-We provide a few IPython magic commands (``%px``, ``%autopx`` and ``%result``) that make it more pleasant to execute Python commands on the engines interactively. These are simply shortcuts to ``execute`` and ``get_result``. The ``%px`` magic executes a single Python command on the engines specified by the `magicTargets``targets` attribute of the ``MultiEngineClient`` instance (by default this is 'all')::
+We provide a few IPython magic commands (``%px``, ``%autopx`` and ``%result``)
+that make it more pleasant to execute Python commands on the engines
+interactively. These are simply shortcuts to :meth:`execute` and
+:meth:`get_result`. The ``%px`` magic executes a single Python command on the
+engines specified by the :attr:`targets` attribute of the
+:class:`MultiEngineClient` instance (by default this is ``'all'``)::
 
 	# Make this MultiEngineClient active for parallel magic commands
 	In [23]: mec.activate()
 
 	In [24]: mec.block=True
 
 	In [25]: import numpy
 
 	In [26]: %px import numpy
 	Executing command on Controller
 	Out[26]:
 	<Results List>
 	[0] In [8]: import numpy
 	[1] In [7]: import numpy
 	[2] In [8]: import numpy
 	[3] In [7]: import numpy
 
 
 	In [27]: %px a = numpy.random.rand(2,2)
 	Executing command on Controller
 	Out[27]:
 	<Results List>
 	[0] In [9]: a = numpy.random.rand(2,2)
 	[1] In [8]: a = numpy.random.rand(2,2)
 	[2] In [9]: a = numpy.random.rand(2,2)
 	[3] In [8]: a = numpy.random.rand(2,2)
 
 
 	In [28]: %px print numpy.linalg.eigvals(a)
 	Executing command on Controller
 	Out[28]:
 	<Results List>
 	[0] In [10]: print numpy.linalg.eigvals(a)
 	[0] Out[10]: [ 1.28167017  0.14197338]
 
 	[1] In [9]: print numpy.linalg.eigvals(a)
 	[1] Out[9]: [-0.14093616  1.27877273]
 
 	[2] In [10]: print numpy.linalg.eigvals(a)
 	[2] Out[10]: [-0.37023573  1.06779409]
 
 	[3] In [9]: print numpy.linalg.eigvals(a)
 	[3] Out[9]: [ 0.83664764 -0.25602658]
 
-The ``%result`` magic gets and prints the stdin/stdout/stderr of the last command executed on each engine.  It is simply a shortcut to the ``get_result`` method::
+The ``%result`` magic gets and prints the stdin/stdout/stderr of the last
+command executed on each engine. It is simply a shortcut to the
+:meth:`get_result` method::
 
 	In [29]: %result
 	Out[29]:
 	<Results List>
 	[0] In [10]: print numpy.linalg.eigvals(a)
 	[0] Out[10]: [ 1.28167017  0.14197338]
 
 	[1] In [9]: print numpy.linalg.eigvals(a)
 	[1] Out[9]: [-0.14093616  1.27877273]
 
 	[2] In [10]: print numpy.linalg.eigvals(a)
 	[2] Out[10]: [-0.37023573  1.06779409]
 
 	[3] In [9]: print numpy.linalg.eigvals(a)
 	[3] Out[9]: [ 0.83664764 -0.25602658]
 
-The ``%autopx`` magic switches to a mode where everything you type is executed on the engines given by the ``targets`` attribute::
+The ``%autopx`` magic switches to a mode where everything you type is executed
+on the engines given by the :attr:`targets` attribute::
 
 	In [30]: mec.block=False
 
 	In [31]: %autopx
 	Auto Parallel Enabled
 	Type %autopx to disable
 
 	In [32]: max_evals = []
 	<IPython.kernel.multiengineclient.PendingResult object at 0x17b8a70>
 
 	In [33]: for i in range(100):
 	   ....:     a = numpy.random.rand(10,10)
 	   ....:     a = a+a.transpose()
 	   ....:     evals = numpy.linalg.eigvals(a)
 	   ....:     max_evals.append(evals[0].real)
 	   ....:
 	   ....:
 	<IPython.kernel.multiengineclient.PendingResult object at 0x17af8f0>
 
 	In [34]: %autopx
 	Auto Parallel Disabled
 
 	In [35]: mec.block=True
 
 	In [36]: px print "Average max eigenvalue is: ", sum(max_evals)/len(max_evals)
 	Executing command on Controller
 	Out[36]:
 	<Results List>
 	[0] In [13]: print "Average max eigenvalue is: ", sum(max_evals)/len(max_evals)
 	[0] Out[13]: Average max eigenvalue is:  10.1387247332
 
 	[1] In [12]: print "Average max eigenvalue is: ", sum(max_evals)/len(max_evals)
 	[1] Out[12]: Average max eigenvalue is:  10.2076902286
 
 	[2] In [13]: print "Average max eigenvalue is: ", sum(max_evals)/len(max_evals)
 	[2] Out[13]: Average max eigenvalue is:  10.1891484655
 
 	[3] In [12]: print "Average max eigenvalue is: ", sum(max_evals)/len(max_evals)
 	[3] Out[12]: Average max eigenvalue is:  10.1158837784
 
-Using the ``with`` statement of Python 2.5
-------------------------------------------
 
-Python 2.5 introduced the ``with`` statement.  The ``MultiEngineClient`` can be used with the ``with`` statement to execute a block of code on the engines indicated by the ``targets`` attribute::
+Moving Python objects around
+============================
 
-	In [3]: with mec:
-	   ...:     client.remote()    # Required so the following code is not run locally
-	   ...:     a = 10
-	   ...:     b = 30
-	   ...:     c = a+b
-	   ...:     
-	   ...:     
-
-	In [4]: mec.get_result()
-	Out[4]: 
-	<Results List>
-	[0] In [1]: a = 10
-	b = 30
-	c = a+b
-
-	[1] In [1]: a = 10
-	b = 30
-	c = a+b
-
-	[2] In [1]: a = 10
-	b = 30
-	c = a+b
-
-	[3] In [1]: a = 10
-	b = 30
-	c = a+b
-
-This is basically another way of calling execute, but one with allows you to avoid writing code in strings.  When used in this way, the attributes ``targets`` and ``block`` are used to control how the code is executed.  For now, if you run code in non-blocking mode you won't have access to the ``PendingResult``.
-
-Moving Python object around
-===========================
-
-In addition to executing code on engines, you can transfer Python objects to and from your
-IPython session and the engines. In IPython, these operations are called ``push`` (sending an
-object to the engines) and ``pull`` (getting an object from the engines).
+In addition to executing code on engines, you can transfer Python objects to
+and from your IPython session and the engines. In IPython, these operations
+are called :meth:`push` (sending an object to the engines) and :meth:`pull`
+(getting an object from the engines).
 
 Basic push and pull
 -------------------
 
-Here are some examples of how you use ``push`` and ``pull``::
+Here are some examples of how you use :meth:`push` and :meth:`pull`::
 
 	In [38]: mec.push(dict(a=1.03234,b=3453))
 	Out[38]: [None, None, None, None]
 
 	In [39]: mec.pull('a')
 	Out[39]: [1.03234, 1.03234, 1.03234, 1.03234]
 
 	In [40]: mec.pull('b',targets=0)
 	Out[40]: [3453]
 
 	In [41]: mec.pull(('a','b'))
 	Out[41]: [[1.03234, 3453], [1.03234, 3453], [1.03234, 3453], [1.03234, 3453]]
 
 	In [42]: mec.zip_pull(('a','b'))
 	Out[42]: [(1.03234, 1.03234, 1.03234, 1.03234), (3453, 3453, 3453, 3453)]
 
 	In [43]: mec.push(dict(c='speed'))
 	Out[43]: [None, None, None, None]
 
 	In [44]: %px print c
 	Executing command on Controller
 	Out[44]: 
 	<Results List>
 	[0] In [14]: print c
 	[0] Out[14]: speed
 
 	[1] In [13]: print c
 	[1] Out[13]: speed
 
 	[2] In [14]: print c
 	[2] Out[14]: speed
 
 	[3] In [13]: print c
 	[3] Out[13]: speed
 
-In non-blocking mode ``push`` and ``pull`` also return ``PendingResult`` objects::
+In non-blocking mode :meth:`push` and :meth:`pull` also return
+:class:`PendingResult` objects::
 
 	In [47]: mec.block=False
 
 	In [48]: pr = mec.pull('a')
 
 	In [49]: pr.r
 	Out[49]: [1.03234, 1.03234, 1.03234, 1.03234]
 
 
 Push and pull for functions
 ---------------------------
 
-Functions can also be pushed and pulled using ``push_function`` and ``pull_function``::
+Functions can also be pushed and pulled using :meth:`push_function` and
+:meth:`pull_function`::
+
+
+    In [52]: mec.block=True
 
 	In [53]: def f(x):
 	   ....:     return 2.0*x**4
 	   ....: 
 
 	In [54]: mec.push_function(dict(f=f))
 	Out[54]: [None, None, None, None]
 
 	In [55]: mec.execute('y = f(4.0)')
 	Out[55]: 
 	<Results List>
 	[0] In [15]: y = f(4.0)
 	[1] In [14]: y = f(4.0)
 	[2] In [15]: y = f(4.0)
 	[3] In [14]: y = f(4.0)
 
 
 	In [56]: px print y
 	Executing command on Controller
 	Out[56]: 
 	<Results List>
 	[0] In [16]: print y
 	[0] Out[16]: 512.0
 
 	[1] In [15]: print y
 	[1] Out[15]: 512.0
 
 	[2] In [16]: print y
 	[2] Out[16]: 512.0
 
 	[3] In [15]: print y
 	[3] Out[15]: 512.0
 
 
 Dictionary interface
 --------------------
 
-As a shorthand to ``push`` and ``pull``, the ``MultiEngineClient`` class implements some of the Python dictionary interface. This make the remote namespaces of the engines appear as a local dictionary. Underneath, this uses ``push`` and ``pull``::
+As a shorthand to :meth:`push` and :meth:`pull`, the
+:class:`MultiEngineClient` class implements some of the Python dictionary
+interface. This make the remote namespaces of the engines appear as a local
+dictionary. Underneath, this uses :meth:`push` and :meth:`pull`::
 
 	In [50]: mec.block=True
 
 	In [51]: mec['a']=['foo','bar']
 
 	In [52]: mec['a']
 	Out[52]: [['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar']]
 
 Scatter and gather
 ------------------
 
-Sometimes it is useful to partition a sequence and push the partitions to different engines. In
-MPI language, this is know as scatter/gather and we follow that terminology. However, it is
-important to remember that in IPython ``scatter`` is from the interactive IPython session to
-the engines and ``gather`` is from the engines back to the interactive IPython session. For
-scatter/gather operations between engines, MPI should be used::
+Sometimes it is useful to partition a sequence and push the partitions to
+different engines. In MPI language, this is know as scatter/gather and we
+follow that terminology. However, it is important to remember that in
+IPython's :class:`MultiEngineClient` class, :meth:`scatter` is from the
+interactive IPython session to the engines and :meth:`gather` is from the
+engines back to the interactive IPython session. For scatter/gather operations
+between engines, MPI should be used::
 
 	In [58]: mec.scatter('a',range(16))
 	Out[58]: [None, None, None, None]
 
 	In [59]: px print a
 	Executing command on Controller
 	Out[59]: 
 	<Results List>
 	[0] In [17]: print a
 	[0] Out[17]: [0, 1, 2, 3]
 
 	[1] In [16]: print a
 	[1] Out[16]: [4, 5, 6, 7]
 
 	[2] In [17]: print a
 	[2] Out[17]: [8, 9, 10, 11]
 
 	[3] In [16]: print a
 	[3] Out[16]: [12, 13, 14, 15]
 
 
 	In [60]: mec.gather('a')
 	Out[60]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
 
 Other things to look at
 =======================
 
-Parallel map
-------------
-
-Python's builtin ``map`` functions allows a function to be applied to a sequence element-by-element.  This type of code is typically trivial to parallelize.  In fact, the MultiEngine interface in IPython already has a parallel version of ``map`` that works just like its serial counterpart::
-
-	In [63]: serial_result = map(lambda x:x**10, range(32))
-
-	In [64]: parallel_result = mec.map(lambda x:x**10, range(32))
-
-	In [65]: serial_result==parallel_result
-	Out[65]: True
-
-As you would expect, the parallel version of ``map`` is also influenced by the ``block`` and ``targets`` keyword arguments and attributes.
-
 How to do parallel list comprehensions
 --------------------------------------
 
-In many cases list comprehensions are nicer than using the map function.  While we don't have fully parallel list comprehensions, it is simple to get the basic effect using ``scatter`` and ``gather``::
+In many cases list comprehensions are nicer than using the map function. While
+we don't have fully parallel list comprehensions, it is simple to get the
+basic effect using :meth:`scatter` and :meth:`gather`::
 
 	In [66]: mec.scatter('x',range(64))
 	Out[66]: [None, None, None, None]
 
 	In [67]: px y = [i**10 for i in x]
 	Executing command on Controller
 	Out[67]: 
 	<Results List>
 	[0] In [19]: y = [i**10 for i in x]
 	[1] In [18]: y = [i**10 for i in x]
 	[2] In [19]: y = [i**10 for i in x]
 	[3] In [18]: y = [i**10 for i in x]
 
 
 	In [68]: y = mec.gather('y')
 
 	In [69]: print y
 	[0, 1, 1024, 59049, 1048576, 9765625, 60466176, 282475249, 1073741824,...]
 
-Parallel Exceptions
+Parallel exceptions
 -------------------
 
-In the MultiEngine interface, parallel commands can raise Python exceptions, just like serial commands.  But, it is a little subtle, because a single parallel command can actually raise multiple exceptions (one for each engine the command was run on).  To express this idea, the MultiEngine interface has a ``CompositeError`` exception class that will be raised in most cases.  The ``CompositeError`` class is a special type of exception that wraps one or more other types of exceptions.  Here is how it works::
+In the multiengine interface, parallel commands can raise Python exceptions,
+just like serial commands. But, it is a little subtle, because a single
+parallel command can actually raise multiple exceptions (one for each engine
+the command was run on). To express this idea, the MultiEngine interface has a
+:exc:`CompositeError` exception class that will be raised in most cases. The
+:exc:`CompositeError` class is a special type of exception that wraps one or
+more other types of exceptions. Here is how it works::
 
 	In [76]: mec.block=True
 
 	In [77]: mec.execute('1/0')
 	---------------------------------------------------------------------------
 	CompositeError                            Traceback (most recent call last)
 
 	/ipython1-client-r3021/docs/examples/<ipython console> in <module>()
 
 	/ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in execute(self, lines, targets, block)
 	    432         targets, block = self._findTargetsAndBlock(targets, block)
 	    433         result = blockingCallFromThread(self.smultiengine.execute, lines,
 	--> 434             targets=targets, block=block)
 	    435         if block:
 	    436             result = ResultList(result)
 
 	/ipython1-client-r3021/ipython1/kernel/twistedutil.pyc in blockingCallFromThread(f, *a, **kw)
 	     72             result.raiseException()
 	     73         except Exception, e:
 	---> 74             raise e
 	     75     return result
 	     76 
 
 	CompositeError: one or more exceptions from call to method: execute
 	[0:execute]: ZeroDivisionError: integer division or modulo by zero
 	[1:execute]: ZeroDivisionError: integer division or modulo by zero
 	[2:execute]: ZeroDivisionError: integer division or modulo by zero
 	[3:execute]: ZeroDivisionError: integer division or modulo by zero
 
-Notice how the error message printed when ``CompositeError`` is raised has information about the individual exceptions that were raised on each engine.  If you want, you can even raise one of these original exceptions::
+Notice how the error message printed when :exc:`CompositeError` is raised has information about the individual exceptions that were raised on each engine.  If you want, you can even raise one of these original exceptions::
 
 	In [80]: try:
 	   ....:     mec.execute('1/0')
 	   ....: except client.CompositeError, e:
 	   ....:     e.raise_exception()
 	   ....:     
 	   ....:     
 	---------------------------------------------------------------------------
 	ZeroDivisionError                         Traceback (most recent call last)
 
 	/ipython1-client-r3021/docs/examples/<ipython console> in <module>()
 
 	/ipython1-client-r3021/ipython1/kernel/error.pyc in raise_exception(self, excid)
 	    156             raise IndexError("an exception with index %i does not exist"%excid)
 	    157         else:
 	--> 158             raise et, ev, etb
 	    159 
 	    160 def collect_exceptions(rlist, method):
 
 	ZeroDivisionError: integer division or modulo by zero
 
-If you are working in IPython, you can simple type ``%debug`` after one of these ``CompositeError`` is raised, and inspect the exception instance::
+If you are working in IPython, you can simple type ``%debug`` after one of
+these :exc:`CompositeError` exceptions is raised, and inspect the exception
+instance::
 
 	In [81]: mec.execute('1/0')
 	---------------------------------------------------------------------------
 	CompositeError                            Traceback (most recent call last)
 
 	/ipython1-client-r3021/docs/examples/<ipython console> in <module>()
 
 	/ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in execute(self, lines, targets, block)
 	    432         targets, block = self._findTargetsAndBlock(targets, block)
 	    433         result = blockingCallFromThread(self.smultiengine.execute, lines,
 	--> 434             targets=targets, block=block)
 	    435         if block:
 	    436             result = ResultList(result)
 
 	/ipython1-client-r3021/ipython1/kernel/twistedutil.pyc in blockingCallFromThread(f, *a, **kw)
 	     72             result.raiseException()
 	     73         except Exception, e:
 	---> 74             raise e
 	     75     return result
 	     76 
 
 	CompositeError: one or more exceptions from call to method: execute
 	[0:execute]: ZeroDivisionError: integer division or modulo by zero
 	[1:execute]: ZeroDivisionError: integer division or modulo by zero
 	[2:execute]: ZeroDivisionError: integer division or modulo by zero
 	[3:execute]: ZeroDivisionError: integer division or modulo by zero
 
 	In [82]: %debug
 	> 
 	
 	/ipython1-client-r3021/ipython1/kernel/twistedutil.py(74)blockingCallFromThread()
 	     73         except Exception, e:
 	---> 74             raise e
 	     75     return result
 
 	# With the debugger running, e is the exceptions instance.  We can tab complete
 	# on it and see the extra methods that are available.
 	ipdb> e.
 	e.__class__         e.__getitem__       e.__new__           e.__setstate__      e.args
 	e.__delattr__       e.__getslice__      e.__reduce__        e.__str__           e.elist
 	e.__dict__          e.__hash__          e.__reduce_ex__     e.__weakref__       e.message
 	e.__doc__           e.__init__          e.__repr__          e._get_engine_str   e.print_tracebacks
 	e.__getattribute__  e.__module__        e.__setattr__       e._get_traceback    e.raise_exception
 	ipdb> e.print_tracebacks()
 	[0:execute]: 
 	---------------------------------------------------------------------------
 	ZeroDivisionError                         Traceback (most recent call last)
 
 	/ipython1-client-r3021/docs/examples/<string> in <module>()
 
 	ZeroDivisionError: integer division or modulo by zero
 
 	[1:execute]: 
 	---------------------------------------------------------------------------
 	ZeroDivisionError                         Traceback (most recent call last)
 
 	/ipython1-client-r3021/docs/examples/<string> in <module>()
 
 	ZeroDivisionError: integer division or modulo by zero
 
 	[2:execute]: 
 	---------------------------------------------------------------------------
 	ZeroDivisionError                         Traceback (most recent call last)
 
 	/ipython1-client-r3021/docs/examples/<string> in <module>()
 
 	ZeroDivisionError: integer division or modulo by zero
 
 	[3:execute]: 
 	---------------------------------------------------------------------------
 	ZeroDivisionError                         Traceback (most recent call last)
 
 	/ipython1-client-r3021/docs/examples/<string> in <module>()
 
 	ZeroDivisionError: integer division or modulo by zero
 
+.. note::
+
+    The above example appears to be broken right now because of a change in 
+    how we are using Twisted.
+
 All of this same error handling magic even works in non-blocking mode::
 
 	In [83]: mec.block=False
 
 	In [84]: pr = mec.execute('1/0')
 
 	In [85]: pr.r
 	---------------------------------------------------------------------------
 	CompositeError                            Traceback (most recent call last)
 
 	/ipython1-client-r3021/docs/examples/<ipython console> in <module>()
 
 	/ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in _get_r(self)
 	    170 
 	    171     def _get_r(self):
 	--> 172         return self.get_result(block=True)
 	    173 
 	    174     r = property(_get_r)
 
 	/ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in get_result(self, default, block)
 	    131                 return self.result
 	    132         try:
 	--> 133             result = self.client.get_pending_deferred(self.result_id, block)
 	    134         except error.ResultNotCompleted:
 	    135             return default
 
 	/ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in get_pending_deferred(self, deferredID, block)
 	    385 
 	    386     def get_pending_deferred(self, deferredID, block):
 	--> 387         return blockingCallFromThread(self.smultiengine.get_pending_deferred, deferredID, block)
 	    388 
 	    389     def barrier(self, pendingResults):
 
 	/ipython1-client-r3021/ipython1/kernel/twistedutil.pyc in blockingCallFromThread(f, *a, **kw)
 	     72             result.raiseException()
 	     73         except Exception, e:
 	---> 74             raise e
 	     75     return result
 	     76 
 
 	CompositeError: one or more exceptions from call to method: execute
 	[0:execute]: ZeroDivisionError: integer division or modulo by zero
 	[1:execute]: ZeroDivisionError: integer division or modulo by zero
 	[2:execute]: ZeroDivisionError: integer division or modulo by zero
 	[3:execute]: ZeroDivisionError: integer division or modulo by zero
 
 

docs/source/parallel/parallel_task.txt

@@ -1,240 +1,93 b''
 .. _paralleltask:
 
-=================================
-The IPython Task interface
-=================================
+==========================
+The IPython task interface
+==========================
 
 .. contents::
 
-The ``Task`` interface to the controller presents the engines as a fault tolerant, dynamic load-balanced system or workers. Unlike the ``MultiEngine`` interface, in the ``Task`` interface, the user have no direct access to individual engines. In some ways, this interface is simpler, but in other ways it is more powerful. Best of all the user can use both of these interfaces at the same time to take advantage or both of their strengths.  When the user can break up the user's work into segments that do not depend on previous execution, the ``Task`` interface is ideal.  But it also has more power and flexibility, allowing the user to guide the distribution of jobs, without having to assign Tasks to engines explicitly.
+The task interface to the controller presents the engines as a fault tolerant, dynamic load-balanced system or workers. Unlike the multiengine interface, in the task interface, the user have no direct access to individual engines. In some ways, this interface is simpler, but in other ways it is more powerful.
+
+Best of all the user can use both of these interfaces running at the same time to take advantage or both of their strengths.  When the user can break up the user's work into segments that do not depend on previous execution, the task interface is ideal.  But it also has more power and flexibility, allowing the user to guide the distribution of jobs, without having to assign tasks to engines explicitly.
 
 Starting the IPython controller and engines
 ===========================================
 
-To follow along with this tutorial, the user will need to start the IPython
-controller and four IPython engines. The simplest way of doing this is to
-use the ``ipcluster`` command::
+To follow along with this tutorial, you will need to start the IPython
+controller and four IPython engines. The simplest way of doing this is to use
+the :command:`ipcluster` command::
 
 	$ ipcluster -n 4
 	
-For more detailed information about starting the controller and engines, see our :ref:`introduction <ip1par>` to using IPython for parallel computing.
+For more detailed information about starting the controller and engines, see
+our :ref:`introduction <ip1par>` to using IPython for parallel computing.
+
+Creating a ``TaskClient`` instance
+=========================================
+
+The first step is to import the IPython :mod:`IPython.kernel.client` module
+and then create a :class:`TaskClient` instance::
+
+	In [1]: from IPython.kernel import client
+	
+	In [2]: tc = client.TaskClient()
+
+This form assumes that the :file:`ipcontroller-tc.furl` is in the
+:file:`~./ipython/security` directory on the client's host. If not, the
+location of the ``.furl`` file must be given as an argument to the
+constructor::
+
+    In[2]: mec = client.TaskClient('/path/to/my/ipcontroller-tc.furl')
+
+Quick and easy parallelism
+==========================
+
+In many cases, you simply want to apply a Python function to a sequence of objects, but *in parallel*.  Like the multiengine interface, the task interface provides two simple ways of accomplishing this:  a parallel version of :func:`map` and ``@parallel`` function decorator.  However, the verions in the task interface have one important difference:  they are dynamically load balanced.  Thus, if the execution time per item varies significantly, you should use the versions in the task interface.
+
+Parallel map
+------------
+
+The parallel :meth:`map` in the task interface is similar to that in the multiengine interface::
+
+	In [63]: serial_result = map(lambda x:x**10, range(32))
+
+	In [64]: parallel_result = tc.map(lambda x:x**10, range(32))
+
+	In [65]: serial_result==parallel_result
+	Out[65]: True
+
+Parallel function decorator
+---------------------------
+
+Parallel functions are just like normal function, but they can be called on sequences and *in parallel*.  The multiengine interface provides a decorator that turns any Python function into a parallel function::
 
-The magic here is that this single controller and set of engines is running both the MultiEngine and ``Task`` interfaces simultaneously.
+    In [10]: @tc.parallel()
+       ....: def f(x):
+       ....:     return 10.0*x**4
+       ....: 
 
-QuickStart Task Farming
-=======================
+    In [11]: f(range(32))    # this is done in parallel
+    Out[11]: 
+    [0.0,10.0,160.0,...]
 
-First, a quick example of how to start running the most basic Tasks.
-The first step is to import the IPython ``client`` module and then create a ``TaskClient`` instance::
-    
-    In [1]: from IPython.kernel import client
-    
-    In [2]: tc = client.TaskClient()
+More details
+============
 
-Then the user wrap the commands the user want to run in Tasks::
+The :class:`TaskClient` has many more powerful features that allow quite a bit of flexibility in how tasks are defined and run.  The next places to look are in the following classes:
 
-    In [3]: tasklist = []
-    In [4]: for n in range(1000):
-        ...     tasklist.append(client.Task("a = %i"%n, pull="a"))
+* :class:`IPython.kernel.client.TaskClient`
+* :class:`IPython.kernel.client.StringTask`
+* :class:`IPython.kernel.client.MapTask`
 
-The first argument of the ``Task`` constructor is a string, the command to be executed.  The most important optional keyword argument is ``pull``, which can be a string or list of strings, and it specifies the variable names to be saved as results of the ``Task``.
+The following is an overview of how to use these classes together:
 
-Next, the user need to submit the Tasks to the ``TaskController`` with the ``TaskClient``::
+1. Create a :class:`TaskClient`.
+2. Create one or more instances of :class:`StringTask` or :class:`MapTask`
+   to define your tasks.
+3. Submit your tasks to using the :meth:`run` method of your
+   :class:`TaskClient` instance.
+4. Use :meth:`TaskClient.get_task_result` to get the results of the
+   tasks.
 
-    In [5]: taskids = [ tc.run(t) for t in tasklist ]
+We are in the process of developing more detailed information about the task interface.  For now, the docstrings of the :class:`TaskClient`, :class:`StringTask` and :class:`MapTask` classes should be consulted.
 
-This will give the user a list of the TaskIDs used by the controller to keep track of the Tasks and their results.  Now at some point the user are going to want to get those results back.  The ``barrier`` method allows the user to wait for the Tasks to finish running::
-
-    In [6]: tc.barrier(taskids)
-
-This command will block until all the Tasks in ``taskids`` have finished.  Now, the user probably want to look at the user's results::
-
-    In [7]: task_results = [ tc.get_task_result(taskid) for taskid in taskids ]
-
-Now the user have a list of ``TaskResult`` objects, which have the actual result as a dictionary, but also keep track of some useful metadata about the ``Task``::
-
-    In [8]: tr = ``Task``_results[73]
-    
-    In [9]: tr
-    Out[9]: ``TaskResult``[ID:73]:{'a':73}
-    
-    In [10]: tr.engineid
-    Out[10]: 1
-    
-    In [11]: tr.submitted, tr.completed, tr.duration
-    Out[11]: ("2008/03/08 03:41:42", "2008/03/08 03:41:44", 2.12345)
-
-The actual results are stored in a dictionary, ``tr.results``, and a namespace object ``tr.ns`` which accesses the result keys by attribute::
-
-    In [12]: tr.results['a']
-    Out[12]: 73
-    
-    In [13]: tr.ns.a
-    Out[13]: 73
-
-That should cover the basics of running simple Tasks.  There are several more powerful things the user can do with Tasks covered later.  The most useful probably being using a ``MutiEngineClient`` interface to initialize all the engines with the import dependencies necessary to run the user's Tasks.
-
-There are many options for running and managing Tasks. The best way to learn further about the ``Task`` interface is to study the examples in ``docs/examples``. If the user do so and learn a lots about this interface, we encourage the user to expand this documentation about the ``Task`` system.
-
-Overview of the Task System
-===========================
-
-The user's view of the ``Task`` system has three basic objects:  The ``TaskClient``, the ``Task``, and the ``TaskResult``.  The names of these three objects well indicate their role.
-
-The ``TaskClient`` is the user's ``Task`` farming connection to the IPython cluster.  Unlike the ``MultiEngineClient``, the ``TaskControler`` handles all the scheduling and distribution of work, so the ``TaskClient`` has no notion of engines, it just submits Tasks and requests their results.  The Tasks are described as ``Task`` objects, and their results are wrapped in ``TaskResult`` objects.  Thus, there are very few necessary methods for the user to manage.
-
-Inside the task system is a Scheduler object, which assigns tasks to workers.  The default scheduler is a simple FIFO queue.  Subclassing the Scheduler should be easy, just implementing your own priority system.
-
-The TaskClient
-==============
-
-The ``TaskClient`` is the object the user use to connect to the ``Controller`` that is managing the user's Tasks.  It is the analog of the ``MultiEngineClient`` for the standard IPython multiplexing interface.  As with all client interfaces, the first step is to import the IPython Client Module::
-
-    In [1]: from IPython.kernel import client
-
-Just as with the ``MultiEngineClient``, the user create the ``TaskClient`` with a tuple, containing the ip-address and port of the ``Controller``.  the ``client`` module conveniently has the default address of the ``Task`` interface of the controller.  Creating a default ``TaskClient`` object would be done with this::
-
-    In [2]: tc = client.TaskClient(client.default_task_address)
-
-or, if the user want to specify a non default location of the ``Controller``, the user can specify explicitly::
-
-    In [3]: tc = client.TaskClient(("192.168.1.1", 10113))
-
-As discussed earlier, the ``TaskClient`` only has a few basic methods.
-
- * ``tc.run(task)``
-    ``run`` is the method by which the user submits Tasks.  It takes exactly one argument, a ``Task`` object.  All the advanced control of ``Task`` behavior is handled by properties of the ``Task`` object, rather than the submission command, so they will be discussed later in the `Task`_ section.  ``run`` returns an integer, the ``Task``ID by which the ``Task`` and its results can be tracked and retrieved::
-    
-        In [4]: ``Task``ID = tc.run(``Task``)
-    
- * ``tc.get_task_result(taskid, block=``False``)``
-    ``get_task_result`` is the method by which results are retrieved.  It takes a single integer argument, the ``Task``ID`` of the result the user wish to retrieve.  ``get_task_result`` also takes a keyword argument ``block``.  ``block`` specifies whether the user actually want to wait for the result.  If ``block`` is false, as it is by default, ``get_task_result`` will return immediately.  If the ``Task`` has completed, it will return the ``TaskResult`` object for that ``Task``.  But if the ``Task`` has not completed, it will return ``None``.  If the user specify ``block=``True``, then ``get_task_result`` will wait for the ``Task`` to complete, and always return the ``TaskResult`` for the requested ``Task``.
- * ``tc.barrier(taskid(s))``
-    ``barrier`` is a synchronization method.  It takes exactly one argument, a ``Task``ID or list of taskIDs.  ``barrier`` will block until all the specified Tasks have completed.  In practice, a barrier is often called between the ``Task`` submission section of the code and the result gathering section::
-        
-        In [5]: taskIDs = [ tc.run(``Task``) for ``Task`` in myTasks ]
-        
-        In [6]: tc.get_task_result(taskIDs[-1]) is None
-        Out[6]: ``True``
-        
-        In [7]: tc.barrier(``Task``ID)
-        
-        In [8]: results = [ tc.get_task_result(tid) for tid in taskIDs ]
-        
- * ``tc.queue_status(verbose=``False``)``
-    ``queue_status`` is a method for querying the state of the ``TaskControler``.  ``queue_status`` returns a dict of the form::
-        
-        {'scheduled': Tasks that have been submitted but yet run
-         'pending'  : Tasks that are currently running
-         'succeeded': Tasks that have completed successfully
-         'failed'   : Tasks that have finished with a failure
-        }
-    
-    if @verbose is not specified (or is ``False``), then the values of the dict are integers - the number of Tasks in each state.  if @verbose is ``True``, then each element in the dict is a list of the taskIDs in that state::
-    
-        In [8]: tc.queue_status()
-        Out[8]: {'scheduled': 4,
-                'pending'  : 2,
-                'succeeded': 5,
-                'failed'   : 1
-                }
-        
-        In [9]: tc.queue_status(verbose=True)
-        Out[9]: {'scheduled': [8,9,10,11],
-                'pending'  : [6,7],
-                'succeeded': [0,1,2,4,5],
-                'failed'   : [3]
-                }
-        
- * ``tc.abort(taskid)``
-    ``abort`` allows the user to abort Tasks that have already been submitted.  ``abort`` will always return immediately.  If the ``Task`` has completed, ``abort`` will raise an ``IndexError ``Task`` Already Completed``.  An obvious case for ``abort`` would be where the user submits a long-running ``Task`` with a number of retries (see ``Task``_ section for how to specify retries) in an interactive session, but realizes there has been a typo.  The user can then abort the ``Task``, preventing certain failures from cluttering up the queue.  It can also be used for parallel search-type problems, where only one ``Task`` will give the solution, so once the user find the solution, the user would want to abort all remaining Tasks to prevent wasted work.
- * ``tc.spin()``
-    ``spin`` simply triggers the scheduler in the ``TaskControler``.  Under most normal circumstances, this will do nothing.  The primary known usage case involves the ``Task`` dependency (see `Dependencies`_).  The dependency is a function of an Engine's ``properties``, but changing the ``properties`` via the ``MutliEngineClient`` does not trigger a reschedule event.  The main example case for this requires the following event sequence:
-     * ``engine`` is available, ``Task`` is submitted, but ``engine`` does not have ``Task``'s dependencies.
-     * ``engine`` gets necessary dependencies while no new Tasks are submitted or completed.
-     * now ``engine`` can run ``Task``, but a ``Task`` event is required for the ``TaskControler`` to try scheduling ``Task`` again.
-     
-    ``spin`` is just an empty ping method to ensure that the Controller has scheduled all available Tasks, and should not be needed under most normal circumstances.
-
-That covers the ``TaskClient``, a simple interface to the cluster.  With this, the user can submit jobs (and abort if necessary), request their results, synchronize on arbitrary subsets of jobs.
-
-.. _task: The Task Object
-
-The Task Object
-===============
-
-The ``Task`` is the basic object for describing a job.  It can be used in a very simple manner, where the user just specifies a command string to be executed as the ``Task``.  The usage of this first argument is exactly the same as the ``execute`` method of the ``MultiEngine`` (in fact, ``execute`` is called to run the code)::
-
-    In [1]: t = client.Task("a = str(id)")
-    
-This ``Task`` would run, and store the string representation of the ``id`` element in ``a`` in each worker's namespace, but it is fairly useless because the user does not know anything about the state of the ``worker`` on which it ran at the time of retrieving results.  It is important that each ``Task`` not expect the state of the ``worker`` to persist after the ``Task`` is completed.  
-There are many different situations for using ``Task`` Farming, and the ``Task`` object has many attributes for use in customizing the ``Task`` behavior.  All of a ``Task``'s attributes may be specified in the constructor, through keyword arguments, or after ``Task`` construction through attribute assignment.
-
-Data Attributes
-***************
-It is likely that the user may want to move data around before or after executing the ``Task``.  We provide methods of sending data to initialize the worker's namespace, and specifying what data to bring back as the ``Task``'s results.
-
- * pull = []
-    The obvious case is as above, where ``t`` would execute and store the result of ``myfunc`` in ``a``, it is likely that the user would want to bring ``a`` back to their namespace.  This is done through the ``pull`` attribute.  ``pull`` can be a string or list of strings, and it specifies the names of variables to be retrieved.  The ``TaskResult`` object retrieved by ``get_task_result`` will have a dictionary of keys and values, and the ``Task``'s ``pull`` attribute determines what goes into it::
-        
-        In [2]: t = client.Task("a = str(id)", pull = "a")
-        
-        In [3]: t = client.Task("a = str(id)", pull = ["a", "id"])
-        
- * push = {}
-    A user might also want to initialize some data into the namespace before the code part of the ``Task`` is run.  Enter ``push``.  ``push`` is a dictionary of key/value pairs to be loaded from the user's namespace into the worker's immediately before execution::
-
-        In [4]: t = client.Task("a = f(submitted)", push=dict(submitted=time.time()), pull="a")
-
-push and pull result directly in calling an ``engine``'s ``push`` and ``pull`` methods before and after ``Task`` execution respectively, and thus their api is the same.
-
-Namespace Cleaning
-******************
-When a user is running a large number of Tasks, it is likely that the namespace of the worker's could become cluttered.  Some Tasks might be sensitive to clutter, while others might be known to cause namespace pollution.  For these reasons, Tasks have two boolean attributes for cleaning up the namespace.
-
- * ``clear_after``
-    if clear_after is specified ``True``, the worker on which the ``Task`` was run will be reset (via ``engine.reset``) upon completion of the ``Task``.  This can be useful for both Tasks that produce clutter or Tasks whose intermediate data one might wish to be kept private::
-    
-        In [5]: t = client.Task("a = range(1e10)", pull = "a",clear_after=True)
-        
-    
- * ``clear_before``
-    as one might guess, clear_before is identical to ``clear_after``, but it takes place before the ``Task`` is run.  This ensures that the ``Task`` runs on a fresh worker::
-
-        In [6]: t = client.Task("a = globals()", pull = "a",clear_before=True)
-
-Of course, a user can both at the same time, ensuring that all workers are clear except when they are currently running a job.  Both of these default to ``False``.
-
-Fault Tolerance
-***************
-It is possible that Tasks might fail, and there are a variety of reasons this could happen.  One might be that the worker it was running on disconnected, and there was nothing wrong with the ``Task`` itself.  With the fault tolerance attributes of the ``Task``, the user can specify how many times to resubmit the ``Task``, and what to do if it never succeeds.
-
- * ``retries``
-    ``retries`` is an integer, specifying the number of times a ``Task`` is to be retried.  It defaults to zero.  It is often a good idea for this number to be 1 or 2, to protect the ``Task`` from disconnecting engines, but not a large number.  If a ``Task`` is failing 100 times, there is probably something wrong with the ``Task``.  The canonical bad example:
-    
-        In [7]: t = client.Task("os.kill(os.getpid(), 9)", retries=99)
-        
-    This would actually take down 100 workers.
-    
- * ``recovery_task``
-    ``recovery_task`` is another ``Task`` object, to be run in the event of the original ``Task`` still failing after running out of retries.  Since ``recovery_task`` is another ``Task`` object, it can have its own ``recovery_task``.  The chain of Tasks is limitless, except loops are not allowed (that would be bad!).
-
-Dependencies
-************
-Dependencies are the most powerful part of the ``Task`` farming system, because it allows the user to do some classification of the workers, and guide the ``Task`` distribution without meddling with the controller directly.  It makes use of two objects - the ``Task``'s ``depend`` attribute, and the engine's ``properties``.  See the `MultiEngine`_ reference for how to use engine properties.  The engine properties api exists for extending IPython, allowing conditional execution and new controllers that make decisions based on properties of its engines.  Currently the ``Task`` dependency is the only internal use of the properties api.
-
-.. _MultiEngine: ./parallel_multiengine
-
-The ``depend`` attribute of a ``Task`` must be a function of exactly one argument, the worker's properties dictionary, and it should return ``True`` if the ``Task`` should be allowed to run on the worker and ``False`` if not.  The usage in the controller is fault tolerant, so exceptions raised by ``Task.depend`` will be ignored and functionally equivalent to always returning ``False``.  Tasks`` with invalid ``depend`` functions will never be assigned to a worker::
-
-    In [8]: def dep(properties):
-        ...     return properties["RAM"] > 2**32 # have at least 4GB
-    In [9]: t = client.Task("a = bigfunc()", depend=dep)
-    
-It is important to note that assignment of values to the properties dict is done entirely by the user, either locally (in the engine) using the EngineAPI, or remotely, through the ``MultiEngineClient``'s get/set_properties methods.
-
-
-
-
-
-    

tools/release

@@ -1,60 +1,58 b''
 #!/bin/sh
 # IPython release script
 
 PYVER=`python -V 2>&1 | awk '{print $2}' | awk -F '.' '{print $1$2}' `
 version=`ipython -Version`
 ipdir=~/ipython/ipython
 ipbackupdir=~/ipython/backup
 
 echo
 echo "Releasing IPython version $version"
 echo "=================================="
 
-echo "Marking ChangeLog with release information and making NEWS file..."
-
-# Clean up build/dist directories
-rm -rf $ipdir/build/*
-rm -rf $ipdir/dist/*
-
 # Perform local backup
 cd $ipdir/tools
 ./make_tarball.py
 mv ipython-*.tgz $ipbackupdir
 
+# Clean up build/dist directories
+rm -rf $ipdir/build/*
+rm -rf $ipdir/dist/*
+
 # Build source and binary distros
 cd $ipdir
 ./setup.py sdist --formats=gztar
 
 # Build version-specific RPMs, where we must use the --python option to ensure
 # that the resulting RPM is really built with the requested python version (so
 # things go to lib/python2.X/...)
-python2.4 ./setup.py bdist_rpm --binary-only --release=py24 --python=/usr/bin/python2.4
-python2.5 ./setup.py bdist_rpm --binary-only --release=py25 --python=/usr/bin/python2.5
+#python2.4 ./setup.py bdist_rpm --binary-only --release=py24 --python=/usr/bin/python2.4
+#python2.5 ./setup.py bdist_rpm --binary-only --release=py25 --python=/usr/bin/python2.5
 
 # Build eggs
 python2.4 ./setup_bdist_egg.py
 python2.5 ./setup_bdist_egg.py
 
 # Call the windows build separately, so that the extra Windows scripts don't
 # get pulled into Unix builds (setup.py has code which checks for
 # bdist_wininst)
 ./setup.py bdist_wininst --install-script=ipython_win_post_install.py
 
 # Change name so retarded Vista runs the installer correctly
 rename 's/win32/win32-setup/' $ipdir/dist/*.exe
 
 # Register with the Python Package Index (PyPI)
 echo "Registering with PyPI..."
 cd $ipdir
 ./setup.py register
 
 # Upload all files
 cd $ipdir/dist
 echo "Uploading distribution files..."
 scp * [e-mail]:www/dist/
 
 echo "Uploading backup files..."
 cd $ipbackupdir
 scp `ls -1tr *tgz | tail -1` [e-mail]:www/backup/
 
 echo "Done!"

tools/testrel

@@ -1,31 +1,31 b''
 #!/bin/sh
 
 # release test
 
 ipdir=$PWD/..
 
 cd $ipdir
 
 # Clean up build/dist directories
 rm -rf $ipdir/build/*
 rm -rf $ipdir/dist/*
 
 # build source distros
 cd $ipdir
 ./setup.py sdist --formats=gztar
 
 # Build rpms
-#python2.4 ./setup.py bdist_rpm --binary-only --release=py24 --python=/usr/bin/python2.4
-#python2.5 ./setup.py bdist_rpm --binary-only --release=py25 --python=/usr/bin/python2.5
+python2.4 ./setup.py bdist_rpm --binary-only --release=py24 --python=/usr/bin/python2.4
+python2.5 ./setup.py bdist_rpm --binary-only --release=py25 --python=/usr/bin/python2.5
 
 # Build eggs
 python2.4 ./setup_bdist_egg.py
 python2.5 ./setup_bdist_egg.py
 
 # Call the windows build separately, so that the extra Windows scripts don't
 # get pulled into Unix builds (setup.py has code which checks for
 # bdist_wininst)
 ./setup.py bdist_wininst --install-script=ipython_win_post_install.py
 
 # Change name so retarded Vista runs the installer correctly
 rename 's/win32/win32-setup/' $ipdir/dist/*.exe