Update doc references to .config/ipython

docs/source/config/overview.rst

@@ -1,535 +1,531 b''
 .. _config_overview:
 
 ============================================
 Overview of the IPython configuration system
 ============================================
 
 This section describes the IPython configuration system. 
 
 The following discussion is for users who want to configure
 IPython to their liking.  Developers who want to know how they can
 enable their objects to take advantage of the configuration system
 should consult the :ref:`developer guide <developer_guide>`
 
 The main concepts
 =================
 
 There are a number of abstractions that the IPython configuration system uses.
 Each of these abstractions is represented by a Python class.
 
 Configuration object: :class:`~IPython.config.loader.Config`
     A configuration object is a simple dictionary-like class that holds
     configuration attributes and sub-configuration objects. These classes
     support dotted attribute style access (``Foo.bar``) in addition to the
     regular dictionary style access (``Foo['bar']``). Configuration objects
     are smart. They know how to merge themselves with other configuration
     objects and they automatically create sub-configuration objects.
 
 Application: :class:`~IPython.config.application.Application`
     An application is a process that does a specific job. The most obvious
     application is the :command:`ipython` command line program. Each
     application reads *one or more* configuration files and a single set of
     command line options
     and then produces a master configuration object for the application. This
     configuration object is then passed to the configurable objects that the
     application creates. These configurable objects implement the actual logic
     of the application and know how to configure themselves given the
     configuration object.
     
     Applications always have a `log` attribute that is a configured Logger.
     This allows centralized logging configuration per-application.
 
 Configurable: :class:`~IPython.config.configurable.Configurable`
     A configurable is a regular Python class that serves as a base class for
     all main classes in an application. The
     :class:`~IPython.config.configurable.Configurable` base class is
     lightweight and only does one things.
 
     This :class:`~IPython.config.configurable.Configurable` is a subclass
     of :class:`~IPython.utils.traitlets.HasTraits` that knows how to configure
     itself. Class level traits with the metadata ``config=True`` become
     values that can be configured from the command line and configuration
     files.
     
     Developers create :class:`~IPython.config.configurable.Configurable`
     subclasses that implement all of the logic in the application. Each of
     these subclasses has its own configuration information that controls how
     instances are created.
 
 Singletons: :class:`~IPython.config.configurable.SingletonConfigurable`
     Any object for which there is a single canonical instance. These are
     just like Configurables, except they have a class method 
     :meth:`~IPython.config.configurable.SingletonConfigurable.instance`,
     that returns the current active instance (or creates one if it
     does not exist).  Examples of singletons include
     :class:`~IPython.config.application.Application`s and
     :class:`~IPython.core.interactiveshell.InteractiveShell`.  This lets
     objects easily connect to the current running Application without passing
     objects around everywhere.  For instance, to get the current running 
     Application instance, simply do: ``app = Application.instance()``.
 
 
 .. note::
 
     Singletons are not strictly enforced - you can have many instances
     of a given singleton class, but the :meth:`instance` method will always
     return the same one.
 
 Having described these main concepts, we can now state the main idea in our
 configuration system: *"configuration" allows the default values of class
 attributes to be controlled on a class by class basis*. Thus all instances of
 a given class are configured in the same way. Furthermore, if two instances
 need to be configured differently, they need to be instances of two different
 classes. While this model may seem a bit restrictive, we have found that it
 expresses most things that need to be configured extremely well. However, it
 is possible to create two instances of the same class that have different
 trait values. This is done by overriding the configuration.
 
 Now, we show what our configuration objects and files look like.
 
 Configuration objects and files
 ===============================
 
 A configuration file is simply a pure Python file that sets the attributes
 of a global, pre-created configuration object.  This configuration object is a 
 :class:`~IPython.config.loader.Config` instance.  While in a configuration
 file, to get a reference to this object, simply call the :func:`get_config`
 function.  We inject this function into the global namespace that the 
 configuration file is executed in.
 
 Here is an example of a super simple configuration file that does nothing::
 
     c = get_config()
 
 Once you get a reference to the configuration object, you simply set
 attributes on it.  All you have to know is:
 
 * The name of each attribute.
 * The type of each attribute.
 
 The answers to these two questions are provided by the various
 :class:`~IPython.config.configurable.Configurable` subclasses that an
 application uses. Let's look at how this would work for a simple configurable
 subclass::
 
     # Sample configurable:
     from IPython.config.configurable import Configurable
     from IPython.utils.traitlets import Int, Float, Unicode, Bool
     
     class MyClass(Configurable):
         name = Unicode(u'defaultname', config=True)
         ranking = Int(0, config=True)
         value = Float(99.0)
         # The rest of the class implementation would go here..
 
 In this example, we see that :class:`MyClass` has three attributes, two
 of whom (``name``, ``ranking``) can be configured.  All of the attributes
 are given types and default values.  If a :class:`MyClass` is instantiated,
 but not configured, these default values will be used.  But let's see how
 to configure this class in a configuration file::
 
     # Sample config file
     c = get_config()
     
     c.MyClass.name = 'coolname'
     c.MyClass.ranking = 10
 
 After this configuration file is loaded, the values set in it will override
 the class defaults anytime a :class:`MyClass` is created.  Furthermore,
 these attributes will be type checked and validated anytime they are set.
 This type checking is handled by the :mod:`IPython.utils.traitlets` module,
 which provides the :class:`Unicode`, :class:`Int` and :class:`Float` types.
 In addition to these traitlets, the :mod:`IPython.utils.traitlets` provides
 traitlets for a number of other types.
 
 .. note::
 
     Underneath the hood, the :class:`Configurable` base class is a subclass of
     :class:`IPython.utils.traitlets.HasTraits`. The
     :mod:`IPython.utils.traitlets` module is a lightweight version of
     :mod:`enthought.traits`. Our implementation is a pure Python subset
     (mostly API compatible) of :mod:`enthought.traits` that does not have any
     of the automatic GUI generation capabilities. Our plan is to achieve 100%
     API compatibility to enable the actual :mod:`enthought.traits` to
     eventually be used instead. Currently, we cannot use
     :mod:`enthought.traits` as we are committed to the core of IPython being
     pure Python.
 
 It should be very clear at this point what the naming convention is for 
 configuration attributes::
 
     c.ClassName.attribute_name = attribute_value
 
 Here, ``ClassName`` is the name of the class whose configuration attribute you
 want to set, ``attribute_name`` is the name of the attribute you want to set
 and ``attribute_value`` the the value you want it to have. The ``ClassName``
 attribute of ``c`` is not the actual class, but instead is another
 :class:`~IPython.config.loader.Config` instance.
 
 .. note::
 
     The careful reader may wonder how the ``ClassName`` (``MyClass`` in
     the above example) attribute of the configuration object ``c`` gets
     created. These attributes are created on the fly by the
     :class:`~IPython.config.loader.Config` instance, using a simple naming
     convention. Any attribute of a :class:`~IPython.config.loader.Config`
     instance whose name begins with an uppercase character is assumed to be a
     sub-configuration and a new empty :class:`~IPython.config.loader.Config`
     instance is dynamically created for that attribute. This allows deeply
     hierarchical information created easily (``c.Foo.Bar.value``) on the fly.
 
 Configuration files inheritance
 ===============================
 
 Let's say you want to have different configuration files for various purposes.
 Our configuration system makes it easy for one configuration file to inherit
 the information in another configuration file. The :func:`load_subconfig`
 command can be used in a configuration file for this purpose. Here is a simple
 example that loads all of the values from the file :file:`base_config.py`::
 
     # base_config.py
     c = get_config()
     c.MyClass.name = 'coolname'
     c.MyClass.ranking = 100
 
 into the configuration file :file:`main_config.py`::
 
     # main_config.py
     c = get_config()
     
     # Load everything from base_config.py
     load_subconfig('base_config.py')
     
     # Now override one of the values
     c.MyClass.name = 'bettername'
 
 In a situation like this the :func:`load_subconfig` makes sure that the
 search path for sub-configuration files is inherited from that of the parent.
 Thus, you can typically put the two in the same directory and everything will
 just work.
 
 You can also load configuration files by profile, for instance:
 
 .. sourcecode:: python
 
     load_subconfig('ipython_config.py', profile='default')
 
 to inherit your default configuration as a starting point.
 
 
 Class based configuration inheritance
 =====================================
 
 There is another aspect of configuration where inheritance comes into play.
 Sometimes, your classes will have an inheritance hierarchy that you want
 to be reflected in the configuration system.  Here is a simple example::
 
     from IPython.config.configurable import Configurable
     from IPython.utils.traitlets import Int, Float, Unicode, Bool
     
     class Foo(Configurable):
         name = Unicode(u'fooname', config=True)
         value = Float(100.0, config=True)
     
     class Bar(Foo):
         name = Unicode(u'barname', config=True)
         othervalue = Int(0, config=True)
 
 Now, we can create a configuration file to configure instances of :class:`Foo`
 and :class:`Bar`::
 
     # config file
     c = get_config()
     
     c.Foo.name = u'bestname'
     c.Bar.othervalue = 10
 
 This class hierarchy and configuration file accomplishes the following:
 
 * The default value for :attr:`Foo.name` and :attr:`Bar.name` will be
   'bestname'.  Because :class:`Bar` is a :class:`Foo` subclass it also
   picks up the configuration information for :class:`Foo`.
 * The default value for :attr:`Foo.value` and :attr:`Bar.value` will be
   ``100.0``, which is the value specified as the class default.
 * The default value for :attr:`Bar.othervalue` will be 10 as set in the
   configuration file.  Because :class:`Foo` is the parent of :class:`Bar`
   it doesn't know anything about the :attr:`othervalue` attribute.
 
 
 .. _ipython_dir:
 
 Configuration file location
 ===========================
 
 So where should you put your configuration files? IPython uses "profiles" for
 configuration, and by default, all profiles will be stored in the so called
 "IPython directory". The location of this directory is determined by the
 following algorithm:
 
 * If the ``ipython-dir`` command line flag is given, its value is used.
 
 * If not, the value returned by :func:`IPython.utils.path.get_ipython_dir`
   is used. This function will first look at the :envvar:`IPYTHONDIR`
-  environment variable and then default to a platform-specific default.
+  environment variable and then default to :file:`~/.ipython`.
   Historical support for the :envvar:`IPYTHON_DIR` environment variable will
   be removed in a future release.
 
-On posix systems (Linux, Unix, etc.), IPython respects the ``$XDG_CONFIG_HOME``
-part of the `XDG Base Directory`_ specification. If ``$XDG_CONFIG_HOME`` is
-defined and exists ( ``XDG_CONFIG_HOME`` has a default interpretation of
-:file:`$HOME/.config`), then IPython's config directory will be located in
-:file:`$XDG_CONFIG_HOME/ipython`.  If users still have an IPython directory
-in :file:`$HOME/.ipython`, then that will be used. in preference to the 
-system default.
-
-For most users, the default value will simply be something like 
-:file:`$HOME/.config/ipython` on Linux, or :file:`$HOME/.ipython`
-elsewhere.
+For most users, the configuration directory will be :file:`~/.ipython`.
+
+Previous versions of IPython on Linux would use the XDG config directory,
+creating :file:`~/.config/ipython` by default. We have decided to go
+back to :file:`~/.ipython` for consistency among systems. IPython will
+issue a warning if it finds the XDG location, and will move it to the new
+location if there isn't already a directory there.
 
 Once the location of the IPython directory has been determined, you need to know
 which profile you are using. For users with a single configuration, this will
 simply be 'default', and will be located in 
 :file:`<IPYTHONDIR>/profile_default`.
 
 The next thing you need to know is what to call your configuration file. The
 basic idea is that each application has its own default configuration filename.
 The default named used by the :command:`ipython` command line program is
 :file:`ipython_config.py`, and *all* IPython applications will use this file.
 Other applications, such as the parallel :command:`ipcluster` scripts or the
 QtConsole will load their own config files *after* :file:`ipython_config.py`. To
 load a particular configuration file instead of the default, the name can be
 overridden by the ``config_file`` command line flag.
 
 To generate the default configuration files, do::
 
     $ ipython profile create
 
 and you will have a default :file:`ipython_config.py` in your IPython directory
 under :file:`profile_default`. If you want the default config files for the
 :mod:`IPython.parallel` applications, add ``--parallel`` to the end of the
 command-line args.
 
 
 Locating these files
 --------------------
 
 From the command-line, you can quickly locate the IPYTHONDIR or a specific
 profile with:
 
 .. sourcecode:: bash
 
     $ ipython locate
     /home/you/.ipython
     
     $ ipython locate profile foo
     /home/you/.ipython/profile_foo
 
 These map to the utility functions: :func:`IPython.utils.path.get_ipython_dir`
 and :func:`IPython.utils.path.locate_profile` respectively.
 
 
 .. _Profiles:
 
 Profiles
 ========
 
 A profile is a directory containing configuration and runtime files, such as
 logs, connection info for the parallel apps, and your IPython command history.
 
 The idea is that users often want to maintain a set of configuration files for
 different purposes: one for doing numerical computing with NumPy and SciPy and
 another for doing symbolic computing with SymPy. Profiles make it easy to keep a
 separate configuration files, logs, and histories for each of these purposes.
 
 Let's start by showing how a profile is used:
 
 .. code-block:: bash
 
     $ ipython --profile=sympy
 
 This tells the :command:`ipython` command line program to get its configuration
 from the "sympy" profile. The file names for various profiles do not change. The
 only difference is that profiles are named in a special way. In the case above,
 the "sympy" profile means looking for :file:`ipython_config.py` in :file:`<IPYTHONDIR>/profile_sympy`.
 
 The general pattern is this: simply create a new profile with:
 
 .. code-block:: bash
 
     $ ipython profile create <name>
 
 which adds a directory called ``profile_<name>`` to your IPython directory. Then
 you can load this profile by adding ``--profile=<name>`` to your command line
 options. Profiles are supported by all IPython applications.
 
 IPython ships with some sample profiles in :file:`IPython/config/profile`. If
 you create profiles with the name of one of our shipped profiles, these config
 files will be copied over instead of starting with the automatically generated
 config files.
 
 Security Files
 --------------
 
 If you are using the notebook, qtconsole, or parallel code, IPython stores
 connection information in small JSON files in the active profile's security
 directory. This directory is made private, so only you can see the files inside. If
 you need to move connection files around to other computers, this is where they will
 be. If you want your code to be able to open security files by name, we have a
 convenience function :func:`IPython.utils.path.get_security_file`, which will return
 the absolute path to a security file from its filename and [optionally] profile
 name.
 
 .. _startup_files:
 
 Startup Files
 -------------
 
 If you want some code to be run at the beginning of every IPython session with
 a particular profile, the easiest way is to add Python (``.py``) or 
 IPython (``.ipy``) scripts to your :file:`<profile>/startup` directory. Files
 in this directory will always be executed as soon as the IPython shell is 
 constructed, and before any other code or scripts you have specified. If you 
 have multiple files in the startup directory, they will be run in 
 lexicographical order, so you can control the ordering by adding a '00-' 
 prefix.
 
 
 .. _commandline:
 
 Command-line arguments
 ======================
 
 IPython exposes *all* configurable options on the command-line. The command-line
 arguments are generated from the Configurable traits of the classes associated
 with a given Application.  Configuring IPython from the command-line may look
 very similar to an IPython config file
 
 IPython applications use a parser called
 :class:`~IPython.config.loader.KeyValueLoader` to load values into a Config
 object.  Values are assigned in much the same way as in a config file:
 
 .. code-block:: bash
 
     $ ipython --InteractiveShell.use_readline=False --BaseIPythonApplication.profile='myprofile'
 
 Is the same as adding:
 
 .. sourcecode:: python
 
     c.InteractiveShell.use_readline=False
     c.BaseIPythonApplication.profile='myprofile'
 
 to your config file. Key/Value arguments *always* take a value, separated by '='
 and no spaces.
 
 Common Arguments
 ----------------
 
 Since the strictness and verbosity of the KVLoader above are not ideal for everyday
 use, common arguments can be specified as flags_ or aliases_.
 
 Flags and Aliases are handled by :mod:`argparse` instead, allowing for more flexible
 parsing. In general, flags and aliases are prefixed by ``--``, except for those
 that are single characters, in which case they can be specified with a single ``-``, e.g.:
 
 .. code-block:: bash
 
     $ ipython -i -c "import numpy; x=numpy.linspace(0,1)" --profile testing --colors=lightbg
 
 Aliases
 *******
 
 For convenience, applications have a mapping of commonly used traits, so you don't have
 to specify the whole class name:
 
 .. code-block:: bash
 
     $ ipython --profile myprofile
     # and
     $ ipython --profile='myprofile'
     # are equivalent to
     $ ipython --BaseIPythonApplication.profile='myprofile'
 
 Flags
 *****
 
 Applications can also be passed **flags**. Flags are options that take no
 arguments. They are simply wrappers for
 setting one or more configurables with predefined values, often True/False.
 
 For instance:
 
 .. code-block:: bash
 
     $ ipcontroller --debug
     # is equivalent to
     $ ipcontroller --Application.log_level=DEBUG
     # and
     $ ipython --matploitlib
     # is equivalent to
     $ ipython --matplotlib auto
     # or
     $ ipython --no-banner
     # is equivalent to
     $ ipython --TerminalIPythonApp.display_banner=False
 
 Subcommands
 -----------
 
 
 Some IPython applications have **subcommands**. Subcommands are modeled after
 :command:`git`, and are called with the form :command:`command subcommand
 [...args]`.  Currently, the QtConsole is a subcommand of terminal IPython:
 
 .. code-block:: bash
 
     $ ipython qtconsole --profile myprofile
 
 and :command:`ipcluster` is simply a wrapper for its various subcommands (start,
 stop, engines).
 
 .. code-block:: bash
 
     $ ipcluster start --profile=myprofile -n 4
 
 
 To see a list of the available aliases, flags, and subcommands for an IPython application, simply pass ``-h`` or ``--help``.  And to see the full list of configurable options (*very* long), pass ``--help-all``.
 
 
 Design requirements
 ===================
 
 Here are the main requirements we wanted our configuration system to have:
 
 * Support for hierarchical configuration information.
 
 * Full integration with command line option parsers.  Often, you want to read
   a configuration file, but then override some of the values with command line
   options.  Our configuration system automates this process and allows each
   command line option to be linked to a particular attribute in the 
   configuration hierarchy that it will override.
 
 * Configuration files that are themselves valid Python code. This accomplishes
   many things. First, it becomes possible to put logic in your configuration
   files that sets attributes based on your operating system, network setup,
   Python version, etc. Second, Python has a super simple syntax for accessing
   hierarchical data structures, namely regular attribute access
   (``Foo.Bar.Bam.name``). Third, using Python makes it easy for users to
   import configuration attributes from one configuration file to another.
   Fourth, even though Python is dynamically typed, it does have types that can
   be checked at runtime. Thus, a ``1`` in a config file is the integer '1',
   while a ``'1'`` is a string.
 
 * A fully automated method for getting the configuration information to the
   classes that need it at runtime. Writing code that walks a configuration
   hierarchy to extract a particular attribute is painful. When you have
   complex configuration information with hundreds of attributes, this makes
   you want to cry.
 
 * Type checking and validation that doesn't require the entire configuration
   hierarchy to be specified statically before runtime. Python is a very
   dynamic language and you don't always know everything that needs to be
   configured when a program starts.
 
 
 .. _`XDG Base Directory`: http://standards.freedesktop.org/basedir-spec/basedir-spec-latest.html

docs/source/interactive/reference.rst

@@ -1,1164 +1,1164 b''
 =================
 IPython reference
 =================
 
 .. _command_line_options:
 
 Command-line usage
 ==================
 
 You start IPython with the command::
 
     $ ipython [options] files
 
 .. note::
 
   For IPython on Python 3, use ``ipython3`` in place of ``ipython``.
 
 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 ipython_config.py. 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 configuration files for details on those. There are separate configuration 
 files for each profile, and the files look like "ipython_config.py" or 
 "ipython_config_<frontendname>.py".  Profile directories look like 
-"profile_profilename" and are typically installed in the IPYTHONDIR directory.
-For Linux users, this will be $HOME/.config/ipython, and for other users it 
-will be $HOME/.ipython.  For Windows users, $HOME resolves to C:\\Documents and
-Settings\\YourUserName in most instances.
+"profile_profilename" and are typically installed in the IPYTHONDIR directory,
+which defaults to :file:`$HOME/.ipython`. For Windows users, :envvar:`HOME`
+resolves to :file:`C:\\Documents and Settings\\YourUserName` in most
+instances.
 
 
 Eventloop integration
 ---------------------
 
 Previously IPython had command line options for controlling GUI event loop
 integration (-gthread, -qthread, -q4thread, -wthread, -pylab). As of IPython
 version 0.11, these have been removed. Please see the new ``%gui``
 magic command or :ref:`this section <gui_support>` for details on the new
 interface, or specify the gui at the commandline::
 
     $ ipython --gui=qt
 
 
 Command-line Options
 --------------------
 
 To see the options IPython accepts, use ``ipython --help`` (and you probably
 should run the output through a pager such as ``ipython --help | less`` for
 more convenient reading).  This shows all the options that have a single-word
 alias to control them, but IPython lets you configure all of its objects from
 the command-line by passing the full class name and a corresponding value; type
 ``ipython --help-all`` to see this full list.  For example::
 
   ipython --matplotlib qt
 
 is equivalent to::
 
   ipython --TerminalIPythonApp.matplotlib='qt'
 
 Note that in the second form, you *must* use the equal sign, as the expression
 is evaluated as an actual Python assignment.  While in the above example the
 short form is more convenient, only the most common options have a short form,
 while any configurable variable in IPython can be set at the command-line by
 using the long form.  This long form is the same syntax used in the
 configuration files, if you want to set these options permanently.
 
 
 Interactive use
 ===============
 
 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.
 
 Lines that begin with ``%%`` signal a *cell magic*: they take as arguments not
 only the rest of the current line, but all lines below them as well, in the
 current execution block.  Cell magics can in fact make arbitrary modifications
 to the input they receive, which need not even be valid Python code at all.
 They receive the whole block as a single string.
 
 As a line magic example, the ``%cd`` magic works just like the OS command of
 the same name::
 
       In [8]: %cd
       /home/fperez
 
 The following uses the builtin ``timeit`` in cell mode::
   
   In [10]: %%timeit x = range(10000)
       ...: min(x)
       ...: max(x)
       ...: 
   1000 loops, best of 3: 438 us per loop
 
 In this case, ``x = range(10000)`` is called as the line argument, and the
 block with ``min(x)`` and ``max(x)`` is called as the cell body.  The
 ``timeit`` magic receives both.
   
 If you have 'automagic' enabled (as it by default), you don't need to type in
 the single ``%`` explicitly for line magics; 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'::
 
       In [9]: cd mydir
       /home/fperez/mydir
 
 Note that cell magics *always* require an explicit ``%%`` prefix, automagic
 calling only works for line magics.
       
 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:
 
 .. sourcecode:: ipython
 
     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 "<ipython-input-3-9fedb3aff56c>", 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, automagic works again
 
     In [6]: cd ipython
 
     /home/fperez/ipython
 
 Defining your own magics
 ++++++++++++++++++++++++
 
 There are two main ways to define your own magic functions: from standalone
 functions and by inheriting from a base class provided by IPython:
 :class:`IPython.core.magic.Magics`.  Below we show code you can place in a file
 that you load from your configuration, such as any file in the ``startup``
 subdirectory of your default IPython profile.
 
 First, let us see the simplest case.  The following shows how to create a line
 magic, a cell one and one that works in both modes, using just plain functions:
 
 .. sourcecode:: python
 
     from IPython.core.magic import (register_line_magic, register_cell_magic,
                                     register_line_cell_magic)
 		
     @register_line_magic
     def lmagic(line):
         "my line magic"
 	return line
 
     @register_cell_magic
     def cmagic(line, cell):
         "my cell magic"
         return line, cell
 
     @register_line_cell_magic
     def lcmagic(line, cell=None):
         "Magic that works both as %lcmagic and as %%lcmagic"
 	if cell is None:
 	    print "Called as line magic"
 	    return line
 	else:
 	    print "Called as cell magic"
             return line, cell
 
     # We delete these to avoid name conflicts for automagic to work
     del lmagic, lcmagic
 
 
 You can also create magics of all three kinds by inheriting from the
 :class:`IPython.core.magic.Magics` class.  This lets you create magics that can
 potentially hold state in between calls, and that have full access to the main
 IPython object:
     
 .. sourcecode:: python
 		
     # This code can be put in any Python module, it does not require IPython
     # itself to be running already.  It only creates the magics subclass but
     # doesn't instantiate it yet.
     from IPython.core.magic import (Magics, magics_class, line_magic,
                                     cell_magic, line_cell_magic)
 
     # The class MUST call this class decorator at creation time
     @magics_class
     class MyMagics(Magics):
 
         @line_magic
         def lmagic(self, line):
             "my line magic"
             print "Full access to the main IPython object:", self.shell
             print "Variables in the user namespace:", self.shell.user_ns.keys()
             return line
 
         @cell_magic
         def cmagic(self, line, cell):
             "my cell magic"
             return line, cell
 
         @line_cell_magic
         def lcmagic(self, line, cell=None):
             "Magic that works both as %lcmagic and as %%lcmagic"
             if cell is None:
                 print "Called as line magic"
                 return line
             else:
                 print "Called as cell magic"
                 return line, cell
 
 
     # In order to actually use these magics, you must register them with a
     # running IPython.  This code must be placed in a file that is loaded once
     # IPython is up and running:
     ip = get_ipython()
     # You can register the class itself without instantiating it.  IPython will
     # call the default constructor on it.
     ip.register_magics(MyMagics)
 
 If you want to create a class with a different constructor that holds
 additional state, then you should always call the parent constructor and
 instantiate the class yourself before registration:
 
 .. sourcecode:: python
 		
     @magics_class
     class StatefulMagics(Magics):
         "Magics that hold additional state"
 	
         def __init__(self, shell, data):
             # You must call the parent constructor
             super(StatefulMagics, self).__init__(shell)
             self.data = data
         
         # etc...
 
     # This class must then be registered with a manually created instance,
     # since its constructor has different arguments from the default:
     ip = get_ipython()
     magics = StatefulMagics(ip, some_data)
     ip.register_magics(magics)
     
 
 In earlier versions, IPython had an API for the creation of line magics (cell
 magics did not exist at the time) that required you to create functions with a
 method-looking signature and to manually pass both the function and the name.
 While this API is no longer recommended, it remains indefinitely supported for
 backwards compatibility purposes.  With the old API, you'd create a magic as
 follows:
 
 .. sourcecode:: python
 
     def func(self, line):
         print "Line magic called with line:", line
 	print "IPython object:", self.shell
 
     ip = get_ipython()
     # Declare this function as the magic %mycommand
     ip.define_magic('mycommand', func)
 
 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 :ref:`below <dynamic_object_info>` for
 information on the '?' system) to get information about any particular magic
 function you are interested in.
 
 The API documentation for the :mod:`IPython.core.magic` module contains the full
 docstrings of all currently available magic commands.
 
 
 Access to the standard Python help
 ----------------------------------
 
 Simply type ``help()`` to access Python's standard help system. You can
 also type ``help(object)`` for information about a given object, or
 ``help('keyword')`` for information on a keyword. You may need to configure your
 PYTHONDOCS environment variable 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 (e.g. function signatures) they get
 snipped in the center for brevity. This system gives access variable types and
 values, docstrings, function prototypes and other useful information.
 
 If the information will not fit in the terminal, it is displayed in a pager
 (``less`` if available, otherwise a basic internal pager).
 
 Typing ``??word`` or ``word??`` gives access to the full information, including
 the source code where possible. Long strings are not snipped.
 
 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 (``%function_name?``);
 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 call signature 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``) work on object attributes, as well as
 directly on variables. For example, after doing ``import os``, you can use
 ``os.path.abspath??``.
 
 .. _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/profile_<name>/history.sqlite. 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
 :file:`~/.inputrc` configuration (or whatever file your INPUTRC variable points
 to). Adding the following lines to your :file:`.inputrc` file can make
 indenting/unindenting more convenient (M-i indents, M-u unindents)::
 
     # if you don't already have a ~/.inputrc file, you need this include:
     $include /etc/inputrc
     
     $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::
 
     Setting the above indents will cause problems with unicode text entry in
     the terminal.
 
 .. warning::
 
     Autoindent 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 :file:`ipython_config.py` file
     (set TerminalInteractiveShell.autoindent=False).
     
     If you want to paste multiple lines in the terminal, it is recommended that
     you use ``%paste``.
 
 
 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 configuration file (note
 that these options can not be specified at the command line):
 
     * **readline_parse_and_bind**: this holds a list of strings 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.
 
 You will find the default values in your configuration file.
 
 
 Session logging and restoring
 -----------------------------
 
 You can log all input from a session either by starting IPython with the
 command line switch ``--logfile=foo.py`` (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 by running them as scripts 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 'ipython_log.py' in your
 current working 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
 --------------------------------
 
 You can assign the result of a system command to a Python variable with the
 syntax ``myfiles = !ls``. This gets machine readable output from stdout 
 (e.g. without colours), and splits on newlines. To explicitly get this sort of
 output without assigning to a variable, use two exclamation marks (``!!ls``) or
 the ``%sx`` magic command.
 
 The captured list has some convenience features. ``myfiles.n`` or ``myfiles.s``
 returns a string delimited by newlines or spaces, respectively. ``myfiles.p``
 produces `path objects <http://pypi.python.org/pypi/path.py>`_ from the list items.
 See :ref:`string_lists` for details.
 
 IPython also allows you to expand the value of python variables when
 making system calls. Wrap variables or expressions in {braces}::
 
     In [1]: pyvar = 'Hello world' 
     In [2]: !echo "A python variable: {pyvar}"  
     A python variable: Hello world
     In [3]: import math
     In [4]: x = 8
     In [5]: !echo {math.factorial(x)}
     40320
 
 For simple cases, you can alternatively prepend $ to a variable name::
 
     In [6]: !echo $sys.argv  
     [/home/fperez/usr/bin/ipython]
     In [7]: !echo "A system variable: $$HOME"  # Use $$ for literal $
     A system variable: /home/fperez
 
 System command aliases
 ----------------------
 
 The %alias magic function allows 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  
     ERROR: Alias <parts> requires 2 arguments, 1 given.
 
 If called with no parameters, %alias prints the table of currently
 defined aliases.
 
 The %rehashx magic allows you to load your entire $PATH as
 ipython aliases. See its docstring for further details.
 
 
 .. _dreload:
 
 Recursive reload
 ----------------
 
 The :mod:`IPython.lib.deepreload` module allows you to recursively reload a
 module: changes made to any of its dependencies will be reloaded without
 having to exit. To start using it, do::
 
     from IPython.lib.deepreload import reload as dreload
 
 
 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 system
 --------------------
 
 IPython offers numbered prompts (In/Out) with input and output caching
 (also referred to as 'input history'). All input is saved and can be 
 retrieved as variables (besides the usual arrow key recall), in 
 addition to the %rep magic command that brings a history entry
 up for editing on the next  command line.
 
 The following GLOBAL variables always exist (so don't overwrite them!):
 
 * _i, _ii, _iii: store previous, next previous and next-next previous inputs.
 * In, _ih : a list of all inputs; _ih[n] is the input from line n. 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), so ``_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.
 
 You can also re-execute multiple lines of input easily by using the
 magic %rerun or %macro functions. The macro system also allows you to re-execute
 previous lines which include magic function calls (which require special
 processing). Type %macro? 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.
 
 You can also search ('grep') through your history by typing 
 ``%hist -g somestring``. This is handy for searching for URLs, IP addresses,
 etc. You can bring history entries listed by '%hist -g' up for editing
 with the %recall command, or run them immediately with %rerun.
 
 .. _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 variables are also 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 configuration
 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
 conveniently 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)::
 
     In [1]: callable_ob arg1, arg2, arg3
     ------> callable_ob(arg1, arg2, arg3)
 
 You can force automatic parentheses by using '/' as the first character
 of a line. For example::
 
     In [2]: /globals # becomes 'globals()'
 
 Note that the '/' MUST be the first character on the line! This won't work::
 
     In [3]: 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 [4]: zip (1,2,3),(4,5,6) # won't work
 
 but this will work::
 
     In [5]: /zip (1,2,3),(4,5,6) 
     ------> zip ((1,2,3),(4,5,6))  
     Out[5]: [(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 [6]: 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::
 
     In [1]: ,my_function /home/me  # becomes my_function("/home/me")
 
 If you use ';' the whole argument is quoted as a single string, while ',' splits
 on whitespace::
 
     In [2]: ,my_function a b c    # becomes my_function("a","b","c")
 
     In [3]: ;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::
 
     In [4]: 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 the following code at
 the end of that file, then IPython will be your working environment anytime you
 start Python::
 
     from IPython.frontend.terminal.ipapp import launch_new_instance
     launch_new_instance()
     raise SystemExit
 
 The ``raise SystemExit`` is needed to exit Python 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
 =================
 
 You can start a regular IPython session with
 
 .. sourcecode:: python
 
     import IPython
     IPython.start_ipython()
 
 at any point in your program.  This will load IPython configuration,
 startup files, and everything, just as if it were a normal IPython session.
 In addition to this,
 it is possible to embed 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.
 
 .. note::
 
   At present, embedding IPython cannot be done from inside IPython.
   Run the code samples below outside IPython.
 
 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 import embed
 
     embed() # this call anywhere in your program will start IPython
 
 .. note::
 
     As of 0.13, you can embed an IPython *kernel*, for use with qtconsole,
     etc. via ``IPython.embed_kernel()`` instead of ``IPython.embed()``.
     It should function just the same as regular embed, but you connect
     an external frontend rather than IPython starting up in the local
     terminal.
 
 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 :mod:`~IPython.frontend.terminal.embed` 
 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:
 
 .. literalinclude:: ../../../examples/core/example-embed.py
     :language: python
 
 Once you understand how the system functions, you can use the following
 code fragments in your programs which are ready for cut and paste:
 
 .. literalinclude:: ../../../examples/core/example-embed-short.py
     :language: python
 
 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 config 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 pdb will automatically 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
     from IPython.core import ultratb
     sys.excepthook = 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_with_prompts:
 
 Pasting of code starting with Python or IPython prompts
 -------------------------------------------------------
 
 IPython is smart enough to filter out input prompts, be they plain Python ones
 (``>>>`` and ``...``) or IPython ones (``In [N]:`` and ``...:``).  You can
 therefore copy and paste from existing interactive sessions without worry.
 
 The following is a 'screenshot' of how things work, copying an example from the
 standard Python tutorial::
 
     In [1]: >>> # Fibonacci series:
 
     In [2]: ... # the sum of two elements defines the next
 
     In [3]: ... a, b = 0, 1
 
     In [4]: >>> while b < 10:
        ...:     ...     print b
        ...:     ...     a, b = b, a+b
        ...:     
     1
     1
     2
     3
     5
     8
 
 And pasting from IPython sessions works equally well::
 
     In [1]: In [5]: def f(x):
        ...:        ...:     "A simple function"
        ...:        ...:     return x**2
        ...:    ...: 
 
     In [2]: f(3)
     Out[2]: 9
 
 .. _gui_support:
 
 GUI event loop support
 ======================
 
 .. versionadded:: 0.11
     The ``%gui`` magic and :mod:`IPython.lib.inputhook`.
 
 IPython has excellent support for working interactively with Graphical User
 Interface (GUI) toolkits, such as wxPython, PyQt4/PySide, PyGTK and Tk. This is
 implemented using Python's builtin ``PyOSInputHook`` hook. This implementation
 is extremely robust compared to our previous thread-based version. The
 advantages of this are:
 
 * GUIs can be enabled and disabled dynamically at runtime.
 * The active GUI can be switched dynamically at runtime.
 * In some cases, multiple GUIs can run simultaneously with no problems.
 * There is a developer API in :mod:`IPython.lib.inputhook` for customizing 
   all of these things.
 
 For users, enabling GUI event loop integration is simple.  You simple use the
 ``%gui`` magic as follows::
 
     %gui [GUINAME]
 
 With no arguments, ``%gui`` removes all GUI support.  Valid ``GUINAME``
 arguments are ``wx``, ``qt``, ``gtk`` and ``tk``.
 
 Thus, to use wxPython interactively and create a running :class:`wx.App`
 object, do::
 
     %gui wx
 
 For information on IPython's matplotlib_ integration (and the ``matplotlib``
 mode) see :ref:`this section <matplotlib_support>`.
 
 For developers that want to use IPython's GUI event loop integration in the
 form of a library, these capabilities are exposed in library form in the
 :mod:`IPython.lib.inputhook` and :mod:`IPython.lib.guisupport` modules.
 Interested developers should see the module docstrings for more information,
 but there are a few points that should be mentioned here.
 
 First, the ``PyOSInputHook`` approach only works in command line settings 
 where readline is activated.  The integration with various eventloops
 is handled somewhat differently (and more simply) when using the standalone
 kernel, as in the qtconsole and notebook.
 
 Second, when using the ``PyOSInputHook`` approach, a GUI application should
 *not* start its event loop. Instead all of this is handled by the
 ``PyOSInputHook``. This means that applications that are meant to be used both
 in IPython and as standalone apps need to have special code to detects how the
 application is being run. We highly recommend using IPython's support for this.
 Since the details vary slightly between toolkits, we point you to the various
 examples in our source directory :file:`examples/lib` that demonstrate
 these capabilities.
 
 Third, unlike previous versions of IPython, we no longer "hijack" (replace
 them with no-ops) the event loops. This is done to allow applications that
 actually need to run the real event loops to do so. This is often needed to
 process pending events at critical points.
 
 Finally, we also have a number of examples in our source directory
 :file:`examples/lib` that demonstrate these capabilities.
 
 PyQt and PySide
 ---------------
 
 .. attempt at explanation of the complete mess that is Qt support
 
 When you use ``--gui=qt`` or ``--matplotlib=qt``, IPython can work with either
 PyQt4 or PySide.  There are three options for configuration here, because
 PyQt4 has two APIs for QString and QVariant - v1, which is the default on
 Python 2, and the more natural v2, which is the only API supported by PySide.
 v2 is also the default for PyQt4 on Python 3.  IPython's code for the QtConsole
 uses v2, but you can still use any interface in your code, since the
 Qt frontend is in a different process.
 
 The default will be to import PyQt4 without configuration of the APIs, thus
 matching what most applications would expect. It will fall back of PySide if
 PyQt4 is unavailable.
 
 If specified, IPython will respect the environment variable ``QT_API`` used
 by ETS.  ETS 4.0 also works with both PyQt4 and PySide, but it requires
 PyQt4 to use its v2 API.  So if ``QT_API=pyside`` PySide will be used,
 and if ``QT_API=pyqt`` then PyQt4 will be used *with the v2 API* for
 QString and QVariant, so ETS codes like MayaVi will also work with IPython.
 
 If you launch IPython in matplotlib mode with ``ipython --matplotlib=qt``,
 then IPython will ask matplotlib which Qt library to use (only if QT_API is
 *not set*), via the 'backend.qt4' rcParam.  If matplotlib is version 1.0.1 or
 older, then IPython will always use PyQt4 without setting the v2 APIs, since
 neither v2 PyQt nor PySide work.
 
 .. warning::
 
     Note that this means for ETS 4 to work with PyQt4, ``QT_API`` *must* be set
     to work with IPython's qt integration, because otherwise PyQt4 will be
     loaded in an incompatible mode.
     
     It also means that you must *not* have ``QT_API`` set if you want to
     use ``--gui=qt`` with code that requires PyQt4 API v1.
 
 
 .. _matplotlib_support:
 
 Plotting with matplotlib
 ========================
 
 matplotlib_ provides high quality 2D and 3D plotting for Python. matplotlib_
 can produce plots on screen using a variety of GUI toolkits, including Tk,
 PyGTK, PyQt4 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.
 
 To start IPython with matplotlib support, use the ``--matplotlib`` switch. If
 IPython is already running, you can run the ``%matplotlib`` magic.  If no
 arguments are given, IPython will automatically detect your choice of
 matplotlib backend.  You can also request a specific backend with
 ``%matplotlib backend``, where ``backend`` must be one of: 'tk', 'qt', 'wx',
 'gtk', 'osx'.  In the web notebook and Qt console, 'inline' is also a valid
 backend value, which produces static figures inlined inside the application
 window instead of matplotlib's interactive figures that live in separate
 windows.
 
 .. _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:
 
 .. literalinclude:: ../../../examples/lib/example-demo.py
     :language: python
 
 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.lib.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.lib import demo
 
     demo?
 
 Limitations: It is important to note that these demos are limited to
 fairly simple uses. In particular, you cannot break up sections within
 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, see :func:`IPython.embed` for details.
 
 .. include:: ../links.txt

docs/source/interactive/tutorial.rst

@@ -1,202 +1,201 b''
 .. _tutorial:
 
 ======================
 Introducing IPython
 ======================
 
 You don't need to know anything beyond Python to start using IPython – just type
 commands as you would at the standard Python prompt. But IPython can do much
 more than the standard prompt. Some key features are described here. For more
 information, check the :ref:`tips page <tips>`, or look at examples in the
 `IPython cookbook <https://github.com/ipython/ipython/wiki/Cookbook%3A-Index>`_.
 
 If you've never used Python before, you might want to look at `the official
 tutorial <http://docs.python.org/tutorial/>`_ or an alternative, `Dive into
 Python <http://diveintopython.net/toc/index.html>`_.
 
 The four most helpful commands 
 ===============================
 
 The four most helpful commands, as well as their brief description, is shown
 to you in a banner, every time you start IPython:
 
 ==========    =========================================================
 command       description
 ==========    =========================================================
 ?             Introduction and overview of IPython's features.
 %quickref     Quick reference.
 help          Python's own help system.
 object?       Details about 'object', use 'object??' for extra details.
 ==========    =========================================================
 
 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>``
 to view the object's attributes (see :ref:`the readline section <readline>` for
 more). Besides Python objects and keywords, tab completion also works on file
 and directory names.
 
 Exploring 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. To get specific information on an object, you
 can use the magic commands ``%pdoc``, ``%pdef``, ``%psource`` and ``%pfile``
 
 .. _magics_explained:
 
 Magic functions
 ===============
 
 IPython has a set of predefined 'magic functions' that you can call with a
 command line style syntax.  There are two kinds of magics, line-oriented and
 cell-oriented.  **Line magics** are prefixed with the ``%`` character and work much
 like OS command-line calls: they get as an argument the rest of the line, where
 arguments are passed without parentheses or quotes.  **Cell magics** are
 prefixed with a double ``%%``, and they are functions that get as an argument
 not only the rest of the line, but also the lines below it in a separate
 argument.
 
 The following examples show how to call the builtin ``timeit`` magic, both in
 line and cell mode::
 
       In [1]: %timeit range(1000)
       100000 loops, best of 3: 7.76 us per loop
 
       In [2]: %%timeit x = range(10000)
 	 ...: max(x)
 	 ...: 
       1000 loops, best of 3: 223 us per loop
 
 The builtin magics include:
 
 - Functions that work with code: ``%run``, ``%edit``, ``%save``, ``%macro``,
   ``%recall``, etc.
 - Functions which affect the shell: ``%colors``, ``%xmode``, ``%autoindent``,
   ``%automagic``, etc.
 - Other functions such as ``%reset``, ``%timeit``, ``%%file``, ``%load``, or
   ``%paste``.
 
 You can always call them using the ``%`` prefix, and if you're calling a line
 magic on a line by itself, you can omit even that::
 
     run thescript.py
 
 You can toggle this behavior by running the ``%automagic`` magic.  Cell magics
 must always have the ``%%`` prefix.
 
 A more detailed explanation of the magic system can be obtained by calling
 ``%magic``, and for more details on any magic function, call ``%somemagic?`` to
 read its docstring. To see all the available magic functions, call
 ``%lsmagic``.
 
 .. seealso::
 
     `Cell magics`_ example notebook
 
 Running and Editing
 -------------------
 
 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 (unlike
 imported modules, which have to be specifically reloaded). IPython also
 includes :ref:`dreload <dreload>`, a recursive reload function.
 
 ``%run`` has special flags for timing the execution of your scripts (-t), or
 for running them under the control of either Python's pdb debugger (-d) or
 profiler (-p).
 
 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.
 
 Debugging
 ---------
 
 After an exception occurs, you can call ``%debug`` to jump into the Python
 debugger (pdb) and examine the problem. Alternatively, if you call ``%pdb``,
 IPython will automatically start the debugger on any uncaught exception. 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. This can be an efficient
 way to develop and debug code, in many cases eliminating the need for print
 statements or external debugging tools.
 
 You can also step through a program from the beginning by calling
 ``%run -d theprogram.py``.
 
 History
 =======
 
 IPython stores both the commands you enter, and the results it produces. You
 can easily go through previous commands with the up- and down-arrow keys, or
 access your history in more sophisticated ways.
 
 Input and output history are kept in variables called ``In`` and ``Out``, keyed
 by the prompt numbers, e.g. ``In[4]``. The last three objects in output history
 are also kept in variables named ``_``, ``__`` and ``___``.
 
 You can use the ``%history`` magic function to examine past input and output.
 Input history from previous sessions is saved in a database, and IPython can be
 configured to save output history.
 
 Several other magic functions can use your input history, including ``%edit``, 
 ``%rerun``, ``%recall``, ``%macro``, ``%save`` and ``%pastebin``. You can use a
 standard format to refer to lines::
 
     %pastebin 3 18-20 ~1/1-5
     
 This will take line 3 and lines 18 to 20 from the current session, and lines
 1-5 from the previous session.
 
 System shell commands
 =====================
 
 To run any command at the system shell, simply prefix it with !, e.g.::
 
     !ping www.bbc.co.uk
     
 You can capture the output into a Python list, e.g.: ``files = !ls``. To pass
 the values of Python variables or expressions to system commands, prefix them
 with $: ``!grep -rF $pattern ipython/*``. See :ref:`our shell section
 <system_shell_access>` for more details.
 	
 Define your own system aliases
 ------------------------------
 
 It's 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.
 
 
 Configuration
 =============
 
 Much of IPython can be tweaked through :ref:`configuration <config_overview>`.
 To get started, use the command ``ipython profile create`` to produce the
 default config files. These will be placed in
-:file:`~/.ipython/profile_default` or
-:file:`~/.config/ipython/profile_default`, and contain comments explaining
+:file:`~/.ipython/profile_default`, and contain comments explaining
 what the various options do.
 
 Profiles allow you to use IPython for different tasks, keeping separate config
 files and history for each one. More details in :ref:`the profiles section
 <profiles>`.
 
 Startup Files
 -------------
 
 If you want some code to be run at the beginning of every IPython session, the
 easiest way is to add Python (.py) or IPython (.ipy) scripts to your
 :file:`profile_default/startup/` directory. Files here will be executed as soon
 as the IPython shell is constructed, before any other code or scripts you have
 specified. The files will be run in order of their names, so you can control the
 ordering with prefixes, like ``10-myimports.py``.
 
 .. include:: ../links.txt

docs/source/parallel/parallel_process.rst

@@ -1,884 +1,884 b''
 .. _parallel_process:
 
 ===========================================
 Starting the IPython controller and engines
 ===========================================
 
 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.
 
 Broadly speaking, there are two ways of going about starting a controller and engines:
 
 * In an automated manner using the :command:`ipcluster` command.
 * In a more manual way using the :command:`ipcontroller` and 
   :command:`ipengine` commands.
 
 This document describes both of these methods. We recommend that new users
 start with the :command:`ipcluster` command as it simplifies many common usage
 cases.
 
 General considerations
 ======================
 
 Before delving into the details about how you can start a controller and
 engines using the various methods, we outline some of the general issues that
 come up when starting the controller and engines. These things come up no
 matter which method you use to start your IPython cluster.
 
 If you are running engines on multiple machines, you will likely need to instruct the
 controller to listen for connections on an external interface. This can be done by specifying
 the ``ip`` argument on the command-line, or the ``HubFactory.ip`` configurable in
 :file:`ipcontroller_config.py`.
 
 If your machines are on a trusted network, you can safely instruct the controller to listen
 on all interfaces with::
 
     $> ipcontroller --ip=*
 
 
 Or you can set the same behavior as the default by adding the following line to your :file:`ipcontroller_config.py`:
 
 .. sourcecode:: python
 
     c.HubFactory.ip = '*'
     # c.HubFactory.location = '10.0.1.1'
 
 
 .. note::
 
     ``--ip=*`` instructs ZeroMQ to listen on all interfaces,
     but it does not contain the IP needed for engines / clients
     to know where the controller actually is.
     This can be specified with ``--location=10.0.0.1``,
     the specific IP address of the controller, as seen from engines and/or clients.
     IPython tries to guess this value by default, but it will not always guess correctly.
     Check the ``location`` field in your connection files if you are having connection trouble.
 
 .. note::
 
     Due to the lack of security in ZeroMQ, the controller will only listen for connections on
     localhost by default. If you see Timeout errors on engines or clients, then the first
     thing you should check is the ip address the controller is listening on, and make sure
     that it is visible from the timing out machine.
     
 .. seealso::
 
     Our `notes <parallel_security>`_ on security in the new parallel computing code.
 
 Let's say that you want to start the controller on ``host0`` and engines on
 hosts ``host1``-``hostn``. The following steps are then required:
 
 1. Start the controller on ``host0`` by running :command:`ipcontroller` on
    ``host0``.  The controller must be instructed to listen on an interface visible
    to the engine machines, via the ``ip`` command-line argument or ``HubFactory.ip``
    in :file:`ipcontroller_config.py`.
 2. Move the JSON file (:file:`ipcontroller-engine.json`) created by the 
    controller from ``host0`` to hosts ``host1``-``hostn``.
 3. Start the engines on hosts ``host1``-``hostn`` by running 
    :command:`ipengine`.  This command has to be told where the JSON file 
    (:file:`ipcontroller-engine.json`) is located.
 
 At this point, the controller and engines will be connected. By default, the JSON files
 created by the controller are put into the :file:`IPYTHONDIR/profile_default/security`
 directory. If the engines share a filesystem with the controller, step 2 can be skipped as
 the engines will automatically look at that location.
 
 The final step required to actually use the running controller from a client is to move
 the JSON file :file:`ipcontroller-client.json` from ``host0`` to any host where clients
 will be run. If these file are put into the :file:`IPYTHONDIR/profile_default/security`
 directory of the client's host, they will be found automatically. Otherwise, the full path
 to them has to be passed to the client's constructor.
 
 Using :command:`ipcluster`
 ===========================
 
 The :command:`ipcluster` command provides a simple way of starting a
 controller and engines in the following situations:
 
 1. When the controller and engines are all run on localhost. This is useful
    for testing or running on a multicore computer.
 2. When engines are started using the :command:`mpiexec` command that comes 
    with most MPI [MPI]_ implementations
 3. When engines are started using the PBS [PBS]_ batch system 
    (or other `qsub` systems, such as SGE).
 4. When the controller is started on localhost and the engines are started on 
    remote nodes using :command:`ssh`.
 5. When engines are started using the Windows HPC Server batch system.
 
 .. note::
 
     Currently :command:`ipcluster` requires that the
     :file:`IPYTHONDIR/profile_<name>/security` directory live on a shared filesystem that is
     seen by both the controller and engines. If you don't have a shared file
     system you will need to use :command:`ipcontroller` and
     :command:`ipengine` directly.
 
 Under the hood, :command:`ipcluster` just uses :command:`ipcontroller`
 and :command:`ipengine` to perform the steps described above.
 
 The simplest way to use ipcluster requires no configuration, and will
 launch a controller and a number of engines on the local machine. For instance, 
 to start one controller and 4 engines on localhost, just do::
 
     $ ipcluster start -n 4
 
 To see other command line options, do::
 
     $ ipcluster -h
 
 
 Configuring an IPython cluster
 ==============================
 
 Cluster configurations are stored as `profiles`.  You can create a new profile with::
 
     $ ipython profile create --parallel --profile=myprofile
 
 This will create the directory :file:`IPYTHONDIR/profile_myprofile`, and populate it
 with the default configuration files for the three IPython cluster commands. Once
 you edit those files, you can continue to call ipcluster/ipcontroller/ipengine
 with no arguments beyond ``profile=myprofile``, and any configuration will be maintained.
 
 There is no limit to the number of profiles you can have, so you can maintain a profile for each
 of your common use cases. The default profile will be used whenever the
 profile argument is not specified, so edit :file:`IPYTHONDIR/profile_default/*_config.py` to
 represent your most common use case.
 
 The configuration files are loaded with commented-out settings and explanations,
 which should cover most of the available possibilities.
 
 Using various batch systems with :command:`ipcluster`
 -----------------------------------------------------
 
 :command:`ipcluster` has a notion of Launchers that can start controllers
 and engines with various remote execution schemes.  Currently supported
 models include :command:`ssh`, :command:`mpiexec`, PBS-style (Torque, SGE, LSF),
 and Windows HPC Server.
 
 In general, these are configured by the :attr:`IPClusterEngines.engine_set_launcher_class`,
 and :attr:`IPClusterStart.controller_launcher_class` configurables, which can be the
 fully specified object name (e.g. ``'IPython.parallel.apps.launcher.LocalControllerLauncher'``),
 but if you are using IPython's builtin launchers, you can specify just the class name,
 or even just the prefix e.g:
 
 .. sourcecode:: python
 
     c.IPClusterEngines.engine_launcher_class = 'SSH'
     # equivalent to
     c.IPClusterEngines.engine_launcher_class = 'SSHEngineSetLauncher'
     # both of which expand to
     c.IPClusterEngines.engine_launcher_class = 'IPython.parallel.apps.launcher.SSHEngineSetLauncher'
 
 The shortest form being of particular use on the command line, where all you need to do to
 get an IPython cluster running with engines started with MPI is:
 
 .. sourcecode:: bash
 
     $> ipcluster start --engines=MPI
 
 Assuming that the default MPI config is sufficient.
 
 .. note::
 
     shortcuts for builtin launcher names were added in 0.12, as was the ``_class`` suffix
     on the configurable names.  If you use the old 0.11 names (e.g. ``engine_set_launcher``),
     they will still work, but you will get a deprecation warning that the name has changed.
 
 
 .. note::
 
     The Launchers and configuration are designed in such a way that advanced
     users can subclass and configure them to fit their own system that we
     have not yet supported (such as Condor)
 
 Using :command:`ipcluster` in mpiexec/mpirun mode
 -------------------------------------------------
 
 
 The mpiexec/mpirun mode is useful if you:
 
 1. Have MPI installed.
 2. Your systems are configured to use the :command:`mpiexec` or
    :command:`mpirun` commands to start MPI processes.
 
 If these are satisfied, you can create a new profile::
 
     $ ipython profile create --parallel --profile=mpi
 
 and edit the file :file:`IPYTHONDIR/profile_mpi/ipcluster_config.py`.
 
 There, instruct ipcluster to use the MPI launchers by adding the lines:
 
 .. sourcecode:: python
 
     c.IPClusterEngines.engine_launcher_class = 'MPIEngineSetLauncher'
 
 If the default MPI configuration is correct, then you can now start your cluster, with::
 
     $ ipcluster start -n 4 --profile=mpi
 
 This does the following:
 
 1. Starts the IPython controller on current host.
 2. Uses :command:`mpiexec` to start 4 engines.
 
 If you have a reason to also start the Controller with mpi, you can specify:
 
 .. sourcecode:: python
 
     c.IPClusterStart.controller_launcher_class = 'MPIControllerLauncher'
 
 .. note::
 
     The Controller *will not* be in the same MPI universe as the engines, so there is not
     much reason to do this unless sysadmins demand it.
 
 On newer MPI implementations (such as OpenMPI), this will work even if you
 don't make any calls to MPI or call :func:`MPI_Init`. However, older MPI
 implementations actually require each process to call :func:`MPI_Init` upon
 starting. The easiest way of having this done is to install the mpi4py
 [mpi4py]_ package and then specify the ``c.MPI.use`` option in :file:`ipengine_config.py`:
 
 .. sourcecode:: python
 
     c.MPI.use = 'mpi4py'
 
 Unfortunately, even this won't work for some MPI implementations. If you are
 having problems with this, you will likely have to use a custom Python
 executable that itself calls :func:`MPI_Init` at the appropriate time.
 Fortunately, mpi4py comes with such a custom Python executable that is easy to
 install and use. However, this custom Python executable approach will not work
 with :command:`ipcluster` currently.
 
 More details on using MPI with IPython can be found :ref:`here <parallelmpi>`.
 
 
 Using :command:`ipcluster` in PBS mode
 --------------------------------------
 
 The PBS mode uses the Portable Batch System (PBS) to start the engines. 
 
 As usual, we will start by creating a fresh profile::
 
     $ ipython profile create --parallel --profile=pbs
 
 And in :file:`ipcluster_config.py`, we will select the PBS launchers for the controller
 and engines:
 
 .. sourcecode:: python
 
     c.IPClusterStart.controller_launcher_class = 'PBSControllerLauncher'
     c.IPClusterEngines.engine_launcher_class = 'PBSEngineSetLauncher'
 
 .. note::
 
     Note that the configurable is IPClusterEngines for the engine launcher, and
     IPClusterStart for the controller launcher. This is because the start command is a
     subclass of the engine command, adding a controller launcher. Since it is a subclass,
     any configuration made in IPClusterEngines is inherited by IPClusterStart unless it is
     overridden.
 
 IPython does provide simple default batch templates for PBS and SGE, but you may need
 to specify your own. Here is a sample PBS script template:
 
 .. sourcecode:: bash
 
     #PBS -N ipython
     #PBS -j oe
     #PBS -l walltime=00:10:00
     #PBS -l nodes={n/4}:ppn=4
     #PBS -q {queue}
 
     cd $PBS_O_WORKDIR
     export PATH=$HOME/usr/local/bin
     export PYTHONPATH=$HOME/usr/local/lib/python2.7/site-packages
     /usr/local/bin/mpiexec -n {n} ipengine --profile-dir={profile_dir}
 
 There are a few important points about this template:
 
 1. This template will be rendered at runtime using IPython's :class:`EvalFormatter`.
    This is simply a subclass of :class:`string.Formatter` that allows simple expressions
    on keys.
 
 2. Instead of putting in the actual number of engines, use the notation
    ``{n}`` to indicate the number of engines to be started. You can also use
    expressions like ``{n/4}`` in the template to indicate the number of nodes.
    There will always be ``{n}`` and ``{profile_dir}`` variables passed to the formatter.
    These allow the batch system to know how many engines, and where the configuration
    files reside. The same is true for the batch queue, with the template variable
    ``{queue}``.
 
 3. Any options to :command:`ipengine` can be given in the batch script
    template, or in :file:`ipengine_config.py`.
 
 4. Depending on the configuration of you system, you may have to set
    environment variables in the script template.
 
 The controller template should be similar, but simpler:
 
 .. sourcecode:: bash
 
     #PBS -N ipython
     #PBS -j oe
     #PBS -l walltime=00:10:00
     #PBS -l nodes=1:ppn=4
     #PBS -q {queue}
 
     cd $PBS_O_WORKDIR
     export PATH=$HOME/usr/local/bin
     export PYTHONPATH=$HOME/usr/local/lib/python2.7/site-packages
     ipcontroller --profile-dir={profile_dir}
 
 
 Once you have created these scripts, save them with names like
 :file:`pbs.engine.template`. Now you can load them into the :file:`ipcluster_config` with:
 
 .. sourcecode:: python
 
     c.PBSEngineSetLauncher.batch_template_file = "pbs.engine.template"
     
     c.PBSControllerLauncher.batch_template_file = "pbs.controller.template"
         
 
 Alternately, you can just define the templates as strings inside :file:`ipcluster_config`.
 
 Whether you are using your own templates or our defaults, the extra configurables available are
 the number of engines to launch (``{n}``, and the batch system queue to which the jobs are to be
 submitted (``{queue}``)). These are configurables, and can be specified in
 :file:`ipcluster_config`:
 
 .. sourcecode:: python
 
     c.PBSLauncher.queue = 'veryshort.q'
     c.IPClusterEngines.n = 64
 
 Note that assuming you are running PBS on a multi-node cluster, the Controller's default behavior
 of listening only on localhost is likely too restrictive.  In this case, also assuming the
 nodes are safely behind a firewall, you can simply instruct the Controller to listen for
 connections on all its interfaces, by adding in :file:`ipcontroller_config`:
 
 .. sourcecode:: python
 
     c.HubFactory.ip = '*'
 
 You can now run the cluster with::
 
     $ ipcluster start --profile=pbs -n 128
 
 Additional configuration options can be found in the PBS section of :file:`ipcluster_config`.
 
 .. note::
 
     Due to the flexibility of configuration, the PBS launchers work with simple changes
     to the template for other :command:`qsub`-using systems, such as Sun Grid Engine,
     and with further configuration in similar batch systems like Condor.
 
 
 Using :command:`ipcluster` in SSH mode
 --------------------------------------
 
 
 The SSH mode uses :command:`ssh` to execute :command:`ipengine` on remote
 nodes and :command:`ipcontroller` can be run remotely as well, or on localhost.
 
 .. note::
 
     When using this mode it highly recommended that you have set up SSH keys
     and are using ssh-agent [SSH]_ for password-less logins.
 
 As usual, we start by creating a clean profile::
 
     $ ipython profile create --parallel --profile=ssh
 
 To use this mode, select the SSH launchers in :file:`ipcluster_config.py`:
 
 .. sourcecode:: python
 
     c.IPClusterEngines.engine_launcher_class = 'SSHEngineSetLauncher'
     # and if the Controller is also to be remote:
     c.IPClusterStart.controller_launcher_class = 'SSHControllerLauncher'
 
 
 
 The controller's remote location and configuration can be specified:
 
 .. sourcecode:: python
 
     # Set the user and hostname for the controller
     # c.SSHControllerLauncher.hostname = 'controller.example.com'
     # c.SSHControllerLauncher.user = os.environ.get('USER','username')
 
     # Set the arguments to be passed to ipcontroller
     # note that remotely launched ipcontroller will not get the contents of 
     # the local ipcontroller_config.py unless it resides on the *remote host*
     # in the location specified by the `profile-dir` argument.
     # c.SSHControllerLauncher.controller_args = ['--reuse', '--ip=*', '--profile-dir=/path/to/cd']
 
 Engines are specified in a dictionary, by hostname and the number of engines to be run
 on that host.
 
 .. sourcecode:: python
 
     c.SSHEngineSetLauncher.engines = { 'host1.example.com' : 2,
                 'host2.example.com' : 5,
                 'host3.example.com' : (1, ['--profile-dir=/home/different/location']),
                 'host4.example.com' : 8 }
 
 * The `engines` dict, where the keys are the host we want to run engines on and
   the value is the number of engines to run on that host.
 * on host3, the value is a tuple, where the number of engines is first, and the arguments
   to be passed to :command:`ipengine` are the second element.
 
 For engines without explicitly specified arguments, the default arguments are set in
 a single location:
 
 .. sourcecode:: python
 
     c.SSHEngineSetLauncher.engine_args = ['--profile-dir=/path/to/profile_ssh']
 
 Current limitations of the SSH mode of :command:`ipcluster` are:
 
 * Untested and unsupported on Windows.  Would require a working :command:`ssh` on Windows.
   Also, we are using shell scripts to setup and execute commands on remote hosts.
 
 
 Moving files with SSH
 *********************
 
 SSH launchers will try to move connection files, controlled by the ``to_send`` and
 ``to_fetch`` configurables.  If your machines are on a shared filesystem, this step is
 unnecessary, and can be skipped by setting these to empty lists:
 
 .. sourcecode:: python
 
     c.SSHLauncher.to_send = []
     c.SSHLauncher.to_fetch = []
 
 If our default guesses about paths don't work for you, or other files 
 should be moved, you can manually specify these lists as tuples of (local_path, 
 remote_path) for to_send, and (remote_path, local_path) for to_fetch.  If you do
 specify these lists explicitly, IPython *will not* automatically send connection files,
 so you must include this yourself if they should still be sent/retrieved.
 
 
 IPython on EC2 with StarCluster
 ===============================
 
 The excellent StarCluster_ toolkit for managing `Amazon EC2`_ clusters has a plugin
 which makes deploying IPython on EC2 quite simple.  The starcluster plugin uses
 :command:`ipcluster` with the SGE launchers to distribute engines across the
 EC2 cluster.  See their `ipcluster plugin documentation`_ for more information.
 
 .. _StarCluster: http://web.mit.edu/starcluster
 .. _Amazon EC2: http://aws.amazon.com/ec2/
 .. _ipcluster plugin documentation: http://web.mit.edu/starcluster/docs/latest/plugins/ipython.html
 
 
 Using the :command:`ipcontroller` and :command:`ipengine` commands
 ==================================================================
 
 It is also possible to use the :command:`ipcontroller` and :command:`ipengine`
 commands to start your controller and engines. This approach gives you full
 control over all aspects of the startup process.
 
 Starting the controller and engine on your local machine
 --------------------------------------------------------
 
 To use :command:`ipcontroller` and :command:`ipengine` to start things on your
 local machine, do the following.
 
 First start the controller::
 
     $ ipcontroller
     
 Next, start however many instances of the engine you want using (repeatedly)
 the command::
 
     $ ipengine
 
 The engines should start and automatically connect to the controller using the
 JSON files in :file:`IPYTHONDIR/profile_default/security`. You are now ready to use the
 controller and engines from IPython.
 
 .. warning:: 
     
     The order of the above operations may be important.  You *must*
     start the controller before the engines, unless you are reusing connection
     information (via ``--reuse``), in which case ordering is not important.
 
 .. note::
 
     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.
 
 Starting the controller and engines on different hosts
 ------------------------------------------------------
 
 When the controller and engines are running on different hosts, things are
 slightly more complicated, but the underlying ideas are the same:
 
 1. Start the controller on a host using :command:`ipcontroller`. The controller must be
    instructed to listen on an interface visible to the engine machines, via the ``ip``
    command-line argument or ``HubFactory.ip`` in :file:`ipcontroller_config.py`::
    
         $ ipcontroller --ip=192.168.1.16
    
    .. sourcecode:: python
    
         # in ipcontroller_config.py
         HubFactory.ip = '192.168.1.16'
 
 2. Copy :file:`ipcontroller-engine.json` from :file:`IPYTHONDIR/profile_<name>/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.
 
 The only thing you have to be careful of is to tell :command:`ipengine` where
 the :file:`ipcontroller-engine.json` file is located. There are two ways you
 can do this:
 
 * Put :file:`ipcontroller-engine.json` in the :file:`IPYTHONDIR/profile_<name>/security`
   directory on the engine's host, where it will be found automatically.
 * Call :command:`ipengine` with the ``--file=full_path_to_the_file``
   flag.
 
 The ``file`` flag works like this::
 
     $ ipengine --file=/path/to/my/ipcontroller-engine.json
 
 .. note::
     
     If the controller's and engine's hosts all have a shared file system  
     (:file:`IPYTHONDIR/profile_<name>/security` is the same on all of them), then things
     will just work!
 
 SSH Tunnels
 ***********
 
 If your engines are not on the same LAN as the controller, or you are on a highly
 restricted network where your nodes cannot see each others ports, then you can
 use SSH tunnels to connect engines to the controller.
 
 .. note::
 
     This does not work in all cases.  Manual tunnels may be an option, but are
     highly inconvenient. Support for manual tunnels will be improved.
 
 You can instruct all engines to use ssh, by specifying the ssh server in 
 :file:`ipcontroller-engine.json`:
 
 .. I know this is really JSON, but the example is a subset of Python:
 .. sourcecode:: python
 
     {
       "url":"tcp://192.168.1.123:56951",
       "exec_key":"26f4c040-587d-4a4e-b58b-030b96399584",
       "ssh":"[e-mail]",
       "location":"192.168.1.123"
     }
 
 This will be specified if you give the ``--enginessh=[e-mail]`` argument when
 starting :command:`ipcontroller`.
 
 Or you can specify an ssh server on the command-line when starting an engine::
 
     $> ipengine --profile=foo --ssh=my.login.node
 
 For example, if your system is totally restricted, then all connections will actually be
 loopback, and ssh tunnels will be used to connect engines to the controller::
 
     [node1] $> ipcontroller --enginessh=node1
     [node2] $> ipengine
     [node3] $> ipcluster engines --n=4
 
 Or if you want to start many engines on each node, the command `ipcluster engines --n=4`
 without any configuration is equivalent to running ipengine 4 times.
 
 An example using ipcontroller/engine with ssh
 ---------------------------------------------
 
 No configuration files are necessary to use ipcontroller/engine in an SSH environment
 without a shared filesystem. You simply need to make sure that the controller is listening
 on an interface visible to the engines, and move the connection file from the controller to
 the engines.
 
 1. start the controller, listening on an ip-address visible to the engine machines::
 
     [controller.host] $ ipcontroller --ip=192.168.1.16
     
     [IPControllerApp] Using existing profile dir: u'/Users/me/.ipython/profile_default'
     [IPControllerApp] Hub listening on tcp://192.168.1.16:63320 for registration.
     [IPControllerApp] Hub using DB backend: 'IPython.parallel.controller.dictdb.DictDB'
     [IPControllerApp] hub::created hub
     [IPControllerApp] writing connection info to /Users/me/.ipython/profile_default/security/ipcontroller-client.json
     [IPControllerApp] writing connection info to /Users/me/.ipython/profile_default/security/ipcontroller-engine.json
     [IPControllerApp] task::using Python leastload Task scheduler
     [IPControllerApp] Heartmonitor started
     [IPControllerApp] Creating pid file: /Users/me/.ipython/profile_default/pid/ipcontroller.pid
     Scheduler started [leastload]
 
 2. on each engine, fetch the connection file with scp::
 
     [engine.host.n] $ scp controller.host:.ipython/profile_default/security/ipcontroller-engine.json ./
 
    .. note::
 
         The log output of ipcontroller above shows you where the json files were written.
-        They will be in :file:`~/.ipython` (or :file:`~/.config/ipython`) under
+        They will be in :file:`~/.ipython` under
         :file:`profile_default/security/ipcontroller-engine.json`
 
 3. start the engines, using the connection file::
 
     [engine.host.n] $ ipengine --file=./ipcontroller-engine.json
 
 A couple of notes:
 
 * You can avoid having to fetch the connection file every time by adding ``--reuse`` flag
   to ipcontroller, which instructs the controller to read the previous connection file for
   connection info, rather than generate a new one with randomized ports.
 
 * In step 2, if you fetch the connection file directly into the security dir of a profile,
   then you need not specify its path directly, only the profile (assumes the path exists,
   otherwise you must create it first)::
 
-    [engine.host.n] $ scp controller.host:.ipython/profile_default/security/ipcontroller-engine.json ~/.config/ipython/profile_ssh/security/
+    [engine.host.n] $ scp controller.host:.ipython/profile_default/security/ipcontroller-engine.json ~/.ipython/profile_ssh/security/
     [engine.host.n] $ ipengine --profile=ssh
 
   Of course, if you fetch the file into the default profile, no arguments must be passed to
   ipengine at all.
 
 * Note that ipengine *did not* specify the ip argument. In general, it is unlikely for any
   connection information to be specified at the command-line to ipengine, as all of this
   information should be contained in the connection file written by ipcontroller.
 
 Make JSON files persistent
 --------------------------
 
 At fist glance it may seem that that managing the JSON files is a bit
 annoying. Going back to the house and key analogy, copying the JSON around
 each time you start the controller is like having to make a new key every time
 you want to unlock the door and enter your house. As with your house, you want
 to be able to create the key (or JSON file) once, and then simply use it at
 any point in the future.
 
 To do this, the only thing you have to do is specify the `--reuse` flag, so that
 the connection information in the JSON files remains accurate::
 
     $ ipcontroller --reuse
 
 Then, just copy the JSON 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 reuse the file.
 
 .. 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
     obscurity and ii) to multiple controllers on a given host to start and
     automatically use different ports.
 
 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 :file:`IPYTHONDIR/profile_<name>/log`.
 Sending the log files to us will often help us to debug any problems.
 
 
 Configuring `ipcontroller`
 ---------------------------
 
 The IPython Controller takes its configuration from the file :file:`ipcontroller_config.py`
 in the active profile directory.
 
 Ports and addresses
 *******************
 
 In many cases, you will want to configure the Controller's network identity.  By default,
 the Controller listens only on loopback, which is the most secure but often impractical.
 To instruct the controller to listen on a specific interface, you can set the 
 :attr:`HubFactory.ip` trait.  To listen on all interfaces, simply specify:
 
 .. sourcecode:: python
 
     c.HubFactory.ip = '*'
 
 When connecting to a Controller that is listening on loopback or behind a firewall, it may
 be necessary to specify an SSH server to use for tunnels, and the external IP of the
 Controller. If you specified that the HubFactory listen on loopback, or all interfaces,
 then IPython will try to guess the external IP. If you are on a system with VM network
 devices, or many interfaces, this guess may be incorrect. In these cases, you will want
 to specify the 'location' of the Controller. This is the IP of the machine the Controller
 is on, as seen by the clients, engines, or the SSH server used to tunnel connections.
 
 For example, to set up a cluster with a Controller on a work node, using ssh tunnels
 through the login node, an example :file:`ipcontroller_config.py` might contain:
 
 .. sourcecode:: python
 
     # allow connections on all interfaces from engines
     # engines on the same node will use loopback, while engines
     # from other nodes will use an external IP
     c.HubFactory.ip = '*'
     
     # you typically only need to specify the location when there are extra
     # interfaces that may not be visible to peer nodes (e.g. VM interfaces)
     c.HubFactory.location = '10.0.1.5'
     # or to get an automatic value, try this:
     import socket
     hostname = socket.gethostname()
     # alternate choices for hostname include `socket.getfqdn()`
     # or `socket.gethostname() + '.local'`
     
     ex_ip = socket.gethostbyname_ex(hostname)[-1][-1]
     c.HubFactory.location = ex_ip
     
     # now instruct clients to use the login node for SSH tunnels:
     c.HubFactory.ssh_server = 'login.mycluster.net'
 
 After doing this, your :file:`ipcontroller-client.json` file will look something like this:
 
 .. this can be Python, despite the fact that it's actually JSON, because it's
 .. still valid Python
 
 .. sourcecode:: python
 
     {
       "url":"tcp:\/\/*:43447",
       "exec_key":"9c7779e4-d08a-4c3b-ba8e-db1f80b562c1",
       "ssh":"login.mycluster.net",
       "location":"10.0.1.5"
     }
 
 Then this file will be all you need for a client to connect to the controller, tunneling
 SSH connections through login.mycluster.net.
 
 Database Backend
 ****************
 
 The Hub stores all messages and results passed between Clients and Engines.
 For large and/or long-running clusters, it would be unreasonable to keep all
 of this information in memory. For this reason, we have two database backends:
 [MongoDB]_ via PyMongo_, and SQLite with the stdlib :py:mod:`sqlite`.
 
 MongoDB is our design target, and the dict-like model it uses has driven our design. As far
 as we are concerned, BSON can be considered essentially the same as JSON, adding support
 for binary data and datetime objects, and any new database backend must support the same
 data types.
 
 .. seealso::
 
     MongoDB `BSON doc <http://www.mongodb.org/display/DOCS/BSON>`_
 
 To use one of these backends, you must set the :attr:`HubFactory.db_class` trait:
 
 .. sourcecode:: python
 
     # for a simple dict-based in-memory implementation, use dictdb
     # This is the default and the fastest, since it doesn't involve the filesystem
     c.HubFactory.db_class = 'IPython.parallel.controller.dictdb.DictDB'
     
     # To use MongoDB:
     c.HubFactory.db_class = 'IPython.parallel.controller.mongodb.MongoDB'
     
     # and SQLite:
     c.HubFactory.db_class = 'IPython.parallel.controller.sqlitedb.SQLiteDB'
     
     # You can use NoDB to disable the database altogether, in case you don't need
     # to reuse tasks or results, and want to keep memory consumption under control.
     c.HubFactory.db_class = 'IPython.parallel.controller.dictdb.NoDB'
 
 When using the proper databases, you can actually allow for tasks to persist from
 one session to the next by specifying the MongoDB database or SQLite table in
 which tasks are to be stored.  The default is to use a table named for the Hub's Session,
 which is a UUID, and thus different every time.
 
 .. sourcecode:: python
 
     # To keep persistant task history in MongoDB:
     c.MongoDB.database = 'tasks'
     
     # and in SQLite:
     c.SQLiteDB.table = 'tasks'
 
 
 Since MongoDB servers can be running remotely or configured to listen on a particular port,
 you can specify any arguments you may need to the PyMongo `Connection 
 <http://api.mongodb.org/python/1.9/api/pymongo/connection.html#pymongo.connection.Connection>`_:
 
 .. sourcecode:: python
 
     # positional args to pymongo.Connection
     c.MongoDB.connection_args = []
     
     # keyword args to pymongo.Connection
     c.MongoDB.connection_kwargs = {}
 
 But sometimes you are moving lots of data around quickly, and you don't need
 that information to be stored for later access, even by other Clients to this
 same session. For this case, we have a dummy database, which doesn't actually
 store anything. This lets the Hub stay small in memory, at the obvious expense
 of being able to access the information that would have been stored in the
 database (used for task resubmission, requesting results of tasks you didn't
 submit, etc.). To use this backend, simply pass ``--nodb`` to
 :command:`ipcontroller` on the command-line, or specify the :class:`NoDB` class
 in your :file:`ipcontroller_config.py` as described above.
 
 
 .. seealso::
 
     For more information on the database backends, see the :ref:`db backend reference <parallel_db>`.
 
 
 .. _PyMongo: http://api.mongodb.org/python/1.9/
 
 Configuring `ipengine`
 -----------------------
 
 The IPython Engine takes its configuration from the file :file:`ipengine_config.py`
 
 The Engine itself also has some amount of configuration. Most of this
 has to do with initializing MPI or connecting to the controller.
 
 To instruct the Engine to initialize with an MPI environment set up by
 mpi4py, add:
 
 .. sourcecode:: python
 
     c.MPI.use = 'mpi4py'
 
 In this case, the Engine will use our default mpi4py init script to set up
 the MPI environment prior to exection.  We have default init scripts for
 mpi4py and pytrilinos.  If you want to specify your own code to be run
 at the beginning, specify `c.MPI.init_script`.
 
 You can also specify a file or python command to be run at startup of the
 Engine:
 
 .. sourcecode:: python
 
     c.IPEngineApp.startup_script = u'/path/to/my/startup.py'
     
     c.IPEngineApp.startup_command = 'import numpy, scipy, mpi4py'
 
 These commands/files will be run again, after each
 
 It's also useful on systems with shared filesystems to run the engines
 in some scratch directory.  This can be set with:
 
 .. sourcecode:: python
 
     c.IPEngineApp.work_dir = u'/path/to/scratch/'
 
 
 
 .. [MongoDB] MongoDB database http://www.mongodb.org
 
 .. [PBS] Portable Batch System http://www.openpbs.org
 
 .. [SSH] SSH-Agent http://en.wikipedia.org/wiki/ssh-agent