Various small fixes to docs

docs/source/interactive/index.rst

@@ -1,18 +1,19 b''
 ==================================
 Using IPython for interactive work
 ==================================
 
 .. toctree::
    :maxdepth: 2
 
    tutorial
    magics
    plotting
    reference
    shell
    tips
+   python-ipython-diff
 
 .. seealso::
 
     `A Qt Console for Jupyter <http://jupyter.org/qtconsole/>`__
     `The Jupyter Notebook <http://jupyter-notebook.readthedocs.io/en/latest/>`__

docs/source/interactive/python-ipython-diff.rst

@@ -1,249 +1,249 b''
 =================
 Python vs IPython
 =================
 
 This document is meant to highlight the main differences between the Python
 language and what are the specific construct you can do only in IPython.
 
 Unless expressed otherwise all of the construct you will see here will raise a
 ``SyntaxError`` if run in a pure Python shell, or if executing in a Python
 script. 
 
 Each of these features are describe more in details in further part of the documentation.
 
 
 Quick overview:
 ===============
 
 
 All the following construct are valid IPython syntax:
 
 .. code-block:: ipython
 
     In [1]: ?
 
 .. code-block:: ipython
 
     In [1]: ?object
 
 
 .. code-block:: ipython
 
     In [1]: object?
 
 .. code-block:: ipython
 
     In [1]: *pattern*?
 
 .. code-block:: ipython
 
     In [1]: %shell like --syntax
 
 .. code-block:: ipython
 
     In [1]: !ls
 
 .. code-block:: ipython
 
     In [1]: my_files =! ls ~/
     In [1]: for i,file in enumerate(my_file):
        ...:     raw = !echo $file
        ...:     !echo {files[0].upper()} $raw
 
 
 .. code-block:: ipython
 
     In [1]: %%perl magic --function
        ...: @months = ("July", "August", "September");
        ...: print $months[0];
     
 
 Each of these construct is compile by IPython into valid python code and will
 do most of the time what you expect it will do. Let see each of these example
 in more detail.
 
 
 Accessing help
 ==============
 
 As IPython is mostly an interactive shell, the question mark is a simple
 shortcut to get help. A question mark alone will bring up the IPython help:
 
 .. code-block:: ipython
 
     In [1]: ?
 
     IPython -- An enhanced Interactive Python
     =========================================
 
     IPython offers a combination of convenient shell features, special commands
     and a history mechanism for both input (command history) and output (results
     caching, similar to Mathematica). It is intended to be a fully compatible
     replacement for the standard Python interpreter, while offering vastly
     improved functionality and flexibility.
 
     At your system command line, type 'ipython -h' to see the command line
     options available. This document only describes interactive features.
 
     MAIN FEATURES
     -------------
     ...
 
 A single question mark before, or after an object available in current
 namespace will show help relative to this object:
 
 .. code-block:: ipython
 
     In [6]: object?
     Docstring: The most base type
     Type:      type
 
 
 A double question mark will try to pull out more information about the object,
 and if possible display the python source code of this object. 
 
 .. code-block:: ipython
 
     In[1]: import collections
     In[2]: collection.Counter??
 
     Init signature: collections.Counter(*args, **kwds)
     Source:
     class Counter(dict):
         '''Dict subclass for counting hashable items.  Sometimes called a bag
         or multiset.  Elements are stored as dictionary keys and their counts
         are stored as dictionary values.
 
         >>> c = Counter('abcdeabcdabcaba')  # count elements from a string
 
         >>> c.most_common(3)                # three most common elements
         [('a', 5), ('b', 4), ('c', 3)]
         >>> sorted(c)                       # list all unique elements
         ['a', 'b', 'c', 'd', 'e']
         >>> ''.join(sorted(c.elements()))   # list elements with repetitions
         'aaaaabbbbcccdde'
         ...
 
 
 
 If you are looking for an object, the use of wildcards ``*`` in conjunction
 with question mark will allow you to search current namespace for object with
 matching names:
 
 .. code-block:: ipython
 
     In [24]: *int*?
     FloatingPointError
     int
     print
 
 
 Shell Assignment
 ================
 
 
 When doing interactive computing it is common to need to access the underlying shell.
 This is doable through the use of the exclamation mark ``!`` (or bang). 
 
 This allow to execute simple command when present in beginning of line:
 
 .. code-block:: ipython
 
     In[1]: !pwd
     /User/home/
 
 Change directory:
 
 .. code-block:: ipython
 
     In[1]: !cd /var/etc
 
 Or edit file:
 
 .. code-block:: ipython
 
     In[1]: !mvim myfile.txt
 
 
 The line after the bang can call any program installed in the underlying
 shell, and support variable expansion in the form of ``$variable`` or ``{variable}``.
 The later form of expansion supports arbitrary python expression:
 
 .. code-block:: ipython
 
     In[1]: file = 'myfile.txt'
 
     In[2]: !mv $file {file.upper()}
 
 
 The bang can also be present in the right hand side of an assignment, just
 after the equal sign, or separated from it by a white space. In which case the
 standard output of the command after the bang ``!`` will be split out into lines
-in a list-like object (:see:`IPython Slist`) and assign to the left hand side. 
+in a list-like object (:ref:`IPython Slist`) and assign to the left hand side.
 
 This allow you for example to put the list of files of the current working directory in a variable:
 
 .. code-block:: ipython
 
     In[1]: my_files != ls
 
 
 You can combine the different possibilities in for loops, condition, functions...:
 
 .. code-block:: ipython
 
     my_files =! ls ~/
     b = "backup file"
     for i,file in enumerate(my_file):
         raw = !echo $backup $file
         !cp $file {file.split('.')[0]+'.bak'}
 
 
 Magics
 ------
 
 Magics function are often present in the form of shell-like syntax, but are
 under the hood python function. The syntax and assignment possibility are
 similar to the one with the bang (``!``) syntax, but with more flexibility and
 power. Magic function start with a percent sign (``%``) or double percent (``%%``). 
 
 A magic call with a sign percent will act only one line:
 
 .. code-block:: ipython
 
     In[1]: %xmode
     Exception reporting mode: Verbose
 
 And support assignment:
 
 .. code-block:: ipython
 
     In [1]: results = %timeit -r1 -n1 -o list(range(1000))
     1 loops, best of 1: 21.1 µs per loop
 
     In [2]: results
     Out[2]: <TimeitResult : 1 loops, best of 1: 21.1 µs per loop>
 
 Magic with two percent sign can spread over multiple lines, but do not support assignment:
 
 .. code-block:: ipython
 
     In[1]: %%bash
     ...  : echo "My shell is:" $SHELL
     ...  : echo "My disk usage is:"
     ...  : df -h
     My shell is: /usr/local/bin/bash
     My disk usage is:
     Filesystem      Size   Used  Avail Capacity  iused   ifree %iused  Mounted on
     /dev/disk1     233Gi  216Gi   16Gi    94% 56788108 4190706   93%   /
     devfs          190Ki  190Ki    0Bi   100%      656       0  100%   /dev
     map -hosts       0Bi    0Bi    0Bi   100%        0       0  100%   /net
     map auto_home    0Bi    0Bi    0Bi   100%        0       0  100%   /hom
 
 
 Combining it all
 ----------------
 
 ::
 
     find a snippet that combine all that into one thing!

docs/source/interactive/reference.rst

@@ -1,998 +1,998 b''
 =================
 IPython reference
 =================
 
 .. _command_line_options:
 
 Command-line usage
 ==================
 
 You start IPython with the command::
 
     $ ipython [options] files
 
 If invoked with no options, it executes all the files listed in sequence
 and drops you into the interpreter while still acknowledging any options
 you may have set in your 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 :file:`ipython_config.py` or
 :file:`ipython_config_{frontendname}.py`.  Profile directories look like
 :file:`profile_{profilename}` and are typically installed in the :envvar:`IPYTHONDIR` directory,
 which defaults to :file:`$HOME/.ipython`. For Windows users, :envvar:`HOME`
 resolves to :file:`C:\\Users\\{YourUserName}` in most instances.
 
 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 :magic:`cd` magic works just like the OS command of
 the same name::
 
       In [8]: %cd
       /home/fperez
 
 The following uses the builtin :magic:`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
 :magic:`timeit` magic receives both.
   
 If you have 'automagic' enabled (as it is 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
 
 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
 you can freely use variables with the same names as magic commands. If a magic
 command is 'shadowed' by a variable, you will need the explicit ``%`` prefix to
 use it:
 
 .. 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
 
 Line magics, if they return a value, can be assigned to a variable using the syntax
 ``l = %sx ls`` (which in this particular case returns the result of `ls` as a python list).  
 See :ref:`below <manual_capture>` for more information.
 
 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.
 
 .. seealso::
 
    :doc:`magics`
      A list of the line and cell magics available in IPython by default
 
    :ref:`defining_magics`
      How to define and register additional magic functions
 
 
 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:
 
     * :magic:`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.
     * :magic:`pdef` **<object>**: Print the call signature for any callable
       object. If the object is a class, print the constructor information.
     * :magic:`psource` **<object>**: Print (or run through a pager if too long)
       the source code for an object.
     * :magic:`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.
     * :magic:`who`/:magic:`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.
 
 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 the up and down arrow keys (or :kbd:`Ctrl-p`
       and :kbd:`Ctrl-n`) to search through only the history items that match
       what you've typed so far.
    2. Hit :kbd:`Ctrl-r`: to open 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.
 
 IPython will save your input history when it leaves and reload it next
 time you restart it. By default, the history file is named
 :file:`.ipython/profile_{name}/history.sqlite`.
 
 Autoindent
 ++++++++++
 
 Starting with 5.0, IPython uses `prompt_toolkit` in place of ``readline``,
 it thus can recognize lines ending in ':' and indent the next line,
 while also un-indenting automatically after 'raise' or 'return',
 and support real multi-line editing as well as syntactic coloration
 during edition.
 
 This feature does not use the ``readline`` library anymore, so it will
 not honor your :file:`~/.inputrc` configuration (or whatever
 file your :envvar:`INPUTRC` environment variable points to).
 
 In particular if you want to change the input mode to ``vi``, you will need to
 set the ``TerminalInteractiveShell.editing_mode`` configuration  option of IPython.
 
 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 :magic:`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 :magic:`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 :magic:`logoff` and :magic:`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:
 
 Manual capture of command output and magic output
 -------------------------------------------------
 
 You can assign the result of a system command to a Python variable with the
 syntax ``myfiles = !ls``. Similarly, the result of a magic (as long as it returns
 a value) can be assigned to a variable.  For example, the syntax ``myfiles = %sx ls``
 is equivalent to the above system command example (the :magic:`sx` magic runs a shell command
 and captures the output).  Each of these 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 :magic:`sx` magic command without an assignment.
 (However, ``!!`` commands cannot be assigned to a variable.)
 
 The captured list in this example 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
 
 Note that `$$` is used to represent a literal `$`.
 
 System command aliases
 ----------------------
 
 The :magic:`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, :magic:`alias` prints the table of currently
 defined aliases.
 
 The :magic:`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 :magic:`xmode` and :magic:`colors` functions for details.
 
 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 :magic:`rep` magic command that brings a history entry
 up for editing on the next command line.
 
 The following variables always exist:
 
 * _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.
 
 You can also re-execute multiple lines of input easily by using the
 magic :magic:`rerun` or :magic:`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 :magic:`history` 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 :magic:`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 variables always exist:
 
     * [_] (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 configuration option ``InteractiveShell.cache_size``.
 If you set it to 0, output caching is disabled. You can also use the :magic:`reset`
 and :magic:`xdel` magics to clear large items from memory.
 
 Directory history
 -----------------
 
 Your history of visited directories is kept in the global list _dh, and
 the magic :magic:`cd` command can be used to go to any entry in that list. The
 :magic:`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.
 
 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)
 
 .. note::
    This feature is disabled by default. To enable it, use the ``%autocall``
    magic command. The commands below with special prefixes will always work,
    however.
 
 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 ``--->``.
 
 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 :envvar:`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::
 
     import os, IPython
     os.environ['PYTHONSTARTUP'] = ''  # Prevent running this again
     IPython.start_ipython()
     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(argv=[])
 
 at any point in your program.  This will load IPython configuration,
 startup files, and everything, just as if it were a normal IPython session.
 
 It is also possible to embed an IPython shell in a namespace in your Python code.
 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
 
 You can also embed an IPython *kernel*, for use with qtconsole, etc. via
 ``IPython.embed_kernel()``. This should function work the same way, but you can
 connect an external frontend (``ipython qtconsole`` or ``ipython console``),
 rather than interacting with it in the 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 embed_class_long.py.
 It should be fairly self-explanatory:
 
 .. literalinclude:: ../../../examples/Embedding/embed_class_long.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/Embedding/embed_class_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 :magic:`run` command's documentation for more details, including
 how to control where pdb will stop execution first.
 
 For more information on the use of the pdb debugger, see :ref:`debugger-commands`
 in the Python documentation.
 
 IPython extends the debugger with a few useful additions, like coloring of
 tracebacks. The debugger will adopt the color scheme selected for IPython. 
 
 The ``where`` command has also been extended to take as argument the number of
 context line to show. This allows to a many line of context on shallow stack trace:
 
 .. code::
     In [5]: def foo(x):
     ...:     1
     ...:     2
     ...:     3
     ...:     return 1/x+foo(x-1)
     ...:     5
     ...:     6
     ...:     7
     ...:
     
     In[6]: foo(1)
     # ...
     ipdb> where 8
     <ipython-input-6-9e45007b2b59>(1)<module>()
     ----> 1 foo(1)
 
     <ipython-input-5-7baadc3d1465>(5)foo()
         1 def foo(x):
         2     1
         3     2
         4     3
     ----> 5     return 1/x+foo(x-1)
         6     5
         7     6
         8     7
 
     > <ipython-input-5-7baadc3d1465>(5)foo()
         1 def foo(x):
         2     1
         3     2
         4     3
     ----> 5     return 1/x+foo(x-1)
         6     5
         7     6
         8     7
 
 
 And less context on shallower Stack Trace:
 
 .. code::
     ipdb> where 1
     <ipython-input-13-afa180a57233>(1)<module>()
     ----> 1 foo(7)
 
     <ipython-input-5-7baadc3d1465>(5)foo()
     ----> 5     return 1/x+foo(x-1)
 
     <ipython-input-5-7baadc3d1465>(5)foo()
     ----> 5     return 1/x+foo(x-1)
 
     <ipython-input-5-7baadc3d1465>(5)foo()
     ----> 5     return 1/x+foo(x-1)
 
     <ipython-input-5-7baadc3d1465>(5)foo()
     ----> 5     return 1/x+foo(x-1)
 
 
 Post-mortem debugging
 ---------------------
 
 Going into a debugger when an exception occurs 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.
 
 You can use the :magic:`debug` magic after an exception has occurred to start
 post-mortem debugging. IPython can also call debugger every time your code
 triggers an uncaught exception. This feature can be toggled with the :magic:`pdb` magic
 command, or you can start IPython with the ``--pdb`` option.
 
 For a post-mortem debugger in your programs outside IPython,
 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.
 
 .. _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
 :magic:`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
 
-You can also start IPython with an event loop set up using the :option:`--gui`
+You can also start IPython with an event loop set up using the `--gui`
 flag::
 
     $ ipython --gui=qt
 
 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/Embedding` 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/Embedding` 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 to 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 :magic:`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/IPython Kernel/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. Then call it to run each step
 of the demo::
 
     mydemo()
 
 Demo objects can be
 restarted, you can move forward or back skipping blocks, re-execute the
 last block, etc. See the :mod:`IPython.lib.demo` module and the
 :class:`~IPython.lib.demo.Demo` class for details.
 
 Limitations: 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
 :ref:`embedding facilities <Embedding>`.
 
 .. include:: ../links.txt

docs/source/whatsnew/github-stats-3.rst

@@ -1,269 +1,269 b''
 .. _issues_list_3:
 
 Issues closed in the 3.x development cycle
 ==========================================
 
 
 Issues closed in 3.2.1
 ----------------------
 
 GitHub stats for 2015/06/22 - 2015/07/12 (since 3.2)
 
 These lists are automatically generated, and may be incomplete or contain duplicates.
 
 We closed 1 issue and merged 3 pull requests.
-The full list can be seen `on GitHub <https://github.com/ipython/ipython/milestones/3.2.1>`_
+The full list can be seen `on GitHub <https://github.com/ipython/ipython/milestones/3.2.1>`__
 
 The following 5 authors contributed 9 commits.
 
 * Benjamin Ragan-Kelley
 * Matthias Bussonnier
 * Nitin Dahyabhai
 * Sebastiaan Mathot
 * Thomas Kluyver
 
 
 Issues closed in 3.2
 --------------------
 
 GitHub stats for 2015/04/03 - 2015/06/21 (since 3.1)
 
 These lists are automatically generated, and may be incomplete or contain duplicates.
 
 We closed 7 issues and merged 30 pull requests.
-The full list can be seen `on GitHub <https://github.com/ipython/ipython/milestones/3.2>`_
+The full list can be seen `on GitHub <https://github.com/ipython/ipython/milestones/3.2>`__
 
 The following 15 authors contributed 74 commits.
 
 * Benjamin Ragan-Kelley
 * Brian Gough
 * Damián Avila
 * Ian Barfield
 * Jason Grout
 * Jeff Hussmann
 * Jessica B. Hamrick
 * Kyle Kelley
 * Matthias Bussonnier
 * Nicholas Bollweg
 * Randy Lai
 * Scott Sanderson
 * Sylvain Corlay
 * Thomas A Caswell
 * Thomas Kluyver
 
 
 Issues closed in 3.1
 --------------------
 
 GitHub stats for 2015/02/27 - 2015/04/03 (since 3.0)
 
 These lists are automatically generated, and may be incomplete or contain duplicates.
 
 We closed 46 issues and merged 133 pull requests.
 The full list can be seen `on GitHub <https://github.com/ipython/ipython/milestones/3.1>`__.
 
 The following 33 authors contributed 344 commits:
 
 * Abe Guerra
 * Adal Chiriliuc
 * Benjamin Ragan-Kelley
 * Brian Drawert
 * Fernando Perez
 * Gareth Elston
 * Gert-Ludwig Ingold
 * Giuseppe Venturini
 * Jakob Gager
 * Jan Schulz
 * Jason Grout
 * Jessica B. Hamrick
 * Jonathan Frederic
 * Justin Tyberg
 * Lorena Pantano
 * mashenjun
 * Mathieu
 * Matthias Bussonnier
 * Morten Enemark Lund
 * Naveen Nathan
 * Nicholas Bollweg
 * onesandzeroes
 * Patrick Snape
 * Peter Parente
 * RickWinter
 * Robert Smith
 * Ryan Nelson
 * Scott Sanderson
 * Sylvain Corlay
 * Thomas Kluyver
 * tmtabor
 * Wieland Hoffmann
 * Yuval Langer
 
 
 Issues closed in 3.0
 --------------------
 
 GitHub stats for 2014/04/02 - 2015/02/13 (since 2.0)
 
 These lists are automatically generated, and may be incomplete or contain duplicates.
 
 We closed 469 issues and merged 925 pull requests.
 The full list can be seen `on GitHub <https://github.com/ipython/ipython/milestones/3.0>`__.
 
 The following 155 authors contributed 5975 commits.
 
 * A.J. Holyoake
 * abalkin
 * Adam Hodgen
 * Adrian Price-Whelan
 * Amin Bandali
 * Andreas Amann
 * Andrew Dawes
 * Andrew Jesaitis
 * Andrew Payne
 * AnneTheAgile
 * Aron Ahmadia
 * Ben Duffield
 * Benjamin ABEL
 * Benjamin Ragan-Kelley
 * Benjamin Schultz
 * Björn Grüning
 * Björn Linse
 * Blake Griffith
 * Boris Egorov
 * Brian E. Granger
 * bsvh
 * Carlos Cordoba
 * Cedric GESTES
 * cel
 * chebee7i
 * Christoph Gohlke
 * CJ Carey
 * Cyrille Rossant
 * Dale Jung
 * Damián Avila
 * Damon Allen
 * Daniel B. Vasquez
 * Daniel Rocco
 * Daniel Wehner
 * Dav Clark
 * David Hirschfeld
 * David Neto
 * dexterdev
 * Dimitry Kloper
 * dongweiming
 * Doug Blank
 * drevicko
 * Dustin Rodriguez
 * Eric Firing
 * Eric Galloway
 * Erik M. Bray
 * Erik Tollerud
 * Ezequiel (Zac) Panepucci
 * Fernando Perez
 * foogunlana
 * Francisco de la Peña
 * George Titsworth
 * Gordon Ball
 * gporras
 * Grzegorz Rożniecki
 * Helen ST
 * immerrr
 * Ingolf Becker
 * Jakob Gager
 * James Goppert
 * James Porter
 * Jan Schulz
 * Jason Goad
 * Jason Gors
 * Jason Grout
 * Jason Newton
 * jdavidheiser
 * Jean-Christophe Jaskula
 * Jeff Hemmelgarn
 * Jeffrey Bush
 * Jeroen Demeyer
 * Jessica B. Hamrick
 * Jessica Frazelle
 * jhemmelg
 * Jim Garrison
 * Joel Nothman
 * Johannes Feist
 * John Stowers
 * John Zwinck
 * jonasc
 * Jonathan Frederic
 * Juergen Hasch
 * Julia Evans
 * Justyna Ilczuk
 * Jörg Dietrich
 * K.-Michael Aye
 * Kalibri
 * Kester Tong
 * Kyle Kelley
 * Kyle Rawlins
 * Lev Abalkin
 * Manuel Riel
 * Martin Bergtholdt
 * Martin Spacek
 * Mateusz Paprocki
 * Mathieu
 * Matthias Bussonnier
 * Maximilian Albert
 * mbyt
 * MechCoder
 * Mohan Raj Rajamanickam
 * mvr
 * Narahari
 * Nathan Goldbaum
 * Nathan Heijermans
 * Nathaniel J. Smith
 * ncornette
 * Nicholas Bollweg
 * Nick White
 * Nikolay Koldunov
 * Nile Geisinger
 * Olga Botvinnik
 * Osada Paranaliyanage
 * Pankaj Pandey
 * Pascal Bugnion
 * patricktokeeffe
 * Paul Ivanov
 * Peter Odding
 * Peter Parente
 * Peter Würtz
 * Phil Elson
 * Phillip Nordwall
 * Pierre Gerold
 * Pierre Haessig
 * Raffaele De Feo
 * Ramiro Gómez
 * Reggie Pierce
 * Remi Rampin
 * Renaud Richardet
 * Richard Everson
 * Scott Sanderson
 * Silvia Vinyes
 * Simon Guillot
 * Spencer Nelson
 * Stefan Zimmermann
 * Steve Chan
 * Steven Anton
 * Steven Silvester
 * sunny
 * Susan Tan
 * Sylvain Corlay
 * Tarun Gaba
 * Thomas Ballinger
 * Thomas Kluyver
 * Thomas Robitaille
 * Thomas Spura
 * Tobias Oberstein
 * Torsten Bittner
 * unknown
 * v923z
 * vaibhavsagar
 * W. Trevor King
 * weichm
 * Xiuming Chen
 * Yaroslav Halchenko
 * zah

docs/source/whatsnew/index.rst

@@ -1,43 +1,44 b''
 .. Developers should add in this file, during each release cycle, information
 .. about important changes they've made, in a summary format that's meant for
 .. end users.  For each release we normally have three sections: features,  bug
 .. fixes and api breakage.
 .. Please remember to credit the authors of the contributions by name,
 .. especially when they are new users or developers who do not regularly
 .. participate  in IPython's development.
 
 .. _whatsnew_index:
 
 =====================
 What's new in IPython
 =====================
 
 This section documents the changes that have been made in various versions of
 IPython. Users should consult these pages to learn about new features, bug
 fixes and backwards incompatibilities. Developers should summarize the
 development work they do here in a user friendly format.
 
 .. toctree::
    :maxdepth: 1
 
+   version5
    development
    version4
    github-stats-4
    version3
    github-stats-3
    version3_widget_migration
    version2.0
    github-stats-2.0
    version1.0
    github-stats-1.0
    version0.13
    github-stats-0.13
    version0.12
    github-stats-0.12
    version0.11
    github-stats-0.11
    version0.10
    version0.9
    version0.8