Merge pull request #3812 from minrk/start_ip

Describe differences between start_ipython and embed

in what's new and the embedding section

closes  #3726

docs/source/interactive/reference.rst

@@ -1,1155 +1,1165
 =================
 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.
 
 
 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
 =================
 
-It is possible to start an IPython instance inside your own Python
-programs. This allows you to evaluate dynamically the state of your
-code, operate with your variables, analyze them, etc. Note however that
+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, trying to embed IPython from inside IPython causes problems. Run
-  the code samples below outside IPython.
+  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:`docs/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:`docs/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.
 
 .. _Matplotlib: http://matplotlib.sourceforge.net
 
 .. _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.
 

docs/source/whatsnew/version1.0.rst

@@ -1,295 +1,298
 ============
  1.0 Series
 ============
 
 Release 1.0
 ===========
 
 .. note::
 
    This document describes a pre-release version of IPython.
 
 IPython 1.0 requires Python ≥ 2.6.5 or ≥ 3.2.1.
 It does not support Python 3.0, 3.1, or 2.5.
 
 This is a big release.  The principal milestone is the addition of :mod:`IPython.nbconvert`,
 but there has been a great deal of work improving all parts of IPython as well.
 
 The previous version (0.13) was released on June 30, 2012,
 and in this development cycle we had:
 
 - ~12 months of work.
 - ~700 pull requests merged.
 - ~600 issues closed (non-pull requests).
 - contributions from ~150 authors.
 - ~4000 commits.
 
 The amount of work included in this release is so large that we can only cover
 here the main highlights; please see our :ref:`detailed release statistics
 <issues_list_100>` for links to every issue and pull request closed on GitHub
 as well as a full list of individual contributors.
 It includes
 
 Reorganization
 --------------
 
 There have been two major reorganizations in IPython 1.0:
 
 - Added :mod:`IPython.kernel` for all kernel-related code.
   This means that :mod:`IPython.zmq` has been removed,
   and much of it is now in :mod:`IPython.kernel.zmq`,
   some of it being in the top-level :mod:`IPython.kernel`.
 - We have removed the `frontend` subpackage,
   as it caused unnecessary depth.  So what was :mod:`IPython.frontend.qt`
   is now :mod:`IPython.qt`, and so on.  The one difference is that
   the notebook has been further flattened, so that
   :mod:`IPython.frontend.html.notebook` is now just `IPython.html`.
   There is a shim module, so :mod:`IPython.frontend` is still
   importable in 1.0, but there will be a warning.
 - The IPython sphinx directives are now installed in :mod:`IPython.sphinx`,
   so they can be imported by other projects.
 
 
 Public APIs
 -----------
 
 For the first time since 0.10 (sorry, everyone),
 there is an official public API for starting IPython:
 
 .. sourcecode:: python
 
     from IPython import start_ipython
     start_ipython()
 
 This is what packages should use that start their own IPython session,
 but don't actually want embedded IPython (most cases).
+:func:`IPython.embed()` is used for embedding IPython into the calling namespace,
+similar to calling :func:`Pdb.set_trace`, whereas :func:`start_ipython`
+will start a plain IPython session, loading config and startup files as normal.
 
 We also have added:
 
 .. sourcecode:: python
 
     from IPython import get_ipython
 
 
 Which is a *library* function for getting the current IPython instance,
 and will return ``None`` if no IPython instance is running.
 This is the official way to check whether your code is called from inside an IPython session.
 If you want to check for IPython without unnecessarily importing IPython,
 use this function:
 
 .. sourcecode:: python
 
     def get_ipython():
         """return IPython instance if there is one, None otherwise"""
         import sys
         if "IPython" in sys.modules:
             import IPython
             return IPython.get_ipython()
 
 Core
 ----
 
 - The input transformation framework has been reworked. This fixes some corner
   cases, and adds more flexibility for projects which use IPython, like SymPy &
   SAGE. For more details, see :doc:`/config/inputtransforms`.
 - Exception types can now be displayed with a custom traceback, by defining a
   ``_render_traceback_()`` method which returns a list of strings, each
   containing one line of the traceback.
 - A new command, ``ipython history trim`` can be used to delete everything but
   the last 1000 entries in the history database.
 - ``__file__`` is defined in both config files at load time,
   and ``.ipy`` files executed with ``%run``.
 - ``%logstart`` and ``%logappend`` are no longer broken.
 - Add glob expansion for ``%run``, e.g. ``%run -g script.py *.txt``.
 - Expand variables (``$foo``) in Cell Magic argument line.
 - By default, :command:`iptest` will exclude various slow tests.
   All tests can be run with :command:`iptest --all`.
 - SQLite history can be disabled in the various cases that it does not behave well.
 - ``%edit`` works on interactively defined variables.
 - editor hooks have been restored from quarantine, enabling TextMate as editor,
   etc.
 - The env variable PYTHONSTARTUP is respected by IPython.
 - A ``%matplotlib`` magic is added, which is like the old ``%pylab`` magic,
   but it does not import anything to the interactive namespace.
   It is recommended that users switch to ``%matplotlib`` and explicit imports.
 
 
 Backwards incompatible changes
 ******************************
 
 - Calling :meth:`InteractiveShell.prefilter` will no longer perform static
   transformations - the processing of escaped commands such as ``%magic`` and
   ``!system``, and stripping input prompts from code blocks. This functionality
   was duplicated in :mod:`IPython.core.inputsplitter`, and the latter version
   was already what IPython relied on. A new API to transform input will be ready
   before release.
 - Functions from :mod:`IPython.lib.inputhook` to control integration with GUI
   event loops are no longer exposed in the top level of :mod:`IPython.lib`.
   Code calling these should make sure to import them from
   :mod:`IPython.lib.inputhook`.
 - For all kernel managers, the ``sub_channel`` attribute has been renamed to
   ``iopub_channel``.
 - Users on Python versions before 2.6.6, 2.7.1 or 3.2 will now need to call
   :func:`IPython.utils.doctestreload.doctest_reload` to make doctests run 
   correctly inside IPython. Python releases since those versions are unaffected.
   For details, see :ghpull:`3068` and `Python issue 8048 <http://bugs.python.org/issue8048>`_.
 - The ``InteractiveShell.cache_main_mod()`` method has been removed, and
   :meth:`~IPython.core.interactiveshell.InteractiveShell.new_main_mod` has a
   different signature, expecting a filename where earlier versions expected
   a namespace. See :ghpull:`3555` for details.
 - The short-lived plugin system has been removed. Extensions are the way to go.
 
 
 .. _nbconvert1:
 
 NbConvert
 ---------
 
 The major milestone for IPython 1.0 is the addition of :mod:`IPython.nbconvert` - tools for converting
 IPython notebooks to various other formats.
 
 .. warning::
 
     nbconvert is α-level preview code in 1.0
 
 To use nbconvert to convert various file formats::
 
     ipython nbconvert --format full_html *.ipynb
 
 See ``ipython nbconvert --help`` for more information.
 nbconvert depends on `pandoc`_ for many of the translations to and from various formats.
 
 .. _pandoc: http://johnmacfarlane.net/pandoc/
 
 Notebook
 --------
 
 Major changes to the IPython Notebook in 1.0:
 
 - The notebook is now autosaved, by default at an interval of two minutes.
   When you press 'save' or Ctrl-S, a *checkpoint* is made, in a hidden folder.
   This checkpoint can be restored, so that the autosave model is strictly safer
   than traditional save. If you change nothing about your save habits,
   you will always have a checkpoint that you have written,
   and an autosaved file that is kept up to date.
 - You can load custom javascript and CSS in the notebook by editing the files
   :file:`$(ipython locate profile)/static/custom/custom.{js,css}`.
 - Add ``%%html``, ``%%svg``, ``%%javascript``, and ``%%latex`` cell magics
   for writing raw output in notebook cells.
 - add a redirect handler and anchors on heading cells, so you can link
   across notebooks, directly to heading cells in other notebooks.
 - Images support width and height metadata,
   and thereby 2x scaling (retina support).
 - ``_repr_foo_`` methods can return a tuple of (data, metadata),
   where metadata is a dict containing metadata about the displayed object.
   This is used to set size, etc. for retina graphics. To enable retina matplotlib figures,
   simply set ``InlineBackend.figure_format = 'retina'`` for 2x PNG figures.
 - Add display.FileLink and FileLinks for quickly displaying HTML links to local files.
 - Cells have metadata, which can be edited via cell toolbars.
   This metadata can be used by external code (e.g. reveal.js or exporters),
   when examining the notebook.
 - Fix an issue parsing LaTeX in markdown cells, which required users to type ``\\\``,
   instead of ``\\``.
 - Notebook templates are rendered with Jinja instead of Tornado.
 - ``%%file`` has been renamed ``%%writefile`` (``%%file``) is deprecated.
 - ANSI (and VT100) color parsing has been improved in both performance and
   supported values.
 - The static files path can be found as ``IPython.html.DEFAULT_STATIC_FILES_PATH``,
   which may be changed by package managers.
 - The notebook supports :func:`raw_input`, and thus also ``%debug``.
 - IPython's CSS is installed in :file:`static/css/style.min.css`
   (all style, including bootstrap), and :file:`static/css/ipython.min.css`,
   which only has IPython's own CSS. The latter file should be useful for embedding
   IPython notebooks in other pages, blogs, etc.
 - The Print View has been removed. Users are encouraged to test :ref:`ipython
   nbconvert <nbconvert1>` to generate a static view.
 
 Javascript Components
 *********************
 
 The javascript components used in the notebook have been updated significantly.
 
 - updates to jQuery (2.0) and jQueryUI (1.10)
 - Update CodeMirror to 3.14
 - Twitter Bootstrap (2.3) for layout
 - Font-Awesome (3.1) for icons
 - highlight.js (7.3) for syntax highlighting
 - marked (0.2.8) for markdown rendering
 - require.js (2.1) for loading javascript
 
 Some relevant changes that are results of this:
 
 - Markdown cells now support GitHub-flavored Markdown (GFM),
   which includes ``\`\`\`python`` code blocks and tables.
 - Notebook UI behaves better on more screen sizes.
 - Various code cell input issues have been fixed.
 
 
 Kernel
 ------
 
 The kernel code has been substantially reorganized.
 
 New features in the kernel:
 
 - Kernels support ZeroMQ IPC transport, not just TCP
 - The message protocol has added a top-level metadata field,
   used for information about messages.
 - Add a `data_pub` message that functions much like `display_pub`,
   but publishes raw (usually pickled) data, rather than representations.
 - Ensure that ``sys.stdout.encoding`` is defined in Kernels.
 - Stdout from forked subprocesses should be forwarded to frontends (instead of crashing).
 
 IPEP 13
 *******
 
 The KernelManager has been split into a :class:`~.KernelManager` and a :class:`~.KernelClient`.
 The Manager owns a kernel and starts / signals / restarts it. There is always zero or one
 KernelManager per Kernel.  Clients communicate with Kernels via zmq channels,
 and there can be zero-to-many Clients connected to a Kernel at any given time.
 
 The KernelManager now automatically restarts the kernel when it dies,
 rather than requiring user input at the notebook or QtConsole UI
 (which may or may not exist at restart time).
 
 In-process kernels
 ******************
 
 The Python-language frontends, particularly the Qt console, may now communicate
 with in-process kernels, in addition to the traditional out-of-process
 kernels. An in-process kernel permits direct access to the kernel namespace,
 which is necessary in some applications. It should be understood, however, that
 the in-process kernel is not robust to bad user input and will block the main
 (GUI) thread while executing. Developers must decide on a case-by-case basis
 whether this tradeoff is appropriate for their application.
 
 
 
 Parallel
 --------
 
 IPython.parallel has had some refactoring as well.  
 There are many improvements and fixes, but these are the major changes:
 
 - Connections have been simplified. All ports and the serialization in use
   are written to the connection file, rather than the initial two-stage system.
 - Serialization has been rewritten, fixing many bugs and dramatically improving
   performance serializing large containers.
 - Load-balancing scheduler performance with large numbers of tasks has been dramatically improved.
 - There should be fewer (hopefully zero) false-positives for engine failures.
 - Increased compatibility with various use cases that produced serialization / argument errors
   with map, etc.
 - The controller can attempt to resume operation if it has crashed,
   by passing ``ipcontroller --restore``.
 - Engines can monitor the Hub heartbeat, and shutdown if the Hub disappears for too long.
 - add HTCondor support in launchers
 
 
 QtConsole
 ---------
 
 Various fixes, including improved performance with lots of text output,
 and better drag and drop support.
 The initial window size of the qtconsole is now configurable via ``IPythonWidget.width``
 and ``IPythonWidget.height``.