docs/source/interactive/reference.rst

=================
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
exits. If you add the ``-i`` flag, it 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 --help-all
    <...snip...>
    --matplotlib=<CaselessStrEnum> (InteractiveShellApp.matplotlib)
        Default: None
        Choices: ['auto', 'gtk', 'gtk3', 'inline', 'nbagg', 'notebook', 'osx', 'qt', 'qt4', 'qt5', 'tk', 'wx']
        Configure matplotlib for interactive use with the default matplotlib
        backend.
    <...snip...>


Indicate that the following::

   $ 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??``.


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.
For information on setting configuration options when running IPython from
python, see :ref:`configure_start_ipython`.

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
======================

IPython has excellent support for working interactively with Graphical User
Interface (GUI) toolkits, such as wxPython, PyQt4/PySide, PyGTK and Tk. This is
implemented by running the toolkit's event loop while IPython is waiting for
input.

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 include ``wx``, ``qt``, ``qt5``, ``gtk``, ``gtk3`` 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 `--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 integrate additional event loops with IPython, see
:doc:`/config/eventloops`.

When running inside IPython with an integrated event loop, a GUI application
should *not* start its own event loop. 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/IPython Kernel/gui/` 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