Fix example inclusions in docs

docs/source/interactive/reference.rst

@@ -1,1093 +1,1093 b''
 =================
 IPython reference
 =================
 
 .. _command_line_options:
 
 Command-line usage
 ==================
 
 You start IPython with the command::
 
     $ ipython [options] files
 
 If invoked with no options, it executes all the files listed in sequence
 and drops you into the interpreter while still acknowledging any options
 you may have set in your ipython_config.py. This behavior is different from
 standard Python, which when called as python -i will only execute one
 file and ignore your configuration setup.
 
 Please note that some of the configuration options are not available at
 the command line, simply because they are not practical here. Look into
 your configuration files for details on those. There are separate configuration 
 files for each profile, and the files look like :file:`ipython_config.py` or
 :file:`ipython_config_{frontendname}.py`.  Profile directories look like
 :file:`profile_{profilename}` and are typically installed in the :envvar:`IPYTHONDIR` directory,
 which defaults to :file:`$HOME/.ipython`. For Windows users, :envvar:`HOME`
 resolves to :file:`C:\\Users\\{YourUserName}` in most instances.
 
 Command-line Options
 --------------------
 
 To see the options IPython accepts, use ``ipython --help`` (and you probably
 should run the output through a pager such as ``ipython --help | less`` for
 more convenient reading).  This shows all the options that have a single-word
 alias to control them, but IPython lets you configure all of its objects from
 the command-line by passing the full class name and a corresponding value; type
 ``ipython --help-all`` to see this full list.  For example::
 
   ipython --matplotlib qt
 
 is equivalent to::
 
   ipython --TerminalIPythonApp.matplotlib='qt'
 
 Note that in the second form, you *must* use the equal sign, as the expression
 is evaluated as an actual Python assignment.  While in the above example the
 short form is more convenient, only the most common options have a short form,
 while any configurable variable in IPython can be set at the command-line by
 using the long form.  This long form is the same syntax used in the
 configuration files, if you want to set these options permanently.
 
 
 Interactive use
 ===============
 
 IPython is meant to work as a drop-in replacement for the standard interactive
 interpreter. As such, any code which is valid python should execute normally
 under IPython (cases where this is not true should be reported as bugs). It
 does, however, offer many features which are not available at a standard python
 prompt. What follows is a list of these.
 
 
 Caution for Windows users
 -------------------------
 
 Windows, unfortunately, uses the '\\' character as a path separator. This is a
 terrible choice, because '\\' also represents the escape character in most
 modern programming languages, including Python. For this reason, using '/'
 character is recommended if you have problems with ``\``.  However, in Windows
 commands '/' flags options, so you can not use it for the root directory. This
 means that paths beginning at the root must be typed in a contrived manner
 like: ``%copy \opt/foo/bar.txt \tmp``
 
 .. _magic:
 
 Magic command system
 --------------------
 
 IPython will treat any line whose first character is a % as a special
 call to a 'magic' function. These allow you to control the behavior of
 IPython itself, plus a lot of system-type features. They are all
 prefixed with a % character, but parameters are given without
 parentheses or quotes.
 
 Lines that begin with ``%%`` signal a *cell magic*: they take as arguments not
 only the rest of the current line, but all lines below them as well, in the
 current execution block.  Cell magics can in fact make arbitrary modifications
 to the input they receive, which need not even be valid Python code at all.
 They receive the whole block as a single string.
 
 As a line magic example, the ``%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
 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
 
 .. _defining_magics:
 
 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 __future__ import print_function
     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:", list(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 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
 ++++++++++
 
 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 :envvar:`INPUTRC` environment 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:`.inputrc` file. IPython respects this, and you can also customise readline
 by setting the following :doc:`configuration </config/intro>` options:
 
     * ``InteractiveShell.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.
     * ``InteractiveShell.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.
 
 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 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 %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 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 ``%reset``
 and ``%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 %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.
 
 Callable objects (i.e. functions, methods, etc) can be invoked like this
 (notice the commas between the arguments)::
 
     In [1]: callable_ob arg1, arg2, arg3
     ------> callable_ob(arg1, arg2, arg3)
 
 .. note::
    This feature is disabled by default. To enable it, use the ``%autocall``
    magic command. The commands below with special prefixes will always work,
    however.
 
 You can force automatic parentheses by using '/' as the first character
 of a line. For example::
 
     In [2]: /globals # becomes 'globals()'
 
 Note that the '/' MUST be the first character on the line! This won't work::
 
     In [3]: print /globals # syntax error
 
 In most cases the automatic algorithm should work, so you should rarely
 need to explicitly invoke /. One notable exception is if you are trying
 to call a function with a list of tuples as arguments (the parenthesis
 will confuse IPython)::
 
     In [4]: zip (1,2,3),(4,5,6) # won't work
 
 but this will work::
 
     In [5]: /zip (1,2,3),(4,5,6) 
     ------> zip ((1,2,3),(4,5,6))  
     Out[5]: [(1, 4), (2, 5), (3, 6)]
 
 IPython tells you that it has altered your command line by displaying
 the new command line preceded by ``--->``.
 
 You can force automatic quoting of a function's arguments by using ``,``
 or ``;`` as the first character of a line. For example::
 
     In [1]: ,my_function /home/me  # becomes my_function("/home/me")
 
 If you use ';' the whole argument is quoted as a single string, while ',' splits
 on whitespace::
 
     In [2]: ,my_function a b c    # becomes my_function("a","b","c")
 
     In [3]: ;my_function a b c    # becomes my_function("a b c")
 
 Note that the ',' or ';' MUST be the first character on the line! This
 won't work::
 
     In [4]: x = ,my_function /home/me # syntax error
 
 IPython as your default Python environment
 ==========================================
 
 Python honors the environment variable :envvar:`PYTHONSTARTUP` and will
 execute at startup the file referenced by this variable. If you put the
 following code at the end of that file, then IPython will be your working
 environment anytime you start Python::
 
     import os, IPython
     os.environ['PYTHONSTARTUP'] = ''  # Prevent running this again
     IPython.start_ipython()
     raise SystemExit
 
 The ``raise SystemExit`` is needed to exit Python when
 it finishes, otherwise you'll be back at the normal Python ``>>>``
 prompt.
 
 This is probably useful to developers who manage multiple Python
 versions and don't want to have correspondingly multiple IPython
 versions. Note that in this mode, there is no way to pass IPython any
 command-line options, as those are trapped first by Python itself.
 
 .. _Embedding:
 
 Embedding IPython
 =================
 
 You can start a regular IPython session with
 
 .. sourcecode:: python
 
     import IPython
     IPython.start_ipython(argv=[])
 
 at any point in your program.  This will load IPython configuration,
 startup files, and everything, just as if it were a normal IPython session.
 
 It is also possible to embed an IPython shell in a namespace in your Python code.
 This allows you to evaluate dynamically the state of your code,
 operate with your variables, analyze them, etc. Note however that
 any changes you make to values while in the shell do not propagate back
 to the running code, so it is safe to modify your values because you
 won't break your code in bizarre ways by doing so.
 
 .. note::
 
   At present, embedding IPython cannot be done from inside IPython.
   Run the code samples below outside IPython.
 
 This feature allows you to easily have a fully functional python
 environment for doing object introspection anywhere in your code with a
 simple function call. In some cases a simple print statement is enough,
 but if you need to do more detailed analysis of a code fragment this
 feature can be very valuable.
 
 It can also be useful in scientific computing situations where it is
 common to need to do some automatic, computationally intensive part and
 then stop to look at data, plots, etc.
 Opening an IPython instance will give you full access to your data and
 functions, and you can resume program execution once you are done with
 the interactive part (perhaps to stop again later, as many times as
 needed).
 
 The following code snippet is the bare minimum you need to include in
 your Python programs for this to work (detailed examples follow later)::
 
     from IPython import embed
 
     embed() # this call anywhere in your program will start IPython
 
 You can also embed an IPython *kernel*, for use with qtconsole, etc. via
 ``IPython.embed_kernel()``. This should function work the same way, but you can
 connect an external frontend (``ipython qtconsole`` or ``ipython console``),
 rather than interacting with it in the terminal.
 
 You can run embedded instances even in code which is itself being run at
 the IPython interactive prompt with '%run <filename>'. Since it's easy
 to get lost as to where you are (in your top-level IPython or in your
 embedded one), it's a good idea in such cases to set the in/out prompts
 to something different for the embedded instances. The code examples
 below illustrate this.
 
 You can also have multiple IPython instances in your program and open
 them separately, for example with different options for data
 presentation. If you close and open the same instance multiple times,
 its prompt counters simply continue from each execution to the next.
 
 Please look at the docstrings in the :mod:`~IPython.frontend.terminal.embed` 
 module for more details on the use of this system.
 
 The following sample file illustrating how to use the embedding
-functionality is provided in the examples directory as example-embed.py.
+functionality is provided in the examples directory as embed_class_long.py.
 It should be fairly self-explanatory:
 
-.. literalinclude:: ../../../examples/core/example-embed.py
+.. 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/core/example-embed-short.py
+.. 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 %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.
 
 
 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 ``%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 %pdb magic
 command, or you can start IPython with the ``--pdb`` option.
 
 For a post-mortem debugger in your programs outside IPython,
 put the following lines toward the top of your 'main' routine::
 
     import sys
     from IPython.core import ultratb
     sys.excepthook = ultratb.FormattedTB(mode='Verbose',
     color_scheme='Linux', call_pdb=1)
 
 The mode keyword can be either 'Verbose' or 'Plain', giving either very
 detailed or normal tracebacks respectively. The color_scheme keyword can
 be one of 'NoColor', 'Linux' (default) or 'LightBG'. These are the same
 options which can be set in IPython with ``--colors`` and ``--xmode``.
 
 This will give any of your programs detailed, colored tracebacks with
 automatic invocation of pdb.
 
 .. _pasting_with_prompts:
 
 Pasting of code starting with Python or IPython prompts
 =======================================================
 
 IPython is smart enough to filter out input prompts, be they plain Python ones
 (``>>>`` and ``...``) or IPython ones (``In [N]:`` and ``...:``).  You can
 therefore copy and paste from existing interactive sessions without worry.
 
 The following is a 'screenshot' of how things work, copying an example from the
 standard Python tutorial::
 
     In [1]: >>> # Fibonacci series:
 
     In [2]: ... # the sum of two elements defines the next
 
     In [3]: ... a, b = 0, 1
 
     In [4]: >>> while b < 10:
        ...:     ...     print(b)
        ...:     ...     a, b = b, a+b
        ...:     
     1
     1
     2
     3
     5
     8
 
 And pasting from IPython sessions works equally well::
 
     In [1]: In [5]: def f(x):
        ...:        ...:     "A simple function"
        ...:        ...:     return x**2
        ...:    ...: 
 
     In [2]: f(3)
     Out[2]: 9
 
 .. _gui_support:
 
 GUI event loop support
 ======================
 
 .. versionadded:: 0.11
     The ``%gui`` magic and :mod:`IPython.lib.inputhook`.
 
 IPython has excellent support for working interactively with Graphical User
 Interface (GUI) toolkits, such as wxPython, PyQt4/PySide, PyGTK and Tk. This is
 implemented using Python's builtin ``PyOSInputHook`` hook. This implementation
 is extremely robust compared to our previous thread-based version. The
 advantages of this are:
 
 * GUIs can be enabled and disabled dynamically at runtime.
 * The active GUI can be switched dynamically at runtime.
 * In some cases, multiple GUIs can run simultaneously with no problems.
 * There is a developer API in :mod:`IPython.lib.inputhook` for customizing 
   all of these things.
 
 For users, enabling GUI event loop integration is simple.  You simple use the
 ``%gui`` magic as follows::
 
     %gui [GUINAME]
 
 With no arguments, ``%gui`` removes all GUI support.  Valid ``GUINAME``
 arguments are ``wx``, ``qt``, ``gtk`` and ``tk``.
 
 Thus, to use wxPython interactively and create a running :class:`wx.App`
 object, do::
 
     %gui wx
 
 You can also start IPython with an event loop set up using the :option:`--gui`
 flag::
 
     $ ipython --gui=qt
 
 For information on IPython's matplotlib_ integration (and the ``matplotlib``
 mode) see :ref:`this section <matplotlib_support>`.
 
 For developers that want to use IPython's GUI event loop integration in the
 form of a library, these capabilities are exposed in library form in the
 :mod:`IPython.lib.inputhook` and :mod:`IPython.lib.guisupport` modules.
 Interested developers should see the module docstrings for more information,
 but there are a few points that should be mentioned here.
 
 First, the ``PyOSInputHook`` approach only works in command line settings 
 where readline is activated.  The integration with various eventloops
 is handled somewhat differently (and more simply) when using the standalone
 kernel, as in the qtconsole and notebook.
 
 Second, when using the ``PyOSInputHook`` approach, a GUI application should
 *not* start its event loop. Instead all of this is handled by the
 ``PyOSInputHook``. This means that applications that are meant to be used both
 in IPython and as standalone apps need to have special code to detects how the
 application is being run. We highly recommend using IPython's support for this.
 Since the details vary slightly between toolkits, we point you to the various
 examples in our source directory :file:`examples/lib` that demonstrate
 these capabilities.
 
 Third, unlike previous versions of IPython, we no longer "hijack" (replace
 them with no-ops) the event loops. This is done to allow applications that
 actually need to run the real event loops to do so. This is often needed to
 process pending events at critical points.
 
 Finally, we also have a number of examples in our source directory
 :file:`examples/lib` that demonstrate these capabilities.
 
 PyQt and PySide
 ---------------
 
 .. attempt at explanation of the complete mess that is Qt support
 
 When you use ``--gui=qt`` or ``--matplotlib=qt``, IPython can work with either
 PyQt4 or PySide.  There are three options for configuration here, because
 PyQt4 has two APIs for QString and QVariant - v1, which is the default on
 Python 2, and the more natural v2, which is the only API supported by PySide.
 v2 is also the default for PyQt4 on Python 3.  IPython's code for the QtConsole
 uses v2, but you can still use any interface in your code, since the
 Qt frontend is in a different process.
 
 The default will be to import PyQt4 without configuration of the APIs, thus
 matching what most applications would expect. It will fall back of PySide if
 PyQt4 is unavailable.
 
 If specified, IPython will respect the environment variable ``QT_API`` used
 by ETS.  ETS 4.0 also works with both PyQt4 and PySide, but it requires
 PyQt4 to use its v2 API.  So if ``QT_API=pyside`` PySide will be used,
 and if ``QT_API=pyqt`` then PyQt4 will be used *with the v2 API* for
 QString and QVariant, so ETS codes like MayaVi will also work with IPython.
 
 If you launch IPython in matplotlib mode with ``ipython --matplotlib=qt``,
 then IPython will ask matplotlib which Qt library to use (only if QT_API is
 *not set*), via the 'backend.qt4' rcParam.  If matplotlib is version 1.0.1 or
 older, then IPython will always use PyQt4 without setting the v2 APIs, since
 neither v2 PyQt nor PySide work.
 
 .. warning::
 
     Note that this means for ETS 4 to work with PyQt4, ``QT_API`` *must* be set
     to work with IPython's qt integration, because otherwise PyQt4 will be
     loaded in an incompatible mode.
     
     It also means that you must *not* have ``QT_API`` set if you want to
     use ``--gui=qt`` with code that requires PyQt4 API v1.
 
 
 .. _matplotlib_support:
 
 Plotting with matplotlib
 ========================
 
 matplotlib_ provides high quality 2D and 3D plotting for Python. matplotlib_
 can produce plots on screen using a variety of GUI toolkits, including Tk,
 PyGTK, PyQt4 and wxPython. It also provides a number of commands useful for
 scientific computing, all with a syntax compatible with that of the popular
 Matlab program.
 
 To start IPython with matplotlib support, use the ``--matplotlib`` switch. If
 IPython is already running, you can run the ``%matplotlib`` magic.  If no
 arguments are given, IPython will automatically detect your choice of
 matplotlib backend.  You can also request a specific backend with
 ``%matplotlib backend``, where ``backend`` must be one of: 'tk', 'qt', 'wx',
 'gtk', 'osx'.  In the web notebook and Qt console, 'inline' is also a valid
 backend value, which produces static figures inlined inside the application
 window instead of matplotlib's interactive figures that live in separate
 windows.
 
 .. _interactive_demos:
 
 Interactive demos with IPython
 ==============================
 
 IPython ships with a basic system for running scripts interactively in
 sections, useful when presenting code to audiences. A few tags embedded
 in comments (so that the script remains valid Python code) divide a file
 into separate blocks, and the demo can be run one block at a time, with
 IPython printing (with syntax highlighting) the block before executing
 it, and returning to the interactive prompt after each block. The
 interactive namespace is updated after each block is run with the
 contents of the demo's namespace.
 
 This allows you to show a piece of code, run it and then execute
 interactively commands based on the variables just created. Once you
 want to continue, you simply execute the next block of the demo. The
 following listing shows the markup necessary for dividing a script into
 sections for execution as a demo:
 
-.. literalinclude:: ../../../examples/lib/example-demo.py
+.. literalinclude:: ../../../examples/IPython Kernel/example-demo.py
     :language: python
 
 In order to run a file as a demo, you must first make a Demo object out
 of it. If the file is named myscript.py, the following code will make a
 demo::
 
     from IPython.lib.demo import Demo
 
     mydemo = Demo('myscript.py')
 
 This creates the mydemo object, whose blocks you run one at a time by
 simply calling the object with no arguments. Then call it to run each step
 of the demo::
 
     mydemo()
 
 Demo objects can be
 restarted, you can move forward or back skipping blocks, re-execute the
 last block, etc. See the :mod:`IPython.lib.demo` module and the
 :class:`~IPython.lib.demo.Demo` class for details.
 
 Limitations: These demos are limited to
 fairly simple uses. In particular, you cannot break up sections within
 indented code (loops, if statements, function definitions, etc.)
 Supporting something like this would basically require tracking the
 internal execution state of the Python interpreter, so only top-level
 divisions are allowed. If you want to be able to open an IPython
 instance at an arbitrary point in a program, you can use IPython's
 :ref:`embedding facilities <Embedding>`.
 
 .. include:: ../links.txt

docs/source/parallel/asyncresult.rst

@@ -1,150 +1,150 b''
 .. _parallel_asyncresult:
 
 ======================
 The AsyncResult object
 ======================
 
 In non-blocking mode, :meth:`apply` submits the command to be executed and
 then returns a :class:`~.AsyncResult` object immediately. The
 AsyncResult object gives you a way of getting a result at a later
 time through its :meth:`get` method, but it also collects metadata
 on execution.
 
 
 Beyond multiprocessing's AsyncResult
 ====================================
 
 .. Note::
 
     The :class:`~.AsyncResult` object provides a superset of the interface in 
     :py:class:`multiprocessing.pool.AsyncResult`.  See the 
     `official Python documentation <http://docs.python.org/library/multiprocessing#multiprocessing.pool.AsyncResult>`_
     for more on the basics of this interface.
 
 Our AsyncResult objects add a number of convenient features for working with
 parallel results, beyond what is provided by the original AsyncResult.
 
 
 get_dict
 --------
 
 First, is :meth:`.AsyncResult.get_dict`, which pulls results as a dictionary
 keyed by engine_id, rather than a flat list.  This is useful for quickly
 coordinating or distributing information about all of the engines.
 
 As an example, here is a quick call that gives every engine a dict showing
 the PID of every other engine:
 
 .. sourcecode:: ipython
 
     In [10]: ar = rc[:].apply_async(os.getpid)
     In [11]: pids = ar.get_dict()
     In [12]: rc[:]['pid_map'] = pids
 
 This trick is particularly useful when setting up inter-engine communication,
 as in IPython's :file:`examples/parallel/interengine` examples.
 
 
 Metadata
 ========
 
 IPython.parallel tracks some metadata about the tasks, which is stored
 in the :attr:`.Client.metadata` dict.  The AsyncResult object gives you an
 interface for this information as well, including timestamps stdout/err,
 and engine IDs.
 
 
 Timing
 ------
 
 IPython tracks various timestamps as :py:class:`.datetime` objects,
 and the AsyncResult object has a few properties that turn these into useful
 times (in seconds as floats).
 
 For use while the tasks are still pending:
 
 * :attr:`ar.elapsed` is just the elapsed seconds since submission, for use
   before the AsyncResult is complete.
 * :attr:`ar.progress` is the number of tasks that have completed.  Fractional progress
   would be::
 
     1.0 * ar.progress / len(ar)
 
 * :meth:`AsyncResult.wait_interactive` will wait for the result to finish, but
   print out status updates on progress and elapsed time while it waits.
 
 For use after the tasks are done:
 
 * :attr:`ar.serial_time` is the sum of the computation time of all of the tasks
   done in parallel.
 * :attr:`ar.wall_time` is the time between the first task submitted and last result
   received.  This is the actual cost of computation, including IPython overhead. 
   
 
 .. note::
 
   wall_time is only precise if the Client is waiting for results when
   the task finished, because the `received` timestamp is made when the result is
   unpacked by the Client, triggered by the :meth:`~Client.spin` call. If you
   are doing work in the Client, and not waiting/spinning, then `received` might
   be artificially high.
 
 An often interesting metric is the time it actually cost to do the work in parallel
 relative to the serial computation, and this can be given simply with
 
 .. sourcecode:: python
 
     speedup = ar.serial_time / ar.wall_time
 
 
 Map results are iterable!
 =========================
 
 When an AsyncResult object has multiple results (e.g. the :class:`~AsyncMapResult`
 object), you can actually iterate through results themselves, and act on them as they arrive:
 
-.. literalinclude:: ../../../examples/parallel/itermapresult.py
+.. literalinclude:: ../../../examples/Parallel Computing/itermapresult.py
     :language: python
     :lines: 20-67
     
 That is to say, if you treat an AsyncMapResult as if it were a list of your actual
 results, it should behave as you would expect, with the only difference being
 that you can start iterating through the results before they have even been computed.
 
 This lets you do a dumb version of map/reduce with the builtin Python functions,
 and the only difference between doing this locally and doing it remotely in parallel
 is using the asynchronous view.map instead of the builtin map.
 
 
 Here is a simple one-line RMS (root-mean-square) implemented with Python's builtin map/reduce.
 
 .. sourcecode:: ipython
 
     In [38]: X = np.linspace(0,100)
 
     In [39]: from math import sqrt
 
     In [40]: add = lambda a,b: a+b
 
     In [41]: sq = lambda x: x*x
 
     In [42]: sqrt(reduce(add, map(sq, X)) / len(X))
     Out[42]: 58.028845747399714
 
     In [43]: sqrt(reduce(add, view.map(sq, X)) / len(X))
     Out[43]: 58.028845747399714
 
 To break that down:
 
 1. ``map(sq, X)`` Compute the square of each element in the list (locally, or in parallel)
 2. ``reduce(add, sqX) / len(X)`` compute the mean by summing over the list (or AsyncMapResult)
    and dividing by the size
 3. take the square root of the resulting number
 
 .. seealso::
 
     When AsyncResult or the AsyncMapResult don't provide what you need (for instance,
     handling individual results as they arrive, but with metadata), you can always
     just split the original result's ``msg_ids`` attribute, and handle them as you like.
     
     For an example of this, see :file:`examples/parallel/customresult.py`

docs/source/parallel/dag_dependencies.rst

@@ -1,177 +1,177 b''
 .. _dag_dependencies:
 
 ================
 DAG Dependencies
 ================
 
 Often, parallel workflow is described in terms of a `Directed Acyclic Graph
 <http://en.wikipedia.org/wiki/Directed_acyclic_graph>`_ or DAG.  A popular library
 for working with Graphs is NetworkX_.  Here, we will walk through a demo mapping
 a nx DAG to task dependencies.
 
 The full script that runs this demo can be found in
 :file:`examples/parallel/dagdeps.py`.
 
 Why are DAGs good for task dependencies?
 ----------------------------------------
 
 The 'G' in DAG is 'Graph'. A Graph is a collection of **nodes** and **edges** that connect
 the nodes. For our purposes, each node would be a task, and each edge would be a
 dependency. The 'D' in DAG stands for 'Directed'. This means that each edge has a
 direction associated with it. So we can interpret the edge (a,b) as meaning that b depends
 on a, whereas the edge (b,a) would mean a depends on b. The 'A' is 'Acyclic', meaning that
 there must not be any closed loops in the graph. This is important for dependencies,
 because if a loop were closed, then a task could ultimately depend on itself, and never be
 able to run. If your workflow can be described as a DAG, then it is impossible for your
 dependencies to cause a deadlock.
 
 A Sample DAG
 ------------
 
 Here, we have a very simple 5-node DAG:
 
 .. figure:: figs/simpledag.*
     :width: 600px
 
 With NetworkX, an arrow is just a fattened bit on the edge. Here, we can see that task 0
 depends on nothing, and can run immediately. 1 and 2 depend on 0; 3 depends on
 1 and 2; and 4 depends only on 1.
 
 A possible sequence of events for this workflow:
 
 0. Task 0 can run right away
 1. 0 finishes, so 1,2 can start
 2. 1 finishes, 3 is still waiting on 2, but 4 can start right away
 3. 2 finishes, and 3 can finally start
 
 
 Further, taking failures into account, assuming all dependencies are run with the default
 `success=True,failure=False`, the following cases would occur for each node's failure:
 
 0. fails: all other tasks fail as Impossible
 1. 2 can still succeed, but 3,4 are unreachable
 2. 3 becomes unreachable, but 4 is unaffected
 3. and 4. are terminal, and can have no effect on other nodes
 
 The code to generate the simple DAG:
 
 .. sourcecode:: python
 
     import networkx as nx
 
     G = nx.DiGraph()
 
     # add 5 nodes, labeled 0-4:
     map(G.add_node, range(5))
     # 1,2 depend on 0:
     G.add_edge(0,1)
     G.add_edge(0,2)
     # 3 depends on 1,2
     G.add_edge(1,3)
     G.add_edge(2,3)
     # 4 depends on 1
     G.add_edge(1,4)
 
     # now draw the graph:
     pos = { 0 : (0,0), 1 : (1,1), 2 : (-1,1),
             3 : (0,2), 4 : (2,2)}
     nx.draw(G, pos, edge_color='r')
 
 
 For demonstration purposes, we have a function that generates a random DAG with a given
 number of nodes and edges.
 
-.. literalinclude:: ../../../examples/parallel/dagdeps.py
+.. literalinclude:: ../../../examples/Parallel Computing/dagdeps.py
     :language: python
     :lines: 20-36
 
 So first, we start with a graph of 32 nodes, with 128 edges:
 
 .. sourcecode:: ipython
 
     In [2]: G = random_dag(32,128)
 
 Now, we need to build our dict of jobs corresponding to the nodes on the graph:
 
 .. sourcecode:: ipython
 
     In [3]: jobs = {}
 
     # in reality, each job would presumably be different
     # randomwait is just a function that sleeps for a random interval
     In [4]: for node in G:
        ...:     jobs[node] = randomwait
 
 Once we have a dict of jobs matching the nodes on the graph, we can start submitting jobs,
 and linking up the dependencies. Since we don't know a job's msg_id until it is submitted,
 which is necessary for building dependencies, it is critical that we don't submit any jobs
 before other jobs it may depend on. Fortunately, NetworkX provides a
 :meth:`topological_sort` method which ensures exactly this. It presents an iterable, that
 guarantees that when you arrive at a node, you have already visited all the nodes it
 on which it depends:
 
 .. sourcecode:: ipython
 
     In [5]: rc = Client()
     In [5]: view = rc.load_balanced_view()
 
     In [6]: results = {}
 
     In [7]: for node in nx.topological_sort(G):
        ...:    # get list of AsyncResult objects from nodes
        ...:    # leading into this one as dependencies
        ...:    deps = [ results[n] for n in G.predecessors(node) ]
        ...:    # submit and store AsyncResult object
        ...:    with view.temp_flags(after=deps, block=False):
        ...:         results[node] = view.apply_with_flags(jobs[node])
 
 
 Now that we have submitted all the jobs, we can wait for the results:
 
 .. sourcecode:: ipython
 
     In [8]: view.wait(results.values())
 
 Now, at least we know that all the jobs ran and did not fail (``r.get()`` would have
 raised an error if a task failed).  But we don't know that the ordering was properly
 respected.  For this, we can use the :attr:`metadata` attribute of each AsyncResult.
 
 These objects store a variety of metadata about each task, including various timestamps.
 We can validate that the dependencies were respected by checking that each task was
 started after all of its predecessors were completed:
 
-.. literalinclude:: ../../../examples/parallel/dagdeps.py
+.. literalinclude:: ../../../examples/Parallel Computing/dagdeps.py
     :language: python
     :lines: 64-70
 
 We can also validate the graph visually. By drawing the graph with each node's x-position
 as its start time, all arrows must be pointing to the right if dependencies were respected.
 For spreading, the y-position will be the runtime of the task, so long tasks
 will be at the top, and quick, small tasks will be at the bottom.
 
 .. sourcecode:: ipython
 
     In [10]: from matplotlib.dates import date2num
 
     In [11]: from matplotlib.cm import gist_rainbow
 
     In [12]: pos = {}; colors = {}
 
     In [12]: for node in G:
        ....:    md = results[node].metadata
        ....:    start = date2num(md.started)
        ....:    runtime = date2num(md.completed) - start
        ....:    pos[node] = (start, runtime)
        ....:    colors[node] = md.engine_id
 
     In [13]: nx.draw(G, pos, node_list=colors.keys(), node_color=colors.values(),
        ....:    cmap=gist_rainbow)
 
 .. figure:: figs/dagdeps.*
     :width: 600px
 
     Time started on x, runtime on y, and color-coded by engine-id (in this case there
     were four engines). Edges denote dependencies.
 
 
 .. _NetworkX: http://networkx.lanl.gov/

docs/source/parallel/parallel_demos.rst

@@ -1,208 +1,208 b''
 .. _parallel_examples:
 
 =================
 Parallel examples
 =================
 
 In this section we describe two more involved examples of using an IPython
 cluster to perform a parallel computation. We will be doing some plotting,
 so we start IPython with matplotlib integration by typing::
 
     ipython --matplotlib
 
 at the system command line.
 Or you can enable matplotlib integration at any point with:
 
 .. sourcecode:: ipython
 
     In [1]: %matplotlib
 
 
 150 million digits of pi
 ========================
 
 In this example we would like to study the distribution of digits in the
 number pi (in base 10). While it is not known if pi is a normal number (a
 number is normal in base 10 if 0-9 occur with equal likelihood) numerical
 investigations suggest that it is. We will begin with a serial calculation on
 10,000 digits of pi and then perform a parallel calculation involving 150
 million digits.
 
 In both the serial and parallel calculation we will be using functions defined
 in the :file:`pidigits.py` file, which is available in the
 :file:`examples/parallel` directory of the IPython source distribution.
 These functions provide basic facilities for working with the digits of pi and
 can be loaded into IPython by putting :file:`pidigits.py` in your current
 working directory and then doing:
 
 .. sourcecode:: ipython
 
     In [1]: run pidigits.py
 
 Serial calculation
 ------------------
 
 For the serial calculation, we will use `SymPy <http://www.sympy.org>`_ to
 calculate 10,000 digits of pi and then look at the frequencies of the digits
 0-9. Out of 10,000 digits, we expect each digit to occur 1,000 times. While
 SymPy is capable of calculating many more digits of pi, our purpose here is to
 set the stage for the much larger parallel calculation.
 
 In this example, we use two functions from :file:`pidigits.py`: 
 :func:`one_digit_freqs` (which calculates how many times each digit occurs)
 and :func:`plot_one_digit_freqs` (which uses Matplotlib to plot the result).
 Here is an interactive IPython session that uses these functions with
 SymPy:
 
 .. sourcecode:: ipython
 
     In [7]: import sympy
 
     In [8]: pi = sympy.pi.evalf(40)
 
     In [9]: pi
     Out[9]: 3.141592653589793238462643383279502884197
 
     In [10]: pi = sympy.pi.evalf(10000)
 
     In [11]: digits = (d for d in str(pi)[2:])  # create a sequence of digits
 
     In [13]: freqs = one_digit_freqs(digits)
 
     In [14]: plot_one_digit_freqs(freqs)
     Out[14]: [<matplotlib.lines.Line2D object at 0x18a55290>]
 
 The resulting plot of the single digit counts shows that each digit occurs
 approximately 1,000 times, but that with only 10,000 digits the
 statistical fluctuations are still rather large:
 
 .. image:: figs/single_digits.*
 
 It is clear that to reduce the relative fluctuations in the counts, we need
 to look at many more digits of pi. That brings us to the parallel calculation.
 
 Parallel calculation
 --------------------
 
 Calculating many digits of pi is a challenging computational problem in itself. 
 Because we want to focus on the distribution of digits in this example, we
 will use pre-computed digit of pi from the website of Professor Yasumasa
 Kanada at the University of Tokyo (http://www.super-computing.org). These
 digits come in a set of text files (ftp://pi.super-computing.org/.2/pi200m/)
 that each have 10 million digits of pi.
 
 For the parallel calculation, we have copied these files to the local hard
 drives of the compute nodes. A total of 15 of these files will be used, for a
 total of 150 million digits of pi. To make things a little more interesting we
 will calculate the frequencies of all 2 digits sequences (00-99) and then plot
 the result using a 2D matrix in Matplotlib.
 
 The overall idea of the calculation is simple: each IPython engine will
 compute the two digit counts for the digits in a single file. Then in a final
 step the counts from each engine will be added up. To perform this
 calculation, we will need two top-level functions from :file:`pidigits.py`:
 
-.. literalinclude:: ../../../examples/parallel/pi/pidigits.py
+.. literalinclude:: ../../../examples/Parallel Computing/pi/pidigits.py
    :language: python
    :lines: 47-62
 
 We will also use the :func:`plot_two_digit_freqs` function to plot the
 results. The code to run this calculation in parallel is contained in
 :file:`examples/parallel/parallelpi.py`. This code can be run in parallel
 using IPython by following these steps:
 
 1. Use :command:`ipcluster` to start 15 engines. We used 16 cores of an SGE linux
    cluster (1 controller + 15 engines).
 2. With the file :file:`parallelpi.py` in your current working directory, open
    up IPython, enable matplotlib, and type ``run parallelpi.py``.  This will download
    the pi files via ftp the first time you run it, if they are not
    present in the Engines' working directory.
 
 When run on our 16 cores, we observe a speedup of 14.2x. This is slightly
 less than linear scaling (16x) because the controller is also running on one of
 the cores.
 
 To emphasize the interactive nature of IPython, we now show how the
 calculation can also be run by simply typing the commands from
 :file:`parallelpi.py` interactively into IPython:
 
 .. sourcecode:: ipython
 
     In [1]: from IPython.parallel import Client
 
     # The Client allows us to use the engines interactively.
     # We simply pass Client the name of the cluster profile we
     # are using.
     In [2]: c = Client(profile='mycluster')
     In [3]: v = c[:]
 
     In [3]: c.ids
     Out[3]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
 
     In [4]: run pidigits.py
 
     In [5]: filestring = 'pi200m.ascii.%(i)02dof20'
 
     # Create the list of files to process.
     In [6]: files = [filestring % {'i':i} for i in range(1,16)]
 
     In [7]: files
     Out[7]: 
     ['pi200m.ascii.01of20',
      'pi200m.ascii.02of20',
      'pi200m.ascii.03of20',
      'pi200m.ascii.04of20',
      'pi200m.ascii.05of20',
      'pi200m.ascii.06of20',
      'pi200m.ascii.07of20',
      'pi200m.ascii.08of20',
      'pi200m.ascii.09of20',
      'pi200m.ascii.10of20',
      'pi200m.ascii.11of20',
      'pi200m.ascii.12of20',
      'pi200m.ascii.13of20',
      'pi200m.ascii.14of20',
      'pi200m.ascii.15of20']
     
     # download the data files if they don't already exist:
     In [8]: v.map(fetch_pi_file, files)
     
     # This is the parallel calculation using the Client.map method 
     # which applies compute_two_digit_freqs to each file in files in parallel.
     In [9]: freqs_all = v.map(compute_two_digit_freqs, files)
 
     # Add up the frequencies from each engine.
     In [10]: freqs = reduce_freqs(freqs_all)
 
     In [11]: plot_two_digit_freqs(freqs)
     Out[11]: <matplotlib.image.AxesImage object at 0x18beb110>
 
     In [12]: plt.title('2 digit counts of 150m digits of pi')
     Out[12]: <matplotlib.text.Text object at 0x18d1f9b0>
 
 The resulting plot generated by Matplotlib is shown below. The colors indicate
 which two digit sequences are more (red) or less (blue) likely to occur in the
 first 150 million digits of pi. We clearly see that the sequence "41" is 
 most likely and that "06" and "07" are least likely. Further analysis would
 show that the relative size of the statistical fluctuations have decreased
 compared to the 10,000 digit calculation.
 
 .. image:: figs/two_digit_counts.*
 
 Conclusion
 ==========
 
 To conclude these examples, we summarize the key features of IPython's
 parallel architecture that have been demonstrated:
 
 * Serial code can be parallelized often with only a few extra lines of code.
   We have used the :class:`DirectView` and :class:`LoadBalancedView` classes
   for this purpose.
 * The resulting parallel code can be run without ever leaving the IPython's
   interactive shell.
 * Any data computed in parallel can be explored interactively through 
   visualization or further numerical calculations.
 * We have run these examples on a cluster running RHEL 5 and Sun GridEngine.
   IPython's built in support for SGE (and other batch systems) makes it easy
   to get started with IPython's parallel capabilities.