Merge pull request #4022 from ivanov/doc-links

DOC: fix links to matplotlib, notebook docs

IPython/html/templates/notebook.html

@@ -1,258 +1,259 b''
 {% extends "page.html" %}
 
 {% block stylesheet %}
 
 {% if mathjax_url %}
 <script type="text/javascript" src="{{mathjax_url}}?config=TeX-AMS_HTML-full&delayStartupUntil=configured" charset="utf-8"></script>
 {% endif %}
 <script type="text/javascript">
 // MathJax disabled, set as null to distingish from *missing* MathJax,
 // where it will be undefined, and should prompt a dialog later.
 window.mathjax_url = "{{mathjax_url}}";
 </script>
 
 <link rel="stylesheet" href="{{ static_url("components/codemirror/lib/codemirror.css") }}">
 
 {{super()}}
 
 <link rel="stylesheet" href="{{ static_url("notebook/css/override.css") }}" type="text/css" />
 
 {% endblock %}
 
 {% block params %}
 
 data-project={{project}}
 data-base-project-url={{base_project_url}}
 data-base-kernel-url={{base_kernel_url}}
 data-notebook-id={{notebook_id}}
 class="notebook_app"
 
 {% endblock %}
 
 
 {% block header %}
 
 <span id="save_widget" class="nav pull-left">
     <span id="notebook_name"></span>
     <span id="checkpoint_status"></span>
     <span id="autosave_status"></span>
 </span>
 
 {% endblock %}
 
 
 {% block site %}
 
 <div id="menubar-container" class="container">
 <div id="menubar">
 <div class="navbar">
   <div class="navbar-inner">
     <div class="container">
     <ul id="menus" class="nav">
         <li class="dropdown"><a href="#" class="dropdown-toggle" data-toggle="dropdown">File</a>
             <ul class="dropdown-menu">
                 <li id="new_notebook"><a href="#">New</a></li>
                 <li id="open_notebook"><a href="#">Open...</a></li>
                 <!-- <hr/> -->
                 <li class="divider"></li>
                 <li id="copy_notebook"><a href="#">Make a Copy...</a></li>
                 <li id="rename_notebook"><a href="#">Rename...</a></li>
                 <li id="save_checkpoint"><a href="#">Save and Checkpoint</a></li>
                 <!-- <hr/> -->
                 <li class="divider"></li>
                 <li id="restore_checkpoint" class="dropdown-submenu"><a href="#">Revert to Checkpoint</a>
                   <ul class="dropdown-menu">
                     <li><a href="#"></a></li>
                     <li><a href="#"></a></li>
                     <li><a href="#"></a></li>
                     <li><a href="#"></a></li>
                     <li><a href="#"></a></li>
                   </ul>
                 </li>
                 <li class="divider"></li>
                 <li class="dropdown-submenu"><a href="#">Download as</a>
                     <ul class="dropdown-menu">
                         <li id="download_ipynb"><a href="#">IPython (.ipynb)</a></li>
                         <li id="download_py"><a href="#">Python (.py)</a></li>
                     </ul>
                 </li>
                 <li class="divider"></li>
                 
                 <li id="kill_and_exit"><a href="#" >Close and halt</a></li>
             </ul>
         </li>
         <li class="dropdown"><a href="#" class="dropdown-toggle" data-toggle="dropdown">Edit</a>
             <ul class="dropdown-menu">
                 <li id="cut_cell"><a href="#">Cut Cell</a></li>
                 <li id="copy_cell"><a href="#">Copy Cell</a></li>
                 <li id="paste_cell_above" class="disabled"><a href="#">Paste Cell Above</a></li>
                 <li id="paste_cell_below" class="disabled"><a href="#">Paste Cell Below</a></li>
                 <li id="paste_cell_replace" class="disabled"><a href="#">Paste Cell &amp; Replace</a></li>
                 <li id="delete_cell"><a href="#">Delete Cell</a></li>
                 <li id="undelete_cell" class="disabled"><a href="#">Undo Delete Cell</a></li>
                 <li class="divider"></li>
                 <li id="split_cell"><a href="#">Split Cell</a></li>
                 <li id="merge_cell_above"><a href="#">Merge Cell Above</a></li>
                 <li id="merge_cell_below"><a href="#">Merge Cell Below</a></li>
                 <li class="divider"></li>
                 <li id="move_cell_up"><a href="#">Move Cell Up</a></li>
                 <li id="move_cell_down"><a href="#">Move Cell Down</a></li>
                 <li class="divider"></li>
                 <li id="select_previous"><a href="#">Select Previous Cell</a></li>
                 <li id="select_next"><a href="#">Select Next Cell</a></li>
             </ul>
         </li>
         <li class="dropdown"><a href="#" class="dropdown-toggle" data-toggle="dropdown">View</a>
             <ul class="dropdown-menu">
                 <li id="toggle_header"><a href="#">Toggle Header</a></li>
                 <li id="toggle_toolbar"><a href="#">Toggle Toolbar</a></li>
             </ul>
         </li>
         <li class="dropdown"><a href="#" class="dropdown-toggle" data-toggle="dropdown">Insert</a>
             <ul class="dropdown-menu">
                 <li id="insert_cell_above"><a href="#">Insert Cell Above</a></li>
                 <li id="insert_cell_below"><a href="#">Insert Cell Below</a></li>
             </ul>
         </li>
         <li class="dropdown"><a href="#" class="dropdown-toggle" data-toggle="dropdown">Cell</a>
             <ul class="dropdown-menu">
                 <li id="run_cell"><a href="#">Run</a></li>
                 <li id="run_cell_in_place"><a href="#">Run in Place</a></li>
                 <li id="run_all_cells"><a href="#">Run All</a></li>
                 <li id="run_all_cells_above"><a href="#">Run All Above</a></li>
                 <li id="run_all_cells_below"><a href="#">Run All Below</a></li>
                 <li class="divider"></li>
                 <li id="change_cell_type" class="dropdown-submenu"><a href="#">Cell Type</a>
                     <ul class="dropdown-menu">
                       <li id="to_code"><a href="#">Code</a></li>
                       <li id="to_markdown"><a href="#">Markdown </a></li>
                       <li id="to_raw"><a href="#">Raw Text</a></li>
                       <li id="to_heading1"><a href="#">Heading 1</a></li>
                       <li id="to_heading2"><a href="#">Heading 2</a></li>
                       <li id="to_heading3"><a href="#">Heading 3</a></li>
                       <li id="to_heading4"><a href="#">Heading 4</a></li>
                       <li id="to_heading5"><a href="#">Heading 5</a></li>
                       <li id="to_heading6"><a href="#">Heading 6</a></li>
                     </ul>
                 </li>
                 <li class="divider"></li>
                 <li id="toggle_output"><a href="#">Toggle Current Output</a></li>
                 <li id="all_outputs" class="dropdown-submenu"><a href="#">All Output</a>
                     <ul class="dropdown-menu">
                         <li id="expand_all_output"><a href="#">Expand</a></li>
                         <li id="scroll_all_output"><a href="#">Scroll Long</a></li>
                         <li id="collapse_all_output"><a href="#">Collapse</a></li>
                         <li id="clear_all_output"><a href="#">Clear</a></li>
                     </ul>
                 </li>
             </ul>
         </li>
         <li class="dropdown"><a href="#" class="dropdown-toggle" data-toggle="dropdown">Kernel</a>
             <ul class="dropdown-menu">
                 <li id="int_kernel"><a href="#">Interrupt</a></li>
                 <li id="restart_kernel"><a href="#">Restart</a></li>
             </ul>
         </li>
         <li class="dropdown"><a href="#" class="dropdown-toggle" data-toggle="dropdown">Help</a>
             <ul class="dropdown-menu">
                 <li><a href="http://ipython.org/documentation.html" target="_blank">IPython Help</a></li>
-                <li><a href="http://ipython.org/ipython-doc/stable/interactive/htmlnotebook.html" target="_blank">Notebook Help</a></li>
+                <li><a href="http://ipython.org/ipython-doc/stable/interactive/notebook.html" target="_blank">Notebook Help</a></li>
                 <li id="keyboard_shortcuts"><a href="#">Keyboard Shortcuts</a></li>
                 <li class="divider"></li>
                 <li><a href="http://docs.python.org" target="_blank">Python</a></li>
                 <li><a href="http://docs.scipy.org/doc/numpy/reference/" target="_blank">NumPy</a></li>
                 <li><a href="http://docs.scipy.org/doc/scipy/reference/" target="_blank">SciPy</a></li>
+                <li><a href="http://matplotlib.org/" target="_blank">Matplotlib</a></li>
                 <li><a href="http://docs.sympy.org/dev/index.html" target="_blank">SymPy</a></li>
-                <li><a href="http://matplotlib.sourceforge.net/" target="_blank">Matplotlib</a></li>
+                <li><a href="http://pandas.pydata.org/pandas-docs/stable/" target="_blank">pandas</a></li>
             </ul>
         </li>
     </ul>
     <div id="notification_area"></div>
     </div>
   </div>
 </div>
 </div>
 <div id="maintoolbar" class="navbar">
   <div class="toolbar-inner navbar-inner navbar-nobg">
     <div id="maintoolbar-container" class="container"></div>
   </div>
 </div>
 </div>
 
 <div id="ipython-main-app">
 
     <div id="notebook_panel">
         <div id="notebook"></div>
         <div id="pager_splitter"></div>
         <div id="pager">
             <div id='pager_button_area'>
             </div>
             <div id="pager-container" class="container"></div>
         </div>
     </div>
 
 </div>
 <div id='tooltip' class='ipython_tooltip' style='display:none'></div>
 
 
 {% endblock %}
 
 
 {% block script %}
 
 {{super()}}
 
 <script src="{{ static_url("components/codemirror/lib/codemirror.js") }}" charset="utf-8"></script>
 <script type="text/javascript">
     CodeMirror.modeURL = "{{ static_url("components/codemirror/mode/%N/%N.js") }}";
 </script>
 <script src="{{ static_url("components/codemirror/addon/mode/loadmode.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("components/codemirror/addon/mode/multiplex.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("components/codemirror/addon/mode/overlay.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("components/codemirror/addon/edit/matchbrackets.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("components/codemirror/addon/comment/comment.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("components/codemirror/mode/htmlmixed/htmlmixed.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("components/codemirror/mode/xml/xml.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("components/codemirror/mode/javascript/javascript.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("components/codemirror/mode/css/css.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("components/codemirror/mode/rst/rst.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("components/codemirror/mode/markdown/markdown.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("components/codemirror/mode/gfm/gfm.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("components/codemirror/mode/python/python.js") }}" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/codemirror-ipython.js") }}" charset="utf-8"></script>
 
 <script src="{{ static_url("components/highlight.js/build/highlight.pack.js") }}" charset="utf-8"></script>
 
 <script src="{{ static_url("dateformat/date.format.js") }}" charset="utf-8"></script>
 
 <script src="{{ static_url("base/js/events.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("base/js/utils.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("base/js/dialog.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/layoutmanager.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/mathjaxutils.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/outputarea.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/cell.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/celltoolbar.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/codecell.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/completer.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/textcell.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("services/kernels/js/kernel.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/savewidget.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/quickhelp.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/pager.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/menubar.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/toolbar.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/maintoolbar.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/notebook.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/notificationwidget.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/notificationarea.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/tooltip.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/config.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/main.js") }}" type="text/javascript" charset="utf-8"></script>
 
 <script src="{{ static_url("notebook/js/contexthint.js") }}" charset="utf-8"></script>
 
 <script src="{{ static_url("notebook/js/celltoolbarpresets/default.js") }}" type="text/javascript" charset="utf-8"></script>
 <script src="{{ static_url("notebook/js/celltoolbarpresets/slideshow.js") }}" type="text/javascript" charset="utf-8"></script>
 
 {% endblock %}

docs/source/interactive/qtconsole.rst

@@ -1,610 +1,610 b''
 .. _qtconsole:
 
 =========================
 A Qt Console for IPython
 =========================
 
 We now have a version of IPython, using the new two-process :ref:`ZeroMQ Kernel
 <ipythonzmq>`, running in a PyQt_ GUI.  This is a very lightweight widget that
 largely feels like a terminal, but provides a number of enhancements only
 possible in a GUI, such as inline figures, proper multiline editing with syntax
 highlighting, graphical calltips, and much more.
 
 .. figure:: ../../_images/qtconsole.png
     :width: 400px
     :alt: IPython Qt console with embedded plots
     :align: center
     :target: ../_images/qtconsole.png
 
     The Qt console for IPython, using inline matplotlib plots.
 
 To get acquainted with the Qt console, type `%guiref` to see a quick
 introduction of its main features.
 
 The Qt frontend has hand-coded emacs-style bindings for text navigation. This
 is not yet configurable.
 
 .. tip::
 
    Since the Qt console tries hard to behave like a terminal, by default it
    immediately executes single lines of input that are complete.  If you want
    to force multiline input, hit :kbd:`Ctrl-Enter` at the end of the first line
    instead of :kbd:`Enter`, and it will open a new line for input.  At any
    point in a multiline block, you can force its execution (without having to
    go to the bottom) with :kbd:`Shift-Enter`.
 
 ``%load``
 =========
 
 The new ``%load`` magic (previously ``%loadpy``) takes any script, and pastes
 its contents as your next input, so you can edit it before executing. The
 script may be on your machine, but you can also specify an history range, or a
 url, and it will download the script from the web. This is particularly useful
 for playing with examples from documentation, such as matplotlib.
 
 .. sourcecode:: ipython
 
-    In [6]: %load http://matplotlib.sourceforge.net/plot_directive/mpl_examples/mplot3d/contour3d_demo.py
+    In [6]: %load http://matplotlib.org/plot_directive/mpl_examples/mplot3d/contour3d_demo.py
 
     In [7]: from mpl_toolkits.mplot3d import axes3d
        ...: import matplotlib.pyplot as plt
        ...:
        ...: fig = plt.figure()
        ...: ax = fig.add_subplot(111, projection='3d')
        ...: X, Y, Z = axes3d.get_test_data(0.05)
        ...: cset = ax.contour(X, Y, Z)
        ...: ax.clabel(cset, fontsize=9, inline=1)
        ...:
        ...: plt.show()
 
 Inline Matplotlib
 =================
 
 One of the most exciting features of the QtConsole is embedded matplotlib
 figures. You can use any standard matplotlib GUI backend
 to draw the figures, and since there is now a two-process model, there is no
 longer a conflict between user input and the drawing eventloop.
 
 .. image:: figs/besselj.png
     :width: 519px
 
 .. _display:
 
 :func:`display`
 ***************
 
 IPython provides a function :func:`display` for displaying rich representations
 of objects if they are available. The IPython display
 system provides a mechanism for specifying PNG or SVG (and more)
 representations of objects for GUI frontends.
 When you enable matplotlib integration via the ``%matplotlib`` magic, IPython registers
 convenient PNG and SVG renderers for matplotlib figures, so you can embed them
 in your document by calling :func:`display` on one or more of them. This is
 especially useful for saving_ your work.
 
 .. sourcecode:: ipython
 
     In [4]: from IPython.display import display
     
     In [5]: plt.plot(range(5)) # plots in the matplotlib window
     
     In [6]: display(plt.gcf()) # embeds the current figure in the qtconsole
     
     In [7]: display(*getfigs()) # embeds all active figures in the qtconsole
 
 If you have a reference to a matplotlib figure object, you can always display
 that specific figure:
 
 .. sourcecode:: ipython
 
    In [1]: f = plt.figure()
 
    In [2]: plt.plot(np.rand(100))
    Out[2]: [<matplotlib.lines.Line2D at 0x7fc6ac03dd90>]
 
    In [3]: display(f)
 
    # Plot is shown here
 
    In [4]: plt.title('A title')
    Out[4]: <matplotlib.text.Text at 0x7fc6ac023450>
 
    In [5]: display(f)
 
    # Updated plot with title is shown here.
 
 .. _inline:
 
 ``--matplotlib inline``
 ***********************
 
 If you want to have all of your figures embedded in your session, instead of
 calling :func:`display`, you can specify ``--matplotlib inline`` when you start the
 console, and each time you make a plot, it will show up in your document, as if
 you had called :func:`display(fig)`.
 
 The inline backend can use either SVG or PNG figures (PNG being the default).
 It also supports the special key ``'retina'``, which is 2x PNG for high-DPI displays.
 To switch between them, set the ``InlineBackend.figure_format`` configurable
 in a config file, or via the ``%config`` magic:
 
 .. sourcecode:: ipython
 
     In [10]: %config InlineBackend.figure_format = 'svg'
 
 .. note::
 
     Changing the inline figure format also affects calls to :func:`display` above,
     even if you are not using the inline backend for all figures.
 
 By default, IPython closes all figures at the completion of each execution. This means you
 don't have to manually close figures, which is less convenient when figures aren't attached
 to windows with an obvious close button.  It also means that the first matplotlib call in
 each cell will always create a new figure:
 
 .. sourcecode:: ipython
 
     In [11]: plt.plot(range(100))
     <single-line plot>
     
     In [12]: plt.plot([1,3,2])
     <another single-line plot>
 
 
 However, it does prevent the list of active figures surviving from one input cell to the
 next, so if you want to continue working with a figure, you must hold on to a reference to
 it:
 
 .. sourcecode:: ipython
 
     In [11]: fig = gcf()
        ....: fig.plot(rand(100))
     <plot>
     In [12]: fig.title('Random Title')
     <redraw plot with title>
 
 This behavior is controlled by the :attr:`InlineBackend.close_figures` configurable, and
 if you set it to False, via %config or config file, then IPython will *not* close figures,
 and tools like :func:`gcf`, :func:`gca`, :func:`getfigs` will behave the same as they
 do with other backends.  You will, however, have to manually close figures:
 
 .. sourcecode:: ipython
 
     # close all active figures:
     In [13]: [ fig.close() for fig in getfigs() ]
 
 
 
 .. _saving:
 
 Saving and Printing
 ===================
 
 IPythonQt has the ability to save your current session, as either HTML or
 XHTML. If you have been using :func:`display` or inline_ matplotlib, your figures
 will be PNG in HTML, or inlined as SVG in XHTML. PNG images have the option to
 be either in an external folder, as in many browsers' "Webpage, Complete"
 option, or inlined as well, for a larger, but more portable file.
 
 .. note::
 
     Export to SVG+XHTML requires that you are using SVG figures, which is *not*
     the default.  To switch the inline figure format to use SVG during an active
     session, do:
     
     .. sourcecode:: ipython
     
         In [10]: %config InlineBackend.figure_format = 'svg'
     
     Or, you can add the same line (c.Inline... instead of %config Inline...) to
     your config files.
     
     This will only affect figures plotted after making this call
 
 
 The widget also exposes the ability to print directly, via the default print
 shortcut or context menu.
 
 
 .. Note:: 
 
     Saving is only available to richtext Qt widgets, which are used by default,
     but if you pass the ``--plain`` flag, saving will not be available to you.
 
 
 See these examples of :download:`png/html<figs/jn.html>` and
 :download:`svg/xhtml <figs/jn.xhtml>` output. Note that syntax highlighting
 does not survive export. This is a known issue, and is being investigated.
 
 
 Colors and Highlighting
 =======================
 
 Terminal IPython has always had some coloring, but never syntax
 highlighting. There are a few simple color choices, specified by the ``colors``
 flag or ``%colors`` magic:
 
 * LightBG for light backgrounds
 * Linux for dark backgrounds
 * NoColor for a simple colorless terminal
 
 The Qt widget has full support for the ``colors`` flag used in the terminal shell.
 
 The Qt widget, however, has full syntax highlighting as you type, handled by
 the `pygments`_ library. The ``style`` argument exposes access to any style by
 name that can be found by pygments, and there are several already
 installed. The ``colors`` argument, if unspecified, will be guessed based on
 the chosen style. Similarly, there are default styles associated with each
 ``colors`` option.
 
 
 Screenshot of ``ipython qtconsole --colors=linux``, which uses the 'monokai'
 theme by default:
 
 .. image:: figs/colors_dark.png
     :width: 627px
     
 .. Note::
 
     Calling ``ipython qtconsole -h`` will show all the style names that
     pygments can find on your system.
 
 You can also pass the filename of a custom CSS stylesheet, if you want to do
 your own coloring, via the ``stylesheet`` argument.  The default LightBG
 stylesheet:
 
 .. sourcecode:: css
 
     QPlainTextEdit, QTextEdit { background-color: white;
             color: black ;
             selection-background-color: #ccc}
     .error { color: red; }
     .in-prompt { color: navy; }
     .in-prompt-number { font-weight: bold; }
     .out-prompt { color: darkred; }
     .out-prompt-number { font-weight: bold; }
     /* .inverted is used to highlight selected completion */
     .inverted { background-color: black ; color: white; }
 
 Fonts
 =====
 
 The QtConsole has configurable via the ConsoleWidget. To change these, set the
 ``font_family`` or ``font_size`` traits of the ConsoleWidget. For instance, to
 use 9pt Anonymous Pro::
 
     $> ipython qtconsole --ConsoleWidget.font_family="Anonymous Pro" --ConsoleWidget.font_size=9
 
 Process Management
 ==================
 
 With the two-process ZMQ model, the frontend does not block input during
 execution. This means that actions can be taken by the frontend while the
 Kernel is executing, or even after it crashes. The most basic such command is
 via 'Ctrl-.', which restarts the kernel.  This can be done in the middle of a
 blocking execution. The frontend can also know, via a heartbeat mechanism, that
 the kernel has died. This means that the frontend can safely restart the
 kernel.
 
 .. _multiple_consoles:
 
 Multiple Consoles
 *****************
 
 Since the Kernel listens on the network, multiple frontends can connect to it.
 These do not have to all be qt frontends - any IPython frontend can connect and
 run code.  When you start ipython qtconsole, there will be an output line,
 like::
 
     [IPKernelApp] To connect another client to this kernel, use:
     [IPKernelApp] --existing kernel-12345.json
 
 Other frontends can connect to your kernel, and share in the execution. This is
 great for collaboration.  The ``--existing`` flag means connect to a kernel
 that already exists.  Starting other consoles
 with that flag will not try to start their own kernel, but rather connect to
 yours.  :file:`kernel-12345.json` is a small JSON file with the ip, port, and
 authentication information necessary to connect to your kernel. By default, this file
 will be in your default profile's security directory.  If it is somewhere else,
 the output line will print the full path of the connection file, rather than
 just its filename.
 
 If you need to find the connection info to send, and don't know where your connection file
 lives, there are a couple of ways to get it. If you are already running an IPython console
 connected to the kernel, you can use the ``%connect_info`` magic to display the information
 necessary to connect another frontend to the kernel.
 
 .. sourcecode:: ipython
 
     In [2]: %connect_info
     {
       "stdin_port":50255,
       "ip":"127.0.0.1",
       "hb_port":50256,
       "key":"70be6f0f-1564-4218-8cda-31be40a4d6aa",
       "shell_port":50253,
       "iopub_port":50254
     }
 
     Paste the above JSON into a file, and connect with:
         $> ipython <app> --existing <file>
     or, if you are local, you can connect with just:
         $> ipython <app> --existing kernel-12345.json
     or even just:
         $> ipython <app> --existing
     if this is the most recent IPython session you have started.
 
 Otherwise, you can find a connection file by name (and optionally profile) with
 :func:`IPython.lib.kernel.find_connection_file`:
 
 .. sourcecode:: bash
 
     $> python -c "from IPython.lib.kernel import find_connection_file;\
     print find_connection_file('kernel-12345.json')"
     /home/you/.ipython/profile_default/security/kernel-12345.json
 
 And if you are using a particular IPython profile:
 
 .. sourcecode:: bash
 
     $> python -c "from IPython.lib.kernel import find_connection_file;\
     print find_connection_file('kernel-12345.json', profile='foo')"
     /home/you/.ipython/profile_foo/security/kernel-12345.json
 
 You can even launch a standalone kernel, and connect and disconnect Qt Consoles
 from various machines.  This lets you keep the same running IPython session
 on your work machine (with matplotlib plots and everything), logging in from home,
 cafés, etc.::
 
     $> ipython kernel
     [IPKernelApp] To connect another client to this kernel, use:
     [IPKernelApp] --existing kernel-12345.json
 
 This is actually exactly the same as the subprocess launched by the qtconsole, so
 all the information about connecting to a standalone kernel is identical to that
 of connecting to the kernel attached to a running console.
 
 .. _kernel_security:
 
 Security
 --------
 
 .. warning::
 
     Since the ZMQ code currently has no encryption, listening on an
     external-facing IP is dangerous.  You are giving any computer that can see
     you on the network the ability to connect to your kernel, and view your traffic.
     Read the rest of this section before listening on external ports
     or running an IPython kernel on a shared machine.
 
 By default (for security reasons), the kernel only listens on localhost, so you
 can only connect multiple frontends to the kernel from your local machine. You
 can specify to listen on an external interface by specifying the ``ip``
 argument::
 
     $> ipython qtconsole --ip=192.168.1.123
 
 If you specify the ip as 0.0.0.0 or '*', that means all interfaces, so any
 computer that can see yours on the network can connect to the kernel.
 
 Messages are not encrypted, so users with access to the ports your kernel is using will be
 able to see any output of the kernel. They will **NOT** be able to issue shell commands as
 you due to message signatures, which are enabled by default as of IPython 0.12.
 
 .. warning::
 
     If you disable message signatures, then any user with access to the ports your
     kernel is listening on can issue arbitrary code as you. **DO NOT** disable message
     signatures unless you have a lot of trust in your environment.
 
 The one security feature IPython does provide is protection from unauthorized execution.
 IPython's messaging system will sign messages with HMAC digests using a shared-key. The key
 is never sent over the network, it is only used to generate a unique hash for each message,
 based on its content. When IPython receives a message, it will check that the digest
 matches, and discard the message. You can use any file that only you have access to to
 generate this key, but the default is just to generate a new UUID. You can generate a random
 private key with::
 
     # generate 1024b of random data, and store in a file only you can read:
     # (assumes IPYTHONDIR is defined, otherwise use your IPython directory)
     $> python -c "import os; print os.urandom(128).encode('base64')" > $IPYTHONDIR/sessionkey
     $> chmod 600 $IPYTHONDIR/sessionkey
 
 The *contents* of this file will be stored in the JSON connection file, so that file
 contains everything you need to connect to and use a kernel.
 
 To use this generated key, simply specify the ``Session.keyfile`` configurable
 in :file:`ipython_config.py` or at the command-line, as in::
 
     # instruct IPython to sign messages with that key, instead of a new UUID
     $> ipython qtconsole --Session.keyfile=$IPYTHONDIR/sessionkey
 
 .. _ssh_tunnels:
 
 SSH Tunnels
 -----------
 
 Sometimes you want to connect to machines across the internet, or just across
 a LAN that either doesn't permit open ports or you don't trust the other
 machines on the network.  To do this, you can use SSH tunnels.  SSH tunnels
 are a way to securely forward ports on your local machine to ports on another
 machine, to which you have SSH access.
 
 In simple cases, IPython's tools can forward ports over ssh by simply adding the
 ``--ssh=remote`` argument to the usual ``--existing...`` set of flags for connecting
 to a running kernel, after copying the JSON connection file (or its contents) to
 the second computer.
 
 .. warning::
 
     Using SSH tunnels does *not* increase localhost security.  In fact, when
     tunneling from one machine to another *both* machines have open
     ports on localhost available for connections to the kernel.
 
 There are two primary models for using SSH tunnels with IPython.  The first
 is to have the Kernel listen only on localhost, and connect to it from
 another machine on the same LAN.
 
 First, let's start a kernel on machine **worker**, listening only
 on loopback::
 
     user@worker $> ipython kernel
     [IPKernelApp] To connect another client to this kernel, use:
     [IPKernelApp] --existing kernel-12345.json
 
 In this case, the IP that you would connect
 to would still be 127.0.0.1, but you want to specify the additional ``--ssh`` argument
 with the hostname of the kernel (in this example, it's 'worker')::
 
     user@client $> ipython qtconsole  --ssh=worker --existing /path/to/kernel-12345.json
 
 Which will write a new connection file with the forwarded ports, so you can reuse them::
 
     [IPythonQtConsoleApp] To connect another client via this tunnel, use:
     [IPythonQtConsoleApp] --existing kernel-12345-ssh.json
 
 Note again that this opens ports on the *client* machine that point to your kernel.
 
 .. note::
 
     the ssh argument is simply passed to openssh, so it can be fully specified ``user@host:port``
     but it will also respect your aliases, etc. in :file:`.ssh/config` if you have any.
 
 The second pattern is for connecting to a machine behind a firewall across the internet
 (or otherwise wide network). This time, we have a machine **login** that you have ssh access
 to, which can see **kernel**, but **client** is on another network. The important difference
 now is that **client** can see **login**, but *not* **worker**. So we need to forward ports from
 client to worker *via* login. This means that the kernel must be started listening
 on external interfaces, so that its ports are visible to `login`::
 
     user@worker $> ipython kernel --ip=0.0.0.0
     [IPKernelApp] To connect another client to this kernel, use:
     [IPKernelApp] --existing kernel-12345.json
 
 Which we can connect to from the client with::
 
     user@client $> ipython qtconsole --ssh=login --ip=192.168.1.123 --existing /path/to/kernel-12345.json
 
 .. note::
 
     The IP here is the address of worker as seen from *login*, and need only be specified if
     the kernel used the ambiguous 0.0.0.0 (all interfaces) address. If it had used
     192.168.1.123 to start with, it would not be needed.
 
 
 Manual SSH tunnels
 ------------------
 
 It's possible that IPython's ssh helper functions won't work for you, for various
 reasons.  You can still connect to remote machines, as long as you set up the tunnels
 yourself.  The basic format of forwarding a local port to a remote one is::
 
     [client] $> ssh <server> <localport>:<remoteip>:<remoteport> -f -N
 
 This will forward local connections to **localport** on client to **remoteip:remoteport**
 *via* **server**. Note that remoteip is interpreted relative to *server*, not the client.
 So if you have direct ssh access to the machine to which you want to forward connections,
 then the server *is* the remote machine, and remoteip should be server's IP as seen from the
 server itself, i.e. 127.0.0.1.  Thus, to forward local port 12345 to remote port 54321 on
 a machine you can see, do::
 
     [client] $> ssh machine 12345:127.0.0.1:54321 -f -N
 
 But if your target is actually on a LAN at 192.168.1.123, behind another machine called **login**,
 then you would do::
 
     [client] $> ssh login 12345:192.168.1.16:54321 -f -N
 
 The ``-f -N`` on the end are flags that tell ssh to run in the background,
 and don't actually run any commands beyond creating the tunnel.
 
 .. seealso::
 
     A short discussion of ssh tunnels: http://www.revsys.com/writings/quicktips/ssh-tunnel.html
 
 
 
 Stopping Kernels and Consoles
 *****************************
 
 Since there can be many consoles per kernel, the shutdown mechanism and dialog
 are probably more complicated than you are used to. Since you don't always want
 to shutdown a kernel when you close a window, you are given the option to just
 close the console window or also close the Kernel and *all other windows*. Note
 that this only refers to all other *local* windows, as remote Consoles are not
 allowed to shutdown the kernel, and shutdowns do not close Remote consoles (to
 allow for saving, etc.).
 
 Rules:
 
     * Restarting the kernel automatically clears all *local* Consoles, and prompts remote
       Consoles about the reset.
     * Shutdown closes all *local* Consoles, and notifies remotes that
       the Kernel has been shutdown.
     * Remote Consoles may not restart or shutdown the kernel.
 
 Qt and the QtConsole
 ====================
 
 An important part of working with the QtConsole when you are writing your own
 Qt code is to remember that user code (in the kernel) is *not* in the same
 process as the frontend.  This means that there is not necessarily any Qt code
 running in the kernel, and under most normal circumstances there isn't. If,
 however, you specify ``--matplotlib qt`` at the command-line, then there *will* be a
 :class:`QCoreApplication` instance running in the kernel process along with
 user-code. To get a reference to this application, do:
 
 .. sourcecode:: python
 
     from PyQt4 import QtCore
     app = QtCore.QCoreApplication.instance()
     # app will be None if there is no such instance
 
 A common problem listed in the PyQt4 Gotchas_ is the fact that Python's garbage
 collection will destroy Qt objects (Windows, etc.) once there is no longer a
 Python reference to them, so you have to hold on to them.  For instance, in:
 
 .. sourcecode:: python
 
     def make_window():
         win = QtGui.QMainWindow()
     
     def make_and_return_window():
         win = QtGui.QMainWindow()
         return win
 
 :func:`make_window` will never draw a window, because garbage collection will
 destroy it before it is drawn, whereas :func:`make_and_return_window` lets the
 caller decide when the window object should be destroyed.  If, as a developer,
 you know that you always want your objects to last as long as the process, you
 can attach them to the QApplication instance itself:
 
 .. sourcecode:: python
     
     # do this just once:
     app = QtCore.QCoreApplication.instance()
     app.references = set()
     # then when you create Windows, add them to the set
     def make_window():
         win = QtGui.QMainWindow()
         app.references.add(win)
 
 Now the QApplication itself holds a reference to ``win``, so it will never be
 garbage collected until the application itself is destroyed.
 
 .. _Gotchas: http://www.riverbankcomputing.co.uk/static/Docs/PyQt4/html/gotchas.html#garbage-collection
 
 Regressions
 ===========
 
 There are some features, where the qt console lags behind the Terminal
 frontend:
 
 * !cmd input: Due to our use of pexpect, we cannot pass input to subprocesses
   launched using the '!' escape, so you should never call a command that
   requires interactive input.  For such cases, use the terminal IPython.  This
   will not be fixed, as abandoning pexpect would significantly degrade the
   console experience.
 
 .. _PyQt: http://www.riverbankcomputing.co.uk/software/pyqt/download
 .. _pygments: http://pygments.org/

docs/source/interactive/reference.rst

@@ -1,1165 +1,1164 b''
 =================
 IPython reference
 =================
 
 .. _command_line_options:
 
 Command-line usage
 ==================
 
 You start IPython with the command::
 
     $ ipython [options] files
 
 .. note::
 
   For IPython on Python 3, use ``ipython3`` in place of ``ipython``.
 
 If invoked with no options, it executes all the files listed in sequence
 and drops you into the interpreter while still acknowledging any options
 you may have set in your ipython_config.py. This behavior is different from
 standard Python, which when called as python -i will only execute one
 file and ignore your configuration setup.
 
 Please note that some of the configuration options are not available at
 the command line, simply because they are not practical here. Look into
 your configuration files for details on those. There are separate configuration 
 files for each profile, and the files look like "ipython_config.py" or 
 "ipython_config_<frontendname>.py".  Profile directories look like 
 "profile_profilename" and are typically installed in the IPYTHONDIR directory.
 For Linux users, this will be $HOME/.config/ipython, and for other users it 
 will be $HOME/.ipython.  For Windows users, $HOME resolves to C:\\Documents and
 Settings\\YourUserName in most instances.
 
 
 Eventloop integration
 ---------------------
 
 Previously IPython had command line options for controlling GUI event loop
 integration (-gthread, -qthread, -q4thread, -wthread, -pylab). As of IPython
 version 0.11, these have been removed. Please see the new ``%gui``
 magic command or :ref:`this section <gui_support>` for details on the new
 interface, or specify the gui at the commandline::
 
     $ ipython --gui=qt
 
 
 Command-line Options
 --------------------
 
 To see the options IPython accepts, use ``ipython --help`` (and you probably
 should run the output through a pager such as ``ipython --help | less`` for
 more convenient reading).  This shows all the options that have a single-word
 alias to control them, but IPython lets you configure all of its objects from
 the command-line by passing the full class name and a corresponding value; type
 ``ipython --help-all`` to see this full list.  For example::
 
   ipython --matplotlib qt
 
 is equivalent to::
 
   ipython --TerminalIPythonApp.matplotlib='qt'
 
 Note that in the second form, you *must* use the equal sign, as the expression
 is evaluated as an actual Python assignment.  While in the above example the
 short form is more convenient, only the most common options have a short form,
 while any configurable variable in IPython can be set at the command-line by
 using the long form.  This long form is the same syntax used in the
 configuration files, if you want to set these options permanently.
 
 
 Interactive use
 ===============
 
 IPython is meant to work as a drop-in replacement for the standard interactive
 interpreter. As such, any code which is valid python should execute normally
 under IPython (cases where this is not true should be reported as bugs). It
 does, however, offer many features which are not available at a standard python
 prompt. What follows is a list of these.
 
 
 Caution for Windows users
 -------------------------
 
 Windows, unfortunately, uses the '\\' character as a path separator. This is a
 terrible choice, because '\\' also represents the escape character in most
 modern programming languages, including Python. For this reason, using '/'
 character is recommended if you have problems with ``\``.  However, in Windows
 commands '/' flags options, so you can not use it for the root directory. This
 means that paths beginning at the root must be typed in a contrived manner
 like: ``%copy \opt/foo/bar.txt \tmp``
 
 .. _magic:
 
 Magic command system
 --------------------
 
 IPython will treat any line whose first character is a % as a special
 call to a 'magic' function. These allow you to control the behavior of
 IPython itself, plus a lot of system-type features. They are all
 prefixed with a % character, but parameters are given without
 parentheses or quotes.
 
 Lines that begin with ``%%`` signal a *cell magic*: they take as arguments not
 only the rest of the current line, but all lines below them as well, in the
 current execution block.  Cell magics can in fact make arbitrary modifications
 to the input they receive, which need not even be valid Python code at all.
 They receive the whole block as a single string.
 
 As a line magic example, the ``%cd`` magic works just like the OS command of
 the same name::
 
       In [8]: %cd
       /home/fperez
 
 The following uses the builtin ``timeit`` in cell mode::
   
   In [10]: %%timeit x = range(10000)
       ...: min(x)
       ...: max(x)
       ...: 
   1000 loops, best of 3: 438 us per loop
 
 In this case, ``x = range(10000)`` is called as the line argument, and the
 block with ``min(x)`` and ``max(x)`` is called as the cell body.  The
 ``timeit`` magic receives both.
   
 If you have 'automagic' enabled (as it by default), you don't need to type in
 the single ``%`` explicitly for line magics; IPython will scan its internal
 list of magic functions and call one if it exists. With automagic on you can
 then just type ``cd mydir`` to go to directory 'mydir'::
 
       In [9]: cd mydir
       /home/fperez/mydir
 
 Note that cell magics *always* require an explicit ``%%`` prefix, automagic
 calling only works for line magics.
       
 The automagic system has the lowest possible precedence in name searches, so
 defining an identifier with the same name as an existing magic function will
 shadow it for automagic use. You can still access the shadowed magic function
 by explicitly using the ``%`` character at the beginning of the line.
 
 An example (with automagic on) should clarify all this:
 
 .. sourcecode:: ipython
 
     In [1]: cd ipython     # %cd is called by automagic
     /home/fperez/ipython
 
     In [2]: cd=1 	   # now cd is just a variable
 
     In [3]: cd .. 	   # and doesn't work as a function anymore
     File "<ipython-input-3-9fedb3aff56c>", line 1
       cd ..
           ^
     SyntaxError: invalid syntax
 
 
     In [4]: %cd .. 	   # but %cd always works
     /home/fperez
 
     In [5]: del cd     # if you remove the cd variable, automagic works again
 
     In [6]: cd ipython
 
     /home/fperez/ipython
 
 Defining your own magics
 ++++++++++++++++++++++++
 
 There are two main ways to define your own magic functions: from standalone
 functions and by inheriting from a base class provided by IPython:
 :class:`IPython.core.magic.Magics`.  Below we show code you can place in a file
 that you load from your configuration, such as any file in the ``startup``
 subdirectory of your default IPython profile.
 
 First, let us see the simplest case.  The following shows how to create a line
 magic, a cell one and one that works in both modes, using just plain functions:
 
 .. sourcecode:: python
 
     from IPython.core.magic import (register_line_magic, register_cell_magic,
                                     register_line_cell_magic)
 		
     @register_line_magic
     def lmagic(line):
         "my line magic"
 	return line
 
     @register_cell_magic
     def cmagic(line, cell):
         "my cell magic"
         return line, cell
 
     @register_line_cell_magic
     def lcmagic(line, cell=None):
         "Magic that works both as %lcmagic and as %%lcmagic"
 	if cell is None:
 	    print "Called as line magic"
 	    return line
 	else:
 	    print "Called as cell magic"
             return line, cell
 
     # We delete these to avoid name conflicts for automagic to work
     del lmagic, lcmagic
 
 
 You can also create magics of all three kinds by inheriting from the
 :class:`IPython.core.magic.Magics` class.  This lets you create magics that can
 potentially hold state in between calls, and that have full access to the main
 IPython object:
     
 .. sourcecode:: python
 		
     # This code can be put in any Python module, it does not require IPython
     # itself to be running already.  It only creates the magics subclass but
     # doesn't instantiate it yet.
     from IPython.core.magic import (Magics, magics_class, line_magic,
                                     cell_magic, line_cell_magic)
 
     # The class MUST call this class decorator at creation time
     @magics_class
     class MyMagics(Magics):
 
         @line_magic
         def lmagic(self, line):
             "my line magic"
             print "Full access to the main IPython object:", self.shell
             print "Variables in the user namespace:", self.shell.user_ns.keys()
             return line
 
         @cell_magic
         def cmagic(self, line, cell):
             "my cell magic"
             return line, cell
 
         @line_cell_magic
         def lcmagic(self, line, cell=None):
             "Magic that works both as %lcmagic and as %%lcmagic"
             if cell is None:
                 print "Called as line magic"
                 return line
             else:
                 print "Called as cell magic"
                 return line, cell
 
 
     # In order to actually use these magics, you must register them with a
     # running IPython.  This code must be placed in a file that is loaded once
     # IPython is up and running:
     ip = get_ipython()
     # You can register the class itself without instantiating it.  IPython will
     # call the default constructor on it.
     ip.register_magics(MyMagics)
 
 If you want to create a class with a different constructor that holds
 additional state, then you should always call the parent constructor and
 instantiate the class yourself before registration:
 
 .. sourcecode:: python
 		
     @magics_class
     class StatefulMagics(Magics):
         "Magics that hold additional state"
 	
         def __init__(self, shell, data):
             # You must call the parent constructor
             super(StatefulMagics, self).__init__(shell)
             self.data = data
         
         # etc...
 
     # This class must then be registered with a manually created instance,
     # since its constructor has different arguments from the default:
     ip = get_ipython()
     magics = StatefulMagics(ip, some_data)
     ip.register_magics(magics)
     
 
 In earlier versions, IPython had an API for the creation of line magics (cell
 magics did not exist at the time) that required you to create functions with a
 method-looking signature and to manually pass both the function and the name.
 While this API is no longer recommended, it remains indefinitely supported for
 backwards compatibility purposes.  With the old API, you'd create a magic as
 follows:
 
 .. sourcecode:: python
 
     def func(self, line):
         print "Line magic called with line:", line
 	print "IPython object:", self.shell
 
     ip = get_ipython()
     # Declare this function as the magic %mycommand
     ip.define_magic('mycommand', func)
 
 Type ``%magic`` for more information, including a list of all available magic
 functions at any time and their docstrings. You can also type
 ``%magic_function_name?`` (see :ref:`below <dynamic_object_info>` for
 information on the '?' system) to get information about any particular magic
 function you are interested in.
 
 The API documentation for the :mod:`IPython.core.magic` module contains the full
 docstrings of all currently available magic commands.
 
 
 Access to the standard Python help
 ----------------------------------
 
 Simply type ``help()`` to access Python's standard help system. You can
 also type ``help(object)`` for information about a given object, or
 ``help('keyword')`` for information on a keyword. You may need to configure your
 PYTHONDOCS environment variable for this feature to work correctly.
 
 .. _dynamic_object_info:
 
 Dynamic object information
 --------------------------
 
 Typing ``?word`` or ``word?`` prints detailed information about an object. If
 certain strings in the object are too long (e.g. function signatures) they get
 snipped in the center for brevity. This system gives access variable types and
 values, docstrings, function prototypes and other useful information.
 
 If the information will not fit in the terminal, it is displayed in a pager
 (``less`` if available, otherwise a basic internal pager).
 
 Typing ``??word`` or ``word??`` gives access to the full information, including
 the source code where possible. Long strings are not snipped.
 
 The following magic functions are particularly useful for gathering
 information about your working environment. You can get more details by
 typing ``%magic`` or querying them individually (``%function_name?``);
 this is just a summary:
 
     * **%pdoc <object>**: Print (or run through a pager if too long) the
       docstring for an object. If the given object is a class, it will
       print both the class and the constructor docstrings.
     * **%pdef <object>**: Print the call signature for any callable
       object. If the object is a class, print the constructor information.
     * **%psource <object>**: Print (or run through a pager if too long)
       the source code for an object.
     * **%pfile <object>**: Show the entire source file where an object was
       defined via a pager, opening it at the line where the object
       definition begins.
     * **%who/%whos**: These functions give information about identifiers
       you have defined interactively (not things you loaded or defined
       in your configuration files). %who just prints a list of
       identifiers and %whos prints a table with some basic details about
       each identifier.
 
 Note that the dynamic object information functions (?/??, ``%pdoc``,
 ``%pfile``, ``%pdef``, ``%psource``) work on object attributes, as well as
 directly on variables. For example, after doing ``import os``, you can use
 ``os.path.abspath??``.
 
 .. _readline:
 
 Readline-based features
 -----------------------
 
 These features require the GNU readline library, so they won't work if your
 Python installation lacks readline support. We will first describe the default
 behavior IPython uses, and then how to change it to suit your preferences.
 
 
 Command line completion
 +++++++++++++++++++++++
 
 At any time, hitting TAB will complete any available python commands or
 variable names, and show you a list of the possible completions if
 there's no unambiguous one. It will also complete filenames in the
 current directory if no python names match what you've typed so far.
 
 
 Search command history
 ++++++++++++++++++++++
 
 IPython provides two ways for searching through previous input and thus
 reduce the need for repetitive typing:
 
    1. Start typing, and then use Ctrl-p (previous,up) and Ctrl-n
       (next,down) to search through only the history items that match
       what you've typed so far. If you use Ctrl-p/Ctrl-n at a blank
       prompt, they just behave like normal arrow keys.
    2. Hit Ctrl-r: opens a search prompt. Begin typing and the system
       searches your history for lines that contain what you've typed so
       far, completing as much as it can.
 
 
 Persistent command history across sessions
 ++++++++++++++++++++++++++++++++++++++++++
 
 IPython will save your input history when it leaves and reload it next
 time you restart it. By default, the history file is named
 $IPYTHONDIR/profile_<name>/history.sqlite. This allows you to keep
 separate histories related to various tasks: commands related to
 numerical work will not be clobbered by a system shell history, for
 example.
 
 
 Autoindent
 ++++++++++
 
 IPython can recognize lines ending in ':' and indent the next line,
 while also un-indenting automatically after 'raise' or 'return'.
 
 This feature uses the readline library, so it will honor your
 :file:`~/.inputrc` configuration (or whatever file your INPUTRC variable points
 to). Adding the following lines to your :file:`.inputrc` file can make
 indenting/unindenting more convenient (M-i indents, M-u unindents)::
 
     # if you don't already have a ~/.inputrc file, you need this include:
     $include /etc/inputrc
     
     $if Python 
     "\M-i": "    "  
     "\M-u": "\d\d\d\d"  
     $endif
 
 Note that there are 4 spaces between the quote marks after "M-i" above.
 
 .. warning::
 
     Setting the above indents will cause problems with unicode text entry in
     the terminal.
 
 .. warning::
 
     Autoindent is ON by default, but it can cause problems with the pasting of
     multi-line indented code (the pasted code gets re-indented on each line). A
     magic function %autoindent allows you to toggle it on/off at runtime. You
     can also disable it permanently on in your :file:`ipython_config.py` file
     (set TerminalInteractiveShell.autoindent=False).
     
     If you want to paste multiple lines in the terminal, it is recommended that
     you use ``%paste``.
 
 
 Customizing readline behavior
 +++++++++++++++++++++++++++++
 
 All these features are based on the GNU readline library, which has an
 extremely customizable interface. Normally, readline is configured via a
 file which defines the behavior of the library; the details of the
 syntax for this can be found in the readline documentation available
 with your system or on the Internet. IPython doesn't read this file (if
 it exists) directly, but it does support passing to readline valid
 options via a simple interface. In brief, you can customize readline by
 setting the following options in your configuration file (note
 that these options can not be specified at the command line):
 
     * **readline_parse_and_bind**: this holds a list of strings to be executed
       via a readline.parse_and_bind() command. The syntax for valid commands
       of this kind can be found by reading the documentation for the GNU
       readline library, as these commands are of the kind which readline
       accepts in its configuration file.
     * **readline_remove_delims**: a string of characters to be removed
       from the default word-delimiters list used by readline, so that
       completions may be performed on strings which contain them. Do not
       change the default value unless you know what you're doing.
 
 You will find the default values in your configuration file.
 
 
 Session logging and restoring
 -----------------------------
 
 You can log all input from a session either by starting IPython with the
 command line switch ``--logfile=foo.py`` (see :ref:`here <command_line_options>`)
 or by activating the logging at any moment with the magic function %logstart.
 
 Log files can later be reloaded by running them as scripts and IPython
 will attempt to 'replay' the log by executing all the lines in it, thus
 restoring the state of a previous session. This feature is not quite
 perfect, but can still be useful in many cases.
 
 The log files can also be used as a way to have a permanent record of
 any code you wrote while experimenting. Log files are regular text files
 which you can later open in your favorite text editor to extract code or
 to 'clean them up' before using them to replay a session.
 
 The `%logstart` function for activating logging in mid-session is used as
 follows::
 
     %logstart [log_name [log_mode]]
 
 If no name is given, it defaults to a file named 'ipython_log.py' in your
 current working directory, in 'rotate' mode (see below).
 
 '%logstart name' saves to file 'name' in 'backup' mode. It saves your
 history up to that point and then continues logging.
 
 %logstart takes a second optional parameter: logging mode. This can be
 one of (note that the modes are given unquoted):
 
     * [over:] overwrite existing log_name.
     * [backup:] rename (if exists) to log_name~ and start log_name.
     * [append:] well, that says it.
     * [rotate:] create rotating logs log_name.1~, log_name.2~, etc.
 
 The %logoff and %logon functions allow you to temporarily stop and
 resume logging to a file which had previously been started with
 %logstart. They will fail (with an explanation) if you try to use them
 before logging has been started.
 
 .. _system_shell_access:
 
 System shell access
 -------------------
 
 Any input line beginning with a ! character is passed verbatim (minus
 the !, of course) to the underlying operating system. For example,
 typing ``!ls`` will run 'ls' in the current directory.
 
 Manual capture of command output
 --------------------------------
 
 You can assign the result of a system command to a Python variable with the
 syntax ``myfiles = !ls``. This gets machine readable output from stdout 
 (e.g. without colours), and splits on newlines. To explicitly get this sort of
 output without assigning to a variable, use two exclamation marks (``!!ls``) or
 the ``%sx`` magic command.
 
 The captured list has some convenience features. ``myfiles.n`` or ``myfiles.s``
 returns a string delimited by newlines or spaces, respectively. ``myfiles.p``
 produces `path objects <http://pypi.python.org/pypi/path.py>`_ from the list items.
 See :ref:`string_lists` for details.
 
 IPython also allows you to expand the value of python variables when
 making system calls. Wrap variables or expressions in {braces}::
 
     In [1]: pyvar = 'Hello world' 
     In [2]: !echo "A python variable: {pyvar}"  
     A python variable: Hello world
     In [3]: import math
     In [4]: x = 8
     In [5]: !echo {math.factorial(x)}
     40320
 
 For simple cases, you can alternatively prepend $ to a variable name::
 
     In [6]: !echo $sys.argv  
     [/home/fperez/usr/bin/ipython]
     In [7]: !echo "A system variable: $$HOME"  # Use $$ for literal $
     A system variable: /home/fperez
 
 System command aliases
 ----------------------
 
 The %alias magic function allows you to define magic functions which are in fact
 system shell commands. These aliases can have parameters.
 
 ``%alias alias_name cmd`` defines 'alias_name' as an alias for 'cmd'
 
 Then, typing ``alias_name params`` will execute the system command 'cmd
 params' (from your underlying operating system).
 
 You can also define aliases with parameters using %s specifiers (one per
 parameter). The following example defines the parts function as an
 alias to the command 'echo first %s second %s' where each %s will be
 replaced by a positional parameter to the call to %parts::
 
     In [1]: %alias parts echo first %s second %s
     In [2]: parts A B
     first A second B
     In [3]: parts A  
     ERROR: Alias <parts> requires 2 arguments, 1 given.
 
 If called with no parameters, %alias prints the table of currently
 defined aliases.
 
 The %rehashx magic allows you to load your entire $PATH as
 ipython aliases. See its docstring for further details.
 
 
 .. _dreload:
 
 Recursive reload
 ----------------
 
 The :mod:`IPython.lib.deepreload` module allows you to recursively reload a
 module: changes made to any of its dependencies will be reloaded without
 having to exit. To start using it, do::
 
     from IPython.lib.deepreload import reload as dreload
 
 
 Verbose and colored exception traceback printouts
 -------------------------------------------------
 
 IPython provides the option to see very detailed exception tracebacks,
 which can be especially useful when debugging large programs. You can
 run any Python file with the %run function to benefit from these
 detailed tracebacks. Furthermore, both normal and verbose tracebacks can
 be colored (if your terminal supports it) which makes them much easier
 to parse visually.
 
 See the magic xmode and colors functions for details (just type %magic).
 
 These features are basically a terminal version of Ka-Ping Yee's cgitb
 module, now part of the standard Python library.
 
 
 .. _input_caching:
 
 Input caching system
 --------------------
 
 IPython offers numbered prompts (In/Out) with input and output caching
 (also referred to as 'input history'). All input is saved and can be 
 retrieved as variables (besides the usual arrow key recall), in 
 addition to the %rep magic command that brings a history entry
 up for editing on the next  command line.
 
 The following GLOBAL variables always exist (so don't overwrite them!):
 
 * _i, _ii, _iii: store previous, next previous and next-next previous inputs.
 * In, _ih : a list of all inputs; _ih[n] is the input from line n. If you
   overwrite In with a variable of your own, you can remake the assignment to the
   internal list with a simple ``In=_ih``.
 
 Additionally, global variables named _i<n> are dynamically created (<n>
 being the prompt counter), so ``_i<n> == _ih[<n>] == In[<n>]``.
 
 For example, what you typed at prompt 14 is available as _i14, _ih[14]
 and In[14].
 
 This allows you to easily cut and paste multi line interactive prompts
 by printing them out: they print like a clean string, without prompt
 characters. You can also manipulate them like regular variables (they
 are strings), modify or exec them (typing ``exec _i9`` will re-execute the
 contents of input prompt 9.
 
 You can also re-execute multiple lines of input easily by using the
 magic %rerun or %macro functions. The macro system also allows you to re-execute
 previous lines which include magic function calls (which require special
 processing). Type %macro? for more details on the macro system.
 
 A history function %hist allows you to see any part of your input
 history by printing a range of the _i variables.
 
 You can also search ('grep') through your history by typing 
 ``%hist -g somestring``. This is handy for searching for URLs, IP addresses,
 etc. You can bring history entries listed by '%hist -g' up for editing
 with the %recall command, or run them immediately with %rerun.
 
 .. _output_caching:
 
 Output caching system
 ---------------------
 
 For output that is returned from actions, a system similar to the input
 cache exists but using _ instead of _i. Only actions that produce a
 result (NOT assignments, for example) are cached. If you are familiar
 with Mathematica, IPython's _ variables behave exactly like
 Mathematica's % variables.
 
 The following GLOBAL variables always exist (so don't overwrite them!):
 
     * [_] (a single underscore) : stores previous output, like Python's
       default interpreter.
     * [__] (two underscores): next previous.
     * [___] (three underscores): next-next previous.
 
 Additionally, global variables named _<n> are dynamically created (<n>
 being the prompt counter), such that the result of output <n> is always
 available as _<n> (don't use the angle brackets, just the number, e.g.
 _21).
 
 These variables are also stored in a global dictionary (not a
 list, since it only has entries for lines which returned a result)
 available under the names _oh and Out (similar to _ih and In). So the
 output from line 12 can be obtained as _12, Out[12] or _oh[12]. If you
 accidentally overwrite the Out variable you can recover it by typing
 'Out=_oh' at the prompt.
 
 This system obviously can potentially put heavy memory demands on your
 system, since it prevents Python's garbage collector from removing any
 previously computed results. You can control how many results are kept
 in memory with the option (at the command line or in your configuration
 file) cache_size. If you set it to 0, the whole system is completely
 disabled and the prompts revert to the classic '>>>' of normal Python.
 
 
 Directory history
 -----------------
 
 Your history of visited directories is kept in the global list _dh, and
 the magic %cd command can be used to go to any entry in that list. The
 %dhist command allows you to view this history. Do ``cd -<TAB>`` to
 conveniently view the directory history.
 
 
 Automatic parentheses and quotes
 --------------------------------
 
 These features were adapted from Nathan Gray's LazyPython. They are
 meant to allow less typing for common situations.
 
 
 Automatic parentheses
 +++++++++++++++++++++
 
 Callable objects (i.e. functions, methods, etc) can be invoked like this
 (notice the commas between the arguments)::
 
     In [1]: callable_ob arg1, arg2, arg3
     ------> callable_ob(arg1, arg2, arg3)
 
 You can force automatic parentheses by using '/' as the first character
 of a line. For example::
 
     In [2]: /globals # becomes 'globals()'
 
 Note that the '/' MUST be the first character on the line! This won't work::
 
     In [3]: print /globals # syntax error
 
 In most cases the automatic algorithm should work, so you should rarely
 need to explicitly invoke /. One notable exception is if you are trying
 to call a function with a list of tuples as arguments (the parenthesis
 will confuse IPython)::
 
     In [4]: zip (1,2,3),(4,5,6) # won't work
 
 but this will work::
 
     In [5]: /zip (1,2,3),(4,5,6) 
     ------> zip ((1,2,3),(4,5,6))  
     Out[5]: [(1, 4), (2, 5), (3, 6)]
 
 IPython tells you that it has altered your command line by displaying
 the new command line preceded by ->. e.g.::
 
     In [6]: callable list 
     ------> callable(list)
 
 
 Automatic quoting
 +++++++++++++++++
 
 You can force automatic quoting of a function's arguments by using ','
 or ';' as the first character of a line. For example::
 
     In [1]: ,my_function /home/me  # becomes my_function("/home/me")
 
 If you use ';' the whole argument is quoted as a single string, while ',' splits
 on whitespace::
 
     In [2]: ,my_function a b c    # becomes my_function("a","b","c")
 
     In [3]: ;my_function a b c    # becomes my_function("a b c")
 
 Note that the ',' or ';' MUST be the first character on the line! This
 won't work::
 
     In [4]: x = ,my_function /home/me # syntax error
 
 IPython as your default Python environment
 ==========================================
 
 Python honors the environment variable PYTHONSTARTUP and will execute at
 startup the file referenced by this variable. If you put the following code at
 the end of that file, then IPython will be your working environment anytime you
 start Python::
 
     from IPython.frontend.terminal.ipapp import launch_new_instance
     launch_new_instance()
     raise SystemExit
 
 The ``raise SystemExit`` is needed to exit Python when
 it finishes, otherwise you'll be back at the normal Python '>>>'
 prompt.
 
 This is probably useful to developers who manage multiple Python
 versions and don't want to have correspondingly multiple IPython
 versions. Note that in this mode, there is no way to pass IPython any
 command-line options, as those are trapped first by Python itself.
 
 .. _Embedding:
 
 Embedding IPython
 =================
 
 You can start a regular IPython session with
 
 .. sourcecode:: python
 
     import IPython
     IPython.start_ipython()
 
 at any point in your program.  This will load IPython configuration,
 startup files, and everything, just as if it were a normal IPython session.
 In addition to this,
 it is possible to embed an IPython instance inside your own Python programs.
 This allows you to evaluate dynamically the state of your code,
 operate with your variables, analyze them, etc. Note however that
 any changes you make to values while in the shell do not propagate back
 to the running code, so it is safe to modify your values because you
 won't break your code in bizarre ways by doing so.
 
 .. note::
 
   At present, embedding IPython cannot be done from inside IPython.
   Run the code samples below outside IPython.
 
 This feature allows you to easily have a fully functional python
 environment for doing object introspection anywhere in your code with a
 simple function call. In some cases a simple print statement is enough,
 but if you need to do more detailed analysis of a code fragment this
 feature can be very valuable.
 
 It can also be useful in scientific computing situations where it is
 common to need to do some automatic, computationally intensive part and
 then stop to look at data, plots, etc.
 Opening an IPython instance will give you full access to your data and
 functions, and you can resume program execution once you are done with
 the interactive part (perhaps to stop again later, as many times as
 needed).
 
 The following code snippet is the bare minimum you need to include in
 your Python programs for this to work (detailed examples follow later)::
 
     from IPython import embed
 
     embed() # this call anywhere in your program will start IPython
 
 .. note::
 
     As of 0.13, you can embed an IPython *kernel*, for use with qtconsole,
     etc. via ``IPython.embed_kernel()`` instead of ``IPython.embed()``.
     It should function just the same as regular embed, but you connect
     an external frontend rather than IPython starting up in the local
     terminal.
 
 You can run embedded instances even in code which is itself being run at
 the IPython interactive prompt with '%run <filename>'. Since it's easy
 to get lost as to where you are (in your top-level IPython or in your
 embedded one), it's a good idea in such cases to set the in/out prompts
 to something different for the embedded instances. The code examples
 below illustrate this.
 
 You can also have multiple IPython instances in your program and open
 them separately, for example with different options for data
 presentation. If you close and open the same instance multiple times,
 its prompt counters simply continue from each execution to the next.
 
 Please look at the docstrings in the :mod:`~IPython.frontend.terminal.embed` 
 module for more details on the use of this system.
 
 The following sample file illustrating how to use the embedding
 functionality is provided in the examples directory as example-embed.py.
 It should be fairly self-explanatory:
 
 .. literalinclude:: ../../../examples/core/example-embed.py
     :language: python
 
 Once you understand how the system functions, you can use the following
 code fragments in your programs which are ready for cut and paste:
 
 .. literalinclude:: ../../../examples/core/example-embed-short.py
     :language: python
 
 Using the Python debugger (pdb)
 ===============================
 
 Running entire programs via pdb
 -------------------------------
 
 pdb, the Python debugger, is a powerful interactive debugger which
 allows you to step through code, set breakpoints, watch variables,
 etc.  IPython makes it very easy to start any script under the control
 of pdb, regardless of whether you have wrapped it into a 'main()'
 function or not. For this, simply type '%run -d myscript' at an
 IPython prompt. See the %run command's documentation (via '%run?' or
 in Sec. magic_ for more details, including how to control where pdb
 will stop execution first.
 
 For more information on the use of the pdb debugger, read the included
 pdb.doc file (part of the standard Python distribution). On a stock
 Linux system it is located at /usr/lib/python2.3/pdb.doc, but the
 easiest way to read it is by using the help() function of the pdb module
 as follows (in an IPython prompt)::
 
     In [1]: import pdb
     In [2]: pdb.help()
 
 This will load the pdb.doc document in a file viewer for you automatically.
 
 
 Automatic invocation of pdb on exceptions
 -----------------------------------------
 
 IPython, if started with the ``--pdb`` option (or if the option is set in
 your config file) can call the Python pdb debugger every time your code
 triggers an uncaught exception. This feature
 can also be toggled at any time with the %pdb magic command. This can be
 extremely useful in order to find the origin of subtle bugs, because pdb
 opens up at the point in your code which triggered the exception, and
 while your program is at this point 'dead', all the data is still
 available and you can walk up and down the stack frame and understand
 the origin of the problem.
 
 Furthermore, you can use these debugging facilities both with the
 embedded IPython mode and without IPython at all. For an embedded shell
 (see sec. Embedding_), simply call the constructor with
 ``--pdb`` in the argument string and pdb will automatically be called if an
 uncaught exception is triggered by your code.
 
 For stand-alone use of the feature in your programs which do not use
 IPython at all, put the following lines toward the top of your 'main'
 routine::
 
     import sys
     from IPython.core import ultratb
     sys.excepthook = ultratb.FormattedTB(mode='Verbose',
     color_scheme='Linux', call_pdb=1)
 
 The mode keyword can be either 'Verbose' or 'Plain', giving either very
 detailed or normal tracebacks respectively. The color_scheme keyword can
 be one of 'NoColor', 'Linux' (default) or 'LightBG'. These are the same
 options which can be set in IPython with ``--colors`` and ``--xmode``.
 
 This will give any of your programs detailed, colored tracebacks with
 automatic invocation of pdb.
 
 
 Extensions for syntax processing
 ================================
 
 This isn't for the faint of heart, because the potential for breaking
 things is quite high. But it can be a very powerful and useful feature.
 In a nutshell, you can redefine the way IPython processes the user input
 line to accept new, special extensions to the syntax without needing to
 change any of IPython's own code.
 
 In the IPython/extensions directory you will find some examples
 supplied, which we will briefly describe now. These can be used 'as is'
 (and both provide very useful functionality), or you can use them as a
 starting point for writing your own extensions.
 
 .. _pasting_with_prompts:
 
 Pasting of code starting with Python or IPython prompts
 -------------------------------------------------------
 
 IPython is smart enough to filter out input prompts, be they plain Python ones
 (``>>>`` and ``...``) or IPython ones (``In [N]:`` and ``...:``).  You can
 therefore copy and paste from existing interactive sessions without worry.
 
 The following is a 'screenshot' of how things work, copying an example from the
 standard Python tutorial::
 
     In [1]: >>> # Fibonacci series:
 
     In [2]: ... # the sum of two elements defines the next
 
     In [3]: ... a, b = 0, 1
 
     In [4]: >>> while b < 10:
        ...:     ...     print b
        ...:     ...     a, b = b, a+b
        ...:     
     1
     1
     2
     3
     5
     8
 
 And pasting from IPython sessions works equally well::
 
     In [1]: In [5]: def f(x):
        ...:        ...:     "A simple function"
        ...:        ...:     return x**2
        ...:    ...: 
 
     In [2]: f(3)
     Out[2]: 9
 
 .. _gui_support:
 
 GUI event loop support
 ======================
 
 .. versionadded:: 0.11
     The ``%gui`` magic and :mod:`IPython.lib.inputhook`.
 
 IPython has excellent support for working interactively with Graphical User
 Interface (GUI) toolkits, such as wxPython, PyQt4/PySide, PyGTK and Tk. This is
 implemented using Python's builtin ``PyOSInputHook`` hook. This implementation
 is extremely robust compared to our previous thread-based version. The
 advantages of this are:
 
 * GUIs can be enabled and disabled dynamically at runtime.
 * The active GUI can be switched dynamically at runtime.
 * In some cases, multiple GUIs can run simultaneously with no problems.
 * There is a developer API in :mod:`IPython.lib.inputhook` for customizing 
   all of these things.
 
 For users, enabling GUI event loop integration is simple.  You simple use the
 ``%gui`` magic as follows::
 
     %gui [GUINAME]
 
 With no arguments, ``%gui`` removes all GUI support.  Valid ``GUINAME``
 arguments are ``wx``, ``qt``, ``gtk`` and ``tk``.
 
 Thus, to use wxPython interactively and create a running :class:`wx.App`
 object, do::
 
     %gui wx
 
-For information on IPython's Matplotlib integration (and the ``matplotlib``
+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
+matplotlib_ provides high quality 2D and 3D plotting for Python. matplotlib_
 can produce plots on screen using a variety of GUI toolkits, including Tk,
 PyGTK, PyQt4 and wxPython. It also provides a number of commands useful for
 scientific computing, all with a syntax compatible with that of the popular
 Matlab program.
 
 To start IPython with matplotlib support, use the ``--matplotlib`` switch. If
 IPython is already running, you can run the ``%matplotlib`` magic.  If no
 arguments are given, IPython will automatically detect your choice of
 matplotlib backend.  You can also request a specific backend with
 ``%matplotlib backend``, where ``backend`` must be one of: 'tk', 'qt', 'wx',
 'gtk', 'osx'.  In the web notebook and Qt console, 'inline' is also a valid
 backend value, which produces static figures inlined inside the application
 window instead of matplotlib's interactive figures that live in separate
 windows.
 
-.. _Matplotlib: http://matplotlib.sourceforge.net
-
 .. _interactive_demos:
 
 Interactive demos with IPython
 ==============================
 
 IPython ships with a basic system for running scripts interactively in
 sections, useful when presenting code to audiences. A few tags embedded
 in comments (so that the script remains valid Python code) divide a file
 into separate blocks, and the demo can be run one block at a time, with
 IPython printing (with syntax highlighting) the block before executing
 it, and returning to the interactive prompt after each block. The
 interactive namespace is updated after each block is run with the
 contents of the demo's namespace.
 
 This allows you to show a piece of code, run it and then execute
 interactively commands based on the variables just created. Once you
 want to continue, you simply execute the next block of the demo. The
 following listing shows the markup necessary for dividing a script into
 sections for execution as a demo:
 
 .. literalinclude:: ../../../examples/lib/example-demo.py
     :language: python
 
 In order to run a file as a demo, you must first make a Demo object out
 of it. If the file is named myscript.py, the following code will make a
 demo::
 
     from IPython.lib.demo import Demo
 
     mydemo = Demo('myscript.py')
 
 This creates the mydemo object, whose blocks you run one at a time by
 simply calling the object with no arguments. If you have autocall active
 in IPython (the default), all you need to do is type::
 
     mydemo 
 
 and IPython will call it, executing each block. Demo objects can be
 restarted, you can move forward or back skipping blocks, re-execute the
 last block, etc. Simply use the Tab key on a demo object to see its
 methods, and call '?' on them to see their docstrings for more usage
 details. In addition, the demo module itself contains a comprehensive
 docstring, which you can access via::
 
     from IPython.lib import demo
 
     demo?
 
 Limitations: It is important to note that these demos are limited to
 fairly simple uses. In particular, you cannot break up sections within
 indented code (loops, if statements, function definitions, etc.)
 Supporting something like this would basically require tracking the
 internal execution state of the Python interpreter, so only top-level
 divisions are allowed. If you want to be able to open an IPython
 instance at an arbitrary point in a program, you can use IPython's
 embedding facilities, see :func:`IPython.embed` for details.
 
+.. include:: ../links.txt

docs/source/links.txt

@@ -1,101 +1,101 b''
 .. This (-*- rst -*-) format file contains commonly used link targets
    and name substitutions.  It may be included in many files,
    therefore it should only contain link targets and name
    substitutions.  Try grepping for "^\.\. _" to find plausible
    candidates for this list.  
 
    NOTE: this file must have an extension *opposite* to that of the main reST
    files in the manuals, so that we can include it with ".. include::"
    directives, but without triggering warnings from Sphinx for not being listed
    in any toctree.  Since IPython uses .txt for the main files, this wone will
    use .rst.
    
    NOTE: reST targets are
    __not_case_sensitive__, so only one target definition is needed for
    ipython, IPython, etc.
 
    NOTE: Some of these were taken from the nipy links compendium.
 
 .. Main IPython links
 .. _ipython: http://ipython.org
 .. _`ipython manual`: http://ipython.org/documentation.html
 .. _ipython_github: http://github.com/ipython/ipython/
 .. _ipython_github_repo: http://github.com/ipython/ipython/
 .. _ipython_downloads: http://ipython.org/download.html
 .. _ipython_pypi: http://pypi.python.org/pypi/ipython
 .. _nbviewer: http://nbviewer.ipython.org
 
 .. _ZeroMQ: http://zeromq.org
 
 .. Documentation tools and related links
 .. _graphviz: http://www.graphviz.org
 .. _Sphinx: http://sphinx.pocoo.org
 .. _`Sphinx reST`: http://sphinx.pocoo.org/rest.html
-.. _sampledoc: http://matplotlib.sourceforge.net/sampledoc
+.. _sampledoc: http://matplotlib.org/sampledoc
 .. _reST: http://docutils.sourceforge.net/rst.html
 .. _docutils: http://docutils.sourceforge.net
 .. _lyx: http://www.lyx.org
 .. _pep8: http://www.python.org/dev/peps/pep-0008
 .. _numpy_coding_guide: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt
 
 .. Licenses
 .. _GPL: http://www.gnu.org/licenses/gpl.html
 .. _BSD: http://www.opensource.org/licenses/bsd-license.php
 .. _LGPL: http://www.gnu.org/copyleft/lesser.html
 
 .. Other python projects
 .. _numpy: http://numpy.scipy.org
 .. _scipy: http://www.scipy.org
 .. _scipy_conference: http://conference.scipy.org
 .. _matplotlib: http://matplotlib.org
 .. _pythonxy: http://www.pythonxy.com
 .. _ETS: http://code.enthought.com/projects/tool-suite.php
 .. _EPD: http://www.enthought.com/products/epd.php
 .. _python: http://www.python.org
 .. _mayavi: http://code.enthought.com/projects/mayavi
 .. _sympy: http://code.google.com/p/sympy
 .. _sage: http://sagemath.org
 .. _pydy: http://code.google.com/p/pydy
 .. _vpython: http://vpython.org
 .. _cython: http://cython.org
 .. _software carpentry: http://software-carpentry.org
 
 .. Not so python scientific computing tools
 .. _matlab: http://www.mathworks.com
 .. _VTK: http://vtk.org
 
 .. Other organizations
 .. _enthought: http://www.enthought.com
 .. _kitware: http://www.kitware.com
 .. _netlib: http://netlib.org
 
 .. Other tools and projects
 .. _indefero: http://www.indefero.net
 .. _git: http://git-scm.com
 .. _github: http://github.com
 .. _Markdown: http://daringfireball.net/projects/markdown/syntax
 
 .. _Running Code in the IPython Notebook: notebook_p1_
 .. _notebook_p1: http://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Part%25201%2520-%2520Running%2520Code.ipynb
 
 .. _Basic Output: notebook_p2_
 .. _notebook_p2: http://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Part%202%20-%20Basic%20Output.ipynb
 
 .. _Plotting with Matplotlib: notebook_p3_
 .. _notebook_p3: http://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Part%203%20-%20Plotting%20with%20Matplotlib.ipynb
 
 .. _Markdown Cells: notebook_p4
 .. _notebook_p4: http://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Part%204%20-%20Markdown%20Cells.ipynb
 
 .. _Rich Display System: notebook_p5_
 .. _notebook_p5: http://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Part%205%20-%20Rich%20Display%20System.ipynb
 
 .. _notebook_custom_display: http://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Custom%20Display%20Logic.ipynb
 
 .. _Frontend/Kernel Model: notebook_two_proc_
 .. _notebook_two_proc: http://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Frontend-Kernel%20Model.ipynb
 
 .. _Cell magics: notebook_cell_magics_
 .. _notebook_cell_magics: http://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Cell%20Magics.ipynb
 
 

docs/source/parallel/parallel_winhpc.rst

@@ -1,360 +1,361 b''
 ============================================
 Getting started with Windows HPC Server 2008
 ============================================
 
 Introduction
 ============
 
 The Python programming language is an increasingly popular language for
 numerical computing. This is due to a unique combination of factors. First,
 Python is a high-level and *interactive* language that is well matched to
 interactive numerical work. Second, it is easy (often times trivial) to
 integrate legacy C/C++/Fortran code into Python. Third, a large number of
 high-quality open source projects provide all the needed building blocks for
 numerical computing: numerical arrays (NumPy), algorithms (SciPy), 2D/3D
-Visualization (Matplotlib, Mayavi, Chaco), Symbolic Mathematics (Sage, Sympy)
+Visualization (matplotlib_, Mayavi, Chaco), Symbolic Mathematics (Sage, Sympy)
 and others.
 
 The IPython project is a core part of this open-source toolchain and is
 focused on creating a comprehensive environment for interactive and
 exploratory computing in the Python programming language. It enables all of
 the above tools to be used interactively and consists of two main components:
 
 * An enhanced interactive Python shell with support for interactive plotting
   and visualization.
 * An architecture for interactive parallel computing.
 
 With these components, it is possible to perform all aspects of a parallel
 computation interactively. This type of workflow is particularly relevant in
 scientific and numerical computing where algorithms, code and data are
 continually evolving as the user/developer explores a problem. The broad
 threads in computing (commodity clusters, multicore, cloud computing, etc.)
 make these capabilities of IPython particularly relevant.
 
 While IPython is a cross platform tool, it has particularly strong support for
 Windows based compute clusters running Windows HPC Server 2008. This document
 describes how to get started with IPython on Windows HPC Server 2008. The
 content and emphasis here is practical: installing IPython, configuring
 IPython to use the Windows job scheduler and running example parallel programs
 interactively. A more complete description of IPython's parallel computing
 capabilities can be found in IPython's online documentation
 (http://ipython.org/documentation.html).
 
 Setting up your Windows cluster
 ===============================
 
 This document assumes that you already have a cluster running Windows
 HPC Server 2008. Here is a broad overview of what is involved with setting up
 such a cluster:
 
 1. Install Windows Server 2008 on the head and compute nodes in the cluster.
 2. Setup the network configuration on each host. Each host should have a 
    static IP address.
 3. On the head node, activate the "Active Directory Domain Services" role 
    and make the head node the domain controller.
 4. Join the compute nodes to the newly created Active Directory (AD) domain.
 5. Setup user accounts in the domain with shared home directories.
 6. Install the HPC Pack 2008 on the head node to create a cluster.
 7. Install the HPC Pack 2008 on the compute nodes.
 
 More details about installing and configuring Windows HPC Server 2008 can be
 found on the Windows HPC Home Page (http://www.microsoft.com/hpc). Regardless
 of what steps you follow to set up your cluster, the remainder of this
 document will assume that:
 
 * There are domain users that can log on to the AD domain and submit jobs
   to the cluster scheduler.
 * These domain users have shared home directories. While shared home
   directories are not required to use IPython, they make it much easier to
   use IPython.
 
 Installation of IPython and its dependencies
 ============================================
 
 IPython and all of its dependencies are freely available and open source.
 These packages provide a powerful and cost-effective approach to numerical and
 scientific computing on Windows. The following dependencies are needed to run
 IPython on Windows:
 
 * Python 2.6 or 2.7 (http://www.python.org)
 * pywin32 (http://sourceforge.net/projects/pywin32/)
 * PyReadline (https://launchpad.net/pyreadline)
 * pyzmq (http://github.com/zeromq/pyzmq/downloads)
 * IPython (http://ipython.org)
 
 In addition, the following dependencies are needed to run the demos described
 in this document.
 
 * NumPy and SciPy (http://www.scipy.org)
-* Matplotlib (http://matplotlib.sourceforge.net/)
+* matplotlib_ (http://matplotlib.org)
 
 The easiest way of obtaining these dependencies is through the Enthought
 Python Distribution (EPD) (http://www.enthought.com/products/epd.php). EPD is
 produced by Enthought, Inc. and contains all of these packages and others in a
 single installer and is available free for academic users. While it is also
 possible to download and install each package individually, this is a tedious
 process. Thus, we highly recommend using EPD to install these packages on
 Windows.
 
 Regardless of how you install the dependencies, here are the steps you will
 need to follow:
 
 1. Install all of the packages listed above, either individually or using EPD
    on the head node, compute nodes and user workstations.
 
 2. Make sure that :file:`C:\\Python27` and :file:`C:\\Python27\\Scripts` are
    in the system :envvar:`%PATH%` variable on each node.
 
 3. Install the latest development version of IPython. This can be done by
    downloading the the development version from the IPython website
    (http://ipython.org) and following the installation instructions.
 
 Further details about installing IPython or its dependencies can be found in
 the online IPython documentation (http://ipython.org/documentation.html)
 Once you are finished with the installation, you can try IPython out by
 opening a Windows Command Prompt and typing ``ipython``. This will
 start IPython's interactive shell and you should see something like the
 following::
 
     Microsoft Windows [Version 6.0.6001]
     Copyright (c) 2006 Microsoft Corporation.  All rights reserved.
 
     Z:\>ipython
     Python 2.7.2 (default, Jun 12 2011, 15:08:59) [MSC v.1500 32 bit (Intel)]
     Type "copyright", "credits" or "license" for more information.
 
     IPython 0.12.dev -- An enhanced Interactive Python.
     ?         -> Introduction and overview of IPython's features.
     %quickref -> Quick reference.
     help      -> Python's own help system.
     object?   -> Details about 'object', use 'object??' for extra details.
 
     In [1]:
 
 
 Starting an IPython cluster
 ===========================
 
 To use IPython's parallel computing capabilities, you will need to start an
 IPython cluster. An IPython cluster consists of one controller and multiple
 engines:
 
 IPython controller
     The IPython controller manages the engines and acts as a gateway between 
     the engines and the client, which runs in the user's interactive IPython
     session. The controller is started using the :command:`ipcontroller`
     command.
 
 IPython engine
     IPython engines run a user's Python code in parallel on the compute nodes. 
     Engines are starting using the :command:`ipengine` command.
 
 Once these processes are started, a user can run Python code interactively and
 in parallel on the engines from within the IPython shell using an appropriate
 client. This includes the ability to interact with, plot and visualize data
 from the engines.
 
 IPython has a command line program called :command:`ipcluster` that automates
 all aspects of starting the controller and engines on the compute nodes.
 :command:`ipcluster` has full support for the Windows HPC job scheduler,
 meaning that :command:`ipcluster` can use this job scheduler to start the
 controller and engines. In our experience, the Windows HPC job scheduler is
 particularly well suited for interactive applications, such as IPython. Once
 :command:`ipcluster` is configured properly, a user can start an IPython
 cluster from their local workstation almost instantly, without having to log
 on to the head node (as is typically required by Unix based job schedulers).
 This enables a user to move seamlessly between serial and parallel
 computations.
 
 In this section we show how to use :command:`ipcluster` to start an IPython
 cluster using the Windows HPC Server 2008 job scheduler. To make sure that
 :command:`ipcluster` is installed and working properly, you should first try
 to start an IPython cluster on your local host. To do this, open a Windows
 Command Prompt and type the following command::
 
     ipcluster start -n 2
 
 You should see a number of messages printed to the screen.
 The result should look something like this::
 
     Microsoft Windows [Version 6.1.7600]
     Copyright (c) 2009 Microsoft Corporation.  All rights reserved.
 
     Z:\>ipcluster start --profile=mycluster
     [IPClusterStart] Using existing profile dir: u'\\\\blue\\domainusers$\\bgranger\\.ipython\\profile_mycluster'
     [IPClusterStart] Starting ipcluster with [daemon=False]
     [IPClusterStart] Creating pid file: \\blue\domainusers$\bgranger\.ipython\profile_mycluster\pid\ipcluster.pid
     [IPClusterStart] Writing job description file: \\blue\domainusers$\bgranger\.ipython\profile_mycluster\ipcontroller_job.xml
     [IPClusterStart] Starting Win HPC Job: job submit /jobfile:\\blue\domainusers$\bgranger\.ipython\profile_mycluster\ipcontroller_job.xml /scheduler:HEADNODE
     [IPClusterStart] Starting 15 engines
     [IPClusterStart] Writing job description file: \\blue\domainusers$\bgranger\.ipython\profile_mycluster\ipcontroller_job.xml
     [IPClusterStart] Starting Win HPC Job: job submit /jobfile:\\blue\domainusers$\bgranger\.ipython\profile_mycluster\ipengineset_job.xml /scheduler:HEADNODE
     
 
 At this point, the controller and two engines are running on your local host.
 This configuration is useful for testing and for situations where you want to
 take advantage of multiple cores on your local computer. 
 
 Now that we have confirmed that :command:`ipcluster` is working properly, we
 describe how to configure and run an IPython cluster on an actual compute
 cluster running Windows HPC Server 2008. Here is an outline of the needed
 steps:
 
 1. Create a cluster profile using: ``ipython profile create mycluster --parallel``
 
 2. Edit configuration files in the directory :file:`.ipython\\cluster_mycluster`
 
 3. Start the cluster using: ``ipcluster start --profile=mycluster -n 32``
 
 Creating a cluster profile
 --------------------------
 
 In most cases, you will have to create a cluster profile to use IPython on a
 cluster. A cluster profile is a name (like "mycluster") that is associated
 with a particular cluster configuration. The profile name is used by
 :command:`ipcluster` when working with the cluster.
 
 Associated with each cluster profile is a cluster directory. This cluster
 directory is a specially named directory (typically located in the
 :file:`.ipython` subdirectory of your home directory) that contains the
 configuration files for a particular cluster profile, as well as log files and
 security keys. The naming convention for cluster directories is:
 :file:`profile_<profile name>`. Thus, the cluster directory for a profile named
 "foo" would be :file:`.ipython\\cluster_foo`.
 
 To create a new cluster profile (named "mycluster") and the associated cluster
 directory, type the following command at the Windows Command Prompt::
 
     ipython profile create --parallel --profile=mycluster
 
 The output of this command is shown in the screenshot below. Notice how
 :command:`ipcluster` prints out the location of the newly created profile
 directory::
 
     Z:\>ipython profile create mycluster --parallel
     [ProfileCreate] Generating default config file: u'\\\\blue\\domainusers$\\bgranger\\.ipython\\profile_mycluster\\ipython_config.py'
     [ProfileCreate] Generating default config file: u'\\\\blue\\domainusers$\\bgranger\\.ipython\\profile_mycluster\\ipcontroller_config.py'
     [ProfileCreate] Generating default config file: u'\\\\blue\\domainusers$\\bgranger\\.ipython\\profile_mycluster\\ipengine_config.py'
     [ProfileCreate] Generating default config file: u'\\\\blue\\domainusers$\\bgranger\\.ipython\\profile_mycluster\\ipcluster_config.py'
     [ProfileCreate] Generating default config file: u'\\\\blue\\domainusers$\\bgranger\\.ipython\\profile_mycluster\\iplogger_config.py'
 
     Z:\>
 
 Configuring a cluster profile
 -----------------------------
 
 Next, you will need to configure the newly created cluster profile by editing
 the following configuration files in the cluster directory:
 
 * :file:`ipcluster_config.py`
 * :file:`ipcontroller_config.py`
 * :file:`ipengine_config.py`
 
 When :command:`ipcluster` is run, these configuration files are used to 
 determine how the engines and controller will be started. In most cases,
 you will only have to set a few of the attributes in these files.
 
 To configure :command:`ipcluster` to use the Windows HPC job scheduler, you
 will need to edit the following attributes in the file
 :file:`ipcluster_config.py`::
 
     # Set these at the top of the file to tell ipcluster to use the
     # Windows HPC job scheduler.
     c.IPClusterStart.controller_launcher_class = 'WindowsHPCControllerLauncher'
     c.IPClusterEngines.engine_launcher_class = 'WindowsHPCEngineSetLauncher'
 
     # Set these to the host name of the scheduler (head node) of your cluster.
     c.WindowsHPCControllerLauncher.scheduler = 'HEADNODE'
     c.WindowsHPCEngineSetLauncher.scheduler = 'HEADNODE'
 
 There are a number of other configuration attributes that can be set, but
 in most cases these will be sufficient to get you started.
 
 .. warning::
     If any of your configuration attributes involve specifying the location
     of shared directories or files, you must make sure that you use UNC paths
     like :file:`\\\\host\\share`. It is helpful to specify
     these paths using raw Python strings: ``r'\\host\share'`` to make sure
     that the backslashes are properly escaped.
 
 Starting the cluster profile
 ----------------------------
 
 Once a cluster profile has been configured, starting an IPython cluster using
 the profile is simple::
 
     ipcluster start --profile=mycluster -n 32
 
 The ``-n`` option tells :command:`ipcluster` how many engines to start (in
 this case 32). Stopping the cluster is as simple as typing Control-C.
 
 Using the HPC Job Manager
 -------------------------
 føø
 When ``ipcluster start`` is run the first time, :command:`ipcluster` creates
 two XML job description files in the cluster directory:
 
 * :file:`ipcontroller_job.xml`
 * :file:`ipengineset_job.xml`
 
 Once these files have been created, they can be imported into the HPC Job
 Manager application. Then, the controller and engines for that profile can be
 started using the HPC Job Manager directly, without using :command:`ipcluster`.
 However, anytime the cluster profile is re-configured, ``ipcluster start``
 must be run again to regenerate the XML job description files. The
 following screenshot shows what the HPC Job Manager interface looks like
 with a running IPython cluster.
 
 .. image:: figs/hpc_job_manager.*
 
 Performing a simple interactive parallel computation
 ====================================================
 
 Once you have started your IPython cluster, you can start to use it. To do
 this, open up a new Windows Command Prompt and start up IPython's interactive
 shell by typing::
 
     ipython
 
 Then you can create a :class:`DirectView` instance for your profile and
 use the resulting instance to do a simple interactive parallel computation. In
 the code and screenshot that follows, we take a simple Python function and
 apply it to each element of an array of integers in parallel using the
 :meth:`DirectView.map` method:
 
 .. sourcecode:: ipython
 
     In [1]: from IPython.parallel import *
 
     In [2]: c = Client(profile='mycluster')
 
     In [3]: view = c[:]
 
     In [4]: c.ids
     Out[4]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
 
     In [5]: def f(x):
        ...:     return x**10
 
     In [6]: view.map(f, range(15))  # f is applied in parallel
     Out[6]:
     [0,
      1,
      1024,
      59049,
      1048576,
      9765625,
      60466176,
      282475249,
      1073741824,
      3486784401L,
      10000000000L,
      25937424601L,
      61917364224L,
      137858491849L,
      289254654976L]
 
 The :meth:`map` method has the same signature as Python's builtin :func:`map`
 function, but runs the calculation in parallel. More involved examples of using
 :class:`DirectView` are provided in the examples that follow.
 
+.. include:: ../links.txt