fix a few markup issues and inaccuracies

docs/source/interactive/converting_notebooks.rst

@@ -1,260 +1,261 b''
 
 
 .. _`nbconvert script`:
 
 Converting notebooks to other formats
 =====================================
 
 Newly added in the 1.0 release of IPython is the ``nbconvert`` tool, which 
 allows you to convert an ``.ipynb`` notebook document file into various static 
 formats. 
 
 Currently, ``nbconvert`` is provided as a command line tool, run as a script 
 using IPython. In the future, a direct export capability from within the 
 IPython Notebook web app is planned. 
 
 The command-line syntax to run the ``nbconvert`` script is::
 
   $ ipython nbconvert --format=FORMAT notebook.ipynb
 
 This will convert the IPython document file ``notebook.ipynb`` into the output 
 format given by the ``FORMAT`` string.
 
 The default output format is HTML, for which the ``--format`` modifier may be 
 omitted::
   
   $ ipython nbconvert notebook.ipynb
 
 The currently supported export formats are the following:
 
 * HTML:
 
   - **full_html**:
     Standard HTML
 
   - **simple_html**:
     Simplified HTML
 
   - **reveal**:
     HTML slideshow presentation for use with the ``reveal.js`` package
 
 * PDF:
 
   - **sphinx_howto**:
     The format for Sphinx_ HOWTOs; similar to an ``article`` in LaTeX
 
   - **sphinx_manual**:
     The format for Sphinx_ manuals; similar to a ``book`` in LaTeX 
 
   - **latex**:
     An article formatted completely using LaTeX
 
 * Markup:
 
   - **rst**:
     reStructuredText_ markup
 
   - **markdown**:
     Markdown_ markup
 
 .. _Sphinx: http://sphinx-doc.org/
 .. _reStructuredText: http://docutils.sourceforge.net/rst.html
+.. _Markdown: http://daringfireball.net/projects/markdown/syntax
 
 * Python:
 
     Comments out all the non-Python code to produce a ``.py`` Python
     script with just the code content. Currently the output includes IPython 
     magics, and so can be run with ``ipython``, after changing the extension 
     of the script to ``.ipy``.
     
 The files output file created by ``nbconvert`` will have the same base name as
 the notebook and will be placed in the current working directory. Any
 supporting files (graphics, etc) will be placed in a new directory with the
 same base name as the notebook, suffixed with ``_files``::
 
   $ ipython nbconvert notebook.ipynb
   $ ls
   notebook.ipynb   notebook.html    notebook_files/
 
 Each of the options for PDF export produces as an intermediate step a LaTeX 
 ``.tex`` file with the same basename as the notebook, as well as individual 
 files for each figure, and ``.text`` files with textual output from running
 code cells.
 
 To actually produce the final PDF file, run the following commands::
   
   $ ipython nbconvert --format=latex notebook.ipynb
   $ pdflatex notebook
 
 This requires a local installation of LaTeX on your machine.
 The output is a PDF file ``notebook.pdf``, also placed inside the 
 ``nbconvert_build`` subdirectory.
 
 Alternatively, the output may be sent to standard output with::
     
   $ ipython nbconvert notebook.ipynb --stdout
     
 Multiple notebooks can be specified from the command line::
     
   $ ipython nbconvert notebook*.ipynb
   $ ipython nbconvert notebook1.ipynb notebook2.ipynb
     
 or via a list in a configuration file, say ``mycfg.py``, containing the text::
 
   c = get_config()
   c.NbConvertApp.notebooks = ["notebook1.ipynb", "notebook2.ipynb"]
 
 and using the command::
 
   $ ipython nbconvert --config mycfg.py
 
 
 Extracting standard Python files from notebooks
 -----------------------------------------------
 ``.ipynb`` notebook document files are plain text files which store a 
 representation in JSON format of the contents of a notebook space. As such, 
 they are not valid ``.py`` Python scripts, and so can be neither imported 
 directly with ``import`` in Python, nor run directly as a standard Python 
 script (though both of these are possible with simple workarounds).
 
 
 To extract the Python code from within a notebook document, the simplest 
 method is to use the ``File | Download as | Python (.py)`` menu item; the 
 resulting ``.py`` script will be downloaded to your browser's  default 
 download location.
 
 An alternative is to pass an argument to the IPython Notebook, from the moment 
 when it is originally started, specifying that whenever it saves an ``.ipynb`` 
 notebook document, it should, at the same time, save the corresponding 
  ``.py`` script. To do so, you can execute the following command::
 
   $ ipython notebook --script
 
 or you can set this option permanently in your configuration file with::
 
   c = get_config()
   c.NotebookManager.save_script=True
 
 The result is that standard ``.py`` files are also now generated, which 
 can be ``%run``, imported from regular IPython sessions or other notebooks, or 
 executed at the command line, as usual.  Since the raw code you have typed is 
 exported, you must avoid using syntax such as IPython magics and other 
 IPython-specific extensions to the language for the files to be able to be 
 successfully imported.
 .. or you can change the script's extension to ``.ipy`` and run it with::
 ..
 ..  $ ipython script.ipy
 
 In normal Python practice, the standard way to differentiate importable code 
 in a Python script from the "executable" part of a script is to use the 
 following idiom at the start of the executable part of the code::
 
   if __name__ == '__main__'
 
     # rest of the code...
   
 Since all cells in the notebook are run as top-level code, you will need to
 similarly protect *all* cells that you do not want executed when other scripts
 try to import your notebook.  A convenient shortand for this is to define 
 early on::
 
   script = __name__ == '__main__'
 
 Then in any cell that you need to protect, use::
 
   if script:
     # rest of the cell...
 
 
 
 .. _notebook_format:
 
 Notebook JSON file format
 -------------------------
 Notebook documents are JSON files with an ``.ipynb`` extension, formatted
 as legibly as possible with minimal extra indentation and cell content broken
 across lines to make them reasonably friendly to use in version-control
 workflows.  You should be very careful if you ever manually edit this JSON
 data, as it is extremely easy to corrupt its internal structure and make the
 file impossible to load.  In general, you should consider the notebook as a
 file meant only to be edited by the IPython Notebook app itself, not for 
 hand-editing.
 
 .. note::
 
      Binary data such as figures are also saved directly in the JSON file.  
      This provides convenient single-file portability, but means that the 
      files can be large; a ``diff`` of binary data is also not very 
      meaningful.  Since the binary blobs are encoded in a single line, they 
      affect only one line of the ``diff`` output, but they are typically very 
      long lines.  You can use the ``Cell | All Output | Clear`` menu option to 
      remove all output from a notebook prior to committing it to version 
      control, if this is a concern.
 
 The notebook server can also generate a pure Python version of your notebook, 
 using the ``File | Download as`` menu option. The resulting ``.py`` file will 
 contain all the code cells from your notebook verbatim, and all Markdown cells 
 prepended with a comment marker.  The separation between code and Markdown
 cells is indicated with special comments and there is a header indicating the
 format version.  All output is removed when exporting to Python.
 
 As an example, consider a simple notebook called ``simple.ipynb`` which 
 contains one Markdown cell, with the content ``The simplest notebook.``, one 
 code input cell with the content ``print "Hello, IPython!"``, and the 
 corresponding output.
 
 The contents of the notebook document ``simple.ipynb`` is the following JSON 
 container::
 
   {
    "metadata": {
     "name": "simple"
    },
    "nbformat": 3,
    "nbformat_minor": 0,
    "worksheets": [
     {
      "cells": [
       {
        "cell_type": "markdown",
        "metadata": {},
        "source": "The simplest notebook."
       },
       {
        "cell_type": "code",
        "collapsed": false,
        "input": "print \"Hello, IPython\"",
        "language": "python",
        "metadata": {},
        "outputs": [
         {
          "output_type": "stream",
          "stream": "stdout",
          "text": "Hello, IPython\n"
         }
        ],
        "prompt_number": 1
       }
      ],
      "metadata": {}
     }
    ]
   }
 
 
 The corresponding Python script is::
 
   # -*- coding: utf-8 -*-
   # <nbformat>3.0</nbformat>
 
   # <markdowncell>
 
   # The simplest notebook.
 
   # <codecell>
 
   print "Hello, IPython"
 
 Note that indeed the output of the code cell, which is present in the JSON 
 container, has been removed in the ``.py`` script.
 

docs/source/interactive/notebook.rst

@@ -1,564 +1,561 b''
 .. _htmlnotebook:
 
 The IPython Notebook
 ====================
 
 The IPython Notebook is part of the IPython package, which aims to provide a 
 powerful, interactive approach to scientific computation.
 The IPython Notebook extends the previous text-console-based approach, and the 
 later Qt console, in a qualitatively new diretion, providing a web-based
 application suitable for capturing the whole scientific computation process.
 
 .. seealso::
 
     :ref:`Installation requirements <installnotebook>` for the Notebook.
 
 
 .. Basic structure
 .. ---------------
 
 Introduction
 ------------
 
 The IPython Notebook combines two components:
 
 * **The IPython Notebook web application**:
 
       The *IPython Notebook web app* is a browser-based tool for interactive 
       authoring of literate computations, in which explanatory text, 
       mathematics, computations and rich media output may be combined. Input 
       and output are stored in persistent cells that may be edited in-place.
 
 * **Notebook documents**:
 
       *Notebook documents*, or *notebooks*, are plain text documents which 
       record all inputs and outputs of the computations, interspersed with 
       text, mathematics and HTML 5 representations of objects, in a literate 
       style.
 
 Since the similarity in names can lead to some confusion, in this 
 documentation we will  use capitalization of the word "notebook" to 
-distinguish the *N*otebook app and *n*otebook documents, thinking of the 
+distinguish the Notebook app and notebook documents, thinking of the 
 Notebook app as being a proper noun. We will also always refer to the 
 "Notebook app" when we are referring to the browser-based interface, 
 and usually to "notebook documents", instead of "notebooks", for added
 precision.
 
 We refer to the current state of the computational process taking place in the 
 Notebook app, i.e. the (numbered) sequence of input and output cells, as the 
 *notebook space*. Notebook documents provide an *exact*, *one-to-one* record 
 of all the content in the notebook space, as a plain text file in JSON format. 
 The Notebook app automatically saves, at certain intervals, the contents of 
 the notebook space to a notebook document stored on disk, with the same name 
 as the title of the notebook space, and the file extension ``.ipynb``. For 
 this reason, there is no confusion about using the same word "notebook" for 
-both the notebook space and the corresonding notebook document, since they are 
+both the notebook space and the corresponding notebook document, since they are 
 really one and the same concept (we could say that they are "isomorphic").
 
 
 Main features of the IPython Notebook web app
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The main features of the IPython Notebook app include:
 
 * In-browser editing for code, with automatic syntax highlighting and 
   indentation and tab completion/introspection.
 
 * Literate combination of code with rich text using the Markdown_ markup 
   language.
 
 * Mathematics is easily included within the Markdown using LaTeX notation, and 
   rendered natively by MathJax_.
 
 * Displays rich data representations (e.g. HTML / LaTeX / SVG) as the result 
   of computations.
 
 * Publication-quality figures in a range of formats (SVG / PNG), rendered by 
   the matplotlib_ library, may be included inline and exported.
 
 
 .. _MathJax: http://www.mathjax.org/
 .. _matplotlib: http://matplotlib.org/
 .. _Markdown: http://daringfireball.net/projects/markdown/syntax
 
 
 Notebook documents
 ~~~~~~~~~~~~~~~~~~
 
-Notebook document files are just standard, ASCII-coded text files with the 
-extension ``.ipynb``, stored in the working directory on your computer. 
-Since the contents of the files are just plain text, they can be easily 
-version-controlled and shared with colleagues.
-
-Internally, notebook document files use the JSON_ format, allowing them to 
-store a *complete*, *reproducible*, *one-to-one* copy of the state of the
+Notebook document files are simple JSON_ files with the 
+extension ``.ipynb``.
+Since JSON is just plain text, they can be easily version-controlled and shared with colleagues.
+The notebook stores a *complete*, *reproducible*, *one-to-one* copy of the state of the
 computational state as it is inside the Notebook app. All computations
 carried out, and the corresponding results obtained, can be combined in
 a literate way, interleaving executable code with rich text, mathematics, 
-and HTML 5 representations of objects.
+and rich representations of objects.
 
 .. _JSON: http://en.wikipedia.org/wiki/JSON
 
 Notebooks may easily be exported to a range of static formats, including 
-HTML (for example, for blog posts), PDF and slide shows, via the 
-newly-included `nbconvert script`_ functionality.
+HTML (for example, for blog posts), PDF and slide shows, 
+via the new nbconvert_ command.
 
-Furthermore, any  ``.ipynb`` notebook document with a publicly-available 
-URL can be shared via the `IPython Notebook Viewer`_ service. This service
- loads the notebook document from the URL  which will 
-provide it as a static web page. The results may thus be shared with a 
+Furthermore, any  ``.ipynb`` notebook document available from a public 
+URL can be shared via the `IPython Notebook Viewer <nbviewer>`_ service.
+This service loads the notebook document from the URL and will 
+render it as a static web page. The results may thus be shared with a 
 colleague, or as a public blog post, without other users needing to install 
-IPython themselves.
+IPython themselves.  NbViewer is simply NbConvert as a simple heroku webservice.
 
 See the :ref:`installation documentation <install_index>` for directions on
 how to install the notebook and its dependencies.
 
-.. _`Ipython Notebook Viewer`: http://nbviewer.ipython.org
+.. _nbviewer: http://nbviewer.ipython.org
 
 .. note::
 
    You can start more than one notebook server at the same time, if you want 
    to work on notebooks in different directories.  By default the first 
    notebook server starts on port 8888, and later notebook servers search for  
    ports near that one.  You can also manually specify the port with the 
    ``--port`` option.
    
 
 Basic workflow in the IPython Notebook web app
 ----------------------------------------------
 
 Starting up
 ~~~~~~~~~~~~
 
 You can start running the Notebook web app using the following command::
 
     $ ipython notebook
 
 (Here, and in the sequel, the initial ``$`` represents the shell prompt, 
 indicating that the command is to be run from the command line in a shell.)
 
 The landing page of the IPython Notebook  application, the *dashboard*, shows 
-the notebooks currently available in the *working directory* (the directory 
+the notebooks currently available in the *notebook directory* (By default, the directory 
 from which the notebook was started).
 You can create new notebooks from the dashboard with the ``New Notebook``
 button, or open existing ones by clicking on their name.
 You can also drag and drop ``.ipynb`` notebooks and standard ``.py`` Python 
 source code files into the notebook list area.
 
 
 You can open an existing notebook directly, without having to go via the 
 dashboard, with:
 
   ipython notebook my_notebook
 
 The `.ipynb` extension is assumed if no extension is given.
 
 The `File | Open...` menu option will open the dashboard in a new browser tab, 
 to allow you to select a current notebook 
-from the working directory or to create a new notebook
+from the notebook directory or to create a new notebook.
 
 
 
 Notebook user interface
 ~~~~~~~~~~~~~~~~~~~~~~~
 
 When you open a new notebook document in the Notebook, you will be presented 
 with the title associated to the notebook space/document, a *menu bar*, a 
 *toolbar* and an empty *input cell*.
 
 Notebook title
 ^^^^^^^^^^^^^^
 The title of the notebook document that is currently being edited is displayed 
 at the top of the page, next to the ``IP[y]: Notebook`` logo. This title may 
 be edited directly by clicking on it. The title is reflected in the name of 
 the ``.ipynb`` notebook document file that is saved.
 
 Menu bar
 ^^^^^^^^
 The menu bar presents different options that may be used to manipulate the way 
 the Notebook functions.
 
 Toolbar
 ^^^^^^^
 The tool bar gives a quick way of accessing the most-used operations within 
 the Notebook, by clicking on an icon.
 
 
 Creating a new notebook document
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 A new notebook space/document may be created at any time, either from the 
 dashboard, or using the `File | New` menu option from within an active 
-notebook. The new notebook is created within the same working directory and 
+notebook. The new notebook is created within the same directory and 
 will open in a new browser tab. It will also be reflected as a new entry in 
 the notebook list on the dashboard.
 
 
 Structure of a notebook document
 --------------------------------
 
 Input cells
 ~~~~~~~~~~~
 Input cells are at the core of the functionality of the IPython Notebook.
 They are regions in the document in which you can enter different types of 
 text and commands. To *execute* or *run* the *current cell*, i.e. the cell 
 under the cursor, you can use the :kbd:`Shift-Enter` key combination. 
 This tells the Notebook app to perform the relevant operation for each type of 
 cell (see below), and then to display the resulting output.
 
 The notebook consists of a sequence of input cells, labelled ``In[n]``, which 
 may be executed in a non-linear way, and outputs ``Out[n]``, where ``n`` is a 
 number which denotes the order in which the cells were executed over the 
 history of the computational process. The contents of all of these cells are 
 accessible as Python variables with the same names, forming a complete record 
 of the history of the computation.
 
 
 
 Input cell types
 ~~~~~~~~~~~~~~~~
 Each IPython input cell has a *cell type*, of which there is a restricted 
 number. The type of a cell may be set by using the cell type dropdown on the 
 toolbar, or via the following keyboard shortcuts:
 
 * **code**: :kbd:`Ctrl-m y`
 * **markdown**: :kbd:`Ctrl-m m`
 * **raw**: :kbd:`Ctrl-m t`
 * **heading**: :kbd:`Ctrl-m 1` - :kbd:`Ctrl-m 6`
 
 Upon initial creation, each input cell is by default a code cell.
 
 
 Code cells
 ^^^^^^^^^^
 A *code input cell* allows you to edit code inline within the cell, with full 
 syntax highlighting and autocompletion/introspection. By default, the language 
 associated to a code cell is Python, but other languages, such as ``julia`` 
 and ``R``, can be handled using magic commands (see below).
 
 When a code cell is executed with :kbd:`Shift-Enter`, the code that it 
 contains is transparently exported and run in that language (with automatic 
 compiling, etc., if necessary). The result that is returned from this 
 computation  is then displayed in the notebook space as the cell's 
 *output*. If this output is of a textual nature, it is placed into a 
 numbered *output cell*. However, many other possible forms of output are also 
 possible, including ``matplotlib`` figures and HTML tables (as used, for 
 example, in the ``pandas`` data analyis package). This is known as IPython's 
 *rich display* capability.
 
 
 Markdown cells
 ^^^^^^^^^^^^^^
 You can document the computational process in a literate way, alternating 
 descriptive text with code, using *rich text*. In IPython this is accomplished 
 by marking up text with the Markdown language. The corresponding cells are 
 called *Markdown input cells*. The Markdown language provides a simple way to 
 perform this text markup, that is, to specify which parts of the text should 
 be emphasized (italics), bold, form lists, etc. 
 
 
 When a Markdown input cell is executed, the Markdown code is converted into 
 the corresponding formatted rich text. This output then *replaces* the 
 original Markdown input cell, leaving just the visually-significant marked up 
 rich text.  Markdown allows arbitrary HTML code for formatting.
 
 Within Markdown cells, you can also include *mathematics* in a straightforward 
 way, using standard LaTeX notation: ``$...$`` for inline mathematics and 
 ``$$...$$`` for displayed mathematics. When the Markdown cell is executed, 
 the LaTeX portions are automatically rendered in the HTML output as equations 
 with high quality typography. This is made possible by MathJax_, which 
-supports a `large subset`_ of LaTeX functionality 
+supports a `large subset <mathjax_tex>`_ of LaTeX functionality 
 
-.. _`large subset`: http://docs.mathjax.org/en/latest/tex.html
+.. _mathjax_tex: http://docs.mathjax.org/en/latest/tex.html
 
 Standard mathematics environments defined by LaTeX and AMS-LaTeX (the 
 `amsmath` package) also work, such as 
 ``\begin{equation}...\end{equation}``, and ``\begin{align}...\end{align}``.
 New LaTeX macros may be defined using standard methods, 
 such as ``\newcommand``, by placing them anywhere *between math delimiters* in 
 a Markdown cell. These definitions are then available throughout the rest of 
 the IPython session. (Note, however, that more care must be taken when using 
-the `nbconvert script`_ to output to LaTeX).
+nbconvert_ to output to LaTeX).
 
 Raw input cells
 ~~~~~~~~~~~~~~~
-*Raw* input cells provide a place in which you can put additional information 
-which you do not want to evaluated by the Notebook. This can be used, for 
-example, to include extra information that is needed when exporting to a 
-certain format. The output after evaluating a raw cell is just a verbatim copy 
-of the input.
+
+*Raw* input cells provide a place in which you can write *output* directly.
+Raw cells are not evaluated by the Notebook, and have no output.
+When passed through nbconvert, Raw cells arrive in the destination format unmodified,
+allowing you to type full latex into a raw cell, which will only be rendered
+by latex after conversion by nbconvert.
 
 Heading cells
 ~~~~~~~~~~~~~
+
 You can provide a conceptual structure for your computational document as a 
 whole using different levels of headings; there are 6 levels available, from 
 level 1 (top level) down to level 6 (paragraph). These can be used later for 
 constructing tables of contents, etc.
 
 As with Markdown cells, a heading input cell is replaced by a rich text 
 rendering of the heading when the cell is executed.
 
 
 Basic workflow
 --------------
+
 The normal workflow in a notebook is, then, quite similar to a standard 
 IPython session, with the difference that you can edit cells in-place multiple 
 times until you obtain the desired results, rather than having to 
 rerun separate scripts with the ``%run`` magic command. (Magic commands do, 
 however, also work in the notebook; see below).   
 
 Typically, you will work on a computational problem in pieces, organizing 
 related ideas into cells and moving forward once previous parts work 
 correctly. This is much more convenient for interactive exploration than 
 breaking up a computation into scripts that must be executed together, as was 
 previously necessary, especially if parts of them take a long time to run
 
 The only significant limitation that the Notebook currently has, compared to 
 the Qt console, is that it cannot run any code that expects input from the 
 kernel (such as scripts that call :func:`raw_input`).  Very importantly, this 
 means that the ``%debug`` magic does *not* currently work in the notebook!  
 
 This limitation will be overcome in the future, but in the meantime, there is 
 a simple solution for debugging: you can attach a Qt console to your existing 
 notebook kernel, and run ``%debug`` from the Qt console.
 If your notebook is running on a local computer (i.e. if you are accessing it 
 via your localhost address at ``127.0.0.1``), then you can just type 
 ``%qtconsole`` in the notebook and a Qt console will open up, connected to 
 that same kernel.
 
 At certain moments, it may be necessary to interrupt a calculation which is 
 taking too long to complete. This may be done with the ``Kernel | Interrupt`` 
 menu option, or the :kbd:``Ctrl-i`` keyboard shortcut.
 Similarly, it may be necessary or desirable to restart the whole computational 
 process, with the ``Kernel | Restart`` menu option or :kbd:``Ctrl-.`` 
 shortcut. This gives an equivalent state to loading the notebook document 
 afresh.
 
     
 .. warning::
 
    While in simple cases you can "roundtrip" a notebook to Python, edit the
    Python file, and then import it back without loss of main content, this is 
    in general *not guaranteed to work*.  First, there is extra metadata
    saved in the notebook that may not be saved to the ``.py`` format.  And as
    the notebook format evolves in complexity, there will be attributes of the
    notebook that will not survive a roundtrip through the Python form.  You
    should think of the Python format as a way to output a script version of a
    notebook and the import capabilities as a way to load existing code to get 
    a notebook started.  But the Python version is *not* an alternate notebook
    format.
 
 
 Keyboard shortcuts
 ~~~~~~~~~~~~~~~~~~
 All actions in the notebook can be achieved with the mouse, but keyboard 
 shortcuts are also available for the most common ones, so that productive use 
 of the notebook can be achieved with minimal mouse usage. The main shortcuts 
 to remember are the following:
 
 * :kbd:`Shift-Enter`: 
 
     Execute the current cell, show output (if any), and jump to the next cell 
     below. If :kbd:`Shift-Enter` is invoked on the last input cell, a new code 
     cell will also be created. Note that in the notebook, typing :kbd:`Enter` 
     on its own *never* forces execution, but rather just inserts a new line in 
     the current input cell. In the Notebook it is thus always necessary to use 
     :kbd:`Shift-Enter` to execute the cell (or use the ``Cell | Run`` menu 
     item).
 
 * :kbd:`Ctrl-Enter`: 
     Execute the current cell as if it were in "terminal mode", where any 
     output is shown, but the cursor *remains* in the current cell. This is 
     convenient for doing quick experiments in place, or for querying things 
     like filesystem content, without needing to create additional cells that 
     you may not want to be saved in the notebook.
 
 * :kbd:`Alt-Enter`: 
     Executes the current cell, shows the output, and inserts a *new* input 
     cell between the current cell and the adjacent cell (if one exists). This  
     is thus a shortcut for the sequence :kbd:`Shift-Enter`, :kbd:`Ctrl-m a`.
     (:kbd:`Ctrl-m a` adds a new cell above the current one.)
   
 * :kbd:`Ctrl-m`: 
   This is the prefix for *all* other shortcuts, which consist of :kbd:`Ctrl-m` 
   followed by a single letter or character. For example, if you type 
   :kbd:`Ctrl-m h` (that is, the sole letter :kbd:`h` after :kbd:`Ctrl-m`), 
   IPython will show you all the available keyboard shortcuts.
    
 
 Magic commands
 --------------
 Magic commands, or *magics*, are commands for controlling IPython itself.
 They all begin with ``%`` and are entered into code input cells; the code 
 cells are executed as usual with :kbd:`Shift-Enter`.
 
 The magic commands call special functions defined by IPython which manipulate 
 the computational state in certain ways.
 
 There are two types of magics:
 
   - **line magics**:
 
      These begin with a single ``%`` and take as arguments the rest of the 
      *same line* of the code cell. Any other lines of the code cell are 
      treated as if they were part of a standard code cell.
 
   - **cell magics**:
 
       These begin with ``%%`` and operate on the *entire* remaining contents 
       of the code cell.
 
 Line magics
 ~~~~~~~~~~~
 Some of the available line magics are the following:
 
   * ``%load filename``:
 
         Loads the contents of the file ``filename`` into a new code cell. This 
         can be a URL for a remote file.
 
   * ``%timeit code``: 
 
       An easy way to time how long the single line of code ``code`` takes to 
       run
 
   * ``%config``:
 
       Configuration of the IPython Notebook
 
   * ``%lsmagic``:
 
       Provides a list of all available magic commands
 
 Cell magics
 ~~~~~~~~~~~
 
   * ``%%latex``:
 
       Renders the entire contents of the cell in LaTeX, without needing to use 
       explicit LaTeX delimiters.
 
   * ``%%bash``:
 
       The code cell is executed by sending it to be executed by ``bash``. The 
       output of the ``bash`` commands is captured and displayed in the 
       notebook.
 
   * ``%%file filename``:
 
       Writes the contents of the cell to the file ``filename``.
       **Caution**: The file is over-written without warning!
 
   * ``%%R``:
 
       Execute the contents of the cell using the R language.
 
   * ``%%timeit``:
 
       Version of ``%timeit`` which times the entire block of code in the 
       current code cell.
 
 
 
 Several of the cell magics provide functionality to manipulate the filesystem 
 of a remote server to which you otherwise do not have access.  
 
 
 Plotting
 --------
 One major feature of the Notebook is the ability to interact with 
 plots that are the output of running code cells. IPython is designed to work 
 seamlessly with the ``matplotlib`` plotting library to provide this 
 functionality. 
 
 To set this up, before any plotting is performed you must execute the
 ``%matplotlib`` magic command. This performs the necessary behind-the-scenes 
 setup for IPython to work correctly hand in hand with ``matplotlib``; it does 
 *not*, however, actually execute any Python ``import`` commands, that is, no 
 names are added to the namespace.
 
 For more agile *interactive* use of the notebook space, an alternative magic, 
 ``%pylab``, is provided. This does the same work as the ``%matplotlib`` magic, 
 but *in addition* it automatically executes a standard sequence of ``import`` 
 statements required to work with the ``%matplotlib`` library, importing the 
 following names into the namespace:
 
   ``numpy`` as ``np``; ``matplotlib.pyplot`` as ``plt``;
   ``matplotlib``, ``pylab`` and ``mlab`` from ``matplotlib``; and *all names* 
   from within ``numpy`` and ``pylab``. 
 
 However, the use of ``%pylab`` is discouraged, since names coming from 
 different packages may collide. In general, the use of ``from package import 
 *`` is discouraged. A better option is then::
   
     %pylab --no-import-all
 
 which imports the  names listed above, but does *not* perform this 
 ``import *`` imports.
 
 If the ``%matplotlib`` or ``%pylab` magics are called without an argument, the 
 output of a plotting command is displayed using the default ``matplotlib`` 
 backend in a separate window. Alternatively, the backend can be explicitly 
 requested using, for example::
 
   %matplotlib gtk
 
 A particularly interesting backend is the ``inline`` backend.
 This is applicable only for the IPython Notebook and the IPython Qtconsole. 
 It can be invoked as follows::
 
   %matplotlib inline
 
 With this backend, output of plotting commands is displayed *inline* within 
 the notebook format, directly below the input cell that produced it. The 
 resulting plots will then also be stored in the notebook document. This 
 provides a key part of the functionality for reproducibility_ that the IPython 
 Notebook provides.
 
 .. _reproducibility: https://en.wikipedia.org/wiki/Reproducibility
 
 
 
 Configuring the IPython Notebook
 --------------------------------
 The IPython Notebook can be run with a variety of command line arguments. 
 To see a list of available options enter::
 
   $ ipython notebook --help 
 
 Defaults for these options can also be set by creating a file named 
 ``ipython_notebook_config.py`` in your IPython *profile folder*. The profile 
 folder is a subfolder of your IPython directory; to find out where it is 
 located, run::
 
   $ ipython locate
 
 To create a new set of default configuration files, with lots of information 
 on available options, use::
 
   $ ipython profile create
 
 .. seealso:
 
     :ref:`config_overview`, in particular :ref:`Profiles`.
 
 
 Importing `.py` files
 ----------------------
 
 
 ``.py`` files will be imported into the IPython Notebook as a notebook with 
-the same basename, but an ``.ipynb`` extension, located in the working 
+the same basename, but an ``.ipynb`` extension, located in the notebook 
 directory. The notebook created will have just one cell, which will contain 
 all the code in the ``.py`` file. You can later manually partition this into 
 individual cells using the ``Edit | Split Cell`` menu option, or the 
 :kbd:`Ctrl-m -` keyboard shortcut.
 
 .. Alternatively, prior to importing the ``.py``, you can manually add ``# <
 nbformat>2</nbformat>`` at the start of the file, and then add separators for 
 text and code cells, to get a cleaner import with the file already broken into 
 individual cells.
 
-
-    
-.. _Markdown: http://daringfireball.net/projects/markdown/basics