From 446f347b84e5e8eb5e6c4d5192b05eb9b32983bb 2013-02-13 19:30:13
From: Matthias BUSSONNIER <bussonniermatthias@gmail.com>
Date: 2013-02-13 19:30:13
Subject: [PATCH] start writting more references test

---

diff --git a/profile/full_html.nbcv b/profile/full_html.nbcv
index ab3ec71..88182d4 100644
--- a/profile/full_html.nbcv
+++ b/profile/full_html.nbcv
@@ -1,7 +1,6 @@
 c = get_config()
 
 
-c.ConverterTemplate.extract_figures=False
 c.ConverterTemplate.template_file='fullhtml'
 c.ConverterTemplate.tex_environement=False
 
diff --git a/tests/ipynbref/00_notebook_tour.orig.rst b/tests/ipynbref/00_notebook_tour.orig.rst
new file mode 100644
index 0000000..1bdd22b
--- /dev/null
+++ b/tests/ipynbref/00_notebook_tour.orig.rst
@@ -0,0 +1,698 @@
+A brief tour of the IPython notebook
+====================================
+
+This document will give you a brief tour of the capabilities of the
+IPython notebook.
+You can view its contents by scrolling around, or execute each cell by
+typing ``Shift-Enter``. After you conclude this brief high-level tour,
+you should read the accompanying notebook titled
+``01_notebook_introduction``, which takes a more step-by-step approach
+to the features of the system.
+
+The rest of the notebooks in this directory illustrate various other
+aspects and capabilities of the IPython notebook; some of them may
+require additional libraries to be executed.
+
+**NOTE:** This notebook *must* be run from its own directory, so you
+must ``cd`` to this directory and then start the notebook, but do *not*
+use the ``--notebook-dir`` option to run it from another location.
+
+The first thing you need to know is that you are still controlling the
+same old IPython you're used to, so things like shell aliases and magic
+commands still work:
+
+In[1]:
+
+.. code:: python
+
+    pwd
+
+Out[1]:
+
+.. parsed-literal::
+
+    u'/Users/minrk/dev/ip/mine/docs/examples/notebooks'
+
+In[2]:
+
+.. code:: python
+
+    ls
+
+.. parsed-literal::
+
+    00_notebook_tour.ipynb          callbacks.ipynb                 python-logo.svg
+    01_notebook_introduction.ipynb  cython_extension.ipynb          rmagic_extension.ipynb
+    Animations_and_Progress.ipynb   display_protocol.ipynb          sympy.ipynb
+    Capturing Output.ipynb          formatting.ipynb                sympy_quantum_computing.ipynb
+    Script Magics.ipynb             octavemagic_extension.ipynb     trapezoid_rule.ipynb
+    animation.m4v                   progbar.ipynb
+
+
+In[3]:
+
+.. code:: python
+
+    message = 'The IPython notebook is great!'
+    # note: the echo command does not run on Windows, it's a unix command.
+    !echo $message
+
+.. parsed-literal::
+
+    The IPython notebook is great!
+
+
+Plots with matplotlib
+---------------------
+
+IPython adds an 'inline' matplotlib backend, which embeds any matplotlib
+figures into the notebook.
+
+In[4]:
+
+.. code:: python
+
+    %pylab inline
+
+.. parsed-literal::
+
+    
+    Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
+    For more information, type 'help(pylab)'.
+
+
+In[5]:
+
+.. code:: python
+
+    x = linspace(0, 3*pi, 500)
+    plot(x, sin(x**2))
+    title('A simple chirp');
+
+.. image:: tests/ipynbref/00_notebook_tour_orig_files/00_notebook_tour_orig_fig_00.png
+
+You can paste blocks of input with prompt markers, such as those from
+`the official Python
+tutorial <http://docs.python.org/tutorial/interpreter.html#interactive-mode>`_
+
+In[6]:
+
+.. code:: python
+
+    >>> the_world_is_flat = 1
+    >>> if the_world_is_flat:
+    ...     print "Be careful not to fall off!"
+
+.. parsed-literal::
+
+    Be careful not to fall off!
+
+
+Errors are shown in informative ways:
+
+In[7]:
+
+.. code:: python
+
+    %run non_existent_file
+
+.. parsed-literal::
+
+    ERROR: File `u'non_existent_file.py'` not found.
+
+In[8]:
+
+.. code:: python
+
+    x = 1
+    y = 4
+    z = y/(1-x)
+
+::
+
+    ---------------------------------------------------------------------------
+    ZeroDivisionError                         Traceback (most recent call last)
+    <ipython-input-8-dc39888fd1d2> in <module>()
+          1 x = 1
+          2 y = 4
+    ----> 3 z = y/(1-x)
+    
+    ZeroDivisionError: integer division or modulo by zero
+
+When IPython needs to display additional information (such as providing
+details on an object via ``x?`` it will automatically invoke a pager at
+the bottom of the screen:
+
+In[18]:
+
+.. code:: python
+
+    magic
+
+Non-blocking output of kernel
+-----------------------------
+
+If you execute the next cell, you will see the output arriving as it is
+generated, not all at the end.
+
+In[19]:
+
+.. code:: python
+
+    import time, sys
+    for i in range(8):
+        print i,
+        time.sleep(0.5)
+
+.. parsed-literal::
+
+    0 
+
+.. parsed-literal::
+
+    1 
+
+.. parsed-literal::
+
+    2 
+
+.. parsed-literal::
+
+    3 
+
+.. parsed-literal::
+
+    4 
+
+.. parsed-literal::
+
+    5 
+
+.. parsed-literal::
+
+    6 
+
+.. parsed-literal::
+
+    7
+
+
+Clean crash and restart
+-----------------------
+
+We call the low-level system libc.time routine with the wrong argument
+via ctypes to segfault the Python interpreter:
+
+In[*]:
+
+.. code:: python
+
+    import sys
+    from ctypes import CDLL
+    # This will crash a Linux or Mac system; equivalent calls can be made on Windows
+    dll = 'dylib' if sys.platform == 'darwin' else '.so.6'
+    libc = CDLL("libc.%s" % dll) 
+    libc.time(-1)  # BOOM!!
+
+Markdown cells can contain formatted text and code
+--------------------------------------------------
+
+You can *italicize*, **boldface**
+
+-  build
+-  lists
+
+and embed code meant for illustration instead of execution in Python:
+
+::
+
+    def f(x):
+        """a docstring"""
+        return x**2
+
+or other languages:
+
+::
+
+    if (i=0; i<n; i++) {
+      printf("hello %d\n", i);
+      x += 4;
+    }
+
+
+Courtesy of MathJax, you can include mathematical expressions both
+inline: :math:`e^{i\pi} + 1 = 0` and displayed:
+
+.. math:: e^x=\sum_{i=0}^\infty \frac{1}{i!}x^i
+
+
+
+Rich displays: include anyting a browser can show
+-------------------------------------------------
+
+Note that we have an actual protocol for this, see the
+``display_protocol`` notebook for further details.
+
+Images
+~~~~~~
+
+
+In[1]:
+
+.. code:: python
+
+    from IPython.display import Image
+    Image(filename='../../source/_static/logo.png')
+
+Out[1]:
+
+.. parsed-literal::
+
+    <IPython.core.display.Image at 0x10faeafd0>
+
+An image can also be displayed from raw data or a url
+
+In[2]:
+
+.. code:: python
+
+    Image(url='http://python.org/images/python-logo.gif')
+
+Out[2]:
+
+.. parsed-literal::
+
+    <IPython.core.display.Image at 0x1060e7410>
+
+SVG images are also supported out of the box (since modern browsers do a
+good job of rendering them):
+
+In[3]:
+
+.. code:: python
+
+    from IPython.display import SVG
+    SVG(filename='python-logo.svg')
+
+Out[3]:
+
+.. parsed-literal::
+
+    <IPython.core.display.SVG at 0x10fb998d0>
+
+Embedded vs Non-embedded Images
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+
+As of IPython 0.13, images are embedded by default for compatibility
+with QtConsole, and the ability to still be displayed offline.
+
+Let's look at the differences:
+
+In[4]:
+
+.. code:: python
+
+    # by default Image data are embedded
+    Embed      = Image(    'http://scienceview.berkeley.edu/view/images/newview.jpg')
+    
+    # if kwarg `url` is given, the embedding is assumed to be false
+    SoftLinked = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg')
+    
+    # In each case, embed can be specified explicitly with the `embed` kwarg
+    # ForceEmbed = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg', embed=True)
+
+Today's image from a webcam at Berkeley, (at the time I created this
+notebook). This should also work in the Qtconsole. Drawback is that the
+saved notebook will be larger, but the image will still be present
+offline.
+
+In[5]:
+
+.. code:: python
+
+    Embed
+
+Out[5]:
+
+.. parsed-literal::
+
+    <IPython.core.display.Image at 0x10fb99b50>
+
+Today's image from same webcam at Berkeley, (refreshed every minutes, if
+you reload the notebook), visible only with an active internet
+connexion, that should be different from the previous one. This will not
+work on Qtconsole. Notebook saved with this kind of image will be
+lighter and always reflect the current version of the source, but the
+image won't display offline.
+
+In[6]:
+
+.. code:: python
+
+    SoftLinked
+
+Out[6]:
+
+.. parsed-literal::
+
+    <IPython.core.display.Image at 0x10fb99b10>
+
+Of course, if you re-run the all notebook, the two images will be the
+same again.
+
+Video
+~~~~~
+
+
+And more exotic objects can also be displayed, as long as their
+representation supports the IPython display protocol.
+
+For example, videos hosted externally on YouTube are easy to load (and
+writing a similar wrapper for other hosted content is trivial):
+
+In[7]:
+
+.. code:: python
+
+    from IPython.display import YouTubeVideo
+    # a talk about IPython at Sage Days at U. Washington, Seattle.
+    # Video credit: William Stein.
+    YouTubeVideo('1j_HxD4iLn8')
+
+Out[7]:
+
+.. parsed-literal::
+
+    <IPython.lib.display.YouTubeVideo at 0x10fba2190>
+
+Using the nascent video capabilities of modern browsers, you may also be
+able to display local videos. At the moment this doesn't work very well
+in all browsers, so it may or may not work for you; we will continue
+testing this and looking for ways to make it more robust.
+
+The following cell loads a local file called ``animation.m4v``, encodes
+the raw video as base64 for http transport, and uses the HTML5 video tag
+to load it. On Chrome 15 it works correctly, displaying a control bar at
+the bottom with a play/pause button and a location slider.
+
+In[8]:
+
+.. code:: python
+
+    from IPython.display import HTML
+    video = open("animation.m4v", "rb").read()
+    video_encoded = video.encode("base64")
+    video_tag = '<video controls alt="test" src="data:video/x-m4v;base64,{0}">'.format(video_encoded)
+    HTML(data=video_tag)
+
+Out[8]:
+
+.. parsed-literal::
+
+    <IPython.core.display.HTML at 0x10fba28d0>
+
+Local Files
+-----------
+
+The above examples embed images and video from the notebook filesystem
+in the output areas of code cells. It is also possible to request these
+files directly in markdown cells if they reside in the notebook
+directory via relative urls prefixed with ``files/``:
+
+::
+
+    files/[subdirectory/]<filename>
+
+For example, in the example notebook folder, we have the Python logo,
+addressed as:
+
+::
+
+    <img src="files/python-logo.svg" />
+
+and a video with the HTML5 video tag:
+
+::
+
+    <video controls src="files/animation.m4v" />
+
+These do not embed the data into the notebook file, and require that the
+files exist when you are viewing the notebook.
+
+Security of local files
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Note that this means that the IPython notebook server also acts as a
+generic file server for files inside the same tree as your notebooks.
+Access is not granted outside the notebook folder so you have strict
+control over what files are visible, but for this reason it is highly
+recommended that you do not run the notebook server with a notebook
+directory at a high level in your filesystem (e.g. your home directory).
+
+When you run the notebook in a password-protected manner, local file
+access is restricted to authenticated users unless read-only views are
+active.
+
+Linking to files and directories for viewing in the browser
+-----------------------------------------------------------
+
+It is also possible to link directly to files or directories so they can
+be opened in the browser. This is especially convenient if you're
+interacting with a tool within IPython that generates HTML pages, and
+you'd like to easily be able to open those in a new browser window.
+Alternatively, if your IPython notebook server is on a remote system,
+creating links provides an easy way to download any files that get
+generated.
+
+As we saw above, there are a bunch of ``.ipynb`` files in our current
+directory.
+
+In[1]:
+
+.. code:: python
+
+    ls
+
+.. parsed-literal::
+
+    00_notebook_tour.ipynb          formatting.ipynb
+    01_notebook_introduction.ipynb  octavemagic_extension.ipynb
+    Animations_and_Progress.ipynb   publish_data.ipynb
+    Capturing Output.ipynb          python-logo.svg
+    Script Magics.ipynb             rmagic_extension.ipynb
+    animation.m4v                   sympy.ipynb
+    cython_extension.ipynb          sympy_quantum_computing.ipynb
+    display_protocol.ipynb          trapezoid_rule.ipynb
+
+
+If we want to create a link to one of them, we can call use the
+``FileLink`` object.
+
+In[2]:
+
+.. code:: python
+
+    from IPython.display import FileLink
+    FileLink('00_notebook_tour.ipynb')
+
+Out[2]:
+
+.. parsed-literal::
+
+    <IPython.lib.display.FileLink at 0x10f7ea3d0>
+
+Alternatively, if we want to link to all of them, we can use the
+``FileLinks`` object, passing ``'.'`` to indicate that we want links
+generated for the current working directory. Note that if there were
+other directories under the current directory, ``FileLinks`` would work
+in a recursive manner creating links to files in all sub-directories as
+well.
+
+In[7]:
+
+.. code:: python
+
+    from IPython.display import FileLinks
+    FileLinks('.')
+
+Out[7]:
+
+.. parsed-literal::
+
+    <IPython.lib.display.FileLinks at 0x10f7eaad0>
+
+External sites
+~~~~~~~~~~~~~~
+
+You can even embed an entire page from another site in an iframe; for
+example this is today's Wikipedia page for mobile users:
+
+In[9]:
+
+.. code:: python
+
+    HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')
+
+Out[9]:
+
+.. parsed-literal::
+
+    <IPython.core.display.HTML at 0x1094900d0>
+
+Mathematics
+~~~~~~~~~~~
+
+And we also support the display of mathematical expressions typeset in
+LaTeX, which is rendered in the browser thanks to the `MathJax
+library <http://mathjax.org>`_.
+
+Note that this is *different* from the above examples. Above we were
+typing mathematical expressions in Markdown cells (along with normal
+text) and letting the browser render them; now we are displaying the
+output of a Python computation as a LaTeX expression wrapped by the
+``Math()`` object so the browser renders it. The ``Math`` object will
+add the needed LaTeX delimiters (``$$``) if they are not provided:
+
+In[10]:
+
+.. code:: python
+
+    from IPython.display import Math
+    Math(r'F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx')
+
+Out[10]:
+
+.. math::
+
+    $$F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx$$
+
+.. parsed-literal::
+
+    <IPython.core.display.Math at 0x10fba26d0>
+
+With the ``Latex`` class, you have to include the delimiters yourself.
+This allows you to use other LaTeX modes such as ``eqnarray``:
+
+In[11]:
+
+.. code:: python
+
+    from IPython.display import Latex
+    Latex(r"""\begin{eqnarray}
+    \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
+    \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
+    \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
+    \nabla \cdot \vec{\mathbf{B}} & = 0 
+    \end{eqnarray}""")
+
+Out[11]:
+
+.. math::
+
+    \begin{eqnarray}
+    \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
+    \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
+    \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
+    \nabla \cdot \vec{\mathbf{B}} & = 0 
+    \end{eqnarray}
+
+.. parsed-literal::
+
+    <IPython.core.display.Latex at 0x10fba2c10>
+
+Or you can enter latex directly with the ``%%latex`` cell magic:
+
+In[12]:
+
+.. code:: python
+
+    %%latex
+    \begin{aligned}
+    \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
+    \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
+    \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
+    \nabla \cdot \vec{\mathbf{B}} & = 0
+    \end{aligned}
+
+.. parsed-literal::
+
+    <IPython.core.display.Latex at 0x10a617c90>
+
+There is also a ``%%javascript`` cell magic for running javascript
+directly, and ``%%svg`` for manually entering SVG content.
+
+Loading external codes
+======================
+
+-  Drag and drop a ``.py`` in the dashboard
+-  Use ``%load`` with any local or remote url: `the Matplotlib
+   Gallery! <http://matplotlib.sourceforge.net/gallery.html>`_
+
+In this notebook we've kept the output saved so you can see the result,
+but you should run the next cell yourself (with an active internet
+connection).
+
+Let's make sure we have pylab again, in case we have restarted the
+kernel due to the crash demo above
+
+In[12]:
+
+.. code:: python
+
+    %pylab inline
+
+.. parsed-literal::
+
+    
+    Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
+    For more information, type 'help(pylab)'.
+
+
+In[15]:
+
+.. code:: python
+
+    %load http://matplotlib.sourceforge.net/mpl_examples/pylab_examples/integral_demo.py
+
+In[16]:
+
+.. code:: python
+
+    #!/usr/bin/env python
+    
+    # implement the example graphs/integral from pyx
+    from pylab import *
+    from matplotlib.patches import Polygon
+    
+    def func(x):
+        return (x-3)*(x-5)*(x-7)+85
+    
+    ax = subplot(111)
+    
+    a, b = 2, 9 # integral area
+    x = arange(0, 10, 0.01)
+    y = func(x)
+    plot(x, y, linewidth=1)
+    
+    # make the shaded region
+    ix = arange(a, b, 0.01)
+    iy = func(ix)
+    verts = [(a,0)] + zip(ix,iy) + [(b,0)]
+    poly = Polygon(verts, facecolor='0.8', edgecolor='k')
+    ax.add_patch(poly)
+    
+    text(0.5 * (a + b), 30,
+         r"$\int_a^b f(x)\mathrm{d}x$", horizontalalignment='center',
+         fontsize=20)
+    
+    axis([0,10, 0, 180])
+    figtext(0.9, 0.05, 'x')
+    figtext(0.1, 0.9, 'y')
+    ax.set_xticks((a,b))
+    ax.set_xticklabels(('a','b'))
+    ax.set_yticks([])
+    show()
+
+
+.. image:: tests/ipynbref/00_notebook_tour_orig_files/00_notebook_tour_orig_fig_01.png
diff --git a/tests/ipynbref/IntroNumPy.orig.html b/tests/ipynbref/IntroNumPy.orig.html
index 8267056..06a03e7 100644
--- a/tests/ipynbref/IntroNumPy.orig.html
+++ b/tests/ipynbref/IntroNumPy.orig.html
@@ -1,3 +1,4 @@
+<!DOCTYPE html>
 <html>
 <head>
 <style type="text/css">
@@ -29457,4 +29458,4 @@ Wall time:       0.34 s.
 <p>Finally, as we said earlier, IPython also has a high-level and easy to use set of libraries for parallel computing, that let you control (interactively if desired) not just one IPython but an entire cluster of 'IPython engines'.   Unfortunately a detailed discussion of these tools is beyond the scope of this text, but should you need to parallelize your analysis codes, a quick read of the tutorials and examples provided at the IPython site may prove fruitful.</p>
 </div>
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/tests/ipynbref/data_geeks_team_calendar.orig.rst b/tests/ipynbref/data_geeks_team_calendar.orig.rst
new file mode 100644
index 0000000..5990982
--- /dev/null
+++ b/tests/ipynbref/data_geeks_team_calendar.orig.rst
@@ -0,0 +1,70 @@
+In[1]:
+
+.. code:: python
+
+    #! /usr/bin/env python
+    '''
+    github_team_calendar.py
+    Python program to scrape friends github to build team calendar for github
+    '''
+    
+    
+    import json
+    import requests
+    import pandas as pd
+    
+    
+    def line_draw_target(target):                                                    
+        github_url = 'https://github.com/users/%s/contributions_calendar_data'       
+        r = requests.get(github_url % target)                                                                                                                                                                                                                                                                                   
+        data = json.loads(r.text)                                                    
+        dates, contributions = zip(*data)                                            
+        ts = pd.Series(contributions, index=dates)
+        plt.plot(ts)
+
+In[2]:
+
+.. code:: python
+
+    target = "mikedewar"
+    line_draw_target(target)
+            
+
+.. image:: tests/ipynbref/data_geeks_team_calendar_orig_files/data_geeks_team_calendar_orig_fig_00.png
+
+In[3]:
+
+.. code:: python
+
+    target = "drewconway"
+    line_draw_target(target)
+
+.. image:: tests/ipynbref/data_geeks_team_calendar_orig_files/data_geeks_team_calendar_orig_fig_01.png
+
+In[4]:
+
+.. code:: python
+
+    target = "hmason"
+    line_draw_target(target)
+
+.. image:: tests/ipynbref/data_geeks_team_calendar_orig_files/data_geeks_team_calendar_orig_fig_02.png
+
+In[5]:
+
+.. code:: python
+
+    target = "mbostock"
+    line_draw_target(target)
+
+.. image:: tests/ipynbref/data_geeks_team_calendar_orig_files/data_geeks_team_calendar_orig_fig_03.png
+
+In[6]:
+
+.. code:: python
+
+    target = "amueller"
+    line_draw_target(target)
+
+.. image:: tests/ipynbref/data_geeks_team_calendar_orig_files/data_geeks_team_calendar_orig_fig_04.png
+
diff --git a/tests/template_ref/00_notebook_tour_orig.html b/tests/template_ref/00_notebook_tour_orig.html
new file mode 100644
index 0000000..8e858ec
--- /dev/null
+++ b/tests/template_ref/00_notebook_tour_orig.html
@@ -0,0 +1,4306 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="UTF-8">
+<title>[00_notebook_tour]</title>
+<style type="text/css">
+/**
+ * HTML5 ✰ Boilerplate
+ *
+ * style.css contains a reset, font normalization and some base styles.
+ *
+ * Credit is left where credit is due.
+ * Much inspiration was taken from these projects:
+ * - yui.yahooapis.com/2.8.1/build/base/base.css
+ * - camendesign.com/design/
+ * - praegnanz.de/weblog/htmlcssjs-kickstart
+ */
+
+
+/**
+ * html5doctor.com Reset Stylesheet (Eric Meyer's Reset Reloaded + HTML5 baseline)
+ * v1.6.1 2010-09-17 | Authors: Eric Meyer & Richard Clark
+ * html5doctor.com/html-5-reset-stylesheet/
+ */
+
+html, body, div, span, object, iframe,
+h1, h2, h3, h4, h5, h6, p, blockquote, pre,
+abbr, address, cite, code, del, dfn, em, img, ins, kbd, q, samp,
+small, strong, sub, sup, var, b, i, dl, dt, dd, ol, ul, li,
+fieldset, form, label, legend,
+table, caption, tbody, tfoot, thead, tr, th, td,
+article, aside, canvas, details, figcaption, figure,
+footer, header, hgroup, menu, nav, section, summary,
+time, mark, audio, video {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  font-size: 100%;
+  font: inherit;
+  vertical-align: baseline;
+}
+
+sup { vertical-align: super; }
+sub { vertical-align: sub; }
+
+article, aside, details, figcaption, figure,
+footer, header, hgroup, menu, nav, section {
+  display: block;
+}
+
+blockquote, q { quotes: none; }
+
+blockquote:before, blockquote:after,
+q:before, q:after { content: ""; content: none; }
+
+ins { background-color: #ff9; color: #000; text-decoration: none; }
+
+mark { background-color: #ff9; color: #000; font-style: italic; font-weight: bold; }
+
+del { text-decoration: line-through; }
+
+abbr[title], dfn[title] { border-bottom: 1px dotted; cursor: help; }
+
+table { border-collapse: collapse; border-spacing: 0; }
+
+hr { display: block; height: 1px; border: 0; border-top: 1px solid #ccc; margin: 1em 0; padding: 0; }
+
+input, select { vertical-align: middle; }
+
+
+/**
+ * Font normalization inspired by YUI Library's fonts.css: developer.yahoo.com/yui/
+ */
+
+body { font:13px/1.231 sans-serif; *font-size:small; } /* Hack retained to preserve specificity */
+select, input, textarea, button { font:99% sans-serif; }
+
+/* Normalize monospace sizing:
+   en.wikipedia.org/wiki/MediaWiki_talk:Common.css/Archive_11#Teletype_style_fix_for_Chrome */
+pre, code, kbd, samp { font-family: monospace, sans-serif; }
+
+em,i { font-style: italic; }
+b,strong { font-weight: bold; }
+
+</style>
+<style type="text/css">
+article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block;}
+audio,canvas,video{display:inline-block;*display:inline;*zoom:1;}
+audio:not([controls]){display:none;}
+html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%;}
+a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px;}
+a:hover,a:active{outline:0;}
+sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline;}
+sup{top:-0.5em;}
+sub{bottom:-0.25em;}
+img{max-width:100%;width:auto\9;height:auto;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic;}
+#map_canvas img,.google-maps img{max-width:none;}
+button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle;}
+button,input{*overflow:visible;line-height:normal;}
+button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0;}
+button,html input[type="button"],input[type="reset"],input[type="submit"]{-webkit-appearance:button;cursor:pointer;}
+label,select,button,input[type="button"],input[type="reset"],input[type="submit"],input[type="radio"],input[type="checkbox"]{cursor:pointer;}
+input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield;}
+input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none;}
+textarea{overflow:auto;vertical-align:top;}
+@media print{*{text-shadow:none !important;color:#000 !important;background:transparent !important;box-shadow:none !important;} a,a:visited{text-decoration:underline;} a[href]:after{content:" (" attr(href) ")";} abbr[title]:after{content:" (" attr(title) ")";} .ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:"";} pre,blockquote{border:1px solid #999;page-break-inside:avoid;} thead{display:table-header-group;} tr,img{page-break-inside:avoid;} img{max-width:100% !important;} @page {margin:0.5cm;}p,h2,h3{orphans:3;widows:3;} h2,h3{page-break-after:avoid;}}.clearfix{*zoom:1;}.clearfix:before,.clearfix:after{display:table;content:"";line-height:0;}
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+.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0;}
+.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;}
+body{margin:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;line-height:20px;color:#000000;background-color:#ffffff;}
+a{color:#0088cc;text-decoration:none;}
+a:hover{color:#005580;text-decoration:underline;}
+.img-rounded{-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;}
+.img-polaroid{padding:4px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0, 0, 0, 0.2);-webkit-box-shadow:0 1px 3px rgba(0, 0, 0, 0.1);-moz-box-shadow:0 1px 3px rgba(0, 0, 0, 0.1);box-shadow:0 1px 3px rgba(0, 0, 0, 0.1);}
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+.row{margin-left:-20px;*zoom:1;}.row:before,.row:after{display:table;content:"";line-height:0;}
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+[class*="span"]{float:left;min-height:1px;margin-left:20px;}
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+.span12{width:940px;}
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+.row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.127659574468085%;*margin-left:2.074468085106383%;}
+.row-fluid [class*="span"]:first-child{margin-left:0;}
+.row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.127659574468085%;}
+.row-fluid .span12{width:100%;*width:99.94680851063829%;}
+.row-fluid .span11{width:91.48936170212765%;*width:91.43617021276594%;}
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+.row-fluid .span1{width:6.382978723404255%;*width:6.329787234042553%;}
+.row-fluid .offset12{margin-left:104.25531914893617%;*margin-left:104.14893617021275%;}
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+.row-fluid .offset9{margin-left:78.72340425531914%;*margin-left:78.61702127659572%;}
+.row-fluid .offset9:first-child{margin-left:76.59574468085106%;*margin-left:76.48936170212764%;}
+.row-fluid .offset8{margin-left:70.2127659574468%;*margin-left:70.10638297872339%;}
+.row-fluid .offset8:first-child{margin-left:68.08510638297872%;*margin-left:67.9787234042553%;}
+.row-fluid .offset7{margin-left:61.70212765957446%;*margin-left:61.59574468085106%;}
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+.row-fluid .offset6{margin-left:53.191489361702125%;*margin-left:53.085106382978715%;}
+.row-fluid .offset6:first-child{margin-left:51.063829787234035%;*margin-left:50.95744680851063%;}
+.row-fluid .offset5{margin-left:44.68085106382979%;*margin-left:44.57446808510638%;}
+.row-fluid .offset5:first-child{margin-left:42.5531914893617%;*margin-left:42.4468085106383%;}
+.row-fluid .offset4{margin-left:36.170212765957444%;*margin-left:36.06382978723405%;}
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+.row-fluid .offset3:first-child{margin-left:25.53191489361702%;*margin-left:25.425531914893618%;}
+.row-fluid .offset2{margin-left:19.148936170212764%;*margin-left:19.04255319148936%;}
+.row-fluid .offset2:first-child{margin-left:17.02127659574468%;*margin-left:16.914893617021278%;}
+.row-fluid .offset1{margin-left:10.638297872340425%;*margin-left:10.53191489361702%;}
+.row-fluid .offset1:first-child{margin-left:8.51063829787234%;*margin-left:8.404255319148938%;}
+[class*="span"].hide,.row-fluid [class*="span"].hide{display:none;}
+[class*="span"].pull-right,.row-fluid [class*="span"].pull-right{float:right;}
+.container{margin-right:auto;margin-left:auto;*zoom:1;}.container:before,.container:after{display:table;content:"";line-height:0;}
+.container:after{clear:both;}
+.container-fluid{padding-right:20px;padding-left:20px;*zoom:1;}.container-fluid:before,.container-fluid:after{display:table;content:"";line-height:0;}
+.container-fluid:after{clear:both;}
+p{margin:0 0 10px;}
+.lead{margin-bottom:20px;font-size:19.5px;font-weight:200;line-height:30px;}
+small{font-size:85%;}
+strong{font-weight:bold;}
+em{font-style:italic;}
+cite{font-style:normal;}
+.muted{color:#999999;}
+a.muted:hover{color:#808080;}
+.text-warning{color:#c09853;}
+a.text-warning:hover{color:#a47e3c;}
+.text-error{color:#b94a48;}
+a.text-error:hover{color:#953b39;}
+.text-info{color:#3a87ad;}
+a.text-info:hover{color:#2d6987;}
+.text-success{color:#468847;}
+a.text-success:hover{color:#356635;}
+h1,h2,h3,h4,h5,h6{margin:10px 0;font-family:inherit;font-weight:bold;line-height:20px;color:inherit;text-rendering:optimizelegibility;}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small{font-weight:normal;line-height:1;color:#999999;}
+h1,h2,h3{line-height:40px;}
+h1{font-size:35.75px;}
+h2{font-size:29.25px;}
+h3{font-size:22.75px;}
+h4{font-size:16.25px;}
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+h6{font-size:11.049999999999999px;}
+h1 small{font-size:22.75px;}
+h2 small{font-size:16.25px;}
+h3 small{font-size:13px;}
+h4 small{font-size:13px;}
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+ul,ol{padding:0;margin:0 0 10px 25px;}
+ul ul,ul ol,ol ol,ol ul{margin-bottom:0;}
+li{line-height:20px;}
+ul.unstyled,ol.unstyled{margin-left:0;list-style:none;}
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+dl{margin-bottom:20px;}
+dt,dd{line-height:20px;}
+dt{font-weight:bold;}
+dd{margin-left:10px;}
+.dl-horizontal{*zoom:1;}.dl-horizontal:before,.dl-horizontal:after{display:table;content:"";line-height:0;}
+.dl-horizontal:after{clear:both;}
+.dl-horizontal dt{float:left;width:160px;clear:left;text-align:right;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;}
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+hr{margin:20px 0;border:0;border-top:1px solid #eeeeee;border-bottom:1px solid #ffffff;}
+abbr[title],abbr[data-original-title]{cursor:help;border-bottom:1px dotted #999999;}
+abbr.initialism{font-size:90%;text-transform:uppercase;}
+blockquote{padding:0 0 0 15px;margin:0 0 20px;border-left:5px solid #eeeeee;}blockquote p{margin-bottom:0;font-size:16px;font-weight:300;line-height:25px;}
+blockquote small{display:block;line-height:20px;color:#999999;}blockquote small:before{content:'\2014 \00A0';}
+blockquote.pull-right{float:right;padding-right:15px;padding-left:0;border-right:5px solid #eeeeee;border-left:0;}blockquote.pull-right p,blockquote.pull-right small{text-align:right;}
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+q:before,q:after,blockquote:before,blockquote:after{content:"";}
+address{display:block;margin-bottom:20px;font-style:normal;line-height:20px;}
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+fieldset{padding:0;margin:0;border:0;}
+legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:19.5px;line-height:40px;color:#333333;border:0;border-bottom:1px solid #e5e5e5;}legend small{font-size:15px;color:#999999;}
+label,input,button,select,textarea{font-size:13px;font-weight:normal;line-height:20px;}
+input,button,select,textarea{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;}
+label{display:block;margin-bottom:5px;}
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+textarea{height:auto;}
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+.uneditable-textarea{width:auto;height:auto;}
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+input:-ms-input-placeholder,textarea:-ms-input-placeholder{color:#999999;}
+input::-webkit-input-placeholder,textarea::-webkit-input-placeholder{color:#999999;}
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+.radio input[type="radio"],.checkbox input[type="checkbox"]{float:left;margin-left:-20px;}
+.controls>.radio:first-child,.controls>.checkbox:first-child{padding-top:5px;}
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+.radio.inline+.radio.inline,.checkbox.inline+.checkbox.inline{margin-left:10px;}
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+.input-small{width:90px;}
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+input,textarea,.uneditable-input{margin-left:0;}
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+.tooltip.right{margin-left:3px;}
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+.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid;}
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+a.thumbnail:hover{border-color:#0088cc;-webkit-box-shadow:0 1px 4px rgba(0, 105, 214, 0.25);-moz-box-shadow:0 1px 4px rgba(0, 105, 214, 0.25);box-shadow:0 1px 4px rgba(0, 105, 214, 0.25);}
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+.thumbnail .caption{padding:9px;color:#555555;}
+.media,.media-body{overflow:hidden;*overflow:visible;zoom:1;}
+.media,.media .media{margin-top:15px;}
+.media:first-child{margin-top:0;}
+.media-object{display:block;}
+.media-heading{margin:0 0 5px;}
+.media .pull-left{margin-right:10px;}
+.media .pull-right{margin-left:10px;}
+.media-list{margin-left:0;list-style:none;}
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+.label{-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px;}
+.badge{padding-left:9px;padding-right:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px;}
+.label:empty,.badge:empty{display:none;}
+a.label:hover,a.badge:hover{color:#ffffff;text-decoration:none;cursor:pointer;}
+.label-important,.badge-important{background-color:#b94a48;}
+.label-important[href],.badge-important[href]{background-color:#953b39;}
+.label-warning,.badge-warning{background-color:#f89406;}
+.label-warning[href],.badge-warning[href]{background-color:#c67605;}
+.label-success,.badge-success{background-color:#468847;}
+.label-success[href],.badge-success[href]{background-color:#356635;}
+.label-info,.badge-info{background-color:#3a87ad;}
+.label-info[href],.badge-info[href]{background-color:#2d6987;}
+.label-inverse,.badge-inverse{background-color:#333333;}
+.label-inverse[href],.badge-inverse[href]{background-color:#1a1a1a;}
+.btn .label,.btn .badge{position:relative;top:-1px;}
+.btn-mini .label,.btn-mini .badge{top:0;}
+@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0;} to{background-position:0 0;}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0;} to{background-position:0 0;}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0;} to{background-position:0 0;}}@-o-keyframes progress-bar-stripes{from{background-position:0 0;} to{background-position:40px 0;}}@keyframes progress-bar-stripes{from{background-position:40px 0;} to{background-position:0 0;}}.progress{overflow:hidden;height:20px;margin-bottom:20px;background-color:#f7f7f7;background-image:-moz-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));background-image:-webkit-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-o-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:linear-gradient(to bottom, #f5f5f5, #f9f9f9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0, 0, 0, 0.1);-moz-box-shadow:inset 0 1px 2px rgba(0, 0, 0, 0.1);box-shadow:inset 0 1px 2px rgba(0, 0, 0, 0.1);-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;}
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+.progress.active .bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-moz-animation:progress-bar-stripes 2s linear infinite;-ms-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite;}
+.progress-danger .bar,.progress .bar-danger{background-color:#dd514c;background-image:-moz-linear-gradient(top, #ee5f5b, #c43c35);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35));background-image:-webkit-linear-gradient(top, #ee5f5b, #c43c35);background-image:-o-linear-gradient(top, #ee5f5b, #c43c35);background-image:linear-gradient(to bottom, #ee5f5b, #c43c35);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0);}
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+.progress-success .bar,.progress .bar-success{background-color:#5eb95e;background-image:-moz-linear-gradient(top, #62c462, #57a957);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957));background-image:-webkit-linear-gradient(top, #62c462, #57a957);background-image:-o-linear-gradient(top, #62c462, #57a957);background-image:linear-gradient(to bottom, #62c462, #57a957);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0);}
+.progress-success.progress-striped .bar,.progress-striped .bar-success{background-color:#62c462;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}
+.progress-info .bar,.progress .bar-info{background-color:#4bb1cf;background-image:-moz-linear-gradient(top, #5bc0de, #339bb9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9));background-image:-webkit-linear-gradient(top, #5bc0de, #339bb9);background-image:-o-linear-gradient(top, #5bc0de, #339bb9);background-image:linear-gradient(to bottom, #5bc0de, #339bb9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0);}
+.progress-info.progress-striped .bar,.progress-striped .bar-info{background-color:#5bc0de;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}
+.progress-warning .bar,.progress .bar-warning{background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0);}
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+.rendered_html p+p{margin-top:1em;}
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+.tooltipbuttons{position:absolute;padding-right:15px;top:0px;right:0px;}
+.tooltiptext{padding-right:30px;}
+.ipython_tooltip{max-width:700px;-webkit-animation:fadeOut 400ms;-moz-animation:fadeOut 400ms;animation:fadeOut 400ms;-webkit-animation:fadeIn 400ms;-moz-animation:fadeIn 400ms;animation:fadeIn 400ms;vertical-align:middle;background-color:#f7f7f7;overflow:visible;border:#ababab 1px solid;outline:none;padding:3px;margin:0px;padding-left:7px;font-family:monospace;min-height:50px;-moz-box-shadow:0px 6px 10px -1px #adadad;-webkit-box-shadow:0px 6px 10px -1px #adadad;box-shadow:0px 6px 10px -1px #adadad;border-radius:4px;position:absolute;}.ipython_tooltip a{float:right;}
+.pretooltiparrow{left:0px;margin:0px;top:-16px;width:40px;height:16px;overflow:hidden;position:absolute;}
+.pretooltiparrow:before{background-color:#f7f7f7;border:1px #ababab solid;z-index:11;content:"";position:absolute;left:15px;top:10px;width:25px;height:25px;-webkit-transform:rotate(45deg);-moz-transform:rotate(45deg);-ms-transform:rotate(45deg);-o-transform:rotate(45deg);}
+
+</style>
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export
+
+*/
+body {
+  overflow: visible;
+  padding: 8px;
+}
+.input_area {
+  padding: 0.4em;
+}
+
+</style>
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+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
+</style>
+
+<script src="https://c328740.ssl.cf1.rackcdn.com/mathjax/latest/MathJax.js?config=TeX-AMS_HTML" type="text/javascript">
+
+</script>
+<script type="text/javascript">
+init_mathjax = function() {
+    if (window.MathJax) {
+        // MathJax loaded
+        MathJax.Hub.Config({
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+            "HTML-CSS": {
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+        MathJax.Hub.Queue(["Typeset",MathJax.Hub]);
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+init_mathjax();
+</script>
+</head>
+<body>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1>A brief tour of the IPython notebook</h1>
+<p>This document will give you a brief tour of the capabilities of the IPython notebook.<br />
+You can view its contents by scrolling around, or execute each cell by typing <code>Shift-Enter</code>.
+After you conclude this brief high-level tour, you should read the accompanying notebook 
+titled <code>01_notebook_introduction</code>, which takes a more step-by-step approach to the features of the
+system.<br />
+</p>
+<p>The rest of the notebooks in this directory illustrate various other aspects and 
+capabilities of the IPython notebook; some of them may require additional libraries to be executed.</p>
+<p><strong>NOTE:</strong> This notebook <em>must</em> be run from its own directory, so you must <code>cd</code>
+to this directory and then start the notebook, but do <em>not</em> use the <code>--notebook-dir</code>
+option to run it from another location.</p>
+<p>The first thing you need to know is that you are still controlling the same old IPython you're used to,
+so things like shell aliases and magic commands still work:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">pwd</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[1]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>u&apos;/Users/minrk/dev/ip/mine/docs/examples/notebooks&apos;</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">ls</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>00_notebook_tour.ipynb          callbacks.ipynb                 python-logo.svg
+01_notebook_introduction.ipynb  cython_extension.ipynb          rmagic_extension.ipynb
+Animations_and_Progress.ipynb   display_protocol.ipynb          sympy.ipynb
+Capturing Output.ipynb          formatting.ipynb                sympy_quantum_computing.ipynb
+Script Magics.ipynb             octavemagic_extension.ipynb     trapezoid_rule.ipynb
+animation.m4v                   progbar.ipynb
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">message</span> <span class="o">=</span> <span class="s">&#39;The IPython notebook is great!&#39;</span>
+<span class="c"># note: the echo command does not run on Windows, it&#39;s a unix command.</span>
+<span class="o">!</span><span class="nb">echo</span> <span class="nv">$message</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>The IPython notebook is great!
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  Plots with matplotlib
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>IPython adds an 'inline' matplotlib backend,
+which embeds any matplotlib figures into the notebook.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[4]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="o">%</span><span class="k">pylab</span> <span class="n">inline</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
+For more information, type &apos;help(pylab)&apos;.
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">x</span> <span class="o">=</span> <span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="o">*</span><span class="n">pi</span><span class="p">,</span> <span class="mi">500</span><span class="p">)</span>
+<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+<span class="n">title</span><span class="p">(</span><span class="s">&#39;A simple chirp&#39;</span><span class="p">);</span>
+</pre></div>
+
+</div>
+</div>
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+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
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+"></img>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>You can paste blocks of input with prompt markers, such as those from
+<a href="http://docs.python.org/tutorial/interpreter.html#interactive-mode">the official Python tutorial</a></p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="o">&gt;&gt;&gt;</span> <span class="n">the_world_is_flat</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="o">&gt;&gt;&gt;</span> <span class="k">if</span> <span class="n">the_world_is_flat</span><span class="p">:</span>
+<span class="o">...</span>     <span class="k">print</span> <span class="s">&quot;Be careful not to fall off!&quot;</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>Be careful not to fall off!
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Errors are shown in informative ways:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="o">%</span><span class="k">run</span> <span class="n">non_existent_file</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stderr">
+<pre>ERROR: File &#96;u&apos;non_existent_file.py&apos;&#96; not found.</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">x</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">y</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">z</span> <span class="o">=</span> <span class="n">y</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyerr">
+<pre>
+<span class="ansired">---------------------------------------------------------------------------</span>
+<span class="ansired">ZeroDivisionError</span>                         Traceback (most recent call last)
+<span class="ansigreen">&lt;ipython-input-8-dc39888fd1d2&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
+<span class="ansigreen">      1</span> x <span class="ansiyellow">=</span> <span class="ansicyan">1</span><span class="ansiyellow"></span>
+<span class="ansigreen">      2</span> y <span class="ansiyellow">=</span> <span class="ansicyan">4</span><span class="ansiyellow"></span>
+<span class="ansigreen">----&gt; 3</span><span class="ansiyellow"> </span>z <span class="ansiyellow">=</span> y<span class="ansiyellow">/</span><span class="ansiyellow">(</span><span class="ansicyan">1</span><span class="ansiyellow">-</span>x<span class="ansiyellow">)</span><span class="ansiyellow"></span>
+
+<span class="ansired">ZeroDivisionError</span>: integer division or modulo by zero</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>When IPython needs to display additional information (such as providing details on an object via <code>x?</code>
+it will automatically invoke a pager at the bottom of the screen:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[18]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">magic</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>Non-blocking output of kernel</h2>
+<p>If you execute the next cell, you will see the output arriving as it is generated, not all at the end.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[19]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">time</span><span class="o">,</span> <span class="nn">sys</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">8</span><span class="p">):</span>
+    <span class="k">print</span> <span class="n">i</span><span class="p">,</span>
+    <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>0 </pre>
+</div>
+</div><div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>1 </pre>
+</div>
+</div><div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>2 </pre>
+</div>
+</div><div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>3 </pre>
+</div>
+</div><div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>4 </pre>
+</div>
+</div><div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>5 </pre>
+</div>
+</div><div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>6 </pre>
+</div>
+</div><div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>7
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>Clean crash and restart</h2>
+<p>We call the low-level system libc.time routine with the wrong argument via
+ctypes to segfault the Python interpreter:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[*]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">from</span> <span class="nn">ctypes</span> <span class="kn">import</span> <span class="n">CDLL</span>
+<span class="c"># This will crash a Linux or Mac system; equivalent calls can be made on Windows</span>
+<span class="n">dll</span> <span class="o">=</span> <span class="s">&#39;dylib&#39;</span> <span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span> <span class="o">==</span> <span class="s">&#39;darwin&#39;</span> <span class="k">else</span> <span class="s">&#39;.so.6&#39;</span>
+<span class="n">libc</span> <span class="o">=</span> <span class="n">CDLL</span><span class="p">(</span><span class="s">&quot;libc.</span><span class="si">%s</span><span class="s">&quot;</span> <span class="o">%</span> <span class="n">dll</span><span class="p">)</span> 
+<span class="n">libc</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>  <span class="c"># BOOM!!</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>Markdown cells can contain formatted text and code</h2>
+<p>You can <em>italicize</em>, <strong>boldface</strong></p>
+<ul>
+<li>build</li>
+<li>lists</li>
+</ul>
+<p>and embed code meant for illustration instead of execution in Python:</p>
+<pre><code>def f(x):
+    """a docstring"""
+    return x**2
+</code></pre>
+<p>or other languages:</p>
+<pre><code>if (i=0; i&lt;n; i++) {
+  printf("hello %d\n", i);
+  x += 4;
+}
+</code></pre>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Courtesy of MathJax, you can include mathematical expressions both inline: 
+$e^{i\pi} + 1 = 0$  and displayed:</p>
+<p>$$e^x=\sum_{i=0}^\infty \frac{1}{i!}x^i$$</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>Rich displays: include anyting a browser can show</h2>
+<p>Note that we have an actual protocol for this, see the <code>display_protocol</code> notebook for further details.</p>
+<h3>Images</h3>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Image</span>
+<span class="n">Image</span><span class="p">(</span><span class="n">filename</span><span class="o">=</span><span class="s">&#39;../../source/_static/logo.png&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[1]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
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+"></img>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>An image can also be displayed from raw data or a url</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">Image</span><span class="p">(</span><span class="n">url</span><span class="o">=</span><span class="s">&#39;http://python.org/images/python-logo.gif&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[2]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+<div class="output_html rendered_html">
+<img src="http://python.org/images/python-logo.gif" />
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>SVG images are also supported out of the box (since modern browsers do a good job of rendering them):</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">SVG</span>
+<span class="n">SVG</span><span class="p">(</span><span class="n">filename</span><span class="o">=</span><span class="s">&#39;python-logo.svg&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[3]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
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+  <metadata id="metadata2193">
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+      <cc:Work rdf:about="">
+        <dc:format>image/svg+xml</dc:format>
+        <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
+      </cc:Work>
+    </rdf:RDF>
+  </metadata>
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+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4>Embedded vs Non-embedded Images</h4>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>As of IPython 0.13, images are embedded by default for compatibility with QtConsole, and the ability to still be displayed offline.</p>
+<p>Let's look at the differences:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[4]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="c"># by default Image data are embedded</span>
+<span class="n">Embed</span>      <span class="o">=</span> <span class="n">Image</span><span class="p">(</span>    <span class="s">&#39;http://scienceview.berkeley.edu/view/images/newview.jpg&#39;</span><span class="p">)</span>
+
+<span class="c"># if kwarg `url` is given, the embedding is assumed to be false</span>
+<span class="n">SoftLinked</span> <span class="o">=</span> <span class="n">Image</span><span class="p">(</span><span class="n">url</span><span class="o">=</span><span class="s">&#39;http://scienceview.berkeley.edu/view/images/newview.jpg&#39;</span><span class="p">)</span>
+
+<span class="c"># In each case, embed can be specified explicitly with the `embed` kwarg</span>
+<span class="c"># ForceEmbed = Image(url=&#39;http://scienceview.berkeley.edu/view/images/newview.jpg&#39;, embed=True)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Today's image from a webcam at Berkeley, (at the time I created this notebook). This should also work in the Qtconsole.
+Drawback is that the saved notebook will be larger, but the image will still be present offline.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">Embed</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[5]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
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+"></img>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Today's image from same webcam at Berkeley, (refreshed every minutes, if you reload the notebook), visible only with an active internet connexion, that should be different from the previous one. This will not work on Qtconsole.
+Notebook saved with this kind of image will be lighter and always reflect the current version of the source, but the image won't display offline.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">SoftLinked</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[6]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+<div class="output_html rendered_html">
+<img src="http://scienceview.berkeley.edu/view/images/newview.jpg" />
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Of course, if you re-run the all notebook, the two images will be the same again.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3>Video</h3>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>And more exotic objects can also be displayed, as long as their representation supports 
+the IPython display protocol.</p>
+<p>For example, videos hosted externally on YouTube are easy to load (and writing a similar wrapper for other
+hosted content is trivial):</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">YouTubeVideo</span>
+<span class="c"># a talk about IPython at Sage Days at U. Washington, Seattle.</span>
+<span class="c"># Video credit: William Stein.</span>
+<span class="n">YouTubeVideo</span><span class="p">(</span><span class="s">&#39;1j_HxD4iLn8&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[7]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+<div class="output_html rendered_html">
+
+            <iframe
+                width="400"
+                height="300"
+                src="http://www.youtube.com/embed/1j_HxD4iLn8"
+                frameborder="0"
+                allowfullscreen
+            ></iframe>
+        
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Using the nascent video capabilities of modern browsers, you may also be able to display local
+videos.  At the moment this doesn't work very well in all browsers, so it may or may not work for you;
+we will continue testing this and looking for ways to make it more robust.<br />
+</p>
+<p>The following cell loads a local file called  <code>animation.m4v</code>, encodes the raw video as base64 for http
+transport, and uses the HTML5 video tag to load it. On Chrome 15 it works correctly, displaying a control
+bar at the bottom with a play/pause button and a location slider.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="n">video</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s">&quot;animation.m4v&quot;</span><span class="p">,</span> <span class="s">&quot;rb&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+<span class="n">video_encoded</span> <span class="o">=</span> <span class="n">video</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s">&quot;base64&quot;</span><span class="p">)</span>
+<span class="n">video_tag</span> <span class="o">=</span> <span class="s">&#39;&lt;video controls alt=&quot;test&quot; src=&quot;data:video/x-m4v;base64,{0}&quot;&gt;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">video_encoded</span><span class="p">)</span>
+<span class="n">HTML</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">video_tag</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[8]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+<div class="output_html rendered_html">
+<video controls alt="test" src="data:video/x-m4v;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAAqiW1kYXQAAAKMBgX//4jcRem9
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+">
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>Local Files</h2>
+<p>The above examples embed images and video from the notebook filesystem in the output
+areas of code cells.  It is also possible to request these files directly in markdown cells
+if they reside in the notebook directory via relative urls prefixed with <code>files/</code>:</p>
+<pre><code>files/[subdirectory/]&lt;filename&gt;
+</code></pre>
+<p>For example, in the example notebook folder, we have the Python logo, addressed as:</p>
+<pre><code>&lt;img src="files/python-logo.svg" /&gt;
+</code></pre>
+<p><img src="python-logo.svg" /></p>
+<p>and a video with the HTML5 video tag:</p>
+<pre><code>&lt;video controls src="files/animation.m4v" /&gt;
+</code></pre>
+<video controls src="animation.m4v" />
+
+These do not embed the data into the notebook file,
+and require that the files exist when you are viewing the notebook.
+
+### Security of local files
+
+Note that this means that the IPython notebook server also acts as a generic file server
+for files inside the same tree as your notebooks.  Access is not granted outside the
+notebook folder so you have strict control over what files are visible, but for this
+reason it is highly recommended that you do not run the notebook server with a notebook
+directory at a high level in your filesystem (e.g. your home directory).
+
+When you run the notebook in a password-protected manner, local file access is restricted
+to authenticated users unless read-only views are active.
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>Linking to files and directories for viewing in the browser</h2>
+<p>It is also possible to link directly to files or directories so they can be opened in the browser. This is especially convenient if you're interacting with a tool within IPython that generates HTML pages, and you'd like to easily be able to open those in a new browser window. Alternatively, if your IPython notebook server is on a remote system, creating links provides an easy way to download any files that get generated.</p>
+<p>As we saw above, there are a bunch of <code>.ipynb</code> files in our current directory.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">ls</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>00_notebook_tour.ipynb          formatting.ipynb
+01_notebook_introduction.ipynb  octavemagic_extension.ipynb
+Animations_and_Progress.ipynb   publish_data.ipynb
+Capturing Output.ipynb          python-logo.svg
+Script Magics.ipynb             rmagic_extension.ipynb
+animation.m4v                   sympy.ipynb
+cython_extension.ipynb          sympy_quantum_computing.ipynb
+display_protocol.ipynb          trapezoid_rule.ipynb
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>If we want to create a link to one of them, we can call use the <code>FileLink</code> object.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">FileLink</span>
+<span class="n">FileLink</span><span class="p">(</span><span class="s">&#39;00_notebook_tour.ipynb&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[2]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+<div class="output_html rendered_html">
+<a href='files/00_notebook_tour.ipynb' target='_blank'>00_notebook_tour.ipynb</a><br>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Alternatively, if we want to link to all of them, we can use the <code>FileLinks</code> object, passing <code>'.'</code> to indicate that we want links generated for the current working directory. Note that if there were other directories under the current directory, <code>FileLinks</code> would work in a recursive manner creating links to files in all sub-directories as well.</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">FileLinks</span>
+<span class="n">FileLinks</span><span class="p">(</span><span class="s">&#39;.&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[7]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+<div class="output_html rendered_html">
+<a href='files/./00_notebook_tour.ipynb' target='_blank'>00_notebook_tour.ipynb</a><br>
+<a href='files/./01_notebook_introduction.ipynb' target='_blank'>01_notebook_introduction.ipynb</a><br>
+<a href='files/./animation.m4v' target='_blank'>animation.m4v</a><br>
+<a href='files/./Animations_and_Progress.ipynb' target='_blank'>Animations_and_Progress.ipynb</a><br>
+<a href='files/./Capturing Output.ipynb' target='_blank'>Capturing Output.ipynb</a><br>
+<a href='files/./cython_extension.ipynb' target='_blank'>cython_extension.ipynb</a><br>
+<a href='files/./display_protocol.ipynb' target='_blank'>display_protocol.ipynb</a><br>
+<a href='files/./formatting.ipynb' target='_blank'>formatting.ipynb</a><br>
+<a href='files/./octavemagic_extension.ipynb' target='_blank'>octavemagic_extension.ipynb</a><br>
+<a href='files/./publish_data.ipynb' target='_blank'>publish_data.ipynb</a><br>
+<a href='files/./python-logo.svg' target='_blank'>python-logo.svg</a><br>
+<a href='files/./rmagic_extension.ipynb' target='_blank'>rmagic_extension.ipynb</a><br>
+<a href='files/./Script Magics.ipynb' target='_blank'>Script Magics.ipynb</a><br>
+<a href='files/./sympy.ipynb' target='_blank'>sympy.ipynb</a><br>
+<a href='files/./sympy_quantum_computing.ipynb' target='_blank'>sympy_quantum_computing.ipynb</a><br>
+<a href='files/./trapezoid_rule.ipynb' target='_blank'>trapezoid_rule.ipynb</a><br>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3>External sites</h3>
+<p>You can even embed an entire page from another site in an iframe; for example this is today's Wikipedia
+page for mobile users:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">HTML</span><span class="p">(</span><span class="s">&#39;&lt;iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350&gt;&lt;/iframe&gt;&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[9]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+<div class="output_html rendered_html">
+<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3>Mathematics</h3>
+<p>And we also support the display of mathematical expressions typeset in LaTeX, which is rendered
+in the browser thanks to the <a href="http://mathjax.org">MathJax library</a>.<br />
+</p>
+<p>Note that this is <em>different</em> from the above examples.  Above we were typing mathematical expressions
+in Markdown cells (along with normal text) and letting the browser render them; now we are displaying
+the output of a Python computation as a LaTeX expression wrapped by the <code>Math()</code> object so the browser
+renders it.  The <code>Math</code> object will add the needed LaTeX delimiters (<code>$$</code>) if they are not provided:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Math</span>
+<span class="n">Math</span><span class="p">(</span><span class="s">r&#39;F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[10]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+$$F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx$$
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>With the <code>Latex</code> class, you have to include the delimiters yourself.  This allows you to use other LaTeX modes such as <code>eqnarray</code>:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Latex</span>
+<span class="n">Latex</span><span class="p">(</span><span class="s">r&quot;&quot;&quot;\begin{eqnarray}</span>
+<span class="s">\nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} &amp; = \frac{4\pi}{c}\vec{\mathbf{j}} \\</span>
+<span class="s">\nabla \cdot \vec{\mathbf{E}} &amp; = 4 \pi \rho \\</span>
+<span class="s">\nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} &amp; = \vec{\mathbf{0}} \\</span>
+<span class="s">\nabla \cdot \vec{\mathbf{B}} &amp; = 0 </span>
+<span class="s">\end{eqnarray}&quot;&quot;&quot;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[11]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+\begin{eqnarray}
+\nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
+\nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
+\nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
+\nabla \cdot \vec{\mathbf{B}} & = 0 
+\end{eqnarray}
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Or you can enter latex directly with the <code>%%latex</code> cell magic:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="o">%%</span><span class="k">latex</span>
+\<span class="n">begin</span><span class="p">{</span><span class="n">aligned</span><span class="p">}</span>
+\<span class="n">nabla</span> \<span class="n">times</span> \<span class="n">vec</span><span class="p">{</span>\<span class="n">mathbf</span><span class="p">{</span><span class="n">B</span><span class="p">}}</span> <span class="o">-</span>\<span class="p">,</span> \<span class="n">frac1c</span>\<span class="p">,</span> \<span class="n">frac</span><span class="p">{</span>\<span class="n">partial</span>\<span class="n">vec</span><span class="p">{</span>\<span class="n">mathbf</span><span class="p">{</span><span class="n">E</span><span class="p">}}}{</span>\<span class="n">partial</span> <span class="n">t</span><span class="p">}</span> <span class="o">&amp;</span> <span class="o">=</span> \<span class="n">frac</span><span class="p">{</span><span class="mi">4</span>\<span class="n">pi</span><span class="p">}{</span><span class="n">c</span><span class="p">}</span>\<span class="n">vec</span><span class="p">{</span>\<span class="n">mathbf</span><span class="p">{</span><span class="n">j</span><span class="p">}}</span> \\
+\<span class="n">nabla</span> \<span class="n">cdot</span> \<span class="n">vec</span><span class="p">{</span>\<span class="n">mathbf</span><span class="p">{</span><span class="n">E</span><span class="p">}}</span> <span class="o">&amp;</span> <span class="o">=</span> <span class="mi">4</span> \<span class="n">pi</span> \<span class="n">rho</span> \\
+\<span class="n">nabla</span> \<span class="n">times</span> \<span class="n">vec</span><span class="p">{</span>\<span class="n">mathbf</span><span class="p">{</span><span class="n">E</span><span class="p">}}</span>\<span class="p">,</span> <span class="o">+</span>\<span class="p">,</span> \<span class="n">frac1c</span>\<span class="p">,</span> \<span class="n">frac</span><span class="p">{</span>\<span class="n">partial</span>\<span class="n">vec</span><span class="p">{</span>\<span class="n">mathbf</span><span class="p">{</span><span class="n">B</span><span class="p">}}}{</span>\<span class="n">partial</span> <span class="n">t</span><span class="p">}</span> <span class="o">&amp;</span> <span class="o">=</span> \<span class="n">vec</span><span class="p">{</span>\<span class="n">mathbf</span><span class="p">{</span><span class="mi">0</span><span class="p">}}</span> \\
+\<span class="n">nabla</span> \<span class="n">cdot</span> \<span class="n">vec</span><span class="p">{</span>\<span class="n">mathbf</span><span class="p">{</span><span class="n">B</span><span class="p">}}</span> <span class="o">&amp;</span> <span class="o">=</span> <span class="mi">0</span>
+\<span class="n">end</span><span class="p">{</span><span class="n">aligned</span><span class="p">}</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_display_data">
+
+\begin{aligned}
+\nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
+\nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
+\nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
+\nabla \cdot \vec{\mathbf{B}} & = 0
+\end{aligned}
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>There is also a <code>%%javascript</code> cell magic for running javascript directly,
+and <code>%%svg</code> for manually entering SVG content.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1>Loading external codes</h1>
+<ul>
+<li>Drag and drop a <code>.py</code> in the dashboard</li>
+<li>Use <code>%load</code> with any local or remote url: <a href="http://matplotlib.sourceforge.net/gallery.html">the Matplotlib Gallery!</a></li>
+</ul>
+<p>In this notebook we've kept the output saved so you can see the result, but you should run the next
+cell yourself (with an active internet connection).</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Let's make sure we have pylab again, in case we have restarted the kernel due to the crash demo above</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="o">%</span><span class="k">pylab</span> <span class="n">inline</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>
+Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
+For more information, type &apos;help(pylab)&apos;.
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[15]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="o">%</span><span class="k">load</span> <span class="n">http</span><span class="p">:</span><span class="o">//</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">sourceforge</span><span class="o">.</span><span class="n">net</span><span class="o">/</span><span class="n">mpl_examples</span><span class="o">/</span><span class="n">pylab_examples</span><span class="o">/</span><span class="n">integral_demo</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[16]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="c">#!/usr/bin/env python</span>
+
+<span class="c"># implement the example graphs/integral from pyx</span>
+<span class="kn">from</span> <span class="nn">pylab</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">Polygon</span>
+
+<span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="mi">3</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="mi">5</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="mi">7</span><span class="p">)</span><span class="o">+</span><span class="mi">85</span>
+
+<span class="n">ax</span> <span class="o">=</span> <span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
+
+<span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">9</span> <span class="c"># integral area</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c"># make the shaded region</span>
+<span class="n">ix</span> <span class="o">=</span> <span class="n">arange</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
+<span class="n">iy</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="n">ix</span><span class="p">)</span>
+<span class="n">verts</span> <span class="o">=</span> <span class="p">[(</span><span class="n">a</span><span class="p">,</span><span class="mi">0</span><span class="p">)]</span> <span class="o">+</span> <span class="nb">zip</span><span class="p">(</span><span class="n">ix</span><span class="p">,</span><span class="n">iy</span><span class="p">)</span> <span class="o">+</span> <span class="p">[(</span><span class="n">b</span><span class="p">,</span><span class="mi">0</span><span class="p">)]</span>
+<span class="n">poly</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span><span class="n">verts</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;0.8&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s">&#39;k&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">add_patch</span><span class="p">(</span><span class="n">poly</span><span class="p">)</span>
+
+<span class="n">text</span><span class="p">(</span><span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">a</span> <span class="o">+</span> <span class="n">b</span><span class="p">),</span> <span class="mi">30</span><span class="p">,</span>
+     <span class="s">r&quot;$\int_a^b f(x)\mathrm{d}x$&quot;</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s">&#39;center&#39;</span><span class="p">,</span>
+     <span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+
+<span class="n">axis</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">180</span><span class="p">])</span>
+<span class="n">figtext</span><span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="s">&#39;x&#39;</span><span class="p">)</span>
+<span class="n">figtext</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="s">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">((</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">((</span><span class="s">&#39;a&#39;</span><span class="p">,</span><span class="s">&#39;b&#39;</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([])</span>
+<span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_display_data">
+
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+</div>
+</div>
+</div>
+</div>
+</div>
+</body>
+</html>
\ No newline at end of file
diff --git a/tests/template_ref/00_notebook_tour_orig.rst b/tests/template_ref/00_notebook_tour_orig.rst
new file mode 100644
index 0000000..aa558bf
--- /dev/null
+++ b/tests/template_ref/00_notebook_tour_orig.rst
@@ -0,0 +1,662 @@
+A brief tour of the IPython notebook
+====================================
+
+This document will give you a brief tour of the capabilities of the
+IPython notebook.
+You can view its contents by scrolling around, or execute each cell by
+typing ``Shift-Enter``. After you conclude this brief high-level tour,
+you should read the accompanying notebook titled
+``01_notebook_introduction``, which takes a more step-by-step approach
+to the features of the system.
+
+The rest of the notebooks in this directory illustrate various other
+aspects and capabilities of the IPython notebook; some of them may
+require additional libraries to be executed.
+
+**NOTE:** This notebook *must* be run from its own directory, so you
+must ``cd`` to this directory and then start the notebook, but do *not*
+use the ``--notebook-dir`` option to run it from another location.
+
+The first thing you need to know is that you are still controlling the
+same old IPython you're used to, so things like shell aliases and magic
+commands still work:
+
+In[1]:
+
+.. code:: python
+
+    pwd
+
+Out[1]:
+.. parsed-literal::
+
+    u'/Users/minrk/dev/ip/mine/docs/examples/notebooks'
+
+
+In[2]:
+
+.. code:: python
+
+    ls
+
+
+.. parsed-literal::
+
+    00_notebook_tour.ipynb          callbacks.ipynb                 python-logo.svg
+    01_notebook_introduction.ipynb  cython_extension.ipynb          rmagic_extension.ipynb
+    Animations_and_Progress.ipynb   display_protocol.ipynb          sympy.ipynb
+    Capturing Output.ipynb          formatting.ipynb                sympy_quantum_computing.ipynb
+    Script Magics.ipynb             octavemagic_extension.ipynb     trapezoid_rule.ipynb
+    animation.m4v                   progbar.ipynb
+
+In[3]:
+
+.. code:: python
+
+    message = 'The IPython notebook is great!'
+    # note: the echo command does not run on Windows, it's a unix command.
+    !echo $message
+
+
+.. parsed-literal::
+
+    The IPython notebook is great!
+
+Plots with matplotlib
+---------------------
+
+
+IPython adds an 'inline' matplotlib backend, which embeds any matplotlib
+figures into the notebook.
+
+In[4]:
+
+.. code:: python
+
+    %pylab inline
+
+
+.. parsed-literal::
+
+    
+    Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
+    For more information, type 'help(pylab)'.
+
+In[5]:
+
+.. code:: python
+
+    x = linspace(0, 3*pi, 500)
+    plot(x, sin(x**2))
+    title('A simple chirp');
+
+.. image:: _fig_07.png
+
+You can paste blocks of input with prompt markers, such as those from
+`the official Python
+tutorial <http://docs.python.org/tutorial/interpreter.html#interactive-mode>`_
+
+In[6]:
+
+.. code:: python
+
+    >>> the_world_is_flat = 1
+    >>> if the_world_is_flat:
+    ...     print "Be careful not to fall off!"
+
+
+.. parsed-literal::
+
+    Be careful not to fall off!
+
+Errors are shown in informative ways:
+
+In[7]:
+
+.. code:: python
+
+    %run non_existent_file
+
+
+.. parsed-literal::
+
+    ERROR: File `u'non_existent_file.py'` not found.
+In[8]:
+
+.. code:: python
+
+    x = 1
+    y = 4
+    z = y/(1-x)
+
+::
+
+    ---------------------------------------------------------------------------
+    ZeroDivisionError                         Traceback (most recent call last)
+    <ipython-input-8-dc39888fd1d2> in <module>()
+          1 x = 1
+          2 y = 4
+    ----> 3 z = y/(1-x)
+    
+    ZeroDivisionError: integer division or modulo by zero
+When IPython needs to display additional information (such as providing
+details on an object via ``x?`` it will automatically invoke a pager at
+the bottom of the screen:
+
+In[18]:
+
+.. code:: python
+
+    magic
+
+Non-blocking output of kernel
+-----------------------------
+
+If you execute the next cell, you will see the output arriving as it is
+generated, not all at the end.
+
+In[19]:
+
+.. code:: python
+
+    import time, sys
+    for i in range(8):
+        print i,
+        time.sleep(0.5)
+
+
+.. parsed-literal::
+
+    0 
+
+.. parsed-literal::
+
+    1 
+
+.. parsed-literal::
+
+    2 
+
+.. parsed-literal::
+
+    3 
+
+.. parsed-literal::
+
+    4 
+
+.. parsed-literal::
+
+    5 
+
+.. parsed-literal::
+
+    6 
+
+.. parsed-literal::
+
+    7
+
+Clean crash and restart
+-----------------------
+
+We call the low-level system libc.time routine with the wrong argument
+via ctypes to segfault the Python interpreter:
+
+In[*]:
+
+.. code:: python
+
+    import sys
+    from ctypes import CDLL
+    # This will crash a Linux or Mac system; equivalent calls can be made on Windows
+    dll = 'dylib' if sys.platform == 'darwin' else '.so.6'
+    libc = CDLL("libc.%s" % dll) 
+    libc.time(-1)  # BOOM!!
+
+Markdown cells can contain formatted text and code
+--------------------------------------------------
+
+You can *italicize*, **boldface**
+
+-  build
+-  lists
+
+and embed code meant for illustration instead of execution in Python:
+
+::
+
+    def f(x):
+        """a docstring"""
+        return x**2
+
+or other languages:
+
+::
+
+    if (i=0; i<n; i++) {
+      printf("hello %d\n", i);
+      x += 4;
+    }
+
+
+Courtesy of MathJax, you can include mathematical expressions both
+inline: :math:`e^{i\pi} + 1 = 0` and displayed:
+
+.. math:: e^x=\sum_{i=0}^\infty \frac{1}{i!}x^i
+
+
+
+Rich displays: include anyting a browser can show
+-------------------------------------------------
+
+Note that we have an actual protocol for this, see the
+``display_protocol`` notebook for further details.
+
+Images
+~~~~~~
+
+
+In[1]:
+
+.. code:: python
+
+    from IPython.display import Image
+    Image(filename='../../source/_static/logo.png')
+
+Out[1]:
+.. image:: _fig_22.png
+
+
+An image can also be displayed from raw data or a url
+
+In[2]:
+
+.. code:: python
+
+    Image(url='http://python.org/images/python-logo.gif')
+
+Out[2]:
+
+SVG images are also supported out of the box (since modern browsers do a
+good job of rendering them):
+
+In[3]:
+
+.. code:: python
+
+    from IPython.display import SVG
+    SVG(filename='python-logo.svg')
+
+Out[3]:
+.. image:: _fig_26.svg
+
+
+Embedded vs Non-embedded Images
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+
+As of IPython 0.13, images are embedded by default for compatibility
+with QtConsole, and the ability to still be displayed offline.
+
+Let's look at the differences:
+
+In[4]:
+
+.. code:: python
+
+    # by default Image data are embedded
+    Embed      = Image(    'http://scienceview.berkeley.edu/view/images/newview.jpg')
+    
+    # if kwarg `url` is given, the embedding is assumed to be false
+    SoftLinked = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg')
+    
+    # In each case, embed can be specified explicitly with the `embed` kwarg
+    # ForceEmbed = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg', embed=True)
+
+Today's image from a webcam at Berkeley, (at the time I created this
+notebook). This should also work in the Qtconsole. Drawback is that the
+saved notebook will be larger, but the image will still be present
+offline.
+
+In[5]:
+
+.. code:: python
+
+    Embed
+
+Out[5]:
+..jpg image:: 
+
+
+Today's image from same webcam at Berkeley, (refreshed every minutes, if
+you reload the notebook), visible only with an active internet
+connexion, that should be different from the previous one. This will not
+work on Qtconsole. Notebook saved with this kind of image will be
+lighter and always reflect the current version of the source, but the
+image won't display offline.
+
+In[6]:
+
+.. code:: python
+
+    SoftLinked
+
+Out[6]:
+
+Of course, if you re-run the all notebook, the two images will be the
+same again.
+
+Video
+~~~~~
+
+
+And more exotic objects can also be displayed, as long as their
+representation supports the IPython display protocol.
+
+For example, videos hosted externally on YouTube are easy to load (and
+writing a similar wrapper for other hosted content is trivial):
+
+In[7]:
+
+.. code:: python
+
+    from IPython.display import YouTubeVideo
+    # a talk about IPython at Sage Days at U. Washington, Seattle.
+    # Video credit: William Stein.
+    YouTubeVideo('1j_HxD4iLn8')
+
+Out[7]:
+
+Using the nascent video capabilities of modern browsers, you may also be
+able to display local videos. At the moment this doesn't work very well
+in all browsers, so it may or may not work for you; we will continue
+testing this and looking for ways to make it more robust.
+
+The following cell loads a local file called ``animation.m4v``, encodes
+the raw video as base64 for http transport, and uses the HTML5 video tag
+to load it. On Chrome 15 it works correctly, displaying a control bar at
+the bottom with a play/pause button and a location slider.
+
+In[8]:
+
+.. code:: python
+
+    from IPython.display import HTML
+    video = open("animation.m4v", "rb").read()
+    video_encoded = video.encode("base64")
+    video_tag = '<video controls alt="test" src="data:video/x-m4v;base64,{0}">'.format(video_encoded)
+    HTML(data=video_tag)
+
+Out[8]:
+
+Local Files
+-----------
+
+The above examples embed images and video from the notebook filesystem
+in the output areas of code cells. It is also possible to request these
+files directly in markdown cells if they reside in the notebook
+directory via relative urls prefixed with ``files/``:
+
+::
+
+    files/[subdirectory/]<filename>
+
+For example, in the example notebook folder, we have the Python logo,
+addressed as:
+
+::
+
+    <img src="files/python-logo.svg" />
+
+and a video with the HTML5 video tag:
+
+::
+
+    <video controls src="files/animation.m4v" />
+
+These do not embed the data into the notebook file, and require that the
+files exist when you are viewing the notebook.
+
+Security of local files
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Note that this means that the IPython notebook server also acts as a
+generic file server for files inside the same tree as your notebooks.
+Access is not granted outside the notebook folder so you have strict
+control over what files are visible, but for this reason it is highly
+recommended that you do not run the notebook server with a notebook
+directory at a high level in your filesystem (e.g. your home directory).
+
+When you run the notebook in a password-protected manner, local file
+access is restricted to authenticated users unless read-only views are
+active.
+
+Linking to files and directories for viewing in the browser
+-----------------------------------------------------------
+
+It is also possible to link directly to files or directories so they can
+be opened in the browser. This is especially convenient if you're
+interacting with a tool within IPython that generates HTML pages, and
+you'd like to easily be able to open those in a new browser window.
+Alternatively, if your IPython notebook server is on a remote system,
+creating links provides an easy way to download any files that get
+generated.
+
+As we saw above, there are a bunch of ``.ipynb`` files in our current
+directory.
+
+In[1]:
+
+.. code:: python
+
+    ls
+
+
+.. parsed-literal::
+
+    00_notebook_tour.ipynb          formatting.ipynb
+    01_notebook_introduction.ipynb  octavemagic_extension.ipynb
+    Animations_and_Progress.ipynb   publish_data.ipynb
+    Capturing Output.ipynb          python-logo.svg
+    Script Magics.ipynb             rmagic_extension.ipynb
+    animation.m4v                   sympy.ipynb
+    cython_extension.ipynb          sympy_quantum_computing.ipynb
+    display_protocol.ipynb          trapezoid_rule.ipynb
+
+If we want to create a link to one of them, we can call use the
+``FileLink`` object.
+
+In[2]:
+
+.. code:: python
+
+    from IPython.display import FileLink
+    FileLink('00_notebook_tour.ipynb')
+
+Out[2]:
+
+Alternatively, if we want to link to all of them, we can use the
+``FileLinks`` object, passing ``'.'`` to indicate that we want links
+generated for the current working directory. Note that if there were
+other directories under the current directory, ``FileLinks`` would work
+in a recursive manner creating links to files in all sub-directories as
+well.
+
+In[7]:
+
+.. code:: python
+
+    from IPython.display import FileLinks
+    FileLinks('.')
+
+Out[7]:
+
+External sites
+~~~~~~~~~~~~~~
+
+You can even embed an entire page from another site in an iframe; for
+example this is today's Wikipedia page for mobile users:
+
+In[9]:
+
+.. code:: python
+
+    HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')
+
+Out[9]:
+
+Mathematics
+~~~~~~~~~~~
+
+And we also support the display of mathematical expressions typeset in
+LaTeX, which is rendered in the browser thanks to the `MathJax
+library <http://mathjax.org>`_.
+
+Note that this is *different* from the above examples. Above we were
+typing mathematical expressions in Markdown cells (along with normal
+text) and letting the browser render them; now we are displaying the
+output of a Python computation as a LaTeX expression wrapped by the
+``Math()`` object so the browser renders it. The ``Math`` object will
+add the needed LaTeX delimiters (``$$``) if they are not provided:
+
+In[10]:
+
+.. code:: python
+
+    from IPython.display import Math
+    Math(r'F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx')
+
+Out[10]:
+.. math::
+
+    $$F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx$$
+
+
+With the ``Latex`` class, you have to include the delimiters yourself.
+This allows you to use other LaTeX modes such as ``eqnarray``:
+
+In[11]:
+
+.. code:: python
+
+    from IPython.display import Latex
+    Latex(r"""\begin{eqnarray}
+    \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
+    \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
+    \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
+    \nabla \cdot \vec{\mathbf{B}} & = 0 
+    \end{eqnarray}""")
+
+Out[11]:
+.. math::
+
+    \begin{eqnarray}
+    \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
+    \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
+    \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
+    \nabla \cdot \vec{\mathbf{B}} & = 0 
+    \end{eqnarray}
+
+
+Or you can enter latex directly with the ``%%latex`` cell magic:
+
+In[12]:
+
+.. code:: python
+
+    %%latex
+    \begin{aligned}
+    \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
+    \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
+    \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
+    \nabla \cdot \vec{\mathbf{B}} & = 0
+    \end{aligned}
+
+.. math::
+
+    \begin{aligned}
+    \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
+    \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
+    \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
+    \nabla \cdot \vec{\mathbf{B}} & = 0
+    \end{aligned}
+
+There is also a ``%%javascript`` cell magic for running javascript
+directly, and ``%%svg`` for manually entering SVG content.
+
+Loading external codes
+======================
+
+-  Drag and drop a ``.py`` in the dashboard
+-  Use ``%load`` with any local or remote url: `the Matplotlib
+   Gallery! <http://matplotlib.sourceforge.net/gallery.html>`_
+
+In this notebook we've kept the output saved so you can see the result,
+but you should run the next cell yourself (with an active internet
+connection).
+
+Let's make sure we have pylab again, in case we have restarted the
+kernel due to the crash demo above
+
+In[12]:
+
+.. code:: python
+
+    %pylab inline
+
+
+.. parsed-literal::
+
+    
+    Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
+    For more information, type 'help(pylab)'.
+
+In[15]:
+
+.. code:: python
+
+    %load http://matplotlib.sourceforge.net/mpl_examples/pylab_examples/integral_demo.py
+
+In[16]:
+
+.. code:: python
+
+    #!/usr/bin/env python
+    
+    # implement the example graphs/integral from pyx
+    from pylab import *
+    from matplotlib.patches import Polygon
+    
+    def func(x):
+        return (x-3)*(x-5)*(x-7)+85
+    
+    ax = subplot(111)
+    
+    a, b = 2, 9 # integral area
+    x = arange(0, 10, 0.01)
+    y = func(x)
+    plot(x, y, linewidth=1)
+    
+    # make the shaded region
+    ix = arange(a, b, 0.01)
+    iy = func(ix)
+    verts = [(a,0)] + zip(ix,iy) + [(b,0)]
+    poly = Polygon(verts, facecolor='0.8', edgecolor='k')
+    ax.add_patch(poly)
+    
+    text(0.5 * (a + b), 30,
+         r"$\int_a^b f(x)\mathrm{d}x$", horizontalalignment='center',
+         fontsize=20)
+    
+    axis([0,10, 0, 180])
+    figtext(0.9, 0.05, 'x')
+    figtext(0.1, 0.9, 'y')
+    ax.set_xticks((a,b))
+    ax.set_xticklabels(('a','b'))
+    ax.set_yticks([])
+    show()
+
+
+.. image:: _fig_60.png
+
diff --git a/tests/template_ref/00_notebook_tour_orig.tex b/tests/template_ref/00_notebook_tour_orig.tex
new file mode 100644
index 0000000..3608916
--- /dev/null
+++ b/tests/template_ref/00_notebook_tour_orig.tex
@@ -0,0 +1,853 @@
+
+
+%% This file was auto-generated by IPython.
+%% Conversion from the original notebook file:
+%% tests/ipynbref/Gun_Data.orig.ipynb
+%%
+\documentclass[11pt,english]{article}
+
+%% This is the automatic preamble used by IPython.  Note that it does *not*
+%% include a documentclass declaration, that is added at runtime to the overall
+%% document.
+
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{graphicx}
+\usepackage{ucs}
+\usepackage[utf8x]{inputenc}
+
+% needed for markdown enumerations to work
+\usepackage{enumerate}
+
+% Slightly bigger margins than the latex defaults
+\usepackage{geometry}
+\geometry{verbose,tmargin=3cm,bmargin=3cm,lmargin=2.5cm,rmargin=2.5cm}
+
+% Define a few colors for use in code, links and cell shading
+\usepackage{color}
+\definecolor{orange}{cmyk}{0,0.4,0.8,0.2}
+\definecolor{darkorange}{rgb}{.71,0.21,0.01}
+\definecolor{darkgreen}{rgb}{.12,.54,.11}
+\definecolor{myteal}{rgb}{.26, .44, .56}
+\definecolor{gray}{gray}{0.45}
+\definecolor{lightgray}{gray}{.95}
+\definecolor{mediumgray}{gray}{.8}
+\definecolor{inputbackground}{rgb}{.95, .95, .85}
+\definecolor{outputbackground}{rgb}{.95, .95, .95}
+\definecolor{traceback}{rgb}{1, .95, .95}
+
+% Framed environments for code cells (inputs, outputs, errors, ...).  The
+% various uses of \unskip (or not) at the end were fine-tuned by hand, so don't
+% randomly change them unless you're sure of the effect it will have.
+\usepackage{framed}
+
+% remove extraneous vertical space in boxes
+\setlength\fboxsep{0pt}
+
+% codecell is the whole input+output set of blocks that a Code cell can
+% generate.
+
+% TODO: unfortunately, it seems that using a framed codecell environment breaks
+% the ability of the frames inside of it to be broken across pages.  This
+% causes at least the problem of having lots of empty space at the bottom of
+% pages as new frames are moved to the next page, and if a single frame is too
+% long to fit on a page, will completely stop latex from compiling the
+% document.  So unless we figure out a solution to this, we'll have to instead
+% leave the codecell env. as empty.  I'm keeping the original codecell
+% definition here (a thin vertical bar) for reference, in case we find a
+% solution to the page break issue.
+
+%% \newenvironment{codecell}{%
+%%     \def\FrameCommand{\color{mediumgray} \vrule width 1pt \hspace{5pt}}%
+%%    \MakeFramed{\vspace{-0.5em}}}
+%%  {\unskip\endMakeFramed}
+
+% For now, make this a no-op...
+\newenvironment{codecell}{}
+
+ \newenvironment{codeinput}{%
+   \def\FrameCommand{\colorbox{inputbackground}}%
+   \MakeFramed{\advance\hsize-\width \FrameRestore}}
+ {\unskip\endMakeFramed}
+
+\newenvironment{codeoutput}{%
+   \def\FrameCommand{\colorbox{outputbackground}}%
+   \vspace{-1.4em}
+   \MakeFramed{\advance\hsize-\width \FrameRestore}}
+ {\unskip\medskip\endMakeFramed}
+
+\newenvironment{traceback}{%
+   \def\FrameCommand{\colorbox{traceback}}%
+   \MakeFramed{\advance\hsize-\width \FrameRestore}}
+ {\endMakeFramed}
+
+% Use and configure listings package for nicely formatted code
+\usepackage{listingsutf8}
+\lstset{
+  language=python,
+  inputencoding=utf8x,
+  extendedchars=\true,
+  aboveskip=\smallskipamount,
+  belowskip=\smallskipamount,
+  xleftmargin=2mm,
+  breaklines=true,
+  basicstyle=\small \ttfamily,
+  showstringspaces=false,
+  keywordstyle=\color{blue}\bfseries,
+  commentstyle=\color{myteal},
+  stringstyle=\color{darkgreen},
+  identifierstyle=\color{darkorange},
+  columns=fullflexible,  % tighter character kerning, like verb
+}
+
+% The hyperref package gives us a pdf with properly built
+% internal navigation ('pdf bookmarks' for the table of contents,
+% internal cross-reference links, web links for URLs, etc.)
+\usepackage{hyperref}
+\hypersetup{
+  breaklinks=true,  % so long urls are correctly broken across lines
+  colorlinks=true,
+  urlcolor=blue,
+  linkcolor=darkorange,
+  citecolor=darkgreen,
+  }
+
+% hardcode size of all verbatim environments to be a bit smaller
+\makeatletter 
+\g@addto@macro\@verbatim\small\topsep=0.5em\partopsep=0pt
+\makeatother 
+
+% Prevent overflowing lines due to urls and other hard-to-break entities.
+\sloppy
+
+\begin{document}
+\section{A brief tour of the IPython notebook}
+
+This document will give you a brief tour of the capabilities of the
+IPython notebook.\\You can view its contents by scrolling around, or
+execute each cell by typing \texttt{Shift-Enter}. After you conclude
+this brief high-level tour, you should read the accompanying notebook
+titled \texttt{01\_notebook\_introduction}, which takes a more
+step-by-step approach to the features of the system.
+
+The rest of the notebooks in this directory illustrate various other
+aspects and capabilities of the IPython notebook; some of them may
+require additional libraries to be executed.
+
+\textbf{NOTE:} This notebook \emph{must} be run from its own directory,
+so you must \texttt{cd} to this directory and then start the notebook,
+but do \emph{not} use the \texttt{-{}-notebook-dir} option to run it
+from another location.
+
+The first thing you need to know is that you are still controlling the
+same old IPython you're used to, so things like shell aliases and magic
+commands still work:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+pwd
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+u'/Users/minrk/dev/ip/mine/docs/examples/notebooks'
+
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+ls
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+00_notebook_tour.ipynb          callbacks.ipynb                 python-logo.svg
+01_notebook_introduction.ipynb  cython_extension.ipynb          rmagic_extension.ipynb
+Animations_and_Progress.ipynb   display_protocol.ipynb          sympy.ipynb
+Capturing Output.ipynb          formatting.ipynb                sympy_quantum_computing.ipynb
+Script Magics.ipynb             octavemagic_extension.ipynb     trapezoid_rule.ipynb
+animation.m4v                   progbar.ipynb
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+message = 'The IPython notebook is great!'
+# note: the echo command does not run on Windows, it's a unix command.
+!echo $message
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+The IPython notebook is great!
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+
+\section{Plots with matplotlib}
+IPython adds an `inline' matplotlib backend, which embeds any matplotlib
+figures into the notebook.
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+%pylab inline
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+
+Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
+For more information, type 'help(pylab)'.
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+x = linspace(0, 3*pi, 500)
+plot(x, sin(x**2))
+title('A simple chirp');
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+\begin{center}
+\includegraphics[width=0.7\textwidth]{_fig_07.png}
+\par
+\end{center}
+\end{codeoutput}
+\end{codecell}
+You can paste blocks of input with prompt markers, such as those from
+\href{http://docs.python.org/tutorial/interpreter.html\#interactive-mode}{the
+official Python tutorial}
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+>>> the_world_is_flat = 1
+>>> if the_world_is_flat:
+...     print "Be careful not to fall off!"
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+Be careful not to fall off!
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+Errors are shown in informative ways:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+%run non_existent_file
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+ERROR: File `u'non_existent_file.py'` not found.
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+x = 1
+y = 4
+z = y/(1-x)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+    ---------------------------------------------------------------------------
+    ZeroDivisionError                         Traceback (most recent call last)
+    <ipython-input-8-dc39888fd1d2> in <module>()
+          1 x = 1
+          2 y = 4
+    ----> 3 z = y/(1-x)
+    
+    ZeroDivisionError: integer division or modulo by zero
+
+\end{codeoutput}
+\end{codecell}
+When IPython needs to display additional information (such as providing
+details on an object via \texttt{x?} it will automatically invoke a
+pager at the bottom of the screen:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+magic
+\end{lstlisting}
+\end{codeinput}
+
+\end{codecell}
+\subsection{Non-blocking output of kernel}
+
+If you execute the next cell, you will see the output arriving as it is
+generated, not all at the end.
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+import time, sys
+for i in range(8):
+    print i,
+    time.sleep(0.5)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+0 
+\end{verbatim}
+
+\begin{verbatim}
+1 
+\end{verbatim}
+
+\begin{verbatim}
+2 
+\end{verbatim}
+
+\begin{verbatim}
+3 
+\end{verbatim}
+
+\begin{verbatim}
+4 
+\end{verbatim}
+
+\begin{verbatim}
+5 
+\end{verbatim}
+
+\begin{verbatim}
+6 
+\end{verbatim}
+
+\begin{verbatim}
+7
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+\subsection{Clean crash and restart}
+
+We call the low-level system libc.time routine with the wrong argument
+via ctypes to segfault the Python interpreter:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+import sys
+from ctypes import CDLL
+# This will crash a Linux or Mac system; equivalent calls can be made on Windows
+dll = 'dylib' if sys.platform == 'darwin' else '.so.6'
+libc = CDLL("libc.%s" % dll) 
+libc.time(-1)  # BOOM!!
+\end{lstlisting}
+\end{codeinput}
+
+\end{codecell}
+\subsection{Markdown cells can contain formatted text and code}
+
+You can \emph{italicize}, \textbf{boldface}
+
+\begin{itemize}
+\item
+  build
+\item
+  lists
+\end{itemize}
+and embed code meant for illustration instead of execution in Python:
+
+\begin{verbatim}
+def f(x):
+    """a docstring"""
+    return x**2
+\end{verbatim}
+or other languages:
+
+\begin{verbatim}
+if (i=0; i<n; i++) {
+  printf("hello %d\n", i);
+  x += 4;
+}
+\end{verbatim}
+
+
+Courtesy of MathJax, you can include mathematical expressions both
+inline: $e^{i\pi} + 1 = 0$ and displayed:
+
+\[e^x=\sum_{i=0}^\infty \frac{1}{i!}x^i\]
+
+\subsection{Rich displays: include anyting a browser can show}
+
+Note that we have an actual protocol for this, see the
+\texttt{display\_protocol} notebook for further details.
+
+\subsubsection{Images}
+
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+from IPython.display import Image
+Image(filename='../../source/_static/logo.png')
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+<IPython.core.display.Image at 0x10faeafd0>
+
+\end{codeoutput}
+\end{codecell}
+An image can also be displayed from raw data or a url
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+Image(url='http://python.org/images/python-logo.gif')
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+<IPython.core.display.Image at 0x1060e7410>
+
+\end{codeoutput}
+\end{codecell}
+SVG images are also supported out of the box (since modern browsers do a
+good job of rendering them):
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+from IPython.display import SVG
+SVG(filename='python-logo.svg')
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+<IPython.core.display.SVG at 0x10fb998d0>
+
+\end{codeoutput}
+\end{codecell}
+\paragraph{Embedded vs Non-embedded Images}
+
+
+As of IPython 0.13, images are embedded by default for compatibility
+with QtConsole, and the ability to still be displayed offline.
+
+Let's look at the differences:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+# by default Image data are embedded
+Embed      = Image(    'http://scienceview.berkeley.edu/view/images/newview.jpg')
+
+# if kwarg `url` is given, the embedding is assumed to be false
+SoftLinked = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg')
+
+# In each case, embed can be specified explicitly with the `embed` kwarg
+# ForceEmbed = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg', embed=True)
+\end{lstlisting}
+\end{codeinput}
+
+\end{codecell}
+Today's image from a webcam at Berkeley, (at the time I created this
+notebook). This should also work in the Qtconsole. Drawback is that the
+saved notebook will be larger, but the image will still be present
+offline.
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+Embed
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+<IPython.core.display.Image at 0x10fb99b50>
+
+\end{codeoutput}
+\end{codecell}
+Today's image from same webcam at Berkeley, (refreshed every minutes, if
+you reload the notebook), visible only with an active internet
+connexion, that should be different from the previous one. This will not
+work on Qtconsole. Notebook saved with this kind of image will be
+lighter and always reflect the current version of the source, but the
+image won't display offline.
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+SoftLinked
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+<IPython.core.display.Image at 0x10fb99b10>
+
+\end{codeoutput}
+\end{codecell}
+Of course, if you re-run the all notebook, the two images will be the
+same again.
+
+\subsubsection{Video}
+
+
+And more exotic objects can also be displayed, as long as their
+representation supports the IPython display protocol.
+
+For example, videos hosted externally on YouTube are easy to load (and
+writing a similar wrapper for other hosted content is trivial):
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+from IPython.display import YouTubeVideo
+# a talk about IPython at Sage Days at U. Washington, Seattle.
+# Video credit: William Stein.
+YouTubeVideo('1j_HxD4iLn8')
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+<IPython.lib.display.YouTubeVideo at 0x10fba2190>
+
+\end{codeoutput}
+\end{codecell}
+Using the nascent video capabilities of modern browsers, you may also be
+able to display local videos. At the moment this doesn't work very well
+in all browsers, so it may or may not work for you; we will continue
+testing this and looking for ways to make it more robust.
+
+The following cell loads a local file called \texttt{animation.m4v},
+encodes the raw video as base64 for http transport, and uses the HTML5
+video tag to load it. On Chrome 15 it works correctly, displaying a
+control bar at the bottom with a play/pause button and a location
+slider.
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+from IPython.display import HTML
+video = open("animation.m4v", "rb").read()
+video_encoded = video.encode("base64")
+video_tag = '<video controls alt="test" src="data:video/x-m4v;base64,{0}">'.format(video_encoded)
+HTML(data=video_tag)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+<IPython.core.display.HTML at 0x10fba28d0>
+
+\end{codeoutput}
+\end{codecell}
+\subsection{Local Files}
+
+The above examples embed images and video from the notebook filesystem
+in the output areas of code cells. It is also possible to request these
+files directly in markdown cells if they reside in the notebook
+directory via relative urls prefixed with \texttt{files/}:
+
+\begin{verbatim}
+files/[subdirectory/]<filename>
+\end{verbatim}
+For example, in the example notebook folder, we have the Python logo,
+addressed as:
+
+\begin{verbatim}
+<img src="files/python-logo.svg" />
+\end{verbatim}
+and a video with the HTML5 video tag:
+
+\begin{verbatim}
+<video controls src="files/animation.m4v" />
+\end{verbatim}
+These do not embed the data into the notebook file, and require that the
+files exist when you are viewing the notebook.
+
+\subsubsection{Security of local files}
+
+Note that this means that the IPython notebook server also acts as a
+generic file server for files inside the same tree as your notebooks.
+Access is not granted outside the notebook folder so you have strict
+control over what files are visible, but for this reason it is highly
+recommended that you do not run the notebook server with a notebook
+directory at a high level in your filesystem (e.g.~your home directory).
+
+When you run the notebook in a password-protected manner, local file
+access is restricted to authenticated users unless read-only views are
+active.
+
+\subsection{Linking to files and directories for viewing in the browser}
+
+It is also possible to link directly to files or directories so they can
+be opened in the browser. This is especially convenient if you're
+interacting with a tool within IPython that generates HTML pages, and
+you'd like to easily be able to open those in a new browser window.
+Alternatively, if your IPython notebook server is on a remote system,
+creating links provides an easy way to download any files that get
+generated.
+
+As we saw above, there are a bunch of \texttt{.ipynb} files in our
+current directory.
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+ls
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+00_notebook_tour.ipynb          formatting.ipynb
+01_notebook_introduction.ipynb  octavemagic_extension.ipynb
+Animations_and_Progress.ipynb   publish_data.ipynb
+Capturing Output.ipynb          python-logo.svg
+Script Magics.ipynb             rmagic_extension.ipynb
+animation.m4v                   sympy.ipynb
+cython_extension.ipynb          sympy_quantum_computing.ipynb
+display_protocol.ipynb          trapezoid_rule.ipynb
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+If we want to create a link to one of them, we can call use the
+\texttt{FileLink} object.
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+from IPython.display import FileLink
+FileLink('00_notebook_tour.ipynb')
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+<IPython.lib.display.FileLink at 0x10f7ea3d0>
+
+\end{codeoutput}
+\end{codecell}
+Alternatively, if we want to link to all of them, we can use the
+\texttt{FileLinks} object, passing \texttt{'.'} to indicate that we want
+links generated for the current working directory. Note that if there
+were other directories under the current directory, \texttt{FileLinks}
+would work in a recursive manner creating links to files in all
+sub-directories as well.
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+from IPython.display import FileLinks
+FileLinks('.')
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+<IPython.lib.display.FileLinks at 0x10f7eaad0>
+
+\end{codeoutput}
+\end{codecell}
+\subsubsection{External sites}
+
+You can even embed an entire page from another site in an iframe; for
+example this is today's Wikipedia page for mobile users:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+<IPython.core.display.HTML at 0x1094900d0>
+
+\end{codeoutput}
+\end{codecell}
+\subsubsection{Mathematics}
+
+And we also support the display of mathematical expressions typeset in
+LaTeX, which is rendered in the browser thanks to the
+\href{http://mathjax.org}{MathJax library}.
+
+Note that this is \emph{different} from the above examples. Above we
+were typing mathematical expressions in Markdown cells (along with
+normal text) and letting the browser render them; now we are displaying
+the output of a Python computation as a LaTeX expression wrapped by the
+\texttt{Math()} object so the browser renders it. The \texttt{Math}
+object will add the needed LaTeX delimiters (\texttt{\$\$}) if they are
+not provided:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+from IPython.display import Math
+Math(r'F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx')
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+<IPython.core.display.Math at 0x10fba26d0>
+
+\end{codeoutput}
+\end{codecell}
+With the \texttt{Latex} class, you have to include the delimiters
+yourself. This allows you to use other LaTeX modes such as
+\texttt{eqnarray}:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+from IPython.display import Latex
+Latex(r"""\begin{eqnarray}
+\nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
+\nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
+\nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
+\nabla \cdot \vec{\mathbf{B}} & = 0 
+\end{eqnarray}""")
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+<IPython.core.display.Latex at 0x10fba2c10>
+
+\end{codeoutput}
+\end{codecell}
+Or you can enter latex directly with the \texttt{\%\%latex} cell magic:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+%%latex
+\begin{aligned}
+\nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
+\nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
+\nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
+\nabla \cdot \vec{\mathbf{B}} & = 0
+\end{aligned}
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\end{codeoutput}
+\end{codecell}
+There is also a \texttt{\%\%javascript} cell magic for running
+javascript directly, and \texttt{\%\%svg} for manually entering SVG
+content.
+
+\section{Loading external codes}
+
+\begin{itemize}
+\item
+  Drag and drop a \texttt{.py} in the dashboard
+\item
+  Use \texttt{\%load} with any local or remote url:
+  \href{http://matplotlib.sourceforge.net/gallery.html}{the Matplotlib
+  Gallery!}
+\end{itemize}
+In this notebook we've kept the output saved so you can see the result,
+but you should run the next cell yourself (with an active internet
+connection).
+
+Let's make sure we have pylab again, in case we have restarted the
+kernel due to the crash demo above
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+%pylab inline
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+
+Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
+For more information, type 'help(pylab)'.
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+%load http://matplotlib.sourceforge.net/mpl_examples/pylab_examples/integral_demo.py
+\end{lstlisting}
+\end{codeinput}
+
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+#!/usr/bin/env python
+
+# implement the example graphs/integral from pyx
+from pylab import *
+from matplotlib.patches import Polygon
+
+def func(x):
+    return (x-3)*(x-5)*(x-7)+85
+
+ax = subplot(111)
+
+a, b = 2, 9 # integral area
+x = arange(0, 10, 0.01)
+y = func(x)
+plot(x, y, linewidth=1)
+
+# make the shaded region
+ix = arange(a, b, 0.01)
+iy = func(ix)
+verts = [(a,0)] + zip(ix,iy) + [(b,0)]
+poly = Polygon(verts, facecolor='0.8', edgecolor='k')
+ax.add_patch(poly)
+
+text(0.5 * (a + b), 30,
+     r"$\int_a^b f(x)\mathrm{d}x$", horizontalalignment='center',
+     fontsize=20)
+
+axis([0,10, 0, 180])
+figtext(0.9, 0.05, 'x')
+figtext(0.1, 0.9, 'y')
+ax.set_xticks((a,b))
+ax.set_xticklabels(('a','b'))
+ax.set_yticks([])
+show()
+
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+\begin{center}
+\includegraphics[width=0.7\textwidth]{_fig_60.png}
+\par
+\end{center}
+\end{codeoutput}
+\end{codecell}
+
+\end{document}
diff --git a/tests/template_ref/IntroNumPy_orig.html b/tests/template_ref/IntroNumPy_orig.html
new file mode 100644
index 0000000..14139b8
--- /dev/null
+++ b/tests/template_ref/IntroNumPy_orig.html
@@ -0,0 +1,30061 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="UTF-8">
+<title>[IntroNumPy]</title>
+<style type="text/css">
+/**
+ * HTML5 ✰ Boilerplate
+ *
+ * style.css contains a reset, font normalization and some base styles.
+ *
+ * Credit is left where credit is due.
+ * Much inspiration was taken from these projects:
+ * - yui.yahooapis.com/2.8.1/build/base/base.css
+ * - camendesign.com/design/
+ * - praegnanz.de/weblog/htmlcssjs-kickstart
+ */
+
+
+/**
+ * html5doctor.com Reset Stylesheet (Eric Meyer's Reset Reloaded + HTML5 baseline)
+ * v1.6.1 2010-09-17 | Authors: Eric Meyer & Richard Clark
+ * html5doctor.com/html-5-reset-stylesheet/
+ */
+
+html, body, div, span, object, iframe,
+h1, h2, h3, h4, h5, h6, p, blockquote, pre,
+abbr, address, cite, code, del, dfn, em, img, ins, kbd, q, samp,
+small, strong, sub, sup, var, b, i, dl, dt, dd, ol, ul, li,
+fieldset, form, label, legend,
+table, caption, tbody, tfoot, thead, tr, th, td,
+article, aside, canvas, details, figcaption, figure,
+footer, header, hgroup, menu, nav, section, summary,
+time, mark, audio, video {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  font-size: 100%;
+  font: inherit;
+  vertical-align: baseline;
+}
+
+sup { vertical-align: super; }
+sub { vertical-align: sub; }
+
+article, aside, details, figcaption, figure,
+footer, header, hgroup, menu, nav, section {
+  display: block;
+}
+
+blockquote, q { quotes: none; }
+
+blockquote:before, blockquote:after,
+q:before, q:after { content: ""; content: none; }
+
+ins { background-color: #ff9; color: #000; text-decoration: none; }
+
+mark { background-color: #ff9; color: #000; font-style: italic; font-weight: bold; }
+
+del { text-decoration: line-through; }
+
+abbr[title], dfn[title] { border-bottom: 1px dotted; cursor: help; }
+
+table { border-collapse: collapse; border-spacing: 0; }
+
+hr { display: block; height: 1px; border: 0; border-top: 1px solid #ccc; margin: 1em 0; padding: 0; }
+
+input, select { vertical-align: middle; }
+
+
+/**
+ * Font normalization inspired by YUI Library's fonts.css: developer.yahoo.com/yui/
+ */
+
+body { font:13px/1.231 sans-serif; *font-size:small; } /* Hack retained to preserve specificity */
+select, input, textarea, button { font:99% sans-serif; }
+
+/* Normalize monospace sizing:
+   en.wikipedia.org/wiki/MediaWiki_talk:Common.css/Archive_11#Teletype_style_fix_for_Chrome */
+pre, code, kbd, samp { font-family: monospace, sans-serif; }
+
+em,i { font-style: italic; }
+b,strong { font-weight: bold; }
+
+</style>
+<style type="text/css">
+article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block;}
+audio,canvas,video{display:inline-block;*display:inline;*zoom:1;}
+audio:not([controls]){display:none;}
+html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%;}
+a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px;}
+a:hover,a:active{outline:0;}
+sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline;}
+sup{top:-0.5em;}
+sub{bottom:-0.25em;}
+img{max-width:100%;width:auto\9;height:auto;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic;}
+#map_canvas img,.google-maps img{max-width:none;}
+button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle;}
+button,input{*overflow:visible;line-height:normal;}
+button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0;}
+button,html input[type="button"],input[type="reset"],input[type="submit"]{-webkit-appearance:button;cursor:pointer;}
+label,select,button,input[type="button"],input[type="reset"],input[type="submit"],input[type="radio"],input[type="checkbox"]{cursor:pointer;}
+input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield;}
+input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none;}
+textarea{overflow:auto;vertical-align:top;}
+@media print{*{text-shadow:none !important;color:#000 !important;background:transparent !important;box-shadow:none !important;} a,a:visited{text-decoration:underline;} a[href]:after{content:" (" attr(href) ")";} abbr[title]:after{content:" (" attr(title) ")";} .ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:"";} pre,blockquote{border:1px solid #999;page-break-inside:avoid;} thead{display:table-header-group;} tr,img{page-break-inside:avoid;} img{max-width:100% !important;} @page {margin:0.5cm;}p,h2,h3{orphans:3;widows:3;} h2,h3{page-break-after:avoid;}}.clearfix{*zoom:1;}.clearfix:before,.clearfix:after{display:table;content:"";line-height:0;}
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+.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0;}
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+.img-circle{-webkit-border-radius:500px;-moz-border-radius:500px;border-radius:500px;}
+.row{margin-left:-20px;*zoom:1;}.row:before,.row:after{display:table;content:"";line-height:0;}
+.row:after{clear:both;}
+[class*="span"]{float:left;min-height:1px;margin-left:20px;}
+.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px;}
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+.popover.bottom{margin-top:10px;}
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+.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid;}
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+.popover .arrow:after{border-width:10px;content:"";}
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+.media,.media .media{margin-top:15px;}
+.media:first-child{margin-top:0;}
+.media-object{display:block;}
+.media-heading{margin:0 0 5px;}
+.media .pull-left{margin-right:10px;}
+.media .pull-right{margin-left:10px;}
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+.badge{padding-left:9px;padding-right:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px;}
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+a.label:hover,a.badge:hover{color:#ffffff;text-decoration:none;cursor:pointer;}
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+.label-success[href],.badge-success[href]{background-color:#356635;}
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+.label-inverse[href],.badge-inverse[href]{background-color:#1a1a1a;}
+.btn .label,.btn .badge{position:relative;top:-1px;}
+.btn-mini .label,.btn-mini .badge{top:0;}
+@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0;} to{background-position:0 0;}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0;} to{background-position:0 0;}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0;} to{background-position:0 0;}}@-o-keyframes progress-bar-stripes{from{background-position:0 0;} to{background-position:40px 0;}}@keyframes progress-bar-stripes{from{background-position:40px 0;} to{background-position:0 0;}}.progress{overflow:hidden;height:20px;margin-bottom:20px;background-color:#f7f7f7;background-image:-moz-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));background-image:-webkit-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-o-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:linear-gradient(to bottom, #f5f5f5, #f9f9f9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0, 0, 0, 0.1);-moz-box-shadow:inset 0 1px 2px rgba(0, 0, 0, 0.1);box-shadow:inset 0 1px 2px rgba(0, 0, 0, 0.1);-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;}
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+.accordion-group{margin-bottom:2px;border:1px solid #e5e5e5;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;}
+.accordion-heading{border-bottom:0;}
+.accordion-heading .accordion-toggle{display:block;padding:8px 15px;}
+.accordion-toggle{cursor:pointer;}
+.accordion-inner{padding:9px 15px;border-top:1px solid #e5e5e5;}
+.carousel{position:relative;margin-bottom:20px;line-height:1;}
+.carousel-inner{overflow:hidden;width:100%;position:relative;}
+.carousel-inner>.item{display:none;position:relative;-webkit-transition:0.6s ease-in-out left;-moz-transition:0.6s ease-in-out left;-o-transition:0.6s ease-in-out left;transition:0.6s ease-in-out left;}
+.carousel-inner>.item>img{display:block;line-height:1;}
+.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block;}
+.carousel-inner>.active{left:0;}
+.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%;}
+.carousel-inner>.next{left:100%;}
+.carousel-inner>.prev{left:-100%;}
+.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0;}
+.carousel-inner>.active.left{left:-100%;}
+.carousel-inner>.active.right{left:100%;}
+.carousel-control{position:absolute;top:40%;left:15px;width:40px;height:40px;margin-top:-20px;font-size:60px;font-weight:100;line-height:30px;color:#ffffff;text-align:center;background:#222222;border:3px solid #ffffff;-webkit-border-radius:23px;-moz-border-radius:23px;border-radius:23px;opacity:0.5;filter:alpha(opacity=50);}.carousel-control.right{left:auto;right:15px;}
+.carousel-control:hover{color:#ffffff;text-decoration:none;opacity:0.9;filter:alpha(opacity=90);}
+.carousel-caption{position:absolute;left:0;right:0;bottom:0;padding:15px;background:#333333;background:rgba(0, 0, 0, 0.75);}
+.carousel-caption h4,.carousel-caption p{color:#ffffff;line-height:20px;}
+.carousel-caption h4{margin:0 0 5px;}
+.carousel-caption p{margin-bottom:0;}
+.hero-unit{padding:60px;margin-bottom:30px;font-size:18px;font-weight:200;line-height:30px;color:inherit;background-color:#eeeeee;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;}.hero-unit h1{margin-bottom:0;font-size:60px;line-height:1;color:inherit;letter-spacing:-1px;}
+.hero-unit li{line-height:30px;}
+.pull-right{float:right;}
+.pull-left{float:left;}
+.hide{display:none;}
+.show{display:block;}
+.invisible{visibility:hidden;}
+.affix{position:fixed;}
+.corner-all{border-radius:4px;}
+.hbox{display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;}
+.hbox>*{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;}
+.vbox{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;}
+.vbox>*{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;}
+.reverse{-webkit-box-direction:reverse;-moz-box-direction:reverse;box-direction:reverse;}
+.box-flex0{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;}
+.box-flex1,.box-flex{-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;}
+.box-flex2{-webkit-box-flex:2;-moz-box-flex:2;box-flex:2;}
+.box-group1{-webkit-box-flex-group:1;-moz-box-flex-group:1;box-flex-group:1;}
+.box-group2{-webkit-box-flex-group:2;-moz-box-flex-group:2;box-flex-group:2;}
+.start{-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;}
+.end{-webkit-box-pack:end;-moz-box-pack:end;box-pack:end;}
+.center{-webkit-box-pack:center;-moz-box-pack:center;box-pack:center;}
+.corner-all{border-radius:4px;}
+body{background-color:#ffffff;}
+body.notebook_app{overflow:hidden;}
+blockquote{border-left:4px solid #DDD;padding:0 15px;color:#777;}
+span#save_widget{padding:5px;margin:0px 0px 0px 300px;display:inline-block;}
+span#notebook_name{height:1em;line-height:1em;padding:3px;border:none;font-size:146.5%;}
+.ui-menubar-item .ui-button .ui-button-text{padding:0.4em 1.0em;font-size:100%;}
+.ui-menu{-moz-box-shadow:0px 6px 10px -1px #adadad;-webkit-box-shadow:0px 6px 10px -1px #adadad;box-shadow:0px 6px 10px -1px #adadad;}
+.ui-menu .ui-menu-item a{border:1px solid transparent;padding:2px 1.6em;}
+.ui-menu .ui-menu-item a.ui-state-focus{margin:0;}
+.ui-menu hr{margin:0.3em 0;}
+#menubar_container{position:relative;}
+#notification_area{position:absolute;right:0px;top:0px;height:25px;padding:3px 0px;padding-right:3px;z-index:10;}
+.notification_widget{float:right;right:0px;top:1px;height:25px;padding:3px 6px;z-index:10;}
+.toolbar{padding:3px 15px;border-bottom:1px #ababab solid;}.toolbar button{margin-top:2px;margin-bottom:2px;}
+.toolbar select,.toolbar label{height:19px;vertical-align:middle;margin-right:2px;margin-bottom:0;display:inline;font-size:92%;margin-left:0.3em;margin-right:0.3em;padding:0px;}
+.toolbar select{width:auto;}
+#ipython-main-app{width:100%;position:relative;font-size:110%;}
+span#quick_help_area{position:static;padding:5px 0px;margin:0px 0px 0px 0px;}
+.help_string{float:right;width:170px;padding:0px 5px;text-align:left;font-size:85%;}
+.help_string_label{float:right;font-size:85%;}
+div#notebook_panel{margin:0px 0px 0px 0px;padding:0px;}
+div#notebook{overflow-y:scroll;overflow-x:auto;width:100%;padding:5px 5px 15px 5px;margin:0px;}
+div#pager_splitter{height:8px;}
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+
+</script>
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+init_mathjax = function() {
+    if (window.MathJax) {
+        // MathJax loaded
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+<div class="text_cell_render border-box-sizing rendered_html">
+<h1>
+  An Introduction to the Scientific Python Ecosystem
+</h1>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>While the Python language is an excellent tool for general-purpose programming, with a highly readable syntax, rich and powerful data types (strings, lists, sets, dictionaries, arbitrary length integers, etc) and a very comprehensive standard library, it was not designed specifically for mathematical and scientific computing.  Neither the language nor its standard library have facilities for the efficient representation of multidimensional datasets, tools for linear algebra and general matrix manipulations (an essential building block of virtually all technical computing), nor any data visualization facilities.</p>
+<p>In particular, Python lists are very flexible containers that can be nested arbitrarily deep and which can hold any Python object in them, but they are poorly suited to represent efficiently common mathematical constructs like vectors and matrices.  In contrast, much of our modern heritage of scientific computing has been built on top of libraries written in the Fortran language, which has native support for vectors and matrices as well as a library of mathematical functions that can efficiently operate on entire arrays at once.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  Scientific Python: a collaboration of projects built by scientists
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The scientific community has developed a set of related Python libraries that provide powerful array facilities, linear algebra, numerical algorithms, data visualization and more.  In this appendix, we will briefly outline the tools most frequently used for this purpose, that make "Scientific Python" something far more powerful than the Python language alone.</p>
+<p>For reasons of space, we can only describe in some detail the central Numpy library, but below we provide links to the websites of each project where you can read their documentation in more detail.</p>
+<p>First, let's look at an overview of the basic tools that most scientists use in daily research with Python.  The core of this ecosystem is composed of:</p>
+<ul>
+<li>
+<p>Numpy: the basic library that most others depend on, it provides a powerful array type that can represent multidmensional datasets of many different kinds and that supports arithmetic operations. Numpy also provides a library of common mathematical functions, basic linear algebra, random number generation and Fast Fourier Transforms.  Numpy can be found at <a href="http://numpy.scipy.org">numpy.scipy.org</a></p>
+</li>
+<li>
+<p>Scipy: a large collection of numerical algorithms that operate on numpy arrays and provide facilities for many common tasks in scientific computing, including dense and sparse linear algebra support, optimization, special functions, statistics, n-dimensional image processing, signal processing and more. Scipy can be found at <a href="http://scipy.org">scipy.org</a>.</p>
+</li>
+<li>
+<p>Matplotlib: a data visualization library with a strong focus on producing high-quality output, it supports a variety of common scientific plot types in two and three dimensions, with precise control over the final output and format for publication-quality results.  Matplotlib can also be controlled interactively allowing graphical manipulation of your data (zooming, panning, etc) and can be used with most modern user interface toolkits.  It can be found at <a href="http://matplotlib.sf.net">matplotlib.sf.net</a>.</p>
+</li>
+<li>
+<p>IPython: while not strictly scientific in nature, IPython is the interactive environment in which many scientists spend their time.  IPython provides a powerful Python shell that integrates tightly with Matplotlib and with easy access to the files and operating system, and which can execute in a terminal or in a graphical Qt console. IPython also has a web-based notebook interface that can combine code with text, mathematical expressions, figures and multimedia.  It can be found at <a href="http://ipython.org">ipython.org</a>.</p>
+</li>
+</ul>
+<p>While each of these tools can be installed separately, in our opinion the most convenient way today of accessing them (especially on Windows and Mac computers) is to install the <a href="http://www.enthought.com/products/epd_free.php">Free Edition of the Enthought Python Distribution</a> which contain all the above.  Other free alternatives on Windows (but not on Macs) are <a href="http://code.google.com/p/pythonxy">Python(x,y)</a> and <a href="http://www.lfd.uci.edu/~gohlke/pythonlibs"> Christoph Gohlke's packages page</a>.</p>
+<p>These four 'core' libraries are in practice complemented by a number of other tools for more specialized work.  We will briefly list here the ones that we think are the most commonly needed:</p>
+<ul>
+<li>
+<p>Sympy: a symbolic manipulation tool that turns a Python session into a computer algebra system.  It integrates with the IPython notebook, rendering results in properly typeset mathematical notation.  <a href="http://sympy.org">sympy.org</a>.</p>
+</li>
+<li>
+<p>Mayavi: sophisticated 3d data visualization; <a href="http://code.enthought.com/projects/mayavi">code.enthought.com/projects/mayavi</a>.</p>
+</li>
+<li>
+<p>Cython: a bridge language between Python and C, useful both to optimize performance bottlenecks in Python and to access C libraries directly; <a href="http://cython.org">cython.org</a>.</p>
+</li>
+<li>
+<p>Pandas: high-performance data structures and data analysis tools, with powerful data alignment and structural manipulation capabilities; <a href="http://pandas.pydata.org">pandas.pydata.org</a>.</p>
+</li>
+<li>
+<p>Statsmodels: statistical data exploration and model estimation; <a href="http://statsmodels.sourceforge.net">statsmodels.sourceforge.net</a>.</p>
+</li>
+<li>
+<p>Scikit-learn: general purpose machine learning algorithms with a common interface; <a href="http://scikit-learn.org">scikit-learn.org</a>.</p>
+</li>
+<li>
+<p>Scikits-image: image processing toolbox; <a href="http://scikits-image.org">scikits-image.org</a>.</p>
+</li>
+<li>
+<p>NetworkX: analysis of complex networks (in the graph theoretical sense); <a href="http://networkx.lanl.gov">networkx.lanl.gov</a>.</p>
+</li>
+<li>
+<p>PyTables: management of hierarchical datasets using the industry-standard HDF5 format; <a href="http://www.pytables.org">www.pytables.org</a>.</p>
+</li>
+</ul>
+<p>Beyond these, for any specific problem you should look on the internet first, before starting to write code from scratch.  There's a good chance that someone, somewhere, has written an open source library that you can use for part or all of your problem.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  A note about the examples below
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>In all subsequent examples, you will see blocks of input code, followed by the results of the code if the code generated output.  This output may include text, graphics and other result objects.  These blocks of input can be pasted into your interactive IPython session or notebook for you to execute.  In the print version of this document, a thin vertical bar on the left of the blocks of input and output shows which blocks go together.</p>
+<p>If you are reading this text as an actual IPython notebook, you can press <code>Shift-Enter</code> or use the 'play' button on the toolbar (right-pointing triangle) to execute each block of code, known as a 'cell' in IPython:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[71]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="c"># This is a block of code, below you&#39;ll see its output</span>
+<span class="k">print</span> <span class="s">&quot;Welcome to the world of scientific computing with Python!&quot;</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>Welcome to the world of scientific computing with Python!
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1>
+  Motivation: the trapezoidal rule
+</h1>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>In subsequent sections we'll provide a basic introduction to the nuts and bolts of the basic scientific python tools; but we'll first motivate it with a brief example that illustrates what you can do in a few lines with these tools.  For this, we will use the simple problem of approximating a definite integral with the trapezoid rule:</p>
+<p>$$
+\int_{a}^{b} f(x)\, dx \approx \frac{1}{2} \sum_{k=1}^{N} \left( x_{k} - x_{k-1} \right) \left( f(x_{k}) + f(x_{k-1}) \right).
+$$</p>
+<p>Our task will be to compute this formula for a function such as:</p>
+<p>$$
+f(x) = (x-3)(x-5)(x-7)+85
+$$</p>
+<p>integrated between $a=1$ and $b=9$.</p>
+<p>First, we define the function and sample it evenly between 0 and 10 at 200 points:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="mi">3</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="mi">5</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="mi">7</span><span class="p">)</span><span class="o">+</span><span class="mi">85</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>We select $a$ and $b$, our integration limits, and we take only a few points in that region to illustrate the error behavior of the trapezoid approximation:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">9</span>
+<span class="n">xint</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">logical_and</span><span class="p">(</span><span class="n">x</span><span class="o">&gt;=</span><span class="n">a</span><span class="p">,</span> <span class="n">x</span><span class="o">&lt;=</span><span class="n">b</span><span class="p">)][::</span><span class="mi">30</span><span class="p">]</span>
+<span class="n">yint</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">logical_and</span><span class="p">(</span><span class="n">x</span><span class="o">&gt;=</span><span class="n">a</span><span class="p">,</span> <span class="n">x</span><span class="o">&lt;=</span><span class="n">b</span><span class="p">)][::</span><span class="mi">30</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Let's plot both the function and the area below it in the trapezoid approximation:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">140</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">xint</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">yint</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;gray&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">a</span> <span class="o">+</span> <span class="n">b</span><span class="p">),</span> <span class="mi">30</span><span class="p">,</span><span class="s">r&quot;$\int_a^b f(x)dx$&quot;</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s">&#39;center&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">);</span>
+</pre></div>
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+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Compute the integral both at high accuracy and with the trapezoid approximation</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[4]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="kn">import</span> <span class="n">quad</span><span class="p">,</span> <span class="n">trapz</span>
+<span class="n">integral</span><span class="p">,</span> <span class="n">error</span> <span class="o">=</span> <span class="n">quad</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">9</span><span class="p">)</span>
+<span class="n">trap_integral</span> <span class="o">=</span> <span class="n">trapz</span><span class="p">(</span><span class="n">yint</span><span class="p">,</span> <span class="n">xint</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&quot;The integral is: </span><span class="si">%g</span><span class="s"> +/- </span><span class="si">%.1e</span><span class="s">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">integral</span><span class="p">,</span> <span class="n">error</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&quot;The trapezoid approximation with&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">xint</span><span class="p">),</span> <span class="s">&quot;points is:&quot;</span><span class="p">,</span> <span class="n">trap_integral</span>
+<span class="k">print</span> <span class="s">&quot;The absolute error is:&quot;</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">integral</span> <span class="o">-</span> <span class="n">trap_integral</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>The integral is: 680 +/- 7.5e-12
+The trapezoid approximation with 6 points is: 621.286411141
+The absolute error is: 58.7135888589
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>This simple example showed us how, combining the numpy, scipy and matplotlib libraries we can provide an illustration of a standard method in elementary calculus with just a few lines of code.  We will now discuss with more detail the basic usage of these tools.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1>
+  NumPy arrays: the right data structure for scientific computing
+</h1>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  Basics of Numpy arrays
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>We now turn our attention to the Numpy library, which forms the base layer for the entire 'scipy ecosystem'.  Once you have installed numpy, you can import it as</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">numpy</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>though in this book we will use the common shorthand</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>As mentioned above, the main object provided by numpy is a powerful array.  We'll start by exploring how the numpy array differs from Python lists.  We start by creating a simple list and an array with the same contents of the list:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">lst</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">]</span>
+<span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Elements of a one-dimensional array are accessed with the same syntax as a list:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">lst</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[8]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>10</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[9]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>10</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[10]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>40</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[11]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([30, 40])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The first difference to note between lists and arrays is that arrays are <em>homogeneous</em>; i.e. all elements of an array must be of the same type.  In contrast, lists can contain elements of arbitrary type. For example, we can change the last element in our list above to be a string:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">lst</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s">&#39;a string inside a list&#39;</span>
+<span class="n">lst</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[12]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>[10, 20, 30, &apos;a string inside a list&apos;]</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>but the same can not be done with an array, as we get an error message:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[13]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="s">&#39;a string inside an array&#39;</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyerr">
+<pre>
+<span class="ansired">---------------------------------------------------------------------------</span>
+<span class="ansired">ValueError</span>                                Traceback (most recent call last)
+<span class="ansigreen">/home/fperez/teach/book-math-labtool/&lt;ipython-input-13-29c0bfa5fa8a&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
+<span class="ansigreen">----&gt; 1</span><span class="ansiyellow"> </span>arr<span class="ansiyellow">[</span><span class="ansiyellow">-</span><span class="ansicyan">1</span><span class="ansiyellow">]</span> <span class="ansiyellow">=</span> <span class="ansiblue">&apos;a string inside an array&apos;</span><span class="ansiyellow"></span>
+
+<span class="ansired">ValueError</span>: invalid literal for long() with base 10: &apos;a string inside an array&apos;</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The information about the type of an array is contained in its <em>dtype</em> attribute:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[14]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr</span><span class="o">.</span><span class="n">dtype</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[14]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>dtype(&apos;int32&apos;)</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Once an array has been created, its dtype is fixed and it can only store elements of the same type.  For this example where the dtype is integer, if we store a floating point number it will be automatically converted into an integer:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[15]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.234</span>
+<span class="n">arr</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[15]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([10, 20, 30,  1])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Above we created an array from an existing list; now let us now see other ways in which we can create arrays, which we'll illustrate next.  A common need is to have an array initialized with a constant value, and very often this value is 0 or 1 (suitable as starting value for additive and multiplicative loops respectively); <code>zeros</code> creates arrays of all zeros, with any desired dtype:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[16]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[16]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([ 0.,  0.,  0.,  0.,  0.])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[17]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[17]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([0, 0, 0])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[18]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="nb">complex</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[18]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([ 0.+0.j,  0.+0.j,  0.+0.j])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>and similarly for <code>ones</code>:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[19]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;5 ones:&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>5 ones: [ 1.  1.  1.  1.  1.]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>If we want an array initialized with an arbitrary value, we can create an empty array and then use the fill method to put the value we want into the array:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[20]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">a</span> <span class="o">=</span> <span class="n">empty</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+<span class="n">a</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="mf">5.5</span><span class="p">)</span>
+<span class="n">a</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[20]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([ 5.5,  5.5,  5.5,  5.5])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Numpy also offers the <code>arange</code> function, which works like the builtin <code>range</code> but returns an array instead of a list:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[21]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[21]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([0, 1, 2, 3, 4])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>and the <code>linspace</code> and <code>logspace</code> functions to create linearly and logarithmically-spaced grids respectively, with a fixed number of points and including both ends of the specified interval:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[22]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&quot;A linear grid between 0 and 1:&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&quot;A logarithmic grid between 10**1 and 10**4: &quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>A linear grid between 0 and 1: [ 0.    0.25  0.5   0.75  1.  ]
+A logarithmic grid between 10**1 and 10**4:  [    10.    100.   1000.  10000.]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Finally, it is often useful to create arrays with random numbers that follow a specific distribution.  The <code>np.random</code> module contains a number of functions that can be used to this effect, for example this will produce an array of 5 random samples taken from a standard normal distribution (0 mean and variance 1):</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[23]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[23]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([-0.08633343, -0.67375434,  1.00589536,  0.87081651,  1.65597822])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>whereas this will also give 5 samples, but from a normal distribution with a mean of 10 and a variance of 3:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[24]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">norm10</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">norm10</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[24]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([  8.94879575,   5.53038269,   8.24847281,  12.14944165,  11.56209294])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  Indexing with other arrays
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Above we saw how to index arrays with single numbers and slices, just like Python lists.  But arrays allow for a more sophisticated kind of indexing which is very powerful: you can index an array with another array, and in particular with an array of boolean values.  This is particluarly useful to extract information from an array that matches a certain condition.</p>
+<p>Consider for example that in the array <code>norm10</code> we want to replace all values above 9 with the value 0.  We can do so by first finding the <em>mask</em> that indicates where this condition is true or false:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[25]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">mask</span> <span class="o">=</span> <span class="n">norm10</span> <span class="o">&gt;</span> <span class="mi">9</span>
+<span class="n">mask</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[25]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([False, False, False,  True,  True], dtype=bool)</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Now that we have this mask, we can use it to either read those values or to reset them to 0:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[26]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Values above 9:&#39;</span><span class="p">,</span> <span class="n">norm10</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>Values above 9: [ 12.14944165  11.56209294]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[27]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Resetting all values above 9 to 0...&#39;</span>
+<span class="n">norm10</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="k">print</span> <span class="n">norm10</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>Resetting all values above 9 to 0...
+[ 8.94879575  5.53038269  8.24847281  0.          0.        ]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  Arrays with more than one dimension
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Up until now all our examples have used one-dimensional arrays.  But Numpy can create arrays of aribtrary dimensions, and all the methods illustrated in the previous section work with more than one dimension.  For example, a list of lists can be used to initialize a two dimensional array:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[28]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">lst2</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span>
+<span class="n">arr2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
+<span class="n">arr2</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[28]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([[1, 2],
+       [3, 4]])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>With two-dimensional arrays we start seeing the power of numpy: while a nested list can be indexed using repeatedly the <code>[ ]</code> operator, multidimensional arrays support a much more natural indexing syntax with a single <code>[ ]</code> and a set of indices separated by commas:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[29]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="n">lst2</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+<span class="k">print</span> <span class="n">arr2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>2
+2
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Most of the array creation functions listed above can be used with more than one dimension, for example:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[30]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[30]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([[ 0.,  0.,  0.],
+       [ 0.,  0.,  0.]])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[31]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[31]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([[ 11.26788826,   4.29619866,  11.09346496,   9.73861307],
+       [ 10.54025996,   9.5146268 ,  10.80367214,  13.62204505]])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>In fact, the shape of an array can be changed at any time, as long as the total number of elements is unchanged.  For example, if we want a 2x4 array with numbers increasing from 0, the easiest way to create it is:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[32]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
+<span class="k">print</span> <span class="n">arr</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>[[0 1 2 3]
+ [4 5 6 7]]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>With multidimensional arrays, you can also use slices, and you can mix and match slices and single indices in the different dimensions (using the same array as above):</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[33]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Slicing in the second row:&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>
+<span class="k">print</span> <span class="s">&#39;All rows, third column   :&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>Slicing in the second row: [6 7]
+All rows, third column   : [2 6]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>If you only provide one index, then you will get an array with one less dimension containing that row:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[34]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;First row:  &#39;</span><span class="p">,</span> <span class="n">arr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="k">print</span> <span class="s">&#39;Second row: &#39;</span><span class="p">,</span> <span class="n">arr</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>First row:   [0 1 2 3]
+Second row:  [4 5 6 7]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Now that we have seen how to create arrays with more than one dimension, it's a good idea to look at some of the most useful properties and methods that arrays have.  The following provide basic information about the size, shape and data in the array:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[35]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Data type                :&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">dtype</span>
+<span class="k">print</span> <span class="s">&#39;Total number of elements :&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">size</span>
+<span class="k">print</span> <span class="s">&#39;Number of dimensions     :&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">ndim</span>
+<span class="k">print</span> <span class="s">&#39;Shape (dimensionality)   :&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span>
+<span class="k">print</span> <span class="s">&#39;Memory used (in bytes)   :&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">nbytes</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>Data type                : int32
+Total number of elements : 8
+Number of dimensions     : 2
+Shape (dimensionality)   : (2, 4)
+Memory used (in bytes)   : 32
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Arrays also have many useful methods, some especially useful ones are:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[36]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Minimum and maximum             :&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">arr</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+<span class="k">print</span> <span class="s">&#39;Sum and product of all elements :&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="n">arr</span><span class="o">.</span><span class="n">prod</span><span class="p">()</span>
+<span class="k">print</span> <span class="s">&#39;Mean and standard deviation     :&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">arr</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>Minimum and maximum             : 0 7
+Sum and product of all elements : 28 0
+Mean and standard deviation     : 3.5 2.29128784748
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>For these methods, the above operations area all computed on all the elements of the array.  But for a multidimensional array, it's possible to do the computation along a single dimension, by passing the <code>axis</code> parameter; for example:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[37]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;For the following array:</span><span class="se">\n</span><span class="s">&#39;</span><span class="p">,</span> <span class="n">arr</span>
+<span class="k">print</span> <span class="s">&#39;The sum of elements along the rows is    :&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;The sum of elements along the columns is :&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>For the following array:
+[[0 1 2 3]
+ [4 5 6 7]]
+The sum of elements along the rows is    : [ 6 22]
+The sum of elements along the columns is : [ 4  6  8 10]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>As you can see in this example, the value of the <code>axis</code> parameter is the dimension which will be <em>consumed</em> once the operation has been carried out.  This is why to sum along the rows we use <code>axis=0</code>.</p>
+<p>This can be easily illustrated with an example that has more dimensions; we create an array with 4 dimensions and shape <code>(3,4,5,6)</code> and sum along the axis number 2 (i.e. the <em>third</em> axis, since in Python all counts are 0-based).  That consumes the dimension whose length was 5, leaving us with a new array that has shape <code>(3,4,6)</code>:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[38]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[38]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>(3, 4, 6)</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Another widely used property of arrays is the <code>.T</code> attribute, which allows you to access the transpose of the array:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[39]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Array:</span><span class="se">\n</span><span class="s">&#39;</span><span class="p">,</span> <span class="n">arr</span>
+<span class="k">print</span> <span class="s">&#39;Transpose:</span><span class="se">\n</span><span class="s">&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">T</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>Array:
+[[0 1 2 3]
+ [4 5 6 7]]
+Transpose:
+[[0 4]
+ [1 5]
+ [2 6]
+ [3 7]]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>We don't have time here to look at all the methods and properties of arrays, here's a complete list.  Simply try exploring some of these IPython to learn more, or read their description in the full Numpy documentation:</p>
+<pre><code>arr.T             arr.copy          arr.getfield      arr.put           arr.squeeze
+arr.all           arr.ctypes        arr.imag          arr.ravel         arr.std
+arr.any           arr.cumprod       arr.item          arr.real          arr.strides
+arr.argmax        arr.cumsum        arr.itemset       arr.repeat        arr.sum
+arr.argmin        arr.data          arr.itemsize      arr.reshape       arr.swapaxes
+arr.argsort       arr.diagonal      arr.max           arr.resize        arr.take
+arr.astype        arr.dot           arr.mean          arr.round         arr.tofile
+arr.base          arr.dtype         arr.min           arr.searchsorted  arr.tolist
+arr.byteswap      arr.dump          arr.nbytes        arr.setasflat     arr.tostring
+arr.choose        arr.dumps         arr.ndim          arr.setfield      arr.trace
+arr.clip          arr.fill          arr.newbyteorder  arr.setflags      arr.transpose
+arr.compress      arr.flags         arr.nonzero       arr.shape         arr.var
+arr.conj          arr.flat          arr.prod          arr.size          arr.view
+arr.conjugate     arr.flatten       arr.ptp           arr.sort
+</code></pre>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  Operating with arrays
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Arrays support all regular arithmetic operators, and the numpy library also contains a complete collection of basic mathematical functions that operate on arrays.  It is important to remember that in general, all operations with arrays are applied <em>element-wise</em>, i.e., are applied to all the elements of the array at the same time.  Consider for example:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[40]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+<span class="n">arr2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">14</span><span class="p">)</span>
+<span class="k">print</span> <span class="n">arr1</span><span class="p">,</span> <span class="s">&#39;+&#39;</span><span class="p">,</span> <span class="n">arr2</span><span class="p">,</span> <span class="s">&#39;=&#39;</span><span class="p">,</span> <span class="n">arr1</span><span class="o">+</span><span class="n">arr2</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>[0 1 2 3] + [10 11 12 13] = [10 12 14 16]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Importantly, you must remember that even the multiplication operator is by default applied element-wise, it is <em>not</em> the matrix multiplication from linear algebra (as is the case in Matlab, for example):</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[41]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="n">arr1</span><span class="p">,</span> <span class="s">&#39;*&#39;</span><span class="p">,</span> <span class="n">arr2</span><span class="p">,</span> <span class="s">&#39;=&#39;</span><span class="p">,</span> <span class="n">arr1</span><span class="o">*</span><span class="n">arr2</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>[0 1 2 3] * [10 11 12 13] = [ 0 11 24 39]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>While this means that in principle arrays must always match in their dimensionality in order for an operation to be valid, numpy will <em>broadcast</em> dimensions when possible.  For example, suppose that you want to add the number 1.5 to <code>arr1</code>; the following would be a valid way to do it:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[42]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr1</span> <span class="o">+</span> <span class="mf">1.5</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[42]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([ 1.5,  2.5,  3.5,  4.5])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>But thanks to numpy's broadcasting rules, the following is equally valid:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[43]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr1</span> <span class="o">+</span> <span class="mf">1.5</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[43]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([ 1.5,  2.5,  3.5,  4.5])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>In this case, numpy looked at both operands and saw that the first (<code>arr1</code>) was a one-dimensional array of length 4 and the second was a scalar, considered a zero-dimensional object. The broadcasting rules allow numpy to:</p>
+<ul>
+<li><em>create</em> new dimensions of length 1 (since this doesn't change the size of the array)</li>
+<li>'stretch' a dimension of length 1 that needs to be matched to a dimension of a different size.</li>
+</ul>
+<p>So in the above example, the scalar 1.5 is effectively:</p>
+<ul>
+<li>first 'promoted' to a 1-dimensional array of length 1</li>
+<li>then, this array is 'stretched' to length 4 to match the dimension of <code>arr1</code>.</li>
+</ul>
+<p>After these two operations are complete, the addition can proceed as now both operands are one-dimensional arrays of length 4.</p>
+<p>This broadcasting behavior is in practice enormously powerful, especially because when numpy broadcasts to create new dimensions or to 'stretch' existing ones, it doesn't actually replicate the data.  In the example above the operation is carried <em>as if</em> the 1.5 was a 1-d array with 1.5 in all of its entries, but no actual array was ever created.  This can save lots of memory in cases when the arrays in question are large and can have significant performance implications.</p>
+<p>The general rule is: when operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions, and works its way forward, creating dimensions of length 1 as needed. Two dimensions are considered compatible when</p>
+<ul>
+<li>they are equal to begin with, or</li>
+<li>one of them is 1; in this case numpy will do the 'stretching' to make them equal.</li>
+</ul>
+<p>If these conditions are not met, a <code>ValueError: frames are not aligned</code> exception is thrown, indicating that the arrays have incompatible shapes. The size of the resulting array is the maximum size along each dimension of the input arrays.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>This shows how the broadcasting rules work in several dimensions:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[44]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+<span class="k">print</span> <span class="n">arr</span><span class="p">,</span> <span class="s">&#39;</span><span class="se">\n\n</span><span class="s">+&#39;</span><span class="p">,</span> <span class="n">b</span> <span class="p">,</span> <span class="s">&#39;</span><span class="se">\n</span><span class="s">----------------</span><span class="se">\n</span><span class="s">&#39;</span><span class="p">,</span> <span class="n">arr</span> <span class="o">+</span> <span class="n">b</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>[[0 1 2 3]
+ [4 5 6 7]] 
+
++ [2 3 4 5] 
+----------------
+[[ 2  4  6  8]
+ [ 6  8 10 12]]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Now, how could you use broadcasting to say add <code>[4, 6]</code> along the rows to <code>arr</code> above?  Simply performing the direct addition will produce the error we previously mentioned:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[45]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
+<span class="n">arr</span> <span class="o">+</span> <span class="n">c</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyerr">
+<pre>
+<span class="ansired">---------------------------------------------------------------------------</span>
+<span class="ansired">ValueError</span>                                Traceback (most recent call last)
+<span class="ansigreen">/home/fperez/teach/book-math-labtool/&lt;ipython-input-45-62aa20ac1980&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
+<span class="ansigreen">      1</span> c <span class="ansiyellow">=</span> np<span class="ansiyellow">.</span>array<span class="ansiyellow">(</span><span class="ansiyellow">[</span><span class="ansicyan">4</span><span class="ansiyellow">,</span> <span class="ansicyan">6</span><span class="ansiyellow">]</span><span class="ansiyellow">)</span><span class="ansiyellow"></span>
+<span class="ansigreen">----&gt; 2</span><span class="ansiyellow"> </span>arr <span class="ansiyellow">+</span> c<span class="ansiyellow"></span>
+
+<span class="ansired">ValueError</span>: operands could not be broadcast together with shapes (2,4) (2) </pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>According to the rules above, the array <code>c</code> would need to have a <em>trailing</em> dimension of 1 for the broadcasting to work.  It turns out that numpy allows you to 'inject' new dimensions anywhere into an array on the fly, by indexing it with the special object <code>np.newaxis</code>:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[46]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="p">(</span><span class="n">c</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[46]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>(2, 1)</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>This is exactly what we need, and indeed it works:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[47]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr</span> <span class="o">+</span> <span class="n">c</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[47]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>array([[ 4,  5,  6,  7],
+       [10, 11, 12, 13]])</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>For the full broadcasting rules, please see the official Numpy docs, which describe them in detail and with more complex examples.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>As we mentioned before, Numpy ships with a full complement of mathematical functions that work on entire arrays, including logarithms, exponentials, trigonometric and hyperbolic trigonometric functions, etc.  Furthermore, scipy ships a rich special function library in the <code>scipy.special</code> module that includes Bessel, Airy, Fresnel, Laguerre and other classical special functions.  For example, sampling the sine function at 100 points between $0$ and $2\pi$ is as simple as:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[48]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  Linear algebra in numpy
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Numpy ships with a basic linear algebra library, and all arrays have a <code>dot</code> method whose behavior is that of the scalar dot product when its arguments are vectors (one-dimensional arrays) and the traditional matrix multiplication when one or both of its arguments are two-dimensional arrays:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[49]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">v1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+<span class="n">v2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+<span class="k">print</span> <span class="n">v1</span><span class="p">,</span> <span class="s">&#39;.&#39;</span><span class="p">,</span> <span class="n">v2</span><span class="p">,</span> <span class="s">&#39;=&#39;</span><span class="p">,</span> <span class="n">v1</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">v2</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>[2 3 4] . [1 0 1] = 6
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Here is a regular matrix-vector multiplication, note that the array <code>v1</code> should be viewed as a <em>column</em> vector in traditional linear algebra notation; numpy makes no distinction between row and column vectors and simply verifies that the dimensions match the required rules of matrix multiplication, in this case we have a $2 \times 3$ matrix multiplied by a 3-vector, which produces a 2-vector:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[50]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="k">print</span> <span class="n">A</span><span class="p">,</span> <span class="s">&#39;x&#39;</span><span class="p">,</span> <span class="n">v1</span><span class="p">,</span> <span class="s">&#39;=&#39;</span><span class="p">,</span> <span class="n">A</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">v1</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>[[0 1 2]
+ [3 4 5]] x [2 3 4] = [11 38]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>For matrix-matrix multiplication, the same dimension-matching rules must be satisfied, e.g. consider the difference between $A \times A^T$:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[51]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="n">A</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>[[ 5 14]
+ [14 50]]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>and $A^T \times A$:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[52]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="n">A</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>[[ 9 12 15]
+ [12 17 22]
+ [15 22 29]]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Furthermore, the <code>numpy.linalg</code> module includes additional functionality such as determinants, matrix norms, Cholesky, eigenvalue and singular value decompositions, etc.  For even more linear algebra tools, <code>scipy.linalg</code> contains the majority of the tools in the classic LAPACK libraries as well as functions to operate on sparse matrices.  We refer the reader to the Numpy and Scipy documentations for additional details on these.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  Reading and writing arrays to disk
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Numpy lets you read and write arrays into files in a number of ways.  In order to use these tools well, it is critical to understand the difference between a <em>text</em> and a <em>binary</em> file containing numerical data.  In a text file, the number $\pi$ could be written as "3.141592653589793", for example: a string of digits that a human can read, with in this case 15 decimal digits.  In contrast, that same number written to a binary file would be encoded as 8 characters (bytes) that are not readable by a human but which contain the exact same data that the variable <code>pi</code> had in the computer's memory.<br />
+</p>
+<p>The tradeoffs between the two modes are thus:</p>
+<ul>
+<li>
+<p>Text mode: occupies more space, precision can be lost (if not all digits are written to disk), but is readable and editable by hand with a text editor.  Can <em>only</em> be used for one- and two-dimensional arrays.</p>
+</li>
+<li>
+<p>Binary mode: compact and exact representation of the data in memory, can't be read or edited by hand.  Arrays of any size and dimensionality can be saved and read without loss of information.</p>
+</li>
+</ul>
+<p>First, let's see how to read and write arrays in text mode.  The <code>np.savetxt</code> function saves an array to a text file, with options to control the precision, separators and even adding a header:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[53]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">np</span><span class="o">.</span><span class="n">savetxt</span><span class="p">(</span><span class="s">&#39;test.out&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s">&#39;</span><span class="si">%.2e</span><span class="s">&#39;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="s">&quot;My dataset&quot;</span><span class="p">)</span>
+<span class="o">!</span>cat test.out
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre># My dataset
+0.00e+00 1.00e+00 2.00e+00 3.00e+00 4.00e+00
+5.00e+00 6.00e+00 7.00e+00 8.00e+00 9.00e+00
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>And this same type of file can then be read with the matching <code>np.loadtxt</code> function:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[54]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">arr2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s">&#39;test.out&#39;</span><span class="p">)</span>
+<span class="k">print</span> <span class="n">arr2</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>[[ 0.  1.  2.  3.  4.]
+ [ 5.  6.  7.  8.  9.]]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>For binary data, Numpy provides the <code>np.save</code> and <code>np.savez</code> routines.  The first saves a single array to a file with <code>.npy</code> extension, while the latter can be used to save a <em>group</em> of arrays into a single file with <code>.npz</code> extension.  The files created with these routines can then be read with the <code>np.load</code> function.</p>
+<p>Let us first see how to use the simpler <code>np.save</code> function to save a single array:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[55]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s">&#39;test.npy&#39;</span><span class="p">,</span> <span class="n">arr2</span><span class="p">)</span>
+<span class="c"># Now we read this back</span>
+<span class="n">arr2n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s">&#39;test.npy&#39;</span><span class="p">)</span>
+<span class="c"># Let&#39;s see if any element is non-zero in the difference.</span>
+<span class="c"># A value of True would be a problem.</span>
+<span class="k">print</span> <span class="s">&#39;Any differences?&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">arr2</span><span class="o">-</span><span class="n">arr2n</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>Any differences? False
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Now let us see how the <code>np.savez</code> function works.  You give it a filename and either a sequence of arrays or a set of keywords.  In the first mode, the function will auotmatically name the saved arrays in the archive as <code>arr_0</code>, <code>arr_1</code>, etc:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[56]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="s">&#39;test.npz&#39;</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">arr2</span><span class="p">)</span>
+<span class="n">arrays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s">&#39;test.npz&#39;</span><span class="p">)</span>
+<span class="n">arrays</span><span class="o">.</span><span class="n">files</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[56]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>[&apos;arr_1&apos;, &apos;arr_0&apos;]</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Alternatively, we can explicitly choose how to name the arrays we save:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[57]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="s">&#39;test.npz&#39;</span><span class="p">,</span> <span class="n">array1</span><span class="o">=</span><span class="n">arr</span><span class="p">,</span> <span class="n">array2</span><span class="o">=</span><span class="n">arr2</span><span class="p">)</span>
+<span class="n">arrays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s">&#39;test.npz&#39;</span><span class="p">)</span>
+<span class="n">arrays</span><span class="o">.</span><span class="n">files</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox">Out[57]:</div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_pyout">
+
+<pre>[&apos;array2&apos;, &apos;array1&apos;]</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The object returned by <code>np.load</code> from an <code>.npz</code> file works like a dictionary, though you can also access its constituent files by attribute using its special <code>.f</code> field; this is best illustrated with an example with the <code>arrays</code> object from above:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[58]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;First row of first array:&#39;</span><span class="p">,</span> <span class="n">arrays</span><span class="p">[</span><span class="s">&#39;array1&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+<span class="c"># This is an equivalent way to get the same field</span>
+<span class="k">print</span> <span class="s">&#39;First row of first array:&#39;</span><span class="p">,</span> <span class="n">arrays</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">array1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+<div class="output_wrapper">
+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>First row of first array: [0 1 2 3 4]
+First row of first array: [0 1 2 3 4]
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>This <code>.npz</code> format is a very convenient way to package compactly and without loss of information, into a single file, a group of related arrays that pertain to a specific problem.  At some point, however, the complexity of your dataset may be such that the optimal approach is to use one of the standard formats in scientific data processing that have been designed to handle complex datasets, such as NetCDF or HDF5.<br />
+</p>
+<p>Fortunately, there are tools for manipulating these formats in Python, and for storing data in other ways such as databases.  A complete discussion of the possibilities is beyond the scope of this discussion, but of particular interest for scientific users we at least mention the following:</p>
+<ul>
+<li>
+<p>The <code>scipy.io</code> module contains routines to read and write Matlab files in <code>.mat</code> format and files in the NetCDF format that is widely used in certain scientific disciplines.</p>
+</li>
+<li>
+<p>For manipulating files in the HDF5 format, there are two excellent options in Python: The PyTables project offers a high-level, object oriented approach to manipulating HDF5 datasets, while the h5py project offers a more direct mapping to the standard HDF5 library interface.  Both are excellent tools; if you need to work with HDF5 datasets you should read some of their documentation and examples and decide which approach is a better match for your needs.</p>
+</li>
+</ul>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1>
+  High quality data visualization with Matplotlib
+</h1>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The <a href="http://matplotlib.sf.net">matplotlib</a> library is a powerful tool capable of producing complex publication-quality figures with fine layout control in two and three dimensions; here we will only provide a minimal self-contained introduction to its usage that covers the functionality needed for the rest of the book.  We encourage the reader to read the tutorials included with the matplotlib documentation as well as to browse its extensive gallery of examples that include source code.</p>
+<p>Just as we typically use the shorthand <code>np</code> for Numpy, we will use <code>plt</code> for the <code>matplotlib.pyplot</code> module where the easy-to-use plotting functions reside (the library contains a rich object-oriented architecture that we don't have the space to discuss here):</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[59]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The most frequently used function is simply called <code>plot</code>, here is how you can make a simple plot of $\sin(x)$ for $x \in [0, 2\pi]$ with labels and a grid (we use the semicolon in the last line to suppress the display of some information that is unnecessary right now):</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[60]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;sin(x)&#39;</span><span class="p">)</span>
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+<div class="text_cell_render border-box-sizing rendered_html">
+<p>You can control the style, color and other properties of the markers, for example:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[61]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">);</span>
+</pre></div>
+
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+</div>
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+<div class="text_cell_render border-box-sizing rendered_html">
+<p>We will now see how to create a few other common plot types, such as a simple error plot:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[63]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="c"># example data</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">)</span>
+
+<span class="c"># example variable error bar values</span>
+<span class="n">yerr</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="o">+</span> <span class="mf">0.2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">xerr</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="o">+</span> <span class="n">yerr</span>
+
+<span class="c"># First illustrate basic pyplot interface, using defaults where possible.</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">xerr</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">yerr</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&quot;Simplest errorbars, 0.2 in x, 0.4 in y&quot;</span><span class="p">);</span>
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+<div class="text_cell_render border-box-sizing rendered_html">
+<p>A simple log plot</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[64]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">);</span>
+</pre></div>
+
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+<p>A histogram annotated with text inside the plot, using the <code>text</code> function:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[65]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">15</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">+</span> <span class="n">sigma</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10000</span><span class="p">)</span>
+
+<span class="c"># the histogram of the data</span>
+<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;g&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">&#39;Smarts&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;Probability&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&#39;Histogram of IQ&#39;</span><span class="p">)</span>
+<span class="c"># This will put a text fragment at the position given:</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mi">55</span><span class="p">,</span> <span class="o">.</span><span class="mo">027</span><span class="p">,</span> <span class="s">r&#39;$\mu=100,\ \sigma=15$&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="mi">40</span><span class="p">,</span> <span class="mi">160</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.03</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span>
+</pre></div>
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+<h2>
+  Image display
+</h2>
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+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The <code>imshow</code> command can display single or multi-channel images.  A simple array of random numbers, plotted in grayscale:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[66]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cm</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s">&#39;nearest&#39;</span><span class="p">);</span>
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+<div class="text_cell_render border-box-sizing rendered_html">
+<p>A real photograph is a multichannel image, <code>imshow</code> interprets it correctly:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[67]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="n">img</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s">&#39;stinkbug.png&#39;</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;Dimensions of the array img:&#39;</span><span class="p">,</span> <span class="n">img</span><span class="o">.</span><span class="n">shape</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">);</span>
+</pre></div>
+
+</div>
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+<div class="output vbox">
+<div class="prompt output_prompt">
+<div class="output vbox"></div>
+</div>
+<div class="hbox output_area">
+<div class="prompt"></div>
+<div class="box-flex1 output_subarea output_stream output_stdout">
+<pre>Dimensions of the array img: (375, 500, 3)
+</pre>
+</div>
+</div><div class="hbox output_area">
+<div class="prompt"></div>
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+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  Simple 3d plotting with matplotlib
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Note that you must execute at least once in your session:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[68]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">mpl_toolkits.mplot3d</span> <span class="kn">import</span> <span class="n">Axes3D</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>One this has been done, you can create 3d axes with the <code>projection='3d'</code> keyword to <code>add_subplot</code>:</p>
+<pre><code>fig = plt.figure()
+fig.add_subplot(&lt;other arguments here&gt;, projection='3d')
+</code></pre>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>A simple surface plot:</p>
+</div>
+<div class="cell border-box-sizing code_cell vbox">
+<div class="input hbox">
+<div class="prompt input_prompt">In&nbsp;[72]:</div>
+<div class="input_area box-flex1">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">mpl_toolkits.mplot3d.axes3d</span> <span class="kn">import</span> <span class="n">Axes3D</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cm</span>
+
+<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="s">&#39;3d&#39;</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">)</span>
+<span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">)</span>
+<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
+<span class="n">R</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">X</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">Y</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">R</span><span class="p">)</span>
+<span class="n">surf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot_surface</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">rstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">jet</span><span class="p">,</span>
+        <span class="n">linewidth</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">antialiased</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_zlim3d</span><span class="p">(</span><span class="o">-</span><span class="mf">1.01</span><span class="p">,</span> <span class="mf">1.01</span><span class="p">);</span>
+</pre></div>
+
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+</div>
+</div>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1>
+  IPython: a powerful interactive environment
+</h1>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>A key component of the everyday workflow of most scientific computing environments is a good interactive environment, that is, a system in which you can execute small amounts of code and view the results immediately, combining both printing out data and opening graphical visualizations.  All modern systems for scientific computing, commercial and open source, include such functionality.<br />
+</p>
+<p>Out of the box, Python also offers a simple interactive shell with very limited capabilities.  But just like the scientific community built Numpy to provide arrays suited for scientific work (since Pytyhon's lists aren't optimal for this task), it has also developed an interactive environment much more sophisticated than the built-in one.  The <a href="http://ipython.org">IPython project</a> offers a set of tools to make productive use of the Python language, all the while working interactively and with immedate feedback on your results.  The basic tools that IPython provides are:</p>
+<ol>
+<li>
+<p>A powerful terminal shell, with many features designed to increase the fluidity and productivity of everyday scientific workflows, including:</p>
+<ul>
+<li>rich introspection of all objects and variables including easy access to the source code of any function</li>
+<li>powerful and extensible tab completion of variables and filenames,</li>
+<li>tight integration with matplotlib, supporting interactive figures that don't block the terminal,</li>
+<li>direct access to the filesystem and underlying operating system,</li>
+<li>an extensible system for shell-like commands called 'magics' that reduce the work needed to perform many common tasks,</li>
+<li>tools for easily running, timing, profiling and debugging your codes,</li>
+<li>syntax highlighted error messages with much more detail than the default Python ones,</li>
+<li>logging and access to all previous history of inputs, including across sessions</li>
+</ul>
+</li>
+<li>
+<p>A Qt console that provides the look and feel of a terminal, but adds support for inline figures, graphical calltips, a persistent session that can survive crashes (even segfaults) of the kernel process, and more.</p>
+</li>
+<li>
+<p>A web-based notebook that can execute code and also contain rich text and figures, mathematical equations and arbitrary HTML. This notebook presents a document-like view with cells where code is executed but that can be edited in-place, reordered, mixed with explanatory text and figures, etc.</p>
+</li>
+<li>
+<p>A high-performance, low-latency system for parallel computing that supports the control of a cluster of IPython engines communicating over a network, with optimizations that minimize unnecessary copying of large objects (especially numpy arrays).</p>
+</li>
+</ol>
+<p>We will now discuss the highlights of the tools 1-3 above so that you can make them an effective part of your workflow.  The topic of parallel computing is beyond the scope of this document, but we encourage you to read the extensive <a href="http://ipython.org/ipython-doc/rel-0.12.1/parallel/index.html">documentation</a> and <a href="http://minrk.github.com/scipy-tutorial-2011/">tutorials</a> on this available on the IPython website.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  The IPython terminal
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>You can start IPython at the terminal simply by typing:</p>
+<pre><code>$ ipython
+</code></pre>
+<p>which will provide you some basic information about how to get started and will then open a prompt labeled <code>In [1]:</code> for you to start typing.  Here we type $2^{64}$ and Python computes the result for us in exact arithmetic, returning it as <code>Out[1]</code>:</p>
+<pre><code>$ ipython
+Python 2.7.2+ (default, Oct  4 2011, 20:03:08) 
+Type "copyright", "credits" or "license" for more information.
+
+IPython 0.13.dev -- An enhanced Interactive Python.
+?         -&gt; Introduction and overview of IPython's features.
+%quickref -&gt; Quick reference.
+help      -&gt; Python's own help system.
+object?   -&gt; Details about 'object', use 'object??' for extra details.
+
+In [1]: 2**64
+Out[1]: 18446744073709551616L
+</code></pre>
+<p>The first thing you should know about IPython is that all your inputs and outputs are saved. There are two variables named <code>In</code> and <code>Out</code> which are filled as you work with your results.  Furthermore, all outputs are also saved to auto-created variables of the form <code>_NN</code> where <code>NN</code> is the prompt number, and inputs to <code>_iNN</code>.  This allows you to recover quickly the result of a prior computation by referring to its number even if you forgot to store it as a variable.  For example, later on in the above session you can do:</p>
+<pre><code>In [6]: print _1
+18446744073709551616
+</code></pre>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>We strongly recommend that you take a few minutes to read at least the basic introduction provided by the <code>?</code> command, and keep in mind that the <code>%quickref</code> command at all times can be used as a quick reference "cheat sheet" of the most frequently used features of IPython.</p>
+<p>At the IPython prompt, any valid Python code that you type will be executed similarly to the default Python shell (though often with more informative feedback).  But since IPython is a <em>superset</em> of the default Python shell; let's have a brief look at some of its additional functionality.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Object introspection</strong></p>
+<p>A simple <code>?</code> command provides a general introduction to IPython, but as indicated in the banner above, you can use the <code>?</code> syntax to ask for details about any object.  For example, if we type <code>_1?</code>, IPython will print the following details about this variable:</p>
+<pre><code>In [14]: _1?
+Type:       long
+Base Class: &lt;type 'long'&gt;
+String Form:18446744073709551616
+Namespace:  Interactive
+Docstring:
+long(x[, base]) -&gt; integer
+
+Convert a string or number to a long integer, if possible.  A floating
+
+[etc... snipped for brevity]
+</code></pre>
+<p>If you add a second <code>?</code> and for any oobject <code>x</code> type <code>x??</code>, IPython will try to provide an even more detailed analsysi of the object, including its syntax-highlighted source code when it can be found.  It's possible that <code>x??</code> returns the same information as <code>x?</code>, but in many cases <code>x??</code> will indeed provide additional details.</p>
+<p>Finally, the <code>?</code> syntax is also useful to search <em>namespaces</em> with wildcards.  Suppose you are wondering if there is any function in Numpy that may do text-related things; with <code>np.*txt*?</code>, IPython will print all the names in the <code>np</code> namespace (our Numpy shorthand) that have 'txt' anywhere in their name:</p>
+<pre><code>In [17]: np.*txt*?
+np.genfromtxt
+np.loadtxt
+np.mafromtxt
+np.ndfromtxt
+np.recfromtxt
+np.savetxt
+</code></pre>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Tab completion</strong></p>
+<p>IPython makes the tab key work extra hard for you as a way to rapidly inspect objects and libraries.  Whenever you have typed something at the prompt, by hitting the <code>&lt;tab&gt;</code> key IPython will try to complete the rest of the line.  For this, IPython will analyze the text you had so far and try to search for Python data or files that may match the context you have already provided.</p>
+<p>For example, if you type <code>np.load</code> and hit the <tab> key, you'll see:</p>
+<pre><code>In [21]: np.load&lt;TAB HERE&gt;
+np.load     np.loads    np.loadtxt
+</code></pre>
+<p>so you can quickly find all the load-related functionality in numpy.  Tab completion works even for function arguments, for example consider this function definition:</p>
+<pre><code>In [20]: def f(x, frobinate=False):
+   ....:     if frobinate:
+   ....:         return x**2
+   ....:
+</code></pre>
+<p>If you now use the <code>&lt;tab&gt;</code> key after having typed 'fro' you'll get all valid Python completions, but those marked with <code>=</code> at the end are known to be keywords of your function:</p>
+<pre><code>In [21]: f(2, fro&lt;TAB HERE&gt;
+frobinate=    frombuffer    fromfunction  frompyfunc    fromstring    
+from          fromfile      fromiter      fromregex     frozenset
+</code></pre>
+<p>at this point you can add the <code>b</code> letter and hit <code>&lt;tab&gt;</code> once more, and IPython will finish the line for you:</p>
+<pre><code>In [21]: f(2, frobinate=
+</code></pre>
+<p>As a beginner, simply get into the habit of using <code>&lt;tab&gt;</code> after most objects; it should quickly become second nature as you will see how helps keep a fluid workflow and discover useful information.  Later on you can also customize this behavior by writing your own completion code, if you so desire.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Matplotlib integration</strong></p>
+<p>One of the most useful features of IPython for scientists is its tight integration with matplotlib: at the terminal IPython lets you open matplotlib figures without blocking your typing (which is what happens if you try to do the same thing at the default Python shell), and in the Qt console and notebook you can even view your figures embedded in your workspace next to the code that created them.</p>
+<p>The matplotlib support can be either activated when you start IPython by passing the <code>--pylab</code> flag, or at any point later in your session by using the <code>%pylab</code> command.  If you start IPython with <code>--pylab</code>, you'll see something like this (note the extra message about pylab):</p>
+<pre><code>$ ipython --pylab
+Python 2.7.2+ (default, Oct  4 2011, 20:03:08) 
+Type "copyright", "credits" or "license" for more information.
+
+IPython 0.13.dev -- An enhanced Interactive Python.
+?         -&gt; Introduction and overview of IPython's features.
+%quickref -&gt; Quick reference.
+help      -&gt; Python's own help system.
+object?   -&gt; Details about 'object', use 'object??' for extra details.
+
+Welcome to pylab, a matplotlib-based Python environment [backend: Qt4Agg].
+For more information, type 'help(pylab)'.
+
+In [1]:
+</code></pre>
+<p>Furthermore, IPython will import <code>numpy</code> with the <code>np</code> shorthand, <code>matplotlib.pyplot</code> as <code>plt</code>, and it will also load all of the numpy and pyplot top-level names so that you can directly type something like:</p>
+<pre><code>In [1]: x = linspace(0, 2*pi, 200)
+
+In [2]: plot(x, sin(x))
+Out[2]: [&lt;matplotlib.lines.Line2D at 0x9e7c16c&gt;]
+</code></pre>
+<p>instead of having to prefix each call with its full signature (as we have been doing in the examples thus far):</p>
+<pre><code>In [3]: x = np.linspace(0, 2*np.pi, 200)
+
+In [4]: plt.plot(x, np.sin(x))
+Out[4]: [&lt;matplotlib.lines.Line2D at 0x9e900ac&gt;]
+</code></pre>
+<p>This shorthand notation can be a huge time-saver when working interactively (it's a few characters but you are likely to type them hundreds of times in a session).  But we should note that as you develop persistent scripts and notebooks meant for reuse, it's best to get in the habit of using the longer notation (known as <em>fully qualified names</em> as it's clearer where things come from and it makes for more robust, readable and maintainable code in the long run).</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Access to the operating system and files</strong></p>
+<p>In IPython, you can type <code>ls</code> to see your files or <code>cd</code> to change directories, just like you would at a regular system prompt:</p>
+<pre><code>In [2]: cd tests
+/home/fperez/ipython/nbconvert/tests
+
+In [3]: ls test.*
+test.aux  test.html  test.ipynb  test.log  test.out  test.pdf  test.rst  test.tex
+</code></pre>
+<p>Furthermore, if you use the <code>!</code> at the beginning of a line, any commands you pass afterwards go directly to the operating system:</p>
+<pre><code>In [4]: !echo "Hello IPython"
+Hello IPython
+</code></pre>
+<p>IPython offers a useful twist in this feature: it will substitute in the command the value of any <em>Python</em> variable you may have if you prepend it with a <code>$</code> sign:</p>
+<pre><code>In [5]: message = 'IPython interpolates from Python to the shell'
+
+In [6]: !echo $message
+IPython interpolates from Python to the shell
+</code></pre>
+<p>This feature can be extremely useful, as it lets you combine the power and clarity of Python for complex logic with the immediacy and familiarity of many shell commands.  Additionally, if you start the line with <em>two</em> <code>$$</code> signs, the output of the command will be automatically captured as a list of lines, e.g.:</p>
+<pre><code>In [10]: !!ls test.*
+Out[10]: 
+['test.aux',
+ 'test.html',
+ 'test.ipynb',
+ 'test.log',
+ 'test.out',
+ 'test.pdf',
+ 'test.rst',
+ 'test.tex']
+</code></pre>
+<p>As explained above, you can now use this as the variable <code>_10</code>.  If you directly want to capture the output of a system command to a Python variable, you can use the syntax <code>=!</code>:</p>
+<pre><code>In [11]: testfiles =! ls test.*
+
+In [12]: print testfiles
+['test.aux', 'test.html', 'test.ipynb', 'test.log', 'test.out', 'test.pdf', 'test.rst', 'test.tex']
+</code></pre>
+<p>Finally, the special <code>%alias</code> command lets you define names that are shorthands for system commands, so that you can type them without having to prefix them via <code>!</code> explicitly (for example, <code>ls</code> is an alias that has been predefined for you at startup).</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Magic commands</strong></p>
+<p>IPython has a system for special commands, called 'magics', that let you control IPython itself and perform many common tasks with a more shell-like syntax: it uses spaces for delimiting arguments, flags can be set with dashes and all arguments are treated as strings, so no additional quoting is required.  This kind of syntax is invalid in the Python language but very convenient for interactive typing (less parentheses, commans and quoting everywhere); IPython distinguishes the two by detecting lines that start with the <code>%</code> character.<br />
+</p>
+<p>You can learn more about the magic system by simply typing <code>%magic</code> at the prompt, which will give you a short description plus the documentation on <em>all</em> available magics.  If you want to see only a listing of existing magics, you can use <code>%lsmagic</code>:</p>
+<pre><code>In [4]: lsmagic
+Available magic functions:
+%alias  %autocall  %autoindent  %automagic  %bookmark  %c  %cd  %colors  %config  %cpaste
+%debug  %dhist  %dirs  %doctest_mode  %ds  %ed  %edit  %env  %gui  %hist  %history
+%install_default_config  %install_ext  %install_profiles  %load_ext  %loadpy  %logoff  %logon  
+%logstart  %logstate  %logstop  %lsmagic  %macro  %magic  %notebook  %page  %paste  %pastebin  
+%pd  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pop  %popd  %pprint  %precision  %profile  
+%prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %quickref  %recall  %rehashx  
+%reload_ext  %rep  %rerun  %reset  %reset_selective  %run  %save  %sc  %stop  %store  %sx  %tb
+%time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode
+
+Automagic is ON, % prefix NOT needed for magic functions.
+</code></pre>
+<p>Note how the example above omitted the eplicit <code>%</code> marker and simply uses <code>lsmagic</code>.  As long as the 'automagic' feature is on (which it is by default), you can omit the <code>%</code> marker as long as there is no ambiguity with a Python variable of the same name.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Running your code</strong></p>
+<p>While it's easy to type a few lines of code in IPython, for any long-lived work you should keep your codes in Python scripts (or in IPython notebooks, see below).  Consider that you have a script, in this case trivially simple for the sake of brevity, named <code>simple.py</code>:</p>
+<pre><code>In [12]: !cat simple.py
+import numpy as np
+
+x = np.random.normal(size=100)
+
+print 'First elment of x:', x[0]
+</code></pre>
+<p>The typical workflow with IPython is to use the <code>%run</code> magic to execute your script (you can omit the .py extension if you want).  When you run it, the script will execute just as if it had been run at the system prompt with <code>python simple.py</code> (though since modules don't get re-executed on new imports by Python, all system initialization is essentially free, which can have a significant run time impact in some cases):</p>
+<pre><code>In [13]: run simple
+First elment of x: -1.55872256289
+</code></pre>
+<p>Once it completes, all variables defined in it become available for you to use interactively:</p>
+<pre><code>In [14]: x.shape
+Out[14]: (100,)
+</code></pre>
+<p>This allows you to plot data, try out ideas, etc, in a <code>%run</code>/interact/edit cycle that can be very productive.  As you start understanding your problem better you can refine your script further, incrementally improving it based on the work you do at the IPython prompt.  At any point you can use the <code>%hist</code> magic to print out your history without prompts, so that you can copy useful fragments back into the script.</p>
+<p>By default, <code>%run</code> executes scripts in a completely empty namespace, to better mimic how they would execute at the system prompt with plain Python.  But if you use the <code>-i</code> flag, the script will also see your interactively defined variables.  This lets you edit in a script larger amounts of code that still behave as if you had typed them at the IPython prompt.</p>
+<p>You can also get a summary of the time taken by your script with the <code>-t</code> flag; consider a different script <code>randsvd.py</code> that takes a bit longer to run:</p>
+<pre><code>In [21]: run -t randsvd.py
+
+IPython CPU timings (estimated):
+  User   :       0.38 s.
+  System :       0.04 s.
+Wall time:       0.34 s.
+</code></pre>
+<p><code>User</code> is the time spent by the computer executing your code, while <code>System</code> is the time the operating system had to work on your behalf, doing things like memory allocation that are needed by your code but that you didn't explicitly program and that happen inside the kernel.  The <code>Wall time</code> is the time on a 'clock on the wall' between the start and end of your program.<br />
+</p>
+<p>If <code>Wall &gt; User+System</code>, your code is most likely waiting idle for certain periods.  That could be waiting for data to arrive from a remote source or perhaps because the operating system has to swap large amounts of virtual memory.  If you know that your code doesn't explicitly wait for remote data to arrive, you should investigate further to identify possible ways of improving the performance profile.</p>
+<p>If you only want to time how long a single statement takes, you don't need to put it into a script as you can use the <code>%timeit</code> magic, which uses Python's <code>timeit</code> module to very carefully measure timig data; <code>timeit</code> can measure even short statements that execute extremely fast:</p>
+<pre><code>In [27]: %timeit a=1
+10000000 loops, best of 3: 23 ns per loop
+</code></pre>
+<p>and for code that runs longer, it automatically adjusts so the overall measurement doesn't take too long:</p>
+<pre><code>In [28]: %timeit np.linalg.svd(x)
+1 loops, best of 3: 310 ms per loop
+</code></pre>
+<p>The <code>%run</code> magic still has more options for debugging and profiling data; you should read its documentation for many useful details (as always, just type <code>%run?</code>).</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  The graphical Qt console
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>If you type at the system prompt (see the IPython website for installation details, as this requires some additional libraries):</p>
+<pre><code>$ ipython qtconsole
+</code></pre>
+<p>instead of opening in a terminal as before, IPython will start a graphical console that at first sight appears just like a terminal, but which is in fact much more capable than a text-only terminal.  This is a specialized terminal designed for interactive scientific work, and it supports full multi-line editing with color highlighting and graphical calltips for functions, it can keep multiple IPython sessions open simultaneously in tabs, and when scripts run it can display the figures inline directly in the work area.</p>
+<p><center><img src="ipython_qtconsole2.png" width=400px></center></p>
+</div>
+% This cell is for the pdflatex output only
+\begin{figure}[htbp]
+\centering
+\includegraphics[width=3in]{ipython_qtconsole2.png}
+\caption{The IPython Qt console: a lightweight terminal for scientific exploration, with code, results and graphics in a soingle environment.}
+\end{figure}
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The Qt console accepts the same <code>--pylab</code> startup flags as the terminal, but you can additionally supply the value <code>--pylab inline</code>, which enables the support for inline graphics shown in the figure.  This is ideal for keeping all the code and figures in the same session, given that the console can save the output of your entire session to HTML or PDF.</p>
+<p>Since the Qt console makes it far more convenient than the terminal to edit blocks of code with multiple lines, in this environment it's worth knowing about the <code>%loadpy</code> magic function.  <code>%loadpy</code> takes a path to a local file or remote URL, fetches its contents, and puts it in the work area for you to further edit and execute.  It can be an extremely fast and convenient way of loading code from local disk or remote examples from sites such as the <a href="http://matplotlib.sourceforge.net/gallery.html">Matplotlib gallery</a>.</p>
+<p>Other than its enhanced capabilities for code and graphics, all of the features of IPython we've explained before remain functional in this graphical console.</p>
+</div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2>
+  The IPython Notebook
+</h2>
+</div>
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The third way to interact with IPython, in addition to the terminal and graphical Qt console, is a powerful web interface called the "IPython Notebook".  If you run at the system console (you can omit the <code>pylab</code> flags if you don't need plotting support):</p>
+<pre><code>$ ipython notebook --pylab inline
+</code></pre>
+<p>IPython will start a process that runs a web server in your local machine and to which a web browser can connect.  The Notebook is a workspace that lets you execute code in blocks called 'cells' and displays any results and figures, but which can also contain arbitrary text (including LaTeX-formatted mathematical expressions) and any rich media that a modern web browser is capable of displaying.</p>
+<p><center><img src="ipython-notebook-specgram-2.png" width=400px></center></p>
+</div>
+% This cell is for the pdflatex output only
+\begin{figure}[htbp]
+\centering
+\includegraphics[width=3in]{ipython-notebook-specgram-2.png}
+\caption{The IPython Notebook: text, equations, code, results, graphics and other multimedia in an open format for scientific exploration and collaboration}
+\end{figure}
+
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>In fact, this document was written as a Notebook, and only exported to LaTeX for printing.  Inside of each cell, all the features of IPython that we have discussed before remain functional, since ultimately this web client is communicating with the same IPython code that runs in the terminal.  But this interface is a much more rich and powerful environment for maintaining long-term "live and executable" scientific documents.<br />
+</p>
+<p>Notebook environments have existed in commercial systems like Mathematica(TM) and Maple(TM) for a long time; in the open source world the <a href="http://sagemath.org">Sage</a> project blazed this particular trail starting in 2006, and now we bring all the features that have made IPython such a widely used tool to a Notebook model.</p>
+<p>Since the Notebook runs as a web application, it is possible to configure it for remote access, letting you run your computations on a persistent server close to your data, which you can then access remotely from any browser-equipped computer.  We encourage you to read the extensive documentation provided by the IPython project for details on how to do this and many more features of the notebook.</p>
+<p>Finally, as we said earlier, IPython also has a high-level and easy to use set of libraries for parallel computing, that let you control (interactively if desired) not just one IPython but an entire cluster of 'IPython engines'.   Unfortunately a detailed discussion of these tools is beyond the scope of this text, but should you need to parallelize your analysis codes, a quick read of the tutorials and examples provided at the IPython site may prove fruitful.</p>
+</div>
+</body>
+</html>
\ No newline at end of file
diff --git a/tests/template_ref/IntroNumPy_orig.rst b/tests/template_ref/IntroNumPy_orig.rst
new file mode 100644
index 0000000..f6723cc
--- /dev/null
+++ b/tests/template_ref/IntroNumPy_orig.rst
@@ -0,0 +1,2089 @@
+An Introduction to the Scientific Python Ecosystem
+==================================================
+
+While the Python language is an excellent tool for general-purpose
+programming, with a highly readable syntax, rich and powerful data types
+(strings, lists, sets, dictionaries, arbitrary length integers, etc) and
+a very comprehensive standard library, it was not designed specifically
+for mathematical and scientific computing. Neither the language nor its
+standard library have facilities for the efficient representation of
+multidimensional datasets, tools for linear algebra and general matrix
+manipulations (an essential building block of virtually all technical
+computing), nor any data visualization facilities.
+
+In particular, Python lists are very flexible containers that can be
+nested arbitrarily deep and which can hold any Python object in them,
+but they are poorly suited to represent efficiently common mathematical
+constructs like vectors and matrices. In contrast, much of our modern
+heritage of scientific computing has been built on top of libraries
+written in the Fortran language, which has native support for vectors
+and matrices as well as a library of mathematical functions that can
+efficiently operate on entire arrays at once.
+
+Scientific Python: a collaboration of projects built by scientists
+------------------------------------------------------------------
+
+
+The scientific community has developed a set of related Python libraries
+that provide powerful array facilities, linear algebra, numerical
+algorithms, data visualization and more. In this appendix, we will
+briefly outline the tools most frequently used for this purpose, that
+make "Scientific Python" something far more powerful than the Python
+language alone.
+
+For reasons of space, we can only describe in some detail the central
+Numpy library, but below we provide links to the websites of each
+project where you can read their documentation in more detail.
+
+First, let's look at an overview of the basic tools that most scientists
+use in daily research with Python. The core of this ecosystem is
+composed of:
+
+-  Numpy: the basic library that most others depend on, it provides a
+   powerful array type that can represent multidmensional datasets of
+   many different kinds and that supports arithmetic operations. Numpy
+   also provides a library of common mathematical functions, basic
+   linear algebra, random number generation and Fast Fourier Transforms.
+   Numpy can be found at `numpy.scipy.org <http://numpy.scipy.org>`_
+
+-  Scipy: a large collection of numerical algorithms that operate on
+   numpy arrays and provide facilities for many common tasks in
+   scientific computing, including dense and sparse linear algebra
+   support, optimization, special functions, statistics, n-dimensional
+   image processing, signal processing and more. Scipy can be found at
+   `scipy.org <http://scipy.org>`_.
+
+-  Matplotlib: a data visualization library with a strong focus on
+   producing high-quality output, it supports a variety of common
+   scientific plot types in two and three dimensions, with precise
+   control over the final output and format for publication-quality
+   results. Matplotlib can also be controlled interactively allowing
+   graphical manipulation of your data (zooming, panning, etc) and can
+   be used with most modern user interface toolkits. It can be found at
+   `matplotlib.sf.net <http://matplotlib.sf.net>`_.
+
+-  IPython: while not strictly scientific in nature, IPython is the
+   interactive environment in which many scientists spend their time.
+   IPython provides a powerful Python shell that integrates tightly with
+   Matplotlib and with easy access to the files and operating system,
+   and which can execute in a terminal or in a graphical Qt console.
+   IPython also has a web-based notebook interface that can combine code
+   with text, mathematical expressions, figures and multimedia. It can
+   be found at `ipython.org <http://ipython.org>`_.
+
+While each of these tools can be installed separately, in our opinion
+the most convenient way today of accessing them (especially on Windows
+and Mac computers) is to install the `Free Edition of the Enthought
+Python Distribution <http://www.enthought.com/products/epd_free.php>`_
+which contain all the above. Other free alternatives on Windows (but not
+on Macs) are `Python(x,y) <http://code.google.com/p/pythonxy>`_ and
+`Christoph Gohlke's packages
+page <http://www.lfd.uci.edu/~gohlke/pythonlibs>`_.
+
+These four 'core' libraries are in practice complemented by a number of
+other tools for more specialized work. We will briefly list here the
+ones that we think are the most commonly needed:
+
+-  Sympy: a symbolic manipulation tool that turns a Python session into
+   a computer algebra system. It integrates with the IPython notebook,
+   rendering results in properly typeset mathematical notation.
+   `sympy.org <http://sympy.org>`_.
+
+-  Mayavi: sophisticated 3d data visualization;
+   `code.enthought.com/projects/mayavi <http://code.enthought.com/projects/mayavi>`_.
+
+-  Cython: a bridge language between Python and C, useful both to
+   optimize performance bottlenecks in Python and to access C libraries
+   directly; `cython.org <http://cython.org>`_.
+
+-  Pandas: high-performance data structures and data analysis tools,
+   with powerful data alignment and structural manipulation
+   capabilities; `pandas.pydata.org <http://pandas.pydata.org>`_.
+
+-  Statsmodels: statistical data exploration and model estimation;
+   `statsmodels.sourceforge.net <http://statsmodels.sourceforge.net>`_.
+
+-  Scikit-learn: general purpose machine learning algorithms with a
+   common interface; `scikit-learn.org <http://scikit-learn.org>`_.
+
+-  Scikits-image: image processing toolbox;
+   `scikits-image.org <http://scikits-image.org>`_.
+
+-  NetworkX: analysis of complex networks (in the graph theoretical
+   sense); `networkx.lanl.gov <http://networkx.lanl.gov>`_.
+
+-  PyTables: management of hierarchical datasets using the
+   industry-standard HDF5 format;
+   `www.pytables.org <http://www.pytables.org>`_.
+
+Beyond these, for any specific problem you should look on the internet
+first, before starting to write code from scratch. There's a good chance
+that someone, somewhere, has written an open source library that you can
+use for part or all of your problem.
+
+A note about the examples below
+-------------------------------
+
+
+In all subsequent examples, you will see blocks of input code, followed
+by the results of the code if the code generated output. This output may
+include text, graphics and other result objects. These blocks of input
+can be pasted into your interactive IPython session or notebook for you
+to execute. In the print version of this document, a thin vertical bar
+on the left of the blocks of input and output shows which blocks go
+together.
+
+If you are reading this text as an actual IPython notebook, you can
+press ``Shift-Enter`` or use the 'play' button on the toolbar
+(right-pointing triangle) to execute each block of code, known as a
+'cell' in IPython:
+
+In[71]:
+
+.. code:: python
+
+    # This is a block of code, below you'll see its output
+    print "Welcome to the world of scientific computing with Python!"
+
+
+.. parsed-literal::
+
+    Welcome to the world of scientific computing with Python!
+
+Motivation: the trapezoidal rule
+================================
+
+In subsequent sections we'll provide a basic introduction to the nuts
+and bolts of the basic scientific python tools; but we'll first motivate
+it with a brief example that illustrates what you can do in a few lines
+with these tools. For this, we will use the simple problem of
+approximating a definite integral with the trapezoid rule:
+
+.. math::
+
+
+   \int_{a}^{b} f(x)\, dx \approx \frac{1}{2} \sum_{k=1}^{N} \left( x_{k} - x_{k-1} \right) \left( f(x_{k}) + f(x_{k-1}) \right).
+
+Our task will be to compute this formula for a function such as:
+
+.. math::
+
+
+   f(x) = (x-3)(x-5)(x-7)+85
+
+integrated between :math:`a=1` and :math:`b=9`\ .
+
+First, we define the function and sample it evenly between 0 and 10 at
+200 points:
+
+In[1]:
+
+.. code:: python
+
+    def f(x):
+        return (x-3)*(x-5)*(x-7)+85
+    
+    import numpy as np
+    x = np.linspace(0, 10, 200)
+    y = f(x)
+
+We select :math:`a` and :math:`b`\ , our integration limits, and we take
+only a few points in that region to illustrate the error behavior of the
+trapezoid approximation:
+
+In[2]:
+
+.. code:: python
+
+    a, b = 1, 9
+    xint = x[logical_and(x>=a, x<=b)][::30]
+    yint = y[logical_and(x>=a, x<=b)][::30]
+
+Let's plot both the function and the area below it in the trapezoid
+approximation:
+
+In[3]:
+
+.. code:: python
+
+    import matplotlib.pyplot as plt
+    plt.plot(x, y, lw=2)
+    plt.axis([0, 10, 0, 140])
+    plt.fill_between(xint, 0, yint, facecolor='gray', alpha=0.4)
+    plt.text(0.5 * (a + b), 30,r"$\int_a^b f(x)dx$", horizontalalignment='center', fontsize=20);
+
+.. image:: _fig_13.svg
+
+Compute the integral both at high accuracy and with the trapezoid
+approximation
+
+In[4]:
+
+.. code:: python
+
+    from scipy.integrate import quad, trapz
+    integral, error = quad(f, 1, 9)
+    trap_integral = trapz(yint, xint)
+    print "The integral is: %g +/- %.1e" % (integral, error)
+    print "The trapezoid approximation with", len(xint), "points is:", trap_integral
+    print "The absolute error is:", abs(integral - trap_integral)
+
+
+.. parsed-literal::
+
+    The integral is: 680 +/- 7.5e-12
+    The trapezoid approximation with 6 points is: 621.286411141
+    The absolute error is: 58.7135888589
+
+This simple example showed us how, combining the numpy, scipy and
+matplotlib libraries we can provide an illustration of a standard method
+in elementary calculus with just a few lines of code. We will now
+discuss with more detail the basic usage of these tools.
+
+NumPy arrays: the right data structure for scientific computing
+===============================================================
+
+Basics of Numpy arrays
+----------------------
+
+
+We now turn our attention to the Numpy library, which forms the base
+layer for the entire 'scipy ecosystem'. Once you have installed numpy,
+you can import it as
+
+In[5]:
+
+.. code:: python
+
+    import numpy
+
+though in this book we will use the common shorthand
+
+In[6]:
+
+.. code:: python
+
+    import numpy as np
+
+As mentioned above, the main object provided by numpy is a powerful
+array. We'll start by exploring how the numpy array differs from Python
+lists. We start by creating a simple list and an array with the same
+contents of the list:
+
+In[7]:
+
+.. code:: python
+
+    lst = [10, 20, 30, 40]
+    arr = np.array([10, 20, 30, 40])
+
+Elements of a one-dimensional array are accessed with the same syntax as
+a list:
+
+In[8]:
+
+.. code:: python
+
+    lst[0]
+
+Out[8]:
+.. parsed-literal::
+
+    10
+
+
+In[9]:
+
+.. code:: python
+
+    arr[0]
+
+Out[9]:
+.. parsed-literal::
+
+    10
+
+
+In[10]:
+
+.. code:: python
+
+    arr[-1]
+
+Out[10]:
+.. parsed-literal::
+
+    40
+
+
+In[11]:
+
+.. code:: python
+
+    arr[2:]
+
+Out[11]:
+.. parsed-literal::
+
+    array([30, 40])
+
+
+The first difference to note between lists and arrays is that arrays are
+*homogeneous*; i.e. all elements of an array must be of the same type.
+In contrast, lists can contain elements of arbitrary type. For example,
+we can change the last element in our list above to be a string:
+
+In[12]:
+
+.. code:: python
+
+    lst[-1] = 'a string inside a list'
+    lst
+
+Out[12]:
+.. parsed-literal::
+
+    [10, 20, 30, 'a string inside a list']
+
+
+but the same can not be done with an array, as we get an error message:
+
+In[13]:
+
+.. code:: python
+
+    arr[-1] = 'a string inside an array'
+
+::
+
+    ---------------------------------------------------------------------------
+    ValueError                                Traceback (most recent call last)
+    /home/fperez/teach/book-math-labtool/<ipython-input-13-29c0bfa5fa8a> in <module>()
+    ----> 1 arr[-1] = 'a string inside an array'
+    
+    ValueError: invalid literal for long() with base 10: 'a string inside an array'
+The information about the type of an array is contained in its *dtype*
+attribute:
+
+In[14]:
+
+.. code:: python
+
+    arr.dtype
+
+Out[14]:
+.. parsed-literal::
+
+    dtype('int32')
+
+
+Once an array has been created, its dtype is fixed and it can only store
+elements of the same type. For this example where the dtype is integer,
+if we store a floating point number it will be automatically converted
+into an integer:
+
+In[15]:
+
+.. code:: python
+
+    arr[-1] = 1.234
+    arr
+
+Out[15]:
+.. parsed-literal::
+
+    array([10, 20, 30,  1])
+
+
+Above we created an array from an existing list; now let us now see
+other ways in which we can create arrays, which we'll illustrate next. A
+common need is to have an array initialized with a constant value, and
+very often this value is 0 or 1 (suitable as starting value for additive
+and multiplicative loops respectively); ``zeros`` creates arrays of all
+zeros, with any desired dtype:
+
+In[16]:
+
+.. code:: python
+
+    np.zeros(5, float)
+
+Out[16]:
+.. parsed-literal::
+
+    array([ 0.,  0.,  0.,  0.,  0.])
+
+
+In[17]:
+
+.. code:: python
+
+    np.zeros(3, int)
+
+Out[17]:
+.. parsed-literal::
+
+    array([0, 0, 0])
+
+
+In[18]:
+
+.. code:: python
+
+    np.zeros(3, complex)
+
+Out[18]:
+.. parsed-literal::
+
+    array([ 0.+0.j,  0.+0.j,  0.+0.j])
+
+
+and similarly for ``ones``:
+
+In[19]:
+
+.. code:: python
+
+    print '5 ones:', np.ones(5)
+
+
+.. parsed-literal::
+
+    5 ones: [ 1.  1.  1.  1.  1.]
+
+If we want an array initialized with an arbitrary value, we can create
+an empty array and then use the fill method to put the value we want
+into the array:
+
+In[20]:
+
+.. code:: python
+
+    a = empty(4)
+    a.fill(5.5)
+    a
+
+Out[20]:
+.. parsed-literal::
+
+    array([ 5.5,  5.5,  5.5,  5.5])
+
+
+Numpy also offers the ``arange`` function, which works like the builtin
+``range`` but returns an array instead of a list:
+
+In[21]:
+
+.. code:: python
+
+    np.arange(5)
+
+Out[21]:
+.. parsed-literal::
+
+    array([0, 1, 2, 3, 4])
+
+
+and the ``linspace`` and ``logspace`` functions to create linearly and
+logarithmically-spaced grids respectively, with a fixed number of points
+and including both ends of the specified interval:
+
+In[22]:
+
+.. code:: python
+
+    print "A linear grid between 0 and 1:", np.linspace(0, 1, 5)
+    print "A logarithmic grid between 10**1 and 10**4: ", np.logspace(1, 4, 4)
+
+
+.. parsed-literal::
+
+    A linear grid between 0 and 1: [ 0.    0.25  0.5   0.75  1.  ]
+    A logarithmic grid between 10**1 and 10**4:  [    10.    100.   1000.  10000.]
+
+Finally, it is often useful to create arrays with random numbers that
+follow a specific distribution. The ``np.random`` module contains a
+number of functions that can be used to this effect, for example this
+will produce an array of 5 random samples taken from a standard normal
+distribution (0 mean and variance 1):
+
+In[23]:
+
+.. code:: python
+
+    np.random.randn(5)
+
+Out[23]:
+.. parsed-literal::
+
+    array([-0.08633343, -0.67375434,  1.00589536,  0.87081651,  1.65597822])
+
+
+whereas this will also give 5 samples, but from a normal distribution
+with a mean of 10 and a variance of 3:
+
+In[24]:
+
+.. code:: python
+
+    norm10 = np.random.normal(10, 3, 5)
+    norm10
+
+Out[24]:
+.. parsed-literal::
+
+    array([  8.94879575,   5.53038269,   8.24847281,  12.14944165,  11.56209294])
+
+
+Indexing with other arrays
+--------------------------
+
+
+Above we saw how to index arrays with single numbers and slices, just
+like Python lists. But arrays allow for a more sophisticated kind of
+indexing which is very powerful: you can index an array with another
+array, and in particular with an array of boolean values. This is
+particluarly useful to extract information from an array that matches a
+certain condition.
+
+Consider for example that in the array ``norm10`` we want to replace all
+values above 9 with the value 0. We can do so by first finding the
+*mask* that indicates where this condition is true or false:
+
+In[25]:
+
+.. code:: python
+
+    mask = norm10 > 9
+    mask
+
+Out[25]:
+.. parsed-literal::
+
+    array([False, False, False,  True,  True], dtype=bool)
+
+
+Now that we have this mask, we can use it to either read those values or
+to reset them to 0:
+
+In[26]:
+
+.. code:: python
+
+    print 'Values above 9:', norm10[mask]
+
+
+.. parsed-literal::
+
+    Values above 9: [ 12.14944165  11.56209294]
+
+In[27]:
+
+.. code:: python
+
+    print 'Resetting all values above 9 to 0...'
+    norm10[mask] = 0
+    print norm10
+
+
+.. parsed-literal::
+
+    Resetting all values above 9 to 0...
+    [ 8.94879575  5.53038269  8.24847281  0.          0.        ]
+
+Arrays with more than one dimension
+-----------------------------------
+
+
+Up until now all our examples have used one-dimensional arrays. But
+Numpy can create arrays of aribtrary dimensions, and all the methods
+illustrated in the previous section work with more than one dimension.
+For example, a list of lists can be used to initialize a two dimensional
+array:
+
+In[28]:
+
+.. code:: python
+
+    lst2 = [[1, 2], [3, 4]]
+    arr2 = np.array([[1, 2], [3, 4]])
+    arr2
+
+Out[28]:
+.. parsed-literal::
+
+    array([[1, 2],
+           [3, 4]])
+
+
+With two-dimensional arrays we start seeing the power of numpy: while a
+nested list can be indexed using repeatedly the ``[ ]`` operator,
+multidimensional arrays support a much more natural indexing syntax with
+a single ``[ ]`` and a set of indices separated by commas:
+
+In[29]:
+
+.. code:: python
+
+    print lst2[0][1]
+    print arr2[0,1]
+
+
+.. parsed-literal::
+
+    2
+    2
+
+Most of the array creation functions listed above can be used with more
+than one dimension, for example:
+
+In[30]:
+
+.. code:: python
+
+    np.zeros((2,3))
+
+Out[30]:
+.. parsed-literal::
+
+    array([[ 0.,  0.,  0.],
+           [ 0.,  0.,  0.]])
+
+
+In[31]:
+
+.. code:: python
+
+    np.random.normal(10, 3, (2, 4))
+
+Out[31]:
+.. parsed-literal::
+
+    array([[ 11.26788826,   4.29619866,  11.09346496,   9.73861307],
+           [ 10.54025996,   9.5146268 ,  10.80367214,  13.62204505]])
+
+
+In fact, the shape of an array can be changed at any time, as long as
+the total number of elements is unchanged. For example, if we want a 2x4
+array with numbers increasing from 0, the easiest way to create it is:
+
+In[32]:
+
+.. code:: python
+
+    arr = np.arange(8).reshape(2,4)
+    print arr
+
+
+.. parsed-literal::
+
+    [[0 1 2 3]
+     [4 5 6 7]]
+
+With multidimensional arrays, you can also use slices, and you can mix
+and match slices and single indices in the different dimensions (using
+the same array as above):
+
+In[33]:
+
+.. code:: python
+
+    print 'Slicing in the second row:', arr[1, 2:4]
+    print 'All rows, third column   :', arr[:, 2]
+
+
+.. parsed-literal::
+
+    Slicing in the second row: [6 7]
+    All rows, third column   : [2 6]
+
+If you only provide one index, then you will get an array with one less
+dimension containing that row:
+
+In[34]:
+
+.. code:: python
+
+    print 'First row:  ', arr[0]
+    print 'Second row: ', arr[1]
+
+
+.. parsed-literal::
+
+    First row:   [0 1 2 3]
+    Second row:  [4 5 6 7]
+
+Now that we have seen how to create arrays with more than one dimension,
+it's a good idea to look at some of the most useful properties and
+methods that arrays have. The following provide basic information about
+the size, shape and data in the array:
+
+In[35]:
+
+.. code:: python
+
+    print 'Data type                :', arr.dtype
+    print 'Total number of elements :', arr.size
+    print 'Number of dimensions     :', arr.ndim
+    print 'Shape (dimensionality)   :', arr.shape
+    print 'Memory used (in bytes)   :', arr.nbytes
+
+
+.. parsed-literal::
+
+    Data type                : int32
+    Total number of elements : 8
+    Number of dimensions     : 2
+    Shape (dimensionality)   : (2, 4)
+    Memory used (in bytes)   : 32
+
+Arrays also have many useful methods, some especially useful ones are:
+
+In[36]:
+
+.. code:: python
+
+    print 'Minimum and maximum             :', arr.min(), arr.max()
+    print 'Sum and product of all elements :', arr.sum(), arr.prod()
+    print 'Mean and standard deviation     :', arr.mean(), arr.std()
+
+
+.. parsed-literal::
+
+    Minimum and maximum             : 0 7
+    Sum and product of all elements : 28 0
+    Mean and standard deviation     : 3.5 2.29128784748
+
+For these methods, the above operations area all computed on all the
+elements of the array. But for a multidimensional array, it's possible
+to do the computation along a single dimension, by passing the ``axis``
+parameter; for example:
+
+In[37]:
+
+.. code:: python
+
+    print 'For the following array:\n', arr
+    print 'The sum of elements along the rows is    :', arr.sum(axis=1)
+    print 'The sum of elements along the columns is :', arr.sum(axis=0)
+
+
+.. parsed-literal::
+
+    For the following array:
+    [[0 1 2 3]
+     [4 5 6 7]]
+    The sum of elements along the rows is    : [ 6 22]
+    The sum of elements along the columns is : [ 4  6  8 10]
+
+As you can see in this example, the value of the ``axis`` parameter is
+the dimension which will be *consumed* once the operation has been
+carried out. This is why to sum along the rows we use ``axis=0``.
+
+This can be easily illustrated with an example that has more dimensions;
+we create an array with 4 dimensions and shape ``(3,4,5,6)`` and sum
+along the axis number 2 (i.e. the *third* axis, since in Python all
+counts are 0-based). That consumes the dimension whose length was 5,
+leaving us with a new array that has shape ``(3,4,6)``:
+
+In[38]:
+
+.. code:: python
+
+    np.zeros((3,4,5,6)).sum(2).shape
+
+Out[38]:
+.. parsed-literal::
+
+    (3, 4, 6)
+
+
+Another widely used property of arrays is the ``.T`` attribute, which
+allows you to access the transpose of the array:
+
+In[39]:
+
+.. code:: python
+
+    print 'Array:\n', arr
+    print 'Transpose:\n', arr.T
+
+
+.. parsed-literal::
+
+    Array:
+    [[0 1 2 3]
+     [4 5 6 7]]
+    Transpose:
+    [[0 4]
+     [1 5]
+     [2 6]
+     [3 7]]
+
+We don't have time here to look at all the methods and properties of
+arrays, here's a complete list. Simply try exploring some of these
+IPython to learn more, or read their description in the full Numpy
+documentation:
+
+::
+
+    arr.T             arr.copy          arr.getfield      arr.put           arr.squeeze
+    arr.all           arr.ctypes        arr.imag          arr.ravel         arr.std
+    arr.any           arr.cumprod       arr.item          arr.real          arr.strides
+    arr.argmax        arr.cumsum        arr.itemset       arr.repeat        arr.sum
+    arr.argmin        arr.data          arr.itemsize      arr.reshape       arr.swapaxes
+    arr.argsort       arr.diagonal      arr.max           arr.resize        arr.take
+    arr.astype        arr.dot           arr.mean          arr.round         arr.tofile
+    arr.base          arr.dtype         arr.min           arr.searchsorted  arr.tolist
+    arr.byteswap      arr.dump          arr.nbytes        arr.setasflat     arr.tostring
+    arr.choose        arr.dumps         arr.ndim          arr.setfield      arr.trace
+    arr.clip          arr.fill          arr.newbyteorder  arr.setflags      arr.transpose
+    arr.compress      arr.flags         arr.nonzero       arr.shape         arr.var
+    arr.conj          arr.flat          arr.prod          arr.size          arr.view
+    arr.conjugate     arr.flatten       arr.ptp           arr.sort          
+
+
+Operating with arrays
+---------------------
+
+
+Arrays support all regular arithmetic operators, and the numpy library
+also contains a complete collection of basic mathematical functions that
+operate on arrays. It is important to remember that in general, all
+operations with arrays are applied *element-wise*, i.e., are applied to
+all the elements of the array at the same time. Consider for example:
+
+In[40]:
+
+.. code:: python
+
+    arr1 = np.arange(4)
+    arr2 = np.arange(10, 14)
+    print arr1, '+', arr2, '=', arr1+arr2
+
+
+.. parsed-literal::
+
+    [0 1 2 3] + [10 11 12 13] = [10 12 14 16]
+
+Importantly, you must remember that even the multiplication operator is
+by default applied element-wise, it is *not* the matrix multiplication
+from linear algebra (as is the case in Matlab, for example):
+
+In[41]:
+
+.. code:: python
+
+    print arr1, '*', arr2, '=', arr1*arr2
+
+
+.. parsed-literal::
+
+    [0 1 2 3] * [10 11 12 13] = [ 0 11 24 39]
+
+While this means that in principle arrays must always match in their
+dimensionality in order for an operation to be valid, numpy will
+*broadcast* dimensions when possible. For example, suppose that you want
+to add the number 1.5 to ``arr1``; the following would be a valid way to
+do it:
+
+In[42]:
+
+.. code:: python
+
+    arr1 + 1.5*np.ones(4)
+
+Out[42]:
+.. parsed-literal::
+
+    array([ 1.5,  2.5,  3.5,  4.5])
+
+
+But thanks to numpy's broadcasting rules, the following is equally
+valid:
+
+In[43]:
+
+.. code:: python
+
+    arr1 + 1.5
+
+Out[43]:
+.. parsed-literal::
+
+    array([ 1.5,  2.5,  3.5,  4.5])
+
+
+In this case, numpy looked at both operands and saw that the first
+(``arr1``) was a one-dimensional array of length 4 and the second was a
+scalar, considered a zero-dimensional object. The broadcasting rules
+allow numpy to:
+
+-  *create* new dimensions of length 1 (since this doesn't change the
+   size of the array)
+-  'stretch' a dimension of length 1 that needs to be matched to a
+   dimension of a different size.
+
+So in the above example, the scalar 1.5 is effectively:
+
+-  first 'promoted' to a 1-dimensional array of length 1
+-  then, this array is 'stretched' to length 4 to match the dimension of
+   ``arr1``.
+
+After these two operations are complete, the addition can proceed as now
+both operands are one-dimensional arrays of length 4.
+
+This broadcasting behavior is in practice enormously powerful,
+especially because when numpy broadcasts to create new dimensions or to
+'stretch' existing ones, it doesn't actually replicate the data. In the
+example above the operation is carried *as if* the 1.5 was a 1-d array
+with 1.5 in all of its entries, but no actual array was ever created.
+This can save lots of memory in cases when the arrays in question are
+large and can have significant performance implications.
+
+The general rule is: when operating on two arrays, NumPy compares their
+shapes element-wise. It starts with the trailing dimensions, and works
+its way forward, creating dimensions of length 1 as needed. Two
+dimensions are considered compatible when
+
+-  they are equal to begin with, or
+-  one of them is 1; in this case numpy will do the 'stretching' to make
+   them equal.
+
+If these conditions are not met, a
+``ValueError: frames are not aligned`` exception is thrown, indicating
+that the arrays have incompatible shapes. The size of the resulting
+array is the maximum size along each dimension of the input arrays.
+
+This shows how the broadcasting rules work in several dimensions:
+
+In[44]:
+
+.. code:: python
+
+    b = np.array([2, 3, 4, 5])
+    print arr, '\n\n+', b , '\n----------------\n', arr + b
+
+
+.. parsed-literal::
+
+    [[0 1 2 3]
+     [4 5 6 7]] 
+    
+    + [2 3 4 5] 
+    ----------------
+    [[ 2  4  6  8]
+     [ 6  8 10 12]]
+
+Now, how could you use broadcasting to say add ``[4, 6]`` along the rows
+to ``arr`` above? Simply performing the direct addition will produce the
+error we previously mentioned:
+
+In[45]:
+
+.. code:: python
+
+    c = np.array([4, 6])
+    arr + c
+
+::
+
+    ---------------------------------------------------------------------------
+    ValueError                                Traceback (most recent call last)
+    /home/fperez/teach/book-math-labtool/<ipython-input-45-62aa20ac1980> in <module>()
+          1 c = np.array([4, 6])
+    ----> 2 arr + c
+    
+    ValueError: operands could not be broadcast together with shapes (2,4) (2) 
+According to the rules above, the array ``c`` would need to have a
+*trailing* dimension of 1 for the broadcasting to work. It turns out
+that numpy allows you to 'inject' new dimensions anywhere into an array
+on the fly, by indexing it with the special object ``np.newaxis``:
+
+In[46]:
+
+.. code:: python
+
+    (c[:, np.newaxis]).shape
+
+Out[46]:
+.. parsed-literal::
+
+    (2, 1)
+
+
+This is exactly what we need, and indeed it works:
+
+In[47]:
+
+.. code:: python
+
+    arr + c[:, np.newaxis]
+
+Out[47]:
+.. parsed-literal::
+
+    array([[ 4,  5,  6,  7],
+           [10, 11, 12, 13]])
+
+
+For the full broadcasting rules, please see the official Numpy docs,
+which describe them in detail and with more complex examples.
+
+As we mentioned before, Numpy ships with a full complement of
+mathematical functions that work on entire arrays, including logarithms,
+exponentials, trigonometric and hyperbolic trigonometric functions, etc.
+Furthermore, scipy ships a rich special function library in the
+``scipy.special`` module that includes Bessel, Airy, Fresnel, Laguerre
+and other classical special functions. For example, sampling the sine
+function at 100 points between :math:`0` and :math:`2\pi` is as simple
+as:
+
+In[48]:
+
+.. code:: python
+
+    x = np.linspace(0, 2*np.pi, 100)
+    y = np.sin(x)
+
+Linear algebra in numpy
+-----------------------
+
+
+Numpy ships with a basic linear algebra library, and all arrays have a
+``dot`` method whose behavior is that of the scalar dot product when its
+arguments are vectors (one-dimensional arrays) and the traditional
+matrix multiplication when one or both of its arguments are
+two-dimensional arrays:
+
+In[49]:
+
+.. code:: python
+
+    v1 = np.array([2, 3, 4])
+    v2 = np.array([1, 0, 1])
+    print v1, '.', v2, '=', v1.dot(v2)
+
+
+.. parsed-literal::
+
+    [2 3 4] . [1 0 1] = 6
+
+Here is a regular matrix-vector multiplication, note that the array
+``v1`` should be viewed as a *column* vector in traditional linear
+algebra notation; numpy makes no distinction between row and column
+vectors and simply verifies that the dimensions match the required rules
+of matrix multiplication, in this case we have a :math:`2 \times 3`
+matrix multiplied by a 3-vector, which produces a 2-vector:
+
+In[50]:
+
+.. code:: python
+
+    A = np.arange(6).reshape(2, 3)
+    print A, 'x', v1, '=', A.dot(v1)
+
+
+.. parsed-literal::
+
+    [[0 1 2]
+     [3 4 5]] x [2 3 4] = [11 38]
+
+For matrix-matrix multiplication, the same dimension-matching rules must
+be satisfied, e.g. consider the difference between :math:`A \times A^T`\ :
+
+In[51]:
+
+.. code:: python
+
+    print A.dot(A.T)
+
+
+.. parsed-literal::
+
+    [[ 5 14]
+     [14 50]]
+
+and :math:`A^T \times A`\ :
+
+In[52]:
+
+.. code:: python
+
+    print A.T.dot(A)
+
+
+.. parsed-literal::
+
+    [[ 9 12 15]
+     [12 17 22]
+     [15 22 29]]
+
+Furthermore, the ``numpy.linalg`` module includes additional
+functionality such as determinants, matrix norms, Cholesky, eigenvalue
+and singular value decompositions, etc. For even more linear algebra
+tools, ``scipy.linalg`` contains the majority of the tools in the
+classic LAPACK libraries as well as functions to operate on sparse
+matrices. We refer the reader to the Numpy and Scipy documentations for
+additional details on these.
+
+Reading and writing arrays to disk
+----------------------------------
+
+
+Numpy lets you read and write arrays into files in a number of ways. In
+order to use these tools well, it is critical to understand the
+difference between a *text* and a *binary* file containing numerical
+data. In a text file, the number :math:`\pi` could be written as
+"3.141592653589793", for example: a string of digits that a human can
+read, with in this case 15 decimal digits. In contrast, that same number
+written to a binary file would be encoded as 8 characters (bytes) that
+are not readable by a human but which contain the exact same data that
+the variable ``pi`` had in the computer's memory.
+
+The tradeoffs between the two modes are thus:
+
+-  Text mode: occupies more space, precision can be lost (if not all
+   digits are written to disk), but is readable and editable by hand
+   with a text editor. Can *only* be used for one- and two-dimensional
+   arrays.
+
+-  Binary mode: compact and exact representation of the data in memory,
+   can't be read or edited by hand. Arrays of any size and
+   dimensionality can be saved and read without loss of information.
+
+First, let's see how to read and write arrays in text mode. The
+``np.savetxt`` function saves an array to a text file, with options to
+control the precision, separators and even adding a header:
+
+In[53]:
+
+.. code:: python
+
+    arr = np.arange(10).reshape(2, 5)
+    np.savetxt('test.out', arr, fmt='%.2e', header="My dataset")
+    !cat test.out
+
+
+.. parsed-literal::
+
+    # My dataset
+    0.00e+00 1.00e+00 2.00e+00 3.00e+00 4.00e+00
+    5.00e+00 6.00e+00 7.00e+00 8.00e+00 9.00e+00
+
+And this same type of file can then be read with the matching
+``np.loadtxt`` function:
+
+In[54]:
+
+.. code:: python
+
+    arr2 = np.loadtxt('test.out')
+    print arr2
+
+
+.. parsed-literal::
+
+    [[ 0.  1.  2.  3.  4.]
+     [ 5.  6.  7.  8.  9.]]
+
+For binary data, Numpy provides the ``np.save`` and ``np.savez``
+routines. The first saves a single array to a file with ``.npy``
+extension, while the latter can be used to save a *group* of arrays into
+a single file with ``.npz`` extension. The files created with these
+routines can then be read with the ``np.load`` function.
+
+Let us first see how to use the simpler ``np.save`` function to save a
+single array:
+
+In[55]:
+
+.. code:: python
+
+    np.save('test.npy', arr2)
+    # Now we read this back
+    arr2n = np.load('test.npy')
+    # Let's see if any element is non-zero in the difference.
+    # A value of True would be a problem.
+    print 'Any differences?', np.any(arr2-arr2n)
+
+
+.. parsed-literal::
+
+    Any differences? False
+
+Now let us see how the ``np.savez`` function works. You give it a
+filename and either a sequence of arrays or a set of keywords. In the
+first mode, the function will auotmatically name the saved arrays in the
+archive as ``arr_0``, ``arr_1``, etc:
+
+In[56]:
+
+.. code:: python
+
+    np.savez('test.npz', arr, arr2)
+    arrays = np.load('test.npz')
+    arrays.files
+
+Out[56]:
+.. parsed-literal::
+
+    ['arr_1', 'arr_0']
+
+
+Alternatively, we can explicitly choose how to name the arrays we save:
+
+In[57]:
+
+.. code:: python
+
+    np.savez('test.npz', array1=arr, array2=arr2)
+    arrays = np.load('test.npz')
+    arrays.files
+
+Out[57]:
+.. parsed-literal::
+
+    ['array2', 'array1']
+
+
+The object returned by ``np.load`` from an ``.npz`` file works like a
+dictionary, though you can also access its constituent files by
+attribute using its special ``.f`` field; this is best illustrated with
+an example with the ``arrays`` object from above:
+
+In[58]:
+
+.. code:: python
+
+    print 'First row of first array:', arrays['array1'][0]
+    # This is an equivalent way to get the same field
+    print 'First row of first array:', arrays.f.array1[0]
+
+
+.. parsed-literal::
+
+    First row of first array: [0 1 2 3 4]
+    First row of first array: [0 1 2 3 4]
+
+This ``.npz`` format is a very convenient way to package compactly and
+without loss of information, into a single file, a group of related
+arrays that pertain to a specific problem. At some point, however, the
+complexity of your dataset may be such that the optimal approach is to
+use one of the standard formats in scientific data processing that have
+been designed to handle complex datasets, such as NetCDF or HDF5.
+
+Fortunately, there are tools for manipulating these formats in Python,
+and for storing data in other ways such as databases. A complete
+discussion of the possibilities is beyond the scope of this discussion,
+but of particular interest for scientific users we at least mention the
+following:
+
+-  The ``scipy.io`` module contains routines to read and write Matlab
+   files in ``.mat`` format and files in the NetCDF format that is
+   widely used in certain scientific disciplines.
+
+-  For manipulating files in the HDF5 format, there are two excellent
+   options in Python: The PyTables project offers a high-level, object
+   oriented approach to manipulating HDF5 datasets, while the h5py
+   project offers a more direct mapping to the standard HDF5 library
+   interface. Both are excellent tools; if you need to work with HDF5
+   datasets you should read some of their documentation and examples and
+   decide which approach is a better match for your needs.
+
+
+
+High quality data visualization with Matplotlib
+===============================================
+
+The `matplotlib <http://matplotlib.sf.net>`_ library is a powerful tool
+capable of producing complex publication-quality figures with fine
+layout control in two and three dimensions; here we will only provide a
+minimal self-contained introduction to its usage that covers the
+functionality needed for the rest of the book. We encourage the reader
+to read the tutorials included with the matplotlib documentation as well
+as to browse its extensive gallery of examples that include source code.
+
+Just as we typically use the shorthand ``np`` for Numpy, we will use
+``plt`` for the ``matplotlib.pyplot`` module where the easy-to-use
+plotting functions reside (the library contains a rich object-oriented
+architecture that we don't have the space to discuss here):
+
+In[59]:
+
+.. code:: python
+
+    import matplotlib.pyplot as plt
+
+The most frequently used function is simply called ``plot``, here is how
+you can make a simple plot of :math:`\sin(x)` for
+:math:`x \in [0, 2\pi]` with labels and a grid (we use the semicolon in
+the last line to suppress the display of some information that is
+unnecessary right now):
+
+In[60]:
+
+.. code:: python
+
+    x = np.linspace(0, 2*np.pi)
+    y = np.sin(x)
+    plt.plot(x,y, label='sin(x)')
+    plt.legend()
+    plt.grid()
+    plt.title('Harmonic')
+    plt.xlabel('x')
+    plt.ylabel('y');
+
+.. image:: _fig_134.svg
+
+You can control the style, color and other properties of the markers,
+for example:
+
+In[61]:
+
+.. code:: python
+
+    plt.plot(x, y, linewidth=2);
+
+.. image:: _fig_136.svg
+
+In[62]:
+
+.. code:: python
+
+    plt.plot(x, y, 'o', markersize=5, color='r');
+
+.. image:: _fig_137.svg
+
+We will now see how to create a few other common plot types, such as a
+simple error plot:
+
+In[63]:
+
+.. code:: python
+
+    # example data
+    x = np.arange(0.1, 4, 0.5)
+    y = np.exp(-x)
+    
+    # example variable error bar values
+    yerr = 0.1 + 0.2*np.sqrt(x)
+    xerr = 0.1 + yerr
+    
+    # First illustrate basic pyplot interface, using defaults where possible.
+    plt.figure()
+    plt.errorbar(x, y, xerr=0.2, yerr=0.4)
+    plt.title("Simplest errorbars, 0.2 in x, 0.4 in y");
+
+.. image:: _fig_139.svg
+
+A simple log plot
+
+In[64]:
+
+.. code:: python
+
+    x = np.linspace(-5, 5)
+    y = np.exp(-x**2)
+    plt.semilogy(x, y);
+
+.. image:: _fig_141.svg
+
+A histogram annotated with text inside the plot, using the ``text``
+function:
+
+In[65]:
+
+.. code:: python
+
+    mu, sigma = 100, 15
+    x = mu + sigma * np.random.randn(10000)
+    
+    # the histogram of the data
+    n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)
+    
+    plt.xlabel('Smarts')
+    plt.ylabel('Probability')
+    plt.title('Histogram of IQ')
+    # This will put a text fragment at the position given:
+    plt.text(55, .027, r'$\mu=100,\ \sigma=15$', fontsize=14)
+    plt.axis([40, 160, 0, 0.03])
+    plt.grid(True)
+
+.. image:: _fig_143.svg
+
+Image display
+-------------
+
+
+The ``imshow`` command can display single or multi-channel images. A
+simple array of random numbers, plotted in grayscale:
+
+In[66]:
+
+.. code:: python
+
+    from matplotlib import cm
+    plt.imshow(np.random.rand(5, 10), cmap=cm.gray, interpolation='nearest');
+
+.. image:: _fig_146.svg
+
+A real photograph is a multichannel image, ``imshow`` interprets it
+correctly:
+
+In[67]:
+
+.. code:: python
+
+    img = plt.imread('stinkbug.png')
+    print 'Dimensions of the array img:', img.shape
+    plt.imshow(img);
+
+
+.. parsed-literal::
+
+    Dimensions of the array img: (375, 500, 3)
+
+.. image:: _fig_149.svg
+
+Simple 3d plotting with matplotlib
+----------------------------------
+
+
+Note that you must execute at least once in your session:
+
+In[68]:
+
+.. code:: python
+
+    from mpl_toolkits.mplot3d import Axes3D
+
+One this has been done, you can create 3d axes with the
+``projection='3d'`` keyword to ``add_subplot``:
+
+::
+
+    fig = plt.figure()
+    fig.add_subplot(<other arguments here>, projection='3d')
+
+
+A simple surface plot:
+
+In[72]:
+
+.. code:: python
+
+    from mpl_toolkits.mplot3d.axes3d import Axes3D
+    from matplotlib import cm
+    
+    fig = plt.figure()
+    ax = fig.add_subplot(1, 1, 1, projection='3d')
+    X = np.arange(-5, 5, 0.25)
+    Y = np.arange(-5, 5, 0.25)
+    X, Y = np.meshgrid(X, Y)
+    R = np.sqrt(X**2 + Y**2)
+    Z = np.sin(R)
+    surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet,
+            linewidth=0, antialiased=False)
+    ax.set_zlim3d(-1.01, 1.01);
+
+.. image:: _fig_154.svg
+
+IPython: a powerful interactive environment
+===========================================
+
+A key component of the everyday workflow of most scientific computing
+environments is a good interactive environment, that is, a system in
+which you can execute small amounts of code and view the results
+immediately, combining both printing out data and opening graphical
+visualizations. All modern systems for scientific computing, commercial
+and open source, include such functionality.
+
+Out of the box, Python also offers a simple interactive shell with very
+limited capabilities. But just like the scientific community built Numpy
+to provide arrays suited for scientific work (since Pytyhon's lists
+aren't optimal for this task), it has also developed an interactive
+environment much more sophisticated than the built-in one. The `IPython
+project <http://ipython.org>`_ offers a set of tools to make productive
+use of the Python language, all the while working interactively and with
+immedate feedback on your results. The basic tools that IPython provides
+are:
+
+1. A powerful terminal shell, with many features designed to increase
+   the fluidity and productivity of everyday scientific workflows,
+   including:
+
+   -  rich introspection of all objects and variables including easy
+      access to the source code of any function
+   -  powerful and extensible tab completion of variables and filenames,
+   -  tight integration with matplotlib, supporting interactive figures
+      that don't block the terminal,
+   -  direct access to the filesystem and underlying operating system,
+   -  an extensible system for shell-like commands called 'magics' that
+      reduce the work needed to perform many common tasks,
+   -  tools for easily running, timing, profiling and debugging your
+      codes,
+   -  syntax highlighted error messages with much more detail than the
+      default Python ones,
+   -  logging and access to all previous history of inputs, including
+      across sessions
+
+2. A Qt console that provides the look and feel of a terminal, but adds
+   support for inline figures, graphical calltips, a persistent session
+   that can survive crashes (even segfaults) of the kernel process, and
+   more.
+
+3. A web-based notebook that can execute code and also contain rich text
+   and figures, mathematical equations and arbitrary HTML. This notebook
+   presents a document-like view with cells where code is executed but
+   that can be edited in-place, reordered, mixed with explanatory text
+   and figures, etc.
+
+4. A high-performance, low-latency system for parallel computing that
+   supports the control of a cluster of IPython engines communicating
+   over a network, with optimizations that minimize unnecessary copying
+   of large objects (especially numpy arrays).
+
+We will now discuss the highlights of the tools 1-3 above so that you
+can make them an effective part of your workflow. The topic of parallel
+computing is beyond the scope of this document, but we encourage you to
+read the extensive
+`documentation <http://ipython.org/ipython-doc/rel-0.12.1/parallel/index.html>`_
+and `tutorials <http://minrk.github.com/scipy-tutorial-2011/>`_ on this
+available on the IPython website.
+
+The IPython terminal
+--------------------
+
+
+You can start IPython at the terminal simply by typing:
+
+::
+
+    $ ipython
+
+which will provide you some basic information about how to get started
+and will then open a prompt labeled ``In [1]:`` for you to start typing.
+Here we type :math:`2^{64}` and Python computes the result for us in
+exact arithmetic, returning it as ``Out[1]``:
+
+::
+
+    $ ipython
+    Python 2.7.2+ (default, Oct  4 2011, 20:03:08) 
+    Type "copyright", "credits" or "license" for more information.
+
+    IPython 0.13.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]: 2**64
+    Out[1]: 18446744073709551616L
+
+The first thing you should know about IPython is that all your inputs
+and outputs are saved. There are two variables named ``In`` and ``Out``
+which are filled as you work with your results. Furthermore, all outputs
+are also saved to auto-created variables of the form ``_NN`` where
+``NN`` is the prompt number, and inputs to ``_iNN``. This allows you to
+recover quickly the result of a prior computation by referring to its
+number even if you forgot to store it as a variable. For example, later
+on in the above session you can do:
+
+::
+
+    In [6]: print _1
+    18446744073709551616
+
+
+We strongly recommend that you take a few minutes to read at least the
+basic introduction provided by the ``?`` command, and keep in mind that
+the ``%quickref`` command at all times can be used as a quick reference
+"cheat sheet" of the most frequently used features of IPython.
+
+At the IPython prompt, any valid Python code that you type will be
+executed similarly to the default Python shell (though often with more
+informative feedback). But since IPython is a *superset* of the default
+Python shell; let's have a brief look at some of its additional
+functionality.
+
+**Object introspection**
+
+A simple ``?`` command provides a general introduction to IPython, but
+as indicated in the banner above, you can use the ``?`` syntax to ask
+for details about any object. For example, if we type ``_1?``, IPython
+will print the following details about this variable:
+
+::
+
+    In [14]: _1?
+    Type:       long
+    Base Class: <type 'long'>
+    String Form:18446744073709551616
+    Namespace:  Interactive
+    Docstring:
+    long(x[, base]) -> integer
+
+    Convert a string or number to a long integer, if possible.  A floating
+
+    [etc... snipped for brevity]
+
+If you add a second ``?`` and for any oobject ``x`` type ``x??``,
+IPython will try to provide an even more detailed analsysi of the
+object, including its syntax-highlighted source code when it can be
+found. It's possible that ``x??`` returns the same information as
+``x?``, but in many cases ``x??`` will indeed provide additional
+details.
+
+Finally, the ``?`` syntax is also useful to search *namespaces* with
+wildcards. Suppose you are wondering if there is any function in Numpy
+that may do text-related things; with ``np.*txt*?``, IPython will print
+all the names in the ``np`` namespace (our Numpy shorthand) that have
+'txt' anywhere in their name:
+
+::
+
+    In [17]: np.*txt*?
+    np.genfromtxt
+    np.loadtxt
+    np.mafromtxt
+    np.ndfromtxt
+    np.recfromtxt
+    np.savetxt
+
+
+**Tab completion**
+
+IPython makes the tab key work extra hard for you as a way to rapidly
+inspect objects and libraries. Whenever you have typed something at the
+prompt, by hitting the ``<tab>`` key IPython will try to complete the
+rest of the line. For this, IPython will analyze the text you had so far
+and try to search for Python data or files that may match the context
+you have already provided.
+
+For example, if you type ``np.load`` and hit the key, you'll see:
+
+::
+
+    In [21]: np.load<TAB HERE>
+    np.load     np.loads    np.loadtxt  
+
+so you can quickly find all the load-related functionality in numpy. Tab
+completion works even for function arguments, for example consider this
+function definition:
+
+::
+
+    In [20]: def f(x, frobinate=False):
+       ....:     if frobinate:
+       ....:         return x**2
+       ....:     
+
+If you now use the ``<tab>`` key after having typed 'fro' you'll get all
+valid Python completions, but those marked with ``=`` at the end are
+known to be keywords of your function:
+
+::
+
+    In [21]: f(2, fro<TAB HERE>
+    frobinate=    frombuffer    fromfunction  frompyfunc    fromstring    
+    from          fromfile      fromiter      fromregex     frozenset     
+
+at this point you can add the ``b`` letter and hit ``<tab>`` once more,
+and IPython will finish the line for you:
+
+::
+
+    In [21]: f(2, frobinate=
+
+As a beginner, simply get into the habit of using ``<tab>`` after most
+objects; it should quickly become second nature as you will see how
+helps keep a fluid workflow and discover useful information. Later on
+you can also customize this behavior by writing your own completion
+code, if you so desire.
+
+**Matplotlib integration**
+
+One of the most useful features of IPython for scientists is its tight
+integration with matplotlib: at the terminal IPython lets you open
+matplotlib figures without blocking your typing (which is what happens
+if you try to do the same thing at the default Python shell), and in the
+Qt console and notebook you can even view your figures embedded in your
+workspace next to the code that created them.
+
+The matplotlib support can be either activated when you start IPython by
+passing the ``--pylab`` flag, or at any point later in your session by
+using the ``%pylab`` command. If you start IPython with ``--pylab``,
+you'll see something like this (note the extra message about pylab):
+
+::
+
+    $ ipython --pylab
+    Python 2.7.2+ (default, Oct  4 2011, 20:03:08) 
+    Type "copyright", "credits" or "license" for more information.
+
+    IPython 0.13.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.
+
+    Welcome to pylab, a matplotlib-based Python environment [backend: Qt4Agg].
+    For more information, type 'help(pylab)'.
+
+    In [1]: 
+
+Furthermore, IPython will import ``numpy`` with the ``np`` shorthand,
+``matplotlib.pyplot`` as ``plt``, and it will also load all of the numpy
+and pyplot top-level names so that you can directly type something like:
+
+::
+
+    In [1]: x = linspace(0, 2*pi, 200)
+
+    In [2]: plot(x, sin(x))
+    Out[2]: [<matplotlib.lines.Line2D at 0x9e7c16c>]
+
+instead of having to prefix each call with its full signature (as we
+have been doing in the examples thus far):
+
+::
+
+    In [3]: x = np.linspace(0, 2*np.pi, 200)
+
+    In [4]: plt.plot(x, np.sin(x))
+    Out[4]: [<matplotlib.lines.Line2D at 0x9e900ac>]
+
+This shorthand notation can be a huge time-saver when working
+interactively (it's a few characters but you are likely to type them
+hundreds of times in a session). But we should note that as you develop
+persistent scripts and notebooks meant for reuse, it's best to get in
+the habit of using the longer notation (known as *fully qualified names*
+as it's clearer where things come from and it makes for more robust,
+readable and maintainable code in the long run).
+
+**Access to the operating system and files**
+
+In IPython, you can type ``ls`` to see your files or ``cd`` to change
+directories, just like you would at a regular system prompt:
+
+::
+
+    In [2]: cd tests
+    /home/fperez/ipython/nbconvert/tests
+
+    In [3]: ls test.*
+    test.aux  test.html  test.ipynb  test.log  test.out  test.pdf  test.rst  test.tex
+
+Furthermore, if you use the ``!`` at the beginning of a line, any
+commands you pass afterwards go directly to the operating system:
+
+::
+
+    In [4]: !echo "Hello IPython"
+    Hello IPython
+
+IPython offers a useful twist in this feature: it will substitute in the
+command the value of any *Python* variable you may have if you prepend
+it with a ``$`` sign:
+
+::
+
+    In [5]: message = 'IPython interpolates from Python to the shell'
+
+    In [6]: !echo $message
+    IPython interpolates from Python to the shell
+
+This feature can be extremely useful, as it lets you combine the power
+and clarity of Python for complex logic with the immediacy and
+familiarity of many shell commands. Additionally, if you start the line
+with *two* ``$$`` signs, the output of the command will be automatically
+captured as a list of lines, e.g.:
+
+::
+
+    In [10]: !!ls test.*
+    Out[10]: 
+    ['test.aux',
+     'test.html',
+     'test.ipynb',
+     'test.log',
+     'test.out',
+     'test.pdf',
+     'test.rst',
+     'test.tex']
+
+As explained above, you can now use this as the variable ``_10``. If you
+directly want to capture the output of a system command to a Python
+variable, you can use the syntax ``=!``:
+
+::
+
+    In [11]: testfiles =! ls test.*
+
+    In [12]: print testfiles
+    ['test.aux', 'test.html', 'test.ipynb', 'test.log', 'test.out', 'test.pdf', 'test.rst', 'test.tex']
+
+Finally, the special ``%alias`` command lets you define names that are
+shorthands for system commands, so that you can type them without having
+to prefix them via ``!`` explicitly (for example, ``ls`` is an alias
+that has been predefined for you at startup).
+
+**Magic commands**
+
+IPython has a system for special commands, called 'magics', that let you
+control IPython itself and perform many common tasks with a more
+shell-like syntax: it uses spaces for delimiting arguments, flags can be
+set with dashes and all arguments are treated as strings, so no
+additional quoting is required. This kind of syntax is invalid in the
+Python language but very convenient for interactive typing (less
+parentheses, commans and quoting everywhere); IPython distinguishes the
+two by detecting lines that start with the ``%`` character.
+
+You can learn more about the magic system by simply typing ``%magic`` at
+the prompt, which will give you a short description plus the
+documentation on *all* available magics. If you want to see only a
+listing of existing magics, you can use ``%lsmagic``:
+
+::
+
+    In [4]: lsmagic
+    Available magic functions:
+    %alias  %autocall  %autoindent  %automagic  %bookmark  %c  %cd  %colors  %config  %cpaste
+    %debug  %dhist  %dirs  %doctest_mode  %ds  %ed  %edit  %env  %gui  %hist  %history
+    %install_default_config  %install_ext  %install_profiles  %load_ext  %loadpy  %logoff  %logon  
+    %logstart  %logstate  %logstop  %lsmagic  %macro  %magic  %notebook  %page  %paste  %pastebin  
+    %pd  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pop  %popd  %pprint  %precision  %profile  
+    %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %quickref  %recall  %rehashx  
+    %reload_ext  %rep  %rerun  %reset  %reset_selective  %run  %save  %sc  %stop  %store  %sx  %tb
+    %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode
+
+    Automagic is ON, % prefix NOT needed for magic functions.
+
+Note how the example above omitted the eplicit ``%`` marker and simply
+uses ``lsmagic``. As long as the 'automagic' feature is on (which it is
+by default), you can omit the ``%`` marker as long as there is no
+ambiguity with a Python variable of the same name.
+
+**Running your code**
+
+While it's easy to type a few lines of code in IPython, for any
+long-lived work you should keep your codes in Python scripts (or in
+IPython notebooks, see below). Consider that you have a script, in this
+case trivially simple for the sake of brevity, named ``simple.py``:
+
+::
+
+    In [12]: !cat simple.py
+    import numpy as np
+
+    x = np.random.normal(size=100)
+
+    print 'First elment of x:', x[0]
+
+The typical workflow with IPython is to use the ``%run`` magic to
+execute your script (you can omit the .py extension if you want). When
+you run it, the script will execute just as if it had been run at the
+system prompt with ``python simple.py`` (though since modules don't get
+re-executed on new imports by Python, all system initialization is
+essentially free, which can have a significant run time impact in some
+cases):
+
+::
+
+    In [13]: run simple
+    First elment of x: -1.55872256289
+
+Once it completes, all variables defined in it become available for you
+to use interactively:
+
+::
+
+    In [14]: x.shape
+    Out[14]: (100,)
+
+This allows you to plot data, try out ideas, etc, in a
+``%run``/interact/edit cycle that can be very productive. As you start
+understanding your problem better you can refine your script further,
+incrementally improving it based on the work you do at the IPython
+prompt. At any point you can use the ``%hist`` magic to print out your
+history without prompts, so that you can copy useful fragments back into
+the script.
+
+By default, ``%run`` executes scripts in a completely empty namespace,
+to better mimic how they would execute at the system prompt with plain
+Python. But if you use the ``-i`` flag, the script will also see your
+interactively defined variables. This lets you edit in a script larger
+amounts of code that still behave as if you had typed them at the
+IPython prompt.
+
+You can also get a summary of the time taken by your script with the
+``-t`` flag; consider a different script ``randsvd.py`` that takes a bit
+longer to run:
+
+::
+
+    In [21]: run -t randsvd.py
+
+    IPython CPU timings (estimated):
+      User   :       0.38 s.
+      System :       0.04 s.
+    Wall time:       0.34 s.
+
+``User`` is the time spent by the computer executing your code, while
+``System`` is the time the operating system had to work on your behalf,
+doing things like memory allocation that are needed by your code but
+that you didn't explicitly program and that happen inside the kernel.
+The ``Wall time`` is the time on a 'clock on the wall' between the start
+and end of your program.
+
+If ``Wall > User+System``, your code is most likely waiting idle for
+certain periods. That could be waiting for data to arrive from a remote
+source or perhaps because the operating system has to swap large amounts
+of virtual memory. If you know that your code doesn't explicitly wait
+for remote data to arrive, you should investigate further to identify
+possible ways of improving the performance profile.
+
+If you only want to time how long a single statement takes, you don't
+need to put it into a script as you can use the ``%timeit`` magic, which
+uses Python's ``timeit`` module to very carefully measure timig data;
+``timeit`` can measure even short statements that execute extremely
+fast:
+
+::
+
+    In [27]: %timeit a=1
+    10000000 loops, best of 3: 23 ns per loop
+
+and for code that runs longer, it automatically adjusts so the overall
+measurement doesn't take too long:
+
+::
+
+    In [28]: %timeit np.linalg.svd(x)
+    1 loops, best of 3: 310 ms per loop
+
+The ``%run`` magic still has more options for debugging and profiling
+data; you should read its documentation for many useful details (as
+always, just type ``%run?``).
+
+The graphical Qt console
+------------------------
+
+
+If you type at the system prompt (see the IPython website for
+installation details, as this requires some additional libraries):
+
+::
+
+    $ ipython qtconsole
+
+instead of opening in a terminal as before, IPython will start a
+graphical console that at first sight appears just like a terminal, but
+which is in fact much more capable than a text-only terminal. This is a
+specialized terminal designed for interactive scientific work, and it
+supports full multi-line editing with color highlighting and graphical
+calltips for functions, it can keep multiple IPython sessions open
+simultaneously in tabs, and when scripts run it can display the figures
+inline directly in the work area.
+
+.. raw:: html
+
+   <center>
+
+.. raw:: html
+
+   </center>
+
+
+% This cell is for the pdflatex output only
+\begin{figure}[htbp]
+\centering
+\includegraphics[width=3in]{ipython_qtconsole2.png}
+\caption{The IPython Qt console: a lightweight terminal for scientific exploration, with code, results and graphics in a soingle environment.}
+\end{figure}
+The Qt console accepts the same ``--pylab`` startup flags as the
+terminal, but you can additionally supply the value ``--pylab inline``,
+which enables the support for inline graphics shown in the figure. This
+is ideal for keeping all the code and figures in the same session, given
+that the console can save the output of your entire session to HTML or
+PDF.
+
+Since the Qt console makes it far more convenient than the terminal to
+edit blocks of code with multiple lines, in this environment it's worth
+knowing about the ``%loadpy`` magic function. ``%loadpy`` takes a path
+to a local file or remote URL, fetches its contents, and puts it in the
+work area for you to further edit and execute. It can be an extremely
+fast and convenient way of loading code from local disk or remote
+examples from sites such as the `Matplotlib
+gallery <http://matplotlib.sourceforge.net/gallery.html>`_.
+
+Other than its enhanced capabilities for code and graphics, all of the
+features of IPython we've explained before remain functional in this
+graphical console.
+
+The IPython Notebook
+--------------------
+
+
+The third way to interact with IPython, in addition to the terminal and
+graphical Qt console, is a powerful web interface called the "IPython
+Notebook". If you run at the system console (you can omit the ``pylab``
+flags if you don't need plotting support):
+
+::
+
+    $ ipython notebook --pylab inline
+
+IPython will start a process that runs a web server in your local
+machine and to which a web browser can connect. The Notebook is a
+workspace that lets you execute code in blocks called 'cells' and
+displays any results and figures, but which can also contain arbitrary
+text (including LaTeX-formatted mathematical expressions) and any rich
+media that a modern web browser is capable of displaying.
+
+.. raw:: html
+
+   <center>
+
+.. raw:: html
+
+   </center>
+
+
+% This cell is for the pdflatex output only
+\begin{figure}[htbp]
+\centering
+\includegraphics[width=3in]{ipython-notebook-specgram-2.png}
+\caption{The IPython Notebook: text, equations, code, results, graphics and other multimedia in an open format for scientific exploration and collaboration}
+\end{figure}
+In fact, this document was written as a Notebook, and only exported to
+LaTeX for printing. Inside of each cell, all the features of IPython
+that we have discussed before remain functional, since ultimately this
+web client is communicating with the same IPython code that runs in the
+terminal. But this interface is a much more rich and powerful
+environment for maintaining long-term "live and executable" scientific
+documents.
+
+Notebook environments have existed in commercial systems like
+Mathematica(TM) and Maple(TM) for a long time; in the open source world
+the `Sage <http://sagemath.org>`_ project blazed this particular trail
+starting in 2006, and now we bring all the features that have made
+IPython such a widely used tool to a Notebook model.
+
+Since the Notebook runs as a web application, it is possible to
+configure it for remote access, letting you run your computations on a
+persistent server close to your data, which you can then access remotely
+from any browser-equipped computer. We encourage you to read the
+extensive documentation provided by the IPython project for details on
+how to do this and many more features of the notebook.
+
+Finally, as we said earlier, IPython also has a high-level and easy to
+use set of libraries for parallel computing, that let you control
+(interactively if desired) not just one IPython but an entire cluster of
+'IPython engines'. Unfortunately a detailed discussion of these tools is
+beyond the scope of this text, but should you need to parallelize your
+analysis codes, a quick read of the tutorials and examples provided at
+the IPython site may prove fruitful.
+
diff --git a/tests/template_ref/IntroNumPy_orig.tex b/tests/template_ref/IntroNumPy_orig.tex
new file mode 100644
index 0000000..885ff44
--- /dev/null
+++ b/tests/template_ref/IntroNumPy_orig.tex
@@ -0,0 +1,2358 @@
+
+
+%% This file was auto-generated by IPython.
+%% Conversion from the original notebook file:
+%% tests/ipynbref/Gun_Data.orig.ipynb
+%%
+\documentclass[11pt,english]{article}
+
+%% This is the automatic preamble used by IPython.  Note that it does *not*
+%% include a documentclass declaration, that is added at runtime to the overall
+%% document.
+
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{graphicx}
+\usepackage{ucs}
+\usepackage[utf8x]{inputenc}
+
+% needed for markdown enumerations to work
+\usepackage{enumerate}
+
+% Slightly bigger margins than the latex defaults
+\usepackage{geometry}
+\geometry{verbose,tmargin=3cm,bmargin=3cm,lmargin=2.5cm,rmargin=2.5cm}
+
+% Define a few colors for use in code, links and cell shading
+\usepackage{color}
+\definecolor{orange}{cmyk}{0,0.4,0.8,0.2}
+\definecolor{darkorange}{rgb}{.71,0.21,0.01}
+\definecolor{darkgreen}{rgb}{.12,.54,.11}
+\definecolor{myteal}{rgb}{.26, .44, .56}
+\definecolor{gray}{gray}{0.45}
+\definecolor{lightgray}{gray}{.95}
+\definecolor{mediumgray}{gray}{.8}
+\definecolor{inputbackground}{rgb}{.95, .95, .85}
+\definecolor{outputbackground}{rgb}{.95, .95, .95}
+\definecolor{traceback}{rgb}{1, .95, .95}
+
+% Framed environments for code cells (inputs, outputs, errors, ...).  The
+% various uses of \unskip (or not) at the end were fine-tuned by hand, so don't
+% randomly change them unless you're sure of the effect it will have.
+\usepackage{framed}
+
+% remove extraneous vertical space in boxes
+\setlength\fboxsep{0pt}
+
+% codecell is the whole input+output set of blocks that a Code cell can
+% generate.
+
+% TODO: unfortunately, it seems that using a framed codecell environment breaks
+% the ability of the frames inside of it to be broken across pages.  This
+% causes at least the problem of having lots of empty space at the bottom of
+% pages as new frames are moved to the next page, and if a single frame is too
+% long to fit on a page, will completely stop latex from compiling the
+% document.  So unless we figure out a solution to this, we'll have to instead
+% leave the codecell env. as empty.  I'm keeping the original codecell
+% definition here (a thin vertical bar) for reference, in case we find a
+% solution to the page break issue.
+
+%% \newenvironment{codecell}{%
+%%     \def\FrameCommand{\color{mediumgray} \vrule width 1pt \hspace{5pt}}%
+%%    \MakeFramed{\vspace{-0.5em}}}
+%%  {\unskip\endMakeFramed}
+
+% For now, make this a no-op...
+\newenvironment{codecell}{}
+
+ \newenvironment{codeinput}{%
+   \def\FrameCommand{\colorbox{inputbackground}}%
+   \MakeFramed{\advance\hsize-\width \FrameRestore}}
+ {\unskip\endMakeFramed}
+
+\newenvironment{codeoutput}{%
+   \def\FrameCommand{\colorbox{outputbackground}}%
+   \vspace{-1.4em}
+   \MakeFramed{\advance\hsize-\width \FrameRestore}}
+ {\unskip\medskip\endMakeFramed}
+
+\newenvironment{traceback}{%
+   \def\FrameCommand{\colorbox{traceback}}%
+   \MakeFramed{\advance\hsize-\width \FrameRestore}}
+ {\endMakeFramed}
+
+% Use and configure listings package for nicely formatted code
+\usepackage{listingsutf8}
+\lstset{
+  language=python,
+  inputencoding=utf8x,
+  extendedchars=\true,
+  aboveskip=\smallskipamount,
+  belowskip=\smallskipamount,
+  xleftmargin=2mm,
+  breaklines=true,
+  basicstyle=\small \ttfamily,
+  showstringspaces=false,
+  keywordstyle=\color{blue}\bfseries,
+  commentstyle=\color{myteal},
+  stringstyle=\color{darkgreen},
+  identifierstyle=\color{darkorange},
+  columns=fullflexible,  % tighter character kerning, like verb
+}
+
+% The hyperref package gives us a pdf with properly built
+% internal navigation ('pdf bookmarks' for the table of contents,
+% internal cross-reference links, web links for URLs, etc.)
+\usepackage{hyperref}
+\hypersetup{
+  breaklinks=true,  % so long urls are correctly broken across lines
+  colorlinks=true,
+  urlcolor=blue,
+  linkcolor=darkorange,
+  citecolor=darkgreen,
+  }
+
+% hardcode size of all verbatim environments to be a bit smaller
+\makeatletter 
+\g@addto@macro\@verbatim\small\topsep=0.5em\partopsep=0pt
+\makeatother 
+
+% Prevent overflowing lines due to urls and other hard-to-break entities.
+\sloppy
+
+\begin{document}
+
+\section{An Introduction to the Scientific Python Ecosystem}
+While the Python language is an excellent tool for general-purpose
+programming, with a highly readable syntax, rich and powerful data types
+(strings, lists, sets, dictionaries, arbitrary length integers, etc) and
+a very comprehensive standard library, it was not designed specifically
+for mathematical and scientific computing. Neither the language nor its
+standard library have facilities for the efficient representation of
+multidimensional datasets, tools for linear algebra and general matrix
+manipulations (an essential building block of virtually all technical
+computing), nor any data visualization facilities.
+
+In particular, Python lists are very flexible containers that can be
+nested arbitrarily deep and which can hold any Python object in them,
+but they are poorly suited to represent efficiently common mathematical
+constructs like vectors and matrices. In contrast, much of our modern
+heritage of scientific computing has been built on top of libraries
+written in the Fortran language, which has native support for vectors
+and matrices as well as a library of mathematical functions that can
+efficiently operate on entire arrays at once.
+
+
+\section{Scientific Python: a collaboration of projects built by scientists}
+The scientific community has developed a set of related Python libraries
+that provide powerful array facilities, linear algebra, numerical
+algorithms, data visualization and more. In this appendix, we will
+briefly outline the tools most frequently used for this purpose, that
+make ``Scientific Python'' something far more powerful than the Python
+language alone.
+
+For reasons of space, we can only describe in some detail the central
+Numpy library, but below we provide links to the websites of each
+project where you can read their documentation in more detail.
+
+First, let's look at an overview of the basic tools that most scientists
+use in daily research with Python. The core of this ecosystem is
+composed of:
+
+\begin{itemize}
+\item
+  Numpy: the basic library that most others depend on, it provides a
+  powerful array type that can represent multidmensional datasets of
+  many different kinds and that supports arithmetic operations. Numpy
+  also provides a library of common mathematical functions, basic linear
+  algebra, random number generation and Fast Fourier Transforms. Numpy
+  can be found at \href{http://numpy.scipy.org}{numpy.scipy.org}
+\item
+  Scipy: a large collection of numerical algorithms that operate on
+  numpy arrays and provide facilities for many common tasks in
+  scientific computing, including dense and sparse linear algebra
+  support, optimization, special functions, statistics, n-dimensional
+  image processing, signal processing and more. Scipy can be found at
+  \href{http://scipy.org}{scipy.org}.
+\item
+  Matplotlib: a data visualization library with a strong focus on
+  producing high-quality output, it supports a variety of common
+  scientific plot types in two and three dimensions, with precise
+  control over the final output and format for publication-quality
+  results. Matplotlib can also be controlled interactively allowing
+  graphical manipulation of your data (zooming, panning, etc) and can be
+  used with most modern user interface toolkits. It can be found at
+  \href{http://matplotlib.sf.net}{matplotlib.sf.net}.
+\item
+  IPython: while not strictly scientific in nature, IPython is the
+  interactive environment in which many scientists spend their time.
+  IPython provides a powerful Python shell that integrates tightly with
+  Matplotlib and with easy access to the files and operating system, and
+  which can execute in a terminal or in a graphical Qt console. IPython
+  also has a web-based notebook interface that can combine code with
+  text, mathematical expressions, figures and multimedia. It can be
+  found at \href{http://ipython.org}{ipython.org}.
+\end{itemize}
+While each of these tools can be installed separately, in our opinion
+the most convenient way today of accessing them (especially on Windows
+and Mac computers) is to install the
+\href{http://www.enthought.com/products/epd\_free.php}{Free Edition of
+the Enthought Python Distribution} which contain all the above. Other
+free alternatives on Windows (but not on Macs) are
+\href{http://code.google.com/p/pythonxy}{Python(x,y)} and
+\href{http://www.lfd.uci.edu/~gohlke/pythonlibs}{Christoph Gohlke's
+packages page}.
+
+These four `core' libraries are in practice complemented by a number of
+other tools for more specialized work. We will briefly list here the
+ones that we think are the most commonly needed:
+
+\begin{itemize}
+\item
+  Sympy: a symbolic manipulation tool that turns a Python session into a
+  computer algebra system. It integrates with the IPython notebook,
+  rendering results in properly typeset mathematical notation.
+  \href{http://sympy.org}{sympy.org}.
+\item
+  Mayavi: sophisticated 3d data visualization;
+  \href{http://code.enthought.com/projects/mayavi}{code.enthought.com/projects/mayavi}.
+\item
+  Cython: a bridge language between Python and C, useful both to
+  optimize performance bottlenecks in Python and to access C libraries
+  directly; \href{http://cython.org}{cython.org}.
+\item
+  Pandas: high-performance data structures and data analysis tools, with
+  powerful data alignment and structural manipulation capabilities;
+  \href{http://pandas.pydata.org}{pandas.pydata.org}.
+\item
+  Statsmodels: statistical data exploration and model estimation;
+  \href{http://statsmodels.sourceforge.net}{statsmodels.sourceforge.net}.
+\item
+  Scikit-learn: general purpose machine learning algorithms with a
+  common interface; \href{http://scikit-learn.org}{scikit-learn.org}.
+\item
+  Scikits-image: image processing toolbox;
+  \href{http://scikits-image.org}{scikits-image.org}.
+\item
+  NetworkX: analysis of complex networks (in the graph theoretical
+  sense); \href{http://networkx.lanl.gov}{networkx.lanl.gov}.
+\item
+  PyTables: management of hierarchical datasets using the
+  industry-standard HDF5 format;
+  \href{http://www.pytables.org}{www.pytables.org}.
+\end{itemize}
+Beyond these, for any specific problem you should look on the internet
+first, before starting to write code from scratch. There's a good chance
+that someone, somewhere, has written an open source library that you can
+use for part or all of your problem.
+
+
+\section{A note about the examples below}
+In all subsequent examples, you will see blocks of input code, followed
+by the results of the code if the code generated output. This output may
+include text, graphics and other result objects. These blocks of input
+can be pasted into your interactive IPython session or notebook for you
+to execute. In the print version of this document, a thin vertical bar
+on the left of the blocks of input and output shows which blocks go
+together.
+
+If you are reading this text as an actual IPython notebook, you can
+press \texttt{Shift-Enter} or use the `play' button on the toolbar
+(right-pointing triangle) to execute each block of code, known as a
+`cell' in IPython:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+# This is a block of code, below you'll see its output
+print "Welcome to the world of scientific computing with Python!"
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+Welcome to the world of scientific computing with Python!
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+
+\section{Motivation: the trapezoidal rule}
+In subsequent sections we'll provide a basic introduction to the nuts
+and bolts of the basic scientific python tools; but we'll first motivate
+it with a brief example that illustrates what you can do in a few lines
+with these tools. For this, we will use the simple problem of
+approximating a definite integral with the trapezoid rule:
+
+\[
+\int_{a}^{b} f(x)\, dx \approx \frac{1}{2} \sum_{k=1}^{N} \left( x_{k} - x_{k-1} \right) \left( f(x_{k}) + f(x_{k-1}) \right).
+\]
+
+Our task will be to compute this formula for a function such as:
+
+\[
+f(x) = (x-3)(x-5)(x-7)+85
+\]
+
+integrated between $a=1$ and $b=9$.
+
+First, we define the function and sample it evenly between 0 and 10 at
+200 points:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+def f(x):
+    return (x-3)*(x-5)*(x-7)+85
+
+import numpy as np
+x = np.linspace(0, 10, 200)
+y = f(x)
+\end{lstlisting}
+\end{codeinput}
+
+\end{codecell}
+We select $a$ and $b$, our integration limits, and we take only a few
+points in that region to illustrate the error behavior of the trapezoid
+approximation:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+a, b = 1, 9
+xint = x[logical_and(x>=a, x<=b)][::30]
+yint = y[logical_and(x>=a, x<=b)][::30]
+\end{lstlisting}
+\end{codeinput}
+
+\end{codecell}
+Let's plot both the function and the area below it in the trapezoid
+approximation:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+import matplotlib.pyplot as plt
+plt.plot(x, y, lw=2)
+plt.axis([0, 10, 0, 140])
+plt.fill_between(xint, 0, yint, facecolor='gray', alpha=0.4)
+plt.text(0.5 * (a + b), 30,r"$\int_a^b f(x)dx$", horizontalalignment='center', fontsize=20);
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+\begin{center}
+\includegraphics[width=0.7\textwidth]{_fig_13.pdf}
+\par
+\end{center}
+\end{codeoutput}
+\end{codecell}
+Compute the integral both at high accuracy and with the trapezoid
+approximation
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+from scipy.integrate import quad, trapz
+integral, error = quad(f, 1, 9)
+trap_integral = trapz(yint, xint)
+print "The integral is: %g +/- %.1e" % (integral, error)
+print "The trapezoid approximation with", len(xint), "points is:", trap_integral
+print "The absolute error is:", abs(integral - trap_integral)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+The integral is: 680 +/- 7.5e-12
+The trapezoid approximation with 6 points is: 621.286411141
+The absolute error is: 58.7135888589
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+This simple example showed us how, combining the numpy, scipy and
+matplotlib libraries we can provide an illustration of a standard method
+in elementary calculus with just a few lines of code. We will now
+discuss with more detail the basic usage of these tools.
+
+
+\section{NumPy arrays: the right data structure for scientific computing}
+
+\section{Basics of Numpy arrays}
+We now turn our attention to the Numpy library, which forms the base
+layer for the entire `scipy ecosystem'. Once you have installed numpy,
+you can import it as
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+import numpy
+\end{lstlisting}
+\end{codeinput}
+
+\end{codecell}
+though in this book we will use the common shorthand
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+import numpy as np
+\end{lstlisting}
+\end{codeinput}
+
+\end{codecell}
+As mentioned above, the main object provided by numpy is a powerful
+array. We'll start by exploring how the numpy array differs from Python
+lists. We start by creating a simple list and an array with the same
+contents of the list:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+lst = [10, 20, 30, 40]
+arr = np.array([10, 20, 30, 40])
+\end{lstlisting}
+\end{codeinput}
+
+\end{codecell}
+Elements of a one-dimensional array are accessed with the same syntax as
+a list:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+lst[0]
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+10
+
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr[0]
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+10
+
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr[-1]
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+40
+
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr[2:]
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([30, 40])
+
+\end{codeoutput}
+\end{codecell}
+The first difference to note between lists and arrays is that arrays are
+\emph{homogeneous}; i.e.~all elements of an array must be of the same
+type. In contrast, lists can contain elements of arbitrary type. For
+example, we can change the last element in our list above to be a
+string:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+lst[-1] = 'a string inside a list'
+lst
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+[10, 20, 30, 'a string inside a list']
+
+\end{codeoutput}
+\end{codecell}
+but the same can not be done with an array, as we get an error message:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr[-1] = 'a string inside an array'
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+    ---------------------------------------------------------------------------
+    ValueError                                Traceback (most recent call last)
+    /home/fperez/teach/book-math-labtool/<ipython-input-13-29c0bfa5fa8a> in <module>()
+    ----> 1 arr[-1] = 'a string inside an array'
+    
+    ValueError: invalid literal for long() with base 10: 'a string inside an array'
+
+\end{codeoutput}
+\end{codecell}
+The information about the type of an array is contained in its
+\emph{dtype} attribute:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr.dtype
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+dtype('int32')
+
+\end{codeoutput}
+\end{codecell}
+Once an array has been created, its dtype is fixed and it can only store
+elements of the same type. For this example where the dtype is integer,
+if we store a floating point number it will be automatically converted
+into an integer:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr[-1] = 1.234
+arr
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([10, 20, 30,  1])
+
+\end{codeoutput}
+\end{codecell}
+Above we created an array from an existing list; now let us now see
+other ways in which we can create arrays, which we'll illustrate next. A
+common need is to have an array initialized with a constant value, and
+very often this value is 0 or 1 (suitable as starting value for additive
+and multiplicative loops respectively); \texttt{zeros} creates arrays of
+all zeros, with any desired dtype:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+np.zeros(5, float)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([ 0.,  0.,  0.,  0.,  0.])
+
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+np.zeros(3, int)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([0, 0, 0])
+
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+np.zeros(3, complex)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([ 0.+0.j,  0.+0.j,  0.+0.j])
+
+\end{codeoutput}
+\end{codecell}
+and similarly for \texttt{ones}:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print '5 ones:', np.ones(5)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+5 ones: [ 1.  1.  1.  1.  1.]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+If we want an array initialized with an arbitrary value, we can create
+an empty array and then use the fill method to put the value we want
+into the array:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+a = empty(4)
+a.fill(5.5)
+a
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([ 5.5,  5.5,  5.5,  5.5])
+
+\end{codeoutput}
+\end{codecell}
+Numpy also offers the \texttt{arange} function, which works like the
+builtin \texttt{range} but returns an array instead of a list:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+np.arange(5)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([0, 1, 2, 3, 4])
+
+\end{codeoutput}
+\end{codecell}
+and the \texttt{linspace} and \texttt{logspace} functions to create
+linearly and logarithmically-spaced grids respectively, with a fixed
+number of points and including both ends of the specified interval:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print "A linear grid between 0 and 1:", np.linspace(0, 1, 5)
+print "A logarithmic grid between 10**1 and 10**4: ", np.logspace(1, 4, 4)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+A linear grid between 0 and 1: [ 0.    0.25  0.5   0.75  1.  ]
+A logarithmic grid between 10**1 and 10**4:  [    10.    100.   1000.  10000.]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+Finally, it is often useful to create arrays with random numbers that
+follow a specific distribution. The \texttt{np.random} module contains a
+number of functions that can be used to this effect, for example this
+will produce an array of 5 random samples taken from a standard normal
+distribution (0 mean and variance 1):
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+np.random.randn(5)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([-0.08633343, -0.67375434,  1.00589536,  0.87081651,  1.65597822])
+
+\end{codeoutput}
+\end{codecell}
+whereas this will also give 5 samples, but from a normal distribution
+with a mean of 10 and a variance of 3:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+norm10 = np.random.normal(10, 3, 5)
+norm10
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([  8.94879575,   5.53038269,   8.24847281,  12.14944165,  11.56209294])
+
+\end{codeoutput}
+\end{codecell}
+
+\section{Indexing with other arrays}
+Above we saw how to index arrays with single numbers and slices, just
+like Python lists. But arrays allow for a more sophisticated kind of
+indexing which is very powerful: you can index an array with another
+array, and in particular with an array of boolean values. This is
+particluarly useful to extract information from an array that matches a
+certain condition.
+
+Consider for example that in the array \texttt{norm10} we want to
+replace all values above 9 with the value 0. We can do so by first
+finding the \emph{mask} that indicates where this condition is true or
+false:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+mask = norm10 > 9
+mask
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([False, False, False,  True,  True], dtype=bool)
+
+\end{codeoutput}
+\end{codecell}
+Now that we have this mask, we can use it to either read those values or
+to reset them to 0:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print 'Values above 9:', norm10[mask]
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+Values above 9: [ 12.14944165  11.56209294]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print 'Resetting all values above 9 to 0...'
+norm10[mask] = 0
+print norm10
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+Resetting all values above 9 to 0...
+[ 8.94879575  5.53038269  8.24847281  0.          0.        ]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+
+\section{Arrays with more than one dimension}
+Up until now all our examples have used one-dimensional arrays. But
+Numpy can create arrays of aribtrary dimensions, and all the methods
+illustrated in the previous section work with more than one dimension.
+For example, a list of lists can be used to initialize a two dimensional
+array:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+lst2 = [[1, 2], [3, 4]]
+arr2 = np.array([[1, 2], [3, 4]])
+arr2
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([[1, 2],
+       [3, 4]])
+
+\end{codeoutput}
+\end{codecell}
+With two-dimensional arrays we start seeing the power of numpy: while a
+nested list can be indexed using repeatedly the \texttt{{[} {]}}
+operator, multidimensional arrays support a much more natural indexing
+syntax with a single \texttt{{[} {]}} and a set of indices separated by
+commas:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print lst2[0][1]
+print arr2[0,1]
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+2
+2
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+Most of the array creation functions listed above can be used with more
+than one dimension, for example:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+np.zeros((2,3))
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([[ 0.,  0.,  0.],
+       [ 0.,  0.,  0.]])
+
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+np.random.normal(10, 3, (2, 4))
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([[ 11.26788826,   4.29619866,  11.09346496,   9.73861307],
+       [ 10.54025996,   9.5146268 ,  10.80367214,  13.62204505]])
+
+\end{codeoutput}
+\end{codecell}
+In fact, the shape of an array can be changed at any time, as long as
+the total number of elements is unchanged. For example, if we want a 2x4
+array with numbers increasing from 0, the easiest way to create it is:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr = np.arange(8).reshape(2,4)
+print arr
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+[[0 1 2 3]
+ [4 5 6 7]]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+With multidimensional arrays, you can also use slices, and you can mix
+and match slices and single indices in the different dimensions (using
+the same array as above):
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print 'Slicing in the second row:', arr[1, 2:4]
+print 'All rows, third column   :', arr[:, 2]
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+Slicing in the second row: [6 7]
+All rows, third column   : [2 6]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+If you only provide one index, then you will get an array with one less
+dimension containing that row:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print 'First row:  ', arr[0]
+print 'Second row: ', arr[1]
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+First row:   [0 1 2 3]
+Second row:  [4 5 6 7]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+Now that we have seen how to create arrays with more than one dimension,
+it's a good idea to look at some of the most useful properties and
+methods that arrays have. The following provide basic information about
+the size, shape and data in the array:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print 'Data type                :', arr.dtype
+print 'Total number of elements :', arr.size
+print 'Number of dimensions     :', arr.ndim
+print 'Shape (dimensionality)   :', arr.shape
+print 'Memory used (in bytes)   :', arr.nbytes
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+Data type                : int32
+Total number of elements : 8
+Number of dimensions     : 2
+Shape (dimensionality)   : (2, 4)
+Memory used (in bytes)   : 32
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+Arrays also have many useful methods, some especially useful ones are:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print 'Minimum and maximum             :', arr.min(), arr.max()
+print 'Sum and product of all elements :', arr.sum(), arr.prod()
+print 'Mean and standard deviation     :', arr.mean(), arr.std()
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+Minimum and maximum             : 0 7
+Sum and product of all elements : 28 0
+Mean and standard deviation     : 3.5 2.29128784748
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+For these methods, the above operations area all computed on all the
+elements of the array. But for a multidimensional array, it's possible
+to do the computation along a single dimension, by passing the
+\texttt{axis} parameter; for example:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print 'For the following array:\n', arr
+print 'The sum of elements along the rows is    :', arr.sum(axis=1)
+print 'The sum of elements along the columns is :', arr.sum(axis=0)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+For the following array:
+[[0 1 2 3]
+ [4 5 6 7]]
+The sum of elements along the rows is    : [ 6 22]
+The sum of elements along the columns is : [ 4  6  8 10]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+As you can see in this example, the value of the \texttt{axis} parameter
+is the dimension which will be \emph{consumed} once the operation has
+been carried out. This is why to sum along the rows we use
+\texttt{axis=0}.
+
+This can be easily illustrated with an example that has more dimensions;
+we create an array with 4 dimensions and shape \texttt{(3,4,5,6)} and
+sum along the axis number 2 (i.e.~the \emph{third} axis, since in Python
+all counts are 0-based). That consumes the dimension whose length was 5,
+leaving us with a new array that has shape \texttt{(3,4,6)}:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+np.zeros((3,4,5,6)).sum(2).shape
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+(3, 4, 6)
+
+\end{codeoutput}
+\end{codecell}
+Another widely used property of arrays is the \texttt{.T} attribute,
+which allows you to access the transpose of the array:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print 'Array:\n', arr
+print 'Transpose:\n', arr.T
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+Array:
+[[0 1 2 3]
+ [4 5 6 7]]
+Transpose:
+[[0 4]
+ [1 5]
+ [2 6]
+ [3 7]]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+We don't have time here to look at all the methods and properties of
+arrays, here's a complete list. Simply try exploring some of these
+IPython to learn more, or read their description in the full Numpy
+documentation:
+
+\begin{verbatim}
+arr.T             arr.copy          arr.getfield      arr.put           arr.squeeze
+arr.all           arr.ctypes        arr.imag          arr.ravel         arr.std
+arr.any           arr.cumprod       arr.item          arr.real          arr.strides
+arr.argmax        arr.cumsum        arr.itemset       arr.repeat        arr.sum
+arr.argmin        arr.data          arr.itemsize      arr.reshape       arr.swapaxes
+arr.argsort       arr.diagonal      arr.max           arr.resize        arr.take
+arr.astype        arr.dot           arr.mean          arr.round         arr.tofile
+arr.base          arr.dtype         arr.min           arr.searchsorted  arr.tolist
+arr.byteswap      arr.dump          arr.nbytes        arr.setasflat     arr.tostring
+arr.choose        arr.dumps         arr.ndim          arr.setfield      arr.trace
+arr.clip          arr.fill          arr.newbyteorder  arr.setflags      arr.transpose
+arr.compress      arr.flags         arr.nonzero       arr.shape         arr.var
+arr.conj          arr.flat          arr.prod          arr.size          arr.view
+arr.conjugate     arr.flatten       arr.ptp           arr.sort          
+\end{verbatim}
+
+
+
+\section{Operating with arrays}
+Arrays support all regular arithmetic operators, and the numpy library
+also contains a complete collection of basic mathematical functions that
+operate on arrays. It is important to remember that in general, all
+operations with arrays are applied \emph{element-wise}, i.e., are
+applied to all the elements of the array at the same time. Consider for
+example:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr1 = np.arange(4)
+arr2 = np.arange(10, 14)
+print arr1, '+', arr2, '=', arr1+arr2
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+[0 1 2 3] + [10 11 12 13] = [10 12 14 16]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+Importantly, you must remember that even the multiplication operator is
+by default applied element-wise, it is \emph{not} the matrix
+multiplication from linear algebra (as is the case in Matlab, for
+example):
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print arr1, '*', arr2, '=', arr1*arr2
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+[0 1 2 3] * [10 11 12 13] = [ 0 11 24 39]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+While this means that in principle arrays must always match in their
+dimensionality in order for an operation to be valid, numpy will
+\emph{broadcast} dimensions when possible. For example, suppose that you
+want to add the number 1.5 to \texttt{arr1}; the following would be a
+valid way to do it:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr1 + 1.5*np.ones(4)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([ 1.5,  2.5,  3.5,  4.5])
+
+\end{codeoutput}
+\end{codecell}
+But thanks to numpy's broadcasting rules, the following is equally
+valid:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr1 + 1.5
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([ 1.5,  2.5,  3.5,  4.5])
+
+\end{codeoutput}
+\end{codecell}
+In this case, numpy looked at both operands and saw that the first
+(\texttt{arr1}) was a one-dimensional array of length 4 and the second
+was a scalar, considered a zero-dimensional object. The broadcasting
+rules allow numpy to:
+
+\begin{itemize}
+\item
+  \emph{create} new dimensions of length 1 (since this doesn't change
+  the size of the array)
+\item
+  `stretch' a dimension of length 1 that needs to be matched to a
+  dimension of a different size.
+\end{itemize}
+So in the above example, the scalar 1.5 is effectively:
+
+\begin{itemize}
+\item
+  first `promoted' to a 1-dimensional array of length 1
+\item
+  then, this array is `stretched' to length 4 to match the dimension of
+  \texttt{arr1}.
+\end{itemize}
+After these two operations are complete, the addition can proceed as now
+both operands are one-dimensional arrays of length 4.
+
+This broadcasting behavior is in practice enormously powerful,
+especially because when numpy broadcasts to create new dimensions or to
+`stretch' existing ones, it doesn't actually replicate the data. In the
+example above the operation is carried \emph{as if} the 1.5 was a 1-d
+array with 1.5 in all of its entries, but no actual array was ever
+created. This can save lots of memory in cases when the arrays in
+question are large and can have significant performance implications.
+
+The general rule is: when operating on two arrays, NumPy compares their
+shapes element-wise. It starts with the trailing dimensions, and works
+its way forward, creating dimensions of length 1 as needed. Two
+dimensions are considered compatible when
+
+\begin{itemize}
+\item
+  they are equal to begin with, or
+\item
+  one of them is 1; in this case numpy will do the `stretching' to make
+  them equal.
+\end{itemize}
+If these conditions are not met, a
+\texttt{ValueError: frames are not aligned} exception is thrown,
+indicating that the arrays have incompatible shapes. The size of the
+resulting array is the maximum size along each dimension of the input
+arrays.
+
+This shows how the broadcasting rules work in several dimensions:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+b = np.array([2, 3, 4, 5])
+print arr, '\n\n+', b , '\n----------------\n', arr + b
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+[[0 1 2 3]
+ [4 5 6 7]] 
+
++ [2 3 4 5] 
+----------------
+[[ 2  4  6  8]
+ [ 6  8 10 12]]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+Now, how could you use broadcasting to say add \texttt{{[}4, 6{]}} along
+the rows to \texttt{arr} above? Simply performing the direct addition
+will produce the error we previously mentioned:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+c = np.array([4, 6])
+arr + c
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+    ---------------------------------------------------------------------------
+    ValueError                                Traceback (most recent call last)
+    /home/fperez/teach/book-math-labtool/<ipython-input-45-62aa20ac1980> in <module>()
+          1 c = np.array([4, 6])
+    ----> 2 arr + c
+    
+    ValueError: operands could not be broadcast together with shapes (2,4) (2) 
+
+\end{codeoutput}
+\end{codecell}
+According to the rules above, the array \texttt{c} would need to have a
+\emph{trailing} dimension of 1 for the broadcasting to work. It turns
+out that numpy allows you to `inject' new dimensions anywhere into an
+array on the fly, by indexing it with the special object
+\texttt{np.newaxis}:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+(c[:, np.newaxis]).shape
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+(2, 1)
+
+\end{codeoutput}
+\end{codecell}
+This is exactly what we need, and indeed it works:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr + c[:, np.newaxis]
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+array([[ 4,  5,  6,  7],
+       [10, 11, 12, 13]])
+
+\end{codeoutput}
+\end{codecell}
+For the full broadcasting rules, please see the official Numpy docs,
+which describe them in detail and with more complex examples.
+
+As we mentioned before, Numpy ships with a full complement of
+mathematical functions that work on entire arrays, including logarithms,
+exponentials, trigonometric and hyperbolic trigonometric functions, etc.
+Furthermore, scipy ships a rich special function library in the
+\texttt{scipy.special} module that includes Bessel, Airy, Fresnel,
+Laguerre and other classical special functions. For example, sampling
+the sine function at 100 points between $0$ and $2\pi$ is as simple as:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+x = np.linspace(0, 2*np.pi, 100)
+y = np.sin(x)
+\end{lstlisting}
+\end{codeinput}
+
+\end{codecell}
+
+\section{Linear algebra in numpy}
+Numpy ships with a basic linear algebra library, and all arrays have a
+\texttt{dot} method whose behavior is that of the scalar dot product
+when its arguments are vectors (one-dimensional arrays) and the
+traditional matrix multiplication when one or both of its arguments are
+two-dimensional arrays:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+v1 = np.array([2, 3, 4])
+v2 = np.array([1, 0, 1])
+print v1, '.', v2, '=', v1.dot(v2)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+[2 3 4] . [1 0 1] = 6
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+Here is a regular matrix-vector multiplication, note that the array
+\texttt{v1} should be viewed as a \emph{column} vector in traditional
+linear algebra notation; numpy makes no distinction between row and
+column vectors and simply verifies that the dimensions match the
+required rules of matrix multiplication, in this case we have a
+$2 \times 3$ matrix multiplied by a 3-vector, which produces a 2-vector:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+A = np.arange(6).reshape(2, 3)
+print A, 'x', v1, '=', A.dot(v1)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+[[0 1 2]
+ [3 4 5]] x [2 3 4] = [11 38]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+For matrix-matrix multiplication, the same dimension-matching rules must
+be satisfied, e.g.~consider the difference between $A \times A^T$:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print A.dot(A.T)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+[[ 5 14]
+ [14 50]]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+and $A^T \times A$:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print A.T.dot(A)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+[[ 9 12 15]
+ [12 17 22]
+ [15 22 29]]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+Furthermore, the \texttt{numpy.linalg} module includes additional
+functionality such as determinants, matrix norms, Cholesky, eigenvalue
+and singular value decompositions, etc. For even more linear algebra
+tools, \texttt{scipy.linalg} contains the majority of the tools in the
+classic LAPACK libraries as well as functions to operate on sparse
+matrices. We refer the reader to the Numpy and Scipy documentations for
+additional details on these.
+
+
+\section{Reading and writing arrays to disk}
+Numpy lets you read and write arrays into files in a number of ways. In
+order to use these tools well, it is critical to understand the
+difference between a \emph{text} and a \emph{binary} file containing
+numerical data. In a text file, the number $\pi$ could be written as
+``3.141592653589793'', for example: a string of digits that a human can
+read, with in this case 15 decimal digits. In contrast, that same number
+written to a binary file would be encoded as 8 characters (bytes) that
+are not readable by a human but which contain the exact same data that
+the variable \texttt{pi} had in the computer's memory.
+
+The tradeoffs between the two modes are thus:
+
+\begin{itemize}
+\item
+  Text mode: occupies more space, precision can be lost (if not all
+  digits are written to disk), but is readable and editable by hand with
+  a text editor. Can \emph{only} be used for one- and two-dimensional
+  arrays.
+\item
+  Binary mode: compact and exact representation of the data in memory,
+  can't be read or edited by hand. Arrays of any size and dimensionality
+  can be saved and read without loss of information.
+\end{itemize}
+First, let's see how to read and write arrays in text mode. The
+\texttt{np.savetxt} function saves an array to a text file, with options
+to control the precision, separators and even adding a header:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr = np.arange(10).reshape(2, 5)
+np.savetxt('test.out', arr, fmt='%.2e', header="My dataset")
+!cat test.out
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+# My dataset
+0.00e+00 1.00e+00 2.00e+00 3.00e+00 4.00e+00
+5.00e+00 6.00e+00 7.00e+00 8.00e+00 9.00e+00
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+And this same type of file can then be read with the matching
+\texttt{np.loadtxt} function:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+arr2 = np.loadtxt('test.out')
+print arr2
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+[[ 0.  1.  2.  3.  4.]
+ [ 5.  6.  7.  8.  9.]]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+For binary data, Numpy provides the \texttt{np.save} and
+\texttt{np.savez} routines. The first saves a single array to a file
+with \texttt{.npy} extension, while the latter can be used to save a
+\emph{group} of arrays into a single file with \texttt{.npz} extension.
+The files created with these routines can then be read with the
+\texttt{np.load} function.
+
+Let us first see how to use the simpler \texttt{np.save} function to
+save a single array:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+np.save('test.npy', arr2)
+# Now we read this back
+arr2n = np.load('test.npy')
+# Let's see if any element is non-zero in the difference.
+# A value of True would be a problem.
+print 'Any differences?', np.any(arr2-arr2n)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+Any differences? False
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+Now let us see how the \texttt{np.savez} function works. You give it a
+filename and either a sequence of arrays or a set of keywords. In the
+first mode, the function will auotmatically name the saved arrays in the
+archive as \texttt{arr\_0}, \texttt{arr\_1}, etc:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+np.savez('test.npz', arr, arr2)
+arrays = np.load('test.npz')
+arrays.files
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+['arr_1', 'arr_0']
+
+\end{codeoutput}
+\end{codecell}
+Alternatively, we can explicitly choose how to name the arrays we save:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+np.savez('test.npz', array1=arr, array2=arr2)
+arrays = np.load('test.npz')
+arrays.files
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+['array2', 'array1']
+
+\end{codeoutput}
+\end{codecell}
+The object returned by \texttt{np.load} from an \texttt{.npz} file works
+like a dictionary, though you can also access its constituent files by
+attribute using its special \texttt{.f} field; this is best illustrated
+with an example with the \texttt{arrays} object from above:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+print 'First row of first array:', arrays['array1'][0]
+# This is an equivalent way to get the same field
+print 'First row of first array:', arrays.f.array1[0]
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+First row of first array: [0 1 2 3 4]
+First row of first array: [0 1 2 3 4]
+
+\end{verbatim}
+
+\end{codeoutput}
+\end{codecell}
+This \texttt{.npz} format is a very convenient way to package compactly
+and without loss of information, into a single file, a group of related
+arrays that pertain to a specific problem. At some point, however, the
+complexity of your dataset may be such that the optimal approach is to
+use one of the standard formats in scientific data processing that have
+been designed to handle complex datasets, such as NetCDF or HDF5.
+
+Fortunately, there are tools for manipulating these formats in Python,
+and for storing data in other ways such as databases. A complete
+discussion of the possibilities is beyond the scope of this discussion,
+but of particular interest for scientific users we at least mention the
+following:
+
+\begin{itemize}
+\item
+  The \texttt{scipy.io} module contains routines to read and write
+  Matlab files in \texttt{.mat} format and files in the NetCDF format
+  that is widely used in certain scientific disciplines.
+\item
+  For manipulating files in the HDF5 format, there are two excellent
+  options in Python: The PyTables project offers a high-level, object
+  oriented approach to manipulating HDF5 datasets, while the h5py
+  project offers a more direct mapping to the standard HDF5 library
+  interface. Both are excellent tools; if you need to work with HDF5
+  datasets you should read some of their documentation and examples and
+  decide which approach is a better match for your needs.
+\end{itemize}
+
+
+\section{High quality data visualization with Matplotlib}
+The \href{http://matplotlib.sf.net}{matplotlib} library is a powerful
+tool capable of producing complex publication-quality figures with fine
+layout control in two and three dimensions; here we will only provide a
+minimal self-contained introduction to its usage that covers the
+functionality needed for the rest of the book. We encourage the reader
+to read the tutorials included with the matplotlib documentation as well
+as to browse its extensive gallery of examples that include source code.
+
+Just as we typically use the shorthand \texttt{np} for Numpy, we will
+use \texttt{plt} for the \texttt{matplotlib.pyplot} module where the
+easy-to-use plotting functions reside (the library contains a rich
+object-oriented architecture that we don't have the space to discuss
+here):
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+import matplotlib.pyplot as plt
+\end{lstlisting}
+\end{codeinput}
+
+\end{codecell}
+The most frequently used function is simply called \texttt{plot}, here
+is how you can make a simple plot of $\sin(x)$ for $x \in [0, 2\pi]$
+with labels and a grid (we use the semicolon in the last line to
+suppress the display of some information that is unnecessary right now):
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+x = np.linspace(0, 2*np.pi)
+y = np.sin(x)
+plt.plot(x,y, label='sin(x)')
+plt.legend()
+plt.grid()
+plt.title('Harmonic')
+plt.xlabel('x')
+plt.ylabel('y');
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+\begin{center}
+\includegraphics[width=0.7\textwidth]{_fig_134.pdf}
+\par
+\end{center}
+\end{codeoutput}
+\end{codecell}
+You can control the style, color and other properties of the markers,
+for example:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+plt.plot(x, y, linewidth=2);
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+\begin{center}
+\includegraphics[width=0.7\textwidth]{_fig_136.pdf}
+\par
+\end{center}
+\end{codeoutput}
+\end{codecell}
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+plt.plot(x, y, 'o', markersize=5, color='r');
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+\begin{center}
+\includegraphics[width=0.7\textwidth]{_fig_137.pdf}
+\par
+\end{center}
+\end{codeoutput}
+\end{codecell}
+We will now see how to create a few other common plot types, such as a
+simple error plot:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+# example data
+x = np.arange(0.1, 4, 0.5)
+y = np.exp(-x)
+
+# example variable error bar values
+yerr = 0.1 + 0.2*np.sqrt(x)
+xerr = 0.1 + yerr
+
+# First illustrate basic pyplot interface, using defaults where possible.
+plt.figure()
+plt.errorbar(x, y, xerr=0.2, yerr=0.4)
+plt.title("Simplest errorbars, 0.2 in x, 0.4 in y");
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+\begin{center}
+\includegraphics[width=0.7\textwidth]{_fig_139.pdf}
+\par
+\end{center}
+\end{codeoutput}
+\end{codecell}
+A simple log plot
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+x = np.linspace(-5, 5)
+y = np.exp(-x**2)
+plt.semilogy(x, y);
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+\begin{center}
+\includegraphics[width=0.7\textwidth]{_fig_141.pdf}
+\par
+\end{center}
+\end{codeoutput}
+\end{codecell}
+A histogram annotated with text inside the plot, using the \texttt{text}
+function:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+mu, sigma = 100, 15
+x = mu + sigma * np.random.randn(10000)
+
+# the histogram of the data
+n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)
+
+plt.xlabel('Smarts')
+plt.ylabel('Probability')
+plt.title('Histogram of IQ')
+# This will put a text fragment at the position given:
+plt.text(55, .027, r'$\mu=100,\ \sigma=15$', fontsize=14)
+plt.axis([40, 160, 0, 0.03])
+plt.grid(True)
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+\begin{center}
+\includegraphics[width=0.7\textwidth]{_fig_143.pdf}
+\par
+\end{center}
+\end{codeoutput}
+\end{codecell}
+
+\section{Image display}
+The \texttt{imshow} command can display single or multi-channel images.
+A simple array of random numbers, plotted in grayscale:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+from matplotlib import cm
+plt.imshow(np.random.rand(5, 10), cmap=cm.gray, interpolation='nearest');
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+\begin{center}
+\includegraphics[width=0.7\textwidth]{_fig_146.pdf}
+\par
+\end{center}
+\end{codeoutput}
+\end{codecell}
+A real photograph is a multichannel image, \texttt{imshow} interprets it
+correctly:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+img = plt.imread('stinkbug.png')
+print 'Dimensions of the array img:', img.shape
+plt.imshow(img);
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+
+\begin{verbatim}
+Dimensions of the array img: (375, 500, 3)
+
+\end{verbatim}
+\begin{center}
+\includegraphics[width=0.7\textwidth]{_fig_148.pdf}
+\par
+\end{center}
+\end{codeoutput}
+\end{codecell}
+
+\section{Simple 3d plotting with matplotlib}
+Note that you must execute at least once in your session:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+from mpl_toolkits.mplot3d import Axes3D
+\end{lstlisting}
+\end{codeinput}
+
+\end{codecell}
+One this has been done, you can create 3d axes with the
+\texttt{projection='3d'} keyword to \texttt{add\_subplot}:
+
+\begin{verbatim}
+fig = plt.figure()
+fig.add_subplot(<other arguments here>, projection='3d')
+\end{verbatim}
+
+
+A simple surface plot:
+
+\begin{codecell}
+\begin{codeinput}
+\begin{lstlisting}
+from mpl_toolkits.mplot3d.axes3d import Axes3D
+from matplotlib import cm
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1, projection='3d')
+X = np.arange(-5, 5, 0.25)
+Y = np.arange(-5, 5, 0.25)
+X, Y = np.meshgrid(X, Y)
+R = np.sqrt(X**2 + Y**2)
+Z = np.sin(R)
+surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet,
+        linewidth=0, antialiased=False)
+ax.set_zlim3d(-1.01, 1.01);
+\end{lstlisting}
+\end{codeinput}
+\begin{codeoutput}
+\begin{center}
+\includegraphics[width=0.7\textwidth]{_fig_154.pdf}
+\par
+\end{center}
+\end{codeoutput}
+\end{codecell}
+
+\section{IPython: a powerful interactive environment}
+A key component of the everyday workflow of most scientific computing
+environments is a good interactive environment, that is, a system in
+which you can execute small amounts of code and view the results
+immediately, combining both printing out data and opening graphical
+visualizations. All modern systems for scientific computing, commercial
+and open source, include such functionality.
+
+Out of the box, Python also offers a simple interactive shell with very
+limited capabilities. But just like the scientific community built Numpy
+to provide arrays suited for scientific work (since Pytyhon's lists
+aren't optimal for this task), it has also developed an interactive
+environment much more sophisticated than the built-in one. The
+\href{http://ipython.org}{IPython project} offers a set of tools to make
+productive use of the Python language, all the while working
+interactively and with immedate feedback on your results. The basic
+tools that IPython provides are:
+
+\begin{enumerate}[1.]
+\item
+  A powerful terminal shell, with many features designed to increase the
+  fluidity and productivity of everyday scientific workflows, including:
+
+  \begin{itemize}
+  \item
+    rich introspection of all objects and variables including easy
+    access to the source code of any function
+  \item
+    powerful and extensible tab completion of variables and filenames,
+  \item
+    tight integration with matplotlib, supporting interactive figures
+    that don't block the terminal,
+  \item
+    direct access to the filesystem and underlying operating system,
+  \item
+    an extensible system for shell-like commands called `magics' that
+    reduce the work needed to perform many common tasks,
+  \item
+    tools for easily running, timing, profiling and debugging your
+    codes,
+  \item
+    syntax highlighted error messages with much more detail than the
+    default Python ones,
+  \item
+    logging and access to all previous history of inputs, including
+    across sessions
+  \end{itemize}
+\item
+  A Qt console that provides the look and feel of a terminal, but adds
+  support for inline figures, graphical calltips, a persistent session
+  that can survive crashes (even segfaults) of the kernel process, and
+  more.
+\item
+  A web-based notebook that can execute code and also contain rich text
+  and figures, mathematical equations and arbitrary HTML. This notebook
+  presents a document-like view with cells where code is executed but
+  that can be edited in-place, reordered, mixed with explanatory text
+  and figures, etc.
+\item
+  A high-performance, low-latency system for parallel computing that
+  supports the control of a cluster of IPython engines communicating
+  over a network, with optimizations that minimize unnecessary copying
+  of large objects (especially numpy arrays).
+\end{enumerate}
+We will now discuss the highlights of the tools 1-3 above so that you
+can make them an effective part of your workflow. The topic of parallel
+computing is beyond the scope of this document, but we encourage you to
+read the extensive
+\href{http://ipython.org/ipython-doc/rel-0.12.1/parallel/index.html}{documentation}
+and \href{http://minrk.github.com/scipy-tutorial-2011/}{tutorials} on
+this available on the IPython website.
+
+
+\section{The IPython terminal}
+You can start IPython at the terminal simply by typing:
+
+\begin{verbatim}
+$ ipython
+\end{verbatim}
+which will provide you some basic information about how to get started
+and will then open a prompt labeled \texttt{In {[}1{]}:} for you to
+start typing. Here we type $2^{64}$ and Python computes the result for
+us in exact arithmetic, returning it as \texttt{Out{[}1{]}}:
+
+\begin{verbatim}
+$ ipython
+Python 2.7.2+ (default, Oct  4 2011, 20:03:08) 
+Type "copyright", "credits" or "license" for more information.
+
+IPython 0.13.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]: 2**64
+Out[1]: 18446744073709551616L
+\end{verbatim}
+The first thing you should know about IPython is that all your inputs
+and outputs are saved. There are two variables named \texttt{In} and
+\texttt{Out} which are filled as you work with your results.
+Furthermore, all outputs are also saved to auto-created variables of the
+form \texttt{\_NN} where \texttt{NN} is the prompt number, and inputs to
+\texttt{\_iNN}. This allows you to recover quickly the result of a prior
+computation by referring to its number even if you forgot to store it as
+a variable. For example, later on in the above session you can do:
+
+\begin{verbatim}
+In [6]: print _1
+18446744073709551616
+\end{verbatim}
+
+
+We strongly recommend that you take a few minutes to read at least the
+basic introduction provided by the \texttt{?} command, and keep in mind
+that the \texttt{\%quickref} command at all times can be used as a quick
+reference ``cheat sheet'' of the most frequently used features of
+IPython.
+
+At the IPython prompt, any valid Python code that you type will be
+executed similarly to the default Python shell (though often with more
+informative feedback). But since IPython is a \emph{superset} of the
+default Python shell; let's have a brief look at some of its additional
+functionality.
+
+\textbf{Object introspection}
+
+A simple \texttt{?} command provides a general introduction to IPython,
+but as indicated in the banner above, you can use the \texttt{?} syntax
+to ask for details about any object. For example, if we type
+\texttt{\_1?}, IPython will print the following details about this
+variable:
+
+\begin{verbatim}
+In [14]: _1?
+Type:       long
+Base Class: <type 'long'>
+String Form:18446744073709551616
+Namespace:  Interactive
+Docstring:
+long(x[, base]) -> integer
+
+Convert a string or number to a long integer, if possible.  A floating
+
+[etc... snipped for brevity]
+\end{verbatim}
+If you add a second \texttt{?} and for any oobject \texttt{x} type
+\texttt{x??}, IPython will try to provide an even more detailed analsysi
+of the object, including its syntax-highlighted source code when it can
+be found. It's possible that \texttt{x??} returns the same information
+as \texttt{x?}, but in many cases \texttt{x??} will indeed provide
+additional details.
+
+Finally, the \texttt{?} syntax is also useful to search
+\emph{namespaces} with wildcards. Suppose you are wondering if there is
+any function in Numpy that may do text-related things; with
+\texttt{np.*txt*?}, IPython will print all the names in the \texttt{np}
+namespace (our Numpy shorthand) that have `txt' anywhere in their name:
+
+\begin{verbatim}
+In [17]: np.*txt*?
+np.genfromtxt
+np.loadtxt
+np.mafromtxt
+np.ndfromtxt
+np.recfromtxt
+np.savetxt
+\end{verbatim}
+
+
+\textbf{Tab completion}
+
+IPython makes the tab key work extra hard for you as a way to rapidly
+inspect objects and libraries. Whenever you have typed something at the
+prompt, by hitting the \texttt{\textless{}tab\textgreater{}} key IPython
+will try to complete the rest of the line. For this, IPython will
+analyze the text you had so far and try to search for Python data or
+files that may match the context you have already provided.
+
+For example, if you type \texttt{np.load} and hit the key, you'll see:
+
+\begin{verbatim}
+In [21]: np.load<TAB HERE>
+np.load     np.loads    np.loadtxt  
+\end{verbatim}
+so you can quickly find all the load-related functionality in numpy. Tab
+completion works even for function arguments, for example consider this
+function definition:
+
+\begin{verbatim}
+In [20]: def f(x, frobinate=False):
+   ....:     if frobinate:
+   ....:         return x**2
+   ....:     
+\end{verbatim}
+If you now use the \texttt{\textless{}tab\textgreater{}} key after
+having typed `fro' you'll get all valid Python completions, but those
+marked with \texttt{=} at the end are known to be keywords of your
+function:
+
+\begin{verbatim}
+In [21]: f(2, fro<TAB HERE>
+frobinate=    frombuffer    fromfunction  frompyfunc    fromstring    
+from          fromfile      fromiter      fromregex     frozenset     
+\end{verbatim}
+at this point you can add the \texttt{b} letter and hit
+\texttt{\textless{}tab\textgreater{}} once more, and IPython will finish
+the line for you:
+
+\begin{verbatim}
+In [21]: f(2, frobinate=
+\end{verbatim}
+As a beginner, simply get into the habit of using
+\texttt{\textless{}tab\textgreater{}} after most objects; it should
+quickly become second nature as you will see how helps keep a fluid
+workflow and discover useful information. Later on you can also
+customize this behavior by writing your own completion code, if you so
+desire.
+
+\textbf{Matplotlib integration}
+
+One of the most useful features of IPython for scientists is its tight
+integration with matplotlib: at the terminal IPython lets you open
+matplotlib figures without blocking your typing (which is what happens
+if you try to do the same thing at the default Python shell), and in the
+Qt console and notebook you can even view your figures embedded in your
+workspace next to the code that created them.
+
+The matplotlib support can be either activated when you start IPython by
+passing the \texttt{-{}-pylab} flag, or at any point later in your
+session by using the \texttt{\%pylab} command. If you start IPython with
+\texttt{-{}-pylab}, you'll see something like this (note the extra
+message about pylab):
+
+\begin{verbatim}
+$ ipython --pylab
+Python 2.7.2+ (default, Oct  4 2011, 20:03:08) 
+Type "copyright", "credits" or "license" for more information.
+
+IPython 0.13.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.
+
+Welcome to pylab, a matplotlib-based Python environment [backend: Qt4Agg].
+For more information, type 'help(pylab)'.
+
+In [1]: 
+\end{verbatim}
+Furthermore, IPython will import \texttt{numpy} with the \texttt{np}
+shorthand, \texttt{matplotlib.pyplot} as \texttt{plt}, and it will also
+load all of the numpy and pyplot top-level names so that you can
+directly type something like:
+
+\begin{verbatim}
+In [1]: x = linspace(0, 2*pi, 200)
+
+In [2]: plot(x, sin(x))
+Out[2]: [<matplotlib.lines.Line2D at 0x9e7c16c>]
+\end{verbatim}
+instead of having to prefix each call with its full signature (as we
+have been doing in the examples thus far):
+
+\begin{verbatim}
+In [3]: x = np.linspace(0, 2*np.pi, 200)
+
+In [4]: plt.plot(x, np.sin(x))
+Out[4]: [<matplotlib.lines.Line2D at 0x9e900ac>]
+\end{verbatim}
+This shorthand notation can be a huge time-saver when working
+interactively (it's a few characters but you are likely to type them
+hundreds of times in a session). But we should note that as you develop
+persistent scripts and notebooks meant for reuse, it's best to get in
+the habit of using the longer notation (known as \emph{fully qualified
+names} as it's clearer where things come from and it makes for more
+robust, readable and maintainable code in the long run).
+
+\textbf{Access to the operating system and files}
+
+In IPython, you can type \texttt{ls} to see your files or \texttt{cd} to
+change directories, just like you would at a regular system prompt:
+
+\begin{verbatim}
+In [2]: cd tests
+/home/fperez/ipython/nbconvert/tests
+
+In [3]: ls test.*
+test.aux  test.html  test.ipynb  test.log  test.out  test.pdf  test.rst  test.tex
+\end{verbatim}
+Furthermore, if you use the \texttt{!} at the beginning of a line, any
+commands you pass afterwards go directly to the operating system:
+
+\begin{verbatim}
+In [4]: !echo "Hello IPython"
+Hello IPython
+\end{verbatim}
+IPython offers a useful twist in this feature: it will substitute in the
+command the value of any \emph{Python} variable you may have if you
+prepend it with a \texttt{\$} sign:
+
+\begin{verbatim}
+In [5]: message = 'IPython interpolates from Python to the shell'
+
+In [6]: !echo $message
+IPython interpolates from Python to the shell
+\end{verbatim}
+This feature can be extremely useful, as it lets you combine the power
+and clarity of Python for complex logic with the immediacy and
+familiarity of many shell commands. Additionally, if you start the line
+with \emph{two} \texttt{\$\$} signs, the output of the command will be
+automatically captured as a list of lines, e.g.:
+
+\begin{verbatim}
+In [10]: !!ls test.*
+Out[10]: 
+['test.aux',
+ 'test.html',
+ 'test.ipynb',
+ 'test.log',
+ 'test.out',
+ 'test.pdf',
+ 'test.rst',
+ 'test.tex']
+\end{verbatim}
+As explained above, you can now use this as the variable \texttt{\_10}.
+If you directly want to capture the output of a system command to a
+Python variable, you can use the syntax \texttt{=!}:
+
+\begin{verbatim}
+In [11]: testfiles =! ls test.*
+
+In [12]: print testfiles
+['test.aux', 'test.html', 'test.ipynb', 'test.log', 'test.out', 'test.pdf', 'test.rst', 'test.tex']
+\end{verbatim}
+Finally, the special \texttt{\%alias} command lets you define names that
+are shorthands for system commands, so that you can type them without
+having to prefix them via \texttt{!} explicitly (for example,
+\texttt{ls} is an alias that has been predefined for you at startup).
+
+\textbf{Magic commands}
+
+IPython has a system for special commands, called `magics', that let you
+control IPython itself and perform many common tasks with a more
+shell-like syntax: it uses spaces for delimiting arguments, flags can be
+set with dashes and all arguments are treated as strings, so no
+additional quoting is required. This kind of syntax is invalid in the
+Python language but very convenient for interactive typing (less
+parentheses, commans and quoting everywhere); IPython distinguishes the
+two by detecting lines that start with the \texttt{\%} character.
+
+You can learn more about the magic system by simply typing
+\texttt{\%magic} at the prompt, which will give you a short description
+plus the documentation on \emph{all} available magics. If you want to
+see only a listing of existing magics, you can use \texttt{\%lsmagic}:
+
+\begin{verbatim}
+In [4]: lsmagic
+Available magic functions:
+%alias  %autocall  %autoindent  %automagic  %bookmark  %c  %cd  %colors  %config  %cpaste
+%debug  %dhist  %dirs  %doctest_mode  %ds  %ed  %edit  %env  %gui  %hist  %history
+%install_default_config  %install_ext  %install_profiles  %load_ext  %loadpy  %logoff  %logon  
+%logstart  %logstate  %logstop  %lsmagic  %macro  %magic  %notebook  %page  %paste  %pastebin  
+%pd  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pop  %popd  %pprint  %precision  %profile  
+%prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %quickref  %recall  %rehashx  
+%reload_ext  %rep  %rerun  %reset  %reset_selective  %run  %save  %sc  %stop  %store  %sx  %tb
+%time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode
+
+Automagic is ON, % prefix NOT needed for magic functions.
+\end{verbatim}
+Note how the example above omitted the eplicit \texttt{\%} marker and
+simply uses \texttt{lsmagic}. As long as the `automagic' feature is on
+(which it is by default), you can omit the \texttt{\%} marker as long as
+there is no ambiguity with a Python variable of the same name.
+
+\textbf{Running your code}
+
+While it's easy to type a few lines of code in IPython, for any
+long-lived work you should keep your codes in Python scripts (or in
+IPython notebooks, see below). Consider that you have a script, in this
+case trivially simple for the sake of brevity, named \texttt{simple.py}:
+
+\begin{verbatim}
+In [12]: !cat simple.py
+import numpy as np
+
+x = np.random.normal(size=100)
+
+print 'First elment of x:', x[0]
+\end{verbatim}
+The typical workflow with IPython is to use the \texttt{\%run} magic to
+execute your script (you can omit the .py extension if you want). When
+you run it, the script will execute just as if it had been run at the
+system prompt with \texttt{python simple.py} (though since modules don't
+get re-executed on new imports by Python, all system initialization is
+essentially free, which can have a significant run time impact in some
+cases):
+
+\begin{verbatim}
+In [13]: run simple
+First elment of x: -1.55872256289
+\end{verbatim}
+Once it completes, all variables defined in it become available for you
+to use interactively:
+
+\begin{verbatim}
+In [14]: x.shape
+Out[14]: (100,)
+\end{verbatim}
+This allows you to plot data, try out ideas, etc, in a
+\texttt{\%run}/interact/edit cycle that can be very productive. As you
+start understanding your problem better you can refine your script
+further, incrementally improving it based on the work you do at the
+IPython prompt. At any point you can use the \texttt{\%hist} magic to
+print out your history without prompts, so that you can copy useful
+fragments back into the script.
+
+By default, \texttt{\%run} executes scripts in a completely empty
+namespace, to better mimic how they would execute at the system prompt
+with plain Python. But if you use the \texttt{-i} flag, the script will
+also see your interactively defined variables. This lets you edit in a
+script larger amounts of code that still behave as if you had typed them
+at the IPython prompt.
+
+You can also get a summary of the time taken by your script with the
+\texttt{-t} flag; consider a different script \texttt{randsvd.py} that
+takes a bit longer to run:
+
+\begin{verbatim}
+In [21]: run -t randsvd.py
+
+IPython CPU timings (estimated):
+  User   :       0.38 s.
+  System :       0.04 s.
+Wall time:       0.34 s.
+\end{verbatim}
+\texttt{User} is the time spent by the computer executing your code,
+while \texttt{System} is the time the operating system had to work on
+your behalf, doing things like memory allocation that are needed by your
+code but that you didn't explicitly program and that happen inside the
+kernel. The \texttt{Wall time} is the time on a `clock on the wall'
+between the start and end of your program.
+
+If \texttt{Wall \textgreater{} User+System}, your code is most likely
+waiting idle for certain periods. That could be waiting for data to
+arrive from a remote source or perhaps because the operating system has
+to swap large amounts of virtual memory. If you know that your code
+doesn't explicitly wait for remote data to arrive, you should
+investigate further to identify possible ways of improving the
+performance profile.
+
+If you only want to time how long a single statement takes, you don't
+need to put it into a script as you can use the \texttt{\%timeit} magic,
+which uses Python's \texttt{timeit} module to very carefully measure
+timig data; \texttt{timeit} can measure even short statements that
+execute extremely fast:
+
+\begin{verbatim}
+In [27]: %timeit a=1
+10000000 loops, best of 3: 23 ns per loop
+\end{verbatim}
+and for code that runs longer, it automatically adjusts so the overall
+measurement doesn't take too long:
+
+\begin{verbatim}
+In [28]: %timeit np.linalg.svd(x)
+1 loops, best of 3: 310 ms per loop
+\end{verbatim}
+The \texttt{\%run} magic still has more options for debugging and
+profiling data; you should read its documentation for many useful
+details (as always, just type \texttt{\%run?}).
+
+
+\section{The graphical Qt console}
+If you type at the system prompt (see the IPython website for
+installation details, as this requires some additional libraries):
+
+\begin{verbatim}
+$ ipython qtconsole
+\end{verbatim}
+instead of opening in a terminal as before, IPython will start a
+graphical console that at first sight appears just like a terminal, but
+which is in fact much more capable than a text-only terminal. This is a
+specialized terminal designed for interactive scientific work, and it
+supports full multi-line editing with color highlighting and graphical
+calltips for functions, it can keep multiple IPython sessions open
+simultaneously in tabs, and when scripts run it can display the figures
+inline directly in the work area.
+
+
+# % This cell is for the pdflatex output only
+# \begin{figure}[htbp]
+# \centering
+# \includegraphics[width=3in]{ipython_qtconsole2.png}
+# \caption{The IPython Qt console: a lightweight terminal for scientific exploration, with code, results and graphics in a soingle environment.}
+# \end{figure}
+The Qt console accepts the same \texttt{-{}-pylab} startup flags as the
+terminal, but you can additionally supply the value
+\texttt{-{}-pylab inline}, which enables the support for inline graphics
+shown in the figure. This is ideal for keeping all the code and figures
+in the same session, given that the console can save the output of your
+entire session to HTML or PDF.
+
+Since the Qt console makes it far more convenient than the terminal to
+edit blocks of code with multiple lines, in this environment it's worth
+knowing about the \texttt{\%loadpy} magic function. \texttt{\%loadpy}
+takes a path to a local file or remote URL, fetches its contents, and
+puts it in the work area for you to further edit and execute. It can be
+an extremely fast and convenient way of loading code from local disk or
+remote examples from sites such as the
+\href{http://matplotlib.sourceforge.net/gallery.html}{Matplotlib
+gallery}.
+
+Other than its enhanced capabilities for code and graphics, all of the
+features of IPython we've explained before remain functional in this
+graphical console.
+
+
+\section{The IPython Notebook}
+The third way to interact with IPython, in addition to the terminal and
+graphical Qt console, is a powerful web interface called the ``IPython
+Notebook''. If you run at the system console (you can omit the
+\texttt{pylab} flags if you don't need plotting support):
+
+\begin{verbatim}
+$ ipython notebook --pylab inline
+\end{verbatim}
+IPython will start a process that runs a web server in your local
+machine and to which a web browser can connect. The Notebook is a
+workspace that lets you execute code in blocks called `cells' and
+displays any results and figures, but which can also contain arbitrary
+text (including LaTeX-formatted mathematical expressions) and any rich
+media that a modern web browser is capable of displaying.
+
+
+# % This cell is for the pdflatex output only
+# \begin{figure}[htbp]
+# \centering
+# \includegraphics[width=3in]{ipython-notebook-specgram-2.png}
+# \caption{The IPython Notebook: text, equations, code, results, graphics and other multimedia in an open format for scientific exploration and collaboration}
+# \end{figure}
+In fact, this document was written as a Notebook, and only exported to
+LaTeX for printing. Inside of each cell, all the features of IPython
+that we have discussed before remain functional, since ultimately this
+web client is communicating with the same IPython code that runs in the
+terminal. But this interface is a much more rich and powerful
+environment for maintaining long-term ``live and executable'' scientific
+documents.
+
+Notebook environments have existed in commercial systems like
+Mathematica(TM) and Maple(TM) for a long time; in the open source world
+the \href{http://sagemath.org}{Sage} project blazed this particular
+trail starting in 2006, and now we bring all the features that have made
+IPython such a widely used tool to a Notebook model.
+
+Since the Notebook runs as a web application, it is possible to
+configure it for remote access, letting you run your computations on a
+persistent server close to your data, which you can then access remotely
+from any browser-equipped computer. We encourage you to read the
+extensive documentation provided by the IPython project for details on
+how to do this and many more features of the notebook.
+
+Finally, as we said earlier, IPython also has a high-level and easy to
+use set of libraries for parallel computing, that let you control
+(interactively if desired) not just one IPython but an entire cluster of
+`IPython engines'. Unfortunately a detailed discussion of these tools is
+beyond the scope of this text, but should you need to parallelize your
+analysis codes, a quick read of the tutorials and examples provided at
+the IPython site may prove fruitful.
+
+
+\end{document}
diff --git a/tests/test_templates.py b/tests/test_templates.py
index a512e42..a89f6e8 100644
--- a/tests/test_templates.py
+++ b/tests/test_templates.py
@@ -4,9 +4,17 @@ import os
 from nose.tools import nottest
 from converters.template import ConverterTemplate
 from  IPython.config.loader import PyFileConfigLoader 
+from IPython.config.loader import load_pyconfig_files
 
 
+@nottest
+def cleanfile(stn):
+    return filter(None, map(unicode.strip, stn.split('\n')))
 
+@nottest
+def compfiles(stra, strb):
+    nt.assert_equal(cleanfile(stra),
+                    cleanfile(strb))
 
 def test_evens():
     reflist = [
@@ -20,10 +28,28 @@ def test_evens():
         yield check_null_profile,prof
     ### end null test
 
+    for ipynb in [
+            'IntroNumPy.orig.ipynb',
+            '00_notebook_tour.orig.ipynb'
+            ]:
+        for k,v in {'rst':'.rst','full_html':'.html','latex_base':'.tex'}.iteritems():
+            yield test_profile,k,'tests/ipynbref/'+ipynb,'tests/template_ref/'+ipynb[:-6].replace('.','_')+v
+
+@nottest
 def check_null_profile(profile):
     loader = PyFileConfigLoader(profile, path=[os.path.join(os.getcwdu(),'profile/test')])
     config = loader.load_config()
     C = ConverterTemplate(config=config)
     result,_ = C.from_filename('tests/ipynbref/IntroNumPy.orig.ipynb')
-    nt.assert_equal(result,'')
+    nt.assert_equal(result.strip('\n'),'')
+
+
+@nottest
+def test_profile(profile_name,infile, reference_file):
+    loader = PyFileConfigLoader(profile_name+'.nbcv',path=[os.path.join(os.getcwdu(),'profile/')])
+    config = loader.load_config()
+    C = ConverterTemplate(config=config)
+    output,resources = C.from_filename(infile)
+    with io.open(reference_file,'r') as f:
+        compfiles(output,f.read())