<h2 id="sec-1"><span class="section-number-3"></span>Intended audience </h2>

<h2 id="sec-2"><span class="section-number-3"></span>Prerequisites </h2>

These tutorials are all fairly advanced and require that you be familiar with Python.

For a good introduction it is recommended that you read the <a href="http://docs.python.org/tutorial/">Python Tutorial</a> completely.

Spoken tutorials teaching you Python are also available <a href="http://www.fossee.in/stvideos">here</a> please go through those.

<h2 id="sec-4"><span class="section-number-3"></span>Coverage </h2>

<h3>Jarrod Millman, Git/Github + NumPy/SciPy/MPL basics (2 hrs)</h3>

<ul>

	<li>Git/Github</li>

	<li>NumPy and SciPy basics along with the most important Matplotlib commands.

		This could be thought of as a quick refresher on the basic tools used in Python for scientific computing.

	</li>

</ul>

<h3>Emmanuelle Gouillart, Image processing using NumPy, SciPy and scikits-image (2 hrs)</h3>

<ul>

	<li>This tutorial will show a bag of basic recipes in order to efficiently

	</li>

	<li>Target audience: scientists and engineers working with images

	</li>

	<li>

	Prerequisites : being able to code Python scripts and use an

interactive Python shell + some knowledge of NumPy

	</li>

	<li>

	Software requirements: IPython, NumPy, SciPy, Matplolib, <a href="http://skimage.org">scikits-image</a>, and optionally sklearn

	</li>

	<li>

	Topics covered

		<li>I/O: how to open different image formats, how to open raw images, how to deal with very large raw files.</li>

		<li>Basic visualization of images, and interaction with image data</li>

		<li>Transforming images: changing the size, resolution, orientation of an image; image filtering; image segmentation.</li>

		<li>Extracting information from images: measuring properties of segmented objects; image classification</li>

	<li>

	This tutorial will by no means be a course on digital image processing.It is rather a bag of tricks on how to 

	tinker with images, and how to use the goodies of Python/NumPy/SciPy to make this task easier. A large part 

	of the talk will be devoted to hands-on exercises using the NumPy, SciPy 

	and Matplotlib modules. Some other modules will be mentioned during the 

	tutorial for more advanced uses.

	</li>

	<li>The course materials are available <a href="http://scipy-lectures.github.com/advanced/image_processing/index.html">here</a></li>

</ul>

<h3>Gael Varoquaux,   Machine learning with scikit-learn  (2 hrs)</h3>

<ul>

	<li>

	Introduction to machine learning and statistical data processing with the

	features in scikit-learn, and how to use it to solve real-world problems:

	from handwritten digits classification to stock market prediction.

	</li>

	<li>

	Target audience :   Engineers and scientists using Python for scientific

	and numerical computing. No knowledge needed in statistical learning.

	</li>

	<li>

	Prerequisites: Being able to code scripts and function in Python. Basic

	knowledge of numpy and matplotlib.

	</li>

	<li>

	Software requirements: IPython, scikits.learn, matplotlib.

	</li>

	<li>

	Outline

		<ul>

			<li>The settings: datasets, estimators, and the prediction problem.</li>

			<li>Regression and classification: Support Vector Machines, sparse regressions... Example: recognising hand-written digits</li>

			<li>Model selection: choosing the right estimator, and the right parameters</li>

			<li>Clustering: KMeans, Affinity Propagation. Example: finding structure in the stock market.</li>		

		</ul>

	</li>

</ul>

<h3>Ole Nielsen: Mapping and Geoprocessing with Python (2 hrs)</h3>

<ul>

	<li>

	Putting information on a map and analyzing spatial data are fundamental to a 

	wide range of areas such as navigation, working with climate or geological data, 

	disaster management, presentation of modelling results, demographics, social networking etc.

	</li>

	<li>

	This tutorial will give a practical introduction to tools and techniques 

	available for processing spatial information and, through a few hands-on 

	exercises, give the participants a sense of how to manipulate and visualise 

	spatial data using Python. Topics covered include reading and writing 

	of important data formats for both raster and vector data, looking at the layers, 

	awareness of issues with datums and projections, calculating centroids of polygons, 

	calculation of distance between points on the surface of Earth, interpolation from raster 

	grids to points etc. The tutorial has been developed for Ubuntu Linux and will provide source code, 

	tests and data for this platform. However, the content and messages should be general and apply to any platform.

	</li>

	<li>

	I assume that participants know how to write and run 

	Python scripts and would suggest you install qgis as well as 

	the python dependencies numpy, matplotlib and gdal on your 

	laptop. I don't assume any previous knowledge of mapping or Geographic Information Systems (GIS).		

	</li>

	<li>

	If you have some spatial data you want to manipulate in Python feel free to bring it along and grab me during a lunch break.

	</li>

</ul>

<h3>Eric Jones/Puneeth/Pankaj: Traits + Traits UI (2 hrs)</h3> 

<ul>

	<li>

	Enthought’s traits package provides for a powerful object model which 

	provides a host of useful functionality with a clean and expressive syntax.  

	It is an open source library and forms the basis of the Enthought Tool Suite and many of 

	Enthought’s internal commercial projects.  In this tutorial we will cover the basics of using 

	Traits along with the UI library TraitsUI which makes it very easy to build powerful and 

	interactive, user interfaces using Traits.

	</li>

</ul>

<h3>Prabhu Ramachandran and Gael Varoquaux, Mayavi for 3D visualization (2 hrs)</h3>

	<li>

	At the end of this tutorial attendees will learn how to visualize numpy

	arrays using Mayavi's mlab interface.  They will also learn enough about

	mayavi to be able to create their own simple datasets and visualize

	them.  If this tutorial follows one on traits, then attendees will learn

	how easy it is to embed 3D visualization in their own application UIs

	(provided they are written in wxPython or PyQt).

	</li>

	<li>

	In this tutorial, we first provide a rapid overview of Mayavi_ and its

	features.  We then move on to using Mayavi via IPython_ and mlab.  This

	is done in a hands-on fashion and introduces the audience to visualizing

	numpy arrays and the basic mayavi visualization pipeline.  We then

	introduce the audience to the basic objects and data sources used in

	Mayavi.  We end with an example of creating custom dialogs using Traits

	and embedding 3D visualization in these dialogs with Mayavi.

	</li>

	<li>

	Packages required

		<ul>

			<li><a href="http://code.enthought.com/projects/mayavi">Mayavi</a></li>

			<li><a href="http://ipython.scipy.org">IPython</a></li>

			<li><a href="http://code.enthought.com/projects/traits">Traits</a></li>

			<li>numpy, scipy</li>

		</ul>

	</li>

</ul>

<h3>Pankaj Pandey and Prabhu Ramachandran, An introduction to Cython (1 hrs)</h3>

	<li>

	At some level, Cython (http://www.cython.org) can be thought of a Python to C compiler.  

	It allows someone to write extension modules in a language very similar to Python and 

	therefore makes it rather easy to write C-extensions.  In this tutorial we will cover 

	the basics of building extension modules with Cython.

	</li>

	<li>

		Package requirements: You will require to have Cython, the 

		Python development headers and a working C-compiler to run the hands-on exercises.

	</li>

<h3>Puneeth Chaganti, Sage introduction/tutorial: (1 hr)</h3>

<ul>

	<li>This tutorial will feature a demonstration and a brief review of some of the capabilities of the <a href="http://www.sagemath.org">Sage notebook</a>.</li>

	<li>A rough schedule of the talk would be as follows:

		<ul>

			<li>Introduction</li>

			<li>Starting the server</li>

			<li>The UI</li>

			<li>Getting Help</li>

			<li>Overview of what's available in Sage

				<ul>

					<li>Basic Algebra</li>

					<li>Basic Calculus</li>

					<li>Basic Plotting</li>

				</ul>

	</li>

		</ul>

	</li>

</ul>

<h3>Mateusz Paprocki,  SymPy (2 hrs)</h3>

<ul>

	<li>

	SymPy is a pure Python library for symbolic mathematics. It aims to become a

	full-featured computer algebra system (CAS) while keeping the code as simple

	as possible in order to be comprehensible and easily extensible. SymPy is

	written entirely in Python and does not require any external libraries.

	</li>

	<li>

	In this tutorial we will introduce attendees to SymPy. We will start by

	showing how to install and run SymPy. Then we will proceed with the basics

	of constructing and manipulating mathematical expressions in SymPy. We will

	also discuss the most common issues and differences from other computer

	algebra systems, and how to deal with them. We will also show how to solve

	simple, yet illustrative mathematical problems using SymPy.

	</li>

	<li>

	Outline

		<ul>

			<li>Installing, configuring and running SymPy.</li>

			<li>Basics of mathematical expressions:

 				<ul>

 					<li>symbols, dummy symbols</li>

 					<li>constructing expressions</li>

 					<li>expression traversal</li>

 					<li>pattern matching</li>

				</ul>

			</li>

			<li>Common issues, pitfalls and differences with other CAS:

				<ul>

					<li>1/3 is not a rational number</li>

 					<li>why you shouldn't write 10**(-1000)</li>

 					<li>issues with caching of computation results</li>

				</ul>

			</li>

			<li>Using built-in and implementing customized printers.</li>

			<li>Arbitrary precision numerical computing.</li>

			<li>Interaction with numerical libraries (NumPy, SciPy).</li>

			<li>Examples.</li>

		</ul>

	</li>

</ul>