<!-- <h4 id="sec-1">Day 2 </h4> -->

	Jarrod Millman, Git/Github + NumPy/SciPy/MPL basics: 2 hrs

	<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>

	Emmanuelle Gouillart, Image processing: 2 hrs<br />

<u>Image manipulation and processing using NumPy, SciPy and scikits-image</u>

	<ul>

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

manipulate and process images in the form of NumPy arrays.

		</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

		<ul>

			<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>

		</ul>

		<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>

Gael Varoquaux,   scikit-learn:  2 hrs<br />

<u>Machine learning with scikit-learn</u>

	<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>

Ole Nielsen: Mapping and Geoprocessing with Python, 2 hrs

	<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>

Eric Jones/Puneeth/Pankaj: Traits + Traits UI. 2 hrs. 

	<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>

<li>

Prabhu Ramachandran and  Gael Varoquaux, Mayavi for 3D visualization: 2 hrs

	<ul>

		<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>

</li>

Pankaj Pandey and Prabhu Ramachandran, An introduction to Cython: 1 hrs

	<ul>

		<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>

	</ul>

Puneeth Chaganti, Sage introduction/tutorial: 1 hr.

	<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>

	</ul>

<li>

Mateusz Paprocki,  SymPy:  2 hrs<br />

<b>Details awaited</b>

</li>