--- a/project/templates/_menu.html	Sat Nov 12 16:39:00 2011 +0530
+++ b/project/templates/_menu.html	Tue Nov 15 02:25:12 2011 +0530
@@ -36,12 +36,12 @@
             Conference
           </a>
         </li>
-            <!-- <li>
+        <li>
               <a href="/{{ params.scope }}/talks-cfp/tutorial/">
             Tutorial Schedule
               </a>
-            </li>
-        <<li>
+        </li>
+        <!-- <li>
           <a href="/{{ params.scope }}/talks-cfp/sprint/">
             Sprint Plan &amp; Schedule
           </a>

--- a/project/templates/about/tutorial.html	Sat Nov 12 16:39:00 2011 +0530
+++ b/project/templates/about/tutorial.html	Tue Nov 15 02:25:12 2011 +0530
@@ -46,341 +46,196 @@
 <h3 id="sec-4"><span class="section-number-3"></span>Coverage </h3>
 
 
-<h4 id="sec-1">Day 3 </h4>
-
-
-<ul>
-<li>
-Sage (2 hr 30 min)
-<ul>
-<li>
-getting started with Sage notebook (45 min) (<b>Prabhu</b>)
-<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 calculus
-</li>
-<li>
-basic algebra
-</li>
-<li>
-basic plotting 
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li>
-symbolics &amp; calculus  &amp; basic plotting(1 hr) (<b>Bhanu</b>)
-<ul>
-<li>
-parametric plots
-<ul>
-<li>
-2D
-</li>
-<li>
-3D
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li>
-linear algebra (30 min) (<b>Nishanth</b>)
-</li>
-<li>
-Misc (15 min)
-<ul>
-<li>
-QA
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li>
-Basic Plotting (using pylab) (1 hr 30 min) (<b>Fernando</b>)
-<ul>
-<li>
-getting started with ipython  
-</li>
-<li>
-using the plot command interactively
-</li>
-<li>
-embellishing a plot
-</li>
-<li>
-saving plots
-</li>
-<li>
-multiple plots
-</li>
-<li>
-saving to scripts and running them (from ipython)
-</li>
-<li>
-running the same thing in sage notebook 
-<ul>
-<li>
-change language to python, import pylab, simple plot, savefig
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li>
-Plotting Experimental Data (1 hr) (<b>Puneeth</b>)
-<ul>
-<li>
-plotting points with lists
-<ul>
-<li>
-basic lists
-<ul>
-<li>
-indexing
-</li>
-<li>
-appending
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li>
-loading data from files using loadtxt
-</li>
-<li>
-using for loop with lists
-<ul>
-<li>
-pendulum example
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
-
-
-
+<!-- <h4 id="sec-1">Day 2 </h4> -->
 
 
 
-<h4 id="sec-2">Day 4 </h4>
-
-
-<ul>
 <li>
-Arrays (1 hr) (<b>Perry</b>)
-<ul>
-<li>
-Why use arrays
-<ul>
-<li>
-finding sine of a list of million numbers
-</li>
-</ul>
-</li>
-<li>
-getting started with arrays
-</li>
-<li>
-accessing parts of arrays
-<ul>
-<li>
-1d slicing 
-</li>
-<li>
-1d striding
-</li>
-<li>
-2d slicing
-</li>
-<li>
-2d striding
-</li>
-</ul>
-</li>
-<li>
-lena example of above
-</li>
-<li>
-element wise operations
-</li>
-<li>
-matrices
-<ul>
-<li>
-operations on matrices like det, inv, norm.
-</li>
-</ul>
+	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>
 </li>
-</ul>
-</li>
-<li>
-Scipy (1 hr 30 min) (<b>John</b>)
-<ul>
-<li>
-least square fit
-</li>
-<li>
-Roots
-<ul>
-<li>
-introduction to functions
-</li>
-</ul>
-</li>
-<li>
-Solving Equations
-</li>
-<li>
-ODE
-<ul>
+
 <li>
-revisiting functions
-</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>
 </li>
-<li>
-FFT
-</li>
-</ul>
-</li>
-<li>
-Python Language: Basics (1 hr) (<b>Asokan</b>)
-<ul>
+
 <li>
-basic data-types 
-<ul>
-<li>
-strings
-</li>
-</ul>
-</li>
-<li>
-Operators
-</li>
-<li>
-I/O
+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>
 </li>
 <li>
-conditionals
-</li>
-<li>
-loops
-<ul>
-<li>
-while
-</li>
-<li>
-for loop and its usage with range
-</li>
-</ul>
-</li>
-</ul>
-</li>
-<li>
-Python Language: Data structures (1hr 30 min) (<b>Asokan</b>)
-<ul>
-<li>
-manipulating lists
-</li>
-<li>
-dictionaries
+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>
 </li>
-<li>
-manipulating strings
-</li>
+
 <li>
-getting started with tuples
-</li>
-<li>
-sets
-</li>
-<li>
-examples
+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>
-</ul>
-</li>
-</ul>
 
-
+<li>
+Prabhu Ramachandran and  Gael Varoquaux, Mayavi for 3D visualization: 2 hrs
 
-
-
-
-
-<h4 id="sec-3">Day 5 </h4>
+	<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>
 
 
-<ul>
 <li>
-Python Language: Advanced (1 hr) (<b>Madhu</b>)
-<ul>
-<li>
-functions
-<ul>
-<li>
-defining functions
-</li>
-<li>
-keyword arguments and default arguments
-</li>
-</ul>
-</li>
-<li>
-using python modules
-</li>
-<li>
-writing re-usable python scripts
-</li>
-<li>
-PEP-8?
+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>
 </li>
-</ul>
-</li>
-<li>
-More Numpy? (broadcasting, indexing tricks&hellip;) (1hr) (<b>Stefan</b>)
-</li>
-<li>
-Mayavi (1 hr) (<b>Prabhu</b>)
-</li>
-<li>
-Cython (1 hr) (<b>Stefan</b>)
-</li>
+
 <li>
-Version Control (Hg/Git) (15 min) (<b>Madhu</b>)
+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>
-<li>
-ReST &amp; Scipy/Numpy Documentation Editor (45 min) (<b>Stefan</b>)
-</li>
-</ul>
 
-
-<p>Any participants using their own laptops should have the required
-software installed on their machines, before coming to the venue of
-the tutorials. The installation instructions are available <a href="http://fossee.in/installation-how-to">here</a>.
-</p>
-
+<li>
+Mateusz Paprocki,  SymPy:  2 hrs<br />
+<b>Details awaited</b>
+</li>
 
 <h3 id="sec-5"><span class="section-number-3"></span>Methodology </h3>
 
@@ -396,9 +251,6 @@
 
 </li>
 </ul>
-
-<p>Laptops can be brought by participants, and additional laptops/computers can be provided for use for those required. Charging points will be available.
-</p>
 <p>
 As far as installations go, you would require the following:
 </p>

--- a/project/templates/talk/schedule.html	Sat Nov 12 16:39:00 2011 +0530
+++ b/project/templates/talk/schedule.html	Tue Nov 15 02:25:12 2011 +0530
@@ -11,8 +11,8 @@
       <tr> <td align=center><strong>Date</strong></td><td><strong>Activity</strong></td> </tr>
       <tr > <td align=right>Sunday, Dec. 04 2011</td><td><a href="/{{ params.scope }}/talks-cfp/conference/">Conference</a></td> </tr>
       <tr> <td align=right>Munday, Dec. 05 2011</td><td><a href="/{{ params.scope }}/talks-cfp/conference/">Conference</a></td> </tr>
-      <!-- <tr> <td align=right>Tuesday, Dec. 06 2011</td><td><a href="/{{ params.scope }}/tutorial/">Tutorials</a>/<a href="/{{ params.scope }}/sprints/">Sprint</a></td> </tr>
-      <tr> <td align=right>Wednesday, Dec. 07 2011</td><td><a href="/{{ params.scope }}/tutorial/">Tutorials</a>/<a href="/{{ params.scope }}/sprints/">Sprint</a></td> </tr> -->
+      <tr> <td align=right>Tuesday, Dec. 06 2011</td><td><a href="/{{ params.scope }}/tutorial/">Tutorials</a><!-- /<a href="/{{ params.scope }}/sprints/">Sprint</a> --></td> </tr>
+      <tr> <td align=right>Wednesday, Dec. 07 2011</td><td><a href="/{{ params.scope }}/tutorial/">Tutorials</a><!-- /<a href="/{{ params.scope }}/sprints/">Sprint</a> --></td> </tr>
     </table>
 <br />
 

--- a/project/urls.py	Sat Nov 12 16:39:00 2011 +0530
+++ b/project/urls.py	Tue Nov 15 02:25:12 2011 +0530
@@ -120,9 +120,9 @@
     url(r'^%s/talks-cfp/schedule/$' % (SCOPE_ARG_PATTERN),
         direct_to_template, {"template": "talk/schedule.html"},
         name='scipycon_schedule'),
-    # url(r'^%s/talks-cfp/tutorial/$' % (SCOPE_ARG_PATTERN),
-    #     direct_to_template, {"template": "talk/tutorial-schedule.html"},
-    #     name='scipycon_tutorial_schedule'),
+     url(r'^%s/talks-cfp/tutorial/$' % (SCOPE_ARG_PATTERN),
+         direct_to_template, {"template": "about/tutorial.html"},
+        name='scipycon_tutorial'),
     # url(r'^%s/talks-cfp/sprint/$' % (SCOPE_ARG_PATTERN),
     #     direct_to_template, {"template": "talk/sprint-schedule.html"},
     #     name='scipycon_sprint_schedule'),