# HG changeset patch # User Parth buch # Date 1321304112 -19800 # Node ID f91c329e13b55ab4ce62d9f9a51b96b70aecfc6b # Parent e98f6525c7b08813f8d34939c581371242475143 activated the tutorials schedule diff -r e98f6525c7b0 -r f91c329e13b5 project/templates/_menu.html --- 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 - -

Day 4

- - -

-Arrays (1 hr) (Perry) -
- -Why use arrays -
  - -finding sine of a list of million numbers -
  -
- -getting started with arrays -
- -accessing parts of arrays -
  - -1d slicing -
  - -1d striding -
  - -2d slicing -
  - -2d striding -
  -
- -lena example of above -
- -element wise operations -
- -matrices -
  - -operations on matrices like det, inv, norm. -
  + Jarrod Millman, Git/Github + NumPy/SciPy/MPL basics: 2 hrs +
  - Git/Github
  - 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. +
-
-Scipy (1 hr 30 min) (John) -
- -least square fit -
- -Roots -
  - -introduction to functions -
  -
- -Solving Equations -
- -ODE -
  - -revisiting functions -
  + Emmanuelle Gouillart, Image processing: 2 hrs
  + +Image manipulation and processing using NumPy, SciPy and scikits-image + +
  - This tutorial will show a bag of basic recipes in order to efficiently +manipulate and process images in the form of NumPy arrays. +
  - Target audience: scientists and engineers working with images +
  - + Prerequisites : being able to code Python scripts and use an + interactive Python shell + some knowledge of NumPy +
  - + Software requirements: IPython, NumPy, SciPy, Matplolib, scikits-image, and optionally sklearn +
  - + Topics covered +
    - I/O: how to open different image formats, how to open raw images, how to deal with very large raw files.
    - Basic visualization of images, and interaction with image data
    - Transforming images: changing the size, resolution, orientation of an image; image filtering; image segmentation.
    - Extracting information from images: measuring properties of segmented objects; image classification
    +
  - + 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. +
  - The course materials are available here
- -FFT -
-
-Python Language: Basics (1 hr) (Asokan) -
- -basic data-types -
  - -strings -
  -
- -Operators -
- -I/O +Gael Varoquaux, scikit-learn: 2 hrs
  +Machine learning with scikit-learn +
  - + 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. +
  - + Target audience : Engineers and scientists using Python for scientific + and numerical computing. No knowledge needed in statistical learning. +
  - + Prerequisites: Being able to code scripts and function in Python. Basic + knowledge of numpy and matplotlib. +
  - + Software requirements: IPython, scikits.learn, matplotlib. +
  - + Outline +
    - The settings: datasets, estimators, and the prediction problem.
    - Regression and classification: Support Vector Machines, sparse regressions... Example: recognising hand-written digits
    - Model selection: choosing the right estimator, and the right parameters
    - Clustering: KMeans, Affinity Propagation. Example: finding structure in the stock market.
    + + +
- -conditionals -
- -loops -
  - -while -
  - -for loop and its usage with range -
  -
-
-Python Language: Data structures (1hr 30 min) (Asokan) -
- -manipulating lists -
- -dictionaries +Ole Nielsen: Mapping and Geoprocessing with Python, 2 hrs +
  - + 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. +
  - + 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. +
  - + 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). +
  - + 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. +
- -manipulating strings -
- -getting started with tuples -
- -sets -
- -examples +Eric Jones/Puneeth/Pankaj: Traits + Traits UI. 2 hrs. +
  - + 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. +
-

- +

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

Day 5

+ 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). +
+ 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. +
+ Packages required +
- Mayavi
- IPython
- Traits
- numpy, scipy
+

-Python Language: Advanced (1 hr) (Madhu) -
- -functions -
  - -defining functions -
  - -keyword arguments and default arguments -
  -
- -using python modules -
- -writing re-usable python scripts -
- -PEP-8? +Pankaj Pandey and Prabhu Ramachandran, An introduction to Cython: 1 hrs +
  - + 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. +
  - + Package requirements: You will require to have Cython, the + Python development headers and a working C-compiler to run the hands-on exercises. +
-
-More Numpy? (broadcasting, indexing tricks…) (1hr) (Stefan) -
-Mayavi (1 hr) (Prabhu) -
-Cython (1 hr) (Stefan) -
-Version Control (Hg/Git) (15 min) (Madhu) +Puneeth Chaganti, Sage introduction/tutorial: 1 hr. +
- This tutorial will feature a demonstration and a brief review of some of the capabilities of the Sage notebook.
-ReST & Scipy/Numpy Documentation Editor (45 min) (Stefan) -

- -

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 here. -

- +

+Mateusz Paprocki, SymPy: 2 hrs
+Details awaited +

Methodology

@@ -396,9 +251,6 @@ - -

Laptops can be brought by participants, and additional laptops/computers can be provided for use for those required. Charging points will be available. -

As far as installations go, you would require the following:

diff -r e98f6525c7b0 -r f91c329e13b5 project/templates/talk/schedule.html --- 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 @@ DateActivity Sunday, Dec. 04 2011Conference Munday, Dec. 05 2011Conference - + Tuesday, Dec. 06 2011Tutorials + Wednesday, Dec. 07 2011Tutorials
diff -r e98f6525c7b0 -r f91c329e13b5 project/urls.py --- 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'),