# HG changeset patch # User Parth buch # Date 1321689388 -19800 # Node ID 7167b896d8dec888ae45fa92e84531e63d313bed # Parent f91c329e13b55ab4ce62d9f9a51b96b70aecfc6b Changed the Tutorial page diff -r f91c329e13b5 -r 7167b896d8de project/templates/about/tutorial.html --- a/project/templates/about/tutorial.html Tue Nov 15 02:25:12 2011 +0530 +++ b/project/templates/about/tutorial.html Sat Nov 19 13:26:28 2011 +0530 @@ -2,12 +2,12 @@ {% block content %}

Tutorials

Intended audience

This conference is targeted at anyone who uses Python for work in science/engineering/technology/education. This includes college and university teachers/professors/lecturers from any Engineering or Science domain, students of engineering/science/education who would like to use Python for their basic computing and plotting needs, researchers who use or would like to use Python for their research, and corporate users of Python for scientific computing.

Prerequisites

@@ -20,259 +20,247 @@ Familiarity with using the commandline will be another plus.

- -

Objectives

- -

-At the end of the program the participants will have a good understanding of the Python language, and selected libraries. +These tutorials are all fairly advanced and require that you be familiar with Python.
-They will be able to write good modular procedural code and use objects. -
-They will get a overview of the other major topics, features and libraries and be able to learn these on their own if required. +For a good introduction it is recommended that you read the Python Tutorial completely.
-They will be able to generate 2-D plots using NumPy and Matplotlib, and 3-D plots using MayaVi2. -
-They will be able to incorporate and adapt Python in their lessons - +Spoken tutorials teaching you Python are also available here please go through those.

Coverage

- 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. -

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. +

- 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 +
Emmanuelle Gouillart, Image processing using NumPy, SciPy and scikits-image (2 hrs)
+
- 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
-
-Gael Varoquaux, scikit-learn: 2 hrs
-Machine learning with scikit-learn +
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
- - 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.
  - - -
- 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
-
-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. -
-
+ 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

+ -

-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 +

Gael Varoquaux, Machine learning with scikit-learn (2 hrs)

+ 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.
+ + +

- 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
-

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. +

-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. -

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. +

-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.

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

- -

Methodology

- +

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

-Completely hands on, exploratory mode with minimal lectures introducing essential concepts and techniques. -
-Typically we will have short 15 - 20 minute lectures, followed by series of graduated problems. The participants will solve them exploring the documentation to do so and the solutions will be discussed. -
-We shall be conducting quizzes during the course of the workshop to evaluate the degree to which the objectives have been accomplished. +
+ 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
+

- - -

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

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

-For Debian/ Ubuntu and the like: -(a) IPython -(b) Python doc -(c) the Python Profiler -(d) Scipy/Numpy -(e) Matplotlib -(f) Mayavi2 - -
-For Windows XP (x86), Windows Vista (x86), Mac OS X 10.4+ (x86), RedHat 3 (x86, amd64), RedHat 4 (x86, amd64), RedHat 5 (x86, amd64), and Solaris 10 (x86, amd64) : -(a) get the EPD (http://www.enthought.com/products/epd.php) bundle and you'll have everything you need! This is available for free for those in academia and others can utilize the free 30 day trial version for now. - -
+ 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. +

In any case, we will be providing live DVDs containing all the required installations and some additional tools on site. The iso can also be downloaded from the fossee.in site (http://fossee.in/download#DVDs). -

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.
A rough schedule of the talk would be as follows: +
- Introduction
- Starting the server
- The UI
- Getting Help
- Overview of what's available in Sage +
  - Basic Algebra
  - Basic Calculus
  - Basic Plotting
  +
+

+ + +

Mateusz Paprocki, SymPy (2 hrs)

+ 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. +
+ 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. +
+ Outline +
- Installing, configuring and running SymPy.
- Basics of mathematical expressions: +
  - symbols, dummy symbols
  - constructing expressions
  - expression traversal
  - pattern matching
  +
- Common issues, pitfalls and differences with other CAS: +
  - 1/3 is not a rational number
  - why you shouldn't write 10**(-1000)
  - issues with caching of computation results
  +
- Using built-in and implementing customized printers.
- Arbitrary precision numerical computing.
- Interaction with numerical libraries (NumPy, SciPy).
- Examples.
+

{% endblock content %}

Tutorials

Intended audience

Intended audience

Prerequisites

Prerequisites

Objectives

Coverage

Coverage

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

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

Gael Varoquaux, Machine learning with scikit-learn (2 hrs)

Ole Nielsen: Mapping and Geoprocessing with Python (2 hrs)

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

Methodology

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

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

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

Mateusz Paprocki, SymPy (2 hrs)