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<h1 class="title">SciPy.in 2010 Conference Schedule</h1>

<h2 id="sec-1">Day 1 </h2>

<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">

<caption></caption>

<colgroup><col class="right" /><col class="left" /><col class="left" />

</colgroup>

<thead>

<tr><th scope="col" class="right">Time</th><th scope="col" class="left">Speaker</th><th scope="col" class="left">Title</th></tr>

</thead>

<tbody>

<tr><td class="right">09:00-09:30</td><td class="left"></td><td class="left">Inauguration</td></tr>

<tr><td class="right">09:30-10:30</td><td class="left">Perry Greenfield</td><td class="left"><b>Keynote</b>: <a href="#sec-3_1">How Python Slithered into Astronomy</a></td></tr>

<tr><td class="right">10:30-10:45</td><td class="left"></td><td class="left">Tea Break</td></tr>

<tr><td class="right">10:45-11:30</td><td class="left">Fernando Perez</td><td class="left"><b>Special Talk</b>: <a href="#sec-3_2">IPython : Beyond the Simple Shell</a></td></tr>

<tr><td class="right">11:30-11:50</td><td class="left">Farhat Habib</td><td class="left"><a href="#sec-4_1">Python as a Platform for Scientific Computing Literacy for 10+2 Students: Weighing the Balance</a></td></tr>

<tr><td class="right">11:50-12:10</td><td class="left">Jayesh Gandhi</td><td class="left"><a href="#sec-4_14">Microcontroller experiment and its simulation using Python</a></td></tr>

<tr><td class="right">12:10-12:40</td><td class="left">Vaidhy Mayilrangam</td><td class="left"><a href="#sec-4_17">Natural Language Processing Using Python</a></td></tr>

<tr><td class="right">12:40-13:10</td><td class="left">Georges Khaznadar</td><td class="left"><a href="#sec-4_10">Live media for training in experimental sciences</a></td></tr>

<tr><td class="right">13:10-14:10</td><td class="left"></td><td class="left">Lunch</td></tr>

<tr><td class="right">14:10-14:20</td><td class="left">Shubham Chakraborty</td><td class="left"><a href="#sec-4_11">Use of Python and Phoenix-M interface in Robotics</a></td></tr>

<tr><td class="right">14:20-14:30</td><td class="left">Erroju Rama Krishna</td><td class="left"><a href="#sec-4_8">Simplified and effective Network Simulation using ns-3</a></td></tr>

<tr><td class="right">14:30-14:40</td><td class="left"></td><td class="left">More Lightning Talks</td></tr>

<tr><td class="right">14:40-15:10</td><td class="left">Asokan Pichai</td><td class="left"><b>Invited Talk</b>:  <a href="#sec-3_3">Teaching Programming with Python</a></td></tr>

<tr><td class="right">15:10-15:30</td><td class="left">Hemanth Chandran</td><td class="left"><a href="#sec-4_19">Performance Evaluation of HYBRID MAC for 802.11ad: Next Generation Multi-Gbps Wi-Fi using SimPy</a></td></tr>

<tr><td class="right">15:30-15:50</td><td class="left">Karthikeyan selvaraj</td><td class="left"><a href="#sec-4_9">PyCenter</a></td></tr>

<tr><td class="right">15:50-16:10</td><td class="left"></td><td class="left">Tea Break</td></tr>

<tr><td class="right">16:10-16:40</td><td class="left">Satrajit Ghosh</td><td class="left"><b>Invited Talk</b>: <a href="#sec-3_6">Nipype: Opensource platform for unified and replicable interaction with existing neuroimaging tools</a></td></tr>

<tr><td class="right">16:40-17:00</td><td class="left">Nek Sharan</td><td class="left"><a href="#sec-4_7">Parallel Computation of Axisymmetric Jets</a></td></tr>

<tr><td class="right">17:00-17:20</td><td class="left">pankaj pandey</td><td class="left"><a href="#sec-4_20">PySPH: Smooth Particle Hydrodynamics with Python</a></td></tr>

</tbody>

</table>

<h2 id="sec-2">Day 2 </h2>

<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">

<caption></caption>

<colgroup><col class="right" /><col class="left" /><col class="left" />

</colgroup>

<thead>

<tr><th scope="col" class="right">Time</th><th scope="col" class="left">Speaker</th><th scope="col" class="left">Title</th></tr>

</thead>

<tbody>

<tr><td class="right">09:00-10:00</td><td class="left">John Hunter</td><td class="left"><b>Special Talk</b>: <a href="#sec-3_4">matplotlib: Beyond the simple plot</a></td></tr>

<tr><td class="right">10:00-10:45</td><td class="left">Prabhu Ramachandran</td><td class="left"><b>Invited Talk</b>: <a href="#sec-3_5">Mayavi : Bringing Data to Life</a></td></tr>

<tr><td class="right">10:45-11:00</td><td class="left"></td><td class="left">Tea</td></tr>

<tr><td class="right">11:00-11:45</td><td class="left">Stéfan van der Walt</td><td class="left"><b>Invited Talk</b>: <a href="#sec-3_7">In Pursuit of a Pythonic PhD</a></td></tr>

<tr><td class="right">11:45-12:15</td><td class="left">Dharhas Pothina</td><td class="left"><a href="#sec-4_6">HyPy &amp; HydroPic: Using python to analyze hydrographic survey data</a></td></tr>

<tr><td class="right">12:15-12:35</td><td class="left">Prashant Agrawal</td><td class="left"><a href="#sec-4_18">A Parallel 3D Flow Solver in Python Based on Vortex Methods</a></td></tr>

<tr><td class="right">12:35-13:05</td><td class="left">Ajith Kumar</td><td class="left"><a href="#sec-4_12">Python in Science Experiments using Phoenix</a></td></tr>

<tr><td class="right">13:05-14:05</td><td class="left"></td><td class="left">Lunch</td></tr>

<tr><td class="right">14:15-14:25</td><td class="left">Arun C. H.</td><td class="left"><a href="#sec-4_3">Automation of an Optical Spectrometer</a></td></tr>

<tr><td class="right">14:35-14:55</td><td class="left">Krishnakant Mane</td><td class="left"><a href="#sec-4_22">Convincing Universities to include Python</a></td></tr>

<tr><td class="right">14:55-15:15</td><td class="left">Shantanu Choudhary</td><td class="left"><a href="#sec-4_4">"Python" Swiss army knife for Prototyping, Research and Fun.</a></td></tr>

<tr><td class="right">15:15-15:35</td><td class="left">Puneeth Chaganti</td><td class="left"><a href="#sec-4_21">Pictures, Songs and Python</a></td></tr>

<tr><td class="right">15:35-15:55</td><td class="left">Hrishikesh Deshpande</td><td class="left"><a href="#sec-4_5">Wavelet based denoising of ECG using Python</a></td></tr>

<tr><td class="right">15:55-16:10</td><td class="left"></td><td class="left">Tea-Break</td></tr>

<tr><td class="right">16:10-16:40</td><td class="left">Jarrod Millman</td><td class="left"><b>Invited Talk</b></td></tr>

<tr><td class="right">16:40-17:00</td><td class="left">Ramakrishna Reddy Yekulla</td><td class="left"><a href="#sec-4_13">Building and Packaging your Scientific Python Application For Linux Distributions</a></td></tr>

<tr><td class="right">17:00-17:20</td><td class="left">Yogesh Karpate</td><td class="left"><a href="#sec-4_16">Automatic Proteomic Finger Printing using Scipy</a></td></tr>

<tr><td class="right">17:20-17:40</td><td class="left">Manjusha Joshi</td><td class="left"><a href="#sec-4_15">SAGE for Scientific computing and Education enhancement</a></td></tr>

</tbody>

</table>

<h2 id="sec-3">Invited Talks </h2>

<h3 id="sec-3_1">How Python Slithered into Astronomy </h3>

<p>Perry Greenfield

</p>

<h4 id="sec-3_1_1">Talk/Paper Abstract </h4>

<p>I will talk about how Python was used to solve our problems for

the Hubble Space Telescope. From humble beginnings as a glue

element for our legacy software, it has become a cornerstone of

our scientific software for HST and the next large space

telescope, the James Webb Space Telescope, as well as many other

astronomy projects. The talk will also cover some of the history

of essential elements for scientific Python and where future

work is needed, and why Python is so well suited for scientific

software.

</p>

<h3 id="sec-3_2">IPython : Beyond the Simple Shell </h3>

<p>Fernando Perez

</p>

<h4 id="sec-3_2_1">Talk/Paper Abstract </h4>

<p>IPython is a widely used system for interactive computing in

Python that extends the capabilities of the Python shell with

operating system access, powerful object introspection,

customizable "magic" commands and many more features. It also

contains a set of tools to control parallel computations via

high-level interfaces that can be used either interactively or

in long-running batch mode. In this talk I will outline some of

the main features of IPython as it has been widely adopted by

the scientific Python user base, and will then focus on recent

developments. Using the high performance ZeroMQ networking

library, we have recently restructured IPython to decouple the

kernel executing user code from the control interface. This

allows us to expose multiple clients with different

capabilities, including a terminal-based one, a rich Qt client

and a web-based one with full matplotlib support. In conjunction

with the new HTML5 matplotlib backend, this architecture opens

the door for a rich web-based environment for interactive,

collaborative and parallel computing. There is much interesting

development to be done on this front, and I hope to encourage

participants at the sprints during the conference to join this

effort.

</p>

<h3 id="sec-3_3">Teaching Programming with Python </h3>

<p>Asokan Pichai

</p>

<h4 id="sec-3_3_1">Talk/Paper Abstract </h4>

<p>As a trainer I have been engaged a lot for teaching fresh

Software Engineers and software job aspirants. Before starting

on the language, platform specific areas I teach a part I refer

to as Problem Solving and Programming Logic. I have used Python

for this portion of training in the last 12+years. In this talk

I wish to share my experiences and approaches. This talk is

intended at Teachers, Trainers, Python Evangelists, and HR

Managers [if they lose their way and miraculously find

themselves in SciPy :-)]

</p>

<h3 id="sec-3_4">matplotlib: Beyond the simple plot </h3>

<p>John Hunter

</p>

<h4 id="sec-3_4_1">Talk/Paper Abstract </h4>

<p>matplotlib, a python package for making sophisticated

publication quality 2D graphics, and some 3D, has long supported

a wide variety of basic plotting types such line graphs, bar

charts, images, spectral plots, and more. In this talk, we will

look at some of the new features and performance enhancements in

matplotlib as well as some of the comparatively undiscovered

features such as interacting with your data and graphics, and

animating plot elements with the new animations API. We will

explore the performance with large datasets utilizing the new

path simplification algorithm, and discuss areas where

performance improvements are still needed. Finally, we will

demonstrate the new HTML5 backend, which in combination with the

new HTML5 IPython front-end under development, will enable an

interactive Python shell with interactive graphics in a web

browser.

</p>

<h3 id="sec-3_5">Mayavi : Bringing Data to Life </h3>

<p>Prabhu Ramachandran

</p>

<h4 id="sec-3_5_1">Talk/Paper Abstract </h4>

<p>Mayavi is a powerful 3D plotting package implemented in

Python. It includes both a standalone user interface along with

a powerful yet simple scripting interface. The key feature of

Mayavi though is that it allows a Python user to rapidly

visualize data in the form of NumPy arrays. Apart from these

basic features, Mayavi has some advanced features. These

include, automatic script recording, embedding into a custom

user dialog and application. Mayavi can also be run in an

offscreen mode and be embedded in a sage notebook

(<a href="http://www.sagemath.org">http://www.sagemath.org</a>). We will first rapidly demonstrate

these key features of Mayavi. We will then discuss some of the

underlying technologies like enthought.traits, traitsUI and TVTK

that form the basis of Mayavi. The objective of this is to

demonstrate the wide range of capabilities that both Mayavi and

its underlying technologies provide the Python programmer.

</p>

<h3 id="sec-3_6">Nipype: Opensource platform for unified and replicable interaction with existing neuroimaging tools </h3>

<p>Satrajit Ghosh

</p>

<h4 id="sec-3_6_1">Talk/Paper Abstract </h4>

<p>Current neuroimaging software offer users an incredible

opportunity to analyze their data in different ways, with

different underlying assumptions. However, this has resulted in

a heterogeneous collection of specialized applications without

transparent interoperability or a uniform operating

interface. Nipype, an open-source, community-developed

initiative under the umbrella of Nipy, is a Python project that

solves these issues by providing a uniform interface to existing

neuroimaging software and by facilitating interaction between

these packages within a single workflow. Nipype provides an

environment that encourages interactive exploration of

neuroimaging algorithms from different packages, eases the

design of workflows within and between packages, and reduces the

learning curve necessary to use different packages. Nipype is

creating a collaborative platform for neuroimaging software

development in a high-level language and addressing limitations

of existing pipeline systems.

</p>

<h3 id="sec-3_7">In Pursuit of a Pythonic PhD </h3>

<p>Stéfan van der Walt

</p>

<h4 id="sec-3_7_1">Talk/Paper Abstract </h4>

<p>In May of 2005, I started a pilgrimage to transform myself into

a doctor of engineering. Little did I know, then, that my

journey would bring me in touch with some of the most creative,

vibrant and inspiring minds in the open source world, and that

an opportunity would arise to help realise their (and now my)

dream: a completely free and open environment for performing

cutting edge science. In this talk, I take you on my journey,

and along the way introduce the NumPy and SciPy projects, our

community, the early days of packaging, our documentation

project, the publication of conference proceedings as well as

work-shops and sprints around the world. I may even tell you a

bit about my PhD on super-resolution imaging!

</p>

<h2 id="sec-4">Submitted Talks </h2>

<h3 id="sec-4_1">Python as a Platform for Scientific Computing Literacy for 10+2 Students: Weighing the Balance </h3>

<p>Farhat Habib

</p>

<h4 id="sec-4_1_1">Talk/Paper Abstract </h4>

<p>The use of Python as a language for introducing computing is

becoming increasingly widespread.  Here we report out findings

from two years of running an introduction to computing course

with Python as the programming language, and building upon it,

using SciPy as a scientific computing language in a course on

scientific computing.

</p>

<p>

The course is designed as a general computing course for

introducing computing to first year undergraduate students of

science. We find that a large majority of our incoming students

have no prior exposure to programming and none of the students

had any exposure to Python. Thus, the design of the course is

such that it allows everybody to be brought up to speed with

general programming concepts.  Later, the students will later

specialize in varied topics from Biology to pure Mathematics,

thus, the course emphasizes general computing concepts over

specialized techniques. At a second course in Scien- tific

Computing numerical methods are introduced with the aid of

Scipy. The introduction to computing course has been taught

twice in Fall 2009 and 2010 to batches of around 100 students

each. In this paper we report our experience with teaching

Python and student and faculty feedback related to the course.

</p>

<h3 id="sec-4_2">Usb Connectivity Using Python </h3>

<p>Arun C. H. 

</p>

<h4 id="sec-4_2_1">Talk/Paper Abstract </h4>

<p>Host software using Python interpreter language to communicate

with the USB Mass Storage class device is developed and

tested. The <sub>usic18F4550</sub>.pyd module encapsulating all the

functions needed to configure USB is developed. The Python

extension .pyd using C/C++ functions compatible for Windows make

use of SWIG, distutils and MinGW. SWIG gives the flexibility to

access lower level C/C++ code through more convenient and higher

level languages such as Python, Java, etc. Simplified Wrapper and

Interface Generator (SWIG) is a middle interface between Python

and C/C++. The purpose of the Python interface is to allow the

user to initialize and configure USB through a convenient

scripting layer. The module is built around libusb which can

control an USB device with just a few lines. Libusb-win32 is a

port of the USB library to the Windows operating system. The

library allows user space applications to access any USB device on

Windows in a generic way without writing any line of kernel driver

code. A simple data acquisition system for measuring analog

voltage, setting and reading the status of a particular pin of the

micro controller is fabricated. It is interfaced to PC using USB

port that confirms to library USB win32 device. The USB DAQ

hardware consists of a PIC18F4550 micro-controller and the

essential components needed for USB configuration.

</p>

<h3 id="sec-4_3">Automation of an Optical Spectrometer </h3>

<p>Arun C. H. 

</p>

<h4 id="sec-4_3_1">Talk/Paper Abstract </h4>

<p>This paper describes the automation performed for an Optical

Spectrometer in order to precisely monitor angles, change

dispersing angle and hence measure wave length of light using a

data logger, necessary hardware and Python. Automating instruments

through programs provides great deal of power, flexibility and

precision. Optical Spectrometers are devices which analyze the

wave length of light, and are typically used to identify

materials, and study their optical properties. A broad spectrum of

light is dispersed using a grating and the dispersed light is

measured using a photo transistor. The signal is processed and

acquired using a data logger. Transfer of data, changing angle of

diffraction are all done using the Python. The angle of

diffraction is varied by rotating the detector to pick up lines

using a stepper motor. The Stepper motor has 180 steps or 2

degrees per step. A resolution of 0.1 degree is achieved in the

spectrometer by using the proper gear ratio. The data logger is

interfaced to the computer through a serial port. The stepper

motor is also interfaced to the computer through another serial

port. Python is chosen here for its succinct notation and is

implemented in a Linux environment.

</p>

<h3 id="sec-4_4">"Python" Swiss army knife for Prototyping, Research and Fun. </h3>

<p>Shantanu Choudhary 

</p>

<h4 id="sec-4_4_1">Talk/Paper Abstract </h4>

<p>This talk would be covering usage of Python in different scenarios which helped me through my work:

</p><ul>

<li>

Small mlab(Mayavi) scripts which helped in better understanding

of problem statement.

</li>

<li>

Python3.0 and its blender API's for writing plugins which are

used for Open Source Animation movie project

Tube(tube.freefac.org)

</li>

<li>

PyOpenCL Python's interfacing for OpenCL which helped in

prototyping and speed up of application.

</li>

</ul>

<h3 id="sec-4_5">Wavelet based denoising of ECG using Python </h3>

<p>Hrishikesh Deshpande 

</p>

<h4 id="sec-4_5_1">Talk/Paper Abstract </h4>