Added Jarrod's talk abstract to the website.
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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 & 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">14:05-14:15</td><td class="left">Harikrishna</td><td class="left"><a href="#sec-4_23">Python based Galaxy workflow integration on GARUDA Grid</a></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">14:25-14:35</td><td class="left"></td><td class="left"><a href="#More==Lightning==Talks">More Lightning Talks</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><a href="#sec-3_8">Building an open development community for neuroimaging analysis</a></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>
<h3 id="sec-3_8">Building an open development community for neuroimaging analysis</h3>
<p>Jarrod Millman
</p>
<h4 id="sec-3_8_1">Talk/Paper Abstract </h4>
<p>Programming is becoming increasingly important to scientific activity. As its
importance grows, the need for better software tools becomes more and more
central to scientific practice. However, many fields of science rely on
badly written, poorly documented, and insufficiently tested codebases.
Moreover, scientific software packages often implement only the approaches
and algorithms needed or promoted by the specific lab where the software
was written.</p>
<p>In this talk, I will illustrate this situation by discussing some of the
weaknesses of the software ecosystem for neuroimaging analysis circa 2004.
I will then describe how several of my colleagues and I are attempting
to rectify this situation with a project called Neuroimaging in Python
(http://nipy.org). Specifically, I will discuss the approach we've taken
(e.g., using Python) and the lessons we've learned.
</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>
<p>The python module "RemNoise" is presented. It allows user to
automatically denoise one-dimensional signal using wavelet
transform. It also removes baseline wandering and motion
artifacts. While RemNoise is developed primarily for biological
signals like ECG, its design is generic enough that it should be
useful to applications involving one-dimensional signals. The
basic idea behind this work is to use multi-resolution property of
wavelet transform that allows to study non-stationary signals in
greater depth. Any signal can be decomposed into detail and
approximation coefficients, which can further be decomposed into
higher levels and this approach can be used to analyze the signal
in time-frequency domain. The very first step in any
data-processing application is to pre-process the data to make it
noise-free. Removing noise using wavelet transform involves
transforming the dataset into wavelet domain, zero out all
transform coefficients using suitable thresholding method and
reconstruct the data by taking its inverse wavelet transform. This
module makes use of PyWavelets, Numpy and Matplotlib libraries in
Python, and involves thresholding wavelet coefficients of the data
using one of the several thresholding methods. It also allows
multiplicative threshold rescaling to take into consideration
detail coefficients in each level of wavelet decomposition. The
user can select wavelet family and level of decompositions as
required. To evaluate the module, we experimented with several
complex one-dimensional signals and compared the results with
equivalent procedures in MATLAB. The results showed that RemNoise
is excellent module to preprocess data for noise-removal.
</p>
<h3 id="sec-4_6">HyPy & HydroPic: Using python to analyze hydrographic survey data </h3>
<p>Dharhas Pothina
</p>
<h4 id="sec-4_6_1">Talk/Paper Abstract </h4>
<p>
The Texas Water Development Board(TWDB) collects hydrographic
survey data in lakes, rivers and estuaries. The data collected
includes single, dual and tri-frequency echo sounder data
collected in conjunction with survey grade GPS systems. This raw
data is processed to develop accurate representations of
bathymetry and sedimentation in the water bodies surveyed.
</p>
<p>
This talk provides an overview of how the Texas Water Development
Board (TWDB) is using python to streamline and automate the
process of converting raw hydrographic survey data to finished
products that can then be used in other engineering applications
such as hydrodynamic models, determining lake
elevation-area-capacity relationships and sediment contour maps,
etc.
</p>
<p>
The first part of this talk will present HyPy, a python module
(i.e. function library) for hydrographic survey data
analysis. This module contains functions to read in data from
several brands of depth sounders, conduct anisotropic
interpolations along river channels, apply tidal and elevation
corrections, apply corrections to boat path due to loss of GPS
signals as well as a variety of convenience functions for dealing
with spatial data.
</p>
<p>
In the second part of the talk we present HydroPic, a simple
Traits based application built of top of HyPy. HydroPic is
designed to semi-automate the determination of sediment volume in
a lake. Current techniques require the visual inspection of images
of echo sounder returns along each individual profile. We show
that this current methodology is slow and subject to high human
variability. We present a new technique that uses computer vision
edge detection algorithms available in python to semi-automate
this process. HydroPic wraps these algorithms into a easy to use
interface that allows efficient processing of data for an entire
lake.
</p>
<h3 id="sec-4_7">Parallel Computation of Axisymmetric Jets </h3>
<p>Nek Sharan
</p>
<h4 id="sec-4_7_1">Talk/Paper Abstract </h4>
<p>Flow field for imperfectly expanded jet has been simulated using
Python for prediction of jet screech frequency. This plays an
important role in the design of advanced aircraft engine nozzle,
since screech could cause sonic fatigue failure. For computation,
unsteady axisymmetric Navier-Stokes equation is solved using fifth
order Weighted Essentially Non-Oscillatory (WENO) scheme with a
subgrid scale Large-Eddy Simulation (LES) model. Smagorinsky’s
eddy viscosity model is used for subgrid scale modeling with
second order (Total Variation Diminishing) TVD Runge Kutta time
stepping. The performance of Python code is enhanced by using
different Cython constructs like declaration of variables and
numpy arrays, switching off bound check and wrap around etc. Speed
up obtained from these methods have been individually clocked and
compared with the Python code as well as an existing in-house C
code. Profiling was used to highlight and eliminate the expensive
sections of the code.
</p>
<p>
Further, both shared and distributed memory architectures have
been employed for parallelization. Shared memory parallel
processing is implemented through a thread based model by manual
release of Global Interpreter Lock (GIL). GIL ensures safe and
exclusive access of Python interpreter internals to running
thread. Hence while one thread is running with GIL the other
threads are put on hold until the running thread ends or is forced
to wait. Therefore to run two threads simultaneously, GIL was
manually released using "with nogil" statement. The relative
independence of radial and axial spatial derivative computation
provides an option of putting them in parallel threads. On the
other hand, distributed memory parallel processing is through MPI
based domain decomposition, where the domain is split radially
with an interface of three grid points. Each sub-domain is
delegated to a different processor and communication, in the form
of message transmission, ensures update of interface grid
points. Performance analyses with increase in number of processors
indicate a trade-off between computation and communication. A
combined thread and MPI based model is attempted to harness the
benefits from both forms of architectures.
</p>
<h3 id="sec-4_8">Simplified and effective Network Simulation using ns-3 </h3>
<p>Erroju Rama Krishna
</p>
<h4 id="sec-4_8_1">Talk/Paper Abstract </h4>
<p>
Network simulation has great significance in the research areas of
modern networks. The ns-2 is the popular simulation tool which
proved this, in the successive path of ns-2 by maintaining the
efficiency of the existing mechanism it has been explored with a
new face and enhanced power of python scripting in ns-3. Python
scripting can be added to legacy projects just as well as new
ones, so developers don't have to abandon their old C/C++ code
libraries, but in the ns-2 it is not possible to run a simulation
purely from C++ (i.e., as a main() program without any OTcl), ns-3
does have new capabilities (such as handling multiple interfaces
on nodes correctly, use of IP addressing and more alignment with
Internet protocols and designs, more detailed 802.11 models, etc.)
</p>
<p>
In ns-3, the simulator is written entirely in C++, with optional
Python bindings. Simulation scripts can therefore be written in
C++ or in Python. The results of some simulations can be
visualized by nam, but new animators are under development. Since
ns-3 generates pcap packet trace files, other utilities can be
used to analyze traces as well.
</p>
<p>
In this paper the efficiency and effectiveness of IP addressing
simulation model of ns-3 is compared with the ns-2 simulation
model,ns-3 model consisting of the scripts written in Python which
makes the modeling simpler and effective
</p>
<h3 id="sec-4_9">PyCenter </h3>
<p>Karthikeyan selvaraj
</p>
<h4 id="sec-4_9_1">Talk/Paper Abstract </h4>
<p>The primary objective is defining a centralized testing
environment and a model of testing framework which integrates all
projects in testing in a single unit.
</p>
<p>
The implementation of concurrent processing systems and adopting
client server architecture and with partitioned server zones for
environment manipulation, allows the server to run test requests
from different projects with different environment and testing
requests. The implementation provides features of auto-test
generation, scheduled job run from server, thin and thick clients.
</p>
<p>
The core engine facilitates the management of tests from all the
clients with priority and remote scheduling. It has an extended
configuration utility to manipulate test parameters and watch
dynamic changes. It not only acts as a request pre-preprocessor
but also a sophisticated test bed by its implementation. It is
provided with storage and manipulation segment for every
registered project in the server zone. The system schedules and
records events and user activities thereby the results can be
drilled and examined to core code level with activates and system
states at the test event point.
</p>
<p>
The system generates test cases both in human readable as well as
executable system formats. The generated tests are based on a
pre-defined logic in the system which can be extended to adopt new
cases based on user requests. These are facilitated by a template
system which has a predefined set of cases for various test types
like compatibility, load, performance, code coverage, dependency
and compliance testing. It is also extended with capabilities like
centralized directory systems for user management with roles and
privileges for authentication and authorization, global mailer
utilities, Result consolidator and Visualizer.
</p>
<p>
With the effective implementation of the system with its minimal
requirements, the entire testing procedure can be automated with
the testers being effectively used for configuring, ideating and
managing the test system and scenarios. The overhead of managing
the test procedures like environment pre-processing, test
execution, results collection and presentation are completely
evaded from the testing life cycle.
</p>
<h3 id="sec-4_10">Live media for training in experimental sciences </h3>
<p>Georges Khaznadar
</p>
<h4 id="sec-4_10_1">Talk/Paper Abstract </h4>
<p>A system for distance learning in the field of Physics and
Electricity has been used for three years with some success for 15
years old students. The students are given a little case
containing a PHOENIX box (see
<a href="http://www.iuac.res.in/~elab/phoenix/">http://www.iuac.res.in/~elab/phoenix/</a>) featuring electric analog
and digital I/O interfaces, some unexpensive discrete components
and a live (bootable) USB stick.
</p>
<p>
The PHOENIX project was started by Inter University Accelerator
Centre in New Delhi, with the objective of improving the
laboratory facilities at Indian Universities, and growing with the
support of the user community. PHOENIX depends heavily on Python
language. The data acquisition, analysis and writing simulation
programs to teach science and computation.
</p>
<p>
The hardware design of PHOENIX box is freely available.
</p>
<p>
The live bootable stick provides a free/libre operating system,
and a few dozens educational applications, including applications
developed with Scipy to drive the PHOENIX box and manage the
acquired measurements. The user interface has been made as
intuitive as possible: the main window shows a photo of the front
face of the PHOENIX acquisition device, its connections behaving
like widgets to express their states, and a subwindow displays in
real time the signals connected to it. A booklet gives
general-purpose hints for the usage of the acquisition device. The
educational interaction is done with a free learning management
system.
</p>
<p>
The talk will show how such live media can be used as powerful
training systems, allowing students to access at home exactly the
same environment they can find in the school, and providing them a
lot of structured examples.
</p>
<p>
This talk addresses people who are involved in education and
training in scientific fields. It describes one method which
allows distance learning (however requiring a few initial lessons
to be given non-remotely), and enables students to become fluent
with Python and its scientific extensions, while learning physics
and electricity. This method uses Internet connections to allow
remote interactions, but does not rely on a wide bandwidth, as the
complete learning environment is provided by the live medium,
which is shared by teacher and students after their beginning
lessons.
</p>
<h3 id="sec-4_11">Use of Python and Phoenix-M interface in Robotics </h3>
<p>Shubham Chakraborty
</p>
<h4 id="sec-4_11_1">Talk/Paper Abstract </h4>
<p>In this paper I will show how to use Python programming with a
computer interface such as Phoenix-M to drive simple robots. In my
quest towards Artificial Intelligence (AI) I am experimenting with
a lot of different possibilities in Robotics. This one is trying
to mimic the working of a simple insect's autonomous nervous
system using hard wiring and some minimal software usage. This is
the precursor to my advanced robotics and AI integration where I
plan to use an new paradigm of AI based on Machine Learning and
Self Consciousness via Knowledge Feedback and Update process.
</p>
<h3 id="sec-4_12">Python in Science Experiments using Phoenix </h3>
<p>Ajith Kumar
</p>
<h4 id="sec-4_12_1">Talk/Paper Abstract </h4>
<p>Phoenix is a hardware plus software framework for developing
computer interfaced science experiments. Sensor and control
elements connected to Phoenix can be accessed using Python. Text
based and GUI programs are available for several
experiments. Python programming language is used as a tool for
data acquisition, analysis and visualization.
</p>
<p>
Objective of the project is to improve the laboratory facilities
at the Universities and also to utilize computers in a better
manner to teach science. The hardware design is freely
available. The project is based on Free Software tools and the
code is distributed under GNU General Public License.
</p>
<h3 id="sec-4_13">Building and Packaging your Scientific Python Application For Linux Distributions </h3>
<p>Ramakrishna Reddy Yekulla
</p>
<h4 id="sec-4_13_1">Talk/Paper Abstract </h4>
<p>If you are an Independent Researcher, Academic Project or an
Enterprise software Company building large scale scientific python
applications, there is a huge community of packagers who look at
upstream python projects to get those packages into upstream
distributions. This talk focuses on practices, making your
applications easy to package so that they can be bundled with
Linux distributions. Additionally this talk would be more hands
on, more like a workshop. The audience are encouraged to bring as
many python applications possible, using the techniques showed in
the talk and help them package it for fedora.
</p>
<h3 id="sec-4_14">Microcontroller experiment and its simulation using Python </h3>
<p>Jayesh Gandhi
</p>
<h4 id="sec-4_14_1">Talk/Paper Abstract </h4>
<p>Electronics in industrial has been passing through revolution due
to extensive use of Microcontroller. These electronic devices are
having a high capability to handle multiple events. Their
capability to communicate with the computers has made the
revolution possible. Therefore it is very important to have
trained Personnel in Microcontroller. In the present work
experiments for study of Microcontrollers and its peripherals with
Simulation using Python is carried out. This facilitates the
teachers to demonstrate the experiments in the classroom sessions
using simulations. Then the same experiments can be carried out in
the labs (using the same simulation setup) and the microcontroller
hardware to visualize and understand the experiments. Python is
selected due to its versatility and also to promote the use of
open source software in the education.
</p>
<p>
Here we demonstrate the experiment of driving seven segment
displays by microcontroller. Four seven segment displays are
interfaced with the microcontroller through a single BCD to seven
segments Display Decoder/Driver (74LS47) and switching
transistors. The microcontroller switches on the first transistor
connected to the first display and puts the number to be displayed
on 74LS47. Then it pause a while, switches off the first display
and puts the number to be displayed on the second display and
switches it on. A similar action is carried out for all the
display and the cycle is repeated again and again. Now we can
control the microcontroller action using the serial port of the
computer through python. Simulating the seven segment display
using VPYTHON module and communicating the same action to the
microcontroller, we can demonstrate the switching action of the
display at a very slow rate. It is possible to actually see each
display glowing individually one after another. Now we can
gradually increase the rate of switching the display. You see each
display glowing for a few milliseconds. Finally the refresh rate
is taken very high to around more than 25 times a second we see
that all the display glowing simultaneously.
</p>
<p>
Hence it is possible to simulate and demonstrate experiments and
understand the capabilities of the microcontroller with a lot of
ease and at a very low cost.
</p>
<h3 id="sec-4_15">SAGE for Scientific computing and Education enhancement </h3>
<p>Manjusha Joshi
</p>
<h4 id="sec-4_15_1">Talk/Paper Abstract </h4>
<p>
Sage is Free open source software for Mathematics.
</p>
<p>
Sage can handle long integer computations, symbolic computing,
Matrices etc. Sage is used for Cryptography, Number Theory, Graph
Theory in education field. Note book feature in Sage, allow user
to record all work on worksheet for future use. These worksheets
can be publish for information sharing, students and trainer can
exchange knowledge, share, experiment through worksheets.
</p>
<p>
Sage is an advanced computing tool which can enhance education in
India.
</p>
<h3 id="sec-4_16">Automatic Proteomic Finger Printing using Scipy </h3>
<p>Yogesh Karpate
</p>
<h4 id="sec-4_16_1">Talk/Paper Abstract </h4>
<p>The idea is to demonstrate the PyProt (Python Proteomics), an
approach to classify mass spectrometry data and efficient use of
statistical methods to look for the potential prevalent disease
markers and proteomic pattern diagnostics. Serum proteomic pattern
diagnostics can be used to differentiate samples from the patients
with and without disease. Profile patterns are generated using
surface-enhanced laser desorption and ionization (SELDI) protein
mass spectrometry. This technology has the potential to improve
clinical diagnostic tests for cancer pathologies. There are two
datasets used in this study which are taken from the FDA-NCI
Clinical Proteomics Program Databank. First data is of ovarian
cancer and second is of Premalignant Pancreatic Cancer .The Pyprot
uses the high-resolution ovarian cancer data set that was
generated using the WCX2 protein array. The ovarian cancer dataset
includes 95 controls and 121 ovarian cancer sets, where as
pancreatic cancer dataset has 101 controls and 80 pancreatic
cancer sets. There are two modules designed and implemented in
python using Numpy , Scipy and Matplotlib. There are two different
kinds of classifications implemented here, first to classify the
ovarian cancer data set. Second type focuses on randomly
commingled study set of murine sera. it explores the ability of
the low molecular weight information archive to classify and
discriminate premalignant pancreatic cancer compared to the
control animals.
</p>
<p>
A crucial issue for classification is feature selection which
selects the relevant features in order to focus the learning
search. A relaxed setting for feature selection is known as
feature ranking, which ranks the features with respect to their
relevance. Pyprot comprises of two modules; First includes
implementation of feature ranking in Python using fisher ratio and
t square statistical test to avoid large feature space. In second
module, Multilayer perceptron (MLP) feed forward neural network
model with static back propagation algorithm is used to classify
.The results are excellent and matched with databank results and
concludes that PyProt is useful tool for proteomic finger
printing.
</p>
<h3 id="sec-4_17">Natural Language Processing Using Python </h3>
<p>Vaidhy Mayilrangam
</p>
<h4 id="sec-4_17_1">Talk/Paper Abstract </h4>
<p>The purpose of this talk is to give a high-level overview of
various text mining techniques, the statistical approaches and the
interesting problems.
</p>
<p>
The talk will start with a short summary of two key areas – namely
information retrieval (IR) and information extraction (IE). We
will then discuss how to use the knowledge gained for
summarization and translation. We will talk about how to measure
the correctness of results. As part of measuring the correctness,
we will discuss about different kinds of statistical approaches
for classifying and clustering data.
</p>
<p>
We will do a short dive into NLP specific problems - identifying
sentence boundaries, parts of speech, noun and verb phrases and
named entities. We will also have a sample session on how to use
Python’s NLTK to accomplish these tasks.
</p>
<h3 id="sec-4_18">A Parallel 3D Flow Solver in Python Based on Vortex Methods </h3>
<p>Prashant Agrawal
</p>
<h4 id="sec-4_18_1">Talk/Paper Abstract </h4>
<p>A 3D flow solver for incompressible flow around arbitrary 3D
bodies is developed. The solver is based on vortex methods whose
grid-free nature makes it very general. It uses vortex particles
to represent the flow-field. Vortex particles (or blobs) are
released from the boundary, and these advect, stretch and diffuse
according to the Navier-Stokes equations.
</p>
<p>
The solver is based on a generic and extensible design. This has
been made possible mainly by following a universal theme of using
blobs in every component of the solver. Advection of the
particles is implemented using a parallel fast multipole
method. Diffusion is simulated using the Vorticity Redistribution
Technique (VRT). To control the number of blobs, merging of nearby
blobs is also performed.
</p>
<p>
Each component of the solver is parallelized. The boundary,
advection and stretching algorithms are based on the same parallel
velocity algorithm. Domain decomposition for parallel velocity
calculator is performed using Space Filling Curves. Diffusion,
which requires knowledge of each particle's neighbours, uses a
parallelized fast neighbour finder which is based on a bin data
structure. The same neighbour finder is used in merging also.
</p>
<p>
The code is written completely in Python. It is well-documented
and well-tested. The code base is around 4500 lines long. The
design follows an object oriented approach which makes it
extensible enough to add new features and alternate algorithms to
perform specific tasks.
</p>
<p>
The solver is also designed to run in a parallel environment
involving multiple processors. This parallel implementation is
written using mpi4py, an MPI implementation in Python.
</p>
<p>
Rigorous testing is performed using Python's unittest module. Some
standard example cases are also solved using the present solver.
</p>
<p>
In this talk we will outline the overall design of the solver and
the algorithms used. We discuss the benefits of Python and also
some of the current limitations with respect to parallel testing.
</p>
<h3 id="sec-4_19">Performance Evaluation of HYBRID MAC for 802.11ad: Next Generation Multi-Gbps Wi-Fi using SimPy </h3>
<p>Hemanth Chandran
</p>
<h4 id="sec-4_19_1">Talk/Paper Abstract </h4>
<p>Next generation Wireless Local Area Networks (WLAN) is targeting
at multi giga bits per second throughput by utilizing the
unlicensed spectrum available at 60 GHz, millimeter wavelength
(mmwave).Towards achieving the above goal a new standard namely
the 802.11ad is under consideration. Due to the limited range and
other typical characteristics like high path loss etc., of these
mmwave radios the requirement of the Medium Access Control (MAC)
are totally different.
</p>
<p>
The conventional MAC protocols tend to achieve different
objectives under different conditions. For example, the (Carrier
Sense Multiple Access / Collision Avoidance) CSMA/CA technique is
robust and simple and works well in overlapping network
scenarios. It is also suitable for bursty type of traffic. On the
other hand CSMA/CA is not suitable for power management since it
needs the stations to be awake always. Moreover it requires an
omni directional antenna pattern for the receiver which is
practically not feasible in 60 GHz band.
</p>
<p>
A Time Division Multiple Access (TDMA) based MAC is efficient for
Quality of Service (QoS) sensitive traffic. It is also useful for
power saving since the station knows their schedule and can
therefore power down in non scheduled periods.
</p>
<p>
For 60 GHz usages especially applications like wireless display,
sync and go, and large file transfer, TDMA appears to be a
suitable choice. Whereas for applications that require low latency
channel access (e.g. Internet access etc.)TDMA appears to be
inefficient due to the latency involved in bandwidth reservation.
</p>
<p>
Another choice is the polling MAC which is highly efficient for
the directional communication in the 60 GHz band. This provides an
improved data rates with directional communication as well as acts
as an interference mitigation scheme. On the contrary polling may
not be efficient for power saving and also not efficient to take
advantage of statistical traffic multiplexing. This technique also
leads to wastage of power due to polling the stations without
traffic to transmit.
</p>
<p>
Having the above facts in mind and considering the variety of
applications involved in the next generation WLAN systems
operating at 60 GHz, it can be concluded that no individual MAC
scheme can support the traffic requirements.
</p>
<p>
In this paper we use SimPy to do a Discrete Event Simulation
modeling of a proposed hybrid MAC protocol which dynamically
adjusts the channel times between contention and reservation based
MAC schemes, based on the traffic demand in the network.
</p>
<p>
We plan to model the problem of admission control and scheduling
using DES using SimPy. SimPy v2.1.0 is being used for the
simulation purposes of the proposed Hybrid MAC. We are new to
using Python for scientific purposes and have just begun using
this powerful tool to get meaningful and useful results. We plan
to share our learning experience and how SimPy is increasingly
becoming a useful tool (apart from regular modeling tools like
Opnet / NS2).
</p>
<h3 id="sec-4_20">PySPH: Smooth Particle Hydrodynamics with Python </h3>
<p>pankaj pandey
</p>
<h4 id="sec-4_20_1">Talk/Paper Abstract </h4>
<p>
We present a python/cython implementation of an SPH framework
called PySPH. SPH (Smooth Particle Hydrodynamics) is a numerical
technique for the solution of the continuum equations of fluid and
solid mechanics.
</p>
<p>
PySPH was written to be a tool which requires only a basic working
knowledge of python. Although PySPH may be run on distributed
memory machines, no working knowledge of parallelism is required
of the user as the same code may be run either in serial or in
parallel only by proper invocation of the mpirun command.
</p>
<p>
In PySPH, we follow the message passing paradigm, using the mpi4py
python binding. The performance critical aspects of the SPH
algorithm are optimized with cython which provides the look and
feel of python but the performance near to that of a C/C++
implementation.
</p>
<p>
PySPH is divided into three main modules. The base module provides
the data structures for the particles, and algorithms for nearest
neighbor retrieval. The sph module builds on this to describe the
interactions between particles and defines classes to manage this
interaction. These two modules provide the basic functionality as
dictated by the SPH algorithm and of these, a developer would most
likely be working with the sph module to enhance the functionality
of PySPH. The solver module typically manages the simulation being
run. Most of the functions and classes in this module are written
in pure python which makes is relatively easy to write new solvers
based on the provided functionality.
</p>
<p>
We use PySPH to solve the shock tube problem in gas dynamics and
the classical dam break problem for incompressible fluids. We also
demonstrate how to extend PySPH to solve a problem in solid
mechanics which requires additions to the sph module.
</p>
<h3 id="sec-4_21">Pictures, Songs and Python </h3>
<p>Puneeth Chaganti
</p>
<h4 id="sec-4_21_1">Talk/Paper Abstract </h4>
<p>The aim of this talk is to get students, specially undergrads
excited about Python. Most of what will be shown, is out there on
the Open web. We just wish to draw attention of the students and
get them excited about Python and possibly image processing and
may be even cognition. We hope that this talk will help retain
more participants for the tutorials and sprint sessions.
</p>
<p>
The talk will have two parts. The talk will not consist of any
deep research or amazing code. It's a mash-up of some weekend
hacks, if they could be called so. We reiterate that the idea is
not to show the algorithms or the code and ideas. It is, to show
the power that Python gives.
</p>
<p>
The first part of the talk will deal with the colour Blue. We'll
show some code to illustrate how our eyes suck at blue (1), if
they really do. But, ironically, a statistical analysis that we
did on "Rolling Stones Magazine's Top 500 Songs of All time" (2),
revealed that the occurrences of blue are more than twice the
number of occurrences of red and green! We'll show the code used
to fetch the lyrics and count the occurrences.
</p>
<p>
The second part of the talk will show some simple hacks with
images. First, a simple script that converts images into ASCII
art. We hacked up a very rudimentary algo to convert images to
ASCII and it works well for "machine generated images." Next, a
sample program that uses OpenCV (3) that can detect faces. We wish
to show OpenCV since it has some really powerful stuff for image
processing.
</p>
<p>
(1) <a href="http://nfggames.com/games/ntsc/visual.shtm">http://nfggames.com/games/ntsc/visual.shtm</a>
(2) <a href="http://web.archive.org/web/20080622145429/www.rollingstone.com/news/coverstory/500songs">http://web.archive.org/web/20080622145429/www.rollingstone.com/news/coverstory/500songs</a>
(3) <a href="http://en.wikipedia.org/wiki/OpenCV">http://en.wikipedia.org/wiki/OpenCV</a>
</p>
<h3 id="sec-4_22">Convincing Universities to include Python </h3>
<p>Krishnakant Mane
</p>
<h4 id="sec-4_22_1">Talk/Paper Abstract </h4>
<p>Python has been around for a long enough time now that it needs
serious attention from the educational institutes which teach
computer science. Today Python is known for its simple syntax
yet powerful performance (if not the fastest performance which
is any ways not needed all the time ). From Scientific computing
till graphical user interfaces and from system administration
till web application development, it is used in many
domains. However due to Industrial propaganda leading to
promotion of other interpreted languages (free or proprietary)?
Python has not got the justice in educational sector which it
deserves. This paper will talk on methodologies which can be
adopted to convince the universities for including Python in
their curriculum. The speaker will provide an insight into his
experience on success in getting Python included in some
Universities. A case of SNDT University will be discussed where
the curriculum designers have decided to have Python in their
courses from the next year. The speaker will share his ideas
which led to this inclusion. these will include,
</p>
<ul>
<li>
Begin by doing series of Python workshops
</li>
<li>
Provide information and opportunities for python based projects
</li>
<li>
make the faculties aware of teaching ease
</li>
<li>
clear the FUD regarding jobs
</li>
</ul>
<h3 id="sec-4_23">Python based Galaxy workflow integration on GARUDA Grid </h3>
<p>Harikrishna
</p>
<h4 id="sec-4_23_1">Talk/Paper Abstract </h4>
<p>Bioinformatics applications being complex problem involving
multiple comparisons, alignment, mapping and analysis can be
managed better using workflow solutions. Galaxy is an open web
based platform developed in Python for genomic research. Python
is a light weight dynamic language making Galaxy to be modular
and expandable. Bioinformatics applications being compute and
data intensive scale well in grid computing environments. In
this paper we describe bringing the Galaxy workflow to the
Garuda Grid computing infrastructure for enabling bioinformatics
applications. GAURDA grid is an aggregation of heterogeneous
resources and advanced capabilities for scientific
applications. Here we present the integration of galaxy workflow
tool with GARUDA grid middleware to enable computational
biologists to perform complex problems on the grid environment
through a web browser.
</p>
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