Perry Greenfield -
- - - -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. -
- - - - - - -Fernando Perez -
- - - -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. -
- - - - - - -Asokan Pichai -
- - - -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 :-)] -
- - - - - - -John Hunter -
- - - -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. -
- - - - - -Prabhu Ramachandran -
- - - -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 -(http://www.sagemath.org). 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. -
- - - - - -Satrajit Ghosh -
- - - -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. -
- - - - - - - - - -Stéfan van der Walt -
- - - -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! -
- - - - - - -Jarrod Millman -
- - - -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.
- -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. -
- - - - - - - - -Farhat Habib -
- - - -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. -
- --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. -
- - - - - - -Arun C. H. -
- - - -Host software using Python interpreter language to communicate -with the USB Mass Storage class device is developed and -tested. The usic18F4550.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. -
- - - - - - - - -Arun C. H. -
- - - -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. -
- - - - - - - - -Shantanu Choudhary -
- - - -This talk would be covering usage of Python in different scenarios which helped me through my work: -
Hrishikesh Deshpande -
- - - -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. -
- - - - - - - - -Dharhas Pothina -
- - - --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. -
--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. -
--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. -
--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. -
- - - - - - -Nek Sharan -
- - - -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. -
--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. -
- - - - - - - - -Erroju Rama Krishna -
- - - --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.) -
--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. -
--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 -
- - - - - - - -Karthikeyan selvaraj -
- - - -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. -
--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. -
- --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. -
--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. -
--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. -
- - - - - - - - -Georges Khaznadar -
- - - -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 -http://www.iuac.res.in/~elab/phoenix/) featuring electric analog -and digital I/O interfaces, some unexpensive discrete components -and a live (bootable) USB stick. -
--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. -
--The hardware design of PHOENIX box is freely available. -
--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. -
--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. -
--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. -
- - - - - - - -Shubham Chakraborty -
- - - -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. -
- - - - - - - - -Ajith Kumar -
- - - -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. -
--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. -
- - - - - - -Ramakrishna Reddy Yekulla -
- - - -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. -
- - - - - - - - -Jayesh Gandhi -
- - - -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. -
--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. -
--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. -
- - - - - - - -Manjusha Joshi -
- - - --Sage is Free open source software for Mathematics. -
--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. -
--Sage is an advanced computing tool which can enhance education in -India. -
- - - - - - - - - -Yogesh Karpate -
- - - -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. -
--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. -
- - - - - - - - - - -Vaidhy Mayilrangam -
- - - -The purpose of this talk is to give a high-level overview of -various text mining techniques, the statistical approaches and the -interesting problems. -
--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. -
--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. -
- - - - - - - -Prashant Agrawal -
- - - -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. -
--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. -
--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. -
--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. -
--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. -
--Rigorous testing is performed using Python's unittest module. Some -standard example cases are also solved using the present solver. -
--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. -
- - - - - - - -Hemanth Chandran -
- - - -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. -
--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. -
--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. -
--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. -
--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. -
--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. -
--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. -
--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). -
- - - - - - - - - -pankaj pandey -
- - - --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. -
--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. -
--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. -
--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. -
--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. -
- - - - - - -Puneeth Chaganti -
- - - -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. -
--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. -
--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. -
--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. -
--(1) http://nfggames.com/games/ntsc/visual.shtm -(2) http://web.archive.org/web/20080622145429/www.rollingstone.com/news/coverstory/500songs -(3) http://en.wikipedia.org/wiki/OpenCV -
- - - - - - - - - -Krishnakant Mane -
- - - -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, -
-Harikrishna -
- - - -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. -
- +