| Time | Agenda | Speaker | Title |
|---|---|---|---|
| Time | Speaker | Title | |
| 9:00-9:30 | Inauguration | ||
| 9:30-10:30 | Keynote | Perry Greenfield | How Python Slithered into Astronomy |
| 10:30-10:45 | Tea Break | ||
| 10:45-11:30 | Special Talk 1 | Fernando Perez | IPython : Beyond the Simple Shell |
| 11:30-12:00 | Invited Talk 1 | Asokan Pichai | Teaching Programming with Python |
| 12:00-13:15 | Talks | ||
| 13:15-14:15 | Lunch | ||
| 14:15-14:45 | Lightning Talks | ||
| 14:45-15:55 | Talks | ||
| 15:55-16:10 | Tea Break | ||
| 16:10-17:30 | Talks | ||
| 09:00-09:30 | Inauguration | ||
| 09:30-10:30 | Perry Greenfield | Keynote: How Python Slithered into Astronomy | |
| 10:30-10:45 | Tea Break | ||
| 10:45-11:30 | Fernando Perez | IPython : Beyond the Simple Shell | |
| 11:30-12:00 | Asim Mittal | Interactive interfaces and Gesture recognition using Python | |
| 12:00-12:20 | Jayesh Gandhi | Microcontroller experiment and its simulation using Python | |
| 12:20-12:50 | Vaidhy Mayilrangam | Natural Language Processing Using Python | |
| 12:50-13:20 | Georges Khaznadar | Live media for training in experimental sciences | |
| 13:20-14:20 | Lunch | ||
| 14:20-14:30 | Shubham Chakraborty | Use of Python and Phoenix-M interface in Robotics | |
| 14:30-14:40 | Erroju Rama Krishna | Simplified and effective Network Simulation using ns-3 | |
| 14:40-14:50 | More Lightning Talks | ||
| 14:50-15:20 | Asokan Pichai | Teaching Programming with Python | |
| 15:20-15:40 | Hemanth Chandran | Performance Evaluation of HYBRID MAC for 802.11ad: Next Generation Multi-Gbps Wi-Fi using SimPy | |
| 15:40-16:00 | Karthikeyan selvaraj | PyCenter | |
| 16:00-16:15 | Tea Break | ||
| 16:15-16:45 | Satrajit Ghosh | Nipype: Opensource platform for unified and replicable interaction with existing neuroimaging tools | |
| 16:45-17:05 | Nek Sharan | Parallel Computation of Axisymmetric Jets | |
| 17:05-17:25 | pankaj pandey | PySPH: Smooth Particle Hydrodynamics with Python |
| Time | Agenda | Speaker | Title |
|---|---|---|---|
| Time | Speaker | Title | |
| 9:00-10:00 | Special Talk 2 | John Hunter | matplotlib: Beyond the simple plot |
| 10:00-10:45 | Invited Talk 2 | Prabhu Ramachandran | Mayavi : Bringing Data to Life |
| 10:45-11:00 | Tea Break | ||
| 11:00-13:15 | Talks | ||
| 13:15-14:15 | Lunch | ||
| 14:15-14:45 | Lightning Talks | ||
| 14:45-15:55 | Talks | ||
| 15:55-16:10 | Tea Break | ||
| 16:10-17:30 | Talks | ||
| 09:00-10:00 | John Hunter | matplotlib: Beyond the simple plot | |
| 10:00-10:45 | Prabhu Ramachandran | Mayavi : Bringing Data to Life | |
| 10:45-11:00 | Tea | ||
| 11:00-11:45 | Stéfan van der Walt | ||
| 11:45-12:15 | Dharhas Pothina | HyPy & HydroPic: Using python to analyze hydrographic survey data | |
| 12:15-12:35 | Prashant Agrawal | A Parallel 3D Flow Solver in Python Based on Vortex Methods | |
| 12:35-13:05 | Ajith Kumar | Python in Science Experiments using Phoenix | |
| 13:05-14:05 | Lunch | ||
| 14:05-14:15 | Arun C. H. | USB CONNECTIVITY USING PYTHON | |
| 14:15-14:25 | Arun C. H. | Automation of an Optical Spectrometer | |
| 14:25-14:35 | More Lightning Talks | ||
| 14:35-14:55 | Krishnakant Mane | ||
| 14:55-15:15 | Shantanu Choudhary | "Python" Swiss army knife for Prototyping, Research and Fun. | |
| 15:15-15:35 | Puneeth Chaganti | Pictures, Songs and Python | |
| 15:35-15:55 | Hrishikesh Deshpande | Wavelet based denoising of ECG using Python | |
| 15:55-16:10 | Tea-Break | ||
| 16:10-16:40 | Jarrod Millman | ||
| 16:40-17:00 | Ramakrishna Reddy Yekulla | Building and Packaging your Scientific Python Application For Linux Distributions | |
| 17:00-17:20 | Yogesh Karpate | Automatic Proteomic Finger Printing using Scipy | |
| 17:20-17:40 | Manjusha Joshi | SAGE for Scientific computing and Education enhancement |
Perry Greenfield
-How Python Slithered into Astronomy -
+-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. +
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 : Beyond the Simple Shell +
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.
-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
-Teaching Programming with Python -
+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 :-)] - +
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: Beyond the simple plot -
+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. +
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. +
+ + + + + + + + + + +Asim Mittal +
+ + + +Gesture recognition has caught on in a big way, but methods of +integrating it with intuitive control still remain largely +expensive and closed source. +
++This talk aims at combining the IR tracking ability of the +Nintendo Wiimote along with a little scientific computing in +Python (Linux) to create a means of intuitively controlling +applications and the operating system, using gestures drawn in 2D +space using your fingers. +
++This talk is an extension of the work that I have done from my +talk at PyCon India. +
++You can find out more about my work and ongoing research on my +blog: http://baniyakiduniya.blogspot.com +
+ + + + + + + + +Arun C. H. +
-Mayavi : Bringing Data to Life + + +
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 +
+ + + +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). +
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 +
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