11 <colgroup><col align="right" /><col align="left" /><col align="left" /><col align="left" />

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

    14 <tr><th scope="col">Time</th><th scope="col">Agenda</th><th scope="col">Speaker</th><th scope="col">Title</th></tr>

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

    17 <tr><td>9:00-9:30</td><td>Inauguration</td><td></td><td></td></tr>

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

    18 <tr><td>9:30-10:30</td><td>Keynote</td><td>Perry Greenfield</td><td><a href="#sec-3">How Python Slithered into Astronomy</a></td></tr>

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

    19 <tr><td>10:30-10:45</td><td>Tea Break</td><td></td><td></td></tr>

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

    20 <tr><td>10:45-11:30</td><td>Special Talk 1</td><td>Fernando Perez</td><td><a href="#sec-4">IPython : Beyond the Simple Shell</a></td></tr>

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

    21 <tr><td>11:30-12:00</td><td>Invited Talk 1</td><td>Asokan Pichai</td><td><a href="#sec-5">Teaching Programming with Python</a></td></tr>

    19 <tr><td class="right">11:30-12:00</td><td class="left">Asim Mittal</td><td class="left"><a href="#sec-4_1">Interactive interfaces and Gesture recognition using Python</a></td></tr>

    22 <tr><td>12:00-13:15</td><td>Talks</td><td></td><td></td></tr>

    20 <tr><td class="right">12:00-12:20</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>

    23 <tr><td>13:15-14:15</td><td>Lunch</td><td></td><td></td></tr>

    21 <tr><td class="right">12:20-12:50</td><td class="left">Vaidhy Mayilrangam</td><td class="left"><a href="#sec-4_17">Natural Language Processing Using Python</a></td></tr>

    24 <tr><td>14:15-14:45</td><td>Lightning Talks</td><td></td><td></td></tr>

    22 <tr><td class="right">12:50-13:20</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>

    25 <tr><td>14:45-15:55</td><td>Talks</td><td></td><td></td></tr>

    23 <tr><td class="right">13:20-14:20</td><td class="left"></td><td class="left">Lunch</td></tr>

    26 <tr><td>15:55-16:10</td><td>Tea Break</td><td></td><td></td></tr>

    24 <tr><td class="right">14:20-14:30</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>

    27 <tr><td>16:10-17:30</td><td>Talks</td><td></td><td></td></tr>

    25 <tr><td class="right">14:30-14:40</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>

    26 <tr><td class="right">14:40-14:50</td><td class="left"></td><td class="left">More Lightning Talks</td></tr>

    27 <tr><td class="right">14:50-15:20</td><td class="left">Asokan Pichai</td><td class="left"><a href="#sec-3_3">Teaching Programming with Python</a></td></tr>

    28 <tr><td class="right">15:20-15:40</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>

    29 <tr><td class="right">15:40-16:00</td><td class="left">Karthikeyan selvaraj</td><td class="left"><a href="#sec-4_9">PyCenter</a></td></tr>

    30 <tr><td class="right">16:00-16:15</td><td class="left"></td><td class="left">Tea Break</td></tr>

    31 <tr><td class="right">16:15-16:45</td><td class="left">Satrajit Ghosh</td><td class="left"><a href="#sec-3_6">Nipype: Opensource platform for unified and replicable interaction with existing neuroimaging tools</a></td></tr>

    32 <tr><td class="right">16:45-17:05</td><td class="left">Nek Sharan</td><td class="left"><a href="#sec-4_7">Parallel Computation of Axisymmetric Jets</a></td></tr>

    33 <tr><td class="right">17:05-17:25</td><td class="left">pankaj pandey</td><td class="left"><a href="#sec-4_20">PySPH: Smooth Particle Hydrodynamics with Python</a></td></tr>

    35 <colgroup><col align="right" /><col align="left" /><col align="left" /><col align="left" />

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

    38 <tr><th scope="col">Time</th><th scope="col">Agenda</th><th scope="col">Speaker</th><th scope="col">Title</th></tr>

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

    41 <tr><td>9:00-10:00</td><td>Special Talk 2</td><td>John Hunter</td><td><a href="#sec-6">matplotlib: Beyond the simple plot</a></td></tr>

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

    42 <tr><td>10:00-10:45</td><td>Invited Talk 2</td><td>Prabhu Ramachandran</td><td><a href="#sec-7">Mayavi : Bringing Data to Life</a></td></tr>

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

    43 <tr><td>10:45-11:00</td><td>Tea Break</td><td></td><td></td></tr>

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

    44 <tr><td>11:00-13:15</td><td>Talks</td><td></td><td></td></tr>

    57 <tr><td class="right">11:00-11:45</td><td class="left">Stéfan van der Walt</td><td class="left"></td></tr>

    45 <tr><td>13:15-14:15</td><td>Lunch</td><td></td><td></td></tr>

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

    46 <tr><td>14:15-14:45</td><td>Lightning Talks</td><td></td><td></td></tr>

    59 <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>

    47 <tr><td>14:45-15:55</td><td>Talks</td><td></td><td></td></tr>

    60 <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>

    48 <tr><td>15:55-16:10</td><td>Tea Break</td><td></td><td></td></tr>

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

    49 <tr><td>16:10-17:30</td><td>Talks</td><td></td><td></td></tr>

    62 <tr><td class="right">14:05-14:15</td><td class="left">Arun C. H.</td><td class="left"><a href="#sec-4_2">USB CONNECTIVITY USING PYTHON</a></td></tr>

    63 <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>

    64 <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>

    65 <tr><td class="right">14:35-14:55</td><td class="left">Krishnakant Mane</td><td class="left"></td></tr>

    66 <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>

    67 <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>

    68 <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>

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

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

    71 <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>

    72 <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>

    73 <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>

    53 <h2 id="sec-3">Keynote by Perry Greenfield </h2>

    77 

    78 

    79 

    80 

    81 

    82 

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

    84 

    85 

    86 

    87 

    88 

    89 

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

    91 

    58 <h3 id="sec-3_1">Title </h3>

    96 

    59 

    97 

    60 <p>How Python Slithered into Astronomy

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

    61 </p>

    99 

    62 

   100 

    63 <h3 id="sec-3_2">Talk/Paper Abstract </h3>

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

    64 

   102 the Hubble Space Telescope. From humble beginnings as a glue

    65 <p>

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

    66 I will talk about how Python was used to solve our problems for the

   104 our scientific software for HST and the next large space

    67 Hubble Space Telescope. From humble beginnings as a glue element for

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

    68 our legacy software, it has become a cornerstone of our scientific

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

    69 software for HST and the next large space telescope, the James Webb

   107 of essential elements for scientific Python and where future

    70 Space Telescope, as well as many other astronomy projects. The talk

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

    71 will also cover some of the history of essential elements for

   109 software.

    72 scientific Python and where future work is needed, and why Python is

   110 </p>

    73 so well suited for scientific software.

   111 

    74 </p>

   112 

    75 

   113 

    76 

   114 

    77 <h2 id="sec-4">Special Talk 1 </h2>

   115 

   116 

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

   118 

    82 <h3 id="sec-4_1">Title </h3>

   123 

    83 

   124 

    84 <p>IPython : Beyond the Simple Shell

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

    85 </p>

   126 

    86 

   127 

    87 <h3 id="sec-4_2">Talk/Paper Abstract: </h3>

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

    88 

   129 Python that extends the capabilities of the Python shell with

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

   130 operating system access, powerful object introspection,

    90 that extends the capabilities of the Python shell with operating

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

    91 system access, powerful object introspection, customizable "magic"

   132 contains a set of tools to control parallel computations via

    92 commands and many more features.  It also contains a set of tools to

   133 high-level interfaces that can be used either interactively or

    93 control parallel computations via high-level interfaces that can be

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

    94 used either interactively or in long-running batch mode.

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

    95 

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

    96 In this talk I will outline some of the main features of IPython as it

   137 developments. Using the high performance ZeroMQ networking

    97 has been widely adopted by the scientific Python user base, and will

   138 library, we have recently restructured IPython to decouple the

    98 then focus on recent developments.  Using the high performance ZeroMQ

   139 kernel executing user code from the control interface. This

    99 networking library, we have recently restructured IPython to decouple

   140 allows us to expose multiple clients with different

   100 the kernel executing user code from the control interface.  This

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

   101 allows us to expose multiple clients with different capabilities,

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

   102 including a terminal-based one, a rich Qt client and a web-based one

   143 with the new HTML5 matplotlib backend, this architecture opens

   103 with full matplotlib support. In conjunction with the new HTML5

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

   104 matplotlib backend, this architecture opens the door for a rich

   145 collaborative and parallel computing. There is much interesting

   105 web-based environment for interactive, collaborative and parallel

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

   106 computing.  

   147 participants at the sprints during the conference to join this

   107 

   148 effort.

   108 There is much interesting development to be done on this front, and I

   149 </p>

   109 hope to encourage participants at the sprints during the conference to

   150 

   110 join this effort.

   151 

   111 

   152 

   112 </p>

   153 

   113 

   154 

   114 <h2 id="sec-5">Invited Talk 1 </h2>

   155 

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

   157 

   119 <h3 id="sec-5_1">Title </h3>

   162 

   120 

   163 

   121 <p>Teaching Programming with Python

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

   122 </p>

   165 

   123 

   166 

   124 <h3 id="sec-5_2">Talk/Paper Abstract: </h3>

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

   125 

   168 Software Engineers and software job aspirants. Before starting

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

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

   127 Engineers and software job aspirants. Before starting on the language,

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

   128 platform specific areas I teach a part I refer to as Problem Solving

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

   129 and Programming Logic. I have used Python for this portion of training

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

   130 in the last 12+years. In this talk I wish to share my experiences and

   173 intended at Teachers, Trainers, Python Evangelists, and HR

   131 approaches.

   174 Managers [if they lose their way and miraculously find

   132 

   175 themselves in SciPy :-)]

   133 This talk is intended at Teachers, Trainers, Python Evangelists, and

   176 </p>

   134 HR Managers [if they lose their way and miraculously find themselves in SciPy :-)]

   177 

   135 

   178 

   136 </p>

   179 

   137 

   180 

   138 

   181 

   139 <h2 id="sec-6">Special Talk 2 </h2>

   182 

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

   184 

   144 <h3 id="sec-6_1">Title </h3>

   189 

   145 

   190 

   146 <p>matplotlib: Beyond the simple plot

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

   147 </p>

   192 

   148 

   193 

   149 <h3 id="sec-6_2">Talk/Paper Abstract: </h3>

   194 <p>matplotlib, a python package for making sophisticated

   150 

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

   151 <p>matplotlib, a python package for making sophisticated publication

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

   152 quality 2D graphics, and some 3D, has long supported a wide variety

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

   153 of basic plotting types such line graphs, bar charts, images,

   198 look at some of the new features and performance enhancements in

   154 spectral plots, and more.  In this talk, we will look at some of the

   199 matplotlib as well as some of the comparatively undiscovered

   155 new features and performance enhancements in matplotlib as well as

   200 features such as interacting with your data and graphics, and

   156 some of the comparatively undiscovered features such as interacting

   201 animating plot elements with the new animations API. We will

   157 with your data and graphics, and animating plot elements with the

   202 explore the performance with large datasets utilizing the new

   158 new animations API.  We will explore the performance with large

   203 path simplification algorithm, and discuss areas where

   159 datasets utilizing the new path simplification algorithm, and

   204 performance improvements are still needed. Finally, we will

   160 discuss areas where performance improvements are still needed.

   205 demonstrate the new HTML5 backend, which in combination with the

   161 Finally, we will demonstrate the new HTML5 backend, which in

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

   162 combination with the new HTML5 IPython front-end under development,

   207 interactive Python shell with interactive graphics in a web

   163 will enable an interactive Python shell with interactive graphics in

   208 browser.

   164 a web browser.

   209 </p>

   165 </p>

   210 

   166 

   211 

   167 

   212 

   168 <h2 id="sec-7">Invited Talk 2 </h2>

   213 

   214 

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

   216 

   173 <h3 id="sec-7_1">Title </h3>

   221 

   174 

   222 

   175 <p>Mayavi : Bringing Data to Life

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

   176 </p>

   224 

   177 

   225 

   178 <h3 id="sec-7_2">Talk/Paper Abstract: </h3>

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

   179 

   227 Python. It includes both a standalone user interface along with

   180 <p>Mayavi is a powerful 3D plotting package implemented in Python.  It

   228 a powerful yet simple scripting interface. The key feature of

   181 includes both a standalone user interface along with a powerful yet

   229 Mayavi though is that it allows a Python user to rapidly

   182 simple scripting interface.  The key feature of Mayavi though is that it

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

   183 allows a Python user to rapidly visualize data in the form of NumPy

   231 basic features, Mayavi has some advanced features. These

   184 arrays.  Apart from these basic features, Mayavi has some advanced

   232 include, automatic script recording, embedding into a custom

   185 features.  These include, automatic script recording, embedding into a

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

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

   188 (http://www.sagemath.org).

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

   189 

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

   190 We will first rapidly demonstrate these key features of Mayavi.  We will

   237 underlying technologies like enthought.traits, traitsUI and TVTK

   191 then discuss some of the underlying technologies like enthought.traits,

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

   192 traitsUI and TVTK that form the basis of Mayavi.  The objective of this

   239 demonstrate the wide range of capabilities that both Mayavi and

   193 is to demonstrate the wide range of capabilities that both Mayavi and

   195 

   241 </p>

   196 </p>

   242 

   243 

   244 

   245 

   246 

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

   248 

   249 

   250 <p>Satrajit Ghosh

   251 </p>

   252 

   253 

   254 

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

   256 

   257 

   258 <p>Current neuroimaging software offer users an incredible

   259 opportunity to analyze their data in different ways, with

   260 different underlying assumptions. However, this has resulted in

   261 a heterogeneous collection of specialized applications without

   262 transparent interoperability or a uniform operating

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

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

   265 solves these issues by providing a uniform interface to existing

   266 neuroimaging software and by facilitating interaction between

   267 these packages within a single workflow. Nipype provides an

   268 environment that encourages interactive exploration of

   269 neuroimaging algorithms from different packages, eases the

   270 design of workflows within and between packages, and reduces the

   271 learning curve necessary to use different packages. Nipype is

   272 creating a collaborative platform for neuroimaging software

   273 development in a high-level language and addressing limitations

   274 of existing pipeline systems.

   275 </p>

   276 

   277 

   278 

   279 

   280 

   281 

   282 

   283 

   284 

   285 

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

   287 

   288 

   289 

   290 

   291 

   292 

   293 <h3 id="sec-4_1">Interactive interfaces and Gesture recognition using Python </h3>

   294 

   295 

   296 <p>Asim Mittal 

   297 </p>

   298 

   299 

   300 

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

   302 

   303 

   304 <p>Gesture recognition has caught on in a big way, but methods of

   305 integrating it with intuitive control still remain largely

   306 expensive and closed source.

   307 </p>

   308 <p>

   309 This talk aims at combining the IR tracking ability of the

   310 Nintendo Wiimote along with a little scientific computing in

   311 Python (Linux) to create a means of intuitively controlling

   312 applications and the operating system, using gestures drawn in 2D

   313 space using your fingers.

   314 </p>

   315 <p>

   316 This talk is an extension of the work that I have done from my

   317 talk at PyCon India.

   318 </p>

   319 <p>

   320 You can find out more about my work and ongoing research on my

   321 blog: <a href="http://baniyakiduniya.blogspot.com">http://baniyakiduniya.blogspot.com</a>

   322 </p>

   323 

   324 

   325 

   326 

   327 

   328 

   329 

   330 

   331 <h3 id="sec-4_2">USB CONNECTIVITY USING PYTHON </h3>

   332 

   333 

   334 <p>Arun C. H. 

   335 </p>

   336 

   337 

   338 

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

   340 

   341 

   342 <p>Host software using Python interpreter language to communicate

   343 with the USB Mass Storage class device is developed and

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

   345 functions needed to configure USB is developed. The Python

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

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

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

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

   350 Interface Generator (SWIG) is a middle interface between Python

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

   352 user to initialize and configure USB through a convenient

   353 scripting layer. The module is built around libusb which can

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

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

   356 library allows user space applications to access any USB device on

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

   358 code. A simple data acquisition system for measuring analog

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

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

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

   362 hardware consists of a PIC18F4550 micro-controller and the

   363 essential components needed for USB configuration.

   364 </p>

   365 

   366 

   367 

   368 

   369 

   370 

   371 

   372 

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

   374 

   375 

   376 <p>Arun C. H. 

   377 </p>

   378 

   379 

   380 

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

   382 

   383 

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

   385 Spectrometer in order to precisely monitor angles, change

   386 dispersing angle and hence measure wave length of light using a

   387 data logger, necessary hardware and Python. Automating instruments

   388 through programs provides great deal of power, flexibility and

   389 precision. Optical Spectrometers are devices which analyze the

   390 wave length of light, and are typically used to identify

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

   392 light is dispersed using a grating and the dispersed light is

   393 measured using a photo transistor. The signal is processed and

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

   395 diffraction are all done using the Python. The angle of

   396 diffraction is varied by rotating the detector to pick up lines

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

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

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

   400 interfaced to the computer through a serial port. The stepper

   401 motor is also interfaced to the computer through another serial

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

   403 implemented in a Linux environment.

   404 </p>

   405 

   406 

   407 

   408 

   409 

   410 

   411 

   412 

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

   414 

   415 

   416 <p>Shantanu Choudhary 

   417 </p>

   418 

   419 

   420 

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

   422 

   423 

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

   425 </p><ul>

   426 <li>

   427 Small mlab(Mayavi) scripts which helped in better understanding

   428 of problem statement.

   429 </li>

   430 <li>

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

   432 used for Open Source Animation movie project

   433 Tube(tube.freefac.org)

   434 </li>

   435 <li>

   436 PyOpenCL Python's interfacing for OpenCL which helped in

   437 prototyping and speed up of application.

   438 </li>

   439 </ul>

   440 

   441 

   442 

   443 

   444 

   445 

   446 

   447 

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

   449 

   450 

   451 <p>Hrishikesh Deshpande 

   452 </p>

   453 

   454 

   455 

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

   457 

   458 

   459 <p>The python module "RemNoise" is presented. It allows user to

   460 automatically denoise one-dimensional signal using wavelet

   461 transform. It also removes baseline wandering and motion

   462 artifacts. While RemNoise is developed primarily for biological

   463 signals like ECG, its design is generic enough that it should be

   464 useful to applications involving one-dimensional signals. The

   465 basic idea behind this work is to use multi-resolution property of

   466 wavelet transform that allows to study non-stationary signals in

   467 greater depth. Any signal can be decomposed into detail and

   468 approximation coefficients, which can further be decomposed into

   469 higher levels and this approach can be used to analyze the signal

   470 in time-frequency domain. The very first step in any

   471 data-processing application is to pre-process the data to make it

   472 noise-free. Removing noise using wavelet transform involves

   473 transforming the dataset into wavelet domain, zero out all

   474 transform coefficients using suitable thresholding method and

   475 reconstruct the data by taking its inverse wavelet transform. This

   476 module makes use of PyWavelets, Numpy and Matplotlib libraries in

   477 Python, and involves thresholding wavelet coefficients of the data

   478 using one of the several thresholding methods. It also allows

   479 multiplicative threshold rescaling to take into consideration

   480 detail coefficients in each level of wavelet decomposition. The

   481 user can select wavelet family and level of decompositions as

   482 required. To evaluate the module, we experimented with several

   483 complex one-dimensional signals and compared the results with

   484 equivalent procedures in MATLAB. The results showed that RemNoise

   485 is excellent module to preprocess data for noise-removal.

   486 </p>

   487 

   488 

   489 

   490 

   491 

   492 

   493 

   494 

   495 <h3 id="sec-4_6">HyPy &amp; HydroPic: Using python to analyze hydrographic survey data </h3>

   496 

   497 

   498 <p>Dharhas Pothina 

   499 </p>

   500 

   501 

   502 

   503 <h4 id="sec-4_6_1">Talk/Paper Abstract </h4>

   504 

   505 

   506 

   507 <p>

   508 The Texas Water Development Board(TWDB) collects hydrographic

   509 survey data in lakes, rivers and estuaries. The data collected

   510 includes single, dual and tri-frequency echo sounder data

   511 collected in conjunction with survey grade GPS systems. This raw

   512 data is processed to develop accurate representations of

   513 bathymetry and sedimentation in the water bodies surveyed.

   514 </p>

   515 <p>

   516 This talk provides an overview of how the Texas Water Development

   517 Board (TWDB) is using python to streamline and automate the

   518 process of converting raw hydrographic survey data to finished

   519 products that can then be used in other engineering applications

   520 such as hydrodynamic models, determining lake

   521 elevation-area-capacity relationships and sediment contour maps,

   522 etc.

   523 </p>

   524 <p>

   525 The first part of this talk will present HyPy, a python module

   526 (i.e. function library) for hydrographic survey data

   527 analysis. This module contains functions to read in data from

   528 several brands of depth sounders, conduct anisotropic

   529 interpolations along river channels, apply tidal and elevation

   530 corrections, apply corrections to boat path due to loss of GPS

   531 signals as well as a variety of convenience functions for dealing

   532 with spatial data.

   533 </p>

   534 <p>

   535 In the second part of the talk we present HydroPic, a simple

   536 Traits based application built of top of HyPy. HydroPic is

   537 designed to semi-automate the determination of sediment volume in

   538 a lake. Current techniques require the visual inspection of images

   539 of echo sounder returns along each individual profile. We show

   540 that this current methodology is slow and subject to high human

   541 variability. We present a new technique that uses computer vision

   542 edge detection algorithms available in python to semi-automate

   543 this process. HydroPic wraps these algorithms into a easy to use

   544 interface that allows efficient processing of data for an entire

   545 lake.

   546 </p>

   547 

   548 

   549 

   550 

   551 

   552 

   553 <h3 id="sec-4_7">Parallel Computation of Axisymmetric Jets </h3>

   554 

   555 

   556 <p>Nek Sharan 

   557 </p>

   558 

   559 

   560 

   561 <h4 id="sec-4_7_1">Talk/Paper Abstract </h4>

   562 

   563 

   564 <p>Flow field for imperfectly expanded jet has been simulated using

   565 Python for prediction of jet screech frequency. This plays an

   566 important role in the design of advanced aircraft engine nozzle,

   567 since screech could cause sonic fatigue failure. For computation,

   568 unsteady axisymmetric Navier-Stokes equation is solved using fifth

   569 order Weighted Essentially Non-Oscillatory (WENO) scheme with a

   570 subgrid scale Large-Eddy Simulation (LES) model. Smagorinsky’s

   571 eddy viscosity model is used for subgrid scale modeling with

   572 second order (Total Variation Diminishing) TVD Runge Kutta time

   573 stepping. The performance of Python code is enhanced by using

   574 different Cython constructs like declaration of variables and

   575 numpy arrays, switching off bound check and wrap around etc. Speed

   576 up obtained from these methods have been individually clocked and

   577 compared with the Python code as well as an existing in-house C

   578 code. Profiling was used to highlight and eliminate the expensive

   579 sections of the code.

   580 </p>

   581 <p>

   582 Further, both shared and distributed memory architectures have

   583 been employed for parallelization. Shared memory parallel

   584 processing is implemented through a thread based model by manual

   585 release of Global Interpreter Lock (GIL). GIL ensures safe and

   586 exclusive access of Python interpreter internals to running

   587 thread. Hence while one thread is running with GIL the other

   588 threads are put on hold until the running thread ends or is forced

   589 to wait. Therefore to run two threads simultaneously, GIL was

   590 manually released using "with nogil" statement. The relative

   591 independence of radial and axial spatial derivative computation

   592 provides an option of putting them in parallel threads. On the

   593 other hand, distributed memory parallel processing is through MPI

   594 based domain decomposition, where the domain is split radially

   595 with an interface of three grid points. Each sub-domain is

   596 delegated to a different processor and communication, in the form

   597 of message transmission, ensures update of interface grid

   598 points. Performance analyses with increase in number of processors

   599 indicate a trade-off between computation and communication. A

   600 combined thread and MPI based model is attempted to harness the

   601 benefits from both forms of architectures.

   602 </p>

   603 

   604 

   605 

   606 

   607 

   608 

   609 

   610 

   611 <h3 id="sec-4_8">Simplified and effective Network Simulation using ns-3 </h3>

   612 

   613 

   614 <p>Erroju Rama Krishna 

   615 </p>

   616 

   617 

   618 

   619 <h4 id="sec-4_8_1">Talk/Paper Abstract </h4>

   620 

   621 

   622 

   623 <p>

   624 Network simulation has great significance in the research areas of

   625 modern networks. The ns-2 is the popular simulation tool which

   626 proved this, in the successive path of ns-2 by maintaining the

   627 efficiency of the existing mechanism it has been explored with a

   628 new face and enhanced power of python scripting in ns-3. Python

   629 scripting can be added to legacy projects just as well as new

   630 ones, so developers don't have to abandon their old C/C++ code

   631 libraries, but in the ns-2 it is not possible to run a simulation

   632 purely from C++ (i.e., as a main() program without any OTcl), ns-3

   633 does have new capabilities (such as handling multiple interfaces

   634 on nodes correctly, use of IP addressing and more alignment with

   635 Internet protocols and designs, more detailed 802.11 models, etc.)

   636 </p>

   637 <p>

   638 In ns-3, the simulator is written entirely in C++, with optional

   639 Python bindings. Simulation scripts can therefore be written in

   640 C++ or in Python. The results of some simulations can be

   641 visualized by nam, but new animators are under development. Since

   642 ns-3 generates pcap packet trace files, other utilities can be

   643 used to analyze traces as well.

   644 </p>

   645 <p>

   646 In this paper the efficiency and effectiveness of IP addressing

   647 simulation model of ns-3 is compared with the ns-2 simulation

   648 model,ns-3 model consisting of the scripts written in Python which

   649 makes the modeling simpler and effective

   650 </p>

   651 

   652 

   653 

   654 

   655 

   656 

   657 

   658 <h3 id="sec-4_9">PyCenter </h3>

   659 

   660 

   661 <p>Karthikeyan selvaraj 

   662 </p>

   663 

   664 

   665 

   666 <h4 id="sec-4_9_1">Talk/Paper Abstract </h4>

   667 

   668 

   669 <p>The primary objective is defining a centralized testing

   670 environment and a model of testing framework which integrates all

   671 projects in testing in a single unit. 

   672 </p>

   673 <p>

   674 The implementation of concurrent processing systems and adopting

   675 client server architecture and with partitioned server zones for

   676 environment manipulation, allows the server to run test requests

   677 from different projects with different environment and testing

   678 requests. The implementation provides features of auto-test

   679 generation, scheduled job run from server, thin and thick clients.

   680 </p>

   681 

   682 <p>

   683 The core engine facilitates the management of tests from all the

   684 clients with priority and remote scheduling. It has an extended

   685 configuration utility to manipulate test parameters and watch

   686 dynamic changes. It not only acts as a request pre-preprocessor

   687 but also a sophisticated test bed by its implementation. It is

   688 provided with storage and manipulation segment for every

   689 registered project in the server zone. The system schedules and

   690 records events and user activities thereby the results can be

   691 drilled and examined to core code level with activates and system

   692 states at the test event point.

   693 </p>

   694 <p>

   695 The system generates test cases both in human readable as well as

   696 executable system formats. The generated tests are based on a

   697 pre-defined logic in the system which can be extended to adopt new

   698 cases based on user requests. These are facilitated by a template

   699 system which has a predefined set of cases for various test types

   700 like compatibility, load, performance, code coverage, dependency

   701 and compliance testing. It is also extended with capabilities like

   702 centralized directory systems for user management with roles and

   703 privileges for authentication and authorization, global mailer

   704 utilities, Result consolidator and Visualizer.

   705 </p>

   706 <p>

   707 With the effective implementation of the system with its minimal

   708 requirements, the entire testing procedure can be automated with

   709 the testers being effectively used for configuring, ideating and

   710 managing the test system and scenarios. The overhead of managing

   711 the test procedures like environment pre-processing, test

   712 execution, results collection and presentation are completely

   713 evaded from the testing life cycle.

   714 </p>

   715 

   716 

   717 

   718 

   719 

   720 

   721 

   722 

   723 <h3 id="sec-4_10">Live media for training in experimental sciences </h3>

   724 

   725 

   726 <p>Georges Khaznadar 

   727 </p>

   728 

   729 

   730 

   731 <h4 id="sec-4_10_1">Talk/Paper Abstract </h4>

   732 

   733 

   734 <p>A system for distance learning in the field of Physics and

   735 Electricity has been used for three years with some success for 15

   736 years old students. The students are given a little case

   737 containing a PHOENIX box (see

   738 <a href="http://www.iuac.res.in/~elab/phoenix/">http://www.iuac.res.in/~elab/phoenix/</a>) featuring electric analog

   739 and digital I/O interfaces, some unexpensive discrete components

   740 and a live (bootable) USB stick.

   741 </p>

   742 <p>

   743 The PHOENIX project was started by Inter University Accelerator

   744 Centre in New Delhi, with the objective of improving the

   745 laboratory facilities at Indian Universities, and growing with the

   746 support of the user community. PHOENIX depends heavily on Python

   747 language. The data acquisition, analysis and writing simulation

   748 programs to teach science and computation.

   749 </p>

   750 <p>

   751 The hardware design of PHOENIX box is freely available. 

   752 </p>

   753 <p>

   754 The live bootable stick provides a free/libre operating system,

   755 and a few dozens educational applications, including applications

   756 developed with Scipy to drive the PHOENIX box and manage the

   757 acquired measurements. The user interface has been made as

   758 intuitive as possible: the main window shows a photo of the front

   759 face of the PHOENIX acquisition device, its connections behaving

   760 like widgets to express their states, and a subwindow displays in

   761 real time the signals connected to it. A booklet gives

   762 general-purpose hints for the usage of the acquisition device. The

   763 educational interaction is done with a free learning management

   764 system.

   765 </p>

   766 <p>

   767 The talk will show how such live media can be used as powerful

   768 training systems, allowing students to access at home exactly the

   769 same environment they can find in the school, and providing them a

   770 lot of structured examples.

   771 </p>

   772 <p>

   773 This talk addresses people who are involved in education and

   774 training in scientific fields. It describes one method which

   775 allows distance learning (however requiring a few initial lessons

   776 to be given non-remotely), and enables students to become fluent

   777 with Python and its scientific extensions, while learning physics

   778 and electricity. This method uses Internet connections to allow

   779 remote interactions, but does not rely on a wide bandwidth, as the

   780 complete learning environment is provided by the live medium,

   781 which is shared by teacher and students after their beginning

   782 lessons.

   783 </p>

   784 

   785 

   786 

   787 

   788 

   789 

   790 

   791 <h3 id="sec-4_11">Use of Python and Phoenix-M interface in Robotics </h3>

   792 

   793 

   794 <p>Shubham Chakraborty 

   795 </p>

   796 

   797 

   798 

   799 <h4 id="sec-4_11_1">Talk/Paper Abstract </h4>

   800 

   801 

   802 <p>In this paper I will show how to use Python programming with a

   803 computer interface such as Phoenix-M to drive simple robots. In my

   804 quest towards Artificial Intelligence (AI) I am experimenting with

   805 a lot of different possibilities in Robotics. This one is trying

   806 to mimic the working of a simple insect's autonomous nervous

   807 system using hard wiring and some minimal software usage. This is

   808 the precursor to my advanced robotics and AI integration where I

   809 plan to use an new paradigm of AI based on Machine Learning and

   810 Self Consciousness via Knowledge Feedback and Update process.

   811 </p>

   812 

   813 

   814 

   815 

   816 

   817 

   818 

   819 

   820 <h3 id="sec-4_12">Python in Science Experiments using Phoenix </h3>

   821 

   822 

   823 <p>Ajith Kumar 

   824 </p>

   825 

   826 

   827 

   828 <h4 id="sec-4_12_1">Talk/Paper Abstract </h4>

   829 

   830 

   831 <p>Phoenix is a hardware plus software framework for developing

   832 computer interfaced science experiments. Sensor and control

   833 elements connected to Phoenix can be accessed using Python. Text

   834 based and GUI programs are available for several

   835 experiments. Python programming language is used as a tool for

   836 data acquisition, analysis and visualization.

   837 </p>

   838 <p>

   839 Objective of the project is to improve the laboratory facilities

   840 at the Universities and also to utilize computers in a better

   841 manner to teach science. The hardware design is freely

   842 available. The project is based on Free Software tools and the

   843 code is distributed under GNU General Public License.

   844 </p>

   845 

   846 

   847 

   848 

   849 

   850 

   851 <h3 id="sec-4_13">Building and Packaging your Scientific Python Application For Linux Distributions </h3>

   852 

   853 

   854 <p>Ramakrishna Reddy  Yekulla 

   855 </p>

   856 

   857 

   858 

   859 <h4 id="sec-4_13_1">Talk/Paper Abstract </h4>

   860 

   861 

   862 <p>If you are an Independent Researcher, Academic Project or an

   863 Enterprise software Company building large scale scientific python

   864 applications, there is a huge community of packagers who look at

   865 upstream python projects to get those packages into upstream

   866 distributions. This talk focuses on practices, making your

   867 applications easy to package so that they can be bundled with

   868 Linux distributions. Additionally this talk would be more hands

   869 on, more like a workshop. The audience are encouraged to bring as

   870 many python applications possible, using the techniques showed in

   871 the talk and help them package it for fedora.

   872 </p>

   873 

   874 

   875 

   876 

   877 

   878 

   879 

   880 

   881 <h3 id="sec-4_14">Microcontroller experiment and its simulation using Python </h3>

   882 

   883 

   884 <p>Jayesh Gandhi 

   885 </p>

   886 

   887 

   888 

   889 <h4 id="sec-4_14_1">Talk/Paper Abstract </h4>

   890 

   891 

   892 <p>Electronics in industrial has been passing through revolution due

   893 to extensive use of Microcontroller. These electronic devices are

   894 having a high capability to handle multiple events. Their

   895 capability to communicate with the computers has made the

   896 revolution possible. Therefore it is very important to have

   897 trained Personnel in Microcontroller. In the present work

   898 experiments for study of Microcontrollers and its peripherals with

   899 Simulation using Python is carried out. This facilitates the

   900 teachers to demonstrate the experiments in the classroom sessions

   901 using simulations. Then the same experiments can be carried out in

   902 the labs (using the same simulation setup) and the microcontroller

   903 hardware to visualize and understand the experiments. Python is

   904 selected due to its versatility and also to promote the use of

   905 open source software in the education.

   906 </p>

   907 <p>

   908 Here we demonstrate the experiment of driving seven segment

   909 displays by microcontroller. Four seven segment displays are

   910 interfaced with the microcontroller through a single BCD to seven

   911 segments Display Decoder/Driver (74LS47) and switching

   912 transistors. The microcontroller switches on the first transistor

   913 connected to the first display and puts the number to be displayed

   914 on 74LS47. Then it pause a while, switches off the first display

   915 and puts the number to be displayed on the second display and

   916 switches it on. A similar action is carried out for all the

   917 display and the cycle is repeated again and again. Now we can

   918 control the microcontroller action using the serial port of the

   919 computer through python. Simulating the seven segment display

   920 using VPYTHON module and communicating the same action to the

   921 microcontroller, we can demonstrate the switching action of the

   922 display at a very slow rate. It is possible to actually see each

   923 display glowing individually one after another. Now we can

   924 gradually increase the rate of switching the display. You see each

   925 display glowing for a few milliseconds. Finally the refresh rate

   926 is taken very high to around more than 25 times a second we see

   927 that all the display glowing simultaneously.

   928 </p>

   929 <p>

   930 Hence it is possible to simulate and demonstrate experiments and

   931 understand the capabilities of the microcontroller with a lot of

   932 ease and at a very low cost.

   933 </p>

   934 

   935 

   936 

   937 

   938 

   939 

   940 

   941 <h3 id="sec-4_15">SAGE for Scientific computing and Education enhancement </h3>

   942 

   943 

   944 <p>Manjusha Joshi 

   945 </p>

   946 

   947 

   948 

   949 <h4 id="sec-4_15_1">Talk/Paper Abstract </h4>

   950 

   951 

   952 

   953 <p>

   954 Sage is Free open source software for Mathematics.

   955 </p>

   956 <p>

   957 Sage can handle long integer computations, symbolic computing,

   958 Matrices etc. Sage is used for Cryptography, Number Theory, Graph

   959 Theory in education field. Note book feature in Sage, allow user

   960 to record all work on worksheet for future use. These worksheets

   961 can be publish for information sharing, students and trainer can

   962 exchange knowledge, share, experiment through worksheets.

   963 </p>

   964 <p>

   965 Sage is an advanced computing tool which can enhance education in

   966 India.

   967 </p>

   968 

   969 

   970 

   971 

   972 

   973 

   974 

   975 

   976 

   977 <h3 id="sec-4_16">Automatic Proteomic Finger Printing using Scipy </h3>

   978 

   979 

   980 <p>Yogesh Karpate 

   981 </p>

   982 

   983 

   984 

   985 <h4 id="sec-4_16_1">Talk/Paper Abstract </h4>

   986 

   987 

   988 <p>The idea is to demonstrate the PyProt (Python Proteomics), an

   989 approach to classify mass spectrometry data and efficient use of

   990 statistical methods to look for the potential prevalent disease

   991 markers and proteomic pattern diagnostics. Serum proteomic pattern

   992 diagnostics can be used to differentiate samples from the patients

   993 with and without disease. Profile patterns are generated using

   994 surface-enhanced laser desorption and ionization (SELDI) protein

   995 mass spectrometry. This technology has the potential to improve

   996 clinical diagnostic tests for cancer pathologies. There are two

   997 datasets used in this study which are taken from the FDA-NCI

   998 Clinical Proteomics Program Databank. First data is of ovarian

   999 cancer and second is of Premalignant Pancreatic Cancer .The Pyprot

  1000 uses the high-resolution ovarian cancer data set that was

  1001 generated using the WCX2 protein array. The ovarian cancer dataset

  1002 includes 95 controls and 121 ovarian cancer sets, where as

  1003 pancreatic cancer dataset has 101 controls and 80 pancreatic

  1004 cancer sets. There are two modules designed and implemented in

  1005 python using Numpy , Scipy and Matplotlib. There are two different

  1006 kinds of classifications implemented here, first to classify the

  1007 ovarian cancer data set. Second type focuses on randomly

  1008 commingled study set of murine sera. it explores the ability of

  1009 the low molecular weight information archive to classify and

  1010 discriminate premalignant pancreatic cancer compared to the

  1011 control animals.

  1012 </p>

  1013 <p>

  1014 A crucial issue for classification is feature selection which

  1015 selects the relevant features in order to focus the learning

  1016 search. A relaxed setting for feature selection is known as

  1017 feature ranking, which ranks the features with respect to their

  1018 relevance. Pyprot comprises of two modules; First includes

  1019 implementation of feature ranking in Python using fisher ratio and

  1020 t square statistical test to avoid large feature space. In second

  1021 module, Multilayer perceptron (MLP) feed forward neural network

  1022 model with static back propagation algorithm is used to classify

  1023 .The results are excellent and matched with databank results and

  1024 concludes that PyProt is useful tool for proteomic finger

  1025 printing.

  1026 </p>

  1027 

  1028 

  1029 

  1030 

  1031 

  1032 

  1033 

  1034 

  1035 

  1036 

  1037 <h3 id="sec-4_17">Natural Language Processing Using Python </h3>

  1038 

  1039 

  1040 <p>Vaidhy Mayilrangam 

  1041 </p>

  1042 

  1043 

  1044 

  1045 <h4 id="sec-4_17_1">Talk/Paper Abstract </h4>

  1046 

  1047 

  1048 <p>The purpose of this talk is to give a high-level overview of

  1049 various text mining techniques, the statistical approaches and the

  1050 interesting problems.

  1051 </p>

  1052 <p>

  1053 The talk will start with a short summary of two key areas – namely

  1054 information retrieval (IR) and information extraction (IE). We

  1055 will then discuss how to use the knowledge gained for

  1056 summarization and translation. We will talk about how to measure

  1057 the correctness of results. As part of measuring the correctness,

  1058 we will discuss about different kinds of statistical approaches

  1059 for classifying and clustering data.

  1060 </p>

  1061 <p>

  1062 We will do a short dive into NLP specific problems - identifying

  1063 sentence boundaries, parts of speech, noun and verb phrases and

  1064 named entities. We will also have a sample session on how to use

  1065 Python’s NLTK to accomplish these tasks.

  1066 </p>

  1067 

  1068 

  1069 

  1070 

  1071 

  1072 

  1073 

  1074 <h3 id="sec-4_18">A Parallel 3D Flow Solver in Python Based on Vortex Methods </h3>

  1075 

  1076 

  1077 <p>Prashant Agrawal 

  1078 </p>

  1079 

  1080 

  1081 

  1082 <h4 id="sec-4_18_1">Talk/Paper Abstract </h4>

  1083 

  1084 

  1085 <p>A 3D flow solver for incompressible flow around arbitrary 3D

  1086 bodies is developed. The solver is based on vortex methods whose

  1087 grid-free nature makes it very general. It uses vortex particles

  1088 to represent the flow-field. Vortex particles (or blobs) are

  1089 released from the boundary, and these advect, stretch and diffuse

  1090 according to the Navier-Stokes equations.

  1091 </p>

  1092 <p>

  1093 The solver is based on a generic and extensible design. This has

  1094 been made possible mainly by following a universal theme of using

  1095 blobs in every component of the solver.  Advection of the

  1096 particles is implemented using a parallel fast multipole

  1097 method. Diffusion is simulated using the Vorticity Redistribution

  1098 Technique (VRT). To control the number of blobs, merging of nearby

  1099 blobs is also performed.

  1100 </p>

  1101 <p>

  1102 Each component of the solver is parallelized. The boundary,

  1103 advection and stretching algorithms are based on the same parallel

  1104 velocity algorithm. Domain decomposition for parallel velocity

  1105 calculator is performed using Space Filling Curves. Diffusion,

  1106 which requires knowledge of each particle's neighbours, uses a

  1107 parallelized fast neighbour finder which is based on a bin data

  1108 structure. The same neighbour finder is used in merging also.

  1109 </p>

  1110 <p>

  1111 The code is written completely in Python. It is well-documented

  1112 and well-tested. The code base is around 4500 lines long. The

  1113 design follows an object oriented approach which makes it

  1114 extensible enough to add new features and alternate algorithms to

  1115 perform specific tasks.

  1116 </p>

  1117 <p>

  1118 The solver is also designed to run in a parallel environment

  1119 involving multiple processors. This parallel implementation is

  1120 written using mpi4py, an MPI implementation in Python.

  1121 </p>

  1122 <p>

  1123 Rigorous testing is performed using Python's unittest module. Some

  1124 standard example cases are also solved using the present solver.

  1125 </p>

  1126 <p>

  1127 In this talk we will outline the overall design of the solver and

  1128 the algorithms used. We discuss the benefits of Python and also

  1129 some of the current limitations with respect to parallel testing.

  1130 </p>