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

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

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

<caption></caption>

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

</thead>

<tbody>

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

<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>

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

<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>

<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>

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

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

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

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

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

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

</tbody>

</table>

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

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

<caption></caption>

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

</colgroup>

<thead>

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

</thead>

<tbody>

<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>

<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>

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

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

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

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

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

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

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

</tbody>

</table>

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

<p>Perry Greenfield

</p>

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

<p>How Python Slithered into Astronomy

</p>

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

<p>

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

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

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

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

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

will also cover some of the history of essential elements for

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

so well suited for scientific software.

</p>

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

<p>Fernando Perez

</p>

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

<p>IPython : Beyond the Simple Shell

</p>

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

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

that extends the capabilities of the Python shell with operating

system access, powerful object introspection, customizable "magic"

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

control parallel computations via high-level interfaces that can be

used either interactively or in long-running batch mode.

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

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

then focus on recent developments.  Using the high performance ZeroMQ

networking library, we have recently restructured IPython to decouple

the kernel executing user code from the control interface.  This

allows us to expose multiple clients with different capabilities,

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

with full matplotlib support. In conjunction with the new HTML5

matplotlib backend, this architecture opens the door for a rich

web-based environment for interactive, collaborative and parallel

computing.  

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

hope to encourage participants at the sprints during the conference to

join this effort.

</p>

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

<p>Asokan Pichai

</p>

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

<p>Teaching Programming with Python

</p>

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

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

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

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

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

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

approaches.

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

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

</p>

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

<p>John Hunter

</p>

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

<p>matplotlib: Beyond the simple plot

</p>

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

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

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

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

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

new features and performance enhancements in matplotlib as well as

some of the comparatively undiscovered features such as interacting

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

new animations API.  We will explore the performance with large

datasets utilizing the new path simplification algorithm, and

discuss areas where performance improvements are still needed.

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

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

will enable an interactive Python shell with interactive graphics in

a web browser.

</p>

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

<p>Prabhu Ramachandran

</p>

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

<p>Mayavi : Bringing Data to Life

</p>

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

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

includes both a standalone user interface along with a powerful yet

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

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

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

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

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

offscreen mode and be embedded in a sage notebook

(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.

</p>

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