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

<h3 id="sec-1">A detailed list of talks will be announced after accepting the Call for Papers is complete. The schedule of invited talks given below may change. The final schedule for the conference will be put up after evaluating the submitted talks.</h3>

<h2 id="sec-1">Day 1 </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>9:00-9:30</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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