Proceedings of SciPy 2012

SciPy 2012, the 11th annual Scientific Computing with Python conference, was held July 16-21, 2012 in Austin, Texas. 13 peer reviewed articles were published in the conference proceedings.

OpenMG: A New Multigrid Implementation in Python

In many large-scale computations, systems of equations arise in the form , where is a linear operation to be performed on the unknown data , producing the known right-hand side, , which represents some constraint of known or assumed behavior of the system being modeled.
Tom S. Bertalan, Akand W. Islam, Roger B. Sidje, +1
https://doi.org/10.25080/Majora-54c7f2c8-00c

cphVB: A System for Automated Runtime Optimization and Parallelization of Vectorized Applications

Modern processor architectures, in addition to having still more cores, also require still more consideration to memory-layout in order to run at full capacity. The usefulness of most languages is deprecating as their abstractions, structures or objects are hard to map onto modern processor architectures efficiently.
Mads Ruben Burgdorff Kristensen, Simon Andreas Frimann Lund, Troels Blum, +1
https://doi.org/10.25080/Majora-54c7f2c8-00b

QuTiP: A framework for the dynamics of open quantum systems using SciPy and Cython

We present QuTiP (http://www.qutip.org), an object-oriented, open-source framework for solving the dynamics of open quantum systems. Written in Python, and using a combination of Cython, NumPy, SciPy, and matplotlib, QuTiP provides an environment for computational quantum mechanics that is both easy and efficient to use.
Robert J. Johansson, Paul D. Nation
https://doi.org/10.25080/Majora-54c7f2c8-00a

Uncertainty Modeling with SymPy Stats

We add a random variable type to a mathematical modeling language. We demonstrate through examples how this is a highly separable way to introduce uncertainty and produce and query stochastic models. We motivate the use of symbolics and thin compilers in scientific computing.
Matthew Rocklin
https://doi.org/10.25080/Majora-54c7f2c8-009

Fcm - A python library for flow cytometry

Flow cytometry has the ability to measure multiple parameters of a heterogeneous mix of cells at single cell resolution. This has lead flow cytometry to become an integral tool in immunology and biology.
Jacob Frelinger, Adam Richards, Cliburn Chan
https://doi.org/10.25080/Majora-54c7f2c8-008

The Reference Model for Disease Progression

The Reference Model for disease progression is based on a modeling framework written in Python. It is a prototype that demonstrates the use of computing power to aid in chronic disease forecast. The model uses references to publicly available data as a source of information, hence the name for the model.
Jacob Barhak
https://doi.org/10.25080/Majora-54c7f2c8-007

Self-driving Lego Mindstorms Robot

In this paper, I describe the workings of my personal hobby project - a self-driving lego mindstorms robot. The body of the robot is built with Lego Mindstorms. An Android smartphone is used to capture the view in front of the robot.
Iqbal Mohomed
https://doi.org/10.25080/Majora-54c7f2c8-006

PythonTeX: Fast Access to Python from within LaTeX

PythonTeX is a new LaTeX package that provides access to the full power of Python from within LaTeX documents. It allows Python code entered within a LaTeX document to be executed, and provides access to the output.
Geoffrey M. Poore
https://doi.org/10.25080/Majora-54c7f2c8-005

Python's Role in VisIt

VisIt is an open source, turnkey application for scientific data analysis and visualization that runs on a wide variety of platforms from desktops to petascale class supercomputers. VisIt's core software infrastructure is written in C++, however Python plays a vital role in enabling custom workflows.
Cyrus Harrison, Harinarayan Krishnan
https://doi.org/10.25080/Majora-54c7f2c8-004

Total Recall: flmake and the Quest for Reproducibility

FLASH is a high-performance computing (HPC) multi-physics code which is used to perform astrophysical and high-energy density physics simulations. To run a FLASH simulation, the user must go through three basic steps: setup, build, and execution.
Anthony Scopatz
https://doi.org/10.25080/Majora-54c7f2c8-003

A Tale of Four Libraries

This work describes the use some scientific Python tools to solve information gathering problems using Reinforcement Learning. In particular, we focus on the problem of designing an agent able to learn how to gather information in linked datasets.
Alejandro Weinstein, Michael Wakin
https://doi.org/10.25080/Majora-54c7f2c8-002

A Computational Framework for Plasmonic Nanobiosensing

Basic principles in biosensing and nanomaterials precede the introduction of a novel fiber optic sensor. Software limitations in the biosensing domain are presented, followed by the development of a Python-based simulation environment.
Adam Hughes
https://doi.org/10.25080/Majora-54c7f2c8-001

Parallel High Performance Bootstrapping in Python

.
Aakash Prasad, David Howard, Shoaib Kamil, +1
https://doi.org/10.25080/Majora-54c7f2c8-000