Proceedings of SciPy 2013

SciPy 2013, the 12th annual Scientific Computing with Python conference, was held June 24-29, 2013 in Austin, Texas. 16 peer reviewed articles were published in the conference proceedings.

Modeling the Earth with Fatiando a Terra

Geophysics is the science of using physical observations of the Earth to infer its inner structure. Generally, this is done with a variety of numerical modeling techniques and inverse problems. The development of new algorithms usually involves copy and pasting of code, which leads to errors and poor code reuse.
Leonardo Uieda, Vanderlei C. Oliveira Jr, Valéria C. F. Barbosa
https://doi.org/10.25080/Majora-8b375195-010

GraphTerm: A notebook-like graphical terminal interface for collaboration and inline data visualization

The notebook interface, which blends text and graphics, has been in use for a number of years in commercial mathematical software and is now finding more widespread usage in scientific Python with the availability browser-based front-ends like the Sage and IPython notebooks.
Ramalingam Saravanan
https://doi.org/10.25080/Majora-8b375195-00f

lpEdit: an editor to facilitate reproducible analysis via literate programming

There is evidence to suggest that a surprising proportion of published experiments in science are difficult if not impossible to reproduce. The concepts of data sharing, leaving an audit trail and extensive documentation are fundamental to reproducible research, whether it is in the laboratory or as part of an analysis.
Adam J Richards, Andrzej S. Kosinski, Camille Bonneaud, +2
https://doi.org/10.25080/Majora-8b375195-00e

Reproducible Documents with PythonTeX

PythonTeX is a LaTeX package that allows Python code in a LaTeX document to be executed. This makes possible reproducible documents that combine analysis with the code required to perform it. Writing such documents can be more efficient because code is adjacent to its output.
Geoffrey M Poore
https://doi.org/10.25080/Majora-8b375195-00d

SunPy: Python for Solar Physicists

SunPy is a data analysis toolkit which provides the necessary software for analyzing solar and heliospheric datasets in Python. SunPy aims to provide a free and open-source alternative to the current standard, an IDL-based solar data analysis environment known as SolarSoft (SSW).
Stuart Mumford, David Pérez-Suárez, Steven Christe, +2
https://doi.org/10.25080/Majora-8b375195-00c

Exploring Collaborative HPC Visualization Workflows using VisIt and Python

As High Performance Computing (HPC) environments expand to address the larger computational needs of massive simulations and specialized data analysis and visualization routines, the complexity of these environments brings many challenges for scientists hoping to capture and publish their work in a reproducible manner.
Hari Krishnan, Cyrus Harrison, Brad Whitlock, +2
https://doi.org/10.25080/Majora-8b375195-00b

Ginga: an open-source astronomical image viewer and toolkit

Ginga is a new astronomical image viewer written in Python. It uses and inter-operates with several key scientific Python packages: NumPy, Astropy, and SciPy. A key differentiator for this image viewer, compared to older-generation FITS viewers, is that all the key components are written as Python classes, allowing for the first time a powerful FITS image display widget to be directly embedded in, and tightly coupled with, Python code.
Eric Jeschke
https://doi.org/10.25080/Majora-8b375195-00a

Adapted G-mode Clustering Method applied to Asteroid Taxonomy

The original G-mode was a clustering method developed by A. I. Gavrishin in the late 60's for geochemical classification of rocks, but was also applied to asteroid photometry, cosmic rays, lunar sample and planetary science spectroscopy data.
Pedro Henrique Hasselmann, Jorge Márcio Carvano, Daniela Lazzaro
https://doi.org/10.25080/Majora-8b375195-009

Pythran: Enabling Static Optimization of Scientific Python Programs

Pythran is a young open source static compiler that turns modules written in a subset of Python into native ones. Based on the fact that scientific modules do not rely much on the dynamic features of the language, it trades them in favor of powerful, eventually inter procedural, optimizations.
Serge Guelton, Pierrick Brunet, Alan Raynaud, +2
https://doi.org/10.25080/Majora-8b375195-008

Detection and characterization of interactions of genetic risk factors in disease

It is well known that two or more genes can interact so as to enhance or suppress incidence of disease, such that the observed phenotype differs from when the genes act independently. The effect of a gene allele at one locus can mask or modify the effect of alleles at one or more other loci.
Patricia Francis-Lyon, Shashank Belvadi, Fu-Yuan Cheng
https://doi.org/10.25080/Majora-8b375195-007

Automating Quantitative Confocal Microscopy Analysis

Quantitative confocal microscopy is a powerful analytical tool used to visualize the associations between cellular processes and anatomical structures. In our biological experiments, we use quantitative confocal microscopy to study the association of three cellular components: binding proteins, receptors, and organelles.
Mark E Fenner, Barbara M. Fenner
https://doi.org/10.25080/Majora-8b375195-006

Using Python to Study Rotational Velocity Distributions of Hot Stars

Stars are fundamental pieces that compose our Universe. By studying them we can better comprehend the environment in which we live. In this work, we have studied a sample of 350 nearby O and B stars and have characterized them in aspects of their multiplicity, temperature, spectral classifications, and projected rotational velocity.
Gustavo Bragança, Simone Daflon, Katia Cunha, +3
https://doi.org/10.25080/Majora-8b375195-005

SkData: Data Sets and Algorithm Evaluation Protocols in Python

Machine learning benchmark data sets come in all shapes and sizes, whereas classification algorithms assume sanitized input, such as (x, y) pairs with vector-valued input x and integer class label y. Researchers and practitioners know all too well how tedious it can be to get from the URL of a new data set to a NumPy ndarray suitable for e.
James Bergstra, Nicolas Pinto, David D. Cox
https://doi.org/10.25080/Majora-8b375195-004

Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms

Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train.
James Bergstra, Dan Yamins, David D. Cox
https://doi.org/10.25080/Majora-8b375195-003

Multidimensional Data Exploration with Glue

Modern research projects incorporate data from several sources, and new insights are increasingly driven by the ability to interpret data in the context of other data. Glue is an interactive environment built on top of the standard Python science stack to visualize relationships within and between datasets.
Christopher Beaumont, Thomas Robitaille, Alyssa Goodman, +1
https://doi.org/10.25080/Majora-8b375195-002

DMTCP: Bringing Checkpoint-Restart to Python

DMTCP (Distributed MultiThreaded CheckPointing) is a mature checkpoint-restart package. It operates in user-space without kernel privilege, and adapts to application-specific requirements through plugins.
Kapil Arya, Gene Cooperman
https://doi.org/10.25080/Majora-8b375195-001

Preface

SciPy 2013, the twelfth annual Scientific Computing with Python conference, was held June 24th-29th 2013 in Austin, Texas, USA.
Andy Terrel, Jonathan Rocher
https://doi.org/10.25080/Majora-8b375195-000