Proceedings of SciPy 2013

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

SciPy 2013, the twelfth annual Scientific Computing with Python conference, was held June 24th-29th 2013 in Austin, Texas, USA.