Proceedings of SciPy 2014

SciPy 2014, the 13th annual Scientific Computing with Python conference, was held July 6-12, 2014 in Austin, Texas. 16 peer reviewed articles were published in the conference proceedings.

Simulating X-ray Observations with Python

X-ray astronomy is an important tool in the astrophysicist's toolkit to investigate high-energy astrophysical phenomena. Theoretical numerical simulations of astrophysical sources are fully three-dimensional representations of physical quantities such as density, temperature, and pressure, whereas astronomical observations are two-dimensional projections of the emission generated via mechanisms dependent on these quantities.
John A. ZuHone, Veronica Biffi, Eric J. Hallman, +3
https://doi.org/10.25080/Majora-14bd3278-010

Blaze: Building A Foundation for Array-Oriented Computing in Python

We present the motivation and architecture of Blaze, a library for cross-backend data-oriented computation. Blaze provides a standard interface to connect users familiar with NumPy and Pandas to other data analytics libraries like SQLAlchemy and Spark.
Mark Wiebe, Matthew Rocklin, TJ Alumbaugh, +1
https://doi.org/10.25080/Majora-14bd3278-00f

Frequentism and Bayesianism: A Python-driven Primer

This paper presents a brief, semi-technical comparison of the essential features of the frequentist and Bayesian approaches to statistical inference, with several illustrative examples implemented in Python.
Jake VanderPlas
https://doi.org/10.25080/Majora-14bd3278-00e

TracPy: Wrapping the Fortran Lagrangian trajectory model TRACMASS

Numerical Lagrangian trajectory modeling is a natural method of investigating transport in a circulation system and understanding the physics on the wide range of length scales that are actually experienced by a drifter.
Kristen M. Thyng, Robert D. Hetland
https://doi.org/10.25080/Majora-14bd3278-00d

Creating a browser-based virtual computer lab for classroom instruction

With laptops and tablets becoming more powerful and more ubiquitous in the classroom, traditional computer labs with rows of expensive desktop computers are slowly beginning to lose their relevance. An alternative approach for teaching Python is to use a browser-based virtual computer lab, with a notebook interface.
Ramalingam Saravanan
https://doi.org/10.25080/Majora-14bd3278-00c

Validated numerics with Python: the ValidiPy package

We introduce the ValidiPy package for validated numerics in Python. This suite of tools, which includes interval arithmetic and automatic differentiation, enables rigorous and guaranteed results using floating-point arithmetic.
David P. Sanders, Luis Benet
https://doi.org/10.25080/Majora-14bd3278-00b

Python for research and teaching economics

Together with theory and experimentation, computational modeling and simulation has become a “third pillar” of scientific inquiry. I am developing a curriculum for a three part, graduate level course on computational methods designed to increase the exposure of graduate students and researchers in the School of Economics at the University of Edinburgh to basic techniques used in computational modeling and simulation using the Python programming language.
David R. Pugh
https://doi.org/10.25080/Majora-14bd3278-00a

Campaign for IT literacy through FOSS and Spoken Tutorials

This article explains an approach to promote Information Technology (IT) literacy in India, which has evolved into a pyramid structure. We begin this article by explaining the design decisions, such as the use of FOSS and being a friendly interface between beginners and experts, in undertaking this activity.
Kannan M. Moudgalya
https://doi.org/10.25080/Majora-14bd3278-009

Scaling Polygon Adjacency Algorithms to Big Data Geospatial Analysis

Adjacency and neighbor structures play an essential role in many spatial analytical tasks. The computation of adjacenecy structures is non-trivial and can form a significant processing bottleneck as the total number of observations increases.
Jason Laura, Sergio J. Rey
https://doi.org/10.25080/Majora-14bd3278-008

Python Coding of Geospatial Processing in Web-based Mapping Applications

Python has powerful capabilities for coding elements of Web-based mapping applications. This paper highlights examples of analytical geospatial processing services that we have implemented for several Open Source-based development projects, including the Eastern Interconnection States' Planning Council (EISPC) Energy Zones Mapping Tool (http://eispctools.
James A. Kuiper, Andrew J. Ayers, Michael E. Holm, +1
https://doi.org/10.25080/Majora-14bd3278-007

Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn

Hyperopt-sklearn is a new software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem.
Brent Komer, James Bergstra, Chris Eliasmith
https://doi.org/10.25080/Majora-14bd3278-006

Project-based introduction to scientific computing for physics majors

This paper presents an overview of a project-based course in computing for physics majors using Python and the IPython Notebook that was developed at Cal Poly San Luis Obispo. The course materials are made freely available on GitHub as a project under the Computing4Physics C4P organization.
Jennifer Klay
https://doi.org/10.25080/Majora-14bd3278-005

Teaching numerical methods with IPython notebooks and inquiry-based learning

A course in numerical methods should teach both the mathematical theory of numerical analysis and the craft of implementing numerical algorithms. The IPython notebook provides a single medium in which mathematics, explanations, executable code, and visualizations can be combined, and with which the student can interact in order to learn both the theory and the craft of numerical methods.
David I. Ketcheson
https://doi.org/10.25080/Majora-14bd3278-004

Measuring rainshafts: Bringing Python to bear on remote sensing data

Remote sensing data is complicated, very complicated! It is not only geometrically tricky but also, unlike in-situ methods, indirect as the sensor measures the interaction of the scattering media (eg raindrops) with the probing radiation, not the geophysics.
Scott Collis, Scott Giangrande, Jonathan Helmus, +4
https://doi.org/10.25080/Majora-14bd3278-003

BCE: Berkeley's Common Scientific Compute Environment for Research and Education

There are numerous barriers to the use of scientific computing toolsets. These barriers are becoming more apparent as we increasingly see mixing of different academic backgrounds, and compute ranging from laptops to cloud platforms.
Dav Clark, Aaron Culich, Brian Hamlin, +1
https://doi.org/10.25080/Majora-14bd3278-002

Scientific Computing with SciPy for Undergraduate Physics Majors

The physics community is working to improve the undergraduate curriculum to include computer skills that graduates will need in the workforce. At Penn State Erie, The Behrend College, we have added computational tools to our Junior/Senior physics laboratory, PHYS421w Research Methods.
G William Baxter
https://doi.org/10.25080/Majora-14bd3278-001

Preface

SciPy 2014, the thirteenth annual Scientific Computing with Python conference, was held July 6–12th in Austin, Texas. SciPyis a community dedicated to the advancement of scientific computing through open source Python software for mathematics,science, and engineering
Andy Terrel, Jonathan Rocher, Stéfan van der Walt, +1
https://doi.org/10.25080/Majora-14bd3278-000