SunPy - Python for Solar Physics

This paper presents SunPy (version 0.5), a community-developed Python package for solar physics. Python, a free, cross-platform, general-purpose, high-level programming language, has seen widespread adoption among the scientific community, resulting in the availability of a large number of software packages, from numerical computation (NumPy, SciPy) and machine learning (scikit-learn) to visualisation and plotting (matplotlib). SunPy is a data-analysis environment specialising in providing the software necessary to analyse solar and heliospheric data in Python. SunPy is open-source software (BSD licence) and has an open and transparent development workflow that anyone can contribute to. SunPy provides access to solar data through integration with the Virtual Solar Observatory (VSO), the Heliophysics Event Knowledgebase (HEK), and the HELiophysics Integrated Observatory (HELIO) webservices. It currently supports image data from major solar missions (e.g., SDO, SOHO, STEREO, and IRIS), time-series data from missions such as GOES, SDO/EVE, and PROBA2/LYRA, and radio spectra from e-Callisto and STEREO/SWAVES. We describe SunPy's functionality, provide examples of solar data analysis in SunPy, and show how Python-based solar data-analysis can leverage the many existing tools already available in Python. We discuss the future goals of the project and encourage interested users to become involved in the planning and development of SunPy

DEVELOPMENT AND OPTIMIZATION OF TOOLS FOR CO-EXPRESSION NETWORK ANALYSES OF HOST-PATHOGEN SYSTEMS

High-throughput transcriptomics has provided a powerful new approach for studying host-pathogen interactions. While popular techniques such as differential expression and gene set enrichment analysis can yield informative results, they do not always make full use of information available in multi-condition experiments. Co-expression networks provide a novel way of analyzing these datasets which can lead to new discoveries that are not readily detectable using the more popular approaches. While significant work has been done in recent years on the construction of coexpression networks, less is known about how to measure the quality of such networks. Here, I describe an approach for evaluating the quality of a co-expression network, based on enrichment of biological function across the network. The approach is used to measure the influence of various data transformations and algorithmic parameters on the resulting network quality, leading to several unexpected findings regarding commonly-used techniques, as well as to the development of a novel similarity metric used to assess the degree of co-expression between two genes. Next, I describe a simple approach for aggregating information across multiple network parameterizations, in order to arrive at a robust “consensus” co-expression network. This approach is used to generate independent host and parasite networks for two host-trypanosomatid transcriptomics datasets, resulting in the detection of both previously known disease pathways and novel gene networks potentially related to infection. Finally, a differential network analysis approach is developed and used to explore the impact of infection on the host co-expression network, and to elucidate shared transcriptional signatures of infection by different intracellular pathogens. The approaches developed in this work provide a powerful set of tools and techniques for the rigorous generation and evaluation of co-expression networks, and have significant implications for co-expression network-based research. The application of these approaches to several host-pathogen systems demonstrates their utility for host-pathogen transcriptomics research, and has resulted in the creation of a number of valuable resources for understanding systems-levels processes that occur during the process of infection

pacman: pacman version 0.4.1

NEWS Versioning Releases will be numbered with the following semantic versioning format: <major>.<minor>.<patch> And constructed with the following guidelines: Breaking backward compatibility bumps the major (and resets the minor and patch) New additions without breaking backward compatibility bumps the minor (and resets the patch) Bug fixes and misc changes bumps the patch CHANGES IN pacman VERSION 0.3.1 - 0.4.1 NEW FEATURES Support for Bioconductor packages added compiments of Keith Hughitt. See #62 p_boot added to generate a string for the standard pacman script header that, when added to scripts, will ensure pacman is installed before attempting to use it. pacman will attempt to copy this string (standard script header) to the clipboard for easy cut and paste. p_version_cran (p_ver_cran) added to check R/package version currently available on CRAN. p_version_diff (p_ver_diff) added to determine version difference between a local package and CRAN. p_old added to search for outdated packages. p_install_version_gh and p_install_current_gh added as partners to p_install_version for GitHub packages. Thanks to Steve Simpson for this: https://github.com/trinker/pacman/issues/70 IMPROVEMENTS p_functions sorts the functions alphabetically before returning them. p_path now takes a package as an agument, allowing the user to return the path to individual packages as well. CHANGES p_version returns a numeric version for p_version("R") to allow for logical comparisons. CHANGES IN pacman VERSION 0.2.0 - 0.3.0 The first installation of pacman Package designed to assist in package related tasks

AWARE: An Algorithm for the Automated Characterization of EUV Waves in the Solar Atmosphere

Conference Poster-Introduction Extreme ultraviolet (EUV) waves are large-scale propagating disturbances observed in the solar corona, frequently associated with coronal mass ejections and flares (Thompson et al., 1999, Thompson & Myers 2009). They appear as faint, extended structures propagating from a source region across the structured solar corona, making them difficult to isolate and measure. To further the understanding of EUV waves, we have constructed the Automated Wave Analysis and REduction (AWARE) algorithm for the measurement of EUV waves (Ireland et al, submitted). AWARE is implemented using the persistence transform, simple image processing operations and the RANSAC algorithm

AWARE: an Algorithm for the Automated Characterization of EUV Waves in the Solar Atmosphere

Extreme ultraviolet (EUV) waves are large-scale propagating disturbances observed in the solar corona, frequently associated with coronal mass ejections and flares. They appear as faint, extended structures propagating from a source region across the structured solar corona. To measure these waves, we have constructed the Automated Wave Analysis and REduction (AWARE) algorithm. AWARE is implemented in two stages. In the first stage, we use simple image processing techniques to isolate the propagating, brightening wave fronts as they move across the corona. In the second stage, AWARE measures the distance, velocity and acceleration of that wave front across the Sun. We explore the use of the Huygens principle, dynamic time warping and a simple parametric representation of the wave front as potential methods for detecting and tracking non-radial EUV wave propagation

Introduction to Scientific Python and SunPy

<p>Originally written by Florian Mayer, this talk introduces Python for a scientific audience and the SunPy project.</p

Solar physics data analysis using SunPy

<p>A walk through an e-Callisto dynamic radio spectra analysis.</p

ISCB-Student Council narratives : strategical development of the ISCB-Regional Student Groups in 2016

Regional Student Groups are groups established and managed by the ISCB-Student Council in different regions of the world. The article highlights some of the initiatives and management lessons from our 'top-performing' Spotlight Regional Student Groups (RSGs), RSG-Argentina and RSG-UK, for the current year (2016). In addition, it details some of the operational hurdles faced by RSGs and possible solutions