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 Computational Science and Discovery   [SJR: 0.423]   [H-I: 11]   [2 followers]  Follow        Subscription journal    ISSN (Print) 1749-4680 - ISSN (Online) 1749-4699    Published by IOP  [71 journals]
• A multi-scale geometric flow method for molecular structure reconstruction
• Authors: Guoliang Xu; Ming Li Chong Chen
First page: 014002
Abstract: We have previously reported an L 2 -gradient flow (L2GF) method for cryo-electron tomography and single-particle reconstruction, which has a reasonably good performance. The aim of this paper is to further upgrade both the computational efficiency and accuracy of the L2GF method. In a finite-dimensional space spanned by the radial basis functions, a minimization problem combining a fourth-order geometric flow with an energy decreasing constraint is solved by a bi-gradient method. The bi-gradient method involves a free parameter ##IMG## [http://ej.iop.org/images/1749-4699/8/1/014002/csd510374ieqn1.gif] {$\beta \in [0,1].$} As β increases from 0 to 1, the structures of the reconstructed function from coarse to fine are captured. The experimental results show that the proposed method yields more desirable results.
Citation: Computational Science & Discovery
PubDate: 2015-03-26T00:00:00Z
DOI: 10.1088/1749-4680/8/1/014002
Issue No: Vol. 8, No. 1 (2015)

• ObsPy: a bridge for seismology into the scientific Python ecosystem
• Authors: Lion Krischer; Tobias Megies, Robert Barsch, Moritz Beyreuther, Thomas Lecocq, Corentin Caudron Joachim Wassermann
First page: 014003
Abstract: The Python libraries NumPy and SciPy are extremely powerful tools for numerical processing and analysis well suited to a large variety of applications. We developed ObsPy ( http://obspy.org [http://obspy.org] ), a Python library for seismology intended to facilitate the development of seismological software packages and workflows, to utilize these abilities and provide a bridge for seismology into the larger scientific Python ecosystem. Scientists in many domains who wish to convert their existing tools and applications to take advantage of a platform like the one Python provides are confronted with several hurdles such as special file formats, unknown terminology, and no suitable replacement for a non-trivial piece of software. We present an approach to implement a domain-specific time series library on top of the scientific NumPy stack. In so doing, we show a realization of an abstract internal representation of time series data permitting I/O support for a diverse co...
Citation: Computational Science & Discovery
PubDate: 2015-05-18T00:00:00Z
DOI: 10.1088/1749-4699/8/1/014003
Issue No: Vol. 8, No. 1 (2015)

• A multi-model Python wrapper for operational oil spill transport forecasts
• Authors: X Hou; B R Hodges, S Negusse C Barker
First page: 014004
Abstract: The Hydrodynamic and oil spill modeling system for Python (HyosPy) is presented as an example of a multi-model wrapper that ties together existing models, web access to forecast data and visualization techniques as part of an adaptable operational forecast system. The system is designed to automatically run a continual sequence of hindcast/forecast hydrodynamic models so that multiple predictions of the time-and-space-varying velocity fields are already available when a spill is reported. Once the user provides the estimated spill parameters, the system runs multiple oil spill prediction models using the output from the hydrodynamic models. As new wind and tide data become available, they are downloaded from the web, used as forcing conditions for a new instance of the hydrodynamic model and then applied to a new instance of the oil spill model. The predicted spill trajectories from multiple oil spill models are visualized through Python methods invoking Google Map TM a...
Citation: Computational Science & Discovery
PubDate: 2015-06-12T00:00:00Z
DOI: 10.1088/1749-4699/8/1/014004
Issue No: Vol. 8, No. 1 (2015)

• DMTCP: bringing interactive checkpoint–restart to Python
• Authors: Kapil Arya; Gene Cooperman
First page: 014005
Abstract: 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. While DMTCP has been able to checkpoint Python and IPython ‘from the outside’ for many years, a Python module has recently been created to support DMTCP. IPython support is included through a new DMTCP plugin. A checkpoint can be requested interactively within a Python session or under the control of a specific Python program. Further, the Python program can execute specific Python code prior to checkpoint, upon resuming (within the original process) and upon restarting (from a checkpoint image). Applications of DMTCP are demonstrated for: (i) Python-based graphics using virtual network client, (ii) a fast/slow technique to use multiple hosts or cores to check one (Cython Behnel S et al 2011 Comput. Sci. Eng. 13
Citation: Computational Science & Discovery
PubDate: 2015-07-17T00:00:00Z
DOI: 10.1088/1749-4699/8/1/014005
Issue No: Vol. 8, No. 1 (2015)

• GPU computing with OpenCL to model 2D elastic wave propagation: exploring
memory usage
• Authors: Ursula Iturrar?n-Viveros; Miguel Molero-Armenta
First page: 014006
Abstract: Graphics processing units (GPUs) have become increasingly powerful in recent years. Programs exploring the advantages of this architecture could achieve large performance gains and this is the aim of new initiatives in high performance computing. The objective of this work is to develop an efficient tool to model 2D elastic wave propagation on parallel computing devices. To this end, we implement the elastodynamic finite integration technique, using the industry open standard open computing language (OpenCL) for cross-platform, parallel programming of modern processors, and an open-source toolkit called [Py]OpenCL. The code written with [Py]OpenCL can run on a wide variety of platforms; it can be used on AMD or NVIDIA GPUs as well as classical multicore CPUs, adapting to the underlying architecture. Our main contribution is its implementation with local and global memory and the performance analysis using five different computing devices (including Kepler, one of the fastest and ...
Citation: Computational Science & Discovery
PubDate: 2015-07-27T00:00:00Z
DOI: 10.1088/1749-4699/8/1/014006
Issue No: Vol. 8, No. 1 (2015)

• SkData: data sets and algorithm evaluation protocols in Python
• Authors: James Bergstra; Nicolas Pinto David D Cox
First page: 014007
Abstract: 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.g. pandas or sklearn. The SkData library handles that work for a growing number of benchmark data sets (small and large) so that one-off in-house scripts for downloading and parsing data sets can be replaced with library code that is reliable, community-tested, and documented. The SkData library also introduces an open-ended formalization of training and testing protocols that facilitates direct comparison with published research. This paper describes the usage and architecture of the SkData library.
Citation: Computational Science & Discovery
PubDate: 2015-07-28T00:00:00Z
DOI: 10.1088/1749-4699/8/1/014007
Issue No: Vol. 8, No. 1 (2015)

• Hyperopt: a Python library for model selection and hyperparameter
optimization
• Authors: James Bergstra; Brent Komer, Chris Eliasmith, Dan Yamins David D Cox
First page: 014008
Abstract: 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. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a 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 ...
Citation: Computational Science & Discovery
PubDate: 2015-07-28T00:00:00Z
DOI: 10.1088/1749-4699/8/1/014008
Issue No: Vol. 8, No. 1 (2015)

• SunPy?Python for solar physics
• Authors: The SunPy Community; Stuart J Mumford, Steven Christe, David P?rez-Su?rez, Jack Ireland, Albert Y Shih, Andrew R Inglis, Simon Liedtke, Russell J Hewett, Florian Mayer, Keith Hughitt, Nabil Freij, Tomas Meszaros, Samuel M Bennett, Michael Malocha, John Evans, Ankit Agrawal, Andrew J Leonard, Thomas P Robitaille, Benjamin Mampaey, Jose Iv?n Campos-Rozo Michael S Kirk
First page: 014009
Abstract: 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 visualization and plotting ( matplotlib ). SunPy is a data-analysis environment specializing 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 missi...
Citation: Computational Science & Discovery
PubDate: 2015-07-30T00:00:00Z
DOI: 10.1088/1749-4699/8/1/014009
Issue No: Vol. 8, No. 1 (2015)

• PythonTeX: reproducible documents with LaTeX, Python, and more
• Authors: Geoffrey M Poore
First page: 014010
Abstract: PythonTeX is a LaTeX package that allows Python code in LaTeX documents to be executed and provides access to the output. This makes possible reproducible documents that combine results with the code required to generate them. Calculations and figures may be next to the code that created them. Since code is adjacent to its output in the document, editing may be more efficient. Since code output may be accessed programmatically in the document, copy-and-paste errors are avoided and output is always guaranteed to be in sync with the code that generated it. This paper provides an introduction to PythonTeX and an overview of major features, including performance optimizations, debugging tools, and dependency tracking. Several complete examples are presented. Finally, advanced features are summarized. Though PythonTeX was designed for Python, it may be extended to support additional languages; support for the Ruby and Julia languages is already included. PythonTeX contains a utility f...
Citation: Computational Science & Discovery
PubDate: 2015-07-30T00:00:00Z
DOI: 10.1088/1749-4699/8/1/014010
Issue No: Vol. 8, No. 1 (2015)

• Analysis of high performance conjugate heat transfer with the OpenPALM
coupler
• Authors: Florent Duchaine; St?phan Jaur?, Damien Poitou, Eric Qu?merais, Gabriel Staffelbach, Thierry Morel Laurent Gicquel
First page: 015003
Abstract: In many communities such as climate science or industrial design, to solve complex coupled problems with high fidelity external coupling of legacy solvers puts a lot of pressure on the tool used for the coupling. The precision of such predictions not only largely depends on simulation resolutions and the use of huge meshes but also on high performance computing to reduce restitution times. In this context, the current work aims at studying the scalability of code coupling on high performance computing architectures for a conjugate heat transfer problem. The flow solver is a Large Eddy Simulation code that has been already ported on massively parallel architectures. The conduction solver is based on the same data structure and thus shares the flow solver scalability properties. Accurately coupling solvers on massively parallel architectures while maintaining their scalability is challenging. It requires exchanging and treating information based on two different computational grids...
Citation: Computational Science & Discovery
PubDate: 2015-07-27T00:00:00Z
DOI: 10.1088/1749-4699/8/1/015003
Issue No: Vol. 8, No. 1 (2015)

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