Computational science & discovery最新文献

PythonTeX: reproducible documents with LaTeX, Python, and more PythonTeX:使用LaTeX、Python等可复制的文档

Computational science & discovery Pub Date : 2015-07-30 DOI: 10.1088/1749-4699/8/1/014010

Geoffrey M. Poore

引用次数: 11

Hyperopt: a Python library for model selection and hyperparameter optimization Hyperopt:用于模型选择和超参数优化的Python库

Computational science & discovery Pub Date : 2015-07-28 DOI: 10.1088/1749-4699/8/1/014008

J. Bergstra, Brent Komer, C. Eliasmith, Daniel L. K. Yamins, David D. Cox

{"title":"Hyperopt: a Python library for model selection and hyperparameter optimization","authors":"J. Bergstra, Brent Komer, C. Eliasmith, Daniel L. K. Yamins, David D. Cox","doi":"10.1088/1749-4699/8/1/014008","DOIUrl":"https://doi.org/10.1088/1749-4699/8/1/014008","url":null,"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 single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.","PeriodicalId":89345,"journal":{"name":"Computational science & discovery","volume":"8 1","pages":"014008"},"PeriodicalIF":0.0,"publicationDate":"2015-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1088/1749-4699/8/1/014008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60597530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 614

Analysis of high performance conjugate heat transfer with the OpenPALM coupler OpenPALM耦合器的高性能共轭传热分析

Computational science & discovery Pub Date : 2015-07-27 DOI: 10.1088/1749-4699/8/1/015003

F. Duchaine, S. Jaure, D. Poitou, E. Quémerais, G. Staffelbach, T. Morel, L. Gicquel

{"title":"Analysis of high performance conjugate heat transfer with the OpenPALM coupler","authors":"F. Duchaine, S. Jaure, D. Poitou, E. Quémerais, G. Staffelbach, T. Morel, L. Gicquel","doi":"10.1088/1749-4699/8/1/015003","DOIUrl":"https://doi.org/10.1088/1749-4699/8/1/015003","url":null,"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 that are partitioned differently on a different number of cores. Such transfers have to be thought to maintain code scalabilities while maintaining numerical accuracy. This raises communication and high performance computing issues: transferring data from a distributed interface to another distributed interface in a parallel way and on a very large number of processors is not straightforward and solutions are not clear. Performance tests have been carried out up to 12 288 cores on the CURIE supercomputer (TGCC/CEA). Results show a good behavior of the coupled model when increasing the number of cores thanks to the fully distributed exchange process implemented in the coupler. Advanced analyses are carried out to draw new paths for future developments for coupled simulations: i.e. optimization of the data transfer protocols through asynchronous communications or coupling-aware preprocessing of the coupled models (mesh partitioning phase).","PeriodicalId":89345,"journal":{"name":"Computational science & discovery","volume":"8 1","pages":"015003"},"PeriodicalIF":0.0,"publicationDate":"2015-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1088/1749-4699/8/1/015003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60597639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 373

GPU computing with OpenCL to model 2D elastic wave propagation: exploring memory usage GPU计算与OpenCL建模二维弹性波传播:探索内存使用

Computational science & discovery Pub Date : 2015-07-27 DOI: 10.1088/1749-4699/8/1/014006

U. Iturrarán-Viveros, M. Molero-Armenta

引用次数: 4

DMTCP: bringing interactive checkpoint–restart to Python DMTCP:为Python带来交互式检查点重启

Computational science & discovery Pub Date : 2015-07-17 DOI: 10.1088/1749-4699/8/1/014005

K. Arya, G. Cooperman

引用次数: 0

A multi-model Python wrapper for operational oil spill transport forecasts 用于操作溢油运输预测的多模型Python包装器

Computational science & discovery Pub Date : 2015-06-12 DOI: 10.1088/1749-4699/8/1/014004

Xianlong Hou, B. Hodges, S. Negusse, C. Barker

{"title":"A multi-model Python wrapper for operational oil spill transport forecasts","authors":"Xianlong Hou, B. Hodges, S. Negusse, C. Barker","doi":"10.1088/1749-4699/8/1/014004","DOIUrl":"https://doi.org/10.1088/1749-4699/8/1/014004","url":null,"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 MapTM and Google EarthTM functions. HyosPy is designed in modules that allow easy future adaptation to new models, new data sources or new visualization tools.","PeriodicalId":89345,"journal":{"name":"Computational science & discovery","volume":"8 1","pages":"014004"},"PeriodicalIF":0.0,"publicationDate":"2015-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1088/1749-4699/8/1/014004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60597080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

ObsPy: a bridge for seismology into the scientific Python ecosystem ObsPy:地震学进入科学Python生态系统的桥梁

Computational science & discovery Pub Date : 2015-05-18 DOI: 10.1088/1749-4699/8/1/014003

L. Krischer, T. Megies, R. Barsch, M. Beyreuther, T. Lecocq, C. Caudron, J. Wassermann

{"title":"ObsPy: a bridge for seismology into the scientific Python ecosystem","authors":"L. Krischer, T. Megies, R. Barsch, M. Beyreuther, T. Lecocq, C. Caudron, J. Wassermann","doi":"10.1088/1749-4699/8/1/014003","DOIUrl":"https://doi.org/10.1088/1749-4699/8/1/014003","url":null,"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), 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 collection of file formats. Then we detail the integration and repurposing of well established legacy codes, enabling them to be used in modern workflows composed in Python. Finally we present a case study on how to integrate research code into ObsPy, opening it to the broader community. While the implementations presented in this work are specific to seismology, many of the described concepts and abstractions are directly applicable to other sciences, especially to those with an emphasis on time series analysis.","PeriodicalId":89345,"journal":{"name":"Computational science & discovery","volume":"8 1","pages":"014003"},"PeriodicalIF":0.0,"publicationDate":"2015-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1088/1749-4699/8/1/014003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60597006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 533

SunPy—Python for solar physics 太阳物理的SunPy-Python

Computational science & discovery Pub Date : 2015-05-11 DOI: 10.1088/1749-4699/8/1/014009

The SunPy Community, S. Mumford, S. Christe, D. P'erez-Su'arez, J. Ireland, A. Shih, A. Inglis, Simon Liedtke, Russell J. Hewett, F. Mayer, Keith Hughitt, N. Freij, T. Mészáros, Samuel Bennett, Michael Malocha, John G Evans, Ankit Agrawal, Andrew Leonard, T. Robitaille, B. Mampaey, Jose Iván Campos Rozo, M. Kirk

{"title":"SunPy—Python for solar physics","authors":"The SunPy Community, S. Mumford, S. Christe, D. P'erez-Su'arez, J. Ireland, A. Shih, A. Inglis, Simon Liedtke, Russell J. Hewett, F. Mayer, Keith Hughitt, N. Freij, T. Mészáros, Samuel Bennett, Michael Malocha, John G Evans, Ankit Agrawal, Andrew Leonard, T. Robitaille, B. Mampaey, Jose Iván Campos Rozo, M. Kirk","doi":"10.1088/1749-4699/8/1/014009","DOIUrl":"https://doi.org/10.1088/1749-4699/8/1/014009","url":null,"abstract":"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). We present the latest release of SunPy, version 0.3. Though still in active development, SunPy already provides important functionality for solar data analysis. SunPy provides data structures for representing the most common solar data types: images, lightcurves, and spectra. To enable the acquisition of scientific data, SunPy provides integration with the Virtual Solar Observatory (VSO), a single source for accessing most solar data sets, and integration with the Heliophysics Event Knowledgebase (HEK), a database of transient solar events such as solar flares or coronal mass ejections. SunPy utilizes many packages from the greater scientific Python community, including NumPy and SciPy for core data types and analysis routines, PyFITS for opening image files, in FITS format, from major solar missions (e.g., SDO/AIA, SOHO/EIT, SOHO/LASCO, and STEREO) into WCS-aware map objects, and pandas for advanced time-series analysis tools for data from missions such as GOES, SDO/EVE, and Proba2/LYRA, as well as support for radio spectra (e.g., e-Callisto). Future releases will build upon and integrate with current work in the Astropy project and the rest of the scientific python community, to bring greater functionality to SunPy users.","PeriodicalId":89345,"journal":{"name":"Computational science & discovery","volume":"8 1","pages":"014009"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1088/1749-4699/8/1/014009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60597571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 104

An MHD Code for the Study of Magnetic Structures in the Solar Wind 太阳风磁结构研究的MHD代码

Computational science & discovery Pub Date : 2015-03-26 DOI: 10.1088/1749-4680/8/1/015002

J. Allred, P. MacNeice

{"title":"An MHD Code for the Study of Magnetic Structures in the Solar Wind","authors":"J. Allred, P. MacNeice","doi":"10.1088/1749-4680/8/1/015002","DOIUrl":"https://doi.org/10.1088/1749-4680/8/1/015002","url":null,"abstract":"We have developed a 2.5D MHD code designed to study how the solar wind influences the evolution of transient events in the solar corona and inner heliosphere. The code includes thermal conduction, coronal heating and radiative cooling. Thermal conduction is assumed to be magnetic field-aligned in the inner corona and transitions to a collisionless formulation in the outer corona. We have developed a stable method to handle field-aligned conduction around magnetic null points. The inner boundary is placed in the upper transition region, and the mass flux across the boundary is determined from 1D field-aligned characteristics and a 'radiative energy balance' condition. The 2.5D nature of this code makes it ideal for parameter studies not yet possible with 3D codes. We have made this code publicly available as a tool for the community. To this end we have developed a graphical interface to aid in the selection of appropriate options and a graphical interface that can process and visualize the data produced by the simulation. As an example, we show a simulation of a dipole field stretched into a helmet streamer by the solar wind. Plasmoids periodically erupt from the streamer, and we perform a parameter study of how the frequency and location of these eruptions changed in response to different levels of coronal heating. As a further example, we show the solar wind stretching a compact multi-polar flux system. This flux system will be used to study breakout coronal mass ejections in the presence of the solar wind.","PeriodicalId":89345,"journal":{"name":"Computational science & discovery","volume":"8 1","pages":"015002"},"PeriodicalIF":0.0,"publicationDate":"2015-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1088/1749-4680/8/1/015002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60596428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

A multi-scale geometric flow method for molecular structure reconstruction 分子结构重建的多尺度几何流方法

Computational science & discovery Pub Date : 2015-03-26 DOI: 10.1088/1749-4680/8/1/014002

Guoliang Xu, Ming Li, C. Chen

引用次数: 3