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A photovoltaic power output dataset: Multi-source photovoltaic power output dataset with python toolkit

SOLAR ENERGY 2021年 230卷 122-130页

作者： Yao, Tiechui Wang, Jue Wu, Haoyan Zhang, Pei Li, Shigang Wang, Yangang Chi, Xuebin Shi, Min Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China East China Jiaotong Univ Sch Elect & Automat Engn Nanchang Jiangxi Peoples R China Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland State Grid Hebei Elect Power Co Ltd Shijiazhuang Hebei Peoples R China

The power output of photovoltaic (PV) systems is chiefly affected by climate and weather conditions. In that, PV farm requires accurate weather data, particularly, solar irradiance, in order to predict its power output as a means to improve solar energy utilization. Nevertheless, publicly available datasets which consist both power and weather data are exceptionally few. This may be a combined effect of data propriety and cumbersome collection procedure. And the rarity of such data greatly hinders the progress of solar PV research. Indeed, most solar energy meteorology applications, such as solar forecasting or PV performance evaluation, can benefit from multi-source high-quality datasets. In view of that, we release a PV power output dataset (PVOD), which contains metadata, numerical weather prediction data, and local measurements data from 10 PV systems located in China. In PVOD, a python toolkit with basic functions for data access and preprocessing is provided. Additionally, a case study on PV power output estimation is depicted to demonstrate the potential usage of the dataset.

关键词： Photovoltaic power output dataset Open source python toolkit Data article

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A New Method and python toolkit for General Access to Spatiotemporal N-Dimensional Raster Data

A New Method and Python Toolkit for General Access to Spatio...

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作者： Hales, Riley Chad Brigham Young University

学位级别：M.Sc., Master of Science

Scientific datasets from global-scale scientific models and remote sensing instruments are becoming available at greater spatial and temporal resolutions with shorter lag times. These data are frequently gridded measurements spanning two or three spatial dimensions, the time dimension, and often several data dimensions which vary by the specific dataset. These data are useful in many modeling and analysis applications across the geosciences. Unlike vector spatial datasets, raster spatial datasets lack widely adopted conventions in file formats, data organization, and dissemination mechanisms. Raster datasets are often saved using the Network Common Data Format (NetCDF), Gridded Binary (GRIB), Hierarchical Data Format (HDF), or Geographic Tagged Image File Format (GeoTIFF) file formats. Several of these are entirely or partially incompatible with common GIS software which introduces additional complexity in extracting values from these datasets. We present a method and companion python package as a general-purpose tool for extracting time series subsets from these files using various spatial geometries. This method and tool enable efficient access to multidimensional data regardless of the format of the data. This research builds on existing file formats and software rather than suggesting new alternatives. We also present an analysis of optimizations and performance.

关键词： python toolkit General access Spatiotemporal N-Dimensional

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Introduction to PySPLIT: A python toolkit for NOAA ARL's HYSPLIT Model

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COMPUTING IN SCIENCE & ENGINEERING 2018年第5期20卷 47-62页

作者： Warner, Mellissa S. C. Mayo Clinic Radiology Informatics Laboratory

The US National Oceanic and Atmospheric Administration Air Research Laboratory's HYSPLIT (HYbrid Single Particle Lagrangian Integrated Trajectory) model uses a hybrid Lagrangian and Eulerian calculation method to compute particle dispersion and deposition simulations as well as air parcel paths (trajectories), forward or backward in time. This model is used worldwide in a variety of scientific contexts. The author presents the first python package designed from the ground up to facilitate and expedite HYSPLIT trajectory analysis workflows by providing an intuitive and efficient API for generating, inspecting, and plotting trajectory paths and data. PySPLIT enables fully reproducible workflows, with orders of magnitude superior efficiency compared to what was previously possible with HYSPLIT alone, and leverages the capabilities of the scientific python ecosystem and matplotlib to generate reproducible, publication- quality figures.

关键词： Aerosols Atmospheric Composition Atmospheric Techniques Geophysics Computing Software Packages python Ecosystem python Package Lagrangian Calculation Method HYSPLIT Model US National Oceanic And Atmospheric Administration Air Research Laboratory HYSPLIT Trajectory Analysis Hybrid Lagrangian Calculation Method H Ybrid Single Particle Lagrangian Integrated Trajectory NOAA AR Ls HYSPLIT Model python toolkit Matplotlib Py SPLIT Air Parcel Paths Deposition Simulations Particle Dispersion Eulerian Calculation Method Research And Development Atmospheric Modeling Moisture Measurement Computational Modeling Humidity Task Analysis Scientific Computing python Earth And Atmospheric Sciences python HYSPLIT Py SPLIT Scientific Computing NOAA

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python Indian Weather Radar toolkit (pyiwr): An open-source python library for processing, analyzing and visualizing weather radar data

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JOURNAL OF COMPUTATIONAL SCIENCE 2024年 81卷

作者： Singh, Nitig Tyagi, Vaibhav Das, Saurabh Sahoo, Udaya Kumar Kundu, Shyam Sundar Indian Inst Technol Indore Indore Madhya Pradesh India IITM Sehore Madhya Pradesh India NESAC Umiam Meghalaya India

The python Indian Weather Radar toolkit, abbreviated as "pyiwr", is an open -source python library tailored for the purpose of handling data from the Indian Doppler Weather Radar (DWR). This paper provides a comprehensive overview of the pyiwr, which serves as a toolkit to read, analyze, process, and visualize weather radar data. Apart from this, the toolkit offers a range of robust functions implementing various algorithms covering several aspects of the radar data processing and quality control that facilitate the manipulation and analysis of weather radar data. To demonstrate the practical applicability of pyiwr, various case studies are presented, focusing on processing raw reflectivity data (clutter correction), Quantitative Precipitation Estimation (QPE) using Z -R relationship and time -series analysis of reflectivity and rain intensity, both spatially as well as at a specific location, during various meteorological events. This module enhances the accessibility and compatibility of radar data, enabling researchers, weather forecasters, and hydrologists to efficiently work with DWR data (particularly Indian DWR) that fosters advancements in weather radar research and applications. The open availability of pyiwr 's source code on GitHub ensures that researchers and practitioners can not only access the toolkit but also contribute to its ongoing development.

关键词： python toolkit DWR CAPPI QPE Data processing Time series analysis

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Pygmtools: a python graph matching toolkit

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 1644-1650页

作者： Runzhong Wang Ziao Guo Wenzheng Pan Jiale Ma Yikai Zhang Nan Yang Qi Liu Longxuan Wei Hanxue Zhang Chang Liu Zetian Jiang Xiaokang Yang Junchi Yan CSE Department and MoE Key Lab of AI Shanghai Jiao Tong University Shanghai China

Graph matching aims to find node-to-node matching among multiple graphs, which is a fundamental yet challenging problem. To facilitate graph matching in scientific research and industrial applications, pygmtools is released, which is a python graph matching toolkit that implements a comprehensive collection of two-graph matching and multigraph matching solvers, covering both learning-free solvers as well as learning-based neural graph matching solvers. Our implementation supports numerical backends including Numpy, PyTorch, Jittor, Paddle, runs on Windows, MacOS and Linux, and is friendly to install and configure. Comprehensive documentations covering beginner's guide, API reference and examples are available online. pygmtools is open-sourced under Mulan PSL v2 license.

关键词： graph matching combinatorial optimization graph learning python toolkit

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Portable, heterogeneous ensemble workflows at scale using libEnsemble

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2025年第1期39卷 177-192页

作者： Hudson, Stephen Larson, Jeffrey Navarro, John-Luke Wild, Stefan M. Argonne Natl Lab MCS Lemont IL USA Lawrence Berkeley Natl Lab AMCR Berkeley CA USA Northwestern Univ IEMS Evanston IL USA

libEnsemble is a python-based toolkit for running dynamic ensembles, developed as part of the DOE Exascale Computing Project. The toolkit utilizes a unique generator-simulator-allocator paradigm, where generators produce input for simulators, simulators evaluate those inputs, and allocators decide whether and when a simulator or generator should be called. The generator steers the ensemble based on simulation results. Generators may, for example, apply methods for numerical optimization, machine learning, or statistical calibration. libEnsemble communicates between a manager and workers. Flexibility is provided through multiple manager-worker communication substrates each of which has different benefits. These include python's multiprocessing, mpi4py, and TCP. Multisite ensembles are supported using Balsam or Globus Compute. We overview the unique characteristics of libEnsemble as well as current and potential interoperability with other packages in the workflow ecosystem. We highlight libEnsemble's dynamic resource features: libEnsemble can detect system resources, such as available nodes, cores, and GPUs, and assign these in a portable way. These features allow users to specify the number of processors and GPUs required for each simulation;and resources will be automatically assigned on a wide range of systems, including Frontier, Aurora, and Perlmutter. Such ensembles can include multiple simulation types, some using GPUs and others using only CPUs, sharing nodes for maximum efficiency. We also describe the benefits of libEnsemble's generator-simulator coupling, which easily exposes to the user the ability to cancel, and portably kill, running simulations based on models that are updated with intermediate simulation output. We demonstrate libEnsemble's capabilities, scalability, and scientific impact via a Gaussian process surrogate training problem for the longitudinal density profile at the exit of a plasma accelerator stage. The study uses gpCAM for the surrog

关键词： Dynamic ensembles python toolkit exascale computing portable workflows

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SOWISP-A retrospective high spatial and temporal resolution database of the installed wind and solar PV power in Spain

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SOLAR ENERGY 2023年 256卷 44-54页

作者： Jimenez-Garrote, Antonio Sanchez-Hernandez, Guadalupe Lopez-Cuesta, Miguel Pozo-Vazquez, David Univ Jaen Andalusian Inst Earth Syst Res IISTA CEAMA Jaen 23071 Spain Univ Jaen Dept Phys Jaen 23071 Spain

The proposal of new energy systems based on renewable energies requires thorough research in order to derive technically reliable and economically sustainable systems. One of the key inputs of such research is constituted by reliable databases of renewable resources. Despite the great effort of the scientific community in recent years, most current databases are far from optimal. Although some databases are based on real data, they lack adequate spatial resolution and/or temporal coverage. Other databases are obtained by estimating renewable energy potential from meteorological reanalysis;however, these estimates are subject to high uncertainty. One of the main problems when building these renewable resource databases is the lack of actual values of installed capacity. In this study we present the SOlar and Wind Installed Spanish Power (SOWISP) database. SOWISP provides the actual installed capacity of wind and photovoltaic solar energy in each Spanish town, with a monthly resolution, and covering the period of 2015-2020. SOWISP has been developed and validated based on a careful and thorough compilation of different public databases. It covers the need for a publicly available database with sufficient spatial and temporal resolution suitable for the analysis of energy systems. Moreover, SOWISP, along with other freely available datasets, supports many modern applications. In addition, a python package (available on GitHub) was developed for managing this database.

关键词： Solar PV and wind power dataset Publicly available python toolkit Data article

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A novel software framework for magnetic particle imaging reconstruction

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022年第4期32卷 1119-1132页

作者： Shen, Yusong Hu, Chaoen Zhang, Peng Tian, Jie Hui, Hui Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Key Lab Mol Imaging Beijing Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

Magnetic particle imaging is a novel tomographic imaging technique that enables noninvasive and highly sensitive imaging of superparamagnetic iron oxide nanoparticles distributed in living subjects. Several studies have reported on the development of reconstruction algorithms;however, a unified software framework for magnetic particle imaging reconstruction has yet to be developed. Herein, we propose a high-performance, flexible, and easy-to-use magnetic particle imaging reconstruction framework using the python programming language. The magnetic particle imaging reconstruction framework consists of the data access, preprocessing, image reconstruction, and postprocessing phases. We used the proposed framework to simulate the x-space and system matrix-based reconstruction methods with Cartesian and Lissajous scan trajectories. The reconstruction results of an open magnetic particle imaging dataset and a numerically simulated phantom demonstrated that the magnetic particle imaging reconstruction framework provides a reliable and accessible environment for magnetic particle imaging reconstruction, which can be extended and customized.

关键词： magnetic particle imaging python toolkit reconstruction scan simulation software framework

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word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs 12

word2word: A Collection of Bilingual Lexicons for 3,564 Lang...

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12th International Conference on Language Resources and Evaluation (LREC)

作者： Choe, Yo Joong Park, Kyubyong Kim, Dongwoo Kakao Brain 20 Pangyoyeok Ro 241 Seongnam Si Gyeonggi Do South Korea

ISBN: (纸本)9791095546344

We present word2word, a publicly available dataset and an open-source python package for cross-lingual word translations extracted from sentence-level parallel corpora. Our dataset provides top-k word translations in 3,564 (directed) language pairs across 62 languages in OpenSubtitles2018 (Lison et al., 2018). To obtain this dataset, we use a count-based bilingual lexicon extraction model based on the observation that not only source and target words but also source words themselves can be highly correlated. We illustrate that the resulting bilingual lexicons have high coverage and attain competitive translation quality for several language pairs. We wrap our dataset and model in an easy-to-use python library, which supports downloading and retrieving top-k word translations in any of the supported language pairs as well as computing top-k word translations for custom parallel corpora.

关键词： bilingual lexicon word translation python toolkit

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