MASS2, Modular Aquatic Simulation System in Two Dimensions, User
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A New Data Model, Programming Interface, and Format Using HDF5
NetCDF-4: A New Data Model, Programming Interface, and Format Using HDF5 Russ Rew, Ed Hartnett, John Caron UCAR Unidata Program Center Mike Folk, Robert McGrath, Quincey Kozial NCSA and The HDF Group, Inc. Final Project Review, August 9, 2005 THG, Inc. 1 Motivation: Why is this area of work important? While the commercial world has standardized on the relational data model and SQL, no single standard or tool has critical mass in the scientific community. There are many parallel and competing efforts to build these tool suites – at least one per discipline. Data interchange outside each group is problematic. In the next decade, as data interchange among scientific disciplines becomes increasingly important, a common HDF-like format and package for all the sciences will likely emerge. Jim Gray, Distinguished “Scientific Data Management in the Coming Decade,” Jim Gray, David Engineer at T. Liu, Maria A. Nieto-Santisteban, Alexander S. Szalay, Gerd Heber, Microsoft, David DeWitt, Cyberinfrastructure Technology Watch Quarterly, 1998 Turing Award Volume 1, Number 2, February 2005 winner 2 Preservation of scientific data … the ephemeral nature of both data formats and storage media threatens our very ability to maintain scientific, legal, and cultural continuity, not on the scale of centuries, but considering the unrelenting pace of technological change, from one decade to the next. … And that's true not just for the obvious items like images, documents, and audio files, but also for scientific images, … and MacKenzie Smith, simulations. In the scientific research community, Associate Director standards are emerging here and there—HDF for Technology at (Hierarchical Data Format), NetCDF (network the MIT Libraries, Common Data Form), FITS (Flexible Image Project director at Transport System)—but much work remains to be MIT for DSpace, a groundbreaking done to define a common cyberinfrastructure. -
Using Netcdf and HDF in Arcgis
Using netCDF and HDF in ArcGIS Nawajish Noman Dan Zimble Kevin Sigwart Outline • NetCDF and HDF in ArcGIS • Visualization and Analysis • Sharing • Customization using Python • Demo • Future Directions Scientific Data and Esri • Direct support - NetCDF and HDF • OPeNDAP/THREDDS – a framework for scientific data networking, integrated use by our customers • Users of Esri technology • National Climate Data Center • National Weather Service • National Center for Atmospheric Research • U. S. Navy (NAVO) • Air Force Weather • USGS • Australian Navy • Australian Bur.of Met. • UK Met Office NetCDF Support in ArcGIS • ArcGIS reads/writes netCDF since version 9.2 • An array based data structure for storing multidimensional data. T • N-dimensional coordinates systems • X, Y, Z, time, and other dimensions Z Y • Variables – support for multiple variables X • Temperature, humidity, pressure, salinity, etc • Geometry – implicit or explicit • Regular grid (implicit) • Irregular grid • Points Gridded Data Regular Grid Irregular Grid Reading netCDF data in ArcGIS • NetCDF data is accessed as • Raster • Feature • Table • Direct read • Exports GIS data to netCDF CF Convention Climate and Forecast (CF) Convention http://cf-pcmdi.llnl.gov/ Initially developed for • Climate and forecast data • Atmosphere, surface and ocean model-generated data • Also for observational datasets • The CF conventions generalize and extend the COARDS (Cooperative Ocean/Atmosphere Research Data Service) convention. • CF is now the most widely used conventions for geospatial netCDF data. It has the best coordinate system handling. NetCDF and Coordinate Systems • Geographic Coordinate Systems (GCS) • X dimension units: degrees_east • Y dimension units: degrees_north • Projected Coordinate Systems (PCS) • X dimension standard_name: projection_x_coordinate • Y dimension standard_name: projection_y_coordinate • Variable has a grid_mapping attribute. -
Introduction CFD General Notation System (CGNS)
CGNS Tutorial Introduction CFD General Notation System (CGNS) Christopher L. Rumsey NASA Langley Research Center Outline • Introduction • Overview of CGNS – What it is – History – How it works, and how it can help – The future • Basic usage – Getting it and making it work for you – Simple example – Aspects for data longevity 2 Introduction • CGNS provides a general, portable, and extensible standard for the description, storage, and retrieval of CFD analysis data • Principal target is data normally associated with computed solutions of the Navier-Stokes equations & its derivatives • But applicable to computational field physics in general (with augmentation of data definitions and storage conventions) 3 What is CGNS? • Standard for defining & storing CFD data – Self-descriptive – Machine-independent – Very general and extendable – Administered by international steering committee • AIAA recommended practice (AIAA R-101A-2005) • Free and open software • Well-documented • Discussion forum: [email protected] • Website: http://www.cgns.org 4 History • CGNS was started in the mid-1990s as a joint effort between NASA, Boeing, and McDonnell Douglas – Under NASA’s Advanced Subsonic Technology (AST) program • Arose from need for common CFD data format for improved collaborative analyses between multiple organizations – Existing formats, such as PLOT3D, were incomplete, cumbersome to share between different platforms, and not self-descriptive (poor for archival purposes) • Initial development was heavily influenced by McDonnell Douglas’ “Common -
A COMMON DATA MODEL APPROACH to NETCDF and GRIB DATA HARMONISATION Alessandro Amici, B-Open, Rome @Alexamici
A COMMON DATA MODEL APPROACH TO NETCDF AND GRIB DATA HARMONISATION Alessandro Amici, B-Open, Rome @alexamici - http://bopen.eu Workshop on developing Python frameworks for earth system sciences, 2017-11-28, ECMWF, Reading. alexamici / talks MOTIVATION: FORECAST DATA AND TOOLS ECMWF Weather forecasts, re-analyses, satellite and in- situ observations N-dimensional gridded data in GRIB Archive: Meteorological Archival and Retrieval System (MARS) A lot of tools: Metview, Magics, ecCodes... alexamici / talks MOTIVATION: CLIMATE DATA Copernicus Climate Change Service (C3S / ECMWF) Re-analyses, seasonal forecasts, climate projections, satellite and in-situ observations N-dimensional gridded data in many dialects of NetCDF and GRIB Archive: Climate Data Store (CDS) alexamici / talks HARMONISATION STRATEGIC CHOICES Metview Python Framework and CDS Toolbox projects Python 3 programming language scientic ecosystems xarray data structures NetCDF data model: variables and coordinates support for arbitrary metadata CF Conventions support on IO label-matching broadcast rules on coordinates parallelized and out-of-core computations with dask alexamici / talks ECMWF NETCDF DIALECT >>> import xarray as xr >>> ta_era5 = xr.open_dataset('ERA5-t-2016-06.nc', chunks={}).t >>> ta_era5 <xarray.DataArray 't' (time: 60, level: 3, latitude: 241, longitude: 480)> dask.array<open_dataset-..., shape=(60, 3, 241, 480), dtype=float64, chunksize=(60, 3, Coordinates: * longitude (longitude) float32 0.0 0.75 1.5 2.25 3.0 3.75 4.5 5.25 6.0 ... * latitude (latitude) float32 90.0 89.25 88.5 87.75 87.0 86.25 85.5 ... * level (level) int32 250 500 850 * time (time) datetime64[ns] 2017-06-01 2017-06-01T12:00:00 .. -
Development of a Coupling Approach for Multi-Physics Analyses of Fusion Reactors
Development of a coupling approach for multi-physics analyses of fusion reactors Zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften (Dr.-Ing.) bei der Fakultat¨ fur¨ Maschinenbau des Karlsruher Instituts fur¨ Technologie (KIT) genehmigte DISSERTATION von Yuefeng Qiu Datum der mundlichen¨ Prufung:¨ 12. 05. 2016 Referent: Prof. Dr. Stieglitz Korreferent: Prof. Dr. Moslang¨ This document is licensed under the Creative Commons Attribution – Share Alike 3.0 DE License (CC BY-SA 3.0 DE): http://creativecommons.org/licenses/by-sa/3.0/de/ Abstract Fusion reactors are complex systems which are built of many complex components and sub-systems with irregular geometries. Their design involves many interdependent multi- physics problems which require coupled neutronic, thermal hydraulic (TH) and structural mechanical (SM) analyses. In this work, an integrated system has been developed to achieve coupled multi-physics analyses of complex fusion reactor systems. An advanced Monte Carlo (MC) modeling approach has been first developed for converting complex models to MC models with hybrid constructive solid and unstructured mesh geometries. A Tessellation-Tetrahedralization approach has been proposed for generating accurate and efficient unstructured meshes for describing MC models. For coupled multi-physics analyses, a high-fidelity coupling approach has been developed for the physical conservative data mapping from MC meshes to TH and SM meshes. Interfaces have been implemented for the MC codes MCNP5/6, TRIPOLI-4 and Geant4, the CFD codes CFX and Fluent, and the FE analysis platform ANSYS Workbench. Furthermore, these approaches have been implemented and integrated into the SALOME simulation platform. Therefore, a coupling system has been developed, which covers the entire analysis cycle of CAD design, neutronic, TH and SM analyses. -
MATLAB Tutorial - Input and Output (I/O)
MATLAB Tutorial - Input and Output (I/O) Mathieu Dever NOTE: at any point, typing help functionname in the Command window will give you a description and examples for the specified function 1 Importing data There are many different ways to import data into MATLAB, mostly depending on the format of the datafile. Below are a few tips on how to import the most common data formats into MATLAB. • MATLAB format (*.mat files) This is the easiest to import, as it is already in MATLAB format. The function load(filename) will do the trick. • ASCII format (*.txt, *.csv, etc.) An easy strategy is to use MATLAB's GUI for data import. To do that, right-click on the datafile in the "Current Folder" window in MATLAB and select "Import data ...". The GUI gives you a chance to select the appropriate delimiter (space, tab, comma, etc.), the range of data you want to extract, how to deal with missing values, etc. Once you selected the appropriate options, you can either import the data, or you can generate a script that imports the data. This is very useful in showing the low-level code that takes care of reading the data. You will notice that the textscan is the key function that reads the data into MATLAB. Higher-level functions exist for the different datatype: csvread, xlsread, etc. As a rule of thumb, it is preferable to use lower-level function are they are "simpler" to understand (i.e., no bells and whistles). \Black-box" functions are dangerous! • NetCDF format (*.nc, *.cdf) MATLAB comes with a netcdf library that includes all the functions necessary to read netcdf files. -
R Programming for Climate Data Analysis and Visualization: Computing and Plotting for NOAA Data Applications
Citation of this work: Shen, S.S.P., 2017: R Programming for Climate Data Analysis and Visualization: Computing and plotting for NOAA data applications. The first revised edition. San Diego State University, San Diego, USA, 152pp. R Programming for Climate Data Analysis and Visualization: Computing and plotting for NOAA data applications SAMUEL S.P. SHEN Samuel S.P. Shen San Diego State University, and Scripps Institution of Oceanography, University of California, San Diego USA Contents Preface page vi Acknowledgements viii 1 Basics of R Programming 1 1.1 Download and install R and R-Studio 1 1.2 R Tutorial 2 1.2.1 R as a smart calculator 3 1.2.2 Define a sequence in R 4 1.2.3 Define a function in R 5 1.2.4 Plot with R 5 1.2.5 Symbolic calculations by R 6 1.2.6 Vectors and matrices 7 1.2.7 Simple statistics by R 10 1.3 Online Tutorials 12 1.3.1 Youtube tutorial: for true beginners 12 1.3.2 Youtube tutorial: for some basic statistical summaries 12 1.3.3 Youtube tutorial: Input data by reading a csv file into R 12 References 14 Exercises 14 2 R Analysis of Incomplete Climate Data 17 2.1 The missing data problem 17 2.2 Read NOAAGlobalTemp and form the space-time data matrix 19 2.2.1 Read the downloaded data 19 2.2.2 Plot the temperature data map of a given month 20 2.2.3 Extract the data for a specified region 22 2.2.4 Extract data from only one grid box 23 2.3 Spatial averages and their trends 23 2.3.1 Compute and plot the global area-weighted average of monthly data 23 2.3.2 Percent coverage of the NOAAGlobalTemp 24 2.3.3 Difference from the -
4.04 Netcdf.Pptx
Network Common Data Form NetCDF An Indroduction NetCDF is a set of software libraries and self- describing, machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. 1 The Purpose of NetCDF ● The purpose of the Network Common Data Form (netCDF) interface is to allow you to create, access, and share array- oriented data in a form that is self-describing and portable. ● Self-describing means that a dataset includes information defining the data it contains. ● Portable means that the data in a dataset is represented in a form that can be accessed by computers with different ways of storing integers, characters, and floating-point numbers. ● The netCDF software includes C, Fortran 77, Fortran 90, and C++ interfaces for accessing netCDF data. ● These libraries are available for many common computing platforms. 2 NETCDF Features ● Self-Describing: A netCDF file may include metadata as well as data: names of variables, data locations in time and space, units of measure, and other useful information. ● Portable: Data written on one platform can be read on other platforms. ● Direct-access: A small subset of a large dataset may be accessed efficiently, without first reading through all the preceding data. ● Appendable: Data may be efficiently appended to a netCDF file without copying the dataset or redefining its structure. ● Networkable: The netCDF library provides client access to structured data on remote servers through OPeNDAP protocols. ● Extensible: Adding new dimensions, variables, or attributes to netCDF files does not require changes to existing programs that read the files. ● Sharable: One writer and multiple readers may simultaneously access the same netCDF file. -
Scaling a Content Delivery System for Open Source Software
Scaling a Content Delivery system for Open Source Software Niklas Edmundsson May 25, 2015 Master’s Thesis in Computing Science, 30 credits Supervisor at CS-UmU: Per-Olov Ostberg¨ Examiner: Fredrik Georgsson Ume˚a University Department of Computing Science SE-901 87 UMEA˚ SWEDEN Abstract This master’s thesis addresses scaling of content distribution sites. In a case study, the thesis investigates issues encountered on ftp.acc.umu.se,acontentdistributionsiterun by the Academic Computer Club (ACC) of Ume˚aUniversity. Thissiteischaracterized by the unusual situation of the external network connectivity having higher bandwidth than the components of the system, which differs from the norm of the external con- nectivity being the limiting factor. To address this imbalance, a caching approach is proposed to architect a system that is able to fully utilize the available network capac- ity, while still providing a homogeneous resource to the end user. A set of modifications are made to standard open source solutions to make caching perform as required, and results from production deployment of the system are evaluated. In addition, time se- ries analysis and forecasting techniques are introduced as tools to improve the system further, resulting in the implementation of a method to automatically detect bursts and handle load distribution of unusually popular files. Contents 1Introduction 1 2Background 3 3 Architecture analysis 5 3.1 Overview ................................... 5 3.2 Components . 7 3.2.1 mod cache disk largefile - Apache httpd disk cache . 8 3.2.2 libhttpcacheopen - using the httpd disk cache for otherservices. 9 3.2.3 redirprg.pl-redirectionsubsystem . ... 10 3.3 Results.................................... -
Effective Perl Programming
Effective Perl Programming Second Edition The Effective Software Development Series Scott Meyers, Consulting Editor Visit informit.com/esds for a complete list of available publications. he Effective Software Development Series provides expert advice on Tall aspects of modern software development. Books in the series are well written, technically sound, and of lasting value. Each describes the critical things experts always do—or always avoid—to produce outstanding software. Scott Meyers, author of the best-selling books Effective C++ (now in its third edition), More Effective C++, and Effective STL (all available in both print and electronic versions), conceived of the series and acts as its consulting editor. Authors in the series work with Meyers to create essential reading in a format that is familiar and accessible for software developers of every stripe. Effective Perl Programming Ways to Write Better, More Idiomatic Perl Second Edition Joseph N. Hall Joshua A. McAdams brian d foy Upper Saddle River, NJ • Boston • Indianapolis • San Francisco New York • Toronto • Montreal • London • Munich • Paris • Madrid Capetown • Sydney • Tokyo • Singapore • Mexico City Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed with initial capital letters or in all capitals. The authors and publisher have taken care in the preparation of this book, but make no expressed or implied warranty of any kind and assume no responsibility for errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of the use of the information or programs contained herein. -
Integrated Tool Development for Used Fuel Disposition Natural System Evaluation –
Integrated Tool Development for Used Fuel Disposition Natural System Evaluation – Phase I Report Prepared for U.S. Department of Energy Used Fuel Disposition Yifeng Wang & Teklu Hadgu Sandia National Laboratories Scott Painter, Dylan R. Harp & Shaoping Chu Los Alamos National Laboratory Thomas Wolery Lawrence Livermore National Laboratory Jim Houseworth Lawrence Berkeley National Laboratory September 28, 2012 FCRD-UFD-2012-000229 SAND2012-7073P DISCLAIMER This information was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor any agency thereof, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness, of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. References herein to any specific commercial product, process, or service by trade name, trade mark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the U.S. Government or any agency thereof. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy‘s National Nuclear Security Administration under contract DE-AC04-94AL85000. Integrated Tool Development for UFD Natural System Evaluation 9/1/2012 iii Integrated Tool Development for UFD Natural System Evaluation iv 9/1/2012 Integrated Tool Development for UFD Natural System Evaluation 9/1/2012 v Executive Summary The natural barrier system (NBS) is an integral part of a geologic nuclear waste repository. -
Mastering Algorithms with Perl
Page iii Mastering Algorithms with Perl Jon Orwant, Jarkko Hietaniemi, and John Macdonald Page iv Mastering Algorithms with Perl by Jon Orwant, Jarkko Hietaniemi. and John Macdonald Copyright © 1999 O'Reilly & Associates, Inc. All rights reserved. Printed in the United States of America. Cover illustration by Lorrie LeJeune, Copyright © 1999 O'Reilly & Associates, Inc. Published by O'Reilly & Associates, Inc., 101 Morris Street, Sebastopol, CA 95472. Editors: Andy Oram and Jon Orwant Production Editor: Melanie Wang Printing History: August 1999: First Edition. Nutshell Handbook, the Nutshell Handbook logo, and the O'Reilly logo are registered trademarks of O'Reilly & Associates, Inc. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O'Reilly & Associates, Inc. was aware of a trademark claim, the designations have been printed in caps or initial caps. The association between the image of a wolf and the topic of Perl algorithms is a trademark of O'Reilly & Associates, Inc. While every precaution has been taken in the preparation of this book, the publisher assumes no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein. ISBN: 1-56592-398-7 [1/00] [M]]break Page v Table of Contents Preface xi 1. Introduction 1 What Is an Algorithm? 1 Efficiency 8 Recurrent Themes in Algorithms 20 2. Basic Data Structures 24 Perl's Built-in Data Structures 25 Build Your Own Data Structure 26 A Simple Example 27 Perl Arrays: Many Data Structures in One 37 3.