(EOSSA) DATA PRODUCTS Tamara Payne, Shaylah Mutschler, Neal S
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Parallel Data Analysis Directly on Scientific File Formats
Parallel Data Analysis Directly on Scientific File Formats Spyros Blanas 1 Kesheng Wu 2 Surendra Byna 2 Bin Dong 2 Arie Shoshani 2 1 The Ohio State University 2 Lawrence Berkeley National Laboratory [email protected] {kwu, sbyna, dbin, ashosani}@lbl.gov ABSTRACT and physics, produce massive amounts of data. The size of Scientific experiments and large-scale simulations produce these datasets typically ranges from hundreds of gigabytes massive amounts of data. Many of these scientific datasets to tens of petabytes. For example, the Intergovernmen- are arrays, and are stored in file formats such as HDF5 tal Panel on Climate Change (IPCC) multi-model CMIP-5 and NetCDF. Although scientific data management systems, archive, which is used for the AR-5 report [22], contains over petabytes such as SciDB, are designed to manipulate arrays, there are 10 of climate model data. Scientific experiments, challenges in integrating these systems into existing analy- such as the LHC experiment routinely store many gigabytes sis workflows. Major barriers include the expensive task of of data per second for future analysis. As the resolution of preparing and loading data before querying, and convert- scientific data is increasing rapidly due to novel measure- ing the final results to a format that is understood by the ment techniques for experimental data and computational existing post-processing and visualization tools. As a con- advances for simulation data, the data volume is expected sequence, integrating a data management system into an to grow even further in the near future. existing scientific data analysis workflow is time-consuming Scientific data are often stored in data formats that sup- and requires extensive user involvement. -
Panstamps Documentation Release V0.5.3
panstamps Documentation Release v0.5.3 Dave Young 2020 Getting Started 1 Installation 3 1.1 Troubleshooting on Mac OSX......................................3 1.2 Development...............................................3 1.2.1 Sublime Snippets........................................4 1.3 Issues...................................................4 2 Command-Line Usage 5 3 Documentation 7 4 Command-Line Tutorial 9 4.1 Command-Line..............................................9 4.1.1 JPEGS.............................................. 12 4.1.2 Temporal Constraints (Useful for Moving Objects)...................... 17 4.2 Importing to Your Own Python Script.................................. 18 5 Installation 19 5.1 Troubleshooting on Mac OSX...................................... 19 5.2 Development............................................... 19 5.2.1 Sublime Snippets........................................ 20 5.3 Issues................................................... 20 6 Command-Line Usage 21 7 Documentation 23 8 Command-Line Tutorial 25 8.1 Command-Line.............................................. 25 8.1.1 JPEGS.............................................. 28 8.1.2 Temporal Constraints (Useful for Moving Objects)...................... 33 8.2 Importing to Your Own Python Script.................................. 34 8.2.1 Subpackages.......................................... 35 8.2.1.1 panstamps.commonutils (subpackage)........................ 35 8.2.1.2 panstamps.image (subpackage)............................ 35 8.2.2 Classes............................................ -
A Metadata Based Approach for Supporting Subsetting Queries Over Parallel HDF5 Datasets
A Metadata Based Approach For Supporting Subsetting Queries Over Parallel HDF5 Datasets Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Vignesh Santhanagopalan, B.S. Graduate Program in Computer Science and Engineering The Ohio State University 2011 Thesis Committee: Dr. Gagan Agrawal, Advisor Dr. Radu Teodorescu ABSTRACT A key challenge in scientific data management is to manage the data as the data sizes are growing at a very rapid speed. Scientific datasets are typically stored using low-level formats which store the data as binary. This makes specification of the data and processing very hard. Also, as the volume of data is huge, parallel configurations must be used to process the data to enable efficient access. We have developed a data virtualization approach for supporting subsetting queries on scientific datasets stored in native format. The data is stored in Hierarchical Data Format (HDF5) which is one of the popular formats for storing scientific data. Our system supports SQL queries using the Select, From and Where clauses. We support queries based on the dimensions of the dataset and also queries which are based on the dimensions and attributes (which provide extra information about the dataset) of the dataset. In order to support the different types of queries, we have the pre-processing and post-processing modules. We also parallelize the selection queries involving the dimensions and the attributes. Our system offers the following advantages. We provide SQL like abstraction for specifying subsets of interest which is a powerful mechanism. -
HUDDL for Description and Archive of Hydrographic Binary Data
University of New Hampshire University of New Hampshire Scholars' Repository Center for Coastal and Ocean Mapping Center for Coastal and Ocean Mapping 4-2014 HUDDL for description and archive of hydrographic binary data Giuseppe Masetti University of New Hampshire, Durham, [email protected] Brian R. Calder University of New Hampshire, Durham, [email protected] Follow this and additional works at: https://scholars.unh.edu/ccom Recommended Citation G. Masetti and Calder, Brian R., “Huddl for description and archive of hydrographic binary data”, Canadian Hydrographic Conference 2014. St. John's, NL, Canada, 2014. This Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New Hampshire Scholars' Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of New Hampshire Scholars' Repository. For more information, please contact [email protected]. Canadian Hydrographic Conference April 14-17, 2014 St. John's N&L HUDDL for description and archive of hydrographic binary data Giuseppe Masetti and Brian Calder Center for Coastal and Ocean Mapping & Joint Hydrographic Center – University of New Hampshire (USA) Abstract Many of the attempts to introduce a universal hydrographic binary data format have failed or have been only partially successful. In essence, this is because such formats either have to simplify the data to such an extent that they only support the lowest common subset of all the formats covered, or they attempt to be a superset of all formats and quickly become cumbersome. Neither choice works well in practice. -
Towards Interactive, Reproducible Analytics at Scale on HPC Systems
Towards Interactive, Reproducible Analytics at Scale on HPC Systems Shreyas Cholia Lindsey Heagy Matthew Henderson Lawrence Berkeley National Laboratory University Of California, Berkeley Lawrence Berkeley National Laboratory Berkeley, USA Berkeley, USA Berkeley, USA [email protected] [email protected] [email protected] Drew Paine Jon Hays Ludovico Bianchi Lawrence Berkeley National Laboratory University Of California, Berkeley Lawrence Berkeley National Laboratory Berkeley, USA Berkeley, USA Berkeley, USA [email protected] [email protected] [email protected] Devarshi Ghoshal Fernando Perez´ Lavanya Ramakrishnan Lawrence Berkeley National Laboratory University Of California, Berkeley Lawrence Berkeley National Laboratory Berkeley, USA Berkeley, USA Berkeley, USA [email protected] [email protected] [email protected] Abstract—The growth in scientific data volumes has resulted analytics at scale. Our work addresses these gaps. There are in a need to scale up processing and analysis pipelines using High three key components driving our work: Performance Computing (HPC) systems. These workflows need interactive, reproducible analytics at scale. The Jupyter platform • Reproducible Analytics: Reproducibility is a key com- provides core capabilities for interactivity but was not designed ponent of the scientific workflow. We wish to to capture for HPC systems. In this paper, we outline our efforts that the entire software stack that goes into a scientific analy- bring together core technologies based on the Jupyter Platform to create interactive, reproducible analytics at scale on HPC sis, along with the code for the analysis itself, so that this systems. Our work is grounded in a real world science use case can then be re-run anywhere. In particular it is important - applying geophysical simulations and inversions for imaging to be able reproduce workflows on HPC systems, against the subsurface. -
Parsing Hierarchical Data Format (HDF) Files Karl Nyberg Grebyn Corporation P
Parsing Hierarchical Data Format (HDF) Files Karl Nyberg Grebyn Corporation P. O. Box 47 Sterling, VA 20167-0047 703-406-4161 [email protected] ABSTRACT This paper presents a description of the creation of a library to parse Hierarchical Data Format (HDF) Files in Ada. It describes a “work in progress” with discussion of current performance, limitations and future plans. Categories and Subject Descriptors D.2.2 [Design Tools and Techniques]: Software Libraries; E.2 [Data Storage Representations]: Object Representation; I.2.10 [Vision and Scene Understanding]: Representations, data structures, and transforms. General Terms Algorithms, Performance, Experimentation, Data Formats. Keywords Ada, HDF. 1. INTRODUCTION Large quantities of scientific data are published each year by NASA. These data are often accompanied by metadata files that describe the contents of individual files of the data. One example of this data is the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) [1]. Each file of data (consisting of satellite imagery) in HDF (Hierarchical Data Format) [2] is accompanied by a metadata file in XML (Extensible Markup Language) [3], encoded according to a published DTD (Document Type Description) that indicates the components and types of data in the metadata file. Each ASTER data file consists of an image of the satellite as it passes over the earth [4]. Information on the location of the data collected as the satellite passes is contained in the metadata file. Over time, multiple images of the same location on earth are obtained. For many purposes of analysis (erosion, building patterns, deforestation, glacier movement, etc.), these images of the same location are compared over time. -
Making Color Images with the GIMP Las Cumbres Observatory Global Telescope Network Color Imaging: the GIMP Introduction
Las Cumbres Observatory Global Telescope Network Making Color Images with The GIMP Las Cumbres Observatory Global Telescope Network Color Imaging: The GIMP Introduction These instructions will explain how to use The GIMP to take those three images and composite them to make a color image. Users will also learn how to deal with minor imperfections in their images. Note: The GIMP cannot handle FITS files effectively, so to produce a color image, users will have needed to process FITS files and saved them as grayscale TIFF or JPEG files as outlined in the Basic Imaging section. Separately filtered FITS files are available for you to use on the Color Imaging page. The GIMP can be downloaded for Windows, Mac, and Linux from: www.gimp.org Loading Images Loading TIFF/JPEG Files Users will be processing three separate images to make the RGB color images. When opening files in The GIMP, you can select multiple files at once by holding the Ctrl button and clicking on the TIFF or JPEG files you wish to use to make a color image. Go to File > Open. Image Mode RGB Mode Because these images are saved as grayscale, all three images need to be converted to RGB. This is because color images are made from (R)ed, (G)reen, and (B)lue picture elements (pixels). The different shades of gray in the image show the intensity of light in each of the wavelengths through the red, green, and blue filters. The colors themselves are not recorded in the image. Adding Color Information For the moment, these images are just grayscale. -
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. -
A Very Useful Enhancement for MSC Nastran and Patran
Tips & Tricks HDF5: A Very Useful Enhancement for MSC Nastran and Patran et’s face it, as FEA engineers we’ve all been As a premier FEA solver that is widely used by the in that situation where the finite element Aerospace and Automotive industries, MSC Nastran L model has been prepared, boundary now takes advantage of the HDF5 file to manage conditions have been checked and the model has and store your FEA data. This can simplify your post been solved, but then it comes time to investigate processing tasks and make data management much the results. It’s not unusual on a large project to easier and simpler (see below). produce different outputs for different purposes: an XDB or OP2 for Patran, a Punch file to pass to So what is HDF5? It is an open source file format, Excel, and the f06 to read the printed output. In data model, and library developed by the HDF Group. addition, your company may have programs that read one of these files to perform special purpose HDF5 has three significant advantages compared to calculations. Managing all these files becomes a previous result file formats: 1) The HDF5 file is project of its own. smaller than XDB and OP2, 2) accessing results is significantly faster with HDF5, and 3) the input and Well let me tell you, there is a better way to do this!! output datablocks are stored in a single, high 82 | Engineering Reality Magazine Before HDF5 After HDF5 precision file. With input and output in the same file, party extensions are available for Python, .Net, and you eliminate confusion and potential errors keeping many other languages. -
Arxiv:2002.01657V1 [Eess.IV] 5 Feb 2020 Port Lossless Model to Compress Images Lossless
LEARNED LOSSLESS IMAGE COMPRESSION WITH A HYPERPRIOR AND DISCRETIZED GAUSSIAN MIXTURE LIKELIHOODS Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto Department of Computer Science and Communications Engineering, Waseda University, Tokyo, Japan. ABSTRACT effectively in [12, 13, 14]. Some methods decorrelate each Lossless image compression is an important task in the field channel of latent codes and apply deep residual learning to of multimedia communication. Traditional image codecs improve the performance as [15, 16, 17]. However, deep typically support lossless mode, such as WebP, JPEG2000, learning based lossless compression has rarely discussed. FLIF. Recently, deep learning based approaches have started One related work is L3C [18] to propose a hierarchical archi- to show the potential at this point. HyperPrior is an effective tecture with 3 scales to compress images lossless. technique proposed for lossy image compression. This paper In this paper, we propose a learned lossless image com- generalizes the hyperprior from lossy model to lossless com- pression using a hyperprior and discretized Gaussian mixture pression, and proposes a L2-norm term into the loss function likelihoods. Our contributions mainly consist of two aspects. to speed up training procedure. Besides, this paper also in- First, we generalize the hyperprior from lossy model to loss- vestigated different parameterized models for latent codes, less compression model, and propose a loss function with L2- and propose to use Gaussian mixture likelihoods to achieve norm for lossless compression to speed up training. Second, adaptive and flexible context models. Experimental results we investigate four parameterized distributions and propose validate our method can outperform existing deep learning to use Gaussian mixture likelihoods for the context model. -
Image Formats
Image Formats Ioannis Rekleitis Many different file formats • JPEG/JFIF • Exif • JPEG 2000 • BMP • GIF • WebP • PNG • HDR raster formats • TIFF • HEIF • PPM, PGM, PBM, • BAT and PNM • BPG CSCE 590: Introduction to Image Processing https://en.wikipedia.org/wiki/Image_file_formats 2 Many different file formats • JPEG/JFIF (Joint Photographic Experts Group) is a lossy compression method; JPEG- compressed images are usually stored in the JFIF (JPEG File Interchange Format) >ile format. The JPEG/JFIF >ilename extension is JPG or JPEG. Nearly every digital camera can save images in the JPEG/JFIF format, which supports eight-bit grayscale images and 24-bit color images (eight bits each for red, green, and blue). JPEG applies lossy compression to images, which can result in a signi>icant reduction of the >ile size. Applications can determine the degree of compression to apply, and the amount of compression affects the visual quality of the result. When not too great, the compression does not noticeably affect or detract from the image's quality, but JPEG iles suffer generational degradation when repeatedly edited and saved. (JPEG also provides lossless image storage, but the lossless version is not widely supported.) • JPEG 2000 is a compression standard enabling both lossless and lossy storage. The compression methods used are different from the ones in standard JFIF/JPEG; they improve quality and compression ratios, but also require more computational power to process. JPEG 2000 also adds features that are missing in JPEG. It is not nearly as common as JPEG, but it is used currently in professional movie editing and distribution (some digital cinemas, for example, use JPEG 2000 for individual movie frames). -
Importing and Exporting Data
Chapter II-9 II-9Importing and Exporting Data Importing Data................................................................................................................................................ 117 Load Waves Submenu ............................................................................................................................ 119 Line Terminators...................................................................................................................................... 120 LoadWave Text Encodings..................................................................................................................... 120 Loading Delimited Text Files ........................................................................................................................ 120 Determining Column Formats............................................................................................................... 120 Date/Time Formats .................................................................................................................................. 121 Custom Date Formats ...................................................................................................................... 122 Column Labels ......................................................................................................................................... 122 Examples of Delimited Text ................................................................................................................... 123 The Load