ENVI/IDL for HDF
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ENVI and IDL software support HDF and HDF-EOS. Capabilities and the HDF tools built on ENVI and IDL will be reviewed. The current development will be discussed and demonstrated.
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This document provides an overview of HSDS (HDF Server and Data Service), which allows HDF5 files to be stored and accessed from the cloud. Key points include:
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This document discusses updates and performance improvements to the HDF5 OPeNDAP data handler. It provides a history of the handler since 2001 and describes recent updates including supporting DAP4, new data types, and NetCDF data models. A performance study showed that passing compressed HDF5 data through the handler without decompressing/recompressing led to speedups of around 17-30x by leveraging HDF5 direct I/O APIs. This allows outputting HDF5 files as NetCDF files much faster through the handler.
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This document provides instructions for using the Hyrax software to serve scientific data files stored on Amazon S3 using the OPeNDAP data access protocol. It describes how to generate ancillary metadata files called DMR++ files using the get_dmrpp tool that provide information about the data file structure and locations. The document explains how to run get_dmrpp inside a Docker container to process data files on S3 and generate customized DMR++ files that the Hyrax server can use to serve the files to clients.
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HDF - Current status and Future Directions
The HDF5 Roadmap and New Features document outlines upcoming changes and improvements to the HDF5 library. Key points include:
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2) Performance benchmarks show some improvements but also regressions compared to the previous HDF5 version, and work continues to optimize code and support future versions.
3) There are compatibility considerations for Linux filter plugins, but interim solutions are provided until MathWorks can ship a single HDF5 version.
HDF for the Cloud - Serverless HDF
HSDS provides HDF as a service through a REST API that can scale across nodes. New releases will enable serverless operation using AWS Lambda or direct client access without a server. This allows HDF data to be accessed remotely without managing servers. HSDS stores each HDF object separately, making it compatible with cloud object storage. Performance on AWS Lambda is slower than a dedicated server but has no management overhead. Direct client access has better performance but limits collaboration between clients.
HDF5 <-> Zarr
HDF5 and Zarr are data formats that can be used to store and access scientific data. This presentation discusses approaches to translating between the two formats. It describes how HDF5 files were translated to the Zarr format by creating a separate Zarr store to hold HDF5 file chunks, and storing chunk location metadata. It also discusses an implementation that translates Zarr data to the HDF5 format by using a special chunking layout and storing chunk information in an HDF5 compound dataset. Limitations of the translations include lack of support for some HDF5 dataset properties in Zarr, and lack of support for some Zarr compression methods in the HDF5 implementation.
HDF for the Cloud - New HDF Server Features
The document discusses HDF for the cloud, including new features of the HDF Server and what's next. Key points:
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Apache Drill and Unidata THREDDS Data Server for NASA HDF-EOS on S3
Apache Drill and Unidata THREDDS Data Server for NASA HDF-EOS on S3The HDF-EOS Tools and Information Center
This document compares different methods for accessing HDF and netCDF files stored on Amazon S3, including Apache Drill, THREDDS Data Server (TDS), and HDF5 Virtual File Driver (VFD). A benchmark test of accessing a 24GB HDF5/netCDF-4 file on S3 from Amazon EC2 found that TDS performed the best, responding within 2 minutes, while Apache Drill failed after 7 minutes. The document concludes that TDS 5.0 is the clear winner based on performance and support for role-based access control and HDF4 files, but the best solution depends on use case and software.STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...
STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...The HDF-EOS Tools and Information Center
This document discusses STARE-PODS, a proposal to NASA/ACCESS-19 to develop a scalable data store for earth science data using the SpatioTemporal Adaptive Resolution Encoding (STARE) indexing scheme. STARE allows diverse earth science data to be unified and indexed, enabling the data to be partitioned and stored in a Parallel Optimized Data Store (PODS) for efficient analysis. The HDF Virtual Object Layer and Virtual Data Set technologies can then provide interfaces to access the data in STARE-PODS in a familiar way. The goal is for STARE-PODS to organize diverse data for alignment and parallel/distributed storage and processing to enable integrative analysis at scale.HDF5 and Ecosystem: What Is New?
This document provides an overview and update on HDF5 and its ecosystem. Key points include:
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- The HDF5 library now supports accessing data in the cloud using connectors like S3 VFD and REST VOL without needing to modify applications.
- Projects like HDFql and H5CPP provide additional interfaces for querying and working with HDF5 files from languages like SQL, C++, and Python.
- The HDF5 community is moving development to GitHub and improving documentation resources on the HDF wiki site.
HDF5 Roadmap 2019-2020
This document summarizes new features in HDF5 1.12.0, including support for storing references to objects and attributes across files, new storage backends using a virtual object layer (VOL), and virtual file drivers (VFDs) for Amazon S3 and HDFS. It outlines the HDF5 roadmap for 2019-2022, which includes continued support for HDF5 1.8 and 1.10, and new features in future 1.12.x releases like querying, indexing, and provenance tracking.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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ENVI/IDL for HDF
- 1. ENVI/IDL For HDF Bill Okubo, IDL Product Manager November 17, 2008
- 2. Toolkits & Enhancements For ENVI/IDL • • • • MODIS Conversion Toolkit ENVI Plugin for Ocean Color Hyperion Tools ASTER Level 2 RPC Orthorectification • NOAA CoastWatch AVHRR • AIRS Level 1B Radiance (prototype) Devin White – Senior Programming Consultant, PhD dwhite@ittvis.com • H5_Browser GUI to open and browse HDF5 files • H5_Parse automatically reads entire file into an IDL structure 2
- 3. The MODIS Conversion Toolkit • The MODIS Conversion Toolkit (MCTK) is a plugin for ENVI that can ingest, process, and georeference every known MODIS product (currently 143) through your choice of an easy-to-use interactive widget interface or a fully-accessible programmatic interface. Supported products include: - Level 1A Uncalibrated Radiance - Level 1B Calibrated Radiance - Level 2 Swath - Level 2G, Level 3, and Level 4 Grid • Works with MODIS data from the Land, Cryosphere, Atmosphere, and Calibration groups. 3
- 4. MCTK 4
- 5. ENVI 4.6 Themes This release expands the value of ENVI in two primary areas: • Automated Workflows: – Even more workflows to support a variety of tasks for all – of their panchromatic, MSI and HSI data needs. Customers of all ability levels and backgrounds will find that the new workflows provide an intuitive way to perform rigorous image analysis. • Feature Extraction: – Enhanced results by adding additional datasets and – ground truth data. Prepare results for sharing by adding pertinent annotations to the extracted features of interest. • Release scheduled for January 12, 2009 Enhanced Results • Faster Workflows • Share Results 5
- 6. IDL Product Goals Key problems for today’s IDL user – Cannot read even the most common formats of data into IDL without using commands or programming – Cannot interact with visualizations and also control them using simple procedural commands or programs • Improve overall usability via Workbench integration – Make it easier to read data & interact with graphics – Provide easy-to-use modern, widget controls & behavior – Modernize the user experience • Enhance core functionality 6
- 7. IDL 7.1 Feature Highlights • IDL Workbench Visualization Manager – Open common file formats from File->Open – Drag & drop to create visualizations – Extensible – user-defined categories and action bars • iTools Enhancements – Procedural API with simplified ID strings – Generate code to recreate visualizations – Better margins and zooming • Internationalization phase 2 – Fix File I/O issues – Multibyte text for object graphics – Support Unicode input for widgets • IDL Workbench – Plug-in Update Site Wizard – Improvements & bug fixes 7
- 9. IDL 7.1 Feature Highlights (cont.) • Data Access – – – – Dataminer upgrade to latest version - MySQL DICOMex for 64-bit Windows AVI Read/Write Plugin – 3rd party Ronn Kling CSV reader • Image Processing – new algorithms TBD • Integration – IDL Python Bridge – Jacquette Consulting • Cross Platform Support – – – – Windows Command Line Solaris 10 x86 64-bit, IDL command-line only OMB FDCC non-admin mandate for Windows XP & Vista Mac OS X 64-bit Intel (IDL 7.0.4) • Scientific Data Formats – CDF-3.2, HDF4-r2.3, HDF5-1.6.7 (IDL 7.0.3) 9