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The HDF5 1.8.20 release is now available. It can be obtained from The HDF Group Support Download page: https://support.hdfgroup.org/downloads/ It can also be obtained directly from: https://support.hdfgroup.org/HDF5/release/obtain518.html HDF5 1.8.20 is a minor release with a few new features and changes. Important changes include: An issue with H5Zfilter_avail was fixed where it was not finding available plugins. Improvements were made to the h5repack, h5ls, h5dump, h5diff, and h5import utilities.In particular, please be aware that the behavior of the h5repack utility changed. A parameter was added to the "UD=" option of h5repack to allow the user defined filter flag to be changed to either H5Z_FLAG_MANDATORY (0) or H5Z_FLAG_OPTIONAL (1).An example of the command with the new parameter is shown here: h5repack -f UD=307,0,1,9 h5repack_layout.h5 out.h5repack_layout.h5 Previously, the...

November 30, 2017

The HDF Group is pleased to announce Ann Johnson has joined as the new Director of Engineering, reporting to David Pearah, CEO. Ann was most recently the Vice President of Engineering at Reservoir Labs, responsible for global engineering operations, personnel, and project management. Prior to Reservoir Labs, Ann held several executive management positions at SiCortex, ClearSpeed Technology, and Silicon Graphics. In her new role as Director of Engineering, Ann will be part of The HDF Group’s senior management team—working with all facets of the organization while overseeing and managing software development, quality assurance, and dev ops teams. Her specialized skillsets and past experiences building and directing global technical teams will greatly support The HDF Group’s expansion into new product and service verticals. “We...

November 29, 2017

Greetings! The HDF Group is pleased to launch our new website on the evening of Tuesday, October 17th. Our new site features a new design, a new logo, and other improvements—all based on feedback from our users. You’ll find improved and simplified navigation with a better layout focused on the needs of our customers and users. The new site also clears the way for future updates: In the next year, we will also launch an updated support site, a new forum for user support, new products, and more. No big growth is complete without growing pains, but fortunately all you’ll experience is a brief period of site downtime from 5-7 p.m. CDT on Tuesday, October 17th. The URL will remain the same:...

October 16, 2017

Version 2.2.3 of the h4toh5 Conversion Library and Tools is now available from the HDF Downloads page: https://support.hdfgroup.org/downloads/index.html#h4h5 It can be obtained directly from: https://support.hdfgroup.org/products/hdf5_tools/h4toh5/download.html This release was tested with HDF 4.2.13, HDF5-1.8.19, and HDF5-1.10.1, and contains many changes, including:  Numerous improvements were made to the tools. For example: h4toh5 now follows the HDF5 dimension scale specification to handle HDF4 SDS dimensions “h4toh5 -eos” can now convert a 1D HDF-EOS2 swath to HDF5. Memory leaks in the library and tools were fixed. العاب لربح المال الحقيقي Please see the Release Notes for complete details on what is new with this release: https://support.hdfgroup.org/ftp/HDF5/h4toh5/src/h4h5tools-2.2.3-RELEASE.txt ...

September 12, 2017

By Francesc Alted. He is a freelance consultant and developing author of different open source libraries like PyTables, Blosc, bcolz and numexpr and an experienced programmer in Python and C. Francesc collaborates regularly with the The HDF Group in different projects. We explain our solution for handling big data streams using HDF5 (with a little help from other tools). In the ubiquitously connected world that we live in, there are good reasons to understand what data is transferred across a network and how to extract information out of it. Being able to capture and log the different network packets can be used for many tasks including: Protecting against cyber threats Enforcing policy Extracting and consolidating valuable information Debugging protocols/services Understanding how users use...

August 24, 2017

The HDFView 2.14 and HDF Java 3.3.2 release is now available. This release supports HDF5-1.8 and 32-bit object identifiers and was tested with HDF5-1.8.19 and HDF 4.2.13. It can be obtained from: https://support.hdfgroup.org/downloads/index.html It can also be obtained directly from: https://support.hdfgroup.org/products/java/release/download.html More information on this release can be found on the HDF Java home page. This is a maintenance release with a few bug fixes. It includes changes to the error exception handling in the Java HDF wrappers. These changes fix an issue that affected the display of HDF4 files in HDFView....

July 27, 2017

CONTENTS Release of HDF 4.2.13 Release of HDF 4.2.13 The HDF 4.2.13 release is now available. It can be obtained from the HDF4 home page: https://support.hdfgroup.org/products/hdf4/ HDF 4.2.13 is a minor release with a few changes, including: Support was added for macOS Sierra 10.12.5. Several memory leaks were fixed. The minimum CMake version supported is 3.2.2. For detailed information regarding this release, please see the Release Notes....

June 29, 2017

The HDF5 1.8.19 release is now available. It can be obtained from the HDF5 Download page: https://www.hdfgroup.org/downloads/hdf5/ It can also be obtained directly from: https://support.hdfgroup.org/HDF5/release/obtain518.html HDF5 1.8.19 is a minor release with a few new features and changes. Important changes include: Several H5PL (C) APIs were added to manipulate the entries of the plugin path table: H5PLappend, H5PLget, H5PLinsert, H5PLprepend, H5PLremove, H5PLreplace, and H5PLsize. H5Dget_chunk_storage_size (C) was added to obtain the storage size of a chunk in the file. This API was specifically added in support of H5DOread_chunk, but may also be useful for other purposes. H5DOread_chunk (High Level C) was added to read a raw data chunk directly from a dataset in a file, bypassing HDF5's internal data transfer pipeline, including filters. ...

June 16, 2017

Tobias Weinzierl, Durham University, UK, Sven Köppel, FIAS, Germany, Michael Bader, TUM, Germany, HDF Guest Bloggers ExaHyPE develops a solver engine for hyperbolic differential equations solved on adaptive Cartesian meshes. It supports various HDF5 output formats. Exascale computing is expected to allow scientists and engineers to simulate, and ultimately understand, wave phenomena with unprecedented accuracy over unprecedented time spans. To harvest the power of exascale machines, well-suited software however has to become available. ExaHyPE is a H2020 project writing a PDE solver engine—similar to a 3D computer game engine—that will allow groups with a decent CSE expertise to write their own solver for hyperbolic equation systems within a year. The resulting solver will scale to exascale. This is made possible by the unique...

June 14, 2017

Internal compression is one of several powerful HDF5 features that distinguish HDF5 from other binary formats and make it very attractive for storing and organizing data. Internal HDF5 compression saves storage space and I/O bandwidth and allows efficient partial access to data. Chunk storage must be used when HDF5 compression is enabled....

May 24, 2017
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