digitalmars.D.announce - HDF5 bindings for D
- Laeeth Isharc (59/59) Dec 21 2014 https://github.com/Laeeth/d_hdf5
- Rikki Cattermole (4/59) Dec 21 2014 You seem to be missing your dub file. Would be rather hard to get it
- Laeeth Isharc (5/9) Dec 22 2014 Thanks - added now.
- John Colvin (2/61) Dec 22 2014 Also relevant to some: http://code.dlang.org/packages/netcdf
https://github.com/Laeeth/d_hdf5 HDF5 is a very valuable tool for those working with large data sets. From HDF5group.org HDF5 is a unique technology suite that makes possible the management of extremely large and complex data collections. The HDF5 technology suite includes: * A versatile data model that can represent very complex data objects and a wide variety of metadata. * A completely portable file format with no limit on the number or size of data objects in the collection. * A software library that runs on a range of computational platforms, from laptops to massively parallel systems, and implements a high-level API with C, C++, Fortran 90, and Java interfaces. * A rich set of integrated performance features that allow for access time and storage space optimizations. * Tools and applications for managing, manipulating, viewing, and analyzing the data in the collection. * The HDF5 data model, file format, API, library, and tools are open and distributed without charge. From h5py.org: [HDF5] lets you store huge amounts of numerical data, and easily manipulate that data from NumPy. For example, you can slice into multi-terabyte datasets stored on disk, as if they were real NumPy arrays. Thousands of datasets can be stored in a single file, categorized and tagged however you want. H5py uses straightforward NumPy and Python metaphors, like dictionary and NumPy array syntax. For example, you can iterate over datasets in a file, or check out the .shape or .dtype attributes of datasets. You don't need to know anything special about HDF5 to get started. In addition to the easy-to-use high level interface, h5py rests on a object-oriented Cython wrapping of the HDF5 C API. Almost anything you can do from C in HDF5, you can do from h5py. Best of all, the files you create are in a widely-used standard binary format, which you can exchange with other people, including those who use programs like IDL and MATLAB. =========== As far as I know there has not really been a complete set of HDF5 bindings for D yet. Bindings should have three levels: 1. pure C API declaration 2. 'nice' D wrapper around C API (eg that knows about strings, not just char*) 3. idiomatic D interface that uses CTFE/templates I borrowed Stefan Frijter's work on (1) above to get started. I cannot keep track of things when split over too many source files, so I put everything in one file - hdf5.d. Have implemented a basic version of 2. Includes throwOnError rather than forcing checking status C style, but the exception code is not very good/complete (time + lack of experience with D exceptions). (3) will have to come later. It's more or less complete, and the examples I have translated so far mostly work. But still a work in progress. Any help/suggestions appreciated. [I am doing this for myself, so project is not as pretty as I would like in an ideal world]. https://github.com/Laeeth/d_hdf5
Dec 21 2014
On 22/12/2014 5:51 p.m., Laeeth Isharc wrote:https://github.com/Laeeth/d_hdf5 HDF5 is a very valuable tool for those working with large data sets. From HDF5group.org HDF5 is a unique technology suite that makes possible the management of extremely large and complex data collections. The HDF5 technology suite includes: * A versatile data model that can represent very complex data objects and a wide variety of metadata. * A completely portable file format with no limit on the number or size of data objects in the collection. * A software library that runs on a range of computational platforms, from laptops to massively parallel systems, and implements a high-level API with C, C++, Fortran 90, and Java interfaces. * A rich set of integrated performance features that allow for access time and storage space optimizations. * Tools and applications for managing, manipulating, viewing, and analyzing the data in the collection. * The HDF5 data model, file format, API, library, and tools are open and distributed without charge. From h5py.org: [HDF5] lets you store huge amounts of numerical data, and easily manipulate that data from NumPy. For example, you can slice into multi-terabyte datasets stored on disk, as if they were real NumPy arrays. Thousands of datasets can be stored in a single file, categorized and tagged however you want. H5py uses straightforward NumPy and Python metaphors, like dictionary and NumPy array syntax. For example, you can iterate over datasets in a file, or check out the .shape or .dtype attributes of datasets. You don't need to know anything special about HDF5 to get started. In addition to the easy-to-use high level interface, h5py rests on a object-oriented Cython wrapping of the HDF5 C API. Almost anything you can do from C in HDF5, you can do from h5py. Best of all, the files you create are in a widely-used standard binary format, which you can exchange with other people, including those who use programs like IDL and MATLAB. =========== As far as I know there has not really been a complete set of HDF5 bindings for D yet. Bindings should have three levels: 1. pure C API declaration 2. 'nice' D wrapper around C API (eg that knows about strings, not just char*) 3. idiomatic D interface that uses CTFE/templates I borrowed Stefan Frijter's work on (1) above to get started. I cannot keep track of things when split over too many source files, so I put everything in one file - hdf5.d. Have implemented a basic version of 2. Includes throwOnError rather than forcing checking status C style, but the exception code is not very good/complete (time + lack of experience with D exceptions). (3) will have to come later. It's more or less complete, and the examples I have translated so far mostly work. But still a work in progress. Any help/suggestions appreciated. [I am doing this for myself, so project is not as pretty as I would like in an ideal world]. https://github.com/Laeeth/d_hdf5You seem to be missing your dub file. Would be rather hard to get it onto dub repository without it ;) Oh and keep the bindings separate from wrappers in terms of subpackages.
Dec 21 2014
"Laeeth Isharc" <laeethnospam nospamlaeeth.com> On Monday, 22 December 2014 at 05:04:10 UTC, Rikki Cattermole wrote:You seem to be missing your dub file. Would be rather hard to get it onto dub repository without it ;) Oh and keep the bindings separate from wrappers in terms of subpackages.Thanks - added now. Will work on separating out bindings when have a bit more time, but it should be easy enough.
Dec 22 2014
On Monday, 22 December 2014 at 04:51:44 UTC, Laeeth Isharc wrote:https://github.com/Laeeth/d_hdf5 HDF5 is a very valuable tool for those working with large data sets. From HDF5group.org HDF5 is a unique technology suite that makes possible the management of extremely large and complex data collections. The HDF5 technology suite includes: * A versatile data model that can represent very complex data objects and a wide variety of metadata. * A completely portable file format with no limit on the number or size of data objects in the collection. * A software library that runs on a range of computational platforms, from laptops to massively parallel systems, and implements a high-level API with C, C++, Fortran 90, and Java interfaces. * A rich set of integrated performance features that allow for access time and storage space optimizations. * Tools and applications for managing, manipulating, viewing, and analyzing the data in the collection. * The HDF5 data model, file format, API, library, and tools are open and distributed without charge. From h5py.org: [HDF5] lets you store huge amounts of numerical data, and easily manipulate that data from NumPy. For example, you can slice into multi-terabyte datasets stored on disk, as if they were real NumPy arrays. Thousands of datasets can be stored in a single file, categorized and tagged however you want. H5py uses straightforward NumPy and Python metaphors, like dictionary and NumPy array syntax. For example, you can iterate over datasets in a file, or check out the .shape or .dtype attributes of datasets. You don't need to know anything special about HDF5 to get started. In addition to the easy-to-use high level interface, h5py rests on a object-oriented Cython wrapping of the HDF5 C API. Almost anything you can do from C in HDF5, you can do from h5py. Best of all, the files you create are in a widely-used standard binary format, which you can exchange with other people, including those who use programs like IDL and MATLAB. =========== As far as I know there has not really been a complete set of HDF5 bindings for D yet. Bindings should have three levels: 1. pure C API declaration 2. 'nice' D wrapper around C API (eg that knows about strings, not just char*) 3. idiomatic D interface that uses CTFE/templates I borrowed Stefan Frijter's work on (1) above to get started. I cannot keep track of things when split over too many source files, so I put everything in one file - hdf5.d. Have implemented a basic version of 2. Includes throwOnError rather than forcing checking status C style, but the exception code is not very good/complete (time + lack of experience with D exceptions). (3) will have to come later. It's more or less complete, and the examples I have translated so far mostly work. But still a work in progress. Any help/suggestions appreciated. [I am doing this for myself, so project is not as pretty as I would like in an ideal world]. https://github.com/Laeeth/d_hdf5Also relevant to some: http://code.dlang.org/packages/netcdf
Dec 22 2014