Virtual Datasets (VDS) — h5py 3.5.0 documentation
docs.h5py.org › en › stableTo make a virtual dataset using h5py, you need to: Create a VirtualLayout object representing the dimensions and data type of the virtual dataset. Create a number of VirtualSource objects, representing the datasets the array will be built from. These objects can be created either from an h5py Dataset, or from a filename, dataset name and shape.
HDF5 Datasets — h5py 2.3.1 documentation
https://docs.h5py.org/en/2.3/high/dataset.htmlThey are represented in h5py by a thin proxy class which supports familiar NumPy operations like slicing, along with a variety of descriptive attributes: shapeattribute sizeattribute dtypeattribute Creating datasets¶ New datasets are created using either Group.create_dataset()or Group.require_dataset(). Existing datasets should be retrieved using
Quick Start Guide — h5py 3.5.0 documentation
docs.h5py.org › en › stableCreating a subgroup is accomplished via the aptly-named create_group. But we need to open the file in the “append” mode first (Read/write if exists, create otherwise) >>> f = h5py.File('mydataset.hdf5', 'a') >>> grp = f.create_group("subgroup") All Group objects also have the create_* methods like File:
HDF5 for Python - h5py
https://www.h5py.orgThe h5py package is a Pythonic interface to the HDF5 binary data format. It 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.
Datasets — h5py 3.5.0 documentation
docs.h5py.org › en › stableSee FAQ for the list of dtypes h5py supports. Creating datasets ¶ New datasets are created using either Group.create_dataset () or Group.require_dataset (). Existing datasets should be retrieved using the group indexing syntax ( dset = group ["name"] ).
Groups — h5py 3.5.0 documentation
https://docs.h5py.org/en/stable/high/group.htmlAlso like a UNIX filesystem, HDF5 groups can contain “soft” or symbolic links, which contain a text path instead of a pointer to the object itself. You can easily create these in h5py by using h5py.SoftLink: >>> myfile = h5py.File('foo.hdf5','w') >>> group = myfile.create_group("somegroup") >>> myfile["alias"] = h5py.SoftLink('/somegroup')
Create a simple HDF5 dataset using h5Py - Pythontic.com
pythontic.com › HDF5 › h5PyH5Py Create_simple_dataset Overview: HDF5 is a hierarchical data model used for describing and defining data from sources that generate large volumes of data. Examples of sources that create huge datasets include sensors in a laboratory or factory, particle experiments, terrestrial and extra-terrestrial experiments and the similar kind.